GabsGear

196 ['Very Natural Thing, A (1974)', 'Walk in the Sun, A (1945)', 'War at Home, The (1996)']
186 ['Mamma Roma (1962)', 'Conspiracy Theory (1997)', 'Toy Story (1995)']
22 ['Entertaining Angels: The Dorothy Day Story (1996)', 'King of New York (1990)', 'Usual Suspects, The (1995)']
244 ['Other Voices, Other Rooms (1997)', 'Big Bang Theory, The (1994)', 'Godfather, The (1972)']
166 ['Mamma Roma (1962)', 'Delta of Venus (1994)', 'Carmen Miranda: Bananas Is My Business (1994)']
298 ['North by Northwest (1959)', 'Pinocchio (1940)', 'Amadeus (1984)']
115 ['2001: A Space Odyssey (1968)', 'Clockwork Orange, A (1971)', 'Three Colors: White (1994)']
253 ['Entertaining Angels: The Dorothy Day Story (1996)', 'Michael (1996)', 'Empire Strikes Back, The (1980)']
305 ['Lone Star (1996)', 'African Queen, The (1951)', 'My Left Foot (1989)']
6 ['Bullets Over Broadway (1994)', 'Rosewood (1997)', 'Rear Window (1954)']

GabsGear

import os, io
 
def read_item_names():
    """Read the u.item file from MovieLens 100-k dataset and returns a
    mapping to convert raw ids into movie names.
    """
 
    file_name = (os.path.expanduser('~') +
                 '/.surprise_data/ml-100k/ml-100k/u.item')
    rid_to_name = {}

GabsGear

import os, io
 
def read_item_names():
    """Read the u.item file from MovieLens 100-k dataset and returns a
    mapping to convert raw ids into movie names.
    """
 
    file_name = (os.path.expanduser('~') +
                 '/.surprise_data/ml-100k/ml-100k/u.item')
    rid_to_name = {}

GabsGear

testSet = trainingSet.build_anti_testset()
predictions = knn.test(testSet)

from collections import defaultdict
 
def get_top3_recommendations(predictions, topN = 3):
     
    top_recs = defaultdict(list)
    for uid, iid, true_r, est, _ in predictions:
        top_recs[uid].append((iid, est))

GabsGear

knn = KNNBasic(sim_options={
                'name': 'cosine',
                'user_based': False })
knn.train(trainingSet)

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from surprise import Dataset, evaluate
from surprise import KNNBasic

data = Dataset.load_builtin("ml-100k")
trainingSet = data.build_full_trainset()

GabsGear