Decision Tree Classifier recursively generates rule to split data so as to minimize the impurity of each subset until every sample in subset belongs to the same class

from __future__ import divisionimport numpy as npfrom sklearn.datasets import load_irisfrom IPython.display import Imagefrom sklearn import treeimport pydotplus data = load_iris()X = data.datay = data.targetclass myDecisionTreeClassifier():        def gini(self, X, y, idx_data):        if idx_data.shape[0] == 0:            return 0        else:            p = 1            for v in np.unique(y):                y_sub = y[idx_data]                p -= (y_sub[y_sub==v].shape[0] / idx_data.shape[0])**2            return p            def split_data(self, X, y, idx_data, idx_feat, val_feat):        idx_left = idx_data[np.flatnonzero(X[idx_data][:, idx_feat] < val_feat)]        idx_right = idx_data[np.flatnonzero(X[idx_data][:, idx_feat] >= val_feat)]        return idx_left, idx_right        def best_split_data(self, X, y, idx_data):        igs = {}        for f in range(X.shape[1]):            for v in np.unique(X[:,f]):                idx_left, idx_right = self.split_data(X, y, idx_data, f, v)                gini_left = self.gini(X,y,idx_left)                gini_right = self.gini(X,y,idx_right)                igs[(f,v)] = (idx_left.shape[0]*gini_left + idx_right.shape[0]*gini_right) / idx_data.shape[0]        idx_feat, val_feat = min(igs, key=igs.get)        return idx_feat, val_feat        def build_tree(self, X, y, idx_data):        if idx_data.shape[0] == 0:            return None        node_tree = {            'idx_feat': None,            'val_feat': None,            'node_left': None,            'node_right': None,            'target': None        }        if np.unique(y[idx_data]).shape[0] == 1:            node_tree['target'] = np.unique(y[idx_data])[0]            return node_tree        idx_feat, val_feat = self.best_split_data(X, y, idx_data)        node_tree['idx_feat'] = idx_feat        node_tree['val_feat'] = val_feat        idx_left, idx_right = self.split_data(X, y, idx_data, idx_feat, val_feat)        node_tree['node_left'] = self.build_tree(X, y, idx_left)        node_tree['node_right'] = self.build_tree(X, y, idx_right)        return node_tree            def fit(self, X, y):        self.node_tree = self.build_tree(X, y, np.array(range(X.shape[0])))        def predict_single(self, node_tree, x):        target = node_tree['target']        if target != None:            return target        idx_feat = node_tree['idx_feat']        val_feat = node_tree['val_feat']        node_left = node_tree['node_left']        node_right = node_tree['node_right']        if x[idx_feat] < val_feat:            return self.predict_single(node_left, x)        else:            return self.predict_single(node_right, x)    def predict(self, X):        return np.array(map(lambda x: self.predict_single(self.node_tree, x), X))    def score(self, X, y):        return np.count_nonzero(self.predict(X) == y) / y.shape[0]        def plot_tree_level(self, node_tree, level):        idx_feat = node_tree['idx_feat']        val_feat = node_tree['val_feat']        node_left = node_tree['node_left']        node_right = node_tree['node_right']        target = node_tree['target']        if level == 0:            indent = '|--'        else:            indent =  '      '*level+'  |--'        if idx_feat != None:            print indent, data.feature_names[idx_feat], 'by', val_feat        else:            print indent, '[', data.target_names[target], ']'            return        self.plot_tree_level(node_left, level+1)        self.plot_tree_level(node_right, level+1)            def plot_tree(self):        self.plot_tree_level(self.node_tree, 0)        idx_data = np.array(range(X.shape[0]))dt = myDecisionTreeClassifier()dt.fit(X,y)print 'score:', dt.score(X,y)dt.plot_tree()#score: 1.0#|-- petal width (cm) by 1.0#        |-- [ setosa ]#        |-- petal width (cm) by 1.8#              |-- petal length (cm) by 5.0#                    |-- petal width (cm) by 1.7#                          |-- [ versicolor ]#                          |-- [ virginica ]#                    |-- petal width (cm) by 1.6#                          |-- [ virginica ]#                          |-- sepal length (cm) by 6.8#                                |-- [ versicolor ]#                                |-- [ virginica ]#              |-- petal length (cm) by 4.9#                    |-- sepal width (cm) by 3.1#                          |-- [ virginica ]#                          |-- [ versicolor ]#                    |-- [ virginica ]clf = tree.DecisionTreeClassifier()clf.fit(X,y)print 'scikit score:', np.count_nonzero(clf.predict(X) == y) / y.shape[0]dot_data = tree.export_graphviz(clf, out_file=None,     feature_names=data.feature_names,      class_names=data.target_names,      filled=True, rounded=True,      special_characters=True)  graph = pydotplus.graph_from_dot_data(dot_data)  Image(graph.create_png())#scikit score: 1.0