From 01663eb05f963525a23a7b49ec907c738adef929 Mon Sep 17 00:00:00 2001 From: wassname Date: Sat, 24 Dec 2022 12:36:51 +0800 Subject: [PATCH] some poor results --- examples/Time-Grad-Electricity.ipynb | 336 +++++++++++++++++--------- examples/Time-Grad2-Electricity.ipynb | 7 +- 2 files changed, 229 insertions(+), 114 deletions(-) diff --git a/examples/Time-Grad-Electricity.ipynb b/examples/Time-Grad-Electricity.ipynb index 556895f..0a9a5c8 100644 --- a/examples/Time-Grad-Electricity.ipynb +++ b/examples/Time-Grad-Electricity.ipynb @@ -5,8 +5,8 @@ "execution_count": 1, "metadata": { "ExecuteTime": { - "end_time": "2022-12-23T09:57:39.738715Z", - "start_time": "2022-12-23T09:57:39.730077Z" + "end_time": "2022-12-23T12:53:57.712458Z", + "start_time": "2022-12-23T12:53:57.704099Z" } }, "outputs": [], @@ -21,8 +21,8 @@ "execution_count": 2, "metadata": { "ExecuteTime": { - "end_time": "2022-12-23T09:57:40.738250Z", - "start_time": "2022-12-23T09:57:39.739677Z" + "end_time": "2022-12-23T12:53:59.259208Z", + "start_time": "2022-12-23T12:53:57.713340Z" } }, "outputs": [], @@ -41,11 +41,20 @@ "execution_count": 3, "metadata": { "ExecuteTime": { - "end_time": "2022-12-23T09:57:40.909476Z", - "start_time": "2022-12-23T09:57:40.739741Z" + "end_time": "2022-12-23T12:53:59.433098Z", + "start_time": "2022-12-23T12:53:59.261001Z" } }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/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", + " warnings.warn(\n" + ] + } + ], "source": [ "from gluonts.dataset.multivariate_grouper import MultivariateGrouper\n", "from gluonts.dataset.repository.datasets import dataset_recipes, get_dataset\n", @@ -58,8 +67,8 @@ "execution_count": 4, "metadata": { "ExecuteTime": { - "end_time": "2022-12-23T09:57:40.966206Z", - "start_time": "2022-12-23T09:57:40.910477Z" + "end_time": "2022-12-23T12:53:59.545657Z", + "start_time": "2022-12-23T12:53:59.434450Z" } }, "outputs": [], @@ -75,8 +84,8 @@ "execution_count": 5, "metadata": { "ExecuteTime": { - "end_time": "2022-12-23T09:57:40.980883Z", - "start_time": "2022-12-23T09:57:40.967157Z" + "end_time": "2022-12-23T12:53:59.589097Z", + "start_time": "2022-12-23T12:53:59.546862Z" } }, "outputs": [], @@ -89,8 +98,8 @@ "execution_count": 6, "metadata": { "ExecuteTime": { - "end_time": "2022-12-23T09:57:41.003468Z", - "start_time": "2022-12-23T09:57:40.981830Z" + "end_time": "2022-12-23T12:53:59.620352Z", + "start_time": "2022-12-23T12:53:59.590581Z" } }, "outputs": [], @@ -157,8 +166,8 @@ "execution_count": 7, "metadata": { "ExecuteTime": { - "end_time": "2022-12-23T09:57:41.025224Z", - "start_time": "2022-12-23T09:57:41.004330Z" + "end_time": "2022-12-23T12:53:59.648105Z", + "start_time": "2022-12-23T12:53:59.621542Z" } }, "outputs": [ @@ -179,8 +188,8 @@ "execution_count": 8, "metadata": { "ExecuteTime": { - "end_time": "2022-12-23T09:57:41.041326Z", - "start_time": "2022-12-23T09:57:41.026069Z" + "end_time": "2022-12-23T12:53:59.674244Z", + "start_time": "2022-12-23T12:53:59.649143Z" }, "scrolled": true }, @@ -195,8 +204,8 @@ "execution_count": 9, "metadata": { "ExecuteTime": { - "end_time": "2022-12-23T09:57:41.057694Z", - "start_time": "2022-12-23T09:57:41.042892Z" + "end_time": "2022-12-23T12:53:59.691198Z", + "start_time": "2022-12-23T12:53:59.676310Z" } }, "outputs": [ @@ -232,8 +241,8 @@ "execution_count": 10, "metadata": { "ExecuteTime": { - "end_time": "2022-12-23T09:57:41.073075Z", - "start_time": "2022-12-23T09:57:41.058526Z" + "end_time": "2022-12-23T12:53:59.715182Z", + "start_time": "2022-12-23T12:53:59.692190Z" } }, "outputs": [], @@ -246,8 +255,8 @@ "execution_count": 11, "metadata": { "ExecuteTime": { - "end_time": "2022-12-23T09:57:44.134871Z", - "start_time": "2022-12-23T09:57:41.073880Z" + "end_time": "2022-12-23T12:54:01.039272Z", + "start_time": "2022-12-23T12:53:59.716235Z" } }, "outputs": [], @@ -260,8 +269,8 @@ "execution_count": 12, "metadata": { "ExecuteTime": { - "end_time": "2022-12-23T09:57:44.153700Z", - "start_time": "2022-12-23T09:57:44.136099Z" + "end_time": "2022-12-23T12:54:01.052821Z", + "start_time": "2022-12-23T12:54:01.040398Z" } }, "outputs": [], @@ -288,17 +297,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": { "ExecuteTime": { - "start_time": "2022-12-23T09:57:39.746Z" + "end_time": "2022-12-23T13:17:23.518463Z", + "start_time": "2022-12-23T12:54:01.053838Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "744b148440674ef3902ca56622cb001b", + "model_id": "194804949f884a888bfc91f4d908d90b", "version_major": 2, "version_minor": 0 }, @@ -312,7 +322,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "88abd67eb98d4569b276f98dadd73205", + "model_id": "ecd692a5a7414039b956e859e0b57495", "version_major": 2, "version_minor": 0 }, @@ -326,7 +336,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f5e08c9306c647cd8589f29d8652177b", + "model_id": "af6f1e845efa447391b8c28d1ae9353a", "version_major": 2, "version_minor": 0 }, @@ -340,7 +350,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "10a4ac2006db4f6090dc3f08cc3e3e9e", + "model_id": "12c5b95d86a543519cf057b3febf6344", "version_major": 2, "version_minor": 0 }, @@ -354,7 +364,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6458a98361854786bc4fa81da37471d2", + "model_id": "1724ddc42b3849d0bfb873ed6f94cb53", "version_major": 2, "version_minor": 0 }, @@ -368,7 +378,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "676d661e21ec42acbb544a8848264674", + "model_id": "65ad802537d246609ec3448d1fab3831", "version_major": 2, "version_minor": 0 }, @@ -382,7 +392,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "36010cebcd884d248de158890d2ba324", + "model_id": "b137dcfd271441df960ca5bdf37d434e", "version_major": 2, "version_minor": 0 }, @@ -396,7 +406,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f7487fae1ae54dae8c71d23ea5721e7f", + "model_id": "1bf81c1871af44d18f4aa424f7187b9d", "version_major": 2, "version_minor": 0 }, @@ -410,7 +420,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6a995bf552bb487cbca2a0f3e45098b3", + "model_id": "a747d5b3f9d0452380008b6752a3d8a9", "version_major": 2, "version_minor": 0 }, @@ -424,7 +434,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3ee8b7cdd0b94465a78c43d0917910ae", + "model_id": "28c0a56808d54d31b14e880d78d367e8", "version_major": 2, "version_minor": 0 }, @@ -438,7 +448,133 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "958283558c6a4e958c5c67188e735ed9", + "model_id": "18109c16fc1848deb34cf8dd3ed488f3", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/99 [00:00\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": { diff --git a/examples/Time-Grad2-Electricity.ipynb b/examples/Time-Grad2-Electricity.ipynb index 335d65b..e596398 100644 --- a/examples/Time-Grad2-Electricity.ipynb +++ b/examples/Time-Grad2-Electricity.ipynb @@ -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",