weles

The Python client package for communication with model governance base weles. weles supports virtualization with all versions of Python and R languages from version 3.0 and package versions. At this moment supports all:

• scikit-learn
• keras
• mlr
• caret
• parsnip

models.

During the development weles is only accessible via MINI network. However you can access it from other places via ssh, if you have account at this network.

Installation

Python

git clone https://github.com/WojciechKretowicz/weles.git 
pip install weles/python

R

devtools::install_github("WojciechKretowicz/weles/r")

Usage in Python

Creating an account

To be able to upload your model you need to have an account on weles. So here is what you need to do:

from weles import users

users.create('Example user', 'example password', 'example_mail@gmail.com')

You will receive information if your account was created correctly.

Uploading model

First you need to have a trained scikit-learn or keras model. Let's make a scikit random forest.

Example

from sklearn import datasets
from sklearn.ensemble import RandomForestClassifier

data = datasets.load_wine()
model = RandomForestClassifier(n_estimators = 777)
model.fit(data.data, data.target)

To upload the model to the base you need to import client package and pass classifier, its name that will be visible in the weles, its description, list of tags, training dataset with target column and with the column names, its name, its description, requirements file, your user name and password.

So first let's prepare our data.

import pandas as pd

train_data_to_upload = pd.DataFrame(data.data, colums=data.feature_names)
train_data_to_upload['target'] = data.target

Due to our respect for your privacy we do not investigate your environment. To allow us to make a virtualization please run in command line:

pip freeze > requirements.txt

This will make a Python style requirements file.

Now we are ready to push our model to the base. Its name has to be unique in the base. Dataset name either.

from weles import models

models.upload(model, 'example_model', 'This is an example model.', 'target', ['example', 'easy'], train_data_to_upload, 'example_data', 'This is an example dataset', 'requirements.txt', 'Example user', 'example password')

In this moment model is being uploaded to the weles. If requested environment had not been already created in the weles, it will be created. During this time your Python sesion will be suspended. You will get the message if the uploading was successful.

Summary

You can also pass your model as the path (must contain / sign) to Python pickle. Training data parameter can be a path to .csv file (must contain / sign) or hash of already uploaded dataset in the weles.

Reading an info about model

If you want to read an info about the model already uploaded in weles you can run:

from weles import models

models.info("example_model")

"example_model" is a name of weles model.

Returned value is a Python dictionary containing all information about the model.

Making predictions

If you want to make a prediction, type:

from weles import models

models.predict("example_model", data)

"example_model" is the name of weles model, data is the data frame with named columns without target column, or path to .csv (must contain / sign) file or hash of already uploaded data.

Be aware that some models may require from you exactly the same column names in passed data. If you are passing data as an object then by default columns are fetched from original dataset. If you do not want this behaviour set prepare_data to False. You may easily manually obtain columns with:

columns = models.info("example_model")['columns']

Searching model

You can also search models in weles satisfying some restrictions.

from weles import models

models.search(row = '>1000;<10000;', column='=12;', user='Example user', tags = ['example', 'easy'])

You will get list of all models satisfying your restrictions.

Usage in R

Creating an account

To upload your model you need to create your account.

library(weles)

user_create('Example user', 'example password', 'example_mail@gmail.com')

In response you will get information if your account was created succesfully.

Uploading model

First you need to have a trained mlr, caret or parsnip model. Let's make an mlr random forest.

Example

library(mlr)

task <- makeClassifTask(data = iris, target = 'Species')
model <- makeLearner("classif.randomForest")
model <- train(model, task)

To upload the model to the base you need to import client package and pass classifier, its name that will be visible in the weles, its description, tags, training dataset with target column and column names, its name, its description, your user name and password. Names should be unique.

library('weles')

model_upload(model, 'example_model', 'This is an example model.', 'Species', c('example', 'easy'), iris, 'example_data', 'This is an example data', 'Example user', 'example_password')

In this moment model is being uploaded to the weles. If requested environment had not been already created in the weles, it will be created. During this time your R sesion will be suspended.

Summary

You can also pass your model as the path (must contain / sign) to .rds file. Training data parameter can be a path to .csv file (must constain / sign) or hash of already uploaded dataset in the weles.

Please, have loaded in your namespace only packages that are required to make a model, because creating an environment will take a while

Reading an info about model

If you want to read an info about the model already uploaded in weles you can run:

library('weles')

model_info("example_model")

"example_model" is a name of weles model.

Returned value is a R named list containing all information about the model.

Making predictions

If you want to make a prediction, type:

library('weles')

model_predict("example_model", data)

"example_model" is the name of weles model, data is the data frame with named columns without target column, or path to .csv (must contain / sign) file or hash of already uploaded data.

Be aware that some models may require from you exactly the same column names in passed data. If you passed data as an object then column names will be fetched by default. If you do not want this behaviour pass as argument prepare_data value False. You may also easily manually obtain columns with:

columns <- model_info("example_model")$columns

Searching for model

You can also search for model with some specific restrictions.

Just type:

library(weles)

model_search(row = '>1000;<10000;', column='=12;', user='Example user', tags = c('example', 'easily'))

You will receive in response all models with at least one of these tags.