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Test pr #1

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f806d79
teat-pr
sunkanggao Aug 18, 2017
48cffd8
master
sunkanggao Aug 18, 2017
b490e6b
test-pr
sunkanggao Aug 18, 2017
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9 changes: 7 additions & 2 deletions demo.py
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Original file line number Diff line number Diff line change
Expand Up @@ -8,7 +8,7 @@
#

#fetch data and format it
data = fetch_movielens(min_rating=4.0)
data = fetch_movielens(data_home='', min_rating=4.0)

#print training and testing data
print(repr(data['train']))
Expand Down Expand Up @@ -41,8 +41,13 @@ def sample_recommendation(model, data, user_ids):

#movies our model predicts they will like
scores = model.predict(user_id, np.arange(n_items))

#remove the movie that user has seen
unseen_mask = np.in1d(scores, data['train'].tocsr()[user_id].toarray(), assume_unique=True, invert=True)
unseen_scores = scores[unseen_mask]

#rank them in order of most liked to least
top_items = data['item_labels'][np.argsort(-scores)]
top_items = data['item_labels'][np.argsort(-unseen_scores)]

#print out the results
print("User %s" % user_id)
Expand Down