# %%
# Plot the training iterations vs. the training loss, the valid data mean-absolute-difference,
# and the valid data correlation with predicted and true (y_vald) labels.
file_name = os.getcwd() + "/models/plot_metrics.png"
# file_name = os.path.join(os.getcwd(), name)
E2Nnet_sml.plot_iter_metrics(True, file_name)
# %%
# Predict labels of test data
preds = E2Nnet_sml.predict(x_val)
preds = np.reshape(preds, (len(preds), 1))
# %%
# Compute the metrics.
E2Nnet_sml.print_results(preds, y_val)
print("predictions raw", preds)
print("y_test", y_val)
preds_trans = np.zeros((preds.shape))
preds_trans[preds >= 0.5] = 1
preds_trans[preds < 0.5] = 0
print("predictions", preds_trans)
accuracy = np.sum(np.sum(y_val != preds_trans))
print("accuracy", accuracy)
# %%
# We can save the model like this.
# test_data = (x_test, y_test)
# file_name = "models/test_data.pkl"
# with open(file_name, 'wb') as pkl_file:
# WARNING: If you have a high max_iter and no GPU, this could take awhile...
E2Nnet_sml.fit(x_train, y_train, x_valid,
y_valid) # If no valid data, could put test data here.
# %%
# Plot the training iterations vs. the training loss, the valid data mean-absolute-difference,
# and the valid data correlation with predicted and true (y_vald) labels.
E2Nnet_sml.plot_iter_metrics()
# %%
# Predict labels of test data
preds = E2Nnet_sml.predict(x_test)
# %%
# Compute the metrics.
E2Nnet_sml.print_results(preds, y_test)
# %%
# We can save the model like this.
E2Nnet_sml.save('models/E2Nnet_sml.pkl')
# %%
# Now let's try removing and loading the saved model.
del E2Nnet_sml
del preds
# %%
# Load the model like this.
E2Nnet_sml = load_model('models/E2Nnet_sml.pkl')
# %%