blocktorch 1.33.2

Blocktorch is a library that builds, optimizes, and evaluates machine learning pipelines

blocktorch is an AutoML library which builds, optimizes, and evaluates machine learning pipelines using domain-specific objective functions.

Key Functionality

• Automation - Makes machine learning easier. Avoid training and tuning models by hand. Includes data quality checks, cross-validation and more.
• Data Checks - Catches and warns of problems with your data and problem setup before modeling.
• End-to-end - Constructs and optimizes pipelines that include state-of-the-art preprocessing, feature engineering, feature selection, and a variety of modeling techniques.
• Model Understanding - Provides tools to understand and introspect on models, to learn how they'll behave in your problem domain.
• Domain-specific - Includes repository of domain-specific objective functions and an interface to define your own.

Install from PyPI

pip install blocktorch

Add-ons

Update checker

Receive automatic notifications of new blocktorch releases

pip install blocktorch[update_checker]

Start

Load and split example data

import blocktorch
X, y = blocktorch.demos.load_breast_cancer()
X_train, X_test, y_train, y_test = blocktorch.preprocessing.split_data(X, y, problem_type='binary')

Run AutoML

from blocktorch.automl import AutoMLSearch
automl = AutoMLSearch(X_train=X_train, y_train=y_train, problem_type='binary')
automl.search()

View pipeline rankings

automl.rankings

Get best pipeline and predict on new data

pipeline = automl.best_pipeline
pipeline.predict(X_test)

Next Steps

Read more about blocktorch on our documentation page:

• Installation and getting started.
• Tutorials on how to use blocktorch.
• User guide which describes blocktorch's features.
• Full API reference

Built by Blocktorch