python算法演练_One Rule 算法(详解)

# OneR算法实现import numpy as npfrom sklearn.datasets import load_iris# 加载iris数据集dataset = load_iris()# 加载iris数据集中的data数组（数据集的特征）X = dataset.data# 加载iris数据集中的target数组（数据集的类别）y_true = dataset.target# 计算每一项特征的平均值attribute_means = X.mean(axis=0)# 与平均值比较，大于等于的为“1”，小于的为“0”.将连续性的特征值变为离散性的类别型。x = np.array(X >= attribute_means, dtype="int")from sklearn.model_selection import train_test_splitx_train, x_test, y_train, y_test = train_test_split(x, y_true, random_state=14)from operator import itemgetterfrom collections import defaultdict# 找到一个特征下的不同值的所属的类别。def train_feature_class(x, y_true, feature_index, feature_values):  num_class = defaultdict(int)  for sample, y in zip(x, y_true):    if sample[feature_index] == feature_values:      num_class[y] += 1  # 进行排序，找出最多的类别。按从大到小排列  sorted_num_class = sorted(num_class.items(), key=itemgetter(1), reverse=True)  most_frequent_class = sorted_num_class[0][0]  error = sum(value_num for class_num , value_num in sorted_num_class if class_num != most_frequent_class)  return most_frequent_class, error# print train_feature_class(x_train, y_train, 0, 1)# 接着定义一个以特征为自变量的函数，找出错误率最低的最佳的特征，以及该特征下的各特征值所属的类别。def train_feature(x, y_true, feature_index):  n_sample, n_feature = x.shape  assert 0 <= feature_index < n_feature  value = set(x[:, feature_index])  predictors = {}  errors = []  for current_value in value:    most_frequent_class, error = train_feature_class(x, y_true, feature_index, current_value)    predictors[current_value] = most_frequent_class    errors.append(error)  total_error = sum(errors)  return predictors, total_error# 找到所有特征下的各特征值的类别，格式就如：{0：({0: 0, 1: 2}, 41)}首先为一个字典，字典的键是某个特征，字典的值由一个集合构成，这个集合又是由一个字典和一个值组成，字典的键是特征值，字典的值为类别，最后一个单独的值是错误率。all_predictors = {feature: train_feature(x_train, y_train, feature) for feature in xrange(x_train.shape[1])}# print all_predictors# 筛选出每个特征下的错误率出来errors = {feature: error for feature, (mapping, error) in all_predictors.items()}# 对错误率排序，得到最优的特征和最低的错误率，以此为模型和规则。这就是one Rule（OneR）算法。best_feature, best_error = sorted(errors.items(), key=itemgetter(1), reverse=False)[0]# print "The best model is based on feature {0} and has error {1:.2f}".format(best_feature, best_error)# print all_predictors[best_feature][0]# 建立模型model = {"feature": best_feature, "predictor": all_predictors[best_feature][0]}# print model# 开始测试——对最优特征下的特征值所属类别进行分类。def predict(x_test, model):  feature = model["feature"]  predictor = model["predictor"]  y_predictor = np.array([predictor[int(sample[feature])] for sample in x_test])  return y_predictory_predictor = predict(x_test, model)# print y_predictor# 在这个最优特征下，各特征值的所属类别与测试数据集相对比，得到准确率。accuracy = np.mean(y_predictor == y_test) * 100print "The test accuracy is {0:.2f}%".format(accuracy)from sklearn.metrics import classification_report# print(classification_report(y_test, y_predictor))