add aflow wyckoff labels to 2022-10-19-wbm-summary.csv

data/mp/get_mp_energies.py

@@ -46,7 +46,7 @@
 
 df["spacegroup_number"] = df.pop("symmetry").map(lambda x: x.number)
 
-df["wyckoff"] = [get_aflow_label_from_spglib(x) for x in tqdm(df.structure)]
+df["wyckoff_spglib"] = [get_aflow_label_from_spglib(x) for x in tqdm(df.structure)]
 
 df.to_json(f"{module_dir}/{today}-mp-energies.json.gz", default_handler=as_dict_handler)
 

data/wbm/fetch_process_wbm_dataset.py

@@ -79,9 +79,10 @@
 # %%
 json_paths = sorted(glob(f"{module_dir}/raw/wbm-structures-step-*.json.bz2"))
 step_lens = (61848, 52800, 79205, 40328, 23308)
-# step 3 has 79,211 structures but only 79,205 ComputedStructureEntries
+# step 3 has 79,211 initial structures but only 79,205 ComputedStructureEntries
 # i.e. 6 extra structures which have missing energy, volume, etc. in the summary file
 bad_struct_ids = (70802, 70803, 70825, 70826, 70828, 70829)
+# step 5 has 2 missing initial structures: 23166, 23294
 
 
 assert len(json_paths) == len(step_lens), "Mismatch in WBM steps and JSON files"
@@ -229,6 +230,14 @@ def increment_wbm_material_id(wbm_id: str) -> str:
 ).value_counts().to_dict() == {"GGA": 248481, "GGA+U": 9008}
 
 
+# drop two materials with missing initial structures
+assert list(df_wbm.query("initial_structure.isna()").index) == [
+    "wbm-step-5-23166",
+    "wbm-step-5-23294",
+]
+df_wbm = df_wbm.dropna(subset=["initial_structure"])
+
+
 # %% get composition from CSEs
 df_wbm["composition_from_cse"] = [
     ComputedStructureEntry.from_dict(cse).composition
@@ -273,12 +282,13 @@ def increment_wbm_material_id(wbm_id: str) -> str:
     x.alphabetical_formula for x in df_wbm.pop("composition_from_cse")
 ]
 
-for key, col_name in (
-    ("cses", "computed_structure_entry"),
-    ("init-structs", "initial_structure"),
+for fname, cols in (
+    ("cses", ["computed_structure_entry"]),
+    ("init-structs", ["initial_structure"]),
+    ("cses+init-structs", ["initial_structure", "computed_structure_entry"]),
 ):
-    cols = ["initial_structure", "formula_from_cse", col_name]
-    df_wbm[cols].reset_index().to_json(f"{module_dir}/{today}-wbm-{key}.json.bz2")
+    cols = ["formula_from_cse", *cols]
+    df_wbm[cols].reset_index().to_json(f"{module_dir}/{today}-wbm-{fname}.json.bz2")
 
 
 # %%
@@ -486,26 +496,32 @@ def increment_wbm_material_id(wbm_id: str) -> str:
     f"{module_dir}/2022-10-19-wbm-cses+init-structs.json.bz2"
 ).set_index("material_id")
 
-df_init_struct = pd.read_json(
-    f"{module_dir}/2022-10-19-wbm-init-structs.json.bz2"
-).set_index("material_id")
-
 df_wbm["cse"] = [
     ComputedStructureEntry.from_dict(x) for x in tqdm(df_wbm.computed_structure_entry)
 ]
 
 
 # %%
-df_wbm["init_struct"] = [
-    Structure.from_dict(x) if x else None for x in tqdm(df_wbm.initial_structure)
-]
+df_init_struct = pd.read_json(
+    f"{module_dir}/2022-10-19-wbm-init-structs.json.bz2"
+).set_index("material_id")
 
 wyckoff_col = "wyckoff_spglib"
-for idx, struct in tqdm(df_wbm.init_struct.items(), total=len(df_wbm)):
-    if struct is None:
+if wyckoff_col not in df_init_struct:
+    df_init_struct[wyckoff_col] = None
+
+for idx, struct in tqdm(
+    df_init_struct.initial_structure.items(), total=len(df_init_struct)
+):
+    if not pd.isna(df_summary.at[idx, wyckoff_col]):
         continue
-    if not df_wbm.at[idx, wyckoff_col]:
-        df_wbm.at[idx, wyckoff_col] = get_aflow_label_from_spglib(struct)
+    try:
+        struct = Structure.from_dict(struct)
+        df_summary.at[idx, wyckoff_col] = get_aflow_label_from_spglib(struct)
+    except Exception as exc:
+        print(f"{idx=} {exc=}")
+
+assert df_summary[wyckoff_col].isna().sum() == 0
 
 
 # %% make sure material IDs within each step are consecutive

matbench_discovery/plot_scripts/hist_classified_stable_as_func_of_hull_dist.py

@@ -72,5 +72,4 @@
 ax.legend(loc="center left", frameon=False)
 
 fig_name = f"wren-wbm-hull-dist-hist-{which_energy=}-{stability_crit=}"
-img_path = f"{ROOT}/figures/{today}-{fig_name}.pdf"
-# fig.savefig(img_path)
+# fig.savefig(f"{ROOT}/figures/{today}-{fig_name}.pdf")

matbench_discovery/plot_scripts/precision_recall.py

@@ -15,8 +15,6 @@
 
 
 # %%
-rare = "all"
-
 dfs: dict[str, pd.DataFrame] = {}
 for model_name in ("wren", "cgcnn", "voronoi"):
     csv_path = (
@@ -118,7 +116,6 @@
     ax.set(xlim=(0, None))
 
 
-img_name = f"{today}-precision-recall-vs-calc-count-{rare=}"
 # x-ticks every 10k materials
 # ax.set(xticks=range(0, int(ax.get_xlim()[1]), 10_000))
 
@@ -128,4 +125,4 @@
 
 
 # %%
-fig.savefig(f"{ROOT}/figures/{img_name}.pdf")
+# fig.savefig(f"{ROOT}/figures/{today}-precision-recall-curves.pdf")

models/wrenformer/mp/use_wrenformer_ensemble.py

@@ -39,10 +39,10 @@
 
 # %%
 data_path = f"{ROOT}/data/wbm/2022-10-19-wbm-summary.csv"
-df = pd.read_csv(data_path).set_index("material_id")
+target_col = "e_form_per_atom_mp2020_corrected"
+input_col = "wyckoff_spglib"
+df = pd.read_csv(data_path).dropna(subset=input_col).set_index("material_id")
 
-target_col = "e_form_per_atom"
-input_col = "wyckoff"
 assert target_col in df, f"{target_col=} not in {list(df)}"
 assert input_col in df, f"{input_col=} not in {list(df)}"
 

models/wrenformer/slurm_train_wrenformer_ensemble.py

@@ -3,9 +3,9 @@
 from datetime import datetime
 
 import pandas as pd
-from aviary import ROOT
 from aviary.train import df_train_test_split, train_wrenformer
 
+from matbench_discovery import ROOT
 from matbench_discovery.slurm import slurm_submit_python
 
 """
@@ -45,13 +45,15 @@
 batch_size = 128
 slurm_array_task_id = int(os.environ.get("SLURM_ARRAY_TASK_ID", 0))
 timestamp = f"{datetime.now():%Y-%m-%d@%H-%M-%S}"
+input_col = "wyckoff_spglib"
 
 print(f"Job started running {timestamp}")
 print(f"{run_name=}")
 print(f"{data_path=}")
 
 df = pd.read_json(data_path).set_index("material_id", drop=False)
-assert target_col in df
+assert target_col in df, f"{target_col=} not in {list(df)}"
+assert input_col in df, f"{input_col=} not in {list(df)}"
 train_df, test_df = df_train_test_split(df, test_size=0.3)
 
 run_params = dict(
@@ -70,6 +72,7 @@
     # folds=(n_folds, slurm_array_task_id),
     epochs=epochs,
     checkpoint="wandb",  # None | 'local' | 'wandb',
+    input_col=input_col,
     learning_rate=learning_rate,
     batch_size=batch_size,
     wandb_path="janosh/matbench-discovery",