Practical Deep Learning for Coders, v3¶

Lesson4_collab¶

In [ ]:

from fastai.collab import *
from fastai.tabular import *

Collaborative filtering example¶

协同过滤案例¶

collab models use data in a DataFrame of user, items, and ratings.

collab模型使用的是DataFrame中的一个（包含）用户、电影和评分的数据集。

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user,item,title = 'userId','movieId','title'

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path = untar_data(URLs.ML_SAMPLE)
path

Out[ ]:

PosixPath('/home/ubuntu/.fastai/data/movie_lens_sample')

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ratings = pd.read_csv(path/'ratings.csv')
ratings.head()

Out[ ]:

	userId	movieId	rating	timestamp
0	73	1097	4.0	1255504951
1	561	924	3.5	1172695223
2	157	260	3.5	1291598691
3	358	1210	5.0	957481884
4	130	316	2.0	1138999234

That's all we need to create and train a model:

以上就是我们用来训练模型的全部（数据）：

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data = CollabDataBunch.from_df(ratings, seed=42)

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y_range = [0,5.5]

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learn = collab_learner(data, n_factors=50, y_range=y_range)

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learn.fit_one_cycle(3, 5e-3)

Total time: 00:03

epoch	train_loss	valid_loss
1	1.629454	0.982241
2	0.856353	0.678751
3	0.655987	0.669647

Movielens 100k¶

Let's try with the full Movielens 100k data dataset, available from http://files.grouplens.org/datasets/movielens/ml-100k.zip

让我们尝试一下用Movielens的全部数据进行建模。

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path=Config.data_path()/'ml-100k'

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ratings = pd.read_csv(path/'u.data', delimiter='\t', header=None,
                      names=[user,item,'rating','timestamp'])
ratings.head()

Out[ ]:

	userId	movieId	rating	timestamp
0	196	242	3	881250949
1	186	302	3	891717742
2	22	377	1	878887116
3	244	51	2	880606923
4	166	346	1	886397596

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movies = pd.read_csv(path/'u.item',  delimiter='|', encoding='latin-1', header=None,
                    names=[item, 'title', 'date', 'N', 'url', *[f'g{i}' for i in range(19)]])
movies.head()

Out[ ]:

	movieId	title	date	N	url	g1	g2	g3	g4	...	g16
0	1	Toy Story (1995)	01-Jan-1995	NaN	http://us.imdb.com/M/title-exact?Toy%20Story%2...	0	0	1	1	...	0
1	2	GoldenEye (1995)	01-Jan-1995	NaN	http://us.imdb.com/M/title-exact?GoldenEye%20(...	1	1	0	0	...	1
2	3	Four Rooms (1995)	01-Jan-1995	NaN	http://us.imdb.com/M/title-exact?Four%20Rooms%...	0	0	0	0	...	1
3	4	Get Shorty (1995)	01-Jan-1995	NaN	http://us.imdb.com/M/title-exact?Get%20Shorty%...	1	0	0	0	...	0
4	5	Copycat (1995)	01-Jan-1995	NaN	http://us.imdb.com/M/title-exact?Copycat%20(1995)	0	0	0	0	...	1

5 rows × 24 columns

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len(ratings)

Out[ ]:

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rating_movie = ratings.merge(movies[[item, title]])
rating_movie.head()

Out[ ]:

	userId	movieId	rating	timestamp	title
0	196	242	3	881250949	Kolya (1996)
1	63	242	3	875747190	Kolya (1996)
2	226	242	5	883888671	Kolya (1996)
3	154	242	3	879138235	Kolya (1996)
4	306	242	5	876503793	Kolya (1996)

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data = CollabDataBunch.from_df(rating_movie, seed=42, valid_pct=0.1, item_name=title)

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data.show_batch()

userId	title	target
126	Event Horizon (1997)	1.0
44	Young Frankenstein (1974)	4.0
718	Star Trek: First Contact (1996)	4.0
506	Magnificent Seven, The (1954)	5.0
373	Good, The Bad and The Ugly, The (1966)	3.0

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y_range = [0,5.5]

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learn = collab_learner(data, n_factors=40, y_range=y_range, wd=1e-1)

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learn.lr_find()
learn.recorder.plot(skip_end=15)

LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.

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learn.fit_one_cycle(5, 5e-3)

Total time: 00:30

epoch	train_loss	valid_loss
1	0.923900	0.946068
2	0.865458	0.890646
3	0.783896	0.836753
4	0.638374	0.815428
5	0.561979	0.814652

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learn.save('dotprod')

Here's some benchmarks on the same dataset for the popular Librec system for collaborative filtering. They show best results based on RMSE of 0.91, which corresponds to an MSE of 0.91**2 = 0.83.

这里是一些在同一数据集上建模的基准数据。在表格中我们可以看到最好的模型的RMSE是0.91，对应的MSE是0.91**2 = 0.83。

Interpretation¶

模型释义¶

Setup 调用¶

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learn.load('dotprod');

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learn.model

Out[ ]:

EmbeddingDotBias(
  (u_weight): Embedding(944, 40)
  (i_weight): Embedding(1654, 40)
  (u_bias): Embedding(944, 1)
  (i_bias): Embedding(1654, 1)
)

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g = rating_movie.groupby(title)['rating'].count()
top_movies = g.sort_values(ascending=False).index.values[:1000]
top_movies[:10]

Out[ ]:

array(['Star Wars (1977)', 'Contact (1997)', 'Fargo (1996)', 'Return of the Jedi (1983)', 'Liar Liar (1997)',
       'English Patient, The (1996)', 'Scream (1996)', 'Toy Story (1995)', 'Air Force One (1997)',
       'Independence Day (ID4) (1996)'], dtype=object)

Movie bias¶

电影模型的偏差¶

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movie_bias = learn.bias(top_movies, is_item=True)
movie_bias.shape

Out[ ]:

torch.Size([1000])

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mean_ratings = rating_movie.groupby(title)['rating'].mean()
movie_ratings = [(b, i, mean_ratings.loc[i]) for i,b in zip(top_movies,movie_bias)]

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item0 = lambda o:o[0]

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sorted(movie_ratings, key=item0)[:15]

Out[ ]:

[(tensor(-0.3667),
  'Children of the Corn: The Gathering (1996)',
  1.3157894736842106),
 (tensor(-0.3142),
  'Lawnmower Man 2: Beyond Cyberspace (1996)',
  1.7142857142857142),
 (tensor(-0.2926), 'Mortal Kombat: Annihilation (1997)', 1.9534883720930232),
 (tensor(-0.2708), 'Cable Guy, The (1996)', 2.339622641509434),
 (tensor(-0.2669), 'Striptease (1996)', 2.2388059701492535),
 (tensor(-0.2641), 'Free Willy 3: The Rescue (1997)', 1.7407407407407407),
 (tensor(-0.2511), 'Beautician and the Beast, The (1997)', 2.313953488372093),
 (tensor(-0.2418), 'Bio-Dome (1996)', 1.903225806451613),
 (tensor(-0.2345), "Joe's Apartment (1996)", 2.2444444444444445),
 (tensor(-0.2324), 'Island of Dr. Moreau, The (1996)', 2.1578947368421053),
 (tensor(-0.2266), 'Barb Wire (1996)', 1.9333333333333333),
 (tensor(-0.2219), 'Crow: City of Angels, The (1996)', 1.9487179487179487),
 (tensor(-0.2208), 'Grease 2 (1982)', 2.0),
 (tensor(-0.2151), 'Home Alone 3 (1997)', 1.894736842105263),
 (tensor(-0.2089), "McHale's Navy (1997)", 2.1884057971014492)]

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sorted(movie_ratings, key=lambda o: o[0], reverse=True)[:15]

Out[ ]:

[(tensor(0.5913), "Schindler's List (1993)", 4.466442953020135),
 (tensor(0.5700), 'Titanic (1997)', 4.2457142857142856),
 (tensor(0.5623), 'Shawshank Redemption, The (1994)', 4.445229681978798),
 (tensor(0.5412), 'L.A. Confidential (1997)', 4.161616161616162),
 (tensor(0.5368), 'Rear Window (1954)', 4.3875598086124405),
 (tensor(0.5193), 'Star Wars (1977)', 4.3584905660377355),
 (tensor(0.5149), 'As Good As It Gets (1997)', 4.196428571428571),
 (tensor(0.5114), 'Silence of the Lambs, The (1991)', 4.28974358974359),
 (tensor(0.5097), 'Good Will Hunting (1997)', 4.262626262626263),
 (tensor(0.4946), 'Vertigo (1958)', 4.251396648044692),
 (tensor(0.4899), 'Godfather, The (1972)', 4.283292978208232),
 (tensor(0.4855), 'Boot, Das (1981)', 4.203980099502488),
 (tensor(0.4769), 'Usual Suspects, The (1995)', 4.385767790262173),
 (tensor(0.4743), 'Casablanca (1942)', 4.45679012345679),
 (tensor(0.4665), 'Close Shave, A (1995)', 4.491071428571429)]

Movie weights¶

电影模型权重¶

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movie_w = learn.weight(top_movies, is_item=True)
movie_w.shape

Out[ ]:

torch.Size([1000, 40])

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movie_pca = movie_w.pca(3)
movie_pca.shape

Out[ ]:

torch.Size([1000, 3])

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fac0,fac1,fac2 = movie_pca.t()
movie_comp = [(f, i) for f,i in zip(fac0, top_movies)]

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sorted(movie_comp, key=itemgetter(0), reverse=True)[:10]

Out[ ]:

[(tensor(1.2412), 'Home Alone 3 (1997)'),
 (tensor(1.2072), 'Jungle2Jungle (1997)'),
 (tensor(1.2000), 'Bio-Dome (1996)'),
 (tensor(1.1883), 'Leave It to Beaver (1997)'),
 (tensor(1.1570), 'Children of the Corn: The Gathering (1996)'),
 (tensor(1.1309), "McHale's Navy (1997)"),
 (tensor(1.1187), 'D3: The Mighty Ducks (1996)'),
 (tensor(1.0956), 'Congo (1995)'),
 (tensor(1.0950), 'Free Willy 3: The Rescue (1997)'),
 (tensor(1.0524), 'Cutthroat Island (1995)')]

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sorted(movie_comp, key=itemgetter(0))[:10]

Out[ ]:

[(tensor(-1.0692), 'Casablanca (1942)'),
 (tensor(-1.0523), 'Close Shave, A (1995)'),
 (tensor(-1.0142), 'When We Were Kings (1996)'),
 (tensor(-1.0075), 'Lawrence of Arabia (1962)'),
 (tensor(-1.0034), 'Wrong Trousers, The (1993)'),
 (tensor(-0.9905), 'Chinatown (1974)'),
 (tensor(-0.9692), 'Ran (1985)'),
 (tensor(-0.9541), 'Apocalypse Now (1979)'),
 (tensor(-0.9523), 'Wallace & Gromit: The Best of Aardman Animation (1996)'),
 (tensor(-0.9369), 'Some Folks Call It a Sling Blade (1993)')]

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movie_comp = [(f, i) for f,i in zip(fac1, top_movies)]

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sorted(movie_comp, key=itemgetter(0), reverse=True)[:10]

Out[ ]:

[(tensor(0.8788), 'Ready to Wear (Pret-A-Porter) (1994)'),
 (tensor(0.8263), 'Keys to Tulsa (1997)'),
 (tensor(0.8066), 'Nosferatu (Nosferatu, eine Symphonie des Grauens) (1922)'),
 (tensor(0.7730), 'Dead Man (1995)'),
 (tensor(0.7513), 'Three Colors: Blue (1993)'),
 (tensor(0.7492), 'Trainspotting (1996)'),
 (tensor(0.7414), 'Cable Guy, The (1996)'),
 (tensor(0.7330), 'Jude (1996)'),
 (tensor(0.7246), 'Clockwork Orange, A (1971)'),
 (tensor(0.7195), 'Stupids, The (1996)')]

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sorted(movie_comp, key=itemgetter(0))[:10]

Out[ ]:

[(tensor(-1.2148), 'Braveheart (1995)'),
 (tensor(-1.1153), 'Titanic (1997)'),
 (tensor(-1.1148), 'Raiders of the Lost Ark (1981)'),
 (tensor(-0.8795), "It's a Wonderful Life (1946)"),
 (tensor(-0.8644), "Mr. Holland's Opus (1995)"),
 (tensor(-0.8619), 'Star Wars (1977)'),
 (tensor(-0.8558), 'Return of the Jedi (1983)'),
 (tensor(-0.8526), 'Pretty Woman (1990)'),
 (tensor(-0.8453), 'Independence Day (ID4) (1996)'),
 (tensor(-0.8450), 'Forrest Gump (1994)')]

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idxs = np.random.choice(len(top_movies), 50, replace=False)
idxs = list(range(50))
X = fac0[idxs]
Y = fac2[idxs]
plt.figure(figsize=(15,15))
plt.scatter(X, Y)
for i, x, y in zip(top_movies[idxs], X, Y):
    plt.text(x,y,i, color=np.random.rand(3)*0.7, fontsize=11)
plt.show()