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Utilities for pandas.

Project description

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scikit-learn wrappers for Python fastText.

>>> from skift import FirstColFtClassifier
>>> df = pandas.DataFrame([['woof', 0], ['meow', 1]], columns=['txt', 'lbl'])
>>> sk_clf = FirstColFtClassifier(lr=0.3, epoch=10)
>>> sk_clf.fit(df[['txt']], df['lbl'])
>>> sk_clf.predict([['woof']])
[0]

1   Installation

Dependencies:

  • numpy
  • scikit-learn
  • fastText Python package
pip install skift

NOTICE: Installing skift will not install any of its dependencies. They should be installed separately.

2   Features

  • Adheres to the scikit-learn classifier API, including predict_proba.
  • Also caters to the common use case of pandas.DataFrame inputs.
  • Enables easy stacking of fastText with other types of scikit-learn-compliant classifiers.
  • Pickle-able classifier objects.
  • Pure python.
  • Supports Python 3.4+.
  • Fully tested.

3   Wrappers

fastText works only on text data, which means that it will only use a single column from a dataset which might contain many feature columns of different types. As such, a common use case is to have the fastText classifier use a single column as input, ignoring other columns. This is especially true when fastText is to be used as one of several classifiers in a stacking classifier, with other classifiers using non-textual features.

skift includes several scikit-learn-compatible wrappers for the fastText Python package which cater to these use cases.

NOTICE: Any additional keyword arguments provided to the classifier constructor, besides those required, will be forwarded to the fastText.train_supervised method on every call to fit.

3.1   Standard wrappers

These wrappers do not make additional assumptions on input besides those commonly made by scikit-learn classifies; i.e. that input is a 2d ndarray object and such.

  • FirstColFtClassifier - An sklearn classifier adapter for fasttext that takes the first column of input ndarray objects as input.
>>> from skift import FirstColFtClassifier
>>> df = pandas.DataFrame([['woof', 0], ['meow', 1]], columns=['txt', 'lbl'])
>>> sk_clf = FirstColFtClassifier(lr=0.3, epoch=10)
>>> sk_clf.fit(df[['txt']], df['lbl'])
>>> sk_clf.predict([['woof']])
[0]
  • IdxBasedFtClassifier - An sklearn classifier adapter for fasttext that takes input by column index. This is set on object construction by providing the input_ix parameter to the constructor.
>>> from skift import IdxBasedFtClassifier
>>> df = pandas.DataFrame([[5, 'woof', 0], [83, 'meow', 1]], columns=['count', 'txt', 'lbl'])
>>> sk_clf = IdxBasedFtClassifier(input_ix=1, lr=0.4, epoch=6)
>>> sk_clf.fit(df[['count', 'txt']], df['lbl'])
>>> sk_clf.predict([['woof']])
[0]

3.2   pandas-dependent wrappers

These wrappers assume the X parameters given to fit, predict, and predict_proba methods is a pandas.DataFrame object:

  • FirstObjFtClassifier - An sklearn adapter for fasttext using the first column of dtype == object as input.
>>> from skift import FirstObjFtClassifier
>>> df = pandas.DataFrame([['woof', 0], ['meow', 1]], columns=['txt', 'lbl'])
>>> sk_clf = FirstObjFtClassifier(lr=0.2)
>>> sk_clf.fit(df[['txt']], df['lbl'])
>>> sk_clf.predict([['woof']])
[0]
  • ColLblBasedFtClassifier - An sklearn adapter for fasttext taking input by column label. This is set on object construction by providing the input_col_lbl parameter to the constructor.
>>> from skift import ColLblBasedFtClassifier
>>> df = pandas.DataFrame([['woof', 0], ['meow', 1]], columns=['txt', 'lbl'])
>>> sk_clf = ColLblBasedFtClassifier(input_col_lbl='txt', epoch=8)
>>> sk_clf.fit(df[['txt']], df['lbl'])
>>> sk_clf.predict([['woof']])
[0]

4   Contributing

Package author and current maintainer is Shay Palachy (shay.palachy@gmail.com); You are more than welcome to approach him for help. Contributions are very welcomed.

4.1   Installing for development

Clone:

git clone git@github.com:shaypal5/skift.git

Install in development mode, including test dependencies:

cd skift
pip install -e '.[test]'

4.2   Running the tests

To run the tests use:

cd skift
pytest

4.3   Adding documentation

The project is documented using the numpy docstring conventions, which were chosen as they are perhaps the most widely-spread conventions that are both supported by common tools such as Sphinx and result in human-readable docstrings. When documenting code you add to this project, follow these conventions.

5   Credits

Created by Shay Palachy (shay.palachy@gmail.com).

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