import numpy as np import pandas as pd import pytest from casuallab.interference import ( ExposureMappingConfig, TwoStageSaturationConfig, add_mapped_exposures, estimate_exposure_response, two_stage_saturation_assignment, ) def _units(n_zones: int = 12, n_periods: int = 8) -> pd.DataFrame: return pd.MultiIndex.from_product( [range(n_periods), range(n_zones)], names=["period_id", "zone_id"], ).to_frame(index=False) def _ring_edges(n_zones: int) -> pd.DataFrame: return pd.DataFrame( { "focal_zone_id": np.arange(n_zones), "neighbor_zone_id": np.roll(np.arange(n_zones), -1), "weight": 1.0, } ) def test_two_stage_saturation_is_deterministic_balanced_and_records_probabilities() -> None: config = TwoStageSaturationConfig( n_clusters=6, individuals_per_cell=40, saturation_levels=(0.0, 0.5, 1.0), seed=91, ) first = two_stage_saturation_assignment(_units(), config) second = two_stage_saturation_assignment(_units(), config) pd.testing.assert_frame_equal(first, second) cluster_arms = first.groupby("cluster_id")["cluster_saturation"].nunique() assert cluster_arms.eq(1).all() assert ( first[["cluster_id", "cluster_saturation"]] .drop_duplicates()["cluster_saturation"] .value_counts() .sort_index() .to_dict() == {0.0: 2, 0.5: 2, 1.0: 2} ) assert set(first["saturation_assignment_probability"]) == {1 / 3} assert first.loc[first["cluster_saturation"] == 0, "treated_units"].eq(0).all() assert first.loc[first["cluster_saturation"] == 1, "treated_units"].eq(40).all() assert first["assigned_treatment"].between(0, 1).all() assert first["randomization_cluster"].nunique() == 6 assert set(first["evidence_type"]) == {"randomized_design_assignment"} def test_two_stage_saturation_config_rejects_unsupported_arm_geometry() -> None: with pytest.raises(ValueError, match="number of saturation levels"): TwoStageSaturationConfig(n_clusters=2, saturation_levels=(0.0, 0.5, 1.0)) with pytest.raises(ValueError, match="sum to one"): TwoStageSaturationConfig( n_clusters=4, saturation_levels=(0.0, 1.0), saturation_probabilities=(0.2, 0.2), ) def test_mapped_exposure_uses_predeclared_neighbors_and_exact_time_lags() -> None: assignments = _units(n_zones=4, n_periods=4) assignments["treatment"] = ( assignments["zone_id"] + assignments["period_id"] ) % 2 mapped = add_mapped_exposures( assignments, _ring_edges(4), history_lags=1, ) lookup = assignments.set_index(["zone_id", "period_id"])["treatment"] for row in mapped.itertuples(index=False): expected_neighbor = lookup.loc[((row.zone_id + 1) % 4, row.period_id)] assert row.neighbor_exposure == expected_neighbor if row.period_id == 0: assert np.isnan(row.history_exposure) assert row.history_support == 0 else: assert row.history_exposure == lookup.loc[(row.zone_id, row.period_id - 1)] assert row.history_support == 1 assert mapped["exposure_mapping_id"].nunique() == 1 def test_unmapped_focal_zone_remains_unknown_not_zero() -> None: assignments = _units(n_zones=3, n_periods=2) assignments["treatment"] = 0.0 edges = pd.DataFrame( { "focal_zone_id": [0, 1], "neighbor_zone_id": [1, 2], "weight": [1.0, 1.0], } ) mapped = add_mapped_exposures(assignments, edges) assert mapped.loc[mapped["zone_id"] == 2, "neighbor_exposure"].isna().all() assert mapped.loc[mapped["zone_id"].isin([0, 1]), "neighbor_exposure"].eq(0).all() def test_exposure_mapped_regression_recovers_own_neighbor_and_history_slopes() -> None: assignment = two_stage_saturation_assignment( _units(n_zones=12, n_periods=40), TwoStageSaturationConfig( n_clusters=12, individuals_per_cell=80, saturation_levels=(0.0, 0.35, 0.7, 1.0), seed=81, ), ) mapped = add_mapped_exposures(assignment, _ring_edges(12), history_lags=1) mapped["baseline"] = np.sin(mapped["zone_id"]) rng = np.random.default_rng(719) mapped["outcome"] = ( 4.0 + 2.0 * mapped["treatment"] + 1.5 * mapped["neighbor_exposure"] + 0.7 * mapped["history_exposure"] + 0.25 * mapped["baseline"] + rng.normal(0.0, 0.03, len(mapped)) ) result = estimate_exposure_response( mapped, ExposureMappingConfig(covariates=("baseline",)), ).set_index("exposure_term") assert result.loc["treatment", "estimate"] == pytest.approx(2.0, abs=0.05) assert result.loc["neighbor_exposure", "estimate"] == pytest.approx(1.5, abs=0.05) assert result.loc["history_exposure", "estimate"] == pytest.approx(0.7, abs=0.05) assert result["inference_valid"].all() assert set(result["target_estimand"]) == { "controlled_zone_direct_effect", "spillover_effect", "controlled_history_exposure_response", } assert result["effect_scale"].str.contains("not the full-policy").all() def test_exposure_estimator_rejects_collinear_own_and_neighbor_exposure() -> None: frame = _units(n_zones=4, n_periods=4) frame["treatment"] = np.tile([0.0, 1.0, 0.0, 1.0], 4) frame["neighbor_exposure"] = frame["treatment"] frame["outcome"] = frame["treatment"] frame["randomization_cluster"] = "z_" + frame["zone_id"].astype(str) with pytest.raises(ValueError, match="rank deficient"): estimate_exposure_response( frame, ExposureMappingConfig(history_exposure=None), ) def test_exposure_estimator_fails_closed_when_configured_history_is_missing() -> None: frame = _units(n_zones=4, n_periods=4) frame["treatment"] = np.tile([0.0, 1.0, 0.0, 1.0], 4) frame["neighbor_exposure"] = np.tile([1.0, 0.0, 1.0, 0.0], 4) frame["outcome"] = frame["treatment"] frame["randomization_cluster"] = "z_" + frame["zone_id"].astype(str) with pytest.raises(ValueError, match="missing configured history column"): estimate_exposure_response(frame) def test_exposure_estimator_rejects_mixed_mapping_versions() -> None: assignment = two_stage_saturation_assignment( _units(n_zones=8, n_periods=10), TwoStageSaturationConfig( n_clusters=8, individuals_per_cell=20, saturation_levels=(0.0, 0.5, 1.0), seed=18, ), ) frame = add_mapped_exposures(assignment, _ring_edges(8), history_lags=1) frame["outcome"] = ( 2.0 * frame["treatment"] + frame["neighbor_exposure"] + 0.5 * frame["history_exposure"] ) frame.loc[frame["period_id"] >= 5, "exposure_mapping_id"] = "second-map" with pytest.raises(ValueError, match="one predeclared exposure mapping"): estimate_exposure_response(frame)