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wayfair / pylift

Uplift modeling package.
http://pylift.readthedocs.io
BSD 2-Clause "Simplified" License
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WOE-explore.base._get_counts #44

Open alg-jmx opened 3 years ago

alg-jmx commented 3 years ago

counts_dict = {} y = df_new[col_treatment] trt = df_new[col_outcome] for feat in feats: bin_feat = str(feat) + '_bin' counts1_t1 = df_new[(y == 1) & (trt == 1)][[feat, bin_feat]].groupby(bin_feat).count().rename( columns={feat: 'counts_y1t1'}) counts1_t0 = df_new[(y == 1) & (trt == 0)][[feat, bin_feat]].groupby(bin_feat).count().rename( columns={feat: 'counts_y1t0'}) counts0_t1 = df_new[(y == 0) & (trt == 1)][[feat, bin_feat]].groupby(bin_feat).count().rename( columns={feat: 'counts_y0t1'}) counts0_t0 = df_new[(y == 0) & (trt == 0)][[feat, bin_feat]].groupby(bin_feat).count().rename( columns={feat: 'counts_y0t0'})

creating a dataframe with all of these results

    counts_dict[feat] = pd.concat([counts1_t1, counts1_t0, counts0_t1, counts0_t0], axis=1).fillna(
        0) + 1  # replace any empty slots with zeros (and add 1 to everything)

should be

counts_dict = {} y = df_new[col_outcome] trt = df_new[col_treatment] for feat in feats: bin_feat = str(feat) + '_bin' counts1_t1 = df_new[(y == 1) & (trt == 1)][[feat, bin_feat]].groupby(bin_feat).count().rename( columns={feat: 'counts_y1t1'}) counts1_t0 = df_new[(y == 1) & (trt == 0)][[feat, bin_feat]].groupby(bin_feat).count().rename( columns={feat: 'counts_y1t0'}) counts0_t1 = df_new[(y == 0) & (trt == 1)][[feat, bin_feat]].groupby(bin_feat).count().rename( columns={feat: 'counts_y0t1'}) counts0_t0 = df_new[(y == 0) & (trt == 0)][[feat, bin_feat]].groupby(bin_feat).count().rename( columns={feat: 'counts_y0t0'})

creating a dataframe with all of these results

    counts_dict[feat] = pd.concat([counts1_t1, counts1_t0, counts0_t1, counts0_t0], axis=1).fillna(
        0) + 1  # replace any empty slots with zeros (and add 1 to everything)