使用sklearn进行量纲缩放的程序

朱雀 2022-08-30 11:58 195阅读 0赞

# 使用sklearn进行量纲缩放的程序 #

# -*- coding: utf-8 -*-
    """ 演示内容：量纲的特征缩放 （两种方法：标准化缩放法和区间缩放法。每种方法举了两个例子：简单二维矩阵和iris数据集） """
    #方法1：标准化缩放法 例1：对简单示例二维矩阵的列数据进行
    from sklearn import preprocessing   
    import numpy as np  
    #采用numpy的array表示，因为要用到其mean等函数，而list没有这些函数
    X = np.array([[0, 0], 
            [0, 0], 
            [100, 1], 
            [1, 1]])  
    # calculate mean 
    X_mean = X.mean(axis=0)  
    # calculate variance 
    X_std = X.std(axis=0)  
    #print (X_std)
    # standardize X 
    X1 = (X-X_mean)/X_std
    print (X1)
    print ("")
    
    # we can also use function preprocessing.scale to standardize X 
    X_scale = preprocessing.scale(X)  
    print (X_scale)

[[-0.58504784 -1.        ]
     [-0.58504784 -1.        ]
     [ 1.73197332  1.        ]
     [-0.56187763  1.        ]]
    
    [[-0.58504784 -1.        ]
     [-0.58504784 -1.        ]
     [ 1.73197332  1.        ]
     [-0.56187763  1.        ]]

#方法1： 标准化缩放法 例2：对iris数据二维矩阵的列数据进行。这次采用一个集成的方法StandardScaler
    from sklearn import datasets
    iris = datasets.load_iris()
    X_scale = preprocessing.scale(iris.data)  
    print (X_scale)

[[-9.00681170e-01  1.01900435e+00 -1.34022653e+00 -1.31544430e+00]
     [-1.14301691e+00 -1.31979479e-01 -1.34022653e+00 -1.31544430e+00]
     [-1.38535265e+00  3.28414053e-01 -1.39706395e+00 -1.31544430e+00]
     [-1.50652052e+00  9.82172869e-02 -1.28338910e+00 -1.31544430e+00]
     [-1.02184904e+00  1.24920112e+00 -1.34022653e+00 -1.31544430e+00]
     [-5.37177559e-01  1.93979142e+00 -1.16971425e+00 -1.05217993e+00]
     [-1.50652052e+00  7.88807586e-01 -1.34022653e+00 -1.18381211e+00]
     [-1.02184904e+00  7.88807586e-01 -1.28338910e+00 -1.31544430e+00]
     [-1.74885626e+00 -3.62176246e-01 -1.34022653e+00 -1.31544430e+00]
     [-1.14301691e+00  9.82172869e-02 -1.28338910e+00 -1.44707648e+00]
     [-5.37177559e-01  1.47939788e+00 -1.28338910e+00 -1.31544430e+00]
     [-1.26418478e+00  7.88807586e-01 -1.22655167e+00 -1.31544430e+00]
     [-1.26418478e+00 -1.31979479e-01 -1.34022653e+00 -1.44707648e+00]
     [-1.87002413e+00 -1.31979479e-01 -1.51073881e+00 -1.44707648e+00]
     [-5.25060772e-02  2.16998818e+00 -1.45390138e+00 -1.31544430e+00]
     [-1.73673948e-01  3.09077525e+00 -1.28338910e+00 -1.05217993e+00]
     [-5.37177559e-01  1.93979142e+00 -1.39706395e+00 -1.05217993e+00]
     [-9.00681170e-01  1.01900435e+00 -1.34022653e+00 -1.18381211e+00]
     [-1.73673948e-01  1.70959465e+00 -1.16971425e+00 -1.18381211e+00]
     [-9.00681170e-01  1.70959465e+00 -1.28338910e+00 -1.18381211e+00]
     [-5.37177559e-01  7.88807586e-01 -1.16971425e+00 -1.31544430e+00]
     [-9.00681170e-01  1.47939788e+00 -1.28338910e+00 -1.05217993e+00]
     [-1.50652052e+00  1.24920112e+00 -1.56757623e+00 -1.31544430e+00]
     [-9.00681170e-01  5.58610819e-01 -1.16971425e+00 -9.20547742e-01]
     [-1.26418478e+00  7.88807586e-01 -1.05603939e+00 -1.31544430e+00]
     [-1.02184904e+00 -1.31979479e-01 -1.22655167e+00 -1.31544430e+00]
     [-1.02184904e+00  7.88807586e-01 -1.22655167e+00 -1.05217993e+00]
     [-7.79513300e-01  1.01900435e+00 -1.28338910e+00 -1.31544430e+00]
     [-7.79513300e-01  7.88807586e-01 -1.34022653e+00 -1.31544430e+00]
     [-1.38535265e+00  3.28414053e-01 -1.22655167e+00 -1.31544430e+00]
     [-1.26418478e+00  9.82172869e-02 -1.22655167e+00 -1.31544430e+00]
     [-5.37177559e-01  7.88807586e-01 -1.28338910e+00 -1.05217993e+00]
     [-7.79513300e-01  2.40018495e+00 -1.28338910e+00 -1.44707648e+00]
     [-4.16009689e-01  2.63038172e+00 -1.34022653e+00 -1.31544430e+00]
     [-1.14301691e+00  9.82172869e-02 -1.28338910e+00 -1.31544430e+00]
     [-1.02184904e+00  3.28414053e-01 -1.45390138e+00 -1.31544430e+00]
     [-4.16009689e-01  1.01900435e+00 -1.39706395e+00 -1.31544430e+00]
     [-1.14301691e+00  1.24920112e+00 -1.34022653e+00 -1.44707648e+00]
     [-1.74885626e+00 -1.31979479e-01 -1.39706395e+00 -1.31544430e+00]
     [-9.00681170e-01  7.88807586e-01 -1.28338910e+00 -1.31544430e+00]
     [-1.02184904e+00  1.01900435e+00 -1.39706395e+00 -1.18381211e+00]
     [-1.62768839e+00 -1.74335684e+00 -1.39706395e+00 -1.18381211e+00]
     [-1.74885626e+00  3.28414053e-01 -1.39706395e+00 -1.31544430e+00]
     [-1.02184904e+00  1.01900435e+00 -1.22655167e+00 -7.88915558e-01]
     [-9.00681170e-01  1.70959465e+00 -1.05603939e+00 -1.05217993e+00]
     [-1.26418478e+00 -1.31979479e-01 -1.34022653e+00 -1.18381211e+00]
     [-9.00681170e-01  1.70959465e+00 -1.22655167e+00 -1.31544430e+00]
     [-1.50652052e+00  3.28414053e-01 -1.34022653e+00 -1.31544430e+00]
     [-6.58345429e-01  1.47939788e+00 -1.28338910e+00 -1.31544430e+00]
     [-1.02184904e+00  5.58610819e-01 -1.34022653e+00 -1.31544430e+00]
     [ 1.40150837e+00  3.28414053e-01  5.35408562e-01  2.64141916e-01]
     [ 6.74501145e-01  3.28414053e-01  4.21733708e-01  3.95774101e-01]
     [ 1.28034050e+00  9.82172869e-02  6.49083415e-01  3.95774101e-01]
     [-4.16009689e-01 -1.74335684e+00  1.37546573e-01  1.32509732e-01]
     [ 7.95669016e-01 -5.92373012e-01  4.78571135e-01  3.95774101e-01]
     [-1.73673948e-01 -5.92373012e-01  4.21733708e-01  1.32509732e-01]
     [ 5.53333275e-01  5.58610819e-01  5.35408562e-01  5.27406285e-01]
     [-1.14301691e+00 -1.51316008e+00 -2.60315415e-01 -2.62386821e-01]
     [ 9.16836886e-01 -3.62176246e-01  4.78571135e-01  1.32509732e-01]
     [-7.79513300e-01 -8.22569778e-01  8.07091462e-02  2.64141916e-01]
     [-1.02184904e+00 -2.43394714e+00 -1.46640561e-01 -2.62386821e-01]
     [ 6.86617933e-02 -1.31979479e-01  2.51221427e-01  3.95774101e-01]
     [ 1.89829664e-01 -1.97355361e+00  1.37546573e-01 -2.62386821e-01]
     [ 3.10997534e-01 -3.62176246e-01  5.35408562e-01  2.64141916e-01]
     [-2.94841818e-01 -3.62176246e-01 -8.98031345e-02  1.32509732e-01]
     [ 1.03800476e+00  9.82172869e-02  3.64896281e-01  2.64141916e-01]
     [-2.94841818e-01 -1.31979479e-01  4.21733708e-01  3.95774101e-01]
     [-5.25060772e-02 -8.22569778e-01  1.94384000e-01 -2.62386821e-01]
     [ 4.32165405e-01 -1.97355361e+00  4.21733708e-01  3.95774101e-01]
     [-2.94841818e-01 -1.28296331e+00  8.07091462e-02 -1.30754636e-01]
     [ 6.86617933e-02  3.28414053e-01  5.92245988e-01  7.90670654e-01]
     [ 3.10997534e-01 -5.92373012e-01  1.37546573e-01  1.32509732e-01]
     [ 5.53333275e-01 -1.28296331e+00  6.49083415e-01  3.95774101e-01]
     [ 3.10997534e-01 -5.92373012e-01  5.35408562e-01  8.77547895e-04]
     [ 6.74501145e-01 -3.62176246e-01  3.08058854e-01  1.32509732e-01]
     [ 9.16836886e-01 -1.31979479e-01  3.64896281e-01  2.64141916e-01]
     [ 1.15917263e+00 -5.92373012e-01  5.92245988e-01  2.64141916e-01]
     [ 1.03800476e+00 -1.31979479e-01  7.05920842e-01  6.59038469e-01]
     [ 1.89829664e-01 -3.62176246e-01  4.21733708e-01  3.95774101e-01]
     [-1.73673948e-01 -1.05276654e+00 -1.46640561e-01 -2.62386821e-01]
     [-4.16009689e-01 -1.51316008e+00  2.38717193e-02 -1.30754636e-01]
     [-4.16009689e-01 -1.51316008e+00 -3.29657076e-02 -2.62386821e-01]
     [-5.25060772e-02 -8.22569778e-01  8.07091462e-02  8.77547895e-04]
     [ 1.89829664e-01 -8.22569778e-01  7.62758269e-01  5.27406285e-01]
     [-5.37177559e-01 -1.31979479e-01  4.21733708e-01  3.95774101e-01]
     [ 1.89829664e-01  7.88807586e-01  4.21733708e-01  5.27406285e-01]
     [ 1.03800476e+00  9.82172869e-02  5.35408562e-01  3.95774101e-01]
     [ 5.53333275e-01 -1.74335684e+00  3.64896281e-01  1.32509732e-01]
     [-2.94841818e-01 -1.31979479e-01  1.94384000e-01  1.32509732e-01]
     [-4.16009689e-01 -1.28296331e+00  1.37546573e-01  1.32509732e-01]
     [-4.16009689e-01 -1.05276654e+00  3.64896281e-01  8.77547895e-04]
     [ 3.10997534e-01 -1.31979479e-01  4.78571135e-01  2.64141916e-01]
     [-5.25060772e-02 -1.05276654e+00  1.37546573e-01  8.77547895e-04]
     [-1.02184904e+00 -1.74335684e+00 -2.60315415e-01 -2.62386821e-01]
     [-2.94841818e-01 -8.22569778e-01  2.51221427e-01  1.32509732e-01]
     [-1.73673948e-01 -1.31979479e-01  2.51221427e-01  8.77547895e-04]
     [-1.73673948e-01 -3.62176246e-01  2.51221427e-01  1.32509732e-01]
     [ 4.32165405e-01 -3.62176246e-01  3.08058854e-01  1.32509732e-01]
     [-9.00681170e-01 -1.28296331e+00 -4.30827696e-01 -1.30754636e-01]
     [-1.73673948e-01 -5.92373012e-01  1.94384000e-01  1.32509732e-01]
     [ 5.53333275e-01  5.58610819e-01  1.27429511e+00  1.71209594e+00]
     [-5.25060772e-02 -8.22569778e-01  7.62758269e-01  9.22302838e-01]
     [ 1.52267624e+00 -1.31979479e-01  1.21745768e+00  1.18556721e+00]
     [ 5.53333275e-01 -3.62176246e-01  1.04694540e+00  7.90670654e-01]
     [ 7.95669016e-01 -1.31979479e-01  1.16062026e+00  1.31719939e+00]
     [ 2.12851559e+00 -1.31979479e-01  1.61531967e+00  1.18556721e+00]
     [-1.14301691e+00 -1.28296331e+00  4.21733708e-01  6.59038469e-01]
     [ 1.76501198e+00 -3.62176246e-01  1.44480739e+00  7.90670654e-01]
     [ 1.03800476e+00 -1.28296331e+00  1.16062026e+00  7.90670654e-01]
     [ 1.64384411e+00  1.24920112e+00  1.33113254e+00  1.71209594e+00]
     [ 7.95669016e-01  3.28414053e-01  7.62758269e-01  1.05393502e+00]
     [ 6.74501145e-01 -8.22569778e-01  8.76433123e-01  9.22302838e-01]
     [ 1.15917263e+00 -1.31979479e-01  9.90107977e-01  1.18556721e+00]
     [-1.73673948e-01 -1.28296331e+00  7.05920842e-01  1.05393502e+00]
     [-5.25060772e-02 -5.92373012e-01  7.62758269e-01  1.58046376e+00]
     [ 6.74501145e-01  3.28414053e-01  8.76433123e-01  1.44883158e+00]
     [ 7.95669016e-01 -1.31979479e-01  9.90107977e-01  7.90670654e-01]
     [ 2.24968346e+00  1.70959465e+00  1.67215710e+00  1.31719939e+00]
     [ 2.24968346e+00 -1.05276654e+00  1.78583195e+00  1.44883158e+00]
     [ 1.89829664e-01 -1.97355361e+00  7.05920842e-01  3.95774101e-01]
     [ 1.28034050e+00  3.28414053e-01  1.10378283e+00  1.44883158e+00]
     [-2.94841818e-01 -5.92373012e-01  6.49083415e-01  1.05393502e+00]
     [ 2.24968346e+00 -5.92373012e-01  1.67215710e+00  1.05393502e+00]
     [ 5.53333275e-01 -8.22569778e-01  6.49083415e-01  7.90670654e-01]
     [ 1.03800476e+00  5.58610819e-01  1.10378283e+00  1.18556721e+00]
     [ 1.64384411e+00  3.28414053e-01  1.27429511e+00  7.90670654e-01]
     [ 4.32165405e-01 -5.92373012e-01  5.92245988e-01  7.90670654e-01]
     [ 3.10997534e-01 -1.31979479e-01  6.49083415e-01  7.90670654e-01]
     [ 6.74501145e-01 -5.92373012e-01  1.04694540e+00  1.18556721e+00]
     [ 1.64384411e+00 -1.31979479e-01  1.16062026e+00  5.27406285e-01]
     [ 1.88617985e+00 -5.92373012e-01  1.33113254e+00  9.22302838e-01]
     [ 2.49201920e+00  1.70959465e+00  1.50164482e+00  1.05393502e+00]
     [ 6.74501145e-01 -5.92373012e-01  1.04694540e+00  1.31719939e+00]
     [ 5.53333275e-01 -5.92373012e-01  7.62758269e-01  3.95774101e-01]
     [ 3.10997534e-01 -1.05276654e+00  1.04694540e+00  2.64141916e-01]
     [ 2.24968346e+00 -1.31979479e-01  1.33113254e+00  1.44883158e+00]
     [ 5.53333275e-01  7.88807586e-01  1.04694540e+00  1.58046376e+00]
     [ 6.74501145e-01  9.82172869e-02  9.90107977e-01  7.90670654e-01]
     [ 1.89829664e-01 -1.31979479e-01  5.92245988e-01  7.90670654e-01]
     [ 1.28034050e+00  9.82172869e-02  9.33270550e-01  1.18556721e+00]
     [ 1.03800476e+00  9.82172869e-02  1.04694540e+00  1.58046376e+00]
     [ 1.28034050e+00  9.82172869e-02  7.62758269e-01  1.44883158e+00]
     [-5.25060772e-02 -8.22569778e-01  7.62758269e-01  9.22302838e-01]
     [ 1.15917263e+00  3.28414053e-01  1.21745768e+00  1.44883158e+00]
     [ 1.03800476e+00  5.58610819e-01  1.10378283e+00  1.71209594e+00]
     [ 1.03800476e+00 -1.31979479e-01  8.19595696e-01  1.44883158e+00]
     [ 5.53333275e-01 -1.28296331e+00  7.05920842e-01  9.22302838e-01]
     [ 7.95669016e-01 -1.31979479e-01  8.19595696e-01  1.05393502e+00]
     [ 4.32165405e-01  7.88807586e-01  9.33270550e-01  1.44883158e+00]
     [ 6.86617933e-02 -1.31979479e-01  7.62758269e-01  7.90670654e-01]]

#方法2： 区间缩放法 例3：对简单示例二维矩阵的列数据进行
    from sklearn.preprocessing import MinMaxScaler
    
    data = [[0, 0], 
            [0, 0], 
            [100, 1], 
            [1, 1]]
    
    scaler = MinMaxScaler()
    print(scaler.fit(data))
    print(scaler.transform(data))

MinMaxScaler(copy=True, feature_range=(0, 1))
    [[0.   0.  ]
     [0.   0.  ]
     [1.   1.  ]
     [0.01 1.  ]]

#方法2： 区间缩放法 例4：对iris数据二维矩阵的列数据进行
    from sklearn.preprocessing import MinMaxScaler
    
    data = iris.data
    
    scaler = MinMaxScaler()
    print(scaler.fit(data))
    print(scaler.transform(data))

MinMaxScaler(copy=True, feature_range=(0, 1))
    [[0.22222222 0.625      0.06779661 0.04166667]
     [0.16666667 0.41666667 0.06779661 0.04166667]
     [0.11111111 0.5        0.05084746 0.04166667]
     [0.08333333 0.45833333 0.08474576 0.04166667]
     [0.19444444 0.66666667 0.06779661 0.04166667]
     [0.30555556 0.79166667 0.11864407 0.125     ]
     [0.08333333 0.58333333 0.06779661 0.08333333]
     [0.19444444 0.58333333 0.08474576 0.04166667]
     [0.02777778 0.375      0.06779661 0.04166667]
     [0.16666667 0.45833333 0.08474576 0.        ]
     [0.30555556 0.70833333 0.08474576 0.04166667]
     [0.13888889 0.58333333 0.10169492 0.04166667]
     [0.13888889 0.41666667 0.06779661 0.        ]
     [0.         0.41666667 0.01694915 0.        ]
     [0.41666667 0.83333333 0.03389831 0.04166667]
     [0.38888889 1.         0.08474576 0.125     ]
     [0.30555556 0.79166667 0.05084746 0.125     ]
     [0.22222222 0.625      0.06779661 0.08333333]
     [0.38888889 0.75       0.11864407 0.08333333]
     [0.22222222 0.75       0.08474576 0.08333333]
     [0.30555556 0.58333333 0.11864407 0.04166667]
     [0.22222222 0.70833333 0.08474576 0.125     ]
     [0.08333333 0.66666667 0.         0.04166667]
     [0.22222222 0.54166667 0.11864407 0.16666667]
     [0.13888889 0.58333333 0.15254237 0.04166667]
     [0.19444444 0.41666667 0.10169492 0.04166667]
     [0.19444444 0.58333333 0.10169492 0.125     ]
     [0.25       0.625      0.08474576 0.04166667]
     [0.25       0.58333333 0.06779661 0.04166667]
     [0.11111111 0.5        0.10169492 0.04166667]
     [0.13888889 0.45833333 0.10169492 0.04166667]
     [0.30555556 0.58333333 0.08474576 0.125     ]
     [0.25       0.875      0.08474576 0.        ]
     [0.33333333 0.91666667 0.06779661 0.04166667]
     [0.16666667 0.45833333 0.08474576 0.04166667]
     [0.19444444 0.5        0.03389831 0.04166667]
     [0.33333333 0.625      0.05084746 0.04166667]
     [0.16666667 0.66666667 0.06779661 0.        ]
     [0.02777778 0.41666667 0.05084746 0.04166667]
     [0.22222222 0.58333333 0.08474576 0.04166667]
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