From 9a20a4d91baac293c7eaa7b2becde986f95c7f8a Mon Sep 17 00:00:00 2001 From: willow Date: Wed, 8 Jul 2026 15:26:09 +0100 Subject: [PATCH] Revert "feat: built-in default QRF k-point model with HuggingFace resolution" --- .gitignore | 12 - pyproject.toml | 10 - src/goldilocks_core/advisors/__init__.py | 7 +- .../advisors/kdistance_advisor.py | 260 ---- .../{kindex_advisor.py => kmesh_advisor.py} | 0 src/goldilocks_core/cli/core.py | 35 +- src/goldilocks_core/jobs.py | 14 - src/goldilocks_core/ml/atom_features.py | 39 - src/goldilocks_core/ml/cgcnn.py | 141 -- src/goldilocks_core/ml/cgcnn_graph.py | 53 - src/goldilocks_core/ml/kdistance_features.py | 175 --- src/goldilocks_core/ml/metallicity.py | 52 - src/goldilocks_core/ml/models.py | 57 +- tests/test_cgcnn.py | 64 - ...ndex_advisor.py => test_k_mesh_advisor.py} | 10 +- tests/test_kdistance_advisor.py | 261 ---- tests/test_kdistance_features.py | 33 - tests/test_ml_models.py | 43 +- uv.lock | 1296 ----------------- 19 files changed, 40 insertions(+), 2522 deletions(-) delete mode 100644 src/goldilocks_core/advisors/kdistance_advisor.py rename src/goldilocks_core/advisors/{kindex_advisor.py => kmesh_advisor.py} (100%) delete mode 100644 src/goldilocks_core/ml/atom_features.py delete mode 100644 src/goldilocks_core/ml/cgcnn.py delete mode 100644 src/goldilocks_core/ml/cgcnn_graph.py delete mode 100644 src/goldilocks_core/ml/kdistance_features.py delete mode 100644 src/goldilocks_core/ml/metallicity.py delete mode 100644 tests/test_cgcnn.py rename tests/{test_kindex_advisor.py => test_k_mesh_advisor.py} (91%) delete mode 100644 tests/test_kdistance_advisor.py delete mode 100644 tests/test_kdistance_features.py diff --git a/.gitignore b/.gitignore index c94811f..7e7c1dc 100644 --- a/.gitignore +++ b/.gitignore @@ -15,15 +15,3 @@ notebooks/ local_data/ # Pi harness state .pi/ - -# macOS -.DS_Store - -# Built docs site (mkdocs output) -site/ - -# Pipeline run outputs -goldilocks_output/ - -# Stray planning scratch files -plan-*.md diff --git a/pyproject.toml b/pyproject.toml index a52dfad..323a266 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -5,20 +5,10 @@ description = "Materials machine learning tools for recommending DFT calculation readme = "README.md" requires-python = ">=3.12" dependencies = [ - "dscribe>=2.1", - "huggingface_hub>=0.23", "joblib>=1.5.3", "matminer>=0.10.1", "numpy>=2.4.4", - "pandas>=2.0", "pymatgen>=2026.3.23", - "sklearn-quantile>=0.1.1", - # The default k-points model (QRF + CGCNN metallicity) is a core capability, - # so its ML stack ships in the base install (decision: stfc/goldilocks-core#32). - # torch-geometric requires torch; on GPU hosts install the matching CUDA wheel - # of torch first, then sync. - "torch>=2.0", - "torch-geometric>=2.5", ] [project.scripts] diff --git a/src/goldilocks_core/advisors/__init__.py b/src/goldilocks_core/advisors/__init__.py index d7b7e2b..7cff485 100644 --- a/src/goldilocks_core/advisors/__init__.py +++ b/src/goldilocks_core/advisors/__init__.py @@ -1,7 +1,6 @@ """Advisor interfaces for calculation recommendations.""" -from .kdistance_advisor import default_kmesh_advisor as default_kmesh_advisor -from .kindex_advisor import advise_kpoints as advise_kpoints -from .kindex_advisor import ml_kmesh_advisor as ml_kmesh_advisor +from .kmesh_advisor import advise_kpoints as advise_kpoints +from .kmesh_advisor import ml_kmesh_advisor as ml_kmesh_advisor -__all__ = ["advise_kpoints", "default_kmesh_advisor", "ml_kmesh_advisor"] +__all__ = ["advise_kpoints", "ml_kmesh_advisor"] diff --git a/src/goldilocks_core/advisors/kdistance_advisor.py b/src/goldilocks_core/advisors/kdistance_advisor.py deleted file mode 100644 index d950193..0000000 --- a/src/goldilocks_core/advisors/kdistance_advisor.py +++ /dev/null @@ -1,260 +0,0 @@ -"""Quantile Random Forest (QRF) k-distance advisor. - -The QRF model predicts a VASP-style k-point distance (Å⁻¹) as three quantiles -(lower, median, upper). The median drives the concrete mesh; the interval and -the model's confidence level are recorded in provenance. This module holds the -model-agnostic post-prediction logic; feature extraction (which pulls heavy ML -dependencies) is supplied separately. -""" - -from __future__ import annotations - -import os -from dataclasses import replace - -import numpy as np -from pymatgen.core import Structure - -from goldilocks_core.contracts import ( - CalculationHints, - KMeshAdvisor, - KPointAdvice, - KPointSelection, - ModelSpec, - Provenance, - StructureFeatureVector, -) -from goldilocks_core.kmesh import k_distance_to_mesh, resolve_kpoints_from_advice - -# Built-in default k-point model: the STFC QRF at 95% confidence, resolved from -# the Hugging Face Hub. Feature set names the extractor the model was trained on. -DEFAULT_KPOINTS_MODEL = ModelSpec( - name="kpoints-goldilocks-QRF", - version="QRF95", - model_type="random_forest", - target="k_distance", - feature_set="qrf_comp_struct_soap_lattice_metal", - source="huggingface", - location="STFC-SCD/kpoints-goldilocks-QRF::QRF95.pkl", - revision=None, -) - -# Confidence level of the default model and the calibrated interval correction -# (widens the interval), from the trained QRF at 0.95. See the goldilocks -# k-distance reference implementation. -DEFAULT_KPOINTS_CONFIDENCE = 0.95 -DEFAULT_KPOINTS_CORRECTION = -0.0016 - -# The QRF k-distance features include a CGCNN metallicity block. Its checkpoint -# and atom-embedding table are published to Hugging Face and resolved by default -# from this repo; local paths (env-overridable) take precedence for offline dev. -# When nothing resolves, the advisor falls back to heuristic advice. -DEFAULT_METALLICITY_REPO = "JunwenYin/metallicity-goldilocks-CGCNN" -_METALLICITY_CHECKPOINT_FILE = "is_metal.ckpt" -_METALLICITY_ATOM_INIT_FILE = "atom_init.json" - - -def predict_kdistance_quantiles( - model: object, - features: StructureFeatureVector, - correction: float = DEFAULT_KPOINTS_CORRECTION, -) -> tuple[float, float, float]: - """Return (median, lower, upper) k-distance in Å⁻¹ from a QRF prediction. - - The QRF returns three quantiles ``[lower, median, upper]`` for the single - input row. ``correction`` calibrates (widens) the interval bounds. - - Raises: - AttributeError: If the model has no ``predict`` method. - ValueError: If the prediction does not yield three quantiles. - """ - if not hasattr(model, "predict"): - raise AttributeError("QRF model does not provide a 'predict' method.") - - raw = np.asarray(model.predict(features.values.reshape(1, -1)), dtype=float) - if raw.size != 3: - raise ValueError( - f"Expected 3 quantiles from the QRF prediction; got {raw.size}." - ) - - lower, median, upper = raw.reshape(3, -1)[:, 0] - return float(median), float(lower) - correction, float(upper) + correction - - -def kdistance_to_selection( - structure: Structure, - median: float, - lower: float, - upper: float, - *, - data_source: str, - confidence: float = DEFAULT_KPOINTS_CONFIDENCE, - mesh_type: str = "monkhorst-pack", -) -> KPointSelection: - """Build a concrete k-point selection from a predicted k-distance interval. - - The median distance sets the mesh; the interval and confidence level are - recorded in provenance so callers can judge the prediction. - """ - return KPointSelection( - grid=k_distance_to_mesh(structure, median), - shift=(0, 0, 0), - mesh_type=mesh_type, - provenance=Provenance( - source="model", - reason=( - f"ML-predicted k-point distance {median:.4f} Å⁻¹ " - f"(interval {lower:.4f}-{upper:.4f} Å⁻¹)." - ), - data_source=data_source, - confidence=confidence, - ), - ) - - -def _heuristic_fallback( - structure: Structure, - hints: CalculationHints, - advice: KPointAdvice, - error: Exception, -) -> KPointSelection: - """Resolve k-points from heuristic advice, flagging that the QRF was skipped. - - The mesh comes from the Advise stage, so its provenance source stays honest - (``default``/``analysis``, never ``model``); a warning records that the ML - model was attempted and could not be used. - """ - detail = str(error) or error.__class__.__name__ - selection = resolve_kpoints_from_advice(structure, hints, advice) - warning = f"ML k-point model unavailable; used heuristic k-point advice ({detail})." - return replace( - selection, - provenance=replace( - selection.provenance, - warnings=(*selection.provenance.warnings, warning), - ), - ) - - -def _resolve_metallicity_artifacts( - checkpoint: str | None, - atom_init: str | None, - repo: str | None, - revision: str | None, -) -> tuple[str, str]: - """Resolve the metallicity checkpoint and atom-embedding table to local paths. - - Explicit local paths win; any path left as ``None`` is downloaded (and cached) - from the Hugging Face ``repo``. Raising here is intentional — the caller runs - this inside its guarded load so a download failure degrades to the heuristic. - """ - if checkpoint is not None and atom_init is not None: - return checkpoint, atom_init - if repo is None: - raise ValueError( - "Metallicity artifacts unresolved: provide local paths or a HF repo." - ) - from huggingface_hub import hf_hub_download - - checkpoint = checkpoint or hf_hub_download( - repo, _METALLICITY_CHECKPOINT_FILE, revision=revision - ) - atom_init = atom_init or hf_hub_download( - repo, _METALLICITY_ATOM_INIT_FILE, revision=revision - ) - return checkpoint, atom_init - - -def qrf_kdistance_advisor( - metallicity_checkpoint: str | None = None, - metallicity_atom_init: str | None = None, - *, - metallicity_repo: str | None = None, - metallicity_revision: str | None = None, - spec: ModelSpec = DEFAULT_KPOINTS_MODEL, - correction: float = DEFAULT_KPOINTS_CORRECTION, - confidence: float = DEFAULT_KPOINTS_CONFIDENCE, -) -> KMeshAdvisor: - """Return a Kmesh-stage backend that predicts k-distance with the QRF. - - Loads the QRF and the CGCNN metallicity model once; the returned advisor - reuses them. The metallicity checkpoint and atom-embedding table resolve from - explicit local paths when given, otherwise from ``metallicity_repo`` on - Hugging Face. An explicit ``k_grid`` or ``k_spacing`` hint bypasses the model - and resolves from advice instead. - - Never hard-fails: if the models or their dependencies cannot be loaded (or - downloaded), or a per-structure prediction raises, the advisor degrades to - the heuristic advice and records the reason in provenance warnings. - """ - from goldilocks_core.ml.kdistance_features import extract_qrf_features - from goldilocks_core.ml.metallicity import load_metallicity_model - from goldilocks_core.ml.models import load_model - - try: - qrf = load_model(spec) - checkpoint, atom_init = _resolve_metallicity_artifacts( - metallicity_checkpoint, - metallicity_atom_init, - metallicity_repo, - metallicity_revision, - ) - metal_model = load_metallicity_model(checkpoint) - except Exception as error: # deps missing, download fails, bad checkpoint - load_error = error # bind outside the except scope for the closure - - def unavailable_advisor(structure, hints, kpoint_advice): - if hints.k_grid is not None or hints.k_spacing is not None: - return resolve_kpoints_from_advice(structure, hints, kpoint_advice) - return _heuristic_fallback(structure, hints, kpoint_advice, load_error) - - return unavailable_advisor - - def advisor(structure, hints, kpoint_advice): - if hints.k_grid is not None or hints.k_spacing is not None: - return resolve_kpoints_from_advice(structure, hints, kpoint_advice) - try: - features = extract_qrf_features(structure, metal_model, atom_init) - median, lower, upper = predict_kdistance_quantiles( - qrf, features, correction - ) - except Exception as predict_error: - return _heuristic_fallback(structure, hints, kpoint_advice, predict_error) - return kdistance_to_selection( - structure, - median, - lower, - upper, - data_source=spec.name, - confidence=confidence, - ) - - return advisor - - -def default_kmesh_advisor( - *, - metallicity_checkpoint: str | None = None, - metallicity_atom_init: str | None = None, - metallicity_repo: str | None = None, -) -> KMeshAdvisor: - """Return the built-in default Kmesh backend: the QRF model with fallback. - - Resolves the QRF and the CGCNN metallicity model from Hugging Face - (``DEFAULT_KPOINTS_MODEL`` and ``DEFAULT_METALLICITY_REPO``). For offline - development, ``GOLDILOCKS_METALLICITY_CHECKPOINT`` / - ``GOLDILOCKS_METALLICITY_ATOM_INIT`` point at local files, and - ``GOLDILOCKS_METALLICITY_REPO`` overrides the source repo. This never - hard-fails: if the models or their dependencies are unavailable, the advisor - degrades to heuristic advice (see ``qrf_kdistance_advisor``). - """ - checkpoint = metallicity_checkpoint or os.environ.get( - "GOLDILOCKS_METALLICITY_CHECKPOINT" - ) - atom_init = metallicity_atom_init or os.environ.get( - "GOLDILOCKS_METALLICITY_ATOM_INIT" - ) - repo = metallicity_repo or os.environ.get( - "GOLDILOCKS_METALLICITY_REPO", DEFAULT_METALLICITY_REPO - ) - return qrf_kdistance_advisor(checkpoint, atom_init, metallicity_repo=repo) diff --git a/src/goldilocks_core/advisors/kindex_advisor.py b/src/goldilocks_core/advisors/kmesh_advisor.py similarity index 100% rename from src/goldilocks_core/advisors/kindex_advisor.py rename to src/goldilocks_core/advisors/kmesh_advisor.py diff --git a/src/goldilocks_core/cli/core.py b/src/goldilocks_core/cli/core.py index 4c24ba2..ff61165 100644 --- a/src/goldilocks_core/cli/core.py +++ b/src/goldilocks_core/cli/core.py @@ -14,7 +14,7 @@ CoreResult, ModelSpec, ) -from goldilocks_core.jobs import Pipeline, default_pipeline, run_core_job +from goldilocks_core.jobs import Pipeline, run_core_job from goldilocks_core.pseudo.pp_registry import load_pseudo_metadata @@ -160,29 +160,20 @@ def _request_from_args(args: argparse.Namespace) -> CoreJobRequest: def _pipeline_from_args(args: argparse.Namespace) -> Pipeline | None: - """Build the pipeline for the requested k-point backend. - - Precedence: an explicit ``--model`` (local CSLR k-index model) wins; an - explicit k-point hint (``--k-grid``/``--k-spacing``) resolves from advice - without loading any model; otherwise the built-in default QRF pipeline runs - (with heuristic fallback). - """ - if args.model is not None: - spec = ModelSpec( - name=args.model_name, - version=args.model_version, - model_type="random_forest", - target="k_index", - feature_set="cslr", - source="local", - location=args.model, - ) - return Pipeline(kmesh=ml_kmesh_advisor(spec)) - - if args.k_grid is not None or args.k_spacing is not None: + """Build a custom pipeline from CLI backend options.""" + if args.model is None: return None - return default_pipeline() + spec = ModelSpec( + name=args.model_name, + version=args.model_version, + model_type="random_forest", + target="k_index", + feature_set="cslr", + source="local", + location=args.model, + ) + return Pipeline(kmesh=ml_kmesh_advisor(spec)) def _parse_optional_bool(value: str | None) -> bool | None: diff --git a/src/goldilocks_core/jobs.py b/src/goldilocks_core/jobs.py index 38cee88..bc9f57a 100644 --- a/src/goldilocks_core/jobs.py +++ b/src/goldilocks_core/jobs.py @@ -56,20 +56,6 @@ class Pipeline: bundle: BundleStage = write_bundle_directory -def default_pipeline() -> Pipeline: - """Return a Pipeline whose Kmesh stage uses the built-in default ML model. - - Unlike the bare ``Pipeline()`` (whose Kmesh stage is the spacing heuristic), - this resolves the default QRF k-point model and predicts k-points out of the - box, degrading to heuristic advice when the model or its dependencies are - unavailable. This is the pipeline the CLI uses when no ``--model`` and no - explicit k-point hint are given. - """ - from goldilocks_core.advisors import default_kmesh_advisor - - return Pipeline(kmesh=default_kmesh_advisor()) - - def run_core_job( request: CoreJobRequest, *, diff --git a/src/goldilocks_core/ml/atom_features.py b/src/goldilocks_core/ml/atom_features.py deleted file mode 100644 index ce70809..0000000 --- a/src/goldilocks_core/ml/atom_features.py +++ /dev/null @@ -1,39 +0,0 @@ -"""Per-atom embedding features for the CGCNN graph. - -Looks up each atom's feature vector from an ``atom_init`` JSON (atomic number --> embedding), as used by the pretrained CGCNN metallicity model. -""" - -from __future__ import annotations - -import json - -from pymatgen.core.structure import Structure - - -def load_atom_embeddings(atom_init_path: str) -> dict[str, list[float]]: - """Load the atomic-number -> embedding-vector map from a JSON file.""" - with open(atom_init_path) as handle: - return json.load(handle) - - -def atom_features_from_structure( - structure: Structure, - atom_init_path: str, -) -> list[list[float]]: - """Return the embedding vector for each atom in ``structure``. - - Raises: - ValueError: If the embedding file has no entry for an element. - """ - embeddings = load_atom_embeddings(atom_init_path) - - features: list[list[float]] = [] - for site in structure: - number = site.specie.number - feature = embeddings.get(str(number)) - if feature is None: - raise ValueError(f"No atom embedding for atomic number {number}.") - features.append(feature) - - return features diff --git a/src/goldilocks_core/ml/cgcnn.py b/src/goldilocks_core/ml/cgcnn.py deleted file mode 100644 index 1af3327..0000000 --- a/src/goldilocks_core/ml/cgcnn.py +++ /dev/null @@ -1,141 +0,0 @@ -"""CGCNN (Crystal Graph Convolutional Neural Network) in PyTorch Geometric. - -Ported from the goldilocks k-points models. Used here to load the pretrained -metallicity model and extract a per-crystal representation (the "metal -features" block of the QRF k-distance feature vector). torch / torch_geometric -are heavy optional dependencies imported at module load. -""" - -from __future__ import annotations - -import torch -import torch.nn.functional as F -from torch import nn -from torch.nn import BatchNorm1d, Linear -from torch_geometric.nn import MessagePassing, global_mean_pool - - -class RBFExpansion(nn.Module): - """Expand scalar edge distances into Gaussian radial basis features.""" - - def __init__(self, vmin: float = 0.0, vmax: float = 8.0, bins: int = 40): - super().__init__() - centers = torch.linspace(vmin, vmax, bins) - self.register_buffer("centers", centers) - self.gamma = 1 / ((centers[1] - centers[0]).item() ** 2) - - def forward(self, distance: torch.Tensor) -> torch.Tensor: - return torch.exp(-self.gamma * (distance.unsqueeze(1) - self.centers) ** 2) - - -class CGCNNConv(MessagePassing): - """CGCNN gated graph convolution layer.""" - - def __init__(self, node_dim: int, edge_dim: int, out_dim: int): - super().__init__(aggr="add") - self.lin_f = Linear(2 * node_dim + edge_dim, out_dim) - self.lin_s = Linear(2 * node_dim + edge_dim, out_dim) - self.batch_norm = BatchNorm1d(out_dim) - - def forward(self, x, edge_index, edge_attr): - return self.propagate(edge_index, x=x, edge_attr=edge_attr) - - def message(self, x_i, x_j, edge_attr): - z = torch.cat([x_i, x_j, edge_attr], dim=1) - gate = torch.sigmoid(self.lin_f(z)) - msg = F.softplus(self.lin_s(z)) - return gate * msg - - def update(self, aggr_out, x): - return self.batch_norm(aggr_out + x) - - -class CGCNN_PyG(nn.Module): - """CGCNN model. Only the pieces needed to load the checkpoint and extract - the pooled crystal representation are exercised here; other task heads are - reconstructed from the checkpoint hyper-parameters so ``load_state_dict`` - matches. - """ - - def __init__( - self, - orig_atom_fea_len: int, - edge_feat_dim: int = 64, - name: str = "cgcnn", - h_fea_len: int = 128, - atom_fea_len: int = 64, - n_conv: int = 3, - n_h: int = 3, - robust_regression: bool = False, - classification: bool = False, - quantile_regression: bool = False, - num_quantiles: int = 1, - pooling_type: str = "mean_pool", - num_classes: int = 2, - additional_compound_features: bool = False, - add_feat_len: int | None = None, - ): - super().__init__() - self.name = name - self.classification = classification - self.robust_regression = robust_regression - self.quantile_regression = quantile_regression - - if classification: - self.num_classes = num_classes - elif quantile_regression: - self.num_quantiles = num_quantiles - - self.global_pooling = pooling_type - self.additional_compound_features = additional_compound_features - if self.additional_compound_features: - self.add_feat_len = add_feat_len - - self.embedding = nn.Linear(orig_atom_fea_len, atom_fea_len) - self.rbf = RBFExpansion(vmin=0, vmax=8.0, bins=edge_feat_dim) - self.convs = nn.ModuleList( - [ - CGCNNConv(atom_fea_len, edge_feat_dim, out_dim=atom_fea_len) - for _ in range(n_conv) - ] - ) - self.conv_to_fc_softplus = nn.Softplus() - - if self.additional_compound_features: - self.add_feat_norm = nn.BatchNorm1d(add_feat_len) - self.proj_add_feat = nn.Linear(add_feat_len, atom_fea_len) - self.conv_to_fc = nn.Linear(2 * atom_fea_len, h_fea_len) - self.softplus = nn.Softplus() - else: - self.conv_to_fc = nn.Linear(atom_fea_len, h_fea_len) - - if n_h > 1: - self.fcs = nn.ModuleList( - [nn.Linear(h_fea_len, h_fea_len) for _ in range(n_h - 1)] - ) - self.softpluses = nn.ModuleList([nn.Softplus() for _ in range(n_h - 1)]) - - if self.classification: - self.fc_out = nn.Linear(h_fea_len, self.num_classes) - elif self.robust_regression: - self.fc_out = nn.Linear(h_fea_len, 2) - elif self.quantile_regression: - self.fc_out = nn.Linear(h_fea_len, self.num_quantiles) - else: - self.fc_out = nn.Linear(h_fea_len, 1) - - def extract_crystal_repr(self, data) -> torch.Tensor: - """Return the pooled per-crystal representation [n_graphs, atom_fea_len].""" - x, edge_index, edge_attr, batch = ( - data.x, - data.edge_index, - data.edge_attr, - data.batch, - ) - x = self.embedding(x) - edge_attr = self.rbf(edge_attr.view(-1)) - for conv in self.convs: - x = conv(x, edge_index, edge_attr) - if self.global_pooling == "mean_pool": - x = global_mean_pool(x, batch) - return x diff --git a/src/goldilocks_core/ml/cgcnn_graph.py b/src/goldilocks_core/ml/cgcnn_graph.py deleted file mode 100644 index 31c84e0..0000000 --- a/src/goldilocks_core/ml/cgcnn_graph.py +++ /dev/null @@ -1,53 +0,0 @@ -"""Build a PyTorch Geometric graph from a structure for the CGCNN model. - -Radius-based neighbor search: nodes are atoms, edges connect atoms within a -cutoff (closest ``max_neighbors``), edge attributes are interatomic distances. -""" - -from __future__ import annotations - -import warnings -from collections.abc import Sequence - -import torch -from pymatgen.core.structure import Structure -from torch_geometric.data import Data - - -def build_radius_cgcnn_graph_from_structure( - structure: Structure, - atom_features: Sequence, - radius: float = 10.0, - max_neighbors: int = 12, -) -> Data: - """Return a PyG ``Data`` graph (x, edge_index, edge_attr) for the structure.""" - x = torch.tensor(atom_features, dtype=torch.float32) - - edge_index: list[list[int]] = [] - edge_attr: list[list[float]] = [] - disconnected: list[int] = [] - - all_neighbors = structure.get_all_neighbors(radius, include_index=True) - for i, neighbors in enumerate(all_neighbors): - neighbors = sorted(neighbors, key=lambda n: n[1])[:max_neighbors] - if not neighbors: - disconnected.append(i) - for neighbor in neighbors: - edge_index.append([i, neighbor[2]]) - edge_attr.append([neighbor[1]]) - - if disconnected: - warnings.warn( - f"{len(disconnected)} atoms had no neighbors within radius {radius}: " - f"{disconnected}", - stacklevel=2, - ) - - if edge_index: - edge_index_t = torch.tensor(edge_index, dtype=torch.long).t().contiguous() - edge_attr_t = torch.tensor(edge_attr, dtype=torch.float32) - else: - edge_index_t = torch.empty((2, 0), dtype=torch.long) - edge_attr_t = torch.empty((0, 1), dtype=torch.float32) - - return Data(x=x, edge_index=edge_index_t, edge_attr=edge_attr_t) diff --git a/src/goldilocks_core/ml/kdistance_features.py b/src/goldilocks_core/ml/kdistance_features.py deleted file mode 100644 index b757ca1..0000000 --- a/src/goldilocks_core/ml/kdistance_features.py +++ /dev/null @@ -1,175 +0,0 @@ -"""Composition + structure + SOAP + lattice features for the QRF k-distance model. - -Reproduces the non-metallicity half of the feature vector the STFC QRF was -trained on (matminer composition/structure descriptors, dscribe SOAP, and -lattice/symmetry values). The CGCNN metallicity block is appended separately. - -Feature order within the block: composition, structure, SOAP, lattice. Heavy -dependencies (matminer, dscribe) are imported lazily so importing this module -stays cheap. -""" - -from __future__ import annotations - -import numpy as np -from pymatgen.core import Structure - -from goldilocks_core.contracts import StructureFeatureVector - -_COMPOSITION_FEATURES = ("ElementProperty", "Stoichiometry", "ValenceOrbital") -_STRUCTURE_FEATURES = ("GlobalSymmetryFeatures", "DensityFeatures") -_SOAP_PARAMS = {"r_cut": 10.0, "n_max": 8, "l_max": 6, "sigma": 1.0} - -_CRYSTAL_SYSTEM_ID = { - "triclinic": 0, - "monoclinic": 1, - "orthorhombic": 2, - "tetragonal": 3, - "trigonal": 4, - "hexagonal": 5, - "cubic": 6, -} -_SYSTEM_ABBREVIATION = { - "triclinic": "a", - "monoclinic": "m", - "orthorhombic": "o", - "tetragonal": "t", - "trigonal": "h", - "hexagonal": "h", - "cubic": "c", -} -_BRAVAIS_ID = { - "aP": 0, - "mP": 1, - "mC": 2, - "oP": 3, - "oC": 4, - "oI": 5, - "oF": 6, - "tP": 7, - "tI": 8, - "hP": 9, - "hR": 10, - "cP": 11, - "cI": 12, - "cF": 13, -} - - -def extract_structure_features(structure: Structure) -> np.ndarray: - """Return the composition+structure+SOAP+lattice feature block (1-D). - - Order matches the trained QRF: composition, structure, SOAP, lattice. The - CGCNN metallicity block is concatenated by the caller. - """ - return np.concatenate( - [ - _composition_features(structure), - _structure_features(structure), - _soap_features(structure), - _lattice_features(structure), - ] - ) - - -def extract_qrf_features( - structure: Structure, - metal_model: object, - atom_init_path: str, -) -> StructureFeatureVector: - """Assemble the full 483-dim QRF feature vector for one structure. - - Concatenates the composition+structure+SOAP+lattice block (419) with the - CGCNN metallicity crystal representation (64), in the trained order. - """ - from goldilocks_core.ml.metallicity import metal_features - - structure_block = extract_structure_features(structure) - metal_block = metal_features(structure, metal_model, atom_init_path) - values = np.concatenate([structure_block, metal_block]) - return StructureFeatureVector( - values=values, - feature_names=[f"qrf_{index}" for index in range(values.size)], - ) - - -def _clean(values: np.ndarray) -> np.ndarray: - """Return a finite float array (NaN/inf -> 0.0).""" - return np.nan_to_num(np.asarray(values, dtype=float), nan=0.0) - - -def _composition_features(structure: Structure) -> np.ndarray: - import matminer.featurizers.composition as composition_featurizers - from matminer.featurizers.base import MultipleFeaturizer - from pymatgen.core.composition import Composition - - integer_formula = Composition(structure.formula).get_integer_formula_and_factor()[0] - composition = Composition(Composition(integer_formula).iupac_formula) - - methods = [] - for name in _COMPOSITION_FEATURES: - featurizer_cls = getattr(composition_featurizers, name) - if name == "ElementProperty": - method = featurizer_cls.from_preset("magpie", impute_nan=True) - else: - try: - method = featurizer_cls(impute_nan=True) - except TypeError: - method = featurizer_cls() - methods.append(method) - - featurizer = MultipleFeaturizer(methods) - return _clean(featurizer.featurize(composition)) - - -def _structure_features(structure: Structure) -> np.ndarray: - import matminer.featurizers.structure as structure_featurizers - from matminer.featurizers.base import MultipleFeaturizer - - methods = [ - structure_featurizers.GlobalSymmetryFeatures( - ["spacegroup_num", "crystal_system_int", "is_centrosymmetric"] - ), - structure_featurizers.DensityFeatures(["density", "vpa", "packing fraction"]), - ] - featurizer = MultipleFeaturizer(methods) - return _clean(featurizer.featurize(structure)) - - -def _soap_features(structure: Structure) -> np.ndarray: - from dscribe.descriptors import SOAP - from pymatgen.io.ase import AseAtomsAdaptor - - soap = SOAP( - species=["X"], - r_cut=_SOAP_PARAMS["r_cut"], - n_max=_SOAP_PARAMS["n_max"], - l_max=_SOAP_PARAMS["l_max"], - sigma=_SOAP_PARAMS["sigma"], - periodic=True, - sparse=False, - ) - atoms = AseAtomsAdaptor.get_atoms(structure) - atoms.set_chemical_symbols(["X"] * len(atoms)) - return _clean(soap.create(atoms).mean(axis=0)) - - -def _lattice_features(structure: Structure) -> np.ndarray: - from pymatgen.symmetry.analyzer import SpacegroupAnalyzer - - values: list[float] = [] - values.extend(structure.lattice.abc) - values.extend(structure.lattice.angles) - values.extend(structure.lattice.reciprocal_lattice.abc) - values.extend(structure.lattice.reciprocal_lattice.angles) - - analyzer = SpacegroupAnalyzer(structure, symprec=0.01) - crystal_system = analyzer.get_crystal_system() - space_group_symbol = analyzer.get_space_group_symbol() - bravais = _SYSTEM_ABBREVIATION[crystal_system] + space_group_symbol[0] - - values.append(_CRYSTAL_SYSTEM_ID[crystal_system]) - values.append(_BRAVAIS_ID.get(bravais, -1)) - values.append(analyzer.get_space_group_number()) - - return _clean(values) diff --git a/src/goldilocks_core/ml/metallicity.py b/src/goldilocks_core/ml/metallicity.py deleted file mode 100644 index eb7c9d5..0000000 --- a/src/goldilocks_core/ml/metallicity.py +++ /dev/null @@ -1,52 +0,0 @@ -"""Pretrained CGCNN metallicity model: loading and crystal-representation features. - -The QRF k-distance feature vector includes a "metal" block: the pooled crystal -representation from a CGCNN metallicity classifier. The same model is the basis -for a future Analyze-stage metallicity prediction. torch is a heavy optional -dependency imported lazily. -""" - -from __future__ import annotations - -import numpy as np -from pymatgen.core.structure import Structure - -from goldilocks_core.ml.atom_features import atom_features_from_structure -from goldilocks_core.ml.cgcnn_graph import build_radius_cgcnn_graph_from_structure - - -def load_metallicity_model(checkpoint_path: str) -> object: - """Load the pretrained CGCNN metallicity model from a Lightning checkpoint. - - Reconstructs ``CGCNN_PyG`` from the checkpoint's hyper-parameters and loads - the weights (stripping the Lightning ``model.`` prefix). Returns the model - in eval mode. - """ - import torch - - from goldilocks_core.ml.cgcnn import CGCNN_PyG - - checkpoint = torch.load(checkpoint_path, map_location="cpu", weights_only=True) - model = CGCNN_PyG(**checkpoint["hyper_parameters"]["model"]) - weights = { - key.replace("model.", ""): value - for key, value in checkpoint["state_dict"].items() - } - model.load_state_dict(weights) - model.eval() - return model - - -def metal_features( - structure: Structure, - model: object, - atom_init_path: str, -) -> np.ndarray: - """Return the CGCNN pooled crystal representation for ``structure`` (1-D).""" - import torch - - atom_feats = atom_features_from_structure(structure, atom_init_path) - graph = build_radius_cgcnn_graph_from_structure(structure, atom_feats) - with torch.no_grad(): - representation = model.extract_crystal_repr(graph) - return representation.numpy().reshape(-1) diff --git a/src/goldilocks_core/ml/models.py b/src/goldilocks_core/ml/models.py index a7443cf..a93bb91 100644 --- a/src/goldilocks_core/ml/models.py +++ b/src/goldilocks_core/ml/models.py @@ -12,62 +12,35 @@ def load_model(spec: ModelSpec) -> object: """Load a trained model from a model specification. - Sources - ------- - ``local`` - ``spec.location`` is a filesystem path to a joblib artifact. - ``huggingface`` - ``spec.location`` is ``"::"`` (e.g. - ``"STFC-SCD/kpoints-goldilocks-QRF::QRF95.pkl"``). The file is - downloaded from the Hub and cached locally, honoring ``spec.revision`` - when set. + Parameters + ---------- + spec + Metadata describing the model source, type, target, and feature set. Returns ------- object - Loaded model object. The concrete type depends on the backend. + Loaded model object. The concrete type depends on the model backend. Raises ------ FileNotFoundError If a local model path does not exist. NotImplementedError - If the requested model source is not supported. - ValueError - If a huggingface location is not ``"::"``. + If the requested model source or model type is not yet supported. """ - if spec.source == "local": - return _load_local(spec.location) - - if spec.source == "huggingface": - return _load_huggingface(spec.location, spec.revision) - - raise NotImplementedError(f"Model source '{spec.source}' is not implemented yet.") + if spec.source != "local": + raise NotImplementedError( + f"Model source '{spec.source}' is not implemented yet." + ) + if spec.model_type != "random_forest": + raise NotImplementedError( + f"Model type '{spec.model_type}' is not implemented yet." + ) -def _load_local(location: str) -> object: - """Load a joblib model from a local filesystem path.""" - model_path = Path(location) + model_path = Path(spec.location) if not model_path.exists(): raise FileNotFoundError(f"Model file not found: {model_path}") return joblib.load(model_path) - - -def _load_huggingface(location: str, revision: str | None) -> object: - """Download a joblib model from the Hugging Face Hub and load it.""" - from huggingface_hub import hf_hub_download - - repo_id, separator, filename = location.partition("::") - if not separator or not filename: - raise ValueError( - "HuggingFace model location must be '::'; " - f"got {location!r}" - ) - - model_path = hf_hub_download( - repo_id=repo_id, - filename=filename, - revision=revision, - ) - return joblib.load(model_path) diff --git a/tests/test_cgcnn.py b/tests/test_cgcnn.py deleted file mode 100644 index 0fdd837..0000000 --- a/tests/test_cgcnn.py +++ /dev/null @@ -1,64 +0,0 @@ -import json - -import pytest -import torch -from pymatgen.core import Lattice, Structure - -from goldilocks_core.ml.atom_features import atom_features_from_structure -from goldilocks_core.ml.cgcnn import CGCNN_PyG -from goldilocks_core.ml.cgcnn_graph import build_radius_cgcnn_graph_from_structure - - -def make_pair() -> Structure: - return Structure( - Lattice.cubic(3.0), ["Si", "Si"], [[0.0, 0.0, 0.0], [0.5, 0.5, 0.5]] - ) - - -def test_atom_features_from_structure_looks_up_embeddings(tmp_path) -> None: - """Each atom gets its embedding by atomic number from the atom_init map.""" - atom_init = tmp_path / "atom_init.json" - atom_init.write_text(json.dumps({"14": [1.0, 2.0, 3.0]})) # Si = 14 - - features = atom_features_from_structure(make_pair(), str(atom_init)) - - assert features == [[1.0, 2.0, 3.0], [1.0, 2.0, 3.0]] - - -def test_atom_features_missing_element_raises(tmp_path) -> None: - """A missing element in the embedding map is a clear error.""" - atom_init = tmp_path / "atom_init.json" - atom_init.write_text(json.dumps({"14": [1.0]})) # no oxygen - structure = Structure(Lattice.cubic(4.0), ["O"], [[0.0, 0.0, 0.0]]) - - with pytest.raises(ValueError, match="No atom embedding"): - atom_features_from_structure(structure, str(atom_init)) - - -def test_build_radius_graph_has_expected_shapes() -> None: - """The graph carries node features, a 2xE edge index, and scalar distances.""" - data = build_radius_cgcnn_graph_from_structure( - make_pair(), [[1.0, 0.0], [0.0, 1.0]], radius=5.0 - ) - - assert data.x.shape == (2, 2) - assert data.edge_index.shape[0] == 2 - assert data.edge_index.shape[1] == data.edge_attr.shape[0] - assert data.edge_attr.shape[1] == 1 - - -def test_cgcnn_extract_crystal_repr_pools_to_atom_fea_len() -> None: - """The pooled crystal representation has width atom_fea_len for one graph.""" - model = CGCNN_PyG( - orig_atom_fea_len=4, atom_fea_len=8, edge_feat_dim=16, n_conv=1, n_h=1 - ) - model.eval() - data = build_radius_cgcnn_graph_from_structure( - make_pair(), [[1.0, 0.0, 0.0, 0.0], [0.0, 1.0, 0.0, 0.0]], radius=5.0 - ) - - with torch.no_grad(): - representation = model.extract_crystal_repr(data) - - assert representation.shape == (1, 8) - assert torch.isfinite(representation).all() diff --git a/tests/test_kindex_advisor.py b/tests/test_k_mesh_advisor.py similarity index 91% rename from tests/test_kindex_advisor.py rename to tests/test_k_mesh_advisor.py index 3612431..4bc9d09 100644 --- a/tests/test_kindex_advisor.py +++ b/tests/test_k_mesh_advisor.py @@ -1,6 +1,6 @@ from pymatgen.core import Lattice, Structure -from goldilocks_core.advisors.kindex_advisor import advise_kpoints, ml_kmesh_advisor +from goldilocks_core.advisors.kmesh_advisor import advise_kpoints, ml_kmesh_advisor from goldilocks_core.contracts import ( CalculationHints, KPointAdvice, @@ -55,7 +55,7 @@ def test_advise_kpoints_returns_selected_mesh(monkeypatch) -> None: spec = make_spec() monkeypatch.setattr( - "goldilocks_core.advisors.kindex_advisor.load_model", + "goldilocks_core.advisors.kmesh_advisor.load_model", lambda _: DummyModel(), ) @@ -73,7 +73,7 @@ def test_ml_kmesh_advisor_uses_model_when_no_hint_is_set(monkeypatch) -> None: structure = make_structure() spec = make_spec() monkeypatch.setattr( - "goldilocks_core.advisors.kindex_advisor.load_model", + "goldilocks_core.advisors.kmesh_advisor.load_model", lambda _: DummyModel(), ) @@ -96,7 +96,7 @@ def fail_if_called(_): return DummyModel() monkeypatch.setattr( - "goldilocks_core.advisors.kindex_advisor.load_model", + "goldilocks_core.advisors.kmesh_advisor.load_model", fail_if_called, ) @@ -123,7 +123,7 @@ def fail_if_called(_): return DummyModel() monkeypatch.setattr( - "goldilocks_core.advisors.kindex_advisor.load_model", + "goldilocks_core.advisors.kmesh_advisor.load_model", fail_if_called, ) diff --git a/tests/test_kdistance_advisor.py b/tests/test_kdistance_advisor.py deleted file mode 100644 index 12e246e..0000000 --- a/tests/test_kdistance_advisor.py +++ /dev/null @@ -1,261 +0,0 @@ -import numpy as np -from pymatgen.core import Lattice, Structure - -from goldilocks_core.advisors.kdistance_advisor import ( - DEFAULT_KPOINTS_MODEL, - default_kmesh_advisor, - kdistance_to_selection, - predict_kdistance_quantiles, - qrf_kdistance_advisor, -) -from goldilocks_core.contracts import ( - CalculationHints, - KPointAdvice, - Provenance, - StructureFeatureVector, -) -from goldilocks_core.kmesh import k_distance_to_mesh - - -class FakeQRF: - """Minimal QRF stub returning fixed (lower, median, upper) quantiles.""" - - def __init__(self, lower, median, upper): - self._quantiles = np.array([[lower], [median], [upper]]) - - def predict(self, X): - return self._quantiles - - -def make_features() -> StructureFeatureVector: - return StructureFeatureVector( - values=np.zeros(4), feature_names=["a", "b", "c", "d"] - ) - - -def make_structure() -> Structure: - return Structure(Lattice.cubic(4.0), ["Si"], [[0.0, 0.0, 0.0]]) - - -def test_predict_kdistance_quantiles_returns_median_and_corrected_interval() -> None: - """Median passes through; correction widens the interval bounds.""" - model = FakeQRF(lower=0.20, median=0.25, upper=0.30) - - median, lower, upper = predict_kdistance_quantiles( - model, make_features(), correction=0.01 - ) - - assert median == 0.25 - assert lower == 0.20 - 0.01 - assert upper == 0.30 + 0.01 - - -def test_predict_kdistance_quantiles_rejects_wrong_quantile_count() -> None: - """Reject a prediction that is not three quantiles.""" - - class TwoQuantiles: - def predict(self, X): - return np.array([[0.2], [0.3]]) - - try: - predict_kdistance_quantiles(TwoQuantiles(), make_features()) - except ValueError as error: - assert "3 quantiles" in str(error) - else: - raise AssertionError("expected ValueError for wrong quantile count") - - -def test_kdistance_to_selection_builds_grid_with_model_provenance() -> None: - """Median distance sets the mesh; provenance records model + confidence.""" - selection = kdistance_to_selection( - make_structure(), - median=0.25, - lower=0.19, - upper=0.31, - data_source="kpoints-goldilocks-QRF", - confidence=0.95, - ) - - assert selection.grid == (7, 7, 7) - assert selection.provenance.source == "model" - assert selection.provenance.confidence == 0.95 - assert selection.provenance.data_source == "kpoints-goldilocks-QRF" - - -def test_default_kpoints_model_targets_hf_qrf95() -> None: - """The built-in default resolves the QRF95 artifact from Hugging Face.""" - assert DEFAULT_KPOINTS_MODEL.source == "huggingface" - assert DEFAULT_KPOINTS_MODEL.location.endswith("::QRF95.pkl") - assert DEFAULT_KPOINTS_MODEL.target == "k_distance" - - -def _make_advice() -> KPointAdvice: - return KPointAdvice( - spacing=0.2, - explicit_grid=None, - mesh_type="monkhorst-pack", - provenance=Provenance(source="default", reason="baseline"), - ) - - -def _patch_models(monkeypatch, qrf: FakeQRF) -> None: - monkeypatch.setattr("goldilocks_core.ml.models.load_model", lambda spec: qrf) - monkeypatch.setattr( - "goldilocks_core.ml.metallicity.load_metallicity_model", lambda path: object() - ) - monkeypatch.setattr( - "goldilocks_core.ml.kdistance_features.extract_qrf_features", - lambda structure, model, atom_init: StructureFeatureVector( - values=np.zeros(483), feature_names=[] - ), - ) - - -def test_qrf_kdistance_advisor_predicts_with_model_provenance(monkeypatch) -> None: - """No hint: assemble features, run the QRF, and record model provenance.""" - _patch_models(monkeypatch, FakeQRF(lower=0.20, median=0.25, upper=0.30)) - advisor = qrf_kdistance_advisor("ckpt.pkl", "atom.json", correction=0.0) - - structure = make_structure() - selection = advisor(structure, CalculationHints(), _make_advice()) - - assert selection.grid == k_distance_to_mesh(structure, 0.25) - assert selection.provenance.source == "model" - assert selection.provenance.confidence == 0.95 - - -def test_qrf_kdistance_advisor_respects_grid_hint(monkeypatch) -> None: - """An explicit k-grid hint bypasses the model and wins.""" - _patch_models(monkeypatch, FakeQRF(lower=0.20, median=0.25, upper=0.30)) - advisor = qrf_kdistance_advisor("ckpt.pkl", "atom.json") - - selection = advisor( - make_structure(), CalculationHints(k_grid=(2, 2, 2)), _make_advice() - ) - - assert selection.grid == (2, 2, 2) - assert selection.provenance.source == "user_hint" - - -def test_qrf_kdistance_advisor_falls_back_when_model_load_fails(monkeypatch) -> None: - """Missing deps/checkpoint: degrade to heuristic advice, warn, never crash.""" - - def boom(spec): - raise ModuleNotFoundError("No module named 'torch'") - - monkeypatch.setattr("goldilocks_core.ml.models.load_model", boom) - advisor = qrf_kdistance_advisor("ckpt.pkl", "atom.json") - - structure = make_structure() - selection = advisor(structure, CalculationHints(), _make_advice()) - - # Mesh comes from the heuristic advice (spacing 0.2), not the model. - assert selection.grid == k_distance_to_mesh(structure, 0.2) - assert selection.provenance.source != "model" - assert any("torch" in warning for warning in selection.provenance.warnings) - - -def test_qrf_kdistance_advisor_falls_back_when_prediction_fails(monkeypatch) -> None: - """Model loads but per-structure inference raises: fall back with a warning.""" - _patch_models(monkeypatch, FakeQRF(lower=0.20, median=0.25, upper=0.30)) - monkeypatch.setattr( - "goldilocks_core.ml.kdistance_features.extract_qrf_features", - lambda structure, model, atom_init: (_ for _ in ()).throw( - RuntimeError("feature extraction failed") - ), - ) - advisor = qrf_kdistance_advisor("ckpt.pkl", "atom.json") - - structure = make_structure() - selection = advisor(structure, CalculationHints(), _make_advice()) - - assert selection.grid == k_distance_to_mesh(structure, 0.2) - assert selection.provenance.source != "model" - assert any( - "feature extraction failed" in warning - for warning in selection.provenance.warnings - ) - - -def test_qrf_kdistance_advisor_load_failure_still_honors_grid_hint(monkeypatch) -> None: - """Even with the model unavailable, an explicit grid hint wins cleanly.""" - - def boom(spec): - raise ModuleNotFoundError("No module named 'torch'") - - monkeypatch.setattr("goldilocks_core.ml.models.load_model", boom) - advisor = qrf_kdistance_advisor("ckpt.pkl", "atom.json") - - selection = advisor( - make_structure(), CalculationHints(k_grid=(2, 2, 2)), _make_advice() - ) - - assert selection.grid == (2, 2, 2) - assert selection.provenance.source == "user_hint" - assert selection.provenance.warnings == () - - -def test_default_kmesh_advisor_falls_back_without_checkpoint( - monkeypatch, tmp_path -) -> None: - """The built-in default degrades to heuristic advice when nothing resolves.""" - monkeypatch.setenv( - "GOLDILOCKS_METALLICITY_CHECKPOINT", str(tmp_path / "missing.ckpt") - ) - monkeypatch.setenv( - "GOLDILOCKS_METALLICITY_ATOM_INIT", str(tmp_path / "missing.json") - ) - monkeypatch.setattr( - "goldilocks_core.ml.models.load_model", - lambda spec: (_ for _ in ()).throw(RuntimeError("offline")), - ) - - advisor = default_kmesh_advisor() - structure = make_structure() - selection = advisor(structure, CalculationHints(), _make_advice()) - - assert selection.grid == k_distance_to_mesh(structure, 0.2) - assert selection.provenance.source != "model" - assert selection.provenance.warnings - - -def test_default_kmesh_advisor_honors_explicit_grid_hint(monkeypatch) -> None: - """An explicit grid hint wins even for the built-in default backend.""" - monkeypatch.setattr( - "goldilocks_core.ml.models.load_model", - lambda spec: (_ for _ in ()).throw(RuntimeError("offline")), - ) - - advisor = default_kmesh_advisor() - selection = advisor( - make_structure(), CalculationHints(k_grid=(4, 4, 4)), _make_advice() - ) - - assert selection.grid == (4, 4, 4) - assert selection.provenance.source == "user_hint" - - -def test_default_kmesh_advisor_downloads_metallicity_from_hf(monkeypatch) -> None: - """With no local override, the default resolves the checkpoint from HF.""" - monkeypatch.delenv("GOLDILOCKS_METALLICITY_CHECKPOINT", raising=False) - monkeypatch.delenv("GOLDILOCKS_METALLICITY_ATOM_INIT", raising=False) - monkeypatch.delenv("GOLDILOCKS_METALLICITY_REPO", raising=False) - - downloads: list[tuple[str, str]] = [] - - def fake_download(repo_id, filename, revision=None): - downloads.append((repo_id, filename)) - return f"/cache/{repo_id}/{filename}" - - monkeypatch.setattr("huggingface_hub.hf_hub_download", fake_download) - _patch_models(monkeypatch, FakeQRF(lower=0.20, median=0.25, upper=0.30)) - - advisor = default_kmesh_advisor() - structure = make_structure() - selection = advisor(structure, CalculationHints(), _make_advice()) - - assert selection.provenance.source == "model" - downloaded_files = {filename for _, filename in downloads} - assert downloaded_files == {"is_metal.ckpt", "atom_init.json"} - repos = {repo for repo, _ in downloads} - assert repos == {"JunwenYin/metallicity-goldilocks-CGCNN"} diff --git a/tests/test_kdistance_features.py b/tests/test_kdistance_features.py deleted file mode 100644 index f882698..0000000 --- a/tests/test_kdistance_features.py +++ /dev/null @@ -1,33 +0,0 @@ -import numpy as np -from pymatgen.core import Lattice, Structure - -from goldilocks_core.ml.kdistance_features import extract_structure_features - - -def make_diamond_silicon() -> Structure: - a = 5.43 - return Structure( - lattice=Lattice([[0, a / 2, a / 2], [a / 2, 0, a / 2], [a / 2, a / 2, 0]]), - species=["Si", "Si"], - coords=[[0.0, 0.0, 0.0], [0.25, 0.25, 0.25]], - ) - - -def test_extract_structure_features_dimension_and_finiteness() -> None: - """The comp+struct+soap+lattice block has the trained 419-dim length.""" - features = extract_structure_features(make_diamond_silicon()) - - # composition(146) + structure(6) + soap(252) + lattice(15) = 419; the CGCNN - # metallicity block (64) is appended by the caller to reach the QRF's 483. - assert features.shape == (419,) - assert np.isfinite(features).all() - - -def test_extract_structure_features_is_deterministic() -> None: - """The same structure yields the same feature vector.""" - structure = make_diamond_silicon() - - first = extract_structure_features(structure) - second = extract_structure_features(structure) - - assert np.array_equal(first, second) diff --git a/tests/test_ml_models.py b/tests/test_ml_models.py index c0e8527..3d26192 100644 --- a/tests/test_ml_models.py +++ b/tests/test_ml_models.py @@ -27,20 +27,8 @@ def test_load_model_loads_local_random_forest(tmp_path) -> None: assert loaded_model == model -def test_load_model_downloads_huggingface_artifact(tmp_path, monkeypatch) -> None: - """Download a huggingface artifact via hf_hub_download and load it.""" - model = {"kind": "hf-rf"} - model_path = tmp_path / "QRF95.pkl" - joblib.dump(model, model_path) - - calls = {} - - def fake_download(*, repo_id, filename, revision): - calls.update(repo_id=repo_id, filename=filename, revision=revision) - return str(model_path) - - monkeypatch.setattr("huggingface_hub.hf_hub_download", fake_download) - +def test_load_model_rejects_unsupported_source() -> None: + """Reject unsupported model sources.""" spec = ModelSpec( name="test-model", version="v0", @@ -48,34 +36,11 @@ def fake_download(*, repo_id, filename, revision): target="k_index", feature_set="cslr", source="huggingface", - location="STFC-SCD/kpoints-goldilocks-QRF::QRF95.pkl", + location="junwen94/test-model", revision="main", ) - loaded_model = load_model(spec) - - assert loaded_model == model - assert calls == { - "repo_id": "STFC-SCD/kpoints-goldilocks-QRF", - "filename": "QRF95.pkl", - "revision": "main", - } - - -def test_load_model_rejects_malformed_huggingface_location() -> None: - 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