def visualize(self,
predictions,
model,
save_dir='../../save/keras',
plt_name='keras'):
# Evaluate predictions using accuracy metrics
accuracy = accuracy_score(self.y_test, predictions)
print('{} Classification'.format(model))
print("Accuracy: %.2f%%" % (accuracy * 100.0))
# Evaluate predictions using confusion metrics and plot confusion matrix
classification_report = metrics.classification_report(
predictions,
self.y_test,
target_names=['NadaSportswear', 'Sportswear'])
print(classification_report)
# Calculating confusion matrix
cnf_matrix = confusion_matrix(self.y_test, predictions)
np.set_printoptions(precision=2)
# Plot module is used for plotting confusion matrix, classification report
plot = Plot()
plot.plotly(cnf_matrix, classification_report,
os.path.join(self.args.save_dir, embedding_type), plt_name)
def run_pipeline(self):
"""
run_pipeline function runs the actual pipeline.
:return:
"""
# Train & Test data split using sklearn train_test_split module
X_train, X_test, y_train, y_test = train_test_split(
self.data['url'],
self.data['label'],
test_size=0.33,
random_state=21,
stratify=self.data['label'])
print(
"*******************\nTrain set : {} \n Test set : {}\n*******************\n"
.format(X_train.shape[0], X_test.shape[0]))
# Running the pipeline
model = self.pipeline.fit(X_train, y_train)
print('Saving the {} model after fitting on training data.'.format(
str(self.args.model).upper()))
# Dumping tokenizer
joblib.dump(
model,
os.path.join(self.args.checkpoint_dir,
'{}.pickle'.format(self.args.model)))
# Calculating time per prediction
# Start time ******************************************************************************
start = timeit.default_timer()
# Predicting label, confidence probability on the test data set
predictions = model.predict(X_test)
predictions_prob = model.predict_proba(X_test)
# Binary class values : rounding them to 0 or 1
predictions = [round(value) for value in predictions]
end = timeit.default_timer()
# End Time ******************************************************************************
print('Time per prediction : {}'.format(
(end - start) / X_test.shape[0]))
# evaluate predictions using accuracy metrics
accuracy = accuracy_score(y_test, predictions)
print('{} Classification'.format(self.args.model))
print("Accuracy: %.2f%%" % (accuracy * 100.0))
# evaluate predictions using confusion metrics and plot confusion matrix
classification_report = metrics.classification_report(
predictions, y_test, target_names=['NadaSportswear', 'Sportswear'])
print(classification_report)
# Plotting confusion matrix
cnf_matrix = confusion_matrix(y_test, predictions)
np.set_printoptions(precision=2)
# Plot module is used for plotting confusion matrix, classification report
plot = Plot()
plot.plotly(cnf_matrix, classification_report, self.args.save_dir,
self.args.model)