tra-analysis/data analysis/superscript.py
2020-03-04 17:54:30 -06:00

219 lines
5.6 KiB
Python

# Titan Robotics Team 2022: Superscript Script
# Written by Arthur Lu & Jacob Levine
# Notes:
# setup:
__version__ = "0.0.1.003"
# changelog should be viewed using print(analysis.__changelog__)
__changelog__ = """changelog:
0.0.1.003:
- working
0.0.1.002:
- started implement of metrics
0.0.1.001:
- cleaned up imports
0.0.1.000:
- tested working, can push to database
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
0.0.0.007:
- added load_config
- optimized simpleloop for readibility
- added __all__ entries
- added simplestats engine
- pending testing
0.0.0.006:
- fixes
0.0.0.005:
- imported pickle
- created custom database object
0.0.0.004:
- fixed simpleloop to actually return a vector
0.0.0.003:
- added metricsloop which is unfinished
0.0.0.002:
- added simpleloop which is untested until data is provided
0.0.0.001:
- created script
- added analysis, numba, numpy imports
"""
__author__ = (
"Arthur Lu <learthurgo@gmail.com>",
"Jacob Levine <jlevine@imsa.edu>",
)
__all__ = [
"main",
"load_config",
"simpleloop",
"simplestats",
"metricsloop"
]
# imports:
from analysis import analysis as an
import data as d
import time
def testing():
competition, config = load_config("config.csv")
apikey = an.load_csv("keys.txt")[0][0]
tbakey = an.load_csv("keys.txt")[1][0]
metricsloop(tbakey, apikey, "2020mokc", 1583084980)
def main():
while(True):
current_time = time.time()
print("time: " + str(current_time))
print("loading config")
competition, config = load_config("config.csv")
print("config loaded")
print("loading database keys")
apikey = an.load_csv("keys.txt")[0][0]
tbakey = an.load_csv("keys.txt")[1][0]
print("loaded keys")
print("loading data")
data = d.get_data_formatted(apikey, competition)
print("loaded data")
print("running tests")
results = simpleloop(data, config)
print("finished tests")
print("running metrics")
metrics = metricsloop(apikey, competition, current_time)
print("finished metrics")
print("pushing to database")
push_to_database(apikey, competition, results, None)
print("pushed to database")
def load_config(file):
config_vector = {}
file = an.load_csv(file)
for line in file[1:]:
config_vector[line[0]] = line[1:]
return (file[0][0], config_vector)
def simpleloop(data, tests): # expects 3D array with [Team][Variable][Match]
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
return return_vector
def simplestats(data, test):
if(test == "basic_stats"):
return an.basic_stats(data)
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'])
def push_to_database(apikey, competition, results, metrics):
for team in results:
d.push_team_tests_data(apikey, competition, team, results[team])
def metricsloop(tbakey, apikey, competition, timestamp): # listener based metrics update
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):
for match in matches:
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
testing()
"""
Metrics Defaults:
elo starting score = 1500
elo N = 400
elo K = 24
gl2 starting score = 1500
gl2 starting rd = 350
gl2 starting vol = 0.06
"""