def __init__(self, intents=None, testing=False):
self.td = TopicProcessor()
self.cp = ConversationProcessor()
self.convX, self.convy = self.cp.get_dataset()
self.topX, self.topy = self.td.get_dataset()
self.topy = np_utils.to_categorical(self.topy)
self.top_classes = self.td.get_num_categories()
self.top_sentence_length = self.td.get_sentence_length()
self.graph = None
self.seq2seqmodel = seq2seqmodel()
if not testing:
# Try to load an existing model from file
try:
clear_session()
self.model, self.graph, self.sess = self._load_model()
except (FileNotFoundError, OSError):
# If the model doesn't exist, we'll have to build one
model = self._build_model()
self.model = self._train_model(model, self.convX, self.convy,
self.topX, self.topy)
self.model, self.graph, self.sess = self._load_model()
# If the user hasn't supplied a location, use the default
if not intents:
intents = 'chatbot/corpus/output.json'
with open(intents, 'r') as json_file:
self.intents = json.load(json_file)
else:
# self._test_model(self.convX, self.convy, self.topX, self.topy)
self._k_fold_test(self.convX, self.convy, self.topX, self.topy)
def test_get_sentence_length(self):
tp = TopicProcessor()
test_length = 4
tp.max_sentence_length = test_length
max_sentence_length = tp.get_sentence_length()
self.assertEqual(test_length, max_sentence_length)
def test__load_files(self):
tp = TopicProcessor()
statements, topics = tp._load_files()
if not len(statements):
self.fail()
def test_get_dataset(self):
tp = TopicProcessor()
X, y = tp.get_dataset()
if not X[0].size > 1:
self.fail()
def test_get_num_categories(self):
tp = TopicProcessor()
test_num = 4
tp.num_topics = test_num
num_topics = tp.get_num_categories()
self.assertEqual(test_num, num_topics)
def test_init(self):
tp = TopicProcessor()
def test_decode_topic(self):
tp = TopicProcessor()
original_topic = 15
decoded_topic = tp.decode_topic(original_topic)
self.assertNotEqual(original_topic, decoded_topic)
self.assertEqual(decoded_topic, 'abuse')
def test__build_dictionary(self):
tp = TopicProcessor()
_, topics = tp._load_files()
dictionary, reverse_dictionary, num_topics = tp._build_dictionarys(topics)
if not len(dictionary) > 1:
self.fail()
def test_encode_topic(self):
tp = TopicProcessor()
original_topic = 'abuse'
encoded_topic = tp.encode_topic(original_topic)
self.assertNotEqual(original_topic, encoded_topic)
self.assertEqual(encoded_topic, 15)
def test_encode_statement(self):
tp = TopicProcessor()
original_statement = 'My parents hit me'
new_statement = tp.encode_statement(original_statement)
self.assertNotEqual(original_statement, new_statement)
def test__pad_lines(self):
tp = TopicProcessor()
padded_lines = tp._pad_lines([[40]])
if not len(padded_lines[0]) > 39 or not 40 in padded_lines[0]:
self.fail()
def test__vectorize_text(self):
tp = TopicProcessor()
statements, _ = tp._load_files()
vectorized_text = tp._vectorize_text(statements)
self.assertNotEqual(statements, vectorized_text)
def test__create_tokenizer(self):
tp = TopicProcessor()
statements, _ = tp._load_files()
tokenizer = tp._create_tokenizer(statements)
if not isinstance(tokenizer, keras.preprocessing.text.Tokenizer):
self.fail()
def test__clean_text(self):
tp = TopicProcessor()
statements_1, topics_1 = tp._load_files()
statements_2, topics_2 = tp._clean_text(statements_1, topics_1)
self.assertNotEqual(statements_1, statements_2)