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Code/S_NN_eval.py

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+from __future__ import print_function
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+from torchvision import datasets, transforms
+import pandas as pd
+import numpy as np
+import torch
+from torch.utils import data
+import pickle
+from torch.optim.lr_scheduler import CosineAnnealingLR
+from matplotlib import pyplot as plt
+import time
+
+is_cuda = torch.cuda.is_available()
+
+bs = 2048
+
+class MultDataset(data.Dataset):
+    def __init__(self, factors, product):
+        'Initialization'
+        self.factors = factors
+        self.product = product
+
+    def __len__(self):
+        'Denotes the total number of samples'
+        return len(self.product)
+
+    def __getitem__(self, index):
+        # Load data and get label
+        x = self.factors[index]
+        y = self.product[index]
+
+        return x, y
+
+def rmse_loss(pred, targ):
+    denom = targ**2
+    denom = torch.sqrt(denom.sum()/len(denom))
+
+    return torch.sqrt(F.mse_loss(pred, targ))/denom
+
+
+def NN_eval(pathdir,filename):
+    try:
+        n_variables = np.loadtxt(pathdir+filename, dtype='str').shape[1]-1
+        variables = np.loadtxt(pathdir+filename, usecols=(0,))
+
+        if n_variables==0:
+            return 0
+        elif n_variables==1:
+            variables = np.reshape(variables,(len(variables),1))
+        else:
+            for j in range(1,n_variables):
+                v = np.loadtxt(pathdir+filename, usecols=(j,))
+                variables = np.column_stack((variables,v))
+
+        f_dependent = np.loadtxt(pathdir+filename, usecols=(n_variables,))
+        f_dependent = np.reshape(f_dependent,(len(f_dependent),1))
+
+        factors = torch.from_numpy(variables[0:int(5*len(variables)/6)])
+        if is_cuda:
+            factors = factors.cuda()
+        else:
+            factors = factors
+        factors = factors.float()
+        product = torch.from_numpy(f_dependent[0:int(5*len(f_dependent)/6)])
+        if is_cuda:
+            product = product.cuda()
+        else:
+            product = product
+        product = product.float()
+
+        factors_val = torch.from_numpy(variables[int(5*len(variables)/6):int(len(variables))])
+        if is_cuda:
+            factors_val = factors_val.cuda()
+        else:
+            factors_val = factors_val
+        factors_val = factors_val.float()
+        product_val = torch.from_numpy(f_dependent[int(5*len(variables)/6):int(len(variables))])      
+        if is_cuda:
+            product_val = product_val.cuda()
+        else:
+            product_val = product_val
+        product_val = product_val.float()
+
+        class SimpleNet(nn.Module):
+            def __init__(self, ni):
+                super().__init__()
+                self.linear1 = nn.Linear(ni, 128)
+                self.bn1 = nn.BatchNorm1d(128)
+                self.linear2 = nn.Linear(128, 128)
+                self.bn2 = nn.BatchNorm1d(128)
+                self.linear3 = nn.Linear(128, 64)
+                self.bn3 = nn.BatchNorm1d(64)
+                self.linear4 = nn.Linear(64,64)
+                self.bn4 = nn.BatchNorm1d(64)
+                self.linear5 = nn.Linear(64,1)
+            
+            def forward(self, x):
+                x = F.tanh(self.bn1(self.linear1(x)))
+                x = F.tanh(self.bn2(self.linear2(x)))
+                x = F.tanh(self.bn3(self.linear3(x)))
+                x = F.tanh(self.bn4(self.linear4(x)))
+                x = self.linear5(x)
+                return x  
+
+        if is_cuda:
+            model = SimpleNet(n_variables).cuda()
+        else:
+            model = SimpleNet(n_variables)
+                    
+        model.load_state_dict(torch.load("results/NN_trained_models/models/"+filename+".h5"))
+        model.eval()
+        return(rmse_loss(model(factors_val),product_val))
+
+    except Exception as e:
+        print(e)
+        return (100)
+
+
+
+