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#!/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 db
global Match, Roster, Participant, Hero, ParticipantStats, Player
# 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 RoleModel(object):
def __init__(self, db):
self.batches = 3
self.batchsize = 300
self.testsize = 1000
self.steps = 300
self.id = "role-kda"
self._db = db
self._feature_cols = [
tf.contrib.layers.real_valued_column("farm", dimension=1),
tf.contrib.layers.real_valued_column("kills", dimension=1),
tf.contrib.layers.real_valued_column("deaths", dimension=1),
tf.contrib.layers.real_valued_column("assists", dimension=1)
]
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 _batch(self, size=None):
size = size or self.batchsize
# train a model one batch
offset = int(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()
data = {}
labels = []
# populate from db records
data["farm"] = []
data["kills"] = []
data["deaths"] = []
data["assists"] = []
for record in records:
if (record.hero[0].is_jungler):
labels.append(record.winner)
data["farm"].append(record.participant_stats[0].farm)
data["kills"].append(record.participant_stats[0].kills)
data["deaths"].append(record.participant_stats[0].deaths)
data["assists"].append(record.participant_stats[0].assists)
logging.info("---------- %s data points for training ---------", len(labels))
# convert to numpy arrs
for key in data:
data[key] = np.array(data[key])
labels = np.array(labels)
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(size=self.testsize),
eval_steps=1, every_n_steps=20)
for _ in range(self.batches):
self._model.fit(input_fn=self._batch(),
steps=self.steps,
monitors=[monitor])
logging.basicConfig(level=logging.INFO)
if __name__ == "__main__":
connect()
rolemodel = RoleModel(db)
rolemodel.train()
for name in rolemodel._model.get_variable_names():
logging.info("%s: %s", name,
rolemodel._model.get_variable_value(name))
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