# 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__ = "1.1.3.001" # changelog should be viewed using print(analysis.__changelog__) __changelog__ = """changelog: 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 ", "Jacob Levine ", ) __all__ = [ '_init_device', 'load_csv', 'basic_stats', 'z_score', 'z_normalize', 'histo_analysis', 'regression_engine', 'r_squared', 'mse', 'rms', 'regression' # all statistics functions left out due to integration in other functions ] # now back to your regularly scheduled programming: # imports (now in alphabetical order! v 1.0.3.006): import csv import numba from numba import jit import numpy as np import math try: from analysis import regression except: pass from sklearn import metrics from sklearn import preprocessing import torch class error(ValueError): pass def _init_device(): # initiates computation device for ANNs device = 'cuda:0' if torch.cuda.is_available() else 'cpu' return device @jit(forceobj=True) 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) return _mean, _median, _stdev, _variance # 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 = preprocessing.normalize(array, axis = arg) return array @jit(forceobj=True) # expects 2d array of [x,y] def histo_analysis(hist_data): hist_data = np.array(hist_data) derivative = np.array(len(hist_data) - 1, dtype = float) t = np.diff(hist_data) derivative = t[1] / t[0] np.sort(derivative) return basic_stats(derivative)[0], basic_stats(derivative)[3] @jit(forceobj=True) def regression_engine(device, inputs, outputs, args, loss = torch.nn.MSELoss(), _iterations = 10000, lr = 0.01): regressions = [] if 'cuda' in device: regression.set_device(device) if 'linear' in args: model = regression.SGDTrain(regression.LinearRegKernel(len(inputs)), torch.tensor(inputs).to(torch.float).cuda(), torch.tensor([outputs]).to(torch.float).cuda(), iterations=_iterations, learning_rate=lr, return_losses=True) regressions.append([model[0].parameters, model[1][::-1][0]]) if 'log' in args: model = regression.SGDTrain(regression.LogRegKernel(len(inputs)), torch.tensor(inputs).to(torch.float).cuda(), torch.tensor(outputs).to(torch.float).cuda(), iterations=_iterations, learning_rate=lr, return_losses=True) regressions.append([model[0].parameters, model[1][::-1][0]]) if 'exp' in args: model = regression.SGDTrain(regression.ExpRegKernel(len(inputs)), torch.tensor(inputs).to(torch.float).cuda(), torch.tensor(outputs).to(torch.float).cuda(), iterations=_iterations, learning_rate=lr, return_losses=True) regressions.append([model[0].parameters, model[1][::-1][0]]) #if 'poly' in args: #TODO because Jacob hasnt fixed regression.py if 'sig' in args: model = regression.SGDTrain(regression.SigmoidalRegKernelArthur(len(inputs)), torch.tensor(inputs).to(torch.float).cuda(), torch.tensor(outputs).to(torch.float).cuda(), iterations=_iterations, learning_rate=lr, return_losses=True) regressions.append([model[0].parameters, model[1][::-1][0]]) else: regression.set_device(device) if 'linear' in args: model = regression.SGDTrain(regression.LinearRegKernel(len(inputs)), torch.tensor(inputs).to(torch.float), torch.tensor(outputs).to(torch.float), iterations=_iterations, learning_rate=lr, return_losses=True) regressions.append([model[0].parameters, model[1][::-1][0]]) if 'log' in args: model = regression.SGDTrain(regression.LogRegKernel(len(inputs)), torch.tensor(inputs).to(torch.float), torch.tensor(outputs).to(torch.float), iterations=_iterations, learning_rate=lr, return_losses=True) regressions.append([model[0].parameters, model[1][::-1][0]]) if 'exp' in args: model = regression.SGDTrain(regression.ExpRegKernel(len(inputs)), torch.tensor(inputs).to(torch.float), torch.tensor(outputs).to(torch.float), iterations=_iterations, learning_rate=lr, return_losses=True) regressions.append([model[0].parameters, model[1][::-1][0]]) #if 'poly' in args: #TODO because Jacob hasnt fixed regression.py if 'sig' in args: model = regression.SGDTrain(regression.SigmoidalRegKernelArthur(len(inputs)), torch.tensor(inputs).to(torch.float), torch.tensor(outputs).to(torch.float), iterations=_iterations, learning_rate=lr, return_losses=True) regressions.append([model[0].parameters, model[1][::-1][0]]) return regressions @jit(nopython=True) def elo(starting_score, opposing_scores, observed, N, K): expected = 1/(1+10**((np.array(opposing_scores) - starting_score)/N)) return starting_score + K*(np.sum(observed) - np.sum(expected)) def gliko2(starting_score, starting_rd, starting_vol, opposing_scores, opposing_rd, observations): player = gliko2_engine(rating = starting_score, rd = starting_rd, vol = starting_vol) player.update_player([x for x in opposing_scores], [x for x in opposing_rd], observations) return (player.rating, player.rd, player.vol) @jit(forceobj=True) def r_squared(predictions, targets): # assumes equal size inputs return metrics.r2_score(np.array(targets), np.array(predictions)) @jit(forceobj=True) def mse(predictions, targets): return metrics.mean_squared_error(np.array(targets), np.array(predictions)) @jit(forceobj=True) def rms(predictions, targets): return math.sqrt(metrics.mean_squared_error(np.array(targets), np.array(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) class regression: # Titan Robotics Team 2022: CUDA-based Regressions Module # Written by Arthur Lu & Jacob Levine # Notes: # this module has been automatically inegrated into analysis.py, and should be callable as a class from the package # this module is cuda-optimized and vectorized (except for one small part) # setup: __version__ = "1.0.0.002" # changelog should be viewed using print(analysis.regression.__changelog__) __changelog__ = """ 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 ", "Arthur Lu " ) __all__ = [ 'factorial', 'take_all_pwrs', 'num_poly_terms', 'set_device', 'LinearRegKernel', 'SigmoidalRegKernel', 'LogRegKernel', 'PolyRegKernel', 'ExpRegKernel', 'SigmoidalRegKernelArthur', 'SGDTrain', 'CustomTrain' ] # imports (just one for now): import torch device = "cuda:0" if torch.torch.cuda.is_available() else "cpu" #todo: document completely def factorial(n): if n==0: return 1 else: return n*factorial(n-1) def num_poly_terms(num_vars, power): if power == 0: return 0 return int(factorial(num_vars+power-1) / factorial(power) / factorial(num_vars-1)) + num_poly_terms(num_vars, power-1) def take_all_pwrs(vec,pwr): #todo: vectorize (kinda) combins=torch.combinations(vec, r=pwr, with_replacement=True) out=torch.ones(combins.size()[0]) for i in torch.t(combins): out *= i return torch.cat(out,take_all_pwrs(vec, pwr-1)) def set_device(new_device): global 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=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 forward(self,mtx): #TODO: Vectorize the last part cols=[] for i in torch.t(mtx): cols.append(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(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(kernel, optim, data, ground, loss=torch.nn.MSELoss(), iterations=1000, return_losses=False): data_cuda=data.to(device) ground_cuda=ground.to(device) if (return_losses): losses=[] for i in range(iterations): with torch.set_grad_enabled(True): optim.zero_grad() pred=kernel.forward(data) ls=loss(pred,ground) losses.append(ls.item()) ls.backward() optim.step() return [kernel,losses] else: for i in range(iterations): with torch.set_grad_enabled(True): optim.zero_grad() pred=kernel.forward(data_cuda) ls=loss(pred,ground_cuda) ls.backward() optim.step() return kernel class gliko2_engine: _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()