def test_iris_ensemble_iterative_regression():
print('\ntest_iris_ensemble_iterative_regression():')
X_train, X_test, y_train, y_test = train_test_split(X_iris,
y_iris,
test_size=5,
random_state=42)
cls = ESNClassifier(input_to_node=[('tanh',
InputToNode(hidden_layer_size=10,
random_state=42,
activation='identity')),
('bounded_relu',
InputToNode(hidden_layer_size=10,
random_state=42,
activation='bounded_relu'))
],
node_to_node=[('default',
NodeToNode(hidden_layer_size=20,
spectral_radius=0.0))],
regressor=IncrementalRegression(alpha=.01),
random_state=42)
for samples in np.split(np.arange(0, X_train.shape[0]), 5):
cls.partial_fit(X_train[samples, :],
y_train[samples],
classes=np.arange(3, dtype=int))
y_predicted = cls.predict(X_test)
for record in range(len(y_test)):
print('predicted: {0} \ttrue: {1}'.format(y_predicted[record],
y_test[record]))
print('score: {0}'.format(cls.score(X_test, y_test)))
print('proba: {0}'.format(cls.predict_proba(X_test)))
print('log_proba: {0}'.format(cls.predict_log_proba(X_test)))
assert cls.score(X_test, y_test) >= 4. / 5.
def test_esn_classifier_instance_fit() -> None:
X, y = load_digits(return_X_y=True, as_sequence=True)
esn = ESNClassifier(hidden_layer_size=50).fit(X[0], np.repeat(y[0], 8))
assert (esn.sequence_to_value is False)
y_pred = esn.predict_proba(X[0])
assert (y_pred.ndim == 2)
y_pred = esn.predict_log_proba(X[0])
assert (y_pred.ndim == 2)
def test_esn_classifier_sequence_to_value() -> None:
X, y = load_digits(return_X_y=True, as_sequence=True)
esn = ESNClassifier(hidden_layer_size=50).fit(X, y)
y_pred = esn.predict(X)
assert (len(y) == len(y_pred))
assert (len(y_pred[0]) == 1)
assert (esn.sequence_to_value is True)
assert (esn.decision_strategy == "winner_takes_all")
y_pred = esn.predict_proba(X)
assert (y_pred[0].ndim == 1)
y_pred = esn.predict_log_proba(X)
assert (y_pred[0].ndim == 1)
esn.sequence_to_value = False
y_pred = esn.predict(X)
assert (len(y_pred[0]) == 8)
y_pred = esn.predict_proba(X)
assert (y_pred[0].ndim == 2)
y_pred = esn.predict_log_proba(X)
assert (y_pred[0].ndim == 2)