This is the implementation of the LeafInfluence algorithm from the following paper: https://arxiv.org/pdf/1802.06640.pdf Parameters ---------- pool : Pool The pool for which you want to evaluate the object importances. train_pool : Pool
(
self, pool, train_pool, top_size=-1, type='Average', update_method='SinglePoint',
importance_values_sign='All', thread_count=-1, verbose=False, ostr_type=None,
log_cout=None, log_cerr=None)
| 3601 | return np.array(result) |
| 3602 | |
| 3603 | def get_object_importance( |
| 3604 | self, pool, train_pool, top_size=-1, type='Average', update_method='SinglePoint', |
| 3605 | importance_values_sign='All', thread_count=-1, verbose=False, ostr_type=None, |
| 3606 | log_cout=None, log_cerr=None): |
| 3607 | """ |
| 3608 | This is the implementation of the LeafInfluence algorithm from the following paper: |
| 3609 | https://arxiv.org/pdf/1802.06640.pdf |
| 3610 | |
| 3611 | Parameters |
| 3612 | ---------- |
| 3613 | pool : Pool |
| 3614 | The pool for which you want to evaluate the object importances. |
| 3615 | |
| 3616 | train_pool : Pool |
| 3617 | The pool on which the model has been trained. |
| 3618 | |
| 3619 | top_size : int (default=-1) |
| 3620 | Method returns the result of the top_size most important train objects. |
| 3621 | If -1, then the top size is not limited. |
| 3622 | |
| 3623 | type : string, optional (default='Average') |
| 3624 | Possible values: |
| 3625 | - Average (Method returns the mean train objects scores for all input objects) |
| 3626 | - PerObject (Method returns the train objects scores for every input object) |
| 3627 | |
| 3628 | importance_values_sign : string, optional (default='All') |
| 3629 | Method returns only Positive, Negative or All values. |
| 3630 | Possible values: |
| 3631 | - Positive |
| 3632 | - Negative |
| 3633 | - All |
| 3634 | |
| 3635 | update_method : string, optional (default='SinglePoint') |
| 3636 | Possible values: |
| 3637 | - SinglePoint |
| 3638 | - TopKLeaves (It is posible to set top size : TopKLeaves:top=2) |
| 3639 | - AllPoints |
| 3640 | Description of the update set methods are given in section 3.1.3 of the paper. |
| 3641 | |
| 3642 | thread_count : int, optional (default=-1) |
| 3643 | Number of threads. |
| 3644 | If -1, then the number of threads is set to the number of CPU cores. |
| 3645 | |
| 3646 | verbose : bool or int |
| 3647 | If False, then evaluation is not logged. If True, then each possible iteration is logged. |
| 3648 | If a positive integer, then it stands for the size of batch N. After processing each batch, print progress |
| 3649 | and remaining time. |
| 3650 | |
| 3651 | ostr_type : string, deprecated, use type instead |
| 3652 | |
| 3653 | log_cout: output stream or callback for logging (default=None) |
| 3654 | If None is specified, sys.stdout is used |
| 3655 | |
| 3656 | log_cerr: error stream or callback for logging (default=None) |
| 3657 | If None is specified, sys.stderr is used |
| 3658 | |
| 3659 | Returns |
| 3660 | ------- |