def grid_search(model,
X,
y,
C_range=(-5, 15, 2),
gamma_range=(3, -15, -2),
k=5,
num_cores=1):
if not isinstance(model, PredictableModel):
raise TypeError(
"GridSearch expects a PredictableModel. If you want to perform optimization on raw data use facerec.feature.Identity to pass unpreprocessed data!"
)
if not isinstance(model.classifier, SVM):
raise TypeError(
"GridSearch expects a SVM as classifier. Please use a facerec.classifier.SVM!"
)
logger = logging.getLogger("facerec.svm.gridsearch")
logger.info("Performing a Grid Search.")
# best parameter combination to return
best_parameter = svm_parameter("-q")
best_parameter.kernel_type = model.classifier.param.kernel_type
best_parameter.nu = model.classifier.param.nu
best_parameter.coef0 = model.classifier.param.coef0
# either no gamma given or kernel is linear (only C to optimize)
if (gamma_range is None) or (model.classifier.param.kernel_type == LINEAR):
gamma_range = (0, 0, 1)
# best validation error so far
best_accuracy = np.finfo('float').min
# create grid (cartesian product of ranges)
g = grid([C_range, gamma_range])
results = []
for p in g:
C, gamma = p
C, gamma = 2**C, 2**gamma
model.classifier.param.C, model.classifier.param.gamma = C, gamma
# perform a k-fold cross validation
cv = KFoldCrossValidation(model=model, k=k)
cv.validate(X, y)
# append parameter into list with accuracies for all parameter combinations
results.append([C, gamma, cv.accuracy])
# store best parameter combination
if cv.accuracy > best_accuracy:
logger.info("best_accuracy=%s" % (cv.accuracy))
best_accuracy = cv.accuracy
best_parameter.C, best_parameter.gamma = C, gamma
logger.info("%d-CV Result = %.2f." % (k, cv.accuracy))
# set best parameter combination to best found
return best_parameter, results
def grid_search(model, X, y, C_range=(-5, 15, 2), gamma_range=(3, -15, -2), k=5, num_cores=1):
if not isinstance(model, PredictableModel):
raise TypeError(
"GridSearch expects a PredictableModel. If you want to perform optimization on raw data use facerec.feature.Identity to pass unpreprocessed data!")
if not isinstance(model.classifier, SVM):
raise TypeError("GridSearch expects a SVM as classifier. Please use a facerec.classifier.SVM!")
logger = logging.getLogger("facerec.svm.gridsearch")
logger.info("Performing a Grid Search.")
# best parameter combination to return
best_parameter = svm_parameter("-q")
best_parameter.kernel_type = model.classifier.param.kernel_type
best_parameter.nu = model.classifier.param.nu
best_parameter.coef0 = model.classifier.param.coef0
# either no gamma given or kernel is linear (only C to optimize)
if (gamma_range is None) or (model.classifier.param.kernel_type == LINEAR):
gamma_range = (0, 0, 1)
# best validation error so far
best_accuracy = np.finfo('float').min
# create grid (cartesian product of ranges)
g = grid([C_range, gamma_range])
results = []
for p in g:
C, gamma = p
C, gamma = 2 ** C, 2 ** gamma
model.classifier.param.C, model.classifier.param.gamma = C, gamma
# perform a k-fold cross validation
cv = KFoldCrossValidation(model=model, k=k)
cv.validate(X, y)
# append parameter into list with accuracies for all parameter combinations
results.append([C, gamma, cv.accuracy])
# store best parameter combination
if cv.accuracy > best_accuracy:
logger.info("best_accuracy=%s" % (cv.accuracy))
best_accuracy = cv.accuracy
best_parameter.C, best_parameter.gamma = C, gamma
logger.info("%d-CV Result = %.2f." % (k, cv.accuracy))
# set best parameter combination to best found
return best_parameter, results
def train(self):
# Check if the given dataset exists:
if not os.path.exists(self.dataset):
print ">> [Error] No Dataset Found at '%s'." % self.dataset
sys.exit(1)
# Reads the images, labels and folder_names from a given dataset. Images
# are resized to given size on the fly:
print ">> Loading Dataset <-- " + self.dataset
[images, labels, subject_names] = self.read_images(self.dataset, self.image_size)
# Zip us a {label, name} dict from the given data:
list_of_labels = list(xrange(max(labels) + 1))
subject_dictionary = dict(zip(list_of_labels, subject_names))
# Get the model we want to compute:
model = self.get_model(image_size=self.image_size, subject_names=subject_dictionary)
# Sometimes you want to know how good the model may perform on the data
# given, the script allows you to perform a k-fold Cross Validation before
# the Detection & Recognition part starts:
if self.numfolds is not None:
print ">> Validating Model With %s Folds.." % self.numfolds
# We want to have some log output, so set up a new logging handler
# and point it to stdout:
handler = logging.StreamHandler(sys.stdout)
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
handler.setFormatter(formatter)
# Add a handler to facerec modules, so we see what's going on inside:
logger = logging.getLogger("facerec")
logger.addHandler(handler)
logger.setLevel(logging.DEBUG)
# Perform the validation & print results:
crossval = KFoldCrossValidation(model, k=self.numfolds)
crossval.validate(images, labels)
crossval.print_results()
# Compute the model:
print ">> Computing Model.."
model.compute(images, labels)
# And save the model, which uses Pythons pickle module:
print ">> Saving Model.."
save_model(self.model_filename, model)
def train(self):
# Check if the given dataset exists:
if not os.path.exists(self.dataset):
print ">> [Error] No Dataset Found at '%s'." % self.dataset
sys.exit(1)
# Reads the images, labels and folder_names from a given dataset. Images
# are resized to given size on the fly:
print ">> Loading Dataset <-- " + self.dataset
[images, labels,
subject_names] = self.read_images(self.dataset, self.image_size)
# Zip us a {label, name} dict from the given data:
list_of_labels = list(xrange(max(labels) + 1))
subject_dictionary = dict(zip(list_of_labels, subject_names))
# Get the model we want to compute:
model = self.get_model(image_size=self.image_size,
subject_names=subject_dictionary)
# Sometimes you want to know how good the model may perform on the data
# given, the script allows you to perform a k-fold Cross Validation before
# the Detection & Recognition part starts:
if self.numfolds is not None:
print ">> Validating Model With %s Folds.." % self.numfolds
# We want to have some log output, so set up a new logging handler
# and point it to stdout:
handler = logging.StreamHandler(sys.stdout)
formatter = logging.Formatter(
'%(asctime)s - %(name)s - %(levelname)s - %(message)s')
handler.setFormatter(formatter)
# Add a handler to facerec modules, so we see what's going on inside:
logger = logging.getLogger("facerec")
logger.addHandler(handler)
logger.setLevel(logging.DEBUG)
# Perform the validation & print results:
crossval = KFoldCrossValidation(model, k=self.numfolds)
crossval.validate(images, labels)
crossval.print_results()
# Compute the model:
print ">> Computing Model.."
model.compute(images, labels)
# And save the model, which uses Pythons pickle module:
print ">> Saving Model.."
save_model(self.model_filename, model)