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data_gen.py
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data_gen.py
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from pennylane import numpy as np
import pennylane as qml
import qiskit
import qiskit.providers.aer.noise as noise
import os
from tqdm import tqdm
import cv2
import math
import sys
sys.path.append('.')
from circuit import classifier, classifier_embedding
from tools.get_MNIST import load
def data_gen(n_qubits, root, n_depth_classifier=2, index=None, random_label=True, backend=None, net=None):
n_data = 1000
n_depth_embedding = 2
# if backend is None:
# dev = qml.device("default.qubit", wires=n_qubits)
# elif backend == 'qiskit.aer':
# # qiskit.IBMQ.load_account()
# provider = qiskit.IBMQ.providers(group='open')[0]
# backend_ibm = provider.get_backend('ibmq_16_melbourne') # ibmq_qasm_simulator
# noise_model = noise.NoiseModel.from_backend(backend_ibm)
# dev = qml.device('qiskit.aer', wires=n_qubits, noise_model=noise_model)
# else:
dev = qml.device("default.qubit", wires=n_qubits)
if net is None or net=='qnn':
circuit = qml.QNode(classifier, dev)
else:
circuit = qml.QNode(classifier_embedding, dev)
np.random.seed(index)
while True:
param = np.random.uniform(0, math.pi, n_qubits*n_depth_classifier*3)
param = np.reshape(param, (n_depth_classifier, n_qubits, 3))
data = np.random.uniform(0, 2*math.pi, n_data*n_depth_embedding*n_qubits)
data = np.array(data, requires_grad=False)
data = np.reshape(data, (n_data, n_depth_embedding, n_qubits))
gap_low, gap_high = 0.15, 0.15
data_save, label_save = [], []
for i in tqdm(range(n_data), desc='data_gen'):
exp = circuit(param, feat=data[i])
if exp < 0.0 - gap_low:
data_save.append(data[i])
label_save.append(-1)
elif exp > 0.0 + gap_high:
data_save.append(data[i])
label_save.append(1)
if sum(np.array(label_save) == 1) >= 200 and sum(np.array(label_save) == -1) >= 200:
break
print(sum(label_save))
label_save = np.array(label_save)
data_save = np.array(data_save)
dataset_type = '_true_'
if random_label:
np.random.seed(index)
random_index = np.random.permutation(len(label_save))
label_save = label_save[random_index]
dataset_type = '_random_'
np.save(os.path.join(root, 'feat'+dataset_type+net+'_'+str(n_qubits)+'_'+str(index)), data_save)
np.save(os.path.join(root, 'label'+dataset_type+net+'_'+str(n_qubits)+'_'+str(index)), label_save)
def get_data(feat_file=None, label_file=None, name='synthetic', random_label=True):
if name == 'synthetic':
feat = np.load(feat_file)
label = np.load(label_file)
index_neg = label==-1
index_pos = label==1
feat_pos_train = feat[index_pos][:100]
feat_pos_test = feat[index_pos][100:200]
feat_neg_train = feat[index_neg][:100]
feat_neg_test = feat[index_neg][100:200]
label_pos_train = label[index_pos][:100]
label_pos_test = label[index_pos][100:200]
label_neg_train = label[index_neg][:100]
label_neg_test = label[index_neg][100:200]
feat_train = np.array(np.concatenate((feat_pos_train, feat_neg_train), axis=0), requires_grad=False)
label_train = np.array(np.concatenate((label_pos_train, label_neg_train), axis=0), requires_grad=False)
feat_test = np.array(np.concatenate((feat_pos_test, feat_neg_test), axis=0), requires_grad=False)
label_test = np.array(np.concatenate((label_pos_test, label_neg_test), axis=0), requires_grad=False)
return feat_train, label_train, feat_test, label_test
elif name == 'MNIST':
feat_train, label_train, feat_test, label_test = load()
for i in range(len(feat_train)):
feat_train[i] = cv2.resize(feat_train[i], (10, 10), interpolation=cv2.INTER_NEAREST)
for i in range(len(feat_test)):
feat_test[i] = cv2.resize(feat_test[i], (10, 10), interpolation=cv2.INTER_NEAREST)
# transform data from [0, 255] to [-1, 1]
feat_train = feat_train / 255.0
feat_test = feat_test / 255.0
feat_train = math.pi*((feat_train - 0.1307) / 0.3081)
feat_test = math.pi*((feat_test - 0.1307) / 0.3081)
if random_label:
index = np.random.permutation(len(label_train))
label_train = label_train[index]
return feat_train, label_train, feat_test, label_test
elif name == 'WINE':
feat, label = [], []
with open(feat_file, 'r') as f:
for line in f.readlines():
parts = line.strip().split(',')
if float(parts[0]) > 2:
continue
label.append(2*(float(parts[0])-1)-1)
# label.append(float(parts[0])-1)
feat.append([float(part) for part in parts[1:]])
feat = np.array(feat, requires_grad=False)[:, np.newaxis, :]
# feat = 2*math.pi*(feat - np.min(feat, axis=0)) / np.ptp(feat, axis=0)
label = np.array(label, requires_grad=False)
if random_label:
index = np.random.permutation(len(label))
label = label[index]
index_neg = label==-1
index_pos = label==1
n_pos = sum(index_pos)
n_neg = sum(index_neg)
print('n_pos = {}, n_neg = {}'.format(n_pos, n_neg))
feat_pos_train = feat[index_pos][:int(0.5*n_pos)]
feat_pos_test = feat[index_pos][int(0.5*n_pos):]
feat_neg_train = feat[index_neg][:int(0.5*n_neg)]
feat_neg_test = feat[index_neg][int(0.5*n_neg):]
label_pos_train = label[index_pos][:int(0.5*n_pos)]
label_pos_test = label[index_pos][int(0.5*n_pos):]
label_neg_train = label[index_neg][:int(0.5*n_neg)]
label_neg_test = label[index_neg][int(0.5*n_neg):]
feat_train = np.array(np.concatenate((feat_pos_train, feat_neg_train), axis=0), requires_grad=False)
label_train = np.array(np.concatenate((label_pos_train, label_neg_train), axis=0), requires_grad=False)
feat_test = np.array(np.concatenate((feat_pos_test, feat_neg_test), axis=0), requires_grad=False)
label_test = np.array(np.concatenate((label_pos_test, label_neg_test), axis=0), requires_grad=False)
return feat_train, label_train, feat_test, label_test
if __name__ == '__main__':
# n_qubits = 10
# n_data = 1000
# n_depth_classifier = 2
# n_depth_embedding = 2
# dev = qml.device("default.qubit", wires=n_qubits)
# circuit = qml.QNode(classifier, dev)
# np.random.seed(0)
# param = np.random.uniform(0, math.pi, n_qubits*n_depth_classifier*3)
# param = np.reshape(param, (n_depth_classifier, n_qubits, 3))
# data = np.random.uniform(0, 2*math.pi, n_data*n_depth_embedding*n_qubits)
# data = np.array(data, requires_grad=False)
# data = np.reshape(data, (n_data, n_depth_embedding, n_qubits))
# gap_low, gap_high = 0.15, 0.15
# data_save, label_save = [], []
# for i in range(n_data):
# exp = circuit(param, data[i])
# if exp < 0.0 - gap_low:
# data_save.append(data[i])
# label_save.append(-1)
# elif exp > 0.0 + gap_high:
# data_save.append(data[i])
# label_save.append(1)
# print(sum(label_save))
# np.save('data/feat', np.array(data_save))
# np.save('data/label', np.array(label_save))
data_gen(16, 'data', 0)