https://github.com/aajanki/sequence-classification

Scikit-learn compatible sequence classifier
https://github.com/aajanki/sequence-classification

machine-learning scikit-learn sequence-classification

Last synced: 13 days ago
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Scikit-learn compatible sequence classifier

• Host: GitHub
• URL: https://github.com/aajanki/sequence-classification
• Owner: aajanki
• License: other
• Created: 2018-01-14T15:53:16.000Z (about 8 years ago)
• Default Branch: master
• Last Pushed: 2018-02-15T19:35:34.000Z (about 8 years ago)
• Last Synced: 2025-04-25T08:43:30.112Z (11 months ago)
• Topics: machine-learning, scikit-learn, sequence-classification
• Language: Python
• Homepage:
• Size: 28.3 KB
• Stars: 23
• Watchers: 2
• Forks: 3
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Sequence classifier with a scikit-learn interface

[![Build Status](https://travis-ci.org/aajanki/sequence-classification.svg?branch=master)](https://travis-ci.org/aajanki/sequence-classification)

[![PyPI version](https://badge.fury.io/py/sklearn-sequence-classifiers.svg)](https://badge.fury.io/py/sklearn-sequence-classifiers)

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Convolutional neural network sequence classifier in the spirit of [`[1]`](#references). Wraps a Keras implementation as a scikit-learn classifier.

## Software requirements

* Python (2.7 or >= 3.5)

* scikit-learn (tested on 0.19)

* Keras (tested on 2.0.5, with the Theano 0.9.0 backend)

## Installation

[Install Keras](https://keras.io/#installation). This has been tested on the Theano backend, should work on other backends, too.

```

pip3 install --user sklearn-sequence-classifiers

```

Installing from the source code:

```

git clone git@github.com:aajanki/sequence-classification.git

cd sequence-classification

python3 setup.py install --user

```

## Running tests

```

python3 setup.py test

```

## Usage example

Predicting IMDB review sentiments.

```python

from keras.datasets import imdb

from keras.preprocessing import sequence

from sequence_classifiers import CNNSequenceClassifier

maxlen = 400

(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=5000)

x_train = sequence.pad_sequences(x_train, maxlen=maxlen)

x_test = sequence.pad_sequences(x_test, maxlen=maxlen)

clf = CNNSequenceClassifier(epochs=2)

clf.fit(x_train, y_train)

print(clf.score(x_test, y_test))

```

## License

BSD

## References

[1] Yoon Kim: [Convolutional Neural Networks for Sentence Classification](https://arxiv.org/abs/1408.5882)