def test_score(self):
breast_cancer = load_breast_cancer()
X = breast_cancer.data
y = breast_cancer.target
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.2,
random_state=123)
clf = DecisionTreeClassifier(max_depth=1, random_state=14235)
fit_obj = ns.RandomBagClassifier(
clf,
n_estimators=3,
n_hidden_features=2,
activation_name="relu",
a=0.01,
nodes_sim="sobol",
bias=True,
dropout=0,
direct_link=False,
n_clusters=2,
type_clust="kmeans",
type_scaling=("std", "std", "std"),
col_sample=0.9,
row_sample=0.9,
n_jobs=None,
seed=14253,
verbose=0,
)
clf2 = DecisionTreeClassifier(max_depth=1, random_state=15243)
fit_obj2 = ns.RandomBagClassifier(
clf2,
n_estimators=3,
n_hidden_features=2,
activation_name="relu",
a=0.01,
nodes_sim="sobol",
bias=True,
dropout=0,
direct_link=False,
n_clusters=2,
type_clust="gmm",
type_scaling=("std", "std", "std"),
col_sample=0.9,
row_sample=0.9,
n_jobs=None,
seed=19237,
verbose=0,
)
fit_obj.fit(X_train, y_train)
score1 = fit_obj.score(X_test, y_test)
fit_obj2.fit(X_train, y_train)
score2 = fit_obj2.score(X_test, y_test)
self.assertTrue(
np.allclose(score1, 0.9385964912280702)
& np.allclose(score2, 0.9298245614035088))
breast_cancer = load_breast_cancer()
Z = breast_cancer.data
t = breast_cancer.target
np.random.seed(123)
X_train, X_test, y_train, y_test = train_test_split(Z, t, test_size=0.2)
print("\n")
print(f"1 - breast cancer dataset -----")
print("\n")
# decision tree
clf = DecisionTreeClassifier(max_depth=2, random_state=123)
fit_obj = ns.RandomBagClassifier(clf, n_hidden_features=2,
direct_link=True,
n_estimators=100,
col_sample=0.9, row_sample=0.9,
dropout=0.3, n_clusters=0, verbose=1)
start = time()
fit_obj.fit(X_train, y_train)
print(f"Elapsed {time() - start}")
print(fit_obj.score(X_test, y_test))
print(fit_obj.score(X_test, y_test, scoring="roc_auc"))
start = time()
preds = fit_obj.predict(X_test)
print(f"Elapsed {time() - start}")
print(metrics.classification_report(preds, y_test))
def test_RandomBag(self):
breast_cancer = load_breast_cancer()
X = breast_cancer.data
y = breast_cancer.target
np.random.seed(123)
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.2,
random_state=19823)
wine = load_wine()
Z = wine.data
t = wine.target
Z_train, Z_test, t_train, t_test = train_test_split(Z,
t,
test_size=0.2,
random_state=12736)
clf = DecisionTreeClassifier(max_depth=1, random_state=14235)
fit_obj = ns.RandomBagClassifier(
clf,
n_estimators=3,
n_hidden_features=2,
activation_name="relu",
a=0.01,
nodes_sim="sobol",
bias=True,
dropout=0,
direct_link=False,
n_clusters=2,
type_clust="kmeans",
type_scaling=("std", "std", "std"),
col_sample=0.9,
row_sample=0.9,
n_jobs=None,
seed=14253,
verbose=0,
)
clf2 = DecisionTreeClassifier(max_depth=1, random_state=15243)
fit_obj2 = ns.RandomBagClassifier(
clf2,
n_estimators=3,
n_hidden_features=2,
activation_name="relu",
a=0.01,
nodes_sim="sobol",
bias=True,
dropout=0,
direct_link=False,
n_clusters=2,
type_clust="kmeans",
type_scaling=("std", "std", "std"),
col_sample=0.9,
row_sample=0.9,
n_jobs=None,
seed=19237,
verbose=0,
)
clf3 = DecisionTreeClassifier(max_depth=1, random_state=15243)
fit_obj3 = ns.RandomBagClassifier(
clf3,
n_estimators=3,
n_hidden_features=2,
activation_name="relu",
a=0.01,
nodes_sim="sobol",
bias=True,
dropout=0,
direct_link=False,
n_clusters=2,
cluster_encode=False,
type_clust="kmeans",
type_scaling=("std", "std", "std"),
col_sample=0.9,
row_sample=0.9,
n_jobs=None,
seed=19237,
verbose=0,
)
fit_obj.fit(X_train, y_train)
preds1 = fit_obj.predict_proba(X_test)
fit_obj2.fit(Z_train, t_train)
preds2 = fit_obj2.predict_proba(Z_test)
fit_obj3.fit(Z_train, t_train)
preds3 = fit_obj3.predict_proba(Z_test)
self.assertTrue(np.allclose(preds1[0, 0], 0.043789295499125226))
self.assertTrue(np.allclose(preds1[0, 1], 0.9562107045008749))
self.assertTrue(np.allclose(preds2[0, 0], 0.04650031359722393))
self.assertTrue(np.allclose(preds3[0, 0], 0.04650031359722393))
self.assertTrue(
np.allclose(fit_obj.predict(X_test)[0], 1)
& np.allclose(fit_obj2.predict(X_test)[0], 0))