예제 #1
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    def test_min_max_scaler_zero_variance_features(self):
        """Check min max scaler on toy data with zero variance features"""
        X = [[0., 1., +0.5],
             [0., 1., -0.1],
             [0., 1., +1.1]]

        X_new = [[+0., 2., 0.5],
                 [-1., 1., 0.0],
                 [+0., 1., 1.5]]
        # default params
        scaler = MinMaxScaler()
        X_trans = scaler.fit_transform(X)
        X_expected_0_1 = [[0., 0., 0.5],
                          [0., 0., 0.0],
                          [0., 0., 1.0]]
        assert_array_almost_equal(X_trans, X_expected_0_1)
        X_trans_inv = scaler.inverse_transform(X_trans)
        assert_array_almost_equal(X, X_trans_inv)

        X_trans_new = scaler.transform(X_new)
        X_expected_0_1_new = [[+0., 1., 0.500],
                              [-1., 0., 0.083],
                              [+0., 0., 1.333]]
        assert_array_almost_equal(X_trans_new, X_expected_0_1_new, decimal=2)

        # not default params
        scaler = MinMaxScaler(feature_range=(1, 2))
        X_trans = scaler.fit_transform(X)
        X_expected_1_2 = [[1., 1., 1.5],
                          [1., 1., 1.0],
                          [1., 1., 2.0]]
        assert_array_almost_equal(X_trans, X_expected_1_2)
예제 #2
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    def test_min_max_scaler_iris(self):
        iris = load_iris()
        X = iris.data

        scaler = MinMaxScaler()
        # default params
        X_trans = scaler.fit_transform(X)
        assert_array_almost_equal(X_trans.min(axis=0), 0)
        assert_array_almost_equal(X_trans.min(axis=0), 0)
        assert_array_almost_equal(X_trans.max(axis=0), 1)
        X_trans_inv = scaler.inverse_transform(X_trans)
        assert_array_almost_equal(X, X_trans_inv)

        # not default params: min=1, max=2
        scaler = MinMaxScaler(feature_range=(1, 2))
        X_trans = scaler.fit_transform(X)
        assert_array_almost_equal(X_trans.min(axis=0), 1)
        assert_array_almost_equal(X_trans.max(axis=0), 2)
        X_trans_inv = scaler.inverse_transform(X_trans)
        assert_array_almost_equal(X, X_trans_inv)

        # min=-.5, max=.6
        scaler = MinMaxScaler(feature_range=(-.5, .6))
        X_trans = scaler.fit_transform(X)
        assert_array_almost_equal(X_trans.min(axis=0), -.5)
        assert_array_almost_equal(X_trans.max(axis=0), .6)
        X_trans_inv = scaler.inverse_transform(X_trans)
        assert_array_almost_equal(X, X_trans_inv)

        # raises on invalid range
        scaler = MinMaxScaler(feature_range=(2, 1))
        self.assertRaises(ValueError, scaler.fit, X)
예제 #3
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    def test_min_max_scaler_iris(self):
        iris = load_iris()
        X = iris.data

        scaler = MinMaxScaler()
        # default params
        X_trans = scaler.fit_transform(X)
        assert_array_almost_equal(X_trans.min(axis=0), 0)
        assert_array_almost_equal(X_trans.min(axis=0), 0)
        assert_array_almost_equal(X_trans.max(axis=0), 1)
        X_trans_inv = scaler.inverse_transform(X_trans)
        assert_array_almost_equal(X, X_trans_inv)

        # not default params: min=1, max=2
        scaler = MinMaxScaler(feature_range=(1, 2))
        X_trans = scaler.fit_transform(X)
        assert_array_almost_equal(X_trans.min(axis=0), 1)
        assert_array_almost_equal(X_trans.max(axis=0), 2)
        X_trans_inv = scaler.inverse_transform(X_trans)
        assert_array_almost_equal(X, X_trans_inv)

        # min=-.5, max=.6
        scaler = MinMaxScaler(feature_range=(-.5, .6))
        X_trans = scaler.fit_transform(X)
        assert_array_almost_equal(X_trans.min(axis=0), -.5)
        assert_array_almost_equal(X_trans.max(axis=0), .6)
        X_trans_inv = scaler.inverse_transform(X_trans)
        assert_array_almost_equal(X, X_trans_inv)

        # raises on invalid range
        scaler = MinMaxScaler(feature_range=(2, 1))
        self.assertRaises(ValueError, scaler.fit, X)
예제 #4
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    def test_min_max_scaler_zero_variance_features(self):
        """Check min max scaler on toy data with zero variance features"""
        X = [[0., 1., +0.5], [0., 1., -0.1], [0., 1., +1.1]]

        X_new = [[+0., 2., 0.5], [-1., 1., 0.0], [+0., 1., 1.5]]
        # default params
        scaler = MinMaxScaler()
        X_trans = scaler.fit_transform(X)
        X_expected_0_1 = [[0., 0., 0.5], [0., 0., 0.0], [0., 0., 1.0]]
        assert_array_almost_equal(X_trans, X_expected_0_1)
        X_trans_inv = scaler.inverse_transform(X_trans)
        assert_array_almost_equal(X, X_trans_inv)

        X_trans_new = scaler.transform(X_new)
        X_expected_0_1_new = [[+0., 1., 0.500], [-1., 0., 0.083],
                              [+0., 0., 1.333]]
        assert_array_almost_equal(X_trans_new, X_expected_0_1_new, decimal=2)

        # not default params
        scaler = MinMaxScaler(feature_range=(1, 2))
        X_trans = scaler.fit_transform(X)
        X_expected_1_2 = [[1., 1., 1.5], [1., 1., 1.0], [1., 1., 2.0]]
        assert_array_almost_equal(X_trans, X_expected_1_2)