python引入BalanceCascade的几种方法

1. 使用sklearn库中的BalanceCascade函数：

from imblearn.ensemble import BalanceCascade
bc = BalanceCascade()
X_resampled, y_resampled = bc.fit_resample(X, y)

2. 使用imbalanced-learn库中的BalanceCascade函数：

from imblearn.ensemble import BalanceCascade
bc = BalanceCascade(random_state=42)
X_resampled, y_resampled = bc.fit_resample(X, y)

3. 自定义BalanceCascade函数：

def BalanceCascade(X, y, n_estimators=10, random_state=None):
"""
This function implements the BalanceCascade algorithm for imbalanced data.
"""
if random_state:
np.random.seed(random_state)

X_resampled = []
y_resampled = []
X_remaining = X.copy()
y_remaining = y.copy()

for i in range(n_estimators):
clf = DecisionTreeClassifier()
clf.fit(X_remaining, y_remaining)
y_pred = clf.predict(X_remaining)
idx_pos = np.where(y_pred == 1)[0]
idx_neg = np.where(y_pred == 0)[0]
n_samples_pos = len(idx_pos)
n_samples_neg = len(idx_neg)
if n_samples_pos == 0:
break
if n_samples_neg < n_samples_pos:
n_samples_pos = n_samples_neg
idx_pos_resampled = np.random.choice(idx_pos, size=n_samples_pos, replace=False)
X_resampled.append(X_remaining[idx_pos_resampled])
y_resampled.append(y_remaining[idx_pos_resampled])
X_remaining = np.delete(X_remaining, idx_pos_resampled, axis=0)
y_remaining = np.delete(y_remaining, idx_pos_resampled)

X_resampled = np.concatenate(X_resampled, axis=0)
y_resampled = np.concatenate(y_resampled, axis=0)

return X_resampled, y_resampled

X_resampled, y_resampled = BalanceCascade(X, y, n_estimators=10, random_state=42)