"""knn 预测"""import pandas as pdimport numpy as npimport matplotlibfrom matplotlib.patches import Rectanglefrom sklearn.model_selection import train_test_splitfrom sklearn.neighbors import KNeighborsClassifierfrom sklearn.preprocessing import MinMaxScalerdef load_data(): """ 加载数据 :return: """ data = pd.read_csv('./datingTestSet2.txt', delimiter='\t', header=None, names=['A', 'B', 'C', 'label']) return datadef get_trained_model(data): """ 训练模型 :param data: :return: """ y = data.drop(['A', 'B', 'C'], axis=1) X = data.drop(['label'], axis=1) X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.7, test_size=0.3) for i in range(1, 120): neigh = KNeighborsClassifier(n_neighbors=i) neigh.fit(X_train, y_train) y_tmp = neigh.predict(X_test) a = (y_tmp.T == y_test.values.T) accuracy = np.mean(a) * 100 print("当n_neighbors = " + str(i) + "预测正确率 {0:.1f}%".format(accuracy))def data_normalize(data): """ 数据规范化 :param data: :return: """ tmp_label = data['label'] tmp_data = MinMaxScaler().fit_transform(data) data = pd.DataFrame(tmp_data, columns=['A', 'B', 'C', 'label']) data['label'] = tmp_label return datadef cross_validation(data): """ 交叉验证 :param data: :return: """ from sklearn.model_selection import cross_val_score y = data.drop(['A', 'B', 'C'], axis=1).values X = data.drop(['label'], axis=1).values X = X.reshape(1000, 3) y = y.reshape(1000, ) avg_scores = [] all_scores = [] parameter_values = list(range(1, 210)) # Include 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) from matplotlib import pyplot as plt plt.plot(parameter_values, avg_scores, '-o') plt.show()if __name__ == '__main__': data = load_data() data = data_normalize(data) get_trained_model(data) cross_validation(data)
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