Merge pull request #455 from sebp/upgrade-sphinx

Upgrade sphinx

doc/_static/custom.css

@@ -23,7 +23,7 @@ table {
     font-weight: var(--pst-font-weight-heading) !important;
 }
 
-.overview-grid .sd-card-title .fas {
+.overview-grid .sd-card-title svg[data-prefix="fas"] {
     position: absolute;
     right: 1.2rem;
     line-height: 1.4;

doc/conf.py

@@ -175,6 +175,14 @@
     "secondary_sidebar_items": ["page-toc"],
     "navbar_end": ["theme-switcher.html", "navbar-github-links.html"],
     "navbar_persistent": ["search-field.html"],
+    "navbar_align": "left",
+    "navigation_with_keys": False,
+}
+
+html_sidebars = {
+    "cite": [],
+    "contributing": [],
+    "install": [],
 }
 
 # Add any paths that contain custom themes here, relative to this directory.

doc/release_notes/v0.1.rst

@@ -0,0 +1,11 @@
+.. _release_notes_0_1:
+
+What's new in 0.1
+=================
+
+scikit-survival 0.1 (2016-12-29)
+--------------------------------
+
+This is the initial release of scikit-survival.
+It combines the `implementation of survival support vector machines <https://github.com/tum-camp/survival-support-vector-machine>`_
+with the code used in the `Prostate Cancer DREAM challenge <https://f1000research.com/articles/5-2676/>`_.

doc/release_notes/v0.10.rst

@@ -0,0 +1,27 @@
+.. _release_notes_0_10:
+
+What's new in 0.10
+==================
+
+scikit-survival 0.10 (2019-09-02)
+---------------------------------
+
+This release adds the `ties` argument to :class:`sksurv.linear_model.CoxPHSurvivalAnalysis`
+to choose between Breslow's and Efron's likelihood in the presence of tied event times.
+Moreover, :func:`sksurv.compare.compare_survival` has been added, which implements
+the log-rank hypothesis test for comparing the survival function of 2 or more groups.
+
+Enhancements
+^^^^^^^^^^^^
+
+- Update API doc of predict function of boosting estimators (#75).
+- Clarify documentation for GradientBoostingSurvivalAnalysis (#78).
+- Implement Efron's likelihood for handling tied event times.
+- Implement log-rank test for comparing survival curves.
+- Add support for scipy 1.3.1 (#66).
+
+Bug fixes
+^^^^^^^^^
+
+- Re-add `baseline_survival_` and `cum_baseline_hazard_` attributes
+  to :class:`sksurv.linear_model.CoxPHSurvivalAnalysis` (#76).

doc/release_notes/v0.11.rst

@@ -0,0 +1,38 @@
+.. _release_notes_0_11:
+
+What's new in 0.11
+==================
+
+scikit-survival 0.11 (2019-12-21)
+---------------------------------
+
+This release adds :class:`sksurv.tree.SurvivalTree` and :class:`sksurv.ensemble.RandomSurvivalForest`,
+which are based on the log-rank split criterion.
+It also adds the OSQP solver as option to :class:`sksurv.svm.MinlipSurvivalAnalysis`
+and :class:`sksurv.svm.HingeLossSurvivalSVM`, which will replace the now deprecated
+`cvxpy` and `cvxopt` options in a future release.
+
+This release removes support for sklearn 0.20 and requires sklearn 0.21.
+
+Deprecations
+^^^^^^^^^^^^
+
+- The `cvxpy` and `cvxopt` options for `solver` in :class:`sksurv.svm.MinlipSurvivalAnalysis`
+  and :class:`sksurv.svm.HingeLossSurvivalSVM` are deprecated and will be removed in a future
+  version. Choosing `osqp` is the preferred option now.
+
+Enhancements
+^^^^^^^^^^^^
+
+- Add support for pandas 0.25.
+- Add OSQP solver option to :class:`sksurv.svm.MinlipSurvivalAnalysis` and
+  :class:`sksurv.svm.HingeLossSurvivalSVM` which has no additional dependencies.
+- Fix issue when using cvxpy 1.0.16 or later.
+- Explicitly specify utf-8 encoding when reading README.rst (#89).
+- Add :class:`sksurv.tree.SurvivalTree` and :class:`sksurv.ensemble.RandomSurvivalForest` (#90).
+
+Bug fixes
+^^^^^^^^^
+
+- Exclude Cython-generated files from source distribution because
+  they are not forward compatible.

doc/release_notes/v0.12.rst

@@ -0,0 +1,39 @@
+.. _release_notes_0_12:
+
+What's new in 0.12
+==================
+
+scikit-survival 0.12 (2020-04-15)
+---------------------------------
+
+This release adds support for scikit-learn 0.22, thereby dropping support for
+older versions. Moreover, the regularization strength of the ridge penalty
+in :class:`sksurv.linear_model.CoxPHSurvivalAnalysis` can now be set per
+feature. If you want one or more features to enter the model unpenalized,
+set the corresponding penalty weights to zero.
+Finally, :class:`sklearn.pipeline.Pipeline` will now be automatically patched
+to add support for `predict_cumulative_hazard_function` and `predict_survival_function`
+if the underlying estimator supports it.
+
+Deprecations
+^^^^^^^^^^^^
+
+- Add scikit-learn's deprecation of `presort` in :class:`sksurv.tree.SurvivalTree` and
+  :class:`sksurv.ensemble.GradientBoostingSurvivalAnalysis`.
+- Add warning that default `alpha_min_ratio` in :class:`sksurv.linear_model.CoxnetSurvivalAnalysis`
+  will depend on the ratio of the number of samples to the number of features
+  in the future (#41).
+
+Enhancements
+^^^^^^^^^^^^
+
+- Add references to API doc of :class:`sksurv.ensemble.GradientBoostingSurvivalAnalysis` (#91).
+- Add support for pandas 1.0 (#100).
+- Add `ccp_alpha` parameter for
+  `Minimal Cost-Complexity Pruning <https://scikit-learn.org/stable/modules/tree.html#minimal-cost-complexity-pruning>`_
+  to :class:`sksurv.ensemble.GradientBoostingSurvivalAnalysis`.
+- Patch :class:`sklearn.pipeline.Pipeline` to add support for
+  `predict_cumulative_hazard_function` and `predict_survival_function`
+  if the underlying estimator supports it.
+- Allow per-feature regularization for :class:`sksurv.linear_model.CoxPHSurvivalAnalysis` (#102).
+- Clarify API docs of :func:`sksurv.metrics.concordance_index_censored` (#96).

doc/release_notes/v0.13.rst

@@ -0,0 +1,94 @@
+.. _release_notes_0_13:
+
+What's new in 0.13
+==================
+
+scikit-survival 0.13.1 (2020-07-04)
+-----------------------------------
+
+This release fixes warnings that were introduced with 0.13.0.
+
+Bug fixes
+^^^^^^^^^
+
+- Explicitly pass ``return_array=True`` in :meth:`sksurv.tree.SurvivalTree.predict`
+  to avoid FutureWarning.
+- Fix error when fitting :class:`sksurv.tree.SurvivalTree` with non-float
+  dtype for time (#127).
+- Fix RuntimeWarning: invalid value encountered in true_divide
+  in :func:`sksurv.nonparametric.kaplan_meier_estimator`.
+- Fix PendingDeprecationWarning about use of matrix when fitting
+  :class:`sksurv.svm.FastSurvivalSVM` if optimizer is `PRSVM` or `simple`.
+
+
+scikit-survival 0.13.0 (2020-06-28)
+-----------------------------------
+
+The highlights of this release include the addition of
+:func:`sksurv.metrics.brier_score` and
+:func:`sksurv.metrics.integrated_brier_score`
+and compatibility with scikit-learn 0.23.
+
+`predict_survival_function` and `predict_cumulative_hazard_function`
+of :class:`sksurv.ensemble.RandomSurvivalForest` and
+:class:`sksurv.tree.SurvivalTree` can now return an array of
+:class:`sksurv.functions.StepFunction`, similar
+to :class:`sksurv.linear_model.CoxPHSurvivalAnalysis`
+by specifying ``return_array=False``. This will be the default
+behavior starting with 0.14.0.
+
+Note that this release fixes a bug in estimating
+inverse probability of censoring weights (IPCW), which will
+affect all estimators relying on IPCW.
+
+Enhancements
+^^^^^^^^^^^^
+
+- Make build system compatible with PEP-517/518.
+- Added :func:`sksurv.metrics.brier_score` and
+  :func:`sksurv.metrics.integrated_brier_score` (#101).
+- :class:`sksurv.functions.StepFunction` can now be evaluated at multiple points
+  in a single call.
+- Update documentation on usage of `predict_survival_function` and
+  `predict_cumulative_hazard_function` (#118).
+- The default value of `alpha_min_ratio` of
+  :class:`sksurv.linear_model.CoxnetSurvivalAnalysis` will now depend
+  on the `n_samples/n_features` ratio.
+  If ``n_samples > n_features``, the default value is 0.0001
+  If ``n_samples <= n_features``, the default value is 0.01.
+- Add support for scikit-learn 0.23 (#119).
+
+Deprecations
+^^^^^^^^^^^^
+
+- `predict_survival_function` and `predict_cumulative_hazard_function`
+  of :class:`sksurv.ensemble.RandomSurvivalForest` and
+  :class:`sksurv.tree.SurvivalTree` will return an array of
+  :class:`sksurv.functions.StepFunction` in the future
+  (as :class:`sksurv.linear_model.CoxPHSurvivalAnalysis` does).
+  For the old behavior, use `return_array=True`.
+
+Bug fixes
+^^^^^^^^^
+
+- Fix deprecation of importing joblib via sklearn.
+- Fix estimation of censoring distribution for tied times with events.
+  When estimating the censoring distribution,
+  by specifying ``reverse=True`` when calling
+  :func:`sksurv.nonparametric.kaplan_meier_estimator`,
+  we now consider events to occur before censoring.
+  For tied time points with an event, those
+  with an event are not considered at risk anymore and subtracted from
+  the denominator of the Kaplan-Meier estimator.
+  The change affects all functions relying on inverse probability
+  of censoring weights, namely:
+
+  - :class:`sksurv.nonparametric.CensoringDistributionEstimator`
+  - :func:`sksurv.nonparametric.ipc_weights`
+  - :class:`sksurv.linear_model.IPCRidge`
+  - :func:`sksurv.metrics.cumulative_dynamic_auc`
+  - :func:`sksurv.metrics.concordance_index_ipcw`
+
+- Throw an exception when trying to estimate c-index from incomparable data (#117).
+- Estimators in ``sksurv.svm`` will now throw an
+  exception when trying to fit a model to data with incomparable pairs.

doc/release_notes/v0.14.rst

@@ -0,0 +1,49 @@
+.. _release_notes_0_14:
+
+What's new in 0.14
+==================
+
+scikit-survival 0.14.0 (2020-10-07)
+-----------------------------------
+
+This release features a complete overhaul of the :doc:`documentation <../index>`.
+It features a new visual design, and the inclusion of several interactive notebooks
+in the :ref:`User Guide`.
+
+In addition, it includes important bug fixes.
+It fixes several bugs in :class:`sksurv.linear_model.CoxnetSurvivalAnalysis`
+where ``predict``, ``predict_survival_function``, and ``predict_cumulative_hazard_function``
+returned wrong values if features of the training data were not centered.
+Moreover, the `score` function of :class:`sksurv.ensemble.ComponentwiseGradientBoostingSurvivalAnalysis`
+and :class:`sksurv.ensemble.GradientBoostingSurvivalAnalysis` will now
+correctly compute the concordance index if ``loss='ipcwls'`` or ``loss='squared'``.
+
+Bug fixes
+^^^^^^^^^
+
+- :func:`sksurv.column.standardize` modified data in-place. Data is now always copied.
+- :func:`sksurv.column.standardize` works with integer numpy arrays now.
+- :func:`sksurv.column.standardize` used biased standard deviation for numpy arrays (``ddof=0``),
+  but unbiased standard deviation for pandas objects (``ddof=1``). It always uses ``ddof=1`` now.
+  Therefore, the output, if the input is a numpy array, will differ from that of previous versions.
+- Fixed :meth:`sksurv.linear_model.CoxnetSurvivalAnalysis.predict_survival_function`
+  and :meth:`sksurv.linear_model.CoxnetSurvivalAnalysis.predict_cumulative_hazard_function`,
+  which returned wrong values if features of training data were not already centered.
+  This adds an ``offset_`` attribute that accounts for non-centered data and is added to the
+  predicted risk score. Therefore, the outputs of ``predict``, ``predict_survival_function``,
+  and ``predict_cumulative_hazard_function`` will be different to previous versions for
+  non-centered data (#139).
+- Rescale coefficients of :class:`sksurv.linear_model.CoxnetSurvivalAnalysis` if
+  `normalize=True`.
+- Fix `score` function of :class:`sksurv.ensemble.ComponentwiseGradientBoostingSurvivalAnalysis`
+  and :class:`sksurv.ensemble.GradientBoostingSurvivalAnalysis` if ``loss='ipcwls'`` or ``loss='squared'``
+  is used. Previously, it returned ``1.0 - true_cindex``.
+
+Enhancements
+^^^^^^^^^^^^
+
+- Add :func:`sksurv.show_versions` that prints the version of all dependencies.
+- Add support for pandas 1.1
+- Include interactive notebooks in documentation on readthedocs.
+- Add user guide on `penalized Cox models <user_guide/coxnet.ipynb>`_.
+- Add user guide on `gradient boosted models <user_guide/boosting.ipynb>`_.

doc/release_notes/v0.15.rst

@@ -0,0 +1,68 @@
+.. _release_notes_0_15:
+
+What's new in 0.15
+==================
+
+scikit-survival 0.15.0 (2021-03-20)
+-----------------------------------
+
+This release adds support for scikit-learn 0.24 and Python 3.9.
+scikit-survival now requires at least pandas 0.25 and scikit-learn 0.24.
+Moreover, if :class:`sksurv.ensemble.GradientBoostingSurvivalAnalysis`.
+or :class:`sksurv.ensemble.ComponentwiseGradientBoostingSurvivalAnalysis`
+are fit with ``loss='coxph'``,   `predict_cumulative_hazard_function` and
+`predict_survival_function` are now available.
+:func:`sksurv.metrics.cumulative_dynamic_auc` now supports evaluating
+time-dependent predictions, for instance for a :class:`sksurv.ensemble.RandomSurvivalForest`
+as illustrated in the
+:ref:`User Guide </user_guide/evaluating-survival-models.ipynb#Using-Time-dependent-Risk-Scores>`.
+
+Bug fixes
+^^^^^^^^^
+- Allow passing pandas data frames to all ``fit`` and ``predict`` methods (#148).
+- Allow sparse matrices to be passed to
+  :meth:`sksurv.ensemble.GradientBoostingSurvivalAnalysis.predict`.
+- Fix example in user guide using GridSearchCV to determine alphas for CoxnetSurvivalAnalysis (#186).
+
+Enhancements
+^^^^^^^^^^^^
+- Add score method to :class:`sksurv.meta.Stacking`,
+  :class:`sksurv.meta.EnsembleSelection`, and
+  :class:`sksurv.meta.EnsembleSelectionRegressor` (#151).
+- Add support for `predict_cumulative_hazard_function` and
+  `predict_survival_function` to :class:`sksurv.ensemble.GradientBoostingSurvivalAnalysis`.
+  and :class:`sksurv.ensemble.ComponentwiseGradientBoostingSurvivalAnalysis`
+  if model was fit with ``loss='coxph'``.
+- Add support for time-dependent predictions to :func:`sksurv.metrics.cumulative_dynamic_auc`
+  See the :ref:`User Guide </user_guide/evaluating-survival-models.ipynb#Using-Time-dependent-Risk-Scores>`
+  for an example (#134).
+
+Backwards incompatible changes
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+- The score method of :class:`sksurv.linear_model.IPCRidge`,
+  :class:`sksurv.svm.FastSurvivalSVM`, and :class:`sksurv.svm.FastKernelSurvivalSVM`
+  (if ``rank_ratio`` is smaller than 1) now converts predictions on log(time) scale
+  to risk scores prior to computing the concordance index.
+- Support for cvxpy and cvxopt solver in :class:`sksurv.svm.MinlipSurvivalAnalysis`
+  and :class:`sksurv.svm.HingeLossSurvivalSVM` has been dropped. The default solver
+  is now ECOS, which was used by cvxpy (the previous default) internally. Therefore,
+  results should be identical.
+- Dropped the ``presort`` argument from :class:`sksurv.tree.SurvivalTree`
+  and :class:`sksurv.ensemble.GradientBoostingSurvivalAnalysis`.
+- The ``X_idx_sorted`` argument in :meth:`sksurv.tree.SurvivalTree.fit`
+  has been deprecated in scikit-learn 0.24 and has no effect now.
+- `predict_cumulative_hazard_function` and
+  `predict_survival_function` of :class:`sksurv.ensemble.RandomSurvivalForest`
+  and :class:`sksurv.tree.SurvivalTree` now return an array of
+  :class:`sksurv.functions.StepFunction` objects by default.
+  Use ``return_array=True`` to get the old behavior.
+- Support for Python 3.6 has been dropped.
+- Increase minimum supported versions of dependencies. We now require:
+
+   +--------------+-----------------+
+   | Package      | Minimum Version |
+   +==============+=================+
+   | Pandas       | 0.25.0          |
+   +--------------+-----------------+
+   | scikit-learn | 0.24.0          |
+   +--------------+-----------------+