utils.py

import json
import re
from xml.sax.saxutils import escape, unescape
# escape() and unescape() takes care of &, < and >.
html_escape_table = {
    '"': "&quot;",
    "'": "&apos;"
}
html_unescape_table = {v:k for k, v in html_escape_table.items()}

keep_ents = set(["PERSON", # PERSON People, including fictional
"NORP", # NORP Nationalities or religious or political groups
"FAC", # FACILITY Buildings, airports, highways, bridges, etc.
"ORG", # ORGANIZATION Companies, agencies, institutions, etc.
"GPE", # GPE Countries, cities, states
"LOC", # "LOC", # LOCATION Non-GPE locations, mountain ranges, bodies of water
"PRODUCT", # PRODUCT Vehicles, weapons, foods, etc. (Not services)
"EVENT", # EVENT Named hurricanes, battles, wars, sports events, etc.
"WORK_OF_ART", # WORK OF ART Titles of books, songs, etc.
"LAW", # LAW Named documents made into laws 
"LANGUAGE"]) # LANGUAGE Any named language







def html_escape(text):
    return escape(text, html_escape_table)

def html_unescape(text):
    return unescape(text, html_unescape_table)


def html_unescape(text):
    # escape() and unescape() takes care of &, < and >.
    html_escape_table = {
        '"': "&quot;",
        "'": "&apos;",
        " ": "\xa0\xa0\xa0",
        " ": "\xa0"   
    }
    html_unescape_table  = {v:k for k, v in html_escape_table.items()}
    return unescape(text, html_unescape_table)

def clean_sentence(sentence):
    return(html_unescape(sentence))

def skip_page(p):
#     print( p.is_categorypage(), p.isCategoryRedirect(), p.isDisambig(),  p.is_flow_page(), p.is_filepage(), p.isRedirectPage(), p.isStaticRedirect(), p.isTalkPage())
    try:
        return any([ p.is_categorypage(), p.isCategoryRedirect(), p.isDisambig(), p.is_flow_page(), p.is_filepage(), p.isRedirectPage(), p.isStaticRedirect(), p.isTalkPage()])
    except:
        return True
###### Taken from https://github.com/tkipf/gcn and adapted #######
import numpy as np
import pickle as pkl
import networkx as nx
import scipy.sparse as sp
from scipy.sparse.linalg.eigen.arpack import eigsh
import sys


def parse_index_file(filename):
    """Parse index file."""
    index = []
    for line in open(filename):
        index.append(int(line.strip()))
    return index


def sample_mask(idx, l):
    """Create mask."""
    mask = np.zeros(l)
    mask[idx] = 1
    return np.array(mask, dtype=np.bool)

def load_from_file(dataset_str):
    """
    Loads input data from gcn/data directory
    ind.dataset_str.x => the feature vectors of the training instances as scipy.sparse.csr.csr_matrix object;
    ind.dataset_str.tx => the feature vectors of the test instances as scipy.sparse.csr.csr_matrix object;
    ind.dataset_str.allx => the feature vectors of both labeled and unlabeled training instances
        (a superset of ind.dataset_str.x) as scipy.sparse.csr.csr_matrix object;
    ind.dataset_str.y => the one-hot labels of the labeled training instances as numpy.ndarray object;
    ind.dataset_str.ty => the one-hot labels of the test instances as numpy.ndarray object;
    ind.dataset_str.ally => the labels for instances in ind.dataset_str.allx as numpy.ndarray object;
    ind.dataset_str.graph => a dict in the format {index: [index_of_neighbor_nodes]} as collections.defaultdict
        object;
    ind.dataset_str.test.index => the indices of test instances in graph, for the inductive setting as list object.
    All objects above must be saved using python pickle module.
    :param dataset_str: Dataset name
    :return: All data input files loaded (as well the training/test data).
    """
    names = ['x', 'y', 'tx', 'ty', 'allx', 'ally', 'graph']
    objects = []
    # with open("data/ind.91571.x", 'rb') as f:

    #     objects.append(pkl.load(f))
    
    for i in range(len(names)):
        with open("data/ind.{}.{}".format(dataset_str, names[i]), 'rb') as f:
            # print(names[i])
            # print(pkl.load(f))
            if sys.version_info > (3, 0):
                objects.append(pkl.load(f, encoding='latin1'))
            else:
                objects.append(pkl.load(f))
    
    x, y, tx, ty, allx, ally, graph = tuple(objects)
    # for el in objects:
    #     print(el.shape)
    # q = [i for i in range(x.shape[0],allx.shape[0])]
    # for i in range(x.shape[0],allx.shape[0]):
    #     graph[i] = q

    
    # q = [i for i in range(allx.shape[0],allx.shape[0]+tx.shape[0])]
    # for i in range(allx.shape[0],allx.shape[0]+tx.shape[0]):
    #     graph[i] = q 
            


    test_idx_reorder = [i + x.shape[0] for i in range(tx.shape[0])]
    test_idx_range = np.sort(test_idx_reorder)
    if dataset_str == 'citeseer':
        # Fix citeseer dataset (there are some isolated nodes in the graph)
        # Find isolated nodes, add them as zero-vecs into the right position
        test_idx_range_full = range(min(test_idx_reorder), max(test_idx_reorder)+1)
        tx_extended = sp.lil_matrix((len(test_idx_range_full), x.shape[1]))
        tx_extended[test_idx_range-min(test_idx_range), :] = tx
        tx = tx_extended
        ty_extended = np.zeros((len(test_idx_range_full), y.shape[1]))
        ty_extended[test_idx_range-min(test_idx_range), :] = ty
        ty = ty_extended

    features = sp.vstack((allx, tx)).tolil()
    features[test_idx_reorder, :] = features[test_idx_range, :]
    adj = nx.adjacency_matrix(nx.from_dict_of_lists(graph))

    print("With adj" , x.shape, allx.shape, tx.shape, test_idx_range, adj.shape)

    labels = np.vstack((ally, ty))
    labels[test_idx_reorder, :] = labels[test_idx_range, :]

    idx_test = test_idx_range.tolist()
    idx_train = range(len(y))
    idx_val = range(len(y), len(y)+tx.shape[0])

    train_mask = sample_mask(idx_train, labels.shape[0])
    val_mask = sample_mask(idx_val, labels.shape[0])
    test_mask = sample_mask(idx_test, labels.shape[0])
    # print(train_mask, val_mask, test_mask)
    y_train = np.zeros(labels.shape)
    y_val = np.zeros(labels.shape)
    y_test = np.zeros(labels.shape)
    y_train[train_mask, :] = labels[train_mask, :]
    y_val[val_mask, :] = labels[val_mask, :]
    y_test[test_mask, :] = labels[test_mask, :]
    print(adj.shape, features.shape, y_train.shape, y_val.shape, y_test.shape)
    return adj, features, y_train, y_val, y_test, train_mask, val_mask, test_mask

def load_data(allx, ally, graph, x = [], y=[], tx=[], ty=[] ):
    """
    Loads input data from gcn/data directory

    ind.dataset_str.x => the feature vectors of the training instances as scipy.sparse.csr.csr_matrix object;
    ind.dataset_str.tx => the feature vectors of the test instances as scipy.sparse.csr.csr_matrix object;
    ind.dataset_str.allx => the feature vectors of both labeled and unlabeled training instances
        (a superset of ind.dataset_str.x) as scipy.sparse.csr.csr_matrix object;
    ind.dataset_str.y => the one-hot labels of the labeled training instances as numpy.ndarray object;
    ind.dataset_str.ty => the one-hot labels of the test instances as numpy.ndarray object;
    ind.dataset_str.ally => the labels for instances in ind.dataset_str.allx as numpy.ndarray object;
    ind.dataset_str.graph => a dict in the format {index: [index_of_neighbor_nodes]} as collections.defaultdict
        object;
    ind.dataset_str.test.index => the indices of test instances in graph, for the inductive setting as list object.

    All objects above must be saved using python pickle module.

    :param dataset_str: Dataset name
    :return: All data input files loaded (as well the training/test data).
    """
    names = ['x', 'y', 'tx', 'ty', 'allx', 'ally', 'graph']
    objects = [x,y,tx,ty,allx,ally,graph]
#     for i in range(len(names)):
#         with open("data/ind.{}.{}".format(dataset_str, names[i]), 'rb') as f:
#             if sys.version_info > (3, 0):
#                 objects.append(pkl.load(f, encoding='latin1'))
#             else:
#                 objects.append(pkl.load(f))

    x, y, tx, ty, allx, ally, graph = tuple(objects)
    # print(x)
    # print( y)
    # print(tx)
    # print(ty)
    # print(allx) 
    # print(ally)
    # print(graph)
    test_idx_reorder = [i + allx.shape[0] for i in range(tx.shape[0])]
    test_idx_range = np.sort(test_idx_reorder)

#     if dataset_str == 'citeseer':
#         # Fix citeseer dataset (there are some isolated nodes in the graph)
#         # Find isolated nodes, add them as zero-vecs into the right position
#         test_idx_range_full = range(min(test_idx_reorder), max(test_idx_reorder)+1)
#         tx_extended = sp.lil_matrix((len(test_idx_range_full), x.shape[1]))
#         tx_extended[test_idx_range-min(test_idx_range), :] = tx
#         tx = tx_extended
#         ty_extended = np.zeros((len(test_idx_range_full), y.shape[1]))
#         ty_extended[test_idx_range-min(test_idx_range), :] = ty
#         ty = ty_extended

    features = sp.vstack((allx, tx)).tolil()
    features[test_idx_reorder, :] = features[test_idx_range, :]
    adj = nx.adjacency_matrix(nx.from_dict_of_lists(graph))

    labels = np.vstack((ally, ty))
    labels[test_idx_reorder, :] = labels[test_idx_range, :]

    idx_test = test_idx_range.tolist()
    idx_train = range(len(y))
    idx_val = range(len(y), len(y)+tx.shape[0])

    train_mask = sample_mask(idx_train, labels.shape[0])
    val_mask = sample_mask(idx_val, labels.shape[0])
    test_mask = sample_mask(idx_test, labels.shape[0])

    y_train = np.zeros(labels.shape)
    y_val = np.zeros(labels.shape)
    y_test = np.zeros(labels.shape)
    y_train[train_mask, :] = labels[train_mask, :]
    y_val[val_mask, :] = labels[val_mask, :]
    y_test[test_mask, :] = labels[test_mask, :]

    return adj, features, y_train, y_val, y_test, train_mask, val_mask, test_mask


def sparse_to_tuple(sparse_mx):
    """Convert sparse matrix to tuple representation."""
    def to_tuple(mx):
        if not sp.isspmatrix_coo(mx):
            mx = mx.tocoo()
        coords = np.vstack((mx.row, mx.col)).transpose()
        values = mx.data
        shape = mx.shape
        return coords, values, shape

    if isinstance(sparse_mx, list):
        for i in range(len(sparse_mx)):
            sparse_mx[i] = to_tuple(sparse_mx[i])
    else:
        sparse_mx = to_tuple(sparse_mx)

    return sparse_mx


def preprocess_features(features):
    """Row-normalize feature matrix and convert to tuple representation"""
    rowsum = np.array(features.sum(1))
    r_inv = np.power(rowsum, -1).flatten()
    r_inv[np.isinf(r_inv)] = 0.
    r_mat_inv = sp.diags(r_inv)
    features = r_mat_inv.dot(features)
    return sparse_to_tuple(features)


def normalize_adj(adj):
    """Symmetrically normalize adjacency matrix."""
    adj = sp.coo_matrix(adj)
    rowsum = np.array(adj.sum(1))
    d_inv_sqrt = np.power(rowsum, -0.5).flatten()
    d_inv_sqrt[np.isinf(d_inv_sqrt)] = 0.
    d_mat_inv_sqrt = sp.diags(d_inv_sqrt)
    return adj.dot(d_mat_inv_sqrt).transpose().dot(d_mat_inv_sqrt).tocoo()


def preprocess_adj(adj):
    """Preprocessing of adjacency matrix for simple GCN model and conversion to tuple representation."""
    adj_normalized = normalize_adj(adj + sp.eye(adj.shape[0]))
    return sparse_to_tuple(adj_normalized)


def construct_feed_dict(features, support, labels, labels_mask, placeholders):
    """Construct feed dictionary."""
    feed_dict = dict()
    feed_dict.update({placeholders['labels']: labels})
    feed_dict.update({placeholders['labels_mask']: labels_mask})
    feed_dict.update({placeholders['features']: features})
    feed_dict.update({placeholders['support'][i]: support[i] for i in range(len(support))})
    feed_dict.update({placeholders['num_features_nonzero']: features[1].shape})
    return feed_dict


def chebyshev_polynomials(adj, k):
    """Calculate Chebyshev polynomials up to order k. Return a list of sparse matrices (tuple representation)."""
    print("Calculating Chebyshev polynomials up to order {}...".format(k))

    adj_normalized = normalize_adj(adj)
    laplacian = sp.eye(adj.shape[0]) - adj_normalized
    largest_eigval, _ = eigsh(laplacian, 1, which='LM')
    scaled_laplacian = (2. / largest_eigval[0]) * laplacian - sp.eye(adj.shape[0])

    t_k = list()
    t_k.append(sp.eye(adj.shape[0]))
    t_k.append(scaled_laplacian)

    def chebyshev_recurrence(t_k_minus_one, t_k_minus_two, scaled_lap):
        s_lap = sp.csr_matrix(scaled_lap, copy=True)
        return 2 * s_lap.dot(t_k_minus_one) - t_k_minus_two

    for i in range(2, k+1):
        t_k.append(chebyshev_recurrence(t_k[-1], t_k[-2], scaled_laplacian))

    return sparse_to_tuple(t_k)