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[MNT] Code quality updates #5786

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merged 3 commits into from
Jan 22, 2024
Merged

[MNT] Code quality updates #5786

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1f64074
ruff enhancements
yarnabrina Jan 19, 2024
22ff78f
pre-commit autoupdate
yarnabrina Jan 19, 2024
ce8b957
nbqa enhancements
yarnabrina Jan 19, 2024
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4 changes: 2 additions & 2 deletions .pre-commit-config.yaml
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Original file line number Diff line number Diff line change
Expand Up @@ -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
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2 changes: 2 additions & 0 deletions examples/interpolation.ipynb
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Expand Up @@ -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",
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2 changes: 1 addition & 1 deletion sktime/classification/distance_based/_shape_dtw.py
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Expand Up @@ -443,7 +443,7 @@ 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."
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5 changes: 1 addition & 4 deletions sktime/classification/tests/_classification_test_reproduction.py
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Expand Up @@ -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,
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2 changes: 1 addition & 1 deletion sktime/forecasting/base/_base.py
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Expand Up @@ -1161,7 +1161,7 @@ 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
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1 change: 0 additions & 1 deletion sktime/forecasting/base/adapters/_pytorch.py
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Expand Up @@ -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,
}
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1 change: 0 additions & 1 deletion sktime/networks/base.py
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Expand Up @@ -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,
}
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2 changes: 1 addition & 1 deletion sktime/pipeline/step.py
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Expand Up @@ -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
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2 changes: 1 addition & 1 deletion sktime/utils/_testing/scenarios_classification.py
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Expand Up @@ -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",
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