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classes.py
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classes.py
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import numpy as np
import pandas as pd
from sys import exit # used to end program if an invalid keyword is given to addNode
import numpy.random as npr # used for creating random graphs (one of the __init__ functions for Graph class)
import datetime # used for giving default date to graph generator as today's date
import copy # used for our 'copy constructors'
from math import floor # used for generating numberOfConnections when making parameters for a graph generator
class Graph:
# Attributes ------------------------------------------------------------------------
## Meta-data
name = ""
date = ""
description = ""
## Bools
isMultiGraph = False
isDirected = False
isWeighted = False
## Data Structures
adjacencyLists = {} # a dictionary - each key's value is a set of tuples where the first value in the tuple is the destination node's name and every value after that is a weight
adjacencyMatrix = pd.DataFrame() # for directed graphs rows are sources, columns are destinations
# Methods ------------------------------------------------------------------------
## Constructors
def __init__(self, **kwargs):
# The __init__ constructor wasn't setting the default values by default, so we added the next 8 lines to force the default values at the beginning of each constructor call
self.name = ""
self.date = ""
self.description = ""
self.isMultiGraph = False
self.isDirected = False
self.isWeighted = False
self.adjacencyLists = {}
self.adjacencyMatrix = pd.DataFrame()
if len(kwargs) == 1 or len(kwargs) == 2:
self.makeFromTxt(**kwargs)
else:
self.generateGraph(**kwargs)
def generateGraph(self, seed, numNodes, numConnections, name="", date="", description="", weightsRange=None,
isMultiGraph=False, isDirected=False, isWeighted=False): # random graph generator
def helperMakeRandEdge(numNodes, source, destination, weightsRange):
if not self.isMultiGraph: # if it's not a multigraph then (destination cannot be the same as source) AND (source, destination pair must not be in adjacencyLists already), while either constraint is broken, reselect source and destination
counter = 0
while source == destination or destination in set([x[0] for x in self.adjacencyLists.get(source)]):
if counter > 10000:
print(f"Breaking an infinite while-loop in the construction of Graph: {self.name}, with source: {source}, and destination: {destination}")
exit(1)
source = str(npr.randint(0,numNodes))
destination = str(npr.randint(0, numNodes))
counter += 1
if self.isWeighted:
weight = npr.randint(weightsRange[0], weightsRange[1] + 1)
else:
weight = 1
self.addEdges(source, [destination, weight])
#print(f"adding an edge between {source} and {destination} with weight {weight}","\n",self.adjacencyLists,"\n",self.adjacencyMatrix,"\n")
# set attributes
self.name = name
if date == "":
self.date = datetime.date.today()
else:
self.date = date
self.description = description
self.isMultiGraph = isMultiGraph
self.isDirected = isDirected
self.isWeighted = isWeighted
# For each numNodes initialize adjacencyMatrix and adjacencyLists
for i in range(numNodes):
self.addNode(str(i), "source")
self.addNode(str(i), "destination")
# For each numConnections, use rng to determine the source and destination (bound by numNodes) and weight (bound by weightsRange) then add that edge to adjLis and adjMat
npr.seed(seed=seed) # for repeatability
for edge in range(numConnections):
source = str(npr.randint(0, numNodes))
destination = str(npr.randint(0, numNodes))
helperMakeRandEdge(numNodes, source, destination, weightsRange)
def makeFromTxt(self, file, f=None): # this will read files directly
needToClose = False
if None == f:
f = open(file)
needToClose = True
self.name = f.readline()[len("Graph Name: "):].split("\n")[0]
self.date = f.readline()[len("Date: "):].split("\n")[0]
self.description = f.readline()[len("Description: "):].split("\n")[0]
self.isMultiGraph = True if f.readline()[len("MultiGraph: "):] == "T\n" else False
self.isDirected = True if f.readline()[len("Directed: "):] == "T\n" else False
self.isWeighted = True if f.readline()[len("Weighted: "):] == "T\n" else False
# the remaining lines in f should be the adjacency lists
rows = []
for eachRow in f: # example of eachRow "A -> B 1 2, D 2\n"
# if the row is empty (other than the new line marker) then return to calling function - necessary to enable CollectionOfGraphs.makeFromTxt() to call this function
if eachRow == "\n":
return
# if the line ends in "\n", then remove it - example result: "A -> B 1 2, D 2"
if "\n" == eachRow[len(eachRow) - 1:]:
row = eachRow[:len(eachRow) - 1]
else:
row = eachRow # otherwise do just get the line as is
# element 0 is source node, element 2 is destination nodes and associated weights - example result: ["A ", " B 1 2, D 2"]
row = row.split("->")
row[0] = row[0].split(" ") # isolate source node's name from the space - example result: ["A",""]
row[1] = row[1].split(",") # split destination nodes - example result: [" B 1 2"," D 2"]
# split destination nodes up from their weights - example of result:[["","B","1","2"],["","D","2"]]]
for i in range(len(row[1])):
row[1][i] = row[1][i].split(" ")
# row = [['A', ''], [['', 'B', '1', '2'], ['', 'D', '2']]]
sourceNode = row[0][0]
destinationNodes = row[1]
for i in range(len(destinationNodes)): # ['', 'B', '1', '2'] -> ['B', '1', '2']
destinationNodes[i] = destinationNodes[i][1:]
for i in range(len(destinationNodes)): # this converts the weights from being strings to floats
for j in range(len(destinationNodes[i])):
if j > 0: # only changes weights
if float(destinationNodes[i][j]) == int(destinationNodes[i][j]):
destinationNodes[i][j] = int(destinationNodes[i][j])
else:
destinationNodes[i][j] = float(destinationNodes[i][j])
self.addNode(sourceNode, "source")
self.addNode(sourceNode, "destination")
if len(destinationNodes[0]) == 0: # for "Source ->" rows in .txt
continue
for destination in destinationNodes:
self.addEdges(sourceNode, destination)
if needToClose:
f.close()
def copy(self):
return copy.deepcopy(self)
## Adders
def addSourceToAdjacencyMatrix(self, source):
self.adjacencyMatrix.loc[source] = pd.Series(name=source, dtype=object)
def addDestinationToAdjacencyMatrix(self, destination):
self.adjacencyMatrix.insert(loc=len(self.adjacencyMatrix.columns), column=destination, value=np.NaN)
self.adjacencyMatrix[destination] = self.adjacencyMatrix[destination].astype(object)
def addNode(self, node, nodePurpose="source"):
# first we add it to the adjacencyLists as a source
if self.adjacencyLists.get(node) == None: # if the adjacency List doesn't have the node present
self.adjacencyLists.update({node: {}}) # then add it
# now we add to the adjacencyMatrix
if self.isDirected:
if nodePurpose == "source": # if the node is a source, then...
if node not in set(self.adjacencyMatrix.index): # if the node is not already a row, then...
# add it as a row (index)
if len(self.adjacencyMatrix.columns) == 0: # if we have no columns (i.e. no destinations yet)
if len(self.adjacencyMatrix.index) != 0: # for when we already have indexes
newIndex = self.adjacencyMatrix.index
newIndex.append(node)
else: # for when we have no indexes
newIndex = [node]
self.adjacencyMatrix = pd.DataFrame(
index=newIndex) # re-initialize the adjMat and update the index
else: # we already have columns
self.addSourceToAdjacencyMatrix(node)
elif nodePurpose == "destination": # if the node is a destination, then...
if node not in set(self.adjacencyMatrix.columns): # if we don't already have it as a column, then...
# add it as a column
self.addDestinationToAdjacencyMatrix(node) # adds node as a column
else:
print("ERROR: Invalid keyword given as `nodePurpose` in call to `addNode()`")
exit()
else: # undirected, we don't care about nodePurpose
# for undirected graphs when we add a node it will get added as both a row(source) and column(destination)
if node not in set(self.adjacencyMatrix.columns) and node not in set(self.adjacencyMatrix.index):
self.addDestinationToAdjacencyMatrix(
node) # add as destination first so we ensure we have a column so we can...
self.addSourceToAdjacencyMatrix(node) # add as source
def addEdges(self, source, destList): # doesn't matter if it's directed or not, does matter if it's weighted
destination = destList[0]
if not self.isDirected and not self.isMultiGraph: # if it's undirected or is not a multigraph and we already have source->destination then return (don't want to double-add that edge)
# the first condition is to prevent an exception when checking the second condition (NoneType is not iterable)
if None != self.adjacencyLists.get(destination) and source in set([x[0] for x in self.adjacencyLists.get(destination)]):
return
if self.isMultiGraph or self.isWeighted:
weights = destList[1:]
else: # unweighted and not a multiGraph
weights = [1]
weights = np.asarray(weights)
self.addNode(source, "source") # if the node is already in adjacencyLists then this will do nothing. We have this line for when this function is used later on
self.addNode(destination, "destination")
# do adjacencyLists first
# first check if destination is in source's set
found = ()
for eachDest in self.adjacencyLists[source]: # for each destination in source's destinations
if destination == eachDest[0]: # try to find the destination
found = eachDest # found is now a tuple
break # once we find it we can break this loop
if 0 == len(found): # we never found the destination so the destination is not yet there for source
updatedDestinations = set(self.adjacencyLists[source]) # need to keep the other destinations
updatedDestinations.add(tuple(destList))
self.adjacencyLists.update({source: updatedDestinations})
else: # found the destination
updated = list(found)
for i in destList[1:]: # update the weights of the destination
updated.append(i)
updated = tuple(updated)
self.adjacencyLists[source].remove(found)
self.adjacencyLists[source].add(updated)
# if it's undirected we need to do the above but for the reverse, i.e. destination->source
revDestList = destList # reverse destination list (i.e. the `source` is used as the destination
revDestList[0] = source
found = ()
if not self.isDirected and source != destination:
for eachDest in self.adjacencyLists[destination]: # for each destination in `destination`'s destinations
if source == eachDest[0]: # try to find `source` (as a destination)
found = eachDest
break # once we find it we can break this loop
if 0 == len(
found): # we never found `source` as a destination of `destination`'s so `source` is not yet a destination of `destination`
updatedDestinations = set(self.adjacencyLists[destination]) # need to keep the other destinations
updatedDestinations.add(tuple(revDestList))
self.adjacencyLists.update({destination: updatedDestinations})
else: # found the destination
updated = list(found)
for i in revDestList[1:]: # update the weights of the destination
updated.append(i)
updated = tuple(updated)
self.adjacencyLists[destination].remove(found)
self.adjacencyLists[destination].add(updated)
if source == '1' and destination == '0':
mark = 'hey'
# whether directed or not we add the weights to adjacencyMatrix
# but are we initializing it or just appending to already existent weights?
# if np.isnan(self.adjacencyMatrix.at[source,destination]): # we are initializing it
if isinstance(self.adjacencyMatrix.at[source, destination],
type(np.NaN)): # then it's nan so we are initializing it
self.adjacencyMatrix.at[source, destination] = weights
else: # we are adding a new weights to already existing weights
self.adjacencyMatrix.at[source, destination] = np.append(self.adjacencyMatrix.at[source, destination],
weights)
if not self.isDirected and source != destination: # undirected -> add weights to both places
# if np.isnan(self.adjacencyMatrix.at[destination,source]): # we are initializing all weights
if isinstance(self.adjacencyMatrix.at[destination, source],
type(np.NaN)): # then it's nan so we are initializing it
self.adjacencyMatrix.at[destination, source] = weights
else: # we are adding a new weights to already existing weights
# self.adjacencyMatrix[destination][source] = np.append(self.adjacencyMatrix[source][destination], weights)
self.adjacencyMatrix.at[destination, source] = np.append(self.adjacencyMatrix.at[destination, source],
weights)
## Deleters
def deleteNode(self, node):
# first remove all edges involving node
sources = self.adjacencyMatrix.columns
destinations = self.adjacencyMatrix.index
for source in sources:
self.deleteEdges(source=source, destination=node, all=True)
for destination in destinations:
self.deleteEdges(source=node, destination=destination, all=True)
#remove the node from the adjacencyLists' keys
self.adjacencyLists.pop(node)
#remove node from adjacencyMatrix's rows (sources)
self.adjacencyMatrix.drop(index=node, inplace=True)
self.adjacencyMatrix.drop(columns=node, inplace=True)
def deleteEdges(self, source, destination, all=False, weightsToRemove=None, secondCall=False):
#a helper function: basically set difference but with duplicate values
def removeFromList(original, removables):
#a helper function that sorts an arrayLike and then searches for a val, if found returns the index, otherwise returns None
def sort_n_search(arrayLike, val):
sortedArrayLike = np.sort(arrayLike)
insertionIndex = np.searchsorted(sortedArrayLike, val)
if insertionIndex >= len(sortedArrayLike) or sortedArrayLike[insertionIndex] != val:
return None
else:
return insertionIndex
removables = removables.copy() #we don't want to alter the original object so we get a copy
original = np.sort(original) #sort the original list
returnMe = [] #where we will accumulate values not in removables
#for each value, i, in original, add only so many instances to returnMe as do not exist in removables - i.e. removeFromList([1,1], [1]) returns [1] - kind of like a set difference, but with duplicates
j = 0
for i in range(len(original)):
if sort_n_search(removables[j:], original[i]) == None:
returnMe.append(original[i])
else: j+=1
return np.asarray(returnMe)
# if all is True or self.isMultigraph is False then we remove all edges between source and destination
if all or not self.isMultiGraph:
# adjacencyLists
destinations = self.adjacencyLists.get(source)
newDestinations = set([])
for dest in destinations:
if dest[0] != destination:
newDestinations.add(dest)
self.adjacencyLists.update({source:newDestinations})
# adjacencyMatrix
self.adjacencyMatrix.at[source, destination] = np.NaN
# else if weights != None then delete 1 edge from source to destination for every weight specified in weightsToRemove, having the weight specified in weightsToRemove
elif weightsToRemove != None:
# adjacencyLists
destinations = self.adjacencyLists.get(source)
newDestinations = set([])
for dest in destinations:
if dest[0] != destination:
newDestinations.add(dest)
else:
newDest = [dest[0], *list(removeFromList(dest[1:], weightsToRemove))]
newDestinations.add(tuple(newDest))
self.adjacencyLists.update({source:newDestinations})
# adjacencyMatrix
newWeights = removeFromList(self.adjacencyMatrix.at[source,destination], weightsToRemove)
self.adjacencyMatrix.at[source,destination] = newWeights
# else if all==False and self.isMultiGraph==True and weights==None, then...
# print error message
else:
print("ERROR: deleteEdges() doesn't know which edges to remove. Please specify weightsToRemove as a list or array-like. If the graph is unweighted, pass as many 1's as you want removed.")
exit()
if secondCall: # ends recursion - dont' need a third call
return
# if it's undirected, call delete edge in the opposite order, pass down other parameters
if not self.isDirected:
self.deleteEdges(source=destination, destination=source, all=all, weightsToRemove=weightsToRemove, secondCall=True)
## Output
def formattedAdjacencyList(self):
'''
formats the following:
{"a":{("b", 1, 2),("c", 1)},
"b":{("a", 1, 2)},
"c":{("a", 1)}}
to a string like this:
"a -> b 1 2, c 1
b -> a 1 2
c -> a 1"
and returns it
'''
formAdjLists = ""
counter1 = 0
for key in self.adjacencyLists:
formAdjLists += key + " ->"
destinations = self.adjacencyLists.get(key)
counter2 = 0 #used to determine if we are at the last element in the set - we don't want to add a comma if we are at the last destination for that key(source)
for dest in destinations:
for val in dest:
formAdjLists += " " + str(val)
if counter2 != len(destinations)-1:
formAdjLists += ","
counter2 += 1
if counter1 != len(self.adjacencyLists)-1:
formAdjLists += "\n"
counter1 += 1
return formAdjLists
def display(self):
print(f"Name: {self.name}")
print(f"Date: {self.date}")
print(f"Description: {self.description}")
print(f"Settings: isMultiGraph={self.isMultiGraph}, isDirected={self.isDirected}, isWeighted={self.isWeighted}")
print("Adjacency Lists:")
print(self.formattedAdjacencyList())
print("Adjacency Matrix:")
print(self.adjacencyMatrix)
def writeToTxt(self, fileName, flag="w"):
thisFile = open(fileName, flag)
thisFile.write("Graph Name: " + self.name + "\n")
thisFile.write("Date: " + str(self.date) + "\n")
thisFile.write("Description: " + self.description + "\n")
isMultiGraph = "T" if self.isMultiGraph else "F"
thisFile.write("MultiGraph: " + isMultiGraph + "\n")
isDirected = "T" if self.isDirected else "F"
thisFile.write("Directed: "+isDirected + "\n")
isWeighted = "T" if self.isWeighted else "F"
thisFile.write("Weighted: " + isWeighted + "\n")
thisFile.write(self.formattedAdjacencyList())
thisFile.close()
## Evaluation
def isSymmetric(self):
return self.adjacencyMatrix.size > 0 and self.adjacencyMatrix.equals(self.adjacencyMatrix.transpose())
def evaluateSymmetry(self):
#based on whether the graph is directed or not we should be able to predict if it's symmetric or not.
#Here we print our prediction and the reality, ex:
#"Expected: symmetric
#Actual: asymmetric"
expected = "symmetric" if not self.isDirected else "asymmetric"
if self.isSymmetric():
actual = "symmetric"
else:
actual = "asymmetric"
print(f"Expected: {expected}\nActual: {actual}")
print("\n")
def equals(self, graph2):
# this compares two graphs and returns true iff they have all the same node names and same connections between said nodes
# this returns false even if the two graphs are equivalent (but have different node names)
# furthermore, this function assumes that the adjacencyLists objects in each graph will correspond with their respective adjacencyMatrix so
# only the adjacencyMatrices are compared
#first need to sort the columns and rows of both
self.adjacencyMatrix.sort_index(axis=1, inplace=True)
self.adjacencyMatrix.sort_index(axis=0, inplace=True)
graph2.adjacencyMatrix.sort_index(axis=1, inplace=True)
graph2.adjacencyMatrix.sort_index(axis=0, inplace=True)
#now that both are sorted we compare with pd.DataFrame.equals()
return self.adjacencyMatrix.equals(graph2.adjacencyMatrix)
class CollectionOfGraphs:
# Attributes ------------------------------------------------------------------------
## Meta-data
name = ""
date = ""
description = ""
## Data-structure
Graphs = [] # a list of our graph objects
# Methods ------------------------------------------------------------------------
## Constructors
def __init__(self, *arg):
self.name = ""
self.date = ""
self.description = ""
self.Graphs = []
if len(arg) == 1:
self.makeFromTxt(arg[0])
else:
self.makeFromGenerator(arg[0],arg[1],arg[2],arg[3])
def makeFromTxt(self, fileName):
f = open(fileName)
self.name = f.readline()[len("Graph Collection Name: "):].split("\n")[0]
self.date = f.readline()[len("Date: "):].split("\n")[0]
self.description = f.readline()[len("Description: "):].split("\n")[0]
# need to move read-stream marker forward to where the graph specifications begin
f.readline()
f.readline()
while True: # there's probably a better way to do this, but we're running out of time. Will fix later
g = Graph(file=fileName, f=f) # make the graph from the text file
# if we read in an empty line, our g object will be empty - that's how we detect the eof and break the while loop
if len(g.adjacencyLists)==0 and len(g.adjacencyMatrix.index)==0 and len(g.adjacencyMatrix.columns)==0 and g.name=="" and g.description=="":
del g
break
self.Graphs.append(g)
f.close()
def makeFromGenerator(self, name, date, description, graphParams):
self.name = name
if date == "":
self.date = datetime.date.today()
else:
self.date = date
self.description = description
# def generateGraph(self, seed, numNodes, numConnections, name="", date="", description="", weightsRange=None, isMultiGraph=False, isDirected=False, isWeighted=False)
for i in range(len(graphParams)):
g = Graph(seed=graphParams[i][0], numNodes=graphParams[i][1], numConnections=graphParams[i][2], name=graphParams[i][3], date=graphParams[i][4], description=graphParams[i][5],
weightsRange=graphParams[i][6], isMultiGraph=graphParams[i][7], isDirected=graphParams[i][8], isWeighted=graphParams[i][9])
self.Graphs.append(g)
def copy(self):
return copy.deepcopy(self)
## Output
def display(self):
for each in self.Graphs:
each.display()
print("\n")
def writeToTxt(self, fileName):
f = open(fileName, "w")
f.write(f"Graph Collection Name: {self.name}\nDate: {self.date}\nDescription: {self.description}\n") # write our metadata
f.close()
for i in range(len(self.Graphs)):
f = open(fileName, "a")
f.write("\n\n")
f.close()
self.Graphs[i].writeToTxt(fileName, flag="a")
# if i != len(self.Graphs)-1: # if it's not the last graph in the collection then output two new lines for formatting
# thisFile = open(fileName, "a")
# thisFile.write("\n\n")
# #thisFile.close()
## Ordering
def sort(self):
self.Graphs.sort(key=lambda graph: graph.name)
## Evaluation
def equals(self, collection2):
# this function seeks to compare two collection of graphs. All names must be the same, as well as all node names, connections, and weights, but dates and descriptions don't matter
# first we'll compare their lengths, if the lengths are different, obviously they're two different collections
if len(self.Graphs) != len(collection2.Graphs): return False
# else we sort both and then compare each
self.sort()
collection2.sort()
# now iterate through both at once and compare each object
for i,j in zip(self.Graphs, collection2.Graphs):
if not i.equals(j):
return False
return True