| import numpy as np | |
| from sklearn.base import BaseEstimator, TransformerMixin | |
| class IQRCapper(BaseEstimator, TransformerMixin): | |
| """Winsorise each feature at the IQR fence learned from the fitted (training) data only.""" | |
| def __init__(self, factor=1.5): | |
| self.factor = factor | |
| def fit(self, X, y=None): | |
| X = np.asarray(X, dtype=float) | |
| q1, q3 = np.percentile(X, 25, axis=0), np.percentile(X, 75, axis=0) | |
| iqr = q3 - q1 | |
| self.lower_ = q1 - self.factor * iqr | |
| self.upper_ = q3 + self.factor * iqr | |
| return self | |
| def transform(self, X): | |
| return np.clip(np.asarray(X, dtype=float), self.lower_, self.upper_) | |