mirror of
https://github.com/titanscouting/tra-superscript.git
synced 2024-11-09 22:44:44 +00:00
Arthur Lu
parent
9ddf61621e
commit
bd0c0d99f9
@ -1,188 +0,0 @@
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import numpy as np
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from tra_analysis import Analysis as an
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from data import pull_new_tba_matches, push_metric, load_metric
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import signal
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def simplestats(data_test):
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signal.signal(signal.SIGINT, signal.SIG_IGN)
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data = np.array(data_test[3])
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data = data[np.isfinite(data)]
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ranges = list(range(len(data)))
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test = data_test[2]
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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 matchloop(client, competition, data, tests, exec_threads):
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short_mapping = {"regression_linear": "lin", "regression_logarithmic": "log", "regression_exponential": "exp", "regression_polynomial": "ply", "regression_sigmoidal": "sig"}
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class AutoVivification(dict):
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def __getitem__(self, item):
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try:
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return dict.__getitem__(self, item)
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except KeyError:
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value = self[item] = type(self)()
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return value
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input_vector = []
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return_vector = AutoVivification()
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for team in data:
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for variable in data[team]:
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if variable in tests:
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for test in tests[variable]:
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input_vector.append((team, variable, test, data[team][variable]))
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result_filtered = exec_threads.map(simplestats, input_vector)
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i = 0
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result_filtered = list(result_filtered)
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for result in result_filtered:
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filtered = input_vector[i][2]
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try:
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short = short_mapping[filtered]
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return_vector[input_vector[i][0]][input_vector[i][1]][input_vector[i][2]] = result[short]
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except KeyError: # not in mapping
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return_vector[input_vector[i][0]][input_vector[i][1]][input_vector[i][2]] = result
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i += 1
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return return_vector
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def metricloop(client, competition, data, metrics): # listener based metrics update
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elo_N = metrics["elo"]["N"]
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elo_K = metrics["elo"]["K"]
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matches = data
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#matches = 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_metric(client, competition, match, "red", metrics)
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blu = load_metric(client, competition, match, "blue", metrics)
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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.Metric().elo(red_elo["score"], blu_elo["score"], observations["red"], elo_N, elo_K) - red_elo["score"]
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blu_elo_delta = an.Metric().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.Metric().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.Metric().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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push_metric(client, competition, temp_vector)
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def pitloop(client, competition, 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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@ -165,7 +165,6 @@ import zmq
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from interface import splash, log, ERR, INF, stdout, stderr
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from data import get_previous_time, set_current_time, get_database_config, set_database_config, check_new_database_matches
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from module import Match, Metric, Pit
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#from processing import matchloop, metricloop, pitloop
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config_path = "config.json"
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sample_json = """{
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