TudorandClaude Fable 5
1d855f3c17
The FSM chip anchored against disadvantaged_pct (a different measure, FSM6+CLA) whenever fsm_pct was null — which it always was, since the performance df has no fsm_pct. New fact_census_benchmarks mart supplies pupil-weighted FSM/EAL means per phase; the KS2-column medians that produced a bogus 50% 'secondary disadvantaged' anchor are gone. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0146VHeLAWjDVE2B5uU67jCB
103 lines
4.8 KiB
Python
103 lines
4.8 KiB
Python
"""compute_benchmarks: state-school benchmarks computed from our dataset
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(spec §5/§8.6). The disadvantaged average must be weighted by cohort size,
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medians must ignore NaN, and only the latest year counts."""
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import numpy as np
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import pandas as pd
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from backend.data_loader import compute_benchmarks
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LATEST = 202425
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def _df():
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rows = [
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# Six primary schools, latest year. Disadvantaged RWM chosen so the
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# weighted average differs clearly from the unweighted mean:
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# weighted = (40*100 + 60*300) / 400 = 55.0 ; unweighted mean = 50.0
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dict(year=LATEST, attainment_8_score=np.nan, eligible_pupils=100,
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rwm_expected_disadvantaged_pct=40.0, eal_pct=10.0,
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sen_support_pct=10.0, disadvantaged_pct=20.0, fsm_pct=15.0, total_pupils=200),
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dict(year=LATEST, attainment_8_score=np.nan, eligible_pupils=300,
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rwm_expected_disadvantaged_pct=60.0, eal_pct=20.0,
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sen_support_pct=14.0, disadvantaged_pct=24.0, fsm_pct=17.0, total_pupils=280),
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dict(year=LATEST, attainment_8_score=np.nan, eligible_pupils=np.nan,
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rwm_expected_disadvantaged_pct=99.0, eal_pct=30.0,
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sen_support_pct=18.0, disadvantaged_pct=30.0, fsm_pct=19.0, total_pupils=300),
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dict(year=LATEST, attainment_8_score=np.nan, eligible_pupils=50,
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rwm_expected_disadvantaged_pct=np.nan, eal_pct=np.nan,
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sen_support_pct=np.nan, disadvantaged_pct=np.nan, fsm_pct=np.nan, total_pupils=np.nan),
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dict(year=LATEST, attainment_8_score=np.nan, eligible_pupils=40,
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rwm_expected_disadvantaged_pct=np.nan, eal_pct=40.0,
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sen_support_pct=20.0, disadvantaged_pct=40.0, fsm_pct=21.0, total_pupils=350),
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dict(year=LATEST, attainment_8_score=np.nan, eligible_pupils=60,
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rwm_expected_disadvantaged_pct=np.nan, eal_pct=50.0,
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sen_support_pct=22.0, disadvantaged_pct=44.0, fsm_pct=23.0, total_pupils=400),
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# Two secondary schools (attainment_8 non-null)
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dict(year=LATEST, attainment_8_score=45.0, eligible_pupils=180,
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rwm_expected_disadvantaged_pct=np.nan, eal_pct=15.0,
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sen_support_pct=12.0, disadvantaged_pct=22.0, fsm_pct=12.0, total_pupils=1000),
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dict(year=LATEST, attainment_8_score=50.0, eligible_pupils=200,
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rwm_expected_disadvantaged_pct=np.nan, eal_pct=25.0,
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sen_support_pct=16.0, disadvantaged_pct=26.0, fsm_pct=14.0, total_pupils=1200),
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# An older-year primary row that must NOT influence anything
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dict(year=202324, attainment_8_score=np.nan, eligible_pupils=500,
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rwm_expected_disadvantaged_pct=1.0, eal_pct=99.0,
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sen_support_pct=99.0, disadvantaged_pct=99.0, fsm_pct=99.0, total_pupils=9999),
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]
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return pd.DataFrame(rows)
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def test_weighted_disadvantaged_average():
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b = compute_benchmarks(_df())
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# Row 3 has NaN eligible_pupils and must be excluded from the weighting.
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assert b["primary"]["disadvantaged_rwm_expected_pct"] == 55.0
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def test_medians_ignore_nan_and_older_years():
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b = compute_benchmarks(_df())
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assert b["year"] == LATEST
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# sen medians over [10,14,18,20,22] = 18 — the only context measure still
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# sourced from the performance df (the rest come from the census mart).
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assert b["primary"]["sen_support_pct"] == 18.0
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# disadvantaged_pct medians over [20,24,30,40,44] = 30
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assert b["primary"]["disadvantaged_pct"] == 30.0
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def test_benchmarks_use_census_mart_for_context():
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census = {
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"primary": {"year": LATEST, "fsm_pct": 25.3, "eal_pct": 21.8, "median_pupils": 240},
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"secondary": {"year": LATEST, "fsm_pct": 24.1, "eal_pct": 18.9, "median_pupils": 980},
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}
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b = compute_benchmarks(_df(), census_benchmarks=census)
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assert b["primary"]["fsm_pct"] == 25.3
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assert b["primary"]["eal_pct"] == 21.8
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assert b["secondary"]["eal_pct"] == 18.9
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assert b["secondary"]["median_pupils"] == 980
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def test_benchmarks_context_none_when_mart_missing():
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# The performance df has no fsm_pct and its eal/disadvantaged columns are
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# KS2-only — never silently fall back to medianing them for context.
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b = compute_benchmarks(_df(), census_benchmarks=None)
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assert b["primary"]["fsm_pct"] is None
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assert b["primary"]["eal_pct"] is None
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assert b["primary"]["median_pupils"] is None
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def test_secondary_block_has_no_disadvantaged_rwm():
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b = compute_benchmarks(_df())
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assert "disadvantaged_rwm_expected_pct" not in b["secondary"]
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# KS2-only columns must not produce a fake secondary disadvantaged anchor
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# (the old median over all-through schools' KS2 rows produced 50%).
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assert b["secondary"]["disadvantaged_pct"] is None
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def test_provenance_string():
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b = compute_benchmarks(_df())
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assert b["source"] == "state-school average (computed from our dataset)"
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def test_empty_df():
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assert compute_benchmarks(pd.DataFrame()) == {}
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