diff --git a/pyproject.toml b/pyproject.toml index cc2e44f..10ba1ce 100644 --- a/pyproject.toml +++ b/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" diff --git a/src/rtichoke/helpers/plotly_helper_functions.py b/src/rtichoke/helpers/plotly_helper_functions.py index 98ffe65..5999534 100644 --- a/src/rtichoke/helpers/plotly_helper_functions.py +++ b/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) diff --git a/src/rtichoke/utility/decision.py b/src/rtichoke/utility/decision.py index 62436e8..ca2b71c 100644 --- a/src/rtichoke/utility/decision.py +++ b/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. 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