机器学习与数据挖掘系列算法之--knn的python实现

Posted 2023-02-03 疯孑456258

K近邻算法原理较为简单，不多介绍，直接上python实现，有什么疑问或错误的地方敬请指出探讨，谢谢。

Knn.py

from algorithm.classification.common.PredictBase import PredictBase
from algorithm.classification.common import Utils
# author:chenhq
# create date:2017/12/8

'''
    K近邻算法：
        思想：基于最近k个样例的分类，分类最多的类别即为该类确定的类别，以下为加权k近邻算法
        步骤：
            1.计算x与所有样本X的距离，取topK近邻样本
            2.y = argmax(j)∑[1/dis(x,xi)*I(yi = yj)], i∈topK
                *: I(yi = yj) -> 指示函数，true->1, false->0
'''


class Knn(PredictBase):
    def __init__(self):
        self.sample = []
        self.labels = []
        self.k = 5

    def train_set(self, train_data, class_vec, k):
        self.sample = train_data
        self.labels = class_vec
        self.k = k

    def predict(self, row):
        dis_list = [(self.dis(vec, row), label) for (vec, label) in list(zip(self.sample, self.labels))]
        top_k = sorted(dis_list, key=lambda d: d[0])[:self.k]
        label_dict = 
        [label_dict.update(label: label_dict.get(label, 0) + 1.0 / (distance + 1))for (distance, label) in top_k]
        return sorted(label_dict.items(), key=lambda ld: -ld[0])[0]

    def dis(self, vec1, vec2):
        s = 0.0
        for (v1, v2) in zip(vec1, vec2):
            s += pow((v1 - v2), 2)
        return pow(s, 0.5)

if __name__ == '__main__':
    # load_data-->split(train&test)
    source_data, class_vec = Utils.load_classify_data_set()
    train_data, train_class_vec, eva_data, eva_class_vec = Utils.split_data(source_data, class_vec, 0.8)
    print(eva_data, eva_class_vec)

    # train & evaluate & show
    model = Knn()
    model.train_set(train_data=train_data, class_vec=train_class_vec, k=5)
    prediction, recall, f = model.evaluate(evaluate_set=eva_data, evaluate_label=eva_class_vec)
    print("prediction:\\t%f\\nrecall:\\t%f\\nf-measure:\\t%f" % (prediction, recall, f))

相关的基类及辅助方法

PredictBase.py

class PredictBase(object):
    # __metaclass__ = ABCMeta #指定这是一个抽象类

    def evaluate(self, evaluate_set, evaluate_label):
        evaluate_list = list()
        for (features, label) in list(zip(evaluate_set, evaluate_label)):
            p_max = self.predict(features)
            evaluate_list.append((label, p_max[0]))
        tp = 0      # true-positive     真-->真
        fp = 0      # false-positive    假-->真
        fn = 0      # false-negative    真-->假
        tn = 0      # true-negative     假-->假
        for (label, predict_label) in evaluate_list:
            if label == 1:
                if predict_label == 1:
                    tp += 1
                else:
                    fn += 1
            else:
                if predict_label == 1:
                    fp += 1
                else:
                    tn += 1
        if tp == 0:
            return 0, 0, 0
        prediction = float(tp) / (tp + fp)
        recall = float(tp) / (tp + fn)
        f = 2 * prediction * recall / (prediction + recall)
        return prediction, recall, f

    def predict(self, row):
        pass

Utils.py

def split_data(data, label, rate):
    train_lens = int(len(label) * 0.9)
    (train_data, train_class_vec) = (data[0: train_lens], label[0: train_lens])
    (eva_data, eva_class_vec) = (data[train_lens:], label[train_lens:])
    return train_data, train_class_vec, eva_data, eva_class_vec


# 准数据
def load_classify_data_set():
    source_data = [[7, 8, 10, 8, 6, 10, 1, 2, 0, 2, 1, 1],
                   [0, 1, 1, 2, 2, 1, 8, 9, 9, 9, 7, 8],
                   [0, 0, 2, 2, 2, 1, 8, 9, 9, 9, 7, 8],
                   [7, 8, 7, 8, 7, 8, 1, 2, 0, 2, 1, 1],
                   [0, 0, 1, 3, 2, 1, 8, 9, 9, 9, 7, 8],
                   [0, 2, 1, 2, 2, 1, 8, 9, 9, 9, 7, 8],
                   [0, 0, 1, 2, 4, 1, 8, 9, 9, 9, 7, 8],
                   [7, 9, 10, 8, 6, 9, 1, 2, 0, 2, 1, 1],
                   [1, 0, 1, 2, 2, 1, 8, 9, 9, 9, 7, 8],
                   [7, 7, 9, 8, 6, 8, 1, 2, 0, 2, 1, 1],
                   [7, 9, 9, 8, 9, 0, 11, 2, 0, 2, 1, 1],
                   [7, 8, 9, 8, 6, 9, 1, 2, 0, 2, 2, 1],
                   [0, 0, 1, 4, 2, 1, 8, 9, 9, 9, 7, 8],
                   [8, 8, 9, 8, 8, 5, 1, 2, 0, 2, 1, 0],
                   [7, 8, 9, 8, 6, 5, 1, 2, 0, 2, 0, 1],
                   [0, 0, 3, 2, 2, 1, 8, 9, 9, 9, 7, 8],
                   [7, 8, 9, 8, 6, 9, 1, 2, 0, 2, 2, 1],]
    class_vec = [1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1]
    return source_data, class_vec