From 3d85cf5e4d82b7d9e369f907562ae80aa7ce1cc8 Mon Sep 17 00:00:00 2001
From: schneefux <schneefux+commit@schneefux.xyz>
Date: Mon, 6 Feb 2017 10:55:06 +0100
Subject: rework, add wip kda rating

---
 tf.py | 102 ------------------------------------------------------------------
 1 file changed, 102 deletions(-)
 delete mode 100644 tf.py

(limited to 'tf.py')

diff --git a/tf.py b/tf.py
deleted file mode 100644
index ab72ad1..0000000
--- a/tf.py
+++ /dev/null
@@ -1,102 +0,0 @@
-#!/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())
-- 
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