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| """The admission predicted-vs-observed figure. | |
| Design §15 phase 4 lists "admission predicted-vs-observed plot generated" as an | |
| exit criterion, and it did not exist. These tests cover the part that can go | |
| wrong silently: the plot must not hide the finding. | |
| The finding is that the fit has a negative intercept and the measured marginal | |
| peaks are mostly zero. A chart with a truncated y-axis, or one that clips | |
| negative values, would render that as an ordinary-looking graph and defeat the | |
| point of generating it. | |
| """ | |
| from __future__ import annotations | |
| import re | |
| import xml.etree.ElementTree as ET | |
| import pytest | |
| from forgesight.bench.plot import Series, _nice_ticks, predicted_vs_observed_svg | |
| # The real shape from data/reports/report-2026-10-05.json: the measured marginal | |
| # peaks are ~0 for b=1,2,4 and jump at b=8, and the fitted intercept is negative. | |
| REAL = Series( | |
| label="egret-medium / onnxruntime", | |
| samples={1: 81_920, 2: 0, 4: 0, 8: 128_909_312}, | |
| m0=-39_183_405.0, | |
| m_item=19_050_000.0, | |
| valid=False, | |
| ) | |
| def test_it_is_wellformed_svg(): | |
| svg = predicted_vs_observed_svg([REAL]) | |
| # Parses as XML: a malformed chart is worse than no chart, because a broken | |
| # image in a tracked report is easy to miss. | |
| root = ET.fromstring(svg) | |
| assert root.tag.endswith("svg") | |
| def test_it_is_deterministic(): | |
| """The figure is tracked in git, so it must not churn between runs.""" | |
| assert predicted_vs_observed_svg([REAL]) == predicted_vs_observed_svg([REAL]) | |
| def test_the_negative_intercept_is_not_clipped_away(): | |
| """A truncated axis would hide the entire finding. | |
| The fit's intercept is below zero, which is what makes it invalid. Clamping | |
| the axis at zero would draw a dashed line starting at the floor and make the | |
| claim look ordinary. | |
| Asserted on the rendered y-axis tick labels rather than by scraping | |
| coordinates out of the `points` attribute. An earlier version regexed the | |
| polyline for `[\d.]+` before a comma, which cannot match a leading minus -- | |
| so the assertion passed for the wrong reason on a chart that did clip. The | |
| axis labels are what a reader actually sees, and a negative one on the scale | |
| is direct evidence the range was not clamped. | |
| """ | |
| svg = predicted_vs_observed_svg([REAL]) | |
| ticks = re.findall(r'text-anchor="end">(-?[\d.]+[KM]?)</text>', svg) | |
| assert ticks, "no y-axis ticks were rendered" | |
| assert any(t.startswith("-") for t in ticks), ( | |
| f"no negative tick on the y-axis; the negative intercept was clipped " | |
| f"(ticks={ticks})" | |
| ) | |
| def test_the_zero_marginal_peaks_are_plotted_not_skipped(): | |
| """Three of the four samples are zero, and those zeros are the result. | |
| Filtering them would produce a tidy rising line through three points and | |
| hide the fact that batch size 1 through 4 cost nothing measurable. | |
| """ | |
| svg = predicted_vs_observed_svg([REAL]) | |
| titles = re.findall(r"<title>([^<]+)</title>", svg) | |
| assert len(titles) == len(REAL.samples), ( | |
| f"expected one marker per sample, got {len(titles)} for {len(REAL.samples)}" | |
| ) | |
| # b=2 and b=4 measured exactly zero. b=1 measured 81920 bytes, which is not | |
| # zero and must not be labelled as such: an earlier version rounded to whole | |
| # megabytes and rendered all three as "0 MB", collapsing a real 82 KB sample | |
| # into the very thing the chart is about. | |
| zeros = [t for t in titles if t.endswith("measured 0")] | |
| assert len(zeros) == 2, f"expected exactly the two true zeros, got {zeros}" | |
| tiny = [t for t in titles if "measured 80K" in t] | |
| assert tiny, f"the 82 KB sample was not labelled in KB; titles={titles}" | |
| def test_an_invalid_fit_is_drawn_dashed_and_labelled(): | |
| svg = predicted_vs_observed_svg([REAL]) | |
| assert "stroke-dasharray" in svg, "an unusable fit is not marked as unusable" | |
| assert "not usable" in svg, "the figure does not say the fit is unusable" | |
| def test_a_valid_fit_is_not_marked_unusable(): | |
| ok = Series(label="x", samples={1: 10, 2: 20, 4: 40}, m0=5, m_item=10, valid=True) | |
| svg = predicted_vs_observed_svg([ok]) | |
| assert "not usable" not in svg | |
| def test_a_single_sample_still_renders(): | |
| """One point has no line to join, so the polyline must degrade to markers.""" | |
| svg = predicted_vs_observed_svg([Series(label="one", samples={2: 5}, | |
| m0=1, m_item=2, valid=True)]) | |
| assert "<circle" in svg | |
| ET.fromstring(svg) | |
| def test_multiple_series_are_distinguished(): | |
| a = Series(label="torch", samples={1: 10, 2: 20}, m0=5, m_item=8, valid=True) | |
| b = Series(label="ort", samples={1: 12, 2: 22}, m0=6, m_item=8, valid=True) | |
| svg = predicted_vs_observed_svg([a, b]) | |
| assert "torch" in svg and "ort" in svg | |
| ET.fromstring(svg) | |
| def test_empty_series_is_refused(): | |
| """Better to fail than to emit a chart with no axes labelled.""" | |
| with pytest.raises(ValueError, match="no series"): | |
| predicted_vs_observed_svg([]) | |
| def test_ticks_cover_the_range_and_are_ordered(): | |
| ticks = _nice_ticks(0, 100) | |
| assert ticks == sorted(ticks) | |
| assert ticks[0] <= 0 and ticks[-1] >= 100 | |
| def test_ticks_survive_a_degenerate_range(): | |
| """All samples identical: the axis must still render rather than divide by zero.""" | |
| flat = Series(label="flat", samples={1: 5, 2: 5}, m0=5, m_item=0, valid=True) | |
| svg = predicted_vs_observed_svg([flat]) | |
| ET.fromstring(svg) | |
| def test_no_nan_or_inf_reaches_the_output(): | |
| """A NaN coordinate silently drops the whole shape in most renderers.""" | |
| for s in (REAL, Series(label="z", samples={1: 0}, m0=0, m_item=0, valid=True)): | |
| svg = predicted_vs_observed_svg([s]) | |
| assert "nan" not in svg.lower() | |
| assert "inf" not in svg.lower() |