Merge branch 'issue/MSX-34' into 'develop'

Issue MSX-34

See merge request !12

JBM_Intersection/Graph_Expantion_by_Intersection.py

-# -*- coding: utf-8 -*-
-"""
-Created on Wed Mar 31 12:09:38 2021
-@author: Julian Bianco-Martinez
-
-Graph Expansion by Intersection of Concepts (GEIC)
-
-"""
-
-import pandas as pd
-import numpy as np
-
-
-def concepts_extraction_list(lst1):
-    '''
-    Extract concepts from word2vec output
-    '''
-    lst3 = [value[0] for value in lst1]
-    return lst3
-
-def GEIC(concepts, words_to_exclude = [], topX_intersect_concepts = 5, topXsimilarConcepts = 100, threshold = 0.5 ):
-    '''
-     From a set of concepts = C0, this algorithm collects and weight similar concepts that intersect with concepts in  C0
-     
-     Inputs:
-       concepts: Original Concepts (C0)
-       words_to_exclude: The code will remove new concepts that are duplicated in this list. If it is empty words_to_exclude = concepts
-       topX_intersect_concepts: Only collect the top X concepts found from the intersection of similar concepts between pairs of original concepts C0. ie.
-           C0(i) int C0(j) = IC(i,j)[::topX_intersect_concepts]
-       topXsimilarConcepts: Retrieve top X similar concepts per concept in C0
-       threshold: cut of threshold that retrieves only intersected concepts that apear in theshold * 100 percent of all combine C0 pairs.
-     
-    Output:
-        Data frame with 4 columns:
-            Column 1: C0(i)
-            Column 2: C0(j)
-            Column 3: IC(i,j)[::topX_intersect_concepts]
-            Column 4: Importance of the intersected concept defined as the percentage of time the concept appear in pairwise combination of C0 concepts. 
-    '''
-    
-    if len(words_to_exclude) == 0: # words_to_exclude = concepts
-        words_to_exclude = concepts.copy()
-    df_temp = pd.DataFrame({'Concept 1' : [], 'Concept 2' :[], 'Intersection' : []})
-    # Creation of triangular data (due to symmetry).
-    for i in range(len(concepts)-1):
-        for j in range(i+1, len(concepts)):
-             concepts1 = concepts_extraction_list(model.most_similar(concepts[i], topn=topXsimilarConcepts))
-             concepts2 = concepts_extraction_list(model.most_similar(concepts[j], topn=topXsimilarConcepts))
-             inter =  [v for v in concepts1 if v in concepts2][0:topX_intersect_concepts]
-             if len(inter) > 0:
-                 df_temp = df_temp.append(pd.DataFrame({'Concept 1' : concepts[i],
-                                                                  'Concept 2' : concepts[j], 
-                                                                  'Intersection' : inter}))
-    
-    df_extension = df_temp 
-    
-    #[v for v in df_extention['Intersect'].values if v not in words_to_exclude]
-    #Remove words that contain less than 4 characters.
-    logical_temp = [True if len(v) > 3 else False for v in df_extension['Intersection'].values ]
-    df_extension = df_extension[logical_temp]
-    
-    #Weight Creation
-    weights = df_temp['Intersection'].value_counts().rename_axis(['Intersection']).reset_index(name='weight')
-    weights['weight'] = weights['weight'] /(0.5 * np.math.factorial(len(concepts))/(np.math.factorial(len(concepts)-2) )) #Use for normalization.
-    logical_temp = [True if v not in words_to_exclude else False for v in df_extension['Intersection'].values ]
-    df_extension = df_extension[logical_temp]
-    df_extension = df_extension.merge(weights, on = "Intersection")
-    df_extension = df_extension.loc[df_extension['weight'] > threshold]
-    return(df_extension)
-
-#Example
-concepts = ['king', 'queen', 'prince']
-GEIC(concepts, topX_intersect_concepts = 15)

dash_app/callbacks.py

@@ -39,7 +39,7 @@ def update_base_word(submit_word_button, base_word_input):
     State(component_id='base-word-input', component_property='value'),
     State(component_id='add-word-input', component_property='value'),
     State(component_id='graph-elements-div', component_property='children'),
-    State(component_id='msx-graph', component_property='tapNodeData'),
+    State(component_id='msx-graph', component_property='selectedNodeData'),
     State(component_id='base-word-div', component_property='children'),
     prevent_initial_call=True
 )
@@ -74,14 +74,16 @@ def update_graph_elements(submit_word_button, add_word_button, extend_graph_butt
 
     if button_id == 'remove-word-button':
         graph.set_nodes_and_edges(json.loads(nodes_and_edges))
-        if selected_nodes is not None:
-            selected_word = selected_nodes['label']
-            if selected_word in graph.get_all_words() and selected_word != base_word_state:
-                graph.remove_node(selected_nodes['label'])
-                new_nodes_and_edges = json.dumps(graph.get_nodes_and_edges())
-                return new_nodes_and_edges
-            else:
-                raise PreventUpdate
+
+        if len(selected_nodes) > 0:
+            for selected_node in selected_nodes:
+                selected_word = selected_node['id']
+                if selected_word in graph.get_all_words() and selected_word != base_word_state:
+                    graph.remove_node(selected_word)
+                else:
+                    raise PreventUpdate
+            new_nodes_and_edges = json.dumps(graph.get_nodes_and_edges())
+            return new_nodes_and_edges
         else:
             raise PreventUpdate
 

dash_app/words.py

@@ -5,7 +5,7 @@ import stringdist as sdi
 class AssociatedWords:
 
     def __init__(self):
-        print("\n Word2Vec model is loading.This can take a couple of minutes.")
+        print("\n Word2Vec model is loading. This can take a couple of minutes.")
         self.model = api.load('glove-twitter-200')
         print(" Word2Vec model is ready. Enjoy!!!\n")