def main():
# load data
mat = scipy.io.loadmat('../data/COIL20.mat')
X = mat['X'] # data
X = X.astype(float)
y = mat['Y'] # label
y = y[:, 0]
# construct affinity matrix
kwargs = {"metric": "euclidean", "neighborMode": "knn", "weightMode": "heatKernel", "k": 5, 't': 1}
W = construct_W.construct_W(X, **kwargs)
num_fea = 100 # specify the number of selected features
num_cluster = 20 # specify the number of clusters, it is usually set as the number of classes in the ground truth
# obtain the feature weight matrix
Weight = MCFS.mcfs(X, n_selected_features=num_fea, W=W, n_clusters=20)
# sort the feature scores in an ascending order according to the feature scores
idx = MCFS.feature_ranking(Weight)
# obtain the dataset on the selected features
selected_features = X[:, idx[0:num_fea]]
# perform kmeans clustering based on the selected features and repeats 20 times
nmi_total = 0
acc_total = 0
for i in range(0, 20):
nmi, acc = unsupervised_evaluation.evaluation(X_selected=selected_features, n_clusters=num_cluster, y=y)
nmi_total += nmi
acc_total += acc
# output the average NMI and average ACC
print 'NMI:', float(nmi_total)/20
print 'ACC:', float(acc_total)/20
def test_spec():
# load data
mat = scipy.io.loadmat('./data/COIL20.mat')
X = mat['X'] # data
X = X.astype(float)
y = mat['Y'] # label
y = y[:, 0]
# perform evaluation on clustering task
num_fea = 100 # number of selected features
num_cluster = 20 # number of clusters, it is usually set as the number of classes in the ground truth
kwargs = {'style': 0}
pipeline = []
spec_partial = partial(SPEC.spec, **kwargs)
pipeline.append(
('select top k', SelectKBest(score_func=spec_partial, k=num_fea)))
model = Pipeline(pipeline)
# set y param to be 0 to demonstrate that this works in unsupervised sense.
selected_features = model.fit_transform(X, y=np.zeros(X.shape[0]))
# perform kmeans clustering based on the selected features and repeats 20 times
nmi_total = 0
acc_total = 0
for i in range(0, 20):
nmi, acc = unsupervised_evaluation.evaluation(
X_selected=selected_features, n_clusters=num_cluster, y=y)
nmi_total += nmi
acc_total += acc
# output the average NMI and average ACC
print(('NMI:', float(nmi_total) / 20))
print(('ACC:', float(acc_total) / 20))
def main():
# load data
mat = scipy.io.loadmat('../data/BASEHOCK.mat')
X = mat['X'] # data
X = X.astype(float)
y = mat['Y'] # label
y = y[:, 0]
p = 0.1 # specify the threshold p to be 0.1
num_cluster = 2 # specify the number of clusters to be 2
# perform feature selection and obtain the dataset on the selected features
selected_features = low_variance.low_variance_feature_selection(X, p*(1-p))
# perform kmeans clustering based on the selected features and repeats 20 times
nmi_total = 0
acc_total = 0
for i in range(0, 20):
nmi, acc = unsupervised_evaluation.evaluation(X_selected=selected_features, n_clusters=num_cluster, y=y)
nmi_total += nmi
acc_total += acc
# output the average NMI and average ACC
print 'NMI:', float(nmi_total)/20
print 'ACC:', float(acc_total)/20
def main():
# load data
mat = scipy.io.loadmat('../data/COIL20.mat')
X = mat['X'] # data
X = X.astype(float)
y = mat['Y'] # label
y = y[:, 0]
# perform evaluation on clustering task
num_fea = 100 # number of selected features
num_cluster = 20 # number of clusters, it is usually set as the number of classes in the ground truth
# obtain the feature weight matrix
Weight = UDFS.udfs(X, gamma=0.1, n_clusters=num_cluster)
# sort the feature scores in an ascending order according to the feature scores
idx = feature_ranking(Weight)
# obtain the dataset on the selected features
selected_features = X[:, idx[0:num_fea]]
# perform kmeans clustering based on the selected features and repeats 20 times
nmi_total = 0
acc_total = 0
for i in range(0, 20):
nmi, acc = unsupervised_evaluation.evaluation(X_selected=selected_features, n_clusters=num_cluster, y=y)
nmi_total += nmi
acc_total += acc
# output the average NMI and average ACC
print 'NMI:', float(nmi_total)/20
print 'ACC:', float(acc_total)/20
def eval_subset(train, test):
n_clusters = len(np.unique(train[2]))
clf = ExtraTreesClassifier(n_estimators=50, n_jobs=-1)
clf.fit(train[0], train[2])
DTacc = float(clf.score(test[0], test[2]))
clf = KNeighborsClassifier(n_neighbors=1, algorithm='brute', n_jobs=1)
clf.fit(train[0], train[2])
acc = float(clf.score(test[0], test[2]))
LR = LinearRegression(n_jobs=-1)
LR.fit(train[0], train[1])
MSELR = float(((LR.predict(test[0]) - test[1])**2).mean())
MSE = float((((decoder((train[0], train[1]),
(test[0], test[1])) - test[1])**2).mean()))
max_iters = 10
cnmi, cacc = 0.0, 0.0
for iter in range(max_iters):
nmi, acc = unsupervised_evaluation.evaluation(train[0],
n_clusters=n_clusters,
y=train[2])
cnmi += nmi / max_iters
cacc += acc / max_iters
print('nmi = {:.3f}, acc = {:.3f}'.format(cnmi, cacc))
print('acc = {:.3f}, DTacc = {:.3f}, MSELR = {:.3f}, MSE = {:.3f}'.format(
acc, DTacc, MSELR, MSE))
return MSELR, MSE, acc, DTacc, float(cnmi), float(cacc)
def test_low_variance():
# load data
from functools import partial
mat = scipy.io.loadmat('./data/BASEHOCK.mat')
X = mat['X'] # data
X = X.astype(float)
y = mat['Y'] # label
y = y[:, 0]
p = 0.1 # specify the threshold p to be 0.1
num_cluster = 2 # specify the number of clusters to be 2
# build pipeline
pipeline = []
# this is equivalent to `pipeline.append(('low_variance', VarianceThreshold(threshold=p*(1-p))))`
pipeline.append(('low_variance', low_variance.low_variance_feature_selection(threshold=p*(1-p))))
model = Pipeline(pipeline)
# set y param to be 0 to demonstrate that this works in unsupervised sense.
# perform feature selection and obtain the dataset on the selected features
selected_features = model.fit_transform(X, y=np.zeros(X.shape[0]))
# perform kmeans clustering based on the selected features and repeats 20 times
nmi_total = 0
acc_total = 0
for i in range(0, 20):
nmi, acc = unsupervised_evaluation.evaluation(X_selected=selected_features, n_clusters=num_cluster, y=y)
nmi_total += nmi
acc_total += acc
# output the average NMI and average ACC
print(('NMI:', float(nmi_total)/20))
print(('ACC:', float(acc_total)/20))
assert_true(float(nmi_total)/20 > 0.0)
assert_true(float(acc_total)/20 > 0.5)
def main():
# load data
mat = scipy.io.loadmat('../data/COIL20.mat')
X = mat['X'] # data
X = X.astype(float)
y = mat['Y'] # label
y = y[:, 0]
# specify the second ranking function which uses all except the 1st eigenvalue
kwargs = {'style': 0}
# obtain the scores of features
score = SPEC.spec(X, **kwargs)
# sort the feature scores in an descending order according to the feature scores
idx = SPEC.feature_ranking(score, **kwargs)
# perform evaluation on clustering task
num_fea = 100 # number of selected features
num_cluster = 20 # number of clusters, it is usually set as the number of classes in the ground truth
# obtain the dataset on the selected features
selected_features = X[:, idx[0:num_fea]]
# perform kmeans clustering based on the selected features and repeats 20 times
nmi_total = 0
acc_total = 0
for i in range(0, 20):
nmi, acc = unsupervised_evaluation.evaluation(X_selected=selected_features, n_clusters=num_cluster, y=y)
nmi_total += nmi
acc_total += acc
# output the average NMI and average ACC
print('NMI:', old_div(float(nmi_total),20))
print('ACC:', old_div(float(acc_total),20))
def main():
# load data
mat = scipy.io.loadmat('../data/COIL20.mat')
X = mat['X'] # data
X = X.astype(float)
y = mat['Y'] # label
y = y[:, 0]
# specify the second ranking function which uses all except the 1st eigenvalue
kwargs = {'style': 0}
# obtain the scores of features
score = SPEC.spec(X, **kwargs)
# sort the feature scores in an descending order according to the feature scores
idx = SPEC.feature_ranking(score, **kwargs)
# perform evaluation on clustering task
num_fea = 100 # number of selected features
num_cluster = 20 # number of clusters, it is usually set as the number of classes in the ground truth
# obtain the dataset on the selected features
selected_features = X[:, idx[0:num_fea]]
# perform kmeans clustering based on the selected features and repeats 20 times
nmi_total = 0
acc_total = 0
for i in range(0, 20):
nmi, acc = unsupervised_evaluation.evaluation(X_selected=selected_features, n_clusters=num_cluster, y=y)
nmi_total += nmi
acc_total += acc
# output the average NMI and average ACC
print 'NMI:', float(nmi_total)/20
print 'ACC:', float(acc_total)/20
def main():
# load data
mat = scipy.io.loadmat('../data/COIL20.mat')
X = mat['X'] # data
X = X.astype(float)
y = mat['Y'] # label
y = y[:, 0]
# perform evaluation on clustering task
num_fea = 100 # number of selected features
num_cluster = 20 # number of clusters, it is usually set as the number of classes in the ground truth
# obtain the feature weight matrix
Weight = UDFS.udfs(X, gamma=0.1, n_clusters=num_cluster)
# sort the feature scores in an ascending order according to the feature scores
idx = feature_ranking(Weight)
# obtain the dataset on the selected features
selected_features = X[:, idx[0:num_fea]]
# perform kmeans clustering based on the selected features and repeats 20 times
nmi_total = 0
acc_total = 0
for i in range(0, 20):
nmi, acc = unsupervised_evaluation.evaluation(X_selected=selected_features, n_clusters=num_cluster, y=y)
nmi_total += nmi
acc_total += acc
# output the average NMI and average ACC
print 'NMI:', float(nmi_total)/20
print 'ACC:', float(acc_total)/20
def evaluate_clustering(selected_features,y):
# perform kmeans clustering based on the selected features and repeats 20 times
nmi_total = np.zeros(20)
acc_total = np.zeros(20)
for i in range(0, 20):
nmi, acc = unsupervised_evaluation.evaluation(X_selected=selected_features, n_clusters=len(np.unique(y)), y=y)
nmi_total[i]= nmi
acc_total[i]= acc
# output the average NMI and average ACC
return (np.mean(nmi_total), np.std(nmi_total)), (np.mean(acc_total),np.std(acc_total))
def test_lap_score():
# load data
from functools import partial
mat = scipy.io.loadmat('./data/COIL20.mat')
X = mat['X'] # data
X = X.astype(float)
y = mat['Y'] # label
y = y[:, 0]
# construct affinity matrix
kwargs_W = {
"metric": "euclidean",
"neighbor_mode": "knn",
"weight_mode": "heat_kernel",
"k": 5,
't': 1
}
W = construct_W.construct_W(X, **kwargs_W)
num_fea = 100 # number of selected features
pipeline = []
# partial function required for SelectKBest to work correctly.
lap_score_partial = partial(lap_score.lap_score, W=W)
pipeline.append(
('select top k', SelectKBest(score_func=lap_score_partial, k=num_fea)))
model = Pipeline(pipeline)
# set y param to be 0 to demonstrate that this works in unsupervised sense.
selected_features = model.fit_transform(X, y=np.zeros(X.shape[0]))
print(selected_features.shape)
# perform evaluation on clustering task
num_cluster = 20 # number of clusters, it is usually set as the number of classes in the ground truth
# perform kmeans clustering based on the selected features and repeats 20 times
nmi_total = 0
acc_total = 0
for i in range(0, 20):
nmi, acc = unsupervised_evaluation.evaluation(
X_selected=selected_features, n_clusters=num_cluster, y=y)
nmi_total += nmi
acc_total += acc
# output the average NMI and average ACC
print(('NMI:', float(nmi_total) / 20))
print(('ACC:', float(acc_total) / 20))
assert_true(float(nmi_total) / 20 > 0.5)
assert_true(float(acc_total) / 20 > 0.5)
def main():
# load data
mat = scipy.io.loadmat('../data/COIL20.mat')
X = mat['X'] # data
X = X.astype(float)
y = mat['Y'] # label
y = y[:, 0]
# construct affinity matrix
kwargs = {
"metric": "euclidean",
"neighborMode": "knn",
"weightMode": "heatKernel",
"k": 5,
't': 1
}
W = construct_W.construct_W(X, **kwargs)
# obtain the feature weight matrix
Weight = NDFS.ndfs(X, W=W, n_clusters=20)
# sort the feature scores in an ascending order according to the feature scores
idx = feature_ranking(Weight)
# perform evaluation on clustering task
num_fea = 100 # number of selected features
num_cluster = 20 # number of clusters, it is usually set as the number of classes in the ground truth
# obtain the dataset on the selected features
selected_features = X[:, idx[0:num_fea]]
# perform kmeans clustering based on the selected features and repeats 20 times
nmi_total = 0
acc_total = 0
for i in range(0, 20):
nmi, acc = unsupervised_evaluation.evaluation(
X_selected=selected_features, n_clusters=num_cluster, y=y)
nmi_total += nmi
acc_total += acc
# output the average NMI and average ACC
print('NMI:', float(nmi_total) / 20)
print('ACC:', float(acc_total) / 20)
def main():
# load data
mat = scipy.io.loadmat("../data/COIL20.mat")
X = mat["X"] # data
X = X.astype(float)
y = mat["Y"] # label
y = y[:, 0]
# construct affinity matrix
kwargs_W = {"metric": "euclidean", "neighbor_mode": "knn", "weight_mode": "heat_kernel", "k": 5, "t": 1}
W = construct_W.construct_W(X, **kwargs_W)
# obtain the scores of features
score = lap_score.lap_score(X, W=W)
# sort the feature scores in an ascending order according to the feature scores
idx = lap_score.feature_ranking(score)
# perform evaluation on clustering task
num_fea = 100 # number of selected features
num_cluster = 20 # number of clusters, it is usually set as the number of classes in the ground truth
# obtain the dataset on the selected features
selected_features = X[:, idx[0:num_fea]]
# perform kmeans clustering based on the selected features and repeats 20 times
nmi_total = 0
acc_total = 0
for i in range(0, 20):
nmi, acc = unsupervised_evaluation.evaluation(X_selected=selected_features, n_clusters=num_cluster, y=y)
nmi_total += nmi
acc_total += acc
# output the average NMI and average ACC
print "NMI:", float(nmi_total) / 20
print "ACC:", float(acc_total) / 20
import scipy.io
#from skfeature.function.statistical_based import low_variance
from skfeature.utility import unsupervised_evaluation
mat = scipy.io.loadmat('../Datasets/BASEHOCK.mat')
X = mat['X'] # data
X = X.astype(float)
y = mat['Y'] # label
y = y[:, 0]
p = 0.1 # specify the threshold p to be 0.1
num_cluster = 2 # specify the number of clusters to be 2
# perform feature selection and obtain the dataset on the selected features
from Statistical_Based.Low_Variance.LowVarianceZeal import Low_Variance_FS
selected_features = Low_Variance_FS(X, p * (1 - p))
# perform kmeans clustering based on the selected features and repeats 20 times
nmi_total = 0
acc_total = 0
for i in range(0, 20):
nmi, acc = unsupervised_evaluation.evaluation(X_selected=selected_features,
n_clusters=num_cluster,
y=y)
nmi_total += nmi
acc_total += acc
# output the average NMI and average ACC
print('NMI:', float(nmi_total) / 20)
print('ACC:', float(acc_total) / 20)
