analysis pkg v 1.0.0.11

analysis.py v 1.1.13.009
superscript.py v 0.0.5.002

.devcontainer/Dockerfile

@@ -0,0 +1,2 @@
+FROM python
+WORKDIR ~/

.devcontainer/devcontainer.json

@@ -0,0 +1,26 @@
+{
+    "name": "TRA Analysis Development Environment",
+    "build": {
+		"dockerfile": "Dockerfile",
+    },
+    "settings": { 
+		"terminal.integrated.shell.linux": "/bin/bash",
+		"python.pythonPath": "/usr/local/bin/python",
+		"python.linting.enabled": true,
+		"python.linting.pylintEnabled": true,
+		"python.formatting.autopep8Path": "/usr/local/py-utils/bin/autopep8",
+		"python.formatting.blackPath": "/usr/local/py-utils/bin/black",
+		"python.formatting.yapfPath": "/usr/local/py-utils/bin/yapf",
+		"python.linting.banditPath": "/usr/local/py-utils/bin/bandit",
+		"python.linting.flake8Path": "/usr/local/py-utils/bin/flake8",
+		"python.linting.mypyPath": "/usr/local/py-utils/bin/mypy",
+		"python.linting.pycodestylePath": "/usr/local/py-utils/bin/pycodestyle",
+		"python.linting.pydocstylePath": "/usr/local/py-utils/bin/pydocstyle",
+		"python.linting.pylintPath": "/usr/local/py-utils/bin/pylint",
+		"python.testing.pytestPath": "/usr/local/py-utils/bin/pytest"
+	},
+	"extensions": [
+		"mhutchie.git-graph",
+	],
+	"postCreateCommand": "pip install -r analysis-master/analysis-amd64/requirements.txt"
+}

.gitignore

@@ -19,3 +19,6 @@ data analysis/keys.txt
 data analysis/check_for_new_matches.ipynb
 data analysis/test.ipynb
 data analysis/visualize_pit.ipynb
+data analysis/config/keys.config
+analysis-master/analysis/__pycache__/
+data analysis/__pycache__/

analysis-master/analysis-amd64/analysis.egg-info/PKG-INFO

@@ -1,6 +1,6 @@
 Metadata-Version: 2.1
 Name: analysis
-Version: 1.0.0.8
+Version: 1.0.0.11
 Summary: analysis package developed by Titan Scouting for The Red Alliance
 Home-page: https://github.com/titanscout2022/tr2022-strategy
 Author: The Titan Scouting Team

analysis-master/analysis-amd64/analysis.egg-info/SOURCES.txt

@@ -1,6 +1,7 @@
 setup.py
 analysis/__init__.py
 analysis/analysis.py
+analysis/glicko2.py
 analysis/regression.py
 analysis/titanlearn.py
 analysis/trueskill.py

analysis-master/analysis-amd64/analysis/analysis.py

@@ -7,10 +7,16 @@
 #    current benchmark of optimization: 1.33 times faster
 # setup:
 
-__version__ = "1.1.13.006"
+__version__ = "1.1.13.009"
 
 # changelog should be viewed using print(analysis.__changelog__)
 __changelog__ = """changelog:
+    1.1.13.009:
+        - moved elo, glicko2, trueskill functions under class Metrics
+    1.1.13.008:
+        - moved Glicko2 to a seperate package
+    1.1.13.007:
+        - fixed bug with trueskill
     1.1.13.006:
         - cleaned up imports
     1.1.13.005:
@@ -269,7 +275,6 @@ __all__ = [
     'SVM',
     'random_forest_classifier',
     'random_forest_regressor',
-    'Glicko2',
     # all statistics functions left out due to integration in other functions
 ]
 
@@ -278,6 +283,7 @@ __all__ = [
 # imports (now in alphabetical order! v 1.0.3.006):
 
 import csv
+from analysis import glicko2 as Glicko2
 import numba
 from numba import jit
 import numpy as np
@@ -442,32 +448,34 @@ def regression(inputs, outputs, args): # inputs, outputs expects N-D array
 
     return regressions
 
-def elo(starting_score, opposing_score, observed, N, K):
+class Metrics:
 
-    expected = 1/(1+10**((np.array(opposing_score) - starting_score)/N))
+    def elo(starting_score, opposing_score, observed, N, K):
 
-    return starting_score + K*(np.sum(observed) - np.sum(expected))
+        expected = 1/(1+10**((np.array(opposing_score) - starting_score)/N))
 
-def glicko2(starting_score, starting_rd, starting_vol, opposing_score, opposing_rd, observations):
+        return starting_score + K*(np.sum(observed) - np.sum(expected))
 
-    player = Glicko2(rating = starting_score, rd = starting_rd, vol = starting_vol)
+    def glicko2(starting_score, starting_rd, starting_vol, opposing_score, opposing_rd, observations):
 
-    player.update_player([x for x in opposing_score], [x for x in opposing_rd], observations)
+        player = Glicko2.Glicko2(rating = starting_score, rd = starting_rd, vol = starting_vol)
 
-    return (player.rating, player.rd, player.vol)
+        player.update_player([x for x in opposing_score], [x for x in opposing_rd], observations)
 
-def trueskill(teams_data, observations): # teams_data is array of array of tuples ie. [[(mu, sigma), (mu, sigma), (mu, sigma)], [(mu, sigma), (mu, sigma), (mu, sigma)]]
+        return (player.rating, player.rd, player.vol)
 
-    team_ratings = []
+    def trueskill(teams_data, observations): # teams_data is array of array of tuples ie. [[(mu, sigma), (mu, sigma), (mu, sigma)], [(mu, sigma), (mu, sigma), (mu, sigma)]]
 
-    for team in teams_data:
+        team_ratings = []
-        team_temp = []
-        for player in team:
-            player = Trueskill.Rating(player[0], player[1])
-            team_temp.append(player)
-        team_ratings.append(team_temp)
 
-    return Trueskill.rate(teams_data, observations)
+        for team in teams_data:
+            team_temp = ()
+            for player in team:
+                player = Trueskill.Rating(player[0], player[1])
+                team_temp = team_temp + (player,)
+            team_ratings.append(team_temp)
+
+        return Trueskill.rate(team_ratings, ranks=observations)
 
 class RegressionMetrics():
 
@@ -559,24 +567,25 @@ def decisiontree(data, labels, test_size = 0.3, criterion = "gini", splitter = "
 
     return model, metrics
 
-@jit(forceobj=True)
+class KNN:
-def knn_classifier(data, labels, test_size = 0.3, algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=None, n_neighbors=5, p=2, weights='uniform'): #expects *2d data and 1d labels post-scaling
 
-    data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1)
+    def knn_classifier(data, labels, test_size = 0.3, algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=None, n_neighbors=5, p=2, weights='uniform'): #expects *2d data and 1d labels post-scaling
-    model = sklearn.neighbors.KNeighborsClassifier()
-    model.fit(data_train, labels_train)
-    predictions = model.predict(data_test)
 
-    return model, ClassificationMetrics(predictions, labels_test)
+        data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1)
+        model = sklearn.neighbors.KNeighborsClassifier()
+        model.fit(data_train, labels_train)
+        predictions = model.predict(data_test)
 
-def knn_regressor(data, outputs, test_size, n_neighbors = 5, weights = "uniform", algorithm = "auto", leaf_size = 30, p = 2, metric = "minkowski", metric_params = None, n_jobs = None):
+        return model, ClassificationMetrics(predictions, labels_test)
 
-    data_train, data_test, outputs_train, outputs_test = sklearn.model_selection.train_test_split(data, outputs, test_size=test_size, random_state=1)
+    def knn_regressor(data, outputs, test_size, n_neighbors = 5, weights = "uniform", algorithm = "auto", leaf_size = 30, p = 2, metric = "minkowski", metric_params = None, n_jobs = None):
-    model = sklearn.neighbors.KNeighborsRegressor(n_neighbors = n_neighbors, weights = weights, algorithm = algorithm, leaf_size = leaf_size, p = p, metric = metric, metric_params = metric_params, n_jobs = n_jobs)
-    model.fit(data_train, outputs_train)
-    predictions = model.predict(data_test)
 
-    return model, RegressionMetrics(predictions, outputs_test)
+        data_train, data_test, outputs_train, outputs_test = sklearn.model_selection.train_test_split(data, outputs, test_size=test_size, random_state=1)
+        model = sklearn.neighbors.KNeighborsRegressor(n_neighbors = n_neighbors, weights = weights, algorithm = algorithm, leaf_size = leaf_size, p = p, metric = metric, metric_params = metric_params, n_jobs = n_jobs)
+        model.fit(data_train, outputs_train)
+        predictions = model.predict(data_test)
+
+        return model, RegressionMetrics(predictions, outputs_test)
 
 class NaiveBayes:
 
@@ -689,102 +698,3 @@ def random_forest_regressor(data, outputs, test_size, n_estimators="warn", crite
     predictions = kernel.predict(data_test)
 
     return kernel, RegressionMetrics(predictions, outputs_test)
-
-class Glicko2:
-
-    _tau = 0.5
-
-    def getRating(self):
-        return (self.__rating * 173.7178) + 1500 
-
-    def setRating(self, rating):
-        self.__rating = (rating - 1500) / 173.7178
-
-    rating = property(getRating, setRating)
-
-    def getRd(self):
-        return self.__rd * 173.7178
-
-    def setRd(self, rd):
-        self.__rd = rd / 173.7178
-
-    rd = property(getRd, setRd)
-     
-    def __init__(self, rating = 1500, rd = 350, vol = 0.06):
-
-        self.setRating(rating)
-        self.setRd(rd)
-        self.vol = vol
-            
-    def _preRatingRD(self):
-
-        self.__rd = math.sqrt(math.pow(self.__rd, 2) + math.pow(self.vol, 2))
-        
-    def update_player(self, rating_list, RD_list, outcome_list):
-
-        rating_list = [(x - 1500) / 173.7178 for x in rating_list]
-        RD_list = [x / 173.7178 for x in RD_list]
-
-        v = self._v(rating_list, RD_list)
-        self.vol = self._newVol(rating_list, RD_list, outcome_list, v)
-        self._preRatingRD()
-        
-        self.__rd = 1 / math.sqrt((1 / math.pow(self.__rd, 2)) + (1 / v))
-        
-        tempSum = 0
-        for i in range(len(rating_list)):
-            tempSum += self._g(RD_list[i]) * \
-                       (outcome_list[i] - self._E(rating_list[i], RD_list[i]))
-        self.__rating += math.pow(self.__rd, 2) * tempSum
-        
-        
-    def _newVol(self, rating_list, RD_list, outcome_list, v):
-
-        i = 0
-        delta = self._delta(rating_list, RD_list, outcome_list, v)
-        a = math.log(math.pow(self.vol, 2))
-        tau = self._tau
-        x0 = a
-        x1 = 0
-        
-        while x0 != x1:
-            # New iteration, so x(i) becomes x(i-1)
-            x0 = x1
-            d = math.pow(self.__rating, 2) + v + math.exp(x0)
-            h1 = -(x0 - a) / math.pow(tau, 2) - 0.5 * math.exp(x0) \
-            / d + 0.5 * math.exp(x0) * math.pow(delta / d, 2)
-            h2 = -1 / math.pow(tau, 2) - 0.5 * math.exp(x0) * \
-            (math.pow(self.__rating, 2) + v) \
-            / math.pow(d, 2) + 0.5 * math.pow(delta, 2) * math.exp(x0) \
-            * (math.pow(self.__rating, 2) + v - math.exp(x0)) / math.pow(d, 3)
-            x1 = x0 - (h1 / h2)
-
-        return math.exp(x1 / 2)
-        
-    def _delta(self, rating_list, RD_list, outcome_list, v):
-
-        tempSum = 0
-        for i in range(len(rating_list)):
-            tempSum += self._g(RD_list[i]) * (outcome_list[i] - self._E(rating_list[i], RD_list[i]))
-        return v * tempSum
-        
-    def _v(self, rating_list, RD_list):
-
-        tempSum = 0
-        for i in range(len(rating_list)):
-            tempE = self._E(rating_list[i], RD_list[i])
-            tempSum += math.pow(self._g(RD_list[i]), 2) * tempE * (1 - tempE)
-        return 1 / tempSum
-        
-    def _E(self, p2rating, p2RD):
-
-        return 1 / (1 + math.exp(-1 * self._g(p2RD) * \
-                                 (self.__rating - p2rating)))
-        
-    def _g(self, RD):
-
-        return 1 / math.sqrt(1 + 3 * math.pow(RD, 2) / math.pow(math.pi, 2))
-        
-    def did_not_compete(self):
-
-        self._preRatingRD()

analysis-master/analysis-amd64/analysis/glicko2.py

@@ -0,0 +1,99 @@
+import math
+
+class Glicko2:
+    _tau = 0.5
+
+    def getRating(self):
+        return (self.__rating * 173.7178) + 1500 
+
+    def setRating(self, rating):
+        self.__rating = (rating - 1500) / 173.7178
+
+    rating = property(getRating, setRating)
+
+    def getRd(self):
+        return self.__rd * 173.7178
+
+    def setRd(self, rd):
+        self.__rd = rd / 173.7178
+
+    rd = property(getRd, setRd)
+        
+    def __init__(self, rating = 1500, rd = 350, vol = 0.06):
+
+        self.setRating(rating)
+        self.setRd(rd)
+        self.vol = vol
+            
+    def _preRatingRD(self):
+
+        self.__rd = math.sqrt(math.pow(self.__rd, 2) + math.pow(self.vol, 2))
+        
+    def update_player(self, rating_list, RD_list, outcome_list):
+
+        rating_list = [(x - 1500) / 173.7178 for x in rating_list]
+        RD_list = [x / 173.7178 for x in RD_list]
+
+        v = self._v(rating_list, RD_list)
+        self.vol = self._newVol(rating_list, RD_list, outcome_list, v)
+        self._preRatingRD()
+        
+        self.__rd = 1 / math.sqrt((1 / math.pow(self.__rd, 2)) + (1 / v))
+        
+        tempSum = 0
+        for i in range(len(rating_list)):
+            tempSum += self._g(RD_list[i]) * \
+                        (outcome_list[i] - self._E(rating_list[i], RD_list[i]))
+        self.__rating += math.pow(self.__rd, 2) * tempSum
+        
+        
+    def _newVol(self, rating_list, RD_list, outcome_list, v):
+
+        i = 0
+        delta = self._delta(rating_list, RD_list, outcome_list, v)
+        a = math.log(math.pow(self.vol, 2))
+        tau = self._tau
+        x0 = a
+        x1 = 0
+        
+        while x0 != x1:
+            # New iteration, so x(i) becomes x(i-1)
+            x0 = x1
+            d = math.pow(self.__rating, 2) + v + math.exp(x0)
+            h1 = -(x0 - a) / math.pow(tau, 2) - 0.5 * math.exp(x0) \
+            / d + 0.5 * math.exp(x0) * math.pow(delta / d, 2)
+            h2 = -1 / math.pow(tau, 2) - 0.5 * math.exp(x0) * \
+            (math.pow(self.__rating, 2) + v) \
+            / math.pow(d, 2) + 0.5 * math.pow(delta, 2) * math.exp(x0) \
+            * (math.pow(self.__rating, 2) + v - math.exp(x0)) / math.pow(d, 3)
+            x1 = x0 - (h1 / h2)
+
+        return math.exp(x1 / 2)
+        
+    def _delta(self, rating_list, RD_list, outcome_list, v):
+
+        tempSum = 0
+        for i in range(len(rating_list)):
+            tempSum += self._g(RD_list[i]) * (outcome_list[i] - self._E(rating_list[i], RD_list[i]))
+        return v * tempSum
+        
+    def _v(self, rating_list, RD_list):
+
+        tempSum = 0
+        for i in range(len(rating_list)):
+            tempE = self._E(rating_list[i], RD_list[i])
+            tempSum += math.pow(self._g(RD_list[i]), 2) * tempE * (1 - tempE)
+        return 1 / tempSum
+        
+    def _E(self, p2rating, p2RD):
+
+        return 1 / (1 + math.exp(-1 * self._g(p2RD) * \
+                                    (self.__rating - p2rating)))
+        
+    def _g(self, RD):
+
+        return 1 / math.sqrt(1 + 3 * math.pow(RD, 2) / math.pow(math.pi, 2))
+        
+    def did_not_compete(self):
+
+        self._preRatingRD()

analysis-master/analysis-amd64/build.sh

@@ -0,0 +1 @@
+python setup.py sdist bdist_wheel || python3 setup.py sdist bdist_wheel

analysis-master/analysis-amd64/build/lib/analysis/analysis.py

@@ -7,10 +7,16 @@
 #    current benchmark of optimization: 1.33 times faster
 # setup:
 
-__version__ = "1.1.13.006"
+__version__ = "1.1.13.009"
 
 # changelog should be viewed using print(analysis.__changelog__)
 __changelog__ = """changelog:
+    1.1.13.009:
+        - moved elo, glicko2, trueskill functions under class Metrics
+    1.1.13.008:
+        - moved Glicko2 to a seperate package
+    1.1.13.007:
+        - fixed bug with trueskill
     1.1.13.006:
         - cleaned up imports
     1.1.13.005:
@@ -269,7 +275,6 @@ __all__ = [
     'SVM',
     'random_forest_classifier',
     'random_forest_regressor',
-    'Glicko2',
     # all statistics functions left out due to integration in other functions
 ]
 
@@ -278,6 +283,7 @@ __all__ = [
 # imports (now in alphabetical order! v 1.0.3.006):
 
 import csv
+from analysis import glicko2 as Glicko2
 import numba
 from numba import jit
 import numpy as np
@@ -442,32 +448,34 @@ def regression(inputs, outputs, args): # inputs, outputs expects N-D array
 
     return regressions
 
-def elo(starting_score, opposing_score, observed, N, K):
+class Metrics:
 
-    expected = 1/(1+10**((np.array(opposing_score) - starting_score)/N))
+    def elo(starting_score, opposing_score, observed, N, K):
 
-    return starting_score + K*(np.sum(observed) - np.sum(expected))
+        expected = 1/(1+10**((np.array(opposing_score) - starting_score)/N))
 
-def glicko2(starting_score, starting_rd, starting_vol, opposing_score, opposing_rd, observations):
+        return starting_score + K*(np.sum(observed) - np.sum(expected))
 
-    player = Glicko2(rating = starting_score, rd = starting_rd, vol = starting_vol)
+    def glicko2(starting_score, starting_rd, starting_vol, opposing_score, opposing_rd, observations):
 
-    player.update_player([x for x in opposing_score], [x for x in opposing_rd], observations)
+        player = Glicko2.Glicko2(rating = starting_score, rd = starting_rd, vol = starting_vol)
 
-    return (player.rating, player.rd, player.vol)
+        player.update_player([x for x in opposing_score], [x for x in opposing_rd], observations)
 
-def trueskill(teams_data, observations): # teams_data is array of array of tuples ie. [[(mu, sigma), (mu, sigma), (mu, sigma)], [(mu, sigma), (mu, sigma), (mu, sigma)]]
+        return (player.rating, player.rd, player.vol)
 
-    team_ratings = []
+    def trueskill(teams_data, observations): # teams_data is array of array of tuples ie. [[(mu, sigma), (mu, sigma), (mu, sigma)], [(mu, sigma), (mu, sigma), (mu, sigma)]]
 
-    for team in teams_data:
+        team_ratings = []
-        team_temp = []
-        for player in team:
-            player = Trueskill.Rating(player[0], player[1])
-            team_temp.append(player)
-        team_ratings.append(team_temp)
 
-    return Trueskill.rate(teams_data, observations)
+        for team in teams_data:
+            team_temp = ()
+            for player in team:
+                player = Trueskill.Rating(player[0], player[1])
+                team_temp = team_temp + (player,)
+            team_ratings.append(team_temp)
+
+        return Trueskill.rate(team_ratings, ranks=observations)
 
 class RegressionMetrics():
 
@@ -559,24 +567,25 @@ def decisiontree(data, labels, test_size = 0.3, criterion = "gini", splitter = "
 
     return model, metrics
 
-@jit(forceobj=True)
+class KNN:
-def knn_classifier(data, labels, test_size = 0.3, algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=None, n_neighbors=5, p=2, weights='uniform'): #expects *2d data and 1d labels post-scaling
 
-    data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1)
+    def knn_classifier(data, labels, test_size = 0.3, algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=None, n_neighbors=5, p=2, weights='uniform'): #expects *2d data and 1d labels post-scaling
-    model = sklearn.neighbors.KNeighborsClassifier()
-    model.fit(data_train, labels_train)
-    predictions = model.predict(data_test)
 
-    return model, ClassificationMetrics(predictions, labels_test)
+        data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1)
+        model = sklearn.neighbors.KNeighborsClassifier()
+        model.fit(data_train, labels_train)
+        predictions = model.predict(data_test)
 
-def knn_regressor(data, outputs, test_size, n_neighbors = 5, weights = "uniform", algorithm = "auto", leaf_size = 30, p = 2, metric = "minkowski", metric_params = None, n_jobs = None):
+        return model, ClassificationMetrics(predictions, labels_test)
 
-    data_train, data_test, outputs_train, outputs_test = sklearn.model_selection.train_test_split(data, outputs, test_size=test_size, random_state=1)
+    def knn_regressor(data, outputs, test_size, n_neighbors = 5, weights = "uniform", algorithm = "auto", leaf_size = 30, p = 2, metric = "minkowski", metric_params = None, n_jobs = None):
-    model = sklearn.neighbors.KNeighborsRegressor(n_neighbors = n_neighbors, weights = weights, algorithm = algorithm, leaf_size = leaf_size, p = p, metric = metric, metric_params = metric_params, n_jobs = n_jobs)
-    model.fit(data_train, outputs_train)
-    predictions = model.predict(data_test)
 
-    return model, RegressionMetrics(predictions, outputs_test)
+        data_train, data_test, outputs_train, outputs_test = sklearn.model_selection.train_test_split(data, outputs, test_size=test_size, random_state=1)
+        model = sklearn.neighbors.KNeighborsRegressor(n_neighbors = n_neighbors, weights = weights, algorithm = algorithm, leaf_size = leaf_size, p = p, metric = metric, metric_params = metric_params, n_jobs = n_jobs)
+        model.fit(data_train, outputs_train)
+        predictions = model.predict(data_test)
+
+        return model, RegressionMetrics(predictions, outputs_test)
 
 class NaiveBayes:
 
@@ -689,102 +698,3 @@ def random_forest_regressor(data, outputs, test_size, n_estimators="warn", crite
     predictions = kernel.predict(data_test)
 
     return kernel, RegressionMetrics(predictions, outputs_test)
-
-class Glicko2:
-
-    _tau = 0.5
-
-    def getRating(self):
-        return (self.__rating * 173.7178) + 1500 
-
-    def setRating(self, rating):
-        self.__rating = (rating - 1500) / 173.7178
-
-    rating = property(getRating, setRating)
-
-    def getRd(self):
-        return self.__rd * 173.7178
-
-    def setRd(self, rd):
-        self.__rd = rd / 173.7178
-
-    rd = property(getRd, setRd)
-     
-    def __init__(self, rating = 1500, rd = 350, vol = 0.06):
-
-        self.setRating(rating)
-        self.setRd(rd)
-        self.vol = vol
-            
-    def _preRatingRD(self):
-
-        self.__rd = math.sqrt(math.pow(self.__rd, 2) + math.pow(self.vol, 2))
-        
-    def update_player(self, rating_list, RD_list, outcome_list):
-
-        rating_list = [(x - 1500) / 173.7178 for x in rating_list]
-        RD_list = [x / 173.7178 for x in RD_list]
-
-        v = self._v(rating_list, RD_list)
-        self.vol = self._newVol(rating_list, RD_list, outcome_list, v)
-        self._preRatingRD()
-        
-        self.__rd = 1 / math.sqrt((1 / math.pow(self.__rd, 2)) + (1 / v))
-        
-        tempSum = 0
-        for i in range(len(rating_list)):
-            tempSum += self._g(RD_list[i]) * \
-                       (outcome_list[i] - self._E(rating_list[i], RD_list[i]))
-        self.__rating += math.pow(self.__rd, 2) * tempSum
-        
-        
-    def _newVol(self, rating_list, RD_list, outcome_list, v):
-
-        i = 0
-        delta = self._delta(rating_list, RD_list, outcome_list, v)
-        a = math.log(math.pow(self.vol, 2))
-        tau = self._tau
-        x0 = a
-        x1 = 0
-        
-        while x0 != x1:
-            # New iteration, so x(i) becomes x(i-1)
-            x0 = x1
-            d = math.pow(self.__rating, 2) + v + math.exp(x0)
-            h1 = -(x0 - a) / math.pow(tau, 2) - 0.5 * math.exp(x0) \
-            / d + 0.5 * math.exp(x0) * math.pow(delta / d, 2)
-            h2 = -1 / math.pow(tau, 2) - 0.5 * math.exp(x0) * \
-            (math.pow(self.__rating, 2) + v) \
-            / math.pow(d, 2) + 0.5 * math.pow(delta, 2) * math.exp(x0) \
-            * (math.pow(self.__rating, 2) + v - math.exp(x0)) / math.pow(d, 3)
-            x1 = x0 - (h1 / h2)
-
-        return math.exp(x1 / 2)
-        
-    def _delta(self, rating_list, RD_list, outcome_list, v):
-
-        tempSum = 0
-        for i in range(len(rating_list)):
-            tempSum += self._g(RD_list[i]) * (outcome_list[i] - self._E(rating_list[i], RD_list[i]))
-        return v * tempSum
-        
-    def _v(self, rating_list, RD_list):
-
-        tempSum = 0
-        for i in range(len(rating_list)):
-            tempE = self._E(rating_list[i], RD_list[i])
-            tempSum += math.pow(self._g(RD_list[i]), 2) * tempE * (1 - tempE)
-        return 1 / tempSum
-        
-    def _E(self, p2rating, p2RD):
-
-        return 1 / (1 + math.exp(-1 * self._g(p2RD) * \
-                                 (self.__rating - p2rating)))
-        
-    def _g(self, RD):
-
-        return 1 / math.sqrt(1 + 3 * math.pow(RD, 2) / math.pow(math.pi, 2))
-        
-    def did_not_compete(self):
-
-        self._preRatingRD()

analysis-master/analysis-amd64/build/lib/analysis/glicko2.py

@@ -0,0 +1,99 @@
+import math
+
+class Glicko2:
+    _tau = 0.5
+
+    def getRating(self):
+        return (self.__rating * 173.7178) + 1500 
+
+    def setRating(self, rating):
+        self.__rating = (rating - 1500) / 173.7178
+
+    rating = property(getRating, setRating)
+
+    def getRd(self):
+        return self.__rd * 173.7178
+
+    def setRd(self, rd):
+        self.__rd = rd / 173.7178
+
+    rd = property(getRd, setRd)
+        
+    def __init__(self, rating = 1500, rd = 350, vol = 0.06):
+
+        self.setRating(rating)
+        self.setRd(rd)
+        self.vol = vol
+            
+    def _preRatingRD(self):
+
+        self.__rd = math.sqrt(math.pow(self.__rd, 2) + math.pow(self.vol, 2))
+        
+    def update_player(self, rating_list, RD_list, outcome_list):
+
+        rating_list = [(x - 1500) / 173.7178 for x in rating_list]
+        RD_list = [x / 173.7178 for x in RD_list]
+
+        v = self._v(rating_list, RD_list)
+        self.vol = self._newVol(rating_list, RD_list, outcome_list, v)
+        self._preRatingRD()
+        
+        self.__rd = 1 / math.sqrt((1 / math.pow(self.__rd, 2)) + (1 / v))
+        
+        tempSum = 0
+        for i in range(len(rating_list)):
+            tempSum += self._g(RD_list[i]) * \
+                        (outcome_list[i] - self._E(rating_list[i], RD_list[i]))
+        self.__rating += math.pow(self.__rd, 2) * tempSum
+        
+        
+    def _newVol(self, rating_list, RD_list, outcome_list, v):
+
+        i = 0
+        delta = self._delta(rating_list, RD_list, outcome_list, v)
+        a = math.log(math.pow(self.vol, 2))
+        tau = self._tau
+        x0 = a
+        x1 = 0
+        
+        while x0 != x1:
+            # New iteration, so x(i) becomes x(i-1)
+            x0 = x1
+            d = math.pow(self.__rating, 2) + v + math.exp(x0)
+            h1 = -(x0 - a) / math.pow(tau, 2) - 0.5 * math.exp(x0) \
+            / d + 0.5 * math.exp(x0) * math.pow(delta / d, 2)
+            h2 = -1 / math.pow(tau, 2) - 0.5 * math.exp(x0) * \
+            (math.pow(self.__rating, 2) + v) \
+            / math.pow(d, 2) + 0.5 * math.pow(delta, 2) * math.exp(x0) \
+            * (math.pow(self.__rating, 2) + v - math.exp(x0)) / math.pow(d, 3)
+            x1 = x0 - (h1 / h2)
+
+        return math.exp(x1 / 2)
+        
+    def _delta(self, rating_list, RD_list, outcome_list, v):
+
+        tempSum = 0
+        for i in range(len(rating_list)):
+            tempSum += self._g(RD_list[i]) * (outcome_list[i] - self._E(rating_list[i], RD_list[i]))
+        return v * tempSum
+        
+    def _v(self, rating_list, RD_list):
+
+        tempSum = 0
+        for i in range(len(rating_list)):
+            tempE = self._E(rating_list[i], RD_list[i])
+            tempSum += math.pow(self._g(RD_list[i]), 2) * tempE * (1 - tempE)
+        return 1 / tempSum
+        
+    def _E(self, p2rating, p2RD):
+
+        return 1 / (1 + math.exp(-1 * self._g(p2RD) * \
+                                    (self.__rating - p2rating)))
+        
+    def _g(self, RD):
+
+        return 1 / math.sqrt(1 + 3 * math.pow(RD, 2) / math.pow(math.pi, 2))
+        
+    def did_not_compete(self):
+
+        self._preRatingRD()

analysis-master/analysis-amd64/docker/Dockerfile

@@ -0,0 +1,5 @@
+FROM python
+WORKDIR ~/
+COPY ./ ./
+RUN pip install -r requirements.txt
+CMD ["bash"]

analysis-master/analysis-amd64/docker/start-docker.sh

@@ -0,0 +1,3 @@
+cd ..
+docker build -t tra-analysis-amd64-dev -f docker/Dockerfile .
+docker run -it tra-analysis-amd64-dev

analysis-master/analysis-amd64/requirements.txt

@@ -0,0 +1,6 @@
+numba
+numpy
+scipy
+scikit-learn
+six
+matplotlib

analysis-master/analysis-amd64/setup.py

@@ -1,8 +1,14 @@
 import setuptools
 
+requirements = []
+
+with open("requirements.txt", 'r') as file:
+    for line in file:
+        requirements.append(line)
+
 setuptools.setup(
-    name="analysis", # Replace with your own username
+    name="analysis",
-    version="1.0.0.008",
+    version="1.0.0.011",
     author="The Titan Scouting Team",
     author_email="titanscout2022@gmail.com",
     description="analysis package developed by Titan Scouting for The Red Alliance",
@@ -10,14 +16,7 @@ setuptools.setup(
     long_description_content_type="text/markdown",
     url="https://github.com/titanscout2022/tr2022-strategy",
     packages=setuptools.find_packages(),
-    install_requires=[
+    install_requires=requirements,
-        "numba",
-        "numpy",
-        "scipy",
-        "scikit-learn",
-        "six",
-        "matplotlib"
-    ],
     license = "GNU General Public License v3.0",
     classifiers=[
         "Programming Language :: Python :: 3",

analysis-master/analysis-arm64/docker/start-docker.sh

@@ -0,0 +1,3 @@
+cd ..
+docker build -t tra-analysis-amd64-dev -f docker/Dockerfile .
+docker run -it tra-analysis-amd64-dev

analysis-master/build.sh

@@ -1 +0,0 @@
-python3 setup.py sdist bdist_wheel

data analysis/config/competition.config

@@ -0,0 +1 @@
+2020ilch

data analysis/config/stats.config

@@ -1,4 +1,3 @@
-2020ilch
 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

data analysis/requirements.txt

@@ -0,0 +1,4 @@
+requests
+pymongo
+pandas
+dnspython

data analysis/superscript.py

@@ -3,10 +3,17 @@
 # Notes:
 # setup:
 
-__version__ = "0.0.4.002"
+__version__ = "0.0.5.002"
 
 # changelog should be viewed using print(analysis.__changelog__)
 __changelog__ = """changelog:
+    0.0.5.002:
+        - made changes due to refactoring of analysis
+    0.0.5.001:
+        - text fixes
+        - removed matplotlib requirement
+    0.0.5.000:
+        - improved user interface
     0.0.4.002:
         - removed unessasary code
     0.0.4.001:
@@ -82,7 +89,8 @@ __all__ = [
 from analysis import analysis as an
 import data as d
 import numpy as np
-import matplotlib.pyplot as plt
+from os import system, name
+from pathlib import Path
 import time
 import warnings
 
@@ -91,16 +99,16 @@ def main():
     while(True):
 
         current_time = time.time()
-        print("time: " + str(current_time))
+        print("[OK] time: " + str(current_time))
 
-        print(" loading config")
+        start = time.time()
-        competition, config = load_config("config.csv")
+        config = load_config(Path("config/stats.config"))
-        print(" config loaded")
+        competition = an.load_csv(Path("config/competition.config"))[0][0]
+        print("[OK] configs loaded")
 
-        print(" loading database keys")
+        apikey = an.load_csv(Path("config/keys.config"))[0][0]
-        apikey = an.load_csv("keys.txt")[0][0]
+        tbakey = an.load_csv(Path("config/keys.config"))[1][0]
-        tbakey = an.load_csv("keys.txt")[1][0]
+        print("[OK] loaded keys")
-        print(" loaded keys")
 
         previous_time = d.get_analysis_flags(apikey, "latest_update")
 
@@ -113,38 +121,55 @@ def main():
 
             previous_time = previous_time["latest_update"]
 
-        print(" analysis backtimed to: " + str(previous_time))
+        print("[OK] analysis backtimed to: " + str(previous_time))
 
-        print(" loading data")
+        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(" loaded data")
+        print("[OK] loaded data in " + str(time.time() - start) + " seconds")
 
-        print(" running tests")
+        print("[OK] running tests")
+        start = time.time()
         results = simpleloop(data, config)
-        print(" finished tests")
+        print("[OK] finished tests in " + str(time.time() - start) + " seconds")
 
-        print(" running metrics")
+        print("[OK] running metrics")
+        start = time.time()
         metricsloop(tbakey, apikey, competition, previous_time)
-        print(" finished metrics")
+        print("[OK] finished metrics in " + str(time.time() - start) + " seconds")
 
-        print(" running pit analysis")
+        print("[OK] running pit analysis")
+        start = time.time()
         pit = pitloop(pit_data, config)
-        print(" finished pit analysis")
+        print("[OK] finished pit analysis in " + str(time.time() - start) + " seconds")
 
         d.set_analysis_flags(apikey, "latest_update", {"latest_update":current_time})
         
-        print(" pushing to database")
+        print("[OK] pushing to database")
+        start = time.time()
         push_to_database(apikey, competition, results, pit)
-        print(" pushed to database")
+        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[1:]:
+    for line in file:
         config_vector[line[0]] = line[1:]
 
-    return (file[0][0], config_vector)
+    return config_vector
 
 def simpleloop(data, tests): # expects 3D array with [Team][Variable][Match]
 
@@ -263,11 +288,11 @@ def metricsloop(tbakey, apikey, competition, timestamp): # listener based metric
 
             observations = {"red": 0.5, "blu": 0.5}
 
-        red_elo_delta = an.elo(red_elo["score"], blu_elo["score"], observations["red"], elo_N, elo_K) - red_elo["score"]
+        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.elo(blu_elo["score"], red_elo["score"], observations["blu"], elo_N, elo_K) - blu_elo["score"]
+        blu_elo_delta = an.Metrics.elo(blu_elo["score"], red_elo["score"], observations["blu"], elo_N, elo_K) - blu_elo["score"]
 
-        new_red_gl2_score, new_red_gl2_rd, new_red_gl2_vol = an.glicko2(red_gl2["score"], red_gl2["rd"], red_gl2["vol"], [blu_gl2["score"]], [blu_gl2["rd"]], [observations["red"], observations["blu"]])
+        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.glicko2(blu_gl2["score"], blu_gl2["rd"], blu_gl2["vol"], [red_gl2["score"]], [red_gl2["rd"]], [observations["blu"], observations["red"]])
+        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"]])
 
         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"]}