def test_sample_weight(self):
lgbm = LGBMClassifier(n_estimators=2000, verbose=100)
sample_weight = calc_balanced_sample_weight(self.y_train)
lgbm.fit(self.X_train,
self.y_train,
self.X_test,
self.y_test,
sample_weight=sample_weight)
print(lgbm.score(self.X_test, self.y_test))
def test_use_categorical_feature(self):
# 测试category
lgbm = LGBMClassifier(n_estimators=2000, verbose=100)
lgbm.fit(self.X_train,
self.y_train,
self.X_test,
self.y_test,
categorical_feature=self.cat_indexes.tolist())
print(lgbm.score(self.X_test, self.y_test))
def test_warm_start(self):
lgbm = LGBMClassifier(verbose=16)
# 0.8764 1618
# 0.8749 1557
for n_estimators in [128, 512, 2048, 4096]:
lgbm.n_estimators = n_estimators
lgbm.fit(self.X_train, self.y_train, self.X_test, self.y_test)
acc = lgbm.score(self.X_test, self.y_test)
print(f"n_estimator = {n_estimators}, accuracy = {acc:.4f}")
def test_multiclass(self):
X, y = load_digits(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.33,
random_state=42)
lgbm = LGBMClassifier(n_estimators=5000, verbose=100)
lgbm.fit(X_train, y_train, X_test, y_test)
print(lgbm.score(X_test, y_test))
y_score = lgbm.predict_proba(X_test)
assert y_score.shape[1] == 10
assert np.all(np.abs(y_score.sum(axis=1) - 1) < 1e5)
def test_no_validate_set(self):
lgbm = LGBMClassifier(n_estimators=100, verbose=10)
lgbm.fit(self.X_train, self.y_train)
print(lgbm.score(self.X_test, self.y_test))