def get_label_feature_of_date(label_field): """提取标记区间日期相关特征""" # 源数据 data = label_field.copy() data['Coupon_id'] = data['Coupon_id'].map(int) # 将Coupon_id列中float类型的元素转换为int类型,因为列中存在np.nan即空值会让整列的元素变为float data['Date_received'] = data['Date_received'].map(int) # 将Date_received列中float类型的元素转换为int类型,因为列中存在np.nan即空值会让整列的元素变为float # 返回的特征数据集 feature = data.copy() feature['Week_received'] = feature['date_received'].map(lambda x: x.isoweekday()) # 星期几,星期一为1,星期天为7 feature['is_weekend_reveived'] = feature['Week_received'].map(lambda x: 1 if x == 6 or x == 7 else 0) # 判断领券日是否为休息日 feature['Month_received'] = feature['date_received'].map(lambda x: x.month)# 月份 feature = pd.concat([feature, pd.get_dummies(feature['Week_received'], prefix='Week_received')], axis=1) # one-hot离散星期几 feature.index = range(len(feature)) # 重置index # 返回 return feature解释
时间: 2024-04-07 09:28:05 浏览: 75
这段代码的主要作用是提取标记区间日期相关的特征,将标记区间内的数据作为输入。具体来说,该函数首先对输入数据进行复制,然后将Coupon_id和Date_received两列的数据类型从float转换为int,以便后续的处理。接着,通过lambda函数分别计算领券日是星期几、是否为休息日、月份等特征,并将星期几进行one-hot编码。最后,通过concat()函数将编码后的特征数据集与原始数据集进行合并,并通过reset_index()函数重置数据集的行索引。最终将特征数据集作为输出返回。
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