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添加决策树，朴素贝叶斯和回归的sklearn版本，logistic回归的sklearn版本
This commit is contained in:
chenyyx
@@ -19,36 +19,38 @@ import matplotlib.pyplot as plt
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# 创建一个随机的数据集
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# 参考 https://docs.scipy.org/doc/numpy-1.6.0/reference/generated/numpy.random.mtrand.RandomState.html
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rng = np.random.RandomState(1)
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print 'lalalalala===', rng
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# print 'lalalalala===', rng
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# rand() 是给定形状的随机值，rng.rand(80, 1)即矩阵的形状是 80行，1列
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# sort()
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X = np.sort(5 * rng.rand(80, 1), axis=0)
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print 'X=', X
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# print 'X=', X
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y = np.sin(X).ravel()
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print 'y=', y
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# print 'y=', y
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y[::5] += 3 * (0.5 - rng.rand(16))
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print 'yyy=', y
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# print 'yyy=', y
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# 拟合回归模型
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regr_1 = DecisionTreeRegressor(max_depth=2)
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# regr_1 = DecisionTreeRegressor(max_depth=2)
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# 保持 max_depth=5 不变，增加 min_samples_leaf=6 的参数，效果进一步提升了
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regr_2 = DecisionTreeRegressor(max_depth=5)
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regr_3 = DecisionTreeRegressor(max_depth=3)
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regr_1.fit(X, y)
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regr_2 = DecisionTreeRegressor(min_samples_leaf=6)
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# regr_3 = DecisionTreeRegressor(max_depth=4)
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# regr_1.fit(X, y)
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regr_2.fit(X, y)
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regr_3.fit(X, y)
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# regr_3.fit(X, y)
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# 预测
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X_test = np.arange(0.0, 5.0, 0.01)[:, np.newaxis]
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y_1 = regr_1.predict(X_test)
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# y_1 = regr_1.predict(X_test)
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y_2 = regr_2.predict(X_test)
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y_3 = regr_3.predict(X_test)
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# y_3 = regr_3.predict(X_test)
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# 绘制结果
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plt.figure()
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plt.scatter(X, y, c="darkorange", label="data")
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plt.plot(X_test, y_1, color="cornflowerblue", label="max_depth=2", linewidth=2)
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# plt.plot(X_test, y_1, color="cornflowerblue", label="max_depth=2", linewidth=2)
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plt.plot(X_test, y_2, color="yellowgreen", label="max_depth=5", linewidth=2)
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plt.plot(X_test, y_3, color="red", label="max_depth=3", linewidth=2)
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# plt.plot(X_test, y_3, color="red", label="max_depth=3", linewidth=2)
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plt.xlabel("data")
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plt.ylabel("target")
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plt.title("Decision Tree Regression")
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@@ -8,73 +8,38 @@ NaiveBayes：朴素贝叶斯
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@author: 小瑶
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《机器学习实战》更新地址：https://github.com/apachecn/MachineLearning
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"""
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# GaussianNB_高斯朴素贝叶斯
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import numpy as np
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import matplotlib.pyplot as plt
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from sklearn import svm
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print(__doc__)
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# 创建40个分离点
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np.random.seed(0)
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# X = np.r_[np.random.randn(20, 2) - [2, 2], np.random.randn(20, 2) + [2, 2]]
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# Y = [0] * 20 + [1] * 20
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def loadDataSet(fileName):
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"""
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对文件进行逐行解析，从而得到第行的类标签和整个数据矩阵
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Args:
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fileName 文件名
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Returns:
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dataMat 数据矩阵
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labelMat 类标签
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"""
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dataMat = []
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labelMat = []
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fr = open(fileName)
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for line in fr.readlines():
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lineArr = line.strip().split('\t')
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dataMat.append([float(lineArr[0]), float(lineArr[1])])
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labelMat.append(float(lineArr[2]))
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return dataMat, labelMat
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X, Y = loadDataSet('input/6.SVM/testSet.txt')
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X = np.mat(X)
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print("X=", X)
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print("Y=", Y)
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# 拟合一个SVM模型
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clf = svm.SVC(kernel='linear')
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X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])
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Y = np.array([1, 1, 1, 2, 2, 2])
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from sklearn.naive_bayes import GaussianNB
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clf = GaussianNB()
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clf.fit(X, Y)
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print clf.predict([[-0.8, -1]])
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clf_pf = GaussianNB()
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clf_pf.partial_fit(X, Y, np.unique(Y))
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print clf_pf.predict([[-0.8, -1]])
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# 获取分割超平面
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w = clf.coef_[0]
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# 斜率
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a = -w[0] / w[1]
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# 从-5到5，顺序间隔采样50个样本，默认是num=50
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# xx = np.linspace(-5, 5) # , num=50)
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xx = np.linspace(-2, 10) # , num=50)
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# 二维的直线方程
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yy = a * xx - (clf.intercept_[0]) / w[1]
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print("yy=", yy)
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# MultinomialNB_多项朴素贝叶斯
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'''
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import numpy as np
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X = np.random.randint(5, size=(6, 100))
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y = np.array([1, 2, 3, 4, 5, 6])
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from sklearn.naive_bayes import MultinomialNB
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clf = MultinomialNB()
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clf.fit(X, y)
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print clf.predict(X[2:3])
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'''
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# plot the parallels to the separating hyperplane that pass through the support vectors
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# 通过支持向量绘制分割超平面
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print("support_vectors_=", clf.support_vectors_)
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b = clf.support_vectors_[0]
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yy_down = a * xx + (b[1] - a * b[0])
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b = clf.support_vectors_[-1]
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yy_up = a * xx + (b[1] - a * b[0])
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# plot the line, the points, and the nearest vectors to the plane
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plt.plot(xx, yy, 'k-')
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plt.plot(xx, yy_down, 'k--')
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plt.plot(xx, yy_up, 'k--')
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plt.scatter(clf.support_vectors_[:, 0], clf.support_vectors_[:, 1], s=80, facecolors='none')
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plt.scatter(X[:, 0], X[:, 1], c=Y, cmap=plt.cm.Paired)
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plt.axis('tight')
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plt.show()
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# BernoulliNB_伯努利朴素贝叶斯
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'''
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import numpy as np
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X = np.random.randint(2, size=(6, 100))
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Y = np.array([1, 2, 3, 4, 4, 5])
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from sklearn.naive_bayes import BernoulliNB
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clf = BernoulliNB()
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clf.fit(X, Y)
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print clf.predict(X[2:3])
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'''
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280
src/python/5.Logistic/sklearn_logisticRegression_demo.py
Normal file
280
src/python/5.Logistic/sklearn_logisticRegression_demo.py
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@@ -0,0 +1,280 @@
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#!/usr/bin/python
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# coding: utf8
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'''
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Created on Oct 27, 2010
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Update on 2017-05-18
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Logistic Regression Working Module
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@author: 小瑶
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《机器学习实战》更新地址：https://github.com/apachecn/MachineLearning
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scikit-learn的例子地址：http://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
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'''
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# 逻辑回归中的 L1 惩罚和稀缺性 L1 Penalty and Sparsity in Logistic Regression
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'''
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print(__doc__)
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import numpy as np
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import matplotlib.pyplot as plt
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from sklearn.linear_model import LogisticRegression
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from sklearn import datasets
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from sklearn.preprocessing import StandardScaler
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digits = datasets.load_digits()
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X, y = digits.data, digits.target
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X = StandardScaler().fit_transform(X)
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# 将大小数字分类为小
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y = (y > 4).astype(np.int)
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# 设置正则化参数
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for i, C in enumerate((100, 1, 0.01)):
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# 减少训练时间短的容忍度
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clf_l1_LR = LogisticRegression(C=C, penalty='l1', tol=0.01)
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clf_l2_LR = LogisticRegression(C=C, penalty='l2', tol=0.01)
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clf_l1_LR.fit(X, y)
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clf_l2_LR.fit(X, y)
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coef_l1_LR = clf_l1_LR.coef_.ravel()
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coef_l2_LR = clf_l2_LR.coef_.ravel()
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# coef_l1_LR contains zeros due to the
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# L1 sparsity inducing norm
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# 由于 L1 稀疏诱导规范，coef_l1_LR 包含零
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sparsity_l1_LR = np.mean(coef_l1_LR == 0) * 100
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sparsity_l2_LR = np.mean(coef_l2_LR == 0) * 100
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print("C=%.2f" % C)
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print("Sparsity with L1 penalty: %.2f%%" % sparsity_l1_LR)
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print("score with L1 penalty: %.4f" % clf_l1_LR.score(X, y))
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print("Sparsity with L2 penalty: %.2f%%" % sparsity_l2_LR)
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print("score with L2 penalty: %.4f" % clf_l2_LR.score(X, y))
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l1_plot = plt.subplot(3, 2, 2 * i + 1)
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l2_plot = plt.subplot(3, 2, 2 * (i + 1))
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if i == 0:
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l1_plot.set_title("L1 penalty")
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l2_plot.set_title("L2 penalty")
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l1_plot.imshow(np.abs(coef_l1_LR.reshape(8, 8)), interpolation='nearest',
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cmap='binary', vmax=1, vmin=0)
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l2_plot.imshow(np.abs(coef_l2_LR.reshape(8, 8)), interpolation='nearest',
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cmap='binary', vmax=1, vmin=0)
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plt.text(-8, 3, "C = %.2f" % C)
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l1_plot.set_xticks(())
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l1_plot.set_yticks(())
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l2_plot.set_xticks(())
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l2_plot.set_yticks(())
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plt.show()
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'''
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# 具有 L1-逻辑回归的路径
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'''
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print(__doc__)
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from datetime import datetime
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import numpy as np
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import matplotlib.pyplot as plt
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from sklearn import linear_model
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from sklearn import datasets
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from sklearn.svm import l1_min_c
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iris = datasets.load_iris()
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X = iris.data
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y = iris.target
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X = X[y != 2]
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y = y[y != 2]
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X -= np.mean(X, 0)
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cs = l1_min_c(X, y, loss='log') * np.logspace(0, 3)
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print("Computing regularization path ...")
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start = datetime.now()
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clf = linear_model.LogisticRegression(C=1.0, penalty='l1', tol=1e-6)
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coefs_ = []
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for c in cs:
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clf.set_params(C=c)
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clf.fit(X, y)
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coefs_.append(clf.coef_.ravel().copy())
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print("This took ", datetime.now() - start)
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coefs_ = np.array(coefs_)
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plt.plot(np.log10(cs), coefs_)
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ymin, ymax = plt.ylim()
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plt.xlabel('log(C)')
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plt.ylabel('Coefficients')
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plt.title('Logistic Regression Path')
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plt.axis('tight')
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plt.show()
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'''
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# 绘制多项式和一对二的逻辑回归 Plot multinomial and One-vs-Rest Logistic Regression
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'''
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print(__doc__)
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import numpy as np
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import matplotlib.pyplot as plt
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from sklearn.datasets import make_blobs
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from sklearn.linear_model import LogisticRegression
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# 制作 3 类数据集进行分类
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centers = [[-5, 0], [0, 1.5], [5, -1]]
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X, y = make_blobs(n_samples=1000, centers=centers, random_state=40)
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transformation = [[0.4, 0.2], [-0.4, 1.2]]
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X = np.dot(X, transformation)
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for multi_class in ('multinomial', 'ovr'):
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clf = LogisticRegression(solver='sag', max_iter=100, random_state=42,
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multi_class=multi_class).fit(X, y)
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# 打印训练分数
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print("training score : %.3f (%s)" % (clf.score(X, y), multi_class))
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# 创建一个网格来绘制
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h = .02 # 网格中的步长
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x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
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y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
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xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
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np.arange(y_min, y_max, h))
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# 绘制决策边界。为此，我们将为网格 [x_min, x_max]x[y_min, y_max]中的每个点分配一个颜色。
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Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
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# 将结果放入彩色图
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Z = Z.reshape(xx.shape)
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plt.figure()
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plt.contourf(xx, yy, Z, cmap=plt.cm.Paired)
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plt.title("Decision surface of LogisticRegression (%s)" % multi_class)
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plt.axis('tight')
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# 将训练点也绘制进入
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colors = "bry"
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for i, color in zip(clf.classes_, colors):
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idx = np.where(y == i)
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plt.scatter(X[idx, 0], X[idx, 1], c=color, cmap=plt.cm.Paired)
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# 绘制三个一对数分类器
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xmin, xmax = plt.xlim()
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ymin, ymax = plt.ylim()
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coef = clf.coef_
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intercept = clf.intercept_
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def plot_hyperplane(c, color):
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def line(x0):
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return (-(x0 * coef[c, 0]) - intercept[c]) / coef[c, 1]
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plt.plot([xmin, xmax], [line(xmin), line(xmax)],
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ls="--", color=color)
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for i, color in zip(clf.classes_, colors):
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plot_hyperplane(i, color)
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plt.show()
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'''
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# Logistic Regression 3-class Classifier 逻辑回归 3-类 分类器
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'''
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print(__doc__)
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import numpy as np
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import matplotlib.pyplot as plt
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from sklearn import linear_model, datasets
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# 引入一些数据来玩
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iris = datasets.load_iris()
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# 我们只采用样本数据的前两个feature
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X = iris.data[:, :2]
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Y = iris.target
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h = .02 # 网格中的步长
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logreg = linear_model.LogisticRegression(C=1e5)
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# 我们创建了一个 Neighbours Classifier 的实例，并拟合数据。
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logreg.fit(X, Y)
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# 绘制决策边界。为此我们将为网格 [x_min, x_max]x[y_min, y_max] 中的每个点分配一个颜色。
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x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5
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y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5
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xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
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Z = logreg.predict(np.c_[xx.ravel(), yy.ravel()])
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# 将结果放入彩色图中
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Z = Z.reshape(xx.shape)
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plt.figure(1, figsize=(4, 3))
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plt.pcolormesh(xx, yy, Z, cmap=plt.cm.Paired)
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# 将训练点也同样放入彩色图中
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plt.scatter(X[:, 0], X[:, 1], c=Y, edgecolors='k', cmap=plt.cm.Paired)
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plt.xlabel('Sepal length')
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plt.ylabel('Sepal width')
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plt.xlim(xx.min(), xx.max())
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plt.ylim(yy.min(), yy.max())
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plt.xticks(())
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plt.yticks(())
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plt.show()
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'''
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# Logistic function 逻辑回归函数
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# 这个类似于咱们之前讲解 logistic 回归的 Sigmoid 函数，模拟的阶跃函数
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print(__doc__)
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import numpy as np
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import matplotlib.pyplot as plt
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from sklearn import linear_model
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# 这是我们的测试集，它只是一条直线，带有一些高斯噪声。
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xmin, xmax = -5, 5
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n_samples = 100
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np.random.seed(0)
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X = np.random.normal(size=n_samples)
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y = (X > 0).astype(np.float)
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X[X > 0] *= 4
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X += .3 * np.random.normal(size=n_samples)
|
||||
|
||||
X = X[:, np.newaxis]
|
||||
# 运行分类器
|
||||
clf = linear_model.LogisticRegression(C=1e5)
|
||||
clf.fit(X, y)
|
||||
|
||||
# 并且画出我们的结果
|
||||
plt.figure(1, figsize=(4, 3))
|
||||
plt.clf()
|
||||
plt.scatter(X.ravel(), y, color='black', zorder=20)
|
||||
X_test = np.linspace(-5, 10, 300)
|
||||
|
||||
|
||||
def model(x):
|
||||
return 1 / (1 + np.exp(-x))
|
||||
loss = model(X_test * clf.coef_ + clf.intercept_).ravel()
|
||||
plt.plot(X_test, loss, color='red', linewidth=3)
|
||||
|
||||
ols = linear_model.LinearRegression()
|
||||
ols.fit(X, y)
|
||||
plt.plot(X_test, ols.coef_ * X_test + ols.intercept_, linewidth=1)
|
||||
plt.axhline(.5, color='.5')
|
||||
|
||||
plt.ylabel('y')
|
||||
plt.xlabel('X')
|
||||
plt.xticks(range(-5, 10))
|
||||
plt.yticks([0, 0.5, 1])
|
||||
plt.ylim(-.25, 1.25)
|
||||
plt.xlim(-4, 10)
|
||||
plt.legend(('Logistic Regression Model', 'Linear Regression Model'),
|
||||
loc="lower right", fontsize='small')
|
||||
plt.show()
|
||||
|
||||
|
||||
@@ -4,7 +4,7 @@
|
||||
'''
|
||||
Created on Jan 8, 2011
|
||||
Update on 2017-05-18
|
||||
@author: Peter Harrington/ApacheCN-小瑶
|
||||
@author: Peter Harrington/小瑶
|
||||
《机器学习实战》更新地址：https://github.com/apachecn/MachineLearning
|
||||
'''
|
||||
|
||||
@@ -12,37 +12,74 @@ Update on 2017-05-18
|
||||
from numpy import *
|
||||
import matplotlib.pylab as plt
|
||||
|
||||
def loadDataSet(fileName): # 解析以tab键分隔的文件中的浮点数
|
||||
def loadDataSet(fileName):
|
||||
""" 加载数据
|
||||
解析以tab键分隔的文件中的浮点数
|
||||
Returns：
|
||||
dataMat feature 对应的数据集
|
||||
labelMat feature 对应的分类标签，即类别标签
|
||||
dataMat ： feature 对应的数据集
|
||||
labelMat ： feature 对应的分类标签，即类别标签
|
||||
|
||||
"""
|
||||
numFeat = len(open(fileName).readline().split('\t')) - 1 # 获得每一行的输入数据，最后一个代表真实值
|
||||
dataMat = []; labelMat = []
|
||||
# 获取样本特征的总数，不算最后的目标变量
|
||||
numFeat = len(open(fileName).readline().split('\t')) - 1
|
||||
dataMat = []
|
||||
labelMat = []
|
||||
fr = open(fileName)
|
||||
for line in fr.readlines(): # 读取每一行
|
||||
for line in fr.readlines():
|
||||
# 读取每一行
|
||||
lineArr =[]
|
||||
curLine = line.strip().split('\t') # 删除一行中以tab分隔的数据前后的空白符号
|
||||
for i in range(numFeat): # 从0到2，不包括2
|
||||
lineArr.append(float(curLine[i]))# 将数据添加到lineArr List中，每一行数据测试数据组成一个行向量
|
||||
dataMat.append(lineArr) # 将测试数据的输入数据部分存储到dataMat矩阵中
|
||||
labelMat.append(float(curLine[-1]))# 将每一行的最后一个数据，即真实的目标变量存储到labelMat矩阵中
|
||||
# 删除一行中以tab分隔的数据前后的空白符号
|
||||
curLine = line.strip().split('\t')
|
||||
# i 从0到2，不包括2
|
||||
for i in range(numFeat):
|
||||
# 将数据添加到lineArr List中，每一行数据测试数据组成一个行向量
|
||||
lineArr.append(float(curLine[i]))
|
||||
# 将测试数据的输入数据部分存储到dataMat 的List中
|
||||
dataMat.append(lineArr)
|
||||
# 将每一行的最后一个数据，即类别，或者叫目标变量存储到labelMat List中
|
||||
labelMat.append(float(curLine[-1]))
|
||||
return dataMat,labelMat
|
||||
|
||||
def standRegres(xArr,yArr): # 线性回归
|
||||
xMat = mat(xArr); yMat = mat(yArr).T # mat()函数将xArr，yArr转换为矩阵
|
||||
xTx = xMat.T*xMat # 矩阵乘法的条件是左矩阵的列数等于右矩阵的行数
|
||||
if linalg.det(xTx) == 0.0: # 因为要用到xTx的逆矩阵，所以事先需要确定计算得到的xTx是否可逆，条件是矩阵的行列式不为0
|
||||
print ("This matrix is singular, cannot do inverse")
|
||||
def standRegres(xArr,yArr):
|
||||
'''
|
||||
Description：
|
||||
线性回归
|
||||
Args:
|
||||
xArr ：输入的样本数据，包含每个样本数据的 feature
|
||||
yArr ：对应于输入数据的类别标签，也就是每个样本对应的目标变量
|
||||
Returns:
|
||||
|
||||
'''
|
||||
|
||||
# mat()函数将xArr，yArr转换为矩阵 mat().T 代表的是对矩阵进行转置操作
|
||||
xMat = mat(xArr)
|
||||
yMat = mat(yArr).T
|
||||
# 矩阵乘法的条件是左矩阵的列数等于右矩阵的行数
|
||||
xTx = xMat.T*xMat
|
||||
# 因为要用到xTx的逆矩阵，所以事先需要确定计算得到的xTx是否可逆，条件是矩阵的行列式不为0
|
||||
# linalg.det() 函数是用来求得矩阵的行列式的，如果矩阵的行列式为0，则这个矩阵是不可逆的，就无法进行接下来的运算
|
||||
if linalg.det(xTx) == 0.0:
|
||||
print "This matrix is singular, cannot do inverse"
|
||||
return
|
||||
# 最小二乘法
|
||||
# http://www.apache.wiki/pages/viewpage.action?pageId=5505133
|
||||
ws = xTx.I * (xMat.T*yMat) # 书中的公式，求得w的最优解
|
||||
# 书中的公式，求得w的最优解
|
||||
ws = xTx.I * (xMat.T*yMat)
|
||||
return ws
|
||||
|
||||
def lwlr(testPoint,xArr,yArr,k=1.0): # 局部加权线性回归
|
||||
# 局部加权线性回归
|
||||
def lwlr(testPoint,xArr,yArr,k=1.0):
|
||||
'''
|
||||
Description：
|
||||
局部加权线性回归
|
||||
Args：
|
||||
testPoint：
|
||||
xArr：
|
||||
yArr：
|
||||
k:
|
||||
Returns:
|
||||
|
||||
'''
|
||||
xMat = mat(xArr); yMat = mat(yArr).T
|
||||
m = shape(xMat)[0] # 获得xMat矩阵的行数
|
||||
weights = mat(eye((m))) # eye()返回一个对角线元素为1，其他元素为0的二维数组，创建权重矩阵
|
||||
|
||||
Reference in New Issue
Block a user