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spam-classifier

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Scikit-learn
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scikit-learn
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spam-classifier
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import datetime
from pathlib import Path

import numpy as np
import pytest

import pandas as pd
import pandas._testing as tm
from pandas.util.version import Version

pyreadstat = pytest.importorskip("pyreadstat")


# TODO(CoW) - detection of chained assignment in cython
# https://github.com/pandas-dev/pandas/issues/51315
@pytest.mark.filterwarnings("ignore::pandas.errors.ChainedAssignmentError")
@pytest.mark.filterwarnings("ignore:ChainedAssignmentError:FutureWarning")
@pytest.mark.parametrize("path_klass", [lambda p: p, Path])
def test_spss_labelled_num(path_klass, datapath):
    # test file from the Haven project (https://haven.tidyverse.org/)
    # Licence at LICENSES/HAVEN_LICENSE, LICENSES/HAVEN_MIT
    fname = path_klass(datapath("io", "data", "spss", "labelled-num.sav"))

    df = pd.read_spss(fname, convert_categoricals=True)
    expected = pd.DataFrame({"VAR00002": "This is one"}, index=[0])
    expected["VAR00002"] = pd.Categorical(expected["VAR00002"])
    tm.assert_frame_equal(df, expected)

    df = pd.read_spss(fname, convert_categoricals=False)
    expected = pd.DataFrame({"VAR00002": 1.0}, index=[0])
    tm.assert_frame_equal(df, expected)


@pytest.mark.filterwarnings("ignore::pandas.errors.ChainedAssignmentError")
@pytest.mark.filterwarnings("ignore:ChainedAssignmentError:FutureWarning")
def test_spss_labelled_num_na(datapath):
    # test file from the Haven project (https://haven.tidyverse.org/)
    # Licence at LICENSES/HAVEN_LICENSE, LICENSES/HAVEN_MIT
    fname = datapath("io", "data", "spss", "labelled-num-na.sav")

    df = pd.read_spss(fname, convert_categoricals=True)
    expected = pd.DataFrame({"VAR00002": ["This is one", None]})
    expected["VAR00002"] = pd.Categorical(expected["VAR00002"])
    tm.assert_frame_equal(df, expected)

    df = pd.read_spss(fname, convert_categoricals=False)
    expected = pd.DataFrame({"VAR00002": [1.0, np.nan]})
    tm.assert_frame_equal(df, expected)


@pytest.mark.filterwarnings("ignore::pandas.errors.ChainedAssignmentError")
@pytest.mark.filterwarnings("ignore:ChainedAssignmentError:FutureWarning")
def test_spss_labelled_str(datapath):
    # test file from the Haven project (https://haven.tidyverse.org/)
    # Licence at LICENSES/HAVEN_LICENSE, LICENSES/HAVEN_MIT
    fname = datapath("io", "data", "spss", "labelled-str.sav")

    df = pd.read_spss(fname, convert_categoricals=True)
    expected = pd.DataFrame({"gender": ["Male", "Female"]})
    expected["gender"] = pd.Categorical(expected["gender"])
    tm.assert_frame_equal(df, expected)

    df = pd.read_spss(fname, convert_categoricals=False)
    expected = pd.DataFrame({"gender": ["M", "F"]})
    tm.assert_frame_equal(df, expected)


@pytest.mark.filterwarnings("ignore::pandas.errors.ChainedAssignmentError")
@pytest.mark.filterwarnings("ignore:ChainedAssignmentError:FutureWarning")
def test_spss_umlauts(datapath):
    # test file from the Haven project (https://haven.tidyverse.org/)
    # Licence at LICENSES/HAVEN_LICENSE, LICENSES/HAVEN_MIT
    fname = datapath("io", "data", "spss", "umlauts.sav")

    df = pd.read_spss(fname, convert_categoricals=True)
    expected = pd.DataFrame(
        {"var1": ["the ä umlaut", "the ü umlaut", "the ä umlaut", "the ö umlaut"]}
    )
    expected["var1"] = pd.Categorical(expected["var1"])
    tm.assert_frame_equal(df, expected)

    df = pd.read_spss(fname, convert_categoricals=False)
    expected = pd.DataFrame({"var1": [1.0, 2.0, 1.0, 3.0]})
    tm.assert_frame_equal(df, expected)


def test_spss_usecols(datapath):
    # usecols must be list-like
    fname = datapath("io", "data", "spss", "labelled-num.sav")

    with pytest.raises(TypeError, match="usecols must be list-like."):
        pd.read_spss(fname, usecols="VAR00002")


def test_spss_umlauts_dtype_backend(datapath, dtype_backend):
    # test file from the Haven project (https://haven.tidyverse.org/)
    # Licence at LICENSES/HAVEN_LICENSE, LICENSES/HAVEN_MIT
    fname = datapath("io", "data", "spss", "umlauts.sav")

    df = pd.read_spss(fname, convert_categoricals=False, dtype_backend=dtype_backend)
    expected = pd.DataFrame({"var1": [1.0, 2.0, 1.0, 3.0]}, dtype="Int64")

    if dtype_backend == "pyarrow":
        pa = pytest.importorskip("pyarrow")

        from pandas.arrays import ArrowExtensionArray

        expected = pd.DataFrame(
            {
                col: ArrowExtensionArray(pa.array(expected[col], from_pandas=True))
                for col in expected.columns
            }
        )

    tm.assert_frame_equal(df, expected)


def test_invalid_dtype_backend():
    msg = (
        "dtype_backend numpy is invalid, only 'numpy_nullable' and "
        "'pyarrow' are allowed."
    )
    with pytest.raises(ValueError, match=msg):
        pd.read_spss("test", dtype_backend="numpy")


@pytest.mark.filterwarnings("ignore::pandas.errors.ChainedAssignmentError")
@pytest.mark.filterwarnings("ignore:ChainedAssignmentError:FutureWarning")
def test_spss_metadata(datapath):
    # GH 54264
    fname = datapath("io", "data", "spss", "labelled-num.sav")

    df = pd.read_spss(fname)
    metadata = {
        "column_names": ["VAR00002"],
        "column_labels": [None],
        "column_names_to_labels": {"VAR00002": None},
        "file_encoding": "UTF-8",
        "number_columns": 1,
        "number_rows": 1,
        "variable_value_labels": {"VAR00002": {1.0: "This is one"}},
        "value_labels": {"labels0": {1.0: "This is one"}},
        "variable_to_label": {"VAR00002": "labels0"},
        "notes": [],
        "original_variable_types": {"VAR00002": "F8.0"},
        "readstat_variable_types": {"VAR00002": "double"},
        "table_name": None,
        "missing_ranges": {},
        "missing_user_values": {},
        "variable_storage_width": {"VAR00002": 8},
        "variable_display_width": {"VAR00002": 8},
        "variable_alignment": {"VAR00002": "unknown"},
        "variable_measure": {"VAR00002": "unknown"},
        "file_label": None,
        "file_format": "sav/zsav",
    }
    if Version(pyreadstat.__version__) >= Version("1.2.4"):
        metadata.update(
            {
                "creation_time": datetime.datetime(2015, 2, 6, 14, 33, 36),
                "modification_time": datetime.datetime(2015, 2, 6, 14, 33, 36),
            }
        )
    assert df.attrs == metadata