Kaggle Solo第一！

数据STUDIO

发布于 2024-05-20 13:53:42

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发布于 2024-05-20 13:53:42

文章被收录于专栏：数据STUDIO数据STUDIO

简介

第一名方案主要由4个XGBoost模型（2个不同的标签）以及2个GRU模型（2个不同模型）组成。

验证策略

使用前500天数据进行训练并且将剩余的时间用做验证。

特征

county+datetime+data_block_id的组合特征。
fw_new_feature可以提升大致 0.1~0.2

# client_df
client_df = client_df.drop(["date"]).sort(["data_block_id"], descending=False)
df = df.join(client_df.select(["county", "is_business", "product_type", "data_block_id","eic_count","installed_capacity"]), how="left", on=["county", "is_business", "product_type", "data_block_id"])
# electricity
electricity_df = electricity_df.drop(["origin_date"]).rename({"forecast_date":"datetime"}).with_columns([(pl.col("datetime").str.to_datetime()+pl.duration(days=1)).alias("datetime"), (pl.col("euros_per_mwh").abs()+0.1).alias("euros_per_mwh"),])
df = df.join(electricity_df[["data_block_id","datetime","euros_per_mwh"]], how="left", on=["data_block_id","datetime"])

# fw_df 
fw_df = fw_df.with_columns(
                (pl.col("origin_datetime").str.to_datetime()+pl.duration(hours="hours_ahead")).alias("datetime"),
                pl.col("latitude").cast(pl.datatypes.Float32).round(1),
                pl.col("longitude").cast(pl.datatypes.Float32).round(1),
                pl.col("data_block_id").cast(pl.datatypes.Int64),
            ).join(weather_station_to_county_mapping.drop(["county_name"]).with_columns(
                pl.col("latitude").cast(pl.datatypes.Float32).round(1),
                pl.col("longitude").cast(pl.datatypes.Float32).round(1),
            ), how="left",on=["longitude","latitude"]).drop(["longitude","latitude","origin_datetime"])
fw_df = fw_df.group_by(["county","datetime","data_block_id"]).agg([pl.mean(col).alias("fw_{}".format(col)) for col in forecast_weather_cols]).with_columns([pl.col("county").cast(pl.datatypes.Int64),    
                                                                    pl.col("data_block_id").cast(pl.datatypes.Int64),])
df = df.join(fw_df, how="left", on=["county","datetime","data_block_id"]).with_columns([(pl.col("installed_capacity")*pl.col("fw_surface_solar_radiation_downwards") / (pl.col("fw_temperature") + 273.15)).alias("fw_new_feature"),])

# hw_df 
hw_df = hw_df.with_columns(
            (pl.col("datetime").str.to_datetime()+pl.duration(days=1)).alias("datetime"),
                pl.col("latitude").cast(pl.datatypes.Float32).round(1),
                pl.col("longitude").cast(pl.datatypes.Float32).round(1),
                pl.col("data_block_id").cast(pl.datatypes.Int64),
            ).join(weather_station_to_county_mapping.drop(["county_name"]).with_columns(
                pl.col("latitude").cast(pl.datatypes.Float32).round(1),
                pl.col("longitude").cast(pl.datatypes.Float32).round(1),
            ), how="left",on=["longitude","latitude"]).drop(["longitude","latitude"])
hw_df = hw_df.group_by(["county","datetime","data_block_id"]).agg([pl.mean(col).alias("hw_{}".format(col)) for col in historical_weather_cols]).with_columns([                                                            pl.when(pl.col("datetime").dt.hour()>10).then(pl.col("datetime")+pl.duration(days=1)).otherwise(pl.col("datetime")).alias("datetime"),
                                                                pl.col("county").cast(pl.datatypes.Int64),    
                                                                pl.col("data_block_id").cast(pl.datatypes.Int64),])
df = df.join(hw_df, how="left", on=["county","datetime","data_block_id"])

Target

(target-target_shift2)/installed_capacity.
target/installed_capacity.

df = df.with_columns([
pl.when(pl.col('target').shift(2).over(['prediction_unit_id','hour']).is_not_null()).then(pl.col('target').shift(2).over(['prediction_unit_id','hour'])).when(pl.col('target').shift(3).over(['prediction_unit_id','hour']).is_not_null()).then(pl.col('target').shift(3).over(['prediction_unit_id','hour'])).when(pl.col('target').shift(4).over(['prediction_unit_id','hour']).is_not_null()).then(pl.col('target').shift(4).over(['prediction_unit_id','hour'])).when(pl.col('target').shift(2).mean().over(['datetime']).is_null()).then(pl.col('target').mean()).otherwise(pl.col('target').shift(2).mean().over(['datetime'])).fill_null(0.0).cast(pl.Float32).alias('target_shift_tmp'),

        pl.when(pl.col('data_block_id')<=1).then(pl.col('installed_capacity').shift(-2).over(['prediction_unit_id','hour'])).when(pl.col('installed_capacity').shift(1).over(['prediction_unit_id','hour']).is_not_null()).then(pl.col('installed_capacity').shift(1).over(['prediction_unit_id','hour'])).when(pl.col('installed_capacity').shift(2).over(['prediction_unit_id','hour']).is_not_null()).then(pl.col('installed_capacity').shift(2).over(['prediction_unit_id','hour'])).when(pl.col('installed_capacity').shift(3).over(['prediction_unit_id','hour']).is_not_null()).then(pl.col('installed_capacity').shift(3).over(['prediction_unit_id','hour'])).when(pl.col('installed_capacity').mean().over(['datetime']).is_null()).then(pl.col('installed_capacity').mean()).otherwise(pl.col('installed_capacity').mean().over(['datetime'])).cast(pl.Float32).alias('installed_capacity_tmp'),
        ])
df = df.with_columns([   pl.when(pl.col('installed_capacity').is_null()).then(pl.col('installed_capacity_tmp')).otherwise(pl.col('installed_capacity')).clip(10,None).cast(pl.Float32).alias('installed_capacity_tmp'),   #clip(10,None) 
        ]).with_columns([
        pl.when(pl.col('installed_capacity_tmp')<2*pl.col('target_shift_tmp').abs()).then(2*pl.col('target_shift_tmp').abs()).otherwise(pl.col('installed_capacity_tmp')).cast(pl.Float32).alias('installed_capacity_tmp'),
        ])
df = df.with_columns([
        ((pl.col('target')-pl.col('target_shift_tmp')) / pl.col('installed_capacity_tmp')).alias('target2'),
        (pl.col('target') / pl.col('installed_capacity_tmp')).alias('target3'),
        ])

模型

XGBoost
GRU模型，输入的tensor大小为：(batch_size, 24 hours, 600 dense_feature_dim + 6*16 cat_feature_dim)，输出的tensor的shape为(batch_size, 24 hours)，此处类别特征为：['county', 'product_type', 'hour', 'month', 'weekday', 'day']，NN模型可以将最终分数提升0.9。

在线学习策略

在线学习策略可以使用更多数据，这对public learderboard帮助较小，但private leaderboard可能会带来更多的帮助。

参考文献

https://www.kaggle.com/competitions/predict-energy-behavior-of-prosumers/discussion/472793
https://www.kaggle.com/competitions/predict-energy-behavior-of-prosumers/overview

作者：hyd

本文参与腾讯云自媒体同步曝光计划，分享自微信公众号。

原始发表：2024-05-13，如有侵权请联系 cloudcommunity@tencent.com 删除

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