From fd991401c45b9122c1294f0bdf661a6f1306b2b0 Mon Sep 17 00:00:00 2001 From: ltcptgeneral Date: Mon, 30 Sep 2019 10:11:53 -0500 Subject: [PATCH] analysis.py v 1.1.1.000 --- data analysis/analysis/analysis.py | 67 +++++++++++++++++++++++++++++- 1 file changed, 65 insertions(+), 2 deletions(-) diff --git a/data analysis/analysis/analysis.py b/data analysis/analysis/analysis.py index 92a0ebf1..17c2cd55 100644 --- a/data analysis/analysis/analysis.py +++ b/data analysis/analysis/analysis.py @@ -7,10 +7,13 @@ # current benchmark of optimization: 1.33 times faster # setup: -__version__ = "1.1.0.007" +__version__ = "1.1.1.000" # changelog should be viewed using print(analysis.__changelog__) __changelog__ = """changelog: +1.1.1.000: + - added regression_engine() + - added all regressions except polynomial 1.1.0.007: - updated _init_device() 1.1.0.006: @@ -154,6 +157,7 @@ import numba from numba import jit import numpy as np import math +import regression from sklearn import metrics from sklearn import preprocessing import torch @@ -219,7 +223,66 @@ def histo_analysis(hist_data): return basic_stats(derivative)[0], basic_stats(derivative)[3] -#regressions +@jit(forceobj=True) +def regression_engine(device, inputs, outputs, loss = torch.nn.MSELoss(), _iterations = 10000, lr = 0.1, *args): + + 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].parameter, 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].parameter, 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].parameter, 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].parameter, 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].parameter, 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].parameter, 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].parameter, 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].parameter, model[1][::-1][0]]) @jit(forceobj=True) def r_squared(predictions, targets): # assumes equal size inputs