def test_subdataset_rows(self):
order = "obtain a subset by rows from random0 from 10 to 60"
data = response(self, order)
self.assertEqual(data['queryResult']['intent']['displayName'],
'SubDatasetRow')
self.assertGreater(data['queryResult']['intentDetectionConfidence'],
0.8)
self.assertEqual(data['queryResult']['parameters']['Dataset'],
'random0')
self.assertEqual(data['queryResult']['parameters']['from'], 10)
self.assertEqual(data['queryResult']['parameters']['to'], 60)
al.create_dataset({'columns': 10, 'rows': 200, 'values': [0, 1]})
al.get_subdataset_rows(data['queryResult']['parameters'])
dataset = al.Workspace().get_dataset('subrow0random0')
index = dataset.index
nrow = dataset.index.size
self.assertEqual(index.min(),
data['queryResult']['parameters']['from'])
self.assertEqual(index.max(),
data['queryResult']['parameters']['to'] - 1)
self.assertEqual(nrow, 50)
def test_change_name(self):
order = "rename random0 to r_a_n_d_o_m_0"
data = response(self, order)
self.assertEqual(data['queryResult']['intent']['displayName'],
'ChangeName')
self.assertGreater(data['queryResult']['intentDetectionConfidence'],
0.95)
self.assertEqual(data['queryResult']['parameters']['Dataset'],
'random0')
self.assertEqual(data['queryResult']['parameters']['NameInto'],
'r_a_n_d_o_m_0')
al.create_dataset({'columns': 10, 'rows': 200, 'values': [0, 1]})
self.assertTrue(al.Workspace().get_dataset('random0') is not None)
self.assertTrue(al.Workspace().get_dataset('r_a_n_d_o_m_0') is None)
data1 = al.Workspace().get_dataset('random0')
al.change_name(data['queryResult']['parameters'])
self.assertTrue(al.Workspace().get_dataset('random0') is None)
self.assertTrue(
al.Workspace().get_dataset('r_a_n_d_o_m_0') is not None)
data2 = al.Workspace().get_dataset('r_a_n_d_o_m_0')
self.assertTrue(pd.DataFrame.equals(data1, data2))
def test_split_cols(self):
order = "split the dataset random0 by 2 columns"
data = response(self, order)
self.assertEqual(data['queryResult']['intent']['displayName'],
'SplitByCols')
self.assertGreater(data['queryResult']['intentDetectionConfidence'],
0.8)
self.assertEqual(data['queryResult']['parameters']['Dataset'],
'random0')
self.assertEqual(data['queryResult']['parameters']['split'], 2)
al.create_dataset({'columns': 10, 'rows': 200, 'values': [0, 1]})
al.split_by_cols(data['queryResult']['parameters'])
datasets = re.findall('random0c[0-9]*',
str(al.Workspace().get_all_dataset()))
num_datasets = len(datasets)
self.assertEqual(num_datasets, 5)
workspace = al.Workspace()
for num in range(num_datasets):
dataset = workspace.get_dataset(datasets[num])
ncol = dataset.columns.size
nrow = dataset.index.size
self.assertEqual(ncol, 2)
self.assertEqual(nrow, 200)
def test_join_rows(self):
order = "join by rows the datasets random0 and random1"
data = response(self, order)
self.assertEqual(data['queryResult']['intent']['displayName'],
'JoinByRows')
self.assertGreater(data['queryResult']['intentDetectionConfidence'],
0.8)
self.assertEqual(data['queryResult']['parameters']['Dataset'],
'random0')
self.assertEqual(data['queryResult']['parameters']['Dataset2'],
'random1')
al.create_dataset({'columns': 10, 'rows': 200, 'values': [0, 1]})
al.create_dataset({'columns': 10, 'rows': 200, 'values': [0, 1]})
al.join_by_rows(data['queryResult']['parameters'])
dataset = al.Workspace().get_dataset('join0')
ncol = dataset.columns.size
nrow = dataset.index.size
self.assertEqual(ncol, 10)
self.assertEqual(nrow, 400)
def test_subdataset_cols(self):
order = "obtain a subset from random0 by columns at col0, col2, and col7"
data = response(self, order)
self.assertEqual(data['queryResult']['intent']['displayName'],
'SubDatasetCols')
self.assertGreater(data['queryResult']['intentDetectionConfidence'],
0.8)
self.assertEqual(data['queryResult']['parameters']['Dataset'],
'random0')
self.assertEqual(data['queryResult']['parameters']['cols'],
['col0', 'col2', 'col7'])
al.create_dataset({'columns': 10, 'rows': 200, 'values': [0, 1]})
al.get_subdataset_columns(data['queryResult']['parameters'])
dataset = al.Workspace().get_dataset('subcol0random0')
ncol = dataset.columns.size
cols = dataset.columns.to_list()
expected = ['col0', 'col2', 'col7']
self.assertEqual(ncol, 3)
for n in range(3):
self.assertEqual(cols[n], expected[n])
def test_create_random(self):
order = "create random dataset for 5 row and 10 columns between -12.1 and 80"
data = response(self, order)
self.assertEqual(data['queryResult']['intent']['displayName'],
'RandomDataset')
self.assertGreater(data['queryResult']['intentDetectionConfidence'],
0.8)
al.create_dataset(data['queryResult']['parameters'])
self.workspace = al.Workspace()
random = self.workspace.get_dataset('current')
self.assertEqual(random.shape,
(int(data['queryResult']['parameters']['rows']),
int(data['queryResult']['parameters']['columns'])),
'(n_row, n_column) do not match')
self.assertGreaterEqual(
random.values.min(),
float(data['queryResult']['parameters']['values'][0]))
self.assertLessEqual(
random.values.max(),
float(data['queryResult']['parameters']['values'][1]))
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
