From fe2d5e71b384536afad093c1c10ea6f3c198098b Mon Sep 17 00:00:00 2001 From: levtelyatnikov Date: Thu, 31 Oct 2024 19:35:09 +0100 Subject: [PATCH 01/24] some random error --- tutorials/batching.ipynb | 49 ++++++++++++++++++++++++++++++++++++++++ 1 file changed, 49 insertions(+) create mode 100644 tutorials/batching.ipynb diff --git a/tutorials/batching.ipynb b/tutorials/batching.ipynb new file mode 100644 index 00000000..0270acbd --- /dev/null +++ b/tutorials/batching.ipynb @@ -0,0 +1,49 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "outputs": [], + "source": [ + "import rootutils\n", + "\n", + "rootutils.setup_root(\"./\", indicator=\".project-root\", pythonpath=True)\n", + "\n", + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "import hydra\n", + "import torch\n", + "import torch_geometric\n", + "from hydra import compose, initialize\n", + "from omegaconf import OmegaConf\n", + "\n", + "from topobenchmarkx.data.preprocessor import PreProcessor\n", + "from topobenchmarkx.dataloader.dataloader import TBXDataloader\n", + "from topobenchmarkx.data.loaders import GraphLoader\n", + "\n", + "from topobenchmarkx.utils.config_resolvers import (\n", + " get_default_transform,\n", + " get_monitor_metric,\n", + " get_monitor_mode,\n", + " infer_in_channels,\n", + ")\n", + "\n", + "\n", + "initialize(config_path=\"../configs\", job_name=\"job\")" + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 82b3edebeb20898fe8af7a71ea0f406e40b423de Mon Sep 17 00:00:00 2001 From: levtelyatnikov Date: Thu, 31 Oct 2024 20:06:08 +0100 Subject: [PATCH 02/24] run --- configs/run.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/configs/run.yaml b/configs/run.yaml index 049db711..2bb5d1b2 100755 --- a/configs/run.yaml +++ b/configs/run.yaml @@ -5,7 +5,7 @@ defaults: - _self_ - dataset: graph/cocitation_cora - - model: hypergraph/unignn2 + - model: graph/gcn - transforms: ${get_default_transform:${dataset},${model}} #no_transform - optimizer: default - loss: default From 9cb5defee7953e686afef620643c7e907ab4d2c8 Mon Sep 17 00:00:00 2001 From: levtelyatnikov Date: Thu, 31 Oct 2024 20:37:47 +0100 Subject: [PATCH 03/24] start the developments of node level batching --- tutorials/batching.ipynb | 126 +++++++++++++++++++++++---------------- 1 file changed, 74 insertions(+), 52 deletions(-) diff --git a/tutorials/batching.ipynb b/tutorials/batching.ipynb index 4b4e2c4f..97259c69 100644 --- a/tutorials/batching.ipynb +++ b/tutorials/batching.ipynb @@ -9,7 +9,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_3187732/2455096930.py:26: UserWarning: \n", + "/tmp/ipykernel_3192954/2455096930.py:26: UserWarning: \n", "The version_base parameter is not specified.\n", "Please specify a compatability version level, or None.\n", "Will assume defaults for version 1.1\n", @@ -58,64 +58,86 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 17, "metadata": {}, "outputs": [ { - "ename": "ConfigCompositionException", - "evalue": "Error resolving interpolation '${get_default_transform:${dataset},${model}}', possible interpolation keys: debug, hparams_search, experiment, hydra, extras, paths, trainer, logger, callbacks, evaluator, loss, optimizer, model, dataset", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mInterpolationResolutionError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m~/miniconda3/envs/tbx/lib/python3.11/site-packages/hydra/core/default_element.py:238\u001b[0m, in \u001b[0;36mInputDefault._resolve_interpolation_impl\u001b[0;34m(self, known_choices, val)\u001b[0m\n\u001b[1;32m 237\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 238\u001b[0m ret \u001b[38;5;241m=\u001b[39m \u001b[43mnode\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m_dummy_\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\n\u001b[1;32m 239\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(ret, \u001b[38;5;28mstr\u001b[39m)\n", - "File \u001b[0;32m~/miniconda3/envs/tbx/lib/python3.11/site-packages/omegaconf/dictconfig.py:375\u001b[0m, in \u001b[0;36mDictConfig.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 374\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m--> 375\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_format_and_raise\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcause\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43me\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/miniconda3/envs/tbx/lib/python3.11/site-packages/omegaconf/base.py:231\u001b[0m, in \u001b[0;36mNode._format_and_raise\u001b[0;34m(self, key, value, cause, msg, type_override)\u001b[0m\n\u001b[1;32m 223\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_format_and_raise\u001b[39m(\n\u001b[1;32m 224\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 225\u001b[0m key: Any,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 229\u001b[0m type_override: Any \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 230\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 231\u001b[0m \u001b[43mformat_and_raise\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 232\u001b[0m \u001b[43m \u001b[49m\u001b[43mnode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 233\u001b[0m \u001b[43m \u001b[49m\u001b[43mkey\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 234\u001b[0m \u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 235\u001b[0m \u001b[43m \u001b[49m\u001b[43mmsg\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mcause\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mmsg\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mmsg\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 236\u001b[0m \u001b[43m \u001b[49m\u001b[43mcause\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcause\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 237\u001b[0m \u001b[43m \u001b[49m\u001b[43mtype_override\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtype_override\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 238\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 239\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m\n", - "File \u001b[0;32m~/miniconda3/envs/tbx/lib/python3.11/site-packages/omegaconf/_utils.py:899\u001b[0m, in \u001b[0;36mformat_and_raise\u001b[0;34m(node, key, value, msg, cause, type_override)\u001b[0m\n\u001b[1;32m 897\u001b[0m ex\u001b[38;5;241m.\u001b[39mref_type_str \u001b[38;5;241m=\u001b[39m ref_type_str\n\u001b[0;32m--> 899\u001b[0m \u001b[43m_raise\u001b[49m\u001b[43m(\u001b[49m\u001b[43mex\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcause\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/miniconda3/envs/tbx/lib/python3.11/site-packages/omegaconf/_utils.py:797\u001b[0m, in \u001b[0;36m_raise\u001b[0;34m(ex, cause)\u001b[0m\n\u001b[1;32m 796\u001b[0m ex\u001b[38;5;241m.\u001b[39m__cause__ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m--> 797\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m ex\u001b[38;5;241m.\u001b[39mwith_traceback(sys\u001b[38;5;241m.\u001b[39mexc_info()[\u001b[38;5;241m2\u001b[39m])\n", - "File \u001b[0;32m~/miniconda3/envs/tbx/lib/python3.11/site-packages/omegaconf/dictconfig.py:369\u001b[0m, in \u001b[0;36mDictConfig.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 368\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 369\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_get_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdefault_value\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_DEFAULT_MARKER_\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 370\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n", - "File \u001b[0;32m~/miniconda3/envs/tbx/lib/python3.11/site-packages/omegaconf/dictconfig.py:451\u001b[0m, in \u001b[0;36mDictConfig._get_impl\u001b[0;34m(self, key, default_value, validate_key)\u001b[0m\n\u001b[1;32m 450\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(node, Node)\n\u001b[0;32m--> 451\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_resolve_with_default\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 452\u001b[0m \u001b[43m \u001b[49m\u001b[43mkey\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdefault_value\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdefault_value\u001b[49m\n\u001b[1;32m 453\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/miniconda3/envs/tbx/lib/python3.11/site-packages/omegaconf/basecontainer.py:98\u001b[0m, in \u001b[0;36mBaseContainer._resolve_with_default\u001b[0;34m(self, key, value, default_value)\u001b[0m\n\u001b[1;32m 96\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m MissingMandatoryValue(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMissing mandatory value: $FULL_KEY\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m---> 98\u001b[0m resolved_node \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_maybe_resolve_interpolation\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 99\u001b[0m \u001b[43m \u001b[49m\u001b[43mparent\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 100\u001b[0m \u001b[43m \u001b[49m\u001b[43mkey\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 101\u001b[0m \u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 102\u001b[0m \u001b[43m \u001b[49m\u001b[43mthrow_on_resolution_failure\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 103\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 105\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _get_value(resolved_node)\n", - "File \u001b[0;32m~/miniconda3/envs/tbx/lib/python3.11/site-packages/omegaconf/base.py:719\u001b[0m, in \u001b[0;36mContainer._maybe_resolve_interpolation\u001b[0;34m(self, parent, key, value, throw_on_resolution_failure, memo)\u001b[0m\n\u001b[1;32m 718\u001b[0m parse_tree \u001b[38;5;241m=\u001b[39m parse(_get_value(value))\n\u001b[0;32m--> 719\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_resolve_interpolation_from_parse_tree\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 720\u001b[0m \u001b[43m \u001b[49m\u001b[43mparent\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparent\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 721\u001b[0m \u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 722\u001b[0m \u001b[43m \u001b[49m\u001b[43mkey\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 723\u001b[0m \u001b[43m \u001b[49m\u001b[43mparse_tree\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparse_tree\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 724\u001b[0m \u001b[43m \u001b[49m\u001b[43mthrow_on_resolution_failure\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mthrow_on_resolution_failure\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 725\u001b[0m \u001b[43m \u001b[49m\u001b[43mmemo\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmemo\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mmemo\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mset\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 726\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/miniconda3/envs/tbx/lib/python3.11/site-packages/omegaconf/base.py:584\u001b[0m, in \u001b[0;36mContainer._resolve_interpolation_from_parse_tree\u001b[0;34m(self, parent, value, key, parse_tree, throw_on_resolution_failure, memo)\u001b[0m\n\u001b[1;32m 583\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 584\u001b[0m resolved \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresolve_parse_tree\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 585\u001b[0m \u001b[43m \u001b[49m\u001b[43mparse_tree\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparse_tree\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkey\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmemo\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmemo\u001b[49m\n\u001b[1;32m 586\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 587\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m InterpolationResolutionError:\n", - "File \u001b[0;32m~/miniconda3/envs/tbx/lib/python3.11/site-packages/omegaconf/base.py:769\u001b[0m, in \u001b[0;36mContainer.resolve_parse_tree\u001b[0;34m(self, parse_tree, node, memo, key)\u001b[0m\n\u001b[1;32m 767\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[1;32m 768\u001b[0m \u001b[38;5;66;03m# Other kinds of exceptions are wrapped in an `InterpolationResolutionError`.\u001b[39;00m\n\u001b[0;32m--> 769\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m InterpolationResolutionError(\n\u001b[1;32m 770\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(exc)\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m raised while resolving interpolation: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mexc\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 771\u001b[0m )\u001b[38;5;241m.\u001b[39mwith_traceback(sys\u001b[38;5;241m.\u001b[39mexc_info()[\u001b[38;5;241m2\u001b[39m])\n", - "File \u001b[0;32m~/miniconda3/envs/tbx/lib/python3.11/site-packages/omegaconf/base.py:764\u001b[0m, in \u001b[0;36mContainer.resolve_parse_tree\u001b[0;34m(self, parse_tree, node, memo, key)\u001b[0m\n\u001b[1;32m 763\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 764\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mvisitor\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvisit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mparse_tree\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 765\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m InterpolationResolutionError:\n", - "File \u001b[0;32m~/miniconda3/envs/tbx/lib/python3.11/site-packages/antlr4/tree/Tree.py:34\u001b[0m, in \u001b[0;36mParseTreeVisitor.visit\u001b[0;34m(self, tree)\u001b[0m\n\u001b[1;32m 33\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mvisit\u001b[39m(\u001b[38;5;28mself\u001b[39m, tree):\n\u001b[0;32m---> 34\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mtree\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43maccept\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/miniconda3/envs/tbx/lib/python3.11/site-packages/omegaconf/grammar/gen/OmegaConfGrammarParser.py:206\u001b[0m, in \u001b[0;36mOmegaConfGrammarParser.ConfigValueContext.accept\u001b[0;34m(self, visitor)\u001b[0m\n\u001b[1;32m 205\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m( visitor, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvisitConfigValue\u001b[39m\u001b[38;5;124m\"\u001b[39m ):\n\u001b[0;32m--> 206\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mvisitor\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvisitConfigValue\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 207\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n", - "File \u001b[0;32m~/miniconda3/envs/tbx/lib/python3.11/site-packages/omegaconf/grammar_visitor.py:101\u001b[0m, in \u001b[0;36mGrammarVisitor.visitConfigValue\u001b[0;34m(self, ctx)\u001b[0m\n\u001b[1;32m 100\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m ctx\u001b[38;5;241m.\u001b[39mgetChildCount() \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m2\u001b[39m\n\u001b[0;32m--> 101\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[43mvisit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mctx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgetChild\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/miniconda3/envs/tbx/lib/python3.11/site-packages/antlr4/tree/Tree.py:34\u001b[0m, in \u001b[0;36mParseTreeVisitor.visit\u001b[0;34m(self, tree)\u001b[0m\n\u001b[1;32m 33\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mvisit\u001b[39m(\u001b[38;5;28mself\u001b[39m, tree):\n\u001b[0;32m---> 34\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mtree\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43maccept\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/miniconda3/envs/tbx/lib/python3.11/site-packages/omegaconf/grammar/gen/OmegaConfGrammarParser.py:342\u001b[0m, in \u001b[0;36mOmegaConfGrammarParser.TextContext.accept\u001b[0;34m(self, visitor)\u001b[0m\n\u001b[1;32m 341\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m( visitor, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvisitText\u001b[39m\u001b[38;5;124m\"\u001b[39m ):\n\u001b[0;32m--> 342\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mvisitor\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvisitText\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 343\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n", - "File \u001b[0;32m~/miniconda3/envs/tbx/lib/python3.11/site-packages/omegaconf/grammar_visitor.py:298\u001b[0m, in \u001b[0;36mGrammarVisitor.visitText\u001b[0;34m(self, ctx)\u001b[0m\n\u001b[1;32m 297\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(c, OmegaConfGrammarParser\u001b[38;5;241m.\u001b[39mInterpolationContext):\n\u001b[0;32m--> 298\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[43mvisitInterpolation\u001b[49m\u001b[43m(\u001b[49m\u001b[43mc\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 300\u001b[0m \u001b[38;5;66;03m# Otherwise, concatenate string representations together.\u001b[39;00m\n", - "File \u001b[0;32m~/miniconda3/envs/tbx/lib/python3.11/site-packages/omegaconf/grammar_visitor.py:125\u001b[0m, in \u001b[0;36mGrammarVisitor.visitInterpolation\u001b[0;34m(self, ctx)\u001b[0m\n\u001b[1;32m 124\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m ctx\u001b[38;5;241m.\u001b[39mgetChildCount() \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m \u001b[38;5;66;03m# interpolationNode | interpolationResolver\u001b[39;00m\n\u001b[0;32m--> 125\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[43mvisit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mctx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgetChild\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/miniconda3/envs/tbx/lib/python3.11/site-packages/antlr4/tree/Tree.py:34\u001b[0m, in \u001b[0;36mParseTreeVisitor.visit\u001b[0;34m(self, tree)\u001b[0m\n\u001b[1;32m 33\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mvisit\u001b[39m(\u001b[38;5;28mself\u001b[39m, tree):\n\u001b[0;32m---> 34\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mtree\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43maccept\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/miniconda3/envs/tbx/lib/python3.11/site-packages/omegaconf/grammar/gen/OmegaConfGrammarParser.py:1041\u001b[0m, in \u001b[0;36mOmegaConfGrammarParser.InterpolationResolverContext.accept\u001b[0;34m(self, visitor)\u001b[0m\n\u001b[1;32m 1040\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m( visitor, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvisitInterpolationResolver\u001b[39m\u001b[38;5;124m\"\u001b[39m ):\n\u001b[0;32m-> 1041\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mvisitor\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvisitInterpolationResolver\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1042\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n", - "File \u001b[0;32m~/miniconda3/envs/tbx/lib/python3.11/site-packages/omegaconf/grammar_visitor.py:179\u001b[0m, in \u001b[0;36mGrammarVisitor.visitInterpolationResolver\u001b[0;34m(self, ctx)\u001b[0m\n\u001b[1;32m 177\u001b[0m args_str\u001b[38;5;241m.\u001b[39mappend(txt)\n\u001b[0;32m--> 179\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresolver_interpolation_callback\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 180\u001b[0m \u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mresolver_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 181\u001b[0m \u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mtuple\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 182\u001b[0m \u001b[43m \u001b[49m\u001b[43margs_str\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mtuple\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43margs_str\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 183\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/miniconda3/envs/tbx/lib/python3.11/site-packages/omegaconf/base.py:750\u001b[0m, in \u001b[0;36mContainer.resolve_parse_tree..resolver_interpolation_callback\u001b[0;34m(name, args, args_str)\u001b[0m\n\u001b[1;32m 747\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mresolver_interpolation_callback\u001b[39m(\n\u001b[1;32m 748\u001b[0m name: \u001b[38;5;28mstr\u001b[39m, args: Tuple[Any, \u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m], args_str: Tuple[\u001b[38;5;28mstr\u001b[39m, \u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m]\n\u001b[1;32m 749\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Any:\n\u001b[0;32m--> 750\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_evaluate_custom_resolver\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 751\u001b[0m \u001b[43m \u001b[49m\u001b[43mkey\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 752\u001b[0m \u001b[43m \u001b[49m\u001b[43mnode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 753\u001b[0m \u001b[43m \u001b[49m\u001b[43minter_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 754\u001b[0m \u001b[43m \u001b[49m\u001b[43minter_args\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 755\u001b[0m \u001b[43m \u001b[49m\u001b[43minter_args_str\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43margs_str\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 756\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/miniconda3/envs/tbx/lib/python3.11/site-packages/omegaconf/base.py:694\u001b[0m, in \u001b[0;36mContainer._evaluate_custom_resolver\u001b[0;34m(self, key, node, inter_type, inter_args, inter_args_str)\u001b[0m\n\u001b[1;32m 693\u001b[0m root_node \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_root()\n\u001b[0;32m--> 694\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mresolver\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 695\u001b[0m \u001b[43m \u001b[49m\u001b[43mroot_node\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 696\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 697\u001b[0m \u001b[43m \u001b[49m\u001b[43mnode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 698\u001b[0m \u001b[43m \u001b[49m\u001b[43minter_args\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 699\u001b[0m \u001b[43m \u001b[49m\u001b[43minter_args_str\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 700\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 701\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n", - "File \u001b[0;32m~/miniconda3/envs/tbx/lib/python3.11/site-packages/omegaconf/omegaconf.py:445\u001b[0m, in \u001b[0;36mOmegaConf.register_new_resolver..resolver_wrapper\u001b[0;34m(config, parent, node, args, args_str)\u001b[0m\n\u001b[1;32m 443\u001b[0m kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_root_\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m config\n\u001b[0;32m--> 445\u001b[0m ret \u001b[38;5;241m=\u001b[39m \u001b[43mresolver\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 447\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m use_cache:\n", - "File \u001b[0;32m~/projects/TopoBenchmark/topobenchmarkx/utils/config_resolvers.py:33\u001b[0m, in \u001b[0;36mget_default_transform\u001b[0;34m(dataset, model)\u001b[0m\n\u001b[1;32m 28\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m data_domain \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgraph\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m model_domain \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcombinatorial\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m 29\u001b[0m \u001b[38;5;66;03m# Check if there is a default transform for the dataset at ./configs/transforms/dataset_defaults/\u001b[39;00m\n\u001b[1;32m 30\u001b[0m \u001b[38;5;66;03m# If not, use the default lifting transform for the dataset to be compatible with the model\u001b[39;00m\n\u001b[1;32m 31\u001b[0m datasets_with_defaults \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m 32\u001b[0m f\u001b[38;5;241m.\u001b[39msplit(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m)[\u001b[38;5;241m0\u001b[39m]\n\u001b[0;32m---> 33\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m f \u001b[38;5;129;01min\u001b[39;00m \u001b[43mos\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlistdir\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m./configs/transforms/dataset_defaults/\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 34\u001b[0m ]\n\u001b[1;32m 35\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m dataset \u001b[38;5;129;01min\u001b[39;00m datasets_with_defaults:\n", - "\u001b[0;31mInterpolationResolutionError\u001b[0m: FileNotFoundError raised while resolving interpolation: [Errno 2] No such file or directory: './configs/transforms/dataset_defaults/'\n full_key: _dummy_\n object_type=dict", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[0;31mConfigCompositionException\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[2], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m cfg \u001b[38;5;241m=\u001b[39m \u001b[43mhydra\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcompose\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconfig_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrun.yaml\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moverrides\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdataset=graph/NCI1\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/miniconda3/envs/tbx/lib/python3.11/site-packages/hydra/compose.py:38\u001b[0m, in \u001b[0;36mcompose\u001b[0;34m(config_name, overrides, return_hydra_config, strict)\u001b[0m\n\u001b[1;32m 36\u001b[0m gh \u001b[38;5;241m=\u001b[39m GlobalHydra\u001b[38;5;241m.\u001b[39minstance()\n\u001b[1;32m 37\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m gh\u001b[38;5;241m.\u001b[39mhydra \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m---> 38\u001b[0m cfg \u001b[38;5;241m=\u001b[39m \u001b[43mgh\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhydra\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcompose_config\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 39\u001b[0m \u001b[43m \u001b[49m\u001b[43mconfig_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 40\u001b[0m \u001b[43m \u001b[49m\u001b[43moverrides\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moverrides\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 41\u001b[0m \u001b[43m \u001b[49m\u001b[43mrun_mode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mRunMode\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mRUN\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 42\u001b[0m \u001b[43m \u001b[49m\u001b[43mfrom_shell\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 43\u001b[0m \u001b[43m \u001b[49m\u001b[43mwith_log_configuration\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 44\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 45\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(cfg, DictConfig)\n\u001b[1;32m 47\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m return_hydra_config:\n", - "File \u001b[0;32m~/miniconda3/envs/tbx/lib/python3.11/site-packages/hydra/_internal/hydra.py:594\u001b[0m, in \u001b[0;36mHydra.compose_config\u001b[0;34m(self, config_name, overrides, run_mode, with_log_configuration, from_shell, validate_sweep_overrides)\u001b[0m\n\u001b[1;32m 576\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcompose_config\u001b[39m(\n\u001b[1;32m 577\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 578\u001b[0m config_name: Optional[\u001b[38;5;28mstr\u001b[39m],\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 583\u001b[0m validate_sweep_overrides: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m 584\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m DictConfig:\n\u001b[1;32m 585\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 586\u001b[0m \u001b[38;5;124;03m :param config_name:\u001b[39;00m\n\u001b[1;32m 587\u001b[0m \u001b[38;5;124;03m :param overrides:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 591\u001b[0m \u001b[38;5;124;03m :return:\u001b[39;00m\n\u001b[1;32m 592\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 594\u001b[0m cfg \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconfig_loader\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload_configuration\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 595\u001b[0m \u001b[43m \u001b[49m\u001b[43mconfig_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 596\u001b[0m \u001b[43m \u001b[49m\u001b[43moverrides\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moverrides\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 597\u001b[0m \u001b[43m \u001b[49m\u001b[43mrun_mode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrun_mode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 598\u001b[0m \u001b[43m \u001b[49m\u001b[43mfrom_shell\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfrom_shell\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 599\u001b[0m \u001b[43m \u001b[49m\u001b[43mvalidate_sweep_overrides\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvalidate_sweep_overrides\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 600\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 601\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m with_log_configuration:\n\u001b[1;32m 602\u001b[0m configure_log(cfg\u001b[38;5;241m.\u001b[39mhydra\u001b[38;5;241m.\u001b[39mhydra_logging, cfg\u001b[38;5;241m.\u001b[39mhydra\u001b[38;5;241m.\u001b[39mverbose)\n", - "File \u001b[0;32m~/miniconda3/envs/tbx/lib/python3.11/site-packages/hydra/_internal/config_loader_impl.py:142\u001b[0m, in \u001b[0;36mConfigLoaderImpl.load_configuration\u001b[0;34m(self, config_name, overrides, run_mode, from_shell, validate_sweep_overrides)\u001b[0m\n\u001b[1;32m 133\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mload_configuration\u001b[39m(\n\u001b[1;32m 134\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 135\u001b[0m config_name: Optional[\u001b[38;5;28mstr\u001b[39m],\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 139\u001b[0m validate_sweep_overrides: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m 140\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m DictConfig:\n\u001b[1;32m 141\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 142\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_load_configuration_impl\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 143\u001b[0m \u001b[43m \u001b[49m\u001b[43mconfig_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 144\u001b[0m \u001b[43m \u001b[49m\u001b[43moverrides\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moverrides\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 145\u001b[0m \u001b[43m \u001b[49m\u001b[43mrun_mode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrun_mode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 146\u001b[0m \u001b[43m \u001b[49m\u001b[43mfrom_shell\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfrom_shell\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 147\u001b[0m \u001b[43m \u001b[49m\u001b[43mvalidate_sweep_overrides\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvalidate_sweep_overrides\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 148\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 149\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m OmegaConfBaseException \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 150\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m ConfigCompositionException()\u001b[38;5;241m.\u001b[39mwith_traceback(sys\u001b[38;5;241m.\u001b[39mexc_info()[\u001b[38;5;241m2\u001b[39m]) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n", - "File \u001b[0;32m~/miniconda3/envs/tbx/lib/python3.11/site-packages/hydra/_internal/config_loader_impl.py:253\u001b[0m, in \u001b[0;36mConfigLoaderImpl._load_configuration_impl\u001b[0;34m(self, config_name, overrides, run_mode, from_shell, validate_sweep_overrides)\u001b[0m\n\u001b[1;32m 248\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m validate_sweep_overrides:\n\u001b[1;32m 249\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvalidate_sweep_overrides_legal(\n\u001b[1;32m 250\u001b[0m overrides\u001b[38;5;241m=\u001b[39mparsed_overrides, run_mode\u001b[38;5;241m=\u001b[39mrun_mode, from_shell\u001b[38;5;241m=\u001b[39mfrom_shell\n\u001b[1;32m 251\u001b[0m )\n\u001b[0;32m--> 253\u001b[0m defaults_list \u001b[38;5;241m=\u001b[39m \u001b[43mcreate_defaults_list\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 254\u001b[0m \u001b[43m \u001b[49m\u001b[43mrepo\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcaching_repo\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 255\u001b[0m \u001b[43m \u001b[49m\u001b[43mconfig_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 256\u001b[0m \u001b[43m \u001b[49m\u001b[43moverrides_list\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparsed_overrides\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 257\u001b[0m \u001b[43m \u001b[49m\u001b[43mprepend_hydra\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 258\u001b[0m \u001b[43m \u001b[49m\u001b[43mskip_missing\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrun_mode\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m==\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mRunMode\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mMULTIRUN\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 259\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 261\u001b[0m config_overrides \u001b[38;5;241m=\u001b[39m defaults_list\u001b[38;5;241m.\u001b[39mconfig_overrides\n\u001b[1;32m 263\u001b[0m cfg \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compose_config_from_defaults_list(\n\u001b[1;32m 264\u001b[0m defaults\u001b[38;5;241m=\u001b[39mdefaults_list\u001b[38;5;241m.\u001b[39mdefaults, repo\u001b[38;5;241m=\u001b[39mcaching_repo\n\u001b[1;32m 265\u001b[0m )\n", - "File \u001b[0;32m~/miniconda3/envs/tbx/lib/python3.11/site-packages/hydra/_internal/defaults_list.py:745\u001b[0m, in \u001b[0;36mcreate_defaults_list\u001b[0;34m(repo, config_name, overrides_list, prepend_hydra, skip_missing)\u001b[0m\n\u001b[1;32m 736\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 737\u001b[0m \u001b[38;5;124;03m:param repo:\u001b[39;00m\n\u001b[1;32m 738\u001b[0m \u001b[38;5;124;03m:param config_name:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 742\u001b[0m \u001b[38;5;124;03m:return:\u001b[39;00m\n\u001b[1;32m 743\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 744\u001b[0m overrides \u001b[38;5;241m=\u001b[39m Overrides(repo\u001b[38;5;241m=\u001b[39mrepo, overrides_list\u001b[38;5;241m=\u001b[39moverrides_list)\n\u001b[0;32m--> 745\u001b[0m defaults, tree \u001b[38;5;241m=\u001b[39m \u001b[43m_create_defaults_list\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 746\u001b[0m \u001b[43m \u001b[49m\u001b[43mrepo\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 747\u001b[0m \u001b[43m \u001b[49m\u001b[43mconfig_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 748\u001b[0m \u001b[43m \u001b[49m\u001b[43moverrides\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 749\u001b[0m \u001b[43m \u001b[49m\u001b[43mprepend_hydra\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprepend_hydra\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 750\u001b[0m \u001b[43m \u001b[49m\u001b[43mskip_missing\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskip_missing\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 751\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 752\u001b[0m overrides\u001b[38;5;241m.\u001b[39mensure_overrides_used()\n\u001b[1;32m 753\u001b[0m overrides\u001b[38;5;241m.\u001b[39mensure_deletions_used()\n", - "File \u001b[0;32m~/miniconda3/envs/tbx/lib/python3.11/site-packages/hydra/_internal/defaults_list.py:715\u001b[0m, in \u001b[0;36m_create_defaults_list\u001b[0;34m(repo, config_name, overrides, prepend_hydra, skip_missing)\u001b[0m\n\u001b[1;32m 706\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_create_defaults_list\u001b[39m(\n\u001b[1;32m 707\u001b[0m repo: IConfigRepository,\n\u001b[1;32m 708\u001b[0m config_name: Optional[\u001b[38;5;28mstr\u001b[39m],\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 711\u001b[0m skip_missing: \u001b[38;5;28mbool\u001b[39m,\n\u001b[1;32m 712\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tuple[List[ResultDefault], DefaultsTreeNode]:\n\u001b[1;32m 713\u001b[0m root \u001b[38;5;241m=\u001b[39m _create_root(config_name\u001b[38;5;241m=\u001b[39mconfig_name, with_hydra\u001b[38;5;241m=\u001b[39mprepend_hydra)\n\u001b[0;32m--> 715\u001b[0m defaults_tree \u001b[38;5;241m=\u001b[39m \u001b[43m_create_defaults_tree\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 716\u001b[0m \u001b[43m \u001b[49m\u001b[43mrepo\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrepo\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 717\u001b[0m \u001b[43m \u001b[49m\u001b[43mroot\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mroot\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 718\u001b[0m \u001b[43m \u001b[49m\u001b[43moverrides\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moverrides\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 719\u001b[0m \u001b[43m \u001b[49m\u001b[43mis_root_config\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 720\u001b[0m \u001b[43m \u001b[49m\u001b[43minterpolated_subtree\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 721\u001b[0m \u001b[43m \u001b[49m\u001b[43mskip_missing\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskip_missing\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 722\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 724\u001b[0m output \u001b[38;5;241m=\u001b[39m _tree_to_list(tree\u001b[38;5;241m=\u001b[39mdefaults_tree)\n\u001b[1;32m 725\u001b[0m ensure_no_duplicates_in_list(output)\n", - "File \u001b[0;32m~/miniconda3/envs/tbx/lib/python3.11/site-packages/hydra/_internal/defaults_list.py:356\u001b[0m, in \u001b[0;36m_create_defaults_tree\u001b[0;34m(repo, root, is_root_config, skip_missing, interpolated_subtree, overrides)\u001b[0m\n\u001b[1;32m 348\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_create_defaults_tree\u001b[39m(\n\u001b[1;32m 349\u001b[0m repo: IConfigRepository,\n\u001b[1;32m 350\u001b[0m root: DefaultsTreeNode,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 354\u001b[0m overrides: Overrides,\n\u001b[1;32m 355\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m DefaultsTreeNode:\n\u001b[0;32m--> 356\u001b[0m ret \u001b[38;5;241m=\u001b[39m \u001b[43m_create_defaults_tree_impl\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 357\u001b[0m \u001b[43m \u001b[49m\u001b[43mrepo\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrepo\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 358\u001b[0m \u001b[43m \u001b[49m\u001b[43mroot\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mroot\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 359\u001b[0m \u001b[43m \u001b[49m\u001b[43mis_root_config\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mis_root_config\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 360\u001b[0m \u001b[43m \u001b[49m\u001b[43mskip_missing\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskip_missing\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 361\u001b[0m \u001b[43m \u001b[49m\u001b[43minterpolated_subtree\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minterpolated_subtree\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 362\u001b[0m \u001b[43m \u001b[49m\u001b[43moverrides\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moverrides\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 363\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 365\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ret\n", - "File \u001b[0;32m~/miniconda3/envs/tbx/lib/python3.11/site-packages/hydra/_internal/defaults_list.py:457\u001b[0m, in \u001b[0;36m_create_defaults_tree_impl\u001b[0;34m(repo, root, is_root_config, skip_missing, interpolated_subtree, overrides)\u001b[0m\n\u001b[1;32m 455\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m parent\u001b[38;5;241m.\u001b[39mis_virtual():\n\u001b[1;32m 456\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_root_config:\n\u001b[0;32m--> 457\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_expand_virtual_root\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrepo\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mroot\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moverrides\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskip_missing\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 458\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 459\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m root\n", - "File \u001b[0;32m~/miniconda3/envs/tbx/lib/python3.11/site-packages/hydra/_internal/defaults_list.py:280\u001b[0m, in \u001b[0;36m_expand_virtual_root\u001b[0;34m(repo, root, overrides, skip_missing)\u001b[0m\n\u001b[1;32m 277\u001b[0m new_root \u001b[38;5;241m=\u001b[39m DefaultsTreeNode(node\u001b[38;5;241m=\u001b[39md, parent\u001b[38;5;241m=\u001b[39mroot)\n\u001b[1;32m 278\u001b[0m d\u001b[38;5;241m.\u001b[39mupdate_parent(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 280\u001b[0m subtree \u001b[38;5;241m=\u001b[39m \u001b[43m_create_defaults_tree_impl\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 281\u001b[0m \u001b[43m \u001b[49m\u001b[43mrepo\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrepo\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 282\u001b[0m \u001b[43m \u001b[49m\u001b[43mroot\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnew_root\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 283\u001b[0m \u001b[43m \u001b[49m\u001b[43mis_root_config\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43md\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprimary\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 284\u001b[0m \u001b[43m \u001b[49m\u001b[43mskip_missing\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskip_missing\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 285\u001b[0m \u001b[43m \u001b[49m\u001b[43minterpolated_subtree\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 286\u001b[0m \u001b[43m \u001b[49m\u001b[43moverrides\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moverrides\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 287\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 288\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m subtree\u001b[38;5;241m.\u001b[39mchildren \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 289\u001b[0m children\u001b[38;5;241m.\u001b[39mappend(d)\n", - "File \u001b[0;32m~/miniconda3/envs/tbx/lib/python3.11/site-packages/hydra/_internal/defaults_list.py:580\u001b[0m, in \u001b[0;36m_create_defaults_tree_impl\u001b[0;34m(repo, root, is_root_config, skip_missing, interpolated_subtree, overrides)\u001b[0m\n\u001b[1;32m 578\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m idx, dd \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(children):\n\u001b[1;32m 579\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(dd, InputDefault) \u001b[38;5;129;01mand\u001b[39;00m dd\u001b[38;5;241m.\u001b[39mis_interpolation():\n\u001b[0;32m--> 580\u001b[0m \u001b[43mdd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresolve_interpolation\u001b[49m\u001b[43m(\u001b[49m\u001b[43mknown_choices\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 581\u001b[0m new_root \u001b[38;5;241m=\u001b[39m DefaultsTreeNode(node\u001b[38;5;241m=\u001b[39mdd, parent\u001b[38;5;241m=\u001b[39mroot)\n\u001b[1;32m 582\u001b[0m dd\u001b[38;5;241m.\u001b[39mupdate_parent(parent\u001b[38;5;241m.\u001b[39mget_group_path(), parent\u001b[38;5;241m.\u001b[39mget_final_package())\n", - "File \u001b[0;32m~/miniconda3/envs/tbx/lib/python3.11/site-packages/hydra/core/default_element.py:558\u001b[0m, in \u001b[0;36mGroupDefault.resolve_interpolation\u001b[0;34m(self, known_choices)\u001b[0m\n\u001b[1;32m 555\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 556\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m ConfigCompositionException(msg)\n\u001b[0;32m--> 558\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvalue \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_resolve_interpolation_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[43mknown_choices\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/miniconda3/envs/tbx/lib/python3.11/site-packages/hydra/core/default_element.py:252\u001b[0m, in \u001b[0;36mInputDefault._resolve_interpolation_impl\u001b[0;34m(self, known_choices, val)\u001b[0m\n\u001b[1;32m 250\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 251\u001b[0m msg \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mError resolving interpolation \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mval\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m--> 252\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m ConfigCompositionException(msg)\n", - "\u001b[0;31mConfigCompositionException\u001b[0m: Error resolving interpolation '${get_default_transform:${dataset},${model}}', possible interpolation keys: debug, hparams_search, experiment, hydra, extras, paths, trainer, logger, callbacks, evaluator, loss, optimizer, model, dataset" + "name": "stderr", + "output_type": "stream", + "text": [ + "Processing...\n", + "/home/lev/miniconda3/envs/tbx/lib/python3.11/site-packages/scipy/sparse/_index.py:143: SparseEfficiencyWarning: Changing the sparsity structure of a csr_matrix is expensive. lil_matrix is more efficient.\n", + " self._set_arrayXarray(i, j, x)\n", + "Done!\n" ] } ], "source": [ - "cfg = hydra.compose(config_name=\"run.yaml\", overrides=[\"dataset=graph/NCI1\"])" + "cfg = compose(config_name=\"run.yaml\", \n", + " overrides=[\"dataset=graph/cocitation_cora\", \"model=simplicial/scn\"], \n", + " return_hydra_config=True)\n", + "graph_loader = GraphLoader(cfg.dataset.loader.parameters)\n", + "dataset, dataset_dir = graph_loader.load()\n", + "preprocessed_dataset = PreProcessor(dataset, dataset_dir, cfg['transforms'])" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "data = preprocessed_dataset[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['y',\n", + " 'up_laplacian_3',\n", + " 'adjacency_1',\n", + " 'x',\n", + " 'up_laplacian_2',\n", + " 'down_laplacian_0',\n", + " 'hodge_laplacian_1',\n", + " 'x_3',\n", + " 'incidence_0',\n", + " 'up_laplacian_1',\n", + " 'x_2',\n", + " 'hodge_laplacian_0',\n", + " 'val_mask',\n", + " 'shape',\n", + " 'train_mask',\n", + " 'test_mask',\n", + " 'hodge_laplacian_2',\n", + " 'incidence_1',\n", + " 'down_laplacian_1',\n", + " 'incidence_3',\n", + " 'incidence_2',\n", + " 'edge_index',\n", + " 'hodge_laplacian_3',\n", + " 'x_1',\n", + " 'adjacency_0',\n", + " 'down_laplacian_2',\n", + " 'adjacency_2',\n", + " 'down_laplacian_3',\n", + " 'x_0',\n", + " 'up_laplacian_0',\n", + " 'adjacency_3']" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.keys()" ] }, { From c988b2d85d1e24059d57cf0ac3aebd31e12261d5 Mon Sep 17 00:00:00 2001 From: Coerulatus Date: Thu, 14 Nov 2024 16:18:01 +0000 Subject: [PATCH 04/24] Marco - added batching functions --- tutorials/batching.ipynb | 787 ++++++++++++++++++++++++++++++++++++--- 1 file changed, 745 insertions(+), 42 deletions(-) diff --git a/tutorials/batching.ipynb b/tutorials/batching.ipynb index 97259c69..607f630c 100644 --- a/tutorials/batching.ipynb +++ b/tutorials/batching.ipynb @@ -9,7 +9,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_3192954/2455096930.py:26: UserWarning: \n", + "/tmp/ipykernel_272091/2455096930.py:26: UserWarning: \n", "The version_base parameter is not specified.\n", "Please specify a compatability version level, or None.\n", "Will assume defaults for version 1.1\n", @@ -58,23 +58,20 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 2, "metadata": {}, "outputs": [ { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "Processing...\n", - "/home/lev/miniconda3/envs/tbx/lib/python3.11/site-packages/scipy/sparse/_index.py:143: SparseEfficiencyWarning: Changing the sparsity structure of a csr_matrix is expensive. lil_matrix is more efficient.\n", - " self._set_arrayXarray(i, j, x)\n", - "Done!\n" + "Transform parameters are the same, using existing data_dir: /TopoBenchmarkX/datasets/graph/cocitation/Cora/graph2hypergraph_lifting/1273654097\n" ] } ], "source": [ "cfg = compose(config_name=\"run.yaml\", \n", - " overrides=[\"dataset=graph/cocitation_cora\", \"model=simplicial/scn\"], \n", + " overrides=[\"dataset=graph/cocitation_cora\", \"model=hypergraph/allsettransformer\"], \n", " return_hydra_config=True)\n", "graph_loader = GraphLoader(cfg.dataset.loader.parameters)\n", "dataset, dataset_dir = graph_loader.load()\n", @@ -83,7 +80,24 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'graph2hypergraph_lifting': {'_target_': 'topobenchmarkx.transforms.data_transform.DataTransform', 'transform_type': 'lifting', 'transform_name': 'HypergraphKHopLifting', 'k_value': 1}}\n" + ] + } + ], + "source": [ + "print(cfg['transforms'])" + ] + }, + { + "cell_type": "code", + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -92,46 +106,25 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "['y',\n", - " 'up_laplacian_3',\n", - " 'adjacency_1',\n", - " 'x',\n", - " 'up_laplacian_2',\n", - " 'down_laplacian_0',\n", - " 'hodge_laplacian_1',\n", - " 'x_3',\n", - " 'incidence_0',\n", - " 'up_laplacian_1',\n", - " 'x_2',\n", - " 'hodge_laplacian_0',\n", - " 'val_mask',\n", - " 'shape',\n", - " 'train_mask',\n", - " 'test_mask',\n", - " 'hodge_laplacian_2',\n", - " 'incidence_1',\n", - " 'down_laplacian_1',\n", - " 'incidence_3',\n", - " 'incidence_2',\n", + "['val_mask',\n", " 'edge_index',\n", - " 'hodge_laplacian_3',\n", - " 'x_1',\n", - " 'adjacency_0',\n", - " 'down_laplacian_2',\n", - " 'adjacency_2',\n", - " 'down_laplacian_3',\n", + " 'x',\n", + " 'x_hyperedges',\n", + " 'y',\n", + " 'num_hyperedges',\n", " 'x_0',\n", - " 'up_laplacian_0',\n", - " 'adjacency_3']" + " 'train_mask',\n", + " 'incidence_hyperedges',\n", + " 'test_mask']" ] }, - "execution_count": 20, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -142,10 +135,720 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# shape is a list, it breaks everything if we keep it\n", + "if hasattr(data, \"shape\"):\n", + " del data[\"shape\"]\n", + " \n", + " \n", + "# replace adjacency keys with temp\n", + "n_incidences = len([key for key in data.keys() if \"incidence\" in key])\n", + "for i in range(n_incidences):\n", + " if f\"adjacency_{i}\" in data.keys():\n", + " data[f\"temp_{i}\"] = data[f\"adjacency_{i}\"]\n", + " del data[f\"adjacency_{i}\"]\n", + "\n", + "# For some reason we need to call the adjacency matrices something else because the __cat_dim__ function will return a tuple for attributes with the adjacency or adj keys. This behaviour breaks stuff in the GlobalStorage module.\n", + "# for key in data.keys():\n", + "# value = data[key]\n", + "# print(key)\n", + "# print(value.shape)\n", + "# print(data._parent().__cat_dim__(key, value, data))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# Training, validation and split idxs should be defined somewhere, here we use a toy example\n", + "rank = 1\n", + "if hasattr(data, \"x_hyperedges\") and rank==1:\n", + " n_cells = data.x_hyperedges.shape[0]\n", + "else:\n", + " n_cells = data[f'x_{rank}'].shape[0]\n", + "\n", + "train_prop = 0.5\n", + "n_train = int(train_prop * n_cells)\n", + "train_mask = torch.zeros(n_cells, dtype=torch.bool)\n", + "train_mask[:n_train] = 1\n", + "\n", + "if rank != 0:\n", + " y = torch.zeros(n_cells, dtype=torch.long)\n", + " data.y = y\n", + "batch_size = 2" + ] + }, + { + "cell_type": "code", + "execution_count": 21, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "from torch_geometric.loader import NeighborLoader\n", + "from torch_sparse import SparseTensor\n", + "import torch_sparse\n", + "\n", + "def change_sparse(tensor):\n", + " r\"\"\" Change from SparseTensor to torch_sparse_coo_tensor or viceversa.\n", + " \n", + " Parameters\n", + " ----------\n", + " tensor: torch.Tensor or SparseTensor\n", + " The input tensor.\n", + " \n", + " Returns\n", + " -------\n", + " torch.Tensor or SparseTensor\n", + " The output tensor.\n", + " \n", + " \"\"\"\n", + " if isinstance(tensor, SparseTensor):\n", + " return tensor.to_torch_sparse_coo_tensor().to(device=tensor.device())\n", + " elif tensor.is_sparse:\n", + " tensor = tensor.coalesce()\n", + " return SparseTensor(row=tensor.indices()[0], \n", + " col=tensor.indices()[1], \n", + " value=tensor.values(), \n", + " sparse_sizes=tensor.size()).to_device(tensor.device)\n", + " else:\n", + " raise NotImplementedError(f\"Type {type(tensor)} not supported\")\n", + " \n", + "def clique_expansion(data, rank=0, is_hypergraph=False):\n", + " ''' This function adds edges between cells that belong to the same higher-order cells.\n", + " \n", + " This function is needed so that the NeighborLoader can select all the nodes of the cells that contain the nodes of interest. In general nodes belonging to the same higher-order cell do not need to be directly connected. E.g. in a cell complex a face of 4 nodes does not have edges between opposite nodes.\n", + " \n", + " Parameters\n", + " ----------\n", + " data: torch_geometric.data.Data\n", + " The input data.\n", + " rank: int\n", + " The rank of the cells that you want to batch over.\n", + " is_hypergraph: bool\n", + " Whether the data represents an hypergraph.\n", + " \n", + " Returns\n", + " -------\n", + " torch_geometric.data.Data\n", + " The output data with the added edges.\n", + " '''\n", + " if is_hypergraph:\n", + " P = data.incidence_hyperedges\n", + " Q = torch.sparse.mm(P,P.T)\n", + " edges = Q.indices()\n", + " else:\n", + " # get number of incidences\n", + " max_rank = len([key for key in data.keys() if \"incidence\" in key])-1\n", + " if rank > max_rank:\n", + " raise ValueError(f\"Rank {rank} is greater than the maximum rank {max_rank} in the data.\")\n", + " if rank == max_rank:\n", + " edges = torch.empty((2, 0), dtype=torch.long)\n", + " else:\n", + " P = data[f\"incidence_{rank+1}\"]\n", + " Q = torch.sparse.mm(P,P.T)\n", + " edges = Q.indices()\n", + " \n", + " for i in range(rank+1, max_rank):\n", + " P = torch.sparse.mm(P, data[f\"incidence_{i+1}\"])\n", + " Q = torch.sparse.mm(P,P.T)\n", + " edges = torch.cat((edges, Q.indices()), dim=1)\n", + " \n", + " if rank == 0:\n", + " edges = torch.cat((edges, data.edge_index), dim=1)\n", + " else:\n", + " P = data[f\"incidence_{rank}\"]\n", + " for i in range(rank-1, 0, -1):\n", + " P = torch.sparse.mm(data[f\"incidence_{i}\"], P)\n", + " Q = torch.sparse.mm(P.T,P)\n", + " edges = torch.cat((edges, Q.indices()), dim=1)\n", + " \n", + " edges = torch.unique(edges, dim=1)\n", + " # Remove self edges\n", + " mask = edges[0, :] != edges[1, :]\n", + " edges = edges[:, mask]\n", + " \n", + " data.edge_index = edges\n", + " \n", + " # We need to set x to x_rank since NeighborLoader will take the number of nodes from the x attribute\n", + " if is_hypergraph and rank == 1:\n", + " data.x = data.x_hyperedges\n", + " else:\n", + " data.x = data[f'x_{rank}']\n", + " \n", + " return data\n", + "\n", + "def reduce_higher_ranks_incidences(batch, cells_ids, rank, max_rank, is_hypergraph=False):\n", + " \"\"\" Reduce the incidences with higher rank than the specified one.\n", + " \n", + " Parameters\n", + " ----------\n", + " batch: torch_geometric.data.Data\n", + " The input data.\n", + " cells_ids: list[torch.Tensor]\n", + " List of tensors containing the ids of the cells. The length of the list should be equal to the maximum rank.\n", + " rank: int\n", + " The rank to select the higher order incidences.\n", + " max_rank: int\n", + " The maximum rank of the incidences.\n", + " is_hypergraph: bool\n", + " Whether the data represents an hypergraph.\n", + " \n", + " Returns\n", + " -------\n", + " torch_geometric.data.Data\n", + " The output data with the reduced incidences.\n", + " \"\"\"\n", + " for i in range(1, max_rank+1):\n", + " if is_hypergraph:\n", + " incidence = change_sparse(batch.incidence_hyperedges)\n", + " else:\n", + " incidence = change_sparse(batch[f\"incidence_{i}\"])\n", + " if i != rank+1:\n", + " incidence = incidence[cells_ids[i-1], :]\n", + " cells_ids[i] = torch.where(torch_sparse.sum(incidence, dim=0).to_dense() > 1)[0]\n", + " incidence = incidence[:, cells_ids[i]]\n", + " batch[f\"incidence_{i}\"] = change_sparse(incidence)\n", + " if not is_hypergraph:\n", + " incidence = change_sparse(batch[f\"incidence_0\"])\n", + " incidence = incidence[:, cells_ids[0]]\n", + " batch[f\"incidence_0\"] = change_sparse(incidence)\n", + " \n", + " return batch, cells_ids\n", + "\n", + "def get_node_indices(batch, cells_ids, rank, is_hypergraph=False):\n", + " \"\"\" Get the indices of the nodes contained by the cells specified in cells_ids and rank.\n", + " \n", + " Parameters\n", + " ----------\n", + " batch: torch_geometric.data.Data\n", + " The input data.\n", + " cells_ids: list[torch.Tensor]\n", + " List of tensors containing the ids of the cells. The length of the list should be equal to the maximum rank.\n", + " rank: int\n", + " The rank of the cells to consider.\n", + " is_hypergraph: bool\n", + " Whether the data represents an hypergraph.\n", + " \n", + " Returns\n", + " -------\n", + " torch.Tensor\n", + " The indices of the nodes contained by the cells.\n", + " \"\"\"\n", + " cells_ids_new = [c_i for c_i in cells_ids]\n", + " for i in range(rank, 0, -1):\n", + " if is_hypergraph:\n", + " incidence = change_sparse(batch.incidence_hyperedges)\n", + " else:\n", + " incidence = change_sparse(batch[f\"incidence_{i}\"].clone())\n", + " incidence = incidence[:, cells_ids_new[i]]\n", + " cells_ids_new[i-1] = torch.where(torch_sparse.sum(incidence, dim=1).to_dense() > 0)[0]\n", + " return cells_ids_new[0]\n", + "\n", + "def reduce_matrices(batch, cells_ids, names, rank, max_rank):\n", + " \"\"\" Reduce the matrices using the indices in cells_ids. \n", + " \n", + " The matrices are assumed to be in the batch with the names specified in the list names.\n", + " \n", + " Parameters\n", + " ----------\n", + " batch: torch_geometric.data.Data\n", + " The input data.\n", + " cells_ids: list[torch.Tensor]\n", + " List of tensors containing the ids of the cells. The length of the list should be equal to the maximum rank.\n", + " names: list[str]\n", + " List of names of the matrices in the batch. They should appear in the format f\"{name}{i}\" where i is the rank of the matrix.\n", + " rank: int\n", + " The rank over which you are batching.\n", + " max_rank: int\n", + " The maximum rank of the matrices.\n", + " \n", + " Returns\n", + " -------\n", + " torch_geometric.data.Data\n", + " The output data with the reduced matrices.\n", + " \"\"\"\n", + " for i in range(max_rank+1):\n", + " for name in names:\n", + " if f\"{name}{i}\" in batch.keys():\n", + " matrix = change_sparse(batch[f\"{name}{i}\"])\n", + " if i==rank:\n", + " matrix = matrix[:, cells_ids[i]]\n", + " else:\n", + " matrix = matrix[cells_ids[i], cells_ids[i]]\n", + " batch[f\"{name}{i}\"] = change_sparse(matrix)\n", + " return batch\n", + "\n", + "def reduce_neighborhoods(batch, rank=0, remove_self_loops=True):\n", + " \"\"\" Reduce the neighborhoods of the cells in the batch.\n", + " \n", + " Parameters\n", + " ----------\n", + " batch: torch_geometric.data.Data\n", + " The input data.\n", + " rank: int\n", + " The rank of the cells to batch over.\n", + " remove_self_loops: bool\n", + " Whether to remove self loops from the edge_index.\n", + " \n", + " Returns\n", + " -------\n", + " torch_geometric.data.Data\n", + " The output data with the reduced neighborhoods.\n", + " \"\"\"\n", + " is_hypergraph = False\n", + " if hasattr(batch, 'incidence_hyperedges'):\n", + " is_hypergraph = True\n", + " max_rank = 1\n", + " else:\n", + " max_rank = len([key for key in batch.keys() if \"incidence\" in key])-1\n", + " \n", + " if rank > max_rank:\n", + " raise ValueError(f\"Rank {rank} is greater than the maximum rank {max_rank} in the dataset.\")\n", + " \n", + " cells_ids = [None for _ in range(max_rank+1)]\n", + " # the ids of the cells are saved in the batch\n", + " cells_ids[rank] = batch.n_id\n", + " \n", + " if rank != 0:\n", + " cells_ids[0] = get_node_indices(batch, cells_ids, rank, is_hypergraph)\n", + " else:\n", + " cells_ids[0] = batch.n_id\n", + " batch, cells_ids = reduce_higher_ranks_incidences(batch, cells_ids, rank, max_rank, is_hypergraph)\n", + "\n", + " batch = reduce_matrices(batch, \n", + " cells_ids, \n", + " names=['down_laplacian_', 'up_laplacian_', 'hodge_laplacian_', 'temp_'],\n", + " rank=rank,\n", + " max_rank=max_rank)\n", + " \n", + " # reduce the feature matrices\n", + " for i in range(max_rank+1):\n", + " if i != rank:\n", + " if f\"x_{i}\" in batch.keys():\n", + " batch[f\"x_{i}\"] = batch[f\"x_{i}\"][cells_ids[i]]\n", + " \n", + " # change the temp matrices back to adjacency\n", + " for i in range(max_rank+1):\n", + " if f\"temp_{i}\" in batch.keys():\n", + " batch[f\"adjacency_{i}\"] = batch[f\"temp_{i}\"]\n", + " del batch[f\"temp_{i}\"]\n", + " \n", + " # fix edge_index\n", + " if hasattr(batch, 'adjacency_0'):\n", + " adjacency_0 = batch.adjacency_0.coalesce()\n", + " edge_index = adjacency_0.indices()\n", + " if remove_self_loops:\n", + " edge_index = torch_geometric.utils.remove_self_loops(edge_index)[0]\n", + " batch.edge_index = edge_index\n", + " \n", + " # fix x\n", + " batch.x = batch[f\"x_0\"]\n", + " \n", + " return batch\n", + "\n", + "class ReduceNeighborhoods():\n", + " \"\"\" Reduce the neighborhoods of the cells in the batch.\n", + " \n", + " Parameters\n", + " ----------\n", + " rank: int\n", + " The rank of the cells to batch over.\n", + " remove_self_loops: bool\n", + " Whether to remove self loops from the edge_index.\n", + " \"\"\"\n", + " \n", + " def __init__(self, rank=0, remove_self_loops=True):\n", + " self.rank = rank\n", + " self.remove_self_loops = remove_self_loops\n", + " \n", + " def __call__(self, batch):\n", + " \"\"\" Call reduce_neighborhoods.\n", + " \n", + " Parameters\n", + " ----------\n", + " batch: torch_geometric.data.Data\n", + " The input data.\n", + " \n", + " Returns\n", + " -------\n", + " torch_geometric.data.Data\n", + " The output data with the reduced neighborhoods.\n", + " \"\"\"\n", + " return reduce_neighborhoods(batch, self.rank, self.remove_self_loops)\n", + "\n", + "class NeighborLoaderWrapper(NeighborLoader):\n", + " \"\"\" NeighborLoader with clique expansion.\n", + " \n", + " Parameters\n", + " ----------\n", + " dataset: torch_geometric.data.Dataset\n", + " The input dataset.\n", + " rank: int\n", + " The rank of the cells to batch over.\n", + " **kwargs: dict\n", + " Additional arguments for the NeighborLoader.\n", + " \"\"\"\n", + " def __init__(self, dataset, rank=0, **kwargs):\n", + " is_hypergraph = hasattr(dataset, 'incidence_hyperedges')\n", + " dataset = clique_expansion(dataset, rank, is_hypergraph)\n", + " if 'num_neighbors' in kwargs.keys():\n", + " if len(kwargs['num_neighbors']) > 1:\n", + " raise NotImplementedError(\"NeighborLoaderWrapper only supports one-hop neighborhood selection.\")\n", + " super(NeighborLoaderWrapper, self).__init__(dataset, **kwargs)\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.11/site-packages/torch_geometric/sampler/neighbor_sampler.py:61: UserWarning: Using 'NeighborSampler' without a 'pyg-lib' installation is deprecated and will be removed soon. Please install 'pyg-lib' for accelerated neighborhood sampling\n", + " warnings.warn(f\"Using '{self.__class__.__name__}' without a \"\n" + ] + } + ], + "source": [ + "# num_neighbors controls also the number of hops (for 2 hops do num_neighbors=[-1, -1])\n", + "reduce = ReduceNeighborhoods(rank=rank, remove_self_loops=True)\n", + "\n", + "loader = NeighborLoaderWrapper(data,\n", + " rank=rank,\n", + " num_neighbors=[-1],\n", + " input_nodes=train_mask,\n", + " batch_size=batch_size,\n", + " shuffle=False,\n", + " transform=reduce)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data(x=[17, 1433], edge_index=[2, 15], y=[17], train_mask=[17], val_mask=[17], test_mask=[17], incidence_hyperedges=[17, 2708], num_hyperedges=2708, x_0=[17, 1433], x_hyperedges=[17, 1433], n_id=[17], e_id=[15], input_id=[2], batch_size=2, incidence_1=[17, 29])\n", + "tensor([ 0, 1, 926, 1862, 2582, 1166, 633, 1701, 1866, 332, 1986, 470,\n", + " 1666, 652, 654, 2, 1454])\n", + "tensor([[ 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16],\n", + " [ 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1]])\n", + "tensor([[1., 0., 0., ..., 0., 0., 0.],\n", + " [0., 1., 1., ..., 0., 0., 0.],\n", + " [0., 0., 0., ..., 0., 0., 0.],\n", + " ...,\n", + " [0., 1., 0., ..., 0., 0., 0.],\n", + " [0., 1., 1., ..., 0., 0., 0.],\n", + " [0., 0., 1., ..., 0., 0., 0.]])\n" + ] + } + ], + "source": [ + "for batch in loader:\n", + " print(batch)\n", + " print(batch.n_id)\n", + " print(batch.edge_index)\n", + " if hasattr(batch, 'incidence_hyperedges'):\n", + " print(batch.incidence_hyperedges.to_dense())\n", + " else:\n", + " print(batch.incidence_3.to_dense())\n", + " print(batch.incidence_2.to_dense())\n", + " print(batch.incidence_1.to_dense())\n", + " break" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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bVldXH3EpWF98cSDLstjZlI/dLa0xqKIshlWVi3mgKMQMUPDGG28UDul85plnoqysLD7xiU9EXV1dzJw5M0477bRiD7FX2LhxY0ydOjVOPfXUWLRoUQwaNKjYQ6KLsiyLLVu2HHZ2ZfPmzdHe3h4REaWlpTFmzJjDLgUbOXKkJ/IARSJmgEN666234qGHHor6+vpYuHBhtLe3x9SpUwtbPvfXM1R27NgRV1xxRTQ1NcXSpUvj1FNPLfaQOIxdu3bFunXrDhkr69ati+bm5sJtR44cedjZldGjR5t5A+ilxAxwVNu3b48FCxZEfX19/PznP499+/bFxRdfXNgZ7fzzz+8Xr0zv3bs3PvnJT8bLL78cS5YsibPPPrvYQ+rXWltbY9OmTYddCrZt27bCbQcOHHjYWBk/fnwMHjy4iI8EgGMlZoAu2b17dzzyyCNRX18fDz/8cOzatSvOOuuswozNxz72sW4526K3aW9vj9/93d+NefPmxRNPPBG1tbXFHlKfl2VZbNu27bBLwTZu3Bhtbft31srlcjF69OjDLgU75ZRT+kVwA/Q3YgY4Zvv27Ysnnngi6uvr46GHHopt27bFRz7ykcKMzZVXXhllZWXFHma3+MIXvhB///d/H/fff3/MmjWr2MPpM5qamg67FGzt2rWxZ8+ewm1ramoOO7syduxY24sD9ENi5hhkWRY7mvKxp6U1qivKosYuLhCtra2xePHiws5omzdvjhEjRsSMGTOirq4urrvuuhN2wF93+853vhN/9md/FnfffXd8/vOfL/ZwktLW1hZvvfXWYZeCbdmypXDbioqKwrKvQy0FGzZsWPEeCAC9kpjpgsbmfNy/cnP86BD7699eOy5mTxoVQytdJApZlsWKFSuivr4+7r///lizZk0MGjQoPvWpT0VdXV186lOfSuYahZ/+9Kdx6623xhe/+MX41re+Vezh9DpZlsWOHTsOuxRsw4YNkc/nC7c//fTTY8KECYecYTnttNP65BJFAHqOmOmkzp58/N3bJsfVTj6GgizLYvXq1YUtn1euXBkDBgyI66+/PmbNmhUzZsyIkSNHFnuYh/TUU0/F9ddfH3V1dfGTn/yk3z7R3rt3b6xfv/6wS8Hef//9wm2HDBlyxDNXUp2dA6B3EjOdsGjN1rhj7rLIIuJIf1q53P6w+eGcKYIGDmP9+vXxwAMPxAMPPBBPP/105HK5uPrqq2PWrFkxa9asGDVqVLGHGBERq1evjssvvzwuvvjieOSRR2LAgAHFHlKPaW9vj3feeadDoHzw12+//XbhtmVlZTFu3LjDXmhfU1Nj2S0AJ4yYOYrG5nxM/eYT0ZxvO2LIHJDLRVSWl8bSL11ryRkcxZYtWwpn2fzyl7+MfD4fU6ZMKeyMNnHixKKM65133ompU6fG4MGD46mnnuoT12o0NjYedinY+vXrY9++fYXbnnLKKYddCvaRj3wkSktLi/hIAOA3xMxR/GDxuvjaglejK39IuYj4yi3nxR2Xj++pYUGfs3Pnznj44Yejvr4+HnnkkWhubo7zzz8/6urqoq6uLi666KJue8X/SJt47Nq1K6666qrYunVrLF26NEaPHt0tX7OntbS0xIYNGw67FGzHjh2F21ZXVx9xKVh1dXURHwkAdJ6YOYIsy2LatxfGxoamLsfMmOFVsfCL0yy3gGPQ1NQUv/jFL6K+vj7mz58fO3fujPHjxxe2fJ46deoxXb9ytE08Zl54SvyH/9eseOaZZ+Lpp5+OCy+8sDsf1nHJsiy2bNly2KVgb731VrS3t0dERGlpaYwZM+awS8FGjhzp3yYA+gQxcwQNe1pi0tcfO+b7/17VSzG4oiQqKioKb+Xl5cf9+9LSUk9E6DdaWlpi4cKFhetstmzZEqeeemrMnDkz6urqYtq0aZ06X6Qzm3jk2ltja/3fxkP/9M245ppruv/BHMWuXbsOe+bKunXrorm5uXDbkSNHHnYp2KhRo6K83DJXAPo+MXMEm3Y0xZXf+tUx33/ffX8R+xreiZaWlsjn89HS0tIt48rlcoeMne4Ipe76/Yd/3V93gaJ7tbW1xTPPPFPYGW3dunUxbNiwmD59etTV1cUNN9wQVVVVB92vs5t4ZO3tUVKSi7l3XNYjm3jk8/nYtGnTYZeCbdu2rXDbysrKI565ksrW1gDQk8TMERzvzMzzX74+aqp/84pxlmXR2traIW4+/Ovu/v3xfq62trbu+KOM0tLSXhlah/t9WVmZ2a9eLsuyePHFF6O+vj4eeOCBePnll6OysjJuuummqKuri5tvvjmGDRt2QjfxyLIstm3bdtilYJs2bSr8ncrlcjF69OjDLgU75ZRTfA8CwFGImSNwzcz+LVsPF0e9Kbo++Pvu+pbuLWHV2d/399mvNWvWFGZsli1bFuXl5XHNNdfEqGt/P55oGNZtm3g0NTUddinY2rVrY8+ePYXb1tTUHHYp2JgxYzq1PA4AODwxcxR2M0tLlmXR1taWzMxXPp/vcDr68SgtLe01YdWZ3/fk7NfmzZvjwQcfjPvr62PNxP8QZcNOiVyu87GXi4iTKnPxHz/ybqxf3zFWtmzZUrhdRUXFEZeC9YUtnQGgNxMzR9HVJSoluYiBzpmhC7IsK0ROKhF2YNes43Wk66y6I5zaygbGP20945jHt+k7vxunjRhy2KVgp512Wr+fEQOAYhIzndDZi4dzuf2v6M6dMyWu6oGLh6G3ONLsV2+KsLaBw6Lm9ruP+XFOeXd+jB05OEaOHNnhbcSIETFy5MioqalxgCQAFJGY6aTObOtaWVEa99w2WchAETU1NcWKFSvi2WefjcXLX4znx//OMX+uU57+n9Hw7qbYvn17h0MnD8jlcjF8+PAOgXOo6Png27Bhw8zmAEA3ETNd0Nicj/qVm2PuIQ7cm1M7LmZPHhVDBlpaBidKe3t7/PrXv45nn3228LZq1apoa2uLqqqqmDR5cjTU/knsyVXGb152OLpDbeLR2toaDQ0NsW3btti2bVts37698OvDva+xsfGgz11SUlIIoCNFzwffN3ToUAEEAIcgZo5BlmWxsykfu1taY1BFWQyrKk9+1zJIwbvvvtshXJ577rnYtWtX5HK5OO+88+Kyyy4rvJ1//vlRVlZW1E088vl8hwDqTAi9//77B32e0tLSGDFiRKdnf0aMGBFDhw717xIAfZ6YAXqlDy4XW7ZsWTz77LOxcePGiIg47bTT4rLLLospU6bEZZddFpdeemkMGTLkkJ+nq5t4ZO3tMbC8JJb91xuKsolHS0tLh9jpzAzQ7t27D/o8ZWVlHUKnMyE0ePBgAQRAUsQMUHRHWy42efLkDrMuo0aN6tKT7k5v4hERWdYe2+//m/jOl/44/vAP//C4H9uJsG/fvk5Fzwff98HzcA4oLy8/ZPQcKYYGDRokgAAoGjEDnHDHslzseHV2E4//729fFPfd/Tdxzz33xJ/8yZ/E3/3d33XL1+9tmpubC2HT2RBqbm4+6PNUVFR06fqfkSNHRlVVlQACoFuIGaBHfXC52IElY8eyXKw7dGUTj+9+97vxn/7Tf4qrrroq/u3f/i1GjBjRY+NKRVNT00GRc6QQ2rp1a+zbt++gzzNw4MAuXf9zIIAA4MPEDNBtenq5WHfp7CYeixYtitmzZ8fQoUPjoYceigsuuOCEjzVlWZZFU1NTl2Z/tm3bFi0tLQd9rsrKyi7N/owYMSIqKyuL8KgBOJHEDHDMirFc7ERbt25dzJw5M9atWxc/+clPYubMmcUeUp+WZVns2bOnS9f/bNu2LfL5/EGfq6qqqkuzPyNHjowBAwYU4VEDcKzEDNApvWm52Im2e/fuuP3226O+vj6+/vWvx1/91V+55qMXybIsdu3a1aUZoO3bt0dra+tBn2vQoEGHjZ7DzQBVVFQU4VGnLcuy2NGUjz0trVFdURY1jjgAjpGYAQ7S3t4eq1evLmyJ3FuXi51I7e3t8bWvfS3uuuuuuPXWW+MHP/hBVFdXF3tYHKMsy+L999/v0uzP9u3bo62t7aDPNXjw4C7NAI0YMSLKy/vnAcuNzfm4f+Xm+NEhrlu7vXZczJ40qihbogPpEjNAv1gu1l3q6+vjM5/5TJx11lnx0EMPxZgxY4o9JE6Q9vb2aGxs7NLsz/bt26O9vf2gzzV06NBOX/8zcuTIGD58ePJ/7zq7o+B3b5scV0886YSPD0iTmIF+pj8vF+suL730UsycOTP27NkT9fX1ccUVVxR7SPRS7e3tsXPnzi7NADU0NMShfjQPGzasS9f/DB8+PEpLS4vwqA/W6bOecvvD5odzpggaoFPEDPRhB5aLHYgWy8W6z7Zt2+K3fuu3YsmSJfEP//AP8f/8P/9PsYdEH9HW1hY7duzo0gxQQ0PDQZ8nl8tFTU1Nl7bBrqmp6fYAamzOx9RvPhHN+bYjhsxvxh1RWV4aS790rSVnwFGJGehDLBc7sfL5fPzpn/5p/OM//mN8/vOfj7/7u7/rt9dCUFytra2xY8eOTs/+bNu2LXbu3HnQ58nlcjF8+PAjRs+H3z9s2LAoKSk57Nh+sHhdfG3Bq9GVJxu5iPjKLefFHZeP7/KfBdC/iBlIlOVivcc//dM/xec///m48sor47777nPAJklobW2NhoaGLm2D3djYeNDnKSkpiREjRhzmWp8R8X92TYwd+ZL4zZUxR5eLiDHDq2LhF6eZLQaOSMxAAiwX6/2efPLJmD17dgwePDgeeuihuPDCC4s9JOh2LS0thQDqzOzP9t37ouYPv3fMX+/5L18fNdW2vgYOT8xAL2S5WJrWr18fM2fOjDfffDN+8pOfxKc//eliDwmKatOOprjyW7865vs/9RefiNE1Vd04IqCv8QwIiuzDy8WeffbZ2LRpU0REnHrqqXHZZZfFX/7lX1ouloBx48bFkiVL4vbbb49Zs2bF3/zN38SXv/xls2T0W9UVx/c0Y9Bx3h/o+/wrASdQZ5aL/fZv/7blYgmrrq6Of/u3f4u//du/ja985Svx0ksvxdy5cx2wSb9UU1UeY4dXxcaGpi5vADBmeFUMq7KhBnBklplBD7JcrH974IEH4vd///fjzDPPjIceeijGjh1b7CHBCWc3M6AniRnoJp1ZLnbgzXKx/mPVqlUxc+bM2L17d9x///1x5ZVXFntIcEJ19ZyZklzEQOfMAJ0kZuAYfHC52IElY3YX43C2bdsWt956azz11FPxD//wD/FHf/RHxR4SnFCL1myNO+YuiyziiEGTy+2flZk7Z0pcNfGkEzU8IGFiBjrBcjGOVz6fjz/7sz+Lf/iHf4jPfvaz8Z3vfMcBm/Qri9ZsjTvvXRHNLW0RER2WnR14qaeyojTuuW2ykAE6TczAh1guRk/63ve+F5/73OfiiiuuiPvuuy9GjhxZ7CHBCdPYnI/6lZtj7pL1saGhqfD+scOrYk7tuJg9eVQMGSjygc4TM/RrlotRDE899VTMnj07qqurY968eQ7YpN/Jsix2NuVjd0trDKooi2FV5f5tBY6JmKFfeeeddwpbIh9pudiUKVPiggsusFyMHrNhw4b49Kc/Ha+//nr8+Mc/jlmzZhV7SACQHDFDj8qyLHY05WNPS2tUV5RFzQl89c1yMXq7PXv2xB133BH33Xdf3HXXXfHXf/3XUVJSUuxhAUAyxAw9orE5H/ev3Bw/OsS66Ntrx8XsSaO6dctNy8VIVZZl8bd/+7fx13/91zF79uyYO3duDBo0qNjDAoAkiBm6XWd3rPnubZPj6mPcseadd94pRIvlYvQFDz30UPze7/1eTJgwIR566KEYN25csYcEAL2emKFbdfUsgR/OmXLUoLFcjP7i5ZdfjpkzZ8b7778fP/3pT+Pqq68u9pAAoFcTM3Sbrp7ynMtFVH7olOcPLxd79tln4+WXX7ZcjH5j+/btceutt8aTTz4Zd999d/zxH/9xsYcEAL2WmKHb/GDxuvjaglejK99QuYj49Ni2qNz4jOVi8O/y+Xx84QtfiLvvvjvuvPPO+F//6385YBMADkHM0C2yLItp314YGxuauhQzWXt7tDa+G+0PfcVyMfiQ73//+/HZz342amtr47777ouTTnIqOgB8kJihWzTsaYlJX3/smO+/8svXxfDqAd04Iugbnn766Zg9e3ZUVlbGvHnz4qMf/WixhwQAvYYDDegWe1paj/P+bd00Euhbrrjiinjuuedi+PDhUVtbG/X19cUeEgD0GmKGblFdcXzXsgw6zvtDXzZmzJh4+umn4+abb47Zs2fHXXfdFe3t7cUeFgAUnWeQdIuaqvIYO7yqy9fM5CJizPCqGFbl4mY4kqqqqviXf/mXuOiii+K//tf/Gi+99FL87//9vx2wCUC/ZmaGbpHL5eL22nHHdN85teNsrwydkMvl4q/+6q/ioYceisceeyxqa2tj3bp1B90uy7Jo2NMSm3Y0RcOelnBpJAB9lQ0A6DZdPWemJBcx8EPnzACd88orr8TMmTNj586d8dOf/jSmTZsWjc35uH/l5vjRkvWxoaGpcNuxw6vi9tpxMXvSKH/XAOhTxAzdatGarXHH3GWRRRwxaHK5/UvM5s6ZEldNtN0sHIuGhoa49dZbY9GiRfGn/+N78XDjadH875tpfPCv34F5z8qK0vjubZPjan/nAOgjxAzdbtGarXHnvSuO+qTqntsmCxk4Tq2trfH7/+W/x5KKiyOXK9n/SsFhHHgR4YdzpggaAPoEMUOPaGzOR/3KzTH3EMtd5tSOi9mTR8WQgZa7wPE6sLyzqaU1fvNyweHlchGVlncC0EeIGXpUlmWxsykfu1taY1BFWQyrKnexP3SjHyxeF19b8GqXdxH8yi3nxR2Xj++pYQHACWE3M3pULpeLmuqKGF1TFTXVFUIGulGWZfGjJeuP6b5zl6y3yxkAyRMzAIna0ZSPDV082yli/3VsGxqaYmdTvieGBQAnjJgBSNSeltbjuv/u47w/ABSbmAFIVHVF2XHdf9Bx3h8Aik3MACSqpqo8xg6v6sQeZh3lYv/OgsOq7GYGQNrEDECicrlc3F477pjuO6d2nA05AEiemAFI2OxJo6KyovRIZ2V2UJLbf2ht3aRRPTswADgBxAxAwoZWlsd3b5scuYijBs2Bj99z22QHZgLQJ4gZgMRdPfGk+OGcKVFZXro/aj708QPvqywvjblzpsRVE0868YMEgB6Qy5yaBtAnNDbno37l5pi7ZH1saGgqvH/s8KqYUzsuZk8eFUMGmpEBoO8QMwB9TJZlsbMpH7tbWmNQRVkMqyp3sT8AfZKYAQAAkuSaGQAAIEliBgAASJKYAQAAkiRmAACAJIkZAAAgSWIGAABIkpgBAACSJGYAAIAkiRkAACBJYgYAAEiSmAEAAJIkZgAAgCSJGQAAIEliBgAASJKYAQAAkiRmAACAJIkZAAAgSWIGAABIkpgBAACSJGYAAIAkiRkAACBJYgYAAEiSmAEAAJIkZgAAgCSJGQAAIEliBgAASJKYAQAAkiRmAACAJIkZAAAgSWIGAABIkpgBAACSJGYAAIAkiRkAACBJYgYAAEiSmAEAAJIkZgAAgCSJGQAAIEliBgAASJKYAQAAkiRmAACAJIkZAAAgSWIGAABIkpgBAACSJGYAAIAkiRkAACBJYgYAAEiSmAEAAJIkZgAAgCSJGQAAIEliBgAASJKYAQAAkiRmAACAJIkZAAAgSWIGAABIkpgBAACSJGYAAIAkiRkAACBJYgYAAEiSmAEAAJIkZgAAgCSJGQAAIEliBgAASJKYAQAAkiRmAACAJIkZAAAgSWIGAABIkpgBAACSJGYAAIAkiRkAACBJYgYAAEiSmAEAAJIkZgAAgCSJGQAAIEliBgAASJKYAQAAkiRmAACAJIkZAAAgSWIGAABIkpgBAACSJGYAAIAkiRkAACBJYgYAAEiSmAEAAJIkZgAAgCSJGQAAIEliBgAASJKYAQAAkiRmAACAJIkZAAAgSWIGAABIkpgBAACSJGYAAIAkiRkAACBJYgYAAEiSmAEAAJIkZgAAgCSJGQAAIEliBgAASJKYAQAAkiRmAACAJIkZAAAgSWIGAABIkpgBAACSJGYAAIAkiRkAACBJYgYAAEiSmAEAAJIkZgAAgCSJGQAAIEliBgAASJKYAQAAkiRmAACAJIkZAAAgSWIGAABIkpgBAACSJGYAAIAkiRkAACBJYgYAAEiSmAEAAJIkZgAAgCSJGQAAIEliBgAASJKYAQAAkiRmAACAJIkZAAAgSWIGAABIkpgBAACSJGYAAIAkiRkAACBJYgYAAEiSmAEAAJIkZgAAgCSJGQAAIEliBgAASJKYAQAAkiRmAACAJIkZAAAgSWIGAABIkpgBAACSJGYAAIAkiRkAACBJYgYAAEiSmAEAAJIkZgAAgCSJGQAAIEliBgAASJKYAQAAkiRmAACAJIkZAAAgSWIGAABIkpgBAACSJGYAAIAkiRkAACBJYgYAAEiSmAEAAJIkZgAAgCSJGQAAIEliBgAASJKYAQAAkiRmAACAJIkZAAAgSWIGAABIkpgBAACSJGYAAIAkiRkAACBJYgYAAEiSmAEAAJIkZgAAgCSJGQAAIEliBgAASJKYAQAAkiRmAACAJIkZAAAgSWIGAABIkpgBAACSJGYAAIAkiRkAACBJYgYAAEiSmAEAAJIkZgAAgCSJGQAAIEliBgAASJKYAQAAkiRmAACAJIkZAAAgSWIGAABIkpgBAACSJGYAAIAkiRkAACBJYgYAAEiSmAEAAJIkZgAAgCSJGQAAIEliBgAASJKYAQAAkiRmAACAJIkZAAAgSWIGAABIkpgBAACSJGYAAIAkiRkAACBJYgYAAEiSmAEAAJIkZgAAgCSJGQAAIEliBgAASJKYAQAAkiRmAACAJIkZAAAgSWIGAABIkpgBAACSJGYAAIAkiRkAACBJYgYAAEiSmAEAAJIkZgAAgCSJGQAAIEliBgAASJKYAQAAkiRmAACAJIkZAAAgSWIGAABIkpgBAACSJGYAAIAkiRkAACBJYgYAAEiSmAEAAJIkZgAAgCSJGQAAIEliBgAASJKYAQAAkiRmAACAJIkZAAAgSWIGAABIkpgBAACSJGYAAIAkiRkAACBJYgYAAEiSmAEAAJIkZgAAgCSJGQAAIEliBgAASJKYAQAAkiRmAACAJIkZAAAgSWIGAABIkpgBAACSJGYAAIAkiRkAACBJYgYAAEiSmAEAAJIkZgAAgCSJGQAAIEliBgAASJKYAQAAkiRmAACAJIkZAAAgSWIGAABIkpgBAACSJGYAAIAkiRkAACBJYgYAAEiSmAEAAJIkZgAAgCSJGQAAIEliBgAASJKYAQAAkiRmAACAJIkZAAAgSWIGAABIkpgBAACSJGYAAIAkiRkAACBJYgYAAEiSmAEAAJIkZgAAgCSJGQAAIEliBgAASJKYAQAAkiRmAACAJIkZAAAgSWIGAABIkpgBAACSJGYAAIAkiRkAACBJYgYAAEiSmAEAAJIkZgAAgCSJGQAAIEliBgAASJKYAQAAkiRmAACAJIkZAAAgSWIGAABIkpgBAACSJGYAAIAkiRkAACBJYgYAAEiSmAEAAJIkZgAAgCSJGQAAIEliBgAASJKYAQAAkiRmAACAJIkZAAAgSWIGAABIkpgBAACSJGYAAIAkiRkAACBJYgYAAEiSmAEAAJIkZgAAgCSJGQAAIEliBgAASJKYAQAAkiRmAACAJIkZAAAgSWIGAABIkpgBAACSJGYAAIAkiRkAACBJYgYAAEiSmAEAAJIkZgAAgCSJGQAAIEliBgAASJKYAQAAkiRmAACAJIkZAAAgSWIGAABIkpgBAACSJGYAAIAkiRkAACBJYgYAAEiSmAEAAJIkZgAAgCSJGQAAIEliBgAASJKYAQAAkiRmAACAJIkZAAAgSWIGAABIkpgBAACSJGYAAIAkiRkAACBJYgYAAEiSmAEAAJIkZgAAgCSJGQAAIEliBgAASJKYAQAAkiRmAACAJIkZAAAgSWIGAABIkpgBAACSJGYAAIAkiRkAACBJYgYAAEiSmAEAAJIkZgAAgCSJGQAAIEliBgAASJKYAQAAkiRmAACAJIkZAAAgSWIGAABIkpgBAACSJGYAAIAkiRkAACBJYgYAAEiSmAEAAJIkZgAAgCSJGQAAIEliBgAASJKYAQAAkiRmAACAJIkZAAAgSWIGAABIkpgBAACSJGYAAIAkiRkAACBJYgYAAEiSmAEAAJIkZgAAgCSJGQAAIEliBgAASJKYAQAAkiRmAACAJIkZAAAgSWIGAABIkpgBAACSJGYAAIAkiRkAACBJYgYAAEiSmAEAAJIkZgAAgCSJGQAAIEliBgAASJKYAQAAkiRmAACAJIkZAAAgSWIGAABIkpgBAACSJGYAAIAkiRkAACBJYgYAAEiSmAEAAJIkZgAAgCSJGQAAIEliBgAASJKYAQAAkiRmAACAJIkZAAAgSWIGAABIkpgBAACSJGYAAIAkiRkAACBJYgYAAEiSmAEAAJIkZgAAgCSJGQAAIEliBgAASJKYAQAAkiRmAACAJIkZAAAgSWIGAABIkpgBAACSJGYAAIAkiRkAACBJYgYAAEiSmAEAAJIkZgAAgCSJGQAAIEliBgAASFJZsQcAAKnJsix2NOVjT0trVFeURU1VeeRyuWIPC6DfETMA0EmNzfm4f+Xm+NGS9bGhoanw/rHDq+L22nExe9KoGFpZXsQRAvQvuSzLsmIPAgB6u0Vrtsad966I5pa2iIj44A/PA3MylRWl8d3bJsfVE0864eMD6I/EDAAcxaI1W+OOucsii4gj/dTM5faHzQ/nTBE0ACeADQAA4Agam/Nx570rjhoy8e8fzyLizntXRGNz/kQMD6BfEzMAcAT3r9wczS1tRw2ZA7IsormlLepXbu7ZgQEgZgDgcLIsix8tWX9M9527ZH1YyQ3Qs8QMABzGjqZ8bGhoiq4mSRYRGxqaYmeTpWYAPUnMAMBh7GlpPa777z7O+wNwZGIGAA6juuL4jmMbdJz3B+DIxAwAHEZNVXmMHV5VOEems3Kx/yDNYVUO0AToSWIGAA4jl8vF7bXjjum+c2rHRS7X1QyCdGRZFg17WmLTjqZo2NNiwwuKwvw3ABzB7Emj4tu/eC2a853bnrkkFzGwvDTqJo3q+cFBETQ25+P+lZvjR0vWx4aGpsL7xw6vittrx8XsSaNiaKVZSU6MXCajAeCIFq3ZGnfMXXbUgzNzuf1LzObOmRJXTTzpRA0PTphFa7bGnfeuiOaWtoiIDjv9HZiHrKwoje/eNjmu9neAE0DMAEAnHOlJXGTtEZGLqgFlcc9tk4UMfVJXo/6Hc6YIGnqca2YAoBOunnhSLP3StfGVW86LMcOrOnxs+ICIhse/F3dfO1jI0Cc1NufjzntXHDVk4t8/nkXEnfeuiMZmZy3Rs8zMAEAXZVkWO5vysbulNQZVlMWQgaVx0UUXxemnnx6/+MUvij086HY/WLwuvrbg1S4dIJuLiK/ccl7ccfn4nhoWmJkBgK7K5XJRU10Ro2uqoqa6IkpLS+OrX/1qPPbYY7F48eJiDw+6VZZl8aMl64/pvnOXrLfLGT1KzABAN5g9e3ZceOGFcddddxV7KNCtdjTlY0NDU5dmZSL2LzXb0NAUO5ssNaPniBkA6AYlJSXx1a9+NR5//PF4+umniz0c6DZ7WlqP6/67j/P+cCSumQFOqCzLYkdTPva0tEZ1RVnUVJU7WJA+o729PS655JIYOXJkPPHEE8UeDhyzTZs2xeLFi2PJkiXx1LLnY8cnvnTMn+v5L18fNdUV3Tg6+A2HZgInhEPW6A9KSkrirrvuirq6unjyySfjqquuKvaQ4KhaW1vjxRdfjCVLlsTixYtj8eLFsXnz5oiIOPPMM2NqbW28VNYaja1lXd4AYMzwqhhW5d92eo6ZGaDHOWSN/iTLspg0aVIMGzYsfvWrXxV7OHCQnTt3xtKlSwvx8uyzz0ZTU1NUVFTE5MmT4/LLL4/a2tqora2NU045JSLsZkbvJWaAHuWQNfqjBx98MGbNmhW/+tWvYtq0acUeDv1YlmXx5ptvFsJlyZIl8corr0SWZXHSSSdFbW1tIV4mT54cAwcOPOTnaWzOx9RvPhHN+bajnjMTEVGSixhYXhpLv3StWXd6lJgBekxXf/jlchGVfvjRB2RZFpMnT47BgwfHwoULXRfGCbNv375YsWJFh3h57733IiLivPPOK4TL5ZdfHmeeeWaXvje7+uLU3DlTHCJLjxMzQI+xLIH+bN68eTFz5sx44okn4pprrin2cOijtm7d2iFcli9fHvv27YvKysq47LLLCvEyderUqKmpOe6v19llw/fcNlnIcEKIGaBHZFkW0769MDZ28WyCAxeMLvziNK9mk7Qsy+JjH/tYVFZWxpNPPun7mePW3t4ev/71rwvhsnjx4nj99dcjIuIjH/lIh1mXiy66KMrLe2aGu7E5H/UrN8fcQ2zoMqd2XMyePCqGDDS7zokhZoAe0bCnJSZ9/bFjvr+tPOkLFixYENOnT4/HHnssrrvuumIPh8Q0NTXFsmXLCuGydOnS2LFjR5SUlMRFF11UCJfLL788Ro8efcKDOcuy2NmUj90trTGooiyG2WqfIhAzQI/YtKMprvzWse/kNHLp/yfOG3NKTJw4Mc4+++yYOHFinHnmmVFdXd2No4SelWVZXHbZZVFeXh5PP/20J3oc0VtvvdVh1uWFF16I1tbWGDJkSEydOrUQL1OmTInBgwcXe7jQK4gZoEcc78zM5e8+FOteeznWrFkT27dvL7x/1KhRHQLnwNu4ceOirMzRWfQ+P/vZz+Lmm2+On//853HDDTcUezj0Eq2trbFq1aoOZ7ts3LgxIiImTJjQYZex888/P0pLS4s8YuidxAzQI7rzmpnt27fHmjVrDvm2d+/eiIgoLy+PM844o0PgHIieU045xSviFE2WZTF16tQoKSmJxYsX+17spxobG+OZZ57pcLbL7t27o7y8PCZNmlRYLjZ16tQ47bTTij1cSIaYAXpMT+9m1t7eHps3b+4QN6+99lqsWbMm1q9fH+3t7RERMXjw4ENGzllnnRVDhgw5tgcHXfDoo4/GTTfdFI8++mh88pOfLPZw6GFZlsW6des67DK2atWqyLIsRowY0WHW5dJLL43KyspiDxmSJWaAHlPMQ9b27dsXa9eu7RA4B962bNlSuN2pp57aIXAO/HrChAlRUWEDArpHlmVRW1sbWZbF0qVLzc70MS0tLfH8888XlostWbIk3n333YiIOOeccwqzLrW1tTFx4kT//6EbiRmgR/XGQ9YaGxsPu2xt9+7dERFRUlIS48ePP+janIkTJ8ZHPvKRKCkp6dEx0vf84he/iE9+8pPxs5/9LG666aZiD4fjsG3btli6dGkhXJ577rnYu3dvDBw4MKZMmdLhbJcRI0YUe7jQp4kZoMelcshalmXxzjvvHBQ4r732WqxduzZaW1sjIqKqqirOOuusg5atTZw4sVsOpaNvyrIsrrjiisjn8/Hss896dT4RWZbFa6+91mGXsddeey0iIk477bQOZ7tcfPHFZnThBBMzwAmR+iFr+Xw+1q9ff9C1OWvWrIm33nqrcLuRI0ceMnLOOOMM6+KJxx9/PK6//vpYsGBB3HzzzcUeDofQ1NQUy5cvL8TLkiVLoqGhIUpKSuLCCy/sEC9jx44VpVBkYgY4ofriIWu7d++ON95446Brc1577bVobGyMiIhcLhdjxow55LbSY8aMse1qP5FlWVx55ZWxb9++WLZsWfLf+33BO++802HWZeXKldHa2hqDBw+Oj3/844Vwueyyy2wYAr2QmAHoIVmWxdatWw95bc7rr78eLS0tERExYMCAOPPMMw+549rIkSM94e1jnnjiibjuuuti3rx5MX369GIPp19pa2uLl19+uUO8rF+/PiIixo0b12HW5YILLvAiAyRAzAAUQVtbW2zcuPGQ20pv3LgxDvzTPGzYsMNuK11dXV3kR8GxyLIsrr766tizZ08sX75crPagXbt2dTjb5Zlnnoldu3ZFWVlZTJo0qcMWyaeffnqxhwscAzED0Ms0NzfHm2++echtpbdt21a43Uc+8pFDbis9bty4KC/vvdcfEfGrX/0qrrnmmnjwwQdj5syZxR5On5BlWWzYsKEQLosXL45Vq1ZFe3t7DB8+PGprawvxcumll0ZVVVWxhwx0AzEDkJCGhobDbivd3NwcERFlZWUxYcKEQ24rfdppp5kJ6CWmTZsWjY2NsXLlSv9PjkE+n48XXnihw9kub7/9dkREnH322R1mXc4++2zbqUMfJWYA+oD29vZ46623Drmt9Lp166K9vT0iIgYNGnRQ4Bx4Gzp0aJEfRf+yaNGimDZtWtTX18esWbOKPZxer6GhocPZLsuWLYvm5uYYOHBgfOxjHyvEy9SpU2PkyJHFHi5wgogZgD6upaUl1q5de8htpQ+cUh4RccoppxxyW+kJEybEgAEDivgI+q5rrrkmtm/fHs8//7yZgw/Isixef/31Dhfqr169OiL2f59efvnlhVmXSZMmOdsF+jExA9CPvf/++/H6668fclvp3bt3R0RESUlJjBs37pDbSo8aNcqT8OPw1FNPxVVXXRX3339/1NXVFXs4RbN3796DznbZtm1b5HK5uOCCCzrEy/jx4y3LAwrEDAAHybIs3n333UNem/Pmm29GPp+PiIjKyso466yzDrnj2vDhw4v8KNJw3XXXxXvvvRcvvPBCvwnDLVu2dJh1WbFiReTz+aiuro6Pf/zjhXD5+Mc/bvkjcERiBoAuaW1tjfXr1x9yW+nNmzcXbjdixIhDRs6ZZ54ZlZWVRXwEvcvixYvjiiuuiPvuuy9+67d+q9jD6Xbt7e3xyiuvdIiXtWvXRkTEmDFjOpztcuGFF0ZZWVmRRwykRMwA0G327NkTb7zxxkHX5rz22muxc+fOwu3GjBlzyG2lx44d2y8PKrzhhhvinXfeiRdffDH52Zndu3fHs88+2+Fsl8bGxigtLY1LLrmkEC+1tbUxatSoYg8XSJyYAaDHZVkW27dvP+janDVr1sTrr78e+/bti4iIioqKOOOMMw65rfTJJ5/cZ6+VWLp0adTW1sa//uu/xq233lrs4XTJxo0bO5zt8uKLL0Z7e3sMGzasw9kuH/vYxxz0CnQ7MQNAUbW1tcWmTZsOua30hg0b4sCPqaFDhx52W+lBgwYV+VEcvxtvvDE2bdoUL730Uq+dncrn8/Hiiy8W4mXJkiWFpYVnnXVWhyVj55xzTvKzTHAiZFkWO5rysaelNaoryqKmqrzPvnDTE8QMAL3W3r1748033zzkttJbt24t3O70008/5LbS48ePj/Ly8iI+gs575plnYurUqfF//+//jd/5nd8p9nAiImLHjh3xzDPPFMLl2WefjaampqioqDjobJeTTz652MOFpDQ25+P+lZvjR0vWx4aGpsL7xw6vittrx8XsSaNiaGUa/34Vk5gBIEk7duw45LbSa9asiaam/U8MSktLY8KECYfcVvr000/vda9+3nTTTbF+/fp4+eWXo6Sk5IS+WptlWbz55puF5WJLliyJV155JSIiTj755EK4XH755TFp0iRnD8FxWLRma9x574pobmmLiIgPPhk/8Le8sqI0vnvb5Lh64kknfHwpETMA9ClZlsXbb799yMhZu3ZttLXtf/JQXV192GVrw4YNK8rYly1bFh+/6pq481tz4+V9I3r01dp9+/bFihUrOpzt8t5770Uul4vzzz+/EC+1tbVxxhln9Lrwg1QtWrM17pi7LLKIONKz8Fxuf9j8cM4UQXMEYgaAfqOlpSXWrVt3yG2l33nnncLtTj755ENuK33GGWf06IzEojVbY873F0dWUha5XK5bX6197733Olzrsnz58mhpaYmqqqq47LLLCuEyderUosUc9HWNzfmY+s0nojnfdsSQOSCXi6gsL42lX7rWkrPDEDMAEBG7du2K119//ZDbSu/atSsiIkpKSmLs2LGH3FZ69OjRx3XBe+HV2qzjkpMP68yrte3t7bF69eoOZ7u88cYbERExatSownKx2trauOiii5ztAifIDxavi68tePWIf8c/LBcRX7nlvLjj8vE9NaykiRkAOIIsy+K999475LK1N954I/L5fEREDBw4MM4888xDbis9cuTII36N4321ds+ePbFs2bJCuCxdujR27twZpaWlcdFFF3XYZWz06NHd8ccCdFGWZTHt2wtjY0NTl2NmzPCqWPjFaZZ7HoKYAYBj1NraGhs2bDjkttKbNm0q3G748OGHvDbnrLPOiqqqqmN6tTYiiwtaXostT/5LPP/889HW1hZDhw6NqVOnFuJlypQpfWLbavq2LMuivb092tvbD/vrI32sJ37dE593dz6L/7nulGP+c3r+y9dHTXVFN/7J9w1iBgB6QFNTU7zxxhsHXZvz2muvxY4dOwq3Gz16dFTM/ka0Dhi6f8qlk7L29sg1bY+r3v9VXP7vsy7nnXdet5/tkmVZkk8c++KT4b749frT09DSoSfHqDt/cMz3f+ovPhGja6q6cUR9g5gBgG6SZVnk8/loamo66G3Pnj2FX7/33nuxfv362Lx5c2x6b0e8/fH/fMxfc8DDfx3Z3t099kS1PyopKYlcLhclJSXH/evu/FzF/Bq+3vH/ekdTPi79708c8/elmZlDc8UfAP1ClmXR3Nx8yNA4Unh09XYHtn4+moqKiqiqqoqqk0bH8exRdMW062LEwOj1T+RS+XquSaCnjBhUEmOHVx3zNTPDquxmdihiBoCia21tPSg0jicoDvfWWVVVVUd8q6mpOeptqqqqorq6+pDvr6ysjHw+H0888UT8dP6jsfA4/uzm/v/uiTPHnBaXXnppTJ48OS699NK45JJLYvDgwcfxWYHulsvl4vbacfG1Ba92+b5zascJ7cOwzAwSl2XZCT0lnP4ly7JoaWnp0ZmMpqamaGlp6dR4SkpKOgTC4WKhs1FxqNsNHDiwx/4ObdmyJRYsWBDz5s2Lxx57LJqbm+PMs86Kirq/jaZcVZdfrT11UFn88agtsWLF8li+fHm88MIL0dzcHLlcLs4+++wOgXPxxRfbDACKrKs7F5bkIgY6Z+aIxAwkqrE5H/ev3Bw/WrK+R08Jp/dqb2+PvXv39thMxoHbdfa6iQEDBnRrVBzq/eXlacV6lmXxyiuvxLx582LevHmxbNmyyOVyUVtbGzNmzIgZM2bE2Wef3W1nT7S2tsbq1atjxYoVsXz58lixYkW88MILsXfv3sjlcnHuuecW4mby5Mlx8cUXR3V1dbc/buDwCmdKRRwxaA6cKTV3zpS4qouH5PYnYgYStGjN1rjz3hXR3LJ/bX53nhJO92htbe3RmYympqZobm7u9Hi6OyoO9VZaWtqDf6LpyOfz8eSTT8a8efNi/vz5sW7duqiuro4bb7wxZsyYEZ/61KcOOnemJ1+tzefzsXr16kLcLF++PF588cXYt29flJSUxHnnndchcC666KKoqrJjEvSkzv4cv+e2yULmKMQMJKarr+gc6ZTw/ujAsqmuXGdxLOFx4CDFoyktLe1UMBxPfPTksin227FjRzzyyCMxf/78eOSRR6KxsTFGjRoVM2bMiOnTp8e0adNi4MCBR/wcJ/LV2nw+H6+88kqHwHnppZeipaUlSktL47zzzuuwRO2jH/1oVFZWHtPXAg6tsTkf9Ss3x9xDrLCYUzsuZk8eFUMGWmFxNGIGEnK8p4T3du3t7Yfdbao7l1Adz7Kp7p7RqKiwzWaq3nzzzZg/f37MmzcvnnzyyWhra4vJkyfH9OnTY8aMGXHxxRd3OSKP9Gpt1t4euVwuqgaU9cirtS0tLfHyyy8X4mb58uWxatWqyOfzUVpaGhdccEGHwLnwwguPGmjA0WVZFjub8rG7pTUGVZTFMNe+domYgYR017r6Y3G4szO6Mzz27t3buceUy3V66dOxxkdlZaVlU3TQ1tYWy5YtK1z/8uqrr8aAAQPimmuuiRkzZsQtt9wSo0aNOu6vc7hXawdFc2x56l/j+fvviVGnjDjur9MZ+/bti1WrVnW4BmfVqlXR2toaZWVlceGFFxbi5tJLL40LLrggBgwYcELGBhAhZiAZWZbFtG8v7PL+9BFZDK9oj8+O3hrNzcceHse7bKo7ZzQsm+JE2bNnTzz22GMxb968WLBgQWzdujVOOumkuPnmm2PGjBlx/fXX99gOYR9+tbZp59YYP358fOMb34gvfOELPfI1O2Pv3r2xatWqwuzNihUr4uWXX462trYoLy+Pj370ox2uwbngggvMQAI9RsxAIhr2tMSkrz92zPff9J3fjfKspUe2sv3g78vLe/9yNjiSt956q7B98hNPPBH79u2Lc889t7D72GWXXVa0Wbs77rgjHn/88Vi7dm2v+rvW3NwcL730UodrcF599dVoa2uLioqK+OhHP9phidr555/fq8YPpEvMQCI27WiKK7/1q2O+/6IvXBVjRzpEDz4sy7J48cUXC7uPLV++PEpLS+PKK68sXMB/5plnFnuYERHx8ssvx4UXXhg/+clP4rbbbiv2cI6oqakpXnzxxQ7X4KxevTra29tjwIABcdFFF3UInPPOOy/KypzlDXSNmIFEHO/MzPNfvj5qqi31gIj914IsXLiwEDCbNm2KIUOGxE033RQzZsyIG2+8MYYPH17sYR7STTfdFO+++26sXLkyueWWe/bsiRdeeKHDNTirV6+OLMti4MCBcfHFF3dYonbuuecKHOCIxAwk4livmclFxJjhVbHwi9OSe+ID3Wnbtm3xs5/9LObPnx+PPvpo7N69O8aNG1eYfbnqqquSuLbjiSeeiOuuuy4ef/zxuPbaa4s9nOO2e/fueP755zsEzmuvvRZZlkVlZWVcfPHFhQ0GJk+eHOecc47NOYACMQMJKeZuZpCi1157rbB98uLFi6O9vT0uu+yywvbJF1xwQXKRn2VZTJo0KU499dR45JFHij2cHrFr1654/vnnO1yDs2bNmojYfwDsJZdc0mGJ2sSJEwUO9FNiBhLSk6eEQ1/Q2toaS5cuLWyfvGbNmqisrIzrrrsuZsyYETfffHOcdtppxR7mcbv33nvj937v92LVqlVxwQUXFHs4J0RjY+NBgfPGG29ERMSgQYMOCpyzzjorSkpKijxqoKeJGUjMiTwlHFKwa9eu+PnPfx7z5s2Lhx9+OBoaGuKUU04pzL5ce+21UVVVVexhdqt8Ph8TJkyI6667Ln74wx8WezhFs3Pnzli5cmWHTQbWrl0bERGDBw+OSZMmdbgG58wzzxQ40MeIGUjQkU4JP7BgprKitEdOCYfeYOPGjYXlYwsXLoyWlpa48MILC9snX3rppX3+Sev//J//M/7yL/8y1q9fH6effnqxh9NrNDQ0dAicFStWxLp16yIiYsiQITF58uQOgXPGGWckt9QQ+A0xA4k63CnhY4dXxZzacTF78qgYMtDSMvqG9vb2WLlyZWH3sRdeeCHKyspi2rRphQv4x40bV+xhnlDvv/9+jB49Oj772c/GN77xjWIPp1fbvn17rFy5ssNBnxs2bIiIiGHDhsWkSZM6LFEbP368wIFEiBlI3IdPCR9WVe6HMH1Cc3Nz/PKXv4z58+fH/Pnz4+23345hw4bFzTffHDNmzIhPfvKTMXTo0GIPs6j+y3/5L/H9738/Nm7cGIMHO0eqK7Zt29Zh9mb58uWxadOmiIioqanpMHtz6aWXxtixY/3bCr2QmAGg19iyZUs8/PDDMX/+/PjFL34RTU1NccYZZ8TMmTNj+vTpcfnllzs5/gM2bdoUEyZMiG9/+9vxn//zfy72cJL33nvvHRQ4b731VkREDB8+vEPcTJ48OcaMGSNwoMjEDABFk2VZvPrqq4XrX5555pmIiKitrS1cwH/OOed4wngEv//7vx9PPfVUvPHGGw6Y7AHvvvturFixosMmA++8805ERIwcOfKgwBk1apTvVziBxAwAJ1Q+n4+nn366sH3y2rVro7q6Om644YaYMWNGfOpTn4qTTz652MNMxgsvvBCXXHJJ/Mu//Ev89m//drGH0y+8/fbbBwXOli1bIiLi5JNPPmiJ2umnny5woIeIGQB63M6dO+PRRx+NefPmxSOPPBI7d+6M008/vbD72Cc+8YkYOHBgsYeZrOuuuy527twZzz33nCfNRZBlWSFwPrhE7b333ouIiFNOOSUuvfTSDoHTF847gt5AzADQI9auXVtYPvbkk09Ga2trXHLJJYWAueSSSzzx7iaPPvpo3HTTTbFw4cK4+uqriz0cYn/gbN68+aDA2bZtW0REnHbaaQctUTv11FOLPGpIj5gBoFu0t7fHsmXLCtsnv/zyy1FRURHXXHNNzJgxI2655ZYYPXp0sYfZJ2VZFh/96Edj3LhxMX/+/GIPh8PIsiw2bdrUIW6WL18eDQ0NERHxkY985KDAseQSjkzMAHDM9uzZE48//njMnz8/FixYEFu2bIkRI0bELbfcEtOnT48bbrjBlsEnyNy5c+OOO+6I1atXxznnnFPs4dBJWZbFhg0bOgTOihUrYseOHRERMXr06A5xM3ny5DjpJIchwwFiBoAueeedd2LBggUxb968ePzxx2Pv3r1xzjnnFA6vnDp1apSWlhZ7mP3Ovn37Yvz48XHLLbfE9773vWIPh+OQZVmsW7euw+zNihUrorGxMSIixo4de1DgjBgxosijhuIQMwAcUZZlsWrVqsLuY88991yUlJTElVdeGdOnT4/p06fHxIkTiz1MIuKb3/xm3HXXXbFhw4Y45ZRTij0culGWZfHmm292mL1ZsWJFvP/++xERMW7cuIOWqNXU1BR51NDzxAwAB2lpaYlFixYVAubACfM33nhjzJgxI2666SavBPdCO3bsiNGjR8ef//mfx9/8zd8Uezj0sPb29njzzTc7zN6sWLEidu/eHREREyZMKMTNpZdeGpMmTYphw4YVd9DQzcQMABER0dDQED/72c9i3rx58eijj8auXbti7NixhcMrr7766qioqCj2MDmKP/3TP40f//jHsWnTpqiqqir2cDjB2tvb4/XXX+9wDc7KlStjz549ERFx5plndpi9mTRpUgwdOrTIo4ZjJ2YA+rHXX3+9MPuyePHiaGtri4997GOF7ZMvvPBC2ycnZv369XHGGWfE3XffHZ/97GeLPRx6gba2tlizZk2HwHn++eejqakpIiImTpzYIXAuueSSGDJkyAkbX5ZlsaMpH3taWqO6oixqqsr9u0OniRmAfqStrS2WLl1a2D7517/+dQwcODCuu+66wvbJDvNL3+/8zu/E8uXL47XXXrMZA4fU1tYWv/71rztsMvDCCy9Ec3Nz5HK5mDhxYodrcC655JIYNGhQt46hsTkf96/cHD9asj42NDQV3j92eFXcXjsuZk8aFUMry7v1a9L3iBmAPm7Xrl3xi1/8IubNmxcPP/xwbN++PU4++eTC8rHrrrvOcqQ+Zvny5fGxj30s7r///qirqyv2cEhEa2trrF69usMmAy+88ELs3bs3crlcnHPOOR0C5+KLL47q6upj+lqL1myNO+9dEc0tbRER8cEnowfmZCorSuO7t02OqyfaiprDEzMAfdCmTZti/vz5MX/+/PjlL38ZLS0tccEFFxS2T54yZUqUlJQUe5j0oGnTpkVLS0ssWbKk2EMhYfl8PlavXt1hidqLL74Y+/bti5KSkjj33HM7LFG7+OKLj/riyKI1W+OOucsii4gjPQvN5faHzQ/nTBE0HJaYAegDsiyLlStXxvz582PevHnx/PPPR1lZWVx99dWFgBk/fnyxh8kJtGDBgpg+fXosXrw4amtriz0c+pB8Ph+vvPJKh8B56aWXoqWlJUpKSuL888/vEDgXXXRRVFZWRsT+pWVTv/lENOfbjhgyB+RyEZXlpbH0S9dacsYhiRmARO3duzd++ctfFmZg3nrrrRg2bFh86lOfiunTp8eNN95oG9Z+rL29Pc4///w499xzo76+vtjDoY9raWmJl19+ucM1OKtWrYp8Ph+lpaVx/vnnx6WXXhrZxGnxq53DoytPPnMR8ZVbzos7LveCDAcTMwAJ2bp1azz88MMxb968+MUvfhF79uyJCRMmFHYfu+KKK6K83KuX7Pf9738//uiP/ihee+21OOuss4o9HPqZffv2xapVq34TOCtWxNYpn43SYadELtf5Za65iBgzvCoWfnGaXc44iJgBOE49ua1olmXx61//urB98tKlSyMi4uMf/3ghYM4991w/4DmkvXv3xtixY2P27Nnxj//4j8UeDv1cw56WmPT1x475/s9/+fqoqXbWFR2VFXsAAKnqqW1FW1tb4+mnny4EzJtvvhlVVVVxww03xD//8z/HzTffHCeffHJ3PhT6qIEDB8bnP//5+O///b/Hf/tv/y1OOslF1BTPnpbW47r/7pZWMcNBzMwAHIPu3la0sbExHn300Zg3b1488sgjsWPHjjj99NML2yd/4hOfKFxAC12xbdu2GDNmTHzpS1+Kr3zlK8UeDv2YmRl6gpgB6KLu2lZ03bp1hYv3Fy5cGK2trXHxxRcXdh+bNGmS7ZPpFp/73Ofivvvuiw0bNohiiibLspj27YWxsaGpyxsAuGaGwxEzAF1wPNuKDh5QGs8991xh++RVq1ZFeXl5XHPNNTF9+vSYPn16jBkzpucfBP3OG2+8ERMnTox77rkn/uiP/qjYw6Ef+8HidfG1Ba/azYxuI2YAuuBYfhBHZHH27lXx4n3/K7Zs2RLDhw+PW265JaZPnx433HBDDBkypIdGC78xe/bseOWVV+LVV18140fRdPUFoZJcxEDnzHAEYgagk451iUTW3h4lTQ0xe+DLMXPGjJg6dWqUldl/hRNr6dKlUVtbG/PmzYvp06cXezj0Y11dqjt3zpS4qhPXHtI/iRmATnLxKqm7/PLLo6ysLBYtWlTsodDPdXYTlXtumyxkOCLzzACd1B3bikIxffGLX4wnn3wyli1bVuyh0M9dPfGkWPqla+Mrt5wXY4ZXdfjYmOFV8ZVbzotn/vJaIcNRmZkB6CQzM6Sura0tzjnnnJg0aVL867/+a7GHAxGxfwnvzqZ87G5pjUEVZTGsGw8epu8zMwPQSTVV5TF2eFV09UdsLvYfpDmsysWrFFdpaWn8+Z//efz0pz+NdevWFXs4EBERuVwuaqorYnRNVdRUVwgZukTMAHRSLpeL22vHHdN959SO8wOaXuH222+Pmpqa+M53vlPsoQAcNzED0AWzJ42KyorS6GyXlOT2X8RaN2lUzw4MOqmqqio+97nPxT//8z9HQ0NDsYcDcFzEDEAXDK0sj+/eNjlyEUcNmgMfv+e2yc5HoFf53Oc+F62trfFP//RPkWVZNOxpiU07mqJhT0u4lBZIiQ0AAI6BbUVJ3R/88efiF2saY8KNfxAbdzQX3j92eFXcXjsuZk8aJcKBXk/MAByjxuZ81K/cHHOXrI8NDU2F948dXhVzasfF7MmjYshATwbpfRat2Rr/8cfPRXO+7d+v5frNNOMHY/y7t02Oq8U40IuJGYDjZFtRUtLV09d/OGeKoAF6LTEDAP1EY3M+pn7ziWjOtx0xZA7I5SIqy0tj6ZeuteQM6JVsAAAA/cT9KzdHc0vnQiZi/8xNc0tb1K/c3LMDAzhGYgYA+oEsy+JHS9Yf033nLllvlzOgVxIzANAP7GjKx4aGpuhqkmQRsaGhKXY25XtiWADHRcwAQD+wp6X1uO6/+zjvD9ATxAwA9APVFWXHdf9Bx3l/gJ4gZgCgH6ipKo+xw6uiq5uG52L/2UnDquxmBvQ+YgYA+oFcLhe31447pvvOqR3n7CSgVxIzANBPzJ40KiorSqOzXVKSi6isKI26SaN6dmAAx0jMAEA/MbSyPL572+TIRRw1aA58/J7bJjswE+i1xAwA9CNXTzwpfjhnSlSWl+6Pmg99/MD7KstLY+6cKXHVxJNO/CABOimXOQULAPqdxuZ81K/cHHOXrI8NDU2F948dXhVzasfF7MmjYshAMzJA7yZmAKAfy7IsdjblY3dLawyqKIthVeUu9geSIWYAAIAkuWYGAABIkpgBAACSJGYAAIAkiRkAACBJYgYAAEiSmAEAAJIkZgAAgCSJGQAAIEliBgAASJKYAQAAkiRmAACAJIkZAAAgSWIGAABIkpgBAACSJGYAAIAkiRkAACBJYgYAAEiSmAEAAJIkZgAAgCSJGQAAIEliBgAASJKYAQAAkiRmAACAJIkZAAAgSWIGAABIkpgBAACSJGYAAIAkiRkAACBJYgYAAEiSmAEAAJIkZgAAgCSJGQAAIEliBgAASJKYAQAAkiRmAACAJIkZAAAgSWIGAABIkpgBAACSJGYAAIAkiRkAACBJYgYAAEiSmAEAAJIkZgAAgCSJGQAAIEliBgAASJKYAQAAkiRmAACAJIkZAAAgSWIGAABIkpgBAACSJGYAAIAkiRkAACBJYgYAAEiSmAEAAJIkZgAAgCSJGQAAIEliBgAASJKYAQAAkiRmAACAJIkZAAAgSWIGAABIkpgBAACSJGYAAIAkiRkAACBJYgYAAEiSmAEAAJIkZgAAgCSJGQAAIEliBgAASNL/H5yGQDEQrHCaAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#use networkx to plot the batch graph\n", + "import networkx as nx\n", + "import matplotlib.pyplot as plt\n", + "\n", + "def plot_graph(data):\n", + " G = nx.Graph()\n", + " G.add_edges_from(data.edge_index.T.numpy())\n", + " list_nodes = dict(G.nodes.data())\n", + "\n", + " pos = nx.spring_layout(G)\n", + " labels = nx.get_node_attributes(G, 'label')\n", + " nx.draw(G, pos, labels=labels, with_labels=True, node_size=100)\n", + " \n", + " plt.show()\n", + "\n", + "for batch in loader:\n", + " plot_graph(batch)\n", + " break" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Data(x=[2708, 1433], edge_index=[2, 96888], y=[2708], x_0=[2708, 1433])" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "if hasattr(data, 'incidence_3'):\n", + " del data['incidence_3']\n", + "if hasattr(data, 'x_3'):\n", + " del data['x_3']\n", + "for key in list(data.keys()):\n", + " if 'laplacian' in key or 'temp' in key or 'mask' in key or 'hyperedges' in key:\n", + " del data[key]\n", + "\n", + "incidence_3 = torch.tensor([[],[]]).to_sparse()\n", + "incidence_2 = torch.tensor([[1,0],[1,0],[1,0],[0,0],[0,1],[0,1],[0,1]]).float().to_sparse()\n", + "incidence_1 = torch.tensor([[1,0,1,0,0,0,0],[1,1,0,0,0,0,0],[0,1,1,1,0,0,0],[0,0,0,1,1,0,1],[0,0,0,0,1,1,0],[0,0,0,0,0,1,1]]).float().to_sparse()\n", + "incidence_0 = torch.tensor([[1,1,1,1,1,1]]).float().to_sparse()\n", + "data " + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data(x=[6, 6], edge_index=[2, 14], y=[6], x_0=[6, 6], incidence_3=[2, 0], incidence_2=[7, 2], incidence_1=[6, 7], incidence_0=[1, 6], x_3=[0], x_2=[2, 2], x_1=[7, 3], temp_0=[6, 6])\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "rank = 0\n", + "\n", + "data['incidence_3'] = incidence_3\n", + "data['incidence_2'] = incidence_2\n", + "data['incidence_1'] = incidence_1\n", + "data['incidence_0'] = incidence_0\n", + "\n", + "data['x_3'] = torch.tensor([]).float()\n", + "data['x_2'] = torch.tensor([[1,0],[0,1]]).float()\n", + "data['x_1'] = torch.tensor([[1,0,0],[0,1,0],[0,0,1],[1,0,0],[0,1,0],[0,0,1],[1,0,0]]).float()\n", + "data['x_0'] = torch.tensor([[1,0,0,0,0,0],[0,1,0,0,0,0],[0,0,1,0,0,0],[0,0,0,1,0,0],[0,0,0,0,1,0],[0,0,0,0,0,1]]).float()\n", + "data['x'] = data[f'x_{rank}']\n", + "data['y'] = torch.zeros(data[f'x_{rank}'].shape[0], dtype=torch.long)\n", + "\n", + "data['edge_index'] = torch.tensor([[0,0,1,1,2,2,2,3,3,3,4,4,5,5],[1,2,0,2,0,1,3,2,4,5,3,5,3,4]])\n", + "data['temp_0'] = torch.sparse_coo_tensor(data['edge_index'], torch.ones(data['edge_index'].shape[1]), data['x_0'].shape)\n", + "print(data)\n", + "plot_graph(data)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.11/site-packages/torch_geometric/sampler/neighbor_sampler.py:61: UserWarning: Using 'NeighborSampler' without a 'pyg-lib' installation is deprecated and will be removed soon. Please install 'pyg-lib' for accelerated neighborhood sampling\n", + " warnings.warn(f\"Using '{self.__class__.__name__}' without a \"\n" + ] + } + ], + "source": [ + "# num_neighbors controls also the number of hops (for 2 hops do num_neighbors=[-1, -1])\n", + "reduce = ReduceNeighborhoods(rank=rank, remove_self_loops=True)\n", + "batch_size = 1\n", + "loader = NeighborLoaderWrapper(data,\n", + " rank=rank,\n", + " num_neighbors=[-1],\n", + " input_nodes=train_mask,\n", + " batch_size=batch_size,\n", + " shuffle=False,\n", + " transform=reduce)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor([[0., 1., 1., 1.],\n", + " [1., 0., 1., 0.],\n", + " [1., 1., 0., 0.],\n", + " [1., 0., 0., 0.]])\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data(x=[4, 6], edge_index=[2, 8], y=[4], x_0=[4, 6], incidence_3=[1, 0], incidence_2=[4, 1], incidence_1=[4, 4], incidence_0=[1, 4], x_3=[0], x_2=[1, 2], x_1=[4, 3], n_id=[4], e_id=[3], input_id=[1], batch_size=1, adjacency_0=[4, 4])\n", + "tensor([2, 0, 1, 3])\n" + ] + } + ], + "source": [ + "for i, batch in enumerate(loader):\n", + " if i==2:\n", + " print(batch.adjacency_0.to_dense())\n", + " plot_graph(batch)\n", + " print(batch)\n", + " print(batch.n_id)\n", + " break\n" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data(x=[4, 2], edge_index=[2, 12], y=[4], x_0=[4, 4], incidence_3=[4, 1], incidence_2=[6, 4], incidence_1=[4, 6], incidence_0=[1, 4], x_3=[1, 2], x_2=[4, 2], x_1=[6, 3], temp_0=[4, 4])\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "incidence_3 = torch.tensor([[1],[1],[1],[1]]).float().to_sparse()\n", + "incidence_2 = torch.tensor([[1,0,1,0],[1,1,0,0],[0,1,1,0],[0,0,1,1],[1,0,0,1],[0,1,0,1]]).float().to_sparse()\n", + "incidence_1 = torch.tensor([[1,1,1,0,0,0],[1,0,0,1,1,0],[0,1,0,0,1,1],[0,0,1,1,0,1]]).float().to_sparse()\n", + "incidence_0 = torch.tensor([[1,1,1,1]]).float().to_sparse()\n", + "\n", + "x_3 = torch.tensor([[1,0]]).float()\n", + "x_2 = torch.tensor([[1,0],[0,1],[1,1],[0,0]]).float()\n", + "x_1 = torch.tensor([[1,0,0],[0,1,0],[0,0,1],[1,0,0],[0,1,0],[0,0,1]]).float()\n", + "x_0 = torch.tensor([[1,0,0,0],[0,1,0,0],[0,0,1,0],[0,0,0,1]]).float()\n", + "\n", + "rank = 2\n", + "\n", + "data['incidence_3'] = incidence_3\n", + "data['incidence_2'] = incidence_2\n", + "data['incidence_1'] = incidence_1\n", + "data['incidence_0'] = incidence_0\n", + "\n", + "data['x_3'] = x_3\n", + "data['x_2'] = x_2\n", + "data['x_1'] = x_1\n", + "data['x_0'] = x_0\n", + "data['x'] = data[f'x_{rank}']\n", + "data['y'] = torch.zeros(data[f'x_{rank}'].shape[0], dtype=torch.long)\n", + "\n", + "data['edge_index'] = torch.tensor([[0,0,0,1,1,1,2,2,2,3,3,3],[1,2,3,0,2,3,0,1,3,0,1,2]])\n", + "data['temp_0'] = torch.sparse_coo_tensor(data['edge_index'], torch.ones(data['edge_index'].shape[1]), data['x_0'].shape)\n", + "print(data)\n", + "plot_graph(data)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data(x=[4, 4], edge_index=[2, 12], y=[4], x_0=[4, 4], incidence_3=[4, 1], incidence_2=[6, 4], incidence_1=[4, 6], incidence_0=[1, 4], x_3=[1, 2], x_2=[4, 2], x_1=[6, 3], n_id=[4], e_id=[3], input_id=[1], batch_size=1, adjacency_0=[4, 4])\n", + "tensor([0, 1, 2, 3])\n" + ] + } + ], + "source": [ + "# num_neighbors controls also the number of hops (for 2 hops do num_neighbors=[-1, -1])\n", + "reduce = ReduceNeighborhoods(rank=rank, remove_self_loops=True)\n", + "batch_size = 1\n", + "loader = NeighborLoaderWrapper(data,\n", + " rank=rank,\n", + " num_neighbors=[-1],\n", + " input_nodes=train_mask,\n", + " batch_size=batch_size,\n", + " shuffle=False,\n", + " transform=reduce)\n", + "\n", + "for i, batch in enumerate(loader):\n", + " if i==0:\n", + " plot_graph(batch)\n", + " print(batch)\n", + " print(batch.n_id)\n", + " break" + ] } ], "metadata": { From 877fce10e759a4ad76e4d455f1666b6ee38a8663 Mon Sep 17 00:00:00 2001 From: Coerulatus Date: Thu, 14 Nov 2024 22:05:17 +0000 Subject: [PATCH 05/24] Marco - get_sampled_neighborhood reworked --- tutorials/batching.ipynb | 218 ++++++++++++++++++--------------------- 1 file changed, 101 insertions(+), 117 deletions(-) diff --git a/tutorials/batching.ipynb b/tutorials/batching.ipynb index 607f630c..c5fce106 100644 --- a/tutorials/batching.ipynb +++ b/tutorials/batching.ipynb @@ -9,7 +9,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_272091/2455096930.py:26: UserWarning: \n", + "/tmp/ipykernel_60814/2455096930.py:26: UserWarning: \n", "The version_base parameter is not specified.\n", "Please specify a compatability version level, or None.\n", "Will assume defaults for version 1.1\n", @@ -65,7 +65,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Transform parameters are the same, using existing data_dir: /TopoBenchmarkX/datasets/graph/cocitation/Cora/graph2hypergraph_lifting/1273654097\n" + "Transform parameters are the same, using existing data_dir: /TopoBenchmark/datasets/graph/cocitation/Cora/graph2hypergraph_lifting/1273654097\n" ] } ], @@ -112,16 +112,16 @@ { "data": { "text/plain": [ - "['val_mask',\n", - " 'edge_index',\n", - " 'x',\n", - " 'x_hyperedges',\n", - " 'y',\n", + "['test_mask',\n", " 'num_hyperedges',\n", + " 'x_hyperedges',\n", + " 'x',\n", " 'x_0',\n", - " 'train_mask',\n", + " 'val_mask',\n", + " 'y',\n", + " 'edge_index',\n", " 'incidence_hyperedges',\n", - " 'test_mask']" + " 'train_mask']" ] }, "execution_count": 5, @@ -145,11 +145,13 @@ " \n", " \n", "# replace adjacency keys with temp\n", - "n_incidences = len([key for key in data.keys() if \"incidence\" in key])\n", - "for i in range(n_incidences):\n", - " if f\"adjacency_{i}\" in data.keys():\n", - " data[f\"temp_{i}\"] = data[f\"adjacency_{i}\"]\n", - " del data[f\"adjacency_{i}\"]\n", + "def workaround_adj(data):\n", + " n_incidences = len([key for key in data.keys() if \"incidence\" in key])\n", + " for i in range(n_incidences):\n", + " if f\"adjacency_{i}\" in data.keys():\n", + " data[f\"temp_{i}\"] = data[f\"adjacency_{i}\"]\n", + " del data[f\"adjacency_{i}\"]\n", + " return data\n", "\n", "# For some reason we need to call the adjacency matrices something else because the __cat_dim__ function will return a tuple for attributes with the adjacency or adj keys. This behaviour breaks stuff in the GlobalStorage module.\n", "# for key in data.keys():\n", @@ -161,7 +163,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -185,43 +187,16 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "from torch_geometric.loader import NeighborLoader\n", - "from torch_sparse import SparseTensor\n", - "import torch_sparse\n", - "\n", - "def change_sparse(tensor):\n", - " r\"\"\" Change from SparseTensor to torch_sparse_coo_tensor or viceversa.\n", " \n", - " Parameters\n", - " ----------\n", - " tensor: torch.Tensor or SparseTensor\n", - " The input tensor.\n", - " \n", - " Returns\n", - " -------\n", - " torch.Tensor or SparseTensor\n", - " The output tensor.\n", + "def get_sampled_neighborhood(data, rank=0, is_hypergraph=False):\n", + " ''' This function updates the edge_index attribute of torch_geometric.data.Data. \n", " \n", - " \"\"\"\n", - " if isinstance(tensor, SparseTensor):\n", - " return tensor.to_torch_sparse_coo_tensor().to(device=tensor.device())\n", - " elif tensor.is_sparse:\n", - " tensor = tensor.coalesce()\n", - " return SparseTensor(row=tensor.indices()[0], \n", - " col=tensor.indices()[1], \n", - " value=tensor.values(), \n", - " sparse_sizes=tensor.size()).to_device(tensor.device)\n", - " else:\n", - " raise NotImplementedError(f\"Type {type(tensor)} not supported\")\n", - " \n", - "def clique_expansion(data, rank=0, is_hypergraph=False):\n", - " ''' This function adds edges between cells that belong to the same higher-order cells.\n", - " \n", - " This function is needed so that the NeighborLoader can select all the nodes of the cells that contain the nodes of interest. In general nodes belonging to the same higher-order cell do not need to be directly connected. E.g. in a cell complex a face of 4 nodes does not have edges between opposite nodes.\n", + " The function finds cells, of the specified rank K, that are either upper or lower neighbors.\n", " \n", " Parameters\n", " ----------\n", @@ -235,12 +210,17 @@ " Returns\n", " -------\n", " torch_geometric.data.Data\n", - " The output data with the added edges.\n", + " The output data with updated edge_index.\n", + " edge_index contains indices of connected cells of the specified rank K. \n", + " Two cells of rank K are connected if they are either lower or upper neighbors. \n", " '''\n", - " if is_hypergraph:\n", - " P = data.incidence_hyperedges\n", - " Q = torch.sparse.mm(P,P.T)\n", - " edges = Q.indices()\n", + " # TODO: add upper adj\n", + " if rank == 0:\n", + " return data\n", + " if is_hypergraph: #TODO: add rank=1 case\n", + " I = data.incidence_hyperedges\n", + " A = torch.sparse.mm(I,I.T) # lower adj matrix\n", + " edges = A.indices() \n", " else:\n", " # get number of incidences\n", " max_rank = len([key for key in data.keys() if \"incidence\" in key])-1\n", @@ -253,19 +233,22 @@ " Q = torch.sparse.mm(P,P.T)\n", " edges = Q.indices()\n", " \n", - " for i in range(rank+1, max_rank):\n", - " P = torch.sparse.mm(P, data[f\"incidence_{i+1}\"])\n", - " Q = torch.sparse.mm(P,P.T)\n", - " edges = torch.cat((edges, Q.indices()), dim=1)\n", - " \n", - " if rank == 0:\n", - " edges = torch.cat((edges, data.edge_index), dim=1)\n", - " else:\n", - " P = data[f\"incidence_{rank}\"]\n", - " for i in range(rank-1, 0, -1):\n", - " P = torch.sparse.mm(data[f\"incidence_{i}\"], P)\n", - " Q = torch.sparse.mm(P.T,P)\n", - " edges = torch.cat((edges, Q.indices()), dim=1)\n", + " # This is for selecting the whole upper cells\n", + " # for i in range(rank+1, max_rank):\n", + " # P = torch.sparse.mm(P, data[f\"incidence_{i+1}\"])\n", + " # Q = torch.sparse.mm(P,P.T)\n", + " # edges = torch.cat((edges, Q.indices()), dim=1)\n", + " \n", + " # This considers the lower adjacency \n", + " P = data[f\"incidence_{rank}\"]\n", + " Q = torch.sparse.mm(P.T,P)\n", + " edges = torch.cat((edges, Q.indices()), dim=1)\n", + " \n", + " # This is for selecting if the cells share any node\n", + " # for i in range(rank-1, 0, -1):\n", + " # P = torch.sparse.mm(data[f\"incidence_{i}\"], P)\n", + " # Q = torch.sparse.mm(P.T,P)\n", + " # edges = torch.cat((edges, Q.indices()), dim=1)\n", " \n", " edges = torch.unique(edges, dim=1)\n", " # Remove self edges\n", @@ -274,7 +257,8 @@ " \n", " data.edge_index = edges\n", " \n", - " # We need to set x to x_rank since NeighborLoader will take the number of nodes from the x attribute\n", + " # We need to set x to x_{rank} since NeighborLoader will take the number of nodes from the x attribute\n", + " # The correct x is given after the reduce_neighborhoods function\n", " if is_hypergraph and rank == 1:\n", " data.x = data.x_hyperedges\n", " else:\n", @@ -305,18 +289,19 @@ " \"\"\"\n", " for i in range(1, max_rank+1):\n", " if is_hypergraph:\n", - " incidence = change_sparse(batch.incidence_hyperedges)\n", + " incidence = batch.incidence_hyperedges\n", " else:\n", - " incidence = change_sparse(batch[f\"incidence_{i}\"])\n", + " incidence = batch[f\"incidence_{i}\"]\n", + " \n", " if i != rank+1:\n", - " incidence = incidence[cells_ids[i-1], :]\n", - " cells_ids[i] = torch.where(torch_sparse.sum(incidence, dim=0).to_dense() > 1)[0]\n", - " incidence = incidence[:, cells_ids[i]]\n", - " batch[f\"incidence_{i}\"] = change_sparse(incidence)\n", + " incidence = torch.index_select(incidence, 0, cells_ids[i-1])\n", + " cells_ids[i] = torch.where(torch.sum(incidence, dim=0).to_dense() > 1)[0]\n", + " incidence = torch.index_select(incidence, 1, cells_ids[i])\n", + " batch[f\"incidence_{i}\"] = incidence\n", " if not is_hypergraph:\n", - " incidence = change_sparse(batch[f\"incidence_0\"])\n", - " incidence = incidence[:, cells_ids[0]]\n", - " batch[f\"incidence_0\"] = change_sparse(incidence)\n", + " incidence = batch[f\"incidence_0\"]\n", + " incidence = torch.index_select(incidence, 1, cells_ids[0])\n", + " batch[f\"incidence_0\"] = incidence\n", " \n", " return batch, cells_ids\n", "\n", @@ -342,11 +327,11 @@ " cells_ids_new = [c_i for c_i in cells_ids]\n", " for i in range(rank, 0, -1):\n", " if is_hypergraph:\n", - " incidence = change_sparse(batch.incidence_hyperedges)\n", + " incidence = batch.incidence_hyperedges\n", " else:\n", - " incidence = change_sparse(batch[f\"incidence_{i}\"].clone())\n", - " incidence = incidence[:, cells_ids_new[i]]\n", - " cells_ids_new[i-1] = torch.where(torch_sparse.sum(incidence, dim=1).to_dense() > 0)[0]\n", + " incidence = batch[f\"incidence_{i}\"]\n", + " incidence = torch.index_select(incidence, 1, cells_ids_new[i])\n", + " cells_ids_new[i-1] = torch.where(torch.sum(incidence, dim=1).to_dense() > 0)[0]\n", " return cells_ids_new[0]\n", "\n", "def reduce_matrices(batch, cells_ids, names, rank, max_rank):\n", @@ -375,12 +360,14 @@ " for i in range(max_rank+1):\n", " for name in names:\n", " if f\"{name}{i}\" in batch.keys():\n", - " matrix = change_sparse(batch[f\"{name}{i}\"])\n", + " # matrix = change_sparse(batch[f\"{name}{i}\"])\n", + " matrix = batch[f\"{name}{i}\"]\n", " if i==rank:\n", - " matrix = matrix[:, cells_ids[i]]\n", + " matrix = torch.index_select(matrix, 1, cells_ids[i])\n", " else:\n", - " matrix = matrix[cells_ids[i], cells_ids[i]]\n", - " batch[f\"{name}{i}\"] = change_sparse(matrix)\n", + " matrix = torch.index_select(matrix, 0, cells_ids[i])\n", + " matrix = torch.index_select(matrix, 1, cells_ids[i])\n", + " batch[f\"{name}{i}\"] = matrix\n", " return batch\n", "\n", "def reduce_neighborhoods(batch, rank=0, remove_self_loops=True):\n", @@ -411,13 +398,15 @@ " raise ValueError(f\"Rank {rank} is greater than the maximum rank {max_rank} in the dataset.\")\n", " \n", " cells_ids = [None for _ in range(max_rank+1)]\n", - " # the ids of the cells are saved in the batch\n", + " \n", + " # the indices of the cells selected by the NeighborhoodLoader are saved in the batch in the attribute n_id\n", " cells_ids[rank] = batch.n_id\n", " \n", - " if rank != 0:\n", - " cells_ids[0] = get_node_indices(batch, cells_ids, rank, is_hypergraph)\n", - " else:\n", + " if rank == 0:\n", " cells_ids[0] = batch.n_id\n", + " else:\n", + " cells_ids[0] = get_node_indices(batch, cells_ids, rank, is_hypergraph)\n", + " \n", " batch, cells_ids = reduce_higher_ranks_incidences(batch, cells_ids, rank, max_rank, is_hypergraph)\n", "\n", " batch = reduce_matrices(batch, \n", @@ -439,7 +428,7 @@ " del batch[f\"temp_{i}\"]\n", " \n", " # fix edge_index\n", - " if hasattr(batch, 'adjacency_0'):\n", + " if not is_hypergraph:\n", " adjacency_0 = batch.adjacency_0.coalesce()\n", " edge_index = adjacency_0.indices()\n", " if remove_self_loops:\n", @@ -482,7 +471,7 @@ " return reduce_neighborhoods(batch, self.rank, self.remove_self_loops)\n", "\n", "class NeighborLoaderWrapper(NeighborLoader):\n", - " \"\"\" NeighborLoader with clique expansion.\n", + " \"\"\" NeighborLoader with get_sampled_neighborhood.\n", " \n", " Parameters\n", " ----------\n", @@ -493,25 +482,29 @@ " **kwargs: dict\n", " Additional arguments for the NeighborLoader.\n", " \"\"\"\n", - " def __init__(self, dataset, rank=0, **kwargs):\n", - " is_hypergraph = hasattr(dataset, 'incidence_hyperedges')\n", - " dataset = clique_expansion(dataset, rank, is_hypergraph)\n", + " def __init__(self, data, rank=0, **kwargs):\n", + " is_hypergraph = hasattr(data, 'incidence_hyperedges')\n", + " data = get_sampled_neighborhood(data, rank, is_hypergraph)\n", + " # This workaround is needed because torch_geometric treats any attribute of data with adj in the name differently and it raises errors.\n", + " data = workaround_adj(data)\n", " if 'num_neighbors' in kwargs.keys():\n", " if len(kwargs['num_neighbors']) > 1:\n", " raise NotImplementedError(\"NeighborLoaderWrapper only supports one-hop neighborhood selection.\")\n", - " super(NeighborLoaderWrapper, self).__init__(dataset, **kwargs)\n", + " super(NeighborLoaderWrapper, self).__init__(data, **kwargs)\n", " " ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ + "/tmp/ipykernel_60814/2824364303.py:56: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:54.)\n", + " A = torch.sparse.mm(I,I.T) # lower adj matrix\n", "/usr/local/lib/python3.11/site-packages/torch_geometric/sampler/neighbor_sampler.py:61: UserWarning: Using 'NeighborSampler' without a 'pyg-lib' installation is deprecated and will be removed soon. Please install 'pyg-lib' for accelerated neighborhood sampling\n", " warnings.warn(f\"Using '{self.__class__.__name__}' without a \"\n" ] @@ -532,7 +525,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -570,12 +563,12 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -607,7 +600,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -616,7 +609,7 @@ "Data(x=[2708, 1433], edge_index=[2, 96888], y=[2708], x_0=[2708, 1433])" ] }, - "execution_count": 26, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -639,7 +632,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -651,7 +644,7 @@ }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -683,18 +676,9 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 14, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/lib/python3.11/site-packages/torch_geometric/sampler/neighbor_sampler.py:61: UserWarning: Using 'NeighborSampler' without a 'pyg-lib' installation is deprecated and will be removed soon. Please install 'pyg-lib' for accelerated neighborhood sampling\n", - " warnings.warn(f\"Using '{self.__class__.__name__}' without a \"\n" - ] - } - ], + "outputs": [], "source": [ "# num_neighbors controls also the number of hops (for 2 hops do num_neighbors=[-1, -1])\n", "reduce = ReduceNeighborhoods(rank=rank, remove_self_loops=True)\n", @@ -710,7 +694,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -725,7 +709,7 @@ }, { "data": { - "image/png": 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", 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", 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" ] @@ -754,7 +738,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -766,7 +750,7 @@ }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -808,12 +792,12 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 17, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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" ] From 09bab78f8d15d94e415580df17578f1cae577b27 Mon Sep 17 00:00:00 2001 From: levtelyatnikov Date: Fri, 15 Nov 2024 01:16:34 +0100 Subject: [PATCH 06/24] added proper plot function --- tutorials/batching.ipynb | 628 ++++++++++++++++++++------------------- 1 file changed, 321 insertions(+), 307 deletions(-) diff --git a/tutorials/batching.ipynb b/tutorials/batching.ipynb index c5fce106..6c449f42 100644 --- a/tutorials/batching.ipynb +++ b/tutorials/batching.ipynb @@ -2,31 +2,9 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_60814/2455096930.py:26: UserWarning: \n", - "The version_base parameter is not specified.\n", - "Please specify a compatability version level, or None.\n", - "Will assume defaults for version 1.1\n", - " initialize(config_path=\"../configs\", job_name=\"job\")\n" - ] - }, - { - "data": { - "text/plain": [ - "hydra.initialize()" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "import rootutils\n", "\n", @@ -60,90 +38,141 @@ "cell_type": "code", "execution_count": 2, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Transform parameters are the same, using existing data_dir: /TopoBenchmark/datasets/graph/cocitation/Cora/graph2hypergraph_lifting/1273654097\n" - ] - } - ], - "source": [ - "cfg = compose(config_name=\"run.yaml\", \n", - " overrides=[\"dataset=graph/cocitation_cora\", \"model=hypergraph/allsettransformer\"], \n", - " return_hydra_config=True)\n", - "graph_loader = GraphLoader(cfg.dataset.loader.parameters)\n", - "dataset, dataset_dir = graph_loader.load()\n", - "preprocessed_dataset = PreProcessor(dataset, dataset_dir, cfg['transforms'])" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'graph2hypergraph_lifting': {'_target_': 'topobenchmarkx.transforms.data_transform.DataTransform', 'transform_type': 'lifting', 'transform_name': 'HypergraphKHopLifting', 'k_value': 1}}\n" - ] - } - ], - "source": [ - "print(cfg['transforms'])" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, "outputs": [], "source": [ - "data = preprocessed_dataset[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['test_mask',\n", - " 'num_hyperedges',\n", - " 'x_hyperedges',\n", - " 'x',\n", - " 'x_0',\n", - " 'val_mask',\n", - " 'y',\n", - " 'edge_index',\n", - " 'incidence_hyperedges',\n", - " 'train_mask']" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data.keys()" + "import matplotlib.pyplot as plt\n", + "import networkx as nx\n", + "import numpy as np\n", + "import torch\n", + "from matplotlib.patches import Polygon\n", + "from itertools import combinations\n", + "from typing import Optional, Dict, List\n", + "\n", + "def plot_graph(\n", + " data,\n", + " face_color_map: Optional[Dict[int, str]] = None,\n", + " node_size: int = 500,\n", + " font_size: int = 12,\n", + " seed: int = 5,\n", + " show: bool = True\n", + ") -> plt.Figure:\n", + " \"\"\"\n", + " Visualize a simplicial complex from a PyTorch Geometric Data object.\n", + " \n", + " Args:\n", + " data: torch_geometric.data.Data object containing the simplicial complex\n", + " face_color_map: Dictionary mapping number of tetrahedrons to colors\n", + " node_size: Size of nodes in the visualization\n", + " font_size: Size of font for labels\n", + " seed: Random seed for layout\n", + " show: Whether to display the plot immediately\n", + " \n", + " Returns:\n", + " matplotlib.figure.Figure: The generated figure\n", + " \"\"\"\n", + " # Default color map if none provided\n", + " if face_color_map is None:\n", + " face_color_map = {\n", + " 0: \"pink\",\n", + " 1: \"gray\",\n", + " 2: \"blue\",\n", + " 3: \"blue\",\n", + " 4: \"orange\",\n", + " 5: \"purple\",\n", + " 6: \"red\",\n", + " 7: \"brown\",\n", + " 8: \"black\",\n", + " 9: \"gray\",\n", + " }\n", + " \n", + " # Extract vertices\n", + " num_vertices = data.num_nodes if hasattr(data, 'num_nodes') else data.x.shape[0]\n", + " vertices = list(range(num_vertices))\n", + " \n", + " # Extract edges from incidence matrix\n", + " edges = []\n", + " for edge in abs(data.incidence_1.to_dense().T):\n", + " edges.append(torch.where(edge == 1)[0].numpy())\n", + " edges = np.array(edges)\n", + " \n", + " # Extract tetrahedrons if available\n", + " tetrahedrons = []\n", + " if hasattr(data, 'tetrahedrons'):\n", + " tetrahedrons = data.tetrahedrons\n", + " elif hasattr(data, 'incidence_2'):\n", + " # Extract tetrahedrons from incidence_2 matrix if available\n", + " for tetra in abs(data.incidence_2.to_dense().T):\n", + " tetrahedrons.append(torch.where(tetra == 1)[0].tolist())\n", + " \n", + " # Create graph\n", + " G = nx.Graph()\n", + " G.add_nodes_from(vertices)\n", + " G.add_edges_from(edges)\n", + " \n", + " # Find triangular cliques\n", + " cliques = list(nx.enumerate_all_cliques(G))\n", + " cliques = [triangle for triangle in cliques if len(triangle) == 3]\n", + " \n", + " # Create layout\n", + " pos = nx.spring_layout(G, seed=seed)\n", + " \n", + " # Create figure\n", + " fig = plt.figure(figsize=(10, 8))\n", + " \n", + " # Draw nodes and labels\n", + " nx.draw(\n", + " G,\n", + " pos,\n", + " labels={i: f\"v_{i}\" for i in G.nodes()},\n", + " node_size=node_size,\n", + " node_color=\"skyblue\",\n", + " font_size=font_size,\n", + " )\n", + " \n", + " # Draw edges\n", + " nx.draw_networkx_edges(G, pos, edgelist=edges, edge_color=\"g\", width=2, alpha=0.5)\n", + " \n", + " # Add edge labels\n", + " for i, (u, v) in enumerate(edges):\n", + " x = (pos[u][0] + pos[v][0]) / 2\n", + " y = (pos[u][1] + pos[v][1]) / 2\n", + " plt.text(x, y, f\"e_{i}\", fontsize=font_size - 2, color=\"r\")\n", + " \n", + " # Color the faces (cliques)\n", + " for clique in cliques:\n", + " # Count tetrahedrons containing this clique\n", + " counter = 0\n", + " for tetrahedron in tetrahedrons:\n", + " for comb in combinations(tetrahedron, 3):\n", + " if set(clique) == set(comb):\n", + " counter += 1\n", + " \n", + " # Create and add polygon\n", + " polygon = [pos[v] for v in clique]\n", + " poly = Polygon(\n", + " polygon,\n", + " closed=True,\n", + " facecolor=face_color_map.get(counter, \"gray\"), # Default to gray if counter not in map\n", + " edgecolor=\"pink\",\n", + " alpha=0.3,\n", + " )\n", + " plt.gca().add_patch(poly)\n", + " \n", + " plt.title(f\"Graph with cliques colored ({num_vertices} vertices)\")\n", + " \n", + " if show:\n", + " plt.show()\n", + " " ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ - "# shape is a list, it breaks everything if we keep it\n", - "if hasattr(data, \"shape\"):\n", - " del data[\"shape\"]\n", - " \n", - " \n", + "from torch_geometric.loader import NeighborLoader\n", + "\n", "# replace adjacency keys with temp\n", "def workaround_adj(data):\n", " n_incidences = len([key for key in data.keys() if \"incidence\" in key])\n", @@ -153,46 +182,6 @@ " del data[f\"adjacency_{i}\"]\n", " return data\n", "\n", - "# For some reason we need to call the adjacency matrices something else because the __cat_dim__ function will return a tuple for attributes with the adjacency or adj keys. This behaviour breaks stuff in the GlobalStorage module.\n", - "# for key in data.keys():\n", - "# value = data[key]\n", - "# print(key)\n", - "# print(value.shape)\n", - "# print(data._parent().__cat_dim__(key, value, data))" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Training, validation and split idxs should be defined somewhere, here we use a toy example\n", - "rank = 1\n", - "if hasattr(data, \"x_hyperedges\") and rank==1:\n", - " n_cells = data.x_hyperedges.shape[0]\n", - "else:\n", - " n_cells = data[f'x_{rank}'].shape[0]\n", - "\n", - "train_prop = 0.5\n", - "n_train = int(train_prop * n_cells)\n", - "train_mask = torch.zeros(n_cells, dtype=torch.bool)\n", - "train_mask[:n_train] = 1\n", - "\n", - "if rank != 0:\n", - " y = torch.zeros(n_cells, dtype=torch.long)\n", - " data.y = y\n", - "batch_size = 2" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "from torch_geometric.loader import NeighborLoader\n", - " \n", "def get_sampled_neighborhood(data, rank=0, is_hypergraph=False):\n", " ''' This function updates the edge_index attribute of torch_geometric.data.Data. \n", " \n", @@ -494,23 +483,61 @@ " " ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Manual Graph" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from topobenchmarkx.data.utils.utils import load_manual_graph\n", + "\n", + "cfg = compose(config_name=\"run.yaml\", \n", + " overrides=[\"dataset=graph/manual_dataset\", \"model=simplicial/san\"], \n", + " return_hydra_config=True)\n", + "data = load_manual_graph()\n", + "preprocessed_dataset = PreProcessor(data, './', cfg['transforms'])\n", + "data = preprocessed_dataset[0]\n", + "print(data)\n", + "plot_graph(data)" + ] + }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_60814/2824364303.py:56: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:54.)\n", - " A = torch.sparse.mm(I,I.T) # lower adj matrix\n", - "/usr/local/lib/python3.11/site-packages/torch_geometric/sampler/neighbor_sampler.py:61: UserWarning: Using 'NeighborSampler' without a 'pyg-lib' installation is deprecated and will be removed soon. Please install 'pyg-lib' for accelerated neighborhood sampling\n", - " warnings.warn(f\"Using '{self.__class__.__name__}' without a \"\n" - ] - } - ], + "outputs": [], "source": [ + "\n", + "# shape is a list, it breaks everything if we keep it\n", + "# TODO: add somehow to workaround\n", + "if hasattr(data, \"shape\"):\n", + " del data[\"shape\"]\n", + " \n", + "\n", + "# Training, validation and split idxs should be defined somewhere, here we use a toy example\n", + "rank = 1\n", + "if hasattr(data, \"x_hyperedges\") and rank==1:\n", + " n_cells = data.x_hyperedges.shape[0]\n", + "else:\n", + " n_cells = data[f'x_{rank}'].shape[0]\n", + "\n", + "train_prop = 0.5\n", + "n_train = int(train_prop * n_cells)\n", + "train_mask = torch.zeros(n_cells, dtype=torch.bool)\n", + "train_mask[:n_train] = 1\n", + "\n", + "if rank != 0:\n", + " y = torch.zeros(n_cells, dtype=torch.long)\n", + " data.y = y\n", + "batch_size = 2\n", + "\n", "# num_neighbors controls also the number of hops (for 2 hops do num_neighbors=[-1, -1])\n", "reduce = ReduceNeighborhoods(rank=rank, remove_self_loops=True)\n", "\n", @@ -520,34 +547,48 @@ " input_nodes=train_mask,\n", " batch_size=batch_size,\n", " shuffle=False,\n", - " transform=reduce)" + " transform=reduce)\n", + "\n", + "for batch in loader:\n", + " print(batch)\n", + " print(batch.n_id)\n", + " print(batch.edge_index)\n", + " if hasattr(batch, 'incidence_hyperedges'):\n", + " print(batch.incidence_hyperedges.to_dense())\n", + " else:\n", + " print(batch.incidence_3.to_dense())\n", + " print(batch.incidence_2.to_dense())\n", + " print(batch.incidence_1.to_dense())\n", + " break\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plot_graph(batch)" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Data(x=[17, 1433], edge_index=[2, 15], y=[17], train_mask=[17], val_mask=[17], test_mask=[17], incidence_hyperedges=[17, 2708], num_hyperedges=2708, x_0=[17, 1433], x_hyperedges=[17, 1433], n_id=[17], e_id=[15], input_id=[2], batch_size=2, incidence_1=[17, 29])\n", - "tensor([ 0, 1, 926, 1862, 2582, 1166, 633, 1701, 1866, 332, 1986, 470,\n", - " 1666, 652, 654, 2, 1454])\n", - "tensor([[ 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16],\n", - " [ 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1]])\n", - "tensor([[1., 0., 0., ..., 0., 0., 0.],\n", - " [0., 1., 1., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " ...,\n", - " [0., 1., 0., ..., 0., 0., 0.],\n", - " [0., 1., 1., ..., 0., 0., 0.],\n", - " [0., 0., 1., ..., 0., 0., 0.]])\n" - ] - } - ], + "outputs": [], "source": [ + "batch_size = 1\n", + "\n", + "# num_neighbors controls also the number of hops (for 2 hops do num_neighbors=[-1, -1])\n", + "reduce = ReduceNeighborhoods(rank=rank, remove_self_loops=True)\n", + "\n", + "loader = NeighborLoaderWrapper(data,\n", + " rank=rank,\n", + " num_neighbors=[-1],\n", + " input_nodes=train_mask,\n", + " batch_size=batch_size,\n", + " shuffle=False,\n", + " transform=reduce)\n", "for batch in loader:\n", " print(batch)\n", " print(batch.n_id)\n", @@ -558,62 +599,119 @@ " print(batch.incidence_3.to_dense())\n", " print(batch.incidence_2.to_dense())\n", " print(batch.incidence_1.to_dense())\n", - " break" + " break\n", + "\n", + "plot_graph(batch)" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "#use networkx to plot the batch graph\n", - "import networkx as nx\n", - "import matplotlib.pyplot as plt\n", + "visualize_simplicial_complex(batch)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cfg = compose(config_name=\"run.yaml\", \n", + " overrides=[\"dataset=graph/cocitation_cora\", \"model=hypergraph/allsettransformer\"], \n", + " return_hydra_config=True)\n", + "graph_loader = GraphLoader(cfg.dataset.loader.parameters)\n", + "dataset, dataset_dir = graph_loader.load()\n", + "preprocessed_dataset = PreProcessor(dataset, './', cfg['transforms'])\n", + "data = preprocessed_dataset[0]\n", + "# shape is a list, it breaks everything if we keep it\n", + "# TODO: add somehow to workaround\n", + "if hasattr(data, \"shape\"):\n", + " del data[\"shape\"]\n", + " \n", "\n", - "def plot_graph(data):\n", - " G = nx.Graph()\n", - " G.add_edges_from(data.edge_index.T.numpy())\n", - " list_nodes = dict(G.nodes.data())\n", + "# Training, validation and split idxs should be defined somewhere, here we use a toy example\n", + "rank = 1\n", + "if hasattr(data, \"x_hyperedges\") and rank==1:\n", + " n_cells = data.x_hyperedges.shape[0]\n", + "else:\n", + " n_cells = data[f'x_{rank}'].shape[0]\n", "\n", - " pos = nx.spring_layout(G)\n", - " labels = nx.get_node_attributes(G, 'label')\n", - " nx.draw(G, pos, labels=labels, with_labels=True, node_size=100)\n", - " \n", - " plt.show()\n", + "train_prop = 0.5\n", + "n_train = int(train_prop * n_cells)\n", + "train_mask = torch.zeros(n_cells, dtype=torch.bool)\n", + "train_mask[:n_train] = 1\n", + "\n", + "if rank != 0:\n", + " y = torch.zeros(n_cells, dtype=torch.long)\n", + " data.y = y\n", + "batch_size = 1\n", "\n", + "# num_neighbors controls also the number of hops (for 2 hops do num_neighbors=[-1, -1])\n", + "reduce = ReduceNeighborhoods(rank=rank, remove_self_loops=True)\n", + "\n", + "loader = NeighborLoaderWrapper(data,\n", + " rank=rank,\n", + " num_neighbors=[-1],\n", + " input_nodes=train_mask,\n", + " batch_size=batch_size,\n", + " shuffle=False,\n", + " transform=reduce)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ "for batch in loader:\n", - " plot_graph(batch)\n", + " print(batch)\n", + " print(batch.n_id)\n", + " print(batch.edge_index)\n", + " if hasattr(batch, 'incidence_hyperedges'):\n", + " print(batch.incidence_hyperedges.to_dense())\n", + " else:\n", + " print(batch.incidence_3.to_dense())\n", + " print(batch.incidence_2.to_dense())\n", + " print(batch.incidence_1.to_dense())\n", " break" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Data(x=[2708, 1433], edge_index=[2, 96888], y=[2708], x_0=[2708, 1433])" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], + "source": [ + "batch" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "# for batch in loader:\n", + "# plot_graph(batch)\n", + "# break" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "if hasattr(data, 'incidence_3'):\n", " del data['incidence_3']\n", @@ -632,27 +730,9 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Data(x=[6, 6], edge_index=[2, 14], y=[6], x_0=[6, 6], incidence_3=[2, 0], incidence_2=[7, 2], incidence_1=[6, 7], incidence_0=[1, 6], x_3=[0], x_2=[2, 2], x_1=[7, 3], temp_0=[6, 6])\n" - ] - }, - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Data(x=[4, 6], edge_index=[2, 8], y=[4], x_0=[4, 6], incidence_3=[1, 0], incidence_2=[4, 1], incidence_1=[4, 4], incidence_0=[1, 4], x_3=[0], x_2=[1, 2], x_1=[4, 3], n_id=[4], e_id=[3], input_id=[1], batch_size=1, adjacency_0=[4, 4])\n", - "tensor([2, 0, 1, 3])\n" - ] - } - ], + "outputs": [], "source": [ "for i, batch in enumerate(loader):\n", " if i==2:\n", @@ -738,27 +789,9 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Data(x=[4, 2], edge_index=[2, 12], y=[4], x_0=[4, 4], incidence_3=[4, 1], incidence_2=[6, 4], incidence_1=[4, 6], incidence_0=[1, 4], x_3=[1, 2], x_2=[4, 2], x_1=[6, 3], temp_0=[4, 4])\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "incidence_3 = torch.tensor([[1],[1],[1],[1]]).float().to_sparse()\n", "incidence_2 = torch.tensor([[1,0,1,0],[1,1,0,0],[0,1,1,0],[0,0,1,1],[1,0,0,1],[0,1,0,1]]).float().to_sparse()\n", @@ -792,28 +825,9 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Data(x=[4, 4], edge_index=[2, 12], y=[4], x_0=[4, 4], incidence_3=[4, 1], incidence_2=[6, 4], incidence_1=[4, 6], incidence_0=[1, 4], x_3=[1, 2], x_2=[4, 2], x_1=[6, 3], n_id=[4], e_id=[3], input_id=[1], batch_size=1, adjacency_0=[4, 4])\n", - "tensor([0, 1, 2, 3])\n" - ] - } - ], + "outputs": [], "source": [ "# num_neighbors controls also the number of hops (for 2 hops do num_neighbors=[-1, -1])\n", "reduce = ReduceNeighborhoods(rank=rank, remove_self_loops=True)\n", From 72f92ecff6a5808954cac8fa4d623ca97c026379 Mon Sep 17 00:00:00 2001 From: Coerulatus Date: Fri, 15 Nov 2024 11:11:58 +0000 Subject: [PATCH 07/24] Marco - batching done --- tutorials/batching.ipynb | 302 +++++++++++++++++++++++++++++---------- 1 file changed, 223 insertions(+), 79 deletions(-) diff --git a/tutorials/batching.ipynb b/tutorials/batching.ipynb index 6c449f42..3cb816cb 100644 --- a/tutorials/batching.ipynb +++ b/tutorials/batching.ipynb @@ -2,9 +2,31 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_45596/2455096930.py:26: UserWarning: \n", + "The version_base parameter is not specified.\n", + "Please specify a compatability version level, or None.\n", + "Will assume defaults for version 1.1\n", + " initialize(config_path=\"../configs\", job_name=\"job\")\n" + ] + }, + { + "data": { + "text/plain": [ + "hydra.initialize()" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "import rootutils\n", "\n", @@ -132,7 +154,7 @@ " # Draw edges\n", " nx.draw_networkx_edges(G, pos, edgelist=edges, edge_color=\"g\", width=2, alpha=0.5)\n", " \n", - " # Add edge labels\n", + " # # Add edge labels\n", " for i, (u, v) in enumerate(edges):\n", " x = (pos[u][0] + pos[v][0]) / 2\n", " y = (pos[u][1] + pos[v][1]) / 2\n", @@ -174,18 +196,36 @@ "from torch_geometric.loader import NeighborLoader\n", "\n", "# replace adjacency keys with temp\n", - "def workaround_adj(data):\n", - " n_incidences = len([key for key in data.keys() if \"incidence\" in key])\n", - " for i in range(n_incidences):\n", + "def workaround_data(data):\n", + " \"\"\" The function is a workaround to change the data to work with NeighborLoader. \n", + " \n", + " The function replaces the keys with adjacency in the name with temp. It also removes the shape attribute if present.\n", + " \n", + " Parameters\n", + " ----------\n", + " data: torch_geometric.data.Data\n", + " The input data.\n", + " \n", + " Returns\n", + " -------\n", + " torch_geometric.data.Data\n", + " The output data with the keys replaced and the shape attribute removed.\n", + " \"\"\"\n", + " n_adjacencies = len([key for key in data.keys() if \"adjacency\" in key])\n", + " for i in range(n_adjacencies):\n", " if f\"adjacency_{i}\" in data.keys():\n", " data[f\"temp_{i}\"] = data[f\"adjacency_{i}\"]\n", " del data[f\"adjacency_{i}\"]\n", + " \n", + " # shape is a list, it breaks the NeighborLoader if we keep it\n", + " if hasattr(data, 'shape'):\n", + " del data.shape\n", " return data\n", "\n", "def get_sampled_neighborhood(data, rank=0, is_hypergraph=False):\n", " ''' This function updates the edge_index attribute of torch_geometric.data.Data. \n", " \n", - " The function finds cells, of the specified rank K, that are either upper or lower neighbors.\n", + " The function finds cells, of the specified rank, that are either upper or lower neighbors.\n", " \n", " Parameters\n", " ----------\n", @@ -203,24 +243,32 @@ " edge_index contains indices of connected cells of the specified rank K. \n", " Two cells of rank K are connected if they are either lower or upper neighbors. \n", " '''\n", - " # TODO: add upper adj\n", " if rank == 0:\n", " return data\n", - " if is_hypergraph: #TODO: add rank=1 case\n", - " I = data.incidence_hyperedges\n", - " A = torch.sparse.mm(I,I.T) # lower adj matrix\n", - " edges = A.indices() \n", + " if is_hypergraph: \n", + " if rank > 1:\n", + " raise ValueError(\"Hypergraphs are not supported for ranks greater than 1.\")\n", + " if rank == 1:\n", + " I = data.incidence_hyperedges\n", + " A = torch.sparse.mm(I,I.T) # lower adj matrix\n", + " edges = A.indices()\n", + " else:\n", + " I = data.incidence_hyperedges\n", + " A = torch.sparse.mm(I.T,I)\n", + " edges = A.indices() \n", " else:\n", " # get number of incidences\n", " max_rank = len([key for key in data.keys() if \"incidence\" in key])-1\n", " if rank > max_rank:\n", " raise ValueError(f\"Rank {rank} is greater than the maximum rank {max_rank} in the data.\")\n", + " \n", + " # This considers the upper adjacencies\n", " if rank == max_rank:\n", " edges = torch.empty((2, 0), dtype=torch.long)\n", " else:\n", - " P = data[f\"incidence_{rank+1}\"]\n", - " Q = torch.sparse.mm(P,P.T)\n", - " edges = Q.indices()\n", + " I = data[f\"incidence_{rank+1}\"]\n", + " A = torch.sparse.mm(I,I.T)\n", + " edges = A.indices()\n", " \n", " # This is for selecting the whole upper cells\n", " # for i in range(rank+1, max_rank):\n", @@ -228,12 +276,13 @@ " # Q = torch.sparse.mm(P,P.T)\n", " # edges = torch.cat((edges, Q.indices()), dim=1)\n", " \n", - " # This considers the lower adjacency \n", - " P = data[f\"incidence_{rank}\"]\n", - " Q = torch.sparse.mm(P.T,P)\n", - " edges = torch.cat((edges, Q.indices()), dim=1)\n", + " # This considers the lower adjacencies\n", + " if rank != 0: \n", + " I = data[f\"incidence_{rank}\"]\n", + " A = torch.sparse.mm(I.T,I)\n", + " edges = torch.cat((edges, A.indices()), dim=1)\n", " \n", - " # This is for selecting if the cells share any node\n", + " # This is for selecting cells if they share any node\n", " # for i in range(rank-1, 0, -1):\n", " # P = torch.sparse.mm(data[f\"incidence_{i}\"], P)\n", " # Q = torch.sparse.mm(P.T,P)\n", @@ -253,6 +302,8 @@ " else:\n", " data.x = data[f'x_{rank}']\n", " \n", + " if hasattr(data, 'num_nodes'):\n", + " data.num_nodes = data.x.shape[0]\n", " return data\n", "\n", "def reduce_higher_ranks_incidences(batch, cells_ids, rank, max_rank, is_hypergraph=False):\n", @@ -275,8 +326,10 @@ " -------\n", " torch_geometric.data.Data\n", " The output data with the reduced incidences.\n", + " list[torch.Tensor]\n", + " The updated indices of the cells. Each element of the list is a tensor containing the ids of the cells of the corresponding rank.\n", " \"\"\"\n", - " for i in range(1, max_rank+1):\n", + " for i in range(rank+1, max_rank+1):\n", " if is_hypergraph:\n", " incidence = batch.incidence_hyperedges\n", " else:\n", @@ -287,15 +340,11 @@ " cells_ids[i] = torch.where(torch.sum(incidence, dim=0).to_dense() > 1)[0]\n", " incidence = torch.index_select(incidence, 1, cells_ids[i])\n", " batch[f\"incidence_{i}\"] = incidence\n", - " if not is_hypergraph:\n", - " incidence = batch[f\"incidence_0\"]\n", - " incidence = torch.index_select(incidence, 1, cells_ids[0])\n", - " batch[f\"incidence_0\"] = incidence\n", " \n", " return batch, cells_ids\n", "\n", - "def get_node_indices(batch, cells_ids, rank, is_hypergraph=False):\n", - " \"\"\" Get the indices of the nodes contained by the cells specified in cells_ids and rank.\n", + "def reduce_lower_ranks_incidences(batch, cells_ids, rank, is_hypergraph=False):\n", + " \"\"\" Reduce the incidences with lower rank than the specified one.\n", " \n", " Parameters\n", " ----------\n", @@ -312,16 +361,24 @@ " -------\n", " torch.Tensor\n", " The indices of the nodes contained by the cells.\n", + " list[torch.Tensor]\n", + " The updated indices of the cells. Each element of the list is a tensor containing the ids of the cells of the corresponding rank.\n", " \"\"\"\n", - " cells_ids_new = [c_i for c_i in cells_ids]\n", " for i in range(rank, 0, -1):\n", " if is_hypergraph:\n", " incidence = batch.incidence_hyperedges\n", " else:\n", " incidence = batch[f\"incidence_{i}\"]\n", - " incidence = torch.index_select(incidence, 1, cells_ids_new[i])\n", - " cells_ids_new[i-1] = torch.where(torch.sum(incidence, dim=1).to_dense() > 0)[0]\n", - " return cells_ids_new[0]\n", + " incidence = torch.index_select(incidence, 1, cells_ids[i])\n", + " cells_ids[i-1] = torch.where(torch.sum(incidence, dim=1).to_dense() > 0)[0]\n", + " incidence = torch.index_select(incidence, 0, cells_ids[i-1])\n", + " batch[f\"incidence_{i}\"] = incidence\n", + " \n", + " if not is_hypergraph:\n", + " incidence = batch[f\"incidence_0\"]\n", + " incidence = torch.index_select(incidence, 1, cells_ids[0])\n", + " batch[f\"incidence_0\"] = incidence\n", + " return batch, cells_ids\n", "\n", "def reduce_matrices(batch, cells_ids, names, rank, max_rank):\n", " \"\"\" Reduce the matrices using the indices in cells_ids. \n", @@ -349,7 +406,6 @@ " for i in range(max_rank+1):\n", " for name in names:\n", " if f\"{name}{i}\" in batch.keys():\n", - " # matrix = change_sparse(batch[f\"{name}{i}\"])\n", " matrix = batch[f\"{name}{i}\"]\n", " if i==rank:\n", " matrix = torch.index_select(matrix, 1, cells_ids[i])\n", @@ -390,14 +446,10 @@ " \n", " # the indices of the cells selected by the NeighborhoodLoader are saved in the batch in the attribute n_id\n", " cells_ids[rank] = batch.n_id\n", - " \n", - " if rank == 0:\n", - " cells_ids[0] = batch.n_id\n", - " else:\n", - " cells_ids[0] = get_node_indices(batch, cells_ids, rank, is_hypergraph)\n", " \n", " batch, cells_ids = reduce_higher_ranks_incidences(batch, cells_ids, rank, max_rank, is_hypergraph)\n", - "\n", + " batch, cells_ids = reduce_lower_ranks_incidences(batch, cells_ids, rank, is_hypergraph)\n", + " \n", " batch = reduce_matrices(batch, \n", " cells_ids, \n", " names=['down_laplacian_', 'up_laplacian_', 'hodge_laplacian_', 'temp_'],\n", @@ -426,6 +478,8 @@ " \n", " # fix x\n", " batch.x = batch[f\"x_0\"]\n", + " if hasattr(batch, 'num_nodes'):\n", + " batch.num_nodes = batch.x.shape[0]\n", " \n", " return batch\n", "\n", @@ -460,7 +514,7 @@ " return reduce_neighborhoods(batch, self.rank, self.remove_self_loops)\n", "\n", "class NeighborLoaderWrapper(NeighborLoader):\n", - " \"\"\" NeighborLoader with get_sampled_neighborhood.\n", + " \"\"\" Wrapper that applies the needed transformations to the data before passing it to NeighborLoader.\n", " \n", " Parameters\n", " ----------\n", @@ -475,7 +529,7 @@ " is_hypergraph = hasattr(data, 'incidence_hyperedges')\n", " data = get_sampled_neighborhood(data, rank, is_hypergraph)\n", " # This workaround is needed because torch_geometric treats any attribute of data with adj in the name differently and it raises errors.\n", - " data = workaround_adj(data)\n", + " data = workaround_data(data)\n", " if 'num_neighbors' in kwargs.keys():\n", " if len(kwargs['num_neighbors']) > 1:\n", " raise NotImplementedError(\"NeighborLoaderWrapper only supports one-hop neighborhood selection.\")\n", @@ -492,9 +546,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Transform parameters are the same, using existing data_dir: ./graph2simplicial_lifting/131528455\n", + "Data(x=[8, 1], edge_index=[2, 13], y=[8], num_nodes=8, incidence_0=[1, 8], down_laplacian_0=[8, 8], up_laplacian_0=[8, 8], adjacency_0=[8, 8], hodge_laplacian_0=[8, 8], incidence_1=[8, 13], down_laplacian_1=[13, 13], up_laplacian_1=[13, 13], adjacency_1=[13, 13], hodge_laplacian_1=[13, 13], incidence_2=[13, 6], down_laplacian_2=[6, 6], up_laplacian_2=[6, 6], adjacency_2=[6, 6], hodge_laplacian_2=[6, 6], incidence_3=[6, 1], down_laplacian_3=[1, 1], up_laplacian_3=[1, 1], adjacency_3=[1, 1], hodge_laplacian_3=[1, 1], shape=[4], x_0=[8, 1], x_1=[13, 1], x_2=[6, 1], x_3=[1, 1])\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "from topobenchmarkx.data.utils.utils import load_manual_graph\n", "\n", @@ -510,17 +583,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Data(x=[8, 1], edge_index=[2, 13], y=[13], num_nodes=8, incidence_0=[1, 8], down_laplacian_0=[8, 8], up_laplacian_0=[8, 8], adjacency_0=[8, 8], hodge_laplacian_0=[8, 8], incidence_1=[8, 13], down_laplacian_1=[13, 13], up_laplacian_1=[13, 13], adjacency_1=[13, 13], hodge_laplacian_1=[13, 13], incidence_2=[13, 6], down_laplacian_2=[6, 6], up_laplacian_2=[6, 6], adjacency_2=[6, 6], hodge_laplacian_2=[6, 6], incidence_3=[6, 1], down_laplacian_3=[1, 1], up_laplacian_3=[1, 1], adjacency_3=[1, 1], hodge_laplacian_3=[1, 1], x_0=[8, 1], x_1=[13, 1], x_2=[6, 1], x_3=[1, 1])" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "\n", - "# shape is a list, it breaks everything if we keep it\n", - "# TODO: add somehow to workaround\n", - "if hasattr(data, \"shape\"):\n", - " del data[\"shape\"]\n", - " \n", - "\n", "# Training, validation and split idxs should be defined somewhere, here we use a toy example\n", "rank = 1\n", "if hasattr(data, \"x_hyperedges\") and rank==1:\n", @@ -536,9 +613,18 @@ "if rank != 0:\n", " y = torch.zeros(n_cells, dtype=torch.long)\n", " data.y = y\n", + "\n", + "data" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ "batch_size = 2\n", "\n", - "# num_neighbors controls also the number of hops (for 2 hops do num_neighbors=[-1, -1])\n", "reduce = ReduceNeighborhoods(rank=rank, remove_self_loops=True)\n", "\n", "loader = NeighborLoaderWrapper(data,\n", @@ -547,10 +633,37 @@ " input_nodes=train_mask,\n", " batch_size=batch_size,\n", " shuffle=False,\n", - " transform=reduce)\n", - "\n", + " transform=reduce)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data(x=[7, 1], edge_index=[2, 22], y=[10], num_nodes=7, incidence_0=[1, 7], down_laplacian_0=[7, 7], up_laplacian_0=[7, 7], hodge_laplacian_0=[7, 7], incidence_1=[7, 10], down_laplacian_1=[10, 10], up_laplacian_1=[10, 10], hodge_laplacian_1=[10, 10], incidence_2=[10, 6], down_laplacian_2=[6, 6], up_laplacian_2=[6, 6], hodge_laplacian_2=[6, 6], incidence_3=[6, 1], down_laplacian_3=[1, 1], up_laplacian_3=[1, 1], hodge_laplacian_3=[1, 1], x_0=[7, 1], x_1=[10, 1], x_2=[6, 1], x_3=[1, 1], n_id=[10], e_id=[13], input_id=[2], batch_size=2, adjacency_0=[7, 7], adjacency_1=[10, 10], adjacency_2=[6, 6], adjacency_3=[1, 1])\n", + "The cells of rank 1 that were originally selected are tensor([0, 1])\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ "for batch in loader:\n", " print(batch)\n", + " print(f\"The cells of rank {rank} that were originally selected are {batch.n_id[:batch_size]}\")\n", " print(batch.n_id)\n", " print(batch.edge_index)\n", " if hasattr(batch, 'incidence_hyperedges'):\n", @@ -559,16 +672,8 @@ " print(batch.incidence_3.to_dense())\n", " print(batch.incidence_2.to_dense())\n", " print(batch.incidence_1.to_dense())\n", - " break\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plot_graph(batch)" + " plot_graph(batch)\n", + " break" ] }, { @@ -606,7 +711,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, "outputs": [], "source": [] @@ -622,9 +727,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Transform parameters are the same, using existing data_dir: ./graph2hypergraph_lifting/1273654097\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.11/site-packages/torch_geometric/sampler/neighbor_sampler.py:61: UserWarning: Using 'NeighborSampler' without a 'pyg-lib' installation is deprecated and will be removed soon. Please install 'pyg-lib' for accelerated neighborhood sampling\n", + " warnings.warn(f\"Using '{self.__class__.__name__}' without a \"\n" + ] + } + ], "source": [ "cfg = compose(config_name=\"run.yaml\", \n", " overrides=[\"dataset=graph/cocitation_cora\", \"model=hypergraph/allsettransformer\"], \n", @@ -633,14 +754,9 @@ "dataset, dataset_dir = graph_loader.load()\n", "preprocessed_dataset = PreProcessor(dataset, './', cfg['transforms'])\n", "data = preprocessed_dataset[0]\n", - "# shape is a list, it breaks everything if we keep it\n", - "# TODO: add somehow to workaround\n", - "if hasattr(data, \"shape\"):\n", - " del data[\"shape\"]\n", - " \n", "\n", "# Training, validation and split idxs should be defined somewhere, here we use a toy example\n", - "rank = 1\n", + "rank = 0\n", "if hasattr(data, \"x_hyperedges\") and rank==1:\n", " n_cells = data.x_hyperedges.shape[0]\n", "else:\n", @@ -670,9 +786,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data(x=[4, 1433], edge_index=[2, 3], y=[4], train_mask=[4], val_mask=[4], test_mask=[4], incidence_hyperedges=[4, 2708], num_hyperedges=2708, x_0=[4, 1433], x_hyperedges=[4, 1433], n_id=[4], e_id=[3], input_id=[1], batch_size=1, incidence_1=[4, 5], num_nodes=4)\n", + "tensor([ 0, 1862, 633, 2582])\n", + "tensor([[1, 2, 3],\n", + " [0, 0, 0]])\n", + "tensor([[1., 0., 0., ..., 0., 0., 0.],\n", + " [1., 0., 0., ..., 0., 0., 0.],\n", + " [1., 0., 0., ..., 0., 0., 0.],\n", + " [1., 0., 0., ..., 0., 0., 0.]])\n" + ] + } + ], "source": [ "for batch in loader:\n", " print(batch)\n", @@ -684,6 +815,7 @@ " print(batch.incidence_3.to_dense())\n", " print(batch.incidence_2.to_dense())\n", " print(batch.incidence_1.to_dense())\n", + " \n", " break" ] }, @@ -698,7 +830,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -709,9 +841,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'data' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[4], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(\u001b[43mdata\u001b[49m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mincidence_3\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mincidence_3\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(data, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mx_3\u001b[39m\u001b[38;5;124m'\u001b[39m):\n", + "\u001b[0;31mNameError\u001b[0m: name 'data' is not defined" + ] + } + ], "source": [ "if hasattr(data, 'incidence_3'):\n", " del data['incidence_3']\n", @@ -756,7 +900,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ From 76e1d0314a6e1b0bba3f9f1cd35928ce4545a379 Mon Sep 17 00:00:00 2001 From: levtelyatnikov Date: Fri, 15 Nov 2024 21:23:16 +0100 Subject: [PATCH 08/24] added some comments --- tutorials/batching.ipynb | 27 ++++++++++++++++-- .../131528455/data.pt | Bin 0 -> 22921 bytes .../path_transform_parameters_dict.json | 11 +++++++ .../131528455/pre_filter.pt | Bin 0 -> 864 bytes .../131528455/pre_transform.pt | Bin 0 -> 864 bytes 5 files changed, 35 insertions(+), 3 deletions(-) create mode 100644 tutorials/graph2simplicial_lifting/131528455/data.pt create mode 100644 tutorials/graph2simplicial_lifting/131528455/path_transform_parameters_dict.json create mode 100644 tutorials/graph2simplicial_lifting/131528455/pre_filter.pt create mode 100644 tutorials/graph2simplicial_lifting/131528455/pre_transform.pt diff --git a/tutorials/batching.ipynb b/tutorials/batching.ipynb index 3cb816cb..a822f091 100644 --- a/tutorials/batching.ipynb +++ b/tutorials/batching.ipynb @@ -9,7 +9,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_45596/2455096930.py:26: UserWarning: \n", + "/tmp/ipykernel_461589/2455096930.py:26: UserWarning: \n", "The version_base parameter is not specified.\n", "Please specify a compatability version level, or None.\n", "Will assume defaults for version 1.1\n", @@ -334,9 +334,12 @@ " incidence = batch.incidence_hyperedges\n", " else:\n", " incidence = batch[f\"incidence_{i}\"]\n", - " \n", + "\n", + " # Having rank 2 to be sampled, then range(2+1, max_rank+1)\n", + " # Hence i start from 3 and i == rank+1\n", " if i != rank+1:\n", " incidence = torch.index_select(incidence, 0, cells_ids[i-1])\n", + "\n", " cells_ids[i] = torch.where(torch.sum(incidence, dim=0).to_dense() > 1)[0]\n", " incidence = torch.index_select(incidence, 1, cells_ids[i])\n", " batch[f\"incidence_{i}\"] = incidence\n", @@ -364,13 +367,22 @@ " list[torch.Tensor]\n", " The updated indices of the cells. Each element of the list is a tensor containing the ids of the cells of the corresponding rank.\n", " \"\"\"\n", + " \n", + " # Start iterating from the rank chosen for sampling and go down to 1\n", " for i in range(rank, 0, -1):\n", + " # incidence_i \\in R^{i-1, i}, incidence_{1} describes {node x edges} relations\n", " if is_hypergraph:\n", " incidence = batch.incidence_hyperedges\n", " else:\n", " incidence = batch[f\"incidence_{i}\"]\n", + "\n", + " # Select i-cell indexes: assuming we choosen rank=2 for sampling \n", + " # hence select all 2-cells in incidence_2 \\in R^{1, 2}, describing {1-cells x 2-cells} relations)\n", " incidence = torch.index_select(incidence, 1, cells_ids[i])\n", + " \n", + " # For the selected 2-cells find all all 1-cells that belong to 2-cells\n", " cells_ids[i-1] = torch.where(torch.sum(incidence, dim=1).to_dense() > 0)[0]\n", + " # Reduce the incidence to the selected 1-cells as well, getting sampled {1-cells x 2-cells}\n", " incidence = torch.index_select(incidence, 0, cells_ids[i-1])\n", " batch[f\"incidence_{i}\"] = incidence\n", " \n", @@ -553,10 +565,19 @@ "name": "stdout", "output_type": "stream", "text": [ - "Transform parameters are the same, using existing data_dir: ./graph2simplicial_lifting/131528455\n", "Data(x=[8, 1], edge_index=[2, 13], y=[8], num_nodes=8, incidence_0=[1, 8], down_laplacian_0=[8, 8], up_laplacian_0=[8, 8], adjacency_0=[8, 8], hodge_laplacian_0=[8, 8], incidence_1=[8, 13], down_laplacian_1=[13, 13], up_laplacian_1=[13, 13], adjacency_1=[13, 13], hodge_laplacian_1=[13, 13], incidence_2=[13, 6], down_laplacian_2=[6, 6], up_laplacian_2=[6, 6], adjacency_2=[6, 6], hodge_laplacian_2=[6, 6], incidence_3=[6, 1], down_laplacian_3=[1, 1], up_laplacian_3=[1, 1], adjacency_3=[1, 1], hodge_laplacian_3=[1, 1], shape=[4], x_0=[8, 1], x_1=[13, 1], x_2=[6, 1], x_3=[1, 1])\n" ] }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Processing...\n", + "/home/lev/miniconda3/envs/tbx/lib/python3.11/site-packages/scipy/sparse/_index.py:143: SparseEfficiencyWarning: Changing the sparsity structure of a csr_matrix is expensive. lil_matrix is more efficient.\n", + " self._set_arrayXarray(i, j, x)\n", + "Done!\n" + ] + }, { "data": { "image/png": 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zW6TAz`{KOxP$r-8Jn4y znwuM&Tbh`e7#JFu8X6mbfswJLrICS=g{6hLp@AjHl~u+{s}SMO2y!eu=8@w@03?9| z(9=PHH>z&ruopzpSpnq1bi-p4T|08{@uO(JhNK-DUFe#TeFaL22oS`Gup4`L2Y9ow z=|DBeG3&y$0<#1dz-Tt0Ng(h8N(1$=gZTfzVaUJ$5)SZY1tmQO79a$vho}VrRjQ}5 From f623d24ae8be02344e18a25c5388cc4fe50b0963 Mon Sep 17 00:00:00 2001 From: levtelyatnikov Date: Fri, 15 Nov 2024 22:40:57 +0100 Subject: [PATCH 10/24] added just sampling over the graph --- tutorials/batching.ipynb | 164 +++++++++++++++++- .../131528455/data.pt | Bin 0 -> 22921 bytes .../path_transform_parameters_dict.json | 11 ++ .../131528455/pre_filter.pt | Bin 0 -> 864 bytes .../131528455/pre_transform.pt | Bin 0 -> 864 bytes 5 files changed, 168 insertions(+), 7 deletions(-) create mode 100644 tutorials/graph2simplicial_lifting/131528455/data.pt create mode 100644 tutorials/graph2simplicial_lifting/131528455/path_transform_parameters_dict.json create mode 100644 tutorials/graph2simplicial_lifting/131528455/pre_filter.pt create mode 100644 tutorials/graph2simplicial_lifting/131528455/pre_transform.pt diff --git a/tutorials/batching.ipynb b/tutorials/batching.ipynb index a822f091..c93c0d23 100644 --- a/tutorials/batching.ipynb +++ b/tutorials/batching.ipynb @@ -9,7 +9,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_461589/2455096930.py:26: UserWarning: \n", + "/tmp/ipykernel_482697/2455096930.py:26: UserWarning: \n", "The version_base parameter is not specified.\n", "Please specify a compatability version level, or None.\n", "Will assume defaults for version 1.1\n", @@ -610,7 +610,7 @@ { "data": { "text/plain": [ - "Data(x=[8, 1], edge_index=[2, 13], y=[13], num_nodes=8, incidence_0=[1, 8], down_laplacian_0=[8, 8], up_laplacian_0=[8, 8], adjacency_0=[8, 8], hodge_laplacian_0=[8, 8], incidence_1=[8, 13], down_laplacian_1=[13, 13], up_laplacian_1=[13, 13], adjacency_1=[13, 13], hodge_laplacian_1=[13, 13], incidence_2=[13, 6], down_laplacian_2=[6, 6], up_laplacian_2=[6, 6], adjacency_2=[6, 6], hodge_laplacian_2=[6, 6], incidence_3=[6, 1], down_laplacian_3=[1, 1], up_laplacian_3=[1, 1], adjacency_3=[1, 1], hodge_laplacian_3=[1, 1], x_0=[8, 1], x_1=[13, 1], x_2=[6, 1], x_3=[1, 1])" + "Data(x=[8, 1], edge_index=[2, 13], y=[13], num_nodes=8, incidence_0=[1, 8], down_laplacian_0=[8, 8], up_laplacian_0=[8, 8], adjacency_0=[8, 8], hodge_laplacian_0=[8, 8], incidence_1=[8, 13], down_laplacian_1=[13, 13], up_laplacian_1=[13, 13], adjacency_1=[13, 13], hodge_laplacian_1=[13, 13], incidence_2=[13, 6], down_laplacian_2=[6, 6], up_laplacian_2=[6, 6], adjacency_2=[6, 6], hodge_laplacian_2=[6, 6], incidence_3=[6, 1], down_laplacian_3=[1, 1], up_laplacian_3=[1, 1], adjacency_3=[1, 1], hodge_laplacian_3=[1, 1], shape=[4], x_0=[8, 1], x_1=[13, 1], x_2=[6, 1], x_3=[1, 1])" ] }, "execution_count": 5, @@ -640,9 +640,18 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_482697/3633582491.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)\n", + " A = torch.sparse.mm(I,I.T)\n" + ] + } + ], "source": [ "batch_size = 2\n", "\n", @@ -659,15 +668,41 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Data(x=[7, 1], edge_index=[2, 22], y=[10], num_nodes=7, incidence_0=[1, 7], down_laplacian_0=[7, 7], up_laplacian_0=[7, 7], hodge_laplacian_0=[7, 7], incidence_1=[7, 10], down_laplacian_1=[10, 10], up_laplacian_1=[10, 10], hodge_laplacian_1=[10, 10], incidence_2=[10, 6], down_laplacian_2=[6, 6], up_laplacian_2=[6, 6], hodge_laplacian_2=[6, 6], incidence_3=[6, 1], down_laplacian_3=[1, 1], up_laplacian_3=[1, 1], hodge_laplacian_3=[1, 1], x_0=[7, 1], x_1=[10, 1], x_2=[6, 1], x_3=[1, 1], n_id=[10], e_id=[13], input_id=[2], batch_size=2, adjacency_0=[7, 7], adjacency_1=[10, 10], adjacency_2=[6, 6], adjacency_3=[1, 1])\n", - "The cells of rank 1 that were originally selected are tensor([0, 1])\n" + "Data(x=[7, 1], edge_index=[2, 22], y=[10], num_nodes=7, incidence_0=[1, 7], down_laplacian_0=[7, 7], up_laplacian_0=[7, 7], hodge_laplacian_0=[7, 7], incidence_1=[7, 10], down_laplacian_1=[10, 10], up_laplacian_1=[10, 10], hodge_laplacian_1=[10, 10], incidence_2=[10, 6], down_laplacian_2=[6, 6], up_laplacian_2=[6, 6], hodge_laplacian_2=[6, 6], incidence_3=[6, 1], down_laplacian_3=[1, 1], up_laplacian_3=[1, 1], hodge_laplacian_3=[1, 1], x_0=[7, 1], x_1=[10, 1], x_2=[6, 1], x_3=[1, 1], n_id=[10], e_id=[13], num_sampled_nodes=[2], num_sampled_edges=[1], input_id=[2], batch_size=2, adjacency_0=[7, 7], adjacency_1=[10, 10], adjacency_2=[6, 6], adjacency_3=[1, 1])\n", + "The cells of rank 1 that were originally selected are tensor([0, 1])\n", + "tensor([0, 1, 5, 4, 3, 2, 6, 7, 9, 8])\n", + "tensor([[0, 0, 0, 0, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 4, 4, 4, 5, 5, 6, 6, 6],\n", + " [1, 2, 4, 6, 0, 2, 4, 0, 1, 3, 4, 5, 6, 2, 0, 1, 2, 2, 6, 0, 2, 5]])\n", + "tensor([[1.],\n", + " [1.],\n", + " [1.],\n", + " [0.],\n", + " [1.],\n", + " [0.]])\n", + "tensor([[1., 1., 0., 0., 0., 0.],\n", + " [1., 0., 1., 1., 0., 0.],\n", + " [0., 1., 0., 0., 1., 0.],\n", + " [1., 0., 0., 0., 1., 0.],\n", + " [0., 0., 0., 1., 0., 0.],\n", + " [0., 1., 1., 0., 0., 0.],\n", + " [0., 0., 0., 0., 0., 0.],\n", + " [0., 0., 1., 0., 1., 0.],\n", + " [0., 0., 0., 1., 0., 1.],\n", + " [0., 0., 0., 0., 0., 1.]])\n", + "tensor([[1., 1., 0., 0., 1., 1., 0., 0., 0., 0.],\n", + " [1., 0., 1., 1., 0., 0., 0., 0., 0., 0.],\n", + " [0., 1., 0., 1., 0., 0., 1., 1., 1., 1.],\n", + " [0., 0., 0., 0., 0., 0., 1., 0., 0., 0.],\n", + " [0., 0., 1., 0., 0., 1., 0., 1., 0., 0.],\n", + " [0., 0., 0., 0., 0., 0., 0., 0., 0., 1.],\n", + " [0., 0., 0., 0., 1., 0., 0., 0., 1., 0.]])\n" ] }, { @@ -697,6 +732,121 @@ " break" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Investigate how NodeSampler works" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Transform parameters are the same, using existing data_dir: ./graph2simplicial_lifting/131528455\n" + ] + } + ], + "source": [ + "cfg = compose(config_name=\"run.yaml\", \n", + " overrides=[\"dataset=graph/manual_dataset\", \"model=simplicial/san\"], \n", + " return_hydra_config=True)\n", + "data = load_manual_graph()\n", + "preprocessed_dataset = PreProcessor(data, './', cfg['transforms'])\n", + "data = preprocessed_dataset[0]\n", + "\n", + "data.edge_index = data.edge_index.contiguous()" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "loader = NeighborLoader(\n", + " data=data,\n", + " num_neighbors=[-1], \n", + " batch_size=2,\n", + " input_nodes=None\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "tuple indices must be integers or slices, not tuple", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[31], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43miter\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mloader\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/miniconda3/envs/tbx/lib/python3.11/site-packages/torch/utils/data/dataloader.py:631\u001b[0m, in \u001b[0;36m_BaseDataLoaderIter.__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 628\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sampler_iter \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 629\u001b[0m \u001b[38;5;66;03m# TODO(https://github.com/pytorch/pytorch/issues/76750)\u001b[39;00m\n\u001b[1;32m 630\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reset() \u001b[38;5;66;03m# type: ignore[call-arg]\u001b[39;00m\n\u001b[0;32m--> 631\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_next_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 632\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_yielded \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m 633\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dataset_kind \u001b[38;5;241m==\u001b[39m _DatasetKind\u001b[38;5;241m.\u001b[39mIterable \u001b[38;5;129;01mand\u001b[39;00m \\\n\u001b[1;32m 634\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_IterableDataset_len_called \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \\\n\u001b[1;32m 635\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_yielded \u001b[38;5;241m>\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_IterableDataset_len_called:\n", + "File \u001b[0;32m~/miniconda3/envs/tbx/lib/python3.11/site-packages/torch/utils/data/dataloader.py:675\u001b[0m, in \u001b[0;36m_SingleProcessDataLoaderIter._next_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 673\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_next_data\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 674\u001b[0m index \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_next_index() \u001b[38;5;66;03m# may raise StopIteration\u001b[39;00m\n\u001b[0;32m--> 675\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_dataset_fetcher\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfetch\u001b[49m\u001b[43m(\u001b[49m\u001b[43mindex\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# may raise StopIteration\u001b[39;00m\n\u001b[1;32m 676\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pin_memory:\n\u001b[1;32m 677\u001b[0m data \u001b[38;5;241m=\u001b[39m _utils\u001b[38;5;241m.\u001b[39mpin_memory\u001b[38;5;241m.\u001b[39mpin_memory(data, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pin_memory_device)\n", + "File \u001b[0;32m~/miniconda3/envs/tbx/lib/python3.11/site-packages/torch/utils/data/_utils/fetch.py:54\u001b[0m, in \u001b[0;36m_MapDatasetFetcher.fetch\u001b[0;34m(self, possibly_batched_index)\u001b[0m\n\u001b[1;32m 52\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 53\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdataset[possibly_batched_index]\n\u001b[0;32m---> 54\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[43mcollate_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/miniconda3/envs/tbx/lib/python3.11/site-packages/torch_geometric/loader/node_loader.py:150\u001b[0m, in \u001b[0;36mNodeLoader.collate_fn\u001b[0;34m(self, index)\u001b[0m\n\u001b[1;32m 147\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnode_sampler\u001b[38;5;241m.\u001b[39msample_from_nodes(input_data)\n\u001b[1;32m 149\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfilter_per_worker: \u001b[38;5;66;03m# Execute `filter_fn` in the worker process\u001b[39;00m\n\u001b[0;32m--> 150\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfilter_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[43mout\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 152\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m out\n", + "File \u001b[0;32m~/miniconda3/envs/tbx/lib/python3.11/site-packages/torch_geometric/loader/node_loader.py:167\u001b[0m, in \u001b[0;36mNodeLoader.filter_fn\u001b[0;34m(self, out)\u001b[0m\n\u001b[1;32m 165\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(out, SamplerOutput):\n\u001b[1;32m 166\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata, Data):\n\u001b[0;32m--> 167\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[43mfilter_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m#\u001b[39;49;00m\n\u001b[1;32m 168\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrow\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcol\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43medge\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 169\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnode_sampler\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43medge_permutation\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 171\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m: \u001b[38;5;66;03m# Tuple[FeatureStore, GraphStore]\u001b[39;00m\n\u001b[1;32m 172\u001b[0m \n\u001b[1;32m 173\u001b[0m \u001b[38;5;66;03m# Hack to detect whether we are in a distributed setting.\u001b[39;00m\n\u001b[1;32m 174\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnode_sampler\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m \u001b[38;5;241m==\u001b[39m\n\u001b[1;32m 175\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDistNeighborSampler\u001b[39m\u001b[38;5;124m'\u001b[39m):\n", + "File \u001b[0;32m~/miniconda3/envs/tbx/lib/python3.11/site-packages/torch_geometric/loader/utils.py:163\u001b[0m, in \u001b[0;36mfilter_data\u001b[0;34m(data, node, row, col, edge, perm)\u001b[0m\n\u001b[1;32m 159\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfilter_data\u001b[39m(data: Data, node: Tensor, row: Tensor, col: Tensor,\n\u001b[1;32m 160\u001b[0m edge: OptTensor, perm: OptTensor \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Data:\n\u001b[1;32m 161\u001b[0m \u001b[38;5;66;03m# Filters a data object to only hold nodes in `node` and edges in `edge`:\u001b[39;00m\n\u001b[1;32m 162\u001b[0m out \u001b[38;5;241m=\u001b[39m copy\u001b[38;5;241m.\u001b[39mcopy(data)\n\u001b[0;32m--> 163\u001b[0m \u001b[43mfilter_node_store_\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_store\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_store\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnode\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 164\u001b[0m filter_edge_store_(data\u001b[38;5;241m.\u001b[39m_store, out\u001b[38;5;241m.\u001b[39m_store, row, col, edge, perm)\n\u001b[1;32m 165\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m out\n", + "File \u001b[0;32m~/miniconda3/envs/tbx/lib/python3.11/site-packages/torch_geometric/loader/utils.py:92\u001b[0m, in \u001b[0;36mfilter_node_store_\u001b[0;34m(store, out_store, index)\u001b[0m\n\u001b[1;32m 89\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m key \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnum_nodes\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[1;32m 90\u001b[0m out_store\u001b[38;5;241m.\u001b[39mnum_nodes \u001b[38;5;241m=\u001b[39m index\u001b[38;5;241m.\u001b[39mnumel()\n\u001b[0;32m---> 92\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[43mstore\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mis_node_attr\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[1;32m 93\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(value, (Tensor, TensorFrame)):\n\u001b[1;32m 94\u001b[0m index \u001b[38;5;241m=\u001b[39m index\u001b[38;5;241m.\u001b[39mto(value\u001b[38;5;241m.\u001b[39mdevice)\n", + "File \u001b[0;32m~/miniconda3/envs/tbx/lib/python3.11/site-packages/torch_geometric/data/storage.py:811\u001b[0m, in \u001b[0;36mGlobalStorage.is_node_attr\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 808\u001b[0m cat_dim \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_parent()\u001b[38;5;241m.\u001b[39m__cat_dim__(key, value, \u001b[38;5;28mself\u001b[39m)\n\u001b[1;32m 809\u001b[0m num_nodes, num_edges \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnum_nodes, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnum_edges\n\u001b[0;32m--> 811\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[43mvalue\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshape\u001b[49m\u001b[43m[\u001b[49m\u001b[43mcat_dim\u001b[49m\u001b[43m]\u001b[49m \u001b[38;5;241m!=\u001b[39m num_nodes:\n\u001b[1;32m 812\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m value\u001b[38;5;241m.\u001b[39mshape[cat_dim] \u001b[38;5;241m==\u001b[39m num_edges:\n\u001b[1;32m 813\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_cached_attr[AttrType\u001b[38;5;241m.\u001b[39mEDGE]\u001b[38;5;241m.\u001b[39madd(key)\n", + "\u001b[0;31mTypeError\u001b[0m: tuple indices must be integers or slices, not tuple" + ] + } + ], + "source": [ + "iter(loader)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "tuple indices must be integers or slices, not tuple", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[30], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43miter\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mloader\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/miniconda3/envs/tbx/lib/python3.11/site-packages/torch/utils/data/dataloader.py:631\u001b[0m, in \u001b[0;36m_BaseDataLoaderIter.__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 628\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sampler_iter \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 629\u001b[0m \u001b[38;5;66;03m# TODO(https://github.com/pytorch/pytorch/issues/76750)\u001b[39;00m\n\u001b[1;32m 630\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reset() \u001b[38;5;66;03m# type: ignore[call-arg]\u001b[39;00m\n\u001b[0;32m--> 631\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_next_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 632\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_yielded \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m 633\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dataset_kind \u001b[38;5;241m==\u001b[39m _DatasetKind\u001b[38;5;241m.\u001b[39mIterable \u001b[38;5;129;01mand\u001b[39;00m \\\n\u001b[1;32m 634\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_IterableDataset_len_called \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \\\n\u001b[1;32m 635\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_yielded \u001b[38;5;241m>\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_IterableDataset_len_called:\n", + "File \u001b[0;32m~/miniconda3/envs/tbx/lib/python3.11/site-packages/torch/utils/data/dataloader.py:675\u001b[0m, in \u001b[0;36m_SingleProcessDataLoaderIter._next_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 673\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_next_data\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 674\u001b[0m index \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_next_index() \u001b[38;5;66;03m# may raise StopIteration\u001b[39;00m\n\u001b[0;32m--> 675\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_dataset_fetcher\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfetch\u001b[49m\u001b[43m(\u001b[49m\u001b[43mindex\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# may raise StopIteration\u001b[39;00m\n\u001b[1;32m 676\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pin_memory:\n\u001b[1;32m 677\u001b[0m data \u001b[38;5;241m=\u001b[39m _utils\u001b[38;5;241m.\u001b[39mpin_memory\u001b[38;5;241m.\u001b[39mpin_memory(data, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pin_memory_device)\n", + "File \u001b[0;32m~/miniconda3/envs/tbx/lib/python3.11/site-packages/torch/utils/data/_utils/fetch.py:54\u001b[0m, in \u001b[0;36m_MapDatasetFetcher.fetch\u001b[0;34m(self, possibly_batched_index)\u001b[0m\n\u001b[1;32m 52\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 53\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdataset[possibly_batched_index]\n\u001b[0;32m---> 54\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[43mcollate_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/miniconda3/envs/tbx/lib/python3.11/site-packages/torch_geometric/loader/node_loader.py:150\u001b[0m, in \u001b[0;36mNodeLoader.collate_fn\u001b[0;34m(self, index)\u001b[0m\n\u001b[1;32m 147\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnode_sampler\u001b[38;5;241m.\u001b[39msample_from_nodes(input_data)\n\u001b[1;32m 149\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfilter_per_worker: \u001b[38;5;66;03m# Execute `filter_fn` in the worker process\u001b[39;00m\n\u001b[0;32m--> 150\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfilter_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[43mout\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 152\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m out\n", + "File \u001b[0;32m~/miniconda3/envs/tbx/lib/python3.11/site-packages/torch_geometric/loader/node_loader.py:167\u001b[0m, in \u001b[0;36mNodeLoader.filter_fn\u001b[0;34m(self, out)\u001b[0m\n\u001b[1;32m 165\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(out, SamplerOutput):\n\u001b[1;32m 166\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata, Data):\n\u001b[0;32m--> 167\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[43mfilter_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m#\u001b[39;49;00m\n\u001b[1;32m 168\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrow\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcol\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43medge\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 169\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnode_sampler\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43medge_permutation\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 171\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m: \u001b[38;5;66;03m# Tuple[FeatureStore, GraphStore]\u001b[39;00m\n\u001b[1;32m 172\u001b[0m \n\u001b[1;32m 173\u001b[0m \u001b[38;5;66;03m# Hack to detect whether we are in a distributed setting.\u001b[39;00m\n\u001b[1;32m 174\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnode_sampler\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m \u001b[38;5;241m==\u001b[39m\n\u001b[1;32m 175\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDistNeighborSampler\u001b[39m\u001b[38;5;124m'\u001b[39m):\n", + "File \u001b[0;32m~/miniconda3/envs/tbx/lib/python3.11/site-packages/torch_geometric/loader/utils.py:163\u001b[0m, in \u001b[0;36mfilter_data\u001b[0;34m(data, node, row, col, edge, perm)\u001b[0m\n\u001b[1;32m 159\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfilter_data\u001b[39m(data: Data, node: Tensor, row: Tensor, col: Tensor,\n\u001b[1;32m 160\u001b[0m edge: OptTensor, perm: OptTensor \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Data:\n\u001b[1;32m 161\u001b[0m \u001b[38;5;66;03m# Filters a data object to only hold nodes in `node` and edges in `edge`:\u001b[39;00m\n\u001b[1;32m 162\u001b[0m out \u001b[38;5;241m=\u001b[39m copy\u001b[38;5;241m.\u001b[39mcopy(data)\n\u001b[0;32m--> 163\u001b[0m \u001b[43mfilter_node_store_\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_store\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_store\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnode\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 164\u001b[0m filter_edge_store_(data\u001b[38;5;241m.\u001b[39m_store, out\u001b[38;5;241m.\u001b[39m_store, row, col, edge, perm)\n\u001b[1;32m 165\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m out\n", + "File \u001b[0;32m~/miniconda3/envs/tbx/lib/python3.11/site-packages/torch_geometric/loader/utils.py:92\u001b[0m, in \u001b[0;36mfilter_node_store_\u001b[0;34m(store, out_store, index)\u001b[0m\n\u001b[1;32m 89\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m key \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnum_nodes\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[1;32m 90\u001b[0m out_store\u001b[38;5;241m.\u001b[39mnum_nodes \u001b[38;5;241m=\u001b[39m index\u001b[38;5;241m.\u001b[39mnumel()\n\u001b[0;32m---> 92\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[43mstore\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mis_node_attr\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[1;32m 93\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(value, (Tensor, TensorFrame)):\n\u001b[1;32m 94\u001b[0m index \u001b[38;5;241m=\u001b[39m index\u001b[38;5;241m.\u001b[39mto(value\u001b[38;5;241m.\u001b[39mdevice)\n", + "File \u001b[0;32m~/miniconda3/envs/tbx/lib/python3.11/site-packages/torch_geometric/data/storage.py:811\u001b[0m, in \u001b[0;36mGlobalStorage.is_node_attr\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 808\u001b[0m cat_dim \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_parent()\u001b[38;5;241m.\u001b[39m__cat_dim__(key, value, \u001b[38;5;28mself\u001b[39m)\n\u001b[1;32m 809\u001b[0m num_nodes, num_edges \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnum_nodes, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnum_edges\n\u001b[0;32m--> 811\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[43mvalue\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshape\u001b[49m\u001b[43m[\u001b[49m\u001b[43mcat_dim\u001b[49m\u001b[43m]\u001b[49m \u001b[38;5;241m!=\u001b[39m num_nodes:\n\u001b[1;32m 812\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m value\u001b[38;5;241m.\u001b[39mshape[cat_dim] \u001b[38;5;241m==\u001b[39m num_edges:\n\u001b[1;32m 813\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_cached_attr[AttrType\u001b[38;5;241m.\u001b[39mEDGE]\u001b[38;5;241m.\u001b[39madd(key)\n", + "\u001b[0;31mTypeError\u001b[0m: tuple indices must be integers or slices, not tuple" + ] + } + ], + "source": [] + }, { "cell_type": "code", "execution_count": null, diff --git a/tutorials/graph2simplicial_lifting/131528455/data.pt b/tutorials/graph2simplicial_lifting/131528455/data.pt new file mode 100644 index 0000000000000000000000000000000000000000..aadadd0d48261820956510bd3991d4a9c69ee35e GIT binary patch literal 22921 zcmdU13w#_^^`53}T0$S86fCb&3P?drv%5)}NTJ)bgu-GiQ@~J=kS1%B5|Z4TDbNZS zA3H6IfDaV4V#ODVN_{`5TEqvwQ9)4=tB4}LDk2~%_?@}?r89SSb~kPL|9=1Z{pOxC zckXx2ckVs+?#$gyR^=v6a-6ACo!C+D%yPPQPhbD#`GvjRUU$Rrr2`%9Gn_S%W3=XK zrzQmk8`b2_iD=t1yg}9Grh011IXNdcH8;UmC`24^ZBNh8z(Bso z>mM2%se763&Fg&c^8OyLPSu_0sslYW%~jJo>tH3{yDs0=KiHeUQqAZCjXt_QduVXo znCF>+=aj1NtIZwmse{C0O>VlcX2D~PuMWPpPYloY)FG}qG-$k0y};P(GvKtE12E_w z7SJuI!*de~>Iefo(o;vdYHsHNU^KYlvaZ3Q-u#Fydo1PwUh%#bzUU-QPXXzo}cgqONOQ-s>9Z-Z-=YJu$zt9=6TU6wy6HL+aRq zI?h$cFNl!Os275Ky{Aqvkx%v20@LLungCAn)QenoauH+(s!6@rMBGq}c%ekxXaFfs 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b#Qz5lLk0$raDX=}DCq(9urPqsL(~EQ2^^-_ literal 0 HcmV?d00001 diff --git a/tutorials/graph2simplicial_lifting/131528455/pre_transform.pt b/tutorials/graph2simplicial_lifting/131528455/pre_transform.pt new file mode 100644 index 0000000000000000000000000000000000000000..8625d2634a8da5c3da8246d7bafd2d77e7bdb562 GIT binary patch literal 864 zcmWIWW@cev;NW1u00Im`42ea_8JT6N`YDMeiFyUuIc`pT3{fbcfhj@`sMR??w;;bb zRU?{9LBR#6IHV{suQ)BgC|5(1D^|0RK`+3Youf>5_j(PWVh|3%X|EuIB;4Ml%97Ol zqLkDkHz!dvi=nQ_$t)?!Nd=kSYWrA{4QMh5<2Ie2!4__MS!z*nW`3TVlO=YuQ9O!+ zW6TAz`{KOxP$r-8Jn4y znwuM&Tbh`e7#JFu8X6mbfswJLrICS=g{6hLp@AjHl~u+{s}SMO2y!eu=8@w@03?9| z(9=PHH>z&ruopzpSpnq1bi-p4T|08{@uO(JhNK-DUFe#TeFaL22oS`Gup4`L2Y9ow z=|DBeG3&y$0<#1dz-Tt0Ng(h8N(1$=gZTfzVaUJ$5)SZY1tmQO79a$vho}VrRjQ}5 literal 0 HcmV?d00001 From 8519fef962a9a6cb9f22f9d7834e36f97e09caf5 Mon Sep 17 00:00:00 2001 From: Coerulatus Date: Sat, 16 Nov 2024 15:37:31 +0000 Subject: [PATCH 11/24] Marco - defined NeighborCellsLoader --- topobenchmarkx/data/batching/cell_loader.py | 191 +++++ .../data/batching/neighbor_cells_loader.py | 163 +++++ topobenchmarkx/data/batching/utils.py | 284 ++++++++ tutorials/batching.ipynb | 676 ++---------------- 4 files changed, 709 insertions(+), 605 deletions(-) create mode 100644 topobenchmarkx/data/batching/cell_loader.py create mode 100644 topobenchmarkx/data/batching/neighbor_cells_loader.py create mode 100644 topobenchmarkx/data/batching/utils.py diff --git a/topobenchmarkx/data/batching/cell_loader.py b/topobenchmarkx/data/batching/cell_loader.py new file mode 100644 index 00000000..15239060 --- /dev/null +++ b/topobenchmarkx/data/batching/cell_loader.py @@ -0,0 +1,191 @@ +from typing import Any, Callable, Iterator, List, Optional, Tuple, Union + +import torch +from torch import Tensor + +from torch_geometric.data import Data, FeatureStore, GraphStore, HeteroData +from torch_geometric.loader.base import DataLoaderIterator +from torch_geometric.loader.mixin import ( + AffinityMixin, + LogMemoryMixin, + MultithreadingMixin, +) + +from topobenchmarkx.data.batching.utils import filter_data + +from torch_geometric.loader.utils import ( + filter_custom_hetero_store, + filter_custom_store, + filter_hetero_data, + get_input_nodes, + infer_filter_per_worker, +) +from torch_geometric.sampler import ( + BaseSampler, + HeteroSamplerOutput, + NodeSamplerInput, + SamplerOutput, +) +from torch_geometric.typing import InputNodes, OptTensor + + +class CellLoader( + torch.utils.data.DataLoader, + AffinityMixin, + MultithreadingMixin, + LogMemoryMixin, +): + r"""A data loader that performs mini-batch sampling from cell information, + using a generic :class:`~torch_geometric.sampler.BaseSampler` + implementation that defines a + :meth:`~torch_geometric.sampler.BaseSampler.sample_from_nodes` function and + is supported on the provided input :obj:`data` object. + + Args: + data (Any): A :class:`~torch_geometric.data.Data`, + :class:`~torch_geometric.data.HeteroData`, or + (:class:`~torch_geometric.data.FeatureStore`, + :class:`~torch_geometric.data.GraphStore`) data object. + cell_sampler (torch_geometric.sampler.BaseSampler): The sampler + implementation to be used with this loader. + Needs to implement + :meth:`~torch_geometric.sampler.BaseSampler.sample_from_cells`. + The sampler implementation must be compatible with the input + :obj:`data` object. + input_cells (torch.Tensor or str or Tuple[str, torch.Tensor]): The + indices of seed cells to start sampling from. + Needs to be either given as a :obj:`torch.LongTensor` or + :obj:`torch.BoolTensor`. + If set to :obj:`None`, all cells will be considered. + In heterogeneous graphs, needs to be passed as a tuple that holds + the cell type and cell indices. (default: :obj:`None`) + input_time (torch.Tensor, optional): Optional values to override the + timestamp for the input cells given in :obj:`input_cells`. If not + set, will use the timestamps in :obj:`time_attr` as default (if + present). The :obj:`time_attr` needs to be set for this to work. + (default: :obj:`None`) + transform (callable, optional): A function/transform that takes in + a sampled mini-batch and returns a transformed version. + (default: :obj:`None`) + transform_sampler_output (callable, optional): A function/transform + that takes in a :class:`torch_geometric.sampler.SamplerOutput` and + returns a transformed version. (default: :obj:`None`) + filter_per_worker (bool, optional): If set to :obj:`True`, will filter + the returned data in each worker's subprocess. + If set to :obj:`False`, will filter the returned data in the main + process. + If set to :obj:`None`, will automatically infer the decision based + on whether data partially lives on the GPU + (:obj:`filter_per_worker=True`) or entirely on the CPU + (:obj:`filter_per_worker=False`). + There exists different trade-offs for setting this option. + Specifically, setting this option to :obj:`True` for in-memory + datasets will move all features to shared memory, which may result + in too many open file handles. (default: :obj:`None`) + custom_cls (HeteroData, optional): A custom + :class:`~torch_geometric.data.HeteroData` class to return for + mini-batches in case of remote backends. (default: :obj:`None`) + **kwargs (optional): Additional arguments of + :class:`torch.utils.data.DataLoader`, such as :obj:`batch_size`, + :obj:`shuffle`, :obj:`drop_last` or :obj:`num_workers`. + """ + def __init__( + self, + data: Union[Data, HeteroData, Tuple[FeatureStore, GraphStore]], + cell_sampler: BaseSampler, + input_cells: InputNodes = None, + input_time: OptTensor = None, + transform: Optional[Callable] = None, + transform_sampler_output: Optional[Callable] = None, + filter_per_worker: Optional[bool] = None, + custom_cls: Optional[HeteroData] = None, + input_id: OptTensor = None, + **kwargs, + ): + if filter_per_worker is None: + filter_per_worker = infer_filter_per_worker(data) + + self.data = data + self.cell_sampler = cell_sampler + self.input_cells = input_cells + self.input_time = input_time + self.transform = transform + self.transform_sampler_output = transform_sampler_output + self.filter_per_worker = filter_per_worker + self.custom_cls = custom_cls + self.input_id = input_id + + kwargs.pop('dataset', None) + kwargs.pop('collate_fn', None) + + # Get cell type (or `None` for homogeneous graphs): + input_type, input_cells, input_id = get_input_nodes( + data, input_cells, input_id) + + self.input_data = NodeSamplerInput( + input_id=input_id, + node=input_cells, + time=input_time, + input_type=input_type, + ) + + iterator = range(input_cells.size(0)) + super().__init__(iterator, collate_fn=self.collate_fn, **kwargs) + + def __call__( + self, + index: Union[Tensor, List[int]], + ) -> Union[Data, HeteroData]: + r"""Samples a subgraph from a batch of input cells.""" + out = self.collate_fn(index) + if not self.filter_per_worker: + out = self.filter_fn(out) + return out + + def collate_fn(self, index: Union[Tensor, List[int]]) -> Any: + r"""Samples a subgraph from a batch of input cells.""" + input_data: NodeSamplerInput = self.input_data[index] + + out = self.cell_sampler.sample_from_nodes(input_data) + + if self.filter_per_worker: # Execute `filter_fn` in the worker process + out = self.filter_fn(out) + + return out + + def filter_fn( + self, + out: Union[SamplerOutput, HeteroSamplerOutput], + ) -> Union[Data, HeteroData]: + r"""Joins the sampled cells with their corresponding features, + returning the resulting :class:`~torch_geometric.data.Data` + object to be used downstream. + """ + if self.transform_sampler_output: + out = self.transform_sampler_output(out) + + if isinstance(out, SamplerOutput) and isinstance(self.data, Data): + data = filter_data( # + self.data, out.node, self.rank) + else: + raise TypeError(f"'{self.__class__.__name__}'' found invalid " + f"type: '{type(data)}'") + + return data if self.transform is None else self.transform(data) + + def _get_iterator(self) -> Iterator: + if self.filter_per_worker: + return super()._get_iterator() + + # if not self.is_cuda_available and not self.cpu_affinity_enabled: + # TODO: Add manual page for best CPU practices + # link = ... + # Warning('Dataloader CPU affinity opt is not enabled, consider ' + # 'switching it on with enable_cpu_affinity() or see CPU ' + # f'best practices for PyG [{link}])') + + # Execute `filter_fn` in the main process: + return DataLoaderIterator(super()._get_iterator(), self.filter_fn) + + def __repr__(self) -> str: + return f'{self.__class__.__name__}()' \ No newline at end of file diff --git a/topobenchmarkx/data/batching/neighbor_cells_loader.py b/topobenchmarkx/data/batching/neighbor_cells_loader.py new file mode 100644 index 00000000..a5c872cf --- /dev/null +++ b/topobenchmarkx/data/batching/neighbor_cells_loader.py @@ -0,0 +1,163 @@ +from typing import Callable, Dict, List, Optional, Tuple, Union + +from topobenchmarkx.data.batching.cell_loader import CellLoader +from topobenchmarkx.data.batching.utils import get_sampled_neighborhood + +from torch_geometric.data import Data, FeatureStore, GraphStore, HeteroData + +from torch_geometric.sampler import NeighborSampler +from torch_geometric.sampler.base import SubgraphType +from torch_geometric.typing import EdgeType, InputNodes, OptTensor + + +class NeighborCellsLoader(CellLoader): + r"""A data loader that samples neighbors for each cell. Cells are considered neighbors if they are upper or lower neighbors. + + Args: + data (Any): A :class:`~torch_geometric.data.Data`, + :class:`~torch_geometric.data.HeteroData`, or + (:class:`~torch_geometric.data.FeatureStore`, + :class:`~torch_geometric.data.GraphStore`) data object. + rank (int): The rank of the cells to consider. + num_neighbors (List[int] or Dict[Tuple[str, str, str], List[int]]): The + number of neighbors to sample for each node in each iteration. + If an entry is set to :obj:`-1`, all neighbors will be included. + In heterogeneous graphs, may also take in a dictionary denoting + the amount of neighbors to sample for each individual edge type. + input_nodes (torch.Tensor or str or Tuple[str, torch.Tensor]): The + indices of nodes for which neighbors are sampled to create + mini-batches. + Needs to be either given as a :obj:`torch.LongTensor` or + :obj:`torch.BoolTensor`. + If set to :obj:`None`, all nodes will be considered. + In heterogeneous graphs, needs to be passed as a tuple that holds + the node type and node indices. (default: :obj:`None`) + input_time (torch.Tensor, optional): Optional values to override the + timestamp for the input nodes given in :obj:`input_nodes`. If not + set, will use the timestamps in :obj:`time_attr` as default (if + present). The :obj:`time_attr` needs to be set for this to work. + (default: :obj:`None`) + replace (bool, optional): If set to :obj:`True`, will sample with + replacement. (default: :obj:`False`) + subgraph_type (SubgraphType or str, optional): The type of the returned + subgraph. + If set to :obj:`"directional"`, the returned subgraph only holds + the sampled (directed) edges which are necessary to compute + representations for the sampled seed nodes. + If set to :obj:`"bidirectional"`, sampled edges are converted to + bidirectional edges. + If set to :obj:`"induced"`, the returned subgraph contains the + induced subgraph of all sampled nodes. + (default: :obj:`"directional"`) + disjoint (bool, optional): If set to :obj: `True`, each seed node will + create its own disjoint subgraph. + If set to :obj:`True`, mini-batch outputs will have a :obj:`batch` + vector holding the mapping of nodes to their respective subgraph. + Will get automatically set to :obj:`True` in case of temporal + sampling. (default: :obj:`False`) + temporal_strategy (str, optional): The sampling strategy when using + temporal sampling (:obj:`"uniform"`, :obj:`"last"`). + If set to :obj:`"uniform"`, will sample uniformly across neighbors + that fulfill temporal constraints. + If set to :obj:`"last"`, will sample the last `num_neighbors` that + fulfill temporal constraints. + (default: :obj:`"uniform"`) + time_attr (str, optional): The name of the attribute that denotes + timestamps for either the nodes or edges in the graph. + If set, temporal sampling will be used such that neighbors are + guaranteed to fulfill temporal constraints, *i.e.* neighbors have + an earlier or equal timestamp than the center node. + (default: :obj:`None`) + weight_attr (str, optional): The name of the attribute that denotes + edge weights in the graph. + If set, weighted/biased sampling will be used such that neighbors + are more likely to get sampled the higher their edge weights are. + Edge weights do not need to sum to one, but must be non-negative, + finite and have a non-zero sum within local neighborhoods. + (default: :obj:`None`) + transform (callable, optional): A function/transform that takes in + a sampled mini-batch and returns a transformed version. + (default: :obj:`None`) + transform_sampler_output (callable, optional): A function/transform + that takes in a :class:`torch_geometric.sampler.SamplerOutput` and + returns a transformed version. (default: :obj:`None`) + is_sorted (bool, optional): If set to :obj:`True`, assumes that + :obj:`edge_index` is sorted by column. + If :obj:`time_attr` is set, additionally requires that rows are + sorted according to time within individual neighborhoods. + This avoids internal re-sorting of the data and can improve + runtime and memory efficiency. (default: :obj:`False`) + filter_per_worker (bool, optional): If set to :obj:`True`, will filter + the returned data in each worker's subprocess. + If set to :obj:`False`, will filter the returned data in the main + process. + If set to :obj:`None`, will automatically infer the decision based + on whether data partially lives on the GPU + (:obj:`filter_per_worker=True`) or entirely on the CPU + (:obj:`filter_per_worker=False`). + There exists different trade-offs for setting this option. + Specifically, setting this option to :obj:`True` for in-memory + datasets will move all features to shared memory, which may result + in too many open file handles. (default: :obj:`None`) + **kwargs (optional): Additional arguments of + :class:`torch.utils.data.DataLoader`, such as :obj:`batch_size`, + :obj:`shuffle`, :obj:`drop_last` or :obj:`num_workers`. + """ + def __init__( + self, + data: Union[Data, HeteroData, Tuple[FeatureStore, GraphStore]], + rank: int, + num_neighbors: Union[List[int], Dict[EdgeType, List[int]]], + input_nodes: InputNodes = None, + input_time: OptTensor = None, + replace: bool = False, + subgraph_type: Union[SubgraphType, str] = 'directional', + disjoint: bool = False, + temporal_strategy: str = 'uniform', + time_attr: Optional[str] = None, + weight_attr: Optional[str] = None, + transform: Optional[Callable] = None, + transform_sampler_output: Optional[Callable] = None, + is_sorted: bool = False, + filter_per_worker: Optional[bool] = None, + neighbor_sampler: Optional[NeighborSampler] = None, + directed: bool = True, # Deprecated. + **kwargs, + ): + if input_time is not None and time_attr is None: + raise ValueError("Received conflicting 'input_time' and " + "'time_attr' arguments: 'input_time' is set " + "while 'time_attr' is not set.") + + is_hypergraph = hasattr(data, 'incidence_hyperedges') + data = get_sampled_neighborhood(data, rank, is_hypergraph) + self.rank = rank + + if len(num_neighbors) > 1: + raise NotImplementedError("NeighborCellsLoader only supports one-hop neighborhood selection.") + + if neighbor_sampler is None: + neighbor_sampler = NeighborSampler( + data, + num_neighbors=num_neighbors, + replace=replace, + subgraph_type=subgraph_type, + disjoint=disjoint, + temporal_strategy=temporal_strategy, + time_attr=time_attr, + weight_attr=weight_attr, + is_sorted=is_sorted, + share_memory=kwargs.get('num_workers', 0) > 0, + directed=directed, + ) + + super().__init__( + data=data, + node_sampler=neighbor_sampler, + input_nodes=input_nodes, + input_time=input_time, + transform=transform, + transform_sampler_output=transform_sampler_output, + filter_per_worker=filter_per_worker, + **kwargs, + ) \ No newline at end of file diff --git a/topobenchmarkx/data/batching/utils.py b/topobenchmarkx/data/batching/utils.py new file mode 100644 index 00000000..b522d361 --- /dev/null +++ b/topobenchmarkx/data/batching/utils.py @@ -0,0 +1,284 @@ +import copy +import logging +import math +from typing import Any, Dict, Optional, Tuple, Union + +import numpy as np +import torch +from torch import Tensor + +import torch_geometric.typing +from torch_geometric.data import Data + +def reduce_higher_ranks_incidences(batch, cells_ids, rank, max_rank, is_hypergraph=False): + """ Reduce the incidences with higher rank than the specified one. + + Parameters + ---------- + batch: torch_geometric.data.Data + The input data. + cells_ids: list[torch.Tensor] + List of tensors containing the ids of the cells. The length of the list should be equal to the maximum rank. + rank: int + The rank to select the higher order incidences. + max_rank: int + The maximum rank of the incidences. + is_hypergraph: bool + Whether the data represents an hypergraph. + + Returns + ------- + torch_geometric.data.Data + The output data with the reduced incidences. + list[torch.Tensor] + The updated indices of the cells. Each element of the list is a tensor containing the ids of the cells of the corresponding rank. + """ + for i in range(rank+1, max_rank+1): + if is_hypergraph: + incidence = batch.incidence_hyperedges + else: + incidence = batch[f"incidence_{i}"] + + # if i != rank+1: + incidence = torch.index_select(incidence, 0, cells_ids[i-1]) + cells_ids[i] = torch.where(torch.sum(incidence, dim=0).to_dense() > 1)[0] + incidence = torch.index_select(incidence, 1, cells_ids[i]) + batch[f"incidence_{i}"] = incidence + + return batch, cells_ids + +def reduce_lower_ranks_incidences(batch, cells_ids, rank, is_hypergraph=False): + """ Reduce the incidences with lower rank than the specified one. + + Parameters + ---------- + batch: torch_geometric.data.Data + The input data. + cells_ids: list[torch.Tensor] + List of tensors containing the ids of the cells. The length of the list should be equal to the maximum rank. + rank: int + The rank of the cells to consider. + is_hypergraph: bool + Whether the data represents an hypergraph. + + Returns + ------- + torch.Tensor + The indices of the nodes contained by the cells. + list[torch.Tensor] + The updated indices of the cells. Each element of the list is a tensor containing the ids of the cells of the corresponding rank. + """ + for i in range(rank, 0, -1): + if is_hypergraph: + incidence = batch.incidence_hyperedges + else: + incidence = batch[f"incidence_{i}"] + incidence = torch.index_select(incidence, 1, cells_ids[i]) + cells_ids[i-1] = torch.where(torch.sum(incidence, dim=1).to_dense() > 0)[0] + incidence = torch.index_select(incidence, 0, cells_ids[i-1]) + batch[f"incidence_{i}"] = incidence + + if not is_hypergraph: + incidence = batch[f"incidence_0"] + incidence = torch.index_select(incidence, 1, cells_ids[0]) + batch[f"incidence_0"] = incidence + return batch, cells_ids + +def reduce_matrices(batch, cells_ids, names, rank, max_rank): + """ Reduce the matrices using the indices in cells_ids. + + The matrices are assumed to be in the batch with the names specified in the list names. + + Parameters + ---------- + batch: torch_geometric.data.Data + The input data. + cells_ids: list[torch.Tensor] + List of tensors containing the ids of the cells. The length of the list should be equal to the maximum rank. + names: list[str] + List of names of the matrices in the batch. They should appear in the format f"{name}{i}" where i is the rank of the matrix. + rank: int + The rank over which you are batching. + max_rank: int + The maximum rank of the matrices. + + Returns + ------- + torch_geometric.data.Data + The output data with the reduced matrices. + """ + for i in range(max_rank+1): + for name in names: + if f"{name}{i}" in batch.keys(): + matrix = batch[f"{name}{i}"] + matrix = torch.index_select(matrix, 0, cells_ids[i]) + matrix = torch.index_select(matrix, 1, cells_ids[i]) + batch[f"{name}{i}"] = matrix + return batch + +def reduce_neighborhoods(batch, node, rank=0, remove_self_loops=True): + """ Reduce the neighborhoods of the cells in the batch. + + Parameters + ---------- + batch: torch_geometric.data.Data + The input data. + rank: int + The rank of the cells to batch over. + remove_self_loops: bool + Whether to remove self loops from the edge_index. + + Returns + ------- + torch_geometric.data.Data + The output data with the reduced neighborhoods. + """ + is_hypergraph = False + if hasattr(batch, 'incidence_hyperedges'): + is_hypergraph = True + max_rank = 1 + else: + max_rank = len([key for key in batch.keys() if "incidence" in key])-1 + + if rank > max_rank: + raise ValueError(f"Rank {rank} is greater than the maximum rank {max_rank} in the dataset.") + + cells_ids = [None for _ in range(max_rank+1)] + + # the indices of the cells selected by the NeighborhoodLoader are saved in the batch in the attribute n_id + cells_ids[rank] = node + + batch, cells_ids = reduce_higher_ranks_incidences(batch, cells_ids, rank, max_rank, is_hypergraph) + batch, cells_ids = reduce_lower_ranks_incidences(batch, cells_ids, rank, is_hypergraph) + + batch = reduce_matrices(batch, + cells_ids, + names=['down_laplacian_', 'up_laplacian_', 'hodge_laplacian_', 'adjacency_'], + rank=rank, + max_rank=max_rank) + + # reduce the feature matrices + for i in range(max_rank+1): + if f"x_{i}" in batch.keys(): + batch[f"x_{i}"] = batch[f"x_{i}"][cells_ids[i]] + + # fix edge_index + if not is_hypergraph: + adjacency_0 = batch.adjacency_0.coalesce() + edge_index = adjacency_0.indices() + if remove_self_loops: + edge_index = torch_geometric.utils.remove_self_loops(edge_index)[0] + batch.edge_index = edge_index + + # fix x + batch.x = batch[f"x_0"] + if hasattr(batch, 'num_nodes'): + batch.num_nodes = batch.x.shape[0] + + if hasattr(batch, 'y'): + batch.y = batch.y[cells_ids[rank]] + + return batch + +def filter_data(data: Data, cells: Tensor, rank: int) -> Data: + ''' The function filters the attributes of the data based on the cells passed. + + The function uses the indices passed to select the cells of the specified rank. The cells of lower or higher ranks are selected using the incidence matrices. + + Parameters + ---------- + data: torch_geometric.data.Data + The input data. + cells: Tensor + Tensor containing the indices of the cells of the specified rank to keep. + rank: int + Rank of the cells of interest. + ''' + out = copy.copy(data) + out = reduce_neighborhoods(out, cells, rank=rank) + return out + +def get_sampled_neighborhood(data, rank=0, is_hypergraph=False): + ''' This function updates the edge_index attribute of torch_geometric.data.Data. + + The function finds cells, of the specified rank, that are either upper or lower neighbors. + + Parameters + ---------- + data: torch_geometric.data.Data + The input data. + rank: int + The rank of the cells that you want to batch over. + is_hypergraph: bool + Whether the data represents an hypergraph. + + Returns + ------- + torch_geometric.data.Data + The output data with updated edge_index. + edge_index contains indices of connected cells of the specified rank K. + Two cells of rank K are connected if they are either lower or upper neighbors. + ''' + if rank == 0: + data.edge_index = torch_geometric.utils.to_undirected(data.edge_index) + return data + if is_hypergraph: + if rank > 1: + raise ValueError("Hypergraphs are not supported for ranks greater than 1.") + if rank == 1: + I = data.incidence_hyperedges + A = torch.sparse.mm(I,I.T) # lower adj matrix + edges = A.indices() + else: + I = data.incidence_hyperedges + A = torch.sparse.mm(I.T,I) + edges = A.indices() + else: + # get number of incidences + max_rank = len([key for key in data.keys() if "incidence" in key])-1 + if rank > max_rank: + raise ValueError(f"Rank {rank} is greater than the maximum rank {max_rank} in the data.") + + # This considers the upper adjacencies + if rank == max_rank: + edges = torch.empty((2, 0), dtype=torch.long) + else: + I = data[f"incidence_{rank+1}"] + A = torch.sparse.mm(I,I.T) + edges = A.indices() + + # This is for selecting the whole upper cells + # for i in range(rank+1, max_rank): + # P = torch.sparse.mm(P, data[f"incidence_{i+1}"]) + # Q = torch.sparse.mm(P,P.T) + # edges = torch.cat((edges, Q.indices()), dim=1) + + # This considers the lower adjacencies + if rank != 0: + I = data[f"incidence_{rank}"] + A = torch.sparse.mm(I.T,I) + edges = torch.cat((edges, A.indices()), dim=1) + + # This is for selecting cells if they share any node + # for i in range(rank-1, 0, -1): + # P = torch.sparse.mm(data[f"incidence_{i}"], P) + # Q = torch.sparse.mm(P.T,P) + # edges = torch.cat((edges, Q.indices()), dim=1) + + edges = torch.unique(edges, dim=1) + # Remove self edges + mask = edges[0, :] != edges[1, :] + edges = edges[:, mask] + + data.edge_index = edges + + # We need to set x to x_{rank} since NeighborLoader will take the number of nodes from the x attribute + # The correct x is given after the reduce_neighborhoods function + if is_hypergraph and rank == 1: + data.x = data.x_hyperedges + else: + data.x = data[f'x_{rank}'] + + if hasattr(data, 'num_nodes'): + data.num_nodes = data.x.shape[0] + return data \ No newline at end of file diff --git a/tutorials/batching.ipynb b/tutorials/batching.ipynb index 3cb816cb..15027087 100644 --- a/tutorials/batching.ipynb +++ b/tutorials/batching.ipynb @@ -9,7 +9,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_45596/2455096930.py:26: UserWarning: \n", + "/tmp/ipykernel_140081/2455096930.py:26: UserWarning: \n", "The version_base parameter is not specified.\n", "Please specify a compatability version level, or None.\n", "Will assume defaults for version 1.1\n", @@ -45,6 +45,8 @@ "from topobenchmarkx.dataloader.dataloader import TBXDataloader\n", "from topobenchmarkx.data.loaders import GraphLoader\n", "\n", + "from topobenchmarkx.data.samplers.neighbor_cells_loader import NeighborCellsLoader\n", + "\n", "from topobenchmarkx.utils.config_resolvers import (\n", " get_default_transform,\n", " get_monitor_metric,\n", @@ -142,10 +144,11 @@ " fig = plt.figure(figsize=(10, 8))\n", " \n", " # Draw nodes and labels\n", + " node_labels = {i: f\"v_{n.item()}\" for i,n in enumerate(data.n_id)} if hasattr(data, 'n_id') else {i: f\"v_{i}\" for i in G.nodes()}\n", " nx.draw(\n", " G,\n", " pos,\n", - " labels={i: f\"v_{i}\" for i in G.nodes()},\n", + " labels=node_labels,\n", " node_size=node_size,\n", " node_color=\"skyblue\",\n", " font_size=font_size,\n", @@ -187,356 +190,6 @@ " " ] }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "from torch_geometric.loader import NeighborLoader\n", - "\n", - "# replace adjacency keys with temp\n", - "def workaround_data(data):\n", - " \"\"\" The function is a workaround to change the data to work with NeighborLoader. \n", - " \n", - " The function replaces the keys with adjacency in the name with temp. It also removes the shape attribute if present.\n", - " \n", - " Parameters\n", - " ----------\n", - " data: torch_geometric.data.Data\n", - " The input data.\n", - " \n", - " Returns\n", - " -------\n", - " torch_geometric.data.Data\n", - " The output data with the keys replaced and the shape attribute removed.\n", - " \"\"\"\n", - " n_adjacencies = len([key for key in data.keys() if \"adjacency\" in key])\n", - " for i in range(n_adjacencies):\n", - " if f\"adjacency_{i}\" in data.keys():\n", - " data[f\"temp_{i}\"] = data[f\"adjacency_{i}\"]\n", - " del data[f\"adjacency_{i}\"]\n", - " \n", - " # shape is a list, it breaks the NeighborLoader if we keep it\n", - " if hasattr(data, 'shape'):\n", - " del data.shape\n", - " return data\n", - "\n", - "def get_sampled_neighborhood(data, rank=0, is_hypergraph=False):\n", - " ''' This function updates the edge_index attribute of torch_geometric.data.Data. \n", - " \n", - " The function finds cells, of the specified rank, that are either upper or lower neighbors.\n", - " \n", - " Parameters\n", - " ----------\n", - " data: torch_geometric.data.Data\n", - " The input data.\n", - " rank: int\n", - " The rank of the cells that you want to batch over.\n", - " is_hypergraph: bool\n", - " Whether the data represents an hypergraph.\n", - " \n", - " Returns\n", - " -------\n", - " torch_geometric.data.Data\n", - " The output data with updated edge_index.\n", - " edge_index contains indices of connected cells of the specified rank K. \n", - " Two cells of rank K are connected if they are either lower or upper neighbors. \n", - " '''\n", - " if rank == 0:\n", - " return data\n", - " if is_hypergraph: \n", - " if rank > 1:\n", - " raise ValueError(\"Hypergraphs are not supported for ranks greater than 1.\")\n", - " if rank == 1:\n", - " I = data.incidence_hyperedges\n", - " A = torch.sparse.mm(I,I.T) # lower adj matrix\n", - " edges = A.indices()\n", - " else:\n", - " I = data.incidence_hyperedges\n", - " A = torch.sparse.mm(I.T,I)\n", - " edges = A.indices() \n", - " else:\n", - " # get number of incidences\n", - " max_rank = len([key for key in data.keys() if \"incidence\" in key])-1\n", - " if rank > max_rank:\n", - " raise ValueError(f\"Rank {rank} is greater than the maximum rank {max_rank} in the data.\")\n", - " \n", - " # This considers the upper adjacencies\n", - " if rank == max_rank:\n", - " edges = torch.empty((2, 0), dtype=torch.long)\n", - " else:\n", - " I = data[f\"incidence_{rank+1}\"]\n", - " A = torch.sparse.mm(I,I.T)\n", - " edges = A.indices()\n", - " \n", - " # This is for selecting the whole upper cells\n", - " # for i in range(rank+1, max_rank):\n", - " # P = torch.sparse.mm(P, data[f\"incidence_{i+1}\"])\n", - " # Q = torch.sparse.mm(P,P.T)\n", - " # edges = torch.cat((edges, Q.indices()), dim=1)\n", - " \n", - " # This considers the lower adjacencies\n", - " if rank != 0: \n", - " I = data[f\"incidence_{rank}\"]\n", - " A = torch.sparse.mm(I.T,I)\n", - " edges = torch.cat((edges, A.indices()), dim=1)\n", - " \n", - " # This is for selecting cells if they share any node\n", - " # for i in range(rank-1, 0, -1):\n", - " # P = torch.sparse.mm(data[f\"incidence_{i}\"], P)\n", - " # Q = torch.sparse.mm(P.T,P)\n", - " # edges = torch.cat((edges, Q.indices()), dim=1)\n", - " \n", - " edges = torch.unique(edges, dim=1)\n", - " # Remove self edges\n", - " mask = edges[0, :] != edges[1, :]\n", - " edges = edges[:, mask]\n", - " \n", - " data.edge_index = edges\n", - " \n", - " # We need to set x to x_{rank} since NeighborLoader will take the number of nodes from the x attribute\n", - " # The correct x is given after the reduce_neighborhoods function\n", - " if is_hypergraph and rank == 1:\n", - " data.x = data.x_hyperedges\n", - " else:\n", - " data.x = data[f'x_{rank}']\n", - " \n", - " if hasattr(data, 'num_nodes'):\n", - " data.num_nodes = data.x.shape[0]\n", - " return data\n", - "\n", - "def reduce_higher_ranks_incidences(batch, cells_ids, rank, max_rank, is_hypergraph=False):\n", - " \"\"\" Reduce the incidences with higher rank than the specified one.\n", - " \n", - " Parameters\n", - " ----------\n", - " batch: torch_geometric.data.Data\n", - " The input data.\n", - " cells_ids: list[torch.Tensor]\n", - " List of tensors containing the ids of the cells. The length of the list should be equal to the maximum rank.\n", - " rank: int\n", - " The rank to select the higher order incidences.\n", - " max_rank: int\n", - " The maximum rank of the incidences.\n", - " is_hypergraph: bool\n", - " Whether the data represents an hypergraph.\n", - " \n", - " Returns\n", - " -------\n", - " torch_geometric.data.Data\n", - " The output data with the reduced incidences.\n", - " list[torch.Tensor]\n", - " The updated indices of the cells. Each element of the list is a tensor containing the ids of the cells of the corresponding rank.\n", - " \"\"\"\n", - " for i in range(rank+1, max_rank+1):\n", - " if is_hypergraph:\n", - " incidence = batch.incidence_hyperedges\n", - " else:\n", - " incidence = batch[f\"incidence_{i}\"]\n", - " \n", - " if i != rank+1:\n", - " incidence = torch.index_select(incidence, 0, cells_ids[i-1])\n", - " cells_ids[i] = torch.where(torch.sum(incidence, dim=0).to_dense() > 1)[0]\n", - " incidence = torch.index_select(incidence, 1, cells_ids[i])\n", - " batch[f\"incidence_{i}\"] = incidence\n", - " \n", - " return batch, cells_ids\n", - "\n", - "def reduce_lower_ranks_incidences(batch, cells_ids, rank, is_hypergraph=False):\n", - " \"\"\" Reduce the incidences with lower rank than the specified one.\n", - " \n", - " Parameters\n", - " ----------\n", - " batch: torch_geometric.data.Data\n", - " The input data.\n", - " cells_ids: list[torch.Tensor]\n", - " List of tensors containing the ids of the cells. The length of the list should be equal to the maximum rank.\n", - " rank: int\n", - " The rank of the cells to consider.\n", - " is_hypergraph: bool\n", - " Whether the data represents an hypergraph.\n", - " \n", - " Returns\n", - " -------\n", - " torch.Tensor\n", - " The indices of the nodes contained by the cells.\n", - " list[torch.Tensor]\n", - " The updated indices of the cells. Each element of the list is a tensor containing the ids of the cells of the corresponding rank.\n", - " \"\"\"\n", - " for i in range(rank, 0, -1):\n", - " if is_hypergraph:\n", - " incidence = batch.incidence_hyperedges\n", - " else:\n", - " incidence = batch[f\"incidence_{i}\"]\n", - " incidence = torch.index_select(incidence, 1, cells_ids[i])\n", - " cells_ids[i-1] = torch.where(torch.sum(incidence, dim=1).to_dense() > 0)[0]\n", - " incidence = torch.index_select(incidence, 0, cells_ids[i-1])\n", - " batch[f\"incidence_{i}\"] = incidence\n", - " \n", - " if not is_hypergraph:\n", - " incidence = batch[f\"incidence_0\"]\n", - " incidence = torch.index_select(incidence, 1, cells_ids[0])\n", - " batch[f\"incidence_0\"] = incidence\n", - " return batch, cells_ids\n", - "\n", - "def reduce_matrices(batch, cells_ids, names, rank, max_rank):\n", - " \"\"\" Reduce the matrices using the indices in cells_ids. \n", - " \n", - " The matrices are assumed to be in the batch with the names specified in the list names.\n", - " \n", - " Parameters\n", - " ----------\n", - " batch: torch_geometric.data.Data\n", - " The input data.\n", - " cells_ids: list[torch.Tensor]\n", - " List of tensors containing the ids of the cells. The length of the list should be equal to the maximum rank.\n", - " names: list[str]\n", - " List of names of the matrices in the batch. They should appear in the format f\"{name}{i}\" where i is the rank of the matrix.\n", - " rank: int\n", - " The rank over which you are batching.\n", - " max_rank: int\n", - " The maximum rank of the matrices.\n", - " \n", - " Returns\n", - " -------\n", - " torch_geometric.data.Data\n", - " The output data with the reduced matrices.\n", - " \"\"\"\n", - " for i in range(max_rank+1):\n", - " for name in names:\n", - " if f\"{name}{i}\" in batch.keys():\n", - " matrix = batch[f\"{name}{i}\"]\n", - " if i==rank:\n", - " matrix = torch.index_select(matrix, 1, cells_ids[i])\n", - " else:\n", - " matrix = torch.index_select(matrix, 0, cells_ids[i])\n", - " matrix = torch.index_select(matrix, 1, cells_ids[i])\n", - " batch[f\"{name}{i}\"] = matrix\n", - " return batch\n", - "\n", - "def reduce_neighborhoods(batch, rank=0, remove_self_loops=True):\n", - " \"\"\" Reduce the neighborhoods of the cells in the batch.\n", - " \n", - " Parameters\n", - " ----------\n", - " batch: torch_geometric.data.Data\n", - " The input data.\n", - " rank: int\n", - " The rank of the cells to batch over.\n", - " remove_self_loops: bool\n", - " Whether to remove self loops from the edge_index.\n", - " \n", - " Returns\n", - " -------\n", - " torch_geometric.data.Data\n", - " The output data with the reduced neighborhoods.\n", - " \"\"\"\n", - " is_hypergraph = False\n", - " if hasattr(batch, 'incidence_hyperedges'):\n", - " is_hypergraph = True\n", - " max_rank = 1\n", - " else:\n", - " max_rank = len([key for key in batch.keys() if \"incidence\" in key])-1\n", - " \n", - " if rank > max_rank:\n", - " raise ValueError(f\"Rank {rank} is greater than the maximum rank {max_rank} in the dataset.\")\n", - " \n", - " cells_ids = [None for _ in range(max_rank+1)]\n", - " \n", - " # the indices of the cells selected by the NeighborhoodLoader are saved in the batch in the attribute n_id\n", - " cells_ids[rank] = batch.n_id\n", - " \n", - " batch, cells_ids = reduce_higher_ranks_incidences(batch, cells_ids, rank, max_rank, is_hypergraph)\n", - " batch, cells_ids = reduce_lower_ranks_incidences(batch, cells_ids, rank, is_hypergraph)\n", - " \n", - " batch = reduce_matrices(batch, \n", - " cells_ids, \n", - " names=['down_laplacian_', 'up_laplacian_', 'hodge_laplacian_', 'temp_'],\n", - " rank=rank,\n", - " max_rank=max_rank)\n", - " \n", - " # reduce the feature matrices\n", - " for i in range(max_rank+1):\n", - " if i != rank:\n", - " if f\"x_{i}\" in batch.keys():\n", - " batch[f\"x_{i}\"] = batch[f\"x_{i}\"][cells_ids[i]]\n", - " \n", - " # change the temp matrices back to adjacency\n", - " for i in range(max_rank+1):\n", - " if f\"temp_{i}\" in batch.keys():\n", - " batch[f\"adjacency_{i}\"] = batch[f\"temp_{i}\"]\n", - " del batch[f\"temp_{i}\"]\n", - " \n", - " # fix edge_index\n", - " if not is_hypergraph:\n", - " adjacency_0 = batch.adjacency_0.coalesce()\n", - " edge_index = adjacency_0.indices()\n", - " if remove_self_loops:\n", - " edge_index = torch_geometric.utils.remove_self_loops(edge_index)[0]\n", - " batch.edge_index = edge_index\n", - " \n", - " # fix x\n", - " batch.x = batch[f\"x_0\"]\n", - " if hasattr(batch, 'num_nodes'):\n", - " batch.num_nodes = batch.x.shape[0]\n", - " \n", - " return batch\n", - "\n", - "class ReduceNeighborhoods():\n", - " \"\"\" Reduce the neighborhoods of the cells in the batch.\n", - " \n", - " Parameters\n", - " ----------\n", - " rank: int\n", - " The rank of the cells to batch over.\n", - " remove_self_loops: bool\n", - " Whether to remove self loops from the edge_index.\n", - " \"\"\"\n", - " \n", - " def __init__(self, rank=0, remove_self_loops=True):\n", - " self.rank = rank\n", - " self.remove_self_loops = remove_self_loops\n", - " \n", - " def __call__(self, batch):\n", - " \"\"\" Call reduce_neighborhoods.\n", - " \n", - " Parameters\n", - " ----------\n", - " batch: torch_geometric.data.Data\n", - " The input data.\n", - " \n", - " Returns\n", - " -------\n", - " torch_geometric.data.Data\n", - " The output data with the reduced neighborhoods.\n", - " \"\"\"\n", - " return reduce_neighborhoods(batch, self.rank, self.remove_self_loops)\n", - "\n", - "class NeighborLoaderWrapper(NeighborLoader):\n", - " \"\"\" Wrapper that applies the needed transformations to the data before passing it to NeighborLoader.\n", - " \n", - " Parameters\n", - " ----------\n", - " dataset: torch_geometric.data.Dataset\n", - " The input dataset.\n", - " rank: int\n", - " The rank of the cells to batch over.\n", - " **kwargs: dict\n", - " Additional arguments for the NeighborLoader.\n", - " \"\"\"\n", - " def __init__(self, data, rank=0, **kwargs):\n", - " is_hypergraph = hasattr(data, 'incidence_hyperedges')\n", - " data = get_sampled_neighborhood(data, rank, is_hypergraph)\n", - " # This workaround is needed because torch_geometric treats any attribute of data with adj in the name differently and it raises errors.\n", - " data = workaround_data(data)\n", - " if 'num_neighbors' in kwargs.keys():\n", - " if len(kwargs['num_neighbors']) > 1:\n", - " raise NotImplementedError(\"NeighborLoaderWrapper only supports one-hop neighborhood selection.\")\n", - " super(NeighborLoaderWrapper, self).__init__(data, **kwargs)\n", - " " - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -546,7 +199,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -583,23 +236,23 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "Data(x=[8, 1], edge_index=[2, 13], y=[13], num_nodes=8, incidence_0=[1, 8], down_laplacian_0=[8, 8], up_laplacian_0=[8, 8], adjacency_0=[8, 8], hodge_laplacian_0=[8, 8], incidence_1=[8, 13], down_laplacian_1=[13, 13], up_laplacian_1=[13, 13], adjacency_1=[13, 13], hodge_laplacian_1=[13, 13], incidence_2=[13, 6], down_laplacian_2=[6, 6], up_laplacian_2=[6, 6], adjacency_2=[6, 6], hodge_laplacian_2=[6, 6], incidence_3=[6, 1], down_laplacian_3=[1, 1], up_laplacian_3=[1, 1], adjacency_3=[1, 1], hodge_laplacian_3=[1, 1], x_0=[8, 1], x_1=[13, 1], x_2=[6, 1], x_3=[1, 1])" + "Data(x=[8, 1], edge_index=[2, 13], y=[8], num_nodes=8, incidence_0=[1, 8], down_laplacian_0=[8, 8], up_laplacian_0=[8, 8], adjacency_0=[8, 8], hodge_laplacian_0=[8, 8], incidence_1=[8, 13], down_laplacian_1=[13, 13], up_laplacian_1=[13, 13], adjacency_1=[13, 13], hodge_laplacian_1=[13, 13], incidence_2=[13, 6], down_laplacian_2=[6, 6], up_laplacian_2=[6, 6], adjacency_2=[6, 6], hodge_laplacian_2=[6, 6], incidence_3=[6, 1], down_laplacian_3=[1, 1], up_laplacian_3=[1, 1], adjacency_3=[1, 1], hodge_laplacian_3=[1, 1], shape=[4], x_0=[8, 1], x_1=[13, 1], x_2=[6, 1], x_3=[1, 1])" ] }, - "execution_count": 5, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Training, validation and split idxs should be defined somewhere, here we use a toy example\n", - "rank = 1\n", + "rank = 0\n", "if hasattr(data, \"x_hyperedges\") and rank==1:\n", " n_cells = data.x_hyperedges.shape[0]\n", "else:\n", @@ -610,48 +263,74 @@ "train_mask = torch.zeros(n_cells, dtype=torch.bool)\n", "train_mask[:n_train] = 1\n", "\n", - "if rank != 0:\n", - " y = torch.zeros(n_cells, dtype=torch.long)\n", - " data.y = y\n", + "y = torch.zeros(n_cells, dtype=torch.long)\n", + "data.y = y\n", "\n", "data" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.11/site-packages/torch_geometric/sampler/neighbor_sampler.py:61: UserWarning: Using 'NeighborSampler' without a 'pyg-lib' installation is deprecated and will be removed soon. Please install 'pyg-lib' for accelerated neighborhood sampling\n", + " warnings.warn(f\"Using '{self.__class__.__name__}' without a \"\n" + ] + } + ], "source": [ - "batch_size = 2\n", - "\n", - "reduce = ReduceNeighborhoods(rank=rank, remove_self_loops=True)\n", + "batch_size = 1\n", "\n", - "loader = NeighborLoaderWrapper(data,\n", - " rank=rank,\n", - " num_neighbors=[-1],\n", - " input_nodes=train_mask,\n", - " batch_size=batch_size,\n", - " shuffle=False,\n", - " transform=reduce)" + "loader = NeighborCellsLoader(data,\n", + " rank=rank,\n", + " num_neighbors=[-1],\n", + " input_nodes=train_mask,\n", + " batch_size=batch_size,\n", + " shuffle=False)" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Data(x=[7, 1], edge_index=[2, 22], y=[10], num_nodes=7, incidence_0=[1, 7], down_laplacian_0=[7, 7], up_laplacian_0=[7, 7], hodge_laplacian_0=[7, 7], incidence_1=[7, 10], down_laplacian_1=[10, 10], up_laplacian_1=[10, 10], hodge_laplacian_1=[10, 10], incidence_2=[10, 6], down_laplacian_2=[6, 6], up_laplacian_2=[6, 6], hodge_laplacian_2=[6, 6], incidence_3=[6, 1], down_laplacian_3=[1, 1], up_laplacian_3=[1, 1], hodge_laplacian_3=[1, 1], x_0=[7, 1], x_1=[10, 1], x_2=[6, 1], x_3=[1, 1], n_id=[10], e_id=[13], input_id=[2], batch_size=2, adjacency_0=[7, 7], adjacency_1=[10, 10], adjacency_2=[6, 6], adjacency_3=[1, 1])\n", - "The cells of rank 1 that were originally selected are tensor([0, 1])\n" + "Data(x=[5, 1], edge_index=[2, 16], y=[5], num_nodes=5, incidence_0=[1, 5], down_laplacian_0=[5, 5], up_laplacian_0=[5, 5], adjacency_0=[5, 5], hodge_laplacian_0=[5, 5], incidence_1=[5, 8], down_laplacian_1=[8, 8], up_laplacian_1=[8, 8], adjacency_1=[8, 8], hodge_laplacian_1=[8, 8], incidence_2=[8, 5], down_laplacian_2=[5, 5], up_laplacian_2=[5, 5], adjacency_2=[5, 5], hodge_laplacian_2=[5, 5], incidence_3=[5, 1], down_laplacian_3=[1, 1], up_laplacian_3=[1, 1], adjacency_3=[1, 1], hodge_laplacian_3=[1, 1], shape=[4], x_0=[5, 1], x_1=[8, 1], x_2=[5, 1], x_3=[1, 1], n_id=[5], e_id=[4], input_id=[1], batch_size=1)\n", + "The cells of rank 0 that were originally selected are tensor([0])\n", + "tensor([0, 7, 1, 2, 4])\n", + "tensor([[0, 0, 0, 0, 1, 1, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4],\n", + " [1, 2, 3, 4, 0, 3, 0, 3, 4, 0, 1, 2, 4, 0, 2, 3]])\n", + "tensor([[1.],\n", + " [1.],\n", + " [1.],\n", + " [0.],\n", + " [1.]])\n", + "tensor([[1., 1., 0., 0., 0.],\n", + " [1., 0., 1., 1., 0.],\n", + " [0., 1., 1., 0., 0.],\n", + " [0., 0., 0., 1., 0.],\n", + " [1., 0., 0., 0., 1.],\n", + " [0., 1., 0., 0., 1.],\n", + " [0., 0., 1., 0., 1.],\n", + " [0., 0., 0., 1., 0.]])\n", + "tensor([[1., 1., 1., 1., 0., 0., 0., 0.],\n", + " [0., 0., 0., 1., 0., 0., 0., 1.],\n", + " [1., 0., 0., 0., 1., 1., 0., 0.],\n", + " [0., 1., 0., 0., 1., 0., 1., 1.],\n", + " [0., 0., 1., 0., 0., 1., 1., 0.]])\n" ] }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -677,57 +356,15 @@ ] }, { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "batch_size = 1\n", - "\n", - "# num_neighbors controls also the number of hops (for 2 hops do num_neighbors=[-1, -1])\n", - "reduce = ReduceNeighborhoods(rank=rank, remove_self_loops=True)\n", - "\n", - "loader = NeighborLoaderWrapper(data,\n", - " rank=rank,\n", - " num_neighbors=[-1],\n", - " input_nodes=train_mask,\n", - " batch_size=batch_size,\n", - " shuffle=False,\n", - " transform=reduce)\n", - "for batch in loader:\n", - " print(batch)\n", - " print(batch.n_id)\n", - " print(batch.edge_index)\n", - " if hasattr(batch, 'incidence_hyperedges'):\n", - " print(batch.incidence_hyperedges.to_dense())\n", - " else:\n", - " print(batch.incidence_3.to_dense())\n", - " print(batch.incidence_2.to_dense())\n", - " print(batch.incidence_1.to_dense())\n", - " break\n", - "\n", - "plot_graph(batch)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "visualize_simplicial_complex(batch)" + "## Cora hypergraph" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -773,34 +410,36 @@ "batch_size = 1\n", "\n", "# num_neighbors controls also the number of hops (for 2 hops do num_neighbors=[-1, -1])\n", - "reduce = ReduceNeighborhoods(rank=rank, remove_self_loops=True)\n", "\n", - "loader = NeighborLoaderWrapper(data,\n", + "\n", + "loader = NeighborCellsLoader(data,\n", " rank=rank,\n", " num_neighbors=[-1],\n", " input_nodes=train_mask,\n", " batch_size=batch_size,\n", - " shuffle=False,\n", - " transform=reduce)" + " shuffle=False)" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Data(x=[4, 1433], edge_index=[2, 3], y=[4], train_mask=[4], val_mask=[4], test_mask=[4], incidence_hyperedges=[4, 2708], num_hyperedges=2708, x_0=[4, 1433], x_hyperedges=[4, 1433], n_id=[4], e_id=[3], input_id=[1], batch_size=1, incidence_1=[4, 5], num_nodes=4)\n", + "Data(x=[4, 1433], edge_index=[2, 10556], y=[4], train_mask=[2708], val_mask=[2708], test_mask=[2708], incidence_hyperedges=[2708, 2708], num_hyperedges=2708, x_0=[4, 1433], x_hyperedges=[2708, 1433], incidence_1=[4, 5], num_nodes=4, n_id=[4], e_id=[3], input_id=[1], batch_size=1)\n", "tensor([ 0, 1862, 633, 2582])\n", - "tensor([[1, 2, 3],\n", - " [0, 0, 0]])\n", + "tensor([[ 0, 0, 0, ..., 2707, 2707, 2707],\n", + " [ 633, 1862, 2582, ..., 598, 1473, 2706]])\n", "tensor([[1., 0., 0., ..., 0., 0., 0.],\n", - " [1., 0., 0., ..., 0., 0., 0.],\n", - " [1., 0., 0., ..., 0., 0., 0.],\n", - " [1., 0., 0., ..., 0., 0., 0.]])\n" + " [0., 1., 1., ..., 0., 0., 0.],\n", + " [0., 1., 1., ..., 0., 0., 0.],\n", + " ...,\n", + " [0., 0., 0., ..., 1., 0., 0.],\n", + " [0., 0., 0., ..., 0., 1., 1.],\n", + " [0., 0., 0., ..., 0., 1., 1.]])\n" ] } ], @@ -818,179 +457,6 @@ " \n", " break" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "batch" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# for batch in loader:\n", - "# plot_graph(batch)\n", - "# break" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'data' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[4], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(\u001b[43mdata\u001b[49m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mincidence_3\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mincidence_3\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(data, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mx_3\u001b[39m\u001b[38;5;124m'\u001b[39m):\n", - "\u001b[0;31mNameError\u001b[0m: name 'data' is not defined" - ] - } - ], - "source": [ - "if hasattr(data, 'incidence_3'):\n", - " del data['incidence_3']\n", - "if hasattr(data, 'x_3'):\n", - " del data['x_3']\n", - "for key in list(data.keys()):\n", - " if 'laplacian' in key or 'temp' in key or 'mask' in key or 'hyperedges' in key:\n", - " del data[key]\n", - "\n", - "incidence_3 = torch.tensor([[],[]]).to_sparse()\n", - "incidence_2 = torch.tensor([[1,0],[1,0],[1,0],[0,0],[0,1],[0,1],[0,1]]).float().to_sparse()\n", - "incidence_1 = torch.tensor([[1,0,1,0,0,0,0],[1,1,0,0,0,0,0],[0,1,1,1,0,0,0],[0,0,0,1,1,0,1],[0,0,0,0,1,1,0],[0,0,0,0,0,1,1]]).float().to_sparse()\n", - "incidence_0 = torch.tensor([[1,1,1,1,1,1]]).float().to_sparse()\n", - "data " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "rank = 0\n", - "\n", - "data['incidence_3'] = incidence_3\n", - "data['incidence_2'] = incidence_2\n", - "data['incidence_1'] = incidence_1\n", - "data['incidence_0'] = incidence_0\n", - "\n", - "data['x_3'] = torch.tensor([]).float()\n", - "data['x_2'] = torch.tensor([[1,0],[0,1]]).float()\n", - "data['x_1'] = torch.tensor([[1,0,0],[0,1,0],[0,0,1],[1,0,0],[0,1,0],[0,0,1],[1,0,0]]).float()\n", - "data['x_0'] = torch.tensor([[1,0,0,0,0,0],[0,1,0,0,0,0],[0,0,1,0,0,0],[0,0,0,1,0,0],[0,0,0,0,1,0],[0,0,0,0,0,1]]).float()\n", - "data['x'] = data[f'x_{rank}']\n", - "data['y'] = torch.zeros(data[f'x_{rank}'].shape[0], dtype=torch.long)\n", - "\n", - "data['edge_index'] = torch.tensor([[0,0,1,1,2,2,2,3,3,3,4,4,5,5],[1,2,0,2,0,1,3,2,4,5,3,5,3,4]])\n", - "data['temp_0'] = torch.sparse_coo_tensor(data['edge_index'], torch.ones(data['edge_index'].shape[1]), data['x_0'].shape)\n", - "print(data)\n", - "plot_graph(data)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# num_neighbors controls also the number of hops (for 2 hops do num_neighbors=[-1, -1])\n", - "reduce = ReduceNeighborhoods(rank=rank, remove_self_loops=True)\n", - "batch_size = 1\n", - "loader = NeighborLoaderWrapper(data,\n", - " rank=rank,\n", - " num_neighbors=[-1],\n", - " input_nodes=train_mask,\n", - " batch_size=batch_size,\n", - " shuffle=False,\n", - " transform=reduce)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for i, batch in enumerate(loader):\n", - " if i==2:\n", - " print(batch.adjacency_0.to_dense())\n", - " plot_graph(batch)\n", - " print(batch)\n", - " print(batch.n_id)\n", - " break\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "incidence_3 = torch.tensor([[1],[1],[1],[1]]).float().to_sparse()\n", - "incidence_2 = torch.tensor([[1,0,1,0],[1,1,0,0],[0,1,1,0],[0,0,1,1],[1,0,0,1],[0,1,0,1]]).float().to_sparse()\n", - "incidence_1 = torch.tensor([[1,1,1,0,0,0],[1,0,0,1,1,0],[0,1,0,0,1,1],[0,0,1,1,0,1]]).float().to_sparse()\n", - "incidence_0 = torch.tensor([[1,1,1,1]]).float().to_sparse()\n", - "\n", - "x_3 = torch.tensor([[1,0]]).float()\n", - "x_2 = torch.tensor([[1,0],[0,1],[1,1],[0,0]]).float()\n", - "x_1 = torch.tensor([[1,0,0],[0,1,0],[0,0,1],[1,0,0],[0,1,0],[0,0,1]]).float()\n", - "x_0 = torch.tensor([[1,0,0,0],[0,1,0,0],[0,0,1,0],[0,0,0,1]]).float()\n", - "\n", - "rank = 2\n", - "\n", - "data['incidence_3'] = incidence_3\n", - "data['incidence_2'] = incidence_2\n", - "data['incidence_1'] = incidence_1\n", - "data['incidence_0'] = incidence_0\n", - "\n", - "data['x_3'] = x_3\n", - "data['x_2'] = x_2\n", - "data['x_1'] = x_1\n", - "data['x_0'] = x_0\n", - "data['x'] = data[f'x_{rank}']\n", - "data['y'] = torch.zeros(data[f'x_{rank}'].shape[0], dtype=torch.long)\n", - "\n", - "data['edge_index'] = torch.tensor([[0,0,0,1,1,1,2,2,2,3,3,3],[1,2,3,0,2,3,0,1,3,0,1,2]])\n", - "data['temp_0'] = torch.sparse_coo_tensor(data['edge_index'], torch.ones(data['edge_index'].shape[1]), data['x_0'].shape)\n", - "print(data)\n", - "plot_graph(data)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# num_neighbors controls also the number of hops (for 2 hops do num_neighbors=[-1, -1])\n", - "reduce = ReduceNeighborhoods(rank=rank, remove_self_loops=True)\n", - "batch_size = 1\n", - "loader = NeighborLoaderWrapper(data,\n", - " rank=rank,\n", - " num_neighbors=[-1],\n", - " input_nodes=train_mask,\n", - " batch_size=batch_size,\n", - " shuffle=False,\n", - " transform=reduce)\n", - "\n", - "for i, batch in enumerate(loader):\n", - " if i==0:\n", - " plot_graph(batch)\n", - " print(batch)\n", - " print(batch.n_id)\n", - " break" - ] } ], "metadata": { From 5ec69b811ffeb751cbce20a6dbfb1cf1aa05c626 Mon Sep 17 00:00:00 2001 From: Coerulatus Date: Sat, 16 Nov 2024 16:00:10 +0000 Subject: [PATCH 12/24] Marco - fixed conflict --- .../data/batching/neighbor_cells_loader.py | 4 +- topobenchmarkx/data/batching/utils.py | 1 + tutorials/batching.ipynb | 550 ++---------------- .../131528455/data.pt | Bin 22921 -> 0 bytes .../path_transform_parameters_dict.json | 11 - .../131528455/pre_filter.pt | Bin 864 -> 0 bytes .../131528455/pre_transform.pt | Bin 864 -> 0 bytes 7 files changed, 55 insertions(+), 511 deletions(-) delete mode 100644 tutorials/graph2simplicial_lifting/131528455/data.pt delete mode 100644 tutorials/graph2simplicial_lifting/131528455/path_transform_parameters_dict.json delete mode 100644 tutorials/graph2simplicial_lifting/131528455/pre_filter.pt delete mode 100644 tutorials/graph2simplicial_lifting/131528455/pre_transform.pt diff --git a/topobenchmarkx/data/batching/neighbor_cells_loader.py b/topobenchmarkx/data/batching/neighbor_cells_loader.py index a5c872cf..62abdb96 100644 --- a/topobenchmarkx/data/batching/neighbor_cells_loader.py +++ b/topobenchmarkx/data/batching/neighbor_cells_loader.py @@ -153,8 +153,8 @@ def __init__( super().__init__( data=data, - node_sampler=neighbor_sampler, - input_nodes=input_nodes, + cell_sampler=neighbor_sampler, + input_cells=input_nodes, input_time=input_time, transform=transform, transform_sampler_output=transform_sampler_output, diff --git a/topobenchmarkx/data/batching/utils.py b/topobenchmarkx/data/batching/utils.py index b522d361..5636f3d2 100644 --- a/topobenchmarkx/data/batching/utils.py +++ b/topobenchmarkx/data/batching/utils.py @@ -196,6 +196,7 @@ def filter_data(data: Data, cells: Tensor, rank: int) -> Data: ''' out = copy.copy(data) out = reduce_neighborhoods(out, cells, rank=rank) + out.n_id = cells return out def get_sampled_neighborhood(data, rank=0, is_hypergraph=False): diff --git a/tutorials/batching.ipynb b/tutorials/batching.ipynb index f0d67ead..0742e617 100644 --- a/tutorials/batching.ipynb +++ b/tutorials/batching.ipynb @@ -9,7 +9,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_45596/2455096930.py:26: UserWarning: \n", + "/tmp/ipykernel_161536/1370418617.py:28: UserWarning: \n", "The version_base parameter is not specified.\n", "Please specify a compatability version level, or None.\n", "Will assume defaults for version 1.1\n", @@ -45,7 +45,7 @@ "from topobenchmarkx.dataloader.dataloader import TBXDataloader\n", "from topobenchmarkx.data.loaders import GraphLoader\n", "\n", - "from topobenchmarkx.data.samplers.neighbor_cells_loader import NeighborCellsLoader\n", + "from topobenchmarkx.data.batching.neighbor_cells_loader import NeighborCellsLoader\n", "\n", "from topobenchmarkx.utils.config_resolvers import (\n", " get_default_transform,\n", @@ -54,7 +54,6 @@ " infer_in_channels,\n", ")\n", "\n", - "\n", "initialize(config_path=\"../configs\", job_name=\"job\")" ] }, @@ -190,356 +189,6 @@ " " ] }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "from torch_geometric.loader import NeighborLoader\n", - "\n", - "# replace adjacency keys with temp\n", - "def workaround_data(data):\n", - " \"\"\" The function is a workaround to change the data to work with NeighborLoader. \n", - " \n", - " The function replaces the keys with adjacency in the name with temp. It also removes the shape attribute if present.\n", - " \n", - " Parameters\n", - " ----------\n", - " data: torch_geometric.data.Data\n", - " The input data.\n", - " \n", - " Returns\n", - " -------\n", - " torch_geometric.data.Data\n", - " The output data with the keys replaced and the shape attribute removed.\n", - " \"\"\"\n", - " n_adjacencies = len([key for key in data.keys() if \"adjacency\" in key])\n", - " for i in range(n_adjacencies):\n", - " if f\"adjacency_{i}\" in data.keys():\n", - " data[f\"temp_{i}\"] = data[f\"adjacency_{i}\"]\n", - " del data[f\"adjacency_{i}\"]\n", - " \n", - " # shape is a list, it breaks the NeighborLoader if we keep it\n", - " if hasattr(data, 'shape'):\n", - " del data.shape\n", - " return data\n", - "\n", - "def get_sampled_neighborhood(data, rank=0, is_hypergraph=False):\n", - " ''' This function updates the edge_index attribute of torch_geometric.data.Data. \n", - " \n", - " The function finds cells, of the specified rank, that are either upper or lower neighbors.\n", - " \n", - " Parameters\n", - " ----------\n", - " data: torch_geometric.data.Data\n", - " The input data.\n", - " rank: int\n", - " The rank of the cells that you want to batch over.\n", - " is_hypergraph: bool\n", - " Whether the data represents an hypergraph.\n", - " \n", - " Returns\n", - " -------\n", - " torch_geometric.data.Data\n", - " The output data with updated edge_index.\n", - " edge_index contains indices of connected cells of the specified rank K. \n", - " Two cells of rank K are connected if they are either lower or upper neighbors. \n", - " '''\n", - " if rank == 0:\n", - " return data\n", - " if is_hypergraph: \n", - " if rank > 1:\n", - " raise ValueError(\"Hypergraphs are not supported for ranks greater than 1.\")\n", - " if rank == 1:\n", - " I = data.incidence_hyperedges\n", - " A = torch.sparse.mm(I,I.T) # lower adj matrix\n", - " edges = A.indices()\n", - " else:\n", - " I = data.incidence_hyperedges\n", - " A = torch.sparse.mm(I.T,I)\n", - " edges = A.indices() \n", - " else:\n", - " # get number of incidences\n", - " max_rank = len([key for key in data.keys() if \"incidence\" in key])-1\n", - " if rank > max_rank:\n", - " raise ValueError(f\"Rank {rank} is greater than the maximum rank {max_rank} in the data.\")\n", - " \n", - " # This considers the upper adjacencies\n", - " if rank == max_rank:\n", - " edges = torch.empty((2, 0), dtype=torch.long)\n", - " else:\n", - " I = data[f\"incidence_{rank+1}\"]\n", - " A = torch.sparse.mm(I,I.T)\n", - " edges = A.indices()\n", - " \n", - " # This is for selecting the whole upper cells\n", - " # for i in range(rank+1, max_rank):\n", - " # P = torch.sparse.mm(P, data[f\"incidence_{i+1}\"])\n", - " # Q = torch.sparse.mm(P,P.T)\n", - " # edges = torch.cat((edges, Q.indices()), dim=1)\n", - " \n", - " # This considers the lower adjacencies\n", - " if rank != 0: \n", - " I = data[f\"incidence_{rank}\"]\n", - " A = torch.sparse.mm(I.T,I)\n", - " edges = torch.cat((edges, A.indices()), dim=1)\n", - " \n", - " # This is for selecting cells if they share any node\n", - " # for i in range(rank-1, 0, -1):\n", - " # P = torch.sparse.mm(data[f\"incidence_{i}\"], P)\n", - " # Q = torch.sparse.mm(P.T,P)\n", - " # edges = torch.cat((edges, Q.indices()), dim=1)\n", - " \n", - " edges = torch.unique(edges, dim=1)\n", - " # Remove self edges\n", - " mask = edges[0, :] != edges[1, :]\n", - " edges = edges[:, mask]\n", - " \n", - " data.edge_index = edges\n", - " \n", - " # We need to set x to x_{rank} since NeighborLoader will take the number of nodes from the x attribute\n", - " # The correct x is given after the reduce_neighborhoods function\n", - " if is_hypergraph and rank == 1:\n", - " data.x = data.x_hyperedges\n", - " else:\n", - " data.x = data[f'x_{rank}']\n", - " \n", - " if hasattr(data, 'num_nodes'):\n", - " data.num_nodes = data.x.shape[0]\n", - " return data\n", - "\n", - "def reduce_higher_ranks_incidences(batch, cells_ids, rank, max_rank, is_hypergraph=False):\n", - " \"\"\" Reduce the incidences with higher rank than the specified one.\n", - " \n", - " Parameters\n", - " ----------\n", - " batch: torch_geometric.data.Data\n", - " The input data.\n", - " cells_ids: list[torch.Tensor]\n", - " List of tensors containing the ids of the cells. The length of the list should be equal to the maximum rank.\n", - " rank: int\n", - " The rank to select the higher order incidences.\n", - " max_rank: int\n", - " The maximum rank of the incidences.\n", - " is_hypergraph: bool\n", - " Whether the data represents an hypergraph.\n", - " \n", - " Returns\n", - " -------\n", - " torch_geometric.data.Data\n", - " The output data with the reduced incidences.\n", - " list[torch.Tensor]\n", - " The updated indices of the cells. Each element of the list is a tensor containing the ids of the cells of the corresponding rank.\n", - " \"\"\"\n", - " for i in range(rank+1, max_rank+1):\n", - " if is_hypergraph:\n", - " incidence = batch.incidence_hyperedges\n", - " else:\n", - " incidence = batch[f\"incidence_{i}\"]\n", - " \n", - " if i != rank+1:\n", - " incidence = torch.index_select(incidence, 0, cells_ids[i-1])\n", - " cells_ids[i] = torch.where(torch.sum(incidence, dim=0).to_dense() > 1)[0]\n", - " incidence = torch.index_select(incidence, 1, cells_ids[i])\n", - " batch[f\"incidence_{i}\"] = incidence\n", - " \n", - " return batch, cells_ids\n", - "\n", - "def reduce_lower_ranks_incidences(batch, cells_ids, rank, is_hypergraph=False):\n", - " \"\"\" Reduce the incidences with lower rank than the specified one.\n", - " \n", - " Parameters\n", - " ----------\n", - " batch: torch_geometric.data.Data\n", - " The input data.\n", - " cells_ids: list[torch.Tensor]\n", - " List of tensors containing the ids of the cells. The length of the list should be equal to the maximum rank.\n", - " rank: int\n", - " The rank of the cells to consider.\n", - " is_hypergraph: bool\n", - " Whether the data represents an hypergraph.\n", - " \n", - " Returns\n", - " -------\n", - " torch.Tensor\n", - " The indices of the nodes contained by the cells.\n", - " list[torch.Tensor]\n", - " The updated indices of the cells. Each element of the list is a tensor containing the ids of the cells of the corresponding rank.\n", - " \"\"\"\n", - " for i in range(rank, 0, -1):\n", - " if is_hypergraph:\n", - " incidence = batch.incidence_hyperedges\n", - " else:\n", - " incidence = batch[f\"incidence_{i}\"]\n", - " incidence = torch.index_select(incidence, 1, cells_ids[i])\n", - " cells_ids[i-1] = torch.where(torch.sum(incidence, dim=1).to_dense() > 0)[0]\n", - " incidence = torch.index_select(incidence, 0, cells_ids[i-1])\n", - " batch[f\"incidence_{i}\"] = incidence\n", - " \n", - " if not is_hypergraph:\n", - " incidence = batch[f\"incidence_0\"]\n", - " incidence = torch.index_select(incidence, 1, cells_ids[0])\n", - " batch[f\"incidence_0\"] = incidence\n", - " return batch, cells_ids\n", - "\n", - "def reduce_matrices(batch, cells_ids, names, rank, max_rank):\n", - " \"\"\" Reduce the matrices using the indices in cells_ids. \n", - " \n", - " The matrices are assumed to be in the batch with the names specified in the list names.\n", - " \n", - " Parameters\n", - " ----------\n", - " batch: torch_geometric.data.Data\n", - " The input data.\n", - " cells_ids: list[torch.Tensor]\n", - " List of tensors containing the ids of the cells. The length of the list should be equal to the maximum rank.\n", - " names: list[str]\n", - " List of names of the matrices in the batch. They should appear in the format f\"{name}{i}\" where i is the rank of the matrix.\n", - " rank: int\n", - " The rank over which you are batching.\n", - " max_rank: int\n", - " The maximum rank of the matrices.\n", - " \n", - " Returns\n", - " -------\n", - " torch_geometric.data.Data\n", - " The output data with the reduced matrices.\n", - " \"\"\"\n", - " for i in range(max_rank+1):\n", - " for name in names:\n", - " if f\"{name}{i}\" in batch.keys():\n", - " matrix = batch[f\"{name}{i}\"]\n", - " if i==rank:\n", - " matrix = torch.index_select(matrix, 1, cells_ids[i])\n", - " else:\n", - " matrix = torch.index_select(matrix, 0, cells_ids[i])\n", - " matrix = torch.index_select(matrix, 1, cells_ids[i])\n", - " batch[f\"{name}{i}\"] = matrix\n", - " return batch\n", - "\n", - "def reduce_neighborhoods(batch, rank=0, remove_self_loops=True):\n", - " \"\"\" Reduce the neighborhoods of the cells in the batch.\n", - " \n", - " Parameters\n", - " ----------\n", - " batch: torch_geometric.data.Data\n", - " The input data.\n", - " rank: int\n", - " The rank of the cells to batch over.\n", - " remove_self_loops: bool\n", - " Whether to remove self loops from the edge_index.\n", - " \n", - " Returns\n", - " -------\n", - " torch_geometric.data.Data\n", - " The output data with the reduced neighborhoods.\n", - " \"\"\"\n", - " is_hypergraph = False\n", - " if hasattr(batch, 'incidence_hyperedges'):\n", - " is_hypergraph = True\n", - " max_rank = 1\n", - " else:\n", - " max_rank = len([key for key in batch.keys() if \"incidence\" in key])-1\n", - " \n", - " if rank > max_rank:\n", - " raise ValueError(f\"Rank {rank} is greater than the maximum rank {max_rank} in the dataset.\")\n", - " \n", - " cells_ids = [None for _ in range(max_rank+1)]\n", - " \n", - " # the indices of the cells selected by the NeighborhoodLoader are saved in the batch in the attribute n_id\n", - " cells_ids[rank] = batch.n_id\n", - " \n", - " batch, cells_ids = reduce_higher_ranks_incidences(batch, cells_ids, rank, max_rank, is_hypergraph)\n", - " batch, cells_ids = reduce_lower_ranks_incidences(batch, cells_ids, rank, is_hypergraph)\n", - " \n", - " batch = reduce_matrices(batch, \n", - " cells_ids, \n", - " names=['down_laplacian_', 'up_laplacian_', 'hodge_laplacian_', 'temp_'],\n", - " rank=rank,\n", - " max_rank=max_rank)\n", - " \n", - " # reduce the feature matrices\n", - " for i in range(max_rank+1):\n", - " if i != rank:\n", - " if f\"x_{i}\" in batch.keys():\n", - " batch[f\"x_{i}\"] = batch[f\"x_{i}\"][cells_ids[i]]\n", - " \n", - " # change the temp matrices back to adjacency\n", - " for i in range(max_rank+1):\n", - " if f\"temp_{i}\" in batch.keys():\n", - " batch[f\"adjacency_{i}\"] = batch[f\"temp_{i}\"]\n", - " del batch[f\"temp_{i}\"]\n", - " \n", - " # fix edge_index\n", - " if not is_hypergraph:\n", - " adjacency_0 = batch.adjacency_0.coalesce()\n", - " edge_index = adjacency_0.indices()\n", - " if remove_self_loops:\n", - " edge_index = torch_geometric.utils.remove_self_loops(edge_index)[0]\n", - " batch.edge_index = edge_index\n", - " \n", - " # fix x\n", - " batch.x = batch[f\"x_0\"]\n", - " if hasattr(batch, 'num_nodes'):\n", - " batch.num_nodes = batch.x.shape[0]\n", - " \n", - " return batch\n", - "\n", - "class ReduceNeighborhoods():\n", - " \"\"\" Reduce the neighborhoods of the cells in the batch.\n", - " \n", - " Parameters\n", - " ----------\n", - " rank: int\n", - " The rank of the cells to batch over.\n", - " remove_self_loops: bool\n", - " Whether to remove self loops from the edge_index.\n", - " \"\"\"\n", - " \n", - " def __init__(self, rank=0, remove_self_loops=True):\n", - " self.rank = rank\n", - " self.remove_self_loops = remove_self_loops\n", - " \n", - " def __call__(self, batch):\n", - " \"\"\" Call reduce_neighborhoods.\n", - " \n", - " Parameters\n", - " ----------\n", - " batch: torch_geometric.data.Data\n", - " The input data.\n", - " \n", - " Returns\n", - " -------\n", - " torch_geometric.data.Data\n", - " The output data with the reduced neighborhoods.\n", - " \"\"\"\n", - " return reduce_neighborhoods(batch, self.rank, self.remove_self_loops)\n", - "\n", - "class NeighborLoaderWrapper(NeighborLoader):\n", - " \"\"\" Wrapper that applies the needed transformations to the data before passing it to NeighborLoader.\n", - " \n", - " Parameters\n", - " ----------\n", - " dataset: torch_geometric.data.Dataset\n", - " The input dataset.\n", - " rank: int\n", - " The rank of the cells to batch over.\n", - " **kwargs: dict\n", - " Additional arguments for the NeighborLoader.\n", - " \"\"\"\n", - " def __init__(self, data, rank=0, **kwargs):\n", - " is_hypergraph = hasattr(data, 'incidence_hyperedges')\n", - " data = get_sampled_neighborhood(data, rank, is_hypergraph)\n", - " # This workaround is needed because torch_geometric treats any attribute of data with adj in the name differently and it raises errors.\n", - " data = workaround_data(data)\n", - " if 'num_neighbors' in kwargs.keys():\n", - " if len(kwargs['num_neighbors']) > 1:\n", - " raise NotImplementedError(\"NeighborLoaderWrapper only supports one-hop neighborhood selection.\")\n", - " super(NeighborLoaderWrapper, self).__init__(data, **kwargs)\n", - " " - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -549,26 +198,17 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ + "Transform parameters are the same, using existing data_dir: ./graph2simplicial_lifting/131528455\n", "Data(x=[8, 1], edge_index=[2, 13], y=[8], num_nodes=8, incidence_0=[1, 8], down_laplacian_0=[8, 8], up_laplacian_0=[8, 8], adjacency_0=[8, 8], hodge_laplacian_0=[8, 8], incidence_1=[8, 13], down_laplacian_1=[13, 13], up_laplacian_1=[13, 13], adjacency_1=[13, 13], hodge_laplacian_1=[13, 13], incidence_2=[13, 6], down_laplacian_2=[6, 6], up_laplacian_2=[6, 6], adjacency_2=[6, 6], hodge_laplacian_2=[6, 6], incidence_3=[6, 1], down_laplacian_3=[1, 1], up_laplacian_3=[1, 1], adjacency_3=[1, 1], hodge_laplacian_3=[1, 1], shape=[4], x_0=[8, 1], x_1=[13, 1], x_2=[6, 1], x_3=[1, 1])\n" ] }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Processing...\n", - "/home/lev/miniconda3/envs/tbx/lib/python3.11/site-packages/scipy/sparse/_index.py:143: SparseEfficiencyWarning: Changing the sparsity structure of a csr_matrix is expensive. lil_matrix is more efficient.\n", - " self._set_arrayXarray(i, j, x)\n", - "Done!\n" - ] - }, { "data": { "image/png": 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@@ -595,16 +235,16 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "Data(x=[8, 1], edge_index=[2, 13], y=[13], num_nodes=8, incidence_0=[1, 8], down_laplacian_0=[8, 8], up_laplacian_0=[8, 8], adjacency_0=[8, 8], hodge_laplacian_0=[8, 8], incidence_1=[8, 13], down_laplacian_1=[13, 13], up_laplacian_1=[13, 13], adjacency_1=[13, 13], hodge_laplacian_1=[13, 13], incidence_2=[13, 6], down_laplacian_2=[6, 6], up_laplacian_2=[6, 6], adjacency_2=[6, 6], hodge_laplacian_2=[6, 6], incidence_3=[6, 1], down_laplacian_3=[1, 1], up_laplacian_3=[1, 1], adjacency_3=[1, 1], hodge_laplacian_3=[1, 1], x_0=[8, 1], x_1=[13, 1], x_2=[6, 1], x_3=[1, 1])" + "Data(x=[8, 1], edge_index=[2, 13], y=[8], num_nodes=8, incidence_0=[1, 8], down_laplacian_0=[8, 8], up_laplacian_0=[8, 8], adjacency_0=[8, 8], hodge_laplacian_0=[8, 8], incidence_1=[8, 13], down_laplacian_1=[13, 13], up_laplacian_1=[13, 13], adjacency_1=[13, 13], hodge_laplacian_1=[13, 13], incidence_2=[13, 6], down_laplacian_2=[6, 6], up_laplacian_2=[6, 6], adjacency_2=[6, 6], hodge_laplacian_2=[6, 6], incidence_3=[6, 1], down_laplacian_3=[1, 1], up_laplacian_3=[1, 1], adjacency_3=[1, 1], hodge_laplacian_3=[1, 1], shape=[4], x_0=[8, 1], x_1=[13, 1], x_2=[6, 1], x_3=[1, 1])" ] }, - "execution_count": 6, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -630,9 +270,18 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.11/site-packages/torch_geometric/sampler/neighbor_sampler.py:61: UserWarning: Using 'NeighborSampler' without a 'pyg-lib' installation is deprecated and will be removed soon. Please install 'pyg-lib' for accelerated neighborhood sampling\n", + " warnings.warn(f\"Using '{self.__class__.__name__}' without a \"\n" + ] + } + ], "source": [ "batch_size = 1\n", "\n", @@ -646,15 +295,36 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Data(x=[7, 1], edge_index=[2, 22], y=[10], num_nodes=7, incidence_0=[1, 7], down_laplacian_0=[7, 7], up_laplacian_0=[7, 7], hodge_laplacian_0=[7, 7], incidence_1=[7, 10], down_laplacian_1=[10, 10], up_laplacian_1=[10, 10], hodge_laplacian_1=[10, 10], incidence_2=[10, 6], down_laplacian_2=[6, 6], up_laplacian_2=[6, 6], hodge_laplacian_2=[6, 6], incidence_3=[6, 1], down_laplacian_3=[1, 1], up_laplacian_3=[1, 1], hodge_laplacian_3=[1, 1], x_0=[7, 1], x_1=[10, 1], x_2=[6, 1], x_3=[1, 1], n_id=[10], e_id=[13], input_id=[2], batch_size=2, adjacency_0=[7, 7], adjacency_1=[10, 10], adjacency_2=[6, 6], adjacency_3=[1, 1])\n", - "The cells of rank 1 that were originally selected are tensor([0, 1])\n" + "Data(x=[5, 1], edge_index=[2, 16], y=[5], num_nodes=5, incidence_0=[1, 5], down_laplacian_0=[5, 5], up_laplacian_0=[5, 5], adjacency_0=[5, 5], hodge_laplacian_0=[5, 5], incidence_1=[5, 8], down_laplacian_1=[8, 8], up_laplacian_1=[8, 8], adjacency_1=[8, 8], hodge_laplacian_1=[8, 8], incidence_2=[8, 5], down_laplacian_2=[5, 5], up_laplacian_2=[5, 5], adjacency_2=[5, 5], hodge_laplacian_2=[5, 5], incidence_3=[5, 1], down_laplacian_3=[1, 1], up_laplacian_3=[1, 1], adjacency_3=[1, 1], hodge_laplacian_3=[1, 1], shape=[4], x_0=[5, 1], x_1=[8, 1], x_2=[5, 1], x_3=[1, 1], n_id=[5])\n", + "The cells of rank 0 that were originally selected are tensor([0])\n", + "tensor([0, 7, 1, 2, 4])\n", + "tensor([[0, 0, 0, 0, 1, 1, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4],\n", + " [1, 2, 3, 4, 0, 3, 0, 3, 4, 0, 1, 2, 4, 0, 2, 3]])\n", + "tensor([[1.],\n", + " [1.],\n", + " [1.],\n", + " [0.],\n", + " [1.]])\n", + "tensor([[1., 1., 0., 0., 0.],\n", + " [1., 0., 1., 1., 0.],\n", + " [0., 1., 1., 0., 0.],\n", + " [0., 0., 0., 1., 0.],\n", + " [1., 0., 0., 0., 1.],\n", + " [0., 1., 0., 0., 1.],\n", + " [0., 0., 1., 0., 1.],\n", + " [0., 0., 0., 1., 0.]])\n", + "tensor([[1., 1., 1., 1., 0., 0., 0., 0.],\n", + " [0., 0., 0., 1., 0., 0., 0., 1.],\n", + " [1., 0., 0., 0., 1., 1., 0., 0.],\n", + " [0., 1., 0., 0., 1., 0., 1., 1.],\n", + " [0., 0., 1., 0., 0., 1., 1., 0.]])\n" ] }, { @@ -684,121 +354,6 @@ " break" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Investigate how NodeSampler works" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Transform parameters are the same, using existing data_dir: ./graph2simplicial_lifting/131528455\n" - ] - } - ], - "source": [ - "cfg = compose(config_name=\"run.yaml\", \n", - " overrides=[\"dataset=graph/manual_dataset\", \"model=simplicial/san\"], \n", - " return_hydra_config=True)\n", - "data = load_manual_graph()\n", - "preprocessed_dataset = PreProcessor(data, './', cfg['transforms'])\n", - "data = preprocessed_dataset[0]\n", - "\n", - "data.edge_index = data.edge_index.contiguous()" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "loader = NeighborLoader(\n", - " data=data,\n", - " num_neighbors=[-1], \n", - " batch_size=2,\n", - " input_nodes=None\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "ename": "TypeError", - "evalue": "tuple indices must be integers or slices, not tuple", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[31], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43miter\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mloader\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/miniconda3/envs/tbx/lib/python3.11/site-packages/torch/utils/data/dataloader.py:631\u001b[0m, in \u001b[0;36m_BaseDataLoaderIter.__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 628\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sampler_iter \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 629\u001b[0m \u001b[38;5;66;03m# TODO(https://github.com/pytorch/pytorch/issues/76750)\u001b[39;00m\n\u001b[1;32m 630\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reset() \u001b[38;5;66;03m# type: ignore[call-arg]\u001b[39;00m\n\u001b[0;32m--> 631\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_next_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 632\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_yielded \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m 633\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dataset_kind \u001b[38;5;241m==\u001b[39m _DatasetKind\u001b[38;5;241m.\u001b[39mIterable \u001b[38;5;129;01mand\u001b[39;00m \\\n\u001b[1;32m 634\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_IterableDataset_len_called \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \\\n\u001b[1;32m 635\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_yielded \u001b[38;5;241m>\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_IterableDataset_len_called:\n", - "File \u001b[0;32m~/miniconda3/envs/tbx/lib/python3.11/site-packages/torch/utils/data/dataloader.py:675\u001b[0m, in \u001b[0;36m_SingleProcessDataLoaderIter._next_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 673\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_next_data\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 674\u001b[0m index \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_next_index() \u001b[38;5;66;03m# may raise StopIteration\u001b[39;00m\n\u001b[0;32m--> 675\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_dataset_fetcher\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfetch\u001b[49m\u001b[43m(\u001b[49m\u001b[43mindex\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# may raise StopIteration\u001b[39;00m\n\u001b[1;32m 676\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pin_memory:\n\u001b[1;32m 677\u001b[0m data \u001b[38;5;241m=\u001b[39m _utils\u001b[38;5;241m.\u001b[39mpin_memory\u001b[38;5;241m.\u001b[39mpin_memory(data, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pin_memory_device)\n", - "File \u001b[0;32m~/miniconda3/envs/tbx/lib/python3.11/site-packages/torch/utils/data/_utils/fetch.py:54\u001b[0m, in \u001b[0;36m_MapDatasetFetcher.fetch\u001b[0;34m(self, possibly_batched_index)\u001b[0m\n\u001b[1;32m 52\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 53\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdataset[possibly_batched_index]\n\u001b[0;32m---> 54\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[43mcollate_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/miniconda3/envs/tbx/lib/python3.11/site-packages/torch_geometric/loader/node_loader.py:150\u001b[0m, in \u001b[0;36mNodeLoader.collate_fn\u001b[0;34m(self, index)\u001b[0m\n\u001b[1;32m 147\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnode_sampler\u001b[38;5;241m.\u001b[39msample_from_nodes(input_data)\n\u001b[1;32m 149\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfilter_per_worker: \u001b[38;5;66;03m# Execute `filter_fn` in the worker process\u001b[39;00m\n\u001b[0;32m--> 150\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfilter_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[43mout\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 152\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m out\n", - "File \u001b[0;32m~/miniconda3/envs/tbx/lib/python3.11/site-packages/torch_geometric/loader/node_loader.py:167\u001b[0m, in \u001b[0;36mNodeLoader.filter_fn\u001b[0;34m(self, out)\u001b[0m\n\u001b[1;32m 165\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(out, SamplerOutput):\n\u001b[1;32m 166\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata, Data):\n\u001b[0;32m--> 167\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[43mfilter_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m#\u001b[39;49;00m\n\u001b[1;32m 168\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrow\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcol\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43medge\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 169\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnode_sampler\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43medge_permutation\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 171\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m: \u001b[38;5;66;03m# Tuple[FeatureStore, GraphStore]\u001b[39;00m\n\u001b[1;32m 172\u001b[0m \n\u001b[1;32m 173\u001b[0m \u001b[38;5;66;03m# Hack to detect whether we are in a distributed setting.\u001b[39;00m\n\u001b[1;32m 174\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnode_sampler\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m \u001b[38;5;241m==\u001b[39m\n\u001b[1;32m 175\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDistNeighborSampler\u001b[39m\u001b[38;5;124m'\u001b[39m):\n", - "File \u001b[0;32m~/miniconda3/envs/tbx/lib/python3.11/site-packages/torch_geometric/loader/utils.py:163\u001b[0m, in \u001b[0;36mfilter_data\u001b[0;34m(data, node, row, col, edge, perm)\u001b[0m\n\u001b[1;32m 159\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfilter_data\u001b[39m(data: Data, node: Tensor, row: Tensor, col: Tensor,\n\u001b[1;32m 160\u001b[0m edge: OptTensor, perm: OptTensor \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Data:\n\u001b[1;32m 161\u001b[0m \u001b[38;5;66;03m# Filters a data object to only hold nodes in `node` and edges in `edge`:\u001b[39;00m\n\u001b[1;32m 162\u001b[0m out \u001b[38;5;241m=\u001b[39m copy\u001b[38;5;241m.\u001b[39mcopy(data)\n\u001b[0;32m--> 163\u001b[0m \u001b[43mfilter_node_store_\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_store\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_store\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnode\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 164\u001b[0m filter_edge_store_(data\u001b[38;5;241m.\u001b[39m_store, out\u001b[38;5;241m.\u001b[39m_store, row, col, edge, perm)\n\u001b[1;32m 165\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m out\n", - "File \u001b[0;32m~/miniconda3/envs/tbx/lib/python3.11/site-packages/torch_geometric/loader/utils.py:92\u001b[0m, in \u001b[0;36mfilter_node_store_\u001b[0;34m(store, out_store, index)\u001b[0m\n\u001b[1;32m 89\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m key \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnum_nodes\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[1;32m 90\u001b[0m out_store\u001b[38;5;241m.\u001b[39mnum_nodes \u001b[38;5;241m=\u001b[39m index\u001b[38;5;241m.\u001b[39mnumel()\n\u001b[0;32m---> 92\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[43mstore\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mis_node_attr\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[1;32m 93\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(value, (Tensor, TensorFrame)):\n\u001b[1;32m 94\u001b[0m index \u001b[38;5;241m=\u001b[39m index\u001b[38;5;241m.\u001b[39mto(value\u001b[38;5;241m.\u001b[39mdevice)\n", - "File \u001b[0;32m~/miniconda3/envs/tbx/lib/python3.11/site-packages/torch_geometric/data/storage.py:811\u001b[0m, in \u001b[0;36mGlobalStorage.is_node_attr\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 808\u001b[0m cat_dim \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_parent()\u001b[38;5;241m.\u001b[39m__cat_dim__(key, value, \u001b[38;5;28mself\u001b[39m)\n\u001b[1;32m 809\u001b[0m num_nodes, num_edges \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnum_nodes, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnum_edges\n\u001b[0;32m--> 811\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[43mvalue\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshape\u001b[49m\u001b[43m[\u001b[49m\u001b[43mcat_dim\u001b[49m\u001b[43m]\u001b[49m \u001b[38;5;241m!=\u001b[39m num_nodes:\n\u001b[1;32m 812\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m value\u001b[38;5;241m.\u001b[39mshape[cat_dim] \u001b[38;5;241m==\u001b[39m num_edges:\n\u001b[1;32m 813\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_cached_attr[AttrType\u001b[38;5;241m.\u001b[39mEDGE]\u001b[38;5;241m.\u001b[39madd(key)\n", - "\u001b[0;31mTypeError\u001b[0m: tuple indices must be integers or slices, not tuple" - ] - } - ], - "source": [ - "iter(loader)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "ename": "TypeError", - "evalue": "tuple indices must be integers or slices, not tuple", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[30], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43miter\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mloader\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/miniconda3/envs/tbx/lib/python3.11/site-packages/torch/utils/data/dataloader.py:631\u001b[0m, in \u001b[0;36m_BaseDataLoaderIter.__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 628\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sampler_iter \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 629\u001b[0m \u001b[38;5;66;03m# TODO(https://github.com/pytorch/pytorch/issues/76750)\u001b[39;00m\n\u001b[1;32m 630\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reset() \u001b[38;5;66;03m# type: ignore[call-arg]\u001b[39;00m\n\u001b[0;32m--> 631\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_next_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 632\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_yielded \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m 633\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dataset_kind \u001b[38;5;241m==\u001b[39m _DatasetKind\u001b[38;5;241m.\u001b[39mIterable \u001b[38;5;129;01mand\u001b[39;00m \\\n\u001b[1;32m 634\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_IterableDataset_len_called \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \\\n\u001b[1;32m 635\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_yielded \u001b[38;5;241m>\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_IterableDataset_len_called:\n", - "File \u001b[0;32m~/miniconda3/envs/tbx/lib/python3.11/site-packages/torch/utils/data/dataloader.py:675\u001b[0m, in \u001b[0;36m_SingleProcessDataLoaderIter._next_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 673\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_next_data\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 674\u001b[0m index \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_next_index() \u001b[38;5;66;03m# may raise StopIteration\u001b[39;00m\n\u001b[0;32m--> 675\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_dataset_fetcher\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfetch\u001b[49m\u001b[43m(\u001b[49m\u001b[43mindex\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# may raise StopIteration\u001b[39;00m\n\u001b[1;32m 676\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pin_memory:\n\u001b[1;32m 677\u001b[0m data \u001b[38;5;241m=\u001b[39m _utils\u001b[38;5;241m.\u001b[39mpin_memory\u001b[38;5;241m.\u001b[39mpin_memory(data, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pin_memory_device)\n", - "File \u001b[0;32m~/miniconda3/envs/tbx/lib/python3.11/site-packages/torch/utils/data/_utils/fetch.py:54\u001b[0m, in \u001b[0;36m_MapDatasetFetcher.fetch\u001b[0;34m(self, possibly_batched_index)\u001b[0m\n\u001b[1;32m 52\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 53\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdataset[possibly_batched_index]\n\u001b[0;32m---> 54\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[43mcollate_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/miniconda3/envs/tbx/lib/python3.11/site-packages/torch_geometric/loader/node_loader.py:150\u001b[0m, in \u001b[0;36mNodeLoader.collate_fn\u001b[0;34m(self, index)\u001b[0m\n\u001b[1;32m 147\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnode_sampler\u001b[38;5;241m.\u001b[39msample_from_nodes(input_data)\n\u001b[1;32m 149\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfilter_per_worker: \u001b[38;5;66;03m# Execute `filter_fn` in the worker process\u001b[39;00m\n\u001b[0;32m--> 150\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfilter_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[43mout\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 152\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m out\n", - "File \u001b[0;32m~/miniconda3/envs/tbx/lib/python3.11/site-packages/torch_geometric/loader/node_loader.py:167\u001b[0m, in \u001b[0;36mNodeLoader.filter_fn\u001b[0;34m(self, out)\u001b[0m\n\u001b[1;32m 165\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(out, SamplerOutput):\n\u001b[1;32m 166\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata, Data):\n\u001b[0;32m--> 167\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[43mfilter_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m#\u001b[39;49;00m\n\u001b[1;32m 168\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrow\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcol\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43medge\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 169\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnode_sampler\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43medge_permutation\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 171\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m: \u001b[38;5;66;03m# Tuple[FeatureStore, GraphStore]\u001b[39;00m\n\u001b[1;32m 172\u001b[0m \n\u001b[1;32m 173\u001b[0m \u001b[38;5;66;03m# Hack to detect whether we are in a distributed setting.\u001b[39;00m\n\u001b[1;32m 174\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnode_sampler\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m \u001b[38;5;241m==\u001b[39m\n\u001b[1;32m 175\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDistNeighborSampler\u001b[39m\u001b[38;5;124m'\u001b[39m):\n", - "File \u001b[0;32m~/miniconda3/envs/tbx/lib/python3.11/site-packages/torch_geometric/loader/utils.py:163\u001b[0m, in \u001b[0;36mfilter_data\u001b[0;34m(data, node, row, col, edge, perm)\u001b[0m\n\u001b[1;32m 159\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfilter_data\u001b[39m(data: Data, node: Tensor, row: Tensor, col: Tensor,\n\u001b[1;32m 160\u001b[0m edge: OptTensor, perm: OptTensor \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Data:\n\u001b[1;32m 161\u001b[0m \u001b[38;5;66;03m# Filters a data object to only hold nodes in `node` and edges in `edge`:\u001b[39;00m\n\u001b[1;32m 162\u001b[0m out \u001b[38;5;241m=\u001b[39m copy\u001b[38;5;241m.\u001b[39mcopy(data)\n\u001b[0;32m--> 163\u001b[0m \u001b[43mfilter_node_store_\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_store\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_store\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnode\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 164\u001b[0m filter_edge_store_(data\u001b[38;5;241m.\u001b[39m_store, out\u001b[38;5;241m.\u001b[39m_store, row, col, edge, perm)\n\u001b[1;32m 165\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m out\n", - "File \u001b[0;32m~/miniconda3/envs/tbx/lib/python3.11/site-packages/torch_geometric/loader/utils.py:92\u001b[0m, in \u001b[0;36mfilter_node_store_\u001b[0;34m(store, out_store, index)\u001b[0m\n\u001b[1;32m 89\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m key \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnum_nodes\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[1;32m 90\u001b[0m out_store\u001b[38;5;241m.\u001b[39mnum_nodes \u001b[38;5;241m=\u001b[39m index\u001b[38;5;241m.\u001b[39mnumel()\n\u001b[0;32m---> 92\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[43mstore\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mis_node_attr\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[1;32m 93\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(value, (Tensor, TensorFrame)):\n\u001b[1;32m 94\u001b[0m index \u001b[38;5;241m=\u001b[39m index\u001b[38;5;241m.\u001b[39mto(value\u001b[38;5;241m.\u001b[39mdevice)\n", - "File \u001b[0;32m~/miniconda3/envs/tbx/lib/python3.11/site-packages/torch_geometric/data/storage.py:811\u001b[0m, in \u001b[0;36mGlobalStorage.is_node_attr\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 808\u001b[0m cat_dim \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_parent()\u001b[38;5;241m.\u001b[39m__cat_dim__(key, value, \u001b[38;5;28mself\u001b[39m)\n\u001b[1;32m 809\u001b[0m num_nodes, num_edges \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnum_nodes, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnum_edges\n\u001b[0;32m--> 811\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[43mvalue\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshape\u001b[49m\u001b[43m[\u001b[49m\u001b[43mcat_dim\u001b[49m\u001b[43m]\u001b[49m \u001b[38;5;241m!=\u001b[39m num_nodes:\n\u001b[1;32m 812\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m value\u001b[38;5;241m.\u001b[39mshape[cat_dim] \u001b[38;5;241m==\u001b[39m num_edges:\n\u001b[1;32m 813\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_cached_attr[AttrType\u001b[38;5;241m.\u001b[39mEDGE]\u001b[38;5;241m.\u001b[39madd(key)\n", - "\u001b[0;31mTypeError\u001b[0m: tuple indices must be integers or slices, not tuple" - ] - } - ], - "source": [] - }, { "cell_type": "markdown", "metadata": {}, @@ -808,7 +363,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -817,14 +372,6 @@ "text": [ "Transform parameters are the same, using existing data_dir: ./graph2hypergraph_lifting/1273654097\n" ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/lib/python3.11/site-packages/torch_geometric/sampler/neighbor_sampler.py:61: UserWarning: Using 'NeighborSampler' without a 'pyg-lib' installation is deprecated and will be removed soon. Please install 'pyg-lib' for accelerated neighborhood sampling\n", - " warnings.warn(f\"Using '{self.__class__.__name__}' without a \"\n" - ] } ], "source": [ @@ -866,14 +413,14 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Data(x=[4, 1433], edge_index=[2, 10556], y=[4], train_mask=[2708], val_mask=[2708], test_mask=[2708], incidence_hyperedges=[2708, 2708], num_hyperedges=2708, x_0=[4, 1433], x_hyperedges=[2708, 1433], incidence_1=[4, 5], num_nodes=4, n_id=[4], e_id=[3], input_id=[1], batch_size=1)\n", + "Data(x=[4, 1433], edge_index=[2, 10556], y=[4], train_mask=[2708], val_mask=[2708], test_mask=[2708], incidence_hyperedges=[2708, 2708], num_hyperedges=2708, x_0=[4, 1433], x_hyperedges=[2708, 1433], incidence_1=[4, 5], num_nodes=4, n_id=[4])\n", "tensor([ 0, 1862, 633, 2582])\n", "tensor([[ 0, 0, 0, ..., 2707, 2707, 2707],\n", " [ 633, 1862, 2582, ..., 598, 1473, 2706]])\n", @@ -901,6 +448,13 @@ " \n", " break" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/tutorials/graph2simplicial_lifting/131528455/data.pt b/tutorials/graph2simplicial_lifting/131528455/data.pt deleted file mode 100644 index aadadd0d48261820956510bd3991d4a9c69ee35e..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 22921 zcmdU13w#_^^`53}T0$S86fCb&3P?drv%5)}NTJ)bgu-GiQ@~J=kS1%B5|Z4TDbNZS zA3H6IfDaV4V#ODVN_{`5TEqvwQ9)4=tB4}LDk2~%_?@}?r89SSb~kPL|9=1Z{pOxC zckXx2ckVs+?#$gyR^=v6a-6ACo!C+D%yPPQPhbD#`GvjRUU$Rrr2`%9Gn_S%W3=XK zrzQmk8`b2_iD=t1yg}9Grh011IXNdcH8;UmC`24^ZBNh8z(Bso z>mM2%se763&Fg&c^8OyLPSu_0sslYW%~jJo>tH3{yDs0=KiHeUQqAZCjXt_QduVXo znCF>+=aj1NtIZwmse{C0O>VlcX2D~PuMWPpPYloY)FG}qG-$k0y};P(GvKtE12E_w z7SJuI!*de~>Iefo(o;vdYHsHNU^KYlvaZ3Q-u#Fydo1PwUh%#bzUU-QPXXzo}cgqONOQ-s>9Z-Z-=YJu$zt9=6TU6wy6HL+aRq 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readout --- topobenchmarkx/nn/readouts/propagate_signal_down.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/topobenchmarkx/nn/readouts/propagate_signal_down.py b/topobenchmarkx/nn/readouts/propagate_signal_down.py index 1d1bf658..d2e28017 100644 --- a/topobenchmarkx/nn/readouts/propagate_signal_down.py +++ b/topobenchmarkx/nn/readouts/propagate_signal_down.py @@ -26,23 +26,23 @@ def __init__(self, **kwargs): self.name = kwargs["readout_name"] self.dimensions = range(kwargs["num_cell_dimensions"] - 1, 0, -1) - hidden_dim = kwargs["hidden_dim"] + self.hidden_dim = kwargs["hidden_dim"] for i in self.dimensions: setattr( self, f"agg_conv_{i}", topomodelx.base.conv.Conv( - hidden_dim, hidden_dim, aggr_norm=False + self.hidden_dim, self.hidden_dim, aggr_norm=False ), ) - setattr(self, f"ln_{i}", torch.nn.LayerNorm(hidden_dim)) + setattr(self, f"ln_{i}", torch.nn.LayerNorm(self.hidden_dim)) setattr( self, f"projector_{i}", - torch.nn.Linear(2 * hidden_dim, hidden_dim), + torch.nn.Linear(2 * self.hidden_dim, self.hidden_dim), ) def forward(self, model_out: dict, batch: torch_geometric.data.Data): From e154231d201c266eabade4d9dc16ed5b050151fd Mon Sep 17 00:00:00 2001 From: Coerulatus Date: Wed, 27 Nov 2024 16:50:15 +0000 Subject: [PATCH 14/24] support for multiple hops --- .../data/batching/neighbor_cells_loader.py | 10 ++++----- topobenchmarkx/data/batching/utils.py | 22 ++++++++++++++----- 2 files changed, 21 insertions(+), 11 deletions(-) diff --git a/topobenchmarkx/data/batching/neighbor_cells_loader.py b/topobenchmarkx/data/batching/neighbor_cells_loader.py index 62abdb96..91079564 100644 --- a/topobenchmarkx/data/batching/neighbor_cells_loader.py +++ b/topobenchmarkx/data/batching/neighbor_cells_loader.py @@ -130,12 +130,12 @@ def __init__( "while 'time_attr' is not set.") is_hypergraph = hasattr(data, 'incidence_hyperedges') - data = get_sampled_neighborhood(data, rank, is_hypergraph) + n_hops = len(num_neighbors) + data = get_sampled_neighborhood(data, rank, n_hops, is_hypergraph) self.rank = rank - - if len(num_neighbors) > 1: - raise NotImplementedError("NeighborCellsLoader only supports one-hop neighborhood selection.") - + if self.rank != 0: + # When rank is different than 0 get_sampled_neighborhood connects cells that are up to n_hops away, meaning that the NeighborhoodSampler needs to consider only one hop. + num_neighbors = [num_neighbors[0]] if neighbor_sampler is None: neighbor_sampler = NeighborSampler( data, diff --git a/topobenchmarkx/data/batching/utils.py b/topobenchmarkx/data/batching/utils.py index 5636f3d2..1a16b350 100644 --- a/topobenchmarkx/data/batching/utils.py +++ b/topobenchmarkx/data/batching/utils.py @@ -178,6 +178,7 @@ def reduce_neighborhoods(batch, node, rank=0, remove_self_loops=True): if hasattr(batch, 'y'): batch.y = batch.y[cells_ids[rank]] + batch.cells_ids = cells_ids return batch def filter_data(data: Data, cells: Tensor, rank: int) -> Data: @@ -199,7 +200,7 @@ def filter_data(data: Data, cells: Tensor, rank: int) -> Data: out.n_id = cells return out -def get_sampled_neighborhood(data, rank=0, is_hypergraph=False): +def get_sampled_neighborhood(data, rank=0, n_hops=1, is_hypergraph=False): ''' This function updates the edge_index attribute of torch_geometric.data.Data. The function finds cells, of the specified rank, that are either upper or lower neighbors. @@ -210,6 +211,8 @@ def get_sampled_neighborhood(data, rank=0, is_hypergraph=False): The input data. rank: int The rank of the cells that you want to batch over. + n_hops: int + Two cells are considered neighbors if they are connected by n hops in the upper or lower neighborhoods. is_hypergraph: bool Whether the data represents an hypergraph. @@ -229,11 +232,12 @@ def get_sampled_neighborhood(data, rank=0, is_hypergraph=False): if rank == 1: I = data.incidence_hyperedges A = torch.sparse.mm(I,I.T) # lower adj matrix - edges = A.indices() else: I = data.incidence_hyperedges A = torch.sparse.mm(I.T,I) - edges = A.indices() + for _ in range(n_hops-1): + A = torch.sparse.mm(A,A) + edges = A.indices() else: # get number of incidences max_rank = len([key for key in data.keys() if "incidence" in key])-1 @@ -241,12 +245,16 @@ def get_sampled_neighborhood(data, rank=0, is_hypergraph=False): raise ValueError(f"Rank {rank} is greater than the maximum rank {max_rank} in the data.") # This considers the upper adjacencies + n_cells = data[f"x_{rank}"].shape[0] + A_sum = torch.sparse_coo_tensor([[],[]], [], (n_cells, n_cells)) if rank == max_rank: edges = torch.empty((2, 0), dtype=torch.long) else: I = data[f"incidence_{rank+1}"] A = torch.sparse.mm(I,I.T) - edges = A.indices() + for _ in range(n_hops-1): + A = torch.sparse.mm(A,A) + A_sum += A # This is for selecting the whole upper cells # for i in range(rank+1, max_rank): @@ -258,7 +266,9 @@ def get_sampled_neighborhood(data, rank=0, is_hypergraph=False): if rank != 0: I = data[f"incidence_{rank}"] A = torch.sparse.mm(I.T,I) - edges = torch.cat((edges, A.indices()), dim=1) + for _ in range(n_hops-1): + A = torch.sparse.mm(A,A) + A_sum += A # This is for selecting cells if they share any node # for i in range(rank-1, 0, -1): @@ -266,7 +276,7 @@ def get_sampled_neighborhood(data, rank=0, is_hypergraph=False): # Q = torch.sparse.mm(P.T,P) # edges = torch.cat((edges, Q.indices()), dim=1) - edges = torch.unique(edges, dim=1) + edges = A_sum.coalesce().indices() # Remove self edges mask = edges[0, :] != edges[1, :] edges = edges[:, mask] From 7bddf5ddd4b33c1b45b31013aa791c3a45aef806 Mon Sep 17 00:00:00 2001 From: Coerulatus Date: Wed, 27 Nov 2024 16:50:56 +0000 Subject: [PATCH 15/24] changed DataloadDataset call --- topobenchmarkx/data/loaders/loaders.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/topobenchmarkx/data/loaders/loaders.py b/topobenchmarkx/data/loaders/loaders.py index d1d13985..b708bb99 100755 --- a/topobenchmarkx/data/loaders/loaders.py +++ b/topobenchmarkx/data/loaders/loaders.py @@ -142,7 +142,7 @@ def load(self) -> tuple[torch_geometric.data.Dataset, str]: elif self.parameters.data_name in ["manual"]: data = load_manual_graph() - dataset = DataloadDataset([data], data_dir) + dataset = DataloadDataset([data]) else: raise NotImplementedError( From 69a0e949464f97d3beaee60202744cfdf99c661d Mon Sep 17 00:00:00 2001 From: Coerulatus Date: Wed, 27 Nov 2024 16:51:39 +0000 Subject: [PATCH 16/24] test batching with multiple hops --- tutorials/batching.ipynb | 243 ++++++++++++++++++++++++++++++--------- 1 file changed, 186 insertions(+), 57 deletions(-) diff --git a/tutorials/batching.ipynb b/tutorials/batching.ipynb index 0742e617..1556c4ae 100644 --- a/tutorials/batching.ipynb +++ b/tutorials/batching.ipynb @@ -9,7 +9,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_161536/1370418617.py:28: UserWarning: \n", + "/tmp/ipykernel_99975/2509310254.py:30: UserWarning: \n", "The version_base parameter is not specified.\n", "Please specify a compatability version level, or None.\n", "Will assume defaults for version 1.1\n", @@ -28,7 +28,7 @@ } ], "source": [ - "import rootutils\n", + "import os, shutil, rootutils\n", "\n", "rootutils.setup_root(\"./\", indicator=\".project-root\", pythonpath=True)\n", "\n", @@ -46,6 +46,9 @@ "from topobenchmarkx.data.loaders import GraphLoader\n", "\n", "from topobenchmarkx.data.batching.neighbor_cells_loader import NeighborCellsLoader\n", + "from topobenchmarkx.dataloader import TBXDataloader\n", + "from topobenchmarkx.data.preprocessor import PreProcessor\n", + "from topomodelx.nn.simplicial.scn2 import SCN2\n", "\n", "from topobenchmarkx.utils.config_resolvers import (\n", " get_default_transform,\n", @@ -193,7 +196,15 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Manual Graph" + "## Test batching" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If batching is done correctly the results on the selected nodes should not change when compared to the results obtained over the whole graph.\n", + "We test this to check that our batching strategy is correct." ] }, { @@ -205,13 +216,132 @@ "name": "stdout", "output_type": "stream", "text": [ - "Transform parameters are the same, using existing data_dir: ./graph2simplicial_lifting/131528455\n", + "Transform parameters are the same, using existing data_dir: /TopoBenchmark/datasets/graph/cocitation/Cora/graph2simplicial_lifting/131528455\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Processing...\n", + "Done!\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Batching works: True\n" + ] + } + ], + "source": [ + "path = \"./graph2simplicial_lifting/\"\n", + "if os.path.isdir(path):\n", + " shutil.rmtree(path)\n", + "cfg = compose(config_name=\"run.yaml\", \n", + " overrides=[\"dataset=graph/cocitation_cora\", \"model=simplicial/scn\"], \n", + " return_hydra_config=True)\n", + "\n", + "dataset_loader = hydra.utils.instantiate(cfg.dataset.loader)\n", + "dataset, dataset_dir = dataset_loader.load()\n", + "# Preprocess dataset and load the splits\n", + "transform_config = cfg.get(\"transforms\", None)\n", + "preprocessor = PreProcessor(dataset, dataset_dir, transform_config)\n", + "dataset_train, dataset_val, dataset_test = (\n", + " preprocessor.load_dataset_splits(cfg.dataset.split_params)\n", + ")\n", + "\n", + "datamodule = TBXDataloader(\n", + " dataset_train=dataset_train,\n", + " dataset_val=dataset_val,\n", + " dataset_test=dataset_test,\n", + " **cfg.dataset.get(\"dataloader_params\", {}),\n", + " )\n", + "\n", + "input_dim = 1433\n", + "hidden_channels = 16\n", + "out_dim = 7\n", + "\n", + "model = SCN2(input_dim, input_dim, input_dim, n_layers=2)\n", + "model.eval()\n", + "\n", + "train_dataloader = datamodule.train_dataloader()\n", + "for data in train_dataloader:\n", + " x_0_full, x_1_full, x_2_full = model(data.x_0, data.x_1, data.x_2, data.hodge_laplacian_0, data.hodge_laplacian_1, data.hodge_laplacian_2)\n", + " break\n", + "\n", + "graph_loader = GraphLoader(cfg.dataset.loader.parameters)\n", + "dataset, dataset_dir = graph_loader.load()\n", + "preprocessed_dataset = PreProcessor(dataset, './', cfg['transforms'])\n", + "data = preprocessed_dataset[0]\n", + "\n", + "# Training, validation and split idxs should be defined somewhere, here we use a toy example\n", + "rank = 0\n", + "if hasattr(data, \"x_hyperedges\") and rank==1:\n", + " n_cells = data.x_hyperedges.shape[0]\n", + "else:\n", + " n_cells = data[f'x_{rank}'].shape[0]\n", + "\n", + "train_prop = 0.5\n", + "n_train = int(train_prop * n_cells)\n", + "train_mask = torch.zeros(n_cells, dtype=torch.bool)\n", + "train_mask[:n_train] = 1\n", + "\n", + "if rank != 0:\n", + " y = torch.zeros(n_cells, dtype=torch.long)\n", + " data.y = y\n", + " \n", + "batch_size = 32\n", + "\n", + "# num_neighbors also controls the number of hops (for 2 hops do num_neighbors=[-1, -1])\n", + "loader = NeighborCellsLoader(data,\n", + " rank=rank,\n", + " num_neighbors=[-1]*3,\n", + " input_nodes=train_mask,\n", + " batch_size=batch_size,\n", + " shuffle=False)\n", + "\n", + "success = []\n", + "for i, batch in enumerate(loader):\n", + " x_0_batch, x_1_batch, x_2_batch = model(batch.x_0, batch.x_1, batch.x_2, batch.hodge_laplacian_0, batch.hodge_laplacian_1, batch.hodge_laplacian_2)\n", + " n_ids = batch.n_id[:batch_size]\n", + " success.append(torch.allclose(x_0_full[n_ids, :], x_0_batch[:batch_size, :],atol=1e-03))\n", + " \n", + "# The last element might be False since the last batch might not be full\n", + "print(f\"Batching works: {all(success[:-1])}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Manual Graph" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ "Data(x=[8, 1], edge_index=[2, 13], y=[8], num_nodes=8, incidence_0=[1, 8], down_laplacian_0=[8, 8], up_laplacian_0=[8, 8], adjacency_0=[8, 8], hodge_laplacian_0=[8, 8], incidence_1=[8, 13], down_laplacian_1=[13, 13], up_laplacian_1=[13, 13], adjacency_1=[13, 13], hodge_laplacian_1=[13, 13], incidence_2=[13, 6], down_laplacian_2=[6, 6], up_laplacian_2=[6, 6], adjacency_2=[6, 6], hodge_laplacian_2=[6, 6], incidence_3=[6, 1], down_laplacian_3=[1, 1], up_laplacian_3=[1, 1], adjacency_3=[1, 1], hodge_laplacian_3=[1, 1], shape=[4], x_0=[8, 1], x_1=[13, 1], x_2=[6, 1], x_3=[1, 1])\n" ] }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Processing...\n", + "Done!\n" + ] + }, { "data": { - "image/png": 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", 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"text/plain": [ "
" ] @@ -221,11 +351,16 @@ } ], "source": [ + "import os, shutil\n", "from topobenchmarkx.data.utils.utils import load_manual_graph\n", "\n", + "path = \"./graph2simplicial_lifting/\"\n", + "if os.path.isdir(path):\n", + " shutil.rmtree(path)\n", "cfg = compose(config_name=\"run.yaml\", \n", " overrides=[\"dataset=graph/manual_dataset\", \"model=simplicial/san\"], \n", " return_hydra_config=True)\n", + "\n", "data = load_manual_graph()\n", "preprocessed_dataset = PreProcessor(data, './', cfg['transforms'])\n", "data = preprocessed_dataset[0]\n", @@ -235,23 +370,23 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "Data(x=[8, 1], edge_index=[2, 13], y=[8], num_nodes=8, incidence_0=[1, 8], down_laplacian_0=[8, 8], up_laplacian_0=[8, 8], adjacency_0=[8, 8], hodge_laplacian_0=[8, 8], incidence_1=[8, 13], down_laplacian_1=[13, 13], up_laplacian_1=[13, 13], adjacency_1=[13, 13], hodge_laplacian_1=[13, 13], incidence_2=[13, 6], down_laplacian_2=[6, 6], up_laplacian_2=[6, 6], adjacency_2=[6, 6], hodge_laplacian_2=[6, 6], incidence_3=[6, 1], down_laplacian_3=[1, 1], up_laplacian_3=[1, 1], adjacency_3=[1, 1], hodge_laplacian_3=[1, 1], shape=[4], x_0=[8, 1], x_1=[13, 1], x_2=[6, 1], x_3=[1, 1])" + "Data(x=[8, 1], edge_index=[2, 13], y=[13], num_nodes=8, incidence_0=[1, 8], down_laplacian_0=[8, 8], up_laplacian_0=[8, 8], adjacency_0=[8, 8], hodge_laplacian_0=[8, 8], incidence_1=[8, 13], down_laplacian_1=[13, 13], up_laplacian_1=[13, 13], adjacency_1=[13, 13], hodge_laplacian_1=[13, 13], incidence_2=[13, 6], down_laplacian_2=[6, 6], up_laplacian_2=[6, 6], adjacency_2=[6, 6], hodge_laplacian_2=[6, 6], incidence_3=[6, 1], down_laplacian_3=[1, 1], up_laplacian_3=[1, 1], adjacency_3=[1, 1], hodge_laplacian_3=[1, 1], shape=[4], x_0=[8, 1], x_1=[13, 1], x_2=[6, 1], x_3=[1, 1])" ] }, - "execution_count": 4, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Training, validation and split idxs should be defined somewhere, here we use a toy example\n", - "rank = 0\n", + "rank = 1\n", "if hasattr(data, \"x_hyperedges\") and rank==1:\n", " n_cells = data.x_hyperedges.shape[0]\n", "else:\n", @@ -270,15 +405,15 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/usr/local/lib/python3.11/site-packages/torch_geometric/sampler/neighbor_sampler.py:61: UserWarning: Using 'NeighborSampler' without a 'pyg-lib' installation is deprecated and will be removed soon. Please install 'pyg-lib' for accelerated neighborhood sampling\n", - " warnings.warn(f\"Using '{self.__class__.__name__}' without a \"\n" + "/TopoBenchmark/topobenchmarkx/data/batching/utils.py:256: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)\n", + " A = torch.sparse.mm(I,I.T)\n" ] } ], @@ -287,7 +422,7 @@ "\n", "loader = NeighborCellsLoader(data,\n", " rank=rank,\n", - " num_neighbors=[-1],\n", + " num_neighbors=[-1,-1],\n", " input_nodes=train_mask,\n", " batch_size=batch_size,\n", " shuffle=False)" @@ -295,47 +430,43 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Data(x=[5, 1], edge_index=[2, 16], y=[5], num_nodes=5, incidence_0=[1, 5], down_laplacian_0=[5, 5], up_laplacian_0=[5, 5], adjacency_0=[5, 5], hodge_laplacian_0=[5, 5], incidence_1=[5, 8], down_laplacian_1=[8, 8], up_laplacian_1=[8, 8], adjacency_1=[8, 8], hodge_laplacian_1=[8, 8], incidence_2=[8, 5], down_laplacian_2=[5, 5], up_laplacian_2=[5, 5], adjacency_2=[5, 5], hodge_laplacian_2=[5, 5], incidence_3=[5, 1], down_laplacian_3=[1, 1], up_laplacian_3=[1, 1], adjacency_3=[1, 1], hodge_laplacian_3=[1, 1], shape=[4], x_0=[5, 1], x_1=[8, 1], x_2=[5, 1], x_3=[1, 1], n_id=[5])\n", - "The cells of rank 0 that were originally selected are tensor([0])\n", - "tensor([0, 7, 1, 2, 4])\n", - "tensor([[0, 0, 0, 0, 1, 1, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4],\n", - " [1, 2, 3, 4, 0, 3, 0, 3, 4, 0, 1, 2, 4, 0, 2, 3]])\n", + "Data(x=[7, 1], edge_index=[2, 22], y=[11], num_nodes=7, incidence_0=[1, 7], down_laplacian_0=[7, 7], up_laplacian_0=[7, 7], adjacency_0=[7, 7], hodge_laplacian_0=[7, 7], incidence_1=[7, 11], down_laplacian_1=[11, 11], up_laplacian_1=[11, 11], adjacency_1=[11, 11], hodge_laplacian_1=[11, 11], incidence_2=[11, 6], down_laplacian_2=[6, 6], up_laplacian_2=[6, 6], adjacency_2=[6, 6], hodge_laplacian_2=[6, 6], incidence_3=[6, 1], down_laplacian_3=[1, 1], up_laplacian_3=[1, 1], adjacency_3=[1, 1], hodge_laplacian_3=[1, 1], shape=[4], x_0=[7, 1], x_1=[11, 1], x_2=[6, 1], x_3=[1, 1], cells_ids=[4], n_id=[11])\n", + "The cells of rank 1 that were originally selected are tensor([0])\n", + "tensor([ 0, 5, 9, 8, 3, 7, 2, 12, 4, 1, 6])\n", + "tensor([[0, 0, 0, 0, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 4, 4, 4, 5, 5, 6, 6, 6],\n", + " [1, 2, 4, 6, 0, 2, 4, 0, 1, 3, 4, 5, 6, 2, 0, 1, 2, 2, 6, 0, 2, 5]])\n", "tensor([[1.],\n", " [1.],\n", " [1.],\n", " [0.],\n", - " [1.]])\n", - "tensor([[1., 1., 0., 0., 0.],\n", - " [1., 0., 1., 1., 0.],\n", - " [0., 1., 1., 0., 0.],\n", - " [0., 0., 0., 1., 0.],\n", - " [1., 0., 0., 0., 1.],\n", - " [0., 1., 0., 0., 1.],\n", - " [0., 0., 1., 0., 1.],\n", - " [0., 0., 0., 1., 0.]])\n", - "tensor([[1., 1., 1., 1., 0., 0., 0., 0.],\n", - " [0., 0., 0., 1., 0., 0., 0., 1.],\n", - " [1., 0., 0., 0., 1., 1., 0., 0.],\n", - " [0., 1., 0., 0., 1., 0., 1., 1.],\n", - " [0., 0., 1., 0., 0., 1., 1., 0.]])\n" + " [1.],\n", + " [0.]])\n", + "tensor([[1., 1., 0., 0., 0., 0.],\n", + " [0., 1., 0., 0., 1., 0.],\n", + " [0., 0., 0., 1., 0., 1.],\n", + " [0., 0., 0., 0., 0., 1.],\n", + " [0., 0., 0., 1., 0., 0.],\n", + " [0., 0., 1., 0., 1., 0.],\n", + " [0., 1., 1., 0., 0., 0.],\n", + " [0., 0., 0., 0., 0., 1.],\n", + " [1., 0., 0., 0., 1., 0.],\n", + " [1., 0., 1., 1., 0., 0.],\n", + " [0., 0., 0., 0., 0., 0.]])\n", + "tensor([[1., 0., 0., 0., 1., 0., 1., 0., 0., 1., 0.],\n", + " [1., 1., 0., 0., 0., 0., 0., 0., 1., 0., 0.],\n", + " [0., 0., 1., 1., 0., 1., 0., 0., 1., 1., 1.],\n", + " [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1.],\n", + " [0., 1., 0., 0., 0., 1., 1., 0., 0., 0., 0.],\n", + " [0., 0., 0., 1., 0., 0., 0., 1., 0., 0., 0.],\n", + " [0., 0., 1., 0., 1., 0., 0., 1., 0., 0., 0.]])\n" ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ @@ -350,7 +481,8 @@ " print(batch.incidence_3.to_dense())\n", " print(batch.incidence_2.to_dense())\n", " print(batch.incidence_1.to_dense())\n", - " plot_graph(batch)\n", + " if rank == 0:\n", + " plot_graph(batch)\n", " break" ] }, @@ -363,18 +495,22 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [ { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "Transform parameters are the same, using existing data_dir: ./graph2hypergraph_lifting/1273654097\n" + "Processing...\n", + "Done!\n" ] } ], "source": [ + "path = \"./graph2hypergraph_lifting/\"\n", + "if os.path.isdir(path):\n", + " shutil.rmtree(path)\n", "cfg = compose(config_name=\"run.yaml\", \n", " overrides=[\"dataset=graph/cocitation_cora\", \"model=hypergraph/allsettransformer\"], \n", " return_hydra_config=True)\n", @@ -413,14 +549,14 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Data(x=[4, 1433], edge_index=[2, 10556], y=[4], train_mask=[2708], val_mask=[2708], test_mask=[2708], incidence_hyperedges=[2708, 2708], num_hyperedges=2708, x_0=[4, 1433], x_hyperedges=[2708, 1433], incidence_1=[4, 5], num_nodes=4, n_id=[4])\n", + "Data(x=[4, 1433], edge_index=[2, 10556], y=[4], train_mask=[2708], val_mask=[2708], test_mask=[2708], incidence_hyperedges=[2708, 2708], num_hyperedges=2708, x_0=[4, 1433], x_hyperedges=[2708, 1433], incidence_1=[4, 5], num_nodes=4, cells_ids=[2], n_id=[4])\n", "tensor([ 0, 1862, 633, 2582])\n", "tensor([[ 0, 0, 0, ..., 2707, 2707, 2707],\n", " [ 633, 1862, 2582, ..., 598, 1473, 2706]])\n", @@ -448,18 +584,11 @@ " \n", " break" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { "kernelspec": { - "display_name": "tbx", + "display_name": "Python 3", "language": "python", "name": "python3" }, From 6a0e4ec69d2deae6438ccd5f7f87b75f07e11df4 Mon Sep 17 00:00:00 2001 From: Coerulatus Date: Wed, 18 Dec 2024 15:39:17 +0000 Subject: [PATCH 17/24] hydra already initialized --- test/data/dataload/test_Dataloaders.py | 4 ---- 1 file changed, 4 deletions(-) diff --git a/test/data/dataload/test_Dataloaders.py b/test/data/dataload/test_Dataloaders.py index 35770d68..26b7de36 100644 --- a/test/data/dataload/test_Dataloaders.py +++ b/test/data/dataload/test_Dataloaders.py @@ -20,10 +20,6 @@ class TestCollateFunction: def setup_method(self): """Setup the test.""" - - hydra.initialize( - version_base="1.3", config_path="../../../configs", job_name="run" - ) cfg = hydra.compose(config_name="run.yaml", overrides=["dataset=graph/NCI1"]) graph_loader = hydra.utils.instantiate(cfg.dataset.loader, _recursive_=False) From 78f5ed299f521af3167fc1a55ca2602490be4187 Mon Sep 17 00:00:00 2001 From: Coerulatus Date: Wed, 18 Dec 2024 15:39:42 +0000 Subject: [PATCH 18/24] added test --- .../batching/test_neighbor_cells_loader.py | 131 ++++++++++++++++++ topobenchmark/data/batching/__init__.py | 7 + topobenchmark/data/batching/cell_loader.py | 2 +- .../data/batching/neighbor_cells_loader.py | 17 ++- topobenchmark/data/batching/utils.py | 14 +- 5 files changed, 161 insertions(+), 10 deletions(-) create mode 100644 test/data/batching/test_neighbor_cells_loader.py create mode 100644 topobenchmark/data/batching/__init__.py diff --git a/test/data/batching/test_neighbor_cells_loader.py b/test/data/batching/test_neighbor_cells_loader.py new file mode 100644 index 00000000..5a02db27 --- /dev/null +++ b/test/data/batching/test_neighbor_cells_loader.py @@ -0,0 +1,131 @@ +import os +import shutil +import rootutils +from hydra import compose +import torch + +from topobenchmark.data.preprocessor import PreProcessor +from topobenchmark.data.utils.utils import load_manual_graph +from topobenchmark.data.batching import NeighborCellsLoader +from topobenchmark.run import initialize_hydra + +initialize_hydra() + +path = "/temp/graph2simplicial_lifting/" +if os.path.isdir(path): + shutil.rmtree(path) +cfg = compose(config_name="run.yaml", + overrides=["dataset=graph/manual_dataset", "model=simplicial/san"], + return_hydra_config=True) + +data = load_manual_graph() +preprocessed_dataset = PreProcessor(data, path, cfg['transforms']) +data = preprocessed_dataset[0] + +batch_size=2 + +rank = 0 +n_cells = data[f'x_{rank}'].shape[0] +train_prop = 0.5 +n_train = int(train_prop * n_cells) +train_mask = torch.zeros(n_cells, dtype=torch.bool) +train_mask[:n_train] = 1 + +y = torch.zeros(n_cells, dtype=torch.long) +data.y = y + +loader = NeighborCellsLoader(data, + rank=rank, + num_neighbors=[-1], + input_nodes=train_mask, + batch_size=batch_size, + shuffle=False) +train_nodes = [] +for batch in loader: + train_nodes += [n for n in batch.n_id[:batch_size]] +for i in range(n_train): + assert i in train_nodes + +rank = 1 +n_cells = data[f'x_{rank}'].shape[0] +train_prop = 0.5 +n_train = int(train_prop * n_cells) +train_mask = torch.zeros(n_cells, dtype=torch.bool) +train_mask[:n_train] = 1 + +y = torch.zeros(n_cells, dtype=torch.long) +data.y = y + +loader = NeighborCellsLoader(data, + rank=rank, + num_neighbors=[-1,-1], + input_nodes=train_mask, + batch_size=batch_size, + shuffle=False) + +train_nodes = [] +for batch in loader: + train_nodes += [n for n in batch.n_id[:batch_size]] +for i in range(n_train): + assert i in train_nodes +shutil.rmtree(path) + + +path = "/temp/graph2hypergraph_lifting/" +if os.path.isdir(path): + shutil.rmtree(path) +cfg = compose(config_name="run.yaml", + overrides=["dataset=graph/manual_dataset", "model=hypergraph/allsettransformer"], + return_hydra_config=True) + +data = load_manual_graph() +preprocessed_dataset = PreProcessor(data, path, cfg['transforms']) +data = preprocessed_dataset[0] + +batch_size=2 + +rank = 0 +n_cells = data[f'x_0'].shape[0] +train_prop = 0.5 +n_train = int(train_prop * n_cells) +train_mask = torch.zeros(n_cells, dtype=torch.bool) +train_mask[:n_train] = 1 + +y = torch.zeros(n_cells, dtype=torch.long) +data.y = y + +loader = NeighborCellsLoader(data, + rank=rank, + num_neighbors=[-1], + input_nodes=train_mask, + batch_size=batch_size, + shuffle=False) +train_nodes = [] +for batch in loader: + train_nodes += [n for n in batch.n_id[:batch_size]] +for i in range(n_train): + assert i in train_nodes + +rank = 1 +n_cells = data[f'x_hyperedges'].shape[0] +train_prop = 0.5 +n_train = int(train_prop * n_cells) +train_mask = torch.zeros(n_cells, dtype=torch.bool) +train_mask[:n_train] = 1 + +y = torch.zeros(n_cells, dtype=torch.long) +data.y = y + +loader = NeighborCellsLoader(data, + rank=rank, + num_neighbors=[-1,-1], + input_nodes=train_mask, + batch_size=batch_size, + shuffle=False) + +train_nodes = [] +for batch in loader: + train_nodes += [n for n in batch.n_id[:batch_size]] +for i in range(n_train): + assert i in train_nodes +shutil.rmtree(path) \ No newline at end of file diff --git a/topobenchmark/data/batching/__init__.py b/topobenchmark/data/batching/__init__.py new file mode 100644 index 00000000..114d82f9 --- /dev/null +++ b/topobenchmark/data/batching/__init__.py @@ -0,0 +1,7 @@ +""" Init file for batching module. """ + +from .neighbor_cells_loader import NeighborCellsLoader + +__all__ = [ + "NeighborCellsLoader", +] \ No newline at end of file diff --git a/topobenchmark/data/batching/cell_loader.py b/topobenchmark/data/batching/cell_loader.py index 592b83c8..645e40a9 100644 --- a/topobenchmark/data/batching/cell_loader.py +++ b/topobenchmark/data/batching/cell_loader.py @@ -165,7 +165,7 @@ def filter_fn( out = self.transform_sampler_output(out) if isinstance(out, SamplerOutput) and isinstance(self.data, Data): - data = filter_data( # + data = filter_data( self.data, out.node, self.rank) else: raise TypeError(f"'{self.__class__.__name__}'' found invalid " diff --git a/topobenchmark/data/batching/neighbor_cells_loader.py b/topobenchmark/data/batching/neighbor_cells_loader.py index 4a71e65e..33ca9bb8 100644 --- a/topobenchmark/data/batching/neighbor_cells_loader.py +++ b/topobenchmark/data/batching/neighbor_cells_loader.py @@ -2,6 +2,7 @@ from topobenchmark.data.batching.cell_loader import CellLoader from topobenchmark.data.batching.utils import get_sampled_neighborhood +from topobenchmark.dataloader import DataloadDataset from torch_geometric.data import Data, FeatureStore, GraphStore, HeteroData @@ -121,7 +122,7 @@ def __init__( is_sorted: bool = False, filter_per_worker: Optional[bool] = None, neighbor_sampler: Optional[NeighborSampler] = None, - directed: bool = True, # Deprecated. + directed: bool = True, **kwargs, ): if input_time is not None and time_attr is None: @@ -129,16 +130,22 @@ def __init__( "'time_attr' arguments: 'input_time' is set " "while 'time_attr' is not set.") - is_hypergraph = hasattr(data, 'incidence_hyperedges') + data_obj = Data() + if isinstance(data, DataloadDataset): + for tensor, name in zip(data[0][0], data[0][1]): + setattr(data_obj, name, tensor) + else: + data_obj = data + is_hypergraph = hasattr(data_obj, 'incidence_hyperedges') n_hops = len(num_neighbors) - data = get_sampled_neighborhood(data, rank, n_hops, is_hypergraph) + data_obj = get_sampled_neighborhood(data_obj, rank, n_hops, is_hypergraph) self.rank = rank if self.rank != 0: # When rank is different than 0 get_sampled_neighborhood connects cells that are up to n_hops away, meaning that the NeighborhoodSampler needs to consider only one hop. num_neighbors = [num_neighbors[0]] if neighbor_sampler is None: neighbor_sampler = NeighborSampler( - data, + data_obj, num_neighbors=num_neighbors, replace=replace, subgraph_type=subgraph_type, @@ -152,7 +159,7 @@ def __init__( ) super().__init__( - data=data, + data=data_obj, cell_sampler=neighbor_sampler, input_cells=input_nodes, input_time=input_time, diff --git a/topobenchmark/data/batching/utils.py b/topobenchmark/data/batching/utils.py index 1a16b350..3e360cc8 100644 --- a/topobenchmark/data/batching/utils.py +++ b/topobenchmark/data/batching/utils.py @@ -43,7 +43,10 @@ def reduce_higher_ranks_incidences(batch, cells_ids, rank, max_rank, is_hypergra incidence = torch.index_select(incidence, 0, cells_ids[i-1]) cells_ids[i] = torch.where(torch.sum(incidence, dim=0).to_dense() > 1)[0] incidence = torch.index_select(incidence, 1, cells_ids[i]) - batch[f"incidence_{i}"] = incidence + if is_hypergraph: + batch.incidence_hyperedges = incidence + else: + batch[f"incidence_{i}"] = incidence return batch, cells_ids @@ -76,7 +79,10 @@ def reduce_lower_ranks_incidences(batch, cells_ids, rank, is_hypergraph=False): incidence = torch.index_select(incidence, 1, cells_ids[i]) cells_ids[i-1] = torch.where(torch.sum(incidence, dim=1).to_dense() > 0)[0] incidence = torch.index_select(incidence, 0, cells_ids[i-1]) - batch[f"incidence_{i}"] = incidence + if is_hypergraph: + batch.incidence_hyperedges = incidence + else: + batch[f"incidence_{i}"] = incidence if not is_hypergraph: incidence = batch[f"incidence_0"] @@ -275,8 +281,8 @@ def get_sampled_neighborhood(data, rank=0, n_hops=1, is_hypergraph=False): # P = torch.sparse.mm(data[f"incidence_{i}"], P) # Q = torch.sparse.mm(P.T,P) # edges = torch.cat((edges, Q.indices()), dim=1) - - edges = A_sum.coalesce().indices() + + edges = A_sum.coalesce().indices() # Remove self edges mask = edges[0, :] != edges[1, :] edges = edges[:, mask] From 9758ff4f923f2639ddfbe2e3aa0d0cec68036a87 Mon Sep 17 00:00:00 2001 From: Coerulatus Date: Wed, 18 Dec 2024 15:40:08 +0000 Subject: [PATCH 19/24] added batching in transductive setting --- topobenchmark/dataloader/dataloader.py | 90 +++++++++++++++++--------- 1 file changed, 58 insertions(+), 32 deletions(-) diff --git a/topobenchmark/dataloader/dataloader.py b/topobenchmark/dataloader/dataloader.py index 30c42689..32d2880d 100755 --- a/topobenchmark/dataloader/dataloader.py +++ b/topobenchmark/dataloader/dataloader.py @@ -7,7 +7,7 @@ from topobenchmark.dataloader.dataload_dataset import DataloadDataset from topobenchmark.dataloader.utils import collate_fn - +from topobenchmark.data.batching import NeighborCellsLoader class TBDataloader(LightningDataModule): r"""This class takes care of returning the dataloaders for the training, validation, and test datasets. @@ -24,6 +24,10 @@ class TBDataloader(LightningDataModule): The test dataset (default: None). batch_size : int, optional The batch size for the dataloader (default: 1). + rank : int, optional + The rank of the cells to consider when batching in the transductive setting (default: 0). + num_neighbors : list[int], optional + The number of neighbors to sample in the transductive setting. To consider n-hop neighborhoods this list should contain n elements. Care should be taken to check that the number of hops is appropriate for your model. With topological models the number of layers might not be enough to determine how far information is propagated. (default: [-1]). num_workers : int, optional The number of worker processes to use for data loading (default: 0). pin_memory : bool, optional @@ -43,6 +47,8 @@ def __init__( dataset_val: DataloadDataset = None, dataset_test: DataloadDataset = None, batch_size: int = 1, + rank: int = 0, + num_neighbors: list[int] = [-1], num_workers: int = 0, pin_memory: bool = False, **kwargs: Any, @@ -57,24 +63,68 @@ def __init__( ) self.dataset_train = dataset_train self.batch_size = batch_size - + self.transductive = False + self.rank = rank + self.num_neighbors = num_neighbors if dataset_val is None and dataset_test is None: # Transductive setting self.dataset_val = dataset_train self.dataset_test = dataset_train - assert ( - self.batch_size == 1 - ), "Batch size must be 1 for transductive setting." + self.transductive = True else: self.dataset_val = dataset_val self.dataset_test = dataset_test self.num_workers = num_workers self.pin_memory = pin_memory self.persistent_workers = kwargs.get("persistent_workers", False) + self.kwargs = kwargs def __repr__(self) -> str: return f"{self.__class__.__name__}(dataset_train={self.dataset_train}, dataset_val={self.dataset_val}, dataset_test={self.dataset_test}, batch_size={self.batch_size})" + def _get_dataloader(self, split: str) -> DataLoader | NeighborCellsLoader: + r""" Create and return the dataloader for the specified split. + + Parameters + ---------- + split : str + The split to create the dataloader for. + + Returns + ------- + torch.utils.data.DataLoader | NeighborCellsLoader + The dataloader for the specified split. + """ + shuffle = (split == "train") + + if not self.transductive or self.batch_size == -1: + if self.batch_size == -1: + batch_size = 1 + else: + batch_size = self.batch_size + + return DataLoader( + dataset=getattr(self, f"dataset_{split}"), + batch_size=batch_size, + num_workers=self.num_workers, + pin_memory=self.pin_memory, + shuffle=shuffle, + collate_fn=collate_fn, + persistent_workers=self.persistent_workers, + **self.kwargs, + ) + mask_idx = self.dataset_train[0][1].index(f'{split}_mask') + mask = self.dataset_train[0][0][mask_idx] + return NeighborCellsLoader( + data=getattr(self, f"dataset_{split}"), + rank=self.rank, + num_neighbors=self.num_neighbors, + input_nodes=mask, + batch_size=self.batch_size, + shuffle=shuffle, + **self.kwargs, + ) + def train_dataloader(self) -> DataLoader: r"""Create and return the train dataloader. @@ -83,15 +133,7 @@ def train_dataloader(self) -> DataLoader: torch.utils.data.DataLoader The train dataloader. """ - return DataLoader( - dataset=self.dataset_train, - batch_size=self.batch_size, - num_workers=self.num_workers, - pin_memory=self.pin_memory, - shuffle=True, - collate_fn=collate_fn, - persistent_workers=self.persistent_workers, - ) + return self._get_dataloader("train") def val_dataloader(self) -> DataLoader: r"""Create and return the validation dataloader. @@ -101,15 +143,7 @@ def val_dataloader(self) -> DataLoader: torch.utils.data.DataLoader The validation dataloader. """ - return DataLoader( - dataset=self.dataset_val, - batch_size=self.batch_size, - num_workers=self.num_workers, - pin_memory=self.pin_memory, - shuffle=False, - collate_fn=collate_fn, - persistent_workers=self.persistent_workers, - ) + return self._get_dataloader("val") def test_dataloader(self) -> DataLoader: r"""Create and return the test dataloader. @@ -121,15 +155,7 @@ def test_dataloader(self) -> DataLoader: """ if self.dataset_test is None: raise ValueError("There is no test dataloader.") - return DataLoader( - dataset=self.dataset_test, - batch_size=self.batch_size, - num_workers=self.num_workers, - pin_memory=self.pin_memory, - shuffle=False, - collate_fn=collate_fn, - persistent_workers=self.persistent_workers, - ) + return self._get_dataloader("test") def teardown(self, stage: str | None = None) -> None: r"""Lightning hook for cleaning up after `trainer.fit()`, `trainer.validate()`, `trainer.test()`, and `trainer.predict()`. From 4d8024188a36dac8e345dbafc17dfe964cf94950 Mon Sep 17 00:00:00 2001 From: Coerulatus Date: Wed, 18 Dec 2024 15:44:03 +0000 Subject: [PATCH 20/24] test mse when batching --- tutorials/batching.ipynb | 280 +++++++++++++++++++++++++-------------- 1 file changed, 179 insertions(+), 101 deletions(-) diff --git a/tutorials/batching.ipynb b/tutorials/batching.ipynb index 5d9c9b7b..b78ea289 100644 --- a/tutorials/batching.ipynb +++ b/tutorials/batching.ipynb @@ -25,7 +25,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_18685/3206793763.py:29: UserWarning: \n", + "/tmp/ipykernel_26947/1154935553.py:31: UserWarning: \n", "The version_base parameter is not specified.\n", "Please specify a compatability version level, or None.\n", "Will assume defaults for version 1.1\n", @@ -57,6 +57,7 @@ "from hydra import compose, initialize\n", "from omegaconf import OmegaConf\n", "\n", + "from topobenchmark.data.utils.utils import load_manual_graph\n", "from topobenchmark.data.preprocessor import PreProcessor\n", "from topobenchmark.dataloader.dataloader import TBDataloader\n", "from topobenchmark.data.loaders import PlanetoidDatasetLoader\n", @@ -64,6 +65,7 @@ "from topobenchmark.data.batching.neighbor_cells_loader import NeighborCellsLoader\n", "from topobenchmark.data.preprocessor import PreProcessor\n", "from topomodelx.nn.simplicial.scn2 import SCN2\n", + "from topomodelx.nn.hypergraph.allset_transformer import AllSetTransformer\n", "\n", "from topobenchmark.utils.config_resolvers import (\n", " get_default_transform,\n", @@ -253,9 +255,6 @@ } ], "source": [ - "import os, shutil\n", - "from topobenchmark.data.utils.utils import load_manual_graph\n", - "\n", "path = \"./graph2simplicial_lifting/\"\n", "if os.path.isdir(path):\n", " shutil.rmtree(path)\n", @@ -278,7 +277,7 @@ { "data": { "text/plain": [ - "Data(x=[8, 1], edge_index=[2, 13], y=[13], num_nodes=8, incidence_0=[1, 8], down_laplacian_0=[8, 8], up_laplacian_0=[8, 8], adjacency_0=[8, 8], coadjacency_0=[8, 8], hodge_laplacian_0=[8, 8], incidence_1=[8, 13], down_laplacian_1=[13, 13], up_laplacian_1=[13, 13], adjacency_1=[13, 13], coadjacency_1=[13, 13], hodge_laplacian_1=[13, 13], incidence_2=[13, 6], down_laplacian_2=[6, 6], up_laplacian_2=[6, 6], adjacency_2=[6, 6], coadjacency_2=[6, 6], hodge_laplacian_2=[6, 6], incidence_3=[6, 1], down_laplacian_3=[1, 1], up_laplacian_3=[1, 1], adjacency_3=[1, 1], coadjacency_3=[1, 1], hodge_laplacian_3=[1, 1], shape=[4], x_0=[8, 1], x_1=[13, 1], x_2=[6, 1], x_3=[1, 1])" + "Data(x=[8, 1], edge_index=[2, 13], y=[8], num_nodes=8, incidence_0=[1, 8], down_laplacian_0=[8, 8], up_laplacian_0=[8, 8], adjacency_0=[8, 8], coadjacency_0=[8, 8], hodge_laplacian_0=[8, 8], incidence_1=[8, 13], down_laplacian_1=[13, 13], up_laplacian_1=[13, 13], adjacency_1=[13, 13], coadjacency_1=[13, 13], hodge_laplacian_1=[13, 13], incidence_2=[13, 6], down_laplacian_2=[6, 6], up_laplacian_2=[6, 6], adjacency_2=[6, 6], coadjacency_2=[6, 6], hodge_laplacian_2=[6, 6], incidence_3=[6, 1], down_laplacian_3=[1, 1], up_laplacian_3=[1, 1], adjacency_3=[1, 1], coadjacency_3=[1, 1], hodge_laplacian_3=[1, 1], shape=[4], x_0=[8, 1], x_1=[13, 1], x_2=[6, 1], x_3=[1, 1])" ] }, "execution_count": 4, @@ -288,7 +287,7 @@ ], "source": [ "# Training, validation and split idxs should be defined somewhere, here we use a toy example\n", - "rank = 1\n", + "rank = 0\n", "if hasattr(data, \"x_hyperedges\") and rank==1:\n", " n_cells = data.x_hyperedges.shape[0]\n", "else:\n", @@ -309,22 +308,13 @@ "cell_type": "code", "execution_count": 5, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/TopoBenchmark/topobenchmark/data/batching/utils.py:254: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)\n", - " A = torch.sparse.mm(I,I.T)\n" - ] - } - ], + "outputs": [], "source": [ "batch_size = 1\n", "\n", "loader = NeighborCellsLoader(data,\n", " rank=rank,\n", - " num_neighbors=[-1,-1],\n", + " num_neighbors=[-1],\n", " input_nodes=train_mask,\n", " batch_size=batch_size,\n", " shuffle=False)" @@ -339,49 +329,58 @@ "name": "stdout", "output_type": "stream", "text": [ - "Data(x=[7, 1], edge_index=[2, 22], y=[11], num_nodes=7, incidence_0=[1, 7], down_laplacian_0=[7, 7], up_laplacian_0=[7, 7], adjacency_0=[7, 7], coadjacency_0=[8, 8], hodge_laplacian_0=[7, 7], incidence_1=[7, 11], down_laplacian_1=[11, 11], up_laplacian_1=[11, 11], adjacency_1=[11, 11], coadjacency_1=[13, 13], hodge_laplacian_1=[11, 11], incidence_2=[11, 6], down_laplacian_2=[6, 6], up_laplacian_2=[6, 6], adjacency_2=[6, 6], coadjacency_2=[6, 6], hodge_laplacian_2=[6, 6], incidence_3=[6, 1], down_laplacian_3=[1, 1], up_laplacian_3=[1, 1], adjacency_3=[1, 1], coadjacency_3=[1, 1], hodge_laplacian_3=[1, 1], shape=[4], x_0=[7, 1], x_1=[11, 1], x_2=[6, 1], x_3=[1, 1], cells_ids=[4], n_id=[11])\n", - "The cells of rank 1 that were originally selected are tensor([0])\n", - "tensor([ 0, 5, 9, 8, 3, 7, 2, 12, 4, 1, 6])\n", - "tensor([[0, 0, 0, 0, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 4, 4, 4, 5, 5, 6, 6, 6],\n", - " [1, 2, 4, 6, 0, 2, 4, 0, 1, 3, 4, 5, 6, 2, 0, 1, 2, 2, 6, 0, 2, 5]])\n", + "Data(x=[5, 1], edge_index=[2, 16], y=[5], num_nodes=5, incidence_0=[1, 5], down_laplacian_0=[5, 5], up_laplacian_0=[5, 5], adjacency_0=[5, 5], coadjacency_0=[8, 8], hodge_laplacian_0=[5, 5], incidence_1=[5, 8], down_laplacian_1=[8, 8], up_laplacian_1=[8, 8], adjacency_1=[8, 8], coadjacency_1=[13, 13], hodge_laplacian_1=[8, 8], incidence_2=[8, 5], down_laplacian_2=[5, 5], up_laplacian_2=[5, 5], adjacency_2=[5, 5], coadjacency_2=[6, 6], hodge_laplacian_2=[5, 5], incidence_3=[5, 1], down_laplacian_3=[1, 1], up_laplacian_3=[1, 1], adjacency_3=[1, 1], coadjacency_3=[1, 1], hodge_laplacian_3=[1, 1], shape=[4], x_0=[5, 1], x_1=[8, 1], x_2=[5, 1], x_3=[1, 1], cells_ids=[4], n_id=[5])\n", + "The cells of rank 0 that were originally selected are [0]\n", + "Selected cells of rank 0: tensor([0, 7, 1, 2, 4])\n", + "Incidence 3:\n", "tensor([[1.],\n", " [1.],\n", " [1.],\n", " [0.],\n", - " [1.],\n", - " [0.]])\n", - "tensor([[1., 1., 0., 0., 0., 0.],\n", - " [0., 1., 0., 0., 1., 0.],\n", - " [0., 0., 0., 1., 0., 1.],\n", - " [0., 0., 0., 0., 0., 1.],\n", - " [0., 0., 0., 1., 0., 0.],\n", - " [0., 0., 1., 0., 1., 0.],\n", - " [0., 1., 1., 0., 0., 0.],\n", - " [0., 0., 0., 0., 0., 1.],\n", - " [1., 0., 0., 0., 1., 0.],\n", - " [1., 0., 1., 1., 0., 0.],\n", - " [0., 0., 0., 0., 0., 0.]])\n", - "tensor([[1., 0., 0., 0., 1., 0., 1., 0., 0., 1., 0.],\n", - " [1., 1., 0., 0., 0., 0., 0., 0., 1., 0., 0.],\n", - " [0., 0., 1., 1., 0., 1., 0., 0., 1., 1., 1.],\n", - " [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1.],\n", - " [0., 1., 0., 0., 0., 1., 1., 0., 0., 0., 0.],\n", - " [0., 0., 0., 1., 0., 0., 0., 1., 0., 0., 0.],\n", - " [0., 0., 1., 0., 1., 0., 0., 1., 0., 0., 0.]])\n" + " [1.]])\n", + "Incidence 2:\n", + "tensor([[1., 1., 0., 0., 0.],\n", + " [1., 0., 1., 1., 0.],\n", + " [0., 1., 1., 0., 0.],\n", + " [0., 0., 0., 1., 0.],\n", + " [1., 0., 0., 0., 1.],\n", + " [0., 1., 0., 0., 1.],\n", + " [0., 0., 1., 0., 1.],\n", + " [0., 0., 0., 1., 0.]])\n", + "Incidence 1:\n", + "tensor([[1., 1., 1., 1., 0., 0., 0., 0.],\n", + " [0., 0., 0., 1., 0., 0., 0., 1.],\n", + " [1., 0., 0., 0., 1., 1., 0., 0.],\n", + " [0., 1., 0., 0., 1., 0., 1., 1.],\n", + " [0., 0., 1., 0., 0., 1., 1., 0.]])\n" ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ "for batch in loader:\n", " print(batch)\n", - " print(f\"The cells of rank {rank} that were originally selected are {batch.n_id[:batch_size]}\")\n", - " print(batch.n_id)\n", - " print(batch.edge_index)\n", + " print(f\"The cells of rank {rank} that were originally selected are {batch.n_id[:batch_size].tolist()}\")\n", + " \n", + " print(f\"Selected cells of rank {rank}: {batch.n_id}\")\n", " if hasattr(batch, 'incidence_hyperedges'):\n", + " print(\"Incidence hyperedges:\")\n", " print(batch.incidence_hyperedges.to_dense())\n", " else:\n", + " print(\"Incidence 3:\")\n", " print(batch.incidence_3.to_dense())\n", + " print(\"Incidence 2:\")\n", " print(batch.incidence_2.to_dense())\n", + " print(\"Incidence 1:\")\n", " print(batch.incidence_1.to_dense())\n", " if rank == 0:\n", " plot_graph(batch)\n", @@ -458,17 +457,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "Data(x=[4, 1433], edge_index=[2, 10556], y=[4], train_mask=[2708], val_mask=[2708], test_mask=[2708], incidence_hyperedges=[2708, 2708], num_hyperedges=2708, x_0=[4, 1433], x_hyperedges=[2708, 1433], incidence_1=[4, 5], num_nodes=4, cells_ids=[2], n_id=[4])\n", + "Data(x=[4, 1433], edge_index=[2, 10556], y=[4], train_mask=[2708], val_mask=[2708], test_mask=[2708], incidence_hyperedges=[4, 5], num_hyperedges=2708, x_0=[4, 1433], x_hyperedges=[2708, 1433], num_nodes=4, cells_ids=[2], n_id=[4])\n", "tensor([ 0, 1862, 633, 2582])\n", "tensor([[ 0, 0, 0, ..., 2707, 2707, 2707],\n", " [ 633, 1862, 2582, ..., 598, 1473, 2706]])\n", - "tensor([[1., 0., 0., ..., 0., 0., 0.],\n", - " [0., 1., 1., ..., 0., 0., 0.],\n", - " [0., 1., 1., ..., 0., 0., 0.],\n", - " ...,\n", - " [0., 0., 0., ..., 1., 0., 0.],\n", - " [0., 0., 0., ..., 0., 1., 1.],\n", - " [0., 0., 0., ..., 0., 1., 1.]])\n" + "tensor([[1., 1., 0., 1., 1.],\n", + " [1., 0., 1., 1., 1.],\n", + " [1., 1., 1., 0., 0.],\n", + " [1., 0., 0., 1., 1.]])\n" ] } ], @@ -498,7 +494,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "If batching is done correctly the results on the selected nodes should not change when compared to the results obtained over the whole graph.\n", + "If batching is done correctly the results on the selected cells should not change when compared to the results obtained over the whole graph.\n", "We test this to check that our batching strategy is correct." ] }, @@ -506,7 +502,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Full batching" + "### Testing simplicial complexes" ] }, { @@ -514,21 +510,16 @@ "execution_count": 9, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Processing...\n", - "Done!\n", - "Processing...\n", - "Done!\n" - ] - }, { "name": "stdout", "output_type": "stream", "text": [ - "Batching works: True\n" + "Transform parameters are the same, using existing data_dir: /TopoBenchmark/datasets/graph/cocitation/Cora/graph2simplicial_lifting/1597083846\n", + "Testing batching for SCN2 using 2 layers.\n", + "We return the MSE between the full batch and the batched version.\n", + " n_hops = 1 MSE: 3.947006937138043\n", + " n_hops = 2 MSE: 9.035350438167982e-12\n", + " n_hops = 3 MSE: 1.0411274301838695e-11\n" ] } ], @@ -549,6 +540,9 @@ " preprocessor.load_dataset_splits(cfg.dataset.split_params)\n", ")\n", "\n", + "### Full batch --------------------------------------------------------\n", + "cfg.dataset.dataloader_params.batch_size = -1\n", + "\n", "datamodule = TBDataloader(\n", " dataset_train=dataset_train,\n", " dataset_val=dataset_val,\n", @@ -566,48 +560,132 @@ "train_dataloader = datamodule.train_dataloader()\n", "for data in train_dataloader:\n", " x_0_full, x_1_full, x_2_full = model(data.x_0, data.x_1, data.x_2, data.hodge_laplacian_0, data.hodge_laplacian_1, data.hodge_laplacian_2)\n", - " break\n", "\n", - "graph_loader = PlanetoidDatasetLoader(cfg.dataset.loader.parameters)\n", - "dataset, dataset_dir = graph_loader.load()\n", - "preprocessed_dataset = PreProcessor(dataset, './', cfg['transforms'])\n", - "data = preprocessed_dataset[0]\n", + "### Batched --------------------------------------------------------\n", + "print(\"Testing batching for SCN2 using 2 layers.\")\n", + "print(\"We return the MSE between the full batch and the batched version.\")\n", + "for n_hops in range(1, 4):\n", + " cfg.dataset.dataloader_params.batch_size = 32\n", + "\n", + " datamodule_batched = TBDataloader(\n", + " dataset_train=dataset_train,\n", + " dataset_val=dataset_val,\n", + " dataset_test=dataset_test,\n", + " num_neighbors = [-1] * n_hops,\n", + " **cfg.dataset.get(\"dataloader_params\", {}),\n", + " )\n", + " train_dataloader_batched = datamodule_batched.train_dataloader()\n", + " mse = 0\n", + " for i, batch in enumerate(train_dataloader_batched):\n", + " x_0_batch, x_1_batch, x_2_batch = model(batch.x_0, batch.x_1, batch.x_2, batch.hodge_laplacian_0, batch.hodge_laplacian_1, batch.hodge_laplacian_2)\n", + " n_ids = batch.n_id[:batch_size]\n", + " mse += torch.mean((x_0_full[n_ids, :] - x_0_batch[:batch_size, :]).pow(2)).item()\n", + " mse = mse / (i + 1)\n", + " \n", + " # The last element might be False since the last batch might not be full\n", + " print(f\" n_hops = {n_hops} MSE: {mse}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Testing hypergraphs" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Transform parameters are the same, using existing data_dir: /TopoBenchmark/datasets/graph/cocitation/Cora/graph2hypergraph_lifting/1010717418\n", + "Testing batching for AllSetTransformer using 3 layers.\n", + "We return the MSE between the full batch and the batched version.\n", + " n_hops = 1 MSE: 0.0002609546898304469\n", + " n_hops = 2 MSE: 1.660647607597814e-05\n", + " n_hops = 3 MSE: 1.1712208103968767e-06\n", + " n_hops = 4 MSE: 1.5722682561617035e-08\n", + " n_hops = 5 MSE: 1.550387910249659e-11\n", + " n_hops = 6 MSE: 8.365642818938425e-15\n", + " n_hops = 7 MSE: 7.258141882427575e-15\n", + " n_hops = 8 MSE: 7.711832348623849e-15\n", + " n_hops = 9 MSE: 4.981246563409259e-15\n" + ] + } + ], + "source": [ + "path = \"./graph2hypergraph_lifting/\"\n", + "if os.path.isdir(path):\n", + " shutil.rmtree(path)\n", + "cfg = compose(config_name=\"run.yaml\", \n", + " overrides=[\"dataset=graph/cocitation_cora\", \"model=hypergraph/allsettransformer\"], \n", + " return_hydra_config=True)\n", "\n", - "# Training, validation and split idxs should be defined somewhere, here we use a toy example\n", - "rank = 0\n", - "if hasattr(data, \"x_hyperedges\") and rank==1:\n", - " n_cells = data.x_hyperedges.shape[0]\n", - "else:\n", - " n_cells = data[f'x_{rank}'].shape[0]\n", + "dataset_loader = hydra.utils.instantiate(cfg.dataset.loader)\n", + "dataset, dataset_dir = dataset_loader.load()\n", + "# Preprocess dataset and load the splits\n", + "transform_config = cfg.get(\"transforms\", None)\n", + "preprocessor = PreProcessor(dataset, dataset_dir, transform_config)\n", + "dataset_train, dataset_val, dataset_test = (\n", + " preprocessor.load_dataset_splits(cfg.dataset.split_params)\n", + ")\n", "\n", - "train_prop = 0.5\n", - "n_train = int(train_prop * n_cells)\n", - "train_mask = torch.zeros(n_cells, dtype=torch.bool)\n", - "train_mask[:n_train] = 1\n", + "### Full batch --------------------------------------------------------\n", + "cfg.dataset.dataloader_params.batch_size = -1\n", "\n", - "if rank != 0:\n", - " y = torch.zeros(n_cells, dtype=torch.long)\n", - " data.y = y\n", - " \n", - "batch_size = 32\n", + "datamodule = TBDataloader(\n", + " dataset_train=dataset_train,\n", + " dataset_val=dataset_val,\n", + " dataset_test=dataset_test,\n", + " **cfg.dataset.get(\"dataloader_params\", {}),\n", + " )\n", "\n", - "# num_neighbors also controls the number of hops (for 2 hops do num_neighbors=[-1, -1])\n", - "loader = NeighborCellsLoader(data,\n", - " rank=rank,\n", - " num_neighbors=[-1]*3,\n", - " input_nodes=train_mask,\n", - " batch_size=batch_size,\n", - " shuffle=False)\n", + "input_dim = 1433\n", + "hidden_channels = 16\n", + "out_dim = 7\n", + "n_layers = 3\n", + "model = AllSetTransformer(input_dim, hidden_channels, n_layers=n_layers)\n", + "model.eval()\n", "\n", - "success = []\n", - "for i, batch in enumerate(loader):\n", - " x_0_batch, x_1_batch, x_2_batch = model(batch.x_0, batch.x_1, batch.x_2, batch.hodge_laplacian_0, batch.hodge_laplacian_1, batch.hodge_laplacian_2)\n", - " n_ids = batch.n_id[:batch_size]\n", - " success.append(torch.allclose(x_0_full[n_ids, :], x_0_batch[:batch_size, :],atol=1e-03))\n", - " \n", - "# The last element might be False since the last batch might not be full\n", - "print(f\"Batching works: {all(success[:-1])}\")" + "train_dataloader = datamodule.train_dataloader()\n", + "for data in train_dataloader:\n", + " x_0_full, x_1_full = model(data.x_0, data.incidence_hyperedges)\n", + "\n", + "### Batched --------------------------------------------------------\n", + "print(f\"Testing batching for AllSetTransformer using {n_layers} layers.\")\n", + "print(\"We return the MSE between the full batch and the batched version.\")\n", + "for n_hops in range(1, 10):\n", + " cfg.dataset.dataloader_params.batch_size = 32\n", + "\n", + " datamodule_batched = TBDataloader(\n", + " dataset_train=dataset_train,\n", + " dataset_val=dataset_val,\n", + " dataset_test=dataset_test,\n", + " num_neighbors = [-1] * n_hops,\n", + " **cfg.dataset.get(\"dataloader_params\", {}),\n", + " )\n", + " train_dataloader_batched = datamodule_batched.train_dataloader()\n", + " mse = 0\n", + " for i, batch in enumerate(train_dataloader_batched):\n", + " x_0_batch, x_1_batch = model(batch.x_0, batch.incidence_hyperedges)\n", + " n_ids = batch.n_id[:batch_size]\n", + " mse += torch.mean((x_0_full[n_ids, :] - x_0_batch[:batch_size, :]).pow(2)).item()\n", + " mse = mse / (i + 1)\n", + " \n", + " # The last element might be False since the last batch might not be full\n", + " print(f\" n_hops = {n_hops} MSE: {mse}\")" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { From 503512f86d28eded47fed803a0732e7e1aac2e7c Mon Sep 17 00:00:00 2001 From: Coerulatus Date: Wed, 18 Dec 2024 16:08:42 +0000 Subject: [PATCH 21/24] formatting --- topobenchmark/data/batching/__init__.py | 4 +- topobenchmark/data/batching/cell_loader.py | 184 +++++++----- .../data/batching/neighbor_cells_loader.py | 133 +++++---- topobenchmark/data/batching/utils.py | 276 ++++++++++-------- topobenchmark/dataloader/dataloader.py | 17 +- 5 files changed, 359 insertions(+), 255 deletions(-) diff --git a/topobenchmark/data/batching/__init__.py b/topobenchmark/data/batching/__init__.py index 114d82f9..71952e48 100644 --- a/topobenchmark/data/batching/__init__.py +++ b/topobenchmark/data/batching/__init__.py @@ -1,7 +1,7 @@ -""" Init file for batching module. """ +"""Init file for batching module.""" from .neighbor_cells_loader import NeighborCellsLoader __all__ = [ "NeighborCellsLoader", -] \ No newline at end of file +] diff --git a/topobenchmark/data/batching/cell_loader.py b/topobenchmark/data/batching/cell_loader.py index 645e40a9..dd056fb6 100644 --- a/topobenchmark/data/batching/cell_loader.py +++ b/topobenchmark/data/batching/cell_loader.py @@ -1,3 +1,5 @@ +"""Cell Loader module from PyTorch Geometric with custom filter_data function.""" + from typing import Any, Callable, Iterator, List, Optional, Tuple, Union import torch @@ -30,65 +32,75 @@ class CellLoader( - torch.utils.data.DataLoader, - AffinityMixin, - MultithreadingMixin, - LogMemoryMixin, + torch.utils.data.DataLoader, + AffinityMixin, + MultithreadingMixin, + LogMemoryMixin, ): - r"""A data loader that performs mini-batch sampling from cell information, - using a generic :class:`~torch_geometric.sampler.BaseSampler` + r"""A data loader that performs mini-batch sampling from cell information. + + It uses a generic :class:`~torch_geometric.sampler.BaseSampler` implementation that defines a :meth:`~torch_geometric.sampler.BaseSampler.sample_from_nodes` function and is supported on the provided input :obj:`data` object. - Args: - data (Any): A :class:`~torch_geometric.data.Data`, - :class:`~torch_geometric.data.HeteroData`, or - (:class:`~torch_geometric.data.FeatureStore`, - :class:`~torch_geometric.data.GraphStore`) data object. - cell_sampler (torch_geometric.sampler.BaseSampler): The sampler - implementation to be used with this loader. - Needs to implement - :meth:`~torch_geometric.sampler.BaseSampler.sample_from_cells`. - The sampler implementation must be compatible with the input - :obj:`data` object. - input_cells (torch.Tensor or str or Tuple[str, torch.Tensor]): The - indices of seed cells to start sampling from. - Needs to be either given as a :obj:`torch.LongTensor` or - :obj:`torch.BoolTensor`. - If set to :obj:`None`, all cells will be considered. - In heterogeneous graphs, needs to be passed as a tuple that holds - the cell type and cell indices. (default: :obj:`None`) - input_time (torch.Tensor, optional): Optional values to override the - timestamp for the input cells given in :obj:`input_cells`. If not - set, will use the timestamps in :obj:`time_attr` as default (if - present). The :obj:`time_attr` needs to be set for this to work. - (default: :obj:`None`) - transform (callable, optional): A function/transform that takes in - a sampled mini-batch and returns a transformed version. - (default: :obj:`None`) - transform_sampler_output (callable, optional): A function/transform - that takes in a :class:`torch_geometric.sampler.SamplerOutput` and - returns a transformed version. (default: :obj:`None`) - filter_per_worker (bool, optional): If set to :obj:`True`, will filter - the returned data in each worker's subprocess. - If set to :obj:`False`, will filter the returned data in the main - process. - If set to :obj:`None`, will automatically infer the decision based - on whether data partially lives on the GPU - (:obj:`filter_per_worker=True`) or entirely on the CPU - (:obj:`filter_per_worker=False`). - There exists different trade-offs for setting this option. - Specifically, setting this option to :obj:`True` for in-memory - datasets will move all features to shared memory, which may result - in too many open file handles. (default: :obj:`None`) - custom_cls (HeteroData, optional): A custom - :class:`~torch_geometric.data.HeteroData` class to return for - mini-batches in case of remote backends. (default: :obj:`None`) - **kwargs (optional): Additional arguments of - :class:`torch.utils.data.DataLoader`, such as :obj:`batch_size`, - :obj:`shuffle`, :obj:`drop_last` or :obj:`num_workers`. + Parameters + ---------- + data : Any + A :class:`~torch_geometric.data.Data`, + :class:`~torch_geometric.data.HeteroData`, or + (:class:`~torch_geometric.data.FeatureStore`, + :class:`~torch_geometric.data.GraphStore`) data object. + cell_sampler : torch_geometric.sampler.BaseSampler + The sampler implementation to be used with this loader. + Needs to implement + :meth:`~torch_geometric.sampler.BaseSampler.sample_from_cells`. + The sampler implementation must be compatible with the input + :obj:`data` object. + input_cells : torch.Tensor or str or Tuple[str, torch.Tensor] + The indices of seed cells to start sampling from. + Needs to be either given as a :obj:`torch.LongTensor` or + :obj:`torch.BoolTensor`. + If set to :obj:`None`, all cells will be considered. + In heterogeneous graphs, needs to be passed as a tuple that holds + the cell type and cell indices. (default: :obj:`None`). + input_time : torch.Tensor, optional + Optional values to override the timestamp for the input cells given in + :obj:`input_cells`. If not set, will use the timestamps in + :obj:`time_attr` as default (if present). The :obj:`time_attr` needs + to be set for this to work. (default: :obj:`None`). + transform : callable, optional + A function/transform that takes in a sampled mini-batch and returns a + transformed version. (default: :obj:`None`). + transform_sampler_output : callable, optional + A function/transform that takes in a + :class:`torch_geometric.sampler.SamplerOutput` and returns a + transformed version. (default: :obj:`None`). + filter_per_worker : bool, optional + If set to :obj:`True`, will filter the returned data in each worker's + subprocess. + If set to :obj:`False`, will filter the returned data in the main + process. + If set to :obj:`None`, will automatically infer the decision based + on whether data partially lives on the GPU + (:obj:`filter_per_worker=True`) or entirely on the CPU + (:obj:`filter_per_worker=False`). + There exists different trade-offs for setting this option. + Specifically, setting this option to :obj:`True` for in-memory + datasets will move all features to shared memory, which may result + in too many open file handles. (default: :obj:`None`). + custom_cls : torch_geometric.data.HeteroData, optional + A custom :class:`~torch_geometric.data.HeteroData` class to return for + mini-batches in case of remote backends. (default: :obj:`None`). + input_id : torch.Tensor, optional + The indices of the input cells in the original data object. + (default: :obj:`None`). + **kwargs : optional + Additional arguments of :class:`torch.utils.data.DataLoader`, such as + :obj:`batch_size`, :obj:`shuffle`, :obj:`drop_last` or + :obj:`num_workers`. """ + def __init__( self, data: Union[Data, HeteroData, Tuple[FeatureStore, GraphStore]], @@ -115,12 +127,13 @@ def __init__( self.custom_cls = custom_cls self.input_id = input_id - kwargs.pop('dataset', None) - kwargs.pop('collate_fn', None) + kwargs.pop("dataset", None) + kwargs.pop("collate_fn", None) # Get cell type (or `None` for homogeneous graphs): input_type, input_cells, input_id = get_input_nodes( - data, input_cells, input_id) + data, input_cells, input_id + ) self.input_data = NodeSamplerInput( input_id=input_id, @@ -136,14 +149,36 @@ def __call__( self, index: Union[Tensor, List[int]], ) -> Union[Data, HeteroData]: - r"""Samples a subgraph from a batch of input cells.""" + r"""Sample a subgraph from a batch of input cells. + + Parameters + ---------- + index : torch.Tensor or List[int] + The indices of cells to sample. + + Returns + ------- + Union[Data, HeteroData] + The sampled subgraph. + """ out = self.collate_fn(index) if not self.filter_per_worker: out = self.filter_fn(out) return out def collate_fn(self, index: Union[Tensor, List[int]]) -> Any: - r"""Samples a subgraph from a batch of input cells.""" + r"""Sample a subgraph from a batch of input cells. + + Parameters + ---------- + index : torch.Tensor or List[int] + The indices of cells to sample. + + Returns + ------- + Any + The sampled subgraph. + """ input_data: NodeSamplerInput = self.input_data[index] out = self.cell_sampler.sample_from_nodes(input_data) @@ -157,23 +192,42 @@ def filter_fn( self, out: Union[SamplerOutput, HeteroSamplerOutput], ) -> Union[Data, HeteroData]: - r"""Joins the sampled cells with their corresponding features, - returning the resulting :class:`~torch_geometric.data.Data` + r"""Join the sampled cells with their corresponding features. + + It returns the resulting :class:`~torch_geometric.data.Data` object to be used downstream. + + Parameters + ---------- + out : Union[SamplerOutput, HeteroSamplerOutput] + The output of the sampler. + + Returns + ------- + Union[Data, HeteroData] + The resulting data object. """ if self.transform_sampler_output: out = self.transform_sampler_output(out) if isinstance(out, SamplerOutput) and isinstance(self.data, Data): - data = filter_data( - self.data, out.node, self.rank) + data = filter_data(self.data, out.node, self.rank) else: - raise TypeError(f"'{self.__class__.__name__}'' found invalid " - f"type: '{type(data)}'") + raise TypeError( + f"'{self.__class__.__name__}'' found invalid " + f"type: '{type(data)}'" + ) return data if self.transform is None else self.transform(data) def _get_iterator(self) -> Iterator: + r"""Return the internal iterator to be used for sampling. + + Returns + ------- + Iterator + The internal iterator to be used for sampling. + """ if self.filter_per_worker: return super()._get_iterator() @@ -188,4 +242,4 @@ def _get_iterator(self) -> Iterator: return DataLoaderIterator(super()._get_iterator(), self.filter_fn) def __repr__(self) -> str: - return f'{self.__class__.__name__}()' \ No newline at end of file + return f"{self.__class__.__name__}()" diff --git a/topobenchmark/data/batching/neighbor_cells_loader.py b/topobenchmark/data/batching/neighbor_cells_loader.py index 33ca9bb8..51dd47e0 100644 --- a/topobenchmark/data/batching/neighbor_cells_loader.py +++ b/topobenchmark/data/batching/neighbor_cells_loader.py @@ -1,3 +1,5 @@ +"""NeighborCellsLoader class to batch in the transductive setting when working with topological domains.""" + from typing import Callable, Dict, List, Optional, Tuple, Union from topobenchmark.data.batching.cell_loader import CellLoader @@ -13,35 +15,34 @@ class NeighborCellsLoader(CellLoader): r"""A data loader that samples neighbors for each cell. Cells are considered neighbors if they are upper or lower neighbors. - - Args: - data (Any): A :class:`~torch_geometric.data.Data`, + + Parameters + ---------- + data : Any + A :class:`~torch_geometric.data.Data`, :class:`~torch_geometric.data.HeteroData`, or (:class:`~torch_geometric.data.FeatureStore`, :class:`~torch_geometric.data.GraphStore`) data object. - rank (int): The rank of the cells to consider. - num_neighbors (List[int] or Dict[Tuple[str, str, str], List[int]]): The - number of neighbors to sample for each node in each iteration. + rank : int + The rank of the cells to consider. + num_neighbors : List[int] or Dict[Tuple[str, str, str], List[int]] + The number of neighbors to sample for each node in each iteration. If an entry is set to :obj:`-1`, all neighbors will be included. - In heterogeneous graphs, may also take in a dictionary denoting - the amount of neighbors to sample for each individual edge type. - input_nodes (torch.Tensor or str or Tuple[str, torch.Tensor]): The - indices of nodes for which neighbors are sampled to create + input_nodes : torch.Tensor or str or Tuple[str, torch.Tensor] + The indices of nodes for which neighbors are sampled to create mini-batches. Needs to be either given as a :obj:`torch.LongTensor` or :obj:`torch.BoolTensor`. If set to :obj:`None`, all nodes will be considered. - In heterogeneous graphs, needs to be passed as a tuple that holds - the node type and node indices. (default: :obj:`None`) - input_time (torch.Tensor, optional): Optional values to override the - timestamp for the input nodes given in :obj:`input_nodes`. If not + input_time : torch.Tensor, optional + Optional values to override the timestamp for the input nodes given in :obj:`input_nodes`. If not set, will use the timestamps in :obj:`time_attr` as default (if present). The :obj:`time_attr` needs to be set for this to work. - (default: :obj:`None`) - replace (bool, optional): If set to :obj:`True`, will sample with - replacement. (default: :obj:`False`) - subgraph_type (SubgraphType or str, optional): The type of the returned - subgraph. + (default: :obj:`None`). + replace : bool, optional + If set to :obj:`True`, will sample with replacement. (default: :obj:`False`). + subgraph_type : SubgraphType or str, optional + The type of the returned subgraph. If set to :obj:`"directional"`, the returned subgraph only holds the sampled (directed) edges which are necessary to compute representations for the sampled seed nodes. @@ -49,49 +50,48 @@ class NeighborCellsLoader(CellLoader): bidirectional edges. If set to :obj:`"induced"`, the returned subgraph contains the induced subgraph of all sampled nodes. - (default: :obj:`"directional"`) - disjoint (bool, optional): If set to :obj: `True`, each seed node will - create its own disjoint subgraph. + (default: :obj:`"directional"`). + disjoint : bool, optional + If set to :obj: `True`, each seed node will create its own disjoint subgraph. If set to :obj:`True`, mini-batch outputs will have a :obj:`batch` vector holding the mapping of nodes to their respective subgraph. Will get automatically set to :obj:`True` in case of temporal - sampling. (default: :obj:`False`) - temporal_strategy (str, optional): The sampling strategy when using - temporal sampling (:obj:`"uniform"`, :obj:`"last"`). + sampling. (default: :obj:`False`). + temporal_strategy : str, optional + The sampling strategy when using temporal sampling (:obj:`"uniform"`, :obj:`"last"`). If set to :obj:`"uniform"`, will sample uniformly across neighbors that fulfill temporal constraints. If set to :obj:`"last"`, will sample the last `num_neighbors` that fulfill temporal constraints. - (default: :obj:`"uniform"`) - time_attr (str, optional): The name of the attribute that denotes - timestamps for either the nodes or edges in the graph. + (default: :obj:`"uniform"`). + time_attr : str, optional + The name of the attribute that denotes timestamps for either the nodes or edges in the graph. If set, temporal sampling will be used such that neighbors are guaranteed to fulfill temporal constraints, *i.e.* neighbors have an earlier or equal timestamp than the center node. - (default: :obj:`None`) - weight_attr (str, optional): The name of the attribute that denotes - edge weights in the graph. + (default: :obj:`None`). + weight_attr : str, optional + The name of the attribute that denotes edge weights in the graph. If set, weighted/biased sampling will be used such that neighbors are more likely to get sampled the higher their edge weights are. Edge weights do not need to sum to one, but must be non-negative, finite and have a non-zero sum within local neighborhoods. - (default: :obj:`None`) - transform (callable, optional): A function/transform that takes in - a sampled mini-batch and returns a transformed version. - (default: :obj:`None`) - transform_sampler_output (callable, optional): A function/transform - that takes in a :class:`torch_geometric.sampler.SamplerOutput` and - returns a transformed version. (default: :obj:`None`) - is_sorted (bool, optional): If set to :obj:`True`, assumes that - :obj:`edge_index` is sorted by column. + (default: :obj:`None`). + transform : callable, optional + A function/transform that takes in a sampled mini-batch and returns a transformed version. + (default: :obj:`None`). + transform_sampler_output : callable, optional + A function/transform that takes in a :class:`torch_geometric.sampler.SamplerOutput` and + returns a transformed version. (default: :obj:`None`). + is_sorted : bool, optional + If set to :obj:`True`, assumes that :obj:`edge_index` is sorted by column. If :obj:`time_attr` is set, additionally requires that rows are sorted according to time within individual neighborhoods. This avoids internal re-sorting of the data and can improve - runtime and memory efficiency. (default: :obj:`False`) - filter_per_worker (bool, optional): If set to :obj:`True`, will filter - the returned data in each worker's subprocess. - If set to :obj:`False`, will filter the returned data in the main - process. + runtime and memory efficiency. (default: :obj:`False`). + filter_per_worker : bool, optional + If set to :obj:`True`, will filter the returned data in each worker's subprocess. + If set to :obj:`False`, will filter the returned data in the main process. If set to :obj:`None`, will automatically infer the decision based on whether data partially lives on the GPU (:obj:`filter_per_worker=True`) or entirely on the CPU @@ -99,11 +99,20 @@ class NeighborCellsLoader(CellLoader): There exists different trade-offs for setting this option. Specifically, setting this option to :obj:`True` for in-memory datasets will move all features to shared memory, which may result - in too many open file handles. (default: :obj:`None`) - **kwargs (optional): Additional arguments of - :class:`torch.utils.data.DataLoader`, such as :obj:`batch_size`, - :obj:`shuffle`, :obj:`drop_last` or :obj:`num_workers`. + in too many open file handles. (default: :obj:`None`). + neighbor_sampler : NeighborSampler, optional + The neighbor sampler implementation to be used with this loader. + If not set, a new :class:`torch_geometric.sampler.NeighborSampler` + instance will be created. (default: :obj:`None`). + directed : bool, optional + If set to :obj:`True`, will consider the graph as directed. + If set to :obj:`False`, will consider the graph as undirected. + (default: :obj:`True`). + **kwargs : optional + Additional arguments of :class:`torch.utils.data.DataLoader`, such as + :obj:`batch_size`, :obj:`shuffle`, :obj:`drop_last` or :obj:`num_workers`. """ + def __init__( self, data: Union[Data, HeteroData, Tuple[FeatureStore, GraphStore]], @@ -112,9 +121,9 @@ def __init__( input_nodes: InputNodes = None, input_time: OptTensor = None, replace: bool = False, - subgraph_type: Union[SubgraphType, str] = 'directional', + subgraph_type: Union[SubgraphType, str] = "directional", disjoint: bool = False, - temporal_strategy: str = 'uniform', + temporal_strategy: str = "uniform", time_attr: Optional[str] = None, weight_attr: Optional[str] = None, transform: Optional[Callable] = None, @@ -126,22 +135,26 @@ def __init__( **kwargs, ): if input_time is not None and time_attr is None: - raise ValueError("Received conflicting 'input_time' and " - "'time_attr' arguments: 'input_time' is set " - "while 'time_attr' is not set.") - + raise ValueError( + "Received conflicting 'input_time' and " + "'time_attr' arguments: 'input_time' is set " + "while 'time_attr' is not set." + ) + data_obj = Data() if isinstance(data, DataloadDataset): for tensor, name in zip(data[0][0], data[0][1]): setattr(data_obj, name, tensor) else: data_obj = data - is_hypergraph = hasattr(data_obj, 'incidence_hyperedges') + is_hypergraph = hasattr(data_obj, "incidence_hyperedges") n_hops = len(num_neighbors) - data_obj = get_sampled_neighborhood(data_obj, rank, n_hops, is_hypergraph) + data_obj = get_sampled_neighborhood( + data_obj, rank, n_hops, is_hypergraph + ) self.rank = rank if self.rank != 0: - # When rank is different than 0 get_sampled_neighborhood connects cells that are up to n_hops away, meaning that the NeighborhoodSampler needs to consider only one hop. + # When rank is different than 0 get_sampled_neighborhood connects cells that are up to n_hops away, meaning that the NeighborhoodSampler needs to consider only one hop. num_neighbors = [num_neighbors[0]] if neighbor_sampler is None: neighbor_sampler = NeighborSampler( @@ -154,7 +167,7 @@ def __init__( time_attr=time_attr, weight_attr=weight_attr, is_sorted=is_sorted, - share_memory=kwargs.get('num_workers', 0) > 0, + share_memory=kwargs.get("num_workers", 0) > 0, directed=directed, ) @@ -167,4 +180,4 @@ def __init__( transform_sampler_output=transform_sampler_output, filter_per_worker=filter_per_worker, **kwargs, - ) \ No newline at end of file + ) diff --git a/topobenchmark/data/batching/utils.py b/topobenchmark/data/batching/utils.py index 3e360cc8..d5e368eb 100644 --- a/topobenchmark/data/batching/utils.py +++ b/topobenchmark/data/batching/utils.py @@ -1,3 +1,5 @@ +"""Utility functions for batching cells of different ranks.""" + import copy import logging import math @@ -10,22 +12,25 @@ import torch_geometric.typing from torch_geometric.data import Data -def reduce_higher_ranks_incidences(batch, cells_ids, rank, max_rank, is_hypergraph=False): - """ Reduce the incidences with higher rank than the specified one. - + +def reduce_higher_ranks_incidences( + batch, cells_ids, rank, max_rank, is_hypergraph=False +): + """Reduce the incidences with higher rank than the specified one. + Parameters ---------- - batch: torch_geometric.data.Data + batch : torch_geometric.data.Data The input data. - cells_ids: list[torch.Tensor] + cells_ids : list[torch.Tensor] List of tensors containing the ids of the cells. The length of the list should be equal to the maximum rank. - rank: int + rank : int The rank to select the higher order incidences. - max_rank: int + max_rank : int The maximum rank of the incidences. - is_hypergraph: bool + is_hypergraph : bool Whether the data represents an hypergraph. - + Returns ------- torch_geometric.data.Data @@ -33,43 +38,46 @@ def reduce_higher_ranks_incidences(batch, cells_ids, rank, max_rank, is_hypergra list[torch.Tensor] The updated indices of the cells. Each element of the list is a tensor containing the ids of the cells of the corresponding rank. """ - for i in range(rank+1, max_rank+1): + for i in range(rank + 1, max_rank + 1): if is_hypergraph: incidence = batch.incidence_hyperedges else: incidence = batch[f"incidence_{i}"] - + # if i != rank+1: - incidence = torch.index_select(incidence, 0, cells_ids[i-1]) - cells_ids[i] = torch.where(torch.sum(incidence, dim=0).to_dense() > 1)[0] + incidence = torch.index_select(incidence, 0, cells_ids[i - 1]) + cells_ids[i] = torch.where(torch.sum(incidence, dim=0).to_dense() > 1)[ + 0 + ] incidence = torch.index_select(incidence, 1, cells_ids[i]) if is_hypergraph: batch.incidence_hyperedges = incidence else: batch[f"incidence_{i}"] = incidence - + return batch, cells_ids + def reduce_lower_ranks_incidences(batch, cells_ids, rank, is_hypergraph=False): - """ Reduce the incidences with lower rank than the specified one. - + """Reduce the incidences with lower rank than the specified one. + Parameters ---------- - batch: torch_geometric.data.Data - The input data. - cells_ids: list[torch.Tensor] - List of tensors containing the ids of the cells. The length of the list should be equal to the maximum rank. - rank: int - The rank of the cells to consider. - is_hypergraph: bool - Whether the data represents an hypergraph. - + batch : torch_geometric.data.Data + The input data. + cells_ids : list[torch.Tensor] + List of tensors containing the ids of the cells. The length of the list should be equal to the maximum rank. + rank : int + The rank of the cells to consider. + is_hypergraph : bool + Whether the data represents an hypergraph. + Returns ------- - torch.Tensor - The indices of the nodes contained by the cells. - list[torch.Tensor] - The updated indices of the cells. Each element of the list is a tensor containing the ids of the cells of the corresponding rank. + torch.Tensor + The indices of the nodes contained by the cells. + list[torch.Tensor] + The updated indices of the cells. Each element of the list is a tensor containing the ids of the cells of the corresponding rank. """ for i in range(rank, 0, -1): if is_hypergraph: @@ -77,43 +85,44 @@ def reduce_lower_ranks_incidences(batch, cells_ids, rank, is_hypergraph=False): else: incidence = batch[f"incidence_{i}"] incidence = torch.index_select(incidence, 1, cells_ids[i]) - cells_ids[i-1] = torch.where(torch.sum(incidence, dim=1).to_dense() > 0)[0] - incidence = torch.index_select(incidence, 0, cells_ids[i-1]) + cells_ids[i - 1] = torch.where( + torch.sum(incidence, dim=1).to_dense() > 0 + )[0] + incidence = torch.index_select(incidence, 0, cells_ids[i - 1]) if is_hypergraph: batch.incidence_hyperedges = incidence else: batch[f"incidence_{i}"] = incidence - + if not is_hypergraph: incidence = batch[f"incidence_0"] incidence = torch.index_select(incidence, 1, cells_ids[0]) batch[f"incidence_0"] = incidence return batch, cells_ids -def reduce_matrices(batch, cells_ids, names, rank, max_rank): - """ Reduce the matrices using the indices in cells_ids. - + +def reduce_matrices(batch, cells_ids, names, max_rank): + """Reduce the matrices using the indices in cells_ids. + The matrices are assumed to be in the batch with the names specified in the list names. - + Parameters ---------- - batch: torch_geometric.data.Data + batch : torch_geometric.data.Data The input data. - cells_ids: list[torch.Tensor] + cells_ids : list[torch.Tensor] List of tensors containing the ids of the cells. The length of the list should be equal to the maximum rank. - names: list[str] + names : list[str] List of names of the matrices in the batch. They should appear in the format f"{name}{i}" where i is the rank of the matrix. - rank: int - The rank over which you are batching. - max_rank: int + max_rank : int The maximum rank of the matrices. - + Returns ------- torch_geometric.data.Data The output data with the reduced matrices. """ - for i in range(max_rank+1): + for i in range(max_rank + 1): for name in names: if f"{name}{i}" in batch.keys(): matrix = batch[f"{name}{i}"] @@ -122,52 +131,68 @@ def reduce_matrices(batch, cells_ids, names, rank, max_rank): batch[f"{name}{i}"] = matrix return batch + def reduce_neighborhoods(batch, node, rank=0, remove_self_loops=True): - """ Reduce the neighborhoods of the cells in the batch. - + """Reduce the neighborhoods of the cells in the batch. + Parameters ---------- - batch: torch_geometric.data.Data + batch : torch_geometric.data.Data The input data. - rank: int + node : torch.Tensor + The indices of the cells to batch over. + rank : int The rank of the cells to batch over. - remove_self_loops: bool + remove_self_loops : bool Whether to remove self loops from the edge_index. - + Returns ------- torch_geometric.data.Data The output data with the reduced neighborhoods. """ is_hypergraph = False - if hasattr(batch, 'incidence_hyperedges'): + if hasattr(batch, "incidence_hyperedges"): is_hypergraph = True max_rank = 1 else: - max_rank = len([key for key in batch.keys() if "incidence" in key])-1 - + max_rank = len([key for key in batch.keys() if "incidence" in key]) - 1 + if rank > max_rank: - raise ValueError(f"Rank {rank} is greater than the maximum rank {max_rank} in the dataset.") - - cells_ids = [None for _ in range(max_rank+1)] - + raise ValueError( + f"Rank {rank} is greater than the maximum rank {max_rank} in the dataset." + ) + + cells_ids = [None for _ in range(max_rank + 1)] + # the indices of the cells selected by the NeighborhoodLoader are saved in the batch in the attribute n_id cells_ids[rank] = node - - batch, cells_ids = reduce_higher_ranks_incidences(batch, cells_ids, rank, max_rank, is_hypergraph) - batch, cells_ids = reduce_lower_ranks_incidences(batch, cells_ids, rank, is_hypergraph) - - batch = reduce_matrices(batch, - cells_ids, - names=['down_laplacian_', 'up_laplacian_', 'hodge_laplacian_', 'adjacency_'], - rank=rank, - max_rank=max_rank) - + + batch, cells_ids = reduce_higher_ranks_incidences( + batch, cells_ids, rank, max_rank, is_hypergraph + ) + batch, cells_ids = reduce_lower_ranks_incidences( + batch, cells_ids, rank, is_hypergraph + ) + + batch = reduce_matrices( + batch, + cells_ids, + names=[ + "down_laplacian_", + "up_laplacian_", + "hodge_laplacian_", + "adjacency_", + ], + rank=rank, + max_rank=max_rank, + ) + # reduce the feature matrices - for i in range(max_rank+1): + for i in range(max_rank + 1): if f"x_{i}" in batch.keys(): batch[f"x_{i}"] = batch[f"x_{i}"][cells_ids[i]] - + # fix edge_index if not is_hypergraph: adjacency_0 = batch.adjacency_0.coalesce() @@ -175,127 +200,138 @@ def reduce_neighborhoods(batch, node, rank=0, remove_self_loops=True): if remove_self_loops: edge_index = torch_geometric.utils.remove_self_loops(edge_index)[0] batch.edge_index = edge_index - + # fix x batch.x = batch[f"x_0"] - if hasattr(batch, 'num_nodes'): + if hasattr(batch, "num_nodes"): batch.num_nodes = batch.x.shape[0] - - if hasattr(batch, 'y'): + + if hasattr(batch, "y"): batch.y = batch.y[cells_ids[rank]] - + batch.cells_ids = cells_ids return batch + def filter_data(data: Data, cells: Tensor, rank: int) -> Data: - ''' The function filters the attributes of the data based on the cells passed. - + """Filter the attributes of the data based on the cells passed. + The function uses the indices passed to select the cells of the specified rank. The cells of lower or higher ranks are selected using the incidence matrices. - + Parameters ---------- - data: torch_geometric.data.Data + data : torch_geometric.data.Data The input data. - cells: Tensor + cells : Tensor Tensor containing the indices of the cells of the specified rank to keep. - rank: int - Rank of the cells of interest. - ''' + rank : int + Rank of the cells of interest. + + Returns + ------- + torch_geometric.data.Data + The output data with the filtered attributes. + """ out = copy.copy(data) out = reduce_neighborhoods(out, cells, rank=rank) out.n_id = cells return out + def get_sampled_neighborhood(data, rank=0, n_hops=1, is_hypergraph=False): - ''' This function updates the edge_index attribute of torch_geometric.data.Data. - + """Update the edge_index attribute of torch_geometric.data.Data. + The function finds cells, of the specified rank, that are either upper or lower neighbors. - + Parameters ---------- - data: torch_geometric.data.Data + data : torch_geometric.data.Data The input data. - rank: int + rank : int The rank of the cells that you want to batch over. - n_hops: int + n_hops : int Two cells are considered neighbors if they are connected by n hops in the upper or lower neighborhoods. - is_hypergraph: bool + is_hypergraph : bool Whether the data represents an hypergraph. - + Returns ------- torch_geometric.data.Data The output data with updated edge_index. - edge_index contains indices of connected cells of the specified rank K. - Two cells of rank K are connected if they are either lower or upper neighbors. - ''' + edge_index contains indices of connected cells of the specified rank K. + Two cells of rank K are connected if they are either lower or upper neighbors. + """ if rank == 0: data.edge_index = torch_geometric.utils.to_undirected(data.edge_index) return data - if is_hypergraph: + if is_hypergraph: if rank > 1: - raise ValueError("Hypergraphs are not supported for ranks greater than 1.") + raise ValueError( + "Hypergraphs are not supported for ranks greater than 1." + ) if rank == 1: I = data.incidence_hyperedges - A = torch.sparse.mm(I,I.T) # lower adj matrix + A = torch.sparse.mm(I, I.T) # lower adj matrix else: I = data.incidence_hyperedges - A = torch.sparse.mm(I.T,I) - for _ in range(n_hops-1): - A = torch.sparse.mm(A,A) + A = torch.sparse.mm(I.T, I) + for _ in range(n_hops - 1): + A = torch.sparse.mm(A, A) edges = A.indices() else: # get number of incidences - max_rank = len([key for key in data.keys() if "incidence" in key])-1 + max_rank = len([key for key in data.keys() if "incidence" in key]) - 1 if rank > max_rank: - raise ValueError(f"Rank {rank} is greater than the maximum rank {max_rank} in the data.") - + raise ValueError( + f"Rank {rank} is greater than the maximum rank {max_rank} in the data." + ) + # This considers the upper adjacencies n_cells = data[f"x_{rank}"].shape[0] - A_sum = torch.sparse_coo_tensor([[],[]], [], (n_cells, n_cells)) + A_sum = torch.sparse_coo_tensor([[], []], [], (n_cells, n_cells)) if rank == max_rank: edges = torch.empty((2, 0), dtype=torch.long) else: I = data[f"incidence_{rank+1}"] - A = torch.sparse.mm(I,I.T) - for _ in range(n_hops-1): - A = torch.sparse.mm(A,A) + A = torch.sparse.mm(I, I.T) + for _ in range(n_hops - 1): + A = torch.sparse.mm(A, A) A_sum += A - + # This is for selecting the whole upper cells # for i in range(rank+1, max_rank): # P = torch.sparse.mm(P, data[f"incidence_{i+1}"]) # Q = torch.sparse.mm(P,P.T) # edges = torch.cat((edges, Q.indices()), dim=1) - + # This considers the lower adjacencies - if rank != 0: + if rank != 0: I = data[f"incidence_{rank}"] - A = torch.sparse.mm(I.T,I) - for _ in range(n_hops-1): - A = torch.sparse.mm(A,A) + A = torch.sparse.mm(I.T, I) + for _ in range(n_hops - 1): + A = torch.sparse.mm(A, A) A_sum += A - + # This is for selecting cells if they share any node # for i in range(rank-1, 0, -1): # P = torch.sparse.mm(data[f"incidence_{i}"], P) # Q = torch.sparse.mm(P.T,P) # edges = torch.cat((edges, Q.indices()), dim=1) - + edges = A_sum.coalesce().indices() # Remove self edges mask = edges[0, :] != edges[1, :] edges = edges[:, mask] - + data.edge_index = edges - + # We need to set x to x_{rank} since NeighborLoader will take the number of nodes from the x attribute # The correct x is given after the reduce_neighborhoods function if is_hypergraph and rank == 1: data.x = data.x_hyperedges else: - data.x = data[f'x_{rank}'] - - if hasattr(data, 'num_nodes'): + data.x = data[f"x_{rank}"] + + if hasattr(data, "num_nodes"): data.num_nodes = data.x.shape[0] - return data \ No newline at end of file + return data diff --git a/topobenchmark/dataloader/dataloader.py b/topobenchmark/dataloader/dataloader.py index 32d2880d..3134f75a 100755 --- a/topobenchmark/dataloader/dataloader.py +++ b/topobenchmark/dataloader/dataloader.py @@ -9,6 +9,7 @@ from topobenchmark.dataloader.utils import collate_fn from topobenchmark.data.batching import NeighborCellsLoader + class TBDataloader(LightningDataModule): r"""This class takes care of returning the dataloaders for the training, validation, and test datasets. @@ -83,26 +84,26 @@ def __repr__(self) -> str: return f"{self.__class__.__name__}(dataset_train={self.dataset_train}, dataset_val={self.dataset_val}, dataset_test={self.dataset_test}, batch_size={self.batch_size})" def _get_dataloader(self, split: str) -> DataLoader | NeighborCellsLoader: - r""" Create and return the dataloader for the specified split. - + r"""Create and return the dataloader for the specified split. + Parameters ---------- split : str The split to create the dataloader for. - + Returns ------- torch.utils.data.DataLoader | NeighborCellsLoader The dataloader for the specified split. """ - shuffle = (split == "train") - + shuffle = split == "train" + if not self.transductive or self.batch_size == -1: if self.batch_size == -1: batch_size = 1 else: batch_size = self.batch_size - + return DataLoader( dataset=getattr(self, f"dataset_{split}"), batch_size=batch_size, @@ -113,7 +114,7 @@ def _get_dataloader(self, split: str) -> DataLoader | NeighborCellsLoader: persistent_workers=self.persistent_workers, **self.kwargs, ) - mask_idx = self.dataset_train[0][1].index(f'{split}_mask') + mask_idx = self.dataset_train[0][1].index(f"{split}_mask") mask = self.dataset_train[0][0][mask_idx] return NeighborCellsLoader( data=getattr(self, f"dataset_{split}"), @@ -124,7 +125,7 @@ def _get_dataloader(self, split: str) -> DataLoader | NeighborCellsLoader: shuffle=shuffle, **self.kwargs, ) - + def train_dataloader(self) -> DataLoader: r"""Create and return the train dataloader. From f92e3783c4d36a01666cdd92ae180e1be39f1800 Mon Sep 17 00:00:00 2001 From: Coerulatus Date: Wed, 18 Dec 2024 16:17:42 +0000 Subject: [PATCH 22/24] changed batch size for new TBDataloader --- configs/dataset/graph/US-county-demos.yaml | 2 +- configs/dataset/graph/amazon_ratings.yaml | 2 +- configs/dataset/graph/cocitation_citeseer.yaml | 2 +- configs/dataset/graph/cocitation_cora.yaml | 2 +- configs/dataset/graph/cocitation_pubmed.yaml | 2 +- configs/dataset/graph/manual_dataset.yaml | 2 +- configs/dataset/graph/minesweeper.yaml | 2 +- configs/dataset/graph/questions.yaml | 2 +- configs/dataset/graph/roman_empire.yaml | 2 +- configs/dataset/graph/tolokers.yaml | 2 +- configs/dataset/hypergraph/coauthorship_cora.yaml | 2 +- configs/dataset/hypergraph/coauthorship_dblp.yaml | 2 +- configs/dataset/hypergraph/cocitation_citeseer.yaml | 2 +- configs/dataset/hypergraph/cocitation_cora.yaml | 2 +- configs/dataset/hypergraph/cocitation_pubmed.yaml | 2 +- 15 files changed, 15 insertions(+), 15 deletions(-) diff --git a/configs/dataset/graph/US-county-demos.yaml b/configs/dataset/graph/US-county-demos.yaml index 6e21a4a9..87b12fea 100755 --- a/configs/dataset/graph/US-county-demos.yaml +++ b/configs/dataset/graph/US-county-demos.yaml @@ -30,6 +30,6 @@ split_params: # Dataloader parameters dataloader_params: - batch_size: 1 # Fixed + batch_size: -1 # Fixed num_workers: 0 pin_memory: False diff --git a/configs/dataset/graph/amazon_ratings.yaml b/configs/dataset/graph/amazon_ratings.yaml index 3e5a9dae..149b20ea 100755 --- a/configs/dataset/graph/amazon_ratings.yaml +++ b/configs/dataset/graph/amazon_ratings.yaml @@ -27,6 +27,6 @@ split_params: # Dataloader parameters dataloader_params: - batch_size: 1 # Fixed + batch_size: -1 # Fixed num_workers: 0 pin_memory: False diff --git a/configs/dataset/graph/cocitation_citeseer.yaml b/configs/dataset/graph/cocitation_citeseer.yaml index cfb1b6fe..b92f31a9 100755 --- a/configs/dataset/graph/cocitation_citeseer.yaml +++ b/configs/dataset/graph/cocitation_citeseer.yaml @@ -28,6 +28,6 @@ split_params: # Dataloader parameters dataloader_params: - batch_size: 1 # Fixed + batch_size: -1 # Fixed num_workers: 1 pin_memory: False diff --git a/configs/dataset/graph/cocitation_cora.yaml b/configs/dataset/graph/cocitation_cora.yaml index d2b9fa3b..64de64e3 100755 --- a/configs/dataset/graph/cocitation_cora.yaml +++ b/configs/dataset/graph/cocitation_cora.yaml @@ -27,6 +27,6 @@ split_params: # Dataloader parameters dataloader_params: - batch_size: 1 # Fixed + batch_size: -1 # Fixed num_workers: 1 pin_memory: False diff --git a/configs/dataset/graph/cocitation_pubmed.yaml b/configs/dataset/graph/cocitation_pubmed.yaml index 7d901437..c974b6b1 100755 --- a/configs/dataset/graph/cocitation_pubmed.yaml +++ b/configs/dataset/graph/cocitation_pubmed.yaml @@ -27,6 +27,6 @@ split_params: # Dataloader parameters dataloader_params: - batch_size: 1 # Fixed + batch_size: -1 # Fixed num_workers: 1 pin_memory: False diff --git a/configs/dataset/graph/manual_dataset.yaml b/configs/dataset/graph/manual_dataset.yaml index e0357d2b..bafe272a 100755 --- a/configs/dataset/graph/manual_dataset.yaml +++ b/configs/dataset/graph/manual_dataset.yaml @@ -28,6 +28,6 @@ split_params: # Dataloader parameters dataloader_params: - batch_size: 1 + batch_size: -1 num_workers: 1 pin_memory: False diff --git a/configs/dataset/graph/minesweeper.yaml b/configs/dataset/graph/minesweeper.yaml index 19119e78..c487de79 100755 --- a/configs/dataset/graph/minesweeper.yaml +++ b/configs/dataset/graph/minesweeper.yaml @@ -28,6 +28,6 @@ split_params: # Dataloader parameters dataloader_params: - batch_size: 1 # Fixed + batch_size: -1 # Fixed num_workers: 0 pin_memory: False diff --git a/configs/dataset/graph/questions.yaml b/configs/dataset/graph/questions.yaml index 25333b75..a10d0f9a 100755 --- a/configs/dataset/graph/questions.yaml +++ b/configs/dataset/graph/questions.yaml @@ -27,6 +27,6 @@ split_params: # Dataloader parameters dataloader_params: - batch_size: 1 # Fixed + batch_size: -1 # Fixed num_workers: 1 pin_memory: False diff --git a/configs/dataset/graph/roman_empire.yaml b/configs/dataset/graph/roman_empire.yaml index 37adfb4b..e40d0e7b 100755 --- a/configs/dataset/graph/roman_empire.yaml +++ b/configs/dataset/graph/roman_empire.yaml @@ -27,6 +27,6 @@ split_params: # Dataloader parameters dataloader_params: - batch_size: 1 # Fixed + batch_size: -1 # Fixed num_workers: 0 pin_memory: False diff --git a/configs/dataset/graph/tolokers.yaml b/configs/dataset/graph/tolokers.yaml index f1657f16..2da6e9af 100755 --- a/configs/dataset/graph/tolokers.yaml +++ b/configs/dataset/graph/tolokers.yaml @@ -27,6 +27,6 @@ split_params: # Dataloader parameters dataloader_params: - batch_size: 1 # Fixed + batch_size: -1 # Fixed num_workers: 1 pin_memory: False diff --git a/configs/dataset/hypergraph/coauthorship_cora.yaml b/configs/dataset/hypergraph/coauthorship_cora.yaml index 80699bbd..2bc0ea7c 100755 --- a/configs/dataset/hypergraph/coauthorship_cora.yaml +++ b/configs/dataset/hypergraph/coauthorship_cora.yaml @@ -27,6 +27,6 @@ split_params: # Dataloader parameters dataloader_params: - batch_size: 1 # Fixed + batch_size: -1 # Fixed num_workers: 1 pin_memory: False diff --git a/configs/dataset/hypergraph/coauthorship_dblp.yaml b/configs/dataset/hypergraph/coauthorship_dblp.yaml index 5f4c4e25..0e378a9b 100755 --- a/configs/dataset/hypergraph/coauthorship_dblp.yaml +++ b/configs/dataset/hypergraph/coauthorship_dblp.yaml @@ -27,6 +27,6 @@ split_params: # Dataloader parameters dataloader_params: - batch_size: 1 # Fixed + batch_size: -1 # Fixed num_workers: 1 pin_memory: False diff --git a/configs/dataset/hypergraph/cocitation_citeseer.yaml b/configs/dataset/hypergraph/cocitation_citeseer.yaml index d51b884f..7823c357 100755 --- a/configs/dataset/hypergraph/cocitation_citeseer.yaml +++ b/configs/dataset/hypergraph/cocitation_citeseer.yaml @@ -27,6 +27,6 @@ split_params: # Dataloader parameters dataloader_params: - batch_size: 1 # Fixed + batch_size: -1 # Fixed num_workers: 1 pin_memory: False diff --git a/configs/dataset/hypergraph/cocitation_cora.yaml b/configs/dataset/hypergraph/cocitation_cora.yaml index 557b0a14..cbe8c613 100755 --- a/configs/dataset/hypergraph/cocitation_cora.yaml +++ b/configs/dataset/hypergraph/cocitation_cora.yaml @@ -27,6 +27,6 @@ split_params: # Dataloader parameters dataloader_params: - batch_size: 1 # Fixed + batch_size: -1 # Fixed num_workers: 1 pin_memory: False diff --git a/configs/dataset/hypergraph/cocitation_pubmed.yaml b/configs/dataset/hypergraph/cocitation_pubmed.yaml index 8aa19826..6fb00abf 100755 --- a/configs/dataset/hypergraph/cocitation_pubmed.yaml +++ b/configs/dataset/hypergraph/cocitation_pubmed.yaml @@ -27,6 +27,6 @@ split_params: # Dataloader parameters dataloader_params: - batch_size: 1 # Fixed + batch_size: -1 # Fixed num_workers: 1 pin_memory: False From 4a53d8fa3ed71dc0aa1efb1ede988cc0879236d6 Mon Sep 17 00:00:00 2001 From: Coerulatus Date: Wed, 18 Dec 2024 17:04:58 +0000 Subject: [PATCH 23/24] ruff fixes --- topobenchmark/data/batching/cell_loader.py | 32 +++++++--------- .../data/batching/neighbor_cells_loader.py | 31 ++++++++------- topobenchmark/data/batching/utils.py | 38 ++++++++----------- topobenchmark/dataloader/dataloader.py | 13 +++---- 4 files changed, 51 insertions(+), 63 deletions(-) diff --git a/topobenchmark/data/batching/cell_loader.py b/topobenchmark/data/batching/cell_loader.py index dd056fb6..21593ec6 100644 --- a/topobenchmark/data/batching/cell_loader.py +++ b/topobenchmark/data/batching/cell_loader.py @@ -1,10 +1,10 @@ """Cell Loader module from PyTorch Geometric with custom filter_data function.""" -from typing import Any, Callable, Iterator, List, Optional, Tuple, Union +from collections.abc import Callable, Iterator +from typing import Any import torch from torch import Tensor - from torch_geometric.data import Data, FeatureStore, GraphStore, HeteroData from torch_geometric.loader.base import DataLoaderIterator from torch_geometric.loader.mixin import ( @@ -12,13 +12,7 @@ LogMemoryMixin, MultithreadingMixin, ) - -from topobenchmark.data.batching.utils import filter_data - from torch_geometric.loader.utils import ( - filter_custom_hetero_store, - filter_custom_store, - filter_hetero_data, get_input_nodes, infer_filter_per_worker, ) @@ -30,6 +24,8 @@ ) from torch_geometric.typing import InputNodes, OptTensor +from topobenchmark.data.batching.utils import filter_data + class CellLoader( torch.utils.data.DataLoader, @@ -103,14 +99,14 @@ class CellLoader( def __init__( self, - data: Union[Data, HeteroData, Tuple[FeatureStore, GraphStore]], + data: Data | HeteroData | tuple[FeatureStore, GraphStore], cell_sampler: BaseSampler, input_cells: InputNodes = None, input_time: OptTensor = None, - transform: Optional[Callable] = None, - transform_sampler_output: Optional[Callable] = None, - filter_per_worker: Optional[bool] = None, - custom_cls: Optional[HeteroData] = None, + transform: Callable | None = None, + transform_sampler_output: Callable | None = None, + filter_per_worker: bool | None = None, + custom_cls: HeteroData | None = None, input_id: OptTensor = None, **kwargs, ): @@ -147,8 +143,8 @@ def __init__( def __call__( self, - index: Union[Tensor, List[int]], - ) -> Union[Data, HeteroData]: + index: Tensor | list[int], + ) -> Data | HeteroData: r"""Sample a subgraph from a batch of input cells. Parameters @@ -166,7 +162,7 @@ def __call__( out = self.filter_fn(out) return out - def collate_fn(self, index: Union[Tensor, List[int]]) -> Any: + def collate_fn(self, index: Tensor | list[int]) -> Any: r"""Sample a subgraph from a batch of input cells. Parameters @@ -190,8 +186,8 @@ def collate_fn(self, index: Union[Tensor, List[int]]) -> Any: def filter_fn( self, - out: Union[SamplerOutput, HeteroSamplerOutput], - ) -> Union[Data, HeteroData]: + out: SamplerOutput | HeteroSamplerOutput, + ) -> Data | HeteroData: r"""Join the sampled cells with their corresponding features. It returns the resulting :class:`~torch_geometric.data.Data` diff --git a/topobenchmark/data/batching/neighbor_cells_loader.py b/topobenchmark/data/batching/neighbor_cells_loader.py index 51dd47e0..be772ef9 100644 --- a/topobenchmark/data/batching/neighbor_cells_loader.py +++ b/topobenchmark/data/batching/neighbor_cells_loader.py @@ -1,17 +1,16 @@ """NeighborCellsLoader class to batch in the transductive setting when working with topological domains.""" -from typing import Callable, Dict, List, Optional, Tuple, Union - -from topobenchmark.data.batching.cell_loader import CellLoader -from topobenchmark.data.batching.utils import get_sampled_neighborhood -from topobenchmark.dataloader import DataloadDataset +from collections.abc import Callable from torch_geometric.data import Data, FeatureStore, GraphStore, HeteroData - from torch_geometric.sampler import NeighborSampler from torch_geometric.sampler.base import SubgraphType from torch_geometric.typing import EdgeType, InputNodes, OptTensor +from topobenchmark.data.batching.cell_loader import CellLoader +from topobenchmark.data.batching.utils import get_sampled_neighborhood +from topobenchmark.dataloader import DataloadDataset + class NeighborCellsLoader(CellLoader): r"""A data loader that samples neighbors for each cell. Cells are considered neighbors if they are upper or lower neighbors. @@ -115,22 +114,22 @@ class NeighborCellsLoader(CellLoader): def __init__( self, - data: Union[Data, HeteroData, Tuple[FeatureStore, GraphStore]], + data: Data | HeteroData | tuple[FeatureStore, GraphStore], rank: int, - num_neighbors: Union[List[int], Dict[EdgeType, List[int]]], + num_neighbors: list[int] | dict[EdgeType, list[int]], input_nodes: InputNodes = None, input_time: OptTensor = None, replace: bool = False, - subgraph_type: Union[SubgraphType, str] = "directional", + subgraph_type: SubgraphType | str = "directional", disjoint: bool = False, temporal_strategy: str = "uniform", - time_attr: Optional[str] = None, - weight_attr: Optional[str] = None, - transform: Optional[Callable] = None, - transform_sampler_output: Optional[Callable] = None, + time_attr: str | None = None, + weight_attr: str | None = None, + transform: Callable | None = None, + transform_sampler_output: Callable | None = None, is_sorted: bool = False, - filter_per_worker: Optional[bool] = None, - neighbor_sampler: Optional[NeighborSampler] = None, + filter_per_worker: bool | None = None, + neighbor_sampler: NeighborSampler | None = None, directed: bool = True, **kwargs, ): @@ -143,7 +142,7 @@ def __init__( data_obj = Data() if isinstance(data, DataloadDataset): - for tensor, name in zip(data[0][0], data[0][1]): + for tensor, name in zip(data[0][0], data[0][1], strict=False): setattr(data_obj, name, tensor) else: data_obj = data diff --git a/topobenchmark/data/batching/utils.py b/topobenchmark/data/batching/utils.py index d5e368eb..78df4fe9 100644 --- a/topobenchmark/data/batching/utils.py +++ b/topobenchmark/data/batching/utils.py @@ -1,15 +1,10 @@ """Utility functions for batching cells of different ranks.""" import copy -import logging -import math -from typing import Any, Dict, Optional, Tuple, Union -import numpy as np import torch -from torch import Tensor - import torch_geometric.typing +from torch import Tensor from torch_geometric.data import Data @@ -95,9 +90,9 @@ def reduce_lower_ranks_incidences(batch, cells_ids, rank, is_hypergraph=False): batch[f"incidence_{i}"] = incidence if not is_hypergraph: - incidence = batch[f"incidence_0"] + incidence = batch["incidence_0"] incidence = torch.index_select(incidence, 1, cells_ids[0]) - batch[f"incidence_0"] = incidence + batch["incidence_0"] = incidence return batch, cells_ids @@ -124,7 +119,7 @@ def reduce_matrices(batch, cells_ids, names, max_rank): """ for i in range(max_rank + 1): for name in names: - if f"{name}{i}" in batch.keys(): + if f"{name}{i}" in batch.keys(): # noqa matrix = batch[f"{name}{i}"] matrix = torch.index_select(matrix, 0, cells_ids[i]) matrix = torch.index_select(matrix, 1, cells_ids[i]) @@ -156,7 +151,7 @@ def reduce_neighborhoods(batch, node, rank=0, remove_self_loops=True): is_hypergraph = True max_rank = 1 else: - max_rank = len([key for key in batch.keys() if "incidence" in key]) - 1 + max_rank = len([key for key in batch.keys() if "incidence" in key]) - 1 # noqa if rank > max_rank: raise ValueError( @@ -184,13 +179,12 @@ def reduce_neighborhoods(batch, node, rank=0, remove_self_loops=True): "hodge_laplacian_", "adjacency_", ], - rank=rank, max_rank=max_rank, ) # reduce the feature matrices for i in range(max_rank + 1): - if f"x_{i}" in batch.keys(): + if f"x_{i}" in batch.keys(): # noqa batch[f"x_{i}"] = batch[f"x_{i}"][cells_ids[i]] # fix edge_index @@ -202,7 +196,7 @@ def reduce_neighborhoods(batch, node, rank=0, remove_self_loops=True): batch.edge_index = edge_index # fix x - batch.x = batch[f"x_0"] + batch.x = batch["x_0"] if hasattr(batch, "num_nodes"): batch.num_nodes = batch.x.shape[0] @@ -270,17 +264,17 @@ def get_sampled_neighborhood(data, rank=0, n_hops=1, is_hypergraph=False): "Hypergraphs are not supported for ranks greater than 1." ) if rank == 1: - I = data.incidence_hyperedges - A = torch.sparse.mm(I, I.T) # lower adj matrix + incidence = data.incidence_hyperedges + A = torch.sparse.mm(incidence, incidence.T) # lower adj matrix else: - I = data.incidence_hyperedges - A = torch.sparse.mm(I.T, I) + incidence = data.incidence_hyperedges + A = torch.sparse.mm(incidence.T, incidence) for _ in range(n_hops - 1): A = torch.sparse.mm(A, A) edges = A.indices() else: # get number of incidences - max_rank = len([key for key in data.keys() if "incidence" in key]) - 1 + max_rank = len([key for key in data.keys() if "incidence" in key]) - 1 # noqa if rank > max_rank: raise ValueError( f"Rank {rank} is greater than the maximum rank {max_rank} in the data." @@ -292,8 +286,8 @@ def get_sampled_neighborhood(data, rank=0, n_hops=1, is_hypergraph=False): if rank == max_rank: edges = torch.empty((2, 0), dtype=torch.long) else: - I = data[f"incidence_{rank+1}"] - A = torch.sparse.mm(I, I.T) + incidence = data[f"incidence_{rank+1}"] + A = torch.sparse.mm(incidence, incidence.T) for _ in range(n_hops - 1): A = torch.sparse.mm(A, A) A_sum += A @@ -306,8 +300,8 @@ def get_sampled_neighborhood(data, rank=0, n_hops=1, is_hypergraph=False): # This considers the lower adjacencies if rank != 0: - I = data[f"incidence_{rank}"] - A = torch.sparse.mm(I.T, I) + incidence = data[f"incidence_{rank}"] + A = torch.sparse.mm(incidence.T, incidence) for _ in range(n_hops - 1): A = torch.sparse.mm(A, A) A_sum += A diff --git a/topobenchmark/dataloader/dataloader.py b/topobenchmark/dataloader/dataloader.py index 3134f75a..b3d50a86 100755 --- a/topobenchmark/dataloader/dataloader.py +++ b/topobenchmark/dataloader/dataloader.py @@ -5,9 +5,9 @@ from lightning import LightningDataModule from torch.utils.data import DataLoader +from topobenchmark.data.batching import NeighborCellsLoader from topobenchmark.dataloader.dataload_dataset import DataloadDataset from topobenchmark.dataloader.utils import collate_fn -from topobenchmark.data.batching import NeighborCellsLoader class TBDataloader(LightningDataModule): @@ -49,7 +49,7 @@ def __init__( dataset_test: DataloadDataset = None, batch_size: int = 1, rank: int = 0, - num_neighbors: list[int] = [-1], + num_neighbors: list[int] | None = None, num_workers: int = 0, pin_memory: bool = False, **kwargs: Any, @@ -66,7 +66,9 @@ def __init__( self.batch_size = batch_size self.transductive = False self.rank = rank - self.num_neighbors = num_neighbors + self.num_neighbors = ( + num_neighbors if num_neighbors is not None else [-1] + ) if dataset_val is None and dataset_test is None: # Transductive setting self.dataset_val = dataset_train @@ -99,10 +101,7 @@ def _get_dataloader(self, split: str) -> DataLoader | NeighborCellsLoader: shuffle = split == "train" if not self.transductive or self.batch_size == -1: - if self.batch_size == -1: - batch_size = 1 - else: - batch_size = self.batch_size + batch_size = self.batch_size if self.batch_size != -1 else 1 return DataLoader( dataset=getattr(self, f"dataset_{split}"), From 3f44880fb93d87bb8e8f15ce8b7fb3ac959f8c28 Mon Sep 17 00:00:00 2001 From: Coerulatus Date: Wed, 18 Dec 2024 17:11:49 +0000 Subject: [PATCH 24/24] fix temp folder --- test/data/batching/test_neighbor_cells_loader.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/test/data/batching/test_neighbor_cells_loader.py b/test/data/batching/test_neighbor_cells_loader.py index 5a02db27..5153183b 100644 --- a/test/data/batching/test_neighbor_cells_loader.py +++ b/test/data/batching/test_neighbor_cells_loader.py @@ -1,3 +1,4 @@ +""" Test for the NeighborCellsLoader class.""" import os import shutil import rootutils @@ -11,7 +12,7 @@ initialize_hydra() -path = "/temp/graph2simplicial_lifting/" +path = "./graph2simplicial_lifting/" if os.path.isdir(path): shutil.rmtree(path) cfg = compose(config_name="run.yaml", @@ -71,7 +72,7 @@ shutil.rmtree(path) -path = "/temp/graph2hypergraph_lifting/" +path = "./graph2hypergraph_lifting/" if os.path.isdir(path): shutil.rmtree(path) cfg = compose(config_name="run.yaml",