[Fix] test_evaluate

Author: franchuterivera

test/test_pipeline/components/training/base.py

@@ -1,6 +1,7 @@
 import logging
 
 from sklearn.datasets import make_classification, make_regression
+from sklearn.preprocessing import StandardScaler
 
 import torch
 
@@ -32,9 +33,11 @@ def prepare_trainer(self,
                 n_repeated=0,
                 n_classes=2,
                 n_clusters_per_class=2,
+                class_sep=3.0,
                 shuffle=True,
                 random_state=0
             )
+            X = StandardScaler().fit_transform(X)
             X = torch.tensor(X, dtype=torch.float)
             y = torch.tensor(y, dtype=torch.long)
             output_type = BINARY
@@ -50,6 +53,7 @@ def prepare_trainer(self,
                 shuffle=True,
                 random_state=0
             )
+            X = StandardScaler().fit_transform(X)
             X = torch.tensor(X, dtype=torch.float)
             y = torch.tensor(y, dtype=torch.float)
             # normalize targets for regression since NNs are better when predicting small outputs

test/test_pipeline/test_traditional_pipeline.py

@@ -4,10 +4,10 @@
 
 import pytest
 
+from autoPyTorch.pipeline.components.setup.traditional_ml.classifier_models import _classifiers
 from autoPyTorch.pipeline.traditional_tabular_classification import (
     TraditionalTabularClassificationPipeline,
 )
-from autoPyTorch.pipeline.components.setup.traditional_ml.classifier_models import _classifiers
 
 
 @pytest.mark.parametrize("fit_dictionary_tabular",