def run():
print("============================================")
print("Testing Multi Table Pipeline")
print("============================================")
orders = pd.read_csv("data/Retail/orders.csv")
order_products = pd.read_csv("data/Retail/order_products.csv")
label_times = pd.read_csv("data/Retail/label_times.csv")
X_train = label_times.sample(frac=0.8)
X_test = label_times.drop(X_train.index)
y_train = X_train["label"]
y_test = X_test["label"]
entity_set = make_entity_set(orders, order_products)
multitable = MLPipeline(['dfs', 'random_forest_classifier'])
updated_hyperparam = MLHyperparam('max_depth', 'int', [1, 10])
updated_hyperparam.block_name = 'dfs'
# multitable.update_tunable_hyperparams([updated_hyperparam])
# Check that the hyperparameters are correct.
for hyperparam in multitable.get_tunable_hyperparams():
print(hyperparam)
# Check that the blocks are correct.
expected_blocks = {'dfs', 'rf_classifier'}
blocks = set(multitable.blocks.keys())
assert expected_blocks == blocks
# Check that we can score properly.
produce_params = {
('dfs', 'entityset'): entity_set,
('dfs', 'cutoff_time_in_index'): True
}
print("\nFitting pipeline...")
fit_params = {
('dfs', 'entityset'): entity_set,
('dfs', 'target_entity'): "users",
('dfs', 'training_window'): ft.Timedelta("60 days")
}
multitable.fit(X_train,
y_train,
fit_params=fit_params,
produce_params=produce_params)
print("\nFit pipeline.")
print("\nScoring pipeline...")
predicted_y_val = multitable.predict(X_test, predict_params=produce_params)
score = f1_score(predicted_y_val, y_test, average='micro')
print("\nf1 micro score: %f" % score)
return score
def run(train_size=160, test_size=40):
print("============================================")
print("Testing Audio Pipeline")
print("============================================")
# Data loading.
classes = [
'street_music', 'siren', 'jackhammer', 'gun_shot', 'engine_idling',
'drilling', 'dog_bark', 'children_playing', 'car_horn',
'air_conditioner'
]
labels = []
all_filepaths = []
for label_class in classes:
for filepath in glob.glob(
os.path.join('data/UrbanSound/data', label_class, '*.wav')):
all_filepaths.append(filepath)
labels.append(label_class)
filepaths, filepaths_test, y, y_test = train_test_split(
all_filepaths, labels, train_size=train_size, test_size=test_size)
audio_pipeline = MLPipeline([
'audio_featurizer', 'audio_padder', 'pca', 'random_forest_classifier'
])
# Check that the hyperparameters are correct.
for hyperparam in audio_pipeline.get_tunable_hyperparams():
print(hyperparam)
# Check that the blocks are correct.
expected_blocks = {
'audio_featurizer', 'audio_padder', 'pca', 'rf_classifier'
}
blocks = set(audio_pipeline.blocks.keys())
assert expected_blocks == blocks
# Check that we can score properly.
print("\nFitting pipeline...")
X, sample_freqs = load_and_segment(filepaths)
produce_params = {('audio_featurizer', 'sample_freqs'): sample_freqs}
audio_pipeline.fit(X, y, produce_params=produce_params)
print("\nFit pipeline.")
print("\nScoring pipeline...")
X_test, sample_freqs_test = load_and_segment(filepaths_test)
predict_params = {('audio_featurizer', 'sample_freqs'): sample_freqs_test}
predicted_y_val = audio_pipeline.predict(X_test, predict_params)
score = f1_score(predicted_y_val, y_test, average='micro')
print("\nf1 micro score: %f" % score)
return score
