if node.value is not None: return node.value if x[node.feature] <= node.threshold: return self.predict_single(x, node.left) else: return self.predict_single(x, node.right)翻译这段代码
时间: 2024-02-14 10:35:39 浏览: 28
这段代码是一个决策树的预测函数,它接收一个样本 x 和一个决策树节点 node,返回该样本的预测值。
如果当前节点 node 的 value 不为 None,则返回该节点的 value,表示已经到达叶子节点,返回预测结果。
如果当前节点的 feature 对应的样本特征值 x[node.feature] 小于等于节点的阈值 node.threshold,则递归调用该函数并传入当前节点的左子节点 node.left,表示往左子树遍历。
如果当前节点的 feature 对应的样本特征值 x[node.feature] 大于节点的阈值 node.threshold,则递归调用该函数并传入当前节点的右子节点 node.right,表示往右子树遍历。
最终到达叶子节点后,返回该节点的 value,即为预测结果。
相关问题
题目三:使用 numpy 编写的 CART 分类/回归树算法,并对 iris 数据集/boston 数据集进行预 测。 具体内容: (1)导入数据集。 (2)划分数据(分成训练集和数据集) (3)训练模型(参考程序模板:cart_numpy_template.py) (4)输出树模型。 (5)进行预测,评估模型性能。 拓展内容(选做): (1)尝试加入 TN样本数量阈值和 TG基尼指数阈值作为终止条件。 (2)尝试对离散特征进行分枝。
好的,针对您的问题,我来进一步回答一下。
首先,我们需要导入 numpy 和相应的数据集,例如 iris 数据集和 boston 数据集。然后,我们需要将数据集划分为训练集和测试集。
对于 CART 分类/回归树算法,我们可以参考以下程序模板:
```python
import numpy as np
# 导入数据集
data = np.loadtxt("iris.data", delimiter=",", usecols=(0, 1, 2, 3))
target = np.loadtxt("iris.data", delimiter=",", usecols=(4), dtype=np.str)
# 划分训练集和测试集
train_data = data[:100]
train_target = target[:100]
test_data = data[100:]
test_target = target[100:]
# 定义 CART 分类/回归树算法
class CART(object):
def __init__(self, min_samples_leaf=1, min_impurity_split=1e-7):
self.min_samples_leaf = min_samples_leaf
self.min_impurity_split = min_impurity_split
self.tree = None
def fit(self, X, y):
self.tree = self.build_tree(X, y)
def predict(self, X):
return np.array([self.predict_one(x, self.tree) for x in X])
def predict_one(self, x, node):
if node.is_leaf:
return node.value
if x[node.feature_index] <= node.threshold:
return self.predict_one(x, node.left)
else:
return self.predict_one(x, node.right)
def build_tree(self, X, y):
n_samples, n_features = X.shape
# 如果样本数小于等于阈值,则返回叶子节点
if n_samples < self.min_samples_leaf:
return Node(value=np.mean(y), is_leaf=True)
# 计算当前节点的基尼指数
current_gini = self.gini(y)
best_gini = np.inf
best_feature_index = None
best_threshold = None
# 遍历所有特征和特征值,选择最优的划分点
for feature_index in range(n_features):
feature_values = X[:, feature_index]
unique_values = np.unique(feature_values)
for threshold in unique_values:
# 根据当前特征和特征值对样本进行划分
left_indices = feature_values <= threshold
right_indices = feature_values > threshold
# 如果左右子树的样本数小于等于阈值,则不进行划分
if len(left_indices) < self.min_samples_leaf or len(right_indices) < self.min_samples_leaf:
continue
# 计算左右子树的基尼指数
left_gini = self.gini(y[left_indices])
right_gini = self.gini(y[right_indices])
# 计算加权基尼指数
weighted_gini = (len(left_indices) / n_samples) * left_gini + (len(right_indices) / n_samples) * right_gini
# 如果加权基尼指数小于当前最优基尼指数,则更新最优基尼指数和划分点
if weighted_gini < best_gini:
best_gini = weighted_gini
best_feature_index = feature_index
best_threshold = threshold
# 如果当前节点的基尼指数减去划分后的基尼指数小于等于阈值,则返回叶子节点
if current_gini - best_gini <= self.min_impurity_split:
return Node(value=np.mean(y), is_leaf=True)
# 根据最优划分点对样本进行划分,并递归构建左右子树
left_indices = X[:, best_feature_index] <= best_threshold
right_indices = X[:, best_feature_index] > best_threshold
left_tree = self.build_tree(X[left_indices], y[left_indices])
right_tree = self.build_tree(X[right_indices], y[right_indices])
# 返回当前节点
return Node(feature_index=best_feature_index, threshold=best_threshold, left=left_tree, right=right_tree)
def gini(self, y):
_, counts = np.unique(y, return_counts=True)
impurity = 1 - np.sum(np.square(counts / len(y)))
return impurity
# 定义节点类
class Node(object):
def __init__(self, feature_index=None, threshold=None, value=None, left=None, right=None, is_leaf=False):
self.feature_index = feature_index
self.threshold = threshold
self.value = value
self.left = left
self.right = right
self.is_leaf = is_leaf
# 训练模型并输出树模型
cart = CART()
cart.fit(train_data, train_target)
print(cart.tree)
# 进行预测并评估模型性能
predict_target = cart.predict(test_data)
accuracy = np.mean(predict_target == test_target)
print("Accuracy:", accuracy)
```
需要注意的是,这里的 CART 分类/回归树算法是一个简化版的实现,并没有包含所有的特性。如果需要加入 TN样本数量阈值和 TG基尼指数阈值作为终止条件,可以在代码中进行相应的修改。如果需要对离散特征进行分枝,可以采用类似独热编码(One-Hot Encoding)的方式将离散特征转换为数值型特征。
希望我的回答对您有所帮助,如有其他问题,欢迎继续提问。
用python写一段代码:利用贷款申请样本数据集,自编程实现决策树的ID3算法
好的,以下是一段用于实现决策树ID3算法的Python代码:
```python
import numpy as np
import pandas as pd
def calc_entropy(y):
"""
计算熵
"""
n = len(y)
if n <= 1:
return 0
_, counts = np.unique(y, return_counts=True)
probs = counts / n
entropy = -np.sum(probs * np.log2(probs))
return entropy
def calc_info_gain(X, y, idx):
"""
计算信息增益
"""
entropy_parent = calc_entropy(y)
_, counts_parent = np.unique(y, return_counts=True)
n_parent = len(y)
vals, counts = np.unique(X[:, idx], return_counts=True)
entropy_children = 0
for i in range(len(vals)):
_, counts_child = np.unique(y[X[:, idx] == vals[i]], return_counts=True)
n_child = np.sum(X[:, idx] == vals[i])
entropy_children += counts[i] / n_parent * calc_entropy(y[X[:, idx] == vals[i]])
info_gain = entropy_parent - entropy_children
return info_gain
def find_best_split(X, y, used_features):
"""
寻找最佳的分裂特征和阈值
"""
best_feature_idx = None
best_threshold = None
best_info_gain = -1
for idx in range(X.shape[1]):
if idx in used_features:
continue
thresh_set = set(X[:, idx])
for threshold in thresh_set:
y_left = y[X[:, idx] <= threshold]
y_right = y[X[:, idx] > threshold]
info_gain = calc_info_gain(X, y, idx)
if info_gain > best_info_gain:
best_feature_idx = idx
best_threshold = threshold
best_info_gain = info_gain
return best_feature_idx, best_threshold
class TreeNode:
"""
决策树节点类
"""
def __init__(self, feature_idx=None, threshold=None, left=None, right=None, value=None):
self.feature_idx = feature_idx
self.threshold = threshold
self.left = left
self.right = right
self.value = value
class DecisionTree:
"""
决策树类
"""
def __init__(self, min_samples_split=2, max_depth=1000):
self.min_samples_split = min_samples_split
self.max_depth = max_depth
def fit(self, X, y):
self.n_classes_ = len(np.unique(y))
self.tree_ = self._grow_tree(X, y)
def predict(self, X):
return [self._predict(inputs) for inputs in X]
def _predict(self, inputs):
node = self.tree_
while node.left:
if inputs[node.feature_idx] <= node.threshold:
node = node.left
else:
node = node.right
return node.value
def _grow_tree(self, X, y, depth=0):
n_samples, n_features = X.shape
n_labels = len(np.unique(y))
# 如果当前数据集中所有数据分类相同或者达到最大深度,则结束递归
if (n_labels == 1) or (n_samples < self.min_samples_split) or (depth >= self.max_depth):
return TreeNode(value=np.argmax(np.bincount(y)))
# 寻找最佳分裂特征和阈值
used_features = set()
while len(used_features) < n_features:
idx, threshold = find_best_split(X, y, used_features)
used_features.add(idx)
left_idxs = X[:, idx] <= threshold
right_idxs = X[:, idx] > threshold
# 如果分裂后有一个子集为空,则结束递归
if np.sum(left_idxs) == 0 or np.sum(right_idxs) == 0:
return TreeNode(value=np.argmax(np.bincount(y)))
# 继续递归建树
left = self._grow_tree(X[left_idxs], y[left_idxs], depth+1)
right = self._grow_tree(X[right_idxs], y[right_idxs], depth+1)
return TreeNode(feature_idx=idx, threshold=threshold, left=left, right=right)
# 加载数据集
data = pd.read_csv('loan_application.csv')
# 预处理
X = data.iloc[:,:-1].values
y = data.iloc[:,-1].values
y = np.where(y=='yes', 1, 0)
# 训练决策树模型
clf = DecisionTree()
clf.fit(X, y)
# 使用模型进行预测
print(clf.predict([[1, 1, -1, 1]])) # 0
print(clf.predict([[1, 1, 1, 1]])) # 1
```
注意:该代码只是一个简单的决策树ID3算法实现,并没有经过优化处理。在实际问题中,可以使用更为成熟的机器学习工具箱或者库来完成决策树的训练和预测。