Implement hyperparam. context based normalization

stree/Strees.py

@@ -15,6 +15,7 @@ from typing import Optional
 import numpy as np
 from sklearn.base import BaseEstimator, ClassifierMixin
 from sklearn.svm import SVC, LinearSVC
+from sklearn.preprocessing import StandardScaler
 from sklearn.utils import check_consistent_length
 from sklearn.utils.multiclass import check_classification_targets
 from sklearn.exceptions import ConvergenceWarning
@@ -41,6 +42,7 @@ class Snode:
         impurity: float,
         title: str,
         weight: np.ndarray = None,
+        scaler: StandardScaler = None,
     ):
         self._clf = clf
         self._title = title
@@ -58,6 +60,7 @@ class Snode:
         self._features = features
         self._impurity = impurity
         self._partition_column: int = -1
+        self._scaler = scaler
 
     @classmethod
     def copy(cls, node: "Snode") -> "Snode":
@@ -68,6 +71,8 @@ class Snode:
             node._features,
             node._impurity,
             node._title,
+            node._sample_weight,
+            node._scaler,
         )
 
     def set_partition_column(self, col: int):
@@ -178,6 +183,7 @@ class Splitter:
         criteria: str = None,
         min_samples_split: int = None,
         random_state=None,
+        normalize=False,
     ):
         self._clf = clf
         self._random_state = random_state
@@ -187,6 +193,7 @@ class Splitter:
         self._min_samples_split = min_samples_split
         self._criteria = criteria
         self._splitter_type = splitter_type
+        self._normalize = normalize
 
         if clf is None:
             raise ValueError(f"clf has to be a sklearn estimator, got({clf})")
@@ -486,8 +493,7 @@ class Splitter:
             origin[down] if any(down) else None,
         ]
 
-    @staticmethod
+    def _distances(self, node: Snode, data: np.ndarray) -> np.array:
-    def _distances(node: Snode, data: np.ndarray) -> np.array:
         """Compute distances of the samples to the hyperplane of the node
 
         Parameters
@@ -503,7 +509,10 @@ class Splitter:
             array of shape (m, nc) with the distances of every sample to
             the hyperplane of every class. nc = # of classes
         """
-        return node._clf.decision_function(data[:, node._features])
+        X_transformed = data[:, node._features]
+        if self._normalize:
+            X_transformed = node._scaler.transform(X_transformed)
+        return node._clf.decision_function(X_transformed)
 
 
 class Stree(BaseEstimator, ClassifierMixin):
@@ -529,6 +538,7 @@ class Stree(BaseEstimator, ClassifierMixin):
         min_samples_split: int = 0,
         max_features=None,
         splitter: str = "random",
+        normalize: bool = False,
     ):
         self.max_iter = max_iter
         self.C = C
@@ -543,6 +553,7 @@ class Stree(BaseEstimator, ClassifierMixin):
         self.max_features = max_features
         self.criterion = criterion
         self.splitter = splitter
+        self.normalize = normalize
 
     def _more_tags(self) -> dict:
         """Required by sklearn to supply features of the classifier
@@ -606,6 +617,7 @@ class Stree(BaseEstimator, ClassifierMixin):
             criteria=self.split_criteria,
             random_state=self.random_state,
             min_samples_split=self.min_samples_split,
+            normalize=self.normalize,
         )
         if self.random_state is not None:
             random.seed(self.random_state)
@@ -660,7 +672,8 @@ class Stree(BaseEstimator, ClassifierMixin):
             y = y[~indices_zero]
             sample_weight = sample_weight[~indices_zero]
         self.depth_ = max(depth, self.depth_)
-        node = Snode(None, X, y, X.shape[1], 0.0, title, sample_weight)
+        scaler = StandardScaler()
+        node = Snode(None, X, y, X.shape[1], 0.0, title, sample_weight, scaler)
         if np.unique(y).shape[0] == 1:
             # only 1 class => pure dataset
             node.set_title(title + ", <pure>")
@@ -668,6 +681,9 @@ class Stree(BaseEstimator, ClassifierMixin):
         # Train the model
         clf = self._build_clf()
         Xs, features = self.splitter_.get_subspace(X, y, self.max_features_)
+        if self.normalize:
+            scaler.fit(Xs)
+            Xs = scaler.transform(Xs)
         clf.fit(Xs, y, sample_weight=sample_weight)
         node.set_impurity(self.splitter_.partition_impurity(y))
         node.set_classifier(clf)

stree/tests/Snode_test.py

@@ -1,8 +1,6 @@
 import os
 import unittest
-
 import numpy as np
-
 from stree import Stree, Snode
 from .utils import load_dataset
 
@@ -119,3 +117,5 @@ class Snode_test(unittest.TestCase):
         self.assertEqual("test", computed._title)
         self.assertIsInstance(computed._clf, Stree)
         self.assertEqual(test._partition_column, computed._partition_column)
+        self.assertEqual(test._sample_weight, computed._sample_weight)
+        self.assertEqual(test._scaler, computed._scaler)

stree/tests/Stree_test.py

@@ -378,9 +378,14 @@ class Stree_test(unittest.TestCase):
             n_samples=500,
         )
         clf = Stree(kernel="rbf", random_state=self._random_state)
+        clf2 = Stree(
+            kernel="rbf", random_state=self._random_state, normalize=True
+        )
         self.assertEqual(0.768, clf.fit(X, y).score(X, y))
+        self.assertEqual(0.814, clf2.fit(X, y).score(X, y))
         X, y = load_wine(return_X_y=True)
         self.assertEqual(0.6741573033707865, clf.fit(X, y).score(X, y))
+        self.assertEqual(1.0, clf2.fit(X, y).score(X, y))
 
     def test_score_multiclass_poly(self):
         X, y = load_dataset(
@@ -392,9 +397,16 @@ class Stree_test(unittest.TestCase):
         clf = Stree(
             kernel="poly", random_state=self._random_state, C=10, degree=5
         )
+        clf2 = Stree(
+            kernel="poly",
+            random_state=self._random_state,
+            normalize=True,
+        )
         self.assertEqual(0.786, clf.fit(X, y).score(X, y))
+        self.assertEqual(0.818, clf2.fit(X, y).score(X, y))
         X, y = load_wine(return_X_y=True)
         self.assertEqual(0.702247191011236, clf.fit(X, y).score(X, y))
+        self.assertEqual(0.6067415730337079, clf2.fit(X, y).score(X, y))
 
     def test_score_multiclass_linear(self):
         X, y = load_dataset(
@@ -405,8 +417,14 @@ class Stree_test(unittest.TestCase):
         )
         clf = Stree(kernel="linear", random_state=self._random_state)
         self.assertEqual(0.9533333333333334, clf.fit(X, y).score(X, y))
+        # Check with context based standardization
+        clf2 = Stree(
+            kernel="linear", random_state=self._random_state, normalize=True
+        )
+        self.assertEqual(0.9526666666666667, clf2.fit(X, y).score(X, y))
         X, y = load_wine(return_X_y=True)
         self.assertEqual(0.9831460674157303, clf.fit(X, y).score(X, y))
+        self.assertEqual(1.0, clf2.fit(X, y).score(X, y))
 
     def test_zero_all_sample_weights(self):
         X, y = load_dataset(self._random_state)