skift 0.0.4

Utilities for pandas.

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 Features

• 3 Wrappers

  • 3.1 Standard wrappers

  • 3.2 pandas-dependent wrappers

• 4 Contributing

  • 4.1 Installing for development

  • 4.2 Running the tests

  • 4.3 Adding documentation

• 5 Credits

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 ([e-mail]); You are more than welcome to approach him for help. Contributions are very welcomed.

4.1 Installing for development

Clone:

git clone [e-mail]: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 ([e-mail]).