split model pred loading from CSV into new module matbench_discovery/…

…preds.py

matbench_discovery/metrics.py

@@ -1,5 +1,3 @@
-"""Centralize data-loading and computing metrics for plotting scripts"""
-
 from __future__ import annotations
 
 from collections.abc import Sequence
@@ -8,7 +6,12 @@
 import pandas as pd
 from sklearn.metrics import r2_score
 
-from matbench_discovery.data import load_df_wbm_preds
+"""Functions to classify energy above convex hull predictions as true/false
+positive/negative and compute performance metrics.
+"""
+
+__author__ = "Janosh Riebesell"
+__date__ = "2023-02-01"
 
 
 def classify_stable(
@@ -98,30 +101,3 @@ def stable_metrics(
         RMSE=((true - pred) ** 2).mean() ** 0.5,
         R2=r2_score(true, pred),
     )
-
-
-models = sorted(
-    "Wrenformer, CGCNN, Voronoi Random Forest, MEGNet, M3GNet + MEGNet, "
-    "BOWSR + MEGNet".split(", ")
-)
-e_form_col = "e_form_per_atom_mp2020_corrected"
-each_true_col = "e_above_hull_mp2020_corrected_ppd_mp"
-each_pred_col = "e_above_hull_pred"
-
-df_wbm = load_df_wbm_preds(models).round(3)
-
-for col in [e_form_col, each_true_col]:
-    assert col in df_wbm, f"{col=} not in {list(df_wbm)=}"
-
-
-df_metrics = pd.DataFrame()
-for model in models:
-    df_metrics[model] = stable_metrics(
-        df_wbm[each_true_col],
-        df_wbm[each_true_col] + df_wbm[e_form_col] - df_wbm[model],
-    )
-
-assert df_metrics.T.MAE.between(0, 0.2).all(), "MAE not in range"
-assert df_metrics.T.R2.between(0.1, 1).all(), "R2 not in range"
-assert df_metrics.T.RMSE.between(0, 0.25).all(), "RMSE not in range"
-assert df_metrics.isna().sum().sum() == 0, "NaNs in metrics"

matbench_discovery/preds.py

@@ -0,0 +1,37 @@
+from __future__ import annotations
+
+import pandas as pd
+
+from matbench_discovery.data import load_df_wbm_preds
+from matbench_discovery.metrics import stable_metrics
+
+"""Centralize data-loading and computing metrics for plotting scripts"""
+
+__author__ = "Janosh Riebesell"
+__date__ = "2023-02-04"
+
+models = sorted(
+    "Wrenformer, CGCNN, Voronoi Random Forest, MEGNet, M3GNet + MEGNet, "
+    "BOWSR + MEGNet".split(", ")
+)
+e_form_col = "e_form_per_atom_mp2020_corrected"
+each_true_col = "e_above_hull_mp2020_corrected_ppd_mp"
+each_pred_col = "e_above_hull_pred"
+
+df_wbm = load_df_wbm_preds(models).round(3)
+
+for col in [e_form_col, each_true_col]:
+    assert col in df_wbm, f"{col=} not in {list(df_wbm)=}"
+
+
+df_metrics = pd.DataFrame()
+for model in models:
+    df_metrics[model] = stable_metrics(
+        df_wbm[each_true_col],
+        df_wbm[each_true_col] + df_wbm[e_form_col] - df_wbm[model],
+    )
+
+assert df_metrics.T.MAE.between(0, 0.2).all(), "MAE not in range"
+assert df_metrics.T.R2.between(0.1, 1).all(), "R2 not in range"
+assert df_metrics.T.RMSE.between(0, 0.25).all(), "RMSE not in range"
+assert df_metrics.isna().sum().sum() == 0, "NaNs in metrics"

scripts/cumulative_clf_metrics.py

@@ -3,8 +3,8 @@
 from pymatviz.utils import save_fig
 
 from matbench_discovery import FIGS, STATIC, today
-from matbench_discovery.metrics import df_wbm, e_form_col, each_true_col, models
 from matbench_discovery.plots import cumulative_precision_recall
+from matbench_discovery.preds import df_wbm, e_form_col, each_true_col, models
 
 __author__ = "Janosh Riebesell, Rhys Goodall"
 __date__ = "2022-12-04"

scripts/hist_classified_stable_vs_hull_dist.py

@@ -2,14 +2,9 @@
 from pymatviz.utils import save_fig
 
 from matbench_discovery import FIGS, today
-from matbench_discovery.metrics import (
-    df_wbm,
-    e_form_col,
-    each_pred_col,
-    each_true_col,
-    stable_metrics,
-)
+from matbench_discovery.metrics import stable_metrics
 from matbench_discovery.plots import WhichEnergy, hist_classified_stable_vs_hull_dist
+from matbench_discovery.preds import df_wbm, e_form_col, each_pred_col, each_true_col
 
 __author__ = "Rhys Goodall, Janosh Riebesell"
 __date__ = "2022-06-18"

scripts/hist_classified_stable_vs_hull_dist_batches.py

@@ -2,19 +2,14 @@
 from pymatviz.utils import save_fig
 
 from matbench_discovery import FIGS, today
-from matbench_discovery.metrics import (
-    df_wbm,
-    e_form_col,
-    each_pred_col,
-    each_true_col,
-    stable_metrics,
-)
+from matbench_discovery.metrics import stable_metrics
 from matbench_discovery.plots import (
     Backend,
     WhichEnergy,
     hist_classified_stable_vs_hull_dist,
     plt,
 )
+from matbench_discovery.preds import df_wbm, e_form_col, each_pred_col, each_true_col
 
 __author__ = "Rhys Goodall, Janosh Riebesell"
 __date__ = "2022-08-25"

scripts/hist_classified_stable_vs_hull_dist_models.py

@@ -2,14 +2,14 @@
 from pymatviz.utils import save_fig
 
 from matbench_discovery import STATIC, today
-from matbench_discovery.metrics import (
+from matbench_discovery.plots import Backend, hist_classified_stable_vs_hull_dist, plt
+from matbench_discovery.preds import (
     df_metrics,
     df_wbm,
     e_form_col,
     each_true_col,
     models,
 )
-from matbench_discovery.plots import Backend, hist_classified_stable_vs_hull_dist, plt
 
 __author__ = "Janosh Riebesell"
 __date__ = "2022-12-01"

scripts/prc_roc_curves_models.py

@@ -5,15 +5,15 @@
 from tqdm import tqdm
 
 from matbench_discovery import FIGS, today
-from matbench_discovery.metrics import (
+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,
-    stable_metrics,
 )
-from matbench_discovery.plots import pio
 
 __author__ = "Janosh Riebesell"
 __date__ = "2023-01-30"

scripts/rolling_mae_vs_hull_dist.py

@@ -1,7 +1,7 @@
 # %%
 from matbench_discovery import FIGS, today
-from matbench_discovery.metrics import df_metrics, df_wbm, e_form_col, each_true_col
 from matbench_discovery.plots import rolling_mae_vs_hull_dist
+from matbench_discovery.preds import df_metrics, df_wbm, e_form_col, each_true_col
 
 __author__ = "Rhys Goodall, Janosh Riebesell"
 __date__ = "2022-06-18"

scripts/rolling_mae_vs_hull_dist_all_models.py

@@ -2,8 +2,8 @@
 from pymatviz.utils import save_fig
 
 from matbench_discovery import FIGS, STATIC, today
-from matbench_discovery.metrics import df_metrics, df_wbm, e_form_col, each_true_col
 from matbench_discovery.plots import Backend, rolling_mae_vs_hull_dist
+from matbench_discovery.preds import df_metrics, df_wbm, e_form_col, each_true_col
 
 __author__ = "Rhys Goodall, Janosh Riebesell"
 __date__ = "2022-06-18"

scripts/rolling_mae_vs_hull_dist_wbm_batches.py

@@ -1,7 +1,7 @@
 # %%
 from matbench_discovery import FIGS, today
-from matbench_discovery.metrics import df_wbm, e_form_col, each_true_col
 from matbench_discovery.plots import plt, rolling_mae_vs_hull_dist
+from matbench_discovery.preds import df_wbm, e_form_col, each_true_col
 
 __author__ = "Rhys Goodall, Janosh Riebesell"
 __date__ = "2022-06-18"

scripts/scatter_e_above_hull_models.py

@@ -3,16 +3,15 @@
 from pymatviz.utils import add_identity_line, save_fig
 
 from matbench_discovery import FIGS, STATIC, today
-from matbench_discovery.metrics import (
-    classify_stable,
+from matbench_discovery.metrics import classify_stable, stable_metrics
+from matbench_discovery.plots import clf_color_map, clf_labels, px
+from matbench_discovery.preds import (
     df_wbm,
     e_form_col,
     each_pred_col,
     each_true_col,
     models,
-    stable_metrics,
 )
-from matbench_discovery.plots import clf_color_map, clf_labels, px
 
 __author__ = "Janosh Riebesell"
 __date__ = "2022-11-28"

site/src/routes/how-to-contribute/+page.md

@@ -147,17 +147,17 @@ and place the above-listed files there. The file structure should look like this
 ```txt
 matbench-discovery-root
 └── models
-    └── <model name>
+    └── <model_name>
         ├── metadata.yml
         ├── <yyyy-mm-dd>-<model_name>-preds.(json|csv).gz
         ├── test_<model_name>.py
-        ├── readme.md # optional
-        └── train_<model_name>.py # optional
+        ├── readme.md  # optional
+        └── train_<model_name>.py  # optional
 ```
 
 You can include arbitrary other supporting files like metadata and model features (below 10MB to keep `git clone` time low) if they are needed to run the model or help others reproduce your results. For larger files, please upload to [Figshare](https://figshare.com) or similar and link them somewhere in your files.
 
-### Step 3: Create a PR to the [Matbench Discovery repo](https://github.com/janosh/matbench-discovery)
+### Step 3: Open a PR to the [Matbench Discovery repo](https://github.com/janosh/matbench-discovery)
 
 Commit your files to the repo on a branch called `<model_name>` and create a pull request (PR) to the Matbench repository.
 

tests/test_energy.py

@@ -6,8 +6,14 @@
 from pymatgen.analysis.phase_diagram import PDEntry
 from pymatgen.core import Lattice, Structure
 from pymatgen.entries.computed_entries import ComputedEntry, Entry
+from pytest import approx
 
-from matbench_discovery.energy import get_e_form_per_atom, get_elemental_ref_entries
+from matbench_discovery.energy import (
+    get_e_form_per_atom,
+    get_elemental_ref_entries,
+    mp_elem_reference_entries,
+    mp_elemental_ref_energies,
+)
 
 dummy_struct = Structure(
     lattice=Lattice.cubic(5),
@@ -49,3 +55,11 @@ def test_get_elemental_ref_entries(
         expected = {"Fe": constructor(*entries[2]), "O": constructor(*entries[3])}
 
     assert elemental_ref_entries == expected
+
+
+def test_mp_ref_energies() -> None:
+    """Test MP elemental reference energies are in sync with PDEntries saved to disk."""
+    for key, val in mp_elemental_ref_energies.items():
+        actual = mp_elem_reference_entries[key].energy_per_atom
+        assert actual == approx(val, abs=1e-3), f"{key=}"
+        assert actual == approx(val, abs=1e-3), f"{key=}"

tests/test_metrics.py

@@ -7,10 +7,6 @@
 import pytest
 from pytest import approx
 
-from matbench_discovery.energy import (
-    mp_elem_reference_entries,
-    mp_elemental_ref_energies,
-)
 from matbench_discovery.metrics import classify_stable, stable_metrics
 
 
@@ -83,11 +79,3 @@ def test_stable_metrics() -> None:
     # test stable_metrics docstring is up to date, all returned metrics should be listed
     assert stable_metrics.__doc__  # for mypy
     assert all(key in stable_metrics.__doc__ for key in metrics)
-
-
-def test_mp_ref_energies() -> None:
-    """Test MP elemental reference energies are in sync with PDEntries saved to disk."""
-    for key, val in mp_elemental_ref_energies.items():
-        actual = mp_elem_reference_entries[key].energy_per_atom
-        assert actual == approx(val, abs=1e-3), f"{key=}"
-        assert actual == approx(val, abs=1e-3), f"{key=}"