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data analysis/keys.txt
data analysis/check_for_new_matches.ipynb
data analysis/test.ipynb
data analysis/visualize_pit.ipynb
data analysis/visualize_pit.ipynb
data analysis/config/keys.config

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Metadata-Version: 2.1
Name: analysis
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
Author-email: titanscout2022@gmail.com
License: GNU General Public License v3.0
Description: UNKNOWN
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown

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setup.py
analysis/__init__.py
analysis/analysis.py
analysis/regression.py
analysis/titanlearn.py
analysis/trueskill.py
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

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numba
numpy
scipy
scikit-learn
six
matplotlib

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analysis

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# 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__ = "1.1.13.006"
# 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
- 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:
- fixed numba issues by removing numba from elo, glicko2 and trueskill
1.1.12.004:
- renamed gliko to glicko
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__ = [
'load_csv',
'basic_stats',
'z_score',
'z_normalize',
'histo_analysis',
'regression',
'elo',
'glicko2',
'trueskill',
'RegressionMetrics',
'ClassificationMetrics',
'kmeans',
'pca',
'decisiontree',
'knn_classifier',
'knn_regressor',
'NaiveBayes',
'SVM',
'random_forest_classifier',
'random_forest_regressor',
'Glicko2',
# 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 scipy
from scipy import *
import sklearn
from sklearn import *
from analysis import trueskill as Trueskill
class error(ValueError):
pass
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):
if(len(hist_data[0]) > 2):
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]
else:
return None
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:
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
try:
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
try:
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 = np.array([inputs])
outputs = np.array([outputs])
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 tanh (b(x + c)) + d
try:
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
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))
def glicko2(starting_score, starting_rd, starting_vol, opposing_score, opposing_rd, observations):
player = Glicko2(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)
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 Glicko2:
_tau = 0.5
def getRating(self):
return (self.__rating * 173.7178) + 1500
def setRating(self, rating):
self.__rating = (rating - 1500) / 173.7178
rating = property(getRating, setRating)
def getRd(self):
return self.__rd * 173.7178
def setRd(self, rd):
self.__rd = rd / 173.7178
rd = property(getRd, setRd)
def __init__(self, rating = 1500, rd = 350, vol = 0.06):
self.setRating(rating)
self.setRd(rd)
self.vol = vol
def _preRatingRD(self):
self.__rd = math.sqrt(math.pow(self.__rd, 2) + math.pow(self.vol, 2))
def update_player(self, rating_list, RD_list, outcome_list):
rating_list = [(x - 1500) / 173.7178 for x in rating_list]
RD_list = [x / 173.7178 for x in RD_list]
v = self._v(rating_list, RD_list)
self.vol = self._newVol(rating_list, RD_list, outcome_list, v)
self._preRatingRD()
self.__rd = 1 / math.sqrt((1 / math.pow(self.__rd, 2)) + (1 / v))
tempSum = 0
for i in range(len(rating_list)):
tempSum += self._g(RD_list[i]) * \
(outcome_list[i] - self._E(rating_list[i], RD_list[i]))
self.__rating += math.pow(self.__rd, 2) * tempSum
def _newVol(self, rating_list, RD_list, outcome_list, v):
i = 0
delta = self._delta(rating_list, RD_list, outcome_list, v)
a = math.log(math.pow(self.vol, 2))
tau = self._tau
x0 = a
x1 = 0
while x0 != x1:
# New iteration, so x(i) becomes x(i-1)
x0 = x1
d = math.pow(self.__rating, 2) + v + math.exp(x0)
h1 = -(x0 - a) / math.pow(tau, 2) - 0.5 * math.exp(x0) \
/ d + 0.5 * math.exp(x0) * math.pow(delta / d, 2)
h2 = -1 / math.pow(tau, 2) - 0.5 * math.exp(x0) * \
(math.pow(self.__rating, 2) + v) \
/ math.pow(d, 2) + 0.5 * math.pow(delta, 2) * math.exp(x0) \
* (math.pow(self.__rating, 2) + v - math.exp(x0)) / math.pow(d, 3)
x1 = x0 - (h1 / h2)
return math.exp(x1 / 2)
def _delta(self, rating_list, RD_list, outcome_list, v):
tempSum = 0
for i in range(len(rating_list)):
tempSum += self._g(RD_list[i]) * (outcome_list[i] - self._E(rating_list[i], RD_list[i]))
return v * tempSum
def _v(self, rating_list, RD_list):
tempSum = 0
for i in range(len(rating_list)):
tempE = self._E(rating_list[i], RD_list[i])
tempSum += math.pow(self._g(RD_list[i]), 2) * tempE * (1 - tempE)
return 1 / tempSum
def _E(self, p2rating, p2RD):
return 1 / (1 + math.exp(-1 * self._g(p2RD) * \
(self.__rating - p2rating)))
def _g(self, RD):
return 1 / math.sqrt(1 + 3 * math.pow(RD, 2) / math.pow(math.pi, 2))
def did_not_compete(self):
self._preRatingRD()

220
analysis-master/analysis/regression.py
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@ -1,220 +0,0 @@
# 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__ = "1.0.0.004"
# changelog should be viewed using print(analysis.regression.__changelog__)
__changelog__ = """
1.0.0.004:
- bug fixes
- fixed 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'
]
import torch
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

122
analysis-master/analysis/titanlearn.py
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# Titan Robotics Team 2022: ML Module
# Written by Arthur Lu & Jacob Levine
# Notes:
# this should be imported as a python module using 'import titanlearn'
# this should be included in the local directory or environment variable
# this module is optimized for multhreaded computing
# this module learns from its mistakes far faster than 2022's captains
# setup:
__version__ = "2.0.1.001"
#changelog should be viewed using print(analysis.__changelog__)
__changelog__ = """changelog:
2.0.1.001:
- removed matplotlib import
- removed graphloss()
2.0.1.000:
- added net, dataset, dataloader, and stdtrain template definitions
- added graphloss function
2.0.0.001:
- added clear functions
2.0.0.000:
- complete rewrite planned
- depreciated 1.0.0.xxx versions
- added simple training loop
1.0.0.xxx:
-added generation of ANNS, basic SGD training
"""
__author__ = (
"Arthur Lu <arthurlu@ttic.edu>,"
"Jacob Levine <jlevine@ttic.edu>,"
)
__all__ = [
'clear',
'net',
'dataset',
'dataloader',
'train',
'stdtrainer',
]
import torch
from os import system, name
import numpy as np
def clear():
if name == 'nt':
_ = system('cls')
else:
_ = system('clear')
class net(torch.nn.Module): #template for standard neural net
def __init__(self):
super(Net, self).__init__()
def forward(self, input):
pass
class dataset(torch.utils.data.Dataset): #template for standard dataset
def __init__(self):
super(torch.utils.data.Dataset).__init__()
def __getitem__(self, index):
pass
def __len__(self):
pass
def dataloader(dataset, batch_size, num_workers, shuffle = True):
return torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=shuffle, num_workers=num_workers)
def train(device, net, epochs, trainloader, optimizer, criterion): #expects standard dataloader, whch returns (inputs, labels)
dataset_len = trainloader.dataset.__len__()
iter_count = 0
running_loss = 0
running_loss_list = []
for epoch in range(epochs): # loop over the dataset multiple times
for i, data in enumerate(trainloader, 0):
inputs = data[0].to(device)
labels = data[1].to(device)
optimizer.zero_grad()
outputs = net(inputs)
loss = criterion(outputs, labels.to(torch.float))
loss.backward()
optimizer.step()
# monitoring steps below
iter_count += 1
running_loss += loss.item()
running_loss_list.append(running_loss)
clear()
print("training on: " + device)
print("iteration: " + str(i) + "/" + str(int(dataset_len / trainloader.batch_size)) + " | " + "epoch: " + str(epoch) + "/" + str(epochs))
print("current batch loss: " + str(loss.item))
print("running loss: " + str(running_loss / iter_count))
return net, running_loss_list
print("finished training")
def stdtrainer(net, criterion, optimizer, dataloader, epochs, batch_size):
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
net = net.to(device)
criterion = criterion.to(device)
optimizer = optimizer.to(device)
trainloader = dataloader
return train(device, net, epochs, trainloader, optimizer, criterion)

907
analysis-master/analysis/trueskill.py
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@ -1,907 +0,0 @@
from __future__ import absolute_import
from itertools import chain
import math
from six import iteritems
from six.moves import map, range, zip
from six import iterkeys
import copy
try:
from numbers import Number
except ImportError:
Number = (int, long, float, complex)
inf = float('inf')
class Gaussian(object):
#: Precision, the inverse of the variance.
pi = 0
#: Precision adjusted mean, the precision multiplied by the mean.
tau = 0
def __init__(self, mu=None, sigma=None, pi=0, tau=0):
if mu is not None:
if sigma is None:
raise TypeError('sigma argument is needed')
elif sigma == 0:
raise ValueError('sigma**2 should be greater than 0')
pi = sigma ** -2
tau = pi * mu
self.pi = pi
self.tau = tau
@property
def mu(self):
return self.pi and self.tau / self.pi
@property
def sigma(self):
return math.sqrt(1 / self.pi) if self.pi else inf
def __mul__(self, other):
pi, tau = self.pi + other.pi, self.tau + other.tau
return Gaussian(pi=pi, tau=tau)
def __truediv__(self, other):
pi, tau = self.pi - other.pi, self.tau - other.tau
return Gaussian(pi=pi, tau=tau)
__div__ = __truediv__ # for Python 2
def __eq__(self, other):
return self.pi == other.pi and self.tau == other.tau
def __lt__(self, other):
return self.mu < other.mu
def __le__(self, other):
return self.mu <= other.mu
def __gt__(self, other):
return self.mu > other.mu
def __ge__(self, other):
return self.mu >= other.mu
def __repr__(self):
return 'N(mu={:.3f}, sigma={:.3f})'.format(self.mu, self.sigma)
def _repr_latex_(self):
latex = r'\mathcal{{ N }}( {:.3f}, {:.3f}^2 )'.format(self.mu, self.sigma)
return '$%s$' % latex
class Matrix(list):
def __init__(self, src, height=None, width=None):
if callable(src):
f, src = src, {}
size = [height, width]
if not height:
def set_height(height):
size[0] = height
size[0] = set_height
if not width:
def set_width(width):
size[1] = width
size[1] = set_width
try:
for (r, c), val in f(*size):
src[r, c] = val
except TypeError:
raise TypeError('A callable src must return an interable '
'which generates a tuple containing '
'coordinate and value')
height, width = tuple(size)
if height is None or width is None:
raise TypeError('A callable src must call set_height and '
'set_width if the size is non-deterministic')
if isinstance(src, list):
is_number = lambda x: isinstance(x, Number)
unique_col_sizes = set(map(len, src))
everything_are_number = filter(is_number, sum(src, []))
if len(unique_col_sizes) != 1 or not everything_are_number:
raise ValueError('src must be a rectangular array of numbers')
two_dimensional_array = src
elif isinstance(src, dict):
if not height or not width:
w = h = 0
for r, c in iterkeys(src):
if not height:
h = max(h, r + 1)
if not width:
w = max(w, c + 1)
if not height:
height = h
if not width:
width = w
two_dimensional_array = []
for r in range(height):
row = []
two_dimensional_array.append(row)
for c in range(width):
row.append(src.get((r, c), 0))
else:
raise TypeError('src must be a list or dict or callable')
super(Matrix, self).__init__(two_dimensional_array)
@property
def height(self):
return len(self)
@property
def width(self):
return len(self[0])
def transpose(self):
height, width = self.height, self.width
src = {}
for c in range(width):
for r in range(height):
src[c, r] = self[r][c]
return type(self)(src, height=width, width=height)
def minor(self, row_n, col_n):
height, width = self.height, self.width
if not (0 <= row_n < height):
raise ValueError('row_n should be between 0 and %d' % height)
elif not (0 <= col_n < width):
raise ValueError('col_n should be between 0 and %d' % width)
two_dimensional_array = []
for r in range(height):
if r == row_n:
continue
row = []
two_dimensional_array.append(row)
for c in range(width):
if c == col_n:
continue
row.append(self[r][c])
return type(self)(two_dimensional_array)
def determinant(self):
height, width = self.height, self.width
if height != width:
raise ValueError('Only square matrix can calculate a determinant')
tmp, rv = copy.deepcopy(self), 1.
for c in range(width - 1, 0, -1):
pivot, r = max((abs(tmp[r][c]), r) for r in range(c + 1))
pivot = tmp[r][c]
if not pivot:
return 0.
tmp[r], tmp[c] = tmp[c], tmp[r]
if r != c:
rv = -rv
rv *= pivot
fact = -1. / pivot
for r in range(c):
f = fact * tmp[r][c]
for x in range(c):
tmp[r][x] += f * tmp[c][x]
return rv * tmp[0][0]
def adjugate(self):
height, width = self.height, self.width
if height != width:
raise ValueError('Only square matrix can be adjugated')
if height == 2:
a, b = self[0][0], self[0][1]
c, d = self[1][0], self[1][1]
return type(self)([[d, -b], [-c, a]])
src = {}
for r in range(height):
for c in range(width):
sign = -1 if (r + c) % 2 else 1
src[r, c] = self.minor(r, c).determinant() * sign
return type(self)(src, height, width)
def inverse(self):
if self.height == self.width == 1:
return type(self)([[1. / self[0][0]]])
return (1. / self.determinant()) * self.adjugate()
def __add__(self, other):
height, width = self.height, self.width
if (height, width) != (other.height, other.width):
raise ValueError('Must be same size')
src = {}
for r in range(height):
for c in range(width):
src[r, c] = self[r][c] + other[r][c]
return type(self)(src, height, width)
def __mul__(self, other):
if self.width != other.height:
raise ValueError('Bad size')
height, width = self.height, other.width
src = {}
for r in range(height):
for c in range(width):
src[r, c] = sum(self[r][x] * other[x][c]
for x in range(self.width))
return type(self)(src, height, width)
def __rmul__(self, other):
if not isinstance(other, Number):
raise TypeError('The operand should be a number')
height, width = self.height, self.width
src = {}
for r in range(height):
for c in range(width):
src[r, c] = other * self[r][c]
return type(self)(src, height, width)
def __repr__(self):
return '{}({})'.format(type(self).__name__, super(Matrix, self).__repr__())
def _repr_latex_(self):
rows = [' && '.join(['%.3f' % cell for cell in row]) for row in self]
latex = r'\begin{matrix} %s \end{matrix}' % r'\\'.join(rows)
return '$%s$' % latex
def _gen_erfcinv(erfc, math=math):
def erfcinv(y):
"""The inverse function of erfc."""
if y >= 2:
return -100.
elif y <= 0:
return 100.
zero_point = y < 1
if not zero_point:
y = 2 - y
t = math.sqrt(-2 * math.log(y / 2.))
x = -0.70711 * \
((2.30753 + t * 0.27061) / (1. + t * (0.99229 + t * 0.04481)) - t)
for i in range(2):
err = erfc(x) - y
x += err / (1.12837916709551257 * math.exp(-(x ** 2)) - x * err)
return x if zero_point else -x
return erfcinv
def _gen_ppf(erfc, math=math):
erfcinv = _gen_erfcinv(erfc, math)
def ppf(x, mu=0, sigma=1):
return mu - sigma * math.sqrt(2) * erfcinv(2 * x)
return ppf
def erfc(x):
z = abs(x)
t = 1. / (1. + z / 2.)
r = t * math.exp(-z * z - 1.26551223 + t * (1.00002368 + t * (
0.37409196 + t * (0.09678418 + t * (-0.18628806 + t * (
0.27886807 + t * (-1.13520398 + t * (1.48851587 + t * (
-0.82215223 + t * 0.17087277
)))
)))
)))
return 2. - r if x < 0 else r
def cdf(x, mu=0, sigma=1):
return 0.5 * erfc(-(x - mu) / (sigma * math.sqrt(2)))
def pdf(x, mu=0, sigma=1):
return (1 / math.sqrt(2 * math.pi) * abs(sigma) *
math.exp(-(((x - mu) / abs(sigma)) ** 2 / 2)))
ppf = _gen_ppf(erfc)
def choose_backend(backend):
if backend is None: # fallback
return cdf, pdf, ppf
elif backend == 'mpmath':
try:
import mpmath
except ImportError:
raise ImportError('Install "mpmath" to use this backend')
return mpmath.ncdf, mpmath.npdf, _gen_ppf(mpmath.erfc, math=mpmath)
elif backend == 'scipy':
try:
from scipy.stats import norm
except ImportError:
raise ImportError('Install "scipy" to use this backend')
return norm.cdf, norm.pdf, norm.ppf
raise ValueError('%r backend is not defined' % backend)
def available_backends():
backends = [None]
for backend in ['mpmath', 'scipy']:
try:
__import__(backend)
except ImportError:
continue
backends.append(backend)
return backends
class Node(object):
pass
class Variable(Node, Gaussian):
def __init__(self):
self.messages = {}
super(Variable, self).__init__()
def set(self, val):
delta = self.delta(val)
self.pi, self.tau = val.pi, val.tau
return delta
def delta(self, other):
pi_delta = abs(self.pi - other.pi)
if pi_delta == inf:
return 0.
return max(abs(self.tau - other.tau), math.sqrt(pi_delta))
def update_message(self, factor, pi=0, tau=0, message=None):
message = message or Gaussian(pi=pi, tau=tau)
old_message, self[factor] = self[factor], message
return self.set(self / old_message * message)
def update_value(self, factor, pi=0, tau=0, value=None):
value = value or Gaussian(pi=pi, tau=tau)
old_message = self[factor]
self[factor] = value * old_message / self
return self.set(value)
def __getitem__(self, factor):
return self.messages[factor]
def __setitem__(self, factor, message):
self.messages[factor] = message
def __repr__(self):
args = (type(self).__name__, super(Variable, self).__repr__(),
len(self.messages), '' if len(self.messages) == 1 else 's')
return '<%s %s with %d connection%s>' % args
class Factor(Node):
def __init__(self, variables):
self.vars = variables
for var in variables:
var[self] = Gaussian()
def down(self):
return 0
def up(self):
return 0
@property
def var(self):
assert len(self.vars) == 1
return self.vars[0]
def __repr__(self):
args = (type(self).__name__, len(self.vars),
'' if len(self.vars) == 1 else 's')
return '<%s with %d connection%s>' % args
class PriorFactor(Factor):
def __init__(self, var, val, dynamic=0):
super(PriorFactor, self).__init__([var])
self.val = val
self.dynamic = dynamic
def down(self):
sigma = math.sqrt(self.val.sigma ** 2 + self.dynamic ** 2)
value = Gaussian(self.val.mu, sigma)
return self.var.update_value(self, value=value)
class LikelihoodFactor(Factor):
def __init__(self, mean_var, value_var, variance):
super(LikelihoodFactor, self).__init__([mean_var, value_var])
self.mean = mean_var
self.value = value_var
self.variance = variance
def calc_a(self, var):
return 1. / (1. + self.variance * var.pi)
def down(self):
# update value.
msg = self.mean / self.mean[self]
a = self.calc_a(msg)
return self.value.update_message(self, a * msg.pi, a * msg.tau)
def up(self):
# update mean.
msg = self.value / self.value[self]
a = self.calc_a(msg)
return self.mean.update_message(self, a * msg.pi, a * msg.tau)
class SumFactor(Factor):
def __init__(self, sum_var, term_vars, coeffs):
super(SumFactor, self).__init__([sum_var] + term_vars)
self.sum = sum_var
self.terms = term_vars
self.coeffs = coeffs
def down(self):
vals = self.terms
msgs = [var[self] for var in vals]
return self.update(self.sum, vals, msgs, self.coeffs)
def up(self, index=0):
coeff = self.coeffs[index]
coeffs = []
for x, c in enumerate(self.coeffs):
try:
if x == index:
coeffs.append(1. / coeff)
else:
coeffs.append(-c / coeff)
except ZeroDivisionError:
coeffs.append(0.)
vals = self.terms[:]
vals[index] = self.sum
msgs = [var[self] for var in vals]
return self.update(self.terms[index], vals, msgs, coeffs)
def update(self, var, vals, msgs, coeffs):
pi_inv = 0
mu = 0
for val, msg, coeff in zip(vals, msgs, coeffs):
div = val / msg
mu += coeff * div.mu
if pi_inv == inf:
continue
try:
# numpy.float64 handles floating-point error by different way.
# For example, it can just warn RuntimeWarning on n/0 problem
# instead of throwing ZeroDivisionError. So div.pi, the
# denominator has to be a built-in float.
pi_inv += coeff ** 2 / float(div.pi)
except ZeroDivisionError:
pi_inv = inf
pi = 1. / pi_inv
tau = pi * mu
return var.update_message(self, pi, tau)
class TruncateFactor(Factor):
def __init__(self, var, v_func, w_func, draw_margin):
super(TruncateFactor, self).__init__([var])
self.v_func = v_func
self.w_func = w_func
self.draw_margin = draw_margin
def up(self):
val = self.var
msg = self.var[self]
div = val / msg
sqrt_pi = math.sqrt(div.pi)
args = (div.tau / sqrt_pi, self.draw_margin * sqrt_pi)
v = self.v_func(*args)
w = self.w_func(*args)
denom = (1. - w)
pi, tau = div.pi / denom, (div.tau + sqrt_pi * v) / denom
return val.update_value(self, pi, tau)
#: Default initial mean of ratings.
MU = 25.
#: Default initial standard deviation of ratings.
SIGMA = MU / 3
#: Default distance that guarantees about 76% chance of winning.
BETA = SIGMA / 2
#: Default dynamic factor.
TAU = SIGMA / 100
#: Default draw probability of the game.
DRAW_PROBABILITY = .10
#: A basis to check reliability of the result.
DELTA = 0.0001
def calc_draw_probability(draw_margin, size, env=None):
if env is None:
env = global_env()
return 2 * env.cdf(draw_margin / (math.sqrt(size) * env.beta)) - 1
def calc_draw_margin(draw_probability, size, env=None):
if env is None:
env = global_env()
return env.ppf((draw_probability + 1) / 2.) * math.sqrt(size) * env.beta
def _team_sizes(rating_groups):
team_sizes = [0]
for group in rating_groups:
team_sizes.append(len(group) + team_sizes[-1])
del team_sizes[0]
return team_sizes
def _floating_point_error(env):
if env.backend == 'mpmath':
msg = 'Set "mpmath.mp.dps" to higher'
else:
msg = 'Cannot calculate correctly, set backend to "mpmath"'
return FloatingPointError(msg)
class Rating(Gaussian):
def __init__(self, mu=None, sigma=None):
if isinstance(mu, tuple):
mu, sigma = mu
elif isinstance(mu, Gaussian):
mu, sigma = mu.mu, mu.sigma
if mu is None:
mu = global_env().mu
if sigma is None:
sigma = global_env().sigma
super(Rating, self).__init__(mu, sigma)
def __int__(self):
return int(self.mu)
def __long__(self):
return long(self.mu)
def __float__(self):
return float(self.mu)
def __iter__(self):
return iter((self.mu, self.sigma))
def __repr__(self):
c = type(self)
args = ('.'.join([c.__module__, c.__name__]), self.mu, self.sigma)
return '%s(mu=%.3f, sigma=%.3f)' % args
class TrueSkill(object):
def __init__(self, mu=MU, sigma=SIGMA, beta=BETA, tau=TAU,
draw_probability=DRAW_PROBABILITY, backend=None):
self.mu = mu
self.sigma = sigma
self.beta = beta
self.tau = tau
self.draw_probability = draw_probability
self.backend = backend
if isinstance(backend, tuple):
self.cdf, self.pdf, self.ppf = backend
else:
self.cdf, self.pdf, self.ppf = choose_backend(backend)
def create_rating(self, mu=None, sigma=None):
if mu is None:
mu = self.mu
if sigma is None:
sigma = self.sigma
return Rating(mu, sigma)
def v_win(self, diff, draw_margin):
x = diff - draw_margin
denom = self.cdf(x)
return (self.pdf(x) / denom) if denom else -x
def v_draw(self, diff, draw_margin):
abs_diff = abs(diff)
a, b = draw_margin - abs_diff, -draw_margin - abs_diff
denom = self.cdf(a) - self.cdf(b)
numer = self.pdf(b) - self.pdf(a)
return ((numer / denom) if denom else a) * (-1 if diff < 0 else +1)
def w_win(self, diff, draw_margin):
x = diff - draw_margin
v = self.v_win(diff, draw_margin)
w = v * (v + x)
if 0 < w < 1:
return w
raise _floating_point_error(self)
def w_draw(self, diff, draw_margin):
abs_diff = abs(diff)
a, b = draw_margin - abs_diff, -draw_margin - abs_diff
denom = self.cdf(a) - self.cdf(b)
if not denom:
raise _floating_point_error(self)
v = self.v_draw(abs_diff, draw_margin)
return (v ** 2) + (a * self.pdf(a) - b * self.pdf(b)) / denom
def validate_rating_groups(self, rating_groups):
# check group sizes
if len(rating_groups) < 2:
raise ValueError('Need multiple rating groups')
elif not all(rating_groups):
raise ValueError('Each group must contain multiple ratings')
# check group types
group_types = set(map(type, rating_groups))
if len(group_types) != 1:
raise TypeError('All groups should be same type')
elif group_types.pop() is Rating:
raise TypeError('Rating cannot be a rating group')
# normalize rating_groups
if isinstance(rating_groups[0], dict):
dict_rating_groups = rating_groups
rating_groups = []
keys = []
for dict_rating_group in dict_rating_groups:
rating_group, key_group = [], []
for key, rating in iteritems(dict_rating_group):
rating_group.append(rating)
key_group.append(key)
rating_groups.append(tuple(rating_group))
keys.append(tuple(key_group))
else:
rating_groups = list(rating_groups)
keys = None
return rating_groups, keys
def validate_weights(self, weights, rating_groups, keys=None):
if weights is None:
weights = [(1,) * len(g) for g in rating_groups]
elif isinstance(weights, dict):
weights_dict, weights = weights, []
for x, group in enumerate(rating_groups):
w = []
weights.append(w)
for y, rating in enumerate(group):
if keys is not None:
y = keys[x][y]
w.append(weights_dict.get((x, y), 1))
return weights
def factor_graph_builders(self, rating_groups, ranks, weights):
flatten_ratings = sum(map(tuple, rating_groups), ())
flatten_weights = sum(map(tuple, weights), ())
size = len(flatten_ratings)
group_size = len(rating_groups)
# create variables
rating_vars = [Variable() for x in range(size)]
perf_vars = [Variable() for x in range(size)]
team_perf_vars = [Variable() for x in range(group_size)]
team_diff_vars = [Variable() for x in range(group_size - 1)]
team_sizes = _team_sizes(rating_groups)
# layer builders
def build_rating_layer():
for rating_var, rating in zip(rating_vars, flatten_ratings):
yield PriorFactor(rating_var, rating, self.tau)
def build_perf_layer():
for rating_var, perf_var in zip(rating_vars, perf_vars):
yield LikelihoodFactor(rating_var, perf_var, self.beta ** 2)
def build_team_perf_layer():
for team, team_perf_var in enumerate(team_perf_vars):
if team > 0:
start = team_sizes[team - 1]
else:
start = 0
end = team_sizes[team]
child_perf_vars = perf_vars[start:end]
coeffs = flatten_weights[start:end]
yield SumFactor(team_perf_var, child_perf_vars, coeffs)
def build_team_diff_layer():
for team, team_diff_var in enumerate(team_diff_vars):
yield SumFactor(team_diff_var,
team_perf_vars[team:team + 2], [+1, -1])
def build_trunc_layer():
for x, team_diff_var in enumerate(team_diff_vars):
if callable(self.draw_probability):
# dynamic draw probability
team_perf1, team_perf2 = team_perf_vars[x:x + 2]
args = (Rating(team_perf1), Rating(team_perf2), self)
draw_probability = self.draw_probability(*args)
else:
# static draw probability
draw_probability = self.draw_probability
size = sum(map(len, rating_groups[x:x + 2]))
draw_margin = calc_draw_margin(draw_probability, size, self)
if ranks[x] == ranks[x + 1]: # is a tie?
v_func, w_func = self.v_draw, self.w_draw
else:
v_func, w_func = self.v_win, self.w_win
yield TruncateFactor(team_diff_var,
v_func, w_func, draw_margin)
# build layers
return (build_rating_layer, build_perf_layer, build_team_perf_layer,
build_team_diff_layer, build_trunc_layer)
def run_schedule(self, build_rating_layer, build_perf_layer,
build_team_perf_layer, build_team_diff_layer,
build_trunc_layer, min_delta=DELTA):
if min_delta <= 0:
raise ValueError('min_delta must be greater than 0')
layers = []
def build(builders):
layers_built = [list(build()) for build in builders]
layers.extend(layers_built)
return layers_built
# gray arrows
layers_built = build([build_rating_layer,
build_perf_layer,
build_team_perf_layer])
rating_layer, perf_layer, team_perf_layer = layers_built
for f in chain(*layers_built):
f.down()
# arrow #1, #2, #3
team_diff_layer, trunc_layer = build([build_team_diff_layer,
build_trunc_layer])
team_diff_len = len(team_diff_layer)
for x in range(10):
if team_diff_len == 1:
# only two teams
team_diff_layer[0].down()
delta = trunc_layer[0].up()
else:
# multiple teams
delta = 0
for x in range(team_diff_len - 1):
team_diff_layer[x].down()
delta = max(delta, trunc_layer[x].up())
team_diff_layer[x].up(1) # up to right variable
for x in range(team_diff_len - 1, 0, -1):
team_diff_layer[x].down()
delta = max(delta, trunc_layer[x].up())
team_diff_layer[x].up(0) # up to left variable
# repeat until to small update
if delta <= min_delta:
break
# up both ends
team_diff_layer[0].up(0)
team_diff_layer[team_diff_len - 1].up(1)
# up the remainder of the black arrows
for f in team_perf_layer:
for x in range(len(f.vars) - 1):
f.up(x)
for f in perf_layer:
f.up()
return layers
def rate(self, rating_groups, ranks=None, weights=None, min_delta=DELTA):
rating_groups, keys = self.validate_rating_groups(rating_groups)
weights = self.validate_weights(weights, rating_groups, keys)
group_size = len(rating_groups)
if ranks is None:
ranks = range(group_size)
elif len(ranks) != group_size:
raise ValueError('Wrong ranks')
# sort rating groups by rank
by_rank = lambda x: x[1][1]
sorting = sorted(enumerate(zip(rating_groups, ranks, weights)),
key=by_rank)
sorted_rating_groups, sorted_ranks, sorted_weights = [], [], []
for x, (g, r, w) in sorting:
sorted_rating_groups.append(g)
sorted_ranks.append(r)
# make weights to be greater than 0
sorted_weights.append(max(min_delta, w_) for w_ in w)
# build factor graph
args = (sorted_rating_groups, sorted_ranks, sorted_weights)
builders = self.factor_graph_builders(*args)
args = builders + (min_delta,)
layers = self.run_schedule(*args)
# make result
rating_layer, team_sizes = layers[0], _team_sizes(sorted_rating_groups)
transformed_groups = []
for start, end in zip([0] + team_sizes[:-1], team_sizes):
group = []
for f in rating_layer[start:end]:
group.append(Rating(float(f.var.mu), float(f.var.sigma)))
transformed_groups.append(tuple(group))
by_hint = lambda x: x[0]
unsorting = sorted(zip((x for x, __ in sorting), transformed_groups),
key=by_hint)
if keys is None:
return [g for x, g in unsorting]
# restore the structure with input dictionary keys
return [dict(zip(keys[x], g)) for x, g in unsorting]
def quality(self, rating_groups, weights=None):
rating_groups, keys = self.validate_rating_groups(rating_groups)
weights = self.validate_weights(weights, rating_groups, keys)
flatten_ratings = sum(map(tuple, rating_groups), ())
flatten_weights = sum(map(tuple, weights), ())
length = len(flatten_ratings)
# a vector of all of the skill means
mean_matrix = Matrix([[r.mu] for r in flatten_ratings])
# a matrix whose diagonal values are the variances (sigma ** 2) of each
# of the players.
def variance_matrix(height, width):
variances = (r.sigma ** 2 for r in flatten_ratings)
for x, variance in enumerate(variances):
yield (x, x), variance
variance_matrix = Matrix(variance_matrix, length, length)
# the player-team assignment and comparison matrix
def rotated_a_matrix(set_height, set_width):
t = 0
for r, (cur, _next) in enumerate(zip(rating_groups[:-1],
rating_groups[1:])):
for x in range(t, t + len(cur)):
yield (r, x), flatten_weights[x]
t += 1
x += 1
for x in range(x, x + len(_next)):
yield (r, x), -flatten_weights[x]
set_height(r + 1)
set_width(x + 1)
rotated_a_matrix = Matrix(rotated_a_matrix)
a_matrix = rotated_a_matrix.transpose()
# match quality further derivation
_ata = (self.beta ** 2) * rotated_a_matrix * a_matrix
_atsa = rotated_a_matrix * variance_matrix * a_matrix
start = mean_matrix.transpose() * a_matrix
middle = _ata + _atsa
end = rotated_a_matrix * mean_matrix
# make result
e_arg = (-0.5 * start * middle.inverse() * end).determinant()
s_arg = _ata.determinant() / middle.determinant()
return math.exp(e_arg) * math.sqrt(s_arg)
def expose(self, rating):
k = self.mu / self.sigma
return rating.mu - k * rating.sigma
def make_as_global(self):
return setup(env=self)
def __repr__(self):
c = type(self)
if callable(self.draw_probability):
f = self.draw_probability
draw_probability = '.'.join([f.__module__, f.__name__])
else:
draw_probability = '%.1f%%' % (self.draw_probability * 100)
if self.backend is None:
backend = ''
elif isinstance(self.backend, tuple):
backend = ', backend=...'
else:
backend = ', backend=%r' % self.backend
args = ('.'.join([c.__module__, c.__name__]), self.mu, self.sigma,
self.beta, self.tau, draw_probability, backend)
return ('%s(mu=%.3f, sigma=%.3f, beta=%.3f, tau=%.3f, '
'draw_probability=%s%s)' % args)
def rate_1vs1(rating1, rating2, drawn=False, min_delta=DELTA, env=None):
if env is None:
env = global_env()
ranks = [0, 0 if drawn else 1]
teams = env.rate([(rating1,), (rating2,)], ranks, min_delta=min_delta)
return teams[0][0], teams[1][0]
def quality_1vs1(rating1, rating2, env=None):
if env is None:
env = global_env()
return env.quality([(rating1,), (rating2,)])
def global_env():
try:
global_env.__trueskill__
except AttributeError:
# setup the default environment
setup()
return global_env.__trueskill__
def setup(mu=MU, sigma=SIGMA, beta=BETA, tau=TAU,
draw_probability=DRAW_PROBABILITY, backend=None, env=None):
if env is None:
env = TrueSkill(mu, sigma, beta, tau, draw_probability, backend)
global_env.__trueskill__ = env
return env
def rate(rating_groups, ranks=None, weights=None, min_delta=DELTA):
return global_env().rate(rating_groups, ranks, weights, min_delta)
def quality(rating_groups, weights=None):
return global_env().quality(rating_groups, weights)
def expose(rating):
return global_env().expose(rating)

34
analysis-master/analysis/visualization.py
View File

@ -1,34 +0,0 @@
# Titan Robotics Team 2022: Visualization Module
# Written by Arthur Lu & Jacob Levine
# Notes:
# this should be imported as a python module using 'import visualization'
# this should be included in the local directory or environment variable
# fancy
# setup:
__version__ = "1.0.0.000"
#changelog should be viewed using print(analysis.__changelog__)
__changelog__ = """changelog:
1.0.0.000:
- created visualization.py
- added graphloss()
- added imports
"""
__author__ = (
"Arthur Lu <arthurlu@ttic.edu>,"
"Jacob Levine <jlevine@ttic.edu>,"
)
__all__ = [
'graphloss',
]
import matplotlib.pyplot as plt
def graphloss(losses):
x = range(0, len(losses))
plt.plot(x, losses)
plt.show()

1
analysis-master/build.sh
View File

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

0
analysis-master/build/lib/analysis/__init__.py
View File

790
analysis-master/build/lib/analysis/analysis.py
View File

@ -1,790 +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__ = "1.1.13.006"
# 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
- 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:
- fixed numba issues by removing numba from elo, glicko2 and trueskill
1.1.12.004:
- renamed gliko to glicko
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__ = [
'load_csv',
'basic_stats',
'z_score',
'z_normalize',
'histo_analysis',
'regression',
'elo',
'glicko2',
'trueskill',
'RegressionMetrics',
'ClassificationMetrics',
'kmeans',
'pca',
'decisiontree',
'knn_classifier',
'knn_regressor',
'NaiveBayes',
'SVM',
'random_forest_classifier',
'random_forest_regressor',
'Glicko2',
# 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 scipy
from scipy import *
import sklearn
from sklearn import *
from analysis import trueskill as Trueskill
class error(ValueError):
pass
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):
if(len(hist_data[0]) > 2):
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]
else:
return None
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:
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
try:
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
try:
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 = np.array([inputs])
outputs = np.array([outputs])
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 tanh (b(x + c)) + d
try:
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
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))
def glicko2(starting_score, starting_rd, starting_vol, opposing_score, opposing_rd, observations):
player = Glicko2(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)
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 Glicko2:
_tau = 0.5
def getRating(self):
return (self.__rating * 173.7178) + 1500
def setRating(self, rating):
self.__rating = (rating - 1500) / 173.7178
rating = property(getRating, setRating)
def getRd(self):
return self.__rd * 173.7178
def setRd(self, rd):
self.__rd = rd / 173.7178
rd = property(getRd, setRd)
def __init__(self, rating = 1500, rd = 350, vol = 0.06):
self.setRating(rating)
self.setRd(rd)
self.vol = vol
def _preRatingRD(self):
self.__rd = math.sqrt(math.pow(self.__rd, 2) + math.pow(self.vol, 2))
def update_player(self, rating_list, RD_list, outcome_list):
rating_list = [(x - 1500) / 173.7178 for x in rating_list]
RD_list = [x / 173.7178 for x in RD_list]
v = self._v(rating_list, RD_list)
self.vol = self._newVol(rating_list, RD_list, outcome_list, v)
self._preRatingRD()
self.__rd = 1 / math.sqrt((1 / math.pow(self.__rd, 2)) + (1 / v))
tempSum = 0
for i in range(len(rating_list)):
tempSum += self._g(RD_list[i]) * \
(outcome_list[i] - self._E(rating_list[i], RD_list[i]))
self.__rating += math.pow(self.__rd, 2) * tempSum
def _newVol(self, rating_list, RD_list, outcome_list, v):
i = 0
delta = self._delta(rating_list, RD_list, outcome_list, v)
a = math.log(math.pow(self.vol, 2))
tau = self._tau
x0 = a
x1 = 0
while x0 != x1:
# New iteration, so x(i) becomes x(i-1)
x0 = x1
d = math.pow(self.__rating, 2) + v + math.exp(x0)
h1 = -(x0 - a) / math.pow(tau, 2) - 0.5 * math.exp(x0) \
/ d + 0.5 * math.exp(x0) * math.pow(delta / d, 2)
h2 = -1 / math.pow(tau, 2) - 0.5 * math.exp(x0) * \
(math.pow(self.__rating, 2) + v) \
/ math.pow(d, 2) + 0.5 * math.pow(delta, 2) * math.exp(x0) \
* (math.pow(self.__rating, 2) + v - math.exp(x0)) / math.pow(d, 3)
x1 = x0 - (h1 / h2)
return math.exp(x1 / 2)
def _delta(self, rating_list, RD_list, outcome_list, v):
tempSum = 0
for i in range(len(rating_list)):
tempSum += self._g(RD_list[i]) * (outcome_list[i] - self._E(rating_list[i], RD_list[i]))
return v * tempSum
def _v(self, rating_list, RD_list):
tempSum = 0
for i in range(len(rating_list)):
tempE = self._E(rating_list[i], RD_list[i])
tempSum += math.pow(self._g(RD_list[i]), 2) * tempE * (1 - tempE)
return 1 / tempSum
def _E(self, p2rating, p2RD):
return 1 / (1 + math.exp(-1 * self._g(p2RD) * \
(self.__rating - p2rating)))
def _g(self, RD):
return 1 / math.sqrt(1 + 3 * math.pow(RD, 2) / math.pow(math.pi, 2))
def did_not_compete(self):
self._preRatingRD()

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analysis-master/build/lib/analysis/regression.py
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@ -1,220 +0,0 @@
# 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__ = "1.0.0.004"
# changelog should be viewed using print(analysis.regression.__changelog__)
__changelog__ = """
1.0.0.004:
- bug fixes
- fixed 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'
]
import torch
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

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analysis-master/build/lib/analysis/titanlearn.py
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# Titan Robotics Team 2022: ML Module
# Written by Arthur Lu & Jacob Levine
# Notes:
# this should be imported as a python module using 'import titanlearn'
# this should be included in the local directory or environment variable
# this module is optimized for multhreaded computing
# this module learns from its mistakes far faster than 2022's captains
# setup:
__version__ = "2.0.1.001"
#changelog should be viewed using print(analysis.__changelog__)
__changelog__ = """changelog:
2.0.1.001:
- removed matplotlib import
- removed graphloss()
2.0.1.000:
- added net, dataset, dataloader, and stdtrain template definitions
- added graphloss function
2.0.0.001:
- added clear functions
2.0.0.000:
- complete rewrite planned
- depreciated 1.0.0.xxx versions
- added simple training loop
1.0.0.xxx:
-added generation of ANNS, basic SGD training
"""
__author__ = (
"Arthur Lu <arthurlu@ttic.edu>,"
"Jacob Levine <jlevine@ttic.edu>,"
)
__all__ = [
'clear',
'net',
'dataset',
'dataloader',
'train',
'stdtrainer',
]
import torch
from os import system, name
import numpy as np
def clear():
if name == 'nt':
_ = system('cls')
else:
_ = system('clear')
class net(torch.nn.Module): #template for standard neural net
def __init__(self):
super(Net, self).__init__()
def forward(self, input):
pass
class dataset(torch.utils.data.Dataset): #template for standard dataset
def __init__(self):
super(torch.utils.data.Dataset).__init__()
def __getitem__(self, index):
pass
def __len__(self):
pass
def dataloader(dataset, batch_size, num_workers, shuffle = True):
return torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=shuffle, num_workers=num_workers)
def train(device, net, epochs, trainloader, optimizer, criterion): #expects standard dataloader, whch returns (inputs, labels)
dataset_len = trainloader.dataset.__len__()
iter_count = 0
running_loss = 0
running_loss_list = []
for epoch in range(epochs): # loop over the dataset multiple times
for i, data in enumerate(trainloader, 0):
inputs = data[0].to(device)
labels = data[1].to(device)
optimizer.zero_grad()
outputs = net(inputs)
loss = criterion(outputs, labels.to(torch.float))
loss.backward()
optimizer.step()
# monitoring steps below
iter_count += 1
running_loss += loss.item()
running_loss_list.append(running_loss)
clear()
print("training on: " + device)
print("iteration: " + str(i) + "/" + str(int(dataset_len / trainloader.batch_size)) + " | " + "epoch: " + str(epoch) + "/" + str(epochs))
print("current batch loss: " + str(loss.item))
print("running loss: " + str(running_loss / iter_count))
return net, running_loss_list
print("finished training")
def stdtrainer(net, criterion, optimizer, dataloader, epochs, batch_size):
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
net = net.to(device)
criterion = criterion.to(device)
optimizer = optimizer.to(device)
trainloader = dataloader
return train(device, net, epochs, trainloader, optimizer, criterion)

907
analysis-master/build/lib/analysis/trueskill.py
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@ -1,907 +0,0 @@
from __future__ import absolute_import
from itertools import chain
import math
from six import iteritems
from six.moves import map, range, zip
from six import iterkeys
import copy
try:
from numbers import Number
except ImportError:
Number = (int, long, float, complex)
inf = float('inf')
class Gaussian(object):
#: Precision, the inverse of the variance.
pi = 0
#: Precision adjusted mean, the precision multiplied by the mean.
tau = 0
def __init__(self, mu=None, sigma=None, pi=0, tau=0):
if mu is not None:
if sigma is None:
raise TypeError('sigma argument is needed')
elif sigma == 0:
raise ValueError('sigma**2 should be greater than 0')
pi = sigma ** -2
tau = pi * mu
self.pi = pi
self.tau = tau
@property
def mu(self):
return self.pi and self.tau / self.pi
@property
def sigma(self):
return math.sqrt(1 / self.pi) if self.pi else inf
def __mul__(self, other):
pi, tau = self.pi + other.pi, self.tau + other.tau
return Gaussian(pi=pi, tau=tau)
def __truediv__(self, other):
pi, tau = self.pi - other.pi, self.tau - other.tau
return Gaussian(pi=pi, tau=tau)
__div__ = __truediv__ # for Python 2
def __eq__(self, other):
return self.pi == other.pi and self.tau == other.tau
def __lt__(self, other):
return self.mu < other.mu
def __le__(self, other):
return self.mu <= other.mu
def __gt__(self, other):
return self.mu > other.mu
def __ge__(self, other):
return self.mu >= other.mu
def __repr__(self):
return 'N(mu={:.3f}, sigma={:.3f})'.format(self.mu, self.sigma)
def _repr_latex_(self):
latex = r'\mathcal{{ N }}( {:.3f}, {:.3f}^2 )'.format(self.mu, self.sigma)
return '$%s$' % latex
class Matrix(list):
def __init__(self, src, height=None, width=None):
if callable(src):
f, src = src, {}
size = [height, width]
if not height:
def set_height(height):
size[0] = height
size[0] = set_height
if not width:
def set_width(width):
size[1] = width
size[1] = set_width
try:
for (r, c), val in f(*size):
src[r, c] = val
except TypeError:
raise TypeError('A callable src must return an interable '
'which generates a tuple containing '
'coordinate and value')
height, width = tuple(size)
if height is None or width is None:
raise TypeError('A callable src must call set_height and '
'set_width if the size is non-deterministic')
if isinstance(src, list):
is_number = lambda x: isinstance(x, Number)
unique_col_sizes = set(map(len, src))
everything_are_number = filter(is_number, sum(src, []))
if len(unique_col_sizes) != 1 or not everything_are_number:
raise ValueError('src must be a rectangular array of numbers')
two_dimensional_array = src
elif isinstance(src, dict):
if not height or not width:
w = h = 0
for r, c in iterkeys(src):
if not height:
h = max(h, r + 1)
if not width:
w = max(w, c + 1)
if not height:
height = h
if not width:
width = w
two_dimensional_array = []
for r in range(height):
row = []
two_dimensional_array.append(row)
for c in range(width):
row.append(src.get((r, c), 0))
else:
raise TypeError('src must be a list or dict or callable')
super(Matrix, self).__init__(two_dimensional_array)
@property
def height(self):
return len(self)
@property
def width(self):
return len(self[0])
def transpose(self):
height, width = self.height, self.width
src = {}
for c in range(width):
for r in range(height):
src[c, r] = self[r][c]
return type(self)(src, height=width, width=height)
def minor(self, row_n, col_n):
height, width = self.height, self.width
if not (0 <= row_n < height):
raise ValueError('row_n should be between 0 and %d' % height)
elif not (0 <= col_n < width):
raise ValueError('col_n should be between 0 and %d' % width)
two_dimensional_array = []
for r in range(height):
if r == row_n:
continue
row = []
two_dimensional_array.append(row)
for c in range(width):
if c == col_n:
continue
row.append(self[r][c])
return type(self)(two_dimensional_array)
def determinant(self):
height, width = self.height, self.width
if height != width:
raise ValueError('Only square matrix can calculate a determinant')
tmp, rv = copy.deepcopy(self), 1.
for c in range(width - 1, 0, -1):
pivot, r = max((abs(tmp[r][c]), r) for r in range(c + 1))
pivot = tmp[r][c]
if not pivot:
return 0.
tmp[r], tmp[c] = tmp[c], tmp[r]
if r != c:
rv = -rv
rv *= pivot
fact = -1. / pivot
for r in range(c):
f = fact * tmp[r][c]
for x in range(c):
tmp[r][x] += f * tmp[c][x]
return rv * tmp[0][0]
def adjugate(self):
height, width = self.height, self.width
if height != width:
raise ValueError('Only square matrix can be adjugated')
if height == 2:
a, b = self[0][0], self[0][1]
c, d = self[1][0], self[1][1]
return type(self)([[d, -b], [-c, a]])
src = {}
for r in range(height):
for c in range(width):
sign = -1 if (r + c) % 2 else 1
src[r, c] = self.minor(r, c).determinant() * sign
return type(self)(src, height, width)
def inverse(self):
if self.height == self.width == 1:
return type(self)([[1. / self[0][0]]])
return (1. / self.determinant()) * self.adjugate()
def __add__(self, other):
height, width = self.height, self.width
if (height, width) != (other.height, other.width):
raise ValueError('Must be same size')
src = {}
for r in range(height):
for c in range(width):
src[r, c] = self[r][c] + other[r][c]
return type(self)(src, height, width)
def __mul__(self, other):
if self.width != other.height:
raise ValueError('Bad size')
height, width = self.height, other.width
src = {}
for r in range(height):
for c in range(width):
src[r, c] = sum(self[r][x] * other[x][c]
for x in range(self.width))
return type(self)(src, height, width)
def __rmul__(self, other):
if not isinstance(other, Number):
raise TypeError('The operand should be a number')
height, width = self.height, self.width
src = {}
for r in range(height):
for c in range(width):
src[r, c] = other * self[r][c]
return type(self)(src, height, width)
def __repr__(self):
return '{}({})'.format(type(self).__name__, super(Matrix, self).__repr__())
def _repr_latex_(self):
rows = [' && '.join(['%.3f' % cell for cell in row]) for row in self]
latex = r'\begin{matrix} %s \end{matrix}' % r'\\'.join(rows)
return '$%s$' % latex
def _gen_erfcinv(erfc, math=math):
def erfcinv(y):
"""The inverse function of erfc."""
if y >= 2:
return -100.
elif y <= 0:
return 100.
zero_point = y < 1
if not zero_point:
y = 2 - y
t = math.sqrt(-2 * math.log(y / 2.))
x = -0.70711 * \
((2.30753 + t * 0.27061) / (1. + t * (0.99229 + t * 0.04481)) - t)
for i in range(2):
err = erfc(x) - y
x += err / (1.12837916709551257 * math.exp(-(x ** 2)) - x * err)
return x if zero_point else -x
return erfcinv
def _gen_ppf(erfc, math=math):
erfcinv = _gen_erfcinv(erfc, math)
def ppf(x, mu=0, sigma=1):
return mu - sigma * math.sqrt(2) * erfcinv(2 * x)
return ppf
def erfc(x):
z = abs(x)
t = 1. / (1. + z / 2.)
r = t * math.exp(-z * z - 1.26551223 + t * (1.00002368 + t * (
0.37409196 + t * (0.09678418 + t * (-0.18628806 + t * (
0.27886807 + t * (-1.13520398 + t * (1.48851587 + t * (
-0.82215223 + t * 0.17087277
)))
)))
)))
return 2. - r if x < 0 else r
def cdf(x, mu=0, sigma=1):
return 0.5 * erfc(-(x - mu) / (sigma * math.sqrt(2)))
def pdf(x, mu=0, sigma=1):
return (1 / math.sqrt(2 * math.pi) * abs(sigma) *
math.exp(-(((x - mu) / abs(sigma)) ** 2 / 2)))
ppf = _gen_ppf(erfc)
def choose_backend(backend):
if backend is None: # fallback
return cdf, pdf, ppf
elif backend == 'mpmath':
try:
import mpmath
except ImportError:
raise ImportError('Install "mpmath" to use this backend')
return mpmath.ncdf, mpmath.npdf, _gen_ppf(mpmath.erfc, math=mpmath)
elif backend == 'scipy':
try:
from scipy.stats import norm
except ImportError:
raise ImportError('Install "scipy" to use this backend')
return norm.cdf, norm.pdf, norm.ppf
raise ValueError('%r backend is not defined' % backend)
def available_backends():
backends = [None]
for backend in ['mpmath', 'scipy']:
try:
__import__(backend)
except ImportError:
continue
backends.append(backend)
return backends
class Node(object):
pass
class Variable(Node, Gaussian):
def __init__(self):
self.messages = {}
super(Variable, self).__init__()
def set(self, val):
delta = self.delta(val)
self.pi, self.tau = val.pi, val.tau
return delta
def delta(self, other):
pi_delta = abs(self.pi - other.pi)
if pi_delta == inf:
return 0.
return max(abs(self.tau - other.tau), math.sqrt(pi_delta))
def update_message(self, factor, pi=0, tau=0, message=None):
message = message or Gaussian(pi=pi, tau=tau)
old_message, self[factor] = self[factor], message
return self.set(self / old_message * message)
def update_value(self, factor, pi=0, tau=0, value=None):
value = value or Gaussian(pi=pi, tau=tau)
old_message = self[factor]
self[factor] = value * old_message / self
return self.set(value)
def __getitem__(self, factor):
return self.messages[factor]
def __setitem__(self, factor, message):
self.messages[factor] = message
def __repr__(self):
args = (type(self).__name__, super(Variable, self).__repr__(),
len(self.messages), '' if len(self.messages) == 1 else 's')
return '<%s %s with %d connection%s>' % args
class Factor(Node):
def __init__(self, variables):
self.vars = variables
for var in variables:
var[self] = Gaussian()
def down(self):
return 0
def up(self):
return 0
@property
def var(self):
assert len(self.vars) == 1
return self.vars[0]
def __repr__(self):
args = (type(self).__name__, len(self.vars),
'' if len(self.vars) == 1 else 's')
return '<%s with %d connection%s>' % args
class PriorFactor(Factor):
def __init__(self, var, val, dynamic=0):
super(PriorFactor, self).__init__([var])
self.val = val
self.dynamic = dynamic
def down(self):
sigma = math.sqrt(self.val.sigma ** 2 + self.dynamic ** 2)
value = Gaussian(self.val.mu, sigma)
return self.var.update_value(self, value=value)
class LikelihoodFactor(Factor):
def __init__(self, mean_var, value_var, variance):
super(LikelihoodFactor, self).__init__([mean_var, value_var])
self.mean = mean_var
self.value = value_var
self.variance = variance
def calc_a(self, var):
return 1. / (1. + self.variance * var.pi)
def down(self):
# update value.
msg = self.mean / self.mean[self]
a = self.calc_a(msg)
return self.value.update_message(self, a * msg.pi, a * msg.tau)
def up(self):
# update mean.
msg = self.value / self.value[self]
a = self.calc_a(msg)
return self.mean.update_message(self, a * msg.pi, a * msg.tau)
class SumFactor(Factor):
def __init__(self, sum_var, term_vars, coeffs):
super(SumFactor, self).__init__([sum_var] + term_vars)
self.sum = sum_var
self.terms = term_vars
self.coeffs = coeffs
def down(self):
vals = self.terms
msgs = [var[self] for var in vals]
return self.update(self.sum, vals, msgs, self.coeffs)
def up(self, index=0):
coeff = self.coeffs[index]
coeffs = []
for x, c in enumerate(self.coeffs):
try:
if x == index:
coeffs.append(1. / coeff)
else:
coeffs.append(-c / coeff)
except ZeroDivisionError:
coeffs.append(0.)
vals = self.terms[:]
vals[index] = self.sum
msgs = [var[self] for var in vals]
return self.update(self.terms[index], vals, msgs, coeffs)
def update(self, var, vals, msgs, coeffs):
pi_inv = 0
mu = 0
for val, msg, coeff in zip(vals, msgs, coeffs):
div = val / msg
mu += coeff * div.mu
if pi_inv == inf:
continue
try:
# numpy.float64 handles floating-point error by different way.
# For example, it can just warn RuntimeWarning on n/0 problem
# instead of throwing ZeroDivisionError. So div.pi, the
# denominator has to be a built-in float.
pi_inv += coeff ** 2 / float(div.pi)
except ZeroDivisionError:
pi_inv = inf
pi = 1. / pi_inv
tau = pi * mu
return var.update_message(self, pi, tau)
class TruncateFactor(Factor):
def __init__(self, var, v_func, w_func, draw_margin):
super(TruncateFactor, self).__init__([var])
self.v_func = v_func
self.w_func = w_func
self.draw_margin = draw_margin
def up(self):
val = self.var
msg = self.var[self]
div = val / msg
sqrt_pi = math.sqrt(div.pi)
args = (div.tau / sqrt_pi, self.draw_margin * sqrt_pi)
v = self.v_func(*args)
w = self.w_func(*args)
denom = (1. - w)
pi, tau = div.pi / denom, (div.tau + sqrt_pi * v) / denom
return val.update_value(self, pi, tau)
#: Default initial mean of ratings.
MU = 25.
#: Default initial standard deviation of ratings.
SIGMA = MU / 3
#: Default distance that guarantees about 76% chance of winning.
BETA = SIGMA / 2
#: Default dynamic factor.
TAU = SIGMA / 100
#: Default draw probability of the game.
DRAW_PROBABILITY = .10
#: A basis to check reliability of the result.
DELTA = 0.0001
def calc_draw_probability(draw_margin, size, env=None):
if env is None:
env = global_env()
return 2 * env.cdf(draw_margin / (math.sqrt(size) * env.beta)) - 1
def calc_draw_margin(draw_probability, size, env=None):
if env is None:
env = global_env()
return env.ppf((draw_probability + 1) / 2.) * math.sqrt(size) * env.beta
def _team_sizes(rating_groups):
team_sizes = [0]
for group in rating_groups:
team_sizes.append(len(group) + team_sizes[-1])
del team_sizes[0]
return team_sizes
def _floating_point_error(env):
if env.backend == 'mpmath':
msg = 'Set "mpmath.mp.dps" to higher'
else:
msg = 'Cannot calculate correctly, set backend to "mpmath"'
return FloatingPointError(msg)
class Rating(Gaussian):
def __init__(self, mu=None, sigma=None):
if isinstance(mu, tuple):
mu, sigma = mu
elif isinstance(mu, Gaussian):
mu, sigma = mu.mu, mu.sigma
if mu is None:
mu = global_env().mu
if sigma is None:
sigma = global_env().sigma
super(Rating, self).__init__(mu, sigma)
def __int__(self):
return int(self.mu)
def __long__(self):
return long(self.mu)
def __float__(self):
return float(self.mu)
def __iter__(self):
return iter((self.mu, self.sigma))
def __repr__(self):
c = type(self)
args = ('.'.join([c.__module__, c.__name__]), self.mu, self.sigma)
return '%s(mu=%.3f, sigma=%.3f)' % args
class TrueSkill(object):
def __init__(self, mu=MU, sigma=SIGMA, beta=BETA, tau=TAU,
draw_probability=DRAW_PROBABILITY, backend=None):
self.mu = mu
self.sigma = sigma
self.beta = beta
self.tau = tau
self.draw_probability = draw_probability
self.backend = backend
if isinstance(backend, tuple):
self.cdf, self.pdf, self.ppf = backend
else:
self.cdf, self.pdf, self.ppf = choose_backend(backend)
def create_rating(self, mu=None, sigma=None):
if mu is None:
mu = self.mu
if sigma is None:
sigma = self.sigma
return Rating(mu, sigma)
def v_win(self, diff, draw_margin):
x = diff - draw_margin
denom = self.cdf(x)
return (self.pdf(x) / denom) if denom else -x
def v_draw(self, diff, draw_margin):
abs_diff = abs(diff)
a, b = draw_margin - abs_diff, -draw_margin - abs_diff
denom = self.cdf(a) - self.cdf(b)
numer = self.pdf(b) - self.pdf(a)
return ((numer / denom) if denom else a) * (-1 if diff < 0 else +1)
def w_win(self, diff, draw_margin):
x = diff - draw_margin
v = self.v_win(diff, draw_margin)
w = v * (v + x)
if 0 < w < 1:
return w
raise _floating_point_error(self)
def w_draw(self, diff, draw_margin):
abs_diff = abs(diff)
a, b = draw_margin - abs_diff, -draw_margin - abs_diff
denom = self.cdf(a) - self.cdf(b)
if not denom:
raise _floating_point_error(self)
v = self.v_draw(abs_diff, draw_margin)
return (v ** 2) + (a * self.pdf(a) - b * self.pdf(b)) / denom
def validate_rating_groups(self, rating_groups):
# check group sizes
if len(rating_groups) < 2:
raise ValueError('Need multiple rating groups')
elif not all(rating_groups):
raise ValueError('Each group must contain multiple ratings')
# check group types
group_types = set(map(type, rating_groups))
if len(group_types) != 1:
raise TypeError('All groups should be same type')
elif group_types.pop() is Rating:
raise TypeError('Rating cannot be a rating group')
# normalize rating_groups
if isinstance(rating_groups[0], dict):
dict_rating_groups = rating_groups
rating_groups = []
keys = []
for dict_rating_group in dict_rating_groups:
rating_group, key_group = [], []
for key, rating in iteritems(dict_rating_group):
rating_group.append(rating)
key_group.append(key)
rating_groups.append(tuple(rating_group))
keys.append(tuple(key_group))
else:
rating_groups = list(rating_groups)
keys = None
return rating_groups, keys
def validate_weights(self, weights, rating_groups, keys=None):
if weights is None:
weights = [(1,) * len(g) for g in rating_groups]
elif isinstance(weights, dict):
weights_dict, weights = weights, []
for x, group in enumerate(rating_groups):
w = []
weights.append(w)
for y, rating in enumerate(group):
if keys is not None:
y = keys[x][y]
w.append(weights_dict.get((x, y), 1))
return weights
def factor_graph_builders(self, rating_groups, ranks, weights):
flatten_ratings = sum(map(tuple, rating_groups), ())
flatten_weights = sum(map(tuple, weights), ())
size = len(flatten_ratings)
group_size = len(rating_groups)
# create variables
rating_vars = [Variable() for x in range(size)]
perf_vars = [Variable() for x in range(size)]
team_perf_vars = [Variable() for x in range(group_size)]
team_diff_vars = [Variable() for x in range(group_size - 1)]
team_sizes = _team_sizes(rating_groups)
# layer builders
def build_rating_layer():
for rating_var, rating in zip(rating_vars, flatten_ratings):
yield PriorFactor(rating_var, rating, self.tau)
def build_perf_layer():
for rating_var, perf_var in zip(rating_vars, perf_vars):
yield LikelihoodFactor(rating_var, perf_var, self.beta ** 2)
def build_team_perf_layer():
for team, team_perf_var in enumerate(team_perf_vars):
if team > 0:
start = team_sizes[team - 1]
else:
start = 0
end = team_sizes[team]
child_perf_vars = perf_vars[start:end]
coeffs = flatten_weights[start:end]
yield SumFactor(team_perf_var, child_perf_vars, coeffs)
def build_team_diff_layer():
for team, team_diff_var in enumerate(team_diff_vars):
yield SumFactor(team_diff_var,
team_perf_vars[team:team + 2], [+1, -1])
def build_trunc_layer():
for x, team_diff_var in enumerate(team_diff_vars):
if callable(self.draw_probability):
# dynamic draw probability
team_perf1, team_perf2 = team_perf_vars[x:x + 2]
args = (Rating(team_perf1), Rating(team_perf2), self)
draw_probability = self.draw_probability(*args)
else:
# static draw probability
draw_probability = self.draw_probability
size = sum(map(len, rating_groups[x:x + 2]))
draw_margin = calc_draw_margin(draw_probability, size, self)
if ranks[x] == ranks[x + 1]: # is a tie?
v_func, w_func = self.v_draw, self.w_draw
else:
v_func, w_func = self.v_win, self.w_win
yield TruncateFactor(team_diff_var,
v_func, w_func, draw_margin)
# build layers
return (build_rating_layer, build_perf_layer, build_team_perf_layer,
build_team_diff_layer, build_trunc_layer)
def run_schedule(self, build_rating_layer, build_perf_layer,
build_team_perf_layer, build_team_diff_layer,
build_trunc_layer, min_delta=DELTA):
if min_delta <= 0:
raise ValueError('min_delta must be greater than 0')
layers = []
def build(builders):
layers_built = [list(build()) for build in builders]
layers.extend(layers_built)
return layers_built
# gray arrows
layers_built = build([build_rating_layer,
build_perf_layer,
build_team_perf_layer])
rating_layer, perf_layer, team_perf_layer = layers_built
for f in chain(*layers_built):
f.down()
# arrow #1, #2, #3
team_diff_layer, trunc_layer = build([build_team_diff_layer,
build_trunc_layer])
team_diff_len = len(team_diff_layer)
for x in range(10):
if team_diff_len == 1:
# only two teams
team_diff_layer[0].down()
delta = trunc_layer[0].up()
else:
# multiple teams
delta = 0
for x in range(team_diff_len - 1):
team_diff_layer[x].down()
delta = max(delta, trunc_layer[x].up())
team_diff_layer[x].up(1) # up to right variable
for x in range(team_diff_len - 1, 0, -1):
team_diff_layer[x].down()
delta = max(delta, trunc_layer[x].up())
team_diff_layer[x].up(0) # up to left variable
# repeat until to small update
if delta <= min_delta:
break
# up both ends
team_diff_layer[0].up(0)
team_diff_layer[team_diff_len - 1].up(1)
# up the remainder of the black arrows
for f in team_perf_layer:
for x in range(len(f.vars) - 1):
f.up(x)
for f in perf_layer:
f.up()
return layers
def rate(self, rating_groups, ranks=None, weights=None, min_delta=DELTA):
rating_groups, keys = self.validate_rating_groups(rating_groups)
weights = self.validate_weights(weights, rating_groups, keys)
group_size = len(rating_groups)
if ranks is None:
ranks = range(group_size)
elif len(ranks) != group_size:
raise ValueError('Wrong ranks')
# sort rating groups by rank
by_rank = lambda x: x[1][1]
sorting = sorted(enumerate(zip(rating_groups, ranks, weights)),
key=by_rank)
sorted_rating_groups, sorted_ranks, sorted_weights = [], [], []
for x, (g, r, w) in sorting:
sorted_rating_groups.append(g)
sorted_ranks.append(r)
# make weights to be greater than 0
sorted_weights.append(max(min_delta, w_) for w_ in w)
# build factor graph
args = (sorted_rating_groups, sorted_ranks, sorted_weights)
builders = self.factor_graph_builders(*args)
args = builders + (min_delta,)
layers = self.run_schedule(*args)
# make result
rating_layer, team_sizes = layers[0], _team_sizes(sorted_rating_groups)
transformed_groups = []
for start, end in zip([0] + team_sizes[:-1], team_sizes):
group = []
for f in rating_layer[start:end]:
group.append(Rating(float(f.var.mu), float(f.var.sigma)))
transformed_groups.append(tuple(group))
by_hint = lambda x: x[0]
unsorting = sorted(zip((x for x, __ in sorting), transformed_groups),
key=by_hint)
if keys is None:
return [g for x, g in unsorting]
# restore the structure with input dictionary keys
return [dict(zip(keys[x], g)) for x, g in unsorting]
def quality(self, rating_groups, weights=None):
rating_groups, keys = self.validate_rating_groups(rating_groups)
weights = self.validate_weights(weights, rating_groups, keys)
flatten_ratings = sum(map(tuple, rating_groups), ())
flatten_weights = sum(map(tuple, weights), ())
length = len(flatten_ratings)
# a vector of all of the skill means
mean_matrix = Matrix([[r.mu] for r in flatten_ratings])
# a matrix whose diagonal values are the variances (sigma ** 2) of each
# of the players.
def variance_matrix(height, width):
variances = (r.sigma ** 2 for r in flatten_ratings)
for x, variance in enumerate(variances):
yield (x, x), variance
variance_matrix = Matrix(variance_matrix, length, length)
# the player-team assignment and comparison matrix
def rotated_a_matrix(set_height, set_width):
t = 0
for r, (cur, _next) in enumerate(zip(rating_groups[:-1],
rating_groups[1:])):
for x in range(t, t + len(cur)):
yield (r, x), flatten_weights[x]
t += 1
x += 1
for x in range(x, x + len(_next)):
yield (r, x), -flatten_weights[x]
set_height(r + 1)
set_width(x + 1)
rotated_a_matrix = Matrix(rotated_a_matrix)
a_matrix = rotated_a_matrix.transpose()
# match quality further derivation
_ata = (self.beta ** 2) * rotated_a_matrix * a_matrix
_atsa = rotated_a_matrix * variance_matrix * a_matrix
start = mean_matrix.transpose() * a_matrix
middle = _ata + _atsa
end = rotated_a_matrix * mean_matrix
# make result
e_arg = (-0.5 * start * middle.inverse() * end).determinant()
s_arg = _ata.determinant() / middle.determinant()
return math.exp(e_arg) * math.sqrt(s_arg)
def expose(self, rating):
k = self.mu / self.sigma
return rating.mu - k * rating.sigma
def make_as_global(self):
return setup(env=self)
def __repr__(self):
c = type(self)
if callable(self.draw_probability):
f = self.draw_probability
draw_probability = '.'.join([f.__module__, f.__name__])
else:
draw_probability = '%.1f%%' % (self.draw_probability * 100)
if self.backend is None:
backend = ''
elif isinstance(self.backend, tuple):
backend = ', backend=...'
else:
backend = ', backend=%r' % self.backend
args = ('.'.join([c.__module__, c.__name__]), self.mu, self.sigma,
self.beta, self.tau, draw_probability, backend)
return ('%s(mu=%.3f, sigma=%.3f, beta=%.3f, tau=%.3f, '
'draw_probability=%s%s)' % args)
def rate_1vs1(rating1, rating2, drawn=False, min_delta=DELTA, env=None):
if env is None:
env = global_env()
ranks = [0, 0 if drawn else 1]
teams = env.rate([(rating1,), (rating2,)], ranks, min_delta=min_delta)
return teams[0][0], teams[1][0]
def quality_1vs1(rating1, rating2, env=None):
if env is None:
env = global_env()
return env.quality([(rating1,), (rating2,)])
def global_env():
try:
global_env.__trueskill__
except AttributeError:
# setup the default environment
setup()
return global_env.__trueskill__
def setup(mu=MU, sigma=SIGMA, beta=BETA, tau=TAU,
draw_probability=DRAW_PROBABILITY, backend=None, env=None):
if env is None:
env = TrueSkill(mu, sigma, beta, tau, draw_probability, backend)
global_env.__trueskill__ = env
return env
def rate(rating_groups, ranks=None, weights=None, min_delta=DELTA):
return global_env().rate(rating_groups, ranks, weights, min_delta)
def quality(rating_groups, weights=None):
return global_env().quality(rating_groups, weights)
def expose(rating):
return global_env().expose(rating)

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analysis-master/build/lib/analysis/visualization.py
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@ -1,34 +0,0 @@
# Titan Robotics Team 2022: Visualization Module
# Written by Arthur Lu & Jacob Levine
# Notes:
# this should be imported as a python module using 'import visualization'
# this should be included in the local directory or environment variable
# fancy
# setup:
__version__ = "1.0.0.000"
#changelog should be viewed using print(analysis.__changelog__)
__changelog__ = """changelog:
1.0.0.000:
- created visualization.py
- added graphloss()
- added imports
"""
__author__ = (
"Arthur Lu <arthurlu@ttic.edu>,"
"Jacob Levine <jlevine@ttic.edu>,"
)
__all__ = [
'graphloss',
]
import matplotlib.pyplot as plt
def graphloss(losses):
x = range(0, len(losses))
plt.plot(x, losses)
plt.show()

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analysis-master/setup.py
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import setuptools
setuptools.setup(
name="analysis", # Replace with your own username
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",
long_description="",
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",
"Operating System :: OS Independent",
],
python_requires='>=3.6',
)

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1
data analysis/config/competition.config
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2020ilch

0
data analysis/config/database.config
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2
data analysis/config/keys.config
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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
UDvKmPjPRfwwUdDX1JxbmkyecYBJhCtXeyVk9vmO2i7K0Zn4wqQPMfzuEINXJ7e5

14
data analysis/config/stats.config
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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
balls-lower-auto,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-teleop,basic_stats,historical_analysis,regression_linear,regression_logarithmic,regression_exponential,regression_polynomial,regression_sigmoidal
balls-upper-auto,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

102
data analysis/data.py
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@ -1,102 +0,0 @@
import requests
import pymongo
import pandas as pd
import time
def pull_new_tba_matches(apikey, competition, cutoff):
api_key= apikey
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"] != 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
def get_team_match_data(apikey, competition, team_num):
client = pymongo.MongoClient(apikey)
db = client.data_scouting
mdata = db.matchdata
out = {}
for i in mdata.find({"competition" : competition, "team_scouted": 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
mdata = db.team_metrics
return mdata.find_one({"competition" : competition, "team": team_num})
def unkeyify_2l(layered_dict):
out = {}
for i in layered_dict.keys():
add = []
sortkey = []
for j in layered_dict[i].keys():
add.append([j,layered_dict[i][j]])
add.sort(key = lambda x: x[0])
out[i] = list(map(lambda x: x[1], add))
return out
def get_match_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)] = unkeyify_2l(get_team_match_data(apikey, competition, int(i)).transpose().to_dict())
except:
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]
mdata = db[colname]
mdata.replace_one({"competition" : competition, "team": team_num}, {"_id": competition+str(team_num)+"am", "competition" : competition, "team" : team_num, "data" : data}, True)
def push_team_metrics_data(apikey, competition, team_num, data, dbname = "data_processing", colname = "team_metrics"):
client = pymongo.MongoClient(apikey)
db = client[dbname]
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
mdata = db.flags
return mdata.find_one({flag:{"$exists":True}})
def set_analysis_flags(apikey, flag, data):
client = pymongo.MongoClient(apikey)
db = client.data_processing
mdata = db.flags
return mdata.replace_one({flag:{"$exists":True}}, data, True)

59
data analysis/get_team_rankings.py
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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()

374
data analysis/superscript.py
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# Titan Robotics Team 2022: Superscript Script
# Written by Arthur Lu & Jacob Levine
# Notes:
# setup:
__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:
- fixed bug where X range for regression was determined before sanitization
- better sanitized data
0.0.4.000:
- fixed spelling issue in __changelog__
- addressed nan bug in regression
- fixed errors on line 335 with metrics calling incorrect key "glicko2"
- fixed errors in metrics computing
0.0.3.000:
- added analysis to pit data
0.0.2.001:
- minor stability patches
- implemented db syncing for timestamps
- fixed bugs
0.0.2.000:
- finalized testing and small fixes
0.0.1.004:
- finished metrics implement, trueskill is bugged
0.0.1.003:
- working
0.0.1.002:
- started implement of metrics
0.0.1.001:
- cleaned up imports
0.0.1.000:
- tested working, can push to database
0.0.0.009:
- tested working
- prints out stats for the time being, will push to database later
0.0.0.008:
- added data import
- removed tba import
- finished main method
0.0.0.007:
- added load_config
- optimized simpleloop for readibility
- added __all__ entries
- added simplestats engine
- pending testing
0.0.0.006:
- fixes
0.0.0.005:
- imported pickle
- created custom database object
0.0.0.004:
- fixed simpleloop to actually return a vector
0.0.0.003:
- added metricsloop which is unfinished
0.0.0.002:
- added simpleloop which is untested until data is provided
0.0.0.001:
- created script
- added analysis, numba, numpy imports
"""
__author__ = (
"Arthur Lu <learthurgo@gmail.com>",
"Jacob Levine <jlevine@imsa.edu>",
)
__all__ = [
"main",
"load_config",
"simpleloop",
"simplestats",
"metricsloop"
]
# imports:
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
def main():
warnings.filterwarnings("ignore")
while(True):
current_time = time.time()
print("[OK] time: " + str(current_time))
start = time.time()
config = load_config(Path("config/stats.config"))
competition = an.load_csv(Path("config/competition.config"))[0][0]
print("[OK] configs loaded")
apikey = an.load_csv(Path("config/keys.config"))[0][0]
tbakey = an.load_csv(Path("config/keys.config"))[1][0]
print("[OK] loaded keys")
previous_time = d.get_analysis_flags(apikey, "latest_update")
if(previous_time == None):
d.set_analysis_flags(apikey, "latest_update", 0)
previous_time = 0
else:
previous_time = previous_time["latest_update"]
print("[OK] analysis backtimed to: " + str(previous_time))
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("[OK] loaded data in " + str(time.time() - start) + " seconds")
print("[OK] running tests")
start = time.time()
results = simpleloop(data, config)
print("[OK] finished tests in " + str(time.time() - start) + " seconds")
print("[OK] running metrics")
start = time.time()
metricsloop(tbakey, apikey, competition, previous_time)
print("[OK] finished metrics in " + str(time.time() - start) + " seconds")
print("[OK] running pit analysis")
start = time.time()
pit = pitloop(pit_data, config)
print("[OK] finished pit analysis in " + str(time.time() - start) + " seconds")
d.set_analysis_flags(apikey, "latest_update", {"latest_update":current_time})
print("[OK] pushing to database")
start = time.time()
push_to_database(apikey, competition, results, pit)
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:
config_vector[line[0]] = line[1:]
return config_vector
def simpleloop(data, tests): # expects 3D array with [Team][Variable][Match]
return_vector = {}
for team in data:
variable_vector = {}
for variable in data[team]:
test_vector = {}
variable_data = data[team][variable]
if(variable in tests):
for test in tests[variable]:
test_vector[test] = simplestats(variable_data, test)
else:
pass
variable_vector[variable] = test_vector
return_vector[team] = variable_vector
return return_vector
def simplestats(data, test):
data = np.array(data)
data = data[np.isfinite(data)]
ranges = list(range(len(data)))
if(test == "basic_stats"):
return an.basic_stats(data)
if(test == "historical_analysis"):
return an.histo_analysis([ranges, data])
if(test == "regression_linear"):
return an.regression(ranges, data, ['lin'])
if(test == "regression_logarithmic"):
return an.regression(ranges, data, ['log'])
if(test == "regression_exponential"):
return an.regression(ranges, data, ['exp'])
if(test == "regression_polynomial"):
return an.regression(ranges, data, ['ply'])
if(test == "regression_sigmoidal"):
return an.regression(ranges, data, ['sig'])
def push_to_database(apikey, competition, results, pit):
for team in results:
d.push_team_tests_data(apikey, competition, team, results[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
elo_K = 24
matches = d.pull_new_tba_matches(tbakey, competition, timestamp)
red = {}
blu = {}
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_score_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:
elo_red_total += red[team]["elo"]["score"]
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 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, "blu": 0}
elif(match["winner"] == "blue"):
observations = {"red": 0, "blu": 1}
else:
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"]
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"]])
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"]
temp_vector = {}
temp_vector.update(red)
temp_vector.update(blu)
for team in temp_vector:
d.push_team_metrics_data(apikey, competition, team, temp_vector[team])
def load_metrics(apikey, competition, match, group_name):
group = {}
for team in match[group_name]:
db_data = d.get_team_metrics_data(apikey, competition, team)
if d.get_team_metrics_data(apikey, competition, team) == None:
elo = {"score": 1500}
gl2 = {"score": 1500, "rd": 250, "vol": 0.06}
ts = {"mu": 25, "sigma": 25/3}
#d.push_team_metrics_data(apikey, competition, team, {"elo":elo, "gl2":gl2,"trueskill":ts})
group[team] = {"elo": elo, "gl2": gl2, "ts": ts}
else:
metrics = db_data["metrics"]
elo = metrics["elo"]
gl2 = metrics["gl2"]
ts = metrics["ts"]
group[team] = {"elo": elo, "gl2": gl2, "ts": ts}
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()
"""
Metrics Defaults:
elo starting score = 1500
elo N = 400
elo K = 24
gl2 starting score = 1500
gl2 starting rd = 350
gl2 starting vol = 0.06
"""

59
data analysis/visualize_pit.py
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@ -1,59 +0,0 @@
# 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=(80,15))
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()
# %%