-
Notifications
You must be signed in to change notification settings - Fork 3
/
recommendation-system.py
475 lines (376 loc) · 18.1 KB
/
recommendation-system.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
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
#!/usr/bin/env python
# coding: utf-8
# In[41]:
from pyspark import SparkContext
from pyspark.sql import SQLContext
from pyspark.sql import SparkSession
from pyspark.sql.types import *
from pyspark.sql.functions import udf
from pyspark.ml.stat import Correlation
from pyspark.ml.linalg import Vectors
import numpy as np
import pandas as pd
from collections import Counter
import re
import time
from pathos.pools import ProcessPool as Pool
num_partitions = 10
num_cores = 2
spark = SparkSession.builder.appName('FootballManager').getOrCreate()
# In[42]:
def readCSV(path):
return spark.read.format("csv").options(header="true", inferSchema="true") .load(path)
teams_df = readCSV("./team_feat.csv")
players_df = readCSV("./data_clean.csv")
_teams_df = pd.read_csv('./team_feat.csv')
_players_df = pd.read_csv('./data_clean.csv')
# In[43]:
def single_workflow(features):
def foo(teams_df, input):
"""
Get 3 features-teams matrixs , transfer the features' score into weights(use Reciprocal Function) and thus we have 3 weighted-teams matrixs.
:param: input, a dic of features vectors
:return a tuple of weighted-teams matrixs
"""
features = input
teams_columns = dict(zip(range(0,teams_df['Club'].size), teams_df['Club']))
features_teams = teams_df.loc[:, features].T
features_teams = features_teams.rename(columns=teams_columns)
weights_teams = features_teams.applymap(lambda x: 1./float(x))
return weights_teams
def getPlayersTeamsMatrix_parallel(players_df, teams_df, input):
"""
Create three sections, each section has a m*n and n*k matrix,
where m is the number of players, n is the number of features' weights,
and k is the number of teams. For all these three pairs of matrices,
do the matrix multiplication. Then we can get 3 MxK matrices for DEF, MID and ATK positions.
:params: input, a dict
: return: a tuple of three players_teams matrixs
"""
features = input['features']
weights_teams = input['weights_teams']
players_rows = dict(zip(range(0, players_df['Name'].size), players_df['Name']))
# DEF
players_features = players_df.loc[:, features]
players_features = players_features.rename(index=players_rows)
players_teams = players_features.dot(weights_teams)
return players_teams
weights_teams = foo(
_teams_df,
features
)
players_teams = getPlayersTeamsMatrix_parallel(
_players_df,
_teams_df,
{
'features': features,
'weights_teams': weights_teams
}
)
return players_teams
# In[44]:
class RecommendationEngine(object):
def __init__(self, players_df, teams_df, _players_df, _teams_df):
self.players_df = players_df
self.teams_df = teams_df
self._teams_df = _teams_df
self._players_df = _players_df
self.result = {}
def readCSVToSparksql(self, path):
return spark.read.format("csv").options(header="true", inferSchema="true") .load(path)
def __convertDFtoRDD(self, df):
rdd = df.rdd
return rdd
# def __groupPosition(self, players_df):
# """
# Function to group the players' positions into three main groups: DEF, MID, FWD
# :param: players_df
# :return: Dataframe
# """
# def _classify(position):
# """
# Classify Position
# :param: position
# :return: string
# """
# # Regex to group
# defs = r'\w*B$'
# mids = r'\w*M$'
# fwds = r'\w*[FSTW]$'
# if re.match(defs, position):
# return "DEF"
# elif re.match(mids, position):
# return "MID"
# elif re.match(fwds, position):
# return "FWD"
# else:
# return None
# # Write an UDF for withColumn
# _classify_udf = udf(_classify, StringType())
# # groupPosition list
# return players_df\
# .withColumn('GroupPosition', _classify_udf(players_df['Position']))
def __groupPosition(self, players_df):
# Regex to group
defs = r'\w*B$'
mids = r'\w*M$'
fwds = r'\w*[FSTW]$'
# groupPosition list
groupPositions = []
for index, row in players_df.iterrows():
position = row['Position']
if re.match(defs, position):
groupPositions.append('DEF')
if re.match(mids, position):
groupPositions.append('MID')
if re.match(fwds, position):
groupPositions.append('FWD')
series = pd.Series(groupPositions)
players_df['GroupPosition'] = series
return players_df
def __findTopRelatedPosition(self, players_df):
"""
Calculate the Pearson Correlation between each specific position,
and specific featuresFind out top characteristics for different position
:param: players_df, Pandas Dataframe
:return:
"""
player_characteristics = ['Crossing','Finishing', 'HeadingAccuracy',
'ShortPassing', 'Volleys', 'Dribbling', 'Curve',
'FKAccuracy', 'LongPassing', 'BallControl',
'Acceleration', 'SprintSpeed', 'Agility', 'Reactions',
'Balance', 'ShotPower', 'Jumping', 'Stamina',
'Strength', 'LongShots', 'Aggression',
'Interceptions', 'Positioning', 'Vision',
'Penalties', 'Composure', 'Marking', 'StandingTackle',
'SlidingTackle']
## Top characteristics for positions
corr_matrix = players_df.corr() # default is pearson
counter_DEF = Counter()
counter_MID = Counter()
counter_ATK = Counter()
defs = r'\w*B$'
mids = r'\w*M$'
fwds = r'\w*[FSTW]$'
for index, row in corr_matrix.loc[player_characteristics, "LS":"RB"].T.iterrows():
largests = tuple(row.nlargest(12).index)
# print('Position {}: {}, {}, {}, {}, {}, {}, {}, {}'.format(index, *tuple(row.nlargest(8).index)))
if re.match(defs, index): # DEF
for feature in largests:
counter_DEF[feature] += 1
if re.match(mids, index): # MID
for feature in largests:
counter_MID[feature] += 1
if re.match(fwds, index): # FWD
for feature in largests:
counter_ATK[feature] += 1
# 3. Group all positions together into only three, ATK, MID and DEF, thus we get the three 1 * 8 vector for three main groups.
features_DEF = [kv[0] for kv in counter_DEF.most_common(12)]
features_MID = [kv[0] for kv in counter_MID.most_common(12)]
features_ATK = [kv[0] for kv in counter_ATK.most_common(12)]
return features_DEF, features_MID, features_ATK
def _getWeightedMatrix(self, teams_df, input):
"""
Get 3 features-teams matrixs , transfer the features' score into weights(use Reciprocal Function) and thus we have 3 weighted-teams matrixs.
:param: input, a dic of features vectors
:return a tuple of weighted-teams matrixs
"""
features_DEF = input['features_DEF']
features_MID = input['features_MID']
features_ATK = input['features_ATK']
teams_columns = dict(zip(range(0,teams_df['Club'].size), teams_df['Club']))
features_teams_DEF = teams_df.loc[:, features_DEF].T
features_teams_DEF = features_teams_DEF.rename(columns=teams_columns)
features_teams_MID = teams_df.loc[:, features_MID].T
features_teams_MID = features_teams_MID.rename(columns=teams_columns)
features_teams_ATK = teams_df.loc[:, features_ATK].T
features_teams_ATK = features_teams_ATK.rename(columns=teams_columns)
weights_teams_DEF = features_teams_DEF.applymap(lambda x: 1./float(x))
weights_teams_MID = features_teams_MID.applymap(lambda x: 1./float(x))
weights_teams_ATK = features_teams_ATK.applymap(lambda x: 1./float(x))
return weights_teams_DEF, weights_teams_MID, weights_teams_ATK
def _getPlayersTeamsMatrix(self, players_df, teams_df, input):
"""
Create three sections, each section has a m*n and n*k matrix,
where m is the number of players, n is the number of features' weights,
and k is the number of teams. For all these three pairs of matrices,
do the matrix multiplication. Then we can get 3 MxK matrices for DEF, MID and ATK positions.
:params: input, a dict
: return: a tuple of three players_teams matrixs
"""
features_DEF = input['features_DEF']
features_MID = input['features_MID']
features_ATK = input['features_ATK']
weights_teams_DEF = input['weights_teams_DEF']
weights_teams_MID = input['weights_teams_MID']
weights_teams_ATK = input['weights_teams_ATK']
players_rows = dict(zip(range(0, players_df['Name'].size), players_df['Name']))
# DEF
players_features_DEF = players_df.loc[:, features_DEF]
players_features_DEF = players_features_DEF.rename(index=players_rows)
players_teams_DEF = players_features_DEF.dot(weights_teams_DEF)
# MID
players_features_MID = players_df.loc[:, features_MID]
players_features_MID = players_features_MID.rename(index=players_rows)
players_teams_MID = players_features_MID.dot(weights_teams_MID)
# ATK
players_features_ATK = players_df.loc[:, features_ATK]
players_features_ATK = players_features_ATK.rename(index=players_rows)
players_teams_ATK = players_features_ATK.dot(weights_teams_ATK)
return players_teams_DEF, players_teams_MID, players_teams_ATK
def _run(self):
"""
Run the recommendation engine
:return: a tuple of three players_teams matrixs
"""
start=time.time()
self._players_df = self.__groupPosition(self._players_df)
features_DEF, features_MID, features_ATK = self.__findTopRelatedPosition(self._players_df)
weights_teams_DEF, weights_teams_MID, weights_teams_ATK = self._getWeightedMatrix(
self._teams_df,
{'features_DEF': features_DEF,
'features_MID': features_MID,
'features_ATK': features_ATK
}
)
players_teams_DEF, players_teams_MID, players_teams_ATK = self._getPlayersTeamsMatrix(
self._players_df,
self._teams_df,
{'features_DEF': features_DEF,
'features_MID': features_MID,
'features_ATK': features_ATK,
'weights_teams_DEF': weights_teams_DEF,
'weights_teams_MID': weights_teams_MID,
'weights_teams_ATK': weights_teams_ATK
}
)
end=time.time()
print("The recommendation running time is: {:.2f} seconds in Serial Mode".format(end-start))
return players_teams_DEF, players_teams_MID, players_teams_ATK
def _run_parallel(self):
"""
Run the recommendation engine
:return: a tuple of three players_teams matrixs
"""
start=time.time()
with Pool(num_cores) as pool:
self._players_df = self.__groupPosition(self._players_df)
features_array = list(self.__findTopRelatedPosition(self._players_df))
players_teams_array = pool.map(single_workflow, features_array)
pool.close()
pool.join()
end=time.time()
print("The recommendation running time is: {:.2f} seconds in Parallel mode".format(end-start))
players_teams_DEF = players_teams_array[0]
players_teams_MID = players_teams_array[1]
players_teams_ATK = players_teams_array[2]
return players_teams_DEF, players_teams_MID, players_teams_ATK
def getRecommendation(self, mode=1):
"""
Get the recommendation result, use lazy load mode
:param: mode, 0 means serial, 1 means parallel, defualt is 0
:return: a tuple of three players_teams matrixs
"""
if len(self.result) == 0:
if mode == 1:
players_teams_DEF, players_teams_MID, players_teams_ATK = self._run_parallel()
else:
players_teams_DEF, players_teams_MID, players_teams_ATK = self._run()
self.result['players_teams_DEF'] = players_teams_DEF
self.result['players_teams_MID'] = players_teams_MID
self.result['players_teams_ATK'] = players_teams_ATK
return self.result
class RecommendationSystem(object):
def __init__(self, recommendation_engine, players_df, teams_df, _players_df, _teams_df):
self.players_df = players_df
self.teams_df = teams_df
self._teams_df = _teams_df
self._players_df = _players_df
self._recommendation_engine = recommendation_engine
self.players_teams_matrixs = self._recommendation_engine.getRecommendation()
def getMVPForTeam(self, team, position, K, isReverse=False):
"""
To recommend K Most Valued People in specific position for specific team if ascending is False,
otherwise show Worst Valued People if ascending is True
:param: team
:param: position
:param: K
:param: isReverse, default False
:return: players -> List[]
"""
if position == 'DEF':
players = self.players_teams_matrixs['players_teams_DEF'][team] .sort_values(ascending=isReverse) .head(K)
return players
elif position == 'MID':
players = self.players_teams_matrixs['players_teams_MID'][team] .sort_values(ascending=isReverse) .head(K)
return players
elif position == 'ATK':
players = self.players_teams_matrixs['players_teams_ATK'][team] .sort_values(ascending=isReverse) .head(K)
return players
else:
raise RuntimeError('Invalid position argument')
def getMVTForPlayer(self, player, position, K, isReverse=False):
"""
To recommend Most Valued Teams in specific position for specific palyer if ascending is False,
otherwise show Worst Valued Teams if ascending is True
:param: player
:param: position
:param: K
:param: isReverse, default False
:return: teams -> List[]
"""
if position == 'DEF':
teams = self.players_teams_matrixs['players_teams_DEF'] .loc[player, :] .sort_values(ascending=isReverse) .head(K)
return teams
elif position == 'MID':
teams = self.players_teams_matrixs['players_teams_MID'] .loc[player, :] .sort_values(ascending=isReverse) .head(K)
return teams
elif position == 'ATK':
teams = self.players_teams_matrixs['players_teams_ATK'] .loc[player, :] .sort_values(ascending=isReverse) .head(K)
return teams
else:
raise RuntimeError('Invalid position argument')
def searchWorstPlayersInPosByTeam(self, position, team):
"""
To find out the Least Valued Players in specific position for specific team,
thus in the future we can replace them with better players.
:params: position
:params: team
:return:
"""
players_df = self._players_df
players_teams_matrix = None
if position == 'DEF':
players_teams_matrix = self.players_teams_matrixs['players_teams_DEF']
elif position == 'MID':
players_teams_matrix = self.players_teams_matrixs['players_teams_MID']
elif position == 'ATK':
players_teams_matrix = self.players_teams_matrixs['players_teams_ATK']
else:
raise RuntimeError('Invalid position argument')
for index, value in players_teams_matrix[team].sort_values(ascending=True).iteritems():
if players_df.loc[(players_df.loc[:, 'Name'] == index), :]['Club'].values[0] == team and players_df.loc[(players_df.loc[:, 'Name'] == index), :]['GroupPosition'].values[0] == position:
print("{}\t\t{}".format(index, value))
class FootballManager(object):
def __init__(self, recommendation_system):
self.players_df = self.readCSVToSparksql("./data_clean.csv")
self.teams_df = self.readCSVToSparksql("./team_feat.csv")
self._teams_df = pd.read_csv('./team_feat.csv')
self._players_df = pd.read_csv('./data_clean.csv')
self.recommendation_system = recommendation_system
def readCSVToSparksql(self, path):
return spark.read.format("csv").options(header="true", inferSchema="true") .load(path)
def matrix_weighted_recommandation(self, input_player=None, input_team=None):
if input_player is None and input_team is None:
raise RuntimeError('No player or team is found, please at least offer one argument')
# In[45]:
recommendation_engine = RecommendationEngine(players_df, teams_df, _players_df, _teams_df)
# In[46]:
recommendationSystem = RecommendationSystem(recommendation_engine, players_df, teams_df, _players_df, _teams_df)
# In[47]:
players = recommendationSystem.getMVPForTeam('LA Galaxy', 'DEF', 10)
print(players)
teams = recommendationSystem.getMVTForPlayer('L. Messi', 'DEF', 10)
print(teams)
recommendationSystem.searchWorstPlayersInPosByTeam('DEF', 'LA Galaxy')
# In[ ]: