Attachment 1978456 Details for Bug 2226012 – build.log

build.log

build.log (text/plain), 32.00 KB, created by Fedora Release Engineering on 2023-07-25 18:36:52 UTC

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>build/BUILD/pingouin-0.5.3
>configfile: setup.cfg
>plugins: cov-4.0.0
>collected 88 items
>pingouin/tests/test_bayesian.py ...                                      [  3%]
>pingouin/tests/test_circular.py ........                                 [ 12%]
>pingouin/tests/test_config.py .                                          [ 13%]
>pingouin/tests/test_contingency.py ....                                  [ 18%]
>pingouin/tests/test_correlation.py ..F.                                  [ 22%]
>pingouin/tests/test_distribution.py ......                               [ 29%]
>pingouin/tests/test_effsize.py .....                                     [ 35%]
>pingouin/tests/test_equivalence.py .                                     [ 36%]
>pingouin/tests/test_multicomp.py .....                                   [ 42%]
>pingouin/tests/test_multivariate.py ....                                 [ 46%]
>pingouin/tests/test_nonparametric.py ........                            [ 55%]
>pingouin/tests/test_pairwise.py F
>=================================== FAILURES ===================================
>______________________ TestCorrelation.test_partial_corr _______________________
>self = <pingouin.tests.test_correlation.TestCorrelation testMethod=test_partial_corr>
>    def test_partial_corr(self):
>        """Test function partial_corr.
>    
>        Compare with the R package ppcor (which is also used by JASP).
>        """
>        df = read_dataset("partial_corr")
>        #######################################################################
>        # PARTIAL CORRELATION
>        #######################################################################
>        # With one covariate
>>       pc = partial_corr(data=df, x="x", y="y", covar="cv1")
>df         =            x         y       cv1       cv2       cv3
>0   5.230740  7.141488  1.825430 -1.085631 -0.255619
>1   6.013931...681  1.754886
>28  3.697089  6.129922  3.138867 -0.140069  1.495644
>29  5.466289  7.747689  1.053789 -0.861755  1.069393
>self       = <pingouin.tests.test_correlation.TestCorrelation testMethod=test_partial_corr>
>pingouin/tests/test_correlation.py:137: 
>_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
>data =            x         y       cv1       cv2       cv3
>0   5.230740  7.141488  1.825430 -1.085631 -0.255619
>1   6.013931...681  1.754886
>28  3.697089  6.129922  3.138867 -0.140069  1.495644
>29  5.466289  7.747689  1.053789 -0.861755  1.069393
>x = 'x', y = 'y', covar = 'cv1', x_covar = None, y_covar = None
>alternative = 'two-sided', method = 'pearson'
>    @pf.register_dataframe_method
>    def partial_corr(
>        data=None,
>        x=None,
>        y=None,
>        covar=None,
>        x_covar=None,
>        y_covar=None,
>        alternative="two-sided",
>        method="pearson",
>    ):
>        """Partial and semi-partial correlation.
>    
>        Parameters
>        ----------
>        data : :py:class:`pandas.DataFrame`
>            Pandas Dataframe. Note that this function can also directly be used
>            as a :py:class:`pandas.DataFrame` method, in which case this argument
>            is no longer needed.
>        x, y : string
>            x and y. Must be names of columns in ``data``.
>        covar : string or list
>            Covariate(s). Must be a names of columns in ``data``. Use a list if
>            there are two or more covariates.
>        x_covar : string or list
>            Covariate(s) for the ``x`` variable. This is used to compute
>            semi-partial correlation (i.e. the effect of ``x_covar`` is removed
>            from ``x`` but not from ``y``). Only one of ``covar``,  ``x_covar`` and
>            ``y_covar`` can be specified.
>        y_covar : string or list
>            Covariate(s) for the ``y`` variable. This is used to compute
>            semi-partial correlation (i.e. the effect of ``y_covar`` is removed
>            from ``y`` but not from ``x``). Only one of ``covar``,  ``x_covar`` and
>            ``y_covar`` can be specified.
>        alternative : string
>            Defines the alternative hypothesis, or tail of the partial correlation. Must be one of
>            "two-sided" (default), "greater" or "less". Both "greater" and "less" return a one-sided
>            p-value. "greater" tests against the alternative hypothesis that the partial correlation is
>            positive (greater than zero), "less" tests against the hypothesis that the partial
>            correlation is negative.
>        method : string
>            Correlation type:
>    
>            * ``'pearson'``: Pearson :math:`r` product-moment correlation
>            * ``'spearman'``: Spearman :math:`\\rho` rank-order correlation
>    
>        Returns
>        -------
>        stats : :py:class:`pandas.DataFrame`
>    
>            * ``'n'``: Sample size (after removal of missing values)
>            * ``'r'``: Partial correlation coefficient
>            * ``'CI95'``: 95% parametric confidence intervals around :math:`r`
>            * ``'p-val'``: p-value
>    
>        See also
>        --------
>        corr, pcorr, pairwise_corr, rm_corr
>    
>        Notes
>        -----
>        Partial correlation [1]_ measures the degree of association between ``x``
>        and ``y``, after removing the effect of one or more controlling variables
>        (``covar``, or :math:`Z`). Practically, this is achieved by calculating the
>        correlation coefficient between the residuals of two linear regressions:
>    
>        .. math:: x \\sim Z, y \\sim Z
>    
>        Like the correlation coefficient, the partial correlation
>        coefficient takes on a value in the range from â1 to 1, where 1 indicates a
>        perfect positive association.
>    
>        The semipartial correlation is similar to the partial correlation,
>        with the exception that the set of controlling variables is only
>        removed for either ``x`` or ``y``, but not both.
>    
>        Pingouin uses the method described in [2]_ to calculate the (semi)partial
>        correlation coefficients and associated p-values. This method is based on
>        the inverse covariance matrix and is significantly faster than the
>        traditional regression-based method. Results have been tested against the
>        `ppcor <https://cran.r-project.org/web/packages/ppcor/index.html>`_
>        R package.
>    
>        .. important:: Rows with missing values are automatically removed from
>            data.
>    
>        References
>        ----------
>        .. [1] https://en.wikipedia.org/wiki/Partial_correlation
>    
>        .. [2] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4681537/
>    
>        Examples
>        --------
>        1. Partial correlation with one covariate
>    
>        >>> import pingouin as pg
>        >>> df = pg.read_dataset('partial_corr')
>        >>> pg.partial_corr(data=df, x='x', y='y', covar='cv1').round(3)
>                  n      r         CI95%  p-val
>        pearson  30  0.568  [0.25, 0.77]  0.001
>    
>        2. Spearman partial correlation with several covariates
>    
>        >>> # Partial correlation of x and y controlling for cv1, cv2 and cv3
>        >>> pg.partial_corr(data=df, x='x', y='y', covar=['cv1', 'cv2', 'cv3'],
>        ...                 method='spearman').round(3)
>                   n      r         CI95%  p-val
>        spearman  30  0.521  [0.18, 0.75]  0.005
>    
>        3. Same but one-sided test
>    
>        >>> pg.partial_corr(data=df, x='x', y='y', covar=['cv1', 'cv2', 'cv3'],
>        ...                 alternative="greater", method='spearman').round(3)
>                   n      r        CI95%  p-val
>        spearman  30  0.521  [0.24, 1.0]  0.003
>    
>        >>> pg.partial_corr(data=df, x='x', y='y', covar=['cv1', 'cv2', 'cv3'],
>        ...                 alternative="less", method='spearman').round(3)
>                   n      r         CI95%  p-val
>        spearman  30  0.521  [-1.0, 0.72]  0.997
>    
>        4. As a pandas method
>    
>        >>> df.partial_corr(x='x', y='y', covar=['cv1'], method='spearman').round(3)
>                   n      r         CI95%  p-val
>        spearman  30  0.578  [0.27, 0.78]  0.001
>    
>        5. Partial correlation matrix (returns only the correlation coefficients)
>    
>        >>> df.pcorr().round(3)
>                 x      y    cv1    cv2    cv3
>        x    1.000  0.493 -0.095  0.130 -0.385
>        y    0.493  1.000 -0.007  0.104 -0.002
>        cv1 -0.095 -0.007  1.000 -0.241 -0.470
>        cv2  0.130  0.104 -0.241  1.000 -0.118
>        cv3 -0.385 -0.002 -0.470 -0.118  1.000
>    
>        6. Semi-partial correlation on x
>    
>        >>> pg.partial_corr(data=df, x='x', y='y', x_covar=['cv1', 'cv2', 'cv3']).round(3)
>                  n      r        CI95%  p-val
>        pearson  30  0.463  [0.1, 0.72]  0.015
>        """
>        from pingouin.utils import _flatten_list
>    
>        # Safety check
>        assert alternative in [
>            "two-sided",
>            "greater",
>            "less",
>        ], "Alternative must be one of 'two-sided' (default), 'greater' or 'less'."
>        assert method in [
>            "pearson",
>            "spearman",
>        ], 'only "pearson" and "spearman" are supported for partial correlation.'
>        assert isinstance(data, pd.DataFrame), "data must be a pandas DataFrame."
>        assert data.shape[0] > 2, "Data must have at least 3 samples."
>        if covar is not None and (x_covar is not None or y_covar is not None):
>            raise ValueError("Cannot specify both covar and {x,y}_covar.")
>        if x_covar is not None and y_covar is not None:
>            raise ValueError("Cannot specify both x_covar and y_covar.")
>        assert x != covar, "x and covar must be independent"
>        assert y != covar, "y and covar must be independent"
>        assert x != y, "x and y must be independent"
>        if isinstance(covar, list):
>            assert x not in covar, "x and covar must be independent"
>            assert y not in covar, "y and covar must be independent"
>        # Check that columns exist
>        col = _flatten_list([x, y, covar, x_covar, y_covar])
>>       assert all([c in data for c in col]), "columns are not in dataframe."
>E       AssertionError: columns are not in dataframe.
>_flatten_list = <function _flatten_list at 0xffff7ea32520>
>alternative = 'two-sided'
>col        = ['x', 'y', 'cv1', None, None]
>covar      = 'cv1'
>data       =            x         y       cv1       cv2       cv3
>0   5.230740  7.141488  1.825430 -1.085631 -0.255619
>1   6.013931...681  1.754886
>28  3.697089  6.129922  3.138867 -0.140069  1.495644
>29  5.466289  7.747689  1.053789 -0.861755  1.069393
>method     = 'pearson'
>x          = 'x'
>x_covar    = None
>y          = 'y'
>y_covar    = None
>pingouin/correlation.py:843: AssertionError
>_______________________ TestPairwise.test_pairwise_corr ________________________
>self = <pingouin.tests.test_pairwise.TestPairwise testMethod=test_pairwise_corr>
>    def test_pairwise_corr(self):
>        """Test function pairwise_corr"""
>        # Load JASP Big 5 DataSets (remove subject column)
>        data = read_dataset("pairwise_corr").iloc[:, 1:]
>        stats = pairwise_corr(data=data, method="pearson", alternative="two-sided")
>        jasp_rval = [-0.350, -0.01, -0.134, -0.368, 0.267, 0.055, 0.065, 0.159, -0.013, 0.159]
>        assert np.allclose(stats["r"].round(3).to_numpy(), jasp_rval)
>        assert stats["n"].to_numpy()[0] == 500
>        # Correct for multiple comparisons
>        pairwise_corr(data=data, method="spearman", alternative="greater", padjust="bonf")
>        # Check with a subset of columns
>        pairwise_corr(data=data, columns=["Neuroticism", "Extraversion"])
>        with pytest.raises(ValueError):
>            pairwise_corr(data=data, columns="wrong")
>        # Check with non-numeric columns
>        data["test"] = "test"
>        pairwise_corr(data=data, method="pearson")
>        # Check different variation of product / combination
>        n = data.shape[0]
>        data["Age"] = np.random.randint(18, 65, n)
>        data["IQ"] = np.random.normal(105, 1, n)
>        data["One"] = 1
>        data["Gender"] = np.repeat(["M", "F"], int(n / 2))
>        pairwise_corr(data, columns=["Neuroticism", "Gender"], method="shepherd")
>        pairwise_corr(data, columns=["Neuroticism", "Extraversion", "Gender"])
>        pairwise_corr(data, columns=["Neuroticism"])
>        pairwise_corr(data, columns="Neuroticism", method="skipped")
>        pairwise_corr(data, columns=[["Neuroticism"]], method="spearman")
>        pairwise_corr(data, columns=[["Neuroticism"], None], method="percbend")
>        pairwise_corr(data, columns=[["Neuroticism", "Gender"], ["Age"]])
>        pairwise_corr(data, columns=[["Neuroticism"], ["Age", "IQ"]])
>        pairwise_corr(data, columns=[["Age", "IQ"], []])
>        pairwise_corr(data, columns=["Age", "Gender", "IQ", "Wrong"])
>        pairwise_corr(data, columns=["Age", "Gender", "Wrong"])
>        # Test with no good combinations
>        with pytest.raises(ValueError):
>            pairwise_corr(data, columns=["Gender", "Gender"])
>        # Test when one column has only one unique value
>        pairwise_corr(data=data, columns=["Age", "One", "Gender"])
>        stats = pairwise_corr(data, columns=["Neuroticism", "IQ", "One"])
>        assert stats.shape[0] == 1
>        # Test with covariate
>>       pairwise_corr(data, covar="Age")
>data       =      Neuroticism  Extraversion  Openness  ...          IQ  One Gender
>0        2.47917       4.20833   3.93750  ...  1...  105.904904    1      F
>499      2.54167       3.56250   3.14583  ...  105.719773    1      F
>[500 rows x 10 columns]
>jasp_rval  = [-0.35, -0.01, -0.134, -0.368, 0.267, 0.055, ...]
>n          = 500
>self       = <pingouin.tests.test_pairwise.TestPairwise testMethod=test_pairwise_corr>
>stats      =              X   Y   method  ...     p-unc   BF10     power
>0  Neuroticism  IQ  pearson  ...  0.670885  0.061  0.070912
>[1 rows x 10 columns]
>pingouin/tests/test_pairwise.py:681: 
>_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
>pingouin/pairwise.py:1458: in pairwise_corr
>    cor_st = partial_corr(
>        X          = array(['Neuroticism', 'Neuroticism', 'Neuroticism', 'Neuroticism',
>       'Neuroticism', 'Neuroticism', 'Extraversion'...eness',
>       'Agreeableness', 'Agreeableness', 'Conscientiousness',
>       'Conscientiousness', 'Age'], dtype='<U17')
>        Y          = array(['Extraversion', 'Openness', 'Agreeableness', 'Conscientiousness',
>       'Age', 'IQ', 'Openness', 'Agreeablenes...ableness', 'Conscientiousness', 'Age', 'IQ',
>       'Conscientiousness', 'Age', 'IQ', 'Age', 'IQ', 'IQ'], dtype='<U17')
>        alternative = 'two-sided'
>        col1       = 'Neuroticism'
>        col2       = 'Extraversion'
>        columns    = None
>        combs      = array([['Neuroticism', 'Extraversion'],
>       ['Neuroticism', 'Openness'],
>       ['Neuroticism', 'Agreeableness'],
> ...', 'IQ'],
>       ['Conscientiousness', 'Age'],
>       ['Conscientiousness', 'IQ'],
>       ['Age', 'IQ']], dtype='<U17')
>        corr       = <function corr at 0xffff7d4e3d80>
>        covar      = ['Age']
>        data       =      Neuroticism  Extraversion  Openness  ...  Conscientiousness  Age          IQ
>0        2.47917       4.20833   3.9...8  105.904904
>499      2.54167       3.56250   3.14583  ...            2.89583   32  105.719773
>[500 rows x 7 columns]
>        i          = 0
>        keys       = ['Neuroticism', 'Extraversion', 'Openness', 'Agreeableness', 'Conscientiousness', 'Age', ...]
>        method     = 'pearson'
>        multi_index = False
>        nan_policy = 'pairwise'
>        old_options = {'round': None, 'round.column.BF10': <function _format_bf at 0xffff811ee700>, 'round.column.CI95%': 2}
>        padjust    = 'none'
>        partial_corr = <function partial_corr at 0xffff7d4e3e20>
>        stats      =                     X                  Y   method  ... p-val BF10 power
>0         Neuroticism       Extraversion  pear...n  ...   NaN  NaN   NaN
>14  Conscientiousness                 IQ  pearson  ...   NaN  NaN   NaN
>[15 rows x 11 columns]
>        traverse   = <function pairwise_corr.<locals>.traverse at 0xffff7252a7a0>
>_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
>data =      Neuroticism  Extraversion  Openness  ...  Conscientiousness  Age          IQ
>0        2.47917       4.20833   3.9...8  105.904904
>499      2.54167       3.56250   3.14583  ...            2.89583   32  105.719773
>[500 rows x 7 columns]
>x = 'Neuroticism', y = 'Extraversion', covar = ['Age'], x_covar = None
>y_covar = None, alternative = 'two-sided', method = 'pearson'
>    @pf.register_dataframe_method
>    def partial_corr(
>        data=None,
>        x=None,
>        y=None,
>        covar=None,
>        x_covar=None,
>        y_covar=None,
>        alternative="two-sided",
>        method="pearson",
>    ):
>        """Partial and semi-partial correlation.
>    
>        Parameters
>        ----------
>        data : :py:class:`pandas.DataFrame`
>            Pandas Dataframe. Note that this function can also directly be used
>            as a :py:class:`pandas.DataFrame` method, in which case this argument
>            is no longer needed.
>        x, y : string
>            x and y. Must be names of columns in ``data``.
>        covar : string or list
>            Covariate(s). Must be a names of columns in ``data``. Use a list if
>            there are two or more covariates.
>        x_covar : string or list
>            Covariate(s) for the ``x`` variable. This is used to compute
>            semi-partial correlation (i.e. the effect of ``x_covar`` is removed
>            from ``x`` but not from ``y``). Only one of ``covar``,  ``x_covar`` and
>            ``y_covar`` can be specified.
>        y_covar : string or list
>            Covariate(s) for the ``y`` variable. This is used to compute
>            semi-partial correlation (i.e. the effect of ``y_covar`` is removed
>            from ``y`` but not from ``x``). Only one of ``covar``,  ``x_covar`` and
>            ``y_covar`` can be specified.
>        alternative : string
>            Defines the alternative hypothesis, or tail of the partial correlation. Must be one of
>            "two-sided" (default), "greater" or "less". Both "greater" and "less" return a one-sided
>            p-value. "greater" tests against the alternative hypothesis that the partial correlation is
>            positive (greater than zero), "less" tests against the hypothesis that the partial
>            correlation is negative.
>        method : string
>            Correlation type:
>    
>            * ``'pearson'``: Pearson :math:`r` product-moment correlation
>            * ``'spearman'``: Spearman :math:`\\rho` rank-order correlation
>    
>        Returns
>        -------
>        stats : :py:class:`pandas.DataFrame`
>    
>            * ``'n'``: Sample size (after removal of missing values)
>            * ``'r'``: Partial correlation coefficient
>            * ``'CI95'``: 95% parametric confidence intervals around :math:`r`
>            * ``'p-val'``: p-value
>    
>        See also
>        --------
>        corr, pcorr, pairwise_corr, rm_corr
>    
>        Notes
>        -----
>        Partial correlation [1]_ measures the degree of association between ``x``
>        and ``y``, after removing the effect of one or more controlling variables
>        (``covar``, or :math:`Z`). Practically, this is achieved by calculating the
>        correlation coefficient between the residuals of two linear regressions:
>    
>        .. math:: x \\sim Z, y \\sim Z
>    
>        Like the correlation coefficient, the partial correlation
>        coefficient takes on a value in the range from â1 to 1, where 1 indicates a
>        perfect positive association.
>    
>        The semipartial correlation is similar to the partial correlation,
>        with the exception that the set of controlling variables is only
>        removed for either ``x`` or ``y``, but not both.
>    
>        Pingouin uses the method described in [2]_ to calculate the (semi)partial
>        correlation coefficients and associated p-values. This method is based on
>        the inverse covariance matrix and is significantly faster than the
>        traditional regression-based method. Results have been tested against the
>        `ppcor <https://cran.r-project.org/web/packages/ppcor/index.html>`_
>        R package.
>    
>        .. important:: Rows with missing values are automatically removed from
>            data.
>    
>        References
>        ----------
>        .. [1] https://en.wikipedia.org/wiki/Partial_correlation
>    
>        .. [2] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4681537/
>    
>        Examples
>        --------
>        1. Partial correlation with one covariate
>    
>        >>> import pingouin as pg
>        >>> df = pg.read_dataset('partial_corr')
>        >>> pg.partial_corr(data=df, x='x', y='y', covar='cv1').round(3)
>                  n      r         CI95%  p-val
>        pearson  30  0.568  [0.25, 0.77]  0.001
>    
>        2. Spearman partial correlation with several covariates
>    
>        >>> # Partial correlation of x and y controlling for cv1, cv2 and cv3
>        >>> pg.partial_corr(data=df, x='x', y='y', covar=['cv1', 'cv2', 'cv3'],
>        ...                 method='spearman').round(3)
>                   n      r         CI95%  p-val
>        spearman  30  0.521  [0.18, 0.75]  0.005
>    
>        3. Same but one-sided test
>    
>        >>> pg.partial_corr(data=df, x='x', y='y', covar=['cv1', 'cv2', 'cv3'],
>        ...                 alternative="greater", method='spearman').round(3)
>                   n      r        CI95%  p-val
>        spearman  30  0.521  [0.24, 1.0]  0.003
>    
>        >>> pg.partial_corr(data=df, x='x', y='y', covar=['cv1', 'cv2', 'cv3'],
>        ...                 alternative="less", method='spearman').round(3)
>                   n      r         CI95%  p-val
>        spearman  30  0.521  [-1.0, 0.72]  0.997
>    
>        4. As a pandas method
>    
>        >>> df.partial_corr(x='x', y='y', covar=['cv1'], method='spearman').round(3)
>                   n      r         CI95%  p-val
>        spearman  30  0.578  [0.27, 0.78]  0.001
>    
>        5. Partial correlation matrix (returns only the correlation coefficients)
>    
>        >>> df.pcorr().round(3)
>                 x      y    cv1    cv2    cv3
>        x    1.000  0.493 -0.095  0.130 -0.385
>        y    0.493  1.000 -0.007  0.104 -0.002
>        cv1 -0.095 -0.007  1.000 -0.241 -0.470
>        cv2  0.130  0.104 -0.241  1.000 -0.118
>        cv3 -0.385 -0.002 -0.470 -0.118  1.000
>    
>        6. Semi-partial correlation on x
>    
>        >>> pg.partial_corr(data=df, x='x', y='y', x_covar=['cv1', 'cv2', 'cv3']).round(3)
>                  n      r        CI95%  p-val
>        pearson  30  0.463  [0.1, 0.72]  0.015
>        """
>        from pingouin.utils import _flatten_list
>    
>        # Safety check
>        assert alternative in [
>            "two-sided",
>            "greater",
>            "less",
>        ], "Alternative must be one of 'two-sided' (default), 'greater' or 'less'."
>        assert method in [
>            "pearson",
>            "spearman",
>        ], 'only "pearson" and "spearman" are supported for partial correlation.'
>        assert isinstance(data, pd.DataFrame), "data must be a pandas DataFrame."
>        assert data.shape[0] > 2, "Data must have at least 3 samples."
>        if covar is not None and (x_covar is not None or y_covar is not None):
>            raise ValueError("Cannot specify both covar and {x,y}_covar.")
>        if x_covar is not None and y_covar is not None:
>            raise ValueError("Cannot specify both x_covar and y_covar.")
>        assert x != covar, "x and covar must be independent"
>        assert y != covar, "y and covar must be independent"
>        assert x != y, "x and y must be independent"
>        if isinstance(covar, list):
>            assert x not in covar, "x and covar must be independent"
>            assert y not in covar, "y and covar must be independent"
>        # Check that columns exist
>        col = _flatten_list([x, y, covar, x_covar, y_covar])
>>       assert all([c in data for c in col]), "columns are not in dataframe."
>E       AssertionError: columns are not in dataframe.
>_flatten_list = <function _flatten_list at 0xffff7ea32520>
>alternative = 'two-sided'
>col        = ['Neuroticism', 'Extraversion', 'Age', None, None]
>covar      = ['Age']
>data       =      Neuroticism  Extraversion  Openness  ...  Conscientiousness  Age          IQ
>0        2.47917       4.20833   3.9...8  105.904904
>499      2.54167       3.56250   3.14583  ...            2.89583   32  105.719773
>[500 rows x 7 columns]
>method     = 'pearson'
>x          = 'Neuroticism'
>x_covar    = None
>y          = 'Extraversion'
>y_covar    = None
>pingouin/correlation.py:843: AssertionError
>=============================== warnings summary ===============================
>../../../../usr/lib64/python3.12/site-packages/coverage/inorout.py:460
>  /usr/lib64/python3.12/site-packages/coverage/inorout.py:460: CoverageWarning: --include is ignored because --source is set (include-ignored)
>    self.warn("--include is ignored because --source is set", slug="include-ignored")
>../../../../usr/lib/python3.12/site-packages/pytz/tzinfo.py:27
>  /usr/lib/python3.12/site-packages/pytz/tzinfo.py:27: DeprecationWarning: datetime.utcfromtimestamp() is deprecated and scheduled for removal in a future version. Use timezone-aware objects to represent datetimes in UTC: datetime.fromtimestamp(timestamp, datetime.UTC).
>    _epoch = datetime.utcfromtimestamp(0)
>../../../../usr/lib/python3.12/site-packages/pkg_resources/__init__.py:121
>  /usr/lib/python3.12/site-packages/pkg_resources/__init__.py:121: DeprecationWarning: pkg_resources is deprecated as an API
>    warnings.warn("pkg_resources is deprecated as an API", DeprecationWarning)
>pingouin/tests/test_contingency.py::TestContingency::test_dichotomous_crosstab
>pingouin/tests/test_contingency.py::TestContingency::test_dichotomous_crosstab
>  /usr/lib/python3.12/site-packages/outdated/utils.py:14: OutdatedCheckFailedWarning: Failed to check for latest version of package.
>  Set the environment variable OUTDATED_RAISE_EXCEPTION=1 for a full traceback.
>  Set the environment variable OUTDATED_IGNORE=1 to disable these warnings.
>    return warn(
>pingouin/tests/test_correlation.py: 9 warnings
>pingouin/tests/test_distribution.py: 48 warnings
>pingouin/tests/test_pairwise.py: 6 warnings
>  /builddir/build/BUILD/pingouin-0.5.3/pingouin/utils.py:301: DeprecationWarning: NotImplemented should not be used in a boolean context
>    x = list(filter(None.__ne__, x))
>-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
>----------- coverage: platform linux, python 3.12.0-beta-4 -----------
>Name                        Stmts   Miss Branch BrPart  Cover   Missing
>-----------------------------------------------------------------------
>pingouin/__init__.py           23      0      0      0   100%
>pingouin/bayesian.py           76      0     20      0   100%
>pingouin/circular.py          103      0     12      0   100%
>pingouin/config.py              9      0      0      0   100%
>pingouin/contingency.py        80      0     40      1    99%   366->368
>pingouin/correlation.py       311     67    100      5    75%   64->71, 148->147, 642-643, 832, 834, 845-904, 952-957, 1073-1117
>pingouin/distribution.py      176      0     56      1    99%   467->470
>pingouin/effsize.py           182      0     90      0   100%
>pingouin/equivalence.py        14      0      0      0   100%
>pingouin/multicomp.py          81      0     24      0   100%
>pingouin/multivariate.py      103      0     14      0   100%
>pingouin/nonparametric.py     156      7     34      3    95%   456->458, 727->739, 816-822
>pingouin/pairwise.py          374    279    148      6    27%   28-29, 268-594, 716-765, 869-931, 1034-1100, 1325-1330, 1334-1340, 1394-1395, 1422-1423, 1432, 1441-1444, 1506
>pingouin/parametric.py        456    346    122      6    27%   228-229, 241, 248-249, 312, 510-669, 678-806, 967-1044, 1054-1131, 1141-1212, 1331-1371, 1467-1606, 1701->1703, 1729-1730
>pingouin/plotting.py          351    337     90      0     3%   115-193, 215-216, 332-407, 523-656, 747-893, 978-1022, 1087-1152
>pingouin/power.py             282    186    118     16    31%   128, 141->143, 144, 175-210, 295-366, 471-538, 693-766, 842, 851->855, 855->857, 858, 859->865, 861-862, 906-938, 1022-1023, 1026->1028, 1028->1030, 1031, 1043-1072
>pingouin/regression.py        331    306    114      0     6%   315-512, 528-599, 826-949, 955-968, 1016-1020, 1203-1330
>pingouin/reliability.py        85     77     10      0     8%   125-156, 259-379
>pingouin/utils.py             159     30     77     15    76%   53, 76-82, 131, 137->142, 141, 233, 299, 313, 349-362, 365, 368, 371, 374, 377, 380, 383-385
>-----------------------------------------------------------------------
>TOTAL                        3352   1635   1069     53    51%
>============================= slowest 10 durations =============================
>51.02s call     pingouin/tests/test_effsize.py::TestEffsize::test_compute_boot_esci
>2.16s call     pingouin/tests/test_pairwise.py::TestPairwise::test_pairwise_corr
>0.47s call     pingouin/tests/test_correlation.py::TestCorrelation::test_corr
>0.31s call     pingouin/tests/test_distribution.py::TestDistribution::test_epsilon
>0.27s call     pingouin/tests/test_equivalence.py::TestEquivalence::test_tost
>0.22s call     pingouin/tests/test_correlation.py::TestCorrelation::test_distance_corr
>0.15s call     pingouin/tests/test_distribution.py::TestDistribution::test_sphericity
>0.14s call     pingouin/tests/test_contingency.py::TestContingency::test_chi2_independence
>0.09s call     pingouin/tests/test_bayesian.py::TestBayesian::test_bayesfactor_binom
>0.09s call     pingouin/tests/test_bayesian.py::TestBayesian::test_bayesfactor_pearson
>=========================== short test summary info ============================
>FAILED pingouin/tests/test_correlation.py::TestCorrelation::test_partial_corr
>FAILED pingouin/tests/test_pairwise.py::TestPairwise::test_pairwise_corr - As...
>!!!!!!!!!!!!!!!!!!!!!!!!!! stopping after 2 failures !!!!!!!!!!!!!!!!!!!!!!!!!!!
>============= 2 failed, 48 passed, 68 warnings in 63.26s (0:01:03) =============
>error: Bad exit status from /var/tmp/rpm-tmp.xLiOc9 (%check)
>RPM build errors:
>    Bad exit status from /var/tmp/rpm-tmp.xLiOc9 (%check)
>Child return code was: 1
>EXCEPTION: [Error('Command failed: \n # /usr/bin/systemd-nspawn -q -M 954974351d88490db0c31ff9e2eec846 -D /var/lib/mock/f39-build-44306099-5275604/root -a -u mockbuild --capability=cap_ipc_lock --bind=/tmp/mock-resolv.hg_7x8tj:/etc/resolv.conf --bind=/dev/btrfs-control --bind=/dev/mapper/control --bind=/dev/loop-control --bind=/dev/loop0 --bind=/dev/loop1 --bind=/dev/loop2 --bind=/dev/loop3 --bind=/dev/loop4 --bind=/dev/loop5 --bind=/dev/loop6 --bind=/dev/loop7 --bind=/dev/loop8 --bind=/dev/loop9 --bind=/dev/loop10 --bind=/dev/loop11 --console=pipe --setenv=TERM=vt100 --setenv=SHELL=/bin/bash --setenv=HOME=/builddir --setenv=HOSTNAME=mock --setenv=PATH=/usr/bin:/bin:/usr/sbin:/sbin --setenv=PROMPT_COMMAND=printf "\\033]0;<mock-chroot>\\007" --setenv=PS1=<mock-chroot> \\s-\\v\\$  --setenv=LANG=C.UTF-8 --resolv-conf=off bash --login -c /usr/bin/rpmbuild -ba --noprep --noclean --target aarch64 --nodeps /builddir/build/SPECS/python-pingouin.spec\n', 1)]
>Traceback (most recent call last):
>  File "/usr/lib/python3.11/site-packages/mockbuild/trace_decorator.py", line 93, in trace
>    result = func(*args, **kw)
>             ^^^^^^^^^^^^^^^^^
>  File "/usr/lib/python3.11/site-packages/mockbuild/util.py", line 597, in do_with_status
>    raise exception.Error("Command failed: \n # %s\n%s" % (command, output), child.returncode)
>mockbuild.exception.Error: Command failed: 
> # /usr/bin/systemd-nspawn -q -M 954974351d88490db0c31ff9e2eec846 -D /var/lib/mock/f39-build-44306099-5275604/root -a -u mockbuild --capability=cap_ipc_lock --bind=/tmp/mock-resolv.hg_7x8tj:/etc/resolv.conf --bind=/dev/btrfs-control --bind=/dev/mapper/control --bind=/dev/loop-control --bind=/dev/loop0 --bind=/dev/loop1 --bind=/dev/loop2 --bind=/dev/loop3 --bind=/dev/loop4 --bind=/dev/loop5 --bind=/dev/loop6 --bind=/dev/loop7 --bind=/dev/loop8 --bind=/dev/loop9 --bind=/dev/loop10 --bind=/dev/loop11 --console=pipe --setenv=TERM=vt100 --setenv=SHELL=/bin/bash --setenv=HOME=/builddir --setenv=HOSTNAME=mock --setenv=PATH=/usr/bin:/bin:/usr/sbin:/sbin --setenv=PROMPT_COMMAND=printf "\033]0;<mock-chroot>\007" --setenv=PS1=<mock-chroot> \s-\v\$  --setenv=LANG=C.UTF-8 --resolv-conf=off bash --login -c /usr/bin/rpmbuild -ba --noprep --noclean --target aarch64 --nodeps /builddir/build/SPECS/python-pingouin.spec
>

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Attachments on bug 2226012: 1978456 | 1978457 | 1978458