Metadata-Version: 1.1
Name: numeraicb
Version: 1.0.0
Summary: Keras Callback to track Numerai consistency
Home-page: https://github.com/volker48/numeraicb
Author: Marcus McCurdy
Author-email: marcus.mccurdy@gmail.com
License: MIT
Description-Content-Type: UNKNOWN
Description: 
        ==========================
        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))
        
        
        
        
        
        
        
        
        
        
        
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2.6
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Programming Language :: Python :: Implementation :: PyPy
