def test_nested_model(teardown):
x_data = iris.data
y_t_data = iris.target
# Sub-model
x = Input()
y_t = Input()
h = PCA(n_components=2)(x)
y = LogisticRegression()(h, y_t)
submodel = Model(x, y, y_t)
# Model
x = Input()
y_t = Input()
y = submodel(x, y_t)
model = Model(x, y, y_t)
with raises_with_cause(RuntimeError, NotFittedError):
submodel.predict(x_data)
model.fit(x_data, y_t_data)
y_pred = model.predict(x_data)
y_pred_sub = submodel.predict(x_data)
assert_array_equal(y_pred, y_pred_sub)
def test_with_unnecessary_inputs(self, teardown):
x1 = Input()
x2 = Input()
y_t = Input()
h = PCA()(x1)
y = LogisticRegression()(h, y_t)
with pytest.raises(ValueError):
Model([x1, x2], y, y_t)
with pytest.raises(ValueError):
Model([x1, h], y, y_t) # x1 is an unnecessary input upstream of h
def make_naive_stacked_model(n_components, random_state, x_data, y_t_data):
# An unnecessarily complex Model
# Sub-model 1
x1 = Input(name="x1")
y1_t = Input(name="y1_t")
h1 = PCA(n_components=n_components, random_state=random_state, name="pca_sub1")(x1)
y1 = LogisticRegression(
multi_class="multinomial",
solver="lbfgs",
random_state=random_state,
name="logreg_sub1",
)(h1, y1_t)
submodel1 = Model(x1, y1, y1_t, name="submodel1")
# Sub-model 2 (a nested stacked model)
x2 = Input(name="x2")
y2_t = Input(name="y2_t")
y2_1 = RandomForestClassifier(random_state=random_state, name="rforest_sub2")(
x2, y2_t
)
y2_2 = ExtraTreesClassifier(random_state=random_state, name="extrees_sub2")(
x2, y2_t
)
features = Stack(axis=1, name="stack_sub2")([y2_1, y2_2])
y2 = LogisticRegression(
multi_class="multinomial",
solver="lbfgs",
random_state=random_state,
name="logreg_sub2",
)(features, y2_t)
submodel2 = Model(x2, y2, y2_t, name="submodel2")
# Stack of submodels
x = Input(name="x")
y_t = Input(name="y_t")
y1 = submodel1(x, y_t)
y2 = submodel2(x, y_t)
features = Stack(axis=1, name="stack")([y1, y2])
y = LogisticRegression(
multi_class="multinomial",
solver="lbfgs",
random_state=random_state,
name="logreg_stacked",
)(features, y_t)
stacked_model_baikal = Model(x, y, y_t, name="stacked")
stacked_model_baikal.fit(x_data, y_t_data)
return stacked_model_baikal
def test_with_wrong_type(self, teardown):
x = Input()
y_t = Input()
y = LogisticRegression()(x, y_t)
wrong = np.zeros((10,))
with pytest.raises(ValueError):
Model(wrong, y, y_t)
with pytest.raises(ValueError):
Model(x, wrong, y_t)
with pytest.raises(ValueError):
Model(x, y, wrong)
def test_fit_predict_ensemble(teardown):
mask = iris.target != 2 # Reduce to binary problem to avoid ConvergenceWarning
x_data = iris.data
y_t_data = iris.target
random_state = 123
# baikal way
x = Input()
y_t = Input()
y1 = LogisticRegression(random_state=random_state)(x, y_t)
y2 = RandomForestClassifier(random_state=random_state)(x, y_t)
features = Stack(axis=1)([y1, y2])
y = LogisticRegression(random_state=random_state)(features, y_t)
model = Model(x, y, y_t)
model.fit(x_data, y_t_data)
y_pred_baikal = model.predict(x_data)
# traditional way
logreg = sklearn.linear_model.LogisticRegression(random_state=random_state)
logreg.fit(x_data, y_t_data)
logreg_pred = logreg.predict(x_data)
random_forest = sklearn.ensemble.RandomForestClassifier(random_state=random_state)
random_forest.fit(x_data, y_t_data)
random_forest_pred = random_forest.predict(x_data)
features = np.stack([logreg_pred, random_forest_pred], axis=1)
ensemble = sklearn.linear_model.LogisticRegression(random_state=random_state)
ensemble.fit(features, y_t_data)
y_pred_traditional = ensemble.predict(features)
assert_array_equal(y_pred_baikal, y_pred_traditional)
def build_model(step):
x1 = Input()
x2 = Input()
y_t1 = Input()
y_t2 = Input()
y_p = step([x1, x2], [y_t1, y_t2])
return Model([x1, x2], y_p, [y_t1, y_t2])
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_fit_predict_pipeline(teardown):
x_data = iris.data
y_t_data = iris.target
random_state = 123
n_components = 2
# baikal way
x = Input()
y_t = Input()
x_pca = PCA(n_components=n_components, random_state=random_state, name="pca")(x)
y = LogisticRegression(
multi_class="multinomial",
solver="lbfgs",
random_state=random_state,
name="logreg",
)(x_pca, y_t)
model = Model(x, y, y_t)
y_pred_baikal = model.fit(x_data, y_t_data).predict(x_data)
# traditional way
pca = PCA(n_components=n_components, random_state=random_state)
logreg = LogisticRegression(
multi_class="multinomial", solver="lbfgs", random_state=random_state
)
x_data_transformed = pca.fit_transform(x_data)
y_pred_traditional = logreg.fit(x_data_transformed, y_t_data).predict(
x_data_transformed
)
assert_array_equal(y_pred_baikal, y_pred_traditional)
def test_with_missing_inputs(self, teardown):
x1 = Input()
x2 = Input()
c = Concatenate()([x1, x2])
with pytest.raises(ValueError):
Model(x1, c)
def test_with_improperly_defined_step(self, teardown):
x = Input()
y = DummyImproperlyDefined()(x)
model = Model(x, y)
with pytest.raises(RuntimeError):
model.predict(iris.data)
def test_fit_predict_naive_stack(teardown):
x_data = iris.data
y_t_data = iris.target
random_state = 123
# baikal way
x = Input()
y_t = Input()
y1 = LogisticRegression(random_state=random_state, solver="liblinear")(x, y_t)
y2 = RandomForestClassifier(random_state=random_state)(x, y_t)
features = Stack(axis=1)([y1, y2])
y = LogisticRegression(random_state=random_state, solver="liblinear")(features, y_t)
model = Model(x, y, y_t)
model.fit(x_data, y_t_data)
y_pred_baikal = model.predict(x_data)
# traditional way
logreg = LogisticRegression(random_state=random_state, solver="liblinear")
logreg.fit(x_data, y_t_data)
logreg_pred = logreg.predict(x_data)
random_forest = RandomForestClassifier(random_state=random_state)
random_forest.fit(x_data, y_t_data)
random_forest_pred = random_forest.predict(x_data)
features = np.stack([logreg_pred, random_forest_pred], axis=1)
stacked = LogisticRegression(random_state=random_state, solver="liblinear")
stacked.fit(features, y_t_data)
y_pred_traditional = stacked.predict(features)
assert_array_equal(y_pred_baikal, y_pred_traditional)
def test_fit_params(teardown):
x_data = iris.data
y_t_data = iris.target
random_state = 123
n_components = 2
sample_weight = y_t_data + 1 # Just weigh the classes differently
fit_params = {"logreg__sample_weight": sample_weight}
# baikal way
x = Input()
y_t = Input()
x_pca = PCA(n_components=n_components, random_state=random_state, name="pca")(x)
y = LogisticRegression(
multi_class="multinomial",
solver="lbfgs",
random_state=random_state,
name="logreg",
)(x_pca, y_t)
model = Model(x, y, y_t)
model.fit(x_data, y_t_data, **fit_params)
# traditional way
pca = PCA(n_components=n_components, random_state=random_state)
logreg = LogisticRegression(
multi_class="multinomial", solver="lbfgs", random_state=random_state
)
pipe = Pipeline([("pca", pca), ("logreg", logreg)])
pipe.fit(x_data, y_t_data, **fit_params)
# Use assert_allclose instead of all equal due to small numerical differences
# between fit_transform(...) and fit(...).transform(...)
assert_allclose(model.get_step("logreg").coef_, pipe.named_steps["logreg"].coef_)
def build_fn():
x = Input()
y_t = Input()
h = PCA(random_state=random_state, name="pca")(x)
y_p = LogisticRegression(random_state=random_state, name="classifier")(h, y_t)
model = Model(x, y_p, y_t)
return model
def test_with_steps_with_duplicated_names(self, teardown):
x = Input()
h = PCA(name="duplicated-name")(x)
y = LogisticRegression(name="duplicated-name")(h)
with pytest.raises(RuntimeError):
Model(x, y)
def test_fit_with_shared_step(self, teardown):
x = Input()
scaler = StandardScaler()
z = scaler(x, compute_func="transform", trainable=True)
y = scaler(z, compute_func="inverse_transform", trainable=False)
model = Model(x, y)
model.fit(np.array([1, 3, 1, 3]).reshape(-1, 1))
assert (scaler.mean_, scaler.var_) == (2.0, 1.0)
def test_simple(self, teardown):
x1 = Input()
x2 = Input()
y_t = Input()
x1_transformed = PCA()(x1)
y_t_encoded = LabelEncoder()(y_t)
z = Concatenate()([x1_transformed, x2])
y = LogisticRegression()(z, y_t_encoded)
# TODO: support shareable steps to reuse LabelEncoder with compute_func="inverse_transform"
# full model
Model([x1, x2], y, y_t)
# submodels
Model(x1, x1_transformed)
Model(z, y, y_t_encoded)
def test_with_undefined_target(self, teardown):
x = Input()
y = LogisticRegression()(x, trainable=True)
model = Model(inputs=x, outputs=y)
with raises_with_cause(RuntimeError, TypeError):
# LogisticRegression.fit will be called with not enough arguments
# hence the TypeError
model.fit(iris.data)
def test_predict_with_shared_step(self, teardown):
x1 = Input()
x2 = Input()
doubler = Lambda(lambda x: x * 2)
y1 = doubler(x1)
y2 = doubler(x2)
model = Model([x1, x2], [y1, y2])
assert model.predict([2, 3]) == [4, 6]
def test_try_and_raise_with_cause(teardown):
x = Input()
y = DummyStepWithFaultyPredict(name="faultystep")(x)
model = Model(x, y)
with raises_with_cause(RuntimeError, KeyError):
model.predict(123)
x = Input()
y = DummyStepWithFaultyFit(name="faultystep")(x)
model = Model(x, y)
with raises_with_cause(RuntimeError, ValueError):
model.fit(123)
x = Input()
y = DummyStepWithFaultyFitPredict(name="faultystep")(x)
model = Model(x, y)
with raises_with_cause(RuntimeError, ValueError):
model.fit(123)
def build_fn():
x = Input()
y_t = Input()
h = PCA(random_state=random_state, name="pca")(x)
y = LogisticRegression(random_state=random_state,
solver="liblinear",
name="logreg")(h, y_t)
model = Model(x, y, y_t)
return model
def test_lazy_model(teardown):
x_data = np.array([[1, 2], [3, 4]])
x = Input()
model = Model(x, x)
model.fit(x_data) # nothing to fit
x_pred = model.predict(x_data)
assert_array_equal(x_pred, x_data)
def test_plot_model(teardown, tmp_path):
x1 = Input(name="x1")
x2 = Input(name="x2")
y1, y2 = DummyMIMO()([x1, x2])
submodel = Model([x1, x2], [y1, y2], name="submodel")
x = Input(name="x")
h1, h2 = DummySIMO()(x)
z1, z2 = submodel([h1, h2])
u = Input(name="u")
v = DummySISO()(u)
w = DummyMISO()([z1, z2])
model = Model([x, u], [w, v], name="main_model")
filename = str(tmp_path / "test_plot_model.png")
plot_model(model, filename, show=False, expand_nested=True)
def test_single_input(step_class, teardown):
x = Input()
y = step_class()(x)
model = Model(x, y)
x_data = np.array([[1, 2], [3, 4]])
if step_class is Stack:
assert_array_equal(x_data.reshape((2, 2, 1)), model.predict(x_data))
else:
assert_array_equal(x_data, model.predict(x_data))
def test_predict_with_not_fitted_steps(self, teardown):
x_data = iris.data
x = Input(name="x")
xt = PCA(n_components=2)(x)
y = LogisticRegression(multi_class="multinomial", solver="lbfgs")(xt)
model = Model(x, y)
with raises_with_cause(RuntimeError, NotFittedError):
model.predict(x_data)
def test_with_unnecessarily_defined_but_missing_target(self, teardown):
x = Input()
y_t = Input()
pca = PCA()
# The target passed to PCA is unnecessary (see notes in Step.__call__)
y = pca(x, y_t, trainable=True)
model = Model(inputs=x, outputs=y, targets=y_t)
with pytest.raises(ValueError):
# fails because of the model target specification and trainable=True
model.fit(iris.data)
def test_multiedge(teardown):
x = Input()
z1, z2 = DummySIMO()(x)
y = DummyMISO()([z1, z2])
model = Model(x, y)
x_data = np.array([[1], [2]])
y_out = model.predict(x_data)
assert_array_equal(y_out, np.array([[2], [4]]))
def test_fit_predict_with_shared_step(teardown):
x = Input()
scaler = StandardScaler()
z = scaler(x, compute_func="transform", trainable=True)
y = scaler(z, compute_func="inverse_transform", trainable=False)
model = Model(x, y)
X_data = np.array([1, 3, 1, 3]).reshape(-1, 1)
model.fit(X_data)
assert_array_equal(model.predict(X_data), X_data)
def test_with_non_fitted_non_trainable_step(self, teardown):
x = Input()
y_t = Input()
z = PCA()(x, trainable=False)
y = LogisticRegression()(z, y_t)
model = Model(x, y, y_t)
with raises_with_cause(RuntimeError, NotFittedError):
# this will raise an error when calling compute
# on PCA which was flagged as trainable=False but
# hasn't been fitted
model.fit(iris.data, iris.target)
def test_split(x, indices_or_sections, teardown):
x1 = Input()
ys = Split(indices_or_sections, axis=0)(x1)
model = Model(x1, ys)
y_expected = np.split(x, indices_or_sections, axis=0)
y_pred = model.predict(x)
y_pred = listify(y_pred)
for actual, expected in safezip2(y_pred, y_expected):
assert_array_equal(actual, expected)
def test_fit_and_predict_model_with_no_fittable_steps(teardown):
X_data = np.array([[1, 2], [3, 4]])
y_expected = np.array([[2, 4], [6, 8]])
x = Input()
y = DummySISO()(x)
model = Model(x, y)
model.fit(X_data) # nothing to fit
y_pred = model.predict(X_data)
assert_array_equal(y_pred, y_expected)
