def test_get_tree_without_filename(self, output):
configurator = Configurator(self.args)
tree = configurator.get_tree()
output_msg = output.getvalue()
self.assertEqual(
output_msg, "The bk-tree is building...\n"
"The bk-tree built successfully\n")
self.assertEqual(
tree.tree,
BKTree(distance, configurator.get_frequency_words()).tree)
def test_get_tree_correct_load(self, output):
configurator = Configurator(self.args_tree)
tree1 = bk.BuildBKTree.build_tree(["teststring"])
with open(self.tree_file, 'wb') as file:
pickle.dump(tree1, file)
tree2 = configurator.get_tree()
output = output.getvalue()
self.assertEqual(
output, f"The bk-tree is loading from {self.tree_file}...\n"
"The bk-tree loaded successfully\n")
self.assertEqual(tree1.tree, tree2.tree)
self.assertNotEqual(BKTree(distance, ['abcdrasdsf']).tree, tree1.tree)
def test_get_tree_without_existing_file_correct_tree(self, output):
configurator = Configurator(self.args_tree)
tree = configurator.get_tree()
output_msg = output.getvalue()
self.assertEqual(
output_msg, "The bk-tree is building...\n"
"The bk-tree built successfully\n"
f"The bk-tree is saving to {self.tree_file}...\n"
"The bk-tree saved successfully\n")
self.assertTrue(os.path.isfile(self.tree_file))
self.assertEqual(
BKTree(distance, configurator.get_frequency_words()).tree,
tree.tree)
self.assertNotEqual(BKTree(distance, ['abcdrasdsf']).tree, tree.tree)
def test_get_tree_with_existing_empty_file(self, output, err):
configurator = Configurator(self.args_tree)
file = open(self.tree_file, 'w').close()
tree = configurator.get_tree()
output = output.getvalue()
err = err.getvalue()
self.assertEqual(
output, f"The bk-tree is loading from {self.tree_file}...\n"
"The bk-tree is building...\n"
"The bk-tree built successfully\n")
self.assertEqual(
err,
"Error: The bk-tree file you specified is empty and can't be loaded\n"
)
self.assertEqual(
BKTree(distance, configurator.get_frequency_words()).tree,
tree.tree)
self.assertNotEqual(BKTree(distance, ['abcdrasdsf']).tree, tree.tree)
def test_get_tree_with_existing_wrong_pickling_file(self, output, err):
configurator = Configurator(self.args_tree)
with open(self.tree_file, 'wb') as file:
pickle.dump("teststring", file)
tree = configurator.get_tree()
output = output.getvalue()
err = err.getvalue()
self.assertEqual(
output, f"The bk-tree is loading from {self.tree_file}...\n"
"The bk-tree is building...\n"
"The bk-tree built successfully\n")
self.assertEqual(
err,
"Error: You're trying to load from the file not a BK-tree object\n"
)
self.assertEqual(
BKTree(distance, configurator.get_frequency_words()).tree,
tree.tree)
self.assertNotEqual(BKTree(distance, ['abcdrasdsf']).tree, tree.tree)
def test_get_tree_with_existing_not_unpickling_file(self, output, err):
configurator = Configurator(self.args_tree)
with open(self.tree_file, 'w') as file:
file.write("teststring")
tree = configurator.get_tree()
output = output.getvalue()
err = err.getvalue()
self.assertEqual(
output, f"The bk-tree is loading from {self.tree_file}...\n"
"The bk-tree is building...\n"
"The bk-tree built successfully\n")
self.assertEqual(
err,
"Error: This file of the bk-tree structure doesn't contain any bk-tree structure actually\n"
)
self.assertEqual(
BKTree(distance, configurator.get_frequency_words()).tree,
tree.tree)
self.assertNotEqual(BKTree(distance, ['abcdrasdsf']).tree, tree.tree)
def build(configurator: cfg.Configurator):
analyzer = WordAnalyzer()
analyzer.words = configurator.get_words(verbose=True)
analyzer.frequency_words = configurator.get_frequency_words(
verbose=True)
analyzer.splitter = TextSplitter(analyzer.frequency_words)
analyzer.tree = configurator.get_tree()
analyzer.mode = configurator.get_mode()
analyzer.verbose = configurator.get_verbose()
# This flag specifies that's need add extra information with a word when analyzing words
analyzer.verbose_file = False
# If the word isn't in the dictionary, then its cost is default_cost
analyzer.default_cost = 1000
# How many similar words will be returned by get_similar_words method
analyzer.number_similar_words = configurator.get_configuration_values(
)['similar_words']
# What distance will be used to search for similar words
analyzer.distance = configurator.get_configuration_values()['distance']
# How many parts will be spliced in the get_correct_words method
analyzer.threshold = configurator.get_configuration_values(
)['threshold']
# How many words will be returned by get_correct_words method
analyzer.number_of_corrected_words = configurator.get_configuration_values(
)['number_corrected']
analyzer.max_len = max(len(x) for x in analyzer.words)
# Define dictionary with values like (number: list_of_divisors).
# There are defined all numbers from 1 to max length of all words.
# It's necessary to improve efficiency, because divisors won't calculated again for same number
analyzer.divisors = dict((number, factorize(number))
for number in range(1, analyzer.max_len + 1))
return analyzer