alu/tra-analysis
1
0
Fork 0
mirror of https://github.com/titanscouting/tra-analysis.git synced 2024-12-25 17:19:09 +00:00
Code Issues Packages Projects Releases Wiki Activity

analysis.py v 1.1.12.005

Browse Source 
analysis pkg v 1.0.0.002
This commit is contained in:
ltcptgeneral 2020-03-04 18:55:45 -06:00
parent 59508574c9
commit c7031361b0
7 changed files with 109 additions and 39 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
analysis-master/analysis.egg-info/PKG-INFO
View File

@ -1,6 +1,6 @@
Metadata-Version: 2.1
Name: analysis
Version: 1.0.0.1
Version: 1.0.0.2
Summary: analysis package developed by Titan Scouting for The Red Alliance
Home-page: https://github.com/titanscout2022/tr2022-strategy
Author: The Titan Scouting Team

7
analysis-master/analysis/analysis.py
View File

@ -7,10 +7,12 @@
# current benchmark of optimization: 1.33 times faster
# setup:
__version__ = "1.1.12.004"
__version__ = "1.1.12.005"
# changelog should be viewed using print(analysis.__changelog__)
__changelog__ = """changelog:
1.1.12.005:
- fixed numba issues by removing numba from elo, glicko2 and trueskill
1.1.12.004:
- renamed gliko to glicko
1.1.12.003:
@ -384,14 +386,12 @@ def regression(ndevice, inputs, outputs, args, loss = torch.nn.MSELoss(), _itera
return regressions
@jit(nopython=True)
def elo(starting_score, opposing_score, observed, N, K):
expected = 1/(1+10**((np.array(opposing_score) - starting_score)/N))
return starting_score + K*(np.sum(observed) - np.sum(expected))
@jit(forceobj=True)
def glicko2(starting_score, starting_rd, starting_vol, opposing_score, opposing_rd, observations):
player = Glicko2(rating = starting_score, rd = starting_rd, vol = starting_vol)
@ -400,7 +400,6 @@ def glicko2(starting_score, starting_rd, starting_vol, opposing_score, opposing_
return (player.rating, player.rd, player.vol)
@jit(forceobj=True)
def trueskill(teams_data, observations): # teams_data is array of array of tuples ie. [[(mu, sigma), (mu, sigma), (mu, sigma)], [(mu, sigma), (mu, sigma), (mu, sigma)]]
team_ratings = []

7
analysis-master/build/lib/analysis/analysis.py
View File

@ -7,10 +7,12 @@
# current benchmark of optimization: 1.33 times faster
# setup:
__version__ = "1.1.12.004"
__version__ = "1.1.12.005"
# changelog should be viewed using print(analysis.__changelog__)
__changelog__ = """changelog:
1.1.12.005:
- fixed numba issues by removing numba from elo, glicko2 and trueskill
1.1.12.004:
- renamed gliko to glicko
1.1.12.003:
@ -384,14 +386,12 @@ def regression(ndevice, inputs, outputs, args, loss = torch.nn.MSELoss(), _itera
return regressions
@jit(nopython=True)
def elo(starting_score, opposing_score, observed, N, K):
expected = 1/(1+10**((np.array(opposing_score) - starting_score)/N))
return starting_score + K*(np.sum(observed) - np.sum(expected))
@jit(forceobj=True)
def glicko2(starting_score, starting_rd, starting_vol, opposing_score, opposing_rd, observations):
player = Glicko2(rating = starting_score, rd = starting_rd, vol = starting_vol)
@ -400,7 +400,6 @@ def glicko2(starting_score, starting_rd, starting_vol, opposing_score, opposing_
return (player.rating, player.rd, player.vol)
@jit(forceobj=True)
def trueskill(teams_data, observations): # teams_data is array of array of tuples ie. [[(mu, sigma), (mu, sigma), (mu, sigma)], [(mu, sigma), (mu, sigma), (mu, sigma)]]
team_ratings = []

BIN
analysis-master/dist/analysis-1.0.0.2-py3-none-any.whl vendored Normal file
View File

Binary file not shown.

BIN
analysis-master/dist/analysis-1.0.0.2.tar.gz vendored Normal file
View File

Binary file not shown.

2
analysis-master/setup.py
View File

@ -2,7 +2,7 @@ import setuptools
setuptools.setup(
name="analysis", # Replace with your own username
version="1.0.0.001",
version="1.0.0.002",
author="The Titan Scouting Team",
author_email="titanscout2022@gmail.com",
description="analysis package developed by Titan Scouting for The Red Alliance",

130
data analysis/superscript.py
View File

@ -158,49 +158,121 @@ def push_to_database(apikey, competition, results, metrics):
def metricsloop(tbakey, apikey, competition, timestamp): # listener based metrics update
elo_N = 400
elo_K = 24
matches = d.pull_new_tba_matches(tbakey, competition, timestamp)
red = load_metrics(apikey, competition, matches, "red")
blu = load_metrics(apikey, competition, matches, "blue")
elo_red_total = 0
elo_blu_total = 0
gl2_red_total = 0
gl2_blu_total + 0
for team in red:
return
def load_metrics(apikey, competition, matches, group_name):
return_vector = {}
for match in matches:
for team in match[group_name]:
red = load_metrics(apikey, competition, match, "red")
blu = load_metrics(apikey, competition, match, "blue")
group = {}
elo_red_total = 0
elo_blu_total = 0
db_data = d.get_team_metrics_data(apikey, competition, team)
gl2_red_score_total = 0
gl2_blu_score_total = 0
if d.get_team_metrics_data(apikey, competition, team) == None:
gl2_red_rd_total = 0
gl2_blu_rd_total = 0
elo = {"score": 1500}
gl2 = {"score": 1500, "rd": 250, "vol": 0.06}
ts = {"mu": 25, "sigma": 25/3}
gl2_red_vol_total = 0
gl2_blu_vol_total = 0
d.push_team_metrics_data(apikey, competition, team, {"elo":elo, "gliko2":gl2,"trueskill":ts})
for team in red:
group[team] = {"elo": elo, "gl2": gl2, "ts": ts}
elo_red_total += red[team]["elo"]["score"]
else:
gl2_red_score_total += red[team]["gl2"]["score"]
gl2_red_rd_total += red[team]["gl2"]["rd"]
gl2_red_vol_total += red[team]["gl2"]["vol"]
metrics = db_data["metrics"]
elo = metrics["elo"]
gl2 = metrics["gliko2"]
ts = metrics["trueskill"]
for team in blu:
group[team] = {"elo": elo, "gl2": gl2, "ts": ts}
elo_blu_total += blu[team]["elo"]["score"]
gl2_blu_score_total += blu[team]["gl2"]["score"]
gl2_blu_rd_total += blu[team]["gl2"]["rd"]
gl2_blu_vol_total += blu[team]["gl2"]["vol"]
red_elo = {"score": elo_red_total / len(red)}
blu_elo = {"score": elo_blu_total / len(blu)}
red_gl2 = {"score": gl2_red_score_total / len(red), "rd": gl2_red_rd_total / len(red), "vol": gl2_red_vol_total / len(red)}
blu_gl2 = {"score": gl2_blu_score_total / len(blu), "rd": gl2_blu_rd_total / len(blu), "vol": gl2_blu_vol_total / len(blu)}
if(match["winner"] == "red"):
observations = {"red": 1, "blue": 0}
elif(match["winner"] == "blue"):
observations = {"red": 0, "blue": 1}
else:
observations = {"red": 0.5, "blue": 0.5}
red_elo_delta = an.elo(red_elo["score"], blu_elo["score"], [observations["red"], observations["blue"]], elo_N, elo_K) - red_elo["score"]
blu_elo_delta = an.elo(blu_elo["score"], red_elo["score"], [observations["blue"], observations["red"]], elo_N, elo_K) - blu_elo["score"]
new_red_gl2_score, new_red_gl2_rd, new_red_gl2_vol = an.glicko2(red_gl2["score"], red_gl2["rd"], red_gl2["vol"], [blu_gl2["score"]], [blu_gl2["rd"]], [observations["red"], observations["blue"]])
new_blu_gl2_score, new_blu_gl2_rd, new_blu_gl2_vol = an.glicko2(blu_gl2["score"], blu_gl2["rd"], blu_gl2["vol"], [red_gl2["score"]], [red_gl2["rd"]], [observations["blue"], observations["red"]])
red_gl2_delta = {"score": new_red_gl2_score - red_gl2["score"], "rd": new_red_gl2_rd - red_gl2["rd"], "vol": new_red_gl2_vol - red_gl2["vol"]}
blu_gl2_delta = {"score": new_blu_gl2_score - blu_gl2["score"], "rd": new_blu_gl2_rd - blu_gl2["rd"], "vol": new_blu_gl2_vol - blu_gl2["vol"]}
for team in red:
red[team]["elo"]["score"] = red[team]["elo"]["score"] + red_elo_delta
red[team]["gl2"]["score"] = red[team]["gl2"]["score"] + red_gl2_delta["score"]
red[team]["gl2"]["rd"] = red[team]["gl2"]["rd"] + red_gl2_delta["rd"]
red[team]["gl2"]["vol"] = red[team]["gl2"]["vol"] + red_gl2_delta["vol"]
for team in blu:
blu[team]["elo"]["score"] = blu[team]["elo"]["score"] + blu_elo_delta
blu[team]["gl2"]["score"] = blu[team]["gl2"]["score"] + blu_gl2_delta["score"]
blu[team]["gl2"]["rd"] = blu[team]["gl2"]["rd"] + blu_gl2_delta["rd"]
blu[team]["gl2"]["vol"] = blu[team]["gl2"]["vol"] + blu_gl2_delta["vol"]
return_vector.update(red)
return_vector.update(blu)
return return_vector
def load_metrics(apikey, competition, match, group_name):
for team in match[group_name]:
group = {}
db_data = d.get_team_metrics_data(apikey, competition, team)
if d.get_team_metrics_data(apikey, competition, team) == None:
elo = {"score": 1500}
gl2 = {"score": 1500, "rd": 250, "vol": 0.06}
ts = {"mu": 25, "sigma": 25/3}
d.push_team_metrics_data(apikey, competition, team, {"elo":elo, "gliko2":gl2,"trueskill":ts})
group[team] = {"elo": elo, "gl2": gl2, "ts": ts}
else:
metrics = db_data["metrics"]
elo = metrics["elo"]
gl2 = metrics["gliko2"]
ts = metrics["trueskill"]
group[team] = {"elo": elo, "gl2": gl2, "ts": ts}
return group