Merge pull request #17 from adopy/release/v0.3.0

Release v0.3.0

.gitignore

@@ -184,5 +184,8 @@ $RECYCLE.BIN/
 .idea
 .vscode
 
+adopy/version.py
+
 Pipfile.lock
 
+examples/data/

.travis.yml

@@ -1,11 +1,12 @@
+dist: xenial
 language: python
 sudo: false
 cache: pip
 
 python:
   - "3.5"
   - "3.6"
-  - "3.7-dev"
+  - "3.7"
 
 install: "make"
 

Makefile

@@ -7,10 +7,10 @@ endif
 	pipenv install --dev --skip-lock
 
 test:
-	pipenv run py.test tests
+	pipenv run py.test
 
 test-travis:
-	pipenv run py.test tests --doctest-modules --cov=adopy
+	pipenv run py.test --cov=adopy
 	pipenv run codecov --token $(CODECOV_TOKEN)
 
 lint:
@@ -19,6 +19,6 @@ lint:
 
 docs-travis:
 	pipenv run travis-sphinx build
-	pipenv run travis-sphinx deploy
+	pipenv run travis-sphinx deploy -b master
 
 .PHONY: init test test-travis lint docs-travis

Pipfile

@@ -17,9 +17,13 @@ sphinx = "*"
 sphinx_rtd_theme = "*"
 sphinx-autobuild = "*"
 travis-sphinx = "*"
-sphinx-autodoc-typehints = "*"
 recommonmark = "*"
 sphinxcontrib-rawfiles = "*"
+# Psychopy (won't be installed for linux)
+psychopy = {version = "*",sys_platform = "!= 'linux'"}
+# Jupyter
+jupyter = "*"
+jupyterlab = "*"
 
 [packages]
 pandas = "*"

README.md

@@ -1,32 +1,180 @@
-# ![ADOpy logo](https://adopy.github.io/logo/adopy-logo-tight.svg)
+# ADOpy <img src="https://adopy.github.io/logo/adopy-logo.svg" align="right" width="300px">
 
 [![Project Status: WIP – Initial development is in progress, but there has not yet been a stable, usable release suitable for the public.](https://www.repostatus.org/badges/latest/wip.svg)](https://www.repostatus.org/#wip)
 [![Travid CI](https://travis-ci.com/adopy/adopy.svg?token=gbyEQoyAYgexeSRwBwj6&branch=master)](https://travis-ci.com/adopy/adopy)
 [![CodeCov](https://codecov.io/gh/adopy/adopy/branch/master/graph/badge.svg?token=jFnJgnVV1k)](https://codecov.io/gh/adopy/adopy)
 
-**ADOpy** is a Python implementation of adaptive design optimization (ADO; Myung, Cavagnaro, & Pitt, 2013).
-Its modular design and simple structure permit easy integration into existing experimentation code.
+**ADOpy** is a Python implementation of Adaptive Design Optimization (ADO; Myung, Cavagnaro, & Pitt, 2013), which computes optimal designs dynamically in an experiment. Its modular structure permit easy integration into existing experimentation code.
 
-ADOpy supports Python 3.5+ using NumPy, SciPy, and Pandas.
+ADOpy supports Python 3.5 or above and relies on NumPy, SciPy, and Pandas.
 
-## Implemented tasks
-ADO versions of the following tasks are currently implemented. Models available for each task are listed. Users can also provide their own model. Additional tasks will be added in future releases.
+### Features
 
-[Insert table?]
+- **Grid-based computation of optimal designs using only three classes**: `adopy.Task`, `adopy.Model`, and `adopy.Engine`.
+- **Easily customizable for your own tasks and models**
+- **Pre-implemented Task and Model classes including**:
+  - Psychometric function estimation for 2AFC tasks (`adopy.tasks.psi`)
+  - Delay discounting task (`adopy.tasks.ddt`)
+  - Choice under risk and ambiguity task (`adopy.tasks.cra`)
+- **Example code for experiments using PsychoPy** ([link][example-code])
 
-## (Not so quick) Start GUide
+[example-code]: https://github.com/adopy/adopy/tree/master/examples
 
-**Full documentation is found  [here](https://adopy.org)**
+### Resources
 
+- [**Getting started**](https://adopy.org/getting-started.html)
+- [**Documentation**](https://adopy.org)
+- [**Bug reports**](https://github.com/adopy/adopy/issues)
 
+## (not so) Quick-start guide ##
+
+### Step 0. Install ADOpy on the terminal
+
+```bash
+# Install the stable version from PyPI
+pip install adopy
+
+# Or install the developmental version from GitHub
+git clone https://github.com/adopy/adopy.git
+cd adopy
+git checkout develop
+pip install .
+```
+
+### Step 1. Define a task using `adopy.Task`
+
+Assume that a user want to use ADOpy for an *arbitrary* task with two design
+variables (`x1` and `x2`) where participants can make a binary choice on each
+trial. Then, the task can be defined with `adopy.Task` as described below:
+
+```python
+from adopy import Task
+
+task = Task(name='My New Experiment',  # Name of the task (optional)
+            designs = ['x1', 'x2'],    # Labels of design variables
+            responses = [0, 1])        # Possible responses
+```
+
+### Step 2. Define a model using `adopy.Model`
+
+To predict partipants' choices, here we assume a logistic regression model
+that calculates the probability to make a positive response using three model
+parameters (`b0`, `b1`, and `b2`):
+
+<img src="https://user-images.githubusercontent.com/11037140/59533069-5f7b7880-8f25-11e9-8440-4d31fb6ac260.png" align="center">
+
+How to compute the probabilty `p` should be defined as a function:
+
+```python
+import numpy as np
+
+def calculate_prob(x1, x2, b0, b1, b2):
+    """A function to compute the probability of a positive response."""
+    logit = b0 + x1 * b1 + x1 * b2
+    p_obs = 1. / (1 + np.exp(-logit))
+    return p_obs
+```
+
+Using the information and the function, the model can be defined with
+`adopy.Model`:
+
+```python
+from adopy import Model
+
+model = Model(name='My Logistic Model',   # Name of the model (optional)
+              params=['b0', 'b1', 'b2'],  # Labels of model parameters
+              func=calculate_prob)        # A probability function
+```
+
+### Step 3. Define grids for design variables and model parameters
+
+Since ADOpy uses grid search to search the design space and parameter space,
+you must define a grid for design variables and model parameters.
+The grid can be defined using the labels (of design variables or model
+parameters) as its key and an array of the corresponding grid points
+as its value.
+
+```python
+import numpy as np
+
+grid_design = {
+    'x1': np.linspace(0, 50, 100),    # 100 grid points within [0, 50]
+    'x2': np.linspace(-20, 30, 100),  # 100 grid points within [-20, 30]
+}
+
+grid_param = {
+    'b0': np.linspace(-5, 5, 100),  # 100 grid points within [-5, 5]
+    'b1': np.linspace(-5, 5, 100),  # 100 grid points within [-5, 5]
+    'b2': np.linspace(-5, 5, 100),  # 100 grid points within [-5, 5]
+}
+```
+
+### Step 4. Initialize an engine using `adopy.Engine`
+
+Using the objects created so far, an engine should be initialized using
+`adopy.Engine`. It contains built-in functions to compute an optimal design
+using ADO.
+
+```python
+from adopy import Engine
+
+engine = Engine(model=model,              # a Model object
+                task=task,                # a Task object
+                grid_design=grid_design,  # a grid for design variables
+                grid_param=grid_param)    # a grid for model parameters
+```
+
+### Step 5. Compute a design using the engine
+
+```python
+# Compute an optimal design using ADO
+design = engine.get_design()
+design = engine.get_design('optimal')
+
+# Compute a randomly chosen design, as is typically done in non-ADO experiments
+design = engine.get_design('random')
+```
+
+### Step 6. Collect an observation in your experiment
+
+```python
+# Get a response from a participant using your own code
+response = ...
+```
+
+### Step 7. Update the engine with the observation
+
+```python
+# Update the engine with the design and the corresponding response
+engine.update(design, response)
+```
+
+### Step 8. Repeat Step 5 through Step 7 until the experiment is over
+
+```python
+NUM_TRIAL = 100  # number of trials
+
+for trial in range(NUM_TRIAL):
+    # Compute an optimal design for the current trial
+    design = engine.get_design('optimal')
+
+    # Get a response using the optimal design
+    response = ...  # Using users' own codes
+
+    # Update the engine
+    engine.update(design, response)
+```
 
 ## Citation
 If you use ADOpy, please cite this package along with the specific version.
 It greatly encourages contributors to continue supporting ADOpy.
 
-> To be announced.
+> Yang, J., Ahn, W.-Y., Pitt., M. A., & Myung, J. I. (2019).
+> *ADOpy: A Python Package for Adaptive Design Optimization*.
+> Retrieved from https://adopy.org
 
 ## References
 - Myung, J. I., Cavagnaro, D. R., and Pitt, M. A. (2013).
   A tutorial on adaptive design optimization.
   *Journal of Mathematical Psychology, 57*, 53–67.
+

adopy/__init__.py

@@ -2,14 +2,22 @@
 ADOpy: Adaptive Design Optimization on Experimental Tasks
 """
 import os
+import numpy as np
 
 from adopy import base
 from adopy import functions
 from adopy import tasks
 
 from adopy.base import Task, Model, Engine
+from .version import version as __version__
 
-__all__ = ['base', 'functions', 'tasks', 'Task', 'Model', 'Engine']
+__all__ = [
+    '__version__',
+    # Submodules
+    'base', 'functions', 'tasks',
+    # Base classes
+    'Task', 'Model', 'Engine',
+]
 
-with open(os.path.join(os.path.dirname(__file__), 'VERSION'), 'r') as f:
-    __version__ = f.read().strip()
+# Ignore overflow and underflow floating-point errors
+np.seterr(over='ignore', under='ignore')