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# Code for making a Two hidden layer multi-label classification model
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from sklearn.metrics import recall_score, precision_score, accuracy_score
class MultiLabelTaskHead(nn.Module):
def __init__(self, input_size, output_size, device):
super(MultiLabelTaskHead, self).__init__()
self.fc1 = nn.Linear(input_size, 50)
self.fc2 = nn.Linear(50, 50)
self.fc3 = nn.Linear(50, output_size)
## Example in case of 5 w analysis output_size = 5
self.sigmoid = nn.Sigmoid()
self.device = device
def forward(self, x):
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
x = self.sigmoid(x)
return x
def predict(self, x):
x = self.forward(x)
x = torch.round(x)
return x
def accuracy(self, prediction, target):
prediction = torch.round(prediction)
return torch.mean((prediction == target).float())
def recall(self, prediction, target):
prediction = torch.round(prediction)
tp = torch.sum(torch.logical_and(prediction == 1, target == 1), axis=0)
fn = torch.sum(torch.logical_and(prediction == 0, target == 1), axis=0)
recall = tp / (tp + fn)
overall_recall = torch.mean(recall)
return overall_recall
def precision(self, prediction, target):
prediction = torch.round(prediction)
tp = torch.sum(torch.logical_and(prediction == 1, target == 1), axis=0)
fp = torch.sum(torch.logical_and(prediction == 1, target == 0), axis=0)
precision = tp / (tp + fp)
overall_precision = torch.mean(precision)
return overall_precision
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