max_ranking_size = np.min((cutoff, data.max_query_size()))
click_model = clk.get_click_model(click_model_name)
alpha, beta = click_model(np.arange(max_ranking_size))
model_params = {'hidden units': [32, 32],}
model = nn.init_model(model_params)
optimizer = tf.keras.optimizers.SGD(learning_rate=0.01)
first_results = evl.evaluate_policy(
model,
data.test,
data.test.label_vector*0.25,
alpha,
beta,
)
# print(first_results)
model, vali_reward = opt.optimize_policy(model, optimizer,
data_train=data.train,
train_doc_weights=data.train.label_vector*0.25,
train_alpha=alpha+beta,
train_beta=np.zeros_like(beta),
data_vali=data.validation,
vali_doc_weights=data.validation.label_vector*0.25,
vali_alpha=alpha+beta,
vali_beta=np.zeros_like(beta),
'run name': 'affine estimator',
'number of updates': n_updates,
'number of evaluation points': n_eval,
'update iterations': [int(x) for x in update_points],
'evaluation iterations': [int(x) for x in eval_points],
'model hyperparameters': model_params,
'results': results,
}
if args.pretrained_model:
output['initial model'] = args.pretrained_model
logging_policy_metrics = evl.evaluate_policy(
model,
data.test,
true_test_doc_weights,
alpha,
beta,
)
train_policy_scores[:] = logging_model(data.train.feature_matrix)[:, 0].numpy()
vali_policy_scores[:] = logging_model(
data.validation.feature_matrix)[:, 0].numpy()
i_update = 0
for n_queries_sampled in eval_points:
(
new_train_clicks,
new_train_displays,
new_train_query_freq,