| |
| 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) |
| |
| 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 |
|
|
|
|
