analysis pkg v 1.0.0.11

analysis.py v 1.1.13.009
superscript.py v 0.0.5.002

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

@@ -1,6 +1,6 @@
 Metadata-Version: 2.1
 Name: analysis
-Version: 1.0.0.10
+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/analysis.py

@@ -7,10 +7,12 @@
 #    current benchmark of optimization: 1.33 times faster
 # setup:
 
-__version__ = "1.1.13.008"
+__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:
@@ -446,32 +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.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_temp = ()
-        for player in team:
-            player = Trueskill.Rating(player[0], player[1])
-            team_temp = team_temp + (player,)
-        team_ratings.append(team_temp)
+        team_ratings = []
 
-    return Trueskill.rate(team_ratings, ranks=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():
 
@@ -563,24 +567,25 @@ def decisiontree(data, labels, test_size = 0.3, criterion = "gini", splitter = "
 
     return model, metrics
 
-@jit(forceobj=True)
-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
+class KNN:
 
-    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_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
 
-    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)
-    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)
+    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, 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:
 

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

@@ -7,10 +7,12 @@
 #    current benchmark of optimization: 1.33 times faster
 # setup:
 
-__version__ = "1.1.13.008"
+__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:
@@ -446,32 +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.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_temp = ()
-        for player in team:
-            player = Trueskill.Rating(player[0], player[1])
-            team_temp = team_temp + (player,)
-        team_ratings.append(team_temp)
+        team_ratings = []
 
-    return Trueskill.rate(team_ratings, ranks=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():
 
@@ -563,24 +567,25 @@ def decisiontree(data, labels, test_size = 0.3, criterion = "gini", splitter = "
 
     return model, metrics
 
-@jit(forceobj=True)
-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
+class KNN:
 
-    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_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
 
-    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)
-    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)
+    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, 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:
 

analysis-master/analysis-amd64/setup.py

@@ -8,7 +8,7 @@ with open("requirements.txt", 'r') as file:
 
 setuptools.setup(
     name="analysis",
-    version="1.0.0.010",
+    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",

data analysis/superscript.py

@@ -3,10 +3,12 @@
 # Notes:
 # setup:
 
-__version__ = "0.0.5.001"
+__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
@@ -286,11 +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"]
-        blu_elo_delta = an.elo(blu_elo["score"], red_elo["score"], observations["blu"], elo_N, elo_K) - blu_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.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_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_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"]])
 
         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"]}