タクシー需要予測

未来のタクシー需要を先読みしよう!

賞金: 100,000 参加ユーザー数: 269 6ヶ月前に終了

3rd place solution + EDA

参加者の皆様お疲れさまでした。
運営様、コンペの開催ありがとうございました。
3rd place solutionを共有します。

今回のコンペではLBのスコアを見ながらモデルを作る方法を試しました。
それがPublicLBとPrivateLBの乖離につながったのかな。と思います。
正直これ以上のスコアは出せなかったので、今の実力の限界です。
モデル作成以降は前回のコンペからほぼコピペなので、伸びしろはあると考えています。
130回目ぐらいまで何をやってもPublicLB20の壁を超えられず足踏みしていましたが、
慣習的に4連休を取る人が多い感謝祭の次の週を当てる課題であると置き換えて以降、スコアが伸びはじめました。
解法に加え、EDAで見つけた感謝祭の特徴と、タクシー需要に関するネットの書き込みについても共有します。
備考:
リファクタリングしたら若干スコアが変わりました。(Private LB 17.54004 -> 17.58470)
3rd place solutionということで、ご了承ください。

from google.colab import drive
drive.mount('/content/drive')
Mounted at /content/drive
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
class CFG:
  SEED = 42
  IMP_TH = 90

class PATHS:
  MAIN_DIR = '/content/drive/MyDrive/share/competition/タクシー需要予測'
  TRAIN_DATA = MAIN_DIR + '/data/train_data.csv'
  WEATHER_DATA = MAIN_DIR + '/data/nyc_weather_2017_2019.csv'
  ZONE_DATA = MAIN_DIR + '/data/taxi_zones.csv'
  SUBMIT_DATA = MAIN_DIR + '/output/3rd_place_solution+EDA.csv'
from scipy import signal

def signal_low_pass_filter(input_signal):
    input_signal = np.nan_to_num(input_signal, nan=0.0)

    b, a = signal.iirfilter(4, 0.1, btype='lowpass', analog=False)
    # 初期状態を計算
    zi = signal.lfilter_zi(b, a)

    filtered_signal, zf = signal.lfilter(b, a, input_signal, zi=zi*input_signal[0])

    return filtered_signal
# trainデータの読み込み
train_data = pd.read_csv(PATHS.TRAIN_DATA, index_col='tpep_pickup_datetime',low_memory=False)
area_cols_name = train_data.columns

# test_dataを生成
date_index = pd.date_range(start='2019/12/01 00:00:00', end='2019/12/07 23:30:00', freq='30T')
test_data = pd.DataFrame(columns=train_data.columns, index=date_index)
train_data = pd.concat([train_data, test_data], axis=0)

# 日時を分解
train_data['Year'] =  pd.to_datetime(train_data.index).year
train_data['Month'] = pd.to_datetime(train_data.index).month
train_data['Day'] = pd.to_datetime(train_data.index).day
train_data['hour'] = pd.to_datetime(train_data.index).hour
train_data['min'] = pd.to_datetime(train_data.index).minute
train_data['weekday'] = pd.to_datetime(train_data.index).weekday
# 強めのローパスフィルタをかけると、Thanksgiving Dayの日はタクシー利用率が下がる傾向が確認できる
# (2017-11-23, 2018-11-22, 2019-11-28)
# 2018-11-15も下降傾向にあるが、最後上昇している、絶対値が大きい。といった異なる特徴を持っている
# Thanksgiving Dayは家でお祝いしている人が多く、4連休を取る人も多いらしい
# https://www.tripadvisor.co.uk/ShowTopic-g60763-i5-k5899614-Taxi_from_JFK_on_black_Friday-New_York_City_New_York.html
# EDAで見つけなくても利用許可された祝日ではある
# https://www.cs.ny.gov/attendance_leave/2019_legal_holidays.cfm

plt_df = train_data[train_data['Month'] == 11]

for index in range(7):
  # 指定した曜日のデータを取得
  weekday_data = plt_df[plt_df['weekday'] == index]

  # 曜日ごとにデータを重ねてプロット(乗車数の多い33を使って特徴を観測)
  plt.figure(figsize=(8, 3))
  for _, plot_data in weekday_data.groupby(weekday_data['Day']):
      plot_data['filtered_33'] = signal_low_pass_filter(plot_data['33'].values)
      plot_data['filtered_33'] = (plot_data['filtered_33'] - plot_data['filtered_33'].min()) / (plot_data['filtered_33'].max() - plot_data['filtered_33'].min())
      plt.plot(plot_data['filtered_33'].values, label=plot_data.index[0])
  # 凡例を左外に表示
  plt.legend(loc='upper left', bbox_to_anchor=(1, 1), bbox_transform=plt.gcf().transFigure)
  plt.show()
import re

# 独自の置換関数を定義。replace関数だとNaNになってしまう行があった
def replace_text_re(text):
   if isinstance(text, str):  # 列の要素が文字列であるかを確認
    # 英語の文字(アルファベット)をスペースに置き換える正規表現
    pattern = re.compile(r'[a-zA-Z]+')

    # 正規表現にマッチする部分をスペースに置き換える
    replaced_text = pattern.sub('', text)

    return replaced_text
   else:
    return text  # 文字列でない場合はそのまま返す

# 天候データの読み込み
weather_data_org = pd.read_csv(PATHS.WEATHER_DATA, index_col='DATE',low_memory=False)
weather_data_org.index = pd.to_datetime(weather_data_org.index)
#欠落している情報を直前の値で補間
lack_weather_info = pd.DataFrame(weather_data_org.loc['2019-05-09 22:51:00']).T.rename_axis('DATE')
lack_weather_info.index = ['2019-05-09 23:00:00']
weather_data_org = pd.concat([weather_data_org, lack_weather_info], axis=0)
weather_data_org.fillna(method='ffill',inplace=True)

# 日時を分解
weather_data_org['Year'] =  pd.to_datetime(weather_data_org.index).year
weather_data_org['Month'] = pd.to_datetime(weather_data_org.index).month
weather_data_org['Day'] = pd.to_datetime(weather_data_org.index).day
weather_data_org['hour'] = pd.to_datetime(weather_data_org.index).hour

# データクレンジング
weather_data_org['HourlyPrecipitation'] = weather_data_org['HourlyPrecipitation'].str.replace('s', '')
weather_data_org['HourlyPrecipitation'] = weather_data_org['HourlyPrecipitation'].str.replace('T', '0')
weather_data_org['HourlyPrecipitation'] = pd.to_numeric(weather_data_org['HourlyPrecipitation'])

#.replaceを使うとうまく行かないので、置換用の関数を使用(理由は不明)
weather_data_org['HourlyDryBulbTemperature'] = weather_data_org['HourlyDryBulbTemperature'].apply(replace_text_re)
weather_data_org['HourlyDryBulbTemperature'] = weather_data_org['HourlyDryBulbTemperature'].astype(np.float16)

weather_data_org['HourlyDewPointTemperature'] = weather_data_org['HourlyDewPointTemperature'].apply(replace_text_re)
weather_data_org['HourlyDewPointTemperature'] = weather_data_org['HourlyDewPointTemperature'].astype(np.float16)

weather_data_org['HourlyWetBulbTemperature'] = weather_data_org['HourlyWetBulbTemperature'].astype(np.float16)

weather_data_org['HourlyAltimeterSetting'] = weather_data_org['HourlyAltimeterSetting'].apply(replace_text_re)
weather_data_org['HourlyAltimeterSetting'] = weather_data_org['HourlyAltimeterSetting'].astype(np.float16)

weather_data_org['HourlySeaLevelPressure'] = weather_data_org['HourlySeaLevelPressure'].apply(replace_text_re)
weather_data_org['HourlySeaLevelPressure'] = weather_data_org['HourlySeaLevelPressure'].astype(np.float16)

weather_data_org['HourlyStationPressure'] = weather_data_org['HourlyStationPressure'].apply(replace_text_re)
weather_data_org['HourlyStationPressure'] = weather_data_org['HourlyStationPressure'].astype(np.float16)

weather_data_org['HourlyVisibility'] = weather_data_org['HourlyVisibility'].apply(replace_text_re)
weather_data_org['HourlyVisibility'] = weather_data_org['HourlyVisibility'].astype(np.float16)

weather_data_org['HourlyWindSpeed'] = weather_data_org['HourlyWindSpeed'].astype(np.int16)
def predict_weather(row):
    if row['HourlyPrecipitation'] < 0.1:
        return 0  # 降水量が少ない場合、晴れと予測
    elif row['HourlyPressureChange'] > 0:
        return 1  # 気圧が上昇している場合、安定した天気と予測
    else:
        return 2  # 気圧が下降している場合、不安定な天気と予測

# 天候データの特徴量追加
# 天候の予測
weather_data_org['predict_weather'] = weather_data_org.apply(predict_weather, axis=1)
# 寒さ指数
weather_data_org['Wind Chill Index'] = 13.12 + 0.6215 * weather_data_org['HourlyWetBulbTemperature'] - 11.37 * weather_data_org['HourlyWindSpeed'] + 0.3965 * weather_data_org['HourlyWetBulbTemperature'] * weather_data_org['HourlyWindSpeed']
# 不快指数
T_w = weather_data_org['HourlyDewPointTemperature'] - (100 - weather_data_org['HourlyRelativeHumidity']) / 5
weather_data_org['discomfort index'] = T_w - 0.55 * (1 - weather_data_org['HourlyRelativeHumidity'] / 100) * (T_w - 58)
# 平均的な値を算出(スコアが良かった特徴量だけ)
feature_cols = ['HourlyAltimeterSetting', 'HourlyPressureTendency', 'HourlyVisibility',
                'HourlyStationPressure', 'predict_weather', 'Wind Chill Index', 'discomfort index',
                'Year',	'Month',	'Day',	'hour']
weather_data = weather_data_org[feature_cols]
group_cols = ['Year',	'Month',	'Day',	'hour']
weather_feat_df = weather_data.groupby(group_cols).mean().reset_index(drop=True)

#train_dataと時間間隔を合わせる
weather_feat_df = weather_feat_df.loc[weather_feat_df.index.repeat(2)].reset_index(drop=True)
weather_feat_df = weather_feat_df[:len(train_data)]
weather_feat_df = weather_feat_df.fillna(0)
weather_cols = weather_feat_df.columns
def add_feats_function(df):
  # 2019年で正規化
  for col in area_cols_name:
    Nov2017_mean = df[(df['Year'] == 2017) & (df['Month'] == 11)][col].mean()
    Nov2018_mean = df[(df['Year'] == 2018) & (df['Month'] == 11)][col].mean()
    Nov2019_mean = df[(df['Year'] == 2019) & (df['Month'] == 11)][col].mean()
    df[col] = pd.concat([
        df[df['Year'] == 2017][col] / Nov2017_mean * Nov2019_mean,
        df[df['Year'] == 2018][col] / Nov2018_mean * Nov2019_mean,
        df[df['Year'] == 2019][col] / Nov2019_mean * Nov2019_mean
    ], axis = 0)

  # 強めのローパスフィルタをかけた波形
  iir_col_list = []
  for col in area_cols_name:
    df[f'{col}_iir'] = signal_low_pass_filter(df[col].values)
    iir_col_list.append(f'{col}_iir')

  df = df.drop(['Year',	'Month',	'Day',], axis=1)

  # スコアがよかった曜日単位の特徴量
  group_cols = ['hour', 'min', 'weekday']
  median_df = df.groupby(group_cols).transform('median').add_suffix('_weekday_median')
  std_df = df.groupby(group_cols).transform('std').add_suffix('_weekday_std')
  df = pd.concat([df, median_df, std_df], axis=1)

  # スコアがよかった天候関係の特徴量
  for col in weather_cols:
    df = pd.concat([df,
                    df[col].diff(1).fillna(method='bfill').rename(f'{col}_diff'),
                    df[col].shift(1).fillna(method='bfill').rename(f'{col}_shift'),
                    df[col].ewm(span = 2 * 24).mean().fillna(method='bfill').rename(f'{col}_ewm_mean24'),
                    ], axis=1)
  df = df.drop(iir_col_list, axis=1)

  return df

df_org = pd.concat([train_data.reset_index(), weather_feat_df,], axis=1)
# スコアが最もよくなる範囲
df_org = df_org[(df_org['Month']==11) | ((df_org['Month']==12) & (df_org['Day']<=7))]
df_org = df_org.set_index('index')
df_org = add_feats_function(df_org)
pd.set_option('display.max_columns', 1000)

display(df_org)
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discomfort index_weekday_median 0_iir_weekday_median 1_iir_weekday_median 2_iir_weekday_median 3_iir_weekday_median 4_iir_weekday_median 5_iir_weekday_median 6_iir_weekday_median 7_iir_weekday_median 8_iir_weekday_median 9_iir_weekday_median 10_iir_weekday_median 11_iir_weekday_median 12_iir_weekday_median 13_iir_weekday_median 14_iir_weekday_median 15_iir_weekday_median 16_iir_weekday_median 17_iir_weekday_median 18_iir_weekday_median 19_iir_weekday_median 20_iir_weekday_median 21_iir_weekday_median 22_iir_weekday_median 23_iir_weekday_median 24_iir_weekday_median 25_iir_weekday_median 26_iir_weekday_median 27_iir_weekday_median 28_iir_weekday_median 29_iir_weekday_median 30_iir_weekday_median 31_iir_weekday_median 32_iir_weekday_median 33_iir_weekday_median 34_iir_weekday_median 35_iir_weekday_median 36_iir_weekday_median 37_iir_weekday_median 38_iir_weekday_median 39_iir_weekday_median 40_iir_weekday_median 41_iir_weekday_median 42_iir_weekday_median 43_iir_weekday_median 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HourlyPressureTendency_weekday_std HourlyVisibility_weekday_std HourlyStationPressure_weekday_std predict_weather_weekday_std Wind Chill Index_weekday_std discomfort index_weekday_std 0_iir_weekday_std 1_iir_weekday_std 2_iir_weekday_std 3_iir_weekday_std 4_iir_weekday_std 5_iir_weekday_std 6_iir_weekday_std 7_iir_weekday_std 8_iir_weekday_std 9_iir_weekday_std 10_iir_weekday_std 11_iir_weekday_std 12_iir_weekday_std 13_iir_weekday_std 14_iir_weekday_std 15_iir_weekday_std 16_iir_weekday_std 17_iir_weekday_std 18_iir_weekday_std 19_iir_weekday_std 20_iir_weekday_std 21_iir_weekday_std 22_iir_weekday_std 23_iir_weekday_std 24_iir_weekday_std 25_iir_weekday_std 26_iir_weekday_std 27_iir_weekday_std 28_iir_weekday_std 29_iir_weekday_std 30_iir_weekday_std 31_iir_weekday_std 32_iir_weekday_std 33_iir_weekday_std 34_iir_weekday_std 35_iir_weekday_std 36_iir_weekday_std 37_iir_weekday_std 38_iir_weekday_std 39_iir_weekday_std 40_iir_weekday_std 41_iir_weekday_std 42_iir_weekday_std 43_iir_weekday_std 44_iir_weekday_std 45_iir_weekday_std 46_iir_weekday_std 47_iir_weekday_std 48_iir_weekday_std 49_iir_weekday_std 50_iir_weekday_std 51_iir_weekday_std 52_iir_weekday_std 53_iir_weekday_std 54_iir_weekday_std 55_iir_weekday_std 56_iir_weekday_std 57_iir_weekday_std 58_iir_weekday_std 59_iir_weekday_std 60_iir_weekday_std 61_iir_weekday_std 62_iir_weekday_std 63_iir_weekday_std 64_iir_weekday_std 65_iir_weekday_std 66_iir_weekday_std 67_iir_weekday_std 68_iir_weekday_std 69_iir_weekday_std 70_iir_weekday_std 71_iir_weekday_std 72_iir_weekday_std 73_iir_weekday_std 74_iir_weekday_std 75_iir_weekday_std 76_iir_weekday_std 77_iir_weekday_std 78_iir_weekday_std HourlyAltimeterSetting_diff HourlyAltimeterSetting_shift HourlyAltimeterSetting_ewm_mean24 HourlyPressureTendency_diff HourlyPressureTendency_shift HourlyPressureTendency_ewm_mean24 HourlyVisibility_diff HourlyVisibility_shift HourlyVisibility_ewm_mean24 HourlyStationPressure_diff HourlyStationPressure_shift HourlyStationPressure_ewm_mean24 predict_weather_diff predict_weather_shift predict_weather_ewm_mean24 Wind Chill Index_diff Wind Chill Index_shift Wind Chill Index_ewm_mean24 discomfort index_diff discomfort index_shift discomfort index_ewm_mean24
index
2017-11-01 00:00:00 19.630871 9.533048 14.638592 9.091916 6.762926 1.363106 1.153912 11.575677 5.600327 14.047530 9.710434 164.716842 54.031829 2.571881 4.211153 2.83912 101.895509 16.569052 13.414603 268.639058 31.971167 10.674007 84.993039 4.491674 78.046596 117.705877 115.647979 131.118541 9.294790 35.596065 3.263226 73.055851 32.959710 2.276816 23.747557 57.005938 69.931318 13.375071 56.143313 8.703673 2.265867 165.952527 17.545353 3.980137 69.831221 137.588645 104.499568 78.617384 100.058711 11.709989 74.838150 2.516647 9.669708 84.187922 1.146313 5.691702 34.825164 1.471723 4.332746 7.617384 57.660599 177.234465 59.637243 15.451669 21.123475 154.132718 30.258532 65.249825 28.101145 58.644200 6.130055 89.479913 217.272291 14.053291 9.862250 4.305671 16.443310 9.341917 41.656190 0 0 2 30.265625 1.0 10.00000 30.234375 0.0 73.883750 29.8684 7.000000 5.958155 14.638592 6.061277 3.416991 2.284799 0.682952 10.748843 4.800280 19.005482 8.100564 134.148453 23.053580 0.733785 3.609560 0.720526 88.000000 11.046035 12.000000 143.396928 19.843560 10.674007 46.359840 5.133341 67.000000 72.164912 46.687441 70.818035 4.000000 14.375334 1.957936 136.718806 32.959710 53.109078 29.109908 38.747417 70.683267 13.375071 40.894512 7.000000 3.398801 84.537407 9.000000 2.388082 41.674711 161.727003 103.000000 89.742485 89.266443 12.546417 71.975242 1.590368 5.722184 94.806219 1.146313 9.000000 32.004410 1.471723 3.000000 6.347820 50.772066 191.769710 58.000000 6.000000 25.493849 109.339771 27.000000 57.490986 25.759382 37.000000 3.000000 33.378561 107.168089 7.728620 4.841480 2.233676 16.000000 7.784931 32.399259 30.078125 6.0 10.00000 30.046875 0.0 76.605781 31.725750 6.812325 3.613433 60.561766 16.963332 5.780486 6.523944 1.993679 19.661100 6.687963 101.293963 10.437159 209.278113 51.914271 2.527183 7.499510 3.843087 165.107756 16.459839 38.520193 147.026199 54.910851 31.584921 128.951668 5.428095 128.249691 156.316556 127.764145 88.687384 4.608434 43.528473 0.784273 234.821209 72.972481 161.851594 115.750912 153.160347 238.781465 77.750653 70.531697 6.360058 3.400005 53.421633 51.967426 5.703993 58.412778 397.668041 349.639183 261.508315 166.424462 47.071774 220.152425 1.007743 5.708137 219.951450 2.199258 13.766747 65.650559 1.519762 7.170791 4.954226 147.007846 316.353150 131.525287 7.378652 98.001887 272.022263 280.686968 378.072322 133.865885 189.197787 5.362518 117.691279 132.392976 3.251849 2.094318 1.109779 41.706529 68.952318 115.298576 4.318990 2.430129 5.353423 2.582437 1.729723 1.060597 0.560357 3.220471 2.239782 7.332757 2.481378 18.325497 11.194086 0.735992 1.698801 0.855863 25.293774 3.301843 3.185054 42.867719 7.214955 4.009135 15.729163 2.039918 15.208532 19.374623 20.498831 19.685791 3.209754 7.082907 1.106708 36.453047 5.624292 43.629394 4.282767 10.201292 16.171494 3.681661 9.421961 2.391430 1.821887 27.949547 4.278236 1.378694 11.015009 31.022528 17.402789 23.266369 24.772772 4.368602 19.361182 0.834436 2.788885 71.568613 2.347385 3.517227 5.051590 1.315393 1.392988 2.784744 8.554363 32.461553 10.809482 4.146019 5.624714 22.574820 12.218797 17.310230 7.581802 11.164934 1.581793 15.232069 35.572986 3.304188 2.637575 1.390295 6.799365 3.843431 6.733452 0.245657 3.087441 0.000000 0.245657 0.000 53.381135 10.258855 4.060750 1.971341 19.657810 5.008503 1.744624 2.471746 0.680596 6.516903 2.438405 34.462229 2.967343 60.203186 15.221727 0.744712 2.480358 1.329882 47.120374 6.058651 12.769574 52.384175 16.115732 10.506716 34.570038 1.868153 34.903034 43.438303 32.999136 26.331000 1.982006 12.540324 0.713816 71.625406 22.475277 59.310205 39.852946 47.657191 81.699140 26.473126 19.338311 2.013504 1.291394 32.353807 16.628825 1.738294 16.619283 129.939838 108.235983 83.201306 48.560970 15.961188 66.358147 0.584442 2.116180 65.627548 1.170491 4.666169 18.267248 0.499436 2.392719 1.613104 44.350427 89.868394 40.096036 4.065614 32.372260 76.830010 98.419365 124.990854 44.921268 59.715816 1.734268 35.778355 41.247539 2.975520 2.111134 0.903688 12.017478 23.680773 35.372281 0.000000 30.265625 30.265625 0.0 1.0 1.000000 0.00000 10.00000 10.000000 0.000000 30.234375 30.234375 0.0 0.0 0.000000e+00 0.000000 73.883750 73.883750 0.0000 29.8684 29.868400
2017-11-01 00:30:00 19.069989 16.087019 7.667834 8.334256 2.459246 0.681553 0.576956 6.614672 6.400373 6.610603 9.139232 146.167648 64.838195 0.000000 2.406373 1.41956 101.140727 9.468030 15.091428 233.291814 18.921711 6.671254 69.539759 4.491674 87.802420 74.967433 54.707726 116.393999 5.421961 27.381589 4.568517 99.147226 25.406443 0.569204 11.490753 36.483800 56.396224 12.588302 63.767713 5.538701 6.231135 177.251423 7.628414 2.388082 95.412955 79.656584 76.336564 57.108854 66.225909 11.709989 67.843931 1.887485 5.119257 65.985128 4.585251 2.439301 26.118873 2.207585 3.791153 2.539128 39.688464 132.355351 40.913922 7.725835 13.839518 116.440874 39.017581 33.027689 28.101145 36.557424 9.632944 99.225250 178.368806 8.848368 6.105202 1.845287 9.294045 4.670959 26.999382 0 30 2 30.265625 1.0 10.00000 30.234375 0.0 73.883750 29.8684 4.487056 6.374273 11.153213 4.000000 4.783787 1.000000 0.682952 6.614672 4.000000 9.000000 7.364149 91.000000 16.569761 0.733785 3.593468 0.720526 60.898049 8.751413 10.906421 101.572824 17.462333 9.000000 40.038043 3.833140 47.549997 50.445376 33.240138 55.258679 2.520965 9.094149 2.610581 90.426388 20.101195 3.415223 16.058048 33.443484 53.071557 7.080920 31.190729 4.346382 4.449256 71.324278 6.102731 1.000000 28.347327 116.668734 72.000000 61.558895 69.000000 9.200705 54.799332 1.590368 4.550451 58.000000 2.000000 4.878602 21.945881 2.207585 1.624780 4.000000 35.944270 132.355351 43.000000 7.131540 16.024705 78.749031 18.000000 45.000000 16.392334 23.610003 2.627167 26.000000 89.551417 6.245907 5.000000 2.000000 8.819529 6.066331 24.467173 30.078125 6.0 10.00000 30.046875 0.0 76.605781 31.725750 7.177127 3.673224 60.409898 16.291422 5.924348 6.335957 2.022818 18.369361 6.298144 93.404620 10.574198 218.760528 54.292040 2.541573 7.265506 3.733715 167.617887 15.893783 34.911045 159.126189 55.260473 31.215607 127.461289 5.913944 128.150411 159.326688 131.583907 95.327630 4.737899 42.892625 0.734305 229.455097 71.590343 160.116865 107.926646 146.128170 239.305648 72.406972 73.728616 5.958433 3.233025 59.807111 47.818451 5.554703 60.279948 399.419445 349.806774 263.750487 172.196681 43.981310 215.798577 1.020742 6.549607 218.335766 2.010536 13.996626 66.215968 1.467211 7.026849 4.879114 147.902689 327.276184 133.253772 7.723193 93.922663 272.998883 251.753258 357.712354 122.208302 179.936712 5.376025 111.901028 141.380420 3.781698 2.439408 1.063624 41.464532 61.404422 110.571357 4.669633 3.119958 3.408262 3.040583 1.931524 1.057201 0.800603 2.986684 1.390712 5.297561 2.106723 17.715646 13.277867 0.627507 1.891616 0.883024 15.926563 2.608835 3.635275 37.964275 5.227277 3.609391 14.074283 1.738031 14.755774 12.628790 10.577210 20.155681 1.944656 6.138198 1.004201 24.212018 6.121111 25.810354 4.425494 8.541496 14.060499 2.749339 10.539675 2.231391 1.812458 32.196111 1.905003 1.279252 20.794809 25.075494 14.559719 16.825223 14.422487 4.731966 10.089716 0.988460 2.852848 32.251328 1.570727 2.085949 5.436347 1.534330 1.243039 2.097329 7.469864 23.945672 7.778225 2.430423 5.040446 15.549835 10.484658 10.763650 6.687510 10.871090 2.378514 20.472649 29.864979 2.362531 1.880870 1.185014 3.461200 2.256493 7.561070 0.245657 3.087441 0.000000 0.245657 0.000 53.381135 10.258855 4.084469 1.980066 19.407660 4.674836 1.783516 2.401033 0.688289 5.994733 2.256427 32.127061 3.005440 63.138420 15.347990 0.716394 2.451342 1.306812 47.707193 5.507352 11.415195 53.356698 16.354345 10.315711 34.332506 2.086922 35.008205 44.528069 33.812556 27.217860 1.944798 12.461306 0.715953 70.351399 22.015509 59.212242 37.159228 45.477116 84.082096 24.652843 20.315041 2.005288 1.275216 31.864704 15.171052 1.600496 17.187449 131.458540 108.778129 83.495584 49.288884 14.859175 65.586593 0.582118 2.337555 66.727340 1.137216 4.843236 18.724453 0.503259 2.304902 1.556788 44.994993 93.766032 41.140846 4.344549 31.447995 76.994360 88.836966 119.749881 40.579613 56.773017 1.673140 34.520261 42.070286 2.942594 2.084044 0.907016 11.765339 21.152633 33.887238 0.000000 30.265625 30.265625 0.0 1.0 1.000000 0.00000 10.00000 10.000000 0.000000 30.234375 30.234375 0.0 0.0 0.000000e+00 0.000000 73.883750 73.883750 0.0000 29.8684 29.868400
2017-11-01 01:00:00 7.291466 12.512126 4.879531 14.395533 2.459246 2.726211 0.576956 9.095174 4.000233 4.131627 6.854424 129.102390 54.031829 0.000000 2.406373 2.12934 76.232936 7.890025 12.576190 188.754286 8.482146 8.672631 62.515541 1.925003 66.200238 68.661761 45.012686 126.210360 7.745659 19.851652 2.610581 85.579711 29.526407 0.000000 7.660502 25.082613 42.109180 11.014765 45.053276 10.286159 1.699400 174.426699 6.102731 2.388082 100.944141 73.219688 62.255062 53.400487 62.626675 10.037133 61.549133 1.887485 4.550451 44.748535 0.000000 0.813100 23.216776 2.943446 3.249559 1.904346 28.455880 115.620767 41.607379 7.725835 19.666684 101.633364 16.721820 19.333281 18.734096 29.702907 1.751444 101.883069 143.869490 9.368861 11.740774 1.845287 10.008971 6.227945 33.942081 1 0 2 30.265625 1.0 9.96875 30.234375 0.0 76.128438 29.8684 5.336062 4.957768 6.576574 1.692016 2.459246 1.363106 0.576956 6.000000 3.110876 7.000000 5.712020 79.989140 16.569761 0.000000 2.156081 0.720526 54.344271 7.101022 9.222539 89.075056 10.439565 8.672631 28.287822 1.925003 34.842230 38.534662 22.160092 45.000000 3.000000 8.214477 2.610581 47.000000 16.479855 1.000000 13.022854 22.802375 30.681994 6.000000 27.031965 6.000000 2.966171 57.841384 5.014197 1.662863 19.969133 80.000000 57.000000 46.000000 56.000000 8.831943 34.971098 0.795184 3.981644 50.057684 2.000000 4.906996 15.000000 0.886455 1.505981 3.000000 28.455880 104.971485 35.840997 4.754360 16.024705 52.499354 12.740435 23.361048 13.269985 16.728450 1.751444 19.818520 68.264605 3.643446 2.817786 1.230192 7.055623 3.892466 16.311449 30.078125 6.0 9.96875 30.039062 0.0 76.084531 31.535525 7.411480 3.646853 58.527455 15.238110 5.801298 6.053563 2.039721 17.190397 5.801083 83.987426 10.659872 225.996563 54.572055 2.405966 6.969360 3.483085 165.925943 15.017238 31.100931 169.933820 54.494490 30.437789 123.814074 6.632535 124.979979 158.475324 132.135697 101.553626 4.901391 41.481771 0.686989 221.719451 67.496367 158.700032 97.945010 136.731850 237.392514 65.370564 75.638403 5.785081 3.127972 66.413158 42.922250 5.132299 62.096531 402.555765 344.274465 257.538545 177.855036 40.355261 209.087054 1.102327 7.256198 218.095040 1.878252 14.031242 65.801154 1.499854 6.748099 4.755293 145.307373 336.184488 133.064941 8.106709 88.500092 270.440085 222.479504 331.725934 109.709381 170.168598 5.061405 104.343067 148.843818 4.247073 2.849307 1.070010 40.614803 52.834720 104.411905 2.118678 2.652052 2.368259 3.978637 1.419644 1.129356 0.605256 2.719702 1.598493 5.357705 3.224847 18.137113 11.317850 0.439828 1.488770 0.663909 10.690479 2.104072 3.163848 32.857574 5.282060 2.825615 11.255008 1.701591 10.801967 11.796006 7.744168 23.061311 1.792393 5.566815 1.657796 24.501350 5.576817 9.067033 4.248906 7.515598 8.991756 2.386963 7.348396 2.406475 1.664901 33.922943 2.151369 1.334445 22.015302 17.454763 11.838866 11.212881 14.840228 3.505194 10.385094 1.094150 2.392132 16.512009 2.050707 1.762254 5.503875 0.966047 1.201582 1.473463 5.938743 22.725300 6.595566 1.647771 4.757105 15.788681 5.446195 8.050606 5.664092 7.922753 1.037095 21.770867 22.821054 2.488838 2.605973 0.764248 1.725744 1.659719 7.706292 0.245652 2.768875 0.008263 0.245995 0.000 54.882958 10.194632 4.094081 1.986260 18.755253 4.285285 1.828604 2.292014 0.683603 5.452559 2.068873 29.619193 3.040174 65.016663 15.392396 0.694105 2.402194 1.263851 47.457965 4.964567 10.021149 54.340083 16.289713 9.956939 33.464466 2.317121 34.566038 44.808006 34.019279 28.122155 1.899530 12.175304 0.715204 68.480961 21.116189 58.682217 33.885740 42.472892 85.178699 22.533067 20.987211 2.007152 1.295266 31.623916 13.496401 1.470379 17.599115 130.176343 107.053098 82.426902 49.397197 13.568566 63.620094 0.581743 2.636312 67.270182 1.103216 4.952446 18.818554 0.509946 2.174106 1.521903 44.460922 96.202894 41.361280 4.587374 29.874527 75.661821 78.093105 112.617291 35.706799 52.979476 1.610867 32.603828 42.917220 2.922943 2.056975 0.910995 11.414417 18.282907 31.782920 0.000000 30.265625 30.265625 0.0 1.0 1.000000 -0.03125 10.00000 9.989146 0.000000 30.234375 30.234375 0.0 0.0 0.000000e+00 2.244687 73.883750 74.663367 0.0000 29.8684 29.868400
2017-11-01 01:30:00 8.413230 11.916310 4.879531 4.545958 1.229623 1.363106 0.000000 7.441506 4.000233 7.436928 6.854424 107.585325 39.623341 0.000000 1.203187 0.70978 70.949465 9.468030 6.707301 190.875120 10.439565 12.008258 61.110698 3.850006 63.412859 56.751048 42.242675 95.358939 5.421961 21.905271 2.610581 14.611170 21.286479 0.000000 9.958653 28.122929 30.077986 2.360307 58.915822 5.538701 1.699400 124.287850 3.051366 1.592055 87.116176 65.978180 42.985638 32.633631 50.389279 9.200705 56.653179 0.629162 3.412838 29.579540 2.292626 4.065501 15.236009 2.207585 4.874339 3.808692 39.688464 93.561541 34.672816 4.160065 8.012353 75.383687 7.166494 23.361048 21.856446 22.848390 2.627167 118.715924 105.700033 8.327876 5.165941 1.845287 1.429853 7.006438 25.456561 1 30 2 30.265625 1.0 9.96875 30.234375 0.0 76.128438 29.8684 2.804410 5.000000 4.182455 3.000000 1.844434 0.681553 0.576956 5.050085 3.000000 4.000000 3.682074 57.873485 8.011371 0.000000 1.437387 0.000000 48.541923 5.523017 5.868889 62.211150 9.524909 5.000000 28.000000 1.000000 28.570629 30.127099 17.000000 42.978973 1.680643 6.613927 2.161321 13.567515 13.046552 0.569204 8.426552 19.785915 14.097132 3.147076 21.000000 3.477106 2.224628 55.171781 3.814207 0.831432 15.000000 58.128550 41.503374 28.925264 48.229738 7.000000 25.000000 1.258323 2.861092 33.000000 1.146313 3.252401 12.333912 0.735862 1.083186 2.347716 25.816305 81.390934 24.437043 3.565770 10.158263 34.528349 7.166494 16.916621 9.367048 12.947421 1.000000 13.560040 50.827203 2.602461 2.766560 1.489117 5.291718 5.000000 17.942594 30.078125 6.0 9.96875 30.039062 0.0 76.084531 31.535525 7.566440 3.700916 54.747252 13.794543 5.731596 5.757900 1.995886 16.018645 5.535998 74.253291 10.493181 229.473439 53.335842 2.162424 6.586291 3.312282 161.760282 14.251683 26.972510 178.615887 52.930564 29.195240 118.386181 7.134580 120.332706 156.327402 130.216924 104.730222 5.013289 39.266000 0.699454 212.740545 63.122744 153.541697 86.124897 125.180315 231.920591 58.500870 75.710942 5.539992 3.173592 72.718422 37.509006 4.938602 63.324012 396.558796 329.534643 246.880862 180.953363 35.896514 200.194845 1.196804 7.804705 215.635640 1.857854 14.018950 64.250192 1.513089 6.369277 4.566935 139.517565 340.906721 130.889526 8.347082 82.773225 260.397612 191.514438 300.468531 95.843435 157.985457 4.610282 95.198594 155.264397 4.865964 3.309221 1.069669 37.678445 44.949772 96.871311 2.250497 2.840966 1.897991 1.584091 1.766886 0.852345 0.697942 1.527358 1.928179 2.603047 1.954817 14.696750 8.432761 0.561778 1.131724 0.249747 13.159646 2.538183 1.519391 38.006659 4.375719 2.739006 11.520127 2.249399 11.198333 10.341492 9.076983 18.835821 2.096981 5.013329 1.309138 11.094320 4.538536 4.246720 2.664030 5.478877 5.870484 2.040409 10.548178 1.796251 1.393580 23.808583 2.129047 1.207267 19.341031 10.461432 8.200582 5.440951 9.062955 2.539056 9.777433 1.005192 1.750306 9.684852 1.937451 1.705209 4.411523 1.000868 1.451649 1.869141 5.688900 10.722726 7.165825 1.204352 3.979095 12.913281 4.960964 4.235129 4.766421 5.534498 1.649029 27.661335 19.262704 1.951593 1.320950 0.957830 2.691908 2.565489 7.741218 0.245652 2.768875 0.008263 0.245995 0.000 54.882958 10.194632 4.083466 1.994097 17.693084 3.861216 1.866369 2.143581 0.667863 4.922167 1.889129 26.901569 3.062384 65.484669 15.340634 0.677295 2.329221 1.199515 46.258625 4.470981 8.644394 55.043732 15.898005 9.434580 31.956891 2.534001 33.528386 44.104164 33.488971 28.854654 1.852891 11.681912 0.711404 65.916616 19.798099 57.494762 30.136479 38.742124 84.511333 20.156398 21.229036 2.014296 1.350221 31.661626 11.688722 1.358393 17.787470 125.881396 102.878312 79.835641 48.746732 12.124339 60.386819 0.585279 2.974930 67.085894 1.065850 4.972793 18.492712 0.515741 2.009611 1.503878 42.701782 96.685148 40.625571 4.758492 27.693265 72.701577 66.558887 103.573109 30.521772 48.358791 1.559126 30.126215 43.562663 2.912410 2.028676 0.912588 10.934360 15.232113 29.118183 0.000000 30.265625 30.265625 0.0 1.0 1.000000 0.00000 9.96875 9.983724 0.000000 30.234375 30.234375 0.0 0.0 0.000000e+00 0.000000 76.128438 75.052838 0.0000 29.8684 29.868400
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2019-12-07 22:30:00 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 22 30 5 30.500000 1.0 9.96875 30.468750 0.0 23.904375 19.7997 19.069989 4.249516 25.484225 13.637874 6.762926 4.089317 1.365903 19.358658 7.777190 48.753194 15.993656 323.289439 53.676189 2.000000 5.000000 1.441052 194.000000 16.569052 25.990793 355.593280 31.971167 18.334750 131.352879 7.058344 122.000000 175.000000 134.120286 177.000000 6.196527 34.226986 1.305291 177.421352 71.900428 55.212776 59.000000 140.150232 313.453882 56.647361 98.932835 6.954212 5.098201 154.836935 33.427983 5.000000 96.795751 256.671214 152.000000 172.126643 174.000000 23.419977 164.364162 1.887485 8.000000 227.000000 1.146313 12.196504 67.666468 1.772909 10.000000 3.130289 111.010111 381.852795 118.112376 14.000000 59.000051 235.000000 136.544681 229.000000 87.554330 172.124536 4.378611 79.734576 258.443404 11.000000 5.635572 1.489117 22.877648 34.253697 115.811287 30.199219 5.0 9.96875 30.164062 0.0 65.811094 31.288225 10.208022 5.384730 55.080449 21.066765 6.169015 5.255173 2.231377 25.833848 8.288318 138.937702 13.161034 200.187226 51.737203 2.732441 7.577155 3.387712 171.411368 23.811459 40.653163 213.650130 42.883509 19.451345 130.973378 5.271681 106.070669 160.340887 113.935685 112.754753 5.212520 33.975257 1.626564 220.353113 72.869944 88.855877 98.394384 182.668692 268.626811 80.288365 92.320207 7.236246 4.723872 78.722956 68.973550 5.911307 77.785374 269.017427 211.832438 200.521748 152.039020 36.246534 180.842963 1.376099 4.875929 205.349299 3.119583 15.383392 78.641274 1.738592 10.080231 6.450640 122.619515 213.601128 99.713491 9.701280 72.634607 203.836135 287.300369 338.480564 137.621655 217.928056 4.134998 124.008208 162.913611 5.506612 3.134230 1.860598 56.213420 73.674045 147.629489 7.466601 2.958074 5.996739 4.648827 3.789131 2.306596 0.959536 6.993422 2.454951 12.939718 2.630187 31.824319 19.480849 1.205051 1.867423 1.431426 26.077427 4.119648 7.183880 65.612587 9.587892 5.061969 27.474557 4.850681 14.764808 39.012981 17.295036 26.286651 2.486271 8.714745 1.112358 62.709988 13.357285 52.942724 15.189207 22.842173 59.725588 13.066453 14.167540 2.377393 2.213875 30.825591 6.661168 2.237820 17.493829 51.368685 18.645020 25.254966 21.064313 9.646655 26.413297 1.244309 4.558643 76.569147 2.106876 4.082955 11.188970 1.312028 4.920037 2.930875 18.949824 75.153284 25.361274 3.028098 10.303203 34.367155 23.961747 41.169224 26.269457 34.986082 4.201720 18.392498 48.435961 3.843088 2.027682 0.879915 7.595429 6.240051 17.994543 0.310918 2.708013 1.805664 0.310470 0.200 47.790673 9.005681 3.530665 1.672189 15.291084 6.159726 2.430550 2.227971 0.943988 8.142947 2.663147 37.806525 4.106598 52.096306 14.976128 1.064869 2.581994 1.405015 44.038068 7.598984 12.154515 62.224251 12.619585 5.425187 36.276793 1.857319 31.260052 45.956685 29.582306 30.997631 2.696054 10.197756 0.606362 60.763207 20.771179 47.001347 27.001011 50.490236 70.614530 23.155687 24.998446 3.276023 1.867262 23.375082 20.010140 2.121100 22.395202 75.794978 54.211449 54.929795 38.781199 11.970554 47.674990 0.611898 2.010658 63.699331 0.997126 4.676537 21.155727 0.819034 3.589369 2.125001 32.741294 57.442586 29.374126 3.359711 19.487052 53.732253 78.212418 91.144642 40.442587 58.634090 2.630664 37.561589 46.525283 2.586310 1.545329 0.634276 15.940934 20.883128 42.013828 0.000000 30.500000 30.340874 0.0 1.0 2.534386 0.00000 9.96875 9.987926 0.000000 30.468750 30.309623 0.0 0.0 4.322234e-07 0.000000 23.904375 42.733965 0.0000 19.7997 22.179615
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2019-12-07 23:30:00 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 23 30 5 30.515625 1.0 10.00000 30.484375 0.0 24.571250 19.5750 29.726747 6.374273 20.551794 12.880214 11.000000 6.000000 2.731807 21.042019 8.554908 41.316266 16.937543 274.962667 78.000000 2.935141 7.000000 2.161579 180.000000 16.707244 24.000000 443.000000 43.000000 19.131913 144.000000 12.191686 97.000000 206.000000 123.085073 198.229547 7.562895 43.810542 1.440881 116.262499 86.589762 60.504013 50.000000 121.188730 251.000000 39.000000 119.945118 7.121187 5.190798 229.585800 27.578086 4.776164 112.006512 207.000000 126.733518 158.465798 190.000000 19.000000 162.000000 1.258323 10.729095 185.376745 2.292626 17.000000 76.905570 2.000000 11.294859 6.000000 98.101959 282.205950 130.369787 23.390798 55.023927 235.574023 99.000000 166.000000 63.140142 122.954107 7.000000 87.708033 268.654251 17.389395 12.449519 1.230192 17.873163 31.918218 114.940228 30.203125 5.0 10.00000 30.171875 0.0 63.074219 30.990000 11.144763 5.133722 49.565666 21.012363 5.807164 5.092345 2.205600 24.707291 7.869593 115.588042 13.145948 213.919174 53.127102 2.831127 7.091409 2.945544 173.177295 22.506592 38.010002 237.470935 43.623486 19.275200 132.493389 5.995033 107.211080 164.888912 119.429225 123.195470 5.626848 34.948171 1.531313 216.515128 75.905275 79.030837 94.237291 181.187201 270.372058 74.775989 94.173664 6.784752 4.749861 86.945964 63.144623 5.661979 79.641889 253.977364 203.726340 193.745989 157.171960 34.797174 179.158212 1.364722 5.093378 213.775899 2.838776 15.263776 75.427898 1.685471 10.798514 6.146477 126.629257 226.338675 100.431953 10.193396 76.344963 207.051916 261.031638 310.345135 133.142805 205.897879 4.391169 112.083483 183.148626 5.850231 3.577680 1.741707 47.580465 69.737366 150.713839 10.140412 2.934205 6.308481 3.283132 5.240700 3.127573 2.002164 6.452973 3.009977 12.739515 5.559293 39.370768 28.375346 1.823261 2.606713 1.938082 38.795346 4.190501 4.496228 91.497337 13.972891 4.709423 34.106315 5.614303 13.986633 51.364713 21.778576 33.957858 2.364258 11.320364 1.958510 49.045388 22.381707 57.050840 10.519540 22.540416 82.659554 10.340308 27.202667 3.987351 2.470172 53.533195 7.346709 1.857385 28.409349 39.004987 27.555970 28.515852 29.027550 7.332470 44.631999 1.201921 6.853170 30.313050 2.043474 6.625921 19.927884 0.992204 4.021646 3.015404 22.373701 35.620035 36.534823 7.321227 12.999579 44.266594 20.991666 36.839905 20.161718 26.871263 4.931681 20.020543 53.985481 6.955190 4.034654 1.234441 10.472796 7.174967 19.284852 0.331164 2.708013 1.231107 0.331164 0.250 57.151742 9.431250 3.856170 1.714206 14.290491 5.888582 2.425542 2.178706 0.937963 7.748302 2.697175 31.348778 4.075311 55.355211 15.491505 1.119189 2.526829 1.263330 44.811551 7.150099 11.297544 69.900218 12.885179 5.374586 37.122829 2.119721 31.132772 47.900882 31.045792 34.327693 2.690139 10.313716 0.593836 61.684514 21.581929 49.414357 25.054070 49.895943 71.564483 22.200555 25.191522 3.033391 1.821983 25.952625 18.426001 2.106726 23.219442 71.416946 52.428570 53.123902 39.604063 11.543214 47.898147 0.677689 2.262770 67.205170 0.996009 4.724551 20.517684 0.777250 3.719409 2.208962 33.891202 60.697216 30.230022 3.465787 20.279379 54.189277 70.856263 82.561315 39.517992 55.862654 2.728388 33.301897 51.673873 2.762717 1.646485 0.710311 13.342595 19.454160 41.594224 0.000000 30.515625 30.354848 0.0 1.0 2.411686 0.00000 10.00000 9.988891 0.000000 30.484375 30.323597 0.0 0.0 3.976599e-07 0.000000 24.571250 41.281553 0.0000 19.5750 21.971333

5328 rows × 440 columns

! pip install -q catboost
2K     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 98.7/98.7 MB 4.4 MB/s eta 0:00:00
?25h
import lightgbm as lgb
from catboost import Pool
from catboost import CatBoostRegressor
from sklearn.ensemble import RandomForestRegressor

from tqdm import tqdm

min_band = '0'
max_band = '78'
TARGET_MIN = 0
TARGET_MAX = 78 + 1

# 感謝祭の翌週のみを学習データとする
train = pd.concat([df_org['2017-11-26 00:00:00':'2017-12-02 23:30:00'],
                 df_org['2018-11-25 00:00:00':'2018-12-01 23:30:00'],], axis=0)
test = df_org[-2 * 24 * 7 :]
df = pd.concat([train, test], axis=0)

predict_list = []

for target in tqdm(range(TARGET_MIN, TARGET_MAX)):
  band_l = target - 1
  band_h = target + 1
  # 目的変数の抽出
  if target == 0 :
    target_df = df.drop(columns=df.loc[:, str(band_h):max_band].columns)
  elif target == 78 :
    target_df = df.drop(columns=df.loc[:, min_band:str(band_l)].columns)
  else :
    if target == 1:
      target_df = df.drop([min_band], axis=1)
      target_df = target_df.drop(columns=df.loc[:, str(band_h):max_band].columns)
    elif target == 77:
      target_df = df.drop(columns=df.loc[:, min_band:str(band_l)].columns)
      target_df = target_df.drop([max_band], axis=1)
    else:
      target_df = df.drop(columns=df.loc[:, min_band:str(band_l)].columns)
      target_df = target_df.drop(columns=df.loc[:, str(band_h):max_band].columns)

  target_train = target_df[:-2 * 24 * 7]
  target_test = target_df[-2 * 24 * 7:]
  target_test = target_test.drop(str(target),axis=1)

  X_train = target_train.drop(str(target),axis=1).copy()    # 学習用のデータフレームから説明変数を抽出
  y_train = target_train[str(target)].copy()    # 学習用のデータフレームから目的変数を抽出

# #light gbm
  lgb_model_10 = lgb.LGBMRegressor(verbosity=-1, seed=CFG.SEED, max_depth=10)
  lgb_model_10 = lgb_model_10.fit(X_train, y_train)
  lgb_predict_10 = lgb_model_10.predict(target_test)

# #light gbm
  lgb_model_50 = lgb.LGBMRegressor(verbosity=-1, seed=CFG.SEED, max_depth=50)
  lgb_model_50 = lgb_model_50.fit(X_train, y_train)
  lgb_predict_50 = lgb_model_50.predict(target_test)

# #catboost
  train_pool = Pool(X_train, y_train)
  cat_model = CatBoostRegressor(logging_level='Silent' , random_seed=CFG.SEED)
  cat_model = cat_model.fit(train_pool)
  cat_predict = cat_model.predict(target_test)

# #random forest
  rg_model = RandomForestRegressor(n_jobs=-1, random_state=CFG.SEED)
  rg_model = rg_model.fit(X_train,y_train)
  rg_predict = rg_model.predict(target_test)

  predict = (lgb_predict_10 + lgb_predict_50 + cat_predict + rg_predict) / 4

# 乗車数は0未満にはならないので、後処理で修正
  predict = [elem if elem > 0 else 0 for elem in predict]
  predict_list.append(predict)
100%|██████████| 79/79 [2:18:56<00:00, 105.53s/it]
# submitの作成
submit_df = pd.DataFrame(np.transpose(predict_list), columns=area_cols_name)
submit_df = submit_df.astype(int)
submit_df.columns = area_cols_name
submit_df.index = pd.date_range(start='2019/12/01 00:00:00', end='2019/12/07 23:30:00', freq='30T')
submit_df.index.name = 'tpep_pickup_datetime'
submit_df.to_csv(PATHS.SUBMIT_DATA)
display(submit_df.describe())
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78
count 336.000000 336.000000 336.000000 336.000000 336.000000 336.000000 336.000000 336.000000 336.000000 336.000000 336.000000 336.000000 336.000000 336.000000 336.000000 336.000000 336.000000 336.000000 336.000000 336.000000 336.000000 336.000000 336.000000 336.000000 336.000000 336.000000 336.000000 336.000000 336.000000 336.000000 336.000000 336.000000 336.000000 336.000000 336.000000 336.000000 336.000000 336.000000 336.000000 336.000000 336.000000 336.000000 336.000000 336.000000 336.000000 336.000000 336.000000 336.000000 336.000000 336.000000 336.000000 336.000000 336.000000 336.000000 336.000000 336.000000 336.000000 336.000000 336.000000 336.000000 336.000000 336.000000 336.000000 336.000000 336.000000 336.000000 336.000000 336.000000 336.000000 336.000000 336.000000 336.000000 336.000000 336.000000 336.000000 336.000000 336.000000 336.000000 336.000000
mean 7.500000 5.220238 40.190476 14.184524 3.633929 4.267857 1.377976 17.044643 6.696429 78.583333 8.175595 151.273810 42.261905 1.776786 4.889881 1.562500 116.607143 15.613095 34.336310 122.202381 31.940476 16.553571 83.020833 3.508929 95.848214 108.285714 75.410714 62.425595 4.315476 24.994048 1.547619 166.928571 53.267857 121.315476 87.642857 114.342262 154.773810 57.687500 48.011905 5.702381 4.357143 59.809524 42.886905 3.901786 46.425595 198.565476 175.806548 137.291667 115.351190 27.630952 146.071429 1.872024 3.848214 183.907738 2.931548 9.363095 38.758929 2.068452 8.270833 6.142857 91.151786 171.270833 80.345238 5.714286 60.934524 146.398810 202.574405 229.913690 99.449405 129.339286 4.360119 80.032738 97.062500 2.526786 1.815476 1.541667 30.139881 61.613095 88.958333
std 5.888517 2.852442 27.288717 8.484676 2.273779 3.516228 1.904431 9.737193 4.212645 58.231747 4.831686 65.885219 20.262085 2.750446 2.821519 1.614013 50.452405 8.394686 19.558485 97.947964 18.272956 10.766870 41.594232 2.961430 43.544633 58.740425 42.240015 46.015706 2.982816 14.041302 1.612672 91.035081 27.257199 80.204227 58.632352 63.789150 97.658907 37.845569 33.018310 3.013521 2.036344 72.568481 27.808176 2.590314 28.618235 121.702262 107.744674 80.414846 54.862612 16.409156 78.820603 1.465521 3.268693 89.901142 2.126889 5.638276 25.551673 1.403009 6.162078 3.842085 49.404431 87.992238 42.640880 4.171893 36.023316 85.475781 146.182835 154.927624 64.597147 80.198137 2.931774 44.227588 60.543402 3.853961 2.779756 1.350972 20.829545 45.218795 48.075358
min 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000 1.000000 0.000000 0.000000 28.000000 3.000000 0.000000 0.000000 0.000000 15.000000 1.000000 2.000000 15.000000 1.000000 0.000000 8.000000 0.000000 7.000000 6.000000 3.000000 5.000000 0.000000 1.000000 0.000000 8.000000 3.000000 0.000000 2.000000 9.000000 2.000000 0.000000 2.000000 1.000000 0.000000 5.000000 0.000000 0.000000 3.000000 7.000000 7.000000 9.000000 10.000000 1.000000 7.000000 0.000000 0.000000 11.000000 0.000000 0.000000 1.000000 0.000000 0.000000 1.000000 5.000000 21.000000 5.000000 0.000000 2.000000 6.000000 1.000000 5.000000 3.000000 3.000000 0.000000 4.000000 13.000000 0.000000 0.000000 0.000000 1.000000 1.000000 5.000000
25% 4.000000 3.000000 10.750000 7.000000 2.000000 2.000000 0.000000 10.000000 4.000000 14.000000 4.000000 118.750000 27.750000 0.000000 3.000000 0.000000 87.500000 9.000000 16.000000 68.000000 15.750000 7.000000 45.750000 2.000000 66.750000 60.750000<