import numpy as np
import warnings
warnings.simplefilter("ignore", DeprecationWarning)
# --------------------------------------------------------------
class SklNMF:
"""
Wrapper class backed by the scikit-learn package NMF implementation.
"""
def __init__( self, max_iters = 100, init_strategy = "random" ):
self.max_iters = 100
self.init_strategy = init_strategy
self.W = None
self.H = None
def apply( self, X, k = 2 ):
"""
Apply NMF to the specified document-term matrix X.
"""
from sklearn import decomposition
self.W = None
self.H = None
model = decomposition.NMF(init=self.init_strategy, n_components=k, max_iter=self.max_iters)
self.W = model.fit_transform(X)
self.H = model.components_
def rank_terms( self, topic_index, top = -1 ):
"""
Return the top ranked terms for the specified topic, generated during the last NMF run.
"""
if self.H is None:
raise ValueError("No results for previous run available")
# NB: reverse
top_indices = np.argsort( self.H[topic_index,:] )[::-1]
# truncate if necessary
if top < 1 or top > len(top_indices):
return top_indices
return top_indices[0:top]
def generate_partition( self ):
if self.W is None:
raise ValueError("No results for previous run available")
return np.argmax( self.W, axis = 1 ).flatten().tolist()
class NimfaNMF:
"""
Wrapper class backed by the Nimfa package NMF implementation.
"""
def __init__( self, max_iters = 100, init_strategy = "random", update = "euclidean" ):
self.max_iters = max_iters
self.init_strategy = init_strategy
self.W = None
self.H = None
self.update = update
self.test_conv = 10
def apply( self, X, k = 2 ):
"""
Apply NMF to the specified document-term matrix X.
"""
import nimfa
self.W = None
self.H = None
initialize_only = self.max_iters < 1
if self.update == "euclidean":
objective = "fro"
else:
objective = "div"
lsnmf = nimfa.Lsnmf(X, max_iter = self.max_iters, rank = k, seed = self.init_strategy, update = self.update, objective = objective, test_conv = self.test_conv )
res = lsnmf()
# TODO: fix
try:
self.W = res.basis().todense()
self.H = res.coef().todense()
except:
self.W = res.basis()
self.H = res.coef()
# last number of iterations
self.n_iter = res.n_iter
def rank_terms( self, topic_index, top = -1 ):
"""
Return the top ranked terms for the specified topic, generated during the last NMF run.
"""
if self.H is None:
raise ValueError("No results for previous run available")
h = np.array( self.H[topic_index,:] ).flatten()
# NB: reverse ordering
top_indices = np.argsort(h)[::-1]
# truncate
if top < 1 or top > len(top_indices):
return top_indices
return top_indices[0:top]
def generate_partition( self ):
if self.W is None:
raise ValueError("No results for previous run available")
return np.argmax( self.W, axis = 1 ).flatten().tolist()[0]
