def get_results(self):
for f in self.parent.upper_frame.right_frame.list_files:
structure = read_file(join("Data/all", f), f in listFiles)
if self.parent.upper_frame.left_frame.algo.get() == 0:
preferences = structure["preferences"]
if self.parent.upper_frame.left_frame.filtered.get():
preferences, nb_voters, nb_unique = remove_last_ballots(
preferences)
candidates = structure["candidates"]
ensemble, best = bnb(len(preferences), preferences, candidates)
axes, card = find_axes2(best[0][0], candidates)
axes = filter_symmetric_axes(axes)
for axis in axes:
self.results.append((f, axis))
else:
if self.parent.upper_frame.left_frame.dissimilarity.get() == 0:
dissimilarity_function = dissimilarity_and_n
elif self.parent.upper_frame.left_frame.dissimilarity.get(
) == 1:
dissimilarity_function = dissimilarity_and_or
else:
dissimilarity_function = dissimilarity_over_over
if f in listFiles:
t, permutations = find_axis_from_structure(
structure, dissimilarity_function,
self.parent.upper_frame.left_frame.weighted.get())
else:
t, permutations = find_axis_from_structure(
structure,
dissimilarity_function,
self.parent.upper_frame.left_frame.weighted.get(),
unwanted_candidates=[2, 3, 7, 11])
permutations = filter_symmetric_axes(permutations[1])
for permutation in permutations:
self.results.append((f, permutation))
def get_results(self):
for f in self.parent.upper_frame.right_frame.list_files:
structure = read_file(join("Data/all", f), f in listFiles)
if self.parent.upper_frame.left_frame.algo.get() == 0:
preferences = structure["preferences"]
if self.parent.upper_frame.left_frame.filtered.get():
preferences, nb_voters, nb_unique = remove_last_ballots(preferences)
candidates = structure["candidates"]
ensemble, best = bnb(len(preferences), preferences, candidates)
axes, card = find_axes2(best[0][0], candidates)
axes = filter_symmetric_axes(axes)
for axis in axes:
self.results.append((f, axis))
else:
if self.parent.upper_frame.left_frame.dissimilarity.get() == 0:
dissimilarity_function = dissimilarity_and_n
elif self.parent.upper_frame.left_frame.dissimilarity.get() == 1:
dissimilarity_function = dissimilarity_and_or
else:
dissimilarity_function = dissimilarity_over_over
if f in listFiles:
t, permutations = find_axis_from_structure(structure, dissimilarity_function,
self.parent.upper_frame.left_frame.weighted.get())
else:
t, permutations = find_axis_from_structure(structure, dissimilarity_function,
self.parent.upper_frame.left_frame.weighted.get(),
unwanted_candidates=[2, 3, 7, 11])
permutations = filter_symmetric_axes(permutations[1])
for permutation in permutations:
self.results.append((f, permutation))
def calculate_bnb(self, file, vrow, filtered):
structure = read_file("Data/all/" + str(file), file in listFiles)
if not filtered:
preferences = structure["preferences"]
candidates = structure["candidates"]
Label(self, text=str(structure["nb_voters"])).grid(row=vrow, column=3)
Label(self, text=str(structure["nb_unique_orders"])).grid(row=vrow,
column=5)
else:
preferences_bis = structure["preferences"]
preferences, nb_voters, nb_unique = remove_last_ballots(
preferences_bis)
candidates = structure["candidates"]
Label(self, text=str(nb_voters)).grid(row=vrow, column=3)
Label(self, text=str(nb_unique)).grid(row=vrow, column=5)
t1 = time()
ensemble, best = bnb(len(preferences), preferences, candidates)
t2 = time()
Label(self, text=str(best[1])).grid(row=vrow, column=7)
Label(self, text=str(len(best[0][0]))).grid(row=vrow, column=9)
if not filtered:
Label(self, text=str(best[1] * 100.0 / structure["nb_voters"])).grid(
row=vrow, column=11)
else:
Label(self, text=str(best[1] * 100.0 / nb_voters)).grid(row=vrow,
column=11)
axes, card = find_axes2(best[0][0], candidates)
Label(self, text=str(len(axes))).grid(row=vrow, column=13)
Label(self, text=str(t2 - t1)).grid(row=vrow, column=15)
def calculate_bnb(self, file, vrow, filtered):
structure = read_file("Data/all/" + str(file), file in listFiles)
if not filtered:
preferences = structure["preferences"]
candidates = structure["candidates"]
Label(self, text=str(structure["nb_voters"])).grid(row=vrow, column=3)
Label(self, text=str(structure["nb_unique_orders"])).grid(row=vrow, column=5)
else:
preferences_bis = structure["preferences"]
preferences, nb_voters, nb_unique = remove_last_ballots(preferences_bis)
candidates = structure["candidates"]
Label(self, text=str(nb_voters)).grid(row=vrow, column=3)
Label(self, text=str(nb_unique)).grid(row=vrow, column=5)
t1 = time()
ensemble, best = bnb(len(preferences), preferences, candidates)
t2 = time()
Label(self, text=str(best[1])).grid(row=vrow, column=7)
Label(self, text=str(len(best[0][0]))).grid(row=vrow, column=9)
if not filtered:
Label(self, text=str(best[1] * 100.0 / structure["nb_voters"])).grid(row=vrow, column=11)
else:
Label(self, text=str(best[1] * 100.0 / nb_voters)).grid(row=vrow, column=11)
axes, card = find_axes2(best[0][0], candidates)
Label(self, text=str(len(axes))).grid(row=vrow, column=13)
Label(self, text=str(t2 - t1)).grid(row=vrow, column=15)
def axes_to_latex_graph_bnb(f, axis, name=None):
"""
Returns the LaTeX code to display the axis
:param structure: structure extracted from a file or directory
:param axis: axis corresponding to the file, according to wikipedia
:param name: name that will be given to the figures
:param dissimilarity_function: function to use to calculate dissimilarity between 2 candidates
:param weighted: if True, matrices scores are calculated with the weighted gradient
:param unwanted_candidates: list of candidates to exclude from the search
:return: string containing the LaTeX code
"""
structure = read_file(f)
preferences = structure["preferences"]
candidates = [i + 1 for i in range(len(structure["candidates"]))]
bb, best = bnb(len(preferences), preferences, candidates)
permutations, card = find_axes2(best[0][0], candidates)
matches = get_matches(axis)
length_unit = str(len(permutations[0]) + 1)
res = "\\section{" + str(name) + "}" if name else ""
res += "\\begin{center}\n"
for permutation in permutations:
# beginning of the environment
res += "\\begin{figure}\n"
res += "\\begin{tikzpicture}[x=\\textwidth/" + length_unit + ", y=\\textwidth/" + length_unit + "]\n"
# Adding the x and y axes
res += "\\draw[->, >=latex] (0,0) -- (" + length_unit + ",0);\n"
res += "\\draw[->, >=latex] (0,0) -- (0, 5.5);\n"
#res += "\\draw[->, >=latex] (0,0) -- (" + length_unit + ",0);\n"
#res += "\\draw[->, >=latex] (0,0) -- (0," + length_unit + ");\n"
# Adding the legend below the x axis
for i in range(len(permutation)):
res += "\\draw (" + str(i + 1) + ",-0.1) node[below] {" + str(
permutation[i]) + "};\n"
res += "\\draw plot[ultra thick]coordinates{"
temp = "" # string for the circles marking the dots
for i in range(len(permutation)):
if permutation[i] in matches:
res += "(" + str(i + 1) + "," + str(
matches[permutation[i]]) + ")"
temp += "\\draw[fill=black] (" + str(i + 1) + "," + str(
matches[permutation[i]]) + ") circle (0.1);\n"
res += "};\n"
res += temp # ading the circles
res += "\\end{tikzpicture}\n"
if name:
res += "\\caption{Permutation " + str(
permutations.index(permutation)) + " of " + name + "}\n"
res += "\\end{figure}\n"
res += "\\end{center}\n\\clearpage\n"
return res
def axes_to_latex_graph_bnb(f, axis, name=None):
"""
Returns the LaTeX code to display the axis
:param structure: structure extracted from a file or directory
:param axis: axis corresponding to the file, according to wikipedia
:param name: name that will be given to the figures
:param dissimilarity_function: function to use to calculate dissimilarity between 2 candidates
:param weighted: if True, matrices scores are calculated with the weighted gradient
:param unwanted_candidates: list of candidates to exclude from the search
:return: string containing the LaTeX code
"""
structure = read_file(f)
preferences = structure["preferences"]
candidates = [i+1 for i in range(len(structure["candidates"]))]
bb, best = bnb(len(preferences), preferences, candidates)
permutations, card = find_axes2(best[0][0], candidates)
matches = get_matches(axis)
length_unit = str(len(permutations[0]) + 1)
res = "\\section{" + str(name) + "}" if name else ""
res += "\\begin{center}\n"
for permutation in permutations:
# beginning of the environment
res += "\\begin{figure}\n"
res += "\\begin{tikzpicture}[x=\\textwidth/" + length_unit + ", y=\\textwidth/" + length_unit + "]\n"
# Adding the x and y axes
res += "\\draw[->, >=latex] (0,0) -- (" + length_unit + ",0);\n"
res += "\\draw[->, >=latex] (0,0) -- (0, 5.5);\n"
#res += "\\draw[->, >=latex] (0,0) -- (" + length_unit + ",0);\n"
#res += "\\draw[->, >=latex] (0,0) -- (0," + length_unit + ");\n"
# Adding the legend below the x axis
for i in range(len(permutation)):
res += "\\draw (" + str(i+1) + ",-0.1) node[below] {" + str(permutation[i]) + "};\n"
res += "\\draw plot[ultra thick]coordinates{"
temp = "" # string for the circles marking the dots
for i in range(len(permutation)):
if permutation[i] in matches:
res += "(" + str(i+1) + "," + str(matches[permutation[i]]) + ")"
temp += "\\draw[fill=black] (" + str(i+1) + "," + str(matches[permutation[i]]) + ") circle (0.1);\n"
res += "};\n"
res += temp # ading the circles
res += "\\end{tikzpicture}\n"
if name:
res += "\\caption{Permutation " + str(permutations.index(permutation)) + " of " + name + "}\n"
res += "\\end{figure}\n"
res += "\\end{center}\n\\clearpage\n"
return res