sklearn中的'transform'和'fit_transform'有什么区别 [英] what is the difference between 'transform' and 'fit_transform' in sklearn

问题描述

在sklearn-python工具箱中，有关于sklearn.decomposition.RandomizedPCA的两个函数transform和fit_transform.两个函数的说明如下

但是它们之间有什么区别呢?

解决方案

.transform 方法适用于当您已经计算了 PCA 时，即如果您已经调用它的 .fit 方法.

在[12]中:pc2 = RandomizedPCA(n_components=3)In [13]: pc2.transform(X) # 不能转换，因为它不知道怎么做.---------------------------------------------------------------------------AttributeError 回溯(最近一次调用最后一次)<ipython-input-13-e3b6b8ea2aff>在 <module>()---->1 pc2.transform(X)/usr/local/lib/python3.4/dist-packages/sklearn/decomposition/pca.py in transform(self, X, y)714 # XXX 在 0.16 中移除 scipy.sparse 支持715 X = atleast2d_or_csr(X)-->716 如果 self.mean_ 不是 None:717 X = X - self.mean_718AttributeError: 'RandomizedPCA' 对象没有属性 'mean_'在 [14]: pc2.ftransform(X)pc2.fit pc2.fit_transform在 [14]: pc2.fit_transform(X)出[14]:数组([[-1.38340578, -0.2935787 ],[-2.22189802, 0.25133484],[-3.6053038，-0.04224385]，[ 1.38340578, 0.2935787 ],[ 2.22189802, -0.25133484],[ 3.6053038 , 0.04224385]])

所以你想 fit RandomizedPCA 然后 transform 为:

在[20]中:pca = RandomizedPCA(n_components=3)在 [21] 中:pca.fit(X)出[21]:RandomizedPCA(copy=True, iterated_power=3, n_components=3, random_state=None,白=假)在 [22]: pca.transform(z)出[22]:数组([[ 2.76681156, 0.58715739],[ 1.92831932, 1.13207093],[ 0.54491354, 0.83849224],[ 5.53362311, 1.17431479],[ 6.37211535, 0.62940125],[ 7.75552113, 0.92297994]])在 [23] 中:

特别是 PCA .transform 将通过矩阵 X 的 PCA 分解获得的基变化应用于矩阵 Z.

In the sklearn-python toolbox, there are two functions transform and fit_transform about sklearn.decomposition.RandomizedPCA. The description of two functions are as follows

But what is the difference between them ?

解决方案

The .transform method is meant for when you have already computed PCA, i.e. if you have already called its .fit method.

In [12]: pc2 = RandomizedPCA(n_components=3)

In [13]: pc2.transform(X) # can't transform because it does not know how to do it.
---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-13-e3b6b8ea2aff> in <module>()
----> 1 pc2.transform(X)

/usr/local/lib/python3.4/dist-packages/sklearn/decomposition/pca.py in transform(self, X, y)
    714         # XXX remove scipy.sparse support here in 0.16
    715         X = atleast2d_or_csr(X)
--> 716         if self.mean_ is not None:
    717             X = X - self.mean_
    718 

AttributeError: 'RandomizedPCA' object has no attribute 'mean_'

In [14]: pc2.ftransform(X) 
pc2.fit            pc2.fit_transform  

In [14]: pc2.fit_transform(X)
Out[14]: 
array([[-1.38340578, -0.2935787 ],
       [-2.22189802,  0.25133484],
       [-3.6053038 , -0.04224385],
       [ 1.38340578,  0.2935787 ],
       [ 2.22189802, -0.25133484],
       [ 3.6053038 ,  0.04224385]])
    
  

So you want to fit RandomizedPCA and then transform as:

In [20]: pca = RandomizedPCA(n_components=3)

In [21]: pca.fit(X)
Out[21]: 
RandomizedPCA(copy=True, iterated_power=3, n_components=3, random_state=None,
       whiten=False)

In [22]: pca.transform(z)
Out[22]: 
array([[ 2.76681156,  0.58715739],
       [ 1.92831932,  1.13207093],
       [ 0.54491354,  0.83849224],
       [ 5.53362311,  1.17431479],
       [ 6.37211535,  0.62940125],
       [ 7.75552113,  0.92297994]])

In [23]: 

In particular PCA .transform applies the change of basis obtained through the PCA decomposition of the matrix X to the matrix Z.

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