pyfasttext 0.2.1

Yet another Python binding for fastText

pyfasttext

Yet another Python binding for fastText.

The binding supports Python 2.7 and Python 3. It requires Cython.

Numpy is also a dependency, but is optional.

pyfasttext has been tested successfully on Linux and Mac OS X.

Table of Contents

• pyfasttext

  • Installation

    • Simplest way to install pyfasttext: use pip

    • Cloning

    • Requirements for Python 2.7

    • Building and installing

      • Building and installing without Numpy

  • Usage

    • How to load the library?

    • How to load an existing model?

    • Word representation learning

      • Training using Skipgram

      • Training using CBoW

    • Word vectors

      • Word vectors access

      • Vector for a given word

        • Numpy ndarray

      • Words for a given vector

      • Get the number of words in the model

      • Get all the word vectors in a model

        • Numpy ndarray

      • Misc operations with word vectors

      • Word similarity

      • Most similar words

      • Analogies

    • Text classification

      • Supervised learning

      • Get all the labels

      • Get the number of labels

      • Prediction

      • Labels and probabilities

        • Normalized probabilities

      • Labels only

      • Quantization

    • Misc utilities

      • Show the model (hyper)parameters

      • Extract labels or classes from a dataset

      • Extract labels

      • Extract classes

    • Exceptions

Installation

To compile pyfasttext, make sure you have a compiler with C++11 support.

Simplest way to install pyfasttext: use pip

Just type this line:

pip install pyfasttext

Cloning

pyfasttext uses git submodules.

So, you need to add the --recursive option when you clone the repository.

git clone --recursive https://github.com/vrasneur/pyfasttext.git
cd pyfasttext

Requirements for Python 2.7

Python 2.7 support relies on the future module: pyfasttext needs bytes objects, which are not available natively in Python2.

You can install the future module with pip.

pip install future

Building and installing

python setup.py install

Building and installing without Numpy

pyfasttext can export word vectors as numpy ndarrays, however this feature can be disabled at compile time.

To compile without numpy, pyfasttext has a USE_NUMPY environment variable. Set this variable to 0 (or empty), like this:

USE_NUMPY=0 python setup.py install

Usage

How to load the library?

>>> from pyfasttext import FastText

How to load an existing model?

>>> model = FastText('/path/to/model.bin')

or

>>> model = FastText()
>>> model.load_model('/path/to/model.bin')

Word representation learning

You can use all the options provided by the fastText binary (input, output, epoch, lr, …).

Just use keyword arguments in the training methods of the FastText object.

Training using Skipgram

>>> model = FastText()
>>> model.skipgram(input='data.txt', output='model', epoch=100, lr=0.7)

Training using CBoW

>>> model = FastText()
>>> model.cbow(input='data.txt', output='model', epoch=100, lr=0.7)

Word vectors

Word vectors access

Vector for a given word

By default, a single word vector is returned as a regular Python array of floats.

>>> model['dog']
array('f', [-1.308749794960022, -1.8326224088668823, ...])

Numpy ndarray

The model.get_numpy_vector(word) method returns the word vector as a numpy ndarray.

>>> model.get_numpy_vector('dog')
array([-1.30874979, -1.83262241, ...], dtype=float32)

If you want a normalized vector (i.e. the vector divided by its norm), there is an optional boolean parameter named normalized.

>>> model.get_numpy_vector('dog', normalized=True)
array([-0.07084749, -0.09920666, ...], dtype=float32)

Words for a given vector

The inverse operation of model[word] or model.get_numpy_vector(word) is model.words_for_vector(vector, k).

It returns a list of the k words closest to the provided vector. The default value for k is 1.

>>> king = model.get_numpy_vector('king')
>>> man = model.get_numpy_vector('man')
>>> woman = model.get_numpy_vector('woman')
>>> model.words_for_vector(king + woman - man, k=1)
[('queen', 0.77121970653533936)]

Get the number of words in the model

>>> model.nwords
500000

Get all the word vectors in a model

>>> for word in model.words:
...   print(word, model[word])

Numpy ndarray

If you want all the word vectors as a big numpy ndarray, you can use the numpy_normalized_vectors member. Note that all these vectors are normalized.

>>> model.nwords
500000
>>> model.numpy_normalized_vectors
array([[-0.07549749, -0.09407753, ...],
       [ 0.00635979, -0.17272158, ...],
       ...,
       [-0.01009259,  0.14604086, ...],
       [ 0.12467574, -0.0609326 , ...]], dtype=float32)
>>> model.numpy_normalized_vectors.shape
(500000, 100) # (number of words, dimension)

Misc operations with word vectors

Word similarity

>>> model.similarity('dog', 'cat')
0.75596606254577637

Most similar words

>>> model.nearest_neighbors('dog', k=2)
[('dogs', 0.7843924736976624), ('cat', 75596606254577637)]

Analogies

The model.most_similar() method works similarly as the one in gensim.

>>> model.most_similar(positive=['woman', 'king'], negative=['man'], k=1)
[('queen', 0.77121970653533936)]

Text classification

Supervised learning

>>> model = FastText()
>>> model.supervised(input='/path/to/input.txt', output='/path/to/model', epoch=100, lr=0.7)

Get all the labels

>>> model.labels
['LABEL1', 'LABEL2', ...]

Get the number of labels

>>> model.nlabels
100

Prediction

To obtain the k most likely labels from test sentences, there are multiple model.predict_*() methods.

The default value for k is 1. If you want to obtain all the possible labels, use None for k.

Labels and probabilities

If you have a list of strings (or an iterable object), use this:

>>> model.predict_proba(['first sentence', 'second sentence'], k=2)
[[('LABEL1', 0.99609375), ('LABEL3', 1.953126549381068e-08)], [('LABEL2', 1.0), ('LABEL3', 1.953126549381068e-08)]]

If your test data is stored inside a file, use this:

>>> model.predict_proba_file('/path/to/test.txt', k=2)
[[('LABEL1', 0.99609375), ('LABEL3', 1.953126549381068e-08)], [('LABEL2', 1.0), ('LABEL3', 1.953126549381068e-08)]]

If you want to test a single string, use this:

>>> model.predict_proba_single('first sentence', k=2)
[('LABEL1', 0.99609375), ('LABEL3', 1.953126549381068e-08)]

Normalized probabilities

For performance reasons, fastText probabilities often do not sum up to 1.0.

If you want normalized probabilities (where the sum is closer to 1.0 than the original probabilities), you can use the normalized=True parameter in all the methods that output probabilities (model.predict_proba(), model.predict_proba_file() and model.predict_proba_single()).

>>> sum(proba for label, proba in model.predict_proba_single('this is a sentence that needs to be classified', k=None))
0.9785203068801335
>>> sum(proba for label, proba in model.predict_proba_single('this is a sentence that needs to be classified', k=None, normalized=True))
0.9999999999999898

Labels only

If you have a list of strings (or an iterable object), use this:

>>> model.predict(['first sentence', 'second sentence'], k=2)
[['LABEL1', 'LABEL3'], ['LABEL2', 'LABEL3']]

If your test data is stored inside a file, use this:

>>> model.predict_file('/path/to/test.txt', k=2)
[['LABEL1', 'LABEL3'], ['LABEL2', 'LABEL3']]

If you want to test a single string, use this:

>>> model.predict_single('first sentence', k=2)
['LABEL1', 'LABEL3']

Quantization

Use keyword arguments in the model.quantize() method.

>>> model.quantize(input='/path/to/input.txt', output='/path/to/model')

You can load quantized models using the FastText constructor or the model.load_model() method.

Misc utilities

Show the model (hyper)parameters

>>> model.args
{'bucket': 11000000,
 'cutoff': 0,
 'dim': 100,
 'dsub': 2,
 'epoch': 100,
...
}

Extract labels or classes from a dataset

You can use the FastText object to extract labels or classes from a dataset.

The label prefix (which is __label__ by default) is set using the label parameter in the constructor.

If you load an existing model, the label prefix will be the one defined in the model.

>>> model = FastText(label='__my_prefix__')

Extract labels

There can be multiple labels per line.

>>> model.extract_labels('/path/to/dataset1.txt')
[['LABEL2', 'LABEL5'], ['LABEL1'], ...]

Extract classes

There can be only one class per line.

>>> model.extract_classes('/path/to/dataset2.txt')
['LABEL3', 'LABEL1', 'LABEL2', ...]

Exceptions

The fastText source code directly calls exit() when something wrong happens (e.g. a model file does not exist, …).

Instead of exiting, pyfasttext raises a Python exception (RuntimeError).

>>> import pyfasttext
>>> model = pyfasttext.FastText('/path/to/non-existing_model.bin')
Model file cannot be opened for loading!
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "src/pyfasttext.pyx", line 124, in pyfasttext.FastText.__cinit__ (src/pyfasttext.cpp:1800)
  File "src/pyfasttext.pyx", line 348, in pyfasttext.FastText.load_model (src/pyfasttext.cpp:5947)
RuntimeError: fastext tried to exit: 1