def test_znorm(self):
order = 'Execute znorm on timeserie'
data = response(self, order)
self.assertEqual(data['queryResult']['intent']['displayName'],
'DoNormalization')
self.assertGreater(data['queryResult']['intentDetectionConfidence'],
0.9)
self.assertEqual(data['queryResult']['parameters']['operation'],
'znorm')
self.assertEqual(data['queryResult']['parameters']['Dataset'],
'timeserie')
tt = pd.DataFrame([[0, 1, 2, 3], [4, 5, 6, 7]])
self.workspace.save_dataset('timeserie', tt)
al.do_normalization(data['queryResult']['parameters'])
znorm_result = al.Workspace().get_dataset(
"znorm0").to_numpy().flatten()
expected = [
-1.341640786499870, -0.447213595499958, 0.447213595499958,
1.341640786499870
]
for i in range(len(expected)):
self.assertAlmostEqual(znorm_result[i],
expected[i],
delta=self.DELTA)
self.assertAlmostEqual(znorm_result[i + 4],
expected[i],
delta=self.DELTA)
def test_decimal_scaling_norm(self):
order = 'Execute the decimal scaling normalization on timeserie'
data = response(self, order)
self.assertEqual(data['queryResult']['intent']['displayName'],
'DoNormalization')
self.assertGreater(data['queryResult']['intentDetectionConfidence'],
0.9)
self.assertEqual(data['queryResult']['parameters']['operation'],
'decimal_scaling_norm')
self.assertEqual(data['queryResult']['parameters']['Dataset'],
'timeserie')
tt = pd.DataFrame([[0, 1, -2, 3], [40, 50, 60, -70]])
self.workspace.save_dataset('timeserie', tt)
al.do_normalization(data['queryResult']['parameters'])
max_min_norm_result = al.Workspace().get_dataset(
"dec_sca_norm0").to_numpy().flatten()
expected = [[0.0, 0.1, -0.2, 0.3], [0.4, 0.5, 0.6, -0.7]]
for i in range(len(expected)):
self.assertAlmostEqual(max_min_norm_result[i],
expected[0][i],
delta=self.DELTA)
self.assertAlmostEqual(max_min_norm_result[i + 4],
expected[1][i],
delta=self.DELTA)
def test_mean_norm(self):
order = 'Execute the mean norm on timeserie'
data = response(self, order)
self.assertEqual(data['queryResult']['intent']['displayName'],
'DoNormalization')
self.assertGreater(data['queryResult']['intentDetectionConfidence'],
0.9)
self.assertEqual(data['queryResult']['parameters']['operation'],
'mean_norm')
self.assertEqual(data['queryResult']['parameters']['Dataset'],
'timeserie')
tt = pd.DataFrame([[0, 1, 2, 3], [4, 5, 6, 7]])
self.workspace.save_dataset('timeserie', tt)
al.do_normalization(data['queryResult']['parameters'])
max_min_norm_result = al.Workspace().get_dataset(
"mean_norm0").to_numpy().flatten()
expected = [-0.5, -0.166666667, 0.166666667, 0.5]
for i in range(len(expected)):
self.assertAlmostEqual(max_min_norm_result[i],
expected[i],
delta=self.DELTA)
self.assertAlmostEqual(max_min_norm_result[i + 4],
expected[i],
delta=self.DELTA)
def test_max_min_norm_with_param(self):
order = 'Execute maximal minimal normalization on timeserie with max value of 2 and min value of 1'
data = response(self, order)
self.assertEqual(data['queryResult']['intent']['displayName'],
'DoNormalization')
self.assertGreater(data['queryResult']['intentDetectionConfidence'],
0.9)
self.assertEqual(data['queryResult']['parameters']['operation'],
'max_min_norm')
self.assertEqual(data['queryResult']['parameters']['Dataset'],
'timeserie')
self.assertEqual(data['queryResult']['parameters']['max'], 2)
self.assertEqual(data['queryResult']['parameters']['min'], 1)
tt = pd.DataFrame([[0, 1, 2, 3], [4, 5, 6, 7]])
self.workspace.save_dataset('timeserie', tt)
al.do_normalization(data['queryResult']['parameters'])
max_min_norm_result = al.Workspace().get_dataset(
"max_min_norm0").to_numpy().flatten()
expected = [1.0, 1.3333333333333, 1.66666667, 2.0]
for i in range(len(expected)):
self.assertAlmostEqual(max_min_norm_result[i],
expected[i],
delta=self.DELTA)
self.assertAlmostEqual(max_min_norm_result[i + 4],
expected[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