def plotCost(number_points=0):
rand_samples = []
for model_index in range(num_models_regions):
region = model_regions[model_index]
if number_points:
samples = region.points[np.random.choice(
region.points.shape[0], number_points, replace=False), :]
else:
samples = region.points
rand_samples.append(samples)
all_costs = []
_, axes = plt.subplots(1, 3)
for box_plot in range(num_models_fitness):
ax = axes.flat[box_plot]
#plt.subplot(1,3,box_plot+1)
ax.set_title(str(box_plot + 1) + '-bit model')
ax.set_xlabel('region id')
if box_plot == 0:
ax.set_ylabel('cost')
model = BioProc(np.array([
"protein_production", "protein_production", "protein_production",
"protein_production", "protein_degradation", "protein_degradation",
"Kd", "hill", "protein_production", "protein_degradation", "Kd",
"hill"
]),
model_mode=models[box_plot])
model_evals = []
for model_index in range(num_models_regions):
evals = []
for sample in rand_samples[model_index]:
evals.append(-model.eval(sample)[0])
model_evals.append(evals)
#plt.boxplot(model_evals)
ax.violinplot(model_evals)
ax.boxplot(model_evals)
ax.set_xticks([1, 2, 3])
all_costs.append(model_evals)
#dump data to file
f = open(os.path.join(base_path_robustness, "cost_distrib.p"), "wb")
pickle.dump(all_costs, f)
f.close()
plt.savefig(os.path.join(base_path_robustness, 'cost_distrib.pdf'),
bbox_inches='tight')
plt.show()
def getCosts(number_points=0, file_name=""):
rand_samples = []
for model_index in range(num_models_regions):
region = model_regions[model_index]
if number_points:
samples = region.points[np.random.choice(
region.points.shape[0], number_points, replace=False), :]
else:
samples = region.points
rand_samples.append(samples)
df = pd.DataFrame(columns=['Model id', 'Region id', 'cost'])
for model_id in range(num_models_fitness):
model = BioProc(np.array([
"protein_production", "protein_production", "protein_production",
"protein_production", "protein_degradation", "protein_degradation",
"Kd", "hill", "protein_production", "protein_degradation", "Kd",
"hill"
]),
model_mode=models[model_id])
for region_id in range(num_models_regions):
for sample in rand_samples[region_id]:
cost = -model.eval(sample)[0]
df = df.append(
{
'Model id': model_id + 1,
'Region id': region_id + 1,
'cost': cost
},
ignore_index=True)
if file_name:
df.to_csv(file_name, index=False)
return df