worked

Testing

.gitignore

@@ -19,3 +19,4 @@ 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.egg-info/PKG-INFO

@@ -1,6 +1,6 @@
 Metadata-Version: 2.1
 Name: analysis
-Version: 1.0.0.7
+Version: 1.0.0.8
 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.egg-info/SOURCES.txt

@@ -8,4 +8,5 @@ analysis/visualization.py
 analysis.egg-info/PKG-INFO
 analysis.egg-info/SOURCES.txt
 analysis.egg-info/dependency_links.txt
+analysis.egg-info/requires.txt
 analysis.egg-info/top_level.txt

analysis-master/analysis.egg-info/requires.txt

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

analysis-master/analysis/analysis.py

@@ -7,10 +7,12 @@
 #    current benchmark of optimization: 1.33 times faster
 # setup:
 
-__version__ = "1.1.13.005"
+__version__ = "1.1.13.006"
 
 # changelog should be viewed using print(analysis.__changelog__)
 __changelog__ = """changelog:
+    1.1.13.006:
+        - cleaned up imports
     1.1.13.005:
         - cleaned up package
     1.1.13.004:
@@ -283,10 +285,7 @@ import scipy
 from scipy import *
 import sklearn
 from sklearn import *
-try:
+from analysis import trueskill as Trueskill
-    from analysis import trueskill as Trueskill
-except:
-    import trueskill as Trueskill
 
 class error(ValueError):
     pass

analysis-master/analysis/regression.py

@@ -5,19 +5,22 @@
 #   this module is cuda-optimized and vectorized (except for one small part)
 # setup:
 
-__version__ = "1.0.0.003"
+__version__ = "1.0.0.004"
 
 # changelog should be viewed using print(analysis.regression.__changelog__)
 __changelog__ = """
-1.0.0.003:
+    1.0.0.004:
-    - bug fixes
+        - bug fixes
-1.0.0.002:
+        - fixed changelog
-    -Added more parameters to log, exponential, polynomial
+    1.0.0.003:
-    -Added SigmoidalRegKernelArthur, because Arthur apparently needs
+        - bug fixes
-    to train the scaling and shifting of sigmoids
+    1.0.0.002:
-1.0.0.001:
+        -Added more parameters to log, exponential, polynomial
-    -initial release, with linear, log, exponential, polynomial, and sigmoid kernels
+        -Added SigmoidalRegKernelArthur, because Arthur apparently needs
-    -already vectorized (except for polynomial generation) and CUDA-optimized
+        to train the scaling and shifting of sigmoids
+    1.0.0.001:
+        -initial release, with linear, log, exponential, polynomial, and sigmoid kernels
+        -already vectorized (except for polynomial generation) and CUDA-optimized
 """
 
 __author__ = (
@@ -40,6 +43,8 @@ __all__ = [
     'CustomTrain'
 ]
 
+import torch
+
 global device
 
 device = "cuda:0" if torch.torch.cuda.is_available() else "cpu"

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

@@ -7,10 +7,20 @@
 #    current benchmark of optimization: 1.33 times faster
 # setup:
 
-__version__ = "1.1.13.001"
+__version__ = "1.1.13.006"
 
 # changelog should be viewed using print(analysis.__changelog__)
 __changelog__ = """changelog:
+    1.1.13.006:
+        - cleaned up imports
+    1.1.13.005:
+        - cleaned up package
+    1.1.13.004:
+        - small fixes to regression to improve performance
+    1.1.13.003:
+        - filtered nans from regression
+    1.1.13.002:
+        - removed torch requirement, and moved Regression back to regression.py
     1.1.13.001:
         - bug fix with linear regression not returning a proper value
         - cleaned up regression
@@ -239,7 +249,6 @@ __author__ = (
 )
 
 __all__ = [
-    '_init_device',
     'load_csv',
     'basic_stats',
     'z_score',
@@ -260,7 +269,6 @@ __all__ = [
     'SVM',
     'random_forest_classifier',
     'random_forest_regressor',
-    'Regression',
     'Glicko2',
     # all statistics functions left out due to integration in other functions
 ]
@@ -273,15 +281,11 @@ import csv
 import numba
 from numba import jit
 import numpy as np
-import math
 import scipy
 from scipy import *
 import sklearn
 from sklearn import *
-try:
+from analysis import trueskill as Trueskill
-    from analysis import trueskill as Trueskill
-except:
-    import trueskill as Trueskill
 
 class error(ValueError):
     pass
@@ -344,15 +348,15 @@ def histo_analysis(hist_data):
 
 def regression(inputs, outputs, args): # inputs, outputs expects N-D array 
 
+    X = np.array(inputs)
+    y = np.array(outputs)
+
     regressions = []
 
     if 'lin' in args: # formula: ax + b
 
         try:
 
-            X = np.array(inputs)
-            y = np.array(outputs)
-
             def func(x, a, b):
 
                 return a * x + b
@@ -369,9 +373,6 @@ def regression(inputs, outputs, args): # inputs, outputs expects N-D array
 
         try:
 
-            X = np.array(inputs)
-            y = np.array(outputs)
-
             def func(x, a, b, c, d):
 
                 return a * np.log(b*(x + c)) + d
@@ -388,9 +389,6 @@ def regression(inputs, outputs, args): # inputs, outputs expects N-D array
 
         try:        
 
-            X = np.array(inputs)
-            y = np.array(outputs)
-
             def func(x, a, b, c, d):
 
                 return a * np.exp(b*(x + c)) + d
@@ -405,8 +403,8 @@ def regression(inputs, outputs, args): # inputs, outputs expects N-D array
 
     if 'ply' in args: # formula: a + bx^1 + cx^2 + dx^3 + ...
         
-        inputs = [inputs]
+        inputs = np.array([inputs])
-        outputs = [outputs]
+        outputs = np.array([outputs])
 
         plys = []
         limit = len(outputs[0])
@@ -430,9 +428,6 @@ def regression(inputs, outputs, args): # inputs, outputs expects N-D array
 
         try:        
 
-            X = np.array(inputs)
-            y = np.array(outputs)
-
             def func(x, a, b, c, d):
 
                 return a * np.tanh(b*(x + c)) + d

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

@@ -5,19 +5,22 @@
 #   this module is cuda-optimized and vectorized (except for one small part)
 # setup:
 
-__version__ = "1.0.0.003"
+__version__ = "1.0.0.004"
 
 # changelog should be viewed using print(analysis.regression.__changelog__)
 __changelog__ = """
-1.0.0.003:
+    1.0.0.004:
-    - bug fixes
+        - bug fixes
-1.0.0.002:
+        - fixed changelog
-    -Added more parameters to log, exponential, polynomial
+    1.0.0.003:
-    -Added SigmoidalRegKernelArthur, because Arthur apparently needs
+        - bug fixes
-    to train the scaling and shifting of sigmoids
+    1.0.0.002:
-1.0.0.001:
+        -Added more parameters to log, exponential, polynomial
-    -initial release, with linear, log, exponential, polynomial, and sigmoid kernels
+        -Added SigmoidalRegKernelArthur, because Arthur apparently needs
-    -already vectorized (except for polynomial generation) and CUDA-optimized
+        to train the scaling and shifting of sigmoids
+    1.0.0.001:
+        -initial release, with linear, log, exponential, polynomial, and sigmoid kernels
+        -already vectorized (except for polynomial generation) and CUDA-optimized
 """
 
 __author__ = (
@@ -40,6 +43,8 @@ __all__ = [
     'CustomTrain'
 ]
 
+import torch
+
 global device
 
 device = "cuda:0" if torch.torch.cuda.is_available() else "cpu"

analysis-master/setup.py

@@ -2,7 +2,7 @@ import setuptools
 
 setuptools.setup(
     name="analysis", # Replace with your own username
-    version="1.0.0.007",
+    version="1.0.0.008",
     author="The Titan Scouting Team",
     author_email="titanscout2022@gmail.com",
     description="analysis package developed by Titan Scouting for The Red Alliance",
@@ -10,6 +10,14 @@ setuptools.setup(
     long_description_content_type="text/markdown",
     url="https://github.com/titanscout2022/tr2022-strategy",
     packages=setuptools.find_packages(),
+    install_requires=[
+        "numba",
+        "numpy",
+        "scipy",
+        "scikit-learn",
+        "six",
+        "matplotlib"
+    ],
     license = "GNU General Public License v3.0",
     classifiers=[
         "Programming Language :: Python :: 3",

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/get_team_rankings.py

@@ -0,0 +1,59 @@
+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()

data analysis/superscript.py

@@ -3,10 +3,12 @@
 # Notes:
 # setup:
 
-__version__ = "0.0.4.002"
+__version__ = "0.0.5.000"
 
 # changelog should be viewed using print(analysis.__changelog__)
 __changelog__ = """changelog:
+    0.0.5.000:
+        improved user interface
     0.0.4.002:
         - removed unessasary code
     0.0.4.001:
@@ -83,6 +85,8 @@ 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 +95,16 @@ 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 +117,55 @@ 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]
 
@@ -208,8 +229,6 @@ def metricsloop(tbakey, apikey, competition, timestamp): # listener based metric
 
     matches = d.pull_new_tba_matches(tbakey, competition, timestamp)
 
-    return_vector = {}
-
     red = {}
     blu = {}