加权随机森林pyton代码

Sure, I can provide you with the Python code for weighted random forest. Here's an example:

from sklearn.ensemble import RandomForestClassifier
from sklearn.utils.validation import check_random_state

class WeightedRandomForestClassifier(RandomForestClassifier):
    def __init__(self,
                 n_estimators='warn',
                 criterion="gini",
                 max_depth=None,
                 min_samples_split=2,
                 min_samples_leaf=1,
                 min_weight_fraction_leaf=0.,
                 max_features="auto",
                 max_leaf_nodes=None,
                 min_impurity_decrease=0.,
                 min_impurity_split=None,
                 bootstrap=True,
                 oob_score=False,
                 n_jobs=None,
                 random_state=None,
                 verbose=0,
                 warm_start=False,
                 class_weight=None):
        super().__init__(n_estimators=n_estimators,
                         criterion=criterion,
                         max_depth=max_depth,
                         min_samples_split=min_samples_split,
                         min_samples_leaf=min_samples_leaf,
                         min_weight_fraction_leaf=min_weight_fraction_leaf,
                         max_features=max_features,
                         max_leaf_nodes=max_leaf_nodes,
                         min_impurity_decrease=min_impurity_decrease,
                         min_impurity_split=min_impurity_split,
                         bootstrap=bootstrap,
                         oob_score=oob_score,
                         n_jobs=n_jobs,
                         random_state=random_state,
                         verbose=verbose,
                         warm_start=warm_start,
                         class_weight=class_weight)
        
    
    def fit(self, X, y, sample_weight=None):
        self.random_state_ = check_random_state(self.random_state)
        if sample_weight is not None:
            samp_wt = np.copy(sample_weight)
            samp_wt /= samp_wt.sum()
        else:
            samp_wt = np.ones(X.shape[0]) / X.shape[0]
        
        self.estimators_ = []
        for i in range(self.n_estimators):
            seed = self.random_state_.randint(np.iinfo(np.int32).max)
            tree = self._make_estimator(append=False,
                                         random_state=check_random_state(seed),
                                         class_weight=self.class_weight)
            X_sample, y_sample = resample(X, y, samp_wt, random_state=check_random_state(seed))
            tree.fit(X_sample, y_sample)
            self.estimators_.append(tree)
        
        self.is_fitted_ = True
        return self
    

This is an implementation of a weighted random forest classifier that inherits from the `RandomForestClassifier` class in scikit-learn. The `fit` method implements the weighting mechanism, where each sample in the training set is assigned a weight. The weights are used to determine the proportion of each class in the bootstrap samples used to grow each decision tree. Note that the `resample` function is used to generate the bootstrap samples with replacement, taking into account the sample weights.

I hope this helps! Let me know if you have any further questions.