class NMF(BaseEstimator):
def __init__(self, n=5, factors=50):
print 'nmf begin'
self.n = n
self.factors = factors
def predict(self, testSamples):
recommend_lists = []
for user_item in testSamples:
uid = self.dataModel.getUidByUser(user_item[0])
iid = self.dataModel.getUidByUser(user_item[1])
recommend_lists.append(np.dot(self.pu[uid], self.qi[iid]))
return recommend_lists
def fit(self, trainSamples, trainTargets):
self.dataModel = MemeryDataModel(trainSamples, trainTargets)
#print 'train user:'******'not in test'
return []
else:
predict_scores = []
for i in range(self.dataModel.getItemsNum()):
predict_scores.append(self.predict_single(uid, i))
topN = np.argsort(np.array(predict_scores))[-1:-self.n - 1:-1]
return [self.dataModel.getItemByIid(i) for i in topN]
def score(self, testSamples, trueLabels):
print 'NMF scoring ...'
trueList = []
recommendList = []
user_unique = list(set(np.array(testSamples)[:, 0]))
#print 'test user:'******'NMF result:' + '(' + str(self.get_params()) + ')' + str(
(result)['F1'])
return (result)['F1']
class TopN(BaseEstimator):
def __init__(self, n=5):
print 'topN begin'
self.n = n
def gen_items_popular(self, trainSamples, trainTargets, hasTimes=False):
self.dataModel = MemeryDataModel(trainSamples, trainTargets)
itempopular = np.zeros(self.dataModel.getItemsNum())
uids = self.dataModel.getData().nonzero()[0]
iids = self.dataModel.getData().nonzero()[1]
for i in range(len(iids)):
iid = iids[i]
itempopular[iid] += 1
self.popItems = itempopular
def predict(self, testSamples):
recommend_lists = []
for user_item in testSamples:
if self.dataModel.getIidByItem(user_item[1]) in self.topN[:self.n]:
recommend_lists.append(1)
else:
recommend_lists.append(0)
return recommend_lists
def fit(self, trainSamples, trainTargets):
#print trainSamples, trainTargets
#print len(trainSamples), len(trainTargets)
self.gen_items_popular(trainSamples, trainTargets)
self.topN = np.argsort(np.array(self.popItems))[-1::-1]
return self
def recommend(self, uid):
return [self.dataModel.getItemByIid(i) for i in self.topN[:self.n]]
def score(self, testSamples, trueLabels):
#print testSamples
#print len(testSamples)
trueList = []
recommendList = []
user_unique = list(set(np.array(testSamples)[:, 0]))
for u in user_unique:
uTrueIndex = np.argwhere(np.array(testSamples)[:, 0] == u)[:, 0]
#true = [self.dataModel.getIidByItem(i) for i in list(np.array(testSamples)[uTrueIndex][:,1])]
true = list(np.array(testSamples)[uTrueIndex][:, 1])
trueList.append(true)
pre = [self.dataModel.getItemByIid(i) for i in self.topN[:self.n]]
recommendList.append(pre)
e = Eval()
result = e.evalAll(recommendList, trueList)
print 'TopN result:' + '(' + str(self.get_params()) + ')' + str(
(result)['F1'])
return (result)['F1']
class TopN(BaseEstimator):
def __init__(self, n=5):
print 'topN begin'
self.n = n
def gen_items_popular(self, trainSamples, trainTargets, hasTimes=False):
self.dataModel = MemeryDataModel(trainSamples, trainTargets)
itempopular = np.zeros(self.dataModel.getItemsNum())
uids = self.dataModel.getData().nonzero()[0]
iids = self.dataModel.getData().nonzero()[1]
for i in range(len(iids)):
iid = iids[i]
itempopular[iid] += 1
self.popItems = itempopular
def predict(self, testSamples):
recommend_lists = []
for user_item in testSamples:
if self.dataModel.getIidByItem(user_item[1]) in self.topN[:self.n]:
recommend_lists.append(1)
else:
recommend_lists.append(0)
return recommend_lists
def fit(self, trainSamples, trainTargets):
#print trainSamples, trainTargets
#print len(trainSamples), len(trainTargets)
self.gen_items_popular(trainSamples, trainTargets)
self.topN = np.argsort(np.array(self.popItems))[-1::-1]
return self
def recommend(self, uid):
return [self.dataModel.getItemByIid(i) for i in self.topN[:self.n]]
def score(self, testSamples, trueLabels):
#print testSamples
#print len(testSamples)
trueList = []
recommendList= []
user_unique = list(set(np.array(testSamples)[:,0]))
for u in user_unique:
uTrueIndex = np.argwhere(np.array(testSamples)[:,0] == u)[:,0]
#true = [self.dataModel.getIidByItem(i) for i in list(np.array(testSamples)[uTrueIndex][:,1])]
true = list(np.array(testSamples)[uTrueIndex][:,1])
trueList.append(true)
pre = [self.dataModel.getItemByIid(i) for i in self.topN[:self.n]]
recommendList.append(pre)
e = Eval()
result = e.evalAll(recommendList, trueList)
print 'TopN result:'+'('+str(self.get_params())+')'+str((result)['F1'])
return (result)['F1']
class NMF(BaseEstimator):
def __init__(self, n=5, factors=50):
print 'nmf begin'
self.n = n
self.factors = factors
def predict(self, testSamples):
recommend_lists = []
for user_item in testSamples:
uid = self.dataModel.getUidByUser(user_item[0])
iid = self.dataModel.getUidByUser(user_item[1])
recommend_lists.append(np.dot(self.pu[uid], self.qi[iid]))
return recommend_lists
def fit(self, trainSamples, trainTargets):
self.dataModel = MemeryDataModel(trainSamples, trainTargets)
#print 'train user:'******'not in test'
return []
else:
predict_scores = []
for i in range(self.dataModel.getItemsNum()):
predict_scores.append(self.predict_single(uid, i))
topN = np.argsort(np.array(predict_scores))[-1:-self.n-1:-1]
return [self.dataModel.getItemByIid(i) for i in topN]
def score(self, testSamples, trueLabels):
print 'NMF scoring ...'
trueList = []
recommendList= []
user_unique = list(set(np.array(testSamples)[:,0]))
#print 'test user:'******'NMF result:'+'('+str(self.get_params())+')' + str((result)['F1'])
return (result)['F1']
