Updated transformer test to correctly test the standard deviation and…

… to use a new json file specific to that test

atomsci/ddm/test/integrative/balancing_trans/jsons/all_transforms.json

@@ -0,0 +1,29 @@
+{
+        "dataset_key" : "replaced",
+        "datastore" : "False",
+        "uncertainty": "False",
+        "splitter": "scaffold",
+        "split_valid_frac": "0.20",
+        "split_test_frac": "0.20",
+        "split_strategy": "train_valid_test",
+        "prediction_type": "classification",
+        "model_choice_score_type": "roc_auc",
+        "response_cols" : "active",
+        "id_col": "compound_id",
+        "smiles_col" : "rdkit_smiles",
+        "result_dir": "replaced",
+        "system": "LC",
+        "transformers": "True",
+        "model_type": "NN",
+        "featurizer": "computed_descriptors",
+        "descriptor_type": "rdkit_raw",
+        "weight_transform_type": "balancing",
+        "learning_rate": ".0007",
+        "layer_sizes": "20,10",
+        "dropouts": "0.3,0.3",
+        "save_results": "False",
+        "max_epochs": "2",
+        "early_stopping_patience": "2",
+        "verbose": "False",
+        "seed":"0"
+    }

atomsci/ddm/test/integrative/balancing_trans/test_balancing_transformer.py

@@ -34,7 +34,11 @@ def test_balancing_transformer():
 def test_all_transformers():
     res_dir = tempfile.mkdtemp()
     dskey = os.path.join(res_dir, 'special_test_dset.csv')
-    params = params_w_balan(dskey, res_dir)
+    params = read_params(
+        make_relative_to_file('jsons/all_transforms.json'),
+        dskey,
+        res_dir
+    )
     make_test_datasets.make_test_dataset_and_split(dskey, params['descriptor_type'])
 
     params['previously_featurized'] = True
@@ -77,7 +81,7 @@ def test_all_transformers():
     # untransformed mean is 10 expected transformed mean is (10 - 0) / 2
     assert abs(np.mean(trans_valid_dset.X) - 5) < 1e-4
     # untransformed std is 5 expected transformed std is 5/2 
-    assert abs(np.std(trans_valid_dset.X) - (2.5))
+    assert abs(np.std(trans_valid_dset.X) - (2.5)) < 1e-4
     # validation has a 50/50 split. Majority class * 4 should equal oversampled minority class
     valid_weights = trans_valid_dset.w
     (valid_weight1, valid_weight2), (valid_count1, valid_count2) = np.unique(valid_weights, return_counts=True)
@@ -132,5 +136,5 @@ def params_w_balan(dset_key, res_dir):
     return params
 
 if __name__ == '__main__':
-    #test_all_transformers()
+    test_all_transformers()
     test_balancing_transformer()