Benjamin Mako Hill
8d8a463f81
Flask application skeleton with SQLAlchemy models for courses (including per-course settings), students, enrollments, opt-outs, and calls; the weighted and cycle selection logic ported from the manual coldcall scripts; alembic migrations; and a pytest suite covering selection behavior and the model query helpers. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
46 lines
1.4 KiB
Python
46 lines
1.4 KiB
Python
import random
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from collections import Counter
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from coldcall_lti import selection
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STUDENTS = ["a", "b", "c", "d"]
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def test_weights_halve_per_answered_call():
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weights = selection.compute_weights(STUDENTS, {"a": 0, "b": 1, "c": 3}, 2.0)
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assert weights == {"a": 1.0, "b": 0.5, "c": 0.125, "d": 1.0}
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def test_weight_factor_one_is_uniform():
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weights = selection.compute_weights(STUDENTS, {"a": 5, "b": 2}, 1.0)
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assert set(weights.values()) == {1.0}
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def test_heavily_called_student_selected_less():
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rng = random.Random(42)
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counts = Counter(
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selection.select_student(STUDENTS, {"a": 4}, 2.0, rng)
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for _ in range(4000)
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)
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# "a" has weight 1/16 against 1 for the others, so it should get
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# roughly 1/49th of the picks; the others roughly a third each.
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assert counts["a"] < 250
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for s in ("b", "c", "d"):
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assert 1000 < counts[s] < 1700
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def test_generate_call_list_downweights_within_list():
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rng = random.Random(7)
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picks = selection.generate_call_list(STUDENTS, {}, 200, 2.0, rng)
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assert len(picks) == 200
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counts = Counter(picks)
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# Sequential downweighting keeps the distribution close to even.
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assert all(35 < counts[s] < 65 for s in STUDENTS)
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def test_generate_cycle_list_covers_everyone_once():
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rng = random.Random(1)
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picks = selection.generate_cycle_list(STUDENTS, rng)
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assert sorted(picks) == sorted(STUDENTS)
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