CollaborativeFilteringModel predict

---

predict(self, frame, input_source_column_name, input_dest_column_name, output_user_column_name='user', output_product_column_name='product', output_rating_column_name='rating')

[BETA] Collaborative Filtering Predict (ALS).

Parameters:

frame : Frame

input_source_column_name : unicode

source column name.

input_dest_column_name : unicode

destination column name.

output_user_column_name : unicode (default=user)

A user column name for the output frame

output_product_column_name : unicode (default=product)

A product column name for the output frame

output_rating_column_name : unicode (default=rating)

A rating column name for the output frame

Returns:

: Frame

Returns a double representing the probability if the user(i) to like product (j)

See Collaborative Filtering Train for more information.

Examples

>>> model = ta.CollaborativeFilteringModel()
[===Job Progress===]
>>> model.train(edge_frame, 'source', 'dest', 'weight')
[===Job Progress===]

>>> result = model.predict(edge_frame_predict, 'source', 'dest')
[===Job Progress===]
>>> result.inspect()
    [#]  user  product  rating
    ====================================
    [0]     1        4   0.0485403053463
    [1]     1        5   0.0300555229187
    [2]     2        5  0.00397346867248
    [3]     1        3   0.0404502525926

[===Job Progress===]