"""
==============================
Wikipedia princial 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:

    http://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:

    http://en.wikipedia.org/wiki/Power_iteration

Here the computation is achieved thanks to Martinsson's Randomized SVD
algoritm implemented in the scikit.

The graph data is fetched from the DBpedia dumps. DBpedia is an extraction
of the latent structured data of the Wikipedia content.
"""
print __doc__

# Author: Olivier Grisel <olivier.grisel@ensta.org>
# License: Simplified BSD

from bz2 import BZ2File
import os
from datetime import datetime
from pprint import pprint
from time import time

import numpy as np

from scipy import sparse

from scikits.learn.utils.extmath import fast_svd
from scikits.learn.externals.joblib import Memory


################################################################################
# Where to download the data, if not already on disk
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):
        import urllib
        print "Downloading data from '%s', please wait..." % url
        opener = urllib.urlopen(url)
        open(filename, 'wb').write(opener.read())
        print


################################################################################
# Loading the redirect files

memory = Memory(cachedir=".")


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 = set([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


# disabling joblib as the pickling of large dicts seems much too slow
#@memory.cache
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 = dict((i, name) for name, i in index_map.iteritems())

print "Computing the principal singular vectors using fast_svd"
t0 = time()
U, s, V = fast_svd(X, 5, q=3)
print "done in %0.3fs" % (time() - t0)

# print the names of the wikipedia related strongest compenents of the 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:]])


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 <hagberg@lanl.gov>
      Dan Schult <dschult@colgate.edu>
      Pieter Swart <swart@lanl.gov>
    """
    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(X.sum(axis=1) == 0, 1.0 / n, 0)).ravel()

    scores = np.ones(n, dtype=np.float32) / n  # 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, tol=1e-10)
print "done in %0.3fs" % (time() - t0)
pprint([names[i] for i in np.abs(scores).argsort()[-10:]])