add asserts for targets

scripts/experiments/run_tabular.py

@@ -41,6 +41,22 @@ def main(dataset_name: str, cpu_limit: int, memory_limit: int, save_model: bool)
     train = pd.read_csv(os.path.join(dataset_local_path, "train.csv"))
     test = pd.read_csv(os.path.join(dataset_local_path, "test.csv"))
 
+    if task_type == "multilabel":
+        target_name = [x for x in test.columns if x.startswith("target")]
+    else:
+        target_name = test.columns[-1]
+
+    if task_type in ["binary", "multiclass", "multilabel"]:
+        assert (
+            train[target_name].nunique() == test[target_name].nunique()
+        ), "train and test has different unique values."
+
+        assert min(train[target_name].nunique() > 1) is True, "Only one class present in train target."
+        assert min(test[target_name].nunique() > 1) is True, "Only one class present in test target."
+
+    assert train.isnull().values.any() is False, "train has nans in target."
+    assert test.isnull().values.any() is False, "test has nans in target."
+
     task = Task(task_type)
 
     # =================================== automl config:
@@ -50,9 +66,9 @@ def main(dataset_name: str, cpu_limit: int, memory_limit: int, save_model: bool)
         cpu_limit=cpu_limit,
         memory_limit=memory_limit,
         timeout=15 * 60,
-        general_params={
-            # "use_algos": [["mlp"]]
-        },  # ['nn', 'mlp', 'dense', 'denselight', 'resnet', 'snn', 'node', 'autoint', 'fttransformer'] or custom torch model
+        # general_params={
+        # "use_algos": [["mlp"]]
+        # },  # ['nn', 'mlp', 'dense', 'denselight', 'resnet', 'snn', 'node', 'autoint', 'fttransformer'] or custom torch model
         # nn_params={"n_epochs": 10, "bs": 512, "num_workers": 0, "path_to_save": None, "freeze_defaults": True},
         # nn_pipeline_params={"use_qnt": True, "use_te": False},
         reader_params={
@@ -65,11 +81,6 @@ def main(dataset_name: str, cpu_limit: int, memory_limit: int, save_model: bool)
 
     cml_task.connect(automl)
 
-    if task_type == "multilabel":
-        target_name = [x for x in test.columns if x.startswith("target")]
-    else:
-        target_name = test.columns[-1]
-
     kwargs = {}
     if save_model:
         kwargs["path_to_save"] = "model"