数据集
这里下载这俩即可
分别读入训练数据和测试数据
# 加载训练集数据以及测试集数据 print('Load Training File Start...') # data = pd.read_csv('optdigits.tra', header=None) # x, y = data[list(range(64))], data[64] # x, y = x.values, y.values data = np.loadtxt('optdigits.tra', dtype=np.float, delimiter=',') x, y = np.split(data, (-1,), axis=1) images = x.reshape(-1, 8, 8) print('images.shape = ', images.shape) y = y.ravel().astype(np.int) print('Load Test Data Start...') data = np.loadtxt('optdigits.tes', dtype=np.float, delimiter=',') x_test, y_test = np.split(data, (-1,), axis=1) print(y_test.shape) images_test = x_test.reshape(-1, 8, 8) y_test = y_test.ravel().astype(np.int) print('Load Data OK...')
绘制部分训练集图像
# 画出部分训练集图像 matplotlib.rcParams['font.sans-serif'] = ['SimHei'] matplotlib.rcParams['axes.unicode_minus'] = False plt.figure(figsize=(15, 9), facecolor='w') for index, image in enumerate(images[:16]): plt.subplot(4, 8, index + 1) plt.imshow(image, cmap=plt.cm.gray_r, interpolation='nearest') plt.title('训练图片: %i' % y[index]) for index, image in enumerate(images_test[:16]): plt.subplot(4, 8, index + 17) plt.imshow(image, cmap=plt.cm.gray_r, interpolation='nearest') # save_image(image.copy(), index) plt.title('测试图片: %i' % y_test[index]) plt.tight_layout() plt.show()
如下图
建模训练并预测
# 建模训练并做预测 model = svm.SVC(C=1, kernel='rbf', gamma=0.001) print('Start Learning...') t0 = time() model.fit(x, y) t1 = time() t = t1 - t0 print('训练+CV耗时：%d分钟%.3f秒' % (int(t / 60), t - 60 * int(t / 60))) # print '最优参数：\t', model.best_params_ # clf.fit(x, y) print('Learning is OK...') print('训练集准确率：', accuracy_score(y, model.predict(x))) y_hat = model.predict(x_test) print('测试集准确率：', accuracy_score(y_test, model.predict(x_test))) print(y_hat) print(y_test)
输出
寻找预测错误的数据
# 寻找预测错误数据 err_images = images_test[y_test != y_hat] err_y_hat = y_hat[y_test != y_hat] err_y = y_test[y_test != y_hat] print(err_y_hat) print(err_y)
绘制部分预测错误的数据与预测值
# 画出部分预测错误数据 plt.figure(figsize=(10, 8), facecolor='w') for index, image in enumerate(err_images): if index >= 12: break plt.subplot(3, 4, index + 1) plt.imshow(image, cmap=plt.cm.gray_r, interpolation='nearest') plt.title('错分为：%i，真实值：%i' % (err_y_hat[index], err_y[index])) plt.tight_layout() plt.show()
如下图
全部代码
if __name__ == "__main__": # 加载训练集数据以及测试集数据 print('Load Training File Start...') # data = pd.read_csv('optdigits.tra', header=None) # x, y = data[list(range(64))], data[64] # x, y = x.values, y.values data = np.loadtxt('optdigits.tra', dtype=np.float, delimiter=',') x, y = np.split(data, (-1,), axis=1) images = x.reshape(-1, 8, 8) print('images.shape = ', images.shape) y = y.ravel().astype(np.int) print('Load Test Data Start...') data = np.loadtxt('optdigits.tes', dtype=np.float, delimiter=',') x_test, y_test = np.split(data, (-1,), axis=1) print(y_test.shape) images_test = x_test.reshape(-1, 8, 8) y_test = y_test.ravel().astype(np.int) print('Load Data OK...') # x, x_test, y, y_test = train_test_split(x, y, test_size=0.4, random_state=1) # images = x.reshape(-1, 8, 8) # images_test = x_test.reshape(-1, 8, 8) # 画出部分训练集图像 matplotlib.rcParams['font.sans-serif'] = ['SimHei'] matplotlib.rcParams['axes.unicode_minus'] = False plt.figure(figsize=(15, 9), facecolor='w') for index, image in enumerate(images[:16]): plt.subplot(4, 8, index + 1) plt.imshow(image, cmap=plt.cm.gray_r, interpolation='nearest') plt.title('训练图片: %i' % y[index]) for index, image in enumerate(images_test[:16]): plt.subplot(4, 8, index + 17) plt.imshow(image, cmap=plt.cm.gray_r, interpolation='nearest') # save_image(image.copy(), index) plt.title('测试图片: %i' % y_test[index]) plt.tight_layout() plt.show() # params = {'C':np.logspace(0, 3, 7), 'gamma':np.logspace(-5, 0, 11)} # model = GridSearchCV(svm.SVC(kernel='rbf'), param_grid=params, cv=3) # 建模训练并做预测 model = svm.SVC(C=1, kernel='rbf', gamma=0.001) print('Start Learning...') t0 = time() model.fit(x, y) t1 = time() t = t1 - t0 print('训练+CV耗时：%d分钟%.3f秒' % (int(t / 60), t - 60 * int(t / 60))) # print '最优参数：\t', model.best_params_ # clf.fit(x, y) print('Learning is OK...') print('训练集准确率：', accuracy_score(y, model.predict(x))) y_hat = model.predict(x_test) print('测试集准确率：', accuracy_score(y_test, model.predict(x_test))) print(y_hat) print(y_test) # 寻找预测错误数据 err_images = images_test[y_test != y_hat] err_y_hat = y_hat[y_test != y_hat] err_y = y_test[y_test != y_hat] print(err_y_hat) print(err_y) # 画出部分预测错误数据 plt.figure(figsize=(10, 8), facecolor='w') for index, image in enumerate(err_images): if index >= 12: break plt.subplot(3, 4, index + 1) plt.imshow(image, cmap=plt.cm.gray_r, interpolation='nearest') plt.title('错分为：%i，真实值：%i' % (err_y_hat[index], err_y[index])) plt.tight_layout() plt.show()
