Clean up metriclearning/evaluation/

Author: Pingchuan Ma

browse_results.py

@@ -1,6 +1,5 @@
 import shelve
 from collections import defaultdict
-import sys
 import os
 import re
 import time
@@ -9,7 +8,6 @@
 import time
 import glob
 import argparse
-import copy
 
 
 def is_match(x):

metriclearning/evaluation/normalized_mutual_information.py

@@ -5,11 +5,8 @@
 TORCH_SKLEARN_BACKEND = 'torch+sklearn'
 FAISS_BACKEND = 'faiss'
 FAISS_GPU_BACKEND = 'faiss-gpu'
-_backends_ = [TORCH_SKLEARN_BACKEND, FAISS_BACKEND, FAISS_GPU_BACKEND]
 
-_DEFAULT_BACKEND_ = FAISS_BACKEND
 _DEFAULT_BACKEND_ = FAISS_GPU_BACKEND
-#_DEFAULT_BACKEND_ = TORCH_SKLEARN_BACKEND
 
 
 def cluster_by_kmeans(X, nb_clusters, gpu_id=None, backend=_DEFAULT_BACKEND_):

metriclearning/evaluation/recall.py

@@ -9,11 +9,8 @@
 TORCH_SKLEARN_BACKEND = 'torch+sklearn'
 FAISS_BACKEND = 'faiss'
 FAISS_GPU_BACKEND = 'faiss-gpu'
-_backends_ = [TORCH_SKLEARN_BACKEND, FAISS_BACKEND, FAISS_GPU_BACKEND]
 
-_DEFAULT_BACKEND_ = FAISS_BACKEND
 _DEFAULT_BACKEND_ = FAISS_GPU_BACKEND
-#_DEFAULT_BACKEND_ = TORCH_SKLEARN_BACKEND
 
 
 def assign_by_euclidian_at_k(X, T, k, gpu_id=None, backend=_DEFAULT_BACKEND_):