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@@ -12,16 +12,25 @@ model._Booster = booster
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# 2. 预测：ad_status = 0, 不出广告
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df_0 = pd.read_csv('./data/predict_data/predict_data_0.csv')
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columns_0 = df_0.columns.values.tolist()
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+columns_0.remove('videoid')
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y_pred_proba_0 = model.predict_proba(df_0[columns_0[2:]])
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-df_0['y_0'] = y_pred_proba_0
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+df_0['y_0'] = [x[1] for x in y_pred_proba_0]
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+pre_df_0 = df_0[['apptype', 'mid', 'videoid', 'y_0']].copy()
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# 3. 预测：ad_status = 1, 不出广告
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df_1 = pd.read_csv('./data/predict_data/predict_data_1.csv')
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columns_1 = df_1.columns.values.tolist()
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+columns_1.remove('videoid')
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y_pred_proba_1 = model.predict_proba(df_1[columns_1[2:]])
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-df_0['y_1'] = y_pred_proba_1
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+df_1['y_1'] = [x[1] for x in y_pred_proba_1]
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+pre_df_1 = df_1[['apptype', 'mid', 'videoid', 'y_1']].copy()
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# 4. merge 结果
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-res_df = pd.merge(df_0, df_1, how='left', on=['apptype', 'mid', 'videoid'])
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-print(res_df.head())
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+res_df = pd.merge(pre_df_0, pre_df_1, how='left', on=['apptype', 'mid', 'videoid'])
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res_df['res_predict'] = res_df['y_0'] - res_df['y_1']
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+print(res_df.head())
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+
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+# 5. to redis
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