move /paper/preprint to /paper

add CHGNet to /si rolling MAE batches model comparison

.pre-commit-config.yaml

@@ -7,13 +7,13 @@ default_install_hook_types: [pre-commit, commit-msg]
 
 repos:
   - repo: https://github.com/charliermarsh/ruff-pre-commit
-    rev: v0.0.258
+    rev: v0.0.260
     hooks:
       - id: ruff
         args: [--fix]
 
   - repo: https://github.com/psf/black
-    rev: 23.1.0
+    rev: 23.3.0
     hooks:
       - id: black
 
@@ -34,13 +34,13 @@ repos:
       - id: trailing-whitespace
 
   - repo: https://github.com/pre-commit/mirrors-mypy
-    rev: v1.0.1
+    rev: v1.1.1
     hooks:
       - id: mypy
         additional_dependencies: [types-pyyaml, types-requests]
 
   - repo: https://github.com/codespell-project/codespell
-    rev: v2.2.2
+    rev: v2.2.4
     hooks:
       - id: codespell
         stages: [commit, commit-msg]
@@ -49,7 +49,7 @@ repos:
         args: [--ignore-words-list, "nd,te,fpr"]
 
   - repo: https://github.com/pre-commit/mirrors-prettier
-    rev: v3.0.0-alpha.4
+    rev: v3.0.0-alpha.6
     hooks:
       - id: prettier
         args: [--write] # edit files in-place
@@ -60,7 +60,7 @@ repos:
         exclude: ^(site/src/figs/.+\.svelte|data/wbm/20.+\..+|site/src/routes/.+\.(yml|yaml|json))$
 
   - repo: https://github.com/pre-commit/mirrors-eslint
-    rev: v8.34.0
+    rev: v8.37.0
     hooks:
       - id: eslint
         types: [file]

matbench_discovery/plots.py

@@ -339,6 +339,7 @@ def rolling_mae_vs_hull_dist(
     with_sem: bool = True,
     show_dft_acc: bool = False,
     show_dummy_mae: bool = False,
+    pbar: bool = True,
     **kwargs: Any,
 ) -> plt.Axes | go.Figure:
     """Rolling mean absolute error as the energy to the convex hull is varied. A scale
@@ -380,6 +381,8 @@ def rolling_mae_vs_hull_dist(
             meV/atom. Defaults to False.
         show_dummy_mae (bool, optional): If True, plot a line at the dummy MAE of always
             predicting the target mean.
+        pbar (bool, optional): If True, show a progress bar during rolling MAE
+            calculation. Defaults to True.
         **kwargs: Additional keyword arguments to pass to df.plot().
 
     Returns:
@@ -396,8 +399,10 @@ def rolling_mae_vs_hull_dist(
         df_rolling_err = pd.DataFrame(columns=models, index=bins)
         df_err_std = df_rolling_err.copy()
 
-        for model in (pbar := tqdm(models, desc="Calculating rolling MAE")):
-            pbar.set_postfix_str(model)
+        for model in (
+            prog_bar := tqdm(models, desc="Calculating rolling MAE", disable=not pbar)
+        ):
+            prog_bar.set_postfix_str(model)
             for idx, bin_center in enumerate(bins):
                 low = bin_center - window
                 high = bin_center + window

scripts/hist_classified_stable_vs_hull_dist_batches.py

@@ -27,7 +27,7 @@
 df_preds[each_pred_col] = (
     df_preds[each_true_col] + df_preds[model_name] - df_preds[e_form_col]
 )
-df_preds[(batch_col := "batch_idx")] = df_preds.index.str.split("-").str[-2].astype(int)
+df_preds[(batch_col := "batch_idx")] = df_preds.index.str.split("-").str[1].astype(int)
 
 
 # %% matplotlib

scripts/hist_classified_stable_vs_hull_dist_models.py

@@ -10,10 +10,7 @@
 from pymatviz.utils import save_fig
 
 from matbench_discovery import FIGS, ROOT, today
-from matbench_discovery.plots import (
-    hist_classified_stable_vs_hull_dist,
-    plt,
-)
+from matbench_discovery.plots import hist_classified_stable_vs_hull_dist, plt
 from matbench_discovery.preds import df_metrics, df_preds, e_form_col, each_true_col
 
 __author__ = "Janosh Riebesell"
@@ -25,8 +22,8 @@
 e_form_preds = "e_form_per_atom_pred"
 each_pred_col = "e_above_hull_pred"
 facet_col = "Model"
-models = list(df_metrics)
-# models = df_metrics.T.MAE.nsmallest(6).index  # top 6 models by MAE
+# sort facet plots by model's F1 scores (optionally only show top n=6)
+models = list(df_metrics.T.F1.sort_values().index)[::-1]
 
 df_melt = df_preds.melt(
     id_vars=hover_cols,
@@ -45,7 +42,7 @@
 rows, cols = len(models) // 2, 2
 which_energy: Final = "true"
 kwds = (
-    dict(facet_col=facet_col, facet_col_wrap=cols)
+    dict(facet_col=facet_col, facet_col_wrap=cols, category_orders={facet_col: models})
     if backend == "plotly"
     else dict(by=facet_col, figsize=(20, 20), layout=(rows, cols), bins=500)
 )

scripts/prc_roc_curves_models.py

@@ -11,14 +11,13 @@
 from tqdm import tqdm
 
 from matbench_discovery import FIGS
-from matbench_discovery.plots import pio
+from matbench_discovery import plots as plots
 from matbench_discovery.preds import df_each_pred, df_preds, each_true_col
 
 __author__ = "Janosh Riebesell"
 __date__ = "2023-01-30"
 
 
-pio.templates.default
 line = dict(dash="dash", width=0.5)
 
 facet_col = "Model"

scripts/rolling_mae_vs_hull_dist_wbm_batches.py

@@ -13,9 +13,8 @@
 __author__ = "Rhys Goodall, Janosh Riebesell"
 __date__ = "2022-06-18"
 
-df_each_pred[(batch_col := "batch_idx")] = (
-    "Batch " + df_each_pred.index.str.split("-").str[1]
-)
+batch_col = "batch_idx"
+df_each_pred[batch_col] = "Batch " + df_each_pred.index.str.split("-").str[1]
 df_err, df_std = None, None  # variables to cache rolling MAE and std
 
 
@@ -43,6 +42,7 @@
         backend="matplotlib",
         ax=ax,
         just_plot_lines=idx > 1,
+        pbar=False,
     )
 
 
@@ -54,7 +54,7 @@
 
 
 # %% plotly
-model = "Wrenformer"  # ["M3GNet", "Wrenformer", "MEGNet", "Voronoi RF"]
+model = "CHGNet"
 df_pivot = df_each_pred.pivot(columns=batch_col, values=model)
 
 # unstack two-level column index into new model column