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 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
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
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())
dists[v] = 0
for w in v.out_neighbours():
if part[w] == pos:
dists[v] += edge_weights[pairs_graph.edge(v, w)]
else:
dists[v] -= edge_weights[pairs_graph.edge(v, w)]
if part[v] == pos and dists[v] < 0:
bad_vertices[v] = dists[v]
elif part[v] == neg and dists[v] > 0:
bad_vertices[v] = -dists[v]
else:
bad_vertices[v] = 0
worst_vertex = min(bad_vertices, key=bad_vertices.get)
cut_value = 0
for e in pairs_graph.edges():
if part[e.source()] != part[e.target()]:
cut_value += edge_weights[e]
print("New cut value = " + str(cut_value))
f1 = open('new4_first_part.txt', 'w', encoding="utf-8")
f2 = open('new4_sec_part.txt', 'w', encoding="utf-8")
count1 = 0
count2 = 0
for key in word_dict.keys():
# print(key)
# print(part[pairs_graph.vertex(word_dict[key])])
v = pairs_graph.vertex(word_dict[key])
if largest_label[v] == 1: # if only in largest component
dists[v] = 0
for w in v.out_neighbours():
if part[w] == pos:
dists[v] += edge_weights[pairs_graph.edge(v, w)]
else:
dists[v] -= edge_weights[pairs_graph.edge(v, w)]
if part[v] == pos and dists[v] < 0:
bad_vertices[v] = dists[v]
elif part[v] == neg and dists[v] > 0:
bad_vertices[v] = -dists[v]
else:
bad_vertices[v] = 0
worst_vertex = min(bad_vertices, key=bad_vertices.get)
cut_value = 0
for e in pairs_graph.edges():
if part[e.source()] != part[e.target()]:
cut_value += edge_weights[e]
print("New cut value = " + str(cut_value))
f1 = open('new4_first_part.txt', 'w', encoding="utf-8")
f2 = open('new4_sec_part.txt', 'w', encoding="utf-8")
count1 = 0
count2 = 0
for key in word_dict.keys():
# print(key)
# print(part[pairs_graph.vertex(word_dict[key])])
v = pairs_graph.vertex(word_dict[key])
if largest_label[v] == 1: # if only in largest component