557 lines
19 KiB
Python
557 lines
19 KiB
Python
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import warnings
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from warnings import warn
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import numpy as np
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import scipy.sparse
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import scipy.sparse.csgraph
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from sklearn.decomposition import TruncatedSVD
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from sklearn.manifold import SpectralEmbedding
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from sklearn.metrics import pairwise_distances
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from sklearn.metrics.pairwise import _VALID_METRICS as SKLEARN_PAIRWISE_VALID_METRICS
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from umap.distances import pairwise_special_metric, SPECIAL_METRICS
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from umap.sparse import SPARSE_SPECIAL_METRICS, sparse_named_distances
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def component_layout(
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data,
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n_components,
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component_labels,
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dim,
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random_state,
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metric="euclidean",
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metric_kwds={},
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):
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"""Provide a layout relating the separate connected components. This is done
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by taking the centroid of each component and then performing a spectral embedding
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of the centroids.
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Parameters
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----------
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data: array of shape (n_samples, n_features)
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The source data -- required so we can generate centroids for each
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connected component of the graph.
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n_components: int
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The number of distinct components to be layed out.
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component_labels: array of shape (n_samples)
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For each vertex in the graph the label of the component to
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which the vertex belongs.
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dim: int
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The chosen embedding dimension.
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metric: string or callable (optional, default 'euclidean')
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The metric used to measure distances among the source data points.
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metric_kwds: dict (optional, default {})
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Keyword arguments to be passed to the metric function.
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If metric is 'precomputed', 'linkage' keyword can be used to specify
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'average', 'complete', or 'single' linkage. Default is 'average'
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Returns
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-------
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component_embedding: array of shape (n_components, dim)
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The ``dim``-dimensional embedding of the ``n_components``-many
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connected components.
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"""
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if data is None:
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# We don't have data to work with; just guess
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return np.random.random(size=(n_components, dim)) * 10.0
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component_centroids = np.empty((n_components, data.shape[1]), dtype=np.float64)
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if metric == "precomputed":
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# cannot compute centroids from precomputed distances
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# instead, compute centroid distances using linkage
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distance_matrix = np.zeros((n_components, n_components), dtype=np.float64)
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linkage = metric_kwds.get("linkage", "average")
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if linkage == "average":
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linkage = np.mean
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elif linkage == "complete":
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linkage = np.max
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elif linkage == "single":
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linkage = np.min
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else:
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raise ValueError(
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"Unrecognized linkage '%s'. Please choose from "
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"'average', 'complete', or 'single'" % linkage
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)
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for c_i in range(n_components):
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dm_i = data[component_labels == c_i]
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for c_j in range(c_i + 1, n_components):
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dist = linkage(dm_i[:, component_labels == c_j])
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distance_matrix[c_i, c_j] = dist
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distance_matrix[c_j, c_i] = dist
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else:
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for label in range(n_components):
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component_centroids[label] = data[component_labels == label].mean(axis=0)
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if scipy.sparse.isspmatrix(component_centroids):
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warn(
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"Forcing component centroids to dense; if you are running out of "
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"memory then consider increasing n_neighbors."
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)
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component_centroids = component_centroids.toarray()
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if metric in SPECIAL_METRICS:
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distance_matrix = pairwise_special_metric(
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component_centroids,
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metric=metric,
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kwds=metric_kwds,
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)
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elif metric in SPARSE_SPECIAL_METRICS:
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distance_matrix = pairwise_special_metric(
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component_centroids,
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metric=SPARSE_SPECIAL_METRICS[metric],
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kwds=metric_kwds,
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)
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else:
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if callable(metric) and scipy.sparse.isspmatrix(data):
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function_to_name_mapping = {
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sparse_named_distances[k]: k
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for k in set(SKLEARN_PAIRWISE_VALID_METRICS)
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& set(sparse_named_distances.keys())
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}
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try:
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metric_name = function_to_name_mapping[metric]
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except KeyError:
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raise NotImplementedError(
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"Multicomponent layout for custom "
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"sparse metrics is not implemented at "
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"this time."
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)
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distance_matrix = pairwise_distances(
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component_centroids, metric=metric_name, **metric_kwds
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)
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else:
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distance_matrix = pairwise_distances(
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component_centroids, metric=metric, **metric_kwds
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)
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affinity_matrix = np.exp(-(distance_matrix**2))
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component_embedding = SpectralEmbedding(
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n_components=dim, affinity="precomputed", random_state=random_state
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).fit_transform(affinity_matrix)
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component_embedding /= component_embedding.max()
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return component_embedding
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def multi_component_layout(
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data,
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graph,
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n_components,
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component_labels,
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dim,
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random_state,
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metric="euclidean",
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metric_kwds={},
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init="random",
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tol=0.0,
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maxiter=0
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):
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"""Specialised layout algorithm for dealing with graphs with many connected components.
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This will first find relative positions for the components by spectrally embedding
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their centroids, then spectrally embed each individual connected component positioning
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them according to the centroid embeddings. This provides a decent embedding of each
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component while placing the components in good relative positions to one another.
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Parameters
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----------
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data: array of shape (n_samples, n_features)
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The source data -- required so we can generate centroids for each
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connected component of the graph.
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graph: sparse matrix
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The adjacency matrix of the graph to be embedded.
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n_components: int
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The number of distinct components to be layed out.
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component_labels: array of shape (n_samples)
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For each vertex in the graph the label of the component to
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which the vertex belongs.
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dim: int
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The chosen embedding dimension.
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metric: string or callable (optional, default 'euclidean')
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The metric used to measure distances among the source data points.
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metric_kwds: dict (optional, default {})
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Keyword arguments to be passed to the metric function.
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init: string, either "random" or "tsvd"
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Indicates to initialize the eigensolver. Use "random" (the default) to
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use uniformly distributed random initialization; use "tsvd" to warm-start the
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eigensolver with singular vectors of the Laplacian associated to the largest
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singular values. This latter option also forces usage of the LOBPCG eigensolver;
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with the former, ARPACK's solver ``eigsh`` will be used for smaller Laplacians.
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tol: float, default chosen by implementation
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Stopping tolerance for the numerical algorithm computing the embedding.
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maxiter: int, default chosen by implementation
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Number of iterations the numerical algorithm will go through at most as it
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attempts to compute the embedding.
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Returns
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-------
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embedding: array of shape (n_samples, dim)
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The initial embedding of ``graph``.
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"""
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result = np.empty((graph.shape[0], dim), dtype=np.float32)
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if n_components > 2 * dim:
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meta_embedding = component_layout(
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data,
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n_components,
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component_labels,
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dim,
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random_state,
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metric=metric,
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metric_kwds=metric_kwds,
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)
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else:
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k = int(np.ceil(n_components / 2.0))
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base = np.hstack([np.eye(k), np.zeros((k, dim - k))])
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meta_embedding = np.vstack([base, -base])[:n_components]
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for label in range(n_components):
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component_graph = graph.tocsr()[component_labels == label, :].tocsc()
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component_graph = component_graph[:, component_labels == label].tocoo()
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distances = pairwise_distances([meta_embedding[label]], meta_embedding)
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data_range = distances[distances > 0.0].min() / 2.0
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if component_graph.shape[0] < 2 * dim or component_graph.shape[0] <= dim + 1:
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result[component_labels == label] = (
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random_state.uniform(
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low=-data_range,
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high=data_range,
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size=(component_graph.shape[0], dim),
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)
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+ meta_embedding[label]
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)
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else:
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component_embedding = _spectral_layout(
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data=None,
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graph=component_graph,
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dim=dim,
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random_state=random_state,
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metric=metric,
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metric_kwds=metric_kwds,
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init=init,
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tol=tol,
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maxiter=maxiter
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)
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expansion = data_range / np.max(np.abs(component_embedding))
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component_embedding *= expansion
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result[component_labels == label] = (
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component_embedding + meta_embedding[label]
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)
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return result
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def spectral_layout(
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data,
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graph,
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dim,
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random_state,
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metric="euclidean",
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metric_kwds={},
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tol=0.0,
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maxiter=0
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):
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"""
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Given a graph compute the spectral embedding of the graph. This is
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simply the eigenvectors of the laplacian of the graph. Here we use the
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normalized laplacian.
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Parameters
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----------
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data: array of shape (n_samples, n_features)
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The source data
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graph: sparse matrix
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The (weighted) adjacency matrix of the graph as a sparse matrix.
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dim: int
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The dimension of the space into which to embed.
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random_state: numpy RandomState or equivalent
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A state capable being used as a numpy random state.
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tol: float, default chosen by implementation
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Stopping tolerance for the numerical algorithm computing the embedding.
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maxiter: int, default chosen by implementation
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Number of iterations the numerical algorithm will go through at most as it
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attempts to compute the embedding.
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Returns
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-------
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embedding: array of shape (n_vertices, dim)
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The spectral embedding of the graph.
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"""
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return _spectral_layout(
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data=data,
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graph=graph,
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dim=dim,
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random_state=random_state,
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metric=metric,
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metric_kwds=metric_kwds,
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init="random",
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tol=tol,
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maxiter=maxiter
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)
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def tswspectral_layout(
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data,
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graph,
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dim,
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random_state,
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metric="euclidean",
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metric_kwds={},
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method=None,
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tol=0.0,
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maxiter=0
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):
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"""Given a graph, compute the spectral embedding of the graph. This is
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simply the eigenvectors of the Laplacian of the graph. Here we use the
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normalized laplacian and a truncated SVD-based guess of the
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eigenvectors to "warm" up the eigensolver. This function should
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give results of similar accuracy to the spectral_layout function, but
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may converge more quickly for graph Laplacians that cause
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spectral_layout to take an excessive amount of time to complete.
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Parameters
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----------
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data: array of shape (n_samples, n_features)
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The source data
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graph: sparse matrix
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The (weighted) adjacency matrix of the graph as a sparse matrix.
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dim: int
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The dimension of the space into which to embed.
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random_state: numpy RandomState or equivalent
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A state capable being used as a numpy random state.
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metric: string or callable (optional, default 'euclidean')
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The metric used to measure distances among the source data points.
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Used only if the multiple connected components are found in the
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graph.
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metric_kwds: dict (optional, default {})
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Keyword arguments to be passed to the metric function.
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If metric is 'precomputed', 'linkage' keyword can be used to specify
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'average', 'complete', or 'single' linkage. Default is 'average'.
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Used only if the multiple connected components are found in the
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graph.
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method: str (optional, default None, values either 'eigsh' or 'lobpcg')
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Name of the eigenvalue computation method to use to compute the spectral
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embedding. If left to None (or empty string), as by default, the method is
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chosen from the number of vectors in play: larger vector collections are
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handled with lobpcg, smaller collections with eigsh. Method names correspond
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to SciPy routines in scipy.sparse.linalg.
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tol: float, default chosen by implementation
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Stopping tolerance for the numerical algorithm computing the embedding.
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maxiter: int, default chosen by implementation
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Number of iterations the numerical algorithm will go through at most as it
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attempts to compute the embedding.
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Returns
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-------
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embedding: array of shape (n_vertices, dim)
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The spectral embedding of the graph.
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"""
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return _spectral_layout(
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data=data,
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graph=graph,
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dim=dim,
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random_state=random_state,
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metric=metric,
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metric_kwds=metric_kwds,
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init="tsvd",
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method=method,
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tol=tol,
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maxiter=maxiter
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)
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def _spectral_layout(
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|
data,
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|
graph,
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|
dim,
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|
random_state,
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|
metric="euclidean",
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|
metric_kwds={},
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|
init="random",
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|
method=None,
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|
tol=0.0,
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|
maxiter=0
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|
|
):
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|
"""General implementation of the spectral embedding of the graph, derived as
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|
a subset of the eigenvectors of the normalized Laplacian of the graph. The numerical
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|
method for computing the eigendecomposition is chosen through heuristics.
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|
|
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|
Parameters
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|
----------
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|
data: array of shape (n_samples, n_features)
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|
The source data
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|
|
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|
graph: sparse matrix
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|
The (weighted) adjacency matrix of the graph as a sparse matrix.
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|
|
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|
dim: int
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|
The dimension of the space into which to embed.
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|
|
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||
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|
random_state: numpy RandomState or equivalent
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|
A state capable being used as a numpy random state.
|
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|
|
|
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|
|
metric: string or callable (optional, default 'euclidean')
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|
The metric used to measure distances among the source data points.
|
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|
Used only if the multiple connected components are found in the
|
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|
|
graph.
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|
|
|
||
|
|
metric_kwds: dict (optional, default {})
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||
|
|
Keyword arguments to be passed to the metric function.
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||
|
|
If metric is 'precomputed', 'linkage' keyword can be used to specify
|
||
|
|
'average', 'complete', or 'single' linkage. Default is 'average'.
|
||
|
|
Used only if the multiple connected components are found in the
|
||
|
|
graph.
|
||
|
|
|
||
|
|
init: string, either "random" or "tsvd"
|
||
|
|
Indicates to initialize the eigensolver. Use "random" (the default) to
|
||
|
|
use uniformly distributed random initialization; use "tsvd" to warm-start the
|
||
|
|
eigensolver with singular vectors of the Laplacian associated to the largest
|
||
|
|
singular values. This latter option also forces usage of the LOBPCG eigensolver;
|
||
|
|
with the former, ARPACK's solver ``eigsh`` will be used for smaller Laplacians.
|
||
|
|
|
||
|
|
method: string -- either "eigsh" or "lobpcg" -- or None
|
||
|
|
Name of the eigenvalue computation method to use to compute the spectral
|
||
|
|
embedding. If left to None (or empty string), as by default, the method is
|
||
|
|
chosen from the number of vectors in play: larger vector collections are
|
||
|
|
handled with lobpcg, smaller collections with eigsh. Method names correspond
|
||
|
|
to SciPy routines in scipy.sparse.linalg.
|
||
|
|
|
||
|
|
tol: float, default chosen by implementation
|
||
|
|
Stopping tolerance for the numerical algorithm computing the embedding.
|
||
|
|
|
||
|
|
maxiter: int, default chosen by implementation
|
||
|
|
Number of iterations the numerical algorithm will go through at most as it
|
||
|
|
attempts to compute the embedding.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
embedding: array of shape (n_vertices, dim)
|
||
|
|
The spectral embedding of the graph.
|
||
|
|
"""
|
||
|
|
n_samples = graph.shape[0]
|
||
|
|
n_components, labels = scipy.sparse.csgraph.connected_components(graph)
|
||
|
|
|
||
|
|
if n_components > 1:
|
||
|
|
return multi_component_layout(
|
||
|
|
data,
|
||
|
|
graph,
|
||
|
|
n_components,
|
||
|
|
labels,
|
||
|
|
dim,
|
||
|
|
random_state,
|
||
|
|
metric=metric,
|
||
|
|
metric_kwds=metric_kwds,
|
||
|
|
)
|
||
|
|
|
||
|
|
sqrt_deg = np.sqrt(np.asarray(graph.sum(axis=0)).squeeze())
|
||
|
|
# standard Laplacian
|
||
|
|
# D = scipy.sparse.spdiags(diag_data, 0, graph.shape[0], graph.shape[0])
|
||
|
|
# L = D - graph
|
||
|
|
# Normalized Laplacian
|
||
|
|
I = scipy.sparse.identity(graph.shape[0], dtype=np.float64)
|
||
|
|
D = scipy.sparse.spdiags(
|
||
|
|
1.0 / sqrt_deg, 0, graph.shape[0], graph.shape[0]
|
||
|
|
)
|
||
|
|
L = I - D * graph * D
|
||
|
|
if not scipy.sparse.issparse(L):
|
||
|
|
L = np.asarray(L)
|
||
|
|
|
||
|
|
k = dim + 1
|
||
|
|
num_lanczos_vectors = max(2 * k + 1, int(np.sqrt(graph.shape[0])))
|
||
|
|
gen = (
|
||
|
|
random_state
|
||
|
|
if isinstance(random_state, (np.random.Generator, np.random.RandomState))
|
||
|
|
else np.random.default_rng(seed=random_state)
|
||
|
|
)
|
||
|
|
if not method:
|
||
|
|
method = "eigsh" if L.shape[0] < 2000000 else "lobpcg"
|
||
|
|
|
||
|
|
try:
|
||
|
|
if init == "random":
|
||
|
|
X = gen.normal(size=(L.shape[0], k))
|
||
|
|
elif init == "tsvd":
|
||
|
|
X = TruncatedSVD(
|
||
|
|
n_components=k,
|
||
|
|
random_state=random_state,
|
||
|
|
# algorithm="arpack"
|
||
|
|
).fit_transform(L)
|
||
|
|
else:
|
||
|
|
raise ValueError(
|
||
|
|
"The init parameter must be either 'random' or 'tsvd': "
|
||
|
|
f"{init} is invalid."
|
||
|
|
)
|
||
|
|
# For such a normalized Laplacian, the first eigenvector is always
|
||
|
|
# proportional to sqrt(degrees). We thus replace the first t-SVD guess
|
||
|
|
# with the exact value.
|
||
|
|
X[:, 0] = sqrt_deg / np.linalg.norm(sqrt_deg)
|
||
|
|
|
||
|
|
if method == "eigsh":
|
||
|
|
eigenvalues, eigenvectors = scipy.sparse.linalg.eigsh(
|
||
|
|
L,
|
||
|
|
k,
|
||
|
|
which="SM",
|
||
|
|
ncv=num_lanczos_vectors,
|
||
|
|
tol=tol or 1e-4,
|
||
|
|
v0=np.ones(L.shape[0]),
|
||
|
|
maxiter=maxiter or graph.shape[0] * 5,
|
||
|
|
)
|
||
|
|
elif method == "lobpcg":
|
||
|
|
with warnings.catch_warnings():
|
||
|
|
warnings.filterwarnings(
|
||
|
|
category=UserWarning,
|
||
|
|
message=r"(?ms).*not reaching the requested tolerance",
|
||
|
|
action="error"
|
||
|
|
)
|
||
|
|
eigenvalues, eigenvectors = scipy.sparse.linalg.lobpcg(
|
||
|
|
L,
|
||
|
|
np.asarray(X),
|
||
|
|
largest=False,
|
||
|
|
tol=tol or 1e-4,
|
||
|
|
maxiter=maxiter or 5 * graph.shape[0]
|
||
|
|
)
|
||
|
|
else:
|
||
|
|
raise ValueError("Method should either be None, 'eigsh' or 'lobpcg'")
|
||
|
|
|
||
|
|
order = np.argsort(eigenvalues)[1:k]
|
||
|
|
return eigenvectors[:, order]
|
||
|
|
except (scipy.sparse.linalg.ArpackError, UserWarning):
|
||
|
|
warn(
|
||
|
|
"Spectral initialisation failed! The eigenvector solver\n"
|
||
|
|
"failed. This is likely due to too small an eigengap. Consider\n"
|
||
|
|
"adding some noise or jitter to your data.\n\n"
|
||
|
|
"Falling back to random initialisation!"
|
||
|
|
)
|
||
|
|
return gen.uniform(low=-10.0, high=10.0, size=(graph.shape[0], dim))
|