def setUp(self):
self.dataset = {
'Lisa Rose': {
'Lady in the Water': 2.5,
'Snakes on a Plane': 3.5,
'Just My Luck': 3.0,
'Superman Returns': 3.5,
'You, Me and Dupree': 2.5,
'The Night Listener': 3.0
},
'Gene Seymour': {
'Lady in the Water': 3.0,
'Snakes on a Plane': 3.5,
'Just My Luck': 1.5,
'Superman Returns': 5.0,
'The Night Listener': 3.0,
'You, Me and Dupree': 3.5
},
'Michael Phillips': {
'Lady in the Water': 2.5,
'Snakes on a Plane': 3.0,
'Superman Returns': 3.5,
'The Night Listener': 4.0
},
'Claudia Puig': {
'Snakes on a Plane': 3.5,
'Just My Luck': 3.0,
'The Night Listener': 4.5,
'Superman Returns': 4.0,
'You, Me and Dupree': 2.5
},
'Mick LaSalle': {
'Lady in the Water': 3.0,
'Snakes on a Plane': 4.0,
'Just My Luck': 2.0,
'Superman Returns': 3.0,
'The Night Listener': 3.0,
'You, Me and Dupree': 2.0
},
'Jack Matthews': {
'Lady in the Water': 3.0,
'Snakes on a Plane': 4.0,
'The Night Listener': 3.0,
'Superman Returns': 5.0,
'You, Me and Dupree': 3.5
},
'Toby': {
'Snakes on a Plane': 4.5,
'You, Me and Dupree': 1.0,
'Superman Returns': 4.0
}
}
self.wrapper = DataWrapper(self.dataset)
def __init__(self,
data=dict(),
numRecommendations=1,
db=None,
metric="rating",
school="gatech"):
self.dataset = DataWrapper(instances=data,
db=db,
school=school,
metric=metric)
self.updated = False
self.sparsedata = None
self.sparseifyData()
try:
self.svd = TruncatedSVD(n_components=numRecommendations)
self.model = self.svd.inverse_transform(
self.svd.fit_transform(self.sparsedata))
except ValueError:
self.svd = None
self.model = None
raise ValueError("Not enough ratings for predictions")
