From f1bfb120ff50c8c4d6e192b1cf59dd841fe5decb Mon Sep 17 00:00:00 2001 From: Michael Banf Date: Fri, 5 Jul 2024 22:40:00 +0200 Subject: [PATCH 01/18] bugfix in plot_manual_graph, switched indexing for nodes instead of hyperedges in incidence matrix results in too many added nodes and hence an out of index error. This error does not happen in the KNN given example, because the number of hyperedges equals the number of nodes and hence doubles the number of nodes anyways. It occurs however, if the number of hyperedges is less then the number of nodes --- modules/utils/utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/utils/utils.py b/modules/utils/utils.py index 1dfcdc2e..54e955d5 100644 --- a/modules/utils/utils.py +++ b/modules/utils/utils.py @@ -233,7 +233,7 @@ def sort_vertices_ccw(vertices): n_hyperedges = incidence.shape[1] vertices += [i + n_vertices for i in range(n_hyperedges)] indices = incidence.indices() - edges = np.array([indices[1].numpy(), indices[0].numpy() + n_vertices]).T + edges = np.array([indices[0].numpy(), indices[1].numpy() + n_vertices]).T pos_n = [[i, 0] for i in range(n_vertices)] pos_he = [[i, 1] for i in range(n_hyperedges)] pos = pos_n + pos_he From 063737b795c7c69b1193cf13acff1ae89b205f9c Mon Sep 17 00:00:00 2001 From: Michael Banf Date: Fri, 5 Jul 2024 22:44:08 +0200 Subject: [PATCH 02/18] graph2hypergraph lifting based on forman-ricci curvature to identify network backbone(s) as hyperedges --- .../forman_ricci_curvature_lifting.yaml | 5 + modules/transforms/data_transform.py | 4 + .../forman_ricci_curvature_lifting.py | 128 ++++++ .../test_forman_ricci_curvature_lifting.py | 49 +++ .../forman_ricci_curvature_lifting.ipynb | 392 ++++++++++++++++++ 5 files changed, 578 insertions(+) create mode 100755 configs/transforms/liftings/graph2hypergraph/forman_ricci_curvature_lifting.yaml create mode 100644 modules/transforms/liftings/graph2hypergraph/forman_ricci_curvature_lifting.py create mode 100644 test/transforms/liftings/graph2hypergraph/test_forman_ricci_curvature_lifting.py create mode 100644 tutorials/graph2hypergraph/forman_ricci_curvature_lifting.ipynb diff --git a/configs/transforms/liftings/graph2hypergraph/forman_ricci_curvature_lifting.yaml b/configs/transforms/liftings/graph2hypergraph/forman_ricci_curvature_lifting.yaml new file mode 100755 index 00000000..22ef7fb0 --- /dev/null +++ b/configs/transforms/liftings/graph2hypergraph/forman_ricci_curvature_lifting.yaml @@ -0,0 +1,5 @@ +transform_type: 'lifting' +transform_name: "HypergraphFormanRicciCurvatureLifting" +network_type: 'weighted' +th_quantile: 0.6 +feature_lifting: ProjectionSum \ No newline at end of file diff --git a/modules/transforms/data_transform.py b/modules/transforms/data_transform.py index 59253ecf..f438c8a3 100755 --- a/modules/transforms/data_transform.py +++ b/modules/transforms/data_transform.py @@ -9,6 +9,9 @@ ) from modules.transforms.feature_liftings.feature_liftings import ProjectionSum from modules.transforms.liftings.graph2cell.cycle_lifting import CellCycleLifting +from modules.transforms.liftings.graph2hypergraph.forman_ricci_curvature_lifting import ( + HypergraphFormanRicciCurvatureLifting, +) from modules.transforms.liftings.graph2hypergraph.knn_lifting import ( HypergraphKNNLifting, ) @@ -19,6 +22,7 @@ TRANSFORMS = { # Graph -> Hypergraph "HypergraphKNNLifting": HypergraphKNNLifting, + "HypergraphFormanRicciCurvatureLifting": HypergraphFormanRicciCurvatureLifting, # Graph -> Simplicial Complex "SimplicialCliqueLifting": SimplicialCliqueLifting, # Graph -> Cell Complex diff --git a/modules/transforms/liftings/graph2hypergraph/forman_ricci_curvature_lifting.py b/modules/transforms/liftings/graph2hypergraph/forman_ricci_curvature_lifting.py new file mode 100644 index 00000000..c53af1c4 --- /dev/null +++ b/modules/transforms/liftings/graph2hypergraph/forman_ricci_curvature_lifting.py @@ -0,0 +1,128 @@ +import math + +import networkx as nx +import numpy as np +import torch +import torch_geometric + +from modules.transforms.liftings.graph2hypergraph.base import Graph2HypergraphLifting + + +class HypergraphFormanRicciCurvatureLifting(Graph2HypergraphLifting): + r"""Lifts graphs to hypergraph domain using Forman-Ricci curvature based backbone estimation. + + Parameters + ---------- + network_type : str + Network type may be weighted or unweighted. Default is "weighted". + th_quantile: float + Quantile to estimate cutoff threshold from Forman-Ricci curvature distribution to prune network and reveal backbone. Default is 0.6 + **kwargs : optional + Additional arguments for the class. + """ + + def __init__(self, network_type="weighted", th_quantile=0.6, **kwargs): + super().__init__(**kwargs) + self.network_type = network_type + self.th_quantile = th_quantile + + def lift_topology(self, data: torch_geometric.data.Data) -> dict: + r"""Lifts the topology of a graph to hypergraph domain using Forman-Ricci curvature based backbone estimation. + + Parameters + ---------- + data : torch_geometric.data.Data + The input data to be lifted. + + Returns + ------- + dict + The lifted topology. + """ + + edge_list = data.edge_index.t().numpy() + + # for unweighted graphs or higher-dimensional edge or node features revert to unweighted network structure + if ( + data.edge_attr is None + or self.network_type == "unweighted" + or data.edge_attr.shape[1] > 1 + ): + edge_attr = np.ones(shape=(len(edge_list), 1)) + elif isinstance(data.edge_attr, torch.Tensor): + edge_attr = data.edge_attr.numpy() + else: + edge_attr = data.edge_attr + + if data.x is None or self.network_type == "unweighted" or data.x.shape[1] > 1: + node_attr = data.x = np.ones(shape=(data.num_nodes, 1)) + elif isinstance(data.x, torch.Tensor): + node_attr = data.x.numpy() + else: + node_attr = data.x + + # create undirected networkx graph from pyg data + G = nx.Graph() + for v in range(len(node_attr)): + G.add_node(v) + G.nodes[v]["w"] = node_attr[v][0] + + for e in range(len(edge_list)): + v1 = edge_list[e][0] + v2 = edge_list[e][1] + G.add_edge(v1, v2, w=edge_attr[e][0]) + G.add_edge(v1, v2, w=edge_attr[e][0]) + + # estimate Forman-Ricci curvature as described in: + # M. Weber, J. Jost, E. Saucan (2018). Detecting the Coarse Geometry of Networks. NeurIPS + for v1, v2 in G.edges(): + v1_neighbors = set(G.neighbors(v1)) + v1_neighbors.remove(v2) + v2_neighbors = set(G.neighbors(v2)) + v2_neighbors.remove(v1) + + w_e = G[v1][v2]["w"] + w_v1 = G.nodes[v1]["w"] + w_v2 = G.nodes[v2]["w"] + ev1_sum = sum([w_v1 / math.sqrt(w_e * G[v1][v]["w"]) for v in v1_neighbors]) + ev2_sum = sum([w_v2 / math.sqrt(w_e * G[v2][v]["w"]) for v in v2_neighbors]) + + G[v1][v2]["w_frc"] = w_e * (w_v1 / w_e + w_v2 / w_e - (ev1_sum + ev2_sum)) + + # estimate cutoff threshold from Forman-Ricci curvature distribution to prune network and reveal backbone(s), i.e. hyperedges + w_frc = list(nx.get_edge_attributes(G, "w_frc").values()) + th_cutoff = np.quantile(w_frc, self.th_quantile) + + edges_to_remove = [] + for v1, v2 in G.edges(): + if G[v1][v2]["w_frc"] > th_cutoff: + edges_to_remove.append((v1, v2)) + + G.remove_edges_from(edges_to_remove) + + # find connected components (hyperedges) + hyperedges = [ + c for c in sorted(nx.connected_components(G), key=len, reverse=True) + ] + shape = (data.num_nodes, len(hyperedges)) + incidence_matrix = np.zeros(shape=shape) + + if len(hyperedges) > 0: + for i, nodes in enumerate(hyperedges): + incidence_matrix[list(nodes), i] = 1 + + incidences = torch.Tensor(incidence_matrix).to_sparse_coo() + + coo_indices = torch.stack( + (incidences.indices()[0], incidences.indices()[1]) # nodes # hyperedges + ) + + coo_values = incidences.values() + + incidence_matrix = torch.sparse_coo_tensor(coo_indices, coo_values) + + return { + "incidence_hyperedges": incidence_matrix, + "num_hyperedges": incidence_matrix.size(1), + "x_0": data.x, + } diff --git a/test/transforms/liftings/graph2hypergraph/test_forman_ricci_curvature_lifting.py b/test/transforms/liftings/graph2hypergraph/test_forman_ricci_curvature_lifting.py new file mode 100644 index 00000000..7a3d35af --- /dev/null +++ b/test/transforms/liftings/graph2hypergraph/test_forman_ricci_curvature_lifting.py @@ -0,0 +1,49 @@ +"""Test Forman-Ricci Curvature Lifting.""" + +import pytest +import torch +import torch.nn.functional as F +import torch_geometric + +from modules.data.utils.utils import load_manual_graph +from modules.transforms.liftings.graph2hypergraph.forman_ricci_curvature_lifting import ( + HypergraphFormanRicciCurvatureLifting, +) + + +class TestHypergraphFormanRicciCurvatureLifting: + """Test the HypergraphFormanRicciCurvatureLifting class.""" + + def setup_method(self): + self.data = load_manual_graph() + + self.lifting = HypergraphFormanRicciCurvatureLifting( + network_type="weighted", + th_quantile=0.6, + ) + + def test_lift_topology(self): + # Test the lift_topology method + lifted_data = self.lifting.forward(self.data.clone()) + + expected_n_hyperedges = 2 + + expected_incidence_1 = torch.tensor( + [ + [1.0, 0.0], + [1.0, 0.0], + [1.0, 0.0], + [0.0, 1.0], + [1.0, 0.0], + [1.0, 0.0], + [1.0, 0.0], + [1.0, 0.0], + ] + ) + + assert ( + expected_incidence_1 == lifted_data.incidence_hyperedges.to_dense() + ).all(), "Something is wrong with incidence_hyperedges (k=1)." + assert ( + expected_n_hyperedges == lifted_data.num_hyperedges + ), "Something is wrong with the number of hyperedges (k=1)." diff --git a/tutorials/graph2hypergraph/forman_ricci_curvature_lifting.ipynb b/tutorials/graph2hypergraph/forman_ricci_curvature_lifting.ipynb new file mode 100644 index 00000000..636115fd --- /dev/null +++ b/tutorials/graph2hypergraph/forman_ricci_curvature_lifting.ipynb @@ -0,0 +1,392 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Graph-to-Hypergraph Forman-Ricci Curvature Lifting Tutorial" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "This notebook shows how to import a dataset, with the desired lifting, and how to run a neural network using the loaded data.\n", + "\n", + "The notebook is divided into sections:\n", + "\n", + "- [Loading the dataset](#loading-the-dataset) loads the config files for the data and the desired tranformation, createsa a dataset object and visualizes it.\n", + "- [Loading and applying the lifting](#loading-and-applying-the-lifting) defines a simple neural network to test that the lifting creates the expected incidence matrices.\n", + "- [Create and run a simplicial nn model](#create-and-run-a-simplicial-nn-model) simply runs a forward pass of the model to check that everything is working as expected.\n", + "\n", + "***\n", + "***\n", + "\n", + "Note that for simplicity the notebook is setup to use a simple graph. However, there is a set of available datasets that you can play with.\n", + "\n", + "To switch to one of the available datasets, simply change the *dataset_name* variable in [Dataset config](#dataset-config) to one of the following names:\n", + "\n", + "* cocitation_cora\n", + "* cocitation_citeseer\n", + "* cocitation_pubmed\n", + "* MUTAG\n", + "* NCI1\n", + "* NCI109\n", + "* PROTEINS_TU\n", + "* AQSOL\n", + "* ZINC\n", + "***" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Imports and utilities" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# With this cell any imported module is reloaded before each cell execution\n", + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "from modules.data.load.loaders import GraphLoader\n", + "from modules.data.preprocess.preprocessor import PreProcessor\n", + "from modules.utils.utils import (\n", + " describe_data,\n", + " load_dataset_config,\n", + " load_model_config,\n", + " load_transform_config,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Loading the Dataset" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we just need to spicify the name of the available dataset that we want to load. First, the dataset config is read from the corresponding yaml file (located at `/configs/datasets/` directory), and then the data is loaded via the implemented `Loaders`.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Dataset configuration for manual_dataset:\n", + "\n", + "{'data_domain': 'graph',\n", + " 'data_type': 'toy_dataset',\n", + " 'data_name': 'manual',\n", + " 'data_dir': 'datasets/graph/toy_dataset',\n", + " 'num_features': 1,\n", + " 'num_classes': 2,\n", + " 'task': 'classification',\n", + " 'loss_type': 'cross_entropy',\n", + " 'monitor_metric': 'accuracy',\n", + " 'task_level': 'node'}\n" + ] + } + ], + "source": [ + "dataset_name = \"manual_dataset\"\n", + "dataset_config = load_dataset_config(dataset_name)\n", + "loader = GraphLoader(dataset_config)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can then access to the data through the `load()`method:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Dataset only contains 1 sample:\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " - Graph with 8 vertices and 13 edges.\n", + " - Features dimensions: [1, 0]\n", + " - There are 0 isolated nodes.\n", + "\n" + ] + } + ], + "source": [ + "dataset = loader.load()\n", + "describe_data(dataset)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "modules.data.utils.custom_dataset.CustomDataset" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(dataset)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Loading and Applying the Lifting" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this section, we will instantiate the lifting we want to apply to the data. In this tutorial, we apply Forman-Ricci curvature based lifting. Curvature is a geometric property to describe the local shape of an object. In the graph, an edge with positive curvature represents an edge within a cluster, while a negative curvature edge tents to be a bridge within clusters [1,2]. FR curvature was found to be especially useful for network analysis since its intuitive notion allows for efficient computation that scales to large networks sizes. In its most general form, FR curvature is defined on CW cell complexes. Network representations of relational data $G = {V (G), E(G)}$ form regular, 1-dimensional cell complexes, in which case the following curvature function can be defined [2]:\n", + "\n", + "${\\rm Ric_F}(e) = \\omega (e) \\left( \\frac{\\omega (v_1)}{\\omega (e)} + \\frac{\\omega (v_2)}{\\omega (e)} - \\sum_{\\substack{e_{v_1}\\ \\sim\\ e \\\\ \\ e_{v_2}\\ \\sim\\ e}} \\left[\\frac{\\omega (v_1)}{\\sqrt{\\omega (e) \\omega (e_{v_1})}} + \\frac{\\omega (v_2)}{\\sqrt{\\omega (e) \\omega (e_{v_2})}} \\right] \\right)\\,.$\n", + "\n", + "The function is defined on each edge $e = (v1, v2) \\in E(G)$ of the network, connecting vertices $v1, v2 \\in V (G)$; w denotes the weights of edges and vertices.\n", + "\n", + "Our approach is straightforward: (1) calculate the Forman-Ricci curvature over the edges of a graph, (2) from the given distribution of curvature weights, define a quantile based threshold, (3) prune the curvature graph using this threshold (4) add remaining coarse graph geometry as hyperedge(s).\n", + "\n", + "***\n", + "[[1]](https://arxiv.org/pdf/1607.08654) Melanie Weber, Emil Saucan, Jürgen Jost, Characterizing complex networks with Forman-Ricci curvature and associated geometric flows, Journal of Complex Networks, Volume 5, Issue 4, August 2017, Pages 527–550.\n", + "\n", + "[[2]](https://web.math.princeton.edu/~mw25/project/files/NIPS-FR.pdf) M. Weber, J. Jost, E. Saucan (2018). Detecting the Coarse Geometry of Networks. NeurIPS R2L.\n", + "\n", + "***\n", + "\n", + "For hypergraphs, creating a lifting involves creating the `incidence_hyperedges` matrix.\n", + "\n", + "Similarly to before, we can specify the transformation we want to apply through its type and id --the corresponding config files located at `/configs/transforms.` \n", + "\n", + "Note that the *tranform_config* dictionary generated below can contain a sequence of transforms if it is needed.\n", + "\n", + "This can also be used to explore liftings from one topological domain to another; for example, using two liftings, it is possible to achieve a sequence such as graph -> simplicial complex -> hypergraph. " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Transform configuration for graph2hypergraph/forman_ricci_curvature_lifting:\n", + "\n", + "{'transform_type': 'lifting',\n", + " 'transform_name': 'HypergraphFormanRicciCurvatureLifting',\n", + " 'network_type': 'weighted',\n", + " 'th_quantile': 0.6,\n", + " 'feature_lifting': 'ProjectionSum'}\n" + ] + } + ], + "source": [ + "# Define transformation type and id\n", + "transform_type = \"liftings\"\n", + "# If the transform is a topological lifting, it should include both the type of the lifting and the identifier\n", + "transform_id = \"graph2hypergraph/forman_ricci_curvature_lifting\"\n", + "\n", + "# Read yaml file\n", + "transform_config = {\n", + " \"lifting\": load_transform_config(transform_type, transform_id)\n", + " # other transforms (e.g. data manipulations, feature liftings) can be added here\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We than apply the transform via our `PreProcessor`:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Processing...\n", + "Done!\n" + ] + } + ], + "source": [ + "lifted_dataset = PreProcessor(dataset, transform_config, loader.data_dir)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Dataset only contains 1 sample:\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " - Hypergraph with 8 vertices and 2 hyperedges.\n", + " - The nodes have feature dimensions 1.\n", + " - The hyperedges have feature dimensions 1.\n", + "\n" + ] + } + ], + "source": [ + "describe_data(lifted_dataset)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create and Run a Simplicial NN Model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this section a simple model is created to test that the used lifting works as intended. In this case the model uses the `incidence_hyperedges` matrix so the lifting should make sure to add it to the data." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Model configuration for hypergraph UNIGCN:\n", + "\n", + "{'in_channels': None,\n", + " 'hidden_channels': 32,\n", + " 'out_channels': None,\n", + " 'n_layers': 2}\n" + ] + } + ], + "source": [ + "from modules.models.hypergraph.unigcn import UniGCNModel\n", + "\n", + "model_type = \"hypergraph\"\n", + "model_id = \"unigcn\"\n", + "model_config = load_model_config(model_type, model_id)\n", + "\n", + "model = UniGCNModel(model_config, dataset_config)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "y_hat = model(lifted_dataset.get(0))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If everything is correct the cell above should execute without errors. " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".topox_env", + "language": "python", + "name": ".topox_env" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From 807860bc9391b849f04c654b5d35482be50ec74f Mon Sep 17 00:00:00 2001 From: Michael Banf Date: Fri, 5 Jul 2024 23:15:40 +0200 Subject: [PATCH 03/18] fixing linting errors --- .../graph2hypergraph/test_forman_ricci_curvature_lifting.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/test/transforms/liftings/graph2hypergraph/test_forman_ricci_curvature_lifting.py b/test/transforms/liftings/graph2hypergraph/test_forman_ricci_curvature_lifting.py index 7a3d35af..955c66ff 100644 --- a/test/transforms/liftings/graph2hypergraph/test_forman_ricci_curvature_lifting.py +++ b/test/transforms/liftings/graph2hypergraph/test_forman_ricci_curvature_lifting.py @@ -1,9 +1,6 @@ """Test Forman-Ricci Curvature Lifting.""" -import pytest import torch -import torch.nn.functional as F -import torch_geometric from modules.data.utils.utils import load_manual_graph from modules.transforms.liftings.graph2hypergraph.forman_ricci_curvature_lifting import ( From 310f7036c8cc2896037eecf22d58a7daa8b36db0 Mon Sep 17 00:00:00 2001 From: Dominik Filipiak <757111+DominikFilipiak@users.noreply.github.com> Date: Mon, 8 Jul 2024 10:51:07 +0200 Subject: [PATCH 04/18] Added nx -> PyG todo --- .../graph2hypergraph/forman_ricci_curvature_lifting.ipynb | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/tutorials/graph2hypergraph/forman_ricci_curvature_lifting.ipynb b/tutorials/graph2hypergraph/forman_ricci_curvature_lifting.ipynb index 636115fd..18f507ee 100644 --- a/tutorials/graph2hypergraph/forman_ricci_curvature_lifting.ipynb +++ b/tutorials/graph2hypergraph/forman_ricci_curvature_lifting.ipynb @@ -36,6 +36,11 @@ "* PROTEINS_TU\n", "* AQSOL\n", "* ZINC\n", + "***\n", + "\n", + "***\n", + "Extension ideas and TODOs\n", + "- Switch from NetworkX to PyG graph to possible speed gains (needs to be tested though)\n", "***" ] }, From f8a7eb96630998ff800586cbf8f3126a03027c7b Mon Sep 17 00:00:00 2001 From: Michael Banf Date: Tue, 9 Jul 2024 14:28:13 +0200 Subject: [PATCH 05/18] bugfixing, adding possibility to define either quantile or absolute thresholds --- .../forman_ricci_curvature_lifting.yaml | 4 +- .../forman_ricci_curvature_lifting.py | 45 ++- .../test_forman_ricci_curvature_lifting.py | 2 +- .../forman_ricci_curvature_lifting.ipynb | 317 ++++++++++++++++-- 4 files changed, 336 insertions(+), 32 deletions(-) diff --git a/configs/transforms/liftings/graph2hypergraph/forman_ricci_curvature_lifting.yaml b/configs/transforms/liftings/graph2hypergraph/forman_ricci_curvature_lifting.yaml index 22ef7fb0..0aa6aea0 100755 --- a/configs/transforms/liftings/graph2hypergraph/forman_ricci_curvature_lifting.yaml +++ b/configs/transforms/liftings/graph2hypergraph/forman_ricci_curvature_lifting.yaml @@ -1,5 +1,7 @@ transform_type: 'lifting' transform_name: "HypergraphFormanRicciCurvatureLifting" network_type: 'weighted' -th_quantile: 0.6 +threshold_type: 'quantile' +threshold_direction: 'upper' +threshold: 0.1 feature_lifting: ProjectionSum \ No newline at end of file diff --git a/modules/transforms/liftings/graph2hypergraph/forman_ricci_curvature_lifting.py b/modules/transforms/liftings/graph2hypergraph/forman_ricci_curvature_lifting.py index c53af1c4..82805e83 100644 --- a/modules/transforms/liftings/graph2hypergraph/forman_ricci_curvature_lifting.py +++ b/modules/transforms/liftings/graph2hypergraph/forman_ricci_curvature_lifting.py @@ -21,10 +21,19 @@ class HypergraphFormanRicciCurvatureLifting(Graph2HypergraphLifting): Additional arguments for the class. """ - def __init__(self, network_type="weighted", th_quantile=0.6, **kwargs): + def __init__( + self, + network_type="weighted", + threshold_type="quantile", + threshold_direction="upper", + threshold=0.1, + **kwargs, + ): super().__init__(**kwargs) self.network_type = network_type - self.th_quantile = th_quantile + self.threshold_type = threshold_type + self.theshold_direction = threshold_direction + self.threshold = threshold def lift_topology(self, data: torch_geometric.data.Data) -> dict: r"""Lifts the topology of a graph to hypergraph domain using Forman-Ricci curvature based backbone estimation. @@ -55,7 +64,8 @@ def lift_topology(self, data: torch_geometric.data.Data) -> dict: edge_attr = data.edge_attr if data.x is None or self.network_type == "unweighted" or data.x.shape[1] > 1: - node_attr = data.x = np.ones(shape=(data.num_nodes, 1)) + node_attr = np.ones(shape=(data.num_nodes, 1)) + # data.x = torch.from_numpy(data.x.astype("f4")) elif isinstance(data.x, torch.Tensor): node_attr = data.x.numpy() else: @@ -91,11 +101,28 @@ def lift_topology(self, data: torch_geometric.data.Data) -> dict: # estimate cutoff threshold from Forman-Ricci curvature distribution to prune network and reveal backbone(s), i.e. hyperedges w_frc = list(nx.get_edge_attributes(G, "w_frc").values()) - th_cutoff = np.quantile(w_frc, self.th_quantile) + + if self.threshold_type == "quantile": + th_cutoff = np.quantile(w_frc, self.threshold) + elif self.threshold_type == "absolute": + th_cutoff = self.threshold + else: + raise NotImplementedError( + f"threshold type {self.threshold_type} not implemented" + ) + + if self.theshold_direction == "upper": + compare_to_threshold = lambda x: x > th_cutoff + elif self.theshold_direction == "lower": + compare_to_threshold = lambda x: x < th_cutoff + else: + raise NotImplementedError( + f"threshold theshold_direction {self.theshold_direction} not implemented" + ) edges_to_remove = [] for v1, v2 in G.edges(): - if G[v1][v2]["w_frc"] > th_cutoff: + if compare_to_threshold(G[v1][v2]["w_frc"]): edges_to_remove.append((v1, v2)) G.remove_edges_from(edges_to_remove) @@ -111,11 +138,11 @@ def lift_topology(self, data: torch_geometric.data.Data) -> dict: for i, nodes in enumerate(hyperedges): incidence_matrix[list(nodes), i] = 1 - incidences = torch.Tensor(incidence_matrix).to_sparse_coo() + assert len(hyperedges) > 0 + + incidences = torch.Tensor(incidence_matrix.astype("f4")).to_sparse_coo() - coo_indices = torch.stack( - (incidences.indices()[0], incidences.indices()[1]) # nodes # hyperedges - ) + coo_indices = torch.stack((incidences.indices()[0], incidences.indices()[1])) coo_values = incidences.values() diff --git a/test/transforms/liftings/graph2hypergraph/test_forman_ricci_curvature_lifting.py b/test/transforms/liftings/graph2hypergraph/test_forman_ricci_curvature_lifting.py index 955c66ff..1272cf7c 100644 --- a/test/transforms/liftings/graph2hypergraph/test_forman_ricci_curvature_lifting.py +++ b/test/transforms/liftings/graph2hypergraph/test_forman_ricci_curvature_lifting.py @@ -16,7 +16,7 @@ def setup_method(self): self.lifting = HypergraphFormanRicciCurvatureLifting( network_type="weighted", - th_quantile=0.6, + threshold=0.6, ) def test_lift_topology(self): diff --git a/tutorials/graph2hypergraph/forman_ricci_curvature_lifting.ipynb b/tutorials/graph2hypergraph/forman_ricci_curvature_lifting.ipynb index 636115fd..9e705ca0 100644 --- a/tutorials/graph2hypergraph/forman_ricci_curvature_lifting.ipynb +++ b/tutorials/graph2hypergraph/forman_ricci_curvature_lifting.ipynb @@ -48,9 +48,18 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" + ] + } + ], "source": [ "# With this cell any imported module is reloaded before each cell execution\n", "%load_ext autoreload\n", @@ -82,7 +91,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -120,7 +129,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -159,7 +168,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -168,7 +177,7 @@ "modules.data.utils.custom_dataset.CustomDataset" ] }, - "execution_count": 4, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -188,13 +197,16 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "In this section, we will instantiate the lifting we want to apply to the data. In this tutorial, we apply Forman-Ricci curvature based lifting. Curvature is a geometric property to describe the local shape of an object. In the graph, an edge with positive curvature represents an edge within a cluster, while a negative curvature edge tents to be a bridge within clusters [1,2]. FR curvature was found to be especially useful for network analysis since its intuitive notion allows for efficient computation that scales to large networks sizes. In its most general form, FR curvature is defined on CW cell complexes. Network representations of relational data $G = {V (G), E(G)}$ form regular, 1-dimensional cell complexes, in which case the following curvature function can be defined [2]:\n", + "In this section, we will instantiate the lifting we want to apply to the data. In this tutorial, we apply an novel lifting based on Forman-Ricci curvature. In general, curvature is a geometric property that describes the local shape of an object. In the graph, an edge with positive curvature represents an edge within a cluster, while a negative curvature edge tents to be a bridge within clusters. Forman-Ricci curvature defines an edge-based network characteristic that reveals properties of a graph's community structure [1,2]. In particular high absolute Forman-Ricci curvature exhibits a network's backbone, a coarse, structure preserving graph geometry that forms connections between major communities, most suitable to form hyperedges. In addition, Forman-Ricci curvature was found to be especially useful for network analysis since its intuitive notion allows for efficient computation that scales to large networks sizes.\n", + "\n", + "Network representations of relational data $G = {V (G), E(G)}$ form regular, 1-dimensional cell complexes, in which case the following curvature function can be defined:\n", "\n", "${\\rm Ric_F}(e) = \\omega (e) \\left( \\frac{\\omega (v_1)}{\\omega (e)} + \\frac{\\omega (v_2)}{\\omega (e)} - \\sum_{\\substack{e_{v_1}\\ \\sim\\ e \\\\ \\ e_{v_2}\\ \\sim\\ e}} \\left[\\frac{\\omega (v_1)}{\\sqrt{\\omega (e) \\omega (e_{v_1})}} + \\frac{\\omega (v_2)}{\\sqrt{\\omega (e) \\omega (e_{v_2})}} \\right] \\right)\\,.$\n", "\n", - "The function is defined on each edge $e = (v1, v2) \\in E(G)$ of the network, connecting vertices $v1, v2 \\in V (G)$; w denotes the weights of edges and vertices.\n", + "The function is defined on each edge $e = (v_1, v_2) \\in E(G)$ of the network, connecting vertices $v_1, v_2 \\in V (G)$; $\\omega$ denotes the weights of edges and vertices. Our approach is applicable to both weighted and unweighted networks. In case of unweighted networks all weights in the above equation reduce to 1, i.e. $\\omega (e) = \\omega (v_1) = \\omega (v_2) = 1$.\n", + "\n", + "Our approach is straightforward: (1) calculate the Forman-Ricci curvature over the edges of a graph, (2) from the given distribution of curvature weights, either define an absolute threshold on Forman-Ricci curvature or give a quantile for automatic, network-dependent threshold estimation for backbone extraction, (3) prune the curvature graph using this threshold (4) identify and add remaining network backbone geometries as hyperedges to lift the original graph.\n", "\n", - "Our approach is straightforward: (1) calculate the Forman-Ricci curvature over the edges of a graph, (2) from the given distribution of curvature weights, define a quantile based threshold, (3) prune the curvature graph using this threshold (4) add remaining coarse graph geometry as hyperedge(s).\n", "\n", "***\n", "[[1]](https://arxiv.org/pdf/1607.08654) Melanie Weber, Emil Saucan, Jürgen Jost, Characterizing complex networks with Forman-Ricci curvature and associated geometric flows, Journal of Complex Networks, Volume 5, Issue 4, August 2017, Pages 527–550.\n", @@ -214,7 +226,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -227,7 +239,9 @@ "{'transform_type': 'lifting',\n", " 'transform_name': 'HypergraphFormanRicciCurvatureLifting',\n", " 'network_type': 'weighted',\n", - " 'th_quantile': 0.6,\n", + " 'threshold_type': 'quantile',\n", + " 'threshold_direction': 'upper',\n", + " 'threshold': 0.1,\n", " 'feature_lifting': 'ProjectionSum'}\n" ] } @@ -254,25 +268,26 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 23, "metadata": {}, "outputs": [ { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "Processing...\n", - "Done!\n" + "Transform parameters are the same, using existing data_dir: /Users/michael/perelyn/research/topological_deep_learning/challenge-icml-2024/datasets/graph/toy_dataset/manual/lifting/3982310697\n" ] } ], "source": [ + "# transform_config[\"lifting\"][\"threshold_type\"] = \"absolute\"\n", + "# transform_config[\"lifting\"][\"threshold\"] = -1\n", "lifted_dataset = PreProcessor(dataset, transform_config, loader.data_dir)" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -285,7 +300,7 @@ }, { "data": { - "image/png": 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", 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", 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" ] @@ -297,7 +312,7 @@ "name": "stdout", "output_type": "stream", "text": [ - " - Hypergraph with 8 vertices and 2 hyperedges.\n", + " - Hypergraph with 8 vertices and 6 hyperedges.\n", " - The nodes have feature dimensions 1.\n", " - The hyperedges have feature dimensions 1.\n", "\n" @@ -308,6 +323,13 @@ "describe_data(lifted_dataset)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Given the automated quantile based threshold estimation, the algorithm collapses nodes adjacent to edges with the most significant curvature absolute values based on the given very low quantile (default: 0.1)." + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -324,7 +346,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -353,7 +375,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -366,13 +388,266 @@ "source": [ "If everything is correct the cell above should execute without errors. " ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's try another example, i.e. chemical structures from the MUTAG dataset, where node an edge features are > 1, hence the algorithm switches to the unweighted version automatically. Further, we change the configuration to allow for the definition of absolute thresholds on Forman-Ricci curvature to explore the evolution of the hyperedge geometry." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Dataset configuration for MUTAG:\n", + "\n", + "{'data_domain': 'graph',\n", + " 'data_type': 'TUDataset',\n", + " 'data_name': 'MUTAG',\n", + " 'data_dir': 'datasets/graph/TUDataset',\n", + " 'num_features': [7, 4],\n", + " 'num_classes': 2,\n", + " 'task': 'classification',\n", + " 'loss_type': 'cross_entropy',\n", + " 'monitor_metric': 'accuracy',\n", + " 'task_level': 'graph',\n", + " 'max_dim_if_lifted': 2,\n", + " 'preserve_edge_attr_if_lifted': False}\n" + ] + } + ], + "source": [ + "dataset_name = \"MUTAG\"\n", + "dataset_config = load_dataset_config(dataset_name)\n", + "loader = GraphLoader(dataset_config)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Dataset contains 188 samples.\n", + "\n", + "Providing more details about sample 0/188:\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " - Graph with 17 vertices and 38 edges.\n", + " - Features dimensions: [7, 4]\n", + " - There are 0 isolated nodes.\n", + "\n" + ] + } + ], + "source": [ + "dataset = loader.load()\n", + "describe_data(dataset)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The Forman-Ricci curvature for this molecular structure is rather small in absolute values ranging from -2 to 0. Hence we can nicely control the creation of hyperedges, using absolute tresholds. " + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Dataset contains 188 samples.\n", + "\n", + "Providing more details about sample 0/188:\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Processing...\n", + "Done!\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " - Hypergraph with 17 vertices and 1 hyperedges.\n", + " - The nodes have feature dimensions 7.\n", + " - The hyperedges have feature dimensions 7.\n", + "\n" + ] + } + ], + "source": [ + "transform_config[\"lifting\"][\"threshold_type\"] = \"absolute\"\n", + "transform_config[\"lifting\"][\"threshold\"] = 0\n", + "lifted_dataset = PreProcessor(dataset, transform_config, loader.data_dir)\n", + "describe_data(lifted_dataset)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For an absolute threshold of 0, no edge of the Forman-Ricci curvature graph is pruned, and hence the whole curvature graph forms a single hyperedge" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Transform parameters are the same, using existing data_dir: /Users/michael/perelyn/research/topological_deep_learning/challenge-icml-2024/datasets/graph/TUDataset/MUTAG/lifting/1394719298\n", + "\n", + "Dataset contains 188 samples.\n", + "\n", + "Providing more details about sample 0/188:\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " - Hypergraph with 17 vertices and 5 hyperedges.\n", + " - The nodes have feature dimensions 7.\n", + " - The hyperedges have feature dimensions 7.\n", + "\n" + ] + } + ], + "source": [ + "transform_config[\"lifting\"][\"threshold_type\"] = \"absolute\"\n", + "transform_config[\"lifting\"][\"threshold\"] = -1\n", + "lifted_dataset = PreProcessor(dataset, transform_config, loader.data_dir)\n", + "describe_data(lifted_dataset)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Setting the absolute thresholds to -1 and -2 respectively we see the emergence of multiple smaller hyperedges forming." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Dataset contains 188 samples.\n", + "\n", + "Providing more details about sample 0/188:\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Processing...\n", + "Done!\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " - Hypergraph with 17 vertices and 13 hyperedges.\n", + " - The nodes have feature dimensions 7.\n", + " - The hyperedges have feature dimensions 7.\n", + "\n" + ] + } + ], + "source": [ + "transform_config[\"lifting\"][\"threshold_type\"] = \"absolute\"\n", + "transform_config[\"lifting\"][\"threshold\"] = -2\n", + "lifted_dataset = PreProcessor(dataset, transform_config, loader.data_dir)\n", + "describe_data(lifted_dataset)" + ] } ], "metadata": { "kernelspec": { "display_name": ".topox_env", "language": "python", - "name": ".topox_env" + "name": "python3" }, "language_info": { "codemirror_mode": { From d8e9720e5960e262c5971a94b39955030fc16e6a Mon Sep 17 00:00:00 2001 From: Michael Banf Date: Tue, 9 Jul 2024 16:46:59 +0200 Subject: [PATCH 06/18] Rewrite compare_to_threshold from lambda to inline def function --- .../forman_ricci_curvature_lifting.py | 10 ++- .../forman_ricci_curvature_lifting.ipynb | 64 ++++++------------- 2 files changed, 29 insertions(+), 45 deletions(-) diff --git a/modules/transforms/liftings/graph2hypergraph/forman_ricci_curvature_lifting.py b/modules/transforms/liftings/graph2hypergraph/forman_ricci_curvature_lifting.py index 82805e83..fd1415ce 100644 --- a/modules/transforms/liftings/graph2hypergraph/forman_ricci_curvature_lifting.py +++ b/modules/transforms/liftings/graph2hypergraph/forman_ricci_curvature_lifting.py @@ -112,9 +112,15 @@ def lift_topology(self, data: torch_geometric.data.Data) -> dict: ) if self.theshold_direction == "upper": - compare_to_threshold = lambda x: x > th_cutoff + + def compare_to_threshold(x): + return x > th_cutoff + elif self.theshold_direction == "lower": - compare_to_threshold = lambda x: x < th_cutoff + + def compare_to_threshold(x): + return x < th_cutoff + else: raise NotImplementedError( f"threshold theshold_direction {self.theshold_direction} not implemented" diff --git a/tutorials/graph2hypergraph/forman_ricci_curvature_lifting.ipynb b/tutorials/graph2hypergraph/forman_ricci_curvature_lifting.ipynb index 350151f9..d0b1ca6e 100644 --- a/tutorials/graph2hypergraph/forman_ricci_curvature_lifting.ipynb +++ b/tutorials/graph2hypergraph/forman_ricci_curvature_lifting.ipynb @@ -53,18 +53,9 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 1, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The autoreload extension is already loaded. To reload it, use:\n", - " %reload_ext autoreload\n" - ] - } - ], + "outputs": [], "source": [ "# With this cell any imported module is reloaded before each cell execution\n", "%load_ext autoreload\n", @@ -96,7 +87,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -134,7 +125,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -173,7 +164,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -182,7 +173,7 @@ "modules.data.utils.custom_dataset.CustomDataset" ] }, - "execution_count": 18, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -231,7 +222,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -273,14 +264,15 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 6, "metadata": {}, "outputs": [ { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "Transform parameters are the same, using existing data_dir: /Users/michael/perelyn/research/topological_deep_learning/challenge-icml-2024/datasets/graph/toy_dataset/manual/lifting/3982310697\n" + "Processing...\n", + "Done!\n" ] } ], @@ -292,7 +284,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -351,7 +343,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -380,7 +372,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -408,7 +400,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -441,7 +433,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -489,27 +481,20 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ + "Transform parameters are the same, using existing data_dir: /Users/michael/perelyn/research/topological_deep_learning/challenge-icml-2024/datasets/graph/TUDataset/MUTAG/lifting/1661761690\n", "\n", "Dataset contains 188 samples.\n", "\n", "Providing more details about sample 0/188:\n" ] }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Processing...\n", - "Done!\n" - ] - }, { "data": { "image/png": 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", @@ -547,7 +532,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -598,27 +583,20 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ + "Transform parameters are the same, using existing data_dir: /Users/michael/perelyn/research/topological_deep_learning/challenge-icml-2024/datasets/graph/TUDataset/MUTAG/lifting/2879822529\n", "\n", "Dataset contains 188 samples.\n", "\n", "Providing more details about sample 0/188:\n" ] }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Processing...\n", - "Done!\n" - ] - }, { "data": { "image/png": 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", From 32d4938ba27d0518c23b9d21d802ccce8ed8fb5e Mon Sep 17 00:00:00 2001 From: Michael Banf Date: Tue, 9 Jul 2024 16:52:38 +0200 Subject: [PATCH 07/18] bugfix --- .../graph2hypergraph/forman_ricci_curvature_lifting.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/modules/transforms/liftings/graph2hypergraph/forman_ricci_curvature_lifting.py b/modules/transforms/liftings/graph2hypergraph/forman_ricci_curvature_lifting.py index fd1415ce..3b6e45d2 100644 --- a/modules/transforms/liftings/graph2hypergraph/forman_ricci_curvature_lifting.py +++ b/modules/transforms/liftings/graph2hypergraph/forman_ricci_curvature_lifting.py @@ -32,7 +32,7 @@ def __init__( super().__init__(**kwargs) self.network_type = network_type self.threshold_type = threshold_type - self.theshold_direction = threshold_direction + self.threshold_direction = threshold_direction self.threshold = threshold def lift_topology(self, data: torch_geometric.data.Data) -> dict: @@ -111,19 +111,19 @@ def lift_topology(self, data: torch_geometric.data.Data) -> dict: f"threshold type {self.threshold_type} not implemented" ) - if self.theshold_direction == "upper": + if self.threshold_direction == "upper": def compare_to_threshold(x): return x > th_cutoff - elif self.theshold_direction == "lower": + elif self.threshold_direction == "lower": def compare_to_threshold(x): return x < th_cutoff else: raise NotImplementedError( - f"threshold theshold_direction {self.theshold_direction} not implemented" + f"threshold threshold_direction {self.threshold_direction} not implemented" ) edges_to_remove = [] From 26e84ee4aa70d27670a0d52d65a15363b33a23a0 Mon Sep 17 00:00:00 2001 From: Michael Banf Date: Wed, 10 Jul 2024 14:20:31 +0200 Subject: [PATCH 08/18] initial commit page rank influential node hyperedges - linting --- .../graph2hypergraph/page_rank_lifting.yaml | 9 + modules/transforms/data_transform.py | 8 +- .../graph2hypergraph/page_rank_lifting.py | 135 ++++ .../test_page_rank_lifting.py | 48 ++ .../graph2hypergraph/page_rank_lifting.ipynb | 625 ++++++++++++++++++ 5 files changed, 821 insertions(+), 4 deletions(-) create mode 100755 configs/transforms/liftings/graph2hypergraph/page_rank_lifting.yaml create mode 100644 modules/transforms/liftings/graph2hypergraph/page_rank_lifting.py create mode 100644 test/transforms/liftings/graph2hypergraph/test_page_rank_lifting.py create mode 100644 tutorials/graph2hypergraph/page_rank_lifting.ipynb diff --git a/configs/transforms/liftings/graph2hypergraph/page_rank_lifting.yaml b/configs/transforms/liftings/graph2hypergraph/page_rank_lifting.yaml new file mode 100755 index 00000000..b30ccf95 --- /dev/null +++ b/configs/transforms/liftings/graph2hypergraph/page_rank_lifting.yaml @@ -0,0 +1,9 @@ +transform_type: 'lifting' +transform_name: "HypergraphPageRankLifting" +network_type: 'weighted' +alpha: 0.85 +th_quantile: 0.95 +n_most_influential: 2 +max_iter: 100 +tol: 1e-06 +feature_lifting: ProjectionSum \ No newline at end of file diff --git a/modules/transforms/data_transform.py b/modules/transforms/data_transform.py index f438c8a3..6c265856 100755 --- a/modules/transforms/data_transform.py +++ b/modules/transforms/data_transform.py @@ -9,12 +9,12 @@ ) from modules.transforms.feature_liftings.feature_liftings import ProjectionSum from modules.transforms.liftings.graph2cell.cycle_lifting import CellCycleLifting -from modules.transforms.liftings.graph2hypergraph.forman_ricci_curvature_lifting import ( - HypergraphFormanRicciCurvatureLifting, -) from modules.transforms.liftings.graph2hypergraph.knn_lifting import ( HypergraphKNNLifting, ) +from modules.transforms.liftings.graph2hypergraph.page_rank_lifting import ( + HypergraphPageRankLifting, +) from modules.transforms.liftings.graph2simplicial.clique_lifting import ( SimplicialCliqueLifting, ) @@ -22,7 +22,7 @@ TRANSFORMS = { # Graph -> Hypergraph "HypergraphKNNLifting": HypergraphKNNLifting, - "HypergraphFormanRicciCurvatureLifting": HypergraphFormanRicciCurvatureLifting, + "HypergraphPageRankLifting": HypergraphPageRankLifting, # Graph -> Simplicial Complex "SimplicialCliqueLifting": SimplicialCliqueLifting, # Graph -> Cell Complex diff --git a/modules/transforms/liftings/graph2hypergraph/page_rank_lifting.py b/modules/transforms/liftings/graph2hypergraph/page_rank_lifting.py new file mode 100644 index 00000000..90ddb480 --- /dev/null +++ b/modules/transforms/liftings/graph2hypergraph/page_rank_lifting.py @@ -0,0 +1,135 @@ +import math + +import networkx as nx +import numpy as np +import torch +import torch_geometric + +from modules.transforms.liftings.graph2hypergraph.base import Graph2HypergraphLifting + + +class HypergraphPageRankLifting(Graph2HypergraphLifting): + r"""Lifts graphs to hypergraph domain using Page Rank. + + Parameters + ---------- + network_type : str + Network type may be weighted or unweighted. Default is "weighted". + alpha: float + Damping parameter for PageRank, default=0.85. + **kwargs : optional + Additional arguments for the class. + """ + + def __init__( + self, + network_type="weighted", + alpha=0.85, + th_quantile=0.95, + n_most_influential=2, + max_iter=100, + tol=1e-06, + **kwargs, + ): + super().__init__(**kwargs) + self.network_type = network_type + self.alpha = alpha + self.max_iter = max_iter + self.tol = tol + self.th_quantile = th_quantile + self.n_most_influential = n_most_influential + + def lift_topology(self, data: torch_geometric.data.Data) -> dict: + r"""Lifts the topology of a graph to hypergraph domain using Page Rank. + + Parameters + ---------- + data : torch_geometric.data.Data + The input data to be lifted. + + Returns + ------- + dict + The lifted topology. + """ + + edge_list = data.edge_index.t().numpy() + + # for unweighted graphs or higher-dimensional edge or node features revert to unweighted network structure + if ( + data.edge_attr is None + or self.network_type == "unweighted" + or data.edge_attr.shape[1] > 1 + ): + edge_attr = np.ones(shape=(len(edge_list), 1)) + elif isinstance(data.edge_attr, torch.Tensor): + edge_attr = data.edge_attr.numpy() + else: + edge_attr = data.edge_attr + + if data.x is None or self.network_type == "unweighted" or data.x.shape[1] > 1: + node_attr = np.ones(shape=(data.num_nodes, 1)) + # data.x = torch.from_numpy(data.x.astype("f4")) + elif isinstance(data.x, torch.Tensor): + node_attr = data.x.numpy() + else: + node_attr = data.x + + # create directed networkx graph from pyg data + G = nx.Graph() + for v in range(len(node_attr)): + G.add_node(v) + G.nodes[v]["w"] = node_attr[v][0] + + for e in range(len(edge_list)): + v1 = edge_list[e][0] + v2 = edge_list[e][1] + G.add_edge(v1, v2, w=edge_attr[e][0]) + + assert self.n_most_influential >= 1 + + # estimate distance between all nodes + if self.network_type == "unweighted": + sp = dict(nx.all_pairs_shortest_path_length(G)) + elif self.network_type == "weighted": + sp = dict(nx.all_pairs_dijkstra_path_length(G)) + else: + raise NotImplementedError( + f"network type {self.network_type} not implemented" + ) + + # estimate page rank per node + pr = nx.pagerank( + G, alpha=self.alpha, max_iter=self.max_iter, tol=self.tol, weight="w" + ) + + # hyperedges based on the number of most influencial nodes + th_cutoff = np.quantile(list(pr.values()), self.th_quantile) + nodes_most_influential = [n for n, v in pr.items() if v >= th_cutoff] + num_hyperedges = len(nodes_most_influential) + hyperedge_map = {v: e for e, v in enumerate(nodes_most_influential)} + + incidence_hyperedges = torch.zeros(data.num_nodes, num_hyperedges) + + # assign to the top n most influential + for v in list(G.nodes()): + if v in nodes_most_influential: + incidence_hyperedges[v, hyperedge_map[v]] = 1 + else: + sp_v_influencial = { + k: v for k, v in sp[v].items() if k in nodes_most_influential + } + v_influencial = [ + k + for i, k in enumerate(sp_v_influencial.keys()) + if i < self.n_most_influential + ] + for v_inf in v_influencial: + incidence_hyperedges[v, hyperedge_map[v_inf]] = 1 + + incidence_hyperedges = incidence_hyperedges.to_sparse_coo() + return { + "incidence_hyperedges": incidence_hyperedges, + "num_hyperedges": num_hyperedges, + "x_0": data.x, + } diff --git a/test/transforms/liftings/graph2hypergraph/test_page_rank_lifting.py b/test/transforms/liftings/graph2hypergraph/test_page_rank_lifting.py new file mode 100644 index 00000000..ef2476f8 --- /dev/null +++ b/test/transforms/liftings/graph2hypergraph/test_page_rank_lifting.py @@ -0,0 +1,48 @@ +"""Test Page Rank Lifting.""" + +import torch + +from modules.data.utils.utils import load_manual_graph +from modules.transforms.liftings.graph2hypergraph.page_rank_lifting import ( + HypergraphPageRankLifting, +) + + +class TestHypergraphPageRankLifting: + """Test the HypergraphPageRankLifting class.""" + + def setup_method(self): + self.data = load_manual_graph() + + self.lifting = HypergraphPageRankLifting( + network_type="weighted", + alpha=0.85, + th_quantile=0.8, + n_most_influential=1, + ) + + def test_lift_topology(self): + # Test the lift_topology method + lifted_data = self.lifting.forward(self.data.clone()) + + expected_n_hyperedges = 2 + + expected_incidence_1 = torch.tensor( + [ + [1.0, 0.0], + [1.0, 0.0], + [0.0, 1.0], + [0.0, 1.0], + [1.0, 0.0], + [0.0, 1.0], + [0.0, 1.0], + [1.0, 0.0], + ] + ) + + assert ( + expected_incidence_1 == lifted_data.incidence_hyperedges.to_dense() + ).all(), "Something is wrong with incidence_hyperedges (k=1)." + assert ( + expected_n_hyperedges == lifted_data.num_hyperedges + ), "Something is wrong with the number of hyperedges (k=1)." diff --git a/tutorials/graph2hypergraph/page_rank_lifting.ipynb b/tutorials/graph2hypergraph/page_rank_lifting.ipynb new file mode 100644 index 00000000..56af1af1 --- /dev/null +++ b/tutorials/graph2hypergraph/page_rank_lifting.ipynb @@ -0,0 +1,625 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Graph-to-Hypergraph Page Rank Lifting Tutorial" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "This notebook shows how to import a dataset, with the desired lifting, and how to run a neural network using the loaded data.\n", + "\n", + "The notebook is divided into sections:\n", + "\n", + "- [Loading the dataset](#loading-the-dataset) loads the config files for the data and the desired tranformation, createsa a dataset object and visualizes it.\n", + "- [Loading and applying the lifting](#loading-and-applying-the-lifting) defines a simple neural network to test that the lifting creates the expected incidence matrices.\n", + "- [Create and run a simplicial nn model](#create-and-run-a-simplicial-nn-model) simply runs a forward pass of the model to check that everything is working as expected.\n", + "\n", + "***\n", + "***\n", + "\n", + "Note that for simplicity the notebook is setup to use a simple graph. However, there is a set of available datasets that you can play with.\n", + "\n", + "To switch to one of the available datasets, simply change the *dataset_name* variable in [Dataset config](#dataset-config) to one of the following names:\n", + "\n", + "* cocitation_cora\n", + "* cocitation_citeseer\n", + "* cocitation_pubmed\n", + "* MUTAG\n", + "* NCI1\n", + "* NCI109\n", + "* PROTEINS_TU\n", + "* AQSOL\n", + "* ZINC\n", + "***\n", + "\n", + "***\n", + "Extension ideas and TODOs\n", + "- Switch from NetworkX to PyG graph to possible speed gains (needs to be tested though)\n", + "***" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Imports and utilities" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" + ] + } + ], + "source": [ + "# With this cell any imported module is reloaded before each cell execution\n", + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "from modules.data.load.loaders import GraphLoader\n", + "from modules.data.preprocess.preprocessor import PreProcessor\n", + "from modules.utils.utils import (\n", + " describe_data,\n", + " load_dataset_config,\n", + " load_model_config,\n", + " load_transform_config,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Loading the Dataset" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we just need to spicify the name of the available dataset that we want to load. First, the dataset config is read from the corresponding yaml file (located at `/configs/datasets/` directory), and then the data is loaded via the implemented `Loaders`.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Dataset configuration for manual_dataset:\n", + "\n", + "{'data_domain': 'graph',\n", + " 'data_type': 'toy_dataset',\n", + " 'data_name': 'manual',\n", + " 'data_dir': 'datasets/graph/toy_dataset',\n", + " 'num_features': 1,\n", + " 'num_classes': 2,\n", + " 'task': 'classification',\n", + " 'loss_type': 'cross_entropy',\n", + " 'monitor_metric': 'accuracy',\n", + " 'task_level': 'node'}\n" + ] + } + ], + "source": [ + "dataset_name = \"manual_dataset\"\n", + "dataset_config = load_dataset_config(dataset_name)\n", + "loader = GraphLoader(dataset_config)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can then access to the data through the `load()`method:" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Dataset only contains 1 sample:\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " - Graph with 8 vertices and 13 edges.\n", + " - Features dimensions: [1, 0]\n", + " - There are 0 isolated nodes.\n", + "\n" + ] + } + ], + "source": [ + "dataset = loader.load()\n", + "describe_data(dataset)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "modules.data.utils.custom_dataset.CustomDataset" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(dataset)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Loading and Applying the Lifting" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this section, we will instantiate the lifting we want to apply to the data. In this tutorial, we apply an novel lifting based on PageRank. PageRank computes a ranking of the nodes in a graph based on the structure of the incoming links. Our approach is applicable to both weighted and unweighted networks. \n", + "\n", + "Our approach is straightforward: (1) calculate the Page Rank of all nodes in the graph, (2) select the top $n$ most influential nodes in the graph as hyperedges. (3) Assign all nodes in the network to $m >= 1$ most influential nodes (with $m <= n$), i.e. their respective hyperedges, based on their distance (shortest path) to each influential node. \n", + "\n", + "\n", + "\n", + "For hypergraphs, creating a lifting involves creating the `incidence_hyperedges` matrix.\n", + "\n", + "Similarly to before, we can specify the transformation we want to apply through its type and id --the corresponding config files located at `/configs/transforms.` \n", + "\n", + "Note that the *tranform_config* dictionary generated below can contain a sequence of transforms if it is needed.\n", + "\n", + "This can also be used to explore liftings from one topological domain to another; for example, using two liftings, it is possible to achieve a sequence such as graph -> simplicial complex -> hypergraph. " + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Transform configuration for graph2hypergraph/page_rank_lifting:\n", + "\n", + "{'transform_type': 'lifting',\n", + " 'transform_name': 'HypergraphPageRankLifting',\n", + " 'network_type': 'weighted',\n", + " 'alpha': 0.85,\n", + " 'th_quantile': 0.95,\n", + " 'n_most_influential': 2,\n", + " 'max_iter': 100,\n", + " 'tol': 1e-06,\n", + " 'feature_lifting': 'ProjectionSum'}\n" + ] + } + ], + "source": [ + "# Define transformation type and id\n", + "transform_type = \"liftings\"\n", + "# If the transform is a topological lifting, it should include both the type of the lifting and the identifier\n", + "transform_id = \"graph2hypergraph/page_rank_lifting\"\n", + "\n", + "# Read yaml file\n", + "transform_config = {\n", + " \"lifting\": load_transform_config(transform_type, transform_id)\n", + " # other transforms (e.g. data manipulations, feature liftings) can be added here\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We than apply the transform via our `PreProcessor`:" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Transform parameters are the same, using existing data_dir: /Users/michael/perelyn/research/topological_deep_learning/challenge-icml-2024/datasets/graph/toy_dataset/manual/lifting/3830843336\n", + "\n", + "Dataset only contains 1 sample:\n" + ] + }, + { + "data": { + "image/png": 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899kXXcyZ/W/gV7/6FQCnve5spo6PA7MD4eiRX3HJ0vMWXE7BTN6npy3D6R165EBERJqXkY8cZD2Py/o6wKPmjxaqef2ll/PKvmfxPA/f9/nVgf/g1950adm39X0fPLisryPwEyJFRERc5PmN/Okbo6PTOe792Si+D60BXka54MTMCQ7/6jA+s8f6+Y/+BYCpsWO09yziDe+4mkWLFtHT3VP6fnkfz4Pff1Mvi9v0yIGIiDQvI3+sALC4Lctlfe38++FJ8j6B6oUiD7yTL6D0xndeA5x6BML3fcbGZp9/UBgIed8nj8/b+jo0DEREpOkZ+WOFgquWdtHbluVEgHKhnGw2SyaTKb6v53lkMrNHHhsbY2x8DN/3OeH79LZluWppV6S3X0RExEZGj4P2bIZV5/aQ8Tym8+EGQiaTIZvNks/n8TyPlpbZB0l83yeTyeB5HmNjY4xPTZPxPFad20N71ugPh4iISCKM/9Pw/EWtXHtOd+iBkM3O/njA931aW1vJ52fDyNaWluJgaO/qYWpyktzTOzh/UWu1ixMREWkaxj7nYK7LTu8AYNtL40z7Pq3Ufg5CNptlZmam+KOFfD5PR0cHU1NTtLS2km3rID9zgqf/59f5twf+By0vD3PrrbfGfxgRERHDWTEOYHYg9LZl2XJgjNHpHBkfWjwq/rPOmUyGXC5HZ2cn01NTtLW1kc/laO3oJNvSyuihF3n10X9kx9A3eetb38qGDRsANBBERKTpGf9jhbnOX9TKBy86jbed3oHnwbTvM5XLkyvzhMVsNovv+7S0tpLHo62rB1rbaWtt5cf/3310793Gw/f/HTfccAM//vGPueaaa9iwYQNf/OIX0zmciIiIIYx9nYNajk7neHpkij0jk4yfmB0Hnucxk88xNTn7SMHU5AQdnV1MHD9OZxZ+8k8P8LrcMX7+1I85fvw4F1xwATt27OC6667jO9/5Dr/5m7/Jd77zHdavX69HEEREpGlZ9cjBXIvbslz5a138tzcv4YMXncb15/fwtjM6ONubZPhH/8xSb4Kn/mmIJQf3Mnz//82er3yad/5aN9/59t/z8Y9/nJ/+9Ke8/e1vp6uriwMHDrBixQq2bNnC7/3e7+kRBBERaWrWjoOCrOdxVmcLb+nr4DfO6eZ/y77GQ3/1p7yt9Rj/sWWIV36yk3de+iZ2/Ou/8lu/9Vt4nsfevXu54YYb+NKXvsTnPvc5fvSjHzEwMMB5553H1q1b+fCHP6yBICIiTcv6cVDNsmXL2L9/P9dccw1TU1P89Kc/5YYbbuAb3/gGn/zkJzl+/DiPP/44H/nIR/jiF7/In/7pnzIzM8POnTv5+Mc/roEgIiJNyelx0N/fz/79+7nwwgs599xzeeSRR7j55pv55S9/yY9//GM++tGPcs8993DjjTdy8cUXc8cdd/DVr36VZ599lmeffZZPfvKTGggiItJ0nB4HF1xwAS+88AIA11xzDY888ghvfvObufLKK7n33nv58Ic/zBlnnMHnPvc5vvzlL/PLX/6SoaEhvvKVr/Dggw8yPj7O+vXrNRBERKSpOD0O+vv7mZiY4OWXX+aaa67hhRde4IUXXuDmm2/m8ccf5/nnn+dTn/oUDz74IIcOHeIv/uIv+Pa3v82JEye48847+fKXv8wZZ5yhgSAiIk3F6XFwwQUXALB//36uuuoqWltbeeSRR3jXu97FOeecw7333sv73vc+BgcHueOOO1izZg3vfve7+cQnPsG73/1ubrrpJm6//Xbe9ra3aSCIiEjTcHocvP71r8fzPPbv3093dzfveMc7eOSRR2hpaeFDH/oQ3/3udxkZGeHOO+/kmWeeYdOmTXz+85+ns7OTP/qjP+LTn/40V199NWvXruX666/XQBARkabg9Dhoa2vjnHPOYf/+/cDs8w527NjB5OQkH/jAB/A8j3/4h39gYGCAG264gc997nNkMhm+9KUv8dhjj/G1r32Nr3zlK5x33nn87u/+Lh/4wAc0EERExHlOjwM4lTMCxaTxscceY8mSJcWs8cSJE/zZn/0Zx48f52//9m+56qqr+MhHPsJdd93Fvn37+Pu//3tmZmb40Ic+xLp16zQQRETEac6Pg0LOCJQkjUAxa3zwwQdZunRpMW184YUXWL9+PRdffDEf+chHWLx4Md/85jd59tln+cM//EM+9rGPaSCIiIiznB8HhZyx8G8vFJJGoCRrBIpp45//+Z/T2tpazBvvuOMOLr300mLi+Jd/+ZfceuutGggiIuIk58fB3JwRKEkagWLW+NRTT9HZ2VlMG3fs2EF/f38xb/zBD37Au971rmLi+K1vfUsDQUREnOT8OJibMwIlSSNQkjUCJWljLpfjt3/7t4t546FDh7j55puLieP27ds1EERExDnOj4O5OSNQkjQCJVnj4cOH8TyvmDbef//9eJ5XzBs/9rGPkc/nufPOO4uJ489+9jMNBBERcYrz42B+zgilSSNQkjUCJWnj0aNH6e3tLeaNX/3qV2lpaSlJHF955RUNBBERcYbz4wBKc0YoTRqBBVkjUJI2AiV5409+8hN6enpKEseJiQkNBBERcUJTjIO5OSMsTBqhNGsEFqSNQEneOD4+ztKlS0sSx3w+r4EgIiLWa4pxMDdnBBYkjbAwa4TStBFYkDcCCxJHQANBRESs1hTjYH7OCAuTRijNGoEFaWPhsubmjcCCxBE0EERExF5NMQ7m54ywMGmEhVkjLEwbgQV5I7AgcQQNBBERsVNTjIP5OSMsTBphYdYILEgbC782N2/M5XLFt5ubOIIGgoiI2KcpxkG5nBEWJo2wMGuEhWkjsCBvBMomjqCBICIidmmKcQALc0ZYmDRC+awRFqaNsDBvBMomjqCBICIi9miacTA/Z4TySSMszBqhfNoIC/PGwtvOTxxBA0FEROzQNONgfs4I5ZNGKJ81wsK0EcrnjVA+cQQNBBERMV/TjINyOSOUTxphYdYI5dPGwmXPzxuhfOIIGggiImK2phkH5XJGKJ80QvmsEcqnjVA+b4TyiSNoIIiIiLmaZhyUyxmhfNII5bNGKJ82Fn59ft449+3nJ46ggSAiImZqmnFQKWeE8kkjlM8aoXzaCOXzRqicOIIGgoiImKdpxgGUzxmhfNIIlbNGKJ82Qvm8ESonjqCBICIiZmmqcVAuZ4TKSSOUzxqhctoI5fPGwvuUSxxBA0FERMzRVOOgXM4IlZNGqJw1Qvm0ESrnjVA5cQQNBBERMUNTjYNKOSNUThqhfNYIldPGwnWVyxuhcuIIGggiIpK+phoHlXJGqJw0QuWsESqnjVA5b4TKiSNoIIiISLqaahxUyhmhctIIlbNGqJw2Fn6vXN449/3KJY6ggSAiIulpqnFQLWeEykkjVM4aoXLaCJXzRqieOIIGgoiIpKOpxgFUzhmhctII1bNGqJw2QuW8EaonjqCBICIiyWu6cVApZ4TqSSNUzhqhetoIlfPGwvtWShxBA0FERJLVdOOgUs4I1ZNGqJ41QuW0EarnjVA9cQQNBBERSU7TjYNqOSNUTxqhctYI1dPGwnVXyhuheuIIGggiIpKMphsH1XJGqJ40QvWsEaqnjVA9b4TqiSNoIIiISPyabhxUyxmhetII1bNGqJ42Fn6/Ut449/0rJY6ggSAiIvFqunFQK2eE6kkjVM8aoXraCNXzRqidOIIGgoiIxKfpxgFUzxmhetIItbNGqJ42QvW8EWonjqCBICIi8WjKcVAtZ4TaSSNUzxqhdtoI1fPGwmVUSxxBA0FERKLXlOOgWs4ItZNGqJ01QvW0EWrnjVA7cQQNBBERiVZTjoNaOSPUThqhetYItdPGwm2pljdC7cQRNBBERCQ6TTkOauWMUDtphNpZI9ROG6F23gi1E0fQQBARkWg05TiolTNC7aQRameNUDttLLxNtbxx7uVUSxxBA0FERBrXlOMgSM4ItZNGqJ01Qu20EWrnjRAscQQNBBERaUxTjgOonTNC7aQRgmWNUDtthNp5IwRLHEEDQURE6te046BWzgjBkkaonTVCsLQRaueNhcuqlTiCBoKIiNSnacdBrZwRgiWNECxrhNppIwTLGyFY4ggaCCIiEl7TjoMgOSMESxqhdtYIwdLGwm2rlTdCsMQRNBBERCScph0HQXJGCJY0QrCsEYKljRAsb4RgiSNoIIiISHBNOw5e//rXk8lkao6DIEkjBMsaIVjaWHi7Wnnj3MurlTiCBoKIiATTtOMgaM4IwZJGCJY1QrC0EYLljRA8cQQNBBERqa1pxwHM/mgh6DiolTRC8KwRgqWNECxvhOCJI2ggiIhIdU09DoLkjBA8aYRTWeMPf/jDqm8XNG2EYHlj4TKDJI6ggSAiIpU19TgoPHJQLWeE4EkjnMoav/71r9d82yBpIwTPGyF44ggaCCIiUl5Tj4P+/n4mJydr5owQPGmEYFkjBE8bC7c1SN4IwRNH0EAQEZGFmnocBM0ZIXjSCMGzRgieNkLwvBGCJ46ggSAiIqWaehwEzRkheNIIwbNGCJ42Ft42SN4493KDJI6ggSAiIqc09TgIkzNC8KQRgmeNEDxthOB5I4RLHEEDQUREZjX1OIDgOSMETxohXNYIwdNGCJ43QrjEETQQRERE4yBwzgjhkkYInjVCuLQRgueNhcsOmjiCBoKISLNr+nEQNGeEcEkjhMsaIXjaCOHyRgiXOIIGgohIM2v6cRAmZ4RwSSMEzxohXNoI4fJGCJc4ggaCiEizavpxECZnhHBJI4TLGiFc2gjh8kYIlziCBoKISDNq+nEQJmeEcEkjhMsaIVzaWHj7oHnj3MsPmjiCBoKISLNp+nEQNmeEcEkjhMsaIVzaCOHyRgifOIIGgohIM2n6cQDhckYIlzRC+KwRwqWNEC5vhPCJI2ggiIg0C40DwuWMED5phHBZI4RPGyFc3li4jjCJI2ggiIg0A40DwuWMED5phPBZI4RLGyF83gjhE0fQQBARcZ3GAeFzRgifNEK4rBHCp40QPm+E8IkjaCCIiLhM44DwOSOETxohfNYI4dNGCJ83QvjEETQQRERcpXFA+JwRwieNED5rhPBpY+F9wuSNc68nTOIIGggiIi7SOKC+nBHCJ40QPmuE8GkjhM8bob7EETQQRERco3FwUticEcInjVBf1gjh00YInzdCfYkjaCCIiLhE4+CksDkj1Jc0QvisEepLGyF83li4rrCJI2ggiIi4QuPgpLA5I9SXNEJ9WSOETxuhvrwR6kscQQNBRMQFGgcn1ZMzQn1JI4TPGqG+tBHqyxuhvsQRNBBERGyncXBSPTkj1Jc0Qn1ZI9SXNkJ9eSPUlziCBoKIiM00Dk6qJ2eE+pJGqC9rhPrSxsL7hc0b515f2MQRNBBERGylcXBSvTkj1Jc0Qn1ZI9SXNkJ9eSPUnziCBoKIiI00DuaoJ2eE+pJGqD9rhPrSRqgvb4T6E0fQQBARsY3GwRz15IxQf9II9WWNUH/aCPXljYXrrCdxBA0EERGbaBzMUU/OCPUnjVB/1gj1pY1Qf94I9SeOoIEgImILjYM56s0Zof6kEerLGqH+tBHqzxuh/sQRNBBERGygcTBHvTkj1J80Qv1ZI9SfNkL9eSPUnziCBoKIiOk0DuaoN2eE+pNGqD9rhPrTxsL71pM3zr3eehJH0EAQETGZxsEcjeSMUH/SCPVnjVB/2gj1543QWOIIGggiIqbSOJin3pwR6k8aobGsEepPG6H+vBEaSxxBA0FExEQaB/PUmzNCY0kj1J81QmNpI9SfNxauu97EETQQRERMo3EwT705IzSWNEJjWSPUnzZCY3kjNJY4ggaCiIhJNA7maSRnhMaSRqg/a4TG0kZoLG+ExhJH0EAQETGFxsE8jeSM0FjSCI1ljdBY2giN5Y3QWOIIGggiIibQOJinkZwRGksaobGsERpLGwvvX2/eOPf6600cQQNBRCRtGgfzNJozQmNJIzSWNUJjaSM0ljdC44kjaCCIiKRJ46CMRnJGaCxphMazRmgsbYTG8kZoPHEEDQQRkbRoHJTRSM4IjSeN0FjWCI2njdBY3li4DY0kjqCBICKSBo2DMhrJGaHxpBEazxqhsbQRGs8bofHEETQQRESSpnFQRqM5IzSeNEJjWSM0njZC43kjNJ44ggaCiEiSNA7KaDRnhMaTRmg8a4TG00ZoPG+ExhNH0EAQEUmKxkEZjeaM0HjSCI1njdB42li4jEbyxrm3o5HEETQQRESSoHFQRhQ5IzSeNELjWSM0njZC43kjRJM4ggaCiEjcNA4qaDRnhMaTRogma4TG00ZoPG+EaBJH0EAQEYmTxkEFjeaMEE3SCI1njRBN2giN542F29Jo4ggaCCIicdE4qKDRnBGiSRohmqwRGk8bIZq8EaJJHEEDQUQkDhoHFUSRM0I0SSM0njVCNGkjRJM3QjSJI2ggiIhETeOggihyRogmaYRoskaIJm2EaPJGiCZxBA0EEZEoaRxUEEXOCNEkjRBN1gjRpI2Fy2k0b5x7expNHEEDQUQkKhoHFUSVM0I0SSNEkzVCNGkjRJM3QnSJI2ggiIhEQeOgiihyRogmaYToskaIJm2EaPJGiC5xBA0EEZFGaRxUEUXOCNEljRBN1gjRpY0QTd5YuE1RJI6ggSAi0giNgyqiyBkhuqQRossaIZq0EaLLGyG6xBE0EERE6qVxUEVUOSNElzRCNFkjRJc2QnR5I0SXOIIGgohIPTQOqogqZ4TokkaILmuE6NJGiC5vhOgSR9BAEBEJS+OgiqhyRoguaYToskaILm0sXFYUeePc2xVF4ggaCCIiYWgcVBFlzgjRJY0QXdYI0aWNEF3eCNEmjqCBICISlMZBDVHljBBd0gjRZo0QXdoI0eWNEG3iCBoIIiJBaBzUEFXOCNEmjRBd1gjRpo0QXd5YuG1RJY6ggSAiUovGQQ1R5YwQbdII0WaNEF3aCNHmjRBt4ggaCCIi1Wgc1BBlzgjRJo1wKmvcu3dvw5cVZdoI0eaNEG3iCBoIIiKVaBzUsGzZMiCanBGiTRrhVNYY1aMHUaaNEG3eCNEmjqCBICJSjsZBDeeff35kOSNEmzRCtFkjRJs2Fi4vqrxx7u2LKnEEDQQRkfk0Dmoo5IzDw8ORXWaUSSNEmzVCtGkjRJs3QvSJI2ggiIjMpXEQwAUXXBDZcwQg2qQRos8aIdq0EaLNGyH6xBE0EERECjQOAogyZ4Tok0aINmuE6NNGiDZvhOgTR9BAEBEBjYNAoswZIfqkEaLPGiHatBGizxsh+sQRNBBERDQOAog6Z4Tok0aINmuE6NNGiD5vhOgTR9BAEJHmpnEQQNQ5I0SfNEL0WSNEnzZC9HkjRJ84ggaCiDQvjYMAos4ZIfqkEaLPGiH6tLFwmVHmjYXLjDpxBA0EEWlOGgcBxJEzQvRJI0SfNUL0aSNEnzdCPIkjaCCISPPROAgo6pwRok8aIZ6sEaJPGyH6vBHiSRxBA0FEmovGQUBR54wQT9II0WeNEE/aCNHnjRBP4ggaCCLSPDQOAoo6Z4R4kkaIJ2uE6NNGiCdvhHgSR9BAEJHmoHEQUBw5I8STNEL0WSPEkzZCPHkjxJM4ggaCiLhP4yCgOHJGiCdphHiyRognbYR48kaIJ3EEDQQRcZvGQUBx5IwQT9II8WSNEE/aWLjcqPPGwuXGkTiCBoKIuEvjIKC4ckaIJ2mEeLJGiCdthHjyRogvcQQNBBFxk8ZBCHHkjBBP0gjxZY0QT9oI8eSNEF/iCBoIIuIejYMQ4sgZIb6kEeLJGiG+tBHiyRshvsQRNBBExC0aByHEkTNCfEkjxJc1QjxpI8SXN0J8iSNoIIiIOzQOQogrZ4T4kkaIJ2uE+NJGiC9vhPgSR9BAEBE3aByEEFfOCPEljRBf1gjxpY0QX94I8SWOoIEgIvbTOAghrpwR4ksaIb6sEeJLGwuXHUfeWLjsuBJH0EAQEbtpHIQQZ84I8SWNEF/WCPGljRBf3gjxJo6ggSAi9tI4CCmunBHiSxoh3qwR4ksbIb68EeJNHEEDQUTspHEQUlw5I8SbNEJ8WSPEmzZCfHkjxJs4ggaCiNhH4yCkuHJGiDdphHizRogvbYR480aIN3EEDQQRsYvGQUhx5owQb9II8WWNEG/aCPHmjRBv4ggaCCJiD42DkOLMGSHepBHizRoh3rQR4s0bId7EETQQRMQOGgchxZkzwqmkcdu2bbFcfpxZI8SbNhYuP668sXD5cSaOoIEgIubTOAgp7pwRZn+08Nhjj8WSNEK8WSPEmzZCvHkjxJ84ggaCiJhN46AOceaMEG/SCPFnjRBv2gjx5o0Qf+IIGggiYi6NgzrEmTNC/EkjxJs1QvxpI8SbN0L8iSNoIIiImTQO6hBnzgjxJ40Qf9YI8aaNEH/eCPEnjqCBICLm0TioQ9w5I8SfNEK8WSPEnzZC/HkjxJ84ggaCiJhF46AOceeMEH/SCPFnjRB/2gjx540Qf+IIGggiYg6NgzrEnTNC/EkjxJ81QvxpY+E64swbC9cRd+IIGggiYgaNgzokkTNC/EkjxJ81QvxpI8SfN0IyiSNoIIhI+jQO6hR3zgjxJ42QTNYI8aeNEH/eCMkkjqCBICLp0jioU9w5IySTNEL8WSMkkzZC/HkjJJM4ggaCiKRH46BOceeMkEzSCMlkjRB/2gjJ5I2QTOIIGggikg6NgzolkTNCMkkjxJ81QjJpIySTN0IyiSNoIIhI8jQO6pREzgjJJI2QTNYIyaSNkEzeCMkkjqCBICLJ0jioUxI5IySTNEIyWSMkkzYWrifuvLFwPUkkjqCBICLJ0TioU1I5IySTNEIyWSMkkzZCMnkjJJc4ggaCiCRD46ABSeSMkEzSCMlljZBM2gjJ5I2QXOIIGggiEj+NgwYkkTNCckkjJJM1QnJpIySTN0JyiSNoIIhIvDQOGpBEzgjJJY2QXNYIyaSNkFzeCMkljqCBICLx0ThoQFI5IySXNEIyWSMklzZCcnkjJJc4ggaCiMRD46ABSeWMkFzSCMlljZBc2gjJ5Y2QXOIIGggiEj2NgwYklTNCckkjJJc1QnJpY+G6ksgbC9eVVOIIGggiEi2NgwYkmTNCckkjJJc1QnJpIySXN0KyiSNoIIhIdDQOGpRUzgjJJY2QbNYIyaWNkFzeCMkmjqCBICLR0DhoUFI5IySbNEJyWSMkmzZCcnkjJJs4ggaCiDRO46BBSeWMkGzSCMlmjZBc2gjJ5o2QbOIIGggi0hiNgwYlmTNCskkjJJc1QrJpIySbN0KyiSNoIIhI/TQOGpRkzgjJJo2QbNYIyaaNkGzeCMkmjqCBICL10ThoUJI5IySbNEKyWSMkmzYWri+pvLFwfUkmjqCBICLhaRw0KOmcEZJNGiHZrBGSTRsh2bwRkk8cQQNBRMLROIhAkjkjJJs0QvJZIySbNkKyeSMknziCBoKIBKdxEIEkc0ZIPmmEZLNGSD5thGTzRkg+cQQNBBEJRuMgAknmjJB80gjJZ42QbNoIyeeNkHziCBoIIlKbxkEEks4ZIfmkEZLNGiH5tBGSzxsh+cQRNBBEpDqNgwgknTNC8kkjJJ81QvJpIySfN0LyiSNoIIhIZRoHEUg6Z4Tkk0ZIPmuE5NPGwnUmmTcWrjPpxBE0EESkPI2DCKSRM0LySSMknzVC8mkjJJ83QjqJI2ggiMhCGgcRSTpnhOSTRjiVNf7d3/1dYlkjJJ82QvJ5I6STOIIGgoiU0jiISNI5I6STNMLsz8cPHTqUWNYI6aSNkHzeCOkkjqCBICKnaBxEZNmyZYnmjJBO0gjpZI2QfNoIpXnjpz/96cSuN43EETQQRGSWxkFEli1blnjOCOkkjZB81gjppI1wKm+87777EssbIZ3EETQQRETjIDKFnDHpJyWmkTRCOlkjpJM2wmze+J73vCfRvBHSSRxBA0Gk2WkcRKSQMyb9N/ju7m7e/va3J5o0QjpZI6STNhau96/+6q8SzRsL15tG4ggaCCLNTOMgImnljADXXntt4kkjpJM1QjppI6STN0J6iSNoIIg0K42DCKWRM0I6SSOklzVCOmkjpJM3QnqJI2ggiDQjjYMIpZEzQnpJI6STNUJ6aSOkkzdCeokjaCCINBuNgwilkTNCekkjpJc1QjppI6SXN0J6iSNoIIg0E42DCKWVM0J6SSOkkzVCemkjpJc3QnqJI2ggiDQLjYMIpZUzQnpJI6SXNUJ6aSOklzdCeokjaCCINAONgwillTNCekkjpJc1QnppY+G608gbC9edVuIIGggirtM4iFCaOSOklzRCelkjpJc2Qnp5I6SbOIIGgojLNA4illbOCOkljZBu1gjppY2QXt4I6SaOoIEg4iqNg4illTNCukkjpJc1QrppI6SXN0K6iSNoIIi4SOMgYmnljJBu0gjpZo2QXtoI6eaNkG7iCBoIIq7ROIhYmjkjpJs0QnpZI6SbNkK6eSOkmziCBoKISzQOIpZmzgjpJo2QbtYI6aaNkG7eCOkmjqCBIOIKjYOIpZkzQrpJI6SbNUK6aWPh+tPKGwvXn2biCBoIIi7QOIhY2jkjpJs0QrpZI6SbNkK6eSOknziCBoKI7TQOYpBmzgjpJo2QftYI6aaNkG7eCOknjqCBIGIzjYMYpJkzQvpJI6SbNUL6aSOkmzdC+okjaCCI2ErjIAZp5oyQftII6WeNkG7aCOnnjZB+4ggaCCI20jiIQdo5I6SfNEK6WSOknzZC+nkjpJ84ggaCiG00DmKQds4I6SeNkH7WCOmnjZB+3gjpJ46ggSBiE42DGKSdM0L6SSOknzVC+mlj4TakmTcWbkPaiSNoIIjYQuMgBibkjJB+0gjpZ42QftoI6eeNYEbiCBoIIjbQOIhJ2jkjpJ80ghlZI6SfNkL6eSOYkTiCBoKI6TQOYpJ2zghmJI2QftYIZqSNkH7eCGYkjqCBIGIyjYOYpJ0zghlJI5iRNUL6aSOYkTeCGYkjaCCImErjICYm5IxgRtII6WeNYEbaCGbkjWBG4ggaCCIm0jiIiQk5I5iRNIIZWSOYkTaCGXkjmJE4ggaCiGk0DmJiQs4IZiSNYEbWCGakjYXbkXbeWLgdJiSOoIEgYhKNg5iYkjOCGUkjmJE1ghlpI5iRN4I5iSNoIIiYQuMgRibkjGBG0gjmZI1gRtoIZuSNYE7iCBoIIibQOIiRCTkjmJM0ghlZI5iTNoIZeSOYkziCBoJI2jQOYmRCzgjmJI1gTtYIZqSNYE7eCOYkjqCBIJImjYMYmZIzgjlJI5iRNYI5aSOYkzeCOYkjaCCIpEXjIEam5IxgTtII5mSNYE7aCObkjWBO4ggaCCJp0DiIkSk5I5iTNII5WSOYkzYWbosJeWPhtpiSOIIGgkjSNA5iZFLOCOYkjWBO1gjmpI1gTt4IZiWOoIEgkiSNg5iZkjOCOUkjmJU1gjlpI5iTN4JZiSNoIIgkReMgZqbkjGBW0gjmZI1gVtoI5uSNYFbiCBoIIknQOIiZKTkjmJU0gllZI5iTNoJZeSOYlTiCBoJI3DQOYmZSzginkkZTHs0wJWsEs9JGMCtvBLMSR9BAEImTxkHMTMoZwaykEczKGsGstBHMyhvBrMQRNBBE4qJxEDOTckY4lTSaMg5MyhrBrLSxcHtMyRsLt8ekxBE0EETioHEQM9NyRjAraQSzskYwK20Es/JGMC9xBA0EkahpHCTApJwRzEoawbysEcxKG8GsvBHMSxxBA0EkShoHCTApZwTzkkYwK2sE89JGMCtvBPMSR9BAEImKxkECTMoZwbykEczLGsGstBHMyxvBvMQRNBBEoqBxkADTckYwL2kEs7JGMC9tBPPyRjAvcQQNBJFGaRwkwLScEcxLGsG8rBHMSxvBvLwRzEscQQNBpBEaBwkwLWcE85JGMC9rBPPSRjAvbyzcJtMSR9BAEKmXxkECTMwZwbykEczLGsG8tBHMyxvBzMQRNBBE6qFxkBDTckYwL2kEM7NGMC9tBPPyRjAzcQQNBJGwNA4SYlrOCGYmjWBe1ghmpo1gXt4IZiaOoIEgEobGQUJMyxnBzKQRzMwawby0EczMG8HMxBE0EESC0jhIiIk5I5iZNIJ5WSOYmTaCmXkjmJk4ggaCSBAaBwkxMWcEM5NGMDNrBDPTRjAzbwQzE0fQQBCpReMgISbmjGBm0ghmZo1gZtoIZuaNYG7iCBoIItVoHCTE1JwRzEwawcysEcxMG8HMvBHMTRxBA0GkEo2DBJmYM4KZSSOYmzWCmWkjmJk3grmJI2ggiJSjcZAgE3NGMDdpBDOzRjA3bQQz80YwN3EEDQSR+TQOEmRizgjmJo1gbtYIZqaNYG7eCOYmjqCBIDKXxkGCTM0ZwdykEczMGsHctBHMzRvB3MQRNBBECjQOEmRqzgjmJo1gbtYI5qaNYG7eCOYmjqCBIAIaB4kyNWcEc5NGMDdrBHPTRjA3bwSzE0fQQBDROEiQyTkjmJs0grlZI5ibNoK5eSOYnTiCBoI0N42DhC1btszIRw7A3KQRzM4awdy0EczNG8HsxBE0EKR5aRwkrFAsmMjkpBHMzRrB7LQRzM0bwezEETQQpDlpHCSsMA5M+wYIZieNYHbWCOamjWB23ghmJ46ggSDNR+MgYYWc0bSfrxaYnDSCuVkjmJ02gtl5I5idOIIGgjQXjYOEmZwzgtlJI5idNYLZaSOYnTeC2YkjaCBI89A4SJjJOSOYnTSC2VkjmJ02gtl5I5ifOIIGgjQHjYOEmZ4zgtlJI5idNYLZaSOYnTeC+YkjaCCI+zQOUmByzghmJ41gftYIZqeNYHbeCOYnjqCBIG7TOEiByTkjmJ80gtlZI5ifNoLZeSOYnziCBoK4S+MgBSbnjGB+0gjmZ41gdtoI5ueNYH7iCBoI4iaNgxSYnjOC+UkjmJ01gvlpI5ifN4L5iSNoIIh7NA5SYHrOCOYnjWB+1gjmp41gft4I5ieOoIEgbtE4SIHpOSOYnzSC+VkjmJ82gvl5I9iROIIGgrhD4yAFNuSMYH7SCOZnjWB+2gjm541gR+IIGgjiBo2DlJieM4L5SSPYkTWC+WkjmJ83gh2JI2ggiP00DlJies4IdiSNYH7WCHakjWB+3gh2JI6ggSB20zhIiek5I9iRNIIdWSOYnzaCHXkj2JE4ggaC2EvjICU25IxgR9II5meNYEfaCHbkjWBH4ggaCGInjYOU2JAzgh1JI9iRNYIdaSPYkTeCHYkjaCCIfTQOUmJDzgh2JI1gR9YIdqSNYEfeCPYkjqCBIHbROEiJLTkj2JE0gh1ZI9iRNoIdeSPYkziCBoLYQ+MgRTbkjGBH0gj2ZI1gR9oIduSNYE/iCBoIYgeNgxTZkDOCPUkj2JE1gj1pI9iRN4I9iSNoIIj5NA5SZEPOCPYkjWBP1gh2pI1gT94I9iSOoIEgZtM4SJEtOSPYkzSCHVkj2JM2gj15I9iTOIIGgphL4yBFtuSMYE/SCPZkjWBP2gj25I1gT+IIGghiJo2DFNmSM4I9SSPYkzWCPWkj2JM3gl2JI2ggiHk0DlJkU84I9iSNYE/WCPakjWBP3gh2JY6ggSBm0ThImS05I9iTNIJdWSPYkzaCPXkj2JU4ggaCmEPjIGW25IxgV9II9mSNYFfaCPbkjWBX4ggaCGIGjYOU2ZIzgl1JI9iVNYI9aSPYlTeCXYkjaCBI+jQOUmZTzgh2JY1gT9YIdqWNYFfeCHYljqCBIOnSOEiZTTkj2JU0gl1ZI9iVNoJdeSPYlTiCBoKkR+MgZTbljGBX0gh2ZY1gV9oIduWNYF/iCBoIkg6Ng5TZljOCXUkj2JU1gl1pI9iVN4J9iSNoIEjyNA4MYFPOCHYljWBf1gh2pY1gV94I9iWOoIEgydI4MIBNOSPYlzSCXVkj2Jc2gl15I9iXOIIGgiRH48AANuWMYF/SCPZljWBX2gj25Y1gX+IIGgiSDI0DA9iWM4J9SSPYlTWCfWkj2Jc3gn2JI2ggSPw0DgxgW84I9iWNYF/WCPaljWBf3gj2JY6ggSDx0jgwgG05I9iXNIJ9WSPYlzaCfXkj2Jk4ggaCxEfjwAA25oxgX9II9mWNYF/aCPbljWBn4ggaCBIPjQND2JYzgn1JI9iZNYJ9aSPYlzeCnYkjaCBI9DQODGFbzgh2Jo1gX9YIdqaNYF/eCHYmjqCBINHSODCEbTkj2Jk0gp1ZI9iXNoKdeSPYmTiCBoJER+PAEDbmjGBn0gj2ZY1gZ9oIduaNYGfiCBoIEg2NA0PYmDOCnUkj2Jk1gp1pI9iZN4KdiSNoIEjjNA4MYWPOCHYmjWBn1gh2po1gZ94I9iaOoIEgjdE4MIStOSPYmTSCnVkj2Jk2gp15I9ibOIIGgtRP48AgNuaMYGfSCPZmjWBn2gh25o1gb+IIGghSH40Dg9iYM4K9SSPYmTWCvWkj2Jk3gr2JI2ggSHgaBwaxMWcEe5NGsDdrBDvTRrA3bwR7E0fQQJBwNA4MYmvOCPYmjWBn1gj2po1gb94I9iaOoIEgwWkcGMTWnBHsTRrB3qwR7E0bwd68EexNHEEDQYLRODCIrTkj2Js0gr1ZI9ibNoK9eSPYnTiCBoLUpnFgEJtzRrA3aQR7s0awN20Ee/NGsDtxBA0EqU7jwDC25oxgb9IIdmeNYG/aCPbmjWB34ggaCFKZxoFhbM0Zwe6kEezNGsHutBHszRvB7sQRNBCkPI0Dw9iaM4LdSSPYnTWCvWkj2J03gt2JI2ggyEIaB4axOWcEu5NGsDdrBLvTRrA7bwS7E0fQQJBSGgeGsTlnBLuTRrA7awS700awO28EuxNH0ECQUzQODGNzzgh2J41gd9YIdqeNYHfeCPYnjqCBILM0Dgxje84IdieNYHfWCHanjWB33gj2J46ggSAaB0ayOWcEu5NGsD9rBLvTRrA7bwT7E0fQQGh2GgcGsjlnBPuTRrA7awT700awO28E+xNH0EBoZhoHBrI5ZwT7k0awP2sEu9NGsD9vBPsTR9BAaFYaBwayPWcE+5NGsDtrBPvTRrA/bwT7E0fQQGhGGgcGsj1nBPuTRrA/awT700awP28E+xNH0EBoNhoHBrI9ZwT7k0awP2sE+9NGsD9vBDcSR9BAaCYaBwZyIWcE+5NGOJU1fvvb3077ptTN9rQR7M8bwY3EETQQmoXGgaFsLxbA/qQRTmWN3/jGN6zNGsH+tBHszxvBjcQRNBCagcaBoWx/rQNwI2kE+7NGcCNtBPvzRnAjcQQNBNdpHBjK9pwR3EgawY2sEexPG8GNvBHcSBxBA8FlGgeGciFnBDeSRrA/awQ30kZwI28ENxJH0EBwlcaBoVzIGcGNpBHcyBrBjbQR3MgbwY3EETQQXKRxYCgXckZwI2kEN7JGcCNtBDfyRnAncQQNBNdoHBjKlZwR3EgawY2sEdxIG8GNvBHcSRxBA8ElGgcGcyFnBDeSRnAnawQ30kZwI28EdxJH0EBwhcaBwVzIGcGdpBHcyBrBnbQR3MgbwZ3EETQQXKBxYDAXckZwJ2kEd7JGcCNtBHfyRnAncQQNBNtpHBjMlZwR3EkawY2sEdxJG8GdvBHcSRxBA8FmGgcGcyVnBHeSRnAnawR30kZwJ28EdxJH0ECwlcaBwVzJGcGdpBHcyRrBnbQR3Mkbwa3EETQQbKRxYDCXckZwJ2kEd7JGcCdtBHfyRnArcQQNBNtoHBjOlZwR3Ekawa2sEdxJG8GdvBHcShxBA8EmGgeGcyVnBLeSRnAnawS30kZwJ28EtxJH0ECwhcaB4VzJGcGtpBHcyhrBnbQR3Mobwa3EETQQbKBxYDiXckZwK2kEd7JGcCttBLfyRnArcQQNBNNpHBjOpZwR3Eoawa2sEdxKG8GtvBHcShxBA8FkGgeGcylnBLeSRnArawS30kZwK28E9xJH0EAwlcaB4VzLGcGtpBHcyhrBrbQR3Mobwb3EETQQTKRxYAGXckZwK2kE97JGcCttBLfyRnAvcQQNBNNoHFjApZwR3Esawa2sEdxLG8GtvBHcSxxBA8EkGgcWcClnBPeSRnAvawS30kZwL28E9xJH0EAwhcaBBVzLGcG9pBHcyhrBvbQR3Msbwb3EETQQTKBxYAHXckZwL2kE97JGcC9tBPfyRnAvcQQNhLRpHFjAtZwR3Esawb2sEdxLG8G9vBHcTBxBAyFNGgcWcDFnBPeSRnAvawT30kZwL28ENxNH0EBIi8aBJVzLGcG9pBHczBrBvbQR3Msbwc3EETQQ0qBxYAnXckZwM2kE97JGcDNtBPfyRnAzcQQNhKRpHFjCtZwR3Ewawc2sEdxLG8HNvBHcTBxBAyFJGgeWcDFnBDeTRnAvawQ300ZwM28ENxNH0EBIisaBJVzMGcHNpBHczBrBzbQR3Mwbwc3EETQQkqBxYAkXc0ZwM2kEN7NGcDNtBDfzRnA3cQQNhLhpHFjC1ZwR3Ewawc2sEdxMG8HNvBHcTRxBAyFOGgcWcTFnBDeTRnA3awQ300ZwM28EdxNH0ECIi8aBRVzMGcHdpBHczBrB3bQR3Mwbwd3EETQQ4qBxYBEXc0ZwN2kEd7NGcDNtBHfzRnA3cQQNhKhpHFjE1ZwR3E0awc2sEdxNG8HdvBHcTRxBAyFKGgcWcTVnBHeTRnA3awR300ZwN28EdxNH0ECIisaBRVzNGcHdpBHczRrB3bQR3M0bwe3EETQQoqBxYBGXc0ZwN2kEd7NGcDdtBHfzRnA7cQQNhEZpHFjG1ZwR3E0awe2sEdxNG8HdvBHcThxBA6ERGgeWcTVnBLeTRnA3awS300ZwN28EtxNH0ECol8aBZVzNGcHtpBHczhrB3bQR3M4bwe3EETQQ6qFxYBmXc0ZwO2kEd7NGcDttBLfzRnA7cQQNhLA0Dizjcs4IbieN4HbWCG6njeB23ghuJ46ggRCGxoFlXM4Zwe2kEdzOGsHttBHczhvB/cQRNBCC0jiwjOs5I7idNILbWSO4nTaC23kjuJ84ggZCEBoHFnI5ZwS3k0ZwP2sEt9NGcDtvBPcTR9BAqEXjwEIu54zgftIIbmeN4H7aCG7njeB+4ggaCNV4vu/7ad+IRuR8n8OTOV6ZmOHViRwvvXqYbf/8L1z7G1dzzpmnc2ZnlrM6Wzi9I0vW89K+uQ0pnHXTP21hx09+ygd//7+RA7KeR3dLxqmz/untf8aPf/4Cn7/7Xl6dyDE+kyfn+06d9YYbbiCXy/E/v/vdks9hV846MTHBr//6r/PWt7615AmY879mbT7v8PAwq1at4n3vex9f+MIXFvy+C2d9+OGHuemmm/jwhz/Mpz71qYpvZ/NZv/jFL7JhwwbWr1/PrbfeWvPtbT5rUNaOg6PTOZ4emWLPyCTjJ3zyvk/G85jJ55ianKK9o52WTLb4692tHpf1dfCWvnYWt2XTvvmhzD/rTG6GyYkJOru68U5+4mU8z6mz/ujFEY5OzdDV3U02kyE/59PUlbN+74dbuPfBR1n1oY9yItNaPJNLZ/3ud7/LRz/6UYaGhrj0ineU/Zq1/bz33Xcff/Inf8I999zDe97zHqDy9ydbz/q1r32NT3/602zYsIEPfvCDJb/nylmDDARXzhqEdeNgKpdnx6Hj7BmZIuf74ENLxiPD7DNtT8yc4PDhw5x++um0trTi+z55YCbvgzf7t+zL+tq5amkX7Vmzf6pS6az5XI7Dh39FX18fba1txbd37axHR4/Q1dlJd1fXgrd35axj48fJZDN0d3QUP4fns/msvu/zX9fcyK/9+rt50zX/pezXbLn3sem8vu+zdu1a/u3f/o0fbn2Eff6iit+fyr2vDWf1fZ9PfepTfPOb3+Rb3/oWK1asqPm9uNxlmH7WSgPBxbPWYtU4+MWxEzx8YIzXpnNk8GjxFt4x88fBXL7vM+NDHp/etiyrzu3h/EWlb2OKamf18Xnl5ZdZvHgxnZ0L/+AEN846cmQEz/NY0ruk6vvbfNapyeOMj41x5plnkvFqf9Ow6awwe95//PmrHJmcob21la72trLfTCux5byjo6O8/w/+iHfc9Mcs/rVzKn5/qsb0s87MzHDTTTfx+OOP8/Xv/ICfZU6v+r24GpPPOn8gBPlzpxqTz1qNNeNgz+FJtr00Tt73afU8MhXunGrjoCDv+5w4+bDPted0c9npHXHe9NCCnPXVX71KR0cHi3oWVb0sm886fnycsbExzjrrLDxqfzHaeNa8n+fVV1+lp7ub7u6ewJdl+lmh9LyT42NMTU5yxhlnBBpB85l+3j2HJ3noP15jYnKKtqxHT1d33Zdl8lnHxsb48F/8DW947wfp6umhLZOp+L04CFPPWhgIf/yFr5J9y1U1/9wJwtSzVtKS9g0IYs/hSbYdmP0m05bxQq22cjKeRxswnffZdmD2Wcam3FFBz9qSbQn0Aiw2n7W9vZ1jx44xPT1Ne1t7zcuz8awZL0NHRwfHJybo6u4ONIJm38/cs8LC87Z0dzM5McH4+HjNQVuOyectnDWTydLq+YyPjdPe1lbxLye1mHzW4akW3vrbf8DxiQnGXxulva+vocsz9ay33norM6/rZ/zcS2mfmqY75CNe5Zh61kqM/+HHL46dKP7tI4phUOB5Hm2Z2SeObHtpnF8cS783D3PWbEuW3MxMoMu19awt2SzZbJapqanAl2vjWbu6usjlcqHOCWaeFcqfN5vJ0t3dzfHjx5nJBfu8nc/E884/a09PDy0tLbz22mv4fv3pn8lnBY/ujnZmZmZmz0ljDz6betbsW66ivaODsdeOMH48mlTVxLNWYvQ4mMrlefjAWOTDoGDuHbXlwBhTufQ63rBnzWazzORygb8w7TyrR3t7O9MN/KFpw1lbW1ppa2vj+PHjoS/fpLNC9fN2d3eTyWQYGxur+/JNOm+5s3p4nHbaaeRyOY4eO9bQ5Zt81raWVnpPO43JycmG7s8CU8/a3d5GT08PY2NjjI03fk4w66zVGD0Odhw6zmvTOVq96IdBged5tHoeo9M5dhwK/805KmHP2pJtmX1GbIgXJrHxrO1tbczkcqH/tmnbWbu6upienubETPi/SZhyVqh+Xo/Zv1lPTk4yPT1d93WYct5KZ23JtrB40WImJiaYnGrsJcBNPmt7eweLFy1ifHyc4xON3zZTz9rT3RPLQDDhrNUYOw6OTufYMzJFhsaeBBJExvPI4LFnZIqj08n/Qyr1nDXbMtvMhv2HX2w7a1vb7M/6pqfC/2Fi01nb29vJZrN1PXoA6Z8Vgp23s6ODttZWjh072tDD0Wmft9ZZOzs76Ojo4OjRo+Tyjd0+k8/a1dVFV1cXx44eY2o63CN85Zh61jgGQtpnrcXYcfD0yZ60JaEXl2rxZl/16umRxj/Bw6rnrNlsFg8CP+9gLpvO6nkZWltb6/7GY8tZPTy6urqYnJwkX+fPqtM8KwQ9r8eiRYs4MTPT8Ov1m33feixevBjP8yL5uby5Z529P9va23ht9DVm6njkaz5TzxrHQEj7a7YaI8dBzvfZMzIJfrietBGe54EPe0YmZ1/kIiH1ntXDI3PyeQdh2XbW9vZ2pqen6/oGa9NZOzs7AZio89GDtM4K4c7b2tpGZ2cnY2NjdQ8hMP++zXgZTlt8GtPT03U/IlRg8lkLz7PIZjMcGR1t+JESk88a9UBI82u2FiPHweHJHOMnfFoytb+pPr9rJ3u3P8QT33+g+Gs/+Js7ePIHm0Nfb0vGY/zE7GtmJ6WRs7ZkW/jOZ28rOXtQNp21vb0d3/f5hzv+yOmz7vrB5mLWeN+dt1pzVgh+3sJZf7b9QXzfZ3x8nPvu/HhdZwWz79vnd+3kuZ3/zHP/+jBjY2OcmDnh7FmHd/8vXnrqCfZs+X8ZHR3Fx3f2rL/82VO8+OP/xc7v3VccCLadNQgjx8ErEzOzr01d4+1GDr5I16LTOPsNF/Po/af+YZfXLX8TRw69GPp6M8y+mtWrE/WlVvVo5KzZlixn9b+Rwwd/Efp6bTprIWlshrMWssbXLX+TNWeFYOede9Z/2/Q/imnj0osuruusYO59O/esT3zvH4p54zlvfIuzZz33okv49+9vKiaO57zxEmfPuvzSAf79B5uKjyDYdtYgjBwHr07kyAR4JvvIoQOcfdHF7H30YZYPXFn89Te8fQVLlp4X+nq9k9f5ykRyC66Rs2azWfr/0/9O39mun3U2aewfvIrTzz4/9PXadNZC1niBRWeFYOedf9ZC2njBwDvrOiuYe9/OP2shbzzf8bNeOPjOYuJ4waDbZ73o8l8v/ohh2eX1fb1Cel+ztRg5DsZn8iX/ylUlFw7O/iH51L88yCUrriv5vf63vR2AvdsfKj48/fyunTUvM+/7HJ9Jrjtt5Kytra10tHcUx8LE2FEevf/rJY+iVGPTWTva22lta6N/4B0Axfv0e3/9Gefu1+6ubjLZbMlZDz73DE98/4FAD10mfVYIdt75Z/XwZl8t0fOKZy3Yu/2hQPcrmHnfzj9rIW/E94tnve/Oj3PwuWc4+Nwz/PDuzwe6bhvOWkgc8/l8yf366P1fL34/DsKGsxaegzAzM1Nyv06MHQ113Wl8zdZi5DgI88SMibGjHPz5M8U7LZPJcPTQi5xx7usZOfgiz+/aySUrruOK997Io/d/LdBlziT8JJig5p+1rbWNY4deLC7Wfbt2cvzokVDXb81Z29qLZz343DMAXPHeG7lu7R9z3523BrpMW87a3t7O2MmzTowdZft993D2RRdzydXX872/+Uygy0zyrBD8vPPP2tHRUTzr3LfZft89TIb4BmvifTv/rJ2dHYz98kDxrCOHXuTrn7iJH97zBVZ8YG3g67fhrF1dXYz98tT9eu8nb+aK967hkhXXsf2+ewJfvw1n7enuYezl2ft15OCL7H30IT7/gVX8+X95O//ntRcH/sta0l+ztRj5bytkQzyT/cjBA/TN+RFCNpOls6ODbCbLvt076eg59Y/ZdPYs5vldO4t3aiUtCRUS0NhZ57tkxXVMHHuNibHgr8xm41mPH3utOPo6exbTtbiXg889w9kXXVz1Mm08a2fPYj761c3Ft5v747NqkjwrBD9vrc9hgL3/8kMuvfr/CHX9Jt63C89a+n4rfueWBY94BmHPWWff9+Bzz9DZs7j4vwufz0HYcVaK/ybKyKEDfOoff1Q87xPff4Ar3ntjoMtN+mu2FiPHQXdL8H/pq2PeP+Kyd/tDxS+4wwd/QdfiU//cb+ei02r+bSTjeXS1JPeASlRnrYetZ71w8MqSgXf86GjNYWDrWQsKPxb7nTv+publJX1WCH7eWmc9+NwzLB+4kr2PPhz4uk29b2ud9cCzTwEwcew1gEB/iNh41pee28vIoRc5cvAAAN/768/wvj/+TM3LtPGsc78vPfH9B7jk6usDXXcaX7O1GDkOzuzMkvd9/ADNdN/Z5/GW/7yKJ77/AJ0nnx1cTeELsRzf9/F9n7M6s3Xd7nrEedZqXDnr9/76M/zXP/m/ql6WC2e94r03smTpeTx0919X/caaxlkh+HlrnXXk0IuhBq/J922ts16/9hPF//2FD17HJVdfX/wbZzm2nnVi7Njsr50c8C89t7fmI322nrVg5OCLs+eucn8WpPU1W4uR4+CszpbZf+seCPLhmvtFNtfsz2xPPcQ+cey1qhVDntlPijM7k/uwRHXWsFw4697tD3Hh4JU1/zCx/awTY0fp7FnMhYNXct+dt3LJiusq/mgsjbNCuPNWOuuj93+dvqXnsnf7Q7z07F5GDr5I39Lzqv4hYvp9W+mse7c/xIFnnyr+fkfPIo4cPECng2ftW3puycPwnYtOY+TQi07erwWPf38TFw5eFeh60/qarcWsxzFOOr0jS3erx0y+sSdoLB+4kpee3Vv875FDL1Z9vsFM3qe71eP0juQWXFRnDcv2sz6/aycdPYu5ZMV1HHzuGUYOVn5dC5vP+sT3H2D7t+8u/nfX4l66Fp1W8e3TOCtEc97//Ns3c8mK67hkxXUsWXoOFw5eWfPHRbbet31Lzyv5w2Ny7JizZ10+eCUjc1535sihAyyv8bwvW89a8PSjW+hbem6gt03ra7YWs6bKSVnP47K+Dh57+Ti+79f9Esp9Z5/HpVdfz97tDzFx7DVW/M4tFd/W933w4LK+jlBPJmtUVGeF2T8wn9+1k4mxo/QtPbfi36htP+vIwRdLCoXJ8WP8923PlH1b2896ydXXs+/k/fr8rh1c8d4bK/4hktZZIfrP4327f8SRQy9x9hsurvg6Hjbft2dfdHEx6zvw7FPctKF6SWXzWTt7FvOf3nsjT3z/ASbGjnHdLX9c88cntp61oKNnEZ2LK4/4gjS/ZmvxfN+wfuKko9M57v3ZKL4PrQFegrZRJ/I+nge//6ZeFrclu+B01vjorMlppvPqrPHRWc1g5I8VABa3Zbmsr508fqAXk2lE3vfJ43NZX3sqd5DOGg+dNVnNdF6dNR46qzmMHQcAVy3torcty4mTz+aMg+/7nPB9etuyXLW0K5brCEJnjZbOmo5mOq/OGi2d1SxGj4P2bIZV5/aQ8Tym89HfUb7vM533yXgeq87toT2b3odDZ42OzpqeZjqvzhodndU8Zt6qOc5f1Mq153RHfkfNvYOuPaeb8xe1RnK5jdBZG6ezpq+ZzquzNk5nNZOxT0icb8/hSba9NE7e92n1vMCvPldO/uRDOoU76LLTOyK8pY3TWeujs5qlmc6rs9ZHZzWXNeMA4BfHTrDlwBij0zkyeLR4tV99bi7f95nxIc/sz3pWndtj7HLTWXXWcmw6KzTXeXVWnbUcm846l1XjAGAql2fHoePsGZma/Ve0fGjJeGQof4f5vk+e2ReawCu0rO1ctbTL2J/1FOisOivYfVZorvPqrDor2H3WAuvGQcHR6RxPj0yxZ2SS8RN+8UUr5uYnGc8r/np36+wLXLzF0GykGp1VZ7X9rNBc59VZdVbbz2rtOCjI+T6HJ3O8OjHDKxM5js/kmfF9WrzZf+XqrM4sZ3a2cHpH1rhXoApLZ9VZbT8rNNd5dVad1dazWj8OREREJFp2/PBDREREEqNxICIiIiU0DkRERKSExoGIiIiU0DgQERGREhoHIiIiUkLjQEREREpoHIiIiEgJjQMREREpoXEgIiIiJTQOREREpITGgYiIiJTQOBAREZESGgciIiJSQuNARERESmgciIiISAmNAxERESmhcSAiIiIlNA5ERESkhMaBiIiIlNA4EBERkRIaByIiIlJC40BERERKaByIiIhICY0DERERKaFxICIiIiU0DkRERKSExoGIiIiU0DgQERGREhoHIiIiUkLjQEREREpoHIiIiEgJjQMREREpoXEgIiIiJTQOREREpMT/D+V40q9LQhyXAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " - Hypergraph with 8 vertices and 1 hyperedges.\n", + " - The nodes have feature dimensions 1.\n", + " - The hyperedges have feature dimensions 1.\n", + "\n" + ] + } + ], + "source": [ + "lifted_dataset = PreProcessor(dataset, transform_config, loader.data_dir)\n", + "describe_data(lifted_dataset)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Given the automated quantile based threshold estimation, the algorithm detects one most influential node, hence one hyperedge." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Transform parameters are the same, using existing data_dir: /Users/michael/perelyn/research/topological_deep_learning/challenge-icml-2024/datasets/graph/toy_dataset/manual/lifting/389497019\n", + "\n", + "Dataset only contains 1 sample:\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " - Hypergraph with 8 vertices and 2 hyperedges.\n", + " - The nodes have feature dimensions 1.\n", + " - The hyperedges have feature dimensions 1.\n", + "\n" + ] + } + ], + "source": [ + "transform_config[\"lifting\"][\"th_quantile\"] = 0.8\n", + "lifted_dataset = PreProcessor(dataset, transform_config, loader.data_dir)\n", + "describe_data(lifted_dataset)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "lowering the quantile leads to the selection of two most influential nodes" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Dataset only contains 1 sample:\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Processing...\n", + "Done!\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " - Hypergraph with 8 vertices and 2 hyperedges.\n", + " - The nodes have feature dimensions 1.\n", + " - The hyperedges have feature dimensions 1.\n", + "\n" + ] + } + ], + "source": [ + "transform_config[\"lifting\"][\"n_most_influential\"] = 1\n", + "transform_config[\"lifting\"][\"th_quantile\"] = 0.8\n", + "lifted_dataset = PreProcessor(dataset, transform_config, loader.data_dir)\n", + "describe_data(lifted_dataset)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Assigning nodes only to one of the two most influential nodes leads to a uniqe assignment to hyperedges" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create and Run a Simplicial NN Model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this section a simple model is created to test that the used lifting works as intended. In this case the model uses the `incidence_hyperedges` matrix so the lifting should make sure to add it to the data." + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Model configuration for hypergraph UNIGCN:\n", + "\n", + "{'in_channels': None,\n", + " 'hidden_channels': 32,\n", + " 'out_channels': None,\n", + " 'n_layers': 2}\n" + ] + } + ], + "source": [ + "from modules.models.hypergraph.unigcn import UniGCNModel\n", + "\n", + "model_type = \"hypergraph\"\n", + "model_id = \"unigcn\"\n", + "model_config = load_model_config(model_type, model_id)\n", + "\n", + "model = UniGCNModel(model_config, dataset_config)" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "y_hat = model(lifted_dataset.get(0))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If everything is correct the cell above should execute without errors. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's try another example, i.e. chemical structures from the MUTAG dataset, where node an edge features are > 1, hence the algorithm switches to the unweighted version automatically. " + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Dataset configuration for MUTAG:\n", + "\n", + "{'data_domain': 'graph',\n", + " 'data_type': 'TUDataset',\n", + " 'data_name': 'MUTAG',\n", + " 'data_dir': 'datasets/graph/TUDataset',\n", + " 'num_features': [7, 4],\n", + " 'num_classes': 2,\n", + " 'task': 'classification',\n", + " 'loss_type': 'cross_entropy',\n", + " 'monitor_metric': 'accuracy',\n", + " 'task_level': 'graph',\n", + " 'max_dim_if_lifted': 2,\n", + " 'preserve_edge_attr_if_lifted': False}\n" + ] + } + ], + "source": [ + "dataset_name = \"MUTAG\"\n", + "dataset_config = load_dataset_config(dataset_name)\n", + "loader = GraphLoader(dataset_config)" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Dataset contains 188 samples.\n", + "\n", + "Providing more details about sample 0/188:\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " - Graph with 17 vertices and 38 edges.\n", + " - Features dimensions: [7, 4]\n", + " - There are 0 isolated nodes.\n", + "\n" + ] + } + ], + "source": [ + "dataset = loader.load()\n", + "describe_data(dataset)" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Transform parameters are the same, using existing data_dir: /Users/michael/perelyn/research/topological_deep_learning/challenge-icml-2024/datasets/graph/TUDataset/MUTAG/lifting/1523334808\n", + "\n", + "Dataset contains 188 samples.\n", + "\n", + "Providing more details about sample 0/188:\n" + ] + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " - Hypergraph with 17 vertices and 4 hyperedges.\n", + " - The nodes have feature dimensions 7.\n", + " - The hyperedges have feature dimensions 7.\n", + "\n" + ] + } + ], + "source": [ + "transform_config[\"lifting\"][\"n_most_influential\"] = 1\n", + "lifted_dataset = PreProcessor(dataset, transform_config, loader.data_dir)\n", + "describe_data(lifted_dataset)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".topox_env", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From 3a080e2c0cc1aca167e2a9c71110da4833fd1bda Mon Sep 17 00:00:00 2001 From: Michael Banf Date: Wed, 10 Jul 2024 14:24:51 +0200 Subject: [PATCH 09/18] clean repo --- .../test_forman_ricci_curvature_lifting.py | 46 -- .../forman_ricci_curvature_lifting.ipynb | 650 ------------------ tutorials/graph2hypergraph/knn_lifting.ipynb | 2 +- 3 files changed, 1 insertion(+), 697 deletions(-) delete mode 100644 test/transforms/liftings/graph2hypergraph/test_forman_ricci_curvature_lifting.py delete mode 100644 tutorials/graph2hypergraph/forman_ricci_curvature_lifting.ipynb diff --git a/test/transforms/liftings/graph2hypergraph/test_forman_ricci_curvature_lifting.py b/test/transforms/liftings/graph2hypergraph/test_forman_ricci_curvature_lifting.py deleted file mode 100644 index 1272cf7c..00000000 --- a/test/transforms/liftings/graph2hypergraph/test_forman_ricci_curvature_lifting.py +++ /dev/null @@ -1,46 +0,0 @@ -"""Test Forman-Ricci Curvature Lifting.""" - -import torch - -from modules.data.utils.utils import load_manual_graph -from modules.transforms.liftings.graph2hypergraph.forman_ricci_curvature_lifting import ( - HypergraphFormanRicciCurvatureLifting, -) - - -class TestHypergraphFormanRicciCurvatureLifting: - """Test the HypergraphFormanRicciCurvatureLifting class.""" - - def setup_method(self): - self.data = load_manual_graph() - - self.lifting = HypergraphFormanRicciCurvatureLifting( - network_type="weighted", - threshold=0.6, - ) - - def test_lift_topology(self): - # Test the lift_topology method - lifted_data = self.lifting.forward(self.data.clone()) - - expected_n_hyperedges = 2 - - expected_incidence_1 = torch.tensor( - [ - [1.0, 0.0], - [1.0, 0.0], - [1.0, 0.0], - [0.0, 1.0], - [1.0, 0.0], - [1.0, 0.0], - [1.0, 0.0], - [1.0, 0.0], - ] - ) - - assert ( - expected_incidence_1 == lifted_data.incidence_hyperedges.to_dense() - ).all(), "Something is wrong with incidence_hyperedges (k=1)." - assert ( - expected_n_hyperedges == lifted_data.num_hyperedges - ), "Something is wrong with the number of hyperedges (k=1)." diff --git a/tutorials/graph2hypergraph/forman_ricci_curvature_lifting.ipynb b/tutorials/graph2hypergraph/forman_ricci_curvature_lifting.ipynb deleted file mode 100644 index d0b1ca6e..00000000 --- a/tutorials/graph2hypergraph/forman_ricci_curvature_lifting.ipynb +++ /dev/null @@ -1,650 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Graph-to-Hypergraph Forman-Ricci Curvature Lifting Tutorial" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "***\n", - "This notebook shows how to import a dataset, with the desired lifting, and how to run a neural network using the loaded data.\n", - "\n", - "The notebook is divided into sections:\n", - "\n", - "- [Loading the dataset](#loading-the-dataset) loads the config files for the data and the desired tranformation, createsa a dataset object and visualizes it.\n", - "- [Loading and applying the lifting](#loading-and-applying-the-lifting) defines a simple neural network to test that the lifting creates the expected incidence matrices.\n", - "- [Create and run a simplicial nn model](#create-and-run-a-simplicial-nn-model) simply runs a forward pass of the model to check that everything is working as expected.\n", - "\n", - "***\n", - "***\n", - "\n", - "Note that for simplicity the notebook is setup to use a simple graph. However, there is a set of available datasets that you can play with.\n", - "\n", - "To switch to one of the available datasets, simply change the *dataset_name* variable in [Dataset config](#dataset-config) to one of the following names:\n", - "\n", - "* cocitation_cora\n", - "* cocitation_citeseer\n", - "* cocitation_pubmed\n", - "* MUTAG\n", - "* NCI1\n", - "* NCI109\n", - "* PROTEINS_TU\n", - "* AQSOL\n", - "* ZINC\n", - "***\n", - "\n", - "***\n", - "Extension ideas and TODOs\n", - "- Switch from NetworkX to PyG graph to possible speed gains (needs to be tested though)\n", - "***" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Imports and utilities" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# With this cell any imported module is reloaded before each cell execution\n", - "%load_ext autoreload\n", - "%autoreload 2\n", - "\n", - "from modules.data.load.loaders import GraphLoader\n", - "from modules.data.preprocess.preprocessor import PreProcessor\n", - "from modules.utils.utils import (\n", - " describe_data,\n", - " load_dataset_config,\n", - " load_model_config,\n", - " load_transform_config,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Loading the Dataset" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here we just need to spicify the name of the available dataset that we want to load. First, the dataset config is read from the corresponding yaml file (located at `/configs/datasets/` directory), and then the data is loaded via the implemented `Loaders`.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Dataset configuration for manual_dataset:\n", - "\n", - "{'data_domain': 'graph',\n", - " 'data_type': 'toy_dataset',\n", - " 'data_name': 'manual',\n", - " 'data_dir': 'datasets/graph/toy_dataset',\n", - " 'num_features': 1,\n", - " 'num_classes': 2,\n", - " 'task': 'classification',\n", - " 'loss_type': 'cross_entropy',\n", - " 'monitor_metric': 'accuracy',\n", - " 'task_level': 'node'}\n" - ] - } - ], - "source": [ - "dataset_name = \"manual_dataset\"\n", - "dataset_config = load_dataset_config(dataset_name)\n", - "loader = GraphLoader(dataset_config)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can then access to the data through the `load()`method:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Dataset only contains 1 sample:\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " - Graph with 8 vertices and 13 edges.\n", - " - Features dimensions: [1, 0]\n", - " - There are 0 isolated nodes.\n", - "\n" - ] - } - ], - "source": [ - "dataset = loader.load()\n", - "describe_data(dataset)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "modules.data.utils.custom_dataset.CustomDataset" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "type(dataset)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Loading and Applying the Lifting" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this section, we will instantiate the lifting we want to apply to the data. In this tutorial, we apply an novel lifting based on Forman-Ricci curvature. In general, curvature is a geometric property that describes the local shape of an object. In the graph, an edge with positive curvature represents an edge within a cluster, while a negative curvature edge tents to be a bridge within clusters. Forman-Ricci curvature defines an edge-based network characteristic that reveals properties of a graph's community structure [1,2]. In particular high absolute Forman-Ricci curvature exhibits a network's backbone, a coarse, structure preserving graph geometry that forms connections between major communities, most suitable to form hyperedges. In addition, Forman-Ricci curvature was found to be especially useful for network analysis since its intuitive notion allows for efficient computation that scales to large networks sizes.\n", - "\n", - "Network representations of relational data $G = {V (G), E(G)}$ form regular, 1-dimensional cell complexes, in which case the following curvature function can be defined:\n", - "\n", - "${\\rm Ric_F}(e) = \\omega (e) \\left( \\frac{\\omega (v_1)}{\\omega (e)} + \\frac{\\omega (v_2)}{\\omega (e)} - \\sum_{\\substack{e_{v_1}\\ \\sim\\ e \\\\ \\ e_{v_2}\\ \\sim\\ e}} \\left[\\frac{\\omega (v_1)}{\\sqrt{\\omega (e) \\omega (e_{v_1})}} + \\frac{\\omega (v_2)}{\\sqrt{\\omega (e) \\omega (e_{v_2})}} \\right] \\right)\\,.$\n", - "\n", - "The function is defined on each edge $e = (v_1, v_2) \\in E(G)$ of the network, connecting vertices $v_1, v_2 \\in V (G)$; $\\omega$ denotes the weights of edges and vertices. Our approach is applicable to both weighted and unweighted networks. In case of unweighted networks all weights in the above equation reduce to 1, i.e. $\\omega (e) = \\omega (v_1) = \\omega (v_2) = 1$.\n", - "\n", - "Our approach is straightforward: (1) calculate the Forman-Ricci curvature over the edges of a graph, (2) from the given distribution of curvature weights, either define an absolute threshold on Forman-Ricci curvature or give a quantile for automatic, network-dependent threshold estimation for backbone extraction, (3) prune the curvature graph using this threshold (4) identify and add remaining network backbone geometries as hyperedges to lift the original graph.\n", - "\n", - "\n", - "***\n", - "[[1]](https://arxiv.org/pdf/1607.08654) Melanie Weber, Emil Saucan, Jürgen Jost, Characterizing complex networks with Forman-Ricci curvature and associated geometric flows, Journal of Complex Networks, Volume 5, Issue 4, August 2017, Pages 527–550.\n", - "\n", - "[[2]](https://web.math.princeton.edu/~mw25/project/files/NIPS-FR.pdf) M. Weber, J. Jost, E. Saucan (2018). Detecting the Coarse Geometry of Networks. NeurIPS R2L.\n", - "\n", - "***\n", - "\n", - "For hypergraphs, creating a lifting involves creating the `incidence_hyperedges` matrix.\n", - "\n", - "Similarly to before, we can specify the transformation we want to apply through its type and id --the corresponding config files located at `/configs/transforms.` \n", - "\n", - "Note that the *tranform_config* dictionary generated below can contain a sequence of transforms if it is needed.\n", - "\n", - "This can also be used to explore liftings from one topological domain to another; for example, using two liftings, it is possible to achieve a sequence such as graph -> simplicial complex -> hypergraph. " - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Transform configuration for graph2hypergraph/forman_ricci_curvature_lifting:\n", - "\n", - "{'transform_type': 'lifting',\n", - " 'transform_name': 'HypergraphFormanRicciCurvatureLifting',\n", - " 'network_type': 'weighted',\n", - " 'threshold_type': 'quantile',\n", - " 'threshold_direction': 'upper',\n", - " 'threshold': 0.1,\n", - " 'feature_lifting': 'ProjectionSum'}\n" - ] - } - ], - "source": [ - "# Define transformation type and id\n", - "transform_type = \"liftings\"\n", - "# If the transform is a topological lifting, it should include both the type of the lifting and the identifier\n", - "transform_id = \"graph2hypergraph/forman_ricci_curvature_lifting\"\n", - "\n", - "# Read yaml file\n", - "transform_config = {\n", - " \"lifting\": load_transform_config(transform_type, transform_id)\n", - " # other transforms (e.g. data manipulations, feature liftings) can be added here\n", - "}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We than apply the transform via our `PreProcessor`:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Processing...\n", - "Done!\n" - ] - } - ], - "source": [ - "# transform_config[\"lifting\"][\"threshold_type\"] = \"absolute\"\n", - "# transform_config[\"lifting\"][\"threshold\"] = -1\n", - "lifted_dataset = PreProcessor(dataset, transform_config, loader.data_dir)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Dataset only contains 1 sample:\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " - Hypergraph with 8 vertices and 6 hyperedges.\n", - " - The nodes have feature dimensions 1.\n", - " - The hyperedges have feature dimensions 1.\n", - "\n" - ] - } - ], - "source": [ - "describe_data(lifted_dataset)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Given the automated quantile based threshold estimation, the algorithm collapses nodes adjacent to edges with the most significant curvature absolute values based on the given very low quantile (default: 0.1)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create and Run a Simplicial NN Model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this section a simple model is created to test that the used lifting works as intended. In this case the model uses the `incidence_hyperedges` matrix so the lifting should make sure to add it to the data." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Model configuration for hypergraph UNIGCN:\n", - "\n", - "{'in_channels': None,\n", - " 'hidden_channels': 32,\n", - " 'out_channels': None,\n", - " 'n_layers': 2}\n" - ] - } - ], - "source": [ - "from modules.models.hypergraph.unigcn import UniGCNModel\n", - "\n", - "model_type = \"hypergraph\"\n", - "model_id = \"unigcn\"\n", - "model_config = load_model_config(model_type, model_id)\n", - "\n", - "model = UniGCNModel(model_config, dataset_config)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "y_hat = model(lifted_dataset.get(0))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If everything is correct the cell above should execute without errors. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's try another example, i.e. chemical structures from the MUTAG dataset, where node an edge features are > 1, hence the algorithm switches to the unweighted version automatically. Further, we change the configuration to allow for the definition of absolute thresholds on Forman-Ricci curvature to explore the evolution of the hyperedge geometry." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Dataset configuration for MUTAG:\n", - "\n", - "{'data_domain': 'graph',\n", - " 'data_type': 'TUDataset',\n", - " 'data_name': 'MUTAG',\n", - " 'data_dir': 'datasets/graph/TUDataset',\n", - " 'num_features': [7, 4],\n", - " 'num_classes': 2,\n", - " 'task': 'classification',\n", - " 'loss_type': 'cross_entropy',\n", - " 'monitor_metric': 'accuracy',\n", - " 'task_level': 'graph',\n", - " 'max_dim_if_lifted': 2,\n", - " 'preserve_edge_attr_if_lifted': False}\n" - ] - } - ], - "source": [ - "dataset_name = \"MUTAG\"\n", - "dataset_config = load_dataset_config(dataset_name)\n", - "loader = GraphLoader(dataset_config)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Dataset contains 188 samples.\n", - "\n", - "Providing more details about sample 0/188:\n" - ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " - Graph with 17 vertices and 38 edges.\n", - " - Features dimensions: [7, 4]\n", - " - There are 0 isolated nodes.\n", - "\n" - ] - } - ], - "source": [ - "dataset = loader.load()\n", - "describe_data(dataset)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The Forman-Ricci curvature for this molecular structure is rather small in absolute values ranging from -2 to 0. Hence we can nicely control the creation of hyperedges, using absolute tresholds. " - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Transform parameters are the same, using existing data_dir: /Users/michael/perelyn/research/topological_deep_learning/challenge-icml-2024/datasets/graph/TUDataset/MUTAG/lifting/1661761690\n", - "\n", - "Dataset contains 188 samples.\n", - "\n", - "Providing more details about sample 0/188:\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " - Hypergraph with 17 vertices and 1 hyperedges.\n", - " - The nodes have feature dimensions 7.\n", - " - The hyperedges have feature dimensions 7.\n", - "\n" - ] - } - ], - "source": [ - "transform_config[\"lifting\"][\"threshold_type\"] = \"absolute\"\n", - "transform_config[\"lifting\"][\"threshold\"] = 0\n", - "lifted_dataset = PreProcessor(dataset, transform_config, loader.data_dir)\n", - "describe_data(lifted_dataset)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For an absolute threshold of 0, no edge of the Forman-Ricci curvature graph is pruned, and hence the whole curvature graph forms a single hyperedge" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Transform parameters are the same, using existing data_dir: /Users/michael/perelyn/research/topological_deep_learning/challenge-icml-2024/datasets/graph/TUDataset/MUTAG/lifting/1394719298\n", - "\n", - "Dataset contains 188 samples.\n", - "\n", - "Providing more details about sample 0/188:\n" - ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " - Hypergraph with 17 vertices and 5 hyperedges.\n", - " - The nodes have feature dimensions 7.\n", - " - The hyperedges have feature dimensions 7.\n", - "\n" - ] - } - ], - "source": [ - "transform_config[\"lifting\"][\"threshold_type\"] = \"absolute\"\n", - "transform_config[\"lifting\"][\"threshold\"] = -1\n", - "lifted_dataset = PreProcessor(dataset, transform_config, loader.data_dir)\n", - "describe_data(lifted_dataset)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Setting the absolute thresholds to -1 and -2 respectively we see the emergence of multiple smaller hyperedges forming." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Transform parameters are the same, using existing data_dir: /Users/michael/perelyn/research/topological_deep_learning/challenge-icml-2024/datasets/graph/TUDataset/MUTAG/lifting/2879822529\n", - "\n", - "Dataset contains 188 samples.\n", - "\n", - "Providing more details about sample 0/188:\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " - Hypergraph with 17 vertices and 13 hyperedges.\n", - " - The nodes have feature dimensions 7.\n", - " - The hyperedges have feature dimensions 7.\n", - "\n" - ] - } - ], - "source": [ - "transform_config[\"lifting\"][\"threshold_type\"] = \"absolute\"\n", - "transform_config[\"lifting\"][\"threshold\"] = -2\n", - "lifted_dataset = PreProcessor(dataset, transform_config, loader.data_dir)\n", - "describe_data(lifted_dataset)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": ".topox_env", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.7" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/tutorials/graph2hypergraph/knn_lifting.ipynb b/tutorials/graph2hypergraph/knn_lifting.ipynb index 40bf15b9..21494321 100644 --- a/tutorials/graph2hypergraph/knn_lifting.ipynb +++ b/tutorials/graph2hypergraph/knn_lifting.ipynb @@ -339,7 +339,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.3" + "version": "3.11.7" } }, "nbformat": 4, From f7d4e934a6d286d4cc1a941134198721aaf612a1 Mon Sep 17 00:00:00 2001 From: Michael Banf Date: Wed, 10 Jul 2024 14:26:34 +0200 Subject: [PATCH 10/18] linting --- .../transforms/liftings/graph2hypergraph/page_rank_lifting.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/modules/transforms/liftings/graph2hypergraph/page_rank_lifting.py b/modules/transforms/liftings/graph2hypergraph/page_rank_lifting.py index 90ddb480..95992588 100644 --- a/modules/transforms/liftings/graph2hypergraph/page_rank_lifting.py +++ b/modules/transforms/liftings/graph2hypergraph/page_rank_lifting.py @@ -1,5 +1,3 @@ -import math - import networkx as nx import numpy as np import torch From 42d634258f2e75338ed4682a65e8f8f8bf73fc7e Mon Sep 17 00:00:00 2001 From: Michael Banf Date: Wed, 10 Jul 2024 14:28:52 +0200 Subject: [PATCH 11/18] clean repo --- .../forman_ricci_curvature_lifting.yaml | 7 - .../forman_ricci_curvature_lifting.py | 161 ------------------ 2 files changed, 168 deletions(-) delete mode 100755 configs/transforms/liftings/graph2hypergraph/forman_ricci_curvature_lifting.yaml delete mode 100644 modules/transforms/liftings/graph2hypergraph/forman_ricci_curvature_lifting.py diff --git a/configs/transforms/liftings/graph2hypergraph/forman_ricci_curvature_lifting.yaml b/configs/transforms/liftings/graph2hypergraph/forman_ricci_curvature_lifting.yaml deleted file mode 100755 index 0aa6aea0..00000000 --- a/configs/transforms/liftings/graph2hypergraph/forman_ricci_curvature_lifting.yaml +++ /dev/null @@ -1,7 +0,0 @@ -transform_type: 'lifting' -transform_name: "HypergraphFormanRicciCurvatureLifting" -network_type: 'weighted' -threshold_type: 'quantile' -threshold_direction: 'upper' -threshold: 0.1 -feature_lifting: ProjectionSum \ No newline at end of file diff --git a/modules/transforms/liftings/graph2hypergraph/forman_ricci_curvature_lifting.py b/modules/transforms/liftings/graph2hypergraph/forman_ricci_curvature_lifting.py deleted file mode 100644 index 3b6e45d2..00000000 --- a/modules/transforms/liftings/graph2hypergraph/forman_ricci_curvature_lifting.py +++ /dev/null @@ -1,161 +0,0 @@ -import math - -import networkx as nx -import numpy as np -import torch -import torch_geometric - -from modules.transforms.liftings.graph2hypergraph.base import Graph2HypergraphLifting - - -class HypergraphFormanRicciCurvatureLifting(Graph2HypergraphLifting): - r"""Lifts graphs to hypergraph domain using Forman-Ricci curvature based backbone estimation. - - Parameters - ---------- - network_type : str - Network type may be weighted or unweighted. Default is "weighted". - th_quantile: float - Quantile to estimate cutoff threshold from Forman-Ricci curvature distribution to prune network and reveal backbone. Default is 0.6 - **kwargs : optional - Additional arguments for the class. - """ - - def __init__( - self, - network_type="weighted", - threshold_type="quantile", - threshold_direction="upper", - threshold=0.1, - **kwargs, - ): - super().__init__(**kwargs) - self.network_type = network_type - self.threshold_type = threshold_type - self.threshold_direction = threshold_direction - self.threshold = threshold - - def lift_topology(self, data: torch_geometric.data.Data) -> dict: - r"""Lifts the topology of a graph to hypergraph domain using Forman-Ricci curvature based backbone estimation. - - Parameters - ---------- - data : torch_geometric.data.Data - The input data to be lifted. - - Returns - ------- - dict - The lifted topology. - """ - - edge_list = data.edge_index.t().numpy() - - # for unweighted graphs or higher-dimensional edge or node features revert to unweighted network structure - if ( - data.edge_attr is None - or self.network_type == "unweighted" - or data.edge_attr.shape[1] > 1 - ): - edge_attr = np.ones(shape=(len(edge_list), 1)) - elif isinstance(data.edge_attr, torch.Tensor): - edge_attr = data.edge_attr.numpy() - else: - edge_attr = data.edge_attr - - if data.x is None or self.network_type == "unweighted" or data.x.shape[1] > 1: - node_attr = np.ones(shape=(data.num_nodes, 1)) - # data.x = torch.from_numpy(data.x.astype("f4")) - elif isinstance(data.x, torch.Tensor): - node_attr = data.x.numpy() - else: - node_attr = data.x - - # create undirected networkx graph from pyg data - G = nx.Graph() - for v in range(len(node_attr)): - G.add_node(v) - G.nodes[v]["w"] = node_attr[v][0] - - for e in range(len(edge_list)): - v1 = edge_list[e][0] - v2 = edge_list[e][1] - G.add_edge(v1, v2, w=edge_attr[e][0]) - G.add_edge(v1, v2, w=edge_attr[e][0]) - - # estimate Forman-Ricci curvature as described in: - # M. Weber, J. Jost, E. Saucan (2018). Detecting the Coarse Geometry of Networks. NeurIPS - for v1, v2 in G.edges(): - v1_neighbors = set(G.neighbors(v1)) - v1_neighbors.remove(v2) - v2_neighbors = set(G.neighbors(v2)) - v2_neighbors.remove(v1) - - w_e = G[v1][v2]["w"] - w_v1 = G.nodes[v1]["w"] - w_v2 = G.nodes[v2]["w"] - ev1_sum = sum([w_v1 / math.sqrt(w_e * G[v1][v]["w"]) for v in v1_neighbors]) - ev2_sum = sum([w_v2 / math.sqrt(w_e * G[v2][v]["w"]) for v in v2_neighbors]) - - G[v1][v2]["w_frc"] = w_e * (w_v1 / w_e + w_v2 / w_e - (ev1_sum + ev2_sum)) - - # estimate cutoff threshold from Forman-Ricci curvature distribution to prune network and reveal backbone(s), i.e. hyperedges - w_frc = list(nx.get_edge_attributes(G, "w_frc").values()) - - if self.threshold_type == "quantile": - th_cutoff = np.quantile(w_frc, self.threshold) - elif self.threshold_type == "absolute": - th_cutoff = self.threshold - else: - raise NotImplementedError( - f"threshold type {self.threshold_type} not implemented" - ) - - if self.threshold_direction == "upper": - - def compare_to_threshold(x): - return x > th_cutoff - - elif self.threshold_direction == "lower": - - def compare_to_threshold(x): - return x < th_cutoff - - else: - raise NotImplementedError( - f"threshold threshold_direction {self.threshold_direction} not implemented" - ) - - edges_to_remove = [] - for v1, v2 in G.edges(): - if compare_to_threshold(G[v1][v2]["w_frc"]): - edges_to_remove.append((v1, v2)) - - G.remove_edges_from(edges_to_remove) - - # find connected components (hyperedges) - hyperedges = [ - c for c in sorted(nx.connected_components(G), key=len, reverse=True) - ] - shape = (data.num_nodes, len(hyperedges)) - incidence_matrix = np.zeros(shape=shape) - - if len(hyperedges) > 0: - for i, nodes in enumerate(hyperedges): - incidence_matrix[list(nodes), i] = 1 - - assert len(hyperedges) > 0 - - incidences = torch.Tensor(incidence_matrix.astype("f4")).to_sparse_coo() - - coo_indices = torch.stack((incidences.indices()[0], incidences.indices()[1])) - - coo_values = incidences.values() - - incidence_matrix = torch.sparse_coo_tensor(coo_indices, coo_values) - - return { - "incidence_hyperedges": incidence_matrix, - "num_hyperedges": incidence_matrix.size(1), - "x_0": data.x, - } From b3fea856751d52e0874df61cbcac25ba1635adc9 Mon Sep 17 00:00:00 2001 From: Michael Banf Date: Thu, 11 Jul 2024 15:49:53 +0200 Subject: [PATCH 12/18] update method and description --- ...ting.yaml => node_centrality_lifting.yaml} | 2 +- modules/transforms/data_transform.py | 6 +- ..._lifting.py => node_centrality_lifting.py} | 15 +++- ...ing.py => test_node_centrality_lifting.py} | 7 +- ...ng.ipynb => node_centrality_lifting.ipynb} | 89 ++++++++++++------- 5 files changed, 77 insertions(+), 42 deletions(-) rename configs/transforms/liftings/graph2hypergraph/{page_rank_lifting.yaml => node_centrality_lifting.yaml} (75%) rename modules/transforms/liftings/graph2hypergraph/{page_rank_lifting.py => node_centrality_lifting.py} (88%) rename test/transforms/liftings/graph2hypergraph/{test_page_rank_lifting.py => test_node_centrality_lifting.py} (86%) rename tutorials/graph2hypergraph/{page_rank_lifting.ipynb => node_centrality_lifting.ipynb} (98%) diff --git a/configs/transforms/liftings/graph2hypergraph/page_rank_lifting.yaml b/configs/transforms/liftings/graph2hypergraph/node_centrality_lifting.yaml similarity index 75% rename from configs/transforms/liftings/graph2hypergraph/page_rank_lifting.yaml rename to configs/transforms/liftings/graph2hypergraph/node_centrality_lifting.yaml index b30ccf95..c3575445 100755 --- a/configs/transforms/liftings/graph2hypergraph/page_rank_lifting.yaml +++ b/configs/transforms/liftings/graph2hypergraph/node_centrality_lifting.yaml @@ -1,5 +1,5 @@ transform_type: 'lifting' -transform_name: "HypergraphPageRankLifting" +transform_name: "HypergraphNodeCentralityLifting" network_type: 'weighted' alpha: 0.85 th_quantile: 0.95 diff --git a/modules/transforms/data_transform.py b/modules/transforms/data_transform.py index 6c265856..e84c1dd9 100755 --- a/modules/transforms/data_transform.py +++ b/modules/transforms/data_transform.py @@ -12,8 +12,8 @@ from modules.transforms.liftings.graph2hypergraph.knn_lifting import ( HypergraphKNNLifting, ) -from modules.transforms.liftings.graph2hypergraph.page_rank_lifting import ( - HypergraphPageRankLifting, +from modules.transforms.liftings.graph2hypergraph.node_centrality_lifting import ( + HypergraphNodeCentralityLifting, ) from modules.transforms.liftings.graph2simplicial.clique_lifting import ( SimplicialCliqueLifting, @@ -22,7 +22,7 @@ TRANSFORMS = { # Graph -> Hypergraph "HypergraphKNNLifting": HypergraphKNNLifting, - "HypergraphPageRankLifting": HypergraphPageRankLifting, + "HypergraphNodeCentralityLifting": HypergraphNodeCentralityLifting, # Graph -> Simplicial Complex "SimplicialCliqueLifting": SimplicialCliqueLifting, # Graph -> Cell Complex diff --git a/modules/transforms/liftings/graph2hypergraph/page_rank_lifting.py b/modules/transforms/liftings/graph2hypergraph/node_centrality_lifting.py similarity index 88% rename from modules/transforms/liftings/graph2hypergraph/page_rank_lifting.py rename to modules/transforms/liftings/graph2hypergraph/node_centrality_lifting.py index 95992588..c33dcec1 100644 --- a/modules/transforms/liftings/graph2hypergraph/page_rank_lifting.py +++ b/modules/transforms/liftings/graph2hypergraph/node_centrality_lifting.py @@ -6,7 +6,7 @@ from modules.transforms.liftings.graph2hypergraph.base import Graph2HypergraphLifting -class HypergraphPageRankLifting(Graph2HypergraphLifting): +class HypergraphNodeCentralityLifting(Graph2HypergraphLifting): r"""Lifts graphs to hypergraph domain using Page Rank. Parameters @@ -15,6 +15,15 @@ class HypergraphPageRankLifting(Graph2HypergraphLifting): Network type may be weighted or unweighted. Default is "weighted". alpha: float Damping parameter for PageRank, default=0.85. + th_quantile: float + Fraction of most influential nodes in the network, default=0.95. + n_most_influential: integer + Number of most influential nodes to assign a node to. default=2. + max_iter: integer + Maximum number of iterations in power method eigenvalue solver. + tol: float + Error tolerance used to check convergence in power method solver. The iteration will stop after a tolerance of len(G) * tol is reached. + **kwargs : optional Additional arguments for the class. """ @@ -67,7 +76,6 @@ def lift_topology(self, data: torch_geometric.data.Data) -> dict: if data.x is None or self.network_type == "unweighted" or data.x.shape[1] > 1: node_attr = np.ones(shape=(data.num_nodes, 1)) - # data.x = torch.from_numpy(data.x.astype("f4")) elif isinstance(data.x, torch.Tensor): node_attr = data.x.numpy() else: @@ -96,7 +104,8 @@ def lift_topology(self, data: torch_geometric.data.Data) -> dict: f"network type {self.network_type} not implemented" ) - # estimate page rank per node + # estimate node centrality + pr = nx.pagerank( G, alpha=self.alpha, max_iter=self.max_iter, tol=self.tol, weight="w" ) diff --git a/test/transforms/liftings/graph2hypergraph/test_page_rank_lifting.py b/test/transforms/liftings/graph2hypergraph/test_node_centrality_lifting.py similarity index 86% rename from test/transforms/liftings/graph2hypergraph/test_page_rank_lifting.py rename to test/transforms/liftings/graph2hypergraph/test_node_centrality_lifting.py index ef2476f8..8e7cff61 100644 --- a/test/transforms/liftings/graph2hypergraph/test_page_rank_lifting.py +++ b/test/transforms/liftings/graph2hypergraph/test_node_centrality_lifting.py @@ -3,8 +3,8 @@ import torch from modules.data.utils.utils import load_manual_graph -from modules.transforms.liftings.graph2hypergraph.page_rank_lifting import ( - HypergraphPageRankLifting, +from modules.transforms.liftings.graph2hypergraph.node_centrality_lifting import ( + HypergraphNodeCentralityLifting, ) @@ -14,9 +14,8 @@ class TestHypergraphPageRankLifting: def setup_method(self): self.data = load_manual_graph() - self.lifting = HypergraphPageRankLifting( + self.lifting = HypergraphNodeCentralityLifting( network_type="weighted", - alpha=0.85, th_quantile=0.8, n_most_influential=1, ) diff --git a/tutorials/graph2hypergraph/page_rank_lifting.ipynb b/tutorials/graph2hypergraph/node_centrality_lifting.ipynb similarity index 98% rename from tutorials/graph2hypergraph/page_rank_lifting.ipynb rename to tutorials/graph2hypergraph/node_centrality_lifting.ipynb index 56af1af1..d08c1cbf 100644 --- a/tutorials/graph2hypergraph/page_rank_lifting.ipynb +++ b/tutorials/graph2hypergraph/node_centrality_lifting.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Graph-to-Hypergraph Page Rank Lifting Tutorial" + "# Graph-to-Hypergraph Node Centrality Lifting Tutorial" ] }, { @@ -53,18 +53,9 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 1, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The autoreload extension is already loaded. To reload it, use:\n", - " %reload_ext autoreload\n" - ] - } - ], + "outputs": [], "source": [ "# With this cell any imported module is reloaded before each cell execution\n", "%load_ext autoreload\n", @@ -96,7 +87,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -134,7 +125,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -202,10 +193,19 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "In this section, we will instantiate the lifting we want to apply to the data. In this tutorial, we apply an novel lifting based on PageRank. PageRank computes a ranking of the nodes in a graph based on the structure of the incoming links. Our approach is applicable to both weighted and unweighted networks. \n", + "In this section, we will instantiate the lifting we want to apply to the data. In this tutorial, we apply an novel lifting that creates hyperedges based on central, i.e. highly influential, nodes in the network. To identify influential nodes in the network, we refer to the variant of the Eigenvector Centrality with an additional jump probability (i.e. PageRank).\n", + "\n", + "Eigenvector Centrality is an algorithm that measures the transitive influence of nodes. Relationships originating from high-scoring nodes contribute more to the score of a node than connections from low-scoring nodes. A high eigenvector score means that a node is connected to many nodes who themselves have high scores.\n", "\n", - "Our approach is straightforward: (1) calculate the Page Rank of all nodes in the graph, (2) select the top $n$ most influential nodes in the graph as hyperedges. (3) Assign all nodes in the network to $m >= 1$ most influential nodes (with $m <= n$), i.e. their respective hyperedges, based on their distance (shortest path) to each influential node. \n", + "The algorithm computes the eigenvector associated with the largest absolute eigenvalue. To compute that eigenvalue, the algorithm applies the power iteration approach. Within each iteration, the centrality score for each node is derived from the scores of its incoming neighbors. In the power iteration method, the eigenvector is L2-normalized after each iteration, leading to normalized results by default.\n", "\n", + "The PageRank [[1]](https://doi.org/10.1137/140976649) variant of Eigenvector Centrality utilises, at any step of the power iteration, an additional jump probability, called dampening factor $\\alpha$, which decides whether to continue following the transition matrix or teleport to random positions in the process.These random teleportations have shown to be an effective way to ensure that the matrix and corresponding Markov chain exhibit ergodicity which makes them easier to analyze and to guarantee convergence.\n", + "\n", + "Our approach is applicable to both directed and undirected as well as weighted and unweighted networks. It is straightforward: (1) calculate the node centrality of all nodes in the graph, (2) select the top $n$ most influential nodes in the graph as hyperedges. (3) Assign all nodes in the network to $m >= 1$ most influential nodes (with $m <= n$), i.e. their respective hyperedges, based on their distance (shortest path) to each influential node. \n", + "\n", + "***\n", + "[[1]](https://doi.org/10.1137/140976649) Gleich, D.F. (2015) PageRank Beyond the Web. SIAM Review, 57, 321-363.\n", + "***\n", "\n", "\n", "For hypergraphs, creating a lifting involves creating the `incidence_hyperedges` matrix.\n", @@ -219,7 +219,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -227,10 +227,10 @@ "output_type": "stream", "text": [ "\n", - "Transform configuration for graph2hypergraph/page_rank_lifting:\n", + "Transform configuration for graph2hypergraph/node_centrality_lifting:\n", "\n", "{'transform_type': 'lifting',\n", - " 'transform_name': 'HypergraphPageRankLifting',\n", + " 'transform_name': 'HypergraphNodeCentralityLifting',\n", " 'network_type': 'weighted',\n", " 'alpha': 0.85,\n", " 'th_quantile': 0.95,\n", @@ -245,7 +245,7 @@ "# Define transformation type and id\n", "transform_type = \"liftings\"\n", "# If the transform is a topological lifting, it should include both the type of the lifting and the identifier\n", - "transform_id = \"graph2hypergraph/page_rank_lifting\"\n", + "transform_id = \"graph2hypergraph/node_centrality_lifting\"\n", "\n", "# Read yaml file\n", "transform_config = {\n", @@ -263,18 +263,25 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Transform parameters are the same, using existing data_dir: /Users/michael/perelyn/research/topological_deep_learning/challenge-icml-2024/datasets/graph/toy_dataset/manual/lifting/3830843336\n", "\n", "Dataset only contains 1 sample:\n" ] }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Processing...\n", + "Done!\n" + ] + }, { "data": { "image/png": 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", @@ -310,18 +317,25 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Transform parameters are the same, using existing data_dir: /Users/michael/perelyn/research/topological_deep_learning/challenge-icml-2024/datasets/graph/toy_dataset/manual/lifting/389497019\n", "\n", "Dataset only contains 1 sample:\n" ] }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Processing...\n", + "Done!\n" + ] + }, { "data": { "image/png": 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", @@ -358,7 +372,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -428,7 +442,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -457,7 +471,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -485,7 +499,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -518,7 +532,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -559,20 +573,33 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 12, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Processing...\n" + ] + }, { "name": "stdout", "output_type": "stream", "text": [ - "Transform parameters are the same, using existing data_dir: /Users/michael/perelyn/research/topological_deep_learning/challenge-icml-2024/datasets/graph/TUDataset/MUTAG/lifting/1523334808\n", "\n", "Dataset contains 188 samples.\n", "\n", "Providing more details about sample 0/188:\n" ] }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Done!\n" + ] + }, { "data": { "image/png": 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", From 9a41e1ee4d60d5fb09e17bd835a644a9f4e40726 Mon Sep 17 00:00:00 2001 From: Michael Banf Date: Thu, 11 Jul 2024 15:55:21 +0200 Subject: [PATCH 13/18] cleanup --- .../liftings/graph2hypergraph/test_node_centrality_lifting.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/test/transforms/liftings/graph2hypergraph/test_node_centrality_lifting.py b/test/transforms/liftings/graph2hypergraph/test_node_centrality_lifting.py index 8e7cff61..9b96aaf0 100644 --- a/test/transforms/liftings/graph2hypergraph/test_node_centrality_lifting.py +++ b/test/transforms/liftings/graph2hypergraph/test_node_centrality_lifting.py @@ -8,8 +8,8 @@ ) -class TestHypergraphPageRankLifting: - """Test the HypergraphPageRankLifting class.""" +class TestHypergraphNodeCentralityLifting: + """Test the HypergraphNodeCentralityLifting class.""" def setup_method(self): self.data = load_manual_graph() From c507611632ebe48a110211ed0a9af602813a65d3 Mon Sep 17 00:00:00 2001 From: Michael Banf Date: Thu, 11 Jul 2024 22:47:18 +0200 Subject: [PATCH 14/18] add hyperedge connection weights --- .../node_centrality_lifting.yaml | 1 + .../node_centrality_lifting.py | 15 ++++++--- .../node_centrality_lifting.ipynb | 32 ++++++++----------- 3 files changed, 25 insertions(+), 23 deletions(-) diff --git a/configs/transforms/liftings/graph2hypergraph/node_centrality_lifting.yaml b/configs/transforms/liftings/graph2hypergraph/node_centrality_lifting.yaml index c3575445..036f03dc 100755 --- a/configs/transforms/liftings/graph2hypergraph/node_centrality_lifting.yaml +++ b/configs/transforms/liftings/graph2hypergraph/node_centrality_lifting.yaml @@ -4,6 +4,7 @@ network_type: 'weighted' alpha: 0.85 th_quantile: 0.95 n_most_influential: 2 +do_weight_hyperedge_influence: False max_iter: 100 tol: 1e-06 feature_lifting: ProjectionSum \ No newline at end of file diff --git a/modules/transforms/liftings/graph2hypergraph/node_centrality_lifting.py b/modules/transforms/liftings/graph2hypergraph/node_centrality_lifting.py index c33dcec1..618cd4fe 100644 --- a/modules/transforms/liftings/graph2hypergraph/node_centrality_lifting.py +++ b/modules/transforms/liftings/graph2hypergraph/node_centrality_lifting.py @@ -19,6 +19,8 @@ class HypergraphNodeCentralityLifting(Graph2HypergraphLifting): Fraction of most influential nodes in the network, default=0.95. n_most_influential: integer Number of most influential nodes to assign a node to. default=2. + do_weight_hyperedge_influence: bool + add a weight to the hyperedge connections per node based on the inverse spath distance to influential node. default=False. max_iter: integer Maximum number of iterations in power method eigenvalue solver. tol: float @@ -34,6 +36,7 @@ def __init__( alpha=0.85, th_quantile=0.95, n_most_influential=2, + do_weight_hyperedge_influence=False, max_iter=100, tol=1e-06, **kwargs, @@ -45,6 +48,7 @@ def __init__( self.tol = tol self.th_quantile = th_quantile self.n_most_influential = n_most_influential + self.do_weight_hyperedge_influence = do_weight_hyperedge_influence def lift_topology(self, data: torch_geometric.data.Data) -> dict: r"""Lifts the topology of a graph to hypergraph domain using Page Rank. @@ -127,12 +131,15 @@ def lift_topology(self, data: torch_geometric.data.Data) -> dict: k: v for k, v in sp[v].items() if k in nodes_most_influential } v_influencial = [ - k - for i, k in enumerate(sp_v_influencial.keys()) + (k, v) + for i, (k, v) in enumerate(sp_v_influencial.items()) if i < self.n_most_influential ] - for v_inf in v_influencial: - incidence_hyperedges[v, hyperedge_map[v_inf]] = 1 + for k_infl, v_infl in v_influencial: + w = 1 + if self.do_weight_hyperedge_influence: + w = 1 / v_infl + incidence_hyperedges[v, hyperedge_map[k_infl]] = w incidence_hyperedges = incidence_hyperedges.to_sparse_coo() return { diff --git a/tutorials/graph2hypergraph/node_centrality_lifting.ipynb b/tutorials/graph2hypergraph/node_centrality_lifting.ipynb index d08c1cbf..66bd0e32 100644 --- a/tutorials/graph2hypergraph/node_centrality_lifting.ipynb +++ b/tutorials/graph2hypergraph/node_centrality_lifting.ipynb @@ -164,7 +164,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -173,7 +173,7 @@ "modules.data.utils.custom_dataset.CustomDataset" ] }, - "execution_count": 5, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -219,7 +219,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -235,6 +235,7 @@ " 'alpha': 0.85,\n", " 'th_quantile': 0.95,\n", " 'n_most_influential': 2,\n", + " 'do_weight_hyperedge_influence': False,\n", " 'max_iter': 100,\n", " 'tol': 1e-06,\n", " 'feature_lifting': 'ProjectionSum'}\n" @@ -263,25 +264,18 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ + "Transform parameters are the same, using existing data_dir: /Users/michael/perelyn/research/topological_deep_learning/challenge-icml-2024/datasets/graph/toy_dataset/manual/lifting/1805445041\n", "\n", "Dataset only contains 1 sample:\n" ] }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Processing...\n", - "Done!\n" - ] - }, { "data": { "image/png": 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", @@ -317,7 +311,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -372,7 +366,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -442,7 +436,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -471,7 +465,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -499,7 +493,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -532,7 +526,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -573,7 +567,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 16, "metadata": {}, "outputs": [ { From 611e5e4a58697bcce4e873e2c4bfba1fce6154b3 Mon Sep 17 00:00:00 2001 From: Michael Banf Date: Fri, 12 Jul 2024 02:53:00 +0200 Subject: [PATCH 15/18] clean --- .../node_centrality_lifting.yaml | 2 +- .../node_centrality_lifting.py | 37 ++++++++-------- .../test_node_centrality_lifting.py | 42 +++++++++++++++++- .../node_centrality_lifting.ipynb | 44 ++++++++++++------- 4 files changed, 90 insertions(+), 35 deletions(-) diff --git a/configs/transforms/liftings/graph2hypergraph/node_centrality_lifting.yaml b/configs/transforms/liftings/graph2hypergraph/node_centrality_lifting.yaml index 036f03dc..82d918b3 100755 --- a/configs/transforms/liftings/graph2hypergraph/node_centrality_lifting.yaml +++ b/configs/transforms/liftings/graph2hypergraph/node_centrality_lifting.yaml @@ -2,7 +2,7 @@ transform_type: 'lifting' transform_name: "HypergraphNodeCentralityLifting" network_type: 'weighted' alpha: 0.85 -th_quantile: 0.95 +th_percentile: 0.05 n_most_influential: 2 do_weight_hyperedge_influence: False max_iter: 100 diff --git a/modules/transforms/liftings/graph2hypergraph/node_centrality_lifting.py b/modules/transforms/liftings/graph2hypergraph/node_centrality_lifting.py index 618cd4fe..bf9fb37b 100644 --- a/modules/transforms/liftings/graph2hypergraph/node_centrality_lifting.py +++ b/modules/transforms/liftings/graph2hypergraph/node_centrality_lifting.py @@ -7,16 +7,18 @@ class HypergraphNodeCentralityLifting(Graph2HypergraphLifting): - r"""Lifts graphs to hypergraph domain using Page Rank. + r"""Lifts graphs to hypergraph domain using node centrality. + + This lifting creates hyperedges based on central, i.e. highly influential, nodes in the network. Mapping a connection between individual nodes to specific nodes in the network architecture that have a specific and potentially competing influence on them is a very convenient scenario to be modelled via hyperedges. Using shortest path distance to identify the most influential nodes on any given node even allows for placing weights on the hyperedge connection to individual, connected nodes (i.e. the inverse shortest path distance to the corresponding most influential node that the hyperedge represents). To define and identify influential nodes in the network, we refer to the variant of the Eigenvector Centrality with an additional jump probability (i.e. PageRank) Parameters ---------- network_type : str Network type may be weighted or unweighted. Default is "weighted". alpha: float - Damping parameter for PageRank, default=0.85. - th_quantile: float - Fraction of most influential nodes in the network, default=0.95. + jump probability, called dampening factor, which decides whether to continue following the transition matrix or teleport to random positions, default=0.85. + th_percentile: float + Fraction of most influential nodes in the network to consider, default=0.05. n_most_influential: integer Number of most influential nodes to assign a node to. default=2. do_weight_hyperedge_influence: bool @@ -32,13 +34,13 @@ class HypergraphNodeCentralityLifting(Graph2HypergraphLifting): def __init__( self, - network_type="weighted", - alpha=0.85, - th_quantile=0.95, - n_most_influential=2, - do_weight_hyperedge_influence=False, - max_iter=100, - tol=1e-06, + network_type: str = "weighted", + alpha: float = 0.85, + th_percentile: float = 0.05, + n_most_influential: float = 2, + do_weight_hyperedge_influence: bool = False, + max_iter: int = 100, + tol: float = 1e-06, **kwargs, ): super().__init__(**kwargs) @@ -46,12 +48,12 @@ def __init__( self.alpha = alpha self.max_iter = max_iter self.tol = tol - self.th_quantile = th_quantile + self.th_percentile = th_percentile self.n_most_influential = n_most_influential self.do_weight_hyperedge_influence = do_weight_hyperedge_influence def lift_topology(self, data: torch_geometric.data.Data) -> dict: - r"""Lifts the topology of a graph to hypergraph domain using Page Rank. + r"""Lifts the topology of a graph to hypergraph domain using node centrality. Parameters ---------- @@ -108,21 +110,20 @@ def lift_topology(self, data: torch_geometric.data.Data) -> dict: f"network type {self.network_type} not implemented" ) - # estimate node centrality - + # estimate node centrality for all nodes pr = nx.pagerank( G, alpha=self.alpha, max_iter=self.max_iter, tol=self.tol, weight="w" ) - # hyperedges based on the number of most influencial nodes - th_cutoff = np.quantile(list(pr.values()), self.th_quantile) + # estimate fraction of most influential nodes in the network to consider, i.e. the hyperedges + th_cutoff = np.quantile(list(pr.values()), (1 - self.th_percentile)) nodes_most_influential = [n for n, v in pr.items() if v >= th_cutoff] num_hyperedges = len(nodes_most_influential) hyperedge_map = {v: e for e, v in enumerate(nodes_most_influential)} incidence_hyperedges = torch.zeros(data.num_nodes, num_hyperedges) - # assign to the top n most influential + # assign each node to the hyeredges corresponding to the top "n_most_influential" most influential nodes for v in list(G.nodes()): if v in nodes_most_influential: incidence_hyperedges[v, hyperedge_map[v]] = 1 diff --git a/test/transforms/liftings/graph2hypergraph/test_node_centrality_lifting.py b/test/transforms/liftings/graph2hypergraph/test_node_centrality_lifting.py index 9b96aaf0..e043fd6e 100644 --- a/test/transforms/liftings/graph2hypergraph/test_node_centrality_lifting.py +++ b/test/transforms/liftings/graph2hypergraph/test_node_centrality_lifting.py @@ -1,5 +1,6 @@ """Test Page Rank Lifting.""" +import pytest import torch from modules.data.utils.utils import load_manual_graph @@ -16,7 +17,7 @@ def setup_method(self): self.lifting = HypergraphNodeCentralityLifting( network_type="weighted", - th_quantile=0.8, + th_percentile=0.2, n_most_influential=1, ) @@ -45,3 +46,42 @@ def test_lift_topology(self): assert ( expected_n_hyperedges == lifted_data.num_hyperedges ), "Something is wrong with the number of hyperedges (k=1)." + + self.lifting.network_type = "unweighted" + lifted_data = self.lifting.forward(self.data.clone()) + + assert ( + expected_incidence_1 == lifted_data.incidence_hyperedges.to_dense() + ).all(), "Something is wrong with incidence_hyperedges (k=1)." + assert ( + expected_n_hyperedges == lifted_data.num_hyperedges + ), "Something is wrong with the number of hyperedges (k=1)." + + expected_incidence_1 = torch.tensor( + [ + [1.0, 0.0], + [1.0, 0.0], + [0.0, 1.0], + [0.0, 1.0], + [1.0, 0.0], + [0.0, 1.0], + [0.0, 0.5], + [1.0, 0.0], + ] + ) + + self.lifting.network_type = "unweighted" + self.lifting.do_weight_hyperedge_influence = True + lifted_data = self.lifting.forward(self.data.clone()) + + assert ( + expected_incidence_1 == lifted_data.incidence_hyperedges.to_dense() + ).all(), "Something is wrong with incidence_hyperedges (k=1)." + assert ( + expected_n_hyperedges == lifted_data.num_hyperedges + ), "Something is wrong with the number of hyperedges (k=1)." + + def test_validations(self): + with pytest.raises(NotImplementedError): + self.lifting.network_type = "mixed" + self.lifting.forward(self.data.clone()) diff --git a/tutorials/graph2hypergraph/node_centrality_lifting.ipynb b/tutorials/graph2hypergraph/node_centrality_lifting.ipynb index 66bd0e32..f611dad9 100644 --- a/tutorials/graph2hypergraph/node_centrality_lifting.ipynb +++ b/tutorials/graph2hypergraph/node_centrality_lifting.ipynb @@ -193,15 +193,22 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "In this section, we will instantiate the lifting we want to apply to the data. In this tutorial, we apply an novel lifting that creates hyperedges based on central, i.e. highly influential, nodes in the network. To identify influential nodes in the network, we refer to the variant of the Eigenvector Centrality with an additional jump probability (i.e. PageRank).\n", + "**Motivation**\n", + "In this section, we will instantiate the lifting we want to apply to the data. This is a novel lifting that creates hyperedges based on central, i.e. highly influential, nodes in the network. Mapping a connection between individual nodes to specific nodes in the network architecture that have a specific and potentially competing influence on them is a very convenient scenario to be modelled via hyperedges. Using shortest path distance to identify the most influential nodes on any given node even allows for placing weights on the hyperedge connection to individual, connected nodes (i.e. the inverse shortest path distance to the corresponding most influential node that the hyperedge represents). To define and identify influential nodes in the network, we refer to the variant of the Eigenvector Centrality with an additional jump probability (i.e. PageRank).\n", "\n", + "**Background**\n", "Eigenvector Centrality is an algorithm that measures the transitive influence of nodes. Relationships originating from high-scoring nodes contribute more to the score of a node than connections from low-scoring nodes. A high eigenvector score means that a node is connected to many nodes who themselves have high scores.\n", "\n", "The algorithm computes the eigenvector associated with the largest absolute eigenvalue. To compute that eigenvalue, the algorithm applies the power iteration approach. Within each iteration, the centrality score for each node is derived from the scores of its incoming neighbors. In the power iteration method, the eigenvector is L2-normalized after each iteration, leading to normalized results by default.\n", "\n", "The PageRank [[1]](https://doi.org/10.1137/140976649) variant of Eigenvector Centrality utilises, at any step of the power iteration, an additional jump probability, called dampening factor $\\alpha$, which decides whether to continue following the transition matrix or teleport to random positions in the process.These random teleportations have shown to be an effective way to ensure that the matrix and corresponding Markov chain exhibit ergodicity which makes them easier to analyze and to guarantee convergence.\n", "\n", - "Our approach is applicable to both directed and undirected as well as weighted and unweighted networks. It is straightforward: (1) calculate the node centrality of all nodes in the graph, (2) select the top $n$ most influential nodes in the graph as hyperedges. (3) Assign all nodes in the network to $m >= 1$ most influential nodes (with $m <= n$), i.e. their respective hyperedges, based on their distance (shortest path) to each influential node. \n", + "**Method**\n", + "Our approach is applicable to both directed and undirected as well as weighted and unweighted networks. It works as follows: (1) calculate the node centrality of all nodes in the graph, (2) select the top $n$ most influential nodes in the graph as hyperedges based on a given quantile. (3) Assign all nodes in the network to $m >= 1$ most influential nodes (with $m <= n$), i.e. their respective hyperedges, based on their shortest path distance $d$ to each influential node. (4, optional) model individual connection weights per node to a hyperedge via the inverse shortest path distance (i.e. $1/d$) to the hyperedge's corresponding most influential node. \n", + "\n", + "**Remarks and outlook**\n", + "Note that we currently utilitze the ProjectionSum feature lifting to model the inverse relationship between all nodes towards their shared most influential node. In order to model the direct influence of the influential node on all individual nodes via the hyperedge, we however plan to further implement a straight-forward feature lifting that utilitzes, per hyperedge, the corresponding most influential node features directly. \n", + "\n", "\n", "***\n", "[[1]](https://doi.org/10.1137/140976649) Gleich, D.F. (2015) PageRank Beyond the Web. SIAM Review, 57, 321-363.\n", @@ -233,7 +240,7 @@ " 'transform_name': 'HypergraphNodeCentralityLifting',\n", " 'network_type': 'weighted',\n", " 'alpha': 0.85,\n", - " 'th_quantile': 0.95,\n", + " 'th_percentile': 0.05,\n", " 'n_most_influential': 2,\n", " 'do_weight_hyperedge_influence': False,\n", " 'max_iter': 100,\n", @@ -264,18 +271,25 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Transform parameters are the same, using existing data_dir: /Users/michael/perelyn/research/topological_deep_learning/challenge-icml-2024/datasets/graph/toy_dataset/manual/lifting/1805445041\n", "\n", "Dataset only contains 1 sample:\n" ] }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Processing...\n", + "Done!\n" + ] + }, { "data": { "image/png": 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", @@ -306,7 +320,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Given the automated quantile based threshold estimation, the algorithm detects one most influential node, hence one hyperedge." + "Given the stringent percentile threshold (i.e. 0.05), the algorithm detects one most influential node, hence one hyperedge." ] }, { @@ -352,7 +366,7 @@ } ], "source": [ - "transform_config[\"lifting\"][\"th_quantile\"] = 0.8\n", + "transform_config[\"lifting\"][\"th_percentile\"] = 0.2\n", "lifted_dataset = PreProcessor(dataset, transform_config, loader.data_dir)\n", "describe_data(lifted_dataset)" ] @@ -361,12 +375,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "lowering the quantile leads to the selection of two most influential nodes" + "a more lenient threshold leads to the selection of two most influential nodes" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -408,7 +422,7 @@ ], "source": [ "transform_config[\"lifting\"][\"n_most_influential\"] = 1\n", - "transform_config[\"lifting\"][\"th_quantile\"] = 0.8\n", + "transform_config[\"lifting\"][\"th_percentile\"] = 0.2\n", "lifted_dataset = PreProcessor(dataset, transform_config, loader.data_dir)\n", "describe_data(lifted_dataset)" ] @@ -436,7 +450,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -465,7 +479,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -493,7 +507,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -526,7 +540,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -567,7 +581,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 14, "metadata": {}, "outputs": [ { From b938817285e262e5dd49cb38870e0b28013fb244 Mon Sep 17 00:00:00 2001 From: Michael Banf Date: Fri, 12 Jul 2024 03:05:55 +0200 Subject: [PATCH 16/18] clean --- .../liftings/graph2hypergraph/node_centrality_lifting.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/transforms/liftings/graph2hypergraph/node_centrality_lifting.py b/modules/transforms/liftings/graph2hypergraph/node_centrality_lifting.py index bf9fb37b..882d3002 100644 --- a/modules/transforms/liftings/graph2hypergraph/node_centrality_lifting.py +++ b/modules/transforms/liftings/graph2hypergraph/node_centrality_lifting.py @@ -139,7 +139,7 @@ def lift_topology(self, data: torch_geometric.data.Data) -> dict: for k_infl, v_infl in v_influencial: w = 1 if self.do_weight_hyperedge_influence: - w = 1 / v_infl + w = max(1 / v_infl, 0.0001) incidence_hyperedges[v, hyperedge_map[k_infl]] = w incidence_hyperedges = incidence_hyperedges.to_sparse_coo() From d858414e7a4cec2a077f314667a2bd0176291a8e Mon Sep 17 00:00:00 2001 From: Michael Banf Date: Fri, 12 Jul 2024 20:17:24 +0200 Subject: [PATCH 17/18] add node assignment feature lifting --- .../node_centrality_lifting.yaml | 1 + .../node_centrality_lifting.py | 14 ++- .../test_node_centrality_lifting.py | 23 ++-- .../node_centrality_lifting.ipynb | 100 +++++++++++++++--- 4 files changed, 115 insertions(+), 23 deletions(-) diff --git a/configs/transforms/liftings/graph2hypergraph/node_centrality_lifting.yaml b/configs/transforms/liftings/graph2hypergraph/node_centrality_lifting.yaml index 82d918b3..07c17e15 100755 --- a/configs/transforms/liftings/graph2hypergraph/node_centrality_lifting.yaml +++ b/configs/transforms/liftings/graph2hypergraph/node_centrality_lifting.yaml @@ -5,6 +5,7 @@ alpha: 0.85 th_percentile: 0.05 n_most_influential: 2 do_weight_hyperedge_influence: False +do_hyperedge_node_assignment_feature_lifting_passthrough: False max_iter: 100 tol: 1e-06 feature_lifting: ProjectionSum \ No newline at end of file diff --git a/modules/transforms/liftings/graph2hypergraph/node_centrality_lifting.py b/modules/transforms/liftings/graph2hypergraph/node_centrality_lifting.py index 882d3002..8bc68596 100644 --- a/modules/transforms/liftings/graph2hypergraph/node_centrality_lifting.py +++ b/modules/transforms/liftings/graph2hypergraph/node_centrality_lifting.py @@ -23,6 +23,8 @@ class HypergraphNodeCentralityLifting(Graph2HypergraphLifting): Number of most influential nodes to assign a node to. default=2. do_weight_hyperedge_influence: bool add a weight to the hyperedge connections per node based on the inverse spath distance to influential node. default=False. + do_hyperedge_node_assignment_feature_lifting_passthrough: bool + assign features of most influential nodes to corresponding hyperedges and pass through feature lifting. default=False. max_iter: integer Maximum number of iterations in power method eigenvalue solver. tol: float @@ -39,6 +41,7 @@ def __init__( th_percentile: float = 0.05, n_most_influential: float = 2, do_weight_hyperedge_influence: bool = False, + do_hyperedge_node_assignment_feature_lifting_passthrough: bool = False, max_iter: int = 100, tol: float = 1e-06, **kwargs, @@ -51,6 +54,9 @@ def __init__( self.th_percentile = th_percentile self.n_most_influential = n_most_influential self.do_weight_hyperedge_influence = do_weight_hyperedge_influence + self.do_hyperedge_node_assignment_feature_lifting_passthrough = ( + do_hyperedge_node_assignment_feature_lifting_passthrough + ) def lift_topology(self, data: torch_geometric.data.Data) -> dict: r"""Lifts the topology of a graph to hypergraph domain using node centrality. @@ -143,8 +149,14 @@ def lift_topology(self, data: torch_geometric.data.Data) -> dict: incidence_hyperedges[v, hyperedge_map[k_infl]] = w incidence_hyperedges = incidence_hyperedges.to_sparse_coo() - return { + lifted_data = { "incidence_hyperedges": incidence_hyperedges, "num_hyperedges": num_hyperedges, "x_0": data.x, } + + if self.do_hyperedge_node_assignment_feature_lifting_passthrough: + # assign features of most influential nodes to corresponding hyperedges and pass through feature lifting. + lifted_data["x_hyperedges"] = data.x[nodes_most_influential] + + return lifted_data diff --git a/test/transforms/liftings/graph2hypergraph/test_node_centrality_lifting.py b/test/transforms/liftings/graph2hypergraph/test_node_centrality_lifting.py index e043fd6e..ac6c1c55 100644 --- a/test/transforms/liftings/graph2hypergraph/test_node_centrality_lifting.py +++ b/test/transforms/liftings/graph2hypergraph/test_node_centrality_lifting.py @@ -42,20 +42,20 @@ def test_lift_topology(self): assert ( expected_incidence_1 == lifted_data.incidence_hyperedges.to_dense() - ).all(), "Something is wrong with incidence_hyperedges (k=1)." + ).all(), "Something is wrong with incidence_hyperedges." assert ( expected_n_hyperedges == lifted_data.num_hyperedges - ), "Something is wrong with the number of hyperedges (k=1)." + ), "Something is wrong with the number of hyperedges." self.lifting.network_type = "unweighted" lifted_data = self.lifting.forward(self.data.clone()) assert ( expected_incidence_1 == lifted_data.incidence_hyperedges.to_dense() - ).all(), "Something is wrong with incidence_hyperedges (k=1)." + ).all(), "Something is wrong with incidence_hyperedges." assert ( expected_n_hyperedges == lifted_data.num_hyperedges - ), "Something is wrong with the number of hyperedges (k=1)." + ), "Something is wrong with the number of hyperedges." expected_incidence_1 = torch.tensor( [ @@ -76,10 +76,21 @@ def test_lift_topology(self): assert ( expected_incidence_1 == lifted_data.incidence_hyperedges.to_dense() - ).all(), "Something is wrong with incidence_hyperedges (k=1)." + ).all(), "Something is wrong with incidence_hyperedges." assert ( expected_n_hyperedges == lifted_data.num_hyperedges - ), "Something is wrong with the number of hyperedges (k=1)." + ), "Something is wrong with the number of hyperedges." + + assert ( + lifted_data.x_hyperedges.to_dense() == torch.tensor([[5106.0], [1060.0]]) + ).all(), "Something is wrong with x_hyperedges." + + self.lifting.do_hyperedge_node_assignment_feature_lifting_passthrough = True + lifted_data = self.lifting.forward(self.data.clone()) + + assert ( + lifted_data.x_hyperedges.to_dense() == torch.tensor([[1.0], [10.0]]) + ).all(), "Something is wrong with x_hyperedges." def test_validations(self): with pytest.raises(NotImplementedError): diff --git a/tutorials/graph2hypergraph/node_centrality_lifting.ipynb b/tutorials/graph2hypergraph/node_centrality_lifting.ipynb index f611dad9..0cab170e 100644 --- a/tutorials/graph2hypergraph/node_centrality_lifting.ipynb +++ b/tutorials/graph2hypergraph/node_centrality_lifting.ipynb @@ -36,12 +36,7 @@ "* PROTEINS_TU\n", "* AQSOL\n", "* ZINC\n", - "***\n", - "\n", - "***\n", - "Extension ideas and TODOs\n", - "- Switch from NetworkX to PyG graph to possible speed gains (needs to be tested though)\n", - "***" + "***\n" ] }, { @@ -206,8 +201,8 @@ "**Method**\n", "Our approach is applicable to both directed and undirected as well as weighted and unweighted networks. It works as follows: (1) calculate the node centrality of all nodes in the graph, (2) select the top $n$ most influential nodes in the graph as hyperedges based on a given quantile. (3) Assign all nodes in the network to $m >= 1$ most influential nodes (with $m <= n$), i.e. their respective hyperedges, based on their shortest path distance $d$ to each influential node. (4, optional) model individual connection weights per node to a hyperedge via the inverse shortest path distance (i.e. $1/d$) to the hyperedge's corresponding most influential node. \n", "\n", - "**Remarks and outlook**\n", - "Note that we currently utilitze the ProjectionSum feature lifting to model the inverse relationship between all nodes towards their shared most influential node. In order to model the direct influence of the influential node on all individual nodes via the hyperedge, we however plan to further implement a straight-forward feature lifting that utilitzes, per hyperedge, the corresponding most influential node features directly. \n", + "**Remarks on feature liftings**\n", + "Note that the algorithm currently supports the utilization of the ProjectionSum feature lifting to model the inverse relationship between all nodes towards their shared most influential node. In order to model the direct influence of the influential node on all individual nodes via the hyperedge, we have, however, further implemented a straight-forward feature lifting via assignment of the hyperedge's corresponding node's features, thereby bypassing the ProjectionSum feature lifting.\n", "\n", "\n", "***\n", @@ -243,6 +238,7 @@ " 'th_percentile': 0.05,\n", " 'n_most_influential': 2,\n", " 'do_weight_hyperedge_influence': False,\n", + " 'do_hyperedge_node_assignment_feature_lifting_passthrough': False,\n", " 'max_iter': 100,\n", " 'tol': 1e-06,\n", " 'feature_lifting': 'ProjectionSum'}\n" @@ -271,7 +267,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -325,7 +321,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -375,13 +371,45 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "a more lenient threshold leads to the selection of two most influential nodes" + "A more lenient threshold leads to the selection of two most influential nodes. Further we observe the effect of passing through the feature lifting (based on projectiveSum) and assigning each influential node's features directly to the corresponding hyperedges. " ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data(x=[8, 1], edge_index=[2, 13], y=[8], num_nodes=8, incidence_hyperedges=[8, 2], num_hyperedges=2, x_0=[8, 1], x_hyperedges=[2, 1])\n", + "tensor([[ 1.],\n", + " [10.]])\n", + "Transform parameters are the same, using existing data_dir: /Users/michael/perelyn/research/topological_deep_learning/challenge-icml-2024/datasets/graph/toy_dataset/manual/lifting/2688954955\n", + "Data(x=[8, 1], edge_index=[2, 13], y=[8], num_nodes=8, incidence_hyperedges=[8, 2], num_hyperedges=2, x_0=[8, 1], x_hyperedges=[2, 1])\n", + "tensor([[ 1.],\n", + " [10.]])\n" + ] + } + ], + "source": [ + "print(lifted_dataset.get(0))\n", + "print(lifted_dataset.get(0).x_hyperedges)\n", + "\n", + "transform_config[\"lifting\"][\n", + " \"do_hyperedge_node_assignment_feature_lifting_passthrough\"\n", + "] = True\n", + "lifted_dataset = PreProcessor(dataset, transform_config, loader.data_dir)\n", + "\n", + "print(lifted_dataset.get(0))\n", + "print(lifted_dataset.get(0).x_hyperedges)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -450,7 +478,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -479,7 +507,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -507,7 +535,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -540,7 +568,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -581,7 +609,46 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Transform configuration for graph2hypergraph/node_centrality_lifting:\n", + "\n", + "{'transform_type': 'lifting',\n", + " 'transform_name': 'HypergraphNodeCentralityLifting',\n", + " 'network_type': 'weighted',\n", + " 'alpha': 0.85,\n", + " 'th_percentile': 0.05,\n", + " 'n_most_influential': 2,\n", + " 'do_weight_hyperedge_influence': False,\n", + " 'do_hyperedge_node_assignment_feature_lifting_passthrough': False,\n", + " 'max_iter': 100,\n", + " 'tol': 1e-06,\n", + " 'feature_lifting': 'ProjectionSum'}\n" + ] + } + ], + "source": [ + "# Define transformation type and id\n", + "transform_type = \"liftings\"\n", + "# If the transform is a topological lifting, it should include both the type of the lifting and the identifier\n", + "transform_id = \"graph2hypergraph/node_centrality_lifting\"\n", + "\n", + "# Read yaml file\n", + "transform_config = {\n", + " \"lifting\": load_transform_config(transform_type, transform_id)\n", + " # other transforms (e.g. data manipulations, feature liftings) can be added here\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -631,6 +698,7 @@ ], "source": [ "transform_config[\"lifting\"][\"n_most_influential\"] = 1\n", + "transform_config[\"lifting\"][\"th_percentile\"] = 0.2\n", "lifted_dataset = PreProcessor(dataset, transform_config, loader.data_dir)\n", "describe_data(lifted_dataset)" ] From 94f218025036056c2e8c23a048d4cf25ac164946 Mon Sep 17 00:00:00 2001 From: Michael Banf Date: Fri, 12 Jul 2024 20:41:14 +0200 Subject: [PATCH 18/18] cleanup --- .../node_centrality_lifting.ipynb | 26 +++++-------------- 1 file changed, 6 insertions(+), 20 deletions(-) diff --git a/tutorials/graph2hypergraph/node_centrality_lifting.ipynb b/tutorials/graph2hypergraph/node_centrality_lifting.ipynb index 0cab170e..36c1ee27 100644 --- a/tutorials/graph2hypergraph/node_centrality_lifting.ipynb +++ b/tutorials/graph2hypergraph/node_centrality_lifting.ipynb @@ -274,18 +274,11 @@ "name": "stdout", "output_type": "stream", "text": [ + "Transform parameters are the same, using existing data_dir: /Users/michael/perelyn/research/topological_deep_learning/challenge-icml-2024/datasets/graph/toy_dataset/manual/lifting/1275153652\n", "\n", "Dataset only contains 1 sample:\n" ] }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Processing...\n", - "Done!\n" - ] - }, { "data": { "image/png": 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", @@ -328,18 +321,11 @@ "name": "stdout", "output_type": "stream", "text": [ + "Transform parameters are the same, using existing data_dir: /Users/michael/perelyn/research/topological_deep_learning/challenge-icml-2024/datasets/graph/toy_dataset/manual/lifting/1661034459\n", "\n", "Dataset only contains 1 sample:\n" ] }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Processing...\n", - "Done!\n" - ] - }, { "data": { "image/png": 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", @@ -376,7 +362,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -384,8 +370,8 @@ "output_type": "stream", "text": [ "Data(x=[8, 1], edge_index=[2, 13], y=[8], num_nodes=8, incidence_hyperedges=[8, 2], num_hyperedges=2, x_0=[8, 1], x_hyperedges=[2, 1])\n", - "tensor([[ 1.],\n", - " [10.]])\n", + "tensor([[6656.],\n", + " [6665.]])\n", "Transform parameters are the same, using existing data_dir: /Users/michael/perelyn/research/topological_deep_learning/challenge-icml-2024/datasets/graph/toy_dataset/manual/lifting/2688954955\n", "Data(x=[8, 1], edge_index=[2, 13], y=[8], num_nodes=8, incidence_hyperedges=[8, 2], num_hyperedges=2, x_0=[8, 1], x_hyperedges=[2, 1])\n", "tensor([[ 1.],\n", @@ -609,7 +595,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "metadata": {}, "outputs": [ {