Section01-04で作った学習データを、多出力データへと変形します。
ベースとなるプログラムはSection01-04で書いたコードです。
レースデータを1つの行データとする
変更するのは、Section01-04で書いたコードのrace_data_to_df関数です。
この関数を変更し、新たにrace_data_to_dict関数を作ります。
行っている処理はほとんど前と同じですが、一つのDataFrameを作るのではなく、一つの辞書を作るようにします。また、keyとして’horse_{data[“horse_number”]}_’を元のkeyに付け足します。この辞書を全レース文集めて、DataFrameに直します。
def race_data_to_dict(race_data, race_id, place_id):
with open('formatted_source_data/horse_name2id_dict.pkl', 'rb') as f:
horse_name_to_id_dict = pickle.load(f)
with open('formatted_source_data/jocky_name2id_dict.pkl', 'rb') as f:
jocky_name_to_id_dict = pickle.load(f)
with open('formatted_source_data/ped_name2id_dict.pkl', 'rb') as f:
ped_name_to_id_dict = pickle.load(f)
race_info = race_data['race_place_data']
horses_data = race_data['race_horses_data']
race_payout_data = race_data['race_payout_data']
# 1st step: calc statistics data of previous race infomation
race_total_prize = sum([data['prize'] for data in horses_data if data['prize'] != None])
ret = {
'race_date':race_info['race_date'],
'race_id':race_id,
'place_id':place_id,
'race_grade':race_info['race_grade'],
'race_distance':race_info['race_distance'],
'race_type':race_type_pre_process(race_info['race_type']),
'race_total_prize':race_total_prize,
'weather':weather_pre_process(race_info['weather']),
'race_condition':race_condition_pre_process(race_info['race_condition']),
'horse_count':len(horses_data),
}
for n,horse_data_dict in enumerate(horses_data):
data = {
'waku':horse_data_dict['waku'],
'horse_number':horse_data_dict['horse_number'],
'name':horse_data_dict['name'],
'sex':horse_data_dict['sex'],
'age':horse_data_dict['age'],
'jocky_weight':horse_data_dict['jocky_weight'],
'jocky_name':horse_data_dict['jocky_name'],
'odds':horse_data_dict['odds'],
'popular':horse_data_dict['popular'],
'weight':horse_data_dict['weight'],
'weight_sub':horse_data_dict['weight_sub'],
'rank':horse_data_dict['rank'],
'time':horse_data_dict['time'],
'prize':horse_data_dict['prize'],
'tansyo_hit':0,
'tansyo_payout':0,
'hukusyo_hit':0,
'hukusyo_payout':0
}
if int(race_payout_data['tansyo_ret']) == data['horse_number']:
data['tansyo_hit'] = 1
data['tansyo_payout'] = string_to_number(race_payout_data['tansyo_payout'])/100
for j, hit_horse_number in enumerate(race_payout_data['hukusyo_ret']):
if int(hit_horse_number) == data['horse_number']:
data['hukusyo_hit'] = 1
data['hukusyo_payout'] = string_to_number(race_payout_data['hukusyo_payout'][j])/100
# add pre_race_info
horse_name = data['name']
horse_id = horse_name_to_id_dict[horse_name]
pre_horse_data = get_horse_pre_info(horse_id, ret['race_date'])
for key, value in pre_horse_data.items():
data[key] = value
# add pre_race_jocky_info
jocky_name = data['jocky_name']
jocky_id = jocky_name_to_id_dict[jocky_name]
pre_jocky_data = get_jocky_pre_info(jocky_id, ret['race_date'])
for key, value in pre_jocky_data.items():
data[key] = value
#add ped name info
for i, ped_name in enumerate(horse_data_dict['ped_data']):
data[f'ped_{i}'] = ped_name_to_id_dict[ped_name]
for key, value in data.items():
ret[f'horse_{data["horse_number"]}_{key}'] = value
return retmain
mainの処理はぼ前と変化していません。保存する名前を’formatted_source_data/analysis_data03.pkl’にすることを忘れないでください。
if __name__ == '__main__':
print('load database')
database = []
race_id = 0
for place_id in range(1,11):
for year in range(25):
database_path = f'keiba_source_data/20{year:02}_{place_id:02}.data'
with open(database_path, 'rb') as f:
#print(database_path)
for data in pickle.load(f):
if data != {} and len(data['race_horses_data']) >= 5:
database.append([data, race_id, place_id])
#print(race_data_to_df(data, race_id, place_id))
#exit()
race_id += 1
if len(database) == 0:
print('database load error : size 0')
exit()
print(f'database num : {len(database)}')
print('race_data to learning_data')
args = [(race_data, race_id, place_id) for race_data, race_id, place_id in database]
rets = joblib.Parallel(n_jobs=10, verbose=1)([joblib.delayed(race_data_to_dict)(*arg) for arg in args])
df = pd.DataFrame(rets)
df = df.sort_values('race_date').reset_index(drop=True)
save_name = f'formatted_source_data/analysis_data03.pkl'
df.to_pickle(save_name)