https://github.com/volker48/numeraicb

Keras callback to calculate Numerai consistency during training
https://github.com/volker48/numeraicb

deep-learning keras machine-learning neural-network numerai

Last synced: about 1 year ago
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Keras callback to calculate Numerai consistency during training

• Host: GitHub
• URL: https://github.com/volker48/numeraicb
• Owner: volker48
• License: gpl-3.0
• Created: 2017-11-11T01:32:59.000Z (over 8 years ago)
• Default Branch: master
• Last Pushed: 2021-10-10T22:19:44.000Z (over 4 years ago)
• Last Synced: 2025-05-13T13:17:52.349Z (about 1 year ago)
• Topics: deep-learning, keras, machine-learning, neural-network, numerai
• Language: Python
• Size: 14.6 KB
• Stars: 10
• Watchers: 2
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.rst
  • License: LICENSE

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README

          
==========================

Keras Consistency Callback

==========================

A Keras callback that calculates your model's consistency during training at

each epoch. The callback prints the consistency and also adds the consistency at

the end of each epoch to the training history under the ``consistency`` key.

Usage

-----

Here is a usage example::

    import pandas as pd

    from numeraicb import Consistency

    from keras.models import Sequential

    from keras.layers.core import Dense

    train = pd.read_csv('numerai_training_data.csv')

    tourn = pd.read_csv('numerai_tournament_data.csv')

    validation = tourn[tourn.data_type == 'validation']

    features = ['feature{}'.format(i) for i in range(1, 51)]

    X = train[features].values

    Y = train.target.values

    X_validation = validation[features].values

    Y_validation = validation.target.values

    model = Sequential()

    model.add(Dense(30, kernel_initializer='uniform', input_dim=X.shape[1], activation='relu'))

    model.add(Dense(1, activation='sigmoid'))

    model.compile(optimizer='adamax', loss='binary_crossentropy')

    cb = Consistency(tourn)

    # Now your models consistency will be printed at each epoch

    history = model.fit(X, Y, callbacks=[cb], validation_data=(X_validation, Y_validation))

    # Consistency is stored in the history as well

    for epoch, consistency in enumerate(history.history['consistency']):

        print('consistency at epoch {}: {:.2%}'.format(epoch, consistency))