Pipeline

Submodules

kale.pipeline.base_nn_trainer module

kale.pipeline.deepdta module

kale.pipeline.domain_adapter module

kale.pipeline.drugban_trainer module

kale.pipeline.mpca_trainer module

Implementation of MPCA->Feature Selection->Linear SVM/LogisticRegression Pipeline

References

[1] Swift, A. J., Lu, H., Uthoff, J., Garg, P., Cogliano, M., Taylor, J., … & Kiely, D. G. (2020). A machine learning cardiac magnetic resonance approach to extract disease features and automate pulmonary arterial hypertension diagnosis. European Heart Journal-Cardiovascular Imaging. [2] Song, X., Meng, L., Shi, Q., & Lu, H. (2015, October). Learning tensor-based features for whole-brain fMRI classification. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 613-620). Springer, Cham. [3] Lu, H., Plataniotis, K. N., & Venetsanopoulos, A. N. (2008). MPCA: Multilinear principal component analysis of tensor objects. IEEE Transactions on Neural Networks, 19(1), 18-39.

class kale.pipeline.mpca_trainer.MPCATrainer(classifier='svc', classifier_params='auto', classifier_param_grid=None, mpca_params=None, n_features=None, search_params=None)

Bases: BaseEstimator, ClassifierMixin

Trainer of pipeline: MPCA->Feature selection->Classifier

Parameters:

  • classifier (str, optional) – Available classifier options: {“svc”, “linear_svc”, “lr”}, where “svc” trains a support vector classifier, supports both linear and non-linear kernels, optimizes with library “libsvm”; “linear_svc” trains a support vector classifier with linear kernel only, and optimizes with library “liblinear”, which suppose to be faster and better in handling large number of samples; and “lr” trains a classifier with logistic regression. Defaults to “svc”.

  • classifier_params (dict, optional) – Parameters of classifier. Defaults to ‘auto’.

  • classifier_param_grid (dict, optional) – Grids for searching the optimal hyper-parameters. Works only when classifier_params == “auto”. Defaults to None by searching from the following hyper-parameter values: 1. svc, {“kernel”: [“linear”], “C”: [0.0001, 0.001, 0.01, 0.1, 1, 10, 100], “max_iter”: [50000]}, 2. linear_svc, {“C”: [0.0001, 0.001, 0.01, 0.1, 1, 10, 100]}, 3. lr, {“C”: [0.0001, 0.001, 0.01, 0.1, 1, 10, 100]}

  • mpca_params (dict, optional) – Parameters of MPCA, e.g., {“var_ratio”: 0.8}. Defaults to None, i.e., using the default parameters (https://pykale.readthedocs.io/en/latest/kale.embed.html#module-kale.embed.mpca).

  • n_features (int, optional) – Number of features for feature selection. Defaults to None, i.e., all features after dimension reduction will be used.

  • search_params (dict, optional) – Parameters of grid search, for more detail please see https://scikit-learn.org/stable/modules/grid_search.html#grid-search . Defaults to None, i.e., using the default params: {“cv”: 5}.

fit(x, y)

Fit a pipeline with the given data x and labels y

Parameters:

  • x (array-like tensor) – input data, shape (n_samples, I_1, I_2, …, I_N)

  • y (array-like) – data labels, shape (n_samples, )

Returns:

self

set_decision_function_request(*, x: bool | None | str = '$UNCHANGED$') → MPCATrainer

Configure whether metadata should be requested to be passed to the decision_function method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to decision_function if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to decision_function.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Parameters:

x (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for x parameter in decision_function.

Returns:

self – The updated object.

Return type:

object

set_fit_request(*, x: bool | None | str = '$UNCHANGED$') → MPCATrainer

Configure whether metadata should be requested to be passed to the fit method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to fit.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Parameters:

x (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for x parameter in fit.

Returns:

self – The updated object.

Return type:

object

set_predict_proba_request(*, x: bool | None | str = '$UNCHANGED$') → MPCATrainer

Configure whether metadata should be requested to be passed to the predict_proba method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to predict_proba if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to predict_proba.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Parameters:

x (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for x parameter in predict_proba.

Returns:

self – The updated object.

Return type:

object

set_predict_request(*, x: bool | None | str = '$UNCHANGED$') → MPCATrainer

Configure whether metadata should be requested to be passed to the predict method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to predict if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to predict.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Parameters:

x (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for x parameter in predict.

Returns:

self – The updated object.

Return type:

object

set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → MPCATrainer

Configure whether metadata should be requested to be passed to the score method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to score.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Parameters:

sample_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_weight parameter in score.

Returns:

self – The updated object.

Return type:

object

predict(x)

Predict the labels for the given data x

Parameters:

x (array-like tensor) – input data, shape (n_samples, I_1, I_2, …, I_N)

Returns:

Predicted labels, shape (n_samples, )

Return type:

array-like

decision_function(x)

Decision scores of each class for the given data x

Parameters:

x (array-like tensor) – input data, shape (n_samples, I_1, I_2, …, I_N)

Returns:

decision scores, shape (n_samples,) for binary case, else (n_samples, n_class)

Return type:

array-like

predict_proba(x)

Probability of each class for the given data x. Not supported by “linear_svc”.

Parameters:

x (array-like tensor) – input data, shape (n_samples, I_1, I_2, …, I_N)

Returns:

probabilities, shape (n_samples, n_class)

Return type:

array-like

kale.pipeline.multimodal_trainer module

kale.pipeline.multi_domain_adapter module

kale.pipeline.multiomics_trainer module

kale.pipeline.video_domain_adapter module

kale.pipeline.fewshot_trainer module

Module contents