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scikit-learn wrappers for Python fastText.
.. code-block:: python
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::
.. section-numbering::
Dependencies:
numpyscipyscikit-learnfasttext Python package.. code-block:: bash
pip install skift
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:
.. code-block:: bash
export SKIFT_TEMP_DIR=/path/to/desired/temp/folder
NOTE: The directory will be created if it does not already exist.
scikit-learn classifier API, including predict_proba.pandas.DataFrame inputs.fastText with other types of scikit-learn-compliant classifiers.official fasttext Python package <https://github.com/facebookresearch/fastText/tree/master/python>_.Fully tested on Linux, OSX and Windows operating systems <https://travis-ci.org/shaypal5/skift>_.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 <https://github.com/facebookresearch/fastText/tree/master/python>_ 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.
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... code-block:: python
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... code-block:: python
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]
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... code-block:: python
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... code-block:: python
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... code-block:: python
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'])
It's possible to pass a validation set to fit() in order to optimize the hyper-parameters.
First, to adjust the auto-tune settings <https://fasttext.cc/docs/en/autotune.html>_, the corresponding keyword arguments can be passed to the constructor (if none are passed the default settings are used):
.. code-block:: python
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(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:
.. code-block:: python
sk_clf.fit(df_train['txt'], df_train['lbl'], X_validation=df_val['txt'], y_validation=df_val['lbl'])
Or simply by position:
.. code-block:: python
sk_clf.fit(df_train['txt'], df_train['lbl'], df_val['txt'], df_val['lbl'])
This is done in the exact same way as with the Python module or the fastText CLI, but not setting the right vector dimensions in the constructor (identical to the dimensions of the pretrained vectors you are using) will crash fastText without explanation, so we provide an example:
.. code-block:: python
from skift import SeriesFtClassifier
ft_clf = SeriesFtClassifier(
autotuneDuration=900,
pretrainedVectors='/Users/myuser/data/word_vectors/crawl-300d-2M.vec',
dim=300,
)In this case, not providing the constructor with dim=300 would bring about a crash when calling ft_clf.fit().
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.
Clone:
.. code-block:: bash
git clone git@github.com:shaypal5/skift.git
Install in development mode, including test dependencies:
.. code-block:: bash
cd skift pip install -e '.[test]'
To also install fasttext, see instructions in the Installation section.
To run the tests use:
.. code-block:: bash
cd skift pytest
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.
.. _numpy docstring conventions: https://github.com/numpy/numpy/blob/master/doc/HOWTO_DOCUMENT.rst.txt
.. _these conventions: https://github.com/numpy/numpy/blob/master/doc/HOWTO_DOCUMENT.rst.txt
Additionally, if you update this README.rst file, use python setup.py checkdocs to validate it compiles.
Created by Shay Palachy (shay.palachy@gmail.com).
Contributions:
Dimid Duchovny <https://github.com/dimidd>_ contributed the SeriesFtClassifier class and the hyperparameter auto-tuning capability.Fixes: uniaz <https://github.com/uniaz>, crouffer <https://github.com/crouffer>, amirzamli <https://github.com/amirzamli> and sgt <https://github.com/sgt>.
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