def test_elmk_diabetes():
# load dataset
data = elm.read("elmTestData/diabetes.data")
# create a regressor
elmk = elm.ELMKernel()
try:
# search for best parameter for this dataset
# elmk.search_param(data, cv="kfold", of="rmse")
# split data in training and testing sets
tr_set, te_set = elm.split_sets(data, training_percent=.8, perm=True)
#train and test
tr_result = elmk.train(tr_set)
te_result = elmk.test(te_set)
except:
ERROR = 1
else:
ERROR = 0
assert (ERROR == 0)
def run_model(X_train, X_test):
print("\t\t\txxxxx ELM (polynomial) model xxxxx")
# set parameters
# params = ['poly', 0.5, [5]]
# create a classifier ** mostly only classifier
elmk = elm.ELMKernel()
elmk.search_param(X_train, cv="kfold", of="accuracy", kf=["poly"], eval=10)
#train and test
# results are Error objects
tr_result = elmk.train(np.array(X_train))
te_result = elmk.test(np.array(X_test))
# print(te_result.expected_targets)
# print(te_result.predicted_targets)
test_result = [1 if x > 0.5 else 0 for x in te_result.predicted_targets]
# print(test_result)
print(
'Accuracy: ' +
str(100 *
metrics.accuracy_score(te_result.expected_targets, test_result)) +
'%')
print('F1 score: ' +
str(metrics.f1_score(te_result.expected_targets, test_result)))
fpr, tpr, thresholds = metrics.roc_curve(te_result.expected_targets,
test_result,
pos_label=1)
print("Area under curve: " + str(metrics.auc(fpr, tpr)))
def main():
# Load in 2D velocity data
velocity = data.load_data()
# data.example_of_data(velocity)
# form testing and training sets for velocity data
X_train, y_train, X_test, y_test = data.form_train_test_sets(velocity)
# Data transformation
#print(X_test[0]['u'].shape)
print("len of y", len(y_test))
# print("shape of y", y_test.shape)
#print(y_train)
#print(X_train['u'].shape)
import elm as standard_elm
# create a classifier
elmk = standard_elm.ELMKernel()
nn_structure = [9, 100, 1]
x, y = utils.transform_dict_for_nn(X_train, y_train, nn_structure[0])
x = np.transpose(x)
y = np.transpose([y])
tr_set = np.concatenate(
(y, x), 1) #standard format for elm function - y_train + x_train
x_test, y_test = utils.transform_dict_for_nn(X_test[0], y_test[0],
nn_structure[0])
#x_test = np.transpose(x_test)
#y_test = np.transpose([y_test])
#te_set = np.concatenate((y_test, x_test), 1)
# load dataset
dataa = standard_elm.read("boston.data")
# create a classifier
elmk = standard_elm.elmk.ELMKernel()
# split data in training and testing sets
# use 80% of dataset to training and shuffle data before splitting
tr_set, te_set = standard_elm.split_sets(dataa,
training_percent=.8,
perm=True)
#train and test
# results are Error objects
tr_result = elmk.train(tr_set)
te_result = elmk.test(te_set)
print(te_result.get_accuracy())
te_result.predicted_targets
3), onehot_encode(
test_diff, 3)
# Concatenate subject and difficulty
train_data = np.concatenate((train_data, train_sub, train_diff),
axis=1)
test_data = np.concatenate((test_data, test_sub, test_diff),
axis=1)
# Classification
if args.clf_model == 'LDA':
clf_model = LDA()
elif args.clf_model == 'SVM':
clf_model = SVC(C=1)
elif args.clf_model == 'ELMK':
clf_model = elm.ELMKernel()
elif args.clf_model == 'ELMR':
clf_model = elm.ELMRandom()
if args.clf_model in ['ELMK', 'ELMR']:
train_elm_data = np.concatenate(
(train_label[:, np.newaxis], train_data), axis=1)
test_elm_data = np.concatenate(
(test_label[:, np.newaxis], test_data), axis=1)
clf_model.search_param(train_elm_data,
cv="kfold",
of="accuracy",
eval=10)
train_acc = clf_model.train(train_elm_data).get_accuracy()
test_acc = clf_model.test(test_elm_data).get_accuracy()
elif args.clf_model in ['pcafc', 'pcafc_sd']:
def main(index_exp=0):
dirName = '%s_%s_data%d_%s' % (args.ext_model, args.rgr_model,
args.data_cate, args.append_name)
fileName = '%s_exp%d' % (dirName, index_exp)
# Create folder for results of this model
if not os.path.exists('./results/%s' % (dirName)):
os.makedirs('./results/%s' % (dirName))
print('Extraction model: %s' % (args.ext_model))
print('Regression model: %s' % (args.rgr_model))
if args.ext_model == 'vgg16':
net = tv_models.vgg16(pretrained=True).to(device=device)
set_parameter_requires_grad(net, True)
net.classifier[6] = Identity()
elif args.ext_model == 'resnet50':
net = tv_models.resnet50(pretrained=True).to(device=device)
set_parameter_requires_grad(net, True)
net.fc = Identity()
# Get dataset
batchSize = 64
input_size = 224
# Load Data
data_transforms = {
'train': transforms.Compose([ndl.Rescale(input_size),
ndl.ToTensor()]),
'test': transforms.Compose([ndl.Rescale(input_size),
ndl.ToTensor()])
}
print("Initializing Datasets and Dataloaders...")
# Create training and testing datasets
image_datasets = {
x: ndl.TopoplotLoader(args.image_folder,
x,
transform=data_transforms[x],
index_exp=index_exp)
for x in ['train', 'test']
}
# Create training and testing dataloaders
dataloaders_dict = {
'train':
Data.DataLoader(image_datasets['train'],
batch_size=batchSize,
shuffle=False,
num_workers=4),
'test':
Data.DataLoader(image_datasets['test'],
batch_size=batchSize,
shuffle=False,
num_workers=4)
}
# Extract features by VGG16
net.eval() # Disable batchnorm, dropout
X_train, Y_train = extract_layer(dataloaders_dict['train'], net)
X_test, Y_test = extract_layer(dataloaders_dict['test'], net)
# Standardize data before PCA
if args.scale:
X_train, X_test = preprocessing.scale(X_train, X_test, mode=args.scale)
# Apply PCA to reduce dimension
if args.n_components > 1:
args.n_components = int(args.n_components)
pca = PCA(n_components=args.n_components, svd_solver='full')
pca.fit(X_train)
X_train = pca.transform(X_train)
X_test = pca.transform(X_test)
print('(X) Number of features after PCA: %d' % (X_train.shape[1]))
print('(X) Explained variance ratio: %.3f' %
(np.sum(pca.explained_variance_ratio_)))
# Add conditional entropy
if args.add_CE and args.data_cate == 2:
print('Add conditional entropy as additional features...')
with open(
'./raw_data/CE_sub%d_channel21_exp%d_train.data' %
(args.subject_ID, index_exp), 'rb') as fp:
CE_train = pickle.load(fp)
with open(
'./raw_data/CE_sub%d_channel21_exp%d_test.data' %
(args.subject_ID, index_exp), 'rb') as fp:
CE_test = pickle.load(fp)
# Scale CE
CE_train, CE_test = preprocessing.scale(CE_train, CE_test)
# Apply PCA
pca = PCA(n_components=30, svd_solver='full')
pca.fit(CE_train)
CE_train = pca.transform(CE_train)
CE_test = pca.transform(CE_test)
print('(CE) Number of features after PCA: %d' % (CE_train.shape[1]))
print('(CE) Explained variance ratio: %.3f' %
(np.sum(pca.explained_variance_ratio_)))
# Concatentate with X
X_train = np.concatenate((X_train, CE_train), axis=1)
X_test = np.concatenate((X_test, CE_test), axis=1)
# Regression to predict solution latency
X_train_Reg = X_train
X_test_Reg = X_test
if args.rgr_model == 'LR':
rgr = linear_model.LinearRegression()
elif args.rgr_model == 'Ridge':
rgr = linear_model.Ridge(alpha=1)
elif args.rgr_model == 'GPR':
kernel = RBF(10, (1e-2, 1e2)) + ConstantKernel(10, (1e-2, 1e2))
rgr = GaussianProcessRegressor(kernel=kernel, random_state=0)
elif args.rgr_model == 'ELMK':
rgr = elm.ELMKernel()
elif args.rgr_model == 'ELMR':
params = ["sigmoid", 1, 500, False]
rgr = elm.ELMRandom(params)
if args.rgr_model not in ['ELMK', 'ELMR']:
rgr.fit(X_train_Reg, Y_train)
pred_train = rgr.predict(X_train_Reg)
pred_test = rgr.predict(X_test_Reg)
else:
# Scale target into -1~1
if args.scale_target == 2:
scaler = TargetScaler(num_step=10)
scaler.fit(Y_train)
Y_train, Y_test = scaler.transform(Y_train), scaler.transform(
Y_test)
elif args.scale_target == 1:
Y_train, Y_test = (Y_train - 30) / 30, (Y_test - 30) / 30
# Concatenate data for extreme learning machine
train_data = np.concatenate((Y_train[:, np.newaxis], X_train), axis=1)
test_data = np.concatenate((Y_test[:, np.newaxis], X_test), axis=1)
rgr.search_param(train_data, cv="kfold", of="rmse", eval=10)
pred_train = rgr.train(train_data).predicted_targets
pred_test = rgr.test(test_data).predicted_targets
# Scale target back to 0~60
if args.scale_target == 2:
[Y_train, Y_test, pred_train, pred_test] = [scaler.transform(x, mode='inverse') for x in \
[Y_train, Y_test, pred_train, pred_test]]
elif args.scale_target == 1:
[Y_train, Y_test, pred_train, pred_test] = [x*30+30 for x in \
[Y_train, Y_test, pred_train, pred_test]]
evaluate_result.plot_scatter(Y_test, pred_test, dirName, fileName)
print('Train std: %.3f' % (mean_squared_error(Y_train, pred_train)**0.5))
print('Test std: %.3f' % (mean_squared_error(Y_test, pred_test)**0.5))
# Save targets and predictions
dict_target = {}
dict_target['target'], dict_target['pred'] = Y_test, pred_test
with open('./results/%s/%s.data' % (dirName, fileName), 'wb') as fp:
pickle.dump(dict_target, fp)
return
from sklearn import svm features = features.reshape(16000,38) clf1 = svm.SVC() clf1.fit(features, y_train) featurestest = featurestest.reshape(4000, 38) accuracy = clf1.score(featurestest,y_test) accuracy *= 100 print(accuracy) import h5py filename = 'resnet_svm_model_color.sav' pickle.dump(clf1, open(filename, 'wb')) !pip3 install elm !pip3 install --upgrade numpy folium imgaug import elm elmk=elm.ELMKernel() elmk.search_param(data, cv="kfold", of="accuracy", eval=10) tr_result = elmk.train(features,y_train) te_result = elmk.test(featurestest,y_test) print(te_result.get_accuracy)
def buildELM(data, iterNum, isNormal, isRegression, isPCA, n_components,normalMethod):
rfList = []
elmr = elm.ELMKernel()
# elmr = elm.ELMRandom()
elmr.search_param(data, eval=100, cv='kfold')
# print("This is X_norm: ", X_norm)
for j in range(iterNum):
# X_train, X_test, y_train, y_test = train_test_split(X_norm, y, test_size=0.2)
# param_test1 = {'n_hidden': range(10, 21, 10)}
# model = GridSearchCV(estimator=elm.ELMRegressor(),
# param_grid=param_test1, cv=5, n_jobs=1)
# model = elm.ELMRegressor(n_hidden=tempDim,activation_func='sine')
# model = elm.ELMRegressor(activation_func='tanh')
# model = elm.GenELMRegressor()
# temp = model.fit(X_train, y_train)
# print("The best parameters are %s with a score of %0.2f"
# % (model.best_params_, model.best_score_))
# Predict on new data
# y_pred = temp.predict(X_test).tolist()
# y_test_list = y_test.tolist()
# create a classifier
# elmr = elm.ELMRandom()
# search for best parameter for this dataset
# define "kfold" cross-validation method, "accuracy" as a objective function
# to be optimized and perform 10 searching steps.
# best parameters will be saved inside 'elmk' object
# split data in training and testing sets
# use 80% of dataset to training and shuffle data before splitting
tr_set, te_set = elm.split_sets(data, training_percent=.9, perm=True)
# print("This is tr_set: ", tr_set)
X_train = tr_set[:, 1:].copy()
# print("This is X_train", X_train)
X_test = te_set[:, 1:].copy()
# print("This is X_test: ", X_test)
if normalMethod == 1:
print("Normalizer() is using..." )
sc = preprocessing.Normalizer()
elif normalMethod == 2:
print("StandardScalar() is using...")
sc = preprocessing.StandardScaler()
elif normalMethod == 3:
print("MinMaxScalar() is using...")
sc = preprocessing.MinMaxScaler()
sc.fit(X_train)
X_train_std = sc.transform(X_train)
X_test_std = sc.transform(X_test)
if isPCA:
X_train, X_test = reduceDim.featureExtraction(X_train_std, X_test_std, n_components)
else:
X_train = X_train_std
X_test = X_test_std
tr_set = np.c_[tr_set[:, 0], X_train]
te_set = np.c_[te_set[:, 0], X_test]
# print("This is new tr_set: ", tr_set)
# print("This is new te_set: ", te_set)
# train and test
# results are Error objects
tr_result = elmr.train(tr_set)
te_result = elmr.test(te_set)
y_test_list = te_result.expected_targets
y_pred = te_result.predicted_targets
# print("This is tr_result: ", tr_result.expected_targets)
# print("This is tr_result pre: ",tr_result.predicted_targets )
# print("This is te_result: ", te_result.expected_targets) # expected is the real value
# print("This is te_result predict: ", te_result.predicted_targets)
# print(te_result.get_accuracy)
if isRegression:
return y_pred
else:
sum_mean = 0
for i in range(len(y_pred)):
# print(
# "This is REAL value %.4f, ======ELM=====> PRED value: %.4f" % (y_test_list[i], y_pred[i]))
# sum_mean += (y_pred[i] - y_test[i]) ** 2 # if the target is np array
sum_mean += (float("{0:.4f}".format(float(y_pred[i]))) - y_test_list[i]) ** 2
# else:
# print("This is REAL value %.4f, ======Random Forest=====> PRED value: %.4f" % (
# y_test.values[i], y_pred[i]))
# # sum_mean += (y_pred[i] - y_test.values[i]) ** 2
# sum_mean += (float("{0:.4f}".format(float(y_pred[i]))) - y_test.values[i]) ** 2
sum_erro = np.sqrt(sum_mean / len(y_pred))
rfList.append(sum_erro)
print("This is RMSE for ELM: ", sum_erro)
print("This is iteration num: ", j + 1)
return rfList
