Make Stree a sklearn estimator

Added check_estimator in notebook test2
Added a Stree test with check_estimator

setup.py

@@ -1,6 +1,6 @@
 import setuptools
 
-__version__ = "0.9rc2"
+__version__ = "0.9rc3"
 __author__ = "Ricardo Montañana Gómez"
 
 def readme():

stree/Strees.py

@@ -15,6 +15,7 @@ from sklearn.base import BaseEstimator, ClassifierMixin
 from sklearn.svm import LinearSVC
 from sklearn.utils.validation import check_X_y, check_array, check_is_fitted
 
+
 class Snode:
     def __init__(self, clf: LinearSVC, X: np.ndarray, y: np.ndarray, title: str):
         self._clf = clf
@@ -97,24 +98,27 @@ class Siterator:
         self._push(node.get_down())
         return node
 
+
 class Stree(BaseEstimator, ClassifierMixin):
     """
     """
+    __folder = 'data/'
 
     def __init__(self, C: float = 1.0, max_iter: int = 1000, random_state: int = 0, use_predictions: bool = False):
-        self._max_iter = max_iter
+        self.max_iter = max_iter
-        self._C = C
+        self.C = C
-        self._random_state = random_state
+        self.random_state = random_state    
-        self._tree = None
+        self.use_predictions = use_predictions
-        self.__folder = 'data/'
-        self.__use_predictions = use_predictions
-        self.__trained = False
-        self.__proba = False
 
     def get_params(self, deep=True):
         """Get dict with hyperparameters and its values to accomplish sklearn rules
         """
-        return {"C": self._C, "random_state": self._random_state, 'max_iter': self._max_iter}
+        return {
+            'C': self.C,
+            'random_state': self.random_state,
+            'max_iter': self.max_iter,
+            'use_predictions': self.use_predictions
+        }
 
     def set_params(self, **parameters):
         """Set hyperparmeters as specified by sklearn, needed in Gridsearchs
@@ -123,12 +127,16 @@ class Stree(BaseEstimator, ClassifierMixin):
             setattr(self, parameter, value)
         return self
 
+    # Added binary_only tag as required by sklearn check_estimator
+    def _more_tags(self):
+        return {'binary_only': True}
+
     def _linear_function(self, data: np.array, node: Snode) -> np.array:
         coef = node._vector[0, :].reshape(-1, data.shape[1])
         return data.dot(coef.T) + node._interceptor[0]
 
     def _split_data(self, node: Snode, data: np.ndarray, indices: np.ndarray) -> list:
-        if self.__use_predictions:
+        if self.use_predictions:
             yp = node._clf.predict(data)
             down = (yp == 1).reshape(-1, 1)
             res = np.expand_dims(node._clf.decision_function(data), 1)
@@ -147,11 +155,16 @@ class Stree(BaseEstimator, ClassifierMixin):
         return [data_up, indices_up, data_down, indices_down, res_up, res_down]
 
     def fit(self, X: np.ndarray, y: np.ndarray, title: str = 'root') -> 'Stree':
-        X, y = check_X_y(X, y.ravel())
+        from sklearn.utils.multiclass import check_classification_targets
+        if type(y).__name__ == 'np.ndarray':
+            y = y.ravel()
+        X, y = check_X_y(X, y)
+        self.classes_ = np.unique(y)
+        self.n_iter_ = self.max_iter
+        check_classification_targets(y)
         self.n_features_in_ = X.shape[1]
-        self._tree = self.train(X, y.ravel(), title)
+        self.tree_ = self.train(X, y.ravel(), title)
         self._build_predictor()
-        self.__trained = True
         return self
 
     def _build_predictor(self):
@@ -165,15 +178,15 @@ class Stree(BaseEstimator, ClassifierMixin):
             run_tree(node.get_down())
             run_tree(node.get_up())
 
-        run_tree(self._tree)
+        run_tree(self.tree_)
 
     def train(self, X: np.ndarray, y: np.ndarray, title: str = 'root') -> Snode:
         if np.unique(y).shape[0] == 1:
             # only 1 class => pure dataset
             return Snode(None, X, y, title + ', <pure>')
         # Train the model
-        clf = LinearSVC(max_iter=self._max_iter, C=self._C,
+        clf = LinearSVC(max_iter=self.max_iter, C=self.C,
-                        random_state=self._random_state)
+                        random_state=self.random_state)
         clf.fit(X, y)
         tree = Snode(clf, X, y, title)
         X_U, y_u, X_D, y_d, _, _ = self._split_data(tree, X, y)
@@ -184,8 +197,13 @@ class Stree(BaseEstimator, ClassifierMixin):
         tree.set_down(self.train(X_D, y_d, title + ' - Down'))
         return tree
 
-    def _reorder_results(self, y: np.array, indices: np.array) -> np.array:
+    def _reorder_results(self, y: np.array, indices: np.array, proba=False) -> np.array:
-        y_ordered = np.zeros(y.shape, dtype=int if y.ndim == 1 else float)
+        if proba:
+            # if predict_proba return np.array of floats
+            y_ordered = np.zeros(y.shape, dtype=float)
+        else:
+            # return array of same type given in y
+            y_ordered = y.copy()
         indices = indices.astype(int)
         for i, index in enumerate(indices):
             y_ordered[index] = y[i]
@@ -205,17 +223,15 @@ class Stree(BaseEstimator, ClassifierMixin):
             return np.append(k, m), np.append(l, n)
 
         # sklearn check
-        check_is_fitted(self)
+        check_is_fitted(self, ['tree_'])
         # Input validation
         X = check_array(X)
         # setup prediction & make it happen
         indices = np.arange(X.shape[0])
-        return self._reorder_results(*predict_class(X, indices, self._tree))
+        return self._reorder_results(*predict_class(X, indices, self.tree_)).ravel()
 
     def predict_proba(self, X: np.array) -> np.array:
-        """Computes an approximation of the probability of samples belonging to class 1 
+        """Computes an approximation of the probability of samples belonging to class 0 and 1
-        (nothing more, nothing less)
-
         :param X: dataset
         :type X: np.array
         """
@@ -247,29 +263,31 @@ class Stree(BaseEstimator, ClassifierMixin):
             return np.append(k, m), np.append(l, n)
 
         # sklearn check
-        check_is_fitted(self)
+        check_is_fitted(self, ['tree_'])
         # Input validation
         X = check_array(X)
         # setup prediction & make it happen
         indices = np.arange(X.shape[0])
-        result, indices = predict_class(X, indices, [], self._tree)
+        empty_dist = np.empty((X.shape[0], 1), dtype=float)
+        result, indices = predict_class(X, indices, empty_dist, self.tree_)
         result = result.reshape(X.shape[0], 2)
         # Turn distances to hyperplane into probabilities based on fitting distances
         # of samples to its hyperplane that classified them, to the sigmoid function
-        result[:, 1] = 1 / (1 + np.exp(-result[:, 1]))
+        result[:, 1] = 1 / (1 + np.exp(-result[:, 1])) # Probability of being 1
-        return self._reorder_results(result, indices)
+        result[:, 0] = 1 - result[:, 1] # Probability of being 0
+        return self._reorder_results(result, indices, proba=True)
 
     def score(self, X: np.array, y: np.array) -> float:
         """Return accuracy
         """
-        if not self.__trained:
+        # sklearn check
-            self.fit(X, y)
+        check_is_fitted(self)
         yp = self.predict(X).reshape(y.shape)
         right = (yp == y).astype(int)
         return np.sum(right) / len(y)
 
     def __iter__(self):
-        return Siterator(self._tree)
+        return Siterator(self.tree_)
 
     def __str__(self) -> str:
         output = ''
@@ -305,7 +323,5 @@ class Stree(BaseEstimator, ClassifierMixin):
         if not os.path.isdir(self.__folder):
             os.mkdir(self.__folder)
         with open(self.get_catalog_name(), 'w', encoding='utf-8') as catalog:
-            self._save_datasets(self._tree, catalog, 1)
+            self._save_datasets(self.tree_, catalog, 1)
-
-
 

stree/Strees_grapher.py

@@ -143,7 +143,7 @@ class Stree_grapher(Stree):
             self._pca = PCA(n_components=3)
             X = self._pca.fit_transform(X)
         res = super().fit(X, y)
-        self._tree_gr = self._copy_tree(self._tree)
+        self._tree_gr = self._copy_tree(self.tree_)
         self._fitted = True
         return res
 

stree/tests/Strees_test.py

@@ -71,7 +71,7 @@ class Stree_test(unittest.TestCase):
     def test_build_tree(self):
         """Check if the tree is built the same way as predictions of models
         """
-        self._check_tree(self._clf._tree)
+        self._check_tree(self._clf.tree_)
 
     def _get_file_data(self, file_name: str) -> tuple:
         """Return X, y from data, y is the last column in array
@@ -145,12 +145,14 @@ class Stree_test(unittest.TestCase):
         """
         # Element 28 has a different prediction than the truth
         decimals = 5
+        prob = 0.29026400766
         X, y = self._get_Xy()
         yp = self._clf.predict_proba(X[28, :].reshape(-1, X.shape[1]))
-        self.assertEqual(0, yp[0:, 0])
+        self.assertEqual(np.round(1 - prob, decimals), np.round(yp[0:, 0], decimals))
         self.assertEqual(1, y[28])
+        
         self.assertAlmostEqual(
-            round(0.29026400766, decimals),
+            round(prob, decimals),
             round(yp[0, 1], decimals),
             decimals
         )
@@ -161,7 +163,7 @@ class Stree_test(unittest.TestCase):
         decimals = 5
         X, y = self._get_Xy()
         yp = self._clf.predict_proba(X[:num, :])
-        self.assertListEqual(y[:num].tolist(), yp[:, 0].tolist())
+        self.assertListEqual(y[:num].tolist(), np.argmax(yp[:num], axis=1).tolist())
         expected_proba = [0.88395641, 0.36746962, 0.84158767, 0.34106833, 0.14269291, 0.85193236,
                           0.29876058, 0.7282164, 0.85958616, 0.89517877, 0.99745224, 0.18860349,
                           0.30756427, 0.8318412, 0.18981198, 0.15564624, 0.25740655, 0.22923355,
@@ -243,6 +245,14 @@ class Stree_test(unittest.TestCase):
             computed.append(str(node))
         self.assertListEqual(expected, computed)
 
+    def test_is_a_sklearn_classifier(self):
+        import warnings
+        from sklearn.exceptions import ConvergenceWarning
+        warnings.filterwarnings('ignore', category=ConvergenceWarning)
+        warnings.filterwarnings('ignore', category=RuntimeWarning)
+        from sklearn.utils.estimator_checks import check_estimator
+        check_estimator(Stree())
+
 class Snode_test(unittest.TestCase):
 
     def __init__(self, *args, **kwargs):
@@ -291,7 +301,7 @@ class Snode_test(unittest.TestCase):
             class_computed = classes[card == max_card]
             self.assertEqual(class_computed, node._class)
 
-        check_leave(self._clf._tree)
+        check_leave(self._clf.tree_)
 
     def test_nodes_coefs(self):
         """Check if the nodes of the tree have the right attributes filled
@@ -309,5 +319,4 @@ class Snode_test(unittest.TestCase):
             run_tree(node.get_down())
             run_tree(node.get_up())
 
-        run_tree(self._clf._tree)
+        run_tree(self._clf.tree_)
-

test2.ipynb

@@ -48,7 +48,7 @@
     {
      "output_type": "stream",
      "name": "stdout",
-     "text": "Fraud: 0.173% 492\nValid: 99.827% 284315\nX.shape (1492, 28)  y.shape (1492,)\nFraud: 33.110% 494\nValid: 66.890% 998\n"
+     "text": "Fraud: 0.173% 492\nValid: 99.827% 284315\nX.shape (1492, 28)  y.shape (1492,)\nFraud: 32.976% 492\nValid: 67.024% 1000\n"
     }
    ],
    "source": [
@@ -94,12 +94,16 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {},
+   "metadata": {
+    "tags": [
+     "outputPrepend"
+    ]
+   },
    "outputs": [
     {
      "output_type": "stream",
      "name": "stdout",
-     "text": "************** C=0.001 ****************************\nClassifier's accuracy (train): 0.9521\nClassifier's accuracy (test) : 0.9598\nroot\nroot - Down, <cgaf> - Leaf class=1 belief=0.980519 counts=(array([0, 1]), array([  6, 302]))\nroot - Up, <cgaf> - Leaf class=0 belief=0.940217 counts=(array([0, 1]), array([692,  44]))\n\n**************************************************\n************** C=0.01 ****************************\nClassifier's accuracy (train): 0.9521\nClassifier's accuracy (test) : 0.9643\nroot\nroot - Down\nroot - Down - Down, <cgaf> - Leaf class=1 belief=0.986842 counts=(array([0, 1]), array([  4, 300]))\nroot - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([1]))\nroot - Up, <cgaf> - Leaf class=0 belief=0.937754 counts=(array([0, 1]), array([693,  46]))\n\n**************************************************\n************** C=1 ****************************\nClassifier's accuracy (train): 0.9636\nClassifier's accuracy (test) : 0.9688\nroot\nroot - Down\nroot - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([308]))\nroot - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([8]))\nroot - Up, <cgaf> - Leaf class=0 belief=0.947802 counts=(array([0, 1]), array([690,  38]))\n\n**************************************************\n************** C=5 ****************************\nClassifier's accuracy (train): 0.9665\nClassifier's accuracy (test) : 0.9621\nroot\nroot - Down\nroot - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([308]))\nroot - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([11]))\nroot - Up\nroot - Up - Down\nroot - Up - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([1]))\nroot - Up - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([1]))\nroot - Up - Up\nroot - Up - Up - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([1]))\nroot - Up - Up - Up\nroot - Up - Up - Up - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([1]))\nroot - Up - Up - Up - Up, <cgaf> - Leaf class=0 belief=0.951456 counts=(array([0, 1]), array([686,  35]))\n\n**************************************************\n************** C=17 ****************************\nClassifier's accuracy (train): 0.9741\nClassifier's accuracy (test) : 0.9576\nroot\nroot - Down\nroot - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([306]))\nroot - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([10]))\nroot - Up\nroot - Up - Down\nroot - Up - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([3]))\nroot - Up - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([2]))\nroot - Up - Up\nroot - Up - Up - Down\nroot - Up - Up - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([1]))\nroot - Up - Up - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([3]))\nroot - Up - Up - Up\nroot - Up - Up - Up - Down\nroot - Up - Up - Up - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([1]))\nroot - Up - Up - Up - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([2]))\nroot - Up - Up - Up - Up\nroot - Up - Up - Up - Up - Down\nroot - Up - Up - Up - Up - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([1]))\nroot - Up - Up - Up - Up - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([2]))\nroot - Up - Up - Up - Up - Up\nroot - Up - Up - Up - Up - Up - Down\nroot - Up - Up - Up - Up - Up - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([7]))\nroot - Up - Up - Up - Up - Up - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([4]))\nroot - Up - Up - Up - Up - Up - Up, <cgaf> - Leaf class=0 belief=0.961538 counts=(array([0, 1]), array([675,  27]))\n\n**************************************************\n0.7816 secs\n"
+     "text": "************** C=0.001 ****************************\nClassifier's accuracy (train): 0.9579\nClassifier's accuracy (test) : 0.9509\nroot\nroot - Down, <cgaf> - Leaf class=1 belief=0.987013 counts=(array([0, 1]), array([  4, 304]))\nroot - Up, <cgaf> - Leaf class=0 belief=0.945652 counts=(array([0, 1]), array([696,  40]))\n\n**************************************************\n************** C=0.01 ****************************\nClassifier's accuracy (train): 0.9579\nClassifier's accuracy (test) : 0.9509\nroot\nroot - Down, <cgaf> - Leaf class=1 belief=0.990196 counts=(array([0, 1]), array([  3, 303]))\nroot - Up, <cgaf> - Leaf class=0 belief=0.944444 counts=(array([0, 1]), array([697,  41]))\n\n**************************************************\n************** C=1 ****************************\nClassifier's accuracy (train): 0.9693\nClassifier's accuracy (test) : 0.9576\nroot\nroot - Down\nroot - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([311]))\nroot - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([6]))\nroot - Up\nroot - Up - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([1]))\nroot - Up - Up, <cgaf> - Leaf class=0 belief=0.955923 counts=(array([0, 1]), array([694,  32]))\n\n**************************************************\n************** C=5 ****************************\nClassifier's accuracy (train): 0.9713\nClassifier's accuracy (test) : 0.9576\nroot\nroot - Down\nroot - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([314]))\nroot - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([6]))\nroot - Up, <cgaf> - Leaf class=0 belief=0.958564 counts=(array([0, 1]), array([694,  30]))\n\n**************************************************\n************** C=17 ****************************\nClassifier's accuracy (train): 0.9780\nClassifier's accuracy (test) : 0.9420\nroot\nroot - Down\nroot - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([301]))\nroot - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([13]))\nroot - Up\nroot - Up - Down\nroot - Up - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([17]))\nroot - Up - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([3]))\nroot - Up - Up\nroot - Up - Up - Down\nroot - Up - Up - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([1]))\nroot - Up - Up - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([1]))\nroot - Up - Up - Up\nroot - Up - Up - Up - Down\nroot - Up - Up - Up - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([2]))\nroot - Up - Up - Up - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([1]))\nroot - Up - Up - Up - Up, <cgaf> - Leaf class=0 belief=0.967376 counts=(array([0, 1]), array([682,  23]))\n\n**************************************************\n0.4537 secs\n"
     }
    ],
    "source": [
@@ -140,7 +144,7 @@
     {
      "output_type": "stream",
      "name": "stdout",
-     "text": "root\nroot - Down\nroot - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([306]))\nroot - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([10]))\nroot - Up\nroot - Up - Down\nroot - Up - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([3]))\nroot - Up - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([2]))\nroot - Up - Up\nroot - Up - Up - Down\nroot - Up - Up - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([1]))\nroot - Up - Up - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([3]))\nroot - Up - Up - Up\nroot - Up - Up - Up - Down\nroot - Up - Up - Up - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([1]))\nroot - Up - Up - Up - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([2]))\nroot - Up - Up - Up - Up\nroot - Up - Up - Up - Up - Down\nroot - Up - Up - Up - Up - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([1]))\nroot - Up - Up - Up - Up - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([2]))\nroot - Up - Up - Up - Up - Up\nroot - Up - Up - Up - Up - Up - Down\nroot - Up - Up - Up - Up - Up - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([7]))\nroot - Up - Up - Up - Up - Up - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([4]))\nroot - Up - Up - Up - Up - Up - Up, <cgaf> - Leaf class=0 belief=0.961538 counts=(array([0, 1]), array([675,  27]))\n"
+     "text": "root\nroot - Down\nroot - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([301]))\nroot - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([13]))\nroot - Up\nroot - Up - Down\nroot - Up - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([17]))\nroot - Up - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([3]))\nroot - Up - Up\nroot - Up - Up - Down\nroot - Up - Up - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([1]))\nroot - Up - Up - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([1]))\nroot - Up - Up - Up\nroot - Up - Up - Up - Down\nroot - Up - Up - Up - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([2]))\nroot - Up - Up - Up - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([1]))\nroot - Up - Up - Up - Up, <cgaf> - Leaf class=0 belief=0.967376 counts=(array([0, 1]), array([682,  23]))\n"
     }
    ],
    "source": [
@@ -157,7 +161,7 @@
     {
      "output_type": "stream",
      "name": "stdout",
-     "text": "root\nroot - Down\nroot - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([306]))\nroot - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([10]))\nroot - Up\nroot - Up - Down\nroot - Up - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([3]))\nroot - Up - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([2]))\nroot - Up - Up\nroot - Up - Up - Down\nroot - Up - Up - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([1]))\nroot - Up - Up - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([3]))\nroot - Up - Up - Up\nroot - Up - Up - Up - Down\nroot - Up - Up - Up - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([1]))\nroot - Up - Up - Up - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([2]))\nroot - Up - Up - Up - Up\nroot - Up - Up - Up - Up - Down\nroot - Up - Up - Up - Up - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([1]))\nroot - Up - Up - Up - Up - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([2]))\nroot - Up - Up - Up - Up - Up\nroot - Up - Up - Up - Up - Up - Down\nroot - Up - Up - Up - Up - Up - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([7]))\nroot - Up - Up - Up - Up - Up - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([4]))\nroot - Up - Up - Up - Up - Up - Up, <cgaf> - Leaf class=0 belief=0.961538 counts=(array([0, 1]), array([675,  27]))\n"
+     "text": "root\nroot - Down\nroot - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([301]))\nroot - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([13]))\nroot - Up\nroot - Up - Down\nroot - Up - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([17]))\nroot - Up - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([3]))\nroot - Up - Up\nroot - Up - Up - Down\nroot - Up - Up - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([1]))\nroot - Up - Up - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([1]))\nroot - Up - Up - Up\nroot - Up - Up - Up - Down\nroot - Up - Up - Up - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([2]))\nroot - Up - Up - Up - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([1]))\nroot - Up - Up - Up - Up, <cgaf> - Leaf class=0 belief=0.967376 counts=(array([0, 1]), array([682,  23]))\n"
     }
    ],
    "source": [
@@ -165,6 +169,38 @@
     "for i in clf:\n",
     "    print(i)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Check if the classifier is a sklearn estimator\n",
+    "from sklearn.utils.estimator_checks import check_estimator\n",
+    "check_estimator(Stree())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": "1 functools.partial(<function check_no_attributes_set_in_init at 0x12d18e0e0>, 'Stree')\n2 functools.partial(<function check_estimators_dtypes at 0x12d185200>, 'Stree')\n3 functools.partial(<function check_fit_score_takes_y at 0x12d1850e0>, 'Stree')\n4 functools.partial(<function check_sample_weights_pandas_series at 0x12d17eb00>, 'Stree')\n5 functools.partial(<function check_sample_weights_not_an_array at 0x12d17ec20>, 'Stree')\n6 functools.partial(<function check_sample_weights_list at 0x12d17ed40>, 'Stree')\n7 functools.partial(<function check_sample_weights_invariance at 0x12d17ee60>, 'Stree')\n8 functools.partial(<function check_estimators_fit_returns_self at 0x12d189200>, 'Stree')\n9 functools.partial(<function check_estimators_fit_returns_self at 0x12d189200>, 'Stree', readonly_memmap=True)\n10 functools.partial(<function check_complex_data at 0x12d181050>, 'Stree')\n11 functools.partial(<function check_dtype_object at 0x12d17ef80>, 'Stree')\n12 functools.partial(<function check_estimators_empty_data_messages at 0x12d185320>, 'Stree')\n13 functools.partial(<function check_pipeline_consistency at 0x12d181f80>, 'Stree')\n14 functools.partial(<function check_estimators_nan_inf at 0x12d185440>, 'Stree')\n15 functools.partial(<function check_estimators_overwrite_params at 0x12d189f80>, 'Stree')\n16 functools.partial(<function check_estimator_sparse_data at 0x12d17e9e0>, 'Stree')\n17 functools.partial(<function check_estimators_pickle at 0x12d185680>, 'Stree')\n18 functools.partial(<function check_classifier_data_not_an_array at 0x12d18e320>, 'Stree')\n19 functools.partial(<function check_classifiers_one_label at 0x12d185d40>, 'Stree')\n20 functools.partial(<function check_classifiers_classes at 0x12d1897a0>, 'Stree')\n21 functools.partial(<function check_estimators_partial_fit_n_features at 0x12d1857a0>, 'Stree')\n22 functools.partial(<function check_classifiers_train at 0x12d185e60>, 'Stree')\n23 functools.partial(<function check_classifiers_train at 0x12d185e60>, 'Stree', readonly_memmap=True)\n24 functools.partial(<function check_classifiers_regression_target at 0x12d18ed40>, 'Stree')\n25 functools.partial(<function check_supervised_y_no_nan at 0x12d17cb00>, 'Stree')\n26 functools.partial(<function check_supervised_y_2d at 0x12d189440>, 'Stree')\n27 functools.partial(<function check_estimators_unfitted at 0x12d189320>, 'Stree')\n28 functools.partial(<function check_non_transformer_estimators_n_iter at 0x12d18e8c0>, 'Stree')\n29 functools.partial(<function check_decision_proba_consistency at 0x12d18ee60>, 'Stree')\n30 functools.partial(<function check_fit2d_predict1d at 0x12d181560>, 'Stree')\n31 functools.partial(<function check_methods_subset_invariance at 0x12d181710>, 'Stree')\n32 functools.partial(<function check_fit2d_1sample at 0x12d181830>, 'Stree')\n33 functools.partial(<function check_fit2d_1feature at 0x12d181950>, 'Stree')\n34 functools.partial(<function check_fit1d at 0x12d181a70>, 'Stree')\n35 functools.partial(<function check_get_params_invariance at 0x12d18eb00>, 'Stree')\n36 functools.partial(<function check_set_params at 0x12d18ec20>, 'Stree')\n37 functools.partial(<function check_dict_unchanged at 0x12d181170>, 'Stree')\n38 functools.partial(<function check_dont_overwrite_parameters at 0x12d181440>, 'Stree')\n39 functools.partial(<function check_fit_idempotent at 0x12d192050>, 'Stree')\n"
+    }
+   ],
+   "source": [
+    "# Make checks one by one\n",
+    "c = 0\n",
+    "checks = check_estimator(Stree(), generate_only=True)\n",
+    "for check in checks:\n",
+    "    c += 1\n",
+    "    print(c, check[1])\n",
+    "    check[1](check[0])"
+   ]
   }
  ],
  "metadata": {