superscript.py v 0.0.3.000

analysis pkg v 1.0.0.006

.gitignore

@@ -17,3 +17,5 @@ data analysis/.ipynb_checkpoints/test-checkpoint.ipynb
 data analysis/arthur_pull.ipynb
 data analysis/keys.txt
 data analysis/check_for_new_matches.ipynb
+data analysis/test.ipynb
+data analysis/visualize_pit.ipynb

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

@@ -1,6 +1,6 @@
 Metadata-Version: 2.1
 Name: analysis
-Version: 1.0.0.3
+Version: 1.0.0.6
 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/.ipynb_checkpoints/analysis-checkpoint.py

@@ -1,952 +0,0 @@
-# Titan Robotics Team 2022: Data Analysis Module
-# Written by Arthur Lu & Jacob Levine
-# Notes:
-#    this should be imported as a python module using 'import analysis'
-#    this should be included in the local directory or environment variable
-#    this module has been optimized for multhreaded computing
-#    current benchmark of optimization: 1.33 times faster
-# setup:
-
-__version__ = "1.1.12.003"
-
-# changelog should be viewed using print(analysis.__changelog__)
-__changelog__ = """changelog:
-    1.1.12.003:
-        - removed depreciated code
-    1.1.12.002:
-        - removed team first time trueskill instantiation in favor of integration in superscript.py
-    1.1.12.001:
-        - improved readibility of regression outputs by stripping tensor data
-        - used map with lambda to acheive the improved readibility
-        - lost numba jit support with regression, and generated_jit hangs at execution
-        - TODO: reimplement correct numba integration in regression
-    1.1.12.000:
-        - temporarily fixed polynomial regressions by using sklearn's PolynomialFeatures
-    1.1.11.010:
-        - alphabeticaly ordered import lists
-    1.1.11.009:
-        - bug fixes
-    1.1.11.008:
-        - bug fixes
-    1.1.11.007:
-        - bug fixes
-    1.1.11.006:
-        - tested min and max
-        - bug fixes 
-    1.1.11.005:
-        - added min and max in basic_stats
-    1.1.11.004:
-        - bug fixes
-    1.1.11.003:
-        - bug fixes
-    1.1.11.002:
-        - consolidated metrics
-        - fixed __all__
-    1.1.11.001:
-        - added test/train split to RandomForestClassifier and RandomForestRegressor
-    1.1.11.000:
-        - added RandomForestClassifier and RandomForestRegressor
-        - note: untested
-    1.1.10.000:
-        - added numba.jit to remaining functions
-    1.1.9.002:
-        - kernelized PCA and KNN
-    1.1.9.001:
-        - fixed bugs with SVM and NaiveBayes
-    1.1.9.000:
-        - added SVM class, subclasses, and functions
-        - note: untested
-    1.1.8.000:
-        - added NaiveBayes classification engine
-        - note: untested
-    1.1.7.000:
-        - added knn()
-        - added confusion matrix to decisiontree()
-    1.1.6.002:
-        - changed layout of __changelog to be vscode friendly
-    1.1.6.001:
-        - added additional hyperparameters to decisiontree()
-    1.1.6.000:
-        - fixed __version__
-        - fixed __all__ order
-        - added decisiontree()
-    1.1.5.003:
-        - added pca
-    1.1.5.002:
-        - reduced import list
-        - added kmeans clustering engine
-    1.1.5.001:
-        - simplified regression by using .to(device)
-    1.1.5.000:
-        - added polynomial regression to regression(); untested
-    1.1.4.000:
-        - added trueskill()
-    1.1.3.002:
-        - renamed regression class to Regression, regression_engine() to regression gliko2_engine class to Gliko2
-    1.1.3.001:
-        - changed glicko2() to return tuple instead of array
-    1.1.3.000:
-        - added glicko2_engine class and glicko()
-        - verified glicko2() accuracy
-    1.1.2.003:
-        - fixed elo()
-    1.1.2.002:
-        - added elo()
-        - elo() has bugs to be fixed
-    1.1.2.001:
-        - readded regrression import
-    1.1.2.000:
-        - integrated regression.py as regression class
-        - removed regression import
-        - fixed metadata for regression class
-        - fixed metadata for analysis class
-    1.1.1.001:
-        - regression_engine() bug fixes, now actaully regresses
-    1.1.1.000:
-        - added regression_engine()
-        - added all regressions except polynomial
-    1.1.0.007:
-        - updated _init_device()
-    1.1.0.006:
-        - removed useless try statements
-    1.1.0.005:
-        - removed impossible outcomes
-    1.1.0.004:
-        - added performance metrics (r^2, mse, rms)
-    1.1.0.003:
-        - resolved nopython mode for mean, median, stdev, variance
-    1.1.0.002:
-        - snapped (removed) majority of uneeded imports
-        - forced object mode (bad) on all jit
-        - TODO: stop numba complaining about not being able to compile in nopython mode
-    1.1.0.001:
-        - removed from sklearn import * to resolve uneeded wildcard imports
-    1.1.0.000:
-        - removed c_entities,nc_entities,obstacles,objectives from __all__
-        - applied numba.jit to all functions
-        - depreciated and removed stdev_z_split
-        - cleaned up histo_analysis to include numpy and numba.jit optimizations
-        - depreciated and removed all regression functions in favor of future pytorch optimizer
-        - depreciated and removed all nonessential functions (basic_analysis, benchmark, strip_data)
-        - optimized z_normalize using sklearn.preprocessing.normalize
-        - TODO: implement kernel/function based pytorch regression optimizer
-    1.0.9.000:
-        - refactored
-        - numpyed everything
-        - removed stats in favor of numpy functions
-    1.0.8.005:
-        - minor fixes
-    1.0.8.004:
-        - removed a few unused dependencies
-    1.0.8.003:
-        - added p_value function
-    1.0.8.002:
-        - updated __all__ correctly to contain changes made in v 1.0.8.000 and v 1.0.8.001
-    1.0.8.001:
-        - refactors
-        - bugfixes
-    1.0.8.000:
-        - depreciated histo_analysis_old
-        - depreciated debug
-        - altered basic_analysis to take array data instead of filepath
-        - refactor
-        - optimization
-    1.0.7.002:
-        - bug fixes
-    1.0.7.001:
-        - bug fixes
-    1.0.7.000:
-        - added tanh_regression (logistical regression)
-        - bug fixes
-    1.0.6.005:
-        - added z_normalize function to normalize dataset
-        - bug fixes
-    1.0.6.004:
-        - bug fixes
-    1.0.6.003:
-        - bug fixes
-    1.0.6.002:
-        - bug fixes
-    1.0.6.001:
-        - corrected __all__ to contain all of the functions
-    1.0.6.000:
-        - added calc_overfit, which calculates two measures of overfit, error and performance
-        - added calculating overfit to optimize_regression
-    1.0.5.000:
-        - added optimize_regression function, which is a sample function to find the optimal regressions
-        - optimize_regression function filters out some overfit funtions (functions with r^2 = 1)
-        - planned addition: overfit detection in the optimize_regression function
-    1.0.4.002:
-        - added __changelog__
-        - updated debug function with log and exponential regressions
-    1.0.4.001:
-        - added log regressions
-        - added exponential regressions
-        - added log_regression and exp_regression to __all__
-    1.0.3.008:
-        - added debug function to further consolidate functions
-    1.0.3.007:
-        - added builtin benchmark function
-        - added builtin random (linear) data generation function
-        - added device initialization (_init_device)
-    1.0.3.006:
-        - reorganized the imports list to be in alphabetical order
-        - added search and regurgitate functions to c_entities, nc_entities, obstacles, objectives
-    1.0.3.005:
-        - major bug fixes
-        - updated historical analysis
-        - depreciated old historical analysis
-    1.0.3.004:
-        - added __version__, __author__, __all__
-        - added polynomial regression
-        - added root mean squared function
-        - added r squared function
-    1.0.3.003:
-        - bug fixes
-        - added c_entities
-    1.0.3.002:
-        - bug fixes
-        - added nc_entities, obstacles, objectives
-        - consolidated statistics.py to analysis.py
-    1.0.3.001:
-        - compiled 1d, column, and row basic stats into basic stats function
-    1.0.3.000:
-        - added historical analysis function
-    1.0.2.xxx:
-        - added z score test
-    1.0.1.xxx:
-        - major bug fixes
-    1.0.0.xxx:
-        - added loading csv
-        - added 1d, column, row basic stats
-"""
-
-__author__ = (
-    "Arthur Lu <learthurgo@gmail.com>",
-    "Jacob Levine <jlevine@imsa.edu>",
-)
-
-__all__ = [
-    '_init_device',
-    'load_csv',
-    'basic_stats',
-    'z_score',
-    'z_normalize',
-    'histo_analysis',
-    'regression',
-    'elo',
-    'gliko2',
-    'trueskill',
-    'RegressionMetrics',
-    'ClassificationMetrics',
-    'kmeans',
-    'pca',
-    'decisiontree',
-    'knn_classifier',
-    'knn_regressor',
-    'NaiveBayes',
-    'SVM',
-    'random_forest_classifier',
-    'random_forest_regressor',
-    'Regression',
-    'Gliko2',
-    # all statistics functions left out due to integration in other functions
-]
-
-# now back to your regularly scheduled programming:
-
-# imports (now in alphabetical order! v 1.0.3.006):
-
-import csv
-import numba
-from numba import jit
-import numpy as np
-import math
-import sklearn
-from sklearn import *
-import torch
-try:
-    from analysis import trueskill as Trueskill
-except:
-    import trueskill as Trueskill
-
-class error(ValueError):
-    pass
-
-def _init_device():  # initiates computation device for ANNs
-    device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
-    return device
-
-def load_csv(filepath):
-    with open(filepath, newline='') as csvfile:
-        file_array = np.array(list(csv.reader(csvfile)))
-        csvfile.close()
-    return file_array
-
-# expects 1d array
-@jit(forceobj=True)
-def basic_stats(data):
-
-    data_t = np.array(data).astype(float)
-
-    _mean = mean(data_t)
-    _median = median(data_t)
-    _stdev = stdev(data_t)
-    _variance = variance(data_t)
-    _min = npmin(data_t)
-    _max = npmax(data_t)
-
-    return _mean, _median, _stdev, _variance, _min, _max
-
-# returns z score with inputs of point, mean and standard deviation of spread
-@jit(forceobj=True)
-def z_score(point, mean, stdev):
-    score = (point - mean) / stdev
-    
-    return score
-
-# expects 2d array, normalizes across all axes
-@jit(forceobj=True)
-def z_normalize(array, *args):
-
-   array = np.array(array)
-   for arg in args:
-       array = sklearn.preprocessing.normalize(array, axis = arg)
-
-   return array
-
-@jit(forceobj=True)
-# expects 2d array of [x,y]
-def histo_analysis(hist_data):
-
-    hist_data = np.array(hist_data)
-    derivative = np.array(len(hist_data) - 1, dtype = float)
-    t = np.diff(hist_data)
-    derivative = t[1] / t[0]
-    np.sort(derivative)
-
-    return basic_stats(derivative)[0], basic_stats(derivative)[3]
-
-def regression(ndevice, inputs, outputs, args, loss = torch.nn.MSELoss(), _iterations = 10000, lr = 0.01, _iterations_ply = 10000, lr_ply = 0.01): # inputs, outputs expects N-D array 
-
-    regressions = []
-    Regression().set_device(ndevice)
-
-    if 'lin' in args: # formula: ax + b
-
-        model = Regression().SGDTrain(Regression.LinearRegKernel(len(inputs)), torch.tensor(inputs).to(torch.float).to(device), torch.tensor([outputs]).to(torch.float).to(device), iterations=_iterations, learning_rate=lr, return_losses=True)
-        params = model[0].parameters
-        params[:] = map(lambda x: x.item(), params)
-        regressions.append((params, model[1][::-1][0]))
-
-    if 'log' in args: # formula: a log (b(x + c)) + d
-
-        model = Regression().SGDTrain(Regression.LogRegKernel(len(inputs)), torch.tensor(inputs).to(torch.float).to(device), torch.tensor(outputs).to(torch.float).to(device), iterations=_iterations, learning_rate=lr, return_losses=True)
-        params = model[0].parameters
-        params[:] = map(lambda x: x.item(), params)
-        regressions.append((params, model[1][::-1][0]))
-
-    if 'exp' in args: # formula: a e ^ (b(x + c)) + d
-
-        model = Regression().SGDTrain(Regression.ExpRegKernel(len(inputs)), torch.tensor(inputs).to(torch.float).to(device), torch.tensor(outputs).to(torch.float).to(device), iterations=_iterations, learning_rate=lr, return_losses=True)
-        params = model[0].parameters
-        params[:] = map(lambda x: x.item(), params)
-        regressions.append((params, model[1][::-1][0]))
-
-    if 'ply' in args: # formula: a + bx^1 + cx^2 + dx^3 + ...
-
-        plys = []
-        limit = len(outputs[0])
-
-        for i in range(2, limit):
-
-            model = sklearn.preprocessing.PolynomialFeatures(degree = i)
-            model = sklearn.pipeline.make_pipeline(model, sklearn.linear_model.LinearRegression())
-            model = model.fit(np.rot90(inputs), np.rot90(outputs))
-
-            params = model.steps[1][1].intercept_.tolist()
-            params = np.append(params, model.steps[1][1].coef_[0].tolist()[1::])
-            params.flatten()
-            params = params.tolist()
-            
-            plys.append(params)
-
-        regressions.append(plys)
-
-    if 'sig' in args: # formula: a sig (b(x + c)) + d | sig() = 1/(1 + e ^ -x)
-
-        model = Regression().SGDTrain(Regression.SigmoidalRegKernelArthur(len(inputs)), torch.tensor(inputs).to(torch.float).to(device), torch.tensor(outputs).to(torch.float).to(device), iterations=_iterations, learning_rate=lr, return_losses=True)
-        params = model[0].parameters
-        params[:] = map(lambda x: x.item(), params)
-        regressions.append((params, model[1][::-1][0]))
-
-    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 gliko2(starting_score, starting_rd, starting_vol, opposing_score, opposing_rd, observations):
-
-    player = Gliko2(rating = starting_score, rd = starting_rd, vol = starting_vol)
-
-    player.update_player([x for x in opposing_score], [x for x in opposing_rd], observations)
-
-    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 = []
-
-    for team in teams_data:
-        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)
-
-class RegressionMetrics():
-
-    def __new__(cls, predictions, targets):
-
-        return cls.r_squared(cls, predictions, targets), cls.mse(cls, predictions, targets), cls.rms(cls, predictions, targets)
-
-    def r_squared(self, predictions, targets):  # assumes equal size inputs
-
-        return sklearn.metrics.r2_score(targets, predictions)
-
-    def mse(self, predictions, targets):
-
-        return sklearn.metrics.mean_squared_error(targets, predictions)
-
-    def rms(self, predictions, targets):
-
-        return math.sqrt(sklearn.metrics.mean_squared_error(targets, predictions))
-
-class ClassificationMetrics():
-
-    def __new__(cls, predictions, targets):
-
-        return cls.cm(cls, predictions, targets), cls.cr(cls, predictions, targets)
-
-    def cm(self, predictions, targets):
-
-        return sklearn.metrics.confusion_matrix(targets, predictions)
-
-    def cr(self, predictions, targets):
-
-        return sklearn.metrics.classification_report(targets, predictions)
-
-@jit(nopython=True)
-def mean(data):
-
-    return np.mean(data)
-
-@jit(nopython=True)
-def median(data):
-
-    return np.median(data)
-
-@jit(nopython=True)
-def stdev(data):
-
-    return np.std(data)
-
-@jit(nopython=True)
-def variance(data):
-
-    return np.var(data)
-
-@jit(nopython=True)
-def npmin(data):
-
-    return np.amin(data)
-
-@jit(nopython=True)
-def npmax(data):
-
-    return np.amax(data)
-
-@jit(forceobj=True)
-def kmeans(data, n_clusters=8, init="k-means++", n_init=10, max_iter=300, tol=0.0001, precompute_distances="auto", verbose=0, random_state=None, copy_x=True, n_jobs=None, algorithm="auto"):
-
-    kernel = sklearn.cluster.KMeans(n_clusters = n_clusters, init = init, n_init = n_init, max_iter = max_iter, tol = tol, precompute_distances = precompute_distances, verbose = verbose, random_state = random_state, copy_x = copy_x, n_jobs = n_jobs, algorithm = algorithm)
-    kernel.fit(data)
-    predictions = kernel.predict(data)
-    centers = kernel.cluster_centers_
-
-    return centers, predictions
-
-@jit(forceobj=True)
-def pca(data, n_components = None, copy = True, whiten = False, svd_solver = "auto", tol = 0.0, iterated_power = "auto", random_state = None):
-
-    kernel = sklearn.decomposition.PCA(n_components = n_components, copy = copy, whiten = whiten, svd_solver = svd_solver, tol = tol, iterated_power = iterated_power, random_state = random_state)
-
-    return kernel.fit_transform(data)
-
-@jit(forceobj=True)
-def decisiontree(data, labels, test_size = 0.3, criterion = "gini", splitter = "default", max_depth = None): #expects *2d data and 1d labels
-
-    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.tree.DecisionTreeClassifier(criterion = criterion, splitter = splitter, max_depth = max_depth)
-    model = model.fit(data_train,labels_train)
-    predictions = model.predict(data_test)
-    metrics = ClassificationMetrics(predictions, labels_test)
-
-    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
-
-    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)
-
-    return model, ClassificationMetrics(predictions, labels_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):
-
-    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:
-
-    def guassian(self, data, labels, test_size = 0.3, priors = None, var_smoothing = 1e-09):
-
-        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.naive_bayes.GaussianNB(priors = priors, var_smoothing = var_smoothing)
-        model.fit(data_train, labels_train)
-        predictions = model.predict(data_test)
-
-        return model, ClassificationMetrics(predictions, labels_test)
-
-    def multinomial(self, data, labels, test_size = 0.3, alpha=1.0, fit_prior=True, class_prior=None):
-
-        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.naive_bayes.MultinomialNB(alpha = alpha, fit_prior = fit_prior, class_prior = class_prior)
-        model.fit(data_train, labels_train)
-        predictions = model.predict(data_test)
-
-        return model, ClassificationMetrics(predictions, labels_test)
-
-    def bernoulli(self, data, labels, test_size = 0.3, alpha=1.0, binarize=0.0, fit_prior=True, class_prior=None):
-
-        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.naive_bayes.BernoulliNB(alpha = alpha, binarize = binarize, fit_prior = fit_prior, class_prior = class_prior)
-        model.fit(data_train, labels_train)
-        predictions = model.predict(data_test)
-
-        return model, ClassificationMetrics(predictions, labels_test)
-
-    def complement(self, data, labels, test_size = 0.3, alpha=1.0, fit_prior=True, class_prior=None, norm=False):
-
-        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.naive_bayes.ComplementNB(alpha = alpha, fit_prior = fit_prior, class_prior = class_prior, norm = norm)
-        model.fit(data_train, labels_train)
-        predictions = model.predict(data_test)
-
-        return model, ClassificationMetrics(predictions, labels_test)
-
-class SVM:
-
-    class CustomKernel:
-
-        def __new__(cls, C, kernel, degre, gamma, coef0, shrinking, probability, tol, cache_size, class_weight, verbose, max_iter, decision_function_shape, random_state):
-
-            return sklearn.svm.SVC(C = C, kernel = kernel, gamma = gamma, coef0 = coef0, shrinking = shrinking, probability = probability, tol = tol, cache_size = cache_size, class_weight = class_weight, verbose = verbose, max_iter = max_iter, decision_function_shape = decision_function_shape, random_state = random_state)
-
-    class StandardKernel:
-
-        def __new__(cls, kernel, C=1.0, degree=3, gamma='auto_deprecated', coef0=0.0, shrinking=True, probability=False, tol=0.001, cache_size=200, class_weight=None, verbose=False, max_iter=-1, decision_function_shape='ovr', random_state=None):
-
-            return sklearn.svm.SVC(C = C, kernel = kernel, gamma = gamma, coef0 = coef0, shrinking = shrinking, probability = probability, tol = tol, cache_size = cache_size, class_weight = class_weight, verbose = verbose, max_iter = max_iter, decision_function_shape = decision_function_shape, random_state = random_state)
-
-    class PrebuiltKernel:
-
-        class Linear:
-
-            def __new__(cls):
-
-                return sklearn.svm.SVC(kernel = 'linear')
-
-        class Polynomial:
-
-            def __new__(cls, power, r_bias):
-
-                return sklearn.svm.SVC(kernel = 'polynomial', degree = power, coef0 = r_bias)
-
-        class RBF:
-
-            def __new__(cls, gamma):
-
-                return sklearn.svm.SVC(kernel = 'rbf', gamma = gamma)
-
-        class Sigmoid:
-
-            def __new__(cls, r_bias):
-
-                return sklearn.svm.SVC(kernel = 'sigmoid', coef0 = r_bias)
-
-    def fit(self, kernel, train_data, train_outputs): # expects *2d data, 1d labels or outputs
-
-        return kernel.fit(train_data, train_outputs)
-
-    def eval_classification(self, kernel, test_data, test_outputs):
-
-        predictions = kernel.predict(test_data)
-
-        return ClassificationMetrics(predictions, test_outputs)
-
-    def eval_regression(self, kernel, test_data, test_outputs):
-
-        predictions = kernel.predict(test_data)
-
-        return RegressionMetrics(predictions, test_outputs)
-
-def random_forest_classifier(data, labels, test_size, n_estimators="warn", criterion="gini", max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features="auto", max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False, class_weight=None):
-
-    data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1)
-    kernel = sklearn.ensemble.RandomForestClassifier(n_estimators = n_estimators, criterion = criterion, max_depth = max_depth, min_samples_split = min_samples_split, min_samples_leaf = min_samples_leaf, min_weight_fraction_leaf = min_weight_fraction_leaf, max_leaf_nodes = max_leaf_nodes, min_impurity_decrease = min_impurity_decrease, bootstrap = bootstrap, oob_score = oob_score, n_jobs = n_jobs, random_state = random_state, verbose = verbose, warm_start = warm_start, class_weight = class_weight)
-    kernel.fit(data_train, labels_train)
-    predictions = kernel.predict(data_test)
-
-    return kernel, ClassificationMetrics(predictions, labels_test)
-
-def random_forest_regressor(data, outputs, test_size, n_estimators="warn", criterion="mse", max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features="auto", max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False):
-
-    data_train, data_test, outputs_train, outputs_test = sklearn.model_selection.train_test_split(data, outputs, test_size=test_size, random_state=1)
-    kernel = sklearn.ensemble.RandomForestRegressor(n_estimators = n_estimators, criterion = criterion, max_depth = max_depth, min_samples_split = min_samples_split, min_weight_fraction_leaf = min_weight_fraction_leaf, max_features = max_features, max_leaf_nodes = max_leaf_nodes, min_impurity_decrease = min_impurity_decrease, min_impurity_split = min_impurity_split, bootstrap = bootstrap, oob_score = oob_score, n_jobs = n_jobs, random_state = random_state, verbose = verbose, warm_start = warm_start)
-    kernel.fit(data_train, outputs_train)
-    predictions = kernel.predict(data_test)
-
-    return kernel, RegressionMetrics(predictions, outputs_test)
-
-class Regression:
-
-    # Titan Robotics Team 2022: CUDA-based Regressions Module
-    # Written by Arthur Lu & Jacob Levine
-    # Notes:
-    #   this module has been automatically inegrated into analysis.py, and should be callable as a class from the package
-    #   this module is cuda-optimized and vectorized (except for one small part)
-    # setup:
-
-    __version__ = "1.0.0.003"
-
-    # changelog should be viewed using print(analysis.regression.__changelog__)
-    __changelog__ = """
-    1.0.0.003:
-        - bug fixes
-    1.0.0.002:
-        -Added more parameters to log, exponential, polynomial
-        -Added SigmoidalRegKernelArthur, because Arthur apparently needs
-        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__ = (
-        "Jacob Levine <jlevine@imsa.edu>",
-        "Arthur Lu <learthurgo@gmail.com>"
-    )
-
-    __all__ = [
-        'factorial',
-        'take_all_pwrs',
-        'num_poly_terms',
-        'set_device',
-        'LinearRegKernel',
-        'SigmoidalRegKernel',
-        'LogRegKernel',
-        'PolyRegKernel',
-        'ExpRegKernel',
-        'SigmoidalRegKernelArthur',
-        'SGDTrain',
-        'CustomTrain'
-    ]
-
-    global device
-
-    device = "cuda:0" if torch.torch.cuda.is_available() else "cpu"
-
-    #todo: document completely
-
-    def set_device(self, new_device):
-        device=new_device
-
-    class LinearRegKernel():
-        parameters= []
-        weights=None
-        bias=None
-        def __init__(self, num_vars):
-            self.weights=torch.rand(num_vars, requires_grad=True, device=device)
-            self.bias=torch.rand(1, requires_grad=True, device=device)
-            self.parameters=[self.weights,self.bias]
-        def forward(self,mtx):
-            long_bias=self.bias.repeat([1,mtx.size()[1]])
-            return torch.matmul(self.weights,mtx)+long_bias
-
-    class SigmoidalRegKernel():
-        parameters= []
-        weights=None
-        bias=None
-        sigmoid=torch.nn.Sigmoid()
-        def __init__(self, num_vars):
-            self.weights=torch.rand(num_vars, requires_grad=True, device=device)
-            self.bias=torch.rand(1, requires_grad=True, device=device)
-            self.parameters=[self.weights,self.bias]
-        def forward(self,mtx):
-            long_bias=self.bias.repeat([1,mtx.size()[1]])
-            return self.sigmoid(torch.matmul(self.weights,mtx)+long_bias)
-
-    class SigmoidalRegKernelArthur():
-        parameters= []
-        weights=None
-        in_bias=None
-        scal_mult=None
-        out_bias=None
-        sigmoid=torch.nn.Sigmoid()
-        def __init__(self, num_vars):
-            self.weights=torch.rand(num_vars, requires_grad=True, device=device)
-            self.in_bias=torch.rand(1, requires_grad=True, device=device)
-            self.scal_mult=torch.rand(1, requires_grad=True, device=device)
-            self.out_bias=torch.rand(1, requires_grad=True, device=device)
-            self.parameters=[self.weights,self.in_bias, self.scal_mult, self.out_bias]
-        def forward(self,mtx):
-            long_in_bias=self.in_bias.repeat([1,mtx.size()[1]])
-            long_out_bias=self.out_bias.repeat([1,mtx.size()[1]])
-            return (self.scal_mult*self.sigmoid(torch.matmul(self.weights,mtx)+long_in_bias))+long_out_bias
-
-    class LogRegKernel():
-        parameters= []
-        weights=None
-        in_bias=None
-        scal_mult=None
-        out_bias=None
-        def __init__(self, num_vars):
-            self.weights=torch.rand(num_vars, requires_grad=True, device=device)
-            self.in_bias=torch.rand(1, requires_grad=True, device=device)
-            self.scal_mult=torch.rand(1, requires_grad=True, device=device)
-            self.out_bias=torch.rand(1, requires_grad=True, device=device)
-            self.parameters=[self.weights,self.in_bias, self.scal_mult, self.out_bias]
-        def forward(self,mtx):
-            long_in_bias=self.in_bias.repeat([1,mtx.size()[1]])
-            long_out_bias=self.out_bias.repeat([1,mtx.size()[1]])
-            return (self.scal_mult*torch.log(torch.matmul(self.weights,mtx)+long_in_bias))+long_out_bias
-
-    class ExpRegKernel():
-        parameters= []
-        weights=None
-        in_bias=None
-        scal_mult=None
-        out_bias=None
-        def __init__(self, num_vars):
-            self.weights=torch.rand(num_vars, requires_grad=True, device=device)
-            self.in_bias=torch.rand(1, requires_grad=True, device=device)
-            self.scal_mult=torch.rand(1, requires_grad=True, device=device)
-            self.out_bias=torch.rand(1, requires_grad=True, device=device)
-            self.parameters=[self.weights,self.in_bias, self.scal_mult, self.out_bias]
-        def forward(self,mtx):
-            long_in_bias=self.in_bias.repeat([1,mtx.size()[1]])
-            long_out_bias=self.out_bias.repeat([1,mtx.size()[1]])
-            return (self.scal_mult*torch.exp(torch.matmul(self.weights,mtx)+long_in_bias))+long_out_bias
-
-    class PolyRegKernel():
-        parameters= []
-        weights=None
-        bias=None
-        power=None
-        def __init__(self, num_vars, power):
-            self.power=power
-            num_terms=self.num_poly_terms(num_vars, power)
-            self.weights=torch.rand(num_terms, requires_grad=True, device=device)
-            self.bias=torch.rand(1, requires_grad=True, device=device)
-            self.parameters=[self.weights,self.bias]
-        def num_poly_terms(self,num_vars, power):
-            if power == 0:
-                return 0
-            return int(self.factorial(num_vars+power-1) / self.factorial(power) / self.factorial(num_vars-1)) + self.num_poly_terms(num_vars, power-1)
-        def factorial(self,n):
-            if n==0:
-                return 1
-            else:
-                return n*self.factorial(n-1)
-        def take_all_pwrs(self, vec, pwr):
-            #todo: vectorize (kinda)
-            combins=torch.combinations(vec, r=pwr, with_replacement=True)
-            out=torch.ones(combins.size()[0]).to(device).to(torch.float)
-            for i in torch.t(combins).to(device).to(torch.float):
-                out *= i
-            if pwr == 1:
-                return out
-            else:
-                return torch.cat((out,self.take_all_pwrs(vec, pwr-1)))
-        def forward(self,mtx):
-            #TODO: Vectorize the last part
-            cols=[]
-            for i in torch.t(mtx):
-                cols.append(self.take_all_pwrs(i,self.power))
-            new_mtx=torch.t(torch.stack(cols))
-            long_bias=self.bias.repeat([1,mtx.size()[1]])
-            return torch.matmul(self.weights,new_mtx)+long_bias
-
-    def SGDTrain(self, kernel, data, ground, loss=torch.nn.MSELoss(), iterations=1000, learning_rate=.1, return_losses=False):
-        optim=torch.optim.SGD(kernel.parameters, lr=learning_rate)
-        data_cuda=data.to(device)
-        ground_cuda=ground.to(device)
-        if (return_losses):
-            losses=[]
-            for i in range(iterations):
-                with torch.set_grad_enabled(True):
-                    optim.zero_grad()
-                    pred=kernel.forward(data_cuda)
-                    ls=loss(pred,ground_cuda)
-                    losses.append(ls.item())
-                    ls.backward()
-                    optim.step()
-            return [kernel,losses]
-        else:
-            for i in range(iterations):
-                with torch.set_grad_enabled(True):
-                    optim.zero_grad()
-                    pred=kernel.forward(data_cuda)
-                    ls=loss(pred,ground_cuda)
-                    ls.backward()
-                    optim.step()
-            return kernel
-
-    def CustomTrain(self, kernel, optim, data, ground, loss=torch.nn.MSELoss(), iterations=1000, return_losses=False):
-        data_cuda=data.to(device)
-        ground_cuda=ground.to(device)
-        if (return_losses):
-            losses=[]
-            for i in range(iterations):
-                with torch.set_grad_enabled(True):
-                    optim.zero_grad()
-                    pred=kernel.forward(data)
-                    ls=loss(pred,ground)
-                    losses.append(ls.item())
-                    ls.backward()
-                    optim.step()
-            return [kernel,losses]
-        else:
-            for i in range(iterations):
-                with torch.set_grad_enabled(True):
-                    optim.zero_grad()
-                    pred=kernel.forward(data_cuda)
-                    ls=loss(pred,ground_cuda)
-                    ls.backward()
-                    optim.step()
-            return kernel
-
-class Gliko2:
-
-    _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/analysis.py

@@ -7,10 +7,16 @@
 #    current benchmark of optimization: 1.33 times faster
 # setup:
 
-__version__ = "1.1.12.006"
+__version__ = "1.1.13.001"
 
 # changelog should be viewed using print(analysis.__changelog__)
 __changelog__ = """changelog:
+    1.1.13.001:
+        - bug fix with linear regression not returning a proper value
+        - cleaned up regression
+        - fixed bug with polynomial regressions
+    1.1.13.000:
+        - fixed all regressions to now properly work
     1.1.12.006:
         - fixed bg with a division by zero in histo_analysis
     1.1.12.005:
@@ -268,6 +274,8 @@ import numba
 from numba import jit
 import numpy as np
 import math
+import scipy
+from scipy import *
 import sklearn
 from sklearn import *
 import torch
@@ -339,34 +347,72 @@ def histo_analysis(hist_data):
 
         return None
 
-def regression(ndevice, inputs, outputs, args, loss = torch.nn.MSELoss(), _iterations = 10000, lr = 0.01, _iterations_ply = 10000, lr_ply = 0.01): # inputs, outputs expects N-D array 
+def regression(inputs, outputs, args): # inputs, outputs expects N-D array 
 
     regressions = []
-    Regression().set_device(ndevice)
 
     if 'lin' in args: # formula: ax + b
 
-        model = Regression().SGDTrain(Regression.LinearRegKernel(len(inputs)), torch.tensor(inputs).to(torch.float).to(device), torch.tensor([outputs]).to(torch.float).to(device), iterations=_iterations, learning_rate=lr, return_losses=True)
-        params = model[0].parameters
-        params[:] = map(lambda x: x.item(), params)
-        regressions.append((params, model[1][::-1][0]))
+        try:
+
+            X = np.array(inputs)
+            y = np.array(outputs)
+
+            def func(x, a, b):
+
+                return a * x + b
+
+            popt, pcov = scipy.optimize.curve_fit(func, X, y)
+
+            regressions.append((popt.flatten().tolist(), None))
+
+        except Exception as e:
+
+            pass
 
     if 'log' in args: # formula: a log (b(x + c)) + d
 
-        model = Regression().SGDTrain(Regression.LogRegKernel(len(inputs)), torch.tensor(inputs).to(torch.float).to(device), torch.tensor(outputs).to(torch.float).to(device), iterations=_iterations, learning_rate=lr, return_losses=True)
-        params = model[0].parameters
-        params[:] = map(lambda x: x.item(), params)
-        regressions.append((params, model[1][::-1][0]))
+        try:
+
+            X = np.array(inputs)
+            y = np.array(outputs)
+
+            def func(x, a, b, c, d):
+
+                return a * np.log(b*(x + c)) + d
+
+            popt, pcov = scipy.optimize.curve_fit(func, X, y)
+
+            regressions.append((popt.flatten().tolist(), None))
+
+        except Exception as e:
+            
+            pass
 
     if 'exp' in args: # formula: a e ^ (b(x + c)) + d
 
-        model = Regression().SGDTrain(Regression.ExpRegKernel(len(inputs)), torch.tensor(inputs).to(torch.float).to(device), torch.tensor(outputs).to(torch.float).to(device), iterations=_iterations, learning_rate=lr, return_losses=True)
-        params = model[0].parameters
-        params[:] = map(lambda x: x.item(), params)
-        regressions.append((params, model[1][::-1][0]))
+        try:
+
+            X = np.array(inputs)
+            y = np.array(outputs)
+
+            def func(x, a, b, c, d):
+
+                return a * np.exp(b*(x + c)) + d
+
+            popt, pcov = scipy.optimize.curve_fit(func, X, y)
+
+            regressions.append((popt.flatten().tolist(), None))
+
+        except Exception as e:
+
+            pass
 
     if 'ply' in args: # formula: a + bx^1 + cx^2 + dx^3 + ...
         
+        inputs = [inputs]
+        outputs = [outputs]
+
         plys = []
         limit = len(outputs[0])
 
@@ -385,12 +431,24 @@ def regression(ndevice, inputs, outputs, args, loss = torch.nn.MSELoss(), _itera
 
         regressions.append(plys)
 
-    if 'sig' in args: # formula: a sig (b(x + c)) + d | sig() = 1/(1 + e ^ -x)
+    if 'sig' in args: # formula: a tanh (b(x + c)) + d
 
-        model = Regression().SGDTrain(Regression.SigmoidalRegKernelArthur(len(inputs)), torch.tensor(inputs).to(torch.float).to(device), torch.tensor(outputs).to(torch.float).to(device), iterations=_iterations, learning_rate=lr, return_losses=True)
-        params = model[0].parameters
-        params[:] = map(lambda x: x.item(), params)
-        regressions.append((params, model[1][::-1][0]))
+        try:
+
+            X = np.array(inputs)
+            y = np.array(outputs)
+
+            def func(x, a, b, c, d):
+
+                return a * np.tanh(b*(x + c)) + d
+
+            popt, pcov = scipy.optimize.curve_fit(func, X, y)
+
+            regressions.append((popt.flatten().tolist(), None))
+
+        except Exception as e:
+           
+            pass
 
     return regressions
 

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

@@ -7,10 +7,16 @@
 #    current benchmark of optimization: 1.33 times faster
 # setup:
 
-__version__ = "1.1.12.006"
+__version__ = "1.1.13.001"
 
 # changelog should be viewed using print(analysis.__changelog__)
 __changelog__ = """changelog:
+    1.1.13.001:
+        - bug fix with linear regression not returning a proper value
+        - cleaned up regression
+        - fixed bug with polynomial regressions
+    1.1.13.000:
+        - fixed all regressions to now properly work
     1.1.12.006:
         - fixed bg with a division by zero in histo_analysis
     1.1.12.005:
@@ -268,6 +274,8 @@ import numba
 from numba import jit
 import numpy as np
 import math
+import scipy
+from scipy import *
 import sklearn
 from sklearn import *
 import torch
@@ -339,34 +347,72 @@ def histo_analysis(hist_data):
 
         return None
 
-def regression(ndevice, inputs, outputs, args, loss = torch.nn.MSELoss(), _iterations = 10000, lr = 0.01, _iterations_ply = 10000, lr_ply = 0.01): # inputs, outputs expects N-D array 
+def regression(inputs, outputs, args): # inputs, outputs expects N-D array 
 
     regressions = []
-    Regression().set_device(ndevice)
 
     if 'lin' in args: # formula: ax + b
 
-        model = Regression().SGDTrain(Regression.LinearRegKernel(len(inputs)), torch.tensor(inputs).to(torch.float).to(device), torch.tensor([outputs]).to(torch.float).to(device), iterations=_iterations, learning_rate=lr, return_losses=True)
-        params = model[0].parameters
-        params[:] = map(lambda x: x.item(), params)
-        regressions.append((params, model[1][::-1][0]))
+        try:
+
+            X = np.array(inputs)
+            y = np.array(outputs)
+
+            def func(x, a, b):
+
+                return a * x + b
+
+            popt, pcov = scipy.optimize.curve_fit(func, X, y)
+
+            regressions.append((popt.flatten().tolist(), None))
+
+        except Exception as e:
+
+            pass
 
     if 'log' in args: # formula: a log (b(x + c)) + d
 
-        model = Regression().SGDTrain(Regression.LogRegKernel(len(inputs)), torch.tensor(inputs).to(torch.float).to(device), torch.tensor(outputs).to(torch.float).to(device), iterations=_iterations, learning_rate=lr, return_losses=True)
-        params = model[0].parameters
-        params[:] = map(lambda x: x.item(), params)
-        regressions.append((params, model[1][::-1][0]))
+        try:
+
+            X = np.array(inputs)
+            y = np.array(outputs)
+
+            def func(x, a, b, c, d):
+
+                return a * np.log(b*(x + c)) + d
+
+            popt, pcov = scipy.optimize.curve_fit(func, X, y)
+
+            regressions.append((popt.flatten().tolist(), None))
+
+        except Exception as e:
+            
+            pass
 
     if 'exp' in args: # formula: a e ^ (b(x + c)) + d
 
-        model = Regression().SGDTrain(Regression.ExpRegKernel(len(inputs)), torch.tensor(inputs).to(torch.float).to(device), torch.tensor(outputs).to(torch.float).to(device), iterations=_iterations, learning_rate=lr, return_losses=True)
-        params = model[0].parameters
-        params[:] = map(lambda x: x.item(), params)
-        regressions.append((params, model[1][::-1][0]))
+        try:
+
+            X = np.array(inputs)
+            y = np.array(outputs)
+
+            def func(x, a, b, c, d):
+
+                return a * np.exp(b*(x + c)) + d
+
+            popt, pcov = scipy.optimize.curve_fit(func, X, y)
+
+            regressions.append((popt.flatten().tolist(), None))
+
+        except Exception as e:
+
+            pass
 
     if 'ply' in args: # formula: a + bx^1 + cx^2 + dx^3 + ...
         
+        inputs = [inputs]
+        outputs = [outputs]
+
         plys = []
         limit = len(outputs[0])
 
@@ -385,12 +431,24 @@ def regression(ndevice, inputs, outputs, args, loss = torch.nn.MSELoss(), _itera
 
         regressions.append(plys)
 
-    if 'sig' in args: # formula: a sig (b(x + c)) + d | sig() = 1/(1 + e ^ -x)
+    if 'sig' in args: # formula: a tanh (b(x + c)) + d
 
-        model = Regression().SGDTrain(Regression.SigmoidalRegKernelArthur(len(inputs)), torch.tensor(inputs).to(torch.float).to(device), torch.tensor(outputs).to(torch.float).to(device), iterations=_iterations, learning_rate=lr, return_losses=True)
-        params = model[0].parameters
-        params[:] = map(lambda x: x.item(), params)
-        regressions.append((params, model[1][::-1][0]))
+        try:
+
+            X = np.array(inputs)
+            y = np.array(outputs)
+
+            def func(x, a, b, c, d):
+
+                return a * np.tanh(b*(x + c)) + d
+
+            popt, pcov = scipy.optimize.curve_fit(func, X, y)
+
+            regressions.append((popt.flatten().tolist(), None))
+
+        except Exception as e:
+           
+            pass
 
     return regressions
 

analysis-master/setup.py

@@ -2,7 +2,7 @@ import setuptools
 
 setuptools.setup(
     name="analysis", # Replace with your own username
-    version="1.0.0.003",
+    version="1.0.0.006",
     author="The Titan Scouting Team",
     author_email="titanscout2022@gmail.com",
     description="analysis package developed by Titan Scouting for The Red Alliance",

data analysis/config.csv

@@ -1,6 +1,13 @@
 2020ilch
-balls-blocked,basic_stats,historical_analysis
-balls-collected,basic_stats,historical_analysis
-balls-lower,basic_stats,historical_analysis
-balls-started,basic_stats,historical_analysis
-balls-upper,basic_stats,historical_analysis
+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,basic_stats,historical_analysis,regression_linear,regression_logarithmic,regression_exponential,regression_polynomial,regression_sigmoidal
+balls-started,basic_stats,historical_analyss,regression_linear,regression_logarithmic,regression_exponential,regression_polynomial,regression_sigmoidal
+balls-upper,basic_stats,historical_analysis,regression_linear,regression_logarithmic,regression_exponential,regression_polynomial,regression_sigmoidal
+wheel-mechanism
+low-balls
+high-balls
+wheel-success
+strategic-focus
+climb-mechanism
+attitude

data analysis/data.py

@@ -8,7 +8,7 @@ def pull_new_tba_matches(apikey, competition, cutoff):
     x=requests.get("https://www.thebluealliance.com/api/v3/event/"+competition+"/matches/simple", headers={"X-TBA-Auth_Key":api_key})
     out = []
     for i in x.json():
-        if (i["actual_time"]-cutoff >= 0 and i["comp_level"] == "qm"):
+        if (i["actual_time"] != None and i["actual_time"]-cutoff >= 0 and i["comp_level"] == "qm"):
             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"]})
     return out
 
@@ -21,6 +21,13 @@ def get_team_match_data(apikey, competition, team_num):
         out[i['match']] = i['data']
     return pd.DataFrame(out)
 
+def get_team_pit_data(apikey, competition, team_num):
+    client = pymongo.MongoClient(apikey)
+    db = client.data_scouting
+    mdata = db.pitdata
+    out = {}
+    return mdata.find_one({"competition" : competition, "team_scouted": team_num})["data"]
+
 def get_team_metrics_data(apikey, competition, team_num):
     client = pymongo.MongoClient(apikey)
     db = client.data_processing
@@ -38,7 +45,7 @@ def unkeyify_2l(layered_dict):
         out[i] = list(map(lambda x: x[1], add))
     return out
 
-def get_data_formatted(apikey, competition):
+def get_match_data_formatted(apikey, competition):
     client = pymongo.MongoClient(apikey)
     db = client.data_scouting
     mdata = db.teamlist
@@ -51,6 +58,19 @@ def get_data_formatted(apikey, competition):
             pass
     return out
 
+def get_pit_data_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)] = get_team_pit_data(apikey, competition, int(i))
+        except:
+            pass
+    return out
+
 def push_team_tests_data(apikey, competition, team_num, data, dbname = "data_processing", colname = "team_tests"):
     client = pymongo.MongoClient(apikey)
     db = client[dbname]
@@ -63,6 +83,12 @@ def push_team_metrics_data(apikey, competition, team_num, data, dbname = "data_p
     mdata = db[colname]
     mdata.replace_one({"competition" : competition, "team": team_num}, {"_id": competition+str(team_num)+"am", "competition" : competition, "team" : team_num, "metrics" : data}, True)
 
+def push_team_pit_data(apikey, competition, variable, data, dbname = "data_processing", colname = "team_pit"):
+    client = pymongo.MongoClient(apikey)
+    db = client[dbname]
+    mdata = db[colname]
+    mdata.replace_one({"competition" : competition, "variable": variable}, {"competition" : competition, "variable" : variable, "data" : data}, True)
+
 def get_analysis_flags(apikey, flag):
     client = pymongo.MongoClient(apikey)
     db = client.data_processing

data analysis/superscript.py

@@ -3,10 +3,12 @@
 # Notes:
 # setup:
 
-__version__ = "0.0.2.001"
+__version__ = "0.0.3.000"
 
 # changelog should be viewed using print(analysis.__changelog__)
 __changelog__ = """changelog:
+    0.0.3.00:
+        - added analysis to pit data
     0.0.2.001:
         - minor stability patches
         - implemented db syncing for timestamps
@@ -69,9 +71,12 @@ __all__ = [
 
 from analysis import analysis as an
 import data as d
+import matplotlib.pyplot as plt
 import time
+import warnings
 
 def main():
+    warnings.filterwarnings("ignore")
     while(True):
 
         current_time = time.time()
@@ -100,7 +105,8 @@ def main():
         print(" analysis backtimed to: " + str(previous_time))
 
         print(" loading data")
-        data = d.get_data_formatted(apikey, competition)
+        data = d.get_match_data_formatted(apikey, competition)
+        pit_data = d.pit = d.get_pit_data_formatted(apikey, competition)
         print(" loaded data")
 
         print(" running tests")
@@ -111,10 +117,14 @@ def main():
         metrics = metricsloop(tbakey, apikey, competition, previous_time)
         print(" finished metrics")
 
+        print(" running pit analysis")
+        pit = pitloop(pit_data, config)
+        print(" finished pit analysis")
+
         d.set_analysis_flags(apikey, "latest_update", {"latest_update":current_time})
         
         print(" pushing to database")
-        push_to_database(apikey, competition, results, metrics)
+        push_to_database(apikey, competition, results, metrics, pit)
         print(" pushed to database")
 
 def load_config(file):
@@ -152,21 +162,21 @@ def simplestats(data, test):
         return an.histo_analysis([list(range(len(data))), data])
 
     if(test == "regression_linear"):
-        return an.regression('cpu', [list(range(len(data)))], [data], ['lin'], _iterations = 5000)
+        return an.regression(list(range(len(data))), data, ['lin'])
 
     if(test == "regression_logarithmic"):
-        return an.regression('cpu', [list(range(len(data)))], [data], ['log'], _iterations = 5000)
+        return an.regression(list(range(len(data))), data, ['log'])
 
     if(test == "regression_exponential"):
-        return an.regression('cpu', [list(range(len(data)))], [data], ['exp'], _iterations = 5000)
+        return an.regression(list(range(len(data))), data, ['exp'])
 
     if(test == "regression_polynomial"):
-        return an.regression('cpu', [list(range(len(data)))], [data], ['ply'], _iterations = 5000)
+        return an.regression(list(range(len(data))), data, ['ply'])
 
     if(test == "regression_sigmoidal"):
-        return an.regression('cpu', [list(range(len(data)))], [data], ['sig'], _iterations = 5000)
+        return an.regression(list(range(len(data))), data, ['sig'])
 
-def push_to_database(apikey, competition, results, metrics):
+def push_to_database(apikey, competition, results, metrics, pit):
 
     for team in results:
 
@@ -176,6 +186,10 @@ def push_to_database(apikey, competition, results, metrics):
 
         d.push_team_metrics_data(apikey, competition, team, metrics[team])
 
+    for variable in pit:
+
+        d.push_team_pit_data(apikey, competition, variable, pit[variable])
+
 def metricsloop(tbakey, apikey, competition, timestamp): # listener based metrics update
 
     elo_N = 400
@@ -325,6 +339,18 @@ def load_metrics(apikey, competition, match, group_name):
 
     return group
 
+def pitloop(pit, tests):
+
+    return_vector = {}
+    for team in pit:
+        for variable in pit[team]:
+            if(variable in tests):
+                if(not variable in return_vector):
+                    return_vector[variable] = []
+                return_vector[variable].append(pit[team][variable])
+
+    return return_vector
+
 main()
 
 """

data analysis/visualize_pit.py

@@ -0,0 +1,59 @@
+# 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=(20,10))
+
+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()
+
+
+# %%
+
+