model.py

#!/usr/bin/env python
# coding: utf-8

# In[1]:


from Data_Preprocessing import Preprocessing
import torch
import torch.nn as nn
import time
from sklearn.pipeline import Pipeline
import matplotlib.pyplot as plt
get_ipython().run_line_magic('matplotlib', 'inline')


# In[2]:


class TabularModel(nn.Module):

    def __init__(self, emb_szs, n_cont, out_sz, layers, p=0.5):
        super().__init__()
        self.embeds = nn.ModuleList([nn.Embedding(ni, nf) for ni,nf in emb_szs])
        self.emb_drop = nn.Dropout(p)
        self.bn_cont = nn.BatchNorm1d(n_cont)
        
        layerlist = []
        n_emb = sum((nf for ni,nf in emb_szs))
        n_in = n_emb + n_cont
        
        for i in layers:
            layerlist.append(nn.Linear(n_in,i)) 
            layerlist.append(nn.ReLU(inplace=True))
            layerlist.append(nn.BatchNorm1d(i))
            layerlist.append(nn.Dropout(p))
            n_in = i
        layerlist.append(nn.Linear(layers[-1],out_sz))
            
        self.layers = nn.Sequential(*layerlist)
    
    def forward(self, x_cat, x_cont):
        embeddings = []
        for i,e in enumerate(self.embeds):
            embeddings.append(e(x_cat[:,i]))
        x = torch.cat(embeddings, 1)
        x = self.emb_drop(x)
        
        x_cont = self.bn_cont(x_cont)
        x = torch.cat([x, x_cont], 1)
        x = self.layers(x)
        return x


# In[ ]: