Merge pull request #133 from sparks-baird/xtal

refactor to accommodate structural inputs

docs/source/readme.md

@@ -163,7 +163,7 @@ How to `fit`/`predict`, use custom or built-in datasets, and perform adaptive de
 ```python
 from mat_discover.mat_discover_ import Discover
 disc = Discover(target_unit="GPa")
-disc.fit(train_df) # DataFrames should have at minimum "formula" and "target" columns
+disc.fit(train_df) # DataFrames should have at minimum ("formula" or "structure") and "target" columns
 scores = disc.predict(val_df)
 disc.plot()
 disc.save()

examples/elmd_densmap_cluster.py

@@ -27,7 +27,7 @@
         x: disc.std_emb[:, 0],
         y: disc.std_emb[:, 1],
         "cluster ID": disc.labels,
-        "formula": disc.all_formula,
+        "formula": disc.all_inputs,
     }
 )
 fig = pareto_plot(

examples/mat_discover_xtal_example.py

@@ -0,0 +1,29 @@
+"""Load some data, fit Discover(), predict on validation data, make some plots, and save the model."""
+# %% imports
+import pandas as pd
+from crabnet.data.materials_data import elasticity
+from mat_discover.mat_discover_ import Discover
+
+# %% setup
+# set dummy to True for a quicker run --> small dataset, MDS instead of UMAP
+dummy = False
+# set gcv to False for a quicker run --> group-cross validation can take a while
+gcv = False
+disc = Discover(dummy_run=dummy, device="cuda", target_unit="GPa")
+train_df, val_df = disc.data(elasticity, fname="train.csv", dummy=dummy)
+cat_df = pd.concat((train_df, val_df), axis=0)
+
+# %% fit
+disc.fit(train_df)
+
+# %% predict
+score = disc.predict(val_df, umap_random_state=42)
+
+# %% leave-one-cluster-out cross-validation
+if gcv:
+    disc.group_cross_val(cat_df, umap_random_state=42)
+    print("scaled test error = ", disc.scaled_error)
+
+# %% plot and save
+disc.plot()
+disc.save(dummy=dummy)