analysis.py v 1.1.12.005

analysis pkg v 1.0.0.002

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

@@ -1,6 +1,6 @@
 Metadata-Version: 2.1
 Name: analysis
-Version: 1.0.0.1
+Version: 1.0.0.2
 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/analysis.py

@@ -7,10 +7,12 @@
 #    current benchmark of optimization: 1.33 times faster
 # setup:
 
-__version__ = "1.1.12.004"
+__version__ = "1.1.12.005"
 
 # changelog should be viewed using print(analysis.__changelog__)
 __changelog__ = """changelog:
+    1.1.12.005:
+        - fixed numba issues by removing numba  from elo, glicko2 and trueskill
     1.1.12.004:
         - renamed gliko to glicko
     1.1.12.003:
@@ -384,14 +386,12 @@ def regression(ndevice, inputs, outputs, args, loss = torch.nn.MSELoss(), _itera
 
     return regressions
 
-@jit(nopython=True)
 def elo(starting_score, opposing_score, observed, N, K):
 
     expected = 1/(1+10**((np.array(opposing_score) - starting_score)/N))
 
     return starting_score + K*(np.sum(observed) - np.sum(expected))
 
-@jit(forceobj=True)
 def glicko2(starting_score, starting_rd, starting_vol, opposing_score, opposing_rd, observations):
 
     player = Glicko2(rating = starting_score, rd = starting_rd, vol = starting_vol)
@@ -400,7 +400,6 @@ def glicko2(starting_score, starting_rd, starting_vol, opposing_score, opposing_
 
     return (player.rating, player.rd, player.vol)
 
-@jit(forceobj=True)
 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)]]
 
     team_ratings = []

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

@@ -7,10 +7,12 @@
 #    current benchmark of optimization: 1.33 times faster
 # setup:
 
-__version__ = "1.1.12.004"
+__version__ = "1.1.12.005"
 
 # changelog should be viewed using print(analysis.__changelog__)
 __changelog__ = """changelog:
+    1.1.12.005:
+        - fixed numba issues by removing numba  from elo, glicko2 and trueskill
     1.1.12.004:
         - renamed gliko to glicko
     1.1.12.003:
@@ -384,14 +386,12 @@ def regression(ndevice, inputs, outputs, args, loss = torch.nn.MSELoss(), _itera
 
     return regressions
 
-@jit(nopython=True)
 def elo(starting_score, opposing_score, observed, N, K):
 
     expected = 1/(1+10**((np.array(opposing_score) - starting_score)/N))
 
     return starting_score + K*(np.sum(observed) - np.sum(expected))
 
-@jit(forceobj=True)
 def glicko2(starting_score, starting_rd, starting_vol, opposing_score, opposing_rd, observations):
 
     player = Glicko2(rating = starting_score, rd = starting_rd, vol = starting_vol)
@@ -400,7 +400,6 @@ def glicko2(starting_score, starting_rd, starting_vol, opposing_score, opposing_
 
     return (player.rating, player.rd, player.vol)
 
-@jit(forceobj=True)
 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)]]
 
     team_ratings = []

analysis-master/setup.py

@@ -2,7 +2,7 @@ import setuptools
 
 setuptools.setup(
     name="analysis", # Replace with your own username
-    version="1.0.0.001",
+    version="1.0.0.002",
     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

@@ -158,24 +158,96 @@ def push_to_database(apikey, competition, results, metrics):
 
 def metricsloop(tbakey, apikey, competition, timestamp): # listener based metrics update
 
+    elo_N = 400
+    elo_K = 24
+
     matches = d.pull_new_tba_matches(tbakey, competition, timestamp)
 
-    red = load_metrics(apikey, competition, matches, "red")
+    return_vector = {}
-    blu = load_metrics(apikey, competition, matches, "blue")
+
+    for match in matches:
+
+        red = load_metrics(apikey, competition, match, "red")
+        blu = load_metrics(apikey, competition, match, "blue")
 
         elo_red_total = 0
         elo_blu_total = 0
 
-    gl2_red_total = 0
+        gl2_red_score_total = 0
-    gl2_blu_total + 0
+        gl2_blu_score_total = 0
+
+        gl2_red_rd_total = 0
+        gl2_blu_rd_total = 0
+
+        gl2_red_vol_total = 0
+        gl2_blu_vol_total = 0
 
         for team in red:
 
-    return
+            elo_red_total += red[team]["elo"]["score"]
 
-def load_metrics(apikey, competition, matches, group_name):
+            gl2_red_score_total += red[team]["gl2"]["score"]
+            gl2_red_rd_total += red[team]["gl2"]["rd"]
+            gl2_red_vol_total += red[team]["gl2"]["vol"]
 
-    for match in matches:
+        for team in blu:
+
+            elo_blu_total += blu[team]["elo"]["score"]
+
+            gl2_blu_score_total += blu[team]["gl2"]["score"]
+            gl2_blu_rd_total += blu[team]["gl2"]["rd"]
+            gl2_blu_vol_total += blu[team]["gl2"]["vol"]
+
+        red_elo = {"score": elo_red_total / len(red)}
+        blu_elo = {"score": elo_blu_total / len(blu)}
+
+        red_gl2 = {"score": gl2_red_score_total / len(red), "rd": gl2_red_rd_total / len(red), "vol": gl2_red_vol_total / len(red)}
+        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"):
+
+            observations = {"red": 1, "blue": 0}
+
+        elif(match["winner"] == "blue"):
+
+            observations = {"red": 0, "blue": 1}
+
+        else:
+
+            observations = {"red": 0.5, "blue": 0.5}
+
+        red_elo_delta = an.elo(red_elo["score"], blu_elo["score"], [observations["red"], observations["blue"]], elo_N, elo_K) - red_elo["score"]
+        blu_elo_delta = an.elo(blu_elo["score"], red_elo["score"], [observations["blue"], observations["red"]], 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["blue"]])
+        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["blue"], 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"]}
+
+        for team in red:
+
+            red[team]["elo"]["score"] = red[team]["elo"]["score"] + red_elo_delta
+
+            red[team]["gl2"]["score"] = red[team]["gl2"]["score"] + red_gl2_delta["score"]
+            red[team]["gl2"]["rd"] = red[team]["gl2"]["rd"] + red_gl2_delta["rd"]
+            red[team]["gl2"]["vol"] = red[team]["gl2"]["vol"] + red_gl2_delta["vol"]
+
+        for team in blu:
+
+            blu[team]["elo"]["score"] = blu[team]["elo"]["score"] + blu_elo_delta
+
+            blu[team]["gl2"]["score"] = blu[team]["gl2"]["score"] + blu_gl2_delta["score"]
+            blu[team]["gl2"]["rd"] = blu[team]["gl2"]["rd"] + blu_gl2_delta["rd"]
+            blu[team]["gl2"]["vol"] = blu[team]["gl2"]["vol"] + blu_gl2_delta["vol"]
+
+    return_vector.update(red)
+    return_vector.update(blu)
+
+    return return_vector
+
+def load_metrics(apikey, competition, match, group_name):
 
     for team in match[group_name]: