def get_features_importance(model, feature_names, top=10):
# graph
terminal.markdown_h2("Top {} Feature Importances".format(top))
print()
feature_importances = model.feature_importances_
df_feature_importances = pd.DataFrame({"importance": feature_importances}, index=feature_names) \
.sort_values('importance', ascending=False)
top_df = df_feature_importances.head(top)
graphs.bars(top_df["importance"],
percentage_on_top=False,
title='Feature Importances',
ylabel="Importance",
xlabel="Feature",
data_processed=True)
# break line
print("\n")
# table
terminal.markdown_h2("Feature Importances")
print()
table = [["Feature", "Importance"]]
content = []
for i, x in enumerate(feature_importances):
content.append([feature_names[i], round(x, 3)])
content.sort(key=lambda x: x[1], reverse=True)
table += content
terminal.markdown_table(table)
def metrics_table(historys):
terminal.markdown_h2("Metrics Table")
print()
header = [
"Neurons", "Accuracy", "Max Accuracy", "Epoch Max Acc.", "Loss",
"Min Loss", "Epoch Min Loss"
]
table = [header]
# metrics table
for neurons, history in historys:
# accuracy
accuracy = history['val_accuracy'][-1]
max_accuracy = max(history['val_accuracy'])
idx_max_accuracy = history['val_accuracy'].index(max_accuracy) + 1
# loss
loss = history['val_loss'][-1]
min_loss = min(history['val_loss'])
idx_min_loss = history['val_loss'].index(min_loss) + 1
row = [
neurons,
round(accuracy, 3),
round(max_accuracy, 3),
round(idx_max_accuracy),
round(loss, 3),
round(min_loss, 3),
round(idx_min_loss, 3),
]
table.append(row)
dskc_terminal.table(table)
def header(section, sub_section, text, increment_sub_section=True):
print("\n")
markdown_h2("{}.{} {}".format(section, sub_section, text))
print("\n")
if increment_sub_section:
sub_section += 1
return sub_section
def loss_graphs(historys):
terminal.markdown_h2("Loss")
print()
_plots(historys, train="loss", test="val_loss", label="Loss")
print("\n")
def accuracy_graphs(historys):
terminal.markdown_h2("Accuracy")
print()
_plots(historys, train="accuracy", test="val_accuracy", label="Accuracy")
print("\n")