def __init__(self, neighbornum=5, n=5):
self.neighbornum = neighbornum
self.similarity = Similarity('COSINE')
self.n = n
class ItemCF(BaseEstimator):
def __init__(self, neighbornum=5, n=5):
self.neighbornum = neighbornum
self.similarity = Similarity('COSINE')
self.n = n
def predict(self, testSamples):
recList = []
for user_item in testSamples:
uid = self.dataModel.getUidByUser(user_item[0])
recList.append(self.recommend(uid))
return recList
def fit(self, trainSamples, trainTargets):
self.dataModel = MemeryDataModel(trainSamples, trainTargets)
itemsNum = self.dataModel.getItemsNum()
self.simiMatrix = np.zeros((itemsNum, itemsNum))
for i in range(itemsNum):
for j in range(i + 1, itemsNum):
s = self.similarity.compute(
self.dataModel.getUserIDsFromIid(i),
self.dataModel.getUserIDsFromIid(j))
self.simiMatrix[i][j] = self.simiMatrix[j][i] = s
def neighborhood(self, itemID):
neighbors = np.argsort(np.array(
self.simiMatrix[itemID]))[-1:-self.neighbornum - 1:-1]
return neighbors
def predict_single(self, userID, itemID):
rating = 0.0
for iid in self.neighborhood(itemID):
if userID in self.dataModel.getUserIDsFromIid(iid):
rating += self.simiMatrix[itemID][
iid] * self.dataModel.getRating(userID, iid)
return rating
def recommend(self, u):
userID = self.dataModel.getUidByUser(u)
if userID == -1:
print 'not in test'
return []
else:
#interactedItems = self.dataModel.getItemIDsFromUid(userID)
ratings = dict()
for iid in self.dataModel.getItemIDsFromUid(userID):
for niid in self.neighborhood(iid):
#if iid in interactedItems:
#continue
r = ratings.get(iid, 0)
ratings[iid] = r + self.simiMatrix[iid][
niid] * self.dataModel.getRating(userID, niid)
r = [
x for (x, y) in sorted(ratings.items(),
lambda a, b: cmp(a[1], b[1]),
reverse=True)[:self.n]
]
return [self.dataModel.getItemByIid(i) for i in r]
def score(self, testSamples, trueLabels):
print 'Item_CF scoring ...'
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.recommend(u)
recommendList.append(pre)
e = Eval()
result = e.evalAll(trueList, recommendList)
print 'ItemCF result:' + '(' + str(self.get_params()) + ')' + str(
(result)['F1'])
return (result)['F1']
def __init__(self, neighbornum=5, n=5):
print 'vsrank begin'
self.neighbornum = neighbornum
self.similarity = Similarity('COSINE')
self.n = n
def __init__(self, neighbornum=5, n=5):
self.neighbornum = neighbornum
self.similarity = Similarity('COSINE')
self.n = n
class ItemCF(BaseEstimator):
def __init__(self, neighbornum=5, n=5):
self.neighbornum = neighbornum
self.similarity = Similarity('COSINE')
self.n = n
def predict(self,testSamples):
recList = []
for user_item in testSamples:
uid = self.dataModel.getUidByUser(user_item[0])
recList.append(self.recommend(uid))
return recList
def fit(self, trainSamples, trainTargets):
self.dataModel = MemeryDataModel(trainSamples, trainTargets)
itemsNum = self.dataModel.getItemsNum()
self.simiMatrix = np.zeros((itemsNum, itemsNum))
for i in range(itemsNum):
for j in range(i+1, itemsNum):
s = self.similarity.compute(self.dataModel.getUserIDsFromIid(i), self.dataModel.getUserIDsFromIid(j))
self.simiMatrix[i][j] = self.simiMatrix[j][i] = s
def neighborhood(self, itemID):
neighbors = np.argsort(np.array(self.simiMatrix[itemID]))[-1:-self.neighbornum-1:-1]
return neighbors
def predict_single(self, userID, itemID):
rating = 0.0
for iid in self.neighborhood(itemID):
if userID in self.dataModel.getUserIDsFromIid(iid):
rating += self.simiMatrix[itemID][iid] * self.dataModel.getRating(userID, iid)
return rating
def recommend(self, u):
userID = self.dataModel.getUidByUser(u)
if userID == -1:
print 'not in test'
return []
else:
#interactedItems = self.dataModel.getItemIDsFromUid(userID)
ratings = dict()
for iid in self.dataModel.getItemIDsFromUid(userID):
for niid in self.neighborhood(iid):
#if iid in interactedItems:
#continue
r = ratings.get(iid, 0)
ratings[iid] = r + self.simiMatrix[iid][niid] * self.dataModel.getRating(userID, niid)
r = [x for (x, y) in sorted(ratings.items(), lambda a, b: cmp(a[1], b[1]), reverse=True)[:self.n]]
return [self.dataModel.getItemByIid(i) for i in r]
def score(self, testSamples, trueLabels):
print 'Item_CF scoring ...'
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.recommend(u)
recommendList.append(pre)
e = Eval()
result = e.evalAll(trueList, recommendList)
print 'ItemCF result:'+'('+str(self.get_params())+')'+str((result)['F1'])
return (result)['F1']
def __init__(self, neighbornum=5, n=5):
print 'ucf begin'
self.neighbornum = neighbornum
self.similarity = Similarity('COSINE')
self.n = n