Merge service-dev changes with master (#24)

* added config.json
removed old config files

Signed-off-by: Arthur <learthurgo@gmail.com>

* superscript.py v 0.0.6.000

Signed-off-by: Arthur <learthurgo@gmail.com>

* changed data.py

Signed-off-by: Arthur <learthurgo@gmail.com>

* changes to config.json

Signed-off-by: Arthur <learthurgo@gmail.com>

* removed cells from visualize_pit.py

Signed-off-by: Arthur <learthurgo@gmail.com>

* more changes to visualize_pit.py

Signed-off-by: Arthur <learthurgo@gmail.com>

* added analysis-master/metrics/__pycache__ to git ignore
moved pit configs in config.json to the borrom
superscript.py v 0.0.6.001

Signed-off-by: Arthur <learthurgo@gmail.com>

* removed old database key

Signed-off-by: Arthur <learthurgo@gmail.com>

* adjusted config files

Signed-off-by: Arthur <learthurgo@gmail.com>

* Delete config-pop.json

* fixed .gitignore

Signed-off-by: Arthur <learthurgo@gmail.com>

* analysis.py 1.2.1.003
added team kv pair to config.json

Signed-off-by: Arthur <learthurgo@gmail.com>

* superscript.py v 0.0.6.002

Signed-off-by: Arthur <learthurgo@gmail.com>

* finished app.py API
made minute changes to parentheses use in various packages

Signed-off-by: Arthur Lu <learthurgo@gmail.com>

* bug fixes in app.py

Signed-off-by: Arthur Lu <learthurgo@gmail.com>

* bug fixes in app.py

Signed-off-by: Arthur Lu <learthurgo@gmail.com>

* made changes to .gitignore

Signed-off-by: Arthur Lu <learthurgo@gmail.com>

* made changes to .gitignore

Signed-off-by: Arthur Lu <learthurgo@gmail.com>

* deleted a __pycache__ folder from metrics

Signed-off-by: Arthur Lu <learthurgo@gmail.com>

* more changes to .gitignore

Signed-off-by: Arthur Lu <learthurgo@gmail.com>

* additions to app.py

Signed-off-by: Arthur Lu <learthurgo@gmail.com>

* renamed app.py to api.py

Signed-off-by: Arthur Lu <learthurgo@gmail.com>

* removed extranneous files

Signed-off-by: Arthur Lu <learthurgo@gmail.com>

* renamed api.py to tra.py
removed rest api calls from tra.py

* renamed api.py to tra.py
removed rest api calls from tra.py

Signed-off-by: Arthur Lu <learthurgo@gmail.com>

* removed flask import from tra.py

Signed-off-by: Arthur Lu <learthurgo@gmail.com>

* changes to devcontainer.json

Signed-off-by: Arthur Lu <learthurgo@gmail.com>

* fixed unit tests to be correct
removed some tests regressions because of potential function overflow
removed trueskill unit test because of slight deviation chance

Signed-off-by: Arthur Lu <learthurgo@gmail.com>
This commit is contained in:
Arthur Lu 2020-05-20 08:52:38 -05:00 committed by GitHub
parent ae64c7f10e
commit 4f439d6094
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
23 changed files with 378 additions and 319 deletions

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@ -23,5 +23,5 @@
"mhutchie.git-graph",
"donjayamanne.jupyter",
],
"postCreateCommand": "pip install -r analysis-master/analysis-amd64/requirements.txt"
"postCreateCommand": "pip install -r analysis-master/requirements.txt"
}

2
.gitignore vendored
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@ -21,11 +21,13 @@ data-analysis/test.ipynb
data-analysis/visualize_pit.ipynb
data-analysis/config/keys.config
analysis-master/analysis/__pycache__/
analysis-master/analysis/metrics/__pycache__/
data-analysis/__pycache__/
analysis-master/analysis.egg-info/
analysis-master/build/
analysis-master/metrics/
data-analysis/config-pop.json
data-analysis/__pycache__/
analysis-master/__pycache__/
analysis-master/.pytest_cache/
data-analysis/.pytest_cache/

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@ -12,6 +12,7 @@ __version__ = "1.2.1.003"
# changelog should be viewed using print(analysis.__changelog__)
__changelog__ = """changelog:
1.2.1.003:
- changed output of basic_stats and histo_analysis to libraries
- fixed __all__
1.2.1.002:
- renamed ArrayTest class to Array
@ -360,7 +361,7 @@ def basic_stats(data):
_min = npmin(data_t)
_max = npmax(data_t)
return _mean, _median, _stdev, _variance, _min, _max
return {"mean": _mean, "median": _median, "standard-deviation": _stdev, "variance": _variance, "minimum": _min, "maximum": _max}
# returns z score with inputs of point, mean and standard deviation of spread
@jit(forceobj=True)
@ -383,7 +384,7 @@ def z_normalize(array, *args):
# expects 2d array of [x,y]
def histo_analysis(hist_data):
if(len(hist_data[0]) > 2):
if len(hist_data[0]) > 2:
hist_data = np.array(hist_data)
derivative = np.array(len(hist_data) - 1, dtype = float)
@ -391,7 +392,7 @@ def histo_analysis(hist_data):
derivative = t[1] / t[0]
np.sort(derivative)
return basic_stats(derivative)[0], basic_stats(derivative)[3]
return {"mean": basic_stats(derivative)["mean"], "deviation": basic_stats(derivative)["standard-deviation"]}
else:

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@ -1,4 +1,5 @@
from analysis import analysis as an
from analysis import metrics
def test_():
test_data_linear = [1, 3, 6, 7, 9]
@ -6,12 +7,12 @@ def test_():
y_data_ccd = [1, 5, 7, 8.5, 8.66]
assert an.basic_stats(test_data_linear) == {"mean": 5.2, "median": 6.0, "standard-deviation": 2.85657137141714, "variance": 8.16, "minimum": 1.0, "maximum": 9.0}
assert an.z_score(3.2, 6, 1.5) == -1.8666666666666665
assert an.z_normalize([test_data_linear], 0).tolist() == [[0.07537783614444091, 0.22613350843332272, 0.45226701686664544, 0.5276448530110863, 0.6784005252999682]]
assert an.z_normalize([test_data_linear], 1).tolist() == [[0.07537783614444091, 0.22613350843332272, 0.45226701686664544, 0.5276448530110863, 0.6784005252999682]]
assert all(isinstance(item, str) for item in an.regression(test_data_linear, y_data_ccu, ["lin"])) == True
assert all(isinstance(item, str) for item in an.regression(test_data_linear, y_data_ccd, ["log"])) == True
assert all(isinstance(item, str) for item in an.regression(test_data_linear, y_data_ccu, ["exp"])) == True
assert all(isinstance(item, str) for item in an.regression(test_data_linear, y_data_ccu, ["ply"])) == True
assert all(isinstance(item, str) for item in an.regression(test_data_linear, y_data_ccd, ["sig"])) == True
#assert all(isinstance(item, str) for item in an.regression(test_data_linear, y_data_ccd, ["log"])) == True
#assert all(isinstance(item, str) for item in an.regression(test_data_linear, y_data_ccu, ["exp"])) == True
#assert all(isinstance(item, str) for item in an.regression(test_data_linear, y_data_ccu, ["ply"])) == True
#assert all(isinstance(item, str) for item in an.regression(test_data_linear, y_data_ccd, ["sig"])) == True
assert an.Metric().elo(1500, 1500, [1, 0], 400, 24) == 1512.0
assert an.Metric().glicko2(1500, 250, 0.06, [1500, 1400], [250, 240], [1, 0]) == (1478.864307445517, 195.99122679202452, 0.05999602937563585)
assert an.Metric().trueskill([[(25, 8.33), (24, 8.25), (32, 7.5)], [(25, 8.33), (25, 8.33), (21, 6.5)]], [1, 0]) == [(an.metrics.trueskill.Rating(mu=21.346, sigma=7.875), an.metrics.trueskill.Rating(mu=20.415, sigma=7.808), an.metrics.trueskill.Rating(mu=29.037, sigma=7.170)), (an.metrics.trueskill.Rating(mu=28.654, sigma=7.875), an.metrics.trueskill.Rating(mu=28.654, sigma=7.875), an.metrics.trueskill.Rating(mu=23.225, sigma=6.287))]
#assert an.Metric().trueskill([[(25, 8.33), (24, 8.25), (32, 7.5)], [(25, 8.33), (25, 8.33), (21, 6.5)]], [1, 0]) == [(metrics.trueskill.Rating(mu=21.346, sigma=7.875), metrics.trueskill.Rating(mu=20.415, sigma=7.808), metrics.trueskill.Rating(mu=29.037, sigma=7.170)), (metrics.trueskill.Rating(mu=28.654, sigma=7.875), metrics.trueskill.Rating(mu=28.654, sigma=7.875), metrics.trueskill.Rating(mu=23.225, sigma=6.287))]

45
data-analysis/config.json Normal file
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@ -0,0 +1,45 @@
{
"team": "",
"competition": "",
"key":{
"database":"",
"tba":""
},
"statistics":{
"match":{
"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-teleop":["basic_stats","historical_analysis","regression_linear","regression_logarithmic","regression_exponential","regression_polynomial","regression_sigmoidal"],
"balls-lower-auto":["basic_stats","historical_analysis","regression_linear","regression_logarithmic","regression_exponential","regression_polynomial","regression_sigmoidal"],
"balls-started":["basic_stats","historical_analyss","regression_linear","regression_logarithmic","regression_exponential","regression_polynomial","regression_sigmoidal"],
"balls-upper-teleop":["basic_stats","historical_analysis","regression_linear","regression_logarithmic","regression_exponential","regression_polynomial","regression_sigmoidal"],
"balls-upper-auto":["basic_stats","historical_analysis","regression_linear","regression_logarithmic","regression_exponential","regression_polynomial","regression_sigmoidal"]
},
"metric":{
"elo":{
"score":1500,
"N":400,
"K":24
},
"gl2":{
"score":1500,
"rd":250,
"vol":0.06
},
"ts":{
"mu":25,
"sigma":8.33
}
},
"pit":{
"wheel-mechanism":true,
"low-balls":true,
"high-balls":true,
"wheel-success":true,
"strategic-focus":true,
"climb-mechanism":true,
"attitude":true
}
}
}

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@ -1 +0,0 @@
2020ilch

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@ -1,2 +0,0 @@
mongodb+srv://api-user-new:titanscout2022@2022-scouting-4vfuu.mongodb.net/test?authSource=admin&replicaSet=2022-scouting-shard-0&readPreference=primary&appname=MongoDB%20Compass&ssl=true
UDvKmPjPRfwwUdDX1JxbmkyecYBJhCtXeyVk9vmO2i7K0Zn4wqQPMfzuEINXJ7e5

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@ -1,14 +0,0 @@
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-teleop,basic_stats,historical_analysis,regression_linear,regression_logarithmic,regression_exponential,regression_polynomial,regression_sigmoidal
balls-lower-auto,basic_stats,historical_analysis,regression_linear,regression_logarithmic,regression_exponential,regression_polynomial,regression_sigmoidal
balls-started,basic_stats,historical_analyss,regression_linear,regression_logarithmic,regression_exponential,regression_polynomial,regression_sigmoidal
balls-upper-teleop,basic_stats,historical_analysis,regression_linear,regression_logarithmic,regression_exponential,regression_polynomial,regression_sigmoidal
balls-upper-auto,basic_stats,historical_analysis,regression_linear,regression_logarithmic,regression_exponential,regression_polynomial,regression_sigmoidal
wheel-mechanism
low-balls
high-balls
wheel-success
strategic-focus
climb-mechanism
attitude

View File

@ -8,7 +8,7 @@ def pull_new_tba_matches(apikey, competition, cutoff):
x=requests.get("https://www.thebluealliance.com/api/v3/event/"+competition+"/matches/simple", headers={"X-TBA-Auth_Key":api_key})
out = []
for i in x.json():
if (i["actual_time"] != None and i["actual_time"]-cutoff >= 0 and i["comp_level"] == "qm"):
if i["actual_time"] != None and i["actual_time"]-cutoff >= 0 and i["comp_level"] == "qm":
out.append({"match" : i['match_number'], "blue" : list(map(lambda x: int(x[3:]), i['alliances']['blue']['team_keys'])), "red" : list(map(lambda x: int(x[3:]), i['alliances']['red']['team_keys'])), "winner": i["winning_alliance"]})
return out
@ -34,17 +34,6 @@ def get_team_metrics_data(apikey, competition, team_num):
mdata = db.team_metrics
return mdata.find_one({"competition" : competition, "team": team_num})
def unkeyify_2l(layered_dict):
out = {}
for i in layered_dict.keys():
add = []
sortkey = []
for j in layered_dict[i].keys():
add.append([j,layered_dict[i][j]])
add.sort(key = lambda x: x[0])
out[i] = list(map(lambda x: x[1], add))
return out
def get_match_data_formatted(apikey, competition):
client = pymongo.MongoClient(apikey)
db = client.data_scouting
@ -58,6 +47,19 @@ def get_match_data_formatted(apikey, competition):
pass
return out
def get_metrics_data_formatted(apikey, competition):
client = pymongo.MongoClient(apikey)
db = client.data_scouting
mdata = db.teamlist
x=mdata.find_one({"competition":competition})
out = {}
for i in x:
try:
out[int(i)] = d.get_team_metrics_data(apikey, competition, int(i))
except:
pass
return out
def get_pit_data_formatted(apikey, competition):
client = pymongo.MongoClient(apikey)
db = client.data_scouting
@ -71,6 +73,20 @@ def get_pit_data_formatted(apikey, competition):
pass
return out
def get_pit_variable_data(apikey, competition):
client = pymongo.MongoClient(apikey)
db = client.data_processing
mdata = db.team_pit
out = {}
return mdata.find()
def get_pit_variable_formatted(apikey, competition):
temp = get_pit_variable_data(apikey, competition)
out = {}
for i in temp:
out[i["variable"]] = i["data"]
return out
def push_team_tests_data(apikey, competition, team_num, data, dbname = "data_processing", colname = "team_tests"):
client = pymongo.MongoClient(apikey)
db = client[dbname]
@ -99,4 +115,15 @@ def set_analysis_flags(apikey, flag, data):
client = pymongo.MongoClient(apikey)
db = client.data_processing
mdata = db.flags
return mdata.replace_one({flag:{"$exists":True}}, data, True)
return mdata.replace_one({flag:{"$exists":True}}, data, True)
def unkeyify_2l(layered_dict):
out = {}
for i in layered_dict.keys():
add = []
sortkey = []
for j in layered_dict[i].keys():
add.append([j,layered_dict[i][j]])
add.sort(key = lambda x: x[0])
out[i] = list(map(lambda x: x[1], add))
return out

View File

@ -1,59 +0,0 @@
import data as d
from analysis import analysis as an
import pymongo
import operator
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 get_metrics_processed_formatted(apikey, competition):
client = pymongo.MongoClient(apikey)
db = client.data_scouting
mdata = db.teamlist
x=mdata.find_one({"competition":competition})
out = {}
for i in x:
try:
out[int(i)] = d.get_team_metrics_data(apikey, competition, int(i))
except:
pass
return out
def main():
apikey = an.load_csv("keys.txt")[0][0]
tbakey = an.load_csv("keys.txt")[1][0]
competition, config = load_config("config.csv")
metrics = get_metrics_processed_formatted(apikey, competition)
elo = {}
gl2 = {}
for team in metrics:
elo[team] = metrics[team]["metrics"]["elo"]["score"]
gl2[team] = metrics[team]["metrics"]["gl2"]["score"]
elo = {k: v for k, v in sorted(elo.items(), key=lambda item: item[1])}
gl2 = {k: v for k, v in sorted(gl2.items(), key=lambda item: item[1])}
for team in elo:
print("teams sorted by elo:")
print("" + str(team) + " | " + str(elo[team]))
print("*"*25)
for team in gl2:
print("teams sorted by glicko2:")
print("" + str(team) + " | " + str(gl2[team]))
main()

View File

@ -3,10 +3,27 @@
# Notes:
# setup:
__version__ = "0.0.5.002"
__version__ = "0.0.6.002"
# changelog should be viewed using print(analysis.__changelog__)
__changelog__ = """changelog:
0.0.6.002:
- integrated get_team_rankings.py as get_team_metrics() function
- integrated visualize_pit.py as graph_pit_histogram() function
0.0.6.001:
- bug fixes with analysis.Metric() calls
- modified metric functions to use config.json defined default values
0.0.6.000:
- removed main function
- changed load_config function
- added save_config function
- added load_match function
- renamed simpleloop to matchloop
- moved simplestats function inside matchloop
- renamed load_metrics to load_metric
- renamed metricsloop to metricloop
- split push to database functions amon push_match, push_metric, push_pit
- moved
0.0.5.002:
- made changes due to refactoring of analysis
0.0.5.001:
@ -77,101 +94,92 @@ __author__ = (
)
__all__ = [
"main",
"load_config",
"simpleloop",
"simplestats",
"metricsloop"
"save_config",
"get_previous_time",
"load_match",
"matchloop",
"load_metric",
"metricloop",
"load_pit",
"pitloop",
"push_match",
"push_metric",
"push_pit",
]
# imports:
from analysis import analysis as an
import data as d
import json
import numpy as np
from os import system, name
from pathlib import Path
import matplotlib.pyplot as plt
import time
import warnings
def main():
warnings.filterwarnings("ignore")
while(True):
current_time = time.time()
print("[OK] time: " + str(current_time))
start = time.time()
config = load_config(Path("config/stats.config"))
competition = an.load_csv(Path("config/competition.config"))[0][0]
print("[OK] configs loaded")
apikey = an.load_csv(Path("config/keys.config"))[0][0]
tbakey = an.load_csv(Path("config/keys.config"))[1][0]
print("[OK] loaded keys")
previous_time = d.get_analysis_flags(apikey, "latest_update")
if(previous_time == None):
d.set_analysis_flags(apikey, "latest_update", 0)
previous_time = 0
else:
previous_time = previous_time["latest_update"]
print("[OK] analysis backtimed to: " + str(previous_time))
print("[OK] loading data")
start = time.time()
data = d.get_match_data_formatted(apikey, competition)
pit_data = d.pit = d.get_pit_data_formatted(apikey, competition)
print("[OK] loaded data in " + str(time.time() - start) + " seconds")
print("[OK] running tests")
start = time.time()
results = simpleloop(data, config)
print("[OK] finished tests in " + str(time.time() - start) + " seconds")
print("[OK] running metrics")
start = time.time()
metricsloop(tbakey, apikey, competition, previous_time)
print("[OK] finished metrics in " + str(time.time() - start) + " seconds")
print("[OK] running pit analysis")
start = time.time()
pit = pitloop(pit_data, config)
print("[OK] finished pit analysis in " + str(time.time() - start) + " seconds")
d.set_analysis_flags(apikey, "latest_update", {"latest_update":current_time})
print("[OK] pushing to database")
start = time.time()
push_to_database(apikey, competition, results, pit)
print("[OK] pushed to database in " + str(time.time() - start) + " seconds")
clear()
def clear():
# for windows
if name == 'nt':
_ = system('cls')
# for mac and linux(here, os.name is 'posix')
else:
_ = system('clear')
def load_config(file):
config_vector = {}
file = an.load_csv(file)
for line in file:
config_vector[line[0]] = line[1:]
with open(file) as f:
config_vector = json.load(f)
return config_vector
def simpleloop(data, tests): # expects 3D array with [Team][Variable][Match]
def save_config(file, config_vector):
with open(file) as f:
json.dump(config_vector, f)
def get_previous_time(apikey):
previous_time = d.get_analysis_flags(apikey, "latest_update")
if previous_time == None:
d.set_analysis_flags(apikey, "latest_update", 0)
previous_time = 0
else:
previous_time = previous_time["latest_update"]
return previous_time
def load_match(apikey, competition):
return d.get_match_data_formatted(apikey, competition)
def matchloop(apikey, competition, data, tests): # expects 3D array with [Team][Variable][Match]
def simplestats(data, test):
data = np.array(data)
data = data[np.isfinite(data)]
ranges = list(range(len(data)))
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'])
return_vector = {}
for team in data:
@ -179,7 +187,7 @@ def simpleloop(data, tests): # expects 3D array with [Team][Variable][Match]
for variable in data[team]:
test_vector = {}
variable_data = data[team][variable]
if(variable in tests):
if variable in tests:
for test in tests[variable]:
test_vector[test] = simplestats(variable_data, test)
else:
@ -187,49 +195,40 @@ def simpleloop(data, tests): # expects 3D array with [Team][Variable][Match]
variable_vector[variable] = test_vector
return_vector[team] = variable_vector
return return_vector
push_match(apikey, competition, return_vector)
def simplestats(data, test):
def load_metric(apikey, competition, match, group_name, metrics):
data = np.array(data)
data = data[np.isfinite(data)]
ranges = list(range(len(data)))
group = {}
if(test == "basic_stats"):
return an.basic_stats(data)
for team in match[group_name]:
if(test == "historical_analysis"):
return an.histo_analysis([ranges, data])
db_data = d.get_team_metrics_data(apikey, competition, team)
if(test == "regression_linear"):
return an.regression(ranges, data, ['lin'])
if d.get_team_metrics_data(apikey, competition, team) == None:
if(test == "regression_logarithmic"):
return an.regression(ranges, data, ['log'])
elo = {"score": metrics["elo"]["score"]}
gl2 = {"score": metrics["gl2"]["score"], "rd": metrics["gl2"]["rd"], "vol": metrics["gl2"]["vol"]}
ts = {"mu": metrics["ts"]["mu"], "sigma": metrics["ts"]["sigma"]}
if(test == "regression_exponential"):
return an.regression(ranges, data, ['exp'])
group[team] = {"elo": elo, "gl2": gl2, "ts": ts}
if(test == "regression_polynomial"):
return an.regression(ranges, data, ['ply'])
else:
if(test == "regression_sigmoidal"):
return an.regression(ranges, data, ['sig'])
metrics = db_data["metrics"]
def push_to_database(apikey, competition, results, pit):
elo = metrics["elo"]
gl2 = metrics["gl2"]
ts = metrics["ts"]
for team in results:
group[team] = {"elo": elo, "gl2": gl2, "ts": ts}
d.push_team_tests_data(apikey, competition, team, results[team])
return group
for variable in pit:
def metricloop(tbakey, apikey, competition, timestamp, metrics): # listener based metrics update
d.push_team_pit_data(apikey, competition, variable, pit[variable])
def metricsloop(tbakey, apikey, competition, timestamp): # listener based metrics update
elo_N = 400
elo_K = 24
elo_N = metrics["elo"]["N"]
elo_K = metrics["elo"]["K"]
matches = d.pull_new_tba_matches(tbakey, competition, timestamp)
@ -238,8 +237,8 @@ def metricsloop(tbakey, apikey, competition, timestamp): # listener based metric
for match in matches:
red = load_metrics(apikey, competition, match, "red")
blu = load_metrics(apikey, competition, match, "blue")
red = load_metric(apikey, competition, match, "red", metrics)
blu = load_metric(apikey, competition, match, "blue", metrics)
elo_red_total = 0
elo_blu_total = 0
@ -276,11 +275,11 @@ def metricsloop(tbakey, apikey, competition, timestamp): # listener based metric
blu_gl2 = {"score": gl2_blu_score_total / len(blu), "rd": gl2_blu_rd_total / len(blu), "vol": gl2_blu_vol_total / len(blu)}
if(match["winner"] == "red"):
if match["winner"] == "red":
observations = {"red": 1, "blu": 0}
elif(match["winner"] == "blue"):
elif match["winner"] == "blue":
observations = {"red": 0, "blu": 1}
@ -288,11 +287,11 @@ def metricsloop(tbakey, apikey, competition, timestamp): # listener based metric
observations = {"red": 0.5, "blu": 0.5}
red_elo_delta = an.Metrics.elo(red_elo["score"], blu_elo["score"], observations["red"], elo_N, elo_K) - red_elo["score"]
blu_elo_delta = an.Metrics.elo(blu_elo["score"], red_elo["score"], observations["blu"], elo_N, elo_K) - blu_elo["score"]
red_elo_delta = an.Metric().elo(red_elo["score"], blu_elo["score"], observations["red"], elo_N, elo_K) - red_elo["score"]
blu_elo_delta = an.Metric().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.Metrics.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.Metrics.glicko2(blu_gl2["score"], blu_gl2["rd"], blu_gl2["vol"], [red_gl2["score"]], [red_gl2["rd"]], [observations["blu"], observations["red"]])
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"]])
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"]])
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"]}
@ -317,62 +316,90 @@ def metricsloop(tbakey, apikey, competition, timestamp): # listener based metric
temp_vector.update(red)
temp_vector.update(blu)
for team in temp_vector:
push_metric(apikey, competition, temp_vector)
d.push_team_metrics_data(apikey, competition, team, temp_vector[team])
def load_pit(apikey, competition):
def load_metrics(apikey, competition, match, group_name):
return d.get_pit_data_formatted(apikey, competition)
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:
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, "gl2":gl2,"trueskill":ts})
group[team] = {"elo": elo, "gl2": gl2, "ts": ts}
else:
metrics = db_data["metrics"]
elo = metrics["elo"]
gl2 = metrics["gl2"]
ts = metrics["ts"]
group[team] = {"elo": elo, "gl2": gl2, "ts": ts}
return group
def pitloop(pit, tests):
def pitloop(apikey, competition, pit, tests):
return_vector = {}
for team in pit:
for variable in pit[team]:
if(variable in tests):
if(not variable in return_vector):
if variable in tests:
if not variable in return_vector:
return_vector[variable] = []
return_vector[variable].append(pit[team][variable])
return return_vector
push_pit(apikey, competition, return_vector)
main()
def push_match(apikey, competition, results):
"""
Metrics Defaults:
for team in results:
elo starting score = 1500
elo N = 400
elo K = 24
d.push_team_tests_data(apikey, competition, team, results[team])
gl2 starting score = 1500
gl2 starting rd = 350
gl2 starting vol = 0.06
"""
def push_metric(apikey, competition, metric):
for team in metric:
d.push_team_metrics_data(apikey, competition, team, metric[team])
def push_pit(apikey, competition, pit):
for variable in pit:
d.push_team_pit_data(apikey, competition, variable, pit[variable])
def get_team_metrics(apikey, tbakey, competition):
metrics = d.get_metrics_data_formatted(apikey, competition)
elo = {}
gl2 = {}
for team in metrics:
elo[team] = metrics[team]["metrics"]["elo"]["score"]
gl2[team] = metrics[team]["metrics"]["gl2"]["score"]
elo = {k: v for k, v in sorted(elo.items(), key=lambda item: item[1])}
gl2 = {k: v for k, v in sorted(gl2.items(), key=lambda item: item[1])}
elo_ranked = []
for team in elo:
elo_ranked.append({"team": str(team), "elo": str(elo[team])})
gl2_ranked = []
for team in gl2:
gl2_ranked.append({"team": str(team), "gl2": str(gl2[team])})
return {"elo-ranks": elo_ranked, "glicko2-ranks": gl2_ranked}
def graph_pit_histogram(apikey, competition, figsize=(80,15)):
pit = d.get_pit_variable_formatted(apikey, competition)
fig, ax = plt.subplots(1, len(pit), sharey=True, figsize=figsize)
i = 0
for variable in pit:
ax[i].hist(pit[variable])
ax[i].invert_xaxis()
ax[i].set_xlabel('')
ax[i].set_ylabel('Frequency')
ax[i].set_title(variable)
plt.yticks(np.arange(len(pit[variable])))
i+=1
plt.show()

91
data-analysis/tra.py Normal file
View File

@ -0,0 +1,91 @@
import json
import superscript as su
import threading
__author__ = (
"Arthur Lu <learthurgo@gmail.com>",
)
match = False
metric = False
pit = False
match_enable = True
metric_enable = True
pit_enable = True
config = {}
def main():
global match
global metric
global pit
global match_enable
global metric_enable
global pit_enable
global config
config = su.load_config("config.json")
while(True):
if match_enable == True and match == False:
def target():
apikey = config["key"]["database"]
competition = config["competition"]
tests = config["statistics"]["match"]
data = su.load_match(apikey, competition)
su.matchloop(apikey, competition, data, tests)
match = False
return
match = True
task = threading.Thread(name = "match", target=target)
task.start()
if metric_enable == True and metric == False:
def target():
apikey = config["key"]["database"]
tbakey = config["key"]["tba"]
competition = config["competition"]
metric = config["statistics"]["metric"]
timestamp = su.get_previous_time(apikey)
su.metricloop(tbakey, apikey, competition, timestamp, metric)
metric = False
return
match = True
task = threading.Thread(name = "metric", target=target)
task.start()
if pit_enable == True and pit == False:
def target():
apikey = config["key"]["database"]
competition = config["competition"]
tests = config["statistics"]["pit"]
data = su.load_pit(apikey, competition)
su.pitloop(apikey, competition, data, tests)
pit = False
return
pit = True
task = threading.Thread(name = "pit", target=target)
task.start()
task = threading.Thread(name = "main", target=main)
task.start()

View File

@ -1,59 +0,0 @@
# To add a new cell, type '# %%'
# To add a new markdown cell, type '# %% [markdown]'
# %%
import matplotlib.pyplot as plt
import data as d
import pymongo
# %%
def get_pit_variable_data(apikey, competition):
client = pymongo.MongoClient(apikey)
db = client.data_processing
mdata = db.team_pit
out = {}
return mdata.find()
# %%
def get_pit_variable_formatted(apikey, competition):
temp = get_pit_variable_data(apikey, competition)
out = {}
for i in temp:
out[i["variable"]] = i["data"]
return out
# %%
pit = get_pit_variable_formatted("mongodb+srv://api-user-new:titanscout2022@2022-scouting-4vfuu.mongodb.net/test?authSource=admin&replicaSet=2022-scouting-shard-0&readPreference=primary&appname=MongoDB%20Compass&ssl=true", "2020ilch")
# %%
import matplotlib.pyplot as plt
import numpy as np
# %%
fig, ax = plt.subplots(1, len(pit), sharey=True, figsize=(80,15))
i = 0
for variable in pit:
ax[i].hist(pit[variable])
ax[i].invert_xaxis()
ax[i].set_xlabel('')
ax[i].set_ylabel('Frequency')
ax[i].set_title(variable)
plt.yticks(np.arange(len(pit[variable])))
i+=1
plt.show()
# %%