How to extract features using PCA in Python?
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How to extract features using PCA in Python?

How to extract features using PCA in Python?

This recipe helps you extract features using PCA in Python

1

Recipe Objective

In many datasets we find that number of features are very large and if we want to train the model it take more computational cost. To decrease the number of features we can use Principal component analysis (PCA). PCA decrease the number of features by selecting dimension of features which have most of the variance.

So this recipe is a short example of how can extract features using PCA in Python

Step 1 - Import the library

from sklearn import decomposition, datasets from sklearn.preprocessing import StandardScaler

Here we have imported various modules like decomposition, datasets and StandardScale from differnt libraries. We will understand the use of these later while using it in the in the code snipet.
For now just have a look on these imports.

Step 2 - Setup the Data

Here we have used datasets to load the inbuilt cancer dataset and we have created objects X and y to store the data and the target value respectively. dataset = datasets.load_breast_cancer() X = dataset.data print(X.shape) print(X)

Step 3 - Using StandardScaler and PCA

StandardScaler is used to remove the outliners and scale the data by making the mean of the data 0 and standard deviation as 1. So we are creating an object std_scl to use standardScaler. std_slc = StandardScaler() X_std = std_slc.fit_transform(X) print(X_std.shape) print(X_std)

We are also using Principal Component Analysis(PCA) which will reduce the dimension of features by creating new features which have most of the varience of the original data. We have passed the parameter n_components as 4 which is the number of feature in final dataset. pca = decomposition.PCA(n_components=4) X_std_pca = pca.fit_transform(X_std) print(X_std_pca.shape) print(X_std_pca) As an output we get:

(569, 30)

[[1.799e+01 1.038e+01 1.228e+02 ... 2.654e-01 4.601e-01 1.189e-01]
 [2.057e+01 1.777e+01 1.329e+02 ... 1.860e-01 2.750e-01 8.902e-02]
 [1.969e+01 2.125e+01 1.300e+02 ... 2.430e-01 3.613e-01 8.758e-02]
 ...
 [1.660e+01 2.808e+01 1.083e+02 ... 1.418e-01 2.218e-01 7.820e-02]
 [2.060e+01 2.933e+01 1.401e+02 ... 2.650e-01 4.087e-01 1.240e-01]
 [7.760e+00 2.454e+01 4.792e+01 ... 0.000e+00 2.871e-01 7.039e-02]]

(569, 30)

[[ 1.09706398 -2.07333501  1.26993369 ...  2.29607613  2.75062224
   1.93701461]
 [ 1.82982061 -0.35363241  1.68595471 ...  1.0870843  -0.24388967
   0.28118999]
 [ 1.57988811  0.45618695  1.56650313 ...  1.95500035  1.152255
   0.20139121]
 ...
 [ 0.70228425  2.0455738   0.67267578 ...  0.41406869 -1.10454895
  -0.31840916]
 [ 1.83834103  2.33645719  1.98252415 ...  2.28998549  1.91908301
   2.21963528]
 [-1.80840125  1.22179204 -1.81438851 ... -1.74506282 -0.04813821
  -0.75120669]]

(569, 4)

[[ 9.19283682  1.94858315 -1.12316659  3.63373524]
 [ 2.3878018  -3.76817178 -0.52929307  1.1182629 ]
 [ 5.73389628 -1.07517381 -0.55174687  0.91208083]
 ...
 [ 1.25617928 -1.90229673  0.56273054 -2.0892281 ]
 [10.37479406  1.67201009 -1.87702907 -2.35603254]
 [-5.4752433  -0.67063675  1.49044361 -2.29915639]]

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