はじめに
最近，Dota2を始めましたが全く勝てません
ハードボットにボコボコにされます．
色々と調べても「死ぬな」くらいのことしか分らず苦戦しています．
データサイエンスでDota2強くなるかも説
そこで，データサイエンスの力を借りて，どのような状況なら勝っているか？や前回に比べてどのように振舞ったから勝てたのか？ということを数値化して分析していけば強くなるのでは！と考えました．本企画はその仮説を検証していく企画です．
前回までのあらすじ
Dota2の情報をPythonで取得できるような環境を作成しました．
今回の概要
今回は，Dota2からデータを取得し，定期的に保存する機構を作っていきます．
Dota2 解析プログラム
Import
import dota2gsi
Demo program
まずはDota2と接続していることを確認．
def demo_handle_state(last_state, state):
# Use nested gets to safely extract data from the state
hero_name = state.get('hero', {}).get('name')
health_percent = state.get('hero', {}).get('health_percent')
max_health = state.get('hero', {}).get('max_health')
max_health = state.get('hero', {}).get('max_health')
health = state.get('hero', {}).get('health')
level = state.get('hero', {}).get('level')
mana = state.get('hero', {}).get('mana')
mana_percent = state.get('hero', {}).get('mana_percent')
gold = state.get('player', {}).get('gold')
xpm = state.get('player', {}).get('xpm')
clock_time = state.get('map', {}).get('clock_time')
# If the attributes exist, print them
if health_percent and max_health:
health = int(max_health * health_percent/100)
# print(f"{hero_name}'s current health: {health}/{max_health}")
print("{}:{}, {}:{}, {}:{}, {}:{}, {}:{}, {}:{}, ".format("clock_time", clock_time, "health", health, "gold", gold, "mana", mana, "level", level, "xpm", xpm))
server = dota2gsi.Server(ip='0.0.0.0', port=3000)
server.on_update(demo_handle_state)
server.start()
DotA 2 GSI server listening on 0.0.0.0:3000 - CTRL+C to stop
clock_time:2887, health:1020, gold:1, mana:375, level:7, xpm:72,
clock_time:2887, health:1020, gold:1, mana:375, level:7, xpm:72,
clock_time:2887, health:1020, gold:1, mana:375, level:7, xpm:72,
clock_time:2887, health:1020, gold:1, mana:375, level:7, xpm:72,
clock_time:2887, health:1020, gold:1, mana:375, level:7, xpm:72,
clock_time:2887, health:1020, gold:1, mana:375, level:7, xpm:72,
clock_time:2887, health:1020, gold:1, mana:375, level:7, xpm:72,
clock_time:2887, health:1020, gold:1, mana:375, level:7, xpm:72,
clock_time:2887, health:1020, gold:1, mana:375, level:7, xpm:72,
clock_time:2887, health:1020, gold:1, mana:375, level:7, xpm:72,
clock_time:2887, health:1020, gold:1, mana:375, level:7, xpm:72,
clock_time:2887, health:1020, gold:1, mana:375, level:7, xpm:72,
clock_time:2887, health:1020, gold:1, mana:375, level:7, xpm:72,
clock_time:2887, health:1020, gold:1, mana:375, level:7, xpm:72,
clock_time:2887, health:1020, gold:1, mana:375, level:7, xpm:72,
clock_time:2887, health:1020, gold:1, mana:375, level:7, xpm:72,
clock_time:2887, health:1020, gold:1, mana:375, level:7, xpm:72,
clock_time:2887, health:1020, gold:1, mana:375, level:7, xpm:72,
clock_time:2887, health:1020, gold:1, mana:375, level:7, xpm:72,
clock_time:2887, health:1020, gold:1, mana:375, level:7, xpm:72,
clock_time:2887, health:1020, gold:1, mana:375, level:7, xpm:72,
clock_time:2887, health:1020, gold:1, mana:375, level:7, xpm:72,
clock_time:2887, health:1020, gold:1, mana:375, level:7, xpm:72,
clock_time:2887, health:1020, gold:1, mana:375, level:7, xpm:72,
clock_time:2887, health:1020, gold:1, mana:375, level:7, xpm:72,
clock_time:2887, health:1020, gold:1, mana:375, level:7, xpm:72,
clock_time:2887, health:1020, gold:1, mana:375, level:7, xpm:72,
clock_time:2887, health:1020, gold:1, mana:375, level:7, xpm:72,
clock_time:2887, health:1020, gold:1, mana:375, level:7, xpm:72,
clock_time:2887, health:1020, gold:1, mana:375, level:7, xpm:72,
clock_time:2887, health:1020, gold:1, mana:375, level:7, xpm:72,
clock_time:2887, health:1020, gold:1, mana:375, level:7, xpm:72,
clock_time:2887, health:1020, gold:1, mana:375, level:7, xpm:72,
clock_time:2887, health:1020, gold:1, mana:375, level:7, xpm:72,
clock_time:2887, health:1020, gold:1, mana:375, level:7, xpm:72,
clock_time:2887, health:1020, gold:1, mana:375, level:7, xpm:72,
clock_time:2887, health:1020, gold:1, mana:375, level:7, xpm:72,
clock_time:2887, health:1020, gold:1, mana:375, level:7, xpm:72,
clock_time:2887, health:1020, gold:1, mana:375, level:7, xpm:72,
clock_time:2887, health:1020, gold:1, mana:375, level:7, xpm:72,
Server stopped.
Read data name file
Create data id csv
detaのIDリストのファイルを作成します．
player
assists
camps_stacked
deaths
denies
gold
gold_reliable
gold_unreliable
gpm
hero_damage
kill_list:victimid_#
kill_streak
kills
last_hits
net_worth
pro_name
runes_activated
support_gold_spent
wards_destroyed
wards_placed
wards_purchased
xpm
Glob file
csv ファイルを探索します．
import glob
import pandas as pd
datasets_path = "datasets"
data_file_list = glob.glob(datasets_path + "/*.csv")
ファイル一覧です．
data_file_list
['datasets\\hero.csv', 'datasets\\player.csv']
Read file
CSVファイルを読み込みます．
for file_path in data_file_list:
df_data = pd.read_csv(file_path)
print("---------------------")
print(df_data.head(5))
---------------------
hero
0 alive
1 break
2 buyback_cost
3 buyback_cooldown
4 disarmed
---------------------
player
0 assists
1 camps_stacked
2 deaths
3 denies
4 gold
dataframe2dict
DataFrameを辞書型に変形します．
data_dict = {}
for file_path in data_file_list:
df_data = pd.read_csv(file_path)
data_dict[df_data.columns[0]] = list(df_data[df_data.columns[0]].values)
変形後のデータです．
data_dict
{'hero': ['alive',
'break',
'buyback_cost',
'buyback_cooldown',
'disarmed',
'has_debuff',
'health',
'health_percent',
'hexed',
'id',
'level',
'magicimmune',
'mana',
'mana_percent',
'max_health',
'max_mana',
'muted',
'name',
'respawn_seconds',
'selected_unit',
'silenced',
'stunned',
'talent_1',
'talent_2',
'talent_3',
'talent_4',
'talent_5',
'talent_6',
'talent_7',
'talent_8',
'xpos',
'ypos'],
'player': ['assists',
'camps_stacked',
'deaths',
'denies',
'gold',
'gold_reliable',
'gold_unreliable',
'gpm',
'hero_damage',
'kill_list:victimid_#',
'kill_streak',
'kills',
'last_hits',
'net_worth',
'pro_name',
'runes_activated',
'support_gold_spent',
'wards_destroyed',
'wards_placed',
'wards_purchased',
'xpm']}
Get Dota2 data
ここから，Dota2のデータを取得します．
data name loop
先ほどの辞書からデータの名前を1つづ取り出すコードを作成します．
for data_kind_name in data_dict.keys():
print("===================")
print(data_kind_name)
print("--------------------")
for data_name in data_dict[data_kind_name]:
print(data_name)
#hero_name = state.get(data_kind_name, {}).get(data_name)
===================
hero
--------------------
alive
break
buyback_cost
buyback_cooldown
disarmed
has_debuff
health
health_percent
hexed
id
level
magicimmune
mana
mana_percent
max_health
max_mana
muted
name
respawn_seconds
selected_unit
silenced
stunned
talent_1
talent_2
talent_3
talent_4
talent_5
talent_6
talent_7
talent_8
xpos
ypos
===================
player
--------------------
assists
camps_stacked
deaths
denies
gold
gold_reliable
gold_unreliable
gpm
hero_damage
kill_list:victimid_#
kill_streak
kills
last_hits
net_worth
pro_name
runes_activated
support_gold_spent
wards_destroyed
wards_placed
wards_purchased
xpm
Define handle
これをハンドラーに組み込みます．
def realtime_handle_state(last_state, state):
# Use nested gets to safely extract data from the state
for data_kind_name in data_dict.keys():
print("===================")
print(data_kind_name)
for data_name in data_dict[data_kind_name]:
print("--------------------")
print(data_name)
data_value = state.get(data_kind_name, {}).get(data_name)
print(data_value)
Run server test
サーバーを起動させます．
server = dota2gsi.Server(ip='0.0.0.0', port=3000)
server.on_update(realtime_handle_state)
server.start()
DotA 2 GSI server listening on 0.0.0.0:3000 - CTRL+C to stop
===================
hero
--------------------
alive
True
--------------------
break
False
--------------------
buyback_cost
233
．．．．
None
--------------------
wards_destroyed
None
--------------------
wards_placed
None
--------------------
wards_purchased
None
--------------------
xpm
23
Server stopped.
．．．．．
これでデータ取得する部分ができました．
Adjust state data
次に，取得したデータを整形していきます．
stateから1行のDataFrameに変形します．
def state2df(state):
df_list = []
for data_kind_name in data_dict.keys():
#print("===================")
#print(data_kind_name)
data_name_list = []
data_value_list = []
for data_name in data_dict[data_kind_name]:
#print("--------------------")
#print(data_name)
data_name_list.append(data_name)
data_value = state.get(data_kind_name, {}).get(data_name)
#print(data_value)
data_value_list.append(data_value)
#df_data = pd.Dataframe(data_value_list)
df_data = pd.DataFrame(data_value_list).T
df_data.columns = data_name_list
#print(df_data)
df_list.append(df_data)
df_merge = pd.concat(df_list, axis=1)
print(df_merge)
これを新しいハンドラ2を作成し組み込みます．
def realtime_handle_state2(last_state, state):
# Use nested gets to safely extract data from the state
df_state = state2df(state)
df_data_base = pd.concat([df_data_base, df_state])
print(df_data_base)
以上を踏まえてクラスを作成します．
Define class
stateを取得し，DataFrameに変形するクラスです．
定期的保存機能
30秒ごとに保存する機構を加えます．
これで，後から解析できます．
if(self.df_data_base['clock_time'].values[-1] % 30 == 0):
print("===================")
print(self.df_data_base['clock_time'].values[-1])
print(self.df_data_base.tail(5))
self.df_data_base.to_csv('log_{}.csv'.format(self.df_data_base['matchid'].values[-1]))
コード全体
import dota2gsi
import glob
import csv
import pprint
pd.set_option('display.max_columns', 5)
class Dota2Analy:
def __init__(self, funname="dota2"):
self.funname = funname
self.df_data_base = pd.DataFrame({})
datasets_path = "datasets"
data_file_list = glob.glob(datasets_path + "/*.csv")
# --------------------------------------
self.data_dict = {}
for file_path in data_file_list:
df_data = pd.read_csv(file_path)
self.data_dict[df_data.columns[0]] = list(df_data[df_data.columns[0]].values)
def state2df(self, state):
df_list = []
for data_kind_name in self.data_dict.keys():
#print("===================")
#print(data_kind_name)
data_name_list = []
data_value_list = []
for data_name in self.data_dict[data_kind_name]:
#print("--------------------")
#print(data_name)
data_name_list.append(data_name)
data_value = state.get(data_kind_name, {}).get(data_name)
#print(data_value)
data_value_list.append(data_value)
#df_data = pd.Dataframe(data_value_list)
df_data = pd.DataFrame(data_value_list).T
df_data.columns = data_name_list
#print(df_data)
df_list.append(df_data)
df_merge = pd.concat(df_list, axis=1)
#print(df_merge)
return df_merge
def _realtime_handle_state2(self, last_state, state):
# Use nested gets to safely extract data from the state
df_state = self.state2df(state)
self.df_data_base = pd.concat([self.df_data_base, df_state])
if(self.df_data_base['clock_time'].values[-1] % 30 == 0):
print("===================")
print(self.df_data_base['clock_time'].values[-1])
print(self.df_data_base.tail(5))
self.df_data_base.to_csv('log_{}.csv'.format(self.df_data_base['matchid'].values[-1]))
def run(self):
server = dota2gsi.Server(ip='0.0.0.0', port=3000)
server.on_update(self._realtime_handle_state2)
server.start()
if __name__ == '__main__':
D2A = Dota2Analy()
D2A.run()
実行するとこのように，時刻ごとに追加されていきます．
DotA 2 GSI server listening on 0.0.0.0:3000 - CTRL+C to stop
===================
270
alive break ... wards_purchased xpm
0 True False ... NaN 328.0
0 True False ... NaN 328.0
0 True False ... NaN 328.0
0 True False ... NaN 328.0
0 True False ... NaN 327.0
[5 rows x 66 columns]
===================
270
alive break ... wards_purchased xpm
0 True False ... NaN 328.0
0 True False ... NaN 328.0
0 True False ... NaN 328.0
0 True False ... NaN 327.0
0 True False ... NaN 327.0
[5 rows x 66 columns]
．．．．
プログラム
コードはこちらです．
おわりに
今回は，Dota2からstateを取得し，csvに記載されているIDを使ってデータを取得しました．
取得したデータが1行のDataFrameに変形し追加し，定期的に保存する機構を作ることができました．
次回は保存したデータをプロットしていこうと思います．
参考サイト
最新情報
速報はTwitterやブログにて