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  • sklearn.preprocessing.StandardScaler 离线使用 不使用pickle如何做

    Having said that, you can query sklearn.preprocessing.StandardScaler for the fit parameters:

    scale_ : ndarray, shape (n_features,) Per feature relative scaling of the data. New in version 0.17: scale_ is recommended instead of deprecated std_. mean_ : array of floats with shape [n_features] The mean value for each feature in the training set.

    The following short snippet illustrates this:

    from sklearn import preprocessing
    import numpy as np
    
    s = preprocessing.StandardScaler()
    s.fit(np.array([[1., 2, 3, 4]]).T)
    >>> s.mean_, s.scale_
    (array([ 2.5]), array([ 1.11803399]))
    

    参考:https://stackoverflow.com/questions/35944783/how-to-store-scaling-parameters-for-later-use

    解法:
    >>> from sklearn import preprocessing
    >>> import numpy as np
    >>> 
    >>> s = preprocessing.StandardScaler()
    >>> s.fit(np.array([[1., 2, 3, 4]]).T)
    StandardScaler(copy=True, with_mean=True, with_std=True)
    >>> s.mean_, s.scale_
    (array([2.5]), array([1.11803399]))
    >>> s.transform(np.array([[1., 2, 3, 4]]).T)
    array([[-1.34164079],
           [-0.4472136 ],
           [ 0.4472136 ],
           [ 1.34164079]])
    >>> (1-s.mean_)/s.scale_
    array([-1.34164079])
    >>> a=np.array([1,2,3])
    >>> b=np.array([1,2,3])
    >>> a==b
    array([ True,  True,  True])

     (np.array([1., 2, 3, 4])-s.mean_)/s.scale_
    array([-1.34164079, -0.4472136 ,  0.4472136 ,  1.34164079]) 和transform效果一样。

    可以看到,离线使用StandardScaler时,只需要s.mean_, s.scale_这两个关键参数即可!

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  • 原文地址:https://www.cnblogs.com/bonelee/p/9095348.html
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