-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathop_dp.py
179 lines (167 loc) · 7.65 KB
/
op_dp.py
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
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
import os
import pandas as pd
import numpy as np
import pickle
from datetime import timezone
import api.util
from sklearn.preprocessing import LabelEncoder,OneHotEncoder,MinMaxScaler
class OpDataProvider:
def __init__(self, include=[],exclude=[], load=False, today=False):
self.LOCAL_TZ = 'Asia/Almaty'
self.SERVER_TZ = 'UTC'
self.DATA_PATH='data/op/'
self.PREREQUISITES_PATH='prerequisites/op/'
self.INCLUDE=include
self.EXCLUDE=exclude
self.COL_CAT=[]
self.COL_NUM=[]
self.COL_LBL=[]
self.COL_INF=[]
self.TODAY=today
self.LOAD=True if today else load
def _load_prerequisites(self,name):
with open(os.path.join(self.PREREQUISITES_PATH, name),'rb') as f:
encoder = pickle.load(f)
return encoder
def _save_prerequisite(self, name, data):
os.makedirs(self.PREREQUISITES_PATH, mode=0o777, exist_ok=True)
with open(os.path.join(self.PREREQUISITES_PATH, name), mode='wb') as f:
pickle.dump(data, f)
def _ff(self, columns):
if len(self.INCLUDE)>0:
return [x for x in columns if x in self.INCLUDE]
else:
return [x for x in columns if x not in self.EXCLUDE]
def _encode_teams(self, df):
teams_name=self.DATA_PATH+'teams.csv'
teams_saved=pd.read_csv(teams_name, index_col=None)
teams=pd.concat([pd.DataFrame(df['t1'].unique(), columns=['name']),pd.DataFrame(df['t2'].unique(), columns=['name'])]).drop_duplicates()
teams_new=teams[~teams.name.isin(teams_saved.name)]
if not teams_new.empty:
print('New teams!')
id=teams_saved.id.max()+1
teams_list=[]
for row in teams_new.itertuples():
if len(row.name)>1:
teams_list.append({'name':row.name, 'id':id})
id+=1
teams_saved=pd.concat([teams_saved,pd.DataFrame(teams_list)])
teams_saved.to_csv(teams_name, index=False)
teams_saved.columns=['t1','tid1']
df=df.merge(teams_saved, on='t1', how='left')
teams_saved.columns=['t2','tid2']
df=df.merge(teams_saved, on='t2', how='left')
return df
def _encode(self, enctype, features, outs, df):
if (len(self.INCLUDE)>0 and outs[0] in self.INCLUDE) or outs[0] in self.EXCLUDE:
return df
name='_'.join(features)
if self.LOAD:
encoder=self._load_prerequisites(f'{enctype}_{name}')
else:
if enctype=='sc':
encoder = MinMaxScaler()
elif enctype=='le':
encoder = LabelEncoder()
elif enctype=='ohe':
encoder = OneHotEncoder()
if len(features)==1:
encoder.fit(df[features].values)
else:
encoder.fit(pd.concat([pd.DataFrame(df[features[0]].unique(), columns=[name]),pd.DataFrame(df[features[1]].unique(), columns=[name])])[name])
self._save_prerequisite(f'{enctype}_{name}', encoder)
if enctype=='ohe':
return encoder.transform(df[features].values).toarray()
if len(features)==1:
df[outs[0]] = encoder.transform(df[features].values)
else:
df[outs[0]] = encoder.transform(df[features[0]])
df[outs[1]] = encoder.transform(df[features[1]])
return df
def _provide_odds(self, df_src):
self.COL_NUM+=['drift_home','drift_draw','drift_away']
#self.COL_NUM+=['oddsprob_home','oddsprob_draw','oddsprob_away','drift_home','drift_draw','drift_away']
if self.TODAY:
df=pd.read_csv(self.DATA_PATH+'odds_today.csv', index_col=False)
else:
df=pd.read_csv(self.DATA_PATH+'odds.csv', index_col=False)
df=df.dropna()
df['w1']=1/df['w1']
df['w2']=1/df['w2']
df['wx']=1/df['wx']
df['open_1']=1/df['open_1']
df['open_2']=1/df['open_2']
df['open_x']=1/df['open_x']
df['margin']=df['w1']+df['w2']+df['wx']
df['oddsprob_home']=df['w1']/df['margin']
df['oddsprob_away']=df['w2']/df['margin']
df['oddsprob_draw']=df['wx']/df['margin']
df=df[(df['margin']>1.01) & (df['margin']<3)]
df['margin']=df['open_1']+df['open_2']+df['open_x']
df['open_1']=df['open_1']/df['margin']
df['open_2']=df['open_2']/df['margin']
df['open_x']=df['open_x']/df['margin']
df=df[(df['margin']>1.01) & (df['margin']<3)]
df['drift_home']=(df['open_1']-df['oddsprob_home'])/df['open_1']
df['drift_away']=(df['open_2']-df['oddsprob_away'])/df['open_2']
df['drift_draw']=(df['open_x']-df['oddsprob_draw'])/df['open_x']
#df.to_csv('data/op/odds2.csv', index=False)
df=df.groupby('mid')[['oddsprob_home','oddsprob_draw','oddsprob_away','drift_home','drift_away','drift_draw']].mean().reset_index()
df_src=df_src.merge(df, on='mid', how='left')
df_src=df_src.dropna(subset=['oddsprob_home'])
return df_src
def _provide_matches(self):
info_colums=['mid','ds','country','liga','t1','t2','tid1','tid2','sc1','sc2', 'odds_home','odds_draw','odds_away',]
#num_colums=['odds_prob_home','odds_prob_draw','odds_prob_away','bn']
num_colums=['bn']
cat_colums=['country_id']
label_colums=['winner']
self.COL_NUM+=num_colums
self.COL_INF+=info_colums
self.COL_CAT+=cat_colums
self.COL_LBL+=label_colums
cols=np.unique(info_colums+num_colums+cat_colums+label_colums)
if self.TODAY:
df=pd.read_csv(self.DATA_PATH+'matches_today.csv', index_col=False)
df['sc1']=0
df['sc2']=0
else:
df=pd.read_csv(self.DATA_PATH+'matches_done.csv', index_col=False)
df = df.rename(columns={'odds1': 'odds_home','oddsdraw': 'odds_draw','odds2': 'odds_away'})
df['t1']=df['t1'].replace('[^a-zA-Z0-9 ]', '', regex=True).str.lower()
df['t2']=df['t2'].replace('[^a-zA-Z0-9 ]', '', regex=True).str.lower()
df=df[~df['t1'].str.contains(' u2')]
df=df[~df['t2'].str.contains(' u2')]
df.loc[df.odds_home=='-','odds_home']='1.01'
df.loc[df.odds_away=='-','odds_away']='1.01'
df.odds_home=df.odds_home.astype(float)
df.odds_draw=df.odds_draw.astype(float)
df.odds_away=df.odds_away.astype(float)
df['ds']=pd.to_datetime(df['ds'])
df['ds']=df['ds'].dt.tz_localize(timezone.utc)
df['sc2']=df['sc2'].apply(lambda x: str(x).replace(' pen.','').replace(' ET',''))
df.sc2=df.sc2.astype(int)
df['winner']='home'
df.loc[df['sc1']== df['sc2'],'winner']='draw'
df.loc[df['sc1'] < df['sc2'],'winner']='away'
df['mid'] = df.link.apply(lambda x: x[-9:-1])
df['season'] = df.season.str.replace('/','-')
df['liga'] = df.apply(lambda x: x.liga.replace('-'+x.season, ''), axis=1)
df=self._encode('sc', ['bn'], ['bn'], df)
df=self._encode('le', ['country'], ['country_id'], df)
#df=self._encode('le', ['t1','t2'], ['home_tid','away_tid'], df)
df=self._encode_teams(df)
return df[self._ff(cols)]
def _load_data(self):
df=self._provide_matches()
df=self._provide_odds(df)
return df
def provide_data(self):
df=self._load_data()
df.reset_index(drop=True, inplace=True)
data=df[self._ff(self.COL_NUM)].values
for col in self._ff(self.COL_CAT):
data=np.hstack([data,self._encode('ohe', [col], [col], df)])
labels=self._encode('ohe', self.COL_LBL, self.COL_LBL, df)
info=df[self.COL_INF]
return data, labels, info, df