hfs.LazyHierarchicalFeatureSelector¶
- class hfs.LazyHierarchicalFeatureSelector(hierarchy: Optional[ndarray] = None)[source]¶
Abstract class used for all lazy hierarchical feature selection methods.
Every method should implement the method select_and_predict.
- __init__(hierarchy: Optional[ndarray] = None)[source]¶
Initialize a LazyHierarchicalFeatureSelector with the required data.
- Parameters
- hierarchynp.ndarray
The hierarchy graph as an adjacency matrix.
- fit(X, y=None)[source]¶
Implementing the fit function for Sklearn compatibility.
- Parameters
- X{array-like, sparse matrix}, shape (n_samples, n_features)
The training input samples.
- yarray-like, shape (n_samples,)
The target values. An array of int.
- Returns
- selfobject
Fitted estimator.
- fit_selector(X_train, y_train, X_test, columns=None)[source]¶
Fit LazyHierarchicalFeatureSelector class.
Due to laziness fitting of parameters as well as predictions are obtained per instance.
- Parameters
- X_train{numpy array} of shape (n_samples, n_features)
The training input samples.
- X_test{numpy array} of shape (n_samples, n_features)
The test input samples. converted into a sparse
csc_matrix.- y_trainarray-like of shape (n_samples, n_levels)
The target values, i.e., hierarchical class labels for classification.
- get_features()[source]¶
Get selected features.
- Parameters
- None
- Returns
- featuresnumpy array
Boolean value at index states if feature is selected.
- get_score(ytest, predictions)[source]¶
Returns score of the predictions.
Note that recall of the positive class is known as “sensitivity”; recall of the negative class is “specificity”
- Parameters
- ytest1d array-like, or label indicator array / sparse matrix
truth values of y.
- predictions1d array-like, or label indicator array / sparse matrix
obtained predictions.
- Returns
- reportdict
metrics of prediction.
- select_and_predict(predict=True, saveFeatures=False, estimator=BernoulliNB())[source]¶
Select features lazy for each test instance amd optionally predict target value of test instances.
To be implemented by children.
- Parameters
- predict{bool}
true if predictions shall be obtained.
- saveFeatures{bool}
true if features selected for each test instance shall be saved.
- estimatorsklearn-compatible estimator.
Estimator to use for predictions.
- Returns
- predictions for test input samples
if predict = false, returns empty array