arbok 0.0.8

A wrapper toolbox that provides compatibility layers between TPOT and Auto-Sklearn and OpenML

Arbok (Automl wrapper toolbox for openml compatibility) provides wrappers for TPOT and Auto-Sklearn, as a compatibility layer between these tools and OpenML.

The wrapper extends Sklearn’s BaseSearchCV and provides all the internal parameters that OpenML needs, such as cv_results_, best_index_, best_params_, best_score_ and classes_.

Installation

pip install arbok

Simple example

import openml
from arbok import AutoSklearnWrapper, TPOTWrapper


task = openml.tasks.get_task(31)
dataset = task.get_dataset()

# Get the AutoSklearn wrapper and pass parameters like you would to AutoSklearn
clf = AutoSklearnWrapper(
    time_left_for_this_task=3600, per_run_time_limit=360
)

# Or get the TPOT wrapper and pass parameters like you would to TPOT
clf = TPOTWrapper(
    generations=100, population_size=100, verbosity=2
)

# Execute the task
run = openml.runs.run_model_on_task(task, clf)
run.publish()

print('URL for run: %s/run/%d' % (openml.config.server, run.run_id))

Preprocessing data

To make the wrapper more robust, we need to preprocess the data. We can fill the missing values, and one-hot encode categorical data.

First, we get a mask that tells us whether a feature is a categorical feature or not.

dataset = task.get_dataset()
_, categorical = dataset.get_data(return_categorical_indicator=True)
categorical = categorical[:-1]  # Remove last index (which is the class)

Next, we setup a pipeline for the preprocessing. We are using a ConditionalImputer, which is an imputer which is able to use different strategies for categorical (nominal) and numerical data.

from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import OneHotEncoder
from arbok import ConditionalImputer

preprocessor = make_pipeline(

    ConditionalImputer(
        categorical_features=categorical,
        strategy="mean",
        strategy_nominal="most_frequent"
    ),

    OneHotEncoder(
        categorical_features=categorical, handle_unknown="ignore", sparse=False
    )
)

And finally, we put everything together in one of the wrappers.

clf = AutoSklearnWrapper(
    preprocessor=preprocessor, time_left_for_this_task=3600, per_run_time_limit=360
)

Limitations

• Currently only the classifiers are implemented. Regression is therefore not possible.

• For TPOT, the config_dict variable can not be set, because this causes problems with the API.