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94 lines
2.9 KiB
94 lines
2.9 KiB
from __future__ import unicode_literals
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from datetime import datetime
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from .logic_adapter import LogicAdapter
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class TimeLogicAdapter(LogicAdapter):
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"""
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The TimeLogicAdapter returns the current time.
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:kwargs:
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* *positive* (``list``) --
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The time-related questions used to identify time questions.
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Defaults to a list of English sentences.
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* *negative* (``list``) --
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The non-time-related questions used to identify time questions.
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Defaults to a list of English sentences.
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"""
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def __init__(self, **kwargs):
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super(TimeLogicAdapter, self).__init__(**kwargs)
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from nltk import NaiveBayesClassifier
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self.positive = kwargs.get('positive', [
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'what time is it',
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'hey what time is it',
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'do you have the time',
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'do you know the time',
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'do you know what time it is',
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'what is the time'
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])
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self.negative = kwargs.get('negative', [
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'it is time to go to sleep',
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'what is your favorite color',
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'i had a great time',
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'thyme is my favorite herb',
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'do you have time to look at my essay',
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'how do you have the time to do all this'
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'what is it'
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])
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labeled_data = (
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[(name, 0) for name in self.negative] +
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[(name, 1) for name in self.positive]
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)
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train_set = [
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(self.time_question_features(text), n) for (text, n) in labeled_data
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]
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self.classifier = NaiveBayesClassifier.train(train_set)
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def time_question_features(self, text):
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"""
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Provide an analysis of significant features in the string.
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"""
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features = {}
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# A list of all words from the known sentences
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all_words = " ".join(self.positive + self.negative).split()
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# A list of the first word in each of the known sentence
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all_first_words = []
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for sentence in self.positive + self.negative:
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all_first_words.append(
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sentence.split(' ', 1)[0]
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)
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for word in text.split():
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features['first_word({})'.format(word)] = (word in all_first_words)
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for word in text.split():
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features['contains({})'.format(word)] = (word in all_words)
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for letter in 'abcdefghijklmnopqrstuvwxyz':
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features['count({})'.format(letter)] = text.lower().count(letter)
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features['has({})'.format(letter)] = (letter in text.lower())
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return features
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def process(self, statement):
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from chatterbot.conversation import Statement
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now = datetime.now()
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time_features = self.time_question_features(statement.text.lower())
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confidence = self.classifier.classify(time_features)
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response = Statement('The current time is ' + now.strftime('%I:%M %p'))
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response.confidence = confidence
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return response
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