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--- a +++ b/Notebooks/12_random_split_test.ipynb @@ -0,0 +1,369 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from wearsed.dataset.WearSEDDataset import WearSEDDataset\n", + "from wearsed.models.vae.VAE import VAE, vae_loss\n", + "\n", + "import torch\n", + "\n", + "\n", + "\n", + "# Dataset\n", + "full_dataset = WearSEDDataset(signals_to_read=['Pleth'])\n", + "train_dataset, test_dataset = torch.utils.data.random_split(full_dataset, [0.8, 0.2])" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 1\n", + "1 2\n", + "2 6\n", + "3 12\n", + "4 14\n", + "5 16\n", + "6 21\n", + "7 27\n", + "8 28\n", + "9 33\n" + ] + } + ], + "source": [ + "for i in range(10):\n", + " print(i, full_dataset[i].id)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 3016\n", + "1 1844\n", + "2 2309\n", + "3 949\n", + "4 5295\n", + "5 4588\n", + "6 3703\n", + "7 837\n", + "8 2227\n", + "9 5369\n" + ] + } + ], + "source": [ + "for i in range(10):\n", + " print(i, train_dataset[i].id)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 -0.025406\n", + "1 -0.024033\n", + "2 -0.022965\n", + "3 -0.022049\n", + "4 -0.021286\n", + " ... \n", + "9057019 0.025254\n", + "9057020 0.020981\n", + "9057021 0.016098\n", + "9057022 0.010758\n", + "9057023 0.004959\n", + "Length: 9057024, dtype: float64" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train_dataset[0].psg['Pleth']" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "data = torch.rand(100, 256) # Example dataset with 1000 samples\n", + "train_loader = torch.utils.data.DataLoader(data, batch_size=16, shuffle=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([16, 256])\n", + "torch.Size([16, 256])\n", + "torch.Size([16, 256])\n", + "torch.Size([16, 256])\n", + "torch.Size([16, 256])\n", + "torch.Size([16, 256])\n", + "torch.Size([4, 256])\n" + ] + } + ], + "source": [ + "for batch in train_loader:\n", + " print(batch.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "a = torch.Tensor([1,2,3])\n", + "b = torch.Tensor([4,5,6])" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([[1., 2., 3.],\n", + " [4., 5., 6.]])" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "torch.stack([a,b])" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "expected Tensor as element 0 in argument 0, but got numpy.ndarray", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[10], line 4\u001b[0m\n\u001b[1;32m 2\u001b[0m a \u001b[38;5;241m=\u001b[39m full[\u001b[38;5;241m3\u001b[39m:\u001b[38;5;241m6\u001b[39m]\n\u001b[1;32m 3\u001b[0m b \u001b[38;5;241m=\u001b[39m full[\u001b[38;5;241m7\u001b[39m:\u001b[38;5;241m10\u001b[39m]\n\u001b[0;32m----> 4\u001b[0m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstack\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[43ma\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mb\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalues\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[0;31mTypeError\u001b[0m: expected Tensor as element 0 in argument 0, but got numpy.ndarray" + ] + } + ], + "source": [ + "full = pd.Series([1,2,3,4,5,6,7,8,9,10,11,12])\n", + "a = full[3:6]\n", + "b = full[7:10]\n", + "torch.stack([a.values, b.values])" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from wearsed.training.vae.train_vae import get_batch\n", + "from wearsed.dataset.WearSEDDataset import WearSEDDataset" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "full_dataset = WearSEDDataset(signals_to_read=['Pleth'])\n", + "r1 = full_dataset[1]" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([[ 0.0141, 0.0150, 0.0159, ..., 0.0555, 0.0555, 0.0555],\n", + " [ 0.0773, 0.0780, 0.0787, ..., 0.1037, 0.1049, 0.1061],\n", + " [-0.1217, -0.1199, -0.1180, ..., -0.0356, -0.0307, -0.0263],\n", + " [ 0.0469, 0.0480, 0.0494, ..., 0.0454, 0.0454, 0.0452]])" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "get_batch(r1, 4)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "signal = r1.psg['Pleth']\n", + "max_time = len(signal) - 256" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4486851" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "list(torch.randint(0, max_time, (4,)))[0].item()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "cannot do slice indexing on RangeIndex with these indexers [5037322] of type Tensor", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[19], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m batch \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m timepoint \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mlist\u001b[39m(torch\u001b[38;5;241m.\u001b[39mrandint(\u001b[38;5;241m0\u001b[39m, max_time, (\u001b[38;5;241m4\u001b[39m,))):\n\u001b[0;32m----> 3\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[43msignal\u001b[49m\u001b[43m[\u001b[49m\u001b[43mtimepoint\u001b[49m\u001b[43m:\u001b[49m\u001b[43mtimepoint\u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[38;5;241;43m256\u001b[39;49m\u001b[43m]\u001b[49m)\n\u001b[1;32m 4\u001b[0m \u001b[38;5;66;03m#batch.append(torch.Tensor(signal[timepoint:timepoint+256].values))\u001b[39;00m\n", + "File \u001b[0;32m~/miniconda3/envs/master/lib/python3.12/site-packages/pandas/core/series.py:1146\u001b[0m, in \u001b[0;36mSeries.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 1142\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_values_tuple(key)\n\u001b[1;32m 1144\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key, \u001b[38;5;28mslice\u001b[39m):\n\u001b[1;32m 1145\u001b[0m \u001b[38;5;66;03m# Do slice check before somewhat-costly is_bool_indexer\u001b[39;00m\n\u001b[0;32m-> 1146\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_getitem_slice\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1148\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m com\u001b[38;5;241m.\u001b[39mis_bool_indexer(key):\n\u001b[1;32m 1149\u001b[0m key \u001b[38;5;241m=\u001b[39m check_bool_indexer(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mindex, key)\n", + "File \u001b[0;32m~/miniconda3/envs/master/lib/python3.12/site-packages/pandas/core/generic.py:4349\u001b[0m, in \u001b[0;36mNDFrame._getitem_slice\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 4344\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 4345\u001b[0m \u001b[38;5;124;03m__getitem__ for the case where the key is a slice object.\u001b[39;00m\n\u001b[1;32m 4346\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 4347\u001b[0m \u001b[38;5;66;03m# _convert_slice_indexer to determine if this slice is positional\u001b[39;00m\n\u001b[1;32m 4348\u001b[0m \u001b[38;5;66;03m# or label based, and if the latter, convert to positional\u001b[39;00m\n\u001b[0;32m-> 4349\u001b[0m slobj \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mindex\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_convert_slice_indexer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkind\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mgetitem\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 4350\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(slobj, np\u001b[38;5;241m.\u001b[39mndarray):\n\u001b[1;32m 4351\u001b[0m \u001b[38;5;66;03m# reachable with DatetimeIndex\u001b[39;00m\n\u001b[1;32m 4352\u001b[0m indexer \u001b[38;5;241m=\u001b[39m lib\u001b[38;5;241m.\u001b[39mmaybe_indices_to_slice(\n\u001b[1;32m 4353\u001b[0m slobj\u001b[38;5;241m.\u001b[39mastype(np\u001b[38;5;241m.\u001b[39mintp, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m), \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m)\n\u001b[1;32m 4354\u001b[0m )\n", + "File \u001b[0;32m~/miniconda3/envs/master/lib/python3.12/site-packages/pandas/core/indexes/base.py:4248\u001b[0m, in \u001b[0;36mIndex._convert_slice_indexer\u001b[0;34m(self, key, kind)\u001b[0m\n\u001b[1;32m 4245\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m key\n\u001b[1;32m 4246\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdtype\u001b[38;5;241m.\u001b[39mkind \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124miu\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m 4247\u001b[0m \u001b[38;5;66;03m# Note: these checks are redundant if we know is_index_slice\u001b[39;00m\n\u001b[0;32m-> 4248\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_indexer\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mslice\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkey\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstart\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mgetitem\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 4249\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_indexer(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mslice\u001b[39m\u001b[38;5;124m\"\u001b[39m, key\u001b[38;5;241m.\u001b[39mstop, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgetitem\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 4250\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_indexer(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mslice\u001b[39m\u001b[38;5;124m\"\u001b[39m, key\u001b[38;5;241m.\u001b[39mstep, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgetitem\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "File \u001b[0;32m~/miniconda3/envs/master/lib/python3.12/site-packages/pandas/core/indexes/base.py:6697\u001b[0m, in \u001b[0;36mIndex._validate_indexer\u001b[0;34m(self, form, key, kind)\u001b[0m\n\u001b[1;32m 6692\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 6693\u001b[0m \u001b[38;5;124;03mIf we are positional indexer, validate that we have appropriate\u001b[39;00m\n\u001b[1;32m 6694\u001b[0m \u001b[38;5;124;03mtyped bounds must be an integer.\u001b[39;00m\n\u001b[1;32m 6695\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 6696\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m lib\u001b[38;5;241m.\u001b[39mis_int_or_none(key):\n\u001b[0;32m-> 6697\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_raise_invalid_indexer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mform\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/miniconda3/envs/master/lib/python3.12/site-packages/pandas/core/indexes/base.py:4301\u001b[0m, in \u001b[0;36mIndex._raise_invalid_indexer\u001b[0;34m(self, form, key, reraise)\u001b[0m\n\u001b[1;32m 4299\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m reraise \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m lib\u001b[38;5;241m.\u001b[39mno_default:\n\u001b[1;32m 4300\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(msg) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mreraise\u001b[39;00m\n\u001b[0;32m-> 4301\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(msg)\n", + "\u001b[0;31mTypeError\u001b[0m: cannot do slice indexing on RangeIndex with these indexers [5037322] of type Tensor" + ] + } + ], + "source": [ + "batch = []\n", + "for timepoint in list(torch.randint(0, max_time, (4,))):\n", + " print(signal[timepoint:timepoint+256])\n", + " #batch.append(torch.Tensor(signal[timepoint:timepoint+256].values))" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import torch.nn.functional as F\n", + "import torch" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor(1.)\n" + ] + } + ], + "source": [ + "a = torch.Tensor([\n", + " [1,2,3],\n", + " [5,6,7]])\n", + "b = torch.Tensor([\n", + " [1,2,1],\n", + " [5,7,8]])\n", + "\n", + "print(F.mse_loss(a, b, reduction='sum')/(2*3))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "master", + "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": "3.12.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}