split stubs file

skore/src/skore/sklearn/_estimator/__init__.pyi

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+from skore.sklearn._estimator.report import EstimatorReport
+
+__all__ = ["EstimatorReport"]

skore/src/skore/sklearn/_estimator/base.pyi

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+from typing import Any, Literal, Optional
+
+import numpy as np
+from rich.panel import Panel
+from rich.tree import Tree
+
+class _HelpMixin:
+    def _get_methods_for_help(self) -> list[tuple[str, Any]]: ...
+    def _sort_methods_for_help(
+        self, methods: list[tuple[str, Any]]
+    ) -> list[tuple[str, Any]]: ...
+    def _format_method_name(self, name: str) -> str: ...
+    def _get_method_description(self, method: Any) -> str: ...
+    def _create_help_panel(self) -> Panel: ...
+    def _get_help_panel_title(self) -> str: ...
+    def _create_help_tree(self) -> Tree: ...
+    def help(self) -> None: ...
+    def __repr__(self) -> str: ...
+
+class _BaseAccessor(_HelpMixin):
+    _parent: Any
+    _icon: str
+
+    def __init__(self, parent: Any, icon: str) -> None: ...
+    def _get_help_panel_title(self) -> str: ...
+    def _create_help_tree(self) -> Tree: ...
+    def _get_X_y_and_data_source_hash(
+        self,
+        *,
+        data_source: Literal["test", "train", "X_y"],
+        X: Optional[np.ndarray] = None,
+        y: Optional[np.ndarray] = None,
+    ) -> tuple[Optional[np.ndarray], Optional[np.ndarray], Optional[str]]: ...

skore/src/skore/sklearn/_estimator/metrics_accessor.pyi

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+from typing import Any, Callable, Literal, Optional, Union
+
+import matplotlib.axes
+import numpy as np
+import pandas as pd
+from sklearn.metrics import PrecisionRecallDisplay, RocCurveDisplay
+
+from skore.sklearn._estimator.base import _BaseAccessor
+from skore.sklearn._plot import PredictionErrorDisplay
+
+class _PlotMetricsAccessor(_BaseAccessor):
+    _metrics_parent: _MetricsAccessor
+
+    def __init__(self, parent: _MetricsAccessor) -> None: ...
+    def _get_display(
+        self,
+        *,
+        X: Optional[np.ndarray],
+        y: Optional[np.ndarray],
+        data_source: Literal["test", "train", "X_y"],
+        response_method: Union[str, list[str]],
+        display_class: Any,
+        display_kwargs: dict[str, Any],
+        display_plot_kwargs: dict[str, Any],
+    ) -> Union[RocCurveDisplay, PrecisionRecallDisplay, PredictionErrorDisplay]: ...
+    def roc(
+        self,
+        *,
+        data_source: Literal["test", "train", "X_y"] = "test",
+        X: Optional[np.ndarray] = None,
+        y: Optional[np.ndarray] = None,
+        pos_label: Optional[Union[str, int]] = None,
+        ax: Optional[matplotlib.axes.Axes] = None,
+    ) -> RocCurveDisplay: ...
+    def precision_recall(
+        self,
+        *,
+        data_source: Literal["test", "train", "X_y"] = "test",
+        X: Optional[np.ndarray] = None,
+        y: Optional[np.ndarray] = None,
+        pos_label: Optional[Union[str, int]] = None,
+        ax: Optional[matplotlib.axes.Axes] = None,
+    ) -> PrecisionRecallDisplay: ...
+    def prediction_error(
+        self,
+        *,
+        data_source: Literal["test", "train", "X_y"] = "test",
+        X: Optional[np.ndarray] = None,
+        y: Optional[np.ndarray] = None,
+        ax: Optional[matplotlib.axes.Axes] = None,
+        kind: Literal[
+            "actual_vs_predicted", "residual_vs_predicted"
+        ] = "residual_vs_predicted",
+        subsample: Optional[Union[int, float]] = 1_000,
+    ) -> PredictionErrorDisplay: ...
+
+class _MetricsAccessor(_BaseAccessor):
+    _SCORE_OR_LOSS_ICONS: dict[str, str]
+    plot: _PlotMetricsAccessor
+
+    def _compute_metric_scores(
+        self,
+        metric_fn: Callable,
+        X: Optional[np.ndarray],
+        y_true: Optional[np.ndarray],
+        *,
+        data_source: Literal["test", "train", "X_y"] = "test",
+        response_method: Union[str, list[str]],
+        pos_label: Optional[Union[str, int]] = None,
+        metric_name: Optional[str] = None,
+        **metric_kwargs: Any,
+    ) -> pd.DataFrame: ...
+    def report_metrics(
+        self,
+        *,
+        data_source: Literal["test", "train", "X_y"] = "test",
+        X: Optional[np.ndarray] = None,
+        y: Optional[np.ndarray] = None,
+        scoring: Optional[Union[list[str], Callable]] = None,
+        pos_label: Optional[Union[str, int]] = None,
+        scoring_kwargs: Optional[dict[str, Any]] = None,
+    ) -> pd.DataFrame: ...
+    def accuracy(
+        self,
+        *,
+        data_source: Literal["test", "train", "X_y"] = "test",
+        X: Optional[np.ndarray] = None,
+        y: Optional[np.ndarray] = None,
+    ) -> pd.DataFrame: ...
+    def precision(
+        self,
+        *,
+        data_source: Literal["test", "train", "X_y"] = "test",
+        X: Optional[np.ndarray] = None,
+        y: Optional[np.ndarray] = None,
+        average: Optional[
+            Literal["binary", "micro", "macro", "weighted", "samples"]
+        ] = None,
+        pos_label: Optional[Union[str, int]] = None,
+    ) -> pd.DataFrame: ...
+    def recall(
+        self,
+        *,
+        data_source: Literal["test", "train", "X_y"] = "test",
+        X: Optional[np.ndarray] = None,
+        y: Optional[np.ndarray] = None,
+        average: Optional[
+            Literal["binary", "micro", "macro", "weighted", "samples"]
+        ] = None,
+        pos_label: Optional[Union[str, int]] = None,
+    ) -> pd.DataFrame: ...
+    def brier_score(
+        self,
+        *,
+        data_source: Literal["test", "train", "X_y"] = "test",
+        X: Optional[np.ndarray] = None,
+        y: Optional[np.ndarray] = None,
+        pos_label: Optional[Union[str, int]] = None,
+    ) -> pd.DataFrame: ...
+    def roc_auc(
+        self,
+        *,
+        data_source: Literal["test", "train", "X_y"] = "test",
+        X: Optional[np.ndarray] = None,
+        y: Optional[np.ndarray] = None,
+        average: Optional[
+            Literal["auto", "micro", "macro", "weighted", "samples"]
+        ] = None,
+        multi_class: Literal["raise", "ovr", "ovo"] = "ovr",
+    ) -> pd.DataFrame: ...
+    def log_loss(
+        self,
+        *,
+        data_source: Literal["test", "train", "X_y"] = "test",
+        X: Optional[np.ndarray] = None,
+        y: Optional[np.ndarray] = None,
+    ) -> pd.DataFrame: ...
+    def r2(
+        self,
+        *,
+        data_source: Literal["test", "train", "X_y"] = "test",
+        X: Optional[np.ndarray] = None,
+        y: Optional[np.ndarray] = None,
+        multioutput: Union[
+            Literal["raw_values", "uniform_average"], np.ndarray
+        ] = "raw_values",
+    ) -> pd.DataFrame: ...
+    def rmse(
+        self,
+        *,
+        data_source: Literal["test", "train", "X_y"] = "test",
+        X: Optional[np.ndarray] = None,
+        y: Optional[np.ndarray] = None,
+        multioutput: Union[
+            Literal["raw_values", "uniform_average"], np.ndarray
+        ] = "raw_values",
+    ) -> pd.DataFrame: ...
+    def custom_metric(
+        self,
+        metric_function: Callable,
+        response_method: Union[str, list[str]],
+        *,
+        metric_name: Optional[str] = None,
+        data_source: Literal["test", "train", "X_y"] = "test",
+        X: Optional[np.ndarray] = None,
+        y: Optional[np.ndarray] = None,
+        **kwargs: Any,
+    ) -> pd.DataFrame: ...