Question

以下是我在您的环境中运行它的代码，我正在使用RandomForestClassifier，我正在尝试为随机林分类器中的选定样本找出 decision_path

import numpy as np
import pandas as pd
from sklearn.datasets import make_classification
from sklearn.ensemble import RandomForestClassifier

X, y = make_classification(n_samples=1000,
                           n_features=6,
                           n_informative=3,
                           n_classes=2,
                           random_state=0,
                           shuffle=False)

# Creating a dataFrame
df = pd.DataFrame({'Feature 1':X[:,0],
                                  'Feature 2':X[:,1],
                                  'Feature 3':X[:,2],
                                  'Feature 4':X[:,3],
                                  'Feature 5':X[:,4],
                                  'Feature 6':X[:,5],
                                  'Class':y})


y_train = df['Class']
X_train = df.drop('Class',axis = 1)

rf = RandomForestClassifier(n_estimators=50,
                               random_state=0)

rf.fit(X_train, y_train)

我得到的最远的是这个

#Extracting the decision path for instance i = 12
i_data = X_train.iloc[12].values.reshape(1,-1)
d_path = rf.decision_path(i_data)

print(d_path)

并且外出没有多大意义

（＆lt; 1x7046类型为''的稀疏矩阵 486个存储元素，压缩稀疏行格式＆gt;，数组（[0,133,282,415,588,761,910,1041,1182,1309,1432， 1569,1728,1869,2000,2143,2284,2419,2572,2711,2856,2987， 3128,3261,3430,3549,3704,3839,3980,4127,4258,4389,4534， 4671,4808,4947,5088,5247,5378,5517,5640,5769,5956,6079， 6226,6385,6524,6655,6780,6925,7046]，dtype = int32））

我试图找出数据框中粒子样本的决策路径。谁能告诉我怎么做？

这个想法是有这样的东西

http://scikit-learn.org/stable/auto_examples/tree/plot_unveil_tree_structure.html

Answer 1

RandomForestClassifier.decision_path方法返回tuple (indicator, n_nodes_ptr)。看文档： here

所以你的变量node_indicator是一个元组而不是你的想法。元组对象没有属性＆＃39;索引＆＃39;这就是你做错误的原因：

node_index = node_indicator.indices[node_indicator.indptr[sample_id]:
                                    node_indicator.indptr[sample_id + 1]]

尝试：

(node_indicator, _) = rf.decision_path(X_train)

您还可以为单个样本ID绘制森林每棵树的决策树：

X_train = X_train.values

sample_id = 0

for j, tree in enumerate(rf.estimators_):

    n_nodes = tree.tree_.node_count
    children_left = tree.tree_.children_left
    children_right = tree.tree_.children_right
    feature = tree.tree_.feature
    threshold = tree.tree_.threshold

    print("Decision path for DecisionTree {0}".format(j))
    node_indicator = tree.decision_path(X_train)
    leave_id = tree.apply(X_train)
    node_index = node_indicator.indices[node_indicator.indptr[sample_id]:
                                        node_indicator.indptr[sample_id + 1]]



    print('Rules used to predict sample %s: ' % sample_id)
    for node_id in node_index:
        if leave_id[sample_id] != node_id:
            continue

        if (X_train[sample_id, feature[node_id]] <= threshold[node_id]):
            threshold_sign = "<="
        else:
            threshold_sign = ">"

        print("decision id node %s : (X_train[%s, %s] (= %s) %s %s)"
              % (node_id,
                 sample_id,
                 feature[node_id],
                 X_train[sample_id, feature[node_id]],
                 threshold_sign,
                 threshold[node_id]))

请注意，在您的情况下，您有50个估算器，因此阅读可能会有点无聊。

随机森林分类器的决策路径

1 个答案: