From a81c597c4600e3cf4cdc7843985c4b8b80e660a7 Mon Sep 17 00:00:00 2001 From: Adam Li Date: Thu, 26 Oct 2023 14:02:26 -0400 Subject: [PATCH] Adding fix to honest forest feature importances (#156) * Adding fix to honest forest feature importances so it can be called `est.feature_importances_` without error --------- Signed-off-by: Adam Li --- doc/whats_new/v0.3.rst | 1 + poetry.lock | 2134 ++++++++++++++--------- pyproject.toml | 2 +- sktree/ensemble/_honest_forest.py | 5 - sktree/ensemble/_unsupervised_forest.py | 6 +- sktree/tests/test_honest_forest.py | 60 + sktree/tree/_honest_tree.py | 2 +- sktree/tree/_marginal.pyx | 2 +- sktree/tree/_marginalize.py | 6 +- 9 files changed, 1414 insertions(+), 804 deletions(-) diff --git a/doc/whats_new/v0.3.rst b/doc/whats_new/v0.3.rst index 7b163ef19..3d1805494 100644 --- a/doc/whats_new/v0.3.rst +++ b/doc/whats_new/v0.3.rst @@ -16,6 +16,7 @@ Changelog - |Fix| Fixes a bug where covariate indices were not shuffled by default when running FeatureImportanceForestClassifier and FeatureImportanceForestRegressor test methods, by `Sambit Panda`_ (:pr:`140`) - |Enhancement| Add multi-view splitter for axis-aligned decision trees, by `Adam Li`_ (:pr:`129`) - |Enhancement| Add stratified sampling option to ``FeatureImportance*`` via the ``stratify`` keyword argument, by `Yuxin Bai`_ (:pr:`143`) +- |Fix| Fixed usage of ``feature_importances_`` property in ``HonestForestClassifier``, by `Adam Li`_ (:pr:`156`) Code and Documentation Contributors ----------------------------------- diff --git a/poetry.lock b/poetry.lock index c6b1fc0b3..f0c904525 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.5.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. 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b/sktree/ensemble/_honest_forest.py index e40773594..e5e735123 100644 --- a/sktree/ensemble/_honest_forest.py +++ b/sktree/ensemble/_honest_forest.py @@ -491,11 +491,6 @@ def honest_indices_(self): check_is_fitted(self) return [tree.honest_indices_ for tree in self.estimators_] - @property - def feature_importances_(self): - """The feature importances.""" - return self.estimator_.feature_importances_ - def _more_tags(self): return {"multioutput": False} diff --git a/sktree/ensemble/_unsupervised_forest.py b/sktree/ensemble/_unsupervised_forest.py index cdadaf62b..610220b64 100644 --- a/sktree/ensemble/_unsupervised_forest.py +++ b/sktree/ensemble/_unsupervised_forest.py @@ -152,7 +152,11 @@ def fit(self, X, y=None, sample_weight=None): # that case. However, for joblib 0.12+ we respect any # parallel_backend contexts set at a higher level, # since correctness does not rely on using threads. - trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose, prefer="threads",)( + trees = Parallel( + n_jobs=self.n_jobs, + verbose=self.verbose, + prefer="threads", + )( delayed(_parallel_build_trees)( t, self.bootstrap, diff --git a/sktree/tests/test_honest_forest.py b/sktree/tests/test_honest_forest.py index 91fce420e..4cf20698b 100644 --- a/sktree/tests/test_honest_forest.py +++ b/sktree/tests/test_honest_forest.py @@ -1,7 +1,9 @@ import numpy as np import pytest +from numpy.testing import assert_array_almost_equal from sklearn import datasets from sklearn.metrics import accuracy_score, r2_score +from sklearn.utils import check_random_state from sklearn.utils.estimator_checks import parametrize_with_checks from sktree._lib.sklearn.tree import DecisionTreeClassifier @@ -18,6 +20,17 @@ iris.data = iris.data[perm] iris.target = iris.target[perm] +# Larger classification sample used for testing feature importances +X_large, y_large = datasets.make_classification( + n_samples=500, + n_features=10, + n_informative=3, + n_redundant=0, + n_repeated=0, + shuffle=False, + random_state=0, +) + def test_toy_accuracy(): clf = HonestForestClassifier(n_estimators=10) @@ -192,3 +205,50 @@ def test_sklearn_compatible_estimator(estimator, check): ]: pytest.skip() check(estimator) + + +@pytest.mark.parametrize("dtype", (np.float64, np.float32)) +@pytest.mark.parametrize("criterion", ["gini", "log_loss"]) +def test_importances(dtype, criterion): + """Ported from sklearn unit-test. + + Used to ensure that honest forest feature importances are consistent with sklearn's. + """ + tolerance = 0.01 + + # cast as dtype + X = X_large.astype(dtype, copy=False) + y = y_large.astype(dtype, copy=False) + + ForestEstimator = HonestForestClassifier + + est = ForestEstimator(n_estimators=10, criterion=criterion, random_state=0) + est.fit(X, y) + + importances = est.feature_importances_ + + # The forest estimator can detect that only the first 3 features of the + # dataset are informative: + n_important = np.sum(importances > 0.1) + assert importances.shape[0] == 10 + assert n_important == 3 + assert np.all(importances[:3] > 0.1) + + # Check with parallel + importances = est.feature_importances_ + est.set_params(n_jobs=2) + importances_parallel = est.feature_importances_ + assert_array_almost_equal(importances, importances_parallel) + + # Check with sample weights + sample_weight = check_random_state(0).randint(1, 10, len(X)) + est = ForestEstimator(n_estimators=10, random_state=0, criterion=criterion) + est.fit(X, y, sample_weight=sample_weight) + importances = est.feature_importances_ + assert np.all(importances >= 0.0) + + for scale in [0.5, 100]: + est = ForestEstimator(n_estimators=10, random_state=0, criterion=criterion) + est.fit(X, y, sample_weight=scale * sample_weight) + importances_bis = est.feature_importances_ + assert np.abs(importances - importances_bis).mean() < tolerance diff --git a/sktree/tree/_honest_tree.py b/sktree/tree/_honest_tree.py index 7d534ddb9..4f053c520 100644 --- a/sktree/tree/_honest_tree.py +++ b/sktree/tree/_honest_tree.py @@ -151,7 +151,7 @@ class HonestTreeClassifier(MetaEstimatorMixin, ClassifierMixin, BaseDecisionTree Read more in the :ref:`User Guide `. tree_estimator : object, default=None - Instatiated tree of type BaseDecisionTree from sktree. + Instantiated tree of type BaseDecisionTree from sktree. If None, then DecisionTreeClassifier with default parameters will be used. Note that one MUST use trees imported from the `sktree.tree` API namespace rather than from `sklearn.tree`. diff --git a/sktree/tree/_marginal.pyx b/sktree/tree/_marginal.pyx index 043ee3263..50b069b56 100644 --- a/sktree/tree/_marginal.pyx +++ b/sktree/tree/_marginal.pyx @@ -171,7 +171,7 @@ cdef inline cnp.ndarray _apply_dense_marginal( n_left_samples = tree.nodes[node.left_child].n_node_samples n_right_samples = tree.nodes[node.right_child].n_node_samples - # compute the probabilies for going left and right + # compute the probabilities for going left and right p_left = (n_left_samples / n_node_samples) # randomly sample a direction diff --git a/sktree/tree/_marginalize.py b/sktree/tree/_marginalize.py index 90fa94b92..0d65fa5c7 100644 --- a/sktree/tree/_marginalize.py +++ b/sktree/tree/_marginalize.py @@ -105,7 +105,11 @@ def _apply_marginal_forest( if est.max_bins is not None: X = est._bin_data(X, is_training_data=False).astype(DTYPE) - results = Parallel(n_jobs=est.n_jobs, verbose=est.verbose, prefer="threads",)( + results = Parallel( + n_jobs=est.n_jobs, + verbose=est.verbose, + prefer="threads", + )( delayed(_apply_marginal_tree)( tree, X,