diff options
| -rw-r--r-- | api.py | 35 |
1 files changed, 14 insertions, 21 deletions
@@ -42,24 +42,22 @@ class KDAClassifier(object): # DNN configuration self._categories = [] - self._continuous = ["kills", "deaths", "assists", "cs", "teamkills"] + self._continuous = ["kills", "deaths", "assists"] self._classes = ["loss", "win"] self._label = "win" # learn configuration - self._steps = 200 # per batch + self._steps = 1000 # per batch self._batchsize = 500 - self._max_batches = 5 # number of batches to train + self._max_batches = 20 # number of batches to train # database mappings # TODO cache, rm duplicated code self._trainquery = """ SELECT - participant.kills, - participant.deaths, - participant.assists, - participant.farm, - roster.hero_kills, + kills/duration::float, + deaths/duration::float, + assists/duration::float, participant.winner, participant.api_id FROM participant @@ -70,11 +68,9 @@ class KDAClassifier(object): """ self._testquery = """ SELECT - participant.kills, - participant.deaths, - participant.assists, - participant.farm, - roster.hero_kills, + kills/duration::float, + deaths/duration::float, + assists/duration::float, participant.winner, participant.api_id FROM participant @@ -100,15 +96,13 @@ class KDAClassifier(object): else: cur.execute(self._testquery, (filter,)) ids = [] - data = {"kills": [], "deaths": [], "assists": [], "win": [], "cs": [], "teamkills": []} + data = {"kills": [], "deaths": [], "assists": [], "win": []} for rec in cur: data["kills"].append(rec[0]) data["deaths"].append(rec[1]) data["assists"].append(rec[2]) - data["cs"].append(rec[3]) - data["teamkills"].append(rec[4]) - data["win"].append(rec[5]) - ids.append(rec[6]) + data["win"].append(rec[3]) + ids.append(rec[4]) cur.close() logging.debug(data) return data, ids @@ -122,7 +116,7 @@ class KDAClassifier(object): emb_columns = [ tf.contrib.layers.embedding_column( sparse_id_column=col, - dimension=5 # log_2(number of unique features) TODO + dimension=2 # log_2(number of unique features) TODO ) for col in feature_columns ] + [ @@ -185,7 +179,7 @@ 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)[0], train=True), + input_fn=lambda: self._toinput(self._get_sample(size=2000)[0], train=True), eval_steps=1, every_n_steps=10 ) @@ -255,7 +249,6 @@ class Analyzer(joblib.worker.Worker): self._pool = await asyncpg.create_pool(**dbconf) self.classifier = KDAClassifier() self.classifier.connect(**dbconf) - self.classifier._step = 1000 # TODO test batch size async def setup(self): """Setup the model.""" |