update tests that used load_boston

.github/workflows/python-pytest.yml

@@ -41,7 +41,7 @@ jobs:
         python -m pip install --upgrade pip
         pip install tensorflow==${{ matrix.tf-version }}
         pip install git+https://github.com/DataCanvasIO/Hypernets
-        pip install -r requirements.txt "protobuf<4.0" "numpy==1.19.5" "featuretools==0.27"
+        pip install -r requirements.txt "protobuf<4.0" "numpy==1.19.5" "featuretools<=0.27"
         pip install pytest-cov==2.4.0  python-coveralls  codacy-coverage
         pip list
     - name: Test with pytest

deeptables/tests/models/deeptable_regression_test.py

@@ -4,26 +4,23 @@
 
 """
 import pandas as pd
-from sklearn.datasets import load_boston
 
 from deeptables.models import deeptable
 from deeptables.tests.misc import r2_c
 from deeptables.utils import consts
 from hypernets.tabular import get_tool_box
+from hypernets.tabular.datasets.dsutils import load_boston
 
 
 class Test_DeepTable_Regression:
 
     @staticmethod
     def load_data():
         print("Loading datasets...")
-
-        boston_dataset = load_boston()
-
-        df_train = pd.DataFrame(boston_dataset.data)
-        df_train.columns = boston_dataset.feature_names
-        target = pd.Series(boston_dataset.target)
-
+        df = load_boston()
+        target = df.pop("target")
+        df_train = df
+        # target = pd.Series(boston_dataset.target)
         return df_train, target
 
     def setup_class(self):

deeptables/tests/models/hyper_dt_regression_test.py

@@ -9,8 +9,9 @@
 from hypernets.core.callbacks import SummaryCallback, FileStorageLoggingCallback
 from hypernets.core.searcher import OptimizeDirection
 from hypernets.searchers import RandomSearcher
-from sklearn.datasets import load_boston
+
 from sklearn.model_selection import train_test_split
+from hypernets.tabular.datasets.dsutils import load_boston
 
 from .. import homedir
 
@@ -19,11 +20,9 @@ class Test_HyperDT_Regression():
 
     def test_boston(self):
         print("Loading datasets...")
-        boston_dataset = load_boston()
-
-        df_train = pd.DataFrame(boston_dataset.data)
-        df_train.columns = boston_dataset.feature_names
-        self.y = pd.Series(boston_dataset.target)
+        df = load_boston()
+        df_train = df
+        self.y = df.pop('target')
         self.X = df_train
 
         self.X_train, \