def __init__(self): self.score_list = [] self.a = {}
时间: 2023-03-24 07:01:18 浏览: 48
这是一个Python类的初始化方法,它创建了两个实例变量:
1. `score_list`:这是一个空列表,可以用于存储分数或其他数值。
2. `a`:这是一个空字典,可以用于存储键值对。
在类的实例化过程中,这个方法会被自动调用,以便初始化这些实例变量。
相关问题
import numpy as np class Node: j = None theta = None p = None left = None right = None class DecisionTreeBase: def __init__(self, max_depth, feature_sample_rate, get_score): self.max_depth = max_depth self.feature_sample_rate = feature_sample_rate self.get_score = get_score def split_data(self, j, theta, X, idx): idx1, idx2 = list(), list() for i in idx: value = X[i][j] if value <= theta: idx1.append(i) else: idx2.append(i) return idx1, idx2 def get_random_features(self, n): shuffled = np.random.permutation(n) size = int(self.feature_sample_rate * n) selected = shuffled[:size] return selected def find_best_split(self, X, y, idx): m, n = X.shape best_score = float("inf") best_j = -1 best_theta = float("inf") best_idx1, best_idx2 = list(), list() selected_j = self.get_random_features(n) for j in selected_j: thetas = set([x[j] for x in X]) for theta in thetas: idx1, idx2 = self.split_data(j, theta, X, idx) if min(len(idx1), len(idx2)) == 0 : continue score1, score2 = self.get_score(y, idx1), self.get_score(y, idx2) w = 1.0 * len(idx1) / len(idx) score = w * score1 + (1-w) * score2 if score < best_score: best_score = score best_j = j best_theta = theta best_idx1 = idx1 best_idx2 = idx2 return best_j, best_theta, best_idx1, best_idx2, best_score def generate_tree(self, X, y, idx, d): r = Node() r.p = np.average(y[idx], axis=0) if d == 0 or len(idx)<2: return r current_score = self.get_score(y, idx) j, theta, idx1, idx2, score = self.find_best_split(X, y, idx) if score >= current_score: return r r.j = j r.theta = theta r.left = self.generate_tree(X, y, idx1, d-1) r.right = self.generate_tree(X, y, idx2, d-1) return r def fit(self, X, y): self.root = self.generate_tree(X, y, range(len(X)), self.max_depth) def get_prediction(self, r, x): if r.left == None and r.right == None: return r.p value = x[r.j] if value <= r.theta: return self.get_prediction(r.left, x) else: return self.get_prediction(r.right, x) def predict(self, X): y = list() for i in range(len(X)): y.append(self.get_prediction(self.root, X[i])) return np.array(y)
这段代码实现了一个基于决策树的分类器,其中包括以下几个类和方法:
1. Node类:表示决策树节点的类,包括属性j表示节点所选择的特征,属性theta表示节点所选择的特征的阈值,属性p表示节点的预测值,属性left和right分别表示左子树和右子树。
2. DecisionTreeBase类:表示决策树分类器的基类,包括方法__init__()、split_data()、get_random_features()、find_best_split()、generate_tree()、fit()、get_prediction()和predict()。
3. __init__(self, max_depth, feature_sample_rate, get_score)方法:初始化决策树分类器的参数,包括最大深度、特征采样率和评价指标。
4. split_data(self, j, theta, X, idx)方法:根据特征j和阈值theta将数据集X中的数据划分为两部分,返回划分后的两部分数据在数据集X中的索引。
5. get_random_features(self, n)方法:从数据集X中随机选择一定比例的特征,返回特征的索引。
6. find_best_split(self, X, y, idx)方法:在数据集X和标签y中,根据评价指标找到最优的特征和阈值,返回最优特征的索引、最优阈值、划分后的两部分数据在数据集X中的索引以及最优评价指标的值。
7. generate_tree(self, X, y, idx, d)方法:根据数据集X、标签y和索引idx生成一棵决策树,返回根节点。
8. fit(self, X, y)方法:训练决策树分类器,生成决策树。
9. get_prediction(self, r, x)方法:对于输入的数据x,根据决策树节点r的特征和阈值进行判断,选择左子树或右子树,并递归调用get_prediction()方法,直到到达叶子节点返回预测值。
10. predict(self, X)方法:对于输入的数据集X,返回预测值。
检查一下:import torch import torch.nn as nn import torch.optim as optim from torch.utils.data import DataLoader, TensorDataset from sklearn.metrics import roc_auc_score # 定义神经网络模型 class Net(nn.Module): def __init__(self): super(Net, self).__init__() self.fc1 = nn.Linear(10, 64) self.fc2 = nn.Linear(64, 32) self.fc3 = nn.Linear(32, 1) self.sigmoid = nn.Sigmoid() def forward(self, x): x = self.fc1(x) x = nn.functional.relu(x) x = self.fc2(x) x = nn.functional.relu(x) x = self.fc3(x) x = self.sigmoid(x) return x # 加载数据集 data = torch.load('data.pt') x_train, y_train, x_test, y_test = data train_dataset = TensorDataset(x_train, y_train) train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True) test_dataset = TensorDataset(x_test, y_test) test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False) # 定义损失函数和优化器 criterion = nn.BCELoss() optimizer = optim.Adam(net.parameters(), lr=0.01) # 训练模型 net = Net() for epoch in range(10): running_loss = 0.0 for i, data in enumerate(train_loader): inputs, labels = data optimizer.zero_grad() outputs = net(inputs) loss = criterion(outputs, labels) loss.backward() optimizer.step() running_loss += loss.item() # 在测试集上计算AUC y_pred = [] y_true = [] with torch.no_grad(): for data in test_loader: inputs, labels = data outputs = net(inputs) y_pred += outputs.tolist() y_true += labels.tolist() auc = roc_auc_score(y_true, y_pred) print('Epoch %d, loss: %.3f, test AUC: %.3f' % (epoch + 1, running_loss / len(train_loader), auc))
在运行代码之前,需要将 `net = Net()` 放在 `optimizer = optim.Adam(net.parameters(), lr=0.01)` 的前面,因为在定义优化器时需要先传入模型的参数。另外,代码中没有定义 `net`,需要先将 `Net` 类定义好再进行实例化。正确的代码如下所示:
```python
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, TensorDataset
from sklearn.metrics import roc_auc_score
# 定义神经网络模型
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.fc1 = nn.Linear(10, 64)
self.fc2 = nn.Linear(64, 32)
self.fc3 = nn.Linear(32, 1)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
x = self.fc1(x)
x = nn.functional.relu(x)
x = self.fc2(x)
x = nn.functional.relu(x)
x = self.fc3(x)
x = self.sigmoid(x)
return x
# 加载数据集
data = torch.load('data.pt')
x_train, y_train, x_test, y_test = data
train_dataset = TensorDataset(x_train, y_train)
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
test_dataset = TensorDataset(x_test, y_test)
test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False)
# 定义损失函数和优化器
criterion = nn.BCELoss()
net = Net()
optimizer = optim.Adam(net.parameters(), lr=0.01)
# 训练模型
for epoch in range(10):
running_loss = 0.0
for i, data in enumerate(train_loader):
inputs, labels = data
optimizer.zero_grad()
outputs = net(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
# 在测试集上计算AUC
y_pred = []
y_true = []
with torch.no_grad():
for data in test_loader:
inputs, labels = data
outputs = net(inputs)
y_pred += outputs.tolist()
y_true += labels.tolist()
auc = roc_auc_score(y_true, y_pred)
print('Epoch %d, loss: %.3f, test AUC: %.3f' % (epoch + 1, running_loss / len(train_loader), auc))
```