Fix typing problems

PyARMViz/PyARMViz.py

@@ -2,7 +2,7 @@
 import networkx as nx
 import numpy as np
 
-from PyARMViz import Rule
+from PyARMViz.Rule import Rule
 
 from typing import List
 import itertools
@@ -292,6 +292,22 @@ def _parallel_category_builder(rules:List, axis_count:int):
 
     return axis_objects
 
+def is_number(s):
+    try:
+        float(s)
+        return True
+    except ValueError:
+        pass
+
+    try:
+        import unicodedata
+        unicodedata.numeric(s)
+        return True
+    except (TypeError, ValueError):
+        pass
+
+    return False
+
 def adjacency_graph_plotly(rules:Rule):
     '''
         This is the plotly version of the 
@@ -328,12 +344,22 @@ def adjacency_graph_plotly(rules:Rule):
         node_y.append(y)
         node_text.append(node)
 
+    hover_text = []
+    for node, adjacencies in enumerate(graph.adjacency()):
+        hover_text.append(' --\n# of Connections: '+str(len(adjacencies[1])))
+
+    nodeI = 0
+    while nodeI < len(node_text):
+        hover_text[nodeI] = node_text[nodeI] + hover_text[nodeI]
+        nodeI = nodeI + 1
 
     node_trace = go.Scatter(
         x=node_x, y=node_y,
-        mode='markers',
+        mode='markers+text',
         hoverinfo='text',
         text=node_text,
+        hovertext = hover_text,
+        textposition = "top center",
         marker=dict(
             showscale=True,
             # colorscale options
@@ -358,11 +384,6 @@ def adjacency_graph_plotly(rules:Rule):
             showlegend=False,
             hovermode='closest',
             margin=dict(b=20,l=5,r=5,t=40),
-            annotations=[ dict(
-                text="Python code: <a href='https://plotly.com/ipython-notebooks/network-graphs/'> https://plotly.com/ipython-notebooks/network-graphs/</a>",
-                showarrow=False,
-                xref="paper", yref="paper",
-                x=0.005, y=-0.002 ) ],
             xaxis=dict(showgrid=False, zeroline=False, showticklabels=False),
             yaxis=dict(showgrid=False, zeroline=False, showticklabels=False))
             )
@@ -391,15 +412,20 @@ def _adjacency_graph_generator(rules:List[Rule]):
     '''
     graph = nx.DiGraph()
     for index, rule in enumerate(rules):
-        graph.add_node (index, Weight=int(rule.confidence*10), type="Association_Rule")
-
-        for entity in rule.lhs:
-            graph.add_node(entity, Weight=1, type="Entity")
-            graph.add_edge(entity, index, Normalized_Lift=int(rule.lift*10))
-
-        for entity in rule.rhs:
-            graph.add_node(entity, Weight=1, type="Entity")
-            graph.add_edge(index, entity, Normalized_Lift=int(rule.lift*10))
+#        graph.add_node (index, Weight=int(rule.confidence*10), type="Association_Rule")
+#
+#        for entity in rule.lhs:
+#            graph.add_node(entity, Weight=1, type="Entity")
+#            graph.add_edge(entity, index, Normalized_Lift=int(rule.lift*10))
+#            
+#        for entity in rule.rhs:
+#            graph.add_node(entity, Weight=1, type="Entity")
+#            graph.add_edge(index, entity, Normalized_Lift=int(rule.lift*10))
+        for entity1 in rule.lhs:
+            graph.add_node(entity1, Weight=1, type="Entity")
+            for entity2 in rule.rhs:
+                graph.add_node(entity2, Weight=1, type="Entity")
+                graph.add_edge(entity1, entity2, Normalized_Lift=int(rule.lift*10))
 
     logging.debug("Generated NetworkX graph for {} rules with {} nodes".format(len(rules), len(graph.nodes)))
     return graph
@@ -436,4 +462,4 @@ def adjacency_scatter_plot(rules:List[Rule], notebook_flag:bool = False):
     ))
 
     fig.show()
-    return fig
+    return fig

PyARMViz/datasets/__init__.py

@@ -13,7 +13,7 @@
 import pandas as pd
 
 from os.path import dirname, exists, expanduser, isdir, join, splitext
-from bokeh.layouts import row
+#from bokeh.layouts import row
 from PyARMViz.Rule import Rule, generate_rule_from_dict
 import json
 
@@ -50,4 +50,4 @@ def load_shopping_rules() -> List[Rule]:
     shopping_rule_data_uri = join(module_path, 'Online_Retail_Rules.json')
     rule_dicts = json.load(open(shopping_rule_data_uri, 'r'))
     rules = list(map(lambda rule_dict: generate_rule_from_dict(rule_dict), rule_dicts))
-    return rules
+    return rules

README.md

@@ -2,6 +2,18 @@
 
 Advanced Python Association Rule Visualization Library
 
+# Changes:
+
+This Repo was forked and then changed to fix two bugs in the original repo:
+  1. Line 5 in PyARMViz throws a TypeError when the file is imported, fixed class reference
+  2. Line 16 throws an import error, line is commented out now
+
+Other changes:
+  1. Removed annotation from network diagram graph
+  2. Removed association rule type nodes from network diagram graph, instead connecting each antecedent with every consequent in a given association rule
+
+# For Original Repo, go to: https://github.com/mazeofthemind/PyARMViz
+
 # Summary
 
 Loosely based on [ARulesViz](https://cran.r-project.org/web/packages/arulesViz/index.html) for R
@@ -197,4 +209,4 @@ git clone https://github.com/Mazeofthemind/PyARMViz.git
 pip install poetry
 cd PyARMViz
 python -m poetry build
-'''
+'''