这是我在您的环境中运行的代码，我使用了RandomForestClassifier，并且正在尝试找出RandomForestClassifier中选定样本的decision_path。
import numpy as npimport pandas as pdfrom sklearn.datasets import make_classificationfrom sklearn.ensemble import RandomForestClassifierX, y = make_classification(n_samples=1000, n_features=6, n_informative=3, n_classes=2, random_state=0, shuffle=False)# 创建数据框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)我目前进展到这一步:
# 提取实例i = 12的决策路径i_data = X_train.iloc[12].values.reshape(1,-1)d_path = rf.decision_path(i_data)print(d_path)但输出结果不太合理:
(<1×7046 sparse matrix of type ‘<class ‘numpy.int64’>’with 486 stored elements in Compressed Sparse Row format>, array([ 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))
我正在尝试找出数据框中某个样本的决策路径。有人能告诉我该怎么做吗？
理想的情况是像这个一样。
回答：
RandomForestClassifier.decision_path方法返回一个tuple，即(indicator, n_nodes_ptr)。请查看文档：这里
因此，您的变量node_indicator是一个元组，而不是您所认为的形式。元组对象没有名为’indices’的属性，这就是为什么当您执行以下操作时会出现错误:
node_index = node_indicator.indices[node_indicator.indptr[sample_id]: node_indicator.indptr[sample_id + 1]]请尝试:
(node_indicator, _) = rf.decision_path(X_train)您还可以为森林中每个树的单个样本ID绘制决策树:
X_train = X_train.valuessample_id = 0for 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("决策树{0}的决策路径".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('用于预测样本%s的规则: ' % 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("决策节点ID %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，在您的案例中，您有50个估计器，阅读起来可能会有些乏味。
