数据增强

数据增强的方式有很多，比如对图像进行几何变换（如翻转、旋转、变形、缩放等）、颜色变换（包括噪声、模糊、颜色变换、檫除、填充等），将有限的数据，进行充分的利用。这里将介绍的仅仅是对图像数据进行任意方向的移动操作（上下左右）来扩充数据。

这里将使用scipy中的shift变换工具（from scipy.ndimage.interpolation import shift）

 　　常用的参数：input输入图像数据为ndarray类型的，

　　　　　　　　shift参数代表表示各个维度的偏移量[1,1]表示第一个第二个维度均偏移1，

　　　　　　　　cval参数代表偏移后原来位置用什么来填充

from scipy.ndimage.interpolation import shift
def shift_digit(digit_array,dx,dy,new = 0):
    return shift(digit_array.reshape(28,28),[dy,dx],cval = new).reshape(784)
plot_digit(shift_digit(some_digit,5,1,new =100))

 　　一个简单的数据偏移完成，接下来对整个训练集进行扩充

X_train_expanded = [X_train]
y_train_expanded = [y_train]
for dx,dy in ((1,0),(-1,0),(0,1),(0,-1)):
    shifted_image = np.apply_along_axis(shift_digit,axis = 1,arr = X_train,dx = dx,dy = dy)
    X_train_expanded.append(shifted_image)
    y_train_expanded.append(y_train)
    
X_train_expanded = np.concatenate(X_train_expanded)
y_train_expanded = np.concatenate(y_train_expanded)
X_train_expanded.shape,y_train_expanded.shape

 　　数据增加大了30万之多，有了更多的数据，接下来进行训练、预测，计算精度

knn_clf.fit(X_train_expanded,y_train_expanded)

y_knn_expanded_pred = knn_clf.predict(X_test)
accuracy_score(y_test,y_knn_expanded_pred)

 另一种表示方式：

def shift_image(image,dx,dy):
    image = image.reshape((28,28))
    shifted_image = shift(image,[dy,dx],cval = 0,mode = 'constant')
    return shifted_image.reshape([-1])

image = X_train[1000]
shifted_image_down = shift_image(image,0,5)
shifted_image_left = shift_image(image,-5,0)

plt.figure(figsize=(12,3))
plt.subplot(131)
plt.title("Original",fontsize= 14)
plt.imshow(image.reshape(28,28),interpolation='nearest',cmap = 'Greys')
plt.subplot(132)
plt.title("shifted down",fontsize= 14)
plt.imshow(shifted_image_down.reshape(28,28),interpolation='nearest',cmap = 'Greys')
plt.subplot(133)
plt.title("shifted left",fontsize= 14)
plt.imshow(shifted_image_left.reshape(28,28),interpolation='nearest',cmap = 'Greys')
plt.show()

X_train_augmented = [image for image in X_train]
y_train_augmented = [label for label in y_train]

for dx,dy in ((1,0),(-1,0),(0,1),(0,-1)):
    for image,label in zip(X_train,y_train):
        X_train_augmented.append(shift_image(image,dx,dy))
        y_train_augmented.append(label)

X_train_augmented = np.array(X_train_augmented)
y_train_augmented = np.array(y_train_augmented)

#打乱顺序
shuffle_idx = np.random.permutation(len(X_train_augmented))
X_train_augmented = X_train_augmented[shuffle_idx]
y_train_augmented = y_train_augmented[shuffle_idx]

knn_clf = KNeighborsClassifier(**grid_search.best_params_)
knn_clf.fit(X_train_augmented,y_train_augmented)

y_pred = knn_clf.predict(X_test)
accuracy_score(y_test,y_pred)

 此时准确率已达到97%以上

关于knn_clf = KNeighborsClassifier(**grid_search.best_params_)中的**犯傻了很久，**代表着该参数中包含了多个参数，在C++中也会有这种参数表示，
也可参看python中*args与**kwargs的介绍（https://pythontips.com/2013/08/04/args-and-kwargs-in-python-explained/）

当然，scipy.ndimage.interpolation 也包含了其他的数据增强的方法，如旋转、缩放等（参考：https://blog.csdn.net/songchunxiao1991/article/details/88531086）