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data_processing.py
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data_processing.py
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from sklearn.decomposition import PCA
import numpy as np
from torchvision import datasets, transforms# 导入量子门RY
from sklearn.model_selection import train_test_split
import torch
from mindquantum.algorithm.library import amplitude_encoder
from mindquantum.algorithm.nisq import IQPEncoding
def sample_data(X, y, label, sample_ratio=0.2):
label_mask = (y == label)
X_label = X[label_mask]
y_label = y[label_mask]
sample_size = int(len(y_label) * sample_ratio)
sample_indices = np.random.choice(len(y_label), sample_size, replace=False)
return X_label[sample_indices], y_label[sample_indices]
def filter_3_and_6(data):
images, labels = data
mask = (labels == 3) | (labels == 6)
return images[mask], labels[mask]
def amplitude_param(pixels):
param_rd = []
_, parameterResolver = amplitude_encoder(pixels, 6)
for _, param in parameterResolver.items():
param_rd.append(param)
param_rd = np.array(param_rd)
return param_rd
def PCA_data_preprocessing(mnist_dataset:datasets.MNIST,PCA_dim:int=10):
'''
将 28*28 的 MNIST 手写数字图像 基于PCA进行压缩
'''
transform = transforms.Compose([
transforms.ToTensor()])
np.random.seed(10)
#mnist_dataset = datasets.MNIST(root='./data', train=True, download=True, transform=None)
filtered_data = filter_3_and_6((mnist_dataset.data, mnist_dataset.targets))
X_data, y = filtered_data # X 图像数据 y 标签
X_data_3, y_data_3 = sample_data(X_data, y, label=3, sample_ratio=0.1)
X_data_6, y_data_6 = sample_data(X_data, y, label=6, sample_ratio=0.1)
#合并抽样后的数据
X_sampled = torch.cat((X_data_3, X_data_6), dim=0)
y_sampled = torch.cat((y_data_3, y_data_6), dim=0)
n_samples = X_sampled.shape[0]
X_flattened = X_sampled.view(n_samples, -1) # 将图像展平为一维向量
X_flattened = X_flattened/255
pca = PCA(n_components=PCA_dim)
X_pca = pca.fit_transform(X_flattened)
# 将 PCA 处理后的值缩放到 [0, π] 之间
X_pca_min = np.min(X_pca)
X_pca_max = np.max(X_pca)
X_pca_scaled = np.pi * (X_pca - X_pca_min) / (X_pca_max - X_pca_min)
X_train, X_test, y_train, y_test = train_test_split(X_pca_scaled, y_sampled, test_size=0.2, random_state=0, shuffle=True) # 将数据集划分为训练集和测试集
y_train[y_train==3]=1
y_train[y_train==6]=0
y_test[y_test==3]=1
y_test[y_test==6]=0
y_train = y_train.numpy()
y_test = y_test.numpy()
return X_train, X_test, y_train, y_test
mnist_dataset = datasets.MNIST(root='./data', train=True, download=True, transform=None)
X_train, X_test, y_train, y_test = PCA_data_preprocessing(mnist_dataset,8)
def PCA_data_preprocessing_micro(mnist_dataset:datasets.MNIST=mnist_dataset,
PCA_dim:int=10,ratio:float=0.9):
'''
将 28*28 的 MNIST 手写数字图像 基于PCA进行压缩
'''
#mnist_dataset = datasets.MNIST(root='./data', train=True, download=True, transform=None)
filtered_data = filter_3_and_6((mnist_dataset.data, mnist_dataset.targets))
X_data, y = filtered_data # X 图像数据 y 标签
X_data_3, y_data_3 = sample_data(X_data, y, label=3, sample_ratio=ratio)
X_data_6, y_data_6 = sample_data(X_data, y, label=6, sample_ratio=ratio)
#合并抽样后的数据
X_sampled = torch.cat((X_data_3, X_data_6), dim=0)
y_sampled = torch.cat((y_data_3, y_data_6), dim=0)
n_samples = X_sampled.shape[0]
X_flattened = X_sampled.view(n_samples, -1) # 将图像展平为一维向量
X_flattened = X_flattened/255
pca = PCA(n_components=PCA_dim)
X_pca = pca.fit_transform(X_flattened)
# 将 PCA 处理后的值缩放到 [0, π] 之间
X_pca_min = np.min(X_pca)
X_pca_max = np.max(X_pca)
X_pca_scaled = np.pi * (X_pca - X_pca_min) / (X_pca_max - X_pca_min)
X_train, X_test, y_train, y_test = train_test_split(X_pca_scaled, y_sampled, test_size=0.2, random_state=0, shuffle=True) # 将数据集划分为训练集和测试集
y_train[y_train==3]=1
y_train[y_train==6]=0
y_test[y_test==3]=1
y_test[y_test==6]=0
y_train = y_train.numpy()
y_test = y_test.numpy()
return X_train, X_test, y_train, y_test
def getfulldata(mnist_dataset:datasets.MNIST,PCA_dim:int=8):
filtered_data = filter_3_and_6((mnist_dataset.data, mnist_dataset.targets))
X_data, y = filtered_data # X 图像数据 y 标签
X_flattened = X_data.view(X_data.shape[0], -1) # 将图像展平为一维向量
X_flattened = X_flattened/255
pca = PCA(n_components=PCA_dim)
X_pca = pca.fit_transform(X_flattened)
X_pca_min = np.min(X_pca)
X_pca_max = np.max(X_pca)
X_pca_scaled = np.pi * (X_pca - X_pca_min) / (X_pca_max - X_pca_min)
y[y==3]=1
y[y==6]=0
y[y==3]=1
y[y==6]=0
return X_pca_scaled,y
x_fulldata, y = getfulldata(mnist_dataset,8)
def amplitude_encoding(X_train:np.array,X_test:np.array):
trian_params = np.array([amplitude_param(pixels=i.flatten()) for i in X_train])
test_params = np.array([amplitude_param(pixels=i.flatten()) for i in X_test])
return trian_params,test_params