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#!/usr/bin/python3
import os
import random
import itertools
import logging
from sqlalchemy.ext.automap import automap_base
from sqlalchemy.orm import Session, relationship
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 = ParticipantExt = Player = None
def connect():
global Match, Roster, Participant, ParticipantExt, Player
# generate schema from db
Base = automap_base()
engine = create_engine(DATABASE_URI)
Base.prepare(engine, reflect=True)
# 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)")
ParticipantExt = Base.classes.participant_ext
Participant.participant_ext = relationship(
"participant_ext", foreign_keys="participant_ext.participant_api_id",
primaryjoin="and_(participant_ext.participant_api_id == participant.api_id)")
Player = Base.classes.player
return 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
for feat in self.features:
self._feature_cols = [tf.contrib.layers.real_valued_column(
feat, dimension=1)]
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_ext":
return vars(record.participant_ext[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=[], size=None):
if len(ids) == 0: # 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)).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)
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.kills", "participant.deaths",
"participant.assists"]
self.label = "participant_ext.rating"
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
logging.basicConfig(level=logging.INFO)
if __name__ == "__main__":
db = connect()
model = MVPScoreModel(db)
model.train(force=True)
print(model._model.evaluate(input_fn=model._batch(), steps=1))
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