mirror of
https://github.com/titanscouting/tra-superscript.git
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518 lines
13 KiB
Python
518 lines
13 KiB
Python
# Titan Robotics Team 2022: Superscript Script
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# Written by Arthur Lu, Jacob Levine, and Dev Singh
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# Notes:
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# setup:
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__version__ = "0.8.1"
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# changelog should be viewed using print(analysis.__changelog__)
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__changelog__ = """changelog:
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0.8.1:
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- optimized matchloop further by bypassing GIL
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0.8.0:
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- added multithreading to matchloop
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- tweaked user log
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0.7.0:
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- finished implementing main function
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0.6.2:
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- integrated get_team_rankings.py as get_team_metrics() function
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- integrated visualize_pit.py as graph_pit_histogram() function
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0.6.1:
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- bug fixes with analysis.Metric() calls
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- modified metric functions to use config.json defined default values
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0.6.0:
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- removed main function
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- changed load_config function
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- added save_config function
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- added load_match function
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- renamed simpleloop to matchloop
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- moved simplestats function inside matchloop
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- renamed load_metrics to load_metric
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- renamed metricsloop to metricloop
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- split push to database functions amon push_match, push_metric, push_pit
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- moved
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0.5.2:
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- made changes due to refactoring of analysis
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0.5.1:
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- text fixes
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- removed matplotlib requirement
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0.5.0:
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- improved user interface
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0.4.2:
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- removed unessasary code
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0.4.1:
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- fixed bug where X range for regression was determined before sanitization
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- better sanitized data
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0.4.0:
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- fixed spelling issue in __changelog__
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- addressed nan bug in regression
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- fixed errors on line 335 with metrics calling incorrect key "glicko2"
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- fixed errors in metrics computing
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0.3.0:
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- added analysis to pit data
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0.2.1:
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- minor stability patches
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- implemented db syncing for timestamps
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- fixed bugs
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0.2.0:
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- finalized testing and small fixes
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0.1.4:
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- finished metrics implement, trueskill is bugged
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0.1.3:
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- working
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0.1.2:
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- started implement of metrics
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0.1.1:
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- cleaned up imports
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0.1.0:
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- tested working, can push to database
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0.0.9:
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- tested working
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- prints out stats for the time being, will push to database later
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0.0.8:
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- added data import
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- removed tba import
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- finished main method
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0.0.7:
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- added load_config
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- optimized simpleloop for readibility
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- added __all__ entries
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- added simplestats engine
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- pending testing
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0.0.6:
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- fixes
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0.0.5:
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- imported pickle
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- created custom database object
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0.0.4:
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- fixed simpleloop to actually return a vector
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0.0.3:
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- added metricsloop which is unfinished
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0.0.2:
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- added simpleloop which is untested until data is provided
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0.0.1:
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- created script
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- added analysis, numba, numpy imports
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"""
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__author__ = (
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"Arthur Lu <learthurgo@gmail.com>",
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"Jacob Levine <jlevine@imsa.edu>",
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)
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__all__ = [
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"load_config",
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"save_config",
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"get_previous_time",
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"load_match",
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"matchloop",
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"load_metric",
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"metricloop",
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"load_pit",
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"pitloop",
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"push_match",
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"push_metric",
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"push_pit",
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]
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# imports:
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from tra_analysis import analysis as an
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import data as d
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from collections import defaultdict
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import json
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import numpy as np
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from os import system, name
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from pathlib import Path
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from multiprocessing import Pool
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import matplotlib.pyplot as plt
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from concurrent.futures import ThreadPoolExecutor
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import time
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import warnings
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global exec_threads
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def main():
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global exec_threads
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warnings.filterwarnings("ignore")
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# while (True):
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current_time = time.time()
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print("[OK] time: " + str(current_time))
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config = load_config("config.json")
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competition = config["competition"]
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match_tests = config["statistics"]["match"]
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pit_tests = config["statistics"]["pit"]
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metrics_tests = config["statistics"]["metric"]
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print("[OK] configs loaded")
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print("[OK] starting threads")
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exec_threads = Pool(processes = config["max-threads"])
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print("[OK] threads started")
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apikey = config["key"]["database"]
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tbakey = config["key"]["tba"]
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print("[OK] loaded keys")
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previous_time = get_previous_time(apikey)
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print("[OK] analysis backtimed to: " + str(previous_time))
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print("[OK] loading data")
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start = time.time()
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match_data = load_match(apikey, competition)
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pit_data = load_pit(apikey, competition)
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print("[OK] loaded data in " + str(time.time() - start) + " seconds")
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print("[OK] running match stats")
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start = time.time()
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matchloop(apikey, competition, match_data, match_tests)
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print("[OK] finished match stats in " + str(time.time() - start) + " seconds")
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print("[OK] running team metrics")
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start = time.time()
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metricloop(tbakey, apikey, competition, previous_time, metrics_tests)
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print("[OK] finished team metrics in " + str(time.time() - start) + " seconds")
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print("[OK] running pit analysis")
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start = time.time()
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pitloop(apikey, competition, pit_data, pit_tests)
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print("[OK] finished pit analysis in " + str(time.time() - start) + " seconds")
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set_current_time(apikey, current_time)
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print("[OK] finished all tests, looping")
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#clear()
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def clear():
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# for windows
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if name == 'nt':
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_ = system('cls')
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# for mac and linux(here, os.name is 'posix')
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else:
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_ = system('clear')
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def load_config(file):
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config_vector = {}
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with open(file) as f:
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config_vector = json.load(f)
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return config_vector
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def save_config(file, config_vector):
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with open(file) as f:
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json.dump(config_vector, f)
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def get_previous_time(apikey):
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previous_time = d.get_analysis_flags(apikey, "latest_update")
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if previous_time == None:
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d.set_analysis_flags(apikey, "latest_update", 0)
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previous_time = 0
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else:
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previous_time = previous_time["latest_update"]
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return previous_time
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def set_current_time(apikey, current_time):
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d.set_analysis_flags(apikey, "latest_update", {"latest_update":current_time})
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def load_match(apikey, competition):
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return d.get_match_data_formatted(apikey, competition)
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def simplestats(data_test):
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data = np.array(data_test[0])
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data = data[np.isfinite(data)]
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ranges = list(range(len(data)))
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test = data_test[1]
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if test == "basic_stats":
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return an.basic_stats(data)
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if test == "historical_analysis":
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return an.histo_analysis([ranges, data])
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if test == "regression_linear":
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return an.regression(ranges, data, ['lin'])
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if test == "regression_logarithmic":
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return an.regression(ranges, data, ['log'])
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if test == "regression_exponential":
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return an.regression(ranges, data, ['exp'])
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if test == "regression_polynomial":
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return an.regression(ranges, data, ['ply'])
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if test == "regression_sigmoidal":
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return an.regression(ranges, data, ['sig'])
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def matchloop(apikey, competition, data, tests): # expects 3D array with [Team][Variable][Match]
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global exec_threads
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class AutoVivification(dict):
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def __getitem__(self, item):
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try:
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return dict.__getitem__(self, item)
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except KeyError:
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value = self[item] = type(self)()
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return value
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return_vector = {}
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team_filtered = []
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variable_filtered = []
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variable_data = []
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test_filtered = []
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result_filtered = []
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return_vector = AutoVivification()
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for team in data:
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for variable in data[team]:
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if variable in tests:
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for test in tests[variable]:
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team_filtered.append(team)
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variable_filtered.append(variable)
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variable_data.append((data[team][variable], test))
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test_filtered.append(test)
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result_filtered = exec_threads.map(simplestats, variable_data)
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i = 0
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result_filtered = list(result_filtered)
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for result in result_filtered:
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return_vector[team_filtered[i]][variable_filtered[i]][test_filtered[i]] = result
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i += 1
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push_match(apikey, competition, return_vector)
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def load_metric(apikey, competition, match, group_name, metrics):
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group = {}
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for team in match[group_name]:
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db_data = d.get_team_metrics_data(apikey, competition, team)
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if d.get_team_metrics_data(apikey, competition, team) == None:
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elo = {"score": metrics["elo"]["score"]}
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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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else:
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metrics = db_data["metrics"]
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elo = metrics["elo"]
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gl2 = metrics["gl2"]
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ts = metrics["ts"]
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group[team] = {"elo": elo, "gl2": gl2, "ts": ts}
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return group
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def metricloop(tbakey, apikey, competition, timestamp, metrics): # listener based metrics update
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elo_N = metrics["elo"]["N"]
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elo_K = metrics["elo"]["K"]
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matches = d.pull_new_tba_matches(tbakey, competition, timestamp)
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red = {}
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blu = {}
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for match in matches:
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red = load_metric(apikey, competition, match, "red", metrics)
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blu = load_metric(apikey, competition, match, "blue", metrics)
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elo_red_total = 0
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elo_blu_total = 0
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gl2_red_score_total = 0
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gl2_blu_score_total = 0
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gl2_red_rd_total = 0
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gl2_blu_rd_total = 0
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gl2_red_vol_total = 0
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gl2_blu_vol_total = 0
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for team in red:
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elo_red_total += red[team]["elo"]["score"]
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gl2_red_score_total += red[team]["gl2"]["score"]
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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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for team in blu:
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elo_blu_total += blu[team]["elo"]["score"]
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gl2_blu_score_total += blu[team]["gl2"]["score"]
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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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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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if match["winner"] == "red":
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observations = {"red": 1, "blu": 0}
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elif match["winner"] == "blue":
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observations = {"red": 0, "blu": 1}
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else:
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observations = {"red": 0.5, "blu": 0.5}
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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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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"]])
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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"]])
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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"]}
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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"]}
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for team in red:
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red[team]["elo"]["score"] = red[team]["elo"]["score"] + red_elo_delta
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red[team]["gl2"]["score"] = red[team]["gl2"]["score"] + red_gl2_delta["score"]
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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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for team in blu:
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blu[team]["elo"]["score"] = blu[team]["elo"]["score"] + blu_elo_delta
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blu[team]["gl2"]["score"] = blu[team]["gl2"]["score"] + blu_gl2_delta["score"]
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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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temp_vector = {}
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temp_vector.update(red)
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temp_vector.update(blu)
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push_metric(apikey, competition, temp_vector)
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def load_pit(apikey, competition):
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return d.get_pit_data_formatted(apikey, competition)
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def pitloop(apikey, competition, pit, tests):
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return_vector = {}
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for team in pit:
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for variable in pit[team]:
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if variable in tests:
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if not variable in return_vector:
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return_vector[variable] = []
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return_vector[variable].append(pit[team][variable])
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push_pit(apikey, competition, return_vector)
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def push_match(apikey, competition, results):
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for team in results:
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d.push_team_tests_data(apikey, competition, team, results[team])
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def push_metric(apikey, competition, metric):
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for team in metric:
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d.push_team_metrics_data(apikey, competition, team, metric[team])
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def push_pit(apikey, competition, pit):
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for variable in pit:
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d.push_team_pit_data(apikey, competition, variable, pit[variable])
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def get_team_metrics(apikey, tbakey, competition):
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metrics = d.get_metrics_data_formatted(apikey, competition)
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elo = {}
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gl2 = {}
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for team in metrics:
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elo[team] = metrics[team]["metrics"]["elo"]["score"]
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gl2[team] = metrics[team]["metrics"]["gl2"]["score"]
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elo = {k: v for k, v in sorted(elo.items(), key=lambda item: item[1])}
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gl2 = {k: v for k, v in sorted(gl2.items(), key=lambda item: item[1])}
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elo_ranked = []
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for team in elo:
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elo_ranked.append({"team": str(team), "elo": str(elo[team])})
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gl2_ranked = []
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for team in gl2:
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gl2_ranked.append({"team": str(team), "gl2": str(gl2[team])})
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return {"elo-ranks": elo_ranked, "glicko2-ranks": gl2_ranked}
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def graph_pit_histogram(apikey, competition, figsize=(80,15)):
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pit = d.get_pit_variable_formatted(apikey, competition)
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fig, ax = plt.subplots(1, len(pit), sharey=True, figsize=figsize)
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i = 0
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for variable in pit:
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ax[i].hist(pit[variable])
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ax[i].invert_xaxis()
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ax[i].set_xlabel('')
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ax[i].set_ylabel('Frequency')
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ax[i].set_title(variable)
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plt.yticks(np.arange(len(pit[variable])))
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i+=1
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plt.show()
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main() |