import logging as log
import numpy as np
from sklearn import decomposition
from sklearn.externals import joblib
# --------------------------------------------------------------
class SklNMF:
"""
Wrapper class backed by the scikit-learn package NMF implementation.
"""
def __init__( self, max_iters = 100, init_strategy = "random", random_seed = 0 ):
self.max_iters = 100
self.init_strategy = init_strategy
self.W = None
self.H = None
self.random_seed = random_seed
def apply( self, X, k = 2 ):
"""
Apply NMF to the specified document-term matrix X.
"""
self.W = None
self.H = None
model = decomposition.NMF(init=self.init_strategy, n_components=k, max_iter=self.max_iters, random_state = self.random_seed)
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()
# --------------------------------------------------------------
def generate_doc_rankings( W ):
'''
Rank document indices, based on values in a W factor matrix produced by NMF.
'''
doc_rankings = []
k = W.shape[1]
for topic_index in range(k):
w = np.array( W[:,topic_index] )
top_indices = np.argsort(w)[::-1]
doc_rankings.append(top_indices)
return doc_rankings
def save_nmf_results( out_path, doc_ids, terms, term_rankings, partition, W, H, topic_labels=None ):
"""
Save output of NMF using Joblib. Note that we use the scikit-learn bundled version of joblib.
"""
# no labels? generate some standard ones
if topic_labels is None:
topic_labels = []
for i in range( len(term_rankings) ):
topic_labels.append( "C%02d" % (i+1) )
log.info( "Saving NMF results to %s" % out_path )
joblib.dump((doc_ids, terms, term_rankings, partition, W, H, topic_labels), out_path )
def load_nmf_results( in_path ):
"""
Load NMF results using Joblib. Note that we use the scikit-learn bundled version of joblib.
"""
(doc_ids, terms, term_rankings, partition, W, H, labels) = joblib.load( in_path )
return (doc_ids, terms, term_rankings, partition, W, H, labels)
