Update to project tool usage per database creation changes

docs/usage/tools.md

@@ -51,19 +51,21 @@ create_db(
 
 ## ECNet .prj file usage
 
-Once an ECNet project has been created, the resulting .prj file can be used to predict properties for new molecules. A text file containing names or SMILES strings of new molecules, one per line, is required in addition to the .prj file.
+Once an ECNet project has been created, the resulting .prj file can be used to predict properties for new molecules. Just supply SMILES strings, a pre-existing ECNet .prj file, and optionally a path to save the results to:
 
 ```python
 from ecnet.tools.project import predict
 
-# From a names txt file
-predict('molecules.txt', 'results.csv', 'my_project.prj', form='name')
+smiles = ['CCC', 'CCCC']
 
-# From a SMILES txt file
-predict('smiles.txt', 'results.csv', 'my_project.prj', form='smiles')
+# obtain results, do not save to file
+results = predict(smiles, 'my_project.prj')
+
+# obtain results, save to file
+results = predict(smiles, 'my_project.prj', 'results.csv')
 ```
 
-Both Open Babel and the Java JRE are required for conversions.
+Java JRE 6.0+ is required for conversions.
 
 ## Constructing parity plots
 

ecnet/tools/project.py

@@ -9,6 +9,7 @@
 #
 
 # Stdlib imports
+from datetime import datetime
 from os import remove
 from shutil import rmtree
 
@@ -19,29 +20,32 @@
 from ecnet.tools.database import create_db
 
 
-def predict(input_txt: str, results_file: str, prj_file: str, form: str='name',
-            temp_db: str='_new_mols.csv', clean_up: bool=True):
-    '''Predicts values for new data using pre-existing .prj file
+def predict(smiles: list, prj_file: str, results_file: str=None,
+            backend: str='padel') -> list:
+    ''' predict: predicts values for supplied molecules (SMILES strings) using
+    pre-existing ECNet project (.prj) file
 
     Args:
-        input_txt (str): path to .txt file containing either molecule names or
-            SMILES strings
-        results_file (str): path to results file generated by this function
-        prj_file (str): path to pre-existing .prj file
-        form (str): `name` if supplying molecule names, `SMILES` if supplying
-            SMILES strings
-        temp_db (str): path to temporary database generated by this function
-        clean_up (bool): if True, cleans up all files generated during this
-            function (except for input/results files)
+        smiles (str): SMILES strings for molecules
+        prj_file (str): path to ECNet .prj file
+        results_file (str): if not none, saves results to this CSV file
+        backend (str): `padel` (default) or `alvadesc`, depending on the data
+            your project was trained with
+
+    Returns:
+        list: predicted values
     '''
 
     sv = Server(prj_file=prj_file)
-    create_db(input_txt, temp_db, form=form, clean_up=clean_up)
-    new_data = DataFrame(temp_db)
+
+    timestamp = datetime.now().strftime('%Y%m%d%H%M%S%f')[:-3]
+    create_db(smiles, '{}.csv'.format(timestamp), backend=backend)
+    new_data = DataFrame('{}.csv'.format(timestamp))
     new_data.set_inputs(sv._df._input_names)
     new_data.create_sets()
     sv._df = new_data
-    sv.use(output_filename=results_file)
-    if clean_up:
-        remove(temp_db)
-        rmtree(prj_file.replace('.prj', ''))
+    sv._sets = sv._df.package_sets()
+    results = sv.use(output_filename=results_file)
+    remove('{}.csv'.format(timestamp))
+    rmtree(prj_file.replace('.prj', ''))
+    return results

tests/tools/test_project.py

@@ -16,14 +16,14 @@ def test_predict(self):
         sv.train()
         sv.save_project()
 
-        with open('smiles.smi', 'w') as smi_file:
-            smi_file.write('CCC')
-        smi_file.close()
-        predict('smiles.smi', 'results.csv', 'test_project', form='smiles')
+        results = predict(['CCC', 'CCCC'], 'test_project.prj', 'results.csv')
+        print(results)
+
+        self.assertEqual(len(results), 2)
         with open('results.csv', 'r') as res_file:
             self.assertGreater(len(res_file.read()), 0)
         res_file.close()
-        remove('smiles.smi')
+
         remove('results.csv')
         remove('test_project.prj')
         remove('config.yml')