use bin counts directly (no KDE) in hull dist density scatter plot

add rolling-mae-vs-hull-dist-wbm-batches-{alignn,bowsr,mace,voronoi-rf}.svelte
fix hull-dist-scatter-wrenformer-failures.pdf (used wrong input dataframe df_preds vs df_each_pred)
add ref wang_framework_2021 (MP2020 correction scheme)

data/wbm/eda.py

@@ -93,7 +93,7 @@
     save_fig(ptable, f"{PDF_FIGS}/{dataset}-element-{count_mode}-counts.pdf")
 
 
-# %% histogram of energy above MP convex hull for WBM
+# %% histogram of energy distance to MP convex hull for WBM
 col = each_true_col  # or e_form_col
 mean, std = df_wbm[col].mean(), df_wbm[col].std()
 

matbench_discovery/plots.py

@@ -521,12 +521,14 @@ def rolling_mae_vs_hull_dist(
         fig.set(xlim=x_lim, ylim=y_lim)
         line_styles = "- -- -. :".split()
         markers = "o s ^ v D * p X".split()
-        combinations = [(ls, mark) for mark in markers for ls in line_styles]
 
         for idx, line in enumerate(fig.lines):
-            ls, marker = combinations[idx % len(combinations)]
+            line_label = line.get_label()
+            if line_label.startswith("_"):
+                continue
+            ls, marker = line_styles[idx], markers[idx]
             line.set(ls=ls, marker=marker, markeredgewidth=0.5, markeredgecolor="black")
-            line.set_markevery(4)
+            line.set_markevery(8)
 
     elif backend == "plotly":
         for idx, model in enumerate(df_rolling_err if with_sem else []):
@@ -614,14 +616,16 @@ def rolling_mae_vs_hull_dist(
         )
         fig.add_shape(type="rect", x0=x0, y0=y0, x1=x0 - window, y1=y0 + window / 5)
 
-        line_styles = "solid dash dot dashdot".split()
-        markers = "circle square triangle-up triangle-down diamond cross star x".split()
         from matbench_discovery.preds import model_styles
 
-        for trace in fig.data:
+        for idx, trace in enumerate(fig.data):
             if style := model_styles.get(trace.name):
                 ls, _marker, color = style
                 trace.line = dict(color=color, dash=ls, width=2)
+            else:
+                trace.line = dict(
+                    color=plotly_colors[idx], dash=plotly_line_styles[idx], width=3
+                )
             # marker_spacing = 2
             # trace = go.Scatter(
             #     x=trace.x[::marker_spacing],

models/wrenformer/analyze_wrenformer.py

@@ -2,11 +2,12 @@
 
 
 # %%
+import numpy as np
 import pandas as pd
 from aviary.wren.utils import get_isopointal_proto_from_aflow
 from pymatviz import spacegroup_hist, spacegroup_sunburst
 from pymatviz.ptable import ptable_heatmap_plotly
-from pymatviz.utils import save_fig
+from pymatviz.utils import add_identity_line, bin_df_cols, save_fig
 
 from matbench_discovery import PDF_FIGS, SITE_FIGS
 from matbench_discovery.data import DATA_FILES, df_wbm
@@ -80,10 +81,14 @@
 
 # %%
 fig = spacegroup_sunburst(df_bad[spg_col], width=350, height=350)
-fig.layout.title.update(text=f"Spacegroup sunburst for {title}", x=0.5, font_size=14)
+# fig.layout.title.update(text=f"Spacegroup sunburst for {title}", x=0.5, font_size=14)
+fig.layout.margin.update(l=1, r=1, t=1, b=1)
 fig.show()
+
+
+# %%
 save_fig(fig, f"{PDF_FIGS}/spacegroup-sunburst-{model.lower()}-failures.pdf")
-# save_fig(fig, f"{FIGS}/spacegroup-sunburst-{model}-failures.svelte")
+save_fig(fig, f"{SITE_FIGS}/spacegroup-sunburst-{model}-failures.svelte")
 
 
 # %%
@@ -92,3 +97,46 @@
 fig.layout.margin = dict(l=0, r=0, t=50, b=0)
 fig.show()
 save_fig(fig, f"{PDF_FIGS}/elements-{model.lower()}-failures.pdf")
+
+
+# %%
+model = "Wrenformer"
+cols = [model, each_true_col]
+bin_cnt_col = "bin counts"
+df_bin = bin_df_cols(
+    df_each_pred, [each_true_col, model], n_bins=200, bin_counts_col=bin_cnt_col
+)
+log_cnt_col = f"log {bin_cnt_col}"
+df_bin[log_cnt_col] = np.log1p(df_bin[bin_cnt_col]).round(2)
+
+
+# %%
+fig = df_bin.reset_index().plot.scatter(
+    x=each_true_col,
+    y=model,
+    hover_data=cols,
+    hover_name=df_preds.index.name,
+    backend="plotly",
+    color=log_cnt_col,
+    color_continuous_scale="turbo",
+)
+
+# title = "Analysis of Wrenformer failure cases in the highlighted rectangle"
+# fig.layout.title.update(text=title, x=0.5)
+fig.layout.margin.update(l=0, r=0, t=0, b=0)
+fig.layout.legend.update(title="", x=1, y=0, xanchor="right")
+add_identity_line(fig)
+fig.layout.coloraxis.colorbar.update(
+    x=1, y=0.5, xanchor="right", thickness=12, title=""
+)
+# add shape shaded rectangle at x < 1, y > 1
+fig.add_shape(
+    type="rect", **dict(x0=1, y0=1, x1=-1, y1=6), fillcolor="gray", opacity=0.2
+)
+fig.show()
+
+
+# %%
+img_name = "hull-dist-scatter-wrenformer-failures"
+save_fig(fig, f"{SITE_FIGS}/{img_name}.svelte")
+save_fig(fig, f"{PDF_FIGS}/{img_name}.pdf", width=600, height=300)

scripts/model_figs/scatter_hull_dist_models.py

@@ -9,13 +9,10 @@
 
 import numpy as np
 import plotly.express as px
-import scipy.stats
 from pymatviz.utils import add_identity_line, bin_df_cols, save_fig
-from tqdm import tqdm
 
 from matbench_discovery import PDF_FIGS, SITE_FIGS
-from matbench_discovery.metrics import classify_stable
-from matbench_discovery.plots import clf_color_map, clf_colors, clf_labels
+from matbench_discovery.plots import clf_colors
 from matbench_discovery.preds import (
     df_metrics,
     df_preds,
@@ -47,22 +44,28 @@
 df_melt[each_pred_col] = (
     df_melt[each_true_col] + df_melt[e_form_pred_col] - df_melt[e_form_col]
 )
-df_bin = bin_df_cols(df_melt, [each_true_col, each_pred_col], [facet_col], n_bins=200)
+df_bin = bin_df_cols(
+    df_melt,
+    bin_by_cols=[each_true_col, each_pred_col],
+    group_by_cols=[facet_col],
+    n_bins=200,
+    bin_counts_col=(bin_cnt_col := "bin counts"),
+)
 df_bin = df_bin.reset_index()
 
 # sort legend and facet plots by MAE
 legend_order = list(df_metrics.T.MAE.sort_values().index)
 
 
 # determine each point's classification to color them by
-true_pos, false_neg, false_pos, true_neg = classify_stable(
-    df_bin[each_true_col], df_bin[each_pred_col]
-)
-
-clf_col = "classified"
-df_bin[clf_col] = np.array(clf_labels)[
-    true_pos * 0 + false_neg * 1 + false_pos * 2 + true_neg * 3
-]
+# now unused, can be used to color points by TP/FP/TN/FN
+# true_pos, false_neg, false_pos, true_neg = classify_stable(
+#     df_bin[each_true_col], df_bin[each_pred_col]
+# )
+# clf_col = "classified"
+# df_bin[clf_col] = np.array(clf_labels)[
+#     true_pos * 0 + false_neg * 1 + false_pos * 2 + true_neg * 3
+# ]
 
 
 # %% scatter plot of actual vs predicted e_form_per_atom
@@ -121,21 +124,8 @@
 
 
 # %%
-clr_col, cnt_col = "density", "counts"
-# compute KDE for each model's predictions separately
-for model in (pbar := tqdm(models)):
-    pbar.set_description(f"KDE for {model=}")
-
-    xy = df_preds[[each_true_col, model]].dropna().T
-    model_kde = scipy.stats.gaussian_kde(xy)
-
-    model_rows = df_bin[df_bin[facet_col] == model]
-    xy_binned = model_rows[[each_true_col, each_pred_col]].T
-    density = model_kde(xy_binned)
-    n_preds = len(df_preds[model].dropna())
-    df_bin.loc[model_rows.index, cnt_col] = density / density.sum() * n_preds
-
-df_bin[clr_col] = np.log1p(df_bin[cnt_col]).round(2)
+log_bin_cnt_col = f"log {bin_cnt_col}"
+df_bin[log_bin_cnt_col] = np.log1p(df_bin[bin_cnt_col]).round(2)
 
 
 # %% scatter plot of DFT vs predicted hull distance with each model in separate subplot
@@ -148,7 +138,7 @@
     y=each_pred_col,
     facet_col=facet_col,
     facet_col_wrap=n_cols,
-    color=clr_col,
+    color=log_bin_cnt_col,
     facet_col_spacing=0.02,
     facet_row_spacing=0.04,
     hover_data=hover_cols,
@@ -259,32 +249,3 @@
 fig_name = f"each-scatter-models-{n_rows}x{n_cols}"
 save_fig(fig, f"{SITE_FIGS}/{fig_name}.svelte")
 save_fig(fig, f"{PDF_FIGS}/{fig_name}.pdf")
-
-
-# %%
-model = "Wrenformer"
-fig = px.scatter(
-    df_bin.query(f"{facet_col} == {model!r}"),
-    x=each_true_col,
-    y=each_pred_col,
-    hover_data=hover_cols,
-    color=clf_col,
-    color_discrete_map=clf_color_map,
-    hover_name=df_preds.index.name,
-    opacity=0.7,
-)
-
-title = "Analysis of Wrenformer failure cases in the highlighted rectangle"
-fig.layout.title.update(text=title, x=0.5)
-fig.layout.legend.update(title="", x=1, y=0, xanchor="right")
-add_identity_line(fig)
-
-# add shape shaded rectangle at x < 1, y > 1
-fig.add_shape(
-    type="rect", **dict(x0=1, y0=1, x1=-1, y1=6), fillcolor="gray", opacity=0.2
-)
-fig.show()
-
-img_name = "hull-dist-scatter-wrenformer-failures"
-# save_fig(fig, f"{FIGS}/{img_name}.svelte")
-save_fig(fig, f"{PDF_FIGS}/{img_name}.pdf")

scripts/rolling_mae_vs_hull_dist_wbm_batches.py

@@ -8,7 +8,13 @@
 
 from matbench_discovery import PDF_FIGS, SITE_FIGS, today
 from matbench_discovery.plots import plt, rolling_mae_vs_hull_dist
-from matbench_discovery.preds import df_each_pred, df_preds, e_form_col, each_true_col
+from matbench_discovery.preds import (
+    df_each_pred,
+    df_preds,
+    e_form_col,
+    each_true_col,
+    models,
+)
 
 __author__ = "Rhys Goodall, Janosh Riebesell"
 __date__ = "2022-06-18"
@@ -39,7 +45,7 @@
         markevery=20,
         markerfacecolor="white",
         markeredgewidth=2.5,
-        backend="matplotlib",
+        backend="matplotlib",  # don't change, code here not plotly compatible
         ax=ax,
         just_plot_lines=idx > 1,
         pbar=False,
@@ -54,7 +60,7 @@
 
 
 # %% plotly
-for model in list(df_each_pred)[:-2]:
+for model in models:
     df_pivot = df_each_pred.pivot(columns=batch_col, values=model)
 
     fig, df_err, df_std = rolling_mae_vs_hull_dist(
@@ -66,9 +72,11 @@
         show_dummy_mae=False,
         with_sem=False,
     )
-    fig.layout.legend.update(title=f"<b>{model}</b>", x=0.02, y=0.02)
+    fig.layout.legend.update(
+        title=f"<b>{model}</b>", x=0.02, y=0.02, bgcolor="rgba(0,0,0,0)"
+    )
     fig.layout.margin.update(l=10, r=10, b=10, t=10)
-    fig.update_layout(hovermode="x unified", hoverlabel_bgcolor="black")
+    fig.layout.update(hovermode="x unified", hoverlabel_bgcolor="black")
     fig.update_traces(
         hovertemplate="y=%{y:.3f} eV",
         selector=lambda trace: trace.name.startswith("Batch"),
@@ -78,4 +86,4 @@
     model_snake_case = model.lower().replace(" + ", "-").replace(" ", "-")
     img_path = f"rolling-mae-vs-hull-dist-wbm-batches-{model_snake_case}"
     save_fig(fig, f"{SITE_FIGS}/{img_path}.svelte")
-    save_fig(fig, f"{PDF_FIGS}/{img_path}.pdf")
+    save_fig(fig, f"{PDF_FIGS}/{img_path}.pdf", width=500, height=330)

site/src/routes/preprint/references.yaml

@@ -2648,6 +2648,62 @@ references:
   URL: https://www.nature.com/articles/s41467-020-18556-9
   volume: '11'
 
+- id: wang_framework_2021
+  abstract: >-
+    In this work, we demonstrate a method to quantify uncertainty in corrections
+    to density functional theory (DFT) energies based on empirical results. Such
+    corrections are commonly used to improve the accuracy of computational
+    enthalpies of formation, phase stability predictions, and other
+    energy-derived properties, for example. We incorporate this method into a
+    new DFT energy correction scheme comprising a mixture of oxidation-state and
+    composition-dependent corrections and show that many chemical systems
+    contain unstable polymorphs that may actually be predicted stable when
+    uncertainty is taken into account. We then illustrate how these
+    uncertainties can be used to estimate the probability that a compound is
+    stable on a compositional phase diagram, thus enabling better-informed
+    assessments of compound stability.
+  accessed:
+    - year: 2023
+      month: 8
+      day: 28
+  author:
+    - family: Wang
+      given: Amanda
+    - family: Kingsbury
+      given: Ryan
+    - family: McDermott
+      given: Matthew
+    - family: Horton
+      given: Matthew
+    - family: Jain
+      given: Anubhav
+    - family: Ong
+      given: Shyue Ping
+    - family: Dwaraknath
+      given: Shyam
+    - family: Persson
+      given: Kristin A.
+  citation-key: wang_framework_2021
+  container-title: Scientific Reports
+  container-title-short: Sci Rep
+  DOI: 10.1038/s41598-021-94550-5
+  ISSN: 2045-2322
+  issue: '1'
+  issued:
+    - year: 2021
+      month: 7
+      day: 29
+  language: en
+  license: 2021 The Author(s)
+  number: '1'
+  page: '15496'
+  publisher: Nature Publishing Group
+  source: www.nature.com
+  title: A framework for quantifying uncertainty in DFT energy corrections
+  type: article-journal
+  URL: https://www.nature.com/articles/s41598-021-94550-5
+  volume: '11'
+
 - id: wang_predicting_2021
   abstract: >-
     We propose an efficient high-throughput scheme for the discovery of stable

site/src/routes/si/+page.md

@@ -81,12 +81,12 @@ The figures below show the rolling MAE as a function of distance to the convex h
 {#if mounted}
 
 <div style="display: grid; grid-template-columns: 1fr 1fr; margin: 0 -1em 0 -4em;">
-  <M3gnetRollingMaeBatches style="margin: -2em 0 0; height: 400px;" />
-  <CHGNetRollingMaeBatches style="margin: -2em 0 0; height: 400px;" />
-  <WrenformerRollingMaeBatches style="margin: -2em 0 0; height: 400px;" />
-  <MegnetRollingMaeBatches style="margin: -2em 0 0; height: 400px;" />
-  <VoronoiRfRollingMaeBatches style="margin: -2em 0 0; height: 400px;" />
-  <CgcnnRollingMaeBatches style="margin: -2em 0 0; height: 400px;" />
+  <M3gnetRollingMaeBatches style="aspect-ratio: 1.2;" />
+  <CHGNetRollingMaeBatches style="aspect-ratio: 1.2;" />
+  <WrenformerRollingMaeBatches style="aspect-ratio: 1.2;" />
+  <MegnetRollingMaeBatches style="aspect-ratio: 1.2;" />
+  <VoronoiRfRollingMaeBatches style="aspect-ratio: 1.2;" />
+  <CgcnnRollingMaeBatches style="aspect-ratio: 1.2;" />
 </div>
 {/if}