def reproduce1(data_path, normalization=1, if_sqrt=False, ylabel='mse'):
data = tb.load_data(data_path)
value_range = data['value_range']
if if_sqrt:
metric = np.sqrt(data['metric'] / normalization)
else:
metric = data['metric'] / normalization
x_label = data['x_label']
plt.plot(value_range, metric)
plt.xlabel(x_label)
plt.ylabel(ylabel)
plt.tight_layout()
plt.grid()
def reproduce2(data_path, normalization=1, if_sqrt=False):
data = tb.load_data(data_path)
X, Y = np.meshgrid(data['c_range'], data['r_range'])
plt.figure()
if if_sqrt:
CS = plt.contour(X, Y, np.sqrt(data['metric'] / normalization))
else:
CS = plt.contour(X, Y, data['metric'] / normalization)
plt.clabel(CS, inline=1, fontsize=10)
# plt.title('MSE contour map')
plt.annotate(data['annotate_text'],
xy=data['annotate_xy'],
xytext=data['annotate_xytext'],
arrowprops=dict(facecolor='black', shrink=0.05), )
plt.plot(data['annotate_xy'][0], data['annotate_xy'][1], 'bo')
plt.xlabel(data['x_label'])
plt.ylabel(data['y_label'])
plt.tight_layout()
plt.grid()
def identifiable_range(data_path,
threshold,
sparsity_weight=0,
sparsity_order=1):
data = tb.load_data(data_path)
x = data['value_range']
y = data['metric']
z = np.poly1d(np.polyfit(x, y, 4))
x_dense = np.linspace(min(x), max(x), 511)
true_value = x[np.argmin(y)]
x_dense = np.sort(np.append(x_dense, true_value))
y_dense = z(x_dense)
true_value = x[np.argmin(y)]
# sparsity = np.power(np.sum(np.power(np.abs(x_dense), sparsity_order)), 1 / sparsity_order)
sparsity = np.power(np.abs(x_dense), sparsity_order)
total_loss = y_dense + sparsity_weight * sparsity - sparsity_weight * abs(
true_value)
x_range = x_dense[total_loss <= threshold]
if x_range.size > 0:
positive_derivation = max(x_range) - true_value
negative_derivation = min(x_range) - true_value
else:
x_range = x_dense[np.argmin(total_loss)]
positive_derivation = x_range - true_value
negative_derivation = x_range - true_value
label = modify_index(data['x_label'])
output = {
'label': label,
'true_value': true_value,
'positive_derivation': positive_derivation,
'negative_derivation': negative_derivation,
'x_axis': x_dense,
'total_loss': total_loss,
}
return output
return 3
elif count == 4:
return 2
elif count == 2:
return 1
else:
print(file_name)
raise(ValueError)
DATA_PATH = os.path.join(PROJECT_DIR, 'experiments', 'cost_landscape', 'data')
SAVE_PATH = os.path.join(PROJECT_DIR, 'experiments', 'cost_landscape', 'images')
file_names = os.listdir(DATA_PATH)
# recover du for reference
data = tb.load_data(os.path.join(DATA_PATH, file_names[0]))
du = data['du']
du.recover_data_unit()
norm_y = tb.mse(du.get('y'), np.zeros(du.get('y').shape))
# rMSE can be seen as the reciprocal of SNR
# SNR = l2(y)/l2(error/noise)
# target_SNR = 10
# noise_MSE = norm_y / np.square(target_SNR)
noise_MSE = 0.05
SNR = np.sqrt(norm_y / noise_MSE)
# find out files that corresponding to one free parameter
target_files = [name for name in file_names if categorize(name) == 1]