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
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, includingpredict_proba
. - Also caters to the common use case of
pandas.DataFrame
inputs. - Enables easy stacking of
fastText
with other types ofscikit-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 inputndarray
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 theinput_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 ofdtype == 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 theinput_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 ([email protected]); You are more than welcome to approach him for help. Contributions are very welcomed.
5.1 Installing for development
Clone:
git clone [email protected]: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 ([email protected]).