Working tree with samples and first test

trees/Stree.py

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+'''
+__author__ = "Ricardo Montañana Gómez"
+__copyright__ = "Copyright 2020, Ricardo Montañana Gómez"
+__license__ = "MIT"
+__version__ = "1.0"
+Create a oblique tree classifier based on SVM Trees
+Uses LinearSVC
+'''
+
+import numpy as np
+from sklearn.svm import LinearSVC
+
+from trees.Snode import Snode
+
+class Stree:
+    """
+    """
+    def __init__(self, max_iter: int=1000, random_state: int=0):
+        self._max_iter = max_iter
+        self._random_state = random_state
+        self._outcomes = None
+        self._tree = None
+
+    def _split_data(self, clf: LinearSVC, X: np.ndarray, y: np.ndarray) -> list:
+        # doesn't work with multiclass as each sample has to do inner product with its own coeficients
+        # computes positition of every sample is w.r.t. the hyperplane
+        coef = clf.coef_[0, :].reshape(-1, X.shape[1])
+        intercept = clf.intercept_[0]
+        res = X.dot(coef.T) + intercept
+        down = res > 0
+        up = ~down
+        X_down = X[down[:, 0]] if any(down) else None
+        y_down = y[down[:, 0]] if any(down) else None
+        X_up = X[up[:, 0]] if any(up) else None
+        y_up = y[up[:, 0]] if any(up) else None
+        return X_up, y_up, X_down, y_down
+
+    def fit(self, X: np.ndarray, y: np.ndarray, title: str = 'root') -> list:
+        self._tree = self.train(X, y, title)
+        return self
+    
+    def train(self: Snode, X: np.ndarray, y: np.ndarray, title: str='') -> list:
+        if np.unique(y).shape[0] == 1:
+            # onlyt 1 class => pure dataset
+            return Snode(np.array([]), 0, X, y, title + f', <pure> class={np.unique(y)} items={y.shape[0]}')
+        # Train the model
+        clf = LinearSVC(max_iter=self._max_iter, random_state=self._random_state)
+        clf.fit(X, y)
+        tree = Snode(clf.coef_, clf.intercept_, X, y, title)
+        #plot_hyperplane(clf, X, y, title)
+        X_T, y_t, X_O, y_o = self._split_data(clf, X, y)
+        if X_T is None or X_O is None:
+            # didn't part anything
+            return Snode(clf.coef_, clf.intercept_, X, y, title + f', <couldn\'t go any further> classes={np.unique(y)} items<0>={y[y==0].shape[0]} items<1>={y[y==1].shape[0]}')
+        tree.set_up( self.train(X_T, y_t, title + ' - Up'))
+        tree.set_down(self.train(X_O, y_o, title + ' - Down'))
+        return tree
+
+    def _print_tree(self, tree: Snode):
+        print(tree)
+        if tree.is_leaf():
+            return
+        self._print_tree(tree.get_down())
+        self._print_tree(tree.get_up())
+    
+    def show_outcomes(self):
+        pointer = self._tree
+        self._print_tree(pointer)
+
+