机器学习模板代码（期末考试复习）自用存档

机器学习复习代码

利用sklearn实现knn

import numpy as np
import pandas as pd
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import GridSearchCV

def model_selection(x_train, y_train):
    ## 第一个是网格搜索
    ## p是选择查找方式:1是欧式距离   2是曼哈顿距离
    params = {'n_neighbors': [3,5,7], 'p': [1,2]}
    model = KNeighborsClassifier()
    gs = GridSearchCV(model, params, verbose=2, cv=5)
    gs.fit(x_train, y_train)
    print("Best Model:", gs.best_params_, "Accuracy:", gs.best_score_)
    print(gs.best_estimator_)
    return gs.best_estimator_

def read():
    filename = r"data/shuixianhua.xlsx"
    data = pd.read_excel(filename, header=None)
    ## iloc[行,列]
    x1 = data.iloc[1:, [0, 1]].values
    x2 = data.iloc[1:, [3, 4]].values
    # print(x2)
    y1 = data.iloc[1:, 2].values
    y2 = data.iloc[1:, 5].values
    x = np.vstack((x1, x2))  # 竖向合并
    print("x:")
    print(x)
    y = np.hstack((y1, y2))  # 横向合并
    print("y:")
    print(y)

## 这里是因为我把excel的y理解成string类型了，如果正常读可以不加这个
    ## 将y转为数值的int
    y = y.astype(int)
    
    return x, y


if __name__ == '__main__':
    x, y = read()
    best_model = model_selection(x, y)

利用sklearn实现线性回归

数据集展示

import pandas as pd
from sklearn.linear_model import LinearRegression
import numpy as np
def MAE(y,y_pre):
    return np.mean(np.abs(y-y_pre))
def MSE(y,y_pred):
    return np.mean((y-y_pred)**2)
def RMSE(y,y_pred):
    return np.sqrt(MSE(y,y_pred))
def MAPE(y,y_pred):
    return np.mean(np.abs(y-y_pred)/y)
def R2(y,y_pred):
    u=np.sum((y-y_pred)**2)
    v=np.sum((y-np.mean(y_pred))**2)
    return 1-(u/v)
def judege(name,y,y_pre):
    mae=MAE(y,y_pre)
    mse=MSE(y,y_pre)
    rmse=RMSE(y,y_pre)
    mape=MAPE(y,y_pre)
    r2=R2(y,y_pre)
    print(f"{name}的MAE:{mae},MSE:{mse},RMSE:{rmse}.MAPE:{mape},R2:{r2}")

def read():
    filename = r"../data/ComposePlot.xlsx"
    data=pd.read_excel(filename,header=None)
    x1 = data.iloc[2:, [0,]].values
    y1 = data.iloc[2:,1].values

    x2 = data.iloc[2:,[2,]].values
    y2 = data.iloc[2:,3].values

    x3 = data.iloc[2:,[4,]].values
    y3 = data.iloc[2:,5].values

    x4 = data.iloc[2:,[6,]].values
    y4 = data.iloc[2:,7].values
    return x1,y1,x2,y2,x3,y3,x4,y4

def getModel(x,y):
    model = LinearRegression()
    model.fit(x,y)
    return model

def main(x1, y1, x2, y2, x3, y3, x4, y4):
    model1 = getModel(x1,y1)
    model2 = getModel(x2, y2)
    model3 =getModel(x3,y3)
    model4 =getModel(x4,y4)
    judege("mode1",y1,model1.predict(x1))
    judege("mode2",y2,model2.predict(x2))
    judege("mode3",y3,model3.predict(x3))
    judege("mode4",y4,model4.predict(x4))




if __name__ == '__main__':
    x1, y1, x2, y2, x3, y3, x4, y4 = read()
    main(x1, y1, x2, y2, x3, y3, x4, y4)

利用sklearn实现逻辑回归

数据集展示

import pandas as pd
import numpy as np
from sklearn.linear_model import LogisticRegression


def main(x,y):
    model=LogisticRegression()
    model.fit(x,y)
    print(model.predict(x))
def read():
    filename = "data/student.xlsx"
    data=pd.read_excel(filename,header=None)
    x=data.iloc[1:,[0,1]].values
    y=data.iloc[1:,2].values
    print(x)
    print(y)
    return x,y
if __name__ =='__main__':
    x,y=read()
    main(x,y)

利用sklearn实现SVM(向量机)

from sklearn.svm import SVC
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
import numpy as np
from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score, \
    f1_score


def load_data(): #导入的尾花
    data = load_iris()
    x, y = data.data, data.target
    x_train, x_test, y_train, y_test = \
        train_test_split(x, y, test_size=0.3,
                         shuffle=True, random_state=20)
    return x, y, x_train, x_test, y_train, y_test

## 无脑写这个就行
def model_selection(x_train, y_train):
    model = SVC()
    paras = {'C': np.arange(1, 10, 5),
             # rbf:高斯核函数   linear:线性核函数  poly:多项式核函数
             'kernel': ['rbf', 'linear', 'poly'],
             'degree': np.arange(1, 10, 2),
             'gamma': ['scale', 'auto'],
             'coef0': np.arange(-10, 10, 5)
             }
    gs = GridSearchCV(model, paras, cv=3, verbose=2, n_jobs=3)
    gs.fit(x_train, y_train)
    print('best score:', gs.best_score_)
    print('best parameters:', gs.best_params_)
    return gs.best_params_


def train(x_train, x_test, y_train, y_test, C, gamma, kernel):
    model = SVC(C=C, kernel=kernel, gamma=gamma)
    model.fit(x_train, y_train)
    y_pred = model.predict(x_test)
    # 生成混淆矩阵
    confusion = confusion_matrix(y_test, y_pred)

    print("混淆矩阵:\n", confusion)
    # 生成分类报告，包括准确率、精度、召回率和 F1 得分
    accuracy = accuracy_score(y_test, y_pred)
    precision = precision_score(y_test, y_pred, average='weighted')
    recall = recall_score(y_test, y_pred, average='weighted')
    f1 = f1_score(y_test, y_pred, average='weighted')
    print("准确率Accuracy:", accuracy)
    print("精度Precision:", precision)
    print("召回率Recall:", recall)
    print("FScore:", f1)

if __name__ == '__main__':
    X, Y, x_train, x_test, y_train, y_test = load_data()
    best_params = model_selection(x_train, y_train)
    ## 这步骤也是无脑写就行
    train(x_train, x_test, y_train, y_test, best_params["C"], best_params["gamma"], best_params["kernel"])

小知识点扩展

将数据分成训练集和测试集

   x_train, x_test, y_train, y_test = \
        train_test_split(x, y, test_size=0.3,
                         shuffle=True, random_state=20)

来自chargpt的解释

是的，这段代码用于将数据集分成训练集和测试集。具体地说，它使用train_test_split函数将输入的特征数据x和标签数据y分成四个部分：x_train（训练集的特征数据）、x_test（测试集的特征数据）、y_train（训练集的标签数据）和y_test（测试集的标签数据）。

以下是这段代码的主要参数和作用：

• x是特征数据。
• y是标签数据。
• test_size=0.3指定了测试集的比例，这里是总数据的30%将被用作测试集。
• shuffle=True表示在分割数据之前对数据进行洗牌，以确保数据的随机性。
• random_state=20是一个随机种子，用于确保每次运行代码时分割数据的结果都相同，这有助于复现实验结果。

综上所诉，只要背就好了，还有参数的意思

归一化

def hypo(x,w,b):
    z=np.matmul(x,w)+b
    h_x=1/(1+np.exp(-z))
    h_x=(h_x>=0.5)*1
    return h_x

书上p49,我也不太懂归一化的用法，其中z=wx+b

从0实现线性回归

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt


# 添加归一化函数
def normalize_data(data):
    min_val = np.min(data)
    max_val = np.max(data)
    normalized_data = (data - min_val) / (max_val - min_val)
    return normalized_data


def prediction(X, W, bias):
    return np.matmul(X, W) + bias


def cost_function(X, y, W, bias):
    m, n = X.shape
    y_hat = prediction(X, W, bias)
    return 0.5 * (1 / m) * np.sum((y - y_hat) ** 2)


def gradient_descent(X, y, W, bias, alpha):
    m, n = X.shape
    y_hat = prediction(X, W, bias)
    grad_w = -(1 / m) * np.matmul(X.T, (y - y_hat))
    grad_b = -(1 / m) * np.sum(y - y_hat)
    W = W - alpha * grad_w
    bias = bias - alpha * grad_b
    return W, bias


def train(X, y, ite=200):
    m, n = X.shape
    W, b, alpha, costs = np.random.randn(n, 1), 0.1, 0.2, []

    for i in range(ite):
        costs.append(cost_function(X, y, W, b))
        W, b = gradient_descent(X, y, W, b, alpha)

    return costs


def read():
    filename = r"../../data/easy_test.xlsx"
    data = pd.read_excel(filename, header=None)
    x = data.iloc[2:, [0, ]].values
    y = data.iloc[2:, 1].values

    # 对特征数据 x 进行归一化
    x_normalized = normalize_data(x)

    return x_normalized, y


if __name__ == '__main__':
    x, y = read()
    costs = train(x, y)
    # print(costs)
  # 绘制损失曲线
    plt.figure()
    plt.plot(range(len(costs)), costs, marker='o', linestyle='-', color='b', label='Training Loss')
    plt.xlabel('Iteration')
    plt.ylabel('Cost')
    plt.title('Training Loss')
    plt.legend()
    plt.grid(True)
    plt.show()

从0实现逻辑回归