[MNT] Code quality updates (#5786)

Automated changes from code quality tools, no manual changes. 1. updated version of pre-commit hooks (`pre-commit autoupdate`) 2. ran ruff with default settings (`ruff sktime`) -> ignored changes in `sktime/_contrib` 3. ran all hooks on all files (`pre-commit run --all-files`) -> ignored changes in `examples/blog_posts`
sktime · Jan 22, 2024 · da59816 · da59816
1 parent adb880c
commit da59816
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Showing 9 changed files with 9 additions and 12 deletions.
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
@@ -32,13 +32,13 @@ repos:
         name: isort
 
   - repo: https://github.com/psf/black
-    rev: 23.12.0
+    rev: 23.12.1
     hooks:
       - id: black
         language_version: python3
 
   - repo: https://github.com/pycqa/flake8
-    rev: 6.1.0
+    rev: 7.0.0
     hooks:
       - id: flake8
         exclude: docs/conf.py

diff --git a/examples/interpolation.ipynb b/examples/interpolation.ipynb
@@ -124,6 +124,8 @@
    ],
    "source": [
     "# randomly cut the data series in-place\n",
+    "\n",
+    "\n",
     "def random_cut(df):\n",
     "    for row_i in range(df.shape[0]):\n",
     "        for dim_i in range(df.shape[1]):\n",

diff --git a/sktime/classification/distance_based/_shape_dtw.py b/sktime/classification/distance_based/_shape_dtw.py
@@ -449,7 +449,7 @@ def _check_metric_params(self, parameters):
         names = list(parameters.keys())
 
         for x in names:
-            if not (x in valid_metric_params):
+            if x not in valid_metric_params:
                 raise ValueError(
                     x
                     + " is not a valid metric parameter."

diff --git a/sktime/classification/tests/_classification_test_reproduction.py b/sktime/classification/tests/_classification_test_reproduction.py
@@ -14,10 +14,7 @@
     ProximityForest,
     ShapeDTW,
 )
-from sktime.classification.early_classification import (
-    TEASER,
-    ProbabilityThresholdEarlyClassifier,
-)
+from sktime.classification.early_classification import TEASER
 from sktime.classification.feature_based import (
     Catch22Classifier,
     MatrixProfileClassifier,

diff --git a/sktime/forecasting/base/_base.py b/sktime/forecasting/base/_base.py
@@ -1163,7 +1163,7 @@ def predict_residuals(self, y=None, X=None):
 
         y_pred = self.predict(fh=fh, X=X)
 
-        if not type(y_pred) is type(y):
+        if type(y_pred) is not type(y):
             y = convert_to(y, self._y_metadata["mtype"])
 
         y_res = y - y_pred

diff --git a/sktime/forecasting/base/adapters/_pytorch.py b/sktime/forecasting/base/adapters/_pytorch.py
@@ -18,7 +18,6 @@ class BaseDeepNetworkPyTorch(BaseForecaster, ABC):
         "y_inner_mtype": "pd.DataFrame",
         "capability:insample": False,
         "capability:pred_int:insample": False,
-        "python_dependencies": "torch",
         "scitype:y": "both",
         "ignores-exogeneous-X": True,
     }

diff --git a/sktime/networks/base.py b/sktime/networks/base.py
@@ -45,7 +45,6 @@ class BaseDeepNetworkPyTorch(BaseForecaster, ABC):
         "y_inner_mtype": "pd.DataFrame",
         "capability:insample": False,
         "capability:pred_int:insample": False,
-        "python_dependencies": "torch",
         "scitype:y": "both",
         "ignores-exogeneous-X": True,
     }

diff --git a/sktime/pipeline/step.py b/sktime/pipeline/step.py
@@ -212,7 +212,7 @@ def _fetch_input_data(self, fit, required_method, mro, kwargs):
                     self.mode = result.mode
                 if result.result is not None:
                     all_none = False
-            if len(results) != 0 and not results[0] is None:
+            if len(results) != 0 and results[0] is not None:
                 if len(results) > 1:
                     input_data[step_name] = pd.concat(
                         results, axis=1, keys=transformer_names

diff --git a/sktime/utils/_testing/scenarios_classification.py b/sktime/utils/_testing/scenarios_classification.py
@@ -72,7 +72,7 @@ def get_tag(obj, tag_name):
         # applicable only if obj inherits from BaseClassifier, BaseEarlyClassifier or
         #   BaseRegressor. currently we test both classifiers and regressors using these
         #   scenarios
-        if not scitype(obj) in (
+        if scitype(obj) not in (
             "classifier",
             "early_classifier",
             "regressor",