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TabNet.py
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TabNet.py
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#!/usr/bin/env python
# coding: utf-8
import torch
import torch.nn as nn
import torch
import torch.nn as nn
import numpy as np
from sparsemax import Sparsemax
sparsemax = Sparsemax(dim=1)
# GLU
def glu(act, n_units):
act[:, :n_units] = act[:, :n_units].clone() * torch.nn.Sigmoid()(act[:, n_units:].clone())
return act
class TabNetModel(nn.Module):
def __init__(
self,
columns = 3,
num_features = 3,
feature_dims = 128,
output_dim =64,
num_decision_steps =6,
relaxation_factor = 0.5,
batch_momentum = 0.001,
virtual_batch_size = 2,
num_classes = 2,
epsilon = 0.00001
):
super().__init__()
self.columns = columns
self.num_features = num_features
self.feature_dims = feature_dims
self.output_dim = output_dim
self.num_decision_steps = num_decision_steps
self.relaxation_factor = relaxation_factor
self.batch_momentum = batch_momentum
self.virtual_batch_size = virtual_batch_size
self.num_classes = num_classes
self.epsilon = epsilon
self.feature_transform_linear1 = torch.nn.Linear(num_features, self.feature_dims * 2, bias=False)
self.BN = torch.nn.BatchNorm1d(num_features, momentum = batch_momentum)
self.BN1 = torch.nn.BatchNorm1d(self.feature_dims * 2, momentum = batch_momentum)
self.feature_transform_linear2 = torch.nn.Linear(self.feature_dims * 2, self.feature_dims * 2, bias=False)
self.feature_transform_linear3 = torch.nn.Linear(self.feature_dims * 2, self.feature_dims * 2, bias=False)
self.feature_transform_linear4 = torch.nn.Linear(self.feature_dims * 2, self.feature_dims * 2, bias=False)
self.mask_linear_layer = torch.nn.Linear(self.feature_dims * 2-output_dim, self.num_features, bias=False)
self.BN2 = torch.nn.BatchNorm1d(self.num_features, momentum = batch_momentum)
self.final_classifier_layer = torch.nn.Linear(self.output_dim, self.num_classes, bias=False)
def encoder(self, data):
batch_size = data.shape[0]
features = self.BN(data)
output_aggregated = torch.zeros([batch_size, self.output_dim])
masked_features = features
mask_values = torch.zeros([batch_size, self.num_features])
aggregated_mask_values = torch.zeros([batch_size, self.num_features])
complemantary_aggregated_mask_values =torch.ones([batch_size, self.num_features])
total_entropy = 0
for ni in range(self.num_decision_steps):
if ni==0:
transform_f1 = self.feature_transform_linear1(masked_features)
norm_transform_f1 = self.BN1(transform_f1)
transform_f2 = self.feature_transform_linear2(norm_transform_f1)
norm_transform_f2 = self.BN1(transform_f2)
else:
transform_f1 = self.feature_transform_linear1(masked_features)
norm_transform_f1 = self.BN1(transform_f1)
transform_f2 = self.feature_transform_linear2(norm_transform_f1)
norm_transform_f2 = self.BN1(transform_f2)
# GLU
transform_f2 = (glu(norm_transform_f2, self.feature_dims) +transform_f1) * np.sqrt(0.5)
transform_f3 = self.feature_transform_linear3(transform_f2)
norm_transform_f3 = self.BN1(transform_f3)
transform_f4 = self.feature_transform_linear4(norm_transform_f3)
norm_transform_f4 = self.BN1(transform_f4)
# GLU
transform_f4 = (glu(norm_transform_f4, self.feature_dims) + transform_f3) * np.sqrt(0.5)
decision_out = torch.nn.ReLU(inplace=True)(transform_f4[:, :self.output_dim])
# Decision aggregation
output_aggregated = torch.add(decision_out, output_aggregated)
scale_agg = torch.sum(decision_out, axis=1, keepdim=True) / (self.num_decision_steps - 1)
aggregated_mask_values = torch.add( aggregated_mask_values, mask_values * scale_agg)
features_for_coef = (transform_f4[:, self.output_dim:])
if ni<(self.num_decision_steps-1):
mask_linear_layer = self.mask_linear_layer(features_for_coef)
mask_linear_norm = self.BN2(mask_linear_layer)
mask_linear_norm = torch.mul(mask_linear_norm, complemantary_aggregated_mask_values)
mask_values = sparsemax(mask_linear_norm)
complemantary_aggregated_mask_values = torch.mul(complemantary_aggregated_mask_values,self.relaxation_factor - mask_values)
total_entropy = torch.add(total_entropy,torch.mean(torch.sum(-mask_values * torch.log(mask_values + self.epsilon),axis=1)) / (self.num_decision_steps - 1))
masked_features = torch.mul(mask_values , features)
return output_aggregated, total_entropy
def classify(self, output_logits):
logits = self.final_classifier_layer(output_logits)
predictions = torch.nn.Softmax(dim=1)(logits)
return logits, predictions