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Diffstat (limited to 'rolerate.py')
| -rw-r--r-- | rolerate.py | 165 |
1 files changed, 165 insertions, 0 deletions
diff --git a/rolerate.py b/rolerate.py new file mode 100644 index 0000000..86ca2d8 --- /dev/null +++ b/rolerate.py @@ -0,0 +1,165 @@ +#!/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 = 15 + self.batchsize = 300 + self.steps = 500 + self.id = "role-kda" + + self._db = db + self._feature_cols = [ + tf.contrib.layers.real_valued_column("kills_per_min", dimension=1), + tf.contrib.layers.real_valued_column("deaths_per_min", dimension=1), + tf.contrib.layers.real_valued_column("assists_per_min", 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, ids=None, size=None): + if ids is None: # training, get random sample + size = size or self.batchsize + # train a model one batch + offset = int(random.random() * self._db.query(Participant)\ + .filter(Participant.hero.any(is_captain=True))\ + .count()) + # have a bit of randomness in the sample + records = self._db.query(Participant)\ + .filter(Participant.hero.any(is_captain=True))\ + .offset(offset).limit(size)\ + .all() + else: + records = self._db.query(Participant)\ + .filter(Hero.is_carry)\ + .filter(Participant.api_id.in_(ids))\ + .order_by(Participant.api_id.desc())\ + .all() + + data = {} + labels = [] + # populate from db records + data["kills_per_min"] = [] + data["deaths_per_min"] = [] + data["assists_per_min"] = [] + for record in records: + labels.append(record.winner) + data["kills_per_min"].append(record.participant_stats[0].kills + / record.roster[0].match[0].duration) + data["deaths_per_min"].append(record.participant_stats[0].deaths + / record.roster[0].match[0].duration) + data["assists_per_min"].append(record.participant_stats[0].assists + / record.roster[0].match[0].duration) + + 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) + + 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 + + +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)) |