diff --git a/src/absinth.py b/src/absinth.py
index 06b74ade9f17363718f20cf8cd368bad5dde7411..294e55c4bc47be478f21cc7ff596ef1f52b525b7 100644
--- a/src/absinth.py
+++ b/src/absinth.py
@@ -24,13 +24,14 @@ Modifiers:
"""
import sys
-print('[A] Loading ' + sys.argv[0] + '.\n')
+print('[a] Loading ' + sys.argv[0] + '.\n')
import config
import networkx as nx # for visualisation
import numpy as np
import os # for reading files
import pprint
import re
+import scipy.special
import spacy # for nlp
import time
@@ -326,6 +327,11 @@ def build_graph(node_freq_dict: dict, edge_freq_dict: dict) -> nx.Graph:
pass
+ # Remove singletons, deepcopy for iteration while being altered.
+ for node in deepcopy(cooccurrence_graph).nodes:
+ if len(cooccurrence_graph.adj[node]) == 0:
+ cooccurrence_graph.remove_node(node)
+
return cooccurrence_graph
@@ -424,11 +430,6 @@ def components(graph: nx.Graph, root_hub_list: list, target_string: str) -> nx.G
minimum_spanning_tree = nx.minimum_spanning_tree(graph_copy)
- # Remove singletons, deepcopy for iteration while being altered.
- for node in deepcopy(minimum_spanning_tree).nodes:
- if len(minimum_spanning_tree.adj[node]) == 0:
- minimum_spanning_tree.remove_node(node)
-
return minimum_spanning_tree
@@ -519,8 +520,8 @@ def induce(topic_name: str, result_list: list) -> (nx.Graph, list, dict):
print('[a]', 'Building graph.\t('+topic_name+')')
graph = build_graph(node_freq_dict, edge_freq_dict)
- stat_dict['node count'] = len(graph.nodes)
- stat_dict['edge count'] = len(graph.edges)
+ stat_dict['nodes'] = len(graph.nodes)
+ stat_dict['edges'] = len(graph.edges)
#finds root hubs (senses) within the graph + more filters for these
print('[a]', 'Collecting root hubs.\t('+topic_name+')')
@@ -807,22 +808,101 @@ def print_stats(stat_dict: dict) -> None:
ts = time.gmtime()
- stat_string.append('[A] Topic:\t{}.'.format(stat_dict['target']))
- stat_string.append('[A] Processed {} at {}.'.format(time.strftime("%Y-%m-%d", ts),time.strftime("%H:%M:%S", ts)))
- stat_string.append('[A] Nodes: {}\tEdges: {}.'.format(stat_dict['node count'],stat_dict['edge count']))
- stat_string.append('[A] Mean cluster length (harmonic):\t{}.'.format(stat_dict['hmean_cluster_length']))
- stat_string.append('[A] Mean cluster length (arithmetic):\t{}.'.format(stat_dict['mean_cluster_length']))
- stat_string.append('[A] Number of clusters: {}.'.format(stat_dict['cluster_count']))
- stat_string.append('[A] Tuples gained through merging: {}.'.format(stat_dict['merge_gain']))
- stat_string.append('[A] Sense inventory:')
+ key_list= ['target','nodes','edges','L','C','L_rand','C_rand','clusters','a_mean_size','h_mean_size','pipe_gain']
+
+ stat_string.append('Topic: {}.'.format(stat_dict['target']))
+ stat_string.append('Processed {} at {}.'.format(time.strftime("%Y-%m-%d", ts),time.strftime("%H:%M:%S", ts)))
+ stat_string.append('Nodes: {}\tEdges: {}.'.format(stat_dict['nodes'],stat_dict['edges']))
+ stat_string.append('Characteristic path length: {}.'.format(stat_dict['L']))
+ stat_string.append('Global clustering coefficient: {}.'.format(stat_dict['C']))
+ stat_string.append('Mean cluster length (arithmetic): {}.'.format(stat_dict['a_mean_size']))
+ stat_string.append('Mean cluster length (harmonic): {}.'.format(stat_dict['h_mean_size']))
+ stat_string.append('Number of clusters: {}.'.format(stat_dict['clusters']))
+ stat_string.append('Tuples gained through merging: {}.'.format(stat_dict['pipe_gain']))
+ stat_string.append('Sense inventory:')
for hub in stat_dict['hubs'].keys():
- stat_string.append('[A] {}:\t{}.'.format(hub, ", ".join(stat_dict['hubs'][hub])))
+ stat_string.append(' -> {}: {}.'.format(hub, ", ".join(stat_dict['hubs'][hub])))
- with open('stats.txt', 'a') as stat_file:
- stat_file.write('\n'.join(stat_string)+'\n\n')
- print('\n'+'\n'.join(stat_string)+'\n')
+ print('\n[A] '+'\n[A] '.join(stat_string)+'\n')
+
+ write_header = not os.path.exists('.statistics.tsv')
+
+ with open('.statistics.tsv', 'a') as stat_file:
+
+ if write_header:
+
+ stat_file.write('\t'.join(key_list)+'\n')
+
+ stat_file.write('\t'.join([str(stat_dict[key]) for key in key_list])+'\n')
+
+def global_clustering_coefficient(graph: nx.Graph) -> float:
+ """Calculates global clustering coefficient from graph.
+
+ Iterates over every node and calculates the global coefficient as a mean
+ of every local clustering coefficient.
+
+ Args:
+ graph: Undirected graph.
+
+ Returns:
+ Global coefficient.
+ """
+
+ local_coefficient_list = list()
+
+ for node in graph.nodes:
+
+ neighbor_list = graph.adj[node]
+
+ neighbor_edge_list = [(x,y) for x in neighbor_list
+ for y in neighbor_list if x<y]
+
+ if len(neighbor_edge_list) == 0:
+
+ local_coefficient_list.append(0)
+
+ else:
+
+ edge_count = 0
+ for x,y in neighbor_edge_list:
+ if graph.has_edge(x,y):
+ edge_count += 1
+
+ local_coefficient_list.append(edge_count/len(neighbor_edge_list))
+
+ return np.mean(local_coefficient_list)
+
+
+def characteristic_path_length(graph: nx.Graph) -> float:
+ """Calculates characteristic path length from graph.
+
+ Iterates over every node tuple and calculates the shortest path between them.
+ The average path length is returned. Tuples without path are ignored.
+
+ Args:
+ graph: Undirected graph.
+
+ Returns:
+ Global coefficient.
+ """
+
+ path_length_list = list()
+
+ path_list = [(x,y) for x in graph.nodes for y in graph.nodes if x<y]
+
+ for path in path_list:
+
+ if nx.has_path(graph,*path):
+
+ shortest_path = nx.shortest_path(graph,*path)
+
+ path_length_list.append(len(shortest_path))
+
+ return np.mean(path_length_list)
+
+
def main(topic_id: int, topic_name: str, result_dict: dict) -> None:
"""Calls induction and disambiguation functions, performs main task.
@@ -849,6 +929,17 @@ 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])
+ stat_dict['L'] = characteristic_path_length(graph)
+ stat_dict['C'] = global_clustering_coefficient(graph)
+
+ edge_count = len(graph.edges)
+ node_count = len(graph.nodes)
+ mean_degree = edge_count/node_count
+
+ stat_dict['L_rand'] = np.log(node_count)/np.log(mean_degree)
+ stat_dict['C_rand'] = 2 * mean_degree/node_count
+
+
colour_rank = config.colour_rank
mst_rank = config.mst_rank
@@ -897,7 +988,7 @@ def main(topic_id: int, topic_name: str, result_dict: dict) -> None:
else:
merged_mapping_dict[topic] = [result]
- stat_dict['merge_gain'] = merged_entry_count
+ stat_dict['pipe_gain'] = merged_entry_count
#collect statistics from result.
cluster_count = 0
@@ -912,9 +1003,9 @@ def main(topic_id: int, topic_name: str, result_dict: dict) -> None:
cluster_count += 1
cluster_length_list.append(cluster_length)
- stat_dict['hmean_cluster_length'] = stats.hmean(cluster_length_list)
- stat_dict['mean_cluster_length'] = np.mean(cluster_length_list)
- stat_dict['cluster_count'] = cluster_count
+ stat_dict['h_mean_size'] = stats.hmean(cluster_length_list)
+ stat_dict['a_mean_size'] = np.mean(cluster_length_list)
+ stat_dict['clusters'] = cluster_count
print('[a]', 'Writing to file.\t('+topic_name+')')