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wassname
2022-12-23 14:34:54 +08:00
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commit 3fb53f620e
11 changed files with 1775 additions and 1017 deletions
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examples/Implicit-Quantile-Network-Example.ipynb
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examples/Multivariate-Flow-Solar.ipynb
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"MetaData(freq='H', target=None, feat_static_cat=[CategoricalFeatureInfo(name='feat_static_cat', cardinality='137')], feat_static_real=[], feat_dynamic_real=[], feat_dynamic_cat=[], prediction_length=24)"
"MetaData(freq='H', target=None, feat_static_cat=[CategoricalFeatureInfo(name='feat_static_cat_0', cardinality='137')], feat_static_real=[], feat_dynamic_real=[], feat_dynamic_cat=[], prediction_length=24)"
]
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"/Users/samuelnorling/Programming/KTH/exjobb/zalandorosettaenv/lib/python3.8/site-packages/gluonts/dataset/multivariate_grouper.py:182: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
"/home/wassname/miniforge3/envs/glounts/lib/python3.9/site-packages/gluonts/dataset/multivariate_grouper.py:191: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
" return {FieldName.TARGET: np.array([funcs(data) for data in dataset])}\n"
]
}
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"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e6b529fa683f4e51b5f8ac63db471849",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
" 0%| | 0/99 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"ename": "Exception",
"evalue": "Reached maximum number of idle transformation calls.\nThis means the transformation looped over 1 inputs without returning any output.\nThis occurred in the following transformation:\ngluonts.transform.split.InstanceSplitter(dummy_value=0.0, forecast_start_field=\"forecast_start\", future_length=24, instance_sampler=gluonts.transform.sampler.ExpectedNumInstanceSampler(axis=-1, min_past=192, min_future=24, num_instances=1.0, total_length=20382, n=3), is_pad_field=\"is_pad\", lead_time=0, output_NTC=True, past_length=192, start_field=\"start\", target_field=\"target\", time_series_fields=[\"time_feat\", \"observed_values\"])",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mException\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m/media/wassname/SGIronWolf/projects5/timeseries/pytorch-ts/examples/Multivariate-Flow-Solar.ipynb Cell 13\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> <a href='vscode-notebook-cell:/media/wassname/SGIronWolf/projects5/timeseries/pytorch-ts/examples/Multivariate-Flow-Solar.ipynb#X15sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m predictor \u001b[39m=\u001b[39m estimator\u001b[39m.\u001b[39;49mtrain(dataset_train)\n\u001b[1;32m <a href='vscode-notebook-cell:/media/wassname/SGIronWolf/projects5/timeseries/pytorch-ts/examples/Multivariate-Flow-Solar.ipynb#X15sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m forecast_it, ts_it \u001b[39m=\u001b[39m make_evaluation_predictions(dataset\u001b[39m=\u001b[39mdataset_test,\n\u001b[1;32m <a href='vscode-notebook-cell:/media/wassname/SGIronWolf/projects5/timeseries/pytorch-ts/examples/Multivariate-Flow-Solar.ipynb#X15sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m predictor\u001b[39m=\u001b[39mpredictor,\n\u001b[1;32m <a href='vscode-notebook-cell:/media/wassname/SGIronWolf/projects5/timeseries/pytorch-ts/examples/Multivariate-Flow-Solar.ipynb#X15sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m num_samples\u001b[39m=\u001b[39m\u001b[39m100\u001b[39m)\n\u001b[1;32m <a href='vscode-notebook-cell:/media/wassname/SGIronWolf/projects5/timeseries/pytorch-ts/examples/Multivariate-Flow-Solar.ipynb#X15sZmlsZQ%3D%3D?line=4'>5</a>\u001b[0m forecasts \u001b[39m=\u001b[39m \u001b[39mlist\u001b[39m(forecast_it)\n",
"File \u001b[0;32m/media/wassname/SGIronWolf/projects5/timeseries/pytorch-ts/pts/model/estimator.py:179\u001b[0m, in \u001b[0;36mPyTorchEstimator.train\u001b[0;34m(self, training_data, validation_data, num_workers, prefetch_factor, shuffle_buffer_length, cache_data, **kwargs)\u001b[0m\n\u001b[1;32m 169\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mtrain\u001b[39m(\n\u001b[1;32m 170\u001b[0m \u001b[39mself\u001b[39m,\n\u001b[1;32m 171\u001b[0m training_data: Dataset,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 177\u001b[0m \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs,\n\u001b[1;32m 178\u001b[0m ) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m PyTorchPredictor:\n\u001b[0;32m--> 179\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mtrain_model(\n\u001b[1;32m 180\u001b[0m training_data,\n\u001b[1;32m 181\u001b[0m validation_data,\n\u001b[1;32m 182\u001b[0m num_workers\u001b[39m=\u001b[39;49mnum_workers,\n\u001b[1;32m 183\u001b[0m prefetch_factor\u001b[39m=\u001b[39;49mprefetch_factor,\n\u001b[1;32m 184\u001b[0m shuffle_buffer_length\u001b[39m=\u001b[39;49mshuffle_buffer_length,\n\u001b[1;32m 185\u001b[0m cache_data\u001b[39m=\u001b[39;49mcache_data,\n\u001b[1;32m 186\u001b[0m \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs,\n\u001b[1;32m 187\u001b[0m )\u001b[39m.\u001b[39mpredictor\n",
"File \u001b[0;32m/media/wassname/SGIronWolf/projects5/timeseries/pytorch-ts/pts/model/estimator.py:151\u001b[0m, in \u001b[0;36mPyTorchEstimator.train_model\u001b[0;34m(self, training_data, validation_data, num_workers, prefetch_factor, shuffle_buffer_length, cache_data, **kwargs)\u001b[0m\n\u001b[1;32m 133\u001b[0m validation_iter_dataset \u001b[39m=\u001b[39m TransformedIterableDataset(\n\u001b[1;32m 134\u001b[0m dataset\u001b[39m=\u001b[39mvalidation_data,\n\u001b[1;32m 135\u001b[0m transform\u001b[39m=\u001b[39mtransformation\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 139\u001b[0m cache_data\u001b[39m=\u001b[39mcache_data,\n\u001b[1;32m 140\u001b[0m )\n\u001b[1;32m 141\u001b[0m validation_data_loader \u001b[39m=\u001b[39m DataLoader(\n\u001b[1;32m 142\u001b[0m validation_iter_dataset,\n\u001b[1;32m 143\u001b[0m batch_size\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtrainer\u001b[39m.\u001b[39mbatch_size,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 148\u001b[0m \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs,\n\u001b[1;32m 149\u001b[0m )\n\u001b[0;32m--> 151\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mtrainer(\n\u001b[1;32m 152\u001b[0m net\u001b[39m=\u001b[39;49mtrained_net,\n\u001b[1;32m 153\u001b[0m train_iter\u001b[39m=\u001b[39;49mtraining_data_loader,\n\u001b[1;32m 154\u001b[0m validation_iter\u001b[39m=\u001b[39;49mvalidation_data_loader,\n\u001b[1;32m 155\u001b[0m )\n\u001b[1;32m 157\u001b[0m \u001b[39mreturn\u001b[39;00m TrainOutput(\n\u001b[1;32m 158\u001b[0m transformation\u001b[39m=\u001b[39mtransformation,\n\u001b[1;32m 159\u001b[0m trained_net\u001b[39m=\u001b[39mtrained_net,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 162\u001b[0m ),\n\u001b[1;32m 163\u001b[0m )\n",
"File \u001b[0;32m/media/wassname/SGIronWolf/projects5/timeseries/pytorch-ts/pts/trainer.py:63\u001b[0m, in \u001b[0;36mTrainer.__call__\u001b[0;34m(self, net, train_iter, validation_iter)\u001b[0m\n\u001b[1;32m 61\u001b[0m \u001b[39m# training loop\u001b[39;00m\n\u001b[1;32m 62\u001b[0m \u001b[39mwith\u001b[39;00m tqdm(train_iter, total\u001b[39m=\u001b[39mtotal) \u001b[39mas\u001b[39;00m it:\n\u001b[0;32m---> 63\u001b[0m \u001b[39mfor\u001b[39;00m batch_no, data_entry \u001b[39min\u001b[39;00m \u001b[39menumerate\u001b[39m(it, start\u001b[39m=\u001b[39m\u001b[39m1\u001b[39m):\n\u001b[1;32m 64\u001b[0m optimizer\u001b[39m.\u001b[39mzero_grad()\n\u001b[1;32m 66\u001b[0m inputs \u001b[39m=\u001b[39m [v\u001b[39m.\u001b[39mto(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdevice) \u001b[39mfor\u001b[39;00m v \u001b[39min\u001b[39;00m data_entry\u001b[39m.\u001b[39mvalues()]\n",
"File \u001b[0;32m~/miniforge3/envs/glounts/lib/python3.9/site-packages/tqdm/notebook.py:259\u001b[0m, in \u001b[0;36mtqdm_notebook.__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 257\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m 258\u001b[0m it \u001b[39m=\u001b[39m \u001b[39msuper\u001b[39m(tqdm_notebook, \u001b[39mself\u001b[39m)\u001b[39m.\u001b[39m\u001b[39m__iter__\u001b[39m()\n\u001b[0;32m--> 259\u001b[0m \u001b[39mfor\u001b[39;00m obj \u001b[39min\u001b[39;00m it:\n\u001b[1;32m 260\u001b[0m \u001b[39m# return super(tqdm...) will not catch exception\u001b[39;00m\n\u001b[1;32m 261\u001b[0m \u001b[39myield\u001b[39;00m obj\n\u001b[1;32m 262\u001b[0m \u001b[39m# NB: except ... [ as ...] breaks IPython async KeyboardInterrupt\u001b[39;00m\n",
"File \u001b[0;32m~/miniforge3/envs/glounts/lib/python3.9/site-packages/tqdm/std.py:1195\u001b[0m, in \u001b[0;36mtqdm.__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1192\u001b[0m time \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_time\n\u001b[1;32m 1194\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m-> 1195\u001b[0m \u001b[39mfor\u001b[39;00m obj \u001b[39min\u001b[39;00m iterable:\n\u001b[1;32m 1196\u001b[0m \u001b[39myield\u001b[39;00m obj\n\u001b[1;32m 1197\u001b[0m \u001b[39m# Update and possibly print the progressbar.\u001b[39;00m\n\u001b[1;32m 1198\u001b[0m \u001b[39m# Note: does not call self.update(1) for speed optimisation.\u001b[39;00m\n",
"File \u001b[0;32m~/miniforge3/envs/glounts/lib/python3.9/site-packages/torch/utils/data/dataloader.py:628\u001b[0m, in \u001b[0;36m_BaseDataLoaderIter.__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 625\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_sampler_iter \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m 626\u001b[0m \u001b[39m# TODO(https://github.com/pytorch/pytorch/issues/76750)\u001b[39;00m\n\u001b[1;32m 627\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_reset() \u001b[39m# type: ignore[call-arg]\u001b[39;00m\n\u001b[0;32m--> 628\u001b[0m data \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_next_data()\n\u001b[1;32m 629\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_num_yielded \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m \u001b[39m1\u001b[39m\n\u001b[1;32m 630\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_dataset_kind \u001b[39m==\u001b[39m _DatasetKind\u001b[39m.\u001b[39mIterable \u001b[39mand\u001b[39;00m \\\n\u001b[1;32m 631\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_IterableDataset_len_called \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39mand\u001b[39;00m \\\n\u001b[1;32m 632\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_num_yielded \u001b[39m>\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_IterableDataset_len_called:\n",
"File \u001b[0;32m~/miniforge3/envs/glounts/lib/python3.9/site-packages/torch/utils/data/dataloader.py:671\u001b[0m, in \u001b[0;36m_SingleProcessDataLoaderIter._next_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 669\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_next_data\u001b[39m(\u001b[39mself\u001b[39m):\n\u001b[1;32m 670\u001b[0m index \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_next_index() \u001b[39m# may raise StopIteration\u001b[39;00m\n\u001b[0;32m--> 671\u001b[0m data \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_dataset_fetcher\u001b[39m.\u001b[39;49mfetch(index) \u001b[39m# may raise StopIteration\u001b[39;00m\n\u001b[1;32m 672\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_pin_memory:\n\u001b[1;32m 673\u001b[0m data \u001b[39m=\u001b[39m _utils\u001b[39m.\u001b[39mpin_memory\u001b[39m.\u001b[39mpin_memory(data, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_pin_memory_device)\n",
"File \u001b[0;32m~/miniforge3/envs/glounts/lib/python3.9/site-packages/torch/utils/data/_utils/fetch.py:34\u001b[0m, in \u001b[0;36m_IterableDatasetFetcher.fetch\u001b[0;34m(self, possibly_batched_index)\u001b[0m\n\u001b[1;32m 32\u001b[0m \u001b[39mfor\u001b[39;00m _ \u001b[39min\u001b[39;00m possibly_batched_index:\n\u001b[1;32m 33\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m---> 34\u001b[0m data\u001b[39m.\u001b[39mappend(\u001b[39mnext\u001b[39;49m(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mdataset_iter))\n\u001b[1;32m 35\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mStopIteration\u001b[39;00m:\n\u001b[1;32m 36\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mended \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m\n",
"File \u001b[0;32m~/miniforge3/envs/glounts/lib/python3.9/site-packages/gluonts/transform/_base.py:103\u001b[0m, in \u001b[0;36mTransformedDataset.__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 102\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__iter__\u001b[39m(\u001b[39mself\u001b[39m) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m Iterator[DataEntry]:\n\u001b[0;32m--> 103\u001b[0m \u001b[39myield from\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtransformation(\n\u001b[1;32m 104\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mbase_dataset, is_train\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mis_train\n\u001b[1;32m 105\u001b[0m )\n",
"File \u001b[0;32m~/miniforge3/envs/glounts/lib/python3.9/site-packages/gluonts/transform/_base.py:124\u001b[0m, in \u001b[0;36mMapTransformation.__call__\u001b[0;34m(self, data_it, is_train)\u001b[0m\n\u001b[1;32m 121\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__call__\u001b[39m(\n\u001b[1;32m 122\u001b[0m \u001b[39mself\u001b[39m, data_it: Iterable[DataEntry], is_train: \u001b[39mbool\u001b[39m\n\u001b[1;32m 123\u001b[0m ) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m Iterator:\n\u001b[0;32m--> 124\u001b[0m \u001b[39mfor\u001b[39;00m data_entry \u001b[39min\u001b[39;00m data_it:\n\u001b[1;32m 125\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m 126\u001b[0m \u001b[39myield\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmap_transform(data_entry\u001b[39m.\u001b[39mcopy(), is_train)\n",
"File \u001b[0;32m~/miniforge3/envs/glounts/lib/python3.9/site-packages/gluonts/transform/_base.py:124\u001b[0m, in \u001b[0;36mMapTransformation.__call__\u001b[0;34m(self, data_it, is_train)\u001b[0m\n\u001b[1;32m 121\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__call__\u001b[39m(\n\u001b[1;32m 122\u001b[0m \u001b[39mself\u001b[39m, data_it: Iterable[DataEntry], is_train: \u001b[39mbool\u001b[39m\n\u001b[1;32m 123\u001b[0m ) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m Iterator:\n\u001b[0;32m--> 124\u001b[0m \u001b[39mfor\u001b[39;00m data_entry \u001b[39min\u001b[39;00m data_it:\n\u001b[1;32m 125\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m 126\u001b[0m \u001b[39myield\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmap_transform(data_entry\u001b[39m.\u001b[39mcopy(), is_train)\n",
"File \u001b[0;32m~/miniforge3/envs/glounts/lib/python3.9/site-packages/gluonts/transform/_base.py:189\u001b[0m, in \u001b[0;36mFlatMapTransformation.__call__\u001b[0;34m(self, data_it, is_train)\u001b[0m\n\u001b[1;32m 182\u001b[0m \u001b[39myield\u001b[39;00m result\n\u001b[1;32m 184\u001b[0m \u001b[39mif\u001b[39;00m (\n\u001b[1;32m 185\u001b[0m \u001b[39m# negative values disable the check\u001b[39;00m\n\u001b[1;32m 186\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmax_idle_transforms \u001b[39m>\u001b[39m \u001b[39m0\u001b[39m\n\u001b[1;32m 187\u001b[0m \u001b[39mand\u001b[39;00m num_idle_transforms \u001b[39m>\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmax_idle_transforms\n\u001b[1;32m 188\u001b[0m ):\n\u001b[0;32m--> 189\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mException\u001b[39;00m(\n\u001b[1;32m 190\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mReached maximum number of idle transformation\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 191\u001b[0m \u001b[39m\"\u001b[39m\u001b[39m calls.\u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39mThis means the transformation looped over\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 192\u001b[0m \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m \u001b[39m\u001b[39m{\u001b[39;00m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmax_idle_transforms\u001b[39m}\u001b[39;00m\u001b[39m inputs without returning any\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 193\u001b[0m \u001b[39m\"\u001b[39m\u001b[39m output.\u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39mThis occurred in the following\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 194\u001b[0m \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m transformation:\u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m{\u001b[39;00m\u001b[39mself\u001b[39m\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m\n\u001b[1;32m 195\u001b[0m )\n",
"\u001b[0;31mException\u001b[0m: Reached maximum number of idle transformation calls.\nThis means the transformation looped over 1 inputs without returning any output.\nThis occurred in the following transformation:\ngluonts.transform.split.InstanceSplitter(dummy_value=0.0, forecast_start_field=\"forecast_start\", future_length=24, instance_sampler=gluonts.transform.sampler.ExpectedNumInstanceSampler(axis=-1, min_past=192, min_future=24, num_instances=1.0, total_length=20382, n=3), is_pad_field=\"is_pad\", lead_time=0, output_NTC=True, past_length=192, start_field=\"start\", target_field=\"target\", time_series_fields=[\"time_feat\", \"observed_values\"])"
]
}
],
@@ -503,7 +213,7 @@
},
{
"cell_type": "code",
"execution_count": 47,
"execution_count": null,
"metadata": {},
"outputs": [
{
@@ -526,7 +236,7 @@
},
{
"cell_type": "code",
"execution_count": 48,
"execution_count": null,
"metadata": {},
"outputs": [
{
@@ -556,7 +266,7 @@
},
{
"cell_type": "code",
"execution_count": 17,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -579,7 +289,7 @@
},
{
"cell_type": "code",
"execution_count": 18,
"execution_count": null,
"metadata": {},
"outputs": [
{
@@ -911,7 +621,7 @@
},
{
"cell_type": "code",
"execution_count": 19,
"execution_count": null,
"metadata": {},
"outputs": [
{
@@ -934,7 +644,7 @@
},
{
"cell_type": "code",
"execution_count": 20,
"execution_count": null,
"metadata": {},
"outputs": [
{
@@ -964,7 +674,7 @@
},
{
"cell_type": "code",
"execution_count": 16,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -990,7 +700,7 @@
},
{
"cell_type": "code",
"execution_count": 17,
"execution_count": null,
"metadata": {},
"outputs": [
{
@@ -1159,7 +869,7 @@
},
{
"cell_type": "code",
"execution_count": 11,
"execution_count": null,
"metadata": {},
"outputs": [
{
@@ -1182,7 +892,7 @@
},
{
"cell_type": "code",
"execution_count": 12,
"execution_count": null,
"metadata": {},
"outputs": [
{
@@ -1213,7 +923,7 @@
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"display_name": "Python 3.9.15 ('glounts')",
"language": "python",
"name": "python3"
},
@@ -1227,7 +937,12 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.2"
"version": "3.9.15"
},
"vscode": {
"interpreter": {
"hash": "7f25a1f13147a60511cf6766827402baf95cbe50d53a241197155306ee38fe70"
}
}
},
"nbformat": 4,
examples/Time-Grad-Electricity.ipynb
+289 -460
View File
File diff suppressed because one or more lines are too long
examples/Time-Grad2-Electricity.ipynb
+631 -87
View File
@@ -2,8 +2,32 @@
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"execution_count": 1,
"metadata": {
"ExecuteTime": {
"end_time": "2022-12-23T06:21:04.900941Z",
"start_time": "2022-12-23T06:21:04.889808Z"
}
},
"outputs": [],
"source": [
"# import warnings\n",
"# warnings.simplefilter(\"ignore\")\n",
"\n",
"# autoreload import your package\n",
"%load_ext autoreload\n",
"%autoreload 2"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"ExecuteTime": {
"end_time": "2022-12-23T06:21:06.416555Z",
"start_time": "2022-12-23T06:21:04.902165Z"
}
},
"outputs": [],
"source": [
"%matplotlib inline\n",
@@ -17,8 +41,13 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"execution_count": 3,
"metadata": {
"ExecuteTime": {
"end_time": "2022-12-23T06:21:06.625752Z",
"start_time": "2022-12-23T06:21:06.418508Z"
}
},
"outputs": [],
"source": [
"from gluonts.dataset.multivariate_grouper import MultivariateGrouper\n",
@@ -29,8 +58,13 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"execution_count": 4,
"metadata": {
"ExecuteTime": {
"end_time": "2022-12-23T06:21:06.738760Z",
"start_time": "2022-12-23T06:21:06.627406Z"
}
},
"outputs": [],
"source": [
"from pts.model.tempflow import TempFlowEstimator\n",
@@ -41,8 +75,13 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"execution_count": 5,
"metadata": {
"ExecuteTime": {
"end_time": "2022-12-23T06:21:06.790762Z",
"start_time": "2022-12-23T06:21:06.740481Z"
}
},
"outputs": [],
"source": [
"device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
@@ -50,8 +89,13 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"execution_count": 6,
"metadata": {
"ExecuteTime": {
"end_time": "2022-12-23T06:21:06.817593Z",
"start_time": "2022-12-23T06:21:06.791945Z"
}
},
"outputs": [],
"source": [
"def plot(target, forecast, prediction_length, prediction_intervals=(50.0, 90.0), color='g', fname=None):\n",
@@ -113,17 +157,34 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"execution_count": 7,
"metadata": {
"ExecuteTime": {
"end_time": "2022-12-23T06:21:06.839629Z",
"start_time": "2022-12-23T06:21:06.818741Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Available datasets: ['constant', 'exchange_rate', 'solar-energy', 'electricity', 'traffic', 'exchange_rate_nips', 'electricity_nips', 'traffic_nips', 'solar_nips', 'wiki-rolling_nips', 'taxi_30min', 'kaggle_web_traffic_with_missing', 'kaggle_web_traffic_without_missing', 'kaggle_web_traffic_weekly', 'm1_yearly', 'm1_quarterly', 'm1_monthly', 'nn5_daily_with_missing', 'nn5_daily_without_missing', 'nn5_weekly', 'tourism_monthly', 'tourism_quarterly', 'tourism_yearly', 'cif_2016', 'london_smart_meters_without_missing', 'wind_farms_without_missing', 'car_parts_without_missing', 'dominick', 'fred_md', 'pedestrian_counts', 'hospital', 'covid_deaths', 'kdd_cup_2018_without_missing', 'weather', 'm3_monthly', 'm3_quarterly', 'm3_yearly', 'm3_other', 'm4_hourly', 'm4_daily', 'm4_weekly', 'm4_monthly', 'm4_quarterly', 'm4_yearly', 'm5', 'uber_tlc_daily', 'uber_tlc_hourly', 'airpassengers']\n"
]
}
],
"source": [
"print(f\"Available datasets: {list(dataset_recipes.keys())}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 8,
"metadata": {
"ExecuteTime": {
"end_time": "2022-12-23T06:21:06.856379Z",
"start_time": "2022-12-23T06:21:06.840657Z"
},
"scrolled": true
},
"outputs": [],
@@ -134,51 +195,57 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"execution_count": 9,
"metadata": {
"ExecuteTime": {
"end_time": "2022-12-23T06:21:06.874723Z",
"start_time": "2022-12-23T06:21:06.858646Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"MetaData(freq='H', target=None, feat_static_cat=[CategoricalFeatureInfo(name='feat_static_cat_0', cardinality='370')], feat_static_real=[], feat_dynamic_real=[], feat_dynamic_cat=[], prediction_length=24)"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dataset.metadata"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"3.5"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
"execution_count": 10,
"metadata": {
"ExecuteTime": {
"end_time": "2022-12-23T06:21:07.663837Z",
"start_time": "2022-12-23T06:21:06.875746Z"
}
],
"source": [
"len(dataset.test)/len(dataset.train)"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
},
"outputs": [],
"source": [
"train_grouper = MultivariateGrouper(max_target_dim=min(2000, int(dataset.metadata.feat_static_cat[0].cardinality)))\n",
"\n",
"test_grouper = MultivariateGrouper(\n",
" # num_test_dates=int(len(dataset.test)/len(dataset.train)), \n",
" num_test_dates=1,\n",
" num_test_dates=int(len(dataset.test)/len(dataset.train)*2),\n",
"# num_test_dates=7,\n",
" max_target_dim=min(2000, int(dataset.metadata.feat_static_cat[0].cardinality)))"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"execution_count": 11,
"metadata": {
"ExecuteTime": {
"end_time": "2022-12-23T06:21:12.079230Z",
"start_time": "2022-12-23T06:21:07.665093Z"
}
},
"outputs": [
{
"name": "stderr",
@@ -187,20 +254,6 @@
"/home/wassname/miniforge3/envs/glounts/lib/python3.9/site-packages/gluonts/dataset/multivariate_grouper.py:191: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
" return {FieldName.TARGET: np.array([funcs(data) for data in dataset])}\n"
]
},
{
"ename": "ValueError",
"evalue": "setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (2590,) + inhomogeneous part.",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m/media/wassname/SGIronWolf/projects5/timeseries/pytorch-ts/examples/Time-Grad2-Electricity.ipynb Cell 13\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m <a href='vscode-notebook-cell:/media/wassname/SGIronWolf/projects5/timeseries/pytorch-ts/examples/Time-Grad2-Electricity.ipynb#X12sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m dataset_train \u001b[39m=\u001b[39m train_grouper(dataset\u001b[39m.\u001b[39mtrain)\n\u001b[0;32m----> <a href='vscode-notebook-cell:/media/wassname/SGIronWolf/projects5/timeseries/pytorch-ts/examples/Time-Grad2-Electricity.ipynb#X12sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m dataset_test \u001b[39m=\u001b[39m test_grouper(dataset\u001b[39m.\u001b[39;49mtest)\n",
"File \u001b[0;32m~/miniforge3/envs/glounts/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[39mdef\u001b[39;00m \u001b[39m__call__\u001b[39m(\u001b[39mself\u001b[39m, dataset: Dataset) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m Dataset:\n\u001b[1;32m 86\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_preprocess(dataset)\n\u001b[0;32m---> 87\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_group_all(dataset)\n",
"File \u001b[0;32m~/miniforge3/envs/glounts/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[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_prepare_train_data(dataset)\n\u001b[1;32m 124\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 125\u001b[0m grouped_dataset \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_prepare_test_data(dataset)\n\u001b[1;32m 126\u001b[0m \u001b[39mreturn\u001b[39;00m grouped_dataset\n",
"File \u001b[0;32m~/miniforge3/envs/glounts/lib/python3.9/site-packages/gluonts/dataset/multivariate_grouper.py:155\u001b[0m, in \u001b[0;36mMultivariateGrouper._prepare_test_data\u001b[0;34m(self, dataset)\u001b[0m\n\u001b[1;32m 153\u001b[0m \u001b[39mfor\u001b[39;00m dataset_at_test_date \u001b[39min\u001b[39;00m split_dataset:\n\u001b[1;32m 154\u001b[0m grouped_data \u001b[39m=\u001b[39m \u001b[39mdict\u001b[39m()\n\u001b[0;32m--> 155\u001b[0m grouped_data[FieldName\u001b[39m.\u001b[39mTARGET] \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39;49marray(\n\u001b[1;32m 156\u001b[0m \u001b[39mlist\u001b[39;49m(dataset_at_test_date), dtype\u001b[39m=\u001b[39;49mnp\u001b[39m.\u001b[39;49mfloat32\n\u001b[1;32m 157\u001b[0m )\n\u001b[1;32m 158\u001b[0m grouped_data \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_restrict_max_dimensionality(grouped_data)\n\u001b[1;32m 159\u001b[0m grouped_data[FieldName\u001b[39m.\u001b[39mSTART] \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfirst_timestamp\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": [
@@ -210,34 +263,63 @@
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"execution_count": 12,
"metadata": {
"ExecuteTime": {
"end_time": "2022-12-23T06:21:12.096888Z",
"start_time": "2022-12-23T06:21:12.080635Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"> \u001b[0;32m/home/wassname/miniforge3/envs/glounts/lib/python3.9/site-packages/gluonts/dataset/multivariate_grouper.py\u001b[0m(155)\u001b[0;36m_prepare_test_data\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32m 153 \u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mdataset_at_test_date\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msplit_dataset\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0m\u001b[0;32m 154 \u001b[0;31m \u001b[0mgrouped_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0m\u001b[0;32m--> 155 \u001b[0;31m grouped_data[FieldName.TARGET] = np.array(\n",
"\u001b[0m\u001b[0;32m 156 \u001b[0;31m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset_at_test_date\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat32\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0m\u001b[0;32m 157 \u001b[0;31m )\n",
"\u001b[0m\n",
"2590\n",
"(2590,)\n",
"(2590,)\n"
]
"data": {
"text/plain": [
"370"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%debug"
"int(dataset.metadata.feat_static_cat[0].cardinality)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"execution_count": 13,
"metadata": {
"ExecuteTime": {
"end_time": "2022-12-23T06:21:12.113235Z",
"start_time": "2022-12-23T06:21:12.098346Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"370"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"int(dataset.metadata.feat_static_cat[0].cardinality)"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"ExecuteTime": {
"end_time": "2022-12-23T06:33:27.308113Z",
"start_time": "2022-12-23T06:33:23.559765Z"
}
},
"outputs": [],
"source": [
"estimator = TimeGradEstimator2(\n",
@@ -248,7 +330,7 @@
" input_size=1484,\n",
" freq=dataset.metadata.freq,\n",
" loss_type='l2',\n",
" scaling=True,\n",
" scaling=False,\n",
" diff_steps=100,\n",
" beta_end=0.1,\n",
" beta_schedule=\"linear\",\n",
@@ -262,17 +344,144 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"execution_count": 58,
"metadata": {
"ExecuteTime": {
"end_time": "2022-12-23T06:33:28.565303Z",
"start_time": "2022-12-23T06:33:27.309629Z"
},
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"cond_length 100\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f7efc1b8dbd24c0d92ecfe54a25359a1",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
" 0%| | 0/99 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"shapes torch.Size([64, 370, 48]) torch.Size([64]) torch.Size([64, 100, 48])\n",
"cond -> cond_up torch.Size([64, 100, 48]) torch.Size([64, 370, 48])\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/media/wassname/SGIronWolf/projects5/timeseries/pytorch-ts/pts/model/time_grad2/gaussian_diffusion_ou.py:283: UserWarning: Using a target size (torch.Size([64, 1, 48])) that is different to the input size (torch.Size([64, 370, 46])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n",
" loss = F.mse_loss(x_recon, noise_rand)\n"
]
},
{
"ename": "RuntimeError",
"evalue": "The size of tensor a (46) must match the size of tensor b (48) at non-singleton dimension 2",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)",
"Input \u001b[0;32mIn [58]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m predictor \u001b[38;5;241m=\u001b[39m \u001b[43mestimator\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdataset_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_workers\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m/media/wassname/SGIronWolf/projects5/timeseries/pytorch-ts/pts/model/estimator.py:179\u001b[0m, in \u001b[0;36mPyTorchEstimator.train\u001b[0;34m(self, training_data, validation_data, num_workers, prefetch_factor, shuffle_buffer_length, cache_data, **kwargs)\u001b[0m\n\u001b[1;32m 169\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mtrain\u001b[39m(\n\u001b[1;32m 170\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 171\u001b[0m training_data: Dataset,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 177\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m 178\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m PyTorchPredictor:\n\u001b[0;32m--> 179\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[43mtrain_model\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 180\u001b[0m \u001b[43m \u001b[49m\u001b[43mtraining_data\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 181\u001b[0m \u001b[43m \u001b[49m\u001b[43mvalidation_data\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 182\u001b[0m \u001b[43m \u001b[49m\u001b[43mnum_workers\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnum_workers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 183\u001b[0m \u001b[43m \u001b[49m\u001b[43mprefetch_factor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprefetch_factor\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 184\u001b[0m \u001b[43m \u001b[49m\u001b[43mshuffle_buffer_length\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mshuffle_buffer_length\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 185\u001b[0m \u001b[43m \u001b[49m\u001b[43mcache_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcache_data\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 186\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 187\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mpredictor\n",
"File \u001b[0;32m/media/wassname/SGIronWolf/projects5/timeseries/pytorch-ts/pts/model/estimator.py:151\u001b[0m, in \u001b[0;36mPyTorchEstimator.train_model\u001b[0;34m(self, training_data, validation_data, num_workers, prefetch_factor, shuffle_buffer_length, cache_data, **kwargs)\u001b[0m\n\u001b[1;32m 133\u001b[0m validation_iter_dataset \u001b[38;5;241m=\u001b[39m TransformedIterableDataset(\n\u001b[1;32m 134\u001b[0m dataset\u001b[38;5;241m=\u001b[39mvalidation_data,\n\u001b[1;32m 135\u001b[0m transform\u001b[38;5;241m=\u001b[39mtransformation\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 139\u001b[0m cache_data\u001b[38;5;241m=\u001b[39mcache_data,\n\u001b[1;32m 140\u001b[0m )\n\u001b[1;32m 141\u001b[0m validation_data_loader \u001b[38;5;241m=\u001b[39m DataLoader(\n\u001b[1;32m 142\u001b[0m validation_iter_dataset,\n\u001b[1;32m 143\u001b[0m batch_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrainer\u001b[38;5;241m.\u001b[39mbatch_size,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 148\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m 149\u001b[0m )\n\u001b[0;32m--> 151\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrainer\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 152\u001b[0m \u001b[43m \u001b[49m\u001b[43mnet\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtrained_net\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 153\u001b[0m \u001b[43m \u001b[49m\u001b[43mtrain_iter\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtraining_data_loader\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 154\u001b[0m \u001b[43m \u001b[49m\u001b[43mvalidation_iter\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvalidation_data_loader\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 155\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 157\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m TrainOutput(\n\u001b[1;32m 158\u001b[0m transformation\u001b[38;5;241m=\u001b[39mtransformation,\n\u001b[1;32m 159\u001b[0m trained_net\u001b[38;5;241m=\u001b[39mtrained_net,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 162\u001b[0m ),\n\u001b[1;32m 163\u001b[0m )\n",
"File \u001b[0;32m/media/wassname/SGIronWolf/projects5/timeseries/pytorch-ts/pts/trainer.py:67\u001b[0m, in \u001b[0;36mTrainer.__call__\u001b[0;34m(self, net, train_iter, validation_iter)\u001b[0m\n\u001b[1;32m 64\u001b[0m optimizer\u001b[38;5;241m.\u001b[39mzero_grad()\n\u001b[1;32m 66\u001b[0m inputs \u001b[38;5;241m=\u001b[39m [v\u001b[38;5;241m.\u001b[39mto(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdevice) \u001b[38;5;28;01mfor\u001b[39;00m v \u001b[38;5;129;01min\u001b[39;00m data_entry\u001b[38;5;241m.\u001b[39mvalues()]\n\u001b[0;32m---> 67\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[43mnet\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43minputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 69\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(output, (\u001b[38;5;28mlist\u001b[39m, \u001b[38;5;28mtuple\u001b[39m)):\n\u001b[1;32m 70\u001b[0m loss \u001b[38;5;241m=\u001b[39m output[\u001b[38;5;241m0\u001b[39m]\n",
"File \u001b[0;32m~/miniforge3/envs/glounts/lib/python3.9/site-packages/torch/nn/modules/module.py:1190\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 1186\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1187\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1188\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[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1189\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1190\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1191\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1192\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
"File \u001b[0;32m/media/wassname/SGIronWolf/projects5/timeseries/pytorch-ts/pts/model/time_grad2/time_grad_network.py:407\u001b[0m, in \u001b[0;36mTimeGradTrainingNetwork2.forward\u001b[0;34m(self, target_dimension_indicator, past_time_feat, past_target_cdf, past_observed_values, past_is_pad, future_time_feat, future_target_cdf, future_observed_values)\u001b[0m\n\u001b[1;32m 405\u001b[0m target \u001b[38;5;241m=\u001b[39m target\u001b[38;5;241m.\u001b[39mpermute(\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m2\u001b[39m, \u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m 406\u001b[0m distr_args \u001b[38;5;241m=\u001b[39m distr_args\u001b[38;5;241m.\u001b[39mpermute(\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m2\u001b[39m, \u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m--> 407\u001b[0m likelihoods \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdiffusion\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlog_prob\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtarget\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdistr_args\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39munsqueeze(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m 409\u001b[0m \u001b[38;5;66;03m# assert_shape(likelihoods, (-1, seq_len, 1))\u001b[39;00m\n\u001b[1;32m 411\u001b[0m past_observed_values \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mmin(\n\u001b[1;32m 412\u001b[0m past_observed_values, \u001b[38;5;241m1\u001b[39m \u001b[38;5;241m-\u001b[39m past_is_pad\u001b[38;5;241m.\u001b[39munsqueeze(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m 413\u001b[0m )\n",
"File \u001b[0;32m/media/wassname/SGIronWolf/projects5/timeseries/pytorch-ts/pts/model/time_grad2/gaussian_diffusion_ou.py:298\u001b[0m, in \u001b[0;36mGaussianDiffusionOU.log_prob\u001b[0;34m(self, x, cond, *args, **kwargs)\u001b[0m\n\u001b[1;32m 295\u001b[0m B, T, _ \u001b[38;5;241m=\u001b[39m x\u001b[38;5;241m.\u001b[39mshape\n\u001b[1;32m 297\u001b[0m time \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mrandint(\u001b[38;5;241m0\u001b[39m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnum_timesteps, (B,), device\u001b[38;5;241m=\u001b[39mx\u001b[38;5;241m.\u001b[39mdevice)\u001b[38;5;241m.\u001b[39mlong()\n\u001b[0;32m--> 298\u001b[0m loss \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mp_losses\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 299\u001b[0m \u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcond\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtime\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[1;32m 300\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 302\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m loss\n",
"File \u001b[0;32m/media/wassname/SGIronWolf/projects5/timeseries/pytorch-ts/pts/model/time_grad2/gaussian_diffusion_ou.py:283\u001b[0m, in \u001b[0;36mGaussianDiffusionOU.p_losses\u001b[0;34m(self, x_start, cond, t)\u001b[0m\n\u001b[1;32m 281\u001b[0m loss \u001b[38;5;241m=\u001b[39m F\u001b[38;5;241m.\u001b[39ml1_loss(x_recon, noise_rand)\n\u001b[1;32m 282\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mloss_type \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124ml2\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m--> 283\u001b[0m loss \u001b[38;5;241m=\u001b[39m \u001b[43mF\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmse_loss\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx_recon\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnoise_rand\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 284\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mloss_type \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhuber\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m 285\u001b[0m loss \u001b[38;5;241m=\u001b[39m F\u001b[38;5;241m.\u001b[39msmooth_l1_loss(x_recon, noise_rand)\n",
"File \u001b[0;32m~/miniforge3/envs/glounts/lib/python3.9/site-packages/torch/nn/functional.py:3291\u001b[0m, in \u001b[0;36mmse_loss\u001b[0;34m(input, target, size_average, reduce, reduction)\u001b[0m\n\u001b[1;32m 3288\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m size_average \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mor\u001b[39;00m reduce \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 3289\u001b[0m reduction \u001b[38;5;241m=\u001b[39m _Reduction\u001b[38;5;241m.\u001b[39mlegacy_get_string(size_average, reduce)\n\u001b[0;32m-> 3291\u001b[0m expanded_input, expanded_target \u001b[38;5;241m=\u001b[39m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbroadcast_tensors\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtarget\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3292\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m torch\u001b[38;5;241m.\u001b[39m_C\u001b[38;5;241m.\u001b[39m_nn\u001b[38;5;241m.\u001b[39mmse_loss(expanded_input, expanded_target, _Reduction\u001b[38;5;241m.\u001b[39mget_enum(reduction))\n",
"File \u001b[0;32m~/miniforge3/envs/glounts/lib/python3.9/site-packages/torch/functional.py:74\u001b[0m, in \u001b[0;36mbroadcast_tensors\u001b[0;34m(*tensors)\u001b[0m\n\u001b[1;32m 72\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m has_torch_function(tensors):\n\u001b[1;32m 73\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m handle_torch_function(broadcast_tensors, tensors, \u001b[38;5;241m*\u001b[39mtensors)\n\u001b[0;32m---> 74\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_VF\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbroadcast_tensors\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtensors\u001b[49m\u001b[43m)\u001b[49m\n",
"\u001b[0;31mRuntimeError\u001b[0m: The size of tensor a (46) must match the size of tensor b (48) at non-singleton dimension 2"
]
}
],
"source": [
"predictor = estimator.train(dataset_train, num_workers=8)"
"predictor = estimator.train(dataset_train, num_workers=0)\n",
"# predictor = estimator.train(dataset_train, num_workers=8)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"ExecuteTime": {
"start_time": "2022-12-23T06:33:31.564Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"> \u001b[0;32m/home/wassname/miniforge3/envs/glounts/lib/python3.9/site-packages/torch/functional.py\u001b[0m(74)\u001b[0;36mbroadcast_tensors\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32m 72 \u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0mhas_torch_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtensors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0m\u001b[0;32m 73 \u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mhandle_torch_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbroadcast_tensors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtensors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mtensors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0m\u001b[0;32m---> 74 \u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_VF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbroadcast_tensors\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtensors\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# type: ignore[attr-defined]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0m\u001b[0;32m 75 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0m\u001b[0;32m 76 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0m\n",
"ipdb> u\n",
"> \u001b[0;32m/home/wassname/miniforge3/envs/glounts/lib/python3.9/site-packages/torch/nn/functional.py\u001b[0m(3291)\u001b[0;36mmse_loss\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32m 3289 \u001b[0;31m \u001b[0mreduction\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_Reduction\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlegacy_get_string\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msize_average\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreduce\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0m\u001b[0;32m 3290 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0m\u001b[0;32m-> 3291 \u001b[0;31m \u001b[0mexpanded_input\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexpanded_target\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbroadcast_tensors\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0m\u001b[0;32m 3292 \u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_C\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_nn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmse_loss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexpanded_input\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexpanded_target\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_Reduction\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_enum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreduction\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0m\u001b[0;32m 3293 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0m\n",
"ipdb> u\n",
"> \u001b[0;32m/media/wassname/SGIronWolf/projects5/timeseries/pytorch-ts/pts/model/time_grad2/gaussian_diffusion_ou.py\u001b[0m(283)\u001b[0;36mp_losses\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32m 281 \u001b[0;31m \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0ml1_loss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_recon\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnoise_rand\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0m\u001b[0;32m 282 \u001b[0;31m \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloss_type\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"l2\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0m\u001b[0;32m--> 283 \u001b[0;31m \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmse_loss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_recon\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnoise_rand\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0m\u001b[0;32m 284 \u001b[0;31m \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloss_type\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"huber\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0m\u001b[0;32m 285 \u001b[0;31m \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msmooth_l1_loss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_recon\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnoise_rand\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0m\n",
"ipdb> u\n",
"> \u001b[0;32m/media/wassname/SGIronWolf/projects5/timeseries/pytorch-ts/pts/model/time_grad2/gaussian_diffusion_ou.py\u001b[0m(298)\u001b[0;36mlog_prob\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32m 296 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0m\u001b[0;32m 297 \u001b[0;31m \u001b[0mtime\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_timesteps\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mB\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlong\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0m\u001b[0;32m--> 298 \u001b[0;31m loss = self.p_losses(\n",
"\u001b[0m\u001b[0;32m 299 \u001b[0;31m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcond\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0m\u001b[0;32m 300 \u001b[0;31m )\n",
"\u001b[0m\n",
"ipdb> x.shape\n",
"torch.Size([64, 370, 48])\n",
"ipdb> cond.shape\n",
"torch.Size([64, 100, 48])\n",
"ipdb> time.shape\n",
"torch.Size([64])\n"
]
}
],
"source": [
"%debug"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2022-12-23T06:21:12.613548Z",
"start_time": "2022-12-23T06:21:12.613540Z"
}
},
"outputs": [],
"source": [
"forecast_it, ts_it = make_evaluation_predictions(dataset=dataset_test,\n",
@@ -283,7 +492,12 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"ExecuteTime": {
"end_time": "2022-12-23T06:21:12.614197Z",
"start_time": "2022-12-23T06:21:12.614190Z"
}
},
"outputs": [],
"source": [
"forecasts = list(forecast_it)\n",
@@ -293,7 +507,12 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"ExecuteTime": {
"end_time": "2022-12-23T06:21:12.614817Z",
"start_time": "2022-12-23T06:21:12.614810Z"
}
},
"outputs": [],
"source": [
"plot(\n",
@@ -307,7 +526,12 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"ExecuteTime": {
"end_time": "2022-12-23T06:21:12.615375Z",
"start_time": "2022-12-23T06:21:12.615368Z"
}
},
"outputs": [],
"source": [
"evaluator = MultivariateEvaluator(quantiles=(np.arange(20)/20.0)[1:], \n",
@@ -318,6 +542,10 @@
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2022-12-23T06:21:12.615882Z",
"start_time": "2022-12-23T06:21:12.615875Z"
},
"scrolled": true
},
"outputs": [],
@@ -328,7 +556,12 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"ExecuteTime": {
"end_time": "2022-12-23T06:21:12.616531Z",
"start_time": "2022-12-23T06:21:12.616525Z"
}
},
"outputs": [],
"source": [
"print(\"CRPS:\", agg_metric[\"mean_wQuantileLoss\"])\n",
@@ -340,6 +573,304 @@
"print(\"NRMSE-Sum:\", agg_metric[\"m_sum_NRMSE\"])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Scratch: debug transforms\n",
"\n",
"See estimator"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2022-12-23T06:21:12.617247Z",
"start_time": "2022-12-23T06:21:12.617240Z"
}
},
"outputs": [],
"source": [
"predictor = estimator.train(dataset_train, num_workers=0)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2022-12-23T06:21:12.617846Z",
"start_time": "2022-12-23T06:21:12.617838Z"
}
},
"outputs": [],
"source": [
"%debug"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2022-12-23T06:21:12.618406Z",
"start_time": "2022-12-23T06:21:12.618396Z"
}
},
"outputs": [],
"source": [
"from tqdm.auto import tqdm"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2022-12-23T06:21:12.619142Z",
"start_time": "2022-12-23T06:21:12.619132Z"
}
},
"outputs": [],
"source": [
"transformation = estimator.create_transformation()\n",
"training_instance_splitter = estimator.create_instance_splitter(\"training\")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2022-12-23T06:21:12.619649Z",
"start_time": "2022-12-23T06:21:12.619642Z"
}
},
"outputs": [],
"source": [
"g1 = trans.apply(dataset_train, is_train=True)\n",
"b = next(iter(g1))\n",
"b.keys()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2022-12-23T06:21:12.620194Z",
"start_time": "2022-12-23T06:21:12.620187Z"
}
},
"outputs": [],
"source": [
"for _ in tqdm(g1):\n",
" pass"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2022-12-23T06:21:12.620996Z",
"start_time": "2022-12-23T06:21:12.620988Z"
}
},
"outputs": [],
"source": [
"transform = transformation + training_instance_splitter\n",
"g2 = transform.apply(dataset_train, is_train=True)\n",
"gg = iter(g2)\n",
"b = next(gg)\n",
"b.keys()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2022-12-23T06:21:12.621464Z",
"start_time": "2022-12-23T06:21:12.621458Z"
}
},
"outputs": [],
"source": [
"for _ in tqdm(g2):\n",
" pass"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2022-12-23T06:21:12.622209Z",
"start_time": "2022-12-23T06:21:12.622202Z"
}
},
"outputs": [],
"source": [
"b = next(gg)\n",
"b.keys()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2022-12-23T06:21:12.622763Z",
"start_time": "2022-12-23T06:21:12.622756Z"
}
},
"outputs": [],
"source": [
"from pts.model import get_module_forward_input_names\n",
"from gluonts.transform import SelectFields, Transformation\n",
"\n",
"trained_net = estimator.create_training_network(estimator.trainer.device)\n",
"input_names = get_module_forward_input_names(trained_net)\n",
"transform = transformation + training_instance_splitter + SelectFields(input_names)\n",
"g = transform.apply(dataset_train, is_train=True)\n",
"b = next(iter(g))\n",
"b.keys()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2022-12-23T05:18:19.966830Z",
"start_time": "2022-12-23T05:18:19.964901Z"
}
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2022-12-23T06:21:12.623279Z",
"start_time": "2022-12-23T06:21:12.623272Z"
}
},
"outputs": [],
"source": [
"from pts.dataset.loader import TransformedIterableDataset\n",
"training_data = dataset_train\n",
"training_iter_dataset = TransformedIterableDataset(\n",
" dataset=training_data,\n",
" transform=transformation\n",
" + training_instance_splitter\n",
" + SelectFields(input_names),\n",
" is_train=True,\n",
" shuffle_buffer_length=None,\n",
"# cache_data=cache_data,\n",
")\n",
"training_iter_dataset"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2022-12-23T06:21:12.623864Z",
"start_time": "2022-12-23T06:21:12.623857Z"
}
},
"outputs": [],
"source": [
"next(iter(training_iter_dataset))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2022-12-23T06:21:12.624379Z",
"start_time": "2022-12-23T06:21:12.624372Z"
}
},
"outputs": [],
"source": [
"from torch.utils.data import DataLoader\n",
"training_data_loader = DataLoader(\n",
" training_iter_dataset,\n",
" batch_size=estimator.trainer.batch_size,\n",
" num_workers=0,\n",
"# prefetch_factor=prefetch_factor,\n",
" pin_memory=True,\n",
" worker_init_fn=estimator._worker_init_fn,\n",
"# **kwargs,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2022-12-23T06:21:12.624842Z",
"start_time": "2022-12-23T06:21:12.624836Z"
}
},
"outputs": [],
"source": [
"next(iter(training_data_loader))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2022-12-23T06:21:12.625415Z",
"start_time": "2022-12-23T06:21:12.625408Z"
}
},
"outputs": [],
"source": [
"for b in tqdm(training_data_loader):\n",
" pass"
]
},
{
"cell_type": "code",
"execution_count": null,
@@ -350,9 +881,9 @@
],
"metadata": {
"kernelspec": {
"display_name": "Python 3.9.15 ('glounts')",
"display_name": "glounts",
"language": "python",
"name": "python3"
"name": "glounts"
},
"language_info": {
"codemirror_mode": {
@@ -366,6 +897,19 @@
"pygments_lexer": "ipython3",
"version": "3.9.15"
},
"toc": {
"base_numbering": 1,
"nav_menu": {},
"number_sections": true,
"sideBar": true,
"skip_h1_title": false,
"title_cell": "Table of Contents",
"title_sidebar": "Contents",
"toc_cell": false,
"toc_position": {},
"toc_section_display": true,
"toc_window_display": true
},
"vscode": {
"interpreter": {
"hash": "7f25a1f13147a60511cf6766827402baf95cbe50d53a241197155306ee38fe70"
examples/m5-tft.ipynb
+37 -25
View File
File diff suppressed because one or more lines are too long
examples/m5.ipynb
+36 -25
View File
File diff suppressed because one or more lines are too long
mjc_notes.md
+28
View File
@@ -0,0 +1,28 @@
install env
```sh
export PROJ=glounts10.0
conda create -n $PROJ python=3.8 -y
conda activate $PROJ
mamba install -y ipykernel pip ipywidgets
# pip install torch==1.10.0+cu113 torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu113
mamba install pytorch==1.9.1 torchvision==0.10.1 torchaudio==0.9.1 cudatoolkit=11.3 -c pytorch -c conda-forge
pip install gluonts==0.10.0
pip install -e .
```
```sh
export PROJ=gluonts10.0
conda create -n $PROJ python=3.9 -y
conda activate $PROJ
mamba install -y ipykernel pip ipywidgets
# torch
mamba install -y ipykernel pip ipywidgets pytorch==1.13.0 torchvision torchaudio pytorch-cuda=11.7 -c pytorch -c nvidia
mamba install -y xformers -c xformers/label/dev
mamba install -y conda-lock
# gluonts
pip install gluonts==0.10.0
pip install -e .
# pip install "gluonts[torch,pro]" pytorchts vectorbt
python -m ipykernel install --user --name $PROJ --display-name $PROJ
```
pts/model/estimator.py
+1 -1
View File
@@ -104,7 +104,7 @@ class PyTorchEstimator(Estimator):
input_names = get_module_forward_input_names(trained_net)
with env._let(max_idle_transforms=maybe_len(training_data) or 0):
with env._let(max_idle_transforms=10):
training_instance_splitter = self.create_instance_splitter("training")
training_iter_dataset = TransformedIterableDataset(
dataset=training_data,
pts/model/time_grad2/epsilon_theta.py
+17 -12
View File
@@ -31,8 +31,9 @@ class DiffusionEmbedding(nn.Module):
class ResidualBlock(nn.Module):
def __init__(self, hidden_size, residual_channels, dilation):
def __init__(self, x_in, t_in, cond_in, dilation):
super().__init__()
residual_channels = x_in
self.dilated_conv = nn.Conv1d(
residual_channels,
2 * residual_channels,
@@ -41,9 +42,9 @@ class ResidualBlock(nn.Module):
dilation=dilation,
padding_mode="circular",
)
self.diffusion_projection = nn.Linear(hidden_size, residual_channels)
self.diffusion_projection = nn.Linear(t_in, residual_channels)
self.conditioner_projection = nn.Conv1d(
1, 2 * residual_channels, 1, padding=2, padding_mode="circular"
cond_in, 2 * residual_channels, 1 #3, padding_mode="circular",
)
self.output_projection = nn.Conv1d(residual_channels, 2 * residual_channels, 1)
@@ -69,13 +70,13 @@ class ResidualBlock(nn.Module):
class CondUpsampler(nn.Module):
def __init__(self, cond_length, target_dim):
super().__init__()
self.linear1 = nn.Linear(cond_length, target_dim // 2)
self.linear2 = nn.Linear(target_dim // 2, target_dim)
self.conv1 = nn.Conv1d(cond_length, target_dim // 2, 1)
self.conv2 = nn.Conv1d(target_dim // 2, target_dim, 1)
def forward(self, x):
x = self.linear1(x)
x = self.conv1(x)
x = F.leaky_relu(x, 0.4)
x = self.linear2(x)
x = self.conv2(x)
x = F.leaky_relu(x, 0.4)
return x
@@ -93,26 +94,30 @@ class EpsilonTheta2(nn.Module):
):
super().__init__()
self.input_projection = nn.Conv1d(
1, residual_channels, 1, padding=2, padding_mode="circular"
target_dim, residual_channels, 1#, padding=2, padding_mode="circular"
)
self.diffusion_embedding = DiffusionEmbedding(
time_emb_dim, proj_dim=residual_hidden
)
# self.cond_upsampler = nn.Conv1d(cond_length, width*3, 1)
self.cond_upsampler = CondUpsampler(
target_dim=target_dim, cond_length=cond_length
)
self.residual_layers = nn.ModuleList(
[
ResidualBlock(
residual_channels=residual_channels,
x_in=residual_channels,
t_in=residual_hidden,
cond_in=target_dim,
# residual_channels=residual_channels,
dilation=2 ** (i % dilation_cycle_length),
hidden_size=residual_hidden,
# hidden_size=residual_hidden,
)
for i in range(residual_layers)
]
)
self.skip_projection = nn.Conv1d(residual_channels, residual_channels, 3)
self.output_projection = nn.Conv1d(residual_channels, 1, 3)
self.skip_projection = nn.Conv1d(residual_channels, residual_channels, 1)
self.output_projection = nn.Conv1d(residual_channels, target_dim, 1)
nn.init.kaiming_normal_(self.input_projection.weight)
nn.init.kaiming_normal_(self.skip_projection.weight)
pts/model/time_grad2/gaussian_diffusion_ou.py
+3 -7
View File
@@ -53,12 +53,8 @@ def mk_noise(shape, device):
noise_ou = noise_ou[:, None]
return C, noise_rand, noise_ou
def noise_like(shape, device, repeat=False):
repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(
shape[0], *((1,) * (len(shape) - 1))
)
noise = lambda: torch.randn(shape, device=device)
return repeat_noise() if repeat else noise()
def noise_like(x):
return mk_noise(x.shape, x.device)
def cosine_beta_schedule(timesteps, s=0.008):
@@ -298,7 +294,7 @@ class GaussianDiffusionOU(nn.Module):
B, T, _ = x.shape
time = torch.randint(0, self.num_timesteps, (B * T,), device=x.device).long()
time = torch.randint(0, self.num_timesteps, (B,), device=x.device).long()
loss = self.p_losses(
x, cond, time, *args, **kwargs
)
pts/model/time_grad2/time_grad_network.py
+3 -2
View File
@@ -3,7 +3,6 @@ from typing import List, Optional, Tuple, Union
import torch
import torch.nn as nn
from gluonts.core.component import validated
from pts.model import weighted_average
@@ -62,6 +61,7 @@ class TimeGradTrainingNetwork2(nn.Module):
)
self.denoise_fn = EpsilonTheta2(
# input_size=input_size,
target_dim=target_dim,
cond_length=conditioning_length,
residual_layers=residual_layers,
@@ -402,7 +402,8 @@ class TimeGradTrainingNetwork2(nn.Module):
# we sum the last axis to have the same shape for all likelihoods
# (batch_size, subseq_length, 1)
target = target.permute(0, 2, 1)
distr_args = distr_args.permute(0, 2, 1)
likelihoods = self.diffusion.log_prob(target, distr_args).unsqueeze(-1)
# assert_shape(likelihoods, (-1, seq_len, 1))