starter code for numerai with keras

example_keras.py

#!/usr/bin/env python
import os
os.environ['CUDA_VISIBLE_DEVICES'] = ''

import pandas as pd
import numpy as np
from keras import optimizers
from keras.utils import to_categorical
from keras.models import Model
from keras.layers import Input, Dense
from keras.layers.normalization import BatchNormalization
from keras.layers.core import Dropout, Activation

def main():
    # Set seed for reproducibility
    np.random.seed(0)

    print("Loading data...")
    # Load the data from the CSV files
    training_data = pd.read_csv('numerai_training_data.csv', header=0)
    prediction_data = pd.read_csv('numerai_tournament_data.csv', header=0)

    # Transform the loaded CSV data into numpy arrays
    features = [f for f in list(training_data) if "feature" in f]
    X = training_data[features]
    Y = training_data["target"]
    x_prediction = prediction_data[features]
    ids = prediction_data["id"]

    # ----- 
    X = X.values
    Y = to_categorical(Y, num_classes=2)
    
    batch_size = 64
    drop_out = 0.3
    
    m_in = Input(shape=(50,))
    m = Dense(50,)(m_in)
    m = BatchNormalization(axis=-1)(m)
    m = Activation('relu')(m)
    m = Dropout(drop_out)(m)
    m = Dense(2,)(m)
    m_out = Activation('softmax')(m)
    model = Model(inputs=m_in, outputs=m_out)
    
    opt = optimizers.SGD(lr=0.001, clipnorm=0.9)
    model.compile(loss='binary_crossentropy',optimizer=opt)
    model.fit(X,Y,batch_size=batch_size,epochs=20,validation_split=0.3)
    
    y_prediction = model.predict(x_prediction)
    results = y_prediction[:, 1]
    # -----
    
    results_df = pd.DataFrame(data={'probability':results})
    joined = pd.DataFrame(ids).join(results_df)

    print("Writing predictions to predictions.csv")
    # Save the predictions out to a CSV file
    joined.to_csv("predictions.csv", index=False)
    # Now you can upload these predictions on numer.ai


if __name__ == '__main__':
    main()