python-2.7 - 为什么在 Turi Create 中没有完成“Recommender 的evaluate() 方法”？

Question

我正在尝试测试 Turi Create Project。

我的 jupyter notebook 浏览器屏幕和 Python 代码如下。

In [1]: import turicreate as tc
In [2]: data = tc.SFrame("data.csv")
In [3]: train, test = data.random_split(0.8)
In [4]: train = train.dropna()
In [5]: test = test.dropna()
In [6]: model = tc.recommender.ranking_factorization_recommender.create(train)
In [*]: eval = model.evaluate(test)

为什么在 Turi Create 中没有完成“Recommender 的evaluate() 方法”？

据我所知，“In[*]”表示该行正在 jupyter notebook 中运行。

第6行的结果如下。

Recsys training: model = ranking_factorization_recommender
Preparing data set.
    Data has 31 observations with 1 users and 27 items.
    Data prepared in: 0.034568s
Training ranking_factorization_recommender for recommendations.
+--------------------------------+--------------------------------------------------+----------+
| Parameter                      | Description                                      | Value    |
+--------------------------------+--------------------------------------------------+----------+
| num_factors                    | Factor Dimension                                 | 32       |
| regularization                 | L2 Regularization on Factors                     | 1e-09    |
| solver                         | Solver used for training                         | adagrad  |
| linear_regularization          | L2 Regularization on Linear Coefficients         | 1e-09    |
| binary_target                  | Assume Binary Targets                            | True     |
| max_iterations                 | Maximum Number of Iterations                     | 25       |
+--------------------------------+--------------------------------------------------+----------+
  Optimizing model using SGD; tuning step size.
  Using 31 / 31 points for tuning the step size.
+---------+-------------------+------------------------------------------+
| Attempt | Initial Step Size | Estimated Objective Value                |
+---------+-------------------+------------------------------------------+
| 0       | 1.16279           | Not Viable                               | 
| 1       | 0.290698          | 0.0198458                                |
| 2       | 0.145349          | 0.109241                                 |
+---------+-------------------+------------------------------------------+
| Final   | 0.290698          | 0.0198458                                |
+---------+-------------------+------------------------------------------+
Starting Optimization.
+---------+--------------+-------------------+-----------------------------------+-------------+
| Iter.   | Elapsed Time | Approx. Objective | Approx. Training Predictive Error | Step Size   |
+---------+--------------+-------------------+-----------------------------------+-------------+
| Initial | 149us        | 0.69303           | 0.69303                           |             |
+---------+--------------+-------------------+-----------------------------------+-------------+
| 1       | 2.987ms      | 4.36077           | 4.36077                           | 0.290698    |
| 2       | 6.12ms       | 0.397951          | 0.397949                          | 0.290698    |
| 3       | 7.599ms      | 0.0258218         | 0.0258208                         | 0.290698    |
| 4       | 9.803ms      | 0.0188815         | 0.0188808                         | 0.290698    |
| 5       | 11.194ms     | 0.0227515         | 0.0227511                         | 0.290698    |
| 10      | 26.505ms     | 1.16849           | 1.16849                           | 0.290698    |
| 12      | 30.94ms      | DIVERGED          | DIVERGED                          | 0.290698    |
| RESET   | 34.911ms     | 0.693064          | 0.693064                          |             |
| 18      | 75.108ms     | 0.0136463         | 0.013646                          | 0.145349    |
+---------+--------------+-------------------+-----------------------------------+-------------+
Optimization Complete: Maximum number of passes through the data reached (hard limit).
Computing final objective value and training Predictive Error.
    Final objective value: 0.0118796
    Final training Predictive Error: 0.0118793

感谢您的阅读。

标签： python-2.7machine-learningrecommender-systemsturi-create