def alignment_graph(lengths=[], pairings=[], alignments=[]):
#print('making graph')
g = Graph(directed=False)
seq_index = g.new_vertex_property("int")
time = g.new_vertex_property("int")
#add vertices
g.add_vertex(sum(lengths))
seq_index.a = np.concatenate([np.repeat(i,l) for i,l in enumerate(lengths)])
time.a = np.concatenate([np.arange(l) for l in lengths])
#add edges (alignments)
alignment_index = g.new_edge_property("int")
segment_index = g.new_edge_property("int")
for i,a in enumerate(alignments):
if len(a) > 0:
j, k = pairings[i]
pairs = np.concatenate(a, axis=0)
indicesJ = (np.arange(lengths[j]) + sum(lengths[:j]))[pairs.T[0]]
indicesK = (np.arange(lengths[k]) + sum(lengths[:k]))[pairs.T[1]]
seg_indices = np.concatenate([np.repeat(i, len(a))
for i,a in enumerate(a)])
g.add_edge_list(np.vstack([indicesJ, indicesK,
np.repeat(i, len(pairs)), seg_indices]).T,
eprops=[alignment_index, segment_index])
#g.add_edge_list([(b, a) for (a, b) in g.edges()])
#print('created alignment graph', g)
#g = prune_isolated_vertices(g)
#print('pruned alignment graph', g)
#g = transitive_closure(g)
#graph_draw(g, output_size=(1000, 1000), output="results/casey_jones_bars.pdf")
return g, seq_index, time, alignment_index, segment_index
def build_word_graph(model_fname, limiar=0.2):
"""
Constroi um grafo de walavras ponderado pela similaridade entre elas
de acordo com o modelo.
:param model_fname: Nome do arquivo com o modelo word2vec como foi salvo
:return: objeto grafo
"""
m = Word2Vec.load(model_fname)
g = Graph()
freq = g.new_vertex_property("int")
weight = g.new_edge_property("float")
i = 0
vdict = {}
for w1, w2 in combinations(m.vocab.keys(), 2):
if w1 == '' or w2 == '':
continue
# print(w1,w2)
v1 = g.add_vertex() if w1 not in vdict else vdict[w1]
vdict[w1] = v1
freq[v1] = m.vocab[w1].count
v2 = g.add_vertex() if w2 not in vdict else vdict[w2]
vdict[w2] = v2
freq[v2] = m.vocab[w2].count
sim = m.similarity(w1, w2)
if sim > 0.1:
e = g.add_edge(v1, v2)
weight[e] = sim
if i > 10000:
break
i += 1
g.vertex_properties['freq'] = freq
g.edge_properties['sim'] = weight
return g
def test_mospp_small():
G = Graph()
G.add_vertex(1)
G.add_vertex(2)
G.add_vertex(3)
G.add_vertex(4)
c1 = G.new_edge_property("int")
c2 = G.new_edge_property("int")
e1 = G.add_edge(1, 3)
e2 = G.add_edge(3, 4)
e3 = G.add_edge(1, 2)
e4 = G.add_edge(2, 4)
e5 = G.add_edge(1, 4)
c1[e1] = 1
c1[e2] = 1
c1[e3] = 0
c1[e4] = 0
c1[e5] = 2
c2[e1] = 1
c2[e2] = 1
c2[e3] = 1
c2[e4] = 1
c2[e5] = 0
assert [[G.vertex_index[r] for r in route]
for route in mospp(G.vertex(1), G.vertex(4), c1, c2)
] == [[1, 4], [1, 2, 4]]
def build_word_graph(model_fname, limiar=0.2):
"""
Constroi um grafo de walavras ponderado pela similaridade entre elas
de acordo com o modelo.
:param model_fname: Nome do arquivo com o modelo word2vec como foi salvo
:return: objeto grafo
"""
m = Word2Vec.load(model_fname)
g = Graph()
freq = g.new_vertex_property("int")
weight = g.new_edge_property("float")
i = 0
vdict = {}
for w1, w2 in combinations(m.vocab.keys(), 2):
if w1 == '' or w2 == '':
continue
# print(w1,w2)
v1 = g.add_vertex() if w1 not in vdict else vdict[w1]
vdict[w1] = v1
freq[v1] = m.vocab[w1].count
v2 = g.add_vertex() if w2 not in vdict else vdict[w2]
vdict[w2] = v2
freq[v2] = m.vocab[w2].count
sim = m.similarity(w1, w2)
if sim > 0.1:
e = g.add_edge(v1, v2)
weight[e] = sim
if i > 10000:
break
i += 1
g.vertex_properties['freq'] = freq
g.edge_properties['sim'] = weight
return g
def mwgm_graph_tool(pairs, sim_mat):
from graph_tool.all import Graph, max_cardinality_matching
if not isinstance(pairs, list):
pairs = list(pairs)
g = Graph()
weight_map = g.new_edge_property("float")
nodes_dict1 = dict()
nodes_dict2 = dict()
edges = list()
for x, y in pairs:
if x not in nodes_dict1.keys():
n1 = g.add_vertex()
nodes_dict1[x] = n1
if y not in nodes_dict2.keys():
n2 = g.add_vertex()
nodes_dict2[y] = n2
n1 = nodes_dict1.get(x)
n2 = nodes_dict2.get(y)
e = g.add_edge(n1, n2)
edges.append(e)
weight_map[g.edge(n1, n2)] = sim_mat[x, y]
print("graph via graph_tool", g)
res = max_cardinality_matching(g,
heuristic=True,
weight=weight_map,
minimize=False)
edge_index = np.where(res.get_array() == 1)[0].tolist()
matched_pairs = set()
for index in edge_index:
matched_pairs.add(pairs[index])
return matched_pairs
class StackGraph(object):
def __init__(self):
self.g = None
def load(self, filename):
# Initialize the graph
self.g = Graph()
# Each node will store a FunctionWrapper() class instance.
self.g.vertex_properties["functions"] = self.g.new_vertex_property("object")
self.g.vertex_properties["display"] = self.g.new_vertex_property("string")
# Each edge will store a [ ..tbd.. ] .
self.g.edge_properties["calls"] = self.g.new_edge_property("object")
# Load the log file and build the graph
i = 0
f = open(filename, "rb")
for line in f:
i += 1
try:
# Skip any informational lines
if "*" in line: continue
# Extract a call stack snapshot
words = line.split()
time = words[0][2:]
depth = words[1][2:]
stack = [FunctionWrapper(instring=item) for item in words[2].split("->")]
# Add the top 2 functions to the graph, if necessary. Format: f1()->f2()
f1, f2 = stack[-2], stack[-1]
v1, v2 = None, None
# Search for the vertices
for v in self.g.vertices():
if self.g.vp.functions[v] == f1: v1 = v
if self.g.vp.functions[v] == f2: v2 = v
if v1 != None and v2 != None: break
# Add new vertices if needed
if v1 == None:
v1 = self.g.add_vertex()
self.g.vp.functions[v1] = f1
self.g.vp.display[v1] = f1.graphDisplayString()
if v2 == None:
v2 = self.g.add_vertex()
self.g.vp.functions[v2] = f2
self.g.vp.display[v2] = f2.graphDisplayString()
# Add the edge if necessary, and then add data to it
if not self.g.edge(v1, v2):
e = self.g.add_edge(v1, v2)
self.g.ep.calls[e] = CallList(v1, v2)
self.g.ep.calls[e].addCall(time, depth)
except Exception as e:
print "Exception on line", i, ":", e
print [str(x) for x in stack]
exit()
def test_graphtool():
g = Graph(directed=True)
g.add_vertex(4)
g.add_edge_list([(0, 1), (1, 2), (2, 3), (3, 0)])
weight = g.new_edge_property('float')
weight[g.edge(0, 1)] = 1
weight[g.edge(1, 2)] = 2
weight[g.edge(2, 3)] = 3
weight[g.edge(3, 0)] = 4
assert set(gt2edges_and_weights(g, weight)) == {
(0, 1, 1), (1, 2, 2), (2, 3, 3), (3, 0, 4)
}
def vytvořím_graph_tool_graf():
from graph_tool.all import Graph
graf = Graph()
u1 = graf.add_vertex()
u2 = graf.add_vertex()
graf.add_edge(u1, u2)
vprop_double = graf.new_vertex_property("double") # Double-precision floating point
vprop_double[graf.vertex(1)] = 3.1416
vprop_vint = graf.new_vertex_property("vector<int>") # Vector of ints
vprop_vint[graf.vertex(0)] = [1, 3, 42, 54]
eprop_dict = graf.new_edge_property("object") # Arbitrary python object. In this case, a dictionary.
eprop_dict[graf.edges().next()] = {"foo": "bar", "gnu": 42}
gprop_bool = graf.new_graph_property("bool") # Boolean
gprop_bool[graf] = True
graf.save('./data/graph_tool.graphml', fmt='xml')
class BiblioNetwork():
"Bibliography network displayer"
def __init__(self, filepath):
self.filepath = filepath
self.db = None
self._auth_betw = None
self._auth_betw_computed_from = 0
self.layout_pos = None
self.graph = None
self.author_list = []
@staticmethod
def _split_authors(row):
"Split authors of the row"
auth = row['Authors'].split(", ")
auth = [", ".join(auth[2*i:2*i+2])
for i in range(int(len(auth)/2))]
return auth
def parse(self, nmb_to_import=None, delimiter=","):
"Parse the database csv file"
# import database
self.db = pd.read_csv(self.filepath, delimiter, index_col=False,
nrows=nmb_to_import, encoding="ISO8859",
error_bad_lines=False, warn_bad_lines=True)
self.db.reset_index()
# separate authors
self.db['Authors'] = self.db.apply(self._split_authors, axis=1)
# Replace missing values
self.db['Cited by'].fillna(0, inplace=True)
# Updat author list
self.update_author_list()
def clean(self, min_citations=10):
"Remove some entries"
len_bef = len(self.db)
self.db.drop(self.db[self.db["Cited by"] < min_citations].index,
inplace=True)
len_after = len(self.db)
print(" Removed {} articles, {} remaining".format(len_bef-len_after,
len_after))
self.update_author_list()
self._auth_betw = None
def remove_anterior(self, year):
"Remove some entries"
len_bef = len(self.db)
self.db.drop(self.db[self.db["Year"] <= year].index,
inplace=True)
len_after = len(self.db)
print(" Removed {} articles, {} remaining".format(len_bef-len_after,
len_after))
self.update_author_list()
self._auth_betw = None
def remove_posterior(self, year):
"Remove some entries"
len_bef = len(self.db)
self.db.drop(self.db[self.db["Year"] > year].index,
inplace=True)
len_after = len(self.db)
print(" Removed {} articles, {} remaining".format(len_bef-len_after,
len_after))
self.update_author_list()
self._auth_betw = None
def update_author_list(self):
"Update author list from database"
auths = list(set(np.concatenate(self.db['Authors'].values)))
self.author_list = np.sort(auths)
@property
def author_betweeness(self):
"Compute authors betweness"
# If already computed, just return it
if self._auth_betw is not None and \
self._auth_betw_computed_from == len(self.db):
return self._auth_betw
# else compute it
self._auth_betw_computed_from = len(self.db)
auth_betw = {auth: {}
for auth in self.author_list}
for auths in self.db['Authors']:
# skip if only one author
if len(auths) == 1:
continue
# Loop on authors couples
for i1, auth1 in enumerate(auths):
for auth2 in auths[i1+1::]:
keys = auth_betw.keys()
# create couple if necessary, or increment
if auth2 not in auth_betw[auth1].keys():
auth_betw[auth1][auth2] = 1
else:
auth_betw[auth1][auth2] += 1
if auth1 not in auth_betw[auth2].keys():
auth_betw[auth2][auth1] = 1
else:
auth_betw[auth2][auth1] += 1
self._auth_betw = auth_betw
return self._auth_betw
@author_betweeness.setter
def author_betweeness(self, val):
raise Exception("You cannot change that")
def get_total_citation(self):
""" Return total number of citations for each author"""
nmbcits = {}
for _, art in self.db.iterrows():
auths = art['Authors']
nmbcit = int(art['Cited by'])
for auth in auths:
if auth in nmbcits.keys():
nmbcits[auth] += nmbcit
else:
nmbcits[auth] = nmbcit
return nmbcits
def get_auth_nmb_of_art(self):
""" Return number of article for each author"""
nmbart = {}
for _, art in self.db.iterrows():
auths = art['Authors']
for auth in auths:
if auth in nmbart.keys():
nmbart[auth] += 1
else:
nmbart[auth] = 1
return nmbart
def _get_author_publication(self):
auth2pub = {}
for _, art in self.db.iterrows():
for auth in art['Authors']:
if auth in auth2pub.keys():
auth2pub[auth] += [art.name]
else:
auth2pub[auth] = [art.name]
return auth2pub
def write_author_list(self, filepath):
with open(filepath, "w") as f:
data = ['{}: {}\n'.format(i, auth)
for i, auth in enumerate(self.author_list)]
f.writelines(data)
def make_article_graph(self, layout="arf"):
"""Make an article graph"""
self.graph = Graph(directed=False)
# add vertex
self.graph.add_vertex(len(self.db))
# add properties
cb = self.graph.new_vertex_property("int", self.db['Cited by'].values)
self.graph.vertex_properties['nmb_citation'] = cb
# Add links
auths = list(self.author_betweeness.keys())
auth2ind = {auths[i]: i
for i in range(len(auths))}
auth2pub = self._get_author_publication()
for _, pubs in auth2pub.items():
if len(pubs) < 2:
continue
combis = itertools.combinations(pubs, 2)
self.graph.add_edge_list(list(combis))
# layout
if layout == "arf":
self.layout_pos = arf_layout(self.graph)
elif layout == "sfpd":
self.layout_pos = sfdp_layout(self.graph)
elif layout == "fr":
self.layout_pos = fruchterman_reingold_layout(self.graph)
elif layout == "radial":
self.layout_pos = radial_tree_layout(self.graph,
auth2ind['Logan, B.E.'])
else:
raise ValueError()
def make_author_graph(self, layout="arf"):
"""Make an author graph"""
self.graph = Graph(directed=False)
# add vertex
auths = self.author_list
self.graph.add_vertex(len(auths))
# add links
auth2ind = {auths[i]: i
for i in range(len(auths))}
abet = []
authbet = copy.deepcopy(self.author_betweeness)
for auth in auths:
for col, weight in authbet[auth].items():
if col == auth:
continue
self.graph.add_edge(auth2ind[auth], auth2ind[col])
del authbet[col][auth] # ensure that edges are not doubled
abet.append(weight)
# add properties
cb = self.graph.new_edge_property("int", abet)
self.graph.edge_properties['weight'] = cb
# layout
if layout == "arf":
self.layout_pos = arf_layout(self.graph,
weight=self.graph.ep.weight,
pos=self.layout_pos,
max_iter=10000)
elif layout == "sfpd":
self.layout_pos = sfdp_layout(self.graph,
eweight=self.graph.ep.weight,
pos=self.layout_pos)
elif layout == "fr":
self.layout_pos = fruchterman_reingold_layout(self.graph,
weight=self.graph.ep.weight,
circular=True,
pos=self.layout_pos)
elif layout == "radial":
nc = self.get_total_citation()
main_auth_ind = np.argmax(list(nc.values()))
main_auth = list(nc.keys())[main_auth_ind]
self.layout_pos = radial_tree_layout(self.graph,
auth2ind[main_auth])
elif layout == "planar":
self.layout_pos = planar_layout(self.graph)
else:
raise ValueError()
def display_article_graph(self, out="graph.pdf", min_size=1,
max_size=10, indice=False):
"""Display an article graph
One point per article.
Size and color corespond to the number of citation.
"""
cb = np.log(np.array(self.graph.vp.nmb_citation.a)+2)
ms = cb/max(cb)*(max_size - min_size) + min_size
ms = self.graph.new_vertex_property('float', ms)
graph_draw(self.graph, pos=self.layout_pos, output=out,
vertex_size=ms,
vertex_fill_color=self.graph.vp.nmb_citation,
vcmap=plt.cm.viridis)
def display_author_graph(self, out="graph.pdf", min_size=1, max_size=10,
indice=False):
"""Display an author graph """
auths = self.author_list
nc = self.get_total_citation()
nc = [int(nc[auth]) for auth in auths]
na = self.get_auth_nmb_of_art()
na = [int(na[auth]) for auth in auths]
# normalize citation number
nc = np.array(nc, dtype=float)
nc /= np.max(nc)
nc *= (max_size - min_size)
nc += min_size
# normalize edge width
weight = np.array(self.graph.ep.weight.a, dtype=float)
weight /= np.max(weight)
weight *= (1 - 0.1)
weight += 0.1
# Get vertex display order
vorder = np.argsort(nc)
# Get index
if indice:
text = range(len(vorder))
textg = self.graph.new_vertex_property('string', text)
else:
textg = None
# plot
ncg = self.graph.new_vertex_property('float', nc)
nag = self.graph.new_vertex_property('int', na)
vorderg = self.graph.new_vertex_property('int', vorder)
weightg = self.graph.new_edge_property('float', weight)
self.graph.vp['nmb_citation'] = ncg
graph_draw(self.graph, pos=self.layout_pos, output=out,
vertex_fill_color=nag, vertex_size=ncg,
edge_pen_width=weightg, vertex_text=textg,
vorder=vorderg,
vertex_text_position=0,
vcmap=plt.cm.PuBu)
def build_region_closure(g,
root,
regions,
infection_times,
obs_nodes,
debug=False):
"""return a closure graph on the the components"""
regions = copy(regions)
root_region = {'nodes': {root}, 'head': root, 'head_time': -float('inf')}
regions[len(regions)] = root_region
gc = Graph(directed=True)
for _ in range(len(regions)):
gc.add_vertex()
# connect each region
gc_edges = []
original_edge_info = {}
for i, j in combinations(regions, 2):
# make group i the one with *later* head
if regions[i]['head_time'] < regions[j]['head_time']:
i, j = j, i
if debug:
print('i, j={}, {}'.format(i, j))
# only need to connect head i to one of the nodes in group j
# where nodes in j have time stamp < head i
# then an edge from region j to region i (because j is earlier)
head_i = regions[i]['head']
def get_pseudo_time(n):
if n == root:
return -float('inf')
else:
return infection_times[n]
targets = [
n for n in regions[j]['nodes']
if get_pseudo_time(n) < regions[i]['head_time']
]
if debug:
print('head_i: {}'.format(head_i))
print('targets: {}'.format(targets))
print('regions[j]["nodes"]: {}'.format(regions[j]['nodes']))
if len(targets) == 0:
continue
visitor = init_visitor(g, head_i)
forbidden_nodes = list(
set(regions[i]['nodes'])
| (set(regions[j]['nodes']) - set(targets)))
if debug:
print('forbidden_nodes: {}'.format(forbidden_nodes))
# NOTE: count_threshold = 1
cpbfs_search(g,
source=head_i,
terminals=targets,
forbidden_nodes=forbidden_nodes,
visitor=visitor,
count_threshold=1)
reachable_targets = [t for t in targets if visitor.dist[t] > 0]
if debug:
print('reachable_targets: {}'.format(reachable_targets))
if len(reachable_targets) == 0:
# cannot reach there
continue
source = min(reachable_targets, key=visitor.dist.__getitem__)
dist = visitor.dist[source]
assert dist > 0
gc_edges.append(((j, i, dist)))
original_edge_info[(j, i)] = {
'dist': dist,
'pred': visitor.pred,
'original_edge': (source, head_i)
}
for u, v, _ in gc_edges:
gc.add_edge(u, v)
eweight = gc.new_edge_property('int')
for u, v, c in gc_edges:
eweight[gc.edge(gc.vertex(u), gc.vertex(v))] = c
return gc, eweight, original_edge_info
class TTC(AbstractMatchingAlgorithm):
"""This class searches for cycles where each student gets his best option.
This takes a list of students, a list of schools and a ruleset
(which is used to calculate priorities).
This works by generating a directed graph, where each student points
at at his best option, and each school points at the student (or students)
with the highest priority.
"""
EDGE_WIDTH_SIZE_FACTOR = 700
"""Size factor (in the image) of each edge that is not part of the main cycle."""
EDGE_WIDTH_CYCLE_SIZE = 10
"""Size factor (in the image) of each edge that takes part of the main cycle."""
def __init__(self,
generate_images=False,
images_folder="TTC_images",
use_longest_cycle=True):
"""Initializes the algorithm.
:param generate_images: If the process generates images or not.
:type generate_images: bool
:param images_folder: Where images are saved.
:type images_folder: str
:param use_longest_cycle: If the algorithm applies the longest cycle available, or the first one encountered.
:type use_longest_cycle: bool
"""
self.generate_images = generate_images
self.images_folder = images_folder
self.use_longest_cycle = use_longest_cycle
self.__graph = None
self.__vertices_by_school_id = None
self.__vertices_by_student_id = None
self.__students_by_id = None
self.__schools_by_id = None
self.__entity_id = None
self.__entity_type = None
def reset_variables(self):
"""Resets all variables."""
self.__graph = Graph()
self.__vertices_by_school_id = {}
self.__vertices_by_student_id = {}
self.__students_by_id = {}
self.__schools_by_id = {}
self.__entity_id = self.__graph.new_vertex_property("int")
self.__graph.vertex_properties["entity_id"] = self.__entity_id
self.__entity_type = self.__graph.new_vertex_property("string")
self.__graph.vertex_properties["entity_type"] = self.__entity_type
def run(self, students, schools, ruleset):
"""Runs the algorithm.
First it creates the graph, then it lists all the cycles available,
after that it selects one cycle, and applies it. Finally, it starts
the process again.
:param students: List of students.
:type students: list
:param schools: List of school.
:type schools: list
:param ruleset: Set of rules used.
:type ruleset: Ruleset
"""
self.reset_variables()
can_improve = True
iteration_counter = 1
while can_improve:
self.structure_graph(students, schools)
cycles = [c for c in all_circuits(self.__graph, unique=True)]
# print("CYCLES", cycles, "iteration", iteration_counter)
cycle_edges = []
if cycles:
for cycle in cycles: # ToDo: Possible optimisation: apply all disjoint cycles at once
for current_v_index in range(len(cycle)):
next_v_index = (current_v_index + 1) % len(cycle)
from_v = self.__graph.vertex(cycle[current_v_index])
target_v = self.__graph.vertex(cycle[next_v_index])
edge = self.__graph.edge(from_v, target_v)
cycle_edges.append(edge)
if self.__entity_type[from_v] == "st":
sel_student = self.__students_by_id[
self.__entity_id[from_v]]
sel_school = self.__schools_by_id[
self.__entity_id[target_v]]
sel_student.assigned_school = sel_school
sel_school.assignation.append(sel_student)
# vertex_school_target_id = self.__entity_id[target_v]
# vertex_school_target = self.__schools_by_id[vertex_school_target_id]
# print("CYCLE: Student", sel_student.id, "School", sel_school.id)
# print("VVV: School {} -> School {} (Student {}) ".format(self.__entity_id[from_v], self.__entity_id[target_v], self.__entity_id[self.__graph.edge(from_v, target_v)]))
if self.generate_images:
self.generate_image(cycle_edges,
iteration_n=iteration_counter)
else:
can_improve = False
self.__graph.clear()
iteration_counter += 1
def structure_graph(self, students, schools):
"""Creates a graph where students points to schools, and schools points to students.
In the graph, each student points at at his best option, and each school points
at the student (or students) with the highest priority.
:param students: List of students.
:type students: list
:param schools:
:type schools: list
"""
if not self.__students_by_id and not self.__schools_by_id:
for student in students:
self.__students_by_id[student.id] = student
for school in schools:
self.__schools_by_id[school.id] = school
for school in schools:
setattr(school, 'preferences',
StudentQueue(school, preference_mode=True))
remaining_students = [
student for student in students if not student.assigned_school
]
for student in remaining_students:
for pref_school in student.preferences:
pref_school.preferences.append(student)
for student in remaining_students:
v_source_student = self.create_vertex_student(student)
pref_school = next(
(school for school in student.preferences if
len(school.assignation.get_all_students()) < school.capacity),
None)
if pref_school:
v_target_school = self.create_vertex_school(pref_school)
self.create_edge(v_source_student, v_target_school)
for school in schools:
if len(school.assignation.get_all_students()) < school.capacity:
v_source_school = self.create_vertex_school(school)
pref_student = next(
iter(school.preferences.get_all_students()), None)
if pref_student:
v_target_student = self.create_vertex_student(pref_student)
self.create_edge(v_source_school, v_target_student)
# graph_draw(self.__graph,
# vertex_text=self.__entity_id, vertex_shape="circle",
# output_size=(1000, 1000), bg_color=[1., 1., 1., 1], output="graph.png")
def create_vertex_student(self, student):
"""Defines a new student as a vertex in the graph (if it did not existed before)."""
if student.id in self.__vertices_by_student_id:
vertex = self.__vertices_by_student_id[student.id]
else:
vertex = self.__graph.add_vertex()
self.__vertices_by_student_id[student.id] = vertex
self.__entity_id[vertex] = student.id
self.__entity_type[
vertex] = "st" # ToDo: There may be other ways to do this.
return vertex
def create_vertex_school(self, school):
"""Defines a new school as a vertex in the graph (if it did not existed before)."""
if school.id in self.__vertices_by_school_id:
vertex = self.__vertices_by_school_id[school.id]
else:
vertex = self.__graph.add_vertex()
self.__vertices_by_school_id[school.id] = vertex
self.__entity_id[vertex] = school.id
self.__entity_type[vertex] = "sc"
return vertex
def create_edge(self, source_v, target_v):
"""Creates a directed edge between two vertices."""
self.__graph.add_edge(source_v, target_v)
def generate_image(self, cycle_edges, iteration_n=0):
"""Generates an image of a graph.
:param cycle_edges: Edges which are part of the main cycle (they will be highlighted in red).
:type cycle_edges: list
:param iteration_n: Number of iteration of the algorithm (this is added in the filename of the image).
:type iteration_n: int
.. DANGER::
This is an experimental feature.
"""
edge_color = self.__graph.new_edge_property("vector<float>")
edge_width = self.__graph.new_edge_property("int")
for edge in self.__graph.edges():
if edge in cycle_edges:
edge_color[edge] = [1., 0.2, 0.2, 0.999]
edge_width[edge] = 7
else:
edge_color[edge] = [0., 0., 0., 0.3]
edge_width[edge] = 4
vertex_shape = self.__graph.new_vertex_property("string")
vertex_size = self.__graph.new_vertex_property("int")
for vertex in self.__graph.vertices():
if self.__entity_type[vertex] == "st":
vertex_shape[vertex] = "circle"
vertex_size[vertex] = 1
else:
vertex_shape[vertex] = "double_circle"
vertex_size[vertex] = 100
# pos = sfdp_layout(self.__graph, C=10, p=5, theta=2, gamma=1)
pos = arf_layout(self.__graph, d=0.2, a=3)
graph_draw(
self.__graph,
pos=pos,
vertex_text=self.__entity_id,
vertex_font_size=
1, # ToDo: Move image related code outside the class.
vertex_fill_color=[0.97, 0.97, 0.97, 1],
vertex_color=[0.05, 0.05, 0.05, 0.95],
vertex_shape=vertex_shape,
edge_color=edge_color,
edge_pen_width=edge_width,
output_size=(1000, 1000),
bg_color=[1., 1., 1., 1],
output=self.generate_filename(iteration_n))
def generate_filename(self, iteration_n): # ToDo: Move this to utils
"""Returns a filename (which is used to generate the images)."""
filename = "Graph (iteration {})".format(
iteration_n) if iteration_n > 0 else "Graph"
output_file = gen_filepath(self.images_folder,
filename=filename,
extension="png")
return output_file
filename = 'buf.txt'
print(filename)
coefficient = 3
word_dict = {}
add_dict = {}
f = open('bad.txt', 'r', encoding="utf-8")
for s in f:
# print(s.split(' ')[0])
add_dict[s.split(' ')[0]] = 1
f = open(filename, 'r', encoding="utf-8")
pairs_graph = Graph(directed=False)
edge_weights = pairs_graph.new_edge_property("int")
ver_names = pairs_graph.new_vertex_property("string")
for line in f:
spl_line = line.split(' ')
if len(spl_line) == 1:
continue
pos = int(spl_line[0])
neg = int(spl_line[1])
cur_weight = pos + coefficient * neg
w1 = spl_line[2].strip(' \n\uefef')
w2 = spl_line[3].strip(' \n\uefef')
if w1 in add_dict or w2 in add_dict:
class GeneralGraph():
"""
General wrapper for graph-tool or networkx graphs to add edges and nodes
according to constraints
"""
def __init__(self, directed=True, verbose=1):
self.graphtool = GRAPH_TOOL
# Initialize graph
if self.graphtool:
self.graph = Graph(directed=directed)
self.weight = self.graph.new_edge_property("float")
else:
if directed:
print("directed graph")
self.graph = nx.DiGraph()
else:
self.graph = nx.Graph()
# set metaparameter
self.time_logs = {}
self.verbose = verbose
def set_edge_costs(self,
layer_classes=["resistance"],
class_weights=[1],
**kwargs):
"""
Initialize edge cost variables
:param classes: list of cost categories
:param weights: list of weights for cost categories - must be of same
shape as classes (if None, then equal weighting)
"""
class_weights = np.array(class_weights)
# set different costs:
self.cost_classes = layer_classes
if self.graphtool:
self.cost_props = [
self.graph.new_edge_property("float")
for _ in range(len(layer_classes))
]
self.cost_weights = class_weights / np.sum(class_weights)
if self.verbose:
print(self.cost_classes, self.cost_weights)
# save weighted instance for plotting
self.instance = np.sum(
np.moveaxis(self.cost_instance, 0, -1) * self.cost_weights,
axis=2) * self.hard_constraints
def set_shift(self,
start,
dest,
pylon_dist_min=3,
pylon_dist_max=5,
max_angle=np.pi / 2,
**kwargs):
"""
Initialize shift variable by getting the donut values
:param lower, upper: min and max distance of pylons
:param vec: vector of diretion of edges
:param max_angle: Maximum angle of edges to vec
"""
vec = dest - start
if self.verbose:
print("SHIFT:", pylon_dist_min, pylon_dist_max, vec, max_angle)
self.shifts = get_half_donut(pylon_dist_min,
pylon_dist_max,
vec,
angle_max=max_angle)
self.shift_tuples = self.shifts
def set_corridor(self,
dist_surface,
start_inds,
dest_inds,
sample_func="mean",
sample_method="simple",
factor_or_n_edges=1):
# set new corridor
corridor = (dist_surface > 0).astype(int)
self.factor = factor_or_n_edges
self.cost_rest = self.cost_instance * (self.hard_constraints >
0).astype(int) * corridor
# downsample
tic = time.time()
if self.factor > 1:
self.cost_rest = CostUtils.downsample(self.cost_rest,
self.factor,
mode=sample_method,
func=sample_func)
self.time_logs["downsample"] = round(time.time() - tic, 3)
# repeat because edge artifacts
self.cost_rest = self.cost_rest * (self.hard_constraints >
0).astype(int) * corridor
# add start and end TODO ugly
self.cost_rest[:, dest_inds[0],
dest_inds[1]] = self.cost_instance[:, dest_inds[0],
dest_inds[1]]
self.cost_rest[:, start_inds[0],
start_inds[1]] = self.cost_instance[:, start_inds[0],
start_inds[1]]
def add_nodes(self, nodes):
"""
Add vertices to the graph
param nodes: list of node names if networkx, integer if graphtool
"""
tic = time.time()
# add nodes to graph
if self.graphtool:
_ = self.graph.add_vertex(nodes)
self.n_nodes = len(list(self.graph.vertices()))
else:
self.graph.add_nodes_from(np.arange(nodes))
self.n_nodes = len(self.graph.nodes())
# verbose
if self.verbose:
print("Added nodes:", nodes, "in time:", time.time() - tic)
self.time_logs["add_nodes"] = round(time.time() - tic, 3)
def add_edges(self):
tic_function = time.time()
n_edges = 0
# kernels, posneg = ConstraintUtils.get_kernel(self.shifts,
# self.shift_vals)
# edge_array = []
times_edge_list = []
times_add_edges = []
if self.verbose:
print("n_neighbors:", len(self.shift_tuples))
for i in range(len(self.shift_tuples)):
tic_edges = time.time()
# set cost rest if necessary (random graph)
self.set_cost_rest()
# compute shift and weights
out = self._compute_edges(self.shift_tuples[i])
# Error if -1 entries because graph-tool crashes with -1 nodes
if np.any(out[:, :2].flatten() < 0):
print(np.where(out[:, :2] < 0))
raise RuntimeError
n_edges += len(out)
times_edge_list.append(round(time.time() - tic_edges, 3))
# add edges to graph
tic_graph = time.time()
if self.graphtool:
self.graph.add_edge_list(out, eprops=self.cost_props)
else:
nx_edge_list = [(e[0], e[1], {
"weight": np.sum(e[2:] * self.cost_weights)
}) for e in out]
self.graph.add_edges_from(nx_edge_list)
times_add_edges.append(round(time.time() - tic_graph, 3))
# alternative: collect edges here and add alltogether
# edge_array.append(out)
# # alternative: add edges all in one go
# tic_concat = time.time()
# edge_lists_concat = np.concatenate(edge_array, axis=0)
# self.time_logs["concatenate"] = round(time.time() - tic_concat, 3)
# print("time for concatenate:", self.time_logs["concatenate"])
# tic_graph = time.time()
# self.graph.add_edge_list(edge_lists_concat, eprops=[self.weight])
# self.time_logs["add_edges"] = round(
# (time.time() - tic_graph) / len(shifts), 3
# )
self.n_edges = len(list(self.graph.edges()))
self._update_time_logs(times_add_edges, times_edge_list, tic_function)
if self.verbose:
print("DONE adding", n_edges, "edges:", time.time() - tic_function)
def _update_time_logs(self, times_add_edges, times_edge_list,
tic_function):
self.time_logs["add_edges"] = round(np.mean(times_add_edges), 3)
self.time_logs["add_edges_times"] = times_add_edges
self.time_logs["edge_list"] = round(np.mean(times_edge_list), 3)
self.time_logs["edge_list_times"] = times_edge_list
self.time_logs["add_all_edges"] = round(time.time() - tic_function, 3)
if self.verbose:
print("Done adding edges:", len(list(self.graph.edges())))
def sum_costs(self):
"""
Additive weighting of costs
Take the individual edge costs, compute weighted sum --> self.weight
"""
# add sum of all costs
if not self.graphtool:
return
tic = time.time()
summed_costs_arr = np.zeros(self.cost_props[0].get_array().shape)
for i in range(len(self.cost_props)):
prop = self.cost_props[i].get_array()
summed_costs_arr += prop * self.cost_weights[i]
self.weight.a = summed_costs_arr
self.time_logs["sum_of_costs"] = round(time.time() - tic, 3)
def remove_vertices(self, dist_surface, delete_padding=0):
"""
Remove edges in a certain corridor (or all) to replace them by
a refined surface
@param dist_surface: a surface where each pixel value corresponds to
the distance of the pixel to the shortest path
@param delete_padding: define padding in which part of the corridor to
delete vertices (cannot delete all because then graph unconnected)
"""
tic = time.time()
self.graph.clear_edges()
self.graph.shrink_to_fit()
self.time_logs["remove_edges"] = round(time.time() - tic, 3)
def get_pareto(self,
vary,
source,
dest,
out_path=None,
compare=[0, 1],
plot=1):
"""
Arguments:
vary: how many weights to explore
e.g 3 --> each cost class can have weight 0, 0.5 or 1
source, dest: as always the source and destination vertex
out_path: where to save the pareto figure(s)
compare: indices of cost classes to compare
Returns:
paths: All found paths
pareto: The costs for each combination of weights
"""
tic = time.time()
# initialize lists
pareto = list()
paths = list()
cost_sum = list()
# get the edge costs
cost_arrs = [cost.get_array() for cost in self.cost_props]
# [self.cost_props[comp].get_array() for comp in compare]
# get vary weights between 0 and 1
var_weights = np.around(np.linspace(0, 1, vary), 2)
# construct weights array
if len(compare) == 2:
weights = [[v, 1 - v] for v in var_weights]
elif len(compare) == 3:
weights = list()
for w0 in var_weights:
for w1 in var_weights[var_weights <= 1 - w0]:
weights.append([w0, w1, 1 - w0 - w1])
else:
raise ValueError("argument compare can only have length 2 or 3")
# w_avail: keep weights of non-compare classes, get leftover amount
w_avail = np.sum(np.asarray(self.cost_weights)[compare])
# compute paths for each combination of weights
for j in range(len(weights)):
# option 2: np.zeros(len(cost_arrs)) + non_compare_weight
w = self.cost_weights.copy()
# replace the ones we want to compare
w[compare] = np.array(weights[j]) * w_avail
# weighted sum of edge costs
self.weight.a = np.sum(
[cost_arrs[i] * w[i] for i in range(len(cost_arrs))], axis=0)
# get shortest path
path, path_costs, _ = self.get_shortest_path(source, dest)
# don't take cost_sum bc this is sum of original weighting
pareto.append(np.sum(path_costs, axis=0)[compare])
paths.append(path)
# take overall sum of costs (unweighted) that this w leads to
cost_sum.append(np.sum(path_costs))
# print best weighting
best_weight = np.argmin(cost_sum)
w = self.cost_weights.copy()
w[compare] = np.array(weights[best_weight]) * w_avail
print("Best weights:", w, "with (unweighted) costs:", np.min(cost_sum))
self.time_logs["pareto"] = round(time.time() - tic, 3)
pareto = np.array(pareto)
classes = [self.cost_classes[comp] for comp in compare]
# Plotting
if plot:
if len(compare) == 2:
plot_pareto_scatter_2d(pareto,
weights,
classes,
cost_sum=cost_sum,
out_path=out_path)
elif len(compare) == 3:
# plot_pareto_3d(pareto, weights, classes)
plot_pareto_scatter_3d(pareto,
weights,
classes,
cost_sum=cost_sum,
out_path=out_path)
return paths, weights, cost_sum
def get_shortest_path(self, source, target):
"""
Compute shortest path from source vertex to target vertex
"""
tic = (time.time())
# #if source and target are given as indices:
if self.graphtool:
vertices_path, _ = shortest_path(self.graph,
source,
target,
weights=self.weight,
negative_weights=True)
else:
try:
vertices_path = nx.dijkstra_path(self.graph, source, target)
except nx.exception.NetworkXNoPath:
return []
self.time_logs["shortest_path"] = round(time.time() - tic, 3)
return vertices_path
def save_graph(self, OUT_PATH):
"""
Save the graph in OUT_PATH
"""
if self.graphtool:
for i, cost_class in enumerate(self.cost_classes):
self.graph.edge_properties[cost_class] = self.cost_props[i]
self.graph.edge_properties["weight"] = self.weight
self.graph.save(OUT_PATH + ".xml.gz")
else:
nx.write_weighted_edgelist(self.graph,
OUT_PATH + '.weighted.edgelist')
def load_graph(self, IN_PATH):
"""
Retrieve graph from IN_PATH
"""
if self.graphtool:
self.g_prev = load_graph(IN_PATH + ".xml.gz")
self.weight_prev = self.g_prev.ep.weight
# weight = G2.ep.weight[G2.edge(66, 69)]
else:
self.g_prev = nx.read_edgelist(IN_PATH + '.weighted.edgelist',
nodetype=int,
data=(('weight', float), ))
# -----------------------------------------------------------------------
# INTERFACE
def single_sp(self, **kwargs):
"""
Function for full processing until shortest path
"""
self.start_inds = kwargs["start_inds"]
self.dest_inds = kwargs["dest_inds"]
self.set_shift(self.start_inds, self.dest_inds, **kwargs)
# self.set_corridor(
# np.ones(self.hard_constraints.shape) * 0.5,
# self.start_inds,
# self.dest_inds,
# factor_or_n_edges=1
# )
if self.verbose:
print("1) Initialize shifts and instance (corridor)")
self.set_edge_costs(**kwargs)
# add vertices
self.add_nodes()
if self.verbose:
print("2) Initialize distances to inf and predecessors")
self.add_edges()
if self.verbose:
print("3) Compute source shortest path tree")
print("number of vertices and edges:", self.n_nodes, self.n_edges)
# weighted sum of all costs
self.sum_costs()
source_v, target_v = self.add_start_and_dest(self.start_inds,
self.dest_inds)
# get actual best path
path, path_costs, cost_sum = self.get_shortest_path(source_v, target_v)
if self.verbose:
print("4) shortest path", cost_sum)
return path, path_costs, cost_sum
plt.plot(range(0, a + 1), ver_attr[:(a + 1)], 'go')
plt.plot(range(a + 1, b + 1), ver_attr[(a + 1):(b + 1)], 'bo')
plt.plot(range(b + 1, len(ver_attr)), ver_attr[(b + 1):], 'ro')
print("-------------------------------------------------")
filename = '../pairparser/results/en_pairs(7).txt'
ftag = '7_3imp'
coefficient = 3
word_dict = {} # dict with indexes of nodes by word
f = open(filename, 'r', encoding="utf-8")
pairs_graph = Graph(directed=False)
edge_weights = pairs_graph.new_edge_property("double")
ver_names = pairs_graph.new_vertex_property("string")
ver_id = pairs_graph.new_vertex_property("int")
for line in f:
spl_line = line.split(' ')
if len(spl_line) == 1:
continue
pos = int(spl_line[0])
neg = int(spl_line[1])
cur_weight = pos + coefficient * neg
w1 = spl_line[2].strip(' \n\uefef')
w2 = spl_line[3].strip(' \n\uefef')
class MemoryGraph:
# "my_graph.xml.gz"
def __init__(self, index_path=None, graph_path=None):
#index
if index_path != None:
self.load_index(index_path)
else:
self.init_index()
#graph
if graph_path != None:
self.load_graph(graph_path)
else:
self.init_graph()
def save_index(self, index_path):
self.index.save_index(index_path)
def load_index(self, index_path):
self.index = hnswlib.Index(space='l2', dim=256)
self.index.load_index(index_path)
self.index.set_ef(10)
def init_index(self):
self.index = hnswlib.Index(space='l2', dim=256)
self.index.init_index(max_elements=50000, ef_construction=100, M=16)
self.index.set_ef(10)
def save_graph(self, graph_path):
self.graph.save(graph_path)
def load_graph(self, graph_path):
self.graph = load_graph(graph_path)
self.vertex_index = dict()
for v in self.graph.vertices():
self.vertex_index[self.graph.vp.id[v]] = v
def init_graph(self):
self.graph = Graph(directed=False)
self.vertex_index = dict()
self.graph.graph_properties["id"] = self.graph.new_graph_property(
"long")
self.graph.graph_properties["id"] = 0
self.graph.vertex_properties["id"] = self.graph.new_vertex_property(
"long")
self.graph.vertex_properties["x"] = self.graph.new_vertex_property(
"double")
self.graph.vertex_properties["y"] = self.graph.new_vertex_property(
"double")
self.graph.vertex_properties["t"] = self.graph.new_vertex_property(
"long")
self.graph.vertex_properties["f"] = self.graph.new_vertex_property(
"vector<double>")
self.graph.edge_properties["d"] = self.graph.new_edge_property(
"double")
def get_observation(self, id):
v = self.vertex_index[id]
return dict(id=id,
x=self.graph.vp.x[v],
y=self.graph.vp.y[v],
t=self.graph.vp.t[v],
f=self.graph.vp.f[v])
def get_observations(self, ids):
return [self.get_observation(id) for id in ids]
def insert_observation(self, t, y, x, f):
v = self.graph.add_vertex()
id = self.graph.graph_properties["id"]
self.graph.graph_properties["id"] = id + 1
self.graph.vp.id[v] = id
self.graph.vp.x[v] = x
self.graph.vp.y[v] = y
self.graph.vp.t[v] = t
self.graph.vp.f[v] = f
self.index.add_items([f], [id])
return id
def get_adjacencies(self, id, radius):
v = self.vertex_index[id]
return [self.vertex_index[n] for n in self._neighbors(v, radius)]
def _neighbors(self, v, radius, depth=0):
result = set()
for w in v.out_neighbors():
result.add(w)
if depth + 1 < radius:
result.update(self._neighbors(w, radius, depth + 1))
return result
def insert_adjacency(self, from_id, to_id, distance):
va = self.vertex_index[from_id]
vb = self.vertex_index[to_id]
e = self.graph.add_edge(va, vb)
self.graph.ep.d[e] = distance
def knn_query(self, feats, k=1):
return self.index.knn_query(feats, k)
def build_closure(g, cand_source, terminals, infection_times, k=-1,
strictly_smaller=True,
debug=False,
verbose=False):
"""
build a clojure graph in which cand_source + terminals are all connected to each other.
the number of neighbors of each node is determined by k
the larger the k, the denser the graph"""
r2pred = {}
edges = {}
terminals = list(terminals)
# from cand_source to terminals
vis = init_visitor(g, cand_source)
cpbfs_search(g, source=cand_source, visitor=vis, terminals=terminals,
forbidden_nodes=terminals,
count_threshold=k)
r2pred[cand_source] = vis.pred
for u, v, c in get_edges(vis.dist, cand_source, terminals):
edges[(u, v)] = c
if debug:
print('cand_source: {}'.format(cand_source))
print('#terminals: {}'.format(len(terminals)))
print('edges from cand_source: {}'.format(edges))
if verbose:
terminals_iter = tqdm(terminals)
print('building closure graph')
else:
terminals_iter = terminals
# from terminal to other terminals
for root in terminals_iter:
if strictly_smaller:
late_terminals = [t for t in terminals
if infection_times[t] > infection_times[root]]
else:
# respect what the paper presents
late_terminals = [t for t in terminals
if infection_times[t] >= infection_times[root]]
late_terminals = set(late_terminals) - {cand_source} # no one can connect to cand_source
if debug:
print('root: {}'.format(root))
print('late_terminals: {}'.format(late_terminals))
vis = init_visitor(g, root)
cpbfs_search(g, source=root, visitor=vis, terminals=list(late_terminals),
forbidden_nodes=list(set(terminals) - set(late_terminals)),
count_threshold=k)
r2pred[root] = vis.pred
for u, v, c in get_edges(vis.dist, root, late_terminals):
if debug:
print('edge ({}, {})'.format(u, v))
edges[(u, v)] = c
if verbose:
print('returning closure graph')
gc = Graph(directed=True)
for _ in range(g.num_vertices()):
gc.add_vertex()
for (u, v) in edges:
gc.add_edge(u, v)
eweight = gc.new_edge_property('int')
eweight.set_2d_array(np.array(list(edges.values())))
# for e, c in edges.items():
# eweight[e] = c
return gc, eweight, r2pred
class Network:
def __init__(self, nodes_info=None, links_info=None, file_name=None):
self.g = Graph()
if nodes_info and links_info:
self.nodes_info = nodes_info
self.links_info = links_info
self.g.vertex_properties["name"] = self.g.new_vertex_property(
'string')
self.g.vertex_properties["id"] = self.g.new_vertex_property(
'int32_t')
self.g.edge_properties["weight"] = self.g.new_edge_property(
'int32_t')
self.create_network()
self.g.vertex_properties["pagerank"] = pagerank(
self.g, weight=self.g.edge_properties["weight"])
self.g.vertex_properties[
"degree_centrality"] = self.degree_centrality()
elif file_name:
self.load_network(file_name)
def create_network(self):
# Add Nodes
for node in self.nodes_info:
self.add_n(node)
# Add Links
for link in self.links_info:
n_loser = 0
n_winner = 0
loser = link['loser']
winner = link['winner']
weight = link['rounds']
for team_id in self.g.vertex_properties.id:
if loser == team_id:
break
n_loser += 1
for team_id in self.g.vertex_properties.id:
if winner == team_id:
break
n_winner += 1
self.add_l(n_loser, n_winner, 16 / weight * 100)
def load_network(self, file_name):
new_file_name = '..' + sep + '..' + sep + 'network-graphs' + sep + file_name
self.g.load(new_file_name, fmt="gt")
def get_normalized_pagerank(self):
max_pgr = 0
for pgr in self.g.vertex_properties.pagerank:
if pgr > max_pgr:
max_pgr = pgr
return [
self.g.vertex_properties.pagerank[v] / max_pgr
for v in self.g.vertices()
]
def add_n(self, node_info):
n = self.g.add_vertex()
self.g.vertex_properties.id[n] = node_info['id']
self.g.vertex_properties.name[n] = node_info['Team_Name']
def add_l(self, loser, winner, weight):
n1 = self.g.vertex(loser)
n2 = self.g.vertex(winner)
l = self.g.add_edge(n1, n2)
self.g.edge_properties.weight[l] = weight
def draw(self, output_file, fmt):
graph_draw(self.g,
vertex_text=self.g.vertex_index,
output=output_file,
fmt=fmt)
def save_network(self, file_name):
try:
new_file_name = '..' + sep + '..' + sep + 'network-graphs' + sep + file_name
self.g.save(new_file_name, fmt="gt")
except:
return False
return True
def vp_pagerank(self):
return self.g.vertex_properties.pagerank
def vp_degree_cent(self):
return self.g.vertex_properties.degree_centrality
def vp_name(self):
return self.g.vertex_properties.name
def vp_id(self):
return self.g.vertex_properties.id
def ep_weight(self):
return self.g.edge_properties.weight
# Calcula as características básicas da rede
def get_basic_info(self):
info = {}
try:
n_vertices = self.g.num_vertices()
n_edges = self.g.num_edges()
density = n_edges / ((n_vertices * (n_vertices - 1)) / 2)
mean_degree = (2 * n_edges) / n_vertices
# Cálculo do coeficiente de clusterização "na mão", usando a média dos
# coeficientes locais calculados pela Graph Tools
local_cc = local_clustering(self.g)
clustering_coef = fsum(
[local_cc[x] for x in self.g.vertices() if local_cc[x] != 0.0])
clustering_coef /= n_vertices
info["Número de times"] = n_vertices
info["Número de confrontos"] = n_edges
info["Densidade"] = density
info["Grau médio"] = mean_degree
info["Coeficiente de Clusterização"] = clustering_coef
except:
info.clear()
return info
def degree_centrality(self):
degree_centrality = self.g.new_vertex_property('float')
for v in self.g.vertices():
degree_centrality[v] = v.in_degree() / (self.g.num_vertices() - 1)
return degree_centrality
# Calcula a distribuição de graus da rede
def degree_distribution(self):
degree_dist = {}
try:
for v in self.g.vertices():
if v.in_degree() not in degree_dist.keys():
degree_dist[v.in_degree()] = 1
else:
degree_dist[v.in_degree()] += 1
for k in degree_dist.keys():
degree_dist[k] /= self.g.num_vertices()
except:
degree_dist.clear()
return degree_dist
def build_closure(g,
cand_source,
terminals,
infection_times,
k=-1,
strictly_smaller=True,
debug=False,
verbose=False):
"""
build a clojure graph in which cand_source + terminals are all connected to each other.
the number of neighbors of each node is determined by k
the larger the k, the denser the graph"""
r2pred = {}
edges = {}
terminals = list(terminals)
# from cand_source to terminals
vis = init_visitor(g, cand_source)
cpbfs_search(g,
source=cand_source,
visitor=vis,
terminals=terminals,
forbidden_nodes=terminals,
count_threshold=k)
r2pred[cand_source] = vis.pred
for u, v, c in get_edges(vis.dist, cand_source, terminals):
edges[(u, v)] = c
if debug:
print('cand_source: {}'.format(cand_source))
print('#terminals: {}'.format(len(terminals)))
print('edges from cand_source: {}'.format(edges))
if verbose:
terminals_iter = tqdm(terminals)
print('building closure graph')
else:
terminals_iter = terminals
# from terminal to other terminals
for root in terminals_iter:
if strictly_smaller:
late_terminals = [
t for t in terminals
if infection_times[t] > infection_times[root]
]
else:
# respect what the paper presents
late_terminals = [
t for t in terminals
if infection_times[t] >= infection_times[root]
]
late_terminals = set(late_terminals) - {
cand_source
} # no one can connect to cand_source
if debug:
print('root: {}'.format(root))
print('late_terminals: {}'.format(late_terminals))
vis = init_visitor(g, root)
cpbfs_search(
g,
source=root,
visitor=vis,
terminals=list(late_terminals),
forbidden_nodes=list(set(terminals) - set(late_terminals)),
count_threshold=k)
r2pred[root] = vis.pred
for u, v, c in get_edges(vis.dist, root, late_terminals):
if debug:
print('edge ({}, {})'.format(u, v))
edges[(u, v)] = c
if verbose:
print('returning closure graph')
gc = Graph(directed=True)
for _ in range(g.num_vertices()):
gc.add_vertex()
for (u, v) in edges:
gc.add_edge(u, v)
eweight = gc.new_edge_property('int')
eweight.set_2d_array(np.array(list(edges.values())))
# for e, c in edges.items():
# eweight[e] = c
return gc, eweight, r2pred
class Network:
def __init__(self):
self.g = Graph(directed=True)
self.player_id_to_vertex = {}
self.pairs = {} # player pair: edge
# property maps for additional information
self.g.vertex_properties['player_id'] = self.g.new_vertex_property(
"string")
self.g.vertex_properties['player_coords'] = self.g.new_vertex_property(
"vector<float>")
self.g.vertex_properties[
'average_player_coords'] = self.g.new_vertex_property(
"vector<float>")
self.g.vertex_properties[
'player_n_coords'] = self.g.new_vertex_property("int")
self.g.edge_properties['weight'] = self.g.new_edge_property("float")
@property
def edge_weights(self):
return self.g.edge_properties['weight']
@property
def player_id_pmap(self):
return self.g.vertex_properties['player_id']
@property
def player_coords_pmap(self):
return self.g.vertex_properties['player_coords']
@property
def player_n_coords_pmap(self):
return self.g.vertex_properties['player_n_coords']
@property
def average_player_coords_pmap(self):
# lazy evaluation of means
for v in self.g.vertices():
self.g.vertex_properties['average_player_coords'][v] = np.asarray(
self.player_coords_pmap[v]) / self.player_n_coords_pmap[v]
return self.g.vertex_properties['average_player_coords']
def add_players(self, pids: List[str]):
n = len(pids)
vs = list(self.g.add_vertex(n))
self.player_id_to_vertex.update({pids[i]: vs[i] for i in range(n)})
for i in range(n):
self.player_id_pmap[vs[i]] = pids[i]
return vs
def add_passes(self,
id_pairs: List[Tuple],
coords_pairs: List[Tuple],
pass_scores=None):
pairs = [(self.player_id_to_vertex[i1], self.player_id_to_vertex[i2])
for i1, i2 in id_pairs]
# append player coordinates
n = len(coords_pairs)
if pass_scores is None:
pass_scores = [1 for _ in range(n)]
for i in range(n):
# remember orig and dest location
# orig player
coords = self.player_coords_pmap[pairs[i][0]]
if len(coords) == 0:
coords = np.asarray([coords_pairs[i][0], coords_pairs[i][1]])
else:
# accumulate
coords += np.asarray([coords_pairs[i][0], coords_pairs[i][1]])
self.player_coords_pmap[pairs[i][0]] = coords
self.player_n_coords_pmap[pairs[i][0]] += 1
# dest player
coords = self.player_coords_pmap[pairs[i][1]]
if len(coords) == 0:
coords = np.asarray([coords_pairs[i][2], coords_pairs[i][3]])
else:
# accumulate
coords += np.asarray([coords_pairs[i][2], coords_pairs[i][3]])
self.player_coords_pmap[pairs[i][1]] = coords
self.player_n_coords_pmap[pairs[i][1]] += 1
# if the edge exists, increment its weight instead of creating a new edge
e = self.pairs.get(pairs[i])
if e is not None:
self.edge_weights[e] += pass_scores[i]
else:
e = self.g.add_edge(*pairs[i])
self.pairs[pairs[i]] = e
self.edge_weights[e] = pass_scores[i]
def cleanup(self):
"""remove isolated vertices"""
to_remove = []
for v in self.g.vertices():
if v.in_degree() + v.out_degree() == 0:
to_remove.append(v)
n = len(to_remove)
self.g.remove_vertex(to_remove, fast=True)
print("Removed {0} isolated vertices".format(n))
def save(self, file: str):
self.g.save(file, fmt='graphml')
class SentenceGraph():
def __init__(self, sentence, directed=False, graph=None):
# Create a SentenceGraph from an existing graph tool graph
if graph is not None:
self.sentence_graph = graph
return
# Create a new SentenceGraph from scratch
self.sentence_graph = Graph(directed=directed)
# Graph properties
sentence_property = self.sentence_graph.new_graph_property("string", sentence)
self.sentence_graph.graph_properties[SENTENCE_KEY] = sentence_property
# Vertex properties
word_property = self.sentence_graph.new_vertex_property("string")
part_of_speech_property = self.sentence_graph.new_vertex_property("string")
vertex_color_property = self.sentence_graph.new_vertex_property("vector<double>")
self.sentence_graph.vertex_properties[WORD_KEY] = word_property
self.sentence_graph.vertex_properties[PART_OF_SPEECH_KEY] = part_of_speech_property
self.sentence_graph.vertex_properties[VERTEX_COLOR_KEY] = vertex_color_property
# Edge properties
sentence_edge_property = self.sentence_graph.new_edge_property("string")
definition_edge_property = self.sentence_graph.new_edge_property("string")
parsed_dependencies_edge_property = self.sentence_graph.new_edge_property("string")
inter_sentence_edge_property = self.sentence_graph.new_edge_property("string")
edge_color_property = self.sentence_graph.new_edge_property("vector<double>")
dependency_edge_property = self.sentence_graph.new_edge_property("string")
self.sentence_graph.edge_properties[SENTENCE_EDGE_KEY] = sentence_edge_property
self.sentence_graph.edge_properties[DEFINITION_EDGE_KEY] = definition_edge_property
self.sentence_graph.edge_properties[PARSED_DEPENDENCIES_EDGE_KEY] = parsed_dependencies_edge_property
self.sentence_graph.edge_properties[INTER_SENTENCE_EDGE_KEY] = inter_sentence_edge_property
self.sentence_graph.edge_properties[EDGE_COLOR_KEY] = edge_color_property
self.sentence_graph.edge_properties[PARSE_TREE_DEPENDENCY_VALUE_KEY] = dependency_edge_property
# Edge filter properties
definition_edge_filter_property = self.sentence_graph.new_edge_property("bool")
inter_sentence_edge_filter_property = self.sentence_graph.new_edge_property("bool")
parsed_dependencies_edge_filter_property = self.sentence_graph.new_edge_property("bool")
sentence_edge_filter_property = self.sentence_graph.new_edge_property("bool")
self.sentence_graph.edge_properties[FILTER_DEFINITION_EDGE_KEY] = definition_edge_filter_property
self.sentence_graph.edge_properties[FILTER_INTER_SENTENCE_EDGE_KEY] = inter_sentence_edge_filter_property
self.sentence_graph.edge_properties[FILTER_PARSED_DEPENDENCIES_EDGE_KEY] = parsed_dependencies_edge_filter_property
self.sentence_graph.edge_properties[FILTER_SENTENCE_EDGE_KEY] = sentence_edge_filter_property
def get_sentence(self):
return self.sentence_graph.graph_properties[SENTENCE_KEY]
def add_vertex(self, word, pos):
word_pos_tuple = (word, pos)
# Create vertex, set properties
word_vertex = self.sentence_graph.add_vertex()
self.sentence_graph.vertex_properties[WORD_KEY][word_vertex] = word
self.sentence_graph.vertex_properties[PART_OF_SPEECH_KEY][word_vertex] = pos
self.sentence_graph.vertex_properties[VERTEX_COLOR_KEY][word_vertex] = [0, 0, 1, 1]
return word_vertex
def set_vertex_color_from_word(self, word, pos, color=[1, 0, 0, 1]):
word_vertex = self.get_vertex(word, pos)
return self.set_vertex_color(word_vertex, color)
def set_vertex_color(self, vertex, color=[1, 0, 0, 1]):
self.sentence_graph.vertex_properties[VERTEX_COLOR_KEY][vertex] = color
def set_vertices_color(self, vertices, color=[1, 0, 0, 1]):
for vertex in vertices:
self.set_vertex_color(vertex, color)
def add_sentence_edge_from_words(self, word1, pos1, word2, pos2):
return self.add_sentence_edge(self.get_vertex(word1, pos1), self.get_vertex(word2, pos2))
def add_sentence_edge(self, word_vertex1, word_vertex2):
sentence_edge = self.sentence_graph.add_edge(word_vertex1, word_vertex2, add_missing=False)
self.sentence_graph.edge_properties[SENTENCE_EDGE_KEY][sentence_edge] = sentence_edge
# Green
self.sentence_graph.edge_properties[EDGE_COLOR_KEY][sentence_edge] = [0.2, 1, 0.2, 1]
self._set_edge_to_zero_in_all_filters(sentence_edge)
self.sentence_graph.edge_properties[FILTER_SENTENCE_EDGE_KEY][sentence_edge] = True
return sentence_edge
def add_sentence_edges(self, sentence_vertices):
for i in range(1, len(sentence_vertices)):
self.add_sentence_edge(sentence_vertices[i - 1], sentence_vertices[i])
def add_parsed_dependency_edge(self, word_vertex1, word_vertex2, dependency_relationship):
parsed_dependency_edge = self.sentence_graph.add_edge(word_vertex1, word_vertex2, add_missing=False)
self.sentence_graph.edge_properties[PARSED_DEPENDENCIES_EDGE_KEY][parsed_dependency_edge] = parsed_dependency_edge
self.sentence_graph.edge_properties[PARSE_TREE_DEPENDENCY_VALUE_KEY][parsed_dependency_edge] = dependency_relationship
# Blue
self.sentence_graph.edge_properties[EDGE_COLOR_KEY][parsed_dependency_edge] = [0, 0, 1, 1]
self._set_edge_to_zero_in_all_filters(parsed_dependency_edge)
self.sentence_graph.edge_properties[FILTER_PARSED_DEPENDENCIES_EDGE_KEY][parsed_dependency_edge] = True
return parsed_dependency_edge
def add_parsed_dependency_edge_from_words(self, word1, pos1, word2, pos2, dependency_relationship):
return self.add_parsed_dependency_edge(
self.get_vertex(word1, pos1),
self.get_vertex(word2, pos2),
dependency_relationship)
def add_definition_edge_from_words(self, word, pos, definition_word, definition_pos):
return self.add_definition_edge(
self.get_vertex(word, pos),
self.get_vertex(definition_word, definition_pos))
def _set_edge_to_zero_in_all_filters(self, edge):
self.sentence_graph.edge_properties[FILTER_DEFINITION_EDGE_KEY][edge] = False
self.sentence_graph.edge_properties[FILTER_INTER_SENTENCE_EDGE_KEY][edge] = False
self.sentence_graph.edge_properties[FILTER_PARSED_DEPENDENCIES_EDGE_KEY][edge] = False
self.sentence_graph.edge_properties[FILTER_SENTENCE_EDGE_KEY][edge] = False
def add_definition_edge(self, word_vertex, definition_word_vertex):
definition_edge = self.sentence_graph.add_edge(word_vertex, definition_word_vertex, add_missing=False)
self.sentence_graph.edge_properties[DEFINITION_EDGE_KEY][definition_edge] = definition_edge
# Red
self.sentence_graph.edge_properties[EDGE_COLOR_KEY][definition_edge] = [1, 0.1, 0.1, 1]
self._set_edge_to_zero_in_all_filters(definition_edge)
self.sentence_graph.edge_properties[FILTER_DEFINITION_EDGE_KEY][definition_edge] = True
return definition_edge
def add_definition_edges(self, word_vertex, definition_word_vertices):
# Add edges from the word_vertex to all definition vertices and set
# the definition edge property on each edge
for definition_word_vertex in definition_word_vertices:
self.add_definition_edge(word_vertex, definition_word_vertex)
return self
def add_inter_sentence_edge(self, sentence1_word_vertex, sentence2_word_vertex):
inter_sentence_edge = self.sentence_graph.add_edge(sentence1_word_vertex, sentence2_word_vertex, add_missing=False)
self.sentence_graph.edge_properties[INTER_SENTENCE_EDGE_KEY][inter_sentence_edge] = inter_sentence_edge
# Pink
self.sentence_graph.edge_properties[EDGE_COLOR_KEY][inter_sentence_edge] = [1, 0.05, 1, 1]
self._set_edge_to_zero_in_all_filters(inter_sentence_edge)
self.sentence_graph.edge_properties[FILTER_INTER_SENTENCE_EDGE_KEY][inter_sentence_edge] = True
return inter_sentence_edge
def add_inter_sentence_edge_from_words(self, word1, pos1, word2, pos2):
return self.add_inter_sentence_edge(
self.get_vertex(word1, pos1),
self.get_vertex(word2, pos2))
def remove_vertex_by_word(self, word, pos):
self.remove_vertex(self.get_vertex(word, pos))
def remove_vertex(self, vertex):
word = self.sentence_graph.vertex_properties[WORD_KEY][vertex]
pos = self.sentence_graph.vertex_properties[PART_OF_SPEECH_KEY][vertex]
self.sentence_graph.remove_vertex(vertex)
def remove_edge(self, word1, pos1, word2, pos2):
self.sentence_graph.remove_edge(self.get_edge(word1, pos1, word2, pos2))
def contains(self, word, pos):
return self.get_vertex(word, pos) is not None
def get_vertex(self, word, pos):
for vertex in self.sentence_graph.vertices():
try:
vertex_word = self.sentence_graph.vertex_properties[WORD_KEY][vertex]
vertex_pos = self.sentence_graph.vertex_properties[PART_OF_SPEECH_KEY][vertex]
if vertex_word == word and vertex_pos == pos:
return vertex
except:
pass
return None
def get_word_pos_tuple(self, vertex):
return self.sentence_graph.vertex_properties[WORD_KEY][vertex],\
self.sentence_graph.vertex_properties[PART_OF_SPEECH_KEY][vertex]
def get_word_pos_tuple_by_index(self, index):
return self.get_word_pos_tuple(self.get_vertex_by_index(index))
def get_vertex_by_index(self, index):
return self.sentence_graph.vertex(index)
def get_vertices_iterator(self):
return self.sentence_graph.vertices()
def get_vertices(self):
return [x for x in self.sentence_graph.vertices()]
def get_vertex_out_neighbor_word_pos_tuples(self, vertex):
return [self.get_word_pos_tuple(neighbor_vertex)
for neighbor_vertex in self.get_vertex_out_neighbors(vertex)]
def get_vertex_in_neighbor_word_pos_tuples(self, vertex):
return [self.get_word_pos_tuple(neighbor_vertex)
for neighbor_vertex in self.get_vertex_in_neighbors(vertex)]
def get_vertex_out_neighbors(self, vertex):
return [neighbor_vertex for neighbor_vertex in vertex.out_neighbours()]
def get_vertex_in_neighbors(self, vertex):
return [neighbor_vertex for neighbor_vertex in vertex.in_neighbours()]
def get_word_pos_tuples(self):
return [self.get_word_pos_tuple(v) for v in self.sentence_graph.vertices()]
def get_num_vertices(self):
return self.sentence_graph.num_vertices()
def get_num_edges(self):
return self.sentence_graph.num_edges()
def get_edge(self, word1, pos1, word2, pos2):
vertex_1 = self.get_vertex(word1, pos1)
vertex_2 = self.get_vertex(word2, pos2)
return None\
if vertex_1 is None or vertex_2 is None\
else self.sentence_graph.edge(vertex_1, vertex_2)
def get_edges_iterator(self):
return self.sentence_graph.edges()
def get_edges(self):
return [x for x in self.sentence_graph.edges()]
def set_definition_edge_filter(self, inverted=False):
self.sentence_graph.set_edge_filter(
self.sentence_graph.edge_properties[FILTER_DEFINITION_EDGE_KEY],
inverted=inverted)
def set_inter_sentence_edge_filter(self, inverted=False):
self.sentence_graph.set_edge_filter(
self.sentence_graph.edge_properties[FILTER_INTER_SENTENCE_EDGE_KEY],
inverted=inverted)
def set_parsed_dependency_edge_filter(self, inverted=False):
self.sentence_edge.set_edge_filter(
self.sentence_graph.edge_properties[FILTER_PARSED_DEPENDENCIES_EDGE_KEY],
inverted=inverted)
def set_sentence_edge_filter(self, inverted=False):
self.sentence_graph.set_edge_filter(
self.sentence_graph.edge_properties[FILTER_SENTENCE_EDGE_KEY],
inverted=inverted)
def clear_filters(self):
self.sentence_graph.clear_filters()
def get_definition_edges(self):
return filter(lambda x: x in self.get_definition_edge_properties(), self.get_edges())
def get_word_vertex_properties(self):
return self.sentence_graph.vertex_properties[WORD_KEY]
def get_pos_vertex_properties(self):
return self.sentence_graph.vertex_properties[PART_OF_SPEECH_KEY]
def get_color_vertex_properties(self):
return self.sentence_graph.vertex_properties[VERTEX_COLOR_KEY]
def get_sentence_edge_properties(self):
return self.sentence_graph.edge_properties[SENTENCE_EDGE_KEY]
def get_definition_edge_properties(self):
return self.sentence_graph.edge_properties[DEFINITION_EDGE_KEY]
def get_inter_sentence_edge_properties(self):
return self.sentence_graph.edge_properties[INTER_SENTENCE_EDGE_KEY]
def get_color_edge_properties(self):
return self.sentence_graph.edge_properties[EDGE_COLOR_KEY]
def get_vertex_index(self, vertex):
return self.sentence_graph.vertex_index[vertex]
def get_degree_properties(self, degree_type):
return self.sentence_graph.degree_property_map(degree_type)
def get_graph(self):
return self.sentence_graph
def copy(self):
return SentenceGraph(
sentence=self.sentence_graph.graph_properties[SENTENCE_KEY],
graph=self.sentence_graph.copy())
def plot_networks(stationary_state,
free_energies,
transition_matrix,
cvs,
save=True,
outputpath="/home/oliverfl/Pictures/network-%s.svg",
description="Transition Network",
simu_id=None):
"""
Do imports only when necessarry to avoid errors with GTK symbols backends
See the error message I get on https://stackoverflow.com/questions/19773190/graph-tool-pyside-gtk-2-x-and-gtk-3-x
"""
# import gtk
import graph_tool as gt
from graph_tool.all import graph_draw, Graph
# from pylab import * # for plotting
if len(stationary_state.shape) == 2:
stationary_state = np.mean(stationary_state, axis=0)
free_energy, boxplot_data = _fix_field_and_data(free_energies, 1000)
# Create graph and labels
graph = Graph(directed=True)
vertices = []
vertex_sizes = graph.new_vertex_property("float")
vertex_labels = graph.new_vertex_property("string")
edge_sizes = graph.new_edge_property("float")
edge_labels = graph.new_edge_property("string")
cluster_to_label = {
0: "M", # "#"Intermediate",
1: "I", # "Inactive",
2: "A" # "Active"
}
#####CREATE VERTICES###########
for i, rho in enumerate(stationary_state):
# print(rho)
v = graph.add_vertex()
vertices.append(v)
vsize = (1 + 1 * rho / max(stationary_state)
) * 50 # np.log(1 + rho / max(stationary_state)) * 100
vertex_sizes[v] = vsize
# Beware that long tables can make the nodes expand to fit the text (and thus override vsize)
vertex_labels[v] = cluster_to_label.get(i) + " ({0:0.01f})".format(
free_energy[i])
####CREATE EDGES##########
max_transition_value = (
transition_matrix - np.diag(transition_matrix.diagonal())
).max() # Max value of matrix excluding diagonal elements
for i, row in enumerate(transition_matrix):
total_traj_count = sum(row)
for j, rhoij in enumerate(row):
if rhoij > 0 and i != j:
e = graph.add_edge(
vertices[i], vertices[j]
) # , weight=rhoij, label="{}->{}".format(i, j))
edge_labels[e] = "{}/{}".format(int(rhoij),
int(total_traj_count))
# The edge width is proportional to the relative number of transition from this starting state
size = (1 + 10 * rhoij / total_traj_count) * 3
edge_sizes[e] = size
# Using matplotlib for rendering if save==False
plt.figure(figsize=(10, 10))
graph_draw(
graph,
pos=gt.draw.sfdp_layout(graph),
# output_size=(400, 400),
output=outputpath % simu_id if save else None,
# inline=True,
mplfig=plt.gcf() if not save else None,
vertex_text=vertex_labels,
vertex_font_size=10,
vertex_size=vertex_sizes,
edge_text=edge_labels,
edge_pen_width=edge_sizes)
if not save:
plt.title(description)
plt.xticks([], [])
plt.yticks([], [])
plt.show()
def build_region_closure(g, root, regions, infection_times, obs_nodes, debug=False):
"""return a closure graph on the the components"""
regions = copy(regions)
root_region = {'nodes': {root}, 'head': root, 'head_time': -float('inf')}
regions[len(regions)] = root_region
gc = Graph(directed=True)
for _ in range(len(regions)):
gc.add_vertex()
# connect each region
gc_edges = []
original_edge_info = {}
for i, j in combinations(regions, 2):
# make group i the one with *later* head
if regions[i]['head_time'] < regions[j]['head_time']:
i, j = j, i
if debug:
print('i, j={}, {}'.format(i, j))
# only need to connect head i to one of the nodes in group j
# where nodes in j have time stamp < head i
# then an edge from region j to region i (because j is earlier)
head_i = regions[i]['head']
def get_pseudo_time(n):
if n == root:
return - float('inf')
else:
return infection_times[n]
targets = [n for n in regions[j]['nodes'] if get_pseudo_time(n) < regions[i]['head_time']]
if debug:
print('head_i: {}'.format(head_i))
print('targets: {}'.format(targets))
print('regions[j]["nodes"]: {}'.format(regions[j]['nodes']))
if len(targets) == 0:
continue
visitor = init_visitor(g, head_i)
forbidden_nodes = list(set(regions[i]['nodes']) | (set(regions[j]['nodes']) - set(targets)))
if debug:
print('forbidden_nodes: {}'.format(forbidden_nodes))
# NOTE: count_threshold = 1
cpbfs_search(g, source=head_i,
terminals=targets,
forbidden_nodes=forbidden_nodes,
visitor=visitor,
count_threshold=1)
reachable_targets = [t for t in targets if visitor.dist[t] > 0]
if debug:
print('reachable_targets: {}'.format(reachable_targets))
if len(reachable_targets) == 0:
# cannot reach there
continue
source = min(reachable_targets, key=visitor.dist.__getitem__)
dist = visitor.dist[source]
assert dist > 0
gc_edges.append(((j, i, dist)))
original_edge_info[(j, i)] = {
'dist': dist,
'pred': visitor.pred,
'original_edge': (source, head_i)
}
for u, v, _ in gc_edges:
gc.add_edge(u, v)
eweight = gc.new_edge_property('int')
for u, v, c in gc_edges:
eweight[gc.edge(gc.vertex(u), gc.vertex(v))] = c
return gc, eweight, original_edge_info
filename = 'buf.txt'
print(filename)
coefficient = 3
word_dict = {}
add_dict = {}
f = open('bad.txt', 'r', encoding="utf-8")
for s in f:
# print(s.split(' ')[0])
add_dict[s.split(' ')[0]] = 1
f = open(filename, 'r', encoding="utf-8")
pairs_graph = Graph(directed=False)
edge_weights = pairs_graph.new_edge_property("int")
ver_names = pairs_graph.new_vertex_property("string")
for line in f:
spl_line = line.split(' ')
if len(spl_line) == 1:
continue
pos = int(spl_line[0])
neg = int(spl_line[1])
cur_weight = pos + coefficient * neg
w1 = spl_line[2].strip(' \n\uefef')
w2 = spl_line[3].strip(' \n\uefef')
if w1 in add_dict or w2 in add_dict:
plt.plot(range(a + 1, b + 1), ver_attr[(a + 1):(b + 1)], 'bo')
plt.plot(range(b + 1, len(ver_attr)), ver_attr[(b + 1):], 'ro')
print("-------------------------------------------------")
filename = '../pairparser/results/en_pairs(7).txt'
ftag = '7_3imp'
coefficient = 3
word_dict = {} # dict with indexes of nodes by word
f = open(filename, 'r', encoding="utf-8")
pairs_graph = Graph(directed=False)
edge_weights = pairs_graph.new_edge_property("double")
ver_names = pairs_graph.new_vertex_property("string")
ver_id = pairs_graph.new_vertex_property("int")
for line in f:
spl_line = line.split(' ')
if len(spl_line) == 1:
continue
pos = int(spl_line[0])
neg = int(spl_line[1])
cur_weight = pos + coefficient * neg
w1 = spl_line[2].strip(' \n\uefef')
w2 = spl_line[3].strip(' \n\uefef')
class ResourceGraph(object):
v3_color = '#1C366B'
v2_color = '#1DACE8'
special_color = '#C4CFD0'
def __init__(self):
self.graph = Graph()
self.v_names = self.graph.new_vertex_property("string")
self.v_colors = self.graph.new_vertex_property("string")
self.e_names = self.graph.new_edge_property("string")
self.vertices = {}
self.edges = {}
def add_resource(self, resource_name, is_v3=True, is_special=False):
vertex = self.graph.add_vertex()
self.vertices[resource_name] = vertex
self.v_names[vertex] = resource_name
self.v_colors[vertex] = self.get_color(is_v3, is_special)
def get_color(self, is_v3, is_special):
if is_special:
return self.__class__.special_color
elif is_v3:
return self.__class__.v3_color
else:
return self.__class__.v2_color
def has_resource(self, resource_name):
return resource_name in self.vertices
def add_link(self, source_name, destination_name, name):
source = self.vertices[source_name]
destination = self.vertices[destination_name]
edge = self.graph.add_edge(source, destination)
edge_id = self.edge_id(source_name, destination_name, name)
self.edges[edge_id] = edge
self.e_names[edge] = name
def has_link(self, source_name, destination_name, name):
return self.edge_id(source_name, destination_name, name) in self.edges
def edge_id(self, source_name, destination_name, name):
return f'{name} -- {source_name} -- {destination_name}'
def draw(self):
graph_draw(
self.graph,
pos=radial_tree_layout(self.graph, self.graph.vertex(0)),
vertex_text=self.v_names,
vertex_fill_color=self.v_colors,
edge_text=self.e_names,
output_size=(2000, 1300),
fit_view=True,
vertex_font_size=10,
vertex_pen_width=1,
vertex_halo=False,
edge_pen_width=3,
)
def phylomemetic_graph(steps,
communities,
min_size=3,
max_size=50,
parent_limit=2,
workers='auto',
chunksize='auto',
method='fast',
min_backwards_containment=0,
min_forward_containment=0):
'''phylomemetic_graph
Parameters
----------
steps : :obj:`iter` of :obj:`int`
communities : :obj:`iter` of :obj:`iter` of :obj:`int`
min_size : :obj:`int`
max_size : :obj:`int`
parent_limit : :obj:`int`
workers : :obj:`int`
chunksize : :obj:`int`
method : :obj:`str`
min_backwards_containment : :obj:`float`
min_forward_containment : :obj:`float`
Returns
-------
g : :obj:`graph_too.Graph`
group_link_strength : :obj:`graph_tool.EdgePropertyMap`
single_link_strength : :obj:`graph_tool.EdgePropertyMap`
vertex_steps : :obj:`graph_tool.VertexPropertyMap`
element_vertex_map : :obj:`dict`
'''
if workers == 'auto':
workers = cpu_count() - 1
communities_filt = []
communities_lengths = []
element_community_mappings = []
for sequences in communities:
s_filt = list(filter_by_size(sequences, min_size, max_size))
communities_filt.append(s_filt)
communities_lengths.append(len(s_filt))
element_community_mappings.append(reverse_index(s_filt))
community_vertex_maps = []
communities_offsets = []
cumsum_lengths = np.cumsum(communities_lengths)
for length, count in zip(communities_lengths, cumsum_lengths):
start = count - length
end = count
communities_offsets.append((start, end))
community_vertex_maps.append(
{c: v
for c, v in zip(range(length), range(start, end))})
n_communities = np.sum(communities_lengths)
phylomemetic_links = []
for i, (cps, cfs) in enumerate(window(communities_filt, 2)):
n_cf = len(cfs)
logger.info(f'Processing {i+1} of {len(communities)-1} periods')
if chunksize == 'auto':
chunksize_i = int(np.ceil((1 / workers) * n_cf))
else:
chunksize_i = chunksize
with Pool(workers) as pool:
phylomemetic_links.append(
pool.map(
find_links,
zip(
cfs,
range(0, len(cfs)),
repeat(cps, n_cf),
repeat(communities_offsets[i], n_cf),
repeat(element_community_mappings[i], n_cf),
repeat(parent_limit, n_cf),
),
chunksize=chunksize_i,
))
pool.close()
pool.join()
g = Graph(directed=True)
g.add_vertex(n_communities)
group_link_strength = g.new_edge_property('float')
single_link_strength = g.new_edge_property('float')
phylomemetic_links = flatten(flatten(phylomemetic_links))
g.add_edge_list(phylomemetic_links,
eprops=[group_link_strength, single_link_strength])
element_vertex_map = reverse_index_communities(flatten(communities_filt))
vertex_steps = g.new_vertex_property('int')
for (start, end), step in zip(communities_offsets, steps):
vertex_steps.a[start:end] = step
return (g, group_link_strength, single_link_strength, vertex_steps,
element_vertex_map)
def gen_graph((repo, events)):
graph = Graph()
repo_on_graph = graph.new_graph_property('string')
repo_on_graph[graph] = repo
graph.graph_properties['repo_on_graph'] = repo_on_graph
language_on_graph = graph.new_graph_property('string')
language_on_graph[graph] = events[0]['language']
graph.graph_properties['language_on_graph'] = language_on_graph
events_on_vertices = graph.new_vertex_property('object')
graph.vertex_properties['events_on_vertices'] = events_on_vertices
actors_on_vertices = graph.new_vertex_property('string')
graph.vertex_properties['actors_on_vertices'] = actors_on_vertices
weights_on_edges = graph.new_edge_property('long double')
graph.edge_properties['weights_on_edges'] = weights_on_edges
# pre_vertices = []
pre_events_map = {}
pre_vertices_map = {}
# owner_vertex = graph.add_vertex()
# owner = repo.split('/')[0]
# actors_on_vertices[owner_vertex] = owner
# pre_vertices_map[owner] = owner_vertex
events = sorted(events, key=lambda x: x['created_at'])
for event in events:
actor = event['actor']
if actor in pre_events_map:
continue
created_at = event['created_at']
vertex = graph.add_vertex()
events_on_vertices[vertex] = event
actors_on_vertices[vertex] = actor
if 'actor-following' not in event:
continue
following = set(event['actor-following'])
commons = following.intersection(pre_vertices_map.keys())
# pre_vertices.append(vertex)
# if len(commons) == 0:
# edge = graph.add_edge(vertex, owner_vertex)
# weights_on_edges[edge] = 1.0
for pre_actor in commons:
edge = graph.add_edge(vertex, pre_vertices_map[pre_actor])
interval =\
(created_at - pre_events_map[pre_actor]['created_at']).days
weight = 1.0 / fib(interval + 2)
weights_on_edges[edge] = weight
pre_events_map[actor] = event
pre_vertices_map[actor] = vertex
return graph
def gen_graph((repo, events)):
graph = Graph()
repo_on_graph = graph.new_graph_property('string')
repo_on_graph[graph] = repo
graph.graph_properties['repo_on_graph'] = repo_on_graph
language_on_graph = graph.new_graph_property('string')
language_on_graph[graph] = events[0]['language']
graph.graph_properties['language_on_graph'] = language_on_graph
events_on_vertices = graph.new_vertex_property('object')
graph.vertex_properties['events_on_vertices'] = events_on_vertices
actors_on_vertices = graph.new_vertex_property('string')
graph.vertex_properties['actors_on_vertices'] = actors_on_vertices
weights_on_edges = graph.new_edge_property('long double')
graph.edge_properties['weights_on_edges'] = weights_on_edges
# pre_vertices = []
pre_events_map = {}
pre_vertices_map = {}
owner_vertex = graph.add_vertex()
owner = repo.split('/')[0]
dummy_event = {'created_at': events[0]['repo-created_at']}
actors_on_vertices[owner_vertex] = owner
events_on_vertices[owner_vertex] = dummy_event
pre_vertices_map[owner] = owner_vertex
pre_events_map[owner] = dummy_event
events = sorted(events, key=lambda x: x['created_at'])
for event in events:
actor = event['actor']
if actor in pre_events_map:
continue
created_at = event['created_at']
vertex = graph.add_vertex()
events_on_vertices[vertex] = event
actors_on_vertices[vertex] = actor
# if 'actor-following' not in event:
# continue
following = set(event['actor-following'])
commons = following.intersection(pre_vertices_map.keys())
# pre_vertices.append(vertex)
# if len(commons) == 0:
# edge = graph.add_edge(vertex, owner_vertex)
# weights_on_edges[edge] = 1.0
for pre_actor in commons:
interval =\
(created_at - pre_events_map[pre_actor]['created_at']).days
if interval < 0:
continue
edge = graph.add_edge(vertex, pre_vertices_map[pre_actor])
if pre_actor == owner:
weight = 1.0
else:
weight = 1.0 / fib(interval + 2)
weights_on_edges[edge] = weight
pre_events_map[actor] = event
pre_vertices_map[actor] = vertex
return graph
def load_instance(filepath):
g = Graph(directed=False)
node_upgraded = g.new_vertex_property("bool")
node_cost = g.new_vertex_property("float")
edge_weight = g.new_edge_property("float")
edge_weight_lv2 = g.new_edge_property("float")
edge_weight_lv3 = g.new_edge_property("float")
edge_upgradeable_weights = g.new_edge_property("vector<float>")
graph_total_cost = g.new_graph_property("float")
graph_total_cost[g] = 0
with open(filepath, "r") as f:
first_line = f.readline()
tokens = first_line.split(" ")
assert len(tokens) == 2
n = int(tokens[0])
m = int(tokens[1])
g.add_vertex(n)
#print (n, m)
for _ in range(m):
line = f.readline()
tokens = line.split(" ")
assert len(tokens) == 5
v1 = int(tokens[0])
v2 = int(tokens[1])
e = g.add_edge(v1, v2)
w1 = float(tokens[2])
w2 = float(tokens[3])
w3 = float(tokens[4])
edge_weight[e] = w1
edge_weight_lv2[e] = w2
edge_weight_lv3[e] = w3
# identify how weights are for vertices
line = f.readline()
v_cost = [float(x) for x in line.split(" ") if x != " "]
if len(v_cost) > 1: # case where formatting is incorrect
for i in range(n):
v = g.vertex(i)
node_cost[v] = v_cost[i]
node_upgraded[i] = False
else:
v = g.vertex(0)
node_cost[v] = float(v_cost[0])
node_upgraded[v] = False
vertices = g.vertices()
vertices.next()
for v in vertices:
line = f.readline()
c = float(line)
node_cost[v] = c
node_upgraded[v] = False
g.vp.is_upgraded = node_upgraded
g.vp.cost = node_cost
g.ep.weight = edge_weight
g.ep.weight_2 = edge_weight_lv2
g.ep.weight_3 = edge_weight_lv3
for v in g.vertices():
graph_total_cost[g] += node_cost[v]
g.gp.total_cost = graph_total_cost
return g
