Make Stree a sklearn estimator

Added check_estimator in notebook test2
Added a Stree test with check_estimator

.travis.yml

@@ -10,4 +10,4 @@ notifications:
     on_success: never # default: change
     on_failure: always # default: always
 # command to run tests
-script: python -m unittest tests.Stree_test tests.Snode_test
+script: python -m unittest stree.tests

README.md

@@ -4,20 +4,38 @@
 
 Oblique Tree classifier based on SVM nodes
 
-## Example
+![Stree](https://raw.github.com/doctorado-ml/stree/master/example.png)
 
-### Jupyter
+## Installation
 
-[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/Doctorado-ML/STree/master?urlpath=lab/tree/test.ipynb)
+```bash
+pip install git+https://github.com/doctorado-ml/stree
+```
+
+## Examples
+
+### Jupyter notebooks
+
+##### Slow launch but better integration
+
+* [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/Doctorado-ML/STree/master?urlpath=lab/tree/test.ipynb) Test notebook
+
+##### Fast launch but have to run first commented out cell for setup
+
+* [![Test](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Doctorado-ML/STree/blob/master/test.ipynb) Test notebook
+
+* [![Test2](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Doctorado-ML/STree/blob/master/test2.ipynb) Another Test notebook
+
+* [![Test Graphics](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Doctorado-ML/STree/blob/master/test_graphs.ipynb) Test Graphics notebook
 
 ### Command line
 
-```python
+```bash
 python main.py
 ```
 
 ## Tests
 
-```python
+```bash
-python -m unittest -v tests.Stree_test tests.Snode_test
+python -m unittest -v stree.tests
 ```

data/.gitignore

@@ -1,2 +1 @@
-*.csv
+*
-*.txt

main.py

@@ -1,6 +1,6 @@
 import time
 from sklearn.model_selection import train_test_split
-from trees.Stree import Stree
+from stree import Stree
 
 random_state=1
 
@@ -50,9 +50,8 @@ print(f"Classifier's accuracy (test) : {clf.score(Xtest, ytest):.4f}")
 proba = clf.predict_proba(Xtest)
 print("Checking that we have correct probabilities, these are probabilities of sample belonging to class 1")
 res0 = proba[proba[:, 0] == 0]
-res1 = proba[proba[:, 0] == 0]
+res1 = proba[proba[:, 0] == 1]
-print("++++++++++res0++++++++++++")
+print("++++++++++res0 > .8++++++++++++")
 print(res0[res0[:, 1] > .8])
-print("**********res1************")
+print("**********res1 < .4************")
 print(res1[res1[:, 1] < .4])
-print(clf.predict_proba(Xtest))

requirements.txt

@@ -1,3 +1,5 @@
-numpy==1.18.2
+numpy
-scikit-learn==0.22.2
+scikit-learn
-pandas==1.0.3
+pandas
+matplotlib
+ipympl

setup.py

@@ -0,0 +1,39 @@
+import setuptools
+
+__version__ = "0.9rc3"
+__author__ = "Ricardo Montañana Gómez"
+
+def readme():
+    with open('README.md') as f:
+        return f.read()
+
+
+setuptools.setup(
+    name='STree',
+    version=__version__,
+    license='MIT License',
+    description='Oblique decision tree with svm nodes',
+    long_description=readme(),
+    long_description_content_type='text/markdown',
+    packages=setuptools.find_packages(),
+    url='https://github.com/doctorado-ml/stree',
+    author=__author__,
+    author_email='ricardo.montanana@alu.uclm.es',
+    keywords='scikit-learn oblique-classifier oblique-decision-tree decision-tree svm svc',
+    classifiers=[
+        'Development Status :: 4 - Beta',
+        'License :: OSI Approved :: MIT License',
+        'Programming Language :: Python :: 3.7',
+        'Natural Language :: English',
+        'Topic :: Scientific/Engineering :: Artificial Intelligence',
+        'Intended Audience :: Science/Research'
+    ],
+    install_requires=[
+        'scikit-learn>=0.23.0',
+        'numpy',
+        'matplotlib',
+        'ipympl'
+    ],
+    test_suite="stree.tests",
+    zip_safe=False
+)

stree/Strees.py

@@ -8,34 +8,117 @@ Uses LinearSVC
 '''
 
 import typing
+import os
 
 import numpy as np
 from sklearn.base import BaseEstimator, ClassifierMixin
 from sklearn.svm import LinearSVC
 from sklearn.utils.validation import check_X_y, check_array, check_is_fitted
 
-from trees.Snode import Snode
+
-from trees.Siterator import Siterator
+class Snode:
+    def __init__(self, clf: LinearSVC, X: np.ndarray, y: np.ndarray, title: str):
+        self._clf = clf
+        self._vector = None if clf is None else clf.coef_
+        self._interceptor = 0. if clf is None else clf.intercept_
+        self._title = title
+        self._belief = 0.  # belief of the prediction in a leaf node based on samples
+        # Only store dataset in Testing
+        self._X = X if os.environ.get('TESTING', 'NS') != 'NS' else None
+        self._y = y
+        self._down = None
+        self._up = None
+        self._class = None
+
+    @classmethod
+    def copy(cls, node: 'Snode') -> 'Snode':
+        return cls(node._clf, node._X, node._y, node._title)
+
+    def set_down(self, son):
+        self._down = son
+
+    def set_up(self, son):
+        self._up = son
+
+    def is_leaf(self) -> bool:
+        return self._up is None and self._down is None
+
+    def get_down(self) -> 'Snode':
+        return self._down
+
+    def get_up(self) -> 'Snode':
+        return self._up
+
+    def make_predictor(self):
+        """Compute the class of the predictor and its belief based on the subdataset of the node
+        only if it is a leaf
+        """
+        if not self.is_leaf():
+            return
+        classes, card = np.unique(self._y, return_counts=True)
+        if len(classes) > 1:
+            max_card = max(card)
+            min_card = min(card)
+            try:
+                self._belief = max_card / (max_card + min_card)
+            except:
+                self._belief = 0.
+            self._class = classes[card == max_card][0]
+        else:
+            self._belief = 1
+            self._class = classes[0]
+
+    def __str__(self) -> str:
+        if self.is_leaf():
+            return f"{self._title} - Leaf class={self._class} belief={self._belief:.6f} counts={np.unique(self._y, return_counts=True)}"
+        else:
+            return f"{self._title}"
+
+
+class Siterator:
+    """Stree preorder iterator
+    """
+
+    def __init__(self, tree: Snode):
+        self._stack = []
+        self._push(tree)
+
+    def __iter__(self):
+        return self
+
+    def _push(self, node: Snode):
+        if node is not None:
+            self._stack.append(node)
+
+    def __next__(self) -> Snode:
+        if len(self._stack) == 0:
+            raise StopIteration()
+        node = self._stack.pop()
+        self._push(node.get_up())
+        self._push(node.get_down())
+        return node
 
 
 class Stree(BaseEstimator, ClassifierMixin):
     """
     """
+    __folder = 'data/'
 
-    def __init__(self, C=1.0, max_iter: int = 1000, random_state: int = 0, use_predictions: bool = False):
+    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
@@ -44,12 +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)
@@ -68,31 +155,38 @@ 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):
         """Process the leaves to make them predictors
         """
+
         def run_tree(node: Snode):
             if node.is_leaf():
                 node.make_predictor()
                 return
             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)
@@ -103,8 +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]
@@ -122,21 +221,21 @@ class Stree(BaseEstimator, ClassifierMixin):
             k, l = predict_class(d, i_d, node.get_down())
             m, n = predict_class(u, i_u, node.get_up())
             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
         """
+
         def predict_class(xp: np.array, indices: np.array, dist: np.array, node: Snode) -> np.array:
             """Run the tree to compute predictions
 
@@ -162,30 +261,33 @@ class Stree(BaseEstimator, ClassifierMixin):
             k, l = predict_class(d, i_d, r_d, node.get_down())
             m, n = predict_class(u, i_u, r_u, node.get_up())
             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 = ''
@@ -218,5 +320,8 @@ class Stree(BaseEstimator, ClassifierMixin):
     def save_sub_datasets(self):
         """Save the every dataset stored in the tree to check with manual classifier
         """
+        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

@@ -0,0 +1,184 @@
+'''
+__author__ = "Ricardo Montañana Gómez"
+__copyright__ = "Copyright 2020, Ricardo Montañana Gómez"
+__license__ = "MIT"
+__version__ = "0.9"
+Plot 3D views of nodes in Stree
+'''
+
+import os
+
+import matplotlib.pyplot as plt
+import numpy as np
+from sklearn.decomposition import PCA
+from mpl_toolkits.mplot3d import Axes3D
+
+from .Strees import Stree, Snode, Siterator
+
+class Snode_graph(Snode):
+
+    def __init__(self, node: Stree):
+        self._plot_size = (8, 8)
+        self._xlimits = (None, None)
+        self._ylimits = (None, None)
+        self._zlimits = (None, None)
+        n = Snode.copy(node)
+        super().__init__(n._clf, n._X, n._y, n._title)
+
+    def set_plot_size(self, size: tuple):
+        self._plot_size = size
+
+    def _is_pure(self) -> bool:
+        """is considered pure a leaf node with one label
+        """
+        if self.is_leaf():
+            return self._belief == 1.
+        return False
+
+    def set_axis_limits(self, limits: tuple):
+        self._xlimits = limits[0]
+        self._ylimits = limits[1]
+        self._zlimits = limits[2]
+
+    def _set_graphics_axis(self, ax: Axes3D):
+        ax.set_xlim(self._xlimits)
+        ax.set_ylim(self._ylimits)
+        ax.set_zlim(self._zlimits)
+
+    def save_hyperplane(self, save_folder: str = './', save_prefix: str = '', save_seq: int = 1):
+        _, fig = self.plot_hyperplane()
+        name = f"{save_folder}{save_prefix}STnode{save_seq}.png"
+        fig.savefig(name, bbox_inches='tight')
+        plt.close(fig)
+
+    def _get_cmap(self):
+        cmap = 'jet'
+        if self._is_pure():
+            if self._class == 1:
+                cmap = 'jet_r'
+        return cmap
+
+    def _graph_title(self):
+        n_class, card = np.unique(self._y, return_counts=True)
+        return f"{self._title} {n_class} {card}"
+
+    def plot_hyperplane(self, plot_distribution: bool = True):
+        fig = plt.figure(figsize=self._plot_size)
+        ax = fig.add_subplot(1, 1, 1, projection='3d')
+        if not self._is_pure():
+            # Can't plot hyperplane of leaves with one label because it hasn't classiffier
+            # get the splitting hyperplane
+            def hyperplane(x, y): return (-self._interceptor - self._vector[0][0] * x
+                                          - self._vector[0][1] * y) / self._vector[0][2]
+
+            tmpx = np.linspace(self._X[:, 0].min(), self._X[:, 0].max())
+            tmpy = np.linspace(self._X[:, 1].min(), self._X[:, 1].max())
+            xx, yy = np.meshgrid(tmpx, tmpy)
+            ax.plot_surface(xx, yy, hyperplane(xx, yy), alpha=.5, antialiased=True,
+                            rstride=1, cstride=1, cmap='seismic')
+            self._set_graphics_axis(ax)
+        if plot_distribution:
+            self.plot_distribution(ax)
+        else:
+            plt.title(self._graph_title())
+            plt.show()
+        return ax, fig
+
+    def plot_distribution(self, ax: Axes3D = None):
+        if ax is None:
+            fig = plt.figure(figsize=self._plot_size)
+            ax = fig.add_subplot(1, 1, 1, projection='3d')
+        plt.title(self._graph_title())
+        cmap = self._get_cmap()
+        ax.scatter(self._X[:, 0], self._X[:, 1],
+                   self._X[:, 2], c=self._y, cmap=cmap)
+        ax.set_xlabel('X0')
+        ax.set_ylabel('X1')
+        ax.set_zlabel('X2')
+        plt.show()
+
+class Stree_grapher(Stree):
+    """Build 3d graphs of any dataset, if it's more than 3 features PCA shall
+    make its magic
+    """
+
+    def __init__(self, params: dict):
+        self._plot_size = (8, 8)
+        self._tree_gr = None
+        # make Snode store X's
+        os.environ['TESTING'] = '1'
+        self._fitted = False
+        self._pca = None
+        super().__init__(**params)
+
+    def __del__(self):
+        try:
+            os.environ.pop('TESTING')
+        except:
+            pass
+        plt.close('all')
+
+    def _copy_tree(self, node: Snode) -> Snode_graph:
+        mirror = Snode_graph(node)
+        # clone node
+        mirror._class = node._class
+        mirror._belief = node._belief
+        if node.get_down() is not None:
+            mirror.set_down(self._copy_tree(node.get_down()))
+        if node.get_up() is not None:
+            mirror.set_up(self._copy_tree(node.get_up()))
+        return mirror
+
+    def fit(self, X: np.array, y: np.array) -> Stree:
+        """Fit the Stree and copy the tree in a Snode_graph tree
+
+        :param X: Dataset
+        :type X: np.array
+        :param y: Labels
+        :type y: np.array
+        :return: Stree model
+        :rtype: Stree
+        """
+        if X.shape[1] != 3:
+            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._fitted = True
+        return res
+
+    def score(self, X: np.array, y: np.array) -> float:
+        self._check_fitted()
+        if X.shape[1] != 3:
+            X = self._pca.transform(X)
+        return super().score(X, y)
+
+    def _check_fitted(self):
+        if not self._fitted:
+            raise Exception('Have to fit the grapher first!')
+
+    def save_all(self, save_folder: str = './', save_prefix: str = ''):
+        """Save all the node plots in png format, each with a sequence number
+
+        :param save_folder: folder where the plots are saved, defaults to './'
+        :type save_folder: str, optional
+        """
+        self._check_fitted()
+        if not os.path.isdir(save_folder):
+            os.mkdir(save_folder)
+        seq = 1
+        for node in self:
+            node.save_hyperplane(save_folder=save_folder,
+                                 save_prefix=save_prefix, save_seq=seq)
+            seq += 1
+
+    def plot_all(self):
+        """Plots all the nodes
+        """
+        self._check_fitted()
+        for node in self:
+            node.plot_hyperplane()
+
+    def __iter__(self):
+        return Siterator(self._tree_gr)
+

stree/__init__.py

@@ -0,0 +1,2 @@
+from .Strees import Stree, Snode, Siterator
+from .Strees_grapher import Stree_grapher, Snode_graph

stree/tests/Strees_test.py

@@ -5,7 +5,7 @@ import unittest
 import numpy as np
 from sklearn.datasets import make_classification
 
-from trees.Stree import Stree, Snode
+from stree import Stree, Snode
 
 
 class Stree_test(unittest.TestCase):
@@ -16,7 +16,7 @@ class Stree_test(unittest.TestCase):
         self._clf = Stree(random_state=self._random_state,
                           use_predictions=False)
         self._clf.fit(*self._get_Xy())
-        super(Stree_test, self).__init__(*args, **kwargs)
+        super().__init__(*args, **kwargs)
 
     @classmethod
     def tearDownClass(cls):
@@ -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
@@ -144,24 +144,35 @@ class Stree_test(unittest.TestCase):
         """Check that element 28 has a prediction different that the current label
         """
         # 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.assertEqual(0.29026400766, round(yp[0, 1], 11))
+        
+        self.assertAlmostEqual(
+            round(prob, decimals),
+            round(yp[0, 1], decimals),
+            decimals
+        )
 
     def test_multiple_predict_proba(self):
         # First 27 elements the predictions are the same as the truth
         num = 27
+        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,
                           0.87365959, 0.49928689, 0.95574351, 0.28761257, 0.28906333, 0.32643692,
                           0.29788483, 0.01657364, 0.81149083]
-        self.assertListEqual(expected_proba, np.round(yp[:, 1], decimals=8).tolist())
+        expected = np.round(expected_proba, decimals=decimals).tolist()
+        computed = np.round(yp[:, 1], decimals=decimals).tolist()
+        for i in range(len(expected)):
+            self.assertAlmostEqual(expected[i], computed[i], decimals)
 
     def build_models(self):
         """Build and train two models, model_clf will use the sklearn classifier to
@@ -217,7 +228,95 @@ class Stree_test(unittest.TestCase):
         #
         self.assertListEqual(yp_line.tolist(), yp_once.tolist())
 
+    def test_iterator(self):
+        """Check preorder iterator
+        """
+        expected = [
+            'root',
+            'root - Down',
+            'root - Down - Down, <cgaf> - Leaf class=1 belief=0.975989 counts=(array([0, 1]), array([ 17, 691]))',
+            'root - Down - Up',
+            'root - Down - Up - Down, <cgaf> - Leaf class=1 belief=0.750000 counts=(array([0, 1]), array([1, 3]))',
+            'root - Down - Up - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([7]))',
+            'root - Up, <cgaf> - Leaf class=0 belief=0.928297 counts=(array([0, 1]), array([725,  56]))',
+        ]
+        computed = []
+        for node in self._clf:
+            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):
+        os.environ['TESTING'] = '1'
+        self._random_state = 1
+        self._clf = Stree(random_state=self._random_state,
+                          use_predictions=True)
+        self._clf.fit(*self._get_Xy())
+        super().__init__(*args, **kwargs)
 
+    @classmethod
+    def tearDownClass(cls):
+        try:
+            os.environ.pop('TESTING')
+        except:
+            pass
+
+    def _get_Xy(self):
+        X, y = make_classification(n_samples=1500, n_features=3, n_informative=3,
+                                   n_redundant=0, n_repeated=0, n_classes=2, n_clusters_per_class=2,
+                                   class_sep=1.5, flip_y=0, weights=[0.5, 0.5], random_state=self._random_state)
+        return X, y
+
+    def test_attributes_in_leaves(self):
+        """Check if the attributes in leaves have correct values so they form a predictor
+        """
+
+        def check_leave(node: Snode):
+            if not node.is_leaf():
+                check_leave(node.get_down())
+                check_leave(node.get_up())
+                return
+            # Check Belief in leave
+            classes, card = np.unique(node._y, return_counts=True)
+            max_card = max(card)
+            min_card = min(card)
+            if len(classes) > 1:
+                try:
+                    belief = max_card / (max_card + min_card)
+                except:
+                    belief = 0.
+            else:
+                belief = 1
+            self.assertEqual(belief, node._belief)
+            # Check Class
+            class_computed = classes[card == max_card]
+            self.assertEqual(class_computed, node._class)
+
+        check_leave(self._clf.tree_)
+
+    def test_nodes_coefs(self):
+        """Check if the nodes of the tree have the right attributes filled
+        """
+
+        def run_tree(node: Snode):
+            if node._belief < 1:
+                # only exclude pure leaves
+                self.assertIsNotNone(node._clf)
+                self.assertIsNotNone(node._clf.coef_)
+                self.assertIsNotNone(node._vector)
+                self.assertIsNotNone(node._interceptor)
+            if node.is_leaf():
+                return
+            run_tree(node.get_down())
+            run_tree(node.get_up())
+
+        run_tree(self._clf.tree_)

stree/tests/__init__.py

@@ -0,0 +1 @@
+from .Strees_test import Stree_test, Snode_test

test2.ipynb

@@ -6,13 +6,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import numpy as np\n",
+    "#\n",
-    "import pandas as pd\n",
+    "# Google Colab setup\n",
-    "from sklearn.svm import LinearSVC\n",
+    "#\n",
-    "from sklearn.tree import DecisionTreeClassifier\n",
+    "#!pip install git+https://github.com/doctorado-ml/stree"
-    "from sklearn.datasets import make_classification, load_iris, load_wine\n",
-    "from trees.Stree import Stree\n",
-    "import time"
    ]
   },
   {
@@ -20,6 +17,22 @@
    "execution_count": 2,
    "metadata": {},
    "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from sklearn.svm import LinearSVC\n",
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "from sklearn.datasets import make_classification, load_iris, load_wine\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from stree import Stree\n",
+    "import time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "import os\n",
     "if not os.path.isfile('data/creditcard.csv'):\n",
@@ -29,7 +42,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -39,10 +52,6 @@
     }
    ],
    "source": [
-    "import time\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from trees.Stree import Stree\n",
-    "\n",
     "random_state=1\n",
     "\n",
     "def load_creditcard(n_examples=0):\n",
@@ -84,20 +93,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 5,
-   "metadata": {},
+   "metadata": {
-   "outputs": [],
+    "tags": [
-   "source": []
+     "outputPrepend"
+    ]
    },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
    "outputs": [
     {
      "output_type": "stream",
      "name": "stdout",
-     "text": "************** C=0.001 ****************************\nClassifier's accuracy (train): 0.9550\nClassifier's accuracy (test) : 0.9487\nroot\nroot - Down\nroot - Down - Down, <cgaf> - Leaf class=1 belief=0.977346 counts=(array([0, 1]), array([  7, 302]))\nroot - Up\nroot - Up - Down, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([1]))\nroot - 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([2]))\nroot - Up - Up - Up, <cgaf> - Leaf class=0 belief=0.945280 counts=(array([0, 1]), array([691,  40]))\n\n**************************************************\n************** C=0.01 ****************************\nClassifier's accuracy (train): 0.9569\nClassifier's accuracy (test) : 0.9576\nroot\nroot - Down, <cgaf> - Leaf class=1 belief=0.986971 counts=(array([0, 1]), array([  4, 303]))\nroot - Up, <cgaf> - Leaf class=0 belief=0.944369 counts=(array([0, 1]), array([696,  41]))\n\n**************************************************\n************** C=1 ****************************\nClassifier's accuracy (train): 0.9674\nClassifier's accuracy (test) : 0.9554\nroot\nroot - Down\nroot - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([310]))\nroot - Up, <cgaf> - Leaf class=0 belief=0.953232 counts=(array([0, 1]), array([693,  34]))\nroot - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([7]))\n\n**************************************************\n************** C=5 ****************************\nClassifier's accuracy (train): 0.9693\nClassifier's accuracy (test) : 0.9487\nroot\nroot - Down\nroot - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([310]))\nroot - Up\nroot - Up - Down, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([1]))\nroot - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([7]))\nroot - Up - Up\nroot - Up - Up - Down, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([2]))\nroot - Up - Up - Up\nroot - Up - Up - Up - Down, <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([2]))\nroot - Up - Up - Up - Up - Up, <cgaf> - Leaf class=0 belief=0.955494 counts=(array([0, 1]), array([687,  32]))\nroot - Up - Up - Up - Up - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([1]))\n\n**************************************************\n************** C=17 ****************************\nClassifier's accuracy (train): 0.9780\nClassifier's accuracy (test) : 0.9487\nroot\nroot - Down\nroot - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([301]))\nroot - Up\nroot - Up - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([2]))\nroot - Down - Up\nroot - Down - Up - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([15]))\nroot - Up - Up\nroot - Up - Up - Down\nroot - Up - Up - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([3]))\nroot - Down - Up - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([15]))\nroot - Up - Up - Up, <cgaf> - Leaf class=0 belief=0.967468 counts=(array([0, 1]), array([684,  23]))\nroot - Up - Up - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([1]))\n\n**************************************************\n0.7277 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": [
@@ -115,7 +121,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -138,10 +144,11 @@
     {
      "output_type": "stream",
      "name": "stdout",
-     "text": "root\nroot - Down\nroot - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([301]))\nroot - Up\nroot - Up - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([2]))\nroot - Down - Up\nroot - Down - Up - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([15]))\nroot - Up - Up\nroot - Up - Up - Down\nroot - Up - Up - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([3]))\nroot - Down - Up - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([15]))\nroot - Up - Up - Up, <cgaf> - Leaf class=0 belief=0.967468 counts=(array([0, 1]), array([684,  23]))\nroot - Up - Up - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([1]))\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": [
+    "#check iterator\n",
     "for i in list(clf):\n",
     "    print(i)"
    ]
@@ -154,75 +161,46 @@
     {
      "output_type": "stream",
      "name": "stdout",
-     "text": "root\nroot - Down\nroot - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([301]))\nroot - Up\nroot - Up - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([2]))\nroot - Down - Up\nroot - Down - Up - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([15]))\nroot - Up - Up\nroot - Up - Up - Down\nroot - Up - Up - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([3]))\nroot - Down - Up - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([15]))\nroot - Up - Up - Up, <cgaf> - Leaf class=0 belief=0.967468 counts=(array([0, 1]), array([684,  23]))\nroot - Up - Up - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([1]))\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": [
+    "#check iterator again\n",
     "for i in clf:\n",
     "    print(i)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [
+   "outputs": [],
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …",
-      "application/vnd.jupyter.widget-view+json": {
-       "version_major": 2,
-       "version_minor": 0,
-       "model_id": "0025f832c1734afc944021e5990c2d11"
-      }
-     },
-     "metadata": {}
-    }
-   ],
    "source": [
-    "%matplotlib widget\n",
+    "# Check if the classifier is a sklearn estimator\n",
-    "from mpl_toolkits.mplot3d import Axes3D\n",
+    "from sklearn.utils.estimator_checks import check_estimator\n",
-    "import matplotlib.pyplot as plt\n",
+    "check_estimator(Stree())"
-    "from matplotlib import cm\n",
-    "from matplotlib.ticker import LinearLocator, FormatStrFormatter\n",
-    "import numpy as np\n",
-    "\n",
-    "fig = plt.figure()\n",
-    "ax = fig.gca(projection='3d')\n",
-    "\n",
-    "scale = 8\n",
-    "# Make data.\n",
-    "X = np.arange(-scale, scale, 0.25)\n",
-    "Y = np.arange(-scale, scale, 0.25)\n",
-    "X, Y = np.meshgrid(X, Y)\n",
-    "Z = X**2 + Y**2\n",
-    "\n",
-    "# Plot the surface.\n",
-    "surf = ax.plot_surface(X, Y, Z, cmap=cm.coolwarm,\n",
-    "                   linewidth=0, antialiased=False)\n",
-    "\n",
-    "# Customize the z axis.\n",
-    "ax.set_zlim(0, 100)\n",
-    "ax.zaxis.set_major_locator(LinearLocator(10))\n",
-    "ax.zaxis.set_major_formatter(FormatStrFormatter('%.02f'))\n",
-    "\n",
-    "# rotate the axes and update\n",
-    "#for angle in range(0, 360):\n",
-    "#   ax.view_init(30, 40)\n",
-    "\n",
-    "# Add a color bar which maps values to colors.\n",
-    "fig.colorbar(surf, shrink=0.5, aspect=5)\n",
-    "\n",
-    "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
-   "source": []
+    {
+     "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": {

tests/Snode_test.py

@@ -1,72 +0,0 @@
-import os
-import unittest
-
-import numpy as np
-from sklearn.datasets import make_classification
-
-from trees.Stree import Stree, Snode
-
-
-class Snode_test(unittest.TestCase):
-
-    def __init__(self, *args, **kwargs):
-        os.environ['TESTING'] = '1'
-        self._random_state = 1
-        self._clf = Stree(random_state=self._random_state,
-                            use_predictions=True)
-        self._clf.fit(*self._get_Xy())
-        super(Snode_test, self).__init__(*args, **kwargs)
-
-    @classmethod
-    def tearDownClass(cls):
-        try:
-            os.environ.pop('TESTING')
-        except:
-            pass
-
-    def _get_Xy(self):
-        X, y = make_classification(n_samples=1500, n_features=3, n_informative=3,
-                                   n_redundant=0, n_repeated=0, n_classes=2, n_clusters_per_class=2,
-                                   class_sep=1.5, flip_y=0, weights=[0.5, 0.5], random_state=self._random_state)
-        return X, y
-
-    def test_attributes_in_leaves(self):
-        """Check if the attributes in leaves have correct values so they form a predictor
-        """
-        def check_leave(node: Snode):
-            if not node.is_leaf():
-                check_leave(node.get_down())
-                check_leave(node.get_up())
-                return
-            # Check Belief in leave
-            classes, card = np.unique(node._y, return_counts=True)
-            max_card = max(card)
-            min_card = min(card)
-            if len(classes) > 1:
-                try:
-                    belief = max_card / (max_card + min_card)
-                except:
-                    belief = 0.
-            else:
-                belief = 1
-            self.assertEqual(belief, node._belief)
-            # Check Class
-            class_computed = classes[card == max_card]
-            self.assertEqual(class_computed, node._class)
-        check_leave(self._clf._tree)
-    
-    def test_nodes_coefs(self):
-        """Check if the nodes of the tree have the right attributes filled
-        """
-        def run_tree(node: Snode):
-            if node._belief < 1:
-                # only exclude pure leaves
-                self.assertIsNotNone(node._clf)
-                self.assertIsNotNone(node._clf.coef_)
-                self.assertIsNotNone(node._vector)
-                self.assertIsNotNone(node._interceptor)
-            if node.is_leaf():
-                return
-            run_tree(node.get_down())
-            run_tree(node.get_up())
-        run_tree(self._clf._tree)

trees/Siterator.py

@@ -1,34 +0,0 @@
-'''
-__author__ = "Ricardo Montañana Gómez"
-__copyright__ = "Copyright 2020, Ricardo Montañana Gómez"
-__license__ = "MIT"
-__version__ = "0.9"
-Inorder iterator for the binary tree of Snodes
-Uses LinearSVC
-'''
-
-from trees.Snode import Snode
-
-
-class Siterator:
-    """Inorder iterator
-    """
-
-    def __init__(self, tree: Snode):
-        self._stack = []
-        self._push(tree)
-
-    def __iter__(self):
-        return self
-
-    def _push(self, node: Snode):
-        while (node is not None):
-            self._stack.insert(0, node)
-            node = node.get_down()
-
-    def __next__(self) -> Snode:
-        if len(self._stack) == 0:
-            raise StopIteration()
-        node = self._stack.pop()
-        self._push(node.get_up())
-        return node

trees/Snode.py

@@ -1,70 +0,0 @@
-'''
-__author__ = "Ricardo Montañana Gómez"
-__copyright__ = "Copyright 2020, Ricardo Montañana Gómez"
-__license__ = "MIT"
-__version__ = "0.9"
-Node of the Stree (binary tree)
-'''
-
-import os
-
-import numpy as np
-from sklearn.svm import LinearSVC
-
-
-class Snode:
-    def __init__(self, clf: LinearSVC, X: np.ndarray, y: np.ndarray, title: str):
-        self._clf = clf
-        self._vector = None if clf is None else clf.coef_
-        self._interceptor = 0. if clf is None else clf.intercept_
-        self._title = title
-        self._belief = 0.  # belief of the prediction in a leaf node based on samples
-        # Only store dataset in Testing 
-        self._X = X if os.environ.get('TESTING', 'NS') != 'NS' else None
-        self._y = y
-        self._down = None
-        self._up = None
-        self._class = None
-
-    def set_down(self, son):
-        self._down = son
-
-    def set_up(self, son):
-        self._up = son
-
-    def is_leaf(self,) -> bool:
-        return self._up is None and self._down is None
-
-    def get_down(self) -> 'Snode':
-        return self._down
-
-    def get_up(self) -> 'Snode':
-        return self._up
-
-    def make_predictor(self):
-        """Compute the class of the predictor and its belief based on the subdataset of the node
-        only if it is a leaf
-        """
-        # Clean memory
-        #self._X = None
-        #self._y = None
-        if not self.is_leaf():
-            return
-        classes, card = np.unique(self._y, return_counts=True)
-        if len(classes) > 1:
-            max_card = max(card)
-            min_card = min(card)
-            try:
-                self._belief = max_card / (max_card + min_card)
-            except:
-                self._belief = 0.
-            self._class = classes[card == max_card][0]
-        else:
-            self._belief = 1
-            self._class = classes[0]
-
-    def __str__(self) -> str:
-        if self.is_leaf():
-            return f"{self._title} - Leaf class={self._class} belief={self._belief:.6f} counts={np.unique(self._y, return_counts=True)}"
-        else:
-            return f"{self._title}"