Merge branch 'master' of github.com:ppdebreuck/modnet

modnet/__init__.py

@@ -1 +1 @@
-__version__ = "0.4.2"
+__version__ = "0.4.3"

modnet/featurizers/featurizers.py

@@ -141,7 +141,10 @@ def _fit_apply_featurizers(
                 _featurizers.set_n_jobs(self._n_jobs)
 
             return _featurizers.featurize_dataframe(
-                df, column, multiindex=True, ignore_errors=True
+                df,
+                column,
+                multiindex=True,
+                ignore_errors=getattr(self, "ignore_errors", True),
             )
         elif mode == "single":
 
@@ -164,7 +167,10 @@ def _fit_apply_featurizers(
                 )
                 start = time.monotonic_ns()
                 df = featurizer.featurize_dataframe(
-                    df, column, multiindex=True, ignore_errors=True
+                    df,
+                    column,
+                    multiindex=True,
+                    ignore_errors=getattr(self, "ignore_errors", True),
                 )
                 LOG.info(
                     f"Applied featurizer {featurizer.__class__.__name__} to column {column!r} in {(time.monotonic_ns() - start) * 1e-9} seconds"
@@ -244,7 +250,11 @@ def featurize_composition(self, df: pd.DataFrame) -> pd.DataFrame:
             else:
                 df = CompositionToOxidComposition(
                     max_sites=-1 if getattr(self, "continuous_only", False) else None
-                ).featurize_dataframe(df, col_id=col_comp, ignore_errors=True)
+                ).featurize_dataframe(
+                    df,
+                    col_id=col_comp,
+                    ignore_errors=getattr(self, "ignore_errors", True),
+                )
             df = self._fit_apply_featurizers(
                 df,
                 self.oxid_composition_featurizers,
@@ -311,7 +321,10 @@ def featurize_site(
                 fingerprint, stats=self.site_stats
             )
             df = site_stats_fingerprint.featurize_dataframe(
-                df, "Input data|structure", multiindex=False, ignore_errors=True
+                df,
+                "Input data|structure",
+                multiindex=False,
+                ignore_errors=getattr(self, "ignore_errors", True),
             )
 
             if aliases:

modnet/models/bayesian.py

@@ -15,7 +15,7 @@
 try:
     import tensorflow_probability as tfp
 except ImportError:
-    raise RuntimeError(
+    raise ImportError(
         "`tensorflow-probability` is required for Bayesian models: install modnet[bayesian]."
     )
 

modnet/models/vanilla.py

@@ -825,7 +825,9 @@ def evaluate(
                         f"Loss {loss} not recognized. Use mae, mse or a callable."
                     )
                 else:
-                    score.append(loss(y_true, y_pred[i]))
+                    pass
+
+                score.append(loss(y_true, y_pred[i]))
 
         return np.mean(score)
 

modnet/tests/conftest.py

@@ -1,7 +1,9 @@
 import pytest
 from pathlib import Path
+from modnet.preprocessing import CompositionContainer
 
 from modnet.utils import get_hash_of_file
+from pymatgen.core import Structure
 
 
 _TEST_DATA_HASHES = {
@@ -41,7 +43,24 @@ def _load_moddata(filename):
     # what it was when created
     assert get_hash_of_file(data_file) == _TEST_DATA_HASHES[filename]
 
-    return MODData.load(data_file)
+    moddata = MODData.load(data_file)
+    # For forwards compatibility with pymatgen, we have to patch our old test data to have the following attributes
+    # to allow for depickling
+    # This is hopefully only a temporary solution, and in future, we should serialize pymatgen objects
+    # with Monty's `from_dict`/`to_dict` to avoid having to hack this private interface
+    for ind, s in enumerate(moddata.structures):
+        if isinstance(s, Structure):
+            # assume all previous data was periodic
+            moddata.structures[ind].lattice._pbc = [True, True, True]
+            for jnd, site in enumerate(s.sites):
+                # assume all of our previous data had ordered sites
+                moddata.structures[ind].sites[jnd].label = str(next(iter(site.species)))
+                # required for the global structure.is_ordered to work
+                moddata.structures[ind].sites[jnd].species._n_atoms = 1.0
+        elif isinstance(s, CompositionContainer):
+            moddata.structures[ind].composition._n_atoms = s.composition._natoms
+
+    return moddata
 
 
 @pytest.fixture(scope="function")

modnet/tests/test_model.py

@@ -1,5 +1,6 @@
 #!/usr/bin/env python
 import pytest
+import numpy as np
 
 
 def test_train_small_model_single_target(subset_moddata, tf_session):
@@ -21,6 +22,7 @@ def test_train_small_model_single_target(subset_moddata, tf_session):
 
     model.fit(data, epochs=2)
     model.predict(data)
+    assert not np.isnan(model.evaluate(data))
 
 
 def test_train_small_model_single_target_classif(subset_moddata, tf_session):
@@ -49,6 +51,7 @@ def is_metal(egap):
     )
 
     model.fit(data, epochs=2)
+    assert not np.isnan(model.evaluate(data))
 
 
 def test_train_small_model_multi_target(subset_moddata, tf_session):
@@ -70,6 +73,7 @@ def test_train_small_model_multi_target(subset_moddata, tf_session):
 
     model.fit(data, epochs=2)
     model.predict(data)
+    assert not np.isnan(model.evaluate(data))
 
 
 def test_train_small_model_presets(subset_moddata, tf_session):
@@ -109,6 +113,7 @@ def test_train_small_model_presets(subset_moddata, tf_session):
         models = results[0]
         assert len(models) == len(modified_presets)
         assert len(models[0]) == num_nested
+    assert not np.isnan(model.evaluate(data))
 
 
 def test_model_integration(subset_moddata, tf_session):
@@ -134,6 +139,7 @@ def test_model_integration(subset_moddata, tf_session):
     loaded_model = MODNetModel.load("test")
 
     assert model.predict(data).equals(loaded_model.predict(data))
+    assert not np.isnan(model.evaluate(data))
 
 
 def test_train_small_bayesian_single_target(subset_moddata, tf_session):
@@ -156,6 +162,7 @@ def test_train_small_bayesian_single_target(subset_moddata, tf_session):
     model.fit(data, epochs=2)
     model.predict(data)
     model.predict(data, return_unc=True)
+    assert not np.isnan(model.evaluate(data))
 
 
 def test_train_small_bayesian_single_target_classif(subset_moddata, tf_session):
@@ -186,6 +193,7 @@ def is_metal(egap):
     model.fit(data, epochs=2)
     model.predict(data)
     model.predict(data, return_unc=True)
+    assert not np.isnan(model.evaluate(data))
 
 
 def test_train_small_bayesian_multi_target(subset_moddata, tf_session):
@@ -208,6 +216,7 @@ def test_train_small_bayesian_multi_target(subset_moddata, tf_session):
     model.fit(data, epochs=2)
     model.predict(data)
     model.predict(data, return_unc=True)
+    assert not np.isnan(model.evaluate(data))
 
 
 def test_train_small_bootstrap_single_target(subset_moddata, tf_session):
@@ -232,6 +241,7 @@ def test_train_small_bootstrap_single_target(subset_moddata, tf_session):
     model.fit(data, epochs=2)
     model.predict(data)
     model.predict(data, return_unc=True)
+    assert not np.isnan(model.evaluate(data))
 
 
 def test_train_small_bootstrap_single_target_classif(small_moddata, tf_session):
@@ -264,6 +274,7 @@ def is_metal(egap):
     model.fit(data, epochs=2)
     model.predict(data)
     model.predict(data, return_unc=True)
+    assert not np.isnan(model.evaluate(data))
 
 
 def test_train_small_bootstrap_multi_target(small_moddata, tf_session):
@@ -333,3 +344,5 @@ def test_train_small_bootstrap_presets(small_moddata, tf_session):
         models = results[0]
         assert len(models) == len(modified_presets)
         assert len(models[0]) == num_nested
+
+        assert not np.isnan(model.evaluate(data))

modnet/tests/test_preprocessing.py

@@ -12,8 +12,14 @@ def check_column_values(new: MODData, reference: MODData, tolerance=0.03):
     Allows for some columns to be checked more loosely (see inline comment below).
 
     """
+    new_cols = set(new.df_featurized.columns)
+    old_cols = set(reference.df_featurized.columns)
+
+    # Check that the new df only adds new columns and is not missing anything
+    assert not (old_cols - new_cols)
+
     error_cols = set()
-    for col in new.df_featurized.columns:
+    for col in old_cols:
         if not (
             np.absolute(
                 (
@@ -349,14 +355,6 @@ def test_small_moddata_featurization(small_moddata_2023, featurizer_mode):
     featurizer.featurizer_mode = featurizer_mode
     new = MODData(structures, targets, target_names=names, featurizer=featurizer)
     new.featurize(fast=False, n_jobs=1)
-
-    new_cols = sorted(new.df_featurized.columns.tolist())
-    old_cols = sorted(old.df_featurized.columns.tolist())
-
-    for i in range(len(old_cols)):
-        assert new_cols[i] == old_cols[i]
-
-    np.testing.assert_array_equal(old_cols, new_cols)
     check_column_values(new, old, tolerance=0.03)
 
 
@@ -376,13 +374,6 @@ def test_small_moddata_composition_featurization(
     new = MODData(materials=compositions, featurizer=featurizer)
     new.featurize(fast=False, n_jobs=1)
 
-    new_cols = sorted(new.df_featurized.columns.tolist())
-    ref_cols = sorted(reference.df_featurized.columns.tolist())
-
-    for i in range(len(ref_cols)):
-        # print(new_cols[i], ref_cols[i])
-        assert new_cols[i] == ref_cols[i]
-
     # assert relative error below 3 percent
     check_column_values(new, reference, tolerance=0.03)
 

requirements.txt

@@ -3,5 +3,5 @@ pandas==1.5.2
 scikit-learn==1.3.2
 matminer==0.9.2
 numpy>=1.25
-pymatgen==2023.11.12
+pymatgen==2024.3.1
 scikit-learn==1.3.2

setup.py

@@ -33,7 +33,7 @@
     packages=setuptools.find_packages(),
     install_requires=[
         "pandas~=1.5",
-        "tensorflow~=2.10",
+        "tensorflow~=2.10,<2.12",
         "pymatgen>=2023",
         "matminer~=0.9",
         "numpy>=1.24",