def test_json_error():
json_ob = '''{
"EMPLOYEE_ID": {
"required": True
}
}'''
with pytest.raises(JSONDeserializationError) as e:
Prep.from_json(json_ob, df)
assert 'Error parsing json' in str(e.value)
def test_mapper_schema_new_key():
error_ob = {
"EMPLOYEE_ID": [{
"required": True,
"encoding": None,
"alias": "EMP_ID",
"imputation": None,
"enforce_type": None,
"derived_eq": None,
"some_new_key": None
}]
}
Prep.from_dict(prep_ob, df)
with pytest.raises(IncorrectSchemaError) as e:
Prep.from_dict(error_ob, df)
assert 'Unknown keys found in schema.' in str(e.value)
def test_required_features():
p = Prep.from_dict(prep_ob, df)
p.get_data()
assert p.get_required_features() == [
'EMPLOYEE_ID', 'AGE', 'GENDER', 'RATING', 'DESIGNATION', 'PROMOTED'
]
def test_alias_features():
p = Prep.from_dict(prep_ob, df)
p.get_data()
assert p.get_alias_features() == ['EMPLOYEE_ID', 'AGE']
def test_mapper_schema_failed():
error_ob = {"EMPLOYEE_ID": "FAIL_VALUE"}
Prep.from_dict(prep_ob, df)
with pytest.raises(IncorrectSchemaError) as e:
Prep.from_dict(error_ob, df)
assert 'Error with mapper schema.' in str(e.value)
def test_imputation_process():
p = Prep.from_dict(prep_ob, df)
rdf = p.get_data()
assert not rdf.isnull().values.any()
def test_data_missing_exception():
with pytest.raises(DataMissingError) as e:
Prep.from_dict(prep_ob)
assert 'Missing file_path and/or dataframe.' in str(e.value)
def test_derived_features():
p = Prep.from_dict(prep_ob, df)
rdf = p.get_data()
assert not all(item in rdf.columns.tolist()
for item in ['FIRST_NAME', 'LAST_NAME'])
def test_derived_features():
p = Prep.from_dict(prep_ob, df)
p.get_data()
assert p.get_derived_features() == ['DOB']
def test_imputation_features():
p = Prep.from_dict(prep_ob, df)
p.get_data()
assert p.get_imputation_features() == ['RATING']
def test_encode_features():
p = Prep.from_dict(prep_ob, df)
p.get_data()
assert p.get_encode_features() == ['GENDER', 'DESIGNATION', 'PROMOTED']
"required":
True,
"alias":
"DOB",
"derived_eq":
"(pd.Timestamp('now') - pd.to_datetime(df.AGE, format='%m/%d/%Y')).astype('<m8[Y]')"
},
"GENDER": {
"required": True,
"encoding": 'ohe'
},
"RATING": {
"required": True,
"imputation": 'most_frequent'
},
"DESIGNATION": {
"required": True,
"encoding": 'label'
},
"PROMOTED": {
"required": True,
"encoding": 'label'
}
}
df = pd.read_csv('tests/MOCK_DATA.csv')
p = Prep.from_dict(prep_ob, df)
rdf = p.get_data()
print(rdf.columns.to_list())
print(rdf.head())