pymfe.landmarking.MFELandmarking

class pymfe.landmarking.MFELandmarking[source]

Keep methods for metafeatures of landmarking group.

The convention adopted for metafeature extraction related methods is to always start with ft_ prefix to allow automatic method detection. This prefix is predefined within _internal module.

All method signature follows the conventions and restrictions listed below:

For independent attribute data, X means every type of attribute, N means Numeric attributes only and C stands for Categorical attributes only. It is important to note that the categorical attribute sets between X and C and the numerical attribute sets between X and N may differ due to data transformations, performed while fitting data into MFE model, enabled by, respectively, transform_num and transform_cat arguments from fit (MFE method).
Only arguments in MFE _custom_args_ft attribute (set up inside fit method) are allowed to be required method arguments. All other arguments must be strictly optional (i.e., has a predefined default value).
The initial assumption is that the user can change any optional argument, without any previous verification of argument value or its type, via kwargs argument of extract method of MFE class.
The return value of all feature extraction methods should be a single value or a generic List (preferably a np.ndarray) type with numeric values.

There is another type of method adopted for automatic detection. It is adopted the prefix precompute_ for automatic detection of these methods. These methods run while fitting some data into an MFE model automatically, and their objective is to precompute some common value shared between more than one feature extraction method. This strategy is a trade-off between more system memory consumption and speeds up of feature extraction. Their return value must always be a dictionary whose keys are possible extra arguments for both feature extraction methods and other precomputation methods. Note that there is a share of precomputed values between all valid feature-extraction modules (e.g., class_freqs computed in module statistical can freely be used for any precomputation or feature extraction method of module landmarking).

__init__(*args, **kwargs)

Methods

`__init__`(args, *kwargs)
`ft_best_node`(N, y, score[, skf, ...])	Performance of a the best single decision tree node.
`ft_elite_nn`(N, y, score[, skf, ...])	Performance of Elite Nearest Neighbor.
`ft_linear_discr`(N, y, score[, skf, ...])	Performance of the Linear Discriminant classifier.
`ft_naive_bayes`(N, y, score[, skf, ...])	Performance of the Naive Bayes classifier.
`ft_one_nn`(N, y, score[, skf, num_cv_folds, ...])	Performance of the 1-Nearest Neighbor classifier.
`ft_random_node`(N, y, score[, skf, ...])	Performance of the single decision tree node model induced by a random attribute.
`ft_worst_node`(N, y, score[, skf, ...])	Performance of the single decision tree node model induced by the worst informative attribute.
`precompute_landmarking_kfolds`(N[, y, ...])	Precompute k-fold cross validation related values.
`precompute_landmarking_sample`(N, lm_sample_frac)	Precompute subsampling landmarking subsample indices.

classmethod ft_best_node(N: ndarray, y: ndarray, score: Callable[[ndarray, ndarray], ndarray], skf: Optional[StratifiedKFold] = None, num_cv_folds: int = 10, shuffle_cv_folds: bool = False, lm_sample_frac: float = 1.0, sample_inds: Optional[ndarray] = None, random_state: Optional[int] = None) → ndarray[source]

Performance of a the best single decision tree node.

Construct a single decision tree node model induced by the most informative attribute to establish the linear separability.

Parameters

Nnp.ndarray: Numerical fitted data.
ynp.ndarray: Target attribute.
scorecallable: Function to compute score of the K-fold evaluations. Possible functions are described in scoring.py module.
skfsklearn.model_selection.StratifiedKFold, optional: Stratified K-Folds cross-validator. Provides train/test indices to split data in train/test sets.
num_cv_foldsint, optional: Number of folds to k-fold cross validation. Used only if skf is None.
shuffle_cv_foldsbool, optional: If True, shuffle the data before splitting into the k-fold cross validation folds. The random seed used for this process is the random_state argument.
lm_sample_fracfloat, optional: Proportion of instances to be sampled before extracting the metafeature. Used only if sample_inds is None.
sample_indsnp.ndarray, optional: Array of indices of instances to be effectively used while extracting this metafeature. If None, then lm_sample_frac is taken into account. Argument used to exploit precomputations.
random_stateint, optional: If given, set the random seed before any pseudo-random calculations to keep the experiments reproducible.

Returns

np.ndarray: The Decision Tree best-node model performance of each fold.

References

1: Hilan Bensusan and Christophe Giraud-Carrier. Discovering task neighbourhoods through landmark learning performances. In 4th European Conference on Principles of Data Mining and Knowledge Discovery (PKDD), pages 325 – 330, 2000.
2: Johannes Furnkranz and Johann Petrak. An evaluation of landmarking variants. In 1st ECML/PKDD International Workshop on Integration and Collaboration Aspects of Data Mining, Decision Support and Meta-Learning (IDDM), pages 57 – 68, 2001.

classmethod ft_elite_nn(N: ndarray, y: ndarray, score: Callable[[ndarray, ndarray], ndarray], skf: Optional[StratifiedKFold] = None, num_cv_folds: int = 10, shuffle_cv_folds: bool = False, lm_sample_frac: float = 1.0, sample_inds: Optional[ndarray] = None, random_state: Optional[int] = None, cv_folds_imp_rank: Optional[ndarray] = None) → ndarray[source]

Performance of Elite Nearest Neighbor.

Elite nearest neighbor uses the most informative attribute in the dataset to induce the 1-nearest neighbor.

With the subset of informative attributes it is expected that the models should be noise tolerant.

Parameters

Nnp.ndarray: Numerical fitted data.
ynp.ndarray: Target attribute.
scorecallable: Function to compute score of the K-fold evaluations. Possible functions are described in scoring.py module.
skfsklearn.model_selection.StratifiedKFold, optional: Stratified K-Folds cross-validator. Provides train/test indices to split data in train/test sets.
num_cv_foldsint, optional: Number of folds to k-fold cross validation. Used only if skf is None.
shuffle_cv_foldsbool, optional: If True, shuffle the data before splitting into the k-fold cross validation folds. The random seed used for this process is the random_state argument.
lm_sample_fracfloat, optional: Proportion of instances to be sampled before extracting the metafeature. Used only if sample_inds is None.
sample_indsnp.ndarray, optional: Array of indices of instances to be effectively used while extracting this metafeature. If None, then lm_sample_frac is taken into account. Argument used to exploit precomputations.
random_stateint, optional: If given, set the random seed before any pseudo-random calculations to keep the experiments reproducible.
cv_folds_imp_ranknp.ndarray, optional: Ranking based on the predictive attribute importance per cross-validation fold. The rows correspond to each fold, and the columns correspond to each predictive attribute. Argument used to take advantage of precomputations. Do not use it if the k-fold cross validation splitter shuffles the data with no random seed fixed.

Returns

np.ndarray: The Elite 1-NN model performance of each fold.

References

1: Hilan Bensusan and Christophe Giraud-Carrier. Discovering task neighbourhoods through landmark learning performances. In 4th European Conference on Principles of Data Mining and Knowledge Discovery (PKDD), pages 325 – 330, 2000.

classmethod ft_linear_discr(N: ndarray, y: ndarray, score: Callable[[ndarray, ndarray], ndarray], skf: Optional[StratifiedKFold] = None, num_cv_folds: int = 10, shuffle_cv_folds: bool = False, lm_sample_frac: float = 1.0, sample_inds: Optional[ndarray] = None, random_state: Optional[int] = None) → ndarray[source]

Performance of the Linear Discriminant classifier.

The Linear Discriminant Classifier is used to construct a linear split (non parallel axis) in the data to establish the linear separability.

Parameters

Nnp.ndarray: Numerical fitted data.
ynp.ndarray: Target attribute.
scorecallable: Function to compute score of the K-fold evaluations. Possible functions are described in scoring.py module.
skfsklearn.model_selection.StratifiedKFold, optional: Stratified K-Folds cross-validator. Provides train/test indices to split data in train/test sets.
num_cv_foldsint, optional: Number of num_cv_folds to k-fold cross validation. Used only if skf is None.
shuffle_cv_foldsbool, optional: If True, shuffle the data before splitting into the k-fold cross validation folds. The random seed used for this process is the random_state argument.
lm_sample_fracfloat, optional: Proportion of instances to be sampled before extracting the metafeature. Used only if sample_inds is None.
sample_indsnp.ndarray, optional: Array of indices of instances to be effectively used while extracting this metafeature. If None, then lm_sample_frac is taken into account. Argument used to exploit precomputations.
random_stateint, optional: If given, set the random seed before any pseudo-random calculations to keep the experiments reproducible.

Returns

np.ndarray: The Linear Discriminant Analysis model performance of each fold.

References

1: Hilan Bensusan and Christophe Giraud-Carrier. Discovering task neighbourhoods through landmark learning performances. In 4th European Conference on Principles of Data Mining and Knowledge Discovery (PKDD), pages 325 – 330, 2000.
2: Johannes Furnkranz and Johann Petrak. An evaluation of landmarking variants. In 1st ECML/PKDD International Workshop on Integration and Collaboration Aspects of Data Mining, Decision Support and Meta-Learning (IDDM), pages 57 – 68, 2001.

classmethod ft_naive_bayes(N: ndarray, y: ndarray, score: Callable[[ndarray, ndarray], ndarray], skf: Optional[StratifiedKFold] = None, num_cv_folds: int = 10, shuffle_cv_folds: bool = False, lm_sample_frac: float = 1.0, sample_inds: Optional[ndarray] = None, random_state: Optional[int] = None) → ndarray[source]

Performance of the Naive Bayes classifier.

It assumes that the attributes are independent and each example belongs to a certain class based on the Bayes probability.

Parameters

Nnp.ndarray: Numerical fitted data.
ynp.ndarray: Target attribute.
scorecallable: Function to compute score of the K-fold evaluations. Possible functions are described in scoring.py module.
skfsklearn.model_selection.StratifiedKFold, optional: Stratified K-Folds cross-validator. Provides train/test indices to split data in train/test sets.
num_cv_foldsint, optional: Number of num_cv_folds to k-fold cross validation. Used only if skf is None.
shuffle_cv_foldsbool, optional: If True, shuffle the data before splitting into the k-fold cross validation folds. The random seed used for this process is the random_state argument.
lm_sample_fracfloat, optional: Proportion of instances to be sampled before extracting the metafeature. Used only if sample_inds is None.
sample_indsnp.ndarray, optional: Array of indices of instances to be effectively used while extracting this metafeature. If None, then lm_sample_frac is taken into account. Argument used to exploit precomputations.
random_stateint, optional: If given, set the random seed before any pseudo-random calculations to keep the experiments reproducible.

Returns

np.ndarray: The Naive Bayes model performance of each fold.

References

1: Hilan Bensusan and Christophe Giraud-Carrier. Discovering task neighbourhoods through landmark learning performances. In 4th European Conference on Principles of Data Mining and Knowledge Discovery (PKDD), pages 325 – 330, 2000.
2: Johannes Furnkranz and Johann Petrak. An evaluation of landmarking variants. In 1st ECML/PKDD International Workshop on Integration and Collaboration Aspects of Data Mining, Decision Support and Meta-Learning (IDDM), pages 57 – 68, 2001.

classmethod ft_one_nn(N: ndarray, y: ndarray, score: Callable[[ndarray, ndarray], ndarray], skf: Optional[StratifiedKFold] = None, num_cv_folds: int = 10, shuffle_cv_folds: bool = False, lm_sample_frac: float = 1.0, sample_inds: Optional[ndarray] = None, random_state: Optional[int] = None) → ndarray[source]

Performance of the 1-Nearest Neighbor classifier.

It uses the euclidean distance of the nearest neighbor to determine how noisy is the data.

Parameters

Nnp.ndarray: Numerical fitted data.
ynp.ndarray: Target attribute.
scorecallable: Function to compute score of the K-fold evaluations. Possible functions are described in scoring.py module.
skfsklearn.model_selection.StratifiedKFold, optional: Stratified K-Folds cross-validator. Provides train/test indices to split data in train/test sets.
num_cv_foldsint, optional: Number of num_cv_folds to k-fold cross validation. Used only if skf is None.
shuffle_cv_foldsbool, optional: If True, shuffle the data before splitting into the k-fold cross validation folds. The random seed used for this process is the random_state argument.
lm_sample_fracfloat, optional: Proportion of instances to be sampled before extracting the metafeature. Used only if sample_inds is None.
sample_indsnp.ndarray, optional: Array of indices of instances to be effectively used while extracting this metafeature. If None, then lm_sample_frac is taken into account. Argument used to exploit precomputations.
random_stateint, optional: If given, set the random seed before any pseudo-random calculations to keep the experiments reproducible.

Returns

np.ndarray: The 1-NN model performance of each fold.

References

1: Hilan Bensusan and Christophe Giraud-Carrier. Discovering task neighbourhoods through landmark learning performances. In 4th European Conference on Principles of Data Mining and Knowledge Discovery (PKDD), pages 325 – 330, 2000.

classmethod ft_random_node(N: ndarray, y: ndarray, score: Callable[[ndarray, ndarray], ndarray], skf: Optional[StratifiedKFold] = None, num_cv_folds: int = 10, shuffle_cv_folds: bool = False, lm_sample_frac: float = 1.0, sample_inds: Optional[ndarray] = None, random_state: Optional[int] = None) → ndarray[source]

Performance of the single decision tree node model induced by a random attribute.

Parameters

Nnp.ndarray: Numerical fitted data.
ynp.ndarray: Target attribute.
scorecallable: Function to compute score of the K-fold evaluations. Possible functions are described in scoring.py module.
skfsklearn.model_selection.StratifiedKFold, optional: Stratified K-Folds cross-validator. Provides train/test indices to split data in train/test sets.
num_cv_foldsint, optional: Number of folds to k-fold cross validation. Used only if skf is None.
shuffle_cv_foldsbool, optional: If True, shuffle the data before splitting into the k-fold cross validation folds. The random seed used for this process is the random_state argument.
lm_sample_fracfloat, optional: Proportion of instances to be sampled before extracting the metafeature. Used only if sample_inds is None.
sample_indsnp.ndarray, optional: Array of indices of instances to be effectively used while extracting this metafeature. If None, then lm_sample_frac is taken into account. Argument used to exploit precomputations.
random_stateint, optional: If given, set the random seed before any pseudo-random calculations to keep the experiments reproducible.

Returns

np.ndarray: The Decision Tree random-node model performance of each fold.

References

1: Hilan Bensusan and Christophe Giraud-Carrier. Discovering task neighbourhoods through landmark learning performances. In 4th European Conference on Principles of Data Mining and Knowledge Discovery (PKDD), pages 325 – 330, 2000.
2: Johannes Furnkranz and Johann Petrak. An evaluation of landmarking variants. In 1st ECML/PKDD International Workshop on Integration and Collaboration Aspects of Data Mining, Decision Support and Meta-Learning (IDDM), pages 57 – 68, 2001.

classmethod ft_worst_node(N: ndarray, y: ndarray, score: Callable[[ndarray, ndarray], ndarray], skf: Optional[StratifiedKFold] = None, num_cv_folds: int = 10, shuffle_cv_folds: bool = False, lm_sample_frac: float = 1.0, sample_inds: Optional[ndarray] = None, random_state: Optional[int] = None, cv_folds_imp_rank: Optional[ndarray] = None) → ndarray[source]

Performance of the single decision tree node model induced by the worst informative attribute.

Parameters

Nnp.ndarray: Numerical fitted data.
ynp.ndarray: Target attribute.
scorecallable: Function to compute score of the K-fold evaluations. Possible functions are described in scoring.py module.
skfsklearn.model_selection.StratifiedKFold, optional: Stratified K-Folds cross-validator. Provides train/test indices to split data in train/test sets.
num_cv_foldsint, optional: Number of folds to k-fold cross validation. Used only if skf is None.
shuffle_cv_foldsbool, optional: If True, shuffle the data before splitting into the k-fold cross validation folds. The random seed used for this process is the random_state argument.
lm_sample_fracfloat, optional: Proportion of instances to be sampled before extracting the metafeature. Used only if sample_inds is None.
sample_indsnp.ndarray, optional: Array of indices of instances to be effectively used while extracting this metafeature. If None, then lm_sample_frac is taken into account. Argument used to exploit precomputations.
random_stateint, optional: If given, set the random seed before any pseudo-random calculations to keep the experiments reproducible.
cv_folds_imp_ranknp.ndarray, optional: Ranking based on the predictive attribute importance per cross-validation fold. The rows correspond to each fold, and the columns correspond to each predictive attribute. Argument used to take advantage of precomputations. Do not use it if the k-fold cross validation splitter shuffles the data with no random seed fixed.

Returns

np.ndarray: The Decision Tree worst-node model performance of each fold.

References

1: Hilan Bensusan and Christophe Giraud-Carrier. Discovering task neighbourhoods through landmark learning performances. In 4th European Conference on Principles of Data Mining and Knowledge Discovery (PKDD), pages 325 – 330, 2000.
2: Johannes Furnkranz and Johann Petrak. An evaluation of landmarking variants. In 1st ECML/PKDD International Workshop on Integration and Collaboration Aspects of Data Mining, Decision Support and Meta-Learning (IDDM), pages 57 – 68, 2001.

classmethod precompute_landmarking_kfolds(N: ndarray, y: Optional[ndarray] = None, num_cv_folds: int = 10, shuffle_cv_folds: Optional[bool] = False, random_state: Optional[int] = None, lm_sample_frac: float = 1.0, **kwargs) → Dict[str, Any][source]

Precompute k-fold cross validation related values.

Parameters

Nnp.ndarray: Numerical fitted data.
ynp.ndarray, optional: Target attribute.
num_cv_foldsint, optional: Number of folds to k-fold cross validation.
shuffle_cv_foldsbool, optional: If True, shuffle the samples before splitting the k-fold cross validation.
random_stateint, optional: If given, set the random seed before any pseudo-random calculations to keep the experiments reproducible.
lm_sample_fracfloat, optional: The percentage of examples subsampled. Value different from default will generate the subsampling-based relative landmarking metafeatures. Used only if sample_inds is not precomputed and if shuffle_cv_folds is False or random_state is given.
kwargs:: Additional arguments. May have previously precomputed before this method from other precomputed methods, so they can help speed up this precomputation.

Returns

dict

With following precomputed items:

skf (sklearn.model_selection.StratifiedKFold): Stratified K-Folds cross-validator. Provides train/test indices to split data in train/test sets.

classmethod precompute_landmarking_sample(N: ndarray, lm_sample_frac: float, random_state: Optional[int] = None, **kwargs) → Dict[str, Any][source]

Precompute subsampling landmarking subsample indices.

Parameters

Nnp.ndarray: Numerical fitted data.
lm_sample_fracfloat: The percentage of examples subsampled. Value different from default will generate the subsampling-based relative landmarking metafeatures.
random_stateint, optional: If given, set the random seed before any pseudo-random calculations to keep the experiments reproducible.

Returns

dict

With following precomputed items:

sample_inds (np.ndarray): indices related to the subsampling of the original dataset. Used only if the subsampling landmarking method is used and, therefore, this value is only precomputed if lm_sample_frac is less than 1.0.