Question

我尝试使用Isolation Forest sklearn implementation训练具有357个功能的数据集。当max features变量设置为1.0（默认值）时，我可以成功训练并获得结果。

但是，当max features设置为2时，会出现以下错误：

ValueError: Number of features of the model must match the input. 
Model n_features is 2 and input n_features is 357

当要素计数为1（整数）而非1.0（浮点数）时，它也会出现相同的错误。

我的理解是，当要素计数为2（int）时，在创建每棵树时应考虑两个特征。这是错的吗？如何更改max features参数？

代码如下：

from sklearn.ensemble.iforest import IsolationForest

def isolation_forest_imp(dataset):

    estimators = 10
    samples = 100
    features = 2
    contamination = 0.1
    bootstrap = False
    random_state = None
    verbosity = 0

    estimator = IsolationForest(n_estimators=estimators, max_samples=samples, contamination=contamination,
                                     max_features=features,
                                     bootstrap=boostrap, random_state=random_state, verbose=verbosity)

    model = estimator.fit(dataset)

Answer 1

在文件中说明：

max_features : int or float, optional (default=1.0)
    The number of features to draw from X to train each base estimator.

        - If int, then draw `max_features` features.
        - If float, then draw `max_features * X.shape[1]` features.

所以，2应该意味着采取两个功能，1.0应该意味着采取所有的功能，0.5取一半，等等，从我的理解。

我认为这可能是一个错误，因为，看看IsolationForest的合适：

# Isolation Forest inherits from BaseBagging 
# and when _fit is called, BaseBagging takes care of the features correctly
super(IsolationForest, self)._fit(X, y, max_samples,
                                          max_depth=max_depth,
                                          sample_weight=sample_weight)
 # however, when after _fit the decision_function is called using X - the whole sample - not taking into account the max_features
 self.threshold_ = -sp.stats.scoreatpercentile(
            -self.decision_function(X), 100. * (1. - self.contamination))

然后：

    # when the decision function _validate_X_predict is called, with X unmodified, 
    # it calls the base estimator's (dt) _validate_X_predict with the whole X
    X = self.estimators_[0]._validate_X_predict(X, check_input=True)

   ... 

   # from tree.py:
   def _validate_X_predict(self, X, check_input):
        """Validate X whenever one tries to predict, apply, predict_proba"""
        if self.tree_ is None:
            raise NotFittedError("Estimator not fitted, "
                                 "call `fit` before exploiting the model.")

        if check_input:
            X = check_array(X, dtype=DTYPE, accept_sparse="csr")
            if issparse(X) and (X.indices.dtype != np.intc or
                                X.indptr.dtype != np.intc):
                raise ValueError("No support for np.int64 index based "
                                 "sparse matrices")
        # so, this check fails because X is the original X, not with the max_features applied
        n_features = X.shape[1]
        if self.n_features_ != n_features:
            raise ValueError("Number of features of the model must "
                             "match the input. Model n_features is %s and "
                             "input n_features is %s "
                             % (self.n_features_, n_features))

        return X

所以，我不知道你如何处理这个问题。也许找出导致你需要的两个功能的百分比 - 即使我不确定它是否会像预期的那样工作。

注意：我使用的是scikit-learn v.0.18

编辑：正如@Vivek Kumar评论说这是一个问题，升级到0.20应该可以解决问题。

使用sklearn在隔离林算法中设置max features参数时出错

1 个答案: