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121 lines
4.8 KiB
121 lines
4.8 KiB
"""Tests for compute_party_bootstrap_cis in analysis.political_axis."""
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import numpy as np
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from analysis.political_axis import compute_party_bootstrap_cis
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# ── Helpers ──────────────────────────────────────────────────────────────────
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def _make_party_vectors(n_mps: int, dim: int = 50, seed: int = 0) -> list:
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"""Generate a list of random MP vectors for a single party."""
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rng = np.random.default_rng(seed)
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return [rng.standard_normal(dim) for _ in range(n_mps)]
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# ── Tests ────────────────────────────────────────────────────────────────────
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class TestBootstrapDeterministic:
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def test_same_seed_gives_identical_output(self):
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"""Same inputs + same seed -> identical outputs."""
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vecs = _make_party_vectors(10, dim=5, seed=99)
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party_vectors = {"PartyA": vecs}
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result1 = compute_party_bootstrap_cis(party_vectors, n_boot=200, seed=42)
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result2 = compute_party_bootstrap_cis(party_vectors, n_boot=200, seed=42)
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np.testing.assert_array_equal(
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result1["PartyA"]["centroid"], result2["PartyA"]["centroid"]
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)
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np.testing.assert_array_equal(
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result1["PartyA"]["ci_lower"], result2["PartyA"]["ci_lower"]
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)
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np.testing.assert_array_equal(
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result1["PartyA"]["ci_upper"], result2["PartyA"]["ci_upper"]
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)
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np.testing.assert_array_equal(
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result1["PartyA"]["std"], result2["PartyA"]["std"]
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)
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assert result1["PartyA"]["n_mps"] == result2["PartyA"]["n_mps"]
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class TestBootstrapSingleMP:
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def test_single_mp_collapses_ci(self):
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"""Party with 1 MP -> ci_lower == ci_upper == centroid, std == 0."""
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vec = np.array([1.0, 2.0, 3.0])
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party_vectors = {"Solo": [vec]}
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result = compute_party_bootstrap_cis(party_vectors, n_boot=500)
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entry = result["Solo"]
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np.testing.assert_array_equal(entry["centroid"], vec)
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np.testing.assert_array_equal(entry["ci_lower"], vec)
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np.testing.assert_array_equal(entry["ci_upper"], vec)
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np.testing.assert_array_equal(entry["std"], np.zeros_like(vec))
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assert entry["n_mps"] == 1
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class TestBootstrapCIWidthScalesWithN:
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def test_larger_party_has_narrower_ci(self):
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"""Party with 3 MPs should have wider CIs than party with 30 MPs
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when sampled from the same distribution."""
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rng = np.random.default_rng(123)
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dim = 10
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# Same underlying distribution, different sample sizes
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small_vecs = [rng.standard_normal(dim) for _ in range(3)]
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large_vecs = [rng.standard_normal(dim) for _ in range(30)]
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party_vectors = {"Small": small_vecs, "Large": large_vecs}
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result = compute_party_bootstrap_cis(party_vectors, n_boot=2000, seed=42)
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small_width = result["Small"]["ci_upper"] - result["Small"]["ci_lower"]
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large_width = result["Large"]["ci_upper"] - result["Large"]["ci_lower"]
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# On average, the small party's CI should be wider
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assert np.mean(small_width) > np.mean(large_width)
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class TestBootstrapEmptyParty:
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def test_empty_list_excluded(self):
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"""Party with empty list -> excluded from output."""
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party_vectors = {
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"HasMPs": _make_party_vectors(5, dim=4),
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"Empty": [],
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}
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result = compute_party_bootstrap_cis(party_vectors, n_boot=100)
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assert "HasMPs" in result
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assert "Empty" not in result
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class TestBootstrapCIContainsCentroid:
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def test_centroid_within_ci_bounds(self):
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"""ci_lower <= centroid <= ci_upper for each dimension."""
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party_vectors = {"A": _make_party_vectors(15, dim=8, seed=7)}
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result = compute_party_bootstrap_cis(party_vectors, n_boot=1000, seed=42)
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entry = result["A"]
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assert np.all(entry["ci_lower"] <= entry["centroid"])
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assert np.all(entry["centroid"] <= entry["ci_upper"])
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class TestBootstrapCustomCILevel:
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def test_wider_ci_at_higher_level(self):
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"""ci=99 produces wider intervals than ci=90."""
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party_vectors = {"X": _make_party_vectors(20, dim=6, seed=55)}
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result_90 = compute_party_bootstrap_cis(
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party_vectors, n_boot=2000, ci=90.0, seed=42
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)
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result_99 = compute_party_bootstrap_cis(
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party_vectors, n_boot=2000, ci=99.0, seed=42
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)
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width_90 = result_90["X"]["ci_upper"] - result_90["X"]["ci_lower"]
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width_99 = result_99["X"]["ci_upper"] - result_99["X"]["ci_lower"]
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# 99% CI should be wider than 90% CI on every dimension
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assert np.all(width_99 >= width_90)
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