skift

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]

Contents

• 1   Installation
• 2   Configuration
• 3   Features
• 4   Wrappers
  • 4.1   Standard wrappers
  • 4.2   pandas-dependent wrappers
• 5   Contributing
  • 5.1   Installing for development
  • 5.2   Running the tests
  • 5.3   Adding documentation
• 6   Credits

1   Installation

Dependencies:

• numpy
• scipy
• scikit-learn
• The fasttext Python package

pip install skift

2   Configuration

Because fasttext reads input data from files, skift has to dump the input data into temporary files for fasttext to use. A dedicated folder is created for those files on the filesystem. By default, this storage is allocated in the system temporary storage location (i.e. /tmp on *nix systems). To override this default location, use the SKIFT_TEMP_DIR environment variable:

export SKIFT_TEMP_DIR=/path/to/desired/temp/folder

NOTE: The directory will be created if it does not already exist.

3   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.
• Built around the official fasttext Python package.
• Pure python.
• Supports Python 3.5+.
• Fully tested on Linux, OSX and Windows operating systems.

4   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 official 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.

4.1   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]

4.2   pandas-dependent wrappers

These wrappers assume the X parameter 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]

5   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.

5.1   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]'

To also install fasttext, see instructions in the Installation section.

5.2   Running the tests

To run the tests use:

cd skift
pytest

5.3   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.

Additionally, if you update this README.rst file, use python setup.py checkdocs to validate it compiles.

6   Credits

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

Fixes: uniaz, crouffer and amirzamli.