def nmf(self):
"""
Performs NMF dimensionality reduction
:return:
"""
constants = self.constants.Nmf()
if self.binary_image_metadata:
data = self.get_binary_image_metadata_matrix()
elif self.subject_subject:
data = self.matrix
else:
if not self.matrix:
data = self.get_object_feature_matrix()
else:
data = self.matrix
if not data.size == 0:
obj_feature = np.array(data['featureVector'].tolist())
if (obj_feature < 0).any():
print("NMF does not accept negative values")
return
model = NMF(n_components=self.k_value,
beta_loss=constants.BETA_LOSS_KL,
init=constants.INIT_MATRIX,
random_state=0,
solver='mu',
max_iter=1000)
w = model.fit_transform(obj_feature)
h = model.components_
if self.save_model:
model = Model()
model.save_model(
w,
"{}_{}_w".format(self.extractor_model.lower(),
self.dimension_reduction_model.lower()))
model.save_model(
h,
"{}_{}_h".format(self.extractor_model.lower(),
self.dimension_reduction_model.lower()))
return self
if not self.conversion:
return w, h
tt1 = time.time()
data_lat = pd.DataFrame({
"imageId": data['imageId'],
"reducedDimensions": w.tolist()
})
# for i in range(h.shape[0]):
# print("Latent Feature: {}\n{}".format(i + 1, sorted(((i, v) for i, v in enumerate(h[i])),
# key=lambda x: x[1], reverse=True)))
# print("\n\nTime Taken for NMF {}\n".format(time.time() - tt1))
return data_lat, h, model
raise \
Exception("Data in database is empty, Run Task 2 of Phase 1 (Insert feature extracted records in db )\n\n")
