-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathmodel.py
executable file
·333 lines (281 loc) · 14.1 KB
/
model.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
import numpy as np
import theano
import theano.tensor as T
LOCATION_HOME = 0
LOCATION_AWAY = 1
LOCATION_TOURNEY = 2
def validation_loss(pred_score1, pred_score2, score1, score2,
method="sqerr"):
if method == "sqerr":
return (pred_score1 - score1)**2 + (pred_score2 - score2)**2
elif method == "zero-one":
return (pred_score1 > pred_score2 and score2 > score1) or \
(pred_score2 > pred_score1 and score1 > score2)
def make_pmf_plus_pace_functions(num_teams, D0, H, D, Hp, reg_param1,
reg_param2, xform_params=None):
# D0 : dimension of base latent vectors
# D : dimension of transformed latent vectors
rng = np.random.RandomState()
# Initialize latent vectors
offense0_vals = np.asarray(rng.uniform(
low=-np.sqrt(6./(num_teams+D0)),
high=np.sqrt(6./(num_teams+D0)),
size=(num_teams, D)), dtype=theano.config.floatX)
defense0_vals = np.asarray(rng.uniform(
low=-np.sqrt(6./(num_teams+D0)),
high=np.sqrt(6./(num_teams+D0)),
size=(num_teams, D)), dtype=theano.config.floatX)
# Initialize transformation weights.
# Different transform for {offense, defense} x {home, away, tourney}
if xform_params is None:
paceW1_vals = np.asarray(rng.uniform(
low=-np.sqrt(1./(4*D+Hp)),
high=np.sqrt(1./(4*D+Hp)),
size=(4*D, Hp)), dtype=theano.config.floatX)
paceb1_vals = np.zeros((Hp,), dtype=theano.config.floatX)
paceW2_vals = np.asarray(rng.uniform(
low=-np.sqrt(1./(Hp+1)),
high=np.sqrt(1./(Hp+1)),
size=(Hp, 1)), dtype=theano.config.floatX)
paceb2_vals = np.zeros((1,), dtype=theano.config.floatX)
mean_score_val = 70*np.ones(1, dtype=theano.config.floatX)
# Create theano variables
paceW1 = theano.shared(value=paceW1_vals, name="paceW1")
paceb1 = theano.shared(value=paceb1_vals, name="paceb1")
paceW2 = theano.shared(value=paceW2_vals, name="paceW2")
paceb2 = theano.shared(value=paceb2_vals, name="paceb2")
mean_score = theano.shared(value=mean_score_val, name="mean_score")
else:
# This allows sharing of transform parameters across seasons
paceW1, paceb1, paceW2, paceb2, mean_score = xform_params
offenses0 = theano.shared(value=offense0_vals, name="offenses0")
defenses0 = theano.shared(value=defense0_vals, name="defenses0")
# inputs
SUPPORT_BATCH_LEARNING = False # theano indexing is not cooperating
if SUPPORT_BATCH_LEARNING:
team1_ids = T.ivector("team1_ids")
team1_locs = T.ivector("team1_locs") # 0:home, 1:away, 2:tourney
team2_ids = T.ivector("team2_ids")
team2_locs = T.ivector("team2_locs") # 0:home, 1:away, 2:tourney
team1_scores = T.dvector("team1_scores")
team2_scores = T.dvector("team2_scores")
else: # only support stochastic gradient training
team1_ids = T.iscalar("team1_ids")
team1_locs = T.iscalar("team1_locs") # 0:home, 1:away, 2:tourney
team2_ids = T.iscalar("team2_ids")
team2_locs = T.iscalar("team2_locs") # 0:home, 1:away, 2:tourney
team1_scores = T.dscalar("team1_scores")
team2_scores = T.dscalar("team2_scores")
# learning parameters
learning_rate = T.scalar("learning_rate")
# select appropriate latent vectors
team1_offenses = offenses0[team1_ids, :]
team1_defenses = defenses0[team1_ids, :]
team2_offenses = offenses0[team2_ids, :]
team2_defenses = defenses0[team2_ids, :]
if SUPPORT_BATCH_LEARNING:
team1_pred_score0 = T.sum(team1_offenses * team2_defenses, axis=1)
team2_pred_score0 = T.sum(team2_offenses * team1_defenses, axis=1)
else:
team1_pred_score0 = T.sum(team1_offenses * team2_defenses)
team2_pred_score0 = T.sum(team2_offenses * team1_defenses)
all_o_and_d_12 = T.concatenate([team1_offenses, team1_defenses,
team2_offenses, team2_defenses])
all_o_and_d_21 = T.concatenate([team2_offenses, team2_defenses,
team1_offenses, team1_defenses])
paceH = T.nnet.sigmoid(T.dot(all_o_and_d_12, paceW1) \
+ T.dot(all_o_and_d_21, paceW1) + paceb1)
pace = .5 + T.nnet.sigmoid(T.dot(paceH, paceW2) + paceb2)
team1_pred_score = (team1_pred_score0 + mean_score) * pace
team2_pred_score = (team2_pred_score0 + mean_score) * pace
# regularization terms
reg1 = T.mean(T.sqr(offenses0)) + T.mean(T.sqr(defenses0))
reg2 = T.mean(T.sqr(paceW1)) + T.mean(T.sqr(paceW2))
# learning objective
obj = T.mean(T.sqr(team1_pred_score - team1_scores)) + \
T.mean(T.sqr(team2_pred_score - team2_scores)) + \
reg_param1 * reg1 + reg_param2 * reg2
# Define updates
params = [offenses0, defenses0,
paceW1, paceb1, paceW2, paceb2, mean_score]
grads = []
for p in params:
g = T.grad(obj, p)
grads.append(g)
updates = {}
for param, grad in zip(params, grads):
updates[param] = param - learning_rate * grad
# Create and return theano functions
out_fn = theano.function([team1_ids, team1_locs, team2_ids, team2_locs],
outputs=[team1_pred_score, team2_pred_score],
on_unused_input='ignore')
train_fn = theano.function([team1_ids, team1_locs, team2_ids, team2_locs,
team1_scores, team2_scores, learning_rate],
outputs=obj,
updates=updates,
on_unused_input='ignore')
return out_fn, train_fn, params
def make_learning_functions(num_teams, D0, H, D, Hp, reg_param1,
reg_param2, xform_params=None):
# D0 : dimension of base latent vectors
# D : dimension of transformed latent vectors
rng = np.random.RandomState()
# Initialize latent vectors
offense0_vals = np.asarray(rng.uniform(
low=-np.sqrt(6./(num_teams+D0)),
high=np.sqrt(6./(num_teams+D0)),
size=(num_teams, D0)), dtype=theano.config.floatX)
defense0_vals = np.asarray(rng.uniform(
low=-np.sqrt(6./(num_teams+D0)),
high=np.sqrt(6./(num_teams+D0)),
size=(num_teams, D0)), dtype=theano.config.floatX)
# Initialize transformation weights.
# Different transform for {offense, defense} x {home, away, tourney}
if xform_params is None:
oxformW1_vals = np.asarray(rng.uniform(
low=-np.sqrt(6./(D0+D)),
high=np.sqrt(6./(D0+D)),
size=(3, D0, H)), dtype=theano.config.floatX)
oxformb1_vals = np.zeros((D,), dtype=theano.config.floatX)
dxformW1_vals = np.asarray(rng.uniform(
low=-np.sqrt(6./(D0+D)),
high=np.sqrt(6./(D0+D)),
size=(3, D0, H)), dtype=theano.config.floatX)
dxformb1_vals = np.zeros((D,), dtype=theano.config.floatX)
oxformW2_vals = np.asarray(rng.uniform(
low=-np.sqrt(6./(D0+D)),
high=np.sqrt(6./(D0+D)),
size=(3, H, D)), dtype=theano.config.floatX)
oxformb2_vals = np.zeros((D,), dtype=theano.config.floatX)
dxformW2_vals = np.asarray(rng.uniform(
low=-np.sqrt(6./(D0+D)),
high=np.sqrt(6./(D0+D)),
size=(3, H, D)), dtype=theano.config.floatX)
dxformb2_vals = np.zeros((D,), dtype=theano.config.floatX)
paceW1_vals = np.asarray(rng.uniform(
low=-np.sqrt(1./(4*D0+1)),
high=np.sqrt(1./(4*D0+1)),
size=(4*D, Hp)), dtype=theano.config.floatX)
paceb1_vals = np.zeros((Hp,), dtype=theano.config.floatX)
paceW2_vals = np.asarray(rng.uniform(
low=-np.sqrt(1./(4*D0+1)),
high=np.sqrt(1./(4*D0+1)),
size=(Hp, 1)), dtype=theano.config.floatX)
paceb2_vals = np.zeros((1,), dtype=theano.config.floatX)
mean_score_val = 70*np.ones(1, dtype=theano.config.floatX)
# Create theano variables
oxformW1 = theano.shared(value=oxformW1_vals, name="oxformW1")
oxformb1 = theano.shared(value=oxformb1_vals, name="oxformb1")
dxformW1 = theano.shared(value=dxformW1_vals, name="dxformW1")
dxformb1 = theano.shared(value=dxformb1_vals, name="dxformb1")
oxformW2 = theano.shared(value=oxformW2_vals, name="oxformW2")
oxformb2 = theano.shared(value=oxformb2_vals, name="oxformb2")
dxformW2 = theano.shared(value=dxformW2_vals, name="dxformW2")
dxformb2 = theano.shared(value=dxformb2_vals, name="dxformb2")
paceW1 = theano.shared(value=paceW1_vals, name="paceW1")
paceb1 = theano.shared(value=paceb1_vals, name="paceb1")
paceW2 = theano.shared(value=paceW2_vals, name="paceW2")
paceb2 = theano.shared(value=paceb2_vals, name="paceb2")
mean_score = theano.shared(value=mean_score_val, name="mean_score")
else:
# This allows sharing of transform parameters across seasons
oxformW1, oxformb1, dxformW1, dxformb1, \
oxformW2, oxformb2, dxformW2, dxformb2, \
paceW1, paceb1, paceW2, paceb2, mean_score \
= xform_params
offenses0 = theano.shared(value=offense0_vals, name="offenses0")
defenses0 = theano.shared(value=defense0_vals, name="defenses0")
# inputs
SUPPORT_BATCH_LEARNING = False # theano indexing is not cooperating
if SUPPORT_BATCH_LEARNING:
team1_ids = T.ivector("team1_ids")
team1_locs = T.ivector("team1_locs") # 0:home, 1:away, 2:tourney
team2_ids = T.ivector("team2_ids")
team2_locs = T.ivector("team2_locs") # 0:home, 1:away, 2:tourney
team1_scores = T.dvector("team1_scores")
team2_scores = T.dvector("team2_scores")
else: # only support stochastic gradient training
team1_ids = T.iscalar("team1_ids")
team1_locs = T.iscalar("team1_locs") # 0:home, 1:away, 2:tourney
team2_ids = T.iscalar("team2_ids")
team2_locs = T.iscalar("team2_locs") # 0:home, 1:away, 2:tourney
team1_scores = T.dscalar("team1_scores")
team2_scores = T.dscalar("team2_scores")
# learning parameters
learning_rate = T.scalar("learning_rate")
# select appropriate latent vectors
team1_offenses0 = offenses0[team1_ids, :]
team1_defenses0 = defenses0[team1_ids, :]
team2_offenses0 = offenses0[team2_ids, :]
team2_defenses0 = defenses0[team2_ids, :]
# apply location-specific transformations
# 0: home; 1:away; 2:tourney
team1_oxformW1s = oxformW1[team1_locs]
team1_oxformb1s = oxformb1[team1_locs]
team2_oxformW1s = oxformW1[team2_locs]
team2_oxformb1s = oxformb1[team2_locs]
team1_dxformW1s = dxformW1[team1_locs]
team1_dxformb1s = dxformb1[team1_locs]
team2_dxformW1s = dxformW1[team2_locs]
team2_dxformb1s = dxformb1[team2_locs]
team1_oxformW2s = oxformW2[team1_locs]
team1_oxformb2s = oxformb2[team1_locs]
team2_oxformW2s = oxformW2[team2_locs]
team2_oxformb2s = oxformb2[team2_locs]
team1_dxformW2s = dxformW2[team1_locs]
team1_dxformb2s = dxformb2[team1_locs]
team2_dxformW2s = dxformW2[team2_locs]
team2_dxformb2s = dxformb2[team2_locs]
# transformed offenses and defenses for each game
team1_offensesH = T.dot(team1_offenses0, team1_oxformW1s) + team1_oxformb1s
team1_defensesH = T.dot(team1_defenses0, team1_dxformW1s) + team1_dxformb1s
team2_offensesH = T.dot(team2_offenses0, team2_oxformW1s) + team2_oxformb1s
team2_defensesH = T.dot(team2_defenses0, team2_dxformW1s) + team2_dxformb1s
team1_offenses = T.dot(team1_offensesH, team1_oxformW2s) + team1_oxformb2s
team1_defenses = T.dot(team1_defensesH, team1_dxformW2s) + team1_dxformb2s
team2_offenses = T.dot(team2_offensesH, team2_oxformW2s) + team2_oxformb2s
team2_defenses = T.dot(team2_defensesH, team2_dxformW2s) + team2_dxformb2s
if SUPPORT_BATCH_LEARNING:
team1_pred_score0 = T.sum(team1_offenses * team2_defenses, axis=1)
team2_pred_score0 = T.sum(team2_offenses * team1_defenses, axis=1)
else:
team1_pred_score0 = T.sum(team1_offenses * team2_defenses)
team2_pred_score0 = T.sum(team2_offenses * team1_defenses)
all_o_and_d_12 = T.concatenate([team1_offenses, team1_defenses,
team2_offenses, team2_defenses])
all_o_and_d_21 = T.concatenate([team2_offenses, team2_defenses,
team1_offenses, team1_defenses])
paceH = T.nnet.sigmoid(T.dot(all_o_and_d_12, paceW1) \
+ T.dot(all_o_and_d_21, paceW1) + paceb1)
pace = .5 + T.nnet.sigmoid(T.dot(paceH, paceW2) + paceb2)
team1_pred_score = (team1_pred_score0 + mean_score) * pace
team2_pred_score = (team2_pred_score0 + mean_score) * pace
# regularization terms
reg1 = T.mean(T.sqr(offenses0)) + T.mean(T.sqr(defenses0))
reg2 = T.mean(T.sqr(oxformW1)) + T.mean(T.sqr(dxformW1)) \
+ T.mean(T.sqr(oxformW2)) + T.mean(T.sqr(dxformW2)) \
+ T.mean(T.sqr(paceW1)) + T.mean(T.sqr(paceW2))
# learning objective
obj = T.mean(T.sqr(team1_pred_score - team1_scores)) + \
T.mean(T.sqr(team2_pred_score - team2_scores)) + \
reg_param1 * reg1 + reg_param2 * reg2
# Define updates
params = [offenses0, defenses0,
oxformW1, oxformb1, dxformW1, dxformb1,
oxformW2, oxformb2, dxformW2, dxformb2,
paceW1, paceb1, paceW2, paceb2, mean_score]
grads = []
for p in params:
g = T.grad(obj, p)
grads.append(g)
updates = {}
for param, grad in zip(params, grads):
updates[param] = param - learning_rate * grad
# Create and return theano functions
out_fn = theano.function([team1_ids, team1_locs, team2_ids, team2_locs],
outputs=[team1_pred_score, team2_pred_score])
train_fn = theano.function([team1_ids, team1_locs, team2_ids, team2_locs,
team1_scores, team2_scores, learning_rate],
outputs=obj,
updates=updates)
return out_fn, train_fn, params