Audit binary and time reference-line isolation

src/rtichoke/utility/decision.py

@@ -5,13 +5,11 @@
 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,
-)
+from rtichoke.processing.plotly_helper_functions import _plot_rtichoke_curve_binary
 from rtichoke.processing.time_reference_lines import (
     _create_rtichoke_plotly_curve_times_reference_safe,
 )
+from rtichoke.performance_data.performance_data import prepare_performance_data
 import numpy as np
 import polars as pl
 
@@ -90,17 +88,21 @@ def create_decision_curve(
     else:
         curve = "interventions avoided"
 
-    fig = _create_rtichoke_plotly_curve_binary(
-        probs,
-        reals,
-        by=by,
+    performance_data = prepare_performance_data(
+        probs=probs,
+        reals=reals,
         stratified_by=stratified_by,
-        size=size,
-        color_values=color_values,
+        by=by,
+    )
+    fig = _plot_rtichoke_curve_binary(
+        performance_data=performance_data,
+        stratified_by=stratified_by[0],
         curve=curve,
+        size=size,
         min_p_threshold=min_p_threshold,
         max_p_threshold=max_p_threshold,
     )
+    fig.update_xaxes(range=[min_p_threshold, max_p_threshold])
     return _apply_color_values_binary(fig, color_values)
 
 
@@ -151,6 +153,7 @@ def plot_decision_curve(
         min_p_threshold=min_p_threshold,
         max_p_threshold=max_p_threshold,
     )
+    fig.update_xaxes(range=[min_p_threshold, max_p_threshold])
     return fig
 
 

tests/test_reference_line_audit.py

@@ -0,0 +1,122 @@
+import numpy as np
+import pytest
+
+from rtichoke import (
+    create_decision_curve,
+    create_gains_curve,
+    create_lift_curve,
+    create_precision_recall_curve,
+    create_gains_curve_times,
+)
+
+
+def _binary_inputs():
+    probs = {
+        "population_a": np.array([0.05, 0.15, 0.35, 0.55, 0.75, 0.95]),
+        "population_b": np.array([0.10, 0.30, 0.50, 0.70]),
+    }
+    reals = {
+        "population_a": np.array([1, 0, 1, 0, 1, 0]),
+        "population_b": np.array([1, 1, 0, 1]),
+    }
+    return probs, reals
+
+
+def _visible_trace(fig, name):
+    matches = [trace for trace in fig.data if trace.name == name and trace.visible is not False]
+    assert len(matches) == 1
+    return matches[0]
+
+
+def _y_at_x(trace, x):
+    matches = [float(y) for tx, y in zip(trace.x, trace.y) if float(tx) == pytest.approx(x)]
+    assert len(matches) == 1
+    return matches[0]
+
+
+def test_binary_precision_recall_reference_is_population_specific():
+    probs, reals = _binary_inputs()
+    fig = create_precision_recall_curve(probs, reals, by=0.1)
+
+    a = _visible_trace(fig, "random_guess_population_a")
+    b = _visible_trace(fig, "random_guess_population_b")
+
+    assert float(a.y[0]) == pytest.approx(3 / 6)
+    assert float(b.y[0]) == pytest.approx(3 / 4)
+
+
+def test_binary_lift_reference_is_population_specific():
+    probs, reals = _binary_inputs()
+    fig = create_lift_curve(probs, reals, by=0.1)
+
+    a = _visible_trace(fig, "perfect_model_population_a")
+    b = _visible_trace(fig, "perfect_model_population_b")
+
+    assert float(a.y[0]) == pytest.approx(2.0)
+    assert float(b.y[0]) == pytest.approx(4 / 3)
+
+
+def test_binary_decision_reference_is_population_specific():
+    probs, reals = _binary_inputs()
+    fig = create_decision_curve(
+        probs, reals, by=0.1, min_p_threshold=0.1, max_p_threshold=0.9
+    )
+
+    a = _visible_trace(fig, "treat_all_population_a")
+    b = _visible_trace(fig, "treat_all_population_b")
+
+    x = float(a.x[0])
+    assert x == pytest.approx(0.1)
+    assert float(a.x[-1]) == pytest.approx(0.9)
+    assert float(b.x[0]) == pytest.approx(x)
+    assert float(a.y[0]) == pytest.approx(0.5 - 0.5 * x / (1 - x))
+    assert float(b.y[0]) == pytest.approx(0.75 - 0.25 * x / (1 - x))
+
+
+def test_binary_interventions_avoided_honors_displayed_threshold_range():
+    probs, reals = _binary_inputs()
+    fig = create_decision_curve(
+        probs,
+        reals,
+        decision_type="interventions avoided",
+        by=0.1,
+        min_p_threshold=0.1,
+        max_p_threshold=0.9,
+    )
+
+    assert tuple(float(x) for x in fig.layout.xaxis.range) == pytest.approx((0.1, 0.9))
+
+
+def test_binary_gains_reference_is_population_specific():
+    probs, reals = _binary_inputs()
+    fig = create_gains_curve(probs, reals, by=0.1)
+
+    a = _visible_trace(fig, "perfect_model_population_a")
+    b = _visible_trace(fig, "perfect_model_population_b")
+
+    assert _y_at_x(a, 0.01) == pytest.approx(0.01 / 0.5)
+    assert _y_at_x(b, 0.01) == pytest.approx(0.01 / 0.75)
+
+
+def test_time_gains_reference_is_population_and_horizon_specific():
+    probs = {
+        "population_a": np.array([0.05, 0.15, 0.35, 0.55, 0.75, 0.95]),
+        "population_b": np.array([0.10, 0.30, 0.50, 0.70]),
+    }
+    reals = {
+        "population_a": np.array([1, 0, 1, 0, 1, 0]),
+        "population_b": np.array([1, 1, 0, 1]),
+    }
+    times = {
+        "population_a": np.array([3.0, 11.0, 7.0, 12.0, 13.0, 14.0]),
+        "population_b": np.array([4.0, 8.0, 11.0, 12.0]),
+    }
+    fig = create_gains_curve_times(
+        probs, reals, times, fixed_time_horizons=[5.0, 10.0], by=0.1
+    )
+
+    traces = {(t.name, t.visible): t for t in fig.data}
+    assert _y_at_x(traces[("perfect_model_population_a", True)], 0.01) == pytest.approx(0.06)
+    assert _y_at_x(traces[("perfect_model_population_b", True)], 0.01) == pytest.approx(0.04)
+    assert _y_at_x(traces[("perfect_model_population_a", False)], 0.01) == pytest.approx(0.03)
+    assert _y_at_x(traces[("perfect_model_population_b", False)], 0.01) == pytest.approx(0.02)