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How to use _validate_variables method in lisa

Best Python code snippet using lisa_python

test__variables_parsing.py

Source:test__variables_parsing.py

...162        )163class TestValidateVariablesParsing:164    def test__empty_dict__should_be_left_as_is(self, parser: ConfigParser) -> None:165        dummy_variables: Dict[str, List[str]] = {}166        result = parser._validate_variables(variables=dummy_variables)167        assert result == {}168    def test__scalar_value__error_should_be_raised(self, parser: ConfigParser) -> None:169        dummy_variables = "I am a string"170        with pytest.raises(ParserError) as exception_info:171            parser._validate_variables(variables=dummy_variables)172        expected = r"The 'variables' section should have the following syntax: 'VARIABLE_NAME' = \['var_1', 'var_2', \.\.\] !"173        assert exception_info.match(expected)174    def test__non_compatible_variable__error_should_be_raised(175        self, parser: ConfigParser176    ) -> None:177        dummy_variables = {178            "VARIABLE": {"this is not a list": 42},179        }180        with pytest.raises(ParserError) as exception_info:181            parser._validate_variables(variables=dummy_variables)182        expected = "The 'variables' section should contain a single string or a list of strings for a variable name!"183        assert exception_info.match(expected)184    def test__non_list_variable_value__should_be_converted_to_a_list(185        self, parser: ConfigParser186    ) -> None:187        dummy_variables = {188            "VARIABLE": "I am not a list",189        }190        result = parser._validate_variables(variables=dummy_variables)191        assert result == {192            "VARIABLE": ["I am not a list"],193        }194    def test__list_variable_values__should_be_left_as_is(195        self, parser: ConfigParser196    ) -> None:197        dummy_variables = {198            "VARIABLE": ["I", "am", "a", "list"],199        }200        result = parser._validate_variables(variables=dummy_variables)201        assert result == {202            "VARIABLE": ["I", "am", "a", "list"],203        }204    def test__non_string_items__error_should_be_raised(205        self, parser: ConfigParser206    ) -> None:207        dummy_variables = {208            "VARIABLE": ["I am a string", 42],209        }210        with pytest.raises(ParserError) as exception_info:211            parser._validate_variables(variables=dummy_variables)212        expected = "The 'variables' section should contain a single string or a list of strings for a variable name!"...

test_transformer.py

Source:test_transformer.py

...58        # no value is overridden, all values are kept59        transformers = self._generate_transformers_runbook(2)60        runbook_builder = self._generate_runbook_builder(transformers)61        result = transformer._run_transformers(runbook_builder)62        self._validate_variables(63            {64                "v0": "original",65                "va": "original",66                "t0_v0": "0_0 processed",67                "t1_v0": "1_0 processed",68                "t1_v1": "1_1 processed",69            },70            result,71        )72    def test_transformer_overridden_values(self) -> None:73        # value is overridden74        transformers = self._generate_transformers_runbook(2)75        transformers[0].rename = {"t0_v0": "v0"}76        transformers[1].rename = {"t1_v0": "v0", "t1_v1": "v1"}77        runbook_builder = self._generate_runbook_builder(transformers)78        result = transformer._run_transformers(runbook_builder)79        self._validate_variables(80            {81                "va": "original",82                "v0": "1_0 processed",83                "v1": "1_1 processed",84            },85            result,86        )87    def test_transformer_rename_not_exist(self) -> None:88        # not exist name raise exception89        transformers = self._generate_transformers_runbook(1)90        transformers[0].rename = {"v0": "v0_1"}91        runbook_builder = self._generate_runbook_builder(transformers)92        with self.assertRaises(LisaException) as cm:93            transformer._run_transformers(runbook_builder)94        self.assertEqual("unmatched rename variable: {'v0': 'v0_1'}", str(cm.exception))95    def test_transformer_customized_prefix(self) -> None:96        # modified prefix97        transformers = self._generate_transformers_runbook(1)98        transformers[0].prefix = "v0_1"99        runbook_builder = self._generate_runbook_builder(transformers)100        result = transformer._run_transformers(runbook_builder)101        self._validate_variables(102            {103                "v0": "original",104                "va": "original",105                "v0_1_v0": "0_0 processed",106            },107            result,108        )109    def test_transformer_no_name(self) -> None:110        # no name, the type will be used. in name111        transformers_data: List[Any] = [{"type": MOCK, "items": {"v0": "v0_1"}}]112        transformers = schema.load_by_type_many(schema.Transformer, transformers_data)113        runbook_builder = self._generate_runbook_builder(transformers)114        result = transformer._run_transformers(runbook_builder)115        self._validate_variables(116            {117                "v0": "original",118                "va": "original",119                "mock_v0": "v0_1 processed",120            },121            result,122        )123    def test_transformer_skip_disabled(self) -> None:124        # the second transformer should be skipped, so the value is original.125        transformers = self._generate_transformers_runbook(2)126        transformers[0].rename = {"t0_v0": "v0"}127        transformers[0].enabled = False128        runbook_builder = self._generate_runbook_builder(transformers)129        result = transformer._run_transformers(runbook_builder)130        self._validate_variables(131            {132                "v0": "original",133                "va": "original",134                "t1_v0": "1_0 processed",135                "t1_v1": "1_1 processed",136            },137            result,138        )139    def _validate_variables(140        self, expected: Dict[str, str], actual: Dict[str, VariableEntry]141    ) -> None:142        actual_pairs = {name: value.data for name, value in actual.items()}143        self.assertDictEqual(expected, actual_pairs)144    def _generate_runbook_builder(145        self, transformers: List[schema.Transformer]146    ) -> RunbookBuilder:147        transformers_data: List[Any] = [148            x.to_dict() for x in transformers  # type:ignore149        ]150        runbook_builder = RunbookBuilder(Path("mock_runbook.yml"))151        runbook_builder._raw_data = {152            constants.TRANSFORMER: transformers_data,153        }...

psi.py

Source:psi.py

...3"""4from typing import Union5import numpy as np6import pandas as pd7def _validate_variables(8    df : pd.DataFrame = None,9    variables : Union[str,list] = None):10    """Validate that `df` is a `pandas.DataFrame` and11    `variables` is a subset of columns"""12    if not isinstance(df, pd.DataFrame):13        raise TypeError("df must be instance of pandas.DataFrame")14    cols = df.columns.tolist()15    cols_to_str = ", ".join(cols)16    if variables is None:17        return cols18    elif isinstance(variables, str):19        if not variables in cols:20            raise ValueError(f"{variables} not in `df.columns`: {cols_to_str}")21        else:22            return [variables]23    elif isinstance(variables, list):24        missing_vars = [i for i in variables if i not in cols]25        if len(missing_vars) > 0:26            missing_vars = ", ".join(missing_vars)27            raise ValueError(f"{missing_vars} not in `df.columns`: {cols_to_str}")28        else:29            return variables30    else:31        t = str(type(variables))32        raise TypeError(f"`variables` must be str or list, but found {t}")33def _psi(e : np.ndarray, o : np.ndarray, k = 1e-6):34    assert e.shape == o.shape35    assert len(e.shape) == 136    if k > 0:37        e = np.maximum(e,k)38        o = np.maximum(o,k)39    psi = np.dot((o - e) , np.log(o / e))40    return psi41def _get_frequencies(df : pd.DataFrame = None, cols : list = None):42    dist = {}43    for c in cols:44        d = df.loc[:,c].value_counts(normalize = True, dropna = False).to_dict()45        dist[c] = d46    return dist47def _dicts_to_df(d1, d2, fillna = 0):48    d1 = pd.DataFrame.from_dict(d1, orient='index').sort_index()[0]49    d2 = pd.DataFrame.from_dict(d2, orient='index').sort_index()[0]50    d = pd.concat([d1,d2],axis=1).fillna(fillna)51    return d52class PSI:53    """Class for calculating Population Stability Index"""54    def __init__(self):55        self._cols = None56        self._dist = None57    def fit(self, df : pd.DataFrame = None, variables : Union[str,list] = None):58        """Fit method59        Parameters60        ----------61        df : pandas.DataFrame62        variables : Union[str,list]63        """64        df = df.copy()65        cols = _validate_variables(df, variables)66        self._cols = cols67        self._dist = _get_frequencies(df, cols)68    def transform(self, df : pd.DataFrame):69        df = df.copy()70        _cols = _validate_variables(df, self._cols)71        dist = _get_frequencies(df, self._cols)72        res = {}73        for col in self._cols:74            d = _dicts_to_df(self._dist[col],dist[col])75            psi = _psi(d.iloc[:,0].values, d.iloc[:,1].values)76            res[col] = psi...

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