Source code for geomstats.datasets.prepare_graph_data

"""Prepare and process graph-structured data."""

import logging
import random

import geomstats.backend as gs
from geomstats.geometry.poincare_ball import PoincareBall

[docs]class Graph: """Class for generating a graph object from a dataset. Prepare Graph object from a dataset file. Parameters ---------- graph_matrix_path : string Path to graph adjacency matrix. labels_path : string Path to labels of the nodes of the graph. Attributes ---------- edges : dict Dictionary with node number as key and edge connected node numbers as values. n_nodes : int Number of nodes in the graph. labels : dict Dictionary with node number as key and the true label number as values. """ def __init__(self, graph_matrix_path, labels_path): self.edges = {} with open(graph_matrix_path, 'r') as edges_file: for i, line in enumerate(edges_file): lsp = line.split() self.edges[i] = [k for k, value in enumerate(lsp) if int(value) == 1] self.n_nodes = len(self.edges) if labels_path is not None: self.labels = {} with open(labels_path, 'r') as labels_file: for i, line in enumerate(labels_file): self.labels[i] = [] self.labels[i].append(int(line))
[docs] def random_walk(self, walk_length=5, n_walks_per_node=1): """Compute a set of random walks on a graph. For each node of the graph, generates a a number of random walks of a specified length. Two consecutive nodes in the random walk, are necessarily related with an edge. The walks capture the structure of the graph. Parameters ---------- walk_length : int Length of a random walk in terms of number of edges. n_walks_per_node : int Number of generated walks starting from each node of the graph. Returns ------- self : array-like, Shape=[n_walks_per_node*self.n_edges), walk_length] array containing random walks. """ paths = [[0] * (walk_length + 1) for i in range(self.n_nodes * n_walks_per_node)] for index in range(len(self.edges)): for i in range(n_walks_per_node): paths[index * n_walks_per_node + i] =\ self._walk(index, walk_length) return gs.array(paths)
def _walk(self, index, walk_length): """Generate a single random walk.""" count_index = index path = [index] for _ in range(walk_length): count_index = self.edges[count_index][random.randint( 0, len(self.edges[count_index]) - 1)] path.append(count_index) return gs.array(path, dtype=gs.int32)
[docs]class HyperbolicEmbedding: """Class for learning embeddings of graphs on hyperbolic space. Parameters ---------- dim : object Dimensions of the used hyperbolic space. max_epochs : int Maximum number of iterations for embedding. lr : int Learning rate for embedding. n_context : int Number of nodes to consider from a neighborhood of nodes around a particular node. n_negative : int Number of nodes to consider when searching for a set of nodes that are far from a particular node. """ def __init__( self, dim=2, max_epochs=100, lr=.05, n_context=1, n_negative=2): self.manifold = PoincareBall(dim) self.max_epochs = max_epochs = lr self.n_context = n_context self.n_negative = n_negative
[docs] @staticmethod def log_sigmoid(vector): """Logsigmoid function. Apply log sigmoid function. Parameters ---------- vector : array-like, shape=[n_samples, dim] Returns ------- result : array-like, shape=[n_samples, dim] """ return gs.log((1 / (1 + gs.exp(-vector))))
[docs] @staticmethod def grad_log_sigmoid(vector): """Gradient of log sigmoid function. Parameters ---------- vector : array-like, shape=[n_samples, dim] Returns ------- gradient : array-like, shape=[n_samples, dim] """ return 1 / (1 + gs.exp(vector))
[docs] def grad_squared_distance(self, point_a, point_b): """Gradient of squared hyperbolic distance. Gradient of the squared distance based on the Ball representation according to point_a. Parameters ---------- point_a : array-like, shape=[n_samples, dim] First point in hyperbolic space. point_b : array-like, shape=[n_samples, dim] Second point in hyperbolic space. Returns ------- dist : array-like, shape=[n_samples, 1] Geodesic squared distance between the two points. """ hyperbolic_metric = self.manifold.metric log_map = hyperbolic_metric.log(point_b, point_a) return -2 * log_map
[docs] def loss( self, example_embedding, context_embedding, negative_embedding): """Compute loss and grad. Compute loss and grad given embedding of the current example, embedding of the context and negative sampling embedding. Parameters ---------- example_embedding : array-like, shape=[dim] Current data sample embedding. context_embedding : array-like, shape=[dim] Current context embedding. negative_embedding: array-like, shape=[dim] Current negative sample embedding. Returns ------- total_loss : int The current value of the loss function. example_grad : array-like, shape=[dim] The gradient of the loss function at the embedding of the current data sample. """ n_edges, dim =\ negative_embedding.shape[0], example_embedding.shape[-1] example_embedding = gs.expand_dims(example_embedding, 0) context_embedding = gs.expand_dims(context_embedding, 0) positive_distance =\ self.manifold.metric.squared_dist( example_embedding, context_embedding) positive_loss =\ self.log_sigmoid(-positive_distance) reshaped_example_embedding =\ gs.repeat(example_embedding, n_edges, axis=0) negative_distance =\ self.manifold.metric.squared_dist( reshaped_example_embedding, negative_embedding) negative_loss = self.log_sigmoid(negative_distance) total_loss = -(positive_loss + gs.sum(negative_loss)) positive_log_sigmoid_grad =\ -self.grad_log_sigmoid(-positive_distance) positive_distance_grad =\ self.grad_squared_distance(example_embedding, context_embedding) positive_grad =\ gs.repeat(positive_log_sigmoid_grad, dim, axis=-1)\ * positive_distance_grad negative_distance_grad =\ self.grad_squared_distance( reshaped_example_embedding, negative_embedding) negative_distance = gs.to_ndarray(negative_distance, to_ndim=2, axis=-1) negative_log_sigmoid_grad =\ self.grad_log_sigmoid(negative_distance) negative_grad = negative_log_sigmoid_grad\ * negative_distance_grad example_grad = -(positive_grad + gs.sum(negative_grad, axis=0)) return total_loss, example_grad
[docs] def embed(self, graph): """Compute embedding. Optimize a loss function to obtain a representable embedding. Parameters ---------- graph : object An instance of the Graph class. Returns ------- embeddings : array-like, shape=[n_samples, dim] Return the embedding of the data. Each data sample is represented as a point belonging to the manifold. """ nb_vertices_by_edges = \ [len(e_2) for _, e_2 in graph.edges.items()]'Number of edges: %s', len(graph.edges)) 'Mean vertices by edges: %s', (sum(nb_vertices_by_edges, 0) / len(graph.edges))) negative_table_parameter = 5 negative_sampling_table = [] for i, nb_v in enumerate(nb_vertices_by_edges): negative_sampling_table += \ ([i] * int((nb_v ** (3. / 4.))) * negative_table_parameter) negative_sampling_table = gs.array(negative_sampling_table) random_walks = graph.random_walk() embeddings = gs.random.normal( size=(graph.n_nodes, self.manifold.dim)) embeddings = embeddings * 0.2 for epoch in range(self.max_epochs): total_loss = [] for path in random_walks: for example_index, one_path in enumerate(path): context_index = path[max( 0, example_index - self.n_context): min(example_index + self.n_context, len(path))] negative_index = \ gs.random.randint(negative_sampling_table.shape[0], size=(len(context_index), self.n_negative)) negative_index = gs.expand_dims( gs.flatten(negative_index), axis=-1) negative_index = gs.get_slice( negative_sampling_table, negative_index) example_embedding = embeddings[gs.cast( one_path, dtype=gs.int64)] for one_context_i, one_negative_i in zip(context_index, negative_index): context_embedding = embeddings[one_context_i] negative_embedding = gs.get_slice( embeddings, gs.squeeze(gs.cast( one_negative_i, dtype=gs.int64))) l, g_ex = self.loss( example_embedding, context_embedding, negative_embedding) total_loss.append(l) example_to_update = embeddings[one_path] valeur = self.manifold.metric.exp( * g_ex, example_to_update) embeddings = gs.assignment( embeddings, valeur, gs.to_ndarray( one_path, to_ndim=1), axis=1) 'iteration %d loss_value %f', epoch, sum(total_loss, 0) / len(total_loss)) return embeddings