def classify(images, classes_list, train_set, test_set, pos_fold, descriptor,
parameters):
"""
Performs the classification of the test_set according to the train_set.
Implementation of Support Vector Machine classifier using sklearn.
"""
print "Classification: SVMDBC"
#Get parameters
SVMDBC_approach = svm_approach(parameters['Approach'])
#Paths
dirname = os.path.abspath(os.path.join(os.path.dirname(__file__)))
temp_path = os.path.abspath(os.path.join(dirname, "..", "..", "temp"))
model_path = os.path.join(temp_path, "iteration:" + str(iteration) + \
"-SVMDBC_" + str(pos_fold) + ".model")
#Preprocess each class to a unique value to the classification
label_encoder = preprocessing.LabelEncoder()
label_encoder.fit(classes_list)
#SVMDBC is a binary classifier, it cannot have more than two classes
print label_encoder.classes_
if len(label_encoder.classes_) != 2:
print "Classifier needs exactly 2 classes"
return None, None, None, None, None
#Read the train list and save the list of class and the list
#of feature vectors
list_class = []
list_fv = []
for img in train_set:
list_class.append(images[img][POS_CLASSES][INDEX_ZERO])
list_fv.append(images[img][POS_FV][INDEX_ZERO])
list_train = numpy.array(list_fv)
list_train_class = numpy.array(list_class)
#Given a list of classes, transform each value in this list to a integer
list_train_class = label_encoder.transform(list_train_class)
print "List of classes of this experiment:", label_encoder.classes_
#Read the test list and save the list of class and the list
#of feature vectors
list_img = test_set
list_class = []
list_fv = []
for img in test_set:
list_class.append(images[img][POS_CLASSES][INDEX_ZERO])
list_fv.append(numpy.array(images[img][POS_FV][INDEX_ZERO]))
list_test = numpy.array(list_fv)
list_test_class = numpy.array(list_class)
#Classification
#--------------------------------------------------------------------------
svmdbc = SVM.SVMDBC(approach=SVMDBC_approach)
#SVMDBC Fit
print "\tFit: Beginning"
svmdbc.fit(list_train, list_train_class)
print "\tFit: Done!"
#Save configuration of the SVMDBC
print svmdbc
model_paths = []
#Predict
print "\tPredict: Beginning"
list_predict = svmdbc.classify(list_test)
print "\tPredict: Done"
#Mapping the results into integers
list_predict = [
int(item) if item is not None else int(label_encoder.transform([None]))
for item in list_predict
]
#Returning the result to strings
list_predict = label_encoder.inverse_transform(list_predict)
list_result = []
for predict in list_predict:
img_result = [0] * len(label_encoder.classes_)
#Find all predict in the list label_encoder.classes_ and grab the
#first index
pos = label_encoder.classes_.tolist().index(predict)
img_result[pos] = 1
list_result.append(img_result)
#--------------------------------------------------------------------------
return list_img, list_test_class, list_result, label_encoder.classes_, \
model_paths
