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| # from https://www.kaggle.com/samratp/wordbatch-ridge-fm-frtl-target-encoding-lgbm/notebook | |
| class TargetEncoder: | |
| # Adapted from https://www.kaggle.com/ogrellier/python-target-encoding-for-categorical-features | |
| def __repr__(self): | |
| return 'TargetEncoder' | |
| def __init__(self, smoothing=1, min_samples_leaf=1, noise_level=0, keep_original=False, suffix='enc'): | |
| self.smoothing = smoothing | |
| self.min_samples_leaf = min_samples_leaf | |
| self.noise_level = noise_level | |
| self.keep_original = keep_original | |
| self.suffix = suffix | |
| @staticmethod | |
| def add_noise(series, noise_level): | |
| return series * (1 + noise_level * np.random.randn(len(series))) | |
| def encode(self, train, test, target, cols, suffix=None): | |
| if suffix is None: | |
| suffix = self.suffix | |
| for col in cols: | |
| if self.keep_original: | |
| train[col + suffix], test[col + suffix] = self.encode_column(train[col], test[col], target) | |
| else: | |
| train[col], test[col] = self.encode_column(train[col], test[col], target) | |
| return train, test | |
| def encode_column(self, trn_series, tst_series, target): | |
| temp = pd.concat([trn_series, target], axis=1) | |
| # Compute target mean | |
| averages = temp.groupby(by=trn_series.name)[target.name].agg(["mean", "count"]) | |
| # Compute smoothing | |
| smoothing = 1 / (1 + np.exp(-(averages["count"] - self.min_samples_leaf) / self.smoothing)) | |
| # Apply average function to all target data | |
| prior = target.mean() | |
| # The bigger the count the less full_avg is taken into account | |
| averages[target.name] = prior * (1 - smoothing) + averages["mean"] * smoothing | |
| averages.drop(['mean', 'count'], axis=1, inplace=True) | |
| # Apply averages to trn and tst series | |
| ft_trn_series = pd.merge( | |
| trn_series.to_frame(trn_series.name), | |
| averages.reset_index().rename(columns={'index': target.name, target.name: 'average'}), | |
| on=trn_series.name, | |
| how='left')['average'].rename(trn_series.name + '_mean').fillna(prior) | |
| # pd.merge does not keep the index so restore it | |
| ft_trn_series.index = trn_series.index | |
| ft_tst_series = pd.merge( | |
| tst_series.to_frame(tst_series.name), | |
| averages.reset_index().rename(columns={'index': target.name, target.name: 'average'}), | |
| on=tst_series.name, | |
| how='left')['average'].rename(trn_series.name + '_mean').fillna(prior) | |
| # pd.merge does not keep the index so restore it | |
| ft_tst_series.index = tst_series.index | |
| return self.add_noise(ft_trn_series, self.noise_level), self.add_noise(ft_tst_series, self.noise_level) |
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