use_histogram=use_historgram)
######## Appending Metrics ########
train_label_list = conversions.tensor_list_to_int_list(
train_labels)
train_pred_list = conversions.tensor_list_to_int_list(train_preds)
val_label_list = conversions.tensor_list_to_int_list(val_labels)
val_pred_list = conversions.tensor_list_to_int_list(val_preds)
train_scores = metrics.precision_recall_fscore_support(
train_label_list, train_pred_list, average='weighted')
val_scores = metrics.precision_recall_fscore_support(
val_label_list, val_pred_list, average='weighted')
validation_user_statistics_over_epochs.append(
statistics.generate_training_statistics_for_user(
val_labels, val_preds, val_users))
if val_scores[2] > best_split_score:
best_split_score = val_scores[2]
epoch_at_best_score = epoch
split_val_scores.append(best_split_score)
best_score_epoch_log.append(epoch_at_best_score)
print("Avg Cross Val Score: {}".format(list_mean(split_val_scores)))
print("Students Used for Training: {}".format(student_filter_ids))
# scores_and_epochs = (split_val_scores, epoch_at_best_score)
# scores_and_epochs_file_name = os.path.join(definitions.DATA_DIR, "cross_val_scores/multitask_autoencoder.pkl")
# write_utils.data_structure_to_pickle(scores_and_epochs, scores_and_epochs_file_name)
alpha=alpha,
beta=beta,
use_histogram=use_historgram,
use_covariates=use_covariates)
######## Appending Metrics ########
train_label_list = conversions.tensor_list_to_int_list(train_labels)
train_pred_list = conversions.tensor_list_to_int_list(train_preds)
val_label_list = conversions.tensor_list_to_int_list(val_labels)
val_pred_list = conversions.tensor_list_to_int_list(val_preds)
train_scores = metrics.precision_recall_fscore_support(train_label_list, train_pred_list, average='weighted')
val_scores = metrics.precision_recall_fscore_support(val_label_list, val_pred_list, average='weighted')
validation_user_statistics_over_epochs.append(statistics.generate_training_statistics_for_user(val_labels,
val_preds,
val_users))
if val_scores[2] > best_split_score:
best_split_score = val_scores[2]
epoch_at_best_score = epoch
best_model = deepcopy(model)
print("Split: {} Score This Epoch: {} Best Score: {}".format(split_no, val_scores[2], best_split_score))
split_val_scores.append(best_split_score)
best_score_epoch_log.append(epoch_at_best_score)
best_models.append(deepcopy(best_model))
print("alpha: {} Beta: {}".format(alpha, beta))
print("Avg Cross Val Score: {}".format(list_mean(split_val_scores)))