In [33]:
sv = Statevector.from_label('0' * n)
feature_map = ZZFeatureMap(n, reps=1)
var_form = RealAmplitudes(n, reps=1)
circuit = feature_map.combine(var_form)
circuit.draw(output='mpl')
Out[33]:
from qiskit.ml.datasets import *
from qiskit import QuantumCircuit
from qiskit.aqua.components.optimizers import COBYLA, ADAM, SPSA, SLSQP, POWELL, L_BFGS_B, TNC, AQGD
from qiskit.circuit.library import ZZFeatureMap, RealAmplitudes
from qiskit.quantum_info import Statevector
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.preprocessing import normalize
from sklearn.model_selection import train_test_split
from sklearn.utils import shuffle
import warnings
warnings.filterwarnings("ignore")
%matplotlib inline
# constants
n = 4
RANDOM_STATE = 42
LR = 1e-3
class_labels = ['yes', 'no']
def normalizeData(DATA_PATH = "../../Data/Processed/data.csv"):
"""
Normalizes the data
"""
# Reads the data
data = pd.read_csv(DATA_PATH)
data = shuffle(data, random_state=RANDOM_STATE)
X, Y = data[['sex', 'cp', 'exang', 'oldpeak']].values, data['num'].values
# normalize the data
X = normalize(X)
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.3, random_state=RANDOM_STATE)
return X_train, X_test, Y_train, Y_test
X_train, X_test, Y_train, Y_test = normalizeData()
sv = Statevector.from_label('0' * n)
feature_map = ZZFeatureMap(n, reps=1)
var_form = RealAmplitudes(n, reps=1)
circuit = feature_map.combine(var_form)
circuit.draw(output='mpl')
def get_data_dict(params, x):
parameters = {}
for i, p in enumerate(feature_map.ordered_parameters):
parameters[p] = x[i]
for i, p in enumerate(var_form.ordered_parameters):
parameters[p] = params[i]
return parameters
X_train[0]
array([0.4472136 , 0.89442719, 0. , 0. ])
data = X_train[0]
params = np.array([0.1, 1.2, 0.02, 0.1, 0.1, 1.2, 0.02, 0.1])
circ_ = circuit.assign_parameters(get_data_dict(params, data))
circ_.draw(plot_barriers=True)
def assign_label(bit_string, class_labels):
hamming_weight = sum([int(k) for k in list(bit_string)])
is_odd_parity = hamming_weight & 1
if is_odd_parity:
return class_labels[1]
else:
return class_labels[0]
def return_probabilities(counts, class_labels):
shots = sum(counts.values())
result = {class_labels[0]: 0,
class_labels[1]: 0}
for key, item in counts.items():
label = assign_label(key, class_labels)
result[label] += counts[key]/shots
return result
return_probabilities({'00' : 10, '01': 10, '11': 20}, class_labels)
{'yes': 0.75, 'no': 0.25}
def classify(x_list, params, class_labels):
qc_list = []
for x in x_list:
circ_ = circuit.assign_parameters(get_data_dict(params, x))
qc = sv.evolve(circ_)
qc_list += [qc]
probs = []
for qc in qc_list:
counts = qc.to_counts()
prob = return_probabilities(counts, class_labels)
probs += [prob]
return probs
x = np.asarray([X_train[0]])
classify(x, params=np.array([0.8, -0.5, 1.5, 0,5, 0.8, -0.5, 1.5, 0,5]), class_labels=class_labels)
[{'yes': 0.6338620140853923, 'no': 0.3661379859146076}]
def cost_estimate_sigmoid(probs, expected_label): # probability of labels vs actual labels
p = probs.get(expected_label)
sig = None
if np.isclose(p, 0.0):
sig = 1
elif np.isclose(p, 1.0):
sig = 0
else:
denominator = np.sqrt(2*p*(1-p))
x = np.sqrt(200)*(0.5-p)/denominator
sig = 1/(1+np.exp(-x))
return sig
def mse_cost(probs, expected_label):
p = probs.get(expected_label)
actual, pred = np.array(1), np.array(p)
return np.square(np.subtract(actual,pred)).mean()
def rmse_cost(probs, expected_label):
p = probs.get(expected_label)
actual, pred = np.array(1), np.array(pred)
return np.sqrt(np.square(np.subtract(actual,pred)).mean())
cost_list = []
def cost_function(X, Y, class_labels, params, shots=100, print_value=False):
# map training input to list of labels and list of samples
cost = 0
training_labels = []
training_samples = []
for sample in X:
training_samples += [sample]
for label in Y:
if label == 0:
training_labels += [class_labels[0]]
elif label == 1:
training_labels += [class_labels[1]]
probs = classify(training_samples, params, class_labels)
# evaluate costs for all classified samples
for i, prob in enumerate(probs):
cost += mse_cost(prob, training_labels[i])
cost /= len(training_samples)
# print resulting objective function
if print_value:
print('%.4f' % cost)
# return objective value
cost_list.append(cost)
return cost
cost_function(X_train, Y_train, class_labels, params)
0.22354962821107346
def compare_optim(optims):
for opt in optims:
if opt == 'SPSA':
optimizer = SPSA(max_trials = 10)
elif opt == 'SLSQP':
optimizer = SLSQP(maxiter = 10)
elif opt == 'TNC':
optimizer = TNC(maxiter = 10)
elif opt == 'POWELL':
optimizer = POWELL(maxiter = 10)
elif opt == 'ADAM':
optimizer = ADAM(maxiter = 10)
elif opt == 'L_BFGS_B':
optimizer = L_BFGS_B(maxfun = 1000 , maxiter = 10)
elif opt == 'COBYLA':
optimizer = COBYLA(maxiter = 10)
elif opt == "AQGD":
optimizer = AQGD(maxiter=10)
cost_list = []
# define objective function for training
objective_function = lambda params: cost_function(X_train, Y_train, class_labels, params, print_value=False)
# randomly initialize the parameters
np.random.seed(RANDOM_STATE)
init_params = 2*np.pi*np.random.rand(n*(1)*2)
# train classifier
opt_params, value, _ = optimizer.optimize(len(init_params), objective_function, initial_point=init_params)
# print results
print()
print('opt_params:', opt_params)
print('opt_value: ', value)
compare_optim(["COBYLA", "ADAM", "SPSA", "SLSQP", "POWELL", "L_BFGS_B", "TNC", "AQGD"])
opt_params: [2.35330497 6.97351416 4.59925358 4.76148219 0.98029403 1.98014248 0.3649501 5.44234523] opt_value: 0.20637154014580933 opt_params: [2.34335531 5.98351175 4.58925731 3.77148558 0.97016639 0.99014585 0.35494922 5.45235819] opt_value: 0.28483386150197443 opt_params: [ 2.04927472 6.15709295 4.97282334 3.95333517 1.30242588 0.97419756 -0.39591884 5.77216108] opt_value: 0.22048075626519356 opt_params: [ 2.67580429 6.73085048 3.67672534 3.86981027 0.77933055 1.82929249 -0.770624 5.59825639] opt_value: 0.17596546056746962 opt_params: [ 0.12309248 7.91780579 8.04906378 3.16355487 -0.49750456 -0.64397726 0.18959737 4.29371839] opt_value: 0.1650836282262177 opt_params: [ 2.90497708 6.41074425 3.89803076 3.09054985 0.22344208 1.9978181 -0.64476327 5.36431209] opt_value: 0.16971839038652609 opt_params: [2.17425836 6.41728759 4.09463744 4.72008948 0.90205469 1.14542438 0.24674838 5.83797257] opt_value: 0.21844982063207888
------------------------------------------------------------ IndexError Traceback (most recent call last) <ipython-input-48-cbe20ebd7e7c> in <module> ----> 1 compare_optim(["COBYLA", "ADAM", "SPSA", "SLSQP", "POWELL", "L_BFGS_B", "TNC", "AQGD"]) <ipython-input-47-b07473820ca6> in compare_optim(optims) 24 init_params = 2*np.pi*np.random.rand(n*(1)*2) 25 # train classifier ---> 26 opt_params, value, _ = optimizer.optimize(len(init_params), objective_function, initial_point=init_params) 27 # print results 28 print() ~/Documents/VENVS/qosf/lib/python3.6/site-packages/qiskit/aqua/components/optimizers/aqgd.py in optimize(self, num_vars, objective_function, gradient_function, variable_bounds, initial_point) 316 # Calculate objective function and estimate of analytical gradient 317 objval, gradient = \ --> 318 self._compute_objective_fn_and_gradient(params, objective_function) 319 320 logger.info(" Iter: %4d | Obj: %11.6f | Grad Norm: %f", ~/Documents/VENVS/qosf/lib/python3.6/site-packages/qiskit/aqua/components/optimizers/aqgd.py in _compute_objective_fn_and_gradient(self, params, obj) 149 150 # return the objective function value --> 151 obj_value = values[0] 152 153 # return the gradient values IndexError: too many indices for array
cost_list = []
optimizer = L_BFGS_B(maxiter=100)
# optimizer = ADAM(maxiter=100, lr=LR)
# define objective function for training
objective_function = lambda params: cost_function(X_train, Y_train, class_labels, params, print_value=True)
# randomly initialize the parameters
np.random.seed(RANDOM_STATE)
init_params = 2*np.pi*np.random.rand(n*(1)*2)
# train classifier
opt_params, value, _ = optimizer.optimize(len(init_params), objective_function, initial_point=init_params)
# print results
print()
print('opt_params:', opt_params)
print('opt_value: ', value)
0.2878 0.2878 0.2878 0.2878 0.2878 0.2878 0.2878 0.2878 0.2878 0.2047 0.2047 0.2047 0.2047 0.2047 0.2047 0.2047 0.2047 0.2047 0.1954 0.1954 0.1954 0.1954 0.1954 0.1954 0.1954 0.1954 0.1954 0.2012 0.2012 0.2012 0.2012 0.2012 0.2012 0.2012 0.2012 0.2012 0.1792 0.1792 0.1792 0.1792 0.1792 0.1792 0.1792 0.1792 0.1792 0.1744 0.1744 0.1744 0.1744 0.1744 0.1744 0.1744 0.1744 0.1744 0.1733 0.1733 0.1733 0.1733 0.1733 0.1733 0.1733 0.1733 0.1733 0.1718 0.1718 0.1718 0.1718 0.1718 0.1718 0.1718 0.1718 0.1718 0.1715 0.1715 0.1715 0.1715 0.1715 0.1715 0.1715 0.1715 0.1715 0.1710 0.1710 0.1710 0.1710 0.1710 0.1710 0.1710 0.1710 0.1710 0.1704 0.1704 0.1704 0.1704 0.1704 0.1704 0.1704 0.1704 0.1704 0.2180 0.2180 0.2180 0.2180 0.2180 0.2180 0.2180 0.2180 0.2180 0.1697 0.1697 0.1697 0.1697 0.1697 0.1697 0.1697 0.1697 0.1697 0.1700 0.1700 0.1700 0.1700 0.1700 0.1700 0.1700 0.1700 0.1700 0.1694 0.1694 0.1694 0.1694 0.1694 0.1694 0.1694 0.1694 0.1694 0.1690 0.1690 0.1690 0.1690 0.1690 0.1690 0.1690 0.1690 0.1690 0.1680 0.1680 0.1680 0.1680 0.1680 0.1680 0.1680 0.1680 0.1680 0.1673 0.1673 0.1673 0.1673 0.1673 0.1673 0.1673 0.1673 0.1673 0.1672 0.1672 0.1672 0.1672 0.1672 0.1672 0.1672 0.1672 0.1672 0.1671 0.1671 0.1671 0.1671 0.1671 0.1671 0.1671 0.1671 0.1671 0.1673 0.1673 0.1673 0.1673 0.1673 0.1673 0.1673 0.1673 0.1673 0.1669 0.1669 0.1669 0.1669 0.1669 0.1669 0.1669 0.1669 0.1669 0.1665 0.1665 0.1665 0.1665 0.1665 0.1665 0.1665 0.1665 0.1665 0.1662 0.1662 0.1662 0.1662 0.1662 0.1662 0.1662 0.1662 0.1662 0.1661 0.1661 0.1661 0.1661 0.1661 0.1661 0.1661 0.1661 0.1661 0.1660 0.1660 0.1660 0.1660 0.1660 0.1660 0.1660 0.1660 0.1660 0.1660 0.1660 0.1660 0.1660 0.1660 0.1660 0.1660 0.1660 0.1660 0.1660 0.1660 0.1660 0.1660 0.1660 0.1660 0.1660 0.1660 0.1660 0.1659 0.1659 0.1659 0.1659 0.1659 0.1659 0.1659 0.1659 0.1659 0.1658 0.1658 0.1658 0.1658 0.1658 0.1658 0.1658 0.1658 0.1658 0.1690 0.1690 0.1690 0.1690 0.1690 0.1690 0.1690 0.1690 0.1690 0.1657 0.1657 0.1657 0.1657 0.1657 0.1657 0.1657 0.1657 0.1657 0.1656 0.1656 0.1656 0.1656 0.1656 0.1656 0.1656 0.1656 0.1656 0.1654 0.1654 0.1654 0.1654 0.1654 0.1654 0.1654 0.1654 0.1654 0.1651 0.1651 0.1651 0.1651 0.1651 0.1651 0.1651 0.1651 0.1651 0.1651 0.1651 0.1651 0.1651 0.1651 0.1651 0.1651 0.1651 0.1651 0.1651 0.1651 0.1651 0.1651 0.1651 0.1651 0.1651 0.1651 0.1651 0.1651 0.1651 0.1651 0.1651 0.1651 0.1651 0.1651 0.1651 0.1651 0.1651 0.1651 0.1651 0.1651 0.1651 0.1651 0.1651 0.1651 0.1651 0.1651 0.1651 0.1651 0.1651 0.1651 0.1651 0.1651 0.1651 0.1651 0.1651 0.1651 0.1651 0.1651 0.1651 0.1651 0.1651 0.1651 0.1651 0.1651 0.1651 0.1651 0.1651 0.1651 0.1651 0.1651 0.1651 0.1651 opt_params: [3.26333934 7.90762422 4.89434665 3.15547364 0.49794644 2.49425875 0.18009411 5.13728541] opt_value: 0.16508072929082526
fig = plt.figure()
plt.plot(range(0,378,1), cost_list)
plt.xlabel('Steps')
plt.ylabel('Cost value')
plt.title("L_BFGS_B Cost value against steps")
plt.show()
fig.savefig('../../Output/Figures/costvssteps.jpeg')
def test_model(X, Y, class_labels, params):
accuracy = 0
training_labels = []
training_samples = []
for sample in X:
training_samples += [sample]
probs = classify(training_samples, params, class_labels)
for i, prob in enumerate(probs):
if (prob.get('yes') >= prob.get('no')) and (Y_test[i] == 0):
accuracy += 1
elif (prob.get('no') >= prob.get('yes')) and (Y_test[i] == 1):
accuracy += 1
accuracy /= len(Y_test)
print("Test accuracy: {}\n".format(accuracy))
test_model(X_test, Y_test, class_labels, opt_params)
Test accuracy: 0.7613636363636364