KNN完整的代码+电离层数据

import os import csv import numpy as np from sklearn.model_selection import train_test_split from sklearn.neighbors import KNeighborsClassifier from sklearn.model_selection import cross_val_score from matplotlib import pyplot as plt from collections import defaultdict data_filename = "E:\机器学习\数据集\电离层数据\ionosphere.data" X = np.zeros((351, 34), dtype='float') y = np.zeros((351,), dtype='bool') with open(data_filename, 'r') as input_file: reader = csv.reader(input_file) # print(reader) # csv.reader类型 for i, row in enumerate(reader): data = [float(datum) for datum in row[:-1]] # Set the appropriate row in our dataset X[i] = data # 将“g”记为1，将“b”记为0。 y[i] = row[-1] == 'g' # 划分训练集、测试集 X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=14) # 即创建估计器（K近邻分类器实例） 默认选择5个近邻作为分类依据 estimator = KNeighborsClassifier() # 进行训练， estimator.fit(X_train, y_train) # 评估在测试集上的表现 y_predicted = estimator.predict(X_test) # 计算准确率 accuracy = np.mean(y_test == y_predicted) * 100 print("The accuracy is {0:.1f}%".format(accuracy)) # 进行交叉检验，计算平均准确率 scores = cross_val_score(estimator, X, y, scoring='accuracy') average_accuracy = np.mean(scores) * 100 print("The average accuracy is {0:.1f}%".format(average_accuracy)) #由于KNN算法对于近邻K的选择依赖度较大，因此需要用实验法确定K值 #在1到20之间确定K值，记录不同K值下的准确率 avg_scores = [] all_scores = [] parameter_values = list(range(1, 21)) # Including 20 for n_neighbors in parameter_values: estimator = KNeighborsClassifier(n_neighbors=n_neighbors) scores = cross_val_score(estimator, X, y, scoring='accuracy') avg_scores.append(np.mean(scores)) all_scores.append(scores) # 绘制n_neighbors的不同取值与分类正确率之间的关系 plt.figure(figsize=(20, 10)) plt.plot(parameter_values, avg_scores, '-o', linewidth=5, markersize=24) plt.show() #交叉检验 all_scores = defaultdict(list) parameter_values = list(range(1, 21)) # Including 20 for n_neighbors in parameter_values: estimator = KNeighborsClassifier(n_neighbors=n_neighbors) scores = cross_val_score(estimator, X, y, scoring='accuracy', cv=10) all_scores[n_neighbors].append(scores) for parameter in parameter_values: scores = all_scores[parameter] n_scores = len(scores) plt.plot([parameter] * n_scores, scores, '-o') plt.plot(parameter_values, avg_scores, '-o') plt.show() #由图可知K取2的情况下准确率最高，因此确定K值为2 #以k值为2重新训练最近邻分类器，并输出结果 Estimator = KNeighborsClassifier(n_neighbors=2) Estimator.fit(X_train, y_train) Y_predicted = Estimator.predict(X_test) accuracy = np.mean(y_test == Y_predicted) * 100 pre_result = np.zeros_like(Y_predicted, dtype=str) pre_result[Y_predicted == 1] = 'g' pre_result[Y_predicted == 0] = 'b' print(pre_result) print("The accuracy is {0:.1f}%".format(accuracy))