Source: scipy, statsmodels Control: found -1 scipy/1.7.1-1 Control: found -1 statsmodels/0.12.2-1 Severity: serious Tags: sid bookworm X-Debbugs-CC: debian-ci@lists.debian.org User: debian-ci@lists.debian.org Usertags: breaks needs-update Dear maintainer(s), With a recent upload of scipy the autopkgtest of statsmodels fails in testing when that autopkgtest is run with the binary packages of scipy from unstable. It passes when run with only packages from testing. In tabular form: pass fail scipy from testing 1.7.1-1 statsmodels from testing 0.12.2-1 all others from testing from testing I copied some of the output at the bottom of this report. Currently this regression is blocking the migration of scipy to testing [1]. Due to the nature of this issue, I filed this bug report against both packages. Can you please investigate the situation and reassign the bug to the right package? More information about this bug and the reason for filing it can be found on https://wiki.debian.org/ContinuousIntegration/RegressionEmailInformation Paul [1] https://qa.debian.org/excuses.php?package=scipy https://ci.debian.net/data/autopkgtest/testing/amd64/s/statsmodels/14751207/log.gz =================================== FAILURES =================================== ______________ TestZeroInflatedPoisson_predict.test_predict_prob _______________ self = <statsmodels.discrete.tests.test_count_model.TestZeroInflatedPoisson_predict object at 0x7f5324966a60> def test_predict_prob(self): res = self.res > pr = res.predict(which='prob') /usr/lib/python3/dist-packages/statsmodels/discrete/tests/test_count_model.py:267: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib/python3/dist-packages/statsmodels/base/model.py:1099: in predict predict_results = self.model.predict(self.params, exog, *args, /usr/lib/python3/dist-packages/statsmodels/discrete/count_model.py:451: in predict return self._predict_prob(params, exog, exog_infl, exposure, offset) /usr/lib/python3/dist-packages/statsmodels/discrete/count_model.py:535: in _predict_prob result = self.distribution.pmf(counts, mu, w) /usr/lib/python3/dist-packages/scipy/stats/_distn_infrastructure.py:3150: in pmf place(output, cond, np.clip(self._pmf(*goodargs), 0, 1)) /usr/lib/python3/dist-packages/statsmodels/distributions/discrete.py:40: in _pmf return np.exp(self._logpmf(x, mu, w)) /usr/lib/python3/dist-packages/statsmodels/distributions/discrete.py:34: in _logpmf return _lazywhere(x != 0, (x, mu, w), /usr/lib/python3/dist-packages/statsmodels/compat/scipy.py:97: in _lazywhere np.place(out, cond, f(*temp)) <__array_function__ internals>:5: in place ??? _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ arr = array([[-1.73119153, -1.73119153, -1.73119153, ..., -1.73119153, -1.73119153, -1.73119153], [-1.7311915...895, -1.28321895], [-1.28321895, -1.28321895, -1.28321895, ..., -1.28321895, -1.28321895, -1.28321895]]) mask = array([[False, True, True, True, True, True, True, True, True, True]]) vals = array([-1.37428414, -1.36072044, -1.75262184, -2.43220532, -3.33493235, -4.41998093, -5.6591802 , -7.03191085, -8.52242455]) @array_function_dispatch(_place_dispatcher) def place(arr, mask, vals): """ Change elements of an array based on conditional and input values. Similar to ``np.copyto(arr, vals, where=mask)``, the difference is that `place` uses the first N elements of `vals`, where N is the number of True values in `mask`, while `copyto` uses the elements where `mask` is True. Note that `extract` does the exact opposite of `place`. Parameters ---------- arr : ndarray Array to put data into. mask : array_like Boolean mask array. Must have the same size as `a`. vals : 1-D sequence Values to put into `a`. Only the first N elements are used, where N is the number of True values in `mask`. If `vals` is smaller than N, it will be repeated, and if elements of `a` are to be masked, this sequence must be non-empty. See Also -------- copyto, put, take, extract Examples -------- >>> arr = np.arange(6).reshape(2, 3) >>> np.place(arr, arr>2, [44, 55]) >>> arr array([[ 0, 1, 2], [44, 55, 44]]) """ if not isinstance(arr, np.ndarray): raise TypeError("argument 1 must be numpy.ndarray, " "not {name}".format(name=type(arr).__name__)) > return _insert(arr, mask, vals) E ValueError: place: mask and data must be the same size /usr/lib/python3/dist-packages/numpy/lib/function_base.py:1742: ValueError _________ TestZeroInflatedGeneralizedPoisson_predict.test_predict_prob _________ self = <statsmodels.discrete.tests.test_count_model.TestZeroInflatedGeneralizedPoisson_predict object at 0x7f5324d31340> def test_predict_prob(self): res = self.res > pr = res.predict(which='prob') /usr/lib/python3/dist-packages/statsmodels/discrete/tests/test_count_model.py:397: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib/python3/dist-packages/statsmodels/base/model.py:1099: in predict predict_results = self.model.predict(self.params, exog, *args, /usr/lib/python3/dist-packages/statsmodels/discrete/count_model.py:451: in predict return self._predict_prob(params, exog, exog_infl, exposure, offset) /usr/lib/python3/dist-packages/statsmodels/discrete/count_model.py:610: in _predict_prob result = self.distribution.pmf(counts, mu, params_main[-1], p, w) /usr/lib/python3/dist-packages/scipy/stats/_distn_infrastructure.py:3150: in pmf place(output, cond, np.clip(self._pmf(*goodargs), 0, 1)) /usr/lib/python3/dist-packages/statsmodels/distributions/discrete.py:83: in _pmf return np.exp(self._logpmf(x, mu, alpha, p, w)) /usr/lib/python3/dist-packages/statsmodels/distributions/discrete.py:76: in _logpmf return _lazywhere(x != 0, (x, mu, alpha, p, w), /usr/lib/python3/dist-packages/statsmodels/compat/scipy.py:97: in _lazywhere np.place(out, cond, f(*temp)) <__array_function__ internals>:5: in place ??? _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ arr = array([[-0.45911613, -0.48614313, -0.5506312 , ..., -0.71569584, -0.71569958, -0.71570203], [-0.4591161...405, -0.715705 ], [-0.3476652 , -0.48199281, -0.58039586, ..., -0.71570259, -0.71570405, -0.715705 ]]) mask = array([[False, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True]]) vals = array([ -2.07030493, -2.43338553, -2.86597292, -3.32288546, -3.78777419, -4.2538559 , -4.71814042, -5.17...55133, -10.45451419, -10.87752249, -11.29869988, -11.71815969, -12.13600587, -12.55233382, -12.96723119]) @array_function_dispatch(_place_dispatcher) def place(arr, mask, vals): """ Change elements of an array based on conditional and input values. Similar to ``np.copyto(arr, vals, where=mask)``, the difference is that `place` uses the first N elements of `vals`, where N is the number of True values in `mask`, while `copyto` uses the elements where `mask` is True. Note that `extract` does the exact opposite of `place`. Parameters ---------- arr : ndarray Array to put data into. mask : array_like Boolean mask array. Must have the same size as `a`. vals : 1-D sequence Values to put into `a`. Only the first N elements are used, where N is the number of True values in `mask`. If `vals` is smaller than N, it will be repeated, and if elements of `a` are to be masked, this sequence must be non-empty. See Also -------- copyto, put, take, extract Examples -------- >>> arr = np.arange(6).reshape(2, 3) >>> np.place(arr, arr>2, [44, 55]) >>> arr array([[ 0, 1, 2], [44, 55, 44]]) """ if not isinstance(arr, np.ndarray): raise TypeError("argument 1 must be numpy.ndarray, " "not {name}".format(name=type(arr).__name__)) > return _insert(arr, mask, vals) E ValueError: place: mask and data must be the same size /usr/lib/python3/dist-packages/numpy/lib/function_base.py:1742: ValueError _________ TestZeroInflatedNegativeBinomialP_predict.test_predict_prob __________ self = <statsmodels.discrete.tests.test_count_model.TestZeroInflatedNegativeBinomialP_predict object at 0x7f5324466c10> def test_predict_prob(self): res = self.res endog = res.model.endog > pr = res.predict(which='prob') /usr/lib/python3/dist-packages/statsmodels/discrete/tests/test_count_model.py:542: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib/python3/dist-packages/statsmodels/base/model.py:1099: in predict predict_results = self.model.predict(self.params, exog, *args, /usr/lib/python3/dist-packages/statsmodels/discrete/count_model.py:451: in predict return self._predict_prob(params, exog, exog_infl, exposure, offset) /usr/lib/python3/dist-packages/statsmodels/discrete/count_model.py:689: in _predict_prob result = self.distribution.pmf(counts, mu, params_main[-1], p, w) /usr/lib/python3/dist-packages/scipy/stats/_distn_infrastructure.py:3150: in pmf place(output, cond, np.clip(self._pmf(*goodargs), 0, 1)) /usr/lib/python3/dist-packages/statsmodels/distributions/discrete.py:115: in _pmf return np.exp(self._logpmf(x, mu, alpha, p, w)) /usr/lib/python3/dist-packages/statsmodels/distributions/discrete.py:108: in _logpmf return _lazywhere(x != 0, (x, s, p, w), /usr/lib/python3/dist-packages/statsmodels/compat/scipy.py:97: in _lazywhere np.place(out, cond, f(*temp)) <__array_function__ internals>:5: in place ??? _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ arr = array([[-1.16651535, -1.09866187, -1.17857054, ..., -1.85937182, -1.85937187, -1.8593719 ], [-1.1665153...622, -1.85793214], [-1.64298738, -1.53672373, -1.48753839, ..., -1.8571836 , -1.85759622, -1.85793214]]) mask = array([False, True, True, True, True, True, True, True, True, True, True, True, True, True, True,... True, True, True, True, True, True, True, True, True, True, True, True, True, True, True]) vals = array([ -1.72852346, -1.88421783, -2.15741638, -2.49498617, -2.87326646, -3.27963799, -3.70656908, -4.14...4252 , -15.76582549, -16.29519967, -16.82548955, -17.35664206, -17.88860857, -18.42134447, -18.95480873]) @array_function_dispatch(_place_dispatcher) def place(arr, mask, vals): """ Change elements of an array based on conditional and input values. Similar to ``np.copyto(arr, vals, where=mask)``, the difference is that `place` uses the first N elements of `vals`, where N is the number of True values in `mask`, while `copyto` uses the elements where `mask` is True. Note that `extract` does the exact opposite of `place`. Parameters ---------- arr : ndarray Array to put data into. mask : array_like Boolean mask array. Must have the same size as `a`. vals : 1-D sequence Values to put into `a`. Only the first N elements are used, where N is the number of True values in `mask`. If `vals` is smaller than N, it will be repeated, and if elements of `a` are to be masked, this sequence must be non-empty. See Also -------- copyto, put, take, extract Examples -------- >>> arr = np.arange(6).reshape(2, 3) >>> np.place(arr, arr>2, [44, 55]) >>> arr array([[ 0, 1, 2], [44, 55, 44]]) """ if not isinstance(arr, np.ndarray): raise TypeError("argument 1 must be numpy.ndarray, " "not {name}".format(name=type(arr).__name__)) > return _insert(arr, mask, vals) E ValueError: place: mask and data must be the same size /usr/lib/python3/dist-packages/numpy/lib/function_base.py:1742: ValueError ______ TestZeroInflatedNegativeBinomialP_predict.test_predict_generic_zi _______ self = <statsmodels.discrete.tests.test_count_model.TestZeroInflatedNegativeBinomialP_predict object at 0x7f5324e73fa0> def test_predict_generic_zi(self): # These tests do not use numbers from other packages. # Tests are on closeness of estimated to true/DGP values # and theoretical relationship between quantities res = self.res endog = self.endog exog = self.res.model.exog prob_infl = self.prob_infl nobs = len(endog) freq = np.bincount(endog.astype(int)) / len(endog) > probs = res.predict(which='prob') /usr/lib/python3/dist-packages/statsmodels/discrete/tests/test_count_model.py:563: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib/python3/dist-packages/statsmodels/base/model.py:1099: in predict predict_results = self.model.predict(self.params, exog, *args, /usr/lib/python3/dist-packages/statsmodels/discrete/count_model.py:451: in predict return self._predict_prob(params, exog, exog_infl, exposure, offset) /usr/lib/python3/dist-packages/statsmodels/discrete/count_model.py:689: in _predict_prob result = self.distribution.pmf(counts, mu, params_main[-1], p, w) /usr/lib/python3/dist-packages/scipy/stats/_distn_infrastructure.py:3150: in pmf place(output, cond, np.clip(self._pmf(*goodargs), 0, 1)) /usr/lib/python3/dist-packages/statsmodels/distributions/discrete.py:115: in _pmf return np.exp(self._logpmf(x, mu, alpha, p, w)) /usr/lib/python3/dist-packages/statsmodels/distributions/discrete.py:108: in _logpmf return _lazywhere(x != 0, (x, s, p, w), /usr/lib/python3/dist-packages/statsmodels/compat/scipy.py:97: in _lazywhere np.place(out, cond, f(*temp)) <__array_function__ internals>:5: in place ??? _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ arr = array([[-1.16651535, -1.09866187, -1.17857054, ..., -1.85937182, -1.85937187, -1.8593719 ], [-1.1665153...622, -1.85793214], [-1.64298738, -1.53672373, -1.48753839, ..., -1.8571836 , -1.85759622, -1.85793214]]) mask = array([False, True, True, True, True, True, True, True, True, True, True, True, True, True, True,... True, True, True, True, True, True, True, True, True, True, True, True, True, True, True]) vals = array([ -1.72852346, -1.88421783, -2.15741638, -2.49498617, -2.87326646, -3.27963799, -3.70656908, -4.14...4252 , -15.76582549, -16.29519967, -16.82548955, -17.35664206, -17.88860857, -18.42134447, -18.95480873]) @array_function_dispatch(_place_dispatcher) def place(arr, mask, vals): """ Change elements of an array based on conditional and input values. Similar to ``np.copyto(arr, vals, where=mask)``, the difference is that `place` uses the first N elements of `vals`, where N is the number of True values in `mask`, while `copyto` uses the elements where `mask` is True. Note that `extract` does the exact opposite of `place`. Parameters ---------- arr : ndarray Array to put data into. mask : array_like Boolean mask array. Must have the same size as `a`. vals : 1-D sequence Values to put into `a`. Only the first N elements are used, where N is the number of True values in `mask`. If `vals` is smaller than N, it will be repeated, and if elements of `a` are to be masked, this sequence must be non-empty. See Also -------- copyto, put, take, extract Examples -------- >>> arr = np.arange(6).reshape(2, 3) >>> np.place(arr, arr>2, [44, 55]) >>> arr array([[ 0, 1, 2], [44, 55, 44]]) """ if not isinstance(arr, np.ndarray): raise TypeError("argument 1 must be numpy.ndarray, " "not {name}".format(name=type(arr).__name__)) > return _insert(arr, mask, vals) E ValueError: place: mask and data must be the same size /usr/lib/python3/dist-packages/numpy/lib/function_base.py:1742: ValueError _____________________________ test_extension_types _____________________________ df = a b c d 0 1.764052 0 NaN <NA> 1 0.400157 1 1.0 1 2 0.978738 2 NaN <NA> 3 2.... NaN <NA> 97 1.785870 7 97.0 97 98 0.126912 8 NaN <NA> 99 0.401989 9 99.0 99 [100 rows x 4 columns] @pytest.mark.skipif(not hasattr(pd, "NA"), reason="Must support NA") def test_extension_types(df): df["c"] = pd.Series(np.arange(100.0)) df["d"] = pd.Series(np.arange(100), dtype=pd.Int64Dtype()) df.loc[df.index[::2], "c"] = np.nan df.loc[df.index[::2], "d"] = pd.NA res = Description(df) > np.testing.assert_allclose(res.frame.c, res.frame.d) /usr/lib/python3/dist-packages/statsmodels/stats/tests/test_descriptivestats.py:212: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ pandas/_libs/properties.pyx:33: in pandas._libs.properties.CachedProperty.__get__ ??? /usr/lib/python3/dist-packages/statsmodels/stats/descriptivestats.py:384: in frame numeric = self.numeric pandas/_libs/properties.pyx:33: in pandas._libs.properties.CachedProperty.__get__ ??? /usr/lib/python3/dist-packages/statsmodels/stats/descriptivestats.py:449: in numeric jb = df.apply( /usr/lib/python3/dist-packages/pandas/core/frame.py:7552: in apply return op.get_result() /usr/lib/python3/dist-packages/pandas/core/apply.py:185: in get_result return self.apply_standard() /usr/lib/python3/dist-packages/pandas/core/apply.py:276: in apply_standard results, res_index = self.apply_series_generator() /usr/lib/python3/dist-packages/pandas/core/apply.py:305: in apply_series_generator results[i] = self.f(v) /usr/lib/python3/dist-packages/statsmodels/stats/descriptivestats.py:450: in <lambda> lambda x: list(jarque_bera(x.dropna())), result_type="expand" /usr/lib/python3/dist-packages/statsmodels/stats/stattools.py:123: in jarque_bera skew = stats.skew(resids, axis=axis) _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ a = array([1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 47, 49, 51, 53, 55, 57, 59, 61, 63, 65, 67, 69, 71, 73, 75, 77, 79, 81, 83, 85, 87, 89, 91, 93, 95, 97, 99], dtype=object) axis = 0, bias = True, nan_policy = 'propagate' def skew(a, axis=0, bias=True, nan_policy='propagate'): r"""Compute the sample skewness of a data set. For normally distributed data, the skewness should be about zero. For unimodal continuous distributions, a skewness value greater than zero means that there is more weight in the right tail of the distribution. The function `skewtest` can be used to determine if the skewness value is close enough to zero, statistically speaking. Parameters ---------- a : ndarray Input array. axis : int or None, optional Axis along which skewness is calculated. Default is 0. If None, compute over the whole array `a`. bias : bool, optional If False, then the calculations are corrected for statistical bias. nan_policy : {'propagate', 'raise', 'omit'}, optional Defines how to handle when input contains nan. The following options are available (default is 'propagate'): * 'propagate': returns nan * 'raise': throws an error * 'omit': performs the calculations ignoring nan values Returns ------- skewness : ndarray The skewness of values along an axis, returning 0 where all values are equal. Notes ----- The sample skewness is computed as the Fisher-Pearson coefficient of skewness, i.e. .. math:: g_1=\frac{m_3}{m_2^{3/2}} where .. math:: m_i=\frac{1}{N}\sum_{n=1}^N(x[n]-\bar{x})^i is the biased sample :math:`i\texttt{th}` central moment, and :math:`\bar{x}` is the sample mean. If ``bias`` is False, the calculations are corrected for bias and the value computed is the adjusted Fisher-Pearson standardized moment coefficient, i.e. .. math:: G_1=\frac{k_3}{k_2^{3/2}}= \frac{\sqrt{N(N-1)}}{N-2}\frac{m_3}{m_2^{3/2}}. References ---------- .. [1] Zwillinger, D. and Kokoska, S. (2000). CRC Standard Probability and Statistics Tables and Formulae. Chapman & Hall: New York. 2000. Section 2.2.24.1 Examples -------- >>> from scipy.stats import skew >>> skew([1, 2, 3, 4, 5]) 0.0 >>> skew([2, 8, 0, 4, 1, 9, 9, 0]) 0.2650554122698573 """ a, axis = _chk_asarray(a, axis) n = a.shape[axis] contains_nan, nan_policy = _contains_nan(a, nan_policy) if contains_nan and nan_policy == 'omit': a = ma.masked_invalid(a) return mstats_basic.skew(a, axis, bias) mean = a.mean(axis, keepdims=True) m2 = _moment(a, 2, axis, mean=mean) m3 = _moment(a, 3, axis, mean=mean) with np.errstate(all='ignore'): > zero = (m2 <= (np.finfo(m2.dtype).resolution * mean.squeeze(axis))**2) E AttributeError: 'float' object has no attribute 'dtype' /usr/lib/python3/dist-packages/scipy/stats/stats.py:1111: AttributeError _________ TestDistDependenceMeasures.test_results_on_the_iris_dataset __________ self = <statsmodels.stats.tests.test_dist_dependant_measures.TestDistDependenceMeasures object at 0x7f5317f90cd0> def test_results_on_the_iris_dataset(self): """ R code example from the `energy` package documentation for `energy::distance_covariance.test`: > x <- iris[1:50, 1:4] > y <- iris[51:100, 1:4] > set.seed(1) > dcov.test(x, y, R=200) dCov independence test (permutation test) data: index 1, replicates 200 nV^2 = 0.5254, p-value = 0.9552 sample estimates: dCov 0.1025087 """ try: iris = get_rdataset("iris", cache=True).data.values[:, :4] except IGNORED_EXCEPTIONS: pytest.skip('Failed with HTTPError or URLError, these are random') x = iris[:50] y = iris[50:100] > stats = ddm.distance_statistics(x, y) /usr/lib/python3/dist-packages/statsmodels/stats/tests/test_dist_dependant_measures.py:147: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib/python3/dist-packages/statsmodels/stats/dist_dependence_measures.py:355: in distance_statistics a = x_dist if x_dist is not None else squareform(pdist(x, "euclidean")) _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ X = array([[5.1, 3.5, 1.4, 0.2], [4.9, 3.0, 1.4, 0.2], [4.7, 3.2, 1.3, 0.2], [4.6, 3.1, 1.5, 0.2], ..., 3.8, 1.6, 0.2], [4.6, 3.2, 1.4, 0.2], [5.3, 3.7, 1.5, 0.2], [5.0, 3.3, 1.4, 0.2]], dtype=object) metric = 'euclidean', out = None, kwargs = {}, s = (50, 4), m = 50, n = 4 mstr = 'euclidean' metric_info = MetricInfo(canonical_name='euclidean', aka={'eu', 'euclidean', 'euclid', 'e'}, dist_func=<function euclidean at 0x7f53...pdist_euclidean of PyCapsule object at 0x7f53360b7390>, validator=None, types=['double'], requires_contiguous_out=True) def pdist(X, metric='euclidean', *, out=None, **kwargs): """ Pairwise distances between observations in n-dimensional space. See Notes for common calling conventions. Parameters ---------- X : array_like An m by n array of m original observations in an n-dimensional space. metric : str or function, optional The distance metric to use. The distance function can be 'braycurtis', 'canberra', 'chebyshev', 'cityblock', 'correlation', 'cosine', 'dice', 'euclidean', 'hamming', 'jaccard', 'jensenshannon', 'kulsinski', 'mahalanobis', 'matching', 'minkowski', 'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'yule'. **kwargs : dict, optional Extra arguments to `metric`: refer to each metric documentation for a list of all possible arguments. Some possible arguments: p : scalar The p-norm to apply for Minkowski, weighted and unweighted. Default: 2. w : ndarray The weight vector for metrics that support weights (e.g., Minkowski). V : ndarray The variance vector for standardized Euclidean. Default: var(X, axis=0, ddof=1) VI : ndarray The inverse of the covariance matrix for Mahalanobis. Default: inv(cov(X.T)).T out : ndarray. The output array If not None, condensed distance matrix Y is stored in this array. Returns ------- Y : ndarray Returns a condensed distance matrix Y. For each :math:`i` and :math:`j` (where :math:`i<j<m`),where m is the number of original observations. The metric ``dist(u=X[i], v=X[j])`` is computed and stored in entry ``m * i + j - ((i + 2) * (i + 1)) // 2``. See Also -------- squareform : converts between condensed distance matrices and square distance matrices. Notes ----- See ``squareform`` for information on how to calculate the index of this entry or to convert the condensed distance matrix to a redundant square matrix. The following are common calling conventions. 1. ``Y = pdist(X, 'euclidean')`` Computes the distance between m points using Euclidean distance (2-norm) as the distance metric between the points. The points are arranged as m n-dimensional row vectors in the matrix X. 2. ``Y = pdist(X, 'minkowski', p=2.)`` Computes the distances using the Minkowski distance :math:`||u-v||_p` (p-norm) where :math:`p \\geq 1`. 3. ``Y = pdist(X, 'cityblock')`` Computes the city block or Manhattan distance between the points. 4. ``Y = pdist(X, 'seuclidean', V=None)`` Computes the standardized Euclidean distance. The standardized Euclidean distance between two n-vectors ``u`` and ``v`` is .. math:: \\sqrt{\\sum {(u_i-v_i)^2 / V[x_i]}} V is the variance vector; V[i] is the variance computed over all the i'th components of the points. If not passed, it is automatically computed. 5. ``Y = pdist(X, 'sqeuclidean')`` Computes the squared Euclidean distance :math:`||u-v||_2^2` between the vectors. 6. ``Y = pdist(X, 'cosine')`` Computes the cosine distance between vectors u and v, .. math:: 1 - \\frac{u \\cdot v} {{||u||}_2 {||v||}_2} where :math:`||*||_2` is the 2-norm of its argument ``*``, and :math:`u \\cdot v` is the dot product of ``u`` and ``v``. 7. ``Y = pdist(X, 'correlation')`` Computes the correlation distance between vectors u and v. This is .. math:: 1 - \\frac{(u - \\bar{u}) \\cdot (v - \\bar{v})} {{||(u - \\bar{u})||}_2 {||(v - \\bar{v})||}_2} where :math:`\\bar{v}` is the mean of the elements of vector v, and :math:`x \\cdot y` is the dot product of :math:`x` and :math:`y`. 8. ``Y = pdist(X, 'hamming')`` Computes the normalized Hamming distance, or the proportion of those vector elements between two n-vectors ``u`` and ``v`` which disagree. To save memory, the matrix ``X`` can be of type boolean. 9. ``Y = pdist(X, 'jaccard')`` Computes the Jaccard distance between the points. Given two vectors, ``u`` and ``v``, the Jaccard distance is the proportion of those elements ``u[i]`` and ``v[i]`` that disagree. 10. ``Y = pdist(X, 'jensenshannon')`` Computes the Jensen-Shannon distance between two probability arrays. Given two probability vectors, :math:`p` and :math:`q`, the Jensen-Shannon distance is .. math:: \\sqrt{\\frac{D(p \\parallel m) + D(q \\parallel m)}{2}} where :math:`m` is the pointwise mean of :math:`p` and :math:`q` and :math:`D` is the Kullback-Leibler divergence. 11. ``Y = pdist(X, 'chebyshev')`` Computes the Chebyshev distance between the points. The Chebyshev distance between two n-vectors ``u`` and ``v`` is the maximum norm-1 distance between their respective elements. More precisely, the distance is given by .. math:: d(u,v) = \\max_i {|u_i-v_i|} 12. ``Y = pdist(X, 'canberra')`` Computes the Canberra distance between the points. The Canberra distance between two points ``u`` and ``v`` is .. math:: d(u,v) = \\sum_i \\frac{|u_i-v_i|} {|u_i|+|v_i|} 13. ``Y = pdist(X, 'braycurtis')`` Computes the Bray-Curtis distance between the points. The Bray-Curtis distance between two points ``u`` and ``v`` is .. math:: d(u,v) = \\frac{\\sum_i {|u_i-v_i|}} {\\sum_i {|u_i+v_i|}} 14. ``Y = pdist(X, 'mahalanobis', VI=None)`` Computes the Mahalanobis distance between the points. The Mahalanobis distance between two points ``u`` and ``v`` is :math:`\\sqrt{(u-v)(1/V)(u-v)^T}` where :math:`(1/V)` (the ``VI`` variable) is the inverse covariance. If ``VI`` is not None, ``VI`` will be used as the inverse covariance matrix. 15. ``Y = pdist(X, 'yule')`` Computes the Yule distance between each pair of boolean vectors. (see yule function documentation) 16. ``Y = pdist(X, 'matching')`` Synonym for 'hamming'. 17. ``Y = pdist(X, 'dice')`` Computes the Dice distance between each pair of boolean vectors. (see dice function documentation) 18. ``Y = pdist(X, 'kulsinski')`` Computes the Kulsinski distance between each pair of boolean vectors. (see kulsinski function documentation) 19. ``Y = pdist(X, 'rogerstanimoto')`` Computes the Rogers-Tanimoto distance between each pair of boolean vectors. (see rogerstanimoto function documentation) 20. ``Y = pdist(X, 'russellrao')`` Computes the Russell-Rao distance between each pair of boolean vectors. (see russellrao function documentation) 21. ``Y = pdist(X, 'sokalmichener')`` Computes the Sokal-Michener distance between each pair of boolean vectors. (see sokalmichener function documentation) 22. ``Y = pdist(X, 'sokalsneath')`` Computes the Sokal-Sneath distance between each pair of boolean vectors. (see sokalsneath function documentation) 23. ``Y = pdist(X, 'wminkowski', p=2, w=w)`` Computes the weighted Minkowski distance between each pair of vectors. (see wminkowski function documentation) 'wminkowski' is deprecated and will be removed in SciPy 1.8.0. Use 'minkowski' instead. 24. ``Y = pdist(X, f)`` Computes the distance between all pairs of vectors in X using the user supplied 2-arity function f. For example, Euclidean distance between the vectors could be computed as follows:: dm = pdist(X, lambda u, v: np.sqrt(((u-v)**2).sum())) Note that you should avoid passing a reference to one of the distance functions defined in this library. For example,:: dm = pdist(X, sokalsneath) would calculate the pair-wise distances between the vectors in X using the Python function sokalsneath. This would result in sokalsneath being called :math:`{n \\choose 2}` times, which is inefficient. Instead, the optimized C version is more efficient, and we call it using the following syntax.:: dm = pdist(X, 'sokalsneath') """ # You can also call this as: # Y = pdist(X, 'test_abc') # where 'abc' is the metric being tested. This computes the distance # between all pairs of vectors in X using the distance metric 'abc' but # with a more succinct, verifiable, but less efficient implementation. X = _asarray_validated(X, sparse_ok=False, objects_ok=True, mask_ok=True, check_finite=False) s = X.shape if len(s) != 2: raise ValueError('A 2-dimensional array must be passed.') m, n = s if callable(metric): mstr = getattr(metric, '__name__', 'UnknownCustomMetric') metric_info = _METRIC_ALIAS.get(mstr, None) if metric_info is not None: X, typ, kwargs = _validate_pdist_input( X, m, n, metric_info, **kwargs) return _pdist_callable(X, metric=metric, out=out, **kwargs) elif isinstance(metric, str): mstr = metric.lower() metric_info = _METRIC_ALIAS.get(mstr, None) if metric_info is not None: pdist_fn = metric_info.pdist_func > return pdist_fn(X, out=out, **kwargs) E ValueError: Unsupported dtype object /usr/lib/python3/dist-packages/scipy/spatial/distance.py:2250: ValueError
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