item_item_collaborative _filtering_4.py

#------------------------
# USER-ITEM CALCULATIONS
#------------------------

# Construct a new dataframe with the 10 closest neighbours (most similar)
# for each artist.
data_neighbours = pd.DataFrame(index=data_matrix.columns, columns=range(1,11))
for i in xrange(0, len(data_matrix.columns)):
    data_neighbours.ix[i,:10] = data_matrix.ix[0:,i].sort_values(ascending=False)[:10].index

user = 5985
user_index = data[data.user == user].index.tolist()[0]

# Get the artists the user has played.
known_user_likes = data_items.ix[user_index]
known_user_likes = known_user_likes[known_user_likes >0].index.values

# Construct the neighbourhood from the most similar items to the
# ones our user has already liked.
most_similar_to_likes = data_neighbours.ix[known_user_likes]
similar_list = most_similar_to_likes.values.tolist()
similar_list = list(set([item for sublist in similar_list for item in sublist]))
neighbourhood = data_matrix[similar_list].ix[similar_list]

# A user vector containing only the neighbourhood items and
# the known user likes.
user_vector = data_items.ix[user_index].ix[similar_list]

# Calculate the score.
score = neighbourhood.dot(user_vector).div(neighbourhood.sum(axis=1))

# Drop the known likes.
score = score.drop(known_user_likes)

print known_user_likes
print score.nlargest(20)