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| -rw-r--r-- | tf.py | 102 |
1 files changed, 102 insertions, 0 deletions
@@ -0,0 +1,102 @@ +#!/usr/bin/python +""" +Work in progress + - Tensorflow Linear Regression to predict win/loss based on KDA +""" + +import asyncio +import pandas +import tensorflow as tf + +import api.database + + +db = api.database.Database() + + +async def get_winrate_sample(): + return await db.select( + """ + SELECT + (data->'attributes'->'stats'->>'winner')::bool AS win, + data->'attributes'->>'actor' AS hero, + (data->'attributes'->'stats'->>'farm')::float AS cs, + (data->'attributes'->'stats'->>'kills')::int AS k, + (data->'attributes'->'stats'->>'deaths')::int AS d, + (data->'attributes'->'stats'->>'assists')::int AS a + FROM participant + """ + ) + +def to_input_winrate(df): + LABEL = "win" + CATEGORIES = ["hero"] + CONTINUOUS = ["cs", "k", "d", "a"] + + # convert constants to tensors + conts = {k: tf.constant(df[k]) + for k in CONTINUOUS} + # convert categories to sparse tensors + cats = {k: tf.SparseTensor( + indices=[[i, 0] for i in range(len(df[k]))], + values=df[k], + shape=[len(df[k]), 1]) + for k in CATEGORIES} + + df[LABEL] = [int(d) for d in df[LABEL]] # bool to 0 / 1 + + features = {**conts, **cats} + label = tf.constant(df[LABEL]) + return features, label + +async def train_winrate(): + data = await get_winrate_sample() # list of dict + trainlimit = int(len(data) * 0.8) + + # convert to dict of list + train_sample = {} + for it in data[:trainlimit]: + for k, v in it.items(): + try: + train_sample[k].append(v) + except KeyError: + train_sample[k] = [v] + test_sample = {} + for it in data[trainlimit:]: + for k, v in it.items(): + try: + test_sample[k].append(v) + except KeyError: + test_sample[k] = [v] + + # create input layers + cs = tf.contrib.layers.real_valued_column("cs") + k = tf.contrib.layers.real_valued_column("k") + d = tf.contrib.layers.real_valued_column("d") + a = tf.contrib.layers.real_valued_column("a") + hero = tf.contrib.layers.sparse_column_with_hash_bucket("hero", hash_bucket_size=50) + + wide_columns = [ + hero, cs, k, d, a + ] + + model = tf.contrib.learn.LinearClassifier( + feature_columns=wide_columns + ) + + def train(): + return to_input_winrate(train_sample) + def test(): + return to_input_winrate(test_sample) + + model.fit(input_fn=train, steps=200) + results = model.evaluate(input_fn=test, steps=1) + for key in sorted(results): + print("%s: %s" % (key, results[key])) + + +tf.logging.set_verbosity(tf.logging.INFO) + +loop = asyncio.get_event_loop() +loop.run_until_complete(db.connect("postgres://vgstats@localhost/vgstats")) +loop.run_until_complete(train_winrate()) |