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Silviu Marian Udrescu
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379
Code/S_separability.py
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379
Code/S_separability.py
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from __future__ import print_function
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import torch
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import os
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import torch.nn as nn
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import torch.nn.functional as F
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import torch.optim as optim
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from torchvision import datasets, transforms
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import pandas as pd
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import numpy as np
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import torch
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from torch.utils import data
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import pickle
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from torch.optim.lr_scheduler import CosineAnnealingLR
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from matplotlib import pyplot as plt
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from itertools import combinations
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import time
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is_cuda = torch.cuda.is_available()
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class SimpleNet(nn.Module):
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def __init__(self, ni):
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super().__init__()
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self.linear1 = nn.Linear(ni, 128)
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self.bn1 = nn.BatchNorm1d(128)
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self.linear2 = nn.Linear(128, 128)
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self.bn2 = nn.BatchNorm1d(128)
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self.linear3 = nn.Linear(128, 64)
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self.bn3 = nn.BatchNorm1d(64)
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self.linear4 = nn.Linear(64,64)
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self.bn4 = nn.BatchNorm1d(64)
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self.linear5 = nn.Linear(64,1)
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def forward(self, x):
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x = F.tanh(self.bn1(self.linear1(x)))
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x = F.tanh(self.bn2(self.linear2(x)))
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x = F.tanh(self.bn3(self.linear3(x)))
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x = F.tanh(self.bn4(self.linear4(x)))
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x = self.linear5(x)
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return x
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def rmse_loss(pred, targ):
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denom = targ**2
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denom = torch.sqrt(denom.sum()/len(denom))
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return torch.sqrt(F.mse_loss(pred, targ))/denom
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def check_separability_plus(pathdir, filename):
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try:
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pathdir_weights = "results/NN_trained_models/models/"
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# load the data
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n_variables = np.loadtxt(pathdir+filename, dtype='str').shape[1]-1
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variables = np.loadtxt(pathdir+filename, usecols=(0,))
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if n_variables==1:
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print(filename, "just one variable for ADD")
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# if there is just one variable you have nothing to separate
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return (-1,-1,-1)
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else:
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for j in range(1,n_variables):
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v = np.loadtxt(pathdir+filename, usecols=(j,))
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variables = np.column_stack((variables,v))
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f_dependent = np.loadtxt(pathdir+filename, usecols=(n_variables,))
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f_dependent = np.reshape(f_dependent,(len(f_dependent),1))
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factors = torch.from_numpy(variables)
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if is_cuda:
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factors = factors.cuda()
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else:
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factors = factors
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factors = factors.float()
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product = torch.from_numpy(f_dependent)
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if is_cuda:
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product = product.cuda()
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else:
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product = product
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product = product.float()
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# load the trained model and put it in evaluation mode
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if is_cuda:
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model = SimpleNet(n_variables).cuda()
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else:
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model = SimpleNet(n_variables)
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model.load_state_dict(torch.load(pathdir_weights+filename+".h5"))
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model.eval()
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# make some variables at the time equal to the median of factors
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models_one = []
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models_rest = []
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with torch.no_grad():
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fact_vary = factors.clone()
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for k in range(len(factors[0])):
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fact_vary[:,k] = torch.full((len(factors),),torch.median(factors[:,k]))
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# loop through all indices combinations
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var_indices_list = np.arange(0,n_variables,1)
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min_error = 1000
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best_i = []
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best_j = []
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for i in range(1,n_variables):
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c = combinations(var_indices_list, i)
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for j in c:
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fact_vary_one = factors.clone()
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fact_vary_rest = factors.clone()
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rest_indx = list(filter(lambda x: x not in j, var_indices_list))
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for t1 in rest_indx:
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fact_vary_one[:,t1] = torch.full((len(factors),),torch.median(factors[:,t1]))
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for t2 in j:
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fact_vary_rest[:,t2] = torch.full((len(factors),),torch.median(factors[:,t2]))
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# check if the equation is separable
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sm = model(fact_vary_one)+model(fact_vary_rest)
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#error = torch.sqrt(torch.mean((product-sm+model(fact_vary))**2))/torch.sqrt(torch.mean(product**2))
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error = 2*torch.median(abs(product-sm+model(fact_vary)))
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if error<min_error:
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min_error = error
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best_i = j
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best_j = rest_indx
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return min_error, best_i, best_j
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except Exception as e:
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print(e)
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return (-1,-1,-1)
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def do_separability_plus(pathdir, filename, list_i,list_j):
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try:
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pathdir_weights = "results/NN_trained_models/models/"
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# load the data
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n_variables = np.loadtxt(pathdir+filename, dtype='str').shape[1]-1
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variables = np.loadtxt(pathdir+filename, usecols=(0,))
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if n_variables==1:
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print(filename, "just one variable for ADD")
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# if there is just one variable you have nothing to separate
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return (-1,-1,-1)
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else:
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for j in range(1,n_variables):
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v = np.loadtxt(pathdir+filename, usecols=(j,))
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variables = np.column_stack((variables,v))
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f_dependent = np.loadtxt(pathdir+filename, usecols=(n_variables,))
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f_dependent = np.reshape(f_dependent,(len(f_dependent),1))
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factors = torch.from_numpy(variables)
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if is_cuda:
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factors = factors.cuda()
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else:
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factors = factors
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factors = factors.float()
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product = torch.from_numpy(f_dependent)
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if is_cuda:
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product = product.cuda()
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else:
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product = product
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product = product.float()
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# load the trained model and put it in evaluation mode
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if is_cuda:
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model = SimpleNet(n_variables).cuda()
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else:
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model = SimpleNet(n_variables)
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model.load_state_dict(torch.load(pathdir_weights+filename+".h5"))
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model.eval()
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# make some variables at the time equal to the median of factors
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models_one = []
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models_rest = []
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fact_vary = factors.clone()
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for k in range(len(factors[0])):
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fact_vary[:,k] = torch.full((len(factors),),torch.median(factors[:,k]))
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fact_vary_one = factors.clone()
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fact_vary_rest = factors.clone()
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for t1 in list_j:
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fact_vary_one[:,t1] = torch.full((len(factors),),torch.median(factors[:,t1]))
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for t2 in list_i:
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fact_vary_rest[:,t2] = torch.full((len(factors),),torch.median(factors[:,t2]))
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with torch.no_grad():
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str1 = filename+"-add_a"
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str2 = filename+"-add_b"
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# save the first half
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data_sep_1 = variables
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data_sep_1 = np.delete(data_sep_1,list_j,axis=1)
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data_sep_1 = np.column_stack((data_sep_1,model(fact_vary_one).cpu()))
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# save the second half
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data_sep_2 = variables
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data_sep_2 = np.delete(data_sep_2,list_i,axis=1)
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data_sep_2 = np.column_stack((data_sep_2,model(fact_vary_rest).cpu()-model(fact_vary).cpu()))
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try:
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os.mkdir("results/separable_add/")
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except:
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pass
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np.savetxt("results/separable_add/"+str1,data_sep_1)
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np.savetxt("results/separable_add/"+str2,data_sep_2)
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# if it is separable, return the 2 new files created and the index of the column with the separable variable
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return ("results/separable_add/",str1,"results/separable_add/",str2)
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except Exception as e:
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print(e)
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return (-1,-1)
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def check_separability_multiply(pathdir, filename):
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try:
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pathdir_weights = "results/NN_trained_models/models/"
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# load the data
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n_variables = np.loadtxt(pathdir+filename, dtype='str').shape[1]-1
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variables = np.loadtxt(pathdir+filename, usecols=(0,))
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if n_variables==1:
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print(filename, "just one variable for ADD")
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# if there is just one variable you have nothing to separate
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return (-1,-1,-1)
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else:
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for j in range(1,n_variables):
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v = np.loadtxt(pathdir+filename, usecols=(j,))
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variables = np.column_stack((variables,v))
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f_dependent = np.loadtxt(pathdir+filename, usecols=(n_variables,))
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f_dependent = np.reshape(f_dependent,(len(f_dependent),1))
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factors = torch.from_numpy(variables)
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if is_cuda:
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factors = factors.cuda()
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else:
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factors = factors
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factors = factors.float()
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product = torch.from_numpy(f_dependent)
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if is_cuda:
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product = product.cuda()
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else:
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product = product
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product = product.float()
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# load the trained model and put it in evaluation mode
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if is_cuda:
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model = SimpleNet(n_variables).cuda()
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else:
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model = SimpleNet(n_variables)
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model.load_state_dict(torch.load(pathdir_weights+filename+".h5"))
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model.eval()
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# make some variables at the time equal to the median of factors
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models_one = []
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models_rest = []
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with torch.no_grad():
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fact_vary = factors.clone()
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for k in range(len(factors[0])):
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fact_vary[:,k] = torch.full((len(factors),),torch.median(factors[:,k]))
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# loop through all indices combinations
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var_indices_list = np.arange(0,n_variables,1)
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min_error = 1000
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best_i = []
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best_j = []
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for i in range(1,n_variables):
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c = combinations(var_indices_list, i)
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for j in c:
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fact_vary_one = factors.clone()
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fact_vary_rest = factors.clone()
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rest_indx = list(filter(lambda x: x not in j, var_indices_list))
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for t1 in rest_indx:
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fact_vary_one[:,t1] = torch.full((len(factors),),torch.median(factors[:,t1]))
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for t2 in j:
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fact_vary_rest[:,t2] = torch.full((len(factors),),torch.median(factors[:,t2]))
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# check if the equation is separable
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pd = model(fact_vary_one)*model(fact_vary_rest)
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#error = torch.sqrt(torch.mean((product-pd/model(fact_vary))**2))/torch.sqrt(torch.mean(product**2))
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error = 2*torch.median(abs(product-pd/model(fact_vary)))
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if error<min_error:
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min_error = error
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best_i = j
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best_j = rest_indx
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return min_error, best_i, best_j
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except Exception as e:
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print(e)
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return (-1,-1,-1)
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def do_separability_multiply(pathdir, filename, list_i,list_j):
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try:
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pathdir_weights = "results/NN_trained_models/models/"
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# load the data
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n_variables = np.loadtxt(pathdir+filename, dtype='str').shape[1]-1
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variables = np.loadtxt(pathdir+filename, usecols=(0,))
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if n_variables==1:
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print(filename, "just one variable for ADD")
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# if there is just one variable you have nothing to separate
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return (-1,-1,-1)
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else:
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for j in range(1,n_variables):
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v = np.loadtxt(pathdir+filename, usecols=(j,))
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variables = np.column_stack((variables,v))
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f_dependent = np.loadtxt(pathdir+filename, usecols=(n_variables,))
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f_dependent = np.reshape(f_dependent,(len(f_dependent),1))
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factors = torch.from_numpy(variables)
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if is_cuda:
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factors = factors.cuda()
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else:
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factors = factors
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factors = factors.float()
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product = torch.from_numpy(f_dependent)
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if is_cuda:
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product = product.cuda()
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else:
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product = product
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product = product.float()
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# load the trained model and put it in evaluation mode
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if is_cuda:
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model = SimpleNet(n_variables).cuda()
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else:
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model = SimpleNet(n_variables)
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model.load_state_dict(torch.load(pathdir_weights+filename+".h5"))
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model.eval()
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# make some variables at the time equal to the median of factors
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models_one = []
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models_rest = []
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fact_vary = factors.clone()
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for k in range(len(factors[0])):
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fact_vary[:,k] = torch.full((len(factors),),torch.median(factors[:,k]))
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fact_vary_one = factors.clone()
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fact_vary_rest = factors.clone()
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for t1 in list_j:
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fact_vary_one[:,t1] = torch.full((len(factors),),torch.median(factors[:,t1]))
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for t2 in list_i:
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fact_vary_rest[:,t2] = torch.full((len(factors),),torch.median(factors[:,t2]))
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with torch.no_grad():
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str1 = filename+"-mult_a"
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str2 = filename+"-mult_b"
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# save the first half
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data_sep_1 = variables
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data_sep_1 = np.delete(data_sep_1,list_j,axis=1)
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data_sep_1 = np.column_stack((data_sep_1,model(fact_vary_one).cpu()))
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# save the second half
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data_sep_2 = variables
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data_sep_2 = np.delete(data_sep_2,list_i,axis=1)
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data_sep_2 = np.column_stack((data_sep_2,model(fact_vary_rest).cpu()/model(fact_vary).cpu()))
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try:
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os.mkdir("results/separable_mult/")
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except:
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pass
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np.savetxt("results/separable_mult/"+str1,data_sep_1)
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np.savetxt("results/separable_mult/"+str2,data_sep_2)
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# if it is separable, return the 2 new files created and the index of the column with the separable variable
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return ("results/separable_mult/",str1,"results/separable_mult/",str2)
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except Exception as e:
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print(e)
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return (-1,-1)
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