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conditional_features.py
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| import pandas as pd | |
| def build_conditional_features(df, cond_series, feat_cols=None): | |
| """ | |
| return a new dataframe with conditional features | |
| """ | |
| if len(cond_series) != len(df): | |
| raise Exception("Condition series isn't same num rows as features") | |
| if feat_cols is None: | |
| feat_cols = list(df.columns) | |
| cond_feat_to_series = {} | |
| for target_cond in cond_series.unique(): | |
| cond_mask = cond_series.map(lambda c: int(c == target_cond)) | |
| for feat in feat_cols: | |
| cond_feat = f"{feat}_||_{target_cond}" | |
| cond_feat_to_series[cond_feat] = df[feat] * cond_mask | |
| return pd.DataFrame(data=cond_feat_to_series) | |
| raw_df = pd.DataFrame(data={'name': ['foo','bar','roo'], 'age': [32, 20, 35]}) | |
| feats_df = pd.DataFrame([[1,1,1],[0,1,1],[1,0,0]], columns=['f1', 'f2', 'f3']) | |
| >>> feats_df | |
| f1 f2 f3 | |
| 0 1 1 1 | |
| 1 0 1 1 | |
| 2 1 0 0 | |
| def age_condition(row): | |
| if row['age'] > 21: | |
| return 'adult' | |
| return 'minor' | |
| cond_series = raw_df.apply(age_condition, axis=1) | |
| cond_feats = build_conditional_features(feats_df, cond_series) | |
| >>> cond_feats | |
| f1_||_adult f2_||_adult f3_||_adult f1_||_minor f2_||_minor f3_||_minor | |
| 0 1 1 1 0 0 0 | |
| 1 0 0 0 0 1 1 | |
| 2 1 0 0 0 0 0 | |
| feats_df = pd.concat([feats_df, cond_feats], axis=1) |
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