analysis.py v 1.1.2.000, quick fixes

data analysis/analysis/analysis.py

@@ -3,14 +3,19 @@
 # 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 not been optimized for multhreaded computing
+#   this module has been optimized for multhreaded computing
 #   current benchmark of optimization: 1.33 times faster
 # setup:
 
-__version__ = "1.1.1.001"
+__version__ = "1.1.2.000"
 
 # changelog should be viewed using print(analysis.__changelog__)
 __changelog__ = """changelog:
+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:
@@ -133,8 +138,8 @@ __changelog__ = """changelog:
 """
 
 __author__ = (
-    "Arthur Lu <arthurlu@ttic.edu>",
-    "Jacob Levine <jlevine@ttic.edu>",
+    "Arthur Lu <learthurgo@gmail.com>",
+    "Jacob Levine <jlevine@imsa.edu>",
 )
 
 __all__ = [
@@ -148,6 +153,7 @@ __all__ = [
     'r_squared',
     'mse',
     'rms',
+    'regression'
     # all statistics functions left out due to integration in other functions
 ]
 
@@ -160,7 +166,6 @@ import numba
 from numba import jit
 import numpy as np
 import math
-from analysis import regression
 from sklearn import metrics
 from sklearn import preprocessing
 import torch
@@ -322,4 +327,224 @@ def stdev(data):
 @jit(nopython=True)
 def variance(data):
 
-    return np.var(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 <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'
+    ]
+
+
+    # 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

data analysis/analysis/regression.py

@@ -8,7 +8,7 @@
 
 __version__ = "1.0.0.002"
 
-# changelog should be viewed using print(cudaregress.__changelog__)
+# changelog should be viewed using print(regression.__changelog__)
 __changelog__ = """
 1.0.0.002:
     -Added more parameters to log, exponential, polynomial