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Autoregressive test score calculated on 96 values

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Victor Mylle
2023-11-13 23:14:07 +00:00
parent caeba6b841
commit 75f1f64c38
4 changed files with 45 additions and 13 deletions
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44
src/trainers/autoregressive_trainer.py
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@@ -8,6 +8,7 @@ import numpy as np
import plotly.subplots as sp
from plotly.subplots import make_subplots
from trainers.trainer import Trainer
from tqdm import tqdm
class AutoRegressiveTrainer(Trainer):
def debug_plots(self, task, train: bool, data_loader, sample_indices, epoch):
@@ -77,4 +78,45 @@ class AutoRegressiveTrainer(Trainer):
prediction = self.model(new_features.unsqueeze(0).to(self.device))
predictions_full.append(prediction.squeeze(-1))
return initial_sequence.cpu(), torch.stack(predictions_full).cpu(), torch.stack(target_full).cpu()
return initial_sequence.cpu(), torch.stack(predictions_full).cpu(), torch.stack(target_full).cpu()
def log_final_metrics(self, task, dataloader, train: bool = True):
metrics = { metric.__class__.__name__: 0.0 for metric in self.metrics_to_track }
transformed_metrics = { metric.__class__.__name__: 0.0 for metric in self.metrics_to_track }
with torch.no_grad():
# iterate idx over dataset
total_amount_samples = len(dataloader.dataset) - 95
for idx in tqdm(range(total_amount_samples)):
_, outputs, targets = self.auto_regressive(dataloader, idx)
inversed_outputs = torch.tensor(self.data_processor.inverse_transform(outputs))
inversed_inputs = torch.tensor(self.data_processor.inverse_transform(targets))
outputs = outputs.to(self.device)
targets = targets.to(self.device)
for metric in self.metrics_to_track:
transformed_metrics[metric.__class__.__name__] += metric(outputs, targets)
metrics[metric.__class__.__name__] += metric(inversed_outputs, inversed_inputs)
for metric in self.metrics_to_track:
metrics[metric.__class__.__name__] /= total_amount_samples
transformed_metrics[metric.__class__.__name__] /= total_amount_samples
for metric_name, metric_value in metrics.items():
if train:
metric_name = f'train_{metric_name}'
else:
metric_name = f'test_{metric_name}'
task.get_logger().report_single_value(name=metric_name, value=metric_value)
for metric_name, metric_value in transformed_metrics.items():
if train:
metric_name = f'train_transformed_{metric_name}'
else:
metric_name = f'test_transformed_{metric_name}'
task.get_logger().report_single_value(name=metric_name, value=metric_value)

2
src/trainers/trainer.py
View File

@@ -180,7 +180,7 @@ class Trainer:
transformed_train_loader, transformed_test_loader = self.data_processor.get_dataloaders(predict_sequence_length=self.model.output_size)
self.log_final_metrics(task, transformed_train_loader, train=True)
# self.log_final_metrics(task, transformed_train_loader, train=True)
self.log_final_metrics(task, transformed_test_loader, train=False)