使用GraphLab进行电影推荐



In [3]:
import graphlab
graphlab.canvas.set_target("ipynb")
# set canvas to show sframes and sgraphs in ipython notebook
import matplotlib.pyplot as plt
%matplotlib inline
In [7]:
# download data from: http://files.grouplens.org/datasets/movielens/ml-1m.zip
In [5]:
data = graphlab.SFrame.read_csv('/Users/chengjun/bigdata/ml-1m/ratings.dat', delimiter='\n', 
                                header=False)['X1'].apply(lambda x: x.split('::')).unpack()
for col in data.column_names():
    data[col] = data[col].astype(int)
data.rename({'X.0': 'user_id', 'X.1': 'movie_id', 'X.2': 'rating', 'X.3': 'timestamp'})
data.save('ratings')

users = graphlab.SFrame.read_csv('/Users/chengjun/bigdata/ml-1m/users.dat', delimiter='\n', 
                                 header=False)['X1'].apply(lambda x: x.split('::')).unpack()
users.rename({'X.0': 'user_id', 'X.1': 'gender', 'X.2': 'age', 'X.3': 'occupation', 'X.4': 'zip-code'})
users['user_id'] = users['user_id'].astype(int)
users.save('users')

items = graphlab.SFrame.read_csv('/Users/chengjun/bigdata/ml-1m/movies.dat', delimiter='\n', 
                                 header=False)['X1'].apply(lambda x: x.split('::')).unpack()
items.rename({'X.0': 'movie_id', 'X.1': 'title', 'X.2': 'genre'})
items['movie_id'] = items['movie_id'].astype(int)
items.save('items')
PROGRESS: Finished parsing file /Users/chengjun/bigdata/ml-1m/ratings.dat
PROGRESS: Parsing completed. Parsed 100 lines in 0.419473 secs.
------------------------------------------------------
Inferred types from first line of file as 
column_type_hints=[str]
If parsing fails due to incorrect types, you can correct
the inferred type list above and pass it to read_csv in
the column_type_hints argument
------------------------------------------------------
PROGRESS: Finished parsing file /Users/chengjun/bigdata/ml-1m/ratings.dat
PROGRESS: Parsing completed. Parsed 1000209 lines in 0.516456 secs.
PROGRESS: Finished parsing file /Users/chengjun/bigdata/ml-1m/users.dat
PROGRESS: Parsing completed. Parsed 100 lines in 0.029414 secs.
------------------------------------------------------
Inferred types from first line of file as 
column_type_hints=[str]
If parsing fails due to incorrect types, you can correct
the inferred type list above and pass it to read_csv in
the column_type_hints argument
------------------------------------------------------
PROGRESS: Finished parsing file /Users/chengjun/bigdata/ml-1m/users.dat
PROGRESS: Parsing completed. Parsed 6040 lines in 0.013402 secs.
PROGRESS: Finished parsing file /Users/chengjun/bigdata/ml-1m/movies.dat
PROGRESS: Parsing completed. Parsed 100 lines in 0.025157 secs.
------------------------------------------------------
Inferred types from first line of file as 
column_type_hints=[str]
If parsing fails due to incorrect types, you can correct
the inferred type list above and pass it to read_csv in
the column_type_hints argument
------------------------------------------------------
PROGRESS: Finished parsing file /Users/chengjun/bigdata/ml-1m/movies.dat
PROGRESS: Parsing completed. Parsed 3883 lines in 0.011876 secs.
In [8]:
data.show()
In [9]:
items.head()
Out[9]:
movie_id title genre
1 Toy Story (1995) Animation|Children's|Come
dy ...
2 Jumanji (1995) Adventure|Children's|Fant
asy ...
3 Grumpier Old Men (1995) Comedy|Romance
4 Waiting to Exhale (1995) Comedy|Drama
5 Father of the Bride Part
II (1995) ...
Comedy
6 Heat (1995) Action|Crime|Thriller
7 Sabrina (1995) Comedy|Romance
8 Tom and Huck (1995) Adventure|Children's
9 Sudden Death (1995) Action
10 GoldenEye (1995) Action|Adventure|Thriller
[10 rows x 3 columns]
In [10]:
data = data.join(items, on='movie_id')
In [11]:
data
Out[11]:
user_id movie_id rating timestamp title genre
1 1193 5 978300760 One Flew Over the
Cuckoo's Nest (1975) ...
Drama
1 661 3 978302109 James and the Giant Peach
(1996) ...
Animation|Children's|Musi
cal ...
1 914 3 978301968 My Fair Lady (1964) Musical|Romance
1 3408 4 978300275 Erin Brockovich (2000) Drama
1 2355 5 978824291 Bug's Life, A (1998) Animation|Children's|Come
dy ...
1 1197 3 978302268 Princess Bride, The
(1987) ...
Action|Adventure|Comedy|R
omance ...
1 1287 5 978302039 Ben-Hur (1959) Action|Adventure|Drama
1 2804 5 978300719 Christmas Story, A (1983) Comedy|Drama
1 594 4 978302268 Snow White and the Seven
Dwarfs (1937) ...
Animation|Children's|Musi
cal ...
1 919 4 978301368 Wizard of Oz, The (1939) Adventure|Children's|Dram
a|Musical ...
[1000209 rows x 6 columns]
Note: Only the head of the SFrame is printed.
You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.
In [27]:
(train_set, test_set) = data.random_split(0.95, seed=1)
In [33]:
m = graphlab.recommender.create(train_set, 'user_id', 'movie_id', 'rating')
PROGRESS: Recsys training: model = ranking_factorization_recommender
PROGRESS: Preparing data set.
PROGRESS:     Data has 949852 observations with 6040 users and 3701 items.
PROGRESS:     Data prepared in: 1.38442s
PROGRESS: Training ranking_factorization_recommender for recommendations.
PROGRESS: +--------------------------------+--------------------------------------------------+----------+
PROGRESS: | Parameter                      | Description                                      | Value    |
PROGRESS: +--------------------------------+--------------------------------------------------+----------+
PROGRESS: | num_factors                    | Factor Dimension                                 | 32       |
PROGRESS: | regularization                 | L2 Regularization on Factors                     | 1e-09    |
PROGRESS: | solver                         | Solver used for training                         | adagrad  |
PROGRESS: | linear_regularization          | L2 Regularization on Linear Coefficients         | 1e-09    |
PROGRESS: | ranking_regularization         | Rank-based Regularization Weight                 | 0.25     |
PROGRESS: | max_iterations                 | Maximum Number of Iterations                     | 25       |
PROGRESS: +--------------------------------+--------------------------------------------------+----------+
PROGRESS:   Optimizing model using SGD; tuning step size.
PROGRESS:   Using 118731 / 949852 points for tuning the step size.
PROGRESS: +---------+-------------------+------------------------------------------+
PROGRESS: | Attempt | Initial Step Size | Estimated Objective Value                |
PROGRESS: +---------+-------------------+------------------------------------------+
PROGRESS: | 0       | 10                | Not Viable                               |
PROGRESS: | 1       | 2.5               | Not Viable                               |
PROGRESS: | 2       | 0.625             | Not Viable                               |
PROGRESS: | 3       | 0.15625           | 0.38911                                  |
PROGRESS: | 4       | 0.078125          | 0.54968                                  |
PROGRESS: | 5       | 0.0390625         | 0.658223                                 |
PROGRESS: | 6       | 0.0195312         | 1.23822                                  |
PROGRESS: +---------+-------------------+------------------------------------------+
PROGRESS: | Final   | 0.15625           | 0.38911                                  |
PROGRESS: +---------+-------------------+------------------------------------------+
PROGRESS: Starting Optimization.
PROGRESS: +---------+--------------+-------------------+-----------------------+-------------+
PROGRESS: | Iter.   | Elapsed Time | Approx. Objective | Approx. Training RMSE | Step Size   |
PROGRESS: +---------+--------------+-------------------+-----------------------+-------------+
PROGRESS: | Initial | 307us        | 2.44719           | 1.1172                |             |
PROGRESS: +---------+--------------+-------------------+-----------------------+-------------+
PROGRESS: | 1       | 1.58s        | DIVERGED          | DIVERGED              | 0.15625     |
PROGRESS: | RESET   | 2.09s        | 2.44725           | 1.11722               |             |
PROGRESS: | 1       | 3.77s        | DIVERGED          | DIVERGED              | 0.078125    |
PROGRESS: | RESET   | 4.39s        | 2.44716           | 1.11722               |             |
PROGRESS: | 1       | 5.67s        | 1.55168           | 1.0281                | 0.0390625   |
PROGRESS: | 2       | 6.96s        | 1.14185           | 0.933767              | 0.0390625   |
PROGRESS: | 3       | 8.10s        | 1.02045           | 0.90251               | 0.0390625   |
PROGRESS: | 4       | 9.36s        | 0.95975           | 0.887199              | 0.0390625   |
PROGRESS: | 5       | 10.51s       | 0.917016          | 0.875537              | 0.0390625   |
PROGRESS: | 6       | 11.64s       | 0.88812           | 0.867364              | 0.0390625   |
PROGRESS: | 7       | 12.84s       | 0.865568          | 0.860687              | 0.0390625   |
PROGRESS: | 8       | 13.92s       | 0.846648          | 0.854981              | 0.0390625   |
PROGRESS: | 9       | 15.06s       | 0.831916          | 0.850301              | 0.0390625   |
PROGRESS: | 10      | 16.12s       | 0.817915          | 0.846041              | 0.0390625   |
PROGRESS: | 11      | 17.48s       | 0.806827          | 0.84242               | 0.0390625   |
PROGRESS: | 12      | 18.59s       | 0.796439          | 0.838696              | 0.0390625   |
PROGRESS: | 13      | 19.70s       | 0.787774          | 0.83584               | 0.0390625   |
PROGRESS: | 14      | 20.94s       | 0.779347          | 0.83306               | 0.0390625   |
PROGRESS: | 15      | 22.23s       | 0.772255          | 0.830361              | 0.0390625   |
PROGRESS: | 16      | 23.43s       | 0.765821          | 0.828197              | 0.0390625   |
PROGRESS: | 17      | 24.54s       | 0.75912           | 0.825862              | 0.0390625   |
PROGRESS: | 18      | 25.60s       | 0.753293          | 0.823827              | 0.0390625   |
PROGRESS: | 19      | 26.71s       | 0.748413          | 0.821838              | 0.0390625   |
PROGRESS: | 20      | 27.74s       | 0.743186          | 0.819837              | 0.0390625   |
PROGRESS: | 21      | 28.85s       | 0.738489          | 0.8181                | 0.0390625   |
PROGRESS: | 22      | 29.92s       | 0.734265          | 0.816403              | 0.0390625   |
PROGRESS: | 23      | 31.00s       | 0.72996           | 0.814651              | 0.0390625   |
PROGRESS: | 24      | 32.30s       | 0.725537          | 0.813209              | 0.0390625   |
PROGRESS: | 25      | 33.47s       | 0.722203          | 0.811926              | 0.0390625   |
PROGRESS: +---------+--------------+-------------------+-----------------------+-------------+
PROGRESS: Optimization Complete: Maximum number of passes through the data reached.
PROGRESS: Computing final objective value and training RMSE.
PROGRESS:        Final objective value: 0.708338
PROGRESS:        Final training RMSE: 0.802685
In [29]:
m
Out[29]:
Class                           : ItemSimilarityRecommender

Schema
------
User ID                         : user_id
Item ID                         : movie_id
Target                          : None
Additional observation features : 0
Number of user side features    : 0
Number of item side features    : 0

Statistics
----------
Number of observations          : 949852
Number of users                 : 6040
Number of items                 : 3701

Training summary
----------------
Training time                   : 0.7314

Settings
--------
only_top_k                      : 100
similarity_type                 : jaccard
threshold                       : 0.001
training_method                 : auto
In [38]:
m2 = graphlab.item_similarity_recommender.create(train_set, 'user_id', 'movie_id', 'rating',
                                 similarity_type='pearson')
PROGRESS: Recsys training: model = item_similarity
PROGRESS: Warning: Ignoring columns timestamp, title, genre;
PROGRESS:     To use these columns in scoring predictions, use a model that allows the use of additional features.
PROGRESS: Preparing data set.
PROGRESS:     Data has 949852 observations with 6040 users and 3701 items.
PROGRESS:     Data prepared in: 0.741166s
PROGRESS: Computing item similarity statistics:
PROGRESS: Computing most similar items for 3701 items:
PROGRESS: +-----------------+-----------------+
PROGRESS: | Number of items | Elapsed Time    |
PROGRESS: +-----------------+-----------------+
PROGRESS: | 1000            | 0.502444        |
PROGRESS: | 2000            | 0.525984        |
PROGRESS: | 3000            | 0.547989        |
PROGRESS: +-----------------+-----------------+
PROGRESS: Finished training in 0.782624s
PROGRESS: Finished prediction in 0.688922s
In [39]:
m2
Out[39]:
Class                           : ItemSimilarityRecommender

Schema
------
User ID                         : user_id
Item ID                         : movie_id
Target                          : rating
Additional observation features : 0
Number of user side features    : 0
Number of item side features    : 0

Statistics
----------
Number of observations          : 949852
Number of users                 : 6040
Number of items                 : 3701

Training summary
----------------
Training time                   : 0.7828

Settings
--------
only_top_k                      : 100
similarity_type                 : pearson
threshold                       : 0.001
training_method                 : auto
In [40]:
result = graphlab.recommender.util.compare_models(test_set, [m, m2],
                                            user_sample=.1, skip_set=train_set)
compare_models: using 562 users to estimate model performance
PROGRESS: Evaluate model M0

Precision and recall summary statistics by cutoff
+--------+-----------------+------------------+
| cutoff |  mean_precision |   mean_recall    |
+--------+-----------------+------------------+
|   2    | 0.0435943060498 | 0.00956472275563 |
|   4    | 0.0333629893238 | 0.0148154269344  |
|   6    | 0.0308422301305 | 0.0200992907447  |
|   8    | 0.0289145907473 | 0.0259425986711  |
|   10   | 0.0274021352313 | 0.0287214600249  |
|   12   | 0.0260972716489 | 0.0337773113572  |
|   14   | 0.0263091001525 | 0.0394111159869  |
|   16   | 0.0256895017794 | 0.0462196778187  |
|   18   | 0.0250098853302 |  0.050977761984  |
|   20   | 0.0248220640569 | 0.0552180941837  |
+--------+-----------------+------------------+
[10 rows x 3 columns]


Overall RMSE:  0.906418088677

Per User RMSE (best)
+---------+-------+-----------------+
| user_id | count |       rmse      |
+---------+-------+-----------------+
|   5909  |   1   | 0.0473437604915 |
+---------+-------+-----------------+
[1 rows x 3 columns]


Per User RMSE (worst)
+---------+-------+---------------+
| user_id | count |      rmse     |
+---------+-------+---------------+
|   2379  |   1   | 3.30603390451 |
+---------+-------+---------------+
[1 rows x 3 columns]


Per Item RMSE (best)
+----------+-------+-------------------+
| movie_id | count |        rmse       |
+----------+-------+-------------------+
|   3407   |   1   | 0.000624169056996 |
+----------+-------+-------------------+
[1 rows x 3 columns]


Per Item RMSE (worst)
+----------+-------+---------------+
| movie_id | count |      rmse     |
+----------+-------+---------------+
|   3747   |   1   | 3.91489813071 |
+----------+-------+---------------+
[1 rows x 3 columns]

PROGRESS: Evaluate model M1

Precision and recall summary statistics by cutoff
+--------+-------------------+-------------------+
| cutoff |   mean_precision  |    mean_recall    |
+--------+-------------------+-------------------+
|   2    | 0.000889679715302 | 0.000296559905101 |
|   4    | 0.000444839857651 | 0.000296559905101 |
|   6    | 0.000593119810202 | 0.000889679715302 |
|   8    | 0.000667259786477 |  0.00133451957295 |
|   10   | 0.000711743772242 |  0.00169039145907 |
|   12   | 0.000593119810202 |  0.00169039145907 |
|   14   | 0.000762582613116 |  0.00215747330961 |
|   16   | 0.000667259786477 |  0.00215747330961 |
|   18   | 0.000691973111902 |  0.00230575326216 |
|   20   | 0.000800711743772 |  0.00236830044214 |
+--------+-------------------+-------------------+
[10 rows x 3 columns]

PROGRESS: Finished prediction in 0.09301s

Overall RMSE:  0.869846693134

Per User RMSE (best)
+---------+-------+-----------------+
| user_id | count |       rmse      |
+---------+-------+-----------------+
|   3350  |   1   | 0.0357205929343 |
+---------+-------+-----------------+
[1 rows x 3 columns]


Per User RMSE (worst)
+---------+-------+---------------+
| user_id | count |      rmse     |
+---------+-------+---------------+
|   200   |   1   | 3.72375859435 |
+---------+-------+---------------+
[1 rows x 3 columns]


Per Item RMSE (best)
+----------+-------+------------------+
| movie_id | count |       rmse       |
+----------+-------+------------------+
|   2273   |   1   | 0.00162381395374 |
+----------+-------+------------------+
[1 rows x 3 columns]


Per Item RMSE (worst)
+----------+-------+---------------+
| movie_id | count |      rmse     |
+----------+-------+---------------+
|   627    |   1   | 4.12012186276 |
+----------+-------+---------------+
[1 rows x 3 columns]

Getting similar items

In [41]:
m.get_similar_items([1287])  # movie_id is Ben-Hur
PROGRESS: Getting similar items completed in 0.002226
Out[41]:
movie_id similar distance rank
1287 3087 1.65043520927 1
1287 54 1.64714694023 2
1287 3473 1.64681369066 3
1287 2690 1.64273333549 4
1287 2014 1.63784432411 5
1287 1950 1.63471919298 6
1287 585 1.63432335854 7
1287 1265 1.62446278334 8
1287 1919 1.62326407433 9
1287 566 1.61960405111 10
[10 rows x 4 columns]
In [42]:
m.get_similar_items([1287]).join(items, on={'similar': 'movie_id'}).sort('rank')
PROGRESS: Getting similar items completed in 0.001121
Out[42]:
movie_id similar distance rank title genre
1287 3087 1.65043520927 1 Scrooged (1988) Comedy
1287 54 1.64714694023 2 Big Green, The (1995) Children's|Comedy
1287 3473 1.64681369066 3 Jonah Who Will Be 25 in
the Year 2000 (1976) ...
Comedy
1287 2690 1.64273333549 4 Ideal Husband, An (1999) Comedy
1287 2014 1.63784432411 5 Freaky Friday (1977) Children's|Comedy
1287 1950 1.63471919298 6 In the Heat of the Night
(1967) ...
Drama|Mystery
1287 585 1.63432335854 7 Brady Bunch Movie, The
(1995) ...
Comedy
1287 1265 1.62446278334 8 Groundhog Day (1993) Comedy|Romance
1287 1919 1.62326407433 9 Madeline (1998) Children's|Comedy
1287 566 1.61960405111 10 Naked in New York (1994) Comedy|Romance
[10 rows x 6 columns]

Making recommendations

In [43]:
recs = m.recommend()
PROGRESS: recommendations finished on 1000/6040 queries. users per second: 7602.42
PROGRESS: recommendations finished on 2000/6040 queries. users per second: 8142.83
PROGRESS: recommendations finished on 3000/6040 queries. users per second: 8330.83
PROGRESS: recommendations finished on 4000/6040 queries. users per second: 8446.43
PROGRESS: recommendations finished on 5000/6040 queries. users per second: 8504.6
PROGRESS: recommendations finished on 6000/6040 queries. users per second: 8163.45
In [44]:
recs
Out[44]:
user_id movie_id score rank
1 356 4.04209059737 1
1 34 4.03408827148 2
1 480 4.00319579504 3
1 2081 3.94718419276 4
1 377 3.92856887243 5
1 2987 3.927424426 6
1 590 3.89587930105 7
1 1387 3.88266849778 8
1 741 3.87735148034 9
1 2006 3.87439003847 10
[60400 rows x 4 columns]
Note: Only the head of the SFrame is printed.
You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.
In [45]:
data[data['user_id'] == 4].join(items, on='movie_id')
Out[45]:
user_id movie_id rating timestamp title genre
4 260 5 978294199 Star Wars: Episode IV - A
New Hope (1977) ...
Action|Adventure|Fantasy
|Sci-Fi ...
4 480 4 978294008 Jurassic Park (1993) Action|Adventure|Sci-Fi
4 1036 4 978294282 Die Hard (1988) Action|Thriller
4 1097 4 978293964 E.T. the Extra-
Terrestrial (1982) ...
Children's|Drama|Fantasy
|Sci-Fi ...
4 1196 2 978294199 Star Wars: Episode V -
The Empire Strikes Back ...
Action|Adventure|Drama
|Sci-Fi|War ...
4 1198 5 978294199 Raiders of the Lost Ark
(1981) ...
Action|Adventure
4 1201 5 978294230 Good, The Bad and The
Ugly, The (1966) ...
Action|Western
4 1210 3 978293924 Star Wars: Episode VI -
Return of the Jedi (1 ...
Action|Adventure|Romance
|Sci-Fi|War ...
4 1214 4 978294260 Alien (1979) Action|Horror|Sci-
Fi|Thriller ...
4 1240 5 978294260 Terminator, The (1984) Action|Sci-Fi|Thriller
title.1 genre.1
Star Wars: Episode IV - A
New Hope (1977) ...
Action|Adventure|Fantasy
|Sci-Fi ...
Jurassic Park (1993) Action|Adventure|Sci-Fi
Die Hard (1988) Action|Thriller
E.T. the Extra-
Terrestrial (1982) ...
Children's|Drama|Fantasy
|Sci-Fi ...
Star Wars: Episode V -
The Empire Strikes Back ...
Action|Adventure|Drama
|Sci-Fi|War ...
Raiders of the Lost Ark
(1981) ...
Action|Adventure
Good, The Bad and The
Ugly, The (1966) ...
Action|Western
Star Wars: Episode VI -
Return of the Jedi (1 ...
Action|Adventure|Romance
|Sci-Fi|War ...
Alien (1979) Action|Horror|Sci-
Fi|Thriller ...
Terminator, The (1984) Action|Sci-Fi|Thriller
[21 rows x 8 columns]
Note: Only the head of the SFrame is printed.
You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.
In [46]:
m.recommend(users=[4], k=20).join(items, on='movie_id')
Out[46]:
user_id movie_id score rank title genre
4 34 4.14169768132 1 Babe (1995) Children's|Comedy|Drama
4 317 3.88899993039 15 Santa Clause, The (1994) Children's|Comedy|Fantasy
4 531 3.86152254678 18 Secret Garden, The (1993) Children's|Drama
4 590 4.12568046785 2 Dances with Wolves (1990) Adventure|Drama|Western
4 741 3.90859223045 12 Ghost in the Shell
(Kokaku kidotai) (1995) ...
Animation|Sci-Fi
4 969 3.92384021617 9 African Queen, The (1951) Action|Adventure|Romance|
War ...
4 1012 3.91079562045 11 Old Yeller (1957) Children's|Drama
4 1013 3.84667891897 19 Parent Trap, The (1961) Children's|Drama
4 1017 3.86540279425 17 Swiss Family Robinson
(1960) ...
Adventure|Children's
4 1204 3.98675022162 5 Lawrence of Arabia (1962) Adventure|War
[20 rows x 6 columns]
Note: Only the head of the SFrame is printed.
You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.
In [47]:
m.recommend?

Recommendations for new users

In [48]:
recent_data = graphlab.SFrame()
recent_data['movie_id'] = [1291] 
recent_data['user_id'] = 99999
In [51]:
m2.recommend(users=[99999], new_observation_data=recent_data).join(items, on='movie_id').sort('rank')
Out[51]:
user_id movie_id score rank title genre
99999 1830 5.0 1 Follow the Bitch (1998) Comedy
99999 572 5.0 2 Foreign Student (1994) Drama
99999 3607 5.0 3 One Little Indian (1973) Comedy|Drama|Western
99999 989 5.0 4 Schlafes Bruder (Brother
of Sleep) (1995) ...
Drama
99999 3172 5.0 5 Ulysses (Ulisse) (1954) Adventure
99999 3233 5.0 6 Smashing Time (1967) Comedy
99999 3382 5.0 7 Song of Freedom (1936) Drama
99999 787 5.0 8 Gate of Heavenly Peace,
The (1995) ...
Documentary
99999 3656 5.0 9 Lured (1947) Crime
99999 3280 5.0 10 Baby, The (1973) Horror
[10 rows x 6 columns]

Saving and loading models

In [ ]:
m.save('my_model')
In [ ]:
m_again = graphlab.load_model('my_model')
In [ ]:
m_again
In [ ]: