yasark

import itertools

def prepro(pairs, filterout=None, hard_lmt=100000):
    pairs['relation'] = 1
    G = nx.from_pandas_edgelist(pairs, 'subject', 'object',
                                create_using=nx.DiGraph())
    if filterout:
        nodes = \
            list(set(pairs[~pairs.subject_type.isin(filterout)]['subject'].tolist()
                 + pairs[~pairs.object_type.isin(filterout)]['object'].tolist()))

yasark

import networkx as nx
import matplotlib.pyplot as plt


def draw_kg(pairs):
    k_graph = nx.from_pandas_edgelist(pairs, 'subject', 'object',
            create_using=nx.MultiDiGraph())
    node_deg = nx.degree(k_graph)
    layout = nx.spring_layout(k_graph, k=0.15, iterations=20)
    plt.figure(num=None, figsize=(120, 90), dpi=80)

yasark

import pandas as pd
import re
import spacy
import neuralcoref

nlp = spacy.load('en_core_web_lg')
neuralcoref.add_to_pipe(nlp)


def get_entity_pairs(text, coref=True):

yasark

import pandas as pd
from collections import Counter
import tensorflow as tf
from tffm import TFFMRegressor
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split
import numpy as np

# Loading datasets'