Make project python package friendly

- Add setup.py
- Move classes to module files
- Move tests folder inside module folder

stree/Strees.py

@@ -0,0 +1,311 @@
+'''
+__author__ = "Ricardo Montañana Gómez"
+__copyright__ = "Copyright 2020, Ricardo Montañana Gómez"
+__license__ = "MIT"
+__version__ = "0.9"
+Build an oblique tree classifier based on SVM Trees
+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
+
+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):
+    """
+    """
+
+    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
+        self._tree = None
+        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}
+
+    def set_params(self, **parameters):
+        """Set hyperparmeters as specified by sklearn, needed in Gridsearchs
+        """
+        for parameter, value in parameters.items():
+            setattr(self, parameter, value)
+        return self
+
+    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:
+            yp = node._clf.predict(data)
+            down = (yp == 1).reshape(-1, 1)
+            res = np.expand_dims(node._clf.decision_function(data), 1)
+        else:
+            # 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
+            res = self._linear_function(data, node)
+            down = res > 0
+        up = ~down
+        data_down = data[down[:, 0]] if any(down) else None
+        indices_down = indices[down[:, 0]] if any(down) else None
+        res_down = res[down[:, 0]] if any(down) else None
+        data_up = data[up[:, 0]] if any(up) else None
+        indices_up = indices[up[:, 0]] if any(up) else None
+        res_up = res[up[:, 0]] if any(up) else None
+        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())
+        self.n_features_in_ = X.shape[1]
+        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)
+
+    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,
+                        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)
+        if X_U is None or X_D is None:
+            # didn't part anything
+            return Snode(clf, X, y, title + ', <cgaf>')
+        tree.set_up(self.train(X_U, y_u, title + ' - Up'))
+        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:
+        y_ordered = np.zeros(y.shape, dtype=int if y.ndim == 1 else float)
+        indices = indices.astype(int)
+        for i, index in enumerate(indices):
+            y_ordered[index] = y[i]
+        return y_ordered
+
+    def predict(self, X: np.array) -> np.array:
+        def predict_class(xp: np.array, indices: np.array, node: Snode) -> np.array:
+            if xp is None:
+                return [], []
+            if node.is_leaf():
+                # set a class for every sample in dataset
+                prediction = np.full((xp.shape[0], 1), node._class)
+                return prediction, indices
+            u, i_u, d, i_d, _, _ = self._split_data(node, xp, indices)
+            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)
+        # 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))
+
+    def predict_proba(self, X: np.array) -> np.array:
+        """Computes an approximation of the probability of samples belonging to class 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
+
+            :param xp: subdataset of samples
+            :type xp: np.array
+            :param indices: indices of subdataset samples to rebuild original order
+            :type indices: np.array
+            :param dist: distances of every sample to the hyperplane or the father node
+            :type dist: np.array
+            :param node: node of the leaf with the class
+            :type node: Snode
+            :return: array of labels and distances, array of indices
+            :rtype: np.array
+            """
+            if xp is None:
+                return [], []
+            if node.is_leaf():
+                # set a class for every sample in dataset
+                prediction = np.full((xp.shape[0], 1), node._class)
+                prediction_proba = dist
+                return np.append(prediction, prediction_proba, axis=1), indices
+            u, i_u, d, i_d, r_u, r_d = self._split_data(node, xp, indices)
+            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)
+        # 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)
+        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]))
+        return self._reorder_results(result, indices)
+
+    def score(self, X: np.array, y: np.array) -> float:
+        """Return accuracy
+        """
+        if not self.__trained:
+            self.fit(X, y)
+        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)
+
+    def __str__(self) -> str:
+        output = ''
+        for i in self:
+            output += str(i) + '\n'
+        return output
+
+    def _save_datasets(self, tree: Snode, catalog: typing.TextIO, number: int):
+        """Save the dataset of the node in a csv file
+
+        :param tree: node with data to save
+        :type tree: Snode
+        :param catalog: catalog file handler
+        :type catalog: typing.TextIO
+        :param number: sequential number for the generated file name
+        :type number: int
+        """
+        data = np.append(tree._X, tree._y.reshape(-1, 1), axis=1)
+        name = f"{self.__folder}dataset{number}.csv"
+        np.savetxt(name, data, delimiter=",")
+        catalog.write(f"{name}, - {str(tree)}")
+        if tree.is_leaf():
+            return
+        self._save_datasets(tree.get_down(), catalog, number + 1)
+        self._save_datasets(tree.get_up(), catalog, number + 2)
+
+    def get_catalog_name(self):
+        return self.__folder + "catalog.txt"
+
+    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)
+
+
+