| import math | |
| from xml.dom import minidom | |
| from xml.etree import ElementTree as ET | |
| from sklearn.base import BaseEstimator, ClassifierMixin | |
| from sklearn.utils.validation import check_array, check_is_fitted, check_X_y | |
| from .c45_utils import decision, grow_tree | |
| class C45(BaseEstimator, ClassifierMixin): | |
| """A C4.5 tree classifier. | |
| Parameters | |
| ---------- | |
| attrNames : list, optional (default=None) | |
| The list of feature names used in printing tree during. If left default, | |
| attributes will be named attr0, attr1... etc | |
| See also | |
| -------- | |
| DecisionTreeClassifier | |
| References | |
| ---------- | |
| .. [1] https://en.wikipedia.org/wiki/Decision_tree_learning | |
| .. [2] https://en.wikipedia.org/wiki/C4.5_algorithm | |
| .. [3] L. Breiman, J. Friedman, R. Olshen, and C. Stone, "Classification | |
| and Regression Trees", Wadsworth, Belmont, CA, 1984. | |
| .. [4] J. R. Quinlain, "C4.5: Programs for Machine Learning", | |
| Morgan Kaufmann Publishers, 1993 | |
| Examples | |
| -------- | |
| >>> from sklearn.datasets import load_iris | |
| >>> from sklearn.model_selection import cross_val_score | |
| >>> from c45 import C45 | |
| >>> iris = load_iris() | |
| >>> clf = C45(attrNames=iris.feature_names) | |
| >>> cross_val_score(clf, iris.data, iris.target, cv=10) | |
| ... # doctest: +SKIP | |
| ... | |
| array([ 1. , 0.93..., 0.86..., 0.93..., 0.93..., | |
| 0.93..., 0.93..., 1. , 0.93..., 1. ]) | |
| """ | |
| def __init__(self, attrNames=None): | |
| if attrNames is not None: | |
| attrNames = [''.join(i for i in x if i.isalnum()).replace(' ', '_') for x in attrNames] | |
| self.attrNames = attrNames | |
| def fit(self, X, y): | |
| X, y = check_X_y(X, y) | |
| self.X_ = X | |
| self.y_ = y | |
| self.resultType = type(y[0]) | |
| if self.attrNames is None: | |
| self.attrNames = [f'attr{x}' for x in range(len(self.X_[0]))] | |
| assert(len(self.attrNames) == len(self.X_[0])) | |
| data = [[] for i in range(len(self.attrNames))] | |
| categories = [] | |
| for i in range(len(self.X_)): | |
| categories.append(str(self.y_[i])) | |
| for j in range(len(self.attrNames)): | |
| data[j].append(self.X_[i][j]) | |
| root = ET.Element('DecisionTree') | |
| grow_tree(data,categories,root,self.attrNames) | |
| self.tree_ = ET.tostring(root, encoding="unicode") | |
| return self | |
| def predict(self, X): | |
| check_is_fitted(self, ['tree_', 'resultType', 'attrNames']) | |
| X = check_array(X) | |
| dom = minidom.parseString(self.tree_) | |
| root = dom.childNodes[0] | |
| prediction = [] | |
| for i in range(len(X)): | |
| answerlist = decision(root,X[i],self.attrNames,1) | |
| answerlist = sorted(answerlist.items(), key=lambda x:x[1], reverse = True ) | |
| answer = answerlist[0][0] | |
| prediction.append((self.resultType)(answer)) | |
| return prediction | |
| def printTree(self): | |
| check_is_fitted(self, ['tree_']) | |
| dom = minidom.parseString(self.tree_) | |
| print(dom.toprettyxml(newl="\r\n")) | |