activity-classification-train.py

total_accuracy = 0.0
total_precision = 0.0
total_recall = 0.0

# Iterate over the cv and fit the decision tree using the training set
# https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html

for i, (train_index, test_index) in enumerate(cv.split(X, Y)):
	X_train, X_test = X[train_index], X[test_index]
	y_train, y_test = Y[train_index], Y[test_index]
	tree = DecisionTreeClassifier(criterion="entropy", max_depth=3)
	print("Fold {} : Training decision tree classifier over {} points...".format(i, len(y_train)))
	sys.stdout.flush()
	tree.fit(X_train, y_train)
	print("Evaluating classifier over {} points...".format(len(y_test)))

	# predict the labels on the test data
	y_pred = tree.predict(X_test)

	# show the comparison between the predicted and ground-truth labels
	conf = confusion_matrix(y_test, y_pred, labels=[0,1])

	accuracy = np.sum(np.diag(conf)) / float(np.sum(conf))
	precision = np.nan_to_num(np.diag(conf) / np.sum(conf, axis=1).astype(float))
	recall = np.nan_to_num(np.diag(conf) / np.sum(conf, axis=0).astype(float))

	total_accuracy += accuracy
	total_precision += precision
	total_recall += recall
   
print("The average accuracy is {}".format(total_accuracy/10.0))  
print("The average precision is {}".format(total_precision/10.0))
print("The average recall is {}".format(total_recall/10.0))


# Set this to the best model we found, trained on all the data:
best_classifier = RandomForestClassifier(n_estimators=100)
best_classifier.fit(X,Y) 

export_graphviz(tree, out_file='tree-random.dot', feature_names = feature_names)

classifier_filename='classifier.pickle'

print("Saving best classifier")

with open(classifier_filename, 'wb') as f:
    pickle.dump(best_classifier, f)