def test_wrapping(self):
''' Verifica se retorna o dataset apenas com o wrapping '''
model = BaseModel()
options = {
**{
"categorias": [
'nm_indicador', 'nu_competencia', 'vl_indicador', 'lat_mun', 'long_mun'
],
"pivot": None
}, **COMMON_OPTIONS
}
result = "".join(model.wrap_result(SAMPLE_DATAFRAME.copy(), options).split())
str_expected = COMMON_EXPECTED_RESPONSE_STRING.format(
"""
"nm_indicador": "Ficticio",
"nu_competencia": 2099,
"vl_indicador": 1.0
},
{
"nm_indicador": "Ficticio",
"nu_competencia": 2047,
"vl_indicador": 0.5
"""
)
expected = "".join(str_expected.split())
self.assertEqual(result, expected)
def test_get_collection_from_invalid_type(self):
''' Test if the method returns None if an invalid type is passed '''
self.assertEqual(
BaseModel.get_collection_from_type(
SAMPLE_DATAFRAME.copy(),
'invalid'
),
None
)
def test_get_collection_from_missing_type(self):
''' Test if the method returns None if no type is passed '''
self.assertEqual(
BaseModel.get_collection_from_type(
SAMPLE_DATAFRAME.copy(),
None
),
None
)
def test_get_collection_from_type_first_occurence(self):
''' Test if the method returns the first item '''
self.assertEqual(
BaseModel.get_collection_from_type(
SAMPLE_DATAFRAME.copy(),
"first_occurence"
).to_dict(),
{"index": 0, "col_1": "d", "col_2": 3, "col_3": 3}
)
def test_get_collection_from_type_max(self):
''' Test if the method returns the item with maximum value in colum '''
self.assertEqual(
BaseModel.get_collection_from_type(
SAMPLE_DATAFRAME.copy(),
"max",
"col_2"
).to_dict(),
{"col_1": "d", "col_2": 3, "col_3": 3}
)
def test_get_collection_from_type_from_invalid_id(self):
''' Test if the method returns the item with the passed string id '''
self.assertRaises(
ValueError,
BaseModel.get_collection_from_type,
SAMPLE_DATAFRAME.copy(),
"from_id",
"col_1",
'a'
)
def test_get_collection_from_type_from_id(self):
''' Test if the method returns the item with the passed numeric id '''
self.assertEqual(
BaseModel.get_collection_from_type(
SAMPLE_DATAFRAME.copy(),
"from_id",
"col_2",
2
).to_dict(),
{"col_1": "b", "col_2": 2, "col_3": 2}
)
def test_apply_coefficients(self):
''' Test if custom coefficients are applied correctly to a dataset '''
self.assertEqual(
TemplateHelper.apply_coefficient(
'col_2-2-test,col_3-3-term',
{"dataset": SAMPLE_DATAFRAME.copy()}
)["dataset"].to_dict(),
{
'col_1': {0: 'd', 1: 'b', 2: 'a', 3: 'c'},
'col_2': {0: 6.0, 1: 4.0, 2: 2.0, 3: 0.0},
'col_3': {0: 9.0, 1: 6.0, 2: 3.0, 3: 0.0}
}
)
def test_build_derivatives_nodata(self):
''' Test the derivate objects is added with no_data flag '''
options = {'cd_analysis_unit': 99}
rules = {
"instances": [{"name": "inst_1", 'type': 'from_id', 'named_prop': 'col_2'}]
}
sources = {"dataset": SAMPLE_DATAFRAME.copy()}
(der_data, der_anynodata) = BaseModel.build_derivatives(
rules,
options,
sources,
{}
)
self.assertEqual(
der_data["inst_1"],
None
)
self.assertEqual(der_anynodata, True)
def test_build_derivatives(self):
''' Test if derivate object is added to the collection '''
options = {'cd_analysis_unit': 2}
rules = {
"instances": [{"name": "inst_1", 'type': 'max', 'named_prop': 'col_2'}]
}
sources = {"dataset": SAMPLE_DATAFRAME.copy()}
(der_data, der_anynodata) = BaseModel.build_derivatives(
rules,
options,
sources,
{}
)
self.assertEqual(
der_data["inst_1"].to_dict(),
{"col_1": "d", "col_2": 3, "col_3": 3}
)
self.assertEqual(der_anynodata, False)