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Silviu Marian Udrescu
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554
Code/S_symmetry.py
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554
Code/S_symmetry.py
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# checks for symmetries in the data
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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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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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# checks if f(x,y)=f(x-y)
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def check_translational_symmetry_minus(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+"/%s" %filename, dtype='str').shape[1]-1
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variables = np.loadtxt(pathdir+"/%s" %filename, usecols=(0,))
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if n_variables==1:
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print(filename, "just one variable for ADD \n")
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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+"/%s" %filename, usecols=(j,))
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variables = np.column_stack((variables,v))
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f_dependent = np.loadtxt(pathdir+"/%s" %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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models_one = []
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models_rest = []
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with torch.no_grad():
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# make the shift x->x+a for 2 variables at a time (different variables)
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min_error = 1000
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best_i = -1
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best_j = -1
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for i in range(0,n_variables,1):
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for j in range(0,n_variables,1):
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if i<j:
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fact_translate = factors.clone()
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a = 0.5*min(torch.std(fact_translate[:,i]),torch.std(fact_translate[:,j]))
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fact_translate[:,i] = fact_translate[:,i] + a
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fact_translate[:,j] = fact_translate[:,j] + a
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error = torch.median(abs(product-model(fact_translate)))
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if error<min_error:
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min_error = error
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best_i = i
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best_j = j
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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_translational_symmetry_minus(pathdir, filename, i,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+"/%s" %filename, dtype='str').shape[1]-1
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variables = np.loadtxt(pathdir+"/%s" %filename, usecols=(0,))
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for k in range(1,n_variables):
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v = np.loadtxt(pathdir+"/%s" %filename, usecols=(k,))
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variables = np.column_stack((variables,v))
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f_dependent = np.loadtxt(pathdir+"/%s" %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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models_one = []
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models_rest = []
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with torch.no_grad():
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file_name = filename + "-translated_minus"
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data_translated = variables
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data_translated[:,i] = variables[:,i]-variables[:,j]
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data_translated = np.delete(data_translated, j, axis=1)
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data_translated = np.column_stack((data_translated,f_dependent))
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try:
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os.mkdir("results/translated_data_minus/")
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except:
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pass
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np.savetxt("results/translated_data_minus/"+file_name , data_translated)
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return ("results/translated_data_minus/",file_name)
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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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# checks if f(x,y)=f(x/y)
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def check_translational_symmetry_divide(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+"/%s" %filename, dtype='str').shape[1]-1
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variables = np.loadtxt(pathdir+"/%s" %filename, usecols=(0,))
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if n_variables==1:
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print(filename, "just one variable for ADD \n")
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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+"/%s" %filename, usecols=(j,))
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variables = np.column_stack((variables,v))
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f_dependent = np.loadtxt(pathdir+"/%s" %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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models_one = []
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models_rest = []
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with torch.no_grad():
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a = 1.2
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min_error = 1000
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best_i = -1
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best_j = -1
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# make the shift x->x*a and y->y*a for 2 variables at a time (different variables)
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for i in range(0,n_variables,1):
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for j in range(0,n_variables,1):
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if i<j:
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fact_translate = factors.clone()
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fact_translate[:,i] = fact_translate[:,i]*a
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fact_translate[:,j] = fact_translate[:,j]*a
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error = torch.median(abs(product-model(fact_translate)))
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if error<min_error:
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min_error = error
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best_i = i
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best_j = j
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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_translational_symmetry_divide(pathdir, filename, i,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+"/%s" %filename, dtype='str').shape[1]-1
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variables = np.loadtxt(pathdir+"/%s" %filename, usecols=(0,))
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for k in range(1,n_variables):
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v = np.loadtxt(pathdir+"/%s" %filename, usecols=(k,))
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variables = np.column_stack((variables,v))
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f_dependent = np.loadtxt(pathdir+"/%s" %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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models_one = []
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models_rest = []
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with torch.no_grad():
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file_name = filename + "-translated_divide"
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data_translated = variables
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data_translated[:,i] = variables[:,i]/variables[:,j]
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data_translated = np.delete(data_translated, j, axis=1)
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data_translated = np.column_stack((data_translated,f_dependent))
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try:
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os.mkdir("results/translated_data_divide/")
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except:
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pass
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np.savetxt("results/translated_data_divide/"+file_name , data_translated)
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return ("results/translated_data_divide/",file_name)
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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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# checks if f(x,y)=f(x*y)
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def check_translational_symmetry_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+"/%s" %filename, dtype='str').shape[1]-1
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variables = np.loadtxt(pathdir+"/%s" %filename, usecols=(0,))
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if n_variables==1:
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print(filename, "just one variable for ADD \n")
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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+"/%s" %filename, usecols=(j,))
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variables = np.column_stack((variables,v))
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f_dependent = np.loadtxt(pathdir+"/%s" %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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models_one = []
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models_rest = []
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with torch.no_grad():
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a = 1.2
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min_error = 1000
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best_i = -1
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best_j = -1
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# make the shift x->x*a and y->y/a for 2 variables at a time (different variables)
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for i in range(0,n_variables,1):
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for j in range(0,n_variables,1):
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if i<j:
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fact_translate = factors.clone()
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fact_translate[:,i] = fact_translate[:,i]*a
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fact_translate[:,j] = fact_translate[:,j]/a
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error = torch.median(abs(product-model(fact_translate)))
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if error<min_error:
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min_error = error
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best_i = i
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best_j = j
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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_translational_symmetry_multiply(pathdir, filename, i,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+"/%s" %filename, dtype='str').shape[1]-1
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variables = np.loadtxt(pathdir+"/%s" %filename, usecols=(0,))
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for k in range(1,n_variables):
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v = np.loadtxt(pathdir+"/%s" %filename, usecols=(k,))
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variables = np.column_stack((variables,v))
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f_dependent = np.loadtxt(pathdir+"/%s" %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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models_one = []
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models_rest = []
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with torch.no_grad():
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file_name = filename + "-translated_multiply"
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data_translated = variables
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data_translated[:,i] = variables[:,i]*variables[:,j]
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data_translated = np.delete(data_translated, j, axis=1)
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data_translated = np.column_stack((data_translated,f_dependent))
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try:
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os.mkdir("results/translated_data_multiply/")
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except:
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pass
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np.savetxt("results/translated_data_multiply/"+file_name , data_translated)
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return ("results/translated_data_multiply/",file_name)
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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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# checks if f(x,y)=f(x+y)
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def check_translational_symmetry_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+"/%s" %filename, dtype='str').shape[1]-1
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variables = np.loadtxt(pathdir+"/%s" %filename, usecols=(0,))
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if n_variables==1:
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print(filename, "just one variable for ADD \n")
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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+"/%s" %filename, usecols=(j,))
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variables = np.column_stack((variables,v))
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f_dependent = np.loadtxt(pathdir+"/%s" %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)
|
||||
model.load_state_dict(torch.load(pathdir_weights+filename+".h5"))
|
||||
model.eval()
|
||||
|
||||
models_one = []
|
||||
models_rest = []
|
||||
|
||||
with torch.no_grad():
|
||||
min_error = 1000
|
||||
best_i = -1
|
||||
best_j = -1
|
||||
for i in range(0,n_variables,1):
|
||||
for j in range(0,n_variables,1):
|
||||
if i<j:
|
||||
fact_translate = factors.clone()
|
||||
a = 0.5*min(torch.std(fact_translate[:,i]),torch.std(fact_translate[:,j]))
|
||||
fact_translate[:,i] = fact_translate[:,i] + a
|
||||
fact_translate[:,j] = fact_translate[:,j] - a
|
||||
error = torch.median(abs(product-model(fact_translate)))
|
||||
if error<min_error:
|
||||
min_error = error
|
||||
best_i = i
|
||||
best_j = j
|
||||
return min_error, best_i, best_j
|
||||
|
||||
except Exception as e:
|
||||
print(e)
|
||||
return (-1,-1,-1)
|
||||
|
||||
def do_translational_symmetry_plus(pathdir, filename, i,j):
|
||||
try:
|
||||
pathdir_weights = "results/NN_trained_models/models/"
|
||||
|
||||
# load the data
|
||||
n_variables = np.loadtxt(pathdir+"/%s" %filename, dtype='str').shape[1]-1
|
||||
variables = np.loadtxt(pathdir+"/%s" %filename, usecols=(0,))
|
||||
|
||||
for k in range(1,n_variables):
|
||||
v = np.loadtxt(pathdir+"/%s" %filename, usecols=(k,))
|
||||
variables = np.column_stack((variables,v))
|
||||
|
||||
f_dependent = np.loadtxt(pathdir+"/%s" %filename, usecols=(n_variables,))
|
||||
f_dependent = np.reshape(f_dependent,(len(f_dependent),1))
|
||||
|
||||
factors = torch.from_numpy(variables)
|
||||
if is_cuda:
|
||||
factors = factors.cuda()
|
||||
else:
|
||||
factors = factors
|
||||
factors = factors.float()
|
||||
|
||||
product = torch.from_numpy(f_dependent)
|
||||
if is_cuda:
|
||||
product = product.cuda()
|
||||
else:
|
||||
product = product
|
||||
product = product.float()
|
||||
|
||||
# load the trained model and put it in evaluation mode
|
||||
if is_cuda:
|
||||
model = SimpleNet(n_variables).cuda()
|
||||
else:
|
||||
model = SimpleNet(n_variables)
|
||||
model.load_state_dict(torch.load(pathdir_weights+filename+".h5"))
|
||||
model.eval()
|
||||
|
||||
models_one = []
|
||||
models_rest = []
|
||||
|
||||
with torch.no_grad():
|
||||
file_name = filename + "-translated_plus"
|
||||
data_translated = variables
|
||||
data_translated[:,i] = variables[:,i]+variables[:,j]
|
||||
data_translated = np.delete(data_translated, j, axis=1)
|
||||
data_translated = np.column_stack((data_translated,f_dependent))
|
||||
try:
|
||||
os.mkdir("results/translated_data_plus/")
|
||||
except:
|
||||
pass
|
||||
np.savetxt("results/translated_data_plus/"+file_name , data_translated)
|
||||
return ("results/translated_data_plus/", file_name)
|
||||
|
||||
except Exception as e:
|
||||
print(e)
|
||||
return (-1,-1)
|
||||
Loading…
Add table
Add a link
Reference in a new issue