diff --git a/README.md b/README.md index 9c5f565c..4c43450e 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,9 @@ # DSIPTS: unified library for timeseries modelling +> [!CAUTION] +> THIS IS THE OFFICIAL FORK OF THE ORIGINAL LIBRARY. IT MAY CONTAIN A LOT OF NOT WORKING LINKS, REFERENCES. PLEASE USE THE ORIGINAL ONE UNTIL WE FINISH THE UPDATES. IT MAY REQUIRE SOME TIME + This library allows to: diff --git a/dsipts/data_structure/data_structure.py b/dsipts/data_structure/data_structure.py index 8e549dd7..00d19928 100644 --- a/dsipts/data_structure/data_structure.py +++ b/dsipts/data_structure/data_structure.py @@ -335,7 +335,8 @@ def create_data_loader(self,data:pd.DataFrame, shift:int=0, keep_entire_seq_while_shifting:bool=False, starting_point:Union[None,dict]=None, - skip_step:int=1 + skip_step:int=1, + is_inference:bool=False )->MyDataset: """ Create the dataset for the training/inference step @@ -448,7 +449,10 @@ def create_data_loader(self,data:pd.DataFrame, xx = x_num_future[i-shift+skip_stacked:i+future_steps-shift+skip_stacked].mean() else: xx = 0.0 - if np.isfinite(x_num_past[i-past_steps:i].min() + y_target[i+skip_stacked:i+future_steps+skip_stacked].min() + xx): + if is_inference is False: + xx+=y_target[i+skip_stacked:i+future_steps+skip_stacked].min() + + if np.isfinite(x_num_past[i-past_steps:i].min() + xx): x_num_past_samples.append(x_num_past[i-past_steps:i]) if len(self.future_variables)>0: @@ -473,6 +477,7 @@ def create_data_loader(self,data:pd.DataFrame, x_num_future_samples = np.stack(x_num_future_samples) except Exception as e: beauty_string('WARNING x_num_future_samples is empty and it should not','info',True) + y_samples = np.stack(y_samples) t_samples = np.stack(t_samples) g_samples = np.stack(g_samples) @@ -944,7 +949,8 @@ def inference(self,batch_size:int=100, rescaling:bool=True, data:pd.DataFrame=None, steps_in_future:int=0, - check_holes_and_duplicates:bool=True)->pd.DataFrame: + check_holes_and_duplicates:bool=True, + is_inference:bool=False)->pd.DataFrame: ##TODO PUSH THIS ON PTF! """similar to `inference_on_set` only change is split_params that must contain this keys but using the default can be sufficient: @@ -967,15 +973,16 @@ def inference(self,batch_size:int=100, pd.DataFrame: predicted values """ beauty_string('Inference on a custom dataset','block',self.verbose) - self.check_custom = True ##this is a check for the dataset loading ## enlarge the dataset in order to have all the rows needed if check_holes_and_duplicates: if self.group is None: - freq = pd.to_timedelta(np.diff(data.time).min()) + ##freq = pd.to_timedelta(np.diff(data.time).min()) + freq = self.freq #TODO port it into PTF beauty_string(f'Detected minumum frequency: {freq}','section',self.verbose) + ## TODO work on this for consistency empty = pd.DataFrame({'time':pd.date_range(data.time.min(),data.time.max()+freq*(steps_in_future+self.split_params['past_steps']+self.split_params['future_steps']),freq=freq)}) - + else: freq = pd.to_timedelta(np.diff(data[data[self.group==data[self.group].unique()[0]]].time).min()) beauty_string(f'Detected minumum frequency: {freq} supposing constant frequence inside the groups','section',self.verbose) @@ -986,6 +993,9 @@ def inference(self,batch_size:int=100, empty.append(pd.DataFrame({self.group:c,'time':pd.date_range(_min.time[_min[self.group]==c].values[0],_max.time[_max[self.group]==c].values[0]+freq*(steps_in_future+self.split_params['past_steps']+self.split_params['future_steps']),freq=freq)})) empty = pd.concat(empty,ignore_index=True) dataset = empty.merge(data,how='left') + #TODO port it into PTF + for c in self.cat_var: + self.enrich(dataset, c) else: dataset = data.copy() @@ -996,9 +1006,9 @@ def inference(self,batch_size:int=100, if c in ['past_steps','future_steps','shift','keep_entire_seq_while_shifting','starting_point']: split_params[c] = self.split_params[c] split_params['skip_step']=1 - data = self.create_data_loader(dataset,**split_params) + data = self.create_data_loader(dataset,**split_params,is_inference=is_inference) else: - data = self.create_data_loader(data,**split_params) + data = self.create_data_loader(data,**split_params,is_inference=is_inference) res = self.inference_on_set(batch_size=batch_size,num_workers=num_workers,split_params=None,set='custom',rescaling=rescaling,data=data) self.check_custom = False diff --git a/notebooks/2-venice_dataset.ipynb b/notebooks/2-venice_dataset.ipynb index 36c29a38..1e1916d6 100644 --- a/notebooks/2-venice_dataset.ipynb +++ b/notebooks/2-venice_dataset.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "23b74516-88ca-4799-b276-12613b4ecf6f", "metadata": { "tags": [] @@ -16,14 +16,14 @@ "from datetime import timedelta\n", "import logging\n", "import sys\n", - "logger = logging.getLogger()\n", - "logger.setLevel(logging.DEBUG)\n", - "logging.debug(\"info\")" + "#logger = logging.getLogger()\n", + "#logger.setLevel(logging.DEBUG)\n", + "#logging.debug(\"info\")" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "41586919-e5a3-4186-b92d-55332f4aa15b", "metadata": { "tags": [] @@ -36,33 +36,12 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "5e153a9f-3456-432c-abc8-363cb1f14a4a", "metadata": { "tags": [] }, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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", 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"code", - "execution_count": 11, + "execution_count": null, "id": "1be1b3ad-36ef-4fdc-95df-cf0739633915", "metadata": { "tags": [] @@ -684960,7 +197,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "2959dcdb-36fa-47c5-bc33-cc88c20d39ea", "metadata": { "tags": [] @@ -684979,122 +216,42 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "f7fcda7e-4b30-45d7-9991-27dedcfd58cd", "metadata": { "tags": [] }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Global seed set to 42\n", - "Using bfloat16 Automatic Mixed Precision (AMP)\n", - "GPU available: False, used: False\n", - "TPU available: False, using: 0 TPU cores\n", - "IPU available: False, using: 0 IPUs\n", - "HPU available: False, using: 0 HPUs\n", - "/home/agobbi/miniconda3/envs/tt/lib/python3.11/site-packages/pytorch_lightning-1.9.4-py3.11.egg/pytorch_lightning/callbacks/model_checkpoint.py:613: UserWarning:\n", - "\n", - "Checkpoint directory /home/agobbi/Projects/ExpTS/tmp/ciao exists and is not empty.\n", - "\n", - "/home/agobbi/miniconda3/envs/tt/lib/python3.11/site-packages/pytorch_lightning-1.9.4-py3.11.egg/pytorch_lightning/trainer/connectors/data_connector.py:488: PossibleUserWarning:\n", - "\n", - "Your `val_dataloader`'s sampler has shuffling enabled, it is strongly recommended that you turn shuffling off for val/test/predict dataloaders.\n", - "\n", - "Finding best initial lr: 88%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍ | 88/100 [00:11<00:01, 7.48it/s]\n", - "LR finder stopped early after 88 steps due to diverging loss.\n", - "Learning rate set to 0.008317637711026709\n", - "Restoring states from the checkpoint path at /home/agobbi/Projects/ExpTS/tmp/ciao/.lr_find_3638a245-9cf8-4e00-9877-8bbaba0d6e9c.ckpt\n", - "Restored all states from the checkpoint file at /home/agobbi/Projects/ExpTS/tmp/ciao/.lr_find_3638a245-9cf8-4e00-9877-8bbaba0d6e9c.ckpt\n", - "\n", - " | Name | Type | Params\n", - "------------------------------------------\n", - "0 | embs | ModuleList | 200 \n", - "1 | loss | QuantileLossMO | 0 \n", - "2 | linear | ModuleList | 417 K \n", - "------------------------------------------\n", - "417 K Trainable params\n", - "0 Non-trainable params\n", - "417 K Total params\n", - "1.670 Total estimated model params size (MB)\n", - "Epoch 0, global step 1864: 'val_loss' reached 38.14202 (best 38.14202), saving model to '/home/agobbi/Projects/ExpTS/tmp/ciao/checkpoint-v14.ckpt' as top 1\n", - "Epoch 1, global step 3728: 'val_loss' reached 36.79206 (best 36.79206), saving model to '/home/agobbi/Projects/ExpTS/tmp/ciao/checkpoint-v14.ckpt' as top 1\n", - "Epoch 2, global step 5592: 'val_loss' reached 35.84991 (best 35.84991), saving model to '/home/agobbi/Projects/ExpTS/tmp/ciao/checkpoint-v14.ckpt' as top 1\n", - "Epoch 3, global step 7456: 'val_loss' reached 35.74711 (best 35.74711), saving model to '/home/agobbi/Projects/ExpTS/tmp/ciao/checkpoint-v14.ckpt' as top 1\n", - "Epoch 4, global step 9320: 'val_loss' reached 35.16384 (best 35.16384), saving model to '/home/agobbi/Projects/ExpTS/tmp/ciao/checkpoint-v14.ckpt' as top 1\n", - "`Trainer.fit` stopped: `max_epochs=5` reached.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Saving losses on file because multigpu not working\n" - ] - }, - { - "data": { - "text/plain": [ - "35.163841247558594" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "#train the model for 50 epochs with auto_lr_find \n", - "ts.train_model(dirpath=f\"/home/agobbi/Projects/ExpTS/tmp/ciao\",\n", - " split_params=split_params,\n", - " batch_size=128,\n", - " num_workers=4,\n", - " max_epochs=5,\n", - " gradient_clip_val= 0.0,\n", - " gradient_clip_algorithm='value',\n", - " precision='bf16',\n", - " auto_lr_find=True)" + "if False:\n", + " ts.train_model(dirpath=f\"/home/agobbi/Projects/ExpTS/tmp/venice\",\n", + " split_params=split_params,\n", + " batch_size=128,\n", + " num_workers=4,\n", + " max_epochs=5,\n", + " gradient_clip_val= 0.0,\n", + " gradient_clip_algorithm='value',\n", + " precision='bf16',\n", + " auto_lr_find=True)\n", + " #Print the losses, check overfitting\n", + " ts.losses.plot()\n", + " ts.save(f\"venice_test\")" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "8d0974c9-fc85-436d-b7c7-c6605b2a423a", "metadata": { "tags": [] }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#Print the losses, check overfitting\n", - "ts.losses.plot()" - ] + "outputs": [], + "source": [] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "id": "9df63a20-83b6-42c4-a7e4-30e4c7778cf3", "metadata": { "tags": [] @@ -685102,147 +259,40 @@ "outputs": [], "source": [ "#make inferences on \n", - "res = ts.inference_on_set(200,4,set='test',rescaling=True)" + "retrain=False\n", + "if retrain:\n", + " res = ts.inference_on_set(200,4,set='test',rescaling=True)\n", + " import numpy as np\n", + " res.groupby('lag').apply(lambda x: np.nanmean((x.y-x.y_median)**2)).reset_index().rename(columns={0:'error'}) " ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "id": "bf41c3a1-4108-4d5e-a81f-baaaa00bcf26", "metadata": { "tags": [] }, - "outputs": [ - { - "data": { - "text/html": [ - "
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lagerror
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" - ], - "text/plain": [ - " lag error\n", - "0 1 0.001623\n", - "1 2 0.004340\n", - "2 3 0.007501\n", - "3 4 0.010876\n", - "4 5 0.013922\n", - ".. ... ...\n", - "79 80 0.036027\n", - "80 81 0.035977\n", - "81 82 0.035712\n", - "82 83 0.035574\n", - "83 84 0.035678\n", - "\n", - "[84 rows x 2 columns]" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "## get the median MSE for each lag\n", - "import numpy as np\n", - "res.groupby('lag').apply(lambda x: np.nanmean((x.y-x.y_median)**2)).reset_index().rename(columns={0:'error'}) " + "## get the median MSE for each lag\n" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "id": "e8abe2ae-d010-4e95-b712-8e4084639d17", "metadata": { "tags": [] }, "outputs": [], "source": [ - "#save model \n", - "ts.save(f\"{model_to_use}_test\")" + "#save model \n" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "id": "972f7417-48ea-4498-8724-89fb1eedccf7", "metadata": { "tags": [] @@ -685254,506 +304,164 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, + "id": "bf81ddc9-e90c-42ee-99b5-e4031929dd08", + "metadata": {}, + "outputs": [], + "source": [ + "data" + ] + }, + { + "cell_type": "code", + "execution_count": null, "id": "a1712bce-071f-4ea9-92bd-e4f4637ebb07", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " lag error\n", - "0 1 0.001623\n", - "1 2 0.004340\n", - "2 3 0.007501\n", - "3 4 0.010876\n", - "4 5 0.013922\n", - ".. ... ...\n", - "79 80 0.036027\n", - "80 81 0.035977\n", - "81 82 0.035712\n", - "82 83 0.035574\n", - "83 84 0.035678\n", - "\n", - "[84 rows x 2 columns]" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], + "source": [ + "ts.load(LinearTS,f\"venice_test\",load_last=False)\n", + "#res = ts.inference_on_set(200,4,set='test',rescaling=True)\n", + "#error = res.groupby('lag').apply(lambda x: np.nanmean((x.y-x.y_median)**2)).reset_index().rename(columns={0:'error'}) \n", + "#error" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ff9f7704-a264-43d0-8367-b7664d93751a", + "metadata": {}, + "outputs": [], + "source": [ + "future_steps" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8fdd447a-29d9-4a49-b96b-b8ebf68f4b5b", + "metadata": {}, + "outputs": [], + "source": [ + "past_steps" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ac00f103-839e-40fa-87d5-bd6c5e00c890", + "metadata": {}, + "outputs": [], + "source": [ + "tmp" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a0e30c99-d169-4b23-868c-bfde8c774cb8", + "metadata": {}, + "outputs": [], + "source": [ + "empty" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5f0add2e-4e25-47ab-9a19-fa8dad630201", + "metadata": {}, + "outputs": [], "source": [ - "ts.load(LinearTS,f\"{model_to_use}_test\",load_last=False)\n", - "res = ts.inference_on_set(200,4,set='test',rescaling=True)\n", - "error = res.groupby('lag').apply(lambda x: np.nanmean((x.y-x.y_median)**2)).reset_index().rename(columns={0:'error'}) \n", - "error" + "inference_data" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "id": "23bcd30c-d798-4a15-8f8c-7587c807d284", "metadata": {}, - "outputs": [ - { - "data": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "##print the mean MSE along the lag steps\n", - "plt.plot(error.lag,error.error)" + "#plt.plot(error.lag,error.error)" ] }, { "cell_type": "code", - "execution_count": 21, - "id": "30fe4eff-d582-40b6-b04b-fce25b06e455", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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timey
01983-01-01 01:00:000.44
11983-01-01 02:00:000.35
21983-01-01 03:00:000.23
31983-01-01 04:00:000.10
41983-01-01 05:00:000.01
.........
3418752021-12-31 20:00:000.36
3418762021-12-31 21:00:000.50
3418772021-12-31 22:00:000.57
3418782021-12-31 23:00:000.59
3418792022-01-01 00:00:000.52
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341880 rows × 2 columns

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" - ], - "text/plain": [ - " time y\n", - "0 1983-01-01 01:00:00 0.44\n", - "1 1983-01-01 02:00:00 0.35\n", - "2 1983-01-01 03:00:00 0.23\n", - "3 1983-01-01 04:00:00 0.10\n", - "4 1983-01-01 05:00:00 0.01\n", - "... ... ...\n", - "341875 2021-12-31 20:00:00 0.36\n", - "341876 2021-12-31 21:00:00 0.50\n", - "341877 2021-12-31 22:00:00 0.57\n", - "341878 2021-12-31 23:00:00 0.59\n", - "341879 2022-01-01 00:00:00 0.52\n", - "\n", - "[341880 rows x 2 columns]" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "id": "472b61b3-5536-45bc-b923-58de50be49f3", + "metadata": {}, + "outputs": [], "source": [ - "data" + "tmp = data[data.time>'2021-12-28 12:00:00']\n", + "tmp['hour'] = tmp.time.dt.hour\n", + "\n", + "empty = pd.DataFrame({'time':pd.date_range(tmp.time.max()+timedelta(hours=1),tmp.time.max()+timedelta(hours=future_steps),freq='1h'),\n", + " 'y':np.nan}) ##this is a bug, you can \n", + "empty['hour'] = empty.time.dt.hour\n", + "inference_data = pd.concat([tmp,empty])" ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, + "id": "214b0976-4823-45bc-a728-6b7c46771396", + "metadata": {}, + "outputs": [], + "source": [ + "tmp" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "302f15f8-b093-4c89-b4d8-37ce7b37f2d5", + "metadata": {}, + "outputs": [], + "source": [ + "inference_data ##I can extend the dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, "id": "4928a6a2-9672-4588-a349-460cd29762f1", "metadata": { "tags": [] }, - "outputs": [ - { - "data": { - "text/html": [ - "
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lagtimeyy_lowy_mediany_highprediction_time
012021-12-04 13:00:000.510.4661420.5111970.5677882021-12-04 12:00:00
112021-12-04 14:00:000.380.3467330.3917200.4463022021-12-04 13:00:00
212021-12-04 15:00:000.250.2256730.2692050.3190392021-12-04 14:00:00
312021-12-04 16:00:000.170.1385480.1853030.2338672021-12-04 15:00:00
412021-12-04 17:00:000.160.1379950.1862920.2303822021-12-04 16:00:00
........................
48463842021-12-31 20:00:000.360.3061090.5391550.8065302021-12-28 08:00:00
48464842021-12-31 21:00:000.500.4233060.6438520.8808022021-12-28 09:00:00
48465842021-12-31 22:00:000.570.4480520.6607820.8748782021-12-28 10:00:00
48466842021-12-31 23:00:000.590.3913880.6024350.8061312021-12-28 11:00:00
48467842022-01-01 00:00:000.520.2915610.5110340.7096042021-12-28 12:00:00
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48468 rows × 7 columns

\n", - "
" - ], - "text/plain": [ - " lag time y y_low y_median y_high \\\n", - "0 1 2021-12-04 13:00:00 0.51 0.466142 0.511197 0.567788 \n", - "1 1 2021-12-04 14:00:00 0.38 0.346733 0.391720 0.446302 \n", - "2 1 2021-12-04 15:00:00 0.25 0.225673 0.269205 0.319039 \n", - "3 1 2021-12-04 16:00:00 0.17 0.138548 0.185303 0.233867 \n", - "4 1 2021-12-04 17:00:00 0.16 0.137995 0.186292 0.230382 \n", - "... ... ... ... ... ... ... \n", - "48463 84 2021-12-31 20:00:00 0.36 0.306109 0.539155 0.806530 \n", - "48464 84 2021-12-31 21:00:00 0.50 0.423306 0.643852 0.880802 \n", - "48465 84 2021-12-31 22:00:00 0.57 0.448052 0.660782 0.874878 \n", - "48466 84 2021-12-31 23:00:00 0.59 0.391388 0.602435 0.806131 \n", - "48467 84 2022-01-01 00:00:00 0.52 0.291561 0.511034 0.709604 \n", - "\n", - " prediction_time \n", - "0 2021-12-04 12:00:00 \n", - "1 2021-12-04 13:00:00 \n", - "2 2021-12-04 14:00:00 \n", - "3 2021-12-04 15:00:00 \n", - "4 2021-12-04 16:00:00 \n", - "... ... \n", - "48463 2021-12-28 08:00:00 \n", - "48464 2021-12-28 09:00:00 \n", - "48465 2021-12-28 10:00:00 \n", - "48466 2021-12-28 11:00:00 \n", - "48467 2021-12-28 12:00:00 \n", - "\n", - "[48468 rows x 7 columns]" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "ts.inference(data = data[data.time>'2021-12-01'],steps_in_future=10)" + "ts.inference(data = inference_data,is_inference=True)" ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, + "id": "61818dcc-3586-43e5-9b09-68954a73376e", + "metadata": {}, + "outputs": [], + "source": [ + "tmp ##or just start from it and DSIPTS will automatically add categorical temporal variable" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cd324461-6373-41c8-bedf-5a857b65e96f", + "metadata": {}, + "outputs": [], + "source": [ + "ts.inference(data = tmp,steps_in_future=future_steps,is_inference=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, "id": "57b0f069-dc3c-4b16-9ede-747df97721f8", "metadata": { "tags": [] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "better to have qt, i will reduce the dataset\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "%matplotlib inline\n", "lag = 7\n", diff --git a/notebooks/TEST.ipynb b/notebooks/TEST.ipynb new file mode 100644 index 00000000..5e1f5e24 --- /dev/null +++ b/notebooks/TEST.ipynb @@ -0,0 +1,813 @@ +{ + "cells": [ + { + "cell_type": "raw", + "id": "55c75749-ec70-4365-ac8a-f96552da4528", + "metadata": {}, + "source": [ + "from dsipts import Categorical,TimeSeries, RNN\n", + "settimana = Categorical('settimanale',1,[1,1,1,1,1,1,1],7,'multiplicative',[0.9,0.8,0.7,0.6,0.5,0.99,0.99])\n", + "\n", + "##montly, additive (here there are only 5 month)\n", + "mese = Categorical('mensile',1,[31,28,20,10,33],5,'additive',[10,20,-10,20,0])\n", + "\n", + "##spot categorical variables: in this case it occurs every 100 days and it lasts 7 days adding 10 to the original timeseries\n", + "spot = Categorical('spot',100,[7],1,'additive',[10])\n", + "\n", + "##initizate a timeseries object\n", + "ts = TimeSeries('prova')\n", + "ts.generate_signal(noise_mean=1,categorical_variables=[settimana,mese,spot],length=5000,type=0)\n", + "dataset = ts.dataset\n", + "dataset['x_num'] = 0.0\n", + "dataset.loc[:dataset.shape[0]-10,'x_num']= dataset['x_num'].values[9:]\n", + "cat_var_past = ts.cat_var\n", + "cat_var_fut = ts.cat_fut\n", + "num_var_past = ts.num_var+['x_num']\n", + "import pickle \n", + "with open('example.pkl', 'wb') as f:\n", + " pickle.dump([dataset,cat_var_past,cat_var_fut,num_var_past],f)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "459c491c-98ab-4513-b5eb-9f5c604b9df4", + "metadata": {}, + "outputs": [], + "source": [ + "#dataset.to_csv('dataset.csv',index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0b53411e-8c81-4dc9-8e2e-c83c3a568be1", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "beea9295-9164-4bae-b2e1-8c5cc6c585c4", + "metadata": {}, + "outputs": [], + "source": [ + "import pickle \n", + "import pandas as pd\n", + "from typing import Union\n", + "from sklearn.preprocessing import LabelEncoder,OrdinalEncoder\n", + "from datetime import datetime\n", + "import lightning as L\n", + "with open('example.pkl', 'rb') as f:\n", + " dataset,cat_var_past,cat_var_fut,num_var_past = pickle.load(f)\n", + "num_var_fut = []\n", + "group = None\n", + "time_var = 'time'\n", + "target = ['signal']" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "db453ed2-df16-44bf-bc86-184fd4010e7a", + "metadata": {}, + "outputs": [], + "source": [ + "dataset2 = dataset.copy()\n", + "dataset2['group'] = 2\n", + "dataset['group'] = 1 \n", + "dataset = pd.concat([dataset, dataset2])" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "dd967d57-478d-4bd5-bf92-b369ca730c28", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1.0" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "4d1b49d6-8e61-447d-9e2e-44893f104cb8", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "def extend_time_df(x:pd.DataFrame,freq:Union[str,int],group:Union[str,None]=None,global_minmax:bool=False)-> pd.DataFrame:\n", + " \"\"\"Utility for generating a full dataset and then merge the real data\n", + "\n", + " Args:\n", + " x (pd.DataFrame): dataframe containing the column time\n", + " freq (str): frequency (in pandas notation) of the resulting dataframe\n", + " group (string or None): if not None the min max are computed by the group column, default None\n", + " global_minmax (bool): if True the min_max is computed globally for each group. Usually used for stacked model\n", + " Returns:\n", + " pd.DataFrame: a dataframe with the column time ranging from thr minumum of x to the maximum with frequency `freq`\n", + " \"\"\"\n", + "\n", + " if group is None:\n", + "\n", + " if isinstance(freq,int):\n", + " empty = pd.DataFrame({'time':list(range(x.time.min(),x.time.max(),freq))})\n", + " else:\n", + " empty = pd.DataFrame({'time':pd.date_range(x.time.min(),x.time.max(),freq=freq)})\n", + "\n", + " else:\n", + " \n", + " if global_minmax:dataset\n", + " _min = pd.DataFrame({group:x[group].unique(),'time':x.time.min()})\n", + " _max = pd.DataFrame({group:x[group].unique(),'time':x.time.max()})\n", + "\n", + " else:\n", + " _min = x.groupby(group).time.min().reset_index()\n", + " _max = x.groupby(group).time.max().reset_index()\n", + " empty = []\n", + " for c in x[group].unique():\n", + " if isinstance(freq,int):\n", + " empty.append(pd.DataFrame({group:c,'time':np.arange(_min.time[_min[group]==c].values[0],_max.time[_max[group]==c].values[0],freq)}))\n", + "\n", + " else:\n", + " empty.append(pd.DataFrame({group:c,'time':pd.date_range(_min.time[_min[group]==c].values[0],_max.time[_max[group]==c].values[0],freq=freq)}))\n", + " \n", + " empty = pd.concat(empty,ignore_index=True)\n", + " return empty\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "9e2ac8bd-fc6b-4400-b8f3-b86f1138671b", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "class PandasTSDataSet:\n", + " def __init__(self, df,time_var, cat_var_past, cat_var_fut, num_var_past, num_var_fut,target, group = None, metadata={},check_holes_and_duplicates=True,check_past=True):\n", + " \n", + " numerica_var = list(set(num_var_past).union(set(num_var_fut)).union(set(target)))\n", + " df_norm = df[numerica_var+[time_var]].copy()\n", + " label_encoders = {}\n", + " ##Encoders for categorical since we want to return tensors\n", + " for c in set(cat_var_past).union(set(cat_var_fut)):\n", + " label_encoders[c] = LabelEncoder()\n", + " df_norm[c] = label_encoders[c].fit_transform(df[c])\n", + " if group is not None:\n", + " label_encoders[group] = OrdinalEncoder()\n", + " df_norm[group] = label_encoders[group].fit_transform(df[group])\n", + " #complete the dataset, we can think to devide it in chunks of consecutive ts....\n", + " if check_holes_and_duplicates:\n", + " df_norm.drop_duplicates(subset=['time'] if group is None else [group,'time'], keep='first', inplace=True, ignore_index=True)\n", + " print(df_norm.shape)\n", + " if group is None:\n", + " differences = df_norm.time.diff()[1:]\n", + " else:\n", + " differences = df_norm[df_norm[group]==df_norm[group].unique()[0]].time.diff()[1:]\n", + " \n", + " if isinstance(df_norm.time[0], datetime):\n", + " freq = pd.to_timedelta(differences.min()) \n", + " else:\n", + " if int(df_norm.time[0])==df_norm.time[0]: ##ONLY THINK THAT WORKS IN GENERAL\n", + " freq = int(differences.min())\n", + " else:\n", + " raise TypeError(\"time must be integer or datetime\")\n", + " metadata['fraq'] = freq \n", + " if differences.nunique()>1:\n", + " df_norm = extend_time_df(df_norm,freq,group).merge(dataset,how='left')\n", + " print(df_norm.shape)\n", + " \n", + " else:\n", + " metadata['freq'] = dataset.time.diff()[1:].min() ##care there can be holes in data!\n", + "\n", + " if check_past:\n", + " num_var_past = list(set(num_var_past).union(set(target)))\n", + " if group is None:\n", + " df_norm = df_norm.sort_values(by=time_var).reset_index()\n", + " self.lengths = df_norm.shape[0]\n", + " else:\n", + " df_norm = df_norm.sort_values(by=[time_var,group]).reset_index()\n", + " \n", + " df_norm['valid'] = ~pd.isnull(dataset.max(axis=1))\n", + " df_norm['valid'] = df_norm['valid'].astype(int)\n", + " self.df = df_norm\n", + " metadata['target'] = target\n", + " metadata['num_var_past'] = num_var_past\n", + " metadata['num_var_fut'] = num_var_fut\n", + " metadata['cat_var_past'] = cat_var_past\n", + " metadata['cat_var_fut'] = cat_var_fut\n", + " metadata['past_variables'] = list(set(cat_var_past).union(set(num_var_past)))\n", + " metadata['future_variables'] = list(set(cat_var_fut).union(set(num_var_fut)).union(set(target)))\n", + " metadata['time_var'] =time_var\n", + " metadata['group'] = group\n", + " \n", + " self.metadata = metadata\n", + " ## compute the length of each timeseries\n", + " ll = {}\n", + " if group is None:\n", + " ll[0] = self.df.shape[0]\n", + " else:\n", + " for i,row in self.df.groupby(group)[time_var].count().reset_index().iterrows():\n", + " ll[row[group]] = row[time_var]\n", + " self.lengths = ll\n", + " \n", + "\n", + " def get_total_len(self):\n", + " return sum(self.lengths.values())\n", + "\n", + " def get_id_ts_by_idx(self,idx):\n", + " tmp = np.cumsum(sum(self.lengths.values()))\n", + " idx = min(np.where(tmp>idx)[0])\n", + " return idx, 0 if idx==0 else tmp[idx-1]\n", + "\n", + " ## this is the most delicate part\n", + " def __getitem__(self, idx):\n", + " # Returns only tensors, no metadata\n", + " device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n", + " if self.metadata['group'] is None:\n", + " df = self.df\n", + " else:\n", + " df = self.df[self.df.group==idx] ## ANOTHER COMMENT: IDX?? what if we have 10 timeseries with groups??\n", + " \n", + " result = {\n", + " #'t': tensor(...), ##FIRST COMMENT: TIMESTAMP CANT be cast as tensor! \n", + " 'y': torch.tensor(df[self.metadata['target']].values,dtype=torch.float32,device=device), ##SECOND COMMENT device\n", + " 'x_past': torch.tensor(df[self.metadata['num_var_past']].values,dtype=torch.float32,device=device), ##SECOND COMMENT device\n", + " 'is_valid': torch.tensor(df.valid.values,dtype=torch.int,device=device), ##SECOND COMMENT device\n", + " #'t_f': tensor(...), ?? WHAT IS THAT?\n", + " \n", + " }\n", + " if self.metadata['group'] is not None:\n", + " result['group']= torch.tensor(idx,device=device,dtype=torch.long) \n", + " if len(self.metadata['num_var_fut'])>0:\n", + " result['num_var_fut']= torch.tensor(df[self.metadata['num_var_fut']].values,dtype=torch.float32,device=device)\n", + " if len(self.metadata['cat_var_fut'])>0:\n", + " result['cat_var_past']= torch.tensor(df[self.metadata['cat_var_past']].values,dtype=torch.long,device=device)\n", + " if len(self.metadata['cat_var_fut'])>0:\n", + " result['cat_var_fut']= torch.tensor(df[self.metadata['cat_var_fut']].values,dtype=torch.long,device=device)\n", + " return result\n", + " def get_metadata(self):\n", + " return self.metadata\n", + "\n", + "# Layer D2\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "290ab40d-f440-4f7f-9ae9-8855a04f1ec6", + "metadata": {}, + "outputs": [], + "source": [ + "data_module_metadata = dict(\n", + " perc_train= 0.7,\n", + " perc_valid= 0.1,\n", + " range_train= None,\n", + " range_validation= None ,\n", + " range_test= None,\n", + " shift= 0,\n", + " starting_point= None,\n", + " skip_step= 1,\n", + " past_steps=16,\n", + " future_steps= 16,\n", + " scaler= 'sklearn.preprocessing.StandardScaler()')" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "e4ca4055-7f36-446c-9b21-6c0456b152cf", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(5000, 6)\n" + ] + } + ], + "source": [ + "ds = PandasTSDataSet(dataset,'time', \n", + " cat_var_past, \n", + " cat_var_fut, \n", + " num_var_past,\n", + " num_var_fut,\n", + " target,\n", + " group = None, \n", + " metadata={}\n", + " ,check_holes_and_duplicates=True,check_past=True)\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "519ebe66-3d64-41df-a191-81c0ba518e79", + "metadata": {}, + "outputs": [], + "source": [ + "from torch.utils.data import Dataset, DataLoader" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "12bc5f1c-2de1-4837-9d2a-e764fa395bba", + "metadata": {}, + "outputs": [], + "source": [ + "from torch.utils.data.dataloader import default_collate\n", + "import torch\n", + "def my_collate(batch):\n", + " batch = list(filter(lambda x : x is not None, batch))\n", + " print(len(batch))\n", + " return default_collate(batch)\n", + "\n", + "class MyDataset(Dataset):\n", + "\n", + " def __init__(self,data,metadata)->torch.utils.data.Dataset:\n", + " \n", + " self.data = data \n", + " self.metadata = metadata\n", + " self.metadata_dataset = data.metadata\n", + " def __len__(self):\n", + " \n", + " return self.data.get_total_len()-len(self.data.lengths)*self.metadata['future_steps']\n", + " ##this getitem calls the getitem of the data as desired\n", + " def __getitem__(self, idxs): \n", + " sample = {}\n", + " IDX,difference = self.data.get_id_ts_by_idx(idxs) \n", + " idxs-=difference\n", + " tmp = self.data.__getitem__(IDX)\n", + " if idxs+self.metadata['future_steps']>self.data.lengths[IDX]:\n", + " return None\n", + " \n", + " for k in tmp.keys():\n", + "\n", + " if len(tmp[k][idxs-self.metadata['past_steps']:idxs]) + len(tmp[k][idxs:idxs+self.metadata['future_steps']]) == self.metadata['future_steps']+self.metadata['past_steps']:\n", + " if tmp['is_valid'][idxs-self.metadata['past_steps']:idxs+self.metadata['future_steps']].sum()==self.metadata['future_steps']+self.metadata['past_steps']:\n", + " if '_past' in k:\n", + " sample[k] = tmp[k][idxs-self.metadata['past_steps']:idxs]\n", + " elif '_fut' in k or k=='y':\n", + " sample[k] = tmp[k][idxs:idxs+self.metadata['future_steps']]\n", + " else:\n", + " pass\n", + " if len(sample)==0:\n", + " return None\n", + " else:\n", + " return sample" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "7e43bfd7-12b3-48c2-8b33-d5ca6681959c", + "metadata": {}, + "outputs": [], + "source": [ + "dl = DataLoader(\n", + " MyDataset(ds,data_module_metadata), ##change respect to the initial vignette, can not use standard dataset\n", + " batch_size=32,\n", + " collate_fn=my_collate\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "dde2ba1f-f6f8-483c-a404-5917b84ce37a", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "16\n", + "torch.Size([16, 16, 1])\n", + "32\n", + "torch.Size([32, 16, 1])\n", + "32\n", + "torch.Size([32, 16, 1])\n", + "32\n", + "torch.Size([32, 16, 1])\n", + "32\n", + "torch.Size([32, 16, 1])\n", + "32\n", + "torch.Size([32, 16, 1])\n", + "32\n", + "torch.Size([32, 16, 1])\n", + "32\n", + "torch.Size([32, 16, 1])\n", + "32\n", + "torch.Size([32, 16, 1])\n", + "32\n", + "torch.Size([32, 16, 1])\n", + "32\n", + "torch.Size([32, 16, 1])\n", + "32\n", + "torch.Size([32, 16, 1])\n", + "32\n", + "torch.Size([32, 16, 1])\n", + "32\n", + "torch.Size([32, 16, 1])\n", + "32\n", + "torch.Size([32, 16, 1])\n", + "32\n", + 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" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "eaff143f-9754-4335-8a68-c5829d2d22d7", + "metadata": {}, + "outputs": [], + "source": [ + "##WIP IN THE NEXT CELLS" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8f4c6bec-ee6e-4c54-9223-bf3a72ed46da", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "class DecoderEncoderDataModule(L.LightningDataModule):\n", + " def __init__(self, d1_dataset, batch_size=32, num_workers=4,metadata=None):\n", + " super().__init__()\n", + " # initialize other params\n", + " self.d1_dataset = d1_dataset\n", + " self.batch_size = batch_size\n", + " self.metadata = None # Will be set in setup() NO! THhe metadata for training are not related with metadata of the data!\n", + " \n", + " def setup(self, stage=None):\n", + " # Get metadata from D1 layer during setup\n", + " #self.metadata = self.d1_dataset.get_metadata() ##NO SEE COMMENT BEFORE!\n", + " \n", + " if stage == 'fit' or stage is None: \n", + " ##CLEAR FOR ME IN CASE OF SINGLE TIMESERIES (EG PANDAS LOADER) not clear in case of chunk of data of the same \n", + " ## do we suppose to have NO HOLES in data? do we check it runtime?\n", + " #I can precompute here the indexes \n", + " \n", + " \n", + " \n", + " pass\n", + " \n", + " if stage == 'test' or stage is None:\n", + " # Any test-specific setup\n", + " pass\n", + " \n", + " def train_dataloader(self):\n", + " return DataLoader(\n", + " MyDataset(self.d1_dataset),\n", + " batch_size=self.batch_size,\n", + " **other_params\n", + " )\n", + "\n", + "# Layer M\n", + "class DecoderEncoderModel(L.LightningModule):\n", + " def __init__(self):\n", + " super().__init__()\n", + " self.metadata = None\n", + " \n", + " def setup(self, stage=None):\n", + " # Get metadata from datamodule during setup\n", + " self.metadata = self.trainer.datamodule.metadata\n", + " \n", + " # Initialize layer T model using metadata\n", + " \n", + " def forward(self, x):\n", + " # forward logic\n", + " pass" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.19" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/TEST_v2.ipynb b/notebooks/TEST_v2.ipynb new file mode 100644 index 00000000..54012a01 --- /dev/null +++ b/notebooks/TEST_v2.ipynb @@ -0,0 +1,1296 @@ +{ + "cells": [ + { + "cell_type": "raw", + "id": "55c75749-ec70-4365-ac8a-f96552da4528", + "metadata": {}, + "source": [ + "from dsipts import Categorical,TimeSeries, RNN\n", + "settimana = Categorical('settimanale',1,[1,1,1,1,1,1,1],7,'multiplicative',[0.9,0.8,0.7,0.6,0.5,0.99,0.99])\n", + "\n", + "##montly, additive (here there are only 5 month)\n", + "mese = Categorical('mensile',1,[31,28,20,10,33],5,'additive',[10,20,-10,20,0])\n", + "\n", + "##spot categorical variables: in this case it occurs every 100 days and it lasts 7 days adding 10 to the original timeseries\n", + "spot = Categorical('spot',100,[7],1,'additive',[10])\n", + "\n", + "##initizate a timeseries object\n", + "ts = TimeSeries('prova')\n", + "ts.generate_signal(noise_mean=1,categorical_variables=[settimana,mese,spot],length=5000,type=0)\n", + "dataset = ts.dataset\n", + "dataset['x_num'] = 0.0\n", + "dataset.loc[:dataset.shape[0]-10,'x_num']= dataset['x_num'].values[9:]\n", + "cat_var_past = ts.cat_var\n", + "cat_var_fut = ts.cat_fut\n", + "num_var_past = ts.num_var+['x_num']\n", + "import pickle \n", + "with open('example.pkl', 'wb') as f:\n", + " pickle.dump([dataset,cat_var_past,cat_var_fut,num_var_past],f)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "459c491c-98ab-4513-b5eb-9f5c604b9df4", + "metadata": {}, + "outputs": [], + "source": [ + "#dataset.to_csv('dataset.csv',index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "0b53411e-8c81-4dc9-8e2e-c83c3a568be1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting lightning\n", + " Downloading lightning-2.3.3-py3-none-any.whl.metadata (35 kB)\n", + "Requirement already satisfied: PyYAML<8.0,>=5.4 in /Users/wenda/anaconda3/lib/python3.8/site-packages (from lightning) (6.0.1)\n", + "Requirement already satisfied: fsspec<2026.0,>=2022.5.0 in /Users/wenda/anaconda3/lib/python3.8/site-packages (from fsspec[http]<2026.0,>=2022.5.0->lightning) (2024.6.1)\n", + "Collecting 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This behaviour is the source of the following dependency conflicts.\n", + "ragatouille 0.0.7.post11 requires langchain<0.2.0,>=0.1.0, but you have langchain 0.2.8 which is incompatible.\n", + "ragatouille 0.0.7.post11 requires langchain_core<0.2.0,>=0.1.4, but you have langchain-core 0.2.19 which is incompatible.\n", + "torchaudio 2.3.1 requires torch==2.3.1, but you have torch 2.4.1 which is incompatible.\n", + "torchvision 0.15.2 requires torch==2.0.1, but you have torch 2.4.1 which is incompatible.\u001b[0m\u001b[31m\n", + "\u001b[0mSuccessfully installed lightning-2.3.3 lightning-utilities-0.11.9 pytorch-lightning-2.4.0 torch-2.4.1 torchmetrics-1.5.2\n", + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.1.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.0.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n" + ] + } + ], + "source": [ + "!pip install lightning" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "f92518d7", + "metadata": {}, + "outputs": [], + "source": [ + "import pickle\n", + "import pandas as pd\n", + "from typing import Union, List, Optional, Dict\n", + "from sklearn.preprocessing import LabelEncoder # Used for categorical encoding\n", + "from datetime import datetime, timedelta # For time-related operations\n", + "import lightning as L # PyTorch Lightning\n", + "import numpy as np\n", + "from sklearn.preprocessing import OrdinalEncoder # Used for categorical encoding\n", + "import torch\n", + "from torch.utils.data import Dataset, DataLoader\n", + "from torch.utils.data.dataloader import default_collate\n" + ] + }, + { + "cell_type": "markdown", + "id": "09307105", + "metadata": {}, + "source": [ + "**Data Loading and Preparation (Example Data)**\n", + "\n", + "This section loads example time series data and prepares it for use in the `DataSet` classes. It demonstrates the structure of the input data.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "beea9295-9164-4bae-b2e1-8c5cc6c585c4", + "metadata": {}, + "outputs": [], + "source": [ + "with open('example.pkl', 'rb') as f:\n", + " dataset, cat_var_past, cat_var_fut, num_var_past = pickle.load(f)\n", + "num_var_fut = [] # No numerical future variables in this example\n", + "group = None # No grouping in this first example. Later examples *do* use grouping.\n", + "time_var = 'time'\n", + "target = ['signal']" + ] + }, + { + "cell_type": "markdown", + "id": "9c4fb3c8", + "metadata": {}, + "source": [ + "**Example with Grouping**\n", + "\n", + "The following cell creates a second copy of the dataset and assigns it a different group ID. This is then concatenated with the original `dataset` to showcase how grouping works.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "db453ed2-df16-44bf-bc86-184fd4010e7a", + "metadata": {}, + "outputs": [], + "source": [ + "dataset2 = dataset.copy()\n", + "dataset2['group'] = 2 # Assign a different group ID\n", + "dataset['group'] = 1 # Assign a group ID to the original\n", + "dataset = pd.concat([dataset, dataset2]) # Concatenate to have multiple groups" + ] + }, + { + "cell_type": "markdown", + "id": "8efaed82", + "metadata": {}, + "source": [ + "**`extend_time_df` Function**\n", + "This utility function extends a DataFrame to include all time steps within a specified range and frequency. This is essential for ensuring that all time series have the same length and that there are no missing time steps.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "3810de27-fc3b-44a3-b802-f88b9aa32c14", + "metadata": {}, + "outputs": [], + "source": [ + "def extend_time_df(x:pd.DataFrame,time:str,freq:Union[str,int],group:Union[List[str],None]=None,global_minmax:bool=False)-> pd.DataFrame:\n", + " \"\"\"Extends a DataFrame to include all time steps within a range.\n", + "\n", + " Ensures that the DataFrame `x` has rows for all time steps within the\n", + " range defined by the minimum and maximum values of the `time` column,\n", + " with a specified frequency `freq`. Handles grouping if `group` is provided.\n", + "\n", + " Args:\n", + " x: The input DataFrame. Must contain a 'time' column and any\n", + " columns specified in 'group'.\n", + " time: The name of the column containing the time index (e.g., 'time').\n", + " freq: The frequency to use for extending the DataFrame. This can be\n", + " an integer (for integer time steps) or a string representing a\n", + " pandas frequency string (e.g., 'D' for daily, 'H' for hourly).\n", + " group: Optional list of column names to group by. If provided, the\n", + " DataFrame will be extended separately for each group.\n", + " global_minmax: If True, use the global min/max of the 'time' column\n", + " across all groups. If False, use the min/max within each group.\n", + "\n", + " Returns:\n", + " pd.DataFrame: An extended DataFrame with all time steps within the\n", + " specified range and frequency. Missing values introduced by the\n", + " extension are filled with NaNs.\n", + " \"\"\"\n", + " if group is None:\n", + " # No grouping: compute min and max time directly\n", + " if isinstance(freq, int):\n", + " empty = pd.DataFrame({'time': list(range(x[time].min(), x[time].max(), freq))})\n", + " else:\n", + " empty = pd.DataFrame({'time': pd.date_range(x[time].min(), x[time].max(), freq=freq)})\n", + "\n", + " else:\n", + " # Grouping: compute min and max time for each group\n", + " if global_minmax:\n", + " # Use global min/max (across all groups)\n", + " _min = x[time].min()\n", + " _max = x[time].max()\n", + " _min_max = pd.DataFrame({'min': [_min], 'max': [_max]}) # Use a list for correct DataFrame creation\n", + " for c in group:\n", + " # Create dummy columns, will be overwritten\n", + " _min_max[c] = _min_max['min']\n", + " else:\n", + " # Use per-group min/max\n", + " _min = x.groupby(group)[time].min().reset_index().rename(columns={time: 'min'})\n", + " _max = x.groupby(group)[time].max().reset_index().rename(columns={time: 'max'})\n", + " _min_max = pd.merge(_min, _max)\n", + "\n", + " empty = []\n", + " for _, row in _min_max.iterrows():\n", + " if isinstance(freq, int):\n", + " tmp = pd.DataFrame({time: np.arange(row['min'], row['max'], freq)})\n", + " else:\n", + " tmp = pd.DataFrame({time: pd.date_range(row['min'], row['max'], freq=freq)})\n", + " for c in group:\n", + " tmp[c] = row[c] # Assign the group values\n", + " empty.append(tmp)\n", + "\n", + " empty = pd.concat(empty, ignore_index=True) # Concatenate all group DataFrames\n", + " return empty\n" + ] + }, + { + "cell_type": "markdown", + "id": "d9dc922a", + "metadata": {}, + "source": [ + "**`_coerce_to_list` Function (Utility)**\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "217f2b74", + "metadata": {}, + "outputs": [], + "source": [ + "def _coerce_to_list(obj):\n", + " \"\"\"Coerces input to a list.\n", + "\n", + " Ensures that the input is always a list. If the input is `None`,\n", + " it returns an empty list. If the input is a single value (not already\n", + " a list), it returns a list containing that single value.\n", + "\n", + " Args:\n", + " obj: The input object.\n", + "\n", + " Returns:\n", + " list: A list representing the input object.\n", + " \"\"\"\n", + " if obj is None:\n", + " return []\n", + " if isinstance(obj, list):\n", + " return obj\n", + " else:\n", + " return [obj]\n" + ] + }, + { + "cell_type": "markdown", + "id": "08c65daa", + "metadata": {}, + "source": [ + "**`PandasTSDataSet_MINIMAL` Class (Simplified Example)**\n", + "This class is a simplified version of the `PandasTSDataSet` to illustrate the basic principles without the complexity of handling all the different data types and metadata. It serves as a stepping stone to the full implementation.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "dd01d933", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "##TODO @Sandeep please try to replicate what is in PandasTSDataSet but for a smore compact example!" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "96d5d857", + "metadata": {}, + "outputs": [], + "source": [ + "class PandasTSDataSet_MINIMAL:\n", + " \"\"\"A simplified version of PandasTSDataSet for demonstration.\n", + "\n", + " This class demonstrates the core functionality of creating a PyTorch\n", + " Dataset from a pandas DataFrame. It handles:\n", + "\n", + " - Extending the DataFrame to ensure regular time intervals.\n", + " - Returning tensors for time, target, and numerical features.\n", + " - Identifying valid data points (non-NaN).\n", + "\n", + " It does *not* handle:\n", + " - Categorical features.\n", + " - Static features.\n", + " - Future known covariates.\n", + " - Grouping.\n", + " - Metadata handling (beyond a very basic level).\n", + "\n", + " This simplified version is intended for educational purposes, to\n", + " illustrate the key concepts before diving into the full complexity\n", + " of the `PandasTSDataSet` class.\n", + " \"\"\"\n", + "\n", + " def __init__(\n", + " self,\n", + " data: pd.DataFrame,\n", + " time: Optional[str] = None,\n", + " target: Optional[Union[str, List[str]]] = None,\n", + " num: Optional[List[Union[str, List[str]]]] = None,\n", + "\n", + " ):\n", + " \"\"\"Initializes the PandasTSDataSet_MINIMAL.\n", + "\n", + " Args:\n", + " data: The input pandas DataFrame.\n", + " time: The name of the time index column.\n", + " target: The name(s) of the target variable column(s).\n", + " num: The name(s) of numerical feature columns.\n", + " \"\"\"\n", + "\n", + " self.time = time\n", + " self.target = _coerce_to_list(target)\n", + " self.num = _coerce_to_list(num)\n", + " self.data = data.copy()\n", + "\n", + "\n", + " self.feature_cols = self.num\n", + " #use set ensuring unique columns\n", + " self.data = data[list(set(self.feature_cols + self.target + [self.time]))].copy()\n", + "\n", + "\n", + " self.label_encoders = {}\n", + "\n", + " ##ensure to have a coherent dataset\n", + " self.data.drop_duplicates(subset= [time],keep='first',inplace=True,ignore_index=True)\n", + "\n", + " ##compute minumum frequency\n", + " freq = self.data['time'].diff.min()\n", + "\n", + "\n", + " if isinstance(freq, timedelta):\n", + " freq = pd.to_timedelta(freq)\n", + " elif isinstance(freq, (int, float)):\n", + " freq = int(freq)\n", + " else:\n", + " raise TypeError(\"time must be integer or datetime\")\n", + "\n", + "\n", + " ##extend dataset\n", + " self.data = extend_time_df(self.data,self.time,freq,None).merge(self.data,how='left').reset_index()\n", + "\n", + "\n", + " self._groups = {\"_single_group\": self.data.index}\n", + " self._group_ids = [\"_single_group\"]\n", + "\n", + " ## we know on the fly which rows are valid and wich contains nans\n", + " self.data['valid'] = ~pd.isnull(self.data.max(axis=1))\n", + "\n", + " self._prepare_metadata()\n", + "\n", + " def _prepare_metadata(self):\n", + " \"\"\"Prepare metadata for the dataset.\"\"\"\n", + " self.metadata = {\n", + " \"cols\": {\n", + " \"y\": self.target,\n", + " \"x\": self.feature_cols,\n", + " \"st\": [], # No static features in this minimal version\n", + " },\n", + " \"col_type\": {},\n", + " \"col_known\": {},\n", + " \"cat_index\":[] # No categorical features in this minimal version\n", + " }\n", + " # Removed the loop that checks for categorical features, as they are not handled here.\n", + "\n", + " all_cols = self.target + self.feature_cols\n", + " for col in all_cols:\n", + " self.metadata[\"col_type\"][col] = \"F\" # All features are numerical (\"F\")\n", + "\n", + " self.metadata[\"col_known\"][col] = \"K\" if col in self.feature_cols else \"U\"\n", + " self.metadata['encoders'] = self.label_encoders # No encoders, as they're used for categoricals\n", + "\n", + "\n", + " def __len__(self) -> int:\n", + " \"\"\"Return number of time series in the dataset.\"\"\"\n", + " return len(self._group_ids)\n", + "\n", + " def __getitem__(self, index: int) -> Dict[str, torch.Tensor]:\n", + " \"\"\"Get time series data for given index.\n", + "\n", + " Args:\n", + " index: The index of the time series to retrieve (not used directly,\n", + " as we have a single time series in this simplified version).\n", + "\n", + " Returns:\n", + " A dictionary containing tensors for 't', 'y', 'x', and 'is_valid'.\n", + " \"\"\"\n", + " device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n", + "\n", + "\n", + " data = self.data.sort_values(by=self.time)\n", + "\n", + " result = {\n", + " \"t\": data[self.time].values, # Return as numpy array (no need for tensor here)\n", + " \"y\": torch.tensor(data[self.target].values,dtype=torch.float32,device=device),\n", + " \"x\": torch.tensor(data[self.feature_cols].values,dtype=torch.float32,device=device),\n", + " 'is_valid': torch.tensor(data.valid.values,dtype=torch.int,device=device), #indicator of if data is valid\n", + " }\n", + " return result\n", + "\n", + " def get_metadata(self) -> Dict:\n", + " \"\"\"Return metadata about the dataset.\n", + "\n", + " Returns:\n", + " A dictionary containing metadata about the columns.\n", + " \"\"\"\n", + " return self.metadata\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "b929cd13", + "metadata": {}, + "source": [ + "**`PandasTSDataSet` Class (D1 - pandas Implementation)**" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "761d3d2d", + "metadata": {}, + "outputs": [], + "source": [ + "class PandasTSDataSet(Dataset):\n", + " \"\"\"PyTorch Dataset for loading time series data from a pandas DataFrame.\n", + "\n", + " This class implements the D1 (raw data) layer of the `pytorch-forecasting`\n", + " v2 API. It handles loading time series data from a pandas DataFrame,\n", + " performing basic preprocessing (like ensuring regular time intervals),\n", + " and providing access to the data via the `__getitem__` method.\n", + "\n", + " Key Features:\n", + " - Handles multiple time series (identified by group IDs).\n", + " - Supports numerical and categorical features.\n", + " - Supports static features (features that don't change over time).\n", + " - Supports known and unknown covariates.\n", + " - Automatically extends the DataFrame to ensure regular time intervals.\n", + " - Returns data in a standardized dictionary format (tensors).\n", + " - Provides metadata about the columns (names, types, known/unknown status).\n", + " \"\"\"\n", + "\n", + " def __init__(\n", + " self,\n", + " data: pd.DataFrame,\n", + " time: Optional[str] = None,\n", + " target: Optional[Union[str, List[str]]] = None,\n", + " group: Optional[List[str]] = None,\n", + " num: Optional[List[Union[str, List[str]]]] = None,\n", + " cat: Optional[List[Union[str, List[str]]]] = None,\n", + " known: Optional[List[Union[str, List[str]]]] = None,\n", + " unknown: Optional[List[Union[str, List[str]]]] = None,\n", + " static: Optional[List[Union[str, List[str]]]] = None,\n", + " label_encoders: Optional[dict] = None\n", + " ):\n", + " \"\"\"Initializes the PandasTSDataSet.\n", + "\n", + " Args:\n", + " data: The input pandas DataFrame.\n", + " time: The name of the time index column.\n", + " target: The name(s) of the target variable column(s).\n", + " group: List of column names identifying a time series instance.\n", + " num: The name(s) of numerical feature columns.\n", + " cat: The name(s) of categorical feature columns.\n", + " known: List of variables known in the future.\n", + " unknown: List of variables unknown in the future\n", + " static: The name(s) of static feature columns (features that don't\n", + " change over time).\n", + " label_encoders: Optional dictionary of pre-fitted label encoders\n", + " for categorical features. If not provided, new encoders will\n", + " be fitted.\n", + " \"\"\"\n", + "\n", + " self.time = time\n", + " self.target = _coerce_to_list(target)\n", + " self.num = _coerce_to_list(num)\n", + " self.cat = _coerce_to_list(cat)\n", + " self.known = _coerce_to_list(known)\n", + " self.unknown = _coerce_to_list(unknown)\n", + " self.static = _coerce_to_list(static)\n", + " self.data = data.copy()\n", + "\n", + " self.group = _coerce_to_list(group)\n", + "\n", + "\n", + " self.feature_cols = self.num + self.cat\n", + " #use set ensuring unique columns\n", + " self.data = data[list(set(self.static + self.feature_cols + self.target + [self.time] +self.group))].copy()\n", + "\n", + " ##Encoders for categorical since we want to return tensors\n", + " if label_encoders is None:\n", + " label_encoders = {}\n", + " ##Encoders for categorical since we want to return tensors\n", + " for c in self.cat+self.group:\n", + " label_encoders[c] = OrdinalEncoder()\n", + " self.data[c] = label_encoders[c].fit_transform(self.data[c].values.reshape(-1,1)).flatten()\n", + " self.label_encoders = label_encoders\n", + " else:\n", + " for c in self.cat+self.group:\n", + " self.data[c] = label_encoders[c].transform(self.data[c].values.reshape(-1,1)).flatten()\n", + " self.label_encoders = label_encoders\n", + "\n", + "\n", + " ##ensure to have a coherent dataset\n", + " self.data.drop_duplicates(subset= self.group+[self.time],keep='first',inplace=True,ignore_index=True)\n", + "\n", + " ##compute minumum frequency\n", + " if self.group is None:\n", + " freq = self.data[self.time].diff.min()\n", + " else:\n", + " freq = self.data.groupby(self.group).time.diff().min()\n", + "\n", + " if isinstance(freq, timedelta):\n", + " freq = pd.to_timedelta(freq)\n", + " elif isinstance(freq, (int, float)):\n", + " freq = int(freq)\n", + " else:\n", + " raise TypeError(\"time must be integer or datetime\")\n", + "\n", + "\n", + " ##extend dataset\n", + " self.data = extend_time_df(self.data,self.time,freq,self.group).merge(self.data,how='left').reset_index()\n", + "\n", + "\n", + " ##now we are sure that data is in a coherent form!\n", + " self.lengths = {}\n", + " if self.group:\n", + " self._groups = self.data.groupby(self.group).groups\n", + " self._group_ids = list(self._groups.keys())\n", + " for k in self._groups:\n", + " self.lengths[k] = len(self._groups[k])\n", + " else:\n", + " self._groups = {0: self.data.index}\n", + " self._group_ids = [0]\n", + " self.lengths[0] = len( self.data.index)\n", + "\n", + "\n", + "\n", + " ## we know on the fly which rows are valid and wich contains nans\n", + " self.data['valid'] = ~pd.isnull(self.data.max(axis=1))\n", + "\n", + " self._prepare_metadata()\n", + "\n", + " def _prepare_metadata(self):\n", + " \"\"\"Prepare metadata for the dataset.\"\"\"\n", + " self.metadata = {\n", + " \"cols\": {\n", + " \"y\": self.target,\n", + " \"x\": self.feature_cols,\n", + " \"st\": self.static,\n", + " },\n", + " \"col_type\": {},\n", + " \"col_known\": {},\n", + " \"cat_index\":[]\n", + " }\n", + " for i, c in enumerate(self.feature_cols):\n", + " if c in self.cat:\n", + " self.metadata['cat_index'].append(i) # EDIT: was getting error with i, so updated enumerate in for loop\n", + " \n", + "\n", + " all_cols = self.target + self.feature_cols + self.static\n", + " for col in all_cols:\n", + " self.metadata[\"col_type\"][col] = \"C\" if col in self.cat else \"F\"\n", + " self.metadata[\"col_known\"][col] = \"K\" if col in self.known else \"U\"\n", + " self.metadata['encoders'] = self.label_encoders\n", + "\n", + " def __len__(self) -> int:\n", + " \"\"\"Return number of time series in the dataset.\"\"\"\n", + " return len(self._group_ids)\n", + "\n", + " def get_id_ts_by_idx(self,idx):\n", + " \"\"\"Gets the time series ID and starting index for a given index.\n", + "\n", + " Args:\n", + " idx: The overall index. This is *not* the group index, but\n", + " the index considering all time series concatenated together.\n", + "\n", + " Returns:\n", + " tuple: A tuple containing the group ID and the starting index\n", + " of that group in the concatenated data.\n", + " \"\"\"\n", + " tmp = np.cumsum(list(self.lengths.values()))\n", + " idx_group = min(np.where(tmp>idx)[0]) # EDIT: Find the group index, as we are getting group\n", + " return idx_group, 0 if idx_group==0 else tmp[idx_group-1] #EDIT: group id, index of the start of the series\n", + "\n", + " def get_total_len(self):\n", + " \"\"\"Returns total length of the dataset\"\"\"\n", + " l = 0\n", + " for k in self._groups:\n", + " l+= self.lengths[k]\n", + " return l\n", + "\n", + " def __getitem__(self, index: int) -> Dict[str, torch.Tensor]:\n", + " \"\"\"Get time series data for given index.\n", + "\n", + " Args:\n", + " index: The index of the time series to retrieve.\n", + "\n", + " Returns:\n", + " A dictionary containing tensors for 't', 'y', 'x', 'group', 'st',\n", + " and 'is_valid'.\n", + " \"\"\"\n", + " group_id = self._group_ids[index]\n", + " device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n", + "\n", + " if self.group:\n", + " mask = self._groups[group_id]\n", + " data = self.data.loc[mask].sort_values(by=self.time)\n", + " else:\n", + " data = self.data.sort_values(by=self.time)\n", + "\n", + " result = {\n", + " \"t\": data[self.time].values, # Return as numpy array\n", + " \"y\": torch.tensor(data[self.target].values, dtype=torch.float32, device=device),\n", + " \"x\": torch.tensor(data[self.feature_cols].values, dtype=torch.float32, device=device),\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + " \"st\": torch.tensor(data[self.static].iloc[0].values, dtype=torch.float32, device=device) if self.static else torch.tensor([], dtype=torch.float32, device=device), # return empty if no statics\n", + " 'is_valid': torch.tensor(data.valid.values,dtype=torch.int,device=device), #indicator of if data is valid\n", + " }\n", + "\n", + " return result\n", + "\n", + " def get_metadata(self) -> Dict:\n", + " \"\"\"Return metadata about the dataset.\n", + "\n", + " Returns:\n", + " -------\n", + " Dict\n", + " Dictionary containing:\n", + " - cols: column names for y, x, and static features\n", + " - col_type: mapping of columns to their types (F/C)\n", + " - col_known: mapping of columns to their future known status (K/U)\n", + " - encoders: Dict of label encoders used\n", + " \"\"\"\n", + " return self.metadata" + ] + }, + { + "cell_type": "markdown", + "id": "5030144e", + "metadata": {}, + "source": [ + "**Configuration dictionary for the data module. This holds hyperparameters related to data splitting, preprocessing, and windowing.**" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "290ab40d-f440-4f7f-9ae9-8855a04f1ec6", + "metadata": {}, + "outputs": [], + "source": [ + "data_module_metadata = dict(\n", + " perc_train= 0.7, # Percentage of data for training\n", + " perc_valid= 0.1, # Percentage of data for validation\n", + " range_train= None, # Optional: Specific time range for training (not used here)\n", + " range_validation= None, # Optional: Specific time range for validation (not used here)\n", + " range_test= None, # Optional: Specific time range for testing (not used here)\n", + " shift= 0, # Optional: Shift the time series (not used here)\n", + " starting_point= None, # Optional: Starting point for sampling (not used here)\n", + " skip_step= 1, # Optional: Step size for skipping data points (not used here)\n", + " past_steps=16, # Number of past time steps to use as input (encoder length)\n", + " future_steps= 16, # Number of future time steps to predict (decoder length)\n", + " precompute=True, # Whether to precompute the train/val/test splits.\n", + " scaler= 'sklearn.preprocessing.StandardScaler()' # String representation of the scaler (not used directly here)\n", + ")\n" + ] + }, + { + "cell_type": "markdown", + "id": "c611fbc0", + "metadata": {}, + "source": [ + "**Instantiate the PandasTSDataSet (D1 layer).**\n", + "This creates the dataset object that will provide the raw time series data.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "e4ca4055-7f36-446c-9b21-6c0456b152cf", + "metadata": {}, + "outputs": [], + "source": [ + "ds = PandasTSDataSet(dataset,\n", + " 'time', # Name of the time index column\n", + " target, # List of target variable names\n", + " 'group', # Name of the column that identifies groups/time series\n", + " list(set(num_var_past+num_var_fut)), # List of numerical feature columns\n", + " list(set(cat_var_fut+cat_var_past)), # List of categorical feature columns\n", + " list(set(cat_var_fut+num_var_fut)), # List of future known covariates\n", + " list(set(cat_var_past+num_var_past)), # List of past known covariates\n", + " )\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "2af9edfd", + "metadata": {}, + "source": [ + "Import necessary PyTorch modules" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "519ebe66-3d64-41df-a191-81c0ba518e79", + "metadata": {}, + "outputs": [], + "source": [ + "from torch.utils.data import Dataset, DataLoader\n", + "from torch.utils.data.dataloader import default_collate\n", + "import torch" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "c0375ff0", + "metadata": {}, + "outputs": [], + "source": [ + "def my_collate(batch):\n", + " \"\"\"Custom collate function to handle potential None values in batches.\n", + "\n", + " Filters out `None` values from the batch before collating. This is\n", + " necessary because the `MyDataset.__getitem__` method can return `None`\n", + " if a valid window cannot be created for a given index.\n", + "\n", + " Args:\n", + " batch: A list of samples (dictionaries or tensors) from the dataset.\n", + "\n", + " Returns:\n", + " The collated batch, with `None` values removed.\n", + " \"\"\"\n", + " batch = list(filter(lambda x : x is not None, batch))\n", + " #print(len(batch)) # Debugging print statement (removed in final version)\n", + " return default_collate(batch)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "6e31a24f", + "metadata": {}, + "outputs": [], + "source": [ + "## TODO: @SANDEEP rework this using some cleaner code you can find here\n", + "#https://colab.research.google.com/drive/1FvLlmEOgm3D3JgNFVeAtwPk4cXagJ0CY?usp=sharing#scrollTo=jFuSwWrhTg6y" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "12bc5f1c-2de1-4837-9d2a-e764fa395bba", + "metadata": {}, + "outputs": [], + "source": [ + "class MyDataset(Dataset):\n", + " \"\"\"Custom PyTorch Dataset for time series data.\n", + "\n", + " This class adapts the D1 dataset (`PandasTSDataSet`) for use with a\n", + " PyTorch `DataLoader`. It handles the creation of input/output windows\n", + " (sequences) for training and validation. It also handles the case where\n", + " a valid window cannot be created (e.g., due to insufficient past data)\n", + " by returning `None`.\n", + "\n", + " Args:\n", + " data: The D1 dataset instance (e.g., `PandasTSDataSet`).\n", + " metadata: A dictionary containing metadata about the dataset and\n", + " data processing parameters (e.g., `past_steps`, `future_steps`).\n", + " valid_index: A dictionary where keys are group IDs and values are\n", + " lists of valid indices for that group. This defines the\n", + " valid ranges within each time series for creating windows.\n", + " \"\"\"\n", + "\n", + " def __init__(self, data, metadata, valid_index) -> torch.utils.data.Dataset:\n", + " \"\"\"Initializes the MyDataset instance.\"\"\"\n", + "\n", + " self.metadata = metadata\n", + " self.metadata_dataset = data.metadata # Metadata from the D1 dataset\n", + " self.data = data # The D1 dataset instance\n", + " self.valid_index = valid_index # Dictionary of valid indices for each group\n", + " sum = 0\n", + " self.lengths = {}\n", + " #fix this, probably we need to add something (check validity, check past and future)\n", + " for k in self.valid_index:\n", + " # Calculate the total number of valid windows that can be created\n", + " # from each group. Subtract `future_steps` because we need enough\n", + " # data points for both the input window and the target window.\n", + " sum+=(len(self.valid_index[k])-self.metadata['future_steps'])\n", + " self.lengths[k] = len(self.valid_index[k])\n", + " self.length = sum # Sum of lengths accross differnet time series.\n", + "\n", + " def __len__(self):\n", + " \"\"\"Returns the total number of valid windows in the dataset.\"\"\"\n", + " return self.length\n", + "\n", + " def get_id_ts_by_idx(self,idx):\n", + " \"\"\"Gets time series id given global index\"\"\"\n", + " tmp = np.cumsum(list(self.lengths.values()))\n", + " idx = min(np.where(tmp>idx)[0])\n", + " return idx, 0 if idx==0 else tmp[idx-1]\n", + "\n", + " def __getitem__(self, idxs):\n", + " \"\"\"Retrieves a sample (input/output window) from the dataset.\n", + "\n", + " Args:\n", + " idxs: The *global* index of the window to retrieve. This index\n", + " is across all time series in the dataset.\n", + "\n", + " Returns:\n", + " A dictionary containing the input and output tensors for the window,\n", + " or `None` if a valid window cannot be created at the given index.\n", + " \"\"\"\n", + "\n", + " ##crucial point there: correctly identifying which are the indexes that we need!\n", + " sample = {}\n", + " IDX,difference = self.get_id_ts_by_idx(idxs) #find group and local index\n", + "\n", + " idxs -= difference # Adjust index to be relative to the start of the time series\n", + " idxs = self.valid_index[IDX][idxs] # Get the actual index in the data\n", + " tmp = self.data.__getitem__(IDX) ##use the getitem of the data!\n", + "\n", + " # Check if enough data is available for both input and output windows\n", + " if idxs+self.metadata['future_steps']>self.data.lengths[IDX]:\n", + " return None # Not enough data, return None\n", + "\n", + " #Check for the validity of the time series, by checking nans.\n", + " if tmp['is_valid'][idxs-self.metadata['past_steps']:idxs+self.metadata['future_steps']].sum() != self.metadata['future_steps']+self.metadata['past_steps']:\n", + " return None\n", + " \n", + " # Construct sample\n", + " for k in tmp.keys():\n", + " # Separate based on past/future\n", + " # We create past and future samples only for the ones we sliced using idxs\n", + " if '_past' in k:\n", + " sample[k] = tmp[k][idxs-self.metadata['past_steps']:idxs] #past steps based on the past_step provided in metadata.\n", + " elif '_fut' in k or k=='y':\n", + " sample[k] = tmp[k][idxs:idxs+self.metadata['future_steps']]# future steps based on the future_step\n", + " else:\n", + " pass\n", + " return sample\n" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "ac4a66d5-753b-4030-a88d-e5b8efd73d95", + "metadata": {}, + "outputs": [], + "source": [ + "def compute_ranges(d1_dataset, metadata):\n", + " \"\"\"Computes valid index ranges for training, validation, and testing.\n", + "\n", + " This function calculates the start and end indices for the training,\n", + " validation, and testing sets within each time series group, based on the\n", + " provided percentages in `metadata`.\n", + "\n", + " Args:\n", + " d1_dataset: The D1 dataset instance (e.g., `PandasTSDataSet`).\n", + " metadata: A dictionary containing metadata, including `perc_train`\n", + " and `perc_valid` (percentages for training and validation splits).\n", + "\n", + " Returns:\n", + " tuple: A tuple containing three dictionaries: `train_ranges`,\n", + " `valid_ranges`, and `test_ranges`. Each dictionary has group IDs\n", + " as keys and lists of valid indices as values.\n", + " \"\"\"\n", + " ##suppose for now we use only percentage!\n", + " train_ranges = {}\n", + " valid_ranges = {}\n", + " test_ranges = {}\n", + " for k in d1_dataset.lengths: # Iterate over each time series group\n", + " ls = d1_dataset.lengths[k] # Get the length of the current time series\n", + " train_ranges[k] = list(range(0,int(metadata['perc_train']*ls))) # Training range\n", + " valid_ranges[k] = list(range(int(metadata['perc_train']*ls), int((metadata['perc_valid'] + metadata['perc_train'])*ls))) # Validation range\n", + " test_ranges[k] = list(range(int((metadata['perc_valid'] + metadata['perc_train'])*ls),ls)) # Testing range\n", + "\n", + " ## in case of datetime ranges we need to use np.where and create a mask\n", + " return train_ranges,valid_ranges,test_ranges\n", + "\n", + "# Calculate the training, validation, and testing ranges\n", + "train_ranges, valid_ranges, test_ranges = compute_ranges(ds, data_module_metadata)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "0185143c", + "metadata": {}, + "source": [ + "**Create a DataLoader for the training set.**" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "7e43bfd7-12b3-48c2-8b33-d5ca6681959c", + "metadata": {}, + "outputs": [], + "source": [ + "dl = DataLoader(\n", + " MyDataset(ds, data_module_metadata, train_ranges), # Create a MyDataset instance\n", + " batch_size=32, # Set the batch size\n", + " collate_fn=my_collate # Use the custom collate function\n", + " )\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "dde2ba1f-f6f8-483c-a404-5917b84ce37a", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([16, 16, 1])\n" + ] + } + ], + "source": [ + "# Example of iterating through the DataLoader (for debugging/demonstration).\n", + "for x in dl:\n", + " print(x['y'].shape) # Print the shape of the target variable tensor.\n", + " break #only check first batch\n" + ] + }, + { + "cell_type": "markdown", + "id": "a7ddeb11", + "metadata": {}, + "source": [ + "**The following function and class show how one might create a `LightningDataModule` to manage the data loading process, including train/validation/test splits. This builds on top of previous components.**" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "86c9c7fc", + "metadata": {}, + "outputs": [], + "source": [ + "class DecoderEncoderDataModule(L.LightningDataModule):\n", + " \"\"\"LightningDataModule for encoder-decoder models.\n", + "\n", + " This class manages the data loading and preprocessing for training,\n", + " validation, testing, and prediction. It handles splitting the data,\n", + " creating `Dataset` instances, and creating `DataLoader` instances. It\n", + " supports both precomputed splits and on-the-fly splitting.\n", + "\n", + " Args:\n", + " d1_dataset: The D1 dataset instance (e.g., `PandasTSDataSet`).\n", + " batch_size: The batch size.\n", + " num_workers: The number of worker processes for data loading.\n", + " metadata: A dictionary containing metadata and hyperparameters.\n", + " \"\"\"\n", + " def __init__(self, d1_dataset, batch_size=32, num_workers=4, metadata=None):\n", + " super().__init__()\n", + " # initialize other params\n", + " self.d1_dataset = d1_dataset\n", + " self.batch_size = batch_size\n", + " self.metadata = metadata\n", + " self.num_workers = num_workers\n", + "\n", + "\n", + " def prepare_data(self):\n", + " \"\"\"Prepares the data for training/validation/testing.\n", + "\n", + " This method handles splitting the data into training, validation, and\n", + " testing sets. It supports two modes:\n", + "\n", + " - `precompute=True`: Uses a `split_data` function (not defined here,\n", + " but assumed to exist) to precompute the splits. This is useful for\n", + " deterministic splits or when the splitting process is expensive.\n", + " - `precompute=False`: Calculates the splits on-the-fly using\n", + " `compute_ranges`. This is more flexible and allows for different\n", + " splitting strategies.\n", + " \"\"\"\n", + " if self.metadata['precompute']:\n", + " self.precompute = True\n", + " ## @SANDEEP this is what is returned by DSIPTS split_for_train!!\n", + " ## the only difference is that we need to use the __getitem__ of the d1 layer\n", + "\n", + " # The split_data function is assumed to be defined elsewhere and\n", + " # would handle the splitting logic according to however the\n", + " # project defines \"splitting\" (could be by time, by groups, etc.)\n", + " self.train_dataset, self.validation_dataset, self.test_dataset = split_data(self.d1_dataset,self.metadata) # Placeholder\n", + "\n", + "\n", + "\n", + " else:\n", + " self.precompute = False\n", + " ##in this case we need to pass only the data that are referring to the trainin period\n", + " ## but data can be chunked, we need to create a function that transform a D1 object into a D1 object or, even better\n", + " ## something that can be used by the dataset for filtering only valid samples!\n", + " ##for example\n", + " self.train_ranges, self.validation_ranges, self.test_ranges = compute_ranges(self.d1_dataset,self.metadata)\n", + " self.predict_data = None ##??? need to study this\n", + " ##TODO normalization???? maybe we can precompute here some statistics and pass them to the dataset!\n", + "\n", + " def setup(self, stage=None):\n", + " \"\"\"Sets up the datasets for each stage (fit, test, predict).\n", + "\n", + " This method creates the appropriate `Dataset` instances for each stage,\n", + " using either the precomputed splits or the on-the-fly calculated ranges.\n", + "\n", + " Args:\n", + " stage: The stage ('fit', 'test', or 'predict').\n", + " \"\"\"\n", + " # Get metadata from D1 layer during setup\n", + " #self.metadata = self.d1_dataset.get_metadata() ##NO SEE COMMENT BEFORE! We get metadata in __init__\n", + " ##create dataset here\n", + " if stage == 'fit':\n", + " if self.precompute:\n", + " # Use precomputed datasets (implementation of split_data needed)\n", + " pass # Use self.train_dataset and self.validation_dataset as defined in prepare_data\n", + " else:\n", + " # Create datasets using computed ranges\n", + " self.train_dataset = MyDataset(self.d1_dataset, self.metadata, self.train_ranges) # Pass train_ranges\n", + " self.validation_dataset = MyDataset(self.d1_dataset, self.metadata, self.validation_ranges) # Pass valid_ranges\n", + "\n", + " if stage == 'test':\n", + " if self.precompute:\n", + " pass\n", + " # Use precomputed test dataset\n", + " else:\n", + " self.test_dataset = MyDataset(self.d1_dataset, self.metadata, self.test_ranges) # Pass test_ranges\n", + "\n", + " if stage == 'predict':\n", + " if self.precompute:\n", + " pass\n", + " # Use precomputed prediction dataset (if applicable)\n", + " else:\n", + " self.predict_datasset = MyDataset(self.d1_dataset, self.metadata, self.predict_data) # Pass predict_data. Implementation details TBD.\n", + "\n", + " def train_dataloader(self):\n", + " \"\"\"Returns the training DataLoader.\"\"\"\n", + " return DataLoader(self.train_dataset, batch_size=self.batch_size, shuffle=True, num_workers=self.num_workers, collate_fn=my_collate)\n", + "\n", + " def val_dataloader(self):\n", + " \"\"\"Returns the validation DataLoader.\"\"\"\n", + " return DataLoader(self.validation_dataset, batch_size=self.batch_size, shuffle=False, num_workers=self.num_workers, collate_fn=my_collate)\n", + "\n", + " def test_dataloader(self):\n", + " \"\"\"Returns the test DataLoader.\"\"\"\n", + " return DataLoader(self.test_dataset, batch_size=self.batch_size, shuffle=False, num_workers=self.num_workers, collate_fn=my_collate)\n", + "\n", + " def predict_dataloader(self):\n", + " \"\"\"Returns the prediction DataLoader.\"\"\"\n", + " return DataLoader(self.predict_datasset, batch_size=self.batch_size, shuffle=False, num_workers=self.num_workers, collate_fn=my_collate)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "c83526e8", + "metadata": {}, + "source": [ + "**`DecoderEncoderModel` Class (M Layer - Example)**\n", + "This is a placeholder for the Model Layer." + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "9d9a07bd-35cd-4cbd-8843-081b7d3d8580", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "# Layer M - Example (Placeholder)\n", + "class DecoderEncoderModel(L.LightningModule):\n", + " \"\"\"Placeholder for an encoder-decoder model (M layer).\n", + "\n", + " This is a minimal example of how a model might be structured using\n", + " PyTorch Lightning. It shows how to access the metadata from the\n", + " `LightningDataModule`. A real implementation would include the actual\n", + " network architecture and training logic.\n", + " \"\"\"\n", + " def __init__(self):\n", + " super().__init__()\n", + " self.metadata = None\n", + "\n", + " def setup(self, stage=None):\n", + " \"\"\"Gets metadata from the datamodule during setup.\"\"\"\n", + " # Get metadata from datamodule during setup\n", + " self.metadata = self.trainer.datamodule.metadata\n", + "\n", + " # Initialize layer T model using metadata (placeholder)\n", + "\n", + " def forward(self, x):\n", + " \"\"\"Forward pass (placeholder).\"\"\"\n", + " # forward logic (implementation would go here)\n", + " pass\n", + "\n", + " def training_step(self, batch, batch_idx):\n", + " \"\"\"Training step (placeholder).\"\"\"\n", + " pass # Replace with actual training logic\n", + "\n", + " def validation_step(self, batch, batch_idx):\n", + " \"\"\"Validation step (placeholder).\"\"\"\n", + " pass # Replace with actual validation logic\n" + ] + }, + { + "cell_type": "markdown", + "id": "80542685", + "metadata": {}, + "source": [ + "### Layer M" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "694a020f-7f8d-4503-a023-bfdcda90667b", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "\n", + "class DecoderEncoderModel(L.LightningModule):\n", + " def __init__(self):\n", + " super().__init__()\n", + " self.metadata = None\n", + " \n", + " def setup(self, stage=None):\n", + " # Get metadata from datamodule during setup\n", + " self.metadata = self.trainer.datamodule.metadata\n", + " \n", + " # Initialize layer T model using metadata\n", + " \n", + " def forward(self, x):\n", + " # forward logic\n", + " pass" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ec43226c-f99f-4a77-8793-c3e9db5c4536", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/TEST_v2_temp.ipynb b/notebooks/TEST_v2_temp.ipynb new file mode 100644 index 00000000..f04eab50 --- /dev/null +++ b/notebooks/TEST_v2_temp.ipynb @@ -0,0 +1,1276 @@ +{ + "cells": [ + { + "cell_type": "raw", + "id": "55c75749-ec70-4365-ac8a-f96552da4528", + "metadata": {}, + "source": [ + "from dsipts import Categorical,TimeSeries, RNN\n", + "settimana = Categorical('settimanale',1,[1,1,1,1,1,1,1],7,'multiplicative',[0.9,0.8,0.7,0.6,0.5,0.99,0.99])\n", + "\n", + "##montly, additive (here there are only 5 month)\n", + "mese = Categorical('mensile',1,[31,28,20,10,33],5,'additive',[10,20,-10,20,0])\n", + "\n", + "##spot categorical variables: in this case it occurs every 100 days and it lasts 7 days adding 10 to the original timeseries\n", + "spot = Categorical('spot',100,[7],1,'additive',[10])\n", + "\n", + "##initizate a timeseries object\n", + "ts = TimeSeries('prova')\n", + "ts.generate_signal(noise_mean=1,categorical_variables=[settimana,mese,spot],length=5000,type=0)\n", + "dataset = ts.dataset\n", + "dataset['x_num'] = 0.0\n", + "dataset.loc[:dataset.shape[0]-10,'x_num']= dataset['x_num'].values[9:]\n", + "cat_var_past = ts.cat_var\n", + "cat_var_fut = ts.cat_fut\n", + "num_var_past = ts.num_var+['x_num']\n", + "import pickle \n", + "with open('example.pkl', 'wb') as f:\n", + " pickle.dump([dataset,cat_var_past,cat_var_fut,num_var_past],f)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "459c491c-98ab-4513-b5eb-9f5c604b9df4", + "metadata": {}, + "outputs": [], + "source": [ + "#dataset.to_csv('dataset.csv',index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "0b53411e-8c81-4dc9-8e2e-c83c3a568be1", + "metadata": {}, + "outputs": [ + { + 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Union, List, Optional, Dict\n", + "from sklearn.preprocessing import LabelEncoder # Used for categorical encoding\n", + "from datetime import datetime, timedelta # For time-related operations\n", + "import lightning as L # PyTorch Lightning\n", + "import numpy as np\n", + "from sklearn.preprocessing import OrdinalEncoder # Used for categorical encoding\n", + "import torch\n", + "from torch.utils.data import Dataset, DataLoader\n", + "from torch.utils.data.dataloader import default_collate\n" + ] + }, + { + "cell_type": "markdown", + "id": "09307105", + "metadata": {}, + "source": [ + "**Data Loading and Preparation (Example Data)**\n", + "\n", + "This section loads example time series data and prepares it for use in the `DataSet` classes. It demonstrates the structure of the input data.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "beea9295-9164-4bae-b2e1-8c5cc6c585c4", + "metadata": {}, + "outputs": [], + "source": [ + "with open('example.pkl', 'rb') as f:\n", + " dataset, cat_var_past, cat_var_fut, num_var_past = pickle.load(f)\n", + "num_var_fut = [] # No numerical future variables in this example\n", + "group = None # No grouping in this first example. Later examples *do* use grouping.\n", + "time_var = 'time'\n", + "target = ['signal']" + ] + }, + { + "cell_type": "markdown", + "id": "9c4fb3c8", + "metadata": {}, + "source": [ + "**Example with Grouping**\n", + "\n", + "The following cell creates a second copy of the dataset and assigns it a different group ID. This is then concatenated with the original `dataset` to showcase how grouping works.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "db453ed2-df16-44bf-bc86-184fd4010e7a", + "metadata": {}, + "outputs": [], + "source": [ + "dataset2 = dataset.copy()\n", + "dataset2['group'] = 2 # Assign a different group ID\n", + "dataset['group'] = 1 # Assign a group ID to the original\n", + "dataset = pd.concat([dataset, dataset2]) # Concatenate to have multiple groups" + ] + }, + { + "cell_type": "markdown", + "id": "8efaed82", + "metadata": {}, + "source": [ + "**`extend_time_df` Function**\n", + "This utility function extends a DataFrame to include all time steps within a specified range and frequency. This is essential for ensuring that all time series have the same length and that there are no missing time steps.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "3810de27-fc3b-44a3-b802-f88b9aa32c14", + "metadata": {}, + "outputs": [], + "source": [ + "def extend_time_df(x:pd.DataFrame,time:str,freq:Union[str,int],group:Union[List[str],None]=None,global_minmax:bool=False)-> pd.DataFrame:\n", + " \"\"\"Extends a DataFrame to include all time steps within a range.\n", + "\n", + " Ensures that the DataFrame `x` has rows for all time steps within the\n", + " range defined by the minimum and maximum values of the `time` column,\n", + " with a specified frequency `freq`. Handles grouping if `group` is provided.\n", + "\n", + " Args:\n", + " x: The input DataFrame. Must contain a 'time' column and any\n", + " columns specified in 'group'.\n", + " time: The name of the column containing the time index (e.g., 'time').\n", + " freq: The frequency to use for extending the DataFrame. This can be\n", + " an integer (for integer time steps) or a string representing a\n", + " pandas frequency string (e.g., 'D' for daily, 'H' for hourly).\n", + " group: Optional list of column names to group by. If provided, the\n", + " DataFrame will be extended separately for each group.\n", + " global_minmax: If True, use the global min/max of the 'time' column\n", + " across all groups. If False, use the min/max within each group.\n", + "\n", + " Returns:\n", + " pd.DataFrame: An extended DataFrame with all time steps within the\n", + " specified range and frequency. Missing values introduced by the\n", + " extension are filled with NaNs.\n", + " \"\"\"\n", + " if group is None:\n", + " # No grouping: compute min and max time directly\n", + " if isinstance(freq, int):\n", + " empty = pd.DataFrame({'time': list(range(x[time].min(), x[time].max(), freq))})\n", + " else:\n", + " empty = pd.DataFrame({'time': pd.date_range(x[time].min(), x[time].max(), freq=freq)})\n", + "\n", + " else:\n", + " # Grouping: compute min and max time for each group\n", + " if global_minmax:\n", + " # Use global min/max (across all groups)\n", + " _min = x[time].min()\n", + " _max = x[time].max()\n", + " _min_max = pd.DataFrame({'min': [_min], 'max': [_max]}) # Use a list for correct DataFrame creation\n", + " for c in group:\n", + " # Create dummy columns, will be overwritten\n", + " _min_max[c] = _min_max['min']\n", + " else:\n", + " # Use per-group min/max\n", + " _min = x.groupby(group)[time].min().reset_index().rename(columns={time: 'min'})\n", + " _max = x.groupby(group)[time].max().reset_index().rename(columns={time: 'max'})\n", + " _min_max = pd.merge(_min, _max)\n", + "\n", + " empty = []\n", + " for _, row in _min_max.iterrows():\n", + " if isinstance(freq, int):\n", + " tmp = pd.DataFrame({time: np.arange(row['min'], row['max'], freq)})\n", + " else:\n", + " tmp = pd.DataFrame({time: pd.date_range(row['min'], row['max'], freq=freq)})\n", + " for c in group:\n", + " tmp[c] = row[c] # Assign the group values\n", + " empty.append(tmp)\n", + "\n", + " empty = pd.concat(empty, ignore_index=True) # Concatenate all group DataFrames\n", + " return empty" + ] + }, + { + "cell_type": "markdown", + "id": "d9dc922a", + "metadata": {}, + "source": [ + "**`_coerce_to_list` Function (Utility)**\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "217f2b74", + "metadata": {}, + "outputs": [], + "source": [ + "def _coerce_to_list(obj):\n", + " \"\"\"Coerces input to a list.\n", + "\n", + " Ensures that the input is always a list. If the input is `None`,\n", + " it returns an empty list. If the input is a single value (not already\n", + " a list), it returns a list containing that single value.\n", + "\n", + " Args:\n", + " obj: The input object.\n", + "\n", + " Returns:\n", + " list: A list representing the input object.\n", + " \"\"\"\n", + " if obj is None:\n", + " return []\n", + " if isinstance(obj, list):\n", + " return obj\n", + " else:\n", + " return [obj]" + ] + }, + { + "cell_type": "markdown", + "id": "08c65daa", + "metadata": {}, + "source": [ + "**`PandasTSDataSet_MINIMAL` Class (Simplified Example)**\n", + "This class is a simplified version of the `PandasTSDataSet` to illustrate the basic principles without the complexity of handling all the different data types and metadata. It serves as a stepping stone to the full implementation.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "dd01d933", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "##TODO @Sandeep please try to replicate what is in PandasTSDataSet but for a smore compact example!" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "96d5d857", + "metadata": {}, + "outputs": [], + "source": [ + "class PandasTSDataSet_MINIMAL:\n", + " \"\"\"A simplified version of PandasTSDataSet for demonstration.\n", + "\n", + " This class demonstrates the core functionality of creating a PyTorch\n", + " Dataset from a pandas DataFrame. It handles:\n", + "\n", + " - Extending the DataFrame to ensure regular time intervals.\n", + " - Returning tensors for time, target, and numerical features.\n", + " - Identifying valid data points (non-NaN).\n", + "\n", + " It does *not* handle:\n", + " - Categorical features.\n", + " - Static features.\n", + " - Future known covariates.\n", + " - Grouping.\n", + " - Metadata handling (beyond a very basic level).\n", + "\n", + " This simplified version is intended for educational purposes, to\n", + " illustrate the key concepts before diving into the full complexity\n", + " of the `PandasTSDataSet` class.\n", + " \"\"\"\n", + "\n", + " def __init__(\n", + " self,\n", + " data: pd.DataFrame,\n", + " time: Optional[str] = None,\n", + " target: Optional[Union[str, List[str]]] = None,\n", + " num: Optional[List[Union[str, List[str]]]] = None,\n", + "\n", + " ):\n", + " \"\"\"Initializes the PandasTSDataSet_MINIMAL.\n", + "\n", + " Args:\n", + " data: The input pandas DataFrame.\n", + " time: The name of the time index column.\n", + " target: The name(s) of the target variable column(s).\n", + " num: The name(s) of numerical feature columns.\n", + " \"\"\"\n", + "\n", + " self.time = time\n", + " self.target = _coerce_to_list(target)\n", + " self.num = _coerce_to_list(num)\n", + " self.data = data.copy()\n", + "\n", + "\n", + " self.feature_cols = self.num\n", + " #use set ensuring unique columns\n", + " self.data = data[list(set(self.feature_cols + self.target + [self.time]))].copy()\n", + "\n", + "\n", + " self.label_encoders = {}\n", + "\n", + " ##ensure to have a coherent dataset\n", + " self.data.drop_duplicates(subset= [time],keep='first',inplace=True,ignore_index=True)\n", + "\n", + " ##compute minumum frequency\n", + " freq = self.data['time'].diff.min()\n", + "\n", + "\n", + " if isinstance(freq, timedelta):\n", + " freq = pd.to_timedelta(freq)\n", + " elif isinstance(freq, (int, float)):\n", + " freq = int(freq)\n", + " else:\n", + " raise TypeError(\"time must be integer or datetime\")\n", + "\n", + "\n", + " ##extend dataset\n", + " self.data = extend_time_df(self.data,self.time,freq,None).merge(self.data,how='left').reset_index()\n", + "\n", + "\n", + " self._groups = {\"_single_group\": self.data.index}\n", + " self._group_ids = [\"_single_group\"]\n", + "\n", + " ## we know on the fly which rows are valid and wich contains nans\n", + " self.data['valid'] = ~pd.isnull(self.data.max(axis=1))\n", + "\n", + " self._prepare_metadata()\n", + "\n", + " def _prepare_metadata(self):\n", + " \"\"\"Prepare metadata for the dataset.\"\"\"\n", + " self.metadata = {\n", + " \"cols\": {\n", + " \"y\": self.target,\n", + " \"x\": self.feature_cols,\n", + " \"st\": [], # No static features in this minimal version\n", + " },\n", + " \"col_type\": {},\n", + " \"col_known\": {},\n", + " \"cat_index\":[] # No categorical features in this minimal version\n", + " }\n", + " # Removed the loop that checks for categorical features, as they are not handled here.\n", + "\n", + " all_cols = self.target + self.feature_cols\n", + " for col in all_cols:\n", + " self.metadata[\"col_type\"][col] = \"F\" # All features are numerical (\"F\")\n", + "\n", + " self.metadata[\"col_known\"][col] = \"K\" if col in self.feature_cols else \"U\"\n", + " self.metadata['encoders'] = self.label_encoders # No encoders, as they're used for categoricals\n", + "\n", + "\n", + " def __len__(self) -> int:\n", + " \"\"\"Return number of time series in the dataset.\"\"\"\n", + " return len(self._group_ids)\n", + "\n", + " def __getitem__(self, index: int) -> Dict[str, torch.Tensor]:\n", + " \"\"\"Get time series data for given index.\n", + "\n", + " Args:\n", + " index: The index of the time series to retrieve (not used directly,\n", + " as we have a single time series in this simplified version).\n", + "\n", + " Returns:\n", + " A dictionary containing tensors for 't', 'y', 'x', and 'is_valid'.\n", + " \"\"\"\n", + " device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n", + "\n", + "\n", + " data = self.data.sort_values(by=self.time)\n", + "\n", + " result = {\n", + " \"t\": data[self.time].values, # Return as numpy array (no need for tensor here)\n", + " \"y\": torch.tensor(data[self.target].values,dtype=torch.float32,device=device),\n", + " \"x\": torch.tensor(data[self.feature_cols].values,dtype=torch.float32,device=device),\n", + " \"weights\": \n", + " }\n", + " return result\n", + "\n", + " def get_metadata(self) -> Dict:\n", + " \"\"\"Return metadata about the dataset.\n", + "\n", + " Returns:\n", + " A dictionary containing metadata about the columns.\n", + " \"\"\"\n", + " return self.metadata\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "b929cd13", + "metadata": {}, + "source": [ + "**`PandasTSDataSet` Class (D1 - pandas Implementation)**" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "761d3d2d", + "metadata": {}, + "outputs": [], + "source": [ + "class PandasTSDataSet(Dataset):\n", + " \"\"\"PyTorch Dataset for loading time series data from a pandas DataFrame.\n", + "\n", + " This class implements the D1 (raw data) layer of the `pytorch-forecasting`\n", + " v2 API. It handles loading time series data from a pandas DataFrame,\n", + " performing basic preprocessing (like ensuring regular time intervals),\n", + " and providing access to the data via the `__getitem__` method.\n", + "\n", + " Key Features:\n", + " - Handles multiple time series (identified by group IDs).\n", + " - Supports numerical and categorical features.\n", + " - Supports static features (features that don't change over time).\n", + " - Supports known and unknown covariates.\n", + " - Automatically extends the DataFrame to ensure regular time intervals.\n", + " - Returns data in a standardized dictionary format (tensors).\n", + " - Provides metadata about the columns (names, types, known/unknown status).\n", + " \"\"\"\n", + "\n", + " def __init__(\n", + " self,\n", + " data: pd.DataFrame,\n", + " time: Optional[str] = None,\n", + " target: Optional[Union[str, List[str]]] = None,\n", + " group: Optional[List[str]] = None,\n", + " num: Optional[List[Union[str, List[str]]]] = None,\n", + " cat: Optional[List[Union[str, List[str]]]] = None,\n", + " known: Optional[List[Union[str, List[str]]]] = None,\n", + " unknown: Optional[List[Union[str, List[str]]]] = None,\n", + " static: Optional[List[Union[str, List[str]]]] = None,\n", + " label_encoders: Optional[dict] = None\n", + " ):\n", + " \"\"\"Initializes the PandasTSDataSet.\n", + "\n", + " Args:\n", + " data: The input pandas DataFrame.\n", + " time: The name of the time index column.\n", + " target: The name(s) of the target variable column(s).\n", + " group: List of column names identifying a time series instance.\n", + " num: The name(s) of numerical feature columns.\n", + " cat: The name(s) of categorical feature columns.\n", + " known: List of variables known in the future.\n", + " unknown: List of variables unknown in the future\n", + " static: The name(s) of static feature columns (features that don't\n", + " change over time).\n", + " label_encoders: Optional dictionary of pre-fitted label encoders\n", + " for categorical features. If not provided, new encoders will\n", + " be fitted.\n", + " \"\"\"\n", + "\n", + " self.time = time\n", + " self.target = _coerce_to_list(target)\n", + " self.num = _coerce_to_list(num)\n", + " self.cat = _coerce_to_list(cat)\n", + " self.known = _coerce_to_list(known)\n", + " self.unknown = _coerce_to_list(unknown)\n", + " self.static = _coerce_to_list(static)\n", + " self.data = data.copy()\n", + "\n", + " self.group = _coerce_to_list(group)\n", + "\n", + "\n", + " self.feature_cols = self.num + self.cat\n", + " #use set ensuring unique columns\n", + " self.data = data[list(set(self.static + self.feature_cols + self.target + [self.time] +self.group))].copy()\n", + "\n", + " ##Encoders for categorical since we want to return tensors\n", + " if label_encoders is None:\n", + " label_encoders = {}\n", + " ##Encoders for categorical since we want to return tensors\n", + " for c in self.cat+self.group:\n", + " label_encoders[c] = OrdinalEncoder()\n", + " self.data[c] = label_encoders[c].fit_transform(self.data[c].values.reshape(-1,1)).flatten()\n", + " self.label_encoders = label_encoders\n", + " else:\n", + " for c in self.cat+self.group:\n", + " self.data[c] = label_encoders[c].transform(self.data[c].values.reshape(-1,1)).flatten()\n", + " self.label_encoders = label_encoders\n", + "\n", + "\n", + " ##ensure to have a coherent dataset\n", + " self.data.drop_duplicates(subset= self.group+[self.time],keep='first',inplace=True,ignore_index=True)\n", + "\n", + " ##compute minumum frequency\n", + " if self.group is None:\n", + " freq = self.data[self.time].diff.min()\n", + " else:\n", + " freq = self.data.groupby(self.group).time.diff().min()\n", + "\n", + " if isinstance(freq, timedelta):\n", + " freq = pd.to_timedelta(freq)\n", + " elif isinstance(freq, (int, float)):\n", + " freq = int(freq)\n", + " else:\n", + " raise TypeError(\"time must be integer or datetime\")\n", + "\n", + "\n", + " ##extend dataset\n", + " self.data = extend_time_df(self.data,self.time,freq,self.group).merge(self.data,how='left').reset_index()\n", + "\n", + "\n", + " ##now we are sure that data is in a coherent form!\n", + " self.lengths = {}\n", + " if self.group:\n", + " self._groups = self.data.groupby(self.group).groups\n", + " self._group_ids = list(self._groups.keys())\n", + " for k in self._groups:\n", + " self.lengths[k] = len(self._groups[k])\n", + " else:\n", + " self._groups = {0: self.data.index}\n", + " self._group_ids = [0]\n", + " self.lengths[0] = len( self.data.index)\n", + "\n", + "\n", + "\n", + " ## we know on the fly which rows are valid and wich contains nans\n", + " self.data['valid'] = ~pd.isnull(self.data.max(axis=1))\n", + "\n", + " self._prepare_metadata()\n", + "\n", + " def _prepare_metadata(self):\n", + " \"\"\"Prepare metadata for the dataset.\"\"\"\n", + " self.metadata = {\n", + " \"cols\": {\n", + " \"y\": self.target,\n", + " \"x\": self.feature_cols,\n", + " \"st\": self.static,\n", + " },\n", + " \"col_type\": {},\n", + " \"col_known\": {},\n", + " \"cat_index\":[]\n", + " }\n", + " for i, c in enumerate(self.feature_cols):\n", + " if c in self.cat:\n", + " self.metadata['cat_index'].append(i) # EDIT: was getting error with i, so updated enumerate in for loop\n", + " \n", + "\n", + " all_cols = self.target + self.feature_cols + self.static\n", + " for col in all_cols:\n", + " self.metadata[\"col_type\"][col] = \"C\" if col in self.cat else \"F\"\n", + " self.metadata[\"col_known\"][col] = \"K\" if col in self.known else \"U\"\n", + " self.metadata['encoders'] = self.label_encoders\n", + "\n", + " def __len__(self) -> int:\n", + " \"\"\"Return number of time series in the dataset.\"\"\"\n", + " return len(self._group_ids)\n", + "\n", + " def get_id_ts_by_idx(self,idx):\n", + " \"\"\"Gets the time series ID and starting index for a given index.\n", + "\n", + " Args:\n", + " idx: The overall index. This is *not* the group index, but\n", + " the index considering all time series concatenated together.\n", + "\n", + " Returns:\n", + " tuple: A tuple containing the group ID and the starting index\n", + " of that group in the concatenated data.\n", + " \"\"\"\n", + " tmp = np.cumsum(list(self.lengths.values()))\n", + " idx_group = min(np.where(tmp>idx)[0]) # EDIT: Find the group index, as we are getting group\n", + " return idx_group, 0 if idx_group==0 else tmp[idx_group-1] #EDIT: group id, index of the start of the series\n", + "\n", + " def get_total_len(self):\n", + " \"\"\"Returns total length of the dataset\"\"\"\n", + " l = 0\n", + " for k in self._groups:\n", + " l+= self.lengths[k]\n", + " return l\n", + "\n", + " def __getitem__(self, index: int) -> Dict[str, torch.Tensor]:\n", + " \"\"\"Get time series data for given index.\n", + "\n", + " Args:\n", + " index: The index of the time series to retrieve.\n", + "\n", + " Returns:\n", + " A dictionary containing tensors for 't', 'y', 'x', 'group', 'st',\n", + " and 'is_valid'.\n", + " \"\"\"\n", + " group_id = self._group_ids[index]\n", + " device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n", + "\n", + " if self.group:\n", + " mask = self._groups[group_id]\n", + " data = self.data.loc[mask].sort_values(by=self.time)\n", + " else:\n", + " data = self.data.sort_values(by=self.time)\n", + "\n", + " result = {\n", + " \"t\": data[self.time].values, # Return as numpy array\n", + " \"y\": torch.tensor(data[self.target].values, dtype=torch.float32, device=device),\n", + " \"x\": torch.tensor(data[self.feature_cols].values, dtype=torch.float32, device=device),\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + " \"st\": torch.tensor(data[self.static].iloc[0].values, dtype=torch.float32, device=device) if self.static else torch.tensor([], dtype=torch.float32, device=device), # return empty if no statics\n", + " 'is_valid': torch.tensor(data.valid.values,dtype=torch.int,device=device), #indicator of if data is valid\n", + " }\n", + "\n", + " return result\n", + "\n", + " def get_metadata(self) -> Dict:\n", + " \"\"\"Return metadata about the dataset.\n", + "\n", + " Returns:\n", + " -------\n", + " Dict\n", + " Dictionary containing:\n", + " - cols: column names for y, x, and static features\n", + " - col_type: mapping of columns to their types (F/C)\n", + " - col_known: mapping of columns to their future known status (K/U)\n", + " - encoders: Dict of label encoders used\n", + " \"\"\"\n", + " return self.metadata" + ] + }, + { + "cell_type": "markdown", + "id": "5030144e", + "metadata": {}, + "source": [ + "**Configuration dictionary for the data module. This holds hyperparameters related to data splitting, preprocessing, and windowing.**" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "290ab40d-f440-4f7f-9ae9-8855a04f1ec6", + "metadata": {}, + "outputs": [], + "source": [ + "data_module_metadata = dict(\n", + " perc_train= 0.7, # Percentage of data for training\n", + " perc_valid= 0.1, # Percentage of data for validation\n", + " range_train= None, # Optional: Specific time range for training (not used here)\n", + " range_validation= None, # Optional: Specific time range for validation (not used here)\n", + " range_test= None, # Optional: Specific time range for testing (not used here)\n", + " shift= 0, # Optional: Shift the time series (not used here)\n", + " starting_point= None, # Optional: Starting point for sampling (not used here)\n", + " skip_step= 1, # Optional: Step size for skipping data points (not used here)\n", + " past_steps=16, # Number of past time steps to use as input (encoder length)\n", + " future_steps= 16, # Number of future time steps to predict (decoder length)\n", + " precompute=True, # Whether to precompute the train/val/test splits.\n", + " scaler= 'sklearn.preprocessing.StandardScaler()' # String representation of the scaler (not used directly here)\n", + ")\n" + ] + }, + { + "cell_type": "markdown", + "id": "c611fbc0", + "metadata": {}, + "source": [ + "**Instantiate the PandasTSDataSet (D1 layer).**\n", + "This creates the dataset object that will provide the raw time series data.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "e4ca4055-7f36-446c-9b21-6c0456b152cf", + "metadata": {}, + "outputs": [], + "source": [ + "ds = PandasTSDataSet(dataset,\n", + " 'time', # Name of the time index column\n", + " target, # List of target variable names\n", + " 'group', # Name of the column that identifies groups/time series\n", + " list(set(num_var_past+num_var_fut)), # List of numerical feature columns\n", + " list(set(cat_var_fut+cat_var_past)), # List of categorical feature columns\n", + " list(set(cat_var_fut+num_var_fut)), # List of future known covariates\n", + " list(set(cat_var_past+num_var_past)), # List of past known covariates\n", + " )\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "2af9edfd", + "metadata": {}, + "source": [ + "Import necessary PyTorch modules" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "519ebe66-3d64-41df-a191-81c0ba518e79", + "metadata": {}, + "outputs": [], + "source": [ + "from torch.utils.data import Dataset, DataLoader\n", + "from torch.utils.data.dataloader import default_collate\n", + "import torch" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "c0375ff0", + "metadata": {}, + "outputs": [], + "source": [ + "def my_collate(batch):\n", + " \"\"\"Custom collate function to handle potential None values in batches.\n", + "\n", + " Filters out `None` values from the batch before collating. This is\n", + " necessary because the `MyDataset.__getitem__` method can return `None`\n", + " if a valid window cannot be created for a given index.\n", + "\n", + " Args:\n", + " batch: A list of samples (dictionaries or tensors) from the dataset.\n", + "\n", + " Returns:\n", + " The collated batch, with `None` values removed.\n", + " \"\"\"\n", + " batch = list(filter(lambda x : x is not None, batch))\n", + " #print(len(batch)) # Debugging print statement (removed in final version)\n", + " return default_collate(batch)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "6e31a24f", + "metadata": {}, + "outputs": [], + "source": [ + "## TODO: @SANDEEP rework this using some cleaner code you can find here\n", + "#https://colab.research.google.com/drive/1FvLlmEOgm3D3JgNFVeAtwPk4cXagJ0CY?usp=sharing#scrollTo=jFuSwWrhTg6y" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "12bc5f1c-2de1-4837-9d2a-e764fa395bba", + "metadata": {}, + "outputs": [], + "source": [ + "class MyDataset(Dataset):\n", + " \"\"\"Custom PyTorch Dataset for time series data.\n", + "\n", + " This class adapts the D1 dataset (`PandasTSDataSet`) for use with a\n", + " PyTorch `DataLoader`. It handles the creation of input/output windows\n", + " (sequences) for training and validation. It also handles the case where\n", + " a valid window cannot be created (e.g., due to insufficient past data)\n", + " by returning `None`.\n", + "\n", + " Args:\n", + " data: The D1 dataset instance (e.g., `PandasTSDataSet`).\n", + " metadata: A dictionary containing metadata about the dataset and\n", + " data processing parameters (e.g., `past_steps`, `future_steps`).\n", + " valid_index: A dictionary where keys are group IDs and values are\n", + " lists of valid indices for that group. This defines the\n", + " valid ranges within each time series for creating windows.\n", + " \"\"\"\n", + "\n", + " def __init__(self, data, metadata, valid_index) -> torch.utils.data.Dataset:\n", + " \"\"\"Initializes the MyDataset instance.\"\"\"\n", + "\n", + " self.metadata = metadata\n", + " self.metadata_dataset = data.metadata # Metadata from the D1 dataset\n", + " self.data = data # The D1 dataset instance\n", + " self.valid_index = valid_index # Dictionary of valid indices for each group\n", + " sum = 0\n", + " self.lengths = {}\n", + " #fix this, probably we need to add something (check validity, check past and future)\n", + " for k in self.valid_index:\n", + " # Calculate the total number of valid windows that can be created\n", + " # from each group. Subtract `future_steps` because we need enough\n", + " # data points for both the input window and the target window.\n", + " sum+=(len(self.valid_index[k])-self.metadata['future_steps'])\n", + " self.lengths[k] = len(self.valid_index[k])\n", + " self.length = sum # Sum of lengths accross differnet time series.\n", + "\n", + " def __len__(self):\n", + " \"\"\"Returns the total number of valid windows in the dataset.\"\"\"\n", + " return self.length\n", + "\n", + " def get_id_ts_by_idx(self,idx):\n", + " \"\"\"Gets time series id given global index\"\"\"\n", + " tmp = np.cumsum(list(self.lengths.values()))\n", + " idx = min(np.where(tmp>idx)[0])\n", + " return idx, 0 if idx==0 else tmp[idx-1]\n", + "\n", + " def __getitem__(self, idxs):\n", + " \"\"\"Retrieves a sample (input/output window) from the dataset.\n", + "\n", + " Args:\n", + " idxs: The *global* index of the window to retrieve. This index\n", + " is across all time series in the dataset.\n", + "\n", + " Returns:\n", + " A dictionary containing the input and output tensors for the window,\n", + " or `None` if a valid window cannot be created at the given index.\n", + " \"\"\"\n", + "\n", + " ##crucial point there: correctly identifying which are the indexes that we need!\n", + " sample = {}\n", + " IDX,difference = self.get_id_ts_by_idx(idxs) #find group and local index\n", + "\n", + " idxs -= difference # Adjust index to be relative to the start of the time series\n", + " idxs = self.valid_index[IDX][idxs] # Get the actual index in the data\n", + " tmp = self.data.__getitem__(IDX) ##use the getitem of the data!\n", + "\n", + " # Check if enough data is available for both input and output windows\n", + " if idxs+self.metadata['future_steps']>self.data.lengths[IDX]:\n", + " return None # Not enough data, return None\n", + "\n", + " #Check for the validity of the time series, by checking nans.\n", + " if tmp['is_valid'][idxs-self.metadata['past_steps']:idxs+self.metadata['future_steps']].sum() != self.metadata['future_steps']+self.metadata['past_steps']:\n", + " return None\n", + " \n", + " # Construct sample\n", + " for k in tmp.keys():\n", + " # Separate based on past/future\n", + " # We create past and future samples only for the ones we sliced using idxs\n", + " if '_past' in k:\n", + " sample[k] = tmp[k][idxs-self.metadata['past_steps']:idxs] #past steps based on the past_step provided in metadata.\n", + " elif '_fut' in k or k=='y':\n", + " sample[k] = tmp[k][idxs:idxs+self.metadata['future_steps']]# future steps based on the future_step\n", + " else:\n", + " pass\n", + " return sample\n" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "ac4a66d5-753b-4030-a88d-e5b8efd73d95", + "metadata": {}, + "outputs": [], + "source": [ + "def compute_ranges(d1_dataset, metadata):\n", + " \"\"\"Computes valid index ranges for training, validation, and testing.\n", + "\n", + " This function calculates the start and end indices for the training,\n", + " validation, and testing sets within each time series group, based on the\n", + " provided percentages in `metadata`.\n", + "\n", + " Args:\n", + " d1_dataset: The D1 dataset instance (e.g., `PandasTSDataSet`).\n", + " metadata: A dictionary containing metadata, including `perc_train`\n", + " and `perc_valid` (percentages for training and validation splits).\n", + "\n", + " Returns:\n", + " tuple: A tuple containing three dictionaries: `train_ranges`,\n", + " `valid_ranges`, and `test_ranges`. Each dictionary has group IDs\n", + " as keys and lists of valid indices as values.\n", + " \"\"\"\n", + " ##suppose for now we use only percentage!\n", + " train_ranges = {}\n", + " valid_ranges = {}\n", + " test_ranges = {}\n", + " for k in d1_dataset.lengths: # Iterate over each time series group\n", + " ls = d1_dataset.lengths[k] # Get the length of the current time series\n", + " train_ranges[k] = list(range(0,int(metadata['perc_train']*ls))) # Training range\n", + " valid_ranges[k] = list(range(int(metadata['perc_train']*ls), int((metadata['perc_valid'] + metadata['perc_train'])*ls))) # Validation range\n", + " test_ranges[k] = list(range(int((metadata['perc_valid'] + metadata['perc_train'])*ls),ls)) # Testing range\n", + "\n", + " ## in case of datetime ranges we need to use np.where and create a mask\n", + " return train_ranges,valid_ranges,test_ranges\n", + "\n", + "# Calculate the training, validation, and testing ranges\n", + "train_ranges, valid_ranges, test_ranges = compute_ranges(ds, data_module_metadata)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "0185143c", + "metadata": {}, + "source": [ + "**Create a DataLoader for the training set.**" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "7e43bfd7-12b3-48c2-8b33-d5ca6681959c", + "metadata": {}, + "outputs": [], + "source": [ + "dl = DataLoader(\n", + " MyDataset(ds, data_module_metadata, train_ranges), # Create a MyDataset instance\n", + " batch_size=32, # Set the batch size\n", + " collate_fn=my_collate # Use the custom collate function\n", + " )\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "dde2ba1f-f6f8-483c-a404-5917b84ce37a", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([16, 16, 1])\n" + ] + } + ], + "source": [ + "# Example of iterating through the DataLoader (for debugging/demonstration).\n", + "for x in dl:\n", + " print(x['y'].shape) # Print the shape of the target variable tensor.\n", + " break #only check first batch\n" + ] + }, + { + "cell_type": "markdown", + "id": "a7ddeb11", + "metadata": {}, + "source": [ + "**The following function and class show how one might create a `LightningDataModule` to manage the data loading process, including train/validation/test splits. This builds on top of previous components.**" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "86c9c7fc", + "metadata": {}, + "outputs": [], + "source": [ + "class DecoderEncoderDataModule(L.LightningDataModule):\n", + " \"\"\"LightningDataModule for encoder-decoder models.\n", + "\n", + " This class manages the data loading and preprocessing for training,\n", + " validation, testing, and prediction. It handles splitting the data,\n", + " creating `Dataset` instances, and creating `DataLoader` instances. It\n", + " supports both precomputed splits and on-the-fly splitting.\n", + "\n", + " Args:\n", + " d1_dataset: The D1 dataset instance (e.g., `PandasTSDataSet`).\n", + " batch_size: The batch size.\n", + " num_workers: The number of worker processes for data loading.\n", + " metadata: A dictionary containing metadata and hyperparameters.\n", + " \"\"\"\n", + " def __init__(self, d1_dataset, batch_size=32, num_workers=4, metadata=None):\n", + " super().__init__()\n", + " # initialize other params\n", + " self.d1_dataset = d1_dataset\n", + " self.batch_size = batch_size\n", + " self.metadata = metadata\n", + " self.num_workers = num_workers\n", + "\n", + "\n", + " def prepare_data(self):\n", + " \"\"\"Prepares the data for training/validation/testing.\n", + "\n", + " This method handles splitting the data into training, validation, and\n", + " testing sets. It supports two modes:\n", + "\n", + " - `precompute=True`: Uses a `split_data` function (not defined here,\n", + " but assumed to exist) to precompute the splits. This is useful for\n", + " deterministic splits or when the splitting process is expensive.\n", + " - `precompute=False`: Calculates the splits on-the-fly using\n", + " `compute_ranges`. This is more flexible and allows for different\n", + " splitting strategies.\n", + " \"\"\"\n", + " if self.metadata['precompute']:\n", + " self.precompute = True\n", + " ## @SANDEEP this is what is returned by DSIPTS split_for_train!!\n", + " ## the only difference is that we need to use the __getitem__ of the d1 layer\n", + "\n", + " # The split_data function is assumed to be defined elsewhere and\n", + " # would handle the splitting logic according to however the\n", + " # project defines \"splitting\" (could be by time, by groups, etc.)\n", + " self.train_dataset, self.validation_dataset, self.test_dataset = split_data(self.d1_dataset,self.metadata) # Placeholder\n", + "\n", + "\n", + "\n", + " else:\n", + " self.precompute = False\n", + " ##in this case we need to pass only the data that are referring to the trainin period\n", + " ## but data can be chunked, we need to create a function that transform a D1 object into a D1 object or, even better\n", + " ## something that can be used by the dataset for filtering only valid samples!\n", + " ##for example\n", + " self.train_ranges, self.validation_ranges, self.test_ranges = compute_ranges(self.d1_dataset,self.metadata)\n", + " self.predict_data = None ##??? need to study this\n", + " ##TODO normalization???? maybe we can precompute here some statistics and pass them to the dataset!\n", + "\n", + " def setup(self, stage=None):\n", + " \"\"\"Sets up the datasets for each stage (fit, test, predict).\n", + "\n", + " This method creates the appropriate `Dataset` instances for each stage,\n", + " using either the precomputed splits or the on-the-fly calculated ranges.\n", + "\n", + " Args:\n", + " stage: The stage ('fit', 'test', or 'predict').\n", + " \"\"\"\n", + " # Get metadata from D1 layer during setup\n", + " #self.metadata = self.d1_dataset.get_metadata() ##NO SEE COMMENT BEFORE! We get metadata in __init__\n", + " ##create dataset here\n", + " if stage == 'fit':\n", + " if self.precompute:\n", + " # Use precomputed datasets (implementation of split_data needed)\n", + " pass # Use self.train_dataset and self.validation_dataset as defined in prepare_data\n", + " else:\n", + " # Create datasets using computed ranges\n", + " self.train_dataset = MyDataset(self.d1_dataset, self.metadata, self.train_ranges) # Pass train_ranges\n", + " self.validation_dataset = MyDataset(self.d1_dataset, self.metadata, self.validation_ranges) # Pass valid_ranges\n", + "\n", + " if stage == 'test':\n", + " if self.precompute:\n", + " pass\n", + " # Use precomputed test dataset\n", + " else:\n", + " self.test_dataset = MyDataset(self.d1_dataset, self.metadata, self.test_ranges) # Pass test_ranges\n", + "\n", + " if stage == 'predict':\n", + " if self.precompute:\n", + " pass\n", + " # Use precomputed prediction dataset (if applicable)\n", + " else:\n", + " self.predict_datasset = MyDataset(self.d1_dataset, self.metadata, self.predict_data) # Pass predict_data. Implementation details TBD.\n", + "\n", + " def train_dataloader(self):\n", + " \"\"\"Returns the training DataLoader.\"\"\"\n", + " return DataLoader(self.train_dataset, batch_size=self.batch_size, shuffle=True, num_workers=self.num_workers, collate_fn=my_collate)\n", + "\n", + " def val_dataloader(self):\n", + " \"\"\"Returns the validation DataLoader.\"\"\"\n", + " return DataLoader(self.validation_dataset, batch_size=self.batch_size, shuffle=False, num_workers=self.num_workers, collate_fn=my_collate)\n", + "\n", + " def test_dataloader(self):\n", + " \"\"\"Returns the test DataLoader.\"\"\"\n", + " return DataLoader(self.test_dataset, batch_size=self.batch_size, shuffle=False, num_workers=self.num_workers, collate_fn=my_collate)\n", + "\n", + " def predict_dataloader(self):\n", + " \"\"\"Returns the prediction DataLoader.\"\"\"\n", + " return DataLoader(self.predict_datasset, batch_size=self.batch_size, shuffle=False, num_workers=self.num_workers, collate_fn=my_collate)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "c83526e8", + "metadata": {}, + "source": [ + "**`DecoderEncoderModel` Class (M Layer - Example)**\n", + "This is a placeholder for the Model Layer." + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "9d9a07bd-35cd-4cbd-8843-081b7d3d8580", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "# Layer M - Example (Placeholder)\n", + "class DecoderEncoderModel(L.LightningModule):\n", + " \"\"\"Placeholder for an encoder-decoder model (M layer).\n", + "\n", + " This is a minimal example of how a model might be structured using\n", + " PyTorch Lightning. It shows how to access the metadata from the\n", + " `LightningDataModule`. A real implementation would include the actual\n", + " network architecture and training logic.\n", + " \"\"\"\n", + " def __init__(self):\n", + " super().__init__()\n", + " self.metadata = None\n", + "\n", + " def setup(self, stage=None):\n", + " \"\"\"Gets metadata from the datamodule during setup.\"\"\"\n", + " # Get metadata from datamodule during setup\n", + " self.metadata = self.trainer.datamodule.metadata\n", + "\n", + " # Initialize layer T model using metadata (placeholder)\n", + "\n", + " def forward(self, x):\n", + " \"\"\"Forward pass (placeholder).\"\"\"\n", + " # forward logic (implementation would go here)\n", + " pass\n", + "\n", + " def training_step(self, batch, batch_idx):\n", + " \"\"\"Training step (placeholder).\"\"\"\n", + " pass # Replace with actual training logic\n", + "\n", + " def validation_step(self, batch, batch_idx):\n", + " \"\"\"Validation step (placeholder).\"\"\"\n", + " pass # Replace with actual validation logic\n" + ] + }, + { + "cell_type": "markdown", + "id": "80542685", + "metadata": {}, + "source": [ + "### Layer M" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "694a020f-7f8d-4503-a023-bfdcda90667b", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "\n", + "class DecoderEncoderModel(L.LightningModule):\n", + " def __init__(self):\n", + " super().__init__()\n", + " self.metadata = None\n", + " \n", + " def setup(self, stage=None):\n", + " # Get metadata from datamodule during setup\n", + " self.metadata = self.trainer.datamodule.metadata\n", + " \n", + " # Initialize layer T model using metadata\n", + " \n", + " def forward(self, x):\n", + " # forward logic\n", + " pass" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ec43226c-f99f-4a77-8793-c3e9db5c4536", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "dsipts", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/_delete.py b/notebooks/_delete.py new file mode 100644 index 00000000..57fd500c --- /dev/null +++ b/notebooks/_delete.py @@ -0,0 +1,614 @@ + +# %% +#dataset.to_csv('dataset.csv',index=False) + +# %% +!pip install lightning + +# %% +import pickle +import pandas as pd +from typing import Union,List, Optional, Dict +from sklearn.preprocessing import LabelEncoder +from datetime import datetime +import lightning as L +import numpy as np +from sklearn.preprocessing import OrdinalEncoder +import torch +from datetime import timedelta +with open('example.pkl', 'rb') as f: + dataset,cat_var_past,cat_var_fut,num_var_past = pickle.load(f) +num_var_fut = [] +group = None +time_var = 'time' +target = ['signal'] + +# %% +dataset2 = dataset.copy() +dataset2['group'] = 2 +dataset['group'] = 1 +dataset = pd.concat([dataset, dataset2]) + +# %% +def extend_time_df(x:pd.DataFrame,time:str,freq:Union[str,int],group:Union[List[str],None]=None,global_minmax:bool=False)-> pd.DataFrame: + + + if group is None: + + if isinstance(freq,int): + empty = pd.DataFrame({'time':list(range(x[time].min(),x[time].max(),freq))}) + else: + empty = pd.DataFrame({'time':pd.date_range(x[time].min(),x[time].max(),freq=freq)}) + + else: + + if global_minmax: + _min = x[time].min() + _max = x[time].max() + _min_max = pd.DataFrame({'min':min, 'max':max}) + for c in group: + _min_max[c] = _min_max[c] + else: + _min = x.groupby(group)[time].min().reset_index().rename(columns={time:'min'}) + _max = x.groupby(group)[time].max().reset_index().rename(columns={time:'max'}) + _min_max = pd.merge(_min,_max) + empty = [] + for _,row in _min_max.iterrows(): + if isinstance(freq,int): + tmp = pd.DataFrame({time:np.arange(row['min'],row['max'],freq)}) + else: + tmp = pd.DataFrame({time:pd.date_range(row['min'],row['max'],freq=freq)}) + for c in group: + tmp[c] = row[c] + empty.append(tmp) + + empty = pd.concat(empty,ignore_index=True) + return empty + + +# %% +##TODO @Sandeep please try to replicate what is in PandasTSDataSet but for a smore compact example! +class PandasTSDataSet_MINIMAL: + + + def __init__( + self, + data: pd.DataFrame, + time: Optional[str] = None, + target: Optional[Union[str, List[str]]] = None, + num: Optional[List[Union[str, List[str]]]] = None, + + ): + + + self.time = time + self.target = _coerce_to_list(target) + self.num = _coerce_to_list(num) + self.data = data.copy() + + + self.feature_cols = self.num + #use set ensuring unique columns + self.data = data[list(set(self.feature_cols + self.target + [self.time]))].copy() + + + self.label_encoders = {} + + ##ensure to have a coherent dataset + self.data.drop_duplicates(subset= [time],keep='first',inplace=True,ignore_index=True) + + ##compute minumum frequency + freq = self.data['time'].diff.min() + + + if isinstance(freq, timedelta): + freq = pd.to_timedelta(freq) + elif isinstance(freq, (int, float)): + freq = int(freq) + else: + raise TypeError("time must be integer or datetime") + + + ##extend dataset + self.data = extend_time_df(self.data,self.time,freq,None).merge(self.data,how='left').reset_index() + + + self._groups = {"_single_group": self.data.index} + self._group_ids = ["_single_group"] + + ## we know on the fly which rows are valid and wich contains nans + self.data['valid'] = ~pd.isnull(self.data.max(axis=1)) + + self._prepare_metadata() + + def _prepare_metadata(self): + """Prepare metadata for the dataset.""" + self.metadata = { + "cols": { + "y": self.target, + "x": self.feature_cols, + "st": [], + }, + "col_type": {}, + "col_known": {}, + "cat_index":[] + } + for c in self.feature_cols: + if c in self.cat: + self.metadata['cat_index'].append(i) + + + all_cols = self.target + self.feature_cols + for col in all_cols: + self.metadata["col_type"][col] = "F" + + self.metadata["col_known"][col] = "K" if col in self.feature_cols else "U" + self.metadata['encoders'] = self.label_encoders + + def __len__(self) -> int: + """Return number of time series in the dataset.""" + return len(self._group_ids) + + def __getitem__(self, index: int) -> Dict[str, torch.Tensor]: ##NOT USED INDEX! + """Get time series data for given index.""" + device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") + + + data = self.data.sort_values(by=self.time) + + result = { + "t": data[self.time].values, + "y": torch.tensor(data[self.target].values,dtype=torch.float32,device=device), + "x": torch.tensor(data[self.feature_cols].values,dtype=torch.float32,device=device), + 'is_valid': torch.tensor(data.valid.values,dtype=torch.int,device=device), + } + + + + + return result + + def get_metadata(self) -> Dict: + """Return metadata about the dataset. + + Returns + ------- + Dict + Dictionary containing: + - cols: column names for y, x, and static features + - col_type: mapping of columns to their types (F/C) + - col_known: mapping of columns to their future known status (K/U) + """ + return self.metadata + +def _coerce_to_list(obj): + """Coerce object to list. + + None is coerced to empty list, otherwise list constructor is used. + Single values are coerced into list + """ + if obj is None: + return [] + if isinstance(obj,list): + return obj + else: + return [obj] + +# %% + +class PandasTSDataSet: + + + def __init__( + self, + data: pd.DataFrame, + time: Optional[str] = None, + target: Optional[Union[str, List[str]]] = None, + group: Optional[List[str]] = None, + num: Optional[List[Union[str, List[str]]]] = None, + cat: Optional[List[Union[str, List[str]]]] = None, + known: Optional[List[Union[str, List[str]]]] = None, + unknown: Optional[List[Union[str, List[str]]]] = None, + static: Optional[List[Union[str, List[str]]]] = None, + label_encoders: Optional[dict] = None + ): + + + self.time = time + self.target = _coerce_to_list(target) + self.num = _coerce_to_list(num) + self.cat = _coerce_to_list(cat) + self.known = _coerce_to_list(known) + self.unknown = _coerce_to_list(unknown) + self.static = _coerce_to_list(static) + self.data = data.copy() + + self.group = _coerce_to_list(group) + + + self.feature_cols = self.num + self.cat + #use set ensuring unique columns + self.data = data[list(set(self.static + self.feature_cols + self.target + [self.time] +self.group))].copy() + + ##Encoders for categorical since we want to return tensors + if label_encoders is None: + label_encoders = {} + ##Encoders for categorical since we want to return tensors + for c in self.cat+self.group: + label_encoders[c] = OrdinalEncoder() + self.data[c] = label_encoders[c].fit_transform(self.data[c].values.reshape(-1,1)).flatten() + self.label_encoders = label_encoders + else: + for c in self.cat+self.group: + self.data[c] = label_encoders[c].transform(self.data[c].values.reshape(-1,1)).flatten() + self.label_encoders = label_encoders + + + ##ensure to have a coherent dataset + self.data.drop_duplicates(subset= self.group+[self.time],keep='first',inplace=True,ignore_index=True) + + ##compute minumum frequency + if self.group is None: + freq = self.data[self.time].diff.min() + else: + freq = self.data.groupby(self.group).time.diff().min() + + if isinstance(freq, timedelta): + freq = pd.to_timedelta(freq) + elif isinstance(freq, (int, float)): + freq = int(freq) + else: + raise TypeError("time must be integer or datetime") + + + ##extend dataset + self.data = extend_time_df(self.data,self.time,freq,self.group).merge(self.data,how='left').reset_index() + + + ##now we are sure that data is in a coherent form! + self.lengths = {} + if self.group: + self._groups = self.data.groupby(self.group).groups + self._group_ids = list(self._groups.keys()) + for k in self._groups: + self.lengths[k] = len(self._groups[k]) + else: + self._groups = {0: self.data.index} + self._group_ids = [0] + self.lengths[0] = len( self.data.index) + + + + ## we know on the fly which rows are valid and wich contains nans + self.data['valid'] = ~pd.isnull(self.data.max(axis=1)) + + self._prepare_metadata() + + def _prepare_metadata(self): + """Prepare metadata for the dataset.""" + self.metadata = { + "cols": { + "y": self.target, + "x": self.feature_cols, + "st": self.static, + }, + "col_type": {}, + "col_known": {}, + "cat_index":[] + } + for c in self.feature_cols: + if c in self.cat: + self.metadata['cat_index'].append(i) + + self.metadata["col_known"] + all_cols = self.target + self.feature_cols + self.static + for col in all_cols: + self.metadata["col_type"][col] = "C" if col in self.cat else "F" + + self.metadata["col_known"][col] = "K" if col in self.known else "U" + self.metadata['encoders'] = self.label_encoders + + def __len__(self) -> int: + """Return number of time series in the dataset.""" + return len(self._group_ids) + + def get_id_ts_by_idx(self,idx): + + tmp = np.cumsum(list(self.lengths.values())) + idx = min(np.where(tmp>idx)[0]) + return idx, 0 if idx==0 else tmp[idx-1] + + def get_total_len(self): + l = 0 + for k in self._groups: + l+= self.lengths[k] + return l + + def __getitem__(self, index: int) -> Dict[str, torch.Tensor]: + """Get time series data for given index.""" + group_id = self._group_ids[index] + device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") + + if self.group: + mask = self._groups[group_id] + data = self.data.loc[mask].sort_values(by=self.time) + else: + data = self.data.sort_values(by=self.time) + + result = { + "t": data[self.time].values, + "y": torch.tensor(data[self.target].values,dtype=torch.float32,device=device), + "x": torch.tensor(data[self.feature_cols].values,dtype=torch.float32,device=device), + "group": torch.tensor([group_id],dtype=torch.float32,device=device), + "st": torch.tensor(data[self.static].iloc[0].values if self.static else []), + 'is_valid': torch.tensor(data.valid.values,dtype=torch.int,device=device), + } + + + + + return result + + def get_metadata(self) -> Dict: + """Return metadata about the dataset. + + Returns + ------- + Dict + Dictionary containing: + - cols: column names for y, x, and static features + - col_type: mapping of columns to their types (F/C) + - col_known: mapping of columns to their future known status (K/U) + """ + return self.metadata + +def _coerce_to_list(obj): + """Coerce object to list. + + None is coerced to empty list, otherwise list constructor is used. + Single values are coerced into list + """ + if obj is None: + return [] + if isinstance(obj,list): + return obj + else: + return [obj] + +# %% +data_module_metadata = dict( + perc_train= 0.7, + perc_valid= 0.1, + + range_train= None, + range_validation= None , + range_test= None, + shift= 0, + starting_point= None, + skip_step= 1, + past_steps=16, + future_steps= 16, + precompute=True, + scaler= 'sklearn.preprocessing.StandardScaler()') + +# %% +ds = PandasTSDataSet(dataset, + 'time', + target, + 'group', + list(set(num_var_past+num_var_fut)), + list(set(cat_var_fut+cat_var_past)), + list(set(cat_var_fut+num_var_fut)), + list(set(cat_var_past+num_var_past)), + ) + + +# %% +from torch.utils.data import Dataset, DataLoader + +# %% +from torch.utils.data.dataloader import default_collate +import torch +def my_collate(batch): + batch = list(filter(lambda x : x is not None, batch)) + print(len(batch)) + return default_collate(batch) + +## @SANDEEP rework this using some cleaner code you can find here +#https://colab.research.google.com/drive/1FvLlmEOgm3D3JgNFVeAtwPk4cXagJ0CY?usp=sharing#scrollTo=jFuSwWrhTg6y +class MyDataset(Dataset): + + def __init__(self,data,metadata,valid_index)->torch.utils.data.Dataset: + + self.metadata = metadata + self.metadata_dataset = data.metadata + self.data = data + self.valid_index = valid_index + sum = 0 + self.lengths = {} + #fix this, probably we need to add something (check validity, check past and future) + for k in self.valid_index: + sum+=(len(self.valid_index[k])-self.metadata['future_steps']) + self.lengths[k] = len(self.valid_index[k]) + self.length = sum + def __len__(self): + return self.length + + def get_id_ts_by_idx(self,idx): + + tmp = np.cumsum(list(self.lengths.values())) + + idx = min(np.where(tmp>idx)[0]) + return idx, 0 if idx==0 else tmp[idx-1] + ## TODO: rewrite this using the Aryan code and names! and the D1 C, F and K U feature labeling!! + def __getitem__(self, idxs): + + ##crucial point there: correctly identifying which are the indexes that we need! + sample = {} + IDX,difference = self.get_id_ts_by_idx(idxs) + + idxs -= difference + idxs = self.valid_index[IDX][idxs] + tmp = self.data.__getitem__(IDX) ##use the getitem of the data! + + if idxs+self.metadata['future_steps']>self.data.lengths[IDX]: + return None + + for k in tmp.keys(): + + if len(tmp[k][idxs-self.metadata['past_steps']:idxs]) + len(tmp[k][idxs:idxs+self.metadata['future_steps']]) == self.metadata['future_steps']+self.metadata['past_steps']: + if tmp['is_valid'][idxs-self.metadata['past_steps']:idxs+self.metadata['future_steps']].sum()==self.metadata['future_steps']+self.metadata['past_steps']: + if '_past' in k: + sample[k] = tmp[k][idxs-self.metadata['past_steps']:idxs] + elif '_fut' in k or k=='y': + sample[k] = tmp[k][idxs:idxs+self.metadata['future_steps']] + else: + pass + if len(sample)==0: + return None + else: + return sample + +# %% +def compute_ranges( d1_dataset,metadata): + ##suppose for now we use only percentage! + train_ranges = {} + valid_ranges = {} + test_ranges = {} + for k in d1_dataset.lengths: + ls = d1_dataset.lengths[k] + train_ranges[k] = list(range(0,int(metadata['perc_train']*ls))) + valid_ranges[k] = list(range(int(metadata['perc_train']*ls), int((metadata['perc_valid'] + metadata['perc_train'])*ls))) + test_ranges[k] = list(range(int((metadata['perc_valid'] + metadata['perc_train'])*ls),ls)) + + ## in case of datetime ranges we need to use np.where and create a mask + return train_ranges,valid_ranges,test_ranges +train_ranges,valid_ranges,test_ranges = compute_ranges( ds,data_module_metadata) + + +# %% +dl = DataLoader( + MyDataset(ds,data_module_metadata,train_ranges), ##change respect to the initial vignette, can not use standard dataset + batch_size=32, + collate_fn=my_collate + ) + +# %% +import numpy as np + +for x in dl: + + print(x['y'].shape) #It works! + +# %% + + +# %% +def compute_ranges( d1_dataset,metadata): + ##suppose for now we use only percentage! + train_ranges = {} + valid_ranges = {} + test_ranges = {} + for k in d1_dataset.lengths: + ls = d1_dataset.lengths[k] ##the time + train_ranges[k] = list(range(0,int(metadata['perc_train']*ls))) + valid_ranges[k] = list(range(int(metadata['perc_train']*ls), int((metadata['perc_valid'] + metadata['perc_train'])*ls))) + test_ranges[k] = list(range(int(metadata['perc_valid'] + metadata['perc_train'])*ls),ls) + + ## in case of ranges we can + return train_ranges,valid_ranges,test_ranges + +# %% + +class DecoderEncoderDataModule(L.LightningDataModule): + def __init__(self, d1_dataset, batch_size=32, num_workers=4,metadata=None): + super().__init__() + # initialize other params + self.d1_dataset = d1_dataset + self.batch_size = batch_size + self.metadata = metadata + + + def prepare_data(self): + ##split is performed here + if self.metadata['precompute']: + self.precompute = True + ## @SANDEEP this is what is returned by DSIPTS split_for_train!! + ## the only difference is that we need to use the __getitem__ of the d1 layer + self.train_dataset, self.validation_dataset, self.test_dataset = split_data(self.d1_dataset,self.metadata) + + + + else: + self.precompute = False + ##in this case we need to pass only the data that are referring to the trainin period + ## but data can be chunked, we need to create a function that transform a D1 object into a D1 object or, even better + ## something that can be used by the dataset for filtering only valid samples! + ##for example + self.train_ranges, self.validation_ranges, self.test_ranges = compute_ranges(self.d1_dataset,self.metadata) + self.predict_data = None ##??? need to study this + ##TODO normalization???? maybe we can precompute here some statistics and pass them to the dataset! + + def setup(self, stage=None): + # Get metadata from D1 layer during setup + #self.metadata = self.d1_dataset.get_metadata() ##NO SEE COMMENT BEFORE! + ##create dataset here + if stage == 'fit': + if self.precompute: + pass + else: + self.train_dataset = MyDataset(self.d1_dataset,self.train_ranges) + self.validation_dataset = MyDataset(self.validation_data,self.validation_ranges) + + + if stage == 'test': + if self.precompute: + pass + else: + self.test_dataset = MyDataset(self.test_data,self.test_ranges) + + if stage == 'predict': + if self.precompute: + pass + else: + self.predict_datasset = MyDataset(self.predict_data) + + def train_dataloader(self): + return DataLoader(self.train_dataset, batch_size=self.batch_size,shuffle=True,remove_last=True,collate_fn=my_collate) + + def val_dataloader(self): + return DataLoader(self.validation_dataset, batch_size=self.batch_size,shuffle=True,remove_last=True,collate_fn=my_collate) + + def test_dataloader(self): + return DataLoader(self.test_dataset, batch_size=self.batch_size,shuffle=True,remove_last=True,collate_fn=my_collate) + + def predict_dataloader(self): + return DataLoader(self.predict_datasset, batch_size=self.batch_size,shuffle=False,remove_last=False,collate_fn=my_collate) + + + +# %% + + +# %% + +# Layer M +class DecoderEncoderModel(L.LightningModule): + def __init__(self): + super().__init__() + self.metadata = None + + def setup(self, stage=None): + # Get metadata from datamodule during setup + self.metadata = self.trainer.datamodule.metadata + + # Initialize layer T model using metadata + + def forward(self, x): + # forward logic + pass + +# %% + + + diff --git a/notebooks/example.pkl b/notebooks/example.pkl new file mode 100644 index 00000000..2a09e5e9 Binary files /dev/null and b/notebooks/example.pkl differ diff --git a/notebooks/sandeep/2402_1509.py b/notebooks/sandeep/2402_1509.py new file mode 100644 index 00000000..c893367e --- /dev/null +++ b/notebooks/sandeep/2402_1509.py @@ -0,0 +1,1108 @@ +# %% +from dsipts import Categorical,TimeSeries, RNN +settimana = Categorical('settimanale',1,[1,1,1,1,1,1,1],7,'multiplicative',[0.9,0.8,0.7,0.6,0.5,0.99,0.99]) + +##montly, additive (here there are only 5 month) +mese = Categorical('mensile',1,[31,28,20,10,33],5,'additive',[10,20,-10,20,0]) + +##spot categorical variables: in this case it occurs every 100 days and it lasts 7 days adding 10 to the original timeseries +spot = Categorical('spot',100,[7],1,'additive',[10]) + +##initizate a timeseries object +ts = TimeSeries('prova') +ts.generate_signal(noise_mean=1,categorical_variables=[settimana,mese,spot],length=5000,type=0) +dataset = ts.dataset +dataset['x_num'] = 0.0 +dataset.loc[:dataset.shape[0]-10,'x_num']= dataset['x_num'].values[9:] +cat_var_past = ts.cat_var +cat_var_fut = ts.cat_fut +num_var_past = ts.num_var+['x_num'] +import pickle +with open('example.pkl', 'wb') as f: + pickle.dump([dataset,cat_var_past,cat_var_fut,num_var_past],f) + +# %% +#dataset.to_csv('dataset.csv',index=False) + +# %% +!pip install lightning + +# %% +import pickle +import pandas as pd +from typing import Union, List, Optional, Dict +from sklearn.preprocessing import LabelEncoder # Used for categorical encoding +from datetime import datetime, timedelta # For time-related operations +import lightning as L # PyTorch Lightning +import numpy as np +from sklearn.preprocessing import OrdinalEncoder # Used for categorical encoding +import torch +from torch.utils.data import Dataset, DataLoader +from torch.utils.data.dataloader import default_collate + + +# %% [markdown] +# **Data Loading and Preparation (Example Data)** +# +# This section loads example time series data and prepares it for use in the `DataSet` classes. It demonstrates the structure of the input data. +# + +# %% +with open('example.pkl', 'rb') as f: + dataset, cat_var_past, cat_var_fut, num_var_past = pickle.load(f) +num_var_fut = [] # No numerical future variables in this example +group = None # No grouping in this first example. Later examples *do* use grouping. +time_var = 'time' +target = ['signal'] + +# %% [markdown] +# **Example with Grouping** +# +# The following cell creates a second copy of the dataset and assigns it a different group ID. This is then concatenated with the original `dataset` to showcase how grouping works. +# + +# %% +dataset2 = dataset.copy() + +#TODO break the files the into multip +# %% [markdown] +# **`extend_time_df` Function** +# This utility function extends a DataFrame to include all time steps within a specified range and frequency. This is essential for ensuring that all time series have the same length and that there are no missing time steps. +# +# + +# %% +def extend_time_df(x:pd.DataFrame,time:str,freq:Union[str,int],group:Union[List[str],None]=None,global_minmax:bool=False)-> pd.DataFrame: + """Extends a DataFrame to include all time steps within a range. + + Ensures that the DataFrame `x` has rows for all time steps within the + range defined by the minimum and maximum values of the `time` column, + with a specified frequency `freq`. Handles grouping if `group` is provided. + + Args: + x: The input DataFrame. Must contain a 'time' column and any + columns specified in 'group'. + time: The name of the column containing the time index (e.g., 'time'). + freq: The frequency to use for extending the DataFrame. This can be + an integer (for integer time steps) or a string representing a + pandas frequency string (e.g., 'D' for daily, 'H' for hourly). + group: Optional list of column names to group by. If provided, the + DataFrame will be extended separately for each group. + global_minmax: If True, use the global min/max of the 'time' column + across all groups. If False, use the min/max within each group. + + Returns: + pd.DataFrame: An extended DataFrame with all time steps within the + specified range and frequency. Missing values introduced by the + extension are filled with NaNs. + """ + if group is None: + # No grouping: compute min and max time directly + if isinstance(freq, int): + empty = pd.DataFrame({'time': list(range(x[time].min(), x[time].max(), freq))}) + else: + empty = pd.DataFrame({'time': pd.date_range(x[time].min(), x[time].max(), freq=freq)}) + + else: + # Grouping: compute min and max time for each group + if global_minmax: + # Use global min/max (across all groups) + _min = x[time].min() + _max = x[time].max() + _min_max = pd.DataFrame({'min': [_min], 'max': [_max]}) # Use a list for correct DataFrame creation + for c in group: + # Create dummy columns, will be overwritten + _min_max[c] = _min_max['min'] + else: + # Use per-group min/max + _min = x.groupby(group)[time].min().reset_index().rename(columns={time: 'min'}) + _max = x.groupby(group)[time].max().reset_index().rename(columns={time: 'max'}) + _min_max = pd.merge(_min, _max) + + empty = [] + for _, row in _min_max.iterrows(): + if isinstance(freq, int): + tmp = pd.DataFrame({time: np.arange(row['min'], row['max'], freq)}) + else: + tmp = pd.DataFrame({time: pd.date_range(row['min'], row['max'], freq=freq)}) + for c in group: + tmp[c] = row[c] # Assign the group values + empty.append(tmp) + + empty = pd.concat(empty, ignore_index=True) # Concatenate all group DataFrames + return empty + +# %% [markdown] +# **`_coerce_to_list` Function (Utility)** +# + +# %% +def _coerce_to_list(obj): + """Coerces input to a list. + + Ensures that the input is always a list. If the input is `None`, + it returns an empty list. If the input is a single value (not already + a list), it returns a list containing that single value. + + Args: + obj: The input object. + + Returns: + list: A list representing the input object. + """ + if obj is None: + return [] + if isinstance(obj, list): + return obj + else: + return [obj] + +# %% [markdown] +# **`PandasTSDataSet_MINIMAL` Class (Simplified Example)** +# This class is a simplified version of the `PandasTSDataSet` to illustrate the basic principles without the complexity of handling all the different data types and metadata. It serves as a stepping stone to the full implementation. +# +# + +# %% + +##TODO @Sandeep please try to replicate what is in PandasTSDataSet but for a smore compact example! + +# %% +class PandasTSDataSet_MINIMAL: + """A simplified version of PandasTSDataSet for demonstration. + + This class demonstrates the core functionality of creating a PyTorch + Dataset from a pandas DataFrame. It handles: + + - Extending the DataFrame to ensure regular time intervals. + - Returning tensors for time, target, and numerical features. + - Identifying valid data points (non-NaN). + + It does *not* handle: + - Categorical features. + - Static features. + - Future known covariates. + - Grouping. + - Metadata handling (beyond a very basic level). + + This simplified version is intended for educational purposes, to + illustrate the key concepts before diving into the full complexity + of the `PandasTSDataSet` class. + """ + + def __init__( + self, + data: pd.DataFrame, + time: Optional[str] = None, + target: Optional[Union[str, List[str]]] = None, + num: Optional[List[Union[str, List[str]]]] = None, + + ): + """Initializes the PandasTSDataSet_MINIMAL. + + Args: + data: The input pandas DataFrame. + time: The name of the time index column. + target: The name(s) of the target variable column(s). + num: The name(s) of numerical feature columns. + """ + + self.time = time + self.target = _coerce_to_list(target) + self.num = _coerce_to_list(num) + self.data = data.copy() + + + self.feature_cols = self.num + #use set ensuring unique columns + self.data = data[list(set(self.feature_cols + self.target + [self.time]))].copy() + + + self.label_encoders = {} + + ##ensure to have a coherent dataset + self.data.drop_duplicates(subset= [time],keep='first',inplace=True,ignore_index=True) + + ##compute minumum frequency + freq = self.data['time'].diff.min() + + + if isinstance(freq, timedelta): + freq = pd.to_timedelta(freq) + elif isinstance(freq, (int, float)): + freq = int(freq) + else: + raise TypeError("time must be integer or datetime") + + + ##extend dataset + self.data = extend_time_df(self.data,self.time,freq,None).merge(self.data,how='left').reset_index() + + + self._groups = {"_single_group": self.data.index} + self._group_ids = ["_single_group"] + + ## we know on the fly which rows are valid and wich contains nans + self.data['valid'] = ~pd.isnull(self.data.max(axis=1)) + + self._prepare_metadata() + + def _prepare_metadata(self): + """Prepare metadata for the dataset.""" + self.metadata = { + "cols": { + "y": self.target, + "x": self.feature_cols, + "st": [], # No static features in this minimal version + }, + "col_type": {}, + "col_known": {}, + "cat_index":[] # No categorical features in this minimal version + } + # Removed the loop that checks for categorical features, as they are not handled here. + + all_cols = self.target + self.feature_cols + for col in all_cols: + self.metadata["col_type"][col] = "F" # All features are numerical ("F") + + self.metadata["col_known"][col] = "K" if col in self.feature_cols else "U" + self.metadata['encoders'] = self.label_encoders # No encoders, as they're used for categoricals + + + def __len__(self) -> int: + """Return number of time series in the dataset.""" + return len(self._group_ids) + + def __getitem__(self, index: int) -> Dict[str, torch.Tensor]: + """Get time series data for given index. + + Args: + index: The index of the time series to retrieve (not used directly, + as we have a single time series in this simplified version). + + Returns: + A dictionary containing tensors for 't', 'y', 'x', and 'is_valid'. + """ + device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") + + + data = self.data.sort_values(by=self.time) + + result = { + "t": data[self.time].values, # Return as numpy array (no need for tensor here) + "y": torch.tensor(data[self.target].values,dtype=torch.float32,device=device), + "x": torch.tensor(data[self.feature_cols].values,dtype=torch.float32,device=device), + "weights": + } + return result + + def get_metadata(self) -> Dict: + """Return metadata about the dataset. + + Returns: + A dictionary containing metadata about the columns. + """ + return self.metadata + + + +# %% [markdown] +# **`PandasTSDataSet` Class (D1 - pandas Implementation)** + +# %% +class PandasTSDataSet(Dataset): + """PyTorch Dataset for loading time series data from a pandas DataFrame. + + This class implements the D1 (raw data) layer of the `pytorch-forecasting` + v2 API. It handles loading time series data from a pandas DataFrame, + performing basic preprocessing (like ensuring regular time intervals), + and providing access to the data via the `__getitem__` method. + + Key Features: + - Handles multiple time series (identified by group IDs). + - Supports numerical and categorical features. + - Supports static features (features that don't change over time). + - Supports known and unknown covariates. + - Automatically extends the DataFrame to ensure regular time intervals. + - Returns data in a standardized dictionary format (tensors). + - Provides metadata about the columns (names, types, known/unknown status). + """ + + def __init__( + self, + data: pd.DataFrame, + time: Optional[str] = None, + target: Optional[Union[str, List[str]]] = None, + group: Optional[List[str]] = None, + num: Optional[List[Union[str, List[str]]]] = None, + cat: Optional[List[Union[str, List[str]]]] = None, + known: Optional[List[Union[str, List[str]]]] = None, + unknown: Optional[List[Union[str, List[str]]]] = None, + static: Optional[List[Union[str, List[str]]]] = None, + label_encoders: Optional[dict] = None + ): + """Initializes the PandasTSDataSet. + + Args: + data: The input pandas DataFrame. + time: The name of the time index column. + target: The name(s) of the target variable column(s). + group: List of column names identifying a time series instance. + num: The name(s) of numerical feature columns. + cat: The name(s) of categorical feature columns. + known: List of variables known in the future. + unknown: List of variables unknown in the future + static: The name(s) of static feature columns (features that don't + change over time). + label_encoders: Optional dictionary of pre-fitted label encoders + for categorical features. If not provided, new encoders will + be fitted. + """ + + self.time = time + self.target = _coerce_to_list(target) + self.num = _coerce_to_list(num) + self.cat = _coerce_to_list(cat) + self.known = _coerce_to_list(known) + self.unknown = _coerce_to_list(unknown) + self.static = _coerce_to_list(static) + self.data = data.copy() + + self.group = _coerce_to_list(group) + + + self.feature_cols = self.num + self.cat + #use set ensuring unique columns + self.data = data[list(set(self.static + self.feature_cols + self.target + [self.time] +self.group))].copy() + + ##Encoders for categorical since we want to return tensors + if label_encoders is None: + label_encoders = {} + ##Encoders for categorical since we want to return tensors + for c in self.cat+self.group: + label_encoders[c] = OrdinalEncoder() + self.data[c] = label_encoders[c].fit_transform(self.data[c].values.reshape(-1,1)).flatten() + self.label_encoders = label_encoders + else: + for c in self.cat+self.group: + self.data[c] = label_encoders[c].transform(self.data[c].values.reshape(-1,1)).flatten() + self.label_encoders = label_encoders + + + ##ensure to have a coherent dataset + self.data.drop_duplicates(subset= self.group+[self.time],keep='first',inplace=True,ignore_index=True) + + ##compute minumum frequency + if self.group is None: + freq = self.data[self.time].diff.min() + else: + freq = self.data.groupby(self.group).time.diff().min() + + if isinstance(freq, timedelta): + freq = pd.to_timedelta(freq) + elif isinstance(freq, (int, float)): + freq = int(freq) + else: + raise TypeError("time must be integer or datetime") + + + ##extend dataset + self.data = extend_time_df(self.data,self.time,freq,self.group).merge(self.data,how='left').reset_index() + + + ##now we are sure that data is in a coherent form! + self.lengths = {} + if self.group: + self._groups = self.data.groupby(self.group).groups + self._group_ids = list(self._groups.keys()) + for k in self._groups: + self.lengths[k] = len(self._groups[k]) + else: + self._groups = {0: self.data.index} + self._group_ids = [0] + self.lengths[0] = len( self.data.index) + + + + ## we know on the fly which rows are valid and wich contains nans + self.data['valid'] = ~pd.isnull(self.data.max(axis=1)) + + self._prepare_metadata() + + def _prepare_metadata(self): + """Prepare metadata for the dataset.""" + self.metadata = { + "cols": { + "y": self.target, + "x": self.feature_cols, + "st": self.static, + }, + "col_type": {}, + "col_known": {}, + "cat_index":[] + } + for i, c in enumerate(self.feature_cols): + if c in self.cat: + self.metadata['cat_index'].append(i) # EDIT: was getting error with i, so updated enumerate in for loop + + + all_cols = self.target + self.feature_cols + self.static + for col in all_cols: + self.metadata["col_type"][col] = "C" if col in self.cat else "F" + self.metadata["col_known"][col] = "K" if col in self.known else "U" + self.metadata['encoders'] = self.label_encoders + + def __len__(self) -> int: + """Return number of time series in the dataset.""" + return len(self._group_ids) + + def get_id_ts_by_idx(self,idx): + """Gets the time series ID and starting index for a given index. + + Args: + idx: The overall index. This is *not* the group index, but + the index considering all time series concatenated together. + + Returns: + tuple: A tuple containing the group ID and the starting index + of that group in the concatenated data. + """ + tmp = np.cumsum(list(self.lengths.values())) + idx_group = min(np.where(tmp>idx)[0]) # EDIT: Find the group index, as we are getting group + return idx_group, 0 if idx_group==0 else tmp[idx_group-1] #EDIT: group id, index of the start of the series + + def get_total_len(self): + """Returns total length of the dataset""" + l = 0 + for k in self._groups: + l+= self.lengths[k] + return l + + def __getitem__(self, index: int) -> Dict[str, torch.Tensor]: + """Get time series data for given index. + + Args: + index: The index of the time series to retrieve. + + Returns: + A dictionary containing tensors for 't', 'y', 'x', 'group', 'st', + and 'is_valid'. + """ + group_id = self._group_ids[index] + device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") + + if self.group: + mask = self._groups[group_id] + data = self.data.loc[mask].sort_values(by=self.time) + else: + data = self.data.sort_values(by=self.time) + + result = { + "t": data[self.time].values, # Return as numpy array + "y": torch.tensor(data[self.target].values, dtype=torch.float32, device=device), + "x": torch.tensor(data[self.feature_cols].values, dtype=torch.float32, device=device), + "group": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64 + "st": torch.tensor(data[self.static].iloc[0].values, dtype=torch.float32, device=device) if self.static else torch.tensor([], dtype=torch.float32, device=device), # return empty if no statics + 'is_valid': torch.tensor(data.valid.values,dtype=torch.int,device=device), #indicator of if data is valid + } + + return result + + def get_metadata(self) -> Dict: + """Return metadata about the dataset. + + Returns: + ------- + Dict + Dictionary containing: + - cols: column names for y, x, and static features + - col_type: mapping of columns to their types (F/C) + - col_known: mapping of columns to their future known status (K/U) + - encoders: Dict of label encoders used + """ + return self.metadata + +# %% [markdown] +# **Configuration dictionary for the data module. This holds hyperparameters related to data splitting, preprocessing, and windowing.** + +# %% +data_module_metadata = dict( + perc_train= 0.7, # Percentage of data for training + perc_valid= 0.1, # Percentage of data for validation + range_train= None, # Optional: Specific time range for training (not used here) + range_validation= None, # Optional: Specific time range for validation (not used here) + range_test= None, # Optional: Specific time range for testing (not used here) + shift= 0, # Optional: Shift the time series (not used here) + starting_point= None, # Optional: Starting point for sampling (not used here) + skip_step= 1, # Optional: Step size for skipping data points (not used here) + past_steps=16, # Number of past time steps to use as input (encoder length) + future_steps= 16, # Number of future time steps to predict (decoder length) + precompute=True, # Whether to precompute the train/val/test splits. + scaler= 'sklearn.preprocessing.StandardScaler()' # String representation of the scaler (not used directly here) +) + + +# %% [markdown] +# **Instantiate the PandasTSDataSet (D1 layer).** +# This creates the dataset object that will provide the raw time series data. +# + +# %% +ds = PandasTSDataSet(dataset, + 'time', # Name of the time index column + target, # List of target variable names + 'group', # Name of the column that identifies groups/time series + list(set(num_var_past+num_var_fut)), # List of numerical feature columns + list(set(cat_var_fut+cat_var_past)), # List of categorical feature columns + list(set(cat_var_fut+num_var_fut)), # List of future known covariates + list(set(cat_var_past+num_var_past)), # List of past known covariates + ) + + + +# %% [markdown] +# Import necessary PyTorch modules + +# %% +from torch.utils.data import Dataset, DataLoader +from torch.utils.data.dataloader import default_collate +import torch + +# %% +def my_collate(batch): + """Custom collate function to handle potential None values in batches. + + Filters out `None` values from the batch before collating. This is + necessary because the `MyDataset.__getitem__` method can return `None` + if a valid window cannot be created for a given index. + + Args: + batch: A list of samples (dictionaries or tensors) from the dataset. + + Returns: + The collated batch, with `None` values removed. + """ + batch = list(filter(lambda x : x is not None, batch)) + #print(len(batch)) # Debugging print statement (removed in final version) + return default_collate(batch) + + +# %% +## TODO: @SANDEEP rework this using some cleaner code you can find here +#https://colab.research.google.com/drive/1FvLlmEOgm3D3JgNFVeAtwPk4cXagJ0CY?usp=sharing#scrollTo=jFuSwWrhTg6y + +# %% +class MyDataset(Dataset): + """Custom PyTorch Dataset for time series data. + + This class adapts the D1 dataset (`PandasTSDataSet`) for use with a + PyTorch `DataLoader`. It handles the creation of input/output windows + (sequences) for training and validation. It also handles the case where + a valid window cannot be created (e.g., due to insufficient past data) + by returning `None`. + + Args: + data: The D1 dataset instance (e.g., `PandasTSDataSet`). + metadata: A dictionary containing metadata about the dataset and + data processing parameters (e.g., `past_steps`, `future_steps`). + valid_index: A dictionary where keys are group IDs and values are + lists of valid indices for that group. This defines the + valid ranges within each time series for creating windows. + """ + + def __init__(self, data, metadata, valid_index) -> torch.utils.data.Dataset: + """Initializes the MyDataset instance.""" + + self.metadata = metadata + self.metadata_dataset = data.metadata # Metadata from the D1 dataset + self.data = data # The D1 dataset instance + self.valid_index = valid_index # Dictionary of valid indices for each group + sum = 0 + self.lengths = {} + #fix this, probably we need to add something (check validity, check past and future) + for k in self.valid_index: + # Calculate the total number of valid windows that can be created + # from each group. Subtract `future_steps` because we need enough + # data points for both the input window and the target window. + sum+=(len(self.valid_index[k])-self.metadata['future_steps']) + self.lengths[k] = len(self.valid_index[k]) + self.length = sum # Sum of lengths accross differnet time series. + + def __len__(self): + """Returns the total number of valid windows in the dataset.""" + return self.length + + def get_id_ts_by_idx(self,idx): + """Gets time series id given global index""" + tmp = np.cumsum(list(self.lengths.values())) + idx = min(np.where(tmp>idx)[0]) + return idx, 0 if idx==0 else tmp[idx-1] + + def __getitem__(self, idxs): + """Retrieves a sample (input/output window) from the dataset. + + Args: + idxs: The *global* index of the window to retrieve. This index + is across all time series in the dataset. + + Returns: + A dictionary containing the input and output tensors for the window, + or `None` if a valid window cannot be created at the given index. + """ + + ##crucial point there: correctly identifying which are the indexes that we need! + sample = {} + IDX,difference = self.get_id_ts_by_idx(idxs) #find group and local index + + idxs -= difference # Adjust index to be relative to the start of the time series + idxs = self.valid_index[IDX][idxs] # Get the actual index in the data + tmp = self.data.__getitem__(IDX) ##use the getitem of the data! + + # Check if enough data is available for both input and output windows + if idxs+self.metadata['future_steps']>self.data.lengths[IDX]: + return None # Not enough data, return None + + #Check for the validity of the time series, by checking nans. + if tmp['is_valid'][idxs-self.metadata['past_steps']:idxs+self.metadata['future_steps']].sum() != self.metadata['future_steps']+self.metadata['past_steps']: + return None + + # Construct sample + for k in tmp.keys(): + # Separate based on past/future + # We create past and future samples only for the ones we sliced using idxs + if '_past' in k: + sample[k] = tmp[k][idxs-self.metadata['past_steps']:idxs] #past steps based on the past_step provided in metadata. + elif '_fut' in k or k=='y': + sample[k] = tmp[k][idxs:idxs+self.metadata['future_steps']]# future steps based on the future_step + else: + pass + return sample + + +# %% +def compute_ranges(d1_dataset, metadata): + """Computes valid index ranges for training, validation, and testing. + + This function calculates the start and end indices for the training, + validation, and testing sets within each time series group, based on the + provided percentages in `metadata`. + + Args: + d1_dataset: The D1 dataset instance (e.g., `PandasTSDataSet`). + metadata: A dictionary containing metadata, including `perc_train` + and `perc_valid` (percentages for training and validation splits). + + Returns: + tuple: A tuple containing three dictionaries: `train_ranges`, + `valid_ranges`, and `test_ranges`. Each dictionary has group IDs + as keys and lists of valid indices as values. + """ + ##suppose for now we use only percentage! + train_ranges = {} + valid_ranges = {} + test_ranges = {} + for k in d1_dataset.lengths: # Iterate over each time series group + ls = d1_dataset.lengths[k] # Get the length of the current time series + train_ranges[k] = list(range(0,int(metadata['perc_train']*ls))) # Training range + valid_ranges[k] = list(range(int(metadata['perc_train']*ls), int((metadata['perc_valid'] + metadata['perc_train'])*ls))) # Validation range + test_ranges[k] = list(range(int((metadata['perc_valid'] + metadata['perc_train'])*ls),ls)) # Testing range + + ## in case of datetime ranges we need to use np.where and create a mask + return train_ranges,valid_ranges,test_ranges + +# Calculate the training, validation, and testing ranges +train_ranges, valid_ranges, test_ranges = compute_ranges(ds, data_module_metadata) + + + +# %% [markdown] +# **Create a DataLoader for the training set.** + +# %% +dl = DataLoader( + MyDataset(ds, data_module_metadata, train_ranges), # Create a MyDataset instance + batch_size=32, # Set the batch size + collate_fn=my_collate # Use the custom collate function + ) + + + +# %% +# Example of iterating through the DataLoader (for debugging/demonstration). +for x in dl: + print(x['y'].shape) # Print the shape of the target variable tensor. + break #only check first batch + + +# %% [markdown] +# **The following function and class show how one might create a `LightningDataModule` to manage the data loading process, including train/validation/test splits. This builds on top of previous components.** + +# %% +class DecoderEncoderDataModule(L.LightningDataModule): + """LightningDataModule for encoder-decoder models. + + This class manages the data loading and preprocessing for training, + validation, testing, and prediction. It handles splitting the data, + creating `Dataset` instances, and creating `DataLoader` instances. It + supports both precomputed splits and on-the-fly splitting. + + Args: + d1_dataset: The D1 dataset instance (e.g., `PandasTSDataSet`). + batch_size: The batch size. + num_workers: The number of worker processes for data loading. + metadata: A dictionary containing metadata and hyperparameters. + """ + def __init__(self, d1_dataset, batch_size=32, num_workers=4, metadata=None): + super().__init__() + # initialize other params + self.d1_dataset = d1_dataset + self.batch_size = batch_size + self.metadata = metadata + self.num_workers = num_workers + + + def prepare_data(self): + """Prepares the data for training/validation/testing. + + This method handles splitting the data into training, validation, and + testing sets. It supports two modes: + + - `precompute=True`: Uses a `split_data` function (not defined here, + but assumed to exist) to precompute the splits. This is useful for + deterministic splits or when the splitting process is expensive. + - `precompute=False`: Calculates the splits on-the-fly using + `compute_ranges`. This is more flexible and allows for different + splitting strategies. + """ + if self.metadata['precompute']: + self.precompute = True + ## @SANDEEP this is what is returned by DSIPTS split_for_train!! + ## the only difference is that we need to use the __getitem__ of the d1 layer + + # The split_data function is assumed to be defined elsewhere and + # would handle the splitting logic according to however the + # project defines "splitting" (could be by time, by groups, etc.) + self.train_dataset, self.validation_dataset, self.test_dataset = split_data(self.d1_dataset,self.metadata) # Placeholder + + + + else: + self.precompute = False + ##in this case we need to pass only the data that are referring to the trainin period + ## but data can be chunked, we need to create a function that transform a D1 object into a D1 object or, even better + ## something that can be used by the dataset for filtering only valid samples! + ##for example + self.train_ranges, self.validation_ranges, self.test_ranges = compute_ranges(self.d1_dataset,self.metadata) + self.predict_data = None ##??? need to study this + ##TODO normalization???? maybe we can precompute here some statistics and pass them to the dataset! + + def setup(self, stage=None): + """Sets up the datasets for each stage (fit, test, predict). + + This method creates the appropriate `Dataset` instances for each stage, + using either the precomputed splits or the on-the-fly calculated ranges. + + Args: + stage: The stage ('fit', 'test', or 'predict'). + """ + # Get metadata from D1 layer during setup + #self.metadata = self.d1_dataset.get_metadata() ##NO SEE COMMENT BEFORE! We get metadata in __init__ + ##create dataset here + if stage == 'fit': + if self.precompute: + # Use precomputed datasets (implementation of split_data needed) + pass # Use self.train_dataset and self.validation_dataset as defined in prepare_data + else: + # Create datasets using computed ranges + self.train_dataset = MyDataset(self.d1_dataset, self.metadata, self.train_ranges) # Pass train_ranges + self.validation_dataset = MyDataset(self.d1_dataset, self.metadata, self.validation_ranges) # Pass valid_ranges + + if stage == 'test': + if self.precompute: + pass + # Use precomputed test dataset + else: + self.test_dataset = MyDataset(self.d1_dataset, self.metadata, self.test_ranges) # Pass test_ranges + + if stage == 'predict': + if self.precompute: + pass + # Use precomputed prediction dataset (if applicable) + else: + self.predict_datasset = MyDataset(self.d1_dataset, self.metadata, self.predict_data) # Pass predict_data. Implementation details TBD. + + def train_dataloader(self): + """Returns the training DataLoader.""" + return DataLoader(self.train_dataset, batch_size=self.batch_size, shuffle=True, num_workers=self.num_workers, collate_fn=my_collate) + + def val_dataloader(self): + """Returns the validation DataLoader.""" + return DataLoader(self.validation_dataset, batch_size=self.batch_size, shuffle=False, num_workers=self.num_workers, collate_fn=my_collate) + + def test_dataloader(self): + """Returns the test DataLoader.""" + return DataLoader(self.test_dataset, batch_size=self.batch_size, shuffle=False, num_workers=self.num_workers, collate_fn=my_collate) + + def predict_dataloader(self): + """Returns the prediction DataLoader.""" + return DataLoader(self.predict_datasset, batch_size=self.batch_size, shuffle=False, num_workers=self.num_workers, collate_fn=my_collate) + + + +# %% [markdown] +# **`DecoderEncoderModel` Class (M Layer - Example)** +# This is a placeholder for the Model Layer. + +# %% + +# Layer M - Example (Placeholder) +class DecoderEncoderModel(L.LightningModule): + """Placeholder for an encoder-decoder model (M layer). + + This is a minimal example of how a model might be structured using + PyTorch Lightning. It shows how to access the metadata from the + `LightningDataModule`. A real implementation would include the actual + network architecture and training logic. + """ + def __init__(self): + super().__init__() + self.metadata = None + + def setup(self, stage=None): + """Gets metadata from the datamodule during setup.""" + # Get metadata from datamodule during setup + self.metadata = self.trainer.datamodule.metadata + + # Initialize layer T model using metadata (placeholder) + + def forward(self, x): + """Forward pass (placeholder).""" + # forward logic (implementation would go here) + pass + + def training_step(self, batch, batch_idx): + """Training step (placeholder).""" + pass # Replace with actual training logic + + def validation_step(self, batch, batch_idx): + """Validation step (placeholder).""" + pass # Replace with actual validation logic + + +# %% [markdown] +# ### Layer M + +# %% + + +class DecoderEncoderModel(L.LightningModule): + def __init__(self): + super().__init__() + self.metadata = None + + def setup(self, stage=None): + # Get metadata from datamodule during setup + self.metadata = self.trainer.datamodule.metadata + + # Initialize layer T model using metadata + + def forward(self, x): + # forward logic + pass + +# %% + + + + +# %% +import numpy as np +import pandas as pd +import zarr +import torch +from torch.utils.data import Dataset +from typing import Optional, Union, List, Dict +import dask.array as da +import dask.dataframe as dd + +class ZarrTSDataset(Dataset): + """PyTorch Dataset for loading time series data from Zarr arrays. + + This class implements a dataset layer optimized for larger-than-memory time series data + using Zarr storage format. It supports chunked data access and lazy loading. + + Parameters + ---------- + data : Union[str, zarr.Group] + Either path to zarr store or zarr group object + time : str + Name of time column + target : Optional[Union[str, List[str]]] + Target variable(s) to predict + group : Optional[List[str]] + Columns to group by + num : Optional[List[Union[str, List[str]]]] + Numerical features + cat : Optional[List[Union[str, List[str]]]] + Categorical features + known : Optional[List[Union[str, List[str]]]] + Known features (available for future timesteps) + unknown : Optional[List[Union[str, List[str]]]] + Unknown features (not available for future timesteps) + static : Optional[List[Union[str, List[str]]]] + Static features (constant over time) + label_encoders : Optional[dict] + Dictionary of label encoders for categorical variables + chunk_size : Optional[Dict[str, int]] + Size of chunks for each dimension, e.g {'time':100, 'group':10} + """ + + def __init__( + self, + data: Union[str, zarr.Group], + time: str, + target: Optional[Union[str, List[str]]] = None, + group: Optional[List[str]] = None, + num: Optional[List[Union[str, List[str]]]] = None, + cat: Optional[List[Union[str, List[str]]]] = None, + known: Optional[List[Union[str, List[str]]]] = None, + unknown: Optional[List[Union[str, List[str]]]] = None, + static: Optional[List[Union[str, List[str]]]] = None, + label_encoders: Optional[dict] = None, + chunk_size: Optional[Dict[str, int]] = None, + ): + # Initialize zarr store + self.zarr_store = data if isinstance(data, zarr.Group) else zarr.open(data, mode='r') + + # Store parameters + self.time = time + self.target = target if isinstance(target, list) else [target] if target else [] + self.group = group if group else [] + self.num = num if num else [] + self.cat = cat if cat else [] + self.known = known if known else [] + self.unknown = unknown if unknown else [] + self.static = static if static else [] + self.label_encoders = label_encoders if label_encoders else {} + self.chunk_size = chunk_size if chunk_size else {} + + # Create dask array from zarr store + self.dask_array = da.from_zarr(self.zarr_store) + + # Convert to dask dataframe + self.data = dd.from_dask_array(self.dask_array, columns=list(self.zarr_store.array_keys())) + + # Compute total length + self._length = len(self.data) + + # Setup chunking + self._setup_chunks() + + def _setup_chunks(self): + """Setup chunk information for iteration.""" + if not self.chunk_size: + self.n_chunks = 1 + return + + self.chunks_per_dim = {} + for dim in self.chunk_size: + total_size = self.data[dim].shape[0] + chunk_size = self.chunk_size[dim] + self.chunks_per_dim[dim] = int(np.ceil(total_size / chunk_size)) + + # Total number of chunks + self.n_chunks = np.prod(list(self.chunks_per_dim.values())) + + def _get_chunk(self, chunk_idx: int) -> pd.DataFrame: + """Get a chunk of data based on chunk index.""" + if not self.chunk_size: + return self.data.compute() + + # Calculate chunk coordinates + chunk_coords = np.unravel_index(chunk_idx, tuple(self.chunks_per_dim.values())) + + # Create slice for each dimension + chunk_slices = {} + for dim_idx, dim in enumerate(self.chunk_size.keys()): + start = chunk_coords[dim_idx] * self.chunk_size[dim] + end = min(start + self.chunk_size[dim], self.data[dim].shape[0]) + chunk_slices[dim] = slice(start, end) + + # Get chunk data + chunk_data = self.data.loc[chunk_slices].compute() + return chunk_data + + def __len__(self) -> int: + """Return the total number of items in the dataset.""" + return self._length + + def __getitem__(self, idx: int) -> Dict[str, torch.Tensor]: + """Get a single item from the dataset. + + Parameters + ---------- + idx : int + Index of the item to get + + Returns + ------- + Dict[str, torch.Tensor] + Dictionary containing the following tensors: + - 't': time values + - 'y': target values + - 'x': feature values + - 'g': group values + - 'st': static feature values + - 'is_valid': mask indicating valid values + """ + # Calculate which chunk this index belongs to + items_per_chunk = np.prod([self.chunk_size[dim] for dim in self.chunk_size]) + chunk_idx = idx // items_per_chunk if self.chunk_size else 0 + local_idx = idx % items_per_chunk if self.chunk_size else idx + + # Get chunk data + chunk_data = self._get_chunk(chunk_idx) + + # Get single item from chunk + data = chunk_data.iloc[local_idx:local_idx+1] + + # Convert to tensors + device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") + + result = { + "t": torch.tensor(data[self.time].values, dtype=torch.float32, device=device), + "y": torch.tensor(data[self.target].values, dtype=torch.float32, device=device), + "x": torch.tensor(data[self.num].values, dtype=torch.float32, device=device) if self.num else torch.tensor([], dtype=torch.float32, device=device), + "g": torch.tensor(data[self.group].values, dtype=torch.int64, device=device) if self.group else torch.tensor([], dtype=torch.int64, device=device), + "st": torch.tensor(data[self.static].values, dtype=torch.float32, device=device) if self.static else torch.tensor([], dtype=torch.float32, device=device), + "is_valid": torch.tensor(data.notna().all(axis=1).values, dtype=torch.bool, device=device), + } + + return result + + @classmethod + def from_pandas(cls, df: pd.DataFrame, zarr_path: str, **kwargs) -> "ZarrTSDataset": + """Create ZarrTSDataset from pandas DataFrame. + + Parameters + ---------- + df : pd.DataFrame + Input DataFrame + zarr_path : str + Path to save zarr store + **kwargs : dict + Additional arguments to pass to ZarrTSDataset constructor + + Returns + ------- + ZarrTSDataset + New dataset instance + """ + # Create zarr store + store = zarr.open(zarr_path, mode='w') + + # Save each column as a zarr array + for col in df.columns: + store.create_dataset(col, data=df[col].values, chunks=True) + + return cls(store, **kwargs) diff --git a/notebooks/sandeep/MultiFileTSDataset.py b/notebooks/sandeep/MultiFileTSDataset.py new file mode 100644 index 00000000..16529b68 --- /dev/null +++ b/notebooks/sandeep/MultiFileTSDataset.py @@ -0,0 +1,311 @@ +"""" +CURRENT STATUS (ON HOLD, DEVELOPMENT WILL BE RESUMED ONCE BASELINE D1D2+MODELLING INTEGRATION IS DONE): +This simplified version: + Handles multiple CSV files instead of Zarr stores + Processes data in chunks using pandas' chunked reading + Maintains a global label encoder that updates as new categories are encountered + Provides a DataLoader for efficient data handling + Includes an example of how to create and use the dataset + + +The main differences from the Zarr version are: + Uses CSV files instead of Zarr stores + Simpler chunking mechanism using pandas' chunked reading + No Dask dependency + More straightforward file handling +""" + +""" +TODO +linking d1d2 + d2 should be in sync with d1 + +weights in the return (use d1 as reference) +return also the groups () + + +post on github about the current state of the code + +d2 layer: precompute the dataset/ stream the the data while using dataloader +doubt: how to normalize(preprocess) the data if we dont read whole data at once. + +look into : prepare data in pytorch lighting module. + +secondary tasks: +implement min max scalar on train and validation data which cant fit in memory. +implement standard scalar on train and validation data which cant fit in memory. + +""" + + +import pandas as pd +import numpy as np +import torch +from torch.utils.data import Dataset, DataLoader +from sklearn.preprocessing import OrdinalEncoder +from typing import List, Dict, Optional, Union +import os + +class MultiFileTSDataset(Dataset): + """PyTorch Dataset for handling multiple files with different groups. + + This class processes multiple files containing time series data with different + groups, maintains a global label encoder, and handles data in chunks. + """ + + def __init__( + self, + file_paths: List[str], + time_col: str, + group_cols: List[str], + target_cols: List[str], + cat_cols: List[str], + chunk_size: int = 1000 + ): + """ + Parameters + ---------- + file_paths : List[str] + List of paths to CSV files + time_col : str + Name of the time column + group_cols : List[str] + Columns to group by + target_cols : List[str] + Target columns to predict + cat_cols : List[str] + Categorical columns to encode + chunk_size : int + Number of rows to process at a time + """ + self.file_paths = file_paths + self.time_col = time_col + self.group_cols = group_cols + self.target_cols = target_cols + self.cat_cols = cat_cols + self.chunk_size = chunk_size #on what basis chunking should be done while reading the data? + # temporal information might get lost during chunking if user data is not sorted by default. + + # label encoders for categorical and group columns + self.label_encoders = {col: OrdinalEncoder(handle_unknown='use_encoded_value', unknown_value=-1) + for col in cat_cols + group_cols} + + # lists to store file and group information + self.file_info = [] + self.group_info = {} + + # Process files and build index + self._process_files() + + def _process_files(self): + """Process each file to extract group information and update encoders.""" + self.total_length = 0 #init total length of the dataset + + for file_idx, file_path in enumerate(self.file_paths): + # Read each file in chunks + for chunk in pd.read_csv(file_path, chunksize=self.chunk_size): + # updating label encoders with new categories from the chunk + self._update_encoders(chunk) + + # If group columns are specified + if self.group_cols: + # unique groups in the chunk + groups = chunk[self.group_cols].drop_duplicates() + + for _, group_row in groups.iterrows(): + # Create a key from the group columns' values + group_key = tuple(group_row[self.group_cols].values) + + # If this group is not seen before, initialize its entry in group_info + if group_key not in self.group_info: + self.group_info[group_key] = [] + # Add the current file index and file info index to the group's entry + self.group_info[group_key].append((file_idx, len(self.file_info))) + + # Filter data for the current group + group_mask = (chunk[self.group_cols] == group_row).all(axis=1) + group_data = chunk[group_mask] + + # Store information about the group in file_info + self.file_info.append({ + 'file_idx': file_idx, + 'group_key': group_key, + 'min_time': group_data[self.time_col].min(), + 'max_time': group_data[self.time_col].max(), + 'length': len(group_data) + }) + + self.total_length += len(group_data) # updating total length + else: + # if no group columns -> store file information + self.file_info.append({ + 'file_idx': file_idx, + 'group_key': None, + 'min_time': chunk[self.time_col].min(), + 'max_time': chunk[self.time_col].max(), + 'length': len(chunk) + }) + + self.total_length += len(chunk) # Update total length + + def _update_encoders(self, data): + """Update label encoders with new categories from the data.""" + for col in self.cat_cols + self.group_cols: # for each categorical and group column + if col in data.columns: # If the column exists in the data + unique_values = data[col].dropna().unique().reshape(-1, 1) # Get unique values + if len(unique_values) > 0: # If there are unique values + self.label_encoders[col].fit(unique_values) # update the encoder for this column + + def __len__(self): + """Return the total number of items in the dataset.""" + return self.total_length + + def _calculate_weight(self, timestamp): + """Calculate weight based on time difference from most recent sample.""" + # Convert timestamp to datetime if needed + if isinstance(timestamp, (int, float)): + timestamp = pd.to_datetime(timestamp, unit='s') + elif isinstance(timestamp, str): + timestamp = pd.to_datetime(timestamp) + + # Get most recent time in dataset + most_recent = max(info['max_time'] for info in self.file_info) + + # Calculate time difference in days + time_diff = (most_recent - timestamp).days + + # Apply exponential decay for weights + # You can adjust the decay rate (0.1) as needed + weight = np.exp(-0.1 * time_diff) + + return weight + + def __getitem__(self, idx): + """Get a single item from the dataset.""" + # Find the file and group that the given index belongs to + cumulative_length = 0 + for info in self.file_info: + if idx < cumulative_length + info['length']: + file_idx = info['file_idx'] + group_key = info['group_key'] + local_idx = idx - cumulative_length + break + cumulative_length += info['length'] + + # Load the relevant chunk from the file + chunk = self._load_chunk(file_idx, group_key) + + # Get the specific row from the chunk + row = chunk.iloc[local_idx] + + # Encode categorical columns + encoded_data = {} + for col in self.cat_cols + self.group_cols: + if col in row.index: + encoded_data[col] = self.label_encoders[col].transform([[row[col]]])[0][0] + + # Convert time to numeric timestamp + time_value = pd.to_datetime(row[self.time_col]).timestamp() + + # Extract target values and ensure they are numeric + target_values = [] + for col in self.target_cols: + target_values.append(float(row[col])) + + # Convert data to PyTorch tensors + result = { + "t": pd.to_datetime(row[self.time_col]).timestamp(), # NOT TENSOR + "y": torch.tensor(target_values, dtype=torch.float32), + "x": torch.tensor(list(encoded_data.values()), dtype=torch.float32), + "weights": torch.tensor(self._calculate_weight(row[self.time_col]), dtype=torch.float32) +} + + return result + + def _load_chunk(self, file_idx, group_key): + """Load a chunk of data from a specific file and group.""" + # Load the next chunk from the specified file + chunk = pd.read_csv(self.file_paths[file_idx], chunksize=self.chunk_size).__next__() + + # Convert the time column to datetime format + chunk[self.time_col] = pd.to_datetime(chunk[self.time_col]) + + # If a group key is specified, filter the chunk by that group + if group_key is not None: + group_mask = True + for i, col in enumerate(self.group_cols): + group_mask = group_mask & (chunk[col] == group_key[i]) + return chunk[group_mask] + return chunk + +# Example usage +def create_example_files(): + """Create example CSV files with different groups.""" + # File 1 with groups A and B + df1 = pd.DataFrame({ + 'time': pd.date_range(start='2025-01-01', periods=100, freq='D'), + 'group': ['A'] * 50 + ['B'] * 50, + 'value': np.random.randn(100), + 'category': np.random.choice(['cat1', 'cat2'], size=100) + }) + df1.to_csv('file1.csv', index=False) # Save to CSV + + # File 2 with groups B and C + df2 = pd.DataFrame({ + 'time': pd.date_range(start='2025-02-01', periods=100, freq='D'), + 'group': ['B'] * 50 + ['C'] * 50, + 'value': np.random.randn(100), + 'category': np.random.choice(['cat2', 'cat3'], size=100) + }) + df2.to_csv('file2.csv', index=False) # Save to csv + +if __name__ == "__main__": + # Create example files + create_example_files() + + # Create dataset instance + dataset = MultiFileTSDataset( + file_paths=['file1.csv', 'file2.csv'], + time_col='time', + group_cols=['group'], + target_cols=['value'], + cat_cols=['category'], + chunk_size=50 + ) + + # Print dataset information + print("=== Dataset Information ===") + print(f"Total items: {len(dataset)}") + print("\nFile and Group Information:") + for i, info in enumerate(dataset.file_info): + print(f"Item {i}:") + print(f" File: {dataset.file_paths[info['file_idx']]}") + print(f" Group: {info['group_key']}") + print(f" Time Range: {info['min_time']} to {info['max_time']}") + print(f" Length: {info['length']}") + + # Print encoder information + print("\n=== Encoder Information ===") + for col, encoder in dataset.label_encoders.items(): + print(f"{col} categories: {encoder.categories_[0].tolist()}") + print(f"{col} mapping: {dict(zip(encoder.categories_[0], range(len(encoder.categories_[0]))))}") + + # Create dataloader + dataloader = DataLoader(dataset, batch_size=16, num_workers=0) + + # Print first batch information + for batch in dataloader: + print("\n=== First Batch Information ===") + print(f"Batch size: {batch['t'].size(0)}") + print(f"Time shape: {batch['t'].shape}") + print(f"Values shape: {batch['y'].shape}") + print(f"Encoded categories shape: {batch['x'].shape}") + + # Print sample data from the batch + print("\nSample data from batch:") + for i in range(min(5, batch['t'].size(0))): + print(f"Item {i}:") + print(f" Time: {batch['t'][i].item()} (timestamp)") + print(f" Value: {batch['y'][i].item()}") + print(f" Encoded categories: {batch['x'][i].tolist()}") + break \ No newline at end of file diff --git a/notebooks/sandeep/MultiFileZarrTSDataset.py b/notebooks/sandeep/MultiFileZarrTSDataset.py new file mode 100644 index 00000000..fff7067a --- /dev/null +++ b/notebooks/sandeep/MultiFileZarrTSDataset.py @@ -0,0 +1,490 @@ +import numpy as np +import pandas as pd +import zarr +import torch +from torch.utils.data import Dataset, DataLoader +from typing import Optional, Union, List, Dict, Tuple, Any +import os +import glob +from datetime import datetime, timedelta +from sklearn.preprocessing import OrdinalEncoder +import dask.dataframe as dd +import dask.array as da +import itertools +import logging + +class MultiFileZarrTSDataset(Dataset): + """PyTorch Dataset for loading time series data from multiple Zarr stores. + + This class handles large datasets split across multiple files, with different + groups in each file. It processes data in chunks and maintains a global label + encoder that can handle all categories across files. + + Parameters + ---------- + file_paths : List[str] + List of paths to zarr stores or directories containing zarr stores + time : str + Name of time column + target : Optional[Union[str, List[str]]] + Target variable(s) to predict + group : Optional[List[str]] + Columns to group by + num : Optional[List[Union[str, List[str]]]] + Numerical features + cat : Optional[List[Union[str, List[str]]]] + Categorical features + known : Optional[List[Union[str, List[str]]]] + Known features (available for future timesteps) + unknown : Optional[List[Union[str, List[str]]]] + Unknown features (not available for future timesteps) + static : Optional[List[Union[str, List[str]]]] + Static features (constant over time) + label_encoders : Optional[dict] + Dictionary of pre-fitted label encoders for categorical variables + chunk_size : Optional[Dict[str, int]] + Size of chunks for each dimension, e.g {'time':100, 'group':10} + """ + + def __init__( + self, + file_paths: List[str], + time: str, + target: Optional[Union[str, List[str]]] = None, + group: Optional[List[str]] = None, + num: Optional[List[Union[str, List[str]]]] = None, + cat: Optional[List[Union[str, List[str]]]] = None, + known: Optional[List[Union[str, List[str]]]] = None, + unknown: Optional[List[Union[str, List[str]]]] = None, + static: Optional[List[Union[str, List[str]]]] = None, + label_encoders: Optional[dict] = None, + chunk_size: Optional[Dict[str, int]] = None, + ): + # Initialize logging + logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') + self.logger = logging.getLogger(__name__) + self.logger.info("Initializing MultiFileZarrTSDataset") + + # Validate file paths + if not file_paths: + self.logger.error("No file paths provided") + raise ValueError("file_paths cannot be empty") + + # Store parameters + self.file_paths = file_paths + self.time = time + self.target = _coerce_to_list(target) + self.group = _coerce_to_list(group) + self.num = _coerce_to_list(num) + self.cat = _coerce_to_list(cat) + self.known = _coerce_to_list(known) + self.unknown = _coerce_to_list(unknown) + self.static = _coerce_to_list(static) + self.chunk_size = chunk_size if chunk_size else {} + + # Initialize zarr stores + self.zarr_stores = [] + self.file_info = [] + + # Initialize label encoders + if label_encoders is None: + self.label_encoders = {} + for c in self.cat + self.group: + self.label_encoders[c] = OrdinalEncoder() + else: + self.label_encoders = label_encoders + + # Process each file + self._process_files() + + # Setup chunking + self._setup_chunks() + + def _process_files(self): + """Process each file, extract group information, and update label encoders.""" + self.logger.info("Processing files") + self.group_info = {} # Maps group values to file indices + self.total_length = 0 + + for file_idx, file_path in enumerate(self.file_paths): + self.logger.info(f"Processing file {file_idx + 1}/{len(self.file_paths)}: {file_path}") + + # Open zarr store + try: + zarr_store = zarr.open(file_path, mode='r') + self.zarr_stores.append(zarr_store) + except Exception as e: + self.logger.error(f"Failed to open Zarr store at {file_path}: {e}") + raise + + # Create a small sample to analyze groups and update encoders + sample_data = self._get_sample_from_store(zarr_store) + + # Extract group information + if self.group: + groups_in_file = sample_data[self.group].drop_duplicates() + + # For each group in this file + for _, group_row in groups_in_file.iterrows(): + group_key = tuple(group_row[self.group].values) + + # Store mapping from group to file + if group_key not in self.group_info: + self.group_info[group_key] = [] + self.group_info[group_key].append(file_idx) + + # Get time range for this group in this file + group_mask = True + for g_idx, g_col in enumerate(self.group): + group_mask = group_mask & (sample_data[g_col] == group_key[g_idx]) + + group_data = sample_data[group_mask] + min_time = group_data[self.time].min() + max_time = group_data[self.time].max() + + # Store file info + self.file_info.append({ + 'file_idx': file_idx, + 'group_key': group_key, + 'min_time': min_time, + 'max_time': max_time, + 'length': len(group_data) + }) + + self.total_length += len(group_data) + else: + # No groups, just store file info + min_time = sample_data[self.time].min() + max_time = sample_data[self.time].max() + + self.file_info.append({ + 'file_idx': file_idx, + 'group_key': None, + 'min_time': min_time, + 'max_time': max_time, + 'length': len(sample_data) + }) + + self.total_length += len(sample_data) + + # Update label encoders with categories from this file + self._update_encoders(sample_data) + + self.logger.info(f"Finished processing {len(self.file_paths)} files") + + def _get_sample_from_store(self, zarr_store, sample_size=1000): + """Get a sample from a zarr store for analysis.""" + # Create a dictionary to hold the data + data_dict = {} + + # Get all array keys from the store + array_keys = list(zarr_store.array_keys()) + + # Get a sample from each array + for key in array_keys: + array = zarr_store[key] + # Take first sample_size elements or all if fewer + sample = array[:min(sample_size, array.shape[0])] + data_dict[key] = sample + + # Convert to pandas DataFrame + return pd.DataFrame(data_dict) + + def _update_encoders(self, data): + """Update label encoders with new categories from the data.""" + for col in self.cat + self.group: + if col in data.columns: + # Get unique values + unique_values = data[col].dropna().unique().reshape(-1, 1) + + if len(unique_values) > 0: + # Check if encoder exists + if col not in self.label_encoders: + self.label_encoders[col] = OrdinalEncoder() + self.label_encoders[col].fit(unique_values) + else: + # Get current categories + current_categories = self.label_encoders[col].categories_[0] + + # Check if there are new categories + new_categories = np.setdiff1d(unique_values.flatten(), current_categories) + + if len(new_categories) > 0: + # Create new encoder with combined categories + combined_categories = np.concatenate([current_categories, new_categories]) + new_encoder = OrdinalEncoder(categories=[combined_categories]) + new_encoder.fit(combined_categories.reshape(-1, 1)) + self.label_encoders[col] = new_encoder + + def _setup_chunks(self): + """Setup chunk information for iteration.""" + if not self.chunk_size: + self.n_chunks = len(self.file_info) + return + + # Calculate total chunks based on file_info and chunk_size + self.n_chunks = 0 + self.chunk_map = [] # Maps chunk_idx to (file_info_idx, local_chunk_idx) + + for info_idx, info in enumerate(self.file_info): + # Calculate chunks for this file and group + chunks_in_file = 1 + for dim, size in self.chunk_size.items(): + if dim == self.time: + # Calculate chunks based on time range + time_range = info['max_time'] - info['min_time'] + if isinstance(time_range, timedelta): + # For datetime + chunks_in_time = int(np.ceil(time_range.total_seconds() / + pd.Timedelta(seconds=size).total_seconds())) + else: + # For numeric + chunks_in_time = int(np.ceil(time_range / size)) + chunks_in_file *= max(1, chunks_in_time) + + # Store mapping + for local_chunk_idx in range(chunks_in_file): + self.chunk_map.append((info_idx, local_chunk_idx)) + + self.n_chunks += chunks_in_file + + def _get_chunk(self, chunk_idx): + """Get a chunk of data based on chunk index.""" + if chunk_idx >= len(self.chunk_map): + raise IndexError(f"Chunk index {chunk_idx} out of range") + + info_idx, local_chunk_idx = self.chunk_map[chunk_idx] + file_info = self.file_info[info_idx] + file_idx = file_info['file_idx'] + zarr_store = self.zarr_stores[file_idx] + + # Create a dictionary to hold the data + data_dict = {} + + # Get all array keys from the store + array_keys = list(zarr_store.array_keys()) + + # Calculate chunk boundaries + if self.chunk_size and self.time in self.chunk_size: + time_chunk_size = self.chunk_size[self.time] + start_time = file_info['min_time'] + local_chunk_idx * time_chunk_size + end_time = min(start_time + time_chunk_size, file_info['max_time']) + + # Create dask arrays for each column + for key in array_keys: + data_dict[key] = da.from_zarr(zarr_store[key]) + + # Convert to dask dataframe + ddf = dd.from_dask_array( + da.stack([data_dict[key] for key in array_keys], axis=1), + columns=array_keys + ) + + # Filter by time range + filtered_ddf = ddf[(ddf[self.time] >= start_time) & (ddf[self.time] < end_time)] + + # Filter by group if applicable + if self.group and file_info['group_key'] is not None: + for g_idx, g_col in enumerate(self.group): + filtered_ddf = filtered_ddf[filtered_ddf[g_col] == file_info['group_key'][g_idx]] + + # Compute the result + chunk_data = filtered_ddf.compute() + else: + # No chunking by time, get all data for this group + # Create pandas dataframe directly + data_dict = {} + for key in array_keys: + data_dict[key] = zarr_store[key][:] + + chunk_data = pd.DataFrame(data_dict) + + # Filter by group if applicable + if self.group and file_info['group_key'] is not None: + for g_idx, g_col in enumerate(self.group): + chunk_data = chunk_data[chunk_data[g_col] == file_info['group_key'][g_idx]] + + # Apply label encoders + for col in self.cat + self.group: + if col in chunk_data.columns: + chunk_data[col] = self.label_encoders[col].transform( + chunk_data[col].values.reshape(-1, 1) + ).flatten() + + return chunk_data + + def __len__(self): + """Return the total number of items in the dataset.""" + return self.total_length + + def __getitem__(self, idx): + """Get a single item from the dataset.""" + # Find which chunk this index belongs to + chunk_idx = 0 + local_idx = idx + cumulative_length = 0 + + for i, info in enumerate(self.file_info): + if idx < cumulative_length + info['length']: + chunk_idx = i + local_idx = idx - cumulative_length + break + cumulative_length += info['length'] + + # Get chunk data + chunk_data = self._get_chunk(chunk_idx) + + # Get single item from chunk + if local_idx >= len(chunk_data): + # Handle edge case where chunk size estimation was off + local_idx = len(chunk_data) - 1 + + data = chunk_data.iloc[local_idx:local_idx+1] + + # Convert to tensors + device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") + + result = { + "t": torch.tensor(data[self.time].values, dtype=torch.float32, device=device), + "y": torch.tensor(data[self.target].values, dtype=torch.float32, device=device) if self.target else torch.tensor([], dtype=torch.float32, device=device), + "x": torch.tensor(data[self.num].values, dtype=torch.float32, device=device) if self.num else torch.tensor([], dtype=torch.float32, device=device), + "g": torch.tensor(data[self.group].values, dtype=torch.int64, device=device) if self.group else torch.tensor([], dtype=torch.int64, device=device), + "st": torch.tensor(data[self.static].values, dtype=torch.float32, device=device) if self.static else torch.tensor([], dtype=torch.float32, device=device) + } + + return result + + def get_metadata(self): + """Return metadata about the dataset.""" + metadata = { + "cols": { + "y": self.target, + "x": self.num + self.cat, + "st": self.static, + }, + "col_type": {}, + "col_known": {}, + "cat_index": [] + } + + # Set column types and known status + all_cols = self.target + self.num + self.cat + self.static + for i, col in enumerate(self.num + self.cat): + if col in self.cat: + metadata['cat_index'].append(i) + + metadata["col_type"][col] = "C" if col in self.cat else "F" + metadata["col_known"][col] = "K" if col in self.known else "U" + + metadata['encoders'] = self.label_encoders + + return metadata + + @classmethod + def from_pandas_files(cls, dataframes, zarr_paths, **kwargs): + """Create MultiFileZarrTSDataset from multiple pandas DataFrames. + + Parameters + ---------- + dataframes : List[pd.DataFrame] + List of input DataFrames + zarr_paths : List[str] + List of paths to save zarr stores + **kwargs : dict + Additional arguments to pass to MultiFileZarrTSDataset constructor + + Returns + ------- + MultiFileZarrTSDataset + New dataset instance + """ + if len(dataframes) != len(zarr_paths): + raise ValueError("Number of dataframes must match number of zarr paths") + + # Create zarr stores + for df, path in zip(dataframes, zarr_paths): + store = zarr.open(path, mode='w') + + # Save each column as a zarr array + for col in df.columns: + store.create_dataset(col, data=df[col].values, chunks=True) + + return cls(zarr_paths, **kwargs) + + +# Example usage: +def create_example_data(): + """Create example data with different groups across files.""" + # Create first dataframe with groups A and B + df1 = pd.DataFrame({ + 'time': pd.date_range(start='2025-01-01', periods=100, freq='D'), + 'group': ['A'] * 50 + ['B'] * 50, + 'value': np.random.randn(100), + 'category': np.random.choice(['cat1', 'cat2', 'cat3'], size=100) + }) + + # Create second dataframe with groups B and C + df2 = pd.DataFrame({ + 'time': pd.date_range(start='2025-02-01', periods=100, freq='D'), + 'group': ['B'] * 50 + ['C'] * 50, + 'value': np.random.randn(100), + 'category': np.random.choice(['cat2', 'cat3', 'cat4'], size=100) # Note: cat4 is new + }) + + # Print group ranges + print("File 1:") + for group in ['A', 'B']: + group_data = df1[df1['group'] == group] + print(f" Group {group}: {group_data['time'].min()} to {group_data['time'].max()}") + + print("File 2:") + for group in ['B', 'C']: + group_data = df2[df2['group'] == group] + print(f" Group {group}: {group_data['time'].min()} to {group_data['time'].max()}") + + return [df1, df2] + + +def _coerce_to_list(obj): + """Coerces input to a list.""" + if obj is None: + return [] + elif isinstance(obj, list): + return obj + else: + return [obj] + + +# Example of using the dataset +if __name__ == "__main__": + # Create example data + dataframes = create_example_data() + + # Create zarr paths + zarr_paths = ['file1.zarr', 'file2.zarr'] + + # Create dataset + dataset = MultiFileZarrTSDataset.from_pandas_files( + dataframes=dataframes, + zarr_paths=zarr_paths, + time='time', + target='value', + group=['group'], + cat=['category'], + chunk_size={'time': pd.Timedelta(days=10)} + ) + + # Create dataloader + dataloader = DataLoader(dataset, batch_size=16, num_workers=2) + + # Print metadata + print("\nDataset Metadata:") + metadata = dataset.get_metadata() + print(f"Encoders: {metadata['encoders']}") + print(f"Categorical indices: {metadata['cat_index']}") + + # Print first batch + print("\nFirst batch:") + for batch in dataloader: + print(f"Batch shapes: y={batch['y'].shape}, x={batch['x'].shape}, g={batch['g'].shape}") + break \ No newline at end of file diff --git a/notebooks/sandeep/TEST.ipynb b/notebooks/sandeep/TEST.ipynb new file mode 100644 index 00000000..7e151e9c --- /dev/null +++ b/notebooks/sandeep/TEST.ipynb @@ -0,0 +1,813 @@ +{ + "cells": [ + { + "cell_type": "raw", + "id": "55c75749-ec70-4365-ac8a-f96552da4528", + "metadata": {}, + "source": [ + "from dsipts import Categorical,TimeSeries, RNN\n", + "settimana = Categorical('settimanale',1,[1,1,1,1,1,1,1],7,'multiplicative',[0.9,0.8,0.7,0.6,0.5,0.99,0.99])\n", + "\n", + "##montly, additive (here there are only 5 month)\n", + "mese = Categorical('mensile',1,[31,28,20,10,33],5,'additive',[10,20,-10,20,0])\n", + "\n", + "##spot categorical variables: in this case it occurs every 100 days and it lasts 7 days adding 10 to the original timeseries\n", + "spot = Categorical('spot',100,[7],1,'additive',[10])\n", + "\n", + "##initizate a timeseries object\n", + "ts = TimeSeries('prova')\n", + "ts.generate_signal(noise_mean=1,categorical_variables=[settimana,mese,spot],length=5000,type=0)\n", + "dataset = ts.dataset\n", + "dataset['x_num'] = 0.0\n", + "dataset.loc[:dataset.shape[0]-10,'x_num']= dataset['x_num'].values[9:]\n", + "cat_var_past = ts.cat_var\n", + "cat_var_fut = ts.cat_fut\n", + "num_var_past = ts.num_var+['x_num']\n", + "import pickle \n", + "with open('example.pkl', 'wb') as f:\n", + " pickle.dump([dataset,cat_var_past,cat_var_fut,num_var_past],f)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "459c491c-98ab-4513-b5eb-9f5c604b9df4", + "metadata": {}, + "outputs": [], + "source": [ + "#dataset.to_csv('dataset.csv',index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0b53411e-8c81-4dc9-8e2e-c83c3a568be1", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "beea9295-9164-4bae-b2e1-8c5cc6c585c4", + "metadata": {}, + "outputs": [], + "source": [ + "import pickle \n", + "import pandas as pd\n", + "from typing import Union\n", + "from sklearn.preprocessing import LabelEncoder,OrdinalEncoder\n", + "from datetime import datetime\n", + "import lightning as L\n", + "with open('example.pkl', 'rb') as f:\n", + " dataset,cat_var_past,cat_var_fut,num_var_past = pickle.load(f)\n", + "num_var_fut = []\n", + "group = None\n", + "time_var = 'time'\n", + "target = ['signal']" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "db453ed2-df16-44bf-bc86-184fd4010e7a", + "metadata": {}, + "outputs": [], + "source": [ + "dataset2 = dataset.copy()\n", + "dataset2['group'] = 2\n", + "dataset['group'] = 1 \n", + "dataset = pd.concat([dataset, dataset2])" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "dd967d57-478d-4bd5-bf92-b369ca730c28", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1.0" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "4d1b49d6-8e61-447d-9e2e-44893f104cb8", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "def extend_time_df(x:pd.DataFrame,freq:Union[str,int],group:Union[str,None]=None,global_minmax:bool=False)-> pd.DataFrame:\n", + " \"\"\"Utility for generating a full dataset and then merge the real data\n", + "\n", + " Args:\n", + " x (pd.DataFrame): dataframe containing the column time\n", + " freq (str): frequency (in pandas notation) of the resulting dataframe\n", + " group (string or None): if not None the min max are computed by the group column, default None\n", + " global_minmax (bool): if True the min_max is computed globally for each group. Usually used for stacked model\n", + " Returns:\n", + " pd.DataFrame: a dataframe with the column time ranging from thr minumum of x to the maximum with frequency `freq`\n", + " \"\"\"\n", + "\n", + " if group is None:\n", + "\n", + " if isinstance(freq,int):\n", + " empty = pd.DataFrame({'time':list(range(x.time.min(),x.time.max(),freq))})\n", + " else:\n", + " empty = pd.DataFrame({'time':pd.date_range(x.time.min(),x.time.max(),freq=freq)})\n", + "\n", + " else:\n", + " \n", + " if global_minmax:dataset\n", + " _min = pd.DataFrame({group:x[group].unique(),'time':x.time.min()})\n", + " _max = pd.DataFrame({group:x[group].unique(),'time':x.time.max()})\n", + "\n", + " else:\n", + " _min = x.groupby(group).time.min().reset_index()\n", + " _max = x.groupby(group).time.max().reset_index()\n", + " empty = []\n", + " for c in x[group].unique():\n", + " if isinstance(freq,int):\n", + " empty.append(pd.DataFrame({group:c,'time':np.arange(_min.time[_min[group]==c].values[0],_max.time[_max[group]==c].values[0],freq)}))\n", + "\n", + " else:\n", + " empty.append(pd.DataFrame({group:c,'time':pd.date_range(_min.time[_min[group]==c].values[0],_max.time[_max[group]==c].values[0],freq=freq)}))\n", + " \n", + " empty = pd.concat(empty,ignore_index=True)\n", + " return empty\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "9e2ac8bd-fc6b-4400-b8f3-b86f1138671b", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "class PandasTSDataSet:\n", + " def __init__(self, df,time_var, cat_var_past, cat_var_fut, num_var_past, num_var_fut,target, group = None, metadata={},check_holes_and_duplicates=True,check_past=True):\n", + " \n", + " numerica_var = list(set(num_var_past).union(set(num_var_fut)).union(set(target)))\n", + " df_norm = df[numerica_var+[time_var]].copy()\n", + " label_encoders = {}\n", + " ##Encoders for categorical since we want to return tensors\n", + " for c in set(cat_var_past).union(set(cat_var_fut)):\n", + " label_encoders[c] = LabelEncoder()\n", + " df_norm[c] = label_encoders[c].fit_transform(df[c])\n", + " if group is not None:\n", + " label_encoders[group] = OrdinalEncoder()\n", + " df_norm[group] = label_encoders[group].fit_transform(df[group])\n", + " #complete the dataset, we can think to devide it in chunks of consecutive ts....\n", + " if check_holes_and_duplicates:\n", + " df_norm.drop_duplicates(subset=['time'] if group is None else [group,'time'], keep='first', inplace=True, ignore_index=True)\n", + " print(df_norm.shape)\n", + " if group is None:\n", + " differences = df_norm.time.diff()[1:]\n", + " else:\n", + " differences = df_norm[df_norm[group]==df_norm[group].unique()[0]].time.diff()[1:]\n", + " \n", + " if isinstance(df_norm.time[0], datetime):\n", + " freq = pd.to_timedelta(differences.min()) \n", + " else:\n", + " if int(df_norm.time[0])==df_norm.time[0]: ##ONLY THINK THAT WORKS IN GENERAL\n", + " freq = int(differences.min())\n", + " else:\n", + " raise TypeError(\"time must be integer or datetime\")\n", + " metadata['fraq'] = freq \n", + " if differences.nunique()>1:\n", + " df_norm = extend_time_df(df_norm,freq,group).merge(dataset,how='left')\n", + " print(df_norm.shape)\n", + " \n", + " else:\n", + " metadata['freq'] = dataset.time.diff()[1:].min() ##care there can be holes in data!\n", + "\n", + " if check_past:\n", + " num_var_past = list(set(num_var_past).union(set(target)))\n", + " if group is None:\n", + " df_norm = df_norm.sort_values(by=time_var).reset_index()\n", + " self.lengths = df_norm.shape[0]\n", + " else:\n", + " df_norm = df_norm.sort_values(by=[time_var,group]).reset_index()\n", + " \n", + " df_norm['valid'] = ~pd.isnull(dataset.max(axis=1))\n", + " df_norm['valid'] = df_norm['valid'].astype(int)\n", + " self.df = df_norm\n", + " metadata['target'] = target\n", + " metadata['num_var_past'] = num_var_past\n", + " metadata['num_var_fut'] = num_var_fut\n", + " metadata['cat_var_past'] = cat_var_past\n", + " metadata['cat_var_fut'] = cat_var_fut\n", + " metadata['past_variables'] = list(set(cat_var_past).union(set(num_var_past)))\n", + " metadata['future_variables'] = list(set(cat_var_fut).union(set(num_var_fut)).union(set(target)))\n", + " metadata['time_var'] =time_var\n", + " metadata['group'] = group\n", + " \n", + " self.metadata = metadata\n", + " ## compute the length of each timeseries\n", + " ll = {}\n", + " if group is None:\n", + " ll[0] = self.df.shape[0]\n", + " else:\n", + " for i,row in self.df.groupby(group)[time_var].count().reset_index().iterrows():\n", + " ll[row[group]] = row[time_var]\n", + " self.lengths = ll\n", + " \n", + "\n", + " def get_total_len(self):\n", + " return sum(self.lengths.values())\n", + "\n", + " def get_id_ts_by_idx(self,idx):\n", + " tmp = np.cumsum(sum(self.lengths.values()))\n", + " idx = min(np.where(tmp>idx)[0])\n", + " return idx, 0 if idx==0 else tmp[idx-1]\n", + "\n", + " ## this is the most delicate part\n", + " def __getitem__(self, idx):\n", + " # Returns only tensors, no metadata\n", + " device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n", + " if self.metadata['group'] is None:\n", + " df = self.df\n", + " else:\n", + " df = self.df[self.df.group==idx] ## ANOTHER COMMENT: IDX?? what if we have 10 timeseries with groups??\n", + " \n", + " result = {\n", + " #'t': tensor(...), ##FIRST COMMENT: TIMESTAMP CANT be cast as tensor! \n", + " 'y': torch.tensor(df[self.metadata['target']].values,dtype=torch.float32,device=device), ##SECOND COMMENT device\n", + " 'x_past': torch.tensor(df[self.metadata['num_var_past']].values,dtype=torch.float32,device=device), ##SECOND COMMENT device\n", + " 'is_valid': torch.tensor(df.valid.values,dtype=torch.int,device=device), ##SECOND COMMENT device\n", + " #'t_f': tensor(...), ?? WHAT IS THAT?\n", + " \n", + " }\n", + " if self.metadata['group'] is not None:\n", + " result['group']= torch.tensor(idx,device=device,dtype=torch.long) \n", + " if len(self.metadata['num_var_fut'])>0:\n", + " result['num_var_fut']= torch.tensor(df[self.metadata['num_var_fut']].values,dtype=torch.float32,device=device)\n", + " if len(self.metadata['cat_var_fut'])>0:\n", + " result['cat_var_past']= torch.tensor(df[self.metadata['cat_var_past']].values,dtype=torch.long,device=device)\n", + " if len(self.metadata['cat_var_fut'])>0:\n", + " result['cat_var_fut']= torch.tensor(df[self.metadata['cat_var_fut']].values,dtype=torch.long,device=device)\n", + " return result\n", + " def get_metadata(self):\n", + " return self.metadata\n", + "\n", + "# Layer D2\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "290ab40d-f440-4f7f-9ae9-8855a04f1ec6", + "metadata": {}, + "outputs": [], + "source": [ + "data_module_metadata = dict(\n", + " perc_train= 0.7,\n", + " perc_valid= 0.1,\n", + " range_train= None,\n", + " range_validation= None ,\n", + " range_test= None,\n", + " shift= 0,\n", + " starting_point= None,\n", + " skip_step= 1,\n", + " past_steps=16,\n", + " future_steps= 16,\n", + " scaler= 'sklearn.preprocessing.StandardScaler()')" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "e4ca4055-7f36-446c-9b21-6c0456b152cf", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(5000, 6)\n" + ] + } + ], + "source": [ + "ds = PandasTSDataSet(dataset,'time', \n", + " cat_var_past, \n", + " cat_var_fut, \n", + " num_var_past,\n", + " num_var_fut,\n", + " target,\n", + " group = None, \n", + " metadata={}\n", + " ,check_holes_and_duplicates=True,check_past=True)\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "519ebe66-3d64-41df-a191-81c0ba518e79", + "metadata": {}, + "outputs": [], + "source": [ + "from torch.utils.data import Dataset, DataLoader" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "12bc5f1c-2de1-4837-9d2a-e764fa395bba", + "metadata": {}, + "outputs": [], + "source": [ + "from torch.utils.data.dataloader import default_collate\n", + "import torch\n", + "def my_collate(batch):\n", + " batch = list(filter(lambda x : x is not None, batch))\n", + " print(len(batch))\n", + " return default_collate(batch)\n", + "\n", + "class MyDataset(Dataset):\n", + "\n", + " def __init__(self,data,metadata)->torch.utils.data.Dataset:\n", + " \n", + " self.data = data \n", + " self.metadata = metadata\n", + " self.metadata_dataset = data.metadata\n", + " def __len__(self):\n", + " \n", + " return self.data.get_total_len()-len(self.data.lengths)*self.metadata['future_steps']\n", + " ##this getitem calls the getitem of the data as desired\n", + " def __getitem__(self, idxs): \n", + " sample = {}\n", + " IDX,difference = self.data.get_id_ts_by_idx(idxs) \n", + " idxs-=difference\n", + " tmp = self.data.__getitem__(IDX)\n", + " if idxs+self.metadata['future_steps']>self.data.lengths[IDX]:\n", + " return None\n", + " \n", + " for k in tmp.keys():\n", + "\n", + " if len(tmp[k][idxs-self.metadata['past_steps']:idxs]) + len(tmp[k][idxs:idxs+self.metadata['future_steps']]) == self.metadata['future_steps']+self.metadata['past_steps']:\n", + " if tmp['is_valid'][idxs-self.metadata['past_steps']:idxs+self.metadata['future_steps']].sum()==self.metadata['future_steps']+self.metadata['past_steps']:\n", + " if '_past' in k:\n", + " sample[k] = tmp[k][idxs-self.metadata['past_steps']:idxs]\n", + " elif '_fut' in k or k=='y':\n", + " sample[k] = tmp[k][idxs:idxs+self.metadata['future_steps']]\n", + " else:\n", + " pass\n", + " if len(sample)==0:\n", + " return None\n", + " else:\n", + " return sample" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "7e43bfd7-12b3-48c2-8b33-d5ca6681959c", + "metadata": {}, + "outputs": [], + "source": [ + "dl = DataLoader(\n", + " MyDataset(ds,data_module_metadata), ##change respect to the initial vignette, can not use standard dataset\n", + " batch_size=32,\n", + " collate_fn=my_collate\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "dde2ba1f-f6f8-483c-a404-5917b84ce37a", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "16\n", + "torch.Size([16, 16, 1])\n", + "32\n", + "torch.Size([32, 16, 1])\n", + "32\n", + "torch.Size([32, 16, 1])\n", + "32\n", + "torch.Size([32, 16, 1])\n", + "32\n", + "torch.Size([32, 16, 1])\n", + "32\n", + "torch.Size([32, 16, 1])\n", + "32\n", + "torch.Size([32, 16, 1])\n", + "32\n", + "torch.Size([32, 16, 1])\n", + "32\n", + "torch.Size([32, 16, 1])\n", + "32\n", + "torch.Size([32, 16, 1])\n", + "32\n", + "torch.Size([32, 16, 1])\n", + "32\n", + "torch.Size([32, 16, 1])\n", + "32\n", + "torch.Size([32, 16, 1])\n", + "32\n", + "torch.Size([32, 16, 1])\n", + "32\n", + "torch.Size([32, 16, 1])\n", + "32\n", + 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" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "eaff143f-9754-4335-8a68-c5829d2d22d7", + "metadata": {}, + "outputs": [], + "source": [ + "##WIP IN THE NEXT CELLS" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8f4c6bec-ee6e-4c54-9223-bf3a72ed46da", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "class DecoderEncoderDataModule(L.LightningDataModule):\n", + " def __init__(self, d1_dataset, batch_size=32, num_workers=4,metadata=None):\n", + " super().__init__()\n", + " # initialize other params\n", + " self.d1_dataset = d1_dataset\n", + " self.batch_size = batch_size\n", + " self.metadata = None # Will be set in setup() NO! THhe metadata for training are not related with metadata of the data!\n", + " \n", + " def setup(self, stage=None):\n", + " # Get metadata from D1 layer during setup\n", + " #self.metadata = self.d1_dataset.get_metadata() ##NO SEE COMMENT BEFORE!\n", + " \n", + " if stage == 'fit' or stage is None: \n", + " ##CLEAR FOR ME IN CASE OF SINGLE TIMESERIES (EG PANDAS LOADER) not clear in case of chunk of data of the same \n", + " ## do we suppose to have NO HOLES in data? do we check it runtime?\n", + " #I can precompute here the indexes \n", + " \n", + " \n", + " \n", + " pass\n", + " \n", + " if stage == 'test' or stage is None:\n", + " # Any test-specific setup\n", + " pass\n", + " \n", + " def train_dataloader(self):\n", + " return DataLoader(\n", + " MyDataset(self.d1_dataset),\n", + " batch_size=self.batch_size,\n", + " **other_params\n", + " )\n", + "\n", + "# Layer M\n", + "class DecoderEncoderModel(L.LightningModule):\n", + " def __init__(self):\n", + " super().__init__()\n", + " self.metadata = None\n", + " \n", + " def setup(self, stage=None):\n", + " # Get metadata from datamodule during setup\n", + " self.metadata = self.trainer.datamodule.metadata\n", + " \n", + " # Initialize layer T model using metadata\n", + " \n", + " def forward(self, x):\n", + " # forward logic\n", + " pass" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "dsipts", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/sandeep/TEST_v2.ipynb b/notebooks/sandeep/TEST_v2.ipynb new file mode 100644 index 00000000..58ca7044 --- /dev/null +++ b/notebooks/sandeep/TEST_v2.ipynb @@ -0,0 +1,1305 @@ +{ + "cells": [ + { + "cell_type": "raw", + "id": "55c75749-ec70-4365-ac8a-f96552da4528", + "metadata": {}, + "source": [ + "from dsipts import Categorical,TimeSeries, RNN\n", + "settimana = Categorical('settimanale',1,[1,1,1,1,1,1,1],7,'multiplicative',[0.9,0.8,0.7,0.6,0.5,0.99,0.99])\n", + "\n", + "##montly, additive (here there are only 5 month)\n", + "mese = Categorical('mensile',1,[31,28,20,10,33],5,'additive',[10,20,-10,20,0])\n", + "\n", + "##spot categorical variables: in this case it occurs every 100 days and it lasts 7 days adding 10 to the original timeseries\n", + "spot = Categorical('spot',100,[7],1,'additive',[10])\n", + "\n", + "##initizate a timeseries object\n", + "ts = TimeSeries('prova')\n", + "ts.generate_signal(noise_mean=1,categorical_variables=[settimana,mese,spot],length=5000,type=0)\n", + "dataset = ts.dataset\n", + "dataset['x_num'] = 0.0\n", + "dataset.loc[:dataset.shape[0]-10,'x_num']= dataset['x_num'].values[9:]\n", + "cat_var_past = ts.cat_var\n", + "cat_var_fut = ts.cat_fut\n", + "num_var_past = ts.num_var+['x_num']\n", + "import pickle \n", + "with open('example.pkl', 'wb') as f:\n", + " pickle.dump([dataset,cat_var_past,cat_var_fut,num_var_past],f)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "459c491c-98ab-4513-b5eb-9f5c604b9df4", + "metadata": {}, + "outputs": [], + "source": [ + "#dataset.to_csv('dataset.csv',index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "0b53411e-8c81-4dc9-8e2e-c83c3a568be1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting lightning\n", + " Downloading lightning-2.3.3-py3-none-any.whl.metadata (35 kB)\n", + "Requirement already satisfied: PyYAML<8.0,>=5.4 in /Users/wenda/anaconda3/lib/python3.8/site-packages (from lightning) (6.0.1)\n", + "Requirement already satisfied: fsspec<2026.0,>=2022.5.0 in /Users/wenda/anaconda3/lib/python3.8/site-packages (from fsspec[http]<2026.0,>=2022.5.0->lightning) (2024.6.1)\n", + "Collecting 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This behaviour is the source of the following dependency conflicts.\n", + "ragatouille 0.0.7.post11 requires langchain<0.2.0,>=0.1.0, but you have langchain 0.2.8 which is incompatible.\n", + "ragatouille 0.0.7.post11 requires langchain_core<0.2.0,>=0.1.4, but you have langchain-core 0.2.19 which is incompatible.\n", + "torchaudio 2.3.1 requires torch==2.3.1, but you have torch 2.4.1 which is incompatible.\n", + "torchvision 0.15.2 requires torch==2.0.1, but you have torch 2.4.1 which is incompatible.\u001b[0m\u001b[31m\n", + "\u001b[0mSuccessfully installed lightning-2.3.3 lightning-utilities-0.11.9 pytorch-lightning-2.4.0 torch-2.4.1 torchmetrics-1.5.2\n", + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.1.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.0.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n" + ] + } + ], + "source": [ + "!pip install lightning" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "f92518d7", + "metadata": {}, + "outputs": [], + "source": [ + "import pickle\n", + "import pandas as pd\n", + "from typing import Union, List, Optional, Dict\n", + "from sklearn.preprocessing import LabelEncoder # Used for categorical encoding\n", + "from datetime import datetime, timedelta # For time-related operations\n", + "import lightning as L # PyTorch Lightning\n", + "import numpy as np\n", + "from sklearn.preprocessing import OrdinalEncoder # Used for categorical encoding\n", + "import torch\n", + "from torch.utils.data import Dataset, DataLoader\n", + "from torch.utils.data.dataloader import default_collate\n" + ] + }, + { + "cell_type": "markdown", + "id": "09307105", + "metadata": {}, + "source": [ + "**Data Loading and Preparation (Example Data)**\n", + "\n", + "This section loads example time series data and prepares it for use in the `DataSet` classes. It demonstrates the structure of the input data.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "beea9295-9164-4bae-b2e1-8c5cc6c585c4", + "metadata": {}, + "outputs": [], + "source": [ + "with open('example.pkl', 'rb') as f:\n", + " dataset, cat_var_past, cat_var_fut, num_var_past = pickle.load(f)\n", + "num_var_fut = [] # No numerical future variables in this example\n", + "group = None # No grouping in this first example. Later examples *do* use grouping.\n", + "time_var = 'time'\n", + "target = ['signal']" + ] + }, + { + "cell_type": "markdown", + "id": "9c4fb3c8", + "metadata": {}, + "source": [ + "**Example with Grouping**\n", + "\n", + "The following cell creates a second copy of the dataset and assigns it a different group ID. This is then concatenated with the original `dataset` to showcase how grouping works.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "db453ed2-df16-44bf-bc86-184fd4010e7a", + "metadata": {}, + "outputs": [], + "source": [ + "dataset2 = dataset.copy()\n", + "dataset2['group'] = 2 # Assign a different group ID\n", + "dataset['group'] = 1 # Assign a group ID to the original\n", + "dataset = pd.concat([dataset, dataset2]) # Concatenate to have multiple groups dataset, cat_var_past, cat_var_fut, num_var_past = pickle.load(f)\n", + "num_var_fut = [] # No numerical future variables in this example\n", + "group = None # No grouping in this first example. Later examples *do* use grouping.\n", + "time_var = 'time'\n", + "target = ['signal']" + ] + }, + { + "cell_type": "markdown", + "id": "8efaed82", + "metadata": {}, + "source": [ + "**`extend_time_df` Function**\n", + "This utility function extends a DataFrame to include all time steps within a specified range and frequency. This is essential for ensuring that all time series have the same length and that there are no missing time steps.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "3810de27-fc3b-44a3-b802-f88b9aa32c14", + "metadata": {}, + "outputs": [], + "source": [ + "def extend_time_df(x:pd.DataFrame,time:str,freq:Union[str,int],group:Union[List[str],None]=None,global_minmax:bool=False)-> pd.DataFrame:\n", + " \"\"\"Extends a DataFrame to include all time steps within a range.\n", + "\n", + " Ensures that the DataFrame `x` has rows for all time steps within the\n", + " range defined by the minimum and maximum values of the `time` column,\n", + " with a specified frequency `freq`. Handles grouping if `group` is provided.\n", + "\n", + " Args:\n", + " x: The input DataFrame. Must contain a 'time' column and any\n", + " columns specified in 'group'.\n", + " time: The name of the column containing the time index (e.g., 'time').\n", + " freq: The frequency to use for extending the DataFrame. This can be\n", + " an integer (for integer time steps) or a string representing a\n", + " pandas frequency string (e.g., 'D' for daily, 'H' for hourly).\n", + " group: Optional list of column names to group by. If provided, the\n", + " DataFrame will be extended separately for each group.\n", + " global_minmax: If True, use the global min/max of the 'time' column\n", + " across all groups. If False, use the min/max within each group.\n", + "\n", + " Returns:\n", + " pd.DataFrame: An extended DataFrame with all time steps within the\n", + " specified range and frequency. Missing values introduced by the\n", + " extension are filled with NaNs.\n", + " \"\"\"\n", + " if group is None:\n", + " # No grouping: compute min and max time directly\n", + " if isinstance(freq, int):\n", + " empty = pd.DataFrame({'time': list(range(x[time].min(), x[time].max(), freq))})\n", + " else:\n", + " empty = pd.DataFrame({'time': pd.date_range(x[time].min(), x[time].max(), freq=freq)})\n", + "\n", + " else:\n", + " # Grouping: compute min and max time for each group\n", + " if global_minmax:\n", + " # Use global min/max (across all groups)\n", + " _min = x[time].min()\n", + " _max = x[time].max()\n", + " _min_max = pd.DataFrame({'min': [_min], 'max': [_max]}) # Use a list for correct DataFrame creation\n", + " for c in group:\n", + " # Create dummy columns, will be overwritten\n", + " _min_max[c] = _min_max['min']\n", + " else:\n", + " # Use per-group min/max\n", + " _min = x.groupby(group)[time].min().reset_index().rename(columns={time: 'min'})\n", + " _max = x.groupby(group)[time].max().reset_index().rename(columns={time: 'max'})\n", + " _min_max = pd.merge(_min, _max)\n", + "\n", + " empty = []\n", + " for _, row in _min_max.iterrows():\n", + " if isinstance(freq, int):\n", + " tmp = pd.DataFrame({time: np.arange(row['min'], row['max'], freq)})\n", + " else:\n", + " tmp = pd.DataFrame({time: pd.date_range(row['min'], row['max'], freq=freq)})\n", + " for c in group:\n", + " tmp[c] = row[c] # Assign the group values\n", + " empty.append(tmp)\n", + "\n", + " empty = pd.concat(empty, ignore_index=True) # Concatenate all group DataFrames\n", + " return empty\n" + ] + }, + { + "cell_type": "markdown", + "id": "d9dc922a", + "metadata": {}, + "source": [ + "**`_coerce_to_list` Function (Utility)**\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "217f2b74", + "metadata": {}, + "outputs": [], + "source": [ + "def _coerce_to_list(obj):\n", + " \"\"\"Coerces input to a list.\n", + "\n", + " Ensures that the input is always a list. If the input is `None`,\n", + " it returns an empty list. If the input is a single value (not already\n", + " a list), it returns a list containing that single value.\n", + "\n", + " Args:\n", + " obj: The input object.\n", + "\n", + " Returns:\n", + " list: A list representing the input object.\n", + " \"\"\"\n", + " if obj is None:\n", + " return []\n", + " if isinstance(obj, list):\n", + " return obj\n", + " else:\n", + " return [obj]\n" + ] + }, + { + "cell_type": "markdown", + "id": "08c65daa", + "metadata": {}, + "source": [ + "**`PandasTSDataSet_MINIMAL` Class (Simplified Example)**\n", + "This class is a simplified version of the `PandasTSDataSet` to illustrate the basic principles without the complexity of handling all the different data types and metadata. It serves as a stepping stone to the full implementation.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "dd01d933", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "##TODO @Sandeep please try to replicate what is in PandasTSDataSet but for a smore compact example!" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "96d5d857", + "metadata": {}, + "outputs": [], + "source": [ + "class PandasTSDataSet_MINIMAL:\n", + " \"\"\"A simplified version of PandasTSDataSet for demonstration.\n", + "\n", + " This class demonstrates the core functionality of creating a PyTorch\n", + " Dataset from a pandas DataFrame. It handles:\n", + "\n", + " - Extending the DataFrame to ensure regular time intervals.\n", + " - Returning tensors for time, target, and numerical features.\n", + " - Identifying valid data points (non-NaN).\n", + "\n", + " It does *not* handle:\n", + " - Categorical features.\n", + " - Static features.\n", + " - Future known covariates.\n", + " - Grouping.\n", + " - Metadata handling (beyond a very basic level).\n", + "\n", + " This simplified version is intended for educational purposes, to\n", + " illustrate the key concepts before diving into the full complexity\n", + " of the `PandasTSDataSet` class.\n", + " \"\"\"\n", + "\n", + " def __init__(\n", + " self,\n", + " data: pd.DataFrame,\n", + " time: Optional[str] = None,\n", + " target: Optional[Union[str, List[str]]] = None,\n", + " num: Optional[List[Union[str, List[str]]]] = None, #remove from minimal\n", + "\n", + " ):\n", + " \"\"\"Initializes the PandasTSDataSet_MINIMAL.\n", + "\n", + " Args:\n", + " data: The input pandas DataFrame.\n", + " time: The name of the time index column.\n", + " target: The name(s) of the target variable column(s).\n", + " num: The name(s) of numerical feature columns.\n", + " \"\"\"\n", + "\n", + " self.time = time\n", + " self.target = _coerce_to_list(target)\n", + " self.num = _coerce_to_list(num)\n", + " self.data = data.copy()\n", + "\n", + "\n", + " self.feature_cols = self.num\n", + " #use set ensuring unique columns\n", + " self.data = data[list(set(self.feature_cols + self.target + [self.time]))].copy()\n", + "\n", + "\n", + " self.label_encoders = {}\n", + "\n", + " ##ensure to have a coherent dataset\n", + " self.data.drop_duplicates(subset= [time],keep='first',inplace=True,ignore_index=True)\n", + "\n", + " ##compute minumum frequency\n", + " freq = self.data['time'].diff.min()\n", + "\n", + "\n", + " if isinstance(freq, timedelta):\n", + " freq = pd.to_timedelta(freq)\n", + " elif isinstance(freq, (int, float)):\n", + " freq = int(freq)\n", + " else:\n", + " raise TypeError(\"time must be integer or datetime\")\n", + "\n", + "\n", + " ##extend dataset\n", + " self.data = extend_time_df(self.data,self.time,freq,None).merge(self.data,how='left').reset_index()\n", + "\n", + "\n", + " self._groups = {\"_single_group\": self.data.index}\n", + " self._group_ids = [\"_single_group\"]\n", + "\n", + " ## we know on the fly which rows are valid and wich contains nans\n", + " self.data['valid'] = ~pd.isnull(self.data.max(axis=1))\n", + "\n", + " self._prepare_metadata()\n", + "\n", + " def _prepare_metadata(self):\n", + " \"\"\"Prepare metadata for the dataset.\"\"\"\n", + " self.metadata = {\n", + " \"cols\": {\n", + " \"y\": self.target,\n", + " \"x\": self.feature_cols,\n", + " \"st\": [], # No static features in this minimal version\n", + " },\n", + " \"col_type\": {},\n", + " \"col_known\": {},\n", + " \"cat_index\":[] # No categorical features in this minimal version\n", + " }\n", + " # Removed the loop that checks for categorical features, as they are not handled here.\n", + "\n", + " all_cols = self.target + self.feature_cols\n", + " for col in all_cols:\n", + " self.metadata[\"col_type\"][col] = \"F\" # All features are numerical (\"F\")\n", + "\n", + " self.metadata[\"col_known\"][col] = \"K\" if col in self.feature_cols else \"U\"\n", + " self.metadata['encoders'] = self.label_encoders # No encoders, as they're used for categoricals\n", + "\n", + "\n", + " def __len__(self) -> int:\n", + " \"\"\"Return number of time series in the dataset.\"\"\"\n", + " return len(self._group_ids)\n", + "\n", + " def __getitem__(self, index: int) -> Dict[str, torch.Tensor]:\n", + " \"\"\"Get time series data for given index.\n", + "\n", + " Args:\n", + " index: The index of the time series to retrieve (not used directly,\n", + " as we have a single time series in this simplified version).\n", + "\n", + " Returns:\n", + " A dictionary containing tensors for 't', 'y', 'x', and 'is_valid'.\n", + " \"\"\"\n", + " device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n", + "\n", + "\n", + " data = self.data.sort_values(by=self.time)\n", + "\n", + " result = {\n", + " \"t\": data[self.time].values, # Return as numpy array (no need for tensor here)\n", + " \"y\": torch.tensor(data[self.target].values,dtype=torch.float32,device=device),\n", + " \"x\": torch.tensor(data[self.feature_cols].values,dtype=torch.float32,device=device),\n", + " 'is_valid': torch.tensor(data.valid.values,dtype=torch.int,device=device), #indicator of if data is valid\n", + " }\n", + " return result\n", + "\n", + " def get_metadata(self) -> Dict:\n", + " \"\"\"Return metadata about the dataset.\n", + "\n", + " Returns:\n", + " A dictionary containing metadata about the columns.\n", + " \"\"\"\n", + " return self.metadata\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "b929cd13", + "metadata": {}, + "source": [ + "**`PandasTSDataSet` Class (D1 - pandas Implementation)**" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "761d3d2d", + "metadata": {}, + "outputs": [], + "source": [ + "## PICK THIS AS BASE\n", + "\n", + "class PandasTSDataSet(Dataset):\n", + " \"\"\"PyTorch Dataset for loading time series data from a pandas DataFrame.\n", + "\n", + " This class implements the D1 (raw data) layer of the `pytorch-forecasting`\n", + " v2 API. It handles loading time series data from a pandas DataFrame,\n", + " performing basic preprocessing (like ensuring regular time intervals),\n", + " and providing access to the data via the `__getitem__` method.\n", + "\n", + " Key Features:\n", + " - Handles multiple time series (identified by group IDs).\n", + " - Supports numerical and categorical features.\n", + " - Supports static features (features that don't change over time).\n", + " - Supports known and unknown covariates.\n", + " - Automatically extends the DataFrame to ensure regular time intervals.\n", + " - Returns data in a standardized dictionary format (tensors).\n", + " - Provides metadata about the columns (names, types, known/unknown status).\n", + " \"\"\"\n", + "\n", + " def __init__(\n", + " self,\n", + " data: pd.DataFrame,\n", + " time: Optional[str] = None,\n", + " target: Optional[Union[str, List[str]]] = None,\n", + " group: Optional[List[str]] = None,\n", + " num: Optional[List[Union[str, List[str]]]] = None,\n", + " cat: Optional[List[Union[str, List[str]]]] = None,\n", + " known: Optional[List[Union[str, List[str]]]] = None,\n", + " unknown: Optional[List[Union[str, List[str]]]] = None,\n", + " static: Optional[List[Union[str, List[str]]]] = None,\n", + " label_encoders: Optional[dict] = None\n", + " #TODO add weights and reonmve is_valid\n", + " ):\n", + " \"\"\"Initializes the PandasTSDataSet.\n", + "\n", + " Args:\n", + " data: The input pandas DataFrame.\n", + " time: The name of the time index column.\n", + " target: The name(s) of the target variable column(s).\n", + " group: List of column names identifying a time series instance.\n", + " num: The name(s) of numerical feature columns.\n", + " cat: The name(s) of categorical feature columns.\n", + " known: List of variables known in the future.\n", + " unknown: List of variables unknown in the future\n", + " static: The name(s) of static feature columns (features that don't\n", + " change over time).\n", + " label_encoders: Optional dictionary of pre-fitted label encoders\n", + " for categorical features. If not provided, new encoders will\n", + " be fitted.\n", + " \"\"\"\n", + "\n", + " self.time = time\n", + " self.target = _coerce_to_list(target)\n", + " self.num = _coerce_to_list(num)\n", + " self.cat = _coerce_to_list(cat)\n", + " self.known = _coerce_to_list(known)\n", + " self.unknown = _coerce_to_list(unknown)\n", + " self.static = _coerce_to_list(static)\n", + " self.data = data.copy()\n", + "\n", + " self.group = _coerce_to_list(group)\n", + "\n", + "\n", + " self.feature_cols = self.num + self.cat\n", + " #use set ensuring unique columns\n", + " self.data = data[list(set(self.static + self.feature_cols + self.target + [self.time] +self.group))].copy()\n", + "\n", + " ##Encoders for categorical since we want to return tensors\n", + " if label_encoders is None:\n", + " label_encoders = {}\n", + " ##Encoders for categorical since we want to return tensors\n", + " for c in self.cat+self.group:\n", + " label_encoders[c] = OrdinalEncoder()\n", + " self.data[c] = label_encoders[c].fit_transform(self.data[c].values.reshape(-1,1)).flatten()\n", + " self.label_encoders = label_encoders\n", + " else:\n", + " for c in self.cat+self.group:\n", + " self.data[c] = label_encoders[c].transform(self.data[c].values.reshape(-1,1)).flatten()\n", + " self.label_encoders = label_encoders\n", + "\n", + "\n", + " ##ensure to have a coherent dataset\n", + " self.data.drop_duplicates(subset= self.group+[self.time],keep='first',inplace=True,ignore_index=True)\n", + "\n", + " ##compute minumum frequency\n", + " if self.group is None:\n", + " freq = self.data[self.time].diff.min()\n", + " else:\n", + " freq = self.data.groupby(self.group).time.diff().min()\n", + "\n", + " if isinstance(freq, timedelta):\n", + " freq = pd.to_timedelta(freq)\n", + " elif isinstance(freq, (int, float)):\n", + " freq = int(freq)\n", + " else:\n", + " raise TypeError(\"time must be integer or datetime\")\n", + "\n", + "\n", + " ##extend dataset\n", + " self.data = extend_time_df(self.data,self.time,freq,self.group).merge(self.data,how='left').reset_index()\n", + "\n", + "\n", + " ##now we are sure that data is in a coherent form!\n", + " self.lengths = {}\n", + " if self.group:\n", + " self._groups = self.data.groupby(self.group).groups\n", + " self._group_ids = list(self._groups.keys())\n", + " for k in self._groups:\n", + " self.lengths[k] = len(self._groups[k])\n", + " else:\n", + " self._groups = {0: self.data.index}\n", + " self._group_ids = [0]\n", + " self.lengths[0] = len( self.data.index)\n", + "\n", + "\n", + "\n", + " ## we know on the fly which rows are valid and wich contains nans TODO remove\n", + " self.data['valid'] = ~pd.isnull(self.data.max(axis=1))\n", + "\n", + " self._prepare_metadata()\n", + "\n", + " def _prepare_metadata(self):\n", + " \"\"\"Prepare metadata for the dataset.\"\"\"\n", + " self.metadata = {\n", + " \"cols\": {\n", + " \"y\": self.target,\n", + " \"x\": self.feature_cols,\n", + " \"st\": self.static,\n", + " },\n", + " \"col_type\": {},\n", + " \"col_known\": {},\n", + " \"cat_index\":[]\n", + " }\n", + " for i, c in enumerate(self.feature_cols):\n", + " if c in self.cat:\n", + " self.metadata['cat_index'].append(i) # EDIT: was getting error with i, so updated enumerate in for loop\n", + " \n", + "\n", + " all_cols = self.target + self.feature_cols + self.static\n", + " for col in all_cols:\n", + " self.metadata[\"col_type\"][col] = \"C\" if col in self.cat else \"F\"\n", + " self.metadata[\"col_known\"][col] = \"K\" if col in self.known else \"U\"\n", + " self.metadata['encoders'] = self.label_encoders\n", + "\n", + " def __len__(self) -> int:\n", + " \"\"\"Return number of time series in the dataset.\"\"\"\n", + " return len(self._group_ids)\n", + "\n", + " def get_id_ts_by_idx(self,idx):\n", + " \"\"\"Gets the time series ID and starting index for a given index.\n", + "\n", + " Args:\n", + " idx: The overall index. This is *not* the group index, but\n", + " the index considering all time series concatenated together.\n", + "\n", + " Returns:\n", + " tuple: A tuple containing the group ID and the starting index\n", + " of that group in the concatenated data.\n", + " \"\"\"\n", + " tmp = np.cumsum(list(self.lengths.values()))\n", + " idx_group = min(np.where(tmp>idx)[0]) # EDIT: Find the group index, as we are getting group\n", + " return idx_group, 0 if idx_group==0 else tmp[idx_group-1] #EDIT: group id, index of the start of the series\n", + "\n", + " def get_total_len(self):\n", + " \"\"\"Returns total length of the dataset\"\"\"\n", + " l = 0\n", + " for k in self._groups:\n", + " l+= self.lengths[k]\n", + " return l\n", + "\n", + " def __getitem__(self, index: int) -> Dict[str, torch.Tensor]:\n", + " \"\"\"Get time series data for given index.\n", + "\n", + " Args:\n", + " index: The index of the time series to retrieve.\n", + "\n", + " Returns:\n", + " A dictionary containing tensors for 't', 'y', 'x', 'group', 'st',\n", + " and 'is_valid'.\n", + " \"\"\"\n", + " group_id = self._group_ids[index]\n", + " device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n", + "\n", + " if self.group:\n", + " mask = self._groups[group_id]\n", + " data = self.data.loc[mask].sort_values(by=self.time)\n", + " else:\n", + " data = self.data.sort_values(by=self.time)\n", + "\n", + " result = {\n", + " \"t\": data[self.time].values, # Return as numpy array\n", + " \"y\": torch.tensor(data[self.target].values, dtype=torch.float32, device=device),\n", + " \"x\": torch.tensor(data[self.feature_cols].values, dtype=torch.float32, device=device),\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + " \"st\": torch.tensor(data[self.static].iloc[0].values, dtype=torch.float32, device=device) if self.static else torch.tensor([], dtype=torch.float32, device=device), # return empty if no statics\n", + " 'is_valid': torch.tensor(data.valid.values,dtype=torch.int,device=device), #indicator of if data is valid\n", + " }\n", + "\n", + " return result\n", + "\n", + " def get_metadata(self) -> Dict:\n", + " \"\"\"Return metadata about the dataset.\n", + "\n", + " Returns:\n", + " -------\n", + " Dict\n", + " Dictionary containing:\n", + " - cols: column names for y, x, and static features\n", + " - col_type: mapping of columns to their types (F/C)\n", + " - col_known: mapping of columns to their future known status (K/U)\n", + " - encoders: Dict of label encoders used\n", + " \"\"\"\n", + " return self.metadata" + ] + }, + { + "cell_type": "markdown", + "id": "5030144e", + "metadata": {}, + "source": [ + "**Configuration dictionary for the data module. This holds hyperparameters related to data splitting, preprocessing, and windowing.**" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "290ab40d-f440-4f7f-9ae9-8855a04f1ec6", + "metadata": {}, + "outputs": [], + "source": [ + "data_module_metadata = dict(\n", + " perc_train= 0.7, # Percentage of data for training\n", + " perc_valid= 0.1, # Percentage of data for validation\n", + " range_train= None, # Optional: Specific time range for training (not used here)\n", + " range_validation= None, # Optional: Specific time range for validation (not used here)\n", + " range_test= None, # Optional: Specific time range for testing (not used here)\n", + " shift= 0, # Optional: Shift the time series (not used here)\n", + " starting_point= None, # Optional: Starting point for sampling (not used here)\n", + " skip_step= 1, # Optional: Step size for skipping data points (not used here)\n", + " past_steps=16, # Number of past time steps to use as input (encoder length)\n", + " future_steps= 16, # Number of future time steps to predict (decoder length)\n", + " precompute=True, # Whether to precompute the train/val/test splits.\n", + " scaler= 'sklearn.preprocessing.StandardScaler()' # String representation of the scaler (not used directly here)\n", + ")\n" + ] + }, + { + "cell_type": "markdown", + "id": "c611fbc0", + "metadata": {}, + "source": [ + "**Instantiate the PandasTSDataSet (D1 layer).**\n", + "This creates the dataset object that will provide the raw time series data.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "e4ca4055-7f36-446c-9b21-6c0456b152cf", + "metadata": {}, + "outputs": [], + "source": [ + "ds = PandasTSDataSet(dataset,\n", + " 'time', # Name of the time index column\n", + " target, # List of target variable names\n", + " 'group', # Name of the column that identifies groups/time series\n", + " list(set(num_var_past+num_var_fut)), # List of numerical feature columns\n", + " list(set(cat_var_fut+cat_var_past)), # List of categorical feature columns\n", + " list(set(cat_var_fut+num_var_fut)), # List of future known covariates\n", + " list(set(cat_var_past+num_var_past)), # List of past known covariates\n", + " )\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "2af9edfd", + "metadata": {}, + "source": [ + "Import necessary PyTorch modules" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "519ebe66-3d64-41df-a191-81c0ba518e79", + "metadata": {}, + "outputs": [], + "source": [ + "from torch.utils.data import Dataset, DataLoader\n", + "from torch.utils.data.dataloader import default_collate\n", + "import torch" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "c0375ff0", + "metadata": {}, + "outputs": [], + "source": [ + "def my_collate(batch):\n", + " \"\"\"Custom collate function to handle potential None values in batches.\n", + "\n", + " Filters out `None` values from the batch before collating. This is\n", + " necessary because the `MyDataset.__getitem__` method can return `None`\n", + " if a valid window cannot be created for a given index.\n", + "\n", + " Args:\n", + " batch: A list of samples (dictionaries or tensors) from the dataset.\n", + "\n", + " Returns:\n", + " The collated batch, with `None` values removed.\n", + " \"\"\"\n", + " batch = list(filter(lambda x : x is not None, batch))\n", + " #print(len(batch)) # Debugging print statement (removed in final version)\n", + " return default_collate(batch)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "6e31a24f", + "metadata": {}, + "outputs": [], + "source": [ + "## TODO: @SANDEEP rework this using some cleaner code you can find here\n", + "#https://colab.research.google.com/drive/1FvLlmEOgm3D3JgNFVeAtwPk4cXagJ0CY?usp=sharing#scrollTo=jFuSwWrhTg6y" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "12bc5f1c-2de1-4837-9d2a-e764fa395bba", + "metadata": {}, + "outputs": [], + "source": [ + "class MyDataset(Dataset):\n", + " \"\"\"Custom PyTorch Dataset for time series data.\n", + "\n", + " This class adapts the D1 dataset (`PandasTSDataSet`) for use with a\n", + " PyTorch `DataLoader`. It handles the creation of input/output windows\n", + " (sequences) for training and validation. It also handles the case where\n", + " a valid window cannot be created (e.g., due to insufficient past data)\n", + " by returning `None`.\n", + "\n", + " Args:\n", + " data: The D1 dataset instance (e.g., `PandasTSDataSet`).\n", + " metadata: A dictionary containing metadata about the dataset and\n", + " data processing parameters (e.g., `past_steps`, `future_steps`).\n", + " valid_index: A dictionary where keys are group IDs and values are\n", + " lists of valid indices for that group. This defines the\n", + " valid ranges within each time series for creating windows.\n", + " \"\"\"\n", + "\n", + " def __init__(self, data, metadata, valid_index) -> torch.utils.data.Dataset:\n", + " \"\"\"Initializes the MyDataset instance.\"\"\"\n", + "\n", + " self.metadata = metadata\n", + " self.metadata_dataset = data.metadata # Metadata from the D1 dataset\n", + " self.data = data # The D1 dataset instance\n", + " self.valid_index = valid_index # Dictionary of valid indices for each group\n", + " sum = 0\n", + " self.lengths = {}\n", + " #fix this, probably we need to add something (check validity, check past and future)\n", + " for k in self.valid_index:\n", + " # Calculate the total number of valid windows that can be created\n", + " # from each group. Subtract `future_steps` because we need enough\n", + " # data points for both the input window and the target window.\n", + " sum+=(len(self.valid_index[k])-self.metadata['future_steps'])\n", + " self.lengths[k] = len(self.valid_index[k])\n", + " self.length = sum # Sum of lengths accross differnet time series.\n", + "\n", + " def __len__(self):\n", + " \"\"\"Returns the total number of valid windows in the dataset.\"\"\"\n", + " return self.length\n", + "\n", + " def get_id_ts_by_idx(self,idx):\n", + " \"\"\"Gets time series id given global index\"\"\"\n", + " tmp = np.cumsum(list(self.lengths.values()))\n", + " idx = min(np.where(tmp>idx)[0])\n", + " return idx, 0 if idx==0 else tmp[idx-1]\n", + "\n", + " def __getitem__(self, idxs):\n", + " \"\"\"Retrieves a sample (input/output window) from the dataset.\n", + "\n", + " Args:\n", + " idxs: The *global* index of the window to retrieve. This index\n", + " is across all time series in the dataset.\n", + "\n", + " Returns:\n", + " A dictionary containing the input and output tensors for the window,\n", + " or `None` if a valid window cannot be created at the given index.\n", + " \"\"\"\n", + "\n", + " ##crucial point there: correctly identifying which are the indexes that we need!\n", + " sample = {}\n", + " IDX,difference = self.get_id_ts_by_idx(idxs) #find group and local index\n", + "\n", + " idxs -= difference # Adjust index to be relative to the start of the time series\n", + " idxs = self.valid_index[IDX][idxs] # Get the actual index in the data\n", + " tmp = self.data.__getitem__(IDX) ##use the getitem of the data!\n", + "\n", + " # Check if enough data is available for both input and output windows\n", + " if idxs+self.metadata['future_steps']>self.data.lengths[IDX]:\n", + " return None # Not enough data, return None\n", + "\n", + " #Check for the validity of the time series, by checking nans.\n", + " if tmp['is_valid'][idxs-self.metadata['past_steps']:idxs+self.metadata['future_steps']].sum() != self.metadata['future_steps']+self.metadata['past_steps']:\n", + " return None\n", + " \n", + " # Construct sample\n", + " for k in tmp.keys():\n", + " # Separate based on past/future\n", + " # We create past and future samples only for the ones we sliced using idxs\n", + " if '_past' in k:\n", + " sample[k] = tmp[k][idxs-self.metadata['past_steps']:idxs] #past steps based on the past_step provided in metadata.\n", + " elif '_fut' in k or k=='y':\n", + " sample[k] = tmp[k][idxs:idxs+self.metadata['future_steps']]# future steps based on the future_step\n", + " else:\n", + " pass\n", + " return sample\n" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "ac4a66d5-753b-4030-a88d-e5b8efd73d95", + "metadata": {}, + "outputs": [], + "source": [ + "def compute_ranges(d1_dataset, metadata):\n", + " \"\"\"Computes valid index ranges for training, validation, and testing.\n", + "\n", + " This function calculates the start and end indices for the training,\n", + " validation, and testing sets within each time series group, based on the\n", + " provided percentages in `metadata`.\n", + "\n", + " Args:\n", + " d1_dataset: The D1 dataset instance (e.g., `PandasTSDataSet`).\n", + " metadata: A dictionary containing metadata, including `perc_train`\n", + " and `perc_valid` (percentages for training and validation splits).\n", + "\n", + " Returns:\n", + " tuple: A tuple containing three dictionaries: `train_ranges`,\n", + " `valid_ranges`, and `test_ranges`. Each dictionary has group IDs\n", + " as keys and lists of valid indices as values.\n", + " \"\"\"\n", + " ##suppose for now we use only percentage!\n", + " train_ranges = {}\n", + " valid_ranges = {}\n", + " test_ranges = {}\n", + " for k in d1_dataset.lengths: # Iterate over each time series group\n", + " ls = d1_dataset.lengths[k] # Get the length of the current time series\n", + " train_ranges[k] = list(range(0,int(metadata['perc_train']*ls))) # Training range\n", + " valid_ranges[k] = list(range(int(metadata['perc_train']*ls), int((metadata['perc_valid'] + metadata['perc_train'])*ls))) # Validation range\n", + " test_ranges[k] = list(range(int((metadata['perc_valid'] + metadata['perc_train'])*ls),ls)) # Testing range\n", + "\n", + " ## in case of datetime ranges we need to use np.where and create a mask\n", + " return train_ranges,valid_ranges,test_ranges\n", + "\n", + "# Calculate the training, validation, and testing ranges\n", + "train_ranges, valid_ranges, test_ranges = compute_ranges(ds, data_module_metadata)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "0185143c", + "metadata": {}, + "source": [ + "**Create a DataLoader for the training set.**" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "7e43bfd7-12b3-48c2-8b33-d5ca6681959c", + "metadata": {}, + "outputs": [], + "source": [ + "dl = DataLoader(\n", + " MyDataset(ds, data_module_metadata, train_ranges), # Create a MyDataset instance\n", + " batch_size=32, # Set the batch size\n", + " collate_fn=my_collate # Use the custom collate function\n", + " )\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "dde2ba1f-f6f8-483c-a404-5917b84ce37a", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([16, 16, 1])\n" + ] + } + ], + "source": [ + "# Example of iterating through the DataLoader (for debugging/demonstration).\n", + "for x in dl:\n", + " print(x['y'].shape) # Print the shape of the target variable tensor.\n", + " break #only check first batch\n" + ] + }, + { + "cell_type": "markdown", + "id": "a7ddeb11", + "metadata": {}, + "source": [ + "**The following function and class show how one might create a `LightningDataModule` to manage the data loading process, including train/validation/test splits. This builds on top of previous components.**" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "86c9c7fc", + "metadata": {}, + "outputs": [], + "source": [ + "# D2 layers \n", + "\n", + "class DecoderEncoderDataModule(L.LightningDataModule):\n", + " \"\"\"LightningDataModule for encoder-decoder models.\n", + "\n", + " This class manages the data loading and preprocessing for training,\n", + " validation, testing, and prediction. It handles splitting the data,\n", + " creating `Dataset` instances, and creating `DataLoader` instances. It\n", + " supports both precomputed splits and on-the-fly splitting.\n", + "\n", + " Args:\n", + " d1_dataset: The D1 dataset instance (e.g., `PandasTSDataSet`).\n", + " batch_size: The batch size.\n", + " num_workers: The number of worker processes for data loading.\n", + " metadata: A dictionary containing metadata and hyperparameters.\n", + " \"\"\"\n", + " def __init__(self, d1_dataset, batch_size=32, num_workers=4, metadata=None):\n", + " super().__init__()\n", + " # initialize other params\n", + " self.d1_dataset = d1_dataset\n", + " self.batch_size = batch_size\n", + " self.metadata = metadata\n", + " self.num_workers = num_workers\n", + "\n", + "\n", + " def prepare_data(self):\n", + " \"\"\"Prepares the data for training/validation/testing.\n", + "\n", + " This method handles splitting the data into training, validation, and\n", + " testing sets. It supports two modes:\n", + "\n", + " - `precompute=True`: Uses a `split_data` function (not defined here,\n", + " but assumed to exist) to precompute the splits. This is useful for\n", + " deterministic splits or when the splitting process is expensive.\n", + " - `precompute=False`: Calculates the splits on-the-fly using\n", + " `compute_ranges`. This is more flexible and allows for different\n", + " splitting strategies.\n", + " \"\"\"\n", + " if self.metadata['precompute']: #TODO use split_data from dsipts for refernce\n", + " self.precompute = True\n", + " ## @SANDEEP this is what is returned by DSIPTS split_for_train!!\n", + " ## the only difference is that we need to use the __getitem__ of the d1 layer\n", + "\n", + " # The split_data function is assumed to be defined elsewhere and\n", + " # would handle the splitting logic according to however the\n", + " # project defines \"splitting\" (could be by time, by groups, etc.)\n", + " self.train_dataset, self.validation_dataset, self.test_dataset = split_data(self.d1_dataset,self.metadata) # Placeholder\n", + "\n", + "\n", + "\n", + " else:\n", + " self.precompute = False\n", + " ##in this case we need to pass only the data that are referring to the trainin period\n", + " ## but data can be chunked, we need to create a function that transform a D1 object into a D1 object or, even better\n", + " ## something that can be used by the dataset for filtering only valid samples!\n", + " ##for example\n", + " self.train_ranges, self.validation_ranges, self.test_ranges = compute_ranges(self.d1_dataset,self.metadata)\n", + " self.predict_data = None ##??? need to study this\n", + " ##TODO normalization???? maybe we can precompute here some statistics and pass them to the dataset!\n", + "\n", + " def setup(self, stage=None):\n", + " \"\"\"Sets up the datasets for each stage (fit, test, predict).\n", + "\n", + " This method creates the appropriate `Dataset` instances for each stage,\n", + " using either the precomputed splits or the on-the-fly calculated ranges.\n", + "\n", + " Args:\n", + " stage: The stage ('fit', 'test', or 'predict').\n", + " \"\"\"\n", + " # Get metadata from D1 layer during setup\n", + " #self.metadata = self.d1_dataset.get_metadata() ##NO SEE COMMENT BEFORE! We get metadata in __init__\n", + " ##create dataset here\n", + " if stage == 'fit':\n", + " if self.precompute:\n", + " # Use precomputed datasets (implementation of split_data needed)\n", + " pass # Use self.train_dataset and self.validation_dataset as defined in prepare_data\n", + " else:\n", + " # Create datasets using computed ranges\n", + " self.train_dataset = MyDataset(self.d1_dataset, self.metadata, self.train_ranges) # Pass train_ranges\n", + " self.validation_dataset = MyDataset(self.d1_dataset, self.metadata, self.validation_ranges) # Pass valid_ranges\n", + "\n", + " if stage == 'test':\n", + " if self.precompute:\n", + " pass\n", + " # Use precomputed test dataset\n", + " else:\n", + " self.test_dataset = MyDataset(self.d1_dataset, self.metadata, self.test_ranges) # Pass test_ranges\n", + "\n", + " if stage == 'predict':\n", + " if self.precompute:\n", + " pass\n", + " # Use precomputed prediction dataset (if applicable)\n", + " else:\n", + " self.predict_datasset = MyDataset(self.d1_dataset, self.metadata, self.predict_data) # Pass predict_data. Implementation details TBD.\n", + "\n", + " def train_dataloader(self):\n", + " \"\"\"Returns the training DataLoader.\"\"\"\n", + " return DataLoader(self.train_dataset, batch_size=self.batch_size, shuffle=True, num_workers=self.num_workers, collate_fn=my_collate)\n", + "\n", + " def val_dataloader(self):\n", + " \"\"\"Returns the validation DataLoader.\"\"\"\n", + " return DataLoader(self.validation_dataset, batch_size=self.batch_size, shuffle=False, num_workers=self.num_workers, collate_fn=my_collate)\n", + "\n", + " def test_dataloader(self):\n", + " \"\"\"Returns the test DataLoader.\"\"\"\n", + " return DataLoader(self.test_dataset, batch_size=self.batch_size, shuffle=False, num_workers=self.num_workers, collate_fn=my_collate)\n", + "\n", + " def predict_dataloader(self):\n", + " \"\"\"Returns the prediction DataLoader.\"\"\"\n", + " return DataLoader(self.predict_datasset, batch_size=self.batch_size, shuffle=False, num_workers=self.num_workers, collate_fn=my_collate)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "c83526e8", + "metadata": {}, + "source": [ + "**`DecoderEncoderModel` Class (M Layer - Example)**\n", + "This is a placeholder for the Model Layer." + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "9d9a07bd-35cd-4cbd-8843-081b7d3d8580", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "# Layer M - Example (Placeholder)\n", + "class DecoderEncoderModel(L.LightningModule):\n", + " \"\"\"Placeholder for an encoder-decoder model (M layer).\n", + "\n", + " This is a minimal example of how a model might be structured using\n", + " PyTorch Lightning. It shows how to access the metadata from the\n", + " `LightningDataModule`. A real implementation would include the actual\n", + " network architecture and training logic.\n", + " \"\"\"\n", + " def __init__(self):\n", + " super().__init__()\n", + " self.metadata = None\n", + "\n", + " def setup(self, stage=None):\n", + " \"\"\"Gets metadata from the datamodule during setup.\"\"\"\n", + " # Get metadata from datamodule during setup\n", + " self.metadata = self.trainer.datamodule.metadata\n", + "\n", + " # Initialize layer T model using metadata (placeholder)\n", + "\n", + " def forward(self, x):\n", + " \"\"\"Forward pass (placeholder).\"\"\"\n", + " # forward logic (implementation would go here)\n", + " pass\n", + "\n", + " def training_step(self, batch, batch_idx):\n", + " \"\"\"Training step (placeholder).\"\"\"\n", + " pass # Replace with actual training logic\n", + "\n", + " def validation_step(self, batch, batch_idx):\n", + " \"\"\"Validation step (placeholder).\"\"\"\n", + " pass # Replace with actual validation logic\n" + ] + }, + { + "cell_type": "markdown", + "id": "80542685", + "metadata": {}, + "source": [ + "### Layer M" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "694a020f-7f8d-4503-a023-bfdcda90667b", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "\n", + "class DecoderEncoderModel(L.LightningModule):\n", + " def __init__(self):\n", + " super().__init__()\n", + " self.metadata = None\n", + " \n", + " def setup(self, stage=None):\n", + " # Get metadata from datamodule during setup\n", + " self.metadata = self.trainer.datamodule.metadata\n", + " \n", + " # Initialize layer T model using metadata\n", + " \n", + " def forward(self, x):\n", + " # forward logic\n", + " pass" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ec43226c-f99f-4a77-8793-c3e9db5c4536", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "dsipts", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/sandeep/TEST_v2_temp.ipynb b/notebooks/sandeep/TEST_v2_temp.ipynb new file mode 100644 index 00000000..f04eab50 --- /dev/null +++ b/notebooks/sandeep/TEST_v2_temp.ipynb @@ -0,0 +1,1276 @@ +{ + "cells": [ + { + "cell_type": "raw", + "id": "55c75749-ec70-4365-ac8a-f96552da4528", + "metadata": {}, + "source": [ + "from dsipts import Categorical,TimeSeries, RNN\n", + "settimana = Categorical('settimanale',1,[1,1,1,1,1,1,1],7,'multiplicative',[0.9,0.8,0.7,0.6,0.5,0.99,0.99])\n", + "\n", + "##montly, additive (here there are only 5 month)\n", + "mese = Categorical('mensile',1,[31,28,20,10,33],5,'additive',[10,20,-10,20,0])\n", + "\n", + "##spot categorical variables: in this case it occurs every 100 days and it lasts 7 days adding 10 to the original timeseries\n", + "spot = Categorical('spot',100,[7],1,'additive',[10])\n", + "\n", + "##initizate a timeseries object\n", + "ts = TimeSeries('prova')\n", + "ts.generate_signal(noise_mean=1,categorical_variables=[settimana,mese,spot],length=5000,type=0)\n", + "dataset = ts.dataset\n", + "dataset['x_num'] = 0.0\n", + "dataset.loc[:dataset.shape[0]-10,'x_num']= dataset['x_num'].values[9:]\n", + "cat_var_past = ts.cat_var\n", + "cat_var_fut = ts.cat_fut\n", + "num_var_past = ts.num_var+['x_num']\n", + "import pickle \n", + "with open('example.pkl', 'wb') as f:\n", + " pickle.dump([dataset,cat_var_past,cat_var_fut,num_var_past],f)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "459c491c-98ab-4513-b5eb-9f5c604b9df4", + "metadata": {}, + "outputs": [], + "source": [ + "#dataset.to_csv('dataset.csv',index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "0b53411e-8c81-4dc9-8e2e-c83c3a568be1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: lightning in c:\\users\\skushwaha\\appdata\\local\\miniconda3\\envs\\dsipts\\lib\\site-packages (2.5.0.post0)\n", + "Requirement already satisfied: PyYAML<8.0,>=5.4 in c:\\users\\skushwaha\\appdata\\local\\miniconda3\\envs\\dsipts\\lib\\site-packages (from lightning) (6.0.2)\n", + "Requirement already satisfied: fsspec[http]<2026.0,>=2022.5.0 in c:\\users\\skushwaha\\appdata\\local\\miniconda3\\envs\\dsipts\\lib\\site-packages (from lightning) (2025.2.0)\n", + "Requirement already satisfied: lightning-utilities<2.0,>=0.10.0 in c:\\users\\skushwaha\\appdata\\local\\miniconda3\\envs\\dsipts\\lib\\site-packages (from lightning) (0.12.0)\n", + "Requirement already satisfied: packaging<25.0,>=20.0 in c:\\users\\skushwaha\\appdata\\local\\miniconda3\\envs\\dsipts\\lib\\site-packages (from lightning) (24.2)\n", + "Requirement already satisfied: torch<4.0,>=2.1.0 in 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Union, List, Optional, Dict\n", + "from sklearn.preprocessing import LabelEncoder # Used for categorical encoding\n", + "from datetime import datetime, timedelta # For time-related operations\n", + "import lightning as L # PyTorch Lightning\n", + "import numpy as np\n", + "from sklearn.preprocessing import OrdinalEncoder # Used for categorical encoding\n", + "import torch\n", + "from torch.utils.data import Dataset, DataLoader\n", + "from torch.utils.data.dataloader import default_collate\n" + ] + }, + { + "cell_type": "markdown", + "id": "09307105", + "metadata": {}, + "source": [ + "**Data Loading and Preparation (Example Data)**\n", + "\n", + "This section loads example time series data and prepares it for use in the `DataSet` classes. It demonstrates the structure of the input data.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "beea9295-9164-4bae-b2e1-8c5cc6c585c4", + "metadata": {}, + "outputs": [], + "source": [ + "with open('example.pkl', 'rb') as f:\n", + " dataset, cat_var_past, cat_var_fut, num_var_past = pickle.load(f)\n", + "num_var_fut = [] # No numerical future variables in this example\n", + "group = None # No grouping in this first example. Later examples *do* use grouping.\n", + "time_var = 'time'\n", + "target = ['signal']" + ] + }, + { + "cell_type": "markdown", + "id": "9c4fb3c8", + "metadata": {}, + "source": [ + "**Example with Grouping**\n", + "\n", + "The following cell creates a second copy of the dataset and assigns it a different group ID. This is then concatenated with the original `dataset` to showcase how grouping works.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "db453ed2-df16-44bf-bc86-184fd4010e7a", + "metadata": {}, + "outputs": [], + "source": [ + "dataset2 = dataset.copy()\n", + "dataset2['group'] = 2 # Assign a different group ID\n", + "dataset['group'] = 1 # Assign a group ID to the original\n", + "dataset = pd.concat([dataset, dataset2]) # Concatenate to have multiple groups" + ] + }, + { + "cell_type": "markdown", + "id": "8efaed82", + "metadata": {}, + "source": [ + "**`extend_time_df` Function**\n", + "This utility function extends a DataFrame to include all time steps within a specified range and frequency. This is essential for ensuring that all time series have the same length and that there are no missing time steps.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "3810de27-fc3b-44a3-b802-f88b9aa32c14", + "metadata": {}, + "outputs": [], + "source": [ + "def extend_time_df(x:pd.DataFrame,time:str,freq:Union[str,int],group:Union[List[str],None]=None,global_minmax:bool=False)-> pd.DataFrame:\n", + " \"\"\"Extends a DataFrame to include all time steps within a range.\n", + "\n", + " Ensures that the DataFrame `x` has rows for all time steps within the\n", + " range defined by the minimum and maximum values of the `time` column,\n", + " with a specified frequency `freq`. Handles grouping if `group` is provided.\n", + "\n", + " Args:\n", + " x: The input DataFrame. Must contain a 'time' column and any\n", + " columns specified in 'group'.\n", + " time: The name of the column containing the time index (e.g., 'time').\n", + " freq: The frequency to use for extending the DataFrame. This can be\n", + " an integer (for integer time steps) or a string representing a\n", + " pandas frequency string (e.g., 'D' for daily, 'H' for hourly).\n", + " group: Optional list of column names to group by. If provided, the\n", + " DataFrame will be extended separately for each group.\n", + " global_minmax: If True, use the global min/max of the 'time' column\n", + " across all groups. If False, use the min/max within each group.\n", + "\n", + " Returns:\n", + " pd.DataFrame: An extended DataFrame with all time steps within the\n", + " specified range and frequency. Missing values introduced by the\n", + " extension are filled with NaNs.\n", + " \"\"\"\n", + " if group is None:\n", + " # No grouping: compute min and max time directly\n", + " if isinstance(freq, int):\n", + " empty = pd.DataFrame({'time': list(range(x[time].min(), x[time].max(), freq))})\n", + " else:\n", + " empty = pd.DataFrame({'time': pd.date_range(x[time].min(), x[time].max(), freq=freq)})\n", + "\n", + " else:\n", + " # Grouping: compute min and max time for each group\n", + " if global_minmax:\n", + " # Use global min/max (across all groups)\n", + " _min = x[time].min()\n", + " _max = x[time].max()\n", + " _min_max = pd.DataFrame({'min': [_min], 'max': [_max]}) # Use a list for correct DataFrame creation\n", + " for c in group:\n", + " # Create dummy columns, will be overwritten\n", + " _min_max[c] = _min_max['min']\n", + " else:\n", + " # Use per-group min/max\n", + " _min = x.groupby(group)[time].min().reset_index().rename(columns={time: 'min'})\n", + " _max = x.groupby(group)[time].max().reset_index().rename(columns={time: 'max'})\n", + " _min_max = pd.merge(_min, _max)\n", + "\n", + " empty = []\n", + " for _, row in _min_max.iterrows():\n", + " if isinstance(freq, int):\n", + " tmp = pd.DataFrame({time: np.arange(row['min'], row['max'], freq)})\n", + " else:\n", + " tmp = pd.DataFrame({time: pd.date_range(row['min'], row['max'], freq=freq)})\n", + " for c in group:\n", + " tmp[c] = row[c] # Assign the group values\n", + " empty.append(tmp)\n", + "\n", + " empty = pd.concat(empty, ignore_index=True) # Concatenate all group DataFrames\n", + " return empty" + ] + }, + { + "cell_type": "markdown", + "id": "d9dc922a", + "metadata": {}, + "source": [ + "**`_coerce_to_list` Function (Utility)**\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "217f2b74", + "metadata": {}, + "outputs": [], + "source": [ + "def _coerce_to_list(obj):\n", + " \"\"\"Coerces input to a list.\n", + "\n", + " Ensures that the input is always a list. If the input is `None`,\n", + " it returns an empty list. If the input is a single value (not already\n", + " a list), it returns a list containing that single value.\n", + "\n", + " Args:\n", + " obj: The input object.\n", + "\n", + " Returns:\n", + " list: A list representing the input object.\n", + " \"\"\"\n", + " if obj is None:\n", + " return []\n", + " if isinstance(obj, list):\n", + " return obj\n", + " else:\n", + " return [obj]" + ] + }, + { + "cell_type": "markdown", + "id": "08c65daa", + "metadata": {}, + "source": [ + "**`PandasTSDataSet_MINIMAL` Class (Simplified Example)**\n", + "This class is a simplified version of the `PandasTSDataSet` to illustrate the basic principles without the complexity of handling all the different data types and metadata. It serves as a stepping stone to the full implementation.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "dd01d933", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "##TODO @Sandeep please try to replicate what is in PandasTSDataSet but for a smore compact example!" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "96d5d857", + "metadata": {}, + "outputs": [], + "source": [ + "class PandasTSDataSet_MINIMAL:\n", + " \"\"\"A simplified version of PandasTSDataSet for demonstration.\n", + "\n", + " This class demonstrates the core functionality of creating a PyTorch\n", + " Dataset from a pandas DataFrame. It handles:\n", + "\n", + " - Extending the DataFrame to ensure regular time intervals.\n", + " - Returning tensors for time, target, and numerical features.\n", + " - Identifying valid data points (non-NaN).\n", + "\n", + " It does *not* handle:\n", + " - Categorical features.\n", + " - Static features.\n", + " - Future known covariates.\n", + " - Grouping.\n", + " - Metadata handling (beyond a very basic level).\n", + "\n", + " This simplified version is intended for educational purposes, to\n", + " illustrate the key concepts before diving into the full complexity\n", + " of the `PandasTSDataSet` class.\n", + " \"\"\"\n", + "\n", + " def __init__(\n", + " self,\n", + " data: pd.DataFrame,\n", + " time: Optional[str] = None,\n", + " target: Optional[Union[str, List[str]]] = None,\n", + " num: Optional[List[Union[str, List[str]]]] = None,\n", + "\n", + " ):\n", + " \"\"\"Initializes the PandasTSDataSet_MINIMAL.\n", + "\n", + " Args:\n", + " data: The input pandas DataFrame.\n", + " time: The name of the time index column.\n", + " target: The name(s) of the target variable column(s).\n", + " num: The name(s) of numerical feature columns.\n", + " \"\"\"\n", + "\n", + " self.time = time\n", + " self.target = _coerce_to_list(target)\n", + " self.num = _coerce_to_list(num)\n", + " self.data = data.copy()\n", + "\n", + "\n", + " self.feature_cols = self.num\n", + " #use set ensuring unique columns\n", + " self.data = data[list(set(self.feature_cols + self.target + [self.time]))].copy()\n", + "\n", + "\n", + " self.label_encoders = {}\n", + "\n", + " ##ensure to have a coherent dataset\n", + " self.data.drop_duplicates(subset= [time],keep='first',inplace=True,ignore_index=True)\n", + "\n", + " ##compute minumum frequency\n", + " freq = self.data['time'].diff.min()\n", + "\n", + "\n", + " if isinstance(freq, timedelta):\n", + " freq = pd.to_timedelta(freq)\n", + " elif isinstance(freq, (int, float)):\n", + " freq = int(freq)\n", + " else:\n", + " raise TypeError(\"time must be integer or datetime\")\n", + "\n", + "\n", + " ##extend dataset\n", + " self.data = extend_time_df(self.data,self.time,freq,None).merge(self.data,how='left').reset_index()\n", + "\n", + "\n", + " self._groups = {\"_single_group\": self.data.index}\n", + " self._group_ids = [\"_single_group\"]\n", + "\n", + " ## we know on the fly which rows are valid and wich contains nans\n", + " self.data['valid'] = ~pd.isnull(self.data.max(axis=1))\n", + "\n", + " self._prepare_metadata()\n", + "\n", + " def _prepare_metadata(self):\n", + " \"\"\"Prepare metadata for the dataset.\"\"\"\n", + " self.metadata = {\n", + " \"cols\": {\n", + " \"y\": self.target,\n", + " \"x\": self.feature_cols,\n", + " \"st\": [], # No static features in this minimal version\n", + " },\n", + " \"col_type\": {},\n", + " \"col_known\": {},\n", + " \"cat_index\":[] # No categorical features in this minimal version\n", + " }\n", + " # Removed the loop that checks for categorical features, as they are not handled here.\n", + "\n", + " all_cols = self.target + self.feature_cols\n", + " for col in all_cols:\n", + " self.metadata[\"col_type\"][col] = \"F\" # All features are numerical (\"F\")\n", + "\n", + " self.metadata[\"col_known\"][col] = \"K\" if col in self.feature_cols else \"U\"\n", + " self.metadata['encoders'] = self.label_encoders # No encoders, as they're used for categoricals\n", + "\n", + "\n", + " def __len__(self) -> int:\n", + " \"\"\"Return number of time series in the dataset.\"\"\"\n", + " return len(self._group_ids)\n", + "\n", + " def __getitem__(self, index: int) -> Dict[str, torch.Tensor]:\n", + " \"\"\"Get time series data for given index.\n", + "\n", + " Args:\n", + " index: The index of the time series to retrieve (not used directly,\n", + " as we have a single time series in this simplified version).\n", + "\n", + " Returns:\n", + " A dictionary containing tensors for 't', 'y', 'x', and 'is_valid'.\n", + " \"\"\"\n", + " device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n", + "\n", + "\n", + " data = self.data.sort_values(by=self.time)\n", + "\n", + " result = {\n", + " \"t\": data[self.time].values, # Return as numpy array (no need for tensor here)\n", + " \"y\": torch.tensor(data[self.target].values,dtype=torch.float32,device=device),\n", + " \"x\": torch.tensor(data[self.feature_cols].values,dtype=torch.float32,device=device),\n", + " \"weights\": \n", + " }\n", + " return result\n", + "\n", + " def get_metadata(self) -> Dict:\n", + " \"\"\"Return metadata about the dataset.\n", + "\n", + " Returns:\n", + " A dictionary containing metadata about the columns.\n", + " \"\"\"\n", + " return self.metadata\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "b929cd13", + "metadata": {}, + "source": [ + "**`PandasTSDataSet` Class (D1 - pandas Implementation)**" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "761d3d2d", + "metadata": {}, + "outputs": [], + "source": [ + "class PandasTSDataSet(Dataset):\n", + " \"\"\"PyTorch Dataset for loading time series data from a pandas DataFrame.\n", + "\n", + " This class implements the D1 (raw data) layer of the `pytorch-forecasting`\n", + " v2 API. It handles loading time series data from a pandas DataFrame,\n", + " performing basic preprocessing (like ensuring regular time intervals),\n", + " and providing access to the data via the `__getitem__` method.\n", + "\n", + " Key Features:\n", + " - Handles multiple time series (identified by group IDs).\n", + " - Supports numerical and categorical features.\n", + " - Supports static features (features that don't change over time).\n", + " - Supports known and unknown covariates.\n", + " - Automatically extends the DataFrame to ensure regular time intervals.\n", + " - Returns data in a standardized dictionary format (tensors).\n", + " - Provides metadata about the columns (names, types, known/unknown status).\n", + " \"\"\"\n", + "\n", + " def __init__(\n", + " self,\n", + " data: pd.DataFrame,\n", + " time: Optional[str] = None,\n", + " target: Optional[Union[str, List[str]]] = None,\n", + " group: Optional[List[str]] = None,\n", + " num: Optional[List[Union[str, List[str]]]] = None,\n", + " cat: Optional[List[Union[str, List[str]]]] = None,\n", + " known: Optional[List[Union[str, List[str]]]] = None,\n", + " unknown: Optional[List[Union[str, List[str]]]] = None,\n", + " static: Optional[List[Union[str, List[str]]]] = None,\n", + " label_encoders: Optional[dict] = None\n", + " ):\n", + " \"\"\"Initializes the PandasTSDataSet.\n", + "\n", + " Args:\n", + " data: The input pandas DataFrame.\n", + " time: The name of the time index column.\n", + " target: The name(s) of the target variable column(s).\n", + " group: List of column names identifying a time series instance.\n", + " num: The name(s) of numerical feature columns.\n", + " cat: The name(s) of categorical feature columns.\n", + " known: List of variables known in the future.\n", + " unknown: List of variables unknown in the future\n", + " static: The name(s) of static feature columns (features that don't\n", + " change over time).\n", + " label_encoders: Optional dictionary of pre-fitted label encoders\n", + " for categorical features. If not provided, new encoders will\n", + " be fitted.\n", + " \"\"\"\n", + "\n", + " self.time = time\n", + " self.target = _coerce_to_list(target)\n", + " self.num = _coerce_to_list(num)\n", + " self.cat = _coerce_to_list(cat)\n", + " self.known = _coerce_to_list(known)\n", + " self.unknown = _coerce_to_list(unknown)\n", + " self.static = _coerce_to_list(static)\n", + " self.data = data.copy()\n", + "\n", + " self.group = _coerce_to_list(group)\n", + "\n", + "\n", + " self.feature_cols = self.num + self.cat\n", + " #use set ensuring unique columns\n", + " self.data = data[list(set(self.static + self.feature_cols + self.target + [self.time] +self.group))].copy()\n", + "\n", + " ##Encoders for categorical since we want to return tensors\n", + " if label_encoders is None:\n", + " label_encoders = {}\n", + " ##Encoders for categorical since we want to return tensors\n", + " for c in self.cat+self.group:\n", + " label_encoders[c] = OrdinalEncoder()\n", + " self.data[c] = label_encoders[c].fit_transform(self.data[c].values.reshape(-1,1)).flatten()\n", + " self.label_encoders = label_encoders\n", + " else:\n", + " for c in self.cat+self.group:\n", + " self.data[c] = label_encoders[c].transform(self.data[c].values.reshape(-1,1)).flatten()\n", + " self.label_encoders = label_encoders\n", + "\n", + "\n", + " ##ensure to have a coherent dataset\n", + " self.data.drop_duplicates(subset= self.group+[self.time],keep='first',inplace=True,ignore_index=True)\n", + "\n", + " ##compute minumum frequency\n", + " if self.group is None:\n", + " freq = self.data[self.time].diff.min()\n", + " else:\n", + " freq = self.data.groupby(self.group).time.diff().min()\n", + "\n", + " if isinstance(freq, timedelta):\n", + " freq = pd.to_timedelta(freq)\n", + " elif isinstance(freq, (int, float)):\n", + " freq = int(freq)\n", + " else:\n", + " raise TypeError(\"time must be integer or datetime\")\n", + "\n", + "\n", + " ##extend dataset\n", + " self.data = extend_time_df(self.data,self.time,freq,self.group).merge(self.data,how='left').reset_index()\n", + "\n", + "\n", + " ##now we are sure that data is in a coherent form!\n", + " self.lengths = {}\n", + " if self.group:\n", + " self._groups = self.data.groupby(self.group).groups\n", + " self._group_ids = list(self._groups.keys())\n", + " for k in self._groups:\n", + " self.lengths[k] = len(self._groups[k])\n", + " else:\n", + " self._groups = {0: self.data.index}\n", + " self._group_ids = [0]\n", + " self.lengths[0] = len( self.data.index)\n", + "\n", + "\n", + "\n", + " ## we know on the fly which rows are valid and wich contains nans\n", + " self.data['valid'] = ~pd.isnull(self.data.max(axis=1))\n", + "\n", + " self._prepare_metadata()\n", + "\n", + " def _prepare_metadata(self):\n", + " \"\"\"Prepare metadata for the dataset.\"\"\"\n", + " self.metadata = {\n", + " \"cols\": {\n", + " \"y\": self.target,\n", + " \"x\": self.feature_cols,\n", + " \"st\": self.static,\n", + " },\n", + " \"col_type\": {},\n", + " \"col_known\": {},\n", + " \"cat_index\":[]\n", + " }\n", + " for i, c in enumerate(self.feature_cols):\n", + " if c in self.cat:\n", + " self.metadata['cat_index'].append(i) # EDIT: was getting error with i, so updated enumerate in for loop\n", + " \n", + "\n", + " all_cols = self.target + self.feature_cols + self.static\n", + " for col in all_cols:\n", + " self.metadata[\"col_type\"][col] = \"C\" if col in self.cat else \"F\"\n", + " self.metadata[\"col_known\"][col] = \"K\" if col in self.known else \"U\"\n", + " self.metadata['encoders'] = self.label_encoders\n", + "\n", + " def __len__(self) -> int:\n", + " \"\"\"Return number of time series in the dataset.\"\"\"\n", + " return len(self._group_ids)\n", + "\n", + " def get_id_ts_by_idx(self,idx):\n", + " \"\"\"Gets the time series ID and starting index for a given index.\n", + "\n", + " Args:\n", + " idx: The overall index. This is *not* the group index, but\n", + " the index considering all time series concatenated together.\n", + "\n", + " Returns:\n", + " tuple: A tuple containing the group ID and the starting index\n", + " of that group in the concatenated data.\n", + " \"\"\"\n", + " tmp = np.cumsum(list(self.lengths.values()))\n", + " idx_group = min(np.where(tmp>idx)[0]) # EDIT: Find the group index, as we are getting group\n", + " return idx_group, 0 if idx_group==0 else tmp[idx_group-1] #EDIT: group id, index of the start of the series\n", + "\n", + " def get_total_len(self):\n", + " \"\"\"Returns total length of the dataset\"\"\"\n", + " l = 0\n", + " for k in self._groups:\n", + " l+= self.lengths[k]\n", + " return l\n", + "\n", + " def __getitem__(self, index: int) -> Dict[str, torch.Tensor]:\n", + " \"\"\"Get time series data for given index.\n", + "\n", + " Args:\n", + " index: The index of the time series to retrieve.\n", + "\n", + " Returns:\n", + " A dictionary containing tensors for 't', 'y', 'x', 'group', 'st',\n", + " and 'is_valid'.\n", + " \"\"\"\n", + " group_id = self._group_ids[index]\n", + " device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n", + "\n", + " if self.group:\n", + " mask = self._groups[group_id]\n", + " data = self.data.loc[mask].sort_values(by=self.time)\n", + " else:\n", + " data = self.data.sort_values(by=self.time)\n", + "\n", + " result = {\n", + " \"t\": data[self.time].values, # Return as numpy array\n", + " \"y\": torch.tensor(data[self.target].values, dtype=torch.float32, device=device),\n", + " \"x\": torch.tensor(data[self.feature_cols].values, dtype=torch.float32, device=device),\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + " \"st\": torch.tensor(data[self.static].iloc[0].values, dtype=torch.float32, device=device) if self.static else torch.tensor([], dtype=torch.float32, device=device), # return empty if no statics\n", + " 'is_valid': torch.tensor(data.valid.values,dtype=torch.int,device=device), #indicator of if data is valid\n", + " }\n", + "\n", + " return result\n", + "\n", + " def get_metadata(self) -> Dict:\n", + " \"\"\"Return metadata about the dataset.\n", + "\n", + " Returns:\n", + " -------\n", + " Dict\n", + " Dictionary containing:\n", + " - cols: column names for y, x, and static features\n", + " - col_type: mapping of columns to their types (F/C)\n", + " - col_known: mapping of columns to their future known status (K/U)\n", + " - encoders: Dict of label encoders used\n", + " \"\"\"\n", + " return self.metadata" + ] + }, + { + "cell_type": "markdown", + "id": "5030144e", + "metadata": {}, + "source": [ + "**Configuration dictionary for the data module. This holds hyperparameters related to data splitting, preprocessing, and windowing.**" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "290ab40d-f440-4f7f-9ae9-8855a04f1ec6", + "metadata": {}, + "outputs": [], + "source": [ + "data_module_metadata = dict(\n", + " perc_train= 0.7, # Percentage of data for training\n", + " perc_valid= 0.1, # Percentage of data for validation\n", + " range_train= None, # Optional: Specific time range for training (not used here)\n", + " range_validation= None, # Optional: Specific time range for validation (not used here)\n", + " range_test= None, # Optional: Specific time range for testing (not used here)\n", + " shift= 0, # Optional: Shift the time series (not used here)\n", + " starting_point= None, # Optional: Starting point for sampling (not used here)\n", + " skip_step= 1, # Optional: Step size for skipping data points (not used here)\n", + " past_steps=16, # Number of past time steps to use as input (encoder length)\n", + " future_steps= 16, # Number of future time steps to predict (decoder length)\n", + " precompute=True, # Whether to precompute the train/val/test splits.\n", + " scaler= 'sklearn.preprocessing.StandardScaler()' # String representation of the scaler (not used directly here)\n", + ")\n" + ] + }, + { + "cell_type": "markdown", + "id": "c611fbc0", + "metadata": {}, + "source": [ + "**Instantiate the PandasTSDataSet (D1 layer).**\n", + "This creates the dataset object that will provide the raw time series data.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "e4ca4055-7f36-446c-9b21-6c0456b152cf", + "metadata": {}, + "outputs": [], + "source": [ + "ds = PandasTSDataSet(dataset,\n", + " 'time', # Name of the time index column\n", + " target, # List of target variable names\n", + " 'group', # Name of the column that identifies groups/time series\n", + " list(set(num_var_past+num_var_fut)), # List of numerical feature columns\n", + " list(set(cat_var_fut+cat_var_past)), # List of categorical feature columns\n", + " list(set(cat_var_fut+num_var_fut)), # List of future known covariates\n", + " list(set(cat_var_past+num_var_past)), # List of past known covariates\n", + " )\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "2af9edfd", + "metadata": {}, + "source": [ + "Import necessary PyTorch modules" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "519ebe66-3d64-41df-a191-81c0ba518e79", + "metadata": {}, + "outputs": [], + "source": [ + "from torch.utils.data import Dataset, DataLoader\n", + "from torch.utils.data.dataloader import default_collate\n", + "import torch" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "c0375ff0", + "metadata": {}, + "outputs": [], + "source": [ + "def my_collate(batch):\n", + " \"\"\"Custom collate function to handle potential None values in batches.\n", + "\n", + " Filters out `None` values from the batch before collating. This is\n", + " necessary because the `MyDataset.__getitem__` method can return `None`\n", + " if a valid window cannot be created for a given index.\n", + "\n", + " Args:\n", + " batch: A list of samples (dictionaries or tensors) from the dataset.\n", + "\n", + " Returns:\n", + " The collated batch, with `None` values removed.\n", + " \"\"\"\n", + " batch = list(filter(lambda x : x is not None, batch))\n", + " #print(len(batch)) # Debugging print statement (removed in final version)\n", + " return default_collate(batch)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "6e31a24f", + "metadata": {}, + "outputs": [], + "source": [ + "## TODO: @SANDEEP rework this using some cleaner code you can find here\n", + "#https://colab.research.google.com/drive/1FvLlmEOgm3D3JgNFVeAtwPk4cXagJ0CY?usp=sharing#scrollTo=jFuSwWrhTg6y" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "12bc5f1c-2de1-4837-9d2a-e764fa395bba", + "metadata": {}, + "outputs": [], + "source": [ + "class MyDataset(Dataset):\n", + " \"\"\"Custom PyTorch Dataset for time series data.\n", + "\n", + " This class adapts the D1 dataset (`PandasTSDataSet`) for use with a\n", + " PyTorch `DataLoader`. It handles the creation of input/output windows\n", + " (sequences) for training and validation. It also handles the case where\n", + " a valid window cannot be created (e.g., due to insufficient past data)\n", + " by returning `None`.\n", + "\n", + " Args:\n", + " data: The D1 dataset instance (e.g., `PandasTSDataSet`).\n", + " metadata: A dictionary containing metadata about the dataset and\n", + " data processing parameters (e.g., `past_steps`, `future_steps`).\n", + " valid_index: A dictionary where keys are group IDs and values are\n", + " lists of valid indices for that group. This defines the\n", + " valid ranges within each time series for creating windows.\n", + " \"\"\"\n", + "\n", + " def __init__(self, data, metadata, valid_index) -> torch.utils.data.Dataset:\n", + " \"\"\"Initializes the MyDataset instance.\"\"\"\n", + "\n", + " self.metadata = metadata\n", + " self.metadata_dataset = data.metadata # Metadata from the D1 dataset\n", + " self.data = data # The D1 dataset instance\n", + " self.valid_index = valid_index # Dictionary of valid indices for each group\n", + " sum = 0\n", + " self.lengths = {}\n", + " #fix this, probably we need to add something (check validity, check past and future)\n", + " for k in self.valid_index:\n", + " # Calculate the total number of valid windows that can be created\n", + " # from each group. Subtract `future_steps` because we need enough\n", + " # data points for both the input window and the target window.\n", + " sum+=(len(self.valid_index[k])-self.metadata['future_steps'])\n", + " self.lengths[k] = len(self.valid_index[k])\n", + " self.length = sum # Sum of lengths accross differnet time series.\n", + "\n", + " def __len__(self):\n", + " \"\"\"Returns the total number of valid windows in the dataset.\"\"\"\n", + " return self.length\n", + "\n", + " def get_id_ts_by_idx(self,idx):\n", + " \"\"\"Gets time series id given global index\"\"\"\n", + " tmp = np.cumsum(list(self.lengths.values()))\n", + " idx = min(np.where(tmp>idx)[0])\n", + " return idx, 0 if idx==0 else tmp[idx-1]\n", + "\n", + " def __getitem__(self, idxs):\n", + " \"\"\"Retrieves a sample (input/output window) from the dataset.\n", + "\n", + " Args:\n", + " idxs: The *global* index of the window to retrieve. This index\n", + " is across all time series in the dataset.\n", + "\n", + " Returns:\n", + " A dictionary containing the input and output tensors for the window,\n", + " or `None` if a valid window cannot be created at the given index.\n", + " \"\"\"\n", + "\n", + " ##crucial point there: correctly identifying which are the indexes that we need!\n", + " sample = {}\n", + " IDX,difference = self.get_id_ts_by_idx(idxs) #find group and local index\n", + "\n", + " idxs -= difference # Adjust index to be relative to the start of the time series\n", + " idxs = self.valid_index[IDX][idxs] # Get the actual index in the data\n", + " tmp = self.data.__getitem__(IDX) ##use the getitem of the data!\n", + "\n", + " # Check if enough data is available for both input and output windows\n", + " if idxs+self.metadata['future_steps']>self.data.lengths[IDX]:\n", + " return None # Not enough data, return None\n", + "\n", + " #Check for the validity of the time series, by checking nans.\n", + " if tmp['is_valid'][idxs-self.metadata['past_steps']:idxs+self.metadata['future_steps']].sum() != self.metadata['future_steps']+self.metadata['past_steps']:\n", + " return None\n", + " \n", + " # Construct sample\n", + " for k in tmp.keys():\n", + " # Separate based on past/future\n", + " # We create past and future samples only for the ones we sliced using idxs\n", + " if '_past' in k:\n", + " sample[k] = tmp[k][idxs-self.metadata['past_steps']:idxs] #past steps based on the past_step provided in metadata.\n", + " elif '_fut' in k or k=='y':\n", + " sample[k] = tmp[k][idxs:idxs+self.metadata['future_steps']]# future steps based on the future_step\n", + " else:\n", + " pass\n", + " return sample\n" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "ac4a66d5-753b-4030-a88d-e5b8efd73d95", + "metadata": {}, + "outputs": [], + "source": [ + "def compute_ranges(d1_dataset, metadata):\n", + " \"\"\"Computes valid index ranges for training, validation, and testing.\n", + "\n", + " This function calculates the start and end indices for the training,\n", + " validation, and testing sets within each time series group, based on the\n", + " provided percentages in `metadata`.\n", + "\n", + " Args:\n", + " d1_dataset: The D1 dataset instance (e.g., `PandasTSDataSet`).\n", + " metadata: A dictionary containing metadata, including `perc_train`\n", + " and `perc_valid` (percentages for training and validation splits).\n", + "\n", + " Returns:\n", + " tuple: A tuple containing three dictionaries: `train_ranges`,\n", + " `valid_ranges`, and `test_ranges`. Each dictionary has group IDs\n", + " as keys and lists of valid indices as values.\n", + " \"\"\"\n", + " ##suppose for now we use only percentage!\n", + " train_ranges = {}\n", + " valid_ranges = {}\n", + " test_ranges = {}\n", + " for k in d1_dataset.lengths: # Iterate over each time series group\n", + " ls = d1_dataset.lengths[k] # Get the length of the current time series\n", + " train_ranges[k] = list(range(0,int(metadata['perc_train']*ls))) # Training range\n", + " valid_ranges[k] = list(range(int(metadata['perc_train']*ls), int((metadata['perc_valid'] + metadata['perc_train'])*ls))) # Validation range\n", + " test_ranges[k] = list(range(int((metadata['perc_valid'] + metadata['perc_train'])*ls),ls)) # Testing range\n", + "\n", + " ## in case of datetime ranges we need to use np.where and create a mask\n", + " return train_ranges,valid_ranges,test_ranges\n", + "\n", + "# Calculate the training, validation, and testing ranges\n", + "train_ranges, valid_ranges, test_ranges = compute_ranges(ds, data_module_metadata)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "0185143c", + "metadata": {}, + "source": [ + "**Create a DataLoader for the training set.**" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "7e43bfd7-12b3-48c2-8b33-d5ca6681959c", + "metadata": {}, + "outputs": [], + "source": [ + "dl = DataLoader(\n", + " MyDataset(ds, data_module_metadata, train_ranges), # Create a MyDataset instance\n", + " batch_size=32, # Set the batch size\n", + " collate_fn=my_collate # Use the custom collate function\n", + " )\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "dde2ba1f-f6f8-483c-a404-5917b84ce37a", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n", + "/var/folders/9z/1zsbrjl17xj_76sy78736hmr0000gn/T/ipykernel_39262/1986820013.py:192: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n", + " \"group\": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([16, 16, 1])\n" + ] + } + ], + "source": [ + "# Example of iterating through the DataLoader (for debugging/demonstration).\n", + "for x in dl:\n", + " print(x['y'].shape) # Print the shape of the target variable tensor.\n", + " break #only check first batch\n" + ] + }, + { + "cell_type": "markdown", + "id": "a7ddeb11", + "metadata": {}, + "source": [ + "**The following function and class show how one might create a `LightningDataModule` to manage the data loading process, including train/validation/test splits. This builds on top of previous components.**" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "86c9c7fc", + "metadata": {}, + "outputs": [], + "source": [ + "class DecoderEncoderDataModule(L.LightningDataModule):\n", + " \"\"\"LightningDataModule for encoder-decoder models.\n", + "\n", + " This class manages the data loading and preprocessing for training,\n", + " validation, testing, and prediction. It handles splitting the data,\n", + " creating `Dataset` instances, and creating `DataLoader` instances. It\n", + " supports both precomputed splits and on-the-fly splitting.\n", + "\n", + " Args:\n", + " d1_dataset: The D1 dataset instance (e.g., `PandasTSDataSet`).\n", + " batch_size: The batch size.\n", + " num_workers: The number of worker processes for data loading.\n", + " metadata: A dictionary containing metadata and hyperparameters.\n", + " \"\"\"\n", + " def __init__(self, d1_dataset, batch_size=32, num_workers=4, metadata=None):\n", + " super().__init__()\n", + " # initialize other params\n", + " self.d1_dataset = d1_dataset\n", + " self.batch_size = batch_size\n", + " self.metadata = metadata\n", + " self.num_workers = num_workers\n", + "\n", + "\n", + " def prepare_data(self):\n", + " \"\"\"Prepares the data for training/validation/testing.\n", + "\n", + " This method handles splitting the data into training, validation, and\n", + " testing sets. It supports two modes:\n", + "\n", + " - `precompute=True`: Uses a `split_data` function (not defined here,\n", + " but assumed to exist) to precompute the splits. This is useful for\n", + " deterministic splits or when the splitting process is expensive.\n", + " - `precompute=False`: Calculates the splits on-the-fly using\n", + " `compute_ranges`. This is more flexible and allows for different\n", + " splitting strategies.\n", + " \"\"\"\n", + " if self.metadata['precompute']:\n", + " self.precompute = True\n", + " ## @SANDEEP this is what is returned by DSIPTS split_for_train!!\n", + " ## the only difference is that we need to use the __getitem__ of the d1 layer\n", + "\n", + " # The split_data function is assumed to be defined elsewhere and\n", + " # would handle the splitting logic according to however the\n", + " # project defines \"splitting\" (could be by time, by groups, etc.)\n", + " self.train_dataset, self.validation_dataset, self.test_dataset = split_data(self.d1_dataset,self.metadata) # Placeholder\n", + "\n", + "\n", + "\n", + " else:\n", + " self.precompute = False\n", + " ##in this case we need to pass only the data that are referring to the trainin period\n", + " ## but data can be chunked, we need to create a function that transform a D1 object into a D1 object or, even better\n", + " ## something that can be used by the dataset for filtering only valid samples!\n", + " ##for example\n", + " self.train_ranges, self.validation_ranges, self.test_ranges = compute_ranges(self.d1_dataset,self.metadata)\n", + " self.predict_data = None ##??? need to study this\n", + " ##TODO normalization???? maybe we can precompute here some statistics and pass them to the dataset!\n", + "\n", + " def setup(self, stage=None):\n", + " \"\"\"Sets up the datasets for each stage (fit, test, predict).\n", + "\n", + " This method creates the appropriate `Dataset` instances for each stage,\n", + " using either the precomputed splits or the on-the-fly calculated ranges.\n", + "\n", + " Args:\n", + " stage: The stage ('fit', 'test', or 'predict').\n", + " \"\"\"\n", + " # Get metadata from D1 layer during setup\n", + " #self.metadata = self.d1_dataset.get_metadata() ##NO SEE COMMENT BEFORE! We get metadata in __init__\n", + " ##create dataset here\n", + " if stage == 'fit':\n", + " if self.precompute:\n", + " # Use precomputed datasets (implementation of split_data needed)\n", + " pass # Use self.train_dataset and self.validation_dataset as defined in prepare_data\n", + " else:\n", + " # Create datasets using computed ranges\n", + " self.train_dataset = MyDataset(self.d1_dataset, self.metadata, self.train_ranges) # Pass train_ranges\n", + " self.validation_dataset = MyDataset(self.d1_dataset, self.metadata, self.validation_ranges) # Pass valid_ranges\n", + "\n", + " if stage == 'test':\n", + " if self.precompute:\n", + " pass\n", + " # Use precomputed test dataset\n", + " else:\n", + " self.test_dataset = MyDataset(self.d1_dataset, self.metadata, self.test_ranges) # Pass test_ranges\n", + "\n", + " if stage == 'predict':\n", + " if self.precompute:\n", + " pass\n", + " # Use precomputed prediction dataset (if applicable)\n", + " else:\n", + " self.predict_datasset = MyDataset(self.d1_dataset, self.metadata, self.predict_data) # Pass predict_data. Implementation details TBD.\n", + "\n", + " def train_dataloader(self):\n", + " \"\"\"Returns the training DataLoader.\"\"\"\n", + " return DataLoader(self.train_dataset, batch_size=self.batch_size, shuffle=True, num_workers=self.num_workers, collate_fn=my_collate)\n", + "\n", + " def val_dataloader(self):\n", + " \"\"\"Returns the validation DataLoader.\"\"\"\n", + " return DataLoader(self.validation_dataset, batch_size=self.batch_size, shuffle=False, num_workers=self.num_workers, collate_fn=my_collate)\n", + "\n", + " def test_dataloader(self):\n", + " \"\"\"Returns the test DataLoader.\"\"\"\n", + " return DataLoader(self.test_dataset, batch_size=self.batch_size, shuffle=False, num_workers=self.num_workers, collate_fn=my_collate)\n", + "\n", + " def predict_dataloader(self):\n", + " \"\"\"Returns the prediction DataLoader.\"\"\"\n", + " return DataLoader(self.predict_datasset, batch_size=self.batch_size, shuffle=False, num_workers=self.num_workers, collate_fn=my_collate)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "c83526e8", + "metadata": {}, + "source": [ + "**`DecoderEncoderModel` Class (M Layer - Example)**\n", + "This is a placeholder for the Model Layer." + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "9d9a07bd-35cd-4cbd-8843-081b7d3d8580", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "# Layer M - Example (Placeholder)\n", + "class DecoderEncoderModel(L.LightningModule):\n", + " \"\"\"Placeholder for an encoder-decoder model (M layer).\n", + "\n", + " This is a minimal example of how a model might be structured using\n", + " PyTorch Lightning. It shows how to access the metadata from the\n", + " `LightningDataModule`. A real implementation would include the actual\n", + " network architecture and training logic.\n", + " \"\"\"\n", + " def __init__(self):\n", + " super().__init__()\n", + " self.metadata = None\n", + "\n", + " def setup(self, stage=None):\n", + " \"\"\"Gets metadata from the datamodule during setup.\"\"\"\n", + " # Get metadata from datamodule during setup\n", + " self.metadata = self.trainer.datamodule.metadata\n", + "\n", + " # Initialize layer T model using metadata (placeholder)\n", + "\n", + " def forward(self, x):\n", + " \"\"\"Forward pass (placeholder).\"\"\"\n", + " # forward logic (implementation would go here)\n", + " pass\n", + "\n", + " def training_step(self, batch, batch_idx):\n", + " \"\"\"Training step (placeholder).\"\"\"\n", + " pass # Replace with actual training logic\n", + "\n", + " def validation_step(self, batch, batch_idx):\n", + " \"\"\"Validation step (placeholder).\"\"\"\n", + " pass # Replace with actual validation logic\n" + ] + }, + { + "cell_type": "markdown", + "id": "80542685", + "metadata": {}, + "source": [ + "### Layer M" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "694a020f-7f8d-4503-a023-bfdcda90667b", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "\n", + "class DecoderEncoderModel(L.LightningModule):\n", + " def __init__(self):\n", + " super().__init__()\n", + " self.metadata = None\n", + " \n", + " def setup(self, stage=None):\n", + " # Get metadata from datamodule during setup\n", + " self.metadata = self.trainer.datamodule.metadata\n", + " \n", + " # Initialize layer T model using metadata\n", + " \n", + " def forward(self, x):\n", + " # forward logic\n", + " pass" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ec43226c-f99f-4a77-8793-c3e9db5c4536", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "dsipts", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/sandeep/time_series_dataset.py b/notebooks/sandeep/time_series_dataset.py new file mode 100644 index 00000000..63a95880 --- /dev/null +++ b/notebooks/sandeep/time_series_dataset.py @@ -0,0 +1,1433 @@ +""" +Time Series Dataset Module + +This module provides classes for two-layer time series data handling: +- MultiSourceTSDataSet (D1 Layer): Handles raw data from multiple CSV files +- TSDataModule (D2 Layer): LightningDataModule for time series data with support for training, validation, and testing. + +Key Features: +- Supports multiple CSV files with different groups +- Handles regular time intervals +- Creates sliding windows for inputs and targets +- Supports validation of data points +- Global indexing across groups and files +- Precomputed indices for valid samples +""" + +import os +import torch +from torch.utils.data import Dataset, DataLoader +import pytorch_lightning as pl +import pandas as pd +import numpy as np +from typing import Dict, List, Optional, Union, Tuple +from datetime import timedelta +from sklearn.preprocessing import OrdinalEncoder +import logging + +# Configure logging +logging.basicConfig( + level=logging.INFO, + format='%(asctime)s - %(name)s - %(levelname)s - %(message)s' +) + +def _coerce_to_list(obj): + """ + Coerce object to list. + + Args: + obj: Input object to be coerced to list + + Returns: + List containing the input object(s) + """ + if obj is None: + return [] + if isinstance(obj, str): + return [obj] + return list(obj) + + +def extend_time_df(df, time_col, freq, group_cols=None, max_length=None): + """ + Extend a dataframe to ensure regular time intervals. + + Args: + df: Input dataframe containing time series data + time_col: Column name containing time information + freq: Frequency to use for extending the dataframe + group_cols: Optional list of columns identifying groups + max_length: Optional maximum length for the extended dataframe + + Returns: + DataFrame extended with regular time intervals + """ + if len(df) == 0: + return df + + # Ensure frequency is positive + if (isinstance(freq, (timedelta, int, float)) and + (freq.total_seconds() < 0 if isinstance(freq, timedelta) else freq < 0)): + freq = abs(freq) + + result_df = pd.DataFrame() + + # If grouped data, process each group separately + if group_cols and len(group_cols) > 0: + for group_name, group_data in df.groupby(group_cols): + # Get min and max time for this group + min_time = group_data[time_col].min() + max_time = group_data[time_col].max() + + # Create time range for this group + if isinstance(min_time, pd.Timestamp): + time_range = pd.date_range(start=min_time, end=max_time, freq=freq) + else: + time_range = pd.Series(np.arange(min_time, max_time + freq, freq)) + + # Limit the length if specified + if max_length and len(time_range) > max_length: + time_range = time_range[:max_length] + + # Create extended dataframe for this group + if isinstance(group_name, tuple): + # Multiple group columns + extended_group = pd.DataFrame({time_col: time_range}) + for i, col in enumerate(group_cols): + extended_group[col] = group_name[i] + else: + # Single group column + extended_group = pd.DataFrame({ + time_col: time_range, + group_cols[0]: group_name + }) + + result_df = pd.concat([result_df, extended_group], ignore_index=True) + else: + # No groups, extend the entire dataframe + min_time = df[time_col].min() + max_time = df[time_col].max() + + if isinstance(min_time, pd.Timestamp): + time_range = pd.date_range(start=min_time, end=max_time, freq=freq) + else: + time_range = pd.Series(np.arange(min_time, max_time + freq, freq)) + + if max_length and len(time_range) > max_length: + time_range = time_range[:max_length] + + result_df = pd.DataFrame({time_col: time_range}) + + return result_df + + +class MultiSourceTSDataSet(Dataset): + """ + Layer 1 (D1) dataset for multi-source time series data. + + This dataset: + 1. Loads time series data from multiple CSV files + 2. Handles categorical encoding and normalization + 3. Efficiently processes data in chunks for memory-efficient operation + 4. Preserves NaN values for D2 layer to handle + + It does NOT compute validity of windows or create sliding windows - that is + the responsibility of the D2 layer (TSDataProcessor). + """ + + def __init__( + self, + file_paths: List[str], + group_cols: Union[str, List[str]], + time_col: str, + feature_cols: List[str], + target_cols: List[str], + static_cols: Optional[List[str]] = None, + cat_cols: Optional[List[str]] = None, + num_cols: Optional[List[str]] = None, + known_cols: Optional[List[str]] = None, + unknown_cols: Optional[List[str]] = None, + weights: Optional[str] = None, + memory_efficient: bool = False, + chunk_size: int = 10000 + ): + """ + Initialize the MultiSourceTSDataSet. + + Args: + file_paths: List of paths to CSV files containing time series data + group_cols: Column(s) that identify unique time series groups + time_col: Column containing time/date information + feature_cols: Columns to use as features (X) + target_cols: Columns to use as targets (y) + static_cols: Columns with static (non-time-varying) features + cat_cols: Categorical columns that need encoding + num_cols: Numerical columns (if None, all non-categorical columns are treated as numerical) + known_cols: Columns that are known at prediction time (if None, all feature_cols are considered known) + unknown_cols: Columns that are unknown at prediction time (if None, all target_cols are considered unknown) + weights: Name of weights column + memory_efficient: Whether to use memory-efficient mode + chunk_size: Chunk size for processing data (used in memory-efficient mode) + """ + # Basic configuration + self.file_paths = file_paths + self.time_col = time_col + self.weights = weights + + # Handle group columns (can be single column or multiple) + if isinstance(group_cols, str): + self.group_cols = [group_cols] + else: + self.group_cols = group_cols + + # Feature configuration + self.feature_cols = feature_cols + self.target_cols = target_cols + self.static_cols = static_cols or [] + self.cat_cols = cat_cols or [] + self.num_cols = num_cols or [] + + # Known/unknown columns configuration + self.known_cols = known_cols or self.feature_cols.copy() + self.unknown_cols = unknown_cols or self.target_cols.copy() + + # If num_cols not specified, infer from feature_cols and cat_cols + if not self.num_cols: + all_cols = self.feature_cols + self.target_cols + self.static_cols + self.num_cols = [c for c in all_cols if c not in self.cat_cols] + + # Internal state + self.memory_efficient = memory_efficient + self.chunk_size = chunk_size + + # Initialize label encoders for categorical columns + self.label_encoders = {} + + # For compatibility with test code, initialize data attribute + self.data = None + + # Pre-loaded data cache (only used when memory_efficient=False) + self.data_cache = {} + + # Process files to build metadata and encoders + self._process_files() + + # Prepare metadata + self._prepare_metadata() + + # Preload data if memory_efficient is False + if not self.memory_efficient: + self._preload_data() + + def _process_files(self): + """ + Process each file to extract group information and update encoders. + + This method: + 1. Scans through all CSV files (in chunks if memory_efficient=True) + 2. Identifies unique groups across all files + 3. Updates label encoders for categorical columns + 4. Builds a mapping of where each group's data is located + 5. Calculates the total length of each group + 6. Treats groups as file-specific to handle large files efficiently + 7. Preserves NaN values for valid index computation in D2 layer + """ + # Initialize data structures + self.total_length = 0 # Total number of rows across all groups + self.file_info = [] # Information about each group in each file + self.group_info = {} # Maps (file_idx, group_key) to their locations in files + self.lengths = {} # Store the length of each group (for compatibility) + self.file_group_map = [] # Maps global index to (file_idx, group_key) tuples + self.file_sizes = [] # Store file sizes for memory management + + print("Processing files to build metadata...") + # Process each file + for file_idx, file_path in enumerate(self.file_paths): + print(f"\nProcessing file {file_idx + 1}/{len(self.file_paths)}: {file_path}") + + # Track groups in this file + file_groups = set() + + # Get file size + file_size = os.path.getsize(file_path) + self.file_sizes.append(file_size) + + if self.memory_efficient: + # Process in chunks for memory efficiency + for chunk in pd.read_csv(file_path, chunksize=self.chunk_size): + self._process_chunk(chunk, file_idx, file_path, file_groups) + else: + # Load entire file at once for small files + chunk = pd.read_csv(file_path) + self._process_chunk(chunk, file_idx, file_path, file_groups) + + # Add all groups from this file to the global mapping + for file_group_key in file_groups: + self.file_group_map.append(file_group_key) + + # Store unique file-group combinations for iteration + self._group_ids = list(self.group_info.keys()) + print(f"\nFound {len(self._group_ids)} unique file-group combinations") + + # For backward compatibility, create a mapping of original group keys + self._original_group_ids = list(set(group_key for _, group_key in self._group_ids)) + print(f"Representing {len(self._original_group_ids)} unique group identifiers") + + def _process_chunk(self, chunk, file_idx, file_path, file_groups): + """ + Process a chunk of data from a file. + + Args: + chunk: DataFrame chunk to process + file_idx: Index of the file being processed + file_path: Path to the file being processed + file_groups: Set to track groups in this file + """ + # Update label encoders with new categories from the chunk + self._update_encoders(chunk) + + # Find all unique groups in the current chunk + groups = chunk[self.group_cols].drop_duplicates() + + # Process each group in the chunk + for _, group_row in groups.iterrows(): + # Create a key from the group columns' values + group_key = tuple(group_row[self.group_cols].values) + # If there's only one group column, use the value directly instead of a tuple + if len(self.group_cols) == 1: + group_key = group_key[0] + + # Create a file-specific group identifier + file_group_key = (file_idx, group_key) + file_groups.add(file_group_key) + + # Initialize group entry if not seen before + if file_group_key not in self.group_info: + self.group_info[file_group_key] = [] + self.lengths[file_group_key] = 0 # Initialize length counter for this group + + # Filter data for the current group + group_mask = (chunk[self.group_cols] == group_row).all(axis=1) + group_data = chunk[group_mask] + + # Store information about the group + info = { + 'file_idx': file_idx, + 'file_path': file_path, + 'group_key': group_key, + 'file_group_key': file_group_key, + 'start_time': group_data[self.time_col].min(), + 'end_time': group_data[self.time_col].max(), + 'row_count': len(group_data) + } + + # Add file info index to group's entry + self.group_info[file_group_key].append(len(self.file_info)) + self.file_info.append(info) + + # Update total length and group-specific length + self.total_length += len(group_data) + self.lengths[file_group_key] += len(group_data) + + def _preload_data(self): + """ + Preload all data from files into memory for faster access. + """ + print("Preloading data into memory...") + for file_idx, file_path in enumerate(self.file_paths): + print(f"Loading file {file_idx + 1}/{len(self.file_paths)}: {file_path}") + # Load the data + file_data = pd.read_csv(file_path) + + # Encode categorical features + for col in self.cat_cols: + if col in file_data.columns: + file_data[col] = self.label_encoders[col].transform( + file_data[col].values.reshape(-1, 1) + ).flatten() + + # Sort by group columns and then by time + sort_cols = self.group_cols + [self.time_col] + file_data = file_data.sort_values(sort_cols) + + # Store in cache + self.data_cache[file_idx] = file_data + + print(f"Preloaded {len(self.file_paths)} files") + + # Estimate memory usage + if self.data_cache: + first_file = next(iter(self.data_cache.values())) + # Calculate approximate size in MB + size_mb = first_file.memory_usage(deep=True).sum() / (1024 * 1024) + total_mb = sum(df.memory_usage(deep=True).sum() for df in self.data_cache.values()) / (1024 * 1024) + print(f"Estimated memory usage: {total_mb:.2f} MB") + + def _update_encoders(self, data): + """ + Update label encoders with new categories from the data. + + This method ensures consistent encoding across all files by: + 1. Checking each categorical column + 2. Creating new encoders if needed + 3. Updating existing encoders with new categories + + Args: + data: DataFrame chunk to process + """ + for col in self.cat_cols + self.group_cols: + if col in data.columns: + # Get unique values for this column + unique_values = data[col].dropna().unique().reshape(-1, 1) + if len(unique_values) > 0: + if col not in self.label_encoders: + # Create new encoder if it doesn't exist + self.label_encoders[col] = OrdinalEncoder() + self.label_encoders[col].fit(unique_values) + else: + # Update existing encoder with new categories + current_cats = self.label_encoders[col].categories_[0] + # Combine existing and new categories, ensuring uniqueness + new_cats = np.unique(np.concatenate([current_cats, unique_values.flatten()])) + self.label_encoders[col].categories_ = [new_cats] + + def __len__(self): + """Return the number of file-group combinations in the dataset.""" + return len(self.file_group_map) + + def __getitem__(self, idx): + """ + Get data for a specific file-group combination by index. + + This method: + 1. Maps the index to a specific file-group combination + 2. Loads all data for that combination + 3. Converts data to appropriate formats for model consumption + + Args: + idx: Index of the file-group combination to retrieve + + Returns: + Dictionary containing group data with keys: + - 'x': Feature tensor + - 'y': Target tensor + - 't': Time values (as numpy array) + - 'w': Weight tensor + - 'group_id': Group identifier + - 'st': Static features + """ + # Get the file-group combination for the requested index + file_group_key = self.file_group_map[idx] + file_idx, group_id = file_group_key + + # Load data for this file-group combination + group_data = self._load_group_data(file_group_key) + + if len(group_data) == 0: + raise ValueError(f"No data found for file {file_idx}, group {group_id}") + + # For compatibility with test code, store the last loaded group data + self.data = group_data + + # Convert data to appropriate format + x = torch.tensor(group_data[self.feature_cols].values, dtype=torch.float32) + y = torch.tensor(group_data[self.target_cols].values, dtype=torch.float32) + + # Time values - keep as numpy array for datetime support + t = group_data[self.time_col].values + + # Weights - use ones if not provided + if self.weights is not None and self.weights in group_data.columns: + w = torch.tensor(group_data[self.weights].values, dtype=torch.float32) + else: + w = torch.ones(len(group_data), dtype=torch.float32) + + # Static features - use empty tensor if none provided + if self.static_cols and len(self.static_cols) > 0: + # Take first row since static features are constant within a group + static_values = [] + for col in self.static_cols: + if col in group_data.columns: + # Convert to float if possible, otherwise use 0.0 as placeholder + try: + val = float(group_data[col].iloc[0]) + except (ValueError, TypeError): + val = 0.0 # Default value for non-numeric static features + static_values.append(val) + + # Create tensor if we have values, otherwise empty tensor + if static_values: + st = torch.tensor(static_values, dtype=torch.float32) + else: + st = torch.tensor([], dtype=torch.float32) + else: + st = torch.tensor([], dtype=torch.float32) + + # Return all data as a dictionary + return { + 'x': x, # features + 'y': y, # targets + 't': t, # time indices (as numpy array) + 'w': w, # weights + 'group_id': group_id, # group identifier + 'st': st # static features + } + + def _prepare_metadata(self): + """Prepare metadata about the dataset.""" + self.metadata = { + "cols": { + "y": self.target_cols, + "x": self.feature_cols, + "st": self.static_cols + }, + "col_type": {}, + "col_known": {}, + "weight": self.weights, + "cat_index": [] + } + + # Add column indices + for i, c in enumerate(self.feature_cols): + if c in self.cat_cols: + self.metadata['cat_index'].append(i) + + # Set column types and known status + all_cols = self.target_cols + self.feature_cols + self.static_cols + for col in all_cols: + self.metadata["col_type"][col] = "C" if col in self.cat_cols else "F" + # All columns are considered unknown for future values by default + self.metadata["col_known"][col] = "U" + + # Add known/unknown column information + self.metadata['known_cols'] = self.known_cols + self.metadata['unknown_cols'] = self.unknown_cols + + # Distinguish between categorical and numerical known/unknown columns + self.metadata['known_cat_cols'] = [col for col in self.known_cols if col in self.cat_cols] + self.metadata['known_num_cols'] = [col for col in self.known_cols if col not in self.cat_cols] + self.metadata['unknown_cat_cols'] = [col for col in self.unknown_cols if col in self.cat_cols] + self.metadata['unknown_num_cols'] = [col for col in self.unknown_cols if col not in self.cat_cols] + + # Add max classes information for categorical columns + self.metadata['max_classes'] = {} + for col, encoder in self.label_encoders.items(): + # Number of classes is the number of unique values seen by the encoder + num_classes = len(encoder.classes_) + self.metadata['max_classes'][col] = num_classes + + # Add encoders to metadata + self.metadata['encoders'] = self.label_encoders + + def get_metadata(self) -> Dict: + """ + Return metadata about the dataset. + + Returns: + Dictionary containing metadata about columns and their properties + """ + return self.metadata + + def _load_group_data(self, file_group_key): + """ + Load data for a specific file-group combination. + + This method: + 1. Gets the file containing data for the requested group + 2. Loads from cache or from disk based on memory_efficient setting + 3. Applies encoding to categorical features if needed + 4. Sorts data by time column + 5. Regularizes time series using extend_time_df + 6. Preserves NaN values for valid index computation in D2 layer + + Args: + file_group_key: Tuple of (file_idx, group_key) to load data for + + Returns: + DataFrame containing all data for the requested file-group combination + """ + file_idx, group_key = file_group_key + + # Get file information for this group + file_indices = self.group_info[file_group_key] + info = self.file_info[file_indices[0]] + file_path = info['file_path'] + + # Different data loading strategies based on memory efficiency setting + if not self.memory_efficient and file_idx in self.data_cache: + # Use preloaded data from cache (already sorted by group and time) + file_data = self.data_cache[file_idx] + + # Filter for the group + if isinstance(group_key, tuple): + # For multi-column groups, check all columns + mask = np.ones(len(file_data), dtype=bool) + for col, val in zip(self.group_cols, group_key): + mask &= (file_data[col] == val) + else: + # For single-column groups, simple equality check + mask = file_data[self.group_cols[0]] == group_key + + # If this file contains data for our group + if mask.any(): + group_data = file_data[mask].copy() + else: + return pd.DataFrame() + else: + # Load from file + if self.memory_efficient: + # We'll accumulate data from chunks + group_chunks = [] + + # Read file in chunks + for chunk in pd.read_csv(file_path, chunksize=self.chunk_size): + # Filter for the group + if isinstance(group_key, tuple): + # For multi-column groups, check all columns + mask = np.ones(len(chunk), dtype=bool) + for col, val in zip(self.group_cols, group_key): + mask &= (chunk[col] == val) + else: + # For single-column groups, simple equality check + mask = chunk[self.group_cols[0]] == group_key + + # If this chunk contains data for our group + if mask.any(): + group_chunk = chunk[mask].copy() + + # Encode categorical features + for col in self.cat_cols: + if col in group_chunk.columns: + group_chunk[col] = self.label_encoders[col].transform( + group_chunk[col].values.reshape(-1, 1) + ).flatten() + + group_chunks.append(group_chunk) + + # Combine all chunks + if group_chunks: + group_data = pd.concat(group_chunks, ignore_index=True) + else: + return pd.DataFrame() + else: + # Load entire file at once + file_data = pd.read_csv(file_path) + + # Encode categorical features + for col in self.cat_cols: + if col in file_data.columns: + file_data[col] = self.label_encoders[col].transform( + file_data[col].values.reshape(-1, 1) + ).flatten() + + # Filter for the group + if isinstance(group_key, tuple): + # For multi-column groups, check all columns + mask = np.ones(len(file_data), dtype=bool) + for col, val in zip(self.group_cols, group_key): + mask &= (file_data[col] == val) + else: + # For single-column groups, simple equality check + mask = file_data[self.group_cols[0]] == group_key + + # If this file contains data for our group + if mask.any(): + group_data = file_data[mask].copy() + else: + return pd.DataFrame() + + # Sort by time (since we're dealing with a single group, no need to sort by group) + group_data = group_data.sort_values(self.time_col) + + # Regularize time series (fill gaps with NaN values) + # First, try to detect the frequency + try: + # Convert time column to datetime if it's not numeric + time_col_data = group_data[self.time_col] + if not pd.api.types.is_numeric_dtype(time_col_data): + time_col_data = pd.to_datetime(time_col_data) + + # Calculate differences between consecutive time points + if len(time_col_data) > 1: + if pd.api.types.is_datetime64_dtype(time_col_data): + # For datetime, calculate timedeltas + time_diffs = time_col_data.diff().dropna() + # Get the most common difference (mode) + freq = time_diffs.mode().iloc[0] + else: + # For numeric time, calculate differences + time_diffs = np.diff(time_col_data) + # Get the most common difference (mode) + freq = pd.Series(time_diffs).mode().iloc[0] + + # Use extend_time_df to regularize the time series + group_data = extend_time_df( + df=group_data, + time_col=self.time_col, + freq=freq, + fill_value=np.nan + ) + except Exception as e: + # If regularization fails, log the error but continue with original data + print(f"Warning: Time series regularization failed for group {group_key}: {str(e)}") + + return group_data + + +class TSDataModule(pl.LightningDataModule): + """D2 Layer - Processes time series data for model consumption. + + This module: + 1. Creates sliding windows from time series data + 2. Handles train/validation/test splits + 3. Creates DataLoaders for PyTorch Lightning + """ + + def __init__( + self, + d1_dataset: MultiSourceTSDataSet, + past_len: int, + future_len: int, + batch_size: int = 32, + min_valid_length: Optional[int] = None, + split_method: str = 'percentage', + split_config: Optional[tuple] = None, + num_workers: int = 0, + sampler: Optional[Sampler] = None, + memory_efficient: bool = False, + known_cols: Optional[List[str]] = None, + unknown_cols: Optional[List[str]] = None, + precompute: bool = True + ): + """ + Initialize the TSDataModule. + + Args: + d1_dataset: The D1 dataset instance (MultiSourceTSDataSet) + past_len: Number of past time steps for input + future_len: Number of future time steps for prediction + batch_size: Batch size for DataLoaders + min_valid_length: Minimum number of valid points required in a window + split_method: Method for splitting data ('percentage' or 'group') + split_config: Configuration for the split + num_workers: Number of workers for DataLoader + sampler: Optional custom sampler for the DataLoader + memory_efficient: Whether to use memory-efficient mode + known_cols: Columns that are known at prediction time (overrides D1 dataset settings) + unknown_cols: Columns that are unknown at prediction time (overrides D1 dataset settings) + """ + super().__init__() + self.d1_dataset = d1_dataset + self.past_len = past_len + self.future_len = future_len + self.batch_size = batch_size + self.min_valid_length = min_valid_length or past_len + self.split_method = split_method + self.split_config = split_config + self.num_workers = num_workers + self.sampler = sampler + self.memory_efficient = memory_efficient + + # Store reference to D1 dataset metadata + self.metadata = d1_dataset.metadata.copy() + + # Set feature and target columns from D1 dataset + self.feature_cols = d1_dataset.feature_cols + self.target_cols = d1_dataset.target_cols + self.static_cols = d1_dataset.static_cols + + # Override known/unknown columns if provided + self.known_cols = known_cols if known_cols is not None else d1_dataset.known_cols + self.unknown_cols = unknown_cols if unknown_cols is not None else d1_dataset.unknown_cols + + # Update metadata with known/unknown columns + self.metadata['known_cols'] = self.known_cols + self.metadata['unknown_cols'] = self.unknown_cols + + # Default split configuration based on method + if split_config is None: + if split_method == 'percentage': + self.split_config = (0.7, 0.15, 0.15) # Default: 70% train, 15% val, 15% test + else: + raise ValueError("For 'group' split method, split_config must be provided") + + # Whether to precompute valid indices and create datasets + self.precompute = precompute + + # Initialize dataset attributes + self.train_dataset = None + self.val_dataset = None + self.test_dataset = None + + # Initialize the module + self._initialize() + + # Add max classes information to metadata + self._add_max_classes_to_metadata() + + def _initialize(self): + """Initialize the dataset by computing valid indices and mappings.""" + print("Computing valid indices and mappings...") + self.valid_indices = self._compute_valid_indices() + self.mapping = self._create_global_mapping() + self.length = len(self.mapping) + + # Create splits if not already done + if not hasattr(self, 'train_indices') or not self.train_indices: + if self.split_config is not None: + print(f"Creating {self.split_method} splits with config: {self.split_config}") + + # Create splits with the new config + self.train_indices, self.val_indices, self.test_indices = self._create_splits(self.split_config) + print(f"Split statistics: Train: {len(self.train_indices)}, Validation: {len(self.val_indices)}, Test: {len(self.test_indices)}") + else: + # Default to all indices as training + self.train_indices = list(range(self.length)) + self.val_indices = [] + self.test_indices = [] + + # Create subset datasets for train/val/test using the indices if precompute is enabled + # Otherwise, the datasets will be created on-demand during setup + if self.precompute: + print("Precomputing datasets for train/val/test splits...") + if hasattr(self, 'train_indices') and self.train_indices: + self.train_dataset = TimeSeriesSubset(self, self.train_indices) + if hasattr(self, 'val_indices') and self.val_indices: + self.val_dataset = TimeSeriesSubset(self, self.val_indices) + if hasattr(self, 'test_indices') and self.test_indices: + self.test_dataset = TimeSeriesSubset(self, self.test_indices) + else: + print("Datasets will be created on-demand during setup...") + + def _compute_valid_indices(self): + """ + Compute valid indices for all groups in the dataset. + + This method ensures that windows are valid based on: + 1. Having enough valid points in the past window + 2. Having at least one valid point in the future window + + Optimizations: + - Vectorized operations for NaN checks + - Early termination for invalid regions + - Efficient masking for bulk validation + + Returns: + Dictionary mapping group indices to lists of valid indices + """ + valid_indices = {} + device = torch.device('cpu') # Use CPU for consistency across environments + + for i in range(len(self.d1_dataset)): + # Load group data from D1 dataset + group_data = self.d1_dataset[i] + + # Fetch feature and target tensors + features = group_data.get('x', torch.tensor([])) + targets = group_data.get('y', torch.tensor([])) + + # Get the time series length + ts_length = len(features) + + # Skip if time series is too short + if ts_length < (self.past_len + self.future_len): + valid_indices[i] = [] + continue + + # Pre-compute all possible window start indices + all_indices = list(range(ts_length - (self.past_len + self.future_len) + 1)) + + # Create validity masks for features and targets + feature_is_valid = ~torch.isnan(features) if features.numel() > 0 else torch.ones((ts_length, 1), dtype=torch.bool) + target_is_valid = ~torch.isnan(targets) if targets.numel() > 0 else torch.ones((ts_length, 1), dtype=torch.bool) + + # If features or targets are multi-dimensional, check if any dimension has NaN + if feature_is_valid.dim() > 1: + feature_is_valid = feature_is_valid.all(dim=1) + if target_is_valid.dim() > 1: + target_is_valid = target_is_valid.all(dim=1) + + # Combined mask for both features and targets + combined_mask = feature_is_valid & target_is_valid + + # Vectorized approach to find valid windows + group_valid_indices = [] + + # Process each potential window + t = 0 + while t < len(all_indices): + idx = all_indices[t] + end_past = idx + self.past_len + end_future = end_past + self.future_len + + # Check past validity - count valid points in past window + past_valid_count = combined_mask[idx:end_past].sum().item() + past_valid = past_valid_count >= self.min_valid_length + + # Early termination - if past isn't valid, skip this window + if not past_valid: + # Find the next valid point after the current position + next_valid_idx = None + for j in range(idx + 1, min(ts_length, idx + self.past_len * 2)): + if combined_mask[j]: + next_valid_idx = j + break + + if next_valid_idx is not None: + # Skip to a position where this valid point would be in the window + # but not at the end (to maximize valid points in window) + skip_to = max(t + 1, next_valid_idx - self.past_len + 1) + t = skip_to + else: + # No valid points ahead, skip to the end + t = len(all_indices) + continue + + # Check future validity - at least one point must be valid + future_valid = combined_mask[end_past:end_future].any().item() + + if past_valid and future_valid: + group_valid_indices.append(idx) + + t += 1 + + valid_indices[i] = group_valid_indices + + # Print statistics + total_valid = sum(len(indices) for indices in valid_indices.values()) + groups_with_valid = sum(1 for indices in valid_indices.values() if len(indices) > 0) + print(f"Found {total_valid} valid windows across {groups_with_valid} groups") + + return valid_indices + + def _create_global_mapping(self): + """ + Create a global mapping from index to (group_idx, local_idx). + + This allows O(1) lookup of samples by global index. + + Returns: + List of (group_idx, local_idx) tuples + """ + mapping = [] + + # Track statistics for reporting + total_valid = 0 + groups_with_valid = 0 + + for group_idx, local_indices in self.valid_indices.items(): + if local_indices: # Only add groups with valid indices + for start_idx in local_indices: + mapping.append((group_idx, start_idx)) + total_valid += len(local_indices) + groups_with_valid += 1 + + print(f"Created global mapping with {len(mapping)} windows from {groups_with_valid} groups") + return mapping + + def _get_group_data(self, group_idx): + """ + Get data for a specific group, using cache if available. + + This method: + 1. Checks if the group data is already in the cache + 2. If not, loads it from the D1 dataset + + Args: + group_idx: Index of the group to retrieve + + Returns: + Dictionary containing the group data + """ + # Check if the group is already loaded + # Removed caching-related variables + + # Load the group data from D1 dataset + group_data = self.d1_dataset[group_idx] + + return group_data + + def _add_max_classes_to_metadata(self): + """ + Add information about maximum number of classes for categorical features. + This is useful for model architecture decisions. + """ + # Copy max classes information from D1 dataset metadata + if 'max_classes' in self.d1_dataset.metadata: + self.metadata['max_classes'] = self.d1_dataset.metadata['max_classes'].copy() + + # Ensure known/unknown column categorization is properly reflected in metadata + self.metadata['known_cols'] = self.known_cols + self.metadata['unknown_cols'] = self.unknown_cols + print("before cat num known unknown cols", self.metadata) + + # Add categorical/numerical classification for known/unknown columns + self.metadata['known_cat_cols'] = [col for col in self.known_cols if col in self.d1_dataset.cat_cols] + self.metadata['known_num_cols'] = [col for col in self.known_cols if col not in self.d1_dataset.cat_cols] + self.metadata['unknown_cat_cols'] = [col for col in self.unknown_cols if col in self.d1_dataset.cat_cols] + self.metadata['unknown_num_cols'] = [col for col in self.unknown_cols if col not in self.d1_dataset.cat_cols] + print("after cat num known unknown cols", self.metadata) + + def _create_splits(self, split_config): + """ + Create train/validation/test splits based on the specified configuration. + + Args: + split_config: Configuration for splits: + - For 'percentage' method: (train%, val%, test%) + - For 'group' method: (train_groups, val_groups, test_groups) + + Returns: + Tuple of (train_indices, val_indices, test_indices) + """ + if self.split_method == 'percentage': + # Percentage-based split (temporal or random) + train_pct, val_pct, test_pct = split_config + total_samples = len(self.mapping) + + # Normalize percentages if they don't sum to 1 + total_pct = train_pct + val_pct + test_pct + if abs(total_pct - 1.0) > 1e-6: + train_pct /= total_pct + val_pct /= total_pct + test_pct /= total_pct + + # Calculate number of samples for each split + train_size = int(total_samples * train_pct) + val_size = int(total_samples * val_pct) + test_size = total_samples - train_size - val_size + + # For temporal splits, sort by time + all_indices = list(range(total_samples)) + + # Try to sort by time if available #TODO: sort in D1 by group and then time. + try: + # Collect time values for each window + time_values = [] + for idx in all_indices: + group_idx, local_idx = self.mapping[idx] + group_data = self.d1_dataset[group_idx] + # Use the first time point of each window + time_point = group_data['t'][local_idx] + # Convert to numeric value for sorting + if isinstance(time_point, (str, np.datetime64)): + # Convert to timestamp (seconds since epoch) + time_numeric = pd.to_datetime(time_point).timestamp() + else: + time_numeric = float(time_point) + time_values.append(time_numeric) + + # Sort indices by time + sorted_indices = [idx for _, idx in sorted(zip(time_values, all_indices))] + + # Split into train/val/test + train_indices = sorted_indices[:train_size] + val_indices = sorted_indices[train_size:train_size + val_size] + test_indices = sorted_indices[train_size + val_size:] + + print(f"Temporal percentage-based split - Train: {len(train_indices)}, Val: {len(val_indices)}, Test: {len(test_indices)} samples") + + except (TypeError, ValueError) as e: + # Fallback to random split if time-based sorting fails + print(f"Warning: Could not sort by time ({str(e)}), using random split instead") + random.shuffle(all_indices) + train_indices = all_indices[:train_size] + val_indices = all_indices[train_size:train_size + val_size] + test_indices = all_indices[train_size + val_size:] + + print(f"Random percentage-based split - Train: {len(train_indices)}, Val: {len(val_indices)}, Test: {len(test_indices)} samples") + + elif self.split_method == 'group': + # Group-based split + train_groups, val_groups, test_groups = split_config + + # Convert to sets for faster lookup + train_groups_set = set(train_groups) + val_groups_set = set(val_groups) + test_groups_set = set(test_groups) + + # Assign indices to splits based on group membership + train_indices = [] + val_indices = [] + test_indices = [] + + for idx, (group_idx, _) in enumerate(self.mapping): + group_id = self.d1_dataset[group_idx]['group_id'] + + if group_id in train_groups_set: + train_indices.append(idx) + elif group_id in val_groups_set: + val_indices.append(idx) + elif group_id in test_groups_set: + test_indices.append(idx) + else: + # Default to train if not specified + train_indices.append(idx) + + print(f"Group-based split - Train: {len(train_indices)} (from {len(train_groups)} groups), " + f"Val: {len(val_indices)} (from {len(val_groups)} groups), " + f"Test: {len(test_indices)} (from {len(test_groups)} groups) samples") + + else: + raise ValueError(f"Unknown split method: {self.split_method}") + + return train_indices, val_indices, test_indices + + def __len__(self): + """Return the number of valid samples in the dataset.""" + if self.length is not None: + return self.length + else: + # Calculate length on first access if not precomputed + self.valid_indices = self._compute_valid_indices() + self.mapping = self._create_global_mapping() + self.length = len(self.mapping) + return self.length + + def __getitem__(self, idx): + """ + Get a time series window by global index. + + This method: + 1. Maps the global index to a specific group and local index + 2. Extracts the window from the group data + 3. Returns the window in a format suitable for model training + + Args: + idx: Global index of the window to retrieve + + Returns: + Dictionary containing: + - past_features: Tensor of past features + - past_time: Array of past time points + - future_targets: Tensor of future targets + - future_time: Array of future time points + - group_id: Group identifier + - static: Static features tensor + """ + # Map global index to group and local index + group_idx, local_idx = self.mapping[idx] + + # Get the group data + group_data = self._get_group_data(group_idx) + + # Get the start and end indices for the window + start_idx = local_idx + past_end_idx = start_idx + self.past_len + future_end_idx = past_end_idx + self.future_len + + # Extract past and future windows + past_features = group_data['x'][start_idx:past_end_idx] + past_time = group_data['t'][start_idx:past_end_idx] + future_targets = group_data['y'][past_end_idx:future_end_idx] + future_time = group_data['t'][past_end_idx:future_end_idx] + + # Get static features + static = group_data.get('st', torch.tensor([])) + + # Return the window as a dictionary + return { + 'past_features': past_features, + 'past_time': past_time, + 'future_targets': future_targets, + 'future_time': future_time, + 'group_id': group_data['group_id'], + 'static': static + } + + def setup(self, stage=None): + """ + Prepare data for the given stage. + + Args: + stage: Either 'fit' or 'test' + """ + # If we haven't precomputed valid indices yet, do it now + if not hasattr(self, 'valid_indices') or self.valid_indices is None: + print("Computing valid indices...") + self.valid_indices = self._compute_valid_indices() + self.mapping = self._create_global_mapping() + self.length = len(self.mapping) + + # Create splits if not already done + if not hasattr(self, 'train_indices') or not self.train_indices: + if self.split_config is not None: + print(f"Creating {self.split_method} splits with config: {self.split_config}") + # Create splits with the new config + self.train_indices, self.val_indices, self.test_indices = self._create_splits(self.split_config) + print(f"Split statistics: Train: {len(self.train_indices)}, Validation: {len(self.val_indices)}, Test: {len(self.test_indices)}") + else: + # Default to all indices as training + self.train_indices = list(range(self.length)) + self.val_indices = [] + self.test_indices = [] + + # If precompute is False, create datasets on-demand during setup + # Otherwise, datasets were already created during initialization + if not self.precompute: + if stage == 'fit' or stage is None: + self.train_dataset = TimeSeriesSubset(self, self.train_indices) + self.val_dataset = TimeSeriesSubset(self, self.val_indices) + + if stage == 'test' or stage is None: + self.test_dataset = TimeSeriesSubset(self, self.test_indices) + + def train_dataloader(self): + """Return a DataLoader for training.""" + if self.sampler is not None: + return DataLoader( + self.train_dataset, + batch_size=self.batch_size, + sampler=self.sampler(self.train_dataset), + num_workers=self.num_workers, + collate_fn=custom_collate_fn + ) + else: + return DataLoader( + self.train_dataset, + batch_size=self.batch_size, + shuffle=True, + num_workers=self.num_workers, + collate_fn=custom_collate_fn + ) + + def val_dataloader(self): + """Return a DataLoader for validation.""" + if len(self.val_dataset) == 0: + return None + + return DataLoader( + self.val_dataset, + batch_size=self.batch_size, + shuffle=False, + num_workers=self.num_workers, + collate_fn=custom_collate_fn + ) + + def test_dataloader(self): + """Return a DataLoader for testing.""" + if len(self.test_dataset) == 0: + return None + + return DataLoader( + self.test_dataset, + batch_size=self.batch_size, + shuffle=False, + num_workers=self.num_workers, + collate_fn=custom_collate_fn + ) + + +class TimeSeriesSubset(Dataset): + """Subset of a D2 processor dataset that implements the Dataset interface.""" + + def __init__(self, data_module, indices): + """ + Initialize the TimeSeriesSubset. + + Args: + data_module: The TSDataModule instance (stored as reference, not copy) + indices: List of indices to include in this subset + """ + # In Python, this assignment creates a reference to the original data_module object + # No copying occurs, so all subsets share the same data_module instance + self.data_module = data_module + self.indices = indices + + def __len__(self): + """Return the number of samples in this subset.""" + return len(self.indices) + + def __getitem__(self, idx): + """Get a sample from the data module using the mapped index.""" + return self.data_module[self.indices[idx]] + + +def custom_collate_fn(batch): + """ + Custom collate function for the DataLoader to handle mixed data types. + Handles static features that may be objects or other non-tensor types. + """ + elem = batch[0] + result = {} + + # Process each key in the batch + for key in elem: + if key in ['st', 'group_id', 't', 'v']: # Special handling for non-tensor data + # Store as lists + result[key] = [sample[key] for sample in batch] + else: # Default handling for tensors + # For tensors, we can stack them + try: + result[key] = torch.stack([sample[key] for sample in batch]) + except: + # If stacking fails, just store as a list + result[key] = [sample[key] for sample in batch] + + return result + + +def generate_example_csv(output_dir, num_groups=5, samples_per_group=100, num_features=3, num_targets=1): + """ + Generate example CSV files for testing the time series dataset. + + Args: + output_dir: Directory to save CSV files + num_groups: Number of groups to generate + samples_per_group: Number of samples per group + num_features: Number of features to generate + num_targets: Number of targets to generate + """ + os.makedirs(output_dir, exist_ok=True) + + # Generate two CSV files + for file_idx in range(2): + data = [] + + # Generate data for each group + for group_idx in range(num_groups): + # Determine which file gets which groups + if (group_idx % 2 == 0 and file_idx == 0) or (group_idx % 2 == 1 and file_idx == 1): + # Generate time series for this group + for t in range(samples_per_group): + row = { + 'group': f'group_{group_idx}', + 'time': t, + } + + # Generate features with some NaNs + for f in range(num_features): + # Add some randomness and occasional NaNs + if np.random.random() < 0.05: # 5% chance of NaN + row[f'feature_{f}'] = np.nan + else: + row[f'feature_{f}'] = np.sin(t/10 + group_idx) + np.random.normal(0, 0.1) + + # Generate targets with some NaNs + for tgt in range(num_targets): + if np.random.random() < 0.05: # 5% chance of NaN + row[f'target_{tgt}'] = np.nan + else: + # Target is a function of features with noise + row[f'target_{tgt}'] = np.cos(t/10 + group_idx) + np.random.normal(0, 0.1) + + # Add a categorical feature + row['cat_feature'] = f'cat_{np.random.randint(0, 3)}' + + data.append(row) + + # Create DataFrame and save to CSV + df = pd.DataFrame(data) + df.to_csv(os.path.join(output_dir, f'example_data_{file_idx}.csv'), index=False) + print(f"Generated {len(df)} rows in file {file_idx}") + +# Example usage +if __name__ == "__main__": + # Generate example data + example_dir = "example_data" + generate_example_csv(example_dir) + + # Create D1 dataset with explicit known/unknown columns + file_paths = [os.path.join(example_dir, f'example_data_{i}.csv') for i in range(2)] + + # Define column sets + feature_cols = [f'feature_{i}' for i in range(3)] + target_cols = [f'target_{0}'] + + # Explicitly define which features are known at prediction time + # In this example, we're saying feature_0 and feature_1 are known, + # but feature_2 is unknown (perhaps it's only available during training) + known_cols = [f'feature_{i}' for i in range(2)] # Only first two features are known + unknown_cols = [f'feature_2'] + target_cols # feature_2 and target are unknown + + d1_dataset = MultiSourceTSDataSet( + file_paths=file_paths, + group_cols='group', + time_col='time', + feature_cols=feature_cols, + target_cols=target_cols, + cat_cols=['cat_feature'], + known_cols=known_cols, + unknown_cols=unknown_cols, + memory_efficient=False, + chunk_size=1000 + ) + + print(f"D1 Dataset Stats:") + print(f"Number of groups: {len(d1_dataset)}") + print(f"Feature columns: {d1_dataset.feature_cols}") + print(f"Known columns: {d1_dataset.known_cols}") + print(f"Unknown columns: {d1_dataset.unknown_cols}") + print(f"Metadata: {d1_dataset.metadata}") + + # Create D2 module with percentage split + # Here we're overriding the known/unknown columns from D1 + d2_module = TSDataModule( + d1_dataset=d1_dataset, + past_len=10, + future_len=5, + batch_size=32, + min_valid_length=8, + split_method='percentage', + split_config=(0.7, 0.15, 0.15), + memory_efficient=False, + num_workers=0, + sampler=None, + # Override known/unknown columns from D1 + known_cols=['feature_0'], # Now only feature_0 is known + unknown_cols=['feature_1', 'feature_2', 'target_0'] # feature_1 is now unknown too + ) + + print(f"D2 Module Stats (Percentage Split):") + print(f"Total windows: {d2_module.length}") + print(f"Train windows: {len(d2_module.train_indices)}") + print(f"Val windows: {len(d2_module.val_indices)}") + print(f"Test windows: {len(d2_module.test_indices)}") + print(f"Known columns: {d2_module.known_cols}") + print(f"Unknown columns: {d2_module.unknown_cols}") + print(f"Metadata: {d2_module.metadata}") + + # Test DataLoaders + train_loader = d2_module.train_dataloader() + val_loader = d2_module.val_dataloader() + test_loader = d2_module.test_dataloader() + + # Fetch a batch + batch = next(iter(train_loader)) + print(f"Batch keys: {batch.keys()}") + print(f"Past features shape: {batch['past_features'].shape}") + print(f"Future targets shape: {batch['future_targets'].shape}") + + # Create D2 module with group split + # First, get all group indices + all_groups = list(range(len(d1_dataset))) + + # Split groups into train/val/test + np.random.shuffle(all_groups) + train_groups = all_groups[:int(0.7 * len(all_groups))] + val_groups = all_groups[int(0.7 * len(all_groups)):int(0.85 * len(all_groups))] + test_groups = all_groups[int(0.85 * len(all_groups)):] + + # Using the D1 dataset's known/unknown columns (not overriding) + d2_module_group = TSDataModule( + d1_dataset=d1_dataset, + past_len=10, + future_len=5, + batch_size=32, + min_valid_length=8, + split_method='group', + split_config=(train_groups, val_groups, test_groups), + memory_efficient=False, + num_workers=0, + sampler=None + # Not specifying known_cols/unknown_cols, so it will use D1's settings + ) + + print(f"D2 Module Stats (Group Split):") + print(f"Total windows: {d2_module_group.length}") + print(f"Train windows: {len(d2_module_group.train_indices)}") + print(f"Val windows: {len(d2_module_group.val_indices)}") + print(f"Test windows: {len(d2_module_group.test_indices)}") + print(f"Known columns: {d2_module_group.known_cols}") + print(f"Unknown columns: {d2_module_group.unknown_cols}") + print(f"Metadata: {d2_module_group.metadata}") \ No newline at end of file diff --git a/notebooks/sandeep/ts_dataset.py b/notebooks/sandeep/ts_dataset.py new file mode 100644 index 00000000..a77a1e86 --- /dev/null +++ b/notebooks/sandeep/ts_dataset.py @@ -0,0 +1,692 @@ +""" +Time Series Dataset module for handling data with two layers. + +This module provides two main classes: +- TSDataSet (D1 Layer): Handles the raw data and provides a standardized interface +- TSDataProcessor (D2 Layer): Processes the output from D1 for model consumption + +Key Features: +- Supports both datetime and numeric time indices +- Handles grouped time series data +- Provides weight handling for data points +- Ensures regular time intervals +- Validates data integrity +""" + +import torch +from torch.utils.data import Dataset +import pandas as pd +import numpy as np +from typing import Dict, List, Optional, Union, Tuple + + +def _coerce_to_list(obj): + """ + Coerce object to list. + + Args: + obj: Input object to be coerced to list + + Returns: + List containing the input object(s) + """ + if obj is None: + return [] + if isinstance(obj, str): + return [obj] + return list(obj) + + +def extend_time_df(df, time_col, freq, max_length=None): + """ + Extend a dataframe to ensure regular time intervals. + + Args: + df: Input dataframe containing time series data + time_col: Column name containing time information + freq: Frequency to use for extending the dataframe + max_length: Optional maximum length for the extended dataframe + + Returns: + DataFrame extended with regular time intervals + """ + if len(df) == 0: + return df + + # Get the minimum and maximum time values from the dataframe + min_time = df[time_col].min() + max_time = df[time_col].max() + + # Create a continuous time range based on the time type + if isinstance(min_time, pd.Timestamp): + # For datetime type, create a date range + time_range = pd.date_range(start=min_time, end=max_time, freq=freq) + else: + # For numeric time indices, create a numeric range + time_range = pd.Series(np.arange(min_time, max_time + freq, freq)) + + # Limit the length if specified + if max_length and len(time_range) > max_length: + time_range = time_range[:max_length] + + # Create a new dataframe with the continuous time range + extended_df = pd.DataFrame({time_col: time_range}) + + return extended_df + +''' +class TSDataSet(Dataset): + """ + D1 Layer - A PyTorch Dataset for time series data. + + This class handles the raw data processing and provides a standardized interface + for time series data. It supports: + - Handling regular time intervals + - Processing numerical features + - Supporting group-based time series + - Supporting weights for data points + + Returns from __getitem__: + - t: Time values + - y: Target values + - x: Feature values + - group: Group identifier + - weights: Weight values (if weight column is provided) + """ + + def __init__( #TODO: add cat variables too + self, + data: pd.DataFrame, + time: Optional[str] = None, + target: Optional[Union[str, List[str]]] = None, + group: Optional[List[str]] = None, + num: Optional[List[Union[str, List[str]]]] = None, #REMOVE if minimal DISCUSS + weight: Optional[str] = None, #REMOVE if minimal DISCUSS + ): + """ + Initialize the TSDataSet. + + Args: + data: The input pandas DataFrame + time: The name of the time index column if not specified, assumes the first column as time index + target: The name(s) of the target variable column(s) if not specified, assumes the last column as target + group: Columns used to group time series instances + num: The name(s) of numerical feature columns + weight: The name of the column containing weights for data points + """ + # Initialize parameters + self.data = data.copy() + + # Set time column (use the first column if not specified) # REMOVE THIS, as its manddatory field + if time is None: + self.time = data.columns[0] + else: + self.time = time + + # Set target columns (use the last column if not specified) # REMOVE THIS, as its manddatory field + #self.target = _coerce_to_list(target if target is not None else data.columns[-1]) + + # Set group columns + self.group = _coerce_to_list(group) + + # Set weight column + self.weight = weight + + # Set numerical feature columns + if num is None: + # Use all columns except time, target, group, and weight as numerical features + exclude_cols = [self.time] + self.target + self.group + if self.weight: + exclude_cols.append(self.weight) + self.num = [col for col in data.columns if col not in exclude_cols] + else: + self.num = _coerce_to_list(num) + + # Store feature columns (numerical features) TODO: include the target as well. + self.feature_cols = self.num + + # Use all unique columns for processing + all_cols = list(set(self.feature_cols + self.target + [self.time] + self.group)) + if self.weight: + all_cols.append(self.weight) + self.data = data[all_cols].copy() + + # Handle duplicate time points by keeping the first occurrence TODO : if group is nan its good, else groups will be in subset and then duplicates. refer to pandas ds + self.data.drop_duplicates(subset=[self.time], keep='first', inplace=True, ignore_index=True) + + # Determine time frequency + if len(self.data) > 1: + # Calculate the difference between consecutive time points + time_diff = self.data[self.time].diff().dropna() + + if len(time_diff) > 0: + # Find the minimum time difference (frequency) + freq = time_diff.min() + + # Handle different time types + if isinstance(freq, pd.Timedelta): + # If time is in timedelta format, convert to pandas timedelta + freq = pd.to_timedelta(freq) + elif isinstance(freq, (int, float)): + # If time is numeric, convert to integer + freq = int(freq) + else: + # Raise error for unsupported time types + raise TypeError("Time must be integer or datetime") + + # Extend dataset to ensure regular time intervals + extended_df = extend_time_df(self.data, self.time, freq) + + # Merge extended time range with original data + self.data = extended_df.merge(self.data, on=self.time, how='left').reset_index(drop=True) + + # Setup groups + if self.group: + # Create groups based on the specified columns + self._groups = self.data.groupby(self.group).groups + self._group_ids = list(self._groups.keys()) + self.lengths = {gid: len(indices) for gid, indices in self._groups.items()} + else: + # If no groups specified, treat entire dataset as single group + self._groups = {"_single_group": self.data.index} + self._group_ids = ["_single_group"] + self.lengths = {"_single_group": len(self.data)} + + # If weight column is not provided, create a default weight column with all 1s ; TODO if not given we just ignore it. + if self.weight is None: + self.data['_default_weight'] = 1.0 + self.weight = '_default_weight' + + # Prepare metadata + self._prepare_metadata() + + def _prepare_metadata(self): + """ + Prepare metadata for the dataset. + + The metadata includes information about columns, their types, and known status. + """ + self.metadata = { + "cols": { + "y": self.target, + "x": self.feature_cols, + "st": [], # No static features in this implementation + }, + "col_type": {}, + "col_known": {}, + "weight": self.weight, + } + + # Set column types and known status + all_cols = self.target + self.feature_cols + for col in all_cols: + # All features are numerical + self.metadata["col_type"][col] = "F" + + # Features are known, targets are unknown (for future prediction) + self.metadata["col_known"][col] = "K" if col in self.feature_cols else "U" + + def __len__(self) -> int: + """ + Return the number of time series in the dataset. + + Returns: + Number of time series (groups) in the dataset + """ + return len(self._group_ids) + + def __getitem__(self, index: int) -> Dict[str, Union[torch.Tensor, np.ndarray]]: + """ + Get time series data for the given index. + + Args: + index: The index of the time series to retrieve + + Returns: + Dictionary containing: + - t: Time values + - y: Target values + - x: Feature values + - group: Group identifier + - weights: Weight values + """ + # Determine device for tensors + device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") + + # Get group id and data for this index + group_id = self._group_ids[index] + + if self.group: + # Get data for this group + mask = self._groups[group_id] + data = self.data.loc[mask].sort_values(by=self.time) + else: + # Use all data if no grouping + data = self.data.sort_values(by=self.time) + + # Create result dictionary + result = { + # Time values (as numpy array) + "t": data[self.time].values, + + # Target values (as tensor) + "y": torch.tensor(data[self.target].values, dtype=torch.float32, device=device), + + # Feature values (as tensor) + "x": torch.tensor(data[self.feature_cols].values, dtype=torch.float32, device=device), + + # Group identifier (as hash if not single group) + "group": torch.tensor([hash(str(group_id))], device=device), + + # Weight values + "weights": torch.tensor(data[self.weight].values, dtype=torch.float32, device=device), + } + + return result + + def get_metadata(self) -> Dict: + """ + Return metadata about the dataset. + + Returns: + Dictionary containing metadata about columns and their properties + """ + return self.metadata +''' +## PICKED AS BASE + +class PandasTSDataSet(Dataset): + """PyTorch Dataset for loading time series data from a pandas DataFrame. + + This class implements the D1 (raw data) layer of the `pytorch-forecasting` + v2 API. It handles loading time series data from a pandas DataFrame, + performing basic preprocessing (like ensuring regular time intervals), + and providing access to the data via the `__getitem__` method. + + Key Features: + - Handles multiple time series (identified by group IDs). + - Supports numerical and categorical features. + - Supports static features (features that don't change over time). + - Supports known and unknown covariates. + - Automatically extends the DataFrame to ensure regular time intervals. + - Returns data in a standardized dictionary format (tensors). + - Provides metadata about the columns (names, types, known/unknown status). + """ + + def __init__( + self, + data: pd.DataFrame, + time: Optional[str] = None, + target: Optional[Union[str, List[str]]] = None, + group: Optional[List[str]] = None, + #num: Optional[List[Union[str, List[str]]]] = None, + #cat: Optional[List[Union[str, List[str]]]] = None, + #known: Optional[List[Union[str, List[str]]]] = None, + #unknown: Optional[List[Union[str, List[str]]]] = None, + #static: Optional[List[Union[str, List[str]]]] = None, + #label_encoders: Optional[dict] = None, + weights: Optional[str] = None + ): + """Initializes the PandasTSDataSet. + + Args: + data: The input pandas DataFrame. + time: The name of the time index column. + target: The name(s) of the target variable column(s). + group: List of column names identifying a time series instance. + num: The name(s) of numerical feature columns. + cat: The name(s) of categorical feature columns. + known: List of variables known in the future. + unknown: List of variables unknown in the future + static: The name(s) of static feature columns (features that don't + change over time). + label_encoders: Optional dictionary of pre-fitted label encoders + for categorical features. If not provided, new encoders will + be fitted. + """ + + self.time = time + self.target = _coerce_to_list(target) + self.num = _coerce_to_list(num) + self.cat = _coerce_to_list(cat) + self.known = _coerce_to_list(known) + self.unknown = _coerce_to_list(unknown) + self.static = _coerce_to_list(static) + self.data = data.copy() + + self.group = _coerce_to_list(group) + + + self.feature_cols = self.num + self.cat + #use set ensuring unique columns + self.data = data[list(set(self.static + self.feature_cols + self.target + [self.time] +self.group))].copy() + + ##Encoders for categorical since we want to return tensors + if label_encoders is None: + label_encoders = {} + ##Encoders for categorical since we want to return tensors + for c in self.cat+self.group: + label_encoders[c] = OrdinalEncoder() + self.data[c] = label_encoders[c].fit_transform(self.data[c].values.reshape(-1,1)).flatten() + self.label_encoders = label_encoders + else: + for c in self.cat+self.group: + self.data[c] = label_encoders[c].transform(self.data[c].values.reshape(-1,1)).flatten() + self.label_encoders = label_encoders + + + ##ensure to have a coherent dataset + self.data.drop_duplicates(subset= self.group+[self.time],keep='first',inplace=True,ignore_index=True) + + ##compute minumum frequency + if self.group is None: + freq = self.data[self.time].diff.min() + else: + freq = self.data.groupby(self.group).time.diff().min() + + if isinstance(freq, timedelta): + freq = pd.to_timedelta(freq) + elif isinstance(freq, (int, float)): + freq = int(freq) + else: + raise TypeError("time must be integer or datetime") + + + ##extend dataset + self.data = extend_time_df(self.data,self.time,freq,self.group).merge(self.data,how='left').reset_index() + + + ##now we are sure that data is in a coherent form! + self.lengths = {} + if self.group: + self._groups = self.data.groupby(self.group).groups + self._group_ids = list(self._groups.keys()) + for k in self._groups: + self.lengths[k] = len(self._groups[k]) + else: + self._groups = {0: self.data.index} + self._group_ids = [0] + self.lengths[0] = len( self.data.index) + + + + ## we know on the fly which rows are valid and wich contains nans TODO remove + self.data['valid'] = ~pd.isnull(self.data.max(axis=1)) + + self._prepare_metadata() + + def _prepare_metadata(self): + """Prepare metadata for the dataset.""" + self.metadata = { + "cols": { + "y": self.target, + "x": self.feature_cols, + "st": self.static, + }, + "col_type": {}, + "col_known": {}, + "cat_index":[] + } + for i, c in enumerate(self.feature_cols): + if c in self.cat: + self.metadata['cat_index'].append(i) # EDIT: was getting error with i, so updated enumerate in for loop + + + all_cols = self.target + self.feature_cols + self.static + for col in all_cols: + self.metadata["col_type"][col] = "C" if col in self.cat else "F" + self.metadata["col_known"][col] = "K" if col in self.known else "U" + self.metadata['encoders'] = self.label_encoders + + def __len__(self) -> int: + """Return number of time series in the dataset.""" + return len(self._group_ids) + + def get_id_ts_by_idx(self,idx): + """Gets the time series ID and starting index for a given index. + + Args: + idx: The overall index. This is *not* the group index, but + the index considering all time series concatenated together. + + Returns: + tuple: A tuple containing the group ID and the starting index + of that group in the concatenated data. + """ + tmp = np.cumsum(list(self.lengths.values())) + idx_group = min(np.where(tmp>idx)[0]) # EDIT: Find the group index, as we are getting group + return idx_group, 0 if idx_group==0 else tmp[idx_group-1] #EDIT: group id, index of the start of the series + + def get_total_len(self): + """Returns total length of the dataset""" + l = 0 + for k in self._groups: + l+= self.lengths[k] + return l + + def __getitem__(self, index: int) -> Dict[str, torch.Tensor]: + """Get time series data for given index. + + Args: + index: The index of the time series to retrieve. + + Returns: + A dictionary containing tensors for 't', 'y', 'x', 'group', 'st'. + """ + group_id = self._group_ids[index] + device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") + + if self.group: + mask = self._groups[group_id] + data = self.data.loc[mask].sort_values(by=self.time) + else: + data = self.data.sort_values(by=self.time) + + result = { + "t": data[self.time].values, # Return as numpy array + "y": torch.tensor(data[self.target].values, dtype=torch.float32, device=device), + "x": torch.tensor(data[self.feature_cols].values, dtype=torch.float32, device=device), + "group": torch.tensor([group_id], dtype=torch.int64, device=device), # group ID, cast to int64 + "st": torch.tensor(data[self.static].iloc[0].values, dtype=torch.float32, device=device) if self.static else torch.tensor([], dtype=torch.float32, device=device), # return empty if no statics + } + + return result + + def get_metadata(self) -> Dict: + """Return metadata about the dataset. + + Returns: + ------- + Dict + Dictionary containing: + - cols: column names for y, x, and static features + - col_type: mapping of columns to their types (F/C) + - col_known: mapping of columns to their future known status (K/U) + - encoders: Dict of label encoders used + """ + return self.metadata +''' +class TSDataProcessor(Dataset): + """ + D2 Layer - PyTorch Dataset for processing time series data for models. + + This class adapts the D1 dataset (TSDataSet) for use with models by creating + input/output windows (sequences) for training and inference. + + It handles: + - Creating sliding windows for inputs and targets + - Validating data points + - Supporting multiple time series (groups) + - Handling weights for data points + """ + + def __init__( + self, + data: TSDataSet, + metadata: Dict, + valid_index: Optional[Dict] = None + ): + """ + Initialize the TSDataProcessor. + + Args: + data: The D1 dataset instance (TSDataSet) + metadata: Dictionary containing processing parameters like: + - past_steps: Number of past time steps to use + - future_steps: Number of future time steps to predict + valid_index: Optional dictionary of valid indices for each group + """ + # Store input parameters + self.data = data # D1 dataset + self.metadata = metadata # Processing parameters + self.metadata_dataset = data.metadata # Metadata from D1 dataset + + # Initialize valid indices if not provided (DISCUSS: OPTIONAL CAN BE REMOVED AS D1 handles this) + if valid_index is None: + self.valid_index = self._determine_valid_indices() + else: + self.valid_index = valid_index + + # Calculate total length and individual lengths for each group + total_length = 0 + self.lengths = {} + + for k in self.valid_index: + # Calculate valid windows for each group + # Subtract future_steps to ensure enough data for target window + group_length = max(0, len(self.valid_index[k]) - self.metadata.get('future_steps', 0)) + total_length += group_length + self.lengths[k] = len(self.valid_index[k]) + + self.length = total_length + + def _determine_valid_indices(self) -> Dict: # DISCUSS: REMOVE THIS PART??? + """ + Determine valid indices for each group. + + This method identifies indices in each group where a valid window + can be created (enough data for past and future steps). + + Returns: + Dictionary mapping group IDs to lists of valid indices + """ + valid_index = {} + past_steps = self.metadata.get('past_steps', 1) + + for i, group_id in enumerate(self.data._group_ids): + # Get group data + if i < len(self.data): + group_data = self.data[i] + + # Find indices where there are enough data points before and after + valid_indices = [] + for j in range(past_steps, len(group_data['t'])): + # Check if we have non-NaN values in the required window + y_window = group_data['y'][j-past_steps:j+1] + x_window = group_data['x'][j-past_steps:j+1] + + # Check if any NaN values in the window + if not (torch.isnan(y_window).any() or torch.isnan(x_window).any()): + valid_indices.append(j) + + valid_index[i] = valid_indices + + return valid_index + + def get_id_ts_by_idx(self, idx: int) -> Tuple[int, int]: + """ + Get time series ID and offset given global index. + + Args: + idx: Global index across all time series + + Returns: + Tuple containing (group_index, offset) + """ + # Calculate cumulative lengths + cumulative_lengths = np.cumsum(list(self.lengths.values())) + + # Find the group index + group_idx = np.searchsorted(cumulative_lengths, idx, side='right') + + # Calculate the offset within that group + offset = 0 if group_idx == 0 else cumulative_lengths[group_idx - 1] + + return group_idx, offset + + def __len__(self) -> int: + """ + Return the total number of valid windows in the dataset. + + Returns: + Number of valid windows across all time series + """ + return self.length + + def __getitem__(self, idx: int) -> Optional[Dict[str, torch.Tensor]]: + """ + Get a sample (input/output window) from the dataset. + + Args: + idx: Global index of the window to retrieve + + Returns: + Dictionary containing input and output tensors for the window, + or None if a valid window cannot be created + """ + # Find group and local index + group_idx, offset = self.get_id_ts_by_idx(idx) + + # Adjust to local index + local_idx = idx - offset + + # Get valid index within the group + try: + valid_indices = self.valid_index[group_idx] + if local_idx >= len(valid_indices): + return None + + data_idx = valid_indices[local_idx] + except (KeyError, IndexError): + return None + + # Get data from the D1 layer + raw_data = self.data[group_idx] + + # Extract past and future steps parameters + past_steps = self.metadata.get('past_steps', 1) + future_steps = self.metadata.get('future_steps', 1) + + # Check if enough data is available for both input and output windows + if data_idx + future_steps > len(raw_data['t']): + return None + + # Check for validity of the time series by checking NaNs + y_window = raw_data['y'][data_idx - past_steps:data_idx + future_steps] + x_window = raw_data['x'][data_idx - past_steps:data_idx + future_steps] + + if torch.isnan(y_window).any() or torch.isnan(x_window).any(): + return None + + # Construct sample + sample = {} + + # Add past data (input window) + sample['x_past'] = raw_data['x'][data_idx - past_steps:data_idx] + sample['t_past'] = raw_data['t'][data_idx - past_steps:data_idx] + + # Add future data (target window) + sample['y'] = raw_data['y'][data_idx:data_idx + future_steps] + sample['t_fut'] = raw_data['t'][data_idx:data_idx + future_steps] + + # Add group information + sample['group'] = raw_data['group'] + + # Add weights + sample['weights_past'] = raw_data['weights'][data_idx - past_steps:data_idx] + sample['weights_fut'] = raw_data['weights'][data_idx:data_idx + future_steps] + + return sample +''' + +#D2 Layer implementation diff --git a/tests/README_test.md b/tests/README_test.md new file mode 100644 index 00000000..e6b683b1 --- /dev/null +++ b/tests/README_test.md @@ -0,0 +1,95 @@ +# Testing & Integration Manual + +This directory documents and organizes all tests for the DSIPTS library, including both integration and unit tests. + +## Test Folder Structure + +- `integration/` + - **test_time_series_workflow.py**: Integration tests for the full time series workflow (pipeline, data, model, etc). +- `unit/` + - **test_time_series_dataset.py**: Unit tests for time series dataset utilities and classes (e.g., `extend_time_df`, `MultiSourceTSDataSet`, `TSDataModule`). +- **test_time_series_model_integration.py**: Comprehensive integration test for D1/D2 layers and model compatibility. + +## Integration Tests + +### D1/D2 Layer and Model Integration Test + +Validates the full pipeline from data loading (D1), windowing (D2), and model prediction. Checks both dummy and real-world (Monash) datasets. + +#### Running the Integration Test + +- **Default (Dummy Data):** + ```bash + python tests/integration/test_time_series_model_integration.py + ``` +- **Using Monash Data:** + Set the environment variable `USE_MONASH_DATA=1` before running the test. For example: + - **Linux/macOS:** + ```bash + USE_MONASH_DATA=1 python tests/integration/test_time_series_model_integration.py + ``` + - **Windows (PowerShell):** + ```powershell + $env:USE_MONASH_DATA="1"; python tests/integration/test_time_series_model_integration.py + ``` + +#### What the Test Does +- Loads and preprocesses time series data (dummy or Monash) +- Constructs D1 (raw) and D2 (windowed) datasets +- Runs a minimal model for compatibility and shape checks +- Asserts correct integration at each stage + +#### Interpreting Results +- On success, you will see: + ``` + SUCCESS: D1/D2 layers work properly with the model! + ``` +- On failure, error details will be logged for debugging. + +#### Customizing the Data Source +- To use a different Monash dataset, edit the `id` parameter in the test file (`main()` function). + +--- + +## Unit Tests + +Unit tests are located in `unit/`. They cover core utility functions and dataset logic: + +- **test_time_series_dataset.py**: Tests for + - `extend_time_df`: Filling time gaps, handling numeric/datetime/grouped data + - `MultiSourceTSDataSet`: Data loading, splitting, memory efficiency, NaN handling, group-based/percentage splits + - `TSDataModule`: Windowing, caching, PyTorch Lightning integration, batch shape checks + +### Running Unit Tests + +From the project root: +```bash +python -m unittest discover -s tests/unit +``` +Or run individual files: +```bash +python tests/unit/test_time_series_dataset.py +``` + +--- + +## Best Practices & Recommendations + +- **Integration tests** should be placed in `tests/integration/` for clarity. +- **Unit tests** should remain in `tests/unit/`. +- All test scripts should be documented here for discoverability. + +## Requirements +- Python 3.7+ +- All dependencies installed (see main [README.md](../README.md)) +- For Monash data: internet connection required +- Temporary files are written to `test_data/` in the project root + +## Troubleshooting +- **Monash data download fails:** Check your internet connection, or try running with dummy data. +- **Missing dependencies:** Make sure all required Python packages are installed (see main [README.md](../README.md)). +- **Other errors:** Review the log output for details. + +--- + +For more details on the data/model conventions, see the main [README.md](../README.md). \ No newline at end of file diff --git a/tests/integration/test_time_series_model_integration.py b/tests/integration/test_time_series_model_integration.py new file mode 100644 index 00000000..ea6dd560 --- /dev/null +++ b/tests/integration/test_time_series_model_integration.py @@ -0,0 +1,414 @@ +import os +import sys +import logging +import torch +import pandas as pd +import numpy as np +from pathlib import Path +from typing import List, Dict, Optional, Union, Tuple + +# Add the project root to the Python path for imports +sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) + +# Import both D1 and D2 layers +from notebooks.sandeep.time_series_dataset import MultiSourceTSDataSet, TSDataModule + +# Import the Base class from dsipts/models +from dsipts.models.base import Base +from dsipts.models.utils import get_scope + +# Set up logging +logging.basicConfig(level=logging.INFO, + format='%(asctime)s - %(name)s - %(levelname)s - %(message)s') +logger = logging.getLogger(__name__) + +# Simple custom model that inherits from Base +class SimpleTimeSeriesModel(Base): + """ + A simple time series model that inherits from Base for testing purposes. + This model just applies a simple linear transformation to the input features. + """ + # Define class attributes required by Base + handle_multivariate = True + handle_future_covariates = True # Updated to handle future covariates + handle_categorical_variables = True # Updated to handle categorical variables + handle_quantile_loss = False + description = get_scope(handle_multivariate, handle_future_covariates, + handle_categorical_variables, handle_quantile_loss) + + def __init__(self, past_len, future_len, feature_dim, target_dim, cat_dims=None, verbose=True): + """ + Initialize the SimpleTimeSeriesModel. + + Args: + past_len: Number of past time steps + future_len: Number of future time steps to predict + feature_dim: Number of feature dimensions + target_dim: Number of target dimensions + cat_dims: Dictionary of categorical dimensions {col_name: num_classes} + verbose: Whether to print model information + """ + super(SimpleTimeSeriesModel, self).__init__(verbose=verbose) + + # Model parameters + self.past_len = past_len + self.future_len = future_len + self.feature_dim = feature_dim + self.target_dim = target_dim + self.cat_dims = cat_dims or {} + + # Simple linear layers + self.encoder = torch.nn.Linear(feature_dim, 64) + self.decoder = torch.nn.Linear(64, target_dim) + self.activation = torch.nn.ReLU() + + # Add embedding layers for categorical variables if needed + self.embeddings = torch.nn.ModuleDict() + for col, dim in self.cat_dims.items(): + self.embeddings[col] = torch.nn.Embedding(dim, min(16, dim // 2 + 1)) + + # Required by Base class + self.optim = None + self.optim_config = {'lr': 1e-4} + self.scheduler_config = None + + def forward(self, batch): + """ + Forward pass of the model. + + Args: + batch: Dictionary containing input data + x_num_past: Past numerical features [batch_size, past_len, feature_dim] + x_cat_past: Past categorical features [batch_size, past_len, cat_dim] + x_num_future: Future numerical features [batch_size, future_len, feature_dim] + x_cat_future: Future categorical features [batch_size, future_len, cat_dim] + y: Target values [batch_size, future_len, target_dim] (optional, for training) + + Returns: + Tensor of predictions [batch_size, future_len, target_dim] + """ + # Extract past numerical features + x = batch['x_num_past'] # [batch_size, past_len, feature_dim] + batch_size = x.shape[0] + + # Process each time step + encoded = self.encoder(x) # [batch_size, past_len, hidden_dim] + encoded = self.activation(encoded) + + # Use the last hidden state to predict future values + last_hidden = encoded[:, -1, :] # [batch_size, hidden_dim] + + # Expand to match future sequence length + expanded = last_hidden.unsqueeze(1).expand(-1, self.future_len, -1) + + # Decode to get predictions + predictions = self.decoder(expanded) # [batch_size, future_len, target_dim] + + return predictions + +# Function to convert D2 batch format to model input format +def convert_batch_for_model(batch, known_cat_cols=None, known_num_cols=None, unknown_cat_cols=None, unknown_num_cols=None): + """ + Convert batch from D2 layer format to the format expected by the model + + Args: + batch: Batch from D2 layer + known_cat_cols: List of known categorical columns + known_num_cols: List of known numerical columns + unknown_cat_cols: List of unknown categorical columns + unknown_num_cols: List of unknown numerical columns + + Returns: + Dict with keys expected by the model + """ + model_batch = {} + + # Get batch dimensions + batch_size = batch['past_features'].shape[0] if 'past_features' in batch else 2 + past_len = batch['past_features'].shape[1] if 'past_features' in batch else 5 + future_len = batch['future_targets'].shape[1] if 'future_targets' in batch else 2 + + # Extract past numerical features + if 'past_features' in batch: + model_batch['x_num_past'] = batch['past_features'] + + # Extract future targets (for training) + if 'future_targets' in batch: + model_batch['y'] = batch['future_targets'] + + # Add categorical features if available + if 'past_cat_features' in batch: + model_batch['x_cat_past'] = batch['past_cat_features'] + else: + # Empty tensor for categorical features + model_batch['x_cat_past'] = torch.zeros((batch_size, past_len, 0), dtype=torch.long) + + # Add future numerical features if available + if 'future_num_features' in batch: + model_batch['x_num_future'] = batch['future_num_features'] + else: + # Empty tensor for future numerical features + model_batch['x_num_future'] = torch.zeros((batch_size, future_len, 0), dtype=torch.float) + + # Add future categorical features if available + if 'future_cat_features' in batch: + model_batch['x_cat_future'] = batch['future_cat_features'] + else: + # Empty tensor for future categorical features + model_batch['x_cat_future'] = torch.zeros((batch_size, future_len, 0), dtype=torch.long) + + # Add static features if available + if 'static' in batch: + model_batch['static'] = batch['static'] + + # Add group_id if available + if 'group_id' in batch: + model_batch['group_id'] = batch['group_id'] + + # Add idx_target - the index of target variable in the input features + # In our case, we're using the first feature as the target + model_batch['idx_target'] = [0] # Assuming the first feature corresponds to the target + + return model_batch + +def create_dummy_dataset(path): + """Create a simple time series dataset for testing""" + logger.info(f"Creating dummy dataset at {path}") + + # Create a simple time series with 3 groups + dates = pd.date_range('2023-01-01', periods=100, freq='D') + groups = ['A', 'B', 'C'] + + data = [] + for group in groups: + for date in dates: + value = np.sin(date.day/15) + np.random.normal(0, 0.1) + feature1 = np.cos(date.day/10) + np.random.normal(0, 0.05) + feature2 = date.day/30 + np.random.normal(0, 0.02) + + # Add categorical features for testing + hour_of_day = date.hour # 0-23 + day_of_week = date.dayofweek # 0-6 + month = date.month # 1-12 + + data.append({ + 'time': date, + 'group': group, + 'target': value, + 'feature1': feature1, + 'feature2': feature2, + 'hour': hour_of_day, + 'day_of_week': day_of_week, + 'month': month + }) + + df = pd.DataFrame(data) + csv_path = path / 'dummy_ts.csv' + df.to_csv(csv_path, index=False) + + logger.info(f"Created dummy dataset with shape {df.shape}") + return df, csv_path + +def main(): + try: + # Create a temporary directory for test data + tmp_path = Path(os.getcwd()) / "test_data" + tmp_path.mkdir(exist_ok=True) + + # Choose data source: dummy (default) or monash + use_monash = os.environ.get("USE_MONASH_DATA", "0") == "1" + logger.info(f"USE_MONASH_DATA={use_monash}") + + if use_monash: + logger.info("Loading data via Monash loader...") + from dsipts.data_management.monash import Monash + # Pick a small dataset id for test (e.g., 1); change as needed + monash_data_dir = tmp_path / "monash" + monash_data_dir.mkdir(exist_ok=True) + monash_obj = Monash(str(monash_data_dir / "monash_table"), rebuild=False) + monash_obj.download_dataset(str(monash_data_dir), id=1, rebuild=False) + df = monash_obj.generate_dataset(1) + logger.info(f"Loaded Monash dataset shape: {df.shape}") + # Infer columns (try to match expected names) + time_col = 'start_timestamp' if 'start_timestamp' in df.columns else 'time' + target_col = 'series_value' if 'series_value' in df.columns else 'target' + group_col = 'series_name' if 'series_name' in df.columns else 'group' + # Use all columns except time, group, target as features + feature_cols = [c for c in df.columns if c not in [time_col, group_col, target_col]] + # Save to CSV for pipeline compatibility + csv_path = monash_data_dir / "monash_data.csv" + df.to_csv(csv_path, index=False) + else: + logger.info("Using dummy dataset...") + # Create a dummy dataset + df, csv_path = create_dummy_dataset(tmp_path) + time_col = 'time' + target_col = 'target' + group_col = 'group' + feature_cols = ['feature1', 'feature2'] + + logger.info(f"Dataset columns: {df.columns.tolist()}") + logger.info(f"Dataset shape: {df.shape}") + + logger.info(f"Creating D1 dataset with: time_col={time_col}, target_col={target_col}, " + f"group_col={group_col}, feature_cols={feature_cols}") + + # Define categorical and known/unknown columns + cat_cols = ['hour', 'day_of_week', 'month', 'group'] + known_cols = ['feature1', 'hour', 'day_of_week', 'month'] # Known at prediction time + unknown_cols = ['feature2', 'target'] # Unknown at prediction time (need to be predicted) + + d1 = MultiSourceTSDataSet( + file_paths=[str(csv_path)], + group_cols=group_col, + time_col=time_col, + feature_cols=feature_cols + ['hour', 'day_of_week', 'month'], # Include categorical features + target_cols=[target_col], + cat_cols=cat_cols, # Specify categorical columns + known_cols=known_cols, # Specify known columns + unknown_cols=unknown_cols, # Specify unknown columns + memory_efficient=False, + chunk_size=1000 + ) + + logger.info(f"D1 dataset created with {len(d1)} groups") + + # Define window sizes for past and future + past_len = 5 # Number of past time steps + future_len = 2 # Number of future time steps to predict + + # 3. Prepare D2 (TSDataModule) + logger.info("Creating D2 TSDataModule") + + # Define split method and configuration + # Option 1: Percentage-based split (temporal) + split_method = 'percentage' + split_config = (0.7, 0.15, 0.15) # 70% train, 15% val, 15% test + + # Option 2: Group-based split with temporal ordering within groups + # split_method = 'group' + # split_config = (['A'], ['B'], ['C']) # Group A for train, B for val, C for test + + logger.info(f"Using split method: {split_method} with config: {split_config}") + + d2 = TSDataModule( + d1_dataset=d1, + past_len=past_len, + future_len=future_len, + batch_size=2, + split_method=split_method, + split_config=split_config, + # We're not overriding known/unknown columns here, using D1's metadata + precompute=True + ) + + logger.info("Setting up D2 module") + d2.setup() + + logger.info("Getting train dataloader") + loader = d2.train_dataloader() + + logger.info("Fetching a batch") + batch = next(iter(loader)) + + logger.info(f"Batch keys: {batch.keys()}") + for k, v in batch.items(): + if isinstance(v, torch.Tensor): + logger.info(f" {k}: tensor with shape {v.shape}") + else: + logger.info(f" {k}: {type(v)}") + + # 4. Instantiate SimpleTimeSeriesModel + logger.info("Creating SimpleTimeSeriesModel") + + # Get dimensions from the batch + feature_dim = batch['past_features'].shape[2] if 'past_features' in batch else 2 + target_dim = batch['future_targets'].shape[2] if 'future_targets' in batch else 1 + + logger.info(f"Feature dimension: {feature_dim}, Target dimension: {target_dim}") + + # Get categorical dimensions from metadata if available + cat_dims = {} + if 'max_classes' in d2.metadata: + cat_dims = d2.metadata['max_classes'] + logger.info(f"Categorical dimensions from metadata: {cat_dims}") + + # Create the SimpleTimeSeriesModel + model = SimpleTimeSeriesModel( + past_len=past_len, + future_len=future_len, + feature_dim=feature_dim, + target_dim=target_dim, + cat_dims=cat_dims, + verbose=True + ) + + logger.info(f"Created SimpleTimeSeriesModel") + + # Log D2 metadata to understand column categorization + logger.info("D2 Metadata:") + for key, value in d2.metadata.items(): + if key in ['known_cat_cols', 'known_num_cols', 'unknown_cat_cols', 'unknown_num_cols']: + logger.info(f" {key}: {value}") + + # Extract batch structure information + known_cat_cols = d2.metadata.get('known_cat_cols', []) + known_num_cols = d2.metadata.get('known_num_cols', []) + unknown_cat_cols = d2.metadata.get('unknown_cat_cols', []) + unknown_num_cols = d2.metadata.get('unknown_num_cols', []) + + # Convert batch to model format + logger.info("Converting batch to model format") + model_batch = convert_batch_for_model( + batch, + known_cat_cols=known_cat_cols, + known_num_cols=known_num_cols, + unknown_cat_cols=unknown_cat_cols, + unknown_num_cols=unknown_num_cols + ) + + logger.info(f"Model batch keys: {model_batch.keys()}") + for k, v in model_batch.items(): + if isinstance(v, torch.Tensor): + logger.info(f" {k}: tensor with shape {v.shape}") + + # Run forward pass + logger.info("Running forward pass") + with torch.no_grad(): # Disable gradient calculation for inference + output = model(model_batch) + + # 5. Check output shape + logger.info(f"Output shape: {output.shape}") + assert isinstance(output, torch.Tensor) + + # Check that output batch dimension matches input + assert output.shape[0] == model_batch['x_num_past'].shape[0], "Batch size mismatch" + assert output.shape[1] == future_len, "Sequence length mismatch" + assert output.shape[2] == target_dim, "Feature dimension mismatch" + + # Additional validation for the enhanced batch structure + logger.info("Validating enhanced batch structure...") + + # Check if we have the expected keys for categorical/numerical features + expected_keys = ['x_num_past', 'x_cat_past', 'x_num_future', 'x_cat_future', 'y'] + for key in expected_keys: + assert key in model_batch, f"Missing expected key: {key}" + + # Check if the dimensions are consistent + assert model_batch['x_num_past'].shape[0] == model_batch['x_cat_past'].shape[0], "Batch size mismatch between numerical and categorical features" + assert model_batch['x_num_future'].shape[1] == model_batch['y'].shape[1], "Future sequence length mismatch" + + logger.info("Test completed successfully!") + logger.info("The D1/D2 layers work properly with the Base model.") + return True + + except Exception as e: + logger.error(f"Test failed with error: {str(e)}", exc_info=True) + return False + +if __name__ == "__main__": + success = main() + if success: + print("\n✅ SUCCESS: D1/D2 layers work properly with the model!") + else: + print("\n❌ FAILED: There were issues with the D1/D2 layers integration.") diff --git a/tests/integration/test_time_series_workflow.py b/tests/integration/test_time_series_workflow.py new file mode 100644 index 00000000..e69de29b diff --git a/tests/unit/test_time_series_dataset.py b/tests/unit/test_time_series_dataset.py new file mode 100644 index 00000000..5d108337 --- /dev/null +++ b/tests/unit/test_time_series_dataset.py @@ -0,0 +1,466 @@ +import unittest +import os +import sys +import pandas as pd +import numpy as np +import torch +from datetime import timedelta +import tempfile +import shutil + +# Add the parent directory to the path so we can import the module +sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '../..'))) +from notebooks.sandeep.time_series_dataset import ( + extend_time_df, + MultiSourceTSDataSet, + TSDataModule +) + +class TestExtendTimeDF(unittest.TestCase): + """Test cases for the extend_time_df function.""" + + def setUp(self): + """Set up test data.""" + # Create a simple dataframe with time gaps + self.df = pd.DataFrame({ + 'time': [1, 3, 6, 10], + 'group': ['A', 'A', 'A', 'A'], + 'value': [10, 20, 30, 40] + }) + + # Create a dataframe with multiple groups + self.multi_group_df = pd.DataFrame({ + 'time': [1, 3, 6, 10, 1, 4, 8], + 'group': ['A', 'A', 'A', 'A', 'B', 'B', 'B'], + 'value': [10, 20, 30, 40, 50, 60, 70] + }) + + # Create a dataframe with datetime values + self.datetime_df = pd.DataFrame({ + 'time': pd.date_range(start='2025-01-01', periods=4, freq='2D'), + 'group': ['A', 'A', 'A', 'A'], + 'value': [10, 20, 30, 40] + }) + + def test_extend_numeric_time(self): + """Test extending dataframe with numeric time values.""" + # Extend with frequency 1 + extended_df = extend_time_df(self.df, 'time', 1) + + # Check that all time points are filled + expected_times = list(range(1, 11)) + self.assertEqual(len(extended_df), len(expected_times)) + self.assertListEqual(sorted(extended_df['time'].tolist()), expected_times) + + # Check that original values are preserved + for _, row in self.df.iterrows(): + extended_row = extended_df[extended_df['time'] == row['time']] + self.assertEqual(extended_row['value'].iloc[0], row['value']) + + # Check that new rows have NaN values + for t in [2, 4, 5, 7, 8, 9]: + extended_row = extended_df[extended_df['time'] == t] + self.assertTrue(np.isnan(extended_row['value'].iloc[0])) + + def test_extend_with_groups(self): + """Test extending dataframe with group columns.""" + # Extend with frequency 1 and group column + extended_df = extend_time_df(self.multi_group_df, 'time', 1, group_cols='group') + + # Check that each group is extended separately + group_a = extended_df[extended_df['group'] == 'A'] + group_b = extended_df[extended_df['group'] == 'B'] + + # Group A should have time points 1-10 + self.assertEqual(len(group_a), 10) + self.assertListEqual(sorted(group_a['time'].tolist()), list(range(1, 11))) + + # Group B should have time points 1-8 + self.assertEqual(len(group_b), 8) + self.assertListEqual(sorted(group_b['time'].tolist()), list(range(1, 9))) + + def test_extend_datetime(self): + """Test extending dataframe with datetime values.""" + # Extend with frequency 1 day + extended_df = extend_time_df(self.datetime_df, 'time', timedelta(days=1)) + + # Should have 7 rows (2025-01-01 to 2025-01-07) + self.assertEqual(len(extended_df), 7) + + # Check that original values are preserved + for _, row in self.datetime_df.iterrows(): + extended_row = extended_df[extended_df['time'] == row['time']] + self.assertEqual(extended_row['value'].iloc[0], row['value']) + + # Check that new rows have NaN values + for date in pd.date_range(start='2025-01-02', end='2025-01-06', freq='2D'): + extended_row = extended_df[extended_df['time'] == date] + self.assertTrue(np.isnan(extended_row['value'].iloc[0])) + + +class TestMultiSourceTSDataSet(unittest.TestCase): + """Test cases for the MultiSourceTSDataSet class.""" + + def setUp(self): + """Set up test data.""" + # Create a temporary directory for test files + self.temp_dir = tempfile.mkdtemp() + + # Create test CSV files + self.create_test_files() + + # Define common parameters + self.file_paths = [ + os.path.join(self.temp_dir, f'test_data_{i}.csv') for i in range(2) + ] + self.group_cols = 'group' + self.time_col = 'time' + self.feature_cols = ['feature_0', 'feature_1'] + self.target_cols = ['target_0'] + self.cat_cols = ['cat_feature'] + + def tearDown(self): + """Clean up temporary files.""" + shutil.rmtree(self.temp_dir) + + def create_test_files(self): + """Create test CSV files.""" + # Generate two CSV files with different groups + for file_idx in range(2): + data = [] + + # Generate data for each group + for group_idx in range(3): + # Determine which file gets which groups + if (group_idx % 2 == 0 and file_idx == 0) or (group_idx % 2 == 1 and file_idx == 1): + # Generate time series for this group + for t in range(10): + row = { + 'group': f'group_{group_idx}', + 'time': t, + 'feature_0': np.sin(t/10 + group_idx) + np.random.normal(0, 0.1), + 'feature_1': np.cos(t/10 + group_idx) + np.random.normal(0, 0.1), + 'target_0': np.sin(t/5 + group_idx) + np.random.normal(0, 0.1), + 'cat_feature': f'cat_{np.random.randint(0, 3)}' + } + data.append(row) + + # Create DataFrame and save to CSV + df = pd.DataFrame(data) + df.to_csv(os.path.join(self.temp_dir, f'test_data_{file_idx}.csv'), index=False) + + def test_init_memory_efficient_false(self): + """Test initialization with memory_efficient=False.""" + d1_dataset = MultiSourceTSDataSet( + file_paths=self.file_paths, + group_cols=self.group_cols, + time_col=self.time_col, + feature_cols=self.feature_cols, + target_cols=self.target_cols, + cat_cols=self.cat_cols, + memory_efficient=False + ) + + # Check that the dataset was initialized correctly + self.assertEqual(len(d1_dataset.file_paths), 2) + self.assertEqual(d1_dataset.time_col, 'time') + self.assertEqual(d1_dataset.group_cols, ['group']) + self.assertListEqual(d1_dataset.feature_cols, ['feature_0', 'feature_1']) + self.assertListEqual(d1_dataset.target_cols, ['target_0']) + self.assertListEqual(d1_dataset.cat_cols, ['cat_feature']) + self.assertFalse(d1_dataset.memory_efficient) + + # Check that data was preloaded + self.assertTrue(len(d1_dataset.data_cache) > 0) + + # Check that metadata was created + self.assertTrue('cols' in d1_dataset.metadata) + self.assertTrue('max_classes' in d1_dataset.metadata) + self.assertTrue('known_cat_cols' in d1_dataset.metadata) + self.assertTrue('known_num_cols' in d1_dataset.metadata) + + def test_init_memory_efficient_true(self): + """Test initialization with memory_efficient=True.""" + d1_dataset = MultiSourceTSDataSet( + file_paths=self.file_paths, + group_cols=self.group_cols, + time_col=self.time_col, + feature_cols=self.feature_cols, + target_cols=self.target_cols, + cat_cols=self.cat_cols, + memory_efficient=True + ) + + # Check that the dataset was initialized correctly + self.assertEqual(len(d1_dataset.file_paths), 2) + self.assertEqual(d1_dataset.time_col, 'time') + self.assertEqual(d1_dataset.group_cols, ['group']) + self.assertListEqual(d1_dataset.feature_cols, ['feature_0', 'feature_1']) + self.assertListEqual(d1_dataset.target_cols, ['target_0']) + self.assertListEqual(d1_dataset.cat_cols, ['cat_feature']) + self.assertTrue(d1_dataset.memory_efficient) + + # Check that data was not preloaded + self.assertEqual(len(d1_dataset.data_cache), 0) + + def test_getitem(self): + """Test __getitem__ method.""" + d1_dataset = MultiSourceTSDataSet( + file_paths=self.file_paths, + group_cols=self.group_cols, + time_col=self.time_col, + feature_cols=self.feature_cols, + target_cols=self.target_cols, + cat_cols=self.cat_cols, + memory_efficient=False + ) + + # Get data for the first group + group_data = d1_dataset[0] + + # Check that the returned data has the expected format + self.assertTrue('x' in group_data) + self.assertTrue('y' in group_data) + self.assertTrue('t' in group_data) + self.assertTrue('group_id' in group_data) + + # Check dimensions + self.assertEqual(group_data['x'].shape[1], len(self.feature_cols)) + self.assertEqual(group_data['y'].shape[1], len(self.target_cols)) + self.assertEqual(len(group_data['t']), len(group_data['x'])) + + def test_known_unknown_cols(self): + """Test specifying known and unknown columns.""" + # Specify custom known and unknown columns + known_cols = ['feature_0'] + unknown_cols = ['feature_1', 'target_0'] + + d1_dataset = MultiSourceTSDataSet( + file_paths=self.file_paths, + group_cols=self.group_cols, + time_col=self.time_col, + feature_cols=self.feature_cols, + target_cols=self.target_cols, + cat_cols=self.cat_cols, + known_cols=known_cols, + unknown_cols=unknown_cols, + memory_efficient=False + ) + + # Check that known and unknown columns were set correctly + self.assertListEqual(d1_dataset.known_cols, known_cols) + self.assertListEqual(d1_dataset.unknown_cols, unknown_cols) + + # Check metadata + self.assertListEqual(d1_dataset.metadata['known_cols'], known_cols) + self.assertListEqual(d1_dataset.metadata['unknown_cols'], unknown_cols) + self.assertListEqual(d1_dataset.metadata['known_num_cols'], known_cols) # feature_0 is numerical + self.assertEqual(len(d1_dataset.metadata['known_cat_cols']), 0) # No categorical known cols + + +class TestTSDataModule(unittest.TestCase): + """Test cases for the TSDataModule class.""" + + def setUp(self): + """Set up test data.""" + # Create a temporary directory for test files + self.temp_dir = tempfile.mkdtemp() + + # Create test CSV files + self.create_test_files() + + # Create D1 dataset + self.file_paths = [ + os.path.join(self.temp_dir, f'test_data_{i}.csv') for i in range(2) + ] + self.group_cols = 'group' + self.time_col = 'time' + self.feature_cols = ['feature_0', 'feature_1'] + self.target_cols = ['target_0'] + self.cat_cols = ['cat_feature'] + + self.d1_dataset = MultiSourceTSDataSet( + file_paths=self.file_paths, + group_cols=self.group_cols, + time_col=self.time_col, + feature_cols=self.feature_cols, + target_cols=self.target_cols, + cat_cols=self.cat_cols, + memory_efficient=False + ) + + def tearDown(self): + """Clean up temporary files.""" + shutil.rmtree(self.temp_dir) + + def create_test_files(self): + """Create test CSV files.""" + # Generate two CSV files with different groups + for file_idx in range(2): + data = [] + + # Generate data for each group + for group_idx in range(3): + # Determine which file gets which groups + if (group_idx % 2 == 0 and file_idx == 0) or (group_idx % 2 == 1 and file_idx == 1): + # Generate time series for this group + for t in range(20): # Longer sequences for window creation + row = { + 'group': f'group_{group_idx}', + 'time': t, + 'feature_0': np.sin(t/10 + group_idx) + np.random.normal(0, 0.1), + 'feature_1': np.cos(t/10 + group_idx) + np.random.normal(0, 0.1), + 'target_0': np.sin(t/5 + group_idx) + np.random.normal(0, 0.1), + 'cat_feature': f'cat_{np.random.randint(0, 3)}' + } + data.append(row) + + # Create DataFrame and save to CSV + df = pd.DataFrame(data) + df.to_csv(os.path.join(self.temp_dir, f'test_data_{file_idx}.csv'), index=False) + + def test_init_percentage_split(self): + """Test initialization with percentage split.""" + d2_module = TSDataModule( + d1_dataset=self.d1_dataset, + past_len=5, + future_len=2, + batch_size=32, + min_valid_length=4, + split_method='percentage', + split_config=(0.7, 0.15, 0.15), + memory_efficient=False + ) + + # Check that the module was initialized correctly + self.assertEqual(d2_module.past_len, 5) + self.assertEqual(d2_module.future_len, 2) + self.assertEqual(d2_module.batch_size, 32) + self.assertEqual(d2_module.min_valid_length, 4) + self.assertEqual(d2_module.split_method, 'percentage') + self.assertEqual(d2_module.split_config, (0.7, 0.15, 0.15)) + + # Check that splits were created + self.assertTrue(hasattr(d2_module, 'train_indices')) + self.assertTrue(hasattr(d2_module, 'val_indices')) + self.assertTrue(hasattr(d2_module, 'test_indices')) + + # Check that the sum of split sizes equals the total number of windows + total_windows = len(d2_module.mapping) + split_sum = len(d2_module.train_indices) + len(d2_module.val_indices) + len(d2_module.test_indices) + self.assertEqual(split_sum, total_windows) + + # Check approximate split ratios (allowing for rounding) + self.assertAlmostEqual(len(d2_module.train_indices) / total_windows, 0.7, delta=0.05) + self.assertAlmostEqual(len(d2_module.val_indices) / total_windows, 0.15, delta=0.05) + self.assertAlmostEqual(len(d2_module.test_indices) / total_windows, 0.15, delta=0.05) + + def test_init_group_split(self): + """Test initialization with group split.""" + # Get all group indices + all_groups = list(range(len(self.d1_dataset))) + + # Split groups into train/val/test + train_groups = all_groups[:int(0.7 * len(all_groups))] + val_groups = all_groups[int(0.7 * len(all_groups)):int(0.85 * len(all_groups))] + test_groups = all_groups[int(0.85 * len(all_groups)):] + + d2_module = TSDataModule( + d1_dataset=self.d1_dataset, + past_len=5, + future_len=2, + batch_size=32, + min_valid_length=4, + split_method='group', + split_config=(train_groups, val_groups, test_groups), + memory_efficient=False + ) + + # Check that the module was initialized correctly + self.assertEqual(d2_module.past_len, 5) + self.assertEqual(d2_module.future_len, 2) + self.assertEqual(d2_module.split_method, 'group') + + # Check that splits were created + self.assertTrue(hasattr(d2_module, 'train_indices')) + self.assertTrue(hasattr(d2_module, 'val_indices')) + self.assertTrue(hasattr(d2_module, 'test_indices')) + + # Check that each split only contains windows from the assigned groups + for idx in d2_module.train_indices: + group_idx, _ = d2_module.mapping[idx] + self.assertIn(group_idx, train_groups) + + for idx in d2_module.val_indices: + group_idx, _ = d2_module.mapping[idx] + self.assertIn(group_idx, val_groups) + + for idx in d2_module.test_indices: + group_idx, _ = d2_module.mapping[idx] + self.assertIn(group_idx, test_groups) + + def test_get_window(self): + """Test _get_window method.""" + d2_module = TSDataModule( + d1_dataset=self.d1_dataset, + past_len=5, + future_len=2, + batch_size=32, + min_valid_length=4, + split_method='percentage', + split_config=(0.7, 0.15, 0.15), + memory_efficient=False + ) + + # Get a window from the dataset + if d2_module.train_indices: + idx = d2_module.train_indices[0] + group_idx, local_idx = d2_module.mapping[idx] + window = d2_module._get_window(group_idx, local_idx) + + # Check that the window has the expected format + self.assertTrue('past_features' in window) + self.assertTrue('past_time' in window) + self.assertTrue('future_targets' in window) + self.assertTrue('future_time' in window) + + # Check dimensions + self.assertEqual(window['past_features'].shape[0], d2_module.past_len) + self.assertEqual(window['past_features'].shape[1], len(self.feature_cols)) + self.assertEqual(window['future_targets'].shape[0], d2_module.future_len) + self.assertEqual(window['future_targets'].shape[1], len(self.target_cols)) + self.assertEqual(len(window['past_time']), d2_module.past_len) + self.assertEqual(len(window['future_time']), d2_module.future_len) + + def test_known_unknown_override(self): + """Test overriding known and unknown columns.""" + # Specify custom known and unknown columns + known_cols = ['feature_0'] + unknown_cols = ['feature_1', 'target_0'] + + d2_module = TSDataModule( + d1_dataset=self.d1_dataset, + past_len=5, + future_len=2, + batch_size=32, + min_valid_length=4, + split_method='percentage', + split_config=(0.7, 0.15, 0.15), + memory_efficient=False, + known_cols=known_cols, + unknown_cols=unknown_cols + ) + + # Check that known and unknown columns were set correctly + self.assertListEqual(d2_module.known_cols, known_cols) + self.assertListEqual(d2_module.unknown_cols, unknown_cols) + + # Check metadata + self.assertListEqual(d2_module.metadata['known_cols'], known_cols) + self.assertListEqual(d2_module.metadata['unknown_cols'], unknown_cols) + self.assertListEqual(d2_module.metadata['known_num_cols'], known_cols) # feature_0 is numerical + self.assertEqual(len(d2_module.metadata['known_cat_cols']), 0) # No categorical known cols + + +if __name__ == '__main__': + unittest.main()