tra-superscript/competition/module.py
2022-03-29 21:17:58 +00:00

306 lines
8.5 KiB
Python

import abc
import data as d
import signal
import numpy as np
from tra_analysis import Analysis as an
from tqdm import tqdm
class Module(metaclass = abc.ABCMeta):
@classmethod
def __subclasshook__(cls, subclass):
return (hasattr(subclass, '__init__') and
callable(subclass.__init__) and
hasattr(subclass, 'validate_config') and
callable(subclass.validate_config) and
hasattr(subclass, 'run') and
callable(subclass.run)
)
@abc.abstractmethod
def __init__(self, config, apikey, tbakey, timestamp, competition, *args, **kwargs):
raise NotImplementedError
@abc.abstractmethod
def validate_config(self, *args, **kwargs):
raise NotImplementedError
@abc.abstractmethod
def run(self, *args, **kwargs):
raise NotImplementedError
class Match (Module):
config = None
apikey = None
tbakey = None
timestamp = None
competition = None
data = None
results = None
def __init__(self, config, apikey, tbakey, timestamp, competition):
self.config = config
self.apikey = apikey
self.tbakey = tbakey
self.timestamp = timestamp
self.competition = competition
def validate_config(self):
return True, ""
def run(self):
self._load_data()
self._process_data()
self._push_results()
def _load_data(self):
self.data = d.load_match(self.apikey, self.competition)
def _simplestats(self, data_test):
signal.signal(signal.SIGINT, signal.SIG_IGN)
data = np.array(data_test[3])
data = data[np.isfinite(data)]
ranges = list(range(len(data)))
test = data_test[2]
if test == "basic_stats":
return an.basic_stats(data)
if test == "historical_analysis":
return an.histo_analysis([ranges, data])
if test == "regression_linear":
return an.regression(ranges, data, ['lin'])
if test == "regression_logarithmic":
return an.regression(ranges, data, ['log'])
if test == "regression_exponential":
return an.regression(ranges, data, ['exp'])
if test == "regression_polynomial":
return an.regression(ranges, data, ['ply'])
if test == "regression_sigmoidal":
return an.regression(ranges, data, ['sig'])
def _process_data(self):
tests = self.config["tests"]
data = self.data
input_vector = []
for team in data:
for variable in data[team]:
if variable in tests:
for test in tests[variable]:
input_vector.append((team, variable, test, data[team][variable]))
self.data = input_vector
self.results = []
for test_var_data in self.data:
self.results.append(self._simplestats(test_var_data))
def _push_results(self):
short_mapping = {"regression_linear": "lin", "regression_logarithmic": "log", "regression_exponential": "exp", "regression_polynomial": "ply", "regression_sigmoidal": "sig"}
class AutoVivification(dict):
def __getitem__(self, item):
try:
return dict.__getitem__(self, item)
except KeyError:
value = self[item] = type(self)()
return value
result_filtered = self.results
input_vector = self.data
return_vector = AutoVivification()
i = 0
for result in result_filtered:
filtered = input_vector[i][2]
try:
short = short_mapping[filtered]
return_vector[input_vector[i][0]][input_vector[i][1]][input_vector[i][2]] = result[short]
except KeyError: # not in mapping
return_vector[input_vector[i][0]][input_vector[i][1]][input_vector[i][2]] = result
i += 1
self.results = return_vector
d.push_match(self.apikey, self.competition, self.results)
class Metric (Module):
config = None
apikey = None
tbakey = None
timestamp = None
competition = None
data = None
results = None
def __init__(self, config, apikey, tbakey, timestamp, competition):
self.config = config
self.apikey = apikey
self.tbakey = tbakey
self.timestamp = timestamp
self.competition = competition
def validate_config(self):
return True, ""
def run(self):
self._load_data()
self._process_data()
self._push_results()
def _load_data(self):
self.data = d.pull_new_tba_matches(self.tbakey, self.competition, self.timestamp)
def _process_data(self):
self.results = {}
matches = self.data
red = {}
blu = {}
for match in tqdm(matches, desc="Metrics"): # grab matches and loop through each one
red = d.load_metric(self.apikey, self.competition, match, "red", self.config["tests"]) # get the current ratings for red
blu = d.load_metric(self.apikey, self.competition, match, "blue", self.config["tests"]) # get the current ratings for blue
gl2_red_score_total = 0
gl2_blu_score_total = 0
gl2_red_rd_total = 0
gl2_blu_rd_total = 0
gl2_red_vol_total = 0
gl2_blu_vol_total = 0
for team in red: # for each team in red, add up gl2 score components
gl2_red_score_total += red[team]["gl2"]["score"]
gl2_red_rd_total += red[team]["gl2"]["rd"]
gl2_red_vol_total += red[team]["gl2"]["vol"]
for team in blu: # for each team in blue, add up gl2 score components
gl2_blu_score_total += blu[team]["gl2"]["score"]
gl2_blu_rd_total += blu[team]["gl2"]["rd"]
gl2_blu_vol_total += blu[team]["gl2"]["vol"]
red_gl2 = {"score": gl2_red_score_total / len(red), "rd": gl2_red_rd_total / len(red), "vol": gl2_red_vol_total / len(red)} # average the scores by dividing by 3
blu_gl2 = {"score": gl2_blu_score_total / len(blu), "rd": gl2_blu_rd_total / len(blu), "vol": gl2_blu_vol_total / len(blu)} # average the scores by dividing by 3
if match["winner"] == "red": # if red won, set observations to {"red": 1, "blu": 0}
observations = {"red": 1, "blu": 0}
elif match["winner"] == "blue": # if blue won, set observations to {"red": 0, "blu": 1}
observations = {"red": 0, "blu": 1}
else: # otherwise it was a tie and observations is {"red": 0.5, "blu": 0.5}
observations = {"red": 0.5, "blu": 0.5}
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"]]) # calculate new scores for gl2 for red
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"]]) # calculate new scores for gl2 for blue
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"]} # calculate gl2 deltas for red
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"]} # calculate gl2 deltas for blue
for team in red: # for each team on red, add the previous score with the delta to find the new score
red[team]["gl2"]["score"] = red[team]["gl2"]["score"] + red_gl2_delta["score"]
red[team]["gl2"]["rd"] = red[team]["gl2"]["rd"] + red_gl2_delta["rd"]
red[team]["gl2"]["vol"] = red[team]["gl2"]["vol"] + red_gl2_delta["vol"]
for team in blu: # for each team on blue, add the previous score with the delta to find the new score
blu[team]["gl2"]["score"] = blu[team]["gl2"]["score"] + blu_gl2_delta["score"]
blu[team]["gl2"]["rd"] = blu[team]["gl2"]["rd"] + blu_gl2_delta["rd"]
blu[team]["gl2"]["vol"] = blu[team]["gl2"]["vol"] + blu_gl2_delta["vol"]
temp_vector = {}
temp_vector.update(red) # update the team's score with the temporay vector
temp_vector.update(blu)
self.results[match['match']] = temp_vector
d.push_metric(self.apikey, self.competition, temp_vector) # push new scores to db
def _push_results(self):
pass
class Pit (Module):
config = None
apikey = None
tbakey = None
timestamp = None
competition = None
data = None
results = None
def __init__(self, config, apikey, tbakey, timestamp, competition):
self.config = config
self.apikey = apikey
self.tbakey = tbakey
self.timestamp = timestamp
self.competition = competition
def validate_config(self):
return True, ""
def run(self):
self._load_data()
self._process_data()
self._push_results()
def _load_data(self):
self.data = d.load_pit(self.apikey, self.competition)
def _process_data(self):
tests = self.config["tests"]
return_vector = {}
for team in self.data:
for variable in self.data[team]:
if variable in tests:
if not variable in return_vector:
return_vector[variable] = []
return_vector[variable].append(self.data[team][variable])
self.results = return_vector
def _push_results(self):
d.push_pit(self.apikey, self.competition, self.results)
class Rating (Module):
pass
class Heatmap (Module):
pass
class Sentiment (Module):
pass