未来已来：全方位掌握【人工智能】的系统学习路线

import numpy as np

# 创建矩阵
A = np.array([[1, 2], [3, 4]])
B = np.array([[5, 6], [7, 8]])

# 矩阵加法
C = A + B
print("矩阵加法结果：\n", C)

# 矩阵乘法
D = np.dot(A, B)
print("矩阵乘法结果：\n", D)

import sympy as sp

# 定义变量和函数
x = sp.symbols('x')
f = x**3 + 2*x**2 + x + 1

# 计算导数
f_prime = sp.diff(f, x)
print("函数的导数：", f_prime)

import numpy as np
import matplotlib.pyplot as plt

# 生成正态分布数据
data = np.random.normal(0, 1, 1000)

# 绘制概率分布图
plt.hist(data, bins=30, density=True)
plt.title("正态分布")
plt.xlabel("值")
plt.ylabel("概率密度")
plt.show()

from collections import deque

# 定义图的邻接表
graph = {
    'A': ['B', 'C'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F'],
    'D': ['B'],
    'E': ['B', 'F'],
    'F': ['C', 'E']
}

# 广度优先搜索算法
def bfs(graph, start):
    visited = set()
    queue = deque([start])
    
    while queue:
        vertex = queue.popleft()
        if vertex not in visited:
            print(vertex, end=" ")
            visited.add(vertex)
            queue.extend(set(graph[vertex]) - visited)

# 执行广度优先搜索
bfs(graph, 'A')

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score

# 加载数据集
iris = load_iris()
X, y = iris.data, iris.target

# 数据集划分
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# 模型训练
model = LogisticRegression(max_iter=200)
model.fit(X_train, y_train)

# 模型预测
y_pred = model.predict(X_test)

# 模型评估
accuracy = accuracy_score(y_test, y_pred)
print("模型准确率：", accuracy)

def quicksort(arr):
    if len(arr) <= 1:
        return arr
    pivot = arr[len(arr) // 2]
    left = [x for x in arr if x < pivot]
    middle = [x for x in arr if x == pivot]
    right = [x for x in arr if x > pivot]
    return quicksort(left) + middle + quicksort(right)

# 测试快速排序算法
arr = [3, 6, 8, 10, 1, 2, 1]
print("排序结果：", quicksort(arr))

import numpy as np
from numba import cuda

# 定义CUDA内核函数
@cuda.jit
def add_arrays(a, b, c):
    idx = cuda.grid(1)
    if idx < a.size:
        c[idx] = a[idx] + b[idx]

# 创建数据
N = 100000
a = np.ones(N, dtype=np.float32)
b = np.ones(N, dtype=np.float32)
c = np.zeros(N, dtype=np.float32)

# 分配设备内存
a_device = cuda.to_device(a)
b_device = cuda.to_device(b)
c_device = cuda.device_array_like(c)

# 配置块和网格
threads_per_block = 256
blocks_per_grid = (a.size + (threads_per_block - 1)) // threads_per_block

# 启动内核
add_arrays[blocks_per_grid, threads_per_block](a_device, b_device, c_device)

# 复制结果回主机
c = c_device.copy_to_host()
print("计算结果：", c[:10])  # 显示前10个结果

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score

# 加载数据集
iris = load_iris()
X, y = iris.data, iris.target

# 数据集划分
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# 模型训练
knn = KNeighborsClassifier(n_neighbors=3)
knn.fit(X_train, y_train)

# 模型预测
y_pred = knn.predict(X_test)

# 模型评估
accuracy = accuracy_score(y_test, y_pred)
print("KNN模型准确率：", accuracy)

import numpy as np
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt

# 生成数据
X = np.array([[1, 2], [1, 4], [1, 0],
              [4, 2], [4, 4], [4, 0]])

# 模型训练
kmeans = KMeans(n_clusters=2, random_state=0).fit(X)

# 预测聚类结果
labels = kmeans.labels_
print("K均值聚类结果：", labels)

# 可视化聚类结果
plt.scatter(X[:, 0], X[:, 1], c=labels, cmap='viridis')
plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s=300, c='red')
plt.show()

import numpy as np
import random

# 环境定义
states = ["A", "B", "C", "D", "E", "F"]
actions = ["left", "right"]
rewards = {
    "A": {"left": 0, "right": 0},
    "B": {"left": 0, "right": 1},
    "C": {"left": 0, "right": 0},
    "D": {"left": 1, "right": 0},
    "E": {"left": 0, "right": 0},
    "F": {"left": 0, "right": 0}
}
Q = {}

# 初始化Q表
for state in states:
    Q[state] = {}
    for action in actions:
        Q[state][action] = 0

# Q学习算法
alpha = 0.1  # 学习率
gamma = 0.9  # 折扣因子
epsilon = 0.1  # 探索率

def choose_action(state):
    if random.uniform(0, 1) < epsilon:
        return random.choice(actions)
    else:
        return max(Q[state], key=Q[state].get)

def update_q(state, action, reward, next_state):
    predict = Q[state][action]
    target = reward + gamma * max(Q[next_state].values())
    Q[state][action] += alpha * (target - predict)

# 训练Q表
episodes = 1000
for _ in range(episodes):
    state = random.choice(states)
    while state != "F":
        action = choose_action(state)
        reward = rewards[state][action]
        next_state = "F" if action == "right" else state
        update_q(state, action, reward, next_state)
        state = next_state

print("Q表：", Q)

import numpy as np
from keras.models import Sequential
from keras.layers import Dense
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder

# 加载数据集
iris = load_iris()
X, y = iris.data, iris.target

# 独热编码标签
encoder = OneHotEncoder(sparse=False)
y = encoder.fit_transform(y.reshape(-1, 1))

# 数据集划分
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# 创建模型
model = Sequential()
model.add(Dense(10, input_dim=4, activation='relu'))
model.add(Dense(10, activation='relu'))
model.add(Dense(3, activation='softmax'))

# 编译模型
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

# 训练模型
model.fit(X_train, y_train, epochs=100, batch_size=10)

# 评估模型
_, accuracy = model.evaluate(X_test, y_test)
print("神经网络模型准确率：", accuracy)

from keras.datasets import mnist
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense

# 加载数据集
(X_train, y_train), (X_test, y_test) = mnist.load_data()

# 数据预处理
X_train = X_train.reshape(X_train.shape[0], 28, 28, 1).astype('float32') / 255
X_test = X_test.reshape(X_test.shape[0], 28, 28, 1).astype('float32') / 255
y_train = np_utils.to_categorical(y_train)
y_test = np_utils.to_categorical(y_test)

# 创建模型
model = Sequential()
model.add(Conv2D(32, (5, 5), input_shape=(28, 28, 1), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, (5, 5), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(1000, activation='relu'))
model.add(Dense(10, activation='softmax'))

# 编译模型
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

# 训练模型
model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=10, batch_size=200)

# 评估模型
_, accuracy = model.evaluate(X_test, y_test)
print("CNN模型准确率：", accuracy)

from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from keras.models import Sequential
from keras.layers import Embedding, LSTM, Dense
from sklearn.model_selection import train_test_split
import numpy as np

# 样本数据
texts = ['I love machine learning', 'Deep learning is awesome', 'I hate spam emails']
labels = [1, 1, 0]

# 文本预处理
tokenizer = Tokenizer(num_words=10000)
tokenizer.fit_on_texts(texts)
sequences = tokenizer.texts_to_sequences(texts)
X = pad_sequences(sequences, maxlen=10)
y = np.array(labels)

# 数据集划分
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# 创建模型
model = Sequential()
model.add(Embedding(10000, 128, input_length=10))
model.add(LSTM(128))
model.add(Dense(1, activation='sigmoid'))

# 编译模型
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

# 训练模型
model.fit(X_train, y_train, epochs=10, batch_size=32)

# 评估模型
_, accuracy = model.evaluate(X_test, y_test)
print("LSTM模型准确率：", accuracy)

import numpy as np
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import Adam

# 生成器模型
def build_generator():
    model = Sequential()
    model.add(Dense(256, input_dim=100, activation='relu'))
    model.add(Dense(512, activation='relu'))
    model.add(Dense(1024, activation='relu'))
    model.add(Dense(28*28, activation='tanh'))
    model.compile(loss='binary_crossentropy', optimizer=Adam(0.0002, 0.5))
    return model

# 判别器模型
def build_discriminator():
    model = Sequential()
    model.add(Dense(1024, input_dim=28*28, activation='relu'))
    model.add(Dense(512, activation='relu'))
    model.add(Dense(256, activation='relu'))
    model.add(Dense(1, activation='sigmoid'))
    model.compile(loss='binary_crossentropy', optimizer=Adam(0.0002, 0.5))
    return model

# 构建GAN模型
def build_gan(generator, discriminator):
    discriminator.trainable = False
    model = Sequential()
    model.add(generator)
    model.add(discriminator)
    model.compile(loss='binary_crossentropy', optimizer=Adam(0.0002, 0.5))
    return model

# 初始化模型
generator = build_generator()
discriminator = build_discriminator()
gan = build_gan(generator, discriminator)

# 训练GAN模型
def train_gan(epochs, batch_size):
    (X_train, _), (_, _) = mnist.load_data()
    X_train = (X_train.astype(np.float32) - 127.5) / 127.5
    X_train = X_train.reshape(X_train.shape[0], 28*28)
    
    for epoch in range(epochs):
        idx = np.random.randint(0, X_train.shape[0], batch_size)
        real_imgs = X_train[idx]
        
        noise = np.random.normal(0, 1, (batch_size, 100))
        fake_imgs = generator.predict(noise)
        
        d_loss_real = discriminator.train_on_batch(real_imgs, np.ones((batch_size, 1)))
        d_loss_fake = discriminator.train_on_batch(fake_imgs, np.zeros((batch_size, 1)))
        d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)
        
        noise = np.random.normal(0, 1, (batch_size, 100))
        g_loss = gan.train_on_batch(noise, np.ones((batch_size, 1)))
        
        if epoch % 1000 == 0:
            print(f"{epoch} [D loss: {d_loss}] [G loss: {g_loss}]")

# 开始训练
train_gan(epochs=10000, batch_size=64)

from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
from nltk.stem import PorterStemmer
import string

# 示例文本
text = "I love natural language processing. It's fascinating!"

# 分词
words = word_tokenize(text)

# 去除停用词
stop_words = set(stopwords.words('english'))
words = [word for word in words if word.lower() not in stop_words]

# 去除标点符号
words = [word for word in words if word not in string.punctuation]

# 词干化
ps = PorterStemmer()
words = [ps.stem(word) for word in words]

print("预处理后的文本：", words)

from transformers import GPT2LMHeadModel, GPT2Tokenizer

# 加载模型和分词器
model_name = "gpt2"
model = GPT2LMHeadModel.from_pretrained(model_name)
tokenizer = GPT2Tokenizer.from_pretrained(model_name)

# 输入文本
input_text = "Once upon a time"
input_ids = tokenizer.encode(input_text, return_tensors='pt')

# 生成文本
output = model.generate(input_ids, max_length=50, num_return_sequences=1)
output_text = tokenizer.decode(output[0], skip_special_tokens=True)

print("生成的文本：", output_text)

from textblob import TextBlob

# 示例文本
text = "I love this product! It's amazing."

# 情感分析
blob = TextBlob(text)
sentiment = blob.sentiment

print("情感分析结果：", sentiment)

import requests
from bs4 import BeautifulSoup

# 目标URL
url = "https://example.com"

# 发起请求
response = requests.get(url)

# 解析HTML内容
soup = BeautifulSoup(response.content, 'html.parser')

# 提取数据
titles = soup.find_all('h2')
for title in titles:
    print("标题：", title.text)

import pandas as pd

# 示例数据
data = {
    'name': ['Alice', 'Bob', 'Charlie', 'David', 'Eve'],
    'age': [24, 27, 22, 32, 29],
    'city': ['New York', 'San Francisco', 'Los Angeles', None, 'Chicago']
}
df = pd.DataFrame(data)

# 缺失值处理
df['city'].fillna('Unknown', inplace=True)

# 数据规范化
df['age'] = (df['age'] - df['age'].mean()) / df['age'].std()

print("清洗后的数据：\n", df)

from keras.preprocessing.image import ImageDataGenerator
import matplotlib.pyplot as plt
from keras.datasets import mnist

# 加载数据集
(X_train, y_train), (_, _) = mnist.load_data()
X_train = X_train.reshape(X_train.shape[0], 28, 28, 1).astype('float32')

# 数据增强
datagen = ImageDataGenerator(
    rotation_range=10,
    zoom_range=0.1,
    width_shift_range=0.1,
    height_shift_range=0.1
)
datagen.fit(X_train)

# 显示增强后的图像
for X_batch, y_batch in datagen.flow(X_train, y_train, batch_size=9):
    for i in range(0, 9):
        plt.subplot(330 + 1 + i)
        plt.imshow(X_batch[i].reshape(28, 28), cmap=plt.get_cmap('gray'))
    plt.show()
    break

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score

# 加载数据集
iris = load_iris()
X, y = iris.data, iris.target

# 数据集划分
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# 模型训练
model = RandomForestClassifier(n_estimators=100)
model.fit(X_train, y_train)

# 模型预测
y_pred = model.predict(X_test)

# 模型评估
accuracy = accuracy_score(y_test, y_pred)
print("随机森林模型准确率：", accuracy)

from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras.optimizers import Adam

# 创建模型
model = Sequential()
model.add(Dense(64, input_dim=20, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(1, activation='sigmoid'))

# 编译模型
model.compile(loss='binary_crossentropy', optimizer=Adam(lr=0.001), metrics=['accuracy'])

# 训练模型
model.fit(X_train, y_train, epochs=50, batch_size=32, validation_split=0.2)

from flask import Flask, request, jsonify
import pickle

# 加载模型
model = pickle.load(open('model.pkl', 'rb'))

# 创建Flask应用
app = Flask(__name__)

# 定义预测接口
@app.route('/predict', methods=['POST'])
def predict():
    data = request.get_json(force=True)
    prediction = model.predict([data['features']])
    output = {'prediction': int(prediction[0])}
    return jsonify(output)

# 启动应用
if __name__ == '__main__':
    app.run(debug=True)

from keras.datasets import cifar10
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
from keras.utils import np_utils

# 加载数据集
(X_train, y_train), (X_test, y_test) = cifar10.load_data()

# 数据预处理
X_train = X_train.astype('float32') / 255
X_test = X_test.astype('float32') / 255
y_train = np_utils.to_categorical(y_train, 10)
y_test = np_utils.to_categorical(y_test, 10)

# 创建模型
model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape=(32, 32, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(512, activation='relu'))
model.add(Dense(10, activation='softmax'))

# 编译模型
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

# 训练模型
model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=10, batch_size=64)

# 评估模型
_, accuracy = model.evaluate(X_test, y_test)
print("CIFAR-10图像分类模型准确率：", accuracy)

from transformers import BertTokenizer, BertForSequenceClassification
from transformers import Trainer, TrainingArguments
from sklearn.model_selection import train_test_split
import torch

# 示例数据
texts = ["I love AI", "AI is the future", "I hate spam emails"]
labels = [1, 1, 0]

# 加载预训练模型和分词器
model_name = "bert-base-uncased"
tokenizer = BertTokenizer.from_pretrained(model_name)
model = BertForSequenceClassification.from_pretrained(model_name, num_labels=2)

# 数据预处理
inputs = tokenizer(texts, return_tensors="pt", padding=True, truncation=True)
inputs['labels'] = torch.tensor(labels)

# 数据集划分
train_inputs, val_inputs, train_labels, val_labels = train_test_split(inputs['input_ids'], inputs['labels'], test_size=0.3, random_state=42)

# 创建数据集
train_dataset = torch.utils.data.TensorDataset(train_inputs, train_labels)
val_dataset = torch.utils.data.TensorDataset(val_inputs, val_labels)

# 设置训练参数
training_args = TrainingArguments(output_dir='./results', num_train_epochs=3, per_device_train_batch_size=16, per_device_eval_batch_size=16, warmup_steps=500, weight_decay=0.01, logging_dir='./logs')

# 创建Trainer
trainer = Trainer(model=model, args=training_args, train_dataset=train_dataset, eval_dataset=val_dataset)

# 训练模型
trainer.train()

import gym
import numpy as np

# 创建环境
env = gym.make('CartPole-v1')

# Q学习算法
Q = np.zeros((env.observation_space.shape[0], env.action_space.n))
alpha = 0.1  # 学习率
gamma = 0.99  # 折扣因子
epsilon = 0.1  # 探索率

def choose_action(state):
    if np.random.uniform(0, 1) < epsilon:
        return env.action_space.sample()
    else:
        return np.argmax(Q[state, :])

def update_q(state, action, reward, next_state):
    predict = Q[state, action]
    target = reward + gamma * np.max(Q[next_state, :])
    Q[state, action] += alpha * (target - predict)

# 训练Q表
episodes = 1000
for _ in range(episodes):
    state = env.reset()
    done = False
    while not done:
        action = choose_action(state)
        next_state, reward, done, _ = env.step(action)
        update_q(state, action, reward, next_state)
        state = next_state

print("Q表：", Q)

import numpy as np

# 模拟本地数据
def generate_data(size):
    X = np.random.rand(size, 10)
    y = (np.sum(X, axis=1) > 5).astype(int)
    return X, y

# 本地模型训练
def train_local_model(X, y):
    model = LogisticRegression()
    model.fit(X, y)
    return model.coef_, model.intercept_

# 模拟客户端数据
clients = 5
local_models = []
for _ in range(clients):
    X, y = generate_data(100)
    coef, intercept = train_local_model(X, y)
    local_models.append((coef, intercept))

# 聚合模型参数
global_coef = np.mean([model[0] for model in local_models], axis=0)
global_intercept = np.mean([model[1] for model in local_models], axis=0)

print("全局模型参数：", global_coef, global_intercept)

from torchvision import datasets, transforms, models
from torch.utils.data import DataLoader
import torch
import torch.nn as nn
import torch.optim as optim

# 数据预处理
transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))])
dataset = datasets.MNIST(root='./data', train=True, download=True, transform=transform)
dataloader = DataLoader(dataset, batch_size=64, shuffle=True)

# 定义自监督学习模型
class Autoencoder(nn.Module):
    def __init__(self):
        super(Autoencoder, self).__init__()
        self.encoder = nn.Sequential(nn.Linear(28*28, 128), nn.ReLU(), nn.Linear(128, 64))
        self.decoder = nn.Sequential(nn.Linear(64, 128), nn.ReLU(), nn.Linear(128, 28*28))
    
    def forward(self, x):
        x = x.view(-1, 28*28)
        encoded = self.encoder(x)
        decoded = self.decoder(encoded)
        return decoded.view(-1, 1, 28, 28)

# 初始化模型、损失函数和优化器
model = Autoencoder()
criterion = nn.MSELoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

# 训练模型
epochs = 5
for epoch in range(epochs):
    for data in dataloader:
        inputs, _ = data
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, inputs)
        loss.backward()
        optimizer.step()
    print(f"Epoch [{epoch+1}/{epochs}], Loss: {loss.item():.4f}")

import numpy as np
from sklearn.datasets import load_iris
from sklearn.ensemble import RandomForestClassifier
import lime
import lime.lime_tabular

# 加载数据集
iris = load_iris()
X, y = iris.data, iris.target

# 模型训练
model = RandomForestClassifier(n_estimators=100)
model.fit(X, y)

# 使用LIME解释模型
explainer = lime.lime_tabular.LimeTabularExplainer(X, feature_names=iris.feature_names, class_names=iris.target_names, discretize_continuous=True)
i = 25
exp = explainer.explain_instance(X[i], model.predict_proba, num_features=2, top_labels=1)
exp.show_in_notebook(show_all=False)

import numpy as np
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
from keras.preprocessing.image import ImageDataGenerator

# 创建模型
model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape=(64, 64, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dense(1, activation='sigmoid'))

# 编译模型
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

# 数据增强
train_datagen = ImageDataGenerator(rescale=1./255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True)
test_datagen = ImageDataGenerator(rescale=1./255)

# 加载训练数据
training_set = train_datagen.flow_from_directory('dataset/training_set', target_size=(64, 64), batch_size=32, class_mode='binary')
test_set = test_datagen.flow_from_directory('dataset/test_set', target_size=(64, 64), batch_size=32, class_mode='binary')

# 训练模型
model.fit(training_set, steps_per_epoch=8000, epochs=25, validation_data=test_set, validation_steps=2000)

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import roc_auc_score

# 加载数据集
data = pd.read_csv('credit_data.csv')
X = data.drop('default', axis=1)
y = data['default']

# 数据集划分
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# 模型训练
model = LogisticRegression()
model.fit(X_train, y_train)

# 模型预测
y_pred_prob = model.predict_proba(X_test)[:, 1]

# 模型评估
auc = roc_auc_score(y_test, y_pred_prob)
print("信用评分模型AUC：", auc)

import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

# 加载数据集
data = pd.read_csv('equipment_data.csv')
X = data.drop('failure', axis=1)
y = data['failure']

# 数据集划分
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# 模型训练
model = RandomForestClassifier(n_estimators=100)
model.fit(X_train, y_train)

# 模型预测
y_pred = model.predict(X_test)

# 模型评估
accuracy = accuracy_score(y_test, y_pred)
print("设备故障预测模型准确率：", accuracy)