.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/applications/wikipedia_principal_eigenvector.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code. or to run this example in your browser via JupyterLite or Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_applications_wikipedia_principal_eigenvector.py: =============================== Wikipedia principal eigenvector =============================== A classical way to assert the relative importance of vertices in a graph is to compute the principal eigenvector of the adjacency matrix so as to assign to each vertex the values of the components of the first eigenvector as a centrality score: https://en.wikipedia.org/wiki/Eigenvector_centrality. On the graph of webpages and links those values are called the PageRank scores by Google. The goal of this example is to analyze the graph of links inside wikipedia articles to rank articles by relative importance according to this eigenvector centrality. The traditional way to compute the principal eigenvector is to use the `power iteration method `_. Here the computation is achieved thanks to Martinsson's Randomized SVD algorithm implemented in scikit-learn. The graph data is fetched from the DBpedia dumps. DBpedia is an extraction of the latent structured data of the Wikipedia content. .. GENERATED FROM PYTHON SOURCE LINES 26-42 .. code-block:: Python # Authors: The scikit-learn developers # SPDX-License-Identifier: BSD-3-Clause import os from bz2 import BZ2File from datetime import datetime from pprint import pprint from time import time from urllib.request import urlopen import numpy as np from scipy import sparse from sklearn.decomposition import randomized_svd .. GENERATED FROM PYTHON SOURCE LINES 43-45 Download data, if not already on disk ------------------------------------- .. GENERATED FROM PYTHON SOURCE LINES 45-65 .. code-block:: Python redirects_url = "http://downloads.dbpedia.org/3.5.1/en/redirects_en.nt.bz2" redirects_filename = redirects_url.rsplit("/", 1)[1] page_links_url = "http://downloads.dbpedia.org/3.5.1/en/page_links_en.nt.bz2" page_links_filename = page_links_url.rsplit("/", 1)[1] resources = [ (redirects_url, redirects_filename), (page_links_url, page_links_filename), ] for url, filename in resources: if not os.path.exists(filename): print("Downloading data from '%s', please wait..." % url) opener = urlopen(url) with open(filename, "wb") as f: f.write(opener.read()) print() .. GENERATED FROM PYTHON SOURCE LINES 66-68 Loading the redirect files -------------------------- .. GENERATED FROM PYTHON SOURCE LINES 68-115 .. code-block:: Python def index(redirects, index_map, k): """Find the index of an article name after redirect resolution""" k = redirects.get(k, k) return index_map.setdefault(k, len(index_map)) DBPEDIA_RESOURCE_PREFIX_LEN = len("http://dbpedia.org/resource/") SHORTNAME_SLICE = slice(DBPEDIA_RESOURCE_PREFIX_LEN + 1, -1) def short_name(nt_uri): """Remove the < and > URI markers and the common URI prefix""" return nt_uri[SHORTNAME_SLICE] def get_redirects(redirects_filename): """Parse the redirections and build a transitively closed map out of it""" redirects = {} print("Parsing the NT redirect file") for l, line in enumerate(BZ2File(redirects_filename)): split = line.split() if len(split) != 4: print("ignoring malformed line: " + line) continue redirects[short_name(split[0])] = short_name(split[2]) if l % 1000000 == 0: print("[%s] line: %08d" % (datetime.now().isoformat(), l)) # compute the transitive closure print("Computing the transitive closure of the redirect relation") for l, source in enumerate(redirects.keys()): transitive_target = None target = redirects[source] seen = {source} while True: transitive_target = target target = redirects.get(target) if target is None or target in seen: break seen.add(target) redirects[source] = transitive_target if l % 1000000 == 0: print("[%s] line: %08d" % (datetime.now().isoformat(), l)) return redirects .. GENERATED FROM PYTHON SOURCE LINES 116-118 Computing the Adjacency matrix ------------------------------ .. GENERATED FROM PYTHON SOURCE LINES 118-166 .. code-block:: Python def get_adjacency_matrix(redirects_filename, page_links_filename, limit=None): """Extract the adjacency graph as a scipy sparse matrix Redirects are resolved first. Returns X, the scipy sparse adjacency matrix, redirects as python dict from article names to article names and index_map a python dict from article names to python int (article indexes). """ print("Computing the redirect map") redirects = get_redirects(redirects_filename) print("Computing the integer index map") index_map = dict() links = list() for l, line in enumerate(BZ2File(page_links_filename)): split = line.split() if len(split) != 4: print("ignoring malformed line: " + line) continue i = index(redirects, index_map, short_name(split[0])) j = index(redirects, index_map, short_name(split[2])) links.append((i, j)) if l % 1000000 == 0: print("[%s] line: %08d" % (datetime.now().isoformat(), l)) if limit is not None and l >= limit - 1: break print("Computing the adjacency matrix") X = sparse.lil_matrix((len(index_map), len(index_map)), dtype=np.float32) for i, j in links: X[i, j] = 1.0 del links print("Converting to CSR representation") X = X.tocsr() print("CSR conversion done") return X, redirects, index_map # stop after 5M links to make it possible to work in RAM X, redirects, index_map = get_adjacency_matrix( redirects_filename, page_links_filename, limit=5000000 ) names = {i: name for name, i in index_map.items()} .. GENERATED FROM PYTHON SOURCE LINES 167-169 Computing Principal Singular Vector using Randomized SVD -------------------------------------------------------- .. GENERATED FROM PYTHON SOURCE LINES 169-181 .. code-block:: Python print("Computing the principal singular vectors using randomized_svd") t0 = time() U, s, V = randomized_svd(X, 5, n_iter=3) print("done in %0.3fs" % (time() - t0)) # print the names of the wikipedia related strongest components of the # principal singular vector which should be similar to the highest eigenvector print("Top wikipedia pages according to principal singular vectors") pprint([names[i] for i in np.abs(U.T[0]).argsort()[-10:]]) pprint([names[i] for i in np.abs(V[0]).argsort()[-10:]]) .. GENERATED FROM PYTHON SOURCE LINES 182-184 Computing Centrality scores --------------------------- .. GENERATED FROM PYTHON SOURCE LINES 184-229 .. code-block:: Python def centrality_scores(X, alpha=0.85, max_iter=100, tol=1e-10): """Power iteration computation of the principal eigenvector This method is also known as Google PageRank and the implementation is based on the one from the NetworkX project (BSD licensed too) with copyrights by: Aric Hagberg Dan Schult Pieter Swart """ n = X.shape[0] X = X.copy() incoming_counts = np.asarray(X.sum(axis=1)).ravel() print("Normalizing the graph") for i in incoming_counts.nonzero()[0]: X.data[X.indptr[i] : X.indptr[i + 1]] *= 1.0 / incoming_counts[i] dangle = np.asarray(np.where(np.isclose(X.sum(axis=1), 0), 1.0 / n, 0)).ravel() scores = np.full(n, 1.0 / n, dtype=np.float32) # initial guess for i in range(max_iter): print("power iteration #%d" % i) prev_scores = scores scores = ( alpha * (scores * X + np.dot(dangle, prev_scores)) + (1 - alpha) * prev_scores.sum() / n ) # check convergence: normalized l_inf norm scores_max = np.abs(scores).max() if scores_max == 0.0: scores_max = 1.0 err = np.abs(scores - prev_scores).max() / scores_max print("error: %0.6f" % err) if err < n * tol: return scores return scores print("Computing principal eigenvector score using a power iteration method") t0 = time() scores = centrality_scores(X, max_iter=100) print("done in %0.3fs" % (time() - t0)) pprint([names[i] for i in np.abs(scores).argsort()[-10:]]) .. _sphx_glr_download_auto_examples_applications_wikipedia_principal_eigenvector.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: binder-badge .. image:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/1.6.X?urlpath=lab/tree/notebooks/auto_examples/applications/wikipedia_principal_eigenvector.ipynb :alt: Launch binder :width: 150 px .. container:: lite-badge .. image:: images/jupyterlite_badge_logo.svg :target: ../../lite/lab/index.html?path=auto_examples/applications/wikipedia_principal_eigenvector.ipynb :alt: Launch JupyterLite :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: wikipedia_principal_eigenvector.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: wikipedia_principal_eigenvector.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: wikipedia_principal_eigenvector.zip ` .. include:: wikipedia_principal_eigenvector.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_