def main():
parser = argparse.ArgumentParser(description='Algorithms traditional ML')
parser.add_argument('--dataset', type=str, required=True, \
choices=["IP", "UP", "SV", "UH", "DIP", "DUP", "DIPr", "DUPr"], \
help='dataset (options: IP, UP, SV, UH, DIP, DUP, DIPr, DUPr)')
parser.add_argument('--repeat', default=1, type=int, help='Number of runs')
parser.add_argument('--components', default=None, type=int, help='dimensionality reduction')
parser.add_argument('--preprocess', default="standard", type=str, help='Preprocessing')
parser.add_argument('--splitmethod', default="sklearn", type=str, help='Method for split datasets')
parser.add_argument('--random_state', default=None, type=int,
help='The seed of the pseudo random number generator to use when shuffling the data')
parser.add_argument('--tr_percent', default=0.15, type=float, help='samples of train set')
#########################################
parser.add_argument('--set_parameters', action='store_false', help='Set some optimal parameters')
############## CHANGE PARAMS ############
parser.add_argument('--n_est', default=200, type=int, help='The number of trees in the forest')
parser.add_argument('--m_s_split', default=2, type=int,
help='The minimum number of samples required to split an internal node')
parser.add_argument('--max_feat', default=40, type=int,
help='The number of features to consider when looking for the best split')
parser.add_argument('--depth', default=60, type=int, help='The maximum depth of the tree')
#########################################
args = parser.parse_args()
state = {k: v for k, v in args._get_kwargs()}
if args.set_parameters: args = set_params(args)
pixels, labels, num_class = \
mydata.loadData(args.dataset, num_components=args.components, preprocessing=args.preprocess)
pixels = pixels.reshape(-1, pixels.shape[-1])
stats = np.ones((args.repeat, num_class+3)) * -1000.0 # OA, AA, K, Aclass
for pos in range(args.repeat):
if args.dataset in ["UH", "DIP", "DUP", "DIPr", "DUPr"]:
x_train, x_test, y_train, y_test = \
mydata.load_split_data_fix(args.dataset, pixels)#, rand_state=args.random_state+pos)
else:
labels = labels.reshape(-1)
pixels = pixels[labels!=0]
labels = labels[labels!=0] - 1
rstate = args.random_state+pos if args.random_state != None else None
x_train, x_test, y_train, y_test = \
mydata.split_data(pixels, labels, args.tr_percent, rand_state=rstate)
clf = RandomForestClassifier(\
n_estimators=args.n_est, min_samples_split=args.m_s_split, \
max_features=args.max_feat, max_depth=args.depth) \
.fit(x_train, y_train)
stats[pos,:] = mymetrics.reports(clf.predict(x_test), y_test)[2]
print(stats[-1])
def main():
parser = argparse.ArgumentParser(description='Algorithms traditional ML')
parser.add_argument(
'--dataset',
type=str,
required=True,
choices=["IP", "UP", "SV", "UH", "DIP", "DUP", "DIPr", "DUPr"],
help='dataset (options: IP, UP, SV, UH, DIP, DUP, DIPr, DUPr)')
parser.add_argument('--repeat', default=1, type=int, help='Number of runs')
parser.add_argument('--components',
default=None,
type=int,
help='dimensionality reduction')
parser.add_argument('--spatialsize',
default=19,
type=int,
help='windows size')
parser.add_argument('--wdecay',
default=0.02,
type=float,
help='apply penalties on layer parameters')
parser.add_argument('--preprocess',
default="standard",
type=str,
help='Preprocessing')
parser.add_argument('--splitmethod',
default="sklearn",
type=str,
help='Method for split datasets')
parser.add_argument(
'--random_state',
default=None,
type=int,
help=
'The seed of the pseudo random number generator to use when shuffling the data'
)
parser.add_argument('--tr_percent',
default=0.15,
type=float,
help='samples of train set')
parser.add_argument('--use_val',
action='store_true',
help='Use validation set')
parser.add_argument('--val_percent',
default=0.1,
type=float,
help='samples of val set')
parser.add_argument('--verbosetrain',
action='store_true',
help='Verbose train')
#########################################
parser.add_argument('--set_parameters',
action='store_false',
help='Set some optimal parameters')
############## CHANGE PARAMS ############
parser.add_argument(
'--batch_size',
default=100,
type=int,
help='Number of training examples in one forward/backward pass.')
parser.add_argument(
'--epochs',
default=300,
type=int,
help='Number of full training cycle on the training set')
#########################################
args = parser.parse_args()
state = {k: v for k, v in args._get_kwargs()}
if args.set_parameters:
args = set_params(args)
pixels, labels, num_class = mydata.loadData(args.dataset,
num_components=args.components,
preprocessing=args.preprocess)
pixels, labels = mydata.createImageCubes(pixels,
labels,
windowSize=args.spatialsize,
removeZeroLabels=False)
stats = np.ones(
(args.repeat, num_class + 3)) * -1000.0 # OA, AA, K, Aclass
for pos in range(args.repeat):
rstate = args.random_state + pos if args.random_state != None else None
if args.dataset in ["UH", "DIP", "DUP", "DIPr", "DUPr"]:
x_train, x_test, y_train, y_test = mydata.load_split_data_fix(
args.dataset, pixels) #, rand_state=args.random_state+pos)
else:
pixels = pixels[labels != 0]
labels = labels[labels != 0] - 1
x_train, x_test, y_train, y_test = mydata.split_data(
pixels, labels, args.tr_percent, rand_state=rstate)
if args.use_val:
x_val, x_test, y_val, y_test = mydata.split_data(x_test,
y_test,
args.val_percent,
rand_state=rstate)
inputshape = x_train.shape[1:]
clf = get_model_compiled(inputshape, num_class, w_decay=args.wdecay)
valdata = (x_val, keras_to_categorical(
y_val, num_class)) if args.use_val else (x_test,
keras_to_categorical(
y_test, num_class))
clf.fit(x_train,
keras_to_categorical(y_train, num_class),
batch_size=args.batch_size,
epochs=args.epochs,
verbose=args.verbosetrain,
validation_data=valdata,
callbacks=[
ModelCheckpoint("/tmp/best_model.h5",
monitor='val_accuracy',
verbose=0,
save_best_only=True)
])
del clf
K.clear_session()
gc.collect()
clf = load_model("/tmp/best_model.h5")
print("PARAMETERS", clf.count_params())
stats[pos, :] = mymetrics.reports(
np.argmax(clf.predict(x_test), axis=1), y_test)[2]
print(args.dataset, list(stats[-1]))
def main():
parser = argparse.ArgumentParser(description='Transfer Learning HSI')
parser.add_argument('--dataset1', type=str, required=True, \
choices=["IP", "UP", "SV", "UH", "DIP", "DUP", "DIPr", "DUPr"], \
help='dataset (options: IP, UP, SV, UH, DIP, DUP, DIPr, DUPr)')
parser.add_argument('--dataset2', type=str, required=True, \
choices=["IP", "UP", "SV", "UH", "DIP", "DUP", "DIPr", "DUPr"], \
help='dataset (options: IP, UP, SV, UH, DIP, DUP, DIPr, DUPr)')
parser.add_argument('--arch', type=str, required=True, \
choices=["CNN1D", "CNN2D", "CNN2D40bands", "CNN3D"], \
help='architecture (options: CNN1D, CNN2D, CNN2D40bands, CNN3D)')
parser.add_argument('--repeat', default=1, type=int, help='Number of runs')
parser.add_argument('--preprocess',
default="standard",
type=str,
help='Preprocessing')
parser.add_argument('--splitmethod',
default="sklearn",
type=str,
help='Method for split datasets')
parser.add_argument(
'--random_state',
default=None,
type=int,
help=
'The seed of the pseudo random number generator to use when shuffling the data'
)
parser.add_argument('--tr_samples',
default=2,
type=int,
help='samples per class train set')
parser.add_argument('--use_val',
action='store_true',
help='Use validation set')
parser.add_argument('--val_percent',
default=0.1,
type=float,
help='samples of val set')
parser.add_argument('--use_transfer_learning',
action='store_true',
help='Use transfer learning')
parser.add_argument('--search_base_model',
action='store_true',
help='Search base model')
parser.add_argument('--lr1',
default=1e-3,
type=float,
help='Learning rate base model')
parser.add_argument('--lr2',
default=8e-4,
type=float,
help='Learning rate fine model')
parser.add_argument('--verbosetrain1',
action='store_true',
help='Verbose train')
parser.add_argument('--verbosetrain2',
action='store_true',
help='Verbose train')
#########################################
parser.add_argument('--set_parameters',
action='store_false',
help='Set some optimal parameters')
############## CHANGE PARAMS ############
parser.add_argument(
'--batch_size1',
default=100,
type=int,
help='Number of training examples in one forward/backward pass.')
parser.add_argument(
'--epochs1',
default=50,
type=int,
help='Number of full training cycle on the training set')
parser.add_argument(
'--batch_size2',
default=100,
type=int,
help='Number of training examples in one forward/backward pass.')
parser.add_argument(
'--epochs2',
default=500,
type=int,
help='Number of full training cycle on the training set')
#########################################
args = parser.parse_args()
state = {k: v for k, v in args._get_kwargs()}
if args.set_parameters: args = set_params(args)
args.components = 1 if "CNN2D" == args.arch else 40
args.spatialsize = 1 if args.arch == "CNN1D" else 19
if args.dataset2 in ["IP", "SV", "DIP", "DIPr"]: num_class = 16
stats = np.ones(
(args.repeat, num_class + 3)) * -1000.0 # OA, AA, K, Aclass
for pos in range(args.repeat):
rstate = args.random_state + pos if args.random_state != None else None
if args.search_base_model:
pixels, labels, num_class = \
mydata.loadData(args.dataset1, num_components=args.components, preprocessing=args.preprocess)
if args.arch != "CNN1D":
pixels, labels = mydata.createImageCubes(
pixels,
labels,
windowSize=args.spatialsize,
removeZeroLabels=True)
if args.arch == "CNN3D":
inputshape = (pixels.shape[1], pixels.shape[2],
pixels.shape[3], 1)
pixels = pixels.reshape(pixels.shape[0], pixels.shape[1],
pixels.shape[2], pixels.shape[3],
1)
else:
inputshape = (pixels.shape[1], pixels.shape[2],
pixels.shape[3])
else:
pixels = pixels.reshape(-1, pixels.shape[-1])
labels = labels.reshape(-1)
pixels = pixels[labels != 0]
labels = labels[labels != 0] - 1
inputshape = (pixels.shape[-1], 1)
pixels = pixels.reshape(pixels.shape[0], pixels.shape[1], 1)
pixels, labels = mydata.random_unison(pixels,
labels,
rstate=rstate)
clf = get_model_compiled(args, inputshape, num_class)
clf.fit(pixels,
keras_to_categorical(labels),
batch_size=args.batch_size1,
epochs=args.epochs1,
verbose=args.verbosetrain1,
callbacks=[
ModelCheckpoint("/tmp/best_base_model.h5",
monitor='loss',
verbose=0,
save_best_only=True)
])
del pixels, labels
del clf
K.clear_session()
gc.collect()
exit()
pixels, labels, num_class = \
mydata.loadData(args.dataset2, num_components=args.components, preprocessing=args.preprocess)
if args.arch != "CNN1D":
pixels, labels = mydata.createImageCubes(
pixels,
labels,
windowSize=args.spatialsize,
removeZeroLabels=False)
if args.arch == "CNN3D":
inputshape = (pixels.shape[1], pixels.shape[2],
pixels.shape[3], 1)
pixels = pixels.reshape(pixels.shape[0], pixels.shape[1],
pixels.shape[2], pixels.shape[3], 1)
else:
inputshape = (pixels.shape[1], pixels.shape[2],
pixels.shape[3])
else:
pixels = pixels.reshape(-1, pixels.shape[-1])
labels = labels.reshape(-1)
pixels = pixels[labels != 0]
labels = labels[labels != 0] - 1
inputshape = (pixels.shape[-1], 1)
pixels = pixels.reshape(pixels.shape[0], pixels.shape[1], 1)
if args.dataset2 in ["UH", "DIP", "DUP", "DIPr", "DUPr"]:
x_train, x_test, y_train, y_test = \
mydata.load_split_data_fix(args.dataset, pixels)
else:
pixels = pixels[labels != 0]
labels = labels[labels != 0] - 1
x_train, x_test, y_train, y_test = \
mydata.split_data(pixels, labels, [args.tr_samples]*num_class, splitdset="custom2", rand_state=rstate)
if args.use_val:
x_val, x_test, y_val, y_test = \
mydata.split_data(x_test, y_test, args.val_percent, rand_state=rstate)
if args.arch == "CNN1D":
x_val = x_val.reshape(x_val.shape[0], x_val.shape[1], 1)
elif args.arch == "CNN3D":
x_val = x_val.reshape(x_val.shape[0], x_val.shape[1],
x_val.shape[2], x_val.shape[3], 1)
if args.arch == "CNN1D":
x_train = x_train.reshape(x_train.shape[0], x_train.shape[1], 1)
x_test = x_test.reshape(x_test.shape[0], x_test.shape[1], 1)
elif args.arch == "CNN3D":
x_train = x_train.reshape(x_train.shape[0], x_train.shape[1],
x_train.shape[2], x_train.shape[3], 1)
x_test = x_test.reshape(x_test.shape[0], x_test.shape[1],
x_test.shape[2], x_test.shape[3], 1)
inputshape = (pixels.shape[1], pixels.shape[2], pixels.shape[3], 1)
valdata = (x_val, keras_to_categorical(
y_val, num_class)) if args.use_val else (x_test,
keras_to_categorical(
y_test, num_class))
clf = get_pretrained_model_compiled(args, inputshape, num_class)
clf.fit(x_train,
keras_to_categorical(y_train),
batch_size=args.batch_size2,
epochs=args.epochs2,
verbose=args.verbosetrain2,
validation_data=valdata,
callbacks=[
ModelCheckpoint("/tmp/best_model.h5",
monitor='val_accuracy',
verbose=0,
save_best_only=True)
])
del clf
K.clear_session()
gc.collect()
clf = load_model("/tmp/best_model.h5")
#print("PARAMETERS", clf.count_params())
stats[pos, :] = mymetrics.reports(
np.argmax(clf.predict(x_test), axis=1), y_test)[2]
print(stats[-1])
def main():
parser = argparse.ArgumentParser(description='Algorithms traditional ML')
parser.add_argument('--dataset', type=str, required=True, \
choices=["IP", "UP", "SV", "UH", "DIP", "DUP", "DIPr", "DUPr"], \
help='dataset (options: IP, UP, SV, UH, DIP, DUP, DIPr, DUPr)')
parser.add_argument('--arch', type=str, required=True, \
choices=["VGG16", "VGG19", "RESNET50", "INCEPTIONV3", "DENSENET121", "MOBILENET", "XCEPTION"], \
help='architecture (options: VGG16, VGG19, RESNET50, INCEPTIONV3, DENSENET121, MOBILENET, XCEPTION)')
parser.add_argument('--repeat', default=1, type=int, help='Number of runs')
parser.add_argument('--components',
default=3,
type=int,
help='dimensionality reduction')
parser.add_argument('--spatialsize',
default=31,
type=int,
help='windows size')
parser.add_argument('--lrate',
default=1e-4,
type=float,
help='Learning rate')
parser.add_argument('--preprocess',
default="standard",
type=str,
help='Preprocessing')
parser.add_argument('--splitmethod',
default="sklearn",
type=str,
help='Method for split datasets')
parser.add_argument(
'--random_state',
default=None,
type=int,
help=
'The seed of the pseudo random number generator to use when shuffling the data'
)
parser.add_argument('--tr_percent',
default=0.15,
type=float,
help='samples of train set')
parser.add_argument('--use_val',
action='store_true',
help='Use validation set')
parser.add_argument('--val_percent',
default=0.1,
type=float,
help='samples of val set')
parser.add_argument('--verbosetrain',
action='store_true',
help='Verbose train')
#########################################
parser.add_argument('--set_parameters',
action='store_false',
help='Set some optimal parameters')
############## CHANGE PARAMS ############
parser.add_argument(
'--batch_size',
default=100,
type=int,
help='Number of training examples in one forward/backward pass.')
parser.add_argument(
'--epochs',
default=200,
type=int,
help='Number of full training cycle on the training set')
#########################################
args = parser.parse_args()
state = {k: v for k, v in args._get_kwargs()}
if args.set_parameters: args = set_params(args)
pixels, labels, num_class = \
mydata.loadData(args.dataset, num_components=args.components, preprocessing=args.preprocess)
pixels, labels = mydata.createImageCubes(pixels,
labels,
windowSize=args.spatialsize,
removeZeroLabels=False)
pixels = np.array([
cv2.resize(a, (a.shape[0] + 1, a.shape[1] + 1),
interpolation=cv2.INTER_CUBIC) for a in pixels
])
stats = np.ones(
(args.repeat, num_class + 3)) * -1000.0 # OA, AA, K, Aclass
for pos in range(args.repeat):
rstate = args.random_state + pos if args.random_state != None else None
if args.dataset in ["UH", "DIP", "DUP", "DIPr", "DUPr"]:
x_train, x_test, y_train, y_test = \
mydata.load_split_data_fix(args.dataset, pixels)#, rand_state=args.random_state+pos)
else:
pixels = pixels[labels != 0]
labels = labels[labels != 0] - 1
x_train, x_test, y_train, y_test = \
mydata.split_data(pixels, labels, args.tr_percent, rand_state=rstate)
if args.use_val:
x_val, x_test, y_val, y_test = \
mydata.split_data(x_test, y_test, args.val_percent, rand_state=rstate)
base_model, modlrate = get_model_pretrain(args.arch)
x = base_model.output
x = GlobalAveragePooling2D()(x)
x = Dense(256, activation='relu')(x)
x = Dropout(0.5)(x)
predictions = Dense(len(np.unique(labels)),
activation='softmax',
name='predictions')(x)
clf = Model(base_model.input, predictions)
valdata = (x_val, keras_to_categorical(
y_val, num_class)) if args.use_val else (x_test,
keras_to_categorical(
y_test, num_class))
#for layer in clf.layers: layer.trainable = True
clf.compile(loss=categorical_crossentropy,
optimizer=Adam(lr=args.lrate),
metrics=['accuracy'])
clf.fit(x_train,
keras_to_categorical(y_train, num_class),
batch_size=args.batch_size,
epochs=5,
verbose=args.verbosetrain,
validation_data=valdata,
callbacks=[
ModelCheckpoint("/tmp/best_model.h5",
monitor='val_loss',
verbose=0,
save_best_only=True)
])
del clf
K.clear_session()
gc.collect()
clf = load_model("/tmp/best_model.h5")
for layer in base_model.layers:
layer.trainable = False
#for layer in clf.layers[:-3]: layer.trainable = False
#for idlayer in [-1,-2,-3]: clf.layers[idlayer].trainable = True
clf.compile(loss=categorical_crossentropy,
optimizer=Adam(lr=args.lrate * modlrate),
metrics=['accuracy'])
clf.fit(x_train,
keras_to_categorical(y_train, num_class),
batch_size=args.batch_size,
epochs=50,
verbose=args.verbosetrain,
validation_data=valdata,
callbacks=[
ModelCheckpoint("/tmp/best_model.h5",
monitor='val_accuracy',
verbose=0,
save_best_only=True)
])
del clf
K.clear_session()
gc.collect()
clf = load_model("/tmp/best_model.h5")
#print("PARAMETERS", clf.count_params())
stats[pos, :] = mymetrics.reports(
np.argmax(clf.predict(x_test), axis=1), y_test)[2]
print(args.dataset, args.arch, args.tr_percent, list(stats[-1]))
def main():
parser = argparse.ArgumentParser(description='Algorithms traditional ML')
parser.add_argument('--dataset', type=str, required=True, \
choices=["IP", "UP", "SV", "UH", "DIP", "DUP", "DIPr", "DUPr"], \
help='dataset (options: IP, UP, SV, UH, DIP, DUP, DIPr, DUPr)')
parser.add_argument('--repeat', default=1, type=int, help='Number of runs')
parser.add_argument('--components',
default=None,
type=int,
help='dimensionality reduction')
parser.add_argument('--preprocess',
default="standard",
type=str,
help='Preprocessing')
parser.add_argument('--splitmethod',
default="sklearn",
type=str,
help='Method for split datasets')
parser.add_argument(
'--random_state',
default=None,
type=int,
help=
'The seed of the pseudo random number generator to use when shuffling the data'
)
parser.add_argument('--tr_percent',
default=0.15,
type=float,
help='samples of train set')
#########################################
parser.add_argument('--set_parameters',
action='store_false',
help='Set some optimal parameters')
############## CHANGE PARAMS ############
parser.add_argument('--C',
default=1,
type=int,
help='Inverse of regularization strength')
#########################################
args = parser.parse_args()
state = {k: v for k, v in args._get_kwargs()}
if args.set_parameters: args = set_params(args)
pixels, labels, num_class = \
mydata.loadData(args.dataset, num_components=args.components, preprocessing=args.preprocess)
pixels = pixels.reshape(-1, pixels.shape[-1])
stats = np.ones(
(args.repeat, num_class + 3)) * -1000.0 # OA, AA, K, Aclass
for pos in range(args.repeat):
if args.dataset in ["UH", "DIP", "DUP", "DIPr", "DUPr"]:
x_train, x_test, y_train, y_test = \
mydata.load_split_data_fix(args.dataset, pixels)#, rand_state=args.random_state+pos)
else:
labels = labels.reshape(-1)
pixels = pixels[labels != 0]
labels = labels[labels != 0] - 1
rstate = args.random_state + pos if args.random_state != None else None
x_train, x_test, y_train, y_test = \
mydata.split_data(pixels, labels, args.tr_percent, rand_state=rstate)
clf = LogisticRegression(penalty='l2', dual=False, tol=1e-20, C=args.C, \
solver='lbfgs', multi_class='multinomial', max_iter=5000,\
fit_intercept=True, n_jobs=-1)\
.fit(x_train, y_train)
stats[pos, :] = np.round(
mymetrics.reports(clf.predict(x_test), y_test)[2], 2)
print(stats[-1])
def main():
parser = argparse.ArgumentParser(description='Algorithms traditional ML')
parser.add_argument(
'--dataset',
type=str,
required=True,
choices=["IP", "UP", "SV", "UH", "DIP", "DUP", "DIPr", "DUPr", "KSC"],
help='dataset (options: IP, UP, SV, UH, DIP, DUP, DIPr, DUPr, KSC)')
parser.add_argument('--repeat', default=1, type=int, help='Number of runs')
parser.add_argument('--components',
default=None,
type=int,
help='dimensionality reduction')
parser.add_argument('--preprocess',
default="minmax",
type=str,
help='Preprocessing')
parser.add_argument('--splitmethod',
default="sklearn",
type=str,
help='Method for split datasets')
parser.add_argument(
'--random_state',
default=None,
type=int,
help=
'The seed of the pseudo random number generator to use when shuffling the data'
)
parser.add_argument('--tr_percent',
default=0.15,
type=float,
help='samples of train set')
#########################################
parser.add_argument('--set_parameters',
action='store_false',
help='Set some optimal parameters')
############## CHANGE PARAMS ############
parser.add_argument('--C',
default=1,
type=int,
help='Inverse of regularization strength')
parser.add_argument(
'--g',
default=1,
type=float,
help='Kernel coefficient for ‘rbf’, ‘poly’ and ‘sigmoid’.')
#########################################
args = parser.parse_args()
state = {k: v for k, v in args._get_kwargs()}
if args.set_parameters:
args = set_params(args)
pixels, labels, num_class = mydata.loadData(args.dataset,
num_components=args.components,
preprocessing=args.preprocess)
pixels = pixels.reshape(-1, pixels.shape[-1])
pixels_bak = np.copy(pixels)
labels_bak = np.copy(labels)
KAPPA = []
OA = []
AA = []
TRAINING_TIME = []
TESTING_TIME = []
ELEMENT_ACC = np.zeros((args.repeat, num_class))
for pos in range(args.repeat):
pixels = pixels_bak
labels = labels_bak
if args.dataset in ["UH", "DIP", "DUP", "DIPr", "DUPr"]:
x_train, x_test, y_train, y_test = mydata.load_split_data_fix(
args.dataset, pixels) #, rand_state=args.random_state+pos)
else:
labels = labels.reshape(-1)
pixels = pixels[labels != 0]
labels = labels[labels != 0] - 1
rstate = args.random_state + pos if args.random_state != None else None
x_train, x_test, y_train, y_test = mydata.split_data(
pixels, labels, args.tr_percent, rand_state=rstate)
tic1 = time()
clf = SVC(gamma=args.g, C=args.C, tol=1e-7).fit(x_train, y_train)
toc1 = time()
tic2 = time()
_, _, overall_acc, average_acc, kappa, each_acc = mymetrics.reports(
clf.predict(x_test), y_test)
toc2 = time()
KAPPA.append(kappa)
OA.append(overall_acc)
AA.append(average_acc)
TRAINING_TIME.append(toc1 - tic1)
TESTING_TIME.append(toc2 - tic2)
ELEMENT_ACC[pos, :] = each_acc
print(OA, AA, KAPPA)
save_report.record_output(
save_report.args_to_text(vars(args)), OA, AA, KAPPA, ELEMENT_ACC,
TRAINING_TIME, TESTING_TIME, './report/' + 'svm_' + str(args.dataset) +
'_' + str(args.tr_percent) + '.txt')
def main():
parser = argparse.ArgumentParser(description='Algorithms traditional ML')
parser.add_argument('--dataset', type=str, required=True, \
choices=["IP", "UP", "SV", "UH", "DIP", "DUP", "DIPr", "DUPr"], \
help='dataset (options: IP, UP, SV, UH, DIP, DUP, DIPr, DUPr)')
parser.add_argument('--type_recurrent', type=str, required=True, \
choices=["RNN", "GRU", "LSTM"], \
help='function (options: "RNN", "GRU", "LSTM")')
parser.add_argument('--repeat', default=1, type=int, help='Number of runs')
parser.add_argument('--components',
default=None,
type=int,
help='dimensionality reduction')
parser.add_argument('--preprocess',
default="standard",
type=str,
help='Preprocessing')
parser.add_argument('--splitmethod',
default="sklearn",
type=str,
help='Method for split datasets')
parser.add_argument(
'--random_state',
default=None,
type=int,
help=
'The seed of the pseudo random number generator to use when shuffling the data'
)
parser.add_argument('--tr_percent',
default=0.15,
type=float,
help='samples of train set')
parser.add_argument('--use_val',
action='store_true',
help='Use validation set')
parser.add_argument('--val_percent',
default=0.1,
type=float,
help='samples of val set')
parser.add_argument('--verbosetrain',
action='store_true',
help='Verbose train')
#########################################
parser.add_argument('--set_parameters',
action='store_false',
help='Set some optimal parameters')
############## CHANGE PARAMS ############
parser.add_argument(
'--batch_size',
default=100,
type=int,
help='Number of training examples in one forward/backward pass.')
parser.add_argument(
'--epochs',
default=10,
type=int,
help='Number of full training cycle on the training set')
#########################################
args = parser.parse_args()
state = {k: v for k, v in args._get_kwargs()}
if args.set_parameters: args = set_params(args)
pixels, labels, num_class = \
mydata.loadData(args.dataset, num_components=args.components, preprocessing=args.preprocess)
pixels = pixels.reshape(-1, pixels.shape[-1])
stats = np.ones(
(args.repeat, num_class + 3)) * -1000.0 # OA, AA, K, Aclass
for pos in range(args.repeat):
rstate = args.random_state + pos if args.random_state != None else None
if args.dataset in ["UH", "DIP", "DUP", "DIPr", "DUPr"]:
x_train, x_test, y_train, y_test = \
mydata.load_split_data_fix(args.dataset, pixels)#, rand_state=args.random_state+pos)
else:
labels = labels.reshape(-1)
pixels = pixels[labels != 0]
labels = labels[labels != 0] - 1
x_train, x_test, y_train, y_test = \
mydata.split_data(pixels, labels, args.tr_percent, rand_state=rstate)
if args.use_val:
x_val, x_test, y_val, y_test = \
mydata.split_data(x_test, y_test, args.val_percent, rand_state=rstate)
x_val = x_val.reshape(x_val.shape[0], x_val.shape[1], 1)
x_train = x_train.reshape(x_train.shape[0], x_train.shape[1], 1)
x_test = x_test.reshape(x_test.shape[0], x_test.shape[1], 1)
n_bands, sequences = x_train.shape[1:]
clf = get_model_compiled(n_bands, sequences, num_class,
args.type_recurrent)
valdata = (x_val, keras_to_categorical(
y_val, num_class)) if args.use_val else (x_test,
keras_to_categorical(
y_test, num_class))
# checkpoint
filepath = "../temp/best_model.h5"
checkpoint = ModelCheckpoint(filepath,
monitor='val_acc',
verbose=0,
save_best_only=True,
mode='max',
period=1)
callbacks_list = [checkpoint]
clf.fit(x_train,
keras_to_categorical(y_train, num_class),
batch_size=args.batch_size,
epochs=args.epochs,
verbose=args.verbosetrain,
validation_data=valdata,
callbacks=callbacks_list)
del clf
K.clear_session()
gc.collect()
clf = load_model("../temp/best_model.h5")
print("PARAMETERS", clf.count_params())
stats[pos, :] = mymetrics.reports(
np.argmax(clf.predict(x_test), axis=1), y_test)[2]
print(args.dataset, args.type_recurrent, list(stats[-1]))
def main():
parser = argparse.ArgumentParser(description='Algorithms traditional ML')
parser.add_argument(
'--dataset',
type=str,
required=True,
choices=["IP", "UP", "SV", "UH", "DIP", "DUP", "DIPr", "DUPr", "KSC"],
help='dataset (options: IP, UP, SV, UH, DIP, DUP, DIPr, DUPr, KSC)')
parser.add_argument('--repeat', default=1, type=int, help='Number of runs')
parser.add_argument('--components',
default=None,
type=int,
help='dimensionality reduction')
parser.add_argument('--preprocess',
default="standard",
type=str,
help='Preprocessing')
parser.add_argument('--splitmethod',
default="sklearn",
type=str,
help='Method for split datasets')
parser.add_argument(
'--random_state',
default=None,
type=int,
help=
'The seed of the pseudo random number generator to use when shuffling the data'
)
parser.add_argument('--tr_percent',
default=0.15,
type=float,
help='samples of train set')
#########################################
parser.add_argument('--set_parameters',
action='store_false',
help='Set some optimal parameters')
############## CHANGE PARAMS ############
parser.add_argument('--n_est',
default=200,
type=int,
help='The number of trees in the forest')
parser.add_argument(
'--m_s_split',
default=2,
type=int,
help='The minimum number of samples required to split an internal node'
)
parser.add_argument(
'--max_feat',
default=40,
type=int,
help=
'The number of features to consider when looking for the best split')
parser.add_argument('--depth',
default=60,
type=int,
help='The maximum depth of the tree')
#########################################
args = parser.parse_args()
state = {k: v for k, v in args._get_kwargs()}
if args.set_parameters:
args = set_params(args)
pixels, labels, num_class = mydata.loadData(args.dataset,
num_components=args.components,
preprocessing=args.preprocess)
pixels = pixels.reshape(-1, pixels.shape[-1])
pixels_bak = np.copy(pixels)
labels_bak = np.copy(labels)
KAPPA = []
OA = []
AA = []
TRAINING_TIME = []
TESTING_TIME = []
ELEMENT_ACC = np.zeros((args.repeat, num_class))
for pos in range(args.repeat):
pixels = pixels_bak
labels = labels_bak
if args.dataset in ["UH", "DIP", "DUP", "DIPr", "DUPr"]:
x_train, x_test, y_train, y_test = mydata.load_split_data_fix(
args.dataset, pixels) #, rand_state=args.random_state+pos)
else:
labels = labels.reshape(-1)
pixels = pixels[labels != 0]
labels = labels[labels != 0] - 1
rstate = args.random_state + pos if args.random_state != None else None
x_train, x_test, y_train, y_test = mydata.split_data(
pixels, labels, args.tr_percent, rand_state=rstate)
tic1 = time()
clf = RandomForestClassifier(n_estimators=args.n_est,
min_samples_split=args.m_s_split,
max_features=args.max_feat,
max_depth=args.depth).fit(
x_train, y_train)
toc1 = time()
tic2 = time()
_, _, overall_acc, average_acc, kappa, each_acc = mymetrics.reports(
clf.predict(x_test), y_test)
toc2 = time()
KAPPA.append(kappa)
OA.append(overall_acc)
AA.append(average_acc)
TRAINING_TIME.append(toc1 - tic1)
TESTING_TIME.append(toc2 - tic2)
ELEMENT_ACC[pos, :] = each_acc
print(OA, AA, KAPPA)
save_report.record_output(
save_report.args_to_text(vars(args)), OA, AA, KAPPA, ELEMENT_ACC,
TRAINING_TIME, TESTING_TIME, './report/' + 'random_forest_' +
args.dataset + '_' + str(args.tr_percent) + '.txt')
def main():
parser = argparse.ArgumentParser(description='Algorithms traditional ML')
parser.add_argument(
'--dataset',
type=str,
required=True,
choices=["IP", "UP", "SV", "UH", "DIP", "DUP", "DIPr", "DUPr", "KSC"],
help='dataset (options: IP, UP, SV, UH, DIP, DUP, DIPr, DUPr, KSC)')
parser.add_argument('--type_recurrent',
type=str,
required=True,
choices=["RNN", "GRU", "LSTM"],
help='function (options: "RNN", "GRU", "LSTM")')
parser.add_argument('--repeat', default=1, type=int, help='Number of runs')
parser.add_argument('--components',
default=None,
type=int,
help='dimensionality reduction')
parser.add_argument('--preprocess',
default="standard",
type=str,
help='Preprocessing')
parser.add_argument('--splitmethod',
default="sklearn",
type=str,
help='Method for split datasets')
parser.add_argument(
'--random_state',
default=None,
type=int,
help=
'The seed of the pseudo random number generator to use when shuffling the data'
)
parser.add_argument('--tr_percent',
default=0.15,
type=float,
help='samples of train set')
parser.add_argument('--use_val',
action='store_true',
help='Use validation set')
parser.add_argument('--val_percent',
default=0.1,
type=float,
help='samples of val set')
parser.add_argument('--verbosetrain',
action='store_false',
help='Verbose train')
#########################################
parser.add_argument('--set_parameters',
action='store_false',
help='Set some optimal parameters')
############## CHANGE PARAMS ############
parser.add_argument(
'--batch_size',
default=100,
type=int,
help='Number of training examples in one forward/backward pass.')
parser.add_argument(
'--epochs',
default=150,
type=int,
help='Number of full training cycle on the training set')
#########################################
args = parser.parse_args()
state = {k: v for k, v in args._get_kwargs()}
if args.set_parameters:
args = set_params(args)
pixels, labels, num_class = mydata.loadData(args.dataset,
num_components=args.components,
preprocessing=args.preprocess)
pixels = pixels.reshape(-1, pixels.shape[-1])
pixels_bak = np.copy(pixels)
labels_bak = np.copy(labels)
KAPPA = []
OA = []
AA = []
TRAINING_TIME = []
TESTING_TIME = []
ELEMENT_ACC = np.zeros((args.repeat, num_class))
for pos in range(args.repeat):
pixels = pixels_bak
labels = labels_bak
rstate = args.random_state + pos if args.random_state != None else None
if args.dataset in ["UH", "DIP", "DUP", "DIPr", "DUPr"]:
x_train, x_test, y_train, y_test = \
mydata.load_split_data_fix(args.dataset, pixels)#, rand_state=args.random_state+pos)
else:
labels = labels.reshape(-1)
pixels = pixels[labels != 0]
labels = labels[labels != 0] - 1
x_train, x_test, y_train, y_test = mydata.split_data(
pixels, labels, args.tr_percent, rand_state=rstate)
if args.use_val:
x_val, x_test, y_val, y_test = mydata.split_data(x_test,
y_test,
args.val_percent,
rand_state=rstate)
x_val = x_val.reshape(x_val.shape[0], x_val.shape[1], 1)
x_train = x_train.reshape(x_train.shape[0], x_train.shape[1], 1)
x_test = x_test.reshape(x_test.shape[0], x_test.shape[1], 1)
n_bands, sequences = x_train.shape[1:]
clf = get_model_compiled(n_bands, sequences, num_class,
args.type_recurrent)
valdata = (x_val, keras_to_categorical(
y_val, num_class)) if args.use_val else (x_test,
keras_to_categorical(
y_test, num_class))
tic1 = time()
clf.fit(x_train,
keras_to_categorical(y_train, num_class),
batch_size=args.batch_size,
epochs=args.epochs,
verbose=args.verbosetrain,
validation_data=valdata,
callbacks=[
ModelCheckpoint("/tmp/best_model.h5",
monitor='val_accuracy',
verbose=0,
save_best_only=True)
])
del clf
K.clear_session()
gc.collect()
clf = load_model("/tmp/best_model.h5")
print("PARAMETERS", clf.count_params())
toc1 = time()
tic2 = time()
_, _, overall_acc, average_acc, kappa, each_acc = mymetrics.reports(
np.argmax(clf.predict(x_test), axis=1), y_test)
toc2 = time()
KAPPA.append(kappa)
OA.append(overall_acc)
AA.append(average_acc)
TRAINING_TIME.append(toc1 - tic1)
TESTING_TIME.append(toc2 - tic2)
ELEMENT_ACC[pos, :] = each_acc
print(OA, AA, KAPPA)
save_report.record_output(
save_report.args_to_text(vars(args)), OA, AA, KAPPA, ELEMENT_ACC,
TRAINING_TIME, TESTING_TIME, './report/' + args.type_recurrent + '_' +
args.dataset + '_' + str(args.tr_percent) + '.txt')
all_mean2 = np.zeros([6,14])
oak = np.zeros([6,3])
num1_class = 16
num2_class = 9
num3_class = 11
for j in range(6):
if j == 0:
stats = np.ones((10, num1_class+3)) * -1000.0 # OA, AA, K, Aclass
for i in range (10):
pres = np.loadtxt("./Prob_MRF/"+add_data1+"dual_outcome/probability/"+str(i)+"labels_pred.txt")
pres = pres.astype(np.float32)
pres.reshape(-1)
stats[i,:] = mymetrics.reports(pres, indian_labels0)[2]
oa_std = np.std(stats[:,0],ddof=1)
aa_std = np.std(stats[:,1],ddof=1)
kappa_std = np.std(stats[:,2],ddof=1)
all_mean0[j,:] = np.mean(stats,axis=0)
oak[j,0]=oa_std
oak[j,1]=aa_std
oak[j,2]=kappa_std
if j == 1:
stats = np.ones((10, num1_class+3)) * -1000.0 # OA, AA, K, Aclass
for i in range (10):
pres = np.loadtxt("./Prob_MRF/"+add_data2+"dual_outcome/probability/"+str(i)+"labels_pred.txt")
pres = pres.astype(np.float32)
pres.reshape(-1)
stats[i,:] = mymetrics.reports(pres, indian_labels)[2]
