def testEvaluate(self):
list_columns_command = StatListColumns()
arg = list_columns_command.argumentTypes()[0]
file_path = os.path.join(package_directory, 'test_data', 'data.csv')
data = pd.read_csv(file_path)
data_object = DataObject(data, ['random', 'dataset'])
user_conv = DataObject('list categorical columns'.split(' '),
['user', 'conv'])
arguments = {arg.keyword: data_object, 'user_conv': user_conv}
result_object = list_columns_command.evaluate(**arguments)
self.assertEqual(result_object.command_status, CommandStatus.Success)
# Try false data
data_object.data = [1, 2, 3]
result_object = list_columns_command.evaluate(**arguments)
self.assertEqual(result_object.command_status, CommandStatus.Error)
def testEvaluate(self):
image_command = ImageDisplay()
arg = image_command.argumentTypes()[0]
# Use None when varstore does not have anything stored
arguments = {arg.keyword: None}
result_object = image_command.evaluate(**arguments)
self.assertEqual(result_object.command_status, CommandStatus.Error)
# Use false data
data_object = DataObject(123, ['random', 'image'])
arguments = {arg.keyword: data_object}
data_object.data = 123
result_object = image_command.evaluate(**arguments)
self.assertEqual(result_object.command_status, CommandStatus.Error)
# No more optional image should be passed by parser:
result_object = image_command.evaluate(**{arg.keyword: None})
self.assertEqual(result_object.command_status, CommandStatus.Error)
# Use real image
image_data = imread(os.path.join(
package_directory, 'test_data', 'image.jpg'))
data_object.data = image_data
result_object = image_command.evaluate(**arguments)
self.assertEqual(result_object.command_status, CommandStatus.Success)
plt.close()
def testTopN(self):
filter_top = FilterTopN()
array_data = DataObject(np.array(
[1, 1, 1, 2, 2, 2, 2, 3, 3, 4, 5, np.nan]),
keyword_list=['array'])
user_conv = 'Find top 2 values in array'
user_conv = user_conv.lower()
target = DataObject(user_conv.split(' '), ['user', 'conversation'])
arguments = {'array_data': array_data, 'target': target}
result_object = filter_top.evaluate(**arguments)
self.assertEqual(result_object.command_status, CommandStatus.Success)
self.assertEqual(result_object.data[-2], True)
self.assertEqual(result_object.data[-3], True)
self.assertEqual(result_object.data[-1], False)
array_data.data = np.array(
['Hello', 'how', 'are', 'you', 'how', 'are'])
result_object = filter_top.evaluate(**arguments)
self.assertEqual(result_object.command_status, CommandStatus.Success)
self.assertEqual(result_object.data[0], False)
self.assertEqual(result_object.data[1], True)
self.assertEqual(result_object.data[-1], True)
def testEvaluate(self):
load_command = Load()
arg = load_command.argumentTypes()[0]
file_name_data_object = FileObject('', DataType.file_name, '', False)
file_name_object = DataObject(file_name_data_object,
['random', 'file'])
arguments = {arg.keyword: file_name_object}
pre_eval_res = load_command.preEvaluate(**arguments)
arguments['pre_evaluate_results'] = pre_eval_res
result_object = load_command.evaluate(**arguments)
self.assertEqual(result_object.command_status, CommandStatus.Error)
file_name_data_object.path = os.path.join(package_directory,
'test_data', 'data.csv')
file_name_data_object.data_type = DataType.csv
file_name_object.data = file_name_data_object
arguments.pop('pre_evaluate_results', None)
pre_eval_res = load_command.preEvaluate(**arguments)
arguments['pre_evaluate_results'] = pre_eval_res
result_objects = load_command.evaluate(**arguments)
self.assertEqual(result_objects[0].command_status,
CommandStatus.Success)