completed google lint. added torch dependencies to conda. tests pass.…

… unskipped all but one.
mims-harvard · Mar 5, 2024 · f42afa6 · f42afa6
1 parent c30bb35
commit f42afa6
Show file tree

Hide file tree

Showing 8 changed files with 45 additions and 30 deletions.
diff --git a/environment.yml b/environment.yml
@@ -2,6 +2,7 @@ name: tdc-conda-env
 channels:
   - conda-forge
   - defaults
+  - pytorch
 dependencies:
   - dataclasses=0.8
   - fuzzywuzzy=0.18.0
@@ -10,10 +11,14 @@ dependencies:
   - python=3.9.13
   - pip=23.3.1
   - pandas=2.1.4
+  - pyg=2.5.0
+  - pytorch=2.2.1
   - requests=2.31.0
   - scikit-learn=1.3.0
   - seaborn=0.12.2
   - tqdm=4.65.0
+  - torchaudio=2.2.1
+  - torchvision=0.17.1
   - pip:
     - cellxgene-census==1.10.2 
     - rdkit==2023.9.5

diff --git a/run_tests.py b/run_tests.py
@@ -6,4 +6,8 @@
     suite = loader.discover(start_dir)
 
     runner = unittest.TextTestRunner()
-    runner.run(suite)
+    res = runner.run(suite)
+    if res.wasSuccessful():
+        print("All base tests passed")
+    else:
+        raise RuntimeError("Some base tests failed")
diff --git a/tdc/test/dev_tests/chem_utils_test/test_molconverter.py b/tdc/test/dev_tests/chem_utils_test/test_molconverter.py
@@ -21,7 +21,7 @@ def setUp(self):
         print(os.getcwd())
         pass
 
-    @unittest.skip("dev test")
+
     def test_MolConvert(self):
         from tdc.chem_utils import MolConvert
 
@@ -35,7 +35,7 @@ def test_MolConvert(self):
 
         MolConvert.eligible_format()
 
-    # @unittest.skip("dev test")
+    # 
     def tearDown(self):
         print(os.getcwd())
 

diff --git a/tdc/test/dev_tests/chem_utils_test/test_molfilter.py b/tdc/test/dev_tests/chem_utils_test/test_molfilter.py
@@ -21,14 +21,14 @@ def setUp(self):
         print(os.getcwd())
         pass
 
-    @unittest.skip("dev test")
+
     def test_MolConvert(self):
         from tdc.chem_utils import MolFilter
 
         filters = MolFilter(filters=["PAINS"], HBD=[0, 6])
         filters(["CCSc1ccccc1C(=O)Nc1onc2c1CCC2"])
 
-    # @unittest.skip("dev test")
+    # 
     def tearDown(self):
         print(os.getcwd())
 

diff --git a/tdc/test/dev_tests/chem_utils_test/test_oracles.py b/tdc/test/dev_tests/chem_utils_test/test_oracles.py
@@ -21,7 +21,7 @@ def setUp(self):
         print(os.getcwd())
         pass
 
-    @unittest.skip("dev test")
+
     def test_Oracle(self):
         from tdc import Oracle
 
@@ -35,7 +35,7 @@ def test_Oracle(self):
         oracle = Oracle(name="Hop")
         x = oracle(["CC(=O)OC1=CC=CC=C1C(=O)O", "C1=CC=C(C=C1)C=O"])
 
-    @unittest.skip("dev test")
+
     def test_distribution(self):
         from tdc import Evaluator
 

diff --git a/tdc/test/dev_tests/utils_tests/test_misc_utils.py b/tdc/test/dev_tests/utils_tests/test_misc_utils.py
@@ -21,7 +21,7 @@ def setUp(self):
         print(os.getcwd())
         pass
 
-    @unittest.skip("dev test")
+    @unittest.skip("long running test")
     def test_neg_sample(self):
         from tdc.multi_pred import PPI
 
@@ -33,7 +33,7 @@ def test_neg_sample(self):
     #    data = ADME(name='Caco2_Wang')
     #    x = data.label_distribution()
 
-    @unittest.skip("dev test")
+
     def test_get_label_map(self):
         from tdc.multi_pred import DDI
         from tdc.utils import get_label_map
@@ -42,26 +42,26 @@ def test_get_label_map(self):
         split = data.get_split()
         get_label_map(name="DrugBank", task="DDI")
 
-    @unittest.skip("dev test")
+
     def test_balanced(self):
         from tdc.single_pred import HTS
 
         data = HTS(name="SARSCoV2_3CLPro_Diamond")
         data.balanced(oversample=True, seed=42)
 
-    @unittest.skip("dev test")
+
     def test_cid2smiles(self):
         from tdc.utils import cid2smiles
 
         smiles = cid2smiles(2248631)
 
-    @unittest.skip("dev test")
+
     def test_uniprot2seq(self):
         from tdc.utils import uniprot2seq
 
         seq = uniprot2seq("P49122")
 
-    @unittest.skip("dev test")
+
     def test_to_graph(self):
         from tdc.multi_pred import DTI
 
@@ -95,7 +95,7 @@ def test_to_graph(self):
         )
         # output: {'pyg_graph': the PyG graph object, 'index_to_entities': a dict map from ID in the data to node ID in the PyG object, 'split': {'train': df, 'valid': df, 'test': df}}
 
-    # @unittest.skip("dev test")
+    # 
     def tearDown(self):
         print(os.getcwd())
 

diff --git a/tdc/test/dev_tests/utils_tests/test_splits.py b/tdc/test/dev_tests/utils_tests/test_splits.py
@@ -21,22 +21,22 @@ def setUp(self):
         print(os.getcwd())
         pass
 
-    @unittest.skip("dev test")
+
     def test_random_split(self):
         from tdc.single_pred import ADME
 
         data = ADME(name="Caco2_Wang")
         split = data.get_split(method="random")
 
-    @unittest.skip("dev test")
+
     def test_scaffold_split(self):
         ## requires RDKit
         from tdc.single_pred import ADME
 
         data = ADME(name="Caco2_Wang")
         split = data.get_split(method="scaffold")
 
-    @unittest.skip("dev test")
+
     def test_cold_start_split(self):
         from tdc.multi_pred import DTI
 
@@ -70,21 +70,21 @@ def test_cold_start_split(self):
             self.assertEqual(0, len(train_entity.intersection(test_entity)))
             self.assertEqual(0, len(valid_entity.intersection(test_entity)))
 
-    @unittest.skip("dev test")
+
     def test_combination_split(self):
         from tdc.multi_pred import DrugSyn
 
         data = DrugSyn(name="DrugComb")
         split = data.get_split(method="combination")
 
-    @unittest.skip("dev test")
+
     def test_time_split(self):
         from tdc.multi_pred import DTI
 
         data = DTI(name="BindingDB_Patent")
         split = data.get_split(method="time", time_column="Year")
 
-    @unittest.skip("dev test")
+
     def test_tearDown(self):
         print(os.getcwd())
 

diff --git a/tdc/utils/label.py b/tdc/utils/label.py
@@ -212,14 +212,17 @@ def NegSample(df, column_names, frac, two_types):
         for i in neg_list:
             neg_list_val.append([i[0], id2seq[i[0]], i[1], id2seq[i[1]], 0])
 
-        df = df.append(
-            pd.DataFrame(neg_list_val).rename(columns={
-                0: id1,
-                1: x1,
-                2: id2,
-                3: x2,
-                4: "Y"
-            })).reset_index(drop=True)
+            df = pd.concat([
+                df,
+                pd.DataFrame(neg_list_val).rename(columns={
+                    0: id1,
+                    1: x1,
+                    2: id2,
+                    3: x2,
+                    4: "Y"
+                })
+            ],
+                           ignore_index=True, sort=False)
         return df
     else:
         df_unique_id1 = np.unique(df[id1].values.reshape(-1))
@@ -254,12 +257,15 @@ def NegSample(df, column_names, frac, two_types):
         for i in neg_list:
             neg_list_val.append([i[0], id2seq1[i[0]], i[1], id2seq2[i[1]], 0])
 
-        df = df.append(
+        df = pd.concat([
+            df,
             pd.DataFrame(neg_list_val).rename(columns={
                 0: id1,
                 1: x1,
                 2: id2,
                 3: x2,
                 4: "Y"
-            })).reset_index(drop=True)
+            })
+        ],
+                       ignore_index=True, sort=False)
         return df