python算法演练——One Rule 算法

# OneR算法实现
import numpy as np
from 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_split
x_train, x_test, y_train, y_test = train_test_split(x, y_true, random_state=14)
from operator import itemgetter
from 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_predictor

y_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) * 100
print "The test accuracy is {0:.2f}%".format(accuracy)

from sklearn.metrics import classification_report

# print(classification_report(y_test, y_predictor))