def test_shape_dataset(self):
order = "Do the kshape of 3 clusters for energy"
data = response(self, order)
self.assertEqual(data['queryResult']['intent']['displayName'],
'DoClustering')
self.assertGreater(data['queryResult']['intentDetectionConfidence'],
0.8)
self.assertEqual(data['queryResult']['parameters']['operation'],
'kshape')
self.assertEqual(data['queryResult']['parameters']['number'], 3)
self.assertEqual(data['queryResult']['parameters']['Dataset'],
'energy')
tts = pd.DataFrame([[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0],
[0.0, 10.0, 4.0, 5.0, 7.0, -3.0, 0.0],
[-1.0, 15.0, -12.0, 8.0, 9.0, 4.0, 5.0],
[2.0, 8.0, 7.0, -6.0, -1.0, 2.0, 9.0],
[-5.0, -5.0, -6.0, 7.0, 9.0, 9.0, 0.0]])
self.workspace.save_dataset('energy', tts)
expected_c = pd.DataFrame(
[[-0.5234, 0.1560, -0.3627, -1.2764, -0.7781, 0.9135, 1.8711],
[-0.7825, 1.5990, 0.1701, 0.4082, 0.8845, -1.4969, -0.7825],
[-0.6278, 1.3812, -2.0090, 0.5022, 0.6278, 0.0000, 0.1256]])
al.do_clustering(data['queryResult']['parameters'])
(centroid, labels) = (al.Workspace().get_dataset('centroids0'),
al.Workspace().get_dataset('labels0'))
for i in range(len(expected_c)):
self.assertAlmostEqual(centroid[0][i],
expected_c[0][i],
delta=self.DELTA)
self.assertAlmostEqual(centroid[1][i],
expected_c[1][i],
delta=self.DELTA)
self.assertAlmostEqual(centroid[2][i],
expected_c[2][i],
delta=self.DELTA)
def test_kmeans_dataset(self):
order = "Do the kmeans of 3 clusters for energy"
data = response(self, order)
self.assertEqual(data['queryResult']['intent']['displayName'],
'DoClustering')
self.assertGreater(data['queryResult']['intentDetectionConfidence'],
0.8)
self.assertEqual(data['queryResult']['parameters']['operation'],
'kmeans')
self.assertEqual(data['queryResult']['parameters']['number'], 3)
self.assertEqual(data['queryResult']['parameters']['Dataset'],
'energy')
tts = pd.DataFrame([[0.0, 1.0, 2.0, 3.0], [6.0, 7.0, 8.0, 9.0],
[2.0, -2.0, 4.0, -4.0], [8.0, 5.0, 3.0, 1.0],
[15.0, 10.0, 5.0, 0.0], [7.0, -7.0, 1.0, -1.0]])
self.workspace.save_dataset('energy', tts)
expected_c = pd.DataFrame([[0.0, 0.1667, 0.3333, 0.5],
[1.5, -1.5, 0.8333, -0.8333],
[4.8333, 3.6667, 2.6667, 1.6667]])
al.do_clustering(data['queryResult']['parameters'])
(centroid, labels) = (al.Workspace().get_dataset('centroids0'),
al.Workspace().get_dataset('labels0'))
for i in range(len(expected_c)):
self.assertAlmostEqual(centroid[0][i],
expected_c[0][i],
delta=self.DELTA)
self.assertAlmostEqual(centroid[1][i],
expected_c[1][i],
delta=self.DELTA)
self.assertAlmostEqual(centroid[2][i],
expected_c[2][i],
delta=self.DELTA)
def detect_intent_text(project_id, session_id, text, language_code):
"""
Detects the intent of the text and execute some instruction
Using the same `session_id` between requests allows continuation of the conversation.
:param project_id: ID of the project
:param session_id: ID of the session
:param text: The text input for analyse
:param language_code: Code of the language
"""
session_client = dialogflow.SessionsClient()
session = session_client.session_path(project_id, session_id)
print('Session path: {}\n'.format(session))
text_input = dialogflow.types.TextInput(text=text, language_code=language_code)
query_input = dialogflow.types.QueryInput(text=text_input)
response = session_client.detect_intent(session=session, query_input=query_input)
"""Conversion of Protocol Buffer to JSON"""
response_json = pbjson.MessageToJson(response)
data = json.loads(response_json)
parameters = data['queryResult']['parameters']
print(parameters)
print('=' * 20)
print('DEBUG: Query text: {}'.format(response.query_result.query_text))
print('DEBUG: Detected intent: {} (confidence: {})\n'.format(
response.query_result.intent.display_name,
response.query_result.intent_detection_confidence))
try:
if response.query_result.intent.display_name == 'RandomDataset':
al.create_dataset(parameters)
elif response.query_result.intent.display_name == 'LoadDataset':
al.load_dataset(parameters)
elif response.query_result.intent.display_name == 'ShowWorkspace':
workspace = al.Workspace()
print(list(workspace.get_all_dataset()))
elif response.query_result.intent.display_name == 'GetBackend':
al.get_library_backend(parameters['library'])
elif response.query_result.intent.display_name == 'SetBackend':
al.set_library_backend(parameters)
elif response.query_result.intent.display_name == 'Exit - yes':
al.exiting_yes(response.query_result.fulfillment_text)
elif response.query_result.intent.display_name == 'Exit - no':
al.exiting_no(response.query_result.fulfillment_text)
elif not re.search("^Default|Exit", response.query_result.intent.display_name):
if not parameters.get("Dataset"):
parameters['Dataset'] = 'current'
if al.check_dataset(parameters):
if response.query_result.intent.display_name == 'ChangeName':
al.change_name(parameters)
elif response.query_result.intent.display_name == 'ShowResult':
al.execute_plot(parameters)
elif response.query_result.intent.display_name == 'PrintResult':
al.execute_print(parameters)
elif response.query_result.intent.display_name == 'SubDatasetRow':
al.get_subdataset_rows(parameters)
elif response.query_result.intent.display_name == 'SubDatasetCols':
al.get_subdataset_columns(parameters)
elif response.query_result.intent.display_name == 'JoinByCols':
al.join_by_cols(parameters)
elif response.query_result.intent.display_name == 'JoinByRows':
al.join_by_rows(parameters)
elif response.query_result.intent.display_name == 'SplitByCols':
al.split_by_cols(parameters)
elif response.query_result.intent.display_name == 'SplitByRows':
al.split_by_rows(parameters)
elif response.query_result.intent.display_name == 'DoDimensionality':
al.do_dimensionality(parameters)
elif response.query_result.intent.display_name == 'DoClustering':
al.do_clustering(parameters)
elif response.query_result.intent.display_name == 'DoMatrix_Stomp':
al.do_matrix(parameters)
elif response.query_result.intent.display_name == 'DoMatrix_Best':
al.do_matrix(parameters)
elif response.query_result.intent.display_name == 'DoNormalization':
al.do_normalization(parameters)
elif response.query_result.intent.display_name == 'DoFeatures':
al.do_features(parameters)
else:
if parameters["Dataset"] != 'current':
print("The object " + parameters["Dataset"] + " does not exist.")
al.voice("The object " + parameters["Dataset"] + " does not exist.")
else:
print("There is no loaded dataset.")
al.voice("There is no loaded dataset.")
print("Please, load a dataset or use a previously stored one before using any function.")
al.voice("Please, load a dataset or use a previously stored one before using any function.")
return
print('DEBUG: Fulfillment text: {}\n'.format(response.query_result.fulfillment_text))
if response.query_result.fulfillment_text:
al.voice(response.query_result.fulfillment_text)
except Exception as e:
print('An error in the execution has been raised.')
print(e)
return