mirror of
https://github.com/wassname/pytorch-ts.git
synced 2026-06-27 18:06:19 +08:00
some poor results
This commit is contained in:
wassname
@@ -5,8 +5,8 @@
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@@ -21,8 +21,8 @@
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@@ -41,11 +41,20 @@
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"/home/wassname/miniforge3/envs/gluonts10.0/lib/python3.9/site-packages/gluonts/json.py:101: UserWarning: Using `json`-module for json-handling. Consider installing one of `orjson`, `ujson` to speed up serialization and deserialization.\n",
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" warnings.warn(\n"
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"from gluonts.dataset.multivariate_grouper import MultivariateGrouper\n",
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"from gluonts.dataset.repository.datasets import dataset_recipes, get_dataset\n",
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@@ -58,8 +67,8 @@
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@@ -75,8 +84,8 @@
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@@ -89,8 +98,8 @@
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@@ -157,8 +166,8 @@
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@@ -179,8 +188,8 @@
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@@ -195,8 +204,8 @@
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@@ -232,8 +241,8 @@
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@@ -246,8 +255,8 @@
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@@ -260,8 +269,8 @@
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@@ -288,17 +297,18 @@
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|
||||
"Input \u001b[0;32mIn [15]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m dataset_test \u001b[38;5;241m=\u001b[39m \u001b[43mtest_grouper\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdataset\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtest\u001b[49m\u001b[43m)\u001b[49m\n",
|
||||
"File \u001b[0;32m~/miniforge3/envs/gluonts10.0/lib/python3.9/site-packages/gluonts/dataset/multivariate_grouper.py:87\u001b[0m, in \u001b[0;36mMultivariateGrouper.__call__\u001b[0;34m(self, dataset)\u001b[0m\n\u001b[1;32m 85\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, dataset: Dataset) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Dataset:\n\u001b[1;32m 86\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_preprocess(dataset)\n\u001b[0;32m---> 87\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_group_all\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdataset\u001b[49m\u001b[43m)\u001b[49m\n",
|
||||
"File \u001b[0;32m~/miniforge3/envs/gluonts10.0/lib/python3.9/site-packages/gluonts/dataset/multivariate_grouper.py:125\u001b[0m, in \u001b[0;36mMultivariateGrouper._group_all\u001b[0;34m(self, dataset)\u001b[0m\n\u001b[1;32m 123\u001b[0m grouped_dataset \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_prepare_train_data(dataset)\n\u001b[1;32m 124\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 125\u001b[0m grouped_dataset \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_prepare_test_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdataset\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 126\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m grouped_dataset\n",
|
||||
"File \u001b[0;32m~/miniforge3/envs/gluonts10.0/lib/python3.9/site-packages/gluonts/dataset/multivariate_grouper.py:145\u001b[0m, in \u001b[0;36mMultivariateGrouper._prepare_test_data\u001b[0;34m(self, dataset)\u001b[0m\n\u001b[1;32m 141\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnum_test_dates \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 143\u001b[0m logging\u001b[38;5;241m.\u001b[39minfo(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgroup test time-series to datasets\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 145\u001b[0m grouped_data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_transform_target\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_left_pad_data\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdataset\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 146\u001b[0m \u001b[38;5;66;03m# splits test dataset with rolling date into N R^d time series where\u001b[39;00m\n\u001b[1;32m 147\u001b[0m \u001b[38;5;66;03m# N is the number of rolling evaluation dates\u001b[39;00m\n\u001b[1;32m 148\u001b[0m split_dataset \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39msplit(\n\u001b[1;32m 149\u001b[0m grouped_data[FieldName\u001b[38;5;241m.\u001b[39mTARGET], \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnum_test_dates\n\u001b[1;32m 150\u001b[0m )\n",
|
||||
"File \u001b[0;32m~/miniforge3/envs/gluonts10.0/lib/python3.9/site-packages/gluonts/dataset/multivariate_grouper.py:191\u001b[0m, in \u001b[0;36mMultivariateGrouper._transform_target\u001b[0;34m(funcs, dataset)\u001b[0m\n\u001b[1;32m 189\u001b[0m \u001b[38;5;129m@staticmethod\u001b[39m\n\u001b[1;32m 190\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_transform_target\u001b[39m(funcs, dataset: Dataset) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m DataEntry:\n\u001b[0;32m--> 191\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m {FieldName\u001b[38;5;241m.\u001b[39mTARGET: \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43marray\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[43mfuncs\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mdataset\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m}\n",
|
||||
"\u001b[0;31mValueError\u001b[0m: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (2590,) + inhomogeneous part."
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"dataset_test = test_grouper(dataset.test)"
|
||||
]
|
||||
@@ -496,60 +643,8 @@
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"start_time": "2022-12-23T09:57:39.750Z"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"next(iter(dataset.test.iter_sequential()))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"start_time": "2022-12-23T09:57:39.751Z"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"len(dataset.test)\n",
|
||||
"x = [x['target'].shape for x in dataset.test.iter_sequential()]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"start_time": "2022-12-23T09:57:39.752Z"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pd.Series(x).value_counts()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"start_time": "2022-12-23T09:57:39.753Z"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%debug"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"start_time": "2022-12-23T09:57:39.754Z"
|
||||
"end_time": "2022-12-23T13:17:36.537956Z",
|
||||
"start_time": "2022-12-23T13:17:36.537946Z"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
@@ -564,7 +659,8 @@
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"start_time": "2022-12-23T09:57:39.756Z"
|
||||
"end_time": "2022-12-23T13:17:36.538906Z",
|
||||
"start_time": "2022-12-23T13:17:36.538898Z"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
@@ -578,7 +674,22 @@
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"start_time": "2022-12-23T09:57:39.756Z"
|
||||
"end_time": "2022-12-23T13:17:36.539968Z",
|
||||
"start_time": "2022-12-23T13:17:36.539960Z"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%debug"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2022-12-23T13:17:36.541030Z",
|
||||
"start_time": "2022-12-23T13:17:36.541021Z"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
@@ -596,7 +707,8 @@
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"start_time": "2022-12-23T09:57:39.757Z"
|
||||
"end_time": "2022-12-23T13:17:36.542095Z",
|
||||
"start_time": "2022-12-23T13:17:36.542086Z"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
@@ -610,7 +722,8 @@
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"start_time": "2022-12-23T09:57:39.758Z"
|
||||
"end_time": "2022-12-23T13:17:36.543150Z",
|
||||
"start_time": "2022-12-23T13:17:36.543141Z"
|
||||
},
|
||||
"scrolled": true
|
||||
},
|
||||
@@ -624,7 +737,8 @@
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"start_time": "2022-12-23T09:57:39.759Z"
|
||||
"end_time": "2022-12-23T13:17:36.544197Z",
|
||||
"start_time": "2022-12-23T13:17:36.544189Z"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
@@ -648,9 +762,9 @@
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "glounts",
|
||||
"display_name": "gluonts10.0",
|
||||
"language": "python",
|
||||
"name": "glounts"
|
||||
"name": "gluonts10.0"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
|
||||
@@ -84,7 +84,8 @@
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
|
||||
"device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
|
||||
"device"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -1162,8 +1163,8 @@
|
||||
],
|
||||
"source": [
|
||||
"print(\"CRPS:\", agg_metric[\"mean_wQuantileLoss\"])\n",
|
||||
"print(\"ND:\", agg_metric[\"ND\"])\n",
|
||||
"print(\"NRMSE:\", agg_metric[\"NRMSE\"])\n",
|
||||
"print(\"ND:\", agg_metric[\"ND\"]) # totals[\"abs_error\"] / totals[\"abs_target_sum\"]\n",
|
||||
"print(\"NRMSE:\", agg_metric[\"NRMSE\"]) # totals[\"RMSE\"] / totals[\"abs_target_mean\"]\n",
|
||||
"print(\"\")\n",
|
||||
"print(\"CRPS-Sum:\", agg_metric[\"m_sum_mean_wQuantileLoss\"])\n",
|
||||
"print(\"ND-Sum:\", agg_metric[\"m_sum_ND\"])\n",
|
||||
|
||||
Reference in New Issue
Block a user