第8章 用scikit-learn库实现机器学习 205
8.1 scikit-learn库 205
8.2 机器学习 205
8.2.1 有监督和无监督学习 205
8.2.2 训练集和测试集 206
8.3 用scikit-learn实现有监督学习 206
8.4 Iris数据集 206
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from sklearn import datasets
iris = datasets.load_iris()
print(iris.data.shape)
print(iris.data[:5])
print()
print(iris.target[:5])
print()
print(iris.target_names)
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(150, 4)
[[5.1 3.5 1.4 0.2]
[4.9 3. 1.4 0.2]
[4.7 3.2 1.3 0.2]
[4.6 3.1 1.5 0.2]
[5. 3.6 1.4 0.2]]
[0 0 0 0 0]
['setosa' 'versicolor' 'virginica']
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import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
from sklearn import datasets
% matplotlib inline
iris = datasets.load_iris()
x = iris.data[:,0] |
([], <a list of 0 Text yticklabel objects>)
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import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
from sklearn import datasets
iris = datasets.load_iris()
x = iris.data[:,2] |
([], <a list of 0 Text yticklabel objects>)
主成分分解
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import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from sklearn import datasets
from sklearn.decomposition import PCA
iris = datasets.load_iris()
x = iris.data[:,1] |
[]
8.5 K-近邻分类器 211
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import numpy as np
from sklearn import datasets
np.random.seed(0)
iris = datasets.load_iris()
x = iris.data
y = iris.target
i = np.random.permutation(len(iris.data))
x_train = x[i[:-10]]
y_train = y[i[:-10]]
x_test = x[i[-10:]]
y_test = y[i[-10:]]
from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier()
knn.fit(x_train,y_train)
y_pre = knn.predict(x_test)
print(y_pre)
print(y_test)
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[1 2 1 0 0 0 2 1 2 0]
[1 1 1 0 0 0 2 1 2 0]
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import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from sklearn import datasets
from sklearn.neighbors import KNeighborsClassifier
iris = datasets.load_iris()
x = iris.data[:,:2] |
(1.5, 4.900000000000003)
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import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from sklearn import datasets
from sklearn.neighbors import KNeighborsClassifier
iris = datasets.load_iris()
x = iris.data[:,2:4] |
(-0.4, 2.980000000000003)
8.6 Diabetes数据集 214
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from sklearn import datasets
diabetes = datasets.load_diabetes()
diabetes.data[0]
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array([ 0.03807591, 0.05068012, 0.06169621, 0.02187235, -0.0442235 ,
-0.03482076, -0.04340085, -0.00259226, 0.01990842, -0.01764613])
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np.sum(diabetes.data[:,0]**2)
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1.0000000000000746
array([151., 75., 141., 206., 135., 97., 138., 63., 110., 310.])
8.7 线性回归：最小平方回归 215
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from sklearn import linear_model
linreg = linear_model.LinearRegression()
from sklearn import datasets
diabetes = datasets.load_diabetes()
x_train = diabetes.data[:-20]
y_train = diabetes.target[:-20]
x_test = diabetes.data[-20:]
y_test = diabetes.target[-20:]
linreg.fit(x_train,y_train)
linreg.coef_
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array([ 3.03499549e-01, -2.37639315e+02, 5.10530605e+02, 3.27736980e+02,
-8.14131709e+02, 4.92814588e+02, 1.02848452e+02, 1.84606489e+02,
7.43519617e+02, 7.60951722e+01])
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y_pre = linreg.predict(x_test)
print(y_pre)
print(y_test)
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[197.61846908 155.43979328 172.88665147 111.53537279 164.80054784
131.06954875 259.12237761 100.47935157 117.0601052 124.30503555
218.36632793 61.19831284 132.25046751 120.3332925 52.54458691
194.03798088 102.57139702 123.56604987 211.0346317 52.60335674]
[233. 91. 111. 152. 120. 67. 310. 94. 183. 66. 173. 72. 49. 64.
48. 178. 104. 132. 220. 57.]
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linreg.score(x_test, y_test)
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0.5850753022690571
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import numpy as np
import matplotlib.pyplot as plt
from sklearn import linear_model
from sklearn import datasets
diabetes = datasets.load_diabetes()
x_train = diabetes.data[:-20]
y_train = diabetes.target[:-20]
x_test = diabetes.data[-20:]
y_test = diabetes.target[-20:]
x0_test = x_test[:,0]
x0_train = x_train[:,0]
x0_test = x0_test[:,np.newaxis]
x0_train = x0_train[:,np.newaxis]
linreg = linear_model.LinearRegression()
linreg.fit(x0_train,y_train)
y = linreg.predict(x0_test)
plt.scatter(x0_test,y_test,color='k')
plt.plot(x0_test,y,color='b',linewidth=3)
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[<matplotlib.lines.Line2D at 0xeecd990>]
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import numpy as np
import matplotlib.pyplot as plt
from sklearn import linear_model
from sklearn import datasets
diabetes = datasets.load_diabetes()
x_train = diabetes.data[:-20]
y_train = diabetes.target[:-20]
x_test = diabetes.data[-20:]
y_test = diabetes.target[-20:]
plt.figure(figsize=(8,12))
for f in range(0,10):
xi_test = x_test[:,f]
xi_train = x_train[:,f]
xi_test = xi_test[:,np.newaxis]
xi_train = xi_train[:,np.newaxis]
linreg.fit(xi_train,y_train)
y = linreg.predict(xi_test)
plt.subplot(5,2,f+1)
plt.scatter(xi_test,y_test,color='k')
plt.plot(xi_test,y,color='b',linewidth=3)
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8.8 支持向量机 219
8.8.1 支持向量分类 219
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import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm
x = np.array([[1,3],[1,2],[1,1.5],[1.5,2],[2,3],[2.5,1.5],
[2,1],[3,1],[3,2],[3.5,1],[3.5,3]])
y = [0]*6 + [1]*5
plt.scatter(x[:,0],x[:,1],c=y,s=50,alpha=0.9)
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<matplotlib.collections.PathCollection at 0xf811110>
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import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm
x = np.array([[1,3],[1,2],[1,1.5],[1.5,2],[2,3],[2.5,1.5],
[2,1],[3,1],[3,2],[3.5,1],[3.5,3]])
y = [0]*6 + [1]*5
svc = svm.SVC(kernel='linear').fit(x,y)
X,Y = np.mgrid[0:4:200j,0:4:200j]
Z = svc.decision_function(np.c_[X.ravel(),Y.ravel()])
Z = Z.reshape(X.shape)
plt.contourf(X,Y,Z > 0,alpha=0.4)
plt.contour(X,Y,Z,colors=['k'], linestyles=['-'],levels=[0])
plt.scatter(x[:,0],x[:,1],c=y,s=50,alpha=0.9)
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<matplotlib.collections.PathCollection at 0xf84ce50>
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svc.predict([[1.5,2.5]])
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array([0])
array([1])
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import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm
x = np.array([[1,3],[1,2],[1,1.5],[1.5,2],[2,3],[2.5,1.5],
[2,1],[3,1],[3,2],[3.5,1],[3.5,3]])
y = [0]*6 + [1]*5
svc = svm.SVC(kernel='linear',C=1).fit(x,y)
X,Y = np.mgrid[0:4:200j,0:4:200j]
Z = svc.decision_function(np.c_[X.ravel(),Y.ravel()])
Z = Z.reshape(X.shape)
plt.contourf(X,Y,Z > 0,alpha=0.4)
plt.contour(X,Y,Z,colors=['k','k','k'], linestyles=['--','-','--'],levels=[-1,0,1])
plt.scatter(svc.support_vectors_[:,0],svc.support_vectors_[:,1],s=120,facecolors='none')
plt.scatter(x[:,0],x[:,1],c=y,s=50,alpha=0.9)
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<matplotlib.collections.PathCollection at 0xfe387b0>
8.8.2 非线性SVC 223
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import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm
x = np.array([[1,3],[1,2],[1,1.5],[1.5,2],[2,3],[2.5,1.5],
[2,1],[3,1],[3,2],[3.5,1],[3.5,3]])
y = [0]*6 + [1]*5
svc = svm.SVC(kernel='linear',C=0.1).fit(x,y)
X,Y = np.mgrid[0:4:200j,0:4:200j]
Z = svc.decision_function(np.c_[X.ravel(),Y.ravel()])
Z = Z.reshape(X.shape)
plt.contourf(X,Y,Z > 0,alpha=0.4)
plt.contour(X,Y,Z,colors=['k','k','k'], linestyles=['--','-','--'],levels=[-1,0,1])
plt.scatter(svc.support_vectors_[:,0],svc.support_vectors_[:,1],s=120,facecolors='none')
plt.scatter(x[:,0],x[:,1],c=y,s=50,alpha=0.9)
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<matplotlib.collections.PathCollection at 0xff1eb90>
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import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm
x = np.array([[1,3],[1,2],[1,1.5],[1.5,2],[2,3],[2.5,1.5],
[2,1],[3,1],[3,2],[3.5,1],[3.5,3]])
y = [0]*6 + [1]*5
svc = svm.SVC(kernel='poly',C=1, degree=3).fit(x,y)
X,Y = np.mgrid[0:4:200j,0:4:200j]
Z = svc.decision_function(np.c_[X.ravel(),Y.ravel()])
Z = Z.reshape(X.shape)
plt.contourf(X,Y,Z > 0,alpha=0.4)
plt.contour(X,Y,Z,colors=['k','k','k'], linestyles=['--','-','--'],levels=[-1,0,1])
plt.scatter(svc.support_vectors_[:,0],svc.support_vectors_[:,1],s=120,facecolors='none')
plt.scatter(x[:,0],x[:,1],c=y,s=50,alpha=0.9)
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<matplotlib.collections.PathCollection at 0xf99b8d0>
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import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm
x = np.array([[1,3],[1,2],[1,1.5],[1.5,2],[2,3],[2.5,1.5],
[2,1],[3,1],[3,2],[3.5,1],[3.5,3]])
y = [0]*6 + [1]*5
svc = svm.SVC(kernel='rbf', C=1, gamma=3).fit(x,y)
X,Y = np.mgrid[0:4:200j,0:4:200j]
Z = svc.decision_function(np.c_[X.ravel(),Y.ravel()])
Z = Z.reshape(X.shape)
plt.contourf(X,Y,Z > 0,alpha=0.4)
plt.contour(X,Y,Z,colors=['k','k','k'], linestyles=['--','-','--'],levels=[-1,0,1])
plt.scatter(svc.support_vectors_[:,0],svc.support_vectors_[:,1],s=120,facecolors='none')
plt.scatter(x[:,0],x[:,1],c=y,s=50,alpha=0.9)
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<matplotlib.collections.PathCollection at 0xe575f90>
8.8.3 绘制SVM分类器对Iris数据集的分类效果图 225
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import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm, datasets
iris = datasets.load_iris()
x = iris.data[:,:2]
y = iris.target
h = .05
svc = svm.SVC(kernel='linear',C=1.0).fit(x,y)
x_min,x_max = x[:,0].min() - .5, x[:,0].max() + .5
y_min,y_max = x[:,1].min() - .5, x[:,1].max() + .5
h = .02
X, Y = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min,y_max,h))
Z = svc.predict(np.c_[X.ravel(),Y.ravel()])
Z = Z.reshape(X.shape)
plt.contourf(X,Y,Z,alpha=0.4)
plt.contour(X,Y,Z,colors='k')
plt.scatter(x[:,0],x[:,1],c=y)
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<matplotlib.collections.PathCollection at 0xd3aac90>
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import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm, datasets
iris = datasets.load_iris()
x = iris.data[:,:2]
y = iris.target
h = .05
svc = svm.SVC(kernel='poly',C=1.0,degree=3).fit(x,y)
x_min,x_max = x[:,0].min() - .5, x[:,0].max() + .5
y_min,y_max = x[:,1].min() - .5, x[:,1].max() + .5
h = .02
X, Y = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min,y_max,h))
Z = svc.predict(np.c_[X.ravel(),Y.ravel()])
Z = Z.reshape(X.shape)
plt.contourf(X,Y,Z,alpha=0.4)
plt.contour(X,Y,Z,colors='k')
plt.scatter(x[:,0],x[:,1],c=y)
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<matplotlib.collections.PathCollection at 0xf705250>
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import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm, datasets
iris = datasets.load_iris()
x = iris.data[:,:2]
y = iris.target
h = .05
svc = svm.SVC(kernel='rbf',C=1.0,degree=3).fit(x,y)
x_min,x_max = x[:,0].min() - .5, x[:,0].max() + .5
y_min,y_max = x[:,1].min() - .5, x[:,1].max() + .5
h = .02
X, Y = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min,y_max,h))
Z = svc.predict(np.c_[X.ravel(),Y.ravel()])
Z = Z.reshape(X.shape)
plt.contourf(X,Y,Z,alpha=0.4)
plt.contour(X,Y,Z,colors='k')
plt.scatter(x[:,0],x[:,1],c=y)
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<matplotlib.collections.PathCollection at 0xe419a50>
8.8.4 支持向量回归 227
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import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm
from sklearn import datasets
diabetes = datasets.load_diabetes()
x_train = diabetes.data[:-20]
y_train = diabetes.target[:-20]
x_test = diabetes.data[-20:]
y_test = diabetes.target[-20:]
x0_test = x_test[:,2]
x0_train = x_train[:,2]
x0_test = x0_test[:,np.newaxis]
x0_train = x0_train[:,np.newaxis]
x0_test.sort(axis=0)
x0_test = x0_test*100
x0_train = x0_train*100
svr = svm.SVR(kernel='linear',C=1000)
svr2 = svm.SVR(kernel='poly',C=1000,degree=2)
svr3 = svm.SVR(kernel='poly',C=1000,degree=3)
svr.fit(x0_train,y_train)
svr2.fit(x0_train,y_train)
svr3.fit(x0_train,y_train)
y = svr.predict(x0_test)
y2 = svr2.predict(x0_test)
y3 = svr3.predict(x0_test)
plt.scatter(x0_test,y_test,color='k')
plt.plot(x0_test,y,color='b')
plt.plot(x0_test,y2,c='r')
plt.plot(x0_test,y3,c='g')
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[<matplotlib.lines.Line2D at 0xf068670>]
8.9 小结 229