add plot_scripts/all_models_scatter.py

centralize model preds loading into matbench_discovery/plot_scripts/__init__.py, now used in most plot scripts
add tests/test_plots_scripts.py

matbench_discovery/plot_scripts/hist_classified_stable_vs_hull_dist_batches.py

@@ -1,9 +1,6 @@
 # %%
-import pandas as pd
-import pymatviz
-
 from matbench_discovery import ROOT, today
-from matbench_discovery.plot_scripts import df_wbm
+from matbench_discovery.plot_scripts import load_df_wbm_with_preds
 from matbench_discovery.plots import (
     StabilityCriterion,
     WhichEnergy,
@@ -27,59 +24,25 @@
 
 
 # %%
-dfs = {}
-dfs["wren"] = pd.read_csv(
-    f"{ROOT}/data/2022-06-11-from-rhys/wren-mp-initial-structures.csv"
-).set_index("material_id")
-dfs["m3gnet"] = pd.read_json(
-    f"{ROOT}/models/m3gnet/2022-10-31-m3gnet-wbm-IS2RE.json.gz"
-).set_index("material_id")
-dfs["wrenformer"] = pd.read_csv(
-    f"{ROOT}/models/wrenformer/2022-11-15-wrenformer-IS2RE-preds.csv"
-).set_index("material_id")
-dfs["bowsr_megnet"] = pd.read_json(
-    f"{ROOT}/models/bowsr/2022-11-22-bowsr-megnet-wbm-IS2RE.json.gz"
-).set_index("material_id")
-
-
-# %%
-pred_col = "e_form_per_atom_pred"
-target_col = "e_form_per_atom"
-if "wren" in dfs:
-    df = dfs["wren"]
-    pred_cols = df.filter(regex=r"_pred_\d").columns
-    # make sure we average the expected number of ensemble member predictions
-    assert len(pred_cols) == 10
-    df[pred_col] = df[pred_cols].mean(axis=1)
-if "m3gnet" in dfs:
-    df = dfs["m3gnet"]
-    df[pred_col] = df.e_form_per_atom_m3gnet
-if "bowsr_megnet" in dfs:
-    df = dfs["bowsr_megnet"]
-    df[pred_col] = df.e_form_per_atom_bowsr_megnet
-if "wrenformer" in dfs:
-    pred_col = "e_form_per_atom_mp2020_corrected_pred_ens"
+df_wbm = load_df_wbm_with_preds(models="Wren Wrenformer".split()).round(3)
+target_col = "e_form_per_atom_mp2020_corrected"
+e_above_hull_col = "e_above_hull_mp2020_corrected_ppd_mp"
 
 
 # %%
 which_energy: WhichEnergy = "true"
 stability_crit: StabilityCriterion = "energy"
 fig, axs = plt.subplots(2, 3, figsize=(18, 9))
 
-model_name = "wrenformer"
-df = dfs[model_name]
-
-df["e_above_hull_mp"] = df_wbm.e_above_hull_mp2020_corrected_ppd_mp
-df[target_col] = df_wbm.e_form_per_atom_mp2020_corrected  # e_form targets
-
+model_name = "Wrenformer"
 
 for batch_idx, ax in zip(range(1, 6), axs.flat):
-    batch_df = df[df.index.str.startswith(f"wbm-step-{batch_idx}-")]
+    batch_df = df_wbm[df_wbm.index.str.startswith(f"wbm-step-{batch_idx}-")]
     assert 1e4 < len(batch_df) < 1e5, print(f"{len(batch_df) = :,}")
 
     ax, metrics = hist_classified_stable_vs_hull_dist(
-        e_above_hull_pred=batch_df[pred_col] - batch_df.e_form_per_atom,
-        e_above_hull_true=batch_df.e_above_hull_mp,
+        e_above_hull_pred=batch_df[model_name] - batch_df[target_col],
+        e_above_hull_true=batch_df[e_above_hull_col],
         which_energy=which_energy,
         stability_crit=stability_crit,
         ax=ax,
@@ -93,8 +56,8 @@
 
 
 ax, metrics = hist_classified_stable_vs_hull_dist(
-    e_above_hull_pred=df[pred_col] - df.e_form_per_atom,
-    e_above_hull_true=df.e_above_hull_mp,
+    e_above_hull_pred=df_wbm[model_name] - df_wbm[target_col],
+    e_above_hull_true=df_wbm[e_above_hull_col],
     which_energy=which_energy,
     stability_crit=stability_crit,
     ax=axs.flat[-1],
@@ -103,7 +66,7 @@
 text = f"Enrichment\nFactor = {metrics['enrichment']:.3}"
 ax.text(0.02, 0.3, text, fontsize=16, transform=ax.transAxes)
 
-axs.flat[-1].set(title=f"All batches ({len(df.filter(like='e_').dropna()):,})")
+axs.flat[-1].set(title=f"All batches ({len(df_wbm[model_name].dropna()):,})")
 axs.flat[0].legend(frameon=False, loc="upper left")
 
 fig.suptitle(f"{today} {model_name}", y=1.07, fontsize=16)
@@ -112,9 +75,3 @@
 # %%
 img_name = f"{today}-{model_name}-wbm-hull-dist-hist-batches"
 ax.figure.savefig(f"{ROOT}/figures/{img_name}.pdf")
-
-
-# %%
-pymatviz.density_scatter(
-    df=dfs[model_name].query(f"{target_col} < 5"), x=target_col, y=pred_col
-)

matbench_discovery/plot_scripts/precision_recall.py

@@ -1,93 +1,45 @@
 # %%
-import pandas as pd
 from sklearn.metrics import f1_score
 
 from matbench_discovery import ROOT, today
-from matbench_discovery.plot_scripts import df_wbm
+from matbench_discovery.plot_scripts import load_df_wbm_with_preds
 from matbench_discovery.plots import StabilityCriterion, cumulative_clf_metric, plt
 
 __author__ = "Rhys Goodall, Janosh Riebesell"
 
 
 # %%
-dfs: dict[str, pd.DataFrame] = {}
-for model_name in ("wren", "cgcnn", "voronoi"):
-    csv_path = (
-        f"{ROOT}/data/2022-06-11-from-rhys/{model_name}-mp-initial-structures.csv"
-    )
-    df = pd.read_csv(csv_path).set_index("material_id")
-    dfs[model_name] = df
-
-dfs["m3gnet"] = pd.read_json(
-    f"{ROOT}/models/m3gnet/2022-10-31-m3gnet-wbm-IS2RE.json.gz"
-).set_index("material_id")
-
-dfs["wrenformer"] = pd.read_csv(
-    f"{ROOT}/models/wrenformer/2022-11-15-wrenformer-IS2RE-preds.csv"
-).set_index("material_id")
+models = (
+    "Wren, CGCNN IS2RE, CGCNN RS2RE, Voronoi IS2RE, Voronoi RS2RE, "
+    "Wrenformer, MEGNet"
+).split(", ")
 
-dfs["bowsr_megnet"] = pd.read_json(
-    f"{ROOT}/models/bowsr/2022-11-22-bowsr-megnet-wbm-IS2RE.json.gz"
-).set_index("material_id")
+df_wbm = load_df_wbm_with_preds(models=models).round(3)
 
-print(f"loaded models: {list(dfs)}")
+target_col = "e_form_per_atom_mp2020_corrected"
+e_above_hull_col = "e_above_hull_mp2020_corrected_ppd_mp"
 
 
 # %%
 stability_crit: StabilityCriterion = "energy"
 colors = "tab:blue tab:orange teal tab:pink black red turquoise tab:purple".split()
-F1s: dict[str, float] = {}
-
-for model_name, df in sorted(dfs.items()):
-    if "std" in stability_crit:
-        # TODO column names to compute standard deviation from are currently hardcoded
-        # needs to be updated when adding non-aviary models with uncertainty estimation
-        var_aleatoric = (df.filter(regex=r"_ale_\d") ** 2).mean(axis=1)
-        var_epistemic = df.filter(regex=r"_pred_\d").var(axis=1, ddof=0)
-        std_total = (var_epistemic + var_aleatoric) ** 0.5
-    else:
-        std_total = None
-
-    try:
-        if model_name == "m3gnet":
-            model_preds = df.e_form_m3gnet
-        elif "wrenformer" in model_name:
-            model_preds = df.e_form_per_atom_pred_ens
-        elif len(pred_cols := df.filter(like="e_form_pred").columns) >= 1:
-            # Voronoi+RF has single prediction column, Wren and CGCNN each have 10
-            # other cases are unexpected
-            assert len(pred_cols) in (1, 10), f"{model_name=} has {len(pred_cols)=}"
-            model_preds = df[pred_cols].mean(axis=1)
-        elif model_name == "bowsr_megnet":
-            model_preds = df.e_form_per_atom_bowsr_megnet
-        else:
-            raise ValueError(f"Unhandled {model_name = }")
-    except AttributeError as exc:
-        raise KeyError(f"{model_name = }") from exc
-
-    df["e_above_hull_mp"] = df_wbm.e_above_hull_mp2020_corrected_ppd_mp
-    df["e_form_per_atom"] = df_wbm.e_form_per_atom_mp2020_corrected
-    df["e_above_hull_pred"] = model_preds - df.e_form_per_atom
-    if n_nans := df.isna().values.sum() > 0:
-        assert n_nans < 10, f"{model_name=} has {n_nans=}"
-        df = df.dropna()
-
-    F1 = f1_score(df.e_above_hull_mp < 0, df.e_above_hull_pred < 0)
-    F1s[model_name] = F1
 
 
 # %%
 fig, (ax_prec, ax_recall) = plt.subplots(1, 2, figsize=(15, 7), sharey=True)
 
-for (model_name, F1), color in zip(sorted(F1s.items(), key=lambda x: x[1]), colors):
-    df = dfs[model_name]
-    e_above_hull_error = df.e_above_hull_pred + df.e_above_hull_mp
-    e_above_hull_true = df.e_above_hull_mp
+for model_name, color in zip(models, colors):
+
+    e_above_hull_pred = df_wbm[model_name] - df_wbm[target_col]
+
+    F1 = f1_score(df_wbm[e_above_hull_col] < 0, e_above_hull_pred < 0)
+
+    e_above_hull_error = e_above_hull_pred + df_wbm[e_above_hull_col]
     cumulative_clf_metric(
         e_above_hull_error,
-        e_above_hull_true,
+        df_wbm[e_above_hull_col],
         color=color,
-        label=f"{model_name}\n{F1=:.2}",
+        label=f"{model_name}\n{F1=:.3}",
         project_end_point="xy",
         stability_crit=stability_crit,
         ax=ax_prec,
@@ -96,9 +48,9 @@
 
     cumulative_clf_metric(
         e_above_hull_error,
-        e_above_hull_true,
+        df_wbm[e_above_hull_col],
         color=color,
-        label=f"{model_name}\n{F1=:.2}",
+        label=f"{model_name}\n{F1=:.3}",
         project_end_point="xy",
         stability_crit=stability_crit,
         ax=ax_recall,

matbench_discovery/plot_scripts/rolling_mae_vs_hull_dist.py

@@ -10,8 +10,6 @@
 
 
 # %%
-markers = ["o", "v", "^", "H", "D", ""]
-
 data_path = (
     f"{ROOT}/data/2022-06-11-from-rhys/wren-mp-initial-structures.csv"
     # f"{ROOT}/models/wrenformer/2022-11-15-wrenformer-IS2RE-preds.csv"
@@ -21,15 +19,6 @@
 
 
 # %%
-# rare = "all"
-# from pymatgen.core import Composition
-# rare = "no-lanthanides"
-# df["contains_rare_earths"] = df.composition.map(
-#     lambda x: any(el.is_rare_earth_metal for el in Composition(x))
-# )
-# df = df.query("~contains_rare_earths")
-
-
 df["e_above_hull_mp"] = df_wbm.e_above_hull_mp2020_corrected_ppd_mp
 
 assert all(n_nans := df.isna().sum() == 0), f"Found {n_nans} NaNs"

matbench_discovery/plots.py

@@ -35,6 +35,11 @@
     e_above_hull_mp="Energy above MP convex hull (eV/atom)",
     e_above_hull_error="Error in energy above convex hull (eV/atom)",
     vol_diff="Volume difference (A^3)",
+    e_form_per_atom_mp2020_corrected="Formation energy (eV/atom)",
+    e_form_per_atom_pred="Predicted formation energy (eV/atom)",
+    material_id="Material ID",
+    band_gap="Band gap (eV)",
+    formula="Formula",
 )
 model_labels = dict(
     wren="Wren",
@@ -254,10 +259,6 @@ def rolling_mae_vs_hull_dist(
     """
     ax = ax or plt.gca()
 
-    for series in (e_above_hull_pred, e_above_hull_true):
-        n_nans = series.isna().sum()
-        assert n_nans == 0, f"{n_nans:,} NaNs in {series.name}"
-
     is_fresh_ax = len(ax.lines) == 0
 
     bins = np.arange(*x_lim, bin_width)
@@ -387,10 +388,6 @@ def cumulative_clf_metric(
     """
     ax = ax or plt.gca()
 
-    for series in (e_above_hull_error, e_above_hull_true):
-        n_nans = series.isna().sum()
-        assert n_nans == 0, f"{n_nans:,} NaNs in {series.name}"
-
     e_above_hull_error = e_above_hull_error.sort_values()
     e_above_hull_true = e_above_hull_true.loc[e_above_hull_error.index]
 

models/m3gnet/eda_wbm_pre_vs_post_m3gnet_relaxation.py

@@ -99,8 +99,7 @@
 df["m3gnet_vol_diff"] = df.m3gnet_volume - df.final_wbm_volume
 df["dft_vol_diff"] = df.initial_wbm_volume - df.final_wbm_volume
 fig = px.histogram(
-    pd.melt(
-        df,
+    df.melt(
         value_vars=["m3gnet", "dft"],
         value_name="vol_diff",
         var_name="method",

models/megnet/test_megnet.py

@@ -17,7 +17,7 @@
 
 """
 To slurm submit this file: python path/to/file.py slurm-submit
-Requires Megnet installation: pip install megnet
+Requires MEGNet installation: pip install megnet
 https://github.com/materialsvirtuallab/megnet
 """