def evaluate_from_model(model_dir, multi_flag=False, eval_data_all=False):
"""
Evaluating interface. 1. Retreive the flags 2. get data 3. initialize network 4. eval
:param model_dir: The folder to retrieve the model
:param eval_data_all: The switch to turn on if you want to put all data in evaluation data
:return: None
"""
# Retrieve the flag object
print("Retrieving flag object for parameters")
if (model_dir.startswith("models")):
model_dir = model_dir[7:]
print("after removing prefix models/, now model_dir is:", model_dir)
flags = helper_functions.load_flags(os.path.join("models", model_dir))
flags.eval_model = model_dir # Reset the eval mode
# Set up the test_ratio
if flags.data_set == 'ballistics':
flags.test_ratio = 0.1
elif flags.data_set == 'sine_wave':
flags.test_ratio = 0.1
elif flags.data_set == 'robotic_arm':
flags.test_ratio = 0.1
# Get the data
train_loader, test_loader = data_reader.read_data(flags, eval_data_all=eval_data_all)
print("Making network now")
# Make Network
ntwk = Network(INN, flags, train_loader, test_loader, inference_mode=True, saved_model=flags.eval_model)
print(ntwk.ckpt_dir)
print("number of trainable parameters is :")
pytorch_total_params = sum(p.numel() for p in ntwk.model.parameters() if p.requires_grad)
print(pytorch_total_params)
# Evaluation process
print("Start eval now:")
if multi_flag:
ntwk.evaluate_multiple_time()
else:
pred_file, truth_file = ntwk.evaluate()
# Plot the MSE distribution
if flags.data_set != 'meta_material' and not multi_flag:
plotMSELossDistrib(pred_file, truth_file, flags)
print("Evaluation finished")
# If gaussian, plot the scatter plot
if flags.data_set == 'gaussian_mixture':
Xpred = helper_functions.get_Xpred(path='data/', name=flags.eval_model)
Ypred = helper_functions.get_Ypred(path='data/', name=flags.eval_model)
# Plot the points scatter
generate_Gaussian.plotData(Xpred, Ypred, save_dir='data/' + flags.eval_model.replace('/','_') + 'generation plot.png', eval_mode=True)
def PlotPairwiseGeometry(figname, Xpred_dir):
"""
Function to plot the pair-wise scattering plot of the geometery file to show
the correlation between the geometry that the network learns
"""
Xpredfile = helper_functions.get_Xpred(Xpred_dir)
Xpred = pd.read_csv(Xpredfile, header=None, delimiter=' ')
f = plt.figure()
axes = pd.plotting.scatter_matrix(Xpred, alpha=0.2)
#plt.tight_layout()
plt.title("Pair-wise scattering of Geometery predictions")
plt.savefig(figname)
def PlotPossibleGeoSpace(figname,
Xpred_dir,
compare_original=False,
calculate_diversity=None):
"""
Function to plot the possible geometry space for a model evaluation result.
It reads from Xpred_dir folder and finds the Xpred result insdie and plot that result
:params figname: The name of the figure to save
:params Xpred_dir: The directory to look for Xpred file which is the source of plotting
:output A plot containing 4 subplots showing the 8 geomoetry dimensions
"""
Xpred = helper_functions.get_Xpred(Xpred_dir)
#Xpredfile = helper_functions.get_Xpred(Xpred_dir)
#Xpred = pd.read_csv(Xpredfile, header=None, delimiter=' ').values
Xtruth = helper_functions.get_Xtruth(Xpred_dir)
#Xtruthfile = helper_functions.get_Xtruth(Xpred_dir)
#Xtruth = pd.read_csv(Xtruthfile, header=None, delimiter=' ').values
f = plt.figure()
ax0 = plt.gca()
print(np.shape(Xpred))
#print(Xpred)
#plt.title(figname)
if (calculate_diversity == 'MST'):
diversity_Xpred, diversity_Xtruth = calculate_MST(Xpred, Xtruth)
elif (calculate_diversity == 'AREA'):
diversity_Xpred, diversity_Xtruth = calculate_AREA(Xpred, Xtruth)
for i in range(4):
ax = plt.subplot(2, 2, i + 1)
ax.scatter(Xpred[:, i], Xpred[:, i + 4], s=3, label="Xpred")
if (compare_original):
ax.scatter(Xtruth[:, i], Xtruth[:, i + 4], s=3, label="Xtruth")
plt.xlabel('h{}'.format(i))
plt.ylabel('r{}'.format(i))
plt.xlim(-1, 1)
plt.ylim(-1, 1)
plt.legend()
if (calculate_diversity != None):
plt.text(-4,
3.5,
'Div_Xpred = {}, Div_Xtruth = {}, under criteria {}'.format(
diversity_Xpred, diversity_Xtruth, calculate_diversity),
zorder=1)
plt.suptitle(figname)
f.savefig(figname + '.png')
