tra-analysis/data analysis/superscript.py

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# Titan Robotics Team 2022: Superscript Script
# Written by Arthur Lu & Jacob Levine
# Notes:
# setup:
__version__ = "0.0.0.009"
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# changelog should be viewed using print(analysis.__changelog__)
__changelog__ = """changelog:
0.0.0.009:
- tested working
- prints out stats for the time being, will push to database later
0.0.0.008:
- added data import
- removed tba import
- finished main method
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0.0.0.007:
- added load_config
- optimized simpleloop for readibility
- added __all__ entries
- added simplestats engine
- pending testing
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0.0.0.006:
- fixes
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0.0.0.005:
- imported pickle
- created custom database object
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0.0.0.004:
- fixed simpleloop to actually return a vector
0.0.0.003:
- added metricsloop which is unfinished
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0.0.0.002:
- added simpleloop which is untested until data is provided
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0.0.0.001:
- created script
- added analysis, numba, numpy imports
"""
__author__ = (
"Arthur Lu <learthurgo@gmail.com>",
"Jacob Levine <jlevine@imsa.edu>",
)
__all__ = [
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"main",
"load_config",
"simpleloop",
"simplestats",
"metricsloop"
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]
# imports:
from analysis import analysis as an
from numba import jit
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import numpy as np
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import pickle
import data as d
try:
from analysis import trueskill as Trueskill
except:
import trueskill as Trueskill
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def main():
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while(True):
competition, config = load_config("config.csv")
apikey = an.load_csv("keys.txt")[0][0]
data = d.get_data_formatted(apikey, competition)
results = simpleloop(data, config)
print(results)
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def load_config(file):
config_vector = {}
file = an.load_csv(file)
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for line in file[1:]:
config_vector[line[0]] = line[1:]
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return (file[0][0], 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 = {}
for variable in data[team]:
test_vector = {}
variable_data = data[team][variable]
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if(variable in tests):
for test in tests[variable]:
test_vector[test] = simplestats(variable_data, test)
else:
pass
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variable_vector[variable] = test_vector
return_vector[team] = variable_vector
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return return_vector
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def simplestats(data, test):
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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"):
return an.histo_analysis(data)
if(test == "regression_linear"):
return an.regression('cpu', list(range(len(data))), data, ['lin'])
if(test == "regression_logarithmic"):
return an.regression('cpu', list(range(len(data))), data, ['log'])
if(test == "regression_exponential"):
return an.regression('cpu', list(range(len(data))), data, ['exp'])
if(test == "regression_polynomial"):
return an.regression('cpu', list(range(len(data))), data, ['ply'])
if(test == "regression_sigmoidal"):
return an.regression('cpu', list(range(len(data))), data, ['sig'])
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def metricsloop(group_data, observations, database, tests): # listener based metrics update
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pass
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class database:
data = {}
elo_starting_score = 1500
N = 1500
K = 32
gl2_starting_score = 1500
gl2_starting_rd = 350
gl2_starting_vol = 0.06
def __init__(self, team_lookup):
super().__init__()
for team in team_lookup:
elo = elo_starting_score
gl2 = {"score": gl2_starting_score, "rd": gl2_starting_rd, "vol": gl2_starting_vol}
ts = Trueskill.Rating()
data[str(team)] = {"elo": elo, "gl2": gl2, "ts": ts}
def get_team(self, team):
return data[team]
def get_elo(self, team):
return data[team]["elo"]
def get_gl2(self, team):
return data[team]["gl2"]
def get_ts(self, team):
return data[team]["ts"]
def set_team(self, team, ndata):
data[team] = ndata
def set_elo(self, team, nelo):
data[team]["elo"] = nelo
def set_gl2(self, team, ngl2):
data[team]["gl2"] = ngl2
def set_ts(self, team, nts):
data[team]["ts"] = nts
def save_database(self, location):
pickle.dump(data, open(location, "wb"))
def load_database(self, location):
data = pickle.load(open(location, "rb"))
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main()