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superscript.py v 0.0.1.004

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ltcptgeneral 2020-03-04 20:12:09 -06:00
parent c7031361b0
commit 72c233649d
2 changed files with 66 additions and 39 deletions
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10
data analysis/config.csv
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@ -1,6 +1,6 @@
2020ilch
balls-blocked,basic_stats
balls-collected,basic_stats
balls-lower,basic_stats
balls-started,basic_stats
balls-upper,basic_stats
balls-blocked,basic_stats, historical_analysis, regression_linear, regression_logarithmic, regression_exponential, regression_polynomial, regression_sigmoidal
balls-collected,basic_stats, historical_analysis, regression_linear, regression_logarithmic, regression_exponential, regression_polynomial, regression_sigmoidal
balls-lower,basic_stats, historical_analysis, regression_linear, regression_logarithmic, regression_exponential, regression_polynomial, regression_sigmoidal
balls-started,basic_stats, historical_analysis, regression_linear, regression_logarithmic, regression_exponential, regression_polynomial, regression_sigmoidal
balls-upper,basic_stats, historical_analysis, regression_linear, regression_logarithmic, regression_exponential, regression_polynomial, regression_sigmoidal
1 2020ilch
2 balls-blocked,basic_stats balls-blocked,basic_stats, historical_analysis, regression_linear, regression_logarithmic, regression_exponential, regression_polynomial, regression_sigmoidal
3 balls-collected,basic_stats balls-collected,basic_stats, historical_analysis, regression_linear, regression_logarithmic, regression_exponential, regression_polynomial, regression_sigmoidal
4 balls-lower,basic_stats balls-lower,basic_stats, historical_analysis, regression_linear, regression_logarithmic, regression_exponential, regression_polynomial, regression_sigmoidal
5 balls-started,basic_stats balls-started,basic_stats, historical_analysis, regression_linear, regression_logarithmic, regression_exponential, regression_polynomial, regression_sigmoidal
6 balls-upper,basic_stats balls-upper,basic_stats, historical_analysis, regression_linear, regression_logarithmic, regression_exponential, regression_polynomial, regression_sigmoidal

95
data analysis/superscript.py
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@ -3,10 +3,12 @@
# Notes:
# setup:
__version__ = "0.0.1.003"
__version__ = "0.0.1.004"
# changelog should be viewed using print(analysis.__changelog__)
__changelog__ = """changelog:
0.0.1.004:
- finished metrics implement, trueskill is bugged
0.0.1.003:
- working
0.0.1.002:
@ -63,44 +65,35 @@ 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")
print(" loading config")
competition, config = load_config("config.csv")
print("config loaded")
print(" config loaded")
print("loading database keys")
print(" loading database keys")
apikey = an.load_csv("keys.txt")[0][0]
tbakey = an.load_csv("keys.txt")[1][0]
print("loaded keys")
print(" loaded keys")
print("loading data")
print(" loading data")
data = d.get_data_formatted(apikey, competition)
print("loaded data")
print(" loaded data")
print("running tests")
print(" running tests")
results = simpleloop(data, config)
print("finished tests")
print(" finished tests")
print("running metrics")
metrics = metricsloop(apikey, competition, current_time)
print("finished metrics")
print(" running metrics")
metrics = metricsloop(tbakey, apikey, competition, 0)
print(" finished metrics")
print("pushing to database")
push_to_database(apikey, competition, results, None)
print("pushed to database")
print(" pushing to database")
push_to_database(apikey, competition, results, metrics)
print(" pushed to database")
def load_config(file):
config_vector = {}
@ -111,6 +104,7 @@ def load_config(file):
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 = {}
@ -156,6 +150,10 @@ def push_to_database(apikey, competition, results, metrics):
d.push_team_tests_data(apikey, competition, team, results[team])
for team in metrics:
d.push_team_metrics_data(apikey, competition, team, metrics[team])
def metricsloop(tbakey, apikey, competition, timestamp): # listener based metrics update
elo_N = 400
@ -165,6 +163,9 @@ def metricsloop(tbakey, apikey, competition, timestamp): # listener based metric
return_vector = {}
red = {}
blu = {}
for match in matches:
red = load_metrics(apikey, competition, match, "red")
@ -207,21 +208,21 @@ def metricsloop(tbakey, apikey, competition, timestamp): # listener based metric
if(match["winner"] == "red"):
observations = {"red": 1, "blue": 0}
observations = {"red": 1, "blu": 0}
elif(match["winner"] == "blue"):
observations = {"red": 0, "blue": 1}
observations = {"red": 0, "blu": 1}
else:
observations = {"red": 0.5, "blue": 0.5}
observations = {"red": 0.5, "blu": 0.5}
red_elo_delta = an.elo(red_elo["score"], blu_elo["score"], [observations["red"], observations["blue"]], elo_N, elo_K) - red_elo["score"]
blu_elo_delta = an.elo(blu_elo["score"], red_elo["score"], [observations["blue"], observations["red"]], elo_N, elo_K) - blu_elo["score"]
red_elo_delta = an.elo(red_elo["score"], blu_elo["score"], observations["red"], elo_N, elo_K) - red_elo["score"]
blu_elo_delta = an.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.glicko2(red_gl2["score"], red_gl2["rd"], red_gl2["vol"], [blu_gl2["score"]], [blu_gl2["rd"]], [observations["red"], observations["blue"]])
new_blu_gl2_score, new_blu_gl2_rd, new_blu_gl2_vol = an.glicko2(blu_gl2["score"], blu_gl2["rd"], blu_gl2["vol"], [red_gl2["score"]], [red_gl2["rd"]], [observations["blue"], observations["red"]])
new_red_gl2_score, new_red_gl2_rd, new_red_gl2_vol = an.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.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"]}
@ -242,6 +243,32 @@ def metricsloop(tbakey, apikey, competition, timestamp): # listener based metric
blu[team]["gl2"]["rd"] = blu[team]["gl2"]["rd"] + blu_gl2_delta["rd"]
blu[team]["gl2"]["vol"] = blu[team]["gl2"]["vol"] + blu_gl2_delta["vol"]
""" not functional for now
red_trueskill = []
blu_trueskill = []
red_ts_team_lookup = []
blu_ts_team_lookup = []
for team in red:
red_trueskill.append((red[team]["ts"]["mu"], red[team]["ts"]["sigma"]))
red_ts_team_lookup.append(team)
for team in blu:
blu_trueskill.append((blu[team]["ts"]["mu"], blu[team]["ts"]["sigma"]))
blu_ts_team_lookup.append(team)
print(red_trueskill)
print(blu_trueskill)
results = an.trueskill([red_trueskill, blu_trueskill], [observations["red"], observations["blu"]])
print(results)
"""
return_vector.update(red)
return_vector.update(blu)
@ -249,10 +276,10 @@ def metricsloop(tbakey, apikey, competition, timestamp): # listener based metric
def load_metrics(apikey, competition, match, group_name):
for team in match[group_name]:
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:
@ -276,7 +303,7 @@ def load_metrics(apikey, competition, match, group_name):
return group
testing()
main()
"""
Metrics Defaults: