Fix EVPI sign bug and release 0.1.0. Added 4 tests covering non-negativity, analytic value, dominance, and budget.

Author: lauravoicu

.gitignore

@@ -4,34 +4,10 @@ __pycache__/
 *$py.class
 
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@@ -40,78 +16,28 @@ htmlcov/
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CITATION.cff

@@ -15,6 +15,6 @@ keywords:
   - causal inference
 repository-code: "https://github.com/security-decision-science/decision-security"
 license: MIT
-version: "0.1.0a1"
+version: "0.1.0"
 date-released: "2025-10-09"
 doi: ""

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "decision-security"                     # pip name (import is decision_security)
-version = "0.1.0a9"                            # bump from prior version
+version = "0.1.0"
 description = "Decision-science utilities for security: Monte Carlo, Bayes, Survival, Value of Information, causal helpers, and viz."
 readme = "README.md"
 requires-python = ">=3.9"

src/voi.py

@@ -4,12 +4,12 @@ def evpi(loss_matrix):
     """
     Expected Value of Perfect Information.
     loss_matrix: shape (S, D) — S scenarios (or posterior draws), D decisions.
-    EVPI = E[min_d L(d,θ)] - min_d E[L(d,θ)]
+    EVPI = min_d E[L(d,θ)] - E[min_d L(d,θ)]  (always >= 0)
     """
     L = np.asarray(loss_matrix, dtype=float)
-    term1 = float(L.min(axis=1).mean())
-    term2 = float(L.mean(axis=0).min())
-    return term1 - term2
+    expected_min = float(L.min(axis=1).mean())
+    min_expected = float(L.mean(axis=0).min())
+    return min_expected - expected_min
 
 def select_controls_by_roi(deltas, costs, budget):
     """

tests/test_voi.py

@@ -0,0 +1,33 @@
+import numpy as np
+import pytest
+
+from decision_security.voi import evpi, select_controls_by_roi
+
+
+def test_evpi_is_nonnegative_random():
+    rng = np.random.default_rng(42)
+    for _ in range(50):
+        L = rng.lognormal(10, 2, size=(200, 4))
+        assert evpi(L) >= 0.0
+
+
+def test_evpi_known_value():
+    # Two equally likely states, two decisions.
+    # E[L(d1)] = 50, E[L(d2)] = 50 -> min_d E[L] = 50
+    # E[min_d L] = (0 + 0) / 2 = 0 -> EVPI = 50
+    L = np.array([[0.0, 100.0], [100.0, 0.0]])
+    assert evpi(L) == pytest.approx(50.0)
+
+
+def test_evpi_zero_when_one_decision_dominates():
+    # Decision 1 is best in every scenario: perfect information changes nothing.
+    L = np.array([[1.0, 5.0], [2.0, 6.0], [3.0, 7.0]])
+    assert evpi(L) == pytest.approx(0.0)
+
+
+def test_select_controls_by_roi_respects_budget():
+    idx, spent, gained = select_controls_by_roi(
+        deltas=[100, 90, 50], costs=[100, 50, 60], budget=110
+    )
+    assert spent <= 110
+    assert 1 in idx  # highest ratio (1.8) always fits first