skift 0.0.23

scikit-learn wrappers for Python fastText

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

  • 4.3 Hyperparameter auto-tuning

• 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]

• SeriesFtClassifier - An sklearn adapter for fasttext taking a Pandas Series as input.

>>> from skift import SeriesFtClassifier
>>> df = pandas.DataFrame([['woof', 0], ['meow', 1]], columns=['txt', 'lbl'])
>>> sk_clf = SeriesFtClassifier(input_col_lbl='txt', epoch=8)
>>> sk_clf.fit(df['txt'], df['lbl'])
>>> sk_clf.predict(['woof'])
>>> sk_clf.predict(df['txt'])

4.3 Hyperparameter auto-tuning

It’s possible to pass a validation set to fit() in order to optimize the hyper-parameters.

First, to adjust the auto-tune settings, the corresponding keyword arguments can be passed to the constructor (if none are passed the default settings are used):

>>> from skift import SeriesFtClassifier
>>> df_train = pandas.DataFrame([['woof', 0], ['meow', 1]], columns=['txt', 'lbl'])
>>> df_val = pandas.DataFrame([['woof woof', 0], ['meow meow', 1]], columns=['txt', 'lbl'])
>>> sk_clf = SeriesFtClassifier(input_col_lbl='txt', epoch=8, autotuneDuration=5)

Then, the validation dataframe (or series, in this case, since we constructed a SeriesFtClassifier) and label column should be provided to the fit() method:

>>> sk_clf.fit(df_train['txt'], df_train['lbl'], X_validation=df_val['txt'], y_validation=df_val['lbl'])

Or simply by position:

>>> sk_clf.fit(df_train['txt'], df_train['lbl'], df_val['txt'], df_val['lbl'])

5 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.

5.1 Installing for development

Clone:

git clone [e-mail]:shaypal5/skift.git

Install in development mode, including test dependencies:

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

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 ([e-mail]).

Contributions:

• Dimid Duchovny <https://github.com/dimidd>_ contributed the SeriesFtClassifier class and the hyperparameter auto-tuning capability.

Fixes: uniaz, crouffer, amirzamli and sgt.