Updated training notebook
This commit is contained in:
Victor Mylle
@@ -7,7 +7,15 @@
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"outputs": [],
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"source": [
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"import sys\n",
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"sys.path.append('../..')\n",
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"sys.path.append('../..')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"from src.data import DataProcessor, DataConfig\n",
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"from src.trainers.quantile_trainer import AutoRegressiveQuantileTrainer, NonAutoRegressiveQuantileRegression\n",
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"from src.trainers.probabilistic_baseline import ProbabilisticBaselineTrainer\n",
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@@ -34,7 +42,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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@@ -159,7 +167,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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@@ -173,23 +181,35 @@
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/workspaces/Thesis/src/notebooks/../../src/trainers/quantile_trainer.py:68: UserWarning:\n",
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"\n",
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"To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
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"\n",
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"/workspaces/Thesis/src/notebooks/../../src/losses/pinball_loss.py:8: UserWarning:\n",
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"\n",
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"To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
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"\n"
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"/workspaces/Thesis/src/notebooks/../../src/trainers/quantile_trainer.py:68: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
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" quantiles_tensor = torch.tensor(quantiles)\n",
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"/workspaces/Thesis/src/notebooks/../../src/losses/pinball_loss.py:8: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
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" self.quantiles_tensor = torch.tensor(quantiles, dtype=torch.float32)\n",
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"InsecureRequestWarning: Certificate verification is disabled! Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"ClearML Task: created new task id=cbf4a5162c604d6ea8f14e71e2d27410\n",
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"ClearML results page: http://192.168.1.182:8080/projects/2e46d4af6f1e4c399cf9f5aa30bc8795/experiments/cbf4a5162c604d6ea8f14e71e2d27410/output/log\n",
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"Early stopping triggered\n"
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"ClearML Task: created new task id=4652507a84f5435fb6bd98c645d15f24\n",
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"ClearML results page: http://192.168.1.182:8080/projects/2e46d4af6f1e4c399cf9f5aa30bc8795/experiments/4652507a84f5435fb6bd98c645d15f24/output/log\n",
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"2023-11-26 22:15:47,860 - clearml.Task - INFO - Storing jupyter notebook directly as code\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Switching to remote execution, output log page http://192.168.1.182:8080/projects/2e46d4af6f1e4c399cf9f5aa30bc8795/experiments/4652507a84f5435fb6bd98c645d15f24/output/log\n"
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]
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},
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{
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"ename": "",
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"evalue": "",
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"output_type": "error",
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"traceback": [
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"\u001b[1;31mThe Kernel crashed while executing code in the the current cell or a previous cell. Please review the code in the cell(s) to identify a possible cause of the failure. Click <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. View Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
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]
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}
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],
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@@ -226,7 +246,7 @@
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")\n",
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"trainer.early_stopping(patience=10)\n",
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"trainer.plot_every(5)\n",
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"trainer.train(epochs=epochs)"
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"trainer.train(epochs=epochs, remotely=True)"
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]
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},
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{
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@@ -315,103 +335,6 @@
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"trainer.plot_every(5)\n",
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"trainer.train(epochs=epochs)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"\n",
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"\n",
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"dataset = data_processor.get_train_dataloader().dataset\n",
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"dataset.predict_sequence_length = 1\n",
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"dataset.data_config.LOAD_HISTORY = True\n",
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"\n",
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"\n",
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"def auto_regressive_batch(dataset, idx_batch, sequence_length):\n",
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" target_full = [] # (batch_size, sequence_length)\n",
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" predictions_samples = [] # (batch_size, sequence_length)\n",
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" predictions_full = [] # (batch_size, sequence_length, quantiles)\n",
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"\n",
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" prev_features, targets = dataset.get_batch(idx_batch)\n",
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"\n",
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" initial_sequence = prev_features[:, :96]\n",
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"\n",
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" target_full = targets[:, 0]\n",
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" self.\n",
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"\n",
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"\n",
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"\n",
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"auto_regressive_batch(dataset, [0, 1, 2], 50)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"def auto_regressive(self, data_loader, idx, sequence_length: int = 96):\n",
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" self.model.eval()\n",
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" target_full = []\n",
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" predictions_sampled = []\n",
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" predictions_full = []\n",
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"\n",
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" prev_features, target = data_loader.dataset[idx]\n",
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" prev_features = prev_features.to(self.device)\n",
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"\n",
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" initial_sequence = prev_features[:96]\n",
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"\n",
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" target_full.append(target)\n",
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" with torch.no_grad():\n",
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" prediction = self.model(prev_features.unsqueeze(0))\n",
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" predictions_full.append(prediction.squeeze(0))\n",
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"\n",
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" # sample from the distribution\n",
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" sample = self.sample_from_dist(\n",
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" self.quantiles.cpu(), prediction.squeeze(-1).cpu().numpy()\n",
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" )\n",
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" predictions_sampled.append(sample)\n",
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"\n",
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" for i in range(sequence_length - 1):\n",
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" new_features = torch.cat(\n",
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" (prev_features[1:96].cpu(), torch.tensor([predictions_sampled[-1]])),\n",
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" dim=0,\n",
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" )\n",
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" new_features = new_features.float()\n",
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"\n",
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" # get the other needed features\n",
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" other_features, new_target = data_loader.dataset.random_day_autoregressive(\n",
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" idx + i + 1\n",
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" )\n",
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"\n",
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" if other_features is not None:\n",
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" prev_features = torch.cat((new_features, other_features), dim=0)\n",
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" else:\n",
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" prev_features = new_features\n",
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"\n",
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" # add target to target_full\n",
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" target_full.append(new_target)\n",
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"\n",
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" # predict\n",
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" with torch.no_grad():\n",
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" prediction = self.model(prev_features.unsqueeze(0).to(self.device))\n",
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" predictions_full.append(prediction.squeeze(0))\n",
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"\n",
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" # sample from the distribution\n",
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" sample = self.sample_from_dist(\n",
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" self.quantiles.cpu(), prediction.squeeze(-1).cpu().numpy()\n",
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" )\n",
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" predictions_sampled.append(sample)\n",
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"\n",
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" return (\n",
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" initial_sequence.cpu(),\n",
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" torch.stack(predictions_full).cpu(),\n",
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" torch.tensor(predictions_sampled).reshape(-1, 1),\n",
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" torch.stack(target_full).cpu(),\n",
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" )"
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]
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}
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],
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"metadata": {
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