Merge remote-tracking branch 'upstream/main' into sparse-permutation-…

…tests-treeple-stats

.pre-commit-config.yaml

@@ -22,7 +22,7 @@ repos:
 
   # Ruff treeple
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.6.1
+    rev: v0.6.4
     hooks:
       - id: ruff
         name: ruff treeple
@@ -31,7 +31,7 @@ repos:
 
   # Ruff tutorials and examples
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.6.1
+    rev: v0.6.4
     hooks:
       - id: ruff
         name: ruff tutorials and examples
@@ -67,7 +67,7 @@ repos:
 
   # mypy
   - repo: https://github.com/pre-commit/mirrors-mypy
-    rev: v1.11.1
+    rev: v1.11.2
     hooks:
       - id: mypy
         # Avoid the conflict between mne/__init__.py and mne/__init__.pyi by ignoring the former

.spin/cmds.py

@@ -5,6 +5,7 @@
 import click
 from spin import util
 from spin.cmds import meson
+from spin.cmds.meson import build_dir_option
 
 
 def get_git_revision_hash(submodule) -> str:
@@ -145,14 +146,18 @@ def setup_submodule(forcesubmodule=False):
 @click.option(
     "--forcesubmodule", is_flag=True, help="Force submodule pull.", envvar="FORCE_SUBMODULE"
 )
+@build_dir_option
 @click.pass_context
 def build(
     ctx,
+    *,
     meson_args,
     jobs=None,
     clean=False,
     verbose=False,
     gcov=False,
+    quiet=False,
+    build_dir=None,
     forcesubmodule=False,
 ):
     """Build treeple using submodules.

DEVELOPING.md

@@ -44,6 +44,10 @@ If you are developing locally, you will need the build dependencies to compile t
 
     pip install -r build_requirements.txt
 
+Additionally, you need to install the latest build of scikit-learn:
+
+    pip install --force -r build_sklearn_requirements.txt
+
 Other requirements can be installed as such:
 
     pip install .
@@ -132,7 +136,7 @@ You can also do the same thing using Meson/Ninja itself. Run the following to bu
     export PYTHONPATH=${PWD}/build/lib/python<YOUR_PYTHON_VERSION>/site-packages
 
     # to check installation, you need to be in a different directory
-    cd docs;  
+    cd docs;
     python -c "from treeple import tree"
     python -c "import sklearn; print(sklearn.__version__);"
 
@@ -188,7 +192,7 @@ GH Actions will build wheels for each Python version and OS. Then the wheels nee
 <https://github.com/neurodata/treeple/actions/workflows/build_wheels.yml> will have all the wheels for common OSes built for each Python version.
 
 2. Upload wheels to test PyPi
-This is to ensure that the wheels are built correctly and can be installed on a fresh environment. For more information, see <https://packaging.python.org/guides/using-testpypi/>. You will need to follow the instructions to create an account and get your API token for testpypi and pypi. 
+This is to ensure that the wheels are built correctly and can be installed on a fresh environment. For more information, see <https://packaging.python.org/guides/using-testpypi/>. You will need to follow the instructions to create an account and get your API token for testpypi and pypi.
 
 ```
 twine upload dist/* --repository testpypi

build_requirements.txt

@@ -8,5 +8,5 @@ click
 rich-click
 doit
 pydevtool
-spin
+spin>=0.12
 build

build_sklearn_requirements.txt

@@ -0,0 +1,3 @@
+--pre
+--extra-index https://pypi.anaconda.org/scientific-python-nightly-wheels/simple
+scikit-learn

meson.build

@@ -8,7 +8,7 @@ project(
   license: 'PolyForm Noncommercial 1.0.0',
   meson_version: '>= 1.1.0',
   default_options: [
-    'c_std=c99',
+    'c_std=c11',
     'cpp_std=c++14',
   ],
 )

pyproject.toml

@@ -68,7 +68,7 @@ build = [
   'twine',
   'meson',
   'meson-python',
-  'spin',
+  'spin>=0.12',
   'doit',
   'scikit-learn>=1.5.0',
   'Cython>=3.0.10',

treeple/__init__.py

@@ -22,6 +22,7 @@
 # https://github.com/ContinuumIO/anaconda-issues/issues/11294
 os.environ.setdefault("KMP_INIT_AT_FORK", "FALSE")
 
+
 try:
     # This variable is injected in the __builtins__ by the build
     # process. It is used to enable importing subpackages of sklearn when
@@ -64,7 +65,8 @@
         msg = """Error importing treeple: you cannot import treeple while
         being in treeple source directory; please exit the treeple source
         tree first and relaunch your Python interpreter."""
-        raise ImportError(msg) from e
+        raise Exception(e)
+        # raise ImportError(msg) from e
 
     __all__ = [
         "_lib",

treeple/_lib/meson.build

@@ -94,3 +94,22 @@ foreach ext: extensions
     subdir: 'treeple/_lib/sklearn/utils/',
   )
 endforeach
+
+
+# python_sources = [
+#   '__init__.py',
+# ]
+
+# py.install_sources(
+#   python_sources,
+#   subdir: 'treeple/_lib'   # Folder relative to site-packages to install to
+# )
+
+# tempita = files('./sklearn/_build_utils/tempita.py')
+
+# # Copy all the .py files to the install dir, rather than using
+# # py.install_sources and needing to list them explicitely one by one
+# # install_subdir('sklearn', install_dir: py.get_install_dir())
+# install_subdir('sklearn', install_dir: join_paths(py.get_install_dir(), 'treeple/_lib'))
+
+# subdir('sklearn')

treeple/ensemble/_honest_forest.py

@@ -720,8 +720,12 @@ def oob_samples_(self):
             oob_samples.append(_oob_samples)
         return oob_samples
 
-    def _more_tags(self):
-        return {"multioutput": False}
+    def __sklearn_tags__(self):
+        # XXX: nans should be supportable in HRF
+        tags = super().__sklearn_tags__()
+        tags.classifier_tags.multi_output = False
+        tags.input_tags.allow_nan = False
+        return tags
 
     def decision_path(self, X):
         """

treeple/ensemble/_unsupervised_forest.py

@@ -21,7 +21,12 @@
 )
 from sklearn.metrics import calinski_harabasz_score
 from sklearn.utils.parallel import Parallel, delayed
-from sklearn.utils.validation import _check_sample_weight, check_is_fitted, check_random_state
+from sklearn.utils.validation import (
+    _check_sample_weight,
+    check_is_fitted,
+    check_random_state,
+    validate_data,
+)
 
 from .._lib.sklearn.ensemble._forest import BaseForest
 from .._lib.sklearn.tree._tree import DTYPE
@@ -85,10 +90,9 @@ def fit(self, X, y=None, sample_weight=None):
         self : object
             Returns the instance itself.
         """
-        self._validate_params()
-
         # Validate or convert input data
-        X = self._validate_data(
+        X = validate_data(
+            self,
             X,
             dtype=DTYPE,  # accept_sparse="csc",
         )

treeple/meson.build

@@ -103,6 +103,7 @@ scikit_learn_cython_args = [
   '-X language_level=3', '-X boundscheck=' + boundscheck, '-X wraparound=False',
   '-X initializedcheck=False', '-X nonecheck=False', '-X cdivision=True',
   '-X profile=False',
+  '-X embedsignature=True',
   # Needed for cython imports across subpackages, e.g. cluster pyx that
   # cimports metrics pxd
   '--include-dir', meson.global_build_root(),

treeple/neighbors.py

@@ -5,8 +5,9 @@
 from sklearn.base import BaseEstimator, MetaEstimatorMixin
 from sklearn.exceptions import NotFittedError
 from sklearn.neighbors import NearestNeighbors
-from sklearn.utils.validation import check_is_fitted
+from sklearn.utils.validation import check_is_fitted, validate_data
 
+from treeple.tree import DecisionTreeClassifier
 from treeple.tree._neighbors import _compute_distance_matrix, compute_forest_similarity_matrix
 
 
@@ -31,13 +32,19 @@ class NearestNeighborsMetaEstimator(BaseEstimator, MetaEstimatorMixin):
         The number of parallel jobs to run for neighbors, by default None.
     """
 
-    def __init__(self, estimator, n_neighbors=5, radius=1.0, algorithm="auto", n_jobs=None):
+    def __init__(self, estimator=None, n_neighbors=5, radius=1.0, algorithm="auto", n_jobs=None):
         self.estimator = estimator
         self.n_neighbors = n_neighbors
         self.algorithm = algorithm
         self.radius = radius
         self.n_jobs = n_jobs
 
+    def get_estimator(self):
+        if self.estimator is not None:
+            return DecisionTreeClassifier(random_state=0)
+        else:
+            return copy(self.estimator)
+
     def fit(self, X, y=None):
         """Fit the nearest neighbors estimator from the training dataset.
 
@@ -56,9 +63,9 @@ def fit(self, X, y=None):
         self : object
             Fitted estimator.
         """
-        X, y = self._validate_data(X, y, accept_sparse="csc")
+        X, y = validate_data(self, X, y, accept_sparse="csc")
 
-        self.estimator_ = copy(self.estimator)
+        self.estimator_ = self.get_estimator()
         try:
             check_is_fitted(self.estimator_)
         except NotFittedError: