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08 Scikit-learn中的Scaler import numpy as np from sklearn import datasets import datetime print(datetime.datetime.now()) 2022-01-16 23:05:58.735705 iris = datasets.load_iris() X = iris.data y = iris.target X[:10,:] array([[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],[5.4, 3.9, 1.7, 0.4],[4.6, 3.4, 1.4, 0.3],[5. , 3.4, 1.5, 0.2],[4.4, 2.9, 1.4, 0.2],[4.9, 3.1, 1.5, 0.1]]) from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.2, random_state=666) scikit-learn中的StandardScaler from sklearn.preprocessing import StandardScaler standardScalar = StandardScaler() standardScalar.fit(X_train) StandardScaler() standardScalar.mean_ array([5.83416667, 3.08666667, 3.70833333, 1.17 ]) standardScalar.scale_ array([0.81019502, 0.44327067, 1.76401924, 0.75317107]) standardScalar.transform(X_train) array([[-0.90616043, 0.93246262, -1.30856471, -1.28788802],[-1.15301457, -0.19551636, -1.30856471, -1.28788802],[-0.16559799, -0.64670795, 0.22203084, 0.17260355],[ 0.45153738, 0.70686683, 0.95898425, 1.50032315],[-0.90616043, -1.32349533, -0.40154513, -0.09294037],[ 1.43895396, 0.25567524, 0.56216318, 0.30537551],[ 0.3281103 , -1.09789954, 1.0723617 , 0.30537551],[ 2.1795164 , -0.19551636, 1.63924894, 1.23477923],[-0.78273335, 2.2860374 , -1.25187599, -1.42065998],[ 0.45153738, -2.00028272, 0.44878573, 0.43814747],[ 1.80923518, -0.42111215, 1.46918276, 0.83646335],[ 0.69839152, 0.25567524, 0.90229552, 1.50032315],[ 0.20468323, 0.70686683, 0.44878573, 0.57091943],[-0.78273335, -0.87230374, 0.10865339, 0.30537551],[-0.53587921, 1.38365421, -1.25187599, -1.28788802],[-0.65930628, 1.38365421, -1.25187599, -1.28788802],[-1.0295875 , 0.93246262, -1.19518726, -0.75680017],[-1.77014994, -0.42111215, -1.30856471, -1.28788802],[-0.04217092, -0.87230374, 0.10865339, 0.03983159],[-0.78273335, 0.70686683, -1.30856471, -1.28788802],[-1.52329579, 0.70686683, -1.30856471, -1.15511606],[ 0.82181859, 0.25567524, 0.78891808, 1.10200727],[-0.16559799, -0.42111215, 0.27871956, 0.17260355],[ 0.94524567, -0.19551636, 0.39209701, 0.30537551],[ 0.20468323, -0.42111215, 0.44878573, 0.43814747],[-1.39986872, 0.25567524, -1.19518726, -1.28788802],[-1.15301457, 1.15805842, -1.30856471, -1.42065998],[ 1.06867274, 0.03007944, 1.0723617 , 1.63309511],[ 0.57496445, -0.87230374, 0.67554063, 0.83646335],[ 0.3281103 , -0.64670795, 0.56216318, 0.03983159],[ 0.45153738, -0.64670795, 0.6188519 , 0.83646335],[-0.16559799, 2.96282478, -1.25187599, -1.0223441 ],[ 0.57496445, -1.32349533, 0.67554063, 0.43814747],[ 0.69839152, -0.42111215, 0.33540828, 0.17260355],[-0.90616043, 1.60925001, -1.02512109, -1.0223441 ],[ 1.19209981, -0.64670795, 0.6188519 , 0.30537551],[-0.90616043, 0.93246262, -1.30856471, -1.15511606],[-1.89357701, -0.19551636, -1.47863088, -1.42065998],[ 0.08125616, -0.19551636, 0.78891808, 0.83646335],[ 0.69839152, -0.64670795, 1.0723617 , 1.23477923],[-0.28902506, -0.64670795, 0.67554063, 1.10200727],[-0.41245214, -1.54909113, -0.00472406, -0.22571233],[ 1.31552689, 0.03007944, 0.67554063, 0.43814747],[ 0.57496445, 0.70686683, 1.0723617 , 1.63309511],[ 0.82181859, -0.19551636, 1.18573914, 1.36755119],[-0.16559799, 1.60925001, -1.13849854, -1.15511606],[ 0.94524567, -0.42111215, 0.50547446, 0.17260355],[ 1.06867274, 0.48127103, 1.12905042, 1.76586707],[-1.27644165, -0.19551636, -1.30856471, -1.42065998],[-1.0295875 , 1.15805842, -1.30856471, -1.28788802],[ 0.20468323, -0.19551636, 0.6188519 , 0.83646335],[-1.0295875 , -0.19551636, -1.19518726, -1.28788802],[ 0.3281103 , -0.19551636, 0.67554063, 0.83646335],[ 0.69839152, 0.03007944, 1.01567297, 0.83646335],[-0.90616043, 1.38365421, -1.25187599, -1.0223441 ],[-0.16559799, -0.19551636, 0.27871956, 0.03983159],[-1.0295875 , 0.93246262, -1.36525344, -1.15511606],[-0.90616043, 1.60925001, -1.25187599, -1.15511606],[-1.52329579, 0.25567524, -1.30856471, -1.28788802],[-0.53587921, -0.19551636, 0.44878573, 0.43814747],[ 0.82181859, -0.64670795, 0.50547446, 0.43814747],[ 0.3281103 , -0.64670795, 0.16534211, 0.17260355],[-1.27644165, 0.70686683, -1.19518726, -1.28788802],[-0.90616043, 0.48127103, -1.13849854, -0.88957213],[-0.04217092, -0.87230374, 0.78891808, 0.96923531],[-0.28902506, -0.19551636, 0.22203084, 0.17260355],[ 0.57496445, -0.64670795, 0.78891808, 0.43814747],[ 1.06867274, 0.48127103, 1.12905042, 1.23477923],[ 1.68580811, -0.19551636, 1.18573914, 0.57091943],[ 1.06867274, -0.19551636, 0.8456068 , 1.50032315],[-1.15301457, 0.03007944, -1.25187599, -1.42065998],[-1.15301457, -1.32349533, 0.44878573, 0.70369139],[-0.16559799, -1.32349533, 0.73222935, 1.10200727],[-1.15301457, -1.54909113, -0.23147896, -0.22571233],[-0.41245214, -1.54909113, 0.05196466, -0.09294037],[ 1.06867274, -1.32349533, 1.18573914, 0.83646335],[ 0.82181859, -0.19551636, 1.01567297, 0.83646335],[-0.16559799, -1.09789954, -0.11810151, -0.22571233],[ 0.20468323, -2.00028272, 0.73222935, 0.43814747],[ 1.06867274, 0.03007944, 0.56216318, 0.43814747],[-1.15301457, 0.03007944, -1.25187599, -1.28788802],[ 0.57496445, -1.32349533, 0.73222935, 0.96923531],[-1.39986872, 0.25567524, -1.36525344, -1.28788802],[ 0.20468323, -0.87230374, 0.78891808, 0.57091943],[-0.04217092, -1.09789954, 0.16534211, 0.03983159],[ 1.31552689, 0.25567524, 1.12905042, 1.50032315],[-1.77014994, -0.19551636, -1.36525344, -1.28788802],[ 1.56238103, -0.19551636, 1.24242787, 1.23477923],[ 1.19209981, 0.25567524, 1.24242787, 1.50032315],[-0.78273335, 0.93246262, -1.25187599, -1.28788802],[ 2.54979762, 1.60925001, 1.52587149, 1.10200727],[ 0.69839152, -0.64670795, 1.0723617 , 1.36755119],[-0.28902506, -0.42111215, -0.06141278, 0.17260355],[-0.41245214, 2.51163319, -1.30856471, -1.28788802],[-1.27644165, -0.19551636, -1.30856471, -1.15511606],[ 0.57496445, -0.42111215, 1.0723617 , 0.83646335],[-1.77014994, 0.25567524, -1.36525344, -1.28788802],[-0.53587921, 1.8348458 , -1.13849854, -1.0223441 ],[-1.0295875 , 0.70686683, -1.19518726, -1.0223441 ],[ 1.06867274, -0.19551636, 0.73222935, 0.70369139],[-0.53587921, 1.8348458 , -1.36525344, -1.0223441 ],[ 2.30294347, -0.64670795, 1.69593766, 1.10200727],[-0.28902506, -0.87230374, 0.27871956, 0.17260355],[ 1.19209981, -0.19551636, 1.01567297, 1.23477923],[-0.41245214, 0.93246262, -1.36525344, -1.28788802],[-1.27644165, 0.70686683, -1.02512109, -1.28788802],[-0.53587921, 0.70686683, -1.13849854, -1.28788802],[ 2.30294347, 1.60925001, 1.69593766, 1.36755119],[ 1.31552689, 0.03007944, 0.95898425, 1.23477923],[-0.28902506, -1.32349533, 0.10865339, -0.09294037],[-0.90616043, 0.70686683, -1.25187599, -1.28788802],[-0.90616043, 1.60925001, -1.19518726, -1.28788802],[ 0.3281103 , -0.42111215, 0.56216318, 0.30537551],[-0.04217092, 2.0604416 , -1.42194216, -1.28788802],[-1.0295875 , -2.45147431, -0.11810151, -0.22571233],[ 0.69839152, 0.25567524, 0.44878573, 0.43814747],[ 0.3281103 , -0.19551636, 0.50547446, 0.30537551],[ 0.08125616, 0.25567524, 0.6188519 , 0.83646335],[ 0.20468323, -2.00028272, 0.16534211, -0.22571233],[ 1.93266225, -0.64670795, 1.35580532, 0.96923531]]) X_train[:10,:] array([[5.1, 3.5, 1.4, 0.2],[4.9, 3. , 1.4, 0.2],[5.7, 2.8, 4.1, 1.3],[6.2, 3.4, 5.4, 2.3],[5.1, 2.5, 3. , 1.1],[7. , 3.2, 4.7, 1.4],[6.1, 2.6, 5.6, 1.4],[7.6, 3. , 6.6, 2.1],[5.2, 4.1, 1.5, 0.1],[6.2, 2.2, 4.5, 1.5]]) X_train = standardScalar.transform(X_train) X_train[:10,:] array([[-0.90616043, 0.93246262, -1.30856471, -1.28788802],[-1.15301457, -0.19551636, -1.30856471, -1.28788802],[-0.16559799, -0.64670795, 0.22203084, 0.17260355],[ 0.45153738, 0.70686683, 0.95898425, 1.50032315],[-0.90616043, -1.32349533, -0.40154513, -0.09294037],[ 1.43895396, 0.25567524, 0.56216318, 0.30537551],[ 0.3281103 , -1.09789954, 1.0723617 , 0.30537551],[ 2.1795164 , -0.19551636, 1.63924894, 1.23477923],[-0.78273335, 2.2860374 , -1.25187599, -1.42065998],[ 0.45153738, -2.00028272, 0.44878573, 0.43814747]]) X_test_standard = standardScalar.transform(X_test) X_test_standard[:10,:] array([[-0.28902506, -0.19551636, 0.44878573, 0.43814747],[-0.04217092, -0.64670795, 0.78891808, 1.63309511],[-1.0295875 , -1.77468693, -0.23147896, -0.22571233],[-0.04217092, -0.87230374, 0.78891808, 0.96923531],[-1.52329579, 0.03007944, -1.25187599, -1.28788802],[-0.41245214, -1.32349533, 0.16534211, 0.17260355],[-0.16559799, -0.64670795, 0.44878573, 0.17260355],[ 0.82181859, -0.19551636, 0.8456068 , 1.10200727],[ 0.57496445, -1.77468693, 0.39209701, 0.17260355],[-0.41245214, -1.09789954, 0.39209701, 0.03983159]]) 使用歸一化后的數據進行knn分類from sklearn.neighbors import KNeighborsClassifier knn_clf = KNeighborsClassifier(n_neighbors=3) knn_clf.fit(X_train, y_train) KNeighborsClassifier(n_neighbors=3) knn_clf.score(X_test_standard, y_test) 1.0 注意，此時不能傳入沒有歸一化的數據！knn_clf.score(X_test, y_test) 0.3333333333333333 實現我們自己的standardScaler 代碼參見：這里X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.2, random_state=666) from playML.preprocessing import StandardScalermy_standardScalar = StandardScaler() my_standardScalar.fit(X_train) <playML.preprocessing.StandardScaler at 0x435ce64340> my_standardScalar.mean_ array([5.83416667, 3.08666667, 3.70833333, 1.17 ]) my_standardScalar.scale_ array([0.81019502, 0.44327067, 1.76401924, 0.75317107]) X_train = standardScalar.transform(X_train) X_train[:10,:] array([[ -8.31938844, -4.85979375, -2.84401549, -3.2633861 ],[ -8.62407329, -7.40446691, -2.84401549, -3.2633861 ],[ -7.4053339 , -8.42233617, -1.9763404 , -1.32426282],[ -6.64362178, -5.36872838, -1.55857092, 0.43857653],[ -8.31938844, -9.94914006, -2.32983766, -1.67683069],[ -5.4248824 , -6.38659765, -1.78352372, -1.14797889],[ -6.79596421, -9.44020543, -1.49429869, -1.14797889],[ -4.51082786, -7.40446691, -1.17293754, 0.08600866],[ -8.16704602, -1.80618597, -2.81187937, -3.43967004],[ -6.64362178, -11.47594395, -1.84779594, -0.97169495]]) Scikit-Learn中的最值歸一化 MinMaxScaler: http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MinMaxScaler.html練習：同學們也可以嘗試實現自己的MinMaxScaler:)

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