外贸网站优势,网站开发虚拟主机系统,建一个区域网站需要多少资金,西安短视频培训班哪个好一、各种熵的计算
entropy_utils.py
import numpy as np # 数值计算
import math # 标量数据的计算class EntropyUtils:决策树中各种熵的计算#xff0c;包括信息熵、信息增益、信息增益率、基尼指数。统一要求#xff1a;按照信息增益最大、信息增益率…一、各种熵的计算
entropy_utils.py
import numpy as np # 数值计算
import math # 标量数据的计算class EntropyUtils:决策树中各种熵的计算包括信息熵、信息增益、信息增益率、基尼指数。统一要求按照信息增益最大、信息增益率最大、基尼指数增益最大staticmethoddef _set_sample_weight(sample_weight, n_samples):扩展到集成学习此处为样本权重的设置:param sample_weight: 各样本的权重:param n_samples: 样本量:return:if sample_weight is None:sample_weight np.asarray([1.0] * n_samples)return sample_weightdef cal_info_entropy(self, y_labels, sample_weightNone):计算样本的信息熵:param y_labels: 递归样本子集中类别集合或特征取值:param sample_weight: 各样本的权重:return:y np.asarray(y_labels)sample_weight self._set_sample_weight(sample_weight, len(y))y_values np.unique(y) # 样本中不同类别值ent_y 0.0for val in y_values:p_i len(y[y val]) * np.mean(sample_weight[y val]) / len(y)ent_y -p_i * math.log2(p_i)return ent_ydef conditional_entropy(self, feature_x, y_labels, sample_weightNone):计算条件熵给定特征属性的情况下信息熵的计算:param feature_x: 某个样本特征:param y_labels: 递归样本子集中的类别集合:param sample_weight: 各样本的权重:return:x, y np.asarray(feature_x), np.asarray(y_labels)sample_weight self._set_sample_weight(sample_weight, len(y))cond_ent 0.0for x_val in np.unique(x):x_idx np.where(x x_val) # 某个特征取值的样本索引集合sub_x, sub_y x[x_idx], y[x_idx]sub_sample_weight sample_weight[x_idx]p_k len(sub_y) / len(y)cond_ent p_k * self.cal_info_entropy(sub_y, sub_sample_weight)return cond_entdef info_gain(self, feature_x, y_labels, sample_weightNone):计算信息增益:param feature_x::param y_labels::param sample_weight::return:return self.cal_info_entropy(y_labels, sample_weight) - \self.conditional_entropy(feature_x, y_labels, sample_weight)def info_gain_rate(self, feature_x, y_labels, sample_weightNone):计算信息增益率:param feature_x::param y_labels::param sample_weight::return:return self.info_gain(feature_x, y_labels, sample_weight) / \self.cal_info_entropy(feature_x, sample_weight)def cal_gini(self, y_label, sample_weightNone):计算当前特征或类别集合的基尼值:param y_label: 递归样本子集中类别集合或特征取值:param sample_weight::return:y np.asarray(y_label)sample_weight self._set_sample_weight(sample_weight, len(y))y_values np.unique(y)gini_val 1.0for val in y_values:p_k len(y[y val]) * np.mean(sample_weight[y val]) / len(y)gini_val - p_k ** 2return gini_valdef conditional_gini(self, feature_x, y_labels, sample_weightNone):计算条件基尼指数:param feature_x::param y_labels::param sample_weight::return:x, y np.asarray(feature_x), np.asarray(y_labels)sample_weight self._set_sample_weight(sample_weight, len(y))cond_gini 0.0for x_val in np.unique(x):x_idx np.where(x x_val) # 某个特征取值的样本索引集合sub_x, sub_y x[x_idx], y[x_idx]sub_sample_weight sample_weight[x_idx]p_k len(sub_y) / len(y)cond_gini p_k * self.cal_gini(sub_y, sub_sample_weight)return cond_ginidef gini_gain(self, feature_x, y_labels, sample_weightNone):计算基尼指数增益:param feature_x::param y_labels::param sample_weight::return:return self.cal_gini(y_labels, sample_weight) - \self.conditional_gini(feature_x, y_labels, sample_weight)# if __name__ __main__:
# y np.random.randint(0, 2, 50)
# entropy EntropyUtils()
# ent entropy.cal_info_entropy(y)
# print(ent)
二、连续特征数据的离散分箱
data_bin_wrapper.py
import numpy as npclass DataBinsWrapper:连续特征数据的离散化分箱分段操作根据用户传参max_bins计算分位数以分位数分箱分段然后根据样本特征取值所在区间段哪个箱位置索引标记当前值1. fit(x)根据样本进行分箱2. transform(x)根据已存在的箱把数据分成max_bins类def __init__(self, max_bins10):self.max_bins max_bins # 分箱数10%20%...90%self.XrangeMap None # 箱区间段def fit(self, x_samples):根据样本进行分箱:param x_samples: 样本二维数组 n * k或一个特征属性的数据二维 n * 1:return:if x_samples.ndim 1: # 一个特征属性转换为二维数组n_features 1x_samples x_samples[:, np.newaxis] # 添加一个轴转换为二维数组else:n_features x_samples.shape[1]# 构建分箱区间段self.XrangeMap [[] for _ in range(n_features)]for idx in range(n_features):x_sorted sorted(x_samples[:, idx]) # 按特征索引取值并从小到大排序for bin in range(1, self.max_bins):p (bin / self.max_bins) * 100 // 1p_val np.percentile(x_sorted, p)self.XrangeMap[idx].append(p_val)self.XrangeMap[idx] sorted(list(set(self.XrangeMap[idx])))def transform(self, x_samples, XrangeMapNone):根据已存在的箱把数据分成max_bins类:param x_samples: 样本二维数组 n * k或一个特征属性的数据二维 n * 1:return:if x_samples.ndim 1:if XrangeMap is not None:return np.asarray(np.digitize(x_samples, XrangeMap[0])).reshape(-1)else:return np.asarray(np.digitize(x_samples, self.XrangeMap[0])).reshape(-1)else:return np.asarray([np.digitize(x_samples[:, i], self.XrangeMap[i])for i in range(x_samples.shape[1])]).T# if __name__ __main__:
# x np.random.randn(10, 5)
# print(x)
# dbw DataBinsWrapper(max_bins5)
# dbw.fit(x)
# print(dbw.XrangeMap)
# print(dbw.transform(x))
三、可视化分类边界函数
plt_decision_funtion.py
import matplotlib.pylab as plt
import numpy as npdef plot_decision_function(X, y, clf, accNone, title_infoNone, is_showTrue, support_vectorsNone):可视化分类边界函数:param X, y: 测试样本与类别:param clf: 分类模型:param acc: 模型分类正确率:param title_info: 可视化标题title的额外信息:param is_show: 是否在当前显示图像用于父函数绘制子图:param support_vectors: 扩展支持向量机:return:if is_show:plt.figure(figsize(7, 5))# 根据特征变量的最小值和最大值生成二维网络用于绘制等值线x_min, x_max X[:, 0].min() - 1, X[:, 0].max() 1y_min, y_max X[:, 1].min() - 1, X[:, 1].max() 1xi, yi np.meshgrid(np.arange(x_min, x_max, 0.02),np.arange(y_min, y_max, 0.02))y_pred clf.predict(np.c_[xi.ravel(), yi.ravel()]) # 模型预测值y_pred y_pred.reshape(xi.shape)plt.contourf(xi, yi, y_pred, alpha0.4)plt.scatter(X[:, 0], X[:, 1], alpha0.8, cy, edgecolorsk)plt.xlabel(Feature 1, fontdict{fontsize: 12})plt.ylabel(Feature 2, fontdict{fontsize: 12})if acc:if title_info:plt.title(Model Classification Boundary %s \n(accuracy %.5f)% (title_info, acc), fontdict{fontsize: 14})else:plt.title(Model Classification Boundary (accuracy %.5f)% acc, fontdict{fontsize: 14})else:if title_info:plt.title(Model Classification Boundary %s% title_info, fontdict{fontsize: 14})else:plt.title(Model Classification Boundary, fontdict{fontsize: 14})if support_vectors is not None: # 可视化支持向量针对SVMplt.scatter(X[support_vectors, 0], X[support_vectors, 1],s50, cNone, alpha0.7, edgecolorsred)if is_show:plt.show()四、熵计算的测试
test_entropy.py
import numpy as np
import pandas as pd
from utils.entropy_utils import EntropyUtils
from utils.data_bin_wrapper import DataBinsWrapperdata pd.read_csv(data/watermelon.csv).iloc[:, 1:]
feat_names data.columns[:6]
y data.iloc[:, -1]
ent_obj EntropyUtils()print(各特征的信息增益如下)
for feat in feat_names:print(feat, :, ent_obj.info_gain(data.loc[:, feat], y))print( * 60)
print(各特征的信息增益率如下)
for feat in feat_names:print(feat, :, ent_obj.info_gain_rate(data.loc[:, feat], y))print( * 60)
print(各特征的基尼指数增益如下)
for feat in feat_names:print(feat, :, ent_obj.gini_gain(data.loc[:, feat], y))print( * 60)
x1 np.asarray(data.loc[:, [密度, 含糖率]])
print(x1)
dbw DataBinsWrapper(max_bins8)
dbw.fit(x1)
print(dbw.transform(x1))
五、树的结点信息封装
tree_node.py class TreeNode_C:决策树分类算法树的结点信息封装实体类setXXX()、getXXX()def __init__(self, feature_idx: int None, feature_valNone, criterion_val: float None,n_samples: int None, target_dist: dict None, weight_dist: dict None,left_child_NodeNone, right_child_NodeNone):决策树结点信息封装:param feature_idx: 特征索引如果指定特征属性的名称可以按照索引取值:param feature_val: 特征取值:param criterion_val: 划分结点的标准信息增益率、基尼指数增益:param n_samples: 当前结点所包含的样本量:param target_dist: 当前结点类别分布0-25%1-50%2-25%:param weight_dist: 当前结点所包含的样本权重分布:param left_child_Node: 左子树:param right_child_Node: 右子树self.feature_idx feature_idxself.feature_val feature_valself.criterion_val criterion_valself.n_samples n_samplesself.target_dist target_distself.weight_dist weight_distself.left_child_Node left_child_Node # 递归self.right_child_Node right_child_Node # 递归def level_order(self):按层次遍历树...:return:pass# def get_feature_idx(self):# return self.get_feature_idx()## def set_feature_idx(self, feature_idx):# self.feature_idx feature_idx
六、分类决策树算法的实现
decision_tree_C.py
import numpy as np
from utils.entropy_utils import EntropyUtils
from utils.tree_node import TreeNode_C
from utils.data_bin_wrapper import DataBinsWrapperclass DecisionTreeClassifier:分类决策树算法实现无论是ID3、C4.5或CART统一按照二叉树构造1. 划分标准信息增益率、基尼指数增益都按照最大值选择特征属性2. 创建决策树fit()递归算法实现注意出口条件3. 预测predict_proba()、predict() -- 对树的搜索4. 数据的预处理操作尤其是连续数据的离散化分箱5. 剪枝处理def __init__(self, criterionCART, is_feature_all_RFalse, dbw_feature_idxNone,max_depthNone, min_sample_split2, min_sample_leaf1,min_impurity_decrease0, max_bins10):self.utils EntropyUtils() # 结点划分类self.criterion criterion # 结点的划分标准if criterion.lower() cart:self.criterion_func self.utils.gini_gain # 基尼指数增益elif criterion.lower() c45:self.criterion_func self.utils.info_gain_rate # 信息增益率elif criterion.lower() id3:self.criterion_func self.utils.info_gain # 信息增益else:raise ValueError(参数criterion仅限cart、c45或id3...)self.is_feature_all_R is_feature_all_R # 所有样本特征是否全是连续数据self.dbw_feature_idx dbw_feature_idx # 混合类型数据可指定连续特征属性的索引self.max_depth max_depth # 树的最大深度不传参则一直划分下去self.min_sample_split min_sample_split # 最小的划分结点的样本量小于则不划分self.min_sample_leaf min_sample_leaf # 叶子结点所包含的最小样本量剩余的样本小于这个值标记叶子结点self.min_impurity_decrease min_impurity_decrease # 最小结点不纯度减少值小于这个值不足以划分self.max_bins max_bins # 连续数据的分箱数越大则划分越细self.root_node: TreeNode_C() None # 分类决策树的根节点self.dbw DataBinsWrapper(max_binsmax_bins) # 连续数据离散化对象self.dbw_XrangeMap {} # 存储训练样本连续特征分箱的端点self.class_values None # 样本的类别取值def _data_bin_wrapper(self, x_samples):针对特定的连续特征属性索引dbw_feature_idx分别进行分箱考虑测试样本与训练样本使用同一个XrangeMap:param x_samples: 样本即可以是训练样本也可以是测试样本:return:self.dbw_feature_idx np.asarray(self.dbw_feature_idx)x_samples_prop [] # 分箱之后的数据if not self.dbw_XrangeMap:# 为空即创建决策树前所做的分箱操作for i in range(x_samples.shape[1]):if i in self.dbw_feature_idx: # 说明当前特征是连续数值self.dbw.fit(x_samples[:, i])self.dbw_XrangeMap[i] self.dbw.XrangeMapx_samples_prop.append(self.dbw.transform(x_samples[:, i]))else:x_samples_prop.append(x_samples[:, i])else: # 针对测试样本的分箱操作for i in range(x_samples.shape[1]):if i in self.dbw_feature_idx: # 说明当前特征是连续数值x_samples_prop.append(self.dbw.transform(x_samples[:, i], self.dbw_XrangeMap[i]))else:x_samples_prop.append(x_samples[:, i])return np.asarray(x_samples_prop).Tdef fit(self, x_train, y_train, sample_weightNone):决策树的创建递归操作前的必要信息处理:param x_train: 训练样本ndarrayn * k:param y_train: 目标集ndarrayn :param sample_weight: 各样本的权重n :return:x_train, y_train np.asarray(x_train), np.asarray(y_train)self.class_values np.unique(y_train) # 样本的类别取值n_samples, n_features x_train.shape # 训练样本的样本量和特征属性数目if sample_weight is None:sample_weight np.asarray([1.0] * n_samples)self.root_node TreeNode_C() # 创建一个空树if self.is_feature_all_R: # 全部是连续数据self.dbw.fit(x_train)x_train self.dbw.transform(x_train)elif self.dbw_feature_idx:x_train self._data_bin_wrapper(x_train)self._build_tree(1, self.root_node, x_train, y_train, sample_weight)# print(x_train)def _build_tree(self, cur_depth, cur_node: TreeNode_C, x_train, y_train, sample_weight):递归创建决策树算法核心算法。按先序中序、后序创建的:param cur_depth: 递归划分后的树的深度:param cur_node: 递归划分后的当前根结点:param x_train: 递归划分后的训练样本:param y_train: 递归划分后的目标集合:param sample_weight: 递归划分后的各样本权重:return:n_samples, n_features x_train.shape # 当前样本子集中的样本量和特征属性数目target_dist, weight_dist {}, {} # 当前样本类别分布和权重分布 0--30%1--70%class_labels np.unique(y_train) # 不同的类别值for label in class_labels:target_dist[label] len(y_train[y_train label]) / n_samplesweight_dist[label] np.mean(sample_weight[y_train label])cur_node.target_dist target_distcur_node.weight_dist weight_distcur_node.n_samples n_samples# 递归出口判断if len(target_dist) 1: # 所有的样本全属于同一个类别递归出口1# 如果为0则表示当前样本集合为空递归出口3returnif n_samples self.min_sample_split: # 当前结点所包含的样本量不足以划分returnif self.max_depth is not None and cur_depth self.max_depth: # 树的深度达到最大深度return# 划分标准选择最佳的划分特征及其取值best_idx, best_val, best_criterion_val None, None, 0.0for k in range(n_features): # 对当前样本集合中每个特征计算划分标准for f_val in np.unique(x_train[:, k]): # 当前特征的不同取值feat_k_values (x_train[:, k] f_val).astype(int) # 是当前取值f_val就是1否则就是0criterion_val self.criterion_func(feat_k_values, y_train, sample_weight)if criterion_val best_criterion_val:best_criterion_val criterion_val # 最佳的划分标准值best_idx, best_val k, f_val # 当前最佳特征索引以及取值# 递归出口的判断if best_idx is None: # 当前属性为空或者所有样本在所有属性上取值相同无法划分returnif best_criterion_val self.min_impurity_decrease: # 小于最小不纯度阈值不划分returncur_node.criterion_val best_criterion_valcur_node.feature_idx best_idxcur_node.feature_val best_val# print(当前划分的特征索引, best_idx, 取值, best_val, 最佳标准值, best_criterion_val)# print(当前结点的类别分布, target_dist)# 创建左子树并递归创建以当前结点为子树根节点的左子树left_idx np.where(x_train[:, best_idx] best_val) # 左子树所包含的样本子集索引if len(left_idx) self.min_sample_leaf: # 小于叶子结点所包含的最少样本量则标记为叶子结点left_child_node TreeNode_C() # 创建左子树空结点# 以当前结点为子树根结点递归创建cur_node.left_child_Node left_child_nodeself._build_tree(cur_depth 1, left_child_node, x_train[left_idx],y_train[left_idx], sample_weight[left_idx])right_idx np.where(x_train[:, best_idx] ! best_val) # 右子树所包含的样本子集索引if len(right_idx) self.min_sample_leaf: # 小于叶子结点所包含的最少样本量则标记为叶子结点right_child_node TreeNode_C() # 创建右子树空结点# 以当前结点为子树根结点递归创建cur_node.right_child_Node right_child_nodeself._build_tree(cur_depth 1, right_child_node, x_train[right_idx],y_train[right_idx], sample_weight[right_idx])def _search_tree_predict(self, cur_node: TreeNode_C, x_test):根据测试样本从根结点到叶子结点搜索路径判定类别搜索按照后续遍历:param x_test: 单个测试样本:return:if cur_node.left_child_Node and x_test[cur_node.feature_idx] cur_node.feature_val:return self._search_tree_predict(cur_node.left_child_Node, x_test)elif cur_node.right_child_Node and x_test[cur_node.feature_idx] ! cur_node.feature_val:return self._search_tree_predict(cur_node.right_child_Node, x_test)else:# 叶子结点类别包含有类别分布# print(cur_node.target_dist)class_p np.zeros(len(self.class_values)) # 测试样本的类别概率for i, c in enumerate(self.class_values):class_p[i] cur_node.target_dist.get(c, 0) * cur_node.weight_dist.get(c, 1.0)class_p / np.sum(class_p) # 归一化return class_pdef predict_proba(self, x_test):预测测试样本x_test的类别概率:param x_test: 测试样本ndarray、numpy数值运算:return:x_test np.asarray(x_test) # 避免传递DataFrame、list...if self.is_feature_all_R:if self.dbw.XrangeMap is not None:x_test self.dbw.transform(x_test)else:raise ValueError(请先创建决策树...)elif self.dbw_feature_idx is not None:x_test self._data_bin_wrapper(x_test)prob_dist [] # 用于存储测试样本的类别概率分布for i in range(x_test.shape[0]):prob_dist.append(self._search_tree_predict(self.root_node, x_test[i]))return np.asarray(prob_dist)def predict(self, x_test):预测测试样本的类别:param x_test: 测试样本:return:x_test np.asarray(x_test) # 避免传递DataFrame、list...return np.argmax(self.predict_proba(x_test), axis1)def _prune_node(self, cur_node: TreeNode_C, alpha):递归剪枝针对决策树中的内部结点自底向上逐个考察方法后序遍历:param cur_node: 当前递归的决策树的内部结点:param alpha: 剪枝阈值:return:# 若左子树存在递归左子树进行剪枝if cur_node.left_child_Node:self._prune_node(cur_node.left_child_Node, alpha)# 若右子树存在递归右子树进行剪枝if cur_node.right_child_Node:self._prune_node(cur_node.right_child_Node, alpha)# 针对决策树的内部结点剪枝非叶结点if cur_node.left_child_Node is not None or cur_node.right_child_Node is not None:for child_node in [cur_node.left_child_Node, cur_node.right_child_Node]:if child_node is None:# 可能存在左右子树之一为空的情况当左右子树划分的样本子集数小于min_samples_leafcontinueif child_node.left_child_Node is not None or child_node.right_child_Node is not None:return# 计算剪枝前的损失值2表示当前结点包含两个叶子结点pre_prune_value 2 * alphafor child_node in [cur_node.left_child_Node, cur_node.right_child_Node]:# 计算左右叶子结点的经验熵 if child_node is None:# 可能存在左右子树之一为空的情况当左右子树划分的样本子集数小于min_samples_leafcontinuefor key, value in child_node.target_dist.items(): # 对每个叶子结点的类别分布pre_prune_value -1 * child_node.n_samples * value * np.log(value) * \child_node.weight_dist.get(key, 1.0)# 计算剪枝后的损失值当前结点即是叶子结点after_prune_value alphafor key, value in cur_node.target_dist.items(): # 当前待剪枝的结点的类别分布after_prune_value -1 * cur_node.n_samples * value * np.log(value) * \cur_node.weight_dist.get(key, 1.0)if after_prune_value pre_prune_value: # 进行剪枝操作cur_node.left_child_Node Nonecur_node.right_child_Node Nonecur_node.feature_idx, cur_node.feature_val None, Nonedef prune(self, alpha0.01):决策树后剪枝算法李航C(T) alpha * |T|:param alpha: 剪枝阈值权衡模型对训练数据的拟合程度与模型的复杂度:return:self._prune_node(self.root_node, alpha)return self.root_node
七、分类决策树算法的测试
test_decision_tree_C.py
import pandas as pd
from decision_tree_C import DecisionTreeClassifier
from sklearn.datasets import load_iris, load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report, accuracy_score
import numpy as np
import matplotlib.pyplot as plt
from sklearn.preprocessing import LabelEncoder# data pd.read_csv(data/watermelon.csv).iloc[:, 1:]
# X data.iloc[:, :-1]
# y data.iloc[:, -1]# iris load_iris()
# X, y iris.data, iris.target# bc_data load_breast_cancer()
# X, y bc_data.data, bc_data.targetnursery pd.read_csv(data/nursery.csv).dropna()
X, y np.asarray(nursery.iloc[:, :-1]), np.asarray(nursery.iloc[:, -1])y LabelEncoder().fit_transform(y)X_train, X_test, y_train, y_test train_test_split(X, y, test_size0.2, random_state0, stratifyy)depth np.linspace(2, 12, 11, dtypenp.int64)
accuracy []for d in depth:dtc DecisionTreeClassifier(is_feature_all_RFalse, max_depthd)dtc.fit(X_train, y_train)y_pred_labels dtc.predict(X_test)acc accuracy_score(y_test, y_pred_labels)# print(acc)accuracy.append(acc)
# dtc DecisionTreeClassifier(dbw_feature_idx[6, 7], max_bins8, max_depth2)
# dtc.fit(X, y)
# y_pred_prob dtc.predict_proba(X)
# print(y_pred_prob)# print(classification_report(y_test, y_pred_labels))plt.figure(figsize(7, 5))
plt.plot(depth, accuracy, ko-, lw1)
plt.show() test_decision_tree_C_2.py
import numpy as np
import matplotlib.pyplot as plt
from decision_tree_C import DecisionTreeClassifier
from sklearn.datasets import make_classification
from sklearn.metrics import classification_report, accuracy_score
from utils.plt_decision_function import plot_decision_function# 生成数据
data, target make_classification(n_samples100, n_features2, n_classes2, n_informative1, n_redundant0,n_clusters_per_class1, class_sep0.8, random_state21)
# print(data)
# print(target)cart_tree DecisionTreeClassifier(is_feature_all_RTrue)
cart_tree.fit(data, target)
y_test_pred cart_tree.predict(data)
print(classification_report(target, y_test_pred))
plt.figure(figsize(14, 10))
plt.subplot(221)
acc accuracy_score(target, y_test_pred)
plot_decision_function(data, target, cart_tree, accacc, is_showFalse, title_infoBy CART UnPrune)# 剪枝处理
alpha [1, 3, 5]
for i in range(3):cart_tree.prune(alphaalpha[i])y_test_pred cart_tree.predict(data)acc accuracy_score(target, y_test_pred)plt.subplot(222 i)plot_decision_function(data, target, cart_tree, accacc, is_showFalse,title_infoBy CART Prune α %.1f % alpha[i])
plt.tight_layout()
plt.show() test_decision_tree_C_3.py
import copyimport numpy as np
import matplotlib.pyplot as plt
from decision_tree_C import DecisionTreeClassifier
from sklearn.datasets import load_breast_cancer, load_iris
from sklearn.metrics import classification_report, accuracy_score
from utils.plt_decision_function import plot_decision_function
from sklearn.model_selection import StratifiedKFoldbc_data load_breast_cancer()
X, y bc_data.data, bc_data.target
alphas np.linspace(0, 10, 30)
accuracy_scores [] # 存储每个alpha阈值下的交叉验证均分
cart DecisionTreeClassifier(criterioncart, is_feature_all_RTrue, max_bins10)
for alpha in alphas:scores []k_fold StratifiedKFold(n_splits10).split(X, y)for train_idx, test_idx in k_fold:tree copy.deepcopy(cart)tree.fit(X[train_idx], y[train_idx])tree.prune(alphaalpha)y_test_pred tree.predict(X[test_idx])scores.append(accuracy_score(y[test_idx], y_test_pred))del treeprint(alpha, :, np.mean(scores))accuracy_scores.append(np.mean(scores))plt.figure(figsize(7, 5))
plt.plot(alphas, accuracy_scores, ko-, lw1)
plt.grid(ls:)
plt.xlabel(Alpha, fontdict{fontsize: 12})
plt.ylabel(Accuracy Scores, fontdict{fontsize: 12})
plt.title(Cross Validation Scores under different Prune Alpha, fontdict{fontsize: 14})
plt.show()
