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In Graphlab, I am working with small set of fitness data, to use recommender functions that could provide recommendations. The dataset has userid's column but not item id's, instead different items arranged in columns and their respective ratings in rows corresponding to each userid. In order to use any graphlab recommender method, I need to have userid's and item id's. Here is what I did:

v = graphlab.SFrame.read_csv('Data.csv')   
userId = v["user_id"]
itemId = v["x","y","z","x1","y1","z1"] //x,y,z,x1,y1,z1 are activities that are actually the columns in Data and contains corresponding ratings given by user

sf= graphlab.SFrame({'UserId':userId,'ItemId':itemId})
print sf.head(5)

Basically, i extracted the user_id col from Data and tried making a column for ItemId using the x,y,z,etc columns extracted from the same data in order to make another sframe with just these 2 columns. This code results in a tabular format sframe with 2 column as expected, but not arranged in the same order I pass arguments in SFrame. So, the output gives ItemId as the first column and then UserId. Even though I tried to change the order of passing these 2 in sframe, it still gives the same output. Does anyone know the reason why ? This is creating a problem further when using any recommender method as it gives the error: Column name user_id does not exist.

Bustikiller
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user6127405
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1 Answers1

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The reason for the column ordering is because you are passing a Python dictionary to the SFrame constructor. Dictionaries in Python will not keep keys in the order they are specified; they have their own order. If you prefer "UserId" to be first, you can call sf.swap_columns('UserId','ItemId').

The order of the columns does not affect the recommender method though. The Column name 'user_id' does not exist error will appear if you don't have a column named exactly user_id AND don't specify what the name of the user_id column is. In your case, you would want to do: graphlab.recommender.create(sf, user_id='UserId', item_id='ItemId').

Also, you may want to look at the stack method, which could help get your data in to the form the recommender method expects. Your current SFrame sf I think will have a column of dictionaries where the item id is the key and the rating is the value. I believe this would work in this case: 

sf.stack('ItemId', new_column_name=['ItemId','Rating'])
Evan Samanas
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  • Thanks Evan , using stack() helped in using the other recommender methods. The code executes fine except it isn't giving any results for the recommender func I used in this way: result=graphlab.recommender.item_similarity_recommender.create(m,user_id='UserId', item_id='ItemId',target='Rating',similarity_type='cosine') recs=result.recommend() print recs – user6127405 Mar 30 '16 at 01:44