diff --git a/auto_disc/auto_disc/wrappers/SaveWrapper.html b/auto_disc/auto_disc/wrappers/SaveWrapper.html index 3055a25..9e65372 100644 --- a/auto_disc/auto_disc/wrappers/SaveWrapper.html +++ b/auto_disc/auto_disc/wrappers/SaveWrapper.html @@ -707,7 +707,7 @@
Usage example:
def - save_leaf( self, resource_uri: str, parent_uid: int = -1) -> <function NewType.<locals>.new_type at 0x7f0ed2ca3670>: + save_leaf( self, resource_uri: str, parent_uid: int = -1) -> <function NewType.<locals>.new_type at 0x7fee26ca1670>: diff --git a/auto_disc/legacy/utils/spaces/dict.html b/auto_disc/legacy/utils/spaces/dict.html index 54ba509..3159440 100644 --- a/auto_disc/legacy/utils/spaces/dict.html +++ b/auto_disc/legacy/utils/spaces/dict.html @@ -507,7 +507,7 @@
Arguments:
def - sample( self) -> Dict[str, <built-in method tensor of type object at 0x7f0f3759f460>]: + sample( self) -> Dict[str, <built-in method tensor of type object at 0x7fee8b64e460>]: diff --git a/auto_disc/utils/leaf/Leaf.html b/auto_disc/utils/leaf/Leaf.html index f6d598f..3251d88 100644 --- a/auto_disc/utils/leaf/Leaf.html +++ b/auto_disc/utils/leaf/Leaf.html @@ -798,7 +798,7 @@

def - save_leaf( self, resource_uri: str = '', *args, **kwargs) -> <function NewType.<locals>.new_type at 0x7f0ed2ca3670>: + save_leaf( self, resource_uri: str = '', *args, **kwargs) -> <function NewType.<locals>.new_type at 0x7fee26ca1670>: diff --git a/auto_disc/utils/leaf/locators/LinearBase.html b/auto_disc/utils/leaf/locators/LinearBase.html index 6b5e966..0bd4ee7 100644 --- a/auto_disc/utils/leaf/locators/LinearBase.html +++ b/auto_disc/utils/leaf/locators/LinearBase.html @@ -881,7 +881,7 @@
Inherited Members
def - store( self, bin: bytes, parent_id: int = -1) -> <function NewType.<locals>.new_type at 0x7f0ed2ca3670>: + store( self, bin: bytes, parent_id: int = -1) -> <function NewType.<locals>.new_type at 0x7fee26ca1670>: @@ -1039,7 +1039,7 @@

Returns:

@staticmethod
def - hash(bin: bytes) -> <function NewType.<locals>.new_type at 0x7f0ed2ca3670>: + hash(bin: bytes) -> <function NewType.<locals>.new_type at 0x7fee26ca1670>: diff --git a/auto_disc/utils/leaf/locators/Locator.html b/auto_disc/utils/leaf/locators/Locator.html index 57fa417..f9b2036 100644 --- a/auto_disc/utils/leaf/locators/Locator.html +++ b/auto_disc/utils/leaf/locators/Locator.html @@ -267,7 +267,7 @@

def - store( self, bin: bytes, *args, **kwargs) -> <function NewType.<locals>.new_type at 0x7f0ed2ca3670>: + store( self, bin: bytes, *args, **kwargs) -> <function NewType.<locals>.new_type at 0x7fee26ca1670>: @@ -320,7 +320,7 @@

@staticmethod
def - hash(bin: bytes) -> <function NewType.<locals>.new_type at 0x7f0ed2ca3670>: + hash(bin: bytes) -> <function NewType.<locals>.new_type at 0x7fee26ca1670>: @@ -393,7 +393,7 @@

def - store( self, bin: bytes, *args, **kwargs) -> <function NewType.<locals>.new_type at 0x7f0ed2ca3670>: + store( self, bin: bytes, *args, **kwargs) -> <function NewType.<locals>.new_type at 0x7fee26ca1670>: @@ -526,7 +526,7 @@
Inherited Members
def - store( self, bin: bytes, *args, **kwargs) -> <function NewType.<locals>.new_type at 0x7f0ed2ca3670>: + store( self, bin: bytes, *args, **kwargs) -> <function NewType.<locals>.new_type at 0x7fee26ca1670>: diff --git a/auto_disc/utils/leaf/locators/locators.html b/auto_disc/utils/leaf/locators/locators.html index b99617b..1f2fa63 100644 --- a/auto_disc/utils/leaf/locators/locators.html +++ b/auto_disc/utils/leaf/locators/locators.html @@ -278,7 +278,7 @@

def - store( self, bin: bytes, *args, **kwargs) -> <function NewType.<locals>.new_type at 0x7f0ed2ca3670>: + store( self, bin: bytes, *args, **kwargs) -> <function NewType.<locals>.new_type at 0x7fee26ca1670>: @@ -438,7 +438,7 @@
Inherited Members
def - store( self, bin: bytes, parent_id: int = -1, *args, **kwargs) -> <function NewType.<locals>.new_type at 0x7f0ed2ca3670>: + store( self, bin: bytes, parent_id: int = -1, *args, **kwargs) -> <function NewType.<locals>.new_type at 0x7fee26ca1670>: @@ -548,7 +548,7 @@
Inherited Members
@staticmethod
def - hash(bin: bytes) -> <function NewType.<locals>.new_type at 0x7f0ed2ca3670>: + hash(bin: bytes) -> <function NewType.<locals>.new_type at 0x7fee26ca1670>: diff --git a/auto_disc/utils/leaf/tests/test_recursive_leaf.html b/auto_disc/utils/leaf/tests/test_recursive_leaf.html index 4193c42..6205fd6 100644 --- a/auto_disc/utils/leaf/tests/test_recursive_leaf.html +++ b/auto_disc/utils/leaf/tests/test_recursive_leaf.html @@ -758,7 +758,7 @@
Inherited Members
def - store( self, bin: bytes, *args, **kwargs) -> <function NewType.<locals>.new_type at 0x7f0ed2ca3670>: + store( self, bin: bytes, *args, **kwargs) -> <function NewType.<locals>.new_type at 0x7fee26ca1670>: diff --git a/search.js b/search.js index 56c2965..970cc1b 100644 --- a/search.js +++ b/search.js @@ -1,6 +1,6 @@ window.pdocSearch = (function(){ /** elasticlunr - http://weixsong.github.io * Copyright (C) 2017 Oliver Nightingale * Copyright (C) 2017 Wei Song * MIT Licensed */!function(){function e(e){if(null===e||"object"!=typeof e)return e;var t=e.constructor();for(var n in e)e.hasOwnProperty(n)&&(t[n]=e[n]);return t}var t=function(e){var n=new t.Index;return n.pipeline.add(t.trimmer,t.stopWordFilter,t.stemmer),e&&e.call(n,n),n};t.version="0.9.5",lunr=t,t.utils={},t.utils.warn=function(e){return function(t){e.console&&console.warn&&console.warn(t)}}(this),t.utils.toString=function(e){return void 0===e||null===e?"":e.toString()},t.EventEmitter=function(){this.events={}},t.EventEmitter.prototype.addListener=function(){var e=Array.prototype.slice.call(arguments),t=e.pop(),n=e;if("function"!=typeof t)throw new TypeError("last argument must be a function");n.forEach(function(e){this.hasHandler(e)||(this.events[e]=[]),this.events[e].push(t)},this)},t.EventEmitter.prototype.removeListener=function(e,t){if(this.hasHandler(e)){var n=this.events[e].indexOf(t);-1!==n&&(this.events[e].splice(n,1),0==this.events[e].length&&delete this.events[e])}},t.EventEmitter.prototype.emit=function(e){if(this.hasHandler(e)){var t=Array.prototype.slice.call(arguments,1);this.events[e].forEach(function(e){e.apply(void 0,t)},this)}},t.EventEmitter.prototype.hasHandler=function(e){return e in this.events},t.tokenizer=function(e){if(!arguments.length||null===e||void 0===e)return[];if(Array.isArray(e)){var n=e.filter(function(e){return null===e||void 0===e?!1:!0});n=n.map(function(e){return t.utils.toString(e).toLowerCase()});var i=[];return n.forEach(function(e){var n=e.split(t.tokenizer.seperator);i=i.concat(n)},this),i}return e.toString().trim().toLowerCase().split(t.tokenizer.seperator)},t.tokenizer.defaultSeperator=/[\s\-]+/,t.tokenizer.seperator=t.tokenizer.defaultSeperator,t.tokenizer.setSeperator=function(e){null!==e&&void 0!==e&&"object"==typeof e&&(t.tokenizer.seperator=e)},t.tokenizer.resetSeperator=function(){t.tokenizer.seperator=t.tokenizer.defaultSeperator},t.tokenizer.getSeperator=function(){return t.tokenizer.seperator},t.Pipeline=function(){this._queue=[]},t.Pipeline.registeredFunctions={},t.Pipeline.registerFunction=function(e,n){n in t.Pipeline.registeredFunctions&&t.utils.warn("Overwriting existing registered function: "+n),e.label=n,t.Pipeline.registeredFunctions[n]=e},t.Pipeline.getRegisteredFunction=function(e){return e in t.Pipeline.registeredFunctions!=!0?null:t.Pipeline.registeredFunctions[e]},t.Pipeline.warnIfFunctionNotRegistered=function(e){var n=e.label&&e.label in this.registeredFunctions;n||t.utils.warn("Function is not registered with pipeline. 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e.elements=this.toArray(),e.length=e.elements.length,e},lunr.SortedSet.prototype.union=function(e){var t,n,i;this.length>=e.length?(t=this,n=e):(t=e,n=this),i=t.clone();for(var o=0,r=n.toArray();oAutomated Discovery Tool

\n\n

NOTE: this README is deprecated

\n\n

We're pleased to introduce Automated Discovery Tool, a software for assisted automated discovery and exploration of complex systems.

\n\n

Installation

\n\n

Docker installation

\n\n

Our software leverages docker to avoid you installing multiple packages.\nTo install docker:

\n\n
    \n
  1. Install Docker: LINK
    2. \n
  2. Install Docker-compose: LINK
    3. \n
\n\n

Using Automated Discovery Tool

\n\n

Our software consists of two primary parts:

\n\n
    \n
  • a Python library called auto_disc where you can\nuse/download/implement systems to explore as well as exploration algorithms.
    • \n
  • a web application allowing you to perform assisted automated discovery\nexperiments using a user-friendly interface.
    • \n
\n\n

The auto_disc library can be used independently of the rest of\nAutomated Discovery Tool, for example to implement some bespoke remote computing\nsolutions, but its main purpose is to present a standardized interface for\nmanaging experimental data and configurational metadata which is\nmodified and displayed by the GUI.

\n\n

Launching the software

\n\n
    \n
  1. Run the following command: sudo ./start_app.sh
    2. \n
  2. Your application should be accessible on http://localhost:4201
    3. \n
  3. If you want to modify the port on which the application starts (as well as other configuration parameters for DataBases, network...), you can modify the .env file in the services directory.
    4. \n
\n\n

Using the web app

\n\n

User documentation

\n\n

Modifying the lib

\n\n

Before being able to modify the lib, you must install it. For this, please refer to this.\nOur lib relies on classes we call Modules. Every system or explorer is a Module.\nHere is a tutorial on how to add a new module to the lib (and then be able to use it in an experiment launched from the web app):\nModule adding tutorial.

\n\n

We also provide callbacks to experiments (e.g. to save discoveries). If you want to add a new one, you can follow this tutorial.

\n\n

Launching experiments on a remote server

\n\n

By default, experiments will be launched locally. However, some large experiments can require large compute. We thus allow users to provide a configuration to reach a remote server: Adding remote server.

\n\n
\n\n

Contributing

\n\n

Please follow these instructions when contributing on the project.

\n\n

Installation

\n\n

The software is implemented in various microservices

\n\n
    \n
  • auto_disc library : a pure Python API for implementing custom experiments \\\nand search algorithms to use in the software
    • \n
  • AutoDiscServer : simple service which performs the operations specified by\nfunctionality in auto_disc library.
    • \n
  • FrontEndApp : web GUI implemented in Angular for interacting with the software
    • \n
  • AppDB & ExpeDB : databases which manage the metadata of experiments (linked\nwith the FrontEnd) and resulting experimental data, respectively
    • \n
  • JupyterLab : Jupyter instance allowing interaction with experimental data
    • \n
\n\n

auto_disc library

\n\n
    \n
  1. If you do not already have it, please install Conda
    2. \n
  2. Create _autoDiscTool_ conda environment: conda env create --name autoDiscTool python=3.7
    3. \n
  3. Activate _autoDiscTool_ conda environment: conda activate autoDiscTool
    4. \n
  4. Go to package: cd libs/auto_disc
    5. \n
  5. Install package: pip install -e .
    6. \n
\n\n

AutoDiscServer

\n\n
    \n
  1. Install flask: pip install -r services/AutoDiscServer/flask_app/requirements.txt
    2. \n
\n\n

FrontEndApp

\n\n
    \n
  1. Install Angular: LINK
    2. \n
  2. Enter the front-end app folder: cd services/FrontEndApp/angular_app/.
    3. \n
  3. Install required packages: npm install
    4. \n
\n\n

AppDB & ExpeDB

\n\n
    \n
  1. Install Docker: LINK
    2. \n
  2. Install Docker-compose: LINK
    3. \n
  3. Go to the service folder: cd services.
    4. \n
  4. Generate containers sudo docker-compose -f services/docker-compose.yml create
    5. \n
  5. Install the Expe DB REST API requirements: pip install -r ExpeDB/API/flask_app/requirements.txt
    6. \n
\n\n

JupyterLab

\n\n
    \n
  1. Install jupyter: pip install -r JupyterLab/requirements.txt
    2. \n
\n\n

Testing the auto_disc library alone

\n\n
    \n
  1. Edit the libs/tests/AutoDiscExperiment.py file to configure the experiment
    2. \n
  2. Launch the experiment: python libs/tests/AutoDiscExperiment.py
    3. \n
\n\n

Commits

\n\n

Please attach every commit to an issue or a merge request. For issues, add #ID at the beginning of your commit message (with ID the id of the issue).

\n\n

Starting the project

\n\n

Prod mode

\n\n

Launch: sudo ./start_app.sh

\n\n

Debug mode

\n\n

Go to the services folder: cd services.

\n\n
AutoDiscServer
\n\n

Launch the flask server: python -m AutoDiscServer.app.

\n\n
App DB
\n\n

Start services: sudo docker-compose up app-db-api\nAdd -d option for daemon.

\n\n
Expe DB
\n\n

Start service: sudo docker-compose up expe-db\nAdd -d option for daemon.\nLaunch flask server for the REST API: python ExpeDB/app.py

\n\n
Front-end app
\n\n

Enter the front-end app folder: cd FrontEndApp.\nStart the angular app: ng serve.

\n\n
Jupyter Lab
\n\n

Enter Jupyter Lab's folder: cd ../JupyterLab\nGive the appropriate rights to the notebook folder: chmod 777 -R Notebooks/\nStart the jupyter lab on port 8887: export PYTHONPATH=$(pwd)/../../libs/auto_disc_db; jupyter lab Notebooks/ --config Config/jupyter_notebook_config.py

\n\n

Using monitoring services

\n\n

We added monitoring applications for the Docker services.\nYou can start then by going in services: cd services and launching the following command:

\n\n
sudo docker-compose --profile monitoring up\n
\n\n

_Warning_: This command will start all the docker services or attach the already launched ones otherwise.

\n\n

Portainer

\n\n

Portainer is a web application allowing to monitor Docker environments.\nYou can access it at: https://localhost:9443.

\n\n

At first startup you'll be asked to create a password for your admin account. Then, you should see your local environment with all the running containers. You can for instance easily see the logs of each container.

\n\n

PgAdmin

\n\n

PgAdmin is a lightweight web application allowing to visually interact with a PostgreSQL database. You can use it to monitor and modify our AppDB (which should be connected by default).\nFor this, go to http://localhost:5050.\nTo login use:

\n\n
    \n
  • user: user@autodisc.com
    • \n
  • password: autodisc
    • \n
\n\n

You should see the server list in the panel on the left. To see the data of a table, unfold the schema/Tables property of the server. Then, right click on the desired table and click \"See data\".

\n\n

MongoExpress

\n\n

MongoExpress is also a lightweight DB GUI but this time made for MongoDB.\nAccess the app at http://localhost:8081.

\n\n

User documentation

\n\n

Home

\n\n

After starting the application and opening it in a browser at http://localhost:4201/home (localhost if you run the application locally), you will access the page presented in the picture below.

\n\n

\n\n

First you can see a list of experiments and for each experience some brief information. If you click on an item in the list you will be redirected to the experiment monitoring page\nThe numbers below correspond to those in red in the picture.\n1: The experiment name\n2: The experiment id\n3: The date of creation\n4: All modules used in the experiment (the system, explorer, the number of input wrapper, the number of output representation and the host i.e.the server where you run the experiment, local if you run it on your pc)\n5: The current state of the experiment: Done if the experiment ended; Preparing if the experiment is in setup or if its waiting the server; Running if the experiment is in progress; Error if the experiment crashed.\n6: Some tools to manage the experiments' list: Refresh to synchronize the list with the database; Date to order by dates; A text zone to filter by the experiments or modules names.\n7: \"New experiment\" or the floating button redirect to the experience creation page.

\n\n

Experiment creation

\n\n

General presentation

\n\n

\n\n

This page consists of 4 parts:

\n\n
1: An accordion to setup the experiment, define each modules and all parameters (we present each part later).\n2: Some tools to import and export the experiment configuration. You could export the current configuration as a JSON file and you could import a JSON file to setup the experiment. You also could re load a past experiment to set the current experiment.\n3: After having setup the experiment you could launch it with the \"Create experiment\" button.\n4: \"Home\" to be redirected to the homepage.\n
\n\n

Now we will see each part of the accordion.

\n\n

General information:\n

\n\n
1: The name of the experiment. Must contain only alpha numeric characters, \"-\" and \"_\".\n2: The number of iterations. How many generations of the system will be explored.\n3: The number of seeds. How many different seeds will be used.\n4: The saving frequency. Every n generations a checkpoint will be created\n5: The host. The computer on which the experiment will be run. Local if it is on your pc; the name of the server otherwise.\n
\n\n

Monitoring experiment

\n\n

The monitoring experiment page give you access to the tools to manage the experiment. You can visualize discoveries and logs generated by the experiment. You have acces to some information about the experiment config. More important you have acces to a jupyter lab link to the experiment to manipulate all things generated by the experiment.

\n\n

The Details part displays information about the experiment config. You have access to all modules used in the experiment, the current system, the current explorer, the list of input wrappers and output representations and the config for each of them and you can see all items the experiment has saved.

\n\n

\n\n

The checkpoints part displays every checkpoint id with its status (Done, running, error...) and the experiment iteration number when the checkpoint was created.

\n\n

\n\n

The jupyter part gives you access to tools to monitor many things. In it you have acces to the database and every results produced by an experiment.\nThe \"dataset_template\" notebook show you how to acces the database and demonstrate a simple way to manipulate data.\nThe yellow/blue popover informs weither the jupyter kernel is available and connected to the database or not.

\n\n

\n\n

The discoveries part displays all discoveries made by the experiment. You can filter them by seed, by iteration range and the number of discoveries per page at will.

\n\n

\n\n

Finally you have a logs part where you can read all logs made by the experiment and filter them by checkpoints, seeds or log levels.

\n\n

\n\n

Add a new module to the libs

\n\n

Four different types of modules can be implemented in the Automated Discovery Tool libs

\n\n
    \n
  • Systems
    • \n
  • Explorers
    • \n
  • Input_wrapper
    • \n
  • Output_representations
    • \n
\n\n

In order to implement a custom module,

\n\n
    \n

  1. Each module has its own folder.

    \n\n

    To add a new module create the file in the associated folder

    \n\n

    Example:

    \n\n
    libs/auto_disc/auto_disc/explorers/myBeautifulNewExplorer.py\n
    \n\n

    or

    \n\n
    libs/auto_disc/auto_disc/systems/python_systems/myBeautifulNewPythonSystems.py\n
    2. \n

  2. The new module must ultimately inherit from its module base class (BaseSystem, BaseOutputRepresentation...).

    \n\n

    Please adhere to the parent class interface during the implementation.

    \n\n

    You can add decorators to your module class, which are useful to set module config parameters. These can be then directly set them in the GUI.

    \n\n

    An example to implement a new explorer :

    \n\n
    from auto_disc.explorers import BaseExplorer\nfrom auto_disc.utils.config_parameters import StringConfigParameter\n\n@StringConfigParameter(name=\"a_string_parameter\", possible_values=[\"first_possible_value\", \"second_possible_value\"], default=\"first_possible_value\")\n\nclass NewExplorer(BaseExplorer):\n\n    CONFIG_DEFINITION = {}\n\n    def __init__(self, **kwargs) -> None:\n        super().__init__(**kwargs)\n\n    def initialize(self, input_space, output_space, input_distance_fn):\n        super().initialize(input_space, output_space, input_distance_fn)\n        \"\"\"do some brilliant stuff\"\"\"\n\n    def sample(self):\n        \"\"\"do some brilliant stuff\"\"\"\n\n    def observe(self, parameters, observations):\n        \"\"\"do some brilliant stuff\"\"\"\n\n    def optimize(self):\n        \"\"\"do some brilliant stuff\"\"\"\n
    \n\n

    Don't forget kwargs argument in the __init__ method and CONFIG_DEFINITION class variable.

    3. \n

  3. Add import in libs/auto_disc/module_cat/subfolder_if_needed/__init__.py

    \n\n

    Example:

    \n\n
    libs/auto_disc/auto_disc/explorers/__init__.py\nlibs/auto_disc/auto_disc/systems/python_systems/__init__.py\n
    4. \n

  4. Add new module in registration.py in REGISTRATION dict

    \n\n

    Modify REGISTRATION starting from this

    \n\n
    REGISTRATION = {\n        'systems':{\n            'PythonLenia': PythonLenia,\n        },...\n    }\n
    \n\n

    to for example the following

    \n\n
    REGISTRATION = {\n    'systems':{\n        'PythonLenia': PythonLenia,\n        'MyBeautifullNewPythonSystems': MyBeautifullNewPythonSystems,\n    },...\n}\n
    \n\n

    The dict keys are used in GUI to choose your module when you setup an experiment.

    5. \n
\n\n

Add a new callback to the libs

\n\n

Six different types of callbacks can be implemented in the Automated Discovery Tool libs, namely:

\n\n
    \n
  • on_cancelled_callbacks : called when user decides to cancel the experiment in the frontend
    • \n
  • on_discovery_callbacks : called each time the experiment makes a new discovery
    • \n
  • on_error_callbacks : called when experiment raises an error during execution
    • \n
  • on_finished_callbacks : called when the experiment ends
    • \n
  • on_save_callbacks : called when the experiment saves its state
    • \n
  • on_save_finished_callbacks : called when the experiment has finished saving
    • \n
\n\n
    \n

  1. Each callback type has its own folder. \nTo add a new callback create the file in the associated folder

    \n\n

    Example:

    \n\n
    libs/auto_disc/auto_disc/utils/callbacks/on_cancelled_callbacks/my_beautiful_new_on_cancelled_callback.py\n
    \n\n

    or

    \n\n
    libs/auto_disc/auto_disc/utils/callbacks/on_discovery_callbacks/my_beautiful_new_on_discovery_callback.py\n
    2. \n

  2. The new callback must inherits base callback class of its own type.

    \n\n

    Example:

    \n\n
         class MyBeautifulNewOnCancelledCallback(BaseOnCancelledCallback):\n         pass\n
    \n\n

    or

    \n\n
         class MyBeautifulNewOnDiscoveryCallback(BaseOnDiscoveryCallback):\n         pass\n
    \n\n

    A full example:

    \n\n
        from auto_disc.legacy.utils.callbacks.on_discovery_callbacks import BaseOnDiscoveryCallback\n\n    class MyBeautifullNewOnDiscoveryCallback(BaseOnDiscoveryCallback):\n\n        def __init__(self, folder_path, to_save_outputs, **kwargs) -> None:\n            super().__init__(to_save_outputs, **kwargs)\n            \"\"\"do some brilliant stuff\"\"\"\n\n        def __call__(self, **kwargs) -> None:\n            \"\"\"do some brilliant stuff\"\"\"\n
    \n\n

    Don't forget to pass **kwargs in the __init__ method.\nEach time our callback will be raised the __call__ method will be executed.

    3. \n

  3. Add import header file, e.g., libs/auto_disc/auto_disc/utils/callbacks/callbacks_sub_folder/__init__.py

    \n\n

    Example:

    \n\n
    libs/auto_disc/auto_disc/utils/callbacks/on_discovery_callbacks/__init__.py\nlibs/auto_disc/auto_disc/utils/callbacks/on_cancelled_callbacks/__init__.py\n
    4. \n

  4. Add new callback in registration.py in REGISTRATION dict

    \n\n

    Example modification:

    \n\n
    Modify REGISTRATION like this:\n\nREGISTRATION = {\n     ...\n     'callbacks': {\n         'on_discovery':{\n             'base': on_discovery_callbacks.BaseOnDiscoveryCallback,\n             'disk': on_discovery_callbacks.OnDiscoverySaveCallbackOnDisk\n         },\n         'on_cancelled':{\n             'base': on_cancelled_callbacks.BaseOnCancelledCallback\n         },\n         'on_error':{\n             'base': on_error_callbacks.BaseOnErrorCallback\n         },\n         'on_finished':{\n             'base': on_finished_callbacks.BaseOnFinishedCallback\n         },\n         'on_saved':{\n             'base': on_save_callbacks.BaseOnSaveCallback,\n             'disk': on_save_callbacks.OnSaveModulesOnDiskCallback\n         },\n         'on_save_finished':{\n             'base': on_save_finished_callbacks.BaseOnSaveFinishedCallback\n         },\n     },...\n }\n\n ==>\n\n REGISTRATION = {\n     ...\n     'callbacks': {\n         'on_discovery':{\n             'base': on_discovery_callbacks.BaseOnDiscoveryCallback,\n             'disk': on_discovery_callbacks.OnDiscoverySaveCallbackOnDisk\n             'new': MyBeautifullNewOnDiscoveryCallback\n         },\n         'on_cancelled':{\n             'base': on_cancelled_callbacks.BaseOnCancelledCallback\n         },\n         'on_error':{\n             'base': on_error_callbacks.BaseOnErrorCallback\n         },\n         'on_finished':{\n             'base': on_finished_callbacks.BaseOnFinishedCallback\n         },\n         'on_saved':{\n             'base': on_save_callbacks.BaseOnSaveCallback,\n             'disk': on_save_callbacks.OnSaveModulesOnDiskCallback\n         },\n         'on_save_finished':{\n             'base': on_save_finished_callbacks.BaseOnSaveFinishedCallback\n         },\n     },...\n }\n
    5. \n

  5. For now the software does not permit to add custom callback via GUI. You may use the libs independently like in libs/test/AutoDiscExperiment.py and add manually your personal callbacks. The other way is to manually add your callback in services/AutodiscServer/flask/experiments/remote_experiments.py on the __init__ method to use your own callbacks with the full software package.

    6. \n
\n\n

Add a new remote server

\n\n

You must have a remote server to start a remote experiment.
\nTo access your server from the software you have to configure it in the configs/remote_experiments folder.
\nHere you have two sub folders.
\nThe first one, additional_files, contains the files we will execute to start the experiment on the remote server.
\nThe second one, profiles, is used to define how to connect and interact with the remote server.

\nWe find there:

\n\n
- The current config name (we will choose it in the GUI).\n- The name of your ssh configuration, defined on your personnal file `/home/user/.ssh/config` (or other path you have set on services/.env file at the `SSH_CONFIG_FOLDER` var).\n- The work folder on the remote server\n- A local folder to store downloaded files created by the remote experiment before they are put in the database \n- The command to start the experiment ( it will execute the associated file in \"additional_file\"). This command must echo `\"[RUN_ID_start]\"id\"[RUN_ID_stop]\"` in the remote shell either directly in the command or at the end of the associated additional_file. The example below does it on the additional file. Here id is equal to every PID the experiment launches (one for each seed).\n- The command to cancel all processes started by the experiment to run it self on the remote server.\n- Finally a number of second (to check that the experiment has started).\n
\n\n

example:

\n\n
        profiles:\n            name: \n                myConfigNameInTheSoftware\n            ssh_configuration: \n                myPersonnalShhConfigName\n            work_path:\n                /path/to/workdirectory/on/the/remote/server\n            local_tmp_path:\n                /tmp/auto_disc/remote_experiment/\n            execution:\n                bash additional_files/docker.sh $NB_SEEDS $ARGS > $EXPE_ID\n            cancellation:\n                scancel $RUN_ID\n            check_experiment_launched_every:\n                60\n\n        additional_files:\n            #!/bin/bash\n            export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/root/anaconda3/lib/;\n\n            PIDS=\"\"\n            for ((i=0; i<$1; i++))\n            do\n            PID=$(/root/anaconda3/envs/autoDiscTool/bin/python3 /home/workdir/auto_disc/auto_disc/run.py --seed ${i} ${@:2} >> experiment_logs.log & echo $!)\n            if [[ $PIDS != \"\" ]]\n            then\n                PIDS+=\" \"\n            fi\n            PIDS+=\"${PID}\"\n            done\n            echo \"[RUN_ID_start]$PIDS[RUN_ID_stop]\"\n
\n\n

Please note that you must first download and install the auto_disc lib on your remote server (follow these steps) to launch experiments on this server.

\n"}, "auto_disc.ExperimentPipeline": {"fullname": "auto_disc.ExperimentPipeline", "modulename": "auto_disc.ExperimentPipeline", "kind": "module", "doc": "

\n"}, "auto_disc.ExperimentPipeline.CancellationToken": {"fullname": "auto_disc.ExperimentPipeline.CancellationToken", "modulename": "auto_disc.ExperimentPipeline", "qualname": "CancellationToken", "kind": "class", "doc": "

Manages the cancellation token which allows you to stop an\nexperiment in progress

\n"}, "auto_disc.ExperimentPipeline.CancellationToken.__init__": {"fullname": "auto_disc.ExperimentPipeline.CancellationToken.__init__", "modulename": "auto_disc.ExperimentPipeline", "qualname": "CancellationToken.__init__", "kind": "function", "doc": "

Init the cancellation token to false

\n", "signature": "()"}, "auto_disc.ExperimentPipeline.CancellationToken.get": {"fullname": "auto_disc.ExperimentPipeline.CancellationToken.get", "modulename": "auto_disc.ExperimentPipeline", "qualname": "CancellationToken.get", "kind": "function", "doc": "

Give access to the cancellation token

\n\n

Returns:

\n\n
    \n
  • token: a boolean indicating if the current experiment\nmust be cancelled
    • \n
\n", "signature": "(self) -> bool:", "funcdef": "def"}, "auto_disc.ExperimentPipeline.CancellationToken.trigger": {"fullname": "auto_disc.ExperimentPipeline.CancellationToken.trigger", "modulename": "auto_disc.ExperimentPipeline", "qualname": "CancellationToken.trigger", "kind": "function", "doc": "

Sets the cancellation token to true\n(the experiment must be cancelled)

\n", "signature": "(self) -> None:", "funcdef": "def"}, "auto_disc.ExperimentPipeline.ExperimentPipeline": {"fullname": "auto_disc.ExperimentPipeline.ExperimentPipeline", "modulename": "auto_disc.ExperimentPipeline", "qualname": "ExperimentPipeline", "kind": "class", "doc": "

Pipeline of an automated discovery experiment.

\n\n

An experiment is at least constituted of a system and an explorer.

\n\n

Additionally, input wrappers and output representations can be added and\ncomposed.

\n\n

In order to monitor the experiment, you must provide callbacks, which\nwill be called every time a discovery has been made.\nPlease see: auto_disc.legacy.utils.callbacks.base_callback.BaseCallback.

\n", "bases": "auto_disc.utils.leaf.Leaf.Leaf"}, "auto_disc.ExperimentPipeline.ExperimentPipeline.__init__": {"fullname": "auto_disc.ExperimentPipeline.ExperimentPipeline.__init__", "modulename": "auto_disc.ExperimentPipeline", "qualname": "ExperimentPipeline.__init__", "kind": "function", "doc": "

Initializes state of experiment pipeline, setting all necessary\nattributes given by the following arguments.

\n\n

Args:

\n\n
    \n
  • experiment_id: ID of current experiment
    • \n
  • seed: Current seed for random number generation
    • \n
  • system: System to be explored
    • \n
  • explorer: Explorer used
    • \n
  • save_frequency: Frequency to save state of the experiment
    • \n
  • on_discovery_callbacks: Called when a discovery is made
    • \n
  • on_save_finished_callbacks: Called when save is complete
    • \n
  • on_finished_callbacks: Called when the experiment is finished
    • \n
  • on_cancelled_callbacks: Called when the experiment is cancelled
    • \n
  • on_save_callbacks: Called when a experiment save is made
    • \n
  • on_error_callbacks: Called when an error is raised
    • \n
\n", "signature": "(\texperiment_id: int = 0,\tseed: int = 0,\tsystem=None,\texplorer=None,\tsave_frequency: int = 100,\ton_discovery_callbacks: List[Callable] = [],\ton_save_finished_callbacks: List[Callable] = [],\ton_finished_callbacks: List[Callable] = [],\ton_cancelled_callbacks: List[Callable] = [],\ton_save_callbacks: List[Callable] = [],\ton_error_callbacks: List[Callable] = [],\tlogger=None,\tresource_uri: str = '',\tdiscovery_saving_keys: List[str] = [])"}, "auto_disc.ExperimentPipeline.ExperimentPipeline.run": {"fullname": "auto_disc.ExperimentPipeline.ExperimentPipeline.run", "modulename": "auto_disc.ExperimentPipeline", "qualname": "ExperimentPipeline.run", "kind": "function", "doc": "

Launches the experiment for n_exploration_runs explorations.

\n\n

n_exploration_runs is specified so more optimized looping routines\ncan be chosen in the inner loop if desired. Interfacing with the\nobjects in the inner loop should be done via the callback interface.

\n\n

Args:

\n\n
    \n
  • n_exploration_runs: number of explorations
    • \n
\n\n

Returns:

\n\n
    \n
  • LeafUID: returns the UID associated to the experiment
    • \n
\n", "signature": "(self, n_exploration_runs: int):", "funcdef": "def"}, "auto_disc.ExperimentPipeline.ExperimentPipeline.save": {"fullname": "auto_disc.ExperimentPipeline.ExperimentPipeline.save", "modulename": "auto_disc.ExperimentPipeline", "qualname": "ExperimentPipeline.save", "kind": "function", "doc": "

\n", "signature": "(self, resource_uri: str):", "funcdef": "def"}, "auto_disc.ExperimentPipeline.ExperimentPipeline.serialize": {"fullname": "auto_disc.ExperimentPipeline.ExperimentPipeline.serialize", "modulename": "auto_disc.ExperimentPipeline", "qualname": "ExperimentPipeline.serialize", "kind": "function", "doc": "

Serializes object to pickle,\nturning all submodules into uniquely identifiable hashes

\n", "signature": "(self, *args, **kwargs) -> bytes:", "funcdef": "def"}, "auto_disc.auto_disc": {"fullname": "auto_disc.auto_disc", "modulename": "auto_disc.auto_disc", "kind": "module", "doc": "

Core modules of the software are located under this module namespace.

\n"}, "auto_disc.auto_disc.explorers": {"fullname": "auto_disc.auto_disc.explorers", "modulename": "auto_disc.auto_disc.explorers", "kind": "module", "doc": "

An Explorer encapsulates and abstracts the algorithmic loop which is run to\nconduct automatic exploration of the system.

\n"}, "auto_disc.auto_disc.explorers.IMGEPExplorer": {"fullname": "auto_disc.auto_disc.explorers.IMGEPExplorer", "modulename": "auto_disc.auto_disc.explorers.IMGEPExplorer", "kind": "module", "doc": "

The simplest possible algorithm of Intrinsically Motivated Goal Exploration Processes

\n"}, "auto_disc.auto_disc.explorers.IMGEPExplorer.IMGEPFactory": {"fullname": "auto_disc.auto_disc.explorers.IMGEPExplorer.IMGEPFactory", "modulename": "auto_disc.auto_disc.explorers.IMGEPExplorer", "qualname": "IMGEPFactory", "kind": "class", "doc": "

Factory class providing interface with config parameters and therefore the\nfrontend

\n"}, "auto_disc.auto_disc.explorers.IMGEPExplorer.IMGEPFactory.__init__": {"fullname": "auto_disc.auto_disc.explorers.IMGEPExplorer.IMGEPFactory.__init__", "modulename": "auto_disc.auto_disc.explorers.IMGEPExplorer", "qualname": "IMGEPFactory.__init__", "kind": "function", "doc": "

\n", "signature": "(*args, **kws)"}, "auto_disc.auto_disc.explorers.IMGEPExplorer.IMGEPFactory.make_behavior_map": {"fullname": "auto_disc.auto_disc.explorers.IMGEPExplorer.IMGEPFactory.make_behavior_map", "modulename": "auto_disc.auto_disc.explorers.IMGEPExplorer", "qualname": "IMGEPFactory.make_behavior_map", "kind": "function", "doc": "

\n", "signature": "(self):", "funcdef": "def"}, "auto_disc.auto_disc.explorers.IMGEPExplorer.IMGEPFactory.make_parameter_map": {"fullname": "auto_disc.auto_disc.explorers.IMGEPExplorer.IMGEPFactory.make_parameter_map", "modulename": "auto_disc.auto_disc.explorers.IMGEPExplorer", "qualname": "IMGEPFactory.make_parameter_map", "kind": "function", "doc": "

\n", "signature": "(self):", "funcdef": "def"}, "auto_disc.auto_disc.explorers.IMGEPExplorer.IMGEPFactory.make_mutator": {"fullname": "auto_disc.auto_disc.explorers.IMGEPExplorer.IMGEPFactory.make_mutator", "modulename": "auto_disc.auto_disc.explorers.IMGEPExplorer", "qualname": "IMGEPFactory.make_mutator", "kind": "function", "doc": "

\n", "signature": "(self, param_map: Any = None):", "funcdef": "def"}, "auto_disc.auto_disc.explorers.IMGEPExplorer.IMGEPExplorer": {"fullname": "auto_disc.auto_disc.explorers.IMGEPExplorer.IMGEPExplorer", "modulename": "auto_disc.auto_disc.explorers.IMGEPExplorer", "qualname": "IMGEPExplorer", "kind": "class", "doc": "

Basic IMGEP that diffuses in goalspace.

\n\n

A class instance of IMGEPExplorer has access to a provided\nparameter_map, behavior_map, and mutator as attributes, whereas it\nreceives data from the system under study through the .map method.

\n", "bases": "auto_disc.utils.leaf.Leaf.Leaf"}, "auto_disc.auto_disc.explorers.IMGEPExplorer.IMGEPExplorer.__init__": {"fullname": "auto_disc.auto_disc.explorers.IMGEPExplorer.IMGEPExplorer.__init__", "modulename": "auto_disc.auto_disc.explorers.IMGEPExplorer", "qualname": "IMGEPExplorer.__init__", "kind": "function", "doc": "

\n", "signature": "(\tpremap_key: str = 'output',\tpostmap_key: str = 'params',\tparameter_map: auto_disc.utils.leaf.Leaf.Leaf = <auto_disc.auto_disc.wrappers.IdentityWrapper.IdentityWrapper object>,\tbehavior_map: auto_disc.utils.leaf.Leaf.Leaf = <auto_disc.auto_disc.wrappers.IdentityWrapper.IdentityWrapper object>,\tmutator: auto_disc.utils.leaf.Leaf.Leaf = Identity(),\tequil_time: int = 0)"}, "auto_disc.auto_disc.explorers.IMGEPExplorer.IMGEPExplorer.bootstrap": {"fullname": "auto_disc.auto_disc.explorers.IMGEPExplorer.IMGEPExplorer.bootstrap", "modulename": "auto_disc.auto_disc.explorers.IMGEPExplorer", "qualname": "IMGEPExplorer.bootstrap", "kind": "function", "doc": "

Return an initial sample needed to bootstrap the exploration loop.

\n", "signature": "(self) -> Dict:", "funcdef": "def"}, "auto_disc.auto_disc.explorers.IMGEPExplorer.IMGEPExplorer.map": {"fullname": "auto_disc.auto_disc.explorers.IMGEPExplorer.IMGEPExplorer.map", "modulename": "auto_disc.auto_disc.explorers.IMGEPExplorer", "qualname": "IMGEPExplorer.map", "kind": "function", "doc": "

Map the raw output of the system rollout to a subsequent parameter\nconfiguration to try.

\n\n
Arguments:
\n\n
    \n
  • system_output: A dictionary where the key self.premap_key indexes the raw\noutput given at the end of the previous system rollout.
    • \n
\n\n
Returns:
\n\n
\n

A dictionary where the key self.postmap_key indexes the parameters to try in the next trial.

\n
\n", "signature": "(self, system_output: Dict) -> Dict:", "funcdef": "def"}, "auto_disc.auto_disc.explorers.IMGEPExplorer.IMGEPExplorer.suggest_trial": {"fullname": "auto_disc.auto_disc.explorers.IMGEPExplorer.IMGEPExplorer.suggest_trial", "modulename": "auto_disc.auto_disc.explorers.IMGEPExplorer", "qualname": "IMGEPExplorer.suggest_trial", "kind": "function", "doc": "

Sample according to the policy a new trial of parameters for the\nsystem.

\n\n
Arguments:
\n\n
    \n

  • lookback_length: number of previous trials to consider when choosing the next\ntrial, i.e., it is a batch size based on the save frequency.

    \n\n

    Note that the default lookback_length = -1 will retrieve the\nentire history.

    • \n
\n\n
Returns:
\n\n
\n

A torch.Tensor containing the parameters to try.

\n
\n", "signature": "(self, lookback_length: int = -1) -> torch.Tensor:", "funcdef": "def"}, "auto_disc.auto_disc.explorers.IMGEPExplorer.IMGEPExplorer.observe_results": {"fullname": "auto_disc.auto_disc.explorers.IMGEPExplorer.IMGEPExplorer.observe_results", "modulename": "auto_disc.auto_disc.explorers.IMGEPExplorer", "qualname": "IMGEPExplorer.observe_results", "kind": "function", "doc": "

Read the raw output observed and process it into a discovered\nbehavior.

\n\n
Arguments:
\n\n
    \n
  • system_output: See arguments for .map.
    • \n
\n\n
Returns:
\n\n
\n

A dictionary of the observed behavior/feature vector associated with\n the raw system_output

\n
\n", "signature": "(self, system_output: Dict) -> Dict:", "funcdef": "def"}, "auto_disc.auto_disc.explorers.IMGEPExplorer.IMGEPExplorer.read_last_discovery": {"fullname": "auto_disc.auto_disc.explorers.IMGEPExplorer.IMGEPExplorer.read_last_discovery", "modulename": "auto_disc.auto_disc.explorers.IMGEPExplorer", "qualname": "IMGEPExplorer.read_last_discovery", "kind": "function", "doc": "

Return last observed discovery.

\n", "signature": "(self) -> Dict:", "funcdef": "def"}, "auto_disc.auto_disc.explorers.IMGEPExplorer.IMGEPExplorer.optimize": {"fullname": "auto_disc.auto_disc.explorers.IMGEPExplorer.IMGEPExplorer.optimize", "modulename": "auto_disc.auto_disc.explorers.IMGEPExplorer", "qualname": "IMGEPExplorer.optimize", "kind": "function", "doc": "

Run optimization step for online learning of the Explorer policy.

\n", "signature": "(self):", "funcdef": "def"}, "auto_disc.auto_disc.maps": {"fullname": "auto_disc.auto_disc.maps", "modulename": "auto_disc.auto_disc.maps", "kind": "module", "doc": "

See the documentation for the auto_disc.auto_disc.maps.Map abstract class for details.

\n"}, "auto_disc.auto_disc.maps.Map": {"fullname": "auto_disc.auto_disc.maps.Map", "modulename": "auto_disc.auto_disc.maps.Map", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.maps.Map.Map": {"fullname": "auto_disc.auto_disc.maps.Map.Map", "modulename": "auto_disc.auto_disc.maps.Map", "qualname": "Map", "kind": "class", "doc": "

An abstract class that defines the interface for a Map, and should\nbe inherited from by concrete implementations of Maps.

\n\n

A Map is a model for a function (in the mathematical sense, i.e., an\ninput-output relation) that is also stateful. It takes a payload of data as\ninput and returns an new payload of data as output, without mutating\nthe input.

\n\n
Attributes:
\n\n
    \n
  • premap_key: key in the input dict for the input (i.e., \"params\")
    • \n
  • postmap_key: key in the output dict for the output (i.e., \"output\")
    • \n
\n", "bases": "auto_disc.utils.leaf.Leaf.Leaf"}, "auto_disc.auto_disc.maps.Map.Map.__init__": {"fullname": "auto_disc.auto_disc.maps.Map.Map.__init__", "modulename": "auto_disc.auto_disc.maps.Map", "qualname": "Map.__init__", "kind": "function", "doc": "

Initialize the Map.

\n\n

A Map minimally sets the premap and postmap keys.

\n", "signature": "(premap_key: str = 'input', postmap_key: str = 'output')"}, "auto_disc.auto_disc.maps.Map.Map.map": {"fullname": "auto_disc.auto_disc.maps.Map.Map.map", "modulename": "auto_disc.auto_disc.maps.Map", "qualname": "Map.map", "kind": "function", "doc": "

Map input to output.

\n\n

A Map operates on regular Python dicts, but it views them as\nstructured. The premap_key and postmap_key are used to define\nthe structured elements that the Map operates on. Often, the\npostmap_key does not exist in the input dict, and is added by the\nMap as output.

\n\n

Whether or not the premap_key exists in the output dict is up to the\nimplementation of the specific Map. We recommend preserving it.

\n\n
Arguments:
\n\n
    \n
  • input: generic dict containing input data to the map at premap_key
    • \n
\n\n
Returns:
\n\n
\n

A dict containing output data from the map at postmap_key

\n
\n", "signature": "(self, input: Dict) -> Dict:", "funcdef": "def"}, "auto_disc.auto_disc.maps.Map.Map.sample": {"fullname": "auto_disc.auto_disc.maps.Map.Map.sample", "modulename": "auto_disc.auto_disc.maps.Map", "qualname": "Map.sample", "kind": "function", "doc": "

Samples from the state of the map, viewed as a stateful record of\ndata which passes through.

\n\n

Curiosity exploration requires that the Map objects are agents in\nsome sense which have a memory of what data has passed through. The\nway in which this is implemented in practice is that Map objects\nmay generally be stateful, often by tracking the history of data that\npasses through. Then, they implement a sample method which returns\na random sample of the latent representation they have encoded.

\n\n
Returns:
\n\n
\n

A random sample of the latent representation, in whatever format\n depending on the implementation of the Map

\n
\n", "signature": "(self) -> Any:", "funcdef": "def"}, "auto_disc.auto_disc.maps.MeanBehaviorMap": {"fullname": "auto_disc.auto_disc.maps.MeanBehaviorMap", "modulename": "auto_disc.auto_disc.maps.MeanBehaviorMap", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.maps.MeanBehaviorMap.MeanBehaviorMap": {"fullname": "auto_disc.auto_disc.maps.MeanBehaviorMap.MeanBehaviorMap", "modulename": "auto_disc.auto_disc.maps.MeanBehaviorMap", "qualname": "MeanBehaviorMap", "kind": "class", "doc": "

A simple BehaviorMap which merely extracts the mean.

\n", "bases": "auto_disc.auto_disc.maps.Map.Map"}, "auto_disc.auto_disc.maps.MeanBehaviorMap.MeanBehaviorMap.__init__": {"fullname": "auto_disc.auto_disc.maps.MeanBehaviorMap.MeanBehaviorMap.__init__", "modulename": "auto_disc.auto_disc.maps.MeanBehaviorMap", "qualname": "MeanBehaviorMap.__init__", "kind": "function", "doc": "

Initialize the Map.

\n\n

A Map minimally sets the premap and postmap keys.

\n", "signature": "(\tpremap_key: str = 'output',\tpostmap_key: str = 'output',\tinput_shape: Tuple[int] = 1)"}, "auto_disc.auto_disc.maps.MeanBehaviorMap.MeanBehaviorMap.map": {"fullname": "auto_disc.auto_disc.maps.MeanBehaviorMap.MeanBehaviorMap.map", "modulename": "auto_disc.auto_disc.maps.MeanBehaviorMap", "qualname": "MeanBehaviorMap.map", "kind": "function", "doc": "

Map input to output.

\n\n

A Map operates on regular Python dicts, but it views them as\nstructured. The premap_key and postmap_key are used to define\nthe structured elements that the Map operates on. Often, the\npostmap_key does not exist in the input dict, and is added by the\nMap as output.

\n\n

Whether or not the premap_key exists in the output dict is up to the\nimplementation of the specific Map. We recommend preserving it.

\n\n
Arguments:
\n\n
    \n
  • input: generic dict containing input data to the map at premap_key
    • \n
\n\n
Returns:
\n\n
\n

A dict containing output data from the map at postmap_key

\n
\n", "signature": "(self, input: Dict) -> Dict:", "funcdef": "def"}, "auto_disc.auto_disc.maps.MeanBehaviorMap.MeanBehaviorMap.sample": {"fullname": "auto_disc.auto_disc.maps.MeanBehaviorMap.MeanBehaviorMap.sample", "modulename": "auto_disc.auto_disc.maps.MeanBehaviorMap", "qualname": "MeanBehaviorMap.sample", "kind": "function", "doc": "

Samples from the state of the map, viewed as a stateful record of\ndata which passes through.

\n\n

Curiosity exploration requires that the Map objects are agents in\nsome sense which have a memory of what data has passed through. The\nway in which this is implemented in practice is that Map objects\nmay generally be stateful, often by tracking the history of data that\npasses through. Then, they implement a sample method which returns\na random sample of the latent representation they have encoded.

\n\n
Returns:
\n\n
\n

A random sample of the latent representation, in whatever format\n depending on the implementation of the Map

\n
\n", "signature": "(self) -> torch.Tensor:", "funcdef": "def"}, "auto_disc.auto_disc.maps.NEATParameterMap": {"fullname": "auto_disc.auto_disc.maps.NEATParameterMap", "modulename": "auto_disc.auto_disc.maps.NEATParameterMap", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.maps.NEATParameterMap.NEATParameterMap": {"fullname": "auto_disc.auto_disc.maps.NEATParameterMap.NEATParameterMap", "modulename": "auto_disc.auto_disc.maps.NEATParameterMap", "qualname": "NEATParameterMap", "kind": "class", "doc": "

An abstract class that defines the interface for a Map, and should\nbe inherited from by concrete implementations of Maps.

\n\n

A Map is a model for a function (in the mathematical sense, i.e., an\ninput-output relation) that is also stateful. It takes a payload of data as\ninput and returns an new payload of data as output, without mutating\nthe input.

\n\n
Attributes:
\n\n
    \n
  • premap_key: key in the input dict for the input (i.e., \"params\")
    • \n
  • postmap_key: key in the output dict for the output (i.e., \"output\")
    • \n
\n", "bases": "auto_disc.auto_disc.maps.Map.Map"}, "auto_disc.auto_disc.maps.NEATParameterMap.NEATParameterMap.__init__": {"fullname": "auto_disc.auto_disc.maps.NEATParameterMap.NEATParameterMap.__init__", "modulename": "auto_disc.auto_disc.maps.NEATParameterMap", "qualname": "NEATParameterMap.__init__", "kind": "function", "doc": "

Initialize the Map.

\n\n

A Map minimally sets the premap and postmap keys.

\n", "signature": "(\tpremap_key: str = 'genome',\tconfig_path: str = './config.cfg',\tconfig_str: Optional[str] = None)"}, "auto_disc.auto_disc.maps.NEATParameterMap.NEATParameterMap.map": {"fullname": "auto_disc.auto_disc.maps.NEATParameterMap.NEATParameterMap.map", "modulename": "auto_disc.auto_disc.maps.NEATParameterMap", "qualname": "NEATParameterMap.map", "kind": "function", "doc": "

Map input to output.

\n\n

A Map operates on regular Python dicts, but it views them as\nstructured. The premap_key and postmap_key are used to define\nthe structured elements that the Map operates on. Often, the\npostmap_key does not exist in the input dict, and is added by the\nMap as output.

\n\n

Whether or not the premap_key exists in the output dict is up to the\nimplementation of the specific Map. We recommend preserving it.

\n\n
Arguments:
\n\n
    \n
  • input: generic dict containing input data to the map at premap_key
    • \n
\n\n
Returns:
\n\n
\n

A dict containing output data from the map at postmap_key

\n
\n", "signature": "(self, input: Dict, override_existing: bool = True) -> Dict:", "funcdef": "def"}, "auto_disc.auto_disc.maps.NEATParameterMap.NEATParameterMap.sample": {"fullname": "auto_disc.auto_disc.maps.NEATParameterMap.NEATParameterMap.sample", "modulename": "auto_disc.auto_disc.maps.NEATParameterMap", "qualname": "NEATParameterMap.sample", "kind": "function", "doc": "

Samples a genome.

\n", "signature": "(self) -> Any:", "funcdef": "def"}, "auto_disc.auto_disc.maps.NEATParameterMap.NEATParameterMap.mutate": {"fullname": "auto_disc.auto_disc.maps.NEATParameterMap.NEATParameterMap.mutate", "modulename": "auto_disc.auto_disc.maps.NEATParameterMap", "qualname": "NEATParameterMap.mutate", "kind": "function", "doc": "

Mutates the genome in the provided dict.

\n", "signature": "(self, dict: Dict) -> Dict:", "funcdef": "def"}, "auto_disc.auto_disc.maps.UniformParameterMap": {"fullname": "auto_disc.auto_disc.maps.UniformParameterMap", "modulename": "auto_disc.auto_disc.maps.UniformParameterMap", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.maps.UniformParameterMap.UniformParameterMap": {"fullname": "auto_disc.auto_disc.maps.UniformParameterMap.UniformParameterMap", "modulename": "auto_disc.auto_disc.maps.UniformParameterMap", "qualname": "UniformParameterMap", "kind": "class", "doc": "

A simple ParameterMap which generates parameters according to a uniform\ndistribution over a box.

\n", "bases": "auto_disc.auto_disc.maps.Map.Map"}, "auto_disc.auto_disc.maps.UniformParameterMap.UniformParameterMap.__init__": {"fullname": "auto_disc.auto_disc.maps.UniformParameterMap.UniformParameterMap.__init__", "modulename": "auto_disc.auto_disc.maps.UniformParameterMap", "qualname": "UniformParameterMap.__init__", "kind": "function", "doc": "

Initialize the Map.

\n\n

A Map minimally sets the premap and postmap keys.

\n", "signature": "(\tpremap_key: str = 'params',\ttensor_low: Union[torch.Tensor, List[float]] = tensor([0.]),\ttensor_high: Union[torch.Tensor, List[float]] = tensor([0.]),\ttensor_bound_low: Union[torch.Tensor, List[float]] = tensor([-inf]),\ttensor_bound_high: Union[torch.Tensor, List[float]] = tensor([inf]),\toverride_existing: bool = True)"}, "auto_disc.auto_disc.maps.UniformParameterMap.UniformParameterMap.map": {"fullname": "auto_disc.auto_disc.maps.UniformParameterMap.UniformParameterMap.map", "modulename": "auto_disc.auto_disc.maps.UniformParameterMap", "qualname": "UniformParameterMap.map", "kind": "function", "doc": "

map() takes an input dict of metadata and adds the\nparams key with a sample if it does not exist

\n", "signature": "(self, input: Dict, override_existing: bool = True) -> Dict:", "funcdef": "def"}, "auto_disc.auto_disc.maps.UniformParameterMap.UniformParameterMap.sample": {"fullname": "auto_disc.auto_disc.maps.UniformParameterMap.UniformParameterMap.sample", "modulename": "auto_disc.auto_disc.maps.UniformParameterMap", "qualname": "UniformParameterMap.sample", "kind": "function", "doc": "

Samples from the state of the map, viewed as a stateful record of\ndata which passes through.

\n\n

Curiosity exploration requires that the Map objects are agents in\nsome sense which have a memory of what data has passed through. The\nway in which this is implemented in practice is that Map objects\nmay generally be stateful, often by tracking the history of data that\npasses through. Then, they implement a sample method which returns\na random sample of the latent representation they have encoded.

\n\n
Returns:
\n\n
\n

A random sample of the latent representation, in whatever format\n depending on the implementation of the Map

\n
\n", "signature": "(self) -> torch.Tensor:", "funcdef": "def"}, "auto_disc.auto_disc.maps.cppn": {"fullname": "auto_disc.auto_disc.maps.cppn", "modulename": "auto_disc.auto_disc.maps.cppn", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.maps.cppn.pytorchneat": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.maps.cppn.pytorchneat.activations": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat.activations", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat.activations", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.maps.cppn.pytorchneat.activations.delphineat_gauss_activation": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat.activations.delphineat_gauss_activation", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat.activations", "qualname": "delphineat_gauss_activation", "kind": "function", "doc": "

PyTorch implementation of gauss activation as defined by SharpNEAT, which is also as in DelphiNEAT.

\n", "signature": "(z):", "funcdef": "def"}, "auto_disc.auto_disc.maps.cppn.pytorchneat.activations.delphineat_sigmoid_activation": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat.activations.delphineat_sigmoid_activation", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat.activations", "qualname": "delphineat_sigmoid_activation", "kind": "function", "doc": "

PyTorch implementation of sigmoidal activation function as defined in DelphiNEAT

\n", "signature": "(z):", "funcdef": "def"}, "auto_disc.auto_disc.maps.cppn.pytorchneat.activations.tanh_activation": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat.activations.tanh_activation", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat.activations", "qualname": "tanh_activation", "kind": "function", "doc": "

\n", "signature": "(z):", "funcdef": "def"}, "auto_disc.auto_disc.maps.cppn.pytorchneat.activations.sin_activation": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat.activations.sin_activation", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat.activations", "qualname": "sin_activation", "kind": "function", "doc": "

\n", "signature": "(z):", "funcdef": "def"}, "auto_disc.auto_disc.maps.cppn.pytorchneat.activations.sigmoid_activation": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat.activations.sigmoid_activation", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat.activations", "qualname": "sigmoid_activation", "kind": "function", "doc": "

\n", "signature": "(z):", "funcdef": "def"}, "auto_disc.auto_disc.maps.cppn.pytorchneat.activations.gauss_activation": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat.activations.gauss_activation", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat.activations", "qualname": "gauss_activation", "kind": "function", "doc": "

\n", "signature": "(z):", "funcdef": "def"}, "auto_disc.auto_disc.maps.cppn.pytorchneat.activations.identity_activation": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat.activations.identity_activation", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat.activations", "qualname": "identity_activation", "kind": "function", "doc": "

\n", "signature": "(z):", "funcdef": "def"}, "auto_disc.auto_disc.maps.cppn.pytorchneat.activations.relu_activation": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat.activations.relu_activation", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat.activations", "qualname": "relu_activation", "kind": "function", "doc": "

\n", "signature": "(z):", "funcdef": "def"}, "auto_disc.auto_disc.maps.cppn.pytorchneat.aggregations": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat.aggregations", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat.aggregations", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.maps.cppn.pytorchneat.aggregations.sum_aggregation": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat.aggregations.sum_aggregation", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat.aggregations", "qualname": "sum_aggregation", "kind": "function", "doc": "

\n", "signature": "(inputs):", "funcdef": "def"}, "auto_disc.auto_disc.maps.cppn.pytorchneat.aggregations.product_aggregation": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat.aggregations.product_aggregation", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat.aggregations", "qualname": "product_aggregation", "kind": "function", "doc": "

\n", "signature": "(inputs):", "funcdef": "def"}, "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn.RecurrentNetwork": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn.RecurrentNetwork", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn", "qualname": "RecurrentNetwork", "kind": "class", "doc": "

Base class for all neural network modules.

\n\n

Your models should also subclass this class.

\n\n

Modules can also contain other Modules, allowing to nest them in\na tree structure. You can assign the submodules as regular attributes::

\n\n
import torch.nn as nn\nimport torch.nn.functional as F\n\nclass Model(nn.Module):\n    def __init__(self):\n        super().__init__()\n        self.conv1 = nn.Conv2d(1, 20, 5)\n        self.conv2 = nn.Conv2d(20, 20, 5)\n\n    def forward(self, x):\n        x = F.relu(self.conv1(x))\n        return F.relu(self.conv2(x))\n
\n\n

Submodules assigned in this way will be registered, and will have their\nparameters converted too when you call to(), etc.

\n\n
\n\n

As per the example above, an __init__() call to the parent class\nmust be made before assignment on the child.

\n\n
\n\n

:ivar training: Boolean represents whether this module is in training or\n evaluation mode.\n:vartype training: bool

\n", "bases": "torch.nn.modules.module.Module"}, "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn.RecurrentNetwork.__init__": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn.RecurrentNetwork.__init__", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn", "qualname": "RecurrentNetwork.__init__", "kind": "function", "doc": "
Parameters
\n\n
    \n
  • input_neuron_ids: list of ids of input neurons
    • \n
  • hidden_neuron_ids: list of ids of hidden neurons
    • \n
  • output_neuron_ids: list of ids of output neurons
    • \n
\n", "signature": "(\tinput_neuron_ids,\thidden_neuron_ids,\toutput_neuron_ids,\tbiases,\tresponses,\tactivations,\tconnections,\tdevice='cpu')"}, "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn.RecurrentNetwork.forward": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn.RecurrentNetwork.forward", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn", "qualname": "RecurrentNetwork.forward", "kind": "function", "doc": "
Parameters
\n\n
    \n
  • inputs: tensor of size (batch_size, n_inputs)\nNote: output of a node as follows: activation(bias+(response\u2217aggregation(inputs)))\nreturns: (batch_size, n_outputs)
    • \n
\n", "signature": "(self, inputs):", "funcdef": "def"}, "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn.RecurrentNetwork.activate": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn.RecurrentNetwork.activate", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn", "qualname": "RecurrentNetwork.activate", "kind": "function", "doc": "
Parameters
\n\n
    \n
  • inputs: tensor of size (..., n_inputs) => only the last dim matter, then CPPN is applied on all coordinates
    • \n
  • n_passes: number of passes in the RNN TODO: currently global passes should it be internal passes as in pytorch-neat by uber research ?\nreturns: (..., n_outputs)
    • \n
\n", "signature": "(self, inputs, n_passes=1):", "funcdef": "def"}, "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn.RecurrentNetwork.draw_net": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn.RecurrentNetwork.draw_net", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn", "qualname": "RecurrentNetwork.draw_net", "kind": "function", "doc": "

\n", "signature": "(\tself,\tview=False,\tfilename=None,\tnode_names=None,\tnode_colors=None,\tfmt='svg'):", "funcdef": "def"}, "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn.RecurrentNetwork.create": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn.RecurrentNetwork.create", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn", "qualname": "RecurrentNetwork.create", "kind": "function", "doc": "

Receives a genome and returns the RecurrentCPPN()

\n", "signature": "(genome, config, device='cpu'):", "funcdef": "def"}, "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn.RecurrentNetwork.update_genome": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn.RecurrentNetwork.update_genome", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn", "qualname": "RecurrentNetwork.update_genome", "kind": "function", "doc": "

\n", "signature": "(self, genome):", "funcdef": "def"}, "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn.str_to_tuple_key": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn.str_to_tuple_key", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn", "qualname": "str_to_tuple_key", "kind": "function", "doc": "

\n", "signature": "(str_key):", "funcdef": "def"}, "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn.tuple_to_str_key": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn.tuple_to_str_key", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn", "qualname": "tuple_to_str_key", "kind": "function", "doc": "

\n", "signature": "(tuple_key):", "funcdef": "def"}, "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn.RecurrentNetworkAgg": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn.RecurrentNetworkAgg", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn", "qualname": "RecurrentNetworkAgg", "kind": "class", "doc": "

Base class for all neural network modules.

\n\n

Your models should also subclass this class.

\n\n

Modules can also contain other Modules, allowing to nest them in\na tree structure. You can assign the submodules as regular attributes::

\n\n
import torch.nn as nn\nimport torch.nn.functional as F\n\nclass Model(nn.Module):\n    def __init__(self):\n        super().__init__()\n        self.conv1 = nn.Conv2d(1, 20, 5)\n        self.conv2 = nn.Conv2d(20, 20, 5)\n\n    def forward(self, x):\n        x = F.relu(self.conv1(x))\n        return F.relu(self.conv2(x))\n
\n\n

Submodules assigned in this way will be registered, and will have their\nparameters converted too when you call to(), etc.

\n\n
\n\n

As per the example above, an __init__() call to the parent class\nmust be made before assignment on the child.

\n\n
\n\n

:ivar training: Boolean represents whether this module is in training or\n evaluation mode.\n:vartype training: bool

\n", "bases": "torch.nn.modules.module.Module"}, "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn.RecurrentNetworkAgg.__init__": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn.RecurrentNetworkAgg.__init__", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn", "qualname": "RecurrentNetworkAgg.__init__", "kind": "function", "doc": "
Parameters
\n\n
    \n
  • input_neuron_ids: list of ids of input neurons
    • \n
  • hidden_neuron_ids: list of ids of hidden neurons
    • \n
  • output_neuron_ids: list of ids of output neurons
    • \n
\n", "signature": "(\tinput_neuron_ids,\thidden_neuron_ids,\toutput_neuron_ids,\tbiases,\tresponses,\tactivations,\taggregations,\tconnections,\tdevice='cpu')"}, "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn.RecurrentNetworkAgg.forward": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn.RecurrentNetworkAgg.forward", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn", "qualname": "RecurrentNetworkAgg.forward", "kind": "function", "doc": "
Parameters
\n\n
    \n
  • inputs: tensor of size (batch_size, n_inputs)
    • \n
\n\n

Note: output of a node as follows: activation(bias+(response\u2217aggregation(inputs)))

\n\n

returns: (batch_size, n_outputs)

\n", "signature": "(self, inputs):", "funcdef": "def"}, "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn.RecurrentNetworkAgg.activate": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn.RecurrentNetworkAgg.activate", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn", "qualname": "RecurrentNetworkAgg.activate", "kind": "function", "doc": "
Parameters
\n\n
    \n
  • inputs: tensor of size (..., n_inputs) => only the last dim matter, then CPPN is applied on all coordinates
    • \n
  • n_passes: number of passes in the RNN TODO: currently global passes should it be internal passes as in pytorch-neat by uber research ?\nreturns: (..., n_outputs)
    • \n
\n", "signature": "(self, inputs, n_passes=1):", "funcdef": "def"}, "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn.RecurrentNetworkAgg.draw_net": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn.RecurrentNetworkAgg.draw_net", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn", "qualname": "RecurrentNetworkAgg.draw_net", "kind": "function", "doc": "

\n", "signature": "(\tself,\tconfig=None,\tview=False,\tfilename=None,\tnode_names=None,\tnode_colors=None,\tfmt='svg'):", "funcdef": "def"}, "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn.RecurrentNetworkAgg.create": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn.RecurrentNetworkAgg.create", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn", "qualname": "RecurrentNetworkAgg.create", "kind": "function", "doc": "

Receives a genome and returns the RecurrentCPPN()

\n", "signature": "(genome, config, device='cpu'):", "funcdef": "def"}, "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn.RecurrentNetworkAgg.update_genome": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn.RecurrentNetworkAgg.update_genome", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn", "qualname": "RecurrentNetworkAgg.update_genome", "kind": "function", "doc": "

\n", "signature": "(self, genome):", "funcdef": "def"}, "auto_disc.auto_disc.maps.cppn.pytorchneat.selfconnectiongenome": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat.selfconnectiongenome", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat.selfconnectiongenome", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.maps.cppn.pytorchneat.selfconnectiongenome.ConnectionWeightAttribute": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat.selfconnectiongenome.ConnectionWeightAttribute", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat.selfconnectiongenome", "qualname": "ConnectionWeightAttribute", "kind": "class", "doc": "

Class for connection weight attributes. Is introduced to allow a differentiation between self connections of a node and connections to other nodes.

\n", "bases": "neat.attributes.BaseAttribute"}, "auto_disc.auto_disc.maps.cppn.pytorchneat.selfconnectiongenome.ConnectionWeightAttribute.clamp": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat.selfconnectiongenome.ConnectionWeightAttribute.clamp", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat.selfconnectiongenome", "qualname": "ConnectionWeightAttribute.clamp", "kind": "function", "doc": "

\n", "signature": "(self, value, is_other_connect, config):", "funcdef": "def"}, "auto_disc.auto_disc.maps.cppn.pytorchneat.selfconnectiongenome.ConnectionWeightAttribute.init_value": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat.selfconnectiongenome.ConnectionWeightAttribute.init_value", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat.selfconnectiongenome", "qualname": "ConnectionWeightAttribute.init_value", "kind": "function", "doc": "

\n", "signature": "(self, is_other_connect, config):", "funcdef": "def"}, "auto_disc.auto_disc.maps.cppn.pytorchneat.selfconnectiongenome.ConnectionWeightAttribute.mutate_value": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat.selfconnectiongenome.ConnectionWeightAttribute.mutate_value", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat.selfconnectiongenome", "qualname": "ConnectionWeightAttribute.mutate_value", "kind": "function", "doc": "

\n", "signature": "(self, value, is_other_connect, config):", "funcdef": "def"}, "auto_disc.auto_disc.maps.cppn.pytorchneat.selfconnectiongenome.ConnectionWeightAttribute.validate": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat.selfconnectiongenome.ConnectionWeightAttribute.validate", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat.selfconnectiongenome", "qualname": "ConnectionWeightAttribute.validate", "kind": "function", "doc": "

\n", "signature": "(self, config):", "funcdef": "def"}, "auto_disc.auto_disc.maps.cppn.pytorchneat.selfconnectiongenome.SelfConnectionGene": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat.selfconnectiongenome.SelfConnectionGene", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat.selfconnectiongenome", "qualname": "SelfConnectionGene", "kind": "class", "doc": "

Handles functions shared by multiple types of genes (both node and connection),\nincluding crossover and calling mutation methods.

\n", "bases": "neat.genes.BaseGene"}, "auto_disc.auto_disc.maps.cppn.pytorchneat.selfconnectiongenome.SelfConnectionGene.__init__": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat.selfconnectiongenome.SelfConnectionGene.__init__", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat.selfconnectiongenome", "qualname": "SelfConnectionGene.__init__", "kind": "function", "doc": "

\n", "signature": "(key)"}, "auto_disc.auto_disc.maps.cppn.pytorchneat.selfconnectiongenome.SelfConnectionGene.distance": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat.selfconnectiongenome.SelfConnectionGene.distance", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat.selfconnectiongenome", "qualname": "SelfConnectionGene.distance", "kind": "function", "doc": "

\n", "signature": "(self, other, config):", "funcdef": "def"}, "auto_disc.auto_disc.maps.cppn.pytorchneat.selfconnectiongenome.SelfConnectionGene.init_attributes": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat.selfconnectiongenome.SelfConnectionGene.init_attributes", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat.selfconnectiongenome", "qualname": "SelfConnectionGene.init_attributes", "kind": "function", "doc": "

\n", "signature": "(self, config):", "funcdef": "def"}, "auto_disc.auto_disc.maps.cppn.pytorchneat.selfconnectiongenome.SelfConnectionGene.mutate": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat.selfconnectiongenome.SelfConnectionGene.mutate", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat.selfconnectiongenome", "qualname": "SelfConnectionGene.mutate", "kind": "function", "doc": "

\n", "signature": "(self, config):", "funcdef": "def"}, "auto_disc.auto_disc.maps.cppn.pytorchneat.selfconnectiongenome.SelfConnectionGenome": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat.selfconnectiongenome.SelfConnectionGenome", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat.selfconnectiongenome", "qualname": "SelfConnectionGenome", "kind": "class", "doc": "

A genome for generalized neural networks. Derived from neat.DefaultGenome.\nExtends the default genome to allow specific configuration for self connections of neurons.

\n\n

Uses the SelfConnectionGene class instead of the neat.genes.DefaultConnectionGene class for connections.

\n\n

Terminology\n pin: Point at which the network is conceptually connected to the external world;\n pins are either input or output.\n node: Analog of a physical neuron.\n connection: Connection between a pin/node output and a node's input, or between a node's\n output and a pin/node input.\n key: Identifier for an object, unique within the set of similar objects.

\n\n

Design assumptions and conventions.\n 1. Each output pin is connected only to the output of its own unique\n neuron by an implicit connection with weight one. This connection\n is permanently enabled.\n 2. The output pin's key is always the same as the key for its\n associated neuron.\n 3. Output neurons can be modified but not deleted.\n 4. The input values are applied to the input pins unmodified.

\n", "bases": "neat.genome.DefaultGenome"}, "auto_disc.auto_disc.maps.cppn.pytorchneat.selfconnectiongenome.SelfConnectionGenome.parse_config": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat.selfconnectiongenome.SelfConnectionGenome.parse_config", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat.selfconnectiongenome", "qualname": "SelfConnectionGenome.parse_config", "kind": "function", "doc": "

\n", "signature": "(cls, param_dict):", "funcdef": "def"}, "auto_disc.auto_disc.maps.cppn.pytorchneat.utils": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat.utils", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat.utils", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.maps.cppn.pytorchneat.utils.create_image_cppn_input": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat.utils.create_image_cppn_input", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat.utils", "qualname": "create_image_cppn_input", "kind": "function", "doc": "

\n", "signature": "(output_size, is_distance_to_center=True, is_bias=True):", "funcdef": "def"}, "auto_disc.auto_disc.maps.lenia": {"fullname": "auto_disc.auto_disc.maps.lenia", "modulename": "auto_disc.auto_disc.maps.lenia", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.maps.lenia.LeniaParameterMap": {"fullname": "auto_disc.auto_disc.maps.lenia.LeniaParameterMap", "modulename": "auto_disc.auto_disc.maps.lenia.LeniaParameterMap", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.maps.lenia.LeniaParameterMap.LeniaHyperParameters": {"fullname": "auto_disc.auto_disc.maps.lenia.LeniaParameterMap.LeniaHyperParameters", "modulename": "auto_disc.auto_disc.maps.lenia.LeniaParameterMap", "qualname": "LeniaHyperParameters", "kind": "class", "doc": "

Holds parameters to initialize Lenia model.

\n"}, "auto_disc.auto_disc.maps.lenia.LeniaParameterMap.LeniaHyperParameters.__init__": {"fullname": "auto_disc.auto_disc.maps.lenia.LeniaParameterMap.LeniaHyperParameters.__init__", "modulename": "auto_disc.auto_disc.maps.lenia.LeniaParameterMap", "qualname": "LeniaHyperParameters.__init__", "kind": "function", "doc": "

\n", "signature": "(\ttensor_low: torch.Tensor = tensor([0.0000, 1.0000, 0.0000, 0.0010, 0.0000, 0.0000, 0.0000, 0.0000]),\ttensor_high: torch.Tensor = tensor([0.0000, 1.0000, 0.0000, 0.0010, 0.0000, 0.0000, 0.0000, 0.0000]),\ttensor_bound_low: torch.Tensor = tensor([0.0000, 1.0000, 0.0000, 0.0010, 0.0000, 0.0000, 0.0000, 0.0000]),\ttensor_bound_high: torch.Tensor = tensor([20.0000, 20.0000, 1.0000, 0.3000, 1.0000, 1.0000, 1.0000, 1.0000]),\tinit_state_dim: Tuple[int, int] = (10, 10),\tcppn_n_passes: int = 2)"}, "auto_disc.auto_disc.maps.lenia.LeniaParameterMap.LeniaParameterMap": {"fullname": "auto_disc.auto_disc.maps.lenia.LeniaParameterMap.LeniaParameterMap", "modulename": "auto_disc.auto_disc.maps.lenia.LeniaParameterMap", "qualname": "LeniaParameterMap", "kind": "class", "doc": "

Due to the complexities of initializing Lenia parameters,\nit's easier to make this custom parameter map.

\n", "bases": "auto_disc.utils.leaf.Leaf.Leaf"}, "auto_disc.auto_disc.maps.lenia.LeniaParameterMap.LeniaParameterMap.__init__": {"fullname": "auto_disc.auto_disc.maps.lenia.LeniaParameterMap.LeniaParameterMap.__init__", "modulename": "auto_disc.auto_disc.maps.lenia.LeniaParameterMap", "qualname": "LeniaParameterMap.__init__", "kind": "function", "doc": "

\n", "signature": "(\tpremap_key: str = 'params',\tparam_obj: auto_disc.auto_disc.maps.lenia.LeniaParameterMap.LeniaHyperParameters = LeniaHyperParameters(tensor_low=tensor([0.0000, 1.0000, 0.0000, 0.0010, 0.0000, 0.0000, 0.0000, 0.0000]), tensor_high=tensor([0.0000, 1.0000, 0.0000, 0.0010, 0.0000, 0.0000, 0.0000, 0.0000]), tensor_bound_low=tensor([0.0000, 1.0000, 0.0000, 0.0010, 0.0000, 0.0000, 0.0000, 0.0000]), tensor_bound_high=tensor([20.0000, 20.0000, 1.0000, 0.3000, 1.0000, 1.0000, 1.0000, 1.0000]), init_state_dim=(10, 10), cppn_n_passes=2),\tneat_config_path: str = './maps/cppn/config.cfg',\tneat_config_str: Optional[str] = None,\t**config_decorator_kwargs)"}, "auto_disc.auto_disc.maps.lenia.LeniaParameterMap.LeniaParameterMap.map": {"fullname": "auto_disc.auto_disc.maps.lenia.LeniaParameterMap.LeniaParameterMap.map", "modulename": "auto_disc.auto_disc.maps.lenia.LeniaParameterMap", "qualname": "LeniaParameterMap.map", "kind": "function", "doc": "

Takes input dictionary and overrides the\npremap_key with generated parameters for Lenia.

\n", "signature": "(self, input: Dict, override_existing: bool = True) -> Dict:", "funcdef": "def"}, "auto_disc.auto_disc.maps.lenia.LeniaParameterMap.LeniaParameterMap.sample": {"fullname": "auto_disc.auto_disc.maps.lenia.LeniaParameterMap.LeniaParameterMap.sample", "modulename": "auto_disc.auto_disc.maps.lenia.LeniaParameterMap", "qualname": "LeniaParameterMap.sample", "kind": "function", "doc": "

Samples from the parameter map to yield the parameters ready\nfor processing by the model (LeniaCPPN), i.e., including the genome.

\n", "signature": "(self) -> Dict:", "funcdef": "def"}, "auto_disc.auto_disc.maps.lenia.LeniaParameterMap.LeniaParameterMap.mutate": {"fullname": "auto_disc.auto_disc.maps.lenia.LeniaParameterMap.LeniaParameterMap.mutate", "modulename": "auto_disc.auto_disc.maps.lenia.LeniaParameterMap", "qualname": "LeniaParameterMap.mutate", "kind": "function", "doc": "

Takes the dictionary of only parameters outputted by\nthe explorer and mutates them.

\n", "signature": "(self, parameter_dict: Dict) -> Dict:", "funcdef": "def"}, "auto_disc.auto_disc.maps.lenia.LeniaStatistics": {"fullname": "auto_disc.auto_disc.maps.lenia.LeniaStatistics", "modulename": "auto_disc.auto_disc.maps.lenia.LeniaStatistics", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.maps.lenia.LeniaStatistics.center_of_mass": {"fullname": "auto_disc.auto_disc.maps.lenia.LeniaStatistics.center_of_mass", "modulename": "auto_disc.auto_disc.maps.lenia.LeniaStatistics", "qualname": "center_of_mass", "kind": "function", "doc": "

\n", "signature": "(input_array: torch.Tensor) -> torch.Tensor:", "funcdef": "def"}, "auto_disc.auto_disc.maps.lenia.LeniaStatistics.calc_distance_matrix": {"fullname": "auto_disc.auto_disc.maps.lenia.LeniaStatistics.calc_distance_matrix", "modulename": "auto_disc.auto_disc.maps.lenia.LeniaStatistics", "qualname": "calc_distance_matrix", "kind": "function", "doc": "

\n", "signature": "(size_y: int, size_x: int) -> torch.Tensor:", "funcdef": "def"}, "auto_disc.auto_disc.maps.lenia.LeniaStatistics.calc_image_moments": {"fullname": "auto_disc.auto_disc.maps.lenia.LeniaStatistics.calc_image_moments", "modulename": "auto_disc.auto_disc.maps.lenia.LeniaStatistics", "qualname": "calc_image_moments", "kind": "function", "doc": "

Calculates the image moments for an image.

\n\n
For more information see:
\n\n
\n \n
\n\n

The code is based on the Javascript implementation of Lenia by Bert Chan.\nThe code had to be adapted, because for the original code the coordinates are (x,y), whereas here they are (y,x).

\n\n
Parameters
\n\n
    \n
  • image: 2d gray scale image in form of a numpy array.
    • \n
\n\n
Returns
\n\n
\n

Namedtupel with the different moments.

\n
\n", "signature": "(image: torch.Tensor) -> Dict[str, torch.Tensor]:", "funcdef": "def"}, "auto_disc.auto_disc.maps.lenia.LeniaStatistics.LeniaStatistics": {"fullname": "auto_disc.auto_disc.maps.lenia.LeniaStatistics.LeniaStatistics", "modulename": "auto_disc.auto_disc.maps.lenia.LeniaStatistics", "qualname": "LeniaStatistics", "kind": "class", "doc": "

Outputs 17-dimensional embedding.

\n", "bases": "auto_disc.utils.leaf.Leaf.Leaf"}, "auto_disc.auto_disc.maps.lenia.LeniaStatistics.LeniaStatistics.__init__": {"fullname": "auto_disc.auto_disc.maps.lenia.LeniaStatistics.LeniaStatistics.__init__", "modulename": "auto_disc.auto_disc.maps.lenia.LeniaStatistics", "qualname": "LeniaStatistics.__init__", "kind": "function", "doc": "

\n", "signature": "(\tpremap_key: str = 'output',\tpostmap_key: str = 'output',\tSX: int = 256,\tSY: int = 256)"}, "auto_disc.auto_disc.maps.lenia.LeniaStatistics.LeniaStatistics.map": {"fullname": "auto_disc.auto_disc.maps.lenia.LeniaStatistics.LeniaStatistics.map", "modulename": "auto_disc.auto_disc.maps.lenia.LeniaStatistics", "qualname": "LeniaStatistics.map", "kind": "function", "doc": "

Compute statistics on Lenia's output

\n\n
Arguments:
\n\n
    \n
  • input: Lenia's output
    • \n
  • is_output_new_discovery: indicates if it is a new discovery
    • \n
\n\n
Returns:
\n\n
\n

Return a torch tensor in dict

\n
\n", "signature": "(self, input: Dict) -> Dict:", "funcdef": "def"}, "auto_disc.auto_disc.maps.lenia.LeniaStatistics.LeniaStatistics.sample": {"fullname": "auto_disc.auto_disc.maps.lenia.LeniaStatistics.LeniaStatistics.sample", "modulename": "auto_disc.auto_disc.maps.lenia.LeniaStatistics", "qualname": "LeniaStatistics.sample", "kind": "function", "doc": "

\n", "signature": "(self):", "funcdef": "def"}, "auto_disc.auto_disc.systems": {"fullname": "auto_disc.auto_disc.systems", "modulename": "auto_disc.auto_disc.systems", "kind": "module", "doc": "

See the documentation for the auto_disc.auto_disc.systems.System abstract class for details.

\n"}, "auto_disc.auto_disc.systems.System": {"fullname": "auto_disc.auto_disc.systems.System", "modulename": "auto_disc.auto_disc.systems.System", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.systems.System.System": {"fullname": "auto_disc.auto_disc.systems.System.System", "modulename": "auto_disc.auto_disc.systems.System", "qualname": "System", "kind": "class", "doc": "

An abstract class that defines the interface for a System, and should\nbe inherited from by concrete implementations of Systems.

\n\n

A System is a model for a dynamical or complex system. In the context\nof the experimental pipeline, it should be stateless and therefore a pure\nfunction. Therefore, while the system parameters may need to be set,\nthese are in fact _hyperparameters_ set by the web interface, and not\nparameters which are explored by the curiosity algorithm.

\n\n

As a pure function, it takes a payload of data as input and returns an\nnew payload of data as output, without mutating the input.

\n", "bases": "auto_disc.utils.leaf.Leaf.Leaf"}, "auto_disc.auto_disc.systems.System.System.__init__": {"fullname": "auto_disc.auto_disc.systems.System.System.__init__", "modulename": "auto_disc.auto_disc.systems.System", "qualname": "System.__init__", "kind": "function", "doc": "

There are no assumptions on the initialization parameters.

\n", "signature": "(*args, **kwargs)"}, "auto_disc.auto_disc.systems.System.System.map": {"fullname": "auto_disc.auto_disc.systems.System.System.map", "modulename": "auto_disc.auto_disc.systems.System", "qualname": "System.map", "kind": "function", "doc": "

Map system inputs to outputs.

\n\n

A System operates on regular Python dicts, but it views them as\nstructured. The premap_key and postmap_key are used to define\nthe structured elements that the System operates on. Often, the\npostmap_key does not exist in the input dict, and is added by the\nMap as output.

\n\n

Whether or not the premap_key exists in the output dict is up to the\nimplementation of the specific System. We recommend preserving it.

\n\n
Arguments:
\n\n
    \n
  • input (dict): generic dict containing input data to the map at premap_key
    • \n
\n\n
Returns:
\n\n
\n

A generic dict containing output data from the map at postmap_key

\n
\n", "signature": "(self, input: Dict) -> Dict:", "funcdef": "def"}, "auto_disc.auto_disc.systems.System.System.render": {"fullname": "auto_disc.auto_disc.systems.System.System.render", "modulename": "auto_disc.auto_disc.systems.System", "qualname": "System.render", "kind": "function", "doc": "

Render system output as an image or video.

\n\n

A System should be able to render useful information about its\nexecution (e.g., a plot, a video, etc.), which may depend on its\ninternal state which is not captured by the map method.

\n\n
Arguments:
\n\n
    \n
  • self (System): If the System can render a graphical output without\nneeding access to its internal state (i.e., only from the\noutput), then it can also be @classmethod.
    • \n
  • data_dict (dict): A dict containing the output of the map\nmethod. Depending on implementation, this may be sufficient to\nrender the output or one may need stateful data from self.
    • \n
\n", "signature": "(self, data_dict: dict) -> bytes:", "funcdef": "def"}, "auto_disc.auto_disc.utils": {"fullname": "auto_disc.auto_disc.utils", "modulename": "auto_disc.auto_disc.utils", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.utils.callbacks": {"fullname": "auto_disc.auto_disc.utils.callbacks", "modulename": "auto_disc.auto_disc.utils.callbacks", "kind": "module", "doc": "

At certain defined points in the exploration loop, the software executes\ncallbacks which may be user defined. Such callbacks are mostly used for doing\nenvironment setup and teardown, for saving output, or for accepting user input.

\n\n

For details on when each callback is called, look in the source for\nauto_disc.ExperimentPipeline

\n"}, "auto_disc.auto_disc.utils.callbacks.interact_callbacks": {"fullname": "auto_disc.auto_disc.utils.callbacks.interact_callbacks", "modulename": "auto_disc.auto_disc.utils.callbacks.interact_callbacks", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.utils.callbacks.interact_callbacks.base_interact_callback": {"fullname": "auto_disc.auto_disc.utils.callbacks.interact_callbacks.base_interact_callback", "modulename": "auto_disc.auto_disc.utils.callbacks.interact_callbacks.base_interact_callback", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.utils.callbacks.interact_callbacks.base_interact_callback.Object": {"fullname": "auto_disc.auto_disc.utils.callbacks.interact_callbacks.base_interact_callback.Object", "modulename": "auto_disc.auto_disc.utils.callbacks.interact_callbacks.base_interact_callback", "qualname": "Object", "kind": "function", "doc": "

\n", "signature": "(**kwargs):", "funcdef": "def"}, "auto_disc.auto_disc.utils.callbacks.interact_callbacks.base_interact_callback.Interaction": {"fullname": "auto_disc.auto_disc.utils.callbacks.interact_callbacks.base_interact_callback.Interaction", "modulename": "auto_disc.auto_disc.utils.callbacks.interact_callbacks.base_interact_callback", "qualname": "Interaction", "kind": "class", "doc": "

\n"}, "auto_disc.auto_disc.utils.callbacks.interact_callbacks.base_interact_callback.Interaction.init_seed": {"fullname": "auto_disc.auto_disc.utils.callbacks.interact_callbacks.base_interact_callback.Interaction.init_seed", "modulename": "auto_disc.auto_disc.utils.callbacks.interact_callbacks.base_interact_callback", "qualname": "Interaction.init_seed", "kind": "function", "doc": "

\n", "signature": "(self, interact_callbacks, config):", "funcdef": "def"}, "auto_disc.auto_disc.utils.callbacks.interact_callbacks.base_interact_callback.Interaction.save": {"fullname": "auto_disc.auto_disc.utils.callbacks.interact_callbacks.base_interact_callback.Interaction.save", "modulename": "auto_disc.auto_disc.utils.callbacks.interact_callbacks.base_interact_callback", "qualname": "Interaction.save", "kind": "function", "doc": "

\n", "signature": "(self, data, dict_info=None):", "funcdef": "def"}, "auto_disc.auto_disc.utils.callbacks.interact_callbacks.base_interact_callback.Interaction.read": {"fullname": "auto_disc.auto_disc.utils.callbacks.interact_callbacks.base_interact_callback.Interaction.read", "modulename": "auto_disc.auto_disc.utils.callbacks.interact_callbacks.base_interact_callback", "qualname": "Interaction.read", "kind": "function", "doc": "

\n", "signature": "(self, filter_attribut=None):", "funcdef": "def"}, "auto_disc.auto_disc.utils.callbacks.interact_callbacks.base_interact_callback.Interaction.feedback": {"fullname": "auto_disc.auto_disc.utils.callbacks.interact_callbacks.base_interact_callback.Interaction.feedback", "modulename": "auto_disc.auto_disc.utils.callbacks.interact_callbacks.base_interact_callback", "qualname": "Interaction.feedback", "kind": "function", "doc": "

\n", "signature": "(self, data, dict_info=None, dict_value_to_change=None):", "funcdef": "def"}, "auto_disc.auto_disc.utils.callbacks.interact_callbacks.base_interact_callback.BaseInteractCallback": {"fullname": "auto_disc.auto_disc.utils.callbacks.interact_callbacks.base_interact_callback.BaseInteractCallback", "modulename": "auto_disc.auto_disc.utils.callbacks.interact_callbacks.base_interact_callback", "qualname": "BaseInteractCallback", "kind": "class", "doc": "

Base class for cancelled callbacks used by the experiment pipelines when the experiment was cancelled.

\n", "bases": "auto_disc.legacy.utils.callbacks.base_callback.BaseCallback"}, "auto_disc.auto_disc.utils.callbacks.interact_callbacks.base_interact_callback.BaseInteractCallback.__init__": {"fullname": "auto_disc.auto_disc.utils.callbacks.interact_callbacks.base_interact_callback.BaseInteractCallback.__init__", "modulename": "auto_disc.auto_disc.utils.callbacks.interact_callbacks.base_interact_callback", "qualname": "BaseInteractCallback.__init__", "kind": "function", "doc": "

initialize attributes common to all cancelled callbacks

\n\n
Arguments:
\n\n
    \n
  • kwargs: some usefull args (e.g. experiment_id...)
    • \n
\n", "signature": "(**kwargs)"}, "auto_disc.auto_disc.utils.callbacks.interact_callbacks.read_disk_interact_callback": {"fullname": "auto_disc.auto_disc.utils.callbacks.interact_callbacks.read_disk_interact_callback", "modulename": "auto_disc.auto_disc.utils.callbacks.interact_callbacks.read_disk_interact_callback", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.utils.callbacks.interact_callbacks.read_disk_interact_callback.ReadDiskInteractCallback": {"fullname": "auto_disc.auto_disc.utils.callbacks.interact_callbacks.read_disk_interact_callback.ReadDiskInteractCallback", "modulename": "auto_disc.auto_disc.utils.callbacks.interact_callbacks.read_disk_interact_callback", "qualname": "ReadDiskInteractCallback", "kind": "class", "doc": "

Base class for cancelled callbacks used by the experiment pipelines when the experiment was cancelled.

\n", "bases": "auto_disc.legacy.utils.callbacks.interact_callbacks.base_interact_callback.BaseInteractCallback"}, "auto_disc.auto_disc.utils.callbacks.interact_callbacks.read_disk_interact_callback.ReadDiskInteractCallback.__init__": {"fullname": "auto_disc.auto_disc.utils.callbacks.interact_callbacks.read_disk_interact_callback.ReadDiskInteractCallback.__init__", "modulename": "auto_disc.auto_disc.utils.callbacks.interact_callbacks.read_disk_interact_callback", "qualname": "ReadDiskInteractCallback.__init__", "kind": "function", "doc": "

initialize attributes common to all cancelled callbacks

\n\n
Arguments:
\n\n
    \n
  • kwargs: some usefull args (e.g. experiment_id...)
    • \n
\n", "signature": "(**kwargs)"}, "auto_disc.auto_disc.utils.callbacks.interact_callbacks.save_disk_interact_callback": {"fullname": "auto_disc.auto_disc.utils.callbacks.interact_callbacks.save_disk_interact_callback", "modulename": "auto_disc.auto_disc.utils.callbacks.interact_callbacks.save_disk_interact_callback", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.utils.callbacks.interact_callbacks.save_disk_interact_callback.SaveDiskInteractCallback": {"fullname": "auto_disc.auto_disc.utils.callbacks.interact_callbacks.save_disk_interact_callback.SaveDiskInteractCallback", "modulename": "auto_disc.auto_disc.utils.callbacks.interact_callbacks.save_disk_interact_callback", "qualname": "SaveDiskInteractCallback", "kind": "class", "doc": "

Base class for cancelled callbacks used by the experiment pipelines when the experiment was cancelled.

\n", "bases": "auto_disc.legacy.utils.callbacks.interact_callbacks.base_interact_callback.BaseInteractCallback"}, "auto_disc.auto_disc.utils.callbacks.interact_callbacks.save_disk_interact_callback.SaveDiskInteractCallback.__init__": {"fullname": "auto_disc.auto_disc.utils.callbacks.interact_callbacks.save_disk_interact_callback.SaveDiskInteractCallback.__init__", "modulename": "auto_disc.auto_disc.utils.callbacks.interact_callbacks.save_disk_interact_callback", "qualname": "SaveDiskInteractCallback.__init__", "kind": "function", "doc": "

initialize attributes common to all cancelled callbacks

\n\n
Arguments:
\n\n
    \n
  • kwargs: some usefull args (e.g. experiment_id...)
    • \n
\n", "signature": "(**kwargs)"}, "auto_disc.auto_disc.utils.callbacks.on_cancelled_callbacks": {"fullname": "auto_disc.auto_disc.utils.callbacks.on_cancelled_callbacks", "modulename": "auto_disc.auto_disc.utils.callbacks.on_cancelled_callbacks", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.utils.callbacks.on_discovery_callbacks": {"fullname": "auto_disc.auto_disc.utils.callbacks.on_discovery_callbacks", "modulename": "auto_disc.auto_disc.utils.callbacks.on_discovery_callbacks", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.utils.callbacks.on_discovery_callbacks.save_discovery": {"fullname": "auto_disc.auto_disc.utils.callbacks.on_discovery_callbacks.save_discovery", "modulename": "auto_disc.auto_disc.utils.callbacks.on_discovery_callbacks.save_discovery", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.utils.callbacks.on_discovery_callbacks.save_discovery.SaveDiscovery": {"fullname": "auto_disc.auto_disc.utils.callbacks.on_discovery_callbacks.save_discovery.SaveDiscovery", "modulename": "auto_disc.auto_disc.utils.callbacks.on_discovery_callbacks.save_discovery", "qualname": "SaveDiscovery", "kind": "class", "doc": "

\n"}, "auto_disc.auto_disc.utils.callbacks.on_discovery_callbacks.save_discovery_in_expedb": {"fullname": "auto_disc.auto_disc.utils.callbacks.on_discovery_callbacks.save_discovery_in_expedb", "modulename": "auto_disc.auto_disc.utils.callbacks.on_discovery_callbacks.save_discovery_in_expedb", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.utils.callbacks.on_discovery_callbacks.save_discovery_in_expedb.SaveDiscoveryInExpeDB": {"fullname": "auto_disc.auto_disc.utils.callbacks.on_discovery_callbacks.save_discovery_in_expedb.SaveDiscoveryInExpeDB", "modulename": "auto_disc.auto_disc.utils.callbacks.on_discovery_callbacks.save_discovery_in_expedb", "qualname": "SaveDiscoveryInExpeDB", "kind": "class", "doc": "

\n", "bases": "auto_disc.legacy.utils.callbacks.on_discovery_callbacks.save_discovery.SaveDiscovery"}, "auto_disc.auto_disc.utils.callbacks.on_discovery_callbacks.save_discovery_on_disk": {"fullname": "auto_disc.auto_disc.utils.callbacks.on_discovery_callbacks.save_discovery_on_disk", "modulename": "auto_disc.auto_disc.utils.callbacks.on_discovery_callbacks.save_discovery_on_disk", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.utils.callbacks.on_discovery_callbacks.save_discovery_on_disk.SaveDiscoveryOnDisk": {"fullname": "auto_disc.auto_disc.utils.callbacks.on_discovery_callbacks.save_discovery_on_disk.SaveDiscoveryOnDisk", "modulename": "auto_disc.auto_disc.utils.callbacks.on_discovery_callbacks.save_discovery_on_disk", "qualname": "SaveDiscoveryOnDisk", "kind": "class", "doc": "

\n", "bases": "auto_disc.legacy.utils.callbacks.on_discovery_callbacks.save_discovery.SaveDiscovery"}, "auto_disc.auto_disc.utils.callbacks.on_error_callbacks": {"fullname": "auto_disc.auto_disc.utils.callbacks.on_error_callbacks", "modulename": "auto_disc.auto_disc.utils.callbacks.on_error_callbacks", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.utils.callbacks.on_finished_callbacks": {"fullname": "auto_disc.auto_disc.utils.callbacks.on_finished_callbacks", "modulename": "auto_disc.auto_disc.utils.callbacks.on_finished_callbacks", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.utils.callbacks.on_save_callbacks": {"fullname": "auto_disc.auto_disc.utils.callbacks.on_save_callbacks", "modulename": "auto_disc.auto_disc.utils.callbacks.on_save_callbacks", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.utils.callbacks.on_save_callbacks.on_save_modules_on_disk_callback": {"fullname": "auto_disc.auto_disc.utils.callbacks.on_save_callbacks.on_save_modules_on_disk_callback", "modulename": "auto_disc.auto_disc.utils.callbacks.on_save_callbacks.on_save_modules_on_disk_callback", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.utils.callbacks.on_save_callbacks.on_save_modules_on_disk_callback.OnSaveModulesOnDiskCallback": {"fullname": "auto_disc.auto_disc.utils.callbacks.on_save_callbacks.on_save_modules_on_disk_callback.OnSaveModulesOnDiskCallback", "modulename": "auto_disc.auto_disc.utils.callbacks.on_save_callbacks.on_save_modules_on_disk_callback", "qualname": "OnSaveModulesOnDiskCallback", "kind": "class", "doc": "

class for save autodisc modules on disk.

\n", "bases": "auto_disc.legacy.utils.callbacks.on_save_callbacks.base_on_save_callback.BaseOnSaveCallback"}, "auto_disc.auto_disc.utils.callbacks.on_save_callbacks.on_save_modules_on_disk_callback.OnSaveModulesOnDiskCallback.__init__": {"fullname": "auto_disc.auto_disc.utils.callbacks.on_save_callbacks.on_save_modules_on_disk_callback.OnSaveModulesOnDiskCallback.__init__", "modulename": "auto_disc.auto_disc.utils.callbacks.on_save_callbacks.on_save_modules_on_disk_callback", "qualname": "OnSaveModulesOnDiskCallback.__init__", "kind": "function", "doc": "

init the callback with a path to save modules on disk

\n\n
Arguments:
\n\n
    \n
  • folder_path: path to folder where we want save the discovery
    • \n
\n", "signature": "(folder_path: str, **kwargs)"}, "auto_disc.auto_disc.utils.callbacks.on_save_callbacks.save_leaf_callback": {"fullname": "auto_disc.auto_disc.utils.callbacks.on_save_callbacks.save_leaf_callback", "modulename": "auto_disc.auto_disc.utils.callbacks.on_save_callbacks.save_leaf_callback", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.utils.callbacks.on_save_callbacks.save_leaf_callback.SaveLeaf": {"fullname": "auto_disc.auto_disc.utils.callbacks.on_save_callbacks.save_leaf_callback.SaveLeaf", "modulename": "auto_disc.auto_disc.utils.callbacks.on_save_callbacks.save_leaf_callback", "qualname": "SaveLeaf", "kind": "class", "doc": "

Simple adapter which calls the save_leaf method from a callback

\n"}, "auto_disc.auto_disc.utils.callbacks.on_save_callbacks.save_leaf_callback.SaveLeaf.__init__": {"fullname": "auto_disc.auto_disc.utils.callbacks.on_save_callbacks.save_leaf_callback.SaveLeaf.__init__", "modulename": "auto_disc.auto_disc.utils.callbacks.on_save_callbacks.save_leaf_callback", "qualname": "SaveLeaf.__init__", "kind": "function", "doc": "

\n", "signature": "(**kwargs)"}, "auto_disc.auto_disc.utils.callbacks.on_save_callbacks.save_leaf_callback_in_expedb": {"fullname": "auto_disc.auto_disc.utils.callbacks.on_save_callbacks.save_leaf_callback_in_expedb", "modulename": "auto_disc.auto_disc.utils.callbacks.on_save_callbacks.save_leaf_callback_in_expedb", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.utils.callbacks.on_save_callbacks.save_leaf_callback_in_expedb.SaveLeafExpeDB": {"fullname": "auto_disc.auto_disc.utils.callbacks.on_save_callbacks.save_leaf_callback_in_expedb.SaveLeafExpeDB", "modulename": "auto_disc.auto_disc.utils.callbacks.on_save_callbacks.save_leaf_callback_in_expedb", "qualname": "SaveLeafExpeDB", "kind": "class", "doc": "

Simple adapter which calls the save_leaf method from a callback

\n", "bases": "auto_disc.legacy.utils.callbacks.on_save_callbacks.save_leaf_callback.SaveLeaf"}, "auto_disc.auto_disc.utils.callbacks.on_save_callbacks.save_leaf_callback_in_expedb.SaveLeafExpeDB.__init__": {"fullname": "auto_disc.auto_disc.utils.callbacks.on_save_callbacks.save_leaf_callback_in_expedb.SaveLeafExpeDB.__init__", "modulename": "auto_disc.auto_disc.utils.callbacks.on_save_callbacks.save_leaf_callback_in_expedb", "qualname": "SaveLeafExpeDB.__init__", "kind": "function", "doc": "

\n", "signature": "(**kwargs)"}, "auto_disc.auto_disc.utils.callbacks.on_save_finished_callbacks": {"fullname": "auto_disc.auto_disc.utils.callbacks.on_save_finished_callbacks", "modulename": "auto_disc.auto_disc.utils.callbacks.on_save_finished_callbacks", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.utils.callbacks.on_save_finished_callbacks.generate_report_callback": {"fullname": "auto_disc.auto_disc.utils.callbacks.on_save_finished_callbacks.generate_report_callback", "modulename": "auto_disc.auto_disc.utils.callbacks.on_save_finished_callbacks.generate_report_callback", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.utils.callbacks.on_save_finished_callbacks.generate_report_callback.GenerateReport": {"fullname": "auto_disc.auto_disc.utils.callbacks.on_save_finished_callbacks.generate_report_callback.GenerateReport", "modulename": "auto_disc.auto_disc.utils.callbacks.on_save_finished_callbacks.generate_report_callback", "qualname": "GenerateReport", "kind": "class", "doc": "

Takes UID which identifies a saved object and annotates the saved data with\nhuman-readable identifying information

\n"}, "auto_disc.auto_disc.utils.callbacks.on_save_finished_callbacks.generate_report_callback.GenerateReport.__init__": {"fullname": "auto_disc.auto_disc.utils.callbacks.on_save_finished_callbacks.generate_report_callback.GenerateReport.__init__", "modulename": "auto_disc.auto_disc.utils.callbacks.on_save_finished_callbacks.generate_report_callback", "qualname": "GenerateReport.__init__", "kind": "function", "doc": "

\n", "signature": "(**kwargs)"}, "auto_disc.auto_disc.utils.logger": {"fullname": "auto_disc.auto_disc.utils.logger", "modulename": "auto_disc.auto_disc.utils.logger", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.utils.logger.ad_logger": {"fullname": "auto_disc.auto_disc.utils.logger.ad_logger", "modulename": "auto_disc.auto_disc.utils.logger.ad_logger", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.utils.logger.ad_logger.AutoDiscLogger": {"fullname": "auto_disc.auto_disc.utils.logger.ad_logger.AutoDiscLogger", "modulename": "auto_disc.auto_disc.utils.logger.ad_logger", "qualname": "AutoDiscLogger", "kind": "class", "doc": "

A logger to manage experiments logs and print them in the console or save them to the database or to disk according to the user's needs.

\n", "bases": "logging.Logger"}, "auto_disc.auto_disc.utils.logger.ad_logger.AutoDiscLogger.__init__": {"fullname": "auto_disc.auto_disc.utils.logger.ad_logger.AutoDiscLogger.__init__", "modulename": "auto_disc.auto_disc.utils.logger.ad_logger", "qualname": "AutoDiscLogger.__init__", "kind": "function", "doc": "

Init the logger for an experiment.

\n\n
Arguments:
\n\n
    \n
  • experiment_id: current experiment id
    • \n
  • seed: current seed number
    • \n
  • handlers: List of all handlers needed by the user to manage logs (e.g. save in database on disk)
    • \n
\n", "signature": "(\texperiment_id: int,\tseed: int,\thandlers: List[auto_disc.auto_disc.utils.logger.ad_logger.AutoDiscLogger])"}, "auto_disc.auto_disc.utils.logger.ad_logger.AutoDiscLogger.debug": {"fullname": "auto_disc.auto_disc.utils.logger.ad_logger.AutoDiscLogger.debug", "modulename": "auto_disc.auto_disc.utils.logger.ad_logger", "qualname": "AutoDiscLogger.debug", "kind": "function", "doc": "

Call the logs method at the debug level and increment the log index to make an unique id for each log

\n", "signature": "(self, *args) -> None:", "funcdef": "def"}, "auto_disc.auto_disc.utils.logger.ad_logger.AutoDiscLogger.info": {"fullname": "auto_disc.auto_disc.utils.logger.ad_logger.AutoDiscLogger.info", "modulename": "auto_disc.auto_disc.utils.logger.ad_logger", "qualname": "AutoDiscLogger.info", "kind": "function", "doc": "

Call the logs method at the info level and increment the log index to make an unique id for each log

\n", "signature": "(self, *args) -> None:", "funcdef": "def"}, "auto_disc.auto_disc.utils.logger.ad_logger.AutoDiscLogger.warning": {"fullname": "auto_disc.auto_disc.utils.logger.ad_logger.AutoDiscLogger.warning", "modulename": "auto_disc.auto_disc.utils.logger.ad_logger", "qualname": "AutoDiscLogger.warning", "kind": "function", "doc": "

Call the logs method at the warning level and increment the log index to make an unique id for each log

\n", "signature": "(self, *args) -> None:", "funcdef": "def"}, "auto_disc.auto_disc.utils.logger.ad_logger.AutoDiscLogger.error": {"fullname": "auto_disc.auto_disc.utils.logger.ad_logger.AutoDiscLogger.error", "modulename": "auto_disc.auto_disc.utils.logger.ad_logger", "qualname": "AutoDiscLogger.error", "kind": "function", "doc": "

Call the logs method at the error level and increment the log index to make an unique id for each log

\n", "signature": "(self, *args) -> None:", "funcdef": "def"}, "auto_disc.auto_disc.utils.logger.ad_logger.AutoDiscLogger.critical": {"fullname": "auto_disc.auto_disc.utils.logger.ad_logger.AutoDiscLogger.critical", "modulename": "auto_disc.auto_disc.utils.logger.ad_logger", "qualname": "AutoDiscLogger.critical", "kind": "function", "doc": "

Call the logs method at the critical level and increment the log index to make an unique id for each log

\n", "signature": "(self, *args) -> None:", "funcdef": "def"}, "auto_disc.auto_disc.utils.logger.ad_logger.ContextFilter": {"fullname": "auto_disc.auto_disc.utils.logger.ad_logger.ContextFilter", "modulename": "auto_disc.auto_disc.utils.logger.ad_logger", "qualname": "ContextFilter", "kind": "class", "doc": "

This is a filter which injects contextual information into the log.

\n", "bases": "logging.Filter"}, "auto_disc.auto_disc.utils.logger.ad_logger.ContextFilter.filter": {"fullname": "auto_disc.auto_disc.utils.logger.ad_logger.ContextFilter.filter", "modulename": "auto_disc.auto_disc.utils.logger.ad_logger", "qualname": "ContextFilter.filter", "kind": "function", "doc": "

Add contextual info to log

\n\n
Arguments:
\n\n
    \n
  • record: Some information
    • \n
\n\n
Returns:
\n\n
\n

The return value is always True

\n
\n", "signature": "(self, record: logging.LogRecord) -> bool:", "funcdef": "def"}, "auto_disc.auto_disc.utils.logger.handlers": {"fullname": "auto_disc.auto_disc.utils.logger.handlers", "modulename": "auto_disc.auto_disc.utils.logger.handlers", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.utils.logger.handlers.file_handler": {"fullname": "auto_disc.auto_disc.utils.logger.handlers.file_handler", "modulename": "auto_disc.auto_disc.utils.logger.handlers.file_handler", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.utils.logger.handlers.file_handler.SetFileHandler": {"fullname": "auto_disc.auto_disc.utils.logger.handlers.file_handler.SetFileHandler", "modulename": "auto_disc.auto_disc.utils.logger.handlers.file_handler", "qualname": "SetFileHandler", "kind": "class", "doc": "

Handler to put logs in file on disk

\n", "bases": "logging.FileHandler"}, "auto_disc.auto_disc.utils.logger.handlers.file_handler.SetFileHandler.__init__": {"fullname": "auto_disc.auto_disc.utils.logger.handlers.file_handler.SetFileHandler.__init__", "modulename": "auto_disc.auto_disc.utils.logger.handlers.file_handler", "qualname": "SetFileHandler.__init__", "kind": "function", "doc": "

Init an handler able to save log in file on disk

\n\n
Arguments:
\n\n
    \n
  • folder_log_path: The path to the folder where the user want save logs
    • \n
  • experiment_id: current experiment id
    • \n
\n", "signature": "(folder_log_path: str, experiment_id: int)"}, "auto_disc.auto_disc.utils.logger.log_levels_enum": {"fullname": "auto_disc.auto_disc.utils.logger.log_levels_enum", "modulename": "auto_disc.auto_disc.utils.logger.log_levels_enum", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.utils.logger.log_levels_enum.LogsLevelEnum": {"fullname": "auto_disc.auto_disc.utils.logger.log_levels_enum.LogsLevelEnum", "modulename": "auto_disc.auto_disc.utils.logger.log_levels_enum", "qualname": "LogsLevelEnum", "kind": "class", "doc": "

An enumeration.

\n", "bases": "enum.Enum"}, "auto_disc.auto_disc.utils.logger.log_levels_enum.LogsLevelEnum.DEBUG": {"fullname": "auto_disc.auto_disc.utils.logger.log_levels_enum.LogsLevelEnum.DEBUG", "modulename": "auto_disc.auto_disc.utils.logger.log_levels_enum", "qualname": "LogsLevelEnum.DEBUG", "kind": "variable", "doc": "

\n", "default_value": "<LogsLevelEnum.DEBUG: 'debug'>"}, "auto_disc.auto_disc.utils.logger.log_levels_enum.LogsLevelEnum.INFO": {"fullname": "auto_disc.auto_disc.utils.logger.log_levels_enum.LogsLevelEnum.INFO", "modulename": "auto_disc.auto_disc.utils.logger.log_levels_enum", "qualname": "LogsLevelEnum.INFO", "kind": "variable", "doc": "

\n", "default_value": "<LogsLevelEnum.INFO: 'info'>"}, "auto_disc.auto_disc.utils.logger.log_levels_enum.LogsLevelEnum.WARNING": {"fullname": "auto_disc.auto_disc.utils.logger.log_levels_enum.LogsLevelEnum.WARNING", "modulename": "auto_disc.auto_disc.utils.logger.log_levels_enum", "qualname": "LogsLevelEnum.WARNING", "kind": "variable", "doc": "

\n", "default_value": "<LogsLevelEnum.WARNING: 'warning'>"}, "auto_disc.auto_disc.utils.logger.log_levels_enum.LogsLevelEnum.ERROR": {"fullname": "auto_disc.auto_disc.utils.logger.log_levels_enum.LogsLevelEnum.ERROR", "modulename": "auto_disc.auto_disc.utils.logger.log_levels_enum", "qualname": "LogsLevelEnum.ERROR", "kind": "variable", "doc": "

\n", "default_value": "<LogsLevelEnum.ERROR: 'error'>"}, "auto_disc.auto_disc.utils.logger.log_levels_enum.LogsLevelEnum.CRITICAL": {"fullname": "auto_disc.auto_disc.utils.logger.log_levels_enum.LogsLevelEnum.CRITICAL", "modulename": "auto_disc.auto_disc.utils.logger.log_levels_enum", "qualname": "LogsLevelEnum.CRITICAL", "kind": "variable", "doc": "

\n", "default_value": "<LogsLevelEnum.CRITICAL: 'critical'>"}, "auto_disc.auto_disc.wrappers": {"fullname": "auto_disc.auto_disc.wrappers", "modulename": "auto_disc.auto_disc.wrappers", "kind": "module", "doc": "

A collection of useful \"pure\" functions which wrap computational steps, for\nconvenience.

\n\n

Essentially identical in functionality to the interface of\nauto_disc.auto_disc.maps.Map, but wrappers are reusable and do not have a\nparticular role in the context of the exploration loop. Consequently, wrappers\nmay be used to do most of the computational work inside a given Map,\nconceptually similar to the layers of a deep neural network. For example:

\n\n
\n
class MyMap(Map):\n\n    def __init__(*args):\n        super().__init__()\n        self.a = MyWrapper(args[0])\n        self.b = MyWrapper(args[1])\n\n    def map(self, input: Dict) -> Dict:\n        return self.b.map(self.a.map(input))\n\n    def sample(self) -> Any:\n        return self.b.sample()\n
\n
\n"}, "auto_disc.auto_disc.wrappers.BoxProjector": {"fullname": "auto_disc.auto_disc.wrappers.BoxProjector", "modulename": "auto_disc.auto_disc.wrappers.BoxProjector", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.wrappers.BoxProjector.BoxProjector": {"fullname": "auto_disc.auto_disc.wrappers.BoxProjector.BoxProjector", "modulename": "auto_disc.auto_disc.wrappers.BoxProjector", "qualname": "BoxProjector", "kind": "class", "doc": "

Projects its input into a box space, i.e., the Cartesian product of N\nreal-valued intervals, where the dimension N is set by the input dimension.

\n\n

Note that it's map() method essentially just passes the received input,\nbut also adds a sampler key to the dict which an Explorer can use to\nsample from the space.

\n", "bases": "auto_disc.utils.leaf.Leaf.Leaf"}, "auto_disc.auto_disc.wrappers.BoxProjector.BoxProjector.__init__": {"fullname": "auto_disc.auto_disc.wrappers.BoxProjector.BoxProjector.__init__", "modulename": "auto_disc.auto_disc.wrappers.BoxProjector", "qualname": "BoxProjector.__init__", "kind": "function", "doc": "

\n", "signature": "(\tpremap_key: str,\tbound_upper: torch.Tensor = tensor([inf]),\tbound_lower: torch.Tensor = tensor([-inf]),\tinit_low: Optional[torch.Tensor] = None,\tinit_high: Optional[torch.Tensor] = None,\ttensor_shape: Optional[Tuple] = None)"}, "auto_disc.auto_disc.wrappers.BoxProjector.BoxProjector.map": {"fullname": "auto_disc.auto_disc.wrappers.BoxProjector.BoxProjector.map", "modulename": "auto_disc.auto_disc.wrappers.BoxProjector", "qualname": "BoxProjector.map", "kind": "function", "doc": "

Passes input, adding a sampler Callable item which Explorer can\nuse to sample from the box space.

\n", "signature": "(self, input: Dict) -> Dict:", "funcdef": "def"}, "auto_disc.auto_disc.wrappers.BoxProjector.BoxProjector.sample": {"fullname": "auto_disc.auto_disc.wrappers.BoxProjector.BoxProjector.sample", "modulename": "auto_disc.auto_disc.wrappers.BoxProjector", "qualname": "BoxProjector.sample", "kind": "function", "doc": "

\n", "signature": "(self) -> torch.Tensor:", "funcdef": "def"}, "auto_disc.auto_disc.wrappers.CPPNWrapper": {"fullname": "auto_disc.auto_disc.wrappers.CPPNWrapper", "modulename": "auto_disc.auto_disc.wrappers.CPPNWrapper", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.wrappers.CPPNWrapper.NEATTuple": {"fullname": "auto_disc.auto_disc.wrappers.CPPNWrapper.NEATTuple", "modulename": "auto_disc.auto_disc.wrappers.CPPNWrapper", "qualname": "NEATTuple", "kind": "class", "doc": "

NEATTuple(genome, neat_config)

\n", "bases": "typing.NamedTuple"}, "auto_disc.auto_disc.wrappers.CPPNWrapper.NEATTuple.__init__": {"fullname": "auto_disc.auto_disc.wrappers.CPPNWrapper.NEATTuple.__init__", "modulename": "auto_disc.auto_disc.wrappers.CPPNWrapper", "qualname": "NEATTuple.__init__", "kind": "function", "doc": "

Create new instance of NEATTuple(genome, neat_config)

\n", "signature": "(genome: str, neat_config: str)"}, "auto_disc.auto_disc.wrappers.CPPNWrapper.NEATTuple.genome": {"fullname": "auto_disc.auto_disc.wrappers.CPPNWrapper.NEATTuple.genome", "modulename": "auto_disc.auto_disc.wrappers.CPPNWrapper", "qualname": "NEATTuple.genome", "kind": "variable", "doc": "

Alias for field number 0

\n", "annotation": ": str"}, "auto_disc.auto_disc.wrappers.CPPNWrapper.NEATTuple.neat_config": {"fullname": "auto_disc.auto_disc.wrappers.CPPNWrapper.NEATTuple.neat_config", "modulename": "auto_disc.auto_disc.wrappers.CPPNWrapper", "qualname": "NEATTuple.neat_config", "kind": "variable", "doc": "

Alias for field number 1

\n", "annotation": ": str"}, "auto_disc.auto_disc.wrappers.CPPNWrapper.CPPNWrapper": {"fullname": "auto_disc.auto_disc.wrappers.CPPNWrapper.CPPNWrapper", "modulename": "auto_disc.auto_disc.wrappers.CPPNWrapper", "qualname": "CPPNWrapper", "kind": "class", "doc": "

\n", "bases": "auto_disc.utils.leaf.Leaf.Leaf"}, "auto_disc.auto_disc.wrappers.CPPNWrapper.CPPNWrapper.__init__": {"fullname": "auto_disc.auto_disc.wrappers.CPPNWrapper.CPPNWrapper.__init__", "modulename": "auto_disc.auto_disc.wrappers.CPPNWrapper", "qualname": "CPPNWrapper.__init__", "kind": "function", "doc": "

\n", "signature": "(\tpremap_key: auto_disc.auto_disc.wrappers.CPPNWrapper.NEATTuple = NEATTuple(genome='genome', neat_config='neat_config'),\tpostmap_key: str = 'init_state',\tpostmap_shape: Tuple[int, int] = (10, 10),\tn_passes: int = 2)"}, "auto_disc.auto_disc.wrappers.CPPNWrapper.CPPNWrapper.map": {"fullname": "auto_disc.auto_disc.wrappers.CPPNWrapper.CPPNWrapper.map", "modulename": "auto_disc.auto_disc.wrappers.CPPNWrapper", "qualname": "CPPNWrapper.map", "kind": "function", "doc": "

\n", "signature": "(self, input: Dict) -> Dict:", "funcdef": "def"}, "auto_disc.auto_disc.wrappers.IdentityWrapper": {"fullname": "auto_disc.auto_disc.wrappers.IdentityWrapper", "modulename": "auto_disc.auto_disc.wrappers.IdentityWrapper", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.wrappers.IdentityWrapper.IdentityWrapper": {"fullname": "auto_disc.auto_disc.wrappers.IdentityWrapper.IdentityWrapper", "modulename": "auto_disc.auto_disc.wrappers.IdentityWrapper", "qualname": "IdentityWrapper", "kind": "class", "doc": "

Wrapper which passes the input without mutation.

\n", "bases": "auto_disc.auto_disc.wrappers.TransformWrapper.TransformWrapper"}, "auto_disc.auto_disc.wrappers.IdentityWrapper.IdentityWrapper.__init__": {"fullname": "auto_disc.auto_disc.wrappers.IdentityWrapper.IdentityWrapper.__init__", "modulename": "auto_disc.auto_disc.wrappers.IdentityWrapper", "qualname": "IdentityWrapper.__init__", "kind": "function", "doc": "

\n", "signature": "(premap_keys: List[str] = [])"}, "auto_disc.auto_disc.wrappers.IdentityWrapper.IdentityWrapper.map": {"fullname": "auto_disc.auto_disc.wrappers.IdentityWrapper.IdentityWrapper.map", "modulename": "auto_disc.auto_disc.wrappers.IdentityWrapper", "qualname": "IdentityWrapper.map", "kind": "function", "doc": "

Transforms the input dictionary with the provided relabelling of keys.

\n", "signature": "(self, input: Dict) -> Dict:", "funcdef": "def"}, "auto_disc.auto_disc.wrappers.SaveWrapper": {"fullname": "auto_disc.auto_disc.wrappers.SaveWrapper", "modulename": "auto_disc.auto_disc.wrappers.SaveWrapper", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.wrappers.SaveWrapper.SaveWrapper": {"fullname": "auto_disc.auto_disc.wrappers.SaveWrapper.SaveWrapper", "modulename": "auto_disc.auto_disc.wrappers.SaveWrapper", "qualname": "SaveWrapper", "kind": "class", "doc": "

Wrapper which does basic processing and\nsaving of captured input history

\n\n
Usage example:
\n\n
\n
    input = {\"in\" : 1}\n    # default setting saves all specified `premap_keys`\n    wrapper = SaveWrapper(premap_keys = [\"in\"],\n                          postmap_keys = [\"out\"])\n    output = wrapper.map(input)\n    assert output[\"out\"] == 1\n
\n
\n", "bases": "auto_disc.auto_disc.wrappers.TransformWrapper.TransformWrapper"}, "auto_disc.auto_disc.wrappers.SaveWrapper.SaveWrapper.__init__": {"fullname": "auto_disc.auto_disc.wrappers.SaveWrapper.SaveWrapper.__init__", "modulename": "auto_disc.auto_disc.wrappers.SaveWrapper", "qualname": "SaveWrapper.__init__", "kind": "function", "doc": "

\n", "signature": "(\tpremap_keys: List[str] = [],\tpostmap_keys: List[str] = [],\tinputs_to_save: List[str] = [])"}, "auto_disc.auto_disc.wrappers.SaveWrapper.SaveWrapper.map": {"fullname": "auto_disc.auto_disc.wrappers.SaveWrapper.SaveWrapper.map", "modulename": "auto_disc.auto_disc.wrappers.SaveWrapper", "qualname": "SaveWrapper.map", "kind": "function", "doc": "

WARN: This wrapper's .map() is stateful.

\n\n

Transforms the input dictionary with the provided relabelling of keys,\nsaving inputs and passing outputs

\n", "signature": "(self, input: Dict) -> Dict:", "funcdef": "def"}, "auto_disc.auto_disc.wrappers.SaveWrapper.SaveWrapper.save_leaf": {"fullname": "auto_disc.auto_disc.wrappers.SaveWrapper.SaveWrapper.save_leaf", "modulename": "auto_disc.auto_disc.wrappers.SaveWrapper", "qualname": "SaveWrapper.save_leaf", "kind": "function", "doc": "

Save entire structure of object. The suggested way to customize\nbehavior is overloading serialize() and the Locator

\n", "signature": "(\tself,\tresource_uri: str,\tparent_uid: int = -1) -> <function NewType.<locals>.new_type at 0x7f0ed2ca3670>:", "funcdef": "def"}, "auto_disc.auto_disc.wrappers.SaveWrapper.SaveWrapper.serialize": {"fullname": "auto_disc.auto_disc.wrappers.SaveWrapper.SaveWrapper.serialize", "modulename": "auto_disc.auto_disc.wrappers.SaveWrapper", "qualname": "SaveWrapper.serialize", "kind": "function", "doc": "

Custom serialize method needed for producing appropriately padded\nbinary with metadata.

\n", "signature": "(self) -> bytes:", "funcdef": "def"}, "auto_disc.auto_disc.wrappers.SaveWrapper.SaveWrapper.get_history": {"fullname": "auto_disc.auto_disc.wrappers.SaveWrapper.SaveWrapper.get_history", "modulename": "auto_disc.auto_disc.wrappers.SaveWrapper", "qualname": "SaveWrapper.get_history", "kind": "function", "doc": "

Retrieve the history of inputs to the wrapper.\nlookback_length = -1 corresponds to retrieving the entire history,\nwhich may be very large as it will query the on-disk SQLite db.

\n", "signature": "(self, lookback_length: int = 1) -> List[Dict]:", "funcdef": "def"}, "auto_disc.auto_disc.wrappers.SaveWrapper.SaveWrapper.generate_dataloader": {"fullname": "auto_disc.auto_disc.wrappers.SaveWrapper.SaveWrapper.generate_dataloader", "modulename": "auto_disc.auto_disc.wrappers.SaveWrapper", "qualname": "SaveWrapper.generate_dataloader", "kind": "function", "doc": "

Query SQLite DB to retrieve an iterable which lazy loads the DB tree.

\n", "signature": "(self, buffer_src_uri: str, cachebuf_size: int = 1) -> Iterable[Dict]:", "funcdef": "def"}, "auto_disc.auto_disc.wrappers.SaveWrapper.BufferStreamer": {"fullname": "auto_disc.auto_disc.wrappers.SaveWrapper.BufferStreamer", "modulename": "auto_disc.auto_disc.wrappers.SaveWrapper", "qualname": "BufferStreamer", "kind": "class", "doc": "

Class for streaming buffers of arbitrary length from SQLite db.

\n\n

mode is set to one of \"serial\" or \"batched\" which specifies whether\nthe cachebuf is returned as a single item or as a list of items.

\n"}, "auto_disc.auto_disc.wrappers.SaveWrapper.BufferStreamer.__init__": {"fullname": "auto_disc.auto_disc.wrappers.SaveWrapper.BufferStreamer.__init__", "modulename": "auto_disc.auto_disc.wrappers.SaveWrapper", "qualname": "BufferStreamer.__init__", "kind": "function", "doc": "

\n", "signature": "(\twrapper: auto_disc.auto_disc.wrappers.SaveWrapper.SaveWrapper,\tresource_uri: str,\tcachebuf_size: int = 1,\tmode: str = 'serial')"}, "auto_disc.auto_disc.wrappers.TransformWrapper": {"fullname": "auto_disc.auto_disc.wrappers.TransformWrapper", "modulename": "auto_disc.auto_disc.wrappers.TransformWrapper", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.wrappers.TransformWrapper.TransformWrapper": {"fullname": "auto_disc.auto_disc.wrappers.TransformWrapper.TransformWrapper", "modulename": "auto_disc.auto_disc.wrappers.TransformWrapper", "qualname": "TransformWrapper", "kind": "class", "doc": "

Wrapper which does basic processing of input

\n\n
Usage example:
\n\n
\n
    input = {\"in\" : 1}\n    wrapper = TransformWrapper(premap_keys = [\"in\"],\n                          postmap_keys = [\"out\"])\n    output = wrapper.map(input)\n    assert output[\"out\"] == 1\n
\n
\n", "bases": "auto_disc.utils.leaf.Leaf.Leaf"}, "auto_disc.auto_disc.wrappers.TransformWrapper.TransformWrapper.__init__": {"fullname": "auto_disc.auto_disc.wrappers.TransformWrapper.TransformWrapper.__init__", "modulename": "auto_disc.auto_disc.wrappers.TransformWrapper", "qualname": "TransformWrapper.__init__", "kind": "function", "doc": "

\n", "signature": "(premap_keys: List[str] = [], postmap_keys: List[str] = [])"}, "auto_disc.auto_disc.wrappers.TransformWrapper.TransformWrapper.map": {"fullname": "auto_disc.auto_disc.wrappers.TransformWrapper.TransformWrapper.map", "modulename": "auto_disc.auto_disc.wrappers.TransformWrapper", "qualname": "TransformWrapper.map", "kind": "function", "doc": "

Transforms the input dictionary with the provided relabelling of keys.

\n", "signature": "(self, input: Dict) -> Dict:", "funcdef": "def"}, "auto_disc.auto_disc.wrappers.WrapperPipeline": {"fullname": "auto_disc.auto_disc.wrappers.WrapperPipeline", "modulename": "auto_disc.auto_disc.wrappers.WrapperPipeline", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.wrappers.WrapperPipeline.WrapperPipeline": {"fullname": "auto_disc.auto_disc.wrappers.WrapperPipeline.WrapperPipeline", "modulename": "auto_disc.auto_disc.wrappers.WrapperPipeline", "qualname": "WrapperPipeline", "kind": "class", "doc": "

Module for composing various wrappers during the input or output processing\nof the experiment.

\n\n
Usage example:
\n\n
\n
    input = {\"in\" : 1}\n    a = alpha_wrapper()\n    b = beta_wrapper()\n    wrapper_list = [a, b]\n    all_wrappers = WrapperPipeline(wrappers=wrapper_list,\n                    inputs_to_save=[\"in\"], outputs_to_save=[\"out\"])\n    assert all_wrappers.map(input) == b.map(a.map(input))\n
\n
\n", "bases": "auto_disc.utils.leaf.Leaf.Leaf"}, "auto_disc.auto_disc.wrappers.WrapperPipeline.WrapperPipeline.__init__": {"fullname": "auto_disc.auto_disc.wrappers.WrapperPipeline.WrapperPipeline.__init__", "modulename": "auto_disc.auto_disc.wrappers.WrapperPipeline", "qualname": "WrapperPipeline.__init__", "kind": "function", "doc": "

\n", "signature": "(\twrappers: List[auto_disc.utils.leaf.Leaf.Leaf] = [],\tresource_uri: str = '',\tlocator: auto_disc.utils.leaf.locators.Locator.Locator = <auto_disc.utils.leaf.locators.Locator.StatelessLocator object>)"}, "auto_disc.auto_disc.wrappers.WrapperPipeline.WrapperPipeline.map": {"fullname": "auto_disc.auto_disc.wrappers.WrapperPipeline.WrapperPipeline.map", "modulename": "auto_disc.auto_disc.wrappers.WrapperPipeline", "qualname": "WrapperPipeline.map", "kind": "function", "doc": "

\n", "signature": "(self, input: Dict) -> Dict:", "funcdef": "def"}, "auto_disc.auto_disc.wrappers.mutators": {"fullname": "auto_disc.auto_disc.wrappers.mutators", "modulename": "auto_disc.auto_disc.wrappers.mutators", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.wrappers.mutators.add_gaussian_noise": {"fullname": "auto_disc.auto_disc.wrappers.mutators.add_gaussian_noise", "modulename": "auto_disc.auto_disc.wrappers.mutators", "qualname": "add_gaussian_noise", "kind": "function", "doc": "

\n", "signature": "(\tinput_tensor: torch.Tensor,\tmean: torch.Tensor = tensor([0.]),\tstd: torch.Tensor = tensor([1.])) -> torch.Tensor:", "funcdef": "def"}, "auto_disc.auto_disc.wrappers.mutators.call_mutate_method": {"fullname": "auto_disc.auto_disc.wrappers.mutators.call_mutate_method", "modulename": "auto_disc.auto_disc.wrappers.mutators", "qualname": "call_mutate_method", "kind": "function", "doc": "

If parameters are given as a complicated dict, then the parameter map which\ncreates it must have a mutate method in order to mutate the underlying\nparameters controlling.

\n", "signature": "(param_dict: Dict, param_map: Any = None) -> Dict:", "funcdef": "def"}, "auto_disc.db": {"fullname": "auto_disc.db", "modulename": "auto_disc.db", "kind": "module", "doc": "

\n"}, "auto_disc.db.dataset": {"fullname": "auto_disc.db.dataset", "modulename": "auto_disc.db.dataset", "kind": "module", "doc": "

\n"}, "auto_disc.db.dataset.Dataset": {"fullname": "auto_disc.db.dataset.Dataset", "modulename": "auto_disc.db.dataset", "qualname": "Dataset", "kind": "class", "doc": "

\n"}, "auto_disc.db.dataset.Dataset.__init__": {"fullname": "auto_disc.db.dataset.Dataset.__init__", "modulename": "auto_disc.db.dataset", "qualname": "Dataset.__init__", "kind": "function", "doc": "

\n", "signature": "(experiment_id, checkpoint_ids=[])"}, "auto_disc.db.dataset.Dataset.filter": {"fullname": "auto_disc.db.dataset.Dataset.filter", "modulename": "auto_disc.db.dataset", "qualname": "Dataset.filter", "kind": "function", "doc": "

\n", "signature": "(self, _filter):", "funcdef": "def"}, "auto_disc.db.get_discoveries_with_filter": {"fullname": "auto_disc.db.get_discoveries_with_filter", "modulename": "auto_disc.db.get_discoveries_with_filter", "kind": "module", "doc": "

\n"}, "auto_disc.db.stream": {"fullname": "auto_disc.db.stream", "modulename": "auto_disc.db.stream", "kind": "module", "doc": "

\n"}, "auto_disc.db.stream.Stream": {"fullname": "auto_disc.db.stream.Stream", "modulename": "auto_disc.db.stream", "qualname": "Stream", "kind": "class", "doc": "

\n"}, "auto_disc.db.stream.Stream.__init__": {"fullname": "auto_disc.db.stream.Stream.__init__", "modulename": "auto_disc.db.stream", "qualname": "Stream.__init__", "kind": "function", "doc": "

\n", "signature": "(initial_filter, size)"}, "auto_disc.db.utils": {"fullname": "auto_disc.db.utils", "modulename": "auto_disc.db.utils", "kind": "module", "doc": "

\n"}, "auto_disc.db.utils.config": {"fullname": "auto_disc.db.utils.config", "modulename": "auto_disc.db.utils.config", "kind": "module", "doc": "

\n"}, "auto_disc.db.utils.config.ConfigError": {"fullname": "auto_disc.db.utils.config.ConfigError", "modulename": "auto_disc.db.utils.config", "qualname": "ConfigError", "kind": "class", "doc": "

Common base class for all non-exit exceptions.

\n", "bases": "builtins.Exception"}, "auto_disc.db.utils.config.Config": {"fullname": "auto_disc.db.utils.config.Config", "modulename": "auto_disc.db.utils.config", "qualname": "Config", "kind": "class", "doc": "

\n"}, "auto_disc.legacy": {"fullname": "auto_disc.legacy", "modulename": "auto_disc.legacy", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.base_autodisc_module": {"fullname": "auto_disc.legacy.base_autodisc_module", "modulename": "auto_disc.legacy.base_autodisc_module", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.base_autodisc_module.BaseAutoDiscModule": {"fullname": "auto_disc.legacy.base_autodisc_module.BaseAutoDiscModule", "modulename": "auto_disc.legacy.base_autodisc_module", "qualname": "BaseAutoDiscModule", "kind": "class", "doc": "

Base class of all modules usable in auto_disc.legacy.

\n"}, "auto_disc.legacy.base_autodisc_module.BaseAutoDiscModule.__init__": {"fullname": "auto_disc.legacy.base_autodisc_module.BaseAutoDiscModule.__init__", "modulename": "auto_disc.legacy.base_autodisc_module", "qualname": "BaseAutoDiscModule.__init__", "kind": "function", "doc": "

initialize attributes common to all auto_disc.legacy modules

\n\n
Arguments:
\n\n
    \n
  • logger: the logger which will make it possible to keep information on the progress of an experiment in the database or on files
    • \n
\n", "signature": "(\tlogger: auto_disc.legacy.utils.logger.ad_logger.AutoDiscLogger = None,\t**kwargs)"}, "auto_disc.legacy.base_autodisc_module.BaseAutoDiscModule.set_history_access_fn": {"fullname": "auto_disc.legacy.base_autodisc_module.BaseAutoDiscModule.set_history_access_fn", "modulename": "auto_disc.legacy.base_autodisc_module", "qualname": "BaseAutoDiscModule.set_history_access_fn", "kind": "function", "doc": "

Set the function allowing module to access (readonly) its history of (input, output) pairs.

\n\n
Arguments:
\n\n
    \n
  • function: The method used to acces to the module history
    • \n
\n", "signature": "(self, function: Callable) -> None:", "funcdef": "def"}, "auto_disc.legacy.base_autodisc_module.BaseAutoDiscModule.set_call_output_history_update_fn": {"fullname": "auto_disc.legacy.base_autodisc_module.BaseAutoDiscModule.set_call_output_history_update_fn", "modulename": "auto_disc.legacy.base_autodisc_module", "qualname": "BaseAutoDiscModule.set_call_output_history_update_fn", "kind": "function", "doc": "

Set the function asking a refresh (raw_outputs will be processed again with output representations) of all outputs in history.

\n\n
Arguments:
\n\n
    \n
  • function: The method used to refresh
    • \n
\n", "signature": "(self, function: Callable) -> None:", "funcdef": "def"}, "auto_disc.legacy.base_autodisc_module.BaseAutoDiscModule.save": {"fullname": "auto_disc.legacy.base_autodisc_module.BaseAutoDiscModule.save", "modulename": "auto_disc.legacy.base_autodisc_module", "qualname": "BaseAutoDiscModule.save", "kind": "function", "doc": "

Return a dict storing everything that has to be pickled.

\n", "signature": "(self) -> Dict:", "funcdef": "def"}, "auto_disc.legacy.base_autodisc_module.BaseAutoDiscModule.load": {"fullname": "auto_disc.legacy.base_autodisc_module.BaseAutoDiscModule.load", "modulename": "auto_disc.legacy.base_autodisc_module", "qualname": "BaseAutoDiscModule.load", "kind": "function", "doc": "

Reload module given a dict storing everything that had to be pickled.

\n\n
Arguments:
\n\n
    \n
  • saved_dict: The dict used to restore the module
    • \n
\n", "signature": "(self, saved_dict: Dict):", "funcdef": "def"}, "auto_disc.legacy.experiment_pipeline": {"fullname": "auto_disc.legacy.experiment_pipeline", "modulename": "auto_disc.legacy.experiment_pipeline", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.experiment_pipeline.CancellationToken": {"fullname": "auto_disc.legacy.experiment_pipeline.CancellationToken", "modulename": "auto_disc.legacy.experiment_pipeline", "qualname": "CancellationToken", "kind": "class", "doc": "

Manages the cancellation token which allows you to stop an experiment in progress

\n"}, "auto_disc.legacy.experiment_pipeline.CancellationToken.__init__": {"fullname": "auto_disc.legacy.experiment_pipeline.CancellationToken.__init__", "modulename": "auto_disc.legacy.experiment_pipeline", "qualname": "CancellationToken.__init__", "kind": "function", "doc": "

Init the cancellation token to false

\n", "signature": "()"}, "auto_disc.legacy.experiment_pipeline.CancellationToken.get": {"fullname": "auto_disc.legacy.experiment_pipeline.CancellationToken.get", "modulename": "auto_disc.legacy.experiment_pipeline", "qualname": "CancellationToken.get", "kind": "function", "doc": "

Give acces to the cancellation token

\n\n
Returns:
\n\n
\n

_token: a boolean indicating if the current experiment must be cancelled

\n
\n", "signature": "(self) -> bool:", "funcdef": "def"}, "auto_disc.legacy.experiment_pipeline.CancellationToken.trigger": {"fullname": "auto_disc.legacy.experiment_pipeline.CancellationToken.trigger", "modulename": "auto_disc.legacy.experiment_pipeline", "qualname": "CancellationToken.trigger", "kind": "function", "doc": "

Set the cancellation token to true (the experiment must be cancelled)

\n", "signature": "(self):", "funcdef": "def"}, "auto_disc.legacy.experiment_pipeline.ExperimentPipeline": {"fullname": "auto_disc.legacy.experiment_pipeline.ExperimentPipeline", "modulename": "auto_disc.legacy.experiment_pipeline", "qualname": "ExperimentPipeline", "kind": "class", "doc": "

Pipeline of an automated discovery experiment.\nAn experiment is at least constitued of a system and an explorer. Additionally, input wrappers and output representations can be added (multiple can be stacked).\nWhen the system requires an action at each timestep, an action_policy must be provided.\nIn order to monitor the experiment, you must provide on_exploration_classbacks, which will be called every time a discovery has been made. Please provide callbacks overriding the libs.auto_disc.legacy.utils.BaseAutoDiscCallback.

\n"}, "auto_disc.legacy.experiment_pipeline.ExperimentPipeline.__init__": {"fullname": "auto_disc.legacy.experiment_pipeline.ExperimentPipeline.__init__", "modulename": "auto_disc.legacy.experiment_pipeline", "qualname": "ExperimentPipeline.__init__", "kind": "function", "doc": "

Init experiment pipeline. Set all attribute used by the pipeline.

\n\n
Arguments:
\n\n
    \n
  • experiment_id: Id of current experiment
    • \n
  • seed: Current seed number
    • \n
  • system: The system we want explore in this experiment
    • \n
  • explorer: The explorer we use to explore the system
    • \n
  • input_wrappers: A list of wrapper who redine the input spaces
    • \n
  • output_representations: A list of wrapper who redine the output spaces
    • \n
  • action_policy: A method that indicate what action should be taken based on the policy
    • \n
  • save_frequency: defines the frequency with which the backups of the elements of the experience are made
    • \n
  • on_discovery_callbacks: Callbacks raised when a discovery was made
    • \n
  • on_save_finished_callbacks: Callbacks raised when a the experiment save is complete
    • \n
  • on_finished_callbacks: Callbacks raised when the experiment is over
    • \n
  • on_cancelled_callbacks: Callbacks raised when the experiment is canccelled
    • \n
  • on_save_callbacks: Callbacks raised when a experiment save was made
    • \n
  • on_error_callbacks: Callbacks raised when an error is raised
    • \n
\n", "signature": "(\texperiment_id: int,\tseed: int,\tsystem: <module 'auto_disc.legacy.systems' from '/home/runner/work/adtool/adtool/services/base/AutoDiscServer/libs/auto_disc/legacy/systems/__init__.py'>,\texplorer: <module 'auto_disc.legacy.explorers' from '/home/runner/work/adtool/adtool/services/base/AutoDiscServer/libs/auto_disc/legacy/explorers/__init__.py'>,\tinput_wrappers: List[auto_disc.legacy.input_wrappers.base_input_wrapper.BaseInputWrapper] = None,\toutput_representations: List[auto_disc.legacy.output_representations.base_output_representation.BaseOutputRepresentation] = None,\taction_policy=None,\tsave_frequency: int = 100,\ton_discovery_callbacks: List[Callable] = [],\ton_save_finished_callbacks: List[Callable] = [],\ton_finished_callbacks: List[Callable] = [],\ton_cancelled_callbacks: List[Callable] = [],\ton_save_callbacks: List[Callable] = [],\ton_error_callbacks: List[Callable] = [],\tinteract_callbacks: List[Callable] = [])"}, "auto_disc.legacy.experiment_pipeline.ExperimentPipeline.run": {"fullname": "auto_disc.legacy.experiment_pipeline.ExperimentPipeline.run", "modulename": "auto_disc.legacy.experiment_pipeline", "qualname": "ExperimentPipeline.run", "kind": "function", "doc": "

Launches the experiment for n_exploration_runs explorations.

\n\n
Arguments:
\n\n
    \n
  • n_exploration_runs: number of explorations
    • \n
\n", "signature": "(self, n_exploration_runs: int) -> None:", "funcdef": "def"}, "auto_disc.legacy.explorers": {"fullname": "auto_disc.legacy.explorers", "modulename": "auto_disc.legacy.explorers", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.explorers.base_explorer": {"fullname": "auto_disc.legacy.explorers.base_explorer", "modulename": "auto_disc.legacy.explorers.base_explorer", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.explorers.base_explorer.BaseExplorer": {"fullname": "auto_disc.legacy.explorers.base_explorer.BaseExplorer", "modulename": "auto_disc.legacy.explorers.base_explorer", "qualname": "BaseExplorer", "kind": "class", "doc": "

Base class for explorers.

\n", "bases": "auto_disc.legacy.base_autodisc_module.BaseAutoDiscModule"}, "auto_disc.legacy.explorers.base_explorer.BaseExplorer.__init__": {"fullname": "auto_disc.legacy.explorers.base_explorer.BaseExplorer.__init__", "modulename": "auto_disc.legacy.explorers.base_explorer", "qualname": "BaseExplorer.__init__", "kind": "function", "doc": "

initialize attributes common to all auto_disc.legacy modules

\n\n
Arguments:
\n\n
    \n
  • logger: the logger which will make it possible to keep information on the progress of an experiment in the database or on files
    • \n
\n", "signature": "(**kwargs)"}, "auto_disc.legacy.explorers.base_explorer.BaseExplorer.initialize": {"fullname": "auto_disc.legacy.explorers.base_explorer.BaseExplorer.initialize", "modulename": "auto_disc.legacy.explorers.base_explorer", "qualname": "BaseExplorer.initialize", "kind": "function", "doc": "

Defines input and output space for the explorer (as well as a distance function for the input space).

\n", "signature": "(\tself,\tinput_space: auto_disc.legacy.utils.spaces.dict.DictSpace,\toutput_space: auto_disc.legacy.utils.spaces.dict.DictSpace,\tinput_distance_fn: Callable) -> None:", "funcdef": "def"}, "auto_disc.legacy.explorers.base_explorer.BaseExplorer.sample": {"fullname": "auto_disc.legacy.explorers.base_explorer.BaseExplorer.sample", "modulename": "auto_disc.legacy.explorers.base_explorer", "qualname": "BaseExplorer.sample", "kind": "function", "doc": "

Emits a new set of parameters to test in the system

\n", "signature": "(self):", "funcdef": "def"}, "auto_disc.legacy.explorers.base_explorer.BaseExplorer.observe": {"fullname": "auto_disc.legacy.explorers.base_explorer.BaseExplorer.observe", "modulename": "auto_disc.legacy.explorers.base_explorer", "qualname": "BaseExplorer.observe", "kind": "function", "doc": "

Stores parameters and the output the system produced using them

\n", "signature": "(self, parameters: Dict, observations: torch.Tensor):", "funcdef": "def"}, "auto_disc.legacy.explorers.base_explorer.BaseExplorer.optimize": {"fullname": "auto_disc.legacy.explorers.base_explorer.BaseExplorer.optimize", "modulename": "auto_disc.legacy.explorers.base_explorer", "qualname": "BaseExplorer.optimize", "kind": "function", "doc": "

Optimizes the explorer's sample policy given the discoveries arhcived

\n", "signature": "(self):", "funcdef": "def"}, "auto_disc.legacy.explorers.imgep_explorer": {"fullname": "auto_disc.legacy.explorers.imgep_explorer", "modulename": "auto_disc.legacy.explorers.imgep_explorer", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.explorers.imgep_explorer.IMGEPExplorer": {"fullname": "auto_disc.legacy.explorers.imgep_explorer.IMGEPExplorer", "modulename": "auto_disc.legacy.explorers.imgep_explorer", "qualname": "IMGEPExplorer", "kind": "class", "doc": "

Basic explorer that samples goals in a goalspace and uses a policy library to generate parameters to reach the goal.

\n", "bases": "auto_disc.legacy.explorers.base_explorer.BaseExplorer"}, "auto_disc.legacy.explorers.imgep_explorer.IMGEPExplorer.__init__": {"fullname": "auto_disc.legacy.explorers.imgep_explorer.IMGEPExplorer.__init__", "modulename": "auto_disc.legacy.explorers.imgep_explorer", "qualname": "IMGEPExplorer.__init__", "kind": "function", "doc": "

initialize attributes common to all auto_disc.legacy modules

\n\n
Arguments:
\n\n
    \n
  • logger: the logger which will make it possible to keep information on the progress of an experiment in the database or on files
    • \n
\n", "signature": "(*args, **kws)"}, "auto_disc.legacy.explorers.imgep_explorer.IMGEPExplorer.initialize": {"fullname": "auto_disc.legacy.explorers.imgep_explorer.IMGEPExplorer.initialize", "modulename": "auto_disc.legacy.explorers.imgep_explorer", "qualname": "IMGEPExplorer.initialize", "kind": "function", "doc": "

Defines input and output space for the explorer (as well as a distance function for the input space).

\n\n
Arguments:
\n\n
    \n
  • input_space: space providing by the last output_representations
    • \n
  • output_space: space providing by the first input_wrappers
    • \n
  • input_distance_fn: a method to calc distance
    • \n
\n", "signature": "(\tself,\tinput_space: auto_disc.legacy.utils.spaces.dict.DictSpace,\toutput_space: auto_disc.legacy.utils.spaces.dict.DictSpace,\tinput_distance_fn: Callable[[torch.Tensor, torch.Tensor], torch.Tensor]) -> None:", "funcdef": "def"}, "auto_disc.legacy.explorers.imgep_explorer.IMGEPExplorer.expand_box_goal_space": {"fullname": "auto_disc.legacy.explorers.imgep_explorer.IMGEPExplorer.expand_box_goal_space", "modulename": "auto_disc.legacy.explorers.imgep_explorer", "qualname": "IMGEPExplorer.expand_box_goal_space", "kind": "function", "doc": "

Expand the goal space in which the explorer will explore

\n\n
Arguments:
\n\n
    \n
  • space: current goal space
    • \n
  • observations: observations/results of previous explorations
    • \n
\n", "signature": "(\tself,\tspace: auto_disc.legacy.utils.spaces.box.BoxSpace,\tobservations: torch.Tensor) -> None:", "funcdef": "def"}, "auto_disc.legacy.explorers.imgep_explorer.IMGEPExplorer.sample": {"fullname": "auto_disc.legacy.explorers.imgep_explorer.IMGEPExplorer.sample", "modulename": "auto_disc.legacy.explorers.imgep_explorer", "qualname": "IMGEPExplorer.sample", "kind": "function", "doc": "

Emits a new set of parameters to test in the system

\n", "signature": "(self) -> addict.addict.Dict:", "funcdef": "def"}, "auto_disc.legacy.explorers.imgep_explorer.IMGEPExplorer.observe": {"fullname": "auto_disc.legacy.explorers.imgep_explorer.IMGEPExplorer.observe", "modulename": "auto_disc.legacy.explorers.imgep_explorer", "qualname": "IMGEPExplorer.observe", "kind": "function", "doc": "

gives to the explorer the exit of the system after using the parameters

\n\n
Arguments:
\n\n
    \n
  • parameters: system's parameters
    • \n
  • observations: observations/results of previous explorations
    • \n
\n", "signature": "(\tself,\tparameters: addict.addict.Dict,\tobservations: Dict[str, torch.Tensor]) -> None:", "funcdef": "def"}, "auto_disc.legacy.explorers.imgep_explorer.IMGEPExplorer.optimize": {"fullname": "auto_disc.legacy.explorers.imgep_explorer.IMGEPExplorer.optimize", "modulename": "auto_disc.legacy.explorers.imgep_explorer", "qualname": "IMGEPExplorer.optimize", "kind": "function", "doc": "

Optimizes the explorer's sample policy given the discoveries arhcived

\n", "signature": "(self):", "funcdef": "def"}, "auto_disc.legacy.explorers.imgep_explorer.IMGEPExplorer.save": {"fullname": "auto_disc.legacy.explorers.imgep_explorer.IMGEPExplorer.save", "modulename": "auto_disc.legacy.explorers.imgep_explorer", "qualname": "IMGEPExplorer.save", "kind": "function", "doc": "

Return a dict storing everything that has to be pickled.

\n", "signature": "(self) -> Dict[str, auto_disc.legacy.utils.spaces.dict.DictSpace]:", "funcdef": "def"}, "auto_disc.legacy.explorers.imgep_explorer.IMGEPExplorer.load": {"fullname": "auto_disc.legacy.explorers.imgep_explorer.IMGEPExplorer.load", "modulename": "auto_disc.legacy.explorers.imgep_explorer", "qualname": "IMGEPExplorer.load", "kind": "function", "doc": "

Reload module given a dict storing everything that had to be pickled.

\n\n
Arguments:
\n\n
    \n
  • saved_dict: The dict used to restore the module
    • \n
\n", "signature": "(self, saved_dict) -> None:", "funcdef": "def"}, "auto_disc.legacy.input_wrappers": {"fullname": "auto_disc.legacy.input_wrappers", "modulename": "auto_disc.legacy.input_wrappers", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.input_wrappers.base_input_wrapper": {"fullname": "auto_disc.legacy.input_wrappers.base_input_wrapper", "modulename": "auto_disc.legacy.input_wrappers.base_input_wrapper", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.input_wrappers.base_input_wrapper.BaseInputWrapper": {"fullname": "auto_disc.legacy.input_wrappers.base_input_wrapper.BaseInputWrapper", "modulename": "auto_disc.legacy.input_wrappers.base_input_wrapper", "qualname": "BaseInputWrapper", "kind": "class", "doc": "

Base class to map the parameters sent by the explorer to the system's input space

\n", "bases": "auto_disc.legacy.base_autodisc_module.BaseAutoDiscModule"}, "auto_disc.legacy.input_wrappers.base_input_wrapper.BaseInputWrapper.__init__": {"fullname": "auto_disc.legacy.input_wrappers.base_input_wrapper.BaseInputWrapper.__init__", "modulename": "auto_disc.legacy.input_wrappers.base_input_wrapper", "qualname": "BaseInputWrapper.__init__", "kind": "function", "doc": "

initialize attributes common to all auto_disc.legacy modules

\n\n
Arguments:
\n\n
    \n
  • logger: the logger which will make it possible to keep information on the progress of an experiment in the database or on files
    • \n
\n", "signature": "(wrapped_output_space_key=None, **kwargs)"}, "auto_disc.legacy.input_wrappers.base_input_wrapper.BaseInputWrapper.initialize": {"fullname": "auto_disc.legacy.input_wrappers.base_input_wrapper.BaseInputWrapper.initialize", "modulename": "auto_disc.legacy.input_wrappers.base_input_wrapper", "qualname": "BaseInputWrapper.initialize", "kind": "function", "doc": "

Defines input and output space for the input wrapper.

\n", "signature": "(self, output_space: auto_disc.legacy.utils.spaces.dict.DictSpace) -> None:", "funcdef": "def"}, "auto_disc.legacy.input_wrappers.base_input_wrapper.BaseInputWrapper.map": {"fullname": "auto_disc.legacy.input_wrappers.base_input_wrapper.BaseInputWrapper.map", "modulename": "auto_disc.legacy.input_wrappers.base_input_wrapper", "qualname": "BaseInputWrapper.map", "kind": "function", "doc": "

Map the input parameters (from the explorer) to the output parameters (sytem input)

\n\n
Arguments:
\n\n
    \n
  • input: input parameters
    • \n
  • is_input_new_discovery: indicates if it is a new discovery
    • \n
\n", "signature": "(self, input, is_input_new_discovery, **kwargs):", "funcdef": "def"}, "auto_disc.legacy.input_wrappers.generic": {"fullname": "auto_disc.legacy.input_wrappers.generic", "modulename": "auto_disc.legacy.input_wrappers.generic", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.input_wrappers.generic.cppn": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.input_wrappers.generic.cppn.cppn_input_wrapper": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.cppn_input_wrapper", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.cppn_input_wrapper", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.input_wrappers.generic.cppn.cppn_input_wrapper.CppnInputWrapper": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.cppn_input_wrapper.CppnInputWrapper", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.cppn_input_wrapper", "qualname": "CppnInputWrapper", "kind": "class", "doc": "

Base class to map the parameters sent by the explorer to the system's input space

\n", "bases": "auto_disc.legacy.input_wrappers.base_input_wrapper.BaseInputWrapper"}, "auto_disc.legacy.input_wrappers.generic.cppn.cppn_input_wrapper.CppnInputWrapper.__init__": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.cppn_input_wrapper.CppnInputWrapper.__init__", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.cppn_input_wrapper", "qualname": "CppnInputWrapper.__init__", "kind": "function", "doc": "

initialize attributes common to all auto_disc.legacy modules

\n\n
Arguments:
\n\n
    \n
  • logger: the logger which will make it possible to keep information on the progress of an experiment in the database or on files
    • \n
\n", "signature": "(*args, **kws)"}, "auto_disc.legacy.input_wrappers.generic.cppn.cppn_input_wrapper.CppnInputWrapper.map": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.cppn_input_wrapper.CppnInputWrapper.map", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.cppn_input_wrapper", "qualname": "CppnInputWrapper.map", "kind": "function", "doc": "

Map the input parameters (from the explorer) to the cppn output parameters (sytem input)

\n\n
Arguments:
\n\n
    \n
  • parameters: cppn input parameters
    • \n
  • is_input_new_discovery: indicates if it is a new discovery
    • \n
\n\n
Returns:
\n\n
\n

parameters: parameters after map to match system input

\n
\n", "signature": "(\tself,\tinput: Dict[str, Any],\tis_input_new_discovery: bool,\t**kwargs) -> Dict[str, torch.Tensor]:", "funcdef": "def"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.activations": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.activations", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.activations", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.activations.delphineat_gauss_activation": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.activations.delphineat_gauss_activation", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.activations", "qualname": "delphineat_gauss_activation", "kind": "function", "doc": "

PyTorch implementation of gauss activation as defined by SharpNEAT, which is also as in DelphiNEAT.

\n", "signature": "(z):", "funcdef": "def"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.activations.delphineat_sigmoid_activation": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.activations.delphineat_sigmoid_activation", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.activations", "qualname": "delphineat_sigmoid_activation", "kind": "function", "doc": "

PyTorch implementation of sigmoidal activation function as defined in DelphiNEAT

\n", "signature": "(z):", "funcdef": "def"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.activations.tanh_activation": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.activations.tanh_activation", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.activations", "qualname": "tanh_activation", "kind": "function", "doc": "

\n", "signature": "(z):", "funcdef": "def"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.activations.sin_activation": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.activations.sin_activation", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.activations", "qualname": "sin_activation", "kind": "function", "doc": "

\n", "signature": "(z):", "funcdef": "def"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.activations.sigmoid_activation": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.activations.sigmoid_activation", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.activations", "qualname": "sigmoid_activation", "kind": "function", "doc": "

\n", "signature": "(z):", "funcdef": "def"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.activations.gauss_activation": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.activations.gauss_activation", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.activations", "qualname": "gauss_activation", "kind": "function", "doc": "

\n", "signature": "(z):", "funcdef": "def"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.activations.identity_activation": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.activations.identity_activation", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.activations", "qualname": "identity_activation", "kind": "function", "doc": "

\n", "signature": "(z):", "funcdef": "def"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.activations.relu_activation": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.activations.relu_activation", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.activations", "qualname": "relu_activation", "kind": "function", "doc": "

\n", "signature": "(z):", "funcdef": "def"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.aggregations": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.aggregations", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.aggregations", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.aggregations.sum_aggregation": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.aggregations.sum_aggregation", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.aggregations", "qualname": "sum_aggregation", "kind": "function", "doc": "

\n", "signature": "(inputs):", "funcdef": "def"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.aggregations.product_aggregation": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.aggregations.product_aggregation", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.aggregations", "qualname": "product_aggregation", "kind": "function", "doc": "

\n", "signature": "(inputs):", "funcdef": "def"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn.RecurrentNetwork": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn.RecurrentNetwork", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn", "qualname": "RecurrentNetwork", "kind": "class", "doc": "

Base class for all neural network modules.

\n\n

Your models should also subclass this class.

\n\n

Modules can also contain other Modules, allowing to nest them in\na tree structure. You can assign the submodules as regular attributes::

\n\n
import torch.nn as nn\nimport torch.nn.functional as F\n\nclass Model(nn.Module):\n    def __init__(self):\n        super().__init__()\n        self.conv1 = nn.Conv2d(1, 20, 5)\n        self.conv2 = nn.Conv2d(20, 20, 5)\n\n    def forward(self, x):\n        x = F.relu(self.conv1(x))\n        return F.relu(self.conv2(x))\n
\n\n

Submodules assigned in this way will be registered, and will have their\nparameters converted too when you call to(), etc.

\n\n
\n\n

As per the example above, an __init__() call to the parent class\nmust be made before assignment on the child.

\n\n
\n\n

:ivar training: Boolean represents whether this module is in training or\n evaluation mode.\n:vartype training: bool

\n", "bases": "torch.nn.modules.module.Module"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn.RecurrentNetwork.__init__": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn.RecurrentNetwork.__init__", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn", "qualname": "RecurrentNetwork.__init__", "kind": "function", "doc": "
Parameters
\n\n
    \n
  • input_neuron_ids: list of ids of input neurons
    • \n
  • hidden_neuron_ids: list of ids of hidden neurons
    • \n
  • output_neuron_ids: list of ids of output neurons
    • \n
\n", "signature": "(\tinput_neuron_ids,\thidden_neuron_ids,\toutput_neuron_ids,\tbiases,\tresponses,\tactivations,\tconnections,\tdevice='cpu')"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn.RecurrentNetwork.forward": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn.RecurrentNetwork.forward", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn", "qualname": "RecurrentNetwork.forward", "kind": "function", "doc": "
Parameters
\n\n
    \n
  • inputs: tensor of size (batch_size, n_inputs)\nNote: output of a node as follows: activation(bias+(response\u2217aggregation(inputs)))\nreturns: (batch_size, n_outputs)
    • \n
\n", "signature": "(self, inputs):", "funcdef": "def"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn.RecurrentNetwork.activate": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn.RecurrentNetwork.activate", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn", "qualname": "RecurrentNetwork.activate", "kind": "function", "doc": "
Parameters
\n\n
    \n
  • inputs: tensor of size (..., n_inputs) => only the last dim matter, then CPPN is applied on all coordinates
    • \n
  • n_passes: number of passes in the RNN TODO: currently global passes should it be internal passes as in pytorch-neat by uber research ?\nreturns: (..., n_outputs)
    • \n
\n", "signature": "(self, inputs, n_passes=1):", "funcdef": "def"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn.RecurrentNetwork.draw_net": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn.RecurrentNetwork.draw_net", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn", "qualname": "RecurrentNetwork.draw_net", "kind": "function", "doc": "

\n", "signature": "(\tself,\tview=False,\tfilename=None,\tnode_names=None,\tnode_colors=None,\tfmt='svg'):", "funcdef": "def"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn.RecurrentNetwork.create": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn.RecurrentNetwork.create", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn", "qualname": "RecurrentNetwork.create", "kind": "function", "doc": "

Receives a genome and returns the RecurrentCPPN()

\n", "signature": "(genome, config, device='cpu'):", "funcdef": "def"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn.RecurrentNetwork.update_genome": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn.RecurrentNetwork.update_genome", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn", "qualname": "RecurrentNetwork.update_genome", "kind": "function", "doc": "

\n", "signature": "(self, genome):", "funcdef": "def"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn.str_to_tuple_key": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn.str_to_tuple_key", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn", "qualname": "str_to_tuple_key", "kind": "function", "doc": "

\n", "signature": "(str_key):", "funcdef": "def"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn.tuple_to_str_key": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn.tuple_to_str_key", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn", "qualname": "tuple_to_str_key", "kind": "function", "doc": "

\n", "signature": "(tuple_key):", "funcdef": "def"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn.RecurrentNetworkAgg": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn.RecurrentNetworkAgg", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn", "qualname": "RecurrentNetworkAgg", "kind": "class", "doc": "

Base class for all neural network modules.

\n\n

Your models should also subclass this class.

\n\n

Modules can also contain other Modules, allowing to nest them in\na tree structure. You can assign the submodules as regular attributes::

\n\n
import torch.nn as nn\nimport torch.nn.functional as F\n\nclass Model(nn.Module):\n    def __init__(self):\n        super().__init__()\n        self.conv1 = nn.Conv2d(1, 20, 5)\n        self.conv2 = nn.Conv2d(20, 20, 5)\n\n    def forward(self, x):\n        x = F.relu(self.conv1(x))\n        return F.relu(self.conv2(x))\n
\n\n

Submodules assigned in this way will be registered, and will have their\nparameters converted too when you call to(), etc.

\n\n
\n\n

As per the example above, an __init__() call to the parent class\nmust be made before assignment on the child.

\n\n
\n\n

:ivar training: Boolean represents whether this module is in training or\n evaluation mode.\n:vartype training: bool

\n", "bases": "torch.nn.modules.module.Module"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn.RecurrentNetworkAgg.__init__": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn.RecurrentNetworkAgg.__init__", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn", "qualname": "RecurrentNetworkAgg.__init__", "kind": "function", "doc": "
Parameters
\n\n
    \n
  • input_neuron_ids: list of ids of input neurons
    • \n
  • hidden_neuron_ids: list of ids of hidden neurons
    • \n
  • output_neuron_ids: list of ids of output neurons
    • \n
\n", "signature": "(\tinput_neuron_ids,\thidden_neuron_ids,\toutput_neuron_ids,\tbiases,\tresponses,\tactivations,\taggregations,\tconnections,\tdevice='cpu')"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn.RecurrentNetworkAgg.forward": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn.RecurrentNetworkAgg.forward", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn", "qualname": "RecurrentNetworkAgg.forward", "kind": "function", "doc": "
Parameters
\n\n
    \n
  • inputs: tensor of size (batch_size, n_inputs)
    • \n
\n\n

Note: output of a node as follows: activation(bias+(response\u2217aggregation(inputs)))

\n\n

returns: (batch_size, n_outputs)

\n", "signature": "(self, inputs):", "funcdef": "def"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn.RecurrentNetworkAgg.activate": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn.RecurrentNetworkAgg.activate", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn", "qualname": "RecurrentNetworkAgg.activate", "kind": "function", "doc": "
Parameters
\n\n
    \n
  • inputs: tensor of size (..., n_inputs) => only the last dim matter, then CPPN is applied on all coordinates
    • \n
  • n_passes: number of passes in the RNN TODO: currently global passes should it be internal passes as in pytorch-neat by uber research ?\nreturns: (..., n_outputs)
    • \n
\n", "signature": "(self, inputs, n_passes=1):", "funcdef": "def"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn.RecurrentNetworkAgg.draw_net": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn.RecurrentNetworkAgg.draw_net", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn", "qualname": "RecurrentNetworkAgg.draw_net", "kind": "function", "doc": "

\n", "signature": "(\tself,\tconfig=None,\tview=False,\tfilename=None,\tnode_names=None,\tnode_colors=None,\tfmt='svg'):", "funcdef": "def"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn.RecurrentNetworkAgg.create": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn.RecurrentNetworkAgg.create", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn", "qualname": "RecurrentNetworkAgg.create", "kind": "function", "doc": "

Receives a genome and returns the RecurrentCPPN()

\n", "signature": "(genome, config, device='cpu'):", "funcdef": "def"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn.RecurrentNetworkAgg.update_genome": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn.RecurrentNetworkAgg.update_genome", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn", "qualname": "RecurrentNetworkAgg.update_genome", "kind": "function", "doc": "

\n", "signature": "(self, genome):", "funcdef": "def"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.selfconnectiongenome": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.selfconnectiongenome", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.selfconnectiongenome", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.selfconnectiongenome.ConnectionWeightAttribute": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.selfconnectiongenome.ConnectionWeightAttribute", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.selfconnectiongenome", "qualname": "ConnectionWeightAttribute", "kind": "class", "doc": "

Class for connection weight attributes. Is introduced to allow a differentiation between self connections of a node and connections to other nodes.

\n", "bases": "neat.attributes.BaseAttribute"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.selfconnectiongenome.ConnectionWeightAttribute.clamp": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.selfconnectiongenome.ConnectionWeightAttribute.clamp", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.selfconnectiongenome", "qualname": "ConnectionWeightAttribute.clamp", "kind": "function", "doc": "

\n", "signature": "(self, value, is_other_connect, config):", "funcdef": "def"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.selfconnectiongenome.ConnectionWeightAttribute.init_value": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.selfconnectiongenome.ConnectionWeightAttribute.init_value", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.selfconnectiongenome", "qualname": "ConnectionWeightAttribute.init_value", "kind": "function", "doc": "

\n", "signature": "(self, is_other_connect, config):", "funcdef": "def"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.selfconnectiongenome.ConnectionWeightAttribute.mutate_value": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.selfconnectiongenome.ConnectionWeightAttribute.mutate_value", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.selfconnectiongenome", "qualname": "ConnectionWeightAttribute.mutate_value", "kind": "function", "doc": "

\n", "signature": "(self, value, is_other_connect, config):", "funcdef": "def"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.selfconnectiongenome.ConnectionWeightAttribute.validate": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.selfconnectiongenome.ConnectionWeightAttribute.validate", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.selfconnectiongenome", "qualname": "ConnectionWeightAttribute.validate", "kind": "function", "doc": "

\n", "signature": "(self, config):", "funcdef": "def"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.selfconnectiongenome.SelfConnectionGene": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.selfconnectiongenome.SelfConnectionGene", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.selfconnectiongenome", "qualname": "SelfConnectionGene", "kind": "class", "doc": "

Handles functions shared by multiple types of genes (both node and connection),\nincluding crossover and calling mutation methods.

\n", "bases": "neat.genes.BaseGene"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.selfconnectiongenome.SelfConnectionGene.__init__": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.selfconnectiongenome.SelfConnectionGene.__init__", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.selfconnectiongenome", "qualname": "SelfConnectionGene.__init__", "kind": "function", "doc": "

\n", "signature": "(key)"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.selfconnectiongenome.SelfConnectionGene.distance": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.selfconnectiongenome.SelfConnectionGene.distance", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.selfconnectiongenome", "qualname": "SelfConnectionGene.distance", "kind": "function", "doc": "

\n", "signature": "(self, other, config):", "funcdef": "def"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.selfconnectiongenome.SelfConnectionGene.init_attributes": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.selfconnectiongenome.SelfConnectionGene.init_attributes", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.selfconnectiongenome", "qualname": "SelfConnectionGene.init_attributes", "kind": "function", "doc": "

\n", "signature": "(self, config):", "funcdef": "def"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.selfconnectiongenome.SelfConnectionGene.mutate": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.selfconnectiongenome.SelfConnectionGene.mutate", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.selfconnectiongenome", "qualname": "SelfConnectionGene.mutate", "kind": "function", "doc": "

\n", "signature": "(self, config):", "funcdef": "def"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.selfconnectiongenome.SelfConnectionGenome": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.selfconnectiongenome.SelfConnectionGenome", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.selfconnectiongenome", "qualname": "SelfConnectionGenome", "kind": "class", "doc": "

A genome for generalized neural networks. Derived from neat.DefaultGenome.\nExtends the default genome to allow specific configuration for self connections of neurons.

\n\n

Uses the SelfConnectionGene class instead of the neat.genes.DefaultConnectionGene class for connections.

\n\n

Terminology\n pin: Point at which the network is conceptually connected to the external world;\n pins are either input or output.\n node: Analog of a physical neuron.\n connection: Connection between a pin/node output and a node's input, or between a node's\n output and a pin/node input.\n key: Identifier for an object, unique within the set of similar objects.

\n\n

Design assumptions and conventions.\n 1. Each output pin is connected only to the output of its own unique\n neuron by an implicit connection with weight one. This connection\n is permanently enabled.\n 2. The output pin's key is always the same as the key for its\n associated neuron.\n 3. Output neurons can be modified but not deleted.\n 4. The input values are applied to the input pins unmodified.

\n", "bases": "neat.genome.DefaultGenome"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.selfconnectiongenome.SelfConnectionGenome.parse_config": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.selfconnectiongenome.SelfConnectionGenome.parse_config", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.selfconnectiongenome", "qualname": "SelfConnectionGenome.parse_config", "kind": "function", "doc": "

\n", "signature": "(cls, param_dict):", "funcdef": "def"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.utils": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.utils", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.utils", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.utils.create_image_cppn_input": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.utils.create_image_cppn_input", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.utils", "qualname": "create_image_cppn_input", "kind": "function", "doc": "

\n", "signature": "(output_size, is_distance_to_center=True, is_bias=True):", "funcdef": "def"}, "auto_disc.legacy.input_wrappers.generic.cppn.utils": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.utils", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.utils", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.input_wrappers.generic.cppn.utils.cppn_genome_space": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.utils.cppn_genome_space", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.utils.cppn_genome_space", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.input_wrappers.generic.cppn.utils.cppn_genome_space.CPPNGenomeSpace": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.utils.cppn_genome_space.CPPNGenomeSpace", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.utils.cppn_genome_space", "qualname": "CPPNGenomeSpace", "kind": "class", "doc": "

Defines the init_space, genome_space and intervention_space of a system

\n", "bases": "auto_disc.legacy.utils.spaces.base_space.BaseSpace"}, "auto_disc.legacy.input_wrappers.generic.cppn.utils.cppn_genome_space.CPPNGenomeSpace.__init__": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.utils.cppn_genome_space.CPPNGenomeSpace.__init__", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.utils.cppn_genome_space", "qualname": "CPPNGenomeSpace.__init__", "kind": "function", "doc": "

Init the elements useful to the spaces

\n\n
Arguments:
\n\n
    \n
  • shape: Space shape
    • \n
  • dtype: torch type
    • \n
  • mutator: current mutator method
    • \n
\n", "signature": "()"}, "auto_disc.legacy.input_wrappers.generic.cppn.utils.cppn_genome_space.CPPNGenomeSpace.sample": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.utils.cppn_genome_space.CPPNGenomeSpace.sample", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.utils.cppn_genome_space", "qualname": "CPPNGenomeSpace.sample", "kind": "function", "doc": "

Randomly sample an element of this space.\nCan be uniform or non-uniform sampling based on boundedness of space.

\n", "signature": "(self):", "funcdef": "def"}, "auto_disc.legacy.input_wrappers.generic.cppn.utils.cppn_genome_space.CPPNGenomeSpace.mutate": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.utils.cppn_genome_space.CPPNGenomeSpace.mutate", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.utils.cppn_genome_space", "qualname": "CPPNGenomeSpace.mutate", "kind": "function", "doc": "

Randomly mutate an element of this space.

\n\n
Arguments:
\n\n
    \n
  • x: The element to be mutated
    • \n
\n", "signature": "(self, genome):", "funcdef": "def"}, "auto_disc.legacy.input_wrappers.generic.cppn.utils.cppn_genome_space.CPPNGenomeSpace.contains": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.utils.cppn_genome_space.CPPNGenomeSpace.contains", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.utils.cppn_genome_space", "qualname": "CPPNGenomeSpace.contains", "kind": "function", "doc": "

Return boolean specifying if x is a valid\nmember of this space

\n\n
Arguments:
\n\n
    \n
  • x: The var which must be part of the space
    • \n
\n", "signature": "(self, x):", "funcdef": "def"}, "auto_disc.legacy.input_wrappers.generic.cppn.utils.cppn_genome_space.CPPNGenomeSpace.clamp": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.utils.cppn_genome_space.CPPNGenomeSpace.clamp", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.utils.cppn_genome_space", "qualname": "CPPNGenomeSpace.clamp", "kind": "function", "doc": "

Return a valid clamped value of x inside space's bounds

\n\n
Arguments:
\n\n
    \n
  • x: a value
    • \n
\n", "signature": "(self, x):", "funcdef": "def"}, "auto_disc.legacy.input_wrappers.generic.dummy_input_wrapper": {"fullname": "auto_disc.legacy.input_wrappers.generic.dummy_input_wrapper", "modulename": "auto_disc.legacy.input_wrappers.generic.dummy_input_wrapper", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.input_wrappers.generic.dummy_input_wrapper.DummyInputWrapper": {"fullname": "auto_disc.legacy.input_wrappers.generic.dummy_input_wrapper.DummyInputWrapper", "modulename": "auto_disc.legacy.input_wrappers.generic.dummy_input_wrapper", "qualname": "DummyInputWrapper", "kind": "class", "doc": "

Empty InputWrapper used when no wrapper should be used.

\n", "bases": "auto_disc.legacy.input_wrappers.base_input_wrapper.BaseInputWrapper"}, "auto_disc.legacy.input_wrappers.generic.dummy_input_wrapper.DummyInputWrapper.__init__": {"fullname": "auto_disc.legacy.input_wrappers.generic.dummy_input_wrapper.DummyInputWrapper.__init__", "modulename": "auto_disc.legacy.input_wrappers.generic.dummy_input_wrapper", "qualname": "DummyInputWrapper.__init__", "kind": "function", "doc": "

initialize attributes common to all auto_disc.legacy modules

\n\n
Arguments:
\n\n
    \n
  • logger: the logger which will make it possible to keep information on the progress of an experiment in the database or on files
    • \n
\n", "signature": "(wrapped_output_space_key: str = None, **kwargs)"}, "auto_disc.legacy.input_wrappers.generic.dummy_input_wrapper.DummyInputWrapper.map": {"fullname": "auto_disc.legacy.input_wrappers.generic.dummy_input_wrapper.DummyInputWrapper.map", "modulename": "auto_disc.legacy.input_wrappers.generic.dummy_input_wrapper", "qualname": "DummyInputWrapper.map", "kind": "function", "doc": "

Map the input parameters (from the explorer) to the output parameters (sytem input)

\n\n
Arguments:
\n\n
    \n
  • input: input parameters
    • \n
  • is_input_new_discovery: indicates if it is a new discovery
    • \n
\n", "signature": "(self, input: Dict, is_input_new_discovery, **kwargs) -> Dict:", "funcdef": "def"}, "auto_disc.legacy.input_wrappers.generic.times_n_input_wrapper": {"fullname": "auto_disc.legacy.input_wrappers.generic.times_n_input_wrapper", "modulename": "auto_disc.legacy.input_wrappers.generic.times_n_input_wrapper", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.input_wrappers.generic.times_n_input_wrapper.TimesNInputWrapper": {"fullname": "auto_disc.legacy.input_wrappers.generic.times_n_input_wrapper.TimesNInputWrapper", "modulename": "auto_disc.legacy.input_wrappers.generic.times_n_input_wrapper", "qualname": "TimesNInputWrapper", "kind": "class", "doc": "

Base class to map the parameters sent by the explorer to the system's input space

\n", "bases": "auto_disc.legacy.input_wrappers.base_input_wrapper.BaseInputWrapper"}, "auto_disc.legacy.input_wrappers.generic.times_n_input_wrapper.TimesNInputWrapper.__init__": {"fullname": "auto_disc.legacy.input_wrappers.generic.times_n_input_wrapper.TimesNInputWrapper.__init__", "modulename": "auto_disc.legacy.input_wrappers.generic.times_n_input_wrapper", "qualname": "TimesNInputWrapper.__init__", "kind": "function", "doc": "

initialize attributes common to all auto_disc.legacy modules

\n\n
Arguments:
\n\n
    \n
  • logger: the logger which will make it possible to keep information on the progress of an experiment in the database or on files
    • \n
\n", "signature": "(*args, **kws)"}, "auto_disc.legacy.input_wrappers.generic.times_n_input_wrapper.TimesNInputWrapper.map": {"fullname": "auto_disc.legacy.input_wrappers.generic.times_n_input_wrapper.TimesNInputWrapper.map", "modulename": "auto_disc.legacy.input_wrappers.generic.times_n_input_wrapper", "qualname": "TimesNInputWrapper.map", "kind": "function", "doc": "

Map the input parameters (from the explorer) to the output parameters (sytem input)

\n\n
Arguments:
\n\n
    \n
  • input: input parameters
    • \n
  • is_input_new_discovery: indicates if it is a new discovery
    • \n
\n", "signature": "(\tself,\tinput: addict.addict.Dict,\tis_input_new_discovery,\t**kwargs) -> addict.addict.Dict:", "funcdef": "def"}, "auto_disc.legacy.input_wrappers.specific": {"fullname": "auto_disc.legacy.input_wrappers.specific", "modulename": "auto_disc.legacy.input_wrappers.specific", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.output_representations": {"fullname": "auto_disc.legacy.output_representations", "modulename": "auto_disc.legacy.output_representations", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.output_representations.base_output_representation": {"fullname": "auto_disc.legacy.output_representations.base_output_representation", "modulename": "auto_disc.legacy.output_representations.base_output_representation", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.output_representations.base_output_representation.BaseOutputRepresentation": {"fullname": "auto_disc.legacy.output_representations.base_output_representation.BaseOutputRepresentation", "modulename": "auto_disc.legacy.output_representations.base_output_representation", "qualname": "BaseOutputRepresentation", "kind": "class", "doc": "

Base class to map the observations of a system to an embedding vector (BC characterization)

\n", "bases": "auto_disc.legacy.base_autodisc_module.BaseAutoDiscModule"}, "auto_disc.legacy.output_representations.base_output_representation.BaseOutputRepresentation.__init__": {"fullname": "auto_disc.legacy.output_representations.base_output_representation.BaseOutputRepresentation.__init__", "modulename": "auto_disc.legacy.output_representations.base_output_representation", "qualname": "BaseOutputRepresentation.__init__", "kind": "function", "doc": "

initialize attributes common to all auto_disc.legacy modules

\n\n
Arguments:
\n\n
    \n
  • logger: the logger which will make it possible to keep information on the progress of an experiment in the database or on files
    • \n
\n", "signature": "(wrapped_input_space_key: str = None, **kwargs)"}, "auto_disc.legacy.output_representations.base_output_representation.BaseOutputRepresentation.initialize": {"fullname": "auto_disc.legacy.output_representations.base_output_representation.BaseOutputRepresentation.initialize", "modulename": "auto_disc.legacy.output_representations.base_output_representation", "qualname": "BaseOutputRepresentation.initialize", "kind": "function", "doc": "

Defines input and output space for the output representation.

\n", "signature": "(self, input_space: auto_disc.legacy.utils.spaces.dict.DictSpace) -> None:", "funcdef": "def"}, "auto_disc.legacy.output_representations.base_output_representation.BaseOutputRepresentation.map": {"fullname": "auto_disc.legacy.output_representations.base_output_representation.BaseOutputRepresentation.map", "modulename": "auto_disc.legacy.output_representations.base_output_representation", "qualname": "BaseOutputRepresentation.map", "kind": "function", "doc": "

Map the input (system's output) to another space (towards the explorer's input space)

\n\n
Arguments:
\n\n
    \n
  • input: input parameters
    • \n
  • is_output_new_discovery: indicates if it is a new discovery
    • \n
\n", "signature": "(self, input, is_output_new_discovery, **kwargs):", "funcdef": "def"}, "auto_disc.legacy.output_representations.base_output_representation.BaseOutputRepresentation.calc_distance": {"fullname": "auto_disc.legacy.output_representations.base_output_representation.BaseOutputRepresentation.calc_distance", "modulename": "auto_disc.legacy.output_representations.base_output_representation", "qualname": "BaseOutputRepresentation.calc_distance", "kind": "function", "doc": "

Compute the distance between 2 embeddings in the latent space\n/!\\ batch mode embedding_a and embedding_b can be N*M or M

\n", "signature": "(self, embedding_a, embedding_b, **kwargs):", "funcdef": "def"}, "auto_disc.legacy.output_representations.generic": {"fullname": "auto_disc.legacy.output_representations.generic", "modulename": "auto_disc.legacy.output_representations.generic", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.output_representations.generic.dummy_output_representation": {"fullname": "auto_disc.legacy.output_representations.generic.dummy_output_representation", "modulename": "auto_disc.legacy.output_representations.generic.dummy_output_representation", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.output_representations.generic.dummy_output_representation.DummyOutputRepresentation": {"fullname": "auto_disc.legacy.output_representations.generic.dummy_output_representation.DummyOutputRepresentation", "modulename": "auto_disc.legacy.output_representations.generic.dummy_output_representation", "qualname": "DummyOutputRepresentation", "kind": "class", "doc": "

Empty OutputRepresentation used when no representation of the system's output mut be used.

\n", "bases": "auto_disc.legacy.output_representations.base_output_representation.BaseOutputRepresentation"}, "auto_disc.legacy.output_representations.generic.dummy_output_representation.DummyOutputRepresentation.__init__": {"fullname": "auto_disc.legacy.output_representations.generic.dummy_output_representation.DummyOutputRepresentation.__init__", "modulename": "auto_disc.legacy.output_representations.generic.dummy_output_representation", "qualname": "DummyOutputRepresentation.__init__", "kind": "function", "doc": "

initialize attributes common to all auto_disc.legacy modules

\n\n
Arguments:
\n\n
    \n
  • logger: the logger which will make it possible to keep information on the progress of an experiment in the database or on files
    • \n
\n", "signature": "(wrapped_input_space_key=None, **kwargs)"}, "auto_disc.legacy.output_representations.generic.dummy_output_representation.DummyOutputRepresentation.map": {"fullname": "auto_disc.legacy.output_representations.generic.dummy_output_representation.DummyOutputRepresentation.map", "modulename": "auto_disc.legacy.output_representations.generic.dummy_output_representation", "qualname": "DummyOutputRepresentation.map", "kind": "function", "doc": "

Map the input (system's output) to another space (towards the explorer's input space)

\n\n
Arguments:
\n\n
    \n
  • input: input parameters
    • \n
  • is_output_new_discovery: indicates if it is a new discovery
    • \n
\n", "signature": "(self, input, is_output_new_discovery, **kwargs):", "funcdef": "def"}, "auto_disc.legacy.output_representations.generic.dummy_output_representation.DummyOutputRepresentation.calc_distance": {"fullname": "auto_disc.legacy.output_representations.generic.dummy_output_representation.DummyOutputRepresentation.calc_distance", "modulename": "auto_disc.legacy.output_representations.generic.dummy_output_representation", "qualname": "DummyOutputRepresentation.calc_distance", "kind": "function", "doc": "

Compute the distance between 2 embeddings in the latent space\n/!\\ batch mode embedding_a and embedding_b can be N*M or M

\n", "signature": "(\tself,\tembedding_a: torch.Tensor,\tembedding_b: torch.Tensor,\t**kwargs) -> torch.Tensor:", "funcdef": "def"}, "auto_disc.legacy.output_representations.specific": {"fullname": "auto_disc.legacy.output_representations.specific", "modulename": "auto_disc.legacy.output_representations.specific", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.output_representations.specific.lenia_output_representation": {"fullname": "auto_disc.legacy.output_representations.specific.lenia_output_representation", "modulename": "auto_disc.legacy.output_representations.specific.lenia_output_representation", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.output_representations.specific.lenia_output_representation.LeniaImageRepresentation": {"fullname": "auto_disc.legacy.output_representations.specific.lenia_output_representation.LeniaImageRepresentation", "modulename": "auto_disc.legacy.output_representations.specific.lenia_output_representation", "qualname": "LeniaImageRepresentation", "kind": "class", "doc": "

Base class to map the observations of a system to an embedding vector (BC characterization)

\n", "bases": "auto_disc.legacy.output_representations.base_output_representation.BaseOutputRepresentation"}, "auto_disc.legacy.output_representations.specific.lenia_output_representation.LeniaImageRepresentation.__init__": {"fullname": "auto_disc.legacy.output_representations.specific.lenia_output_representation.LeniaImageRepresentation.__init__", "modulename": "auto_disc.legacy.output_representations.specific.lenia_output_representation", "qualname": "LeniaImageRepresentation.__init__", "kind": "function", "doc": "

initialize attributes common to all auto_disc.legacy modules

\n\n
Arguments:
\n\n
    \n
  • logger: the logger which will make it possible to keep information on the progress of an experiment in the database or on files
    • \n
\n", "signature": "(*args, **kws)"}, "auto_disc.legacy.output_representations.specific.lenia_output_representation.LeniaImageRepresentation.map": {"fullname": "auto_disc.legacy.output_representations.specific.lenia_output_representation.LeniaImageRepresentation.map", "modulename": "auto_disc.legacy.output_representations.specific.lenia_output_representation", "qualname": "LeniaImageRepresentation.map", "kind": "function", "doc": "

Flatten Lenia's output

\n\n
Arguments:
\n\n
    \n
  • input: Lenia's output
    • \n
  • is_output_new_discovery: indicates if it is a new discovery
    • \n
\n\n
Returns:
\n\n
\n

Return a torch tensor in dict

\n
\n", "signature": "(\tself,\tinput: Dict,\tis_output_new_discovery: bool) -> Dict[str, torch.Tensor]:", "funcdef": "def"}, "auto_disc.legacy.output_representations.specific.lenia_output_representation.LeniaImageRepresentation.calc_distance": {"fullname": "auto_disc.legacy.output_representations.specific.lenia_output_representation.LeniaImageRepresentation.calc_distance", "modulename": "auto_disc.legacy.output_representations.specific.lenia_output_representation", "qualname": "LeniaImageRepresentation.calc_distance", "kind": "function", "doc": "

Compute the distance between 2 embeddings in the latent space\n/!\\ batch mode embedding_a and embedding_b can be N*M or M

\n", "signature": "(\tself,\tembedding_a: torch.Tensor,\tembedding_b: torch.Tensor,\t**kwargs) -> torch.Tensor:", "funcdef": "def"}, "auto_disc.legacy.output_representations.specific.lenia_output_representation_hand_defined": {"fullname": "auto_disc.legacy.output_representations.specific.lenia_output_representation_hand_defined", "modulename": "auto_disc.legacy.output_representations.specific.lenia_output_representation_hand_defined", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.output_representations.specific.lenia_output_representation_hand_defined.center_of_mass": {"fullname": "auto_disc.legacy.output_representations.specific.lenia_output_representation_hand_defined.center_of_mass", "modulename": "auto_disc.legacy.output_representations.specific.lenia_output_representation_hand_defined", "qualname": "center_of_mass", "kind": "function", "doc": "

\n", "signature": "(input_array: torch.Tensor) -> torch.Tensor:", "funcdef": "def"}, "auto_disc.legacy.output_representations.specific.lenia_output_representation_hand_defined.calc_distance_matrix": {"fullname": "auto_disc.legacy.output_representations.specific.lenia_output_representation_hand_defined.calc_distance_matrix", "modulename": "auto_disc.legacy.output_representations.specific.lenia_output_representation_hand_defined", "qualname": "calc_distance_matrix", "kind": "function", "doc": "

\n", "signature": "(size_y: int, size_x: int) -> torch.Tensor:", "funcdef": "def"}, "auto_disc.legacy.output_representations.specific.lenia_output_representation_hand_defined.calc_image_moments": {"fullname": "auto_disc.legacy.output_representations.specific.lenia_output_representation_hand_defined.calc_image_moments", "modulename": "auto_disc.legacy.output_representations.specific.lenia_output_representation_hand_defined", "qualname": "calc_image_moments", "kind": "function", "doc": "

Calculates the image moments for an image.

\n\n
For more information see:
\n\n
\n \n
\n\n

The code is based on the Javascript implementation of Lenia by Bert Chan.\nThe code had to be adapted, because for the original code the coordinates are (x,y), whereas here they are (y,x).

\n\n
Parameters
\n\n
    \n
  • image: 2d gray scale image in form of a numpy array.
    • \n
\n\n
Returns
\n\n
\n

Namedtupel with the different moments.

\n
\n", "signature": "(image: torch.Tensor) -> Dict[str, torch.Tensor]:", "funcdef": "def"}, "auto_disc.legacy.output_representations.specific.lenia_output_representation_hand_defined.LeniaHandDefinedRepresentation": {"fullname": "auto_disc.legacy.output_representations.specific.lenia_output_representation_hand_defined.LeniaHandDefinedRepresentation", "modulename": "auto_disc.legacy.output_representations.specific.lenia_output_representation_hand_defined", "qualname": "LeniaHandDefinedRepresentation", "kind": "class", "doc": "

Base class to map the observations of a system to an embedding vector (BC characterization)

\n", "bases": "auto_disc.legacy.output_representations.base_output_representation.BaseOutputRepresentation"}, "auto_disc.legacy.output_representations.specific.lenia_output_representation_hand_defined.LeniaHandDefinedRepresentation.__init__": {"fullname": "auto_disc.legacy.output_representations.specific.lenia_output_representation_hand_defined.LeniaHandDefinedRepresentation.__init__", "modulename": "auto_disc.legacy.output_representations.specific.lenia_output_representation_hand_defined", "qualname": "LeniaHandDefinedRepresentation.__init__", "kind": "function", "doc": "

initialize attributes common to all auto_disc.legacy modules

\n\n
Arguments:
\n\n
    \n
  • logger: the logger which will make it possible to keep information on the progress of an experiment in the database or on files
    • \n
\n", "signature": "(*args, **kws)"}, "auto_disc.legacy.output_representations.specific.lenia_output_representation_hand_defined.LeniaHandDefinedRepresentation.calc_static_statistics": {"fullname": "auto_disc.legacy.output_representations.specific.lenia_output_representation_hand_defined.LeniaHandDefinedRepresentation.calc_static_statistics", "modulename": "auto_disc.legacy.output_representations.specific.lenia_output_representation_hand_defined", "qualname": "LeniaHandDefinedRepresentation.calc_static_statistics", "kind": "function", "doc": "

Calculates the final statistics for lenia last observation

\n", "signature": "(self, final_obs: torch.Tensor) -> torch.Tensor:", "funcdef": "def"}, "auto_disc.legacy.output_representations.specific.lenia_output_representation_hand_defined.LeniaHandDefinedRepresentation.map": {"fullname": "auto_disc.legacy.output_representations.specific.lenia_output_representation_hand_defined.LeniaHandDefinedRepresentation.map", "modulename": "auto_disc.legacy.output_representations.specific.lenia_output_representation_hand_defined", "qualname": "LeniaHandDefinedRepresentation.map", "kind": "function", "doc": "

Compute statistics on Lenia's output

\n\n
Arguments:
\n\n
    \n
  • input: Lenia's output
    • \n
  • is_output_new_discovery: indicates if it is a new discovery
    • \n
\n\n
Returns:
\n\n
\n

Return a torch tensor in dict

\n
\n", "signature": "(\tself,\tinput: addict.addict.Dict,\tis_output_new_discovery: bool) -> Dict[str, torch.Tensor]:", "funcdef": "def"}, "auto_disc.legacy.output_representations.specific.lenia_output_representation_hand_defined.LeniaHandDefinedRepresentation.calc_distance": {"fullname": "auto_disc.legacy.output_representations.specific.lenia_output_representation_hand_defined.LeniaHandDefinedRepresentation.calc_distance", "modulename": "auto_disc.legacy.output_representations.specific.lenia_output_representation_hand_defined", "qualname": "LeniaHandDefinedRepresentation.calc_distance", "kind": "function", "doc": "

Compute the distance between 2 embeddings in the latent space\n/!\\ batch mode embedding_a and embedding_b can be N*M or M

\n", "signature": "(\tself,\tembedding_a: torch.Tensor,\tembedding_b: torch.Tensor) -> torch.Tensor:", "funcdef": "def"}, "auto_disc.legacy.registration": {"fullname": "auto_disc.legacy.registration", "modulename": "auto_disc.legacy.registration", "kind": "module", "doc": "

This used to be a singleton dictionary mapping between key values and modules\navailable in the software.

\n\n

Those interested may consult the commit 7dea0818 of 5 May 2022

\n"}, "auto_disc.legacy.run": {"fullname": "auto_disc.legacy.run", "modulename": "auto_disc.legacy.run", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.run.create": {"fullname": "auto_disc.legacy.run.create", "modulename": "auto_disc.legacy.run", "qualname": "create", "kind": "function", "doc": "

Setup the whole experiment. Set each modules, logger, callbacks and use them to define the experiment pipeline.

\n\n
Arguments:
\n\n
    \n
  • parameters: Experiment config (define wich systems wich explorer wich callbacks and all other information needed to set an experiment)
    • \n
  • experiment_id: Current experiment id
    • \n
  • seed: current seed number
    • \n
  • additional_callbacks: callbacks we want use in addition to callbacks from parameters arguments
    • \n
  • additional_handlers: handlers we want to use in addition to logger_handlers from parameters arguments
    • \n
\n\n
Returns:
\n\n
\n

experiment: The experiment we have just defined

\n
\n", "signature": "(\tparameters: Dict,\texperiment_id: int,\tseed: int,\tadditional_callbacks: Dict[str, List[Callable]] = None,\tadditional_handlers: List[auto_disc.legacy.utils.logger.ad_logger.AutoDiscLogger] = None,\tinteractMethod: Callable = None) -> auto_disc.legacy.experiment_pipeline.ExperimentPipeline:", "funcdef": "def"}, "auto_disc.legacy.run.start": {"fullname": "auto_disc.legacy.run.start", "modulename": "auto_disc.legacy.run", "qualname": "start", "kind": "function", "doc": "

Runs an experiment for a number of iterations

\n\n
Arguments:
\n\n
    \n
  • experiment: The experiment we want to launch
    • \n
  • nb_iterations: the number explorations
    • \n
\n", "signature": "(\texperiment: auto_disc.legacy.experiment_pipeline.ExperimentPipeline,\tnb_iterations: int) -> None:", "funcdef": "def"}, "auto_disc.legacy.systems": {"fullname": "auto_disc.legacy.systems", "modulename": "auto_disc.legacy.systems", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.systems.base_system": {"fullname": "auto_disc.legacy.systems.base_system", "modulename": "auto_disc.legacy.systems.base_system", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.systems.base_system.BaseSystem": {"fullname": "auto_disc.legacy.systems.base_system.BaseSystem", "modulename": "auto_disc.legacy.systems.base_system", "qualname": "BaseSystem", "kind": "class", "doc": "

The main BaseSystem class. It encapsulates an environment with\narbitrary behind-the-scenes dynamics. An environment can be\npartially or fully observed.

\n\n
The main API methods that users of this class need to know are:
\n\n
\n

reset\n step\n observe\n render\n close

\n
\n\n

The config must be set through ConfigParameters decorators above the system's definition:\n@IntegerConfigParameter(name=\"parameter_name\", default=100, min=1, max=1000)\nThese parameters can then be accessed through self.config.parameter_name

\n", "bases": "auto_disc.legacy.base_autodisc_module.BaseAutoDiscModule"}, "auto_disc.legacy.systems.base_system.BaseSystem.__init__": {"fullname": "auto_disc.legacy.systems.base_system.BaseSystem.__init__", "modulename": "auto_disc.legacy.systems.base_system", "qualname": "BaseSystem.__init__", "kind": "function", "doc": "

initialize attributes common to all auto_disc.legacy modules

\n\n
Arguments:
\n\n
    \n
  • logger: the logger which will make it possible to keep information on the progress of an experiment in the database or on files
    • \n
\n", "signature": "(**kwargs)"}, "auto_disc.legacy.systems.base_system.BaseSystem.reset": {"fullname": "auto_disc.legacy.systems.base_system.BaseSystem.reset", "modulename": "auto_disc.legacy.systems.base_system", "qualname": "BaseSystem.reset", "kind": "function", "doc": "

Resets the environment to an initial state and returns an initial observation.

\n\n
Arguments:
\n\n
    \n
  • run_parameters (AttrDict): the input parameters of the system provided by the agent
    • \n
\n\n
Returns:
\n\n
\n

observation (AttrDict): the initial observation.

\n
\n", "signature": "(self, run_parameters):", "funcdef": "def"}, "auto_disc.legacy.systems.base_system.BaseSystem.step": {"fullname": "auto_disc.legacy.systems.base_system.BaseSystem.step", "modulename": "auto_disc.legacy.systems.base_system", "qualname": "BaseSystem.step", "kind": "function", "doc": "

Run one timestep of the system's dynamics. When end of\nepisode is reached, you are responsible for calling reset()\nto reset this environment's state.\nAccepts an action and returns a tuple (observation, reward, done, info).

\n\n
Arguments:
\n\n
    \n
  • action (AttrDict): an action provided by the agent
    • \n
\n\n
Returns:
\n\n
\n

observation (AttrDict): agent's observation of the current environment\n reward (float) : amount of reward returned after previous action\n done (bool): whether the episode has ended, in which case further step() calls will return undefined results\n info (AttrDict): contains auxiliary diagnostic information (helpful for debugging, and sometimes learning)

\n
\n", "signature": "(self, action=None):", "funcdef": "def"}, "auto_disc.legacy.systems.base_system.BaseSystem.observe": {"fullname": "auto_disc.legacy.systems.base_system.BaseSystem.observe", "modulename": "auto_disc.legacy.systems.base_system", "qualname": "BaseSystem.observe", "kind": "function", "doc": "

Returns the overall output of the system according to the last reset().\nUse this function once the step function has returned done=True to give the system's output to the Output Representation (and then the explorer).

\n", "signature": "(self):", "funcdef": "def"}, "auto_disc.legacy.systems.base_system.BaseSystem.render": {"fullname": "auto_disc.legacy.systems.base_system.BaseSystem.render", "modulename": "auto_disc.legacy.systems.base_system", "qualname": "BaseSystem.render", "kind": "function", "doc": "

Renders the environment.

\n", "signature": "(self, **kwargs):", "funcdef": "def"}, "auto_disc.legacy.systems.base_system.BaseSystem.close": {"fullname": "auto_disc.legacy.systems.base_system.BaseSystem.close", "modulename": "auto_disc.legacy.systems.base_system", "qualname": "BaseSystem.close", "kind": "function", "doc": "

Override close in your subclass to perform any necessary cleanup.\nEnvironments will automatically close() themselves when\ngarbage collected or when the program exits.

\n", "signature": "(self):", "funcdef": "def"}, "auto_disc.legacy.systems.python_systems": {"fullname": "auto_disc.legacy.systems.python_systems", "modulename": "auto_disc.legacy.systems.python_systems", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.systems.python_systems.base_python_system": {"fullname": "auto_disc.legacy.systems.python_systems.base_python_system", "modulename": "auto_disc.legacy.systems.python_systems.base_python_system", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.systems.python_systems.base_python_system.BasePythonSystem": {"fullname": "auto_disc.legacy.systems.python_systems.base_python_system.BasePythonSystem", "modulename": "auto_disc.legacy.systems.python_systems.base_python_system", "qualname": "BasePythonSystem", "kind": "class", "doc": "

The main BaseSystem class. It encapsulates an environment with\narbitrary behind-the-scenes dynamics. An environment can be\npartially or fully observed.

\n\n
The main API methods that users of this class need to know are:
\n\n
\n

reset\n step\n observe\n render\n close

\n
\n\n

The config must be set through ConfigParameters decorators above the system's definition:\n@IntegerConfigParameter(name=\"parameter_name\", default=100, min=1, max=1000)\nThese parameters can then be accessed through self.config.parameter_name

\n", "bases": "auto_disc.legacy.systems.base_system.BaseSystem"}, "auto_disc.legacy.systems.python_systems.lenia": {"fullname": "auto_disc.legacy.systems.python_systems.lenia", "modulename": "auto_disc.legacy.systems.python_systems.lenia", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.systems.python_systems.lenia.Lenia": {"fullname": "auto_disc.legacy.systems.python_systems.lenia.Lenia", "modulename": "auto_disc.legacy.systems.python_systems.lenia", "qualname": "Lenia", "kind": "class", "doc": "

The main BaseSystem class. It encapsulates an environment with\narbitrary behind-the-scenes dynamics. An environment can be\npartially or fully observed.

\n\n
The main API methods that users of this class need to know are:
\n\n
\n

reset\n step\n observe\n render\n close

\n
\n\n

The config must be set through ConfigParameters decorators above the system's definition:\n@IntegerConfigParameter(name=\"parameter_name\", default=100, min=1, max=1000)\nThese parameters can then be accessed through self.config.parameter_name

\n", "bases": "auto_disc.legacy.systems.python_systems.base_python_system.BasePythonSystem"}, "auto_disc.legacy.systems.python_systems.lenia.Lenia.__init__": {"fullname": "auto_disc.legacy.systems.python_systems.lenia.Lenia.__init__", "modulename": "auto_disc.legacy.systems.python_systems.lenia", "qualname": "Lenia.__init__", "kind": "function", "doc": "

initialize attributes common to all auto_disc.legacy modules

\n\n
Arguments:
\n\n
    \n
  • logger: the logger which will make it possible to keep information on the progress of an experiment in the database or on files
    • \n
\n", "signature": "(*args, **kws)"}, "auto_disc.legacy.systems.python_systems.lenia.Lenia.reset": {"fullname": "auto_disc.legacy.systems.python_systems.lenia.Lenia.reset", "modulename": "auto_disc.legacy.systems.python_systems.lenia", "qualname": "Lenia.reset", "kind": "function", "doc": "

Resets the environment to an initial state and returns an initial observation.

\n\n
Arguments:
\n\n
    \n
  • run_parameters (AttrDict): the input parameters of the system provided by the agent
    • \n
\n\n
Returns:
\n\n
\n

observation (AttrDict): the initial observation.

\n
\n", "signature": "(self, run_parameters: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:", "funcdef": "def"}, "auto_disc.legacy.systems.python_systems.lenia.Lenia.step": {"fullname": "auto_disc.legacy.systems.python_systems.lenia.Lenia.step", "modulename": "auto_disc.legacy.systems.python_systems.lenia", "qualname": "Lenia.step", "kind": "function", "doc": "

Run one timestep of the system's dynamics. When end of\nepisode is reached, you are responsible for calling reset()\nto reset this environment's state.\nAccepts an action and returns a tuple (observation, reward, done, info).

\n\n
Arguments:
\n\n
    \n
  • action (AttrDict): an action provided by the agent
    • \n
\n\n
Returns:
\n\n
\n

observation (AttrDict): agent's observation of the current environment\n reward (float) : amount of reward returned after previous action\n done (bool): whether the episode has ended, in which case further step() calls will return undefined results\n info (AttrDict): contains auxiliary diagnostic information (helpful for debugging, and sometimes learning)

\n
\n", "signature": "(self, action=None) -> Tuple[Dict[str, torch.Tensor], int, bool, NoneType]:", "funcdef": "def"}, "auto_disc.legacy.systems.python_systems.lenia.Lenia.observe": {"fullname": "auto_disc.legacy.systems.python_systems.lenia.Lenia.observe", "modulename": "auto_disc.legacy.systems.python_systems.lenia", "qualname": "Lenia.observe", "kind": "function", "doc": "

Returns the overall output of the system according to the last reset().\nUse this function once the step function has returned done=True to give the system's output to the Output Representation (and then the explorer).

\n", "signature": "(self) -> Dict[str, torch.Tensor]:", "funcdef": "def"}, "auto_disc.legacy.systems.python_systems.lenia.Lenia.render": {"fullname": "auto_disc.legacy.systems.python_systems.lenia.Lenia.render", "modulename": "auto_disc.legacy.systems.python_systems.lenia", "qualname": "Lenia.render", "kind": "function", "doc": "

Renders the environment.

\n", "signature": "(self, mode: str = 'PIL_image') -> Any:", "funcdef": "def"}, "auto_disc.legacy.systems.python_systems.lenia.Lenia.close": {"fullname": "auto_disc.legacy.systems.python_systems.lenia.Lenia.close", "modulename": "auto_disc.legacy.systems.python_systems.lenia", "qualname": "Lenia.close", "kind": "function", "doc": "

Override close in your subclass to perform any necessary cleanup.\nEnvironments will automatically close() themselves when\ngarbage collected or when the program exits.

\n", "signature": "(self):", "funcdef": "def"}, "auto_disc.legacy.systems.python_systems.lenia.create_colormap": {"fullname": "auto_disc.legacy.systems.python_systems.lenia.create_colormap", "modulename": "auto_disc.legacy.systems.python_systems.lenia", "qualname": "create_colormap", "kind": "function", "doc": "

\n", "signature": "(colors: numpy.ndarray, is_marker_w: bool = True) -> List[int]:", "funcdef": "def"}, "auto_disc.legacy.systems.python_systems.lenia.im_from_array_with_colormap": {"fullname": "auto_disc.legacy.systems.python_systems.lenia.im_from_array_with_colormap", "modulename": "auto_disc.legacy.systems.python_systems.lenia", "qualname": "im_from_array_with_colormap", "kind": "function", "doc": "

Function that transforms the color palette of a PIL image

\n\n

input:\n - image: the PIL image to transform\n - colormap: the desired colormap\noutput: the transformed PIL image

\n", "signature": "(\tnp_array: numpy.ndarray,\tcolormap: List[int]) -> <module 'PIL.Image' from '/opt/hostedtoolcache/Python/3.9.17/x64/lib/python3.9/site-packages/PIL/Image.py'>:", "funcdef": "def"}, "auto_disc.legacy.systems.python_systems.lenia.LeniaStepFFT": {"fullname": "auto_disc.legacy.systems.python_systems.lenia.LeniaStepFFT", "modulename": "auto_disc.legacy.systems.python_systems.lenia", "qualname": "LeniaStepFFT", "kind": "class", "doc": "

Module pytorch that computes one Lenia Step with the fft version

\n", "bases": "torch.nn.modules.module.Module"}, "auto_disc.legacy.systems.python_systems.lenia.LeniaStepFFT.__init__": {"fullname": "auto_disc.legacy.systems.python_systems.lenia.LeniaStepFFT.__init__", "modulename": "auto_disc.legacy.systems.python_systems.lenia", "qualname": "LeniaStepFFT.__init__", "kind": "function", "doc": "

Initializes internal Module state, shared by both nn.Module and ScriptModule.

\n", "signature": "(\tR: torch.Tensor,\tT: torch.Tensor,\tb: torch.Tensor,\tm: torch.Tensor,\ts: torch.Tensor,\tkn: int,\tgn: int,\tis_soft_clip: bool = False,\tSX: int = 256,\tSY: int = 256,\tdevice: str = 'cpu')"}, "auto_disc.legacy.systems.python_systems.lenia.LeniaStepFFT.compute_kernel": {"fullname": "auto_disc.legacy.systems.python_systems.lenia.LeniaStepFFT.compute_kernel", "modulename": "auto_disc.legacy.systems.python_systems.lenia", "qualname": "LeniaStepFFT.compute_kernel", "kind": "function", "doc": "

\n", "signature": "(self) -> None:", "funcdef": "def"}, "auto_disc.legacy.systems.python_systems.lenia.LeniaStepFFT.forward": {"fullname": "auto_disc.legacy.systems.python_systems.lenia.LeniaStepFFT.forward", "modulename": "auto_disc.legacy.systems.python_systems.lenia", "qualname": "LeniaStepFFT.forward", "kind": "function", "doc": "

Defines the computation performed at every call.

\n\n

Should be overridden by all subclasses.

\n\n
\n\n

Although the recipe for forward pass needs to be defined within\nthis function, one should call the Module instance afterwards\ninstead of this since the former takes care of running the\nregistered hooks while the latter silently ignores them.

\n\n
\n", "signature": "(self, input: torch.Tensor) -> torch.Tensor:", "funcdef": "def"}, "auto_disc.legacy.systems.python_systems.lenia.LeniaStepConv2d": {"fullname": "auto_disc.legacy.systems.python_systems.lenia.LeniaStepConv2d", "modulename": "auto_disc.legacy.systems.python_systems.lenia", "qualname": "LeniaStepConv2d", "kind": "class", "doc": "

Module pytorch that computes one Lenia Step with the conv2d version

\n", "bases": "torch.nn.modules.module.Module"}, "auto_disc.legacy.systems.python_systems.lenia.LeniaStepConv2d.__init__": {"fullname": "auto_disc.legacy.systems.python_systems.lenia.LeniaStepConv2d.__init__", "modulename": "auto_disc.legacy.systems.python_systems.lenia", "qualname": "LeniaStepConv2d.__init__", "kind": "function", "doc": "

Initializes internal Module state, shared by both nn.Module and ScriptModule.

\n", "signature": "(R, T, b, m, s, kn, gn, is_soft_clip=False, device='cpu')"}, "auto_disc.legacy.systems.python_systems.lenia.LeniaStepConv2d.compute_kernel": {"fullname": "auto_disc.legacy.systems.python_systems.lenia.LeniaStepConv2d.compute_kernel", "modulename": "auto_disc.legacy.systems.python_systems.lenia", "qualname": "LeniaStepConv2d.compute_kernel", "kind": "function", "doc": "

\n", "signature": "(self) -> None:", "funcdef": "def"}, "auto_disc.legacy.systems.python_systems.lenia.LeniaStepConv2d.forward": {"fullname": "auto_disc.legacy.systems.python_systems.lenia.LeniaStepConv2d.forward", "modulename": "auto_disc.legacy.systems.python_systems.lenia", "qualname": "LeniaStepConv2d.forward", "kind": "function", "doc": "

Defines the computation performed at every call.

\n\n

Should be overridden by all subclasses.

\n\n
\n\n

Although the recipe for forward pass needs to be defined within\nthis function, one should call the Module instance afterwards\ninstead of this since the former takes care of running the\nregistered hooks while the latter silently ignores them.

\n\n
\n", "signature": "(self, input):", "funcdef": "def"}, "auto_disc.legacy.utils": {"fullname": "auto_disc.legacy.utils", "modulename": "auto_disc.legacy.utils", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.callbacks": {"fullname": "auto_disc.legacy.utils.callbacks", "modulename": "auto_disc.legacy.utils.callbacks", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.callbacks.base_callback": {"fullname": "auto_disc.legacy.utils.callbacks.base_callback", "modulename": "auto_disc.legacy.utils.callbacks.base_callback", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.callbacks.base_callback.BaseCallback": {"fullname": "auto_disc.legacy.utils.callbacks.base_callback.BaseCallback", "modulename": "auto_disc.legacy.utils.callbacks.base_callback", "qualname": "BaseCallback", "kind": "class", "doc": "

Base class for callbacks used by the experiment pipelines when progress is made (e.g. new dicovery, explorer's optimization).

\n"}, "auto_disc.legacy.utils.callbacks.base_callback.BaseCallback.__init__": {"fullname": "auto_disc.legacy.utils.callbacks.base_callback.BaseCallback.__init__", "modulename": "auto_disc.legacy.utils.callbacks.base_callback", "qualname": "BaseCallback.__init__", "kind": "function", "doc": "

initialize attributes common to all auto_disc.legacy callbacks

\n\n
Arguments:
\n\n
    \n
  • logger: the logger which will make it possible to keep information on the progress of an experiment in the database or on files
    • \n
\n", "signature": "(logger=None, **kwargs)"}, "auto_disc.legacy.utils.callbacks.base_expe_db_callback": {"fullname": "auto_disc.legacy.utils.callbacks.base_expe_db_callback", "modulename": "auto_disc.legacy.utils.callbacks.base_expe_db_callback", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.callbacks.base_expe_db_callback.BaseExpeDBCallback": {"fullname": "auto_disc.legacy.utils.callbacks.base_expe_db_callback.BaseExpeDBCallback", "modulename": "auto_disc.legacy.utils.callbacks.base_expe_db_callback", "qualname": "BaseExpeDBCallback", "kind": "class", "doc": "

Base class for callbacks used by the expe_db when progress is made (e.g. new dicovery, explorer's optimization).

\n", "bases": "auto_disc.legacy.utils.callbacks.base_callback.BaseCallback"}, "auto_disc.legacy.utils.callbacks.base_expe_db_callback.BaseExpeDBCallback.__init__": {"fullname": "auto_disc.legacy.utils.callbacks.base_expe_db_callback.BaseExpeDBCallback.__init__", "modulename": "auto_disc.legacy.utils.callbacks.base_expe_db_callback", "qualname": "BaseExpeDBCallback.__init__", "kind": "function", "doc": "

initialize attributes common to all expe_db callbacks

\n\n
Arguments:
\n\n
    \n
  • base_url: the database url
    • \n
  • kwargs: some usefull args (e.g. logger experiment_id...)
    • \n
\n", "signature": "(base_url: str, **kwargs)"}, "auto_disc.legacy.utils.callbacks.custom_print_callback": {"fullname": "auto_disc.legacy.utils.callbacks.custom_print_callback", "modulename": "auto_disc.legacy.utils.callbacks.custom_print_callback", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.callbacks.custom_print_callback.CustomPrintCallback": {"fullname": "auto_disc.legacy.utils.callbacks.custom_print_callback.CustomPrintCallback", "modulename": "auto_disc.legacy.utils.callbacks.custom_print_callback", "qualname": "CustomPrintCallback", "kind": "class", "doc": "

Base class for callbacks used by the experiment pipelines when progress is made (e.g. new dicovery, explorer's optimization).

\n", "bases": "auto_disc.legacy.utils.callbacks.base_callback.BaseCallback"}, "auto_disc.legacy.utils.callbacks.custom_print_callback.CustomPrintCallback.__init__": {"fullname": "auto_disc.legacy.utils.callbacks.custom_print_callback.CustomPrintCallback.__init__", "modulename": "auto_disc.legacy.utils.callbacks.custom_print_callback", "qualname": "CustomPrintCallback.__init__", "kind": "function", "doc": "

Init the callback with a message to print

\n\n
Arguments:
\n\n
    \n
  • custom_message_to_print: The message to print
    • \n
\n", "signature": "(custom_message_to_print: str, **kwargs)"}, "auto_disc.legacy.utils.callbacks.interact_callbacks": {"fullname": "auto_disc.legacy.utils.callbacks.interact_callbacks", "modulename": "auto_disc.legacy.utils.callbacks.interact_callbacks", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.callbacks.interact_callbacks.base_interact_callback": {"fullname": "auto_disc.legacy.utils.callbacks.interact_callbacks.base_interact_callback", "modulename": "auto_disc.legacy.utils.callbacks.interact_callbacks.base_interact_callback", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.callbacks.interact_callbacks.base_interact_callback.Object": {"fullname": "auto_disc.legacy.utils.callbacks.interact_callbacks.base_interact_callback.Object", "modulename": "auto_disc.legacy.utils.callbacks.interact_callbacks.base_interact_callback", "qualname": "Object", "kind": "function", "doc": "

\n", "signature": "(**kwargs):", "funcdef": "def"}, "auto_disc.legacy.utils.callbacks.interact_callbacks.base_interact_callback.Interaction": {"fullname": "auto_disc.legacy.utils.callbacks.interact_callbacks.base_interact_callback.Interaction", "modulename": "auto_disc.legacy.utils.callbacks.interact_callbacks.base_interact_callback", "qualname": "Interaction", "kind": "class", "doc": "

\n"}, "auto_disc.legacy.utils.callbacks.interact_callbacks.base_interact_callback.Interaction.init_seed": {"fullname": "auto_disc.legacy.utils.callbacks.interact_callbacks.base_interact_callback.Interaction.init_seed", "modulename": "auto_disc.legacy.utils.callbacks.interact_callbacks.base_interact_callback", "qualname": "Interaction.init_seed", "kind": "function", "doc": "

\n", "signature": "(self, interact_callbacks, config):", "funcdef": "def"}, "auto_disc.legacy.utils.callbacks.interact_callbacks.base_interact_callback.Interaction.save": {"fullname": "auto_disc.legacy.utils.callbacks.interact_callbacks.base_interact_callback.Interaction.save", "modulename": "auto_disc.legacy.utils.callbacks.interact_callbacks.base_interact_callback", "qualname": "Interaction.save", "kind": "function", "doc": "

\n", "signature": "(self, data, dict_info=None):", "funcdef": "def"}, "auto_disc.legacy.utils.callbacks.interact_callbacks.base_interact_callback.Interaction.read": {"fullname": "auto_disc.legacy.utils.callbacks.interact_callbacks.base_interact_callback.Interaction.read", "modulename": "auto_disc.legacy.utils.callbacks.interact_callbacks.base_interact_callback", "qualname": "Interaction.read", "kind": "function", "doc": "

\n", "signature": "(self, filter_attribut=None):", "funcdef": "def"}, "auto_disc.legacy.utils.callbacks.interact_callbacks.base_interact_callback.Interaction.feedback": {"fullname": "auto_disc.legacy.utils.callbacks.interact_callbacks.base_interact_callback.Interaction.feedback", "modulename": "auto_disc.legacy.utils.callbacks.interact_callbacks.base_interact_callback", "qualname": "Interaction.feedback", "kind": "function", "doc": "

\n", "signature": "(self, data, dict_info=None, dict_value_to_change=None):", "funcdef": "def"}, "auto_disc.legacy.utils.callbacks.interact_callbacks.base_interact_callback.BaseInteractCallback": {"fullname": "auto_disc.legacy.utils.callbacks.interact_callbacks.base_interact_callback.BaseInteractCallback", "modulename": "auto_disc.legacy.utils.callbacks.interact_callbacks.base_interact_callback", "qualname": "BaseInteractCallback", "kind": "class", "doc": "

Base class for cancelled callbacks used by the experiment pipelines when the experiment was cancelled.

\n", "bases": "auto_disc.legacy.utils.callbacks.base_callback.BaseCallback"}, "auto_disc.legacy.utils.callbacks.interact_callbacks.base_interact_callback.BaseInteractCallback.__init__": {"fullname": "auto_disc.legacy.utils.callbacks.interact_callbacks.base_interact_callback.BaseInteractCallback.__init__", "modulename": "auto_disc.legacy.utils.callbacks.interact_callbacks.base_interact_callback", "qualname": "BaseInteractCallback.__init__", "kind": "function", "doc": "

initialize attributes common to all cancelled callbacks

\n\n
Arguments:
\n\n
    \n
  • kwargs: some usefull args (e.g. experiment_id...)
    • \n
\n", "signature": "(**kwargs)"}, "auto_disc.legacy.utils.callbacks.interact_callbacks.read_disk_interact_callback": {"fullname": "auto_disc.legacy.utils.callbacks.interact_callbacks.read_disk_interact_callback", "modulename": "auto_disc.legacy.utils.callbacks.interact_callbacks.read_disk_interact_callback", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.callbacks.interact_callbacks.read_disk_interact_callback.ReadDiskInteractCallback": {"fullname": "auto_disc.legacy.utils.callbacks.interact_callbacks.read_disk_interact_callback.ReadDiskInteractCallback", "modulename": "auto_disc.legacy.utils.callbacks.interact_callbacks.read_disk_interact_callback", "qualname": "ReadDiskInteractCallback", "kind": "class", "doc": "

Base class for cancelled callbacks used by the experiment pipelines when the experiment was cancelled.

\n", "bases": "auto_disc.legacy.utils.callbacks.interact_callbacks.base_interact_callback.BaseInteractCallback"}, "auto_disc.legacy.utils.callbacks.interact_callbacks.read_disk_interact_callback.ReadDiskInteractCallback.__init__": {"fullname": "auto_disc.legacy.utils.callbacks.interact_callbacks.read_disk_interact_callback.ReadDiskInteractCallback.__init__", "modulename": "auto_disc.legacy.utils.callbacks.interact_callbacks.read_disk_interact_callback", "qualname": "ReadDiskInteractCallback.__init__", "kind": "function", "doc": "

initialize attributes common to all cancelled callbacks

\n\n
Arguments:
\n\n
    \n
  • kwargs: some usefull args (e.g. experiment_id...)
    • \n
\n", "signature": "(**kwargs)"}, "auto_disc.legacy.utils.callbacks.interact_callbacks.save_disk_interact_callback": {"fullname": "auto_disc.legacy.utils.callbacks.interact_callbacks.save_disk_interact_callback", "modulename": "auto_disc.legacy.utils.callbacks.interact_callbacks.save_disk_interact_callback", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.callbacks.interact_callbacks.save_disk_interact_callback.SaveDiskInteractCallback": {"fullname": "auto_disc.legacy.utils.callbacks.interact_callbacks.save_disk_interact_callback.SaveDiskInteractCallback", "modulename": "auto_disc.legacy.utils.callbacks.interact_callbacks.save_disk_interact_callback", "qualname": "SaveDiskInteractCallback", "kind": "class", "doc": "

Base class for cancelled callbacks used by the experiment pipelines when the experiment was cancelled.

\n", "bases": "auto_disc.legacy.utils.callbacks.interact_callbacks.base_interact_callback.BaseInteractCallback"}, "auto_disc.legacy.utils.callbacks.interact_callbacks.save_disk_interact_callback.SaveDiskInteractCallback.__init__": {"fullname": "auto_disc.legacy.utils.callbacks.interact_callbacks.save_disk_interact_callback.SaveDiskInteractCallback.__init__", "modulename": "auto_disc.legacy.utils.callbacks.interact_callbacks.save_disk_interact_callback", "qualname": "SaveDiskInteractCallback.__init__", "kind": "function", "doc": "

initialize attributes common to all cancelled callbacks

\n\n
Arguments:
\n\n
    \n
  • kwargs: some usefull args (e.g. experiment_id...)
    • \n
\n", "signature": "(**kwargs)"}, "auto_disc.legacy.utils.callbacks.on_cancelled_callbacks": {"fullname": "auto_disc.legacy.utils.callbacks.on_cancelled_callbacks", "modulename": "auto_disc.legacy.utils.callbacks.on_cancelled_callbacks", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.callbacks.on_cancelled_callbacks.base_on_cancelled_callback": {"fullname": "auto_disc.legacy.utils.callbacks.on_cancelled_callbacks.base_on_cancelled_callback", "modulename": "auto_disc.legacy.utils.callbacks.on_cancelled_callbacks.base_on_cancelled_callback", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.callbacks.on_cancelled_callbacks.base_on_cancelled_callback.BaseOnCancelledCallback": {"fullname": "auto_disc.legacy.utils.callbacks.on_cancelled_callbacks.base_on_cancelled_callback.BaseOnCancelledCallback", "modulename": "auto_disc.legacy.utils.callbacks.on_cancelled_callbacks.base_on_cancelled_callback", "qualname": "BaseOnCancelledCallback", "kind": "class", "doc": "

Base class for cancelled callbacks used by the experiment pipelines when the experiment was cancelled.

\n", "bases": "auto_disc.legacy.utils.callbacks.base_callback.BaseCallback"}, "auto_disc.legacy.utils.callbacks.on_cancelled_callbacks.base_on_cancelled_callback.BaseOnCancelledCallback.__init__": {"fullname": "auto_disc.legacy.utils.callbacks.on_cancelled_callbacks.base_on_cancelled_callback.BaseOnCancelledCallback.__init__", "modulename": "auto_disc.legacy.utils.callbacks.on_cancelled_callbacks.base_on_cancelled_callback", "qualname": "BaseOnCancelledCallback.__init__", "kind": "function", "doc": "

initialize attributes common to all cancelled callbacks

\n\n
Arguments:
\n\n
    \n
  • kwargs: some usefull args (e.g. experiment_id...)
    • \n
\n", "signature": "(**kwargs)"}, "auto_disc.legacy.utils.callbacks.on_discovery_callbacks": {"fullname": "auto_disc.legacy.utils.callbacks.on_discovery_callbacks", "modulename": "auto_disc.legacy.utils.callbacks.on_discovery_callbacks", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.callbacks.on_discovery_callbacks.base_on_discovery_callback": {"fullname": "auto_disc.legacy.utils.callbacks.on_discovery_callbacks.base_on_discovery_callback", "modulename": "auto_disc.legacy.utils.callbacks.on_discovery_callbacks.base_on_discovery_callback", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.callbacks.on_discovery_callbacks.base_on_discovery_callback.BaseOnDiscoveryCallback": {"fullname": "auto_disc.legacy.utils.callbacks.on_discovery_callbacks.base_on_discovery_callback.BaseOnDiscoveryCallback", "modulename": "auto_disc.legacy.utils.callbacks.on_discovery_callbacks.base_on_discovery_callback", "qualname": "BaseOnDiscoveryCallback", "kind": "class", "doc": "

Base class for on discovery callbacks used by the experiment pipelines when new discovery is made.

\n", "bases": "auto_disc.legacy.utils.callbacks.base_callback.BaseCallback"}, "auto_disc.legacy.utils.callbacks.on_discovery_callbacks.base_on_discovery_callback.BaseOnDiscoveryCallback.__init__": {"fullname": "auto_disc.legacy.utils.callbacks.on_discovery_callbacks.base_on_discovery_callback.BaseOnDiscoveryCallback.__init__", "modulename": "auto_disc.legacy.utils.callbacks.on_discovery_callbacks.base_on_discovery_callback", "qualname": "BaseOnDiscoveryCallback.__init__", "kind": "function", "doc": "

initialize attributes common to all on discovery callbacks

\n\n
Arguments:
\n\n
    \n
  • to_save_outputs: Names of all outputs the user want save
    • \n
  • kwargs: some usefull args (e.g. experiment_id...)
    • \n
\n", "signature": "(to_save_outputs: List[str], **kwargs)"}, "auto_disc.legacy.utils.callbacks.on_discovery_callbacks.on_discovery_save_callback_on_disk": {"fullname": "auto_disc.legacy.utils.callbacks.on_discovery_callbacks.on_discovery_save_callback_on_disk", "modulename": "auto_disc.legacy.utils.callbacks.on_discovery_callbacks.on_discovery_save_callback_on_disk", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.callbacks.on_discovery_callbacks.on_discovery_save_callback_on_disk.OnDiscoverySaveCallbackOnDisk": {"fullname": "auto_disc.legacy.utils.callbacks.on_discovery_callbacks.on_discovery_save_callback_on_disk.OnDiscoverySaveCallbackOnDisk", "modulename": "auto_disc.legacy.utils.callbacks.on_discovery_callbacks.on_discovery_save_callback_on_disk", "qualname": "OnDiscoverySaveCallbackOnDisk", "kind": "class", "doc": "

class for save experiment discovery on disk.

\n", "bases": "auto_disc.legacy.utils.callbacks.on_discovery_callbacks.base_on_discovery_callback.BaseOnDiscoveryCallback"}, "auto_disc.legacy.utils.callbacks.on_discovery_callbacks.on_discovery_save_callback_on_disk.OnDiscoverySaveCallbackOnDisk.__init__": {"fullname": "auto_disc.legacy.utils.callbacks.on_discovery_callbacks.on_discovery_save_callback_on_disk.OnDiscoverySaveCallbackOnDisk.__init__", "modulename": "auto_disc.legacy.utils.callbacks.on_discovery_callbacks.on_discovery_save_callback_on_disk", "qualname": "OnDiscoverySaveCallbackOnDisk.__init__", "kind": "function", "doc": "

init the callback with a path to save discoveries on disk

\n\n
Arguments:
\n\n
    \n
  • folder_path: path to folder where we want save the discovery
    • \n
  • to_save_outputs: list, key of \"SAVABLE_OUTPUTS\" (parent's attribute) to select the outputs who we want to save
    • \n
\n", "signature": "(folder_path, to_save_outputs, **kwargs)"}, "auto_disc.legacy.utils.callbacks.on_discovery_callbacks.save_discovery": {"fullname": "auto_disc.legacy.utils.callbacks.on_discovery_callbacks.save_discovery", "modulename": "auto_disc.legacy.utils.callbacks.on_discovery_callbacks.save_discovery", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.callbacks.on_discovery_callbacks.save_discovery.SaveDiscovery": {"fullname": "auto_disc.legacy.utils.callbacks.on_discovery_callbacks.save_discovery.SaveDiscovery", "modulename": "auto_disc.legacy.utils.callbacks.on_discovery_callbacks.save_discovery", "qualname": "SaveDiscovery", "kind": "class", "doc": "

\n"}, "auto_disc.legacy.utils.callbacks.on_discovery_callbacks.save_discovery.SaveDiscovery.__init__": {"fullname": "auto_disc.legacy.utils.callbacks.on_discovery_callbacks.save_discovery.SaveDiscovery.__init__", "modulename": "auto_disc.legacy.utils.callbacks.on_discovery_callbacks.save_discovery", "qualname": "SaveDiscovery.__init__", "kind": "function", "doc": "

Dummy init which accepts any arguments, for backwards compatibility.

\n", "signature": "(**kwargs)"}, "auto_disc.legacy.utils.callbacks.on_discovery_callbacks.save_discovery_in_expedb": {"fullname": "auto_disc.legacy.utils.callbacks.on_discovery_callbacks.save_discovery_in_expedb", "modulename": "auto_disc.legacy.utils.callbacks.on_discovery_callbacks.save_discovery_in_expedb", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.callbacks.on_discovery_callbacks.save_discovery_in_expedb.SaveDiscoveryInExpeDB": {"fullname": "auto_disc.legacy.utils.callbacks.on_discovery_callbacks.save_discovery_in_expedb.SaveDiscoveryInExpeDB", "modulename": "auto_disc.legacy.utils.callbacks.on_discovery_callbacks.save_discovery_in_expedb", "qualname": "SaveDiscoveryInExpeDB", "kind": "class", "doc": "

\n", "bases": "auto_disc.legacy.utils.callbacks.on_discovery_callbacks.save_discovery.SaveDiscovery"}, "auto_disc.legacy.utils.callbacks.on_discovery_callbacks.save_discovery_on_disk": {"fullname": "auto_disc.legacy.utils.callbacks.on_discovery_callbacks.save_discovery_on_disk", "modulename": "auto_disc.legacy.utils.callbacks.on_discovery_callbacks.save_discovery_on_disk", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.callbacks.on_discovery_callbacks.save_discovery_on_disk.SaveDiscoveryOnDisk": {"fullname": "auto_disc.legacy.utils.callbacks.on_discovery_callbacks.save_discovery_on_disk.SaveDiscoveryOnDisk", "modulename": "auto_disc.legacy.utils.callbacks.on_discovery_callbacks.save_discovery_on_disk", "qualname": "SaveDiscoveryOnDisk", "kind": "class", "doc": "

\n", "bases": "auto_disc.legacy.utils.callbacks.on_discovery_callbacks.save_discovery.SaveDiscovery"}, "auto_disc.legacy.utils.callbacks.on_error_callbacks": {"fullname": "auto_disc.legacy.utils.callbacks.on_error_callbacks", "modulename": "auto_disc.legacy.utils.callbacks.on_error_callbacks", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.callbacks.on_error_callbacks.base_on_error_callback": {"fullname": "auto_disc.legacy.utils.callbacks.on_error_callbacks.base_on_error_callback", "modulename": "auto_disc.legacy.utils.callbacks.on_error_callbacks.base_on_error_callback", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.callbacks.on_error_callbacks.base_on_error_callback.BaseOnErrorCallback": {"fullname": "auto_disc.legacy.utils.callbacks.on_error_callbacks.base_on_error_callback.BaseOnErrorCallback", "modulename": "auto_disc.legacy.utils.callbacks.on_error_callbacks.base_on_error_callback", "qualname": "BaseOnErrorCallback", "kind": "class", "doc": "

Base class for on error callbacks used by the experiment pipelines when an error are raise during the experiment progress.

\n", "bases": "auto_disc.legacy.utils.callbacks.base_callback.BaseCallback"}, "auto_disc.legacy.utils.callbacks.on_error_callbacks.base_on_error_callback.BaseOnErrorCallback.__init__": {"fullname": "auto_disc.legacy.utils.callbacks.on_error_callbacks.base_on_error_callback.BaseOnErrorCallback.__init__", "modulename": "auto_disc.legacy.utils.callbacks.on_error_callbacks.base_on_error_callback", "qualname": "BaseOnErrorCallback.__init__", "kind": "function", "doc": "

initialize attributes common to all on error callbacks

\n", "signature": "(**kwargs)"}, "auto_disc.legacy.utils.callbacks.on_finished_callbacks": {"fullname": "auto_disc.legacy.utils.callbacks.on_finished_callbacks", "modulename": "auto_disc.legacy.utils.callbacks.on_finished_callbacks", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.callbacks.on_finished_callbacks.base_on_finished_callback": {"fullname": "auto_disc.legacy.utils.callbacks.on_finished_callbacks.base_on_finished_callback", "modulename": "auto_disc.legacy.utils.callbacks.on_finished_callbacks.base_on_finished_callback", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.callbacks.on_finished_callbacks.base_on_finished_callback.BaseOnFinishedCallback": {"fullname": "auto_disc.legacy.utils.callbacks.on_finished_callbacks.base_on_finished_callback.BaseOnFinishedCallback", "modulename": "auto_disc.legacy.utils.callbacks.on_finished_callbacks.base_on_finished_callback", "qualname": "BaseOnFinishedCallback", "kind": "class", "doc": "

Base class for on finished callbacks used by the experiment pipelines when the experiment is over.

\n", "bases": "auto_disc.legacy.utils.callbacks.base_callback.BaseCallback"}, "auto_disc.legacy.utils.callbacks.on_finished_callbacks.base_on_finished_callback.BaseOnFinishedCallback.__init__": {"fullname": "auto_disc.legacy.utils.callbacks.on_finished_callbacks.base_on_finished_callback.BaseOnFinishedCallback.__init__", "modulename": "auto_disc.legacy.utils.callbacks.on_finished_callbacks.base_on_finished_callback", "qualname": "BaseOnFinishedCallback.__init__", "kind": "function", "doc": "

initialize attributes common to all on finished callbacks

\n", "signature": "(**kwargs)"}, "auto_disc.legacy.utils.callbacks.on_save_callbacks": {"fullname": "auto_disc.legacy.utils.callbacks.on_save_callbacks", "modulename": "auto_disc.legacy.utils.callbacks.on_save_callbacks", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.callbacks.on_save_callbacks.base_on_save_callback": {"fullname": "auto_disc.legacy.utils.callbacks.on_save_callbacks.base_on_save_callback", "modulename": "auto_disc.legacy.utils.callbacks.on_save_callbacks.base_on_save_callback", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.callbacks.on_save_callbacks.base_on_save_callback.BaseOnSaveCallback": {"fullname": "auto_disc.legacy.utils.callbacks.on_save_callbacks.base_on_save_callback.BaseOnSaveCallback", "modulename": "auto_disc.legacy.utils.callbacks.on_save_callbacks.base_on_save_callback", "qualname": "BaseOnSaveCallback", "kind": "class", "doc": "

Base class for on save callbacks used by the experiment pipelines when the experiment save autodisc modules.

\n", "bases": "auto_disc.legacy.utils.callbacks.base_callback.BaseCallback"}, "auto_disc.legacy.utils.callbacks.on_save_callbacks.base_on_save_callback.BaseOnSaveCallback.__init__": {"fullname": "auto_disc.legacy.utils.callbacks.on_save_callbacks.base_on_save_callback.BaseOnSaveCallback.__init__", "modulename": "auto_disc.legacy.utils.callbacks.on_save_callbacks.base_on_save_callback", "qualname": "BaseOnSaveCallback.__init__", "kind": "function", "doc": "

initialize attributes common to all on save callbacks

\n", "signature": "(**kwargs)"}, "auto_disc.legacy.utils.callbacks.on_save_callbacks.on_save_modules_on_disk_callback": {"fullname": "auto_disc.legacy.utils.callbacks.on_save_callbacks.on_save_modules_on_disk_callback", "modulename": "auto_disc.legacy.utils.callbacks.on_save_callbacks.on_save_modules_on_disk_callback", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.callbacks.on_save_callbacks.on_save_modules_on_disk_callback.OnSaveModulesOnDiskCallback": {"fullname": "auto_disc.legacy.utils.callbacks.on_save_callbacks.on_save_modules_on_disk_callback.OnSaveModulesOnDiskCallback", "modulename": "auto_disc.legacy.utils.callbacks.on_save_callbacks.on_save_modules_on_disk_callback", "qualname": "OnSaveModulesOnDiskCallback", "kind": "class", "doc": "

class for save autodisc modules on disk.

\n", "bases": "auto_disc.legacy.utils.callbacks.on_save_callbacks.base_on_save_callback.BaseOnSaveCallback"}, "auto_disc.legacy.utils.callbacks.on_save_callbacks.on_save_modules_on_disk_callback.OnSaveModulesOnDiskCallback.__init__": {"fullname": "auto_disc.legacy.utils.callbacks.on_save_callbacks.on_save_modules_on_disk_callback.OnSaveModulesOnDiskCallback.__init__", "modulename": "auto_disc.legacy.utils.callbacks.on_save_callbacks.on_save_modules_on_disk_callback", "qualname": "OnSaveModulesOnDiskCallback.__init__", "kind": "function", "doc": "

init the callback with a path to save modules on disk

\n\n
Arguments:
\n\n
    \n
  • folder_path: path to folder where we want save the discovery
    • \n
\n", "signature": "(folder_path: str, **kwargs)"}, "auto_disc.legacy.utils.callbacks.on_save_callbacks.save_leaf_callback": {"fullname": "auto_disc.legacy.utils.callbacks.on_save_callbacks.save_leaf_callback", "modulename": "auto_disc.legacy.utils.callbacks.on_save_callbacks.save_leaf_callback", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.callbacks.on_save_callbacks.save_leaf_callback.SaveLeaf": {"fullname": "auto_disc.legacy.utils.callbacks.on_save_callbacks.save_leaf_callback.SaveLeaf", "modulename": "auto_disc.legacy.utils.callbacks.on_save_callbacks.save_leaf_callback", "qualname": "SaveLeaf", "kind": "class", "doc": "

Simple adapter which calls the save_leaf method from a callback

\n"}, "auto_disc.legacy.utils.callbacks.on_save_callbacks.save_leaf_callback.SaveLeaf.__init__": {"fullname": "auto_disc.legacy.utils.callbacks.on_save_callbacks.save_leaf_callback.SaveLeaf.__init__", "modulename": "auto_disc.legacy.utils.callbacks.on_save_callbacks.save_leaf_callback", "qualname": "SaveLeaf.__init__", "kind": "function", "doc": "

\n", "signature": "(**kwargs)"}, "auto_disc.legacy.utils.callbacks.on_save_callbacks.save_leaf_callback_in_expedb": {"fullname": "auto_disc.legacy.utils.callbacks.on_save_callbacks.save_leaf_callback_in_expedb", "modulename": "auto_disc.legacy.utils.callbacks.on_save_callbacks.save_leaf_callback_in_expedb", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.callbacks.on_save_callbacks.save_leaf_callback_in_expedb.SaveLeafExpeDB": {"fullname": "auto_disc.legacy.utils.callbacks.on_save_callbacks.save_leaf_callback_in_expedb.SaveLeafExpeDB", "modulename": "auto_disc.legacy.utils.callbacks.on_save_callbacks.save_leaf_callback_in_expedb", "qualname": "SaveLeafExpeDB", "kind": "class", "doc": "

Simple adapter which calls the save_leaf method from a callback

\n", "bases": "auto_disc.legacy.utils.callbacks.on_save_callbacks.save_leaf_callback.SaveLeaf"}, "auto_disc.legacy.utils.callbacks.on_save_callbacks.save_leaf_callback_in_expedb.SaveLeafExpeDB.__init__": {"fullname": "auto_disc.legacy.utils.callbacks.on_save_callbacks.save_leaf_callback_in_expedb.SaveLeafExpeDB.__init__", "modulename": "auto_disc.legacy.utils.callbacks.on_save_callbacks.save_leaf_callback_in_expedb", "qualname": "SaveLeafExpeDB.__init__", "kind": "function", "doc": "

\n", "signature": "(**kwargs)"}, "auto_disc.legacy.utils.callbacks.on_save_finished_callbacks": {"fullname": "auto_disc.legacy.utils.callbacks.on_save_finished_callbacks", "modulename": "auto_disc.legacy.utils.callbacks.on_save_finished_callbacks", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.callbacks.on_save_finished_callbacks.base_on_save_finished_callback": {"fullname": "auto_disc.legacy.utils.callbacks.on_save_finished_callbacks.base_on_save_finished_callback", "modulename": "auto_disc.legacy.utils.callbacks.on_save_finished_callbacks.base_on_save_finished_callback", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.callbacks.on_save_finished_callbacks.base_on_save_finished_callback.BaseOnSaveFinishedCallback": {"fullname": "auto_disc.legacy.utils.callbacks.on_save_finished_callbacks.base_on_save_finished_callback.BaseOnSaveFinishedCallback", "modulename": "auto_disc.legacy.utils.callbacks.on_save_finished_callbacks.base_on_save_finished_callback", "qualname": "BaseOnSaveFinishedCallback", "kind": "class", "doc": "

Base class for on save finished callbacks used by the experiment pipelines when the experiment save autodisc modules.

\n", "bases": "auto_disc.legacy.utils.callbacks.base_callback.BaseCallback"}, "auto_disc.legacy.utils.callbacks.on_save_finished_callbacks.base_on_save_finished_callback.BaseOnSaveFinishedCallback.__init__": {"fullname": "auto_disc.legacy.utils.callbacks.on_save_finished_callbacks.base_on_save_finished_callback.BaseOnSaveFinishedCallback.__init__", "modulename": "auto_disc.legacy.utils.callbacks.on_save_finished_callbacks.base_on_save_finished_callback", "qualname": "BaseOnSaveFinishedCallback.__init__", "kind": "function", "doc": "

initialize attributes common to all on save finished callbacks

\n", "signature": "(**kwargs)"}, "auto_disc.legacy.utils.callbacks.on_save_finished_callbacks.generate_report_callback": {"fullname": "auto_disc.legacy.utils.callbacks.on_save_finished_callbacks.generate_report_callback", "modulename": "auto_disc.legacy.utils.callbacks.on_save_finished_callbacks.generate_report_callback", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.callbacks.on_save_finished_callbacks.generate_report_callback.GenerateReport": {"fullname": "auto_disc.legacy.utils.callbacks.on_save_finished_callbacks.generate_report_callback.GenerateReport", "modulename": "auto_disc.legacy.utils.callbacks.on_save_finished_callbacks.generate_report_callback", "qualname": "GenerateReport", "kind": "class", "doc": "

Takes UID which identifies a saved object and annotates the saved data with\nhuman-readable identifying information

\n"}, "auto_disc.legacy.utils.callbacks.on_save_finished_callbacks.generate_report_callback.GenerateReport.__init__": {"fullname": "auto_disc.legacy.utils.callbacks.on_save_finished_callbacks.generate_report_callback.GenerateReport.__init__", "modulename": "auto_disc.legacy.utils.callbacks.on_save_finished_callbacks.generate_report_callback", "qualname": "GenerateReport.__init__", "kind": "function", "doc": "

\n", "signature": "(**kwargs)"}, "auto_disc.legacy.utils.config_parameters": {"fullname": "auto_disc.legacy.utils.config_parameters", "modulename": "auto_disc.legacy.utils.config_parameters", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.config_parameters.base_config_parameter": {"fullname": "auto_disc.legacy.utils.config_parameters.base_config_parameter", "modulename": "auto_disc.legacy.utils.config_parameters.base_config_parameter", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.config_parameters.base_config_parameter.BaseConfigParameter": {"fullname": "auto_disc.legacy.utils.config_parameters.base_config_parameter.BaseConfigParameter", "modulename": "auto_disc.legacy.utils.config_parameters.base_config_parameter", "qualname": "BaseConfigParameter", "kind": "class", "doc": "

Decorator to add a config parameter to a class.

\n"}, "auto_disc.legacy.utils.config_parameters.base_config_parameter.BaseConfigParameter.__init__": {"fullname": "auto_disc.legacy.utils.config_parameters.base_config_parameter.BaseConfigParameter.__init__", "modulename": "auto_disc.legacy.utils.config_parameters.base_config_parameter", "qualname": "BaseConfigParameter.__init__", "kind": "function", "doc": "

Init a config parameter

\n\n
Arguments:
\n\n
    \n
  • name: name of config parameter
    • \n
  • default: default value of the config parameter
    • \n
\n", "signature": "(name: str, default: Any)"}, "auto_disc.legacy.utils.config_parameters.base_config_parameter.BaseConfigParameter.check_value_to_set": {"fullname": "auto_disc.legacy.utils.config_parameters.base_config_parameter.BaseConfigParameter.check_value_to_set", "modulename": "auto_disc.legacy.utils.config_parameters.base_config_parameter", "qualname": "BaseConfigParameter.check_value_to_set", "kind": "function", "doc": "

Check if the value is one of the possible values

\n\n
Arguments:
\n\n
    \n
  • value: current value of the config parameter
    • \n
\n\n
Returns:
\n\n
\n

The return value is always True. All values are accepted

\n
\n", "signature": "(self, value: Any) -> bool:", "funcdef": "def"}, "auto_disc.legacy.utils.config_parameters.boolean_config_parameter": {"fullname": "auto_disc.legacy.utils.config_parameters.boolean_config_parameter", "modulename": "auto_disc.legacy.utils.config_parameters.boolean_config_parameter", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.config_parameters.boolean_config_parameter.BooleanConfigParameter": {"fullname": "auto_disc.legacy.utils.config_parameters.boolean_config_parameter.BooleanConfigParameter", "modulename": "auto_disc.legacy.utils.config_parameters.boolean_config_parameter", "qualname": "BooleanConfigParameter", "kind": "class", "doc": "

Decorator to add a boolean config parameter to a class.

\n", "bases": "auto_disc.legacy.utils.config_parameters.base_config_parameter.BaseConfigParameter"}, "auto_disc.legacy.utils.config_parameters.boolean_config_parameter.BooleanConfigParameter.__init__": {"fullname": "auto_disc.legacy.utils.config_parameters.boolean_config_parameter.BooleanConfigParameter.__init__", "modulename": "auto_disc.legacy.utils.config_parameters.boolean_config_parameter", "qualname": "BooleanConfigParameter.__init__", "kind": "function", "doc": "

Init a boolean config parameter. Define all possible values

\n\n
Arguments:
\n\n
    \n
  • name: name of config parameter
    • \n
  • default: default value of the config parameter
    • \n
\n", "signature": "(name: str, default: bool)"}, "auto_disc.legacy.utils.config_parameters.boolean_config_parameter.BooleanConfigParameter.check_value_to_set": {"fullname": "auto_disc.legacy.utils.config_parameters.boolean_config_parameter.BooleanConfigParameter.check_value_to_set", "modulename": "auto_disc.legacy.utils.config_parameters.boolean_config_parameter", "qualname": "BooleanConfigParameter.check_value_to_set", "kind": "function", "doc": "

Check if the value is one of the possible values

\n\n
Arguments:
\n\n
    \n
  • value: current value of the config parameter
    • \n
\n\n
Returns:
\n\n
\n

The return value is True if the value is a boolean else an exception was raised

\n
\n", "signature": "(self, value: bool) -> bool:", "funcdef": "def"}, "auto_disc.legacy.utils.config_parameters.decimal_config_parameter": {"fullname": "auto_disc.legacy.utils.config_parameters.decimal_config_parameter", "modulename": "auto_disc.legacy.utils.config_parameters.decimal_config_parameter", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.config_parameters.decimal_config_parameter.DecimalConfigParameter": {"fullname": "auto_disc.legacy.utils.config_parameters.decimal_config_parameter.DecimalConfigParameter", "modulename": "auto_disc.legacy.utils.config_parameters.decimal_config_parameter", "qualname": "DecimalConfigParameter", "kind": "class", "doc": "

Decorator to add a decimal config parameter to a class.\nUses a 'min' and 'max' to bound the authorized values.\nIf a bound is set to 'None', this means no bound.

\n", "bases": "auto_disc.legacy.utils.config_parameters.base_config_parameter.BaseConfigParameter"}, "auto_disc.legacy.utils.config_parameters.decimal_config_parameter.DecimalConfigParameter.__init__": {"fullname": "auto_disc.legacy.utils.config_parameters.decimal_config_parameter.DecimalConfigParameter.__init__", "modulename": "auto_disc.legacy.utils.config_parameters.decimal_config_parameter", "qualname": "DecimalConfigParameter.__init__", "kind": "function", "doc": "

Init a decimal config parameter. Define the bounds of the value

\n\n
Arguments:
\n\n
    \n
  • name: name of config parameter
    • \n
  • default: default value of the config parameter
    • \n
  • min: the lower bound
    • \n
  • max: the upper limit
    • \n
\n", "signature": "(name: str, default: float, min: float = None, max: float = None)"}, "auto_disc.legacy.utils.config_parameters.decimal_config_parameter.DecimalConfigParameter.check_value_to_set": {"fullname": "auto_disc.legacy.utils.config_parameters.decimal_config_parameter.DecimalConfigParameter.check_value_to_set", "modulename": "auto_disc.legacy.utils.config_parameters.decimal_config_parameter", "qualname": "DecimalConfigParameter.check_value_to_set", "kind": "function", "doc": "

Check if the value is between the two bounds

\n\n
Arguments:
\n\n
    \n
  • value: current value of the config parameter
    • \n
\n\n
Returns:
\n\n
\n

The return value is True if the value is between the two bounds else an exception is thrown

\n
\n", "signature": "(self, value):", "funcdef": "def"}, "auto_disc.legacy.utils.config_parameters.dict_config_parameter": {"fullname": "auto_disc.legacy.utils.config_parameters.dict_config_parameter", "modulename": "auto_disc.legacy.utils.config_parameters.dict_config_parameter", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.config_parameters.dict_config_parameter.DictConfigParameter": {"fullname": "auto_disc.legacy.utils.config_parameters.dict_config_parameter.DictConfigParameter", "modulename": "auto_disc.legacy.utils.config_parameters.dict_config_parameter", "qualname": "DictConfigParameter", "kind": "class", "doc": "

Decorator to add a dict config parameter to a class.

\n", "bases": "auto_disc.legacy.utils.config_parameters.base_config_parameter.BaseConfigParameter"}, "auto_disc.legacy.utils.config_parameters.dict_config_parameter.DictConfigParameter.__init__": {"fullname": "auto_disc.legacy.utils.config_parameters.dict_config_parameter.DictConfigParameter.__init__", "modulename": "auto_disc.legacy.utils.config_parameters.dict_config_parameter", "qualname": "DictConfigParameter.__init__", "kind": "function", "doc": "

Init a dict config parameter.

\n\n
Arguments:
\n\n
    \n
  • name: name of config parameter
    • \n
  • default: default value of the config parameter
    • \n
\n", "signature": "(name: str, default: Dict = {})"}, "auto_disc.legacy.utils.config_parameters.dict_config_parameter.DictConfigParameter.check_value_to_set": {"fullname": "auto_disc.legacy.utils.config_parameters.dict_config_parameter.DictConfigParameter.check_value_to_set", "modulename": "auto_disc.legacy.utils.config_parameters.dict_config_parameter", "qualname": "DictConfigParameter.check_value_to_set", "kind": "function", "doc": "

Check if the value is indeed a dict

\n\n
Arguments:
\n\n
    \n
  • value: current value of the config parameter
    • \n
\n\n
Returns:
\n\n
\n

The return value is True if the value is a dict

\n
\n", "signature": "(self, value: Dict) -> bool:", "funcdef": "def"}, "auto_disc.legacy.utils.config_parameters.integer_config_parameter": {"fullname": "auto_disc.legacy.utils.config_parameters.integer_config_parameter", "modulename": "auto_disc.legacy.utils.config_parameters.integer_config_parameter", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.config_parameters.integer_config_parameter.IntegerConfigParameter": {"fullname": "auto_disc.legacy.utils.config_parameters.integer_config_parameter.IntegerConfigParameter", "modulename": "auto_disc.legacy.utils.config_parameters.integer_config_parameter", "qualname": "IntegerConfigParameter", "kind": "class", "doc": "

Decorator to add an integer config parameter to a class.\nUses a 'min' and 'max' to bound the authorized values.\nIf a bound is set to 'None', this means no bound.\nIf a decimal value is passed, it will be rounded.

\n", "bases": "auto_disc.legacy.utils.config_parameters.decimal_config_parameter.DecimalConfigParameter"}, "auto_disc.legacy.utils.config_parameters.integer_config_parameter.IntegerConfigParameter.__init__": {"fullname": "auto_disc.legacy.utils.config_parameters.integer_config_parameter.IntegerConfigParameter.__init__", "modulename": "auto_disc.legacy.utils.config_parameters.integer_config_parameter", "qualname": "IntegerConfigParameter.__init__", "kind": "function", "doc": "

Init a int config parameter. Define the bounds of the value

\n\n
Arguments:
\n\n
    \n
  • name: name of config parameter
    • \n
  • default: default value of the config parameter
    • \n
  • min: the lower bound
    • \n
  • max: the upper limit
    • \n
\n", "signature": "(name: str, default: int, min: int = None, max: int = None)"}, "auto_disc.legacy.utils.config_parameters.integer_config_parameter.IntegerConfigParameter.check_value_to_set": {"fullname": "auto_disc.legacy.utils.config_parameters.integer_config_parameter.IntegerConfigParameter.check_value_to_set", "modulename": "auto_disc.legacy.utils.config_parameters.integer_config_parameter", "qualname": "IntegerConfigParameter.check_value_to_set", "kind": "function", "doc": "

Check if the value is between the two bounds

\n\n
Arguments:
\n\n
    \n
  • value: current value of the config parameter
    • \n
\n\n
Returns:
\n\n
\n

The return value is True if the value is between the two bounds else an exception is thrown

\n
\n", "signature": "(self, value: int) -> bool:", "funcdef": "def"}, "auto_disc.legacy.utils.config_parameters.string_config_parameter": {"fullname": "auto_disc.legacy.utils.config_parameters.string_config_parameter", "modulename": "auto_disc.legacy.utils.config_parameters.string_config_parameter", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.config_parameters.string_config_parameter.StringConfigParameter": {"fullname": "auto_disc.legacy.utils.config_parameters.string_config_parameter.StringConfigParameter", "modulename": "auto_disc.legacy.utils.config_parameters.string_config_parameter", "qualname": "StringConfigParameter", "kind": "class", "doc": "

Decorator to add a string config parameter to a class.\nUses a list of possible values to choose among.

\n", "bases": "auto_disc.legacy.utils.config_parameters.base_config_parameter.BaseConfigParameter"}, "auto_disc.legacy.utils.config_parameters.string_config_parameter.StringConfigParameter.__init__": {"fullname": "auto_disc.legacy.utils.config_parameters.string_config_parameter.StringConfigParameter.__init__", "modulename": "auto_disc.legacy.utils.config_parameters.string_config_parameter", "qualname": "StringConfigParameter.__init__", "kind": "function", "doc": "

Init a str config parameter. Define the list of all possible values.

\n\n
Arguments:
\n\n
    \n
  • name: name of config parameter
    • \n
  • default: default value of the config parameter
    • \n
  • possible_values: List of all possible string
    • \n
\n", "signature": "(name: str, default: str = '', possible_values: List[str] = None)"}, "auto_disc.legacy.utils.config_parameters.string_config_parameter.StringConfigParameter.check_value_to_set": {"fullname": "auto_disc.legacy.utils.config_parameters.string_config_parameter.StringConfigParameter.check_value_to_set", "modulename": "auto_disc.legacy.utils.config_parameters.string_config_parameter", "qualname": "StringConfigParameter.check_value_to_set", "kind": "function", "doc": "

Check if the value is one of the possible values

\n\n
Arguments:
\n\n
    \n
  • value: current value of the config parameter
    • \n
\n\n
Returns:
\n\n
\n

The return value is True if the value is in possible values

\n
\n", "signature": "(self, value: str) -> bool:", "funcdef": "def"}, "auto_disc.legacy.utils.logger": {"fullname": "auto_disc.legacy.utils.logger", "modulename": "auto_disc.legacy.utils.logger", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.logger.ad_logger": {"fullname": "auto_disc.legacy.utils.logger.ad_logger", "modulename": "auto_disc.legacy.utils.logger.ad_logger", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.logger.ad_logger.AutoDiscLogger": {"fullname": "auto_disc.legacy.utils.logger.ad_logger.AutoDiscLogger", "modulename": "auto_disc.legacy.utils.logger.ad_logger", "qualname": "AutoDiscLogger", "kind": "class", "doc": "

A logger to manage experiments logs and print them in the console or save them to the database or to disk according to the user's needs.

\n", "bases": "logging.Logger"}, "auto_disc.legacy.utils.logger.ad_logger.AutoDiscLogger.__init__": {"fullname": "auto_disc.legacy.utils.logger.ad_logger.AutoDiscLogger.__init__", "modulename": "auto_disc.legacy.utils.logger.ad_logger", "qualname": "AutoDiscLogger.__init__", "kind": "function", "doc": "

Init the logger for an experiment.

\n\n
Arguments:
\n\n
    \n
  • experiment_id: current experiment id
    • \n
  • seed: current seed number
    • \n
  • handlers: List of all handlers needed by the user to manage logs (e.g. save in database on disk)
    • \n
\n", "signature": "(\texperiment_id: int,\tseed: int,\thandlers: List[auto_disc.legacy.utils.logger.ad_logger.AutoDiscLogger])"}, "auto_disc.legacy.utils.logger.ad_logger.AutoDiscLogger.debug": {"fullname": "auto_disc.legacy.utils.logger.ad_logger.AutoDiscLogger.debug", "modulename": "auto_disc.legacy.utils.logger.ad_logger", "qualname": "AutoDiscLogger.debug", "kind": "function", "doc": "

Call the logs method at the debug level and increment the log index to make an unique id for each log

\n", "signature": "(self, *args) -> None:", "funcdef": "def"}, "auto_disc.legacy.utils.logger.ad_logger.AutoDiscLogger.info": {"fullname": "auto_disc.legacy.utils.logger.ad_logger.AutoDiscLogger.info", "modulename": "auto_disc.legacy.utils.logger.ad_logger", "qualname": "AutoDiscLogger.info", "kind": "function", "doc": "

Call the logs method at the info level and increment the log index to make an unique id for each log

\n", "signature": "(self, *args) -> None:", "funcdef": "def"}, "auto_disc.legacy.utils.logger.ad_logger.AutoDiscLogger.warning": {"fullname": "auto_disc.legacy.utils.logger.ad_logger.AutoDiscLogger.warning", "modulename": "auto_disc.legacy.utils.logger.ad_logger", "qualname": "AutoDiscLogger.warning", "kind": "function", "doc": "

Call the logs method at the warning level and increment the log index to make an unique id for each log

\n", "signature": "(self, *args) -> None:", "funcdef": "def"}, "auto_disc.legacy.utils.logger.ad_logger.AutoDiscLogger.error": {"fullname": "auto_disc.legacy.utils.logger.ad_logger.AutoDiscLogger.error", "modulename": "auto_disc.legacy.utils.logger.ad_logger", "qualname": "AutoDiscLogger.error", "kind": "function", "doc": "

Call the logs method at the error level and increment the log index to make an unique id for each log

\n", "signature": "(self, *args) -> None:", "funcdef": "def"}, "auto_disc.legacy.utils.logger.ad_logger.AutoDiscLogger.critical": {"fullname": "auto_disc.legacy.utils.logger.ad_logger.AutoDiscLogger.critical", "modulename": "auto_disc.legacy.utils.logger.ad_logger", "qualname": "AutoDiscLogger.critical", "kind": "function", "doc": "

Call the logs method at the critical level and increment the log index to make an unique id for each log

\n", "signature": "(self, *args) -> None:", "funcdef": "def"}, "auto_disc.legacy.utils.logger.ad_logger.ContextFilter": {"fullname": "auto_disc.legacy.utils.logger.ad_logger.ContextFilter", "modulename": "auto_disc.legacy.utils.logger.ad_logger", "qualname": "ContextFilter", "kind": "class", "doc": "

This is a filter which injects contextual information into the log.

\n", "bases": "logging.Filter"}, "auto_disc.legacy.utils.logger.ad_logger.ContextFilter.filter": {"fullname": "auto_disc.legacy.utils.logger.ad_logger.ContextFilter.filter", "modulename": "auto_disc.legacy.utils.logger.ad_logger", "qualname": "ContextFilter.filter", "kind": "function", "doc": "

Add contextual info to log

\n\n
Arguments:
\n\n
    \n
  • record: Some information
    • \n
\n\n
Returns:
\n\n
\n

The return value is always True

\n
\n", "signature": "(self, record: logging.LogRecord) -> bool:", "funcdef": "def"}, "auto_disc.legacy.utils.logger.handlers": {"fullname": "auto_disc.legacy.utils.logger.handlers", "modulename": "auto_disc.legacy.utils.logger.handlers", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.logger.handlers.file_handler": {"fullname": "auto_disc.legacy.utils.logger.handlers.file_handler", "modulename": "auto_disc.legacy.utils.logger.handlers.file_handler", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.logger.handlers.file_handler.SetFileHandler": {"fullname": "auto_disc.legacy.utils.logger.handlers.file_handler.SetFileHandler", "modulename": "auto_disc.legacy.utils.logger.handlers.file_handler", "qualname": "SetFileHandler", "kind": "class", "doc": "

Handler to put logs in file on disk

\n", "bases": "logging.FileHandler"}, "auto_disc.legacy.utils.logger.handlers.file_handler.SetFileHandler.__init__": {"fullname": "auto_disc.legacy.utils.logger.handlers.file_handler.SetFileHandler.__init__", "modulename": "auto_disc.legacy.utils.logger.handlers.file_handler", "qualname": "SetFileHandler.__init__", "kind": "function", "doc": "

Init an handler able to save log in file on disk

\n\n
Arguments:
\n\n
    \n
  • folder_log_path: The path to the folder where the user want save logs
    • \n
  • experiment_id: current experiment id
    • \n
\n", "signature": "(folder_log_path: str, experiment_id: int)"}, "auto_disc.legacy.utils.logger.log_levels_enum": {"fullname": "auto_disc.legacy.utils.logger.log_levels_enum", "modulename": "auto_disc.legacy.utils.logger.log_levels_enum", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.logger.log_levels_enum.LogsLevelEnum": {"fullname": "auto_disc.legacy.utils.logger.log_levels_enum.LogsLevelEnum", "modulename": "auto_disc.legacy.utils.logger.log_levels_enum", "qualname": "LogsLevelEnum", "kind": "class", "doc": "

An enumeration.

\n", "bases": "enum.Enum"}, "auto_disc.legacy.utils.logger.log_levels_enum.LogsLevelEnum.DEBUG": {"fullname": "auto_disc.legacy.utils.logger.log_levels_enum.LogsLevelEnum.DEBUG", "modulename": "auto_disc.legacy.utils.logger.log_levels_enum", "qualname": "LogsLevelEnum.DEBUG", "kind": "variable", "doc": "

\n", "default_value": "<LogsLevelEnum.DEBUG: 'debug'>"}, "auto_disc.legacy.utils.logger.log_levels_enum.LogsLevelEnum.INFO": {"fullname": "auto_disc.legacy.utils.logger.log_levels_enum.LogsLevelEnum.INFO", "modulename": "auto_disc.legacy.utils.logger.log_levels_enum", "qualname": "LogsLevelEnum.INFO", "kind": "variable", "doc": "

\n", "default_value": "<LogsLevelEnum.INFO: 'info'>"}, "auto_disc.legacy.utils.logger.log_levels_enum.LogsLevelEnum.WARNING": {"fullname": "auto_disc.legacy.utils.logger.log_levels_enum.LogsLevelEnum.WARNING", "modulename": "auto_disc.legacy.utils.logger.log_levels_enum", "qualname": "LogsLevelEnum.WARNING", "kind": "variable", "doc": "

\n", "default_value": "<LogsLevelEnum.WARNING: 'warning'>"}, "auto_disc.legacy.utils.logger.log_levels_enum.LogsLevelEnum.ERROR": {"fullname": "auto_disc.legacy.utils.logger.log_levels_enum.LogsLevelEnum.ERROR", "modulename": "auto_disc.legacy.utils.logger.log_levels_enum", "qualname": "LogsLevelEnum.ERROR", "kind": "variable", "doc": "

\n", "default_value": "<LogsLevelEnum.ERROR: 'error'>"}, "auto_disc.legacy.utils.logger.log_levels_enum.LogsLevelEnum.CRITICAL": {"fullname": "auto_disc.legacy.utils.logger.log_levels_enum.LogsLevelEnum.CRITICAL", "modulename": "auto_disc.legacy.utils.logger.log_levels_enum", "qualname": "LogsLevelEnum.CRITICAL", "kind": "variable", "doc": "

\n", "default_value": "<LogsLevelEnum.CRITICAL: 'critical'>"}, "auto_disc.legacy.utils.misc": {"fullname": "auto_disc.legacy.utils.misc", "modulename": "auto_disc.legacy.utils.misc", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.misc.db": {"fullname": "auto_disc.legacy.utils.misc.db", "modulename": "auto_disc.legacy.utils.misc.db", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.misc.db.DB": {"fullname": "auto_disc.legacy.utils.misc.db.DB", "modulename": "auto_disc.legacy.utils.misc.db", "qualname": "DB", "kind": "class", "doc": "

The main class of TinyDB.

\n\n

The TinyDB class is responsible for creating the storage class instance\nthat will store this database's documents, managing the database\ntables as well as providing access to the default table.

\n\n

For table management, a simple dict is used that stores the table class\ninstances accessible using their table name.

\n\n

Default table access is provided by forwarding all unknown method calls\nand property access operations to the default table by implementing\n__getattr__.

\n\n

When creating a new instance, all arguments and keyword arguments (except\nfor storage) will be passed to the storage class that is provided. If\nno storage class is specified, ~tinydb.storages.JSONStorage will be\nused.

\n\n

.. admonition:: Customization

\n\n
For customization, the following class variables can be set:\n\n- ``table_class`` defines the class that is used to create tables,\n- ``default_table_name`` defines the name of the default table, and\n- ``default_storage_class`` will define the class that will be used to\n  create storage instances if no other storage is passed.\n\n*New in version 4.0.*\n
\n\n

.. admonition:: Data Storage Model

\n\n
Data is stored using a storage class that provides persistence for a\n``dict`` instance. This ``dict`` contains all tables and their data.\nThe data is modelled like this::\n\n    {\n        'table1': {\n            0: {document...},\n            1: {document...},\n        },\n        'table2': {\n            ...\n        }\n    }\n\nEach entry in this ``dict`` uses the table name as its key and a\n``dict`` of documents as its value. The document ``dict`` contains\ndocument IDs as keys and the documents themselves as values.\n
\n\n
Parameters
\n\n
    \n
  • storage: The class of the storage to use. Will be initialized\nwith args and kwargs.
    • \n
\n", "bases": "tinydb.database.TinyDB"}, "auto_disc.legacy.utils.misc.db.DB.__init__": {"fullname": "auto_disc.legacy.utils.misc.db.DB.__init__", "modulename": "auto_disc.legacy.utils.misc.db", "qualname": "DB.__init__", "kind": "function", "doc": "

Create a new instance of TinyDB.

\n", "signature": "()"}, "auto_disc.legacy.utils.misc.db.DB.close": {"fullname": "auto_disc.legacy.utils.misc.db.DB.close", "modulename": "auto_disc.legacy.utils.misc.db", "qualname": "DB.close", "kind": "function", "doc": "

Close the database.

\n\n

This may be needed if the storage instance used for this database\nneeds to perform cleanup operations like closing file handles.

\n\n

To ensure this method is called, the TinyDB instance can be used as a\ncontext manager::

\n\n
with TinyDB('data.json') as db:\n    db.insert({'foo': 'bar'})\n
\n\n

Upon leaving this context, the close method will be called.

\n", "signature": "(self) -> None:", "funcdef": "def"}, "auto_disc.legacy.utils.misc.db.DB.to_autodisc_history": {"fullname": "auto_disc.legacy.utils.misc.db.DB.to_autodisc_history", "modulename": "auto_disc.legacy.utils.misc.db", "qualname": "DB.to_autodisc_history", "kind": "function", "doc": "

Select only some keys in documents and return a History. Use new_keys to rename these keys in the returned History.

\n\n
Arguments:
\n\n
    \n
  • documents: list of db item
    • \n
  • keys: list of key
    • \n
  • new_keys: list of key
    • \n
\n\n
Returns:
\n\n
\n

History

\n
\n", "signature": "(\tself,\tdocuments: list,\tkeys: List[str],\tnew_keys: List[str] = None) -> auto_disc.legacy.utils.misc.history.History:", "funcdef": "def"}, "auto_disc.legacy.utils.misc.db.DB.save": {"fullname": "auto_disc.legacy.utils.misc.db.DB.save", "modulename": "auto_disc.legacy.utils.misc.db", "qualname": "DB.save", "kind": "function", "doc": "

Save DB.

\n", "signature": "(self) -> auto_disc.legacy.utils.misc.db.DB:", "funcdef": "def"}, "auto_disc.legacy.utils.misc.db.DB.load": {"fullname": "auto_disc.legacy.utils.misc.db.DB.load", "modulename": "auto_disc.legacy.utils.misc.db", "qualname": "DB.load", "kind": "function", "doc": "

Reload DB.

\n", "signature": "(self, saved_dict):", "funcdef": "def"}, "auto_disc.legacy.utils.misc.history": {"fullname": "auto_disc.legacy.utils.misc.history", "modulename": "auto_disc.legacy.utils.misc.history", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.misc.history.History": {"fullname": "auto_disc.legacy.utils.misc.history.History", "modulename": "auto_disc.legacy.utils.misc.history", "qualname": "History", "kind": "class", "doc": "

\n"}, "auto_disc.legacy.utils.misc.history.History.__init__": {"fullname": "auto_disc.legacy.utils.misc.history.History.__init__", "modulename": "auto_disc.legacy.utils.misc.history", "qualname": "History.__init__", "kind": "function", "doc": "

\n", "signature": "(columns: List[str], documents: List[Dict] = [])"}, "auto_disc.legacy.utils.misc.history.History.append": {"fullname": "auto_disc.legacy.utils.misc.history.History.append", "modulename": "auto_disc.legacy.utils.misc.history", "qualname": "History.append", "kind": "function", "doc": "

Append document

\n\n
Arguments:
\n\n
    \n
  • document: dict of database element
    • \n
\n", "signature": "(self, document: Dict) -> None:", "funcdef": "def"}, "auto_disc.legacy.utils.misc.torch_utils": {"fullname": "auto_disc.legacy.utils.misc.torch_utils", "modulename": "auto_disc.legacy.utils.misc.torch_utils", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.misc.torch_utils.SphericPad": {"fullname": "auto_disc.legacy.utils.misc.torch_utils.SphericPad", "modulename": "auto_disc.legacy.utils.misc.torch_utils", "qualname": "SphericPad", "kind": "class", "doc": "

Pads spherically the input on all sides with the given padding size.

\n", "bases": "torch.nn.modules.module.Module"}, "auto_disc.legacy.utils.misc.torch_utils.SphericPad.__init__": {"fullname": "auto_disc.legacy.utils.misc.torch_utils.SphericPad.__init__", "modulename": "auto_disc.legacy.utils.misc.torch_utils", "qualname": "SphericPad.__init__", "kind": "function", "doc": "

Initializes internal Module state, shared by both nn.Module and ScriptModule.

\n", "signature": "(padding_size: torch.Tensor)"}, "auto_disc.legacy.utils.misc.torch_utils.SphericPad.forward": {"fullname": "auto_disc.legacy.utils.misc.torch_utils.SphericPad.forward", "modulename": "auto_disc.legacy.utils.misc.torch_utils", "qualname": "SphericPad.forward", "kind": "function", "doc": "

Defines the computation performed at every call.

\n\n

Should be overridden by all subclasses.

\n\n
\n\n

Although the recipe for forward pass needs to be defined within\nthis function, one should call the Module instance afterwards\ninstead of this since the former takes care of running the\nregistered hooks while the latter silently ignores them.

\n\n
\n", "signature": "(self, input):", "funcdef": "def"}, "auto_disc.legacy.utils.misc.torch_utils.complex_mult_torch": {"fullname": "auto_disc.legacy.utils.misc.torch_utils.complex_mult_torch", "modulename": "auto_disc.legacy.utils.misc.torch_utils", "qualname": "complex_mult_torch", "kind": "function", "doc": "

Computes the complex multiplication in Pytorch when the tensor last dimension is 2: 0 is the real component and 1 the imaginary one

\n", "signature": "(X: torch.Tensor, Y: torch.Tensor) -> torch.Tensor:", "funcdef": "def"}, "auto_disc.legacy.utils.misc.torch_utils.roll_n": {"fullname": "auto_disc.legacy.utils.misc.torch_utils.roll_n", "modulename": "auto_disc.legacy.utils.misc.torch_utils", "qualname": "roll_n", "kind": "function", "doc": "

Rolls a tensor with a shift n on the specified axis

\n", "signature": "(X: torch.Tensor, axis: int, n: int) -> torch.Tensor:", "funcdef": "def"}, "auto_disc.legacy.utils.misc.torch_utils.soft_max": {"fullname": "auto_disc.legacy.utils.misc.torch_utils.soft_max", "modulename": "auto_disc.legacy.utils.misc.torch_utils", "qualname": "soft_max", "kind": "function", "doc": "

\n", "signature": "(x, m, k):", "funcdef": "def"}, "auto_disc.legacy.utils.misc.torch_utils.soft_clip": {"fullname": "auto_disc.legacy.utils.misc.torch_utils.soft_clip", "modulename": "auto_disc.legacy.utils.misc.torch_utils", "qualname": "soft_clip", "kind": "function", "doc": "

\n", "signature": "(x, min, max, k):", "funcdef": "def"}, "auto_disc.legacy.utils.mutators": {"fullname": "auto_disc.legacy.utils.mutators", "modulename": "auto_disc.legacy.utils.mutators", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.mutators.base_mutator": {"fullname": "auto_disc.legacy.utils.mutators.base_mutator", "modulename": "auto_disc.legacy.utils.mutators.base_mutator", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.mutators.base_mutator.BaseMutator": {"fullname": "auto_disc.legacy.utils.mutators.base_mutator.BaseMutator", "modulename": "auto_disc.legacy.utils.mutators.base_mutator", "qualname": "BaseMutator", "kind": "class", "doc": "

Base class to mute space var

\n"}, "auto_disc.legacy.utils.mutators.base_mutator.BaseMutator.init_shape": {"fullname": "auto_disc.legacy.utils.mutators.base_mutator.BaseMutator.init_shape", "modulename": "auto_disc.legacy.utils.mutators.base_mutator", "qualname": "BaseMutator.init_shape", "kind": "function", "doc": "

\n", "signature": "(self, shape=None):", "funcdef": "def"}, "auto_disc.legacy.utils.mutators.gaussian_mutator": {"fullname": "auto_disc.legacy.utils.mutators.gaussian_mutator", "modulename": "auto_disc.legacy.utils.mutators.gaussian_mutator", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.mutators.gaussian_mutator.GaussianMutator": {"fullname": "auto_disc.legacy.utils.mutators.gaussian_mutator.GaussianMutator", "modulename": "auto_disc.legacy.utils.mutators.gaussian_mutator", "qualname": "GaussianMutator", "kind": "class", "doc": "

Class to mutate a space with gaussian method

\n", "bases": "auto_disc.legacy.utils.mutators.base_mutator.BaseMutator"}, "auto_disc.legacy.utils.mutators.gaussian_mutator.GaussianMutator.__init__": {"fullname": "auto_disc.legacy.utils.mutators.gaussian_mutator.GaussianMutator.__init__", "modulename": "auto_disc.legacy.utils.mutators.gaussian_mutator", "qualname": "GaussianMutator.__init__", "kind": "function", "doc": "

Init what gaussian method need

\n\n
Arguments:
\n\n
    \n
  • mean: float
    • \n
  • std: float
    • \n
\n", "signature": "(mean: float, std: float)"}, "auto_disc.legacy.utils.mutators.gaussian_mutator.GaussianMutator.init_shape": {"fullname": "auto_disc.legacy.utils.mutators.gaussian_mutator.GaussianMutator.init_shape", "modulename": "auto_disc.legacy.utils.mutators.gaussian_mutator", "qualname": "GaussianMutator.init_shape", "kind": "function", "doc": "

Define the init shape

\n\n
Arguments:
\n\n
    \n
  • shape: tuple
    • \n
\n", "signature": "(self, shape: tuple = None) -> None:", "funcdef": "def"}, "auto_disc.legacy.utils.spaces": {"fullname": "auto_disc.legacy.utils.spaces", "modulename": "auto_disc.legacy.utils.spaces", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.spaces.base_space": {"fullname": "auto_disc.legacy.utils.spaces.base_space", "modulename": "auto_disc.legacy.utils.spaces.base_space", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.spaces.base_space.BaseSpace": {"fullname": "auto_disc.legacy.utils.spaces.base_space.BaseSpace", "modulename": "auto_disc.legacy.utils.spaces.base_space", "qualname": "BaseSpace", "kind": "class", "doc": "

Defines the init_space, genome_space and intervention_space of a system

\n"}, "auto_disc.legacy.utils.spaces.base_space.BaseSpace.__init__": {"fullname": "auto_disc.legacy.utils.spaces.base_space.BaseSpace.__init__", "modulename": "auto_disc.legacy.utils.spaces.base_space", "qualname": "BaseSpace.__init__", "kind": "function", "doc": "

Init the elements useful to the spaces

\n\n
Arguments:
\n\n
    \n
  • shape: Space shape
    • \n
  • dtype: torch type
    • \n
  • mutator: current mutator method
    • \n
\n", "signature": "(\tshape: Tuple[Union[auto_disc.legacy.utils.spaces.utils.config_parameter_binding.ConfigParameterBinding, Any]] = None,\tdtype: torch.dtype = None,\tmutator: auto_disc.legacy.utils.mutators.base_mutator.BaseMutator = None)"}, "auto_disc.legacy.utils.spaces.base_space.BaseSpace.initialize": {"fullname": "auto_disc.legacy.utils.spaces.base_space.BaseSpace.initialize", "modulename": "auto_disc.legacy.utils.spaces.base_space", "qualname": "BaseSpace.initialize", "kind": "function", "doc": "

Initialize the space.

\n\n
Arguments:
\n\n
    \n
  • parent_obj: The current autodisc module
    • \n
\n", "signature": "(\tself,\tparent_obj: auto_disc.legacy.base_autodisc_module.BaseAutoDiscModule) -> None:", "funcdef": "def"}, "auto_disc.legacy.utils.spaces.base_space.BaseSpace.apply_binding_if_existing": {"fullname": "auto_disc.legacy.utils.spaces.base_space.BaseSpace.apply_binding_if_existing", "modulename": "auto_disc.legacy.utils.spaces.base_space", "qualname": "BaseSpace.apply_binding_if_existing", "kind": "function", "doc": "

Get result of config parameter binding operation

\n\n
Arguments:
\n\n
    \n
  • var: the current variable processed
    • \n
  • lookup_obj: current autodisc module in which the binding was made
    • \n
\n\n
Returns:
\n\n
\n

value: The result of the operation binding

\n
\n", "signature": "(\tself,\tvar: Union[auto_disc.legacy.utils.spaces.utils.config_parameter_binding.ConfigParameterBinding, Any],\tlookup_obj: object) -> Any:", "funcdef": "def"}, "auto_disc.legacy.utils.spaces.base_space.BaseSpace.sample": {"fullname": "auto_disc.legacy.utils.spaces.base_space.BaseSpace.sample", "modulename": "auto_disc.legacy.utils.spaces.base_space", "qualname": "BaseSpace.sample", "kind": "function", "doc": "

Randomly sample an element of this space.\nCan be uniform or non-uniform sampling based on boundedness of space.

\n", "signature": "(self):", "funcdef": "def"}, "auto_disc.legacy.utils.spaces.base_space.BaseSpace.mutate": {"fullname": "auto_disc.legacy.utils.spaces.base_space.BaseSpace.mutate", "modulename": "auto_disc.legacy.utils.spaces.base_space", "qualname": "BaseSpace.mutate", "kind": "function", "doc": "

Randomly mutate an element of this space.

\n\n
Arguments:
\n\n
    \n
  • x: The element to be mutated
    • \n
\n", "signature": "(self, x):", "funcdef": "def"}, "auto_disc.legacy.utils.spaces.base_space.BaseSpace.contains": {"fullname": "auto_disc.legacy.utils.spaces.base_space.BaseSpace.contains", "modulename": "auto_disc.legacy.utils.spaces.base_space", "qualname": "BaseSpace.contains", "kind": "function", "doc": "

Return boolean specifying if x is a valid\nmember of this space

\n\n
Arguments:
\n\n
    \n
  • x: The var which must be part of the space
    • \n
\n", "signature": "(self, x):", "funcdef": "def"}, "auto_disc.legacy.utils.spaces.base_space.BaseSpace.clamp": {"fullname": "auto_disc.legacy.utils.spaces.base_space.BaseSpace.clamp", "modulename": "auto_disc.legacy.utils.spaces.base_space", "qualname": "BaseSpace.clamp", "kind": "function", "doc": "

Return a valid clamped value of x inside space's bounds

\n\n
Arguments:
\n\n
    \n
  • x: a value
    • \n
\n", "signature": "(self, x):", "funcdef": "def"}, "auto_disc.legacy.utils.spaces.base_space.BaseSpace.to_json": {"fullname": "auto_disc.legacy.utils.spaces.base_space.BaseSpace.to_json", "modulename": "auto_disc.legacy.utils.spaces.base_space", "qualname": "BaseSpace.to_json", "kind": "function", "doc": "

Convert the space into JSON

\n\n
Returns:
\n\n
\n

The return value are a json containing a readable shape

\n
\n", "signature": "(self) -> Dict:", "funcdef": "def"}, "auto_disc.legacy.utils.spaces.box": {"fullname": "auto_disc.legacy.utils.spaces.box", "modulename": "auto_disc.legacy.utils.spaces.box", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.spaces.box.BoxSpace": {"fullname": "auto_disc.legacy.utils.spaces.box.BoxSpace", "modulename": "auto_disc.legacy.utils.spaces.box", "qualname": "BoxSpace", "kind": "class", "doc": "

A (possibly unbounded) box in R^n. Specifically, a Box represents the\nCartesian product of n closed intervals. Each interval has the form of one\nof [a, b], (-oo, b], [a, oo), or (-oo, oo).

\n\n

There are two common use cases:

\n\n
    \n

  • Identical bound for each dimension::

    \n\n
    \n
    >>> BoxSpace(low=-1.0, high=2.0, shape=(3, 4), dtype=torch.float32)\nBox(3, 4)\n
    \n
    • \n

  • Independent bound for each dimension::

    \n\n
    \n
    >>> BoxSpace(low=torch.tensor([-1.0, -2.0]), high=torch.tensor([2.0, 4.0]), dtype=torch.float32)\nBox(2,)\n
    \n
    • \n
\n", "bases": "auto_disc.legacy.utils.spaces.base_space.BaseSpace"}, "auto_disc.legacy.utils.spaces.box.BoxSpace.__init__": {"fullname": "auto_disc.legacy.utils.spaces.box.BoxSpace.__init__", "modulename": "auto_disc.legacy.utils.spaces.box", "qualname": "BoxSpace.__init__", "kind": "function", "doc": "

Init the elements useful to the spaces

\n\n
Arguments:
\n\n
    \n
  • low: lower bound
    • \n
  • hight: upper bound
    • \n
  • mutator: current mutator method
    • \n
  • shape: Space shape
    • \n
  • dtype: torch type
    • \n
  • indpb: Independent probability for each attribute to be exchanged
    • \n
\n", "signature": "(\tlow: float,\thigh: float,\tmutator: auto_disc.legacy.utils.mutators.base_mutator.BaseMutator = None,\tshape: tuple = None,\tdtype=torch.float32,\tindpb: float = 1.0)"}, "auto_disc.legacy.utils.spaces.box.BoxSpace.initialize": {"fullname": "auto_disc.legacy.utils.spaces.box.BoxSpace.initialize", "modulename": "auto_disc.legacy.utils.spaces.box", "qualname": "BoxSpace.initialize", "kind": "function", "doc": "

Initialize the space.

\n\n
Arguments:
\n\n
    \n
  • parent_obj: The current autodisc module
    • \n
\n", "signature": "(self, parent_obj: object) -> None:", "funcdef": "def"}, "auto_disc.legacy.utils.spaces.box.BoxSpace.is_bounded": {"fullname": "auto_disc.legacy.utils.spaces.box.BoxSpace.is_bounded", "modulename": "auto_disc.legacy.utils.spaces.box", "qualname": "BoxSpace.is_bounded", "kind": "function", "doc": "

Check if the space is bounded (below above or both)

\n\n
Arguments:
\n\n
    \n
  • manner: indicates which bound should be checked
    • \n
\n", "signature": "(self, manner: str = 'both') -> torch.bool:", "funcdef": "def"}, "auto_disc.legacy.utils.spaces.box.BoxSpace.sample": {"fullname": "auto_disc.legacy.utils.spaces.box.BoxSpace.sample", "modulename": "auto_disc.legacy.utils.spaces.box", "qualname": "BoxSpace.sample", "kind": "function", "doc": "

Generates a single random sample inside of the Box.

\n\n

In creating a sample of the box, each coordinate is sampled according to\nthe form of the interval:

\n\n
    \n
  • [a, b] : uniform distribution
    • \n
  • [a, oo) : shifted exponential distribution
    • \n
  • (-oo, b] : shifted negative exponential distribution
    • \n
  • (-oo, oo) : normal distribution
    • \n
\n\n
Return:
\n\n
\n

The return value is torch of the sample value

\n
\n", "signature": "(self):", "funcdef": "def"}, "auto_disc.legacy.utils.spaces.box.BoxSpace.mutate": {"fullname": "auto_disc.legacy.utils.spaces.box.BoxSpace.mutate", "modulename": "auto_disc.legacy.utils.spaces.box", "qualname": "BoxSpace.mutate", "kind": "function", "doc": "

Apply the mutation of the mutator on x

\n\n
Arguments:
\n\n
    \n
  • x: The variable to mutate
    • \n
\n\n
Returns:
\n\n
\n

x: the result of mutation

\n
\n", "signature": "(self, x: torch.Tensor) -> torch.Tensor:", "funcdef": "def"}, "auto_disc.legacy.utils.spaces.box.BoxSpace.contains": {"fullname": "auto_disc.legacy.utils.spaces.box.BoxSpace.contains", "modulename": "auto_disc.legacy.utils.spaces.box", "qualname": "BoxSpace.contains", "kind": "function", "doc": "

Check if x is included in the space

\n\n
Arguments:
\n\n
    \n
  • x: The value to check
    • \n
\n\n
Returns:
\n\n
\n

The return value is True if x is included in the space False otherwise

\n
\n", "signature": "(self, x: Union[torch.Tensor, List]) -> bool:", "funcdef": "def"}, "auto_disc.legacy.utils.spaces.box.BoxSpace.clamp": {"fullname": "auto_disc.legacy.utils.spaces.box.BoxSpace.clamp", "modulename": "auto_disc.legacy.utils.spaces.box", "qualname": "BoxSpace.clamp", "kind": "function", "doc": "

Set x to an acceptable value of the space

\n\n
Arguments:
\n\n
    \n
  • x: The value to set
    • \n
\n\n
Returns:
\n\n
\n

x: After being set

\n
\n", "signature": "(self, x: torch.Tensor) -> torch.Tensor:", "funcdef": "def"}, "auto_disc.legacy.utils.spaces.box.BoxSpace.to_json": {"fullname": "auto_disc.legacy.utils.spaces.box.BoxSpace.to_json", "modulename": "auto_disc.legacy.utils.spaces.box", "qualname": "BoxSpace.to_json", "kind": "function", "doc": "

Convert the object into JSON

\n\n
Returns:
\n\n
\n

The JSON of the object

\n
\n", "signature": "(self) -> Dict[str, <built-in function any>]:", "funcdef": "def"}, "auto_disc.legacy.utils.spaces.dict": {"fullname": "auto_disc.legacy.utils.spaces.dict", "modulename": "auto_disc.legacy.utils.spaces.dict", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.spaces.dict.DictSpace": {"fullname": "auto_disc.legacy.utils.spaces.dict.DictSpace", "modulename": "auto_disc.legacy.utils.spaces.dict", "qualname": "DictSpace", "kind": "class", "doc": "

A Dict dictionary of simpler spaces.

\n\n

Example usage:\nself.genome_space = spaces.DictSpace({\"position\": spaces.Discrete(2), \"velocity\": spaces.Discrete(3)})

\n\n

Example usage [nested]:\nself.nested_genome_space = spaces.DictSpace({\n 'sensors': spaces.DictSpace({\n 'position': spaces.Box(low=-100, high=100, shape=(3,)),\n 'velocity': spaces.Box(low=-1, high=1, shape=(3,)),\n 'front_cam': spaces.Tuple((\n spaces.Box(low=0, high=1, shape=(10, 10, 3)),\n spaces.Box(low=0, high=1, shape=(10, 10, 3))\n )),\n 'rear_cam': spaces.Box(low=0, high=1, shape=(10, 10, 3)),\n }),\n 'ext_controller': spaces.MultiDiscrete((5, 2, 2)),\n 'inner_state':spaces.DictSpace({\n 'charge': spaces.Discrete(100),\n 'system_checks': spaces.MultiBinary(10),\n 'job_status': spaces.DictSpace({\n 'task': spaces.Discrete(5),\n 'progress': spaces.Box(low=0, high=100, shape=()),\n })\n })\n})

\n", "bases": "auto_disc.legacy.utils.spaces.base_space.BaseSpace"}, "auto_disc.legacy.utils.spaces.dict.DictSpace.__init__": {"fullname": "auto_disc.legacy.utils.spaces.dict.DictSpace.__init__", "modulename": "auto_disc.legacy.utils.spaces.dict", "qualname": "DictSpace.__init__", "kind": "function", "doc": "

Init the elements useful to the space. Define spaces as dict of spaces

\n\n
Arguments:
\n\n
    \n
  • spaces: All spaces to be used
    • \n
\n", "signature": "(spaces=None, **spaces_kwargs)"}, "auto_disc.legacy.utils.spaces.dict.DictSpace.initialize": {"fullname": "auto_disc.legacy.utils.spaces.dict.DictSpace.initialize", "modulename": "auto_disc.legacy.utils.spaces.dict", "qualname": "DictSpace.initialize", "kind": "function", "doc": "

Initialize all spaces in dict.

\n\n
Arguments:
\n\n
    \n
  • parent_obj: The current autodisc module
    • \n
\n", "signature": "(self, parent_obj: object) -> None:", "funcdef": "def"}, "auto_disc.legacy.utils.spaces.dict.DictSpace.sample": {"fullname": "auto_disc.legacy.utils.spaces.dict.DictSpace.sample", "modulename": "auto_disc.legacy.utils.spaces.dict", "qualname": "DictSpace.sample", "kind": "function", "doc": "

Generates sample for each spaces according to the space

\n\n
Return:
\n\n
\n

The return value is Dict of sample values

\n
\n", "signature": "(\tself) -> Dict[str, <built-in method tensor of type object at 0x7f0f3759f460>]:", "funcdef": "def"}, "auto_disc.legacy.utils.spaces.dict.DictSpace.mutate": {"fullname": "auto_disc.legacy.utils.spaces.dict.DictSpace.mutate", "modulename": "auto_disc.legacy.utils.spaces.dict", "qualname": "DictSpace.mutate", "kind": "function", "doc": "

Apply the mutation for each spaces according to the space

\n\n
Return:
\n\n
\n

The return value is Dict of mutates values

\n
\n", "signature": "(self, x):", "funcdef": "def"}, "auto_disc.legacy.utils.spaces.dict.DictSpace.contains": {"fullname": "auto_disc.legacy.utils.spaces.dict.DictSpace.contains", "modulename": "auto_disc.legacy.utils.spaces.dict", "qualname": "DictSpace.contains", "kind": "function", "doc": "

Check if each value is correctly set in its space

\n\n
Arguments:
\n\n
    \n
  • x: The dict of spaces
    • \n
\n\n
Returns:
\n\n
\n

The return value is True if each item in x is included in its space False otherwise

\n
\n", "signature": "(self, x: addict.addict.Dict) -> bool:", "funcdef": "def"}, "auto_disc.legacy.utils.spaces.dict.DictSpace.clamp": {"fullname": "auto_disc.legacy.utils.spaces.dict.DictSpace.clamp", "modulename": "auto_disc.legacy.utils.spaces.dict", "qualname": "DictSpace.clamp", "kind": "function", "doc": "

For each element in dict set it to an acceptable value of the space

\n\n
Arguments:
\n\n
    \n
  • x: The dict of value value to set
    • \n
\n\n
Returns:
\n\n
\n

x: After being set

\n
\n", "signature": "(self, x: addict.addict.Dict) -> addict.addict.Dict:", "funcdef": "def"}, "auto_disc.legacy.utils.spaces.dict.DictSpace.to_json": {"fullname": "auto_disc.legacy.utils.spaces.dict.DictSpace.to_json", "modulename": "auto_disc.legacy.utils.spaces.dict", "qualname": "DictSpace.to_json", "kind": "function", "doc": "

Convert the object into JSON

\n\n
Returns:
\n\n
\n

The JSON of the object

\n
\n", "signature": "(self):", "funcdef": "def"}, "auto_disc.legacy.utils.spaces.discrete": {"fullname": "auto_disc.legacy.utils.spaces.discrete", "modulename": "auto_disc.legacy.utils.spaces.discrete", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.spaces.discrete.DiscreteSpace": {"fullname": "auto_disc.legacy.utils.spaces.discrete.DiscreteSpace", "modulename": "auto_disc.legacy.utils.spaces.discrete", "qualname": "DiscreteSpace", "kind": "class", "doc": "

A discrete space in \\( { 0, 1, \\dots, n-1 } \\).\n/!\\ mutation is gaussian by default: please create custom space inheriting from discrete space for custom mutation functions

\n\n

Example::

\n\n
>>> DiscreteSpace(2)\n
\n", "bases": "auto_disc.legacy.utils.spaces.base_space.BaseSpace"}, "auto_disc.legacy.utils.spaces.discrete.DiscreteSpace.__init__": {"fullname": "auto_disc.legacy.utils.spaces.discrete.DiscreteSpace.__init__", "modulename": "auto_disc.legacy.utils.spaces.discrete", "qualname": "DiscreteSpace.__init__", "kind": "function", "doc": "

Init the elements useful to the spaces

\n\n
Arguments:
\n\n
    \n
  • n: discret value
    • \n
  • mutator: current mutator method
    • \n
  • indpb: Independent probability for each attribute to be exchanged
    • \n
\n", "signature": "(\tn: int,\tmutator: auto_disc.legacy.utils.mutators.base_mutator.BaseMutator = None,\tindpb: float = 1.0)"}, "auto_disc.legacy.utils.spaces.discrete.DiscreteSpace.initialize": {"fullname": "auto_disc.legacy.utils.spaces.discrete.DiscreteSpace.initialize", "modulename": "auto_disc.legacy.utils.spaces.discrete", "qualname": "DiscreteSpace.initialize", "kind": "function", "doc": "

Initialize the space.

\n\n
Arguments:
\n\n
    \n
  • parent_obj: The current autodisc module
    • \n
\n", "signature": "(self, parent_obj: object) -> None:", "funcdef": "def"}, "auto_disc.legacy.utils.spaces.discrete.DiscreteSpace.sample": {"fullname": "auto_disc.legacy.utils.spaces.discrete.DiscreteSpace.sample", "modulename": "auto_disc.legacy.utils.spaces.discrete", "qualname": "DiscreteSpace.sample", "kind": "function", "doc": "

Generates a single random sample, upper than 0 and lower than n.

\n\n
Return:
\n\n
\n

The return value is torch of the sample value

\n
\n", "signature": "(self) -> torch.Tensor:", "funcdef": "def"}, "auto_disc.legacy.utils.spaces.discrete.DiscreteSpace.mutate": {"fullname": "auto_disc.legacy.utils.spaces.discrete.DiscreteSpace.mutate", "modulename": "auto_disc.legacy.utils.spaces.discrete", "qualname": "DiscreteSpace.mutate", "kind": "function", "doc": "

Apply the mutation of the mutator on x

\n\n
Arguments:
\n\n
    \n
  • x: The variable to mutate
    • \n
\n\n
Returns:
\n\n
\n

x: the result of mutation

\n
\n", "signature": "(self, x: torch.Tensor) -> torch.Tensor:", "funcdef": "def"}, "auto_disc.legacy.utils.spaces.discrete.DiscreteSpace.contains": {"fullname": "auto_disc.legacy.utils.spaces.discrete.DiscreteSpace.contains", "modulename": "auto_disc.legacy.utils.spaces.discrete", "qualname": "DiscreteSpace.contains", "kind": "function", "doc": "

Check if x is bounded between 0 and n

\n\n
Arguments:
\n\n
    \n
  • x: The value to check
    • \n
\n\n
Returns:
\n\n
\n

The return value is True if x is bounded by 0 and n False otherwise

\n
\n", "signature": "(self, x: torch.Tensor) -> bool:", "funcdef": "def"}, "auto_disc.legacy.utils.spaces.discrete.DiscreteSpace.clamp": {"fullname": "auto_disc.legacy.utils.spaces.discrete.DiscreteSpace.clamp", "modulename": "auto_disc.legacy.utils.spaces.discrete", "qualname": "DiscreteSpace.clamp", "kind": "function", "doc": "

Set x to an acceptable value of the space

\n\n
Arguments:
\n\n
    \n
  • x: The value to set
    • \n
\n\n
Returns:
\n\n
\n

x: After being set

\n
\n", "signature": "(self, x: torch.Tensor) -> torch.Tensor:", "funcdef": "def"}, "auto_disc.legacy.utils.spaces.discrete.DiscreteSpace.to_json": {"fullname": "auto_disc.legacy.utils.spaces.discrete.DiscreteSpace.to_json", "modulename": "auto_disc.legacy.utils.spaces.discrete", "qualname": "DiscreteSpace.to_json", "kind": "function", "doc": "

Convert the object into JSON

\n\n
Returns:
\n\n
\n

The JSON of the object

\n
\n", "signature": "(self) -> Dict[str, Any]:", "funcdef": "def"}, "auto_disc.legacy.utils.spaces.multi_binary": {"fullname": "auto_disc.legacy.utils.spaces.multi_binary", "modulename": "auto_disc.legacy.utils.spaces.multi_binary", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.spaces.multi_binary.MultiBinarySpace": {"fullname": "auto_disc.legacy.utils.spaces.multi_binary.MultiBinarySpace", "modulename": "auto_disc.legacy.utils.spaces.multi_binary", "qualname": "MultiBinarySpace", "kind": "class", "doc": "

An n-shape binary space.

\n\n

The argument to MultiBinarySpace defines n, which could be a number or a list of numbers.

\n\n

Example Usage:

\n\n
\n
\n

self.genome_space = spaces.MultiBinarySpace(5)

\n
\n
\n\n
\n
\n

self.genome_space.sample()

\n\n
array([0,1,0,1,0], dtype =int8)\n
\n \n

self.genome_space = spaces.MultiBinarySpace([3,2])

\n
\n
\n\n
\n
\n

self.genome_space.sample()

\n\n
array([[0, 0],\n       [0, 1],\n       [1, 1]], dtype=int8)\n
\n
\n
\n", "bases": "auto_disc.legacy.utils.spaces.base_space.BaseSpace"}, "auto_disc.legacy.utils.spaces.multi_binary.MultiBinarySpace.__init__": {"fullname": "auto_disc.legacy.utils.spaces.multi_binary.MultiBinarySpace.__init__", "modulename": "auto_disc.legacy.utils.spaces.multi_binary", "qualname": "MultiBinarySpace.__init__", "kind": "function", "doc": "

Init the elements useful to the spaces

\n\n
Arguments:
\n\n
    \n
  • n: number of element
    • \n
  • indpb: Independent probability for each attribute to be exchanged
    • \n
\n", "signature": "(n: int, indpb: float = 1.0)"}, "auto_disc.legacy.utils.spaces.multi_binary.MultiBinarySpace.initialize": {"fullname": "auto_disc.legacy.utils.spaces.multi_binary.MultiBinarySpace.initialize", "modulename": "auto_disc.legacy.utils.spaces.multi_binary", "qualname": "MultiBinarySpace.initialize", "kind": "function", "doc": "

Initialize the space.

\n\n
Arguments:
\n\n
    \n
  • parent_obj: The current autodisc module
    • \n
\n", "signature": "(self, parent_obj: object) -> None:", "funcdef": "def"}, "auto_disc.legacy.utils.spaces.multi_binary.MultiBinarySpace.sample": {"fullname": "auto_disc.legacy.utils.spaces.multi_binary.MultiBinarySpace.sample", "modulename": "auto_disc.legacy.utils.spaces.multi_binary", "qualname": "MultiBinarySpace.sample", "kind": "function", "doc": "

Generates a random sample.

\n\n
Return:
\n\n
\n

The return value is torch of the sample value

\n
\n", "signature": "(self) -> torch.Tensor:", "funcdef": "def"}, "auto_disc.legacy.utils.spaces.multi_binary.MultiBinarySpace.mutate": {"fullname": "auto_disc.legacy.utils.spaces.multi_binary.MultiBinarySpace.mutate", "modulename": "auto_disc.legacy.utils.spaces.multi_binary", "qualname": "MultiBinarySpace.mutate", "kind": "function", "doc": "

Apply a mutation on x

\n\n
Arguments:
\n\n
    \n
  • x: The variable to mutate
    • \n
\n\n
Returns:
\n\n
\n

x: the result of mutation

\n
\n", "signature": "(self, x: Any) -> torch.Tensor:", "funcdef": "def"}, "auto_disc.legacy.utils.spaces.multi_binary.MultiBinarySpace.contains": {"fullname": "auto_disc.legacy.utils.spaces.multi_binary.MultiBinarySpace.contains", "modulename": "auto_disc.legacy.utils.spaces.multi_binary", "qualname": "MultiBinarySpace.contains", "kind": "function", "doc": "

check if x is a tensor of 0 and 1 of a given size

\n\n
Arguments:
\n\n
    \n
  • x: The value to check
    • \n
\n\n
Returns:
\n\n
\n

The return value is True if x is a tensor of 0 and 1 of a given size False otherwise

\n
\n", "signature": "(self, x: Any) -> bool:", "funcdef": "def"}, "auto_disc.legacy.utils.spaces.multi_binary.MultiBinarySpace.clamp": {"fullname": "auto_disc.legacy.utils.spaces.multi_binary.MultiBinarySpace.clamp", "modulename": "auto_disc.legacy.utils.spaces.multi_binary", "qualname": "MultiBinarySpace.clamp", "kind": "function", "doc": "

Return a valid clamped value of x inside space's bounds

\n\n
Arguments:
\n\n
    \n
  • x: a value
    • \n
\n", "signature": "(self, x: Any) -> torch.Tensor:", "funcdef": "def"}, "auto_disc.legacy.utils.spaces.multi_binary.MultiBinarySpace.to_json": {"fullname": "auto_disc.legacy.utils.spaces.multi_binary.MultiBinarySpace.to_json", "modulename": "auto_disc.legacy.utils.spaces.multi_binary", "qualname": "MultiBinarySpace.to_json", "kind": "function", "doc": "

Convert the object into JSON

\n\n
Returns:
\n\n
\n

The JSON of the object

\n
\n", "signature": "(self) -> Dict[str, Any]:", "funcdef": "def"}, "auto_disc.legacy.utils.spaces.multi_discrete": {"fullname": "auto_disc.legacy.utils.spaces.multi_discrete", "modulename": "auto_disc.legacy.utils.spaces.multi_discrete", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.spaces.multi_discrete.MultiDiscreteSpace": {"fullname": "auto_disc.legacy.utils.spaces.multi_discrete.MultiDiscreteSpace", "modulename": "auto_disc.legacy.utils.spaces.multi_discrete", "qualname": "MultiDiscreteSpace", "kind": "class", "doc": "
    \n
  • The multi-discrete space consists of a series of discrete spaces with different number of possible instances in eachs
    • \n

  • Can be initialized as

    \n\n

    MultiDiscreteSpace([ 5, 2, 2 ])

    • \n
\n", "bases": "auto_disc.legacy.utils.spaces.base_space.BaseSpace"}, "auto_disc.legacy.utils.spaces.multi_discrete.MultiDiscreteSpace.__init__": {"fullname": "auto_disc.legacy.utils.spaces.multi_discrete.MultiDiscreteSpace.__init__", "modulename": "auto_disc.legacy.utils.spaces.multi_discrete", "qualname": "MultiDiscreteSpace.__init__", "kind": "function", "doc": "

Init the elements useful to the spaces

\n\n
Arguments:
\n\n
    \n
  • nvec: vector of counts of each categorical variable
    • \n
  • mutator: current mutator method
    • \n
  • indpb: Independent probability for each attribute to be exchanged
    • \n
\n", "signature": "(nvec, mutator=None, indpb=1.0)"}, "auto_disc.legacy.utils.spaces.multi_discrete.MultiDiscreteSpace.initialize": {"fullname": "auto_disc.legacy.utils.spaces.multi_discrete.MultiDiscreteSpace.initialize", "modulename": "auto_disc.legacy.utils.spaces.multi_discrete", "qualname": "MultiDiscreteSpace.initialize", "kind": "function", "doc": "

Initialize the space.

\n\n
Arguments:
\n\n
    \n
  • parent_obj: The current autodisc module
    • \n
\n", "signature": "(self, parent_obj):", "funcdef": "def"}, "auto_disc.legacy.utils.spaces.multi_discrete.MultiDiscreteSpace.sample": {"fullname": "auto_disc.legacy.utils.spaces.multi_discrete.MultiDiscreteSpace.sample", "modulename": "auto_disc.legacy.utils.spaces.multi_discrete", "qualname": "MultiDiscreteSpace.sample", "kind": "function", "doc": "

Generates a single random sample, upper than 0 and lower than n.

\n\n
Return:
\n\n
\n

The return value is torch type of the sample value

\n
\n", "signature": "(self) -> torch.Tensor:", "funcdef": "def"}, "auto_disc.legacy.utils.spaces.multi_discrete.MultiDiscreteSpace.mutate": {"fullname": "auto_disc.legacy.utils.spaces.multi_discrete.MultiDiscreteSpace.mutate", "modulename": "auto_disc.legacy.utils.spaces.multi_discrete", "qualname": "MultiDiscreteSpace.mutate", "kind": "function", "doc": "

Apply the mutation of the mutator on x

\n\n
Arguments:
\n\n
    \n
  • x: The variable to mutate
    • \n
\n\n
Returns:
\n\n
\n

x: the result of mutation

\n
\n", "signature": "(self, x):", "funcdef": "def"}, "auto_disc.legacy.utils.spaces.multi_discrete.MultiDiscreteSpace.contains": {"fullname": "auto_disc.legacy.utils.spaces.multi_discrete.MultiDiscreteSpace.contains", "modulename": "auto_disc.legacy.utils.spaces.multi_discrete", "qualname": "MultiDiscreteSpace.contains", "kind": "function", "doc": "

Check if x is bounded between 0 and n

\n\n
Arguments:
\n\n
    \n
  • x: The value to check
    • \n
\n\n
Returns:
\n\n
\n

The return value is True if x is bounded by 0 and n False otherwise

\n
\n", "signature": "(self, x) -> bool:", "funcdef": "def"}, "auto_disc.legacy.utils.spaces.multi_discrete.MultiDiscreteSpace.clamp": {"fullname": "auto_disc.legacy.utils.spaces.multi_discrete.MultiDiscreteSpace.clamp", "modulename": "auto_disc.legacy.utils.spaces.multi_discrete", "qualname": "MultiDiscreteSpace.clamp", "kind": "function", "doc": "

Set x to an acceptable value of the space

\n\n
Arguments:
\n\n
    \n
  • x: The value to set
    • \n
\n\n
Returns:
\n\n
\n

x: After being set

\n
\n", "signature": "(self, x) -> torch.Tensor:", "funcdef": "def"}, "auto_disc.legacy.utils.spaces.multi_discrete.MultiDiscreteSpace.to_json": {"fullname": "auto_disc.legacy.utils.spaces.multi_discrete.MultiDiscreteSpace.to_json", "modulename": "auto_disc.legacy.utils.spaces.multi_discrete", "qualname": "MultiDiscreteSpace.to_json", "kind": "function", "doc": "

Convert the object into JSON

\n\n
Returns:
\n\n
\n

The JSON of the object

\n
\n", "signature": "(self):", "funcdef": "def"}, "auto_disc.legacy.utils.spaces.tuple": {"fullname": "auto_disc.legacy.utils.spaces.tuple", "modulename": "auto_disc.legacy.utils.spaces.tuple", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.spaces.tuple.TupleSpace": {"fullname": "auto_disc.legacy.utils.spaces.tuple.TupleSpace", "modulename": "auto_disc.legacy.utils.spaces.tuple", "qualname": "TupleSpace", "kind": "class", "doc": "

A tuple (i.e., product) of simpler spaces

\n\n

Example usage:\nself.observation_space = spaces.Tuple((spaces.Discrete(2), spaces.Discrete(3)))

\n", "bases": "auto_disc.legacy.utils.spaces.base_space.BaseSpace"}, "auto_disc.legacy.utils.spaces.tuple.TupleSpace.__init__": {"fullname": "auto_disc.legacy.utils.spaces.tuple.TupleSpace.__init__", "modulename": "auto_disc.legacy.utils.spaces.tuple", "qualname": "TupleSpace.__init__", "kind": "function", "doc": "

Init the elements the spaces

\n\n
Arguments:
\n\n
    \n
  • spaces: elements spaces
    • \n
\n", "signature": "(spaces)"}, "auto_disc.legacy.utils.spaces.tuple.TupleSpace.initialize": {"fullname": "auto_disc.legacy.utils.spaces.tuple.TupleSpace.initialize", "modulename": "auto_disc.legacy.utils.spaces.tuple", "qualname": "TupleSpace.initialize", "kind": "function", "doc": "

Initialize the space.

\n\n
Arguments:
\n\n
    \n
  • parent_obj: The current autodisc module
    • \n
\n", "signature": "(self, parent_obj):", "funcdef": "def"}, "auto_disc.legacy.utils.spaces.tuple.TupleSpace.sample": {"fullname": "auto_disc.legacy.utils.spaces.tuple.TupleSpace.sample", "modulename": "auto_disc.legacy.utils.spaces.tuple", "qualname": "TupleSpace.sample", "kind": "function", "doc": "

Generates samples for each spaces.

\n\n
Return:
\n\n
\n

The return value is tuple of torch of samples values

\n
\n", "signature": "(self) -> Tuple[torch.Tensor]:", "funcdef": "def"}, "auto_disc.legacy.utils.spaces.tuple.TupleSpace.mutate": {"fullname": "auto_disc.legacy.utils.spaces.tuple.TupleSpace.mutate", "modulename": "auto_disc.legacy.utils.spaces.tuple", "qualname": "TupleSpace.mutate", "kind": "function", "doc": "

Apply a mutation on each spaces

\n\n
Arguments:
\n\n
    \n
  • x: The variable to mutate
    • \n
\n\n
Returns:
\n\n
\n

x: the result of mutation

\n
\n", "signature": "(self, x) -> Tuple:", "funcdef": "def"}, "auto_disc.legacy.utils.spaces.tuple.TupleSpace.contains": {"fullname": "auto_disc.legacy.utils.spaces.tuple.TupleSpace.contains", "modulename": "auto_disc.legacy.utils.spaces.tuple", "qualname": "TupleSpace.contains", "kind": "function", "doc": "

Check if x included in spaces

\n\n
Arguments:
\n\n
    \n
  • x: The value to check
    • \n
\n\n
Returns:
\n\n
\n

The return value is True if x is included False otherwise

\n
\n", "signature": "(self, x) -> bool:", "funcdef": "def"}, "auto_disc.legacy.utils.spaces.tuple.TupleSpace.clamp": {"fullname": "auto_disc.legacy.utils.spaces.tuple.TupleSpace.clamp", "modulename": "auto_disc.legacy.utils.spaces.tuple", "qualname": "TupleSpace.clamp", "kind": "function", "doc": "

Set each element of tuple to an accpetable value of his space

\n\n
Arguments:
\n\n
    \n
  • x: tuple elemnts
    • \n
\n\n
Returns:
\n\n
\n

The return value is clamped value of each element

\n
\n", "signature": "(self, x) -> Tuple:", "funcdef": "def"}, "auto_disc.legacy.utils.spaces.utils": {"fullname": "auto_disc.legacy.utils.spaces.utils", "modulename": "auto_disc.legacy.utils.spaces.utils", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.spaces.utils.config_parameter_binding": {"fullname": "auto_disc.legacy.utils.spaces.utils.config_parameter_binding", "modulename": "auto_disc.legacy.utils.spaces.utils.config_parameter_binding", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.spaces.utils.config_parameter_binding.ConfigParameterBinding": {"fullname": "auto_disc.legacy.utils.spaces.utils.config_parameter_binding.ConfigParameterBinding", "modulename": "auto_disc.legacy.utils.spaces.utils.config_parameter_binding", "qualname": "ConfigParameterBinding", "kind": "class", "doc": "

Allows to bind some properties of a space to the value of a config parameter that belongs to the config of the instance.

\n"}, "auto_disc.legacy.utils.spaces.utils.config_parameter_binding.ConfigParameterBinding.__init__": {"fullname": "auto_disc.legacy.utils.spaces.utils.config_parameter_binding.ConfigParameterBinding.__init__", "modulename": "auto_disc.legacy.utils.spaces.utils.config_parameter_binding", "qualname": "ConfigParameterBinding.__init__", "kind": "function", "doc": "

Initialize ConfigParamter by defining its name and the operations to perform on it (basic none)

\n\n
Arguments:
\n\n
    \n
  • parameter_name: the name of the parameter
    • \n
\n", "signature": "(parameter_name: str)"}, "auto_disc.legacy.utils.spaces.utils.config_parameter_binding.ConfigParameterBinding.to_json": {"fullname": "auto_disc.legacy.utils.spaces.utils.config_parameter_binding.ConfigParameterBinding.to_json", "modulename": "auto_disc.legacy.utils.spaces.utils.config_parameter_binding", "qualname": "ConfigParameterBinding.to_json", "kind": "function", "doc": "

transforms the binding into a string to make it readable

\n\n
Returns:
\n\n
\n

binding: the binding definition as string

\n
\n", "signature": "(self) -> str:", "funcdef": "def"}, "auto_disc.legacy.utils.spaces.utils.distance": {"fullname": "auto_disc.legacy.utils.spaces.utils.distance", "modulename": "auto_disc.legacy.utils.spaces.utils.distance", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.spaces.utils.distance.calc_l2": {"fullname": "auto_disc.legacy.utils.spaces.utils.distance.calc_l2", "modulename": "auto_disc.legacy.utils.spaces.utils.distance", "qualname": "calc_l2", "kind": "function", "doc": "

Calculate the Euclidean distance between 2 vectors

\n\n
Ars:
\n\n
\n

embedding_a: first vector\n embedding_b: second vector

\n
\n\n
Returns:
\n\n
\n

dist: The distance between the 2 vectors

\n
\n", "signature": "(embedding_a: torch.Tensor, embedding_b: torch.Tensor) -> torch.Tensor:", "funcdef": "def"}, "auto_disc.run": {"fullname": "auto_disc.run", "modulename": "auto_disc.run", "kind": "module", "doc": "

Helper script which allows creation of ExperimentPipeline.\nThis file run.py can also be run as __main__,\nfor example in remote configurations.

\n"}, "auto_disc.run.create": {"fullname": "auto_disc.run.create", "modulename": "auto_disc.run", "qualname": "create", "kind": "function", "doc": "

Setup the whole experiment. Set each modules, logger, callbacks and use\nthem to define the experiment pipeline.

\n\n

In addition to callbacks and handlers defined in parameters,\nyou can pass extras. as keyword arguments.

\n\n

Args:

\n\n
    \n
  • parameters: Experiment config (define which systems which explorer\nwhich callbacks and all other information needed to set an experiment)
    • \n
  • experiment_id: Current experiment id
    • \n
  • seed: current seed number
    • \n
  • additional_callbacks
    • \n
  • additional_handlers
    • \n
\n\n

Returns:

\n\n
    \n
  • experiment: The experiment we have just defined
    • \n
\n", "signature": "(\tparameters: Dict,\texperiment_id: int,\tseed: int,\tadditional_callbacks: Dict[str, List[Callable]] = None,\tadditional_handlers: List[auto_disc.legacy.utils.logger.ad_logger.AutoDiscLogger] = None,\tinteractMethod: Callable = None) -> auto_disc.ExperimentPipeline.ExperimentPipeline:", "funcdef": "def"}, "auto_disc.run.start": {"fullname": "auto_disc.run.start", "modulename": "auto_disc.run", "qualname": "start", "kind": "function", "doc": "

Runs an experiment for a number of iterations

\n\n

Args:

\n\n
    \n
  • experiment: The experiment we want to launch
    • \n
  • nb_iterations: the number explorations
    • \n
\n", "signature": "(\texperiment: auto_disc.ExperimentPipeline.ExperimentPipeline,\tnb_iterations: int) -> None:", "funcdef": "def"}, "auto_disc.utils": {"fullname": "auto_disc.utils", "modulename": "auto_disc.utils", "kind": "module", "doc": "

\n"}, "auto_disc.utils.depinj": {"fullname": "auto_disc.utils.depinj", "modulename": "auto_disc.utils.depinj", "kind": "module", "doc": "

\n"}, "auto_disc.utils.depinj.injector": {"fullname": "auto_disc.utils.depinj.injector", "modulename": "auto_disc.utils.depinj.injector", "kind": "module", "doc": "

\n"}, "auto_disc.utils.depinj.injector.inject": {"fullname": "auto_disc.utils.depinj.injector.inject", "modulename": "auto_disc.utils.depinj.injector", "qualname": "inject", "kind": "function", "doc": "

Runtime method overrider

\n", "signature": "(instance, function, overwrite: bool = True) -> None:", "funcdef": "def"}, "auto_disc.utils.depinj.injector.inject_callbacks": {"fullname": "auto_disc.utils.depinj.injector.inject_callbacks", "modulename": "auto_disc.utils.depinj.injector", "qualname": "inject_callbacks", "kind": "function", "doc": "

Runtime dependency injector, takes a class and an array of callables

\n", "signature": "(cls, callbacks=[], overwrite=True):", "funcdef": "def"}, "auto_disc.utils.depinj.tests": {"fullname": "auto_disc.utils.depinj.tests", "modulename": "auto_disc.utils.depinj.tests", "kind": "module", "doc": "

\n"}, "auto_disc.utils.depinj.tests.test_injector": {"fullname": "auto_disc.utils.depinj.tests.test_injector", "modulename": "auto_disc.utils.depinj.tests.test_injector", "kind": "module", "doc": "

\n"}, "auto_disc.utils.depinj.tests.test_injector.test_inject_callbacks": {"fullname": "auto_disc.utils.depinj.tests.test_injector.test_inject_callbacks", "modulename": "auto_disc.utils.depinj.tests.test_injector", "qualname": "test_inject_callbacks", "kind": "function", "doc": "

NOTE: pytest is used to capture stderr

\n", "signature": "(capsys):", "funcdef": "def"}, "auto_disc.utils.depinj.tests.test_injector.test_inject_callbacks_args": {"fullname": "auto_disc.utils.depinj.tests.test_injector.test_inject_callbacks_args", "modulename": "auto_disc.utils.depinj.tests.test_injector", "qualname": "test_inject_callbacks_args", "kind": "function", "doc": "

\n", "signature": "(capsys):", "funcdef": "def"}, "auto_disc.utils.depinj.tests.testdeps": {"fullname": "auto_disc.utils.depinj.tests.testdeps", "modulename": "auto_disc.utils.depinj.tests.testdeps", "kind": "module", "doc": "

\n"}, "auto_disc.utils.depinj.tests.testdeps.logger": {"fullname": "auto_disc.utils.depinj.tests.testdeps.logger", "modulename": "auto_disc.utils.depinj.tests.testdeps", "qualname": "logger", "kind": "function", "doc": "

\n", "signature": "(inst, *args, **kwargs):", "funcdef": "def"}, "auto_disc.utils.depinj.tests.testdeps.hw": {"fullname": "auto_disc.utils.depinj.tests.testdeps.hw", "modulename": "auto_disc.utils.depinj.tests.testdeps", "qualname": "hw", "kind": "function", "doc": "

\n", "signature": "(inst):", "funcdef": "def"}, "auto_disc.utils.filetype_converter": {"fullname": "auto_disc.utils.filetype_converter", "modulename": "auto_disc.utils.filetype_converter", "kind": "module", "doc": "

\n"}, "auto_disc.utils.filetype_converter.filetype_converter": {"fullname": "auto_disc.utils.filetype_converter.filetype_converter", "modulename": "auto_disc.utils.filetype_converter.filetype_converter", "kind": "module", "doc": "

\n"}, "auto_disc.utils.filetype_converter.filetype_converter.is_mp4": {"fullname": "auto_disc.utils.filetype_converter.filetype_converter.is_mp4", "modulename": "auto_disc.utils.filetype_converter.filetype_converter", "qualname": "is_mp4", "kind": "function", "doc": "

\n", "signature": "(blob: bytes) -> bool:", "funcdef": "def"}, "auto_disc.utils.filetype_converter.filetype_converter.is_png": {"fullname": "auto_disc.utils.filetype_converter.filetype_converter.is_png", "modulename": "auto_disc.utils.filetype_converter.filetype_converter", "qualname": "is_png", "kind": "function", "doc": "

\n", "signature": "(blob: bytes) -> bool:", "funcdef": "def"}, "auto_disc.utils.filetype_converter.filetype_converter.convert_image": {"fullname": "auto_disc.utils.filetype_converter.filetype_converter.convert_image", "modulename": "auto_disc.utils.filetype_converter.filetype_converter", "qualname": "convert_image", "kind": "function", "doc": "

\n", "signature": "(blob: bytes) -> bytes:", "funcdef": "def"}, "auto_disc.utils.filetype_converter.filetype_converter.convert_video": {"fullname": "auto_disc.utils.filetype_converter.filetype_converter.convert_video", "modulename": "auto_disc.utils.filetype_converter.filetype_converter", "qualname": "convert_video", "kind": "function", "doc": "

\n", "signature": "(blob: bytes) -> bytes:", "funcdef": "def"}, "auto_disc.utils.leaf": {"fullname": "auto_disc.utils.leaf", "modulename": "auto_disc.utils.leaf", "kind": "module", "doc": "

\n"}, "auto_disc.utils.leaf.Leaf": {"fullname": "auto_disc.utils.leaf.Leaf", "modulename": "auto_disc.utils.leaf.Leaf", "kind": "module", "doc": "

\n"}, "auto_disc.utils.leaf.Leaf.prune_state": {"fullname": "auto_disc.utils.leaf.Leaf.prune_state", "modulename": "auto_disc.utils.leaf.Leaf", "qualname": "prune_state", "kind": "function", "doc": "

Decorator allowing specification of what instance variables of a Leaf\nto ignore during the serialization call.

\n", "signature": "(state_vars: Dict[str, Any]):", "funcdef": "def"}, "auto_disc.utils.leaf.Leaf.Leaf": {"fullname": "auto_disc.utils.leaf.Leaf.Leaf", "modulename": "auto_disc.utils.leaf.Leaf", "qualname": "Leaf", "kind": "class", "doc": "

\n"}, "auto_disc.utils.leaf.Leaf.Leaf.serialize": {"fullname": "auto_disc.utils.leaf.Leaf.Leaf.serialize", "modulename": "auto_disc.utils.leaf.Leaf", "qualname": "Leaf.serialize", "kind": "function", "doc": "

Serializes object to pickle,\nturning all submodules into uniquely identifiable hashes

\n", "signature": "(self, *args, **kwargs) -> bytes:", "funcdef": "def"}, "auto_disc.utils.leaf.Leaf.Leaf.deserialize": {"fullname": "auto_disc.utils.leaf.Leaf.Leaf.deserialize", "modulename": "auto_disc.utils.leaf.Leaf", "qualname": "Leaf.deserialize", "kind": "function", "doc": "

Restores object from pickle, without recursively deserializing\nany submodules.

\n", "signature": "(self, bin: bytes) -> auto_disc.utils.leaf.Leaf.Leaf:", "funcdef": "def"}, "auto_disc.utils.leaf.Leaf.Leaf.save_leaf": {"fullname": "auto_disc.utils.leaf.Leaf.Leaf.save_leaf", "modulename": "auto_disc.utils.leaf.Leaf", "qualname": "Leaf.save_leaf", "kind": "function", "doc": "

Save entire structure of object. The suggested way to customize\nbehavior is overloading serialize() and the Locator

\n", "signature": "(\tself,\tresource_uri: str = '',\t*args,\t**kwargs) -> <function NewType.<locals>.new_type at 0x7f0ed2ca3670>:", "funcdef": "def"}, "auto_disc.utils.leaf.Leaf.Leaf.load_leaf": {"fullname": "auto_disc.utils.leaf.Leaf.Leaf.load_leaf", "modulename": "auto_disc.utils.leaf.Leaf", "qualname": "Leaf.load_leaf", "kind": "function", "doc": "

Load entire structure of object, not mutating self

\n", "signature": "(\tself,\tuid: <function NewType.<locals>.new_type>,\tresource_uri: str = '',\t*args,\t**kwargs) -> auto_disc.utils.leaf.Leaf.Leaf:", "funcdef": "def"}, "auto_disc.utils.leaf.LeafUID": {"fullname": "auto_disc.utils.leaf.LeafUID", "modulename": "auto_disc.utils.leaf.LeafUID", "kind": "module", "doc": "

\n"}, "auto_disc.utils.leaf.LeafUID.LeafUID": {"fullname": "auto_disc.utils.leaf.LeafUID.LeafUID", "modulename": "auto_disc.utils.leaf.LeafUID", "qualname": "LeafUID", "kind": "function", "doc": "

\n", "signature": "(x):", "funcdef": "def"}, "auto_disc.utils.leaf.locators": {"fullname": "auto_disc.utils.leaf.locators", "modulename": "auto_disc.utils.leaf.locators", "kind": "module", "doc": "

\n"}, "auto_disc.utils.leaf.locators.LinearBase": {"fullname": "auto_disc.utils.leaf.locators.LinearBase", "modulename": "auto_disc.utils.leaf.locators.LinearBase", "kind": "module", "doc": "

\n"}, "auto_disc.utils.leaf.locators.LinearBase.store_metadata": {"fullname": "auto_disc.utils.leaf.locators.LinearBase.store_metadata", "modulename": "auto_disc.utils.leaf.locators.LinearBase", "qualname": "store_metadata", "kind": "function", "doc": "

\n", "signature": "(subdir: str, data_bin: bytes) -> None:", "funcdef": "def"}, "auto_disc.utils.leaf.locators.LinearBase.store_data": {"fullname": "auto_disc.utils.leaf.locators.LinearBase.store_data", "modulename": "auto_disc.utils.leaf.locators.LinearBase", "qualname": "store_data", "kind": "function", "doc": "

Store the unpadded binary data (i.e., without tag).

\n", "signature": "(subdir: str, data_bin: bytes, parent_id: int) -> str:", "funcdef": "def"}, "auto_disc.utils.leaf.locators.LinearBase.init_db": {"fullname": "auto_disc.utils.leaf.locators.LinearBase.init_db", "modulename": "auto_disc.utils.leaf.locators.LinearBase", "qualname": "init_db", "kind": "function", "doc": "

\n", "signature": "(db_url: str) -> None:", "funcdef": "def"}, "auto_disc.utils.leaf.locators.LinearBase.convert_bytes_to_base64_str": {"fullname": "auto_disc.utils.leaf.locators.LinearBase.convert_bytes_to_base64_str", "modulename": "auto_disc.utils.leaf.locators.LinearBase", "qualname": "convert_bytes_to_base64_str", "kind": "function", "doc": "

\n", "signature": "(bin: bytes) -> str:", "funcdef": "def"}, "auto_disc.utils.leaf.locators.LinearBase.convert_base64_str_to_bytes": {"fullname": "auto_disc.utils.leaf.locators.LinearBase.convert_base64_str_to_bytes", "modulename": "auto_disc.utils.leaf.locators.LinearBase", "qualname": "convert_base64_str_to_bytes", "kind": "function", "doc": "

\n", "signature": "(b64_str: str) -> bytes:", "funcdef": "def"}, "auto_disc.utils.leaf.locators.LinearBase.insert_node": {"fullname": "auto_disc.utils.leaf.locators.LinearBase.insert_node", "modulename": "auto_disc.utils.leaf.locators.LinearBase", "qualname": "insert_node", "kind": "function", "doc": "

\n", "signature": "(\tengine: sqlalchemy.engine.base.Engine,\tdelta: str,\tparent_id: int = -1) -> int:", "funcdef": "def"}, "auto_disc.utils.leaf.locators.LinearBase.retrieve_trajectory": {"fullname": "auto_disc.utils.leaf.locators.LinearBase.retrieve_trajectory", "modulename": "auto_disc.utils.leaf.locators.LinearBase", "qualname": "retrieve_trajectory", "kind": "function", "doc": "

\n", "signature": "(db_url: str, row_id: int = 1, length: int = 1) -> bytes:", "funcdef": "def"}, "auto_disc.utils.leaf.locators.LinearBase.retrieve_packed_trajectory": {"fullname": "auto_disc.utils.leaf.locators.LinearBase.retrieve_packed_trajectory", "modulename": "auto_disc.utils.leaf.locators.LinearBase", "qualname": "retrieve_packed_trajectory", "kind": "function", "doc": "

Simple wrapper which retrieves the raw packed trajectory data\nfrom db_url.

\n", "signature": "(\tdb_url: str,\trow_id: int = 1,\tlength: int = 1) -> Tuple[List[int], List[bytes], List[int]]:", "funcdef": "def"}, "auto_disc.utils.leaf.locators.LinearBase.Stepper": {"fullname": "auto_disc.utils.leaf.locators.LinearBase.Stepper", "modulename": "auto_disc.utils.leaf.locators.LinearBase", "qualname": "Stepper", "kind": "class", "doc": "

\n", "bases": "auto_disc.utils.leaf.Leaf.Leaf"}, "auto_disc.utils.leaf.locators.LinearBase.FileLinearLocator": {"fullname": "auto_disc.utils.leaf.locators.LinearBase.FileLinearLocator", "modulename": "auto_disc.utils.leaf.locators.LinearBase", "qualname": "FileLinearLocator", "kind": "class", "doc": "

Locator which stores branching, linear data\nwith minimal redundancies in a SQLite db.

\n\n

To use, one should override deserialize of\nyour Leaf module class to output a serialized Python object x\nwith x.buffer a List type. I.e., any Leaf which uses LinearLocator\nshould define an instance variable buffer of List type, e.g.,\n 

class A(Leaf):\n\n    def __init__(self, buffer = []):\n        super().__init__()\n        self.buffer = buffer\n

\n\n

NOTE: This means that the save_leaf recursion will not recurse into\n the buffer, and any Leaf types inside will serialize naively.

\n", "bases": "auto_disc.utils.leaf.locators.Locator.Locator"}, "auto_disc.utils.leaf.locators.LinearBase.FileLinearLocator.__init__": {"fullname": "auto_disc.utils.leaf.locators.LinearBase.FileLinearLocator.__init__", "modulename": "auto_disc.utils.leaf.locators.LinearBase", "qualname": "FileLinearLocator.__init__", "kind": "function", "doc": "

\n", "signature": "(resource_uri: str = '')"}, "auto_disc.utils.leaf.locators.LinearBase.FileLinearLocator.store": {"fullname": "auto_disc.utils.leaf.locators.LinearBase.FileLinearLocator.store", "modulename": "auto_disc.utils.leaf.locators.LinearBase", "qualname": "FileLinearLocator.store", "kind": "function", "doc": "

Stores the bin as a child node of the node given by parent_id.

\n\n

Returns:

\n\n
    \n
  • leaf_uid (LeafUID): formatted path indicating the DB UID and the\nSQLite unique key corresponding\nto the inserted node
    • \n
\n", "signature": "(\tself,\tbin: bytes,\tparent_id: int = -1) -> <function NewType.<locals>.new_type at 0x7f0ed2ca3670>:", "funcdef": "def"}, "auto_disc.utils.leaf.locators.LinearBase.FileLinearLocator.retrieve": {"fullname": "auto_disc.utils.leaf.locators.LinearBase.FileLinearLocator.retrieve", "modulename": "auto_disc.utils.leaf.locators.LinearBase", "qualname": "FileLinearLocator.retrieve", "kind": "function", "doc": "

Retrieve trajectory of given length of saved data starting from\nthe leaf node given by uid, then traversing backwards towards the root.\nNOTE: length=0 corresponds to the entire trajectory, and it is not\n recommended to unpack large trajectories in memory

\n\n

Returns:

\n\n
    \n
  • bin (bytes): trajectory packed as a python object x with\nx.buffer being the array of data
    • \n
\n", "signature": "(\tself,\tuid: <function NewType.<locals>.new_type>,\tlength: int = 1) -> bytes:", "funcdef": "def"}, "auto_disc.utils.leaf.locators.LinearBase.FileLinearLocator.parse_bin": {"fullname": "auto_disc.utils.leaf.locators.LinearBase.FileLinearLocator.parse_bin", "modulename": "auto_disc.utils.leaf.locators.LinearBase", "qualname": "FileLinearLocator.parse_bin", "kind": "function", "doc": "

\n", "signature": "(cls, bin: bytes) -> Tuple[str, bytes]:", "funcdef": "def"}, "auto_disc.utils.leaf.locators.LinearBase.FileLinearLocator.parse_leaf_uid": {"fullname": "auto_disc.utils.leaf.locators.LinearBase.FileLinearLocator.parse_leaf_uid", "modulename": "auto_disc.utils.leaf.locators.LinearBase", "qualname": "FileLinearLocator.parse_leaf_uid", "kind": "function", "doc": "

\n", "signature": "(cls, uid: <function NewType.<locals>.new_type>) -> Tuple[str, int]:", "funcdef": "def"}, "auto_disc.utils.leaf.locators.LinearBase.FileLinearLocator.hash": {"fullname": "auto_disc.utils.leaf.locators.LinearBase.FileLinearLocator.hash", "modulename": "auto_disc.utils.leaf.locators.LinearBase", "qualname": "FileLinearLocator.hash", "kind": "function", "doc": "

LinearLocator must use SHA1 hashes

\n", "signature": "(bin: bytes) -> <function NewType.<locals>.new_type at 0x7f0ed2ca3670>:", "funcdef": "def"}, "auto_disc.utils.leaf.locators.LinearBase.FileLinearLocator.set_sqlite_pragma": {"fullname": "auto_disc.utils.leaf.locators.LinearBase.FileLinearLocator.set_sqlite_pragma", "modulename": "auto_disc.utils.leaf.locators.LinearBase", "qualname": "FileLinearLocator.set_sqlite_pragma", "kind": "function", "doc": "

\n", "signature": "(dbapi_connection, connection_record):", "funcdef": "def"}, "auto_disc.utils.leaf.locators.Locator": {"fullname": "auto_disc.utils.leaf.locators.Locator", "modulename": "auto_disc.utils.leaf.locators.Locator", "kind": "module", "doc": "

\n"}, "auto_disc.utils.leaf.locators.Locator.Locator": {"fullname": "auto_disc.utils.leaf.locators.Locator.Locator", "modulename": "auto_disc.utils.leaf.locators.Locator", "qualname": "Locator", "kind": "class", "doc": "

Class which handles data management and the saving/loading of binary data.\nShould have at least an instance variable called resource_uri, although\nit may be an empty string.

\n"}, "auto_disc.utils.leaf.locators.Locator.Locator.__init__": {"fullname": "auto_disc.utils.leaf.locators.Locator.Locator.__init__", "modulename": "auto_disc.utils.leaf.locators.Locator", "qualname": "Locator.__init__", "kind": "function", "doc": "

\n", "signature": "(resource_uri: str = '', *args, **kwargs)"}, "auto_disc.utils.leaf.locators.Locator.Locator.store": {"fullname": "auto_disc.utils.leaf.locators.Locator.Locator.store", "modulename": "auto_disc.utils.leaf.locators.Locator", "qualname": "Locator.store", "kind": "function", "doc": "

Stores bin which generated it somewhere, perhaps on network,\nand returns (redundantly) the hash for the object.

\n", "signature": "(\tself,\tbin: bytes,\t*args,\t**kwargs) -> <function NewType.<locals>.new_type at 0x7f0ed2ca3670>:", "funcdef": "def"}, "auto_disc.utils.leaf.locators.Locator.Locator.retrieve": {"fullname": "auto_disc.utils.leaf.locators.Locator.Locator.retrieve", "modulename": "auto_disc.utils.leaf.locators.Locator", "qualname": "Locator.retrieve", "kind": "function", "doc": "

Retrieve bin identified by uid from somewhere,\nperhaps on network

\n", "signature": "(\tself,\tuid: <function NewType.<locals>.new_type>,\t*args,\t**kwargs) -> bytes:", "funcdef": "def"}, "auto_disc.utils.leaf.locators.Locator.Locator.hash": {"fullname": "auto_disc.utils.leaf.locators.Locator.Locator.hash", "modulename": "auto_disc.utils.leaf.locators.Locator", "qualname": "Locator.hash", "kind": "function", "doc": "

Hashes the bin to give Leaf a uid

\n", "signature": "(bin: bytes) -> <function NewType.<locals>.new_type at 0x7f0ed2ca3670>:", "funcdef": "def"}, "auto_disc.utils.leaf.locators.Locator.StatelessLocator": {"fullname": "auto_disc.utils.leaf.locators.Locator.StatelessLocator", "modulename": "auto_disc.utils.leaf.locators.Locator", "qualname": "StatelessLocator", "kind": "class", "doc": "

Default Locator class, for stateless modules

\n", "bases": "Locator"}, "auto_disc.utils.leaf.locators.Locator.StatelessLocator.__init__": {"fullname": "auto_disc.utils.leaf.locators.Locator.StatelessLocator.__init__", "modulename": "auto_disc.utils.leaf.locators.Locator", "qualname": "StatelessLocator.__init__", "kind": "function", "doc": "

\n", "signature": "(resource_uri: str = '', *args, **kwargs)"}, "auto_disc.utils.leaf.locators.Locator.StatelessLocator.store": {"fullname": "auto_disc.utils.leaf.locators.Locator.StatelessLocator.store", "modulename": "auto_disc.utils.leaf.locators.Locator", "qualname": "StatelessLocator.store", "kind": "function", "doc": "

Stores bin which generated it somewhere, perhaps on network,\nand returns (redundantly) the hash for the object.

\n", "signature": "(\tself,\tbin: bytes,\t*args,\t**kwargs) -> <function NewType.<locals>.new_type at 0x7f0ed2ca3670>:", "funcdef": "def"}, "auto_disc.utils.leaf.locators.Locator.StatelessLocator.retrieve": {"fullname": "auto_disc.utils.leaf.locators.Locator.StatelessLocator.retrieve", "modulename": "auto_disc.utils.leaf.locators.Locator", "qualname": "StatelessLocator.retrieve", "kind": "function", "doc": "

Retrieve bin identified by uid from somewhere,\nperhaps on network

\n", "signature": "(\tself,\tuid: <function NewType.<locals>.new_type>,\t*args,\t**kwargs) -> bytes:", "funcdef": "def"}, "auto_disc.utils.leaf.locators.Locator.FileLocator": {"fullname": "auto_disc.utils.leaf.locators.Locator.FileLocator", "modulename": "auto_disc.utils.leaf.locators.Locator", "qualname": "FileLocator", "kind": "class", "doc": "

Locator which saves modules \u00e0 la Git, to the filesystem,\nwith root directory specified by resource_uri

\n", "bases": "Locator"}, "auto_disc.utils.leaf.locators.Locator.FileLocator.__init__": {"fullname": "auto_disc.utils.leaf.locators.Locator.FileLocator.__init__", "modulename": "auto_disc.utils.leaf.locators.Locator", "qualname": "FileLocator.__init__", "kind": "function", "doc": "

\n", "signature": "(resource_uri: str = '')"}, "auto_disc.utils.leaf.locators.Locator.FileLocator.store": {"fullname": "auto_disc.utils.leaf.locators.Locator.FileLocator.store", "modulename": "auto_disc.utils.leaf.locators.Locator", "qualname": "FileLocator.store", "kind": "function", "doc": "

Stores bin which generated it somewhere, perhaps on network,\nand returns (redundantly) the hash for the object.

\n", "signature": "(\tself,\tbin: bytes,\t*args,\t**kwargs) -> <function NewType.<locals>.new_type at 0x7f0ed2ca3670>:", "funcdef": "def"}, "auto_disc.utils.leaf.locators.Locator.FileLocator.retrieve": {"fullname": "auto_disc.utils.leaf.locators.Locator.FileLocator.retrieve", "modulename": "auto_disc.utils.leaf.locators.Locator", "qualname": "FileLocator.retrieve", "kind": "function", "doc": "

Retrieve bin identified by uid from somewhere,\nperhaps on network

\n", "signature": "(\tself,\tuid: <function NewType.<locals>.new_type>,\t*args,\t**kwargs) -> bytes:", "funcdef": "def"}, "auto_disc.utils.leaf.locators.locators": {"fullname": "auto_disc.utils.leaf.locators.locators", "modulename": "auto_disc.utils.leaf.locators.locators", "kind": "module", "doc": "

\n"}, "auto_disc.utils.leaf.locators.locators.BlobLocator": {"fullname": "auto_disc.utils.leaf.locators.locators.BlobLocator", "modulename": "auto_disc.utils.leaf.locators.locators", "qualname": "BlobLocator", "kind": "class", "doc": "

Default Locator class, for stateful modules.

\n\n

In reality, it is a factory which mimicks the interface of a Locator,\nand therefore lazy-initializes an appropriate Locator based on provided\nkeywords.

\n", "bases": "auto_disc.utils.leaf.locators.Locator.Locator"}, "auto_disc.utils.leaf.locators.locators.BlobLocator.__init__": {"fullname": "auto_disc.utils.leaf.locators.locators.BlobLocator.__init__", "modulename": "auto_disc.utils.leaf.locators.locators", "qualname": "BlobLocator.__init__", "kind": "function", "doc": "

\n", "signature": "(resource_uri: str = '', *args, **kwargs)"}, "auto_disc.utils.leaf.locators.locators.BlobLocator.store": {"fullname": "auto_disc.utils.leaf.locators.locators.BlobLocator.store", "modulename": "auto_disc.utils.leaf.locators.locators", "qualname": "BlobLocator.store", "kind": "function", "doc": "

Stores bin which generated it somewhere, perhaps on network,\nand returns (redundantly) the hash for the object.

\n", "signature": "(\tself,\tbin: bytes,\t*args,\t**kwargs) -> <function NewType.<locals>.new_type at 0x7f0ed2ca3670>:", "funcdef": "def"}, "auto_disc.utils.leaf.locators.locators.BlobLocator.retrieve": {"fullname": "auto_disc.utils.leaf.locators.locators.BlobLocator.retrieve", "modulename": "auto_disc.utils.leaf.locators.locators", "qualname": "BlobLocator.retrieve", "kind": "function", "doc": "

Retrieve bin identified by uid from somewhere,\nperhaps on network

\n", "signature": "(\tself,\tuid: <function NewType.<locals>.new_type>,\t*args,\t**kwargs) -> bytes:", "funcdef": "def"}, "auto_disc.utils.leaf.locators.locators.LinearLocator": {"fullname": "auto_disc.utils.leaf.locators.locators.LinearLocator", "modulename": "auto_disc.utils.leaf.locators.locators", "qualname": "LinearLocator", "kind": "class", "doc": "

Bespoke Locator class, for stateful modules that need to store linear,\npotentially branching data.

\n\n

Also a factory.

\n", "bases": "auto_disc.utils.leaf.locators.Locator.Locator"}, "auto_disc.utils.leaf.locators.locators.LinearLocator.__init__": {"fullname": "auto_disc.utils.leaf.locators.locators.LinearLocator.__init__", "modulename": "auto_disc.utils.leaf.locators.locators", "qualname": "LinearLocator.__init__", "kind": "function", "doc": "

\n", "signature": "(resource_uri: str = '', *args, **kwargs)"}, "auto_disc.utils.leaf.locators.locators.LinearLocator.store": {"fullname": "auto_disc.utils.leaf.locators.locators.LinearLocator.store", "modulename": "auto_disc.utils.leaf.locators.locators", "qualname": "LinearLocator.store", "kind": "function", "doc": "

Stores bin which generated it somewhere, perhaps on network,\nand returns (redundantly) the hash for the object.

\n", "signature": "(\tself,\tbin: bytes,\tparent_id: int = -1,\t*args,\t**kwargs) -> <function NewType.<locals>.new_type at 0x7f0ed2ca3670>:", "funcdef": "def"}, "auto_disc.utils.leaf.locators.locators.LinearLocator.retrieve": {"fullname": "auto_disc.utils.leaf.locators.locators.LinearLocator.retrieve", "modulename": "auto_disc.utils.leaf.locators.locators", "qualname": "LinearLocator.retrieve", "kind": "function", "doc": "

Retrieve bin identified by uid from somewhere,\nperhaps on network

\n", "signature": "(\tself,\tuid: <function NewType.<locals>.new_type>,\tlength: int = 1,\t*args,\t**kwargs) -> bytes:", "funcdef": "def"}, "auto_disc.utils.leaf.locators.locators.LinearLocator.parse_bin": {"fullname": "auto_disc.utils.leaf.locators.locators.LinearLocator.parse_bin", "modulename": "auto_disc.utils.leaf.locators.locators", "qualname": "LinearLocator.parse_bin", "kind": "function", "doc": "

\n", "signature": "(cls, bin: bytes) -> Tuple[str, bytes]:", "funcdef": "def"}, "auto_disc.utils.leaf.locators.locators.LinearLocator.parse_leaf_uid": {"fullname": "auto_disc.utils.leaf.locators.locators.LinearLocator.parse_leaf_uid", "modulename": "auto_disc.utils.leaf.locators.locators", "qualname": "LinearLocator.parse_leaf_uid", "kind": "function", "doc": "

\n", "signature": "(cls, uid: <function NewType.<locals>.new_type>) -> Tuple[str, int]:", "funcdef": "def"}, "auto_disc.utils.leaf.locators.locators.LinearLocator.hash": {"fullname": "auto_disc.utils.leaf.locators.locators.LinearLocator.hash", "modulename": "auto_disc.utils.leaf.locators.locators", "qualname": "LinearLocator.hash", "kind": "function", "doc": "

Hashes the bin to give Leaf a uid

\n", "signature": "(bin: bytes) -> <function NewType.<locals>.new_type at 0x7f0ed2ca3670>:", "funcdef": "def"}, "auto_disc.utils.leaf.tests": {"fullname": "auto_disc.utils.leaf.tests", "modulename": "auto_disc.utils.leaf.tests", "kind": "module", "doc": "

\n"}, "auto_disc.utils.leaf.tests.no_import_load_leaf": {"fullname": "auto_disc.utils.leaf.tests.no_import_load_leaf", "modulename": "auto_disc.utils.leaf.tests.no_import_load_leaf", "kind": "module", "doc": "

\n"}, "auto_disc.utils.leaf.tests.no_import_save_leaf": {"fullname": "auto_disc.utils.leaf.tests.no_import_save_leaf", "modulename": "auto_disc.utils.leaf.tests.no_import_save_leaf", "kind": "module", "doc": "

\n"}, "auto_disc.utils.leaf.tests.test_integration_leaf": {"fullname": "auto_disc.utils.leaf.tests.test_integration_leaf", "modulename": "auto_disc.utils.leaf.tests.test_integration_leaf", "kind": "module", "doc": "

\n"}, "auto_disc.utils.leaf.tests.test_integration_leaf.DiskPipeline": {"fullname": "auto_disc.utils.leaf.tests.test_integration_leaf.DiskPipeline", "modulename": "auto_disc.utils.leaf.tests.test_integration_leaf", "qualname": "DiskPipeline", "kind": "class", "doc": "

\n", "bases": "auto_disc.utils.leaf.Leaf.Leaf"}, "auto_disc.utils.leaf.tests.test_integration_leaf.DiskPipeline.create_locator": {"fullname": "auto_disc.utils.leaf.tests.test_integration_leaf.DiskPipeline.create_locator", "modulename": "auto_disc.utils.leaf.tests.test_integration_leaf", "qualname": "DiskPipeline.create_locator", "kind": "function", "doc": "

\n", "signature": "(self, bin: bytes) -> auto_disc.utils.leaf.locators.Locator.Locator:", "funcdef": "def"}, "auto_disc.utils.leaf.tests.test_integration_leaf.DiskPipeline.store_locator": {"fullname": "auto_disc.utils.leaf.tests.test_integration_leaf.DiskPipeline.store_locator", "modulename": "auto_disc.utils.leaf.tests.test_integration_leaf", "qualname": "DiskPipeline.store_locator", "kind": "function", "doc": "

\n", "signature": "(self, loc):", "funcdef": "def"}, "auto_disc.utils.leaf.tests.test_integration_leaf.DiskPipeline.retrieve_locator": {"fullname": "auto_disc.utils.leaf.tests.test_integration_leaf.DiskPipeline.retrieve_locator", "modulename": "auto_disc.utils.leaf.tests.test_integration_leaf", "qualname": "DiskPipeline.retrieve_locator", "kind": "function", "doc": "

\n", "signature": "(cls, leaf_uid: str) -> auto_disc.utils.leaf.locators.Locator.Locator:", "funcdef": "def"}, "auto_disc.utils.leaf.tests.test_integration_leaf.DiskLocator": {"fullname": "auto_disc.utils.leaf.tests.test_integration_leaf.DiskLocator", "modulename": "auto_disc.utils.leaf.tests.test_integration_leaf", "qualname": "DiskLocator", "kind": "class", "doc": "

Class which handles data management and the saving/loading of binary data.\nShould have at least an instance variable called resource_uri, although\nit may be an empty string.

\n", "bases": "auto_disc.utils.leaf.locators.Locator.Locator"}, "auto_disc.utils.leaf.tests.test_integration_leaf.DiskLocator.__init__": {"fullname": "auto_disc.utils.leaf.tests.test_integration_leaf.DiskLocator.__init__", "modulename": "auto_disc.utils.leaf.tests.test_integration_leaf", "qualname": "DiskLocator.__init__", "kind": "function", "doc": "

\n", "signature": "(bin)"}, "auto_disc.utils.leaf.tests.test_integration_leaf.DiskLocator.store": {"fullname": "auto_disc.utils.leaf.tests.test_integration_leaf.DiskLocator.store", "modulename": "auto_disc.utils.leaf.tests.test_integration_leaf", "qualname": "DiskLocator.store", "kind": "function", "doc": "

Stores bin which generated it somewhere, perhaps on network,\nand returns (redundantly) the hash for the object.

\n", "signature": "(self, bin: bytes, *args, **kwargs) -> None:", "funcdef": "def"}, "auto_disc.utils.leaf.tests.test_integration_leaf.DiskLocator.retrieve": {"fullname": "auto_disc.utils.leaf.tests.test_integration_leaf.DiskLocator.retrieve", "modulename": "auto_disc.utils.leaf.tests.test_integration_leaf", "qualname": "DiskLocator.retrieve", "kind": "function", "doc": "

Retrieve bin identified by uid from somewhere,\nperhaps on network

\n", "signature": "(self) -> bytes:", "funcdef": "def"}, "auto_disc.utils.leaf.tests.test_integration_leaf.setup_function": {"fullname": "auto_disc.utils.leaf.tests.test_integration_leaf.setup_function", "modulename": "auto_disc.utils.leaf.tests.test_integration_leaf", "qualname": "setup_function", "kind": "function", "doc": "

\n", "signature": "(function):", "funcdef": "def"}, "auto_disc.utils.leaf.tests.test_integration_leaf.teardown_function": {"fullname": "auto_disc.utils.leaf.tests.test_integration_leaf.teardown_function", "modulename": "auto_disc.utils.leaf.tests.test_integration_leaf", "qualname": "teardown_function", "kind": "function", "doc": "

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Automated Discovery Tool

\n\n

NOTE: this README is deprecated

\n\n

We're pleased to introduce Automated Discovery Tool, a software for assisted automated discovery and exploration of complex systems.

\n\n

Installation

\n\n

Docker installation

\n\n

Our software leverages docker to avoid you installing multiple packages.\nTo install docker:

\n\n
    \n
  1. Install Docker: LINK
    2. \n
  2. Install Docker-compose: LINK
    3. \n
\n\n

Using Automated Discovery Tool

\n\n

Our software consists of two primary parts:

\n\n
    \n
  • a Python library called auto_disc where you can\nuse/download/implement systems to explore as well as exploration algorithms.
    • \n
  • a web application allowing you to perform assisted automated discovery\nexperiments using a user-friendly interface.
    • \n
\n\n

The auto_disc library can be used independently of the rest of\nAutomated Discovery Tool, for example to implement some bespoke remote computing\nsolutions, but its main purpose is to present a standardized interface for\nmanaging experimental data and configurational metadata which is\nmodified and displayed by the GUI.

\n\n

Launching the software

\n\n
    \n
  1. Run the following command: sudo ./start_app.sh
    2. \n
  2. Your application should be accessible on http://localhost:4201
    3. \n
  3. If you want to modify the port on which the application starts (as well as other configuration parameters for DataBases, network...), you can modify the .env file in the services directory.
    4. \n
\n\n

Using the web app

\n\n

User documentation

\n\n

Modifying the lib

\n\n

Before being able to modify the lib, you must install it. For this, please refer to this.\nOur lib relies on classes we call Modules. Every system or explorer is a Module.\nHere is a tutorial on how to add a new module to the lib (and then be able to use it in an experiment launched from the web app):\nModule adding tutorial.

\n\n

We also provide callbacks to experiments (e.g. to save discoveries). If you want to add a new one, you can follow this tutorial.

\n\n

Launching experiments on a remote server

\n\n

By default, experiments will be launched locally. However, some large experiments can require large compute. We thus allow users to provide a configuration to reach a remote server: Adding remote server.

\n\n
\n\n

Contributing

\n\n

Please follow these instructions when contributing on the project.

\n\n

Installation

\n\n

The software is implemented in various microservices

\n\n
    \n
  • auto_disc library : a pure Python API for implementing custom experiments \\\nand search algorithms to use in the software
    • \n
  • AutoDiscServer : simple service which performs the operations specified by\nfunctionality in auto_disc library.
    • \n
  • FrontEndApp : web GUI implemented in Angular for interacting with the software
    • \n
  • AppDB & ExpeDB : databases which manage the metadata of experiments (linked\nwith the FrontEnd) and resulting experimental data, respectively
    • \n
  • JupyterLab : Jupyter instance allowing interaction with experimental data
    • \n
\n\n

auto_disc library

\n\n
    \n
  1. If you do not already have it, please install Conda
    2. \n
  2. Create _autoDiscTool_ conda environment: conda env create --name autoDiscTool python=3.7
    3. \n
  3. Activate _autoDiscTool_ conda environment: conda activate autoDiscTool
    4. \n
  4. Go to package: cd libs/auto_disc
    5. \n
  5. Install package: pip install -e .
    6. \n
\n\n

AutoDiscServer

\n\n
    \n
  1. Install flask: pip install -r services/AutoDiscServer/flask_app/requirements.txt
    2. \n
\n\n

FrontEndApp

\n\n
    \n
  1. Install Angular: LINK
    2. \n
  2. Enter the front-end app folder: cd services/FrontEndApp/angular_app/.
    3. \n
  3. Install required packages: npm install
    4. \n
\n\n

AppDB & ExpeDB

\n\n
    \n
  1. Install Docker: LINK
    2. \n
  2. Install Docker-compose: LINK
    3. \n
  3. Go to the service folder: cd services.
    4. \n
  4. Generate containers sudo docker-compose -f services/docker-compose.yml create
    5. \n
  5. Install the Expe DB REST API requirements: pip install -r ExpeDB/API/flask_app/requirements.txt
    6. \n
\n\n

JupyterLab

\n\n
    \n
  1. Install jupyter: pip install -r JupyterLab/requirements.txt
    2. \n
\n\n

Testing the auto_disc library alone

\n\n
    \n
  1. Edit the libs/tests/AutoDiscExperiment.py file to configure the experiment
    2. \n
  2. Launch the experiment: python libs/tests/AutoDiscExperiment.py
    3. \n
\n\n

Commits

\n\n

Please attach every commit to an issue or a merge request. For issues, add #ID at the beginning of your commit message (with ID the id of the issue).

\n\n

Starting the project

\n\n

Prod mode

\n\n

Launch: sudo ./start_app.sh

\n\n

Debug mode

\n\n

Go to the services folder: cd services.

\n\n
AutoDiscServer
\n\n

Launch the flask server: python -m AutoDiscServer.app.

\n\n
App DB
\n\n

Start services: sudo docker-compose up app-db-api\nAdd -d option for daemon.

\n\n
Expe DB
\n\n

Start service: sudo docker-compose up expe-db\nAdd -d option for daemon.\nLaunch flask server for the REST API: python ExpeDB/app.py

\n\n
Front-end app
\n\n

Enter the front-end app folder: cd FrontEndApp.\nStart the angular app: ng serve.

\n\n
Jupyter Lab
\n\n

Enter Jupyter Lab's folder: cd ../JupyterLab\nGive the appropriate rights to the notebook folder: chmod 777 -R Notebooks/\nStart the jupyter lab on port 8887: export PYTHONPATH=$(pwd)/../../libs/auto_disc_db; jupyter lab Notebooks/ --config Config/jupyter_notebook_config.py

\n\n

Using monitoring services

\n\n

We added monitoring applications for the Docker services.\nYou can start then by going in services: cd services and launching the following command:

\n\n
sudo docker-compose --profile monitoring up\n
\n\n

_Warning_: This command will start all the docker services or attach the already launched ones otherwise.

\n\n

Portainer

\n\n

Portainer is a web application allowing to monitor Docker environments.\nYou can access it at: https://localhost:9443.

\n\n

At first startup you'll be asked to create a password for your admin account. Then, you should see your local environment with all the running containers. You can for instance easily see the logs of each container.

\n\n

PgAdmin

\n\n

PgAdmin is a lightweight web application allowing to visually interact with a PostgreSQL database. You can use it to monitor and modify our AppDB (which should be connected by default).\nFor this, go to http://localhost:5050.\nTo login use:

\n\n
    \n
  • user: user@autodisc.com
    • \n
  • password: autodisc
    • \n
\n\n

You should see the server list in the panel on the left. To see the data of a table, unfold the schema/Tables property of the server. Then, right click on the desired table and click \"See data\".

\n\n

MongoExpress

\n\n

MongoExpress is also a lightweight DB GUI but this time made for MongoDB.\nAccess the app at http://localhost:8081.

\n\n

User documentation

\n\n

Home

\n\n

After starting the application and opening it in a browser at http://localhost:4201/home (localhost if you run the application locally), you will access the page presented in the picture below.

\n\n

\n\n

First you can see a list of experiments and for each experience some brief information. If you click on an item in the list you will be redirected to the experiment monitoring page\nThe numbers below correspond to those in red in the picture.\n1: The experiment name\n2: The experiment id\n3: The date of creation\n4: All modules used in the experiment (the system, explorer, the number of input wrapper, the number of output representation and the host i.e.the server where you run the experiment, local if you run it on your pc)\n5: The current state of the experiment: Done if the experiment ended; Preparing if the experiment is in setup or if its waiting the server; Running if the experiment is in progress; Error if the experiment crashed.\n6: Some tools to manage the experiments' list: Refresh to synchronize the list with the database; Date to order by dates; A text zone to filter by the experiments or modules names.\n7: \"New experiment\" or the floating button redirect to the experience creation page.

\n\n

Experiment creation

\n\n

General presentation

\n\n

\n\n

This page consists of 4 parts:

\n\n
1: An accordion to setup the experiment, define each modules and all parameters (we present each part later).\n2: Some tools to import and export the experiment configuration. You could export the current configuration as a JSON file and you could import a JSON file to setup the experiment. You also could re load a past experiment to set the current experiment.\n3: After having setup the experiment you could launch it with the \"Create experiment\" button.\n4: \"Home\" to be redirected to the homepage.\n
\n\n

Now we will see each part of the accordion.

\n\n

General information:\n

\n\n
1: The name of the experiment. Must contain only alpha numeric characters, \"-\" and \"_\".\n2: The number of iterations. How many generations of the system will be explored.\n3: The number of seeds. How many different seeds will be used.\n4: The saving frequency. Every n generations a checkpoint will be created\n5: The host. The computer on which the experiment will be run. Local if it is on your pc; the name of the server otherwise.\n
\n\n

Monitoring experiment

\n\n

The monitoring experiment page give you access to the tools to manage the experiment. You can visualize discoveries and logs generated by the experiment. You have acces to some information about the experiment config. More important you have acces to a jupyter lab link to the experiment to manipulate all things generated by the experiment.

\n\n

The Details part displays information about the experiment config. You have access to all modules used in the experiment, the current system, the current explorer, the list of input wrappers and output representations and the config for each of them and you can see all items the experiment has saved.

\n\n

\n\n

The checkpoints part displays every checkpoint id with its status (Done, running, error...) and the experiment iteration number when the checkpoint was created.

\n\n

\n\n

The jupyter part gives you access to tools to monitor many things. In it you have acces to the database and every results produced by an experiment.\nThe \"dataset_template\" notebook show you how to acces the database and demonstrate a simple way to manipulate data.\nThe yellow/blue popover informs weither the jupyter kernel is available and connected to the database or not.

\n\n

\n\n

The discoveries part displays all discoveries made by the experiment. You can filter them by seed, by iteration range and the number of discoveries per page at will.

\n\n

\n\n

Finally you have a logs part where you can read all logs made by the experiment and filter them by checkpoints, seeds or log levels.

\n\n

\n\n

Add a new module to the libs

\n\n

Four different types of modules can be implemented in the Automated Discovery Tool libs

\n\n
    \n
  • Systems
    • \n
  • Explorers
    • \n
  • Input_wrapper
    • \n
  • Output_representations
    • \n
\n\n

In order to implement a custom module,

\n\n
    \n

  1. Each module has its own folder.

    \n\n

    To add a new module create the file in the associated folder

    \n\n

    Example:

    \n\n
    libs/auto_disc/auto_disc/explorers/myBeautifulNewExplorer.py\n
    \n\n

    or

    \n\n
    libs/auto_disc/auto_disc/systems/python_systems/myBeautifulNewPythonSystems.py\n
    2. \n

  2. The new module must ultimately inherit from its module base class (BaseSystem, BaseOutputRepresentation...).

    \n\n

    Please adhere to the parent class interface during the implementation.

    \n\n

    You can add decorators to your module class, which are useful to set module config parameters. These can be then directly set them in the GUI.

    \n\n

    An example to implement a new explorer :

    \n\n
    from auto_disc.explorers import BaseExplorer\nfrom auto_disc.utils.config_parameters import StringConfigParameter\n\n@StringConfigParameter(name=\"a_string_parameter\", possible_values=[\"first_possible_value\", \"second_possible_value\"], default=\"first_possible_value\")\n\nclass NewExplorer(BaseExplorer):\n\n    CONFIG_DEFINITION = {}\n\n    def __init__(self, **kwargs) -> None:\n        super().__init__(**kwargs)\n\n    def initialize(self, input_space, output_space, input_distance_fn):\n        super().initialize(input_space, output_space, input_distance_fn)\n        \"\"\"do some brilliant stuff\"\"\"\n\n    def sample(self):\n        \"\"\"do some brilliant stuff\"\"\"\n\n    def observe(self, parameters, observations):\n        \"\"\"do some brilliant stuff\"\"\"\n\n    def optimize(self):\n        \"\"\"do some brilliant stuff\"\"\"\n
    \n\n

    Don't forget kwargs argument in the __init__ method and CONFIG_DEFINITION class variable.

    3. \n

  3. Add import in libs/auto_disc/module_cat/subfolder_if_needed/__init__.py

    \n\n

    Example:

    \n\n
    libs/auto_disc/auto_disc/explorers/__init__.py\nlibs/auto_disc/auto_disc/systems/python_systems/__init__.py\n
    4. \n

  4. Add new module in registration.py in REGISTRATION dict

    \n\n

    Modify REGISTRATION starting from this

    \n\n
    REGISTRATION = {\n        'systems':{\n            'PythonLenia': PythonLenia,\n        },...\n    }\n
    \n\n

    to for example the following

    \n\n
    REGISTRATION = {\n    'systems':{\n        'PythonLenia': PythonLenia,\n        'MyBeautifullNewPythonSystems': MyBeautifullNewPythonSystems,\n    },...\n}\n
    \n\n

    The dict keys are used in GUI to choose your module when you setup an experiment.

    5. \n
\n\n

Add a new callback to the libs

\n\n

Six different types of callbacks can be implemented in the Automated Discovery Tool libs, namely:

\n\n
    \n
  • on_cancelled_callbacks : called when user decides to cancel the experiment in the frontend
    • \n
  • on_discovery_callbacks : called each time the experiment makes a new discovery
    • \n
  • on_error_callbacks : called when experiment raises an error during execution
    • \n
  • on_finished_callbacks : called when the experiment ends
    • \n
  • on_save_callbacks : called when the experiment saves its state
    • \n
  • on_save_finished_callbacks : called when the experiment has finished saving
    • \n
\n\n
    \n

  1. Each callback type has its own folder. \nTo add a new callback create the file in the associated folder

    \n\n

    Example:

    \n\n
    libs/auto_disc/auto_disc/utils/callbacks/on_cancelled_callbacks/my_beautiful_new_on_cancelled_callback.py\n
    \n\n

    or

    \n\n
    libs/auto_disc/auto_disc/utils/callbacks/on_discovery_callbacks/my_beautiful_new_on_discovery_callback.py\n
    2. \n

  2. The new callback must inherits base callback class of its own type.

    \n\n

    Example:

    \n\n
         class MyBeautifulNewOnCancelledCallback(BaseOnCancelledCallback):\n         pass\n
    \n\n

    or

    \n\n
         class MyBeautifulNewOnDiscoveryCallback(BaseOnDiscoveryCallback):\n         pass\n
    \n\n

    A full example:

    \n\n
        from auto_disc.legacy.utils.callbacks.on_discovery_callbacks import BaseOnDiscoveryCallback\n\n    class MyBeautifullNewOnDiscoveryCallback(BaseOnDiscoveryCallback):\n\n        def __init__(self, folder_path, to_save_outputs, **kwargs) -> None:\n            super().__init__(to_save_outputs, **kwargs)\n            \"\"\"do some brilliant stuff\"\"\"\n\n        def __call__(self, **kwargs) -> None:\n            \"\"\"do some brilliant stuff\"\"\"\n
    \n\n

    Don't forget to pass **kwargs in the __init__ method.\nEach time our callback will be raised the __call__ method will be executed.

    3. \n

  3. Add import header file, e.g., libs/auto_disc/auto_disc/utils/callbacks/callbacks_sub_folder/__init__.py

    \n\n

    Example:

    \n\n
    libs/auto_disc/auto_disc/utils/callbacks/on_discovery_callbacks/__init__.py\nlibs/auto_disc/auto_disc/utils/callbacks/on_cancelled_callbacks/__init__.py\n
    4. \n

  4. Add new callback in registration.py in REGISTRATION dict

    \n\n

    Example modification:

    \n\n
    Modify REGISTRATION like this:\n\nREGISTRATION = {\n     ...\n     'callbacks': {\n         'on_discovery':{\n             'base': on_discovery_callbacks.BaseOnDiscoveryCallback,\n             'disk': on_discovery_callbacks.OnDiscoverySaveCallbackOnDisk\n         },\n         'on_cancelled':{\n             'base': on_cancelled_callbacks.BaseOnCancelledCallback\n         },\n         'on_error':{\n             'base': on_error_callbacks.BaseOnErrorCallback\n         },\n         'on_finished':{\n             'base': on_finished_callbacks.BaseOnFinishedCallback\n         },\n         'on_saved':{\n             'base': on_save_callbacks.BaseOnSaveCallback,\n             'disk': on_save_callbacks.OnSaveModulesOnDiskCallback\n         },\n         'on_save_finished':{\n             'base': on_save_finished_callbacks.BaseOnSaveFinishedCallback\n         },\n     },...\n }\n\n ==>\n\n REGISTRATION = {\n     ...\n     'callbacks': {\n         'on_discovery':{\n             'base': on_discovery_callbacks.BaseOnDiscoveryCallback,\n             'disk': on_discovery_callbacks.OnDiscoverySaveCallbackOnDisk\n             'new': MyBeautifullNewOnDiscoveryCallback\n         },\n         'on_cancelled':{\n             'base': on_cancelled_callbacks.BaseOnCancelledCallback\n         },\n         'on_error':{\n             'base': on_error_callbacks.BaseOnErrorCallback\n         },\n         'on_finished':{\n             'base': on_finished_callbacks.BaseOnFinishedCallback\n         },\n         'on_saved':{\n             'base': on_save_callbacks.BaseOnSaveCallback,\n             'disk': on_save_callbacks.OnSaveModulesOnDiskCallback\n         },\n         'on_save_finished':{\n             'base': on_save_finished_callbacks.BaseOnSaveFinishedCallback\n         },\n     },...\n }\n
    5. \n

  5. For now the software does not permit to add custom callback via GUI. You may use the libs independently like in libs/test/AutoDiscExperiment.py and add manually your personal callbacks. The other way is to manually add your callback in services/AutodiscServer/flask/experiments/remote_experiments.py on the __init__ method to use your own callbacks with the full software package.

    6. \n
\n\n

Add a new remote server

\n\n

You must have a remote server to start a remote experiment.
\nTo access your server from the software you have to configure it in the configs/remote_experiments folder.
\nHere you have two sub folders.
\nThe first one, additional_files, contains the files we will execute to start the experiment on the remote server.
\nThe second one, profiles, is used to define how to connect and interact with the remote server.

\nWe find there:

\n\n
- The current config name (we will choose it in the GUI).\n- The name of your ssh configuration, defined on your personnal file `/home/user/.ssh/config` (or other path you have set on services/.env file at the `SSH_CONFIG_FOLDER` var).\n- The work folder on the remote server\n- A local folder to store downloaded files created by the remote experiment before they are put in the database \n- The command to start the experiment ( it will execute the associated file in \"additional_file\"). This command must echo `\"[RUN_ID_start]\"id\"[RUN_ID_stop]\"` in the remote shell either directly in the command or at the end of the associated additional_file. The example below does it on the additional file. Here id is equal to every PID the experiment launches (one for each seed).\n- The command to cancel all processes started by the experiment to run it self on the remote server.\n- Finally a number of second (to check that the experiment has started).\n
\n\n

example:

\n\n
        profiles:\n            name: \n                myConfigNameInTheSoftware\n            ssh_configuration: \n                myPersonnalShhConfigName\n            work_path:\n                /path/to/workdirectory/on/the/remote/server\n            local_tmp_path:\n                /tmp/auto_disc/remote_experiment/\n            execution:\n                bash additional_files/docker.sh $NB_SEEDS $ARGS > $EXPE_ID\n            cancellation:\n                scancel $RUN_ID\n            check_experiment_launched_every:\n                60\n\n        additional_files:\n            #!/bin/bash\n            export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/root/anaconda3/lib/;\n\n            PIDS=\"\"\n            for ((i=0; i<$1; i++))\n            do\n            PID=$(/root/anaconda3/envs/autoDiscTool/bin/python3 /home/workdir/auto_disc/auto_disc/run.py --seed ${i} ${@:2} >> experiment_logs.log & echo $!)\n            if [[ $PIDS != \"\" ]]\n            then\n                PIDS+=\" \"\n            fi\n            PIDS+=\"${PID}\"\n            done\n            echo \"[RUN_ID_start]$PIDS[RUN_ID_stop]\"\n
\n\n

Please note that you must first download and install the auto_disc lib on your remote server (follow these steps) to launch experiments on this server.

\n"}, "auto_disc.ExperimentPipeline": {"fullname": "auto_disc.ExperimentPipeline", "modulename": "auto_disc.ExperimentPipeline", "kind": "module", "doc": "

\n"}, "auto_disc.ExperimentPipeline.CancellationToken": {"fullname": "auto_disc.ExperimentPipeline.CancellationToken", "modulename": "auto_disc.ExperimentPipeline", "qualname": "CancellationToken", "kind": "class", "doc": "

Manages the cancellation token which allows you to stop an\nexperiment in progress

\n"}, "auto_disc.ExperimentPipeline.CancellationToken.__init__": {"fullname": "auto_disc.ExperimentPipeline.CancellationToken.__init__", "modulename": "auto_disc.ExperimentPipeline", "qualname": "CancellationToken.__init__", "kind": "function", "doc": "

Init the cancellation token to false

\n", "signature": "()"}, "auto_disc.ExperimentPipeline.CancellationToken.get": {"fullname": "auto_disc.ExperimentPipeline.CancellationToken.get", "modulename": "auto_disc.ExperimentPipeline", "qualname": "CancellationToken.get", "kind": "function", "doc": "

Give access to the cancellation token

\n\n

Returns:

\n\n
    \n
  • token: a boolean indicating if the current experiment\nmust be cancelled
    • \n
\n", "signature": "(self) -> bool:", "funcdef": "def"}, "auto_disc.ExperimentPipeline.CancellationToken.trigger": {"fullname": "auto_disc.ExperimentPipeline.CancellationToken.trigger", "modulename": "auto_disc.ExperimentPipeline", "qualname": "CancellationToken.trigger", "kind": "function", "doc": "

Sets the cancellation token to true\n(the experiment must be cancelled)

\n", "signature": "(self) -> None:", "funcdef": "def"}, "auto_disc.ExperimentPipeline.ExperimentPipeline": {"fullname": "auto_disc.ExperimentPipeline.ExperimentPipeline", "modulename": "auto_disc.ExperimentPipeline", "qualname": "ExperimentPipeline", "kind": "class", "doc": "

Pipeline of an automated discovery experiment.

\n\n

An experiment is at least constituted of a system and an explorer.

\n\n

Additionally, input wrappers and output representations can be added and\ncomposed.

\n\n

In order to monitor the experiment, you must provide callbacks, which\nwill be called every time a discovery has been made.\nPlease see: auto_disc.legacy.utils.callbacks.base_callback.BaseCallback.

\n", "bases": "auto_disc.utils.leaf.Leaf.Leaf"}, "auto_disc.ExperimentPipeline.ExperimentPipeline.__init__": {"fullname": "auto_disc.ExperimentPipeline.ExperimentPipeline.__init__", "modulename": "auto_disc.ExperimentPipeline", "qualname": "ExperimentPipeline.__init__", "kind": "function", "doc": "

Initializes state of experiment pipeline, setting all necessary\nattributes given by the following arguments.

\n\n

Args:

\n\n
    \n
  • experiment_id: ID of current experiment
    • \n
  • seed: Current seed for random number generation
    • \n
  • system: System to be explored
    • \n
  • explorer: Explorer used
    • \n
  • save_frequency: Frequency to save state of the experiment
    • \n
  • on_discovery_callbacks: Called when a discovery is made
    • \n
  • on_save_finished_callbacks: Called when save is complete
    • \n
  • on_finished_callbacks: Called when the experiment is finished
    • \n
  • on_cancelled_callbacks: Called when the experiment is cancelled
    • \n
  • on_save_callbacks: Called when a experiment save is made
    • \n
  • on_error_callbacks: Called when an error is raised
    • \n
\n", "signature": "(\texperiment_id: int = 0,\tseed: int = 0,\tsystem=None,\texplorer=None,\tsave_frequency: int = 100,\ton_discovery_callbacks: List[Callable] = [],\ton_save_finished_callbacks: List[Callable] = [],\ton_finished_callbacks: List[Callable] = [],\ton_cancelled_callbacks: List[Callable] = [],\ton_save_callbacks: List[Callable] = [],\ton_error_callbacks: List[Callable] = [],\tlogger=None,\tresource_uri: str = '',\tdiscovery_saving_keys: List[str] = [])"}, "auto_disc.ExperimentPipeline.ExperimentPipeline.run": {"fullname": "auto_disc.ExperimentPipeline.ExperimentPipeline.run", "modulename": "auto_disc.ExperimentPipeline", "qualname": "ExperimentPipeline.run", "kind": "function", "doc": "

Launches the experiment for n_exploration_runs explorations.

\n\n

n_exploration_runs is specified so more optimized looping routines\ncan be chosen in the inner loop if desired. Interfacing with the\nobjects in the inner loop should be done via the callback interface.

\n\n

Args:

\n\n
    \n
  • n_exploration_runs: number of explorations
    • \n
\n\n

Returns:

\n\n
    \n
  • LeafUID: returns the UID associated to the experiment
    • \n
\n", "signature": "(self, n_exploration_runs: int):", "funcdef": "def"}, "auto_disc.ExperimentPipeline.ExperimentPipeline.save": {"fullname": "auto_disc.ExperimentPipeline.ExperimentPipeline.save", "modulename": "auto_disc.ExperimentPipeline", "qualname": "ExperimentPipeline.save", "kind": "function", "doc": "

\n", "signature": "(self, resource_uri: str):", "funcdef": "def"}, "auto_disc.ExperimentPipeline.ExperimentPipeline.serialize": {"fullname": "auto_disc.ExperimentPipeline.ExperimentPipeline.serialize", "modulename": "auto_disc.ExperimentPipeline", "qualname": "ExperimentPipeline.serialize", "kind": "function", "doc": "

Serializes object to pickle,\nturning all submodules into uniquely identifiable hashes

\n", "signature": "(self, *args, **kwargs) -> bytes:", "funcdef": "def"}, "auto_disc.auto_disc": {"fullname": "auto_disc.auto_disc", "modulename": "auto_disc.auto_disc", "kind": "module", "doc": "

Core modules of the software are located under this module namespace.

\n"}, "auto_disc.auto_disc.explorers": {"fullname": "auto_disc.auto_disc.explorers", "modulename": "auto_disc.auto_disc.explorers", "kind": "module", "doc": "

An Explorer encapsulates and abstracts the algorithmic loop which is run to\nconduct automatic exploration of the system.

\n"}, "auto_disc.auto_disc.explorers.IMGEPExplorer": {"fullname": "auto_disc.auto_disc.explorers.IMGEPExplorer", "modulename": "auto_disc.auto_disc.explorers.IMGEPExplorer", "kind": "module", "doc": "

The simplest possible algorithm of Intrinsically Motivated Goal Exploration Processes

\n"}, "auto_disc.auto_disc.explorers.IMGEPExplorer.IMGEPFactory": {"fullname": "auto_disc.auto_disc.explorers.IMGEPExplorer.IMGEPFactory", "modulename": "auto_disc.auto_disc.explorers.IMGEPExplorer", "qualname": "IMGEPFactory", "kind": "class", "doc": "

Factory class providing interface with config parameters and therefore the\nfrontend

\n"}, "auto_disc.auto_disc.explorers.IMGEPExplorer.IMGEPFactory.__init__": {"fullname": "auto_disc.auto_disc.explorers.IMGEPExplorer.IMGEPFactory.__init__", "modulename": "auto_disc.auto_disc.explorers.IMGEPExplorer", "qualname": "IMGEPFactory.__init__", "kind": "function", "doc": "

\n", "signature": "(*args, **kws)"}, "auto_disc.auto_disc.explorers.IMGEPExplorer.IMGEPFactory.make_behavior_map": {"fullname": "auto_disc.auto_disc.explorers.IMGEPExplorer.IMGEPFactory.make_behavior_map", "modulename": "auto_disc.auto_disc.explorers.IMGEPExplorer", "qualname": "IMGEPFactory.make_behavior_map", "kind": "function", "doc": "

\n", "signature": "(self):", "funcdef": "def"}, "auto_disc.auto_disc.explorers.IMGEPExplorer.IMGEPFactory.make_parameter_map": {"fullname": "auto_disc.auto_disc.explorers.IMGEPExplorer.IMGEPFactory.make_parameter_map", "modulename": "auto_disc.auto_disc.explorers.IMGEPExplorer", "qualname": "IMGEPFactory.make_parameter_map", "kind": "function", "doc": "

\n", "signature": "(self):", "funcdef": "def"}, "auto_disc.auto_disc.explorers.IMGEPExplorer.IMGEPFactory.make_mutator": {"fullname": "auto_disc.auto_disc.explorers.IMGEPExplorer.IMGEPFactory.make_mutator", "modulename": "auto_disc.auto_disc.explorers.IMGEPExplorer", "qualname": "IMGEPFactory.make_mutator", "kind": "function", "doc": "

\n", "signature": "(self, param_map: Any = None):", "funcdef": "def"}, "auto_disc.auto_disc.explorers.IMGEPExplorer.IMGEPExplorer": {"fullname": "auto_disc.auto_disc.explorers.IMGEPExplorer.IMGEPExplorer", "modulename": "auto_disc.auto_disc.explorers.IMGEPExplorer", "qualname": "IMGEPExplorer", "kind": "class", "doc": "

Basic IMGEP that diffuses in goalspace.

\n\n

A class instance of IMGEPExplorer has access to a provided\nparameter_map, behavior_map, and mutator as attributes, whereas it\nreceives data from the system under study through the .map method.

\n", "bases": "auto_disc.utils.leaf.Leaf.Leaf"}, "auto_disc.auto_disc.explorers.IMGEPExplorer.IMGEPExplorer.__init__": {"fullname": "auto_disc.auto_disc.explorers.IMGEPExplorer.IMGEPExplorer.__init__", "modulename": "auto_disc.auto_disc.explorers.IMGEPExplorer", "qualname": "IMGEPExplorer.__init__", "kind": "function", "doc": "

\n", "signature": "(\tpremap_key: str = 'output',\tpostmap_key: str = 'params',\tparameter_map: auto_disc.utils.leaf.Leaf.Leaf = <auto_disc.auto_disc.wrappers.IdentityWrapper.IdentityWrapper object>,\tbehavior_map: auto_disc.utils.leaf.Leaf.Leaf = <auto_disc.auto_disc.wrappers.IdentityWrapper.IdentityWrapper object>,\tmutator: auto_disc.utils.leaf.Leaf.Leaf = Identity(),\tequil_time: int = 0)"}, "auto_disc.auto_disc.explorers.IMGEPExplorer.IMGEPExplorer.bootstrap": {"fullname": "auto_disc.auto_disc.explorers.IMGEPExplorer.IMGEPExplorer.bootstrap", "modulename": "auto_disc.auto_disc.explorers.IMGEPExplorer", "qualname": "IMGEPExplorer.bootstrap", "kind": "function", "doc": "

Return an initial sample needed to bootstrap the exploration loop.

\n", "signature": "(self) -> Dict:", "funcdef": "def"}, "auto_disc.auto_disc.explorers.IMGEPExplorer.IMGEPExplorer.map": {"fullname": "auto_disc.auto_disc.explorers.IMGEPExplorer.IMGEPExplorer.map", "modulename": "auto_disc.auto_disc.explorers.IMGEPExplorer", "qualname": "IMGEPExplorer.map", "kind": "function", "doc": "

Map the raw output of the system rollout to a subsequent parameter\nconfiguration to try.

\n\n
Arguments:
\n\n
    \n
  • system_output: A dictionary where the key self.premap_key indexes the raw\noutput given at the end of the previous system rollout.
    • \n
\n\n
Returns:
\n\n
\n

A dictionary where the key self.postmap_key indexes the parameters to try in the next trial.

\n
\n", "signature": "(self, system_output: Dict) -> Dict:", "funcdef": "def"}, "auto_disc.auto_disc.explorers.IMGEPExplorer.IMGEPExplorer.suggest_trial": {"fullname": "auto_disc.auto_disc.explorers.IMGEPExplorer.IMGEPExplorer.suggest_trial", "modulename": "auto_disc.auto_disc.explorers.IMGEPExplorer", "qualname": "IMGEPExplorer.suggest_trial", "kind": "function", "doc": "

Sample according to the policy a new trial of parameters for the\nsystem.

\n\n
Arguments:
\n\n
    \n

  • lookback_length: number of previous trials to consider when choosing the next\ntrial, i.e., it is a batch size based on the save frequency.

    \n\n

    Note that the default lookback_length = -1 will retrieve the\nentire history.

    • \n
\n\n
Returns:
\n\n
\n

A torch.Tensor containing the parameters to try.

\n
\n", "signature": "(self, lookback_length: int = -1) -> torch.Tensor:", "funcdef": "def"}, "auto_disc.auto_disc.explorers.IMGEPExplorer.IMGEPExplorer.observe_results": {"fullname": "auto_disc.auto_disc.explorers.IMGEPExplorer.IMGEPExplorer.observe_results", "modulename": "auto_disc.auto_disc.explorers.IMGEPExplorer", "qualname": "IMGEPExplorer.observe_results", "kind": "function", "doc": "

Read the raw output observed and process it into a discovered\nbehavior.

\n\n
Arguments:
\n\n
    \n
  • system_output: See arguments for .map.
    • \n
\n\n
Returns:
\n\n
\n

A dictionary of the observed behavior/feature vector associated with\n the raw system_output

\n
\n", "signature": "(self, system_output: Dict) -> Dict:", "funcdef": "def"}, "auto_disc.auto_disc.explorers.IMGEPExplorer.IMGEPExplorer.read_last_discovery": {"fullname": "auto_disc.auto_disc.explorers.IMGEPExplorer.IMGEPExplorer.read_last_discovery", "modulename": "auto_disc.auto_disc.explorers.IMGEPExplorer", "qualname": "IMGEPExplorer.read_last_discovery", "kind": "function", "doc": "

Return last observed discovery.

\n", "signature": "(self) -> Dict:", "funcdef": "def"}, "auto_disc.auto_disc.explorers.IMGEPExplorer.IMGEPExplorer.optimize": {"fullname": "auto_disc.auto_disc.explorers.IMGEPExplorer.IMGEPExplorer.optimize", "modulename": "auto_disc.auto_disc.explorers.IMGEPExplorer", "qualname": "IMGEPExplorer.optimize", "kind": "function", "doc": "

Run optimization step for online learning of the Explorer policy.

\n", "signature": "(self):", "funcdef": "def"}, "auto_disc.auto_disc.maps": {"fullname": "auto_disc.auto_disc.maps", "modulename": "auto_disc.auto_disc.maps", "kind": "module", "doc": "

See the documentation for the auto_disc.auto_disc.maps.Map abstract class for details.

\n"}, "auto_disc.auto_disc.maps.Map": {"fullname": "auto_disc.auto_disc.maps.Map", "modulename": "auto_disc.auto_disc.maps.Map", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.maps.Map.Map": {"fullname": "auto_disc.auto_disc.maps.Map.Map", "modulename": "auto_disc.auto_disc.maps.Map", "qualname": "Map", "kind": "class", "doc": "

An abstract class that defines the interface for a Map, and should\nbe inherited from by concrete implementations of Maps.

\n\n

A Map is a model for a function (in the mathematical sense, i.e., an\ninput-output relation) that is also stateful. It takes a payload of data as\ninput and returns an new payload of data as output, without mutating\nthe input.

\n\n
Attributes:
\n\n
    \n
  • premap_key: key in the input dict for the input (i.e., \"params\")
    • \n
  • postmap_key: key in the output dict for the output (i.e., \"output\")
    • \n
\n", "bases": "auto_disc.utils.leaf.Leaf.Leaf"}, "auto_disc.auto_disc.maps.Map.Map.__init__": {"fullname": "auto_disc.auto_disc.maps.Map.Map.__init__", "modulename": "auto_disc.auto_disc.maps.Map", "qualname": "Map.__init__", "kind": "function", "doc": "

Initialize the Map.

\n\n

A Map minimally sets the premap and postmap keys.

\n", "signature": "(premap_key: str = 'input', postmap_key: str = 'output')"}, "auto_disc.auto_disc.maps.Map.Map.map": {"fullname": "auto_disc.auto_disc.maps.Map.Map.map", "modulename": "auto_disc.auto_disc.maps.Map", "qualname": "Map.map", "kind": "function", "doc": "

Map input to output.

\n\n

A Map operates on regular Python dicts, but it views them as\nstructured. The premap_key and postmap_key are used to define\nthe structured elements that the Map operates on. Often, the\npostmap_key does not exist in the input dict, and is added by the\nMap as output.

\n\n

Whether or not the premap_key exists in the output dict is up to the\nimplementation of the specific Map. We recommend preserving it.

\n\n
Arguments:
\n\n
    \n
  • input: generic dict containing input data to the map at premap_key
    • \n
\n\n
Returns:
\n\n
\n

A dict containing output data from the map at postmap_key

\n
\n", "signature": "(self, input: Dict) -> Dict:", "funcdef": "def"}, "auto_disc.auto_disc.maps.Map.Map.sample": {"fullname": "auto_disc.auto_disc.maps.Map.Map.sample", "modulename": "auto_disc.auto_disc.maps.Map", "qualname": "Map.sample", "kind": "function", "doc": "

Samples from the state of the map, viewed as a stateful record of\ndata which passes through.

\n\n

Curiosity exploration requires that the Map objects are agents in\nsome sense which have a memory of what data has passed through. The\nway in which this is implemented in practice is that Map objects\nmay generally be stateful, often by tracking the history of data that\npasses through. Then, they implement a sample method which returns\na random sample of the latent representation they have encoded.

\n\n
Returns:
\n\n
\n

A random sample of the latent representation, in whatever format\n depending on the implementation of the Map

\n
\n", "signature": "(self) -> Any:", "funcdef": "def"}, "auto_disc.auto_disc.maps.MeanBehaviorMap": {"fullname": "auto_disc.auto_disc.maps.MeanBehaviorMap", "modulename": "auto_disc.auto_disc.maps.MeanBehaviorMap", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.maps.MeanBehaviorMap.MeanBehaviorMap": {"fullname": "auto_disc.auto_disc.maps.MeanBehaviorMap.MeanBehaviorMap", "modulename": "auto_disc.auto_disc.maps.MeanBehaviorMap", "qualname": "MeanBehaviorMap", "kind": "class", "doc": "

A simple BehaviorMap which merely extracts the mean.

\n", "bases": "auto_disc.auto_disc.maps.Map.Map"}, "auto_disc.auto_disc.maps.MeanBehaviorMap.MeanBehaviorMap.__init__": {"fullname": "auto_disc.auto_disc.maps.MeanBehaviorMap.MeanBehaviorMap.__init__", "modulename": "auto_disc.auto_disc.maps.MeanBehaviorMap", "qualname": "MeanBehaviorMap.__init__", "kind": "function", "doc": "

Initialize the Map.

\n\n

A Map minimally sets the premap and postmap keys.

\n", "signature": "(\tpremap_key: str = 'output',\tpostmap_key: str = 'output',\tinput_shape: Tuple[int] = 1)"}, "auto_disc.auto_disc.maps.MeanBehaviorMap.MeanBehaviorMap.map": {"fullname": "auto_disc.auto_disc.maps.MeanBehaviorMap.MeanBehaviorMap.map", "modulename": "auto_disc.auto_disc.maps.MeanBehaviorMap", "qualname": "MeanBehaviorMap.map", "kind": "function", "doc": "

Map input to output.

\n\n

A Map operates on regular Python dicts, but it views them as\nstructured. The premap_key and postmap_key are used to define\nthe structured elements that the Map operates on. Often, the\npostmap_key does not exist in the input dict, and is added by the\nMap as output.

\n\n

Whether or not the premap_key exists in the output dict is up to the\nimplementation of the specific Map. We recommend preserving it.

\n\n
Arguments:
\n\n
    \n
  • input: generic dict containing input data to the map at premap_key
    • \n
\n\n
Returns:
\n\n
\n

A dict containing output data from the map at postmap_key

\n
\n", "signature": "(self, input: Dict) -> Dict:", "funcdef": "def"}, "auto_disc.auto_disc.maps.MeanBehaviorMap.MeanBehaviorMap.sample": {"fullname": "auto_disc.auto_disc.maps.MeanBehaviorMap.MeanBehaviorMap.sample", "modulename": "auto_disc.auto_disc.maps.MeanBehaviorMap", "qualname": "MeanBehaviorMap.sample", "kind": "function", "doc": "

Samples from the state of the map, viewed as a stateful record of\ndata which passes through.

\n\n

Curiosity exploration requires that the Map objects are agents in\nsome sense which have a memory of what data has passed through. The\nway in which this is implemented in practice is that Map objects\nmay generally be stateful, often by tracking the history of data that\npasses through. Then, they implement a sample method which returns\na random sample of the latent representation they have encoded.

\n\n
Returns:
\n\n
\n

A random sample of the latent representation, in whatever format\n depending on the implementation of the Map

\n
\n", "signature": "(self) -> torch.Tensor:", "funcdef": "def"}, "auto_disc.auto_disc.maps.NEATParameterMap": {"fullname": "auto_disc.auto_disc.maps.NEATParameterMap", "modulename": "auto_disc.auto_disc.maps.NEATParameterMap", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.maps.NEATParameterMap.NEATParameterMap": {"fullname": "auto_disc.auto_disc.maps.NEATParameterMap.NEATParameterMap", "modulename": "auto_disc.auto_disc.maps.NEATParameterMap", "qualname": "NEATParameterMap", "kind": "class", "doc": "

An abstract class that defines the interface for a Map, and should\nbe inherited from by concrete implementations of Maps.

\n\n

A Map is a model for a function (in the mathematical sense, i.e., an\ninput-output relation) that is also stateful. It takes a payload of data as\ninput and returns an new payload of data as output, without mutating\nthe input.

\n\n
Attributes:
\n\n
    \n
  • premap_key: key in the input dict for the input (i.e., \"params\")
    • \n
  • postmap_key: key in the output dict for the output (i.e., \"output\")
    • \n
\n", "bases": "auto_disc.auto_disc.maps.Map.Map"}, "auto_disc.auto_disc.maps.NEATParameterMap.NEATParameterMap.__init__": {"fullname": "auto_disc.auto_disc.maps.NEATParameterMap.NEATParameterMap.__init__", "modulename": "auto_disc.auto_disc.maps.NEATParameterMap", "qualname": "NEATParameterMap.__init__", "kind": "function", "doc": "

Initialize the Map.

\n\n

A Map minimally sets the premap and postmap keys.

\n", "signature": "(\tpremap_key: str = 'genome',\tconfig_path: str = './config.cfg',\tconfig_str: Optional[str] = None)"}, "auto_disc.auto_disc.maps.NEATParameterMap.NEATParameterMap.map": {"fullname": "auto_disc.auto_disc.maps.NEATParameterMap.NEATParameterMap.map", "modulename": "auto_disc.auto_disc.maps.NEATParameterMap", "qualname": "NEATParameterMap.map", "kind": "function", "doc": "

Map input to output.

\n\n

A Map operates on regular Python dicts, but it views them as\nstructured. The premap_key and postmap_key are used to define\nthe structured elements that the Map operates on. Often, the\npostmap_key does not exist in the input dict, and is added by the\nMap as output.

\n\n

Whether or not the premap_key exists in the output dict is up to the\nimplementation of the specific Map. We recommend preserving it.

\n\n
Arguments:
\n\n
    \n
  • input: generic dict containing input data to the map at premap_key
    • \n
\n\n
Returns:
\n\n
\n

A dict containing output data from the map at postmap_key

\n
\n", "signature": "(self, input: Dict, override_existing: bool = True) -> Dict:", "funcdef": "def"}, "auto_disc.auto_disc.maps.NEATParameterMap.NEATParameterMap.sample": {"fullname": "auto_disc.auto_disc.maps.NEATParameterMap.NEATParameterMap.sample", "modulename": "auto_disc.auto_disc.maps.NEATParameterMap", "qualname": "NEATParameterMap.sample", "kind": "function", "doc": "

Samples a genome.

\n", "signature": "(self) -> Any:", "funcdef": "def"}, "auto_disc.auto_disc.maps.NEATParameterMap.NEATParameterMap.mutate": {"fullname": "auto_disc.auto_disc.maps.NEATParameterMap.NEATParameterMap.mutate", "modulename": "auto_disc.auto_disc.maps.NEATParameterMap", "qualname": "NEATParameterMap.mutate", "kind": "function", "doc": "

Mutates the genome in the provided dict.

\n", "signature": "(self, dict: Dict) -> Dict:", "funcdef": "def"}, "auto_disc.auto_disc.maps.UniformParameterMap": {"fullname": "auto_disc.auto_disc.maps.UniformParameterMap", "modulename": "auto_disc.auto_disc.maps.UniformParameterMap", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.maps.UniformParameterMap.UniformParameterMap": {"fullname": "auto_disc.auto_disc.maps.UniformParameterMap.UniformParameterMap", "modulename": "auto_disc.auto_disc.maps.UniformParameterMap", "qualname": "UniformParameterMap", "kind": "class", "doc": "

A simple ParameterMap which generates parameters according to a uniform\ndistribution over a box.

\n", "bases": "auto_disc.auto_disc.maps.Map.Map"}, "auto_disc.auto_disc.maps.UniformParameterMap.UniformParameterMap.__init__": {"fullname": "auto_disc.auto_disc.maps.UniformParameterMap.UniformParameterMap.__init__", "modulename": "auto_disc.auto_disc.maps.UniformParameterMap", "qualname": "UniformParameterMap.__init__", "kind": "function", "doc": "

Initialize the Map.

\n\n

A Map minimally sets the premap and postmap keys.

\n", "signature": "(\tpremap_key: str = 'params',\ttensor_low: Union[torch.Tensor, List[float]] = tensor([0.]),\ttensor_high: Union[torch.Tensor, List[float]] = tensor([0.]),\ttensor_bound_low: Union[torch.Tensor, List[float]] = tensor([-inf]),\ttensor_bound_high: Union[torch.Tensor, List[float]] = tensor([inf]),\toverride_existing: bool = True)"}, "auto_disc.auto_disc.maps.UniformParameterMap.UniformParameterMap.map": {"fullname": "auto_disc.auto_disc.maps.UniformParameterMap.UniformParameterMap.map", "modulename": "auto_disc.auto_disc.maps.UniformParameterMap", "qualname": "UniformParameterMap.map", "kind": "function", "doc": "

map() takes an input dict of metadata and adds the\nparams key with a sample if it does not exist

\n", "signature": "(self, input: Dict, override_existing: bool = True) -> Dict:", "funcdef": "def"}, "auto_disc.auto_disc.maps.UniformParameterMap.UniformParameterMap.sample": {"fullname": "auto_disc.auto_disc.maps.UniformParameterMap.UniformParameterMap.sample", "modulename": "auto_disc.auto_disc.maps.UniformParameterMap", "qualname": "UniformParameterMap.sample", "kind": "function", "doc": "

Samples from the state of the map, viewed as a stateful record of\ndata which passes through.

\n\n

Curiosity exploration requires that the Map objects are agents in\nsome sense which have a memory of what data has passed through. The\nway in which this is implemented in practice is that Map objects\nmay generally be stateful, often by tracking the history of data that\npasses through. Then, they implement a sample method which returns\na random sample of the latent representation they have encoded.

\n\n
Returns:
\n\n
\n

A random sample of the latent representation, in whatever format\n depending on the implementation of the Map

\n
\n", "signature": "(self) -> torch.Tensor:", "funcdef": "def"}, "auto_disc.auto_disc.maps.cppn": {"fullname": "auto_disc.auto_disc.maps.cppn", "modulename": "auto_disc.auto_disc.maps.cppn", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.maps.cppn.pytorchneat": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.maps.cppn.pytorchneat.activations": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat.activations", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat.activations", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.maps.cppn.pytorchneat.activations.delphineat_gauss_activation": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat.activations.delphineat_gauss_activation", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat.activations", "qualname": "delphineat_gauss_activation", "kind": "function", "doc": "

PyTorch implementation of gauss activation as defined by SharpNEAT, which is also as in DelphiNEAT.

\n", "signature": "(z):", "funcdef": "def"}, "auto_disc.auto_disc.maps.cppn.pytorchneat.activations.delphineat_sigmoid_activation": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat.activations.delphineat_sigmoid_activation", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat.activations", "qualname": "delphineat_sigmoid_activation", "kind": "function", "doc": "

PyTorch implementation of sigmoidal activation function as defined in DelphiNEAT

\n", "signature": "(z):", "funcdef": "def"}, "auto_disc.auto_disc.maps.cppn.pytorchneat.activations.tanh_activation": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat.activations.tanh_activation", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat.activations", "qualname": "tanh_activation", "kind": "function", "doc": "

\n", "signature": "(z):", "funcdef": "def"}, "auto_disc.auto_disc.maps.cppn.pytorchneat.activations.sin_activation": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat.activations.sin_activation", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat.activations", "qualname": "sin_activation", "kind": "function", "doc": "

\n", "signature": "(z):", "funcdef": "def"}, "auto_disc.auto_disc.maps.cppn.pytorchneat.activations.sigmoid_activation": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat.activations.sigmoid_activation", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat.activations", "qualname": "sigmoid_activation", "kind": "function", "doc": "

\n", "signature": "(z):", "funcdef": "def"}, "auto_disc.auto_disc.maps.cppn.pytorchneat.activations.gauss_activation": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat.activations.gauss_activation", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat.activations", "qualname": "gauss_activation", "kind": "function", "doc": "

\n", "signature": "(z):", "funcdef": "def"}, "auto_disc.auto_disc.maps.cppn.pytorchneat.activations.identity_activation": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat.activations.identity_activation", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat.activations", "qualname": "identity_activation", "kind": "function", "doc": "

\n", "signature": "(z):", "funcdef": "def"}, "auto_disc.auto_disc.maps.cppn.pytorchneat.activations.relu_activation": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat.activations.relu_activation", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat.activations", "qualname": "relu_activation", "kind": "function", "doc": "

\n", "signature": "(z):", "funcdef": "def"}, "auto_disc.auto_disc.maps.cppn.pytorchneat.aggregations": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat.aggregations", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat.aggregations", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.maps.cppn.pytorchneat.aggregations.sum_aggregation": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat.aggregations.sum_aggregation", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat.aggregations", "qualname": "sum_aggregation", "kind": "function", "doc": "

\n", "signature": "(inputs):", "funcdef": "def"}, "auto_disc.auto_disc.maps.cppn.pytorchneat.aggregations.product_aggregation": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat.aggregations.product_aggregation", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat.aggregations", "qualname": "product_aggregation", "kind": "function", "doc": "

\n", "signature": "(inputs):", "funcdef": "def"}, "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn.RecurrentNetwork": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn.RecurrentNetwork", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn", "qualname": "RecurrentNetwork", "kind": "class", "doc": "

Base class for all neural network modules.

\n\n

Your models should also subclass this class.

\n\n

Modules can also contain other Modules, allowing to nest them in\na tree structure. You can assign the submodules as regular attributes::

\n\n
import torch.nn as nn\nimport torch.nn.functional as F\n\nclass Model(nn.Module):\n    def __init__(self):\n        super().__init__()\n        self.conv1 = nn.Conv2d(1, 20, 5)\n        self.conv2 = nn.Conv2d(20, 20, 5)\n\n    def forward(self, x):\n        x = F.relu(self.conv1(x))\n        return F.relu(self.conv2(x))\n
\n\n

Submodules assigned in this way will be registered, and will have their\nparameters converted too when you call to(), etc.

\n\n
\n\n

As per the example above, an __init__() call to the parent class\nmust be made before assignment on the child.

\n\n
\n\n

:ivar training: Boolean represents whether this module is in training or\n evaluation mode.\n:vartype training: bool

\n", "bases": "torch.nn.modules.module.Module"}, "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn.RecurrentNetwork.__init__": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn.RecurrentNetwork.__init__", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn", "qualname": "RecurrentNetwork.__init__", "kind": "function", "doc": "
Parameters
\n\n
    \n
  • input_neuron_ids: list of ids of input neurons
    • \n
  • hidden_neuron_ids: list of ids of hidden neurons
    • \n
  • output_neuron_ids: list of ids of output neurons
    • \n
\n", "signature": "(\tinput_neuron_ids,\thidden_neuron_ids,\toutput_neuron_ids,\tbiases,\tresponses,\tactivations,\tconnections,\tdevice='cpu')"}, "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn.RecurrentNetwork.forward": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn.RecurrentNetwork.forward", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn", "qualname": "RecurrentNetwork.forward", "kind": "function", "doc": "
Parameters
\n\n
    \n
  • inputs: tensor of size (batch_size, n_inputs)\nNote: output of a node as follows: activation(bias+(response\u2217aggregation(inputs)))\nreturns: (batch_size, n_outputs)
    • \n
\n", "signature": "(self, inputs):", "funcdef": "def"}, "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn.RecurrentNetwork.activate": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn.RecurrentNetwork.activate", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn", "qualname": "RecurrentNetwork.activate", "kind": "function", "doc": "
Parameters
\n\n
    \n
  • inputs: tensor of size (..., n_inputs) => only the last dim matter, then CPPN is applied on all coordinates
    • \n
  • n_passes: number of passes in the RNN TODO: currently global passes should it be internal passes as in pytorch-neat by uber research ?\nreturns: (..., n_outputs)
    • \n
\n", "signature": "(self, inputs, n_passes=1):", "funcdef": "def"}, "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn.RecurrentNetwork.draw_net": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn.RecurrentNetwork.draw_net", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn", "qualname": "RecurrentNetwork.draw_net", "kind": "function", "doc": "

\n", "signature": "(\tself,\tview=False,\tfilename=None,\tnode_names=None,\tnode_colors=None,\tfmt='svg'):", "funcdef": "def"}, "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn.RecurrentNetwork.create": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn.RecurrentNetwork.create", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn", "qualname": "RecurrentNetwork.create", "kind": "function", "doc": "

Receives a genome and returns the RecurrentCPPN()

\n", "signature": "(genome, config, device='cpu'):", "funcdef": "def"}, "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn.RecurrentNetwork.update_genome": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn.RecurrentNetwork.update_genome", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn", "qualname": "RecurrentNetwork.update_genome", "kind": "function", "doc": "

\n", "signature": "(self, genome):", "funcdef": "def"}, "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn.str_to_tuple_key": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn.str_to_tuple_key", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn", "qualname": "str_to_tuple_key", "kind": "function", "doc": "

\n", "signature": "(str_key):", "funcdef": "def"}, "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn.tuple_to_str_key": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn.tuple_to_str_key", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn", "qualname": "tuple_to_str_key", "kind": "function", "doc": "

\n", "signature": "(tuple_key):", "funcdef": "def"}, "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn.RecurrentNetworkAgg": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn.RecurrentNetworkAgg", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn", "qualname": "RecurrentNetworkAgg", "kind": "class", "doc": "

Base class for all neural network modules.

\n\n

Your models should also subclass this class.

\n\n

Modules can also contain other Modules, allowing to nest them in\na tree structure. You can assign the submodules as regular attributes::

\n\n
import torch.nn as nn\nimport torch.nn.functional as F\n\nclass Model(nn.Module):\n    def __init__(self):\n        super().__init__()\n        self.conv1 = nn.Conv2d(1, 20, 5)\n        self.conv2 = nn.Conv2d(20, 20, 5)\n\n    def forward(self, x):\n        x = F.relu(self.conv1(x))\n        return F.relu(self.conv2(x))\n
\n\n

Submodules assigned in this way will be registered, and will have their\nparameters converted too when you call to(), etc.

\n\n
\n\n

As per the example above, an __init__() call to the parent class\nmust be made before assignment on the child.

\n\n
\n\n

:ivar training: Boolean represents whether this module is in training or\n evaluation mode.\n:vartype training: bool

\n", "bases": "torch.nn.modules.module.Module"}, "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn.RecurrentNetworkAgg.__init__": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn.RecurrentNetworkAgg.__init__", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn", "qualname": "RecurrentNetworkAgg.__init__", "kind": "function", "doc": "
Parameters
\n\n
    \n
  • input_neuron_ids: list of ids of input neurons
    • \n
  • hidden_neuron_ids: list of ids of hidden neurons
    • \n
  • output_neuron_ids: list of ids of output neurons
    • \n
\n", "signature": "(\tinput_neuron_ids,\thidden_neuron_ids,\toutput_neuron_ids,\tbiases,\tresponses,\tactivations,\taggregations,\tconnections,\tdevice='cpu')"}, "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn.RecurrentNetworkAgg.forward": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn.RecurrentNetworkAgg.forward", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn", "qualname": "RecurrentNetworkAgg.forward", "kind": "function", "doc": "
Parameters
\n\n
    \n
  • inputs: tensor of size (batch_size, n_inputs)
    • \n
\n\n

Note: output of a node as follows: activation(bias+(response\u2217aggregation(inputs)))

\n\n

returns: (batch_size, n_outputs)

\n", "signature": "(self, inputs):", "funcdef": "def"}, "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn.RecurrentNetworkAgg.activate": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn.RecurrentNetworkAgg.activate", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn", "qualname": "RecurrentNetworkAgg.activate", "kind": "function", "doc": "
Parameters
\n\n
    \n
  • inputs: tensor of size (..., n_inputs) => only the last dim matter, then CPPN is applied on all coordinates
    • \n
  • n_passes: number of passes in the RNN TODO: currently global passes should it be internal passes as in pytorch-neat by uber research ?\nreturns: (..., n_outputs)
    • \n
\n", "signature": "(self, inputs, n_passes=1):", "funcdef": "def"}, "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn.RecurrentNetworkAgg.draw_net": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn.RecurrentNetworkAgg.draw_net", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn", "qualname": "RecurrentNetworkAgg.draw_net", "kind": "function", "doc": "

\n", "signature": "(\tself,\tconfig=None,\tview=False,\tfilename=None,\tnode_names=None,\tnode_colors=None,\tfmt='svg'):", "funcdef": "def"}, "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn.RecurrentNetworkAgg.create": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn.RecurrentNetworkAgg.create", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn", "qualname": "RecurrentNetworkAgg.create", "kind": "function", "doc": "

Receives a genome and returns the RecurrentCPPN()

\n", "signature": "(genome, config, device='cpu'):", "funcdef": "def"}, "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn.RecurrentNetworkAgg.update_genome": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn.RecurrentNetworkAgg.update_genome", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat.rnn", "qualname": "RecurrentNetworkAgg.update_genome", "kind": "function", "doc": "

\n", "signature": "(self, genome):", "funcdef": "def"}, "auto_disc.auto_disc.maps.cppn.pytorchneat.selfconnectiongenome": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat.selfconnectiongenome", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat.selfconnectiongenome", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.maps.cppn.pytorchneat.selfconnectiongenome.ConnectionWeightAttribute": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat.selfconnectiongenome.ConnectionWeightAttribute", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat.selfconnectiongenome", "qualname": "ConnectionWeightAttribute", "kind": "class", "doc": "

Class for connection weight attributes. Is introduced to allow a differentiation between self connections of a node and connections to other nodes.

\n", "bases": "neat.attributes.BaseAttribute"}, "auto_disc.auto_disc.maps.cppn.pytorchneat.selfconnectiongenome.ConnectionWeightAttribute.clamp": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat.selfconnectiongenome.ConnectionWeightAttribute.clamp", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat.selfconnectiongenome", "qualname": "ConnectionWeightAttribute.clamp", "kind": "function", "doc": "

\n", "signature": "(self, value, is_other_connect, config):", "funcdef": "def"}, "auto_disc.auto_disc.maps.cppn.pytorchneat.selfconnectiongenome.ConnectionWeightAttribute.init_value": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat.selfconnectiongenome.ConnectionWeightAttribute.init_value", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat.selfconnectiongenome", "qualname": "ConnectionWeightAttribute.init_value", "kind": "function", "doc": "

\n", "signature": "(self, is_other_connect, config):", "funcdef": "def"}, "auto_disc.auto_disc.maps.cppn.pytorchneat.selfconnectiongenome.ConnectionWeightAttribute.mutate_value": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat.selfconnectiongenome.ConnectionWeightAttribute.mutate_value", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat.selfconnectiongenome", "qualname": "ConnectionWeightAttribute.mutate_value", "kind": "function", "doc": "

\n", "signature": "(self, value, is_other_connect, config):", "funcdef": "def"}, "auto_disc.auto_disc.maps.cppn.pytorchneat.selfconnectiongenome.ConnectionWeightAttribute.validate": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat.selfconnectiongenome.ConnectionWeightAttribute.validate", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat.selfconnectiongenome", "qualname": "ConnectionWeightAttribute.validate", "kind": "function", "doc": "

\n", "signature": "(self, config):", "funcdef": "def"}, "auto_disc.auto_disc.maps.cppn.pytorchneat.selfconnectiongenome.SelfConnectionGene": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat.selfconnectiongenome.SelfConnectionGene", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat.selfconnectiongenome", "qualname": "SelfConnectionGene", "kind": "class", "doc": "

Handles functions shared by multiple types of genes (both node and connection),\nincluding crossover and calling mutation methods.

\n", "bases": "neat.genes.BaseGene"}, "auto_disc.auto_disc.maps.cppn.pytorchneat.selfconnectiongenome.SelfConnectionGene.__init__": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat.selfconnectiongenome.SelfConnectionGene.__init__", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat.selfconnectiongenome", "qualname": "SelfConnectionGene.__init__", "kind": "function", "doc": "

\n", "signature": "(key)"}, "auto_disc.auto_disc.maps.cppn.pytorchneat.selfconnectiongenome.SelfConnectionGene.distance": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat.selfconnectiongenome.SelfConnectionGene.distance", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat.selfconnectiongenome", "qualname": "SelfConnectionGene.distance", "kind": "function", "doc": "

\n", "signature": "(self, other, config):", "funcdef": "def"}, "auto_disc.auto_disc.maps.cppn.pytorchneat.selfconnectiongenome.SelfConnectionGene.init_attributes": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat.selfconnectiongenome.SelfConnectionGene.init_attributes", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat.selfconnectiongenome", "qualname": "SelfConnectionGene.init_attributes", "kind": "function", "doc": "

\n", "signature": "(self, config):", "funcdef": "def"}, "auto_disc.auto_disc.maps.cppn.pytorchneat.selfconnectiongenome.SelfConnectionGene.mutate": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat.selfconnectiongenome.SelfConnectionGene.mutate", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat.selfconnectiongenome", "qualname": "SelfConnectionGene.mutate", "kind": "function", "doc": "

\n", "signature": "(self, config):", "funcdef": "def"}, "auto_disc.auto_disc.maps.cppn.pytorchneat.selfconnectiongenome.SelfConnectionGenome": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat.selfconnectiongenome.SelfConnectionGenome", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat.selfconnectiongenome", "qualname": "SelfConnectionGenome", "kind": "class", "doc": "

A genome for generalized neural networks. Derived from neat.DefaultGenome.\nExtends the default genome to allow specific configuration for self connections of neurons.

\n\n

Uses the SelfConnectionGene class instead of the neat.genes.DefaultConnectionGene class for connections.

\n\n

Terminology\n pin: Point at which the network is conceptually connected to the external world;\n pins are either input or output.\n node: Analog of a physical neuron.\n connection: Connection between a pin/node output and a node's input, or between a node's\n output and a pin/node input.\n key: Identifier for an object, unique within the set of similar objects.

\n\n

Design assumptions and conventions.\n 1. Each output pin is connected only to the output of its own unique\n neuron by an implicit connection with weight one. This connection\n is permanently enabled.\n 2. The output pin's key is always the same as the key for its\n associated neuron.\n 3. Output neurons can be modified but not deleted.\n 4. The input values are applied to the input pins unmodified.

\n", "bases": "neat.genome.DefaultGenome"}, "auto_disc.auto_disc.maps.cppn.pytorchneat.selfconnectiongenome.SelfConnectionGenome.parse_config": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat.selfconnectiongenome.SelfConnectionGenome.parse_config", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat.selfconnectiongenome", "qualname": "SelfConnectionGenome.parse_config", "kind": "function", "doc": "

\n", "signature": "(cls, param_dict):", "funcdef": "def"}, "auto_disc.auto_disc.maps.cppn.pytorchneat.utils": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat.utils", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat.utils", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.maps.cppn.pytorchneat.utils.create_image_cppn_input": {"fullname": "auto_disc.auto_disc.maps.cppn.pytorchneat.utils.create_image_cppn_input", "modulename": "auto_disc.auto_disc.maps.cppn.pytorchneat.utils", "qualname": "create_image_cppn_input", "kind": "function", "doc": "

\n", "signature": "(output_size, is_distance_to_center=True, is_bias=True):", "funcdef": "def"}, "auto_disc.auto_disc.maps.lenia": {"fullname": "auto_disc.auto_disc.maps.lenia", "modulename": "auto_disc.auto_disc.maps.lenia", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.maps.lenia.LeniaParameterMap": {"fullname": "auto_disc.auto_disc.maps.lenia.LeniaParameterMap", "modulename": "auto_disc.auto_disc.maps.lenia.LeniaParameterMap", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.maps.lenia.LeniaParameterMap.LeniaHyperParameters": {"fullname": "auto_disc.auto_disc.maps.lenia.LeniaParameterMap.LeniaHyperParameters", "modulename": "auto_disc.auto_disc.maps.lenia.LeniaParameterMap", "qualname": "LeniaHyperParameters", "kind": "class", "doc": "

Holds parameters to initialize Lenia model.

\n"}, "auto_disc.auto_disc.maps.lenia.LeniaParameterMap.LeniaHyperParameters.__init__": {"fullname": "auto_disc.auto_disc.maps.lenia.LeniaParameterMap.LeniaHyperParameters.__init__", "modulename": "auto_disc.auto_disc.maps.lenia.LeniaParameterMap", "qualname": "LeniaHyperParameters.__init__", "kind": "function", "doc": "

\n", "signature": "(\ttensor_low: torch.Tensor = tensor([0.0000, 1.0000, 0.0000, 0.0010, 0.0000, 0.0000, 0.0000, 0.0000]),\ttensor_high: torch.Tensor = tensor([0.0000, 1.0000, 0.0000, 0.0010, 0.0000, 0.0000, 0.0000, 0.0000]),\ttensor_bound_low: torch.Tensor = tensor([0.0000, 1.0000, 0.0000, 0.0010, 0.0000, 0.0000, 0.0000, 0.0000]),\ttensor_bound_high: torch.Tensor = tensor([20.0000, 20.0000, 1.0000, 0.3000, 1.0000, 1.0000, 1.0000, 1.0000]),\tinit_state_dim: Tuple[int, int] = (10, 10),\tcppn_n_passes: int = 2)"}, "auto_disc.auto_disc.maps.lenia.LeniaParameterMap.LeniaParameterMap": {"fullname": "auto_disc.auto_disc.maps.lenia.LeniaParameterMap.LeniaParameterMap", "modulename": "auto_disc.auto_disc.maps.lenia.LeniaParameterMap", "qualname": "LeniaParameterMap", "kind": "class", "doc": "

Due to the complexities of initializing Lenia parameters,\nit's easier to make this custom parameter map.

\n", "bases": "auto_disc.utils.leaf.Leaf.Leaf"}, "auto_disc.auto_disc.maps.lenia.LeniaParameterMap.LeniaParameterMap.__init__": {"fullname": "auto_disc.auto_disc.maps.lenia.LeniaParameterMap.LeniaParameterMap.__init__", "modulename": "auto_disc.auto_disc.maps.lenia.LeniaParameterMap", "qualname": "LeniaParameterMap.__init__", "kind": "function", "doc": "

\n", "signature": "(\tpremap_key: str = 'params',\tparam_obj: auto_disc.auto_disc.maps.lenia.LeniaParameterMap.LeniaHyperParameters = LeniaHyperParameters(tensor_low=tensor([0.0000, 1.0000, 0.0000, 0.0010, 0.0000, 0.0000, 0.0000, 0.0000]), tensor_high=tensor([0.0000, 1.0000, 0.0000, 0.0010, 0.0000, 0.0000, 0.0000, 0.0000]), tensor_bound_low=tensor([0.0000, 1.0000, 0.0000, 0.0010, 0.0000, 0.0000, 0.0000, 0.0000]), tensor_bound_high=tensor([20.0000, 20.0000, 1.0000, 0.3000, 1.0000, 1.0000, 1.0000, 1.0000]), init_state_dim=(10, 10), cppn_n_passes=2),\tneat_config_path: str = './maps/cppn/config.cfg',\tneat_config_str: Optional[str] = None,\t**config_decorator_kwargs)"}, "auto_disc.auto_disc.maps.lenia.LeniaParameterMap.LeniaParameterMap.map": {"fullname": "auto_disc.auto_disc.maps.lenia.LeniaParameterMap.LeniaParameterMap.map", "modulename": "auto_disc.auto_disc.maps.lenia.LeniaParameterMap", "qualname": "LeniaParameterMap.map", "kind": "function", "doc": "

Takes input dictionary and overrides the\npremap_key with generated parameters for Lenia.

\n", "signature": "(self, input: Dict, override_existing: bool = True) -> Dict:", "funcdef": "def"}, "auto_disc.auto_disc.maps.lenia.LeniaParameterMap.LeniaParameterMap.sample": {"fullname": "auto_disc.auto_disc.maps.lenia.LeniaParameterMap.LeniaParameterMap.sample", "modulename": "auto_disc.auto_disc.maps.lenia.LeniaParameterMap", "qualname": "LeniaParameterMap.sample", "kind": "function", "doc": "

Samples from the parameter map to yield the parameters ready\nfor processing by the model (LeniaCPPN), i.e., including the genome.

\n", "signature": "(self) -> Dict:", "funcdef": "def"}, "auto_disc.auto_disc.maps.lenia.LeniaParameterMap.LeniaParameterMap.mutate": {"fullname": "auto_disc.auto_disc.maps.lenia.LeniaParameterMap.LeniaParameterMap.mutate", "modulename": "auto_disc.auto_disc.maps.lenia.LeniaParameterMap", "qualname": "LeniaParameterMap.mutate", "kind": "function", "doc": "

Takes the dictionary of only parameters outputted by\nthe explorer and mutates them.

\n", "signature": "(self, parameter_dict: Dict) -> Dict:", "funcdef": "def"}, "auto_disc.auto_disc.maps.lenia.LeniaStatistics": {"fullname": "auto_disc.auto_disc.maps.lenia.LeniaStatistics", "modulename": "auto_disc.auto_disc.maps.lenia.LeniaStatistics", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.maps.lenia.LeniaStatistics.center_of_mass": {"fullname": "auto_disc.auto_disc.maps.lenia.LeniaStatistics.center_of_mass", "modulename": "auto_disc.auto_disc.maps.lenia.LeniaStatistics", "qualname": "center_of_mass", "kind": "function", "doc": "

\n", "signature": "(input_array: torch.Tensor) -> torch.Tensor:", "funcdef": "def"}, "auto_disc.auto_disc.maps.lenia.LeniaStatistics.calc_distance_matrix": {"fullname": "auto_disc.auto_disc.maps.lenia.LeniaStatistics.calc_distance_matrix", "modulename": "auto_disc.auto_disc.maps.lenia.LeniaStatistics", "qualname": "calc_distance_matrix", "kind": "function", "doc": "

\n", "signature": "(size_y: int, size_x: int) -> torch.Tensor:", "funcdef": "def"}, "auto_disc.auto_disc.maps.lenia.LeniaStatistics.calc_image_moments": {"fullname": "auto_disc.auto_disc.maps.lenia.LeniaStatistics.calc_image_moments", "modulename": "auto_disc.auto_disc.maps.lenia.LeniaStatistics", "qualname": "calc_image_moments", "kind": "function", "doc": "

Calculates the image moments for an image.

\n\n
For more information see:
\n\n
\n \n
\n\n

The code is based on the Javascript implementation of Lenia by Bert Chan.\nThe code had to be adapted, because for the original code the coordinates are (x,y), whereas here they are (y,x).

\n\n
Parameters
\n\n
    \n
  • image: 2d gray scale image in form of a numpy array.
    • \n
\n\n
Returns
\n\n
\n

Namedtupel with the different moments.

\n
\n", "signature": "(image: torch.Tensor) -> Dict[str, torch.Tensor]:", "funcdef": "def"}, "auto_disc.auto_disc.maps.lenia.LeniaStatistics.LeniaStatistics": {"fullname": "auto_disc.auto_disc.maps.lenia.LeniaStatistics.LeniaStatistics", "modulename": "auto_disc.auto_disc.maps.lenia.LeniaStatistics", "qualname": "LeniaStatistics", "kind": "class", "doc": "

Outputs 17-dimensional embedding.

\n", "bases": "auto_disc.utils.leaf.Leaf.Leaf"}, "auto_disc.auto_disc.maps.lenia.LeniaStatistics.LeniaStatistics.__init__": {"fullname": "auto_disc.auto_disc.maps.lenia.LeniaStatistics.LeniaStatistics.__init__", "modulename": "auto_disc.auto_disc.maps.lenia.LeniaStatistics", "qualname": "LeniaStatistics.__init__", "kind": "function", "doc": "

\n", "signature": "(\tpremap_key: str = 'output',\tpostmap_key: str = 'output',\tSX: int = 256,\tSY: int = 256)"}, "auto_disc.auto_disc.maps.lenia.LeniaStatistics.LeniaStatistics.map": {"fullname": "auto_disc.auto_disc.maps.lenia.LeniaStatistics.LeniaStatistics.map", "modulename": "auto_disc.auto_disc.maps.lenia.LeniaStatistics", "qualname": "LeniaStatistics.map", "kind": "function", "doc": "

Compute statistics on Lenia's output

\n\n
Arguments:
\n\n
    \n
  • input: Lenia's output
    • \n
  • is_output_new_discovery: indicates if it is a new discovery
    • \n
\n\n
Returns:
\n\n
\n

Return a torch tensor in dict

\n
\n", "signature": "(self, input: Dict) -> Dict:", "funcdef": "def"}, "auto_disc.auto_disc.maps.lenia.LeniaStatistics.LeniaStatistics.sample": {"fullname": "auto_disc.auto_disc.maps.lenia.LeniaStatistics.LeniaStatistics.sample", "modulename": "auto_disc.auto_disc.maps.lenia.LeniaStatistics", "qualname": "LeniaStatistics.sample", "kind": "function", "doc": "

\n", "signature": "(self):", "funcdef": "def"}, "auto_disc.auto_disc.systems": {"fullname": "auto_disc.auto_disc.systems", "modulename": "auto_disc.auto_disc.systems", "kind": "module", "doc": "

See the documentation for the auto_disc.auto_disc.systems.System abstract class for details.

\n"}, "auto_disc.auto_disc.systems.System": {"fullname": "auto_disc.auto_disc.systems.System", "modulename": "auto_disc.auto_disc.systems.System", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.systems.System.System": {"fullname": "auto_disc.auto_disc.systems.System.System", "modulename": "auto_disc.auto_disc.systems.System", "qualname": "System", "kind": "class", "doc": "

An abstract class that defines the interface for a System, and should\nbe inherited from by concrete implementations of Systems.

\n\n

A System is a model for a dynamical or complex system. In the context\nof the experimental pipeline, it should be stateless and therefore a pure\nfunction. Therefore, while the system parameters may need to be set,\nthese are in fact _hyperparameters_ set by the web interface, and not\nparameters which are explored by the curiosity algorithm.

\n\n

As a pure function, it takes a payload of data as input and returns an\nnew payload of data as output, without mutating the input.

\n", "bases": "auto_disc.utils.leaf.Leaf.Leaf"}, "auto_disc.auto_disc.systems.System.System.__init__": {"fullname": "auto_disc.auto_disc.systems.System.System.__init__", "modulename": "auto_disc.auto_disc.systems.System", "qualname": "System.__init__", "kind": "function", "doc": "

There are no assumptions on the initialization parameters.

\n", "signature": "(*args, **kwargs)"}, "auto_disc.auto_disc.systems.System.System.map": {"fullname": "auto_disc.auto_disc.systems.System.System.map", "modulename": "auto_disc.auto_disc.systems.System", "qualname": "System.map", "kind": "function", "doc": "

Map system inputs to outputs.

\n\n

A System operates on regular Python dicts, but it views them as\nstructured. The premap_key and postmap_key are used to define\nthe structured elements that the System operates on. Often, the\npostmap_key does not exist in the input dict, and is added by the\nMap as output.

\n\n

Whether or not the premap_key exists in the output dict is up to the\nimplementation of the specific System. We recommend preserving it.

\n\n
Arguments:
\n\n
    \n
  • input (dict): generic dict containing input data to the map at premap_key
    • \n
\n\n
Returns:
\n\n
\n

A generic dict containing output data from the map at postmap_key

\n
\n", "signature": "(self, input: Dict) -> Dict:", "funcdef": "def"}, "auto_disc.auto_disc.systems.System.System.render": {"fullname": "auto_disc.auto_disc.systems.System.System.render", "modulename": "auto_disc.auto_disc.systems.System", "qualname": "System.render", "kind": "function", "doc": "

Render system output as an image or video.

\n\n

A System should be able to render useful information about its\nexecution (e.g., a plot, a video, etc.), which may depend on its\ninternal state which is not captured by the map method.

\n\n
Arguments:
\n\n
    \n
  • self (System): If the System can render a graphical output without\nneeding access to its internal state (i.e., only from the\noutput), then it can also be @classmethod.
    • \n
  • data_dict (dict): A dict containing the output of the map\nmethod. Depending on implementation, this may be sufficient to\nrender the output or one may need stateful data from self.
    • \n
\n", "signature": "(self, data_dict: dict) -> bytes:", "funcdef": "def"}, "auto_disc.auto_disc.utils": {"fullname": "auto_disc.auto_disc.utils", "modulename": "auto_disc.auto_disc.utils", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.utils.callbacks": {"fullname": "auto_disc.auto_disc.utils.callbacks", "modulename": "auto_disc.auto_disc.utils.callbacks", "kind": "module", "doc": "

At certain defined points in the exploration loop, the software executes\ncallbacks which may be user defined. Such callbacks are mostly used for doing\nenvironment setup and teardown, for saving output, or for accepting user input.

\n\n

For details on when each callback is called, look in the source for\nauto_disc.ExperimentPipeline

\n"}, "auto_disc.auto_disc.utils.callbacks.interact_callbacks": {"fullname": "auto_disc.auto_disc.utils.callbacks.interact_callbacks", "modulename": "auto_disc.auto_disc.utils.callbacks.interact_callbacks", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.utils.callbacks.interact_callbacks.base_interact_callback": {"fullname": "auto_disc.auto_disc.utils.callbacks.interact_callbacks.base_interact_callback", "modulename": "auto_disc.auto_disc.utils.callbacks.interact_callbacks.base_interact_callback", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.utils.callbacks.interact_callbacks.base_interact_callback.Object": {"fullname": "auto_disc.auto_disc.utils.callbacks.interact_callbacks.base_interact_callback.Object", "modulename": "auto_disc.auto_disc.utils.callbacks.interact_callbacks.base_interact_callback", "qualname": "Object", "kind": "function", "doc": "

\n", "signature": "(**kwargs):", "funcdef": "def"}, "auto_disc.auto_disc.utils.callbacks.interact_callbacks.base_interact_callback.Interaction": {"fullname": "auto_disc.auto_disc.utils.callbacks.interact_callbacks.base_interact_callback.Interaction", "modulename": "auto_disc.auto_disc.utils.callbacks.interact_callbacks.base_interact_callback", "qualname": "Interaction", "kind": "class", "doc": "

\n"}, "auto_disc.auto_disc.utils.callbacks.interact_callbacks.base_interact_callback.Interaction.init_seed": {"fullname": "auto_disc.auto_disc.utils.callbacks.interact_callbacks.base_interact_callback.Interaction.init_seed", "modulename": "auto_disc.auto_disc.utils.callbacks.interact_callbacks.base_interact_callback", "qualname": "Interaction.init_seed", "kind": "function", "doc": "

\n", "signature": "(self, interact_callbacks, config):", "funcdef": "def"}, "auto_disc.auto_disc.utils.callbacks.interact_callbacks.base_interact_callback.Interaction.save": {"fullname": "auto_disc.auto_disc.utils.callbacks.interact_callbacks.base_interact_callback.Interaction.save", "modulename": "auto_disc.auto_disc.utils.callbacks.interact_callbacks.base_interact_callback", "qualname": "Interaction.save", "kind": "function", "doc": "

\n", "signature": "(self, data, dict_info=None):", "funcdef": "def"}, "auto_disc.auto_disc.utils.callbacks.interact_callbacks.base_interact_callback.Interaction.read": {"fullname": "auto_disc.auto_disc.utils.callbacks.interact_callbacks.base_interact_callback.Interaction.read", "modulename": "auto_disc.auto_disc.utils.callbacks.interact_callbacks.base_interact_callback", "qualname": "Interaction.read", "kind": "function", "doc": "

\n", "signature": "(self, filter_attribut=None):", "funcdef": "def"}, "auto_disc.auto_disc.utils.callbacks.interact_callbacks.base_interact_callback.Interaction.feedback": {"fullname": "auto_disc.auto_disc.utils.callbacks.interact_callbacks.base_interact_callback.Interaction.feedback", "modulename": "auto_disc.auto_disc.utils.callbacks.interact_callbacks.base_interact_callback", "qualname": "Interaction.feedback", "kind": "function", "doc": "

\n", "signature": "(self, data, dict_info=None, dict_value_to_change=None):", "funcdef": "def"}, "auto_disc.auto_disc.utils.callbacks.interact_callbacks.base_interact_callback.BaseInteractCallback": {"fullname": "auto_disc.auto_disc.utils.callbacks.interact_callbacks.base_interact_callback.BaseInteractCallback", "modulename": "auto_disc.auto_disc.utils.callbacks.interact_callbacks.base_interact_callback", "qualname": "BaseInteractCallback", "kind": "class", "doc": "

Base class for cancelled callbacks used by the experiment pipelines when the experiment was cancelled.

\n", "bases": "auto_disc.legacy.utils.callbacks.base_callback.BaseCallback"}, "auto_disc.auto_disc.utils.callbacks.interact_callbacks.base_interact_callback.BaseInteractCallback.__init__": {"fullname": "auto_disc.auto_disc.utils.callbacks.interact_callbacks.base_interact_callback.BaseInteractCallback.__init__", "modulename": "auto_disc.auto_disc.utils.callbacks.interact_callbacks.base_interact_callback", "qualname": "BaseInteractCallback.__init__", "kind": "function", "doc": "

initialize attributes common to all cancelled callbacks

\n\n
Arguments:
\n\n
    \n
  • kwargs: some usefull args (e.g. experiment_id...)
    • \n
\n", "signature": "(**kwargs)"}, "auto_disc.auto_disc.utils.callbacks.interact_callbacks.read_disk_interact_callback": {"fullname": "auto_disc.auto_disc.utils.callbacks.interact_callbacks.read_disk_interact_callback", "modulename": "auto_disc.auto_disc.utils.callbacks.interact_callbacks.read_disk_interact_callback", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.utils.callbacks.interact_callbacks.read_disk_interact_callback.ReadDiskInteractCallback": {"fullname": "auto_disc.auto_disc.utils.callbacks.interact_callbacks.read_disk_interact_callback.ReadDiskInteractCallback", "modulename": "auto_disc.auto_disc.utils.callbacks.interact_callbacks.read_disk_interact_callback", "qualname": "ReadDiskInteractCallback", "kind": "class", "doc": "

Base class for cancelled callbacks used by the experiment pipelines when the experiment was cancelled.

\n", "bases": "auto_disc.legacy.utils.callbacks.interact_callbacks.base_interact_callback.BaseInteractCallback"}, "auto_disc.auto_disc.utils.callbacks.interact_callbacks.read_disk_interact_callback.ReadDiskInteractCallback.__init__": {"fullname": "auto_disc.auto_disc.utils.callbacks.interact_callbacks.read_disk_interact_callback.ReadDiskInteractCallback.__init__", "modulename": "auto_disc.auto_disc.utils.callbacks.interact_callbacks.read_disk_interact_callback", "qualname": "ReadDiskInteractCallback.__init__", "kind": "function", "doc": "

initialize attributes common to all cancelled callbacks

\n\n
Arguments:
\n\n
    \n
  • kwargs: some usefull args (e.g. experiment_id...)
    • \n
\n", "signature": "(**kwargs)"}, "auto_disc.auto_disc.utils.callbacks.interact_callbacks.save_disk_interact_callback": {"fullname": "auto_disc.auto_disc.utils.callbacks.interact_callbacks.save_disk_interact_callback", "modulename": "auto_disc.auto_disc.utils.callbacks.interact_callbacks.save_disk_interact_callback", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.utils.callbacks.interact_callbacks.save_disk_interact_callback.SaveDiskInteractCallback": {"fullname": "auto_disc.auto_disc.utils.callbacks.interact_callbacks.save_disk_interact_callback.SaveDiskInteractCallback", "modulename": "auto_disc.auto_disc.utils.callbacks.interact_callbacks.save_disk_interact_callback", "qualname": "SaveDiskInteractCallback", "kind": "class", "doc": "

Base class for cancelled callbacks used by the experiment pipelines when the experiment was cancelled.

\n", "bases": "auto_disc.legacy.utils.callbacks.interact_callbacks.base_interact_callback.BaseInteractCallback"}, "auto_disc.auto_disc.utils.callbacks.interact_callbacks.save_disk_interact_callback.SaveDiskInteractCallback.__init__": {"fullname": "auto_disc.auto_disc.utils.callbacks.interact_callbacks.save_disk_interact_callback.SaveDiskInteractCallback.__init__", "modulename": "auto_disc.auto_disc.utils.callbacks.interact_callbacks.save_disk_interact_callback", "qualname": "SaveDiskInteractCallback.__init__", "kind": "function", "doc": "

initialize attributes common to all cancelled callbacks

\n\n
Arguments:
\n\n
    \n
  • kwargs: some usefull args (e.g. experiment_id...)
    • \n
\n", "signature": "(**kwargs)"}, "auto_disc.auto_disc.utils.callbacks.on_cancelled_callbacks": {"fullname": "auto_disc.auto_disc.utils.callbacks.on_cancelled_callbacks", "modulename": "auto_disc.auto_disc.utils.callbacks.on_cancelled_callbacks", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.utils.callbacks.on_discovery_callbacks": {"fullname": "auto_disc.auto_disc.utils.callbacks.on_discovery_callbacks", "modulename": "auto_disc.auto_disc.utils.callbacks.on_discovery_callbacks", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.utils.callbacks.on_discovery_callbacks.save_discovery": {"fullname": "auto_disc.auto_disc.utils.callbacks.on_discovery_callbacks.save_discovery", "modulename": "auto_disc.auto_disc.utils.callbacks.on_discovery_callbacks.save_discovery", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.utils.callbacks.on_discovery_callbacks.save_discovery.SaveDiscovery": {"fullname": "auto_disc.auto_disc.utils.callbacks.on_discovery_callbacks.save_discovery.SaveDiscovery", "modulename": "auto_disc.auto_disc.utils.callbacks.on_discovery_callbacks.save_discovery", "qualname": "SaveDiscovery", "kind": "class", "doc": "

\n"}, "auto_disc.auto_disc.utils.callbacks.on_discovery_callbacks.save_discovery_in_expedb": {"fullname": "auto_disc.auto_disc.utils.callbacks.on_discovery_callbacks.save_discovery_in_expedb", "modulename": "auto_disc.auto_disc.utils.callbacks.on_discovery_callbacks.save_discovery_in_expedb", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.utils.callbacks.on_discovery_callbacks.save_discovery_in_expedb.SaveDiscoveryInExpeDB": {"fullname": "auto_disc.auto_disc.utils.callbacks.on_discovery_callbacks.save_discovery_in_expedb.SaveDiscoveryInExpeDB", "modulename": "auto_disc.auto_disc.utils.callbacks.on_discovery_callbacks.save_discovery_in_expedb", "qualname": "SaveDiscoveryInExpeDB", "kind": "class", "doc": "

\n", "bases": "auto_disc.legacy.utils.callbacks.on_discovery_callbacks.save_discovery.SaveDiscovery"}, "auto_disc.auto_disc.utils.callbacks.on_discovery_callbacks.save_discovery_on_disk": {"fullname": "auto_disc.auto_disc.utils.callbacks.on_discovery_callbacks.save_discovery_on_disk", "modulename": "auto_disc.auto_disc.utils.callbacks.on_discovery_callbacks.save_discovery_on_disk", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.utils.callbacks.on_discovery_callbacks.save_discovery_on_disk.SaveDiscoveryOnDisk": {"fullname": "auto_disc.auto_disc.utils.callbacks.on_discovery_callbacks.save_discovery_on_disk.SaveDiscoveryOnDisk", "modulename": "auto_disc.auto_disc.utils.callbacks.on_discovery_callbacks.save_discovery_on_disk", "qualname": "SaveDiscoveryOnDisk", "kind": "class", "doc": "

\n", "bases": "auto_disc.legacy.utils.callbacks.on_discovery_callbacks.save_discovery.SaveDiscovery"}, "auto_disc.auto_disc.utils.callbacks.on_error_callbacks": {"fullname": "auto_disc.auto_disc.utils.callbacks.on_error_callbacks", "modulename": "auto_disc.auto_disc.utils.callbacks.on_error_callbacks", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.utils.callbacks.on_finished_callbacks": {"fullname": "auto_disc.auto_disc.utils.callbacks.on_finished_callbacks", "modulename": "auto_disc.auto_disc.utils.callbacks.on_finished_callbacks", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.utils.callbacks.on_save_callbacks": {"fullname": "auto_disc.auto_disc.utils.callbacks.on_save_callbacks", "modulename": "auto_disc.auto_disc.utils.callbacks.on_save_callbacks", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.utils.callbacks.on_save_callbacks.on_save_modules_on_disk_callback": {"fullname": "auto_disc.auto_disc.utils.callbacks.on_save_callbacks.on_save_modules_on_disk_callback", "modulename": "auto_disc.auto_disc.utils.callbacks.on_save_callbacks.on_save_modules_on_disk_callback", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.utils.callbacks.on_save_callbacks.on_save_modules_on_disk_callback.OnSaveModulesOnDiskCallback": {"fullname": "auto_disc.auto_disc.utils.callbacks.on_save_callbacks.on_save_modules_on_disk_callback.OnSaveModulesOnDiskCallback", "modulename": "auto_disc.auto_disc.utils.callbacks.on_save_callbacks.on_save_modules_on_disk_callback", "qualname": "OnSaveModulesOnDiskCallback", "kind": "class", "doc": "

class for save autodisc modules on disk.

\n", "bases": "auto_disc.legacy.utils.callbacks.on_save_callbacks.base_on_save_callback.BaseOnSaveCallback"}, "auto_disc.auto_disc.utils.callbacks.on_save_callbacks.on_save_modules_on_disk_callback.OnSaveModulesOnDiskCallback.__init__": {"fullname": "auto_disc.auto_disc.utils.callbacks.on_save_callbacks.on_save_modules_on_disk_callback.OnSaveModulesOnDiskCallback.__init__", "modulename": "auto_disc.auto_disc.utils.callbacks.on_save_callbacks.on_save_modules_on_disk_callback", "qualname": "OnSaveModulesOnDiskCallback.__init__", "kind": "function", "doc": "

init the callback with a path to save modules on disk

\n\n
Arguments:
\n\n
    \n
  • folder_path: path to folder where we want save the discovery
    • \n
\n", "signature": "(folder_path: str, **kwargs)"}, "auto_disc.auto_disc.utils.callbacks.on_save_callbacks.save_leaf_callback": {"fullname": "auto_disc.auto_disc.utils.callbacks.on_save_callbacks.save_leaf_callback", "modulename": "auto_disc.auto_disc.utils.callbacks.on_save_callbacks.save_leaf_callback", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.utils.callbacks.on_save_callbacks.save_leaf_callback.SaveLeaf": {"fullname": "auto_disc.auto_disc.utils.callbacks.on_save_callbacks.save_leaf_callback.SaveLeaf", "modulename": "auto_disc.auto_disc.utils.callbacks.on_save_callbacks.save_leaf_callback", "qualname": "SaveLeaf", "kind": "class", "doc": "

Simple adapter which calls the save_leaf method from a callback

\n"}, "auto_disc.auto_disc.utils.callbacks.on_save_callbacks.save_leaf_callback.SaveLeaf.__init__": {"fullname": "auto_disc.auto_disc.utils.callbacks.on_save_callbacks.save_leaf_callback.SaveLeaf.__init__", "modulename": "auto_disc.auto_disc.utils.callbacks.on_save_callbacks.save_leaf_callback", "qualname": "SaveLeaf.__init__", "kind": "function", "doc": "

\n", "signature": "(**kwargs)"}, "auto_disc.auto_disc.utils.callbacks.on_save_callbacks.save_leaf_callback_in_expedb": {"fullname": "auto_disc.auto_disc.utils.callbacks.on_save_callbacks.save_leaf_callback_in_expedb", "modulename": "auto_disc.auto_disc.utils.callbacks.on_save_callbacks.save_leaf_callback_in_expedb", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.utils.callbacks.on_save_callbacks.save_leaf_callback_in_expedb.SaveLeafExpeDB": {"fullname": "auto_disc.auto_disc.utils.callbacks.on_save_callbacks.save_leaf_callback_in_expedb.SaveLeafExpeDB", "modulename": "auto_disc.auto_disc.utils.callbacks.on_save_callbacks.save_leaf_callback_in_expedb", "qualname": "SaveLeafExpeDB", "kind": "class", "doc": "

Simple adapter which calls the save_leaf method from a callback

\n", "bases": "auto_disc.legacy.utils.callbacks.on_save_callbacks.save_leaf_callback.SaveLeaf"}, "auto_disc.auto_disc.utils.callbacks.on_save_callbacks.save_leaf_callback_in_expedb.SaveLeafExpeDB.__init__": {"fullname": "auto_disc.auto_disc.utils.callbacks.on_save_callbacks.save_leaf_callback_in_expedb.SaveLeafExpeDB.__init__", "modulename": "auto_disc.auto_disc.utils.callbacks.on_save_callbacks.save_leaf_callback_in_expedb", "qualname": "SaveLeafExpeDB.__init__", "kind": "function", "doc": "

\n", "signature": "(**kwargs)"}, "auto_disc.auto_disc.utils.callbacks.on_save_finished_callbacks": {"fullname": "auto_disc.auto_disc.utils.callbacks.on_save_finished_callbacks", "modulename": "auto_disc.auto_disc.utils.callbacks.on_save_finished_callbacks", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.utils.callbacks.on_save_finished_callbacks.generate_report_callback": {"fullname": "auto_disc.auto_disc.utils.callbacks.on_save_finished_callbacks.generate_report_callback", "modulename": "auto_disc.auto_disc.utils.callbacks.on_save_finished_callbacks.generate_report_callback", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.utils.callbacks.on_save_finished_callbacks.generate_report_callback.GenerateReport": {"fullname": "auto_disc.auto_disc.utils.callbacks.on_save_finished_callbacks.generate_report_callback.GenerateReport", "modulename": "auto_disc.auto_disc.utils.callbacks.on_save_finished_callbacks.generate_report_callback", "qualname": "GenerateReport", "kind": "class", "doc": "

Takes UID which identifies a saved object and annotates the saved data with\nhuman-readable identifying information

\n"}, "auto_disc.auto_disc.utils.callbacks.on_save_finished_callbacks.generate_report_callback.GenerateReport.__init__": {"fullname": "auto_disc.auto_disc.utils.callbacks.on_save_finished_callbacks.generate_report_callback.GenerateReport.__init__", "modulename": "auto_disc.auto_disc.utils.callbacks.on_save_finished_callbacks.generate_report_callback", "qualname": "GenerateReport.__init__", "kind": "function", "doc": "

\n", "signature": "(**kwargs)"}, "auto_disc.auto_disc.utils.logger": {"fullname": "auto_disc.auto_disc.utils.logger", "modulename": "auto_disc.auto_disc.utils.logger", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.utils.logger.ad_logger": {"fullname": "auto_disc.auto_disc.utils.logger.ad_logger", "modulename": "auto_disc.auto_disc.utils.logger.ad_logger", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.utils.logger.ad_logger.AutoDiscLogger": {"fullname": "auto_disc.auto_disc.utils.logger.ad_logger.AutoDiscLogger", "modulename": "auto_disc.auto_disc.utils.logger.ad_logger", "qualname": "AutoDiscLogger", "kind": "class", "doc": "

A logger to manage experiments logs and print them in the console or save them to the database or to disk according to the user's needs.

\n", "bases": "logging.Logger"}, "auto_disc.auto_disc.utils.logger.ad_logger.AutoDiscLogger.__init__": {"fullname": "auto_disc.auto_disc.utils.logger.ad_logger.AutoDiscLogger.__init__", "modulename": "auto_disc.auto_disc.utils.logger.ad_logger", "qualname": "AutoDiscLogger.__init__", "kind": "function", "doc": "

Init the logger for an experiment.

\n\n
Arguments:
\n\n
    \n
  • experiment_id: current experiment id
    • \n
  • seed: current seed number
    • \n
  • handlers: List of all handlers needed by the user to manage logs (e.g. save in database on disk)
    • \n
\n", "signature": "(\texperiment_id: int,\tseed: int,\thandlers: List[auto_disc.auto_disc.utils.logger.ad_logger.AutoDiscLogger])"}, "auto_disc.auto_disc.utils.logger.ad_logger.AutoDiscLogger.debug": {"fullname": "auto_disc.auto_disc.utils.logger.ad_logger.AutoDiscLogger.debug", "modulename": "auto_disc.auto_disc.utils.logger.ad_logger", "qualname": "AutoDiscLogger.debug", "kind": "function", "doc": "

Call the logs method at the debug level and increment the log index to make an unique id for each log

\n", "signature": "(self, *args) -> None:", "funcdef": "def"}, "auto_disc.auto_disc.utils.logger.ad_logger.AutoDiscLogger.info": {"fullname": "auto_disc.auto_disc.utils.logger.ad_logger.AutoDiscLogger.info", "modulename": "auto_disc.auto_disc.utils.logger.ad_logger", "qualname": "AutoDiscLogger.info", "kind": "function", "doc": "

Call the logs method at the info level and increment the log index to make an unique id for each log

\n", "signature": "(self, *args) -> None:", "funcdef": "def"}, "auto_disc.auto_disc.utils.logger.ad_logger.AutoDiscLogger.warning": {"fullname": "auto_disc.auto_disc.utils.logger.ad_logger.AutoDiscLogger.warning", "modulename": "auto_disc.auto_disc.utils.logger.ad_logger", "qualname": "AutoDiscLogger.warning", "kind": "function", "doc": "

Call the logs method at the warning level and increment the log index to make an unique id for each log

\n", "signature": "(self, *args) -> None:", "funcdef": "def"}, "auto_disc.auto_disc.utils.logger.ad_logger.AutoDiscLogger.error": {"fullname": "auto_disc.auto_disc.utils.logger.ad_logger.AutoDiscLogger.error", "modulename": "auto_disc.auto_disc.utils.logger.ad_logger", "qualname": "AutoDiscLogger.error", "kind": "function", "doc": "

Call the logs method at the error level and increment the log index to make an unique id for each log

\n", "signature": "(self, *args) -> None:", "funcdef": "def"}, "auto_disc.auto_disc.utils.logger.ad_logger.AutoDiscLogger.critical": {"fullname": "auto_disc.auto_disc.utils.logger.ad_logger.AutoDiscLogger.critical", "modulename": "auto_disc.auto_disc.utils.logger.ad_logger", "qualname": "AutoDiscLogger.critical", "kind": "function", "doc": "

Call the logs method at the critical level and increment the log index to make an unique id for each log

\n", "signature": "(self, *args) -> None:", "funcdef": "def"}, "auto_disc.auto_disc.utils.logger.ad_logger.ContextFilter": {"fullname": "auto_disc.auto_disc.utils.logger.ad_logger.ContextFilter", "modulename": "auto_disc.auto_disc.utils.logger.ad_logger", "qualname": "ContextFilter", "kind": "class", "doc": "

This is a filter which injects contextual information into the log.

\n", "bases": "logging.Filter"}, "auto_disc.auto_disc.utils.logger.ad_logger.ContextFilter.filter": {"fullname": "auto_disc.auto_disc.utils.logger.ad_logger.ContextFilter.filter", "modulename": "auto_disc.auto_disc.utils.logger.ad_logger", "qualname": "ContextFilter.filter", "kind": "function", "doc": "

Add contextual info to log

\n\n
Arguments:
\n\n
    \n
  • record: Some information
    • \n
\n\n
Returns:
\n\n
\n

The return value is always True

\n
\n", "signature": "(self, record: logging.LogRecord) -> bool:", "funcdef": "def"}, "auto_disc.auto_disc.utils.logger.handlers": {"fullname": "auto_disc.auto_disc.utils.logger.handlers", "modulename": "auto_disc.auto_disc.utils.logger.handlers", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.utils.logger.handlers.file_handler": {"fullname": "auto_disc.auto_disc.utils.logger.handlers.file_handler", "modulename": "auto_disc.auto_disc.utils.logger.handlers.file_handler", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.utils.logger.handlers.file_handler.SetFileHandler": {"fullname": "auto_disc.auto_disc.utils.logger.handlers.file_handler.SetFileHandler", "modulename": "auto_disc.auto_disc.utils.logger.handlers.file_handler", "qualname": "SetFileHandler", "kind": "class", "doc": "

Handler to put logs in file on disk

\n", "bases": "logging.FileHandler"}, "auto_disc.auto_disc.utils.logger.handlers.file_handler.SetFileHandler.__init__": {"fullname": "auto_disc.auto_disc.utils.logger.handlers.file_handler.SetFileHandler.__init__", "modulename": "auto_disc.auto_disc.utils.logger.handlers.file_handler", "qualname": "SetFileHandler.__init__", "kind": "function", "doc": "

Init an handler able to save log in file on disk

\n\n
Arguments:
\n\n
    \n
  • folder_log_path: The path to the folder where the user want save logs
    • \n
  • experiment_id: current experiment id
    • \n
\n", "signature": "(folder_log_path: str, experiment_id: int)"}, "auto_disc.auto_disc.utils.logger.log_levels_enum": {"fullname": "auto_disc.auto_disc.utils.logger.log_levels_enum", "modulename": "auto_disc.auto_disc.utils.logger.log_levels_enum", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.utils.logger.log_levels_enum.LogsLevelEnum": {"fullname": "auto_disc.auto_disc.utils.logger.log_levels_enum.LogsLevelEnum", "modulename": "auto_disc.auto_disc.utils.logger.log_levels_enum", "qualname": "LogsLevelEnum", "kind": "class", "doc": "

An enumeration.

\n", "bases": "enum.Enum"}, "auto_disc.auto_disc.utils.logger.log_levels_enum.LogsLevelEnum.DEBUG": {"fullname": "auto_disc.auto_disc.utils.logger.log_levels_enum.LogsLevelEnum.DEBUG", "modulename": "auto_disc.auto_disc.utils.logger.log_levels_enum", "qualname": "LogsLevelEnum.DEBUG", "kind": "variable", "doc": "

\n", "default_value": "<LogsLevelEnum.DEBUG: 'debug'>"}, "auto_disc.auto_disc.utils.logger.log_levels_enum.LogsLevelEnum.INFO": {"fullname": "auto_disc.auto_disc.utils.logger.log_levels_enum.LogsLevelEnum.INFO", "modulename": "auto_disc.auto_disc.utils.logger.log_levels_enum", "qualname": "LogsLevelEnum.INFO", "kind": "variable", "doc": "

\n", "default_value": "<LogsLevelEnum.INFO: 'info'>"}, "auto_disc.auto_disc.utils.logger.log_levels_enum.LogsLevelEnum.WARNING": {"fullname": "auto_disc.auto_disc.utils.logger.log_levels_enum.LogsLevelEnum.WARNING", "modulename": "auto_disc.auto_disc.utils.logger.log_levels_enum", "qualname": "LogsLevelEnum.WARNING", "kind": "variable", "doc": "

\n", "default_value": "<LogsLevelEnum.WARNING: 'warning'>"}, "auto_disc.auto_disc.utils.logger.log_levels_enum.LogsLevelEnum.ERROR": {"fullname": "auto_disc.auto_disc.utils.logger.log_levels_enum.LogsLevelEnum.ERROR", "modulename": "auto_disc.auto_disc.utils.logger.log_levels_enum", "qualname": "LogsLevelEnum.ERROR", "kind": "variable", "doc": "

\n", "default_value": "<LogsLevelEnum.ERROR: 'error'>"}, "auto_disc.auto_disc.utils.logger.log_levels_enum.LogsLevelEnum.CRITICAL": {"fullname": "auto_disc.auto_disc.utils.logger.log_levels_enum.LogsLevelEnum.CRITICAL", "modulename": "auto_disc.auto_disc.utils.logger.log_levels_enum", "qualname": "LogsLevelEnum.CRITICAL", "kind": "variable", "doc": "

\n", "default_value": "<LogsLevelEnum.CRITICAL: 'critical'>"}, "auto_disc.auto_disc.wrappers": {"fullname": "auto_disc.auto_disc.wrappers", "modulename": "auto_disc.auto_disc.wrappers", "kind": "module", "doc": "

A collection of useful \"pure\" functions which wrap computational steps, for\nconvenience.

\n\n

Essentially identical in functionality to the interface of\nauto_disc.auto_disc.maps.Map, but wrappers are reusable and do not have a\nparticular role in the context of the exploration loop. Consequently, wrappers\nmay be used to do most of the computational work inside a given Map,\nconceptually similar to the layers of a deep neural network. For example:

\n\n
\n
class MyMap(Map):\n\n    def __init__(*args):\n        super().__init__()\n        self.a = MyWrapper(args[0])\n        self.b = MyWrapper(args[1])\n\n    def map(self, input: Dict) -> Dict:\n        return self.b.map(self.a.map(input))\n\n    def sample(self) -> Any:\n        return self.b.sample()\n
\n
\n"}, "auto_disc.auto_disc.wrappers.BoxProjector": {"fullname": "auto_disc.auto_disc.wrappers.BoxProjector", "modulename": "auto_disc.auto_disc.wrappers.BoxProjector", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.wrappers.BoxProjector.BoxProjector": {"fullname": "auto_disc.auto_disc.wrappers.BoxProjector.BoxProjector", "modulename": "auto_disc.auto_disc.wrappers.BoxProjector", "qualname": "BoxProjector", "kind": "class", "doc": "

Projects its input into a box space, i.e., the Cartesian product of N\nreal-valued intervals, where the dimension N is set by the input dimension.

\n\n

Note that it's map() method essentially just passes the received input,\nbut also adds a sampler key to the dict which an Explorer can use to\nsample from the space.

\n", "bases": "auto_disc.utils.leaf.Leaf.Leaf"}, "auto_disc.auto_disc.wrappers.BoxProjector.BoxProjector.__init__": {"fullname": "auto_disc.auto_disc.wrappers.BoxProjector.BoxProjector.__init__", "modulename": "auto_disc.auto_disc.wrappers.BoxProjector", "qualname": "BoxProjector.__init__", "kind": "function", "doc": "

\n", "signature": "(\tpremap_key: str,\tbound_upper: torch.Tensor = tensor([inf]),\tbound_lower: torch.Tensor = tensor([-inf]),\tinit_low: Optional[torch.Tensor] = None,\tinit_high: Optional[torch.Tensor] = None,\ttensor_shape: Optional[Tuple] = None)"}, "auto_disc.auto_disc.wrappers.BoxProjector.BoxProjector.map": {"fullname": "auto_disc.auto_disc.wrappers.BoxProjector.BoxProjector.map", "modulename": "auto_disc.auto_disc.wrappers.BoxProjector", "qualname": "BoxProjector.map", "kind": "function", "doc": "

Passes input, adding a sampler Callable item which Explorer can\nuse to sample from the box space.

\n", "signature": "(self, input: Dict) -> Dict:", "funcdef": "def"}, "auto_disc.auto_disc.wrappers.BoxProjector.BoxProjector.sample": {"fullname": "auto_disc.auto_disc.wrappers.BoxProjector.BoxProjector.sample", "modulename": "auto_disc.auto_disc.wrappers.BoxProjector", "qualname": "BoxProjector.sample", "kind": "function", "doc": "

\n", "signature": "(self) -> torch.Tensor:", "funcdef": "def"}, "auto_disc.auto_disc.wrappers.CPPNWrapper": {"fullname": "auto_disc.auto_disc.wrappers.CPPNWrapper", "modulename": "auto_disc.auto_disc.wrappers.CPPNWrapper", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.wrappers.CPPNWrapper.NEATTuple": {"fullname": "auto_disc.auto_disc.wrappers.CPPNWrapper.NEATTuple", "modulename": "auto_disc.auto_disc.wrappers.CPPNWrapper", "qualname": "NEATTuple", "kind": "class", "doc": "

NEATTuple(genome, neat_config)

\n", "bases": "typing.NamedTuple"}, "auto_disc.auto_disc.wrappers.CPPNWrapper.NEATTuple.__init__": {"fullname": "auto_disc.auto_disc.wrappers.CPPNWrapper.NEATTuple.__init__", "modulename": "auto_disc.auto_disc.wrappers.CPPNWrapper", "qualname": "NEATTuple.__init__", "kind": "function", "doc": "

Create new instance of NEATTuple(genome, neat_config)

\n", "signature": "(genome: str, neat_config: str)"}, "auto_disc.auto_disc.wrappers.CPPNWrapper.NEATTuple.genome": {"fullname": "auto_disc.auto_disc.wrappers.CPPNWrapper.NEATTuple.genome", "modulename": "auto_disc.auto_disc.wrappers.CPPNWrapper", "qualname": "NEATTuple.genome", "kind": "variable", "doc": "

Alias for field number 0

\n", "annotation": ": str"}, "auto_disc.auto_disc.wrappers.CPPNWrapper.NEATTuple.neat_config": {"fullname": "auto_disc.auto_disc.wrappers.CPPNWrapper.NEATTuple.neat_config", "modulename": "auto_disc.auto_disc.wrappers.CPPNWrapper", "qualname": "NEATTuple.neat_config", "kind": "variable", "doc": "

Alias for field number 1

\n", "annotation": ": str"}, "auto_disc.auto_disc.wrappers.CPPNWrapper.CPPNWrapper": {"fullname": "auto_disc.auto_disc.wrappers.CPPNWrapper.CPPNWrapper", "modulename": "auto_disc.auto_disc.wrappers.CPPNWrapper", "qualname": "CPPNWrapper", "kind": "class", "doc": "

\n", "bases": "auto_disc.utils.leaf.Leaf.Leaf"}, "auto_disc.auto_disc.wrappers.CPPNWrapper.CPPNWrapper.__init__": {"fullname": "auto_disc.auto_disc.wrappers.CPPNWrapper.CPPNWrapper.__init__", "modulename": "auto_disc.auto_disc.wrappers.CPPNWrapper", "qualname": "CPPNWrapper.__init__", "kind": "function", "doc": "

\n", "signature": "(\tpremap_key: auto_disc.auto_disc.wrappers.CPPNWrapper.NEATTuple = NEATTuple(genome='genome', neat_config='neat_config'),\tpostmap_key: str = 'init_state',\tpostmap_shape: Tuple[int, int] = (10, 10),\tn_passes: int = 2)"}, "auto_disc.auto_disc.wrappers.CPPNWrapper.CPPNWrapper.map": {"fullname": "auto_disc.auto_disc.wrappers.CPPNWrapper.CPPNWrapper.map", "modulename": "auto_disc.auto_disc.wrappers.CPPNWrapper", "qualname": "CPPNWrapper.map", "kind": "function", "doc": "

\n", "signature": "(self, input: Dict) -> Dict:", "funcdef": "def"}, "auto_disc.auto_disc.wrappers.IdentityWrapper": {"fullname": "auto_disc.auto_disc.wrappers.IdentityWrapper", "modulename": "auto_disc.auto_disc.wrappers.IdentityWrapper", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.wrappers.IdentityWrapper.IdentityWrapper": {"fullname": "auto_disc.auto_disc.wrappers.IdentityWrapper.IdentityWrapper", "modulename": "auto_disc.auto_disc.wrappers.IdentityWrapper", "qualname": "IdentityWrapper", "kind": "class", "doc": "

Wrapper which passes the input without mutation.

\n", "bases": "auto_disc.auto_disc.wrappers.TransformWrapper.TransformWrapper"}, "auto_disc.auto_disc.wrappers.IdentityWrapper.IdentityWrapper.__init__": {"fullname": "auto_disc.auto_disc.wrappers.IdentityWrapper.IdentityWrapper.__init__", "modulename": "auto_disc.auto_disc.wrappers.IdentityWrapper", "qualname": "IdentityWrapper.__init__", "kind": "function", "doc": "

\n", "signature": "(premap_keys: List[str] = [])"}, "auto_disc.auto_disc.wrappers.IdentityWrapper.IdentityWrapper.map": {"fullname": "auto_disc.auto_disc.wrappers.IdentityWrapper.IdentityWrapper.map", "modulename": "auto_disc.auto_disc.wrappers.IdentityWrapper", "qualname": "IdentityWrapper.map", "kind": "function", "doc": "

Transforms the input dictionary with the provided relabelling of keys.

\n", "signature": "(self, input: Dict) -> Dict:", "funcdef": "def"}, "auto_disc.auto_disc.wrappers.SaveWrapper": {"fullname": "auto_disc.auto_disc.wrappers.SaveWrapper", "modulename": "auto_disc.auto_disc.wrappers.SaveWrapper", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.wrappers.SaveWrapper.SaveWrapper": {"fullname": "auto_disc.auto_disc.wrappers.SaveWrapper.SaveWrapper", "modulename": "auto_disc.auto_disc.wrappers.SaveWrapper", "qualname": "SaveWrapper", "kind": "class", "doc": "

Wrapper which does basic processing and\nsaving of captured input history

\n\n
Usage example:
\n\n
\n
    input = {\"in\" : 1}\n    # default setting saves all specified `premap_keys`\n    wrapper = SaveWrapper(premap_keys = [\"in\"],\n                          postmap_keys = [\"out\"])\n    output = wrapper.map(input)\n    assert output[\"out\"] == 1\n
\n
\n", "bases": "auto_disc.auto_disc.wrappers.TransformWrapper.TransformWrapper"}, "auto_disc.auto_disc.wrappers.SaveWrapper.SaveWrapper.__init__": {"fullname": "auto_disc.auto_disc.wrappers.SaveWrapper.SaveWrapper.__init__", "modulename": "auto_disc.auto_disc.wrappers.SaveWrapper", "qualname": "SaveWrapper.__init__", "kind": "function", "doc": "

\n", "signature": "(\tpremap_keys: List[str] = [],\tpostmap_keys: List[str] = [],\tinputs_to_save: List[str] = [])"}, "auto_disc.auto_disc.wrappers.SaveWrapper.SaveWrapper.map": {"fullname": "auto_disc.auto_disc.wrappers.SaveWrapper.SaveWrapper.map", "modulename": "auto_disc.auto_disc.wrappers.SaveWrapper", "qualname": "SaveWrapper.map", "kind": "function", "doc": "

WARN: This wrapper's .map() is stateful.

\n\n

Transforms the input dictionary with the provided relabelling of keys,\nsaving inputs and passing outputs

\n", "signature": "(self, input: Dict) -> Dict:", "funcdef": "def"}, "auto_disc.auto_disc.wrappers.SaveWrapper.SaveWrapper.save_leaf": {"fullname": "auto_disc.auto_disc.wrappers.SaveWrapper.SaveWrapper.save_leaf", "modulename": "auto_disc.auto_disc.wrappers.SaveWrapper", "qualname": "SaveWrapper.save_leaf", "kind": "function", "doc": "

Save entire structure of object. The suggested way to customize\nbehavior is overloading serialize() and the Locator

\n", "signature": "(\tself,\tresource_uri: str,\tparent_uid: int = -1) -> <function NewType.<locals>.new_type at 0x7fee26ca1670>:", "funcdef": "def"}, "auto_disc.auto_disc.wrappers.SaveWrapper.SaveWrapper.serialize": {"fullname": "auto_disc.auto_disc.wrappers.SaveWrapper.SaveWrapper.serialize", "modulename": "auto_disc.auto_disc.wrappers.SaveWrapper", "qualname": "SaveWrapper.serialize", "kind": "function", "doc": "

Custom serialize method needed for producing appropriately padded\nbinary with metadata.

\n", "signature": "(self) -> bytes:", "funcdef": "def"}, "auto_disc.auto_disc.wrappers.SaveWrapper.SaveWrapper.get_history": {"fullname": "auto_disc.auto_disc.wrappers.SaveWrapper.SaveWrapper.get_history", "modulename": "auto_disc.auto_disc.wrappers.SaveWrapper", "qualname": "SaveWrapper.get_history", "kind": "function", "doc": "

Retrieve the history of inputs to the wrapper.\nlookback_length = -1 corresponds to retrieving the entire history,\nwhich may be very large as it will query the on-disk SQLite db.

\n", "signature": "(self, lookback_length: int = 1) -> List[Dict]:", "funcdef": "def"}, "auto_disc.auto_disc.wrappers.SaveWrapper.SaveWrapper.generate_dataloader": {"fullname": "auto_disc.auto_disc.wrappers.SaveWrapper.SaveWrapper.generate_dataloader", "modulename": "auto_disc.auto_disc.wrappers.SaveWrapper", "qualname": "SaveWrapper.generate_dataloader", "kind": "function", "doc": "

Query SQLite DB to retrieve an iterable which lazy loads the DB tree.

\n", "signature": "(self, buffer_src_uri: str, cachebuf_size: int = 1) -> Iterable[Dict]:", "funcdef": "def"}, "auto_disc.auto_disc.wrappers.SaveWrapper.BufferStreamer": {"fullname": "auto_disc.auto_disc.wrappers.SaveWrapper.BufferStreamer", "modulename": "auto_disc.auto_disc.wrappers.SaveWrapper", "qualname": "BufferStreamer", "kind": "class", "doc": "

Class for streaming buffers of arbitrary length from SQLite db.

\n\n

mode is set to one of \"serial\" or \"batched\" which specifies whether\nthe cachebuf is returned as a single item or as a list of items.

\n"}, "auto_disc.auto_disc.wrappers.SaveWrapper.BufferStreamer.__init__": {"fullname": "auto_disc.auto_disc.wrappers.SaveWrapper.BufferStreamer.__init__", "modulename": "auto_disc.auto_disc.wrappers.SaveWrapper", "qualname": "BufferStreamer.__init__", "kind": "function", "doc": "

\n", "signature": "(\twrapper: auto_disc.auto_disc.wrappers.SaveWrapper.SaveWrapper,\tresource_uri: str,\tcachebuf_size: int = 1,\tmode: str = 'serial')"}, "auto_disc.auto_disc.wrappers.TransformWrapper": {"fullname": "auto_disc.auto_disc.wrappers.TransformWrapper", "modulename": "auto_disc.auto_disc.wrappers.TransformWrapper", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.wrappers.TransformWrapper.TransformWrapper": {"fullname": "auto_disc.auto_disc.wrappers.TransformWrapper.TransformWrapper", "modulename": "auto_disc.auto_disc.wrappers.TransformWrapper", "qualname": "TransformWrapper", "kind": "class", "doc": "

Wrapper which does basic processing of input

\n\n
Usage example:
\n\n
\n
    input = {\"in\" : 1}\n    wrapper = TransformWrapper(premap_keys = [\"in\"],\n                          postmap_keys = [\"out\"])\n    output = wrapper.map(input)\n    assert output[\"out\"] == 1\n
\n
\n", "bases": "auto_disc.utils.leaf.Leaf.Leaf"}, "auto_disc.auto_disc.wrappers.TransformWrapper.TransformWrapper.__init__": {"fullname": "auto_disc.auto_disc.wrappers.TransformWrapper.TransformWrapper.__init__", "modulename": "auto_disc.auto_disc.wrappers.TransformWrapper", "qualname": "TransformWrapper.__init__", "kind": "function", "doc": "

\n", "signature": "(premap_keys: List[str] = [], postmap_keys: List[str] = [])"}, "auto_disc.auto_disc.wrappers.TransformWrapper.TransformWrapper.map": {"fullname": "auto_disc.auto_disc.wrappers.TransformWrapper.TransformWrapper.map", "modulename": "auto_disc.auto_disc.wrappers.TransformWrapper", "qualname": "TransformWrapper.map", "kind": "function", "doc": "

Transforms the input dictionary with the provided relabelling of keys.

\n", "signature": "(self, input: Dict) -> Dict:", "funcdef": "def"}, "auto_disc.auto_disc.wrappers.WrapperPipeline": {"fullname": "auto_disc.auto_disc.wrappers.WrapperPipeline", "modulename": "auto_disc.auto_disc.wrappers.WrapperPipeline", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.wrappers.WrapperPipeline.WrapperPipeline": {"fullname": "auto_disc.auto_disc.wrappers.WrapperPipeline.WrapperPipeline", "modulename": "auto_disc.auto_disc.wrappers.WrapperPipeline", "qualname": "WrapperPipeline", "kind": "class", "doc": "

Module for composing various wrappers during the input or output processing\nof the experiment.

\n\n
Usage example:
\n\n
\n
    input = {\"in\" : 1}\n    a = alpha_wrapper()\n    b = beta_wrapper()\n    wrapper_list = [a, b]\n    all_wrappers = WrapperPipeline(wrappers=wrapper_list,\n                    inputs_to_save=[\"in\"], outputs_to_save=[\"out\"])\n    assert all_wrappers.map(input) == b.map(a.map(input))\n
\n
\n", "bases": "auto_disc.utils.leaf.Leaf.Leaf"}, "auto_disc.auto_disc.wrappers.WrapperPipeline.WrapperPipeline.__init__": {"fullname": "auto_disc.auto_disc.wrappers.WrapperPipeline.WrapperPipeline.__init__", "modulename": "auto_disc.auto_disc.wrappers.WrapperPipeline", "qualname": "WrapperPipeline.__init__", "kind": "function", "doc": "

\n", "signature": "(\twrappers: List[auto_disc.utils.leaf.Leaf.Leaf] = [],\tresource_uri: str = '',\tlocator: auto_disc.utils.leaf.locators.Locator.Locator = <auto_disc.utils.leaf.locators.Locator.StatelessLocator object>)"}, "auto_disc.auto_disc.wrappers.WrapperPipeline.WrapperPipeline.map": {"fullname": "auto_disc.auto_disc.wrappers.WrapperPipeline.WrapperPipeline.map", "modulename": "auto_disc.auto_disc.wrappers.WrapperPipeline", "qualname": "WrapperPipeline.map", "kind": "function", "doc": "

\n", "signature": "(self, input: Dict) -> Dict:", "funcdef": "def"}, "auto_disc.auto_disc.wrappers.mutators": {"fullname": "auto_disc.auto_disc.wrappers.mutators", "modulename": "auto_disc.auto_disc.wrappers.mutators", "kind": "module", "doc": "

\n"}, "auto_disc.auto_disc.wrappers.mutators.add_gaussian_noise": {"fullname": "auto_disc.auto_disc.wrappers.mutators.add_gaussian_noise", "modulename": "auto_disc.auto_disc.wrappers.mutators", "qualname": "add_gaussian_noise", "kind": "function", "doc": "

\n", "signature": "(\tinput_tensor: torch.Tensor,\tmean: torch.Tensor = tensor([0.]),\tstd: torch.Tensor = tensor([1.])) -> torch.Tensor:", "funcdef": "def"}, "auto_disc.auto_disc.wrappers.mutators.call_mutate_method": {"fullname": "auto_disc.auto_disc.wrappers.mutators.call_mutate_method", "modulename": "auto_disc.auto_disc.wrappers.mutators", "qualname": "call_mutate_method", "kind": "function", "doc": "

If parameters are given as a complicated dict, then the parameter map which\ncreates it must have a mutate method in order to mutate the underlying\nparameters controlling.

\n", "signature": "(param_dict: Dict, param_map: Any = None) -> Dict:", "funcdef": "def"}, "auto_disc.db": {"fullname": "auto_disc.db", "modulename": "auto_disc.db", "kind": "module", "doc": "

\n"}, "auto_disc.db.dataset": {"fullname": "auto_disc.db.dataset", "modulename": "auto_disc.db.dataset", "kind": "module", "doc": "

\n"}, "auto_disc.db.dataset.Dataset": {"fullname": "auto_disc.db.dataset.Dataset", "modulename": "auto_disc.db.dataset", "qualname": "Dataset", "kind": "class", "doc": "

\n"}, "auto_disc.db.dataset.Dataset.__init__": {"fullname": "auto_disc.db.dataset.Dataset.__init__", "modulename": "auto_disc.db.dataset", "qualname": "Dataset.__init__", "kind": "function", "doc": "

\n", "signature": "(experiment_id, checkpoint_ids=[])"}, "auto_disc.db.dataset.Dataset.filter": {"fullname": "auto_disc.db.dataset.Dataset.filter", "modulename": "auto_disc.db.dataset", "qualname": "Dataset.filter", "kind": "function", "doc": "

\n", "signature": "(self, _filter):", "funcdef": "def"}, "auto_disc.db.get_discoveries_with_filter": {"fullname": "auto_disc.db.get_discoveries_with_filter", "modulename": "auto_disc.db.get_discoveries_with_filter", "kind": "module", "doc": "

\n"}, "auto_disc.db.stream": {"fullname": "auto_disc.db.stream", "modulename": "auto_disc.db.stream", "kind": "module", "doc": "

\n"}, "auto_disc.db.stream.Stream": {"fullname": "auto_disc.db.stream.Stream", "modulename": "auto_disc.db.stream", "qualname": "Stream", "kind": "class", "doc": "

\n"}, "auto_disc.db.stream.Stream.__init__": {"fullname": "auto_disc.db.stream.Stream.__init__", "modulename": "auto_disc.db.stream", "qualname": "Stream.__init__", "kind": "function", "doc": "

\n", "signature": "(initial_filter, size)"}, "auto_disc.db.utils": {"fullname": "auto_disc.db.utils", "modulename": "auto_disc.db.utils", "kind": "module", "doc": "

\n"}, "auto_disc.db.utils.config": {"fullname": "auto_disc.db.utils.config", "modulename": "auto_disc.db.utils.config", "kind": "module", "doc": "

\n"}, "auto_disc.db.utils.config.ConfigError": {"fullname": "auto_disc.db.utils.config.ConfigError", "modulename": "auto_disc.db.utils.config", "qualname": "ConfigError", "kind": "class", "doc": "

Common base class for all non-exit exceptions.

\n", "bases": "builtins.Exception"}, "auto_disc.db.utils.config.Config": {"fullname": "auto_disc.db.utils.config.Config", "modulename": "auto_disc.db.utils.config", "qualname": "Config", "kind": "class", "doc": "

\n"}, "auto_disc.legacy": {"fullname": "auto_disc.legacy", "modulename": "auto_disc.legacy", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.base_autodisc_module": {"fullname": "auto_disc.legacy.base_autodisc_module", "modulename": "auto_disc.legacy.base_autodisc_module", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.base_autodisc_module.BaseAutoDiscModule": {"fullname": "auto_disc.legacy.base_autodisc_module.BaseAutoDiscModule", "modulename": "auto_disc.legacy.base_autodisc_module", "qualname": "BaseAutoDiscModule", "kind": "class", "doc": "

Base class of all modules usable in auto_disc.legacy.

\n"}, "auto_disc.legacy.base_autodisc_module.BaseAutoDiscModule.__init__": {"fullname": "auto_disc.legacy.base_autodisc_module.BaseAutoDiscModule.__init__", "modulename": "auto_disc.legacy.base_autodisc_module", "qualname": "BaseAutoDiscModule.__init__", "kind": "function", "doc": "

initialize attributes common to all auto_disc.legacy modules

\n\n
Arguments:
\n\n
    \n
  • logger: the logger which will make it possible to keep information on the progress of an experiment in the database or on files
    • \n
\n", "signature": "(\tlogger: auto_disc.legacy.utils.logger.ad_logger.AutoDiscLogger = None,\t**kwargs)"}, "auto_disc.legacy.base_autodisc_module.BaseAutoDiscModule.set_history_access_fn": {"fullname": "auto_disc.legacy.base_autodisc_module.BaseAutoDiscModule.set_history_access_fn", "modulename": "auto_disc.legacy.base_autodisc_module", "qualname": "BaseAutoDiscModule.set_history_access_fn", "kind": "function", "doc": "

Set the function allowing module to access (readonly) its history of (input, output) pairs.

\n\n
Arguments:
\n\n
    \n
  • function: The method used to acces to the module history
    • \n
\n", "signature": "(self, function: Callable) -> None:", "funcdef": "def"}, "auto_disc.legacy.base_autodisc_module.BaseAutoDiscModule.set_call_output_history_update_fn": {"fullname": "auto_disc.legacy.base_autodisc_module.BaseAutoDiscModule.set_call_output_history_update_fn", "modulename": "auto_disc.legacy.base_autodisc_module", "qualname": "BaseAutoDiscModule.set_call_output_history_update_fn", "kind": "function", "doc": "

Set the function asking a refresh (raw_outputs will be processed again with output representations) of all outputs in history.

\n\n
Arguments:
\n\n
    \n
  • function: The method used to refresh
    • \n
\n", "signature": "(self, function: Callable) -> None:", "funcdef": "def"}, "auto_disc.legacy.base_autodisc_module.BaseAutoDiscModule.save": {"fullname": "auto_disc.legacy.base_autodisc_module.BaseAutoDiscModule.save", "modulename": "auto_disc.legacy.base_autodisc_module", "qualname": "BaseAutoDiscModule.save", "kind": "function", "doc": "

Return a dict storing everything that has to be pickled.

\n", "signature": "(self) -> Dict:", "funcdef": "def"}, "auto_disc.legacy.base_autodisc_module.BaseAutoDiscModule.load": {"fullname": "auto_disc.legacy.base_autodisc_module.BaseAutoDiscModule.load", "modulename": "auto_disc.legacy.base_autodisc_module", "qualname": "BaseAutoDiscModule.load", "kind": "function", "doc": "

Reload module given a dict storing everything that had to be pickled.

\n\n
Arguments:
\n\n
    \n
  • saved_dict: The dict used to restore the module
    • \n
\n", "signature": "(self, saved_dict: Dict):", "funcdef": "def"}, "auto_disc.legacy.experiment_pipeline": {"fullname": "auto_disc.legacy.experiment_pipeline", "modulename": "auto_disc.legacy.experiment_pipeline", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.experiment_pipeline.CancellationToken": {"fullname": "auto_disc.legacy.experiment_pipeline.CancellationToken", "modulename": "auto_disc.legacy.experiment_pipeline", "qualname": "CancellationToken", "kind": "class", "doc": "

Manages the cancellation token which allows you to stop an experiment in progress

\n"}, "auto_disc.legacy.experiment_pipeline.CancellationToken.__init__": {"fullname": "auto_disc.legacy.experiment_pipeline.CancellationToken.__init__", "modulename": "auto_disc.legacy.experiment_pipeline", "qualname": "CancellationToken.__init__", "kind": "function", "doc": "

Init the cancellation token to false

\n", "signature": "()"}, "auto_disc.legacy.experiment_pipeline.CancellationToken.get": {"fullname": "auto_disc.legacy.experiment_pipeline.CancellationToken.get", "modulename": "auto_disc.legacy.experiment_pipeline", "qualname": "CancellationToken.get", "kind": "function", "doc": "

Give acces to the cancellation token

\n\n
Returns:
\n\n
\n

_token: a boolean indicating if the current experiment must be cancelled

\n
\n", "signature": "(self) -> bool:", "funcdef": "def"}, "auto_disc.legacy.experiment_pipeline.CancellationToken.trigger": {"fullname": "auto_disc.legacy.experiment_pipeline.CancellationToken.trigger", "modulename": "auto_disc.legacy.experiment_pipeline", "qualname": "CancellationToken.trigger", "kind": "function", "doc": "

Set the cancellation token to true (the experiment must be cancelled)

\n", "signature": "(self):", "funcdef": "def"}, "auto_disc.legacy.experiment_pipeline.ExperimentPipeline": {"fullname": "auto_disc.legacy.experiment_pipeline.ExperimentPipeline", "modulename": "auto_disc.legacy.experiment_pipeline", "qualname": "ExperimentPipeline", "kind": "class", "doc": "

Pipeline of an automated discovery experiment.\nAn experiment is at least constitued of a system and an explorer. Additionally, input wrappers and output representations can be added (multiple can be stacked).\nWhen the system requires an action at each timestep, an action_policy must be provided.\nIn order to monitor the experiment, you must provide on_exploration_classbacks, which will be called every time a discovery has been made. Please provide callbacks overriding the libs.auto_disc.legacy.utils.BaseAutoDiscCallback.

\n"}, "auto_disc.legacy.experiment_pipeline.ExperimentPipeline.__init__": {"fullname": "auto_disc.legacy.experiment_pipeline.ExperimentPipeline.__init__", "modulename": "auto_disc.legacy.experiment_pipeline", "qualname": "ExperimentPipeline.__init__", "kind": "function", "doc": "

Init experiment pipeline. Set all attribute used by the pipeline.

\n\n
Arguments:
\n\n
    \n
  • experiment_id: Id of current experiment
    • \n
  • seed: Current seed number
    • \n
  • system: The system we want explore in this experiment
    • \n
  • explorer: The explorer we use to explore the system
    • \n
  • input_wrappers: A list of wrapper who redine the input spaces
    • \n
  • output_representations: A list of wrapper who redine the output spaces
    • \n
  • action_policy: A method that indicate what action should be taken based on the policy
    • \n
  • save_frequency: defines the frequency with which the backups of the elements of the experience are made
    • \n
  • on_discovery_callbacks: Callbacks raised when a discovery was made
    • \n
  • on_save_finished_callbacks: Callbacks raised when a the experiment save is complete
    • \n
  • on_finished_callbacks: Callbacks raised when the experiment is over
    • \n
  • on_cancelled_callbacks: Callbacks raised when the experiment is canccelled
    • \n
  • on_save_callbacks: Callbacks raised when a experiment save was made
    • \n
  • on_error_callbacks: Callbacks raised when an error is raised
    • \n
\n", "signature": "(\texperiment_id: int,\tseed: int,\tsystem: <module 'auto_disc.legacy.systems' from '/home/runner/work/adtool/adtool/services/base/AutoDiscServer/libs/auto_disc/legacy/systems/__init__.py'>,\texplorer: <module 'auto_disc.legacy.explorers' from '/home/runner/work/adtool/adtool/services/base/AutoDiscServer/libs/auto_disc/legacy/explorers/__init__.py'>,\tinput_wrappers: List[auto_disc.legacy.input_wrappers.base_input_wrapper.BaseInputWrapper] = None,\toutput_representations: List[auto_disc.legacy.output_representations.base_output_representation.BaseOutputRepresentation] = None,\taction_policy=None,\tsave_frequency: int = 100,\ton_discovery_callbacks: List[Callable] = [],\ton_save_finished_callbacks: List[Callable] = [],\ton_finished_callbacks: List[Callable] = [],\ton_cancelled_callbacks: List[Callable] = [],\ton_save_callbacks: List[Callable] = [],\ton_error_callbacks: List[Callable] = [],\tinteract_callbacks: List[Callable] = [])"}, "auto_disc.legacy.experiment_pipeline.ExperimentPipeline.run": {"fullname": "auto_disc.legacy.experiment_pipeline.ExperimentPipeline.run", "modulename": "auto_disc.legacy.experiment_pipeline", "qualname": "ExperimentPipeline.run", "kind": "function", "doc": "

Launches the experiment for n_exploration_runs explorations.

\n\n
Arguments:
\n\n
    \n
  • n_exploration_runs: number of explorations
    • \n
\n", "signature": "(self, n_exploration_runs: int) -> None:", "funcdef": "def"}, "auto_disc.legacy.explorers": {"fullname": "auto_disc.legacy.explorers", "modulename": "auto_disc.legacy.explorers", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.explorers.base_explorer": {"fullname": "auto_disc.legacy.explorers.base_explorer", "modulename": "auto_disc.legacy.explorers.base_explorer", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.explorers.base_explorer.BaseExplorer": {"fullname": "auto_disc.legacy.explorers.base_explorer.BaseExplorer", "modulename": "auto_disc.legacy.explorers.base_explorer", "qualname": "BaseExplorer", "kind": "class", "doc": "

Base class for explorers.

\n", "bases": "auto_disc.legacy.base_autodisc_module.BaseAutoDiscModule"}, "auto_disc.legacy.explorers.base_explorer.BaseExplorer.__init__": {"fullname": "auto_disc.legacy.explorers.base_explorer.BaseExplorer.__init__", "modulename": "auto_disc.legacy.explorers.base_explorer", "qualname": "BaseExplorer.__init__", "kind": "function", "doc": "

initialize attributes common to all auto_disc.legacy modules

\n\n
Arguments:
\n\n
    \n
  • logger: the logger which will make it possible to keep information on the progress of an experiment in the database or on files
    • \n
\n", "signature": "(**kwargs)"}, "auto_disc.legacy.explorers.base_explorer.BaseExplorer.initialize": {"fullname": "auto_disc.legacy.explorers.base_explorer.BaseExplorer.initialize", "modulename": "auto_disc.legacy.explorers.base_explorer", "qualname": "BaseExplorer.initialize", "kind": "function", "doc": "

Defines input and output space for the explorer (as well as a distance function for the input space).

\n", "signature": "(\tself,\tinput_space: auto_disc.legacy.utils.spaces.dict.DictSpace,\toutput_space: auto_disc.legacy.utils.spaces.dict.DictSpace,\tinput_distance_fn: Callable) -> None:", "funcdef": "def"}, "auto_disc.legacy.explorers.base_explorer.BaseExplorer.sample": {"fullname": "auto_disc.legacy.explorers.base_explorer.BaseExplorer.sample", "modulename": "auto_disc.legacy.explorers.base_explorer", "qualname": "BaseExplorer.sample", "kind": "function", "doc": "

Emits a new set of parameters to test in the system

\n", "signature": "(self):", "funcdef": "def"}, "auto_disc.legacy.explorers.base_explorer.BaseExplorer.observe": {"fullname": "auto_disc.legacy.explorers.base_explorer.BaseExplorer.observe", "modulename": "auto_disc.legacy.explorers.base_explorer", "qualname": "BaseExplorer.observe", "kind": "function", "doc": "

Stores parameters and the output the system produced using them

\n", "signature": "(self, parameters: Dict, observations: torch.Tensor):", "funcdef": "def"}, "auto_disc.legacy.explorers.base_explorer.BaseExplorer.optimize": {"fullname": "auto_disc.legacy.explorers.base_explorer.BaseExplorer.optimize", "modulename": "auto_disc.legacy.explorers.base_explorer", "qualname": "BaseExplorer.optimize", "kind": "function", "doc": "

Optimizes the explorer's sample policy given the discoveries arhcived

\n", "signature": "(self):", "funcdef": "def"}, "auto_disc.legacy.explorers.imgep_explorer": {"fullname": "auto_disc.legacy.explorers.imgep_explorer", "modulename": "auto_disc.legacy.explorers.imgep_explorer", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.explorers.imgep_explorer.IMGEPExplorer": {"fullname": "auto_disc.legacy.explorers.imgep_explorer.IMGEPExplorer", "modulename": "auto_disc.legacy.explorers.imgep_explorer", "qualname": "IMGEPExplorer", "kind": "class", "doc": "

Basic explorer that samples goals in a goalspace and uses a policy library to generate parameters to reach the goal.

\n", "bases": "auto_disc.legacy.explorers.base_explorer.BaseExplorer"}, "auto_disc.legacy.explorers.imgep_explorer.IMGEPExplorer.__init__": {"fullname": "auto_disc.legacy.explorers.imgep_explorer.IMGEPExplorer.__init__", "modulename": "auto_disc.legacy.explorers.imgep_explorer", "qualname": "IMGEPExplorer.__init__", "kind": "function", "doc": "

initialize attributes common to all auto_disc.legacy modules

\n\n
Arguments:
\n\n
    \n
  • logger: the logger which will make it possible to keep information on the progress of an experiment in the database or on files
    • \n
\n", "signature": "(*args, **kws)"}, "auto_disc.legacy.explorers.imgep_explorer.IMGEPExplorer.initialize": {"fullname": "auto_disc.legacy.explorers.imgep_explorer.IMGEPExplorer.initialize", "modulename": "auto_disc.legacy.explorers.imgep_explorer", "qualname": "IMGEPExplorer.initialize", "kind": "function", "doc": "

Defines input and output space for the explorer (as well as a distance function for the input space).

\n\n
Arguments:
\n\n
    \n
  • input_space: space providing by the last output_representations
    • \n
  • output_space: space providing by the first input_wrappers
    • \n
  • input_distance_fn: a method to calc distance
    • \n
\n", "signature": "(\tself,\tinput_space: auto_disc.legacy.utils.spaces.dict.DictSpace,\toutput_space: auto_disc.legacy.utils.spaces.dict.DictSpace,\tinput_distance_fn: Callable[[torch.Tensor, torch.Tensor], torch.Tensor]) -> None:", "funcdef": "def"}, "auto_disc.legacy.explorers.imgep_explorer.IMGEPExplorer.expand_box_goal_space": {"fullname": "auto_disc.legacy.explorers.imgep_explorer.IMGEPExplorer.expand_box_goal_space", "modulename": "auto_disc.legacy.explorers.imgep_explorer", "qualname": "IMGEPExplorer.expand_box_goal_space", "kind": "function", "doc": "

Expand the goal space in which the explorer will explore

\n\n
Arguments:
\n\n
    \n
  • space: current goal space
    • \n
  • observations: observations/results of previous explorations
    • \n
\n", "signature": "(\tself,\tspace: auto_disc.legacy.utils.spaces.box.BoxSpace,\tobservations: torch.Tensor) -> None:", "funcdef": "def"}, "auto_disc.legacy.explorers.imgep_explorer.IMGEPExplorer.sample": {"fullname": "auto_disc.legacy.explorers.imgep_explorer.IMGEPExplorer.sample", "modulename": "auto_disc.legacy.explorers.imgep_explorer", "qualname": "IMGEPExplorer.sample", "kind": "function", "doc": "

Emits a new set of parameters to test in the system

\n", "signature": "(self) -> addict.addict.Dict:", "funcdef": "def"}, "auto_disc.legacy.explorers.imgep_explorer.IMGEPExplorer.observe": {"fullname": "auto_disc.legacy.explorers.imgep_explorer.IMGEPExplorer.observe", "modulename": "auto_disc.legacy.explorers.imgep_explorer", "qualname": "IMGEPExplorer.observe", "kind": "function", "doc": "

gives to the explorer the exit of the system after using the parameters

\n\n
Arguments:
\n\n
    \n
  • parameters: system's parameters
    • \n
  • observations: observations/results of previous explorations
    • \n
\n", "signature": "(\tself,\tparameters: addict.addict.Dict,\tobservations: Dict[str, torch.Tensor]) -> None:", "funcdef": "def"}, "auto_disc.legacy.explorers.imgep_explorer.IMGEPExplorer.optimize": {"fullname": "auto_disc.legacy.explorers.imgep_explorer.IMGEPExplorer.optimize", "modulename": "auto_disc.legacy.explorers.imgep_explorer", "qualname": "IMGEPExplorer.optimize", "kind": "function", "doc": "

Optimizes the explorer's sample policy given the discoveries arhcived

\n", "signature": "(self):", "funcdef": "def"}, "auto_disc.legacy.explorers.imgep_explorer.IMGEPExplorer.save": {"fullname": "auto_disc.legacy.explorers.imgep_explorer.IMGEPExplorer.save", "modulename": "auto_disc.legacy.explorers.imgep_explorer", "qualname": "IMGEPExplorer.save", "kind": "function", "doc": "

Return a dict storing everything that has to be pickled.

\n", "signature": "(self) -> Dict[str, auto_disc.legacy.utils.spaces.dict.DictSpace]:", "funcdef": "def"}, "auto_disc.legacy.explorers.imgep_explorer.IMGEPExplorer.load": {"fullname": "auto_disc.legacy.explorers.imgep_explorer.IMGEPExplorer.load", "modulename": "auto_disc.legacy.explorers.imgep_explorer", "qualname": "IMGEPExplorer.load", "kind": "function", "doc": "

Reload module given a dict storing everything that had to be pickled.

\n\n
Arguments:
\n\n
    \n
  • saved_dict: The dict used to restore the module
    • \n
\n", "signature": "(self, saved_dict) -> None:", "funcdef": "def"}, "auto_disc.legacy.input_wrappers": {"fullname": "auto_disc.legacy.input_wrappers", "modulename": "auto_disc.legacy.input_wrappers", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.input_wrappers.base_input_wrapper": {"fullname": "auto_disc.legacy.input_wrappers.base_input_wrapper", "modulename": "auto_disc.legacy.input_wrappers.base_input_wrapper", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.input_wrappers.base_input_wrapper.BaseInputWrapper": {"fullname": "auto_disc.legacy.input_wrappers.base_input_wrapper.BaseInputWrapper", "modulename": "auto_disc.legacy.input_wrappers.base_input_wrapper", "qualname": "BaseInputWrapper", "kind": "class", "doc": "

Base class to map the parameters sent by the explorer to the system's input space

\n", "bases": "auto_disc.legacy.base_autodisc_module.BaseAutoDiscModule"}, "auto_disc.legacy.input_wrappers.base_input_wrapper.BaseInputWrapper.__init__": {"fullname": "auto_disc.legacy.input_wrappers.base_input_wrapper.BaseInputWrapper.__init__", "modulename": "auto_disc.legacy.input_wrappers.base_input_wrapper", "qualname": "BaseInputWrapper.__init__", "kind": "function", "doc": "

initialize attributes common to all auto_disc.legacy modules

\n\n
Arguments:
\n\n
    \n
  • logger: the logger which will make it possible to keep information on the progress of an experiment in the database or on files
    • \n
\n", "signature": "(wrapped_output_space_key=None, **kwargs)"}, "auto_disc.legacy.input_wrappers.base_input_wrapper.BaseInputWrapper.initialize": {"fullname": "auto_disc.legacy.input_wrappers.base_input_wrapper.BaseInputWrapper.initialize", "modulename": "auto_disc.legacy.input_wrappers.base_input_wrapper", "qualname": "BaseInputWrapper.initialize", "kind": "function", "doc": "

Defines input and output space for the input wrapper.

\n", "signature": "(self, output_space: auto_disc.legacy.utils.spaces.dict.DictSpace) -> None:", "funcdef": "def"}, "auto_disc.legacy.input_wrappers.base_input_wrapper.BaseInputWrapper.map": {"fullname": "auto_disc.legacy.input_wrappers.base_input_wrapper.BaseInputWrapper.map", "modulename": "auto_disc.legacy.input_wrappers.base_input_wrapper", "qualname": "BaseInputWrapper.map", "kind": "function", "doc": "

Map the input parameters (from the explorer) to the output parameters (sytem input)

\n\n
Arguments:
\n\n
    \n
  • input: input parameters
    • \n
  • is_input_new_discovery: indicates if it is a new discovery
    • \n
\n", "signature": "(self, input, is_input_new_discovery, **kwargs):", "funcdef": "def"}, "auto_disc.legacy.input_wrappers.generic": {"fullname": "auto_disc.legacy.input_wrappers.generic", "modulename": "auto_disc.legacy.input_wrappers.generic", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.input_wrappers.generic.cppn": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.input_wrappers.generic.cppn.cppn_input_wrapper": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.cppn_input_wrapper", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.cppn_input_wrapper", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.input_wrappers.generic.cppn.cppn_input_wrapper.CppnInputWrapper": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.cppn_input_wrapper.CppnInputWrapper", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.cppn_input_wrapper", "qualname": "CppnInputWrapper", "kind": "class", "doc": "

Base class to map the parameters sent by the explorer to the system's input space

\n", "bases": "auto_disc.legacy.input_wrappers.base_input_wrapper.BaseInputWrapper"}, "auto_disc.legacy.input_wrappers.generic.cppn.cppn_input_wrapper.CppnInputWrapper.__init__": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.cppn_input_wrapper.CppnInputWrapper.__init__", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.cppn_input_wrapper", "qualname": "CppnInputWrapper.__init__", "kind": "function", "doc": "

initialize attributes common to all auto_disc.legacy modules

\n\n
Arguments:
\n\n
    \n
  • logger: the logger which will make it possible to keep information on the progress of an experiment in the database or on files
    • \n
\n", "signature": "(*args, **kws)"}, "auto_disc.legacy.input_wrappers.generic.cppn.cppn_input_wrapper.CppnInputWrapper.map": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.cppn_input_wrapper.CppnInputWrapper.map", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.cppn_input_wrapper", "qualname": "CppnInputWrapper.map", "kind": "function", "doc": "

Map the input parameters (from the explorer) to the cppn output parameters (sytem input)

\n\n
Arguments:
\n\n
    \n
  • parameters: cppn input parameters
    • \n
  • is_input_new_discovery: indicates if it is a new discovery
    • \n
\n\n
Returns:
\n\n
\n

parameters: parameters after map to match system input

\n
\n", "signature": "(\tself,\tinput: Dict[str, Any],\tis_input_new_discovery: bool,\t**kwargs) -> Dict[str, torch.Tensor]:", "funcdef": "def"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.activations": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.activations", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.activations", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.activations.delphineat_gauss_activation": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.activations.delphineat_gauss_activation", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.activations", "qualname": "delphineat_gauss_activation", "kind": "function", "doc": "

PyTorch implementation of gauss activation as defined by SharpNEAT, which is also as in DelphiNEAT.

\n", "signature": "(z):", "funcdef": "def"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.activations.delphineat_sigmoid_activation": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.activations.delphineat_sigmoid_activation", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.activations", "qualname": "delphineat_sigmoid_activation", "kind": "function", "doc": "

PyTorch implementation of sigmoidal activation function as defined in DelphiNEAT

\n", "signature": "(z):", "funcdef": "def"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.activations.tanh_activation": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.activations.tanh_activation", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.activations", "qualname": "tanh_activation", "kind": "function", "doc": "

\n", "signature": "(z):", "funcdef": "def"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.activations.sin_activation": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.activations.sin_activation", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.activations", "qualname": "sin_activation", "kind": "function", "doc": "

\n", "signature": "(z):", "funcdef": "def"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.activations.sigmoid_activation": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.activations.sigmoid_activation", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.activations", "qualname": "sigmoid_activation", "kind": "function", "doc": "

\n", "signature": "(z):", "funcdef": "def"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.activations.gauss_activation": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.activations.gauss_activation", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.activations", "qualname": "gauss_activation", "kind": "function", "doc": "

\n", "signature": "(z):", "funcdef": "def"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.activations.identity_activation": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.activations.identity_activation", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.activations", "qualname": "identity_activation", "kind": "function", "doc": "

\n", "signature": "(z):", "funcdef": "def"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.activations.relu_activation": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.activations.relu_activation", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.activations", "qualname": "relu_activation", "kind": "function", "doc": "

\n", "signature": "(z):", "funcdef": "def"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.aggregations": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.aggregations", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.aggregations", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.aggregations.sum_aggregation": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.aggregations.sum_aggregation", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.aggregations", "qualname": "sum_aggregation", "kind": "function", "doc": "

\n", "signature": "(inputs):", "funcdef": "def"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.aggregations.product_aggregation": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.aggregations.product_aggregation", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.aggregations", "qualname": "product_aggregation", "kind": "function", "doc": "

\n", "signature": "(inputs):", "funcdef": "def"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn.RecurrentNetwork": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn.RecurrentNetwork", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn", "qualname": "RecurrentNetwork", "kind": "class", "doc": "

Base class for all neural network modules.

\n\n

Your models should also subclass this class.

\n\n

Modules can also contain other Modules, allowing to nest them in\na tree structure. You can assign the submodules as regular attributes::

\n\n
import torch.nn as nn\nimport torch.nn.functional as F\n\nclass Model(nn.Module):\n    def __init__(self):\n        super().__init__()\n        self.conv1 = nn.Conv2d(1, 20, 5)\n        self.conv2 = nn.Conv2d(20, 20, 5)\n\n    def forward(self, x):\n        x = F.relu(self.conv1(x))\n        return F.relu(self.conv2(x))\n
\n\n

Submodules assigned in this way will be registered, and will have their\nparameters converted too when you call to(), etc.

\n\n
\n\n

As per the example above, an __init__() call to the parent class\nmust be made before assignment on the child.

\n\n
\n\n

:ivar training: Boolean represents whether this module is in training or\n evaluation mode.\n:vartype training: bool

\n", "bases": "torch.nn.modules.module.Module"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn.RecurrentNetwork.__init__": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn.RecurrentNetwork.__init__", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn", "qualname": "RecurrentNetwork.__init__", "kind": "function", "doc": "
Parameters
\n\n
    \n
  • input_neuron_ids: list of ids of input neurons
    • \n
  • hidden_neuron_ids: list of ids of hidden neurons
    • \n
  • output_neuron_ids: list of ids of output neurons
    • \n
\n", "signature": "(\tinput_neuron_ids,\thidden_neuron_ids,\toutput_neuron_ids,\tbiases,\tresponses,\tactivations,\tconnections,\tdevice='cpu')"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn.RecurrentNetwork.forward": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn.RecurrentNetwork.forward", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn", "qualname": "RecurrentNetwork.forward", "kind": "function", "doc": "
Parameters
\n\n
    \n
  • inputs: tensor of size (batch_size, n_inputs)\nNote: output of a node as follows: activation(bias+(response\u2217aggregation(inputs)))\nreturns: (batch_size, n_outputs)
    • \n
\n", "signature": "(self, inputs):", "funcdef": "def"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn.RecurrentNetwork.activate": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn.RecurrentNetwork.activate", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn", "qualname": "RecurrentNetwork.activate", "kind": "function", "doc": "
Parameters
\n\n
    \n
  • inputs: tensor of size (..., n_inputs) => only the last dim matter, then CPPN is applied on all coordinates
    • \n
  • n_passes: number of passes in the RNN TODO: currently global passes should it be internal passes as in pytorch-neat by uber research ?\nreturns: (..., n_outputs)
    • \n
\n", "signature": "(self, inputs, n_passes=1):", "funcdef": "def"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn.RecurrentNetwork.draw_net": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn.RecurrentNetwork.draw_net", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn", "qualname": "RecurrentNetwork.draw_net", "kind": "function", "doc": "

\n", "signature": "(\tself,\tview=False,\tfilename=None,\tnode_names=None,\tnode_colors=None,\tfmt='svg'):", "funcdef": "def"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn.RecurrentNetwork.create": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn.RecurrentNetwork.create", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn", "qualname": "RecurrentNetwork.create", "kind": "function", "doc": "

Receives a genome and returns the RecurrentCPPN()

\n", "signature": "(genome, config, device='cpu'):", "funcdef": "def"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn.RecurrentNetwork.update_genome": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn.RecurrentNetwork.update_genome", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn", "qualname": "RecurrentNetwork.update_genome", "kind": "function", "doc": "

\n", "signature": "(self, genome):", "funcdef": "def"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn.str_to_tuple_key": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn.str_to_tuple_key", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn", "qualname": "str_to_tuple_key", "kind": "function", "doc": "

\n", "signature": "(str_key):", "funcdef": "def"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn.tuple_to_str_key": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn.tuple_to_str_key", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn", "qualname": "tuple_to_str_key", "kind": "function", "doc": "

\n", "signature": "(tuple_key):", "funcdef": "def"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn.RecurrentNetworkAgg": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn.RecurrentNetworkAgg", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn", "qualname": "RecurrentNetworkAgg", "kind": "class", "doc": "

Base class for all neural network modules.

\n\n

Your models should also subclass this class.

\n\n

Modules can also contain other Modules, allowing to nest them in\na tree structure. You can assign the submodules as regular attributes::

\n\n
import torch.nn as nn\nimport torch.nn.functional as F\n\nclass Model(nn.Module):\n    def __init__(self):\n        super().__init__()\n        self.conv1 = nn.Conv2d(1, 20, 5)\n        self.conv2 = nn.Conv2d(20, 20, 5)\n\n    def forward(self, x):\n        x = F.relu(self.conv1(x))\n        return F.relu(self.conv2(x))\n
\n\n

Submodules assigned in this way will be registered, and will have their\nparameters converted too when you call to(), etc.

\n\n
\n\n

As per the example above, an __init__() call to the parent class\nmust be made before assignment on the child.

\n\n
\n\n

:ivar training: Boolean represents whether this module is in training or\n evaluation mode.\n:vartype training: bool

\n", "bases": "torch.nn.modules.module.Module"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn.RecurrentNetworkAgg.__init__": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn.RecurrentNetworkAgg.__init__", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn", "qualname": "RecurrentNetworkAgg.__init__", "kind": "function", "doc": "
Parameters
\n\n
    \n
  • input_neuron_ids: list of ids of input neurons
    • \n
  • hidden_neuron_ids: list of ids of hidden neurons
    • \n
  • output_neuron_ids: list of ids of output neurons
    • \n
\n", "signature": "(\tinput_neuron_ids,\thidden_neuron_ids,\toutput_neuron_ids,\tbiases,\tresponses,\tactivations,\taggregations,\tconnections,\tdevice='cpu')"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn.RecurrentNetworkAgg.forward": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn.RecurrentNetworkAgg.forward", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn", "qualname": "RecurrentNetworkAgg.forward", "kind": "function", "doc": "
Parameters
\n\n
    \n
  • inputs: tensor of size (batch_size, n_inputs)
    • \n
\n\n

Note: output of a node as follows: activation(bias+(response\u2217aggregation(inputs)))

\n\n

returns: (batch_size, n_outputs)

\n", "signature": "(self, inputs):", "funcdef": "def"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn.RecurrentNetworkAgg.activate": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn.RecurrentNetworkAgg.activate", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn", "qualname": "RecurrentNetworkAgg.activate", "kind": "function", "doc": "
Parameters
\n\n
    \n
  • inputs: tensor of size (..., n_inputs) => only the last dim matter, then CPPN is applied on all coordinates
    • \n
  • n_passes: number of passes in the RNN TODO: currently global passes should it be internal passes as in pytorch-neat by uber research ?\nreturns: (..., n_outputs)
    • \n
\n", "signature": "(self, inputs, n_passes=1):", "funcdef": "def"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn.RecurrentNetworkAgg.draw_net": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn.RecurrentNetworkAgg.draw_net", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn", "qualname": "RecurrentNetworkAgg.draw_net", "kind": "function", "doc": "

\n", "signature": "(\tself,\tconfig=None,\tview=False,\tfilename=None,\tnode_names=None,\tnode_colors=None,\tfmt='svg'):", "funcdef": "def"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn.RecurrentNetworkAgg.create": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn.RecurrentNetworkAgg.create", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn", "qualname": "RecurrentNetworkAgg.create", "kind": "function", "doc": "

Receives a genome and returns the RecurrentCPPN()

\n", "signature": "(genome, config, device='cpu'):", "funcdef": "def"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn.RecurrentNetworkAgg.update_genome": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn.RecurrentNetworkAgg.update_genome", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.rnn", "qualname": "RecurrentNetworkAgg.update_genome", "kind": "function", "doc": "

\n", "signature": "(self, genome):", "funcdef": "def"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.selfconnectiongenome": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.selfconnectiongenome", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.selfconnectiongenome", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.selfconnectiongenome.ConnectionWeightAttribute": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.selfconnectiongenome.ConnectionWeightAttribute", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.selfconnectiongenome", "qualname": "ConnectionWeightAttribute", "kind": "class", "doc": "

Class for connection weight attributes. Is introduced to allow a differentiation between self connections of a node and connections to other nodes.

\n", "bases": "neat.attributes.BaseAttribute"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.selfconnectiongenome.ConnectionWeightAttribute.clamp": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.selfconnectiongenome.ConnectionWeightAttribute.clamp", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.selfconnectiongenome", "qualname": "ConnectionWeightAttribute.clamp", "kind": "function", "doc": "

\n", "signature": "(self, value, is_other_connect, config):", "funcdef": "def"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.selfconnectiongenome.ConnectionWeightAttribute.init_value": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.selfconnectiongenome.ConnectionWeightAttribute.init_value", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.selfconnectiongenome", "qualname": "ConnectionWeightAttribute.init_value", "kind": "function", "doc": "

\n", "signature": "(self, is_other_connect, config):", "funcdef": "def"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.selfconnectiongenome.ConnectionWeightAttribute.mutate_value": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.selfconnectiongenome.ConnectionWeightAttribute.mutate_value", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.selfconnectiongenome", "qualname": "ConnectionWeightAttribute.mutate_value", "kind": "function", "doc": "

\n", "signature": "(self, value, is_other_connect, config):", "funcdef": "def"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.selfconnectiongenome.ConnectionWeightAttribute.validate": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.selfconnectiongenome.ConnectionWeightAttribute.validate", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.selfconnectiongenome", "qualname": "ConnectionWeightAttribute.validate", "kind": "function", "doc": "

\n", "signature": "(self, config):", "funcdef": "def"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.selfconnectiongenome.SelfConnectionGene": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.selfconnectiongenome.SelfConnectionGene", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.selfconnectiongenome", "qualname": "SelfConnectionGene", "kind": "class", "doc": "

Handles functions shared by multiple types of genes (both node and connection),\nincluding crossover and calling mutation methods.

\n", "bases": "neat.genes.BaseGene"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.selfconnectiongenome.SelfConnectionGene.__init__": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.selfconnectiongenome.SelfConnectionGene.__init__", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.selfconnectiongenome", "qualname": "SelfConnectionGene.__init__", "kind": "function", "doc": "

\n", "signature": "(key)"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.selfconnectiongenome.SelfConnectionGene.distance": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.selfconnectiongenome.SelfConnectionGene.distance", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.selfconnectiongenome", "qualname": "SelfConnectionGene.distance", "kind": "function", "doc": "

\n", "signature": "(self, other, config):", "funcdef": "def"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.selfconnectiongenome.SelfConnectionGene.init_attributes": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.selfconnectiongenome.SelfConnectionGene.init_attributes", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.selfconnectiongenome", "qualname": "SelfConnectionGene.init_attributes", "kind": "function", "doc": "

\n", "signature": "(self, config):", "funcdef": "def"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.selfconnectiongenome.SelfConnectionGene.mutate": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.selfconnectiongenome.SelfConnectionGene.mutate", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.selfconnectiongenome", "qualname": "SelfConnectionGene.mutate", "kind": "function", "doc": "

\n", "signature": "(self, config):", "funcdef": "def"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.selfconnectiongenome.SelfConnectionGenome": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.selfconnectiongenome.SelfConnectionGenome", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.selfconnectiongenome", "qualname": "SelfConnectionGenome", "kind": "class", "doc": "

A genome for generalized neural networks. Derived from neat.DefaultGenome.\nExtends the default genome to allow specific configuration for self connections of neurons.

\n\n

Uses the SelfConnectionGene class instead of the neat.genes.DefaultConnectionGene class for connections.

\n\n

Terminology\n pin: Point at which the network is conceptually connected to the external world;\n pins are either input or output.\n node: Analog of a physical neuron.\n connection: Connection between a pin/node output and a node's input, or between a node's\n output and a pin/node input.\n key: Identifier for an object, unique within the set of similar objects.

\n\n

Design assumptions and conventions.\n 1. Each output pin is connected only to the output of its own unique\n neuron by an implicit connection with weight one. This connection\n is permanently enabled.\n 2. The output pin's key is always the same as the key for its\n associated neuron.\n 3. Output neurons can be modified but not deleted.\n 4. The input values are applied to the input pins unmodified.

\n", "bases": "neat.genome.DefaultGenome"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.selfconnectiongenome.SelfConnectionGenome.parse_config": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.selfconnectiongenome.SelfConnectionGenome.parse_config", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.selfconnectiongenome", "qualname": "SelfConnectionGenome.parse_config", "kind": "function", "doc": "

\n", "signature": "(cls, param_dict):", "funcdef": "def"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.utils": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.utils", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.utils", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.utils.create_image_cppn_input": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.utils.create_image_cppn_input", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.pytorchneat.utils", "qualname": "create_image_cppn_input", "kind": "function", "doc": "

\n", "signature": "(output_size, is_distance_to_center=True, is_bias=True):", "funcdef": "def"}, "auto_disc.legacy.input_wrappers.generic.cppn.utils": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.utils", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.utils", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.input_wrappers.generic.cppn.utils.cppn_genome_space": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.utils.cppn_genome_space", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.utils.cppn_genome_space", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.input_wrappers.generic.cppn.utils.cppn_genome_space.CPPNGenomeSpace": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.utils.cppn_genome_space.CPPNGenomeSpace", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.utils.cppn_genome_space", "qualname": "CPPNGenomeSpace", "kind": "class", "doc": "

Defines the init_space, genome_space and intervention_space of a system

\n", "bases": "auto_disc.legacy.utils.spaces.base_space.BaseSpace"}, "auto_disc.legacy.input_wrappers.generic.cppn.utils.cppn_genome_space.CPPNGenomeSpace.__init__": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.utils.cppn_genome_space.CPPNGenomeSpace.__init__", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.utils.cppn_genome_space", "qualname": "CPPNGenomeSpace.__init__", "kind": "function", "doc": "

Init the elements useful to the spaces

\n\n
Arguments:
\n\n
    \n
  • shape: Space shape
    • \n
  • dtype: torch type
    • \n
  • mutator: current mutator method
    • \n
\n", "signature": "()"}, "auto_disc.legacy.input_wrappers.generic.cppn.utils.cppn_genome_space.CPPNGenomeSpace.sample": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.utils.cppn_genome_space.CPPNGenomeSpace.sample", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.utils.cppn_genome_space", "qualname": "CPPNGenomeSpace.sample", "kind": "function", "doc": "

Randomly sample an element of this space.\nCan be uniform or non-uniform sampling based on boundedness of space.

\n", "signature": "(self):", "funcdef": "def"}, "auto_disc.legacy.input_wrappers.generic.cppn.utils.cppn_genome_space.CPPNGenomeSpace.mutate": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.utils.cppn_genome_space.CPPNGenomeSpace.mutate", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.utils.cppn_genome_space", "qualname": "CPPNGenomeSpace.mutate", "kind": "function", "doc": "

Randomly mutate an element of this space.

\n\n
Arguments:
\n\n
    \n
  • x: The element to be mutated
    • \n
\n", "signature": "(self, genome):", "funcdef": "def"}, "auto_disc.legacy.input_wrappers.generic.cppn.utils.cppn_genome_space.CPPNGenomeSpace.contains": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.utils.cppn_genome_space.CPPNGenomeSpace.contains", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.utils.cppn_genome_space", "qualname": "CPPNGenomeSpace.contains", "kind": "function", "doc": "

Return boolean specifying if x is a valid\nmember of this space

\n\n
Arguments:
\n\n
    \n
  • x: The var which must be part of the space
    • \n
\n", "signature": "(self, x):", "funcdef": "def"}, "auto_disc.legacy.input_wrappers.generic.cppn.utils.cppn_genome_space.CPPNGenomeSpace.clamp": {"fullname": "auto_disc.legacy.input_wrappers.generic.cppn.utils.cppn_genome_space.CPPNGenomeSpace.clamp", "modulename": "auto_disc.legacy.input_wrappers.generic.cppn.utils.cppn_genome_space", "qualname": "CPPNGenomeSpace.clamp", "kind": "function", "doc": "

Return a valid clamped value of x inside space's bounds

\n\n
Arguments:
\n\n
    \n
  • x: a value
    • \n
\n", "signature": "(self, x):", "funcdef": "def"}, "auto_disc.legacy.input_wrappers.generic.dummy_input_wrapper": {"fullname": "auto_disc.legacy.input_wrappers.generic.dummy_input_wrapper", "modulename": "auto_disc.legacy.input_wrappers.generic.dummy_input_wrapper", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.input_wrappers.generic.dummy_input_wrapper.DummyInputWrapper": {"fullname": "auto_disc.legacy.input_wrappers.generic.dummy_input_wrapper.DummyInputWrapper", "modulename": "auto_disc.legacy.input_wrappers.generic.dummy_input_wrapper", "qualname": "DummyInputWrapper", "kind": "class", "doc": "

Empty InputWrapper used when no wrapper should be used.

\n", "bases": "auto_disc.legacy.input_wrappers.base_input_wrapper.BaseInputWrapper"}, "auto_disc.legacy.input_wrappers.generic.dummy_input_wrapper.DummyInputWrapper.__init__": {"fullname": "auto_disc.legacy.input_wrappers.generic.dummy_input_wrapper.DummyInputWrapper.__init__", "modulename": "auto_disc.legacy.input_wrappers.generic.dummy_input_wrapper", "qualname": "DummyInputWrapper.__init__", "kind": "function", "doc": "

initialize attributes common to all auto_disc.legacy modules

\n\n
Arguments:
\n\n
    \n
  • logger: the logger which will make it possible to keep information on the progress of an experiment in the database or on files
    • \n
\n", "signature": "(wrapped_output_space_key: str = None, **kwargs)"}, "auto_disc.legacy.input_wrappers.generic.dummy_input_wrapper.DummyInputWrapper.map": {"fullname": "auto_disc.legacy.input_wrappers.generic.dummy_input_wrapper.DummyInputWrapper.map", "modulename": "auto_disc.legacy.input_wrappers.generic.dummy_input_wrapper", "qualname": "DummyInputWrapper.map", "kind": "function", "doc": "

Map the input parameters (from the explorer) to the output parameters (sytem input)

\n\n
Arguments:
\n\n
    \n
  • input: input parameters
    • \n
  • is_input_new_discovery: indicates if it is a new discovery
    • \n
\n", "signature": "(self, input: Dict, is_input_new_discovery, **kwargs) -> Dict:", "funcdef": "def"}, "auto_disc.legacy.input_wrappers.generic.times_n_input_wrapper": {"fullname": "auto_disc.legacy.input_wrappers.generic.times_n_input_wrapper", "modulename": "auto_disc.legacy.input_wrappers.generic.times_n_input_wrapper", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.input_wrappers.generic.times_n_input_wrapper.TimesNInputWrapper": {"fullname": "auto_disc.legacy.input_wrappers.generic.times_n_input_wrapper.TimesNInputWrapper", "modulename": "auto_disc.legacy.input_wrappers.generic.times_n_input_wrapper", "qualname": "TimesNInputWrapper", "kind": "class", "doc": "

Base class to map the parameters sent by the explorer to the system's input space

\n", "bases": "auto_disc.legacy.input_wrappers.base_input_wrapper.BaseInputWrapper"}, "auto_disc.legacy.input_wrappers.generic.times_n_input_wrapper.TimesNInputWrapper.__init__": {"fullname": "auto_disc.legacy.input_wrappers.generic.times_n_input_wrapper.TimesNInputWrapper.__init__", "modulename": "auto_disc.legacy.input_wrappers.generic.times_n_input_wrapper", "qualname": "TimesNInputWrapper.__init__", "kind": "function", "doc": "

initialize attributes common to all auto_disc.legacy modules

\n\n
Arguments:
\n\n
    \n
  • logger: the logger which will make it possible to keep information on the progress of an experiment in the database or on files
    • \n
\n", "signature": "(*args, **kws)"}, "auto_disc.legacy.input_wrappers.generic.times_n_input_wrapper.TimesNInputWrapper.map": {"fullname": "auto_disc.legacy.input_wrappers.generic.times_n_input_wrapper.TimesNInputWrapper.map", "modulename": "auto_disc.legacy.input_wrappers.generic.times_n_input_wrapper", "qualname": "TimesNInputWrapper.map", "kind": "function", "doc": "

Map the input parameters (from the explorer) to the output parameters (sytem input)

\n\n
Arguments:
\n\n
    \n
  • input: input parameters
    • \n
  • is_input_new_discovery: indicates if it is a new discovery
    • \n
\n", "signature": "(\tself,\tinput: addict.addict.Dict,\tis_input_new_discovery,\t**kwargs) -> addict.addict.Dict:", "funcdef": "def"}, "auto_disc.legacy.input_wrappers.specific": {"fullname": "auto_disc.legacy.input_wrappers.specific", "modulename": "auto_disc.legacy.input_wrappers.specific", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.output_representations": {"fullname": "auto_disc.legacy.output_representations", "modulename": "auto_disc.legacy.output_representations", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.output_representations.base_output_representation": {"fullname": "auto_disc.legacy.output_representations.base_output_representation", "modulename": "auto_disc.legacy.output_representations.base_output_representation", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.output_representations.base_output_representation.BaseOutputRepresentation": {"fullname": "auto_disc.legacy.output_representations.base_output_representation.BaseOutputRepresentation", "modulename": "auto_disc.legacy.output_representations.base_output_representation", "qualname": "BaseOutputRepresentation", "kind": "class", "doc": "

Base class to map the observations of a system to an embedding vector (BC characterization)

\n", "bases": "auto_disc.legacy.base_autodisc_module.BaseAutoDiscModule"}, "auto_disc.legacy.output_representations.base_output_representation.BaseOutputRepresentation.__init__": {"fullname": "auto_disc.legacy.output_representations.base_output_representation.BaseOutputRepresentation.__init__", "modulename": "auto_disc.legacy.output_representations.base_output_representation", "qualname": "BaseOutputRepresentation.__init__", "kind": "function", "doc": "

initialize attributes common to all auto_disc.legacy modules

\n\n
Arguments:
\n\n
    \n
  • logger: the logger which will make it possible to keep information on the progress of an experiment in the database or on files
    • \n
\n", "signature": "(wrapped_input_space_key: str = None, **kwargs)"}, "auto_disc.legacy.output_representations.base_output_representation.BaseOutputRepresentation.initialize": {"fullname": "auto_disc.legacy.output_representations.base_output_representation.BaseOutputRepresentation.initialize", "modulename": "auto_disc.legacy.output_representations.base_output_representation", "qualname": "BaseOutputRepresentation.initialize", "kind": "function", "doc": "

Defines input and output space for the output representation.

\n", "signature": "(self, input_space: auto_disc.legacy.utils.spaces.dict.DictSpace) -> None:", "funcdef": "def"}, "auto_disc.legacy.output_representations.base_output_representation.BaseOutputRepresentation.map": {"fullname": "auto_disc.legacy.output_representations.base_output_representation.BaseOutputRepresentation.map", "modulename": "auto_disc.legacy.output_representations.base_output_representation", "qualname": "BaseOutputRepresentation.map", "kind": "function", "doc": "

Map the input (system's output) to another space (towards the explorer's input space)

\n\n
Arguments:
\n\n
    \n
  • input: input parameters
    • \n
  • is_output_new_discovery: indicates if it is a new discovery
    • \n
\n", "signature": "(self, input, is_output_new_discovery, **kwargs):", "funcdef": "def"}, "auto_disc.legacy.output_representations.base_output_representation.BaseOutputRepresentation.calc_distance": {"fullname": "auto_disc.legacy.output_representations.base_output_representation.BaseOutputRepresentation.calc_distance", "modulename": "auto_disc.legacy.output_representations.base_output_representation", "qualname": "BaseOutputRepresentation.calc_distance", "kind": "function", "doc": "

Compute the distance between 2 embeddings in the latent space\n/!\\ batch mode embedding_a and embedding_b can be N*M or M

\n", "signature": "(self, embedding_a, embedding_b, **kwargs):", "funcdef": "def"}, "auto_disc.legacy.output_representations.generic": {"fullname": "auto_disc.legacy.output_representations.generic", "modulename": "auto_disc.legacy.output_representations.generic", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.output_representations.generic.dummy_output_representation": {"fullname": "auto_disc.legacy.output_representations.generic.dummy_output_representation", "modulename": "auto_disc.legacy.output_representations.generic.dummy_output_representation", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.output_representations.generic.dummy_output_representation.DummyOutputRepresentation": {"fullname": "auto_disc.legacy.output_representations.generic.dummy_output_representation.DummyOutputRepresentation", "modulename": "auto_disc.legacy.output_representations.generic.dummy_output_representation", "qualname": "DummyOutputRepresentation", "kind": "class", "doc": "

Empty OutputRepresentation used when no representation of the system's output mut be used.

\n", "bases": "auto_disc.legacy.output_representations.base_output_representation.BaseOutputRepresentation"}, "auto_disc.legacy.output_representations.generic.dummy_output_representation.DummyOutputRepresentation.__init__": {"fullname": "auto_disc.legacy.output_representations.generic.dummy_output_representation.DummyOutputRepresentation.__init__", "modulename": "auto_disc.legacy.output_representations.generic.dummy_output_representation", "qualname": "DummyOutputRepresentation.__init__", "kind": "function", "doc": "

initialize attributes common to all auto_disc.legacy modules

\n\n
Arguments:
\n\n
    \n
  • logger: the logger which will make it possible to keep information on the progress of an experiment in the database or on files
    • \n
\n", "signature": "(wrapped_input_space_key=None, **kwargs)"}, "auto_disc.legacy.output_representations.generic.dummy_output_representation.DummyOutputRepresentation.map": {"fullname": "auto_disc.legacy.output_representations.generic.dummy_output_representation.DummyOutputRepresentation.map", "modulename": "auto_disc.legacy.output_representations.generic.dummy_output_representation", "qualname": "DummyOutputRepresentation.map", "kind": "function", "doc": "

Map the input (system's output) to another space (towards the explorer's input space)

\n\n
Arguments:
\n\n
    \n
  • input: input parameters
    • \n
  • is_output_new_discovery: indicates if it is a new discovery
    • \n
\n", "signature": "(self, input, is_output_new_discovery, **kwargs):", "funcdef": "def"}, "auto_disc.legacy.output_representations.generic.dummy_output_representation.DummyOutputRepresentation.calc_distance": {"fullname": "auto_disc.legacy.output_representations.generic.dummy_output_representation.DummyOutputRepresentation.calc_distance", "modulename": "auto_disc.legacy.output_representations.generic.dummy_output_representation", "qualname": "DummyOutputRepresentation.calc_distance", "kind": "function", "doc": "

Compute the distance between 2 embeddings in the latent space\n/!\\ batch mode embedding_a and embedding_b can be N*M or M

\n", "signature": "(\tself,\tembedding_a: torch.Tensor,\tembedding_b: torch.Tensor,\t**kwargs) -> torch.Tensor:", "funcdef": "def"}, "auto_disc.legacy.output_representations.specific": {"fullname": "auto_disc.legacy.output_representations.specific", "modulename": "auto_disc.legacy.output_representations.specific", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.output_representations.specific.lenia_output_representation": {"fullname": "auto_disc.legacy.output_representations.specific.lenia_output_representation", "modulename": "auto_disc.legacy.output_representations.specific.lenia_output_representation", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.output_representations.specific.lenia_output_representation.LeniaImageRepresentation": {"fullname": "auto_disc.legacy.output_representations.specific.lenia_output_representation.LeniaImageRepresentation", "modulename": "auto_disc.legacy.output_representations.specific.lenia_output_representation", "qualname": "LeniaImageRepresentation", "kind": "class", "doc": "

Base class to map the observations of a system to an embedding vector (BC characterization)

\n", "bases": "auto_disc.legacy.output_representations.base_output_representation.BaseOutputRepresentation"}, "auto_disc.legacy.output_representations.specific.lenia_output_representation.LeniaImageRepresentation.__init__": {"fullname": "auto_disc.legacy.output_representations.specific.lenia_output_representation.LeniaImageRepresentation.__init__", "modulename": "auto_disc.legacy.output_representations.specific.lenia_output_representation", "qualname": "LeniaImageRepresentation.__init__", "kind": "function", "doc": "

initialize attributes common to all auto_disc.legacy modules

\n\n
Arguments:
\n\n
    \n
  • logger: the logger which will make it possible to keep information on the progress of an experiment in the database or on files
    • \n
\n", "signature": "(*args, **kws)"}, "auto_disc.legacy.output_representations.specific.lenia_output_representation.LeniaImageRepresentation.map": {"fullname": "auto_disc.legacy.output_representations.specific.lenia_output_representation.LeniaImageRepresentation.map", "modulename": "auto_disc.legacy.output_representations.specific.lenia_output_representation", "qualname": "LeniaImageRepresentation.map", "kind": "function", "doc": "

Flatten Lenia's output

\n\n
Arguments:
\n\n
    \n
  • input: Lenia's output
    • \n
  • is_output_new_discovery: indicates if it is a new discovery
    • \n
\n\n
Returns:
\n\n
\n

Return a torch tensor in dict

\n
\n", "signature": "(\tself,\tinput: Dict,\tis_output_new_discovery: bool) -> Dict[str, torch.Tensor]:", "funcdef": "def"}, "auto_disc.legacy.output_representations.specific.lenia_output_representation.LeniaImageRepresentation.calc_distance": {"fullname": "auto_disc.legacy.output_representations.specific.lenia_output_representation.LeniaImageRepresentation.calc_distance", "modulename": "auto_disc.legacy.output_representations.specific.lenia_output_representation", "qualname": "LeniaImageRepresentation.calc_distance", "kind": "function", "doc": "

Compute the distance between 2 embeddings in the latent space\n/!\\ batch mode embedding_a and embedding_b can be N*M or M

\n", "signature": "(\tself,\tembedding_a: torch.Tensor,\tembedding_b: torch.Tensor,\t**kwargs) -> torch.Tensor:", "funcdef": "def"}, "auto_disc.legacy.output_representations.specific.lenia_output_representation_hand_defined": {"fullname": "auto_disc.legacy.output_representations.specific.lenia_output_representation_hand_defined", "modulename": "auto_disc.legacy.output_representations.specific.lenia_output_representation_hand_defined", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.output_representations.specific.lenia_output_representation_hand_defined.center_of_mass": {"fullname": "auto_disc.legacy.output_representations.specific.lenia_output_representation_hand_defined.center_of_mass", "modulename": "auto_disc.legacy.output_representations.specific.lenia_output_representation_hand_defined", "qualname": "center_of_mass", "kind": "function", "doc": "

\n", "signature": "(input_array: torch.Tensor) -> torch.Tensor:", "funcdef": "def"}, "auto_disc.legacy.output_representations.specific.lenia_output_representation_hand_defined.calc_distance_matrix": {"fullname": "auto_disc.legacy.output_representations.specific.lenia_output_representation_hand_defined.calc_distance_matrix", "modulename": "auto_disc.legacy.output_representations.specific.lenia_output_representation_hand_defined", "qualname": "calc_distance_matrix", "kind": "function", "doc": "

\n", "signature": "(size_y: int, size_x: int) -> torch.Tensor:", "funcdef": "def"}, "auto_disc.legacy.output_representations.specific.lenia_output_representation_hand_defined.calc_image_moments": {"fullname": "auto_disc.legacy.output_representations.specific.lenia_output_representation_hand_defined.calc_image_moments", "modulename": "auto_disc.legacy.output_representations.specific.lenia_output_representation_hand_defined", "qualname": "calc_image_moments", "kind": "function", "doc": "

Calculates the image moments for an image.

\n\n
For more information see:
\n\n
\n \n
\n\n

The code is based on the Javascript implementation of Lenia by Bert Chan.\nThe code had to be adapted, because for the original code the coordinates are (x,y), whereas here they are (y,x).

\n\n
Parameters
\n\n
    \n
  • image: 2d gray scale image in form of a numpy array.
    • \n
\n\n
Returns
\n\n
\n

Namedtupel with the different moments.

\n
\n", "signature": "(image: torch.Tensor) -> Dict[str, torch.Tensor]:", "funcdef": "def"}, "auto_disc.legacy.output_representations.specific.lenia_output_representation_hand_defined.LeniaHandDefinedRepresentation": {"fullname": "auto_disc.legacy.output_representations.specific.lenia_output_representation_hand_defined.LeniaHandDefinedRepresentation", "modulename": "auto_disc.legacy.output_representations.specific.lenia_output_representation_hand_defined", "qualname": "LeniaHandDefinedRepresentation", "kind": "class", "doc": "

Base class to map the observations of a system to an embedding vector (BC characterization)

\n", "bases": "auto_disc.legacy.output_representations.base_output_representation.BaseOutputRepresentation"}, "auto_disc.legacy.output_representations.specific.lenia_output_representation_hand_defined.LeniaHandDefinedRepresentation.__init__": {"fullname": "auto_disc.legacy.output_representations.specific.lenia_output_representation_hand_defined.LeniaHandDefinedRepresentation.__init__", "modulename": "auto_disc.legacy.output_representations.specific.lenia_output_representation_hand_defined", "qualname": "LeniaHandDefinedRepresentation.__init__", "kind": "function", "doc": "

initialize attributes common to all auto_disc.legacy modules

\n\n
Arguments:
\n\n
    \n
  • logger: the logger which will make it possible to keep information on the progress of an experiment in the database or on files
    • \n
\n", "signature": "(*args, **kws)"}, "auto_disc.legacy.output_representations.specific.lenia_output_representation_hand_defined.LeniaHandDefinedRepresentation.calc_static_statistics": {"fullname": "auto_disc.legacy.output_representations.specific.lenia_output_representation_hand_defined.LeniaHandDefinedRepresentation.calc_static_statistics", "modulename": "auto_disc.legacy.output_representations.specific.lenia_output_representation_hand_defined", "qualname": "LeniaHandDefinedRepresentation.calc_static_statistics", "kind": "function", "doc": "

Calculates the final statistics for lenia last observation

\n", "signature": "(self, final_obs: torch.Tensor) -> torch.Tensor:", "funcdef": "def"}, "auto_disc.legacy.output_representations.specific.lenia_output_representation_hand_defined.LeniaHandDefinedRepresentation.map": {"fullname": "auto_disc.legacy.output_representations.specific.lenia_output_representation_hand_defined.LeniaHandDefinedRepresentation.map", "modulename": "auto_disc.legacy.output_representations.specific.lenia_output_representation_hand_defined", "qualname": "LeniaHandDefinedRepresentation.map", "kind": "function", "doc": "

Compute statistics on Lenia's output

\n\n
Arguments:
\n\n
    \n
  • input: Lenia's output
    • \n
  • is_output_new_discovery: indicates if it is a new discovery
    • \n
\n\n
Returns:
\n\n
\n

Return a torch tensor in dict

\n
\n", "signature": "(\tself,\tinput: addict.addict.Dict,\tis_output_new_discovery: bool) -> Dict[str, torch.Tensor]:", "funcdef": "def"}, "auto_disc.legacy.output_representations.specific.lenia_output_representation_hand_defined.LeniaHandDefinedRepresentation.calc_distance": {"fullname": "auto_disc.legacy.output_representations.specific.lenia_output_representation_hand_defined.LeniaHandDefinedRepresentation.calc_distance", "modulename": "auto_disc.legacy.output_representations.specific.lenia_output_representation_hand_defined", "qualname": "LeniaHandDefinedRepresentation.calc_distance", "kind": "function", "doc": "

Compute the distance between 2 embeddings in the latent space\n/!\\ batch mode embedding_a and embedding_b can be N*M or M

\n", "signature": "(\tself,\tembedding_a: torch.Tensor,\tembedding_b: torch.Tensor) -> torch.Tensor:", "funcdef": "def"}, "auto_disc.legacy.registration": {"fullname": "auto_disc.legacy.registration", "modulename": "auto_disc.legacy.registration", "kind": "module", "doc": "

This used to be a singleton dictionary mapping between key values and modules\navailable in the software.

\n\n

Those interested may consult the commit 7dea0818 of 5 May 2022

\n"}, "auto_disc.legacy.run": {"fullname": "auto_disc.legacy.run", "modulename": "auto_disc.legacy.run", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.run.create": {"fullname": "auto_disc.legacy.run.create", "modulename": "auto_disc.legacy.run", "qualname": "create", "kind": "function", "doc": "

Setup the whole experiment. Set each modules, logger, callbacks and use them to define the experiment pipeline.

\n\n
Arguments:
\n\n
    \n
  • parameters: Experiment config (define wich systems wich explorer wich callbacks and all other information needed to set an experiment)
    • \n
  • experiment_id: Current experiment id
    • \n
  • seed: current seed number
    • \n
  • additional_callbacks: callbacks we want use in addition to callbacks from parameters arguments
    • \n
  • additional_handlers: handlers we want to use in addition to logger_handlers from parameters arguments
    • \n
\n\n
Returns:
\n\n
\n

experiment: The experiment we have just defined

\n
\n", "signature": "(\tparameters: Dict,\texperiment_id: int,\tseed: int,\tadditional_callbacks: Dict[str, List[Callable]] = None,\tadditional_handlers: List[auto_disc.legacy.utils.logger.ad_logger.AutoDiscLogger] = None,\tinteractMethod: Callable = None) -> auto_disc.legacy.experiment_pipeline.ExperimentPipeline:", "funcdef": "def"}, "auto_disc.legacy.run.start": {"fullname": "auto_disc.legacy.run.start", "modulename": "auto_disc.legacy.run", "qualname": "start", "kind": "function", "doc": "

Runs an experiment for a number of iterations

\n\n
Arguments:
\n\n
    \n
  • experiment: The experiment we want to launch
    • \n
  • nb_iterations: the number explorations
    • \n
\n", "signature": "(\texperiment: auto_disc.legacy.experiment_pipeline.ExperimentPipeline,\tnb_iterations: int) -> None:", "funcdef": "def"}, "auto_disc.legacy.systems": {"fullname": "auto_disc.legacy.systems", "modulename": "auto_disc.legacy.systems", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.systems.base_system": {"fullname": "auto_disc.legacy.systems.base_system", "modulename": "auto_disc.legacy.systems.base_system", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.systems.base_system.BaseSystem": {"fullname": "auto_disc.legacy.systems.base_system.BaseSystem", "modulename": "auto_disc.legacy.systems.base_system", "qualname": "BaseSystem", "kind": "class", "doc": "

The main BaseSystem class. It encapsulates an environment with\narbitrary behind-the-scenes dynamics. An environment can be\npartially or fully observed.

\n\n
The main API methods that users of this class need to know are:
\n\n
\n

reset\n step\n observe\n render\n close

\n
\n\n

The config must be set through ConfigParameters decorators above the system's definition:\n@IntegerConfigParameter(name=\"parameter_name\", default=100, min=1, max=1000)\nThese parameters can then be accessed through self.config.parameter_name

\n", "bases": "auto_disc.legacy.base_autodisc_module.BaseAutoDiscModule"}, "auto_disc.legacy.systems.base_system.BaseSystem.__init__": {"fullname": "auto_disc.legacy.systems.base_system.BaseSystem.__init__", "modulename": "auto_disc.legacy.systems.base_system", "qualname": "BaseSystem.__init__", "kind": "function", "doc": "

initialize attributes common to all auto_disc.legacy modules

\n\n
Arguments:
\n\n
    \n
  • logger: the logger which will make it possible to keep information on the progress of an experiment in the database or on files
    • \n
\n", "signature": "(**kwargs)"}, "auto_disc.legacy.systems.base_system.BaseSystem.reset": {"fullname": "auto_disc.legacy.systems.base_system.BaseSystem.reset", "modulename": "auto_disc.legacy.systems.base_system", "qualname": "BaseSystem.reset", "kind": "function", "doc": "

Resets the environment to an initial state and returns an initial observation.

\n\n
Arguments:
\n\n
    \n
  • run_parameters (AttrDict): the input parameters of the system provided by the agent
    • \n
\n\n
Returns:
\n\n
\n

observation (AttrDict): the initial observation.

\n
\n", "signature": "(self, run_parameters):", "funcdef": "def"}, "auto_disc.legacy.systems.base_system.BaseSystem.step": {"fullname": "auto_disc.legacy.systems.base_system.BaseSystem.step", "modulename": "auto_disc.legacy.systems.base_system", "qualname": "BaseSystem.step", "kind": "function", "doc": "

Run one timestep of the system's dynamics. When end of\nepisode is reached, you are responsible for calling reset()\nto reset this environment's state.\nAccepts an action and returns a tuple (observation, reward, done, info).

\n\n
Arguments:
\n\n
    \n
  • action (AttrDict): an action provided by the agent
    • \n
\n\n
Returns:
\n\n
\n

observation (AttrDict): agent's observation of the current environment\n reward (float) : amount of reward returned after previous action\n done (bool): whether the episode has ended, in which case further step() calls will return undefined results\n info (AttrDict): contains auxiliary diagnostic information (helpful for debugging, and sometimes learning)

\n
\n", "signature": "(self, action=None):", "funcdef": "def"}, "auto_disc.legacy.systems.base_system.BaseSystem.observe": {"fullname": "auto_disc.legacy.systems.base_system.BaseSystem.observe", "modulename": "auto_disc.legacy.systems.base_system", "qualname": "BaseSystem.observe", "kind": "function", "doc": "

Returns the overall output of the system according to the last reset().\nUse this function once the step function has returned done=True to give the system's output to the Output Representation (and then the explorer).

\n", "signature": "(self):", "funcdef": "def"}, "auto_disc.legacy.systems.base_system.BaseSystem.render": {"fullname": "auto_disc.legacy.systems.base_system.BaseSystem.render", "modulename": "auto_disc.legacy.systems.base_system", "qualname": "BaseSystem.render", "kind": "function", "doc": "

Renders the environment.

\n", "signature": "(self, **kwargs):", "funcdef": "def"}, "auto_disc.legacy.systems.base_system.BaseSystem.close": {"fullname": "auto_disc.legacy.systems.base_system.BaseSystem.close", "modulename": "auto_disc.legacy.systems.base_system", "qualname": "BaseSystem.close", "kind": "function", "doc": "

Override close in your subclass to perform any necessary cleanup.\nEnvironments will automatically close() themselves when\ngarbage collected or when the program exits.

\n", "signature": "(self):", "funcdef": "def"}, "auto_disc.legacy.systems.python_systems": {"fullname": "auto_disc.legacy.systems.python_systems", "modulename": "auto_disc.legacy.systems.python_systems", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.systems.python_systems.base_python_system": {"fullname": "auto_disc.legacy.systems.python_systems.base_python_system", "modulename": "auto_disc.legacy.systems.python_systems.base_python_system", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.systems.python_systems.base_python_system.BasePythonSystem": {"fullname": "auto_disc.legacy.systems.python_systems.base_python_system.BasePythonSystem", "modulename": "auto_disc.legacy.systems.python_systems.base_python_system", "qualname": "BasePythonSystem", "kind": "class", "doc": "

The main BaseSystem class. It encapsulates an environment with\narbitrary behind-the-scenes dynamics. An environment can be\npartially or fully observed.

\n\n
The main API methods that users of this class need to know are:
\n\n
\n

reset\n step\n observe\n render\n close

\n
\n\n

The config must be set through ConfigParameters decorators above the system's definition:\n@IntegerConfigParameter(name=\"parameter_name\", default=100, min=1, max=1000)\nThese parameters can then be accessed through self.config.parameter_name

\n", "bases": "auto_disc.legacy.systems.base_system.BaseSystem"}, "auto_disc.legacy.systems.python_systems.lenia": {"fullname": "auto_disc.legacy.systems.python_systems.lenia", "modulename": "auto_disc.legacy.systems.python_systems.lenia", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.systems.python_systems.lenia.Lenia": {"fullname": "auto_disc.legacy.systems.python_systems.lenia.Lenia", "modulename": "auto_disc.legacy.systems.python_systems.lenia", "qualname": "Lenia", "kind": "class", "doc": "

The main BaseSystem class. It encapsulates an environment with\narbitrary behind-the-scenes dynamics. An environment can be\npartially or fully observed.

\n\n
The main API methods that users of this class need to know are:
\n\n
\n

reset\n step\n observe\n render\n close

\n
\n\n

The config must be set through ConfigParameters decorators above the system's definition:\n@IntegerConfigParameter(name=\"parameter_name\", default=100, min=1, max=1000)\nThese parameters can then be accessed through self.config.parameter_name

\n", "bases": "auto_disc.legacy.systems.python_systems.base_python_system.BasePythonSystem"}, "auto_disc.legacy.systems.python_systems.lenia.Lenia.__init__": {"fullname": "auto_disc.legacy.systems.python_systems.lenia.Lenia.__init__", "modulename": "auto_disc.legacy.systems.python_systems.lenia", "qualname": "Lenia.__init__", "kind": "function", "doc": "

initialize attributes common to all auto_disc.legacy modules

\n\n
Arguments:
\n\n
    \n
  • logger: the logger which will make it possible to keep information on the progress of an experiment in the database or on files
    • \n
\n", "signature": "(*args, **kws)"}, "auto_disc.legacy.systems.python_systems.lenia.Lenia.reset": {"fullname": "auto_disc.legacy.systems.python_systems.lenia.Lenia.reset", "modulename": "auto_disc.legacy.systems.python_systems.lenia", "qualname": "Lenia.reset", "kind": "function", "doc": "

Resets the environment to an initial state and returns an initial observation.

\n\n
Arguments:
\n\n
    \n
  • run_parameters (AttrDict): the input parameters of the system provided by the agent
    • \n
\n\n
Returns:
\n\n
\n

observation (AttrDict): the initial observation.

\n
\n", "signature": "(self, run_parameters: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:", "funcdef": "def"}, "auto_disc.legacy.systems.python_systems.lenia.Lenia.step": {"fullname": "auto_disc.legacy.systems.python_systems.lenia.Lenia.step", "modulename": "auto_disc.legacy.systems.python_systems.lenia", "qualname": "Lenia.step", "kind": "function", "doc": "

Run one timestep of the system's dynamics. When end of\nepisode is reached, you are responsible for calling reset()\nto reset this environment's state.\nAccepts an action and returns a tuple (observation, reward, done, info).

\n\n
Arguments:
\n\n
    \n
  • action (AttrDict): an action provided by the agent
    • \n
\n\n
Returns:
\n\n
\n

observation (AttrDict): agent's observation of the current environment\n reward (float) : amount of reward returned after previous action\n done (bool): whether the episode has ended, in which case further step() calls will return undefined results\n info (AttrDict): contains auxiliary diagnostic information (helpful for debugging, and sometimes learning)

\n
\n", "signature": "(self, action=None) -> Tuple[Dict[str, torch.Tensor], int, bool, NoneType]:", "funcdef": "def"}, "auto_disc.legacy.systems.python_systems.lenia.Lenia.observe": {"fullname": "auto_disc.legacy.systems.python_systems.lenia.Lenia.observe", "modulename": "auto_disc.legacy.systems.python_systems.lenia", "qualname": "Lenia.observe", "kind": "function", "doc": "

Returns the overall output of the system according to the last reset().\nUse this function once the step function has returned done=True to give the system's output to the Output Representation (and then the explorer).

\n", "signature": "(self) -> Dict[str, torch.Tensor]:", "funcdef": "def"}, "auto_disc.legacy.systems.python_systems.lenia.Lenia.render": {"fullname": "auto_disc.legacy.systems.python_systems.lenia.Lenia.render", "modulename": "auto_disc.legacy.systems.python_systems.lenia", "qualname": "Lenia.render", "kind": "function", "doc": "

Renders the environment.

\n", "signature": "(self, mode: str = 'PIL_image') -> Any:", "funcdef": "def"}, "auto_disc.legacy.systems.python_systems.lenia.Lenia.close": {"fullname": "auto_disc.legacy.systems.python_systems.lenia.Lenia.close", "modulename": "auto_disc.legacy.systems.python_systems.lenia", "qualname": "Lenia.close", "kind": "function", "doc": "

Override close in your subclass to perform any necessary cleanup.\nEnvironments will automatically close() themselves when\ngarbage collected or when the program exits.

\n", "signature": "(self):", "funcdef": "def"}, "auto_disc.legacy.systems.python_systems.lenia.create_colormap": {"fullname": "auto_disc.legacy.systems.python_systems.lenia.create_colormap", "modulename": "auto_disc.legacy.systems.python_systems.lenia", "qualname": "create_colormap", "kind": "function", "doc": "

\n", "signature": "(colors: numpy.ndarray, is_marker_w: bool = True) -> List[int]:", "funcdef": "def"}, "auto_disc.legacy.systems.python_systems.lenia.im_from_array_with_colormap": {"fullname": "auto_disc.legacy.systems.python_systems.lenia.im_from_array_with_colormap", "modulename": "auto_disc.legacy.systems.python_systems.lenia", "qualname": "im_from_array_with_colormap", "kind": "function", "doc": "

Function that transforms the color palette of a PIL image

\n\n

input:\n - image: the PIL image to transform\n - colormap: the desired colormap\noutput: the transformed PIL image

\n", "signature": "(\tnp_array: numpy.ndarray,\tcolormap: List[int]) -> <module 'PIL.Image' from '/opt/hostedtoolcache/Python/3.9.17/x64/lib/python3.9/site-packages/PIL/Image.py'>:", "funcdef": "def"}, "auto_disc.legacy.systems.python_systems.lenia.LeniaStepFFT": {"fullname": "auto_disc.legacy.systems.python_systems.lenia.LeniaStepFFT", "modulename": "auto_disc.legacy.systems.python_systems.lenia", "qualname": "LeniaStepFFT", "kind": "class", "doc": "

Module pytorch that computes one Lenia Step with the fft version

\n", "bases": "torch.nn.modules.module.Module"}, "auto_disc.legacy.systems.python_systems.lenia.LeniaStepFFT.__init__": {"fullname": "auto_disc.legacy.systems.python_systems.lenia.LeniaStepFFT.__init__", "modulename": "auto_disc.legacy.systems.python_systems.lenia", "qualname": "LeniaStepFFT.__init__", "kind": "function", "doc": "

Initializes internal Module state, shared by both nn.Module and ScriptModule.

\n", "signature": "(\tR: torch.Tensor,\tT: torch.Tensor,\tb: torch.Tensor,\tm: torch.Tensor,\ts: torch.Tensor,\tkn: int,\tgn: int,\tis_soft_clip: bool = False,\tSX: int = 256,\tSY: int = 256,\tdevice: str = 'cpu')"}, "auto_disc.legacy.systems.python_systems.lenia.LeniaStepFFT.compute_kernel": {"fullname": "auto_disc.legacy.systems.python_systems.lenia.LeniaStepFFT.compute_kernel", "modulename": "auto_disc.legacy.systems.python_systems.lenia", "qualname": "LeniaStepFFT.compute_kernel", "kind": "function", "doc": "

\n", "signature": "(self) -> None:", "funcdef": "def"}, "auto_disc.legacy.systems.python_systems.lenia.LeniaStepFFT.forward": {"fullname": "auto_disc.legacy.systems.python_systems.lenia.LeniaStepFFT.forward", "modulename": "auto_disc.legacy.systems.python_systems.lenia", "qualname": "LeniaStepFFT.forward", "kind": "function", "doc": "

Defines the computation performed at every call.

\n\n

Should be overridden by all subclasses.

\n\n
\n\n

Although the recipe for forward pass needs to be defined within\nthis function, one should call the Module instance afterwards\ninstead of this since the former takes care of running the\nregistered hooks while the latter silently ignores them.

\n\n
\n", "signature": "(self, input: torch.Tensor) -> torch.Tensor:", "funcdef": "def"}, "auto_disc.legacy.systems.python_systems.lenia.LeniaStepConv2d": {"fullname": "auto_disc.legacy.systems.python_systems.lenia.LeniaStepConv2d", "modulename": "auto_disc.legacy.systems.python_systems.lenia", "qualname": "LeniaStepConv2d", "kind": "class", "doc": "

Module pytorch that computes one Lenia Step with the conv2d version

\n", "bases": "torch.nn.modules.module.Module"}, "auto_disc.legacy.systems.python_systems.lenia.LeniaStepConv2d.__init__": {"fullname": "auto_disc.legacy.systems.python_systems.lenia.LeniaStepConv2d.__init__", "modulename": "auto_disc.legacy.systems.python_systems.lenia", "qualname": "LeniaStepConv2d.__init__", "kind": "function", "doc": "

Initializes internal Module state, shared by both nn.Module and ScriptModule.

\n", "signature": "(R, T, b, m, s, kn, gn, is_soft_clip=False, device='cpu')"}, "auto_disc.legacy.systems.python_systems.lenia.LeniaStepConv2d.compute_kernel": {"fullname": "auto_disc.legacy.systems.python_systems.lenia.LeniaStepConv2d.compute_kernel", "modulename": "auto_disc.legacy.systems.python_systems.lenia", "qualname": "LeniaStepConv2d.compute_kernel", "kind": "function", "doc": "

\n", "signature": "(self) -> None:", "funcdef": "def"}, "auto_disc.legacy.systems.python_systems.lenia.LeniaStepConv2d.forward": {"fullname": "auto_disc.legacy.systems.python_systems.lenia.LeniaStepConv2d.forward", "modulename": "auto_disc.legacy.systems.python_systems.lenia", "qualname": "LeniaStepConv2d.forward", "kind": "function", "doc": "

Defines the computation performed at every call.

\n\n

Should be overridden by all subclasses.

\n\n
\n\n

Although the recipe for forward pass needs to be defined within\nthis function, one should call the Module instance afterwards\ninstead of this since the former takes care of running the\nregistered hooks while the latter silently ignores them.

\n\n
\n", "signature": "(self, input):", "funcdef": "def"}, "auto_disc.legacy.utils": {"fullname": "auto_disc.legacy.utils", "modulename": "auto_disc.legacy.utils", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.callbacks": {"fullname": "auto_disc.legacy.utils.callbacks", "modulename": "auto_disc.legacy.utils.callbacks", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.callbacks.base_callback": {"fullname": "auto_disc.legacy.utils.callbacks.base_callback", "modulename": "auto_disc.legacy.utils.callbacks.base_callback", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.callbacks.base_callback.BaseCallback": {"fullname": "auto_disc.legacy.utils.callbacks.base_callback.BaseCallback", "modulename": "auto_disc.legacy.utils.callbacks.base_callback", "qualname": "BaseCallback", "kind": "class", "doc": "

Base class for callbacks used by the experiment pipelines when progress is made (e.g. new dicovery, explorer's optimization).

\n"}, "auto_disc.legacy.utils.callbacks.base_callback.BaseCallback.__init__": {"fullname": "auto_disc.legacy.utils.callbacks.base_callback.BaseCallback.__init__", "modulename": "auto_disc.legacy.utils.callbacks.base_callback", "qualname": "BaseCallback.__init__", "kind": "function", "doc": "

initialize attributes common to all auto_disc.legacy callbacks

\n\n
Arguments:
\n\n
    \n
  • logger: the logger which will make it possible to keep information on the progress of an experiment in the database or on files
    • \n
\n", "signature": "(logger=None, **kwargs)"}, "auto_disc.legacy.utils.callbacks.base_expe_db_callback": {"fullname": "auto_disc.legacy.utils.callbacks.base_expe_db_callback", "modulename": "auto_disc.legacy.utils.callbacks.base_expe_db_callback", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.callbacks.base_expe_db_callback.BaseExpeDBCallback": {"fullname": "auto_disc.legacy.utils.callbacks.base_expe_db_callback.BaseExpeDBCallback", "modulename": "auto_disc.legacy.utils.callbacks.base_expe_db_callback", "qualname": "BaseExpeDBCallback", "kind": "class", "doc": "

Base class for callbacks used by the expe_db when progress is made (e.g. new dicovery, explorer's optimization).

\n", "bases": "auto_disc.legacy.utils.callbacks.base_callback.BaseCallback"}, "auto_disc.legacy.utils.callbacks.base_expe_db_callback.BaseExpeDBCallback.__init__": {"fullname": "auto_disc.legacy.utils.callbacks.base_expe_db_callback.BaseExpeDBCallback.__init__", "modulename": "auto_disc.legacy.utils.callbacks.base_expe_db_callback", "qualname": "BaseExpeDBCallback.__init__", "kind": "function", "doc": "

initialize attributes common to all expe_db callbacks

\n\n
Arguments:
\n\n
    \n
  • base_url: the database url
    • \n
  • kwargs: some usefull args (e.g. logger experiment_id...)
    • \n
\n", "signature": "(base_url: str, **kwargs)"}, "auto_disc.legacy.utils.callbacks.custom_print_callback": {"fullname": "auto_disc.legacy.utils.callbacks.custom_print_callback", "modulename": "auto_disc.legacy.utils.callbacks.custom_print_callback", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.callbacks.custom_print_callback.CustomPrintCallback": {"fullname": "auto_disc.legacy.utils.callbacks.custom_print_callback.CustomPrintCallback", "modulename": "auto_disc.legacy.utils.callbacks.custom_print_callback", "qualname": "CustomPrintCallback", "kind": "class", "doc": "

Base class for callbacks used by the experiment pipelines when progress is made (e.g. new dicovery, explorer's optimization).

\n", "bases": "auto_disc.legacy.utils.callbacks.base_callback.BaseCallback"}, "auto_disc.legacy.utils.callbacks.custom_print_callback.CustomPrintCallback.__init__": {"fullname": "auto_disc.legacy.utils.callbacks.custom_print_callback.CustomPrintCallback.__init__", "modulename": "auto_disc.legacy.utils.callbacks.custom_print_callback", "qualname": "CustomPrintCallback.__init__", "kind": "function", "doc": "

Init the callback with a message to print

\n\n
Arguments:
\n\n
    \n
  • custom_message_to_print: The message to print
    • \n
\n", "signature": "(custom_message_to_print: str, **kwargs)"}, "auto_disc.legacy.utils.callbacks.interact_callbacks": {"fullname": "auto_disc.legacy.utils.callbacks.interact_callbacks", "modulename": "auto_disc.legacy.utils.callbacks.interact_callbacks", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.callbacks.interact_callbacks.base_interact_callback": {"fullname": "auto_disc.legacy.utils.callbacks.interact_callbacks.base_interact_callback", "modulename": "auto_disc.legacy.utils.callbacks.interact_callbacks.base_interact_callback", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.callbacks.interact_callbacks.base_interact_callback.Object": {"fullname": "auto_disc.legacy.utils.callbacks.interact_callbacks.base_interact_callback.Object", "modulename": "auto_disc.legacy.utils.callbacks.interact_callbacks.base_interact_callback", "qualname": "Object", "kind": "function", "doc": "

\n", "signature": "(**kwargs):", "funcdef": "def"}, "auto_disc.legacy.utils.callbacks.interact_callbacks.base_interact_callback.Interaction": {"fullname": "auto_disc.legacy.utils.callbacks.interact_callbacks.base_interact_callback.Interaction", "modulename": "auto_disc.legacy.utils.callbacks.interact_callbacks.base_interact_callback", "qualname": "Interaction", "kind": "class", "doc": "

\n"}, "auto_disc.legacy.utils.callbacks.interact_callbacks.base_interact_callback.Interaction.init_seed": {"fullname": "auto_disc.legacy.utils.callbacks.interact_callbacks.base_interact_callback.Interaction.init_seed", "modulename": "auto_disc.legacy.utils.callbacks.interact_callbacks.base_interact_callback", "qualname": "Interaction.init_seed", "kind": "function", "doc": "

\n", "signature": "(self, interact_callbacks, config):", "funcdef": "def"}, "auto_disc.legacy.utils.callbacks.interact_callbacks.base_interact_callback.Interaction.save": {"fullname": "auto_disc.legacy.utils.callbacks.interact_callbacks.base_interact_callback.Interaction.save", "modulename": "auto_disc.legacy.utils.callbacks.interact_callbacks.base_interact_callback", "qualname": "Interaction.save", "kind": "function", "doc": "

\n", "signature": "(self, data, dict_info=None):", "funcdef": "def"}, "auto_disc.legacy.utils.callbacks.interact_callbacks.base_interact_callback.Interaction.read": {"fullname": "auto_disc.legacy.utils.callbacks.interact_callbacks.base_interact_callback.Interaction.read", "modulename": "auto_disc.legacy.utils.callbacks.interact_callbacks.base_interact_callback", "qualname": "Interaction.read", "kind": "function", "doc": "

\n", "signature": "(self, filter_attribut=None):", "funcdef": "def"}, "auto_disc.legacy.utils.callbacks.interact_callbacks.base_interact_callback.Interaction.feedback": {"fullname": "auto_disc.legacy.utils.callbacks.interact_callbacks.base_interact_callback.Interaction.feedback", "modulename": "auto_disc.legacy.utils.callbacks.interact_callbacks.base_interact_callback", "qualname": "Interaction.feedback", "kind": "function", "doc": "

\n", "signature": "(self, data, dict_info=None, dict_value_to_change=None):", "funcdef": "def"}, "auto_disc.legacy.utils.callbacks.interact_callbacks.base_interact_callback.BaseInteractCallback": {"fullname": "auto_disc.legacy.utils.callbacks.interact_callbacks.base_interact_callback.BaseInteractCallback", "modulename": "auto_disc.legacy.utils.callbacks.interact_callbacks.base_interact_callback", "qualname": "BaseInteractCallback", "kind": "class", "doc": "

Base class for cancelled callbacks used by the experiment pipelines when the experiment was cancelled.

\n", "bases": "auto_disc.legacy.utils.callbacks.base_callback.BaseCallback"}, "auto_disc.legacy.utils.callbacks.interact_callbacks.base_interact_callback.BaseInteractCallback.__init__": {"fullname": "auto_disc.legacy.utils.callbacks.interact_callbacks.base_interact_callback.BaseInteractCallback.__init__", "modulename": "auto_disc.legacy.utils.callbacks.interact_callbacks.base_interact_callback", "qualname": "BaseInteractCallback.__init__", "kind": "function", "doc": "

initialize attributes common to all cancelled callbacks

\n\n
Arguments:
\n\n
    \n
  • kwargs: some usefull args (e.g. experiment_id...)
    • \n
\n", "signature": "(**kwargs)"}, "auto_disc.legacy.utils.callbacks.interact_callbacks.read_disk_interact_callback": {"fullname": "auto_disc.legacy.utils.callbacks.interact_callbacks.read_disk_interact_callback", "modulename": "auto_disc.legacy.utils.callbacks.interact_callbacks.read_disk_interact_callback", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.callbacks.interact_callbacks.read_disk_interact_callback.ReadDiskInteractCallback": {"fullname": "auto_disc.legacy.utils.callbacks.interact_callbacks.read_disk_interact_callback.ReadDiskInteractCallback", "modulename": "auto_disc.legacy.utils.callbacks.interact_callbacks.read_disk_interact_callback", "qualname": "ReadDiskInteractCallback", "kind": "class", "doc": "

Base class for cancelled callbacks used by the experiment pipelines when the experiment was cancelled.

\n", "bases": "auto_disc.legacy.utils.callbacks.interact_callbacks.base_interact_callback.BaseInteractCallback"}, "auto_disc.legacy.utils.callbacks.interact_callbacks.read_disk_interact_callback.ReadDiskInteractCallback.__init__": {"fullname": "auto_disc.legacy.utils.callbacks.interact_callbacks.read_disk_interact_callback.ReadDiskInteractCallback.__init__", "modulename": "auto_disc.legacy.utils.callbacks.interact_callbacks.read_disk_interact_callback", "qualname": "ReadDiskInteractCallback.__init__", "kind": "function", "doc": "

initialize attributes common to all cancelled callbacks

\n\n
Arguments:
\n\n
    \n
  • kwargs: some usefull args (e.g. experiment_id...)
    • \n
\n", "signature": "(**kwargs)"}, "auto_disc.legacy.utils.callbacks.interact_callbacks.save_disk_interact_callback": {"fullname": "auto_disc.legacy.utils.callbacks.interact_callbacks.save_disk_interact_callback", "modulename": "auto_disc.legacy.utils.callbacks.interact_callbacks.save_disk_interact_callback", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.callbacks.interact_callbacks.save_disk_interact_callback.SaveDiskInteractCallback": {"fullname": "auto_disc.legacy.utils.callbacks.interact_callbacks.save_disk_interact_callback.SaveDiskInteractCallback", "modulename": "auto_disc.legacy.utils.callbacks.interact_callbacks.save_disk_interact_callback", "qualname": "SaveDiskInteractCallback", "kind": "class", "doc": "

Base class for cancelled callbacks used by the experiment pipelines when the experiment was cancelled.

\n", "bases": "auto_disc.legacy.utils.callbacks.interact_callbacks.base_interact_callback.BaseInteractCallback"}, "auto_disc.legacy.utils.callbacks.interact_callbacks.save_disk_interact_callback.SaveDiskInteractCallback.__init__": {"fullname": "auto_disc.legacy.utils.callbacks.interact_callbacks.save_disk_interact_callback.SaveDiskInteractCallback.__init__", "modulename": "auto_disc.legacy.utils.callbacks.interact_callbacks.save_disk_interact_callback", "qualname": "SaveDiskInteractCallback.__init__", "kind": "function", "doc": "

initialize attributes common to all cancelled callbacks

\n\n
Arguments:
\n\n
    \n
  • kwargs: some usefull args (e.g. experiment_id...)
    • \n
\n", "signature": "(**kwargs)"}, "auto_disc.legacy.utils.callbacks.on_cancelled_callbacks": {"fullname": "auto_disc.legacy.utils.callbacks.on_cancelled_callbacks", "modulename": "auto_disc.legacy.utils.callbacks.on_cancelled_callbacks", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.callbacks.on_cancelled_callbacks.base_on_cancelled_callback": {"fullname": "auto_disc.legacy.utils.callbacks.on_cancelled_callbacks.base_on_cancelled_callback", "modulename": "auto_disc.legacy.utils.callbacks.on_cancelled_callbacks.base_on_cancelled_callback", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.callbacks.on_cancelled_callbacks.base_on_cancelled_callback.BaseOnCancelledCallback": {"fullname": "auto_disc.legacy.utils.callbacks.on_cancelled_callbacks.base_on_cancelled_callback.BaseOnCancelledCallback", "modulename": "auto_disc.legacy.utils.callbacks.on_cancelled_callbacks.base_on_cancelled_callback", "qualname": "BaseOnCancelledCallback", "kind": "class", "doc": "

Base class for cancelled callbacks used by the experiment pipelines when the experiment was cancelled.

\n", "bases": "auto_disc.legacy.utils.callbacks.base_callback.BaseCallback"}, "auto_disc.legacy.utils.callbacks.on_cancelled_callbacks.base_on_cancelled_callback.BaseOnCancelledCallback.__init__": {"fullname": "auto_disc.legacy.utils.callbacks.on_cancelled_callbacks.base_on_cancelled_callback.BaseOnCancelledCallback.__init__", "modulename": "auto_disc.legacy.utils.callbacks.on_cancelled_callbacks.base_on_cancelled_callback", "qualname": "BaseOnCancelledCallback.__init__", "kind": "function", "doc": "

initialize attributes common to all cancelled callbacks

\n\n
Arguments:
\n\n
    \n
  • kwargs: some usefull args (e.g. experiment_id...)
    • \n
\n", "signature": "(**kwargs)"}, "auto_disc.legacy.utils.callbacks.on_discovery_callbacks": {"fullname": "auto_disc.legacy.utils.callbacks.on_discovery_callbacks", "modulename": "auto_disc.legacy.utils.callbacks.on_discovery_callbacks", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.callbacks.on_discovery_callbacks.base_on_discovery_callback": {"fullname": "auto_disc.legacy.utils.callbacks.on_discovery_callbacks.base_on_discovery_callback", "modulename": "auto_disc.legacy.utils.callbacks.on_discovery_callbacks.base_on_discovery_callback", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.callbacks.on_discovery_callbacks.base_on_discovery_callback.BaseOnDiscoveryCallback": {"fullname": "auto_disc.legacy.utils.callbacks.on_discovery_callbacks.base_on_discovery_callback.BaseOnDiscoveryCallback", "modulename": "auto_disc.legacy.utils.callbacks.on_discovery_callbacks.base_on_discovery_callback", "qualname": "BaseOnDiscoveryCallback", "kind": "class", "doc": "

Base class for on discovery callbacks used by the experiment pipelines when new discovery is made.

\n", "bases": "auto_disc.legacy.utils.callbacks.base_callback.BaseCallback"}, "auto_disc.legacy.utils.callbacks.on_discovery_callbacks.base_on_discovery_callback.BaseOnDiscoveryCallback.__init__": {"fullname": "auto_disc.legacy.utils.callbacks.on_discovery_callbacks.base_on_discovery_callback.BaseOnDiscoveryCallback.__init__", "modulename": "auto_disc.legacy.utils.callbacks.on_discovery_callbacks.base_on_discovery_callback", "qualname": "BaseOnDiscoveryCallback.__init__", "kind": "function", "doc": "

initialize attributes common to all on discovery callbacks

\n\n
Arguments:
\n\n
    \n
  • to_save_outputs: Names of all outputs the user want save
    • \n
  • kwargs: some usefull args (e.g. experiment_id...)
    • \n
\n", "signature": "(to_save_outputs: List[str], **kwargs)"}, "auto_disc.legacy.utils.callbacks.on_discovery_callbacks.on_discovery_save_callback_on_disk": {"fullname": "auto_disc.legacy.utils.callbacks.on_discovery_callbacks.on_discovery_save_callback_on_disk", "modulename": "auto_disc.legacy.utils.callbacks.on_discovery_callbacks.on_discovery_save_callback_on_disk", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.callbacks.on_discovery_callbacks.on_discovery_save_callback_on_disk.OnDiscoverySaveCallbackOnDisk": {"fullname": "auto_disc.legacy.utils.callbacks.on_discovery_callbacks.on_discovery_save_callback_on_disk.OnDiscoverySaveCallbackOnDisk", "modulename": "auto_disc.legacy.utils.callbacks.on_discovery_callbacks.on_discovery_save_callback_on_disk", "qualname": "OnDiscoverySaveCallbackOnDisk", "kind": "class", "doc": "

class for save experiment discovery on disk.

\n", "bases": "auto_disc.legacy.utils.callbacks.on_discovery_callbacks.base_on_discovery_callback.BaseOnDiscoveryCallback"}, "auto_disc.legacy.utils.callbacks.on_discovery_callbacks.on_discovery_save_callback_on_disk.OnDiscoverySaveCallbackOnDisk.__init__": {"fullname": "auto_disc.legacy.utils.callbacks.on_discovery_callbacks.on_discovery_save_callback_on_disk.OnDiscoverySaveCallbackOnDisk.__init__", "modulename": "auto_disc.legacy.utils.callbacks.on_discovery_callbacks.on_discovery_save_callback_on_disk", "qualname": "OnDiscoverySaveCallbackOnDisk.__init__", "kind": "function", "doc": "

init the callback with a path to save discoveries on disk

\n\n
Arguments:
\n\n
    \n
  • folder_path: path to folder where we want save the discovery
    • \n
  • to_save_outputs: list, key of \"SAVABLE_OUTPUTS\" (parent's attribute) to select the outputs who we want to save
    • \n
\n", "signature": "(folder_path, to_save_outputs, **kwargs)"}, "auto_disc.legacy.utils.callbacks.on_discovery_callbacks.save_discovery": {"fullname": "auto_disc.legacy.utils.callbacks.on_discovery_callbacks.save_discovery", "modulename": "auto_disc.legacy.utils.callbacks.on_discovery_callbacks.save_discovery", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.callbacks.on_discovery_callbacks.save_discovery.SaveDiscovery": {"fullname": "auto_disc.legacy.utils.callbacks.on_discovery_callbacks.save_discovery.SaveDiscovery", "modulename": "auto_disc.legacy.utils.callbacks.on_discovery_callbacks.save_discovery", "qualname": "SaveDiscovery", "kind": "class", "doc": "

\n"}, "auto_disc.legacy.utils.callbacks.on_discovery_callbacks.save_discovery.SaveDiscovery.__init__": {"fullname": "auto_disc.legacy.utils.callbacks.on_discovery_callbacks.save_discovery.SaveDiscovery.__init__", "modulename": "auto_disc.legacy.utils.callbacks.on_discovery_callbacks.save_discovery", "qualname": "SaveDiscovery.__init__", "kind": "function", "doc": "

Dummy init which accepts any arguments, for backwards compatibility.

\n", "signature": "(**kwargs)"}, "auto_disc.legacy.utils.callbacks.on_discovery_callbacks.save_discovery_in_expedb": {"fullname": "auto_disc.legacy.utils.callbacks.on_discovery_callbacks.save_discovery_in_expedb", "modulename": "auto_disc.legacy.utils.callbacks.on_discovery_callbacks.save_discovery_in_expedb", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.callbacks.on_discovery_callbacks.save_discovery_in_expedb.SaveDiscoveryInExpeDB": {"fullname": "auto_disc.legacy.utils.callbacks.on_discovery_callbacks.save_discovery_in_expedb.SaveDiscoveryInExpeDB", "modulename": "auto_disc.legacy.utils.callbacks.on_discovery_callbacks.save_discovery_in_expedb", "qualname": "SaveDiscoveryInExpeDB", "kind": "class", "doc": "

\n", "bases": "auto_disc.legacy.utils.callbacks.on_discovery_callbacks.save_discovery.SaveDiscovery"}, "auto_disc.legacy.utils.callbacks.on_discovery_callbacks.save_discovery_on_disk": {"fullname": "auto_disc.legacy.utils.callbacks.on_discovery_callbacks.save_discovery_on_disk", "modulename": "auto_disc.legacy.utils.callbacks.on_discovery_callbacks.save_discovery_on_disk", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.callbacks.on_discovery_callbacks.save_discovery_on_disk.SaveDiscoveryOnDisk": {"fullname": "auto_disc.legacy.utils.callbacks.on_discovery_callbacks.save_discovery_on_disk.SaveDiscoveryOnDisk", "modulename": "auto_disc.legacy.utils.callbacks.on_discovery_callbacks.save_discovery_on_disk", "qualname": "SaveDiscoveryOnDisk", "kind": "class", "doc": "

\n", "bases": "auto_disc.legacy.utils.callbacks.on_discovery_callbacks.save_discovery.SaveDiscovery"}, "auto_disc.legacy.utils.callbacks.on_error_callbacks": {"fullname": "auto_disc.legacy.utils.callbacks.on_error_callbacks", "modulename": "auto_disc.legacy.utils.callbacks.on_error_callbacks", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.callbacks.on_error_callbacks.base_on_error_callback": {"fullname": "auto_disc.legacy.utils.callbacks.on_error_callbacks.base_on_error_callback", "modulename": "auto_disc.legacy.utils.callbacks.on_error_callbacks.base_on_error_callback", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.callbacks.on_error_callbacks.base_on_error_callback.BaseOnErrorCallback": {"fullname": "auto_disc.legacy.utils.callbacks.on_error_callbacks.base_on_error_callback.BaseOnErrorCallback", "modulename": "auto_disc.legacy.utils.callbacks.on_error_callbacks.base_on_error_callback", "qualname": "BaseOnErrorCallback", "kind": "class", "doc": "

Base class for on error callbacks used by the experiment pipelines when an error are raise during the experiment progress.

\n", "bases": "auto_disc.legacy.utils.callbacks.base_callback.BaseCallback"}, "auto_disc.legacy.utils.callbacks.on_error_callbacks.base_on_error_callback.BaseOnErrorCallback.__init__": {"fullname": "auto_disc.legacy.utils.callbacks.on_error_callbacks.base_on_error_callback.BaseOnErrorCallback.__init__", "modulename": "auto_disc.legacy.utils.callbacks.on_error_callbacks.base_on_error_callback", "qualname": "BaseOnErrorCallback.__init__", "kind": "function", "doc": "

initialize attributes common to all on error callbacks

\n", "signature": "(**kwargs)"}, "auto_disc.legacy.utils.callbacks.on_finished_callbacks": {"fullname": "auto_disc.legacy.utils.callbacks.on_finished_callbacks", "modulename": "auto_disc.legacy.utils.callbacks.on_finished_callbacks", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.callbacks.on_finished_callbacks.base_on_finished_callback": {"fullname": "auto_disc.legacy.utils.callbacks.on_finished_callbacks.base_on_finished_callback", "modulename": "auto_disc.legacy.utils.callbacks.on_finished_callbacks.base_on_finished_callback", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.callbacks.on_finished_callbacks.base_on_finished_callback.BaseOnFinishedCallback": {"fullname": "auto_disc.legacy.utils.callbacks.on_finished_callbacks.base_on_finished_callback.BaseOnFinishedCallback", "modulename": "auto_disc.legacy.utils.callbacks.on_finished_callbacks.base_on_finished_callback", "qualname": "BaseOnFinishedCallback", "kind": "class", "doc": "

Base class for on finished callbacks used by the experiment pipelines when the experiment is over.

\n", "bases": "auto_disc.legacy.utils.callbacks.base_callback.BaseCallback"}, "auto_disc.legacy.utils.callbacks.on_finished_callbacks.base_on_finished_callback.BaseOnFinishedCallback.__init__": {"fullname": "auto_disc.legacy.utils.callbacks.on_finished_callbacks.base_on_finished_callback.BaseOnFinishedCallback.__init__", "modulename": "auto_disc.legacy.utils.callbacks.on_finished_callbacks.base_on_finished_callback", "qualname": "BaseOnFinishedCallback.__init__", "kind": "function", "doc": "

initialize attributes common to all on finished callbacks

\n", "signature": "(**kwargs)"}, "auto_disc.legacy.utils.callbacks.on_save_callbacks": {"fullname": "auto_disc.legacy.utils.callbacks.on_save_callbacks", "modulename": "auto_disc.legacy.utils.callbacks.on_save_callbacks", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.callbacks.on_save_callbacks.base_on_save_callback": {"fullname": "auto_disc.legacy.utils.callbacks.on_save_callbacks.base_on_save_callback", "modulename": "auto_disc.legacy.utils.callbacks.on_save_callbacks.base_on_save_callback", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.callbacks.on_save_callbacks.base_on_save_callback.BaseOnSaveCallback": {"fullname": "auto_disc.legacy.utils.callbacks.on_save_callbacks.base_on_save_callback.BaseOnSaveCallback", "modulename": "auto_disc.legacy.utils.callbacks.on_save_callbacks.base_on_save_callback", "qualname": "BaseOnSaveCallback", "kind": "class", "doc": "

Base class for on save callbacks used by the experiment pipelines when the experiment save autodisc modules.

\n", "bases": "auto_disc.legacy.utils.callbacks.base_callback.BaseCallback"}, "auto_disc.legacy.utils.callbacks.on_save_callbacks.base_on_save_callback.BaseOnSaveCallback.__init__": {"fullname": "auto_disc.legacy.utils.callbacks.on_save_callbacks.base_on_save_callback.BaseOnSaveCallback.__init__", "modulename": "auto_disc.legacy.utils.callbacks.on_save_callbacks.base_on_save_callback", "qualname": "BaseOnSaveCallback.__init__", "kind": "function", "doc": "

initialize attributes common to all on save callbacks

\n", "signature": "(**kwargs)"}, "auto_disc.legacy.utils.callbacks.on_save_callbacks.on_save_modules_on_disk_callback": {"fullname": "auto_disc.legacy.utils.callbacks.on_save_callbacks.on_save_modules_on_disk_callback", "modulename": "auto_disc.legacy.utils.callbacks.on_save_callbacks.on_save_modules_on_disk_callback", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.callbacks.on_save_callbacks.on_save_modules_on_disk_callback.OnSaveModulesOnDiskCallback": {"fullname": "auto_disc.legacy.utils.callbacks.on_save_callbacks.on_save_modules_on_disk_callback.OnSaveModulesOnDiskCallback", "modulename": "auto_disc.legacy.utils.callbacks.on_save_callbacks.on_save_modules_on_disk_callback", "qualname": "OnSaveModulesOnDiskCallback", "kind": "class", "doc": "

class for save autodisc modules on disk.

\n", "bases": "auto_disc.legacy.utils.callbacks.on_save_callbacks.base_on_save_callback.BaseOnSaveCallback"}, "auto_disc.legacy.utils.callbacks.on_save_callbacks.on_save_modules_on_disk_callback.OnSaveModulesOnDiskCallback.__init__": {"fullname": "auto_disc.legacy.utils.callbacks.on_save_callbacks.on_save_modules_on_disk_callback.OnSaveModulesOnDiskCallback.__init__", "modulename": "auto_disc.legacy.utils.callbacks.on_save_callbacks.on_save_modules_on_disk_callback", "qualname": "OnSaveModulesOnDiskCallback.__init__", "kind": "function", "doc": "

init the callback with a path to save modules on disk

\n\n
Arguments:
\n\n
    \n
  • folder_path: path to folder where we want save the discovery
    • \n
\n", "signature": "(folder_path: str, **kwargs)"}, "auto_disc.legacy.utils.callbacks.on_save_callbacks.save_leaf_callback": {"fullname": "auto_disc.legacy.utils.callbacks.on_save_callbacks.save_leaf_callback", "modulename": "auto_disc.legacy.utils.callbacks.on_save_callbacks.save_leaf_callback", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.callbacks.on_save_callbacks.save_leaf_callback.SaveLeaf": {"fullname": "auto_disc.legacy.utils.callbacks.on_save_callbacks.save_leaf_callback.SaveLeaf", "modulename": "auto_disc.legacy.utils.callbacks.on_save_callbacks.save_leaf_callback", "qualname": "SaveLeaf", "kind": "class", "doc": "

Simple adapter which calls the save_leaf method from a callback

\n"}, "auto_disc.legacy.utils.callbacks.on_save_callbacks.save_leaf_callback.SaveLeaf.__init__": {"fullname": "auto_disc.legacy.utils.callbacks.on_save_callbacks.save_leaf_callback.SaveLeaf.__init__", "modulename": "auto_disc.legacy.utils.callbacks.on_save_callbacks.save_leaf_callback", "qualname": "SaveLeaf.__init__", "kind": "function", "doc": "

\n", "signature": "(**kwargs)"}, "auto_disc.legacy.utils.callbacks.on_save_callbacks.save_leaf_callback_in_expedb": {"fullname": "auto_disc.legacy.utils.callbacks.on_save_callbacks.save_leaf_callback_in_expedb", "modulename": "auto_disc.legacy.utils.callbacks.on_save_callbacks.save_leaf_callback_in_expedb", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.callbacks.on_save_callbacks.save_leaf_callback_in_expedb.SaveLeafExpeDB": {"fullname": "auto_disc.legacy.utils.callbacks.on_save_callbacks.save_leaf_callback_in_expedb.SaveLeafExpeDB", "modulename": "auto_disc.legacy.utils.callbacks.on_save_callbacks.save_leaf_callback_in_expedb", "qualname": "SaveLeafExpeDB", "kind": "class", "doc": "

Simple adapter which calls the save_leaf method from a callback

\n", "bases": "auto_disc.legacy.utils.callbacks.on_save_callbacks.save_leaf_callback.SaveLeaf"}, "auto_disc.legacy.utils.callbacks.on_save_callbacks.save_leaf_callback_in_expedb.SaveLeafExpeDB.__init__": {"fullname": "auto_disc.legacy.utils.callbacks.on_save_callbacks.save_leaf_callback_in_expedb.SaveLeafExpeDB.__init__", "modulename": "auto_disc.legacy.utils.callbacks.on_save_callbacks.save_leaf_callback_in_expedb", "qualname": "SaveLeafExpeDB.__init__", "kind": "function", "doc": "

\n", "signature": "(**kwargs)"}, "auto_disc.legacy.utils.callbacks.on_save_finished_callbacks": {"fullname": "auto_disc.legacy.utils.callbacks.on_save_finished_callbacks", "modulename": "auto_disc.legacy.utils.callbacks.on_save_finished_callbacks", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.callbacks.on_save_finished_callbacks.base_on_save_finished_callback": {"fullname": "auto_disc.legacy.utils.callbacks.on_save_finished_callbacks.base_on_save_finished_callback", "modulename": "auto_disc.legacy.utils.callbacks.on_save_finished_callbacks.base_on_save_finished_callback", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.callbacks.on_save_finished_callbacks.base_on_save_finished_callback.BaseOnSaveFinishedCallback": {"fullname": "auto_disc.legacy.utils.callbacks.on_save_finished_callbacks.base_on_save_finished_callback.BaseOnSaveFinishedCallback", "modulename": "auto_disc.legacy.utils.callbacks.on_save_finished_callbacks.base_on_save_finished_callback", "qualname": "BaseOnSaveFinishedCallback", "kind": "class", "doc": "

Base class for on save finished callbacks used by the experiment pipelines when the experiment save autodisc modules.

\n", "bases": "auto_disc.legacy.utils.callbacks.base_callback.BaseCallback"}, "auto_disc.legacy.utils.callbacks.on_save_finished_callbacks.base_on_save_finished_callback.BaseOnSaveFinishedCallback.__init__": {"fullname": "auto_disc.legacy.utils.callbacks.on_save_finished_callbacks.base_on_save_finished_callback.BaseOnSaveFinishedCallback.__init__", "modulename": "auto_disc.legacy.utils.callbacks.on_save_finished_callbacks.base_on_save_finished_callback", "qualname": "BaseOnSaveFinishedCallback.__init__", "kind": "function", "doc": "

initialize attributes common to all on save finished callbacks

\n", "signature": "(**kwargs)"}, "auto_disc.legacy.utils.callbacks.on_save_finished_callbacks.generate_report_callback": {"fullname": "auto_disc.legacy.utils.callbacks.on_save_finished_callbacks.generate_report_callback", "modulename": "auto_disc.legacy.utils.callbacks.on_save_finished_callbacks.generate_report_callback", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.callbacks.on_save_finished_callbacks.generate_report_callback.GenerateReport": {"fullname": "auto_disc.legacy.utils.callbacks.on_save_finished_callbacks.generate_report_callback.GenerateReport", "modulename": "auto_disc.legacy.utils.callbacks.on_save_finished_callbacks.generate_report_callback", "qualname": "GenerateReport", "kind": "class", "doc": "

Takes UID which identifies a saved object and annotates the saved data with\nhuman-readable identifying information

\n"}, "auto_disc.legacy.utils.callbacks.on_save_finished_callbacks.generate_report_callback.GenerateReport.__init__": {"fullname": "auto_disc.legacy.utils.callbacks.on_save_finished_callbacks.generate_report_callback.GenerateReport.__init__", "modulename": "auto_disc.legacy.utils.callbacks.on_save_finished_callbacks.generate_report_callback", "qualname": "GenerateReport.__init__", "kind": "function", "doc": "

\n", "signature": "(**kwargs)"}, "auto_disc.legacy.utils.config_parameters": {"fullname": "auto_disc.legacy.utils.config_parameters", "modulename": "auto_disc.legacy.utils.config_parameters", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.config_parameters.base_config_parameter": {"fullname": "auto_disc.legacy.utils.config_parameters.base_config_parameter", "modulename": "auto_disc.legacy.utils.config_parameters.base_config_parameter", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.config_parameters.base_config_parameter.BaseConfigParameter": {"fullname": "auto_disc.legacy.utils.config_parameters.base_config_parameter.BaseConfigParameter", "modulename": "auto_disc.legacy.utils.config_parameters.base_config_parameter", "qualname": "BaseConfigParameter", "kind": "class", "doc": "

Decorator to add a config parameter to a class.

\n"}, "auto_disc.legacy.utils.config_parameters.base_config_parameter.BaseConfigParameter.__init__": {"fullname": "auto_disc.legacy.utils.config_parameters.base_config_parameter.BaseConfigParameter.__init__", "modulename": "auto_disc.legacy.utils.config_parameters.base_config_parameter", "qualname": "BaseConfigParameter.__init__", "kind": "function", "doc": "

Init a config parameter

\n\n
Arguments:
\n\n
    \n
  • name: name of config parameter
    • \n
  • default: default value of the config parameter
    • \n
\n", "signature": "(name: str, default: Any)"}, "auto_disc.legacy.utils.config_parameters.base_config_parameter.BaseConfigParameter.check_value_to_set": {"fullname": "auto_disc.legacy.utils.config_parameters.base_config_parameter.BaseConfigParameter.check_value_to_set", "modulename": "auto_disc.legacy.utils.config_parameters.base_config_parameter", "qualname": "BaseConfigParameter.check_value_to_set", "kind": "function", "doc": "

Check if the value is one of the possible values

\n\n
Arguments:
\n\n
    \n
  • value: current value of the config parameter
    • \n
\n\n
Returns:
\n\n
\n

The return value is always True. All values are accepted

\n
\n", "signature": "(self, value: Any) -> bool:", "funcdef": "def"}, "auto_disc.legacy.utils.config_parameters.boolean_config_parameter": {"fullname": "auto_disc.legacy.utils.config_parameters.boolean_config_parameter", "modulename": "auto_disc.legacy.utils.config_parameters.boolean_config_parameter", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.config_parameters.boolean_config_parameter.BooleanConfigParameter": {"fullname": "auto_disc.legacy.utils.config_parameters.boolean_config_parameter.BooleanConfigParameter", "modulename": "auto_disc.legacy.utils.config_parameters.boolean_config_parameter", "qualname": "BooleanConfigParameter", "kind": "class", "doc": "

Decorator to add a boolean config parameter to a class.

\n", "bases": "auto_disc.legacy.utils.config_parameters.base_config_parameter.BaseConfigParameter"}, "auto_disc.legacy.utils.config_parameters.boolean_config_parameter.BooleanConfigParameter.__init__": {"fullname": "auto_disc.legacy.utils.config_parameters.boolean_config_parameter.BooleanConfigParameter.__init__", "modulename": "auto_disc.legacy.utils.config_parameters.boolean_config_parameter", "qualname": "BooleanConfigParameter.__init__", "kind": "function", "doc": "

Init a boolean config parameter. Define all possible values

\n\n
Arguments:
\n\n
    \n
  • name: name of config parameter
    • \n
  • default: default value of the config parameter
    • \n
\n", "signature": "(name: str, default: bool)"}, "auto_disc.legacy.utils.config_parameters.boolean_config_parameter.BooleanConfigParameter.check_value_to_set": {"fullname": "auto_disc.legacy.utils.config_parameters.boolean_config_parameter.BooleanConfigParameter.check_value_to_set", "modulename": "auto_disc.legacy.utils.config_parameters.boolean_config_parameter", "qualname": "BooleanConfigParameter.check_value_to_set", "kind": "function", "doc": "

Check if the value is one of the possible values

\n\n
Arguments:
\n\n
    \n
  • value: current value of the config parameter
    • \n
\n\n
Returns:
\n\n
\n

The return value is True if the value is a boolean else an exception was raised

\n
\n", "signature": "(self, value: bool) -> bool:", "funcdef": "def"}, "auto_disc.legacy.utils.config_parameters.decimal_config_parameter": {"fullname": "auto_disc.legacy.utils.config_parameters.decimal_config_parameter", "modulename": "auto_disc.legacy.utils.config_parameters.decimal_config_parameter", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.config_parameters.decimal_config_parameter.DecimalConfigParameter": {"fullname": "auto_disc.legacy.utils.config_parameters.decimal_config_parameter.DecimalConfigParameter", "modulename": "auto_disc.legacy.utils.config_parameters.decimal_config_parameter", "qualname": "DecimalConfigParameter", "kind": "class", "doc": "

Decorator to add a decimal config parameter to a class.\nUses a 'min' and 'max' to bound the authorized values.\nIf a bound is set to 'None', this means no bound.

\n", "bases": "auto_disc.legacy.utils.config_parameters.base_config_parameter.BaseConfigParameter"}, "auto_disc.legacy.utils.config_parameters.decimal_config_parameter.DecimalConfigParameter.__init__": {"fullname": "auto_disc.legacy.utils.config_parameters.decimal_config_parameter.DecimalConfigParameter.__init__", "modulename": "auto_disc.legacy.utils.config_parameters.decimal_config_parameter", "qualname": "DecimalConfigParameter.__init__", "kind": "function", "doc": "

Init a decimal config parameter. Define the bounds of the value

\n\n
Arguments:
\n\n
    \n
  • name: name of config parameter
    • \n
  • default: default value of the config parameter
    • \n
  • min: the lower bound
    • \n
  • max: the upper limit
    • \n
\n", "signature": "(name: str, default: float, min: float = None, max: float = None)"}, "auto_disc.legacy.utils.config_parameters.decimal_config_parameter.DecimalConfigParameter.check_value_to_set": {"fullname": "auto_disc.legacy.utils.config_parameters.decimal_config_parameter.DecimalConfigParameter.check_value_to_set", "modulename": "auto_disc.legacy.utils.config_parameters.decimal_config_parameter", "qualname": "DecimalConfigParameter.check_value_to_set", "kind": "function", "doc": "

Check if the value is between the two bounds

\n\n
Arguments:
\n\n
    \n
  • value: current value of the config parameter
    • \n
\n\n
Returns:
\n\n
\n

The return value is True if the value is between the two bounds else an exception is thrown

\n
\n", "signature": "(self, value):", "funcdef": "def"}, "auto_disc.legacy.utils.config_parameters.dict_config_parameter": {"fullname": "auto_disc.legacy.utils.config_parameters.dict_config_parameter", "modulename": "auto_disc.legacy.utils.config_parameters.dict_config_parameter", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.config_parameters.dict_config_parameter.DictConfigParameter": {"fullname": "auto_disc.legacy.utils.config_parameters.dict_config_parameter.DictConfigParameter", "modulename": "auto_disc.legacy.utils.config_parameters.dict_config_parameter", "qualname": "DictConfigParameter", "kind": "class", "doc": "

Decorator to add a dict config parameter to a class.

\n", "bases": "auto_disc.legacy.utils.config_parameters.base_config_parameter.BaseConfigParameter"}, "auto_disc.legacy.utils.config_parameters.dict_config_parameter.DictConfigParameter.__init__": {"fullname": "auto_disc.legacy.utils.config_parameters.dict_config_parameter.DictConfigParameter.__init__", "modulename": "auto_disc.legacy.utils.config_parameters.dict_config_parameter", "qualname": "DictConfigParameter.__init__", "kind": "function", "doc": "

Init a dict config parameter.

\n\n
Arguments:
\n\n
    \n
  • name: name of config parameter
    • \n
  • default: default value of the config parameter
    • \n
\n", "signature": "(name: str, default: Dict = {})"}, "auto_disc.legacy.utils.config_parameters.dict_config_parameter.DictConfigParameter.check_value_to_set": {"fullname": "auto_disc.legacy.utils.config_parameters.dict_config_parameter.DictConfigParameter.check_value_to_set", "modulename": "auto_disc.legacy.utils.config_parameters.dict_config_parameter", "qualname": "DictConfigParameter.check_value_to_set", "kind": "function", "doc": "

Check if the value is indeed a dict

\n\n
Arguments:
\n\n
    \n
  • value: current value of the config parameter
    • \n
\n\n
Returns:
\n\n
\n

The return value is True if the value is a dict

\n
\n", "signature": "(self, value: Dict) -> bool:", "funcdef": "def"}, "auto_disc.legacy.utils.config_parameters.integer_config_parameter": {"fullname": "auto_disc.legacy.utils.config_parameters.integer_config_parameter", "modulename": "auto_disc.legacy.utils.config_parameters.integer_config_parameter", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.config_parameters.integer_config_parameter.IntegerConfigParameter": {"fullname": "auto_disc.legacy.utils.config_parameters.integer_config_parameter.IntegerConfigParameter", "modulename": "auto_disc.legacy.utils.config_parameters.integer_config_parameter", "qualname": "IntegerConfigParameter", "kind": "class", "doc": "

Decorator to add an integer config parameter to a class.\nUses a 'min' and 'max' to bound the authorized values.\nIf a bound is set to 'None', this means no bound.\nIf a decimal value is passed, it will be rounded.

\n", "bases": "auto_disc.legacy.utils.config_parameters.decimal_config_parameter.DecimalConfigParameter"}, "auto_disc.legacy.utils.config_parameters.integer_config_parameter.IntegerConfigParameter.__init__": {"fullname": "auto_disc.legacy.utils.config_parameters.integer_config_parameter.IntegerConfigParameter.__init__", "modulename": "auto_disc.legacy.utils.config_parameters.integer_config_parameter", "qualname": "IntegerConfigParameter.__init__", "kind": "function", "doc": "

Init a int config parameter. Define the bounds of the value

\n\n
Arguments:
\n\n
    \n
  • name: name of config parameter
    • \n
  • default: default value of the config parameter
    • \n
  • min: the lower bound
    • \n
  • max: the upper limit
    • \n
\n", "signature": "(name: str, default: int, min: int = None, max: int = None)"}, "auto_disc.legacy.utils.config_parameters.integer_config_parameter.IntegerConfigParameter.check_value_to_set": {"fullname": "auto_disc.legacy.utils.config_parameters.integer_config_parameter.IntegerConfigParameter.check_value_to_set", "modulename": "auto_disc.legacy.utils.config_parameters.integer_config_parameter", "qualname": "IntegerConfigParameter.check_value_to_set", "kind": "function", "doc": "

Check if the value is between the two bounds

\n\n
Arguments:
\n\n
    \n
  • value: current value of the config parameter
    • \n
\n\n
Returns:
\n\n
\n

The return value is True if the value is between the two bounds else an exception is thrown

\n
\n", "signature": "(self, value: int) -> bool:", "funcdef": "def"}, "auto_disc.legacy.utils.config_parameters.string_config_parameter": {"fullname": "auto_disc.legacy.utils.config_parameters.string_config_parameter", "modulename": "auto_disc.legacy.utils.config_parameters.string_config_parameter", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.config_parameters.string_config_parameter.StringConfigParameter": {"fullname": "auto_disc.legacy.utils.config_parameters.string_config_parameter.StringConfigParameter", "modulename": "auto_disc.legacy.utils.config_parameters.string_config_parameter", "qualname": "StringConfigParameter", "kind": "class", "doc": "

Decorator to add a string config parameter to a class.\nUses a list of possible values to choose among.

\n", "bases": "auto_disc.legacy.utils.config_parameters.base_config_parameter.BaseConfigParameter"}, "auto_disc.legacy.utils.config_parameters.string_config_parameter.StringConfigParameter.__init__": {"fullname": "auto_disc.legacy.utils.config_parameters.string_config_parameter.StringConfigParameter.__init__", "modulename": "auto_disc.legacy.utils.config_parameters.string_config_parameter", "qualname": "StringConfigParameter.__init__", "kind": "function", "doc": "

Init a str config parameter. Define the list of all possible values.

\n\n
Arguments:
\n\n
    \n
  • name: name of config parameter
    • \n
  • default: default value of the config parameter
    • \n
  • possible_values: List of all possible string
    • \n
\n", "signature": "(name: str, default: str = '', possible_values: List[str] = None)"}, "auto_disc.legacy.utils.config_parameters.string_config_parameter.StringConfigParameter.check_value_to_set": {"fullname": "auto_disc.legacy.utils.config_parameters.string_config_parameter.StringConfigParameter.check_value_to_set", "modulename": "auto_disc.legacy.utils.config_parameters.string_config_parameter", "qualname": "StringConfigParameter.check_value_to_set", "kind": "function", "doc": "

Check if the value is one of the possible values

\n\n
Arguments:
\n\n
    \n
  • value: current value of the config parameter
    • \n
\n\n
Returns:
\n\n
\n

The return value is True if the value is in possible values

\n
\n", "signature": "(self, value: str) -> bool:", "funcdef": "def"}, "auto_disc.legacy.utils.logger": {"fullname": "auto_disc.legacy.utils.logger", "modulename": "auto_disc.legacy.utils.logger", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.logger.ad_logger": {"fullname": "auto_disc.legacy.utils.logger.ad_logger", "modulename": "auto_disc.legacy.utils.logger.ad_logger", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.logger.ad_logger.AutoDiscLogger": {"fullname": "auto_disc.legacy.utils.logger.ad_logger.AutoDiscLogger", "modulename": "auto_disc.legacy.utils.logger.ad_logger", "qualname": "AutoDiscLogger", "kind": "class", "doc": "

A logger to manage experiments logs and print them in the console or save them to the database or to disk according to the user's needs.

\n", "bases": "logging.Logger"}, "auto_disc.legacy.utils.logger.ad_logger.AutoDiscLogger.__init__": {"fullname": "auto_disc.legacy.utils.logger.ad_logger.AutoDiscLogger.__init__", "modulename": "auto_disc.legacy.utils.logger.ad_logger", "qualname": "AutoDiscLogger.__init__", "kind": "function", "doc": "

Init the logger for an experiment.

\n\n
Arguments:
\n\n
    \n
  • experiment_id: current experiment id
    • \n
  • seed: current seed number
    • \n
  • handlers: List of all handlers needed by the user to manage logs (e.g. save in database on disk)
    • \n
\n", "signature": "(\texperiment_id: int,\tseed: int,\thandlers: List[auto_disc.legacy.utils.logger.ad_logger.AutoDiscLogger])"}, "auto_disc.legacy.utils.logger.ad_logger.AutoDiscLogger.debug": {"fullname": "auto_disc.legacy.utils.logger.ad_logger.AutoDiscLogger.debug", "modulename": "auto_disc.legacy.utils.logger.ad_logger", "qualname": "AutoDiscLogger.debug", "kind": "function", "doc": "

Call the logs method at the debug level and increment the log index to make an unique id for each log

\n", "signature": "(self, *args) -> None:", "funcdef": "def"}, "auto_disc.legacy.utils.logger.ad_logger.AutoDiscLogger.info": {"fullname": "auto_disc.legacy.utils.logger.ad_logger.AutoDiscLogger.info", "modulename": "auto_disc.legacy.utils.logger.ad_logger", "qualname": "AutoDiscLogger.info", "kind": "function", "doc": "

Call the logs method at the info level and increment the log index to make an unique id for each log

\n", "signature": "(self, *args) -> None:", "funcdef": "def"}, "auto_disc.legacy.utils.logger.ad_logger.AutoDiscLogger.warning": {"fullname": "auto_disc.legacy.utils.logger.ad_logger.AutoDiscLogger.warning", "modulename": "auto_disc.legacy.utils.logger.ad_logger", "qualname": "AutoDiscLogger.warning", "kind": "function", "doc": "

Call the logs method at the warning level and increment the log index to make an unique id for each log

\n", "signature": "(self, *args) -> None:", "funcdef": "def"}, "auto_disc.legacy.utils.logger.ad_logger.AutoDiscLogger.error": {"fullname": "auto_disc.legacy.utils.logger.ad_logger.AutoDiscLogger.error", "modulename": "auto_disc.legacy.utils.logger.ad_logger", "qualname": "AutoDiscLogger.error", "kind": "function", "doc": "

Call the logs method at the error level and increment the log index to make an unique id for each log

\n", "signature": "(self, *args) -> None:", "funcdef": "def"}, "auto_disc.legacy.utils.logger.ad_logger.AutoDiscLogger.critical": {"fullname": "auto_disc.legacy.utils.logger.ad_logger.AutoDiscLogger.critical", "modulename": "auto_disc.legacy.utils.logger.ad_logger", "qualname": "AutoDiscLogger.critical", "kind": "function", "doc": "

Call the logs method at the critical level and increment the log index to make an unique id for each log

\n", "signature": "(self, *args) -> None:", "funcdef": "def"}, "auto_disc.legacy.utils.logger.ad_logger.ContextFilter": {"fullname": "auto_disc.legacy.utils.logger.ad_logger.ContextFilter", "modulename": "auto_disc.legacy.utils.logger.ad_logger", "qualname": "ContextFilter", "kind": "class", "doc": "

This is a filter which injects contextual information into the log.

\n", "bases": "logging.Filter"}, "auto_disc.legacy.utils.logger.ad_logger.ContextFilter.filter": {"fullname": "auto_disc.legacy.utils.logger.ad_logger.ContextFilter.filter", "modulename": "auto_disc.legacy.utils.logger.ad_logger", "qualname": "ContextFilter.filter", "kind": "function", "doc": "

Add contextual info to log

\n\n
Arguments:
\n\n
    \n
  • record: Some information
    • \n
\n\n
Returns:
\n\n
\n

The return value is always True

\n
\n", "signature": "(self, record: logging.LogRecord) -> bool:", "funcdef": "def"}, "auto_disc.legacy.utils.logger.handlers": {"fullname": "auto_disc.legacy.utils.logger.handlers", "modulename": "auto_disc.legacy.utils.logger.handlers", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.logger.handlers.file_handler": {"fullname": "auto_disc.legacy.utils.logger.handlers.file_handler", "modulename": "auto_disc.legacy.utils.logger.handlers.file_handler", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.logger.handlers.file_handler.SetFileHandler": {"fullname": "auto_disc.legacy.utils.logger.handlers.file_handler.SetFileHandler", "modulename": "auto_disc.legacy.utils.logger.handlers.file_handler", "qualname": "SetFileHandler", "kind": "class", "doc": "

Handler to put logs in file on disk

\n", "bases": "logging.FileHandler"}, "auto_disc.legacy.utils.logger.handlers.file_handler.SetFileHandler.__init__": {"fullname": "auto_disc.legacy.utils.logger.handlers.file_handler.SetFileHandler.__init__", "modulename": "auto_disc.legacy.utils.logger.handlers.file_handler", "qualname": "SetFileHandler.__init__", "kind": "function", "doc": "

Init an handler able to save log in file on disk

\n\n
Arguments:
\n\n
    \n
  • folder_log_path: The path to the folder where the user want save logs
    • \n
  • experiment_id: current experiment id
    • \n
\n", "signature": "(folder_log_path: str, experiment_id: int)"}, "auto_disc.legacy.utils.logger.log_levels_enum": {"fullname": "auto_disc.legacy.utils.logger.log_levels_enum", "modulename": "auto_disc.legacy.utils.logger.log_levels_enum", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.logger.log_levels_enum.LogsLevelEnum": {"fullname": "auto_disc.legacy.utils.logger.log_levels_enum.LogsLevelEnum", "modulename": "auto_disc.legacy.utils.logger.log_levels_enum", "qualname": "LogsLevelEnum", "kind": "class", "doc": "

An enumeration.

\n", "bases": "enum.Enum"}, "auto_disc.legacy.utils.logger.log_levels_enum.LogsLevelEnum.DEBUG": {"fullname": "auto_disc.legacy.utils.logger.log_levels_enum.LogsLevelEnum.DEBUG", "modulename": "auto_disc.legacy.utils.logger.log_levels_enum", "qualname": "LogsLevelEnum.DEBUG", "kind": "variable", "doc": "

\n", "default_value": "<LogsLevelEnum.DEBUG: 'debug'>"}, "auto_disc.legacy.utils.logger.log_levels_enum.LogsLevelEnum.INFO": {"fullname": "auto_disc.legacy.utils.logger.log_levels_enum.LogsLevelEnum.INFO", "modulename": "auto_disc.legacy.utils.logger.log_levels_enum", "qualname": "LogsLevelEnum.INFO", "kind": "variable", "doc": "

\n", "default_value": "<LogsLevelEnum.INFO: 'info'>"}, "auto_disc.legacy.utils.logger.log_levels_enum.LogsLevelEnum.WARNING": {"fullname": "auto_disc.legacy.utils.logger.log_levels_enum.LogsLevelEnum.WARNING", "modulename": "auto_disc.legacy.utils.logger.log_levels_enum", "qualname": "LogsLevelEnum.WARNING", "kind": "variable", "doc": "

\n", "default_value": "<LogsLevelEnum.WARNING: 'warning'>"}, "auto_disc.legacy.utils.logger.log_levels_enum.LogsLevelEnum.ERROR": {"fullname": "auto_disc.legacy.utils.logger.log_levels_enum.LogsLevelEnum.ERROR", "modulename": "auto_disc.legacy.utils.logger.log_levels_enum", "qualname": "LogsLevelEnum.ERROR", "kind": "variable", "doc": "

\n", "default_value": "<LogsLevelEnum.ERROR: 'error'>"}, "auto_disc.legacy.utils.logger.log_levels_enum.LogsLevelEnum.CRITICAL": {"fullname": "auto_disc.legacy.utils.logger.log_levels_enum.LogsLevelEnum.CRITICAL", "modulename": "auto_disc.legacy.utils.logger.log_levels_enum", "qualname": "LogsLevelEnum.CRITICAL", "kind": "variable", "doc": "

\n", "default_value": "<LogsLevelEnum.CRITICAL: 'critical'>"}, "auto_disc.legacy.utils.misc": {"fullname": "auto_disc.legacy.utils.misc", "modulename": "auto_disc.legacy.utils.misc", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.misc.db": {"fullname": "auto_disc.legacy.utils.misc.db", "modulename": "auto_disc.legacy.utils.misc.db", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.misc.db.DB": {"fullname": "auto_disc.legacy.utils.misc.db.DB", "modulename": "auto_disc.legacy.utils.misc.db", "qualname": "DB", "kind": "class", "doc": "

The main class of TinyDB.

\n\n

The TinyDB class is responsible for creating the storage class instance\nthat will store this database's documents, managing the database\ntables as well as providing access to the default table.

\n\n

For table management, a simple dict is used that stores the table class\ninstances accessible using their table name.

\n\n

Default table access is provided by forwarding all unknown method calls\nand property access operations to the default table by implementing\n__getattr__.

\n\n

When creating a new instance, all arguments and keyword arguments (except\nfor storage) will be passed to the storage class that is provided. If\nno storage class is specified, ~tinydb.storages.JSONStorage will be\nused.

\n\n

.. admonition:: Customization

\n\n
For customization, the following class variables can be set:\n\n- ``table_class`` defines the class that is used to create tables,\n- ``default_table_name`` defines the name of the default table, and\n- ``default_storage_class`` will define the class that will be used to\n  create storage instances if no other storage is passed.\n\n*New in version 4.0.*\n
\n\n

.. admonition:: Data Storage Model

\n\n
Data is stored using a storage class that provides persistence for a\n``dict`` instance. This ``dict`` contains all tables and their data.\nThe data is modelled like this::\n\n    {\n        'table1': {\n            0: {document...},\n            1: {document...},\n        },\n        'table2': {\n            ...\n        }\n    }\n\nEach entry in this ``dict`` uses the table name as its key and a\n``dict`` of documents as its value. The document ``dict`` contains\ndocument IDs as keys and the documents themselves as values.\n
\n\n
Parameters
\n\n
    \n
  • storage: The class of the storage to use. Will be initialized\nwith args and kwargs.
    • \n
\n", "bases": "tinydb.database.TinyDB"}, "auto_disc.legacy.utils.misc.db.DB.__init__": {"fullname": "auto_disc.legacy.utils.misc.db.DB.__init__", "modulename": "auto_disc.legacy.utils.misc.db", "qualname": "DB.__init__", "kind": "function", "doc": "

Create a new instance of TinyDB.

\n", "signature": "()"}, "auto_disc.legacy.utils.misc.db.DB.close": {"fullname": "auto_disc.legacy.utils.misc.db.DB.close", "modulename": "auto_disc.legacy.utils.misc.db", "qualname": "DB.close", "kind": "function", "doc": "

Close the database.

\n\n

This may be needed if the storage instance used for this database\nneeds to perform cleanup operations like closing file handles.

\n\n

To ensure this method is called, the TinyDB instance can be used as a\ncontext manager::

\n\n
with TinyDB('data.json') as db:\n    db.insert({'foo': 'bar'})\n
\n\n

Upon leaving this context, the close method will be called.

\n", "signature": "(self) -> None:", "funcdef": "def"}, "auto_disc.legacy.utils.misc.db.DB.to_autodisc_history": {"fullname": "auto_disc.legacy.utils.misc.db.DB.to_autodisc_history", "modulename": "auto_disc.legacy.utils.misc.db", "qualname": "DB.to_autodisc_history", "kind": "function", "doc": "

Select only some keys in documents and return a History. Use new_keys to rename these keys in the returned History.

\n\n
Arguments:
\n\n
    \n
  • documents: list of db item
    • \n
  • keys: list of key
    • \n
  • new_keys: list of key
    • \n
\n\n
Returns:
\n\n
\n

History

\n
\n", "signature": "(\tself,\tdocuments: list,\tkeys: List[str],\tnew_keys: List[str] = None) -> auto_disc.legacy.utils.misc.history.History:", "funcdef": "def"}, "auto_disc.legacy.utils.misc.db.DB.save": {"fullname": "auto_disc.legacy.utils.misc.db.DB.save", "modulename": "auto_disc.legacy.utils.misc.db", "qualname": "DB.save", "kind": "function", "doc": "

Save DB.

\n", "signature": "(self) -> auto_disc.legacy.utils.misc.db.DB:", "funcdef": "def"}, "auto_disc.legacy.utils.misc.db.DB.load": {"fullname": "auto_disc.legacy.utils.misc.db.DB.load", "modulename": "auto_disc.legacy.utils.misc.db", "qualname": "DB.load", "kind": "function", "doc": "

Reload DB.

\n", "signature": "(self, saved_dict):", "funcdef": "def"}, "auto_disc.legacy.utils.misc.history": {"fullname": "auto_disc.legacy.utils.misc.history", "modulename": "auto_disc.legacy.utils.misc.history", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.misc.history.History": {"fullname": "auto_disc.legacy.utils.misc.history.History", "modulename": "auto_disc.legacy.utils.misc.history", "qualname": "History", "kind": "class", "doc": "

\n"}, "auto_disc.legacy.utils.misc.history.History.__init__": {"fullname": "auto_disc.legacy.utils.misc.history.History.__init__", "modulename": "auto_disc.legacy.utils.misc.history", "qualname": "History.__init__", "kind": "function", "doc": "

\n", "signature": "(columns: List[str], documents: List[Dict] = [])"}, "auto_disc.legacy.utils.misc.history.History.append": {"fullname": "auto_disc.legacy.utils.misc.history.History.append", "modulename": "auto_disc.legacy.utils.misc.history", "qualname": "History.append", "kind": "function", "doc": "

Append document

\n\n
Arguments:
\n\n
    \n
  • document: dict of database element
    • \n
\n", "signature": "(self, document: Dict) -> None:", "funcdef": "def"}, "auto_disc.legacy.utils.misc.torch_utils": {"fullname": "auto_disc.legacy.utils.misc.torch_utils", "modulename": "auto_disc.legacy.utils.misc.torch_utils", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.misc.torch_utils.SphericPad": {"fullname": "auto_disc.legacy.utils.misc.torch_utils.SphericPad", "modulename": "auto_disc.legacy.utils.misc.torch_utils", "qualname": "SphericPad", "kind": "class", "doc": "

Pads spherically the input on all sides with the given padding size.

\n", "bases": "torch.nn.modules.module.Module"}, "auto_disc.legacy.utils.misc.torch_utils.SphericPad.__init__": {"fullname": "auto_disc.legacy.utils.misc.torch_utils.SphericPad.__init__", "modulename": "auto_disc.legacy.utils.misc.torch_utils", "qualname": "SphericPad.__init__", "kind": "function", "doc": "

Initializes internal Module state, shared by both nn.Module and ScriptModule.

\n", "signature": "(padding_size: torch.Tensor)"}, "auto_disc.legacy.utils.misc.torch_utils.SphericPad.forward": {"fullname": "auto_disc.legacy.utils.misc.torch_utils.SphericPad.forward", "modulename": "auto_disc.legacy.utils.misc.torch_utils", "qualname": "SphericPad.forward", "kind": "function", "doc": "

Defines the computation performed at every call.

\n\n

Should be overridden by all subclasses.

\n\n
\n\n

Although the recipe for forward pass needs to be defined within\nthis function, one should call the Module instance afterwards\ninstead of this since the former takes care of running the\nregistered hooks while the latter silently ignores them.

\n\n
\n", "signature": "(self, input):", "funcdef": "def"}, "auto_disc.legacy.utils.misc.torch_utils.complex_mult_torch": {"fullname": "auto_disc.legacy.utils.misc.torch_utils.complex_mult_torch", "modulename": "auto_disc.legacy.utils.misc.torch_utils", "qualname": "complex_mult_torch", "kind": "function", "doc": "

Computes the complex multiplication in Pytorch when the tensor last dimension is 2: 0 is the real component and 1 the imaginary one

\n", "signature": "(X: torch.Tensor, Y: torch.Tensor) -> torch.Tensor:", "funcdef": "def"}, "auto_disc.legacy.utils.misc.torch_utils.roll_n": {"fullname": "auto_disc.legacy.utils.misc.torch_utils.roll_n", "modulename": "auto_disc.legacy.utils.misc.torch_utils", "qualname": "roll_n", "kind": "function", "doc": "

Rolls a tensor with a shift n on the specified axis

\n", "signature": "(X: torch.Tensor, axis: int, n: int) -> torch.Tensor:", "funcdef": "def"}, "auto_disc.legacy.utils.misc.torch_utils.soft_max": {"fullname": "auto_disc.legacy.utils.misc.torch_utils.soft_max", "modulename": "auto_disc.legacy.utils.misc.torch_utils", "qualname": "soft_max", "kind": "function", "doc": "

\n", "signature": "(x, m, k):", "funcdef": "def"}, "auto_disc.legacy.utils.misc.torch_utils.soft_clip": {"fullname": "auto_disc.legacy.utils.misc.torch_utils.soft_clip", "modulename": "auto_disc.legacy.utils.misc.torch_utils", "qualname": "soft_clip", "kind": "function", "doc": "

\n", "signature": "(x, min, max, k):", "funcdef": "def"}, "auto_disc.legacy.utils.mutators": {"fullname": "auto_disc.legacy.utils.mutators", "modulename": "auto_disc.legacy.utils.mutators", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.mutators.base_mutator": {"fullname": "auto_disc.legacy.utils.mutators.base_mutator", "modulename": "auto_disc.legacy.utils.mutators.base_mutator", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.mutators.base_mutator.BaseMutator": {"fullname": "auto_disc.legacy.utils.mutators.base_mutator.BaseMutator", "modulename": "auto_disc.legacy.utils.mutators.base_mutator", "qualname": "BaseMutator", "kind": "class", "doc": "

Base class to mute space var

\n"}, "auto_disc.legacy.utils.mutators.base_mutator.BaseMutator.init_shape": {"fullname": "auto_disc.legacy.utils.mutators.base_mutator.BaseMutator.init_shape", "modulename": "auto_disc.legacy.utils.mutators.base_mutator", "qualname": "BaseMutator.init_shape", "kind": "function", "doc": "

\n", "signature": "(self, shape=None):", "funcdef": "def"}, "auto_disc.legacy.utils.mutators.gaussian_mutator": {"fullname": "auto_disc.legacy.utils.mutators.gaussian_mutator", "modulename": "auto_disc.legacy.utils.mutators.gaussian_mutator", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.mutators.gaussian_mutator.GaussianMutator": {"fullname": "auto_disc.legacy.utils.mutators.gaussian_mutator.GaussianMutator", "modulename": "auto_disc.legacy.utils.mutators.gaussian_mutator", "qualname": "GaussianMutator", "kind": "class", "doc": "

Class to mutate a space with gaussian method

\n", "bases": "auto_disc.legacy.utils.mutators.base_mutator.BaseMutator"}, "auto_disc.legacy.utils.mutators.gaussian_mutator.GaussianMutator.__init__": {"fullname": "auto_disc.legacy.utils.mutators.gaussian_mutator.GaussianMutator.__init__", "modulename": "auto_disc.legacy.utils.mutators.gaussian_mutator", "qualname": "GaussianMutator.__init__", "kind": "function", "doc": "

Init what gaussian method need

\n\n
Arguments:
\n\n
    \n
  • mean: float
    • \n
  • std: float
    • \n
\n", "signature": "(mean: float, std: float)"}, "auto_disc.legacy.utils.mutators.gaussian_mutator.GaussianMutator.init_shape": {"fullname": "auto_disc.legacy.utils.mutators.gaussian_mutator.GaussianMutator.init_shape", "modulename": "auto_disc.legacy.utils.mutators.gaussian_mutator", "qualname": "GaussianMutator.init_shape", "kind": "function", "doc": "

Define the init shape

\n\n
Arguments:
\n\n
    \n
  • shape: tuple
    • \n
\n", "signature": "(self, shape: tuple = None) -> None:", "funcdef": "def"}, "auto_disc.legacy.utils.spaces": {"fullname": "auto_disc.legacy.utils.spaces", "modulename": "auto_disc.legacy.utils.spaces", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.spaces.base_space": {"fullname": "auto_disc.legacy.utils.spaces.base_space", "modulename": "auto_disc.legacy.utils.spaces.base_space", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.spaces.base_space.BaseSpace": {"fullname": "auto_disc.legacy.utils.spaces.base_space.BaseSpace", "modulename": "auto_disc.legacy.utils.spaces.base_space", "qualname": "BaseSpace", "kind": "class", "doc": "

Defines the init_space, genome_space and intervention_space of a system

\n"}, "auto_disc.legacy.utils.spaces.base_space.BaseSpace.__init__": {"fullname": "auto_disc.legacy.utils.spaces.base_space.BaseSpace.__init__", "modulename": "auto_disc.legacy.utils.spaces.base_space", "qualname": "BaseSpace.__init__", "kind": "function", "doc": "

Init the elements useful to the spaces

\n\n
Arguments:
\n\n
    \n
  • shape: Space shape
    • \n
  • dtype: torch type
    • \n
  • mutator: current mutator method
    • \n
\n", "signature": "(\tshape: Tuple[Union[auto_disc.legacy.utils.spaces.utils.config_parameter_binding.ConfigParameterBinding, Any]] = None,\tdtype: torch.dtype = None,\tmutator: auto_disc.legacy.utils.mutators.base_mutator.BaseMutator = None)"}, "auto_disc.legacy.utils.spaces.base_space.BaseSpace.initialize": {"fullname": "auto_disc.legacy.utils.spaces.base_space.BaseSpace.initialize", "modulename": "auto_disc.legacy.utils.spaces.base_space", "qualname": "BaseSpace.initialize", "kind": "function", "doc": "

Initialize the space.

\n\n
Arguments:
\n\n
    \n
  • parent_obj: The current autodisc module
    • \n
\n", "signature": "(\tself,\tparent_obj: auto_disc.legacy.base_autodisc_module.BaseAutoDiscModule) -> None:", "funcdef": "def"}, "auto_disc.legacy.utils.spaces.base_space.BaseSpace.apply_binding_if_existing": {"fullname": "auto_disc.legacy.utils.spaces.base_space.BaseSpace.apply_binding_if_existing", "modulename": "auto_disc.legacy.utils.spaces.base_space", "qualname": "BaseSpace.apply_binding_if_existing", "kind": "function", "doc": "

Get result of config parameter binding operation

\n\n
Arguments:
\n\n
    \n
  • var: the current variable processed
    • \n
  • lookup_obj: current autodisc module in which the binding was made
    • \n
\n\n
Returns:
\n\n
\n

value: The result of the operation binding

\n
\n", "signature": "(\tself,\tvar: Union[auto_disc.legacy.utils.spaces.utils.config_parameter_binding.ConfigParameterBinding, Any],\tlookup_obj: object) -> Any:", "funcdef": "def"}, "auto_disc.legacy.utils.spaces.base_space.BaseSpace.sample": {"fullname": "auto_disc.legacy.utils.spaces.base_space.BaseSpace.sample", "modulename": "auto_disc.legacy.utils.spaces.base_space", "qualname": "BaseSpace.sample", "kind": "function", "doc": "

Randomly sample an element of this space.\nCan be uniform or non-uniform sampling based on boundedness of space.

\n", "signature": "(self):", "funcdef": "def"}, "auto_disc.legacy.utils.spaces.base_space.BaseSpace.mutate": {"fullname": "auto_disc.legacy.utils.spaces.base_space.BaseSpace.mutate", "modulename": "auto_disc.legacy.utils.spaces.base_space", "qualname": "BaseSpace.mutate", "kind": "function", "doc": "

Randomly mutate an element of this space.

\n\n
Arguments:
\n\n
    \n
  • x: The element to be mutated
    • \n
\n", "signature": "(self, x):", "funcdef": "def"}, "auto_disc.legacy.utils.spaces.base_space.BaseSpace.contains": {"fullname": "auto_disc.legacy.utils.spaces.base_space.BaseSpace.contains", "modulename": "auto_disc.legacy.utils.spaces.base_space", "qualname": "BaseSpace.contains", "kind": "function", "doc": "

Return boolean specifying if x is a valid\nmember of this space

\n\n
Arguments:
\n\n
    \n
  • x: The var which must be part of the space
    • \n
\n", "signature": "(self, x):", "funcdef": "def"}, "auto_disc.legacy.utils.spaces.base_space.BaseSpace.clamp": {"fullname": "auto_disc.legacy.utils.spaces.base_space.BaseSpace.clamp", "modulename": "auto_disc.legacy.utils.spaces.base_space", "qualname": "BaseSpace.clamp", "kind": "function", "doc": "

Return a valid clamped value of x inside space's bounds

\n\n
Arguments:
\n\n
    \n
  • x: a value
    • \n
\n", "signature": "(self, x):", "funcdef": "def"}, "auto_disc.legacy.utils.spaces.base_space.BaseSpace.to_json": {"fullname": "auto_disc.legacy.utils.spaces.base_space.BaseSpace.to_json", "modulename": "auto_disc.legacy.utils.spaces.base_space", "qualname": "BaseSpace.to_json", "kind": "function", "doc": "

Convert the space into JSON

\n\n
Returns:
\n\n
\n

The return value are a json containing a readable shape

\n
\n", "signature": "(self) -> Dict:", "funcdef": "def"}, "auto_disc.legacy.utils.spaces.box": {"fullname": "auto_disc.legacy.utils.spaces.box", "modulename": "auto_disc.legacy.utils.spaces.box", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.spaces.box.BoxSpace": {"fullname": "auto_disc.legacy.utils.spaces.box.BoxSpace", "modulename": "auto_disc.legacy.utils.spaces.box", "qualname": "BoxSpace", "kind": "class", "doc": "

A (possibly unbounded) box in R^n. Specifically, a Box represents the\nCartesian product of n closed intervals. Each interval has the form of one\nof [a, b], (-oo, b], [a, oo), or (-oo, oo).

\n\n

There are two common use cases:

\n\n
    \n

  • Identical bound for each dimension::

    \n\n
    \n
    >>> BoxSpace(low=-1.0, high=2.0, shape=(3, 4), dtype=torch.float32)\nBox(3, 4)\n
    \n
    • \n

  • Independent bound for each dimension::

    \n\n
    \n
    >>> BoxSpace(low=torch.tensor([-1.0, -2.0]), high=torch.tensor([2.0, 4.0]), dtype=torch.float32)\nBox(2,)\n
    \n
    • \n
\n", "bases": "auto_disc.legacy.utils.spaces.base_space.BaseSpace"}, "auto_disc.legacy.utils.spaces.box.BoxSpace.__init__": {"fullname": "auto_disc.legacy.utils.spaces.box.BoxSpace.__init__", "modulename": "auto_disc.legacy.utils.spaces.box", "qualname": "BoxSpace.__init__", "kind": "function", "doc": "

Init the elements useful to the spaces

\n\n
Arguments:
\n\n
    \n
  • low: lower bound
    • \n
  • hight: upper bound
    • \n
  • mutator: current mutator method
    • \n
  • shape: Space shape
    • \n
  • dtype: torch type
    • \n
  • indpb: Independent probability for each attribute to be exchanged
    • \n
\n", "signature": "(\tlow: float,\thigh: float,\tmutator: auto_disc.legacy.utils.mutators.base_mutator.BaseMutator = None,\tshape: tuple = None,\tdtype=torch.float32,\tindpb: float = 1.0)"}, "auto_disc.legacy.utils.spaces.box.BoxSpace.initialize": {"fullname": "auto_disc.legacy.utils.spaces.box.BoxSpace.initialize", "modulename": "auto_disc.legacy.utils.spaces.box", "qualname": "BoxSpace.initialize", "kind": "function", "doc": "

Initialize the space.

\n\n
Arguments:
\n\n
    \n
  • parent_obj: The current autodisc module
    • \n
\n", "signature": "(self, parent_obj: object) -> None:", "funcdef": "def"}, "auto_disc.legacy.utils.spaces.box.BoxSpace.is_bounded": {"fullname": "auto_disc.legacy.utils.spaces.box.BoxSpace.is_bounded", "modulename": "auto_disc.legacy.utils.spaces.box", "qualname": "BoxSpace.is_bounded", "kind": "function", "doc": "

Check if the space is bounded (below above or both)

\n\n
Arguments:
\n\n
    \n
  • manner: indicates which bound should be checked
    • \n
\n", "signature": "(self, manner: str = 'both') -> torch.bool:", "funcdef": "def"}, "auto_disc.legacy.utils.spaces.box.BoxSpace.sample": {"fullname": "auto_disc.legacy.utils.spaces.box.BoxSpace.sample", "modulename": "auto_disc.legacy.utils.spaces.box", "qualname": "BoxSpace.sample", "kind": "function", "doc": "

Generates a single random sample inside of the Box.

\n\n

In creating a sample of the box, each coordinate is sampled according to\nthe form of the interval:

\n\n
    \n
  • [a, b] : uniform distribution
    • \n
  • [a, oo) : shifted exponential distribution
    • \n
  • (-oo, b] : shifted negative exponential distribution
    • \n
  • (-oo, oo) : normal distribution
    • \n
\n\n
Return:
\n\n
\n

The return value is torch of the sample value

\n
\n", "signature": "(self):", "funcdef": "def"}, "auto_disc.legacy.utils.spaces.box.BoxSpace.mutate": {"fullname": "auto_disc.legacy.utils.spaces.box.BoxSpace.mutate", "modulename": "auto_disc.legacy.utils.spaces.box", "qualname": "BoxSpace.mutate", "kind": "function", "doc": "

Apply the mutation of the mutator on x

\n\n
Arguments:
\n\n
    \n
  • x: The variable to mutate
    • \n
\n\n
Returns:
\n\n
\n

x: the result of mutation

\n
\n", "signature": "(self, x: torch.Tensor) -> torch.Tensor:", "funcdef": "def"}, "auto_disc.legacy.utils.spaces.box.BoxSpace.contains": {"fullname": "auto_disc.legacy.utils.spaces.box.BoxSpace.contains", "modulename": "auto_disc.legacy.utils.spaces.box", "qualname": "BoxSpace.contains", "kind": "function", "doc": "

Check if x is included in the space

\n\n
Arguments:
\n\n
    \n
  • x: The value to check
    • \n
\n\n
Returns:
\n\n
\n

The return value is True if x is included in the space False otherwise

\n
\n", "signature": "(self, x: Union[torch.Tensor, List]) -> bool:", "funcdef": "def"}, "auto_disc.legacy.utils.spaces.box.BoxSpace.clamp": {"fullname": "auto_disc.legacy.utils.spaces.box.BoxSpace.clamp", "modulename": "auto_disc.legacy.utils.spaces.box", "qualname": "BoxSpace.clamp", "kind": "function", "doc": "

Set x to an acceptable value of the space

\n\n
Arguments:
\n\n
    \n
  • x: The value to set
    • \n
\n\n
Returns:
\n\n
\n

x: After being set

\n
\n", "signature": "(self, x: torch.Tensor) -> torch.Tensor:", "funcdef": "def"}, "auto_disc.legacy.utils.spaces.box.BoxSpace.to_json": {"fullname": "auto_disc.legacy.utils.spaces.box.BoxSpace.to_json", "modulename": "auto_disc.legacy.utils.spaces.box", "qualname": "BoxSpace.to_json", "kind": "function", "doc": "

Convert the object into JSON

\n\n
Returns:
\n\n
\n

The JSON of the object

\n
\n", "signature": "(self) -> Dict[str, <built-in function any>]:", "funcdef": "def"}, "auto_disc.legacy.utils.spaces.dict": {"fullname": "auto_disc.legacy.utils.spaces.dict", "modulename": "auto_disc.legacy.utils.spaces.dict", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.spaces.dict.DictSpace": {"fullname": "auto_disc.legacy.utils.spaces.dict.DictSpace", "modulename": "auto_disc.legacy.utils.spaces.dict", "qualname": "DictSpace", "kind": "class", "doc": "

A Dict dictionary of simpler spaces.

\n\n

Example usage:\nself.genome_space = spaces.DictSpace({\"position\": spaces.Discrete(2), \"velocity\": spaces.Discrete(3)})

\n\n

Example usage [nested]:\nself.nested_genome_space = spaces.DictSpace({\n 'sensors': spaces.DictSpace({\n 'position': spaces.Box(low=-100, high=100, shape=(3,)),\n 'velocity': spaces.Box(low=-1, high=1, shape=(3,)),\n 'front_cam': spaces.Tuple((\n spaces.Box(low=0, high=1, shape=(10, 10, 3)),\n spaces.Box(low=0, high=1, shape=(10, 10, 3))\n )),\n 'rear_cam': spaces.Box(low=0, high=1, shape=(10, 10, 3)),\n }),\n 'ext_controller': spaces.MultiDiscrete((5, 2, 2)),\n 'inner_state':spaces.DictSpace({\n 'charge': spaces.Discrete(100),\n 'system_checks': spaces.MultiBinary(10),\n 'job_status': spaces.DictSpace({\n 'task': spaces.Discrete(5),\n 'progress': spaces.Box(low=0, high=100, shape=()),\n })\n })\n})

\n", "bases": "auto_disc.legacy.utils.spaces.base_space.BaseSpace"}, "auto_disc.legacy.utils.spaces.dict.DictSpace.__init__": {"fullname": "auto_disc.legacy.utils.spaces.dict.DictSpace.__init__", "modulename": "auto_disc.legacy.utils.spaces.dict", "qualname": "DictSpace.__init__", "kind": "function", "doc": "

Init the elements useful to the space. Define spaces as dict of spaces

\n\n
Arguments:
\n\n
    \n
  • spaces: All spaces to be used
    • \n
\n", "signature": "(spaces=None, **spaces_kwargs)"}, "auto_disc.legacy.utils.spaces.dict.DictSpace.initialize": {"fullname": "auto_disc.legacy.utils.spaces.dict.DictSpace.initialize", "modulename": "auto_disc.legacy.utils.spaces.dict", "qualname": "DictSpace.initialize", "kind": "function", "doc": "

Initialize all spaces in dict.

\n\n
Arguments:
\n\n
    \n
  • parent_obj: The current autodisc module
    • \n
\n", "signature": "(self, parent_obj: object) -> None:", "funcdef": "def"}, "auto_disc.legacy.utils.spaces.dict.DictSpace.sample": {"fullname": "auto_disc.legacy.utils.spaces.dict.DictSpace.sample", "modulename": "auto_disc.legacy.utils.spaces.dict", "qualname": "DictSpace.sample", "kind": "function", "doc": "

Generates sample for each spaces according to the space

\n\n
Return:
\n\n
\n

The return value is Dict of sample values

\n
\n", "signature": "(\tself) -> Dict[str, <built-in method tensor of type object at 0x7fee8b64e460>]:", "funcdef": "def"}, "auto_disc.legacy.utils.spaces.dict.DictSpace.mutate": {"fullname": "auto_disc.legacy.utils.spaces.dict.DictSpace.mutate", "modulename": "auto_disc.legacy.utils.spaces.dict", "qualname": "DictSpace.mutate", "kind": "function", "doc": "

Apply the mutation for each spaces according to the space

\n\n
Return:
\n\n
\n

The return value is Dict of mutates values

\n
\n", "signature": "(self, x):", "funcdef": "def"}, "auto_disc.legacy.utils.spaces.dict.DictSpace.contains": {"fullname": "auto_disc.legacy.utils.spaces.dict.DictSpace.contains", "modulename": "auto_disc.legacy.utils.spaces.dict", "qualname": "DictSpace.contains", "kind": "function", "doc": "

Check if each value is correctly set in its space

\n\n
Arguments:
\n\n
    \n
  • x: The dict of spaces
    • \n
\n\n
Returns:
\n\n
\n

The return value is True if each item in x is included in its space False otherwise

\n
\n", "signature": "(self, x: addict.addict.Dict) -> bool:", "funcdef": "def"}, "auto_disc.legacy.utils.spaces.dict.DictSpace.clamp": {"fullname": "auto_disc.legacy.utils.spaces.dict.DictSpace.clamp", "modulename": "auto_disc.legacy.utils.spaces.dict", "qualname": "DictSpace.clamp", "kind": "function", "doc": "

For each element in dict set it to an acceptable value of the space

\n\n
Arguments:
\n\n
    \n
  • x: The dict of value value to set
    • \n
\n\n
Returns:
\n\n
\n

x: After being set

\n
\n", "signature": "(self, x: addict.addict.Dict) -> addict.addict.Dict:", "funcdef": "def"}, "auto_disc.legacy.utils.spaces.dict.DictSpace.to_json": {"fullname": "auto_disc.legacy.utils.spaces.dict.DictSpace.to_json", "modulename": "auto_disc.legacy.utils.spaces.dict", "qualname": "DictSpace.to_json", "kind": "function", "doc": "

Convert the object into JSON

\n\n
Returns:
\n\n
\n

The JSON of the object

\n
\n", "signature": "(self):", "funcdef": "def"}, "auto_disc.legacy.utils.spaces.discrete": {"fullname": "auto_disc.legacy.utils.spaces.discrete", "modulename": "auto_disc.legacy.utils.spaces.discrete", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.spaces.discrete.DiscreteSpace": {"fullname": "auto_disc.legacy.utils.spaces.discrete.DiscreteSpace", "modulename": "auto_disc.legacy.utils.spaces.discrete", "qualname": "DiscreteSpace", "kind": "class", "doc": "

A discrete space in \\( { 0, 1, \\dots, n-1 } \\).\n/!\\ mutation is gaussian by default: please create custom space inheriting from discrete space for custom mutation functions

\n\n

Example::

\n\n
>>> DiscreteSpace(2)\n
\n", "bases": "auto_disc.legacy.utils.spaces.base_space.BaseSpace"}, "auto_disc.legacy.utils.spaces.discrete.DiscreteSpace.__init__": {"fullname": "auto_disc.legacy.utils.spaces.discrete.DiscreteSpace.__init__", "modulename": "auto_disc.legacy.utils.spaces.discrete", "qualname": "DiscreteSpace.__init__", "kind": "function", "doc": "

Init the elements useful to the spaces

\n\n
Arguments:
\n\n
    \n
  • n: discret value
    • \n
  • mutator: current mutator method
    • \n
  • indpb: Independent probability for each attribute to be exchanged
    • \n
\n", "signature": "(\tn: int,\tmutator: auto_disc.legacy.utils.mutators.base_mutator.BaseMutator = None,\tindpb: float = 1.0)"}, "auto_disc.legacy.utils.spaces.discrete.DiscreteSpace.initialize": {"fullname": "auto_disc.legacy.utils.spaces.discrete.DiscreteSpace.initialize", "modulename": "auto_disc.legacy.utils.spaces.discrete", "qualname": "DiscreteSpace.initialize", "kind": "function", "doc": "

Initialize the space.

\n\n
Arguments:
\n\n
    \n
  • parent_obj: The current autodisc module
    • \n
\n", "signature": "(self, parent_obj: object) -> None:", "funcdef": "def"}, "auto_disc.legacy.utils.spaces.discrete.DiscreteSpace.sample": {"fullname": "auto_disc.legacy.utils.spaces.discrete.DiscreteSpace.sample", "modulename": "auto_disc.legacy.utils.spaces.discrete", "qualname": "DiscreteSpace.sample", "kind": "function", "doc": "

Generates a single random sample, upper than 0 and lower than n.

\n\n
Return:
\n\n
\n

The return value is torch of the sample value

\n
\n", "signature": "(self) -> torch.Tensor:", "funcdef": "def"}, "auto_disc.legacy.utils.spaces.discrete.DiscreteSpace.mutate": {"fullname": "auto_disc.legacy.utils.spaces.discrete.DiscreteSpace.mutate", "modulename": "auto_disc.legacy.utils.spaces.discrete", "qualname": "DiscreteSpace.mutate", "kind": "function", "doc": "

Apply the mutation of the mutator on x

\n\n
Arguments:
\n\n
    \n
  • x: The variable to mutate
    • \n
\n\n
Returns:
\n\n
\n

x: the result of mutation

\n
\n", "signature": "(self, x: torch.Tensor) -> torch.Tensor:", "funcdef": "def"}, "auto_disc.legacy.utils.spaces.discrete.DiscreteSpace.contains": {"fullname": "auto_disc.legacy.utils.spaces.discrete.DiscreteSpace.contains", "modulename": "auto_disc.legacy.utils.spaces.discrete", "qualname": "DiscreteSpace.contains", "kind": "function", "doc": "

Check if x is bounded between 0 and n

\n\n
Arguments:
\n\n
    \n
  • x: The value to check
    • \n
\n\n
Returns:
\n\n
\n

The return value is True if x is bounded by 0 and n False otherwise

\n
\n", "signature": "(self, x: torch.Tensor) -> bool:", "funcdef": "def"}, "auto_disc.legacy.utils.spaces.discrete.DiscreteSpace.clamp": {"fullname": "auto_disc.legacy.utils.spaces.discrete.DiscreteSpace.clamp", "modulename": "auto_disc.legacy.utils.spaces.discrete", "qualname": "DiscreteSpace.clamp", "kind": "function", "doc": "

Set x to an acceptable value of the space

\n\n
Arguments:
\n\n
    \n
  • x: The value to set
    • \n
\n\n
Returns:
\n\n
\n

x: After being set

\n
\n", "signature": "(self, x: torch.Tensor) -> torch.Tensor:", "funcdef": "def"}, "auto_disc.legacy.utils.spaces.discrete.DiscreteSpace.to_json": {"fullname": "auto_disc.legacy.utils.spaces.discrete.DiscreteSpace.to_json", "modulename": "auto_disc.legacy.utils.spaces.discrete", "qualname": "DiscreteSpace.to_json", "kind": "function", "doc": "

Convert the object into JSON

\n\n
Returns:
\n\n
\n

The JSON of the object

\n
\n", "signature": "(self) -> Dict[str, Any]:", "funcdef": "def"}, "auto_disc.legacy.utils.spaces.multi_binary": {"fullname": "auto_disc.legacy.utils.spaces.multi_binary", "modulename": "auto_disc.legacy.utils.spaces.multi_binary", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.spaces.multi_binary.MultiBinarySpace": {"fullname": "auto_disc.legacy.utils.spaces.multi_binary.MultiBinarySpace", "modulename": "auto_disc.legacy.utils.spaces.multi_binary", "qualname": "MultiBinarySpace", "kind": "class", "doc": "

An n-shape binary space.

\n\n

The argument to MultiBinarySpace defines n, which could be a number or a list of numbers.

\n\n

Example Usage:

\n\n
\n
\n

self.genome_space = spaces.MultiBinarySpace(5)

\n
\n
\n\n
\n
\n

self.genome_space.sample()

\n\n
array([0,1,0,1,0], dtype =int8)\n
\n \n

self.genome_space = spaces.MultiBinarySpace([3,2])

\n
\n
\n\n
\n
\n

self.genome_space.sample()

\n\n
array([[0, 0],\n       [0, 1],\n       [1, 1]], dtype=int8)\n
\n
\n
\n", "bases": "auto_disc.legacy.utils.spaces.base_space.BaseSpace"}, "auto_disc.legacy.utils.spaces.multi_binary.MultiBinarySpace.__init__": {"fullname": "auto_disc.legacy.utils.spaces.multi_binary.MultiBinarySpace.__init__", "modulename": "auto_disc.legacy.utils.spaces.multi_binary", "qualname": "MultiBinarySpace.__init__", "kind": "function", "doc": "

Init the elements useful to the spaces

\n\n
Arguments:
\n\n
    \n
  • n: number of element
    • \n
  • indpb: Independent probability for each attribute to be exchanged
    • \n
\n", "signature": "(n: int, indpb: float = 1.0)"}, "auto_disc.legacy.utils.spaces.multi_binary.MultiBinarySpace.initialize": {"fullname": "auto_disc.legacy.utils.spaces.multi_binary.MultiBinarySpace.initialize", "modulename": "auto_disc.legacy.utils.spaces.multi_binary", "qualname": "MultiBinarySpace.initialize", "kind": "function", "doc": "

Initialize the space.

\n\n
Arguments:
\n\n
    \n
  • parent_obj: The current autodisc module
    • \n
\n", "signature": "(self, parent_obj: object) -> None:", "funcdef": "def"}, "auto_disc.legacy.utils.spaces.multi_binary.MultiBinarySpace.sample": {"fullname": "auto_disc.legacy.utils.spaces.multi_binary.MultiBinarySpace.sample", "modulename": "auto_disc.legacy.utils.spaces.multi_binary", "qualname": "MultiBinarySpace.sample", "kind": "function", "doc": "

Generates a random sample.

\n\n
Return:
\n\n
\n

The return value is torch of the sample value

\n
\n", "signature": "(self) -> torch.Tensor:", "funcdef": "def"}, "auto_disc.legacy.utils.spaces.multi_binary.MultiBinarySpace.mutate": {"fullname": "auto_disc.legacy.utils.spaces.multi_binary.MultiBinarySpace.mutate", "modulename": "auto_disc.legacy.utils.spaces.multi_binary", "qualname": "MultiBinarySpace.mutate", "kind": "function", "doc": "

Apply a mutation on x

\n\n
Arguments:
\n\n
    \n
  • x: The variable to mutate
    • \n
\n\n
Returns:
\n\n
\n

x: the result of mutation

\n
\n", "signature": "(self, x: Any) -> torch.Tensor:", "funcdef": "def"}, "auto_disc.legacy.utils.spaces.multi_binary.MultiBinarySpace.contains": {"fullname": "auto_disc.legacy.utils.spaces.multi_binary.MultiBinarySpace.contains", "modulename": "auto_disc.legacy.utils.spaces.multi_binary", "qualname": "MultiBinarySpace.contains", "kind": "function", "doc": "

check if x is a tensor of 0 and 1 of a given size

\n\n
Arguments:
\n\n
    \n
  • x: The value to check
    • \n
\n\n
Returns:
\n\n
\n

The return value is True if x is a tensor of 0 and 1 of a given size False otherwise

\n
\n", "signature": "(self, x: Any) -> bool:", "funcdef": "def"}, "auto_disc.legacy.utils.spaces.multi_binary.MultiBinarySpace.clamp": {"fullname": "auto_disc.legacy.utils.spaces.multi_binary.MultiBinarySpace.clamp", "modulename": "auto_disc.legacy.utils.spaces.multi_binary", "qualname": "MultiBinarySpace.clamp", "kind": "function", "doc": "

Return a valid clamped value of x inside space's bounds

\n\n
Arguments:
\n\n
    \n
  • x: a value
    • \n
\n", "signature": "(self, x: Any) -> torch.Tensor:", "funcdef": "def"}, "auto_disc.legacy.utils.spaces.multi_binary.MultiBinarySpace.to_json": {"fullname": "auto_disc.legacy.utils.spaces.multi_binary.MultiBinarySpace.to_json", "modulename": "auto_disc.legacy.utils.spaces.multi_binary", "qualname": "MultiBinarySpace.to_json", "kind": "function", "doc": "

Convert the object into JSON

\n\n
Returns:
\n\n
\n

The JSON of the object

\n
\n", "signature": "(self) -> Dict[str, Any]:", "funcdef": "def"}, "auto_disc.legacy.utils.spaces.multi_discrete": {"fullname": "auto_disc.legacy.utils.spaces.multi_discrete", "modulename": "auto_disc.legacy.utils.spaces.multi_discrete", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.spaces.multi_discrete.MultiDiscreteSpace": {"fullname": "auto_disc.legacy.utils.spaces.multi_discrete.MultiDiscreteSpace", "modulename": "auto_disc.legacy.utils.spaces.multi_discrete", "qualname": "MultiDiscreteSpace", "kind": "class", "doc": "
    \n
  • The multi-discrete space consists of a series of discrete spaces with different number of possible instances in eachs
    • \n

  • Can be initialized as

    \n\n

    MultiDiscreteSpace([ 5, 2, 2 ])

    • \n
\n", "bases": "auto_disc.legacy.utils.spaces.base_space.BaseSpace"}, "auto_disc.legacy.utils.spaces.multi_discrete.MultiDiscreteSpace.__init__": {"fullname": "auto_disc.legacy.utils.spaces.multi_discrete.MultiDiscreteSpace.__init__", "modulename": "auto_disc.legacy.utils.spaces.multi_discrete", "qualname": "MultiDiscreteSpace.__init__", "kind": "function", "doc": "

Init the elements useful to the spaces

\n\n
Arguments:
\n\n
    \n
  • nvec: vector of counts of each categorical variable
    • \n
  • mutator: current mutator method
    • \n
  • indpb: Independent probability for each attribute to be exchanged
    • \n
\n", "signature": "(nvec, mutator=None, indpb=1.0)"}, "auto_disc.legacy.utils.spaces.multi_discrete.MultiDiscreteSpace.initialize": {"fullname": "auto_disc.legacy.utils.spaces.multi_discrete.MultiDiscreteSpace.initialize", "modulename": "auto_disc.legacy.utils.spaces.multi_discrete", "qualname": "MultiDiscreteSpace.initialize", "kind": "function", "doc": "

Initialize the space.

\n\n
Arguments:
\n\n
    \n
  • parent_obj: The current autodisc module
    • \n
\n", "signature": "(self, parent_obj):", "funcdef": "def"}, "auto_disc.legacy.utils.spaces.multi_discrete.MultiDiscreteSpace.sample": {"fullname": "auto_disc.legacy.utils.spaces.multi_discrete.MultiDiscreteSpace.sample", "modulename": "auto_disc.legacy.utils.spaces.multi_discrete", "qualname": "MultiDiscreteSpace.sample", "kind": "function", "doc": "

Generates a single random sample, upper than 0 and lower than n.

\n\n
Return:
\n\n
\n

The return value is torch type of the sample value

\n
\n", "signature": "(self) -> torch.Tensor:", "funcdef": "def"}, "auto_disc.legacy.utils.spaces.multi_discrete.MultiDiscreteSpace.mutate": {"fullname": "auto_disc.legacy.utils.spaces.multi_discrete.MultiDiscreteSpace.mutate", "modulename": "auto_disc.legacy.utils.spaces.multi_discrete", "qualname": "MultiDiscreteSpace.mutate", "kind": "function", "doc": "

Apply the mutation of the mutator on x

\n\n
Arguments:
\n\n
    \n
  • x: The variable to mutate
    • \n
\n\n
Returns:
\n\n
\n

x: the result of mutation

\n
\n", "signature": "(self, x):", "funcdef": "def"}, "auto_disc.legacy.utils.spaces.multi_discrete.MultiDiscreteSpace.contains": {"fullname": "auto_disc.legacy.utils.spaces.multi_discrete.MultiDiscreteSpace.contains", "modulename": "auto_disc.legacy.utils.spaces.multi_discrete", "qualname": "MultiDiscreteSpace.contains", "kind": "function", "doc": "

Check if x is bounded between 0 and n

\n\n
Arguments:
\n\n
    \n
  • x: The value to check
    • \n
\n\n
Returns:
\n\n
\n

The return value is True if x is bounded by 0 and n False otherwise

\n
\n", "signature": "(self, x) -> bool:", "funcdef": "def"}, "auto_disc.legacy.utils.spaces.multi_discrete.MultiDiscreteSpace.clamp": {"fullname": "auto_disc.legacy.utils.spaces.multi_discrete.MultiDiscreteSpace.clamp", "modulename": "auto_disc.legacy.utils.spaces.multi_discrete", "qualname": "MultiDiscreteSpace.clamp", "kind": "function", "doc": "

Set x to an acceptable value of the space

\n\n
Arguments:
\n\n
    \n
  • x: The value to set
    • \n
\n\n
Returns:
\n\n
\n

x: After being set

\n
\n", "signature": "(self, x) -> torch.Tensor:", "funcdef": "def"}, "auto_disc.legacy.utils.spaces.multi_discrete.MultiDiscreteSpace.to_json": {"fullname": "auto_disc.legacy.utils.spaces.multi_discrete.MultiDiscreteSpace.to_json", "modulename": "auto_disc.legacy.utils.spaces.multi_discrete", "qualname": "MultiDiscreteSpace.to_json", "kind": "function", "doc": "

Convert the object into JSON

\n\n
Returns:
\n\n
\n

The JSON of the object

\n
\n", "signature": "(self):", "funcdef": "def"}, "auto_disc.legacy.utils.spaces.tuple": {"fullname": "auto_disc.legacy.utils.spaces.tuple", "modulename": "auto_disc.legacy.utils.spaces.tuple", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.spaces.tuple.TupleSpace": {"fullname": "auto_disc.legacy.utils.spaces.tuple.TupleSpace", "modulename": "auto_disc.legacy.utils.spaces.tuple", "qualname": "TupleSpace", "kind": "class", "doc": "

A tuple (i.e., product) of simpler spaces

\n\n

Example usage:\nself.observation_space = spaces.Tuple((spaces.Discrete(2), spaces.Discrete(3)))

\n", "bases": "auto_disc.legacy.utils.spaces.base_space.BaseSpace"}, "auto_disc.legacy.utils.spaces.tuple.TupleSpace.__init__": {"fullname": "auto_disc.legacy.utils.spaces.tuple.TupleSpace.__init__", "modulename": "auto_disc.legacy.utils.spaces.tuple", "qualname": "TupleSpace.__init__", "kind": "function", "doc": "

Init the elements the spaces

\n\n
Arguments:
\n\n
    \n
  • spaces: elements spaces
    • \n
\n", "signature": "(spaces)"}, "auto_disc.legacy.utils.spaces.tuple.TupleSpace.initialize": {"fullname": "auto_disc.legacy.utils.spaces.tuple.TupleSpace.initialize", "modulename": "auto_disc.legacy.utils.spaces.tuple", "qualname": "TupleSpace.initialize", "kind": "function", "doc": "

Initialize the space.

\n\n
Arguments:
\n\n
    \n
  • parent_obj: The current autodisc module
    • \n
\n", "signature": "(self, parent_obj):", "funcdef": "def"}, "auto_disc.legacy.utils.spaces.tuple.TupleSpace.sample": {"fullname": "auto_disc.legacy.utils.spaces.tuple.TupleSpace.sample", "modulename": "auto_disc.legacy.utils.spaces.tuple", "qualname": "TupleSpace.sample", "kind": "function", "doc": "

Generates samples for each spaces.

\n\n
Return:
\n\n
\n

The return value is tuple of torch of samples values

\n
\n", "signature": "(self) -> Tuple[torch.Tensor]:", "funcdef": "def"}, "auto_disc.legacy.utils.spaces.tuple.TupleSpace.mutate": {"fullname": "auto_disc.legacy.utils.spaces.tuple.TupleSpace.mutate", "modulename": "auto_disc.legacy.utils.spaces.tuple", "qualname": "TupleSpace.mutate", "kind": "function", "doc": "

Apply a mutation on each spaces

\n\n
Arguments:
\n\n
    \n
  • x: The variable to mutate
    • \n
\n\n
Returns:
\n\n
\n

x: the result of mutation

\n
\n", "signature": "(self, x) -> Tuple:", "funcdef": "def"}, "auto_disc.legacy.utils.spaces.tuple.TupleSpace.contains": {"fullname": "auto_disc.legacy.utils.spaces.tuple.TupleSpace.contains", "modulename": "auto_disc.legacy.utils.spaces.tuple", "qualname": "TupleSpace.contains", "kind": "function", "doc": "

Check if x included in spaces

\n\n
Arguments:
\n\n
    \n
  • x: The value to check
    • \n
\n\n
Returns:
\n\n
\n

The return value is True if x is included False otherwise

\n
\n", "signature": "(self, x) -> bool:", "funcdef": "def"}, "auto_disc.legacy.utils.spaces.tuple.TupleSpace.clamp": {"fullname": "auto_disc.legacy.utils.spaces.tuple.TupleSpace.clamp", "modulename": "auto_disc.legacy.utils.spaces.tuple", "qualname": "TupleSpace.clamp", "kind": "function", "doc": "

Set each element of tuple to an accpetable value of his space

\n\n
Arguments:
\n\n
    \n
  • x: tuple elemnts
    • \n
\n\n
Returns:
\n\n
\n

The return value is clamped value of each element

\n
\n", "signature": "(self, x) -> Tuple:", "funcdef": "def"}, "auto_disc.legacy.utils.spaces.utils": {"fullname": "auto_disc.legacy.utils.spaces.utils", "modulename": "auto_disc.legacy.utils.spaces.utils", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.spaces.utils.config_parameter_binding": {"fullname": "auto_disc.legacy.utils.spaces.utils.config_parameter_binding", "modulename": "auto_disc.legacy.utils.spaces.utils.config_parameter_binding", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.spaces.utils.config_parameter_binding.ConfigParameterBinding": {"fullname": "auto_disc.legacy.utils.spaces.utils.config_parameter_binding.ConfigParameterBinding", "modulename": "auto_disc.legacy.utils.spaces.utils.config_parameter_binding", "qualname": "ConfigParameterBinding", "kind": "class", "doc": "

Allows to bind some properties of a space to the value of a config parameter that belongs to the config of the instance.

\n"}, "auto_disc.legacy.utils.spaces.utils.config_parameter_binding.ConfigParameterBinding.__init__": {"fullname": "auto_disc.legacy.utils.spaces.utils.config_parameter_binding.ConfigParameterBinding.__init__", "modulename": "auto_disc.legacy.utils.spaces.utils.config_parameter_binding", "qualname": "ConfigParameterBinding.__init__", "kind": "function", "doc": "

Initialize ConfigParamter by defining its name and the operations to perform on it (basic none)

\n\n
Arguments:
\n\n
    \n
  • parameter_name: the name of the parameter
    • \n
\n", "signature": "(parameter_name: str)"}, "auto_disc.legacy.utils.spaces.utils.config_parameter_binding.ConfigParameterBinding.to_json": {"fullname": "auto_disc.legacy.utils.spaces.utils.config_parameter_binding.ConfigParameterBinding.to_json", "modulename": "auto_disc.legacy.utils.spaces.utils.config_parameter_binding", "qualname": "ConfigParameterBinding.to_json", "kind": "function", "doc": "

transforms the binding into a string to make it readable

\n\n
Returns:
\n\n
\n

binding: the binding definition as string

\n
\n", "signature": "(self) -> str:", "funcdef": "def"}, "auto_disc.legacy.utils.spaces.utils.distance": {"fullname": "auto_disc.legacy.utils.spaces.utils.distance", "modulename": "auto_disc.legacy.utils.spaces.utils.distance", "kind": "module", "doc": "

\n"}, "auto_disc.legacy.utils.spaces.utils.distance.calc_l2": {"fullname": "auto_disc.legacy.utils.spaces.utils.distance.calc_l2", "modulename": "auto_disc.legacy.utils.spaces.utils.distance", "qualname": "calc_l2", "kind": "function", "doc": "

Calculate the Euclidean distance between 2 vectors

\n\n
Ars:
\n\n
\n

embedding_a: first vector\n embedding_b: second vector

\n
\n\n
Returns:
\n\n
\n

dist: The distance between the 2 vectors

\n
\n", "signature": "(embedding_a: torch.Tensor, embedding_b: torch.Tensor) -> torch.Tensor:", "funcdef": "def"}, "auto_disc.run": {"fullname": "auto_disc.run", "modulename": "auto_disc.run", "kind": "module", "doc": "

Helper script which allows creation of ExperimentPipeline.\nThis file run.py can also be run as __main__,\nfor example in remote configurations.

\n"}, "auto_disc.run.create": {"fullname": "auto_disc.run.create", "modulename": "auto_disc.run", "qualname": "create", "kind": "function", "doc": "

Setup the whole experiment. Set each modules, logger, callbacks and use\nthem to define the experiment pipeline.

\n\n

In addition to callbacks and handlers defined in parameters,\nyou can pass extras. as keyword arguments.

\n\n

Args:

\n\n
    \n
  • parameters: Experiment config (define which systems which explorer\nwhich callbacks and all other information needed to set an experiment)
    • \n
  • experiment_id: Current experiment id
    • \n
  • seed: current seed number
    • \n
  • additional_callbacks
    • \n
  • additional_handlers
    • \n
\n\n

Returns:

\n\n
    \n
  • experiment: The experiment we have just defined
    • \n
\n", "signature": "(\tparameters: Dict,\texperiment_id: int,\tseed: int,\tadditional_callbacks: Dict[str, List[Callable]] = None,\tadditional_handlers: List[auto_disc.legacy.utils.logger.ad_logger.AutoDiscLogger] = None,\tinteractMethod: Callable = None) -> auto_disc.ExperimentPipeline.ExperimentPipeline:", "funcdef": "def"}, "auto_disc.run.start": {"fullname": "auto_disc.run.start", "modulename": "auto_disc.run", "qualname": "start", "kind": "function", "doc": "

Runs an experiment for a number of iterations

\n\n

Args:

\n\n
    \n
  • experiment: The experiment we want to launch
    • \n
  • nb_iterations: the number explorations
    • \n
\n", "signature": "(\texperiment: auto_disc.ExperimentPipeline.ExperimentPipeline,\tnb_iterations: int) -> None:", "funcdef": "def"}, "auto_disc.utils": {"fullname": "auto_disc.utils", "modulename": "auto_disc.utils", "kind": "module", "doc": "

\n"}, "auto_disc.utils.depinj": {"fullname": "auto_disc.utils.depinj", "modulename": "auto_disc.utils.depinj", "kind": "module", "doc": "

\n"}, "auto_disc.utils.depinj.injector": {"fullname": "auto_disc.utils.depinj.injector", "modulename": "auto_disc.utils.depinj.injector", "kind": "module", "doc": "

\n"}, "auto_disc.utils.depinj.injector.inject": {"fullname": "auto_disc.utils.depinj.injector.inject", "modulename": "auto_disc.utils.depinj.injector", "qualname": "inject", "kind": "function", "doc": "

Runtime method overrider

\n", "signature": "(instance, function, overwrite: bool = True) -> None:", "funcdef": "def"}, "auto_disc.utils.depinj.injector.inject_callbacks": {"fullname": "auto_disc.utils.depinj.injector.inject_callbacks", "modulename": "auto_disc.utils.depinj.injector", "qualname": "inject_callbacks", "kind": "function", "doc": "

Runtime dependency injector, takes a class and an array of callables

\n", "signature": "(cls, callbacks=[], overwrite=True):", "funcdef": "def"}, "auto_disc.utils.depinj.tests": {"fullname": "auto_disc.utils.depinj.tests", "modulename": "auto_disc.utils.depinj.tests", "kind": "module", "doc": "

\n"}, "auto_disc.utils.depinj.tests.test_injector": {"fullname": "auto_disc.utils.depinj.tests.test_injector", "modulename": "auto_disc.utils.depinj.tests.test_injector", "kind": "module", "doc": "

\n"}, "auto_disc.utils.depinj.tests.test_injector.test_inject_callbacks": {"fullname": "auto_disc.utils.depinj.tests.test_injector.test_inject_callbacks", "modulename": "auto_disc.utils.depinj.tests.test_injector", "qualname": "test_inject_callbacks", "kind": "function", "doc": "

NOTE: pytest is used to capture stderr

\n", "signature": "(capsys):", "funcdef": "def"}, "auto_disc.utils.depinj.tests.test_injector.test_inject_callbacks_args": {"fullname": "auto_disc.utils.depinj.tests.test_injector.test_inject_callbacks_args", "modulename": "auto_disc.utils.depinj.tests.test_injector", "qualname": "test_inject_callbacks_args", "kind": "function", "doc": "

\n", "signature": "(capsys):", "funcdef": "def"}, "auto_disc.utils.depinj.tests.testdeps": {"fullname": "auto_disc.utils.depinj.tests.testdeps", "modulename": "auto_disc.utils.depinj.tests.testdeps", "kind": "module", "doc": "

\n"}, "auto_disc.utils.depinj.tests.testdeps.logger": {"fullname": "auto_disc.utils.depinj.tests.testdeps.logger", "modulename": "auto_disc.utils.depinj.tests.testdeps", "qualname": "logger", "kind": "function", "doc": "

\n", "signature": "(inst, *args, **kwargs):", "funcdef": "def"}, "auto_disc.utils.depinj.tests.testdeps.hw": {"fullname": "auto_disc.utils.depinj.tests.testdeps.hw", "modulename": "auto_disc.utils.depinj.tests.testdeps", "qualname": "hw", "kind": "function", "doc": "

\n", "signature": "(inst):", "funcdef": "def"}, "auto_disc.utils.filetype_converter": {"fullname": "auto_disc.utils.filetype_converter", "modulename": "auto_disc.utils.filetype_converter", "kind": "module", "doc": "

\n"}, "auto_disc.utils.filetype_converter.filetype_converter": {"fullname": "auto_disc.utils.filetype_converter.filetype_converter", "modulename": "auto_disc.utils.filetype_converter.filetype_converter", "kind": "module", "doc": "

\n"}, "auto_disc.utils.filetype_converter.filetype_converter.is_mp4": {"fullname": "auto_disc.utils.filetype_converter.filetype_converter.is_mp4", "modulename": "auto_disc.utils.filetype_converter.filetype_converter", "qualname": "is_mp4", "kind": "function", "doc": "

\n", "signature": "(blob: bytes) -> bool:", "funcdef": "def"}, "auto_disc.utils.filetype_converter.filetype_converter.is_png": {"fullname": "auto_disc.utils.filetype_converter.filetype_converter.is_png", "modulename": "auto_disc.utils.filetype_converter.filetype_converter", "qualname": "is_png", "kind": "function", "doc": "

\n", "signature": "(blob: bytes) -> bool:", "funcdef": "def"}, "auto_disc.utils.filetype_converter.filetype_converter.convert_image": {"fullname": "auto_disc.utils.filetype_converter.filetype_converter.convert_image", "modulename": "auto_disc.utils.filetype_converter.filetype_converter", "qualname": "convert_image", "kind": "function", "doc": "

\n", "signature": "(blob: bytes) -> bytes:", "funcdef": "def"}, "auto_disc.utils.filetype_converter.filetype_converter.convert_video": {"fullname": "auto_disc.utils.filetype_converter.filetype_converter.convert_video", "modulename": "auto_disc.utils.filetype_converter.filetype_converter", "qualname": "convert_video", "kind": "function", "doc": "

\n", "signature": "(blob: bytes) -> bytes:", "funcdef": "def"}, "auto_disc.utils.leaf": {"fullname": "auto_disc.utils.leaf", "modulename": "auto_disc.utils.leaf", "kind": "module", "doc": "

\n"}, "auto_disc.utils.leaf.Leaf": {"fullname": "auto_disc.utils.leaf.Leaf", "modulename": "auto_disc.utils.leaf.Leaf", "kind": "module", "doc": "

\n"}, "auto_disc.utils.leaf.Leaf.prune_state": {"fullname": "auto_disc.utils.leaf.Leaf.prune_state", "modulename": "auto_disc.utils.leaf.Leaf", "qualname": "prune_state", "kind": "function", "doc": "

Decorator allowing specification of what instance variables of a Leaf\nto ignore during the serialization call.

\n", "signature": "(state_vars: Dict[str, Any]):", "funcdef": "def"}, "auto_disc.utils.leaf.Leaf.Leaf": {"fullname": "auto_disc.utils.leaf.Leaf.Leaf", "modulename": "auto_disc.utils.leaf.Leaf", "qualname": "Leaf", "kind": "class", "doc": "

\n"}, "auto_disc.utils.leaf.Leaf.Leaf.serialize": {"fullname": "auto_disc.utils.leaf.Leaf.Leaf.serialize", "modulename": "auto_disc.utils.leaf.Leaf", "qualname": "Leaf.serialize", "kind": "function", "doc": "

Serializes object to pickle,\nturning all submodules into uniquely identifiable hashes

\n", "signature": "(self, *args, **kwargs) -> bytes:", "funcdef": "def"}, "auto_disc.utils.leaf.Leaf.Leaf.deserialize": {"fullname": "auto_disc.utils.leaf.Leaf.Leaf.deserialize", "modulename": "auto_disc.utils.leaf.Leaf", "qualname": "Leaf.deserialize", "kind": "function", "doc": "

Restores object from pickle, without recursively deserializing\nany submodules.

\n", "signature": "(self, bin: bytes) -> auto_disc.utils.leaf.Leaf.Leaf:", "funcdef": "def"}, "auto_disc.utils.leaf.Leaf.Leaf.save_leaf": {"fullname": "auto_disc.utils.leaf.Leaf.Leaf.save_leaf", "modulename": "auto_disc.utils.leaf.Leaf", "qualname": "Leaf.save_leaf", "kind": "function", "doc": "

Save entire structure of object. The suggested way to customize\nbehavior is overloading serialize() and the Locator

\n", "signature": "(\tself,\tresource_uri: str = '',\t*args,\t**kwargs) -> <function NewType.<locals>.new_type at 0x7fee26ca1670>:", "funcdef": "def"}, "auto_disc.utils.leaf.Leaf.Leaf.load_leaf": {"fullname": "auto_disc.utils.leaf.Leaf.Leaf.load_leaf", "modulename": "auto_disc.utils.leaf.Leaf", "qualname": "Leaf.load_leaf", "kind": "function", "doc": "

Load entire structure of object, not mutating self

\n", "signature": "(\tself,\tuid: <function NewType.<locals>.new_type>,\tresource_uri: str = '',\t*args,\t**kwargs) -> auto_disc.utils.leaf.Leaf.Leaf:", "funcdef": "def"}, "auto_disc.utils.leaf.LeafUID": {"fullname": "auto_disc.utils.leaf.LeafUID", "modulename": "auto_disc.utils.leaf.LeafUID", "kind": "module", "doc": "

\n"}, "auto_disc.utils.leaf.LeafUID.LeafUID": {"fullname": "auto_disc.utils.leaf.LeafUID.LeafUID", "modulename": "auto_disc.utils.leaf.LeafUID", "qualname": "LeafUID", "kind": "function", "doc": "

\n", "signature": "(x):", "funcdef": "def"}, "auto_disc.utils.leaf.locators": {"fullname": "auto_disc.utils.leaf.locators", "modulename": "auto_disc.utils.leaf.locators", "kind": "module", "doc": "

\n"}, "auto_disc.utils.leaf.locators.LinearBase": {"fullname": "auto_disc.utils.leaf.locators.LinearBase", "modulename": "auto_disc.utils.leaf.locators.LinearBase", "kind": "module", "doc": "

\n"}, "auto_disc.utils.leaf.locators.LinearBase.store_metadata": {"fullname": "auto_disc.utils.leaf.locators.LinearBase.store_metadata", "modulename": "auto_disc.utils.leaf.locators.LinearBase", "qualname": "store_metadata", "kind": "function", "doc": "

\n", "signature": "(subdir: str, data_bin: bytes) -> None:", "funcdef": "def"}, "auto_disc.utils.leaf.locators.LinearBase.store_data": {"fullname": "auto_disc.utils.leaf.locators.LinearBase.store_data", "modulename": "auto_disc.utils.leaf.locators.LinearBase", "qualname": "store_data", "kind": "function", "doc": "

Store the unpadded binary data (i.e., without tag).

\n", "signature": "(subdir: str, data_bin: bytes, parent_id: int) -> str:", "funcdef": "def"}, "auto_disc.utils.leaf.locators.LinearBase.init_db": {"fullname": "auto_disc.utils.leaf.locators.LinearBase.init_db", "modulename": "auto_disc.utils.leaf.locators.LinearBase", "qualname": "init_db", "kind": "function", "doc": "

\n", "signature": "(db_url: str) -> None:", "funcdef": "def"}, "auto_disc.utils.leaf.locators.LinearBase.convert_bytes_to_base64_str": {"fullname": "auto_disc.utils.leaf.locators.LinearBase.convert_bytes_to_base64_str", "modulename": "auto_disc.utils.leaf.locators.LinearBase", "qualname": "convert_bytes_to_base64_str", "kind": "function", "doc": "

\n", "signature": "(bin: bytes) -> str:", "funcdef": "def"}, "auto_disc.utils.leaf.locators.LinearBase.convert_base64_str_to_bytes": {"fullname": "auto_disc.utils.leaf.locators.LinearBase.convert_base64_str_to_bytes", "modulename": "auto_disc.utils.leaf.locators.LinearBase", "qualname": "convert_base64_str_to_bytes", "kind": "function", "doc": "

\n", "signature": "(b64_str: str) -> bytes:", "funcdef": "def"}, "auto_disc.utils.leaf.locators.LinearBase.insert_node": {"fullname": "auto_disc.utils.leaf.locators.LinearBase.insert_node", "modulename": "auto_disc.utils.leaf.locators.LinearBase", "qualname": "insert_node", "kind": "function", "doc": "

\n", "signature": "(\tengine: sqlalchemy.engine.base.Engine,\tdelta: str,\tparent_id: int = -1) -> int:", "funcdef": "def"}, "auto_disc.utils.leaf.locators.LinearBase.retrieve_trajectory": {"fullname": "auto_disc.utils.leaf.locators.LinearBase.retrieve_trajectory", "modulename": "auto_disc.utils.leaf.locators.LinearBase", "qualname": "retrieve_trajectory", "kind": "function", "doc": "

\n", "signature": "(db_url: str, row_id: int = 1, length: int = 1) -> bytes:", "funcdef": "def"}, "auto_disc.utils.leaf.locators.LinearBase.retrieve_packed_trajectory": {"fullname": "auto_disc.utils.leaf.locators.LinearBase.retrieve_packed_trajectory", "modulename": "auto_disc.utils.leaf.locators.LinearBase", "qualname": "retrieve_packed_trajectory", "kind": "function", "doc": "

Simple wrapper which retrieves the raw packed trajectory data\nfrom db_url.

\n", "signature": "(\tdb_url: str,\trow_id: int = 1,\tlength: int = 1) -> Tuple[List[int], List[bytes], List[int]]:", "funcdef": "def"}, "auto_disc.utils.leaf.locators.LinearBase.Stepper": {"fullname": "auto_disc.utils.leaf.locators.LinearBase.Stepper", "modulename": "auto_disc.utils.leaf.locators.LinearBase", "qualname": "Stepper", "kind": "class", "doc": "

\n", "bases": "auto_disc.utils.leaf.Leaf.Leaf"}, "auto_disc.utils.leaf.locators.LinearBase.FileLinearLocator": {"fullname": "auto_disc.utils.leaf.locators.LinearBase.FileLinearLocator", "modulename": "auto_disc.utils.leaf.locators.LinearBase", "qualname": "FileLinearLocator", "kind": "class", "doc": "

Locator which stores branching, linear data\nwith minimal redundancies in a SQLite db.

\n\n

To use, one should override deserialize of\nyour Leaf module class to output a serialized Python object x\nwith x.buffer a List type. I.e., any Leaf which uses LinearLocator\nshould define an instance variable buffer of List type, e.g.,\n 

class A(Leaf):\n\n    def __init__(self, buffer = []):\n        super().__init__()\n        self.buffer = buffer\n

\n\n

NOTE: This means that the save_leaf recursion will not recurse into\n the buffer, and any Leaf types inside will serialize naively.

\n", "bases": "auto_disc.utils.leaf.locators.Locator.Locator"}, "auto_disc.utils.leaf.locators.LinearBase.FileLinearLocator.__init__": {"fullname": "auto_disc.utils.leaf.locators.LinearBase.FileLinearLocator.__init__", "modulename": "auto_disc.utils.leaf.locators.LinearBase", "qualname": "FileLinearLocator.__init__", "kind": "function", "doc": "

\n", "signature": "(resource_uri: str = '')"}, "auto_disc.utils.leaf.locators.LinearBase.FileLinearLocator.store": {"fullname": "auto_disc.utils.leaf.locators.LinearBase.FileLinearLocator.store", "modulename": "auto_disc.utils.leaf.locators.LinearBase", "qualname": "FileLinearLocator.store", "kind": "function", "doc": "

Stores the bin as a child node of the node given by parent_id.

\n\n

Returns:

\n\n
    \n
  • leaf_uid (LeafUID): formatted path indicating the DB UID and the\nSQLite unique key corresponding\nto the inserted node
    • \n
\n", "signature": "(\tself,\tbin: bytes,\tparent_id: int = -1) -> <function NewType.<locals>.new_type at 0x7fee26ca1670>:", "funcdef": "def"}, "auto_disc.utils.leaf.locators.LinearBase.FileLinearLocator.retrieve": {"fullname": "auto_disc.utils.leaf.locators.LinearBase.FileLinearLocator.retrieve", "modulename": "auto_disc.utils.leaf.locators.LinearBase", "qualname": "FileLinearLocator.retrieve", "kind": "function", "doc": "

Retrieve trajectory of given length of saved data starting from\nthe leaf node given by uid, then traversing backwards towards the root.\nNOTE: length=0 corresponds to the entire trajectory, and it is not\n recommended to unpack large trajectories in memory

\n\n

Returns:

\n\n
    \n
  • bin (bytes): trajectory packed as a python object x with\nx.buffer being the array of data
    • \n
\n", "signature": "(\tself,\tuid: <function NewType.<locals>.new_type>,\tlength: int = 1) -> bytes:", "funcdef": "def"}, "auto_disc.utils.leaf.locators.LinearBase.FileLinearLocator.parse_bin": {"fullname": "auto_disc.utils.leaf.locators.LinearBase.FileLinearLocator.parse_bin", "modulename": "auto_disc.utils.leaf.locators.LinearBase", "qualname": "FileLinearLocator.parse_bin", "kind": "function", "doc": "

\n", "signature": "(cls, bin: bytes) -> Tuple[str, bytes]:", "funcdef": "def"}, "auto_disc.utils.leaf.locators.LinearBase.FileLinearLocator.parse_leaf_uid": {"fullname": "auto_disc.utils.leaf.locators.LinearBase.FileLinearLocator.parse_leaf_uid", "modulename": "auto_disc.utils.leaf.locators.LinearBase", "qualname": "FileLinearLocator.parse_leaf_uid", "kind": "function", "doc": "

\n", "signature": "(cls, uid: <function NewType.<locals>.new_type>) -> Tuple[str, int]:", "funcdef": "def"}, "auto_disc.utils.leaf.locators.LinearBase.FileLinearLocator.hash": {"fullname": "auto_disc.utils.leaf.locators.LinearBase.FileLinearLocator.hash", "modulename": "auto_disc.utils.leaf.locators.LinearBase", "qualname": "FileLinearLocator.hash", "kind": "function", "doc": "

LinearLocator must use SHA1 hashes

\n", "signature": "(bin: bytes) -> <function NewType.<locals>.new_type at 0x7fee26ca1670>:", "funcdef": "def"}, "auto_disc.utils.leaf.locators.LinearBase.FileLinearLocator.set_sqlite_pragma": {"fullname": "auto_disc.utils.leaf.locators.LinearBase.FileLinearLocator.set_sqlite_pragma", "modulename": "auto_disc.utils.leaf.locators.LinearBase", "qualname": "FileLinearLocator.set_sqlite_pragma", "kind": "function", "doc": "

\n", "signature": "(dbapi_connection, connection_record):", "funcdef": "def"}, "auto_disc.utils.leaf.locators.Locator": {"fullname": "auto_disc.utils.leaf.locators.Locator", "modulename": "auto_disc.utils.leaf.locators.Locator", "kind": "module", "doc": "

\n"}, "auto_disc.utils.leaf.locators.Locator.Locator": {"fullname": "auto_disc.utils.leaf.locators.Locator.Locator", "modulename": "auto_disc.utils.leaf.locators.Locator", "qualname": "Locator", "kind": "class", "doc": "

Class which handles data management and the saving/loading of binary data.\nShould have at least an instance variable called resource_uri, although\nit may be an empty string.

\n"}, "auto_disc.utils.leaf.locators.Locator.Locator.__init__": {"fullname": "auto_disc.utils.leaf.locators.Locator.Locator.__init__", "modulename": "auto_disc.utils.leaf.locators.Locator", "qualname": "Locator.__init__", "kind": "function", "doc": "

\n", "signature": "(resource_uri: str = '', *args, **kwargs)"}, "auto_disc.utils.leaf.locators.Locator.Locator.store": {"fullname": "auto_disc.utils.leaf.locators.Locator.Locator.store", "modulename": "auto_disc.utils.leaf.locators.Locator", "qualname": "Locator.store", "kind": "function", "doc": "

Stores bin which generated it somewhere, perhaps on network,\nand returns (redundantly) the hash for the object.

\n", "signature": "(\tself,\tbin: bytes,\t*args,\t**kwargs) -> <function NewType.<locals>.new_type at 0x7fee26ca1670>:", "funcdef": "def"}, "auto_disc.utils.leaf.locators.Locator.Locator.retrieve": {"fullname": "auto_disc.utils.leaf.locators.Locator.Locator.retrieve", "modulename": "auto_disc.utils.leaf.locators.Locator", "qualname": "Locator.retrieve", "kind": "function", "doc": "

Retrieve bin identified by uid from somewhere,\nperhaps on network

\n", "signature": "(\tself,\tuid: <function NewType.<locals>.new_type>,\t*args,\t**kwargs) -> bytes:", "funcdef": "def"}, "auto_disc.utils.leaf.locators.Locator.Locator.hash": {"fullname": "auto_disc.utils.leaf.locators.Locator.Locator.hash", "modulename": "auto_disc.utils.leaf.locators.Locator", "qualname": "Locator.hash", "kind": "function", "doc": "

Hashes the bin to give Leaf a uid

\n", "signature": "(bin: bytes) -> <function NewType.<locals>.new_type at 0x7fee26ca1670>:", "funcdef": "def"}, "auto_disc.utils.leaf.locators.Locator.StatelessLocator": {"fullname": "auto_disc.utils.leaf.locators.Locator.StatelessLocator", "modulename": "auto_disc.utils.leaf.locators.Locator", "qualname": "StatelessLocator", "kind": "class", "doc": "

Default Locator class, for stateless modules

\n", "bases": "Locator"}, "auto_disc.utils.leaf.locators.Locator.StatelessLocator.__init__": {"fullname": "auto_disc.utils.leaf.locators.Locator.StatelessLocator.__init__", "modulename": "auto_disc.utils.leaf.locators.Locator", "qualname": "StatelessLocator.__init__", "kind": "function", "doc": "

\n", "signature": "(resource_uri: str = '', *args, **kwargs)"}, "auto_disc.utils.leaf.locators.Locator.StatelessLocator.store": {"fullname": "auto_disc.utils.leaf.locators.Locator.StatelessLocator.store", "modulename": "auto_disc.utils.leaf.locators.Locator", "qualname": "StatelessLocator.store", "kind": "function", "doc": "

Stores bin which generated it somewhere, perhaps on network,\nand returns (redundantly) the hash for the object.

\n", "signature": "(\tself,\tbin: bytes,\t*args,\t**kwargs) -> <function NewType.<locals>.new_type at 0x7fee26ca1670>:", "funcdef": "def"}, "auto_disc.utils.leaf.locators.Locator.StatelessLocator.retrieve": {"fullname": "auto_disc.utils.leaf.locators.Locator.StatelessLocator.retrieve", "modulename": "auto_disc.utils.leaf.locators.Locator", "qualname": "StatelessLocator.retrieve", "kind": "function", "doc": "

Retrieve bin identified by uid from somewhere,\nperhaps on network

\n", "signature": "(\tself,\tuid: <function NewType.<locals>.new_type>,\t*args,\t**kwargs) -> bytes:", "funcdef": "def"}, "auto_disc.utils.leaf.locators.Locator.FileLocator": {"fullname": "auto_disc.utils.leaf.locators.Locator.FileLocator", "modulename": "auto_disc.utils.leaf.locators.Locator", "qualname": "FileLocator", "kind": "class", "doc": "

Locator which saves modules \u00e0 la Git, to the filesystem,\nwith root directory specified by resource_uri

\n", "bases": "Locator"}, "auto_disc.utils.leaf.locators.Locator.FileLocator.__init__": {"fullname": "auto_disc.utils.leaf.locators.Locator.FileLocator.__init__", "modulename": "auto_disc.utils.leaf.locators.Locator", "qualname": "FileLocator.__init__", "kind": "function", "doc": "

\n", "signature": "(resource_uri: str = '')"}, "auto_disc.utils.leaf.locators.Locator.FileLocator.store": {"fullname": "auto_disc.utils.leaf.locators.Locator.FileLocator.store", "modulename": "auto_disc.utils.leaf.locators.Locator", "qualname": "FileLocator.store", "kind": "function", "doc": "

Stores bin which generated it somewhere, perhaps on network,\nand returns (redundantly) the hash for the object.

\n", "signature": "(\tself,\tbin: bytes,\t*args,\t**kwargs) -> <function NewType.<locals>.new_type at 0x7fee26ca1670>:", "funcdef": "def"}, "auto_disc.utils.leaf.locators.Locator.FileLocator.retrieve": {"fullname": "auto_disc.utils.leaf.locators.Locator.FileLocator.retrieve", "modulename": "auto_disc.utils.leaf.locators.Locator", "qualname": "FileLocator.retrieve", "kind": "function", "doc": "

Retrieve bin identified by uid from somewhere,\nperhaps on network

\n", "signature": "(\tself,\tuid: <function NewType.<locals>.new_type>,\t*args,\t**kwargs) -> bytes:", "funcdef": "def"}, "auto_disc.utils.leaf.locators.locators": {"fullname": "auto_disc.utils.leaf.locators.locators", "modulename": "auto_disc.utils.leaf.locators.locators", "kind": "module", "doc": "

\n"}, "auto_disc.utils.leaf.locators.locators.BlobLocator": {"fullname": "auto_disc.utils.leaf.locators.locators.BlobLocator", "modulename": "auto_disc.utils.leaf.locators.locators", "qualname": "BlobLocator", "kind": "class", "doc": "

Default Locator class, for stateful modules.

\n\n

In reality, it is a factory which mimicks the interface of a Locator,\nand therefore lazy-initializes an appropriate Locator based on provided\nkeywords.

\n", "bases": "auto_disc.utils.leaf.locators.Locator.Locator"}, "auto_disc.utils.leaf.locators.locators.BlobLocator.__init__": {"fullname": "auto_disc.utils.leaf.locators.locators.BlobLocator.__init__", "modulename": "auto_disc.utils.leaf.locators.locators", "qualname": "BlobLocator.__init__", "kind": "function", "doc": "

\n", "signature": "(resource_uri: str = '', *args, **kwargs)"}, "auto_disc.utils.leaf.locators.locators.BlobLocator.store": {"fullname": "auto_disc.utils.leaf.locators.locators.BlobLocator.store", "modulename": "auto_disc.utils.leaf.locators.locators", "qualname": "BlobLocator.store", "kind": "function", "doc": "

Stores bin which generated it somewhere, perhaps on network,\nand returns (redundantly) the hash for the object.

\n", "signature": "(\tself,\tbin: bytes,\t*args,\t**kwargs) -> <function NewType.<locals>.new_type at 0x7fee26ca1670>:", "funcdef": "def"}, "auto_disc.utils.leaf.locators.locators.BlobLocator.retrieve": {"fullname": "auto_disc.utils.leaf.locators.locators.BlobLocator.retrieve", "modulename": "auto_disc.utils.leaf.locators.locators", "qualname": "BlobLocator.retrieve", "kind": "function", "doc": "

Retrieve bin identified by uid from somewhere,\nperhaps on network

\n", "signature": "(\tself,\tuid: <function NewType.<locals>.new_type>,\t*args,\t**kwargs) -> bytes:", "funcdef": "def"}, "auto_disc.utils.leaf.locators.locators.LinearLocator": {"fullname": "auto_disc.utils.leaf.locators.locators.LinearLocator", "modulename": "auto_disc.utils.leaf.locators.locators", "qualname": "LinearLocator", "kind": "class", "doc": "

Bespoke Locator class, for stateful modules that need to store linear,\npotentially branching data.

\n\n

Also a factory.

\n", "bases": "auto_disc.utils.leaf.locators.Locator.Locator"}, "auto_disc.utils.leaf.locators.locators.LinearLocator.__init__": {"fullname": "auto_disc.utils.leaf.locators.locators.LinearLocator.__init__", "modulename": "auto_disc.utils.leaf.locators.locators", "qualname": "LinearLocator.__init__", "kind": "function", "doc": "

\n", "signature": "(resource_uri: str = '', *args, **kwargs)"}, "auto_disc.utils.leaf.locators.locators.LinearLocator.store": {"fullname": "auto_disc.utils.leaf.locators.locators.LinearLocator.store", "modulename": "auto_disc.utils.leaf.locators.locators", "qualname": "LinearLocator.store", "kind": "function", "doc": "

Stores bin which generated it somewhere, perhaps on network,\nand returns (redundantly) the hash for the object.

\n", "signature": "(\tself,\tbin: bytes,\tparent_id: int = -1,\t*args,\t**kwargs) -> <function NewType.<locals>.new_type at 0x7fee26ca1670>:", "funcdef": "def"}, "auto_disc.utils.leaf.locators.locators.LinearLocator.retrieve": {"fullname": "auto_disc.utils.leaf.locators.locators.LinearLocator.retrieve", "modulename": "auto_disc.utils.leaf.locators.locators", "qualname": "LinearLocator.retrieve", "kind": "function", "doc": "

Retrieve bin identified by uid from somewhere,\nperhaps on network

\n", "signature": "(\tself,\tuid: <function NewType.<locals>.new_type>,\tlength: int = 1,\t*args,\t**kwargs) -> bytes:", "funcdef": "def"}, "auto_disc.utils.leaf.locators.locators.LinearLocator.parse_bin": {"fullname": "auto_disc.utils.leaf.locators.locators.LinearLocator.parse_bin", "modulename": "auto_disc.utils.leaf.locators.locators", "qualname": "LinearLocator.parse_bin", "kind": "function", "doc": "

\n", "signature": "(cls, bin: bytes) -> Tuple[str, bytes]:", "funcdef": "def"}, "auto_disc.utils.leaf.locators.locators.LinearLocator.parse_leaf_uid": {"fullname": "auto_disc.utils.leaf.locators.locators.LinearLocator.parse_leaf_uid", "modulename": "auto_disc.utils.leaf.locators.locators", "qualname": "LinearLocator.parse_leaf_uid", "kind": "function", "doc": "

\n", "signature": "(cls, uid: <function NewType.<locals>.new_type>) -> Tuple[str, int]:", "funcdef": "def"}, "auto_disc.utils.leaf.locators.locators.LinearLocator.hash": {"fullname": "auto_disc.utils.leaf.locators.locators.LinearLocator.hash", "modulename": "auto_disc.utils.leaf.locators.locators", "qualname": "LinearLocator.hash", "kind": "function", "doc": "

Hashes the bin to give Leaf a uid

\n", "signature": "(bin: bytes) -> <function NewType.<locals>.new_type at 0x7fee26ca1670>:", "funcdef": "def"}, "auto_disc.utils.leaf.tests": {"fullname": "auto_disc.utils.leaf.tests", "modulename": "auto_disc.utils.leaf.tests", "kind": "module", "doc": "

\n"}, "auto_disc.utils.leaf.tests.no_import_load_leaf": {"fullname": "auto_disc.utils.leaf.tests.no_import_load_leaf", "modulename": "auto_disc.utils.leaf.tests.no_import_load_leaf", "kind": "module", "doc": "

\n"}, "auto_disc.utils.leaf.tests.no_import_save_leaf": {"fullname": "auto_disc.utils.leaf.tests.no_import_save_leaf", "modulename": "auto_disc.utils.leaf.tests.no_import_save_leaf", "kind": "module", "doc": "

\n"}, "auto_disc.utils.leaf.tests.test_integration_leaf": {"fullname": "auto_disc.utils.leaf.tests.test_integration_leaf", "modulename": "auto_disc.utils.leaf.tests.test_integration_leaf", "kind": "module", "doc": "

\n"}, "auto_disc.utils.leaf.tests.test_integration_leaf.DiskPipeline": {"fullname": "auto_disc.utils.leaf.tests.test_integration_leaf.DiskPipeline", "modulename": "auto_disc.utils.leaf.tests.test_integration_leaf", "qualname": "DiskPipeline", "kind": "class", "doc": "

\n", "bases": "auto_disc.utils.leaf.Leaf.Leaf"}, "auto_disc.utils.leaf.tests.test_integration_leaf.DiskPipeline.create_locator": {"fullname": "auto_disc.utils.leaf.tests.test_integration_leaf.DiskPipeline.create_locator", "modulename": "auto_disc.utils.leaf.tests.test_integration_leaf", "qualname": "DiskPipeline.create_locator", "kind": "function", "doc": "

\n", "signature": "(self, bin: bytes) -> auto_disc.utils.leaf.locators.Locator.Locator:", "funcdef": "def"}, "auto_disc.utils.leaf.tests.test_integration_leaf.DiskPipeline.store_locator": {"fullname": "auto_disc.utils.leaf.tests.test_integration_leaf.DiskPipeline.store_locator", "modulename": "auto_disc.utils.leaf.tests.test_integration_leaf", "qualname": "DiskPipeline.store_locator", "kind": "function", "doc": "

\n", "signature": "(self, loc):", "funcdef": "def"}, "auto_disc.utils.leaf.tests.test_integration_leaf.DiskPipeline.retrieve_locator": {"fullname": "auto_disc.utils.leaf.tests.test_integration_leaf.DiskPipeline.retrieve_locator", "modulename": "auto_disc.utils.leaf.tests.test_integration_leaf", "qualname": "DiskPipeline.retrieve_locator", "kind": "function", "doc": "

\n", "signature": "(cls, leaf_uid: str) -> auto_disc.utils.leaf.locators.Locator.Locator:", "funcdef": "def"}, "auto_disc.utils.leaf.tests.test_integration_leaf.DiskLocator": {"fullname": "auto_disc.utils.leaf.tests.test_integration_leaf.DiskLocator", "modulename": "auto_disc.utils.leaf.tests.test_integration_leaf", "qualname": "DiskLocator", "kind": "class", "doc": "

Class which handles data management and the saving/loading of binary data.\nShould have at least an instance variable called resource_uri, although\nit may be an empty string.

\n", "bases": "auto_disc.utils.leaf.locators.Locator.Locator"}, "auto_disc.utils.leaf.tests.test_integration_leaf.DiskLocator.__init__": {"fullname": "auto_disc.utils.leaf.tests.test_integration_leaf.DiskLocator.__init__", "modulename": "auto_disc.utils.leaf.tests.test_integration_leaf", "qualname": "DiskLocator.__init__", "kind": "function", "doc": "

\n", "signature": "(bin)"}, "auto_disc.utils.leaf.tests.test_integration_leaf.DiskLocator.store": {"fullname": "auto_disc.utils.leaf.tests.test_integration_leaf.DiskLocator.store", "modulename": "auto_disc.utils.leaf.tests.test_integration_leaf", "qualname": "DiskLocator.store", "kind": "function", "doc": "

Stores bin which generated it somewhere, perhaps on network,\nand returns (redundantly) the hash for the object.

\n", "signature": "(self, bin: bytes, *args, **kwargs) -> None:", "funcdef": "def"}, "auto_disc.utils.leaf.tests.test_integration_leaf.DiskLocator.retrieve": {"fullname": "auto_disc.utils.leaf.tests.test_integration_leaf.DiskLocator.retrieve", "modulename": "auto_disc.utils.leaf.tests.test_integration_leaf", "qualname": "DiskLocator.retrieve", "kind": "function", "doc": "

Retrieve bin identified by uid from somewhere,\nperhaps on network

\n", "signature": "(self) -> bytes:", "funcdef": "def"}, "auto_disc.utils.leaf.tests.test_integration_leaf.setup_function": {"fullname": "auto_disc.utils.leaf.tests.test_integration_leaf.setup_function", "modulename": "auto_disc.utils.leaf.tests.test_integration_leaf", "qualname": "setup_function", "kind": "function", "doc": "

\n", "signature": "(function):", "funcdef": "def"}, "auto_disc.utils.leaf.tests.test_integration_leaf.teardown_function": {"fullname": "auto_disc.utils.leaf.tests.test_integration_leaf.teardown_function", "modulename": "auto_disc.utils.leaf.tests.test_integration_leaf", "qualname": "teardown_function", "kind": "function", "doc": "

\n", "signature": "(function):", "funcdef": "def"}, "auto_disc.utils.leaf.tests.test_leaf": {"fullname": "auto_disc.utils.leaf.tests.test_leaf", "modulename": "auto_disc.utils.leaf.tests.test_leaf", "kind": "module", "doc": "

\n"}, "auto_disc.utils.leaf.tests.test_leaf.DummyModule": {"fullname": "auto_disc.utils.leaf.tests.test_leaf.DummyModule", "modulename": "auto_disc.utils.leaf.tests.test_leaf", "qualname": "DummyModule", "kind": "class", "doc": "

\n", "bases": "auto_disc.utils.leaf.Leaf.Leaf"}, "auto_disc.utils.leaf.tests.test_leaf.DummyModule.__init__": {"fullname": "auto_disc.utils.leaf.tests.test_leaf.DummyModule.__init__", "modulename": "auto_disc.utils.leaf.tests.test_leaf", "qualname": "DummyModule.__init__", "kind": "function", "doc": "

\n", "signature": "(s=None)"}, "auto_disc.utils.leaf.tests.test_leaf.DummyModule.forward": {"fullname": "auto_disc.utils.leaf.tests.test_leaf.DummyModule.forward", "modulename": "auto_disc.utils.leaf.tests.test_leaf", "qualname": "DummyModule.forward", "kind": "function", "doc": "

\n", "signature": "(self, x):", "funcdef": "def"}, "auto_disc.utils.leaf.tests.test_leaf.DummyLocator": {"fullname": "auto_disc.utils.leaf.tests.test_leaf.DummyLocator", "modulename": "auto_disc.utils.leaf.tests.test_leaf", "qualname": "DummyLocator", "kind": "class", "doc": "

Class which handles data management and the saving/loading of binary data.\nShould have at least an instance variable called resource_uri, although\nit may be an empty string.

\n", "bases": "auto_disc.utils.leaf.locators.Locator.Locator"}, "auto_disc.utils.leaf.tests.test_leaf.DummyLocator.__init__": {"fullname": "auto_disc.utils.leaf.tests.test_leaf.DummyLocator.__init__", "modulename": "auto_disc.utils.leaf.tests.test_leaf", "qualname": "DummyLocator.__init__", "kind": "function", "doc": "

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Retrieve bin identified by uid from somewhere,\nperhaps on network

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