mirror of
https://github.com/titanscouting/tra-analysis.git
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378 lines
9.9 KiB
Python
378 lines
9.9 KiB
Python
# Titan Robotics Team 2022: Superscript Script
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# Written by Arthur Lu & Jacob Levine
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# Notes:
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# setup:
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__version__ = "0.0.5.002"
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# changelog should be viewed using print(analysis.__changelog__)
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__changelog__ = """changelog:
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0.0.5.002:
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- made changes due to refactoring of analysis
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0.0.5.001:
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- text fixes
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- removed matplotlib requirement
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0.0.5.000:
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- improved user interface
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0.0.4.002:
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- removed unessasary code
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0.0.4.001:
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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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0.0.4.000:
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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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0.0.3.000:
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- added analysis to pit data
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0.0.2.001:
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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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0.0.2.000:
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- finalized testing and small fixes
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0.0.1.004:
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- finished metrics implement, trueskill is bugged
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0.0.1.003:
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- working
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0.0.1.002:
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- started implement of metrics
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0.0.1.001:
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- cleaned up imports
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0.0.1.000:
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- tested working, can push to database
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0.0.0.009:
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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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0.0.0.008:
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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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0.0.0.007:
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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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0.0.0.006:
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- fixes
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0.0.0.005:
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- imported pickle
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- created custom database object
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0.0.0.004:
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- fixed simpleloop to actually return a vector
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0.0.0.003:
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- added metricsloop which is unfinished
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0.0.0.002:
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- added simpleloop which is untested until data is provided
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0.0.0.001:
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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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"main",
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"load_config",
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"simpleloop",
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"simplestats",
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"metricsloop"
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]
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# imports:
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from analysis import analysis as an
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import data as d
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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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import time
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import warnings
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def main():
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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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start = time.time()
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config = load_config(Path("config/stats.config"))
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competition = an.load_csv(Path("config/competition.config"))[0][0]
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print("[OK] configs loaded")
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apikey = an.load_csv(Path("config/keys.config"))[0][0]
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tbakey = an.load_csv(Path("config/keys.config"))[1][0]
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print("[OK] loaded keys")
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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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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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data = d.get_match_data_formatted(apikey, competition)
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pit_data = d.pit = d.get_pit_data_formatted(apikey, competition)
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print("[OK] loaded data in " + str(time.time() - start) + " seconds")
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print("[OK] running tests")
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start = time.time()
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results = simpleloop(data, config)
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print("[OK] finished tests in " + str(time.time() - start) + " seconds")
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print("[OK] running metrics")
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start = time.time()
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metricsloop(tbakey, apikey, competition, previous_time)
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print("[OK] finished 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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pit = pitloop(pit_data, config)
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print("[OK] finished pit analysis in " + str(time.time() - start) + " seconds")
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d.set_analysis_flags(apikey, "latest_update", {"latest_update":current_time})
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print("[OK] pushing to database")
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start = time.time()
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push_to_database(apikey, competition, results, pit)
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print("[OK] pushed to database in " + str(time.time() - start) + " seconds")
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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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file = an.load_csv(file)
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for line in file:
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config_vector[line[0]] = line[1:]
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return config_vector
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def simpleloop(data, tests): # expects 3D array with [Team][Variable][Match]
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return_vector = {}
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for team in data:
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variable_vector = {}
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for variable in data[team]:
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test_vector = {}
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variable_data = data[team][variable]
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if(variable in tests):
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for test in tests[variable]:
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test_vector[test] = simplestats(variable_data, test)
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else:
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pass
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variable_vector[variable] = test_vector
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return_vector[team] = variable_vector
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return return_vector
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def simplestats(data, test):
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data = np.array(data)
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data = data[np.isfinite(data)]
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ranges = list(range(len(data)))
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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 push_to_database(apikey, competition, results, pit):
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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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for variable in pit:
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d.push_team_pit_data(apikey, competition, variable, pit[variable])
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def metricsloop(tbakey, apikey, competition, timestamp): # listener based metrics update
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elo_N = 400
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elo_K = 24
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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_metrics(apikey, competition, match, "red")
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blu = load_metrics(apikey, competition, match, "blue")
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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.Metrics.elo(red_elo["score"], blu_elo["score"], observations["red"], elo_N, elo_K) - red_elo["score"]
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blu_elo_delta = an.Metrics.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.Metrics.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.Metrics.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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for team in temp_vector:
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d.push_team_metrics_data(apikey, competition, team, temp_vector[team])
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def load_metrics(apikey, competition, match, group_name):
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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": 1500}
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gl2 = {"score": 1500, "rd": 250, "vol": 0.06}
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ts = {"mu": 25, "sigma": 25/3}
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#d.push_team_metrics_data(apikey, competition, team, {"elo":elo, "gl2":gl2,"trueskill":ts})
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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 pitloop(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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return return_vector
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main()
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"""
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Metrics Defaults:
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elo starting score = 1500
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elo N = 400
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elo K = 24
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gl2 starting score = 1500
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gl2 starting rd = 350
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gl2 starting vol = 0.06
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""" |