Avoid deeply nested control flow statements.

Open

                        for pattern in final_patterns:
                            if patterns[outer_pattern_index] in pattern:
                                outer_add_to_pattern = False

                            if patterns[inner_pattern_index] in pattern:

Avoid deeply nested control flow statements.

Open

                        if end - length < 0:
                            break

Avoid deeply nested control flow statements.

Open

                        if ' '.join(pattern) not in patterns and ' '.join(pattern) not in base_sentence_info:
                            base_sentence_info += [str(' '.join(pattern))]

                            sentence_information[str(' '.join(pattern))] = ['', '', '', [], 2, 0]
                        else:

Avoid deeply nested control flow statements.

Open

                        if end - length < 0:
                            break

Avoid deeply nested control flow statements.

Open

                        if ' '.join(pattern) in str(test_string) and pattern != '' and pattern != []:
                            if ' '.join(pattern) not in patterns and ' '.join(pattern) not in base_sentence_info:
                                base_sentence_info += [str(' '.join(pattern))]

                                sentence_information[str(' '.join(pattern))] = ['', '', '', [], 2, 0]

Avoid deeply nested control flow statements.

Open

                        if inner_add_to_pattern == True and outer_add_to_pattern == True:
                            if len( patterns[outer_pattern_index]) > len(patterns[inner_pattern_index] ):
                                final_patterns.append(patterns[outer_pattern_index])

                                try:

Function find_patterns has 5 arguments (exceeds 4 allowed). Consider refactoring.

Open

    def find_patterns(self, strings, current_index, literal_find, patterns_arg, parser_name):

Function find_previous_instance has 5 arguments (exceeds 4 allowed). Consider refactoring.

Open

    def find_previous_instance(self, instance_to_find, string_containing_instance, current_index, objects_to_fail, updated_object):

Function get_reliability_score has a Cognitive Complexity of 7 (exceeds 5 allowed). Consider refactoring.

Open

    def get_reliability_score(self, pattern):
        """
        Used to return the reliability score of a pattern.
        """

• Read up
  Read up

Avoid too many return statements within this function.

Open

                return self.return_pattern_dependencies(text=kwargs.get("text"))

Avoid too many return statements within this function.

Open

                return self.return_nltk_dependencies(text=kwargs.get("text"))

Avoid too many return statements within this function.

Open

                return self.return_pattern_dependencies(text=kwargs.get("text"))

Avoid too many return statements within this function.

Open

                    return self.return_pattern_dependencies(text=kwargs.get("text"))

Avoid too many return statements within this function.

Open

                    return self.return_pattern_dependencies(text=kwargs.get("text"))

Function process_topics has a Cognitive Complexity of 6 (exceeds 5 allowed). Consider refactoring.

Open

    def process_topics(self, strings, correct_topics):
        """
        process_topics finds the topics of the given strings and returns the topic score.
        """

• Read up
  Read up

Function normalize_text has a Cognitive Complexity of 6 (exceeds 5 allowed). Consider refactoring.

Open

    def normalize_text(self, text):
        """
        Returns the normalized version
        of the passed text variable.
        """

• Read up
  Read up

Function process_raw_data_literal has a Cognitive Complexity of 6 (exceeds 5 allowed). Consider refactoring.

Open

    def process_raw_data_literal(self, parser_name, strings, correct_patterns):
        """
        This function processes the raw data and turns it into useful information.
        That data provides useful statistics about the library's current "health".
        The function returns the overall score for the data, and specific statistics of the data.

• Read up
  Read up