Fix meson build options to remove build type (#307)

Signed-off-by: Adam Li <adam2392@gmail.com>

benchmarks_nonasv/bench_mnist.py

@@ -33,11 +33,14 @@
 from joblib import Memory
 from sklearn.datasets import fetch_openml, get_data_home
 from sklearn.dummy import DummyClassifier
-from sklearn.ensemble import ExtraTreesClassifier, RandomForestClassifier
 from sklearn.metrics import zero_one_loss
 from sklearn.utils import check_array
 
-from treeple import ObliqueRandomForestClassifier, PatchObliqueRandomForestClassifier
+from treeple import (
+    ObliqueRandomForestClassifier,
+    PatchObliqueRandomForestClassifier,
+    RandomForestClassifier,
+)
 
 # Memoize the data extraction and memory map the resulting
 # train / test splits in readonly mode
@@ -70,10 +73,10 @@ def load_data(dtype=np.float32, order="F"):
 
 ESTIMATORS = {
     "dummy": DummyClassifier(),
-    "ExtraTrees": ExtraTreesClassifier(),
+    # "ExtraTrees": ExtraTreesClassifier(),
     "RandomForest": RandomForestClassifier(),
-    "ObliqueRandomForest": ObliqueRandomForestClassifier(),
-    "PatchObliqueRandomForest": PatchObliqueRandomForestClassifier(),
+    # "ObliqueRandomForest": ObliqueRandomForestClassifier(),
+    # "PatchObliqueRandomForest": PatchObliqueRandomForestClassifier(),
 }
 
 

doc/index.rst

@@ -9,7 +9,10 @@ Our package has implemented unsupervised forests (Geodesic Forests
 MORF [Li2023]_), honest forests [Perry2021]_, extended isolation forests [Hariri2019]_, and more.
 
 For all forests, we also support incremental building of the forests, using the
-``partial_fit`` API from scikit-learn [Xu2022]_.
+``partial_fit`` API from scikit-learn [Xu2022]_, and quantile regression by storing
+the training samples in the leaves of the trees [Meinshausen2006]_ (Warning: high memory usage
+will occur in this setting since predicting quantiles stores the training data within the
+leaves of the tree).
 
 We encourage you to use the package for your research and also build on top
 with relevant Pull Requests. See our examples for walk-throughs of how to use the package.
@@ -23,6 +26,9 @@ We are licensed under PolyForm Noncommercial License (see `License <https://gith
    "Extended isolation forest." IEEE transactions on knowledge and data
    engineering 33.4 (2019): 1479-1489.
 
+ .. [Meinshausen2006] Meinshausen, Nicolai, and Greg Ridgeway. "Quantile regression forests."
+   Journal of machine learning research 7.6 (2006). "Quantile regression forests."
+
  .. [Madhyastha2020] Madhyastha, Meghana, et al. :doi:`"Geodesic Forests"
     <10.1145/3394486.3403094>`, KDD 2020, 513-523, 2020.
 

meson.build

@@ -4,11 +4,10 @@ project(
   # Note that the git commit hash cannot be added dynamically here
   # That only happens when importing from a git repository.
   # See `treeple/__init__.py`
-  version: '0.9.1',
+  version: '0.10.0.dev0',
   license: 'PolyForm Noncommercial 1.0.0',
   meson_version: '>= 1.1.0',
   default_options: [
-    'buildtype=debugoptimized',
     'c_std=c99',
     'cpp_std=c++14',
   ],

pyproject.toml

@@ -17,7 +17,7 @@ requires = [
 
 [project]
 name = "treeple"
-version = "0.9.1"
+version = "0.10.0.dev0"
 description = "Modern decision trees in Python"
 maintainers = [
   {name = "Neurodata", email = "adam.li@columbia.edu"}