use all available cores in fit

adapt pre-commit to python 3.8

.pre-commit-config.yaml

@@ -5,7 +5,7 @@ repos:
     - id: black
       language_version: python3.8
 -   repo: https://gitlab.com/pycqa/flake8
-    rev: 3.7.9
+    rev: 3.8.3
     hooks:
     - id: flake8
 -   repo: https://github.com/pre-commit/mirrors-mypy
@@ -17,6 +17,11 @@ repos:
     hooks:
       - id: unittest
         name: unittest
-        entry: python -m unittest discover 
+        entry: python -m coverage run -m unittest discover
+        language: system
+        pass_filenames: false
+      - id: coverage
+        name: coverage
+        entry: python -m coverage report -m --fail-under=100
         language: system
         pass_filenames: false

notebooks/wine_iris.ipynb

@@ -21,7 +21,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -32,7 +32,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -40,14 +40,14 @@
     "clf = {}\n",
     "clf[\"stree\"] = Stree(random_state=random_state, max_depth=5)\n",
     "clf[\"stree\"].set_params(**dict(splitter=\"best\", kernel=\"linear\", max_features=\"auto\"))\n",
-    "clf[\"odte\"] = Odte(base_estimator=clf[\"stree\"], random_state=random_state, n_estimators=n_estimators, max_features=.8)\n",
+    "clf[\"odte\"] = Odte(n_jobs=-1, base_estimator=clf[\"stree\"], random_state=random_state, n_estimators=n_estimators, max_features=.8)\n",
     "clf[\"adaboost\"] = AdaBoostClassifier(base_estimator=clf[\"stree\"], n_estimators=n_estimators, random_state=random_state, algorithm=\"SAMME\")\n",
     "clf[\"bagging\"] = BaggingClassifier(base_estimator=clf[\"stree\"], n_estimators=n_estimators)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 14,
    "metadata": {
     "tags": []
    },
@@ -55,7 +55,7 @@
     {
      "output_type": "stream",
      "name": "stdout",
-     "text": "****************************** Results for wine ******************************\nTraining stree...\nScore: 94.444 in 0.19 seconds\nTraining odte...\nScore: 100.000 in 3.43 seconds\nTraining adaboost...\nScore: 94.444 in 0.76 seconds\nTraining bagging...\nScore: 100.000 in 3.27 seconds\n"
+     "text": "****************************** Results for wine ******************************\nTraining stree...\nScore: 94.444 in 0.18 seconds\nTraining odte...\nScore: 100.000 in 1.33 seconds\nTraining adaboost...\nScore: 94.444 in 0.62 seconds\nTraining bagging...\nScore: 100.000 in 2.88 seconds\n"
     }
    ],
    "source": [
@@ -87,7 +87,7 @@
     "n_estimators = 10\n",
     "clf = {}\n",
     "clf[\"stree\"] = Stree(random_state=random_state, max_depth=3)\n",
-    "clf[\"odte\"] = Odte(random_state=random_state, n_estimators=n_estimators, max_features=1.0)\n",
+    "clf[\"odte\"] = Odte(n_jobs=-1, random_state=random_state, n_estimators=n_estimators, max_features=1.0)\n",
     "clf[\"adaboost\"] = AdaBoostClassifier(base_estimator=clf[\"stree\"], n_estimators=n_estimators, random_state=random_state, algorithm=\"SAMME\")\n",
     "clf[\"bagging\"] = BaggingClassifier(base_estimator=clf[\"stree\"], n_estimators=n_estimators)"
    ]
@@ -102,7 +102,7 @@
     {
      "output_type": "stream",
      "name": "stdout",
-     "text": "****************************** Results for iris ******************************\nTraining stree...\nScore: 100.000 in 0.02 seconds\nTraining odte...\nScore: 100.000 in 0.15 seconds\nTraining adaboost...\nScore: 83.333 in 0.01 seconds\nTraining bagging...\nScore: 96.667 in 0.13 seconds\n"
+     "text": "****************************** Results for iris ******************************\nTraining stree...\nScore: 100.000 in 0.02 seconds\nTraining odte...\nScore: 100.000 in 0.12 seconds\nTraining adaboost...\nScore: 83.333 in 0.01 seconds\nTraining bagging...\nScore: 100.000 in 0.13 seconds\n"
     }
    ],
    "source": [
@@ -124,7 +124,7 @@
     {
      "output_type": "stream",
      "name": "stdout",
-     "text": "{'fit_time': array([0.23599219, 0.22772503, 0.21689606, 0.20017815, 0.22257805]), 'score_time': array([0.01378369, 0.01322389, 0.0125649 , 0.01751685, 0.01062703]), 'test_score': array([1.        , 1.        , 1.        , 0.93333333, 1.        ]), 'train_score': array([0.98333333, 0.96666667, 0.99166667, 0.99166667, 0.975     ])}\n98.667 +- 0.027\n"
+     "text": "{'fit_time': array([0.22121811, 0.21985221, 0.19185114, 0.19187999, 0.20067477]), 'score_time': array([0.01268458, 0.01461887, 0.01160598, 0.01308703, 0.01070738]), 'test_score': array([1.        , 1.        , 1.        , 0.93333333, 1.        ]), 'train_score': array([0.98333333, 0.96666667, 0.99166667, 0.99166667, 0.975     ])}\n98.667 +- 0.027\n"
     }
    ],
    "source": [
@@ -143,7 +143,7 @@
     {
      "output_type": "stream",
      "name": "stdout",
-     "text": "{'fit_time': array([0.02912688, 0.05858397, 0.06724691, 0.02860498, 0.03802919]), 'score_time': array([0.0024271 , 0.0022819 , 0.00219584, 0.00195408, 0.00342584]), 'test_score': array([1.        , 0.93333333, 0.93333333, 0.93333333, 0.96666667]), 'train_score': array([0.93333333, 0.96666667, 0.96666667, 0.96666667, 0.95      ])}\n95.333 +- 0.027\n"
+     "text": "{'fit_time': array([0.02130818, 0.02036786, 0.02829814, 0.02326989, 0.03807497]), 'score_time': array([0.00140715, 0.00173712, 0.00199389, 0.00132608, 0.00199199]), 'test_score': array([1.        , 0.93333333, 0.93333333, 0.93333333, 0.96666667]), 'train_score': array([0.93333333, 0.96666667, 0.96666667, 0.96666667, 0.95      ])}\n95.333 +- 0.027\n"
     }
    ],
    "source": [

odte/Odte.py

@@ -25,6 +25,7 @@ from stree import Stree
 class Odte(BaseEnsemble, ClassifierMixin):  # type: ignore
     def __init__(
         self,
+        # n_jobs = -1 to use all available cores
         n_jobs: int = 1,
         base_estimator: BaseEstimator = None,
         random_state: int = 0,
@@ -38,7 +39,8 @@ class Odte(BaseEnsemble, ClassifierMixin):  # type: ignore
             else base_estimator
         )
         super().__init__(
-            base_estimator=base_estimator, n_estimators=n_estimators,
+            base_estimator=base_estimator,
+            n_estimators=n_estimators,
         )
         self.n_jobs = n_jobs
         self.n_estimators = n_estimators
@@ -48,7 +50,7 @@ class Odte(BaseEnsemble, ClassifierMixin):  # type: ignore
 
     def _initialize_random(self) -> np.random.mtrand.RandomState:
         if self.random_state is None:
-            self.random_state = random.randint(0, sys.maxint)
+            self.random_state = random.randint(0, sys.maxsize)
             return np.random.mtrand._rand
         return np.random.RandomState(self.random_state)
 
@@ -84,9 +86,7 @@ class Odte(BaseEnsemble, ClassifierMixin):  # type: ignore
         self.estimators_: List[BaseEstimator] = []
         self.subspaces_: List[Tuple[int, ...]] = []
         result = self._train(X, y, sample_weight)
-        self.estimators_, self.subspaces_ = tuple(  # type: ignore
+        self.estimators_, self.subspaces_ = tuple(zip(*result))  # type: ignore
-            zip(*result)
-        )
         return self
 
     @staticmethod

odte/tests/Odte_tests.py

@@ -57,7 +57,7 @@ class Odte_test(unittest.TestCase):
         computed = box.randint(0, 1000, 3)
         self.assertListEqual(expected, computed.tolist())
         # test None
-        tclf = Odte()
+        tclf = Odte(random_state=None)
         box = tclf._initialize_random()
         computed = box.randint(101, 1000, 3)
         for value in computed.tolist():
@@ -83,7 +83,7 @@ class Odte_test(unittest.TestCase):
         warnings.filterwarnings("ignore", category=ConvergenceWarning)
         warnings.filterwarnings("ignore", category=RuntimeWarning)
         X, y = [[1, 2], [5, 6], [9, 10], [16, 17]], [0, 1, 1, 2]
-        expected = [0, 1, 1, 1]
+        expected = [0, 1, 1, 2]
         tclf = Odte(
             random_state=self._random_state, n_estimators=10, n_jobs=-1
         )
@@ -102,9 +102,15 @@ class Odte_test(unittest.TestCase):
         X, y = load_dataset(self._random_state)
         expected = y
         tclf = Odte(
-            random_state=self._random_state, max_features=1.0, max_samples=0.1,
+            random_state=self._random_state,
+            max_features=1.0,
+            max_samples=0.1,
+        )
+        tclf.set_params(
+            **dict(
+                base_estimator__kernel="linear",
+            )
         )
-        tclf.set_params(**dict(base_estimator__kernel="linear",))
         computed = tclf.fit(X, y).predict(X)
         self.assertListEqual(expected[:27].tolist(), computed[:27].tolist())
 
@@ -134,7 +140,11 @@ class Odte_test(unittest.TestCase):
                     max_features=max_features,
                     n_estimators=10,
                 )
-                tclf.set_params(**dict(base_estimator__splitter=splitter,))
+                tclf.set_params(
+                    **dict(
+                        base_estimator__splitter=splitter,
+                    )
+                )
                 expected = results.pop(0)
                 computed = tclf.fit(X, y).score(X, y)
                 self.assertAlmostEqual(expected, computed)