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| import pandas as pd | |
| import numpy as np | |
| from sklearn.base import BaseEstimator, TransformerMixin | |
| from sklearn.decomposition import PCA | |
| #from _config import config | |
| class PCAHandler(BaseEstimator, TransformerMixin): | |
| def __init__(self, apply_pca=False, variance=0.95): | |
| self.apply_pca = apply_pca | |
| self.variance = variance | |
| self.pca = None | |
| self.num_cols = None # To store the indices of numerical columns | |
| def fit(self, X, y=None): | |
| if self.apply_pca: | |
| # Select only numerical columns and exclude 'groupid' | |
| self.num_cols = X.select_dtypes(include='number').columns | |
| self.num_cols = self.num_cols.difference(['groupid']) # Exclude 'groupid' | |
| # Apply PCA only to the remaining numerical columns | |
| self.pca = PCA(n_components=self.variance) | |
| self.pca.fit(X[self.num_cols]) | |
| return self | |
| def transform(self, X): | |
| if self.apply_pca and self.pca: | |
| # Transform only the numerical columns excluding 'groupid' | |
| X_transformed = self.pca.transform(X[self.num_cols]) | |
| # Create a DataFrame for the PCA-transformed columns | |
| X_pca = pd.DataFrame(X_transformed, index=X.index, columns=[f'PCA_{i+1}' for i in range(X_transformed.shape[1])]) | |
| # Combine non-PCA columns with PCA-transformed ones | |
| X = X.drop(columns=self.num_cols).join(X_pca) | |
| return X | |