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" ):
		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.
		"""
		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()		

# --------------------------------------------------------------

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)


@@ -328,16 +328,33 @@
 %22random%22
+, random_seed = 0
  ):%0A%09%09se
@@ -440,16 +440,49 @@
 H = None
+%0A%09%09self.random_seed = random_seed
 %0A%0A%09def a
@@ -694,16 +694,49 @@
 ax_iters
+, random_state = self.random_seed
 )%0A%09%09self

Allow specification of random seed to NNDSVD