Python Util.get_num_samples Examples

Example #1

File: Retrieval.py Project: nmonath/NLPProject

def RunEKD(dirname, model, train_test='train',TopK=5):
	if train_test.lower() == 'train':
		Y = Util.LoadClassFile(os.path.join(dirname, 'train_classes.txt'))
	else:
		Y = Util.LoadClassFile(os.path.join(dirname, 'test_classes.txt'))




	# # Manditory settings for the Features Module. Others are up to you
	import Features as FeaturesModule
	FeaturesModule.FUNIT = FeaturesModule.FeatureUnits.WORD
	FeaturesModule.FTYPE = FeaturesModule.FeatureType.BINARY
	FeaturesModule.FREP = FeaturesModule.FeatureRepresentation.STRING
	FeaturesModule.REMOVE_FEATURES_APPEARING_IN_ONLY_ONE_DOCUMENT=False
	FeaturesModule.REMOVE_FEATURES_ONLY_APPEARING_ONE_TIME=False
	FeaturesModule.DisplayConfiguration()

	(feature_def, X) = FeaturesModule.Features(os.path.join(dirname, train_test), frep=FeaturesModule.FREP, ftype=FeaturesModule.FTYPE, funit=FeaturesModule.FUNIT)
	(word_idx, emb) = Word2VecExecuter.Word2VecLoadWordsHashTable(model, list(feature_def.T[0]))
	X = FeaturesModule.Features(os.path.join(dirname, train_test), frep=FeaturesModule.FREP, ftype=FeaturesModule.FTYPE, funit=FeaturesModule.FUNIT, feature=np.array(word_idx.keys()).reshape((len(word_idx), 1)))

 	count1 = 0
	N = Util.get_num_samples(os.path.join(dirname, train_test))
	dists = np.zeros((N,N))
	for i in xrange(0, N):
		Doc1 = EKD.Document(embeddings=emb[X[i, :], :]) 
		for j in xrange(0, N):
			if not i == j:
				Doc2 = EKD.Document(embeddings=emb[X[j, :], :])
				dists[i,j] = Doc1.distance(Doc2)

	precision = 0
	recall = 0
	print("\n**Results**")

	for i in xrange(0, N):
		R = dists[i,:].argsort()
		R = R[1:TopK]
		precision += Metrics.RetrievalPrecision(Y[i], Y[R])
		recall += Metrics.RetrievalRecall(Y[i], Y[R], Y, TopK)
		print("Document Number: " + str(i) + " Class: " + str(Y[i]))
		print("Top Matching Documents: " + str(R))
		print("Top Matching Documents (Associated Class Labels: " + str(Y[R]))
		print(" Precision: " + str(Metrics.RetrievalPrecision(Y[i], Y[R])) + " Recall: " + str(Metrics.RetrievalRecall(Y[i], Y[R], Y, TopK)))
	
	precision = precision / N
	recall = recall / N
	print("Average Precision: " + str(precision) + " Average Recall: " + str(recall))
	return (precision, recall)

	
# # This will be for pairwise
# # Compare document to every other document and say if they are the same or different
# def Run(FeaturesModule, dirname, train_test='train', Score Cut off):
# def RunEKD(dirname, train_test='train'):

Example #2

File: Retrieval.py Project: nmonath/NLPProject

def RunPAD(dirname, model, train_test, TopK=5):
	if train_test.lower() == 'train':
		Y = Util.LoadClassFile(os.path.join(dirname, 'train_classes.txt'))
	else:
		Y = Util.LoadClassFile(os.path.join(dirname, 'test_classes.txt'))


	count1 = 0
	N = Util.get_num_samples(os.path.join(dirname, train_test))
	dists = np.zeros((N,N))
	for filename1 in os.listdir(os.path.join(dirname, train_test)):
		count2 = 0
		if '.srl' in filename1:
			Doc1 = PAD.Document(doc_file_name=os.path.join(dirname, train_test, filename1), model=model, use_lemma=False)
			for filename2 in os.listdir(os.path.join(dirname, train_test)):
				if filename1 == filename2:
					dists[count1, count2] = 0
					count2 += 1
				elif '.srl' in filename2:
					Doc2 = PAD.Document(doc_file_name=os.path.join(dirname, train_test, filename2), model=model, use_lemma=False)
					dists[count1, count2] = Doc1.distance(Doc2)
					count2 += 1
			count1 += 1

	precision = 0
	recall = 0
	print("\n**Results**")
	for i in xrange(0, N):
		R = dists[i,:].argsort()
		R = R[1:TopK]
		precision += Metrics.RetrievalPrecision(Y[i], Y[R])
		recall += Metrics.RetrievalRecall(Y[i], Y[R], Y, TopK)
		print("Document Number: " + str(i) + " Class: " + str(Y[i]))
		print("Top Matching Documents: " + str(R))
		print("Top Matching Documents (Associated Class Labels: " + str(Y[R]))
		print(" Precision: " + str(Metrics.RetrievalPrecision(Y[i], Y[R])) + " Recall: " + str(Metrics.RetrievalRecall(Y[i], Y[R], Y, TopK)))
	
	precision = precision / N
	recall = recall / N
	print("Average Precision: " + str(precision) + " Average Recall: " + str(recall))
	return (precision, recall)