分类:PCA和使用sklearn的逻辑回归 [英] classification: PCA and logistic regression using sklearn

问题描述

我有一个分类问题，即我想在运行主成分分析(PCA)之后，使用逻辑回归，根据一组数字特征预测一个二进制目标.

I have a classification problem, ie I want to predict a binary target based on a collection of numerical features, using logistic regression, and after running a Principal Components Analysis (PCA).

我有2个数据集:df_train和df_valid(分别是训练集和验证集)作为熊猫数据框，其中包含要素和目标.第一步，我使用了get_dummies pandas函数将所有分类变量转换为布尔值.例如，我将拥有:

I have 2 datasets: df_train and df_valid (training set and validation set respectively) as pandas data frame, containing the features and the target. As a first step, I have used get_dummies pandas function to transform all the categorical variables as boolean. For example, I would have:

n_train = 10
np.random.seed(0)
df_train = pd.DataFrame({"f1":np.random.random(n_train), \
                         "f2": np.random.random(n_train), \
                         "f3":np.random.randint(0,2,n_train).astype(bool),\
                         "target":np.random.randint(0,2,n_train).astype(bool)})

In [36]: df_train
Out[36]: 
         f1        f2     f3 target
0  0.548814  0.791725  False  False
1  0.715189  0.528895   True   True
2  0.602763  0.568045  False   True
3  0.544883  0.925597   True   True
4  0.423655  0.071036   True   True
5  0.645894  0.087129   True  False
6  0.437587  0.020218   True   True
7  0.891773  0.832620   True  False
8  0.963663  0.778157  False  False
9  0.383442  0.870012   True   True

n_valid = 3
np.random.seed(1)
df_valid = pd.DataFrame({"f1":np.random.random(n_valid), \
                         "f2": np.random.random(n_valid), \
                         "f3":np.random.randint(0,2,n_valid).astype(bool),\
                         "target":np.random.randint(0,2,n_valid).astype(bool)})

In [44]: df_valid
Out[44]: 
         f1        f2     f3 target
0  0.417022  0.302333  False  False
1  0.720324  0.146756   True  False
2  0.000114  0.092339   True   True

我现在想应用PCA来减少问题的范围，然后使用sklearn中的LogisticRegression进行训练并获得对我的验证集的预测，但是我不确定我遵循的过程是否正确.这是我的工作:

I would like now to apply a PCA to reduce the dimensionality of my problem, then use LogisticRegression from sklearn to train and get prediction on my validation set, but I'm not sure the procedure I follow is correct. Here is what I do:

我的想法是我需要使用PCA以相同的方式转换我的训练和验证集.换句话说，我不能不单独执行PCA.否则，它们将被投影到不同的特征向量上.

The idea is that I need to transform both my training and validation set the same way with PCA. In other words, I can not perform PCA separately. Otherwise, they will be projected on different eigenvectors.

from sklearn.decomposition import PCA

pca = PCA(n_components=2) #assume to keep 2 components, but doesn't matter
newdf_train = pca.fit_transform(df_train.drop("target", axis=1))
newdf_valid = pca.transform(df_valid.drop("target", axis=1)) #not sure here if this is right

第二步:逻辑回归

这不是必需的，但我更喜欢将其保留为数据框:

Step2: Logistic Regression

It's not necessary, but I prefer to keep things as dataframe:

features_train = pd.DataFrame(newdf_train)
features_valid = pd.DataFrame(newdf_valid)  

现在我进行逻辑回归

from sklearn.linear_model import LogisticRegression
cls = LogisticRegression() 
cls.fit(features_train, df_train["target"])
predictions = cls.predict(features_valid)

我认为第2步是正确的，但是我对第1步有更多的疑问:我应该采用这种方式链接PCA，然后分类器吗?

I think step 2 is correct, but I have more doubts about step 1: is this the way I'm supposed to chain PCA, then a classifier ?

推荐答案

有一个管道.

from sklearn.decomposition import PCA
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import Pipeline

pca = PCA(n_components=2)
cls = LogisticRegression() 

pipe = Pipeline([('pca', pca), ('logistic', clf)])
pipe.fit(features_train, df_train["target"])
predictions = pipe.predict(features_valid)

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