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dreamquark-ai / tabnet

PyTorch implementation of TabNet paper : https://arxiv.org/pdf/1908.07442.pdf
https://dreamquark-ai.github.io/tabnet/
MIT License
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fix(deps): update dependency scipy to v1.11.1 - autoclosed #458

Closed renovate[bot] closed 1 year ago

renovate[bot] commented 1 year ago

Mend Renovate

This PR contains the following updates:

Package Change Age Adoption Passing Confidence
scipy (source) 1.5.4 -> 1.11.1 age adoption passing confidence

Release Notes

scipy/scipy (scipy) ### [`v1.11.1`](https://togithub.com/scipy/scipy/releases/tag/v1.11.1): SciPy 1.11.1 [Compare Source](https://togithub.com/scipy/scipy/compare/v1.11.0...v1.11.1) # SciPy 1.11.1 Release Notes SciPy `1.11.1` is a bug-fix release with no new features compared to `1.11.0`. In particular, a licensing issue discovered after the release of `1.11.0` has been addressed. # Authors - Name (commits) - h-vetinari (1) - Robert Kern (1) - Ilhan Polat (4) - Tyler Reddy (8) A total of 4 people contributed to this release. People with a "+" by their names contributed a patch for the first time. This list of names is automatically generated, and may not be fully complete. ### [`v1.11.0`](https://togithub.com/scipy/scipy/releases/tag/v1.11.0): SciPy 1.11.0 [Compare Source](https://togithub.com/scipy/scipy/compare/v1.10.1...v1.11.0) # SciPy 1.11.0 Release Notes SciPy `1.11.0` is the culmination of 6 months of hard work. It contains many new features, numerous bug-fixes, improved test coverage and better documentation. There have been a number of deprecations and API changes in this release, which are documented below. All users are encouraged to upgrade to this release, as there are a large number of bug-fixes and optimizations. Before upgrading, we recommend that users check that their own code does not use deprecated SciPy functionality (to do so, run your code with `python -Wd` and check for `DeprecationWarning` s). Our development attention will now shift to bug-fix releases on the 1.11.x branch, and on adding new features on the main branch. This release requires Python `3.9+` and NumPy `1.21.6` or greater. For running on PyPy, PyPy3 `6.0+` is required. # Highlights of this release - Several `scipy.sparse` array API improvements, including `sparse.sparray`, a new public base class distinct from the older `sparse.spmatrix` class, proper 64-bit index support, and numerous deprecations paving the way to a modern sparse array experience. - `scipy.stats` added tools for survival analysis, multiple hypothesis testing, sensitivity analysis, and working with censored data. - A new function was added for quasi-Monte Carlo integration, and linear algebra functions `det` and `lu` now accept nD-arrays. - An `axes` argument was added broadly to `ndimage` functions, facilitating analysis of stacked image data. # New features # `scipy.integrate` improvements - Added `scipy.integrate.qmc_quad` for quasi-Monte Carlo integration. - For an even number of points, `scipy.integrate.simpson` now calculates a parabolic segment over the last three points which gives improved accuracy over the previous implementation. # `scipy.cluster` improvements - `disjoint_set` has a new method `subset_size` for providing the size of a particular subset. # `scipy.constants` improvements - The `quetta`, `ronna`, `ronto`, and `quecto` SI prefixes were added. # `scipy.linalg` improvements - `scipy.linalg.det` is improved and now accepts nD-arrays. - `scipy.linalg.lu` is improved and now accepts nD-arrays. With the new `p_indices` switch the output permutation argument can be 1D `(n,)` permutation index instead of the full `(n, n)` array. # `scipy.ndimage` improvements - `axes` argument was added to `rank_filter`, `percentile_filter`, `median_filter`, `uniform_filter`, `minimum_filter`, `maximum_filter`, and `gaussian_filter`, which can be useful for processing stacks of image data. # `scipy.optimize` improvements - `scipy.optimize.linprog` now passes unrecognized options directly to HiGHS. - `scipy.optimize.root_scalar` now uses Newton's method to be used without providing `fprime` and the `secant` method to be used without a second guess. - `scipy.optimize.lsq_linear` now accepts `bounds` arguments of type `scipy.optimize.Bounds`. - `scipy.optimize.minimize` `method='cobyla'` now supports simple bound constraints. - Users can opt into a new callback interface for most methods of `scipy.optimize.minimize`: If the provided callback callable accepts a single keyword argument, `intermediate_result`, `scipy.optimize.minimize` now passes both the current solution and the optimal value of the objective function to the callback as an instance of `scipy.optimize.OptimizeResult`. It also allows the user to terminate optimization by raising a `StopIteration` exception from the callback function. `scipy.optimize.minimize` will return normally, and the latest solution information is provided in the result object. - `scipy.optimize.curve_fit` now supports an optional `nan_policy` argument. - `scipy.optimize.shgo` now has parallelization with the `workers` argument, symmetry arguments that can improve performance, class-based design to improve usability, and generally improved performance. # `scipy.signal` improvements - `istft` has an improved warning message when the NOLA condition fails. # `scipy.sparse` improvements - A new public base class `scipy.sparse.sparray` was introduced, allowing further extension of the sparse array API (such as the support for 1-dimensional sparse arrays) without breaking backwards compatibility. `isinstance(x, scipy.sparse.sparray)` to select the new sparse array classes, while `isinstance(x, scipy.sparse.spmatrix)` selects only the old sparse matrix classes. - Division of sparse arrays by a dense array now returns sparse arrays. - `scipy.sparse.isspmatrix` now only returns `True` for the sparse matrices instances. `scipy.sparse.issparse` now has to be used instead to check for instances of sparse arrays or instances of sparse matrices. - Sparse arrays constructed with int64 indices will no longer automatically downcast to int32. - The `argmin` and `argmax` methods now return the correct result when explicit zeros are present. # `scipy.sparse.linalg` improvements - dividing `LinearOperator` by a number now returns a `_ScaledLinearOperator` - `LinearOperator` now supports right multiplication by arrays - `lobpcg` should be more efficient following removal of an extraneous QR decomposition. # `scipy.spatial` improvements - Usage of new C++ backend for additional distance metrics, the majority of which will see substantial performance improvements, though a few minor regressions are known. These are focused on distances between boolean arrays. # `scipy.special` improvements - The factorial functions `factorial`, `factorial2` and `factorialk` were made consistent in their behavior (in terms of dimensionality, errors etc.). Additionally, `factorial2` can now handle arrays with `exact=True`, and `factorialk` can handle arrays. # `scipy.stats` improvements ## New Features - `scipy.stats.sobol_indices`, a method to compute Sobol' sensitivity indices. - `scipy.stats.dunnett`, which performs Dunnett's test of the means of multiple experimental groups against the mean of a control group. - `scipy.stats.ecdf` for computing the empirical CDF and complementary CDF (survival function / SF) from uncensored or right-censored data. This function is also useful for survival analysis / Kaplan-Meier estimation. - `scipy.stats.logrank` to compare survival functions underlying samples. - `scipy.stats.false_discovery_control` for adjusting p-values to control the false discovery rate of multiple hypothesis tests using the Benjamini-Hochberg or Benjamini-Yekutieli procedures. - `scipy.stats.CensoredData` to represent censored data. It can be used as input to the `fit` method of univariate distributions and to the new `ecdf` function. - Filliben's goodness of fit test as `method='Filliben'` of `scipy.stats.goodness_of_fit`. - `scipy.stats.ttest_ind` has a new method, `confidence_interval` for computing a confidence interval of the difference between means. - `scipy.stats.MonteCarloMethod`, `scipy.stats.PermutationMethod`, and `scipy.stats.BootstrapMethod` are new classes to configure resampling and/or Monte Carlo versions of hypothesis tests. They can currently be used with `scipy.stats.pearsonr`. ## Statistical Distributions - Added the von-Mises Fisher distribution as `scipy.stats.vonmises_fisher`. This distribution is the most common analogue of the normal distribution on the unit sphere. - Added the relativistic Breit-Wigner distribution as `scipy.stats.rel_breitwigner`. It is used in high energy physics to model resonances. - Added the Dirichlet multinomial distribution as `scipy.stats.dirichlet_multinomial`. - Improved the speed and precision of several univariate statistical distributions. - `scipy.stats.anglit` `sf` - `scipy.stats.beta` `entropy` - `scipy.stats.betaprime` `cdf`, `sf`, `ppf` - `scipy.stats.chi` `entropy` - `scipy.stats.chi2` `entropy` - `scipy.stats.dgamma` `entropy`, `cdf`, `sf`, `ppf`, and `isf` - `scipy.stats.dweibull` `entropy`, `sf`, and `isf` - `scipy.stats.exponweib` `sf` and `isf` - `scipy.stats.f` `entropy` - `scipy.stats.foldcauchy` `sf` - `scipy.stats.foldnorm` `cdf` and `sf` - `scipy.stats.gamma` `entropy` - `scipy.stats.genexpon` `ppf`, `isf`, `rvs` - `scipy.stats.gengamma` `entropy` - `scipy.stats.geom` `entropy` - `scipy.stats.genlogistic` `entropy`, `logcdf`, `sf`, `ppf`, and `isf` - `scipy.stats.genhyperbolic` `cdf` and `sf` - `scipy.stats.gibrat` `sf` and `isf` - `scipy.stats.gompertz` `entropy`, `sf`. and `isf` - `scipy.stats.halflogistic` `sf`, and `isf` - `scipy.stats.halfcauchy` `sf` and `isf` - `scipy.stats.halfnorm` `cdf`, `sf`, and `isf` - `scipy.stats.invgamma` `entropy` - `scipy.stats.invgauss` `entropy` - `scipy.stats.johnsonsb` `pdf`, `cdf`, `sf`, `ppf`, and `isf` - `scipy.stats.johnsonsu` `pdf`, `sf`, `isf`, and `stats` - `scipy.stats.lognorm` `fit` - `scipy.stats.loguniform` `entropy`, `logpdf`, `pdf`, `cdf`, `ppf`, and `stats` - `scipy.stats.maxwell` `sf` and `isf` - `scipy.stats.nakagami` `entropy` - `scipy.stats.powerlaw` `sf` - `scipy.stats.powerlognorm` `logpdf`, `logsf`, `sf`, and `isf` - `scipy.stats.powernorm` `sf` and `isf` - `scipy.stats.t` `entropy`, `logpdf`, and `pdf` - `scipy.stats.truncexpon` `sf`, and `isf` - `scipy.stats.truncnorm` `entropy` - `scipy.stats.truncpareto` `fit` - `scipy.stats.vonmises` `fit` - `scipy.stats.multivariate_t` now has `cdf` and `entropy` methods. - `scipy.stats.multivariate_normal`, `scipy.stats.matrix_normal`, and `scipy.stats.invwishart` now have an `entropy` method. ## Other Improvements - `scipy.stats.monte_carlo_test` now supports multi-sample statistics. - `scipy.stats.bootstrap` can now produce one-sided confidence intervals. - `scipy.stats.rankdata` performance was improved for `method=ordinal` and `method=dense`. - `scipy.stats.moment` now supports non-central moment calculation. - `scipy.stats.anderson` now supports the `weibull_min` distribution. - `scipy.stats.sem` and `scipy.stats.iqr` now support `axis`, `nan_policy`, and masked array input. # Deprecated features - Multi-Ellipsis sparse matrix indexing has been deprecated and will be removed in SciPy 1.13. - Several methods were deprecated for sparse arrays: `asfptype`, `getrow`, `getcol`, `get_shape`, `getmaxprint`, `set_shape`, `getnnz`, and `getformat`. Additionally, the `.A` and `.H` attributes were deprecated. Sparse matrix types are not affected. - The `scipy.linalg` functions `tri`, `triu` & `tril` are deprecated and will be removed in SciPy 1.13. Users are recommended to use the NumPy versions of these functions with identical names. - The `scipy.signal` functions `bspline`, `quadratic` & `cubic` are deprecated and will be removed in SciPy 1.13. Users are recommended to use `scipy.interpolate.BSpline` instead. - The `even` keyword of `scipy.integrate.simpson` is deprecated and will be removed in SciPy 1.13.0. Users should leave this as the default as this gives improved accuracy compared to the other methods. - Using `exact=True` when passing integers in a float array to `factorial` is deprecated and will be removed in SciPy 1.13.0. - float128 and object dtypes are deprecated for `scipy.signal.medfilt` and `scipy.signal.order_filter` - The functions `scipy.signal.{lsim2, impulse2, step2}` had long been deprecated in documentation only. They now raise a DeprecationWarning and will be removed in SciPy 1.13.0. - Importing window functions directly from `scipy.window` has been soft deprecated since SciPy 1.1.0. They now raise a `DeprecationWarning` and will be removed in SciPy 1.13.0. Users should instead import them from `scipy.signal.window` or use the convenience function `scipy.signal.get_window`. # Backwards incompatible changes - The default for the `legacy` keyword of `scipy.special.comb` has changed from `True` to `False`, as announced since its introduction. # Expired Deprecations There is an ongoing effort to follow through on long-standing deprecations. The following previously deprecated features are affected: - The `n` keyword has been removed from `scipy.stats.moment`. - The `alpha` keyword has been removed from `scipy.stats.interval`. - The misspelt `gilbrat` distribution has been removed (use `scipy.stats.gibrat`). - The deprecated spelling of the `kulsinski` distance metric has been removed (use `scipy.spatial.distance.kulczynski1`). - The `vertices` keyword of `scipy.spatial.Delauney.qhull` has been removed (use simplices). - The `residual` property of `scipy.sparse.csgraph.maximum_flow` has been removed (use `flow`). - The `extradoc` keyword of `scipy.stats.rv_continuous`, `scipy.stats.rv_discrete` and `scipy.stats.rv_sample` has been removed. - The `sym_pos` keyword of `scipy.linalg.solve` has been removed. - The `scipy.optimize.minimize` function now raises an error for `x0` with `x0.ndim > 1`. - In `scipy.stats.mode`, the default value of `keepdims` is now `False`, and support for non-numeric input has been removed. - The function `scipy.signal.lsim` does not support non-uniform time steps anymore. # Other changes - Rewrote the source build docs and restructured the contributor guide. - Improved support for cross-compiling with meson build system. - MyST-NB notebook infrastructure has been added to our documentation. # Authors - h-vetinari (69) - Oriol Abril-Pla (1) + - Tom Adamczewski (1) + - Anton Akhmerov (13) - Andrey Akinshin (1) + - alice (1) + - Oren Amsalem (1) - Ross Barnowski (13) - Christoph Baumgarten (2) - Dawson Beatty (1) + - Doron Behar (1) + - Peter Bell (1) - John Belmonte (1) + - boeleman (1) + - Jack Borchanian (1) + - Matt Borland (3) + - Jake Bowhay (41) - Larry Bradley (1) + - Sienna Brent (1) + - Matthew Brett (1) - Evgeni Burovski (39) - Matthias Bussonnier (2) - Maria Cann (1) + - Alfredo Carella (1) + - CJ Carey (34) - Hood Chatham (2) - Anirudh Dagar (3) - Alberto Defendi (1) + - Pol del Aguila (1) + - Hans Dembinski (1) - Dennis (1) + - Vinayak Dev (1) + - Thomas Duvernay (1) - DWesl (4) - Stefan Endres (66) - Evandro (1) + - Tom Eversdijk (2) + - Isuru Fernando (1) - Franz Forstmayr (4) - Joseph Fox-Rabinovitz (1) - Stefano Frazzetto (1) + - Neil Girdhar (1) - Caden Gobat (1) + - Ralf Gommers (153) - GonVas (1) + - Marco Gorelli (1) - Brett Graham (2) + - Matt Haberland (388) - harshvardhan2707 (1) + - Alex Herbert (1) + - Guillaume Horel (1) - Geert-Jan Huizing (1) + - Jakob Jakobson (2) - Julien Jerphanion (10) - jyuv (2) - Rajarshi Karmakar (1) + - Ganesh Kathiresan (3) + - Robert Kern (4) - Andrew Knyazev (4) - Sergey Koposov (1) - Rishi Kulkarni (2) + - Eric Larson (1) - Zoufiné Lauer-Bare (2) + - Antony Lee (3) - Gregory R. Lee (8) - Guillaume Lemaitre (2) + - lilinjie (2) + - Yannis Linardos (1) + - Christian Lorentzen (5) - Loïc Estève (1) - Adam Lugowski (1) + - Charlie Marsh (2) + - Boris Martin (1) + - Nicholas McKibben (11) - Melissa Weber Mendonça (58) - Michał Górny (1) + - Jarrod Millman (5) - Stefanie Molin (2) + - Mark W. Mueller (1) + - mustafacevik (1) + - Takumasa N (1) + - nboudrie (1) - Andrew Nelson (112) - Nico Schlömer (4) - Lysandros Nikolaou (2) + - Kyle Oman (1) - OmarManzoor (2) + - Simon Ott (1) + - Geoffrey Oxberry (1) + - Geoffrey M. Oxberry (2) + - Sravya papaganti (1) + - Tirth Patel (2) - Ilhan Polat (32) - Quentin Barthélemy (1) - Matteo Raso (12) + - Tyler Reddy (143) - Lucas Roberts (1) - Pamphile Roy (225) - Jordan Rupprecht (1) + - Atsushi Sakai (11) - Omar Salman (7) + - Leo Sandler (1) + - Ujjwal Sarswat (3) + - Saumya (1) + - Daniel Schmitz (79) - Henry Schreiner (2) + - Dan Schult (8) + - Eli Schwartz (6) - Tomer Sery (2) + - Scott Shambaugh (10) + - Gagandeep Singh (1) - Ethan Steinberg (6) + - stepeos (2) + - Albert Steppi (3) - Strahinja Lukić (1) - Kai Striega (4) - suen-bit (1) + - Tartopohm (2) - Logan Thomas (2) + - Jacopo Tissino (1) + - Matus Valo (12) + - Jacob Vanderplas (2) - Christian Veenhuis (1) + - Isaac Virshup (3) - Stefan van der Walt (14) - Warren Weckesser (63) - windows-server-2003 (1) - Levi John Wolf (3) - Nobel Wong (1) + - Benjamin Yeh (1) + - Rory Yorke (1) - Younes (2) + - Zaikun ZHANG (1) + - Alex Zverianskii (1) + A total of 134 people contributed to this release. People with a "+" by their names contributed a patch for the first time. This list of names is automatically generated, and may not be fully complete. ### [`v1.10.1`](https://togithub.com/scipy/scipy/releases/tag/v1.10.1): SciPy 1.10.1 [Compare Source](https://togithub.com/scipy/scipy/compare/v1.10.0...v1.10.1) # SciPy 1.10.1 Release Notes SciPy `1.10.1` is a bug-fix release with no new features compared to `1.10.0`. # Authors - Name (commits) - alice (1) + - Matt Borland (2) + - Evgeni Burovski (2) - CJ Carey (1) - Ralf Gommers (9) - Brett Graham (1) + - Matt Haberland (5) - Alex Herbert (1) + - Ganesh Kathiresan (2) + - Rishi Kulkarni (1) + - Loïc Estève (1) - Michał Górny (1) + - Jarrod Millman (1) - Andrew Nelson (4) - Tyler Reddy (50) - Pamphile Roy (2) - Eli Schwartz (2) - Tomer Sery (1) + - Kai Striega (1) - Jacopo Tissino (1) + - windows-server-2003 (1) A total of 21 people contributed to this release. People with a "+" by their names contributed a patch for the first time. This list of names is automatically generated, and may not be fully complete. ### [`v1.10.0`](https://togithub.com/scipy/scipy/releases/tag/v1.10.0): SciPy 1.10.0 [Compare Source](https://togithub.com/scipy/scipy/compare/v1.9.3...v1.10.0) # SciPy 1.10.0 Release Notes SciPy `1.10.0` is the culmination of `6` months of hard work. It contains many new features, numerous bug-fixes, improved test coverage and better documentation. There have been a number of deprecations and API changes in this release, which are documented below. All users are encouraged to upgrade to this release, as there are a large number of bug-fixes and optimizations. Before upgrading, we recommend that users check that their own code does not use deprecated SciPy functionality (to do so, run your code with `python -Wd` and check for `DeprecationWarning` s). Our development attention will now shift to bug-fix releases on the 1.10.x branch, and on adding new features on the main branch. This release requires Python `3.8+` and NumPy `1.19.5` or greater. For running on PyPy, PyPy3 `6.0+` is required. # Highlights of this release - A new dedicated datasets submodule (`scipy.datasets`) has been added, and is now preferred over usage of `scipy.misc` for dataset retrieval. - A new `scipy.interpolate.make_smoothing_spline` function was added. This function constructs a smoothing cubic spline from noisy data, using the generalized cross-validation (GCV) criterion to find the tradeoff between smoothness and proximity to data points. - `scipy.stats` has three new distributions, two new hypothesis tests, three new sample statistics, a class for greater control over calculations involving covariance matrices, and many other enhancements. # New features # `scipy.datasets` introduction - A new dedicated `datasets` submodule has been added. The submodules is meant for datasets that are relevant to other SciPy submodules ands content (tutorials, examples, tests), as well as contain a curated set of datasets that are of wider interest. As of this release, all the datasets from `scipy.misc` have been added to `scipy.datasets` (and deprecated in `scipy.misc`). - The submodule is based on [Pooch](https://www.fatiando.org/pooch/latest/) (a new optional dependency for SciPy), a Python package to simplify fetching data files. This move will, in a subsequent release, facilitate SciPy to trim down the sdist/wheel sizes, by decoupling the data files and moving them out of the SciPy repository, hosting them externally and downloading them when requested. After downloading the datasets once, the files are cached to avoid network dependence and repeated usage. - Added datasets from `scipy.misc`: `scipy.datasets.face`, `scipy.datasets.ascent`, `scipy.datasets.electrocardiogram` - Added download and caching functionality: - `scipy.datasets.download_all`: a function to download all the `scipy.datasets` associated files at once. - `scipy.datasets.clear_cache`: a simple utility function to clear cached dataset files from the file system. - `scipy/datasets/_download_all.py` can be run as a standalone script for packaging purposes to avoid any external dependency at build or test time. This can be used by SciPy packagers (e.g., for Linux distros) which may have to adhere to rules that forbid downloading sources from external repositories at package build time. # `scipy.integrate` improvements - Added parameter `complex_func` to `scipy.integrate.quad`, which can be set `True` to integrate a complex integrand. # `scipy.interpolate` improvements - `scipy.interpolate.interpn` now supports tensor-product interpolation methods (`slinear`, `cubic`, `quintic` and `pchip`) - Tensor-product interpolation methods (`slinear`, `cubic`, `quintic` and `pchip`) in `scipy.interpolate.interpn` and `scipy.interpolate.RegularGridInterpolator` now allow values with trailing dimensions. - `scipy.interpolate.RegularGridInterpolator` has a new fast path for `method="linear"` with 2D data, and `RegularGridInterpolator` is now easier to subclass - `scipy.interpolate.interp1d` now can take a single value for non-spline methods. - A new `extrapolate` argument is available to `scipy.interpolate.BSpline.design_matrix`, allowing extrapolation based on the first and last intervals. - A new function `scipy.interpolate.make_smoothing_spline` has been added. It is an implementation of the generalized cross-validation spline smoothing algorithm. The `lam=None` (default) mode of this function is a clean-room reimplementation of the classic `gcvspl.f` Fortran algorithm for constructing GCV splines. - A new `method="pchip"` mode was aded to `scipy.interpolate.RegularGridInterpolator`. This mode constructs an interpolator using tensor products of C1-continuous monotone splines (essentially, a `scipy.interpolate.PchipInterpolator` instance per dimension). # `scipy.sparse.linalg` improvements - The spectral 2-norm is now available in `scipy.sparse.linalg.norm`. - The performance of `scipy.sparse.linalg.norm` for the default case (Frobenius norm) has been improved. - LAPACK wrappers were added for `trexc` and `trsen`. - The `scipy.sparse.linalg.lobpcg` algorithm was rewritten, yielding the following improvements: - a simple tunable restart potentially increases the attainable accuracy for edge cases, - internal postprocessing runs one final exact Rayleigh-Ritz method giving more accurate and orthonormal eigenvectors, - output the computed iterate with the smallest max norm of the residual and drop the history of subsequent iterations, - remove the check for `LinearOperator` format input and thus allow a simple function handle of a callable object as an input, - better handling of common user errors with input data, rather than letting the algorithm fail. # `scipy.linalg` improvements - `scipy.linalg.lu_factor` now accepts rectangular arrays instead of being restricted to square arrays. # `scipy.ndimage` improvements - The new `scipy.ndimage.value_indices` function provides a time-efficient method to search for the locations of individual values with an array of image data. - A new `radius` argument is supported by `scipy.ndimage.gaussian_filter1d` and `scipy.ndimage.gaussian_filter` for adjusting the kernel size of the filter. # `scipy.optimize` improvements - `scipy.optimize.brute` now coerces non-iterable/single-value `args` into a tuple. - `scipy.optimize.least_squares` and `scipy.optimize.curve_fit` now accept `scipy.optimize.Bounds` for bounds constraints. - Added a tutorial for `scipy.optimize.milp`. - Improved the pretty-printing of `scipy.optimize.OptimizeResult` objects. - Additional options (`parallel`, `threads`, `mip_rel_gap`) can now be passed to `scipy.optimize.linprog` with `method='highs'`. # `scipy.signal` improvements - The new window function `scipy.signal.windows.lanczos` was added to compute a Lanczos window, also known as a sinc window. # `scipy.sparse.csgraph` improvements - the performance of `scipy.sparse.csgraph.dijkstra` has been improved, and star graphs in particular see a marked performance improvement # `scipy.special` improvements - The new function `scipy.special.powm1`, a ufunc with signature `powm1(x, y)`, computes `x**y - 1`. The function avoids the loss of precision that can result when `y` is close to 0 or when `x` is close to 1\. - `scipy.special.erfinv` is now more accurate as it leverages the Boost equivalent under the hood. # `scipy.stats` improvements - Added `scipy.stats.goodness_of_fit`, a generalized goodness-of-fit test for use with any univariate distribution, any combination of known and unknown parameters, and several choices of test statistic (Kolmogorov-Smirnov, Cramer-von Mises, and Anderson-Darling). - Improved `scipy.stats.bootstrap`: Default method `'BCa'` now supports multi-sample statistics. Also, the bootstrap distribution is returned in the result object, and the result object can be passed into the function as parameter `bootstrap_result` to add additional resamples or change the confidence interval level and type. - Added maximum spacing estimation to `scipy.stats.fit`. - Added the Poisson means test ("E-test") as `scipy.stats.poisson_means_test`. - Added new sample statistics. - Added `scipy.stats.contingency.odds_ratio` to compute both the conditional and unconditional odds ratios and corresponding confidence intervals for 2x2 contingency tables. - Added `scipy.stats.directional_stats` to compute sample statistics of n-dimensional directional data. - Added `scipy.stats.expectile`, which generalizes the expected value in the same way as quantiles are a generalization of the median. - Added new statistical distributions. - Added `scipy.stats.uniform_direction`, a multivariate distribution to sample uniformly from the surface of a hypersphere. - Added `scipy.stats.random_table`, a multivariate distribution to sample uniformly from m x n contingency tables with provided marginals. - Added `scipy.stats.truncpareto`, the truncated Pareto distribution. - Improved the `fit` method of several distributions. - `scipy.stats.skewnorm` and `scipy.stats.weibull_min` now use an analytical solution when `method='mm'`, which also serves a starting guess to improve the performance of `method='mle'`. - `scipy.stats.gumbel_r` and `scipy.stats.gumbel_l`: analytical maximum likelihood estimates have been extended to the cases in which location or scale are fixed by the user. - Analytical maximum likelihood estimates have been added for `scipy.stats.powerlaw`. - Improved random variate sampling of several distributions. - Drawing multiple samples from `scipy.stats.matrix_normal`, `scipy.stats.ortho_group`, `scipy.stats.special_ortho_group`, and `scipy.stats.unitary_group` is faster. - The `rvs` method of `scipy.stats.vonmises` now wraps to the interval `[-np.pi, np.pi]`. - Improved the reliability of `scipy.stats.loggamma` `rvs` method for small values of the shape parameter. - Improved the speed and/or accuracy of functions of several statistical distributions. - Added `scipy.stats.Covariance` for better speed, accuracy, and user control in multivariate normal calculations. - `scipy.stats.skewnorm` methods `cdf`, `sf`, `ppf`, and `isf` methods now use the implementations from Boost, improving speed while maintaining accuracy. The calculation of higher-order moments is also faster and more accurate. - `scipy.stats.invgauss` methods `ppf` and `isf` methods now use the implementations from Boost, improving speed and accuracy. - `scipy.stats.invweibull` methods `sf` and `isf` are more accurate for small probability masses. - `scipy.stats.nct` and `scipy.stats.ncx2` now rely on the implementations from Boost, improving speed and accuracy. - Implemented the `logpdf` method of `scipy.stats.vonmises` for reliability in extreme tails. - Implemented the `isf` method of `scipy.stats.levy` for speed and accuracy. - Improved the robustness of `scipy.stats.studentized_range` for large `df` by adding an infinite degree-of-freedom approximation. - Added a parameter `lower_limit` to `scipy.stats.multivariate_normal`, allowing the user to change the integration limit from -inf to a desired value. - Improved the robustness of `entropy` of `scipy.stats.vonmises` for large concentration values. - Enhanced `scipy.stats.gaussian_kde`. - Added `scipy.stats.gaussian_kde.marginal`, which returns the desired marginal distribution of the original kernel density estimate distribution. - The `cdf` method of `scipy.stats.gaussian_kde` now accepts a `lower_limit` parameter for integrating the PDF over a rectangular region. - Moved calculations for `scipy.stats.gaussian_kde.logpdf` to Cython, improving speed. - The global interpreter lock is released by the `pdf` method of `scipy.stats.gaussian_kde` for improved multithreading performance. - Replaced explicit matrix inversion with Cholesky decomposition for speed and accuracy. - Enhanced the result objects returned by many `scipy.stats` functions - Added a `confidence_interval` method to the result object returned by `scipy.stats.ttest_1samp` and `scipy.stats.ttest_rel`. - The `scipy.stats` functions `combine_pvalues`, `fisher_exact`, `chi2_contingency`, `median_test` and `mood` now return bunch objects rather than plain tuples, allowing attributes to be accessed by name. - Attributes of the result objects returned by `multiscale_graphcorr`, `anderson_ksamp`, `binomtest`, `crosstab`, `pointbiserialr`, `spearmanr`, `kendalltau`, and `weightedtau` have been renamed to `statistic` and `pvalue` for consistency throughout `scipy.stats`. Old attribute names are still allowed for backward compatibility. - `scipy.stats.anderson` now returns the parameters of the fitted distribution in a `scipy.stats._result_classes.FitResult` object. - The `plot` method of `scipy.stats._result_classes.FitResult` now accepts a `plot_type` parameter; the options are `'hist'` (histogram, default), `'qq'` (Q-Q plot), `'pp'` (P-P plot), and `'cdf'` (empirical CDF plot). - Kolmogorov-Smirnov tests (e.g. `scipy.stats.kstest`) now return the location (argmax) at which the statistic is calculated and the variant of the statistic used. - Improved the performance of several `scipy.stats` functions. - Improved the performance of `scipy.stats.cramervonmises_2samp` and `scipy.stats.ks_2samp` with `method='exact'`. - Improved the performance of `scipy.stats.siegelslopes`. - Improved the performance of `scipy.stats.mstats.hdquantile_sd`. - Improved the performance of `scipy.stats.binned_statistic_dd` for several NumPy statistics, and binned statistics methods now support complex data. - Added the `scramble` optional argument to `scipy.stats.qmc.LatinHypercube`. It replaces `centered`, which is now deprecated. - Added a parameter `optimization` to all `scipy.stats.qmc.QMCEngine` subclasses to improve characteristics of the quasi-random variates. - Added tie correction to `scipy.stats.mood`. - Added tutorials for resampling methods in `scipy.stats`. - `scipy.stats.bootstrap`, `scipy.stats.permutation_test`, and `scipy.stats.monte_carlo_test` now automatically detect whether the provided `statistic` is vectorized, so passing the `vectorized` argument explicitly is no longer required to take advantage of vectorized statistics. - Improved the speed of `scipy.stats.permutation_test` for permutation types `'samples'` and `'pairings'`. - Added `axis`, `nan_policy`, and masked array support to `scipy.stats.jarque_bera`. - Added the `nan_policy` optional argument to `scipy.stats.rankdata`. # Deprecated features - `scipy.misc` module and all the methods in `misc` are deprecated in v1.10 and will be completely removed in SciPy v2.0.0. Users are suggested to utilize the `scipy.datasets` module instead for the dataset methods. - `scipy.stats.qmc.LatinHypercube` parameter `centered` has been deprecated. It is replaced by the `scramble` argument for more consistency with other QMC engines. - `scipy.interpolate.interp2d` class has been deprecated. The docstring of the deprecated routine lists recommended replacements. # Expired Deprecations - There is an ongoing effort to follow through on long-standing deprecations. - The following previously deprecated features are affected: - Removed `cond` & `rcond` kwargs in `linalg.pinv` - Removed wrappers `scipy.linalg.blas.{clapack, flapack}` - Removed `scipy.stats.NumericalInverseHermite` and removed `tol` & `max_intervals` kwargs from `scipy.stats.sampling.NumericalInverseHermite` - Removed `local_search_options` kwarg frrom `scipy.optimize.dual_annealing`. # Other changes - `scipy.stats.bootstrap`, `scipy.stats.permutation_test`, and `scipy.stats.monte_carlo_test` now automatically detect whether the provided `statistic` is vectorized by looking for an `axis` parameter in the signature of `statistic`. If an `axis` parameter is present in `statistic` but should not be relied on for vectorized calls, users must pass option `vectorized==False` explicitly. - `scipy.stats.multivariate_normal` will now raise a `ValueError` when the covariance matrix is not positive semidefinite, regardless of which method is called. # Authors - Name (commits) - h-vetinari (10) - Jelle Aalbers (1) - Oriol Abril-Pla (1) + - Alan-Hung (1) + - Tania Allard (7) - Oren Amsalem (1) + - Sven Baars (10) - Balthasar (1) + - Ross Barnowski (1) - Christoph Baumgarten (2) - Peter Bell (2) - Sebastian Berg (1) - Aaron Berk (1) + - boatwrong (1) + - boeleman (1) + - Jake Bowhay (50) - Matthew Brett (4) - Evgeni Burovski (93) - Matthias Bussonnier (6) - Dominic C (2) - Mingbo Cai (1) + - James Campbell (2) + - CJ Carey (4) - cesaregarza (1) + - charlie0389 (1) + - Hood Chatham (5) - Andrew Chin (1) + - Daniel Ching (1) + - Leo Chow (1) + - chris (3) + - John Clow (1) + - cm7S (1) + - cmgodwin (1) + - Christopher Cowden (2) + - Henry Cuzco (2) + - Anirudh Dagar (12) - Hans Dembinski (2) + - Jaiden di Lanzo (24) + - Felipe Dias (1) + - Dieter Werthmüller (1) - Giuseppe Dilillo (1) + - dpoerio (1) + - drpeteb (1) + - Christopher Dupuis (1) + - Jordan Edmunds (1) + - Pieter Eendebak (1) + - Jérome Eertmans (1) + - Fabian Egli (2) + - Sebastian Ehlert (2) + - Kian Eliasi (1) + - Tomohiro Endo (1) + - Stefan Endres (1) - Zeb Engberg (4) + - Jonas Eschle (1) + - Thomas J. 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People with a "+" by their names contributed a patch for the first time. This list of names is automatically generated, and may not be fully complete. ### [`v1.9.3`](https://togithub.com/scipy/scipy/releases/tag/v1.9.3): SciPy 1.9.3 [Compare Source](https://togithub.com/scipy/scipy/compare/v1.9.2...v1.9.3) # SciPy 1.9.3 Release Notes SciPy `1.9.3` is a bug-fix release with no new features compared to `1.9.2`. # Authors - Jelle Aalbers (1) - Peter Bell (1) - Jake Bowhay (3) - Matthew Brett (3) - Evgeni Burovski (5) - drpeteb (1) + - Sebastian Ehlert (1) + - GavinZhang (1) + - Ralf Gommers (2) - Matt Haberland (15) - Lakshaya Inani (1) + - Joseph T. 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Fan (1) - Ralf Gommers (22) - Matt Haberland (5) - Julien Jerphanion (1) - Loïc Estève (1) - Nicholas McKibben (2) - Naoto Mizuno (1) - Andrew Nelson (3) - Tyler Reddy (28) - Pamphile Roy (1) - Ewout ter Hoeven (2) - Warren Weckesser (1) - Meekail Zain (1) + A total of 14 people contributed to this release. People with a "+" by their names contributed a patch for the first time. This list of names is automatically generated, and may not be fully complete. ### [`v1.9.1`](https://togithub.com/scipy/scipy/releases/tag/v1.9.1): SciPy 1.9.1 [Compare Source](https://togithub.com/scipy/scipy/compare/v1.9.0...v1.9.1) # SciPy 1.9.1 Release Notes SciPy `1.9.1` is a bug-fix release with no new features compared to `1.9.0`. Notably, some important meson build fixes are included. # Authors - Anirudh Dagar (1) - Ralf Gommers (12) - Matt Haberland (2) - Andrew Nelson (1) - Tyler Reddy (14) - Atsushi Sakai (1) - Eli Schwartz (1) - Warren Weckesser (2) A total of 8 people contributed to this release. People with a "+" by their names contributed a patch for the first time. This list of names is automatically generated, and may not be fully complete. ### [`v1.9.0`](https://togithub.com/scipy/scipy/releases/tag/v1.9.0): SciPy 1.9.0 [Compare Source](https://togithub.com/scipy/scipy/compare/v1.8.1...v1.9.0) # SciPy 1.9.0 Release Notes SciPy `1.9.0` is the culmination of 6 months of hard work. It contains many new features, numerous bug-fixes, improved test coverage and better documentation. There have been a number of deprecations and API changes in this release, which are documented below. All users are encouraged to upgrade to this release, as there are a large number of bug-fixes and optimizations. Before upgrading, we recommend that users check that their own code does not use deprecated SciPy functionality (to do so, run your code with `python -Wd` and check for `DeprecationWarning` s). Our development attention will now shift to bug-fix releases on the 1.9.x branch, and on adding new features on the main branch. This release requires Python `3.8-3.11` and NumPy `1.18.5` or greater. For running on PyPy, PyPy3 `6.0+` is required. # Highlights of this release - We have modernized our build system to use `meson`, substantially improving our build performance, and providing better build-time configuration and cross-compilation support, - Added `scipy.optimize.milp`, new function for mixed-integer linear programming, - Added `scipy.stats.fit` for fitting discrete and continuous distributions to data, - Tensor-product spline interpolation modes were added to `scipy.interpolate.RegularGridInterpolator`, - A new global optimizer (DIviding RECTangles algorithm) `scipy.optimize.direct`. # New features # `scipy.interpolate` improvements - Speed up the `RBFInterpolator` evaluation with high dimensional interpolants. - Added new spline based interpolation methods for `scipy.interpolate.RegularGridInterpolator` and its tutorial. - `scipy.interpolate.RegularGridInterpolator` and `scipy.interpolate.interpn` now accept descending ordered points. - `RegularGridInterpolator` now handles length-1 grid axes. - The `BivariateSpline` subclasses have a new method `partial_derivative` which constructs a new spline object representing a derivative of an original spline. This mirrors the corresponding functionality for univariate splines, `splder` and `BSpline.derivative`, and can substantially speed up repeated evaluation of derivatives. # `scipy.linalg` improvements - `scipy.linalg.expm` now accepts nD arrays. Its speed is also improved. - Minimum required LAPACK version is bumped to `3.7.1`. # `scipy.fft` improvements - Added `uarray` multimethods for `scipy.fft.fht` and `scipy.fft.ifht` to allow provision of third party backend implementations such as those recently added to CuPy. # `scipy.optimize` improvements - A new global optimizer, `scipy.optimize.direct` (DIviding RECTangles algorithm) was added. For problems with inexpensive function evaluations, like the ones in the SciPy benchmark suite, `direct` is competitive with the best other solvers in SciPy (`dual_annealing` and `differential_evolution`) in terms of execution time. See `gh-14300 `\__ for more details. - Add a `full_output` parameter to `scipy.optimize.curve_fit` to output additional solution information. - Add a `integrality` parameter to `scipy.optimize.differential_evolution`, enabling integer constraints on parameters. - Add a `vectorized` parameter to call a vectorized objective function only once per iteration. This can improve minimization speed by reducing interpreter overhead from the multiple objective function calls. - The default method of `scipy.optimize.linprog` is now `'highs'`. - Added `scipy.optimize.milp`, new function for mixed-integer linear programming. - Added Newton-TFQMR method to `newton_krylov`. - Added support for the `Bounds` class in `shgo` and `dual_annealing` for a more uniform API across `scipy.optimize`. - Added the `vectorized` keyword to `differential_evolution`. - `approx_fprime` now works with vector-valued functions. # `scipy.signal` improvements - The new window function `scipy.signal.windows.kaiser_bessel_derived` was added to compute the Kaiser-Bessel derived window. - Single-precision `hilbert` operations are now faster as a result of more consistent `dtype` handling. # `scipy.sparse` improvements - Add a `copy` parameter to `scipy.sparce.csgraph.laplacian`. Using inplace computation with `copy=False` reduces the memory footprint. - Add a `dtype` parameter to `scipy.sparce.csgraph.laplacian` for type casting. - Add a `symmetrized` parameter to `scipy.sparce.csgraph.laplacian` to produce symmetric Laplacian for directed graphs. - Add a `form` parameter to `scipy.sparce.csgraph.laplacian` taking one of the three values: `array`, or `function`, or `lo` determining the format of the output Laplacian: - `array` is a numpy array (backward compatible default); - `function` is a pointer to a lambda-function evaluating the Laplacian-vector or Laplacian-matrix product; - `lo` results in the format of the `LinearOperator`. # `scipy.sparse.linalg` improvements - `lobpcg` performance improvements for small input cases. # `scipy.spatial` improvements - Add an `order` parameter to `scipy.spatial.transform.Rotation.from_quat` and `scipy.spatial.transform.Rotation.as_quat` to specify quaternion format. # `scipy.stats` improvements - `scipy.stats.monte_carlo_test` performs one-sample Monte Carlo hypothesis tests to assess whether a sample was drawn from a given distribution. Besides reproducing the results of hypothesis tests like `scipy.stats.ks_1samp`, `scipy.stats.normaltest`, and `scipy.stats.cramervonmises` without small sample size limitations, it makes it possible to perform similar tests using arbitrary statistics and distributions. - Several `scipy.stats` functions support new `axis` (integer or tuple of integers) and `nan_policy` ('raise', 'omit', or 'propagate'), and `keepdims` arguments. These functions also support masked arrays as inputs, even if they do not have a `scipy.stats.mstats` counterpart. Edge cases for multidimensional arrays, such as when axis-slices have no unmasked elements or entire inputs are of size zero, are handled consistently. - Add a `weight` parameter to `scipy.stats.hmean`. - Several improvements have been made to `scipy.stats.levy_stable`. Substantial improvement has been made for numerical evaluation of the pdf and cdf, resolving [#​12658](https://togithub.com/scipy/scipy/issues/12658) and [#​14944](https://togithub.com/scipy/scipy/issues/14994). The improvement is particularly dramatic for stability parameter `alpha` close to or equal to 1 and for `alpha` below but approaching its maximum value of 2. The alternative fast Fourier transform based method for pdf calculation has also been updated to use the approach of Wang and Zhang from their 2008 conference paper *Simpson’s rule based FFT method to compute densities of stable distribution*, making this method more competitive with the default method. In addition, users now have the option to change the parametrization of the Levy Stable distribution to Nolan's "S0" parametrization which is used internally by SciPy's pdf and cdf implementations. The "S0" parametrization is described in Nolan's paper [*Numerical calculation of stable densities and distribution functions*](https://doi.org/10.1080/15326349708807450) upon which SciPy's implementation is based. "S0" has the advantage that `delta` and `gamma` are proper location and scale parameters. With `delta` and `gamma` fixed, the location and scale of the resulting distribution remain unchanged as `alpha` and `beta` change. This is not the case for the default "S1" parametrization. Finally, more options have been exposed to allow users to trade off between runtime and accuracy for both the default and FFT methods of pdf and cdf calculation. More information can be found in the documentation here (to be linked). - Added `scipy.stats.fit` for fitting discrete and continuous distributions to data. - The methods `"pearson"` and `"tippet"` from `scipy.stats.combine_pvalues` have been fixed to return the correct p-values, resolving [#​15373](https://togithub.com/scipy/scipy/issues/15373). In addition, the documentation for `scipy.stats.combine_pvalues` has been expanded and improved. - Unlike other reduction functions, `stats.mode` didn't consume the axis being operated on and failed for negative axis inputs. Both the bugs have been fixed. Note that `stats.mode` will now consume the input axis and return an ndarray with the `axis` dimension removed. - Replaced implementation of `scipy.stats.ncf` with the implementation from Boost for improved reliability. - Add a `bits` parameter to `scipy.stats.qmc.Sobol`. It allows to use from 0 to 64 bits to compute the sequence. Default is `None` which corresponds to 30 for backward compatibility. Using a higher value allow to sample more points. Note: `bits` does not affect the output dtype. - Add a `integers` method to `scipy.stats.qmc.QMCEngine`. It allows sampling integers using any QMC sampler. - Improved the fit speed and accuracy of `stats.pareto`. - Added `qrvs` method to `NumericalInversePolynomial` to match the situation for `NumericalInverseHermite`. - Faster random variate generation for `gennorm` and `nakagami`. - `lloyd_centroidal_voronoi_tessellation` has been added to allow improved sample distributions via iterative application of Voronoi diagrams and centering operations - Add `scipy.stats.qmc.PoissonDisk` to sample using the Poisson disk sampling method. It guarantees that samples are separated from each other by a given `radius`. - Add `scipy.stats.pmean` to calculate the weighted power mean also called generalized mean. # Deprecated features - Due to collision with the shape parameter `n` of several distributions, use of the distribution `moment` method with keyword argument `n` is deprecated. Keyword `n` is replaced with keyword `order`. - Similarly, use of the distribution `interval` method with keyword arguments `alpha` is deprecated. Keyword `alpha` is replaced with keyword `confidence`. - The `'simplex'`, `'revised simplex'`, and `'interior-point'` methods of `scipy.optimize.linprog` are deprecated. Methods `highs`, `highs-ds`, or `highs-ipm` should be used in new code. - Support for non-numeric arrays has been deprecated from `stats.mode`. `pandas.DataFrame.mode` can be used instead. - The function `spatial.distance.kulsinski` has been deprecated in favor of `spatial.distance.kulczynski1`. - The `maxiter` keyword of the truncated Newton (TNC) algorithm has been deprecated in favour of `maxfun`. - The `vertices` keyword of `Delauney.qhull` now raises a DeprecationWarning, after having been deprecated in documentation only for a long time. - The `extradoc` keyword of `rv_continuous`, `rv_discrete` and `rv_sample` now raises a DeprecationWarning, after having been deprecated in documentation only for a long time. # Expired Deprecations There is an ongoing effort to follow through on long-standing deprecations. The following previously deprecated features are affected: - Object arrays in sparse matrices now raise an error. - Inexact indices into sparse matrices now raise an error. - Passing `radius=None` to `scipy.spatial.SphericalVoronoi` now raises an error (not adding `radius` defaults to 1, as before). - Several BSpline methods now raise an error if inputs have `ndim > 1`. - The `_rvs` method of statistical distributions now requires a `size` parameter. - Passing a `fillvalue` that cannot be cast to the output type in `scipy.signal.convolve2d` now raises an error. - `scipy.spatial.distance` now enforces that the input vectors are one-dimensional. - Removed `stats.itemfreq`. - Removed `stats.median_absolute_deviation`. - Removed `n_jobs` keyword argument and use of `k=None` from `kdtree.query`. - Removed `right` keyword from `interpolate.PPoly.extend`. - Removed `debug` keyword from `scipy.linalg.solve_*`. - Removed class `_ppform` `scipy.interpolate`. - Removed BSR methods `matvec` and `matmat`. - Removed `mlab` truncation mode from `cluster.dendrogram`. - Removed `cluster.vq.py_vq2`. - Removed keyword arguments `ftol` and `xtol` from `optimize.minimize(method='Nelder-Mead')`. - Removed `signal.windows.hanning`. - Removed LAPACK `gegv` functions from `linalg`; this raises the minimally required LAPACK version to 3.7.1. - Removed `spatial.distance.matching`. - Removed the alias `scipy.random` for `numpy.random`. - Removed docstring related functions from `scipy.misc` (`docformat`, `inherit_docstring_from`, `extend_notes_in_docstring`, `replace_notes_in_docstring`, `indentcount_lines`, `filldoc`, `unindent_dict`, `unindent_string`). - Removed `linalg.pinv2`. # Backwards incompatible changes - Several `scipy.stats` functions now convert `np.matrix` to `np.ndarray`s before the calculation is performed. In this case, the output will be a scalar or `np.ndarray` of appropriate shape rather than a 2D `np.matrix`. Similarly, while masked elements of masked arrays are still ignored, the output will be a scalar or `np.ndarray` rather than a masked array with `mask=False`. - The default method of `scipy.optimize.linprog` is now `'highs'`, not `'interior-point'` (which is now deprecated), so callback functions and some options are no longer supported with the default method. With the default method, the `x` attribute of the returned `OptimizeResult` is now `None` (instead of a non-optimal array) when an optimal solution cannot be found (e.g. infeasible problem). - For `scipy.stats.combine_pvalues`, the sign of the test statistic returned for the method `"pearson"` has been flipped so that higher values of the statistic now correspond to lower p-values, making the statistic more consistent with those of the other methods and with the majority of the literature. - `scipy.linalg.expm` due to historical reasons was using the sparse implementation and thus was accepting sparse arrays. Now it only works with nDarrays. For sparse usage, `scipy.sparse.linal

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