Source code for chatterbot.logic.time_adapter

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


[docs]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) from nltk import NaiveBayesClassifier 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