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Fixing some stuff

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This commit is contained in:
Victor Mylle
2023-12-30 15:22:32 +00:00
parent ef8b5f49ac
commit c26ae76951
6 changed files with 107 additions and 33 deletions
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1
src/data/dataset.py
View File

@@ -71,7 +71,6 @@ class NrvDataset(Dataset):
self.nrv = torch.tensor(dataframe["nrv"].values).float().reshape(-1)
self.datetime = dataframe["datetime"]
print(dataframe.columns)
self.history_features, self.forecast_features = self.preprocess_data(dataframe)
def skip_samples(self, dataframe):

8
src/data/preprocessing.py
View File

@@ -253,7 +253,7 @@ class DataProcessor:
return self.get_dataloader(train_dataset, shuffle=shuffle)
def get_test_dataloader(
self, transform: bool = True, predict_sequence_length: int = 96
self, transform: bool = True, predict_sequence_length: int = 96, full_day_skip: bool = False
):
test_df = self.all_features.copy()
@@ -287,19 +287,19 @@ class DataProcessor:
test_dataset = NrvDataset(
test_df,
data_config=self.data_config,
full_day_skip=self.full_day_skip,
full_day_skip=self.full_day_skip or full_day_skip,
predict_sequence_length=predict_sequence_length,
lstm=self.lstm,
)
return self.get_dataloader(test_dataset, shuffle=False)
def get_dataloaders(
self, transform: bool = True, predict_sequence_length: int = 96
self, transform: bool = True, predict_sequence_length: int = 96, full_day_skip: bool = False
):
return self.get_train_dataloader(
transform=transform, predict_sequence_length=predict_sequence_length
), self.get_test_dataloader(
transform=transform, predict_sequence_length=predict_sequence_length
transform=transform, predict_sequence_length=predict_sequence_length, full_day_skip=full_day_skip
)
def inverse_transform(self, input_data):

60
src/notebooks/training.ipynb
View File

@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
@@ -12,7 +12,7 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
@@ -42,7 +42,7 @@
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
@@ -187,16 +187,17 @@
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ClearML Task: created new task id=d19c767120a24f97b3231f0e8ac9f2b5\n",
"ClearML results page: http://192.168.1.182:8080/projects/2e46d4af6f1e4c399cf9f5aa30bc8795/experiments/d19c767120a24f97b3231f0e8ac9f2b5/output/log\n",
"151780\n"
"2023-12-30 10:58:11,474 - clearml.task - WARNING - Requirement ignored, Task.add_requirements() must be called before Task.init()\n",
"2023-12-30 10:58:11,476 - clearml.task - WARNING - Requirement ignored, Task.ignore_requirements() must be called before Task.init()\n",
"2023-12-30 10:58:11,476 - clearml.task - WARNING - Requirement ignored, Task.ignore_requirements() must be called before Task.init()\n",
"2023-12-30 10:58:11,477 - clearml.task - WARNING - Requirement ignored, Task.ignore_requirements() must be called before Task.init()\n"
]
},
{
@@ -204,7 +205,8 @@
"output_type": "stream",
"text": [
"ERROR: Unexpected bus error encountered in worker. This might be caused by insufficient shared memory (shm).\n",
"\u0000Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f37044039a0>\n",
"\u0000ERROR: Unexpected bus error encountered in worker. This might be caused by insufficient shared memory (shm).\n",
"\u0000Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f6493fb8310>\n",
"Traceback (most recent call last):\n",
" File \"/opt/conda/lib/python3.10/site-packages/torch/utils/data/dataloader.py\", line 1478, in __del__\n",
" self._shutdown_workers()\n",
@@ -220,26 +222,33 @@
" fd_event_list = self._selector.poll(timeout)\n",
" File \"/opt/conda/lib/python3.10/site-packages/torch/utils/data/_utils/signal_handling.py\", line 66, in handler\n",
" _error_if_any_worker_fails()\n",
"RuntimeError: DataLoader worker (pid 326715) is killed by signal: Bus error. It is possible that dataloader's workers are out of shared memory. Please try to raise your shared memory limit.\n"
"RuntimeError: DataLoader worker (pid 610020) is killed by signal: Bus error. It is possible that dataloader's workers are out of shared memory. Please try to raise your shared memory limit.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"151780\n",
"24979\n",
"151780\n",
"24979\n",
"151780\n",
"24979\n",
"Using get_plot_error\n",
"Using get_plot_error\n",
"Using get_plot_error\n",
"Using get_plot_error\n",
"Early stopping triggered\n",
"151780\n",
"24979\n"
"2023-12-30 10:58:28,073 - clearml.Task - INFO - Completed model upload to http://192.168.1.182:8081/Thesis/NrvForecast/Autoregressive%20Non%20Linear%20Quantile%20Regression%20%2B%20Quarter%20%2B%20DoW%20%2B%20Net.79133ec0ca4f497e815cabc31683b626/models/checkpoint.pt\n",
"2023-12-30 10:58:31,506 - clearml.Task - INFO - Completed model upload to http://192.168.1.182:8081/Thesis/NrvForecast/Autoregressive%20Non%20Linear%20Quantile%20Regression%20%2B%20Quarter%20%2B%20DoW%20%2B%20Net.79133ec0ca4f497e815cabc31683b626/models/checkpoint.pt\n"
]
},
{
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[8], line 48\u001b[0m\n\u001b[1;32m 46\u001b[0m trainer\u001b[38;5;241m.\u001b[39mearly_stopping(patience\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m10\u001b[39m)\n\u001b[1;32m 47\u001b[0m trainer\u001b[38;5;241m.\u001b[39mplot_every(\u001b[38;5;241m25\u001b[39m)\n\u001b[0;32m---> 48\u001b[0m \u001b[43mtrainer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtask\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mepochs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mremotely\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m/workspaces/Thesis/src/notebooks/../../src/trainers/trainer.py:182\u001b[0m, in \u001b[0;36mTrainer.train\u001b[0;34m(self, epochs, remotely, task)\u001b[0m\n\u001b[1;32m 177\u001b[0m \u001b[38;5;66;03m# self.plot_quantile_percentages(\u001b[39;00m\n\u001b[1;32m 178\u001b[0m \u001b[38;5;66;03m# task, test_loader, False, epoch, True\u001b[39;00m\n\u001b[1;32m 179\u001b[0m \u001b[38;5;66;03m# )\u001b[39;00m\n\u001b[1;32m 181\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m task:\n\u001b[0;32m--> 182\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfinish_training\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtask\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 183\u001b[0m task\u001b[38;5;241m.\u001b[39mclose()\n\u001b[1;32m 184\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m:\n",
"File \u001b[0;32m/workspaces/Thesis/src/notebooks/../../src/trainers/trainer.py:253\u001b[0m, in \u001b[0;36mTrainer.finish_training\u001b[0;34m(self, task)\u001b[0m\n\u001b[1;32m 250\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mplot_quantile_percentages\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m 251\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlog_final_metrics(task, train_loader, train\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m--> 253\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlog_final_metrics\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtask\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtest_loader\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrain\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m/workspaces/Thesis/src/notebooks/../../src/trainers/quantile_trainer.py:105\u001b[0m, in \u001b[0;36mAutoRegressiveQuantileTrainer.log_final_metrics\u001b[0;34m(self, task, dataloader, train)\u001b[0m\n\u001b[1;32m 103\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m idx \u001b[38;5;129;01min\u001b[39;00m idx_batch:\n\u001b[1;32m 104\u001b[0m computed_idx_batch \u001b[38;5;241m=\u001b[39m [idx] \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m100\u001b[39m\n\u001b[0;32m--> 105\u001b[0m _, outputs, samples, targets \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mauto_regressive\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 106\u001b[0m \u001b[43m \u001b[49m\u001b[43mdataloader\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdataset\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43midx_batch\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcomputed_idx_batch\u001b[49m\n\u001b[1;32m 107\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 108\u001b[0m samples \u001b[38;5;241m=\u001b[39m samples\u001b[38;5;241m.\u001b[39munsqueeze(\u001b[38;5;241m0\u001b[39m)\n\u001b[1;32m 109\u001b[0m targets \u001b[38;5;241m=\u001b[39m targets\u001b[38;5;241m.\u001b[39msqueeze(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m)\n",
"File \u001b[0;32m/workspaces/Thesis/src/notebooks/../../src/trainers/quantile_trainer.py:275\u001b[0m, in \u001b[0;36mAutoRegressiveQuantileTrainer.auto_regressive\u001b[0;34m(self, dataset, idx_batch, sequence_length)\u001b[0m\n\u001b[1;32m 269\u001b[0m new_features \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mcat(\n\u001b[1;32m 270\u001b[0m (prev_features[:, \u001b[38;5;241m1\u001b[39m:\u001b[38;5;241m96\u001b[39m], samples), dim\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m\n\u001b[1;32m 271\u001b[0m ) \u001b[38;5;66;03m# (batch_size, 96)\u001b[39;00m\n\u001b[1;32m 273\u001b[0m new_features \u001b[38;5;241m=\u001b[39m new_features\u001b[38;5;241m.\u001b[39mfloat()\n\u001b[0;32m--> 275\u001b[0m other_features, new_targets \u001b[38;5;241m=\u001b[39m \u001b[43mdataset\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_batch_autoregressive\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 276\u001b[0m \u001b[43m \u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43marray\u001b[49m\u001b[43m(\u001b[49m\u001b[43midx_batch\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mi\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\n\u001b[1;32m 277\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# (batch_size, new_features)\u001b[39;00m\n\u001b[1;32m 279\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m other_features \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 280\u001b[0m prev_features \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mcat(\n\u001b[1;32m 281\u001b[0m (new_features\u001b[38;5;241m.\u001b[39mto(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdevice), other_features\u001b[38;5;241m.\u001b[39mto(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdevice)), dim\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m\n\u001b[1;32m 282\u001b[0m ) \u001b[38;5;66;03m# (batch_size, 96 + new_features)\u001b[39;00m\n",
"File \u001b[0;32m/workspaces/Thesis/src/notebooks/../../src/data/dataset.py:199\u001b[0m, in \u001b[0;36mNrvDataset.get_batch_autoregressive\u001b[0;34m(self, idx)\u001b[0m\n\u001b[1;32m 197\u001b[0m targets \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m 198\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m idx:\n\u001b[0;32m--> 199\u001b[0m f, t \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrandom_day_autoregressive\u001b[49m\u001b[43m(\u001b[49m\u001b[43mi\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 200\u001b[0m features\u001b[38;5;241m.\u001b[39mappend(f)\n\u001b[1;32m 201\u001b[0m targets\u001b[38;5;241m.\u001b[39mappend(t)\n",
"File \u001b[0;32m/workspaces/Thesis/src/notebooks/../../src/data/dataset.py:172\u001b[0m, in \u001b[0;36mNrvDataset.random_day_autoregressive\u001b[0;34m(self, idx)\u001b[0m\n\u001b[1;32m 171\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mrandom_day_autoregressive\u001b[39m(\u001b[38;5;28mself\u001b[39m, idx: \u001b[38;5;28mint\u001b[39m):\n\u001b[0;32m--> 172\u001b[0m all_features, nrv_target, _ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__getitem__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43midx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 174\u001b[0m \u001b[38;5;66;03m# remove the first 96 values of the features (the nrv history)\u001b[39;00m\n\u001b[1;32m 175\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlstm:\n",
"File \u001b[0;32m/workspaces/Thesis/src/notebooks/../../src/data/dataset.py:162\u001b[0m, in \u001b[0;36mNrvDataset.__getitem__\u001b[0;34m(self, idx)\u001b[0m\n\u001b[1;32m 159\u001b[0m nrv_target \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnrv[actual_idx \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msequence_length : actual_idx \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msequence_length \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpredict_sequence_length]\n\u001b[1;32m 161\u001b[0m \u001b[38;5;66;03m# check if nan values are present\u001b[39;00m\n\u001b[0;32m--> 162\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43misnan\u001b[49m\u001b[43m(\u001b[49m\u001b[43mall_features\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43many\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[1;32m 163\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFound nan values in the features of sample \u001b[39m\u001b[38;5;132;01m{\u001b[39;00midx\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 164\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mActual index: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mactual_idx\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n",
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
]
}
],
@@ -262,7 +271,7 @@
"\n",
"model_parameters = {\n",
" \"learning_rate\": 0.0002,\n",
" \"hidden_size\": 1024,\n",
" \"hidden_size\": 512,\n",
" \"num_layers\": 3,\n",
" \"dropout\": 0.2,\n",
" \"time_feature_embedding\": 2,\n",
@@ -301,6 +310,11 @@
"# Non Autoregressive Quantile Regression"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": []
},
{
"cell_type": "code",
"execution_count": 5,

51
src/trainers/quantile_trainer.py
View File

@@ -1,4 +1,6 @@
import torch
from tqdm import tqdm
from src.losses.crps_metric import crps_from_samples
from src.trainers.trainer import Trainer
from src.trainers.autoregressive_trainer import AutoRegressiveTrainer
from src.data.preprocessing import DataProcessor
@@ -81,21 +83,63 @@ class AutoRegressiveQuantileTrainer(AutoRegressiveTrainer):
debug=debug,
)
def calculate_crps_from_samples(self, task, dataloader, epoch: int):
crps_from_samples_metric = []
with torch.no_grad():
for _, _, idx_batch in tqdm(dataloader):
if len(idx_batch) == 0:
continue
for idx in tqdm(idx_batch):
computed_idx_batch = [idx] * 100
_, _, samples, targets = self.auto_regressive(
dataloader.dataset, idx_batch=computed_idx_batch
)
samples = samples.unsqueeze(0)
targets = targets.squeeze(-1)
targets = targets[0].unsqueeze(0)
crps = crps_from_samples(samples, targets)
crps_from_samples_metric.append(crps[0].mean().item())
task.get_logger().report_scalar(
title="CRPS_from_samples", series="test", value=np.mean(crps_from_samples_metric), iteration=epoch
)
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
}
crps_from_samples_metric = []
with torch.no_grad():
total_samples = len(dataloader.dataset) - 96
batches = 0
for _, _, idx_batch in dataloader:
for _, _, idx_batch in tqdm(dataloader):
idx_batch = [idx for idx in idx_batch if idx < total_samples]
if len(idx_batch) == 0:
continue
if train == False:
for idx in tqdm(idx_batch):
computed_idx_batch = [idx] * 100
_, outputs, samples, targets = self.auto_regressive(
dataloader.dataset, idx_batch=computed_idx_batch
)
samples = samples.unsqueeze(0)
targets = targets.squeeze(-1)
targets = targets[0].unsqueeze(0)
crps = crps_from_samples(samples, targets)
crps_from_samples_metric.append(crps[0].mean().item())
_, outputs, samples, targets = self.auto_regressive(
dataloader.dataset, idx_batch=idx_batch
)
@@ -147,6 +191,11 @@ class AutoRegressiveQuantileTrainer(AutoRegressiveTrainer):
)
task.get_logger().report_single_value(name=name, value=metric_value)
if train == False:
task.get_logger().report_single_value(
name="test_CRPS_from_samples_transformed", value=np.mean(crps_from_samples_metric)
)
def get_plot_error(
self,
next_day,

16
src/trainers/trainer.py
View File

@@ -99,6 +99,10 @@ class Trainer:
def train(self, epochs: int, remotely: bool = False, task: Task = None):
try:
_, full_day_skip_test_loader = self.data_processor.get_dataloaders(
predict_sequence_length=self.model.output_size, full_day_skip=True
)
train_loader, test_loader = self.data_processor.get_dataloaders(
predict_sequence_length=self.model.output_size
)
@@ -178,6 +182,11 @@ class Trainer:
# task, test_loader, False, epoch, True
# )
if hasattr(self, "calculate_crps_from_samples"):
self.calculate_crps_from_samples(
task, full_day_skip_test_loader, epoch
)
if task:
self.finish_training(task=task)
task.close()
@@ -243,12 +252,15 @@ class Trainer:
self.model.load_state_dict(torch.load("checkpoint.pt"))
self.model.eval()
# set full day skip
self.data_processor.set_full_day_skip(True)
train_loader, test_loader = self.data_processor.get_dataloaders(
predict_sequence_length=self.model.output_size
)
if not hasattr(self, "plot_quantile_percentages"):
self.log_final_metrics(task, train_loader, train=True)
# if not hasattr(self, "plot_quantile_percentages"):
# self.log_final_metrics(task, train_loader, train=True)
self.log_final_metrics(task, test_loader, train=False)

4
src/training_scripts/autoregressive_quantiles.py
View File

@@ -43,7 +43,7 @@ data_processor.set_full_day_skip(False)
#### Hyperparameters ####
data_processor.set_output_size(1)
inputDim = data_processor.get_input_size()
epochs = 400
epochs = 300
# add parameters to clearml
quantiles = task.get_parameter("general/quantiles", cast=True)
@@ -58,7 +58,7 @@ else:
model_parameters = {
"learning_rate": 0.0001,
"hidden_size": 1024,
"hidden_size": 512,
"num_layers": 2,
"dropout": 0.2,
"time_feature_embedding": 4,