Merge pull request #219 from uriahf/218-update-create_decision_curve-for-the-new-rtichoke-api

feat: migrate decision curves to new api

Author: uriahf

pyproject.toml

@@ -11,9 +11,10 @@ dependencies = [
     "typing>=3.7.4.3",
     "polarstate==0.1.8",
     "marimo>=0.17.0",
+    "pyarrow>=21.0.0",
 ]
 name = "rtichoke"
-version = "0.1.21"
+version = "0.1.22"
 description = "interactive visualizations for performance of predictive models"
 readme = "README.md"
 

src/rtichoke/helpers/plotly_helper_functions.py

@@ -56,12 +56,16 @@ def _plot_rtichoke_curve_binary(
     stratified_by: str = "probability_threshold",
     curve: str = "roc",
     size: int = 600,
+    min_p_threshold: float = 0,
+    max_p_threshold: float = 1,
 ) -> go.Figure:
     rtichoke_curve_list = _create_rtichoke_curve_list_binary(
         performance_data=performance_data,
         stratified_by=stratified_by,
         curve=curve,
         size=size,
+        min_p_threshold=min_p_threshold,
+        max_p_threshold=max_p_threshold,
     )
 
     fig = _create_plotly_curve_binary(rtichoke_curve_list)

src/rtichoke/utility/decision.py

@@ -1,23 +1,26 @@
 """
-A module for Summary Report
+A module for Decision Curves using Plotly helpers
 """
 
-from typing import Dict, List, Optional
-from pandas.core.frame import DataFrame
+from typing import Dict, List, Sequence, Union
 from plotly.graph_objs._figure import Figure
-from rtichoke.helpers.send_post_request_to_r_rtichoke import create_rtichoke_curve
-from rtichoke.helpers.send_post_request_to_r_rtichoke import plot_rtichoke_curve
+from rtichoke.helpers.plotly_helper_functions import (
+    _create_rtichoke_plotly_curve_binary,
+    _plot_rtichoke_curve_binary,
+)
+import numpy as np
+import polars as pl
 
 
 def create_decision_curve(
-    probs: Dict[str, List[float]],
-    reals: Dict[str, List[int]],
+    probs: Dict[str, np.ndarray],
+    reals: Union[np.ndarray, Dict[str, np.ndarray]],
     decision_type: str = "conventional",
     min_p_threshold: float = 0,
     max_p_threshold: float = 1,
     by: float = 0.01,
-    stratified_by: str = "probability_threshold",
-    size: Optional[int] = None,
+    stratified_by: Sequence[str] = ["probability_threshold"],
+    size: int = 600,
     color_values: List[str] = [
         "#1b9e77",
         "#d95f02",
@@ -40,38 +43,63 @@ def create_decision_curve(
         "#D1603D",
         "#585123",
     ],
-    url_api: str = "http://localhost:4242/",
 ) -> Figure:
-    """Create Decision Curve
+    """Create Decision Curve.
 
-    Args:
-        probs (Dict[str, List[float]]): _description_
-        reals (Dict[str, List[int]]): _description_
-        decision_type (str, optional): _description_. Defaults to "conventional".
-        min_p_threshold (float, optional): _description_. Defaults to 0.
-        max_p_threshold (float, optional): _description_. Defaults to 1.
-        by (float, optional): _description_. Defaults to 0.01.
-        stratified_by (str, optional): _description_. Defaults to "probability_threshold".
-        size (Optional[int], optional): _description_. Defaults to None.
-        color_values (List[str], optional): _description_. Defaults to None.
-        url_api (_type_, optional): _description_. Defaults to "http://localhost:4242/".
+    Parameters
+    ----------
+    probs : Dict[str, np.ndarray]
+        Dictionary mapping a label or group name to an array of predicted
+        probabilities for the positive class.
+    reals : Union[np.ndarray, Dict[str, np.ndarray]]
+        Ground-truth binary labels (0/1) as a single array, or a dictionary
+        mapping the same label/group keys used in ``probs`` to arrays of
+        ground-truth labels.
+    decision_type : str, optional
+        Either ``"conventional"`` (decision curve) or another value that
+        implies the "interventions avoided" variant. Default is
+        ``"conventional"``.
+    min_p_threshold : float, optional
+        Minimum probability threshold to include in the curve. Default is 0.
+    max_p_threshold : float, optional
+        Maximum probability threshold to include in the curve. Default is 1.
+    by : float, optional
+        Resolution for probability thresholds when computing the curve
+        (step size). Default is 0.01.
+    stratified_by : Sequence[str], optional
+        Sequence of column names to stratify the performance data by.
+        Default is ["probability_threshold"].
+    size : int, optional
+        Plot size in pixels (width and height). Default is 600.
+    color_values : List[str], optional
+        List of color hex strings to use for the plotted lines. If not
+        provided, a default palette is used.
 
-    Returns:
-        Figure: _description_
+    Returns
+    -------
+    Figure
+        A Plotly ``Figure`` containing the Decision curve.
+
+    Notes
+    -----
+    The function selects the appropriate curve name based on
+    ``decision_type`` and delegates computation and plotting to
+    ``_create_rtichoke_plotly_curve_binary``. Additional keyword arguments
+    (like ``min_p_threshold`` and ``max_p_threshold``) are forwarded to
+    the helper.
     """
     if decision_type == "conventional":
         curve = "decision"
     else:
         curve = "interventions avoided"
 
-    fig = create_rtichoke_curve(
+    fig = _create_rtichoke_plotly_curve_binary(
         probs,
         reals,
         by=by,
         stratified_by=stratified_by,
         size=size,
         color_values=color_values,
-        url_api=url_api,
         curve=curve,
         min_p_threshold=min_p_threshold,
         max_p_threshold=max_p_threshold,
@@ -80,59 +108,55 @@ def create_decision_curve(
 
 
 def plot_decision_curve(
-    performance_data: DataFrame,
-    decision_type: str,
-    min_p_threshold: int = 0,
-    max_p_threshold: int = 1,
-    size: Optional[int] = None,
-    color_values: List[str] = [
-        "#1b9e77",
-        "#d95f02",
-        "#7570b3",
-        "#e7298a",
-        "#07004D",
-        "#E6AB02",
-        "#FE5F55",
-        "#54494B",
-        "#006E90",
-        "#BC96E6",
-        "#52050A",
-        "#1F271B",
-        "#BE7C4D",
-        "#63768D",
-        "#08A045",
-        "#320A28",
-        "#82FF9E",
-        "#2176FF",
-        "#D1603D",
-        "#585123",
-    ],
-    url_api: str = "http://localhost:4242/",
+    performance_data: pl.DataFrame,
+    decision_type: str = "conventional",
+    min_p_threshold: float = 0,
+    max_p_threshold: float = 1,
+    stratified_by: Sequence[str] = ["probability_threshold"],
+    size: int = 600,
 ) -> Figure:
-    """Plot Decision Curve
+    """Plot Decision Curve from performance data.
+
+    Parameters
+    ----------
+    performance_data : pl.DataFrame
+        A Polars DataFrame containing performance metrics for the Decision
+        curve. Expected columns include (but may not be limited to)
+        ``probability_threshold`` and decision-curve metrics, plus any
+        stratification columns.
+    decision_type : str
+        ``"conventional"`` for decision curves, otherwise the
+        "interventions avoided" variant will be used.
+    min_p_threshold : float, optional
+        Minimum probability threshold to include in the curve. Default is 0.
+    max_p_threshold : float, optional
+        Maximum probability threshold to include in the curve. Default is 1.
+    stratified_by : Sequence[str], optional
+        Sequence of column names used for stratification in the
+        ``performance_data``. Default is ["probability_threshold"].
+    size : int, optional
+        Plot size in pixels (width and height). Default is 600.
 
-    Args:
-        performance_data (DataFrame): _description_
-        decision_type (str): _description_
-        min_p_threshold (int, optional): _description_. Defaults to 0.
-        max_p_threshold (int, optional): _description_. Defaults to 1.
-        size (Optional[int], optional): _description_. Defaults to None.
-        color_values (List[str], optional): _description_. Defaults to None.
-        url_api (_type_, optional): _description_. Defaults to "http://localhost:4242/".
+    Returns
+    -------
+    Figure
+        A Plotly ``Figure`` containing the Decision plot.
 
-    Returns:
-        Figure: _description_
+    Notes
+    -----
+    This function wraps ``_plot_rtichoke_curve_binary`` to produce a
+    ready-to-render Plotly figure from precomputed performance data.
+    Additional keyword arguments (``min_p_threshold``, ``max_p_threshold``)
+    are forwarded to the helper.
     """
     if decision_type == "conventional":
         curve = "decision"
     else:
         curve = "interventions avoided"
 
-    fig = plot_rtichoke_curve(
+    fig = _plot_rtichoke_curve_binary(
         performance_data,
         size=size,
-        color_values=color_values,
-        url_api=url_api,
         curve=curve,
         min_p_threshold=min_p_threshold,
         max_p_threshold=max_p_threshold,