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362d103 Feb 20, 2018
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import dataset
import betting
import tensorflow as tf
import numpy as np
import csv
TRAINING_SET_FRACTION = 0.95
def main(argv):
data = dataset.Dataset('data/book.csv')
train_results_len = int(TRAINING_SET_FRACTION * len(data.processed_results))
train_results = data.processed_results[:train_results_len]
test_results = data.processed_results[train_results_len:]
def map_results(results):
features = {}
for result in results:
for key in result.keys():
if key not in features:
features[key] = []
features[key].append(result[key])
for key in features.keys():
features[key] = np.array(features[key])
return features, features['result']
train_features, train_labels = map_results(train_results)
test_features, test_labels = map_results(test_results)
train_input_fn = tf.estimator.inputs.numpy_input_fn(
x=train_features,
y=train_labels,
batch_size=500,
num_epochs=None,
shuffle=True
)
test_input_fn = tf.estimator.inputs.numpy_input_fn(
x=test_features,
y=test_labels,
num_epochs=1,
shuffle=False
)
feature_columns = []
for mode in ['home', 'away']:
feature_columns = feature_columns + [
tf.feature_column.numeric_column(key='{}-wins'.format(mode)),
tf.feature_column.numeric_column(key='{}-draws'.format(mode)),
tf.feature_column.numeric_column(key='{}-losses'.format(mode)),
tf.feature_column.numeric_column(key='{}-goals'.format(mode)),
tf.feature_column.numeric_column(key='{}-opposition-goals'.format(mode)),
tf.feature_column.numeric_column(key='{}-shots'.format(mode)),
tf.feature_column.numeric_column(key='{}-shots-on-target'.format(mode)),
tf.feature_column.numeric_column(key='{}-opposition-shots'.format(mode)),
tf.feature_column.numeric_column(key='{}-opposition-shots-on-target'.format(mode)),
]
model = tf.estimator.DNNClassifier(
model_dir='model/',
hidden_units=[10],
feature_columns=feature_columns,
n_classes=3,
label_vocabulary=['H', 'D', 'A'],
optimizer=tf.train.ProximalAdagradOptimizer(
learning_rate=0.1,
l1_regularization_strength=0.001
))
with open('training-log.csv', 'w') as stream:
csvwriter = csv.writer(stream)
for i in range(0, 200):
model.train(input_fn=train_input_fn, steps=100)
evaluation_result = model.evaluate(input_fn=test_input_fn)
predictions = list(model.predict(input_fn=test_input_fn))
prediction_result = betting.test_betting_stategy(predictions, test_features, test_labels)
csvwriter.writerow([(i + 1) * 100, evaluation_result['accuracy'], evaluation_result['average_loss'], prediction_result['performance']])
if __name__ == '__main__':
tf.logging.set_verbosity(tf.logging.INFO)
tf.app.run(main=main)