import validation_helpers
train_fn = logistic_regression.train_log_reg
test_fn = logistic_regression.test_log_reg
hyperparams = {
'batch_size': [100, 200],
'learning_rate': [0.01, 0.025, 0.05],
'lambda_reg': [0, 0.001, 0.05],
'n_epochs': 100,
'validate_params': ["batch_size", "learning_rate", "lambda_reg"]}
# Cross-validate here
best_params, all_params = validation_helpers.validate_grid_search(train_fn, test_fn,
False, train_samples, train_labels, valid_samples,
valid_labels, hyperparams, num_repeat=1)
# Average results due to non-deterministic nature of the model
f1s = numpy.zeros((1, train_labels.shape[1]))
precisions = numpy.zeros((1, train_labels.shape[1]))
recalls = numpy.zeros((1, train_labels.shape[1]))
num_repeat = 3
print 'All params', all_params
print 'Best params', best_params
for i in range(num_repeat):
model = train_fn(train_labels, train_samples, best_params)
_, _, _, _, f1_c, precision_c, recall_c = test_fn(valid_labels, valid_samples, model)
test_fn = logistic_regression.test_log_reg
hyperparams = {
'batch_size': [100],
'learning_rate': [0.05, 0.1, 0.2],
'lambda_reg': [0, 0.05, 0.1, 0.5],
'n_epochs': 100,
'validate_params': ["batch_size", "learning_rate", "lambda_reg"]
}
# Cross-validate here
best_params, all_params = validation_helpers.validate_grid_search(
train_fn,
test_fn,
False,
train_samples,
train_labels,
valid_samples,
valid_labels,
hyperparams,
num_repeat=3)
# Average results due to non-deterministic nature of the model
f1 = 0
precision = 0
recall = 0
num_repeat = 3
for i in range(num_repeat):
model = train_fn(train_labels, train_samples, best_params)
f1_c, precision_c, recall_c, prediction, _, _, _ = test_fn(
