API: preprocessing

The preprocessing package contains Calculator/Applier nodes and the FeatureEngineer orchestrator.

skyulf.preprocessing

BaseApplier

Bases: ABC

Source code in skyulf-core/skyulf/preprocessing/base.py

class BaseApplier(ABC):
    @abstractmethod
    def apply(self, df: Union[pd.DataFrame, SkyulfDataFrame, tuple], params: Dict[str, Any]) -> Any:
        """
        Applies the transformation using fitted parameters.

        The return type is intentionally `Any` because the concrete shape
        depends on the input: passing a `DataFrame` returns a `DataFrame`;
        passing an `(X, y)` tuple returns a tuple; splitters return
        `SplitDataset`. Encoding every case as a union forces callers to
        defensively narrow on every use, which is worse than `Any` here.
        """

apply(df, params) abstractmethod

Applies the transformation using fitted parameters.

The return type is intentionally Any because the concrete shape depends on the input: passing a DataFrame returns a DataFrame; passing an (X, y) tuple returns a tuple; splitters return SplitDataset. Encoding every case as a union forces callers to defensively narrow on every use, which is worse than Any here.

Source code in skyulf-core/skyulf/preprocessing/base.py

@abstractmethod
def apply(self, df: Union[pd.DataFrame, SkyulfDataFrame, tuple], params: Dict[str, Any]) -> Any:
    """
    Applies the transformation using fitted parameters.

    The return type is intentionally `Any` because the concrete shape
    depends on the input: passing a `DataFrame` returns a `DataFrame`;
    passing an `(X, y)` tuple returns a tuple; splitters return
    `SplitDataset`. Encoding every case as a union forces callers to
    defensively narrow on every use, which is worse than `Any` here.
    """

BaseCalculator

Bases: ABC

Source code in skyulf-core/skyulf/preprocessing/base.py

class BaseCalculator(ABC):
    @abstractmethod
    def fit(
        self, df: Union[pd.DataFrame, SkyulfDataFrame, tuple], config: Dict[str, Any]
    ) -> Mapping[str, Any]:
        """
        Calculates parameters from the training data.
        Returns a Mapping of fitted parameters (typically a TypedDict
        ``*Artifact`` declared in ``preprocessing._artifacts``). The return
        type is ``Mapping`` rather than ``Dict`` so concrete TypedDict
        subclasses are valid LSP-substitutable returns.
        """

    def infer_output_schema(
        self, input_schema: SkyulfSchema, config: Dict[str, Any]
    ) -> Optional[SkyulfSchema]:
        """Best-effort prediction of the output schema from config alone.

        Override this in concrete Calculators when the output columns/dtypes
        can be derived purely from ``input_schema`` and ``config`` (i.e.
        without seeing data). Examples:

        * Scalers — pass through (output == input).
        * Drop columns by name — drop the configured names.
        * One-hot — adds K columns per categorical (K is data-dependent →
          return ``None``).

        Default returns ``None`` to signal "unknown / data-dependent";
        callers should fall back to runtime introspection.
        """
        return None

fit(df, config) abstractmethod

Calculates parameters from the training data. Returns a Mapping of fitted parameters (typically a TypedDict *Artifact declared in preprocessing._artifacts). The return type is Mapping rather than Dict so concrete TypedDict subclasses are valid LSP-substitutable returns.

Source code in skyulf-core/skyulf/preprocessing/base.py

@abstractmethod
def fit(
    self, df: Union[pd.DataFrame, SkyulfDataFrame, tuple], config: Dict[str, Any]
) -> Mapping[str, Any]:
    """
    Calculates parameters from the training data.
    Returns a Mapping of fitted parameters (typically a TypedDict
    ``*Artifact`` declared in ``preprocessing._artifacts``). The return
    type is ``Mapping`` rather than ``Dict`` so concrete TypedDict
    subclasses are valid LSP-substitutable returns.
    """

infer_output_schema(input_schema, config)

Best-effort prediction of the output schema from config alone.

Override this in concrete Calculators when the output columns/dtypes can be derived purely from input_schema and config (i.e. without seeing data). Examples:

• Scalers — pass through (output == input).
• Drop columns by name — drop the configured names.
• One-hot — adds K columns per categorical (K is data-dependent → return None).

Default returns None to signal "unknown / data-dependent"; callers should fall back to runtime introspection.

Source code in skyulf-core/skyulf/preprocessing/base.py

def infer_output_schema(
    self, input_schema: SkyulfSchema, config: Dict[str, Any]
) -> Optional[SkyulfSchema]:
    """Best-effort prediction of the output schema from config alone.

    Override this in concrete Calculators when the output columns/dtypes
    can be derived purely from ``input_schema`` and ``config`` (i.e.
    without seeing data). Examples:

    * Scalers — pass through (output == input).
    * Drop columns by name — drop the configured names.
    * One-hot — adds K columns per categorical (K is data-dependent →
      return ``None``).

    Default returns ``None`` to signal "unknown / data-dependent";
    callers should fall back to runtime introspection.
    """
    return None

CustomBinningCalculator

Bases: BaseCalculator

Apply user-supplied bin edges to selected columns.

Source code in skyulf-core/skyulf/preprocessing/bucketing.py

@NodeRegistry.register("CustomBinning", CustomBinningApplier)
@node_meta(
    id="CustomBinning",
    name="Custom Binning",
    category="Preprocessing",
    description="Bin data using custom edges.",
    params={"bins": [], "columns": []},
)
class CustomBinningCalculator(BaseCalculator):
    """Apply user-supplied bin edges to selected columns."""

    @fit_method
    def fit(self, X: Any, _y: Any, config: Dict[str, Any]) -> GeneralBinningArtifact:
        if user_picked_no_columns(config):
            return cast(GeneralBinningArtifact, {})

        X = _to_pandas_for_fit(X)
        columns = resolve_columns(X, config, detect_numeric_columns)
        bins = config.get("bins")

        bin_edges_map: Dict[str, List[float]] = {}
        if bins:
            sorted_bins = sorted(bins)
            for col in columns:
                if col in X.columns:
                    bin_edges_map[col] = sorted_bins

        artifact: Dict[str, Any] = {
            "type": "general_binning",  # Reuses GeneralBinningApplier.
            "bin_edges": bin_edges_map,
        }
        artifact.update(_passthrough_artifact_options(config))
        return cast(GeneralBinningArtifact, artifact)

FeatureEngineer

Orchestrates a sequence of feature engineering steps.

Source code in skyulf-core/skyulf/preprocessing/pipeline.py

class FeatureEngineer:
    """
    Orchestrates a sequence of feature engineering steps.
    """

    def __init__(
        self,
        steps_config: Sequence[Union[PreprocessingStepConfig, Dict[str, Any]]],
    ):
        # `Sequence` (covariant) accepts list[dict] or list[PreprocessingStepConfig].
        self.steps_config = steps_config
        self.fitted_steps: List[Dict[str, Any]] = []

    def transform(
        self, data: Union[pd.DataFrame, SkyulfDataFrame]
    ) -> Union[pd.DataFrame, SkyulfDataFrame]:
        """
        Apply fitted transformations to new data.
        """
        current_data = data

        for step in self.fitted_steps:
            name = step["name"]
            transformer_type = step["type"]
            applier = step["applier"]
            artifact = step["artifact"]

            # Skip splitters during inference/transform
            if transformer_type in [
                "TrainTestSplitter",
                "feature_target_split",
                "Oversampling",
                "Undersampling",
            ]:
                continue

            logger.debug(f"Applying step: {name} ({transformer_type})")
            current_data = applier.apply(current_data, artifact)

        return current_data

    def fit_transform(
        self, data: Union[pd.DataFrame, SkyulfDataFrame, Any], node_id_prefix=""
    ) -> Any:
        """
        Runs the pipeline on data.
        Returns: (transformed_data, metrics_dict)
        """
        self.fitted_steps = []  # Reset fitted steps
        current_data = data
        metrics: Dict[str, Any] = {}

        for i, step in enumerate(self.steps_config):
            name = step["name"]
            transformer_type = step["transformer"]
            params = step.get("params", {})

            logger.info(f"Running step {i}: {name} ({transformer_type})")
            logger.debug(f"FeatureEngineer running step {i}: {name} ({transformer_type})")
            logger.debug(f"current_data type: {type(current_data)}")

            # Snapshot before for shape-delta + Winsorize value-clipping metrics
            rows_before, cols_before = get_data_stats(current_data)
            data_before = current_data

            calculator, applier = self._get_transformer_components(transformer_type)
            step_node_id = f"{node_id_prefix}_{name}"

            current_data, fitted_params, transformer_inst = self._run_step(
                transformer_type=transformer_type,
                name=name,
                calculator=calculator,
                applier=applier,
                step_node_id=step_node_id,
                current_data=current_data,
                params=params,
            )

            if transformer_inst is not None:
                # Add node-level performance metrics directly into `metrics` dictionary
                metrics["fit_time"] = getattr(transformer_inst, "fit_time", 0.0)
                metrics["peak_memory_bytes"] = getattr(transformer_inst, "peak_memory_bytes", 0)
                metrics["rows_in"] = getattr(transformer_inst, "rows_in", 0)
                metrics["rows_out"] = getattr(transformer_inst, "rows_out", 0)

            logger.debug(f"Step {i} complete. New data type: {type(current_data)}")

            rows_after, cols_after = get_data_stats(current_data)
            self._collect_step_metrics(
                transformer_type=transformer_type,
                fitted_params=fitted_params,
                data_before=data_before,
                current_data=current_data,
                params=params,
                rows_before=rows_before,
                cols_before=cols_before,
                rows_after=rows_after,
                cols_after=cols_after,
                name=name,
                metrics=metrics,
            )

        return current_data, metrics

    # ------------------------------------------------------------------
    # Step execution
    # ------------------------------------------------------------------

    def _run_step(
        self,
        *,
        transformer_type: str,
        name: str,
        calculator: Any,
        applier: Any,
        step_node_id: str,
        current_data: Any,
        params: Dict[str, Any],
    ) -> tuple:  # Returns (data, params, transformer)
        """Execute one pipeline step. Returns (new_data, fitted_params).

        Splitters change the data structure (DataFrame -> SplitDataset / (X, y)),
        so they bypass StatefulTransformer; everything else goes through the
        standard fit_transform wrapper and is appended to fitted_steps.
        """
        transformer = StatefulTransformer(calculator, applier, step_node_id)
        fitted_params: Dict[str, Any] = {}

        if transformer_type == "TrainTestSplitter":
            logger.debug("Handling TrainTestSplitter")
            if isinstance(current_data, (pd.DataFrame, SkyulfDataFrame, tuple)):
                params = calculator.fit(current_data, params)
                current_data = applier.apply(current_data, params)
            else:
                logger.debug(f"Skipping TrainTestSplitter. current_data is {type(current_data)}")
                logger.warning(
                    "Attempting to split an already split dataset. Skipping TrainTestSplitter."
                )
            return current_data, fitted_params, None

        if transformer_type == "feature_target_split":
            logger.debug("Handling feature_target_split")
            params = calculator.fit(current_data, params)
            current_data = applier.apply(current_data, params)
            return current_data, fitted_params, None

        logger.debug("Handling standard transformer via StatefulTransformer")
        current_data = transformer.fit_transform(current_data, params)
        fitted_params = transformer.params
        self.fitted_steps.append(
            {
                "name": name,
                "type": transformer_type,
                "applier": applier,
                "artifact": fitted_params,
            }
        )
        return current_data, fitted_params, transformer

    # ------------------------------------------------------------------
    # Metrics collection
    # ------------------------------------------------------------------

    # Transformer-type groups, kept as class constants so dispatch is data-driven.
    _IMPUTATION_TYPES = {"SimpleImputer", "KNNImputer", "IterativeImputer"}
    _FEATURE_SELECTION_TYPES = {
        "feature_selection",
        "UnivariateSelection",
        "ModelBasedSelection",
        "VarianceThreshold",
    }
    _SCALING_TYPES = {"StandardScaler", "MinMaxScaler", "RobustScaler", "MaxAbsScaler"}
    _OUTLIER_TYPES = {"IQR", "Winsorize", "ZScore", "EllipticEnvelope"}
    _BUCKETING_TYPES = {
        "GeneralBinning",
        "EqualWidthBinning",
        "EqualFrequencyBinning",
        "CustomBinning",
        "KBinsDiscretizer",
    }
    _FEATURE_GEN_TYPES = {"FeatureMath", "FeatureGenerationNode"}
    _ROW_DROP_TYPES = {
        "DropMissingRows",
        "Deduplicate",
        "IQR",
        "ZScore",
        "EllipticEnvelope",
        "Winsorize",
    }
    _ENCODER_TYPES = {
        "OneHotEncoder",
        "LabelEncoder",
        "OrdinalEncoder",
        "TargetEncoder",
        "HashEncoder",
        "DummyEncoder",
    }

    def _collect_step_metrics(
        self,
        *,
        transformer_type: str,
        fitted_params: Dict[str, Any],
        data_before: Any,
        current_data: Any,
        params: Dict[str, Any],
        rows_before: int,
        cols_before: Any,
        rows_after: int,
        cols_after: Any,
        name: str,
        metrics: Dict[str, Any],
    ) -> None:
        """Aggregate per-step metrics into the running metrics dict."""
        try:
            if fitted_params:
                self._metrics_from_fitted_params(
                    transformer_type, fitted_params, data_before, current_data, metrics
                )
        except Exception as e:
            logger.warning(f"Failed to retrieve metrics for step {name}: {e}")

        if transformer_type in {"Oversampling", "Undersampling"}:
            self._metrics_resampling(current_data, params, metrics)

        if rows_after > 0 or cols_after:
            self._metrics_shape_change(
                transformer_type,
                data_before,
                current_data,
                params,
                rows_before,
                cols_before,
                rows_after,
                cols_after,
                metrics,
            )

    def _metrics_from_fitted_params(
        self,
        transformer_type: str,
        fitted_params: Dict[str, Any],
        data_before: Any,
        current_data: Any,
        metrics: Dict[str, Any],
    ) -> None:
        if transformer_type in self._IMPUTATION_TYPES:
            for key in ("missing_counts", "total_missing", "fill_values"):
                if key in fitted_params:
                    metrics[key] = fitted_params[key]

        if transformer_type in self._FEATURE_SELECTION_TYPES:
            for key in (
                "feature_scores",
                "p_values",
                "feature_importances",
                "variances",
                "ranking",
                "selected_columns",
            ):
                if key in fitted_params:
                    metrics[key] = fitted_params[key]

        if transformer_type in self._SCALING_TYPES:
            for key in (
                "mean",
                "scale",
                "var",
                "min",
                "data_min",
                "data_max",
                "center",
                "max_abs",
                "columns",
            ):
                if key in fitted_params:
                    metrics[key] = fitted_params[key]

        if transformer_type in self._OUTLIER_TYPES:
            if "warnings" in fitted_params:
                metrics["warnings"] = fitted_params["warnings"]
        if transformer_type in {"IQR", "Winsorize"} and "bounds" in fitted_params:
            metrics["bounds"] = fitted_params["bounds"]
        if transformer_type == "ZScore" and "stats" in fitted_params:
            metrics["stats"] = fitted_params["stats"]
        if transformer_type == "EllipticEnvelope" and "contamination" in fitted_params:
            metrics["contamination"] = fitted_params["contamination"]

        if transformer_type in self._BUCKETING_TYPES:
            for key in ("bin_edges", "n_bins"):
                if key in fitted_params:
                    metrics[key] = fitted_params[key]

        if transformer_type in self._FEATURE_GEN_TYPES:
            if "operations" in fitted_params:
                metrics["operations_count"] = len(fitted_params["operations"])
                metrics["operations"] = fitted_params["operations"]
            new_cols = self._diff_generated_columns(data_before, current_data)
            if new_cols is not None:
                metrics["generated_features"] = new_cols

    @staticmethod
    def _diff_generated_columns(data_before: Any, current_data: Any):
        """Return the set of newly added columns between two pipeline data objects.

        Handles plain DataFrames, SplitDatasets of DataFrames, and (X, y) tuple variants.
        Returns None if the structures don't allow a meaningful diff.
        """
        if isinstance(data_before, (pd.DataFrame, SkyulfDataFrame)) and isinstance(
            current_data, (pd.DataFrame, SkyulfDataFrame)
        ):
            return list(set(current_data.columns) - set(data_before.columns))

        if isinstance(data_before, SplitDataset) and isinstance(current_data, SplitDataset):
            before_train, after_train = data_before.train, current_data.train
            if isinstance(before_train, (pd.DataFrame, SkyulfDataFrame)) and isinstance(
                after_train, (pd.DataFrame, SkyulfDataFrame)
            ):
                return list(set(after_train.columns) - set(before_train.columns))
            if isinstance(before_train, tuple) and isinstance(after_train, tuple):
                x_before, _ = before_train
                x_after, _ = after_train
                if isinstance(x_before, (pd.DataFrame, SkyulfDataFrame)) and isinstance(
                    x_after, (pd.DataFrame, SkyulfDataFrame)
                ):
                    return list(set(x_after.columns) - set(x_before.columns))
        return None

    @staticmethod
    def _extract_y_for_resampling(current_data: Any, params: Dict[str, Any]):
        """Pull the target Series out of whatever shape the resampler produced."""
        if isinstance(current_data, SplitDataset):
            if isinstance(current_data.train, tuple):
                _, y_res = current_data.train
                return y_res
            if isinstance(current_data.train, (pd.DataFrame, SkyulfDataFrame)):
                target_col = params.get("target_column")
                if target_col and target_col in current_data.train.columns:
                    return current_data.train[target_col]
        elif isinstance(current_data, tuple):
            _, y_res = current_data
            return y_res
        elif isinstance(current_data, (pd.DataFrame, SkyulfDataFrame)):
            target_col = params.get("target_column")
            if target_col and target_col in current_data.columns:
                return current_data[target_col]
        return None

    def _metrics_resampling(
        self, current_data: Any, params: Dict[str, Any], metrics: Dict[str, Any]
    ) -> None:
        try:
            y_res: Any = self._extract_y_for_resampling(current_data, params)
            if y_res is None:
                return
            if hasattr(y_res, "to_pandas"):
                y_res = y_res.to_pandas()
            counts = y_res.value_counts().to_dict()
            metrics["class_counts"] = {str(k): int(v) for k, v in counts.items()}
            metrics["total_samples"] = int(len(y_res))
        except Exception as e:
            logger.warning(f"Failed to calculate resampling metrics: {e}")

    @staticmethod
    def _count_winsorize_diffs(d1: Any, d2: Any) -> int:
        """Count cells that differ between two data objects, for Winsorize clipping metric."""
        d1 = d1.to_pandas() if hasattr(d1, "to_pandas") else d1
        d2 = d2.to_pandas() if hasattr(d2, "to_pandas") else d2

        if isinstance(d1, pd.DataFrame) and isinstance(d2, pd.DataFrame):
            if d1.shape == d2.shape:
                return int(d1.ne(d2).sum().sum())
            return 0

        if isinstance(d1, tuple) and isinstance(d2, tuple) and len(d1) == 2 and len(d2) == 2:
            diffs = 0
            x1 = d1[0].to_pandas() if hasattr(d1[0], "to_pandas") else d1[0]
            x2 = d2[0].to_pandas() if hasattr(d2[0], "to_pandas") else d2[0]
            if (
                isinstance(x1, pd.DataFrame)
                and isinstance(x2, pd.DataFrame)
                and x1.shape == x2.shape
            ):
                diffs += int(x1.ne(x2).sum().sum())
            y1 = d1[1].to_pandas() if hasattr(d1[1], "to_pandas") else d1[1]
            y2 = d2[1].to_pandas() if hasattr(d2[1], "to_pandas") else d2[1]
            if (
                isinstance(y1, (pd.DataFrame, pd.Series))
                and isinstance(y2, (pd.DataFrame, pd.Series))
                and y1.shape == y2.shape
            ):
                diffs += int(y1.ne(y2).sum().sum())
            return diffs
        return 0

    def _metrics_winsorize_clipped(
        self, data_before: Any, current_data: Any, metrics: Dict[str, Any]
    ) -> None:
        try:
            clipped_count = 0
            if isinstance(data_before, (pd.DataFrame, SkyulfDataFrame)) and isinstance(
                current_data, (pd.DataFrame, SkyulfDataFrame)
            ):
                clipped_count = self._count_winsorize_diffs(data_before, current_data)
            elif isinstance(data_before, SplitDataset) and isinstance(current_data, SplitDataset):
                clipped_count += self._count_winsorize_diffs(data_before.train, current_data.train)
                clipped_count += self._count_winsorize_diffs(data_before.test, current_data.test)
                clipped_count += self._count_winsorize_diffs(
                    data_before.validation, current_data.validation
                )
            metrics["values_clipped"] = clipped_count
        except Exception as e:
            logger.warning(f"Failed to calculate values_clipped for Winsorize: {e}")

    def _metrics_shape_change(
        self,
        transformer_type: str,
        data_before: Any,
        current_data: Any,
        params: Dict[str, Any],
        rows_before: int,
        cols_before: Any,
        rows_after: int,
        cols_after: Any,
        metrics: Dict[str, Any],
    ) -> None:
        if transformer_type in self._ROW_DROP_TYPES:
            dropped = rows_before - rows_after
            metrics[f"{transformer_type}_rows_removed"] = dropped
            metrics[f"{transformer_type}_rows_remaining"] = rows_after
            metrics[f"{transformer_type}_rows_total"] = rows_before
            metrics["rows_removed"] = dropped
            metrics["rows_total"] = rows_before
            if transformer_type == "Winsorize":
                self._metrics_winsorize_clipped(data_before, current_data, metrics)

        if transformer_type == "MissingIndicator":
            new_cols_set = cols_after - cols_before
            metrics["missing_indicators_created"] = len(new_cols_set)
            metrics["missing_indicators_columns"] = list(new_cols_set)

        if transformer_type in {"DropMissingColumns", "feature_selection"}:
            dropped_cols_set = cols_before - cols_after
            metrics["dropped_columns"] = list(dropped_cols_set)
            metrics["dropped_columns_count"] = len(dropped_cols_set)

        if transformer_type in self._ENCODER_TYPES:
            new_cols_set = cols_after - cols_before
            metrics["new_features_count"] = len(new_cols_set)
            metrics["encoded_columns_count"] = len(params.get("columns", []))
            if "categories_count" in params:
                metrics["categories_count"] = params["categories_count"]
            if "classes_count" in params:
                metrics["classes_count"] = params["classes_count"]

    def _get_transformer_components(self, type_name: str):
        try:
            return (
                NodeRegistry.get_calculator(type_name)(),
                NodeRegistry.get_applier(type_name)(),
            )
        except ValueError:
            raise ValueError(f"Unknown transformer type: {type_name}")

fit_transform(data, node_id_prefix='')

Runs the pipeline on data. Returns: (transformed_data, metrics_dict)

Source code in skyulf-core/skyulf/preprocessing/pipeline.py

def fit_transform(
    self, data: Union[pd.DataFrame, SkyulfDataFrame, Any], node_id_prefix=""
) -> Any:
    """
    Runs the pipeline on data.
    Returns: (transformed_data, metrics_dict)
    """
    self.fitted_steps = []  # Reset fitted steps
    current_data = data
    metrics: Dict[str, Any] = {}

    for i, step in enumerate(self.steps_config):
        name = step["name"]
        transformer_type = step["transformer"]
        params = step.get("params", {})

        logger.info(f"Running step {i}: {name} ({transformer_type})")
        logger.debug(f"FeatureEngineer running step {i}: {name} ({transformer_type})")
        logger.debug(f"current_data type: {type(current_data)}")

        # Snapshot before for shape-delta + Winsorize value-clipping metrics
        rows_before, cols_before = get_data_stats(current_data)
        data_before = current_data

        calculator, applier = self._get_transformer_components(transformer_type)
        step_node_id = f"{node_id_prefix}_{name}"

        current_data, fitted_params, transformer_inst = self._run_step(
            transformer_type=transformer_type,
            name=name,
            calculator=calculator,
            applier=applier,
            step_node_id=step_node_id,
            current_data=current_data,
            params=params,
        )

        if transformer_inst is not None:
            # Add node-level performance metrics directly into `metrics` dictionary
            metrics["fit_time"] = getattr(transformer_inst, "fit_time", 0.0)
            metrics["peak_memory_bytes"] = getattr(transformer_inst, "peak_memory_bytes", 0)
            metrics["rows_in"] = getattr(transformer_inst, "rows_in", 0)
            metrics["rows_out"] = getattr(transformer_inst, "rows_out", 0)

        logger.debug(f"Step {i} complete. New data type: {type(current_data)}")

        rows_after, cols_after = get_data_stats(current_data)
        self._collect_step_metrics(
            transformer_type=transformer_type,
            fitted_params=fitted_params,
            data_before=data_before,
            current_data=current_data,
            params=params,
            rows_before=rows_before,
            cols_before=cols_before,
            rows_after=rows_after,
            cols_after=cols_after,
            name=name,
            metrics=metrics,
        )

    return current_data, metrics

transform(data)

Apply fitted transformations to new data.

Source code in skyulf-core/skyulf/preprocessing/pipeline.py

def transform(
    self, data: Union[pd.DataFrame, SkyulfDataFrame]
) -> Union[pd.DataFrame, SkyulfDataFrame]:
    """
    Apply fitted transformations to new data.
    """
    current_data = data

    for step in self.fitted_steps:
        name = step["name"]
        transformer_type = step["type"]
        applier = step["applier"]
        artifact = step["artifact"]

        # Skip splitters during inference/transform
        if transformer_type in [
            "TrainTestSplitter",
            "feature_target_split",
            "Oversampling",
            "Undersampling",
        ]:
            continue

        logger.debug(f"Applying step: {name} ({transformer_type})")
        current_data = applier.apply(current_data, artifact)

    return current_data

GeneralBinningCalculator

Bases: BaseCalculator

Master calculator that handles mixed strategies and per-column overrides.

Source code in skyulf-core/skyulf/preprocessing/bucketing.py

@NodeRegistry.register("GeneralBinning", GeneralBinningApplier)
@node_meta(
    id="GeneralBinning",
    name="General Binning",
    category="Preprocessing",
    description="Bin continuous data into intervals.",
    params={"n_bins": 5, "strategy": "uniform", "columns": []},
)
class GeneralBinningCalculator(BaseCalculator):
    """Master calculator that handles mixed strategies and per-column overrides."""

    @fit_method
    def fit(self, X: Any, _y: Any, config: Dict[str, Any]) -> GeneralBinningArtifact:
        if user_picked_no_columns(config):
            return cast(GeneralBinningArtifact, {})

        X = _to_pandas_for_fit(X)
        columns = resolve_columns(X, config, detect_numeric_columns)

        defaults = {
            "default_n_bins": config.get("n_bins", 5),
            "n_bins": config.get("equal_width_bins", config.get("n_bins", 5)),
            "q_bins": config.get("equal_frequency_bins", config.get("n_bins", 5)),
            "duplicates": config.get("duplicates", "drop"),
        }

        valid_cols = [c for c in columns if c in X.columns]
        bin_edges_map: Dict[str, List[float]] = {}
        custom_labels_map: Dict[str, Any] = {}

        for col in valid_cols:
            _fit_one_column_into_maps(X, col, config, defaults, bin_edges_map, custom_labels_map)

        artifact: Dict[str, Any] = {
            "type": "general_binning",
            "bin_edges": bin_edges_map,
            "custom_labels": custom_labels_map,
        }
        artifact.update(_passthrough_artifact_options(config))
        return cast(GeneralBinningArtifact, artifact)

KBinsDiscretizerCalculator

Bases: GeneralBinningCalculator

Thin wrapper around :class:GeneralBinningCalculator with kbins strategy.

Source code in skyulf-core/skyulf/preprocessing/bucketing.py

@NodeRegistry.register("KBinsDiscretizer", KBinsDiscretizerApplier)
@node_meta(
    id="KBinsDiscretizer",
    name="K-Bins Discretizer",
    category="Preprocessing",
    description="Bin continuous data into intervals using sklearn KBinsDiscretizer.",
    params={"n_bins": 5, "encode": "ordinal", "strategy": "quantile", "columns": []},
)
class KBinsDiscretizerCalculator(GeneralBinningCalculator):
    """Thin wrapper around :class:`GeneralBinningCalculator` with ``kbins`` strategy."""

    def fit(
        self,
        df: Union[pd.DataFrame, SkyulfDataFrame, Tuple[Any, ...], Any],
        config: Dict[str, Any],
    ) -> GeneralBinningArtifact:
        new_config = config.copy()
        new_config["strategy"] = "kbins"
        if "n_bins" in config:
            new_config["kbins_n_bins"] = config["n_bins"]
        if "strategy" in config and config["strategy"] != "kbins":
            new_config["kbins_strategy"] = config["strategy"]
        return super().fit(df, new_config)

SkyulfSchema dataclass

Immutable schema description.

Attributes:

Name	Type	Description
columns	Tuple[str, ...]	Ordered list of column names.
dtypes	Dict[str, str]	Mapping of column name → string dtype label (engine-agnostic; e.g. "int64", "float64", "string", "category", "datetime", "bool", or "unknown"). A column may be present in columns but absent from dtypes when its type is unknown.

Source code in skyulf-core/skyulf/preprocessing/_schema.py

@dataclass(frozen=True)
class SkyulfSchema:
    """Immutable schema description.

    Attributes:
        columns: Ordered list of column names.
        dtypes: Mapping of column name → string dtype label
            (engine-agnostic; e.g. ``"int64"``, ``"float64"``, ``"string"``,
            ``"category"``, ``"datetime"``, ``"bool"``, or ``"unknown"``).
            A column may be present in ``columns`` but absent from
            ``dtypes`` when its type is unknown.
    """

    columns: Tuple[str, ...]
    dtypes: Dict[str, str] = field(default_factory=dict)

    # ---- Constructors -----------------------------------------------------

    @classmethod
    def from_columns(
        cls, columns: Iterable[str], dtypes: Optional[Dict[str, str]] = None
    ) -> "SkyulfSchema":
        cols = tuple(columns)
        return cls(columns=cols, dtypes=dict(dtypes or {}))

    @classmethod
    def from_dataframe(cls, df: Any) -> "SkyulfSchema":
        """Best-effort schema extraction from a Pandas/Polars/Wrapper frame."""
        raw_cols = getattr(df, "columns", None)
        cols = list(raw_cols) if raw_cols is not None else []
        dtypes = _extract_pandas_dtypes(df)
        if not dtypes:
            dtypes = _extract_polars_dtypes(df)
        return cls(columns=tuple(cols), dtypes=dtypes)

    # ---- Mutations (return new instances) ---------------------------------

    def drop(self, names: Iterable[str]) -> "SkyulfSchema":
        drop_set = set(names)
        new_cols = tuple(c for c in self.columns if c not in drop_set)
        new_dtypes = {k: v for k, v in self.dtypes.items() if k not in drop_set}
        return replace(self, columns=new_cols, dtypes=new_dtypes)

    def add(self, name: str, dtype: str = "unknown") -> "SkyulfSchema":
        if name in self.columns:
            return self
        new_dtypes = dict(self.dtypes)
        new_dtypes[name] = dtype
        return replace(self, columns=self.columns + (name,), dtypes=new_dtypes)

    def rename(self, mapping: Dict[str, str]) -> "SkyulfSchema":
        new_cols = tuple(mapping.get(c, c) for c in self.columns)
        new_dtypes: Dict[str, str] = {}
        for k, v in self.dtypes.items():
            new_dtypes[mapping.get(k, k)] = v
        return replace(self, columns=new_cols, dtypes=new_dtypes)

    def with_dtype(self, name: str, dtype: str) -> "SkyulfSchema":
        if name not in self.columns:
            return self
        new_dtypes = dict(self.dtypes)
        new_dtypes[name] = dtype
        return replace(self, dtypes=new_dtypes)

    # ---- Queries ----------------------------------------------------------

    def has(self, name: str) -> bool:
        return name in self.columns

    def column_list(self) -> List[str]:
        return list(self.columns)

    def __contains__(self, item: object) -> bool:
        return item in self.columns

    def __len__(self) -> int:
        return len(self.columns)

from_dataframe(df) classmethod

Best-effort schema extraction from a Pandas/Polars/Wrapper frame.

Source code in skyulf-core/skyulf/preprocessing/_schema.py

@classmethod
def from_dataframe(cls, df: Any) -> "SkyulfSchema":
    """Best-effort schema extraction from a Pandas/Polars/Wrapper frame."""
    raw_cols = getattr(df, "columns", None)
    cols = list(raw_cols) if raw_cols is not None else []
    dtypes = _extract_pandas_dtypes(df)
    if not dtypes:
        dtypes = _extract_polars_dtypes(df)
    return cls(columns=tuple(cols), dtypes=dtypes)