iaml.actionables.features_preprocessing.act_feature_agglomeration¶
[STEP] Decompose features with FeatureAgglomeration
Classes¶
[STEP] Apply FeatureAgglomeration for dimensionality reduction |
Module Contents¶
- class iaml.actionables.features_preprocessing.act_feature_agglomeration.ActFeatureAgglomeration¶
Bases:
iaml.actionable.Actionable[STEP] Apply FeatureAgglomeration for dimensionality reduction
- name: str = 'FeatureAgglomeration'¶
Step’s name.
- configuration: dict¶
Configuration of this step (equivalent to hyperparameters for models).
- optimizable: bool = True¶
Whether the configuration’s parameters should be considered optimizable.
- preprocessor: sklearn.cluster.FeatureAgglomeration | None = None¶
- fit(dataset: iaml.dataset.Dataset) iaml.actionable.Actionable¶
Fits the step on the given dataset.
- Parameters:
dataset (Dataset) – Features and labels.
- Returns:
Fitted step.
- transform(X: pandas.DataFrame) pandas.DataFrame¶
Apply FeatureAgglomeration
- Parameters:
X (pd.DataFrame) – DataFrame to transform
- Returns:
Transformed dataset
- suitable(dataset: iaml.dataset.Dataset) bool¶
Evaluates whether the step is suitable to run with the given dataset.
- Parameters:
dataset (Dataset) – Dataset to evaluate on.
- Returns:
Whether the step is suitable to run.
- priorize(candidate: iaml.candidate.Candidate = None) float¶
Evaluates the priority of the step within a pipeline. 0 means the execution of the step should not be prioritized, and 1 means it should be executed early in the pipeline.
- Parameters:
candidate (Candidate, optional) – Candidate on which the priority should be evaluated.
- Returns:
Value between 0 and 1.