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concepts_to_graph.py
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concepts_to_graph.py
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"""
* The MIT License
*
* Copyright 2020 The OpenNARS authors.
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to deal
* in the Software without restriction, including without limitation the rights
* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
* copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in
* all copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
* THE SOFTWARE.
* """
import re
import sys
import ast
import networkx as nx
import matplotlib.pyplot as plt
#Get input and arguments
Simplified = "Simplified" in sys.argv
NoTermlinks = "NoTermlinks" in sys.argv
NoProceduralLinks = "NoProceduralLinks" in sys.argv
NoTemporalLinks = "NoTemporalLinks" in sys.argv
NoLinkLabels = "NoLinkLabels" in sys.argv
NoImages = "NoImages" in sys.argv
lines = []
curline = None
G = nx.MultiDiGraph()
inlines = []
for line in sys.stdin:
inlines.append(line)
def hasImage(line):
return "/1" in line or "/2" in line or "\\1" in line or "\\2" in line
#Utility functions:
def parse_concept(line):
if line.startswith("//{i=") and (not NoImages or not hasImage(line)):
concept = " ".join(line.split(" ")[1:]).split(":")[0]
dictionary = ast.literal_eval("{" + line.split(": {")[1])
return (concept, dictionary)
return (None, None)
def parse_truth(line):
TV = line.split(". {")[1].split("}")[0].split(" ")
return (float(TV[0]), float(TV[1]))
def truth_expectation(truth):
(f,c) = truth
return c * (f - 0.5) + 0.5
def truth_to_color(truth):
truthcol = int(255*truth_expectation(truth))
return '#%02x%02x%02x' % (truthcol,0, 255 - truthcol)
implicationEdges={}
def addImplicationEdge(a, b, operator, truth=[0, 0]):
override = False
UseOp = False if operator == "" else True
if (a,b,UseOp) in implicationEdges:
(operator2, truth2) = implicationEdges[(a,b,UseOp)]
if truth_expectation(truth) > truth_expectation(truth2):
override = True
else:
override = True
if override:
implicationEdges[(a,b,UseOp)] = (operator, truth)
def truthstring(truth):
Format = '{:0,.2f}'
return "{" + Format.format(truth[0]) + " " + Format.format(truth[1]) + "}"
def addImplicationEdges():
for (a,b,UseOp) in implicationEdges:
if NoProceduralLinks and UseOp or NoTemporalLinks and not UseOp:
continue
if Simplified and not UseOp and (a,b,True) in implicationEdges:
continue
if UseOp: #complication: since networkx can only draw 1 link label for (a,b), we merge and make sure the op/non op label is the same with truth info of both
(operator, optruth) = implicationEdges[(a,b,True)]
(_, noptruth) = ("", (0.5, 0)) if (a,b,False) not in implicationEdges else implicationEdges[(a,b,False)][:2]
color = truth_to_color(optruth)
if not UseOp:
(_, noptruth) = implicationEdges[(a,b,False)]
(operator, optruth) = ("", (0.5, 0)) if (a,b,True) not in implicationEdges else implicationEdges[(a,b,True)][:2]
color = truth_to_color(noptruth)
HaveBoth = optruth[1] > 0 and noptruth[1] > 0 and not NoProceduralLinks and not NoTemporalLinks
Top = "" if NoProceduralLinks or optruth[1] == 0 else "with op" + (" (inner)" if HaveBoth and not Simplified else "") + ": " + truthstring(optruth) + "\n"
Tnop = "" if NoTemporalLinks or noptruth[1] == 0 else "w/o op" + (" (outer)" if HaveBoth and not Simplified else "") + ": " + truthstring(noptruth)
label = ("best op: " + operator + "\n" if operator != "" else "") + Top + (Tnop if (a,b,True) not in implicationEdges else "")
if NoLinkLabels:
label = ""
max_rad = 0.0
G.add_edge(a, b, rad=(0.1 if UseOp or Simplified else 0.2), color=color, weight=4, label=label, arrowsize=20)
#Add statement concept nodes:
for line in inlines:
(concept, dictionary) = parse_concept(line)
if concept != None:
truth = (dictionary["frequency"], dictionary["confidence"])
del dictionary['termlinks']
dictionary["color"] = truth_to_color(truth)
dictionary["size"] = 1.0
dictionary["label"] = concept.replace("$","\$")
if dictionary["confidence"] > 0:
dictionary["label"] += "\n" + truthstring(truth)
if concept not in G:
G.add_nodes_from([(concept, dictionary)])
#Add subterm nodes:
for line in inlines:
(concept, dictionary) = parse_concept(line)
if concept != None:
if not NoTermlinks:
for x in dictionary["termlinks"]:
if x not in G and x != "":
variable = False
for vartype in ["\#", "\$", "\?"]:
if re.match(vartype + "[0-9]", x) != None or re.match("\{" + vartype + "[0-9]\}", x) != None or re.match("\[" + vartype + "[0-9]\]", x) != None:
variable = True
if not variable:
tldict = {"color": "gray"}
tldict["size"] = 1.0
tldict["label"] = x.replace("$","\$")
G.add_nodes_from([(x, tldict)])
#Add subterm links:
def AddTermlink(source, target):
if source in G and target in G and source != "" and target != "":
G.add_edge(source, target, color='green', weight=1, label="", arrowsize=1)
G.add_edge(target, source, color='green', weight=1, label="", arrowsize=1)
if not NoTermlinks:
for line in inlines:
(concept, dictionary) = parse_concept(line)
if concept != None:
tldict = dictionary["termlinks"]
AddTermlink(concept, tldict[0])
AddTermlink(concept, tldict[1])
AddTermlink(tldict[0], tldict[2])
AddTermlink(tldict[0], tldict[3])
AddTermlink(tldict[1], tldict[4])
AddTermlink(tldict[1], tldict[5])
#Add implication links:
for line in inlines:
print(line)
if (line.startswith("<") or line.startswith("dt=")) and " =/> " in line:
term = line.split(". {")[0]
if term.startswith("dt="):
timing = float(term.split("dt=")[1].split(" ")[0])
term = " ".join(term.split(" ")[1:])
left = term.split(" =/> ")[0][1:]
right = term.split(" =/> ")[1][:-1]
operator = ""
if " &/ " in left:
operator = left.split(" &/ ")[-1][:-1]
precondition = " &/ ".join(left.split(" &/ ")[:-1])[1:]
if '^' in operator:
left = precondition
else:
operator = ""
truth = parse_truth(line)
if left in G and right in G:
addImplicationEdge(left, right, operator, truth)
addImplicationEdges()
#Draw the graph:
nodecolors = nx.get_node_attributes(G,'color').values()
nodesizes = nx.get_node_attributes(G,'size').values()
labels = nx.get_node_attributes(G, 'label')
degrees = nx.degree(G)
nodesizes = [c * 100 for (node,c) in degrees]
pos = nx.kamada_kawai_layout(G)
nx.draw_networkx_nodes(G, pos, node_color = nodecolors, node_size = nodesizes)
nx.draw_networkx_labels(G, pos, labels)
edgelabels = {}
for edge in G.edges(data=True):
edgelabels[(edge[0],edge[1])] = edge[2].get("label", "")
nx.draw_networkx_edges(G, pos, edgelist=[(edge[0],edge[1])], edge_color=[edge[2]["color"]], width=[edge[2].get("weight", 1.0)], connectionstyle=f'arc3, rad = {edge[2].get("rad", 0.0)}', arrowsize=edge[2].get("arrowsize", 10.0))
nx.draw_networkx_edge_labels(G, pos, edge_labels = edgelabels, label_pos=0.8, bbox=dict(alpha=0))
nx.write_graphml(G, "memory.graphml")
plt.savefig("memory.png")
plt.show()
#Also plot degree distribution:
degrees = [G.degree(n) for n in G.nodes()]
plt.hist(degrees)
plt.xlabel("Degree")
plt.ylabel("Count")
plt.savefig("degrees.png")
plt.show()