HDBSCANClustering

Model

DashAI.back.models.scikit_learn.HDBSCANClustering

---

HDBSCAN clustering model for the Models module.

HDBSCAN is a hierarchical density-based unsupervised algorithm. It builds a minimum spanning tree over core distances, converts it to a cluster hierarchy, and selects the most persistent clusters. Noise samples are labelled -1. Unlike DBSCAN, it supports clusters of varying density without requiring a global eps parameter.

Key hyperparameters include min_cluster_size (smallest group accepted), min_samples (core-point threshold), and metric (distance function). Requires scikit-learn >= 1.3. The implementation wraps scikit-learn's HDBSCAN estimator.

References

• [1] Campello, R. J. G. B., Moulavi, D., & Sander, J. (2013). "Density-Based Clustering Based on Hierarchical Density Estimates."
• [2] https://scikit-learn.org/stable/modules/generated/ sklearn.cluster.HDBSCAN.html

Parameters

min_cluster_size : integer, default=5

Minimum number of samples required to form a cluster.

min_samples : integer, default=5

Number of samples in a neighbourhood for a point to be considered a core point.

metric : string, default=euclidean

Distance metric used to compute core distances.

Methods

get_fit_attributes(self) -> dict

Defined on HDBSCANClustering

Return HDBSCAN post-fit attributes for the converter report.

calculate_metrics(self, split: DashAI.back.core.enums.metrics.SplitEnum = <SplitEnum.VALIDATION: 'validation'>, level: DashAI.back.core.enums.metrics.LevelEnum = <LevelEnum.LAST: 'last'>, log_index: int = None, x_data: 'DashAIDataset' = None, y_data: 'DashAIDataset' = None, fold_index: int = None, inner_fold_index: int = None)

Defined on BaseModel

Calculate metrics for a data split and save them to the database.

Parameters

split : SplitEnum

The data split to evaluate (TRAIN, VALIDATION, or TEST). Defaults to SplitEnum.VALIDATION.

level : LevelEnum

The metric granularity level (LAST, TRIAL, STEP, or BATCH). Defaults to LevelEnum.LAST.

log_index : int, optional

Explicit step index for the metric entry. If None, the next step index is computed automatically. Defaults to None.

x_data : DashAIDataset, optional

Input features. If None, the dataset stored in the model for the given split is used. Defaults to None.

y_data : DashAIDataset, optional

Target labels. If None, the labels stored in the model for the given split are used. Defaults to None.

Returns

Dict[str, float] or None

What was written, so a caller that also wants the numbers does not have to score the same split twice. None when nothing was written: no run to write against, or nothing to score.

compute_metrics(self, split: DashAI.back.core.enums.metrics.SplitEnum = <SplitEnum.VALIDATION: 'validation'>, x_data: 'DashAIDataset' = None, y_data: 'DashAIDataset' = None) -> Optional[Dict[str, float]]

Defined on BaseModel

Score a data split with this model's metrics, without persisting.

Parameters

split : SplitEnum

The data split to evaluate. Defaults to SplitEnum.VALIDATION.

x_data : DashAIDataset, optional

Input features. Defaults to the split stored on the model.

y_data : DashAIDataset, optional

Target labels. Defaults to the split stored on the model.

Returns

Dict[str, float] or None

Metric name to score. A metric that scored a non-finite value is absent from the mapping rather than present with a NaN, so the mapping is empty when every metric returned one. None when there is nothing to score at all: no metrics configured, or no data for this split. Callers that need a particular metric must therefore check that it is there.

get_cluster_labels(self, x: 'DashAIDataset | None' = None) -> 'ndarray'

Defined on SklearnLikeClusterer

Return fitted labels or assign labels to new samples when supported.

Parameters

x : DashAIDataset, optional

Samples to assign to clusters. If omitted, or if the wrapped estimator does not support predict, the labels discovered during fitting are returned.

Returns

numpy.ndarray

Cluster label assigned to each sample.

get_credential(self, name: str)

Defined on ConfigObject

Resolve a registered credential component by name.

Parameters

name : str

Credential component class name (e.g. "HuggingFaceCredential").

Returns

BaseCredential

An instance of the requested credential component.

get_metadata(cls) -> Dict[str, Any]

Defined on BaseModel

Get metadata values for the current model.

Returns

Dict[str, Any]

Dictionary containing UI metadata such as the model icon used in the DashAI frontend.

get_registry(cls) -> Dict[str, Type[ForwardRef('ClusteringModel')]]

Defined on ClusteringModel

Return concrete clustering algorithm classes keyed by class name.

get_schema(cls) -> dict

Defined on ConfigObject

Generates the component related Json Schema.

Returns

dict

Dictionary representing the Json Schema of the component.

load(filename: str) -> Any

Defined on SklearnLikeClusterer

Deserialise a model from disk using joblib.

Parameters

filename : str

Path to the file previously written by :meth:save.

Returns

Any

The loaded model instance.

predict_prepared(self, features: Any) -> Any

Defined on BaseModel

Predict from data that is already in this model's feature space.

Parameters

features : pandas.DataFrame or numpy.ndarray

Feature matrix as returned by prepare_dataset(..., is_fit=False). No further preparation is applied to it.

Returns

Any

The same kind of output as predict: predicted values for regressors, class probabilities for DashAI classifiers.

predict_proba_prepared(self, features: Any) -> Any

Defined on BaseModel

Return class probabilities for data already in the feature space.

Parameters

features : pandas.DataFrame or numpy.ndarray

Feature matrix as returned by prepare_dataset(..., is_fit=False).

Returns

numpy.ndarray

Array of shape (n_samples, n_classes) with class probabilities.

prepare_dataset(self, dataset: 'DashAIDataset', is_fit: bool = False) -> 'DashAIDataset'

Defined on BaseModel

Hook for model specific preprocessing of input features.

Parameters

dataset : DashAIDataset

The input dataset to preprocess.

is_fit : bool

Whether the call is part of a fitting phase. Defaults to False.

Returns

DashAIDataset

The preprocessed dataset ready to be fed into the model.

prepare_output(self, dataset: 'DashAIDataset', is_fit: bool = False) -> 'DashAIDataset'

Defined on BaseModel

Hook for model-specific preprocessing of output targets.

Parameters

dataset : DashAIDataset

The output dataset (target labels) to preprocess.

is_fit : bool

Whether the call is part of a fitting phase. Defaults to False.

Returns

DashAIDataset

The preprocessed output dataset.

save(self, filename: str) -> None

Defined on SklearnLikeClusterer

Serialise the model to disk using joblib.

Parameters

filename : str

Destination file path where the model will be written.

train(self, x_train: 'DashAIDataset') -> 'SklearnLikeClusterer'

Defined on SklearnLikeClusterer

Fit the clustering model using input features only.

Parameters

x_train : DashAIDataset

Dataset containing the input columns selected for clustering.

Returns

SklearnLikeClusterer

The fitted clustering adapter.

validate_and_transform(self, raw_data: dict) -> dict

Defined on ConfigObject

It takes the data given by the user to initialize the model and returns it with all the objects that the model needs to work.

Parameters

raw_data : dict

A dictionary with the data provided by the user to initialize the model.

Returns

dict

A validated dictionary with the necessary objects.

Compatible with

• ClusteringTask