Scipy 和 Sklearn Yeo-Johnson 归一化结果不匹配

Question

我正在运行 Yeo Johnson Transform，并按照 Scipy 网站上给出的示例进行操作。 Scipy link 我还将它与 Sklearn 实现进行了比较。 这是代码： 我

    import seaborn as sns
    from sklearn.preprocessing import PowerTransformer
    from scipy import stats
    import matplotlib.pyplot as plt
    import numpy as np

    fig = plt.figure( figsize=(10,10))
    ax1 = fig.add_subplot(421)
    x = stats.loggamma.rvs(5, size=500) + 5
    prob = stats.probplot(x, dist=stats.norm, plot=ax1)
    ax1.set_xlabel('')
    ax1.set_title('Probplot')

    ax2 = fig.add_subplot(422)
    sns.distplot(x, color="skyblue")
    ax2.set_title('Distribution of Data')

    ax3 = fig.add_subplot(423)
    xt_scipy, lmbda = stats.yeojohnson(x)
    prob = stats.probplot(xt_scipy, dist=stats.norm, plot=ax3)
    ax3.set_xlabel('')
    ax3.set_title('Probplot:Yeo-Johnson:Scipy')

    ax4 = fig.add_subplot(424)
    sns.distplot(xt_scipy, color="skyblue")
    ax4.set_title('Distribution of Transformed Data')

    ax5 = fig.add_subplot(425)
    pt = PowerTransformer(method = 'yeo-johnson',standardize = True)
    xt_sklearn = pt.fit_transform(x.reshape(-1,1))
    prob = stats.probplot(xt_sklearn.flatten(), dist=stats.norm, plot=ax5)
    ax5.set_xlabel('')
    ax5.set_title('Probplot:Yeo-Johnson:Sklearn')

    ax6 = fig.add_subplot(426)
    sns.distplot(xt_sklearn, color="skyblue")
    ax6.set_title('Distribution of Transformed Data')
    plt.tight_layout(h_pad=0.9, w_pad=0.9)
    plt.show()

查看附图，可以看出两种方法似乎都按预期对数据进行了归一化，从分位数可以看出。
但是，两个库的转换数据分布图虽然形状相同，但值范围不同。 为什么转换后的值不同？哪一个对应于真正的 Yeo Johnson 公式？ 赛迪

Answer 1

这是我的错。我没有意识到 Sklearn 在默认情况下在 Power 转换后会进行标准缩放。这是创建匹配结果的代码的修改。

    import seaborn as sns
    import sklearn.preprocessing
    from sklearn.preprocessing import PowerTransformer, StandardScaler
    from scipy import stats
    import matplotlib.pyplot as plt
    import numpy as np

    ss = StandardScaler()
    fig = plt.figure( figsize=(10,10))
    ax1 = fig.add_subplot(441)
    x = stats.loggamma.rvs(5, size=500) + 5
    prob = stats.probplot(x, dist=stats.norm, plot=ax1)
    ax1.set_xlabel('')
    ax1.set_title('Probplot')

    ax2 = fig.add_subplot(442)
    sns.distplot(x, color="skyblue")
    ax2.set_title('Distribution of Data')

    ax5 = fig.add_subplot(445)
    xt_scipy, lmbda = stats.yeojohnson(x)
    prob = stats.probplot(xt_scipy, dist=stats.norm, plot=ax5)
    ax5.set_xlabel('')
    ax5.set_title('Probplot:Yeo-Johnson:Scipy')

    ax6 = fig.add_subplot(446)
    sns.distplot(xt_scipy, color="skyblue")
    ax6.set_title('Distribution of Transformed Data')

    ax7 = fig.add_subplot(447)
    xt_scipy_ss, lmbda = stats.yeojohnson(x)
    xt_scipy_ss = ss.fit_transform(xt_scipy_ss.reshape(-1, 1))
    prob = stats.probplot(xt_scipy_ss.flatten(), dist=stats.norm, plot=ax7)
    ax7.set_xlabel('')
    ax7.set_title('Probplot:Yeo-Johnson + Stand Scal:Scipy')

    ax8 = fig.add_subplot(448)
    sns.distplot(xt_scipy_ss, color="skyblue")
    ax8.set_title('Distribution of Transformed Data')

    ax9 = fig.add_subplot(449)
    pt = PowerTransformer(method = 'yeo-johnson',standardize = False)
    xt_sklearn = pt.fit_transform(x.reshape(-1,1))
    prob = stats.probplot(xt_sklearn.flatten(), dist=stats.norm, plot=ax9)
    ax9.set_xlabel('')
    ax9.set_title('Probplot:Yeo-Johnson:Sklearn')

    ax10 = fig.add_subplot(4,4,10)
    sns.distplot(xt_sklearn, color="skyblue")
    ax10.set_title('Distribution of Transformed Data')

    ax11 = fig.add_subplot(4,4,11)
    pt = PowerTransformer(method='yeo-johnson', standardize=True)
    xt_sklearn_ss = pt.fit_transform(x.reshape(-1, 1))
    prob = stats.probplot(xt_sklearn_ss.flatten(), dist=stats.norm, plot=ax11)
    ax11.set_xlabel('')
    ax11.set_title('Probplot:Yeo-Johnson:Sklearn with Stand Scal')

    ax12 = fig.add_subplot(4, 4, 12)
    sns.distplot(xt_sklearn_ss, color="skyblue")
    ax12.set_title('Distribution of Transformed Data')
    plt.tight_layout(h_pad=0.9, w_pad=0.9)
    plt.show()