Honor custom colors in binary curves

src/rtichoke/discrimination/gains.py

@@ -4,6 +4,7 @@
 
 from typing import Dict, List, Sequence, Union
 from plotly.graph_objs._figure import Figure
+from rtichoke.processing.binary_color_values import _apply_color_values_binary
 from rtichoke.processing.plotly_helper_functions import (
     _create_rtichoke_plotly_curve_times,
     _create_rtichoke_plotly_curve_binary,
@@ -129,7 +130,7 @@ def create_gains_curve(
         color_values=color_values,
         curve="gains",
     )
-    return fig
+    return _apply_color_values_binary(fig, color_values)
 
 
 def plot_gains_curve(

src/rtichoke/discrimination/lift.py

@@ -4,6 +4,7 @@
 
 from typing import Dict, List, Sequence, Union
 from plotly.graph_objs._figure import Figure
+from rtichoke.processing.binary_color_values import _apply_color_values_binary
 from rtichoke.processing.plotly_helper_functions import (
     _create_rtichoke_plotly_curve_binary,
     _plot_rtichoke_curve_binary,
@@ -81,7 +82,7 @@ def create_lift_curve(
         color_values=color_values,
         curve="lift",
     )
-    return fig
+    return _apply_color_values_binary(fig, color_values)
 
 
 def plot_lift_curve(

src/rtichoke/discrimination/precision_recall.py

@@ -4,6 +4,7 @@
 
 from typing import Dict, List, Sequence, Union
 from plotly.graph_objs._figure import Figure
+from rtichoke.processing.binary_color_values import _apply_color_values_binary
 from rtichoke.processing.plotly_helper_functions import (
     _create_rtichoke_plotly_curve_binary,
     _plot_rtichoke_curve_binary,
@@ -82,7 +83,7 @@ def create_precision_recall_curve(
         color_values=color_values,
         curve="precision recall",
     )
-    return fig
+    return _apply_color_values_binary(fig, color_values)
 
 
 def plot_precision_recall_curve(

src/rtichoke/discrimination/roc.py

@@ -4,6 +4,7 @@
 
 from typing import Dict, List, Union, Sequence
 from plotly.graph_objs._figure import Figure
+from rtichoke.processing.binary_color_values import _apply_color_values_binary
 from rtichoke.processing.plotly_helper_functions import (
     _create_rtichoke_plotly_curve_binary,
     _plot_rtichoke_curve_binary,
@@ -87,7 +88,7 @@ def create_roc_curve(
         color_values=color_values,
         curve="roc",
     )
-    return fig
+    return _apply_color_values_binary(fig, color_values)
 
 
 def plot_roc_curve(
@@ -210,4 +211,4 @@ def create_roc_curve_times(
         curve="roc",
     )
 
-    return fig
+    return fig

src/rtichoke/processing/binary_color_values.py

@@ -0,0 +1,56 @@
+"""Helpers for applying custom palettes to binary Plotly curves."""
+
+from plotly.graph_objs._figure import Figure
+
+
+def _apply_color_values_binary(fig: Figure, color_values) -> Figure:
+    """Apply custom colors to multiple binary reference groups.
+
+    rtichoke's R implementation keeps a single model black and uses
+    ``color_values`` only when multiple models or populations are shown.
+    """
+    if color_values is None:
+        return fig
+
+    reference_groups = []
+    for trace in fig.data:
+        if trace.showlegend is True and trace.name is not None:
+            name = str(trace.name)
+            if name not in reference_groups:
+                reference_groups.append(name)
+
+    # A single model intentionally remains black, matching R.
+    if len(reference_groups) <= 1:
+        return fig
+
+    if len(color_values) < len(reference_groups):
+        raise ValueError(
+            "color_values must contain at least one color per reference group"
+        )
+
+    group_colors = dict(zip(reference_groups, color_values))
+
+    for trace in fig.data:
+        legendgroup = "" if trace.legendgroup is None else str(trace.legendgroup)
+        group = next(
+            (
+                reference_group
+                for reference_group in reference_groups
+                if legendgroup == reference_group
+                or legendgroup.endswith(f"_{reference_group}")
+            ),
+            None,
+        )
+        if group is None:
+            continue
+
+        color = group_colors[group]
+        if trace.line is not None:
+            trace.line.color = color
+        if trace.marker is not None and trace.mode and "markers" in trace.mode:
+            trace.marker.color = color
+        if trace.hoverlabel is not None:
+            trace.hoverlabel.bgcolor = color
+            trace.hoverlabel.bordercolor = color
+
+    return fig

src/rtichoke/utility/decision.py

@@ -4,6 +4,7 @@
 
 from typing import Dict, List, Sequence, Union
 from plotly.graph_objs._figure import Figure
+from rtichoke.processing.binary_color_values import _apply_color_values_binary
 from rtichoke.processing.plotly_helper_functions import (
     _create_rtichoke_plotly_curve_binary,
     _plot_rtichoke_curve_binary,
@@ -100,7 +101,7 @@ def create_decision_curve(
         min_p_threshold=min_p_threshold,
         max_p_threshold=max_p_threshold,
     )
-    return fig
+    return _apply_color_values_binary(fig, color_values)
 
 
 def plot_decision_curve(

tests/test_binary_curve_colors.py

@@ -0,0 +1,63 @@
+import numpy as np
+import pytest
+
+from rtichoke.discrimination.gains import create_gains_curve
+from rtichoke.discrimination.lift import create_lift_curve
+from rtichoke.discrimination.precision_recall import create_precision_recall_curve
+from rtichoke.discrimination.roc import create_roc_curve
+from rtichoke.utility.decision import create_decision_curve
+
+
+PROBS = {
+    "model_a": np.array([0.1, 0.3, 0.7, 0.9]),
+    "model_b": np.array([0.2, 0.4, 0.6, 0.8]),
+}
+REALS = np.array([0, 0, 1, 1])
+CUSTOM_COLORS = ["#111111", "#222222"]
+
+
+@pytest.mark.parametrize(
+    "creator",
+    [
+        create_roc_curve,
+        create_precision_recall_curve,
+        create_lift_curve,
+        create_gains_curve,
+        create_decision_curve,
+    ],
+)
+def test_binary_create_curves_honor_custom_colors(creator):
+    fig = creator(
+        probs=PROBS,
+        reals=REALS,
+        by=0.25,
+        color_values=CUSTOM_COLORS,
+    )
+
+    model_traces = [trace for trace in fig.data if trace.showlegend is True]
+
+    assert len(model_traces) == 2
+    assert [trace.line.color for trace in model_traces] == CUSTOM_COLORS
+
+
+def test_binary_single_model_remains_black_like_r():
+    fig = create_roc_curve(
+        probs={"model": PROBS["model_a"]},
+        reals=REALS,
+        by=0.25,
+        color_values=["#123456"],
+    )
+
+    model_trace = next(trace for trace in fig.data if trace.name == "model")
+
+    assert model_trace.line.color == "#000000"
+
+
+def test_binary_custom_colors_require_one_per_reference_group():
+    with pytest.raises(ValueError, match="one color per reference group"):
+        create_roc_curve(
+            probs=PROBS,
+            reals=REALS,
+            by=0.25,
+            color_values=["#111111"],
+        )