In [1]:

    

import graphlab as gl
ratings = gl.SFrame.read_csv("../../data/netflix/netflix_mm.train", delimiter="\t", header=False,
                             column_type_hints=[int,int,int]);
ratings.rename({'X1':'movieid', 'X2':'userid', 'X3':'rating'});


    

    




[INFO] Start server at: ipc:///tmp/graphlab_server-25324 - Server binary: /usr/local/lib/python2.7/dist-packages/graphlab/unity_server - Server log: /tmp/graphlab_server_1431068549.log
[INFO] GraphLab Server Version: 1.3.0






    





PROGRESS: Read 4082259 lines. Lines per second: 4.76371e+06






    





PROGRESS: Read 56182627 lines. Lines per second: 9.38805e+06






    





PROGRESS: Finished parsing file /code/BIDMach/data/netflix/netflix_mm.train






    





PROGRESS: Parsing completed. Parsed 99072124 lines in 10.0376 secs.

In [2]:

    

ratings.head()


    

    
Out[2]:





    

        
movieid
        
userid
        
rating
    
    

        
1
        
1
        
3
    
    

        
8
        
1
        
4
    
    

        
17
        
1
        
2
    
    

        
30
        
1
        
3
    
    

        
44
        
1
        
3
    
    

        
58
        
1
        
5
    
    

        
76
        
1
        
3
    
    

        
80
        
1
        
3
    
    

        
81
        
1
        
3
    
    

        
83
        
1
        
3
    

[10 rows x 3 columns]

In [3]:

    

ratings['userid'].max()


    

    
Out[3]:





480189

In [4]:

    

training, testing = ratings.random_split(0.9)


    

In [5]:

    

#training, testing = gl.recommender.random_split_by_user(ratings, 'userid', 'movieid', max_num_users=1000, item_test_proportion=0.3)
#print training.num_rows(), testing.num_rows()


    

In [6]:

    

m = gl.recommender.factorization_recommender.create(training, 'userid', 'movieid', 'rating',
                                                    num_factors=512, max_iterations=4, solver='sgd')


    

    





PROGRESS: Recsys training: model = factorization_recommender






    





PROGRESS: Preparing data set.






    





PROGRESS:     Data has 89163900 observations with 479957 users and 17770 items.






    





PROGRESS:     Data prepared in: 34.5961s






    





PROGRESS: Training factorization_recommender for recommendations.






    





PROGRESS: +--------------------------------+--------------------------------------------------+----------+






    





PROGRESS: | Parameter                      | Description                                      | Value    |






    





PROGRESS: +--------------------------------+--------------------------------------------------+----------+






    





PROGRESS: | num_factors                    | Factor Dimension                                 | 512      |






    





PROGRESS: | regularization                 | L2 Regularization on Factors                     | 1e-08    |






    





PROGRESS: | solver                         | Solver used for training                         | sgd      |






    





PROGRESS: | linear_regularization          | L2 Regularization on Linear Coefficients         | 1e-10    |






    





PROGRESS: | max_iterations                 | Maximum Number of Iterations                     | 4        |






    





PROGRESS: +--------------------------------+--------------------------------------------------+----------+






    





PROGRESS:   Optimizing model using SGD; tuning step size.






    





PROGRESS:   Using 11145487 / 89163900 points for tuning the step size.






    





PROGRESS: +---------+-------------------+------------------------------------------+






    





PROGRESS: | Attempt | Initial Step Size | Estimated Objective Value                |






    





PROGRESS: +---------+-------------------+------------------------------------------+






    





PROGRESS: | 0       | 25                | Not Viable                               |






    





PROGRESS: | 1       | 6.25              | Not Viable                               |






    





PROGRESS: | 2       | 1.5625            | Not Viable                               |






    





PROGRESS: | 3       | 0.390625          | Not Viable                               |






    





PROGRESS: | 4       | 0.0976562         | Not Viable                               |






    





PROGRESS: | 5       | 0.0244141         | 0.708316                                 |






    





PROGRESS: | 6       | 0.012207          | 0.832388                                 |






    





PROGRESS: | 7       | 0.00610352        | 0.8552                                   |






    





PROGRESS: | 8       | 0.00305176        | 0.884658                                 |






    





PROGRESS: +---------+-------------------+------------------------------------------+






    





PROGRESS: | Final   | 0.0244141         | 0.708316                                 |






    





PROGRESS: +---------+-------------------+------------------------------------------+






    





PROGRESS: Starting Optimization.






    





PROGRESS: +---------+--------------+-------------------+-----------------------+-------------+






    





PROGRESS: | Iter.   | Elapsed Time | Approx. Objective | Approx. Training RMSE | Step Size   |






    





PROGRESS: +---------+--------------+-------------------+-----------------------+-------------+






    





PROGRESS: | Initial | 100us        | 1.17631           | 1.08458               |             |






    





PROGRESS: +---------+--------------+-------------------+-----------------------+-------------+






    





PROGRESS: | 1       | 33.88s       | 0.799822          | 0.891014              | 0.0244141   |






    





PROGRESS: | 2       | 1m 8s        | 0.647667          | 0.7989                | 0.0145167   |






    





PROGRESS: | 3       | 1m 42s       | 0.455859          | 0.665612              | 0.0107102   |






    





PROGRESS: | 4       | 2m 16s       | 0.30782           | 0.54154               | 0.00863167  |






    





PROGRESS: +---------+--------------+-------------------+-----------------------+-------------+






    





PROGRESS: Optimization Complete: Maximum number of passes through the data reached.






    





PROGRESS: Computing final objective value and training RMSE.






    





PROGRESS:        Final objective value: 0.196364






    





PROGRESS:        Final training RMSE: 0.426391

In [18]:

    

opts=m.get_current_options()
opts["regularization"]=1e-4


    

In [20]:

    

opts=m.get_current_options()
opts["regularization"]


    

    
Out[20]:





1e-08

In [19]:

    

# Look at model statistics
m


    

    
Out[19]:





Class                           : FactorizationRecommender

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

Statistics
----------
Number of observations          : 89163900
Number of users                 : 479957
Number of items                 : 17770

Training summary
----------------
Training time                   : 319.24

Settings
--------
additional_iterations_if_unhealthy: 5
sgd_step_adjustment_interval    : 4
num_factors                     : 512
init_random_sigma               : 0.01
max_iterations                  : 4
regularization_type             : normal
side_data_factorization         : 1
regularization                  : 0.0
num_tempering_iterations        : 4
sgd_step_size                   : 0.0
sgd_trial_sample_proportion     : 0.125
sgd_sampling_block_size         : 131072
binary_target                   : 0
nmf                             : 0
track_exact_loss                : 0
sgd_trial_sample_minimum_size   : 10000
sgd_convergence_interval        : 4
solver                          : sgd
tempering_regularization_start_value: 0.0
sgd_convergence_threshold       : 0.0
sgd_max_trial_iterations        : 5
step_size_decrease_rate         : 0.75
linear_regularization           : 0.0

In [8]:

    

# Evaluate rmse (average prediction error) on the test set
m.evaluate(testing)


    

    





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Precision and recall summary statistics by cutoff
+--------+-----------------+------------------+
| cutoff |  mean_precision |   mean_recall    |
+--------+-----------------+------------------+
|   5    | 0.0313541887406 | 0.00944680990716 |
|   10   | 0.0272597036802 | 0.0157296344449  |
|   15   | 0.0249383402792 | 0.0210675314864  |
+--------+-----------------+------------------+
[3 rows x 3 columns]


Overall RMSE:  0.888163413734

Per User RMSE (best)
+--------+-------+------------------+
| userid | count |       rmse       |
+--------+-------+------------------+
| 318568 |   1   | 2.4070233037e-05 |
+--------+-------+------------------+
[1 rows x 3 columns]


Per User RMSE (worst)
+--------+-------+---------------+
| userid | count |      rmse     |
+--------+-------+---------------+
| 419474 |   1   | 4.52736785004 |
+--------+-------+---------------+
[1 rows x 3 columns]


Per Item RMSE (best)
+---------+-------+-----------------+
| movieid | count |       rmse      |
+---------+-------+-----------------+
|   915   |   1   | 0.0340154122413 |
+---------+-------+-----------------+
[1 rows x 3 columns]


Per Item RMSE (worst)
+---------+-------+---------------+
| movieid | count |      rmse     |
+---------+-------+---------------+
|   2564  |   10  | 1.91045491865 |
+---------+-------+---------------+
[1 rows x 3 columns]







    
Out[8]:





{'precision_recall_by_user': Columns:
	userid	int
	cutoff	int
	precision	float
	recall	float
	count	int

Rows: 1359072

Data:
+--------+--------+----------------+-----------------+-------+
| userid | cutoff |   precision    |      recall     | count |
+--------+--------+----------------+-----------------+-------+
|   1    |   5    |      0.8       | 0.0170212765957 |  235  |
|   1    |   10   |      0.5       | 0.0212765957447 |  235  |
|   1    |   15   | 0.466666666667 | 0.0297872340426 |  235  |
|   2    |   5    |      0.0       |       0.0       |   7   |
|   2    |   10   |      0.0       |       0.0       |   7   |
|   2    |   15   |      0.0       |       0.0       |   7   |
|   3    |   5    |      0.0       |       0.0       |   37  |
|   3    |   10   |      0.0       |       0.0       |   37  |
|   3    |   15   |      0.0       |       0.0       |   37  |
|   4    |   5    |      0.0       |       0.0       |   5   |
|  ...   |  ...   |      ...       |       ...       |  ...  |
+--------+--------+----------------+-----------------+-------+
[1359072 rows x 5 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.,
 'precision_recall_overall': Columns:
	cutoff	int
	precision	float
	recall	float

Rows: 3

Data:
+--------+-----------------+------------------+
| cutoff |    precision    |      recall      |
+--------+-----------------+------------------+
|   5    | 0.0313541887406 | 0.00944680990716 |
|   10   | 0.0272597036802 | 0.0157296344449  |
|   15   | 0.0249383402792 | 0.0210675314864  |
+--------+-----------------+------------------+
[3 rows x 3 columns]
,
 'rmse_by_item': Columns:
	movieid	int
	count	int
	rmse	float

Rows: 17761

Data:
+---------+-------+----------------+
| movieid | count |      rmse      |
+---------+-------+----------------+
|   7899  |  1442 | 0.881257028411 |
|   5288  |   21  | 0.929706843965 |
|   5684  |   14  | 0.71641731463  |
|  14515  |   91  | 0.903952079789 |
|  10273  |   92  | 0.795182531985 |
|  11106  |   23  | 0.917332634974 |
|   5531  |  196  | 0.801935640501 |
|   5921  |   14  | 1.51357167965  |
|  14406  |   24  | 0.909400696357 |
|   2871  |   32  | 1.01237323009  |
|   ...   |  ...  |      ...       |
+---------+-------+----------------+
[17761 rows x 3 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.,
 'rmse_by_user': Columns:
	userid	int
	count	int
	rmse	float

Rows: 453024

Data:
+--------+-------+----------------+
| userid | count |      rmse      |
+--------+-------+----------------+
| 211023 |   3   | 0.864035010146 |
| 442699 |   18  | 0.521743281534 |
| 79732  |   11  | 1.21808991455  |
| 333842 |   65  | 0.74295639386  |
|  7899  |   37  | 0.86514529054  |
| 25263  |   2   | 0.585114795906 |
| 453182 |   12  | 0.466558152072 |
| 87629  |   26  | 0.561209620976 |
| 459067 |   34  | 0.700004654829 |
| 43116  |   28  | 1.27584138448  |
|  ...   |  ...  |      ...       |
+--------+-------+----------------+
[453024 rows x 3 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.,
 'rmse_overall': 0.8881634137340938}

In [12]:

    

opts=m.get_current_options()


    

In [13]:

    

opts


    

    
Out[13]:





{'additional_iterations_if_unhealthy': 5,
 'binary_target': 0,
 'init_random_sigma': 0.01,
 'item_id': 'movieid',
 'linear_regularization': 1e-10,
 'max_iterations': 4,
 'nmf': 0,
 'num_factors': 512,
 'num_tempering_iterations': 4,
 'random_seed': 0,
 'regularization': 1e-08,
 'regularization_type': 'normal',
 'sgd_convergence_interval': 4,
 'sgd_convergence_threshold': 1e-05,
 'sgd_max_trial_iterations': 5,
 'sgd_sampling_block_size': 131072,
 'sgd_step_adjustment_interval': 4,
 'sgd_step_size': 0.0,
 'sgd_trial_sample_minimum_size': 10000,
 'sgd_trial_sample_proportion': 0.125,
 'side_data_factorization': 1,
 'solver': 'sgd',
 'step_size_decrease_rate': 0.75,
 'target': 'rating',
 'tempering_regularization_start_value': 1e-08,
 'track_exact_loss': 0,
 'user_id': 'userid'}

In [17]:

    

opts["regularization"]


    

    
Out[17]:





1e-05

In [ ]: