fix psitestats tests

matminer/featurizers/structure/sites.py

@@ -321,7 +321,7 @@ def featurize(self, s):
 
         if not s.is_ordered:
             raise ValueError("Disordered structure support not built yet")
-        if self.elements_ is None:
+        if not hasattr(self, "elements_") or self.elements_ is None:
             raise Exception("You must run 'fit' first!")
 
         output = []
@@ -369,7 +369,7 @@ def compute_pssf(self, s, e):
         return stats
 
     def feature_labels(self):
-        if self.elements_ is None:
+        if not hasattr(self, "elements_") or self.elements_ is None:
             raise Exception("You must run 'fit' first!")
 
         labels = []

matminer/featurizers/structure/tests/test_sites.py

@@ -118,14 +118,19 @@ class PartialStructureSitesFeaturesTest(StructureFeaturesTest):
     def test_partialsitestatsfingerprint(self):
         # Test matrix.
         op_struct_fp = PartialsSiteStatsFingerprint.from_preset("OPSiteFingerprint", stats=None)
+
         op_struct_fp.fit([self.diamond])
         opvals = op_struct_fp.featurize(self.diamond)
         _ = op_struct_fp.feature_labels()
         self.assertAlmostEqual(opvals[10][0], 0.9995, places=7)
         self.assertAlmostEqual(opvals[10][1], 0.9995, places=7)
+
+        op_struct_fp.fit([self.nacl])
         opvals = op_struct_fp.featurize(self.nacl)
         self.assertAlmostEqual(opvals[18][0], 0.9995, places=7)
         self.assertAlmostEqual(opvals[18][1], 0.9995, places=7)
+
+        op_struct_fp.fit([self.cscl])
         opvals = op_struct_fp.featurize(self.cscl)
         self.assertAlmostEqual(opvals[22][0], 0.9995, places=7)
         self.assertAlmostEqual(opvals[22][1], 0.9995, places=7)
@@ -158,69 +163,65 @@ def test_partialsitestatsfingerprint(self):
             stats=["mean"],
             covariance=True,
         )
-        prop_fp.fit([self.diamond])
 
         # Test the feature labels
+        prop_fp.fit([self.diamond])
         labels = prop_fp.feature_labels()
         self.assertEqual(3, len(labels))
 
         #  Test a structure with all the same type (cov should be zero)
+        prop_fp.fit([self.diamond])
         features = prop_fp.featurize(self.diamond)
         self.assertArrayAlmostEqual(features, [6, 12.0107, 0])
 
         #  Test a structure with only one atom (cov should be zero too)
+        prop_fp.fit([self.sc])
         features = prop_fp.featurize(self.sc)
         self.assertArrayAlmostEqual([13, 26.9815386, 0], features)
 
         #  Test a structure with nonzero covariance
+        prop_fp.fit([self.nacl])
         features = prop_fp.featurize(self.nacl)
-        self.assertArrayAlmostEqual([14, 29.22138464, 37.38969216], features)
-
-        # Test soap site featurizer
-        soap_fp = PartialsSiteStatsFingerprint.from_preset("SOAP_formation_energy")
-        soap_fp.fit([self.sc, self.diamond, self.nacl])
-        feats = soap_fp.featurize(self.diamond)
-        self.assertEqual(len(feats), 9504)
-        self.assertAlmostEqual(feats[0], 0.4412608, places=5)
-        self.assertAlmostEqual(feats[1], 0.0)
-        self.assertAlmostEqual(np.sum(feats), 207.88194724, places=5)
+        self.assertArrayAlmostEqual([11, 22.9897693, np.nan, 17, 35.453, np.nan], features)
 
     def test_ward_prb_2017_lpd(self):
         """Test the local property difference attributes from Ward 2017"""
         f = PartialsSiteStatsFingerprint.from_preset("LocalPropertyDifference_ward-prb-2017")
-        f.fit([self.diamond])
 
         # Test diamond
+        f.fit([self.diamond])
         features = f.featurize(self.diamond)
         self.assertArrayAlmostEqual(features, [0] * (22 * 5))
         features = f.featurize(self.diamond_no_oxi)
         self.assertArrayAlmostEqual(features, [0] * (22 * 5))
 
         # Test CsCl
+        f.fit([self.cscl])
         big_face_area = np.sqrt(3) * 3 / 2 * (2 / 4 / 4)
         small_face_area = 0.125
         big_face_diff = 55 - 17
         features = f.featurize(self.cscl)
         labels = f.feature_labels()
-        my_label = "mean local difference in Number"
+        my_label = "Cs mean local difference in Number"
         self.assertAlmostEqual(
             (8 * big_face_area * big_face_diff) / (8 * big_face_area + 6 * small_face_area),
             features[labels.index(my_label)],
             places=3,
         )
-        my_label = "range local difference in Electronegativity"
+        my_label = "Cs range local difference in Electronegativity"
         self.assertAlmostEqual(0, features[labels.index(my_label)], places=3)
 
     def test_ward_prb_2017_efftcn(self):
         """Test the effective coordination number attributes of Ward 2017"""
         f = PartialsSiteStatsFingerprint.from_preset("CoordinationNumber_ward-prb-2017")
 
         # Test Ni3Al
+        f.fit([self.ni3al])
         features = f.featurize(self.ni3al)
         labels = f.feature_labels()
-        my_label = "mean CN_VoronoiNN"
-        self.assertAlmostEqual(12, features[labels.index(my_label)])
-        self.assertArrayAlmostEqual([12, 12, 0, 12, 0], features)
+        self.assertAlmostEqual(12, features[labels.index("Al mean CN_VoronoiNN")])
+        self.assertAlmostEqual(12, features[labels.index("Ni mean CN_VoronoiNN")])
+        self.assertArrayAlmostEqual([12, 12, 0, 12, 0] * 2, features)
 
 
 if __name__ == "__main__":