def test_submission(self):
train_data = extract_features(load_adult_train_data())
valid_data = extract_features(load_adult_valid_data())
model = submission(train_data)
predictions = [predict(model, p) for p in train_data]
print()
print()
print("Training Accuracy:", accuracy(train_data, predictions))
predictions = [predict(model, p) for p in valid_data]
print("Validation Accuracy:", accuracy(valid_data, predictions))
print()
def test_submission(self):
train_data = extract_features(load_adult_train_data())
valid_data = extract_features(load_adult_valid_data())
model = submission(train_data)
predictions = [predict(model, p) for p in train_data]
print
print
print "Training Accuracy:", accuracy(train_data, predictions)
predictions = [predict(model, p) for p in valid_data]
print "Validation Accuracy:", accuracy(valid_data, predictions)
print
def test_submission(self):
"""Overall test.
"""
train_data = extract_features(load_adult_train_data())
valid_data = extract_features(load_adult_valid_data())
model = submission(train_data)
predictions = [predict(model, p) for p in train_data]
print("Training Accuracy: {0}".format(
accuracy(train_data, predictions)))
predictions = [predict(model, p) for p in valid_data]
print("Validation Accuracy: {0}".format(
accuracy(valid_data, predictions)))
def test_submission():
valid_data = test_data
model = submission(train_data)
predictions = [predict(model, p) for p in train_data]
print
print
# print predictions
train_accuracy = accuracy(train_data, predictions)
print "Training Accuracy:", train_accuracy
# print train_data
predictions = [predict(model, p) for p in valid_data]
valid_accuracy = accuracy(valid_data, predictions)
print "Validation Accuracy:", valid_accuracy
print
return train_accuracy, valid_accuracy
max_num_faces = max(max_num_faces, len(item_dict))
# predict for result of one picture
predictions = []
for i in range(0, len(item_dict)):
face = {}
face["faceRectangle"] = item_dict[i]["faceRectangle"]
face["result"] = (predict(
model, extract_features_single_point(item_dict[i]["scores"]))
>= 0.5)
predictions.append(face)
predictions_all.append(predictions)
print max_num_faces
return predictions_all, max_num_faces
if __name__ == "__main__":
# prepare the model
train_data = extract_features(load_adult_train_data())
model = submission(train_data)
print model
predictions = [predict(model, p) for p in train_data]
print
print
print "Training Accuracy:", accuracy(train_data, predictions)
app.run(host='ec2-52-206-17-234.compute-1.amazonaws.com',
port=8000,
threaded=True)
def predict():
train_data = return_train_data()
test_data = return_test_data()
model = submission(train_data)
print test_data[0]