def perform_clustering(seed, m_data, labels, n_clusters):
# Singleview spherical kmeans clustering
# Cluster each view separately
s_kmeans = SphericalKMeans(n_clusters=n_clusters,
random_state=seed,
n_init=100)
s_clusters_v1 = s_kmeans.fit_predict(m_data[0])
s_clusters_v2 = s_kmeans.fit_predict(m_data[1])
# Concatenate the multiple views into a single view
s_data = np.hstack(m_data)
s_clusters = s_kmeans.fit_predict(s_data)
# Compute nmi between true class labels and singleview cluster labels
s_nmi_v1 = nmi_score(labels, s_clusters_v1)
s_nmi_v2 = nmi_score(labels, s_clusters_v2)
s_nmi = nmi_score(labels, s_clusters)
print('Singleview View 1 NMI Score: {0:.3f}\n'.format(s_nmi_v1))
print('Singleview View 2 NMI Score: {0:.3f}\n'.format(s_nmi_v2))
print('Singleview Concatenated NMI Score: {0:.3f}\n'.format(s_nmi))
# Multiview spherical kmeans clustering
# Use the MultiviewKMeans instance to cluster the data
m_kmeans = MultiviewSphericalKMeans(n_clusters=n_clusters,
n_init=100,
random_state=seed)
m_clusters = m_kmeans.fit_predict(m_data)
# Compute nmi between true class labels and multiview cluster labels
m_nmi = nmi_score(labels, m_clusters)
print('Multiview NMI Score: {0:.3f}\n'.format(m_nmi))
return m_clusters
def perform_clustering(seed, m_data, labels, n_clusters, kernel='rbf'):
# Single-view spectral clustering
# Cluster each view separately
s_spectral = SpectralClustering(n_clusters=n_clusters,
random_state=RANDOM_SEED,
affinity=kernel,
n_init=100)
s_clusters_v1 = s_spectral.fit_predict(m_data[0])
s_clusters_v2 = s_spectral.fit_predict(m_data[1])
# Concatenate the multiple views into a single view
s_data = np.hstack(m_data)
s_clusters = s_spectral.fit_predict(s_data)
# Compute nmi between true class labels and single-view cluster labels
s_nmi_v1 = nmi_score(labels, s_clusters_v1)
s_nmi_v2 = nmi_score(labels, s_clusters_v2)
s_nmi = nmi_score(labels, s_clusters)
print('Single-view View 1 NMI Score: {0:.3f}\n'.format(s_nmi_v1))
print('Single-view View 2 NMI Score: {0:.3f}\n'.format(s_nmi_v2))
print('Single-view Concatenated NMI Score: {0:.3f}\n'.format(s_nmi))
# Multi-view spectral clustering
# Use the MultiviewSpectralClustering instance to cluster the data
m_spectral = MultiviewSpectralClustering(n_clusters=n_clusters,
random_state=RANDOM_SEED,
affinity=kernel,
n_init=100)
m_clusters = m_spectral.fit_predict(m_data)
# Compute nmi between true class labels and multi-view cluster labels
m_nmi = nmi_score(labels, m_clusters)
print('Multi-view Concatenated NMI Score: {0:.3f}\n'.format(m_nmi))
return m_clusters
###############################################################################
# Multiview spherical KMeans clustering on 2 views
# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
#
# Here we will demonstrate the performance of the multiview spherical kmeans
# clustering. We will evaluate the purity of the resulting clusters with
# respect to the class labels using the normalized mutual information metric.
#
# Use the MultiviewSphericalKMeans instance to cluster the data
m_kmeans = MultiviewSphericalKMeans(n_clusters=n_class,
random_state=RANDOM_SEED)
m_clusters = m_kmeans.fit_predict(Xs)
# Compute nmi between true class labels and multiview cluster labels
m_nmi = nmi_score(labels, m_clusters)
print('multiview NMI Score: {0:.3f}\n'.format(m_nmi))
###############################################################################
# Multiview spectral clustering results and the true clusters
# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
# We will display the clustering results of the multiview kmeans clustering
# algorithm below, along with the true class labels.
# Running TSNE to display clustering results via low dimensional embedding
tsne = TSNE()
new_data_1 = tsne.fit_transform(Xs[0])
new_data_2 = tsne.fit_transform(Xs[1])
new_data = [new_data_1, new_data_2]
display_plots('True Labels', new_data, labels)
n_class = 5
Xs, labels = load_UCImultifeature(
select_labeled=list(range(n_class)), views=[0, 1])
###############################################################################
# Singleview spectral clustering
# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
#
# Cluster each view separately and compute nmi
s_spectral = SpectralClustering(
n_clusters=n_class, random_state=RANDOM_SEED, n_init=100)
for i in range(len(Xs)):
s_clusters = s_spectral.fit_predict(Xs[i])
s_nmi = nmi_score(labels, s_clusters, average_method='arithmetic')
print('Single-view View {0:d} NMI Score: {1:.3f}\n'.format(i + 1, s_nmi))
# Concatenate the multiple views into a single view and produce clusters
s_data = np.hstack(Xs)
s_clusters = s_spectral.fit_predict(s_data)
s_nmi = nmi_score(labels, s_clusters)
print('Single-view Concatenated NMI Score: {0:.3f}\n'.format(s_nmi))
###############################################################################
# Co-Regularized multiview spectral clustering
# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
#
# Use the MultiviewSpectralClustering instance to cluster the data
m_spectral1 = MultiviewCoRegSpectralClustering(n_clusters=n_class,
# clustering the two views concatenated together.
# Singleview kmeans clustering
# Cluster each view separately
s_kmeans = KMeans(n_clusters=n_class, random_state=RANDOM_SEED)
s_clusters_v1 = s_kmeans.fit_predict(Xs[0])
s_clusters_v2 = s_kmeans.fit_predict(Xs[1])
# Concatenate the multiple views into a single view
s_data = np.hstack(Xs)
s_clusters = s_kmeans.fit_predict(s_data)
# Compute nmi between true class labels and singleview cluster labels
s_nmi_v1 = nmi_score(labels, s_clusters_v1)
s_nmi_v2 = nmi_score(labels, s_clusters_v2)
s_nmi = nmi_score(labels, s_clusters)
print('Singleview View 1 NMI Score: {0:.3f}\n'.format(s_nmi_v1))
print('Singleview View 2 NMI Score: {0:.3f}\n'.format(s_nmi_v2))
print('Singleview Concatenated NMI Score: {0:.3f}\n'.format(s_nmi))
# Multiview kmeans clustering
# Use the MultiviewKMeans instance to cluster the data
m_kmeans = MultiviewKMeans(n_clusters=n_class, random_state=RANDOM_SEED)
m_clusters = m_kmeans.fit_predict(Xs)
# Compute nmi between true class labels and multiview cluster labels
m_nmi = nmi_score(labels, m_clusters)
print('Multiview NMI Score: {0:.3f}\n'.format(m_nmi))
def accuracy_scores_binary(y_true, y_pred):
'''
A function to calculate accuracy measures for a binary classification.
Function written by Osian Roberts.
Parameters:
:param y_true: observed binary labels, where 0 is absence and 1 is presence.
:param y_pred: predicted binary labels, where 0 is absence and 1 is presence.
:returns: a list containing two numpy.arrays - (metrics: name of test metrics, scores: test scores for each metric)
Reference: See pages 253 - 255 in:
Guisan et al. (2017). Habitat suitability and distribution models: with applications in R.
'''
import numpy
# check inputs:
if not isinstance(y_true, numpy.ndarray):
y_true = numpy.array(y_true)
if not isinstance(y_pred, numpy.ndarray):
y_pred = numpy.array(y_pred)
if y_true.ndim != 1:
raise SystemExit('ERROR: the true labels are not in a 1D array.')
if y_pred.ndim != 1:
raise SystemExit('ERROR: the predicted labels are not in a 1D array.')
if y_true.size != y_pred.size:
raise SystemExit('ERROR: unequal number of binary labels.')
# ensure that y_true, y_pred contain binary labels (i.e. 0 or 1 values):
y_true = y_true.astype('uint8')
y_pred = y_pred.astype('uint8')
if numpy.min(y_true) != 0 or numpy.max(y_true) != 1:
raise SystemExit('ERROR: the true labels are not binary (zero or one values).')
if numpy.min(y_pred) != 0 or numpy.max(y_pred) != 1:
raise SystemExit('ERROR: the predicted labels are not binary (zero or one values).')
metrics = numpy.array(['Prevalence', 'Overall Diagnostic Power', 'Correct Classification Rate',
'Misclassification Rate', 'Presence Predictive Power', 'Absence Predictive Power',
'Accuracy', 'Balanced Accuracy', 'Sensitivity', 'Specificity', 'Precision', 'F1 Score',
'Matthews Correlation', 'Cohen Kappa', 'Normalised Mutual Information',
'Hanssen-Kuiper skill'])
try:
n_presence = numpy.where(y_true == 1)[0].size
n_absence = numpy.where(y_true == 0)[0].size
# calculate true-presence, true-absence, false-presence and false-absence:
TP = numpy.where((y_true == 1) & (y_pred == 1))[0].size
TA = numpy.where((y_true == 0) & (y_pred == 0))[0].size
FP = numpy.where((y_true == 1) & (y_pred == 0))[0].size
FA = numpy.where((y_true == 0) & (y_pred == 1))[0].size # aka sweet FA!
# proportion of presence records:
prevalence = (TP / FA) / y_true.size
# proportion of absence records:
ODP = 1 - prevalence
# correct classification & misclassification rate
CCR = (TP + TA) / y_true.size
MR = (FP + FA) / y_true.size
# Sensitivity (aka Recall or True Positive Rate):
sensitivity = TP / n_presence
# false presence rate - inverse of sensitivity (redundant?)
#FPR = 1 - sensitivity
# Presence and absence predictive power:
PPP = TP / (TP + FP)
APP = TA / (TA + FA)
# Specificity (aka True Negative Rate):
specificity = TA / n_absence
# false positive rate - inverse of specificity (redundant?)
#FPR = 1 - specificity
# Accuracy scores:
accuracy = (TP + TA) / (n_presence + n_absence)
balanced_accuracy = ((TP / n_presence) + (TA / n_absence)) / 2
# precision:
precision = TP / (TP + FP)
# F1 score:
f1_score = 2 * TP / ((2*TP) + FP + FA)
# Matthews Correlation Coefficient:
MCC = ((TP * TA) - (FP * FA)) / (((TP + FP) * (TP + FA) * (TA + FP) * (TA + FA))**0.5)
# Hanssen-Kuiper skill (unreliable when TA is very large):
TSS = sensitivity + specificity - 1
del n_presence, n_absence, TP, TA, FP, FA
from sklearn.metrics import normalized_mutual_info_score as nmi_score
nmi_score = nmi_score(y_true, y_pred)
# Cohen's Kappa (caution: sensitive to sample size and proportion of presence records):
from sklearn.metrics import cohen_kappa_score as kappa
kappa = kappa(y_true, y_pred)
scores = numpy.array([prevalence, ODP, CCR, MR, PPP, APP, accuracy, balanced_accuracy, sensitivity,
specificity, precision, f1_score, MCC, kappa, nmi_score, TSS]).round(decimals=6)
del prevalence, ODP, CCR, MR, PPP, APP, accuracy, balanced_accuracy, sensitivity
del specificity, precision, f1_score, MCC, kappa, nmi_score, TSS
except Exception:
scores = numpy.zeros(len(metrics))
if metrics.size == scores.size:
return [metrics, scores]
else:
raise SystemExit('ERROR: unable to calculate accuracy metrics.')
