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
|
#!/usr/bin/python3
import os
import time
import random
import logging
import itertools
from sqlalchemy.orm import Session, relationship
from sqlalchemy.ext.automap import automap_base
from sqlalchemy.exc import OperationalError
from sqlalchemy import create_engine
import tensorflow as tf
import numpy as np
DATABASE_URI = os.environ["DATABASE_URI"]
MODEL_ROOT = os.path.join(os.getcwd(), os.path.dirname(__file__))\
+ "/models/"
# ORM definitions
Match = Roster = Participant = ParticipantStats = Player = None
db = None
# models
mvpmodel = None
def connect():
global Match, Roster, Participant, Hero, ParticipantStats, Player
global db
# generate schema from db
Base = automap_base()
engine = create_engine(DATABASE_URI)
while True:
try:
Base.prepare(engine, reflect=True)
break
except OperationalError as err:
logging.error(err)
time.sleep(5)
# definitions
# TODO check whether the primaryjoin clause is the best method to do this
Match = Base.classes.match
Roster = Base.classes.roster
Roster.match = relationship(
"match", foreign_keys="match.api_id",
primaryjoin="and_(match.api_id == roster.match_api_id)")
Participant = Base.classes.participant
Participant.roster = relationship(
"roster", foreign_keys="roster.api_id",
primaryjoin="and_(roster.api_id == participant.roster_api_id)")
Participant.player = relationship(
"player", foreign_keys="player.api_id",
primaryjoin="and_(player.api_id == participant.player_api_id)")
Participant.hero = relationship(
"hero", foreign_keys="hero.id",
primaryjoin="and_(hero.id == participant.hero_id)")
Hero = Base.classes.hero
ParticipantStats = Base.classes.participant_stats
Participant.participant_stats = relationship(
"participant_stats", foreign_keys="participant_stats.participant_api_id",
primaryjoin="and_(participant_stats.participant_api_id == participant.api_id)")
Player = Base.classes.player
db = Session(engine)
class Model(object):
# override this configuration
features = []
label = ""
type = ""
batches = 1
batchsize = 1
steps = 0
id = "unlabeled"
def __init__(self, db):
self._db = db
self._feature_cols = [tf.contrib.layers.real_valued_column(
feat, dimension=1) for feat in self.features]
if self.type == "linear":
self._model = tf.contrib.learn.LinearClassifier(
feature_columns=self._feature_cols,
model_dir=MODEL_ROOT + self.id,
config=tf.contrib.learn.RunConfig(
save_checkpoints_secs=1))
def _from_record(self, path, record):
table, column = path.split(".")
if table == "participant":
return vars(record)[column]
if table == "participant_stats":
return vars(record.participant_stats[0])[column]
if table == "player":
return vars(record.player[0])[column]
if table == "roster":
return vars(record.roster[0])[column]
if table == "match":
return vars(record.roster[0].match[0])[column]
raise KeyError("Invalid path " + path)
def _batch(self, ids=None, size=None):
if ids is None: # training, get random sample
size = size or self.batchsize
# train a model one batch
offset = random.random() * self._db.query(Participant)\
.count()
# have a bit of randomness in the sample
records = self._db.query(Participant)\
.offset(offset).limit(size)\
.all()
else:
records = self._db.query(Participant)\
.filter(Participant.api_id.in_(ids))\
.order_by(Participant.api_id.desc())\
.all()
data = {}
labels = []
# populate from db records
for record in records:
labels.append(self.estimate(record))
for path in self.features:
if path not in data:
data[path] = []
data[path].append(self._from_record(path, record))
# convert to numpy arrs
for key in data:
data[key] = np.array(data[key])
labels = np.array(labels)
logging.info("sample size: %s", len(labels))
if ids is not None:
assert len(ids) == len(labels), "got nonexisting participant"
return tf.contrib.learn.io.numpy_input_fn(
data, labels, batch_size=self.batchsize,
num_epochs=self.steps)
# TODO at the moment, it's tied to Participant
def train(self, force=False):
if force or not os.path.isdir(MODEL_ROOT + self.id):
monitor = tf.contrib.learn.monitors.ValidationMonitor(
input_fn=self._batch(),
eval_steps=1, every_n_steps=20)
for _ in range(self.batches):
self._model.fit(input_fn=self._batch(),
steps=self.steps,
monitors=[monitor])
def predict(self, ids):
return itertools.islice(
self._model.predict_proba(input_fn=self._batch(ids)),
len(ids))
def estimate(self, record):
# override: calculate or return the label's value
pass
class MVPScoreModel(Model):
def __init__(self, db):
self.features = ["participant_stats.non_jungle_minion_kills"]
self.label = "participant_stats.impact_score"
self.type = "linear"
self.batches = 1
self.batchsize = 500
self.steps = 500
self.id = "kda-win"
super().__init__(db)
def estimate(self, record):
# for training, rating = participant.winner
return record.winner
def rate(self, ids):
return [w for l, w in self.predict(ids)]
logging.basicConfig(level=logging.INFO)
if __name__ == "__main__":
connect()
mvpmodel = MVPScoreModel(db)
mvpmodel.train(force=True)
for name in mvpmodel._model.get_variable_names():
logging.info("%s: %s", name,
mvpmodel._model.get_variable_value(name))
|
