Include classifier output in tests

Author: blokhin

mpds_ml_labs/test_app.py

@@ -8,13 +8,14 @@
 
 from mpds_client import MPDSDataRetrieval, APIError
 
-from prediction import prop_semantics
+from prediction import prop_models
 from struct_utils import detect_format, poscar_to_ase, symmetrize, get_formula, sgn_to_crsystem
 from cif_utils import cif_to_ase
+from common import API_KEY, API_ENDPOINT
 
 
 req = httplib2.Http()
-client = MPDSDataRetrieval()
+client = MPDSDataRetrieval(api_key=API_KEY, endpoint=API_ENDPOINT)
 
 def make_request(address, data={}, httpverb='POST', headers={}):
 
@@ -59,29 +60,29 @@ def make_request(address, data={}, httpverb='POST', headers={}):
         raise RuntimeError(answer['error'])
 
     formulae_categ, lattices_categ = get_formula(ase_obj), sgn_to_crsystem(ase_obj.info['spacegroup'].no)
-    for prop_id, pdata in prop_semantics.items():
+    for prop_id, pdata in prop_models.items():
         try:
             resp = client.get_dataframe({
                 'formulae': formulae_categ,
                 'lattices': lattices_categ,
                 'props': pdata['name']
             })
         except APIError as e:
-            prop_semantics[prop_id]['factual'] = None
+            prop_models[prop_id]['factual'] = None
             if e.code == 1:
                 continue
             else:
                 raise
 
         resp['Value'] = resp['Value'].astype('float64') # to treat values out of bounds given as str
         resp = resp[resp['Units'] == pdata['units']]
-        prop_semantics[prop_id]['factual'] = np.median(resp['Value'])
+        prop_models[prop_id]['factual'] = np.median(resp['Value'])
 
     for prop_id, pdata in answer['prediction'].items():
         print("{0:40} = {1:6}, factual {2:8} (MAE = {3:4}), {4}".format(
-            prop_semantics[prop_id]['name'],
-            pdata['value'],
-            prop_semantics[prop_id]['factual'] or 'absent',
+            prop_models[prop_id]['name'],
+            'conductor' if pdata['value'] == 0 and prop_id == 'w' else pdata['value'],
+            prop_models[prop_id]['factual'] or 'absent',
             pdata['mae'],
-            prop_semantics[prop_id]['units']
+            prop_models[prop_id]['units']
         ))

mpds_ml_labs/test_ml.py

@@ -3,24 +3,27 @@
 
 from struct_utils import detect_format, poscar_to_ase, symmetrize
 from cif_utils import cif_to_ase
-from prediction import ase_to_ml_model, load_ml_model, prop_semantics
+from prediction import ase_to_prediction, load_ml_models, prop_models
 from common import ML_MODELS, DATA_PATH
 
 
 models, structures = [], []
 
 if sys.argv[1:]:
     inputs = [f for f in sys.argv[1:] if os.path.isfile(f)]
-    models, structures = \
-        [f for f in inputs if f.endswith('.pkl')], [f for f in inputs if not f.endswith('.pkl')]
+    models, structures = [
+        f for f in inputs if f.endswith('.pkl')
+    ], [
+        f for f in inputs if not f.endswith('.pkl')
+    ]
 
 if not models:
     models = ML_MODELS
 
 if not structures:
-    structures = [os.path.join(DATA_PATH, f) for f in os.listdir(DATA_PATH) if os.path.isfile(os.path.join(DATA_PATH, f))]
+    structures = [os.path.join(DATA_PATH, f) for f in os.listdir(DATA_PATH) if os.path.isfile(os.path.join(DATA_PATH, f)) and 'settings.ini' not in f]
 
-active_ml_model = load_ml_model(models)
+active_ml_models = load_ml_models(models)
 
 for fname in structures:
     print
@@ -50,15 +53,15 @@
         print(error)
         continue
 
-    prediction, error = ase_to_ml_model(ase_obj, active_ml_model)
+    prediction, error = ase_to_prediction(ase_obj, active_ml_models)
     if error:
         print(error)
         continue
 
     for prop_id, pdata in prediction.items():
         print("{0:40} = {1:6} (MAE = {2:4}), {3}".format(
-            prop_semantics[prop_id]['name'],
-            pdata['value'],
+            prop_models[prop_id]['name'],
+            'conductor' if pdata['value'] == 0 and prop_id == 'w' else pdata['value'],
             pdata['mae'],
-            prop_semantics[prop_id]['units']
+            prop_models[prop_id]['units']
         ))