def test_convert_to_output_format_wrong_order(self):
out = convert_to_output_format({"width": 1, "coeff": "a"})
expected_out = 'coeff_"a"__width_1'
self.assertEqual(out, expected_out)
out = convert_to_output_format({"c": 1, "b": 2, "a": 3})
expected_out = 'a_3__b_2__c_1'
self.assertEqual(out, expected_out)
def test_convert_to_output_format_wrong_order(self):
out = convert_to_output_format({"width": 1, "coeff": "a"})
expected_out = 'coeff_"a"__width_1'
self.assertEqual(out, expected_out)
out = convert_to_output_format({"c": 1, "b": 2, "a": 3})
expected_out = 'a_3__b_2__c_1'
self.assertEqual(out, expected_out)
def test_convert_to_output_format(self):
out = convert_to_output_format({"p1": 1, "p2": "a"})
expected_out = 'p1_1__p2_"a"'
self.assertEqual(out, expected_out)
out = convert_to_output_format({"list": [1, 2, 4]})
expected_out = 'list_[1, 2, 4]'
self.assertEqual(out, expected_out)
out = convert_to_output_format({"list": ["a", "b", "c"]})
expected_out = "list_['a', 'b', 'c']"
self.assertEqual(out, expected_out)
def test_convert_to_output_format(self):
out = convert_to_output_format({"p1": 1, "p2": "a"})
expected_out = 'p1_1__p2_"a"'
self.assertEqual(out, expected_out)
out = convert_to_output_format({"list": [1, 2, 4]})
expected_out = 'list_[1, 2, 4]'
self.assertEqual(out, expected_out)
out = convert_to_output_format({"list": ["a", "b", "c"]})
expected_out = "list_['a', 'b', 'c']"
self.assertEqual(out, expected_out)
def _f():
for function_name, parameter_list in fc_parameters.items():
func = getattr(feature_calculators, function_name)
if func.fctype == "combiner":
result = func(data, param=parameter_list)
else:
if parameter_list:
result = ((convert_to_output_format(param), func(data, **param)) for param in parameter_list)
else:
result = [("", func(data))]
for key, item in result:
feature_name = str(kind) + "__" + func.__name__
if key:
feature_name += "__" + str(key)
yield {"variable": feature_name, "value": item, "id": sample_id}
def _f():
for function_name, parameter_list in fc_parameters.items():
func = getattr(feature_calculators, function_name)
if func.fctype == "combiner":
result = func(data, param=parameter_list)
else:
if parameter_list:
result = ((convert_to_output_format(param), func(data, **param)) for param in parameter_list)
else:
result = [("", func(data))]
for key, item in result:
feature_name = str(kind) + "__" + func.__name__
if key:
feature_name += "__" + str(key)
yield {"variable": feature_name, "value": item, "id": sample_id}
def _f():
for function_name, parameter_list in fc_parameters.items():
func = getattr(feature_calculators, function_name)
# If the function uses the index, pass is at as a pandas Series.
# Otherwise, convert to numpy array
if getattr(func, 'input', False) == 'pd.Series':
# If it has a required index type, check that the data has the right index type.
index_type = getattr(func, 'index_type', None)
if index_type is not None:
try:
assert isinstance(data.index, index_type)
except AssertionError:
warnings.warn(
"{} requires the data to have a index of type {}. Results will "
"not be calculated".format(function_name,
index_type))
continue
x = data
else:
x = data.values
if func.fctype == "combiner":
result = func(x, param=parameter_list)
else:
if parameter_list:
result = ((convert_to_output_format(param),
func(x, **param)) for param in parameter_list)
else:
result = [("", func(x))]
for key, item in result:
feature_name = str(kind) + "__" + func.__name__
if key:
feature_name += "__" + str(key)
yield {
"variable": feature_name,
"value": item,
"id": sample_id
}
