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@@ -87,9 +87,9 @@ def predict_ad_group_video():
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for index, item in group_df.iterrows():
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predict_df[item['group']] = predict_df['video_ad_share_rate'] * item['group_ad_share_rate']
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# 获取分组对应的均值作为阈值
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- threshold_data[item['group']] = predict_df[item['group']].mean() / 48 * 27
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+ threshold_data[item['group']] = predict_df[item['group']].mean() / 24 * 13
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all_group_data.extend(predict_df[item['group']].tolist())
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- threshold_data['mean_group'] = np.mean(all_group_data) / 48 * 27
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+ threshold_data['mean_group'] = np.mean(all_group_data) / 24 * 13
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log_.info(f"threshold_data = {threshold_data}")
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# 将阈值写入redis
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for key, val in threshold_data.items():
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