RelFMTabularPredictor

relationalai.semantics.reasoners.predictive

RelFMTabularPredictor(
    *,
    database: str,
    schema: str,
    context: b.Concept,
    label_column: str,
    task_type: str,
    validation: Optional[b.Concept] = None,
    eval_metric: Optional[str] = None,
    test_batch_size: Optional[int] = None,
    stream_logs: bool = True,
    dataset_alias: Optional[str] = None,
    device: Literal["cpu", "cuda"] = "cpu",
    n_estimators: int = 8,
    random_state: Optional[int] = 42,
    sample_size: Optional[int] = None,
    sampling_strategy: str = "stratified",
    clamp_min: Optional[int] = 0,
    clamp_max: Optional[int] = 100,
    regression_output: Literal["point", "distribution"] = "point",
    quantile_levels: Optional[List[float]] = None,
    norm_methods: Optional[Union[str, List[str]]] = None
)

Classification/regression flat-table RelFM — see RelFMTabular.

Parameters

• database

  (str) - Snowflake database to save predictions in.

• schema

  (str) - Snowflake schema to save predictions in.

• context

  (Concept) - Labeled split, used directly as in-context examples (RelFM has no training step). Must be identified by a single natural-key property and carry label_column among its own properties.

• label_column

  (str) - Name of the label property on context/validation.

• task_type

  (str) - One of "binary_classification", "multiclass_classification", or "regression".

• validation

  (Concept, default: None) - Optional labeled validation split. Does not need to be the same Concept as context — only needs the same feature columns.

• eval_metric

  (str, default: None) - Evaluation metric compatible with the chosen task_type.

• test_batch_size

  (int | None, default: None)

• stream_logs

  (int | None, default: None)

• dataset_alias

  (int | None, default: None)

• device

  (int | None, default: None)

• n_estimators

  (int | None, default: None)

• random_state

  (int | None, default: 42) - See RelFMTabular.

• sample_size

  (int | None, default: 42) - See RelFMTabular.

• sampling_strategy

  (int | None, default: 42) - See RelFMTabular.

• quantile_levels

  (int | None, default: 42) - See RelFMTabular.

• norm_methods

  (int | None, default: 42) - See RelFMTabular.

• clamp_min

  (int, default: 0) - Min percentile clamp for regression predictions. Default is 0.

• clamp_max

  (int, default: 100) - Max percentile clamp for regression predictions. Default is 100.

• regression_output

  (str, default: “point”) - "point" (default) or "distribution".

Examples

relfm_tabular = RelFMTabularPredictor(
    database="MYDB",
    schema="MYSCHEMA",
    context=Customer,
    label_column="lifetime_value",
    task_type="regression",
    eval_metric="rmse",
)
Customer.predictions = relfm_tabular.predictions(domain=TestCustomer)

Methods

.predictions()

RelFMTabularPredictor.predictions(domain: b.Concept) -> b.Relationship

Generate predictions for every row of an unlabeled domain Concept.

Parameters:

• domain

  (Concept) - Unlabeled Concept to predict for — identified by a single natural-key property, carrying the same feature columns as context (no label_column).

Returns:

• Relationship - A prediction relationship: {domain} -> prediction, to be assigned to a field for downstream querying.

Inheritance Hierarchy