Source code for chatterbot.logic.time_adapter

from datetime import datetime
from chatterbot.logic import LogicAdapter
from chatterbot.conversation import Statement
from chatterbot.exceptions import OptionalDependencyImportError


class TimeLogicAdapter(LogicAdapter):
    """
    The TimeLogicAdapter returns the current time.

    :kwargs:
        * *positive* (``list``) --
          The time-related questions used to identify time questions.
          Defaults to a list of English sentences.
        * *negative* (``list``) --
          The non-time-related questions used to identify time questions.
          Defaults to a list of English sentences.
    """

    def __init__(self, chatbot, **kwargs):
        super().__init__(chatbot, **kwargs)
        try:
            from nltk import NaiveBayesClassifier
        except ImportError:
            message = (
                'Unable to import "nltk".\n'
                'Please install "nltk" before using the TimeLogicAdapter:\n'
                'pip3 install nltk'
            )
            raise OptionalDependencyImportError(message)

        self.positive = kwargs.get('positive', [
            'what time is it',
            'hey what time is it',
            'do you have the time',
            'do you know the time',
            'do you know what time it is',
            'what is the time'
        ])

        self.negative = kwargs.get('negative', [
            'it is time to go to sleep',
            'what is your favorite color',
            'i had a great time',
            'thyme is my favorite herb',
            'do you have time to look at my essay',
            'how do you have the time to do all this'
            'what is it'
        ])

        labeled_data = (
            [
                (name, 0) for name in self.negative
            ] + [
                (name, 1) for name in self.positive
            ]
        )

        train_set = [
            (self.time_question_features(text), n) for (text, n) in labeled_data
        ]

        self.classifier = NaiveBayesClassifier.train(train_set)

    def time_question_features(self, text):
        """
        Provide an analysis of significant features in the string.
        """
        features = {}

        # A list of all words from the known sentences
        all_words = " ".join(self.positive + self.negative).split()

        # A list of the first word in each of the known sentence
        all_first_words = []
        for sentence in self.positive + self.negative:
            all_first_words.append(
                sentence.split(' ', 1)[0]
            )

        for word in text.split():
            features['first_word({})'.format(word)] = (word in all_first_words)

        for word in text.split():
            features['contains({})'.format(word)] = (word in all_words)

        for letter in 'abcdefghijklmnopqrstuvwxyz':
            features['count({})'.format(letter)] = text.lower().count(letter)
            features['has({})'.format(letter)] = (letter in text.lower())

        return features

    def process(self, statement, additional_response_selection_parameters=None):
        now = datetime.now()

        time_features = self.time_question_features(statement.text.lower())
        confidence = self.classifier.classify(time_features)
        response = Statement(text='The current time is ' + now.strftime('%I:%M %p'))

        response.confidence = confidence
        return response