Add MACE (#48)

* add scripts/model_figs/update_all_model_figs.py

goal is to be able to run a single script that will update all the model figures
(both the interactive version for the leaderboard and the PDF version for the paper)

* prepare MACE submission (readme.md, metadata.yml, test_mace.py)

* fix model name still set to chgnet in test_mace.py

* wandb collect all dep versions in single dict

* delete matbench_discovery.DEBUG global

* roc_prc_curves_models.py fix n_rows x n_cols in out filename

* test_mace.py add relax trajectory recording

* add MACE + ALIGNN checkpoints figshare urls to class DataFiles

* rename BOWSR + MEGnet -> BOWSR

* ensure out_path matches glob_pattern in join scripts

* load_df_wbm_with_preds() use 1st matching df column

* refactor df_to_pdf() from wkhtmltopdf to weasyprint

* improve Figshare description

* update most figures with MACE results

* extract scripts/model_figs/per_element_errors.py out of scripts/analyze_element_errors.py

to run former as part of update_model_figs.py

* add MACE to site/src/figs/model-run-times-bar.svelte

* revert 'extract scripts/model_figs/per_element_errors.py out of scripts/analyze_element_errors.py'

* add weasyprint to df-pdf-export optional deps

* fix test_df_metrics failing from MACE R^2 of -1.291 being below -0.9

* fix df_to_pdf if weasyprint not installed

data/figshare/1.0.0.json

@@ -1,4 +1,12 @@
 {
+  "alignn_checkpoint": [
+    "https://figshare.com/ndownloader/files/41233560",
+    "2023-06-02-pbenner-best-alignn-model.pth.zip"
+  ],
+  "mace_checkpoint": [
+    "https://figshare.com/ndownloader/files/41565618",
+    "2023-07-14-mace-universal-2-big-128-6.model"
+  ],
   "mp_computed_structure_entries": [
     "https://figshare.com/ndownloader/files/40344436",
     "2023-02-07-mp-computed-structure-entries.json.gz"

data/mp/build_phase_diagram.py

@@ -43,7 +43,7 @@
 df = pd.read_json(data_path).set_index("material_id")
 
 # drop the structure, just load ComputedEntry, makes the PPD faster to build and load
-mp_computed_entries = [ComputedEntry.from_dict(x) for x in tqdm(df.entry)]
+mp_computed_entries = [ComputedEntry.from_dict(dct) for dct in tqdm(df.entry)]
 
 print(f"{len(mp_computed_entries) = :,} on {today}")
 # len(mp_computed_entries) = 146,323 on 2022-09-16

data/wbm/compare_cse_vs_ce_mp_2020_corrections.py

@@ -28,10 +28,11 @@
 )
 
 cses = [
-    ComputedStructureEntry.from_dict(x) for x in tqdm(df_cse.computed_structure_entry)
+    ComputedStructureEntry.from_dict(dct)
+    for dct in tqdm(df_cse.computed_structure_entry)
 ]
 
-ces = [ComputedEntry.from_dict(x) for x in tqdm(df_cse.computed_structure_entry)]
+ces = [ComputedEntry.from_dict(dct) for dct in tqdm(df_cse.computed_structure_entry)]
 
 
 warnings.filterwarnings(action="ignore", category=UserWarning, module="pymatgen")

data/wbm/fetch_process_wbm_dataset.py

@@ -502,7 +502,8 @@ def fix_bad_struct_index_mismatch(material_id: str) -> str:
     assert mat_id == cse["entry_id"], f"{mat_id} != {cse['entry_id']}"
 
 df_wbm["cse"] = [
-    ComputedStructureEntry.from_dict(x) for x in tqdm(df_wbm.computed_structure_entry)
+    ComputedStructureEntry.from_dict(dct)
+    for dct in tqdm(df_wbm.computed_structure_entry)
 ]
 # raw WBM ComputedStructureEntries have no energy corrections applied:
 assert all(cse.uncorrected_energy == cse.energy for cse in df_wbm.cse)
@@ -640,6 +641,6 @@ def fix_bad_struct_index_mismatch(material_id: str) -> str:
     ).set_index("material_id")
 
     df_wbm["cse"] = [
-        ComputedStructureEntry.from_dict(x)
-        for x in tqdm(df_wbm.computed_structure_entry)
+        ComputedStructureEntry.from_dict(dct)
+        for dct in tqdm(df_wbm.computed_structure_entry)
     ]

matbench_discovery/__init__.py

@@ -1,7 +1,6 @@
 """Global variables used all across the matbench_discovery package."""
 
 import os
-import sys
 from datetime import datetime
 
 ROOT = os.path.dirname(os.path.dirname(__file__))  # repo root directory
@@ -13,10 +12,6 @@
 for directory in [FIGS, MODELS, FIGSHARE, PDF_FIGS]:
     os.makedirs(directory, exist_ok=True)
 
-# whether a currently running slurm job is in debug mode
-DEBUG = "DEBUG" in os.environ or (
-    "slurm-submit" not in sys.argv and "SLURM_JOB_ID" not in os.environ
-)
 # directory to store model checkpoints downloaded from wandb cloud storage
 CHECKPOINT_DIR = f"{ROOT}/wandb/checkpoints"
 # wandb <entity>/<project name> to record new runs to

matbench_discovery/data.py

@@ -232,6 +232,7 @@ def _on_not_found(self, key: str, msg: str) -> None:  # type: ignore[override]
         if answer == "y":
             load(key)  # download and cache data file
 
+    # TODO maybe set attrs to None and load file names from Figshare json
     mp_computed_structure_entries = (
         "mp/2023-02-07-mp-computed-structure-entries.json.gz"
     )
@@ -246,6 +247,8 @@ def _on_not_found(self, key: str, msg: str) -> None:  # type: ignore[override]
         "wbm/2022-10-19-wbm-computed-structure-entries+init-structs.json.bz2"
     )
     wbm_summary = "wbm/2022-10-19-wbm-summary.csv.gz"
+    alignn_checkpoint = "2023-06-02-pbenner-best-alignn-model.pth.zip"
+    mace_checkpoint = "2023-07-14-mace-universal-2-big-128-6.model"
 
 
 # data files can be downloaded and cached with matbench_discovery.data.load()

matbench_discovery/plots.py

@@ -4,6 +4,7 @@
 
 import math
 import os
+import subprocess
 from collections import defaultdict
 from collections.abc import Sequence
 from pathlib import Path
@@ -65,7 +66,7 @@ def unit(text: str) -> str:
 model_labels = dict(
     alignn="ALIGNN",
     alignn_pretrained="ALIGNN Pretrained",
-    bowsr_megnet="BOWSR + MEGNet",
+    bowsr_megnet="BOWSR",
     chgnet="CHGNet",
     chgnet_megnet="CHGNet + MEGNet",
     cgcnn_p="CGCNN+P",
@@ -74,6 +75,7 @@ def unit(text: str) -> str:
     m3gnet="M3GNet",
     m3gnet_direct="M3GNet DIRECT",
     m3gnet_ms="M3GNet MS",
+    mace="MACE",
     megnet="MEGNet",
     voronoi_rf="Voronoi RF",
     wrenformer="Wrenformer",
@@ -874,38 +876,81 @@ def df_to_svelte_table(
 def df_to_pdf(
     styler: Styler, file_path: str | Path, crop: bool = True, **kwargs: Any
 ) -> None:
-    """Export a pandas Styler to PDF.
+    """Export a pandas Styler to PDF with WeasyPrint.
 
     Args:
         styler (Styler): Styler object to export.
-        file_path (str): Path to save the PDF to. Requires pdfkit.
-        crop (bool): Whether to crop the PDF margins. Requires pdfCropMargins. Defaults
-            to True.
+        file_path (str): Path to save the PDF to. Requires WeasyPrint.
+        crop (bool): Whether to crop the PDF margins. Requires pdfCropMargins.
+            Defaults to True.
         **kwargs: Keyword arguments passed to Styler.to_html().
     """
     try:
-        # pdfkit used to export pandas Styler to PDF, requires:
-        # pip install pdfkit && brew install homebrew/cask/wkhtmltopdf
-        import pdfkit
+        from weasyprint import HTML
     except ImportError as exc:
-        raise ImportError(
-            "pdfkit not installed\nrun pip install pdfkit && brew install "
-            "homebrew/cask/wkhtmltopdf\n(brew is macOS only, use apt on linux)"
-        ) from exc
-
-    pdfkit.from_string(styler.to_html(**kwargs), file_path)
-    if not crop:
-        return
+        msg = "weasyprint not installed\nrun pip install weasyprint"
+        raise ImportError(msg) from exc
+
+    html_str = styler.to_html(**kwargs)
+
+    # CSS to adjust layout and margins
+    html_str = f"""
+    <style>
+        @page {{ size: landscape; margin: 1cm; }}
+        body {{ margin: 0; padding: 1em; }}
+    </style>
+    {html_str}
+    """
+
+    html = HTML(string=html_str)
+
+    html.write_pdf(file_path)
+
+    if crop:
+        normalize_and_crop_pdf(file_path)
+
+
+def normalize_and_crop_pdf(file_path: str | Path) -> None:
+    """Normalize a PDF using Ghostscript and then crop it.
+    Without gs normalization, pdfCropMargins sometimes corrupts the PDF.
+
+    Args:
+        file_path (str | Path): Path to the PDF file.
+    """
     try:
-        # needed to auto-crop large white margins from PDF
-        # pip install pdfCropMargins
-        from pdfCropMargins import crop as crop_pdf
+        normalized_file_path = f"{file_path}_normalized.pdf"
+        from pdfCropMargins import crop
+
+        # Normalize the PDF with Ghostscript
+        subprocess.run(
+            [
+                "gs",
+                "-sDEVICE=pdfwrite",
+                "-dCompatibilityLevel=1.4",
+                "-dPDFSETTINGS=/default",
+                "-dNOPAUSE",
+                "-dQUIET",
+                "-dBATCH",
+                f"-sOutputFile={normalized_file_path}",
+                str(file_path),
+            ]
+        )
 
-        # Remove PDF margins
-        cropped_file_path, _exit_code, _stdout, _stderr = crop_pdf(
-            ["--percentRetain", "0", file_path]
+        # Crop the normalized PDF
+        cropped_file_path, exit_code, stdout, stderr = crop(
+            ["--percentRetain", "0", normalized_file_path]
         )
-        os.replace(cropped_file_path, file_path)
+
+        if stderr:
+            print(f"pdfCropMargins {stderr=}")
+            # something went wrong, remove the cropped PDF
+            os.remove(cropped_file_path)
+        else:
+            # replace the original PDF with the cropped one
+            os.replace(cropped_file_path, str(file_path))
+
+        os.remove(normalized_file_path)
+
     except ImportError as exc:
         msg = "pdfCropMargins not installed\nrun pip install pdfCropMargins"
         raise ImportError(msg) from exc

matbench_discovery/preds.py

@@ -47,6 +47,9 @@ class PredFiles(Files):
     # m3gnet_direct = "m3gnet/2023-05-30-m3gnet-direct-wbm-IS2RE.csv.gz"
     # m3gnet_ms = "m3gnet/2023-06-01-m3gnet-manual-sampling-wbm-IS2RE.csv.gz"
 
+    # MACE trained on original M3GNet training set
+    mace = "mace/2023-07-23-mace-wbm-IS2RE-FIRE.csv.gz"
+
     # original MEGNet straight from publication, not re-trained
     megnet = "megnet/2022-11-18-megnet-wbm-IS2RE.csv.gz"
     # CHGNet-relaxed structures fed into MEGNet for formation energy prediction
@@ -106,8 +109,15 @@ def load_df_wbm_with_preds(
     df_out = df_wbm.copy()
     for model_name, df in dfs.items():
         model_key = model_name.lower().replace(" + ", "_").replace(" ", "_")
-        if (col := f"e_form_per_atom_{model_key}") in df:
-            df_out[model_name] = df[col]
+
+        cols = [col for col in df if col.startswith(f"e_form_per_atom_{model_key}")]
+        if cols:
+            if len(cols) > 1:
+                print(
+                    f"Warning: multiple pred cols for {model_name=}, using {cols[0]!r} "
+                    f"out of {cols=}"
+                )
+            df_out[model_name] = df[cols[0]]
 
         elif pred_cols := list(df.filter(like="_pred_ens")):
             assert len(pred_cols) == 1

models/alignn/test_alignn.py

@@ -17,7 +17,7 @@
 from sklearn.metrics import r2_score
 from tqdm import tqdm
 
-from matbench_discovery import DEBUG, today
+from matbench_discovery import today
 from matbench_discovery.data import DATA_FILES, df_wbm
 from matbench_discovery.plots import wandb_scatter
 from matbench_discovery.slurm import slurm_submit
@@ -36,7 +36,7 @@
 input_col = "initial_structure"
 id_col = "material_id"
 device = "cuda" if torch.cuda.is_available() else "cpu"
-job_name = f"{model_name}-wbm-{task_type}{'-debug' if DEBUG else ''}"
+job_name = f"{model_name}-wbm-{task_type}"
 out_dir = os.getenv("SBATCH_OUTPUT", f"{module_dir}/{today}-{job_name}")
 
 
@@ -85,15 +85,15 @@
 assert input_col in df_in, f"{input_col=} not in {list(df_in)}"
 
 df_in[input_col] = [
-    JarvisAtomsAdaptor.get_atoms(Structure.from_dict(x))
-    for x in tqdm(df_in[input_col], leave=False, desc="Converting to JARVIS atoms")
+    JarvisAtomsAdaptor.get_atoms(Structure.from_dict(dct))
+    for dct in tqdm(df_in[input_col], leave=False, desc="Converting to JARVIS atoms")
 ]
 
 
 # %%
 run_params = dict(
     data_path=data_path,
-    **{f"{dep}_version": version(dep) for dep in ("megnet", "numpy")},
+    versions={dep: version(dep) for dep in ("megnet", "numpy")},
     model_name=model_name,
     task_type=task_type,
     target_col=target_col,

models/alignn/train_alignn.py

@@ -18,7 +18,7 @@
 from torch.utils.data import DataLoader
 from tqdm import tqdm
 
-from matbench_discovery import DEBUG, today
+from matbench_discovery import today
 from matbench_discovery.data import DATA_FILES
 from matbench_discovery.slurm import slurm_submit
 
@@ -35,7 +35,7 @@
 input_col = "atoms"
 id_col = "material_id"
 device = "cuda" if torch.cuda.is_available() else "cpu"
-job_name = f"train-{model_name}{'-debug' if DEBUG else ''}"
+job_name = f"train-{model_name}"
 
 
 pred_col = "e_form_per_atom_alignn"
@@ -49,7 +49,7 @@
 slurm_vars = slurm_submit(
     job_name=job_name,
     # partition="perlmuttter",
-    account="matgen_g",
+    account="matgen",
     time="4:0:0",
     out_dir=out_dir,
     slurm_flags="--qos regular --constraint gpu --gpus 1",
@@ -79,7 +79,7 @@
 # %%
 run_params = dict(
     data_path=DATA_FILES.mp_energies,
-    **{f"{dep}_version": version(dep) for dep in ("alignn", "numpy", "torch", "dgl")},
+    versions={dep: version(dep) for dep in ("alignn", "numpy", "torch", "dgl")},
     model_name=model_name,
     target_col=target_col,
     df=dict(shape=str(df_in.shape), columns=", ".join(df_in)),

models/bowsr/join_bowsr_results.py

@@ -8,7 +8,6 @@
 import pymatviz
 from tqdm import tqdm
 
-from matbench_discovery import today
 from matbench_discovery.data import DATA_FILES
 
 __author__ = "Janosh Riebesell"
@@ -66,7 +65,7 @@
 
 
 # %%
-out_path = f"{module_dir}/{today}-bowsr-megnet-wbm-{task_type}"
+out_path = f"{module_dir}/{glob_pattern.split('/*')[0]}"
 df_bowsr = df_bowsr.round(4)
 # save energy and formation energy as fast-loading CSV
 df_bowsr.select_dtypes("number").to_csv(f"{out_path}.csv")

models/bowsr/metadata.yml

@@ -1,4 +1,4 @@
-model_name: BOWSR + MEGNet
+model_name: BOWSR
 model_version: 2022.9.20
 matbench_discovery_version: 1.0
 date_added: "2022-11-17"