delete code unrelated to trueskillrelease/2.23.0release/2.22.0develop

1 files changed, 0 insertions, 146 deletions

diff --git a/rolerate.py b/rolerate.py
deleted file mode 100644
index 42ef8a5..0000000
--- a/rolerate.py
+++ /dev/null
@@ -1,146 +0,0 @@
-#!/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))