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https://github.com/chris-santiago/wetsuit

A Scikit-learn style wrapper for H2O estimators.

https://github.com/chris-santiago/wetsuit

Science Score: 36.0%

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    Low similarity (11.0%) to scientific vocabulary

Keywords from Contributors

transformers
Last synced: 10 months ago · JSON representation

Repository

A Scikit-learn style wrapper for H2O estimators.

Basic Info
  • Host: GitHub
  • Owner: chris-santiago
  • License: mit
  • Language: Python
  • Default Branch: master
  • Size: 2.89 MB
Statistics
  • Stars: 0
  • Watchers: 1
  • Forks: 0
  • Open Issues: 1
  • Releases: 1
Created over 3 years ago · Last pushed over 3 years ago
Metadata Files
Readme Changelog License

README.md

Wetsuit

image image

A Scikit-Learn wrapper for H2O Estimators.

Why Wetsuit

While H2O Estimators have the .fit() and .predict() methods of the Scikit-Learn API, they don't always function as expected, especially with Pipeline objects. This package contains two estimators to remedy.

For example. the H2OEstimator.fit() method expects two H2OFrame objects, vice pandas DataFrame or numpy NDArray objects. Wetsuit provides two classes classes that wrap H2OEstimator objects and handle type conversion automatically, within the .fit() and .predict() methods:

  • WetsuitRegressor
  • WetsuitClassifier

Install

Create a virtual environment with Python >= 3.7 and install from PyPI:

bash pip install wetsuit

Use

Basic Pipeline

Here's an example that shows Scikit-Learn Pipeline compatibility. To align with the H2O API, we must instantiate the WetsuitClassifier with a list of feature names and the name of the response variable (these can also be indices). From there, you can plug in to a basic Pipeline object.

```python import h2o from h2o.estimators import H2OXGBoostEstimator from sklearn.datasets import load_iris from sklearn.preprocessing import StandardScaler from sklearn.pipeline import Pipeline

import wetsuit

h2o.init()

data = load_iris()

cls = wetsuit.WetsuitClassifier(H2OXGBoostEstimator(), data['feature_names'], 'target') pl = Pipeline([ ('scaler', StandardScaler()), ('cls', cls) ]) pl.fit(data['data'], data['target']) fitted = pl.predict(data['data'])

h2o.cluster().shutdown() ```

Note: If you're doing feature selection within the pipeline, it's best instantiate the WetsuitClassifier from within the pipeline, so that you can dynamically pass a list of selected features using a selector's .get_feature_names_out() method.

Documentation

Documentation hosted on Github Pages: https://chris-santiago.github.io/wetsuit/

Owner

  • Name: Chris Santiago
  • Login: chris-santiago
  • Kind: user

GitHub Events

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Last synced: over 1 year ago

All Time
  • Total Commits: 14
  • Total Committers: 2
  • Avg Commits per committer: 7.0
  • Development Distribution Score (DDS): 0.071
Past Year
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  • Avg Commits per committer: 0.0
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Top Committers
Name Email Commits
chris-santiago c****o@g****u 13
Chris Santiago 4****o 1
Committer Domains (Top 20 + Academic)
gatech.edu: 1

Issues and Pull Requests

Last synced: 11 months ago

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  • Total issues: 1
  • Total pull requests: 0
  • Average time to close issues: N/A
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  • Total issue authors: 1
  • Total pull request authors: 0
  • Average comments per issue: 0.0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
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  • Average comments per issue: 0
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  • Merged pull requests: 0
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Top Authors
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  • chris-santiago (1)
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bug (1)
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Packages

  • Total packages: 1
  • Total downloads:
    • pypi 11 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 1
  • Total maintainers: 1
pypi.org: wetsuit

A Scikit-learn style wrapper for H2O estimators.

  • Versions: 1
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 11 Last month
Rankings
Dependent packages count: 6.6%
Average: 18.6%
Dependent repos count: 30.6%
Maintainers (1)
cjsantiago
Last synced: 10 months ago