__author__ = 'chunk'

from ..common import *

from ..mdata import MSR, CV ,ILSVRC

from ..mmodel import SVM

from ..mfeat import HOG

from sklearn import cross_validation

timer = Timer()

def test_SVM_CV():
    timer.mark()

    dcv = CV.DataCV()

    X, Y = dcv.load_data(mode='local')  # 90.468586s ->  5.392520s

    # X, Y = dcv.load_data(mode='hbase') # 21.682754s
    # X, Y = dcv.load_data(mode='spark') # 29.549597s
    timer.report()

    timer.mark()
    # msvm = SVM.ModelSVM(toolset='sklearn') # 3.030380s
    # msvm = SVM.ModelSVM(toolset='opencv') # 8.939880s
    # msvm = SVM.ModelSVM(toolset='libsvm') # 185.524023s
    msvm = SVM.ModelSVM(toolset='spark')

    msvm.train(X, Y)
    timer.report()

    timer.mark()
    for path, subdirs, files in os.walk('data/467/'):
        for name in files:
            imgpath = os.path.join(path, name)
            feat = dcv.get_feat(imgpath, 'hog')
            print name, msvm.predict(feat)
    timer.report()

    timer.mark()
    print msvm.test(X, Y)  # 0.948892561983 for svm_cv, 0.989024793388 for svm_sk, 0.9900826446280992 for svm_lib
    timer.report()  # 27.421949s for svm_lib

def test_SVM_ILSVRC():
    timer.mark()
    dil = ILSVRC.DataILSVRC(base_dir='/data/hadoop/ImageNet/ILSVRC/ILSVRC2013_DET_val', category='Test')
    X, Y = dil.load_data(mode='local')  #
    # X, Y = dcv.load_data(mode='hbase') #

    # X, Y = dcv.load_data(mode='spark') #
    print np.array(Y).shape,np.array(X).shape

    # print X[:2]

    X_train, X_test, Y_train, Y_test = cross_validation.train_test_split(X, Y, test_size=0.4, random_state=0)
    print np.array(X_train).shape, np.array(Y_train).shape

    print np.array(X_test).shape, np.array(Y_test).shape

    timer.report()

    timer.mark()
    msvm = SVM.ModelSVM(toolset='sklearn') # 4.884247s 0.777853030816
    # msvm = SVM.ModelSVM(toolset='opencv') #
    # msvm = SVM.ModelSVM(toolset='libsvm') #
    # msvm = SVM.ModelSVM(toolset='spark')

    msvm.train(X_train, Y_train)
    timer.report()

    timer.mark()
    print msvm.test(X_test, Y_test)  #
    timer.report()  #
    
    timer.mark()
    print 'or like this:'
    scores = cross_validation.cross_val_score(msvm.model,X,Y)
    print scores
    timer.report()







if __name__ == '__main__':
    # test_SVM_CV()
    test_SVM_ILSVRC()

    print 'helllo'