Question

出于个人目的，我正在尝试从Random Forest Classifier修改sklearn类以实现我的预期。基本上，我正在尝试让我的随机林中的树采用一些预定义的特征和案例子样本，因此我正在修改默认类。我试图继承原始sklearn的所有方法和结构，以便我的自定义随机森林类的fit方法可以采用sklearn的原始参数

例如，我希望我的自定义类能够采用与原始fit方法相同的参数：

clf = RandomForestClassifier(n_estimators=10, max_depth=2, random_state=None, max_features=None...)


clf = Customized_RF(n_estimators=10, max_depth=2, random_state=None, max_features=None...)

但是我在执行此操作时遇到了一些困难，特别是，它似乎与super().__init__定义有关，但出现了以下错误：TypeError: object.__init__() takes no arguments

我遵循github存储库作为指南

Rf class

我做错了什么或缺少一些明显的步骤吗？

这是我到目前为止的方法：

import numpy as np
from sklearn.tree import DecisionTreeClassifier

class Customized_RF:
    def __init__(self, n_estimators=10, criterion='gini', max_depth=None, random_state=None):

        super().__init__(base_estimator=DecisionTreeClassifier(),
                         n_estimators=n_estimators,
                         estimator_params=("criterion", "max_depth")) # Here's where the error happens

        self.n_estimators = n_estimators

        if random_state is None:
            self.random_state = np.random.RandomState()
        else:
            self.random_state = np.random.RandomState(random_state)

        self.criterion = criterion
        self.max_depth = max_depth

    def fit(self, X, y, max_features=None, cutoff=None, bootstrap_frac=0.8):
        """
        max_features: number of features that each estimator will use,
                      including the fixed features.

        bootstrap_frac: the size of bootstrap sample that each estimator will use.

        cutoff: index feature number from which starting the features subsampling selection. Subsampling for each tree will be done retrieven a random number of features before and after the cutoff. Assuming that the features matrix is not sorted or altered somehow (sparsed).

        """
        self.estimators = []
        self.n_classes  = np.unique(y).shape[0]

        if max_features is None:
            max_features = X.shape[1]  # if max_features is None select all features for every estimator like original

        if cutoff is None:
            cutoff = int(X.shape[1] / 2)  # pick the central index number of the x vector

        print('Cutoff x vector: '.format(cutoff))

        n_samples = X.shape[0]
        n_bs = int(bootstrap_frac*n_samples)  # fraction of samples to be used for every estimator (DT)

        for i in range(self.n_estimators):
                                    replace=False)

            feats_left = self.random_state.choice(cutoff + 1, int(max_features / 2), replace=False)  # inclusive cutoff
            feats_right = self.random_state.choice(range(cutoff + 1, X.shape[1]), int(max_features/2), replace=False)
            # exclusive cutoff

            feats = np.concatenate((feats_left, feats_right)).tolist()

            self.feats_used.append(feats)

            print('Chosen feature indexes for estimator number {0}: {1}'.format(i, feats))

            bs_sample = self.random_state.choice(n_samples, 
                                                 size=n_bs,
                                                 replace=True)

            dtc = DecisionTreeClassifier(random_state=self.random_state)
            dtc.fit(X[bs_sample][:, feats], y[bs_sample])
            self.estimators.append(dtc)

    def predict_proba(self, X):
        out = np.zeros((X.shape[0], self.n_classes))
        for i in range(self.n_estimators):
            out += self.estimators[i].predict_proba(X[:, self.feats_used[i]])
        return out / self.n_estimators

    def predict(self, X):
        return self.predict_proba(X).argmax(axis=1)

    def score(self, X, y):
        return (self.predict(X) == y).mean()

Answer 1

如果您想从另一个类派生自己的类，则该类定义需要引用base class，例如$data = Post::get();。 class MyClass(BaseClass)然后引用基类。

在您的情况下，基类丢失了，Python假定使用了通用类super()。

根据您的问题尚不清楚您想要的基类是object还是DecisionTreeClassifier。无论哪种情况，您都需要更改RandomForestClassifier中使用的类参数。

次要：检查__init__行，它是无效的语法。

自定义随机森林分类器sklearn

1 个答案: