mirror of
https://github.com/titanscouting/tra-superscript.git
synced 2024-09-20 00:47:56 +00:00
405 lines
10 KiB
Python
405 lines
10 KiB
Python
# Titan Robotics Team 2022: Superscript Script
|
|
# Written by Arthur Lu, Jacob Levine, and Dev Singh
|
|
# Notes:
|
|
# setup:
|
|
|
|
__version__ = "0.6.2"
|
|
|
|
# changelog should be viewed using print(analysis.__changelog__)
|
|
__changelog__ = """changelog:
|
|
0.6.2:
|
|
- integrated get_team_rankings.py as get_team_metrics() function
|
|
- integrated visualize_pit.py as graph_pit_histogram() function
|
|
0.6.1:
|
|
- bug fixes with analysis.Metric() calls
|
|
- modified metric functions to use config.json defined default values
|
|
0.6.0:
|
|
- removed main function
|
|
- changed load_config function
|
|
- added save_config function
|
|
- added load_match function
|
|
- renamed simpleloop to matchloop
|
|
- moved simplestats function inside matchloop
|
|
- renamed load_metrics to load_metric
|
|
- renamed metricsloop to metricloop
|
|
- split push to database functions amon push_match, push_metric, push_pit
|
|
- moved
|
|
0.5.2:
|
|
- made changes due to refactoring of analysis
|
|
0.5.1:
|
|
- text fixes
|
|
- removed matplotlib requirement
|
|
0.5.0:
|
|
- improved user interface
|
|
0.4.2:
|
|
- removed unessasary code
|
|
0.4.1:
|
|
- fixed bug where X range for regression was determined before sanitization
|
|
- better sanitized data
|
|
0.4.0:
|
|
- fixed spelling issue in __changelog__
|
|
- addressed nan bug in regression
|
|
- fixed errors on line 335 with metrics calling incorrect key "glicko2"
|
|
- fixed errors in metrics computing
|
|
0.3.0:
|
|
- added analysis to pit data
|
|
0.2.1:
|
|
- minor stability patches
|
|
- implemented db syncing for timestamps
|
|
- fixed bugs
|
|
0.2.0:
|
|
- finalized testing and small fixes
|
|
0.1.4:
|
|
- finished metrics implement, trueskill is bugged
|
|
0.1.3:
|
|
- working
|
|
0.1.2:
|
|
- started implement of metrics
|
|
0.1.1:
|
|
- cleaned up imports
|
|
0.1.0:
|
|
- tested working, can push to database
|
|
0.0.9:
|
|
- tested working
|
|
- prints out stats for the time being, will push to database later
|
|
0.0.8:
|
|
- added data import
|
|
- removed tba import
|
|
- finished main method
|
|
0.0.7:
|
|
- added load_config
|
|
- optimized simpleloop for readibility
|
|
- added __all__ entries
|
|
- added simplestats engine
|
|
- pending testing
|
|
0.0.6:
|
|
- fixes
|
|
0.0.5:
|
|
- imported pickle
|
|
- created custom database object
|
|
0.0.4:
|
|
- fixed simpleloop to actually return a vector
|
|
0.0.3:
|
|
- added metricsloop which is unfinished
|
|
0.0.2:
|
|
- added simpleloop which is untested until data is provided
|
|
0.0.1:
|
|
- created script
|
|
- added analysis, numba, numpy imports
|
|
"""
|
|
|
|
__author__ = (
|
|
"Arthur Lu <learthurgo@gmail.com>",
|
|
"Jacob Levine <jlevine@imsa.edu>",
|
|
)
|
|
|
|
__all__ = [
|
|
"load_config",
|
|
"save_config",
|
|
"get_previous_time",
|
|
"load_match",
|
|
"matchloop",
|
|
"load_metric",
|
|
"metricloop",
|
|
"load_pit",
|
|
"pitloop",
|
|
"push_match",
|
|
"push_metric",
|
|
"push_pit",
|
|
]
|
|
|
|
# imports:
|
|
|
|
from tra_analysis import analysis as an
|
|
import data as d
|
|
import json
|
|
import numpy as np
|
|
from os import system, name
|
|
from pathlib import Path
|
|
import matplotlib.pyplot as plt
|
|
import time
|
|
import warnings
|
|
|
|
def load_config(file):
|
|
|
|
config_vector = {}
|
|
with open(file) as f:
|
|
config_vector = json.load(f)
|
|
|
|
return config_vector
|
|
|
|
def save_config(file, config_vector):
|
|
|
|
with open(file) as f:
|
|
json.dump(config_vector, f)
|
|
|
|
def get_previous_time(apikey):
|
|
|
|
previous_time = d.get_analysis_flags(apikey, "latest_update")
|
|
|
|
if previous_time == None:
|
|
|
|
d.set_analysis_flags(apikey, "latest_update", 0)
|
|
previous_time = 0
|
|
|
|
else:
|
|
|
|
previous_time = previous_time["latest_update"]
|
|
|
|
return previous_time
|
|
|
|
def load_match(apikey, competition):
|
|
|
|
return d.get_match_data_formatted(apikey, competition)
|
|
|
|
def matchloop(apikey, competition, data, tests): # expects 3D array with [Team][Variable][Match]
|
|
|
|
def simplestats(data, test):
|
|
|
|
data = np.array(data)
|
|
data = data[np.isfinite(data)]
|
|
ranges = list(range(len(data)))
|
|
|
|
if test == "basic_stats":
|
|
return an.basic_stats(data)
|
|
|
|
if test == "historical_analysis":
|
|
return an.histo_analysis([ranges, data])
|
|
|
|
if test == "regression_linear":
|
|
return an.regression(ranges, data, ['lin'])
|
|
|
|
if test == "regression_logarithmic":
|
|
return an.regression(ranges, data, ['log'])
|
|
|
|
if test == "regression_exponential":
|
|
return an.regression(ranges, data, ['exp'])
|
|
|
|
if test == "regression_polynomial":
|
|
return an.regression(ranges, data, ['ply'])
|
|
|
|
if test == "regression_sigmoidal":
|
|
return an.regression(ranges, data, ['sig'])
|
|
|
|
return_vector = {}
|
|
for team in data:
|
|
variable_vector = {}
|
|
for variable in data[team]:
|
|
test_vector = {}
|
|
variable_data = data[team][variable]
|
|
if variable in tests:
|
|
for test in tests[variable]:
|
|
test_vector[test] = simplestats(variable_data, test)
|
|
else:
|
|
pass
|
|
variable_vector[variable] = test_vector
|
|
return_vector[team] = variable_vector
|
|
|
|
push_match(apikey, competition, return_vector)
|
|
|
|
def load_metric(apikey, competition, match, group_name, metrics):
|
|
|
|
group = {}
|
|
|
|
for team in match[group_name]:
|
|
|
|
db_data = d.get_team_metrics_data(apikey, competition, team)
|
|
|
|
if d.get_team_metrics_data(apikey, competition, team) == None:
|
|
|
|
elo = {"score": metrics["elo"]["score"]}
|
|
gl2 = {"score": metrics["gl2"]["score"], "rd": metrics["gl2"]["rd"], "vol": metrics["gl2"]["vol"]}
|
|
ts = {"mu": metrics["ts"]["mu"], "sigma": metrics["ts"]["sigma"]}
|
|
|
|
group[team] = {"elo": elo, "gl2": gl2, "ts": ts}
|
|
|
|
else:
|
|
|
|
metrics = db_data["metrics"]
|
|
|
|
elo = metrics["elo"]
|
|
gl2 = metrics["gl2"]
|
|
ts = metrics["ts"]
|
|
|
|
group[team] = {"elo": elo, "gl2": gl2, "ts": ts}
|
|
|
|
return group
|
|
|
|
def metricloop(tbakey, apikey, competition, timestamp, metrics): # listener based metrics update
|
|
|
|
elo_N = metrics["elo"]["N"]
|
|
elo_K = metrics["elo"]["K"]
|
|
|
|
matches = d.pull_new_tba_matches(tbakey, competition, timestamp)
|
|
|
|
red = {}
|
|
blu = {}
|
|
|
|
for match in matches:
|
|
|
|
red = load_metric(apikey, competition, match, "red", metrics)
|
|
blu = load_metric(apikey, competition, match, "blue", metrics)
|
|
|
|
elo_red_total = 0
|
|
elo_blu_total = 0
|
|
|
|
gl2_red_score_total = 0
|
|
gl2_blu_score_total = 0
|
|
|
|
gl2_red_rd_total = 0
|
|
gl2_blu_rd_total = 0
|
|
|
|
gl2_red_vol_total = 0
|
|
gl2_blu_vol_total = 0
|
|
|
|
for team in red:
|
|
|
|
elo_red_total += red[team]["elo"]["score"]
|
|
|
|
gl2_red_score_total += red[team]["gl2"]["score"]
|
|
gl2_red_rd_total += red[team]["gl2"]["rd"]
|
|
gl2_red_vol_total += red[team]["gl2"]["vol"]
|
|
|
|
for team in blu:
|
|
|
|
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, "blu": 0}
|
|
|
|
elif match["winner"] == "blue":
|
|
|
|
observations = {"red": 0, "blu": 1}
|
|
|
|
else:
|
|
|
|
observations = {"red": 0.5, "blu": 0.5}
|
|
|
|
red_elo_delta = an.Metric().elo(red_elo["score"], blu_elo["score"], observations["red"], elo_N, elo_K) - red_elo["score"]
|
|
blu_elo_delta = an.Metric().elo(blu_elo["score"], red_elo["score"], observations["blu"], elo_N, elo_K) - blu_elo["score"]
|
|
|
|
new_red_gl2_score, new_red_gl2_rd, new_red_gl2_vol = an.Metric().glicko2(red_gl2["score"], red_gl2["rd"], red_gl2["vol"], [blu_gl2["score"]], [blu_gl2["rd"]], [observations["red"], observations["blu"]])
|
|
new_blu_gl2_score, new_blu_gl2_rd, new_blu_gl2_vol = an.Metric().glicko2(blu_gl2["score"], blu_gl2["rd"], blu_gl2["vol"], [red_gl2["score"]], [red_gl2["rd"]], [observations["blu"], 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"]
|
|
|
|
temp_vector = {}
|
|
temp_vector.update(red)
|
|
temp_vector.update(blu)
|
|
|
|
push_metric(apikey, competition, temp_vector)
|
|
|
|
def load_pit(apikey, competition):
|
|
|
|
return d.get_pit_data_formatted(apikey, competition)
|
|
|
|
def pitloop(apikey, competition, pit, tests):
|
|
|
|
return_vector = {}
|
|
for team in pit:
|
|
for variable in pit[team]:
|
|
if variable in tests:
|
|
if not variable in return_vector:
|
|
return_vector[variable] = []
|
|
return_vector[variable].append(pit[team][variable])
|
|
|
|
push_pit(apikey, competition, return_vector)
|
|
|
|
def push_match(apikey, competition, results):
|
|
|
|
for team in results:
|
|
|
|
d.push_team_tests_data(apikey, competition, team, results[team])
|
|
|
|
def push_metric(apikey, competition, metric):
|
|
|
|
for team in metric:
|
|
|
|
d.push_team_metrics_data(apikey, competition, team, metric[team])
|
|
|
|
def push_pit(apikey, competition, pit):
|
|
|
|
for variable in pit:
|
|
|
|
d.push_team_pit_data(apikey, competition, variable, pit[variable])
|
|
|
|
def get_team_metrics(apikey, tbakey, competition):
|
|
|
|
metrics = d.get_metrics_data_formatted(apikey, competition)
|
|
|
|
elo = {}
|
|
gl2 = {}
|
|
|
|
for team in metrics:
|
|
|
|
elo[team] = metrics[team]["metrics"]["elo"]["score"]
|
|
gl2[team] = metrics[team]["metrics"]["gl2"]["score"]
|
|
|
|
elo = {k: v for k, v in sorted(elo.items(), key=lambda item: item[1])}
|
|
gl2 = {k: v for k, v in sorted(gl2.items(), key=lambda item: item[1])}
|
|
|
|
elo_ranked = []
|
|
|
|
for team in elo:
|
|
|
|
elo_ranked.append({"team": str(team), "elo": str(elo[team])})
|
|
|
|
gl2_ranked = []
|
|
|
|
for team in gl2:
|
|
|
|
gl2_ranked.append({"team": str(team), "gl2": str(gl2[team])})
|
|
|
|
return {"elo-ranks": elo_ranked, "glicko2-ranks": gl2_ranked}
|
|
|
|
def graph_pit_histogram(apikey, competition, figsize=(80,15)):
|
|
|
|
pit = d.get_pit_variable_formatted(apikey, competition)
|
|
|
|
fig, ax = plt.subplots(1, len(pit), sharey=True, figsize=figsize)
|
|
|
|
i = 0
|
|
|
|
for variable in pit:
|
|
|
|
ax[i].hist(pit[variable])
|
|
ax[i].invert_xaxis()
|
|
|
|
ax[i].set_xlabel('')
|
|
ax[i].set_ylabel('Frequency')
|
|
ax[i].set_title(variable)
|
|
|
|
plt.yticks(np.arange(len(pit[variable])))
|
|
|
|
i+=1
|
|
|
|
plt.show() |