mirror of
https://github.com/titanscouting/tra-superscript.git
synced 2024-09-20 00:47:56 +00:00
Revert "experimental trueskill support"
This reverts commit 3fe2922e97.
This commit is contained in:
Dev Singh
parent
3fe2922e97
commit
25e4babd71
@ -3,32 +3,27 @@ import pull
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import pandas as pd
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import json
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def pull_new_tba_matches(apikey, competition, cutoff):
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api_key = apikey
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x = requests.get("https://www.thebluealliance.com/api/v3/event/" +
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competition+"/matches/simple", headers={"X-TBA-Auth-Key": api_key})
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api_key= apikey
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x=requests.get("https://www.thebluealliance.com/api/v3/event/"+competition+"/matches/simple", headers={"X-TBA-Auth-Key":api_key})
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json = x.json()
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out = []
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for i in json:
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if i["actual_time"] != None and i["comp_level"] == "qm":
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out.append({"match": i['match_number'], "blue": list(map(lambda x: int(x[3:]), i['alliances']['blue']['team_keys'])), "red": list(
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map(lambda x: int(x[3:]), i['alliances']['red']['team_keys'])), "winner": i["winning_alliance"]})
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out.append({"match" : i['match_number'], "blue" : list(map(lambda x: int(x[3:]), i['alliances']['blue']['team_keys'])), "red" : list(map(lambda x: int(x[3:]), i['alliances']['red']['team_keys'])), "winner": i["winning_alliance"]})
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return out
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def pull_new_tba_matches_manual(apikey, competition, cutoff):
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filename = competition+"-wins.json"
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with open(filename, 'r') as f:
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data = json.load(f)
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data = json.load(f)
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return data
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def get_team_match_data(client, competition, team_num):
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db = client.data_scouting
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mdata = db.matchdata
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out = {}
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for i in mdata.find({"competition": competition, "team_scouted": str(team_num)}):
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for i in mdata.find({"competition" : competition, "team_scouted": str(team_num)}):
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out[i['match']] = i['data']
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return pd.DataFrame(out)
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@ -36,27 +31,23 @@ def get_team_pit_data(client, competition, team_num):
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db = client.data_scouting
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mdata = db.pitdata
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out = {}
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return mdata.find_one({"competition": competition, "team_scouted": str(team_num)})["data"]
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return mdata.find_one({"competition" : competition, "team_scouted": str(team_num)})["data"]
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def get_team_metrics_data(client, competition, team_num):
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db = client.data_processing
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mdata = db.team_metrics
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return mdata.find_one({"competition": competition, "team": team_num})
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return mdata.find_one({"competition" : competition, "team": team_num})
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def get_match_data_formatted(client, competition):
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teams_at_comp = pull.get_teams_at_competition(competition)
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out = {}
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for team in teams_at_comp:
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try:
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out[int(team)] = unkeyify_2l(get_team_match_data(
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client, competition, team).transpose().to_dict())
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out[int(team)] = unkeyify_2l(get_team_match_data(client, competition, team).transpose().to_dict())
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except:
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pass
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return out
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def get_metrics_data_formatted(client, competition):
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teams_at_comp = pull.get_teams_at_competition(competition)
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out = {}
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@ -67,10 +58,8 @@ def get_metrics_data_formatted(client, competition):
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pass
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return out
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def get_pit_data_formatted(client, competition):
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x = requests.get(
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"https://titanscouting.epochml.org/api/fetchAllTeamNicknamesAtCompetition?competition="+competition)
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x=requests.get("https://titanscouting.epochml.org/api/fetchAllTeamNicknamesAtCompetition?competition="+competition)
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x = x.json()
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x = x['data']
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x = x.keys()
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@ -82,14 +71,12 @@ def get_pit_data_formatted(client, competition):
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pass
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return out
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def get_pit_variable_data(client, competition):
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db = client.data_processing
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mdata = db.team_pit
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out = {}
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return mdata.find()
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def get_pit_variable_formatted(client, competition):
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temp = get_pit_variable_data(client, competition)
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out = {}
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@ -97,39 +84,30 @@ def get_pit_variable_formatted(client, competition):
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out[i["variable"]] = i["data"]
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return out
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def push_team_tests_data(client, competition, team_num, data, dbname="data_processing", colname="team_tests"):
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def push_team_tests_data(client, competition, team_num, data, dbname = "data_processing", colname = "team_tests"):
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db = client[dbname]
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mdata = db[colname]
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mdata.replace_one({"competition": competition, "team": team_num}, {"_id": competition +
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str(team_num)+"am", "competition": competition, "team": team_num, "data": data}, True)
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mdata.replace_one({"competition" : competition, "team": team_num}, {"_id": competition+str(team_num)+"am", "competition" : competition, "team" : team_num, "data" : data}, True)
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def push_team_metrics_data(client, competition, team_num, data, dbname="data_processing", colname="team_metrics"):
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def push_team_metrics_data(client, competition, team_num, data, dbname = "data_processing", colname = "team_metrics"):
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db = client[dbname]
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mdata = db[colname]
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mdata.replace_one({"competition": competition, "team": team_num}, {"_id": competition +
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str(team_num)+"am", "competition": competition, "team": team_num, "metrics": data}, True)
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mdata.replace_one({"competition" : competition, "team": team_num}, {"_id": competition+str(team_num)+"am", "competition" : competition, "team" : team_num, "metrics" : data}, True)
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def push_team_pit_data(client, competition, variable, data, dbname="data_processing", colname="team_pit"):
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def push_team_pit_data(client, competition, variable, data, dbname = "data_processing", colname = "team_pit"):
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db = client[dbname]
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mdata = db[colname]
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mdata.replace_one({"competition": competition, "variable": variable}, {
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"competition": competition, "variable": variable, "data": data}, True)
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mdata.replace_one({"competition" : competition, "variable": variable}, {"competition" : competition, "variable" : variable, "data" : data}, True)
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def get_analysis_flags(client, flag):
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db = client.data_processing
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mdata = db.flags
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return mdata.find_one({flag: {"$exists": True}})
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return mdata.find_one({flag:{"$exists":True}})
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def set_analysis_flags(client, flag, data):
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db = client.data_processing
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mdata = db.flags
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return mdata.replace_one({flag: {"$exists": True}}, data, True)
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return mdata.replace_one({flag:{"$exists":True}}, data, True)
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def unkeyify_2l(layered_dict):
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out = {}
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@ -137,12 +115,11 @@ def unkeyify_2l(layered_dict):
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add = []
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sortkey = []
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for j in layered_dict[i].keys():
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add.append([j, layered_dict[i][j]])
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add.sort(key=lambda x: x[0])
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add.append([j,layered_dict[i][j]])
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add.sort(key = lambda x: x[0])
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out[i] = list(map(lambda x: x[1], add))
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return out
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def get_previous_time(client):
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previous_time = get_analysis_flags(client, "latest_update")
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@ -158,28 +135,23 @@ def get_previous_time(client):
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return previous_time
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def set_current_time(client, current_time):
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set_analysis_flags(client, "latest_update", {"latest_update": current_time})
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set_analysis_flags(client, "latest_update", {"latest_update":current_time})
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def get_database_config(client):
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remote_config = get_analysis_flags(client, "config")
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return remote_config["config"] if remote_config != None else None
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def set_database_config(client, config):
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set_analysis_flags(client, "config", {"config": config})
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def load_match(client, competition):
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return get_match_data_formatted(client, competition)
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def load_metric(client, competition, match, group_name, metrics):
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group = {}
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@ -191,8 +163,7 @@ def load_metric(client, competition, match, group_name, metrics):
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if db_data == None:
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elo = {"score": metrics["elo"]["score"]}
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gl2 = {"score": metrics["gl2"]["score"],
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"rd": metrics["gl2"]["rd"], "vol": metrics["gl2"]["vol"]}
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gl2 = {"score": metrics["gl2"]["score"], "rd": metrics["gl2"]["rd"], "vol": metrics["gl2"]["vol"]}
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ts = {"mu": metrics["ts"]["mu"], "sigma": metrics["ts"]["sigma"]}
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group[team] = {"elo": elo, "gl2": gl2, "ts": ts}
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@ -209,33 +180,28 @@ def load_metric(client, competition, match, group_name, metrics):
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return group
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def load_pit(client, competition):
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return get_pit_data_formatted(client, competition)
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def push_match(client, competition, results):
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for team in results:
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push_team_tests_data(client, competition, team, results[team])
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def push_metric(client, competition, metric):
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for team in metric:
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push_team_metrics_data(client, competition, team, metric[team])
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def push_pit(client, competition, pit):
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for variable in pit:
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push_team_pit_data(client, competition, variable, pit[variable])
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def check_new_database_matches(client, competition):
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return True
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@ -1,10 +1,8 @@
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import abc
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import data as d
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import signal
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import trueskill
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import numpy as np
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from tra_analysis import Analysis as an
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from ts_predict import win_probability
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class Module(metaclass = abc.ABCMeta):
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@ -200,9 +198,6 @@ class Metric (Module):
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gl2_red_vol_total = 0
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gl2_blu_vol_total = 0
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ts_red_team = {}
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ts_blu_team = {}
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for team in red:
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elo_red_total += red[team]["elo"]["score"]
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@ -211,10 +206,6 @@ class Metric (Module):
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gl2_red_rd_total += red[team]["gl2"]["rd"]
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gl2_red_vol_total += red[team]["gl2"]["vol"]
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tmu = red[team]["ts"]["mu"] or 25
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tsigma = red[team]["ts"]["sigma"] or 8.333
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ts_red_team[team] = trueskill.Rating(mu=tmu, sigma=tsigma)
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for team in blu:
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elo_blu_total += blu[team]["elo"]["score"]
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@ -223,43 +214,25 @@ class Metric (Module):
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gl2_blu_rd_total += blu[team]["gl2"]["rd"]
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gl2_blu_vol_total += blu[team]["gl2"]["vol"]
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tmu = blu[team]["ts"]["mu"] or 25
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tsigma = blu[team]["ts"]["sigma"] or 8.333
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ts_blu_team[team] = trueskill.Rating(mu=tmu, sigma=tsigma)
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red_elo = {"score": elo_red_total / len(red)}
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blu_elo = {"score": elo_blu_total / len(blu)}
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red_gl2 = {"score": gl2_red_score_total / len(red), "rd": gl2_red_rd_total / len(red), "vol": gl2_red_vol_total / len(red)}
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blu_gl2 = {"score": gl2_blu_score_total / len(blu), "rd": gl2_blu_rd_total / len(blu), "vol": gl2_blu_vol_total / len(blu)}
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print('here')
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if match["winner"] == "red":
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observations = {"red": 1, "blu": 0}
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ts_obs = [1,0]
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elif match["winner"] == "blue":
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observations = {"red": 0, "blu": 1}
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ts_obs = [0,1]
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else:
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observations = {"red": 0.5, "blu": 0.5}
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ts_obs = [0,0]
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ts_red = list(ts_red_team.values())
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ts_blu = list(ts_blu_team.values())
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new_red, new_blu = trueskill.rate([ts_red, ts_blu], ranks=ts_obs)
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new_red_ts = {}
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new_blu_ts = {}
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for key, value in zip(ts_red_team.keys(), new_red):
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new_red_ts[key] = value
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for key, value in zip(ts_blu_team.keys(), new_blu):
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new_blu_ts[key] = value
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print("red" if win_probability(new_red, new_blu) > 0.5 else "blue", match["winner"])
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# now there are new trueskll ratings for each team based on the win/loss from DB
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red_elo_delta = an.Metric().elo(red_elo["score"], blu_elo["score"], observations["red"], elo_N, elo_K) - red_elo["score"]
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blu_elo_delta = an.Metric().elo(blu_elo["score"], red_elo["score"], observations["blu"], elo_N, elo_K) - blu_elo["score"]
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@ -277,9 +250,6 @@ class Metric (Module):
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red[team]["gl2"]["rd"] = red[team]["gl2"]["rd"] + red_gl2_delta["rd"]
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red[team]["gl2"]["vol"] = red[team]["gl2"]["vol"] + red_gl2_delta["vol"]
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red[team]["ts"]["mu"] = new_red_ts[team].mu
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red[team]["ts"]["sigma"] = new_red_ts[team].sigma
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for team in blu:
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blu[team]["elo"]["score"] = blu[team]["elo"]["score"] + blu_elo_delta
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@ -288,10 +258,6 @@ class Metric (Module):
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blu[team]["gl2"]["rd"] = blu[team]["gl2"]["rd"] + blu_gl2_delta["rd"]
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blu[team]["gl2"]["vol"] = blu[team]["gl2"]["vol"] + blu_gl2_delta["vol"]
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blu[team]["ts"]["mu"] = new_blu_ts[team].mu
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blu[team]["ts"]["sigma"] = new_blu_ts[team].sigma
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temp_vector = {}
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temp_vector.update(red)
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temp_vector.update(blu)
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@ -13,4 +13,3 @@ scikit-learn
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scipy
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six
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tra-analysis
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trueskill
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@ -1,13 +0,0 @@
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from trueskill import Rating
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import trueskill
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from trueskill import TrueSkill
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import math
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BETA = 8.333/2
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cdf = TrueSkill().cdf
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def win_probability(a, b):
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deltaMu = sum([x.mu for x in a]) - sum([x.mu for x in b])
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sumSigma = sum([x.sigma ** 2 for x in a]) + sum([x.sigma ** 2 for x in b])
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playerCount = len(a) + len(b)
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denominator = math.sqrt(playerCount * (BETA * BETA) + sumSigma)
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return cdf(deltaMu / denominator)
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