tra-analysis/analysis-master/analysis/regression.py

# 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