Merge pull request #4 from Doctorado-ML/be_hyperparams

Add base estimator hyperparameters

.github/workflows/main.yml

@@ -13,7 +13,7 @@ jobs:
     strategy:
       matrix:
         os: [macos-latest, ubuntu-latest, windows-latest]
-        python: [3.8]
+        python: [3.8, 3.9, "3.10"]
 
     steps:
       - uses: actions/checkout@v2

odte/Odte.py

@@ -2,11 +2,13 @@
 __author__ = "Ricardo Montañana Gómez"
 __copyright__ = "Copyright 2020, Ricardo Montañana Gómez"
 __license__ = "MIT"
-Build a forest of oblique trees based on STree
+Build a forest of oblique trees based on STree, admits any base classifier
+as well
 """
 from __future__ import annotations
 import random
 import sys
+import json
 from math import factorial
 from typing import Union, Optional, Tuple, List, Set
 import numpy as np
@@ -33,6 +35,7 @@ class Odte(BaseEnsemble, ClassifierMixin):
         max_features: Optional[Union[str, int, float]] = None,
         max_samples: Optional[Union[int, float]] = None,
         n_estimators: int = 100,
+        be_hyperparams: str = "{}",
     ):
         super().__init__(
             base_estimator=base_estimator,
@@ -44,6 +47,7 @@ class Odte(BaseEnsemble, ClassifierMixin):
         self.random_state = random_state
         self.max_features = max_features
         self.max_samples = max_samples  # size of bootstrap
+        self.be_hyperparams = be_hyperparams
 
     def _initialize_random(self) -> np.random.mtrand.RandomState:
         if self.random_state is None:
@@ -88,8 +92,9 @@ class Odte(BaseEnsemble, ClassifierMixin):
         return self
 
     def _compute_metrics(self) -> None:
-        tdepth = tnodes = tleaves = 0
+        tdepth = tnodes = tleaves = 0.0
         for estimator in self.estimators_:
+            if hasattr(estimator, "nodes_leaves"):
                 nodes, leaves = estimator.nodes_leaves()
                 depth = estimator.depth_
                 tdepth += depth
@@ -109,9 +114,12 @@ class Odte(BaseEnsemble, ClassifierMixin):
         random_seed: int,
         boot_samples: int,
         max_features: int,
+        hyperparams: str,
     ) -> Tuple[BaseEstimator, Tuple[int, ...]]:
         clf = clone(base_estimator_)
-        clf.set_params(random_state=random_seed)
+        hyperparams_ = json.loads(hyperparams)
+        hyperparams_.update(dict(random_state=random_seed))
+        clf.set_params(**hyperparams_)
         n_samples = X.shape[0]
         # bootstrap
         indices = random_box.randint(0, n_samples, boot_samples)
@@ -142,6 +150,7 @@ class Odte(BaseEnsemble, ClassifierMixin):
                 random_seed,
                 boot_samples,
                 self.max_features_,
+                self.be_hyperparams,
             )
             for random_seed in range(
                 self.random_state, self.random_state + self.n_estimators

odte/tests/Odte_tests.py

@@ -3,8 +3,9 @@ import unittest
 import os
 import random
 import warnings
+import json
 from sklearn.exceptions import ConvergenceWarning, NotFittedError
-
+from sklearn.svm import SVC
 from odte import Odte
 from stree import Stree
 from .utils import load_dataset
@@ -215,3 +216,39 @@ class Odte_test(unittest.TestCase):
         nodes, leaves = tclf.nodes_leaves()
         self.assertAlmostEqual(9.333333333333334, leaves)
         self.assertAlmostEqual(17.666666666666668, nodes)
+
+    def test_nodes_leaves_SVC(self):
+        tclf = Odte(
+            base_estimator=SVC(),
+            random_state=self._random_state,
+            n_estimators=3,
+        )
+        X, y = load_dataset(self._random_state, n_features=16, n_samples=500)
+        tclf.fit(X, y)
+        self.assertAlmostEqual(0.0, tclf.leaves_)
+        self.assertAlmostEqual(0.0, tclf.nodes_)
+        nodes, leaves = tclf.nodes_leaves()
+        self.assertAlmostEqual(0.0, leaves)
+        self.assertAlmostEqual(0.0, nodes)
+
+    def test_base_estimator_hyperparams(self):
+        data = [
+            (Stree(), {"max_features": 7, "max_depth": 2}),
+            (SVC(), {"kernel": "linear", "cache_size": 100}),
+        ]
+        for clf, hyperparams in data:
+            hyperparams_ = json.dumps(hyperparams)
+            tclf = Odte(
+                base_estimator=clf,
+                random_state=self._random_state,
+                n_estimators=3,
+                be_hyperparams=hyperparams_,
+            )
+            self.assertEqual(hyperparams_, tclf.be_hyperparams)
+            X, y = load_dataset(
+                self._random_state, n_features=16, n_samples=500
+            )
+            tclf.fit(X, y)
+            for estimator in tclf.estimators_:
+                for key, value in hyperparams.items():
+                    self.assertEqual(value, estimator.get_params()[key])