Derived features: using PCA on a subset of columns

Derived features: using PCA on a subset of columns#

The modelling tools included in ISLP allow for construction of transformers applied to features.

import numpy as np
from sklearn.decomposition import PCA

from ISLP import load_data
from ISLP.models import (ModelSpec, 
                         pca, 
                         Feature, 
                         derived_feature,
                         build_columns)

Carseats = load_data('Carseats')
Carseats.columns

Index(['Sales', 'CompPrice', 'Income', 'Advertising', 'Population', 'Price',
       'ShelveLoc', 'Age', 'Education', 'Urban', 'US'],
      dtype='object')

Let’s create a ModelSpec that is aware of all of the relevant columns.

design = ModelSpec(Carseats.columns.drop(['Sales'])).fit(Carseats)

Suppose we want to make a Feature representing the first 3 principal components of the features ['CompPrice', 'Income', 'Advertising', 'Population', 'Price'].

We first make a Feature that represents these five features columns, then pca can be used to compute a new Feature that returns the first three principal components.

grouped = Feature(('CompPrice', 'Income', 'Advertising', 'Population', 'Price'), name='grouped', encoder=None)
sklearn_pca = PCA(n_components=3, whiten=True)

We can now fit sklearn_pca and create our new feature.

grouped_features = build_columns(design.column_info_,
                                 Carseats,
                                 grouped)[0]
sklearn_pca.fit(grouped_features) 
pca_var = derived_feature(['CompPrice', 'Income', 'Advertising', 'Population', 'Price'],
                           name='pca(grouped)', encoder=sklearn_pca)
derived_features, _ = build_columns(design.column_info_,
                                    Carseats, 
                                    pca_var,
                                    encoders=design.encoders_)

Helper function#

The function pca encompasses these steps into a single function for convenience.

group_pca = pca(['CompPrice', 'Income', 'Advertising', 'Population', 'Price'], 
                n_components=3, 
                whiten=True, 
                name='grouped')

pca_design = ModelSpec([group_pca], intercept=False)
ISLP_features = pca_design.fit_transform(Carseats)
ISLP_features.columns

Index(['pca(grouped, n_components=3, whiten=True)[0]',
       'pca(grouped, n_components=3, whiten=True)[1]',
       'pca(grouped, n_components=3, whiten=True)[2]'],
      dtype='object')

Direct comparison#

X = np.asarray(Carseats[['CompPrice', 'Income', 'Advertising', 'Population', 'Price']])
sklearn_features = sklearn_pca.fit_transform(X)

np.linalg.norm(ISLP_features - sklearn_features), np.linalg.norm(ISLP_features - np.asarray(derived_features))

(0.0, 0.0)
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