first approx to grapher

requirements.txt

@@ -1,3 +1,4 @@
 numpy==1.18.2
 scikit-learn==0.22.2
 pandas==1.0.3
+matplotlib==3.2.1

test2.ipynb

@@ -35,7 +35,7 @@
     {
      "output_type": "stream",
      "name": "stdout",
-     "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"
+     "text": "Fraud: 0.173% 492\nValid: 99.827% 284315\nX.shape (1492, 28)  y.shape (1492,)\nFraud: 33.244% 496\nValid: 66.756% 996\n"
     }
    ],
    "source": [
@@ -97,7 +97,7 @@
     {
      "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): 0.9559\nClassifier's accuracy (test) : 0.9531\nroot\nroot - Down\nroot - Down - Down, <cgaf> - Leaf class=1 belief=0.980769 counts=(array([0, 1]), array([  6, 306]))\nroot - Up\nroot - Up - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([1]))\nroot - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([1]))\nroot - Up - Up, <cgaf> - Leaf class=0 belief=0.945205 counts=(array([0, 1]), array([690,  40]))\n\n**************************************************\n************** C=0.01 ****************************\nClassifier's accuracy (train): 0.9588\nClassifier's accuracy (test) : 0.9509\nroot\nroot - Down\nroot - Down - Down, <cgaf> - Leaf class=1 belief=0.990323 counts=(array([0, 1]), array([  3, 307]))\nroot - Up, <cgaf> - Leaf class=0 belief=0.945205 counts=(array([0, 1]), array([690,  40]))\nroot - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([4]))\n\n**************************************************\n************** C=1 ****************************\nClassifier's accuracy (train): 0.9732\nClassifier's accuracy (test) : 0.9531\nroot\nroot - Down\nroot - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([313]))\nroot - Up\nroot - Up - Down\nroot - Up - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([6]))\nroot - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([9]))\nroot - Up - Up, <cgaf> - Leaf class=0 belief=0.960839 counts=(array([0, 1]), array([687,  28]))\nroot - Up - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([1]))\n\n**************************************************\n************** C=5 ****************************\nClassifier's accuracy (train): 0.9732\nClassifier's accuracy (test) : 0.9509\nroot\nroot - Down\nroot - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([312]))\nroot - Up\nroot - Up - Down\nroot - Up - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([7]))\nroot - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([10]))\nroot - Up - Up, <cgaf> - Leaf class=0 belief=0.960784 counts=(array([0, 1]), array([686,  28]))\nroot - Up - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([1]))\n\n**************************************************\n************** C=17 ****************************\nClassifier's accuracy (train): 0.9741\nClassifier's accuracy (test) : 0.9531\nroot\nroot - Down\nroot - Down - Down\nroot - Down - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([312]))\nroot - Up\nroot - Up - Down\nroot - Up - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([8]))\nroot - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([10]))\nroot - Down - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([1]))\nroot - Up - Up, <cgaf> - Leaf class=0 belief=0.961756 counts=(array([0, 1]), array([679,  27]))\nroot - Up - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([7]))\n\n**************************************************\n0.8116 secs\n"
     }
    ],
    "source": [
@@ -132,97 +132,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "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\nroot - Down - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([312]))\nroot - Up\nroot - Up - Down\nroot - Up - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([8]))\nroot - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([10]))\nroot - Down - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([1]))\nroot - Up - Up, <cgaf> - Leaf class=0 belief=0.961756 counts=(array([0, 1]), array([679,  27]))\nroot - Up - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([7]))\n"
     }
    ],
    "source": [
+    "#check iterator\n",
     "for i in list(clf):\n",
     "    print(i)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "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\nroot - Down - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([312]))\nroot - Up\nroot - Up - Down\nroot - Up - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([8]))\nroot - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([10]))\nroot - Down - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([1]))\nroot - Up - Up, <cgaf> - Leaf class=0 belief=0.961756 counts=(array([0, 1]), array([679,  27]))\nroot - Up - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([7]))\n"
     }
    ],
    "source": [
+    "#check iterator again\n",
     "for i in clf:\n",
     "    print(i)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "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",
-    "from mpl_toolkits.mplot3d import Axes3D\n",
-    "import matplotlib.pyplot as plt\n",
-    "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,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

tests/Snode_test.py

@@ -15,7 +15,7 @@ class Snode_test(unittest.TestCase):
         self._clf = Stree(random_state=self._random_state,
                             use_predictions=True)
         self._clf.fit(*self._get_Xy())
-        super(Snode_test, self).__init__(*args, **kwargs)
+        super().__init__(*args, **kwargs)
 
     @classmethod
     def tearDownClass(cls):

tests/Stree_test.py

@@ -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):
@@ -217,6 +217,23 @@ 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)
+
 
 
 

trees/Siterator.py

@@ -4,14 +4,13 @@ __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
+    """Stree preorder iterator
     """
 
     def __init__(self, tree: Snode):
@@ -22,13 +21,13 @@ class Siterator:
         return self
 
     def _push(self, node: Snode):
-        while (node is not None):
+        if node is not None:
-            self._stack.insert(0, node)
+            self._stack.append(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())
+        self._push(node.get_down())
         return node

trees/Snode.py

@@ -11,7 +11,6 @@ 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
@@ -26,6 +25,10 @@ class Snode:
         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
 
@@ -45,9 +48,6 @@ class Snode:
         """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)

trees/Snode_graph.py

@@ -0,0 +1,49 @@
+'''
+__author__ = "Ricardo Montañana Gómez"
+__copyright__ = "Copyright 2020, Ricardo Montañana Gómez"
+__license__ = "MIT"
+__version__ = "0.9"
+Plot 3D views of nodes in Stree
+'''
+
+import numpy as np
+import matplotlib.pyplot as plt
+from mpl_toolkits.mplot3d import Axes3D
+from trees.Snode import Snode
+from trees.Stree import Stree
+
+
+class Snode_graph(Snode):
+
+    def __init__(self, node: Stree):
+        self._plot_size = (8, 8)
+        n = Snode.copy(node)
+        super().__init__(n._clf, n._X, n._y, n._title)
+
+    def set_plot_size(self, size):
+        self._plot_size = size
+
+    def plot_hyperplane(self):
+        # 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]
+        fig = plt.figure(figsize=self._plot_size)
+        ax = fig.add_subplot(1, 1, 1, projection='3d')
+        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')
+        plt.title(self._title)
+        self.plot_distribution(ax)
+        return ax
+
+    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')
+        ax.scatter(self._X[:, 0], self._X[:, 1], self._X[:, 2], c=self._y)
+        ax.set_xlabel('X0')
+        ax.set_ylabel('X1')
+        ax.set_zlabel('X2')
+        plt.show()

trees/Stree.py

@@ -13,7 +13,6 @@ 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
 
@@ -22,7 +21,7 @@ class Stree(BaseEstimator, ClassifierMixin):
     """
     """
 
-    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._C = C
         self._random_state = random_state

trees/Stree_grapher.py

@@ -0,0 +1,51 @@
+'''
+__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 numpy as np
+from sklearn.decomposition import PCA
+import trees
+import matplotlib.pyplot as plt
+from trees.Snode import Snode
+from trees.Snode_graph import Snode_graph
+from trees.Stree import Stree
+from trees.Siterator import Siterator
+
+class Stree_grapher(Stree):
+    def __init__(self, params: dict):
+        self._plot_size = (8, 8)
+        self._tree_gr = None
+        # make Snode store X's
+        os.environ['TESTING'] = '1'
+        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)
+        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:
+        if X.shape[1] != 3:
+            pca = PCA(n_components=3)
+            X = pca.fit_transform(X)
+        res = super().fit(X, y)
+        self._tree_gr = self._copy_tree(self._tree)
+        return res
+
+    def __iter__(self):
+        return Siterator(self._tree_gr)