def test_custom(self):
np.random.seed(123)
X, y = datasets.make_regression(n_samples=15, n_features=3)
breast_cancer = datasets.load_breast_cancer()
Z = breast_cancer.data
t = breast_cancer.target
regr = linear_model.BayesianRidge()
regr2 = gaussian_process.GaussianProcessClassifier()
fit_obj = ns.CustomRegressor(
obj=regr,
n_hidden_features=10,
direct_link=False,
bias=False,
nodes_sim="sobol",
activation_name="relu",
n_clusters=0,
)
fit_obj2 = ns.CustomRegressor(
obj=regr,
n_hidden_features=9,
direct_link=False,
bias=True,
nodes_sim="halton",
activation_name="sigmoid",
n_clusters=2,
)
fit_obj3 = ns.CustomRegressor(
obj=regr,
n_hidden_features=8,
direct_link=True,
bias=False,
nodes_sim="uniform",
activation_name="tanh",
n_clusters=3,
)
fit_obj4 = ns.CustomRegressor(
obj=regr,
n_hidden_features=7,
direct_link=True,
bias=True,
nodes_sim="hammersley",
activation_name="elu",
n_clusters=2,
)
fit_obj5 = ns.CustomClassifier(
obj=regr2,
n_hidden_features=3,
direct_link=True,
bias=True,
nodes_sim="hammersley",
activation_name="elu",
n_clusters=4,
)
fit_obj6 = ns.CustomClassifier(
obj=regr2,
n_hidden_features=3,
direct_link=True,
bias=True,
nodes_sim="hammersley",
activation_name="elu",
n_clusters=4,
col_sample=0.8,
)
fit_obj7 = ns.CustomClassifier(
obj=regr2,
n_hidden_features=3,
direct_link=True,
bias=True,
nodes_sim="hammersley",
activation_name="elu",
n_clusters=2,
col_sample=0.8,
row_sample=0.9,
)
fit_obj8 = ns.CustomRegressor(
obj=regr,
n_hidden_features=10,
direct_link=False,
bias=False,
nodes_sim="sobol",
activation_name="relu",
n_clusters=0,
col_sample=0.9,
row_sample=1.0,
)
fit_obj9 = ns.CustomRegressor(
obj=regr,
n_hidden_features=10,
direct_link=False,
bias=False,
nodes_sim="sobol",
activation_name="relu",
n_clusters=2,
cluster_encode=False,
col_sample=0.9,
row_sample=1.0,
)
index_train = range(10)
index_test = range(10, 15)
X_train = X[index_train, :]
y_train = y[index_train]
X_test = X[index_test, :]
y_test = y[index_test]
fit_obj.fit(X_train, y_train)
err = fit_obj.predict(X_test) - y_test
rmse = np.sqrt(np.mean(err**2))
fit_obj2.fit(X_train, y_train)
err2 = fit_obj2.predict(X_test) - y_test
rmse2 = np.sqrt(np.mean(err2**2))
fit_obj3.fit(X_train, y_train)
err3 = fit_obj3.predict(X_test) - y_test
rmse3 = np.sqrt(np.mean(err3**2))
fit_obj4.fit(X_train, y_train)
err4 = fit_obj4.predict(X_test) - y_test
rmse4 = np.sqrt(np.mean(err4**2))
err4_1 = fit_obj4.predict(X_test[0, :]) - y_test[0]
rmse4_1 = np.sqrt(np.mean(err4_1**2))
fit_obj8.fit(X_train, y_train)
err8 = fit_obj8.predict(X_test) - y_test
rmse8 = np.sqrt(np.mean(err8**2))
fit_obj5.fit(Z[0:100, :], t[0:100])
pred5 = fit_obj5.predict(Z[106, :])
fit_obj6.fit(Z[0:100, :], t[0:100])
pred6 = fit_obj6.predict(Z[106, :])
pred6_proba1 = fit_obj6.predict_proba(Z[100, :])
pred6_proba2 = fit_obj6.predict_proba(Z[100:120, :])
score6 = fit_obj6.score(Z[100:120, :], t[100:120])
fit_obj7.fit(Z[0:100, :], t[0:100])
score7 = fit_obj7.score(Z[100:120, :], t[100:120])
fit_obj9.fit(X_train, y_train)
err9 = fit_obj9.predict(X_test) - y_test
rmse9 = np.sqrt(np.mean(err9**2))
self.assertTrue(np.allclose(rmse, 64.933610490495667))
self.assertTrue(np.allclose(rmse2, 12.968755131423396))
self.assertTrue(np.allclose(rmse3, 26.716371782298673))
self.assertTrue(np.allclose(rmse4, 3.3480684032382375))
self.assertTrue(np.allclose(rmse4_1, 3.4339085800638713e-05))
self.assertTrue(np.allclose(rmse8, 64.93379560651447))
self.assertTrue(np.allclose(pred5, 1))
self.assertTrue(np.allclose(pred6, 1))
self.assertTrue(np.allclose(pred6_proba1[0], 0.50507336437937767))
self.assertTrue(np.allclose(pred6_proba2[3, 0], 0.4836233209751839))
self.assertTrue(np.allclose(score6, 0.94999999999999996))
self.assertTrue(np.allclose(score7, 0.85))
self.assertTrue(np.allclose(rmse9, 76.12747461579583))
# layer 1 (base layer) ----
print(" \n layer 1 ----- \n")
layer1_regr = RandomForestClassifier(n_estimators=10, random_state=123)
start = time()
layer1_regr.fit(X_train, y_train)
# Accuracy in layer 1
print(layer1_regr.score(X_test, y_test))
# layer 2 using layer 1 ----
print(" \n layer 2 ----- \n")
layer2_regr = ns.CustomClassifier(obj = layer1_regr, n_hidden_features=5,
direct_link=True, bias=True,
nodes_sim='uniform', activation_name='relu',
n_clusters=2, seed=123)
layer2_regr.fit(X_train, y_train)
# Accuracy in layer 2
print(layer2_regr.score(X_test, y_test))
# layer 3 using layer 2 ----
print(" \n layer 3 ----- \n")
layer3_regr = ns.CustomClassifier(obj = layer2_regr, n_hidden_features=10,
direct_link=True, bias=True, dropout=0.7,
nodes_sim='uniform', activation_name='relu',
n_clusters=2, seed=123)
layer3_regr.fit(X_train, y_train)
def test_score(self):
np.random.seed(123)
X, y = datasets.make_regression(n_samples=15, n_features=3)
breast_cancer = datasets.load_breast_cancer()
Z = breast_cancer.data
t = breast_cancer.target
regr = linear_model.BayesianRidge()
regr2 = gaussian_process.GaussianProcessClassifier()
regr3 = gaussian_process.GaussianProcessClassifier()
fit_obj = ns.CustomRegressor(
obj=regr,
n_hidden_features=100,
direct_link=True,
bias=True,
nodes_sim="sobol",
activation_name="relu",
n_clusters=2,
)
fit_obj2 = ns.CustomClassifier(
obj=regr2,
n_hidden_features=10,
direct_link=True,
dropout=0.1,
bias=True,
activation_name="relu",
n_clusters=3,
)
fit_obj3 = ns.CustomRegressor(
obj=regr,
n_hidden_features=100,
dropout=0.6,
direct_link=True,
bias=True,
nodes_sim="sobol",
activation_name="relu",
n_clusters=2,
)
fit_obj4 = ns.CustomClassifier(
obj=regr3,
n_hidden_features=50,
dropout=0.5,
direct_link=True,
bias=True,
activation_name="tanh",
n_clusters=2,
)
fit_obj.fit(X, y)
fit_obj2.fit(Z, t)
fit_obj3.fit(X, y)
fit_obj4.fit(Z, t)
self.assertTrue(
np.allclose(fit_obj.score(X, y), 4846.2057110929481)
& np.allclose(fit_obj2.score(Z, t), 0.99648506151142358)
& np.allclose(fit_obj3.score(X, y), 2.1668647954067132e-11)
& np.allclose(fit_obj4.score(Z, t), 1.0))