fix scripts/prc_roc_curves_models.py and roc-models.svelte fig

sth wrong in stable_metrics() maybe?

data/mp/get_mp_energies.py

@@ -58,7 +58,7 @@
 df["wyckoff_spglib"] = [get_aflow_label_from_spglib(x) for x in tqdm(df.structure)]
 
 df.reset_index().to_json(
-    f"{module_dir}/{today}-mp-energies.json.gz", default_handler=as_dict_handler
+    f"{module_dir}/mp-energies.json.gz", default_handler=as_dict_handler
 )
 
 # df = pd.read_json(f"{module_dir}/2022-08-13-mp-energies.json.gz")
@@ -78,9 +78,9 @@
 )
 
 annotate_mae_r2(df.formation_energy_per_atom, df.decomposition_enthalpy)
-# result on 2023-01-10: plots match. no correlation between formation energy and decomposition
-# enthalpy. R^2 = -1.571, MAE = 1.604
-# ax.figure.savefig(f"{module_dir}/{today}-mp-decomp-enth-vs-e-form.webp", dpi=300)
+# result on 2023-01-10: plots match. no correlation between formation energy and
+# decomposition enthalpy. R^2 = -1.571, MAE = 1.604
+# ax.figure.savefig(f"{module_dir}/mp-decomp-enth-vs-e-form.webp", dpi=300)
 
 
 # %% scatter plot energy above convex hull vs decomposition enthalpy
@@ -99,4 +99,4 @@
     title=f"{n_above_line:,} / {len(df):,} = {n_above_line/len(df):.1%} "
     "MP materials with\nenergy_above_hull - decomposition_enthalpy.clip(0) > 0.1"
 )
-# ax.figure.savefig(f"{module_dir}/{today}-mp-e-above-hull-vs-decomp-enth.webp", dpi=300)
+# ax.figure.savefig(f"{module_dir}/mp-e-above-hull-vs-decomp-enth.webp", dpi=300)

matbench_discovery/plots.py

@@ -699,13 +699,6 @@ def cumulative_precision_recall(
             facet_col="metric",
             facet_col_wrap=2,
             facet_col_spacing=0.03,
-            # pivot df in case we want to show all 3 metrics in each plot's hover tooltip
-            # requires fixing index mismatch due to df sub-sampling above
-            # customdata=dict(
-            #     df_cum.reset_index()
-            #     .pivot(index="index", columns="metric")
-            #     .items()
-            # ),
             **kwargs,
         )
 

matbench_discovery/preds.py

@@ -37,3 +37,8 @@
 df_each_pred = pd.DataFrame()
 for model in df_metrics.T.MAE.sort_values().index:
     df_each_pred[model] = df_wbm[each_true_col] + df_wbm[model] - df_wbm[e_form_col]
+
+
+df_each_err = pd.DataFrame()
+for model in df_metrics.T.MAE.sort_values().index:
+    df_each_err[model] = df_wbm[model] - df_wbm[e_form_col]

scripts/hist_classified_stable_vs_hull_dist_batches.py

@@ -86,7 +86,7 @@
 for anno in fig.layout.annotations:
     if not anno.text.startswith("batch_idx="):
         continue
-    batch_idx = int(anno.text.split("=")[-1])
+    batch_idx = int(anno.text.split("=", 1)[-1])
     len_df = sum(df_wbm[batch_col] == int(batch_idx))
     anno.text = f"Batch {batch_idx} ({len_df:,})"
 

scripts/hist_classified_stable_vs_hull_dist_models.py

@@ -88,7 +88,7 @@
         ax.set(title=f"{model_name} · {F1=:.2f} · {FPR=:.2f} · {FNR=:.2f} · {DAF=:.2f}")
 else:
     for anno in fig.layout.annotations:
-        model_name = anno.text = anno.text.split("=").pop()
+        model_name = anno.text = anno.text.split("=", 1).pop()
         if model_name not in models or not show_metrics:
             continue
         F1, FPR, FNR, DAF = (

scripts/prc_roc_curves_models.py

@@ -1,19 +1,12 @@
 # %%
-import numpy as np
 import pandas as pd
 from pymatviz.utils import save_fig
+from sklearn.metrics import auc, precision_recall_curve, roc_curve
 from tqdm import tqdm
 
 from matbench_discovery import FIGS
-from matbench_discovery.metrics import stable_metrics
 from matbench_discovery.plots import pio
-from matbench_discovery.preds import (
-    df_wbm,
-    e_form_col,
-    each_pred_col,
-    each_true_col,
-    models,
-)
+from matbench_discovery.preds import df_each_pred, df_wbm, each_true_col
 
 __author__ = "Janosh Riebesell"
 __date__ = "2023-01-30"
@@ -34,47 +27,49 @@
 # %%
 df_roc = pd.DataFrame()
 
-for model in (pbar := tqdm(models)):
-    pbar.set_description(model)
-    df_wbm[f"{model}_{each_pred_col}"] = df_wbm[each_true_col] + (
-        df_wbm[model] - df_wbm[e_form_col]
-    )
-    for stab_treshold in np.arange(-0.4, 0.4, 0.01):
-        metrics = stable_metrics(
-            df_wbm[each_true_col], df_wbm[f"{model}_{each_pred_col}"], stab_treshold
-        )
-        df_tmp = pd.DataFrame(
-            {facet_col: model, color_col: stab_treshold, **metrics}, index=[0]
-        )
-        df_roc = pd.concat([df_roc, df_tmp])
-
+for model in (pbar := tqdm(list(df_each_pred), desc="Calculating ROC curves")):
+    pbar.set_postfix_str(model)
+    na_mask = df_wbm[each_true_col].isna() | df_each_pred[model].isna()
+    y_true = (df_wbm[~na_mask][each_true_col] <= 0).astype(int)
+    y_pred = df_each_pred[model][~na_mask]
+    fpr, tpr, thresholds = roc_curve(y_true, y_pred, pos_label=0)
+    AUC = auc(fpr, tpr)
+    title = f"{model} · {AUC=:.2f}"
+    df_tmp = pd.DataFrame(
+        {"FPR": fpr, "TPR": tpr, color_col: thresholds, "AUC": AUC, facet_col: title}
+    ).round(3)
 
-df_roc = df_roc.round(3)
+    df_roc = pd.concat([df_roc, df_tmp])
 
 
 # %%
-fig = df_roc.plot.scatter(
-    x="FPR",
-    y="TPR",
-    facet_col=facet_col,
-    facet_col_wrap=2,
-    backend="plotly",
-    height=800,
-    color=color_col,
-    range_x=(0, 1),
-    range_y=(0, 1),
+fig = (
+    df_roc.iloc[:: len(df_roc) // 500 or 1]
+    .sort_values(["AUC", "FPR"], ascending=False)
+    .plot.scatter(
+        x="FPR",
+        y="TPR",
+        facet_col=facet_col,
+        facet_col_wrap=2,
+        backend="plotly",
+        height=150 * len(df_roc[facet_col].unique()),
+        color=color_col,
+        range_x=(0, 1),
+        range_y=(0, 1),
+        range_color=(-0.5, 0.5),
+        hover_name=facet_col,
+        hover_data={facet_col: False},
+    )
 )
 
 for anno in fig.layout.annotations:
-    anno.text = anno.text.split("=")[1]  # remove Model= from subplot titles
+    anno.text = anno.text.split("=", 1)[1]  # remove Model= from subplot titles
 
 fig.layout.coloraxis.colorbar.update(
-    x=1, y=1, xanchor="right", yanchor="top", thickness=14, len=0.27, title_side="right"
+    x=1, y=1, xanchor="right", yanchor="top", thickness=14, len=0.2, title_side="right"
 )
 fig.add_shape(type="line", x0=0, y0=0, x1=1, y1=1, line=line, row="all", col="all")
-fig.add_annotation(
-    text="No skill", x=0.5, y=0.5, showarrow=False, yshift=-10, textangle=-30
-)
+fig.add_annotation(text="No skill", x=0.5, y=0.5, showarrow=False, yshift=-10)
 # allow scrolling and zooming each subplot individually
 fig.update_xaxes(matches=None)
 fig.update_yaxes(matches=None)
@@ -86,20 +81,44 @@
 
 
 # %%
-fig = df_roc.plot.scatter(
+df_prc = pd.DataFrame()
+
+for model in (pbar := tqdm(list(df_each_pred), desc="Calculating ROC curves")):
+    pbar.set_postfix_str(model)
+    na_mask = df_wbm[each_true_col].isna() | df_each_pred[model].isna()
+    y_true = (df_wbm[~na_mask][each_true_col] <= 0).astype(int)
+    y_pred = df_each_pred[model][~na_mask]
+    prec, recall, thresholds = precision_recall_curve(y_true, y_pred, pos_label=0)
+    df_tmp = pd.DataFrame(
+        {
+            "Precision": prec[:-1],
+            "Recall": recall[:-1],
+            color_col: thresholds,
+            facet_col: model,
+        }
+    ).round(3)
+
+    df_prc = pd.concat([df_prc, df_tmp])
+
+
+# %%
+fig = df_prc.iloc[:: len(df_roc) // 500 or 1].plot.scatter(
     x="Recall",
     y="Precision",
     facet_col=facet_col,
     facet_col_wrap=2,
     backend="plotly",
-    height=800,
+    height=150 * len(df_roc[facet_col].unique()),
     color=color_col,
     range_x=(0, 1),
-    range_y=(0, 1),
+    range_y=(0.5, 1),
+    range_color=(-0.5, 1),
+    hover_name=facet_col,
+    hover_data={facet_col: False},
 )
 
 for anno in fig.layout.annotations:
-    anno.text = anno.text.split("=")[1]  # remove Model= from subplot titles
+    anno.text = anno.text.split("=", 1)[1]  # remove Model= from subplot titles
 
 fig.layout.coloraxis.colorbar.update(
     x=0.5, y=1.1, thickness=14, len=0.4, orientation="h"

scripts/scatter_e_above_hull_models.py

@@ -127,7 +127,7 @@
     traces = [t for t in fig.data if t.xaxis == f"x{idx if idx > 1 else ''}"]
     assert len(traces) == 4, f"Expected 4 traces, got {len(traces)=}"
 
-    model = anno.text.split("=")[1]
+    model = anno.text.split("=", 1)[1]
     assert model in df_wbm, f"Unexpected {model=} not in {list(df_wbm)=}"
     # add MAE and R2 to subplot titles
     MAE, R2 = df_metrics[model][["MAE", "R2"]]
@@ -139,39 +139,39 @@
     fig.layout[f"xaxis{idx}"].title.text = ""
     fig.layout[f"yaxis{idx}"].title.text = ""
 
-    # add transparent rectangle with TN, TP, FN, FP labels in each quadrant
-    for sign_x, sign_y, color, label in zip(
-        [-1, -1, 1, 1], [-1, 1, -1, 1], clf_colors, ("TP", "FN", "FP", "TN")
-    ):
-        # instead of coloring points in each quadrant, we can add a transparent
-        # background to each quadrant (looks worse maybe than coloring points)
-        # fig.add_shape(
-        #     type="rect",
-        #     x0=0,
-        #     y0=0,
-        #     x1=sign_x * 100,
-        #     y1=sign_y * 100,
-        #     fillcolor=color,
-        #     opacity=0.5,
-        #     layer="below",
-        #     xref=f"x{idx}",
-        #     yref=f"y{idx}",
-        # )
-        fig.add_annotation(
-            xref=f"x{idx}",
-            yref=f"y{idx}",
-            x=sign_x * xy_max,
-            y=sign_y * xy_max,
-            xshift=-20 * sign_x,
-            yshift=-20 * sign_y,
-            text=label,
-            showarrow=False,
-            font=dict(size=16, color=color),
-        )
-
-    # add dashed quadrant separators
-    fig.add_vline(x=0, line=dict(width=0.5, dash="dash"))
-    fig.add_hline(y=0, line=dict(width=0.5, dash="dash"))
+# add transparent rectangle with TN, TP, FN, FP labels in each quadrant
+for sign_x, sign_y, color, label in zip(
+    [-1, -1, 1, 1], [-1, 1, -1, 1], clf_colors, ("TP", "FN", "FP", "TN")
+):
+    # instead of coloring points in each quadrant, we can add a transparent
+    # background to each quadrant (looks worse maybe than coloring points)
+    # fig.add_shape(
+    #     type="rect",
+    #     x0=0,
+    #     y0=0,
+    #     x1=sign_x * 100,
+    #     y1=sign_y * 100,
+    #     fillcolor=color,
+    #     opacity=0.2,
+    #     layer="below",
+    #     row="all",
+    #     col="all",
+    # )
+    fig.add_annotation(
+        x=sign_x * xy_max,
+        y=sign_y * xy_max,
+        xshift=-20 * sign_x,
+        yshift=-20 * sign_y,
+        text=label,
+        showarrow=False,
+        font=dict(size=16, color=color),
+        row="all",
+        col="all",
+    )
+
+# add dashed quadrant separators
+fig.add_vline(x=0, line=dict(width=0.5, dash="dash"))
+fig.add_hline(y=0, line=dict(width=0.5, dash="dash"))
 
 fig.update_xaxes(nticks=5)
 fig.update_yaxes(nticks=5)

site/src/routes/about-the-test-set/tmi/+page.svelte

@@ -40,13 +40,12 @@
 
 Stuff that didn't make the cut into the main page describing the WBM test set.
 
-<ColorScaleSelect bind:selected />
-
 <h2>WBM Element Counts for <code>{filter}</code></h2>
 
 Filter WBM element counts by composition arity (how many elements in the formula) or batch
 index (which iteration of elemental substitution the structure was generated in).
 
+<ColorScaleSelect bind:selected />
 <ul>
   <li>
     composition arity:

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

@@ -16,7 +16,7 @@
 <RocModels  />
 {/if}
 
-> @label:fig:roc-models Receiver operating characteristic (ROC) curve for each model. TPR/FPR is the true and false positive rates. Points are colored by their stability threshold. A material is classified as stable if E<sub>above hull</sub> is less than the stability threshold. Since all models predict E<sub>form</sub> (and M3GNet predicted energies are converted to formation energy before stability classification), they are insensitive to changes in the threshold.
+> @label:fig:roc-models Receiver operating characteristic (ROC) curve for each model. TPR/FPR is the true/false positive rate. FPR means the $x$-axis is the fraction of unstable structures classified as stable while TPR on the $y$-axis is the fraction of stable structures classified as stable. Points are colored by stability threshold $t$ which sweeps from $-0.4 \ \frac{\text{eV}}{\text{atom}} \leq t \leq 0.4 \ \frac{\text{eV}}{\text{atom}}$ above the hull. A material is classified as stable if the predicted E<sub>above hull</sub> is less than the stability threshold. Since all models predict E<sub>form</sub> (and M3GNet predicted energies are converted to formation energy before stability classification), they are insensitive to changes in the threshold $t$. M3GNet wins in area under curve (AUC) with 0.87, coming in 34% higher than the worst model Voronoi Random Forest. The diagonal 'No skill' line shows performance of a dummy model that randomly ranks materials stability.
 
 ## Model Run Times
 

tests/test_preds.py

@@ -1,5 +1,6 @@
 from matbench_discovery.data import PRED_FILENAMES
 from matbench_discovery.preds import (
+    df_each_err,
     df_each_pred,
     df_metrics,
     df_wbm,
@@ -23,9 +24,11 @@ def test_df_metrics() -> None:
 
 def test_df_each_pred() -> None:
     assert len(df_each_pred) == len(df_wbm)
-    assert (
-        {*df_each_pred} == {*df_metrics} < {*df_wbm}
-    ), "df_each_pred has wrong columns"
-    assert all(
-        df_each_pred.isna().sum() / len(df_each_pred) < 0.05
-    ), "too many NaNs in df_each_pred"
+    assert {*df_each_pred} == {*df_metrics}, "df_each_pred has wrong columns"
+    assert all(df_each_pred.isna().mean() < 0.05), "too many NaNs in df_each_pred"
+
+
+def test_df_each_err() -> None:
+    assert len(df_each_err) == len(df_wbm)
+    assert {*df_each_err} == {*df_metrics}, "df_each_err has wrong columns"
+    assert all(df_each_err.isna().mean() < 0.05), "too many NaNs in df_each_err"