diff options
| -rw-r--r-- | kdarate.py | 29 |
1 files changed, 13 insertions, 16 deletions
@@ -30,11 +30,12 @@ class KDAClassifier(object): # database mappings self._paths = { "id": "id", - "kills": "(data->'attributes'->'stats'->>'kills')::int", - "deaths": "(data->'attributes'->'stats'->>'deaths')::int", - "assists": "(data->'attributes'->'stats'->>'assists')::int", + "kills": "kills", + "deaths": "deaths", + "assists": "assists", +# "teamkills": "(SELECT hero_kills FROM roster WHERE roster.api_id=participant.roster_api_id)", # "actor": "data->'attributes'->>'actor'", - "win": "(data->'attributes'->'stats'->>'winner')::bool::int" + "win": "winner" } # for n in range(0, 6): # self._paths["item"+str(n)] = \ @@ -47,15 +48,6 @@ class KDAClassifier(object): # TODO with current synchronous implementation, pooling isn't needed self._pool = await asyncpg.create_pool(dbstring) - # create layout - # maps participant -> role jsonb - async with self._pool.acquire() as conn: - async with conn.transaction(): - await conn.execute( - # TODO do this globally etc - "CREATE TABLE IF NOT EXISTS enhanced_participant_wip" + - "(participant_id TEXT PRIMARY KEY, actor JSONB)") - def connect(self, dbstring): """See _async_connect.""" asyncio.get_event_loop().run_until_complete( @@ -159,6 +151,8 @@ class KDAClassifier(object): sample = self._get_sample( size=batchsize, offset=self._step) self._step += batchsize + if len(sample["kills"]) < batchsize: # TODO! + print("!!!!!!!!!! data exhausted !!!!!!!!!!!!!!!") return self._toinput(sample, train=True) def train(self): @@ -172,7 +166,7 @@ class KDAClassifier(object): # get one batch of testing data # TODO either terminate with `eval_steps` or with OutOfRangeError validation_monitor = tf.contrib.learn.monitors.ValidationMonitor( - input_fn=lambda: self._toinput(self._get_sample(size=1000, offset=101000), train=True), + input_fn=lambda: self._toinput(self._get_sample(size=1000, offset=0), train=True), eval_steps=1, every_n_steps=20, early_stopping_metric="accuracy", @@ -234,12 +228,15 @@ class KDAClassifier(object): def main(): classifier = KDAClassifier() - classifier.connect("postgres://vgstats@localhost/vgstats") + classifier.connect("postgres://vainweb:vainweb@localhost/vainsocial-web") + sample = classifier._get_sample(size=10, offset=0) # DEBUG + classifier._step = 1000 # test batch size + print(sample) # TODO train only for the latest patch. print("training") classifier.train() print("done training") - sample = classifier._get_sample(size=10, offset=102000) # DEBUG + sample = classifier._get_sample(size=10, offset=0) # DEBUG print(sample) # TODO train batches/fixed number print("classifying debug data") |