class RCA:
def __init__(self):
self.metric_model = RCA_ml()
self.X_tr = None
self.y_train = None
self.X_te = None
def fit(self, X_tr, y_train):
"""Fits the model to the prescribed data."""
self.X_tr = X_tr
self.y_train = y_train
return self.metric_model.fit(X_tr, y_train)
def transform(self, X):
"""Transforms the test data according to the model"""
return self.metric_model.transform(X)
def predict_proba(self, X_te):
"""Predicts the probabilities of each of the test samples"""
test_samples = X_te.shape[0]
self.X_tr = self.transform(self.X_tr)
clf = NearestCentroid()
clf.fit(self.X_tr, self.y_train)
centroids = clf.centroids_
probabilities = np.zeros((test_samples, centroids.shape[0]))
for sample in xrange(test_samples):
probabilities[sample] = sk_nearest_neighbour_proba(
centroids, X_te[sample, :])
return probabilities
def test_iris(self):
rca = RCA(dim=2)
chunks = RCA.prepare_constraints(self.iris_labels, num_chunks=30,
chunk_size=2, seed=1234)
rca.fit(self.iris_points, chunks)
csep = class_separation(rca.transform(), self.iris_labels)
self.assertLess(csep, 0.25)
def test_iris(self):
rca = RCA(dim=2)
chunks = RCA.prepare_constraints(self.iris_labels,
num_chunks=30,
chunk_size=2,
seed=1234)
rca.fit(self.iris_points, chunks)
csep = class_separation(rca.transform(), self.iris_labels)
self.assertLess(csep, 0.25)
def fit(self, X, y=None, ml=[], cl=[]):
X_transformed = X
if ml:
chunks = np.full(X.shape[0], -1)
ml_graph, cl_graph, neighborhoods = preprocess_constraints(
ml, cl, X.shape[0])
for i, neighborhood in enumerate(neighborhoods):
chunks[neighborhood] = i
# print(chunks)
rca = RCA()
rca.fit(X, chunks=chunks)
X_transformed = rca.transform(X)
# print(rca.metric())
kmeans = KMeans(n_clusters=self.n_clusters, max_iter=self.max_iter)
kmeans.fit(X_transformed)
self.labels_ = kmeans.labels_
return self
