def test_get_set_params_invariance(teardown):
pca = PCA(name="pca")
classifier = RandomForestClassifier(name="classifier")
x = Input()
h = pca(x)
y = classifier(h)
model = Model(x, y)
params1 = model.get_params()
model.set_params(**params1)
params2 = model.get_params()
assert params1 == params2
def test_get_set_params_invariance(teardown):
scaler = StandardScaler(name="scaler")
regressor = LinearRegression(name="regressor")
x = Input()
y_t = Input()
y_t_scaled = scaler(y_t)
y_p_scaled = regressor(x, y_t_scaled)
y_p = scaler(y_p_scaled, compute_func="inverse_transform", trainable=False)
model = Model(x, y_p, y_t)
params1 = model.get_params()
model.set_params(**params1)
params2 = model.get_params()
assert params2 == params1
def test_get_params(teardown):
dummy1 = DummyEstimator(name="dummy1")
dummy2 = DummyEstimator(x=456, y="def", name="dummy2")
concat = Concatenate(name="concat") # a step without get_params/set_params
# a meaningless pipeline that contains shared steps
x1 = Input()
x2 = Input()
h = dummy1(x1)
c = concat([x1, h])
y1 = dummy2(c)
y2 = dummy2(x2, compute_func=lambda X: X * 2, trainable=False)
model = Model([x1, x2], [y1, y2])
expected = {
"dummy1": dummy1,
"dummy2": dummy2,
"concat": concat,
"dummy1__x": 123,
"dummy1__y": "abc",
"dummy2__x": 456,
"dummy2__y": "def",
}
params = model.get_params()
assert params == expected
def test_get_params(teardown):
pca = PCA(name="pca")
logreg = LogisticRegression(name="logreg")
concat = Concatenate(name="concat") # a step without get_params/set_params
x = Input()
h = pca(x)
c = concat([x, h])
y = logreg(c)
model = Model(x, y)
expected = {
"pca": pca,
"logreg": logreg,
"concat": concat,
"pca__n_components": None,
"pca__whiten": False,
"pca__tol": 0.0,
"pca__svd_solver": "auto",
"pca__copy": True,
"pca__random_state": None,
"pca__iterated_power": "auto",
"logreg__C": 1.0,
"logreg__class_weight": None,
"logreg__dual": False,
"logreg__fit_intercept": True,
"logreg__intercept_scaling": 1,
"logreg__max_iter": 100,
"logreg__multi_class": "warn",
"logreg__n_jobs": None,
"logreg__penalty": "l2",
"logreg__random_state": None,
"logreg__solver": "warn",
"logreg__tol": 0.0001,
"logreg__verbose": 0,
"logreg__warm_start": False,
"logreg__l1_ratio": None,
}
params = model.get_params()
assert expected == params
def test_set_params(teardown):
dummy1 = DummyEstimator(name="dummy1")
dummy2 = DummyEstimator(x=456, y="def", name="dummy2")
concat = Concatenate(name="concat") # a step without get_params/set_params
# a meaningless pipeline that contains shared steps
x1 = Input()
x2 = Input()
h = dummy1(x1)
c = concat([x1, h])
y1 = dummy2(c)
y2 = dummy2(x2, compute_func=lambda X: X * 2, trainable=False)
model = Model([x1, x2], [y1, y2])
# Fails when setting params on step that does not implement set_params
new_params_wrong = {"concat__axis": 2}
with pytest.raises(AttributeError):
model.set_params(**new_params_wrong)
# Fails when setting params on step that does not exist
new_params_wrong = {"non_existent_step__param": 42}
with pytest.raises(ValueError):
model.set_params(**new_params_wrong)
# Fails when setting a non-existent param in a step
new_params_wrong = {"dummy1__non_existent_param": 42}
with pytest.raises(ValueError):
model.set_params(**new_params_wrong)
new_dummy = DummyEstimator()
new_params = {
"dummy2": new_dummy,
"dummy1__x": 100,
"dummy1__y": "pqr",
"dummy2__x": 789,
"dummy2__y": "ijk",
}
model.set_params(**new_params)
params = model.get_params()
expected = {
"dummy1": dummy1,
"dummy2": new_dummy,
"concat": concat,
"dummy1__x": 100,
"dummy1__y": "pqr",
"dummy2__x": 789,
"dummy2__y": "ijk",
}
assert params == expected
# Connectivity of the new step should be the same as the old step
assert new_dummy.name is dummy2.name
for port in range(2):
assert new_dummy.get_inputs_at(port) is dummy2.get_inputs_at(port)
assert new_dummy.get_outputs_at(port) is dummy2.get_outputs_at(port)
assert new_dummy.get_targets_at(port) is dummy2.get_targets_at(port)
assert new_dummy.get_trainable_at(port) is dummy2.get_trainable_at(port)
assert new_dummy.get_compute_func_at(port) is dummy2.get_compute_func_at(port)
def test_set_params(teardown):
pca = PCA(name="pca")
classifier = RandomForestClassifier(name="classifier")
concat = Concatenate(name="concat") # a step without get_params/set_params
x = Input()
h = pca(x)
c = concat([x, h])
y = classifier(c)
model = Model(x, y)
# Fails when setting params on step that does not implement set_params
new_params_wrong = {"concat__axis": 2}
with pytest.raises(AttributeError):
model.set_params(**new_params_wrong)
# Fails when setting params on step that does not exist
new_params_wrong = {"non_existent_step__param": 42}
with pytest.raises(ValueError):
model.set_params(**new_params_wrong)
# Fails when setting a non-existent param in a step
new_params_wrong = {"pca__non_existent_param": 42}
with pytest.raises(ValueError):
model.set_params(**new_params_wrong)
new_classifier = LogisticRegression()
new_params = {
"classifier": new_classifier,
"pca__n_components": 4,
"pca__whiten": True,
"classifier__C": 100.0,
"classifier__fit_intercept": False,
"classifier__penalty": "l1",
}
model.set_params(**new_params)
params = model.get_params()
expected = {
"pca": pca,
"classifier": new_classifier,
"concat": concat,
"pca__n_components": 4,
"pca__whiten": True,
"pca__tol": 0.0,
"pca__svd_solver": "auto",
"pca__copy": True,
"pca__random_state": None,
"pca__iterated_power": "auto",
"classifier__C": 100.0,
"classifier__class_weight": None,
"classifier__dual": False,
"classifier__fit_intercept": False,
"classifier__intercept_scaling": 1,
"classifier__max_iter": 100,
"classifier__multi_class": "warn",
"classifier__n_jobs": None,
"classifier__penalty": "l1",
"classifier__random_state": None,
"classifier__solver": "warn",
"classifier__tol": 0.0001,
"classifier__verbose": 0,
"classifier__warm_start": False,
"classifier__l1_ratio": None,
}
assert expected == params
