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Evaluate.py
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Evaluate.py
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'''
Created on Apr 15, 2016
Evaluate the performance of Top-K recommendation:
Protocol: leave-1-out evaluation
Measures: Hit Ratio and NDCG
(more details are in: Xiangnan He, et al. Fast Matrix Factorization for Online Recommendation with Implicit Feedback. SIGIR'16)
@author: hexiangnan
'''
import math
import heapq # for retrieval topK
import multiprocessing
import numpy as np
from time import time
#from numba import jit, autojit
# Global variables that are shared across processes
_model = None
_testRatings = None
_testNegatives = None
_K = None
_DictList = None
_sess = None
def init_evaluate_model(model, sess, testRatings, testNegatives, trainList):
"""
Evaluate the performance (Hit_Ratio, NDCG) of top-K recommendation
Return: score of each test rating.
"""
global _model
global _testRatings
global _testNegatives
global _trainList
global _DictList
global _sess
_sess = sess
_model = model
_testRatings = testRatings
_testNegatives = testNegatives
_trainList = trainList
return load_test_as_list()
def eval(model, sess, testRatings, testNegatives, DictList):
global _model
global _testRatings
global _testNegatives
global _K
global _DictList
global _sess
_model = model
_testRatings = testRatings
_testNegatives = testNegatives
_DictList = DictList
_sess = sess
_K = 10
num_thread = 1#multiprocessing.cpu_count()
hits, ndcgs, losses = [],[],[]
if(num_thread > 1): # Multi-thread
pool = multiprocessing.Pool(num_thread)
res = pool.map(_eval_one_rating, range(len(_testRatings)))
pool.close()
pool.join()
hits = [r[0] for r in res]
ndcgs = [r[1] for r in res]
losses = [r[2] for r in res]
# Single thread
else:
for idx in range(len(_testRatings)):
(hr, ndcg, loss) = _eval_one_rating(idx)
hits.append(hr)
ndcgs.append(ndcg)
losses.append(loss)
return (hits, ndcgs, losses)
def load_test_as_list():
DictList = []
for idx in range(len(_testRatings)):
rating = _testRatings[idx]
items = _testNegatives[idx]
user = _trainList[idx]
num_idx_ = len(user)
gtItem = rating[1]
items.append(gtItem)
# Get prediction scores
num_idx = np.full(len(items),num_idx_, dtype=np.int32 )[:,None]
user_input = []
for i in range(len(items)):
user_input.append(user)
user_input = np.array(user_input)
item_input = np.array(items)[:,None]
feed_dict = {_model.user_input: user_input, _model.num_idx: num_idx,
_model.item_input: item_input, _model.is_train_phase: False}
DictList.append(feed_dict)
print("already load the evaluate model...")
return DictList
def _eval_one_rating(idx):
map_item_score = {}
rating = _testRatings[idx]
items = _testNegatives[idx]
gtItem = rating[1]
labels = np.zeros(len(items))[:, None]
labels[-1] = 1
feed_dict = _DictList[idx]
feed_dict[_model.labels] = labels
predictions, loss = _sess.run([_model.output, _model.loss], feed_dict=feed_dict)
for i in range(len(items)):
item = items[i]
map_item_score[item] = predictions[i]
ranklist = heapq.nlargest(_K, map_item_score, key=map_item_score.get)
hr = _getHitRatio(ranklist, gtItem)
ndcg = _getNDCG(ranklist, gtItem)
return (hr, ndcg, loss)
def _getHitRatio(ranklist, gtItem):
for item in ranklist:
if item == gtItem:
return 1
return 0
def _getNDCG(ranklist, gtItem):
for i in range(len(ranklist)):
item = ranklist[i]
if item == gtItem:
return math.log(2) / math.log(i+2)
return 0