From 677a5f023c31dd9f86e1b77a448f76e374388205 Mon Sep 17 00:00:00 2001
From: Victor Zimmermann <zimmermann@cl.uni-heidelberg.de>
Date: Wed, 21 Mar 2018 11:20:54 +0100
Subject: [PATCH] Minor bugfixes.
---
src/absinth.py | 64 ++++++++++++++++++++++++++++++++++++++++----------
1 file changed, 52 insertions(+), 12 deletions(-)
diff --git a/src/absinth.py b/src/absinth.py
index 13aaa3d..b1a11f1 100644
--- a/src/absinth.py
+++ b/src/absinth.py
@@ -30,7 +30,6 @@ import networkx as nx # for visualisation
import numpy as np
import os # for reading files
import pprint
-import random
import re
import spacy # for nlp
@@ -548,10 +547,12 @@ def induce(topic_name: str, result_list: list) -> (nx.Graph, list, dict):
return graph, root_hub_list, stat_dict
+
def colour_graph(graph: nx.Graph, root_hub_list: list) -> nx.Graph:
"""Colours graph accoring to root hubs.
- Evolving network that colours neighboring nodes iterative.
+ Evolving network that colours neighboring nodes iterative. See sentiment
+ propagation.
Args:
graph: Weighted undirected graph.
@@ -608,6 +609,7 @@ def colour_graph(graph: nx.Graph, root_hub_list: list) -> nx.Graph:
return graph
+
def disambiguate_colour(graph: nx.Graph, root_hub_list: list, context_list: list) -> dict:
"""Clusters senses to root hubs using a coloured graph.
@@ -802,23 +804,61 @@ def main(topic_id: int, topic_name: str, result_dict: dict) -> None:
graph, root_hub_list, stat_dict = induce(topic_name,
result_dict[topic_id])
+ colour_rank = config.colour_rank
+ mst_rank = config.mst_rank
+
+ #Merges Mappings according to pipeline
+ mapping_dict = dict()
+
#matches senses to clusters
- print('[a]', 'Disambiguating result_list.\t('+topic_name+')')
- if config.use_colouring == True:
+ print('[a]', 'Disambiguating results.\t('+topic_name+')')
+ if colour_rank != 0:
+
print('[a]', 'Colouring graph.\t('+topic_name+')')
- mapping_dict = disambiguate_colour(graph, root_hub_list,
- result_dict[topic_id])
- else:
+ mapping_dict[colour_rank] = disambiguate_colour(graph, root_hub_list,
+ result_dict[topic_id])
+
+ if mst_rank != 0:
print('[a]', 'Building minimum spanning tree.\t('+topic_name+')')
- mapping_dict = disambiguate_mst(graph, root_hub_list,
- result_dict[topic_id], topic_name)
+ mapping_dict[mst_rank] = disambiguate_mst(graph,
+ root_hub_list,
+ result_dict[topic_id],
+ topic_name)
+
+ mapping_list = [item[1] for item in sorted(mapping_dict.items())]
+ mapping_count = len(mapping_list)
+
+ merged_mapping_dict = mapping_list[0]
+ merged_entry_count = 0
+
+ for i in range(1,mapping_count):
+
+ result_list = [result for result_list in merged_mapping_dict.values()
+ for result in result_list]
+ #individual mappings
+ relation_list = [(topic,result) for topic in mapping_list[i].keys()
+ for result in mapping_list[i][topic]]
+
+ for topic, result in relation_list:
+
+ if result not in result_list:
+
+ merged_entry_count += 1
+
+ if topic in merged_mapping_dict:
+ merged_mapping_dict[topic].append(result)
+
+ else:
+ merged_mapping_dict[topic] = [result]
+
+ stat_dict['merge_gain'] = merged_entry_count
#collect statistics from result.
cluster_count = 0
cluster_length_list = list()
- for cluster,result_list in mapping_dict.items():
+ for cluster,result_list in merged_mapping_dict.items():
cluster_length = len(result_list)
@@ -839,7 +879,7 @@ def main(topic_id: int, topic_name: str, result_dict: dict) -> None:
output_file.write('subTopicID\tresultID\n')
- for cluster_id,result_list in mapping_dict.items():
+ for cluster_id,result_list in merged_mapping_dict.items():
for result_id in result_list:
output_line = '{}.{}\t{}.{}\n'.format(topic_id, cluster_id,
topic_id, result_id)
@@ -874,4 +914,4 @@ if __name__ == '__main__':
with Pool(process_count) as pool:
parameter_list = [(topic_id, topic_name, result_dict)
for topic_id,topic_name in topic_dict.items()]
- pool.starmap(main, parameter_list)
+ pool.starmap(main, sorted(parameter_list)) #determineate function
--
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