def predict_new_data(graph_file, cons_file, tri_file, other_feature_file):
print 'reading extracted features'
graph_feature = read_feature_data(graph_file)
graph_feature = get_normalized_given_max_min(graph_feature,
'models/grtaph_max_size')
cons_feature = read_feature_data(cons_file)
cons_feature = get_normalized_given_max_min(cons_feature,
'models/cons_max_size')
CC_feature = read_feature_data(tri_file)
CC_feature = get_normalized_given_max_min(CC_feature,
'models/tri_max_size')
ATOS_feature = read_feature_data(other_feature_file)
ATOS_feature = get_normalized_given_max_min(ATOS_feature,
'models/alu_max_size')
width, C, epsilon, num_threads, mkl_epsilon, mkl_norm = 0.5, 1.2, 1e-5, 1, 0.001, 3.5
kernel = CombinedKernel()
feats_train = CombinedFeatures()
feats_test = CombinedFeatures()
#pdb.set_trace()
subkfeats_train = RealFeatures()
subkfeats_test = RealFeatures(np.transpose(np.array(graph_feature)))
subkernel = GaussianKernel(10, width)
feats_test.append_feature_obj(subkfeats_test)
fstream = SerializableAsciiFile("models/graph.dat", "r")
status = subkfeats_train.load_serializable(fstream)
feats_train.append_feature_obj(subkfeats_train)
kernel.append_kernel(subkernel)
subkfeats_train = RealFeatures()
subkfeats_test = RealFeatures(np.transpose(np.array(cons_feature)))
subkernel = GaussianKernel(10, width)
feats_test.append_feature_obj(subkfeats_test)
fstream = SerializableAsciiFile("models/cons.dat", "r")
status = subkfeats_train.load_serializable(fstream)
feats_train.append_feature_obj(subkfeats_train)
kernel.append_kernel(subkernel)
subkfeats_train = RealFeatures()
subkfeats_test = RealFeatures(np.transpose(np.array(CC_feature)))
subkernel = GaussianKernel(10, width)
feats_test.append_feature_obj(subkfeats_test)
fstream = SerializableAsciiFile("models/tri.dat", "r")
status = subkfeats_train.load_serializable(fstream)
feats_train.append_feature_obj(subkfeats_train)
kernel.append_kernel(subkernel)
subkfeats_train = RealFeatures()
subkfeats_test = RealFeatures(np.transpose(np.array(ATOS_feature)))
subkernel = GaussianKernel(10, width)
feats_test.append_feature_obj(subkfeats_test)
fstream = SerializableAsciiFile("models/alu.dat", "r")
status = subkfeats_train.load_serializable(fstream)
feats_train.append_feature_obj(subkfeats_train)
kernel.append_kernel(subkernel)
model_file = "models/mkl.dat"
if not os.path.exists(model_file):
print 'downloading model file'
url_add = 'http://rth.dk/resources/mirnasponge/data/mkl.dat'
urllib.urlretrieve(url_add, model_file)
print 'loading trained model'
fstream = SerializableAsciiFile("models/mkl.dat", "r")
new_mkl = MKLClassification()
status = new_mkl.load_serializable(fstream)
print 'model predicting'
kernel.init(feats_train, feats_test)
new_mkl.set_kernel(kernel)
y_out = new_mkl.apply().get_labels()
return y_out
def predict_new_data(graph_file, cons_file, tri_file, other_feature_file):
print "reading extracted features"
graph_feature = read_feature_data(graph_file)
graph_feature = get_normalized_given_max_min(graph_feature, "models/grtaph_max_size")
cons_feature = read_feature_data(cons_file)
cons_feature = get_normalized_given_max_min(cons_feature, "models/cons_max_size")
CC_feature = read_feature_data(tri_file)
CC_feature = get_normalized_given_max_min(CC_feature, "models/tri_max_size")
ATOS_feature = read_feature_data(other_feature_file)
ATOS_feature = get_normalized_given_max_min(ATOS_feature, "models/alu_max_size")
width, C, epsilon, num_threads, mkl_epsilon, mkl_norm = 0.5, 1.2, 1e-5, 1, 0.001, 3.5
kernel = CombinedKernel()
feats_train = CombinedFeatures()
feats_test = CombinedFeatures()
# pdb.set_trace()
subkfeats_train = RealFeatures()
subkfeats_test = RealFeatures(np.transpose(np.array(graph_feature)))
subkernel = GaussianKernel(10, width)
feats_test.append_feature_obj(subkfeats_test)
fstream = SerializableAsciiFile("models/graph.dat", "r")
status = subkfeats_train.load_serializable(fstream)
feats_train.append_feature_obj(subkfeats_train)
kernel.append_kernel(subkernel)
subkfeats_train = RealFeatures()
subkfeats_test = RealFeatures(np.transpose(np.array(cons_feature)))
subkernel = GaussianKernel(10, width)
feats_test.append_feature_obj(subkfeats_test)
fstream = SerializableAsciiFile("models/cons.dat", "r")
status = subkfeats_train.load_serializable(fstream)
feats_train.append_feature_obj(subkfeats_train)
kernel.append_kernel(subkernel)
subkfeats_train = RealFeatures()
subkfeats_test = RealFeatures(np.transpose(np.array(CC_feature)))
subkernel = GaussianKernel(10, width)
feats_test.append_feature_obj(subkfeats_test)
fstream = SerializableAsciiFile("models/tri.dat", "r")
status = subkfeats_train.load_serializable(fstream)
feats_train.append_feature_obj(subkfeats_train)
kernel.append_kernel(subkernel)
subkfeats_train = RealFeatures()
subkfeats_test = RealFeatures(np.transpose(np.array(ATOS_feature)))
subkernel = GaussianKernel(10, width)
feats_test.append_feature_obj(subkfeats_test)
fstream = SerializableAsciiFile("models/alu.dat", "r")
status = subkfeats_train.load_serializable(fstream)
feats_train.append_feature_obj(subkfeats_train)
kernel.append_kernel(subkernel)
model_file = "models/mkl.dat"
if not os.path.exists(model_file):
print "downloading model file"
url_add = "http://rth.dk/resources/mirnasponge/data/mkl.dat"
urllib.urlretrieve(url_add, model_file)
print "loading trained model"
fstream = SerializableAsciiFile("models/mkl.dat", "r")
new_mkl = MKLClassification()
status = new_mkl.load_serializable(fstream)
print "model predicting"
kernel.init(feats_train, feats_test)
new_mkl.set_kernel(kernel)
y_out = new_mkl.apply().get_labels()
return y_out
