Merge branch 'main' into 4266_msts_search

alteryx · Aug 18, 2023 · 193ef57 · 193ef57
2 parents 890ac12 + 24ba211
commit 193ef57
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Showing 6 changed files with 21 additions and 9 deletions.
diff --git a/docs/source/disable-warnings.py b/docs/source/disable-warnings.py
@@ -1,3 +1,6 @@
+# flake8: noqa 401 imported to force console mode for tqdm in jupyter notebooks
+from tqdm.auto import tqdm
+
 import warnings
 
 warnings.filterwarnings("ignore")
diff --git a/docs/source/release_notes.rst b/docs/source/release_notes.rst
@@ -6,7 +6,9 @@ Release Notes
         * Integrated multiseries time series into AutoMLSearch :pr:`4270`
     * Fixes
     * Changes
+        * Updated ``ARIMARegressor`` to be compatible with sktime v0.22.0 and beyond :pr:`4283`
     * Documentation Changes
+        * Removed erroneous warnings from Data Checks User Guide page and removed ``tqdm`` warning in all notebooks :pr:`4274`
     * Testing Changes
 
 .. warning::

diff --git a/docs/source/user_guide/data_checks.ipynb b/docs/source/user_guide/data_checks.ipynb
@@ -188,9 +188,13 @@
     "from evalml.data_checks import TargetLeakageDataCheck\n",
     "\n",
     "X = pd.DataFrame(\n",
-    "    {\"leak\": [10, 42, 31, 51, 61], \"x\": [42, 54, 12, 64, 12], \"y\": [12, 5, 13, 74, 24]}\n",
+    "    {\n",
+    "        \"leak\": [10, 42, 31, 51, 61] * 5,\n",
+    "        \"x\": [42, 54, 12, 64, 12] * 5,\n",
+    "        \"y\": [12, 5, 13, 74, 24] * 5,\n",
+    "    }\n",
     ")\n",
-    "y = pd.Series([10, 42, 31, 51, 40])\n",
+    "y = pd.Series([10, 42, 31, 51, 40] * 5)\n",
     "\n",
     "target_leakage_check = TargetLeakageDataCheck(pct_corr_threshold=0.8)\n",
     "messages = target_leakage_check.validate(X, y)\n",
@@ -327,15 +331,15 @@
    "source": [
     "from evalml.data_checks import MulticollinearityDataCheck\n",
     "\n",
-    "y = pd.Series([1, 0, 2, 3, 4])\n",
+    "y = pd.Series([1, 0, 2, 3, 4] * 5)\n",
     "X = pd.DataFrame(\n",
     "    {\n",
     "        \"col_1\": y,\n",
     "        \"col_2\": y * 3,\n",
     "        \"col_3\": ~y,\n",
     "        \"col_4\": y / 2,\n",
     "        \"col_5\": y + 1,\n",
-    "        \"not_collinear\": [0, 1, 0, 0, 0],\n",
+    "        \"not_collinear\": [0, 1, 0, 0, 0] * 5,\n",
     "    }\n",
     ")\n",
     "\n",

diff --git a/evalml/pipelines/components/estimators/regressors/arima_regressor.py b/evalml/pipelines/components/estimators/regressors/arima_regressor.py
@@ -229,8 +229,9 @@ def _parse_prediction_intervals(
         y_pred_intervals: pd.DataFrame,
         conf_int: float,
     ) -> Tuple[pd.Series, pd.Series]:
-        preds_lower = y_pred_intervals.loc(axis=1)[("Coverage", conf_int, "lower")]
-        preds_upper = y_pred_intervals.loc(axis=1)[("Coverage", conf_int, "upper")]
+        coverage_name = y_pred_intervals.columns[0][0]
+        preds_lower = y_pred_intervals.loc(axis=1)[(coverage_name, conf_int, "lower")]
+        preds_upper = y_pred_intervals.loc(axis=1)[(coverage_name, conf_int, "upper")]
         preds_lower.name = None
         preds_upper.name = None
         return preds_lower, preds_upper

diff --git a/evalml/tests/component_tests/test_arima_regressor.py b/evalml/tests/component_tests/test_arima_regressor.py
@@ -391,8 +391,10 @@ def test_arima_regressor_respects_use_covariates(mock_predict, mock_fit, ts_data
 
 
 @pytest.mark.parametrize("no_features", [True, False])
-def test_arima_regressor_prediction_intervals(no_features, ts_data):
+@pytest.mark.parametrize("y_name", [None, "y"])
+def test_arima_regressor_prediction_intervals(no_features, ts_data, y_name):
     X_train, X_test, y_train = ts_data(no_features=no_features)
+    y_train.name = y_name
 
     clf = ARIMARegressor(use_covariates=not no_features)
 

diff --git a/evalml/tests/dependency_update_check/latest_dependency_versions.txt b/evalml/tests/dependency_update_check/latest_dependency_versions.txt
@@ -1,7 +1,7 @@
 black==23.7.0
 catboost==1.2
 category-encoders==2.5.1.post0
-click==8.1.6
+click==8.1.7
 cloudpickle==2.2.1
 colorama==0.4.6
 dask==2023.5.0
@@ -29,7 +29,7 @@ scikit-optimize==0.9.0
 scipy==1.10.1
 seaborn==0.12.2
 shap==0.42.1
-sktime==0.21.0
+sktime==0.22.0
 statsmodels==0.14.0
 texttable==1.6.7
 tomli==2.0.1