diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 55fd4cf..78eae46 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -10,5 +10,5 @@ repos: rev: v0.16.9 hooks: - id: ruff - args: ["--fix"] + args: ["--fix", "--select", "E4,E7,E9,F"] - id: ruff-format diff --git a/lambench/metrics/utils.py b/lambench/metrics/utils.py index 6c9fc74..aeed564 100644 --- a/lambench/metrics/utils.py +++ b/lambench/metrics/utils.py @@ -1,7 +1,5 @@ -import json from collections import defaultdict from datetime import datetime -from functools import lru_cache from pathlib import Path from typing import Literal @@ -11,14 +9,6 @@ import lambench from lambench.workflow.entrypoint import gather_model, gather_model_params -_DIATOMICS_JSON = ( - Path(__file__).resolve().parent.parent - / "tasks" - / "calculator" - / "diatomics" - / "diatomics.json" -) - ############################# # General utility functions # ############################# @@ -140,7 +130,7 @@ def aggregated_nve_md_results(results: dict[str, dict[str, float]]) -> dict[str, ## Inference efficiency utility functions def aggregated_inference_efficiency_results( results: dict[str, dict[str, float]], -) -> dict[str, float]: +) -> dict[str, float | None]: system_level_avg = [] system_level_std = [] system_level_success_rate = [] @@ -155,49 +145,106 @@ def aggregated_inference_efficiency_results( if success_count != len(results): return {"average_time": None, "std_time": None, "success_rate": 0.0} return { - "average_time": np.round(np.mean(system_level_avg), 6), - "standard_deviation": np.round( - np.sqrt(np.mean(np.square(system_level_std))), 6 + "average_time": float(np.round(np.mean(system_level_avg), 6)), + "standard_deviation": float( + np.round(np.sqrt(np.mean(np.square(system_level_std))), 6) ), - "success_rate": np.round(np.mean(system_level_success_rate), 2), + "success_rate": float(np.round(np.mean(system_level_success_rate), 2)), } -@lru_cache -def _diatomics_molecule_names() -> tuple[str, ...]: - with open(_DIATOMICS_JSON) as fh: - return tuple(entry["name"] for entry in json.load(fh)) +## Diatomic utility functions +def _empty_diatomics_agg() -> dict[str, float | None]: + return { + "bond_length_mae": None, + "well_depth_mae": None, + "avg_roughness": None, + "coverage": 0.0, + "score": None, + } + +def _mean_or_none(values: list[float]) -> float | None: + if not values: + return None + return float(np.mean(values)) -def _empty_diatomics_agg() -> dict[str, float | None]: - return {"avg_roughness": None} +def _dummy_hat(value: float | None, dummy: float | None) -> float | None: + if ( + value is None + or dummy is None + or not np.isfinite(value) + or not np.isfinite(dummy) + or dummy <= 0 + ): + return None + return float(min(value / dummy, 1.0)) -def aggregated_diatomics_results(results: dict[str, dict]) -> dict[str, float]: - """ - Aggregate per-molecule diatomics results. - avg_roughness: mean slope MAE relative to a constant dummy, capped at 1. - Requires a finite roughness for every molecule in diatomics.json; otherwise None. +def _element_is_excluded(record: dict | None) -> bool: + if not isinstance(record, dict) or record.get("model_excluded", True): + return True + roughness = record.get("roughness") + return roughness is None or not np.isfinite(roughness) or not 0 <= roughness <= 1 + + +def aggregated_diatomics_results(results: dict[str, dict]) -> dict[str, float | None]: + """Aggregate curves using the reference metadata saved during inference. + + bond_length_mae is in Å and well_depth_mae is in eV. avg_roughness is + the mean original finite-difference slope error normalized by a constant + energy dummy. Bond and well errors use their blind dummy scales. Each + term is capped at 1. + score is their equal-weight average, reported only for complete coverage. + Missing or model_excluded elements make score None. Raw metrics and + coverage are retained for diagnostics. A term without samples also makes + score None (for example, a reference set with no bound elements). """ if not results: return _empty_diatomics_agg() - - names = _diatomics_molecule_names() + reference = results["reference"] + names = reference["elements"] + dummy = reference["dummy_scales"] + curves = results["curves"] if not names: return _empty_diatomics_agg() - roughness_values = [] + bond_errors: list[float] = [] + well_errors: list[float] = [] + roughness_values: list[float] = [] + n_excluded = 0 for name in names: - mol_results = results.get(name) - if mol_results is None: - return _empty_diatomics_agg() - roughness = mol_results.get("roughness") - if roughness is None or not np.isfinite(roughness): - return _empty_diatomics_agg() - roughness_values.append(roughness) - - return {"avg_roughness": float(np.mean(roughness_values))} + record = curves.get(name) + if _element_is_excluded(record): + n_excluded += 1 + continue + assert isinstance(record, dict) + bond = record.get("bond_length_error") + well = record.get("well_depth_error") + if bond is not None and np.isfinite(bond): + bond_errors.append(float(bond)) + if well is not None and np.isfinite(well): + well_errors.append(float(well)) + roughness_values.append(float(record["roughness"])) + + coverage = (len(names) - n_excluded) / len(names) + bond_mae = _mean_or_none(bond_errors) + well_mae = _mean_or_none(well_errors) + avg_roughness = _mean_or_none(roughness_values) + bond_hat = _dummy_hat(bond_mae, dummy["bond_length_mae"]) + well_hat = _dummy_hat(well_mae, dummy["well_depth_mae"]) + if n_excluded or bond_hat is None or well_hat is None or avg_roughness is None: + score = None + else: + score = float((bond_hat + well_hat + avg_roughness) / 3.0) + return { + "bond_length_mae": bond_mae, + "well_depth_mae": well_mae, + "avg_roughness": avg_roughness, + "coverage": float(coverage), + "score": score, + } #################################### diff --git a/lambench/metrics/vishelper/metrics_calculations.py b/lambench/metrics/vishelper/metrics_calculations.py index 0fe5d19..7722b2d 100644 --- a/lambench/metrics/vishelper/metrics_calculations.py +++ b/lambench/metrics/vishelper/metrics_calculations.py @@ -199,17 +199,17 @@ def _calculate_instability_error(self, cell: dict, lambda_0: float = 5e-4) -> fl else: return np.clip(np.log10(slope / lambda_0), a_min=0, a_max=None) - def calculate_diatomics_roughness_results(self) -> dict[str, float]: + def calculate_diatomics_results(self) -> dict[str, float]: """ - Leaderboard scores for homonuclear diatomics: avg_roughness (lower is better). - Models with missing results are omitted from this dict; the final ranking - still includes them with a null roughness column. + Leaderboard scores for homonuclear diatomics: complete-coverage score + (lower is better). Models with missing results are omitted from this + dict; the final ranking still includes them with a null column. """ raw = self.fetcher.fetch_diatomics_results() return { - model: metrics["avg_roughness"] + model: metrics["score"] for model, metrics in raw.items() - if metrics is not None and metrics.get("avg_roughness") is not None + if metrics is not None and metrics.get("score") is not None } def calculate_efficiency_results(self) -> dict[str, float]: @@ -238,7 +238,7 @@ def summarize_final_rankings(self): ) stability_results = self.calculate_stability_results() efficiency_results = self.calculate_efficiency_results() - roughness_results = self.calculate_diatomics_roughness_results() + diatomics_results = self.calculate_diatomics_results() if not generalizability_ood or not generalizability_downstream: logging.warning( "Missing data for generalizability metrics (ood or downstream)" @@ -272,8 +272,8 @@ def summarize_final_rankings(self): "Applicability-Efficiency ↑": [ efficiency_results[model] for model in shared_models ], - "Applicability-Roughness ↓": [ - roughness_results.get(model) for model in shared_models + "Applicability-Diatomics ↓": [ + diatomics_results.get(model) for model in shared_models ], } @@ -293,7 +293,7 @@ def summarize_final_rankings(self): "Generalizability-PC Error ↓", "Applicability-Instability ↓", "Applicability-Efficiency ↑", - "Applicability-Roughness ↓", + "Applicability-Diatomics ↓", ], ascending=[True, True, True, False, True], ) diff --git a/lambench/metrics/vishelper/results_fetcher.py b/lambench/metrics/vishelper/results_fetcher.py index e3d56b4..dd0ea76 100644 --- a/lambench/metrics/vishelper/results_fetcher.py +++ b/lambench/metrics/vishelper/results_fetcher.py @@ -130,7 +130,7 @@ def fetch_inference_efficiency_results(self) -> dict[str, dict[str, float]]: return results def fetch_diatomics_results(self) -> dict[str, dict]: - """Returns aggregated diatomics roughness results for all leaderboard models.""" + """Returns aggregated homonuclear-diatomic scores for leaderboard models.""" results = {} for model in self.leaderboard_models: task_results = CalculatorRecord.query( diff --git a/lambench/tasks/calculator/diatomics/diatomics.json b/lambench/tasks/calculator/diatomics/diatomics.json index 1a6e58a..e241873 100644 --- a/lambench/tasks/calculator/diatomics/diatomics.json +++ b/lambench/tasks/calculator/diatomics/diatomics.json @@ -1,1602 +1,8210 @@ [ - { - "name": "AlAl", - "method": "PBE", - "R": [ - 1.21, - 1.41589286, - 1.62178572, - 1.82767858, - 2.03357142, - 2.23946428, - 2.44535714, - 2.65125, - 2.85714286, - 3.06303572, - 3.26892858, - 3.47482142, - 3.68071428, - 3.88660714, - 4.0925, - 4.29839286, - 4.50428572, - 4.71017858, - 4.91607142, - 5.12196428, - 5.32785714, - 5.53375, - 5.73964286, - 5.94553572, - 6.15142858, - 6.35732142, - 6.56321428, - 6.76910714, - 6.975 - ], - "E": [ - 21.69553068, - 11.14410695, - 4.46990509, - 0.69626246, - -1.21676381, - -2.04208305, - -2.27886427, - -2.22546192, - -2.23126744, - -2.13493129, - -1.99294153, - -1.83531198, - -1.67788789, - -1.5284843, - -1.39083808, - -1.26651214, - -1.1571301, - -1.05868295, - -0.97419251, - -0.90180437, - -0.83982298, - -0.78814601, - -0.74514801, - -0.71051327, - -0.68320322, - -0.66276761, - -0.64851475, - -0.64011496, - -0.57647315 - ], - "F": [ - 60.57266875, - 41.354763, - 24.44625509, - 13.06806215, - 6.14128837, - 2.26162033, - 0.25579529, - 0.41602807, - -0.27806619, - -0.61013121, - -0.74631893, - -0.7757976, - -0.7518143, - -0.70137733, - -0.64001526, - -0.57416177, - -0.48706032, - -0.44642018, - -0.38713862, - -0.33408605, - -0.27793413, - -0.22865668, - -0.1892725, - -0.15183375, - -0.11960044, - -0.08665681, - -0.0572195, - -0.02949642, - 0.0 - ], - "S^2": [ - 0.106276, - 0.098596, - 0.058564, - 0.038809, - 0.0289, - 0.023409, - 0.020449, - 0.025281, - 0.022801, - 0.020736, - 0.019321, - 0.018225, - 0.017424, - 0.0169, - 0.016641, - 0.016384, - 0.016384, - 0.016129, - 0.016384, - 0.016384, - 0.016641, - 0.0169, - 0.016641, - 0.0169, - 0.0169, - 0.017161, - 0.017161, - 0.017161, - 0.017161 - ] - }, - { - "name": "SiSi", - "method": "PBE", - "R": [ - 1.11, - 1.31282142, - 1.51564286, - 1.71846428, - 1.92128572, - 2.12410714, - 2.32692858, - 2.52975, - 2.73257142, - 2.93539286, - 3.13821428, - 3.34103572, - 3.54385714, - 3.74667858, - 3.9495, - 4.15232142, - 4.35514286, - 4.55796428, - 4.76078572, - 4.96360714, - 5.16642858, - 5.36925, - 5.57207142, - 5.77489286, - 5.97771428, - 6.18053572, - 6.38335714, - 6.58617858, - 6.789 - ], - "E": [ - 33.12026864, - 13.25774131, - 2.00110273, - -2.49417775, - -4.44198323, - -5.1071608, - -5.24703455, - -4.98742036, - -4.54345777, - -4.04252783, - -3.54839397, - -3.0996546, - -2.68388013, - -2.32916398, - -2.38126229, - -2.24245054, - -2.13768097, - -2.03626856, - -1.9573986, - -1.88489854, - -1.84197426, - -1.80103498, - -1.78620606, - -1.74383145, - -1.68289036, - -1.7109778, - -1.70150633, - -1.69604422, - -0.87555372 - ], - "F": [ - 124.45839432, - 68.53195105, - 33.04559539, - 13.37175758, - 6.21281285, - 0.81988881, - -0.47728931, - -1.88020767, - -2.39683204, - -2.48400236, - -2.35926277, - -2.20970491, - -1.87359833, - -1.61397952, - -0.73841286, - -0.62991633, - -0.52491607, - -0.43387721, - -0.35474778, - -0.31693349, - -0.22822001, - -0.1881757, - -0.13374293, - -0.11164342, - -0.04003388, - -0.0597873, - -0.03960887, - -0.02345417, - 0.0 - ], - "S^2": [ - 0.0, - 0.0, - 0.0, - 0.0, - 0.130321, - 0.114921, - 0.069696, - 0.067081, - 0.066049, - 0.067081, - 0.068644, - 0.071289, - 0.072361, - 0.074529, - 0.310249, - 0.311364, - 0.314721, - 0.3136, - 0.315844, - 0.315844, - 0.318096, - 0.319225, - 0.319225, - 0.321489, - 0.326041, - 0.321489, - 0.322624, - 0.322624, - 0.341056 - ] - }, - { - "name": "SS", - "method": "PBE", - "R": [ - 1.05, - 1.250375, - 1.45075, - 1.651125, - 1.8515, - 2.051875, - 2.25225, - 2.452625, - 2.653, - 2.853375, - 3.05375, - 3.254125, - 3.4545, - 3.654875, - 3.85525, - 4.055625, - 4.256, - 4.456375, - 4.65675, - 4.857125, - 5.0575, - 5.257875, - 5.45825, - 5.658625, - 5.859 - ], - "E": [ - 56.6632406, - 17.59053523, - -0.48155823, - -5.78096745, - -7.12850504, - -6.90163556, - -6.10408707, - -5.1706445, - -4.26951883, - -3.48454744, - -2.82856388, - -2.61429184, - -2.45644616, - -2.31281962, - -2.30469358, - -2.21163025, - -2.13131514, - -2.07984364, - -2.03543091, - -2.00166027, - -2.14408425, - -2.14443081, - -2.14453532, - -2.14455228, - -1.89473979 - ], - "F": [ - 317.15397377, - 120.14746006, - 43.86271234, - 13.66400583, - 1.60573497, - -3.13060913, - -4.51443905, - -4.61397552, - -4.20957069, - -3.59285054, - -2.95131997, - -0.82030173, - -0.74309327, - -0.58769699, - -0.52709542, - -0.41845518, - 0.03331956, - -0.25494417, - -0.18020652, - -0.14812717, - 0.00442569, - 0.00474803, - 0.00328453, - 0.002413, - 0.0 - ], - "S^2": [ - 0.133225, - 0.221857, - 0.252004, - 0.300304, - 0.334084, - 0.351649, - 0.358801, - 0.358801, - 0.356409, - 0.352836, - 0.346921, - 1.340964, - 1.334025, - 1.279161, - 1.311025, - 1.301881, - 1.238769, - 1.288225, - 1.281424, - 1.2769, - 1.245456, - 1.245456, - 1.245456, - 1.245456, - 1.243225 - ] - }, - { - "name": "LiLi", - "method": "PBE", - "R": [ - 1.28, - 1.48353846, - 1.68707692, - 1.89061538, - 2.09415384, - 2.2976923, - 2.50123076, - 2.70476924, - 2.9083077, - 3.11184616, - 3.31538462, - 3.51892308, - 3.72246154, - 3.926, - 4.12953846, - 4.33307692, - 4.53661538, - 4.74015384, - 4.9436923, - 5.14723076, - 5.35076924, - 5.5543077, - 5.75784616, - 5.96138462, - 6.16492308, - 6.36846154, - 6.572 - ], - "E": [ - 2.57567241, - 1.24409369, - 0.21632582, - -0.51468971, - -0.98958252, - -1.26786043, - -1.40617495, - -1.44929497, - -1.42991395, - -1.37109178, - -1.28881753, - -1.19406826, - -1.09431241, - -0.99457511, - -0.89818493, - -0.80729571, - -0.72325252, - -0.64684731, - -0.57850035, - -0.51839306, - -0.46655827, - -0.42294349, - -0.38745548, - -0.35998967, - -0.3404493, - -0.32875772, - -0.32485202 - ], - "F": [ - 7.27327891, - 5.80165488, - 4.30052111, - 2.91692256, - 1.79992067, - 0.98140216, - 0.41437564, - 0.03606571, - -0.20753374, - -0.35715219, - -0.44197593, - -0.4825392, - -0.49314499, - -0.4837446, - -0.4612383, - -0.430389, - -0.39446085, - -0.35568997, - -0.31551901, - -0.2748906, - -0.23433785, - -0.19417691, - -0.15451309, - -0.11535542, - -0.0766322, - -0.03822277, - 0.0 - ], - "S^2": [ - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0 - ] - }, - { - "name": "BB", - "method": "PBE", - "R": [ - 0.84, - 1.04324, - 1.24648, - 1.44972, - 1.65296, - 1.8562, - 2.05944, - 2.26268, - 2.46592, - 2.66916, - 2.8724, - 3.07564, - 3.27888, - 3.48212, - 3.68536, - 3.8886, - 4.09184, - 4.29508, - 4.49832, - 4.70156, - 4.9048, - 5.10804, - 5.31128, - 5.51452, - 5.71776, - 5.921 - ], - "E": [ - 17.92459699, - 5.10213103, - -1.46814943, - -3.82963048, - -4.20597957, - -3.81948386, - -3.2179762, - -2.91230378, - -2.52699665, - -2.1845205, - 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2.49772, + 2.56707, + 2.638346, + 2.711601, + 2.786889, + 2.864268, + 2.943796, + 3.025531, + 3.109536, + 3.195874, + 3.284609, + 3.375807, + 3.469537, + 3.56587, + 3.664878, + 3.766635, + 3.871217, + 3.978703, + 4.089173, + 4.20271, + 4.3194, + 4.43933, + 4.56259, + 4.689272, + 4.819471, + 4.953286, + 5.090815, + 5.232164, + 5.377437, + 5.526743, + 5.680195, + 5.837908, + 6.0 + ], + "E": [ + 26.16394, + 18.422049, + 11.736865, + 6.023865, + 1.288481, + -2.539143, + -5.540092, + -7.807331, + -9.439973, + -10.538813, + -11.201971, + -11.580226, + -11.976865, + -12.187778, + -12.223834, + -12.13293, + -11.985716, + -11.813709, + -11.833163, + -11.875776, + -11.878231, + -11.847671, + -11.786741, + -11.736431, + -11.712511, + -11.655744, + -11.569857, + -11.459325, + -11.327502, + -11.177882, + -11.014081, + -10.839916, + -10.659247, + -10.475741, + -10.292667, + -10.19601, + -10.094126, + -10.009331, + -10.033879, + -9.952288, + -9.894087, + -9.843289, + -9.793471, + -9.745188, + -9.698835, + -9.59743, + -9.573326, + -9.551553, + -9.532262, + -9.535962 + ] + } ] diff --git a/lambench/tasks/calculator/diatomics/diatomics.py b/lambench/tasks/calculator/diatomics/diatomics.py index dda5419..c85dd9e 100644 --- a/lambench/tasks/calculator/diatomics/diatomics.py +++ b/lambench/tasks/calculator/diatomics/diatomics.py @@ -1,76 +1,130 @@ -""" -The reference data (diatomics.json) is derived from the MLIP Arena project: - - MLIP Arena — Benchmark machine learning interatomic potential at scale - Yuan Chiang, Lawrence Berkeley National Laboratory - https://github.com/atomind-ai/mlip-arena - -Licensed under the Apache License, Version 2.0 (the "License"); you may not -use this file except in compliance with the License. You may obtain a copy of -the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software distributed -under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR -CONDITIONS OF ANY KIND, either express or implied. See the License for the -specific language governing permissions and limitations under the License. - ----- - -Homonuclear diatomics dissociation curves (Applicability). - -Same comparison as stacking_fault, on the DFT bond lengths (no PCHIP): -shift each scan so min(E)=0, then MAE of d(E / max(E)) / dr, divided -by a constant-energy dummy (zeros after the shift) and capped at 1: - - 0 = perfect match, 1 = dummy or worse. - - roughness – slope MAE relative to dummy; leaderboard - -The last point of every scan is dropped (PBE tail artifacts). - -Reference data: lambench/tasks/calculator/diatomics/diatomics.json - name, method, R, E, F, S^2 +"""Homonuclear diatomic curves (Applicability). + +Data source +----------- +PBE curves for homonuclear dimers, from the Matbench Discovery diatomic +reference. The published file also contains r2SCAN; this task does not +score it. + + Matbench Discovery diatomic DFT curves (PBE and r2SCAN, H–U) + https://figshare.com/files/68541277 + + Provenance, VASP settings, and spin ladder + https://github.com/janosh/matbench-discovery/blob/main/site/src/lib/diatomics-dft.readme.md + + Riebesell, J., Goodall, R. E. A., Benner, P. et al. A framework to + evaluate machine learning crystal stability predictions. + Nat. Mach. Intell. 7, 836–847 (2025). + https://doi.org/10.1038/s42256-025-01055-1 + +Each curve was computed with VASP 6, PBE_64 PAW potentials, and Materials +Project MP24 static settings in a 15 Å cell. Distances run geometrically +from 0.8 covalent radii to 6 Å. At each distance the stored energy is the +lowest among an even-NUPDOWN spin ladder plus one antiferromagnetic +candidate. The public file has no raw spin-candidate curves and no +per-point edit log, so a deleted distance cannot be restored. Po, At, Rn, +Fr, and Ra are not part of the scored set, and Ni, Zr, Pr, Pm, Sm, Tb, Dy, +Ho, Er, Tm, and Ir are missing 45 design-grid distances. Those curves are +already absent from ``diatomics.json``. The loader reads that file as +stored. + +Three per-element metrics are averaged over finite values: + +- bond_length_error: absolute equilibrium-distance error in Å. The distance + is a quadratic fit of up to five points around the minimum. Reference + binding energies below 0.05 eV are skipped. +- well_depth_error: absolute error in eV of + ``E(r_far) - min(E)`` over the evaluation window. It is skipped with + bond length when the reference binding energy is below 0.05 eV. +- roughness: finite-difference slope MAE between independently min-shifted, + peak-normalized model and reference energies, normalized by a constant-energy + dummy and capped at one. Uses the native grid with the last point dropped. + +Bond length and well depth use distances from 0.9 covalent radii to +min(3.1 Alvarez vdW radii, 6 Å). Roughness uses the native reference grid +with its last point dropped, preserving the original finite-difference metric. +A curve with non-finite values in either scored range is dropped from the +averages and lowers coverage. + +The leaderboard score is the equal-weight average of the dummy-normalized +bond-length MAE, dummy-normalized well-depth MAE, and mean roughness, +and is only reported when every reference element is scored. The bond-length +dummy guesses the midpoint of each element's evaluation window. The well-depth +dummy predicts zero binding energy. Zero is a perfect single-well match. + +Reference file: lambench/tasks/calculator/diatomics/diatomics.json """ from __future__ import annotations import json import logging +from functools import lru_cache from pathlib import Path from typing import TYPE_CHECKING import numpy as np from ase import Atoms from ase.calculators.calculator import Calculator +from ase.data import atomic_numbers, covalent_radii, vdw_alvarez if TYPE_CHECKING: from lambench.models.ase_models import ASEModel _LABEL_FILE = Path(__file__).parent / "diatomics.json" +_N_GRID = 50 +_GRID_R_MAX = 6.0 +_MIN_BINDING_EV = 0.05 +_MIN_WINDOW_POINTS = 5 _DROP_LAST_POINTS = 1 +_FIT_POINTS = 5 +_BOX_ANGSTROM = 30.0 -def _element_from_name(mol_name: str) -> str: - """Extract element symbol: 'AlAl' → 'Al', 'HH' → 'H'.""" - if len(mol_name) % 2: - raise ValueError(f"Expected a repeated homonuclear label, got {mol_name!r}") - half = mol_name[: len(mol_name) // 2] - if mol_name != half + half: - raise ValueError(f"Expected a repeated homonuclear label, got {mol_name!r}") - return half +def design_distances(element: str) -> np.ndarray: + """50 geometric separations from 0.8 covalent radii to 6 Å.""" + r_min = 0.8 * float(covalent_radii[atomic_numbers[element]]) + return r_min * (_GRID_R_MAX / r_min) ** (np.arange(_N_GRID) / (_N_GRID - 1)) -def _scan_arrays(entry: dict) -> tuple[np.ndarray, np.ndarray]: - """Bond lengths (Å) and DFT energies (eV), with the last point dropped.""" - bond_lengths = np.asarray(entry["R"], dtype=float)[:-_DROP_LAST_POINTS] - dft_energies = np.asarray(entry["E"], dtype=float)[:-_DROP_LAST_POINTS] - if bond_lengths.size != dft_energies.size: - raise ValueError( - f"{entry.get('name', '')}: R and E length mismatch after trim" - ) - return bond_lengths, dft_energies +def eval_window( + element: str, seps_max: float, *, r_min_factor: float = 0.9 +) -> tuple[float, float]: + """Element-specific scoring window in Å. + + The default lower bound is 0.9 covalent radii. The upper bound is + min(3.1 Alvarez vdW radii, + ``seps_max``). Missing vdW radii leave the upper bound at ``seps_max``. + """ + atomic_num = atomic_numbers[element] + r_cov = float(covalent_radii[atomic_num]) + r_min = r_min_factor * r_cov if np.isfinite(r_cov) else 0.0 + r_vdw = float(vdw_alvarez.vdw_radii[atomic_num]) + r_max = min(3.1 * r_vdw, seps_max) if np.isfinite(r_vdw) else float(seps_max) + return r_min, r_max + + +def _binding_energy(energies: np.ndarray) -> float: + return float(energies[-1] - np.min(energies)) + + +def _equilibrium_distance(seps: np.ndarray, energies: np.ndarray) -> float: + """Equilibrium separation from a local quadratic fit, in Å.""" + min_idx = int(np.argmin(energies)) + if len(seps) < 3: + return float(seps[min_idx]) + start_idx = min(max(0, min_idx - _FIT_POINTS // 2), max(0, len(seps) - _FIT_POINTS)) + fit_seps = seps[start_idx : start_idx + _FIT_POINTS] + fit_energies = energies[start_idx : start_idx + _FIT_POINTS] + if len(fit_seps) < 3: + return float(seps[min_idx]) + quadratic_coef, linear_coef, _constant = np.polyfit(fit_seps, fit_energies, 2) + if quadratic_coef <= 0: + return float(seps[min_idx]) + equilibrium = -linear_coef / (2 * quadratic_coef) + if fit_seps.min() <= equilibrium <= fit_seps.max(): + return float(equilibrium) + return float(seps[min_idx]) def _shift_to_min(energies: np.ndarray) -> np.ndarray: @@ -86,95 +140,189 @@ def _normalized_slopes(bond_lengths: np.ndarray, y: np.ndarray) -> np.ndarray: return np.zeros_like(slopes) -def _mae(left: np.ndarray, right: np.ndarray) -> float: - return float(np.mean(np.abs(left - right))) - - -def _ratio_capped_at_dummy(value: float, dummy: float) -> float | None: - if dummy <= 0 or not np.isfinite(dummy) or not np.isfinite(value): - return None - return float(min(value / dummy, 1.0)) - - -def _curve_metrics( +def _roughness( bond_lengths: np.ndarray, model_energies: np.ndarray, dft_energies: np.ndarray -) -> dict[str, float] | None: +) -> float | None: + """Original slope MAE relative to a constant-energy dummy, capped at one. + + Inputs use the native reference grid with the final point already dropped. + The caller validates array shapes and strictly increasing distances. + """ if not np.all(np.isfinite(model_energies)) or not np.all(np.isfinite(dft_energies)): return None if bond_lengths.size < 2: return None + ref_slopes = _normalized_slopes(bond_lengths, _shift_to_min(dft_energies)) + model_slopes = _normalized_slopes(bond_lengths, _shift_to_min(model_energies)) + # A constant-energy dummy has zero normalized slopes. + dummy_error = float(np.mean(np.abs(ref_slopes))) + error = float(np.mean(np.abs(model_slopes - ref_slopes))) + if dummy_error <= 0 or not np.isfinite(dummy_error) or not np.isfinite(error): + return None + return float(min(error / dummy_error, 1.0)) + + +def _excluded_record() -> dict[str, float | bool | None]: + return { + "bond_length_error": None, + "well_depth_error": None, + "roughness": None, + "model_excluded": True, + } + + +def score_curve( + element: str, + distances: np.ndarray, + ref_energies: np.ndarray, + model_energies: np.ndarray, +) -> dict[str, float | bool | None]: + """Score one homonuclear curve against its PBE reference. + + ``model_excluded`` is true when the model curve cannot be scored. + """ + distances = np.asarray(distances, dtype=float) + ref_energies = np.asarray(ref_energies, dtype=float) + model_energies = np.asarray(model_energies, dtype=float) + if any(values.ndim != 1 for values in (distances, ref_energies, model_energies)): + return _excluded_record() + if not (distances.size == ref_energies.size == model_energies.size): + return _excluded_record() if ( - bond_lengths.size != model_energies.size - or bond_lengths.size != dft_energies.size + distances.size < 2 + or not np.isfinite(distances).all() + or np.any(np.diff(distances) <= 0) ): - return None - if np.any(np.diff(bond_lengths) == 0): - return None + return _excluded_record() + + seps_max = float(distances[-1]) + r_min, r_max = eval_window(element, seps_max) + general = (distances >= r_min) & (distances <= r_max) + if int(general.sum()) < _MIN_WINDOW_POINTS: + return _excluded_record() + if not ( + np.isfinite(model_energies[general]).all() + and np.isfinite(ref_energies[general]).all() + ): + return _excluded_record() - y_dft = _shift_to_min(dft_energies) - y_model = _shift_to_min(model_energies) - y_dummy = np.zeros_like(y_dft) - - roughness = _ratio_capped_at_dummy( - _mae( - _normalized_slopes(bond_lengths, y_model), - _normalized_slopes(bond_lengths, y_dft), - ), - _mae( - _normalized_slopes(bond_lengths, y_dummy), - _normalized_slopes(bond_lengths, y_dft), - ), + roughness = _roughness( + distances[:-_DROP_LAST_POINTS], + model_energies[:-_DROP_LAST_POINTS], + ref_energies[:-_DROP_LAST_POINTS], ) if roughness is None: - return None - return {"roughness": roughness} + return _excluded_record() + result: dict[str, float | bool | None] = { + "bond_length_error": None, + "well_depth_error": None, + "roughness": roughness, + "model_excluded": False, + } + + ref_general = ref_energies[general] + model_general = model_energies[general] + ref_depth = _binding_energy(ref_general) + if ref_depth >= _MIN_BINDING_EV: + ref_distance = _equilibrium_distance(distances[general], ref_general) + model_distance = _equilibrium_distance(distances[general], model_general) + result["bond_length_error"] = float(abs(model_distance - ref_distance)) + result["well_depth_error"] = abs(_binding_energy(model_general) - ref_depth) + return result + + +@lru_cache +def load_reference( + path: str | None = None, +) -> dict[str, tuple[np.ndarray, np.ndarray]]: + """Scored PBE curves keyed by element, distances then energies in Å and eV.""" + label_path = _LABEL_FILE if path is None else Path(path) + with open(label_path) as fh: + raw: list[dict] = json.load(fh) + return { + entry["element"]: ( + np.asarray(entry["R"], dtype=float), + np.asarray(entry["E"], dtype=float), + ) + for entry in raw + } + + +def scored_element_names(path: str | None = None) -> tuple[str, ...]: + return tuple(load_reference(path)) -def _predict_energies( - calc: Calculator, element: str, bond_lengths: np.ndarray -) -> np.ndarray: - """Evaluate model energy for a homonuclear dimer at each bond length (eV). +@lru_cache +def reference_dummy_scales(path: str | None = None) -> dict[str, float | None]: + """Metric scales from blind bond-position and flat-energy predictions. - Isolated dimer in a 30 Å cubic cell with PBC, matching the MLIP Arena setup. + For each bound reference, the bond dummy guesses the arithmetic midpoint + of that element's evaluation window and the well-depth dummy predicts zero. """ - cell = 30.0 - energies = [] - for r in bond_lengths: + bond_errors: list[float] = [] + well_errors: list[float] = [] + for element, (distances, energies) in load_reference(path).items(): + seps_max = float(np.max(distances)) + r_min, r_max = eval_window(element, seps_max) + general = (distances >= r_min) & (distances <= r_max) + if int(general.sum()) < _MIN_WINDOW_POINTS: + continue + ref_energies = energies[general] + ref_depth = _binding_energy(ref_energies) + if ref_depth < _MIN_BINDING_EV: + continue + ref_distance = _equilibrium_distance(distances[general], ref_energies) + bond_errors.append(abs((r_min + r_max) / 2 - ref_distance)) + well_errors.append(ref_depth) + + return { + "bond_length_mae": float(np.mean(bond_errors)) if bond_errors else None, + "well_depth_mae": float(np.mean(well_errors)) if well_errors else None, + } + + +def _predict_curve(calc: Calculator, element: str, distances: np.ndarray) -> np.ndarray: + """Model energy (eV) at each distance.""" + energies = np.empty(distances.size, dtype=float) + for i, distance in enumerate(distances): atoms = Atoms( symbols=[element, element], - positions=[[0.0, 0.0, 0.0], [r, 0.0, 0.0]], - cell=[cell, cell, cell], + positions=[[0.0, 0.0, 0.0], [float(distance), 0.0, 0.0]], + cell=[_BOX_ANGSTROM, _BOX_ANGSTROM, _BOX_ANGSTROM], pbc=True, ) atoms.calc = calc try: - e = atoms.get_potential_energy() - if not np.isfinite(e): + energy = float(atoms.get_potential_energy()) + if not np.isfinite(energy): raise ValueError("non-finite energy") except Exception as exc: - logging.warning(f"{element}2 @ r={r:.3f} Å failed: {exc}") - e = np.nan - energies.append(e) - return np.array(energies) + logging.warning(f"{element}2 @ r={float(distance):.3f} Å failed: {exc}") + energy = np.nan + energies[i] = energy + return energies def run_inference(model: ASEModel, test_data: Path | None = None) -> dict[str, dict]: - """Compare model and PBE dissociation curves with the stacking_fault metric.""" - label_path = _LABEL_FILE if test_data is None else test_data / "diatomics.json" - - with open(label_path) as fh: - reference_data: list[dict] = json.load(fh) - - results: dict[str, dict] = {} + """Score curves and persist their reference set and dummy normalization.""" + label_path = None if test_data is None else str(test_data / "diatomics.json") + reference = load_reference(label_path) calc = model.calc - - for entry in reference_data: - mol_name: str = entry["name"] - bond_lengths, dft_energies = _scan_arrays(entry) - element = _element_from_name(mol_name) - model_energies = _predict_energies(calc, element, bond_lengths) - mol_result = _curve_metrics(bond_lengths, model_energies, dft_energies) - results[mol_name] = mol_result - logging.info(f"{mol_name}: {mol_result}") - - return results + results: dict[str, dict] = {} + for element, (distances, ref_energies) in reference.items(): + model_energies = _predict_curve(calc, element, distances) + element_result = score_curve( + element, + distances, + ref_energies, + model_energies, + ) + results[element] = element_result + logging.info(f"{element}: {element_result}") + return { + "reference": { + "elements": list(reference), + "dummy_scales": reference_dummy_scales(label_path), + }, + "curves": results, + } diff --git a/lambench/tasks/calculator/elastic/elastic.py b/lambench/tasks/calculator/elastic/elastic.py index 28c08ed..33e891b 100644 --- a/lambench/tasks/calculator/elastic/elastic.py +++ b/lambench/tasks/calculator/elastic/elastic.py @@ -32,9 +32,7 @@ CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -""" -""" The test data is obtained from the following paper: de Jong, M., Chen, W., Angsten, T. et al. Charting the complete elastic properties of inorganic crystalline compounds. diff --git a/lambench/tasks/utils/__init__.py b/lambench/tasks/utils/__init__.py index 1aa881a..e94735f 100644 --- a/lambench/tasks/utils/__init__.py +++ b/lambench/tasks/utils/__init__.py @@ -1,7 +1,6 @@ import logging from math import isnan from pathlib import Path -from typing import Optional def parse_dptest_log_file( diff --git a/lambench/workflow/dflow.py b/lambench/workflow/dflow.py index dd3681f..7cac3b9 100644 --- a/lambench/workflow/dflow.py +++ b/lambench/workflow/dflow.py @@ -1,3 +1,6 @@ +# Environment variables must be loaded before importing dflow and lambench. +# ruff: noqa: E402 + import logging import os from pathlib import Path diff --git a/tests/metrics/test_metrics_calculator.py b/tests/metrics/test_metrics_calculator.py index 9344e1e..01e8793 100644 --- a/tests/metrics/test_metrics_calculator.py +++ b/tests/metrics/test_metrics_calculator.py @@ -92,7 +92,7 @@ def test_summarize_final_rankings(metrics_calculator): metrics_calculator.calculate_stability_results = MagicMock( return_value={"model1": 0.2, "model2": 0.5} ) - metrics_calculator.calculate_diatomics_roughness_results = MagicMock( + metrics_calculator.calculate_diatomics_results = MagicMock( return_value={"model1": 0.05, "model2": 0.03} ) _, result = metrics_calculator.summarize_final_rankings() @@ -101,14 +101,14 @@ def test_summarize_final_rankings(metrics_calculator): assert result.iloc[1]["Model"] == "model1" -def test_calculate_diatomics_roughness_results(metrics_calculator, mock_raw_results): +def test_calculate_diatomics_results(metrics_calculator, mock_raw_results): mock_raw_results.fetch_diatomics_results.return_value = { - "model1": {"avg_roughness": 0.05}, - "model2": {"avg_roughness": 0.03}, + "model1": {"score": 0.05}, + "model2": {"score": 0.03}, "model3": None, - "model4": {"avg_roughness": None}, + "model4": {"score": None}, } - result = metrics_calculator.calculate_diatomics_roughness_results() + result = metrics_calculator.calculate_diatomics_results() assert set(result.keys()) == {"model1", "model2"} np.testing.assert_almost_equal(result["model1"], 0.05) np.testing.assert_almost_equal(result["model2"], 0.03) diff --git a/tests/tasks/calculator/test_diatomics.py b/tests/tasks/calculator/test_diatomics.py index 9a17c29..b5e565e 100644 --- a/tests/tasks/calculator/test_diatomics.py +++ b/tests/tasks/calculator/test_diatomics.py @@ -1,124 +1,356 @@ +import json +from types import SimpleNamespace + import numpy as np import pytest +from ase.data import atomic_numbers, covalent_radii -from lambench.metrics.utils import ( - _diatomics_molecule_names, - aggregated_diatomics_results, -) +from lambench.metrics.utils import aggregated_diatomics_results from lambench.tasks.calculator.diatomics.diatomics import ( - _curve_metrics, - _element_from_name, - _scan_arrays, + design_distances, + eval_window, + load_reference, + reference_dummy_scales, + run_inference, + score_curve, + scored_element_names, ) -def _full_results(**overrides: dict | None) -> dict: - results = {name: {"roughness": 0.02} for name in _diatomics_molecule_names()} - results.update(overrides) - return results +def _parabola(element: str = "Si", shift: float = 0.0, scale: float = 80.0): + r_lo, r_hi = eval_window(element, 6.0, r_min_factor=0.8) + distances = np.linspace(r_lo + 1e-4, r_hi - 1e-4, 60) + r0 = distances[int(len(distances) * 0.65)] + energies = scale * (distances - (r0 + shift)) ** 2 + return element, distances, r0, energies -def _well_scan(): - r = np.linspace(0.8, 4.0, 25) - e = (r - 1.4) ** 2 - 1.0 - return r, e +def _record(**overrides): + record = { + "bond_length_error": 0.0, + "well_depth_error": 0.0, + "roughness": 0.0, + "model_excluded": False, + } + record.update(overrides) + return record -def test_scan_arrays_drops_last_point(): - r, e = _scan_arrays( - {"name": "SiSi", "R": [1.0, 1.2, 1.4, 1.6], "E": [0.0, -1.0, -0.5, 10.0]} +def _full_results(**overrides): + results = {name: _record() for name in scored_element_names()} + results.update(overrides) + return { + "reference": { + "elements": list(scored_element_names()), + "dummy_scales": reference_dummy_scales(), + }, + "curves": results, + } + + +def test_scored_set_and_reference_gate(): + names = scored_element_names() + assert len(names) == 76 + assert names[0] == "H" + assert names[-1] == "U" + for element in ( + "Po", + "At", + "Rn", + "Fr", + "Ra", + "Ni", + "Zr", + "Pr", + "Pm", + "Sm", + "Tb", + "Dy", + "Ho", + "Er", + "Tm", + "Ir", + ): + assert element not in names + distances, energies = load_reference()["H"] + assert distances[0] == pytest.approx( + 0.8 * float(covalent_radii[atomic_numbers["H"]]) ) - np.testing.assert_array_equal(r, [1.0, 1.2, 1.4]) - np.testing.assert_array_equal(e, [0.0, -1.0, -0.5]) - - -def test_perfect_match_is_zero(): - r, e = _well_scan() - metrics = _curve_metrics(r, e, e) - assert metrics is not None + assert distances[-1] == pytest.approx(6.0) + assert len(distances) == len(energies) + + +def test_reference_curves_have_a_wall_and_an_interior_minimum(): + """Every stored PBE curve is a rising short-range wall on a 0.8 r_cov–6 Å grid. + + Elements that bind by at least 0.05 eV also place that minimum inside + the scoring window rather than on an endpoint. + """ + for element, (distances, energies) in load_reference().items(): + rcov = float(covalent_radii[atomic_numbers[element]]) + assert len(distances) == len(energies) == 50 + assert np.allclose(distances, design_distances(element), rtol=0, atol=1e-6) + assert np.isfinite(energies).all() + assert distances[0] == pytest.approx(0.8 * rcov) + assert distances[-1] == pytest.approx(6.0) + assert float(energies[0] - energies.min()) > 1.0 + r_min, r_max = eval_window(element, float(distances[-1])) + window = (distances >= r_min) & (distances <= r_max) + radii = distances[window] + window_energies = energies[window] + if float(window_energies[-1] - window_energies.min()) < 0.05: + continue + minimum = int(np.argmin(window_energies)) + assert 0 < minimum < len(window_energies) - 1 + assert radii[0] < radii[minimum] < radii[-1] + + +def test_dummy_scales_are_positive(): + dummy = reference_dummy_scales() + assert dummy["bond_length_mae"] == pytest.approx(1.1173, abs=1e-3) + assert dummy["well_depth_mae"] == pytest.approx(2.5920, abs=1e-3) + + +def test_identical_parabola_has_zero_reference_error(): + element, distances, _r0, energies = _parabola() + metrics = score_curve( + element, + distances, + energies, + energies, + ) + assert metrics["model_excluded"] is False + assert metrics["bond_length_error"] == pytest.approx(0.0, abs=1e-6) + assert metrics["well_depth_error"] == pytest.approx(0.0, abs=1e-6) assert metrics["roughness"] == pytest.approx(0.0) -def test_constant_offset_is_removed_by_min_shift(): - r, e = _well_scan() - metrics = _curve_metrics(r, e + 1.5, e) - assert metrics is not None - assert metrics["roughness"] == pytest.approx(0.0, abs=1e-10) - - -def test_constant_dummy_scores_one(): - r, e = _well_scan() - dummy = _curve_metrics(r, np.full_like(e, e[-1]), e) - match = _curve_metrics(r, e, e) - assert dummy is not None and match is not None - assert dummy["roughness"] == pytest.approx(1.0) - assert dummy["roughness"] > match["roughness"] - - -def test_worse_than_dummy_is_capped_at_one(): - r, e = _well_scan() - y = e - np.min(e) - inverted = _curve_metrics(r, np.max(y) - y, e) - assert inverted is not None - assert inverted["roughness"] == pytest.approx(1.0) - +def test_shifted_well_moves_bond_length(): + element, distances, _r0, energies = _parabola() + _element, _distances, _shifted_r0, shifted = _parabola(shift=0.15) + metrics = score_curve( + element, + distances, + energies, + shifted, + ) + assert metrics["bond_length_error"] == pytest.approx(0.15, abs=1e-6) + + +def test_scaled_well_changes_well_depth(): + element, distances, _r0, energies = _parabola() + scaled = energies * 0.5 + metrics = score_curve(element, distances, energies, scaled) + r_min, r_max = eval_window(element, float(distances[-1])) + window = (distances >= r_min) & (distances <= r_max) + ref_depth = float(energies[window][-1] - energies[window].min()) + assert metrics["well_depth_error"] == pytest.approx(0.5 * ref_depth) + + +def test_unbound_reference_skips_geometry_and_well_depth(): + element, distances, r0, _energies = _parabola(scale=1e-4) + energies = 1e-4 * (distances - r0) ** 2 + metrics = score_curve(element, distances, energies, energies) + assert metrics["model_excluded"] is False + assert metrics["bond_length_error"] is None + assert metrics["well_depth_error"] is None + + +def test_flat_model_has_zero_depth_and_reference_depth_error(): + element, distances, _r0, energies = _parabola() + flat = np.full_like(energies, energies.min()) + metrics = score_curve(element, distances, energies, flat) + r_min, r_max = eval_window(element, float(distances[-1])) + window = (distances >= r_min) & (distances <= r_max) + ref_depth = float(energies[window][-1] - energies[window].min()) + assert metrics["well_depth_error"] == pytest.approx(ref_depth) + + +def test_roughness_matches_original_finite_difference_formula(): + element, distances, _r0, energies = _parabola() + predicted = energies.copy() + predicted[20:25] += 15.0 + r = distances[:-1] + ref = energies[:-1] - energies[:-1].min() + pred = predicted[:-1] - predicted[:-1].min() + ref_slopes = np.diff(ref) / np.diff(r) / ref.max() + pred_slopes = np.diff(pred) / np.diff(r) / pred.max() + expected = min( + np.mean(np.abs(pred_slopes - ref_slopes)) / np.mean(np.abs(ref_slopes)), + 1.0, + ) + result = score_curve(element, distances, energies, predicted) + assert result["roughness"] == pytest.approx(expected) + assert result["roughness"] > 0 -def test_zero_bond_step_returns_none(): - r = np.array([1.0, 1.0, 1.4]) - e = np.array([0.0, -1.0, -0.5]) - assert _curve_metrics(r, e, e) is None +def test_roughness_flat_dummy_and_energy_scale_invariance(): + element, distances, _r0, energies = _parabola() + flat = score_curve(element, distances, energies, np.zeros_like(energies)) + scaled = score_curve(element, distances, energies, energies * 7 + 10) + assert flat["roughness"] == pytest.approx(1.0) + assert scaled["roughness"] == pytest.approx(0.0, abs=1e-12) -def test_shape_match_is_scale_invariant(): - r, e = _well_scan() - scaled = _curve_metrics(r, 10 * e, e) - assert scaled is not None - assert scaled["roughness"] == pytest.approx(0.0, abs=1e-10) +def test_roughness_drops_last_point(): + element, distances, _r0, energies = _parabola() + changed = energies.copy() + changed[-1] += 100 + result = score_curve(element, distances, energies, changed) + assert result["roughness"] == pytest.approx(0.0) -def test_oscillation_increases_roughness(): - r, e = _well_scan() - match = _curve_metrics(r, e, e) - wiggly = _curve_metrics(r, e + 0.2 * np.sin(25 * (r - r[0])), e) - assert match is not None and wiggly is not None - assert wiggly["roughness"] > match["roughness"] +def test_nonfinite_curve_is_excluded_but_jumpy_curve_is_scored(): + element, distances, _r0, energies = _parabola() + broken = energies.copy() + broken[len(broken) // 2] = np.nan + assert score_curve(element, distances, energies, broken)["model_excluded"] -def test_nonfinite_returns_none(): - r = np.array([1.0, 1.2, 1.4, 1.6]) - e = np.array([0.0, np.nan, -0.5, -0.4]) - assert _curve_metrics(r, e, e) is None + jumpy = np.zeros_like(energies) + jumpy[10:15] += 5.0 + jumpy[25:30] -= 5.0 + jumpy[40:45] += 5.0 + assert not score_curve(element, distances, energies, jumpy)["model_excluded"] -def test_element_from_name(): - assert _element_from_name("HH") == "H" - assert _element_from_name("AlAl") == "Al" +@pytest.mark.parametrize( + "invalid", ["nonfinite", "duplicate", "reversed", "matrix", "length"] +) +def test_invalid_curve_grid_is_excluded(invalid): + element, distances, _r0, energies = _parabola() + if invalid == "nonfinite": + distances[10] = np.nan + elif invalid == "duplicate": + distances[10] = distances[9] + elif invalid == "reversed": + distances = distances[::-1] + elif invalid == "matrix": + distances = distances.reshape(-1, 1) + else: + distances = distances[:-1] + assert score_curve(element, distances, energies, energies)["model_excluded"] + + +def test_constant_reference_has_no_roughness_baseline(): + element, distances, _r0, energies = _parabola() + flat = np.zeros_like(energies) + assert score_curve(element, distances, flat, energies)["model_excluded"] + + +def test_aggregated_perfect_score_is_zero(): + agg = aggregated_diatomics_results(_full_results()) + assert agg["coverage"] == pytest.approx(1.0) + assert agg["bond_length_mae"] == pytest.approx(0.0) + assert agg["well_depth_mae"] == pytest.approx(0.0) + assert agg["avg_roughness"] == pytest.approx(0.0) + assert agg["score"] == pytest.approx(0.0) + + +@pytest.mark.parametrize( + ("metric", "dummy_key"), + ( + ("bond_length_error", "bond_length_mae"), + ("well_depth_error", "well_depth_mae"), + ), +) +def test_aggregated_caps_reference_errors_at_dummy(metric, dummy_key): + dummy = reference_dummy_scales() + agg = aggregated_diatomics_results( + _full_results( + **{ + name: _record(**{metric: dummy[dummy_key] * 5}) + for name in scored_element_names() + } + ) + ) + assert agg["score"] == pytest.approx(1.0 / 3.0) -def test_element_from_name_rejects_invalid(): - with pytest.raises(ValueError): - _element_from_name("H2") - with pytest.raises(ValueError): - _element_from_name("AlH") +def test_aggregated_roughness_and_coverage(): + names = scored_element_names() + results = _full_results(**{name: _record(roughness=1.0) for name in names}) + results["curves"]["H"] = _record(model_excluded=True, roughness=None) + agg = aggregated_diatomics_results(results) + coverage = (len(names) - 1) / len(names) + assert agg["coverage"] == pytest.approx(coverage) + assert agg["avg_roughness"] == pytest.approx(1.0) + assert agg["score"] is None -def test_aggregated_means(): - names = _diatomics_molecule_names() - results = {name: {"roughness": 0.02} for name in names} - results["HH"] = {"roughness": 0.01} - results["NN"] = {"roughness": 0.03} - n = len(names) +def test_aggregated_missing_element_counts_against_coverage(): + results = _full_results() + del results["curves"]["H"] agg = aggregated_diatomics_results(results) - assert agg["avg_roughness"] == pytest.approx((0.01 + 0.03 + 0.02 * (n - 2)) / n) - + assert agg["coverage"] == pytest.approx(75 / 76) + assert agg["score"] is None -def test_aggregated_empty_results(): - agg = aggregated_diatomics_results({}) - assert agg["avg_roughness"] is None +def test_aggregated_empty_or_incomplete_terms_have_no_score(): + empty = aggregated_diatomics_results({}) + assert empty["coverage"] == pytest.approx(0.0) + assert empty["score"] is None -def test_aggregated_incomplete_coverage_is_none(): - assert aggregated_diatomics_results(_full_results(HH=None))["avg_roughness"] is None - incomplete = _full_results() - del incomplete["HH"] - assert aggregated_diatomics_results(incomplete)["avg_roughness"] is None + no_bond = aggregated_diatomics_results( + _full_results( + **{name: _record(bond_length_error=None) for name in scored_element_names()} + ) + ) + assert no_bond["avg_roughness"] == pytest.approx(0.0) + assert no_bond["score"] is None + + +def test_custom_reference_metadata_survives_json_roundtrip(tmp_path, monkeypatch): + reference = { + name: (r, e * 3 + 10) + for name, (r, e) in load_reference().items() + if name in {"H", "Li"} + } + path = tmp_path / "diatomics.json" + path.write_text( + json.dumps( + [ + {"element": name, "R": r.tolist(), "E": e.tolist()} + for name, (r, e) in reference.items() + ] + ) + ) + monkeypatch.setattr( + "lambench.tasks.calculator.diatomics.diatomics._predict_curve", + lambda calc, name, distances: reference[name][1], + ) + results = json.loads( + json.dumps(run_inference(SimpleNamespace(calc=None), tmp_path)) + ) + dummy = reference_dummy_scales(str(path)) + assert results["reference"]["elements"] == ["H", "Li"] + assert results["reference"]["dummy_scales"] == dummy + assert dummy["well_depth_mae"] != reference_dummy_scales()["well_depth_mae"] + agg = aggregated_diatomics_results(results) + assert agg["coverage"] == 1.0 + assert agg["score"] == pytest.approx(0.0) + + for record in results["curves"].values(): + record.update( + roughness=1.0, + bond_length_error=dummy["bond_length_mae"], + well_depth_error=dummy["well_depth_mae"], + ) + assert aggregated_diatomics_results(results)["score"] == pytest.approx(1.0) + del results["curves"]["Li"] + assert aggregated_diatomics_results(results)["coverage"] == 0.5 + assert aggregated_diatomics_results(results)["score"] is None + + +def test_reference_without_bound_elements_has_no_dummy_scale(tmp_path): + r, e = load_reference()["He"] + path = tmp_path / "diatomics.json" + path.write_text(json.dumps([{"element": "He", "R": r.tolist(), "E": e.tolist()}])) + dummy = reference_dummy_scales(str(path)) + assert dummy == {"bond_length_mae": None, "well_depth_mae": None} + results = { + "reference": {"elements": ["He"], "dummy_scales": dummy}, + "curves": {"He": score_curve("He", r, e, e)}, + } + agg = aggregated_diatomics_results(results) + assert agg["coverage"] == 1.0 + assert agg["score"] is None