KNN算法分类示例：基于DAS数据集

#coding = UTF-8 import datetime from sklearn.neighbors import KNeighborsClassifier from sklearn import preprocessing from get_das_data import get_das_data from sklearn.metrics import confusion_matrix import numpy as np import matplotlib.pyplot as plt import matplotlib.font_manager as fm import pandas as pd import seaborn as sns

rootpath = './das_data' train_rootpath = rootpath+'/train' train_labelpath = rootpath+'/train/label.txt' test_rootpath = rootpath+'/test' test_labelpath = rootpath+'/test/label.txt'

start_train = datetime.datetime.now() X_train, y_train = get_das_data(train_rootpath, train_labelpath) X_test, y_test = get_das_data(test_rootpath, test_labelpath)

pre_y_test = y_test[:, np.newaxis]

minMaxScaler = preprocessing.MinMaxScaler() trainingData = minMaxScaler.fit_transform(X_train) testData = minMaxScaler.fit_transform(X_test)

feature_data = np.concatenate((testData, pre_y_test), axis=1) np.savetxt('5km_10km_knn_feature_data.csv', feature_data, delimiter=',')

clf = KNeighborsClassifier(n_neighbors=3) clf.fit(trainingData, y_train) end_train = datetime.datetime.now()

train_result = clf.predict(trainingData)

start_test = datetime.datetime.now() test_result = clf.predict(testData) end_test = datetime.datetime.now()

train_matrix = confusion_matrix(y_train, train_result) test_matrix = confusion_matrix(y_test, test_result) print('train_matrix: ', train_matrix) print('test_matrix: ', test_matrix) print('train time is ', end_train - start_train) print('test time is ', end_test - start_test)

C = test_matrix fig = plt.figure() ax = fig.add_subplot(111) df = pd.DataFrame(C) f1 = fm.FontProperties('Times New Roman', size=15) sns.heatmap(df, fmt='g', annot=True, annot_kws={'size': 10}, xticklabels=['1', '2', '3', '4', '5', '6'], yticklabels=['1', '2', '3', '4', '5', '6'], cmap='Blues') ax.set_xlabel('Predicted label', FontProperties=f1) # x轴 ax.set_ylabel('True label', FontProperties=f1) # y轴 plt.savefig('./5km_10km_knn_confusion_matrix.jpg') plt.show()

Acc = (C[0][0] + C[1][1] + C[2][2] + C[3][3] + C[4][4] + C[5][5]) / sum(C[0] + C[1] + C[2] + C[3] + C[4] + C[5]) print('acc: %.3f' % Acc) lie_he = sum(C, 1) - 1 for i in range(1, 7): Precision = C[i - 1][i - 1] / lie_he[i - 1] NAR = (sum(C[i - 1]) - C[i - 1][i - 1]) / sum(C[i - 1]) F1_score = 2 * C[i - 1][i - 1] / (lie_he[i - 1] + sum(C[i - 1])) print('precision_%d: %.3f' % (i, Precision)) print('NAR_%d: %.3f' % (i, NAR)) print('F1_score_%d: %.3f' % (i, F1_score))