mirror of
https://github.com/titanscouting/tra-analysis.git
synced 2024-09-20 00:47:56 +00:00
Merge pull request #26 from titanscout2022/master
Merge master into master-staged
This commit is contained in:
Arthur Lu
GitHub
commit
e4ab0487d0
@ -23,5 +23,5 @@
|
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"mhutchie.git-graph",
|
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"donjayamanne.jupyter",
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],
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"postCreateCommand": "pip install -r analysis-master/analysis-amd64/requirements.txt"
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"postCreateCommand": "pip install -r analysis-master/requirements.txt"
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}
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2
.gitignore
vendored
2
.gitignore
vendored
@ -21,11 +21,13 @@ data-analysis/test.ipynb
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data-analysis/visualize_pit.ipynb
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data-analysis/config/keys.config
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analysis-master/analysis/__pycache__/
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analysis-master/analysis/metrics/__pycache__/
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data-analysis/__pycache__/
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analysis-master/analysis.egg-info/
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analysis-master/build/
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analysis-master/metrics/
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data-analysis/config-pop.json
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data-analysis/__pycache__/
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analysis-master/__pycache__/
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analysis-master/.pytest_cache/
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data-analysis/.pytest_cache/
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@ -12,6 +12,7 @@ __version__ = "1.2.1.003"
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# changelog should be viewed using print(analysis.__changelog__)
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__changelog__ = """changelog:
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1.2.1.003:
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- changed output of basic_stats and histo_analysis to libraries
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- fixed __all__
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1.2.1.002:
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- renamed ArrayTest class to Array
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@ -360,7 +361,7 @@ def basic_stats(data):
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_min = npmin(data_t)
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_max = npmax(data_t)
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return _mean, _median, _stdev, _variance, _min, _max
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return {"mean": _mean, "median": _median, "standard-deviation": _stdev, "variance": _variance, "minimum": _min, "maximum": _max}
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# returns z score with inputs of point, mean and standard deviation of spread
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@jit(forceobj=True)
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@ -383,7 +384,7 @@ def z_normalize(array, *args):
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# expects 2d array of [x,y]
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def histo_analysis(hist_data):
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|
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if(len(hist_data[0]) > 2):
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if len(hist_data[0]) > 2:
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hist_data = np.array(hist_data)
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derivative = np.array(len(hist_data) - 1, dtype = float)
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@ -391,7 +392,7 @@ def histo_analysis(hist_data):
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derivative = t[1] / t[0]
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np.sort(derivative)
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return basic_stats(derivative)[0], basic_stats(derivative)[3]
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return {"mean": basic_stats(derivative)["mean"], "deviation": basic_stats(derivative)["standard-deviation"]}
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else:
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@ -1,4 +1,5 @@
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from analysis import analysis as an
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from analysis import metrics
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def test_():
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test_data_linear = [1, 3, 6, 7, 9]
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@ -6,12 +7,12 @@ def test_():
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y_data_ccd = [1, 5, 7, 8.5, 8.66]
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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}
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assert an.z_score(3.2, 6, 1.5) == -1.8666666666666665
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assert an.z_normalize([test_data_linear], 0).tolist() == [[0.07537783614444091, 0.22613350843332272, 0.45226701686664544, 0.5276448530110863, 0.6784005252999682]]
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assert an.z_normalize([test_data_linear], 1).tolist() == [[0.07537783614444091, 0.22613350843332272, 0.45226701686664544, 0.5276448530110863, 0.6784005252999682]]
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assert all(isinstance(item, str) for item in an.regression(test_data_linear, y_data_ccu, ["lin"])) == True
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assert all(isinstance(item, str) for item in an.regression(test_data_linear, y_data_ccd, ["log"])) == True
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assert all(isinstance(item, str) for item in an.regression(test_data_linear, y_data_ccu, ["exp"])) == True
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assert all(isinstance(item, str) for item in an.regression(test_data_linear, y_data_ccu, ["ply"])) == True
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assert all(isinstance(item, str) for item in an.regression(test_data_linear, y_data_ccd, ["sig"])) == True
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#assert all(isinstance(item, str) for item in an.regression(test_data_linear, y_data_ccd, ["log"])) == True
|
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#assert all(isinstance(item, str) for item in an.regression(test_data_linear, y_data_ccu, ["exp"])) == True
|
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#assert all(isinstance(item, str) for item in an.regression(test_data_linear, y_data_ccu, ["ply"])) == True
|
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#assert all(isinstance(item, str) for item in an.regression(test_data_linear, y_data_ccd, ["sig"])) == True
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assert an.Metric().elo(1500, 1500, [1, 0], 400, 24) == 1512.0
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assert an.Metric().glicko2(1500, 250, 0.06, [1500, 1400], [250, 240], [1, 0]) == (1478.864307445517, 195.99122679202452, 0.05999602937563585)
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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))]
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#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))]
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45
data-analysis/config.json
Normal file
45
data-analysis/config.json
Normal file
@ -0,0 +1,45 @@
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{
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"team": "",
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"competition": "",
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"key":{
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"database":"",
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"tba":""
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},
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"statistics":{
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"match":{
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"balls-blocked":["basic_stats","historical_analysis","regression_linear","regression_logarithmic","regression_exponential","regression_polynomial","regression_sigmoidal"],
|
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"balls-collected":["basic_stats","historical_analysis","regression_linear","regression_logarithmic","regression_exponential","regression_polynomial","regression_sigmoidal"],
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"balls-lower-teleop":["basic_stats","historical_analysis","regression_linear","regression_logarithmic","regression_exponential","regression_polynomial","regression_sigmoidal"],
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"balls-lower-auto":["basic_stats","historical_analysis","regression_linear","regression_logarithmic","regression_exponential","regression_polynomial","regression_sigmoidal"],
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"balls-started":["basic_stats","historical_analyss","regression_linear","regression_logarithmic","regression_exponential","regression_polynomial","regression_sigmoidal"],
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"balls-upper-teleop":["basic_stats","historical_analysis","regression_linear","regression_logarithmic","regression_exponential","regression_polynomial","regression_sigmoidal"],
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"balls-upper-auto":["basic_stats","historical_analysis","regression_linear","regression_logarithmic","regression_exponential","regression_polynomial","regression_sigmoidal"]
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|
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},
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"metric":{
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"elo":{
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"score":1500,
|
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"N":400,
|
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"K":24
|
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},
|
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"gl2":{
|
||||
"score":1500,
|
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"rd":250,
|
||||
"vol":0.06
|
||||
},
|
||||
"ts":{
|
||||
"mu":25,
|
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"sigma":8.33
|
||||
}
|
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},
|
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"pit":{
|
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"wheel-mechanism":true,
|
||||
"low-balls":true,
|
||||
"high-balls":true,
|
||||
"wheel-success":true,
|
||||
"strategic-focus":true,
|
||||
"climb-mechanism":true,
|
||||
"attitude":true
|
||||
}
|
||||
}
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||||
}
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@ -1 +0,0 @@
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2020ilch
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@ -1,2 +0,0 @@
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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
|
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UDvKmPjPRfwwUdDX1JxbmkyecYBJhCtXeyVk9vmO2i7K0Zn4wqQPMfzuEINXJ7e5
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@ -1,14 +0,0 @@
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balls-blocked,basic_stats,historical_analysis,regression_linear,regression_logarithmic,regression_exponential,regression_polynomial,regression_sigmoidal
|
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balls-collected,basic_stats,historical_analysis,regression_linear,regression_logarithmic,regression_exponential,regression_polynomial,regression_sigmoidal
|
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balls-lower-teleop,basic_stats,historical_analysis,regression_linear,regression_logarithmic,regression_exponential,regression_polynomial,regression_sigmoidal
|
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balls-lower-auto,basic_stats,historical_analysis,regression_linear,regression_logarithmic,regression_exponential,regression_polynomial,regression_sigmoidal
|
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balls-started,basic_stats,historical_analyss,regression_linear,regression_logarithmic,regression_exponential,regression_polynomial,regression_sigmoidal
|
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balls-upper-teleop,basic_stats,historical_analysis,regression_linear,regression_logarithmic,regression_exponential,regression_polynomial,regression_sigmoidal
|
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balls-upper-auto,basic_stats,historical_analysis,regression_linear,regression_logarithmic,regression_exponential,regression_polynomial,regression_sigmoidal
|
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wheel-mechanism
|
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low-balls
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high-balls
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wheel-success
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strategic-focus
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climb-mechanism
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attitude
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@ -8,7 +8,7 @@ def pull_new_tba_matches(apikey, competition, cutoff):
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x=requests.get("https://www.thebluealliance.com/api/v3/event/"+competition+"/matches/simple", headers={"X-TBA-Auth_Key":api_key})
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out = []
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for i in x.json():
|
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if (i["actual_time"] != None and i["actual_time"]-cutoff >= 0 and i["comp_level"] == "qm"):
|
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if i["actual_time"] != None and i["actual_time"]-cutoff >= 0 and i["comp_level"] == "qm":
|
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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"]})
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return out
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@ -34,17 +34,6 @@ def get_team_metrics_data(apikey, competition, team_num):
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mdata = db.team_metrics
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return mdata.find_one({"competition" : competition, "team": team_num})
|
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|
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def unkeyify_2l(layered_dict):
|
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out = {}
|
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for i in layered_dict.keys():
|
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add = []
|
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sortkey = []
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for j in layered_dict[i].keys():
|
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add.append([j,layered_dict[i][j]])
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add.sort(key = lambda x: x[0])
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out[i] = list(map(lambda x: x[1], add))
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return out
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def get_match_data_formatted(apikey, competition):
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client = pymongo.MongoClient(apikey)
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db = client.data_scouting
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@ -58,6 +47,19 @@ def get_match_data_formatted(apikey, competition):
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pass
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return out
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def get_metrics_data_formatted(apikey, competition):
|
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client = pymongo.MongoClient(apikey)
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db = client.data_scouting
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mdata = db.teamlist
|
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x=mdata.find_one({"competition":competition})
|
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out = {}
|
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for i in x:
|
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try:
|
||||
out[int(i)] = d.get_team_metrics_data(apikey, competition, int(i))
|
||||
except:
|
||||
pass
|
||||
return out
|
||||
|
||||
def get_pit_data_formatted(apikey, competition):
|
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client = pymongo.MongoClient(apikey)
|
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db = client.data_scouting
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@ -71,6 +73,20 @@ def get_pit_data_formatted(apikey, competition):
|
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pass
|
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return out
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|
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def get_pit_variable_data(apikey, competition):
|
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client = pymongo.MongoClient(apikey)
|
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db = client.data_processing
|
||||
mdata = db.team_pit
|
||||
out = {}
|
||||
return mdata.find()
|
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|
||||
def get_pit_variable_formatted(apikey, competition):
|
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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"):
|
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client = pymongo.MongoClient(apikey)
|
||||
db = client[dbname]
|
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@ -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
|
||||
@ -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()
|
||||
@ -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
91
data-analysis/tra.py
Normal 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()
|
||||
@ -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()
|
||||
|
||||
|
||||
# %%
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user