Commit 7ff0e703 authored by Victor Zimmermann's avatar Victor Zimmermann
Browse files

Replace try-except blocks with if statements

parent f80f2d90

code/absinth_nx.py

+18 −12
Changes for code/absinth_nx.py: 18 added lines, 12 removed lines.
Original line number Diff line number Diff line
@@ -5,11 +5,12 @@ import spacy # for nlp 
import networkx as nx # for visualisation

import matplotlib.pyplot as plt # for visualisation

from copy import deepcopy

from nltk.corpus import stopwords

import numpy as np # for calculations

nlp = spacy.load('en') # standard english nlp





def frequencies(corpus_path, target, stop_words=['utc', 'new', 'other'], allowed_tags=['NN','NNS','JJ','JJS','JJR','NNP'], min_context_size = 4, max_nodes=100000, max_edges=10000000):

def frequencies(corpus_path, target, stop_words=[], allowed_tags=['NN','NNS','JJ','JJS','JJR','NNP','VBZ','VBG'], min_context_size = 4, max_nodes=100000, max_edges=10000000):

    

    node_freq = dict()

    edge_freq = dict()
@@ -111,7 +112,7 @@ def build_graph(node_freq, edge_freq, min_node_freq=10, min_edge_freq=5, max_wei 
    return G





def root_hubs(graph, edge_freq, min_neighbors=4, theshold=0.8):

def root_hubs(graph, edge_freq, min_neighbors=5, theshold=0.8):

    

    G = deepcopy(graph)

    V = sorted(G.nodes, key=lambda key: G.degree[key], reverse=True) # -1 to sort descending (...3 -> 2 -> 1...)
@@ -178,16 +179,19 @@ def disambiguate(mst, hubs, contexts): 
        

        for h in H:

            

            try:

            if nx.has_path(T,v,h):

                

                path = nx.shortest_path(T,v,h,'weight')

                total_weight = 0

            

                for i in range(1, len(path)):

                    

                    total_weight += T[path[i-1]][path[i]]['weight']

            

                scores.append(1/(1+total_weight))

                

            except:

            else:

                

                scores.append(0)

        

        T.nodes[v]['s'] = np.array([s if s == max(scores) else 0 for s in scores])
@@ -199,14 +203,12 @@ def disambiguate(mst, hubs, contexts): 
        

        idx = contexts.index(c) + 1

        

        try:

            if max(vector) == 0:

        if len(vector) == 0: #if no senses are found -> all in one

            result.append((0, idx))

        elif max(vector) == 0: #if no sense matches -> singletons

            pass

        else: 

                cluster = np.argmax(vector)

                result.append((cluster, idx))

        except:

            result.append((0, idx))

            result.append((np.argmax(vector), idx))

        

    return result
@@ -224,6 +226,8 @@ if __name__ == '__main__': 
    corpus_path = '/proj/absinth/wikipedia.txt.dump.20140615-en.SZTAKI/'

    results_path = '/home/students/zimmermann/Courses/ws17/fsem/absinth/results/'

    

    stop = set(stopwords.words('english') + ['utc', 'new', 'other'])

    

    results = dict()

    

    with open(data_path+'results.txt', 'r') as results_file:
@@ -260,14 +264,16 @@ if __name__ == '__main__': 
        f.write('subTopicID\tresultID\n')

        

        print('[A] Counting Tokens...')

        node_freq, edge_freq = frequencies(corpus_path, target)

        node_freq, edge_freq = frequencies(corpus_path, target, stop)

        

        print('\n[A] Building Graph.\n')

        G = build_graph(node_freq, edge_freq)

        

        print('[A] Collecting Root Hubs...')

        H = root_hubs(G, edge_freq)

        print('Root Hubs:', H, '\n')

        for h in H:

            mfn = sorted(G.adj[h], key=lambda key: edge_freq[h,key] if h < key else edge_freq[key, h], reverse=True)[:6]

            print('{}: {}\n'.format(h, mfn))

        

        print('[A] Building Minimum Spanning Tree.\n')

        T = components(G, H, target)