diff --git a/research/conversion-model/SHARING_EXPERIMENT.md b/research/conversion-model/SHARING_EXPERIMENT.md new file mode 100644 index 0000000..3191209 --- /dev/null +++ b/research/conversion-model/SHARING_EXPERIMENT.md @@ -0,0 +1,120 @@ +# Category evidence-sharing experiment + +October 26, 2026. Research only; no production snapshot, bindings, defaults or +consumer behavior changed. The experiment does not establish a better replacement +for the shipped model. + +The committed [aggregate receipt](sharing-experiment-01.json) records all validation +settings, the frozen shortlist, held-out results and invented query probes. It +omits the full validation subgroup tables available in the local output. + +## Question and design + +Does pulling men's and women's estimates toward a pooled event estimate suppress +useful category differences? Vary that sharing while retaining the current pairing, +noise estimates, event-to-geometry regularization, course reconciliation and +hundredth truncation. + +[`sharing_experiment.py`](sharing_experiment.py) independently multiplies the +category prior's precision for level and log slope by +`0, 0.1, 0.25, 0.5, 1, 2, 4, 10, infinity`: **81 combinations**. One reproduces the +current fit; zero uses the raw category estimate where one exists. Infinite prior +precision uses the pooled event parameters but retains category-specific centers. +A separate **82nd control** removes category centers too and uses a common event +curve and pooled support for both categories. Thus that control actually returns +the same conversion for men and women at the same entered time. + +The training data and its SHA-256 are checked against evaluation checkpoint 08. +The reconstructed baseline's exported cells must exactly equal the shipped +`data/empirical.json`, and vectorized predictions must match every scalar baseline +prediction on validation and main test swimmers. + +All 81 settings are compared on 41,464 validation conversions. Before candidate +test scoring, the script writes a frozen shortlist: the three lowest overall +validation MAEs, category-specific validation winners, and predeclared endpoint +and one-parameter controls. The common-curve endpoint is an additional diagnostic +control, not a candidate selected by test performance. No settings are chosen per +event or from test results. For the harder holdouts, each configuration is refitted +on that holdout's existing training split. + +## Results + +Average absolute error, seconds; lower is better: + +| Setting | Validation | Main test | Later-season test | Regions held out | +| --- | ---: | ---: | ---: | ---: | +| Current sharing (`1, 1`) | 2.717701 | **2.771090** | **2.970586** | 3.137734 | +| No category shrinkage (`0, 0`) | 2.728731 | 2.775502 | 2.984357 | 3.149313 | +| Validation winner (`4, 10`) | **2.715088** | 2.771877 | 2.970993 | **3.135407** | +| One common event curve | 2.718275 | 2.774387 | 2.976008 | 3.137566 | + +The no-shrinkage setting is worse on all three test sets, by 0.0044, 0.0138 and +0.0116 seconds. The validation winner improves validation by just 0.0026 seconds; +on the main test it is 0.0008 seconds worse, with a swimmer-bootstrap 95% interval +for that difference of [-0.0017, +0.0030]. It is also essentially tied on the +later-season test. Its regional gain is 0.0023 seconds, concentrated in men; that +single holdout does not establish an overall improvement. + +Removing category shrinkage raises main-test MAE by 0.0033 seconds for men and +0.0054 seconds for women. On the regional holdout those increases are 0.0143 and +0.0094 seconds. There is no aggregate indication that the current sharing is +harming one category while helping the other. This does not exclude local +category/event/pace weaknesses. + +More sharing also produces event tradeoffs. For the validation winner on the main +test, women's 200 breast MAE increases by 0.0172 seconds and women's 200 fly by +0.0117; men's 200 fly improves by 0.0088. The longest freestyle has much larger +changes in opposing directions but just 42 men's and 24 women's test conversions. +Those small cells do not justify event-specific tuning. + +## Does sharing create the near-equal category outputs? + +These are invented query times, not individual swimmer records. Every output is +SCY to LCM, seconds, truncated by the model's normal arithmetic: + +| Entered event/time | Current men | Current women | No-shrinkage men | No-shrinkage women | +| --- | ---: | ---: | ---: | ---: | +| 100 free, 70.00 | 80.10 | 80.19 | 80.13 | 80.21 | +| 100 back, 75.00 | 87.08 | 87.29 | 87.12 | 87.32 | +| 200 breast, 135.00 | 159.98 | 159.75 | 160.24 | 159.51 | +| 200 fly, 150.00 | 176.36 | 175.10 | 177.25 | 175.29 | + +Similarity persists without category shrinkage, especially in the well-covered +100 free and 100 back. Pooling narrows some differences, particularly 200 breast +and 200 fly, but does not account for the whole pattern. These probes hold clock +time fixed, not age or competitive standing, and do not establish a physiological +explanation. The category-independent noise estimates and modeling assumptions +remain in place even in the no-shrinkage setting. + +## Interpretation and limits + +Keep the current settings. They are in a broad, nearly flat region of performance; +neither removing category sharing nor increasing it establishes a worthwhile, +consistent improvement. Retaining some sharing and some category-specific +information is supported by this experiment, without establishing that the current +strength is optimal. + +The bootstrap uses 2,000 swimmer resamples with fitted models held fixed. Intervals +and normal-approximation p-values in the receipt are unadjusted for multiple +comparisons and do not include refitting or selection uncertainty. The existing +test sets have been examined in earlier research; validation-only selection here +does not make them newly untouched data. The original youth-heavy, sparse-SCM, +source-scoped identity and thin elite-distance limitations all remain. Errors +combined across both directions can hide signed bias; the receipt includes +direction-specific metrics and these aggregate tables should not be read as a +calibration claim within every category/event/pace cell. + +## Reproduce and inspect + +```sh +target/lakehouse-py312/bin/python -m unittest discover \ + -s research/conversion-model -p test_sharing_experiment.py -v +target/lakehouse-py312/bin/python research/conversion-model/sharing_experiment.py +``` + +The local pinned lakehouse is required. The script writes an aggregate +[`receipt.json`](../../target/sharing-experiment/receipt.json) and validation-only +[`selection.json`](../../target/sharing-experiment/selection.json) under ignored +`target/sharing-experiment/`. A private fitted-object cache in that same ignored +directory contains swimmer-level pairs and must remain local. Neither the script +nor this report publishes those rows. diff --git a/research/conversion-model/sharing-experiment-01.json b/research/conversion-model/sharing-experiment-01.json new file mode 100644 index 0000000..4658bf8 --- /dev/null +++ b/research/conversion-model/sharing-experiment-01.json @@ -0,0 +1,14863 @@ +{ + "input_sha256": "dad61c4d47ea265847c6735a73f150e907d02d73bbd0d321cd5ce4a4503b9edc", + "configurations": 81, + "selection": "Validation MAE only; top 3 overall plus category winners and fixed controls.", + "limitations": "Exploratory; existing holdouts have been examined in prior research. No nested selection, refit bootstrap, or multiplicity adjustment. Noise estimates still pool categories; event priors unchanged; missing category lines retain fallback. Infinite precision replaces category parameters with event priors but retains category centers; shared_curve separately removes category centers and uses pooled support. No production artifacts modified.", + "frozen_shortlist": [ + "level=1,slope=1", + "level=0,slope=0", + "level=inf,slope=inf", + "level=1,slope=0", + "level=0,slope=1", + "level=4,slope=10", + "level=4,slope=4", + "level=10,slope=10", + "level=1,slope=inf", + "level=10,slope=4" + ], + "shared_curve_validation": { + "overall": { + "rows": 41464, + "mae": 2.718275, + "bias_seconds": -0.004866, + "bias_log_percent": -0.008563 + }, + "category": { + "men": { + "rows": 19478, + "mae": 2.716435, + "bias_seconds": -0.002048, + "bias_log_percent": -0.009577 + }, + "women": { + "rows": 21986, + "mae": 2.719904, + "bias_seconds": -0.007362, + "bias_log_percent": -0.007664 + } + }, + "event_key": { + "100-BK": { + "rows": 4520, + "mae": 2.25848, + "bias_seconds": -0.002316, + "bias_log_percent": -0.010691 + }, + "100-BR": { + "rows": 4564, + "mae": 2.413734, + "bias_seconds": 0.001521, + "bias_log_percent": -0.00973 + }, + "100-FL": { + "rows": 3666, + "mae": 2.239744, + "bias_seconds": 0.003906, 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"SCM->LCM": { + "rows": 3442, + "mae": 2.702655, + "bias_seconds": -0.00903, + "bias_log_percent": 0.19041 + }, + "SCM->SCY": { + "rows": 3875, + "mae": 3.090782, + "bias_seconds": -0.231148, + "bias_log_percent": -0.27918 + }, + "SCY->LCM": { + "rows": 9733, + "mae": 3.374718, + "bias_seconds": 0.440864, + "bias_log_percent": 0.411674 + }, + "SCY->SCM": { + "rows": 3900, + "mae": 3.336836, + "bias_seconds": 0.249656, + "bias_log_percent": 0.265107 + } + }, + "age_band": { + "10u": { + "rows": 8062, + "mae": 4.11421, + "bias_seconds": -0.00747, + "bias_log_percent": -0.022447 + }, + "11-12": { + "rows": 8078, + "mae": 2.818614, + "bias_seconds": 0.01534, + "bias_log_percent": -0.009553 + }, + "13-14": { + "rows": 9129, + "mae": 3.042356, + "bias_seconds": 0.046192, + "bias_log_percent": 0.028413 + }, + "15-16": { + "rows": 6094, + "mae": 2.646483, + "bias_seconds": 0.025551, + "bias_log_percent": 0.01121 + }, + "17-18": { + "rows": 2828, + "mae": 2.672539, + "bias_seconds": -0.045636, + "bias_log_percent": -0.029323 + }, + "19+": { + "rows": 211, + "mae": 2.567488, + "bias_seconds": -0.165308, + "bias_log_percent": -0.235091 + } + }, + "event_category": { + "100-BK|men": { + "rows": 1885, + "mae": 3.01565, + "bias_seconds": 0.011756, + "bias_log_percent": -0.006499 + }, + "100-BK|women": { + "rows": 2438, + "mae": 2.907756, + "bias_seconds": 0.021177, + "bias_log_percent": 0.028303 + }, + "100-BR|men": { + "rows": 1703, + "mae": 2.630135, + "bias_seconds": 0.035878, + "bias_log_percent": 0.022292 + }, + "100-BR|women": { + "rows": 2163, + "mae": 2.699815, + "bias_seconds": 0.021193, + "bias_log_percent": 0.001086 + }, + "100-FL|men": { + "rows": 1267, + "mae": 2.674783, + "bias_seconds": 0.071468, + "bias_log_percent": 0.039804 + }, + "100-FL|women": { + "rows": 1336, + "mae": 2.913518, + "bias_seconds": 0.064671, + "bias_log_percent": 0.053604 + }, + "100-FR|men": { + "rows": 2823, + "mae": 3.102582, + "bias_seconds": 0.012246, + "bias_log_percent": -0.014451 + }, 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0.065594 + }, + "50-FR|men": { + "rows": 3990, + "mae": 1.435291, + "bias_seconds": -0.001531, + "bias_log_percent": -0.014577 + }, + "50-FR|women": { + "rows": 5251, + "mae": 1.565795, + "bias_seconds": -0.011006, + "bias_log_percent": -0.02079 + }, + "800-FR|men": { + "rows": 28, + "mae": 13.459286, + "bias_seconds": 1.603571, + "bias_log_percent": 0.30387 + }, + "800-FR|women": { + "rows": 34, + "mae": 15.444706, + "bias_seconds": -0.381765, + "bias_log_percent": 0.017293 + } + } + }, + "delta_mae_bootstrap": { + "all": { + "estimate": -0.0002, + "ci95_low": -0.0037, + "ci95_high": 0.0033, + "se": 0.0018, + "z_score": -0.1, + "p_value": 0.925633 + }, + "women": { + "estimate": 0.0007, + "ci95_low": -0.0038, + "ci95_high": 0.0047, + "se": 0.0022, + "z_score": 0.3, + "p_value": 0.761441 + }, + "men": { + "estimate": -0.0012, + "ci95_low": -0.0066, + "ci95_high": 0.0041, + "se": 0.0028, + "z_score": -0.4, + "p_value": 0.668444 + } + } + } + } + } +} diff --git a/research/conversion-model/sharing_experiment.py b/research/conversion-model/sharing_experiment.py new file mode 100644 index 0000000..0bf8096 --- /dev/null +++ b/research/conversion-model/sharing_experiment.py @@ -0,0 +1,253 @@ +"""Research-only category-pooling ablation. Never exports a production model. + +Sweep category-prior precision independently for level and log slope. Zero uses +the raw category line; one reproduces the current fit; infinity uses the pooled +event posterior. Event-to-geometry regularization and course-noise estimates stay +fixed. Missing category lines retain their existing fallback. Select on validation +only, then evaluate the frozen shortlist on test and harder holdouts. +Private pairs and fitted objects are cached only under ignored target/. +""" +from __future__ import annotations + +import argparse +import copy +import hashlib +import itertools +import json +import math +import pickle +from collections import defaultdict +from pathlib import Path + +import numpy as np + +import evaluate_and_train as e +from window_experiment import probe_pair + +STRENGTHS = (0.0, 0.1, 0.25, 0.5, 1.0, 2.0, 4.0, 10.0, math.inf) + + +def adjust(raw: tuple[float, float], post: e.Posterior, strength: float) -> e.Posterior: + """Multiply the original category prior's precision, holding its mean fixed.""" + value, variance = raw + share = post.own_share + if strength == 1: + return post + if strength == 0: + return e.Posterior(value, variance, 1.0) + prior_value = (post.value - share * value) / (1 - share) + prior_var = share * variance / (1 - share) + if math.isinf(strength): + return e.Posterior(prior_value, prior_var, 0.0) + new_share = share / (share + strength * (1 - share)) + return e.Posterior(new_share * value + (1 - new_share) * prior_value, + new_share * variance, new_share) + + +def groups(train): + out = defaultdict(list) + canonical = {f'{a}->{b}' for a, b in e.COURSE_PAIRS} + weights = e.swimmer_weights([p for p in train if p.direction in canonical]) + rows = [p for p in train if p.direction in canonical] + for p, w in zip(rows, weights): + out[(p.direction, p.event_key, p.category)].append( + (math.log(p.source_time_cs / 100), math.log(p.target_time_cs / 100), w)) + return out + + +def variant(base, grouped, level, slope): + model = copy.deepcopy(base) + for key, line in base.lines.items(): + if key[2] == 'all': + continue + model.shift_post[key] = adjust((line.shift, line.shift_var), base.shift_post[key], level) + model.slope_post[key] = adjust((line.log_slope, line.log_slope_var), base.slope_post[key], slope) + for event, category in model.tables: + model.linear[(event, category)] = model._reconcile(event, category) + model.tables[(event, category)] = model._course_tables(event, category, grouped) + return model + + +def shared_curve(base, grouped): + """One common event curve, including common centers and support, for both categories. + + Unlike infinite category-prior precision alone, this also removes the raw + category centers. Use event posteriors directly, preserving event regularization. + """ + model = copy.deepcopy(base) + pooled_groups = defaultdict(list) + for (direction, event, _), rows in grouped.items(): + for category in e.CATEGORIES: + pooled_groups[(direction, event, category)].extend(rows) + for key in model.shift_post: + direction, event, _ = key + pooled_key = (direction, event, 'all') + if pooled_key not in base.lines: + continue + model.lines[key] = base.lines[pooled_key] + model.shift_post[key] = base.event_shift_post[pooled_key] + available = next(k for k in base.slope_post + if k[:2] == key[:2] and k in base.lines) + post = base.slope_post[available] + raw = base.lines[available] + prior = adjust((raw.log_slope, raw.log_slope_var), post, math.inf) + model.slope_post[key] = e.Posterior(prior.value, + max(1e-12, prior.var - base.tau2[direction]['category_log_slope']), 0.) + for event, category in model.tables: + model.linear[(event, category)] = model._reconcile(event, category) + model.tables[(event, category)] = model._course_tables(event, category, pooled_groups) + for event in e.CANONICAL_EVENT_ORDER: + assert model.tables[(event, 'women')] == model.tables[(event, 'men')] + return model + + +def predict(model, pairs): + """Vectorized engine arithmetic; checked against scalar predictions before sweep.""" + indices = defaultdict(list) + for i, p in enumerate(pairs): + indices[(p.event_key, p.category, p.source_course, p.target_course)].append(i) + out = np.empty(len(pairs), dtype=np.int64) + for (event, category, src, dst), ix in indices.items(): + x = np.log(np.array([pairs[i].source_time_cs for i in ix]) / 100) + tables = model.tables[(event, category)] + latent = e.knot_map_np(tables[src], x, True) + y = e.knot_map_np(tables[dst], latent, False) + out[ix] = np.floor(np.exp(y) * 100 + 1e-8).astype(np.int64) + return out + + +def metrics(pairs, pred): + target = np.array([p.target_time_cs for p in pairs]) + err = (pred - target) / 100 + def one(ix): + return {'rows': len(ix), 'mae': round(float(np.abs(err[ix]).mean()), 6), + 'bias_seconds': round(float(err[ix].mean()), 6), + 'bias_log_percent': round(float((100 * np.log(pred[ix] / target[ix])).mean()), 6)} + out = {'overall': one(np.arange(len(pairs)))} + for field in ('category', 'event_key', 'direction', 'age_band'): + sub = defaultdict(list) + for i, p in enumerate(pairs): + sub[getattr(p, field)].append(i) + out[field] = {k: one(v) for k, v in sorted(sub.items())} + sub = defaultdict(list) + for i, p in enumerate(pairs): + sub[f'{p.event_key}|{p.category}'].append(i) + out['event_category'] = {k: one(v) for k, v in sorted(sub.items())} + return out + + +def name(level, slope): + return f'level={level:g},slope={slope:g}' + + +def prepare(db, cache): + receipt = json.loads((e.ROOT / 'research/conversion-model/evaluation-checkpoint-08.json').read_text()) + measured = e.measure_lakehouse_view(db) + assert measured['iceberg_snapshot_id'] == receipt['input_provenance']['iceberg_snapshot_id'] + print('Loading pinned swims', flush=True) + swims = e.load_swims_from_duckdb(db) + digest = e.compute_swims_sha256(swims) + assert digest == receipt['input_provenance']['loaded_swims_sha256'] + print('Verified input hash; reducing rounds', flush=True) + dedup, _ = e.deduplicate_best_of_meet(swims) + del swims + print('Building pairs and repeats', flush=True) + pairs = e.build_directional_pairs(dedup, max_days=30) + repeats = e.build_repeat_swims(dedup, max_days=30) + del dedup + train = [p for p in pairs if p.partition == 'train' and not p.is_ratio_outlier] + val = [p for p in pairs if p.partition == 'validation' and not p.is_ratio_outlier] + test = [p for p in pairs if p.partition == 'test' and not p.is_ratio_outlier] + datasets = {'main': (train, val, test, [r for r in repeats if r.partition == 'train'])} + for label, splitter in [('temporal', e.temporal_holdout_splits), ('regions', e.leave_regions_out_splits)]: + tr, te, rep = splitter(pairs, repeats) + datasets[label] = ([p for p in tr if not p.is_ratio_outlier], [], + [p for p in te if not p.is_ratio_outlier], rep) + fitted = {} + for label, (tr, va, te, rep) in datasets.items(): + print(f'Fitting {label}: train={len(tr)}, validation={len(va)}, test={len(te)}', flush=True) + base = e.EquivalenceModel() + base.fit(tr, e.estimate_course_noise(rep)) + fitted[label] = (base, groups(tr), va, te) + cache.parent.mkdir(parents=True, exist_ok=True) + with cache.open('wb') as f: + pickle.dump((digest, fitted), f) + return digest, fitted + + +def run(args): + cache = args.output / 'private-fit-cache.pkl' + if cache.exists(): + with cache.open('rb') as f: + digest, fitted = pickle.load(f) + else: + digest, fitted = prepare(args.duckdb, cache) + pinned = json.loads((e.ROOT / 'research/conversion-model/evaluation-checkpoint-08.json').read_text()) + assert digest == pinned['input_provenance']['loaded_swims_sha256'] + base, grouped, val, test = fitted['main'] + assert base.export_snapshot_dict('', '')['cells'] == json.loads((e.ROOT / 'data/empirical.json').read_text())['cells'] + for pairs in (val, test): + fast = predict(base, pairs) + assert np.array_equal(fast, [base.predict(p) for p in pairs]), 'Vectorized/scalar mismatch' + main_metrics = metrics(test, predict(base, test)) + assert abs(main_metrics['overall']['mae'] - 2.7711) < 0.00006, main_metrics + sweep = {} + configs = list(itertools.product(STRENGTHS, repeat=2)) + for level, slope in configs: + model = variant(base, grouped, level, slope) + score = metrics(val, predict(model, val)) + sweep[name(level, slope)] = score + print(f'{name(level, slope)} validation MAE={score["overall"]["mae"]}', flush=True) + # Freeze the shortlist before accessing candidate test results. + ordered = sorted(configs, key=lambda c: sweep[name(*c)]['overall']['mae']) + women = min(configs, key=lambda c: sweep[name(*c)]['category']['women']['mae']) + men = min(configs, key=lambda c: sweep[name(*c)]['category']['men']['mae']) + shortlist = list(dict.fromkeys([(1., 1.), (0., 0.), (math.inf, math.inf), + (1., 0.), (0., 1.), *ordered[:3], women, men])) + result = {'input_sha256': digest, 'configurations': len(configs), + 'selection': 'Validation MAE only; top 3 overall plus category winners and fixed controls.', + 'limitations': 'Exploratory; existing holdouts have been examined in prior research. No nested selection, refit bootstrap, or multiplicity adjustment. Noise estimates still pool categories; event priors unchanged; missing category lines retain fallback. Infinite precision replaces category parameters with event priors but retains category centers; shared_curve separately removes category centers and uses pooled support. No production artifacts modified.', + 'validation_sweep': sweep, 'frozen_shortlist': [name(*c) for c in shortlist], + 'evaluations': {}} + (args.output / 'selection.json').write_text(json.dumps({k: v for k, v in result.items() if k != 'evaluations'}, indent=2)) + result['shared_curve_validation'] = metrics(val, predict(shared_curve(base, grouped), val)) + probes = [('100-FR', 7000, 100, 'freestyle'), ('100-BK', 7500, 100, 'backstroke'), + ('200-BR', 13500, 200, 'breaststroke'), ('200-FL', 15000, 200, 'butterfly')] + result['same_entered_time_probes'] = {} + for setting in ((1., 1.), (0., 0.), ordered[0]): + model = variant(base, grouped, *setting) + result['same_entered_time_probes'][name(*setting)] = { + event: {'invented_input_seconds': cs / 100, 'source_course': 'SCY', 'target_course': 'LCM', + **{cat: model.predict(probe_pair(cs, distance, stroke, cat, event)) / 100 + for cat in e.CATEGORIES}} + for event, cs, distance, stroke in probes} + for label, (b, g, _, rows) in fitted.items(): + baseline_pred = predict(b, rows) + target = np.array([p.target_time_cs for p in rows]) + out = {} + candidates = [(name(level, slope), variant(b, g, level, slope)) for level, slope in shortlist] + candidates.append(('shared_curve', shared_curve(b, g))) + for label_model, model in candidates: + pred = predict(model, rows) + score = metrics(rows, pred) + delta = (np.abs(pred - target) - np.abs(baseline_pred - target)) / 100 + bootstrap = {} + for cat in ('all', 'women', 'men'): + ix = [i for i, p in enumerate(rows) if cat == 'all' or p.category == cat] + point, boot = e.clustered_bootstrap([rows[i].swimmer_key for i in ix], + {'delta': delta[ix]}, args.bootstrap, 20261026) + bootstrap[cat] = e._delta_summary(point['delta'], boot['delta']) + out[label_model] = {'metrics': score, 'delta_mae_bootstrap': bootstrap} + print(f'{label} {label_model} test MAE={score["overall"]["mae"]}', flush=True) + result['evaluations'][label] = out + result['script_sha256'] = hashlib.sha256(Path(__file__).read_bytes()).hexdigest() + (args.output / 'receipt.json').write_text(json.dumps(result, indent=2, allow_nan=False)) + print(f'Aggregate receipt: {args.output / "receipt.json"}', flush=True) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument('--duckdb', type=Path, default=e.ROOT / 'target/lakehouse-national-17/queries.duckdb') + parser.add_argument('--output', type=Path, default=e.ROOT / 'target/sharing-experiment') + parser.add_argument('--bootstrap', type=int, default=2000) + run(parser.parse_args()) diff --git a/research/conversion-model/test_sharing_experiment.py b/research/conversion-model/test_sharing_experiment.py new file mode 100644 index 0000000..0991758 --- /dev/null +++ b/research/conversion-model/test_sharing_experiment.py @@ -0,0 +1,42 @@ +"""Checks for the experimental intervention, using invented swims only.""" +import unittest + +import numpy as np + +import evaluate_and_train as e +from sharing_experiment import adjust, groups, predict, shared_curve, variant +from test_evaluate_and_train import known_pairs + + +class SharingExperimentTests(unittest.TestCase): + def test_precision_intervention_and_endpoints(self): + # Raw N(10, 4), prior N(2, 1): posterior N(3.6, .8). + raw = (10., 4.) + post = e.Posterior(3.6, .8, .2) + self.assertEqual(adjust(raw, post, 1), post) + self.assertEqual(adjust(raw, post, 0), e.Posterior(10., 4., 1.)) + pooled = adjust(raw, post, float('inf')) + self.assertAlmostEqual(pooled.value, 2.) + self.assertAlmostEqual(pooled.var, 1.) + half = adjust(raw, post, .5) + self.assertAlmostEqual(half.value, 14 / 3) + self.assertAlmostEqual(half.var, 4 / 3) + self.assertGreater(half.own_share, post.own_share) + + def test_baseline_reconstruction_and_vector_arithmetic(self): + noise = {c: .015 for c in e.COURSES} + pairs = known_pairs(100, noise) + base = e.EquivalenceModel() + base.fit(pairs, {(c, '100-FR'): v ** 2 for c, v in noise.items()}) + clone = variant(base, groups(pairs), 1, 1) + self.assertEqual(base.tables, clone.tables) + for level, slope in ((1, 1), (0, 0), (.25, 4), (float('inf'), float('inf'))): + model = variant(base, groups(pairs), level, slope) + self.assertTrue(np.array_equal(predict(model, pairs), [model.predict(p) for p in pairs])) + self.assertEqual(base.tables, clone.tables, 'Experiment mutated baseline') + pooled = shared_curve(base, groups(pairs)) + self.assertEqual(pooled.tables[('100-FR', 'women')], pooled.tables[('100-FR', 'men')]) + + +if __name__ == '__main__': + unittest.main()