[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
openml · Apr 8, 2024 · 2025c5e · 2025c5e
1 parent f3b8a20
commit 2025c5e
Show file tree

Hide file tree

Showing 12 changed files with 61 additions and 121 deletions.
diff --git a/openml/_api_calls.py b/openml/_api_calls.py
@@ -366,18 +366,15 @@ def _send_request(  # noqa: C901
                     # -- Check if encoding is not UTF-8 perhaps
                     if __is_checksum_equal(response.content, md5_checksum):
                         raise OpenMLHashException(
-                            "Checksum of downloaded file is unequal to the expected checksum {}"
-                            "because the text encoding is not UTF-8 when downloading {}. "
+                            f"Checksum of downloaded file is unequal to the expected checksum {md5_checksum}"
+                            f"because the text encoding is not UTF-8 when downloading {url}. "
                             "There might be a sever-sided issue with the file, "
-                            "see: https://github.com/openml/openml-python/issues/1180.".format(
-                                md5_checksum,
-                                url,
-                            ),
+                            "see: https://github.com/openml/openml-python/issues/1180.",
                         )
 
                     raise OpenMLHashException(
-                        "Checksum of downloaded file is unequal to the expected checksum {} "
-                        "when downloading {}.".format(md5_checksum, url),
+                        f"Checksum of downloaded file is unequal to the expected checksum {md5_checksum} "
+                        f"when downloading {url}.",
                     )
 
                 return response

diff --git a/openml/cli.py b/openml/cli.py
@@ -1,4 +1,5 @@
-""""Command Line Interface for `openml` to configure its settings."""
+""" "Command Line Interface for `openml` to configure its settings."""
+
 from __future__ import annotations
 
 import argparse

diff --git a/openml/datasets/dataset.py b/openml/datasets/dataset.py
@@ -156,14 +156,14 @@ def find_invalid_characters(string: str, pattern: str) -> str:
             )
 
         if dataset_id is None:
-            pattern = "^[\x00-\x7F]*$"
+            pattern = "^[\x00-\x7f]*$"
             if description and not re.match(pattern, description):
                 # not basiclatin (XSD complains)
                 invalid_characters = find_invalid_characters(description, pattern)
                 raise ValueError(
                     f"Invalid symbols {invalid_characters} in description: {description}",
                 )
-            pattern = "^[\x00-\x7F]*$"
+            pattern = "^[\x00-\x7f]*$"
             if citation and not re.match(pattern, citation):
                 # not basiclatin (XSD complains)
                 invalid_characters = find_invalid_characters(citation, pattern)

diff --git a/openml/datasets/functions.py b/openml/datasets/functions.py
@@ -85,8 +85,7 @@ def list_datasets(
     *,
     output_format: Literal["dataframe"],
     **kwargs: Any,
-) -> pd.DataFrame:
-    ...
+) -> pd.DataFrame: ...
 
 
 @overload
@@ -98,8 +97,7 @@ def list_datasets(
     tag: str | None,
     output_format: Literal["dataframe"],
     **kwargs: Any,
-) -> pd.DataFrame:
-    ...
+) -> pd.DataFrame: ...
 
 
 @overload
@@ -111,8 +109,7 @@ def list_datasets(
     tag: str | None = ...,
     output_format: Literal["dict"] = "dict",
     **kwargs: Any,
-) -> pd.DataFrame:
-    ...
+) -> pd.DataFrame: ...
 
 
 def list_datasets(
@@ -207,17 +204,15 @@ def _list_datasets(
     data_id: list | None = ...,
     output_format: Literal["dict"] = "dict",
     **kwargs: Any,
-) -> dict:
-    ...
+) -> dict: ...
 
 
 @overload
 def _list_datasets(
     data_id: list | None = ...,
     output_format: Literal["dataframe"] = "dataframe",
     **kwargs: Any,
-) -> pd.DataFrame:
-    ...
+) -> pd.DataFrame: ...
 
 
 def _list_datasets(
@@ -261,13 +256,11 @@ def _list_datasets(
 
 
 @overload
-def __list_datasets(api_call: str, output_format: Literal["dict"] = "dict") -> dict:
-    ...
+def __list_datasets(api_call: str, output_format: Literal["dict"] = "dict") -> dict: ...
 
 
 @overload
-def __list_datasets(api_call: str, output_format: Literal["dataframe"]) -> pd.DataFrame:
-    ...
+def __list_datasets(api_call: str, output_format: Literal["dataframe"]) -> pd.DataFrame: ...
 
 
 def __list_datasets(
@@ -804,10 +797,7 @@ def create_dataset(  # noqa: C901, PLR0912, PLR0915
         if not is_row_id_an_attribute:
             raise ValueError(
                 "'row_id_attribute' should be one of the data attribute. "
-                " Got '{}' while candidates are {}.".format(
-                    row_id_attribute,
-                    [attr[0] for attr in attributes_],
-                ),
+                f" Got '{row_id_attribute}' while candidates are {[attr[0] for attr in attributes_]}.",
             )
 
     if isinstance(data, pd.DataFrame):

diff --git a/openml/evaluations/functions.py b/openml/evaluations/functions.py
@@ -32,8 +32,7 @@ def list_evaluations(
     per_fold: bool | None = ...,
     sort_order: str | None = ...,
     output_format: Literal["dict", "object"] = "dict",
-) -> dict:
-    ...
+) -> dict: ...
 
 
 @overload
@@ -51,8 +50,7 @@ def list_evaluations(
     per_fold: bool | None = ...,
     sort_order: str | None = ...,
     output_format: Literal["dataframe"] = ...,
-) -> pd.DataFrame:
-    ...
+) -> pd.DataFrame: ...
 
 
 def list_evaluations(

diff --git a/openml/extensions/sklearn/extension.py b/openml/extensions/sklearn/extension.py
@@ -979,9 +979,7 @@ def flatten_all(list_):
                         # length 2 is for {VotingClassifier.estimators,
                         # Pipeline.steps, FeatureUnion.transformer_list}
                         # length 3 is for ColumnTransformer
-                        msg = "Length of tuple of type {} does not match assumptions".format(
-                            sub_component_type,
-                        )
+                        msg = f"Length of tuple of type {sub_component_type} does not match assumptions"
                         raise ValueError(msg)
 
                     if isinstance(sub_component, str):
@@ -1008,9 +1006,9 @@ def flatten_all(list_):
 
                     if identifier in reserved_keywords:
                         parent_model = f"{model.__module__}.{model.__class__.__name__}"
-                        msg = "Found element shadowing official " "parameter for {}: {}".format(
-                            parent_model,
-                            identifier,
+                        msg = (
+                            "Found element shadowing official "
+                            f"parameter for {parent_model}: {identifier}"
                         )
                         raise PyOpenMLError(msg)
 
@@ -1035,9 +1033,9 @@ def flatten_all(list_):
                             model=None,
                         )
                         component_reference: OrderedDict[str, str | dict] = OrderedDict()
-                        component_reference[
-                            "oml-python:serialized_object"
-                        ] = COMPOSITION_STEP_CONSTANT
+                        component_reference["oml-python:serialized_object"] = (
+                            COMPOSITION_STEP_CONSTANT
+                        )
                         cr_value: dict[str, Any] = OrderedDict()
                         cr_value["key"] = identifier
                         cr_value["step_name"] = identifier
@@ -1812,10 +1810,7 @@ def _prediction_to_probabilities(
                     # then we need to add a column full of zeros into the probabilities
                     # for class 3 because the rest of the library expects that the
                     # probabilities are ordered the same way as the classes are ordered).
-                    message = "Estimator only predicted for {}/{} classes!".format(
-                        proba_y.shape[1],
-                        len(task.class_labels),
-                    )
+                    message = f"Estimator only predicted for {proba_y.shape[1]}/{len(task.class_labels)} classes!"
                     warnings.warn(message, stacklevel=2)
                     openml.config.logger.warning(message)
 
@@ -2008,9 +2003,7 @@ def is_subcomponent_specification(values):
                                 pass
                             else:
                                 raise TypeError(
-                                    "Subcomponent flow should be of type flow, but is {}".format(
-                                        type(subcomponent_flow),
-                                    ),
+                                    f"Subcomponent flow should be of type flow, but is {type(subcomponent_flow)}",
                                 )
 
                         current = {

diff --git a/openml/flows/functions.py b/openml/flows/functions.py
@@ -140,8 +140,7 @@ def list_flows(
     tag: str | None = ...,
     output_format: Literal["dict"] = "dict",
     **kwargs: Any,
-) -> dict:
-    ...
+) -> dict: ...
 
 
 @overload
@@ -152,8 +151,7 @@ def list_flows(
     *,
     output_format: Literal["dataframe"],
     **kwargs: Any,
-) -> pd.DataFrame:
-    ...
+) -> pd.DataFrame: ...
 
 
 @overload
@@ -163,8 +161,7 @@ def list_flows(
     tag: str | None,
     output_format: Literal["dataframe"],
     **kwargs: Any,
-) -> pd.DataFrame:
-    ...
+) -> pd.DataFrame: ...
 
 
 def list_flows(
@@ -243,18 +240,15 @@ def list_flows(
 
 
 @overload
-def _list_flows(output_format: Literal["dict"] = ..., **kwargs: Any) -> dict:
-    ...
+def _list_flows(output_format: Literal["dict"] = ..., **kwargs: Any) -> dict: ...
 
 
 @overload
-def _list_flows(*, output_format: Literal["dataframe"], **kwargs: Any) -> pd.DataFrame:
-    ...
+def _list_flows(*, output_format: Literal["dataframe"], **kwargs: Any) -> pd.DataFrame: ...
 
 
 @overload
-def _list_flows(output_format: Literal["dataframe"], **kwargs: Any) -> pd.DataFrame:
-    ...
+def _list_flows(output_format: Literal["dataframe"], **kwargs: Any) -> pd.DataFrame: ...
 
 
 def _list_flows(
@@ -391,13 +385,11 @@ def get_flow_id(
 
 
 @overload
-def __list_flows(api_call: str, output_format: Literal["dict"] = "dict") -> dict:
-    ...
+def __list_flows(api_call: str, output_format: Literal["dict"] = "dict") -> dict: ...
 
 
 @overload
-def __list_flows(api_call: str, output_format: Literal["dataframe"]) -> pd.DataFrame:
-    ...
+def __list_flows(api_call: str, output_format: Literal["dataframe"]) -> pd.DataFrame: ...
 
 
 def __list_flows(

diff --git a/openml/runs/functions.py b/openml/runs/functions.py
@@ -680,9 +680,9 @@ def _calculate_local_measure(  # type: ignore
             user_defined_measures_per_fold[measure][rep_no][fold_no] = user_defined_measures_fold[
                 measure
             ]
-            user_defined_measures_per_sample[measure][rep_no][fold_no][
-                sample_no
-            ] = user_defined_measures_fold[measure]
+            user_defined_measures_per_sample[measure][rep_no][fold_no][sample_no] = (
+                user_defined_measures_fold[measure]
+            )
 
     trace: OpenMLRunTrace | None = None
     if len(traces) > 0:
@@ -784,13 +784,7 @@ def _run_task_get_arffcontent_parallel_helper(  # noqa: PLR0913
         raise NotImplementedError(task.task_type)
 
     config.logger.info(
-        "Going to run model {} on dataset {} for repeat {} fold {} sample {}".format(
-            str(model),
-            openml.datasets.get_dataset(task.dataset_id).name,
-            rep_no,
-            fold_no,
-            sample_no,
-        ),
+        f"Going to run model {model!s} on dataset {openml.datasets.get_dataset(task.dataset_id).name} for repeat {rep_no} fold {fold_no} sample {sample_no}",
     )
     (
         pred_y,

diff --git a/openml/runs/trace.py b/openml/runs/trace.py
@@ -504,10 +504,7 @@ def merge_traces(cls, traces: list[OpenMLRunTrace]) -> OpenMLRunTrace:
                     if list(param_keys) != list(trace_itr_keys):
                         raise ValueError(
                             "Cannot merge traces because the parameters are not equal: "
-                            "{} vs {}".format(
-                                list(trace_itr.parameters.keys()),
-                                list(iteration.parameters.keys()),
-                            ),
+                            f"{list(trace_itr.parameters.keys())} vs {list(iteration.parameters.keys())}",
                         )
 
                 if key in merged_trace:
@@ -521,10 +518,7 @@ def merge_traces(cls, traces: list[OpenMLRunTrace]) -> OpenMLRunTrace:
         return cls(None, merged_trace)
 
     def __repr__(self) -> str:
-        return "[Run id: {}, {} trace iterations]".format(
-            -1 if self.run_id is None else self.run_id,
-            len(self.trace_iterations),
-        )
+        return f"[Run id: {-1 if self.run_id is None else self.run_id}, {len(self.trace_iterations)} trace iterations]"
 
     def __iter__(self) -> Iterator[OpenMLTraceIteration]:
         yield from self.trace_iterations.values()