机器学习之逻辑回归(Logistic Regression)

标签：预测   导致   多项式   metrics   datasets   plot   test   cti   nts   

1.  
   """逻辑回归中的Sigmoid函数"""
2.  
   import numpy as np
3.  
   import matplotlib.pyplot as plt
4.  
    
5.  
   def sigmoid(t):
6.  
   return 1/(1+np.exp(-t))
7.  
    
8.  
   x=np.linspace(-10,10,500)
9.  
   y=sigmoid(x)
10.  
     
11.  
    plt.plot(x,y)
12.  
    plt.show()

 结果：

 逻辑回归损失函数的梯度：

逻辑回归算法： 

1.  
   import numpy as np
2.  
   from metrics import accuracy_score
3.  
    
4.  
   class LogisticRegression:
5.  
    
6.  
   def __init__(self):
7.  
   """初始化Logistic Regression模型"""
8.  
   self.coef_ = None
9.  
   self.intercept_ = None
10.  
    self._theta = None
11.  
     
12.  
    def _sigmoid(self,t):
13.  
    return 1. / (1. + np.exp(-t))
14.  
     
15.  
    def fit(self, X_train, y_train, eta=0.01, n_iters=1e4):
16.  
    """根据训练数据集X_train, y_train, 使用梯度下降法训练Linear Regression模型"""
17.  
    assert X_train.shape[0] == y_train.shape[0], \
18.  
    "the size of X_train must be equal to the size of y_train"
19.  
     
20.  
     
21.  
     
22.  
    def J(theta, X_b, y):
23.  
    """求损失函数"""
24.  
    y_hat=self._sigmoid(X_b.dot(theta))
25.  
    try:
26.  
    return -np.sum(y*np.log(y_hat) + (1-y)*np.log(1-y_hat))/ len(y)
27.  
    except:
28.  
    return float(‘inf‘)
29.  
     
30.  
    def dJ(theta, X_b, y):
31.  
    """求梯度"""
32.  
    # res = np.empty(len(theta))
33.  
    # res[0] = np.sum(X_b.dot(theta) - y)
34.  
    # for i in range(1, len(theta)):
35.  
    # res[i] = (X_b.dot(theta) - y).dot(X_b[:, i])
36.  
    # return res * 2 / len(X_b)
37.  
    return X_b.T.dot(self._sigmoid(X_b.dot(theta)) - y) / len(X_b)
38.  
     
39.  
    def gradient_descent(X_b, y, initial_theta, eta, n_iters=1e4, epsilon=1e-8):
40.  
    """使用批量梯度下降法寻找theta"""
41.  
    theta = initial_theta
42.  
    cur_iter = 0
43.  
     
44.  
    while cur_iter < n_iters:
45.  
    gradient = dJ(theta, X_b, y)
46.  
    last_theta = theta
47.  
    theta = theta - eta * gradient
48.  
    if (abs(J(theta, X_b, y) - J(last_theta, X_b, y)) < epsilon):
49.  
    break
50.  
     
51.  
    cur_iter += 1
52.  
     
53.  
    return theta
54.  
     
55.  
    X_b = np.hstack([np.ones((len(X_train), 1)), X_train])
56.  
    initial_theta = np.zeros(X_b.shape[1])
57.  
    self._theta = gradient_descent(X_b, y_train, initial_theta, eta, n_iters)
58.  
     
59.  
    self.intercept_ = self._theta[0]
60.  
    self.coef_ = self._theta[1:]
61.  
     
62.  
    return self
63.  
     
64.  
    def predict_proba(self, X_predict):
65.  
    """给定待预测数据集X_predict，返回表示X_predict的结果概率向量"""
66.  
    assert self.intercept_ is not None and self.coef_ is not None, \
67.  
    "must fit before predict!"
68.  
    assert X_predict.shape[1] == len(self.coef_), \
69.  
    "the feature number of X_predict must be equal to X_train"
70.  
     
71.  
    X_b = np.hstack([np.ones((len(X_predict), 1)), X_predict])
72.  
    return self._sigmoid(X_b.dot(self._theta))
73.  
     
74.  
    def predict(self, X_predict):
75.  
    """给定待预测数据集X_predict，返回表示X_predict的结果向量"""
76.  
    assert self.intercept_ is not None and self.coef_ is not None, \
77.  
    "must fit before predict!"
78.  
    assert X_predict.shape[1] == len(self.coef_), \
79.  
    "the feature number of X_predict must be equal to X_train"
80.  
     
81.  
    proba=self.predict_proba(X_predict)
82.  
    return np.array(proba>=0.5,dtype=‘int‘)
83.  
     
84.  
    def score(self, X_test, y_test):
85.  
    """根据测试数据集 X_test 和 y_test 确定当前模型的准确度"""
86.  
     
87.  
    y_predict = self.predict(X_test)
88.  
    return accuracy_score(y_test, y_predict)
89.  
     
90.  
    def __repr__(self):
91.  
    return "LogisticRegression()"

1.  
   """实现逻辑回归"""
2.  
   import numpy as np
3.  
   import matplotlib.pyplot as plt
4.  
   from sklearn import datasets
5.  
    
6.  
   iris=datasets.load_iris()
7.  
   X=iris.data
8.  
   y=iris.target
9.  
    
10.  
    X=X[y<2,:2]
11.  
    y=y[y<2]
12.  
     
13.  
    plt.scatter(X[y==0,0],X[y==0,1],color=‘red‘)
14.  
    plt.scatter(X[y==1,0],X[y==1,1],color=‘blue‘)
15.  
    plt.show()
16.  
     
17.  
    """使用逻辑回归"""
18.  
    from model_selection import train_test_split
19.  
    from LogisticRegression import LogisticRegression
20.  
     
21.  
    X_train,X_test,y_train,y_test=train_test_split(X,y,seed=666)
22.  
    log_reg=LogisticRegression()
23.  
    log_reg.fit(X_train,y_train)
24.  
    print(log_reg.score(X_test,y_test))
25.  
    print(log_reg.predict_proba(X_test))

 结果：

1.  
   E:\pythonspace\KNN_function\venv\Scripts\python.exe E:/pythonspace/KNN_function/try.py
2.  
   1.0
3.  
   [0.92972035 0.98664939 0.14852024 0.17601199 0.0369836 0.0186637
4.  
   0.04936918 0.99669244 0.97993941 0.74524655 0.04473194 0.00339285
5.  
   0.26131273 0.0369836 0.84192923 0.79892262 0.82890209 0.32358166
6.  
   0.06535323 0.20735334]
7.  
    
8.  
   Process finished with exit code 0

逻辑回归中的决策边界和添加多项式特征：

1.  
   """在逻辑回归中添加多项式特征"""
2.  
   import numpy as np
3.  
   import matplotlib.pyplot as plt
4.  
    
5.  
   np.random.seed(666)
6.  
   X=np.random.normal(0,1,size=(100,2))
7.  
   y=np.array(X[:,0]**2+X[:,1]**2<1.5,dtype=‘int‘)
8.  
    
9.  
    
10.  
    """使用逻辑回归"""
11.  
    from LogisticRegression import LogisticRegression
12.  
     
13.  
    log_reg=LogisticRegression()
14.  
    log_reg.fit(X,y)
15.  
     
16.  
    """绘制思路"""
17.  
    def plot_decision_boundary(model,axis):
18.  
    x0,x1 = np.meshgrid(
19.  
    np.linspace(axis[0],axis[1],int((axis[1]-axis[0])*100)),
20.  
    np.linspace(axis[2],axis[3],int((axis[3]-axis[2])*100))
21.  
    )
22.  
    X_new = np.c_[x0.ravel(),x1.ravel()]
23.  
    y_predict = model.predict(X_new)
24.  
    zz = y_predict.reshape(x0.shape)
25.  
    from matplotlib.colors import ListedColormap
26.  
    custom_cmap = ListedColormap([‘#EF9A9A‘,‘#FFF59D‘,‘#90CAF9‘])
27.  
    plt.contourf(x0,x1,zz,linewidth=5,cmap=custom_cmap)
28.  
     
29.  
    plot_decision_boundary(log_reg,axis=[-4,4,-4,4])
30.  
    plt.scatter(X[y==0,0],X[y==0,1])
31.  
    plt.scatter(X[y==1,0],X[y==1,1])
32.  
    plt.show()
33.  
     
34.  
     
35.  
    """添加特征值，即升维"""
36.  
    from sklearn.preprocessing import PolynomialFeatures
37.  
    from sklearn.preprocessing import StandardScaler
38.  
    from sklearn.pipeline import Pipeline
39.  
    def PolynomialLogisticRegression(degree):
40.  
    return Pipeline([
41.  
    (‘Poly‘,PolynomialFeatures(degree=degree)),
42.  
    (‘std_scaler‘,StandardScaler()),
43.  
    (‘Logistic‘,LogisticRegression())
44.  
    ])
45.  
    poly_log_reg = PolynomialLogisticRegression(degree=2)
46.  
    poly_log_reg.fit(X,y)
47.  
    plot_decision_boundary(poly_log_reg,axis=[-4,4,-4,4])
48.  
    plt.scatter(X[y==0,0],X[y==0,1])
49.  
    plt.scatter(X[y==1,0],X[y==1,1])
50.  
    plt.show()

 结果：

1.  
   """逻辑回归中使用正则化"""
2.  
   import numpy as np
3.  
   import matplotlib.pyplot as plt
4.  
   from sklearn.model_selection import train_test_split
5.  
   from sklearn.linear_model import LogisticRegression
6.  
   from sklearn.preprocessing import StandardScaler
7.  
   from sklearn.pipeline import Pipeline
8.  
   from sklearn.preprocessing import PolynomialFeatures
9.  
    
10.  
    np.random.seed(666)
11.  
    X=np.random.normal(0,1,size=(200,2))
12.  
    y=np.array(X[:,0]**2+X[:,1]<1.5,dtype=‘int‘)
13.  
    for _ in range(20):
14.  
    y[np.random.randint(200)] = 1
15.  
    plt .scatter(X[y==0,0],X[y==0,1])
16.  
    plt .scatter(X[y==1,0],X[y==1,1])
17.  
    plt.show()
18.  
     
19.  
    X_train,X_test,y_train,y_test=train_test_split(X,y)
20.  
    log_reg=LogisticRegression()
21.  
    log_reg.fit(X,y)
22.  
    def plot_decision_boundary(model,axis):
23.  
    x0,x1 = np.meshgrid(
24.  
    np.linspace(axis[0],axis[1],int((axis[1]-axis[0])*100)),
25.  
    np.linspace(axis[2],axis[3],int((axis[3]-axis[2])*100))
26.  
    )
27.  
    X_new = np.c_[x0.ravel(),x1.ravel()]
28.  
    y_predict = model.predict(X_new)
29.  
    zz = y_predict.reshape(x0.shape)
30.  
    from matplotlib.colors import ListedColormap
31.  
    custom_cmap = ListedColormap([‘#EF9A9A‘,‘#FFF59D‘,‘#90CAF9‘])
32.  
    plt.contourf(x0,x1,zz,linewidth=5,cmap=custom_cmap)
33.  
     
34.  
    def PolynomialLogisticRegression(degree,C=1.0,penalty=‘l2‘):
35.  
    return Pipeline([
36.  
    (‘Poly‘,PolynomialFeatures(degree=degree)),
37.  
    (‘std_scaler‘,StandardScaler()),
38.  
    (‘Logistic‘,LogisticRegression(C=C,penalty=penalty))
39.  
    ])
40.  
    poly_log_reg = PolynomialLogisticRegression(degree=20,C=0.1,penalty=‘l1‘)
41.  
    poly_log_reg.fit(X_train,y_train)
42.  
     
43.  
    plot_decision_boundary(poly_log_reg,axis=[-4,4,-4,4])
44.  
    plt.scatter(X[y==0,0],X[y==0,1])
45.  
    plt.scatter(X[y==1,0],X[y==1,1])
46.  
    plt.show()

 结果

应用OVR和OVO使逻辑回归处理多分类问题
 

1.  
   """OVR和OVO"""
2.  
   #为了数据可视化方便，我们只使用鸢尾花数据集的前两列特征
3.  
   from sklearn import datasets
4.  
   from sklearn.linear_model import LogisticRegression
5.  
   from sklearn.model_selection import train_test_split
6.  
   import matplotlib.pyplot as plt
7.  
   import numpy as np
8.  
    
9.  
   iris = datasets.load_iris()
10.  
    X = iris[‘data‘][:,:2]
11.  
    y = iris[‘target‘]
12.  
    X_train,X_test,y_train,y_test=train_test_split(X,y,random_state=666)
13.  
     
14.  
     
15.  
    #log_reg = LogisticRegression(multi_class=‘ovr‘) #传入multi_class参数可以指定使用ovr或ovo，默认ovr #由于只使用前两列特征，导致分类准确度较低
16.  
    log_reg = LogisticRegression(multi_class=‘ovr‘,solver=‘newton-cg‘)
17.  
    log_reg.fit(X_train,y_train)
18.  
    log_reg.score(X_test,y_test)
19.  
    def plot_decision_boundary(model,axis):
20.  
    x0,x1 = np.meshgrid(
21.  
    np.linspace(axis[0],axis[1],int((axis[1]-axis[0])*100)),
22.  
    np.linspace(axis[2],axis[3],int((axis[3]-axis[2])*100))
23.  
    )
24.  
    X_new = np.c_[x0.ravel(),x1.ravel()]
25.  
    y_predict = model.predict(X_new)
26.  
    zz = y_predict.reshape(x0.shape)
27.  
    from matplotlib.colors import ListedColormap
28.  
    custom_cmap = ListedColormap([‘#EF9A9A‘,‘#FFF59D‘,‘#90CAF9‘])
29.  
    plt.contourf(x0,x1,zz,linewidth=5,cmap=custom_cmap)
30.  
     
31.  
    plot_decision_boundary(log_reg,axis=[4,8.5,1.5,4.5])
32.  
    plt.scatter(X[y==0,0],X[y==0,1])
33.  
    plt.scatter(X[y==1,0],X[y==1,1])
34.  
    plt.scatter(X[y==2,0],X[y==2,1])
35.  
    plt.show()
36.  
     
37.  
     
38.  
     
39.  
    """使用全部数据 OVR and OVO"""
40.  
    from sklearn.multiclass import OneVsOneClassifier
41.  
    from sklearn.multiclass import OneVsRestClassifier
42.  
     
43.  
    from sklearn import datasets
44.  
    from sklearn.linear_model import LogisticRegression
45.  
    from sklearn.model_selection import train_test_split
46.  
    iris = datasets.load_iris()
47.  
    X = iris.data
48.  
    y = iris.target
49.  
    X_train,X_test,y_train,y_test=train_test_split(X,y,random_state=666)
50.  
     
51.  
    ovr = OneVsRestClassifier(log_reg) #参数为二分类器
52.  
    ovr.fit(X_train,y_train)
53.  
    print(ovr.score(X_test,y_test))
54.  
    ovo = OneVsOneClassifier(log_reg)
55.  
    ovo.fit(X_train,y_train)
56.  
    print(ovo.score(X_test,y_test))

结果：

1.  
   E:\pythonspace\KNN_function\venv\Scripts\python.exe E:/pythonspace/KNN_function/try.py
2.  
   E:\pythonspace\KNN_function\venv\lib\site-packages\matplotlib\contour.py:960: UserWarning: The following kwargs were not used by contour: ‘linewidth‘
3.  
   s)
4.  
   0.9736842105263158
5.  
   1.0
6.  
    
7.  
   Process finished with exit code 0

机器学习之逻辑回归(Logistic Regression)

标签：预测   导致   多项式   metrics   datasets   plot   test   cti   nts   

原文地址：https://www.cnblogs.com/kekexuanxaun/p/9459365.html