From 52ed09e7d63a81907be294e256aaecf73e2b2ddb Mon Sep 17 00:00:00 2001 From: Christian Lorentzen Date: Tue, 5 Aug 2025 18:27:51 +0200 Subject: [PATCH 01/18] ENH add gap safe screening rule for enet_coordinate_descent --- sklearn/linear_model/_cd_fast.pyx | 220 ++++++++++++------ .../tests/test_coordinate_descent.py | 18 +- .../tests/test_sparse_coordinate_descent.py | 2 +- 3 files changed, 166 insertions(+), 74 deletions(-) diff --git a/sklearn/linear_model/_cd_fast.pyx b/sklearn/linear_model/_cd_fast.pyx index 82a7e75cb884d..3702498fa5f6a 100644 --- a/sklearn/linear_model/_cd_fast.pyx +++ b/sklearn/linear_model/_cd_fast.pyx @@ -1,7 +1,7 @@ # Authors: The scikit-learn developers # SPDX-License-Identifier: BSD-3-Clause -from libc.math cimport fabs +from libc.math cimport fabs, sqrt import numpy as np from cython cimport floating @@ -12,7 +12,7 @@ from ..utils._cython_blas cimport ( _axpy, _dot, _asum, _gemv, _nrm2, _copy, _scal ) from ..utils._cython_blas cimport ColMajor, Trans, NoTrans -from ..utils._typedefs cimport uint32_t +from ..utils._typedefs cimport uint8_t, uint32_t from ..utils._random cimport our_rand_r @@ -47,7 +47,7 @@ cdef inline floating fsign(floating f) noexcept nogil: return -1.0 -cdef floating abs_max(int n, const floating* a) noexcept nogil: +cdef inline floating abs_max(int n, const floating* a) noexcept nogil: """np.max(np.abs(a))""" cdef int i cdef floating m = fabs(a[0]) @@ -59,7 +59,7 @@ cdef floating abs_max(int n, const floating* a) noexcept nogil: return m -cdef floating max(int n, floating* a) noexcept nogil: +cdef inline floating max(int n, floating* a) noexcept nogil: """np.max(a)""" cdef int i cdef floating m = a[0] @@ -71,7 +71,7 @@ cdef floating max(int n, floating* a) noexcept nogil: return m -cdef floating diff_abs_max(int n, const floating* a, floating* b) noexcept nogil: +cdef inline floating diff_abs_max(int n, const floating* a, floating* b) noexcept nogil: """np.max(np.abs(a - b))""" cdef int i cdef floating m = fabs(a[0] - b[0]) @@ -98,6 +98,62 @@ message_ridge = ( ) +cdef (floating, floating) gap_enet( + int n_samples, + int n_features, + const floating[::1] w, + floating alpha, # L1 penalty + floating beta, # L2 penalty + const floating[::1, :] X, + const floating[::1] y, + const floating[::1] R, # current residuals = y - X@w + floating[::1] XtA, # XtA = X.T @ R - beta * w is calculated inplace + bint positive, +) noexcept nogil: + """Compute dual gap for use in enet_coordinate_descent.""" + cdef floating gap = 0.0 + cdef floating dual_norm_XtA + cdef floating R_norm2 + cdef floating w_norm2 = 0.0 + cdef floating l1_norm + cdef floating A_norm2 + + # XtA = np.dot(X.T, R) - beta * w + _copy(n_features, &w[0], 1, &XtA[0], 1) + _gemv(ColMajor, Trans, n_samples, n_features, 1.0, &X[0, 0], + n_samples, &R[0], 1, + -beta, &XtA[0], 1) + + if positive: + dual_norm_XtA = max(n_features, &XtA[0]) + else: + dual_norm_XtA = abs_max(n_features, &XtA[0]) + + # R_norm2 = np.dot(R, R) + R_norm2 = _dot(n_samples, &R[0], 1, &R[0], 1) + + # w_norm2 = np.dot(w, w) + if beta > 0: + w_norm2 = _dot(n_features, &w[0], 1, &w[0], 1) + + if (dual_norm_XtA > alpha): + const_ = alpha / dual_norm_XtA + A_norm2 = R_norm2 * (const_ ** 2) + gap = 0.5 * (R_norm2 + A_norm2) + else: + const_ = 1.0 + gap = R_norm2 + + l1_norm = _asum(n_features, &w[0], 1) + + gap += ( + alpha * l1_norm + - const_ * _dot(n_samples, &R[0], 1, &y[0], 1) # np.dot(R.T, y) + + 0.5 * beta * (1 + const_ ** 2) * w_norm2 + ) + return gap, dual_norm_XtA + + def enet_coordinate_descent( floating[::1] w, floating alpha, @@ -108,7 +164,8 @@ def enet_coordinate_descent( floating tol, object rng, bint random=0, - bint positive=0 + bint positive=0, + bint do_screening=1, ): """Cython version of the coordinate descent algorithm for Elastic-Net regression @@ -138,9 +195,9 @@ def enet_coordinate_descent( cdef unsigned int n_samples = X.shape[0] cdef unsigned int n_features = X.shape[1] - # compute norms of the columns of X - # same as norm_cols_X = np.square(X).sum(axis=0) - cdef floating[::1] norm_cols_X = np.einsum( + # compute squared norms of the columns of X + # same as norm2_cols_X = np.square(X).sum(axis=0) + cdef floating[::1] norm2_cols_X = np.einsum( "ij,ij->j", X, X, dtype=dtype, order="C" ) @@ -148,20 +205,21 @@ def enet_coordinate_descent( cdef floating[::1] R = np.empty(n_samples, dtype=dtype) cdef floating[::1] XtA = np.empty(n_features, dtype=dtype) + cdef floating d_j + cdef floating Xj_theta cdef floating tmp - cdef floating w_ii + cdef floating w_j cdef floating d_w_max cdef floating w_max - cdef floating d_w_ii + cdef floating d_w_j cdef floating gap = tol + 1.0 cdef floating d_w_tol = tol cdef floating dual_norm_XtA - cdef floating R_norm2 - cdef floating w_norm2 - cdef floating l1_norm - cdef floating const_ - cdef floating A_norm2 - cdef unsigned int ii + cdef unsigned int n_active = n_features + cdef uint32_t[::1] active_set + # TODO: use binset insteaf of array of bools + cdef uint8_t[::1] excluded_set + cdef unsigned int j cdef unsigned int n_iter = 0 cdef unsigned int f_iter cdef uint32_t rand_r_state_seed = rng.randint(0, RAND_R_MAX) @@ -171,6 +229,10 @@ def enet_coordinate_descent( warnings.warn("Coordinate descent with no regularization may lead to " "unexpected results and is discouraged.") + if do_screening: + active_set = np.empty(n_features, dtype=np.uint32) # map [:n_active] -> j + excluded_set = np.empty(n_features, dtype=np.uint8) + with nogil: # R = y - np.dot(X, w) _copy(n_samples, &y[0], 1, &R[0], 1) @@ -180,42 +242,74 @@ def enet_coordinate_descent( # tol *= np.dot(y, y) tol *= _dot(n_samples, &y[0], 1, &y[0], 1) + # Check convergence before entering the main loop. + gap, dual_norm_XtA = gap_enet( + n_samples, n_features, w, alpha, beta, X, y, R, XtA, positive + ) + if gap < tol: + with gil: + return np.asarray(w), gap, tol, 0 + + # Gap Safe Screening Rules, see https://arxiv.org/abs/1802.07481, Eq. 11 + if do_screening: + n_active = 0 + for j in range(n_features): + if norm2_cols_X[j] == 0: + w[j] = 0 + excluded_set[j] = 1 + continue + Xj_theta = XtA[j] / fmax(alpha, dual_norm_XtA) # X[:,j] @ dual_theta + d_j = (1 - fabs(Xj_theta)) / sqrt(norm2_cols_X[j] + beta) + if d_j <= sqrt(2 * gap) / alpha: + # include feature j + active_set[n_active] = j + excluded_set[j] = 0 + n_active += 1 + else: + # R += w_j * X[:,j] + _axpy(n_samples, w[j], &X[0, j], 1, &R[0], 1) + w[j] = 0 + excluded_set[j] = 1 + for n_iter in range(max_iter): w_max = 0.0 d_w_max = 0.0 - for f_iter in range(n_features): # Loop over coordinates + for f_iter in range(n_active): # Loop over coordinates if random: - ii = rand_int(n_features, rand_r_state) + j = rand_int(n_active, rand_r_state) else: - ii = f_iter + j = f_iter - if norm_cols_X[ii] == 0.0: + if do_screening: + j = active_set[j] + + if norm2_cols_X[j] == 0.0: continue - w_ii = w[ii] # Store previous value + w_j = w[j] # Store previous value - if w_ii != 0.0: - # R += w_ii * X[:,ii] - _axpy(n_samples, w_ii, &X[0, ii], 1, &R[0], 1) + if w_j != 0.0: + # R += w_j * X[:,j] + _axpy(n_samples, w_j, &X[0, j], 1, &R[0], 1) - # tmp = (X[:,ii]*R).sum() - tmp = _dot(n_samples, &X[0, ii], 1, &R[0], 1) + # tmp = (X[:,j]*R).sum() + tmp = _dot(n_samples, &X[0, j], 1, &R[0], 1) if positive and tmp < 0: - w[ii] = 0.0 + w[j] = 0.0 else: - w[ii] = (fsign(tmp) * fmax(fabs(tmp) - alpha, 0) - / (norm_cols_X[ii] + beta)) + w[j] = (fsign(tmp) * fmax(fabs(tmp) - alpha, 0) + / (norm2_cols_X[j] + beta)) - if w[ii] != 0.0: - # R -= w[ii] * X[:,ii] # Update residual - _axpy(n_samples, -w[ii], &X[0, ii], 1, &R[0], 1) + if w[j] != 0.0: + # R -= w[j] * X[:,j] # Update residual + _axpy(n_samples, -w[j], &X[0, j], 1, &R[0], 1) # update the maximum absolute coefficient update - d_w_ii = fabs(w[ii] - w_ii) - d_w_max = fmax(d_w_max, d_w_ii) + d_w_j = fabs(w[j] - w_j) + d_w_max = fmax(d_w_max, d_w_j) - w_max = fmax(w_max, fabs(w[ii])) + w_max = fmax(w_max, fabs(w[j])) if ( w_max == 0.0 @@ -225,43 +319,33 @@ def enet_coordinate_descent( # the biggest coordinate update of this iteration was smaller # than the tolerance: check the duality gap as ultimate # stopping criterion - - # XtA = np.dot(X.T, R) - beta * w - _copy(n_features, &w[0], 1, &XtA[0], 1) - _gemv(ColMajor, Trans, - n_samples, n_features, 1.0, &X[0, 0], n_samples, - &R[0], 1, - -beta, &XtA[0], 1) - - if positive: - dual_norm_XtA = max(n_features, &XtA[0]) - else: - dual_norm_XtA = abs_max(n_features, &XtA[0]) - - # R_norm2 = np.dot(R, R) - R_norm2 = _dot(n_samples, &R[0], 1, &R[0], 1) - - # w_norm2 = np.dot(w, w) - w_norm2 = _dot(n_features, &w[0], 1, &w[0], 1) - - if (dual_norm_XtA > alpha): - const_ = alpha / dual_norm_XtA - A_norm2 = R_norm2 * (const_ ** 2) - gap = 0.5 * (R_norm2 + A_norm2) - else: - const_ = 1.0 - gap = R_norm2 - - l1_norm = _asum(n_features, &w[0], 1) - - gap += (alpha * l1_norm - - const_ * _dot(n_samples, &R[0], 1, &y[0], 1) # np.dot(R.T, y) - + 0.5 * beta * (1 + const_ ** 2) * (w_norm2)) + gap, dual_norm_XtA = gap_enet( + n_samples, n_features, w, alpha, beta, X, y, R, XtA, positive + ) if gap < tol: # return if we reached desired tolerance break + # Gap Safe Screening Rules, see https://arxiv.org/abs/1802.07481, Eq. 11 + if do_screening: + n_active = 0 + for j in range(n_features): + if excluded_set[j]: + continue + Xj_theta = XtA[j] / fmax(alpha, dual_norm_XtA) # X @ dual_theta + d_j = (1 - fabs(Xj_theta)) / sqrt(norm2_cols_X[j] + beta) + if d_j <= sqrt(2 * gap) / alpha: + # include feature j + active_set[n_active] = j + excluded_set[j] = 0 + n_active += 1 + else: + # R += w_j * X[:,j] + _axpy(n_samples, w[j], &X[0, j], 1, &R[0], 1) + w[j] = 0 + excluded_set[j] = 1 + else: # for/else, runs if for doesn't end with a `break` with gil: diff --git a/sklearn/linear_model/tests/test_coordinate_descent.py b/sklearn/linear_model/tests/test_coordinate_descent.py index 70226210c010d..74dd82f212092 100644 --- a/sklearn/linear_model/tests/test_coordinate_descent.py +++ b/sklearn/linear_model/tests/test_coordinate_descent.py @@ -814,8 +814,11 @@ def test_elasticnet_precompute_gram(): assert_allclose(clf1.coef_, clf2.coef_) -def test_warm_start_convergence(): +@pytest.mark.parametrize("sparse_X", [True, False]) +def test_warm_start_convergence(sparse_X): X, y, _, _ = build_dataset() + if sparse_X: + X = sparse.csr_matrix(X) model = ElasticNet(alpha=1e-3, tol=1e-3).fit(X, y) n_iter_reference = model.n_iter_ @@ -828,12 +831,17 @@ def test_warm_start_convergence(): n_iter_cold_start = model.n_iter_ assert n_iter_cold_start == n_iter_reference - # Fit the same model again, using a warm start: the optimizer just performs - # a single pass before checking that it has already converged model.set_params(warm_start=True) model.fit(X, y) n_iter_warm_start = model.n_iter_ - assert n_iter_warm_start == 1 + if sparse_X: + # TODO: sparse_enet_coordinate_descent is not yet updated. + # Fit the same model again, using a warm start: the optimizer just performs + # a single pass before checking that it has already converged + assert n_iter_warm_start == 1 + else: + # enet_coordinate_descent checks dual gap before entering the main loop + assert n_iter_warm_start == 0 def test_warm_start_convergence_with_regularizer_decrement(): @@ -1483,7 +1491,7 @@ def test_enet_alpha_max_sample_weight(X_is_sparse, fit_intercept, sample_weight) assert_allclose(reg.coef_, 0, atol=1e-5) alpha_max = reg.alpha_ # Test smaller alpha makes coefs nonzero. - reg = ElasticNet(alpha=0.99 * alpha_max, fit_intercept=fit_intercept) + reg = ElasticNet(alpha=0.95 * alpha_max, fit_intercept=fit_intercept) reg.fit(X, y, sample_weight=sample_weight) assert_array_less(1e-3, np.max(np.abs(reg.coef_))) diff --git a/sklearn/linear_model/tests/test_sparse_coordinate_descent.py b/sklearn/linear_model/tests/test_sparse_coordinate_descent.py index 1aab9babeeb40..5186178997eac 100644 --- a/sklearn/linear_model/tests/test_sparse_coordinate_descent.py +++ b/sklearn/linear_model/tests/test_sparse_coordinate_descent.py @@ -291,7 +291,7 @@ def test_sparse_dense_equality( else: sw = None Xs = csc_container(X) - params = {"fit_intercept": fit_intercept} + params = {"fit_intercept": fit_intercept, "tol": 1e-6} reg_dense = Model(**params).fit(X, y, sample_weight=sw) reg_sparse = Model(**params).fit(Xs, y, sample_weight=sw) if fit_intercept: From 36e7dbdccc522ec4cf135f6fc519e47eb8328ed2 Mon Sep 17 00:00:00 2001 From: Christian Lorentzen Date: Tue, 5 Aug 2025 18:46:27 +0200 Subject: [PATCH 02/18] ENH add private _do_screening to Lasso and relatives --- sklearn/linear_model/_coordinate_descent.py | 22 ++++++++++++++++++++- 1 file changed, 21 insertions(+), 1 deletion(-) diff --git a/sklearn/linear_model/_coordinate_descent.py b/sklearn/linear_model/_coordinate_descent.py index 0db90c7b21b02..7cbf339785bfb 100644 --- a/sklearn/linear_model/_coordinate_descent.py +++ b/sklearn/linear_model/_coordinate_descent.py @@ -554,6 +554,7 @@ def enet_path( max_iter = params.pop("max_iter", 1000) random_state = params.pop("random_state", None) selection = params.pop("selection", "cyclic") + do_screening = params.pop("do_screening", False) if len(params) > 0: raise ValueError("Unexpected parameters in params", params.keys()) @@ -694,7 +695,17 @@ def enet_path( ) elif precompute is False: model = cd_fast.enet_coordinate_descent( - coef_, l1_reg, l2_reg, X, y, max_iter, tol, rng, random, positive + coef_, + l1_reg, + l2_reg, + X, + y, + max_iter, + tol, + rng, + random, + positive, + do_screening, ) else: raise ValueError( @@ -928,6 +939,7 @@ def __init__( self.positive = positive self.random_state = random_state self.selection = selection + self._do_screening = True @_fit_context(prefer_skip_nested_validation=True) def fit(self, X, y, sample_weight=None, check_input=True): @@ -1105,6 +1117,7 @@ def fit(self, X, y, sample_weight=None, check_input=True): random_state=self.random_state, selection=self.selection, sample_weight=sample_weight, + do_screening=self._do_screening, ) coef_[k] = this_coef[:, 0] dual_gaps_[k] = this_dual_gap[0] @@ -1559,6 +1572,7 @@ def __init__( self.positive = positive self.random_state = random_state self.selection = selection + self._do_screening = True @abstractmethod def _get_estimator(self): @@ -1745,6 +1759,9 @@ def fit(self, X, y, sample_weight=None, **params): path_params.pop("cv", None) path_params.pop("n_jobs", None) + if hasattr(self, "_do_screening"): + path_params["do_screening"] = self._do_screening + n_l1_ratio = len(l1_ratios) check_scalar_alpha = partial( @@ -1877,6 +1894,8 @@ def fit(self, X, y, sample_weight=None, **params): model.alpha = best_alpha model.l1_ratio = best_l1_ratio model.copy_X = copy_X + if hasattr(model, "_do_screening"): + model._do_screening = self._do_screening precompute = getattr(self, "precompute", None) if isinstance(precompute, str) and precompute == "auto": model.precompute = False @@ -2433,6 +2452,7 @@ def __init__( self.positive = positive self.random_state = random_state self.selection = selection + self._do_screening = True def _get_estimator(self): return ElasticNet() From 41e97a6492c3c09cb9a59df843908f4acdba1607 Mon Sep 17 00:00:00 2001 From: Christian Lorentzen Date: Thu, 7 Aug 2025 16:34:22 +0200 Subject: [PATCH 03/18] TST decrease tol instead of alpha in test_enet_alpha_max_sample_weight --- sklearn/linear_model/tests/test_coordinate_descent.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/sklearn/linear_model/tests/test_coordinate_descent.py b/sklearn/linear_model/tests/test_coordinate_descent.py index 74dd82f212092..f5b149496a5a0 100644 --- a/sklearn/linear_model/tests/test_coordinate_descent.py +++ b/sklearn/linear_model/tests/test_coordinate_descent.py @@ -1491,7 +1491,7 @@ def test_enet_alpha_max_sample_weight(X_is_sparse, fit_intercept, sample_weight) assert_allclose(reg.coef_, 0, atol=1e-5) alpha_max = reg.alpha_ # Test smaller alpha makes coefs nonzero. - reg = ElasticNet(alpha=0.95 * alpha_max, fit_intercept=fit_intercept) + reg = ElasticNet(alpha=0.99 * alpha_max, fit_intercept=fit_intercept, tol=1e-8) reg.fit(X, y, sample_weight=sample_weight) assert_array_less(1e-3, np.max(np.abs(reg.coef_))) From 53ac9dcddc2faf0f2082e98eee316bc64e13e2dc Mon Sep 17 00:00:00 2001 From: Christian Lorentzen Date: Fri, 8 Aug 2025 08:31:54 +0200 Subject: [PATCH 04/18] MNT update Cython 3.0.10 to 3.1.2 - fixes for fused ctuples --- asv_benchmarks/asv.conf.json | 2 +- ...latest_conda_forge_mkl_linux-64_conda.lock | 46 +++--- ...onda_forge_mkl_no_openmp_osx-64_conda.lock | 12 +- ...pylatest_conda_forge_mkl_osx-64_conda.lock | 12 +- ...pylatest_free_threaded_linux-64_conda.lock | 10 +- ...st_pip_openblas_pandas_linux-64_conda.lock | 6 +- ...pylatest_pip_scipy_dev_linux-64_conda.lock | 4 +- ..._openblas_min_dependencies_environment.yml | 2 +- ...nblas_min_dependencies_linux-64_conda.lock | 18 +- ...e_openblas_ubuntu_2204_linux-64_conda.lock | 18 +- ...min_conda_forge_openblas_win-64_conda.lock | 28 ++-- build_tools/azure/ubuntu_atlas_lock.txt | 2 +- .../azure/ubuntu_atlas_requirements.txt | 2 +- build_tools/circle/doc_linux-64_conda.lock | 38 ++--- .../doc_min_dependencies_environment.yml | 2 +- .../doc_min_dependencies_linux-64_conda.lock | 72 ++++---- ...a_forge_cuda_array-api_linux-64_conda.lock | 154 +++++++++--------- ...n_conda_forge_arm_linux-aarch64_conda.lock | 22 +-- pyproject.toml | 4 +- sklearn/_min_dependencies.py | 2 +- 20 files changed, 230 insertions(+), 226 deletions(-) diff --git a/asv_benchmarks/asv.conf.json b/asv_benchmarks/asv.conf.json index 3b16389139c0c..8da45b58b27bc 100644 --- a/asv_benchmarks/asv.conf.json +++ b/asv_benchmarks/asv.conf.json @@ -68,7 +68,7 @@ "matrix": { "numpy": ["2.0.0"], "scipy": ["1.14.0"], - "cython": ["3.0.10"], + "cython": ["3.1.2"], "joblib": ["1.3.2"], "threadpoolctl": ["3.2.0"], "pandas": ["2.2.2"] diff --git a/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock b/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock index f58d6df794e48..25859b5572772 100644 --- a/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock +++ b/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock @@ -1,6 +1,6 @@ # Generated by conda-lock. # platform: linux-64 -# input_hash: 193ec0257842997716ceb9bf419cbc54d52357ac3159daf1465c788e8bcf0c13 +# input_hash: f524d159a11a0a80ead3448f16255169f24edde269f6b81e8e28453bc4f7fc53 @EXPLICIT https://conda.anaconda.org/conda-forge/noarch/font-ttf-dejavu-sans-mono-2.37-hab24e00_0.tar.bz2#0c96522c6bdaed4b1566d11387caaf45 https://conda.anaconda.org/conda-forge/noarch/font-ttf-inconsolata-3.000-h77eed37_0.tar.bz2#34893075a5c9e55cdafac56607368fc6 @@ -42,7 +42,7 @@ https://conda.anaconda.org/conda-forge/linux-64/libuv-1.51.0-hb03c661_1.conda#0f https://conda.anaconda.org/conda-forge/linux-64/libwebp-base-1.6.0-hd42ef1d_0.conda#aea31d2e5b1091feca96fcfe945c3cf9 https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.3.1-hb9d3cd8_2.conda#edb0dca6bc32e4f4789199455a1dbeb8 https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.5-h2d0b736_3.conda#47e340acb35de30501a76c7c799c41d7 -https://conda.anaconda.org/conda-forge/linux-64/openssl-3.5.1-h7b32b05_0.conda#c87df2ab1448ba69169652ab9547082d +https://conda.anaconda.org/conda-forge/linux-64/openssl-3.5.2-h26f9b46_0.conda#ffffb341206dd0dab0c36053c048d621 https://conda.anaconda.org/conda-forge/linux-64/pthread-stubs-0.4-hb9d3cd8_1002.conda#b3c17d95b5a10c6e64a21fa17573e70e https://conda.anaconda.org/conda-forge/linux-64/xorg-libice-1.1.2-hb9d3cd8_0.conda#fb901ff28063514abb6046c9ec2c4a45 https://conda.anaconda.org/conda-forge/linux-64/xorg-libxau-1.0.12-hb9d3cd8_0.conda#f6ebe2cb3f82ba6c057dde5d9debe4f7 @@ -123,18 +123,18 @@ https://conda.anaconda.org/conda-forge/linux-64/lcms2-2.17-h717163a_0.conda#000e 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a/build_tools/azure/pylatest_conda_forge_mkl_no_openmp_osx-64_conda.lock +++ b/build_tools/azure/pylatest_conda_forge_mkl_no_openmp_osx-64_conda.lock @@ -31,9 +31,9 @@ https://conda.anaconda.org/conda-forge/osx-64/libgfortran5-14.2.0-h51e75f0_103.c https://conda.anaconda.org/conda-forge/osx-64/libpng-1.6.50-h84aeda2_1.conda#1fe32bb16991a24e112051cc0de89847 https://conda.anaconda.org/conda-forge/osx-64/libsqlite-3.50.4-h39a8b3b_0.conda#156bfb239b6a67ab4a01110e6718cbc4 https://conda.anaconda.org/conda-forge/osx-64/libxcb-1.17.0-hf1f96e2_0.conda#bbeca862892e2898bdb45792a61c4afc -https://conda.anaconda.org/conda-forge/osx-64/libxml2-2.13.8-h93c44a6_0.conda#e42a93a31cbc6826620144343d42f472 +https://conda.anaconda.org/conda-forge/osx-64/libxml2-2.13.8-he1bc88e_1.conda#1d31029d8d2685d56a812dec48083483 https://conda.anaconda.org/conda-forge/osx-64/ninja-1.13.1-h0ba0a54_0.conda#71576ca895305a20c73304fcb581ae1a -https://conda.anaconda.org/conda-forge/osx-64/openssl-3.5.1-hc426f3f_0.conda#f1ac2dbc36ce2017bd8f471960b1261d +https://conda.anaconda.org/conda-forge/osx-64/openssl-3.5.2-h6e31bce_0.conda#22f5d63e672b7ba467969e9f8b740ecd https://conda.anaconda.org/conda-forge/osx-64/qhull-2020.2-h3c5361c_5.conda#dd1ea9ff27c93db7c01a7b7656bd4ad4 https://conda.anaconda.org/conda-forge/osx-64/readline-8.2-h7cca4af_2.conda#342570f8e02f2f022147a7f841475784 https://conda.anaconda.org/conda-forge/osx-64/tk-8.6.13-hf689a15_2.conda#9864891a6946c2fe037c02fca7392ab4 @@ -41,7 +41,7 @@ https://conda.anaconda.org/conda-forge/osx-64/zstd-1.5.7-h8210216_2.conda#cd60a4 https://conda.anaconda.org/conda-forge/osx-64/brotli-bin-1.1.0-h6e16a3a_3.conda#a240d09be7c84cb1d33535ebd36fe422 https://conda.anaconda.org/conda-forge/osx-64/libfreetype6-2.13.3-h40dfd5c_1.conda#c76e6f421a0e95c282142f820835e186 https://conda.anaconda.org/conda-forge/osx-64/libgfortran-5.0.0-14_2_0_h51e75f0_103.conda#090b3c9ae1282c8f9b394ac9e4773b10 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https://conda.anaconda.org/conda-forge/noarch/munkres-1.1.4-pyhd8ed1ab_1.conda#37293a85a0f4f77bbd9cf7aaefc62609 -https://conda.anaconda.org/conda-forge/osx-64/openjpeg-2.5.3-h7fd6d84_0.conda#025c711177fc3309228ca1a32374458d +https://conda.anaconda.org/conda-forge/osx-64/openjpeg-2.5.3-h036ada5_1.conda#38f264b121a043cf379980c959fb2d75 https://conda.anaconda.org/conda-forge/noarch/packaging-25.0-pyh29332c3_1.conda#58335b26c38bf4a20f399384c33cbcf9 https://conda.anaconda.org/conda-forge/noarch/pip-25.2-pyh145f28c_0.conda#e7ab34d5a93e0819b62563c78635d937 https://conda.anaconda.org/conda-forge/noarch/pluggy-1.6.0-pyhd8ed1ab_0.conda#7da7ccd349dbf6487a7778579d2bb971 @@ -66,14 +66,14 @@ https://conda.anaconda.org/conda-forge/noarch/python-tzdata-2025.2-pyhd8ed1ab_0. https://conda.anaconda.org/conda-forge/noarch/pytz-2025.2-pyhd8ed1ab_0.conda#bc8e3267d44011051f2eb14d22fb0960 https://conda.anaconda.org/conda-forge/noarch/setuptools-80.9.0-pyhff2d567_0.conda#4de79c071274a53dcaf2a8c749d1499e https://conda.anaconda.org/conda-forge/noarch/six-1.17.0-pyhe01879c_1.conda#3339e3b65d58accf4ca4fb8748ab16b3 -https://conda.anaconda.org/conda-forge/osx-64/tbb-2021.13.0-hb890de9_1.conda#284892942cdddfded53d090050b639a5 +https://conda.anaconda.org/conda-forge/osx-64/tbb-2021.13.0-hc025b3e_2.conda#dc40bce4a1c208ab17d570b49d88b649 https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.6.0-pyhecae5ae_0.conda#9d64911b31d57ca443e9f1e36b04385f https://conda.anaconda.org/conda-forge/noarch/toml-0.10.2-pyhd8ed1ab_1.conda#b0dd904de08b7db706167240bf37b164 https://conda.anaconda.org/conda-forge/noarch/tomli-2.2.1-pyhe01879c_2.conda#30a0a26c8abccf4b7991d590fe17c699 https://conda.anaconda.org/conda-forge/osx-64/tornado-6.5.1-py313h63b0ddb_0.conda#7554d07cbe64f41c73a403e99bccf3c6 https://conda.anaconda.org/conda-forge/noarch/typing_extensions-4.14.1-pyhe01879c_0.conda#e523f4f1e980ed7a4240d7e27e9ec81f https://conda.anaconda.org/conda-forge/osx-64/ccache-4.11.3-h33566b8_0.conda#b65cad834bd6c1f660c101cca09430bf -https://conda.anaconda.org/conda-forge/osx-64/coverage-7.10.1-py313h4db2fa4_0.conda#82ec1dabd8bbdfe1f418447e2a6d20c6 +https://conda.anaconda.org/conda-forge/osx-64/coverage-7.10.2-py313h4db2fa4_0.conda#306339f10c125b6ab64944c6b771611b https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.3.0-pyhd8ed1ab_0.conda#72e42d28960d875c7654614f8b50939a https://conda.anaconda.org/conda-forge/osx-64/fonttools-4.59.0-py313h4db2fa4_0.conda#1dab5b45690c319aba7d846f9267349c https://conda.anaconda.org/conda-forge/osx-64/freetype-2.13.3-h694c41f_1.conda#126dba1baf5030cb6f34533718924577 diff --git a/build_tools/azure/pylatest_conda_forge_mkl_osx-64_conda.lock b/build_tools/azure/pylatest_conda_forge_mkl_osx-64_conda.lock index 5c28d3e975940..60cb83836afdc 100644 --- a/build_tools/azure/pylatest_conda_forge_mkl_osx-64_conda.lock +++ b/build_tools/azure/pylatest_conda_forge_mkl_osx-64_conda.lock @@ -35,9 +35,9 @@ https://conda.anaconda.org/conda-forge/osx-64/libgfortran5-14.2.0-h51e75f0_103.c https://conda.anaconda.org/conda-forge/osx-64/libpng-1.6.50-h84aeda2_1.conda#1fe32bb16991a24e112051cc0de89847 https://conda.anaconda.org/conda-forge/osx-64/libsqlite-3.50.4-h39a8b3b_0.conda#156bfb239b6a67ab4a01110e6718cbc4 https://conda.anaconda.org/conda-forge/osx-64/libxcb-1.17.0-hf1f96e2_0.conda#bbeca862892e2898bdb45792a61c4afc -https://conda.anaconda.org/conda-forge/osx-64/libxml2-2.13.8-h93c44a6_0.conda#e42a93a31cbc6826620144343d42f472 +https://conda.anaconda.org/conda-forge/osx-64/libxml2-2.13.8-he1bc88e_1.conda#1d31029d8d2685d56a812dec48083483 https://conda.anaconda.org/conda-forge/osx-64/ninja-1.13.1-h0ba0a54_0.conda#71576ca895305a20c73304fcb581ae1a -https://conda.anaconda.org/conda-forge/osx-64/openssl-3.5.1-hc426f3f_0.conda#f1ac2dbc36ce2017bd8f471960b1261d +https://conda.anaconda.org/conda-forge/osx-64/openssl-3.5.2-h6e31bce_0.conda#22f5d63e672b7ba467969e9f8b740ecd https://conda.anaconda.org/conda-forge/osx-64/qhull-2020.2-h3c5361c_5.conda#dd1ea9ff27c93db7c01a7b7656bd4ad4 https://conda.anaconda.org/conda-forge/osx-64/readline-8.2-h7cca4af_2.conda#342570f8e02f2f022147a7f841475784 https://conda.anaconda.org/conda-forge/osx-64/tapi-1300.6.5-h390ca13_0.conda#c6ee25eb54accb3f1c8fc39203acfaf1 @@ -47,7 +47,7 @@ https://conda.anaconda.org/conda-forge/osx-64/zstd-1.5.7-h8210216_2.conda#cd60a4 https://conda.anaconda.org/conda-forge/osx-64/brotli-bin-1.1.0-h6e16a3a_3.conda#a240d09be7c84cb1d33535ebd36fe422 https://conda.anaconda.org/conda-forge/osx-64/libfreetype6-2.13.3-h40dfd5c_1.conda#c76e6f421a0e95c282142f820835e186 https://conda.anaconda.org/conda-forge/osx-64/libgfortran-5.0.0-14_2_0_h51e75f0_103.conda#090b3c9ae1282c8f9b394ac9e4773b10 -https://conda.anaconda.org/conda-forge/osx-64/libhwloc-2.11.2-default_h8c32e24_1002.conda#a9f64b764e16b830465ae64364394f36 +https://conda.anaconda.org/conda-forge/osx-64/libhwloc-2.12.1-default_h8c32e24_1000.conda#622d2b076d7f0588ab1baa962209e6dd https://conda.anaconda.org/conda-forge/osx-64/libllvm19-19.1.7-hc29ff6c_1.conda#a937150d07aa51b50ded6a0816df4a5a https://conda.anaconda.org/conda-forge/osx-64/libtiff-4.7.0-h1167cee_5.conda#fc84af14a09e779f1d37ab1d16d5c4e2 https://conda.anaconda.org/conda-forge/osx-64/mpfr-4.2.1-haed47dc_3.conda#d511e58aaaabfc23136880d9956fa7a6 @@ -69,7 +69,7 @@ https://conda.anaconda.org/conda-forge/osx-64/llvm-tools-19-19.1.7-he90a8e3_1.co https://conda.anaconda.org/conda-forge/noarch/meson-1.8.3-pyhe01879c_0.conda#ed40b34242ec6d216605db54d19c6df5 https://conda.anaconda.org/conda-forge/osx-64/mpc-1.3.1-h9d8efa1_1.conda#0520855aaae268ea413d6bc913f1384c https://conda.anaconda.org/conda-forge/noarch/munkres-1.1.4-pyhd8ed1ab_1.conda#37293a85a0f4f77bbd9cf7aaefc62609 -https://conda.anaconda.org/conda-forge/osx-64/openjpeg-2.5.3-h7fd6d84_0.conda#025c711177fc3309228ca1a32374458d +https://conda.anaconda.org/conda-forge/osx-64/openjpeg-2.5.3-h036ada5_1.conda#38f264b121a043cf379980c959fb2d75 https://conda.anaconda.org/conda-forge/noarch/packaging-25.0-pyh29332c3_1.conda#58335b26c38bf4a20f399384c33cbcf9 https://conda.anaconda.org/conda-forge/noarch/pip-25.2-pyh145f28c_0.conda#e7ab34d5a93e0819b62563c78635d937 https://conda.anaconda.org/conda-forge/noarch/pluggy-1.6.0-pyhd8ed1ab_0.conda#7da7ccd349dbf6487a7778579d2bb971 @@ -79,7 +79,7 @@ https://conda.anaconda.org/conda-forge/noarch/python-tzdata-2025.2-pyhd8ed1ab_0. https://conda.anaconda.org/conda-forge/noarch/pytz-2025.2-pyhd8ed1ab_0.conda#bc8e3267d44011051f2eb14d22fb0960 https://conda.anaconda.org/conda-forge/noarch/setuptools-80.9.0-pyhff2d567_0.conda#4de79c071274a53dcaf2a8c749d1499e https://conda.anaconda.org/conda-forge/noarch/six-1.17.0-pyhe01879c_1.conda#3339e3b65d58accf4ca4fb8748ab16b3 -https://conda.anaconda.org/conda-forge/osx-64/tbb-2021.13.0-hb890de9_1.conda#284892942cdddfded53d090050b639a5 +https://conda.anaconda.org/conda-forge/osx-64/tbb-2021.13.0-hc025b3e_2.conda#dc40bce4a1c208ab17d570b49d88b649 https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.6.0-pyhecae5ae_0.conda#9d64911b31d57ca443e9f1e36b04385f https://conda.anaconda.org/conda-forge/noarch/toml-0.10.2-pyhd8ed1ab_1.conda#b0dd904de08b7db706167240bf37b164 https://conda.anaconda.org/conda-forge/noarch/tomli-2.2.1-pyhe01879c_2.conda#30a0a26c8abccf4b7991d590fe17c699 @@ -87,7 +87,7 @@ https://conda.anaconda.org/conda-forge/osx-64/tornado-6.5.1-py313h63b0ddb_0.cond https://conda.anaconda.org/conda-forge/noarch/typing_extensions-4.14.1-pyhe01879c_0.conda#e523f4f1e980ed7a4240d7e27e9ec81f https://conda.anaconda.org/conda-forge/osx-64/ccache-4.11.3-h33566b8_0.conda#b65cad834bd6c1f660c101cca09430bf https://conda.anaconda.org/conda-forge/osx-64/clang-19-19.1.7-default_h3571c67_3.conda#5bd5cda534488611b3970b768139127c -https://conda.anaconda.org/conda-forge/osx-64/coverage-7.10.1-py313h4db2fa4_0.conda#82ec1dabd8bbdfe1f418447e2a6d20c6 +https://conda.anaconda.org/conda-forge/osx-64/coverage-7.10.2-py313h4db2fa4_0.conda#306339f10c125b6ab64944c6b771611b https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.3.0-pyhd8ed1ab_0.conda#72e42d28960d875c7654614f8b50939a https://conda.anaconda.org/conda-forge/osx-64/fonttools-4.59.0-py313h4db2fa4_0.conda#1dab5b45690c319aba7d846f9267349c https://conda.anaconda.org/conda-forge/osx-64/freetype-2.13.3-h694c41f_1.conda#126dba1baf5030cb6f34533718924577 diff --git a/build_tools/azure/pylatest_free_threaded_linux-64_conda.lock b/build_tools/azure/pylatest_free_threaded_linux-64_conda.lock index 4697ad30614be..d2fb22f3b8765 100644 --- a/build_tools/azure/pylatest_free_threaded_linux-64_conda.lock +++ b/build_tools/azure/pylatest_free_threaded_linux-64_conda.lock @@ -1,6 +1,6 @@ # Generated by conda-lock. # platform: linux-64 -# input_hash: 369e1662562a0dd933bde8db136f7a3e1600dd1d12b8cc9d9a45519c74253276 +# input_hash: b76364b5635e8c36a0fc0777955b5664a336ba94ac96f3ade7aad842ab7e15c5 @EXPLICIT https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2#d7c89558ba9fa0495403155b64376d81 https://conda.anaconda.org/conda-forge/noarch/python_abi-3.13-8_cp313t.conda#e1dd2408e4ff08393fbc3502fbe4316d @@ -19,7 +19,7 @@ https://conda.anaconda.org/conda-forge/linux-64/libmpdec-4.0.0-hb9d3cd8_0.conda# https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-15.1.0-h8f9b012_4.conda#3c376af8888c386b9d3d1c2701e2f3ab https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.3.1-hb9d3cd8_2.conda#edb0dca6bc32e4f4789199455a1dbeb8 https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.5-h2d0b736_3.conda#47e340acb35de30501a76c7c799c41d7 -https://conda.anaconda.org/conda-forge/linux-64/openssl-3.5.1-h7b32b05_0.conda#c87df2ab1448ba69169652ab9547082d +https://conda.anaconda.org/conda-forge/linux-64/openssl-3.5.2-h26f9b46_0.conda#ffffb341206dd0dab0c36053c048d621 https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-h4bc722e_7.conda#62ee74e96c5ebb0af99386de58cf9553 https://conda.anaconda.org/conda-forge/linux-64/libgfortran-15.1.0-h69a702a_4.conda#53e876bc2d2648319e94c33c57b9ec74 https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.50.4-h0c1763c_0.conda#0b367fad34931cb79e0d6b7e5c06bb1c @@ -37,7 +37,7 @@ https://conda.anaconda.org/conda-forge/noarch/cpython-3.13.5-py313hd8ed1ab_2.con https://conda.anaconda.org/conda-forge/noarch/cython-3.1.2-pyh2c78169_102.conda#e250288041263e65630a5802c72fa76b https://conda.anaconda.org/conda-forge/noarch/execnet-2.1.1-pyhd8ed1ab_1.conda#a71efeae2c160f6789900ba2631a2c90 https://conda.anaconda.org/conda-forge/noarch/iniconfig-2.0.0-pyhd8ed1ab_1.conda#6837f3eff7dcea42ecd714ce1ac2b108 -https://conda.anaconda.org/conda-forge/linux-64/libblas-3.9.0-32_h59b9bed_openblas.conda#2af9f3d5c2e39f417ce040f5a35c40c6 +https://conda.anaconda.org/conda-forge/linux-64/libblas-3.9.0-33_h59b9bed_openblas.conda#b43d5de8fe73c2a5fb2b43f45301285b https://conda.anaconda.org/conda-forge/linux-64/libhiredis-1.0.2-h2cc385e_0.tar.bz2#b34907d3a81a3cd8095ee83d174c074a https://conda.anaconda.org/conda-forge/noarch/meson-1.8.3-pyhe01879c_0.conda#ed40b34242ec6d216605db54d19c6df5 https://conda.anaconda.org/conda-forge/noarch/packaging-25.0-pyh29332c3_1.conda#58335b26c38bf4a20f399384c33cbcf9 @@ -51,8 +51,8 @@ https://conda.anaconda.org/conda-forge/noarch/typing_extensions-4.14.1-pyhe01879 https://conda.anaconda.org/conda-forge/linux-64/ccache-4.11.3-h80c52d3_0.conda#eb517c6a2b960c3ccb6f1db1005f063a https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.3.0-pyhd8ed1ab_0.conda#72e42d28960d875c7654614f8b50939a https://conda.anaconda.org/conda-forge/noarch/joblib-1.5.1-pyhd8ed1ab_0.conda#fb1c14694de51a476ce8636d92b6f42c -https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.9.0-32_he106b2a_openblas.conda#3d3f9355e52f269cd8bc2c440d8a5263 -https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.9.0-32_h7ac8fdf_openblas.conda#6c3f04ccb6c578138e9f9899da0bd714 +https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.9.0-33_he106b2a_openblas.conda#28052b5e6ea5bd283ac343c5c064b950 +https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.9.0-33_h7ac8fdf_openblas.conda#e598bb54c4a4b45c3d83c72984f79dbb https://conda.anaconda.org/conda-forge/noarch/pyproject-metadata-0.9.1-pyhd8ed1ab_0.conda#22ae7c6ea81e0c8661ef32168dda929b https://conda.anaconda.org/conda-forge/noarch/python-freethreading-3.13.5-h92d6c8b_2.conda#32180e39991faf3fd42b4d74ef01daa0 https://conda.anaconda.org/conda-forge/noarch/meson-python-0.18.0-pyh70fd9c4_0.conda#576c04b9d9f8e45285fb4d9452c26133 diff --git a/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock b/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock index 5919a5401a692..eb270fbe29f42 100644 --- a/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock +++ b/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock @@ -1,6 +1,6 @@ # Generated by conda-lock. # platform: linux-64 -# input_hash: 86e2072dbf3e21dd40532da22c0f58dbd4905ce1a1250b64571702c6845d712c +# input_hash: 0668d85ecef342f1056dfe3d1fd8d677c967d4037f6f95fff49c097fec0cd624 @EXPLICIT https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2#d7c89558ba9fa0495403155b64376d81 https://conda.anaconda.org/conda-forge/noarch/python_abi-3.13-8_cp313.conda#94305520c52a4aa3f6c2b1ff6008d9f8 @@ -19,7 +19,7 @@ https://conda.anaconda.org/conda-forge/linux-64/libmpdec-4.0.0-hb9d3cd8_0.conda# https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-15.1.0-h8f9b012_4.conda#3c376af8888c386b9d3d1c2701e2f3ab https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.3.1-hb9d3cd8_2.conda#edb0dca6bc32e4f4789199455a1dbeb8 https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.5-h2d0b736_3.conda#47e340acb35de30501a76c7c799c41d7 -https://conda.anaconda.org/conda-forge/linux-64/openssl-3.5.1-h7b32b05_0.conda#c87df2ab1448ba69169652ab9547082d +https://conda.anaconda.org/conda-forge/linux-64/openssl-3.5.2-h26f9b46_0.conda#ffffb341206dd0dab0c36053c048d621 https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-h4bc722e_7.conda#62ee74e96c5ebb0af99386de58cf9553 https://conda.anaconda.org/conda-forge/linux-64/libgfortran-15.1.0-h69a702a_4.conda#53e876bc2d2648319e94c33c57b9ec74 https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.50.4-h0c1763c_0.conda#0b367fad34931cb79e0d6b7e5c06bb1c @@ -91,6 +91,6 @@ https://conda.anaconda.org/conda-forge/linux-64/ccache-4.11.3-h80c52d3_0.conda#e # pip pytest-cov @ https://files.pythonhosted.org/packages/bc/16/4ea354101abb1287856baa4af2732be351c7bee728065aed451b678153fd/pytest_cov-6.2.1-py3-none-any.whl#sha256=f5bc4c23f42f1cdd23c70b1dab1bbaef4fc505ba950d53e0081d0730dd7e86d5 # pip pytest-xdist @ https://files.pythonhosted.org/packages/ca/31/d4e37e9e550c2b92a9cbc2e4d0b7420a27224968580b5a447f420847c975/pytest_xdist-3.8.0-py3-none-any.whl#sha256=202ca578cfeb7370784a8c33d6d05bc6e13b4f25b5053c30a152269fd10f0b88 # pip scikit-image @ https://files.pythonhosted.org/packages/cd/9b/c3da56a145f52cd61a68b8465d6a29d9503bc45bc993bb45e84371c97d94/scikit_image-0.25.2-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=b8abd3c805ce6944b941cfed0406d88faeb19bab3ed3d4b50187af55cf24d147 -# pip scipy-doctest @ https://files.pythonhosted.org/packages/c9/13/cd25d1875f3804b73fd4a4ae00e2c76e274e1e0608d79148cac251b644b1/scipy_doctest-1.8.0-py3-none-any.whl#sha256=5863208368c35486e143ce3283ab2f517a0d6b0c63d0d5f19f38a823fc82016f +# pip scipy-doctest @ https://files.pythonhosted.org/packages/22/a8/0b7c959d13f664c92673024a42573518d2474525e5298ca8714f91a72a79/scipy_doctest-2.0.0-py3-none-any.whl#sha256=8d45bb33cfa4e1b45599436fbc48033aeda12d6a818c0dd6da94425c624fe3b5 # pip sphinx @ https://files.pythonhosted.org/packages/31/53/136e9eca6e0b9dc0e1962e2c908fbea2e5ac000c2a2fbd9a35797958c48b/sphinx-8.2.3-py3-none-any.whl#sha256=4405915165f13521d875a8c29c8970800a0141c14cc5416a38feca4ea5d9b9c3 # pip numpydoc @ https://files.pythonhosted.org/packages/6c/45/56d99ba9366476cd8548527667f01869279cedb9e66b28eb4dfb27701679/numpydoc-1.8.0-py3-none-any.whl#sha256=72024c7fd5e17375dec3608a27c03303e8ad00c81292667955c6fea7a3ccf541 diff --git a/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock b/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock index 1a24d95d4cc78..d3e7aadecbd59 100644 --- a/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock +++ b/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock @@ -1,6 +1,6 @@ # Generated by conda-lock. # platform: linux-64 -# input_hash: 94d00db2415f525f6a8902cfb08b959e58ea906230fb5acac0be202ef8fcfba8 +# input_hash: 66c01323547a35e8550a7303dac1f0cb19e0af6173e62d689006d7ca8f1cd385 @EXPLICIT https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2#d7c89558ba9fa0495403155b64376d81 https://conda.anaconda.org/conda-forge/noarch/python_abi-3.13-8_cp313.conda#94305520c52a4aa3f6c2b1ff6008d9f8 @@ -19,7 +19,7 @@ https://conda.anaconda.org/conda-forge/linux-64/libmpdec-4.0.0-hb9d3cd8_0.conda# https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-15.1.0-h8f9b012_4.conda#3c376af8888c386b9d3d1c2701e2f3ab https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.3.1-hb9d3cd8_2.conda#edb0dca6bc32e4f4789199455a1dbeb8 https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.5-h2d0b736_3.conda#47e340acb35de30501a76c7c799c41d7 -https://conda.anaconda.org/conda-forge/linux-64/openssl-3.5.1-h7b32b05_0.conda#c87df2ab1448ba69169652ab9547082d +https://conda.anaconda.org/conda-forge/linux-64/openssl-3.5.2-h26f9b46_0.conda#ffffb341206dd0dab0c36053c048d621 https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-h4bc722e_7.conda#62ee74e96c5ebb0af99386de58cf9553 https://conda.anaconda.org/conda-forge/linux-64/libgfortran-15.1.0-h69a702a_4.conda#53e876bc2d2648319e94c33c57b9ec74 https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.50.4-h0c1763c_0.conda#0b367fad34931cb79e0d6b7e5c06bb1c diff --git a/build_tools/azure/pymin_conda_forge_openblas_min_dependencies_environment.yml b/build_tools/azure/pymin_conda_forge_openblas_min_dependencies_environment.yml index 1e7c36708ee30..8c10cec910bf1 100644 --- a/build_tools/azure/pymin_conda_forge_openblas_min_dependencies_environment.yml +++ b/build_tools/azure/pymin_conda_forge_openblas_min_dependencies_environment.yml @@ -8,7 +8,7 @@ dependencies: - numpy=1.22.0 # min - blas[build=openblas] - scipy=1.8.0 # min - - cython=3.0.10 # min + - cython=3.1.2 # min - joblib=1.2.0 # min - threadpoolctl=3.1.0 # min - matplotlib=3.5.0 # min diff --git a/build_tools/azure/pymin_conda_forge_openblas_min_dependencies_linux-64_conda.lock b/build_tools/azure/pymin_conda_forge_openblas_min_dependencies_linux-64_conda.lock index e0fdda45688fb..879b7b46c2916 100644 --- a/build_tools/azure/pymin_conda_forge_openblas_min_dependencies_linux-64_conda.lock +++ b/build_tools/azure/pymin_conda_forge_openblas_min_dependencies_linux-64_conda.lock @@ -1,6 +1,6 @@ # Generated by conda-lock. # platform: linux-64 -# input_hash: 97a1191dcfb0ec679b12b7ba4cea261ae7ff6bd372a7b26cfe443f3e18b5b8df +# input_hash: d6b142fd975427575778d1d015e16fe1fb879c94e34153e605ff104e9219c04a @EXPLICIT https://conda.anaconda.org/conda-forge/noarch/font-ttf-dejavu-sans-mono-2.37-hab24e00_0.tar.bz2#0c96522c6bdaed4b1566d11387caaf45 https://conda.anaconda.org/conda-forge/noarch/font-ttf-inconsolata-3.000-h77eed37_0.tar.bz2#34893075a5c9e55cdafac56607368fc6 @@ -39,7 +39,7 @@ https://conda.anaconda.org/conda-forge/linux-64/libutf8proc-2.8.0-hf23e847_1.con https://conda.anaconda.org/conda-forge/linux-64/libwebp-base-1.6.0-hd42ef1d_0.conda#aea31d2e5b1091feca96fcfe945c3cf9 https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.3.1-hb9d3cd8_2.conda#edb0dca6bc32e4f4789199455a1dbeb8 https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.5-h2d0b736_3.conda#47e340acb35de30501a76c7c799c41d7 -https://conda.anaconda.org/conda-forge/linux-64/openssl-3.5.1-h7b32b05_0.conda#c87df2ab1448ba69169652ab9547082d +https://conda.anaconda.org/conda-forge/linux-64/openssl-3.5.2-h26f9b46_0.conda#ffffb341206dd0dab0c36053c048d621 https://conda.anaconda.org/conda-forge/linux-64/pthread-stubs-0.4-hb9d3cd8_1002.conda#b3c17d95b5a10c6e64a21fa17573e70e https://conda.anaconda.org/conda-forge/linux-64/xorg-libice-1.1.2-hb9d3cd8_0.conda#fb901ff28063514abb6046c9ec2c4a45 https://conda.anaconda.org/conda-forge/linux-64/xorg-libxau-1.0.12-hb9d3cd8_0.conda#f6ebe2cb3f82ba6c057dde5d9debe4f7 @@ -124,7 +124,7 @@ https://conda.anaconda.org/conda-forge/noarch/certifi-2025.8.3-pyhd8ed1ab_0.cond https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_1.conda#962b9857ee8e7018c22f2776ffa0b2d7 https://conda.anaconda.org/conda-forge/noarch/cycler-0.12.1-pyhd8ed1ab_1.conda#44600c4667a319d67dbe0681fc0bc833 https://conda.anaconda.org/conda-forge/linux-64/cyrus-sasl-2.1.28-hd9c7081_0.conda#cae723309a49399d2949362f4ab5c9e4 -https://conda.anaconda.org/conda-forge/linux-64/cython-3.0.10-py310hc6cd4ac_0.conda#bd1d71ee240be36f1d85c86177d6964f +https://conda.anaconda.org/conda-forge/linux-64/cython-3.1.2-py310had8cdd9_2.conda#be416b1d5ffef48c394cbbb04bc864ae https://conda.anaconda.org/conda-forge/noarch/execnet-2.1.1-pyhd8ed1ab_1.conda#a71efeae2c160f6789900ba2631a2c90 https://conda.anaconda.org/conda-forge/linux-64/gettext-0.25.1-h3f43e3d_1.conda#c42356557d7f2e37676e121515417e3b https://conda.anaconda.org/conda-forge/noarch/iniconfig-2.0.0-pyhd8ed1ab_1.conda#6837f3eff7dcea42ecd714ce1ac2b108 @@ -133,16 +133,16 @@ https://conda.anaconda.org/conda-forge/linux-64/lcms2-2.17-h717163a_0.conda#000e https://conda.anaconda.org/conda-forge/linux-64/libcups-2.3.3-hb8b1518_5.conda#d4a250da4737ee127fb1fa6452a9002e https://conda.anaconda.org/conda-forge/linux-64/libcurl-8.14.1-h332b0f4_0.conda#45f6713cb00f124af300342512219182 https://conda.anaconda.org/conda-forge/linux-64/libfreetype-2.13.3-ha770c72_1.conda#51f5be229d83ecd401fb369ab96ae669 -https://conda.anaconda.org/conda-forge/linux-64/libglib-2.84.2-h3618099_0.conda#072ab14a02164b7c0c089055368ff776 +https://conda.anaconda.org/conda-forge/linux-64/libglib-2.84.3-hf39c6af_0.conda#467f23819b1ea2b89c3fc94d65082301 https://conda.anaconda.org/conda-forge/linux-64/libglx-1.7.0-ha4b6fd6_2.conda#c8013e438185f33b13814c5c488acd5c https://conda.anaconda.org/conda-forge/linux-64/libgrpc-1.54.3-hb20ce57_0.conda#7af7c59ab24db007dfd82e0a3a343f66 https://conda.anaconda.org/conda-forge/linux-64/libhiredis-1.0.2-h2cc385e_0.tar.bz2#b34907d3a81a3cd8095ee83d174c074a https://conda.anaconda.org/conda-forge/linux-64/libopenblas-0.3.25-pthreads_h413a1c8_0.conda#d172b34a443b95f86089e8229ddc9a17 https://conda.anaconda.org/conda-forge/linux-64/libsystemd0-256.9-h2774228_0.conda#7b283ff97a87409a884bc11283855c17 -https://conda.anaconda.org/conda-forge/linux-64/libxml2-2.13.8-h4bc477f_0.conda#14dbe05b929e329dbaa6f2d0aa19466d +https://conda.anaconda.org/conda-forge/linux-64/libxml2-2.13.8-h04c0eec_1.conda#10bcbd05e1c1c9d652fccb42b776a9fa https://conda.anaconda.org/conda-forge/noarch/meson-1.8.3-pyhe01879c_0.conda#ed40b34242ec6d216605db54d19c6df5 https://conda.anaconda.org/conda-forge/noarch/munkres-1.1.4-pyhd8ed1ab_1.conda#37293a85a0f4f77bbd9cf7aaefc62609 -https://conda.anaconda.org/conda-forge/linux-64/openjpeg-2.5.3-h5fbd93e_0.conda#9e5816bc95d285c115a3ebc2f8563564 +https://conda.anaconda.org/conda-forge/linux-64/openjpeg-2.5.3-h55fea9a_1.conda#01243c4aaf71bde0297966125aea4706 https://conda.anaconda.org/conda-forge/linux-64/orc-1.8.4-h2f23424_0.conda#4bb92585a250e67d49b46c073d29f9dd https://conda.anaconda.org/conda-forge/noarch/packaging-25.0-pyh29332c3_1.conda#58335b26c38bf4a20f399384c33cbcf9 https://conda.anaconda.org/conda-forge/noarch/pluggy-1.6.0-pyhd8ed1ab_0.conda#7da7ccd349dbf6487a7778579d2bb971 @@ -169,11 +169,11 @@ https://conda.anaconda.org/conda-forge/linux-64/aws-c-event-stream-0.3.1-h1e0337 https://conda.anaconda.org/conda-forge/linux-64/aws-c-http-0.7.10-h9ab9c9b_2.conda#cf49873da2e59f876a2ad4794b05801b https://conda.anaconda.org/conda-forge/linux-64/brotli-1.0.9-h166bdaf_9.conda#4601544b4982ba1861fa9b9c607b2c06 https://conda.anaconda.org/conda-forge/linux-64/ccache-4.11.3-h80c52d3_0.conda#eb517c6a2b960c3ccb6f1db1005f063a -https://conda.anaconda.org/conda-forge/linux-64/coverage-7.10.1-py310h3406613_0.conda#ac2715e7efc966c105f45d0cc8dfc4cb +https://conda.anaconda.org/conda-forge/linux-64/coverage-7.10.2-py310h3406613_0.conda#c03cc1310dc3059463e52ba1062e1689 https://conda.anaconda.org/conda-forge/linux-64/dbus-1.16.2-h3c4dab8_0.conda#679616eb5ad4e521c83da4650860aba7 https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.3.0-pyhd8ed1ab_0.conda#72e42d28960d875c7654614f8b50939a https://conda.anaconda.org/conda-forge/linux-64/freetype-2.13.3-ha770c72_1.conda#9ccd736d31e0c6e41f54e704e5312811 -https://conda.anaconda.org/conda-forge/linux-64/glib-tools-2.84.2-h4833e2c_0.conda#f2ec1facec64147850b7674633978050 +https://conda.anaconda.org/conda-forge/linux-64/glib-tools-2.84.3-hf516916_0.conda#39f817fb8e0bb88a63bbdca0448143ea https://conda.anaconda.org/conda-forge/noarch/joblib-1.2.0-pyhd8ed1ab_0.tar.bz2#7583652522d71ad78ba536bba06940eb https://conda.anaconda.org/conda-forge/linux-64/libblas-3.9.0-20_linux64_openblas.conda#2b7bb4f7562c8cf334fc2e20c2d28abc https://conda.anaconda.org/conda-forge/linux-64/libflac-1.4.3-h59595ed_0.conda#ee48bf17cc83a00f59ca1494d5646869 @@ -195,7 +195,7 @@ https://conda.anaconda.org/conda-forge/linux-64/aws-c-auth-0.7.0-h435f46f_0.cond https://conda.anaconda.org/conda-forge/linux-64/aws-c-mqtt-0.8.13-hd4f18eb_5.conda#860fb8c0efec64a4a678eb2ea066ff65 https://conda.anaconda.org/conda-forge/linux-64/fontconfig-2.15.0-h7e30c49_1.conda#8f5b0b297b59e1ac160ad4beec99dbee https://conda.anaconda.org/conda-forge/linux-64/fonttools-4.59.0-py310h3406613_0.conda#dc2e5602e20bbffb18314a70094b3c4a -https://conda.anaconda.org/conda-forge/linux-64/glib-2.84.2-h6287aef_0.conda#704648df3a01d4d24bc2c0466b718d63 +https://conda.anaconda.org/conda-forge/linux-64/glib-2.84.3-h89d24bf_0.conda#9d1844ab51651cc3d034bb55fff83b99 https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.9.0-20_linux64_openblas.conda#36d486d72ab64ffea932329a1d3729a3 https://conda.anaconda.org/conda-forge/linux-64/libclang-cpp20.1-20.1.8-default_hddf928d_0.conda#b939740734ad5a8e8f6c942374dee68d https://conda.anaconda.org/conda-forge/linux-64/libclang13-20.1.8-default_ha444ac7_0.conda#783f9cdcb0255ed00e3f1be22e16de40 diff --git a/build_tools/azure/pymin_conda_forge_openblas_ubuntu_2204_linux-64_conda.lock b/build_tools/azure/pymin_conda_forge_openblas_ubuntu_2204_linux-64_conda.lock index 4195ae4bd5044..1027301dff4b5 100644 --- a/build_tools/azure/pymin_conda_forge_openblas_ubuntu_2204_linux-64_conda.lock +++ b/build_tools/azure/pymin_conda_forge_openblas_ubuntu_2204_linux-64_conda.lock @@ -1,6 +1,6 @@ # Generated by conda-lock. # platform: linux-64 -# input_hash: eca51d0b31006b26e8a75b2be7389e6909a81ca3c7647651d7e54f9013aedbde +# input_hash: 4abfb998e26e3beaa198409ac1ebc1278024921c4b3c6fc8de5c93be1b6193ba @EXPLICIT https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2#d7c89558ba9fa0495403155b64376d81 https://conda.anaconda.org/conda-forge/noarch/python_abi-3.10-8_cp310.conda#05e00f3b21e88bb3d658ac700b2ce58c @@ -22,7 +22,7 @@ https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-15.1.0-h8f9b012_4.cond https://conda.anaconda.org/conda-forge/linux-64/libwebp-base-1.6.0-hd42ef1d_0.conda#aea31d2e5b1091feca96fcfe945c3cf9 https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.3.1-hb9d3cd8_2.conda#edb0dca6bc32e4f4789199455a1dbeb8 https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.5-h2d0b736_3.conda#47e340acb35de30501a76c7c799c41d7 -https://conda.anaconda.org/conda-forge/linux-64/openssl-3.5.1-h7b32b05_0.conda#c87df2ab1448ba69169652ab9547082d +https://conda.anaconda.org/conda-forge/linux-64/openssl-3.5.2-h26f9b46_0.conda#ffffb341206dd0dab0c36053c048d621 https://conda.anaconda.org/conda-forge/linux-64/pthread-stubs-0.4-hb9d3cd8_1002.conda#b3c17d95b5a10c6e64a21fa17573e70e https://conda.anaconda.org/conda-forge/linux-64/xorg-libxau-1.0.12-hb9d3cd8_0.conda#f6ebe2cb3f82ba6c057dde5d9debe4f7 https://conda.anaconda.org/conda-forge/linux-64/xorg-libxdmcp-1.1.5-hb9d3cd8_0.conda#8035c64cb77ed555e3f150b7b3972480 @@ -58,13 +58,13 @@ https://conda.anaconda.org/conda-forge/noarch/idna-3.10-pyhd8ed1ab_1.conda#39a4f https://conda.anaconda.org/conda-forge/noarch/imagesize-1.4.1-pyhd8ed1ab_0.tar.bz2#7de5386c8fea29e76b303f37dde4c352 https://conda.anaconda.org/conda-forge/noarch/iniconfig-2.0.0-pyhd8ed1ab_1.conda#6837f3eff7dcea42ecd714ce1ac2b108 https://conda.anaconda.org/conda-forge/linux-64/lcms2-2.17-h717163a_0.conda#000e85703f0fd9594c81710dd5066471 -https://conda.anaconda.org/conda-forge/linux-64/libblas-3.9.0-32_h59b9bed_openblas.conda#2af9f3d5c2e39f417ce040f5a35c40c6 +https://conda.anaconda.org/conda-forge/linux-64/libblas-3.9.0-33_h59b9bed_openblas.conda#b43d5de8fe73c2a5fb2b43f45301285b https://conda.anaconda.org/conda-forge/linux-64/libfreetype-2.13.3-ha770c72_1.conda#51f5be229d83ecd401fb369ab96ae669 https://conda.anaconda.org/conda-forge/linux-64/libhiredis-1.0.2-h2cc385e_0.tar.bz2#b34907d3a81a3cd8095ee83d174c074a https://conda.anaconda.org/conda-forge/linux-64/markupsafe-3.0.2-py310h89163eb_1.conda#8ce3f0332fd6de0d737e2911d329523f https://conda.anaconda.org/conda-forge/noarch/meson-1.8.3-pyhe01879c_0.conda#ed40b34242ec6d216605db54d19c6df5 https://conda.anaconda.org/conda-forge/linux-64/openblas-0.3.30-pthreads_h6ec200e_1.conda#611fcf119d77a78439794c43f7667664 -https://conda.anaconda.org/conda-forge/linux-64/openjpeg-2.5.3-h5fbd93e_0.conda#9e5816bc95d285c115a3ebc2f8563564 +https://conda.anaconda.org/conda-forge/linux-64/openjpeg-2.5.3-h55fea9a_1.conda#01243c4aaf71bde0297966125aea4706 https://conda.anaconda.org/conda-forge/noarch/packaging-25.0-pyh29332c3_1.conda#58335b26c38bf4a20f399384c33cbcf9 https://conda.anaconda.org/conda-forge/noarch/pluggy-1.6.0-pyhd8ed1ab_0.conda#7da7ccd349dbf6487a7778579d2bb971 https://conda.anaconda.org/conda-forge/noarch/pycparser-2.22-pyh29332c3_1.conda#12c566707c80111f9799308d9e265aef @@ -88,23 +88,23 @@ https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.3.0-pyhd8ed1ab_0. https://conda.anaconda.org/conda-forge/noarch/h2-4.2.0-pyhd8ed1ab_0.conda#b4754fb1bdcb70c8fd54f918301582c6 https://conda.anaconda.org/conda-forge/noarch/jinja2-3.1.6-pyhd8ed1ab_0.conda#446bd6c8cb26050d528881df495ce646 https://conda.anaconda.org/conda-forge/noarch/joblib-1.5.1-pyhd8ed1ab_0.conda#fb1c14694de51a476ce8636d92b6f42c -https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.9.0-32_he106b2a_openblas.conda#3d3f9355e52f269cd8bc2c440d8a5263 -https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.9.0-32_h7ac8fdf_openblas.conda#6c3f04ccb6c578138e9f9899da0bd714 +https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.9.0-33_he106b2a_openblas.conda#28052b5e6ea5bd283ac343c5c064b950 +https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.9.0-33_h7ac8fdf_openblas.conda#e598bb54c4a4b45c3d83c72984f79dbb https://conda.anaconda.org/conda-forge/linux-64/pillow-11.3.0-py310h7e6dc6c_0.conda#e609995f031bc848be8ea159865e8afc https://conda.anaconda.org/conda-forge/noarch/pip-25.2-pyh8b19718_0.conda#dfce4b2af4bfe90cdcaf56ca0b28ddf5 https://conda.anaconda.org/conda-forge/noarch/pyproject-metadata-0.9.1-pyhd8ed1ab_0.conda#22ae7c6ea81e0c8661ef32168dda929b https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0.post0-pyhe01879c_2.conda#5b8d21249ff20967101ffa321cab24e8 -https://conda.anaconda.org/conda-forge/linux-64/liblapacke-3.9.0-32_he2f377e_openblas.conda#54e7f7896d0dbf56665bcb0078bfa9d2 +https://conda.anaconda.org/conda-forge/linux-64/liblapacke-3.9.0-33_he2f377e_openblas.conda#912372f43a7430828cca8e86f680e741 https://conda.anaconda.org/conda-forge/noarch/meson-python-0.18.0-pyh70fd9c4_0.conda#576c04b9d9f8e45285fb4d9452c26133 https://conda.anaconda.org/conda-forge/linux-64/numpy-2.2.6-py310hefbff90_0.conda#b0cea2c364bf65cd19e023040eeab05d https://conda.anaconda.org/conda-forge/noarch/pytest-8.4.1-pyhd8ed1ab_0.conda#a49c2283f24696a7b30367b7346a0144 https://conda.anaconda.org/conda-forge/linux-64/zstandard-0.23.0-py310ha75aee5_2.conda#f9254b5b0193982416b91edcb4b2676f -https://conda.anaconda.org/conda-forge/linux-64/blas-devel-3.9.0-32_h1ea3ea9_openblas.conda#34cb4b6753b38a62ae25f3a73efd16b0 +https://conda.anaconda.org/conda-forge/linux-64/blas-devel-3.9.0-33_h1ea3ea9_openblas.conda#bc300d05fd629ecaccc25885275b4500 https://conda.anaconda.org/conda-forge/linux-64/pandas-2.3.1-py310h0158d43_0.conda#94eb2db0b8f769a1e554843e3586504d https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.8.0-pyhd8ed1ab_0.conda#8375cfbda7c57fbceeda18229be10417 https://conda.anaconda.org/conda-forge/linux-64/scipy-1.15.2-py310h1d65ade_0.conda#8c29cd33b64b2eb78597fa28b5595c8d https://conda.anaconda.org/conda-forge/noarch/urllib3-2.5.0-pyhd8ed1ab_0.conda#436c165519e140cb08d246a4472a9d6a -https://conda.anaconda.org/conda-forge/linux-64/blas-2.132-openblas.conda#9c4a27ab2463f9b1d9019e0a798a5b81 +https://conda.anaconda.org/conda-forge/linux-64/blas-2.133-openblas.conda#59bd46ccdcaabbf2d5202a358bdb000c https://conda.anaconda.org/conda-forge/linux-64/pyamg-5.2.1-py310ha2bacc8_1.conda#817d32861729e14f474249f1036291c4 https://conda.anaconda.org/conda-forge/noarch/requests-2.32.4-pyhd8ed1ab_0.conda#f6082eae112814f1447b56a5e1f6ed05 https://conda.anaconda.org/conda-forge/noarch/numpydoc-1.8.0-pyhd8ed1ab_1.conda#5af206d64d18d6c8dfb3122b4d9e643b diff --git a/build_tools/azure/pymin_conda_forge_openblas_win-64_conda.lock b/build_tools/azure/pymin_conda_forge_openblas_win-64_conda.lock index 1ba106605ccf8..4a6682b32aa47 100644 --- a/build_tools/azure/pymin_conda_forge_openblas_win-64_conda.lock +++ b/build_tools/azure/pymin_conda_forge_openblas_win-64_conda.lock @@ -1,6 +1,6 @@ # Generated by conda-lock. # platform: win-64 -# input_hash: 2c3fe1c37ac2b2ad6a1b18ab6881baec49f52a712bd6f5d3c29268a4b92ca179 +# input_hash: 4ff41dadb8a7a77d0b784bfc6b32126b8e1a41c8b9a87375b48c18c9aee4ea2a @EXPLICIT https://conda.anaconda.org/conda-forge/noarch/font-ttf-dejavu-sans-mono-2.37-hab24e00_0.tar.bz2#0c96522c6bdaed4b1566d11387caaf45 https://conda.anaconda.org/conda-forge/noarch/font-ttf-inconsolata-3.000-h77eed37_0.tar.bz2#34893075a5c9e55cdafac56607368fc6 @@ -31,23 +31,23 @@ https://conda.anaconda.org/conda-forge/win-64/libgcc-15.1.0-h1383e82_4.conda#59f https://conda.anaconda.org/conda-forge/win-64/libiconv-1.18-h135ad9c_1.conda#21fc5dba2cbcd8e5e26ff976a312122c https://conda.anaconda.org/conda-forge/win-64/libjpeg-turbo-3.1.0-h2466b09_0.conda#7c51d27540389de84852daa1cdb9c63c https://conda.anaconda.org/conda-forge/win-64/liblzma-5.8.1-h2466b09_2.conda#c15148b2e18da456f5108ccb5e411446 -https://conda.anaconda.org/conda-forge/win-64/libopenblas-0.3.30-pthreads_ha4fe6b2_0.conda#c09864590782cb17fee135db4796bdcb +https://conda.anaconda.org/conda-forge/win-64/libopenblas-0.3.30-pthreads_ha4fe6b2_1.conda#872413a5fcf08fcd0a2dce87fb0ded70 https://conda.anaconda.org/conda-forge/win-64/libsqlite-3.50.4-hf5d6505_0.conda#ccb20d946040f86f0c05b644d5eadeca https://conda.anaconda.org/conda-forge/win-64/libwebp-base-1.6.0-h4d5522a_0.conda#f9bbae5e2537e3b06e0f7310ba76c893 https://conda.anaconda.org/conda-forge/win-64/libzlib-1.3.1-h2466b09_2.conda#41fbfac52c601159df6c01f875de31b9 https://conda.anaconda.org/conda-forge/win-64/ninja-1.13.1-h477610d_0.conda#b8a603d4b32e113e3551b257b677de67 -https://conda.anaconda.org/conda-forge/win-64/openssl-3.5.1-h725018a_0.conda#d124fc2fd7070177b5e2450627f8fc1a +https://conda.anaconda.org/conda-forge/win-64/openssl-3.5.2-h725018a_0.conda#150d3920b420a27c0848acca158f94dc https://conda.anaconda.org/conda-forge/win-64/pixman-0.46.4-hc614b68_0.conda#04170282e8afb5a5e0d7168b0840f91b https://conda.anaconda.org/conda-forge/win-64/qhull-2020.2-hc790b64_5.conda#854fbdff64b572b5c0b470f334d34c11 https://conda.anaconda.org/conda-forge/win-64/tk-8.6.13-h2c6b04d_2.conda#ebd0e761de9aa879a51d22cc721bd095 https://conda.anaconda.org/conda-forge/win-64/krb5-1.21.3-hdf4eb48_0.conda#31aec030344e962fbd7dbbbbd68e60a9 -https://conda.anaconda.org/conda-forge/win-64/libblas-3.9.0-32_h11dc60a_openblas.conda#0696abde82f7b82d4f74e963ebdd430c +https://conda.anaconda.org/conda-forge/win-64/libblas-3.9.0-33_h11dc60a_openblas.conda#de70cf0c3460df403940d81090ce5be3 https://conda.anaconda.org/conda-forge/win-64/libbrotlidec-1.1.0-h2466b09_3.conda#a342933dbc6d814541234c7c81cb5205 https://conda.anaconda.org/conda-forge/win-64/libbrotlienc-1.1.0-h2466b09_3.conda#7ef0af55d70cbd9de324bb88b7f9d81e https://conda.anaconda.org/conda-forge/win-64/libintl-0.22.5-h5728263_3.conda#2cf0cf76cc15d360dfa2f17fd6cf9772 https://conda.anaconda.org/conda-forge/win-64/libpng-1.6.50-h7351971_1.conda#3ae6e9f5c47c495ebeed95651518be61 -https://conda.anaconda.org/conda-forge/win-64/libxml2-2.13.8-h442d1da_0.conda#833c2dbc1a5020007b520b044c713ed3 -https://conda.anaconda.org/conda-forge/win-64/openblas-0.3.30-pthreads_h4a7f399_0.conda#2773d23da17eb31ed3a0911334a08805 +https://conda.anaconda.org/conda-forge/win-64/libxml2-2.13.8-h741aa76_1.conda#aeb49dc1f5531de13d2c0d57ffa6d0c8 +https://conda.anaconda.org/conda-forge/win-64/openblas-0.3.30-pthreads_h4a7f399_1.conda#ad69c0075127ea83cec563b5e8f30994 https://conda.anaconda.org/conda-forge/win-64/pcre2-10.45-h99c9b8b_0.conda#f4c483274001678e129f5cbaf3a8d765 https://conda.anaconda.org/conda-forge/win-64/pthread-stubs-0.4-h0e40799_1002.conda#3c8f2573569bb816483e5cf57efbbe29 https://conda.anaconda.org/conda-forge/win-64/python-3.10.18-h8c5b53a_0_cpython.conda#f1775dab55c8a073ebd024bfb2f689c1 @@ -61,11 +61,11 @@ https://conda.anaconda.org/conda-forge/win-64/cython-3.1.2-py310h6bd2d47_2.conda https://conda.anaconda.org/conda-forge/noarch/execnet-2.1.1-pyhd8ed1ab_1.conda#a71efeae2c160f6789900ba2631a2c90 https://conda.anaconda.org/conda-forge/noarch/iniconfig-2.0.0-pyhd8ed1ab_1.conda#6837f3eff7dcea42ecd714ce1ac2b108 https://conda.anaconda.org/conda-forge/win-64/kiwisolver-1.4.8-py310he9f1925_1.conda#e2755283837d9bd45838564cf54872c8 -https://conda.anaconda.org/conda-forge/win-64/libcblas-3.9.0-32_h9bd4c3b_openblas.conda#69e8e83a9ed37d070b0c5ed4996648a8 +https://conda.anaconda.org/conda-forge/win-64/libcblas-3.9.0-33_h9bd4c3b_openblas.conda#8ac0e84fb704fe6ff6484edc413964d1 https://conda.anaconda.org/conda-forge/win-64/libclang13-20.1.8-default_hadf22e1_0.conda#cf1a9a4c7395c5d6cc0dcf8f7c40acb3 https://conda.anaconda.org/conda-forge/win-64/libfreetype6-2.13.3-h0b5ce68_1.conda#a84b7d1a13060a9372bea961a8131dbc -https://conda.anaconda.org/conda-forge/win-64/libglib-2.84.2-hbc94333_0.conda#fee05801cc5db97bec20a5e78fb3905b -https://conda.anaconda.org/conda-forge/win-64/liblapack-3.9.0-32_h2526c6b_openblas.conda#13c3da761e89eec8a40bf8c877dd7a71 +https://conda.anaconda.org/conda-forge/win-64/libglib-2.84.3-h1c1036b_0.conda#2bcc00752c158d4a70e1eaccbf6fe8ae +https://conda.anaconda.org/conda-forge/win-64/liblapack-3.9.0-33_h2526c6b_openblas.conda#856fab9484c2e0ce0bee0171421ff9a4 https://conda.anaconda.org/conda-forge/win-64/libtiff-4.7.0-h05922d8_5.conda#75370aba951b47ec3b5bfe689f1bcf7f https://conda.anaconda.org/conda-forge/win-64/libxcb-1.17.0-h0e4246c_0.conda#a69bbf778a462da324489976c84cfc8c https://conda.anaconda.org/conda-forge/win-64/libxslt-1.1.43-h25c3957_0.conda#e84f36aa02735c140099d992d491968d @@ -85,18 +85,18 @@ https://conda.anaconda.org/conda-forge/noarch/typing_extensions-4.14.1-pyhe01879 https://conda.anaconda.org/conda-forge/win-64/unicodedata2-16.0.0-py310ha8f682b_0.conda#b28aead44c6e19a1fbba7752aa242b34 https://conda.anaconda.org/conda-forge/noarch/wheel-0.45.1-pyhd8ed1ab_1.conda#75cb7132eb58d97896e173ef12ac9986 https://conda.anaconda.org/conda-forge/win-64/brotli-1.1.0-h2466b09_3.conda#c2a23d8a8986c72148c63bdf855ac99a -https://conda.anaconda.org/conda-forge/win-64/coverage-7.10.1-py310hdb0e946_0.conda#0092c0f10b7473d481070ad5f3b789f0 +https://conda.anaconda.org/conda-forge/win-64/coverage-7.10.2-py310hdb0e946_0.conda#df6cd1aa316df7c199030a889cd8886f https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.3.0-pyhd8ed1ab_0.conda#72e42d28960d875c7654614f8b50939a https://conda.anaconda.org/conda-forge/noarch/joblib-1.5.1-pyhd8ed1ab_0.conda#fb1c14694de51a476ce8636d92b6f42c https://conda.anaconda.org/conda-forge/win-64/lcms2-2.17-hbcf6048_0.conda#3538827f77b82a837fa681a4579e37a1 https://conda.anaconda.org/conda-forge/win-64/libfreetype-2.13.3-h57928b3_1.conda#410ba2c8e7bdb278dfbb5d40220e39d2 -https://conda.anaconda.org/conda-forge/win-64/liblapacke-3.9.0-32_h1d0e49f_openblas.conda#cca697e07375fde34cced92d66e8bdf2 +https://conda.anaconda.org/conda-forge/win-64/liblapacke-3.9.0-33_h1d0e49f_openblas.conda#6cd916c1c97af7138d22fd4af15ede90 https://conda.anaconda.org/conda-forge/win-64/numpy-2.2.6-py310h4987827_0.conda#d2596785ac2cf5bab04e2ee9e5d04041 -https://conda.anaconda.org/conda-forge/win-64/openjpeg-2.5.3-h4d64b90_0.conda#fc050366dd0b8313eb797ed1ffef3a29 +https://conda.anaconda.org/conda-forge/win-64/openjpeg-2.5.3-h24db6dd_1.conda#25f45acb1a234ad1c9b9a20e1e6c559e https://conda.anaconda.org/conda-forge/noarch/pip-25.2-pyh8b19718_0.conda#dfce4b2af4bfe90cdcaf56ca0b28ddf5 https://conda.anaconda.org/conda-forge/noarch/pyproject-metadata-0.9.1-pyhd8ed1ab_0.conda#22ae7c6ea81e0c8661ef32168dda929b https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0.post0-pyhe01879c_2.conda#5b8d21249ff20967101ffa321cab24e8 -https://conda.anaconda.org/conda-forge/win-64/blas-devel-3.9.0-32_hc0f8095_openblas.conda#c07c54d62ee5a9886933051e10ad4b1e +https://conda.anaconda.org/conda-forge/win-64/blas-devel-3.9.0-33_hc0f8095_openblas.conda#2f43b14f3011449f579a2ae03424b18f https://conda.anaconda.org/conda-forge/win-64/contourpy-1.3.2-py310hc19bc0b_0.conda#039416813b5290e7d100a05bb4326110 https://conda.anaconda.org/conda-forge/win-64/fonttools-4.59.0-py310hdb0e946_0.conda#eae900c4fcb37e4a3f9fe9417c669f11 https://conda.anaconda.org/conda-forge/win-64/freetype-2.13.3-h57928b3_1.conda#633504fe3f96031192e40e3e6c18ef06 @@ -104,7 +104,7 @@ https://conda.anaconda.org/conda-forge/noarch/meson-python-0.18.0-pyh70fd9c4_0.c https://conda.anaconda.org/conda-forge/win-64/pillow-11.3.0-py310h6d647b9_0.conda#246b33a0eb812754b529065262aeb1c5 https://conda.anaconda.org/conda-forge/noarch/pytest-8.4.1-pyhd8ed1ab_0.conda#a49c2283f24696a7b30367b7346a0144 https://conda.anaconda.org/conda-forge/win-64/scipy-1.15.2-py310h15c175c_0.conda#81798168111d1021e3d815217c444418 -https://conda.anaconda.org/conda-forge/win-64/blas-2.132-openblas.conda#b59780f3fbd2bf992d3702e59d8d1653 +https://conda.anaconda.org/conda-forge/win-64/blas-2.133-openblas.conda#5f06959474ec0b2b40ca8de380859f7f https://conda.anaconda.org/conda-forge/win-64/fontconfig-2.15.0-h765892d_1.conda#9bb0026a2131b09404c59c4290c697cd https://conda.anaconda.org/conda-forge/win-64/matplotlib-base-3.10.5-py310h0bdd906_0.conda#a26309db5dc93b40f5e6bf69187f631e https://conda.anaconda.org/conda-forge/noarch/pytest-cov-6.2.1-pyhd8ed1ab_0.conda#ce978e1b9ed8b8d49164e90a5cdc94cd diff --git a/build_tools/azure/ubuntu_atlas_lock.txt b/build_tools/azure/ubuntu_atlas_lock.txt index 993b7d8627557..fb49fb3bab1d9 100644 --- a/build_tools/azure/ubuntu_atlas_lock.txt +++ b/build_tools/azure/ubuntu_atlas_lock.txt @@ -4,7 +4,7 @@ # # pip-compile --output-file=build_tools/azure/ubuntu_atlas_lock.txt build_tools/azure/ubuntu_atlas_requirements.txt # -cython==3.0.10 +cython==3.1.2 # via -r build_tools/azure/ubuntu_atlas_requirements.txt exceptiongroup==1.3.0 # via pytest diff --git a/build_tools/azure/ubuntu_atlas_requirements.txt b/build_tools/azure/ubuntu_atlas_requirements.txt index dfb0cfebc54d1..4e0edd877dea7 100644 --- a/build_tools/azure/ubuntu_atlas_requirements.txt +++ b/build_tools/azure/ubuntu_atlas_requirements.txt @@ -1,7 +1,7 @@ # DO NOT EDIT: this file is generated from the specification found in the # following script to centralize the configuration for CI builds: # build_tools/update_environments_and_lock_files.py -cython==3.0.10 # min +cython==3.1.2 # min joblib==1.2.0 # min threadpoolctl==3.1.0 # min pytest diff --git a/build_tools/circle/doc_linux-64_conda.lock b/build_tools/circle/doc_linux-64_conda.lock index d179ba70af52c..a62944d452a95 100644 --- a/build_tools/circle/doc_linux-64_conda.lock +++ b/build_tools/circle/doc_linux-64_conda.lock @@ -1,6 +1,6 @@ # Generated by conda-lock. # platform: linux-64 -# input_hash: 9bc9ca426bc05685148b1ae7e671907e9d514e40b6bb1c8d7c916d4fdc8b70f2 +# input_hash: 207a7209ba4771c5fc039939c36a47d93b9e5478fbdf6fe01c4ac5837581d49a @EXPLICIT https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2#d7c89558ba9fa0495403155b64376d81 https://conda.anaconda.org/conda-forge/noarch/font-ttf-dejavu-sans-mono-2.37-hab24e00_0.tar.bz2#0c96522c6bdaed4b1566d11387caaf45 @@ -43,7 +43,7 @@ https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-15.1.0-h8f9b012_4.cond https://conda.anaconda.org/conda-forge/linux-64/libwebp-base-1.6.0-hd42ef1d_0.conda#aea31d2e5b1091feca96fcfe945c3cf9 https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.3.1-hb9d3cd8_2.conda#edb0dca6bc32e4f4789199455a1dbeb8 https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.5-h2d0b736_3.conda#47e340acb35de30501a76c7c799c41d7 -https://conda.anaconda.org/conda-forge/linux-64/openssl-3.5.1-h7b32b05_0.conda#c87df2ab1448ba69169652ab9547082d +https://conda.anaconda.org/conda-forge/linux-64/openssl-3.5.2-h26f9b46_0.conda#ffffb341206dd0dab0c36053c048d621 https://conda.anaconda.org/conda-forge/linux-64/pthread-stubs-0.4-hb9d3cd8_1002.conda#b3c17d95b5a10c6e64a21fa17573e70e https://conda.anaconda.org/conda-forge/linux-64/rav1e-0.7.1-h8fae777_3.conda#2c42649888aac645608191ffdc80d13a https://conda.anaconda.org/conda-forge/linux-64/xorg-libice-1.1.2-hb9d3cd8_0.conda#fb901ff28063514abb6046c9ec2c4a45 @@ -82,7 +82,7 @@ https://conda.anaconda.org/conda-forge/linux-64/svt-av1-3.0.2-h5888daf_0.conda#0 https://conda.anaconda.org/conda-forge/linux-64/tk-8.6.13-noxft_hd72426e_102.conda#a0116df4f4ed05c303811a837d5b39d8 https://conda.anaconda.org/conda-forge/linux-64/wayland-1.24.0-h3e06ad9_0.conda#0f2ca7906bf166247d1d760c3422cb8a https://conda.anaconda.org/conda-forge/linux-64/zfp-1.0.1-h5888daf_2.conda#e0409515c467b87176b070bff5d9442e -https://conda.anaconda.org/conda-forge/linux-64/zlib-ng-2.2.4-h7955e40_0.conda#c8a816dbf59eb8ba6346a8f10014b302 +https://conda.anaconda.org/conda-forge/linux-64/zlib-ng-2.2.5-hde8ca8f_0.conda#1920c3502e7f6688d650ab81cd3775fd https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.7-hb8e6e7a_2.conda#6432cb5d4ac0046c3ac0a8a0f95842f9 https://conda.anaconda.org/conda-forge/linux-64/aom-3.9.1-hac33072_0.conda#346722a0be40f6edc53f12640d301338 https://conda.anaconda.org/conda-forge/linux-64/blosc-1.21.6-he440d0b_1.conda#2c2fae981fd2afd00812c92ac47d023d @@ -113,7 +113,7 @@ https://conda.anaconda.org/conda-forge/linux-64/brotli-python-1.1.0-py310hf71b8c https://conda.anaconda.org/conda-forge/noarch/cached_property-1.5.2-pyha770c72_1.tar.bz2#576d629e47797577ab0f1b351297ef4a https://conda.anaconda.org/conda-forge/noarch/certifi-2025.8.3-pyhd8ed1ab_0.conda#11f59985f49df4620890f3e746ed7102 https://conda.anaconda.org/conda-forge/noarch/charset-normalizer-3.4.2-pyhd8ed1ab_0.conda#40fe4284b8b5835a9073a645139f35af -https://conda.anaconda.org/conda-forge/noarch/click-8.2.2-pyh707e725_0.conda#2cc16494e4ce28efc52fb29ec3c348a1 +https://conda.anaconda.org/conda-forge/noarch/click-8.2.1-pyh707e725_0.conda#94b550b8d3a614dbd326af798c7dfb40 https://conda.anaconda.org/conda-forge/noarch/cloudpickle-3.1.1-pyhd8ed1ab_0.conda#364ba6c9fb03886ac979b482f39ebb92 https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_1.conda#962b9857ee8e7018c22f2776ffa0b2d7 https://conda.anaconda.org/conda-forge/linux-64/conda-gcc-specs-14.3.0-hb991d5c_4.conda#b6025bc20bf223d68402821f181707fb @@ -138,12 +138,12 @@ https://conda.anaconda.org/conda-forge/linux-64/kiwisolver-1.4.8-py310h3788b33_1 https://conda.anaconda.org/conda-forge/noarch/lark-1.2.2-pyhd8ed1ab_1.conda#3a8063b25e603999188ed4bbf3485404 https://conda.anaconda.org/conda-forge/linux-64/lcms2-2.17-h717163a_0.conda#000e85703f0fd9594c81710dd5066471 https://conda.anaconda.org/conda-forge/linux-64/libavif16-1.3.0-h766b0b6_0.conda#f17f2d0e5c9ad6b958547fd67b155771 -https://conda.anaconda.org/conda-forge/linux-64/libblas-3.9.0-32_h59b9bed_openblas.conda#2af9f3d5c2e39f417ce040f5a35c40c6 +https://conda.anaconda.org/conda-forge/linux-64/libblas-3.9.0-33_h59b9bed_openblas.conda#b43d5de8fe73c2a5fb2b43f45301285b https://conda.anaconda.org/conda-forge/linux-64/libcups-2.3.3-hb8b1518_5.conda#d4a250da4737ee127fb1fa6452a9002e https://conda.anaconda.org/conda-forge/linux-64/libfreetype-2.13.3-ha770c72_1.conda#51f5be229d83ecd401fb369ab96ae669 -https://conda.anaconda.org/conda-forge/linux-64/libglib-2.84.2-h3618099_0.conda#072ab14a02164b7c0c089055368ff776 +https://conda.anaconda.org/conda-forge/linux-64/libglib-2.84.3-hf39c6af_0.conda#467f23819b1ea2b89c3fc94d65082301 https://conda.anaconda.org/conda-forge/linux-64/libglx-1.7.0-ha4b6fd6_2.conda#c8013e438185f33b13814c5c488acd5c -https://conda.anaconda.org/conda-forge/linux-64/libxml2-2.13.8-h4bc477f_0.conda#14dbe05b929e329dbaa6f2d0aa19466d +https://conda.anaconda.org/conda-forge/linux-64/libxml2-2.13.8-h04c0eec_1.conda#10bcbd05e1c1c9d652fccb42b776a9fa https://conda.anaconda.org/conda-forge/linux-64/markupsafe-3.0.2-py310h89163eb_1.conda#8ce3f0332fd6de0d737e2911d329523f https://conda.anaconda.org/conda-forge/noarch/mdurl-0.1.2-pyhd8ed1ab_1.conda#592132998493b3ff25fd7479396e8351 https://conda.anaconda.org/conda-forge/noarch/meson-1.8.3-pyhe01879c_0.conda#ed40b34242ec6d216605db54d19c6df5 @@ -151,7 +151,7 @@ https://conda.anaconda.org/conda-forge/noarch/munkres-1.1.4-pyhd8ed1ab_1.conda#3 https://conda.anaconda.org/conda-forge/noarch/narwhals-2.0.1-pyhe01879c_0.conda#5f0dea40791cecf0f82882b9eea7f7c1 https://conda.anaconda.org/conda-forge/noarch/networkx-3.4.2-pyh267e887_2.conda#fd40bf7f7f4bc4b647dc8512053d9873 https://conda.anaconda.org/conda-forge/linux-64/openblas-0.3.30-pthreads_h6ec200e_1.conda#611fcf119d77a78439794c43f7667664 -https://conda.anaconda.org/conda-forge/linux-64/openjpeg-2.5.3-h5fbd93e_0.conda#9e5816bc95d285c115a3ebc2f8563564 +https://conda.anaconda.org/conda-forge/linux-64/openjpeg-2.5.3-h55fea9a_1.conda#01243c4aaf71bde0297966125aea4706 https://conda.anaconda.org/conda-forge/noarch/packaging-25.0-pyh29332c3_1.conda#58335b26c38bf4a20f399384c33cbcf9 https://conda.anaconda.org/conda-forge/noarch/pandocfilters-1.5.0-pyhd8ed1ab_0.tar.bz2#457c2c8c08e54905d6954e79cb5b5db9 https://conda.anaconda.org/conda-forge/noarch/pkginfo-1.12.1.2-pyhd8ed1ab_0.conda#dc702b2fae7ebe770aff3c83adb16b63 @@ -170,7 +170,7 @@ https://conda.anaconda.org/conda-forge/noarch/python-tzdata-2025.2-pyhd8ed1ab_0. https://conda.anaconda.org/conda-forge/noarch/pytz-2025.2-pyhd8ed1ab_0.conda#bc8e3267d44011051f2eb14d22fb0960 https://conda.anaconda.org/conda-forge/linux-64/pyyaml-6.0.2-py310h89163eb_2.conda#fd343408e64cf1e273ab7c710da374db https://conda.anaconda.org/conda-forge/noarch/rfc3986-validator-0.1.1-pyh9f0ad1d_0.tar.bz2#912a71cc01012ee38e6b90ddd561e36f -https://conda.anaconda.org/conda-forge/linux-64/rpds-py-0.26.0-py310hbcd0ec0_0.conda#e59b1ae4bfd0e42664fa3336bff5b4f0 +https://conda.anaconda.org/conda-forge/linux-64/rpds-py-0.27.0-py310hd8f68c5_0.conda#40a2626d9988362dfaa3c5e888735bc8 https://conda.anaconda.org/conda-forge/noarch/send2trash-1.8.3-pyh0d859eb_1.conda#938c8de6b9de091997145b3bf25cdbf9 https://conda.anaconda.org/conda-forge/noarch/setuptools-80.9.0-pyhff2d567_0.conda#4de79c071274a53dcaf2a8c749d1499e https://conda.anaconda.org/conda-forge/noarch/six-1.17.0-pyhe01879c_1.conda#3339e3b65d58accf4ca4fb8748ab16b3 @@ -219,9 +219,9 @@ https://conda.anaconda.org/conda-forge/noarch/jinja2-3.1.6-pyhd8ed1ab_0.conda#44 https://conda.anaconda.org/conda-forge/noarch/joblib-1.5.1-pyhd8ed1ab_0.conda#fb1c14694de51a476ce8636d92b6f42c https://conda.anaconda.org/conda-forge/noarch/jupyter_core-5.8.1-pyh31011fe_0.conda#b7d89d860ebcda28a5303526cdee68ab https://conda.anaconda.org/conda-forge/noarch/jupyterlab_pygments-0.3.0-pyhd8ed1ab_2.conda#fd312693df06da3578383232528c468d -https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.9.0-32_he106b2a_openblas.conda#3d3f9355e52f269cd8bc2c440d8a5263 +https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.9.0-33_he106b2a_openblas.conda#28052b5e6ea5bd283ac343c5c064b950 https://conda.anaconda.org/conda-forge/linux-64/libgl-1.7.0-ha4b6fd6_2.conda#928b8be80851f5d8ffb016f9c81dae7a -https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.9.0-32_h7ac8fdf_openblas.conda#6c3f04ccb6c578138e9f9899da0bd714 +https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.9.0-33_h7ac8fdf_openblas.conda#e598bb54c4a4b45c3d83c72984f79dbb https://conda.anaconda.org/conda-forge/linux-64/libllvm20-20.1.8-hecd9e04_0.conda#59a7b967b6ef5d63029b1712f8dcf661 https://conda.anaconda.org/conda-forge/linux-64/libxkbcommon-1.10.0-h65c71a3_0.conda#fedf6bfe5d21d21d2b1785ec00a8889a https://conda.anaconda.org/conda-forge/linux-64/libxslt-1.1.43-h7a3aeb2_0.conda#31059dc620fa57d787e3899ed0421e6d @@ -251,7 +251,7 @@ https://conda.anaconda.org/conda-forge/linux-64/xorg-libxi-1.8.2-hb9d3cd8_0.cond https://conda.anaconda.org/conda-forge/linux-64/xorg-libxrandr-1.5.4-hb9d3cd8_0.conda#2de7f99d6581a4a7adbff607b5c278ca https://conda.anaconda.org/conda-forge/linux-64/xorg-libxxf86vm-1.1.6-hb9d3cd8_0.conda#5efa5fa6243a622445fdfd72aee15efa https://conda.anaconda.org/conda-forge/noarch/_python_abi3_support-1.0-hd8ed1ab_2.conda#aaa2a381ccc56eac91d63b6c1240312f -https://conda.anaconda.org/conda-forge/noarch/anyio-4.9.0-pyh29332c3_0.conda#9749a2c77a7c40d432ea0927662d7e52 +https://conda.anaconda.org/conda-forge/noarch/anyio-4.10.0-pyhe01879c_0.conda#cc2613bfa71dec0eb2113ee21ac9ccbf https://conda.anaconda.org/conda-forge/linux-64/argon2-cffi-bindings-25.1.0-py310h7c4b9e2_0.conda#3fd41ccdb9263ad51cf89b05cade6fb7 https://conda.anaconda.org/conda-forge/noarch/arrow-1.3.0-pyhd8ed1ab_1.conda#46b53236fdd990271b03c3978d4218a9 https://conda.anaconda.org/conda-forge/noarch/beautifulsoup4-4.13.4-pyha770c72_0.conda#9f07c4fc992adb2d6c30da7fab3959a7 @@ -269,7 +269,7 @@ https://conda.anaconda.org/conda-forge/noarch/jupyter_server_terminals-0.5.3-pyh https://conda.anaconda.org/conda-forge/noarch/lazy-loader-0.4-pyhd8ed1ab_2.conda#d10d9393680734a8febc4b362a4c94f2 https://conda.anaconda.org/conda-forge/linux-64/libclang-cpp20.1-20.1.8-default_hddf928d_0.conda#b939740734ad5a8e8f6c942374dee68d https://conda.anaconda.org/conda-forge/linux-64/libclang13-20.1.8-default_ha444ac7_0.conda#783f9cdcb0255ed00e3f1be22e16de40 -https://conda.anaconda.org/conda-forge/linux-64/liblapacke-3.9.0-32_he2f377e_openblas.conda#54e7f7896d0dbf56665bcb0078bfa9d2 +https://conda.anaconda.org/conda-forge/linux-64/liblapacke-3.9.0-33_he2f377e_openblas.conda#912372f43a7430828cca8e86f680e741 https://conda.anaconda.org/conda-forge/linux-64/libpq-17.5-h27ae623_0.conda#6458be24f09e1b034902ab44fe9de908 https://conda.anaconda.org/conda-forge/noarch/mdit-py-plugins-0.4.2-pyhd8ed1ab_1.conda#af2060041d4f3250a7eb6ab3ec0e549b https://conda.anaconda.org/conda-forge/noarch/meson-python-0.18.0-pyh70fd9c4_0.conda#576c04b9d9f8e45285fb4d9452c26133 @@ -278,7 +278,7 @@ https://conda.anaconda.org/conda-forge/noarch/pytest-8.4.1-pyhd8ed1ab_0.conda#a4 https://conda.anaconda.org/conda-forge/linux-64/xorg-libxtst-1.2.5-hb9d3cd8_3.conda#7bbe9a0cc0df0ac5f5a8ad6d6a11af2f https://conda.anaconda.org/conda-forge/linux-64/zstandard-0.23.0-py310ha75aee5_2.conda#f9254b5b0193982416b91edcb4b2676f https://conda.anaconda.org/conda-forge/noarch/argon2-cffi-25.1.0-pyhd8ed1ab_0.conda#8ac12aff0860280ee0cff7fa2cf63f3b -https://conda.anaconda.org/conda-forge/linux-64/blas-devel-3.9.0-32_h1ea3ea9_openblas.conda#34cb4b6753b38a62ae25f3a73efd16b0 +https://conda.anaconda.org/conda-forge/linux-64/blas-devel-3.9.0-33_h1ea3ea9_openblas.conda#bc300d05fd629ecaccc25885275b4500 https://conda.anaconda.org/conda-forge/linux-64/cairo-1.18.4-h3394656_0.conda#09262e66b19567aff4f592fb53b28760 https://conda.anaconda.org/conda-forge/linux-64/contourpy-1.3.2-py310h3788b33_0.conda#b6420d29123c7c823de168f49ccdfe6a https://conda.anaconda.org/conda-forge/linux-64/cxx-compiler-1.11.0-hfcd1e18_0.conda#5da8c935dca9186673987f79cef0b2a5 @@ -290,20 +290,20 @@ https://conda.anaconda.org/conda-forge/noarch/jsonschema-4.25.0-pyhe01879c_0.con https://conda.anaconda.org/conda-forge/noarch/jupyterlite-core-0.6.3-pyhe01879c_0.conda#36ebdbf67840763b491045b5a36a2b78 https://conda.anaconda.org/conda-forge/linux-64/pandas-2.3.1-py310h0158d43_0.conda#94eb2db0b8f769a1e554843e3586504d https://conda.anaconda.org/conda-forge/noarch/patsy-1.0.1-pyhd8ed1ab_1.conda#ee23fabfd0a8c6b8d6f3729b47b2859d -https://conda.anaconda.org/conda-forge/linux-64/polars-default-1.31.0-py39hf521cc8_1.conda#85f9f61975ba5a8f3d40b477aef457cb +https://conda.anaconda.org/conda-forge/linux-64/polars-default-1.32.0-py39hf521cc8_1.conda#e70c9d78efe27cc42c6e205f19707a4b https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.8.0-pyhd8ed1ab_0.conda#8375cfbda7c57fbceeda18229be10417 https://conda.anaconda.org/conda-forge/linux-64/pywavelets-1.8.0-py310hf462985_0.conda#4c441eff2be2e65bd67765c5642051c5 https://conda.anaconda.org/conda-forge/linux-64/scipy-1.15.2-py310h1d65ade_0.conda#8c29cd33b64b2eb78597fa28b5595c8d https://conda.anaconda.org/conda-forge/noarch/towncrier-24.8.0-pyhd8ed1ab_1.conda#820b6a1ddf590fba253f8204f7200d82 https://conda.anaconda.org/conda-forge/noarch/urllib3-2.5.0-pyhd8ed1ab_0.conda#436c165519e140cb08d246a4472a9d6a -https://conda.anaconda.org/conda-forge/linux-64/blas-2.132-openblas.conda#9c4a27ab2463f9b1d9019e0a798a5b81 +https://conda.anaconda.org/conda-forge/linux-64/blas-2.133-openblas.conda#59bd46ccdcaabbf2d5202a358bdb000c https://conda.anaconda.org/conda-forge/linux-64/compilers-1.11.0-ha770c72_0.conda#fdcf2e31dd960ef7c5daa9f2c95eff0e https://conda.anaconda.org/conda-forge/linux-64/harfbuzz-11.3.3-hbb57e21_0.conda#0f69590f0c89bed08abc54d86cd87be5 https://conda.anaconda.org/conda-forge/noarch/jsonschema-with-format-nongpl-4.25.0-he01879c_0.conda#f4c7afaf838ab5bb1c4e73eb3095fb26 https://conda.anaconda.org/conda-forge/noarch/jupyterlite-pyodide-kernel-0.6.1-pyhe01879c_0.conda#b55913693e8934299585267ce95af06e https://conda.anaconda.org/conda-forge/linux-64/matplotlib-base-3.10.5-py310hfde16b3_0.conda#4478c9e8038113b9f68904200ec80385 https://conda.anaconda.org/conda-forge/noarch/nbformat-5.10.4-pyhd8ed1ab_1.conda#bbe1963f1e47f594070ffe87cdf612ea -https://conda.anaconda.org/conda-forge/linux-64/polars-1.31.0-default_h70f2ef1_1.conda#0217d9e4176cf33942996a7ee3afac0e +https://conda.anaconda.org/conda-forge/linux-64/polars-1.32.0-default_hac8f6d3_1.conda#92cb09b7f68ea274695292217cd79c9e https://conda.anaconda.org/conda-forge/linux-64/pyamg-5.2.1-py310ha2bacc8_1.conda#817d32861729e14f474249f1036291c4 https://conda.anaconda.org/conda-forge/noarch/requests-2.32.4-pyhd8ed1ab_0.conda#f6082eae112814f1447b56a5e1f6ed05 https://conda.anaconda.org/conda-forge/linux-64/statsmodels-0.14.5-py310haaf2d95_0.conda#92b4b51b83f2cfded298f1b8c7a99e32 @@ -311,7 +311,7 @@ https://conda.anaconda.org/conda-forge/noarch/tifffile-2025.5.10-pyhd8ed1ab_0.co https://conda.anaconda.org/conda-forge/noarch/jupyter_events-0.12.0-pyh29332c3_0.conda#f56000b36f09ab7533877e695e4e8cb0 https://conda.anaconda.org/conda-forge/noarch/jupytext-1.17.2-pyh80e38bb_0.conda#6d0652a97ef103de0c77b9c610d0c20d https://conda.anaconda.org/conda-forge/noarch/nbclient-0.10.2-pyhd8ed1ab_0.conda#6bb0d77277061742744176ab555b723c -https://conda.anaconda.org/conda-forge/noarch/pooch-1.8.2-pyhd8ed1ab_1.conda#b3e783e8e8ed7577cf0b6dee37d1fbac +https://conda.anaconda.org/conda-forge/noarch/pooch-1.8.2-pyhd8ed1ab_2.conda#7b18edf3ed17052a85681d30fa09bf48 https://conda.anaconda.org/conda-forge/linux-64/qt6-main-6.9.1-h6ac528c_2.conda#34ccdb55340a25761efbac1ff1504091 https://conda.anaconda.org/conda-forge/linux-64/scikit-image-0.25.2-py310h5eaa309_1.conda#ed21ab72d049ecdb60f829f04b4dca1c https://conda.anaconda.org/conda-forge/noarch/seaborn-base-0.13.2-pyhd8ed1ab_3.conda#fd96da444e81f9e6fcaac38590f3dd42 @@ -335,6 +335,6 @@ https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-htmlhelp-2.1.0-pyhd8 https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-qthelp-2.0.0-pyhd8ed1ab_1.conda#00534ebcc0375929b45c3039b5ba7636 https://conda.anaconda.org/conda-forge/noarch/sphinx-8.1.3-pyhd8ed1ab_1.conda#1a3281a0dc355c02b5506d87db2d78ac https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-serializinghtml-1.1.10-pyhd8ed1ab_1.conda#3bc61f7161d28137797e038263c04c54 -https://conda.anaconda.org/conda-forge/noarch/sphinxext-opengraph-0.11.0-pyhd8ed1ab_0.conda#d77bd353b3a8e8e2a5aa6f4d2c9f5488 +https://conda.anaconda.org/conda-forge/noarch/sphinxext-opengraph-0.12.0-pyhd8ed1ab_0.conda#1a4d14313b64f8eac388f6742c18a58c # pip libsass @ https://files.pythonhosted.org/packages/fd/5a/eb5b62641df0459a3291fc206cf5bd669c0feed7814dded8edef4ade8512/libsass-0.23.0-cp38-abi3-manylinux_2_5_x86_64.manylinux1_x86_64.whl#sha256=4a218406d605f325d234e4678bd57126a66a88841cb95bee2caeafdc6f138306 # pip sphinxcontrib-sass @ https://files.pythonhosted.org/packages/3f/ec/194f2dbe55b3fe0941b43286c21abb49064d9d023abfb99305c79ad77cad/sphinxcontrib_sass-0.3.5-py2.py3-none-any.whl#sha256=850c83a36ed2d2059562504ccf496ca626c9c0bb89ec642a2d9c42105704bef6 diff --git a/build_tools/circle/doc_min_dependencies_environment.yml b/build_tools/circle/doc_min_dependencies_environment.yml index 2e16632152d1f..3424a9d931fc3 100644 --- a/build_tools/circle/doc_min_dependencies_environment.yml +++ b/build_tools/circle/doc_min_dependencies_environment.yml @@ -8,7 +8,7 @@ dependencies: - numpy=1.22.0 # min - blas - scipy=1.8.0 # min - - cython=3.0.10 # min + - cython=3.1.2 # min - joblib - threadpoolctl - matplotlib=3.5.0 # min diff --git a/build_tools/circle/doc_min_dependencies_linux-64_conda.lock b/build_tools/circle/doc_min_dependencies_linux-64_conda.lock index 8934e6f0f725a..3168a6ccaeb31 100644 --- a/build_tools/circle/doc_min_dependencies_linux-64_conda.lock +++ b/build_tools/circle/doc_min_dependencies_linux-64_conda.lock @@ -1,13 +1,13 @@ # Generated by conda-lock. # platform: linux-64 -# input_hash: d07657e3ddf551b0cfcb8979d3525cd7b043f143170c33c4d33d4a4db2869281 +# input_hash: 1aec67c9ed6cd00477ef687dc63d6860b0f2dc3ee94a92cdc6daa87fa1dfbe8d @EXPLICIT -https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2#d7c89558ba9fa0495403155b64376d81 https://conda.anaconda.org/conda-forge/noarch/font-ttf-dejavu-sans-mono-2.37-hab24e00_0.tar.bz2#0c96522c6bdaed4b1566d11387caaf45 https://conda.anaconda.org/conda-forge/noarch/font-ttf-inconsolata-3.000-h77eed37_0.tar.bz2#34893075a5c9e55cdafac56607368fc6 https://conda.anaconda.org/conda-forge/noarch/font-ttf-source-code-pro-2.038-h77eed37_0.tar.bz2#4d59c254e01d9cde7957100457e2d5fb https://conda.anaconda.org/conda-forge/noarch/font-ttf-ubuntu-0.83-h77eed37_3.conda#49023d73832ef61042f6a237cb2687e7 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https://conda.anaconda.org/conda-forge/noarch/packaging-25.0-pyh29332c3_1.conda#58335b26c38bf4a20f399384c33cbcf9 https://conda.anaconda.org/conda-forge/noarch/pluggy-1.6.0-pyhd8ed1ab_0.conda#7da7ccd349dbf6487a7778579d2bb971 https://conda.anaconda.org/conda-forge/noarch/pygments-2.19.2-pyhd8ed1ab_0.conda#6b6ece66ebcae2d5f326c77ef2c5a066 @@ -121,9 +121,9 @@ https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.3.0-pyhd8ed1ab_0. https://conda.anaconda.org/conda-forge/linux-aarch64/fonttools-4.59.0-py310h2d8da20_0.conda#5f93264842d77827a0dac712d0fd188e https://conda.anaconda.org/conda-forge/linux-aarch64/freetype-2.13.3-h8af1aa0_1.conda#71c4cbe1b384a8e7b56993394a435343 https://conda.anaconda.org/conda-forge/noarch/joblib-1.5.1-pyhd8ed1ab_0.conda#fb1c14694de51a476ce8636d92b6f42c -https://conda.anaconda.org/conda-forge/linux-aarch64/libcblas-3.9.0-32_hab92f65_openblas.conda#2f02a3ea0960118a0a8d45cdd348b039 +https://conda.anaconda.org/conda-forge/linux-aarch64/libcblas-3.9.0-33_hab92f65_openblas.conda#50c94d8f4030118d35fd9fd35a1ed9a2 https://conda.anaconda.org/conda-forge/linux-aarch64/libgl-1.7.0-hd24410f_2.conda#0d00176464ebb25af83d40736a2cd3bb -https://conda.anaconda.org/conda-forge/linux-aarch64/liblapack-3.9.0-32_h411afd4_openblas.conda#8d143759d5a22e9975a996bd13eeb8f0 +https://conda.anaconda.org/conda-forge/linux-aarch64/liblapack-3.9.0-33_h411afd4_openblas.conda#2a303c1821ca533a324c5617540cc8a2 https://conda.anaconda.org/conda-forge/linux-aarch64/libllvm20-20.1.8-h2b567e5_0.conda#b2ae284ba64d978316177c9ab68e3da5 https://conda.anaconda.org/conda-forge/linux-aarch64/libxkbcommon-1.10.0-hbab7b08_0.conda#36cd1db31e923c6068b7e0e6fce2cd7b https://conda.anaconda.org/conda-forge/linux-aarch64/libxslt-1.1.43-h4552c8e_0.conda#fcf40dcbe5841e9b125ca98858e24205 @@ -142,18 +142,18 @@ https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libxxf86vm-1.1.6-h86ec https://conda.anaconda.org/conda-forge/linux-aarch64/fontconfig-2.15.0-h8dda3cd_1.conda#112b71b6af28b47c624bcbeefeea685b https://conda.anaconda.org/conda-forge/linux-aarch64/libclang-cpp20.1-20.1.8-default_hf07bfb7_0.conda#c7a64cd7dd2bf72956d2f3b1b1aa13a7 https://conda.anaconda.org/conda-forge/linux-aarch64/libclang13-20.1.8-default_h173080d_0.conda#c9a9e8c0477f9c915f106fd6254b2a9c -https://conda.anaconda.org/conda-forge/linux-aarch64/liblapacke-3.9.0-32_hc659ca5_openblas.conda#1cd2cbdb80386aae8c584ab9f1175ca6 +https://conda.anaconda.org/conda-forge/linux-aarch64/liblapacke-3.9.0-33_hc659ca5_openblas.conda#3c72eb1e2f35cdc37583cd8e8d210d4f https://conda.anaconda.org/conda-forge/linux-aarch64/libpq-17.5-hf590da8_0.conda#b5a01e5aa04651ccf5865c2d029affa3 https://conda.anaconda.org/conda-forge/noarch/meson-python-0.18.0-pyh70fd9c4_0.conda#576c04b9d9f8e45285fb4d9452c26133 https://conda.anaconda.org/conda-forge/linux-aarch64/numpy-2.2.6-py310h6e5608f_0.conda#9e9f1f279eb02c41bda162a42861adc0 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-https://conda.anaconda.org/conda-forge/linux-aarch64/blas-2.132-openblas.conda#2c1e3662c8c5e7b92a49fd6372bb659f +https://conda.anaconda.org/conda-forge/linux-aarch64/blas-2.133-openblas.conda#a92a7666159cc612f8489943eec6edcf https://conda.anaconda.org/conda-forge/linux-aarch64/harfbuzz-11.3.3-h81c6d19_0.conda#68c8991c65d01f682819950d969f266a https://conda.anaconda.org/conda-forge/linux-aarch64/matplotlib-base-3.10.5-py310hc06f52e_0.conda#6b7cfe985a25928b86a127453ffec2e2 https://conda.anaconda.org/conda-forge/linux-aarch64/qt6-main-6.9.1-haa40e84_2.conda#b388e58798370884d5226b2ae9209edc diff --git a/pyproject.toml b/pyproject.toml index aa69f85073b5c..9415a7ee99d64 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -43,7 +43,7 @@ tracker = "https://github.com/scikit-learn/scikit-learn/issues" "release notes" = "https://scikit-learn.org/stable/whats_new" [project.optional-dependencies] -build = ["numpy>=1.22.0", "scipy>=1.8.0", "cython>=3.0.10", "meson-python>=0.17.1"] +build = ["numpy>=1.22.0", "scipy>=1.8.0", "cython>=3.1.2", "meson-python>=0.17.1"] install = ["numpy>=1.22.0", "scipy>=1.8.0", "joblib>=1.2.0", "threadpoolctl>=3.1.0"] benchmark = ["matplotlib>=3.5.0", "pandas>=1.4.0", "memory_profiler>=0.57.0"] docs = [ @@ -98,7 +98,7 @@ build-backend = "mesonpy" # Minimum requirements for the build system to execute. requires = [ "meson-python>=0.16.0", - "Cython>=3.0.10", + "Cython>=3.1.2", "numpy>=2", "scipy>=1.8.0", ] diff --git a/sklearn/_min_dependencies.py b/sklearn/_min_dependencies.py index ac58820686914..7f5e1c52f044d 100644 --- a/sklearn/_min_dependencies.py +++ b/sklearn/_min_dependencies.py @@ -12,7 +12,7 @@ JOBLIB_MIN_VERSION = "1.2.0" THREADPOOLCTL_MIN_VERSION = "3.1.0" PYTEST_MIN_VERSION = "7.1.2" -CYTHON_MIN_VERSION = "3.0.10" +CYTHON_MIN_VERSION = "3.1.2" # 'build' and 'install' is included to have structured metadata for CI. From 4a002aaea746a9ecb765c7041aa7df687712735c Mon Sep 17 00:00:00 2001 From: Christian Lorentzen Date: Fri, 8 Aug 2025 08:41:47 +0200 Subject: [PATCH 05/18] DOC fix docstring example --- sklearn/linear_model/_coordinate_descent.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/sklearn/linear_model/_coordinate_descent.py b/sklearn/linear_model/_coordinate_descent.py index 7cbf339785bfb..3ab8326a663c8 100644 --- a/sklearn/linear_model/_coordinate_descent.py +++ b/sklearn/linear_model/_coordinate_descent.py @@ -2124,7 +2124,7 @@ class LassoCV(RegressorMixin, LinearModelCV): >>> reg.score(X, y) 0.9993 >>> reg.predict(X[:1,]) - array([-78.4951]) + array([-79.4755331]) """ path = staticmethod(lasso_path) From 0b92338873fb0c9727d2eedeac49c2155fcea0b6 Mon Sep 17 00:00:00 2001 From: Christian Lorentzen Date: Fri, 8 Aug 2025 23:30:01 +0200 Subject: [PATCH 06/18] DOC user guide entry to for CD and Gap Safe Screening Rules --- doc/modules/linear_model.rst | 93 +++++++++++++++++---- sklearn/linear_model/_coordinate_descent.py | 20 ++++- 2 files changed, 98 insertions(+), 15 deletions(-) diff --git a/doc/modules/linear_model.rst b/doc/modules/linear_model.rst index 48acba45fec17..383ad23851d7d 100644 --- a/doc/modules/linear_model.rst +++ b/doc/modules/linear_model.rst @@ -233,24 +233,23 @@ Cross-Validation. Lasso ===== -The :class:`Lasso` is a linear model that estimates sparse coefficients. +The :class:`Lasso` is a linear model that estimates sparse coefficients, i.e., it is +able to set coefficients exactly to zero. It is useful in some contexts due to its tendency to prefer solutions with fewer non-zero coefficients, effectively reducing the number of features upon which the given solution is dependent. For this reason, Lasso and its variants are fundamental to the field of compressed sensing. -Under certain conditions, it can recover the exact set of non-zero -coefficients (see +Under certain conditions, it can recover the exact set of non-zero coefficients (see :ref:`sphx_glr_auto_examples_applications_plot_tomography_l1_reconstruction.py`). Mathematically, it consists of a linear model with an added regularization term. The objective function to minimize is: -.. math:: \min_{w} { \frac{1}{2n_{\text{samples}}} ||X w - y||_2 ^ 2 + \alpha ||w||_1} +.. math:: \min_{w} P(w) = {\frac{1}{2n_{\text{samples}}} ||X w - y||_2 ^ 2 + \alpha ||w||_1} -The lasso estimate thus solves the minimization of the -least-squares penalty with :math:`\alpha ||w||_1` added, where -:math:`\alpha` is a constant and :math:`||w||_1` is the :math:`\ell_1`-norm of -the coefficient vector. +The lasso estimate thus solves the least-squares with added penalty +:math:`\alpha ||w||_1`, where :math:`\alpha` is a constant and :math:`||w||_1` is the +:math:`\ell_1`-norm of the coefficient vector. The implementation in the class :class:`Lasso` uses coordinate descent as the algorithm to fit the coefficients. See :ref:`least_angle_regression` @@ -281,18 +280,84 @@ computes the coefficients along the full path of possible values. .. dropdown:: References - The following two references explain the iterations - used in the coordinate descent solver of scikit-learn, as well as - the duality gap computation used for convergence control. + The following references explain the origin of the Lasso as well as properties + of the Lasso problem and the duality gap computation used for convergence control. - * "Regularization Path For Generalized linear Models by Coordinate Descent", - Friedman, Hastie & Tibshirani, J Stat Softw, 2010 (`Paper - `__). + * :doi:`Robert Tibshirani. (1996) Regression Shrinkage and Selection Via the Lasso. + J. R. Stat. Soc. Ser. B Stat. Methodol., 58(1):267-288 + <10.1111/j.2517-6161.1996.tb02080.x>` * "An Interior-Point Method for Large-Scale L1-Regularized Least Squares," S. J. Kim, K. Koh, M. Lustig, S. Boyd and D. Gorinevsky, in IEEE Journal of Selected Topics in Signal Processing, 2007 (`Paper `__) +.. _coordinate_descent: + +Coordinate Descent with Gap Safe Screening Rules +------------------------------------------------ + +Coordinate descent (CD) is a strategy so solve the minimization problem that considers a +single features :math:`j` at a time. This way, the optimization problem becomes +1-dimensional and is easy to solve: + +.. math:: \min_{w_j} {\frac{1}{2n_{\text{samples}}} ||x_j w_j + X_{-j}w_{-j} - y||_2 ^ 2 + \alpha |w_j|} + +with index :math:`-j` meaning all features but :math:`j`. The solution is + +.. math:: w_j = \frac{S(x_j (y - X_{-j}w_{-j}), \alpha)}{||x_j||_2^2} + +with the soft-thresholding function +:math:`S(z, \alpha) = \operatorname{sign}(z) \max(0, |z|-\alpha)`. +Note that the soft-thresholding function is exactly zero whenever +:math:`\alpha \geq |z|`. +The CD solver then loops over the features either in a cycle, picking one feature after +the other in the order given by `X` (`selection="cyclic"`), or by candomly picking +features (`selection="random"`). +It stops if the duality gap is smaller than the provided tolerance `tol`. + +.. dropdown:: Mathematical details + + The duality gap is an upper bound of the objective function at its minimum. It is + given by :math:`G(w, v) = P(w) - D(v)` with primal :math:`P(w)`, the objective + function of the Lasso, and its dual + + .. math:: D(v) = \frac{1}{2n_{\text{samples}}}(yv - ||v||_2^2) + + subject to :math:`v \in ||X^Tv||_{\infty} \leq n_{\text{samples}}\alpha`. + With (scaled) dual variable :math:`v = c r`, current residual :math:`r = y - Xw` and + dual scaling + + .. math:: + c = \begin{cases} + 1, & ||X^Tr||_{\infty} \leq n_{\text{samples}}\alpha, \\ + \frac{n_{\text{samples}}\alpha}{||X^Tr||_{\infty}}, & \text{otherwise} + \end{cases} + + the stopping criterion is + + .. math:: \text{tol} \frac{||y||_2^2}{n_{\text{samples}}} < G(w, cr)\,. + +A clever method to speedup the coordinate descent algorithm is to screen features such +that we know for certain that :math:`w_j = 0`. Gap safe screening rules are such a +tool. Anywhere during the optimization algorithm, they can tell which feature we can safely exclude, i.e., set to zero with certainty. + +.. dropdown:: References + + The first reference explains the coordinate descent solver used in scikit-learn, the + others treat gap safe screening rules. + + * :doi:`Friedman, Hastie & Tibshirani. (2010). + Regularization Path For Generalized linear Models by Coordinate Descent. + J Stat Softw 33(1), 1-22 <10.18637/jss.v033.i01>` + * arxiv:`O. Fercoq, A. Gramfort, J. Salmon. (2015). + Mind the duality gap: safer rules for the Lasso. + Proceedings of Machine Learning Research 37:333-342, 2015. + <1505.03410>` + * arxiv:`E. Ndiaye, O. Fercoq, A. Gramfort, J. Salmon. (2017). + Gap Safe Screening Rules for Sparsity Enforcing Penalties. + Journal of Machine Learning Research 18(128):1-33, 2017. + <1611.05780>` + Setting regularization parameter -------------------------------- diff --git a/sklearn/linear_model/_coordinate_descent.py b/sklearn/linear_model/_coordinate_descent.py index 3ab8326a663c8..e42a51c842028 100644 --- a/sklearn/linear_model/_coordinate_descent.py +++ b/sklearn/linear_model/_coordinate_descent.py @@ -329,7 +329,10 @@ def lasso_path( Note that in certain cases, the Lars solver may be significantly faster to implement this functionality. In particular, linear interpolation can be used to retrieve model coefficients between the - values output by lars_path + values output by lars_path. + + The underlying coordinate descent solver uses gap safe screening rules so speedup + fitting time, see :ref:`User Guide on coordinate descent `. Examples -------- @@ -528,6 +531,9 @@ def enet_path( :ref:`examples/linear_model/plot_lasso_lasso_lars_elasticnet_path.py `. + The underlying coordinate descent solver uses gap safe screening rules so speedup + fitting time, see :ref:`User Guide on coordinate descent `. + Examples -------- >>> from sklearn.linear_model import enet_path @@ -872,6 +878,9 @@ class ElasticNet(MultiOutputMixin, RegressorMixin, LinearModel): If so, then additionally check whether the dual gap is smaller than `tol` times :math:`||y||_2^2 / n_{\text{samples}}`. + The underlying coordinate descent solver uses gap safe screening rules so speedup + fitting time, see :ref:`User Guide on coordinate descent `. + Examples -------- >>> from sklearn.linear_model import ElasticNet @@ -1312,6 +1321,9 @@ class Lasso(ElasticNet): instead penalizes the :math:`L_{2,1}` norm of the coefficients, yielding row-wise sparsity in the coefficients. + The underlying coordinate descent solver uses gap safe screening rules so speedup + fitting time, see :ref:`User Guide on coordinate descent `. + Examples -------- >>> from sklearn import linear_model @@ -2115,6 +2127,9 @@ class LassoCV(RegressorMixin, LinearModelCV): regularization path. It tends to speed up the hyperparameter search. + The underlying coordinate descent solver uses gap safe screening rules so speedup + fitting time, see :ref:`User Guide on coordinate descent `. + Examples -------- >>> from sklearn.linear_model import LassoCV @@ -2394,6 +2409,9 @@ class ElasticNetCV(RegressorMixin, LinearModelCV): :ref:`examples/linear_model/plot_lasso_model_selection.py `. + The underlying coordinate descent solver uses gap safe screening rules so speedup + fitting time, see :ref:`User Guide on coordinate descent `. + Examples -------- >>> from sklearn.linear_model import ElasticNetCV From 79818c7de6523c8703bc8dfb8dab55259044a91a Mon Sep 17 00:00:00 2001 From: Christian Lorentzen Date: Fri, 8 Aug 2025 23:51:02 +0200 Subject: [PATCH 07/18] DOC add whatsnew --- .../sklearn.linear_model/31882.enhancement.rst | 11 +++++++++++ 1 file changed, 11 insertions(+) create mode 100644 doc/whats_new/upcoming_changes/sklearn.linear_model/31882.enhancement.rst diff --git a/doc/whats_new/upcoming_changes/sklearn.linear_model/31882.enhancement.rst b/doc/whats_new/upcoming_changes/sklearn.linear_model/31882.enhancement.rst new file mode 100644 index 0000000000000..8974cf842c427 --- /dev/null +++ b/doc/whats_new/upcoming_changes/sklearn.linear_model/31882.enhancement.rst @@ -0,0 +1,11 @@ +- :class:`linear_model.ElasticNet`, :class:`linear_model.ElasticNetCV`, + :class:`linear_model.Lasso`, :class:`linear_model.LassoCV` as well as + :func:`linear_model.lasso_path` and :func:`linear_model.enet_path` now implement + gap safe screening rules in the coordinate descent solver for dense `X` and + `precompute=False` or `"auto"` with `n_samples < n_features`. + The speedup of fitting time is particularly pronounced (10-times is possible) when + computing regularization paths like the \*CV-variants of the above estimators do. + There is now an additional check of the stopping criterion before entering the main + loop of descent steps. As the stopping criterion requires the computation of the dual + gap, the screening happens whenever the dual gap is computed.. + By :user:`Christian Lorentzen `. From b5d11cdc0e072aa960eaf9c320873dd12c329445 Mon Sep 17 00:00:00 2001 From: Christian Lorentzen Date: Sat, 9 Aug 2025 09:24:40 +0200 Subject: [PATCH 08/18] TST add test_Cython_solver_equivalence --- .../tests/test_coordinate_descent.py | 67 +++++++++++++++++++ 1 file changed, 67 insertions(+) diff --git a/sklearn/linear_model/tests/test_coordinate_descent.py b/sklearn/linear_model/tests/test_coordinate_descent.py index f5b149496a5a0..92f932934e95f 100644 --- a/sklearn/linear_model/tests/test_coordinate_descent.py +++ b/sklearn/linear_model/tests/test_coordinate_descent.py @@ -32,6 +32,7 @@ lars_path, lasso_path, ) +from sklearn.linear_model import _cd_fast as cd_fast # type: ignore[attr-defined] from sklearn.linear_model._coordinate_descent import _set_order from sklearn.model_selection import ( BaseCrossValidator, @@ -90,6 +91,72 @@ def test_set_order_sparse(order, input_order, coo_container): assert sparse.issparse(y2) and y2.format == format +def test_cython_solver_equivalence(): + """Test that all 3 Cython solvers for 1-d targets give same results.""" + X, y = make_regression() + X_mean = X.mean(axis=0) + X_centered = np.asfortranarray(X - X_mean) + y -= y.mean() + alpha_max = np.linalg.norm(X.T @ y, ord=np.inf) + alpha = alpha_max / 10 + params = { + "beta": 0, + "max_iter": 100, + "tol": 1e-10, + "rng": np.random.RandomState(0), # not used, but needed as argument + "random": False, + "positive": False, + } + + coef_1 = np.zeros(X.shape[1]) + coef_2, coef_3, coef_4 = coef_1.copy(), coef_1.copy(), coef_1.copy() + + # For alpha_max, coefficients must all be zero. + cd_fast.enet_coordinate_descent( + w=coef_1, alpha=alpha_max, X=X_centered, y=y, **params + ) + assert_allclose(coef_1, 0) + + # Without gap safe screening rules + cd_fast.enet_coordinate_descent( + w=coef_1, alpha=alpha, X=X_centered, y=y, **params, do_screening=False + ) + # At least 2 coefficients are non-zero + assert 2 <= np.sum(np.abs(coef_1) > 1e-8) < X.shape[1] + + # With gap safe screening rules + cd_fast.enet_coordinate_descent( + w=coef_2, alpha=alpha, X=X_centered, y=y, **params, do_screening=True + ) + assert_allclose(coef_2, coef_1) + + # Sparse + Xs = sparse.csc_matrix(X) + cd_fast.sparse_enet_coordinate_descent( + w=coef_3, + alpha=alpha, + X_data=Xs.data, + X_indices=Xs.indices, + X_indptr=Xs.indptr, + y=y, + sample_weight=None, + X_mean=X_mean, + **params, + ) + assert_allclose(coef_3, coef_1) + + # Gram + cd_fast.enet_coordinate_descent_gram( + w=coef_4, + alpha=alpha, + Q=X_centered.T @ X_centered, + q=X_centered.T @ y, + y=y, + **params, + ) + assert_allclose(coef_4, coef_1) + + def test_lasso_zero(): # Check that the lasso can handle zero data without crashing X = [[0], [0], [0]] From 86375d5e97b75e213faa59c133ecfd9b1b434af3 Mon Sep 17 00:00:00 2001 From: Christian Lorentzen Date: Mon, 11 Aug 2025 14:19:49 +0200 Subject: [PATCH 09/18] CLN fix typos --- doc/modules/linear_model.rst | 6 +++--- .../sklearn.linear_model/31882.enhancement.rst | 2 +- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/doc/modules/linear_model.rst b/doc/modules/linear_model.rst index 383ad23851d7d..b9efbe176710e 100644 --- a/doc/modules/linear_model.rst +++ b/doc/modules/linear_model.rst @@ -311,7 +311,7 @@ with the soft-thresholding function Note that the soft-thresholding function is exactly zero whenever :math:`\alpha \geq |z|`. The CD solver then loops over the features either in a cycle, picking one feature after -the other in the order given by `X` (`selection="cyclic"`), or by candomly picking +the other in the order given by `X` (`selection="cyclic"`), or by randomly picking features (`selection="random"`). It stops if the duality gap is smaller than the provided tolerance `tol`. @@ -349,11 +349,11 @@ tool. Anywhere during the optimization algorithm, they can tell which feature we * :doi:`Friedman, Hastie & Tibshirani. (2010). Regularization Path For Generalized linear Models by Coordinate Descent. J Stat Softw 33(1), 1-22 <10.18637/jss.v033.i01>` - * arxiv:`O. Fercoq, A. Gramfort, J. Salmon. (2015). + * :arxiv:`O. Fercoq, A. Gramfort, J. Salmon. (2015). Mind the duality gap: safer rules for the Lasso. Proceedings of Machine Learning Research 37:333-342, 2015. <1505.03410>` - * arxiv:`E. Ndiaye, O. Fercoq, A. Gramfort, J. Salmon. (2017). + * :arxiv:`E. Ndiaye, O. Fercoq, A. Gramfort, J. Salmon. (2017). Gap Safe Screening Rules for Sparsity Enforcing Penalties. Journal of Machine Learning Research 18(128):1-33, 2017. <1611.05780>` diff --git a/doc/whats_new/upcoming_changes/sklearn.linear_model/31882.enhancement.rst b/doc/whats_new/upcoming_changes/sklearn.linear_model/31882.enhancement.rst index 8974cf842c427..55e0679b4b375 100644 --- a/doc/whats_new/upcoming_changes/sklearn.linear_model/31882.enhancement.rst +++ b/doc/whats_new/upcoming_changes/sklearn.linear_model/31882.enhancement.rst @@ -7,5 +7,5 @@ computing regularization paths like the \*CV-variants of the above estimators do. There is now an additional check of the stopping criterion before entering the main loop of descent steps. As the stopping criterion requires the computation of the dual - gap, the screening happens whenever the dual gap is computed.. + gap, the screening happens whenever the dual gap is computed. By :user:`Christian Lorentzen `. From 2caf671711bb70f30401aba2a82b0e0e018cd954 Mon Sep 17 00:00:00 2001 From: Christian Lorentzen Date: Mon, 11 Aug 2025 17:42:30 +0200 Subject: [PATCH 10/18] CLN missing declaration of const_ and more modern code comments --- sklearn/linear_model/_cd_fast.pyx | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/sklearn/linear_model/_cd_fast.pyx b/sklearn/linear_model/_cd_fast.pyx index 3702498fa5f6a..21c3c2987aea2 100644 --- a/sklearn/linear_model/_cd_fast.pyx +++ b/sklearn/linear_model/_cd_fast.pyx @@ -106,7 +106,7 @@ cdef (floating, floating) gap_enet( floating beta, # L2 penalty const floating[::1, :] X, const floating[::1] y, - const floating[::1] R, # current residuals = y - X@w + const floating[::1] R, # current residuals = y - X @ w floating[::1] XtA, # XtA = X.T @ R - beta * w is calculated inplace bint positive, ) noexcept nogil: @@ -117,8 +117,9 @@ cdef (floating, floating) gap_enet( cdef floating w_norm2 = 0.0 cdef floating l1_norm cdef floating A_norm2 + cdef floating const_ - # XtA = np.dot(X.T, R) - beta * w + # XtA = X.T @ R - beta * w _copy(n_features, &w[0], 1, &XtA[0], 1) _gemv(ColMajor, Trans, n_samples, n_features, 1.0, &X[0, 0], n_samples, &R[0], 1, @@ -129,10 +130,10 @@ cdef (floating, floating) gap_enet( else: dual_norm_XtA = abs_max(n_features, &XtA[0]) - # R_norm2 = np.dot(R, R) + # R_norm2 = R @ R R_norm2 = _dot(n_samples, &R[0], 1, &R[0], 1) - # w_norm2 = np.dot(w, w) + # w_norm2 = w @ w if beta > 0: w_norm2 = _dot(n_features, &w[0], 1, &w[0], 1) @@ -148,7 +149,7 @@ cdef (floating, floating) gap_enet( gap += ( alpha * l1_norm - - const_ * _dot(n_samples, &R[0], 1, &y[0], 1) # np.dot(R.T, y) + - const_ * _dot(n_samples, &R[0], 1, &y[0], 1) # R @ y + 0.5 * beta * (1 + const_ ** 2) * w_norm2 ) return gap, dual_norm_XtA From d0b052e771764a6a2122b5ed5d89c76bd34d3c14 Mon Sep 17 00:00:00 2001 From: Christian Lorentzen Date: Tue, 12 Aug 2025 22:07:57 +0200 Subject: [PATCH 11/18] DOC whatsnew label enhancement -> efficiency --- .../{31882.enhancement.rst => 31882.efficiency.rst} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename doc/whats_new/upcoming_changes/sklearn.linear_model/{31882.enhancement.rst => 31882.efficiency.rst} (100%) diff --git a/doc/whats_new/upcoming_changes/sklearn.linear_model/31882.enhancement.rst b/doc/whats_new/upcoming_changes/sklearn.linear_model/31882.efficiency.rst similarity index 100% rename from doc/whats_new/upcoming_changes/sklearn.linear_model/31882.enhancement.rst rename to doc/whats_new/upcoming_changes/sklearn.linear_model/31882.efficiency.rst From 90992be1361c34296da7c6398a62e2d8da0a2714 Mon Sep 17 00:00:00 2001 From: Christian Lorentzen Date: Fri, 15 Aug 2025 16:00:30 +0200 Subject: [PATCH 12/18] DOC improve wording based on review --- doc/modules/linear_model.rst | 22 +++++++++++---------- sklearn/linear_model/_coordinate_descent.py | 12 +++++------ 2 files changed, 18 insertions(+), 16 deletions(-) diff --git a/doc/modules/linear_model.rst b/doc/modules/linear_model.rst index b9efbe176710e..cd07cf2e41d13 100644 --- a/doc/modules/linear_model.rst +++ b/doc/modules/linear_model.rst @@ -296,15 +296,15 @@ computes the coefficients along the full path of possible values. Coordinate Descent with Gap Safe Screening Rules ------------------------------------------------ -Coordinate descent (CD) is a strategy so solve the minimization problem that considers a -single features :math:`j` at a time. This way, the optimization problem becomes -1-dimensional and is easy to solve: +Coordinate descent (CD) is a strategy so solve a minimization problem that considers a +single feature :math:`j` at a time. This way, the optimization problem is reduced to a +1-dimensional problem which is easier to solve: .. math:: \min_{w_j} {\frac{1}{2n_{\text{samples}}} ||x_j w_j + X_{-j}w_{-j} - y||_2 ^ 2 + \alpha |w_j|} with index :math:`-j` meaning all features but :math:`j`. The solution is -.. math:: w_j = \frac{S(x_j (y - X_{-j}w_{-j}), \alpha)}{||x_j||_2^2} +.. math:: w_j = \frac{S(x_j^T (y - X_{-j}w_{-j}), \alpha)}{||x_j||_2^2} with the soft-thresholding function :math:`S(z, \alpha) = \operatorname{sign}(z) \max(0, |z|-\alpha)`. @@ -317,11 +317,12 @@ It stops if the duality gap is smaller than the provided tolerance `tol`. .. dropdown:: Mathematical details - The duality gap is an upper bound of the objective function at its minimum. It is - given by :math:`G(w, v) = P(w) - D(v)` with primal :math:`P(w)`, the objective - function of the Lasso, and its dual + The duality gap :math:`G(w, v)` is an upper bound of the difference between the + current primal objective function of the Lasso, :math:`P(w)`, and its minimum + :math:`P(w^\star)`, i.e. :math:`G(w, v) \leq P(w) - P(w^\star)`. It is given by + :math:`G(w, v) = P(w) - D(v)` with dual objective function - .. math:: D(v) = \frac{1}{2n_{\text{samples}}}(yv - ||v||_2^2) + .. math:: D(v) = \frac{1}{2n_{\text{samples}}}(y^Tv - ||v||_2^2) subject to :math:`v \in ||X^Tv||_{\infty} \leq n_{\text{samples}}\alpha`. With (scaled) dual variable :math:`v = c r`, current residual :math:`r = y - Xw` and @@ -338,8 +339,9 @@ It stops if the duality gap is smaller than the provided tolerance `tol`. .. math:: \text{tol} \frac{||y||_2^2}{n_{\text{samples}}} < G(w, cr)\,. A clever method to speedup the coordinate descent algorithm is to screen features such -that we know for certain that :math:`w_j = 0`. Gap safe screening rules are such a -tool. Anywhere during the optimization algorithm, they can tell which feature we can safely exclude, i.e., set to zero with certainty. +that at optimum :math:`w_j = 0`. Gap safe screening rules are such a +tool. Anywhere during the optimization algorithm, they can tell which feature we can +safely exclude, i.e., set to zero with certainty. .. dropdown:: References diff --git a/sklearn/linear_model/_coordinate_descent.py b/sklearn/linear_model/_coordinate_descent.py index e42a51c842028..9c38988495055 100644 --- a/sklearn/linear_model/_coordinate_descent.py +++ b/sklearn/linear_model/_coordinate_descent.py @@ -331,7 +331,7 @@ def lasso_path( interpolation can be used to retrieve model coefficients between the values output by lars_path. - The underlying coordinate descent solver uses gap safe screening rules so speedup + The underlying coordinate descent solver uses gap safe screening rules to speedup fitting time, see :ref:`User Guide on coordinate descent `. Examples @@ -531,7 +531,7 @@ def enet_path( :ref:`examples/linear_model/plot_lasso_lasso_lars_elasticnet_path.py `. - The underlying coordinate descent solver uses gap safe screening rules so speedup + The underlying coordinate descent solver uses gap safe screening rules to speedup fitting time, see :ref:`User Guide on coordinate descent `. Examples @@ -878,7 +878,7 @@ class ElasticNet(MultiOutputMixin, RegressorMixin, LinearModel): If so, then additionally check whether the dual gap is smaller than `tol` times :math:`||y||_2^2 / n_{\text{samples}}`. - The underlying coordinate descent solver uses gap safe screening rules so speedup + The underlying coordinate descent solver uses gap safe screening rules to speedup fitting time, see :ref:`User Guide on coordinate descent `. Examples @@ -1321,7 +1321,7 @@ class Lasso(ElasticNet): instead penalizes the :math:`L_{2,1}` norm of the coefficients, yielding row-wise sparsity in the coefficients. - The underlying coordinate descent solver uses gap safe screening rules so speedup + The underlying coordinate descent solver uses gap safe screening rules to speedup fitting time, see :ref:`User Guide on coordinate descent `. Examples @@ -2127,7 +2127,7 @@ class LassoCV(RegressorMixin, LinearModelCV): regularization path. It tends to speed up the hyperparameter search. - The underlying coordinate descent solver uses gap safe screening rules so speedup + The underlying coordinate descent solver uses gap safe screening rules to speedup fitting time, see :ref:`User Guide on coordinate descent `. Examples @@ -2409,7 +2409,7 @@ class ElasticNetCV(RegressorMixin, LinearModelCV): :ref:`examples/linear_model/plot_lasso_model_selection.py `. - The underlying coordinate descent solver uses gap safe screening rules so speedup + The underlying coordinate descent solver uses gap safe screening rules to speedup fitting time, see :ref:`User Guide on coordinate descent `. Examples From 46d3a09437433872a53903888a7d45721b53435c Mon Sep 17 00:00:00 2001 From: Christian Lorentzen Date: Sun, 17 Aug 2025 18:51:39 +0200 Subject: [PATCH 13/18] Revert "MNT update Cython 3.0.10 to 3.1.2" This reverts commit 53ac9dcddc2faf0f2082e98eee316bc64e13e2dc. --- asv_benchmarks/asv.conf.json | 2 +- ...latest_conda_forge_mkl_linux-64_conda.lock | 46 +++--- ...onda_forge_mkl_no_openmp_osx-64_conda.lock | 12 +- ...pylatest_conda_forge_mkl_osx-64_conda.lock | 12 +- ...pylatest_free_threaded_linux-64_conda.lock | 10 +- ...st_pip_openblas_pandas_linux-64_conda.lock | 6 +- ...pylatest_pip_scipy_dev_linux-64_conda.lock | 4 +- ..._openblas_min_dependencies_environment.yml | 2 +- ...nblas_min_dependencies_linux-64_conda.lock | 18 +- ...e_openblas_ubuntu_2204_linux-64_conda.lock | 18 +- ...min_conda_forge_openblas_win-64_conda.lock | 28 ++-- build_tools/azure/ubuntu_atlas_lock.txt | 2 +- .../azure/ubuntu_atlas_requirements.txt | 2 +- build_tools/circle/doc_linux-64_conda.lock | 38 ++--- .../doc_min_dependencies_environment.yml | 2 +- .../doc_min_dependencies_linux-64_conda.lock | 72 ++++---- ...a_forge_cuda_array-api_linux-64_conda.lock | 154 +++++++++--------- ...n_conda_forge_arm_linux-aarch64_conda.lock | 22 +-- pyproject.toml | 4 +- sklearn/_min_dependencies.py | 2 +- 20 files changed, 226 insertions(+), 230 deletions(-) diff --git a/asv_benchmarks/asv.conf.json b/asv_benchmarks/asv.conf.json index 8da45b58b27bc..3b16389139c0c 100644 --- a/asv_benchmarks/asv.conf.json +++ b/asv_benchmarks/asv.conf.json @@ -68,7 +68,7 @@ "matrix": { "numpy": ["2.0.0"], "scipy": ["1.14.0"], - "cython": ["3.1.2"], + "cython": ["3.0.10"], "joblib": ["1.3.2"], "threadpoolctl": ["3.2.0"], "pandas": ["2.2.2"] diff --git a/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock b/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock index 25859b5572772..f58d6df794e48 100644 --- a/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock +++ b/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock @@ -1,6 +1,6 @@ # Generated by conda-lock. # platform: linux-64 -# input_hash: f524d159a11a0a80ead3448f16255169f24edde269f6b81e8e28453bc4f7fc53 +# input_hash: 193ec0257842997716ceb9bf419cbc54d52357ac3159daf1465c788e8bcf0c13 @EXPLICIT https://conda.anaconda.org/conda-forge/noarch/font-ttf-dejavu-sans-mono-2.37-hab24e00_0.tar.bz2#0c96522c6bdaed4b1566d11387caaf45 https://conda.anaconda.org/conda-forge/noarch/font-ttf-inconsolata-3.000-h77eed37_0.tar.bz2#34893075a5c9e55cdafac56607368fc6 @@ -42,7 +42,7 @@ https://conda.anaconda.org/conda-forge/linux-64/libuv-1.51.0-hb03c661_1.conda#0f https://conda.anaconda.org/conda-forge/linux-64/libwebp-base-1.6.0-hd42ef1d_0.conda#aea31d2e5b1091feca96fcfe945c3cf9 https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.3.1-hb9d3cd8_2.conda#edb0dca6bc32e4f4789199455a1dbeb8 https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.5-h2d0b736_3.conda#47e340acb35de30501a76c7c799c41d7 -https://conda.anaconda.org/conda-forge/linux-64/openssl-3.5.2-h26f9b46_0.conda#ffffb341206dd0dab0c36053c048d621 +https://conda.anaconda.org/conda-forge/linux-64/openssl-3.5.1-h7b32b05_0.conda#c87df2ab1448ba69169652ab9547082d https://conda.anaconda.org/conda-forge/linux-64/pthread-stubs-0.4-hb9d3cd8_1002.conda#b3c17d95b5a10c6e64a21fa17573e70e https://conda.anaconda.org/conda-forge/linux-64/xorg-libice-1.1.2-hb9d3cd8_0.conda#fb901ff28063514abb6046c9ec2c4a45 https://conda.anaconda.org/conda-forge/linux-64/xorg-libxau-1.0.12-hb9d3cd8_0.conda#f6ebe2cb3f82ba6c057dde5d9debe4f7 @@ -123,18 +123,18 @@ https://conda.anaconda.org/conda-forge/linux-64/lcms2-2.17-h717163a_0.conda#000e https://conda.anaconda.org/conda-forge/linux-64/libcups-2.3.3-hb8b1518_5.conda#d4a250da4737ee127fb1fa6452a9002e https://conda.anaconda.org/conda-forge/linux-64/libcurl-8.14.1-h332b0f4_0.conda#45f6713cb00f124af300342512219182 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https://conda.anaconda.org/conda-forge/noarch/packaging-25.0-pyh29332c3_1.conda#58335b26c38bf4a20f399384c33cbcf9 https://conda.anaconda.org/conda-forge/noarch/pip-25.2-pyh145f28c_0.conda#e7ab34d5a93e0819b62563c78635d937 https://conda.anaconda.org/conda-forge/noarch/pluggy-1.6.0-pyhd8ed1ab_0.conda#7da7ccd349dbf6487a7778579d2bb971 @@ -160,7 +160,7 @@ https://conda.anaconda.org/conda-forge/linux-64/aws-c-auth-0.9.0-h0fbd49f_19.con https://conda.anaconda.org/conda-forge/linux-64/aws-c-mqtt-0.13.3-h19deb91_3.conda#1680d64986f8263978c3624f677656c8 https://conda.anaconda.org/conda-forge/linux-64/azure-core-cpp-1.16.0-h3a458e0_0.conda#c09adf9bb0f9310cf2d7af23a4fbf1ff https://conda.anaconda.org/conda-forge/linux-64/ccache-4.11.3-h80c52d3_0.conda#eb517c6a2b960c3ccb6f1db1005f063a -https://conda.anaconda.org/conda-forge/linux-64/coverage-7.10.2-py313h3dea7bd_0.conda#0c644761caf4838743e843343d61cd7e +https://conda.anaconda.org/conda-forge/linux-64/coverage-7.10.1-py313h3dea7bd_0.conda#082db3aff0cf22b5bddfcca9cb13461f https://conda.anaconda.org/conda-forge/linux-64/dbus-1.16.2-h3c4dab8_0.conda#679616eb5ad4e521c83da4650860aba7 https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.3.0-pyhd8ed1ab_0.conda#72e42d28960d875c7654614f8b50939a https://conda.anaconda.org/conda-forge/linux-64/fonttools-4.59.0-py313h3dea7bd_0.conda#9ab0ef93a0904a39910d1835588e25cd @@ -169,7 +169,7 @@ https://conda.anaconda.org/conda-forge/noarch/jinja2-3.1.6-pyhd8ed1ab_0.conda#44 https://conda.anaconda.org/conda-forge/noarch/joblib-1.5.1-pyhd8ed1ab_0.conda#fb1c14694de51a476ce8636d92b6f42c https://conda.anaconda.org/conda-forge/linux-64/libgl-1.7.0-ha4b6fd6_2.conda#928b8be80851f5d8ffb016f9c81dae7a https://conda.anaconda.org/conda-forge/linux-64/libgrpc-1.73.1-h1e535eb_0.conda#8075d8550f773a17288c7ec2cf2f2d56 -https://conda.anaconda.org/conda-forge/linux-64/libhwloc-2.12.1-default_h3d81e11_1000.conda#d821210ab60be56dd27b5525ed18366d +https://conda.anaconda.org/conda-forge/linux-64/libhwloc-2.11.2-default_h3d81e11_1002.conda#56aacccb6356b6b6134a79cdf5688506 https://conda.anaconda.org/conda-forge/linux-64/libllvm20-20.1.8-hecd9e04_0.conda#59a7b967b6ef5d63029b1712f8dcf661 https://conda.anaconda.org/conda-forge/linux-64/libxkbcommon-1.10.0-h65c71a3_0.conda#fedf6bfe5d21d21d2b1785ec00a8889a https://conda.anaconda.org/conda-forge/linux-64/libxslt-1.1.43-h7a3aeb2_0.conda#31059dc620fa57d787e3899ed0421e6d @@ -203,47 +203,47 @@ https://conda.anaconda.org/conda-forge/linux-64/libpq-17.5-h27ae623_0.conda#6458 https://conda.anaconda.org/conda-forge/noarch/meson-python-0.18.0-pyh70fd9c4_0.conda#576c04b9d9f8e45285fb4d9452c26133 https://conda.anaconda.org/conda-forge/linux-64/optree-0.17.0-py313h7037e92_0.conda#21ca2b3ea73b2143033cd87ceadf270e 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-https://conda.anaconda.org/conda-forge/linux-64/libarrow-substrait-21.0.0-h3f74fd7_1_cpu.conda#60dbe0df270e9680eb470add5913c32b +https://conda.anaconda.org/conda-forge/linux-64/libarrow-substrait-21.0.0-h3f74fd7_0_cpu.conda#343b0daf0ddc4acb9abd3438ebaf31ad https://conda.anaconda.org/conda-forge/linux-64/matplotlib-3.10.5-py313h78bf25f_0.conda#0ca5238dd15d01f6609866bb370732e3 https://conda.anaconda.org/conda-forge/linux-64/pyarrow-21.0.0-py313h78bf25f_0.conda#1580ddd94606ccb60270877cb8838562 diff --git a/build_tools/azure/pylatest_conda_forge_mkl_no_openmp_osx-64_conda.lock b/build_tools/azure/pylatest_conda_forge_mkl_no_openmp_osx-64_conda.lock index 83495c2148e61..854fd7bb880d8 100644 --- a/build_tools/azure/pylatest_conda_forge_mkl_no_openmp_osx-64_conda.lock +++ b/build_tools/azure/pylatest_conda_forge_mkl_no_openmp_osx-64_conda.lock @@ -31,9 +31,9 @@ https://conda.anaconda.org/conda-forge/osx-64/libgfortran5-14.2.0-h51e75f0_103.c https://conda.anaconda.org/conda-forge/osx-64/libpng-1.6.50-h84aeda2_1.conda#1fe32bb16991a24e112051cc0de89847 https://conda.anaconda.org/conda-forge/osx-64/libsqlite-3.50.4-h39a8b3b_0.conda#156bfb239b6a67ab4a01110e6718cbc4 https://conda.anaconda.org/conda-forge/osx-64/libxcb-1.17.0-hf1f96e2_0.conda#bbeca862892e2898bdb45792a61c4afc -https://conda.anaconda.org/conda-forge/osx-64/libxml2-2.13.8-he1bc88e_1.conda#1d31029d8d2685d56a812dec48083483 +https://conda.anaconda.org/conda-forge/osx-64/libxml2-2.13.8-h93c44a6_0.conda#e42a93a31cbc6826620144343d42f472 https://conda.anaconda.org/conda-forge/osx-64/ninja-1.13.1-h0ba0a54_0.conda#71576ca895305a20c73304fcb581ae1a -https://conda.anaconda.org/conda-forge/osx-64/openssl-3.5.2-h6e31bce_0.conda#22f5d63e672b7ba467969e9f8b740ecd +https://conda.anaconda.org/conda-forge/osx-64/openssl-3.5.1-hc426f3f_0.conda#f1ac2dbc36ce2017bd8f471960b1261d https://conda.anaconda.org/conda-forge/osx-64/qhull-2020.2-h3c5361c_5.conda#dd1ea9ff27c93db7c01a7b7656bd4ad4 https://conda.anaconda.org/conda-forge/osx-64/readline-8.2-h7cca4af_2.conda#342570f8e02f2f022147a7f841475784 https://conda.anaconda.org/conda-forge/osx-64/tk-8.6.13-hf689a15_2.conda#9864891a6946c2fe037c02fca7392ab4 @@ -41,7 +41,7 @@ https://conda.anaconda.org/conda-forge/osx-64/zstd-1.5.7-h8210216_2.conda#cd60a4 https://conda.anaconda.org/conda-forge/osx-64/brotli-bin-1.1.0-h6e16a3a_3.conda#a240d09be7c84cb1d33535ebd36fe422 https://conda.anaconda.org/conda-forge/osx-64/libfreetype6-2.13.3-h40dfd5c_1.conda#c76e6f421a0e95c282142f820835e186 https://conda.anaconda.org/conda-forge/osx-64/libgfortran-5.0.0-14_2_0_h51e75f0_103.conda#090b3c9ae1282c8f9b394ac9e4773b10 -https://conda.anaconda.org/conda-forge/osx-64/libhwloc-2.12.1-default_h8c32e24_1000.conda#622d2b076d7f0588ab1baa962209e6dd +https://conda.anaconda.org/conda-forge/osx-64/libhwloc-2.11.2-default_h8c32e24_1002.conda#a9f64b764e16b830465ae64364394f36 https://conda.anaconda.org/conda-forge/osx-64/libtiff-4.7.0-h1167cee_5.conda#fc84af14a09e779f1d37ab1d16d5c4e2 https://conda.anaconda.org/conda-forge/osx-64/python-3.13.5-hc3a4c56_102_cp313.conda#afa9492a7d31f6f7189ca8f08aceadac https://conda.anaconda.org/conda-forge/osx-64/brotli-1.1.0-h6e16a3a_3.conda#44903b29bc866576c42d5c0a25e76569 @@ -56,7 +56,7 @@ https://conda.anaconda.org/conda-forge/osx-64/libfreetype-2.13.3-h694c41f_1.cond https://conda.anaconda.org/conda-forge/osx-64/libhiredis-1.0.2-h2beb688_0.tar.bz2#524282b2c46c9dedf051b3bc2ae05494 https://conda.anaconda.org/conda-forge/noarch/meson-1.8.3-pyhe01879c_0.conda#ed40b34242ec6d216605db54d19c6df5 https://conda.anaconda.org/conda-forge/noarch/munkres-1.1.4-pyhd8ed1ab_1.conda#37293a85a0f4f77bbd9cf7aaefc62609 -https://conda.anaconda.org/conda-forge/osx-64/openjpeg-2.5.3-h036ada5_1.conda#38f264b121a043cf379980c959fb2d75 +https://conda.anaconda.org/conda-forge/osx-64/openjpeg-2.5.3-h7fd6d84_0.conda#025c711177fc3309228ca1a32374458d https://conda.anaconda.org/conda-forge/noarch/packaging-25.0-pyh29332c3_1.conda#58335b26c38bf4a20f399384c33cbcf9 https://conda.anaconda.org/conda-forge/noarch/pip-25.2-pyh145f28c_0.conda#e7ab34d5a93e0819b62563c78635d937 https://conda.anaconda.org/conda-forge/noarch/pluggy-1.6.0-pyhd8ed1ab_0.conda#7da7ccd349dbf6487a7778579d2bb971 @@ -66,14 +66,14 @@ https://conda.anaconda.org/conda-forge/noarch/python-tzdata-2025.2-pyhd8ed1ab_0. https://conda.anaconda.org/conda-forge/noarch/pytz-2025.2-pyhd8ed1ab_0.conda#bc8e3267d44011051f2eb14d22fb0960 https://conda.anaconda.org/conda-forge/noarch/setuptools-80.9.0-pyhff2d567_0.conda#4de79c071274a53dcaf2a8c749d1499e https://conda.anaconda.org/conda-forge/noarch/six-1.17.0-pyhe01879c_1.conda#3339e3b65d58accf4ca4fb8748ab16b3 -https://conda.anaconda.org/conda-forge/osx-64/tbb-2021.13.0-hc025b3e_2.conda#dc40bce4a1c208ab17d570b49d88b649 +https://conda.anaconda.org/conda-forge/osx-64/tbb-2021.13.0-hb890de9_1.conda#284892942cdddfded53d090050b639a5 https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.6.0-pyhecae5ae_0.conda#9d64911b31d57ca443e9f1e36b04385f https://conda.anaconda.org/conda-forge/noarch/toml-0.10.2-pyhd8ed1ab_1.conda#b0dd904de08b7db706167240bf37b164 https://conda.anaconda.org/conda-forge/noarch/tomli-2.2.1-pyhe01879c_2.conda#30a0a26c8abccf4b7991d590fe17c699 https://conda.anaconda.org/conda-forge/osx-64/tornado-6.5.1-py313h63b0ddb_0.conda#7554d07cbe64f41c73a403e99bccf3c6 https://conda.anaconda.org/conda-forge/noarch/typing_extensions-4.14.1-pyhe01879c_0.conda#e523f4f1e980ed7a4240d7e27e9ec81f https://conda.anaconda.org/conda-forge/osx-64/ccache-4.11.3-h33566b8_0.conda#b65cad834bd6c1f660c101cca09430bf -https://conda.anaconda.org/conda-forge/osx-64/coverage-7.10.2-py313h4db2fa4_0.conda#306339f10c125b6ab64944c6b771611b +https://conda.anaconda.org/conda-forge/osx-64/coverage-7.10.1-py313h4db2fa4_0.conda#82ec1dabd8bbdfe1f418447e2a6d20c6 https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.3.0-pyhd8ed1ab_0.conda#72e42d28960d875c7654614f8b50939a https://conda.anaconda.org/conda-forge/osx-64/fonttools-4.59.0-py313h4db2fa4_0.conda#1dab5b45690c319aba7d846f9267349c https://conda.anaconda.org/conda-forge/osx-64/freetype-2.13.3-h694c41f_1.conda#126dba1baf5030cb6f34533718924577 diff --git a/build_tools/azure/pylatest_conda_forge_mkl_osx-64_conda.lock b/build_tools/azure/pylatest_conda_forge_mkl_osx-64_conda.lock index 60cb83836afdc..5c28d3e975940 100644 --- a/build_tools/azure/pylatest_conda_forge_mkl_osx-64_conda.lock +++ b/build_tools/azure/pylatest_conda_forge_mkl_osx-64_conda.lock @@ -35,9 +35,9 @@ https://conda.anaconda.org/conda-forge/osx-64/libgfortran5-14.2.0-h51e75f0_103.c https://conda.anaconda.org/conda-forge/osx-64/libpng-1.6.50-h84aeda2_1.conda#1fe32bb16991a24e112051cc0de89847 https://conda.anaconda.org/conda-forge/osx-64/libsqlite-3.50.4-h39a8b3b_0.conda#156bfb239b6a67ab4a01110e6718cbc4 https://conda.anaconda.org/conda-forge/osx-64/libxcb-1.17.0-hf1f96e2_0.conda#bbeca862892e2898bdb45792a61c4afc -https://conda.anaconda.org/conda-forge/osx-64/libxml2-2.13.8-he1bc88e_1.conda#1d31029d8d2685d56a812dec48083483 +https://conda.anaconda.org/conda-forge/osx-64/libxml2-2.13.8-h93c44a6_0.conda#e42a93a31cbc6826620144343d42f472 https://conda.anaconda.org/conda-forge/osx-64/ninja-1.13.1-h0ba0a54_0.conda#71576ca895305a20c73304fcb581ae1a -https://conda.anaconda.org/conda-forge/osx-64/openssl-3.5.2-h6e31bce_0.conda#22f5d63e672b7ba467969e9f8b740ecd +https://conda.anaconda.org/conda-forge/osx-64/openssl-3.5.1-hc426f3f_0.conda#f1ac2dbc36ce2017bd8f471960b1261d https://conda.anaconda.org/conda-forge/osx-64/qhull-2020.2-h3c5361c_5.conda#dd1ea9ff27c93db7c01a7b7656bd4ad4 https://conda.anaconda.org/conda-forge/osx-64/readline-8.2-h7cca4af_2.conda#342570f8e02f2f022147a7f841475784 https://conda.anaconda.org/conda-forge/osx-64/tapi-1300.6.5-h390ca13_0.conda#c6ee25eb54accb3f1c8fc39203acfaf1 @@ -47,7 +47,7 @@ https://conda.anaconda.org/conda-forge/osx-64/zstd-1.5.7-h8210216_2.conda#cd60a4 https://conda.anaconda.org/conda-forge/osx-64/brotli-bin-1.1.0-h6e16a3a_3.conda#a240d09be7c84cb1d33535ebd36fe422 https://conda.anaconda.org/conda-forge/osx-64/libfreetype6-2.13.3-h40dfd5c_1.conda#c76e6f421a0e95c282142f820835e186 https://conda.anaconda.org/conda-forge/osx-64/libgfortran-5.0.0-14_2_0_h51e75f0_103.conda#090b3c9ae1282c8f9b394ac9e4773b10 -https://conda.anaconda.org/conda-forge/osx-64/libhwloc-2.12.1-default_h8c32e24_1000.conda#622d2b076d7f0588ab1baa962209e6dd +https://conda.anaconda.org/conda-forge/osx-64/libhwloc-2.11.2-default_h8c32e24_1002.conda#a9f64b764e16b830465ae64364394f36 https://conda.anaconda.org/conda-forge/osx-64/libllvm19-19.1.7-hc29ff6c_1.conda#a937150d07aa51b50ded6a0816df4a5a https://conda.anaconda.org/conda-forge/osx-64/libtiff-4.7.0-h1167cee_5.conda#fc84af14a09e779f1d37ab1d16d5c4e2 https://conda.anaconda.org/conda-forge/osx-64/mpfr-4.2.1-haed47dc_3.conda#d511e58aaaabfc23136880d9956fa7a6 @@ -69,7 +69,7 @@ https://conda.anaconda.org/conda-forge/osx-64/llvm-tools-19-19.1.7-he90a8e3_1.co https://conda.anaconda.org/conda-forge/noarch/meson-1.8.3-pyhe01879c_0.conda#ed40b34242ec6d216605db54d19c6df5 https://conda.anaconda.org/conda-forge/osx-64/mpc-1.3.1-h9d8efa1_1.conda#0520855aaae268ea413d6bc913f1384c https://conda.anaconda.org/conda-forge/noarch/munkres-1.1.4-pyhd8ed1ab_1.conda#37293a85a0f4f77bbd9cf7aaefc62609 -https://conda.anaconda.org/conda-forge/osx-64/openjpeg-2.5.3-h036ada5_1.conda#38f264b121a043cf379980c959fb2d75 +https://conda.anaconda.org/conda-forge/osx-64/openjpeg-2.5.3-h7fd6d84_0.conda#025c711177fc3309228ca1a32374458d https://conda.anaconda.org/conda-forge/noarch/packaging-25.0-pyh29332c3_1.conda#58335b26c38bf4a20f399384c33cbcf9 https://conda.anaconda.org/conda-forge/noarch/pip-25.2-pyh145f28c_0.conda#e7ab34d5a93e0819b62563c78635d937 https://conda.anaconda.org/conda-forge/noarch/pluggy-1.6.0-pyhd8ed1ab_0.conda#7da7ccd349dbf6487a7778579d2bb971 @@ -79,7 +79,7 @@ https://conda.anaconda.org/conda-forge/noarch/python-tzdata-2025.2-pyhd8ed1ab_0. https://conda.anaconda.org/conda-forge/noarch/pytz-2025.2-pyhd8ed1ab_0.conda#bc8e3267d44011051f2eb14d22fb0960 https://conda.anaconda.org/conda-forge/noarch/setuptools-80.9.0-pyhff2d567_0.conda#4de79c071274a53dcaf2a8c749d1499e https://conda.anaconda.org/conda-forge/noarch/six-1.17.0-pyhe01879c_1.conda#3339e3b65d58accf4ca4fb8748ab16b3 -https://conda.anaconda.org/conda-forge/osx-64/tbb-2021.13.0-hc025b3e_2.conda#dc40bce4a1c208ab17d570b49d88b649 +https://conda.anaconda.org/conda-forge/osx-64/tbb-2021.13.0-hb890de9_1.conda#284892942cdddfded53d090050b639a5 https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.6.0-pyhecae5ae_0.conda#9d64911b31d57ca443e9f1e36b04385f https://conda.anaconda.org/conda-forge/noarch/toml-0.10.2-pyhd8ed1ab_1.conda#b0dd904de08b7db706167240bf37b164 https://conda.anaconda.org/conda-forge/noarch/tomli-2.2.1-pyhe01879c_2.conda#30a0a26c8abccf4b7991d590fe17c699 @@ -87,7 +87,7 @@ https://conda.anaconda.org/conda-forge/osx-64/tornado-6.5.1-py313h63b0ddb_0.cond https://conda.anaconda.org/conda-forge/noarch/typing_extensions-4.14.1-pyhe01879c_0.conda#e523f4f1e980ed7a4240d7e27e9ec81f https://conda.anaconda.org/conda-forge/osx-64/ccache-4.11.3-h33566b8_0.conda#b65cad834bd6c1f660c101cca09430bf https://conda.anaconda.org/conda-forge/osx-64/clang-19-19.1.7-default_h3571c67_3.conda#5bd5cda534488611b3970b768139127c -https://conda.anaconda.org/conda-forge/osx-64/coverage-7.10.2-py313h4db2fa4_0.conda#306339f10c125b6ab64944c6b771611b +https://conda.anaconda.org/conda-forge/osx-64/coverage-7.10.1-py313h4db2fa4_0.conda#82ec1dabd8bbdfe1f418447e2a6d20c6 https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.3.0-pyhd8ed1ab_0.conda#72e42d28960d875c7654614f8b50939a https://conda.anaconda.org/conda-forge/osx-64/fonttools-4.59.0-py313h4db2fa4_0.conda#1dab5b45690c319aba7d846f9267349c https://conda.anaconda.org/conda-forge/osx-64/freetype-2.13.3-h694c41f_1.conda#126dba1baf5030cb6f34533718924577 diff --git a/build_tools/azure/pylatest_free_threaded_linux-64_conda.lock b/build_tools/azure/pylatest_free_threaded_linux-64_conda.lock index d2fb22f3b8765..4697ad30614be 100644 --- a/build_tools/azure/pylatest_free_threaded_linux-64_conda.lock +++ b/build_tools/azure/pylatest_free_threaded_linux-64_conda.lock @@ -1,6 +1,6 @@ # Generated by conda-lock. # platform: linux-64 -# input_hash: b76364b5635e8c36a0fc0777955b5664a336ba94ac96f3ade7aad842ab7e15c5 +# input_hash: 369e1662562a0dd933bde8db136f7a3e1600dd1d12b8cc9d9a45519c74253276 @EXPLICIT https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2#d7c89558ba9fa0495403155b64376d81 https://conda.anaconda.org/conda-forge/noarch/python_abi-3.13-8_cp313t.conda#e1dd2408e4ff08393fbc3502fbe4316d @@ -19,7 +19,7 @@ https://conda.anaconda.org/conda-forge/linux-64/libmpdec-4.0.0-hb9d3cd8_0.conda# https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-15.1.0-h8f9b012_4.conda#3c376af8888c386b9d3d1c2701e2f3ab https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.3.1-hb9d3cd8_2.conda#edb0dca6bc32e4f4789199455a1dbeb8 https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.5-h2d0b736_3.conda#47e340acb35de30501a76c7c799c41d7 -https://conda.anaconda.org/conda-forge/linux-64/openssl-3.5.2-h26f9b46_0.conda#ffffb341206dd0dab0c36053c048d621 +https://conda.anaconda.org/conda-forge/linux-64/openssl-3.5.1-h7b32b05_0.conda#c87df2ab1448ba69169652ab9547082d https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-h4bc722e_7.conda#62ee74e96c5ebb0af99386de58cf9553 https://conda.anaconda.org/conda-forge/linux-64/libgfortran-15.1.0-h69a702a_4.conda#53e876bc2d2648319e94c33c57b9ec74 https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.50.4-h0c1763c_0.conda#0b367fad34931cb79e0d6b7e5c06bb1c @@ -37,7 +37,7 @@ https://conda.anaconda.org/conda-forge/noarch/cpython-3.13.5-py313hd8ed1ab_2.con https://conda.anaconda.org/conda-forge/noarch/cython-3.1.2-pyh2c78169_102.conda#e250288041263e65630a5802c72fa76b https://conda.anaconda.org/conda-forge/noarch/execnet-2.1.1-pyhd8ed1ab_1.conda#a71efeae2c160f6789900ba2631a2c90 https://conda.anaconda.org/conda-forge/noarch/iniconfig-2.0.0-pyhd8ed1ab_1.conda#6837f3eff7dcea42ecd714ce1ac2b108 -https://conda.anaconda.org/conda-forge/linux-64/libblas-3.9.0-33_h59b9bed_openblas.conda#b43d5de8fe73c2a5fb2b43f45301285b +https://conda.anaconda.org/conda-forge/linux-64/libblas-3.9.0-32_h59b9bed_openblas.conda#2af9f3d5c2e39f417ce040f5a35c40c6 https://conda.anaconda.org/conda-forge/linux-64/libhiredis-1.0.2-h2cc385e_0.tar.bz2#b34907d3a81a3cd8095ee83d174c074a https://conda.anaconda.org/conda-forge/noarch/meson-1.8.3-pyhe01879c_0.conda#ed40b34242ec6d216605db54d19c6df5 https://conda.anaconda.org/conda-forge/noarch/packaging-25.0-pyh29332c3_1.conda#58335b26c38bf4a20f399384c33cbcf9 @@ -51,8 +51,8 @@ https://conda.anaconda.org/conda-forge/noarch/typing_extensions-4.14.1-pyhe01879 https://conda.anaconda.org/conda-forge/linux-64/ccache-4.11.3-h80c52d3_0.conda#eb517c6a2b960c3ccb6f1db1005f063a https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.3.0-pyhd8ed1ab_0.conda#72e42d28960d875c7654614f8b50939a https://conda.anaconda.org/conda-forge/noarch/joblib-1.5.1-pyhd8ed1ab_0.conda#fb1c14694de51a476ce8636d92b6f42c -https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.9.0-33_he106b2a_openblas.conda#28052b5e6ea5bd283ac343c5c064b950 -https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.9.0-33_h7ac8fdf_openblas.conda#e598bb54c4a4b45c3d83c72984f79dbb +https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.9.0-32_he106b2a_openblas.conda#3d3f9355e52f269cd8bc2c440d8a5263 +https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.9.0-32_h7ac8fdf_openblas.conda#6c3f04ccb6c578138e9f9899da0bd714 https://conda.anaconda.org/conda-forge/noarch/pyproject-metadata-0.9.1-pyhd8ed1ab_0.conda#22ae7c6ea81e0c8661ef32168dda929b https://conda.anaconda.org/conda-forge/noarch/python-freethreading-3.13.5-h92d6c8b_2.conda#32180e39991faf3fd42b4d74ef01daa0 https://conda.anaconda.org/conda-forge/noarch/meson-python-0.18.0-pyh70fd9c4_0.conda#576c04b9d9f8e45285fb4d9452c26133 diff --git a/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock b/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock index eb270fbe29f42..5919a5401a692 100644 --- a/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock +++ b/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock @@ -1,6 +1,6 @@ # Generated by conda-lock. # platform: linux-64 -# input_hash: 0668d85ecef342f1056dfe3d1fd8d677c967d4037f6f95fff49c097fec0cd624 +# input_hash: 86e2072dbf3e21dd40532da22c0f58dbd4905ce1a1250b64571702c6845d712c @EXPLICIT https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2#d7c89558ba9fa0495403155b64376d81 https://conda.anaconda.org/conda-forge/noarch/python_abi-3.13-8_cp313.conda#94305520c52a4aa3f6c2b1ff6008d9f8 @@ -19,7 +19,7 @@ https://conda.anaconda.org/conda-forge/linux-64/libmpdec-4.0.0-hb9d3cd8_0.conda# https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-15.1.0-h8f9b012_4.conda#3c376af8888c386b9d3d1c2701e2f3ab https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.3.1-hb9d3cd8_2.conda#edb0dca6bc32e4f4789199455a1dbeb8 https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.5-h2d0b736_3.conda#47e340acb35de30501a76c7c799c41d7 -https://conda.anaconda.org/conda-forge/linux-64/openssl-3.5.2-h26f9b46_0.conda#ffffb341206dd0dab0c36053c048d621 +https://conda.anaconda.org/conda-forge/linux-64/openssl-3.5.1-h7b32b05_0.conda#c87df2ab1448ba69169652ab9547082d https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-h4bc722e_7.conda#62ee74e96c5ebb0af99386de58cf9553 https://conda.anaconda.org/conda-forge/linux-64/libgfortran-15.1.0-h69a702a_4.conda#53e876bc2d2648319e94c33c57b9ec74 https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.50.4-h0c1763c_0.conda#0b367fad34931cb79e0d6b7e5c06bb1c @@ -91,6 +91,6 @@ https://conda.anaconda.org/conda-forge/linux-64/ccache-4.11.3-h80c52d3_0.conda#e # pip pytest-cov @ https://files.pythonhosted.org/packages/bc/16/4ea354101abb1287856baa4af2732be351c7bee728065aed451b678153fd/pytest_cov-6.2.1-py3-none-any.whl#sha256=f5bc4c23f42f1cdd23c70b1dab1bbaef4fc505ba950d53e0081d0730dd7e86d5 # pip pytest-xdist @ https://files.pythonhosted.org/packages/ca/31/d4e37e9e550c2b92a9cbc2e4d0b7420a27224968580b5a447f420847c975/pytest_xdist-3.8.0-py3-none-any.whl#sha256=202ca578cfeb7370784a8c33d6d05bc6e13b4f25b5053c30a152269fd10f0b88 # pip scikit-image @ https://files.pythonhosted.org/packages/cd/9b/c3da56a145f52cd61a68b8465d6a29d9503bc45bc993bb45e84371c97d94/scikit_image-0.25.2-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=b8abd3c805ce6944b941cfed0406d88faeb19bab3ed3d4b50187af55cf24d147 -# pip scipy-doctest @ https://files.pythonhosted.org/packages/22/a8/0b7c959d13f664c92673024a42573518d2474525e5298ca8714f91a72a79/scipy_doctest-2.0.0-py3-none-any.whl#sha256=8d45bb33cfa4e1b45599436fbc48033aeda12d6a818c0dd6da94425c624fe3b5 +# pip scipy-doctest @ https://files.pythonhosted.org/packages/c9/13/cd25d1875f3804b73fd4a4ae00e2c76e274e1e0608d79148cac251b644b1/scipy_doctest-1.8.0-py3-none-any.whl#sha256=5863208368c35486e143ce3283ab2f517a0d6b0c63d0d5f19f38a823fc82016f # pip sphinx @ https://files.pythonhosted.org/packages/31/53/136e9eca6e0b9dc0e1962e2c908fbea2e5ac000c2a2fbd9a35797958c48b/sphinx-8.2.3-py3-none-any.whl#sha256=4405915165f13521d875a8c29c8970800a0141c14cc5416a38feca4ea5d9b9c3 # pip numpydoc @ https://files.pythonhosted.org/packages/6c/45/56d99ba9366476cd8548527667f01869279cedb9e66b28eb4dfb27701679/numpydoc-1.8.0-py3-none-any.whl#sha256=72024c7fd5e17375dec3608a27c03303e8ad00c81292667955c6fea7a3ccf541 diff --git a/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock b/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock index d3e7aadecbd59..1a24d95d4cc78 100644 --- a/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock +++ b/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock @@ -1,6 +1,6 @@ # Generated by conda-lock. # platform: linux-64 -# input_hash: 66c01323547a35e8550a7303dac1f0cb19e0af6173e62d689006d7ca8f1cd385 +# input_hash: 94d00db2415f525f6a8902cfb08b959e58ea906230fb5acac0be202ef8fcfba8 @EXPLICIT https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2#d7c89558ba9fa0495403155b64376d81 https://conda.anaconda.org/conda-forge/noarch/python_abi-3.13-8_cp313.conda#94305520c52a4aa3f6c2b1ff6008d9f8 @@ -19,7 +19,7 @@ https://conda.anaconda.org/conda-forge/linux-64/libmpdec-4.0.0-hb9d3cd8_0.conda# https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-15.1.0-h8f9b012_4.conda#3c376af8888c386b9d3d1c2701e2f3ab https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.3.1-hb9d3cd8_2.conda#edb0dca6bc32e4f4789199455a1dbeb8 https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.5-h2d0b736_3.conda#47e340acb35de30501a76c7c799c41d7 -https://conda.anaconda.org/conda-forge/linux-64/openssl-3.5.2-h26f9b46_0.conda#ffffb341206dd0dab0c36053c048d621 +https://conda.anaconda.org/conda-forge/linux-64/openssl-3.5.1-h7b32b05_0.conda#c87df2ab1448ba69169652ab9547082d https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-h4bc722e_7.conda#62ee74e96c5ebb0af99386de58cf9553 https://conda.anaconda.org/conda-forge/linux-64/libgfortran-15.1.0-h69a702a_4.conda#53e876bc2d2648319e94c33c57b9ec74 https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.50.4-h0c1763c_0.conda#0b367fad34931cb79e0d6b7e5c06bb1c diff --git a/build_tools/azure/pymin_conda_forge_openblas_min_dependencies_environment.yml b/build_tools/azure/pymin_conda_forge_openblas_min_dependencies_environment.yml index 8c10cec910bf1..1e7c36708ee30 100644 --- a/build_tools/azure/pymin_conda_forge_openblas_min_dependencies_environment.yml +++ b/build_tools/azure/pymin_conda_forge_openblas_min_dependencies_environment.yml @@ -8,7 +8,7 @@ dependencies: - numpy=1.22.0 # min - blas[build=openblas] - scipy=1.8.0 # min - - cython=3.1.2 # min + - cython=3.0.10 # min - joblib=1.2.0 # min - threadpoolctl=3.1.0 # min - matplotlib=3.5.0 # min diff --git a/build_tools/azure/pymin_conda_forge_openblas_min_dependencies_linux-64_conda.lock b/build_tools/azure/pymin_conda_forge_openblas_min_dependencies_linux-64_conda.lock index 879b7b46c2916..e0fdda45688fb 100644 --- a/build_tools/azure/pymin_conda_forge_openblas_min_dependencies_linux-64_conda.lock +++ b/build_tools/azure/pymin_conda_forge_openblas_min_dependencies_linux-64_conda.lock @@ -1,6 +1,6 @@ # Generated by conda-lock. # platform: linux-64 -# input_hash: d6b142fd975427575778d1d015e16fe1fb879c94e34153e605ff104e9219c04a +# input_hash: 97a1191dcfb0ec679b12b7ba4cea261ae7ff6bd372a7b26cfe443f3e18b5b8df @EXPLICIT https://conda.anaconda.org/conda-forge/noarch/font-ttf-dejavu-sans-mono-2.37-hab24e00_0.tar.bz2#0c96522c6bdaed4b1566d11387caaf45 https://conda.anaconda.org/conda-forge/noarch/font-ttf-inconsolata-3.000-h77eed37_0.tar.bz2#34893075a5c9e55cdafac56607368fc6 @@ -39,7 +39,7 @@ https://conda.anaconda.org/conda-forge/linux-64/libutf8proc-2.8.0-hf23e847_1.con https://conda.anaconda.org/conda-forge/linux-64/libwebp-base-1.6.0-hd42ef1d_0.conda#aea31d2e5b1091feca96fcfe945c3cf9 https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.3.1-hb9d3cd8_2.conda#edb0dca6bc32e4f4789199455a1dbeb8 https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.5-h2d0b736_3.conda#47e340acb35de30501a76c7c799c41d7 -https://conda.anaconda.org/conda-forge/linux-64/openssl-3.5.2-h26f9b46_0.conda#ffffb341206dd0dab0c36053c048d621 +https://conda.anaconda.org/conda-forge/linux-64/openssl-3.5.1-h7b32b05_0.conda#c87df2ab1448ba69169652ab9547082d https://conda.anaconda.org/conda-forge/linux-64/pthread-stubs-0.4-hb9d3cd8_1002.conda#b3c17d95b5a10c6e64a21fa17573e70e https://conda.anaconda.org/conda-forge/linux-64/xorg-libice-1.1.2-hb9d3cd8_0.conda#fb901ff28063514abb6046c9ec2c4a45 https://conda.anaconda.org/conda-forge/linux-64/xorg-libxau-1.0.12-hb9d3cd8_0.conda#f6ebe2cb3f82ba6c057dde5d9debe4f7 @@ -124,7 +124,7 @@ https://conda.anaconda.org/conda-forge/noarch/certifi-2025.8.3-pyhd8ed1ab_0.cond https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_1.conda#962b9857ee8e7018c22f2776ffa0b2d7 https://conda.anaconda.org/conda-forge/noarch/cycler-0.12.1-pyhd8ed1ab_1.conda#44600c4667a319d67dbe0681fc0bc833 https://conda.anaconda.org/conda-forge/linux-64/cyrus-sasl-2.1.28-hd9c7081_0.conda#cae723309a49399d2949362f4ab5c9e4 -https://conda.anaconda.org/conda-forge/linux-64/cython-3.1.2-py310had8cdd9_2.conda#be416b1d5ffef48c394cbbb04bc864ae +https://conda.anaconda.org/conda-forge/linux-64/cython-3.0.10-py310hc6cd4ac_0.conda#bd1d71ee240be36f1d85c86177d6964f https://conda.anaconda.org/conda-forge/noarch/execnet-2.1.1-pyhd8ed1ab_1.conda#a71efeae2c160f6789900ba2631a2c90 https://conda.anaconda.org/conda-forge/linux-64/gettext-0.25.1-h3f43e3d_1.conda#c42356557d7f2e37676e121515417e3b https://conda.anaconda.org/conda-forge/noarch/iniconfig-2.0.0-pyhd8ed1ab_1.conda#6837f3eff7dcea42ecd714ce1ac2b108 @@ -133,16 +133,16 @@ https://conda.anaconda.org/conda-forge/linux-64/lcms2-2.17-h717163a_0.conda#000e https://conda.anaconda.org/conda-forge/linux-64/libcups-2.3.3-hb8b1518_5.conda#d4a250da4737ee127fb1fa6452a9002e https://conda.anaconda.org/conda-forge/linux-64/libcurl-8.14.1-h332b0f4_0.conda#45f6713cb00f124af300342512219182 https://conda.anaconda.org/conda-forge/linux-64/libfreetype-2.13.3-ha770c72_1.conda#51f5be229d83ecd401fb369ab96ae669 -https://conda.anaconda.org/conda-forge/linux-64/libglib-2.84.3-hf39c6af_0.conda#467f23819b1ea2b89c3fc94d65082301 +https://conda.anaconda.org/conda-forge/linux-64/libglib-2.84.2-h3618099_0.conda#072ab14a02164b7c0c089055368ff776 https://conda.anaconda.org/conda-forge/linux-64/libglx-1.7.0-ha4b6fd6_2.conda#c8013e438185f33b13814c5c488acd5c https://conda.anaconda.org/conda-forge/linux-64/libgrpc-1.54.3-hb20ce57_0.conda#7af7c59ab24db007dfd82e0a3a343f66 https://conda.anaconda.org/conda-forge/linux-64/libhiredis-1.0.2-h2cc385e_0.tar.bz2#b34907d3a81a3cd8095ee83d174c074a https://conda.anaconda.org/conda-forge/linux-64/libopenblas-0.3.25-pthreads_h413a1c8_0.conda#d172b34a443b95f86089e8229ddc9a17 https://conda.anaconda.org/conda-forge/linux-64/libsystemd0-256.9-h2774228_0.conda#7b283ff97a87409a884bc11283855c17 -https://conda.anaconda.org/conda-forge/linux-64/libxml2-2.13.8-h04c0eec_1.conda#10bcbd05e1c1c9d652fccb42b776a9fa +https://conda.anaconda.org/conda-forge/linux-64/libxml2-2.13.8-h4bc477f_0.conda#14dbe05b929e329dbaa6f2d0aa19466d https://conda.anaconda.org/conda-forge/noarch/meson-1.8.3-pyhe01879c_0.conda#ed40b34242ec6d216605db54d19c6df5 https://conda.anaconda.org/conda-forge/noarch/munkres-1.1.4-pyhd8ed1ab_1.conda#37293a85a0f4f77bbd9cf7aaefc62609 -https://conda.anaconda.org/conda-forge/linux-64/openjpeg-2.5.3-h55fea9a_1.conda#01243c4aaf71bde0297966125aea4706 +https://conda.anaconda.org/conda-forge/linux-64/openjpeg-2.5.3-h5fbd93e_0.conda#9e5816bc95d285c115a3ebc2f8563564 https://conda.anaconda.org/conda-forge/linux-64/orc-1.8.4-h2f23424_0.conda#4bb92585a250e67d49b46c073d29f9dd https://conda.anaconda.org/conda-forge/noarch/packaging-25.0-pyh29332c3_1.conda#58335b26c38bf4a20f399384c33cbcf9 https://conda.anaconda.org/conda-forge/noarch/pluggy-1.6.0-pyhd8ed1ab_0.conda#7da7ccd349dbf6487a7778579d2bb971 @@ -169,11 +169,11 @@ https://conda.anaconda.org/conda-forge/linux-64/aws-c-event-stream-0.3.1-h1e0337 https://conda.anaconda.org/conda-forge/linux-64/aws-c-http-0.7.10-h9ab9c9b_2.conda#cf49873da2e59f876a2ad4794b05801b https://conda.anaconda.org/conda-forge/linux-64/brotli-1.0.9-h166bdaf_9.conda#4601544b4982ba1861fa9b9c607b2c06 https://conda.anaconda.org/conda-forge/linux-64/ccache-4.11.3-h80c52d3_0.conda#eb517c6a2b960c3ccb6f1db1005f063a -https://conda.anaconda.org/conda-forge/linux-64/coverage-7.10.2-py310h3406613_0.conda#c03cc1310dc3059463e52ba1062e1689 +https://conda.anaconda.org/conda-forge/linux-64/coverage-7.10.1-py310h3406613_0.conda#ac2715e7efc966c105f45d0cc8dfc4cb https://conda.anaconda.org/conda-forge/linux-64/dbus-1.16.2-h3c4dab8_0.conda#679616eb5ad4e521c83da4650860aba7 https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.3.0-pyhd8ed1ab_0.conda#72e42d28960d875c7654614f8b50939a https://conda.anaconda.org/conda-forge/linux-64/freetype-2.13.3-ha770c72_1.conda#9ccd736d31e0c6e41f54e704e5312811 -https://conda.anaconda.org/conda-forge/linux-64/glib-tools-2.84.3-hf516916_0.conda#39f817fb8e0bb88a63bbdca0448143ea +https://conda.anaconda.org/conda-forge/linux-64/glib-tools-2.84.2-h4833e2c_0.conda#f2ec1facec64147850b7674633978050 https://conda.anaconda.org/conda-forge/noarch/joblib-1.2.0-pyhd8ed1ab_0.tar.bz2#7583652522d71ad78ba536bba06940eb https://conda.anaconda.org/conda-forge/linux-64/libblas-3.9.0-20_linux64_openblas.conda#2b7bb4f7562c8cf334fc2e20c2d28abc https://conda.anaconda.org/conda-forge/linux-64/libflac-1.4.3-h59595ed_0.conda#ee48bf17cc83a00f59ca1494d5646869 @@ -195,7 +195,7 @@ https://conda.anaconda.org/conda-forge/linux-64/aws-c-auth-0.7.0-h435f46f_0.cond https://conda.anaconda.org/conda-forge/linux-64/aws-c-mqtt-0.8.13-hd4f18eb_5.conda#860fb8c0efec64a4a678eb2ea066ff65 https://conda.anaconda.org/conda-forge/linux-64/fontconfig-2.15.0-h7e30c49_1.conda#8f5b0b297b59e1ac160ad4beec99dbee https://conda.anaconda.org/conda-forge/linux-64/fonttools-4.59.0-py310h3406613_0.conda#dc2e5602e20bbffb18314a70094b3c4a -https://conda.anaconda.org/conda-forge/linux-64/glib-2.84.3-h89d24bf_0.conda#9d1844ab51651cc3d034bb55fff83b99 +https://conda.anaconda.org/conda-forge/linux-64/glib-2.84.2-h6287aef_0.conda#704648df3a01d4d24bc2c0466b718d63 https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.9.0-20_linux64_openblas.conda#36d486d72ab64ffea932329a1d3729a3 https://conda.anaconda.org/conda-forge/linux-64/libclang-cpp20.1-20.1.8-default_hddf928d_0.conda#b939740734ad5a8e8f6c942374dee68d https://conda.anaconda.org/conda-forge/linux-64/libclang13-20.1.8-default_ha444ac7_0.conda#783f9cdcb0255ed00e3f1be22e16de40 diff --git a/build_tools/azure/pymin_conda_forge_openblas_ubuntu_2204_linux-64_conda.lock b/build_tools/azure/pymin_conda_forge_openblas_ubuntu_2204_linux-64_conda.lock index 1027301dff4b5..4195ae4bd5044 100644 --- a/build_tools/azure/pymin_conda_forge_openblas_ubuntu_2204_linux-64_conda.lock +++ b/build_tools/azure/pymin_conda_forge_openblas_ubuntu_2204_linux-64_conda.lock @@ -1,6 +1,6 @@ # Generated by conda-lock. # platform: linux-64 -# input_hash: 4abfb998e26e3beaa198409ac1ebc1278024921c4b3c6fc8de5c93be1b6193ba +# input_hash: eca51d0b31006b26e8a75b2be7389e6909a81ca3c7647651d7e54f9013aedbde @EXPLICIT https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2#d7c89558ba9fa0495403155b64376d81 https://conda.anaconda.org/conda-forge/noarch/python_abi-3.10-8_cp310.conda#05e00f3b21e88bb3d658ac700b2ce58c @@ -22,7 +22,7 @@ https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-15.1.0-h8f9b012_4.cond https://conda.anaconda.org/conda-forge/linux-64/libwebp-base-1.6.0-hd42ef1d_0.conda#aea31d2e5b1091feca96fcfe945c3cf9 https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.3.1-hb9d3cd8_2.conda#edb0dca6bc32e4f4789199455a1dbeb8 https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.5-h2d0b736_3.conda#47e340acb35de30501a76c7c799c41d7 -https://conda.anaconda.org/conda-forge/linux-64/openssl-3.5.2-h26f9b46_0.conda#ffffb341206dd0dab0c36053c048d621 +https://conda.anaconda.org/conda-forge/linux-64/openssl-3.5.1-h7b32b05_0.conda#c87df2ab1448ba69169652ab9547082d https://conda.anaconda.org/conda-forge/linux-64/pthread-stubs-0.4-hb9d3cd8_1002.conda#b3c17d95b5a10c6e64a21fa17573e70e https://conda.anaconda.org/conda-forge/linux-64/xorg-libxau-1.0.12-hb9d3cd8_0.conda#f6ebe2cb3f82ba6c057dde5d9debe4f7 https://conda.anaconda.org/conda-forge/linux-64/xorg-libxdmcp-1.1.5-hb9d3cd8_0.conda#8035c64cb77ed555e3f150b7b3972480 @@ -58,13 +58,13 @@ https://conda.anaconda.org/conda-forge/noarch/idna-3.10-pyhd8ed1ab_1.conda#39a4f https://conda.anaconda.org/conda-forge/noarch/imagesize-1.4.1-pyhd8ed1ab_0.tar.bz2#7de5386c8fea29e76b303f37dde4c352 https://conda.anaconda.org/conda-forge/noarch/iniconfig-2.0.0-pyhd8ed1ab_1.conda#6837f3eff7dcea42ecd714ce1ac2b108 https://conda.anaconda.org/conda-forge/linux-64/lcms2-2.17-h717163a_0.conda#000e85703f0fd9594c81710dd5066471 -https://conda.anaconda.org/conda-forge/linux-64/libblas-3.9.0-33_h59b9bed_openblas.conda#b43d5de8fe73c2a5fb2b43f45301285b +https://conda.anaconda.org/conda-forge/linux-64/libblas-3.9.0-32_h59b9bed_openblas.conda#2af9f3d5c2e39f417ce040f5a35c40c6 https://conda.anaconda.org/conda-forge/linux-64/libfreetype-2.13.3-ha770c72_1.conda#51f5be229d83ecd401fb369ab96ae669 https://conda.anaconda.org/conda-forge/linux-64/libhiredis-1.0.2-h2cc385e_0.tar.bz2#b34907d3a81a3cd8095ee83d174c074a https://conda.anaconda.org/conda-forge/linux-64/markupsafe-3.0.2-py310h89163eb_1.conda#8ce3f0332fd6de0d737e2911d329523f https://conda.anaconda.org/conda-forge/noarch/meson-1.8.3-pyhe01879c_0.conda#ed40b34242ec6d216605db54d19c6df5 https://conda.anaconda.org/conda-forge/linux-64/openblas-0.3.30-pthreads_h6ec200e_1.conda#611fcf119d77a78439794c43f7667664 -https://conda.anaconda.org/conda-forge/linux-64/openjpeg-2.5.3-h55fea9a_1.conda#01243c4aaf71bde0297966125aea4706 +https://conda.anaconda.org/conda-forge/linux-64/openjpeg-2.5.3-h5fbd93e_0.conda#9e5816bc95d285c115a3ebc2f8563564 https://conda.anaconda.org/conda-forge/noarch/packaging-25.0-pyh29332c3_1.conda#58335b26c38bf4a20f399384c33cbcf9 https://conda.anaconda.org/conda-forge/noarch/pluggy-1.6.0-pyhd8ed1ab_0.conda#7da7ccd349dbf6487a7778579d2bb971 https://conda.anaconda.org/conda-forge/noarch/pycparser-2.22-pyh29332c3_1.conda#12c566707c80111f9799308d9e265aef @@ -88,23 +88,23 @@ https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.3.0-pyhd8ed1ab_0. https://conda.anaconda.org/conda-forge/noarch/h2-4.2.0-pyhd8ed1ab_0.conda#b4754fb1bdcb70c8fd54f918301582c6 https://conda.anaconda.org/conda-forge/noarch/jinja2-3.1.6-pyhd8ed1ab_0.conda#446bd6c8cb26050d528881df495ce646 https://conda.anaconda.org/conda-forge/noarch/joblib-1.5.1-pyhd8ed1ab_0.conda#fb1c14694de51a476ce8636d92b6f42c -https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.9.0-33_he106b2a_openblas.conda#28052b5e6ea5bd283ac343c5c064b950 -https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.9.0-33_h7ac8fdf_openblas.conda#e598bb54c4a4b45c3d83c72984f79dbb +https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.9.0-32_he106b2a_openblas.conda#3d3f9355e52f269cd8bc2c440d8a5263 +https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.9.0-32_h7ac8fdf_openblas.conda#6c3f04ccb6c578138e9f9899da0bd714 https://conda.anaconda.org/conda-forge/linux-64/pillow-11.3.0-py310h7e6dc6c_0.conda#e609995f031bc848be8ea159865e8afc https://conda.anaconda.org/conda-forge/noarch/pip-25.2-pyh8b19718_0.conda#dfce4b2af4bfe90cdcaf56ca0b28ddf5 https://conda.anaconda.org/conda-forge/noarch/pyproject-metadata-0.9.1-pyhd8ed1ab_0.conda#22ae7c6ea81e0c8661ef32168dda929b https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0.post0-pyhe01879c_2.conda#5b8d21249ff20967101ffa321cab24e8 -https://conda.anaconda.org/conda-forge/linux-64/liblapacke-3.9.0-33_he2f377e_openblas.conda#912372f43a7430828cca8e86f680e741 +https://conda.anaconda.org/conda-forge/linux-64/liblapacke-3.9.0-32_he2f377e_openblas.conda#54e7f7896d0dbf56665bcb0078bfa9d2 https://conda.anaconda.org/conda-forge/noarch/meson-python-0.18.0-pyh70fd9c4_0.conda#576c04b9d9f8e45285fb4d9452c26133 https://conda.anaconda.org/conda-forge/linux-64/numpy-2.2.6-py310hefbff90_0.conda#b0cea2c364bf65cd19e023040eeab05d https://conda.anaconda.org/conda-forge/noarch/pytest-8.4.1-pyhd8ed1ab_0.conda#a49c2283f24696a7b30367b7346a0144 https://conda.anaconda.org/conda-forge/linux-64/zstandard-0.23.0-py310ha75aee5_2.conda#f9254b5b0193982416b91edcb4b2676f -https://conda.anaconda.org/conda-forge/linux-64/blas-devel-3.9.0-33_h1ea3ea9_openblas.conda#bc300d05fd629ecaccc25885275b4500 +https://conda.anaconda.org/conda-forge/linux-64/blas-devel-3.9.0-32_h1ea3ea9_openblas.conda#34cb4b6753b38a62ae25f3a73efd16b0 https://conda.anaconda.org/conda-forge/linux-64/pandas-2.3.1-py310h0158d43_0.conda#94eb2db0b8f769a1e554843e3586504d https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.8.0-pyhd8ed1ab_0.conda#8375cfbda7c57fbceeda18229be10417 https://conda.anaconda.org/conda-forge/linux-64/scipy-1.15.2-py310h1d65ade_0.conda#8c29cd33b64b2eb78597fa28b5595c8d https://conda.anaconda.org/conda-forge/noarch/urllib3-2.5.0-pyhd8ed1ab_0.conda#436c165519e140cb08d246a4472a9d6a -https://conda.anaconda.org/conda-forge/linux-64/blas-2.133-openblas.conda#59bd46ccdcaabbf2d5202a358bdb000c +https://conda.anaconda.org/conda-forge/linux-64/blas-2.132-openblas.conda#9c4a27ab2463f9b1d9019e0a798a5b81 https://conda.anaconda.org/conda-forge/linux-64/pyamg-5.2.1-py310ha2bacc8_1.conda#817d32861729e14f474249f1036291c4 https://conda.anaconda.org/conda-forge/noarch/requests-2.32.4-pyhd8ed1ab_0.conda#f6082eae112814f1447b56a5e1f6ed05 https://conda.anaconda.org/conda-forge/noarch/numpydoc-1.8.0-pyhd8ed1ab_1.conda#5af206d64d18d6c8dfb3122b4d9e643b diff --git a/build_tools/azure/pymin_conda_forge_openblas_win-64_conda.lock b/build_tools/azure/pymin_conda_forge_openblas_win-64_conda.lock index 4a6682b32aa47..1ba106605ccf8 100644 --- a/build_tools/azure/pymin_conda_forge_openblas_win-64_conda.lock +++ b/build_tools/azure/pymin_conda_forge_openblas_win-64_conda.lock @@ -1,6 +1,6 @@ # Generated by conda-lock. # platform: win-64 -# input_hash: 4ff41dadb8a7a77d0b784bfc6b32126b8e1a41c8b9a87375b48c18c9aee4ea2a +# input_hash: 2c3fe1c37ac2b2ad6a1b18ab6881baec49f52a712bd6f5d3c29268a4b92ca179 @EXPLICIT https://conda.anaconda.org/conda-forge/noarch/font-ttf-dejavu-sans-mono-2.37-hab24e00_0.tar.bz2#0c96522c6bdaed4b1566d11387caaf45 https://conda.anaconda.org/conda-forge/noarch/font-ttf-inconsolata-3.000-h77eed37_0.tar.bz2#34893075a5c9e55cdafac56607368fc6 @@ -31,23 +31,23 @@ https://conda.anaconda.org/conda-forge/win-64/libgcc-15.1.0-h1383e82_4.conda#59f https://conda.anaconda.org/conda-forge/win-64/libiconv-1.18-h135ad9c_1.conda#21fc5dba2cbcd8e5e26ff976a312122c 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-https://conda.anaconda.org/conda-forge/win-64/openssl-3.5.2-h725018a_0.conda#150d3920b420a27c0848acca158f94dc +https://conda.anaconda.org/conda-forge/win-64/openssl-3.5.1-h725018a_0.conda#d124fc2fd7070177b5e2450627f8fc1a https://conda.anaconda.org/conda-forge/win-64/pixman-0.46.4-hc614b68_0.conda#04170282e8afb5a5e0d7168b0840f91b https://conda.anaconda.org/conda-forge/win-64/qhull-2020.2-hc790b64_5.conda#854fbdff64b572b5c0b470f334d34c11 https://conda.anaconda.org/conda-forge/win-64/tk-8.6.13-h2c6b04d_2.conda#ebd0e761de9aa879a51d22cc721bd095 https://conda.anaconda.org/conda-forge/win-64/krb5-1.21.3-hdf4eb48_0.conda#31aec030344e962fbd7dbbbbd68e60a9 -https://conda.anaconda.org/conda-forge/win-64/libblas-3.9.0-33_h11dc60a_openblas.conda#de70cf0c3460df403940d81090ce5be3 +https://conda.anaconda.org/conda-forge/win-64/libblas-3.9.0-32_h11dc60a_openblas.conda#0696abde82f7b82d4f74e963ebdd430c https://conda.anaconda.org/conda-forge/win-64/libbrotlidec-1.1.0-h2466b09_3.conda#a342933dbc6d814541234c7c81cb5205 https://conda.anaconda.org/conda-forge/win-64/libbrotlienc-1.1.0-h2466b09_3.conda#7ef0af55d70cbd9de324bb88b7f9d81e https://conda.anaconda.org/conda-forge/win-64/libintl-0.22.5-h5728263_3.conda#2cf0cf76cc15d360dfa2f17fd6cf9772 https://conda.anaconda.org/conda-forge/win-64/libpng-1.6.50-h7351971_1.conda#3ae6e9f5c47c495ebeed95651518be61 -https://conda.anaconda.org/conda-forge/win-64/libxml2-2.13.8-h741aa76_1.conda#aeb49dc1f5531de13d2c0d57ffa6d0c8 -https://conda.anaconda.org/conda-forge/win-64/openblas-0.3.30-pthreads_h4a7f399_1.conda#ad69c0075127ea83cec563b5e8f30994 +https://conda.anaconda.org/conda-forge/win-64/libxml2-2.13.8-h442d1da_0.conda#833c2dbc1a5020007b520b044c713ed3 +https://conda.anaconda.org/conda-forge/win-64/openblas-0.3.30-pthreads_h4a7f399_0.conda#2773d23da17eb31ed3a0911334a08805 https://conda.anaconda.org/conda-forge/win-64/pcre2-10.45-h99c9b8b_0.conda#f4c483274001678e129f5cbaf3a8d765 https://conda.anaconda.org/conda-forge/win-64/pthread-stubs-0.4-h0e40799_1002.conda#3c8f2573569bb816483e5cf57efbbe29 https://conda.anaconda.org/conda-forge/win-64/python-3.10.18-h8c5b53a_0_cpython.conda#f1775dab55c8a073ebd024bfb2f689c1 @@ -61,11 +61,11 @@ https://conda.anaconda.org/conda-forge/win-64/cython-3.1.2-py310h6bd2d47_2.conda https://conda.anaconda.org/conda-forge/noarch/execnet-2.1.1-pyhd8ed1ab_1.conda#a71efeae2c160f6789900ba2631a2c90 https://conda.anaconda.org/conda-forge/noarch/iniconfig-2.0.0-pyhd8ed1ab_1.conda#6837f3eff7dcea42ecd714ce1ac2b108 https://conda.anaconda.org/conda-forge/win-64/kiwisolver-1.4.8-py310he9f1925_1.conda#e2755283837d9bd45838564cf54872c8 -https://conda.anaconda.org/conda-forge/win-64/libcblas-3.9.0-33_h9bd4c3b_openblas.conda#8ac0e84fb704fe6ff6484edc413964d1 +https://conda.anaconda.org/conda-forge/win-64/libcblas-3.9.0-32_h9bd4c3b_openblas.conda#69e8e83a9ed37d070b0c5ed4996648a8 https://conda.anaconda.org/conda-forge/win-64/libclang13-20.1.8-default_hadf22e1_0.conda#cf1a9a4c7395c5d6cc0dcf8f7c40acb3 https://conda.anaconda.org/conda-forge/win-64/libfreetype6-2.13.3-h0b5ce68_1.conda#a84b7d1a13060a9372bea961a8131dbc -https://conda.anaconda.org/conda-forge/win-64/libglib-2.84.3-h1c1036b_0.conda#2bcc00752c158d4a70e1eaccbf6fe8ae -https://conda.anaconda.org/conda-forge/win-64/liblapack-3.9.0-33_h2526c6b_openblas.conda#856fab9484c2e0ce0bee0171421ff9a4 +https://conda.anaconda.org/conda-forge/win-64/libglib-2.84.2-hbc94333_0.conda#fee05801cc5db97bec20a5e78fb3905b +https://conda.anaconda.org/conda-forge/win-64/liblapack-3.9.0-32_h2526c6b_openblas.conda#13c3da761e89eec8a40bf8c877dd7a71 https://conda.anaconda.org/conda-forge/win-64/libtiff-4.7.0-h05922d8_5.conda#75370aba951b47ec3b5bfe689f1bcf7f https://conda.anaconda.org/conda-forge/win-64/libxcb-1.17.0-h0e4246c_0.conda#a69bbf778a462da324489976c84cfc8c https://conda.anaconda.org/conda-forge/win-64/libxslt-1.1.43-h25c3957_0.conda#e84f36aa02735c140099d992d491968d @@ -85,18 +85,18 @@ https://conda.anaconda.org/conda-forge/noarch/typing_extensions-4.14.1-pyhe01879 https://conda.anaconda.org/conda-forge/win-64/unicodedata2-16.0.0-py310ha8f682b_0.conda#b28aead44c6e19a1fbba7752aa242b34 https://conda.anaconda.org/conda-forge/noarch/wheel-0.45.1-pyhd8ed1ab_1.conda#75cb7132eb58d97896e173ef12ac9986 https://conda.anaconda.org/conda-forge/win-64/brotli-1.1.0-h2466b09_3.conda#c2a23d8a8986c72148c63bdf855ac99a -https://conda.anaconda.org/conda-forge/win-64/coverage-7.10.2-py310hdb0e946_0.conda#df6cd1aa316df7c199030a889cd8886f +https://conda.anaconda.org/conda-forge/win-64/coverage-7.10.1-py310hdb0e946_0.conda#0092c0f10b7473d481070ad5f3b789f0 https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.3.0-pyhd8ed1ab_0.conda#72e42d28960d875c7654614f8b50939a https://conda.anaconda.org/conda-forge/noarch/joblib-1.5.1-pyhd8ed1ab_0.conda#fb1c14694de51a476ce8636d92b6f42c https://conda.anaconda.org/conda-forge/win-64/lcms2-2.17-hbcf6048_0.conda#3538827f77b82a837fa681a4579e37a1 https://conda.anaconda.org/conda-forge/win-64/libfreetype-2.13.3-h57928b3_1.conda#410ba2c8e7bdb278dfbb5d40220e39d2 -https://conda.anaconda.org/conda-forge/win-64/liblapacke-3.9.0-33_h1d0e49f_openblas.conda#6cd916c1c97af7138d22fd4af15ede90 +https://conda.anaconda.org/conda-forge/win-64/liblapacke-3.9.0-32_h1d0e49f_openblas.conda#cca697e07375fde34cced92d66e8bdf2 https://conda.anaconda.org/conda-forge/win-64/numpy-2.2.6-py310h4987827_0.conda#d2596785ac2cf5bab04e2ee9e5d04041 -https://conda.anaconda.org/conda-forge/win-64/openjpeg-2.5.3-h24db6dd_1.conda#25f45acb1a234ad1c9b9a20e1e6c559e +https://conda.anaconda.org/conda-forge/win-64/openjpeg-2.5.3-h4d64b90_0.conda#fc050366dd0b8313eb797ed1ffef3a29 https://conda.anaconda.org/conda-forge/noarch/pip-25.2-pyh8b19718_0.conda#dfce4b2af4bfe90cdcaf56ca0b28ddf5 https://conda.anaconda.org/conda-forge/noarch/pyproject-metadata-0.9.1-pyhd8ed1ab_0.conda#22ae7c6ea81e0c8661ef32168dda929b https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0.post0-pyhe01879c_2.conda#5b8d21249ff20967101ffa321cab24e8 -https://conda.anaconda.org/conda-forge/win-64/blas-devel-3.9.0-33_hc0f8095_openblas.conda#2f43b14f3011449f579a2ae03424b18f +https://conda.anaconda.org/conda-forge/win-64/blas-devel-3.9.0-32_hc0f8095_openblas.conda#c07c54d62ee5a9886933051e10ad4b1e https://conda.anaconda.org/conda-forge/win-64/contourpy-1.3.2-py310hc19bc0b_0.conda#039416813b5290e7d100a05bb4326110 https://conda.anaconda.org/conda-forge/win-64/fonttools-4.59.0-py310hdb0e946_0.conda#eae900c4fcb37e4a3f9fe9417c669f11 https://conda.anaconda.org/conda-forge/win-64/freetype-2.13.3-h57928b3_1.conda#633504fe3f96031192e40e3e6c18ef06 @@ -104,7 +104,7 @@ https://conda.anaconda.org/conda-forge/noarch/meson-python-0.18.0-pyh70fd9c4_0.c https://conda.anaconda.org/conda-forge/win-64/pillow-11.3.0-py310h6d647b9_0.conda#246b33a0eb812754b529065262aeb1c5 https://conda.anaconda.org/conda-forge/noarch/pytest-8.4.1-pyhd8ed1ab_0.conda#a49c2283f24696a7b30367b7346a0144 https://conda.anaconda.org/conda-forge/win-64/scipy-1.15.2-py310h15c175c_0.conda#81798168111d1021e3d815217c444418 -https://conda.anaconda.org/conda-forge/win-64/blas-2.133-openblas.conda#5f06959474ec0b2b40ca8de380859f7f +https://conda.anaconda.org/conda-forge/win-64/blas-2.132-openblas.conda#b59780f3fbd2bf992d3702e59d8d1653 https://conda.anaconda.org/conda-forge/win-64/fontconfig-2.15.0-h765892d_1.conda#9bb0026a2131b09404c59c4290c697cd https://conda.anaconda.org/conda-forge/win-64/matplotlib-base-3.10.5-py310h0bdd906_0.conda#a26309db5dc93b40f5e6bf69187f631e https://conda.anaconda.org/conda-forge/noarch/pytest-cov-6.2.1-pyhd8ed1ab_0.conda#ce978e1b9ed8b8d49164e90a5cdc94cd diff --git a/build_tools/azure/ubuntu_atlas_lock.txt b/build_tools/azure/ubuntu_atlas_lock.txt index fb49fb3bab1d9..993b7d8627557 100644 --- a/build_tools/azure/ubuntu_atlas_lock.txt +++ b/build_tools/azure/ubuntu_atlas_lock.txt @@ -4,7 +4,7 @@ # # pip-compile --output-file=build_tools/azure/ubuntu_atlas_lock.txt build_tools/azure/ubuntu_atlas_requirements.txt # -cython==3.1.2 +cython==3.0.10 # via -r build_tools/azure/ubuntu_atlas_requirements.txt exceptiongroup==1.3.0 # via pytest diff --git a/build_tools/azure/ubuntu_atlas_requirements.txt b/build_tools/azure/ubuntu_atlas_requirements.txt index 4e0edd877dea7..dfb0cfebc54d1 100644 --- a/build_tools/azure/ubuntu_atlas_requirements.txt +++ b/build_tools/azure/ubuntu_atlas_requirements.txt @@ -1,7 +1,7 @@ # DO NOT EDIT: this file is generated from the specification found in the # following script to centralize the configuration for CI builds: # build_tools/update_environments_and_lock_files.py -cython==3.1.2 # min +cython==3.0.10 # min joblib==1.2.0 # min threadpoolctl==3.1.0 # min pytest diff --git a/build_tools/circle/doc_linux-64_conda.lock b/build_tools/circle/doc_linux-64_conda.lock index a62944d452a95..d179ba70af52c 100644 --- a/build_tools/circle/doc_linux-64_conda.lock +++ b/build_tools/circle/doc_linux-64_conda.lock @@ -1,6 +1,6 @@ # Generated by conda-lock. # platform: linux-64 -# input_hash: 207a7209ba4771c5fc039939c36a47d93b9e5478fbdf6fe01c4ac5837581d49a +# input_hash: 9bc9ca426bc05685148b1ae7e671907e9d514e40b6bb1c8d7c916d4fdc8b70f2 @EXPLICIT https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2#d7c89558ba9fa0495403155b64376d81 https://conda.anaconda.org/conda-forge/noarch/font-ttf-dejavu-sans-mono-2.37-hab24e00_0.tar.bz2#0c96522c6bdaed4b1566d11387caaf45 @@ -43,7 +43,7 @@ https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-15.1.0-h8f9b012_4.cond https://conda.anaconda.org/conda-forge/linux-64/libwebp-base-1.6.0-hd42ef1d_0.conda#aea31d2e5b1091feca96fcfe945c3cf9 https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.3.1-hb9d3cd8_2.conda#edb0dca6bc32e4f4789199455a1dbeb8 https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.5-h2d0b736_3.conda#47e340acb35de30501a76c7c799c41d7 -https://conda.anaconda.org/conda-forge/linux-64/openssl-3.5.2-h26f9b46_0.conda#ffffb341206dd0dab0c36053c048d621 +https://conda.anaconda.org/conda-forge/linux-64/openssl-3.5.1-h7b32b05_0.conda#c87df2ab1448ba69169652ab9547082d https://conda.anaconda.org/conda-forge/linux-64/pthread-stubs-0.4-hb9d3cd8_1002.conda#b3c17d95b5a10c6e64a21fa17573e70e https://conda.anaconda.org/conda-forge/linux-64/rav1e-0.7.1-h8fae777_3.conda#2c42649888aac645608191ffdc80d13a https://conda.anaconda.org/conda-forge/linux-64/xorg-libice-1.1.2-hb9d3cd8_0.conda#fb901ff28063514abb6046c9ec2c4a45 @@ -82,7 +82,7 @@ https://conda.anaconda.org/conda-forge/linux-64/svt-av1-3.0.2-h5888daf_0.conda#0 https://conda.anaconda.org/conda-forge/linux-64/tk-8.6.13-noxft_hd72426e_102.conda#a0116df4f4ed05c303811a837d5b39d8 https://conda.anaconda.org/conda-forge/linux-64/wayland-1.24.0-h3e06ad9_0.conda#0f2ca7906bf166247d1d760c3422cb8a https://conda.anaconda.org/conda-forge/linux-64/zfp-1.0.1-h5888daf_2.conda#e0409515c467b87176b070bff5d9442e -https://conda.anaconda.org/conda-forge/linux-64/zlib-ng-2.2.5-hde8ca8f_0.conda#1920c3502e7f6688d650ab81cd3775fd +https://conda.anaconda.org/conda-forge/linux-64/zlib-ng-2.2.4-h7955e40_0.conda#c8a816dbf59eb8ba6346a8f10014b302 https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.7-hb8e6e7a_2.conda#6432cb5d4ac0046c3ac0a8a0f95842f9 https://conda.anaconda.org/conda-forge/linux-64/aom-3.9.1-hac33072_0.conda#346722a0be40f6edc53f12640d301338 https://conda.anaconda.org/conda-forge/linux-64/blosc-1.21.6-he440d0b_1.conda#2c2fae981fd2afd00812c92ac47d023d @@ -113,7 +113,7 @@ https://conda.anaconda.org/conda-forge/linux-64/brotli-python-1.1.0-py310hf71b8c https://conda.anaconda.org/conda-forge/noarch/cached_property-1.5.2-pyha770c72_1.tar.bz2#576d629e47797577ab0f1b351297ef4a https://conda.anaconda.org/conda-forge/noarch/certifi-2025.8.3-pyhd8ed1ab_0.conda#11f59985f49df4620890f3e746ed7102 https://conda.anaconda.org/conda-forge/noarch/charset-normalizer-3.4.2-pyhd8ed1ab_0.conda#40fe4284b8b5835a9073a645139f35af -https://conda.anaconda.org/conda-forge/noarch/click-8.2.1-pyh707e725_0.conda#94b550b8d3a614dbd326af798c7dfb40 +https://conda.anaconda.org/conda-forge/noarch/click-8.2.2-pyh707e725_0.conda#2cc16494e4ce28efc52fb29ec3c348a1 https://conda.anaconda.org/conda-forge/noarch/cloudpickle-3.1.1-pyhd8ed1ab_0.conda#364ba6c9fb03886ac979b482f39ebb92 https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_1.conda#962b9857ee8e7018c22f2776ffa0b2d7 https://conda.anaconda.org/conda-forge/linux-64/conda-gcc-specs-14.3.0-hb991d5c_4.conda#b6025bc20bf223d68402821f181707fb @@ -138,12 +138,12 @@ https://conda.anaconda.org/conda-forge/linux-64/kiwisolver-1.4.8-py310h3788b33_1 https://conda.anaconda.org/conda-forge/noarch/lark-1.2.2-pyhd8ed1ab_1.conda#3a8063b25e603999188ed4bbf3485404 https://conda.anaconda.org/conda-forge/linux-64/lcms2-2.17-h717163a_0.conda#000e85703f0fd9594c81710dd5066471 https://conda.anaconda.org/conda-forge/linux-64/libavif16-1.3.0-h766b0b6_0.conda#f17f2d0e5c9ad6b958547fd67b155771 -https://conda.anaconda.org/conda-forge/linux-64/libblas-3.9.0-33_h59b9bed_openblas.conda#b43d5de8fe73c2a5fb2b43f45301285b +https://conda.anaconda.org/conda-forge/linux-64/libblas-3.9.0-32_h59b9bed_openblas.conda#2af9f3d5c2e39f417ce040f5a35c40c6 https://conda.anaconda.org/conda-forge/linux-64/libcups-2.3.3-hb8b1518_5.conda#d4a250da4737ee127fb1fa6452a9002e https://conda.anaconda.org/conda-forge/linux-64/libfreetype-2.13.3-ha770c72_1.conda#51f5be229d83ecd401fb369ab96ae669 -https://conda.anaconda.org/conda-forge/linux-64/libglib-2.84.3-hf39c6af_0.conda#467f23819b1ea2b89c3fc94d65082301 +https://conda.anaconda.org/conda-forge/linux-64/libglib-2.84.2-h3618099_0.conda#072ab14a02164b7c0c089055368ff776 https://conda.anaconda.org/conda-forge/linux-64/libglx-1.7.0-ha4b6fd6_2.conda#c8013e438185f33b13814c5c488acd5c -https://conda.anaconda.org/conda-forge/linux-64/libxml2-2.13.8-h04c0eec_1.conda#10bcbd05e1c1c9d652fccb42b776a9fa +https://conda.anaconda.org/conda-forge/linux-64/libxml2-2.13.8-h4bc477f_0.conda#14dbe05b929e329dbaa6f2d0aa19466d https://conda.anaconda.org/conda-forge/linux-64/markupsafe-3.0.2-py310h89163eb_1.conda#8ce3f0332fd6de0d737e2911d329523f https://conda.anaconda.org/conda-forge/noarch/mdurl-0.1.2-pyhd8ed1ab_1.conda#592132998493b3ff25fd7479396e8351 https://conda.anaconda.org/conda-forge/noarch/meson-1.8.3-pyhe01879c_0.conda#ed40b34242ec6d216605db54d19c6df5 @@ -151,7 +151,7 @@ https://conda.anaconda.org/conda-forge/noarch/munkres-1.1.4-pyhd8ed1ab_1.conda#3 https://conda.anaconda.org/conda-forge/noarch/narwhals-2.0.1-pyhe01879c_0.conda#5f0dea40791cecf0f82882b9eea7f7c1 https://conda.anaconda.org/conda-forge/noarch/networkx-3.4.2-pyh267e887_2.conda#fd40bf7f7f4bc4b647dc8512053d9873 https://conda.anaconda.org/conda-forge/linux-64/openblas-0.3.30-pthreads_h6ec200e_1.conda#611fcf119d77a78439794c43f7667664 -https://conda.anaconda.org/conda-forge/linux-64/openjpeg-2.5.3-h55fea9a_1.conda#01243c4aaf71bde0297966125aea4706 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https://conda.anaconda.org/conda-forge/linux-64/libxslt-1.1.43-h7a3aeb2_0.conda#31059dc620fa57d787e3899ed0421e6d @@ -251,7 +251,7 @@ https://conda.anaconda.org/conda-forge/linux-64/xorg-libxi-1.8.2-hb9d3cd8_0.cond https://conda.anaconda.org/conda-forge/linux-64/xorg-libxrandr-1.5.4-hb9d3cd8_0.conda#2de7f99d6581a4a7adbff607b5c278ca https://conda.anaconda.org/conda-forge/linux-64/xorg-libxxf86vm-1.1.6-hb9d3cd8_0.conda#5efa5fa6243a622445fdfd72aee15efa https://conda.anaconda.org/conda-forge/noarch/_python_abi3_support-1.0-hd8ed1ab_2.conda#aaa2a381ccc56eac91d63b6c1240312f -https://conda.anaconda.org/conda-forge/noarch/anyio-4.10.0-pyhe01879c_0.conda#cc2613bfa71dec0eb2113ee21ac9ccbf +https://conda.anaconda.org/conda-forge/noarch/anyio-4.9.0-pyh29332c3_0.conda#9749a2c77a7c40d432ea0927662d7e52 https://conda.anaconda.org/conda-forge/linux-64/argon2-cffi-bindings-25.1.0-py310h7c4b9e2_0.conda#3fd41ccdb9263ad51cf89b05cade6fb7 https://conda.anaconda.org/conda-forge/noarch/arrow-1.3.0-pyhd8ed1ab_1.conda#46b53236fdd990271b03c3978d4218a9 https://conda.anaconda.org/conda-forge/noarch/beautifulsoup4-4.13.4-pyha770c72_0.conda#9f07c4fc992adb2d6c30da7fab3959a7 @@ -269,7 +269,7 @@ https://conda.anaconda.org/conda-forge/noarch/jupyter_server_terminals-0.5.3-pyh https://conda.anaconda.org/conda-forge/noarch/lazy-loader-0.4-pyhd8ed1ab_2.conda#d10d9393680734a8febc4b362a4c94f2 https://conda.anaconda.org/conda-forge/linux-64/libclang-cpp20.1-20.1.8-default_hddf928d_0.conda#b939740734ad5a8e8f6c942374dee68d https://conda.anaconda.org/conda-forge/linux-64/libclang13-20.1.8-default_ha444ac7_0.conda#783f9cdcb0255ed00e3f1be22e16de40 -https://conda.anaconda.org/conda-forge/linux-64/liblapacke-3.9.0-33_he2f377e_openblas.conda#912372f43a7430828cca8e86f680e741 +https://conda.anaconda.org/conda-forge/linux-64/liblapacke-3.9.0-32_he2f377e_openblas.conda#54e7f7896d0dbf56665bcb0078bfa9d2 https://conda.anaconda.org/conda-forge/linux-64/libpq-17.5-h27ae623_0.conda#6458be24f09e1b034902ab44fe9de908 https://conda.anaconda.org/conda-forge/noarch/mdit-py-plugins-0.4.2-pyhd8ed1ab_1.conda#af2060041d4f3250a7eb6ab3ec0e549b https://conda.anaconda.org/conda-forge/noarch/meson-python-0.18.0-pyh70fd9c4_0.conda#576c04b9d9f8e45285fb4d9452c26133 @@ -278,7 +278,7 @@ https://conda.anaconda.org/conda-forge/noarch/pytest-8.4.1-pyhd8ed1ab_0.conda#a4 https://conda.anaconda.org/conda-forge/linux-64/xorg-libxtst-1.2.5-hb9d3cd8_3.conda#7bbe9a0cc0df0ac5f5a8ad6d6a11af2f https://conda.anaconda.org/conda-forge/linux-64/zstandard-0.23.0-py310ha75aee5_2.conda#f9254b5b0193982416b91edcb4b2676f https://conda.anaconda.org/conda-forge/noarch/argon2-cffi-25.1.0-pyhd8ed1ab_0.conda#8ac12aff0860280ee0cff7fa2cf63f3b -https://conda.anaconda.org/conda-forge/linux-64/blas-devel-3.9.0-33_h1ea3ea9_openblas.conda#bc300d05fd629ecaccc25885275b4500 +https://conda.anaconda.org/conda-forge/linux-64/blas-devel-3.9.0-32_h1ea3ea9_openblas.conda#34cb4b6753b38a62ae25f3a73efd16b0 https://conda.anaconda.org/conda-forge/linux-64/cairo-1.18.4-h3394656_0.conda#09262e66b19567aff4f592fb53b28760 https://conda.anaconda.org/conda-forge/linux-64/contourpy-1.3.2-py310h3788b33_0.conda#b6420d29123c7c823de168f49ccdfe6a https://conda.anaconda.org/conda-forge/linux-64/cxx-compiler-1.11.0-hfcd1e18_0.conda#5da8c935dca9186673987f79cef0b2a5 @@ -290,20 +290,20 @@ https://conda.anaconda.org/conda-forge/noarch/jsonschema-4.25.0-pyhe01879c_0.con https://conda.anaconda.org/conda-forge/noarch/jupyterlite-core-0.6.3-pyhe01879c_0.conda#36ebdbf67840763b491045b5a36a2b78 https://conda.anaconda.org/conda-forge/linux-64/pandas-2.3.1-py310h0158d43_0.conda#94eb2db0b8f769a1e554843e3586504d https://conda.anaconda.org/conda-forge/noarch/patsy-1.0.1-pyhd8ed1ab_1.conda#ee23fabfd0a8c6b8d6f3729b47b2859d -https://conda.anaconda.org/conda-forge/linux-64/polars-default-1.32.0-py39hf521cc8_1.conda#e70c9d78efe27cc42c6e205f19707a4b +https://conda.anaconda.org/conda-forge/linux-64/polars-default-1.31.0-py39hf521cc8_1.conda#85f9f61975ba5a8f3d40b477aef457cb https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.8.0-pyhd8ed1ab_0.conda#8375cfbda7c57fbceeda18229be10417 https://conda.anaconda.org/conda-forge/linux-64/pywavelets-1.8.0-py310hf462985_0.conda#4c441eff2be2e65bd67765c5642051c5 https://conda.anaconda.org/conda-forge/linux-64/scipy-1.15.2-py310h1d65ade_0.conda#8c29cd33b64b2eb78597fa28b5595c8d https://conda.anaconda.org/conda-forge/noarch/towncrier-24.8.0-pyhd8ed1ab_1.conda#820b6a1ddf590fba253f8204f7200d82 https://conda.anaconda.org/conda-forge/noarch/urllib3-2.5.0-pyhd8ed1ab_0.conda#436c165519e140cb08d246a4472a9d6a -https://conda.anaconda.org/conda-forge/linux-64/blas-2.133-openblas.conda#59bd46ccdcaabbf2d5202a358bdb000c +https://conda.anaconda.org/conda-forge/linux-64/blas-2.132-openblas.conda#9c4a27ab2463f9b1d9019e0a798a5b81 https://conda.anaconda.org/conda-forge/linux-64/compilers-1.11.0-ha770c72_0.conda#fdcf2e31dd960ef7c5daa9f2c95eff0e https://conda.anaconda.org/conda-forge/linux-64/harfbuzz-11.3.3-hbb57e21_0.conda#0f69590f0c89bed08abc54d86cd87be5 https://conda.anaconda.org/conda-forge/noarch/jsonschema-with-format-nongpl-4.25.0-he01879c_0.conda#f4c7afaf838ab5bb1c4e73eb3095fb26 https://conda.anaconda.org/conda-forge/noarch/jupyterlite-pyodide-kernel-0.6.1-pyhe01879c_0.conda#b55913693e8934299585267ce95af06e https://conda.anaconda.org/conda-forge/linux-64/matplotlib-base-3.10.5-py310hfde16b3_0.conda#4478c9e8038113b9f68904200ec80385 https://conda.anaconda.org/conda-forge/noarch/nbformat-5.10.4-pyhd8ed1ab_1.conda#bbe1963f1e47f594070ffe87cdf612ea -https://conda.anaconda.org/conda-forge/linux-64/polars-1.32.0-default_hac8f6d3_1.conda#92cb09b7f68ea274695292217cd79c9e +https://conda.anaconda.org/conda-forge/linux-64/polars-1.31.0-default_h70f2ef1_1.conda#0217d9e4176cf33942996a7ee3afac0e https://conda.anaconda.org/conda-forge/linux-64/pyamg-5.2.1-py310ha2bacc8_1.conda#817d32861729e14f474249f1036291c4 https://conda.anaconda.org/conda-forge/noarch/requests-2.32.4-pyhd8ed1ab_0.conda#f6082eae112814f1447b56a5e1f6ed05 https://conda.anaconda.org/conda-forge/linux-64/statsmodels-0.14.5-py310haaf2d95_0.conda#92b4b51b83f2cfded298f1b8c7a99e32 @@ -311,7 +311,7 @@ https://conda.anaconda.org/conda-forge/noarch/tifffile-2025.5.10-pyhd8ed1ab_0.co https://conda.anaconda.org/conda-forge/noarch/jupyter_events-0.12.0-pyh29332c3_0.conda#f56000b36f09ab7533877e695e4e8cb0 https://conda.anaconda.org/conda-forge/noarch/jupytext-1.17.2-pyh80e38bb_0.conda#6d0652a97ef103de0c77b9c610d0c20d https://conda.anaconda.org/conda-forge/noarch/nbclient-0.10.2-pyhd8ed1ab_0.conda#6bb0d77277061742744176ab555b723c -https://conda.anaconda.org/conda-forge/noarch/pooch-1.8.2-pyhd8ed1ab_2.conda#7b18edf3ed17052a85681d30fa09bf48 +https://conda.anaconda.org/conda-forge/noarch/pooch-1.8.2-pyhd8ed1ab_1.conda#b3e783e8e8ed7577cf0b6dee37d1fbac https://conda.anaconda.org/conda-forge/linux-64/qt6-main-6.9.1-h6ac528c_2.conda#34ccdb55340a25761efbac1ff1504091 https://conda.anaconda.org/conda-forge/linux-64/scikit-image-0.25.2-py310h5eaa309_1.conda#ed21ab72d049ecdb60f829f04b4dca1c https://conda.anaconda.org/conda-forge/noarch/seaborn-base-0.13.2-pyhd8ed1ab_3.conda#fd96da444e81f9e6fcaac38590f3dd42 @@ -335,6 +335,6 @@ https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-htmlhelp-2.1.0-pyhd8 https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-qthelp-2.0.0-pyhd8ed1ab_1.conda#00534ebcc0375929b45c3039b5ba7636 https://conda.anaconda.org/conda-forge/noarch/sphinx-8.1.3-pyhd8ed1ab_1.conda#1a3281a0dc355c02b5506d87db2d78ac https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-serializinghtml-1.1.10-pyhd8ed1ab_1.conda#3bc61f7161d28137797e038263c04c54 -https://conda.anaconda.org/conda-forge/noarch/sphinxext-opengraph-0.12.0-pyhd8ed1ab_0.conda#1a4d14313b64f8eac388f6742c18a58c +https://conda.anaconda.org/conda-forge/noarch/sphinxext-opengraph-0.11.0-pyhd8ed1ab_0.conda#d77bd353b3a8e8e2a5aa6f4d2c9f5488 # pip libsass @ https://files.pythonhosted.org/packages/fd/5a/eb5b62641df0459a3291fc206cf5bd669c0feed7814dded8edef4ade8512/libsass-0.23.0-cp38-abi3-manylinux_2_5_x86_64.manylinux1_x86_64.whl#sha256=4a218406d605f325d234e4678bd57126a66a88841cb95bee2caeafdc6f138306 # pip sphinxcontrib-sass @ https://files.pythonhosted.org/packages/3f/ec/194f2dbe55b3fe0941b43286c21abb49064d9d023abfb99305c79ad77cad/sphinxcontrib_sass-0.3.5-py2.py3-none-any.whl#sha256=850c83a36ed2d2059562504ccf496ca626c9c0bb89ec642a2d9c42105704bef6 diff --git a/build_tools/circle/doc_min_dependencies_environment.yml b/build_tools/circle/doc_min_dependencies_environment.yml index 3424a9d931fc3..2e16632152d1f 100644 --- a/build_tools/circle/doc_min_dependencies_environment.yml +++ b/build_tools/circle/doc_min_dependencies_environment.yml @@ -8,7 +8,7 @@ dependencies: - numpy=1.22.0 # min - blas - scipy=1.8.0 # min - - cython=3.1.2 # min + - cython=3.0.10 # min - joblib - threadpoolctl - matplotlib=3.5.0 # min diff --git a/build_tools/circle/doc_min_dependencies_linux-64_conda.lock b/build_tools/circle/doc_min_dependencies_linux-64_conda.lock index 3168a6ccaeb31..8934e6f0f725a 100644 --- a/build_tools/circle/doc_min_dependencies_linux-64_conda.lock +++ b/build_tools/circle/doc_min_dependencies_linux-64_conda.lock @@ -1,13 +1,13 @@ # Generated by conda-lock. # platform: linux-64 -# input_hash: 1aec67c9ed6cd00477ef687dc63d6860b0f2dc3ee94a92cdc6daa87fa1dfbe8d +# input_hash: d07657e3ddf551b0cfcb8979d3525cd7b043f143170c33c4d33d4a4db2869281 @EXPLICIT +https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2#d7c89558ba9fa0495403155b64376d81 https://conda.anaconda.org/conda-forge/noarch/font-ttf-dejavu-sans-mono-2.37-hab24e00_0.tar.bz2#0c96522c6bdaed4b1566d11387caaf45 https://conda.anaconda.org/conda-forge/noarch/font-ttf-inconsolata-3.000-h77eed37_0.tar.bz2#34893075a5c9e55cdafac56607368fc6 https://conda.anaconda.org/conda-forge/noarch/font-ttf-source-code-pro-2.038-h77eed37_0.tar.bz2#4d59c254e01d9cde7957100457e2d5fb https://conda.anaconda.org/conda-forge/noarch/font-ttf-ubuntu-0.83-h77eed37_3.conda#49023d73832ef61042f6a237cb2687e7 https://conda.anaconda.org/conda-forge/noarch/kernel-headers_linux-64-4.18.0-he073ed8_8.conda#ff007ab0f0fdc53d245972bba8a6d40c -https://conda.anaconda.org/conda-forge/linux-64/mkl-include-2024.2.2-ha770c72_17.conda#c18fd07c02239a7eb744ea728db39630 https://conda.anaconda.org/conda-forge/noarch/python_abi-3.10-8_cp310.conda#05e00f3b21e88bb3d658ac700b2ce58c https://conda.anaconda.org/conda-forge/noarch/tzdata-2025b-h78e105d_0.conda#4222072737ccff51314b5ece9c7d6f5a https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2025.8.3-hbd8a1cb_0.conda#74784ee3d225fc3dca89edb635b4e5cc @@ -17,9 +17,8 @@ https://conda.anaconda.org/conda-forge/noarch/libgcc-devel_linux-64-14.3.0-h85bb https://conda.anaconda.org/conda-forge/linux-64/libglvnd-1.7.0-ha4b6fd6_2.conda#434ca7e50e40f4918ab701e3facd59a0 https://conda.anaconda.org/conda-forge/linux-64/libgomp-15.1.0-h767d61c_4.conda#3baf8976c96134738bba224e9ef6b1e5 https://conda.anaconda.org/conda-forge/noarch/libstdcxx-devel_linux-64-14.3.0-h85bb3a7_104.conda#c8d0b75a145e4cc3525df0343146c459 -https://conda.anaconda.org/conda-forge/linux-64/llvm-openmp-20.1.8-h4922eb0_1.conda#5d5099916a3659a46cca8f974d0455b9 https://conda.anaconda.org/conda-forge/noarch/sysroot_linux-64-2.28-h4ee821c_8.conda#1bad93f0aa428d618875ef3a588a889e -https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-3_kmp_llvm.conda#ee5c2118262e30b972bc0b4db8ef0ba5 +https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_gnu.tar.bz2#73aaf86a425cc6e73fcf236a5a46396d https://conda.anaconda.org/conda-forge/linux-64/binutils_impl_linux-64-2.44-h4bf12b8_1.conda#e45cfedc8ca5630e02c106ea36d2c5c6 https://conda.anaconda.org/conda-forge/noarch/fonts-conda-ecosystem-1-0.tar.bz2#fee5683a3f04bd15cbd8318b096a27ab https://conda.anaconda.org/conda-forge/linux-64/libegl-1.7.0-ha4b6fd6_2.conda#c151d5eb730e9b7480e6d48c0fc44048 @@ -46,7 +45,7 @@ https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-15.1.0-h8f9b012_4.cond https://conda.anaconda.org/conda-forge/linux-64/libwebp-base-1.6.0-hd42ef1d_0.conda#aea31d2e5b1091feca96fcfe945c3cf9 https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.3.1-hb9d3cd8_2.conda#edb0dca6bc32e4f4789199455a1dbeb8 https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.5-h2d0b736_3.conda#47e340acb35de30501a76c7c799c41d7 -https://conda.anaconda.org/conda-forge/linux-64/openssl-3.5.2-h26f9b46_0.conda#ffffb341206dd0dab0c36053c048d621 +https://conda.anaconda.org/conda-forge/linux-64/openssl-3.5.1-h7b32b05_0.conda#c87df2ab1448ba69169652ab9547082d https://conda.anaconda.org/conda-forge/linux-64/pthread-stubs-0.4-hb9d3cd8_1002.conda#b3c17d95b5a10c6e64a21fa17573e70e https://conda.anaconda.org/conda-forge/linux-64/rav1e-0.7.1-h8fae777_3.conda#2c42649888aac645608191ffdc80d13a https://conda.anaconda.org/conda-forge/linux-64/xorg-libice-1.1.2-hb9d3cd8_0.conda#fb901ff28063514abb6046c9ec2c4a45 @@ -55,6 +54,7 @@ https://conda.anaconda.org/conda-forge/linux-64/xorg-libxdmcp-1.1.5-hb9d3cd8_0.c https://conda.anaconda.org/conda-forge/linux-64/xorg-libxshmfence-1.3.3-hb9d3cd8_0.conda#9a809ce9f65460195777f2f2116bae02 https://conda.anaconda.org/conda-forge/linux-64/yaml-0.2.5-h280c20c_3.conda#a77f85f77be52ff59391544bfe73390a https://conda.anaconda.org/conda-forge/linux-64/attr-2.5.1-h166bdaf_1.tar.bz2#d9c69a24ad678ffce24c6543a0176b00 +https://conda.anaconda.org/conda-forge/linux-64/blis-0.9.0-h4ab18f5_2.conda#6f77ba1352b69c4a6f8a6d20def30e4e https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-h4bc722e_7.conda#62ee74e96c5ebb0af99386de58cf9553 https://conda.anaconda.org/conda-forge/linux-64/dav1d-1.2.1-hd590300_0.conda#418c6ca5929a611cbd69204907a83995 https://conda.anaconda.org/conda-forge/linux-64/gettext-tools-0.25.1-h3f43e3d_1.conda#a59c05d22bdcbb4e984bf0c021a2a02f @@ -93,7 +93,7 @@ https://conda.anaconda.org/conda-forge/linux-64/snappy-1.2.2-h03e3b7b_0.conda#3d https://conda.anaconda.org/conda-forge/linux-64/svt-av1-3.0.2-h5888daf_0.conda#0096882bd623e6cc09e8bf920fc8fb47 https://conda.anaconda.org/conda-forge/linux-64/tk-8.6.13-noxft_hd72426e_102.conda#a0116df4f4ed05c303811a837d5b39d8 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https://conda.anaconda.org/conda-forge/linux-64/libasprintf-devel-0.25.1-h3f43e3d_1.conda#fd9cf4a11d07f0ef3e44fc061611b1ed +https://conda.anaconda.org/conda-forge/linux-64/libblas-3.9.0-32_h66dfbfd_blis.conda#dca8fde8cc52d44049339be5ee888dda https://conda.anaconda.org/conda-forge/linux-64/libcap-2.75-h39aace5_0.conda#c44c16d6976d2aebbd65894d7741e67e https://conda.anaconda.org/conda-forge/linux-64/libfreetype6-2.13.3-h48d6fc4_1.conda#3c255be50a506c50765a93a6644f32fe https://conda.anaconda.org/conda-forge/linux-64/libgcrypt-lib-1.11.1-hb9d3cd8_0.conda#8504a291085c9fb809b66cabd5834307 @@ -127,13 +128,13 @@ https://conda.anaconda.org/conda-forge/linux-64/brotli-1.1.0-hb9d3cd8_3.conda#5d https://conda.anaconda.org/conda-forge/linux-64/brotli-python-1.1.0-py310hf71b8c6_3.conda#63d24a5dd21c738d706f91569dbd1892 https://conda.anaconda.org/conda-forge/noarch/certifi-2025.8.3-pyhd8ed1ab_0.conda#11f59985f49df4620890f3e746ed7102 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-https://conda.anaconda.org/conda-forge/linux-64/cython-3.1.2-py310had8cdd9_2.conda#be416b1d5ffef48c394cbbb04bc864ae +https://conda.anaconda.org/conda-forge/linux-64/cython-3.0.10-py310hc6cd4ac_0.conda#bd1d71ee240be36f1d85c86177d6964f https://conda.anaconda.org/conda-forge/noarch/docutils-0.21.2-pyhd8ed1ab_1.conda#24c1ca34138ee57de72a943237cde4cc https://conda.anaconda.org/conda-forge/noarch/execnet-2.1.1-pyhd8ed1ab_1.conda#a71efeae2c160f6789900ba2631a2c90 https://conda.anaconda.org/conda-forge/noarch/fsspec-2025.7.0-pyhd8ed1ab_0.conda#a31ce802cd0ebfce298f342c02757019 @@ -149,18 +150,20 @@ https://conda.anaconda.org/conda-forge/noarch/iniconfig-2.0.0-pyhd8ed1ab_1.conda https://conda.anaconda.org/conda-forge/linux-64/kiwisolver-1.4.8-py310h3788b33_1.conda#b70dd76da5231e6073fd44c42a1d78c5 https://conda.anaconda.org/conda-forge/linux-64/lcms2-2.17-h717163a_0.conda#000e85703f0fd9594c81710dd5066471 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-https://conda.anaconda.org/conda-forge/linux-64/matplotlib-3.10.5-py313h78bf25f_0.conda#0ca5238dd15d01f6609866bb370732e3 +https://conda.anaconda.org/conda-forge/linux-64/matplotlib-3.10.3-py313h78bf25f_0.conda#cc9324e614a297fdf23439d887d3513d https://conda.anaconda.org/conda-forge/linux-64/libarrow-substrait-19.0.1-h08228c5_3_cpu.conda#a58e4763af8293deaac77b63bc7804d8 https://conda.anaconda.org/conda-forge/linux-64/pyarrow-19.0.1-py313h78bf25f_0.conda#e8efe6998a383dd149787c83d3d6a92e diff --git a/build_tools/github/pymin_conda_forge_arm_linux-aarch64_conda.lock b/build_tools/github/pymin_conda_forge_arm_linux-aarch64_conda.lock index 689cd22cda440..1a523e0c7c762 100644 --- a/build_tools/github/pymin_conda_forge_arm_linux-aarch64_conda.lock +++ b/build_tools/github/pymin_conda_forge_arm_linux-aarch64_conda.lock @@ -1,6 +1,6 @@ # Generated by conda-lock. # platform: linux-aarch64 -# input_hash: f12646c755adbf5f02f95c5d07e868bf1570777923e737bc27273eb1a5e40cd7 +# input_hash: 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https://conda.anaconda.org/conda-forge/noarch/pluggy-1.6.0-pyhd8ed1ab_0.conda#7da7ccd349dbf6487a7778579d2bb971 https://conda.anaconda.org/conda-forge/noarch/pygments-2.19.2-pyhd8ed1ab_0.conda#6b6ece66ebcae2d5f326c77ef2c5a066 @@ -121,9 +121,9 @@ https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.3.0-pyhd8ed1ab_0. https://conda.anaconda.org/conda-forge/linux-aarch64/fonttools-4.59.0-py310h2d8da20_0.conda#5f93264842d77827a0dac712d0fd188e https://conda.anaconda.org/conda-forge/linux-aarch64/freetype-2.13.3-h8af1aa0_1.conda#71c4cbe1b384a8e7b56993394a435343 https://conda.anaconda.org/conda-forge/noarch/joblib-1.5.1-pyhd8ed1ab_0.conda#fb1c14694de51a476ce8636d92b6f42c -https://conda.anaconda.org/conda-forge/linux-aarch64/libcblas-3.9.0-33_hab92f65_openblas.conda#50c94d8f4030118d35fd9fd35a1ed9a2 +https://conda.anaconda.org/conda-forge/linux-aarch64/libcblas-3.9.0-32_hab92f65_openblas.conda#2f02a3ea0960118a0a8d45cdd348b039 https://conda.anaconda.org/conda-forge/linux-aarch64/libgl-1.7.0-hd24410f_2.conda#0d00176464ebb25af83d40736a2cd3bb -https://conda.anaconda.org/conda-forge/linux-aarch64/liblapack-3.9.0-33_h411afd4_openblas.conda#2a303c1821ca533a324c5617540cc8a2 +https://conda.anaconda.org/conda-forge/linux-aarch64/liblapack-3.9.0-32_h411afd4_openblas.conda#8d143759d5a22e9975a996bd13eeb8f0 https://conda.anaconda.org/conda-forge/linux-aarch64/libllvm20-20.1.8-h2b567e5_0.conda#b2ae284ba64d978316177c9ab68e3da5 https://conda.anaconda.org/conda-forge/linux-aarch64/libxkbcommon-1.10.0-hbab7b08_0.conda#36cd1db31e923c6068b7e0e6fce2cd7b https://conda.anaconda.org/conda-forge/linux-aarch64/libxslt-1.1.43-h4552c8e_0.conda#fcf40dcbe5841e9b125ca98858e24205 @@ -142,18 +142,18 @@ https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libxxf86vm-1.1.6-h86ec https://conda.anaconda.org/conda-forge/linux-aarch64/fontconfig-2.15.0-h8dda3cd_1.conda#112b71b6af28b47c624bcbeefeea685b https://conda.anaconda.org/conda-forge/linux-aarch64/libclang-cpp20.1-20.1.8-default_hf07bfb7_0.conda#c7a64cd7dd2bf72956d2f3b1b1aa13a7 https://conda.anaconda.org/conda-forge/linux-aarch64/libclang13-20.1.8-default_h173080d_0.conda#c9a9e8c0477f9c915f106fd6254b2a9c -https://conda.anaconda.org/conda-forge/linux-aarch64/liblapacke-3.9.0-33_hc659ca5_openblas.conda#3c72eb1e2f35cdc37583cd8e8d210d4f +https://conda.anaconda.org/conda-forge/linux-aarch64/liblapacke-3.9.0-32_hc659ca5_openblas.conda#1cd2cbdb80386aae8c584ab9f1175ca6 https://conda.anaconda.org/conda-forge/linux-aarch64/libpq-17.5-hf590da8_0.conda#b5a01e5aa04651ccf5865c2d029affa3 https://conda.anaconda.org/conda-forge/noarch/meson-python-0.18.0-pyh70fd9c4_0.conda#576c04b9d9f8e45285fb4d9452c26133 https://conda.anaconda.org/conda-forge/linux-aarch64/numpy-2.2.6-py310h6e5608f_0.conda#9e9f1f279eb02c41bda162a42861adc0 https://conda.anaconda.org/conda-forge/noarch/pytest-8.4.1-pyhd8ed1ab_0.conda#a49c2283f24696a7b30367b7346a0144 https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libxtst-1.2.5-h57736b2_3.conda#c05698071b5c8e0da82a282085845860 -https://conda.anaconda.org/conda-forge/linux-aarch64/blas-devel-3.9.0-33_h9678261_openblas.conda#1f7e6dca036b2fa9d9e2f7f558b1a467 +https://conda.anaconda.org/conda-forge/linux-aarch64/blas-devel-3.9.0-32_h9678261_openblas.conda#9c18808e64a8557732e664eac92df74d https://conda.anaconda.org/conda-forge/linux-aarch64/cairo-1.18.4-h83712da_0.conda#cd55953a67ec727db5dc32b167201aa6 https://conda.anaconda.org/conda-forge/linux-aarch64/contourpy-1.3.2-py310hf54e67a_0.conda#779694434d1f0a67c5260db76b7b7907 https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.8.0-pyhd8ed1ab_0.conda#8375cfbda7c57fbceeda18229be10417 https://conda.anaconda.org/conda-forge/linux-aarch64/scipy-1.15.2-py310hf37559f_0.conda#5c9b72f10d2118d943a5eaaf2f396891 -https://conda.anaconda.org/conda-forge/linux-aarch64/blas-2.133-openblas.conda#a92a7666159cc612f8489943eec6edcf +https://conda.anaconda.org/conda-forge/linux-aarch64/blas-2.132-openblas.conda#2c1e3662c8c5e7b92a49fd6372bb659f https://conda.anaconda.org/conda-forge/linux-aarch64/harfbuzz-11.3.3-h81c6d19_0.conda#68c8991c65d01f682819950d969f266a https://conda.anaconda.org/conda-forge/linux-aarch64/matplotlib-base-3.10.5-py310hc06f52e_0.conda#6b7cfe985a25928b86a127453ffec2e2 https://conda.anaconda.org/conda-forge/linux-aarch64/qt6-main-6.9.1-haa40e84_2.conda#b388e58798370884d5226b2ae9209edc diff --git a/pyproject.toml b/pyproject.toml index 9415a7ee99d64..aa69f85073b5c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -43,7 +43,7 @@ tracker = "https://github.com/scikit-learn/scikit-learn/issues" "release notes" = "https://scikit-learn.org/stable/whats_new" [project.optional-dependencies] -build = ["numpy>=1.22.0", "scipy>=1.8.0", "cython>=3.1.2", "meson-python>=0.17.1"] +build = ["numpy>=1.22.0", "scipy>=1.8.0", "cython>=3.0.10", "meson-python>=0.17.1"] install = ["numpy>=1.22.0", "scipy>=1.8.0", "joblib>=1.2.0", "threadpoolctl>=3.1.0"] benchmark = ["matplotlib>=3.5.0", "pandas>=1.4.0", "memory_profiler>=0.57.0"] docs = [ @@ -98,7 +98,7 @@ build-backend = "mesonpy" # Minimum requirements for the build system to execute. requires = [ "meson-python>=0.16.0", - "Cython>=3.1.2", + "Cython>=3.0.10", "numpy>=2", "scipy>=1.8.0", ] diff --git a/sklearn/_min_dependencies.py b/sklearn/_min_dependencies.py index 7f5e1c52f044d..ac58820686914 100644 --- a/sklearn/_min_dependencies.py +++ b/sklearn/_min_dependencies.py @@ -12,7 +12,7 @@ JOBLIB_MIN_VERSION = "1.2.0" THREADPOOLCTL_MIN_VERSION = "3.1.0" PYTEST_MIN_VERSION = "7.1.2" -CYTHON_MIN_VERSION = "3.1.2" +CYTHON_MIN_VERSION = "3.0.10" # 'build' and 'install' is included to have structured metadata for CI. From e71e89bb81e8cbf0adb8330c04d7f719be041ddc Mon Sep 17 00:00:00 2001 From: Christian Lorentzen Date: Sun, 17 Aug 2025 19:11:56 +0200 Subject: [PATCH 14/18] MNT struct of 2 float64_t instead of templated ctuples of Cython 3.1 --- sklearn/linear_model/_cd_fast.pyx | 28 +++++++++++++++++++++------- 1 file changed, 21 insertions(+), 7 deletions(-) diff --git a/sklearn/linear_model/_cd_fast.pyx b/sklearn/linear_model/_cd_fast.pyx index 21c3c2987aea2..f9670279541ca 100644 --- a/sklearn/linear_model/_cd_fast.pyx +++ b/sklearn/linear_model/_cd_fast.pyx @@ -12,10 +12,16 @@ from ..utils._cython_blas cimport ( _axpy, _dot, _asum, _gemv, _nrm2, _copy, _scal ) from ..utils._cython_blas cimport ColMajor, Trans, NoTrans -from ..utils._typedefs cimport uint8_t, uint32_t +from ..utils._typedefs cimport float64_t, uint8_t, uint32_t from ..utils._random cimport our_rand_r +# Struct to return 2 float64_t +# TODO(Cython 3.1): remove and use templated ctuples, i.e. return val1, val2 +ctypedef struct float64_pair: + float64_t val1 + float64_t val2 + # The following two functions are shamelessly copied from the tree code. cdef enum: @@ -98,7 +104,7 @@ message_ridge = ( ) -cdef (floating, floating) gap_enet( +cdef float64_pair gap_enet( int n_samples, int n_features, const floating[::1] w, @@ -111,13 +117,14 @@ cdef (floating, floating) gap_enet( bint positive, ) noexcept nogil: """Compute dual gap for use in enet_coordinate_descent.""" - cdef floating gap = 0.0 - cdef floating dual_norm_XtA + cdef float64_t gap = 0.0 + cdef float64_t dual_norm_XtA cdef floating R_norm2 cdef floating w_norm2 = 0.0 cdef floating l1_norm cdef floating A_norm2 cdef floating const_ + cdef float64_pair out # XtA = X.T @ R - beta * w _copy(n_features, &w[0], 1, &XtA[0], 1) @@ -152,7 +159,9 @@ cdef (floating, floating) gap_enet( - const_ * _dot(n_samples, &R[0], 1, &y[0], 1) # R @ y + 0.5 * beta * (1 + const_ ** 2) * w_norm2 ) - return gap, dual_norm_XtA + out.val1 = gap + out.val2 = dual_norm_XtA + return out def enet_coordinate_descent( @@ -216,6 +225,7 @@ def enet_coordinate_descent( cdef floating gap = tol + 1.0 cdef floating d_w_tol = tol cdef floating dual_norm_XtA + cdef float64_pair gap_and_dual_norm cdef unsigned int n_active = n_features cdef uint32_t[::1] active_set # TODO: use binset insteaf of array of bools @@ -244,9 +254,11 @@ def enet_coordinate_descent( tol *= _dot(n_samples, &y[0], 1, &y[0], 1) # Check convergence before entering the main loop. - gap, dual_norm_XtA = gap_enet( + gap_and_dual_norm = gap_enet( n_samples, n_features, w, alpha, beta, X, y, R, XtA, positive ) + gap = gap_and_dual_norm.val1 + dual_norm_XtA = gap_and_dual_norm.val2 if gap < tol: with gil: return np.asarray(w), gap, tol, 0 @@ -320,9 +332,11 @@ def enet_coordinate_descent( # the biggest coordinate update of this iteration was smaller # than the tolerance: check the duality gap as ultimate # stopping criterion - gap, dual_norm_XtA = gap_enet( + gap_and_dual_norm = gap_enet( n_samples, n_features, w, alpha, beta, X, y, R, XtA, positive ) + gap = gap_and_dual_norm.val1 + dual_norm_XtA = gap_and_dual_norm.val2 if gap < tol: # return if we reached desired tolerance From e1cd8f0aee481017e651f8107b6afa64ab6a113c Mon Sep 17 00:00:00 2001 From: Christian Lorentzen Date: Mon, 18 Aug 2025 09:16:22 +0200 Subject: [PATCH 15/18] FIX gap <= tol instead of gap < tol --- sklearn/linear_model/_cd_fast.pyx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/sklearn/linear_model/_cd_fast.pyx b/sklearn/linear_model/_cd_fast.pyx index 853c6b9d7e8c5..232f926544a21 100644 --- a/sklearn/linear_model/_cd_fast.pyx +++ b/sklearn/linear_model/_cd_fast.pyx @@ -207,7 +207,7 @@ def enet_coordinate_descent( The final stopping criterion is based on the duality gap - tol ||y||_2^2 < G(w, v) + tol ||y||_2^2 <= G(w, v) The tolerance here is multiplied by ||y||_2^2 to have an inequality that scales the same on both sides and because one has G(0, 0) = 1/2 ||y||_2^2. @@ -299,7 +299,7 @@ def enet_coordinate_descent( ) gap = gap_and_dual_norm.val1 dual_norm_XtA = gap_and_dual_norm.val2 - if gap < tol: + if gap <= tol: with gil: return np.asarray(w), gap, tol, 0 From 47d6bf3c348e8a282c4fa8187c6f7708327e2d8d Mon Sep 17 00:00:00 2001 From: Christian Lorentzen Date: Mon, 18 Aug 2025 09:23:41 +0200 Subject: [PATCH 16/18] CLN remove self._do_screening * default to do_screening=True in enet_path --- sklearn/linear_model/_coordinate_descent.py | 11 +---------- 1 file changed, 1 insertion(+), 10 deletions(-) diff --git a/sklearn/linear_model/_coordinate_descent.py b/sklearn/linear_model/_coordinate_descent.py index d74b36ba07f80..f31fa1c3ee3a8 100644 --- a/sklearn/linear_model/_coordinate_descent.py +++ b/sklearn/linear_model/_coordinate_descent.py @@ -560,7 +560,7 @@ def enet_path( max_iter = params.pop("max_iter", 1000) random_state = params.pop("random_state", None) selection = params.pop("selection", "cyclic") - do_screening = params.pop("do_screening", False) + do_screening = params.pop("do_screening", True) if len(params) > 0: raise ValueError("Unexpected parameters in params", params.keys()) @@ -947,7 +947,6 @@ def __init__( self.positive = positive self.random_state = random_state self.selection = selection - self._do_screening = True @_fit_context(prefer_skip_nested_validation=True) def fit(self, X, y, sample_weight=None, check_input=True): @@ -1125,7 +1124,6 @@ def fit(self, X, y, sample_weight=None, check_input=True): random_state=self.random_state, selection=self.selection, sample_weight=sample_weight, - do_screening=self._do_screening, ) coef_[k] = this_coef[:, 0] dual_gaps_[k] = this_dual_gap[0] @@ -1582,7 +1580,6 @@ def __init__( self.positive = positive self.random_state = random_state self.selection = selection - self._do_screening = True @abstractmethod def _get_estimator(self): @@ -1769,9 +1766,6 @@ def fit(self, X, y, sample_weight=None, **params): path_params.pop("cv", None) path_params.pop("n_jobs", None) - if hasattr(self, "_do_screening"): - path_params["do_screening"] = self._do_screening - n_l1_ratio = len(l1_ratios) check_scalar_alpha = partial( @@ -1904,8 +1898,6 @@ def fit(self, X, y, sample_weight=None, **params): model.alpha = best_alpha model.l1_ratio = best_l1_ratio model.copy_X = copy_X - if hasattr(model, "_do_screening"): - model._do_screening = self._do_screening precompute = getattr(self, "precompute", None) if isinstance(precompute, str) and precompute == "auto": model.precompute = False @@ -2466,7 +2458,6 @@ def __init__( self.positive = positive self.random_state = random_state self.selection = selection - self._do_screening = True def _get_estimator(self): return ElasticNet() From 537b41a093b52b84027ab667ea43fdd44732c673 Mon Sep 17 00:00:00 2001 From: Christian Lorentzen Date: Mon, 18 Aug 2025 16:11:59 +0200 Subject: [PATCH 17/18] Revert "MNT struct of 2 float64_t instead of templated ctuples of Cython 3.1" This reverts commit e71e89bb81e8cbf0adb8330c04d7f719be041ddc. --- sklearn/linear_model/_cd_fast.pyx | 28 +++++++--------------------- 1 file changed, 7 insertions(+), 21 deletions(-) diff --git a/sklearn/linear_model/_cd_fast.pyx b/sklearn/linear_model/_cd_fast.pyx index 232f926544a21..bcd690fe24e7a 100644 --- a/sklearn/linear_model/_cd_fast.pyx +++ b/sklearn/linear_model/_cd_fast.pyx @@ -12,16 +12,10 @@ from ..utils._cython_blas cimport ( _axpy, _dot, _asum, _gemv, _nrm2, _copy, _scal ) from ..utils._cython_blas cimport ColMajor, Trans, NoTrans -from ..utils._typedefs cimport float64_t, uint8_t, uint32_t +from ..utils._typedefs cimport uint8_t, uint32_t from ..utils._random cimport our_rand_r -# Struct to return 2 float64_t -# TODO(Cython 3.1): remove and use templated ctuples, i.e. return val1, val2 -ctypedef struct float64_pair: - float64_t val1 - float64_t val2 - # The following two functions are shamelessly copied from the tree code. cdef enum: @@ -104,7 +98,7 @@ message_ridge = ( ) -cdef float64_pair gap_enet( +cdef (floating, floating) gap_enet( int n_samples, int n_features, const floating[::1] w, @@ -117,14 +111,13 @@ cdef float64_pair gap_enet( bint positive, ) noexcept nogil: """Compute dual gap for use in enet_coordinate_descent.""" - cdef float64_t gap = 0.0 - cdef float64_t dual_norm_XtA + cdef floating gap = 0.0 + cdef floating dual_norm_XtA cdef floating R_norm2 cdef floating w_norm2 = 0.0 cdef floating l1_norm cdef floating A_norm2 cdef floating const_ - cdef float64_pair out # XtA = X.T @ R - beta * w _copy(n_features, &w[0], 1, &XtA[0], 1) @@ -159,9 +152,7 @@ cdef float64_pair gap_enet( - const_ * _dot(n_samples, &R[0], 1, &y[0], 1) # R @ y + 0.5 * beta * (1 + const_ ** 2) * w_norm2 ) - out.val1 = gap - out.val2 = dual_norm_XtA - return out + return gap, dual_norm_XtA def enet_coordinate_descent( @@ -265,7 +256,6 @@ def enet_coordinate_descent( cdef floating gap = tol + 1.0 cdef floating d_w_tol = tol cdef floating dual_norm_XtA - cdef float64_pair gap_and_dual_norm cdef unsigned int n_active = n_features cdef uint32_t[::1] active_set # TODO: use binset insteaf of array of bools @@ -294,11 +284,9 @@ def enet_coordinate_descent( tol *= _dot(n_samples, &y[0], 1, &y[0], 1) # Check convergence before entering the main loop. - gap_and_dual_norm = gap_enet( + gap, dual_norm_XtA = gap_enet( n_samples, n_features, w, alpha, beta, X, y, R, XtA, positive ) - gap = gap_and_dual_norm.val1 - dual_norm_XtA = gap_and_dual_norm.val2 if gap <= tol: with gil: return np.asarray(w), gap, tol, 0 @@ -372,11 +360,9 @@ def enet_coordinate_descent( # the biggest coordinate update of this iteration was smaller # than the tolerance: check the duality gap as ultimate # stopping criterion - gap_and_dual_norm = gap_enet( + gap, dual_norm_XtA = gap_enet( n_samples, n_features, w, alpha, beta, X, y, R, XtA, positive ) - gap = gap_and_dual_norm.val1 - dual_norm_XtA = gap_and_dual_norm.val2 if gap <= tol: # return if we reached desired tolerance From b17137360968a71120113ebdb9c864062d9926d5 Mon Sep 17 00:00:00 2001 From: Christian Lorentzen Date: Thu, 21 Aug 2025 17:10:10 +0200 Subject: [PATCH 18/18] MNT fix code comments R += w[j] * X[:,j] --- sklearn/linear_model/_cd_fast.pyx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/sklearn/linear_model/_cd_fast.pyx b/sklearn/linear_model/_cd_fast.pyx index bcd690fe24e7a..b5e89cf5716c4 100644 --- a/sklearn/linear_model/_cd_fast.pyx +++ b/sklearn/linear_model/_cd_fast.pyx @@ -307,7 +307,7 @@ def enet_coordinate_descent( excluded_set[j] = 0 n_active += 1 else: - # R += w_j * X[:,j] + # R += w[j] * X[:,j] _axpy(n_samples, w[j], &X[0, j], 1, &R[0], 1) w[j] = 0 excluded_set[j] = 1 @@ -382,7 +382,7 @@ def enet_coordinate_descent( excluded_set[j] = 0 n_active += 1 else: - # R += w_j * X[:,j] + # R += w[j] * X[:,j] _axpy(n_samples, w[j], &X[0, j], 1, &R[0], 1) w[j] = 0 excluded_set[j] = 1