使用sklearn库实现AdaBoost.M1和AdaBoost.M2

好的，下面是使用sklearn库实现AdaBoost.M1和AdaBoost.M2的代码示例：

from sklearn.ensemble import AdaBoostClassifier
from sklearn.base import BaseEstimator
from sklearn.tree import DecisionTreeClassifier
from sklearn.utils.validation import check_X_y, check_array, check_is_fitted

class AdaBoostM2(BaseEstimator):
    def __init__(self, base_estimator=None, n_estimators=50, learning_rate=1.0, algorithm='SAMME.R', random_state=None):
        self.base_estimator = base_estimator
        self.n_estimators = n_estimators
        self.learning_rate = learning_rate
        self.algorithm = algorithm
        self.random_state = random_state

    def _boost(self, i, X, y, sample_weight, estimator_weight, estimator_error):
        # Train base estimator
        estimator = self.base_estimator.fit(X, y, sample_weight=sample_weight)

        y_predict = estimator.predict(X)
        incorrect = y_predict != y

        # Calculate estimator error rate
        estimator_error[i] = np.mean(np.average(incorrect, weights=sample_weight, axis=0))

        # If the error rate is zero, set the estimator weight to infinity
        if estimator_error[i] == 0:
            estimator_weight[i] = float('inf')

        else:
            # Calculate estimator weight
            estimator_weight[i] = self.learning_rate * np.log((1 - estimator_error[i]) / estimator_error[i])

            # Update sample weights
            sample_weight *= np.exp(estimator_weight[i] * incorrect * ((sample_weight > 0) | (estimator_weight[i] < 0)))

        return sample_weight

    def fit(self, X, y, sample_weight=None):
        # Check that X and y have correct shape
        X, y = check_X_y(X, y)

        # Initialize sample weights
        if sample_weight is None:
            sample_weight = np.ones(X.shape[0]) / X.shape[0]

        # Initialize estimators and weights
        self.estimators_ = []
        self.estimator_weights_ = np.zeros(self.n_estimators, dtype=np.float64)
        self.estimator_errors_ = np.ones(self.n_estimators, dtype=np.float64)

        for i in range(self.n_estimators):
            # Boost the ensemble
            sample_weight = self._boost(i, X, y, sample_weight, self.estimator_weights_, self.estimator_errors_)

            # Stop if the estimator error rate is zero
            if self.estimator_errors_[i] == 0:
                break

            # Normalize estimator weights
            self.estimator_weights_ /= np.sum(self.estimator_weights_)

            # Append the current estimator to the ensemble
            self.estimators_.append(self.base_estimator)

        return self

    def predict(self, X):
        # Check is fit had been called
        check_is_fitted(self, ['estimators_', 'estimator_weights_'])

        # Initialize the predictions
        y_predict = np.zeros(X.shape[0])

        # Make predictions using all estimators
        for estimator, weight in zip(self.estimators_, self.estimator_weights_):
            y_predict += weight * estimator.predict(X)

        # Return the class with highest weighted probability
        return np.sign(y_predict)

# Define base estimator
base_estimator = DecisionTreeClassifier(max_depth=1)

# Initialize AdaBoost.M1 and AdaBoost.M2 classifiers
ada_m1 = AdaBoostClassifier(base_estimator=base_estimator, n_estimators=50, learning_rate=1.0, algorithm='SAMME.R', random_state=None)
ada_m2 = AdaBoostM2(base_estimator=base_estimator, n_estimators=50, learning_rate=1.0, algorithm='SAMME.R', random_state=None)

# Train classifiers
ada_m1.fit(X_train, y_train)
ada_m2.fit(X_train, y_train)

# Make predictions using classifiers
y_pred_m1 = ada_m1.predict(X_test)
y_pred_m2 = ada_m2.predict(X_test)

在这个示例中，我们使用了sklearn库的`AdaBoostClassifier`类来实现AdaBoost.M1，使用自定义的`AdaBoostM2`类来实现AdaBoost.M2。其中，`_boost`方法实现了AdaBoost.M2的弱分类器训练过程。在`fit`方法中，我们使用`_boost`方法来训练AdaBoost.M2的弱分类器，并更新样本权重、估计器权重和估计器错误率。在`predict`方法中，我们使用所有弱分类器的预测结果来得出最终的预测结果。