def test_get_data():
# train=True
data_generator = DataGenerator(dataset,
feature_extractor,
folds=['all'],
batch_size=2,
shuffle=False)
X, Y = data_generator.get_data()
assert np.allclose(X, np.concatenate((X_gt[0], X_gt[1], X_gt[2]), axis=0))
assert np.allclose(Y, np.concatenate((Y_gt[0], Y_gt[1], Y_gt[2]), axis=0))
# train=False
data_generator = DataGenerator(dataset,
feature_extractor,
folds=['all'],
batch_size=2,
shuffle=False,
train=False)
X, Y = data_generator.get_data()
assert type(X) is list
assert type(Y) is list
assert len(X) == 3
assert len(Y) == 3
for j in range(3):
assert np.allclose(X[j], X_gt[j])
assert np.allclose(Y[j], Y_gt[j])
def update_plot2D(samples_per_class, x_select, y_select, active_tab, fold_ix,
model_path, dataset_ix, sr):
global X
global X_pca
global Y
global file_names
global feature_extractor
print('start visualization')
if (active_tab == 'tab_visualization'):
fold_name = dataset.fold_list[fold_ix]
exp_folder_fold = conv_path(os.path.join(model_path, fold_name))
scaler_path = os.path.join(exp_folder_fold, 'scaler.pickle')
scaler = load_pickle(scaler_path)
dataset_name = options_datasets[dataset_ix]['label']
params_dataset = params['datasets'][dataset_name]
folds_train, folds_val, _ = evaluation_setup(
fold_name, dataset.fold_list, params_dataset['evaluation_mode'])
print(feature_extractor)
data_gen_train = DataGenerator(
dataset,
feature_extractor,
folds=folds_train,
batch_size=params['train']['batch_size'],
shuffle=True,
train=False,
scaler=scaler)
X_list, Y_list = data_gen_train.get_data()
file_names = data_gen_train.audio_file_list
# file_names = data_gen_train.convert_features_path_to_audio_path(
# file_names, sr=sr)
Xt = []
Yt = []
for j in range(len(X_list)):
ix = int(len(X_list[j]) / 2) if len(X_list[j]) > 1 else 0
Xj = np.expand_dims(X_list[j][ix], 0)
Yj = np.expand_dims(Y_list[j][ix], 0)
Xt.append(Xj)
Yt.append(Yj)
X = np.concatenate(Xt, axis=0)
Yt = np.concatenate(Yt, axis=0)
with graph.as_default():
model_container.load_model_weights(exp_folder_fold)
X_emb = model_container.get_intermediate_output(-2, X)
# output_select
pca = PCA(n_components=4)
pca.fit(X_emb)
X_pca = pca.transform(X_emb)
print('pca', X_pca.shape, Yt.shape)
figure2D = generate_figure2D(X_pca,
Yt,
dataset.label_list,
pca_components=[x_select, y_select],
samples_per_class=samples_per_class)
return [figure2D]
def test_transform():
# minmax
X1 = np.random.randn(100, 32, 128)
scaler = Scaler(normalizer="minmax")
scaler.fit(X1)
X1_scaled = scaler.transform(X1)
assert np.amin(X1_scaled) == -1.0
assert np.amax(X1_scaled) == 1.0
# standard
X2 = np.random.randn(100, 32, 128)
scaler = Scaler(normalizer="standard")
scaler.fit(X2)
X2_scaled = scaler.transform(X2)
X2_scaled_flat = np.reshape(X2_scaled, (-1, X2.shape[-1]))
assert X2_scaled_flat.shape[1] == X2.shape[-1]
mean = np.mean(X2_scaled_flat, axis=0)
std = np.std(X2_scaled_flat, axis=0)
assert np.allclose(mean, np.zeros(128), rtol=0.001, atol=0.001)
assert np.allclose(std, np.ones(128), rtol=0.001, atol=0.001)
# list of scalers
scaler = Scaler(normalizer=["minmax", "standard"])
X_list = [X1, X2]
scaler.fit(X_list)
X_list_scaled = scaler.transform(X_list)
assert type(X_list_scaled) is list
assert len(X_list_scaled) == 2
assert np.allclose(X_list_scaled[0], X1_scaled, rtol=0.001, atol=0.001)
assert np.allclose(X_list_scaled[1], X2_scaled, rtol=0.001, atol=0.001)
# DataGenerator
feature_extractor = MelSpectrogram()
feature_extractor.extract(dataset)
data_generator = DataGenerator(dataset, feature_extractor, folds=["all"])
scaler = Scaler(normalizer="minmax")
scaler.fit(data_generator)
data_generator.set_scaler(scaler)
X, _ = data_generator.get_data()
assert np.amin(X) == -1.0
assert np.amax(X) == 1.0
def evaluate_model(n_clicks, fold_ix, model_path):
global X_test
global X_pca_test
global file_names_test
global Y_test
global predictions
global data_generator_test
print('Change tab evaluation')
# if (active_tab == "tab_evaluation") and (fold_ix is not None):
if (n_clicks is not None) and (fold_ix is not None):
print('Start evaluation')
fold_name = dataset.fold_list[fold_ix]
exp_folder_fold = conv_path(os.path.join(model_path, fold_name))
scaler_path = os.path.join(exp_folder_fold, 'scaler.pickle')
scaler = load_pickle(scaler_path)
data_generator_test = DataGenerator(
dataset,
feature_extractor,
folds=[fold_name],
batch_size=params['train']['batch_size'],
shuffle=True,
train=False,
scaler=scaler)
print('Loading data...')
X_test, Y_test = data_generator_test.get_data()
print('Done')
print(len(X_test), len(Y_test))
with graph.as_default():
model_container.load_model_weights(exp_folder_fold)
results = model_container.evaluate((X_test, Y_test),
label_list=dataset.label_list)
results = results['classification'].results()
accuracy = results['overall']['accuracy']
class_wise = results['class_wise']
metrics = []
for label in dataset.label_list:
metrics.append(class_wise[label]['accuracy']['accuracy'])
print(metrics)
figure_metrics = generate_figure_metrics(dataset.label_list, metrics)
msg = "Accuracy in fold %s is %1.2f" % (fold_name, accuracy)
return [msg, figure_metrics]
return ['Pa']
def test_fit():
# Array, expect same results than partial_fit
X = np.random.randn(100, 32, 128)
scaler = Scaler(normalizer="minmax")
scaler.fit(X)
assert type(scaler.scaler) is list
assert len(scaler.scaler) == 1
assert scaler.scaler[0][0] == np.amin(X)
assert scaler.scaler[0][1] == np.amax(X)
# DataGenerator
feature_extractor = MelSpectrogram()
feature_extractor.extract(dataset)
data_generator = DataGenerator(dataset, feature_extractor, folds=["all"])
scaler = Scaler(normalizer="minmax")
scaler.fit(data_generator)
X, _ = data_generator.get_data()
assert type(scaler.scaler) is list
assert len(scaler.scaler) == 1
assert scaler.scaler[0][0] == np.amin(X)
assert scaler.scaler[0][1] == np.amax(X)