Merge pull request #195 from neulab/e501

Complying with E501

Former-commit-id: ee63b46

Author: Yusuke Oda

explainaboard/explainaboard_main.py

@@ -20,7 +20,10 @@ def main():
         type=str,
         required=True,
         nargs="+",
-        help="the directories of system outputs. Multiple one should be separated by space, for example: system1 system2",
+        help=(
+            "the directories of system outputs. Multiple one should be separated by "
+            "space, for example: system1 system2"
+        ),
     )
 
     parser.add_argument(
@@ -84,11 +87,13 @@ def main():
     # Checks on inputs
     if num_outputs > 2:
         raise ValueError(
-            f'ExplainaBoard currently only supports 1 or 2 system outputs, but received {num_outputs}'
+            f'ExplainaBoard currently only supports 1 or 2 system outputs, but '
+            f'received {num_outputs}'
         )
     if task not in TaskType.list():
         raise ValueError(
-            f'Task name {task} was not recognized. ExplainaBoard currently supports: {TaskType.list()}'
+            f'Task name {task} was not recognized. ExplainaBoard currently supports: '
+            f'{TaskType.list()}'
         )
 
     # Read in data and check validity
@@ -101,7 +106,8 @@ def main():
             num0 = len(system_datasets[0])
             num1 = len(system_datasets[1])
             raise ValueError(
-                f'Data must be identical for pairwise analysis, but length of files {system_datasets[0]} ({num0}) != {system_datasets[1]} ({num1})'
+                f'Data must be identical for pairwise analysis, but length of files '
+                f'{system_datasets[0]} ({num0}) != {system_datasets[1]} ({num1})'
             )
         if (
             loaders[0].user_defined_features_configs

explainaboard/feature.py

@@ -18,8 +18,8 @@
 
 def _arrow_to_datasets_dtype(arrow_type: pa.DataType) -> str:
     """
-    _arrow_to_datasets_dtype takes a pyarrow.DataType and converts it to a datasets string dtype.
-    In effect, `dt == string_to_arrow(_arrow_to_datasets_dtype(dt))`
+    _arrow_to_datasets_dtype takes a pyarrow.DataType and converts it to a datasets
+    string dtype. In effect, `dt == string_to_arrow(_arrow_to_datasets_dtype(dt))`
     """
 
     if pa.types.is_null(arrow_type):
@@ -74,11 +74,11 @@ def string_to_arrow(datasets_dtype: str) -> pa.DataType:
     """
     string_to_arrow takes a datasets string dtype and converts it to a pyarrow.DataType.
     In effect, `dt == string_to_arrow(_arrow_to_datasets_dtype(dt))`
-    This is necessary because the datasets.Value() primitive type is constructed using a string dtype
-    Value(dtype=str)
-    But Features.type (via `get_nested_type()` expects to resolve Features into a pyarrow Schema,
-        which means that each Value() must be able to resolve into a corresponding pyarrow.DataType, which is the
-        purpose of this function.
+    This is necessary because the datasets.Value() primitive type is constructed using a
+    string dtype Value(dtype=str)
+    But Features.type (via `get_nested_type()` expects to resolve Features into a
+    pyarrow Schema, which means that each Value() must be able to resolve into a
+    corresponding pyarrow.DataType, which is the purpose of this function.
     """
     timestamp_regex = re.compile(r"^timestamp\[(.*)\]$")
     timestamp_matches = timestamp_regex.search(datasets_dtype)
@@ -97,16 +97,21 @@ def string_to_arrow(datasets_dtype: str) -> pa.DataType:
             return pa.timestamp(internals_matches.group(1), internals_matches.group(2))
         else:
             raise ValueError(
-                f"{datasets_dtype} is not a validly formatted string representation of a pyarrow timestamp."
-                f"Examples include timestamp[us] or timestamp[us, tz=America/New_York]"
-                f"See: https://arrow.apache.org/docs/python/generated/pyarrow.timestamp.html#pyarrow.timestamp"
+                f"""
+{datasets_dtype} is not a validly formatted string representation of a pyarrow
+timestamp. Examples include timestamp[us] or timestamp[us, tz=America/New_York]
+See:
+https://arrow.apache.org/docs/python/generated/pyarrow.timestamp.html#pyarrow.timestamp
+"""
             )
     elif datasets_dtype not in pa.__dict__:
         if str(datasets_dtype + "_") not in pa.__dict__:
             raise ValueError(
-                f"Neither {datasets_dtype} nor {datasets_dtype + '_'} seems to be a pyarrow data type. "
-                f"Please make sure to use a correct data type, see: "
-                f"https://arrow.apache.org/docs/python/api/datatypes.html#factory-functions"
+                f"""
+Neither {datasets_dtype} nor {datasets_dtype + '_'} seems to be a pyarrow data type.
+Please make sure to use a correct data type, see:
+https://arrow.apache.org/docs/python/api/datatypes.html#factory-functions
+"""
             )
         arrow_data_factory_function_name = str(datasets_dtype + "_")
     else:
@@ -119,17 +124,23 @@ def _cast_to_python_objects(obj: Any, only_1d_for_numpy: bool) -> tuple[Any, boo
     """
     Cast pytorch/tensorflow/pandas objects to python numpy array/lists.
     It works recursively.
-    To avoid iterating over possibly long lists, it first checks if the first element that is not None has to be casted.
-    If the first element needs to be casted, then all the elements of the list will be casted, otherwise they'll stay the same.
-    This trick allows to cast objects that contain tokenizers outputs without iterating over every single token for example.
+    To avoid iterating over possibly long lists, it first checks if the first element
+    that is not None has to be casted.
+    If the first element needs to be casted, then all the elements of the list will be
+    casted, otherwise they'll stay the same.
+    This trick allows to cast objects that contain tokenizers outputs without iterating
+    over every single token for example.
     Args:
         obj: the object (nested struct) to cast
-        only_1d_for_numpy (bool): whether to keep the full multi-dim tensors as multi-dim numpy arrays, or convert them to
-            nested lists of 1-dimensional numpy arrays. This can be useful to keep only 1-d arrays to instantiate Arrow arrays.
-            Indeed Arrow only support converting 1-dimensional array values.
+        only_1d_for_numpy (bool): whether to keep the full multi-dim tensors as
+            multi-dim numpy arrays, or convert them to nested lists of 1-dimensional
+            numpy arrays. This can be useful to keep only 1-d arrays to instantiate
+            Arrow arrays. Indeed Arrow only support converting 1-dimensional array
+            values.
     Returns:
         casted_obj: the casted object
-        has_changed (bool): True if the object has been changed, False if it is identical
+        has_changed (bool): True if the object has been changed, False if it is
+            identical
     """
 
     if config.TF_AVAILABLE and "tensorflow" in sys.modules:
@@ -240,9 +251,12 @@ def cast_to_python_objects(obj: Any, only_1d_for_numpy=False) -> Any:
     """
     Cast numpy/pytorch/tensorflow/pandas objects to python lists.
     It works recursively.
-    To avoid iterating over possibly long lists, it first checks if the first element that is not None has to be casted.
-    If the first element needs to be casted, then all the elements of the list will be casted, otherwise they'll stay the same.
-    This trick allows to cast objects that contain tokenizers outputs without iterating over every single token for example.
+    To avoid iterating over possibly long lists, it first checks if the first element
+    that is not None has to be casted.
+    If the first element needs to be casted, then all the elements of the list will be
+    casted, otherwise they'll stay the same.
+    This trick allows to cast objects that contain tokenizers outputs without iterating
+    over every single token for example.
     Args:
         obj: the object (nested struct) to cast
     Returns:
@@ -552,7 +566,8 @@ def encode_example(self, value):
 
 def encode_nested_example(schema, obj):
     """Encode a nested example.
-    This is used since some features (in particular ClassLabel) have some logic during encoding.
+    This is used since some features (in particular ClassLabel) have some logic during
+    encoding.
     """
     # Nested structures: we allow dict, list/tuples, sequences
     if isinstance(schema, dict):
@@ -598,10 +613,12 @@ def encode_nested_example(schema, obj):
             else None
         )
     # Object with special encoding:
-    # ClassLabel will convert from string to int, TranslationVariableLanguages does some checks
+    # ClassLabel will convert from string to int,
+    # TranslationVariableLanguages does some checks
     elif isinstance(schema, (ClassLabel, Value)):
         return schema.encode_example(obj)
-    # Other object should be directly convertible to a native Arrow type (like Translation and Translation)
+    # Other object should be directly convertible to a native Arrow type
+    # (like Translation and Translation)
     return obj
 
 

explainaboard/info.py

@@ -74,9 +74,10 @@ class SysOutputInfo:
         download_link (str): the url of the system output.
         paper (Paper, optional): the published paper of the system.
         features (Features, optional): the features used to describe system output's
-                                        column type.
+            column type.
         is_print_case (bool): Whether or not to print out cases
-        is_print_confidence_interval (bool): Whether or not to print out confidence intervals
+        is_print_confidence_interval (bool): Whether or not to print out confidence
+            intervals
     """
 
     # set in the system_output scripts
@@ -141,8 +142,8 @@ def _dump_info(self, file):
     def from_directory(cls, sys_output_info_dir: str) -> "SysOutputInfo":
         """Create SysOutputInfo from the JSON file in `sys_output_info_dir`.
         Args:
-            sys_output_info_dir (`str`): The directory containing the metadata file. This
-                should be the root directory of a specific dataset version.
+            sys_output_info_dir (`str`): The directory containing the metadata file.
+                This should be the root directory of a specific dataset version.
         """
         logger.info("Loading Dataset info from %s", sys_output_info_dir)
         if not sys_output_info_dir:
@@ -158,11 +159,6 @@ def from_directory(cls, sys_output_info_dir: str) -> "SysOutputInfo":
             sys_output_info_dict = json.load(f)
         return cls.from_dict(sys_output_info_dict)
 
-    # @classmethod
-    # def from_dict(cls, task_name: str, sys_output_info_dict: dict) -> "SysOutputInfo":
-    #     field_names = set(f.name for f in dataclasses.fields(cls))
-    #     return cls(task_name, **{k: v for k, v in sys_output_info_dict.items() if k in field_names})
-
     @classmethod
     def from_dict(cls, sys_output_info_dict: dict) -> "SysOutputInfo":
         field_names = set(f.name for f in dataclasses.fields(cls))

explainaboard/loaders/file_loader.py

@@ -14,16 +14,19 @@
 class FileLoaderField:
     """
     Args:
-        src_name: field name in the source file. use int for tsv column indices and use str for dict keys
+        src_name: field name in the source file. use int for tsv column indices and use
+            str for dict keys
         target_name: field name expected in the loaded data
-        dtype: data type of the field in the loaded data. It is only intended for simple type conversion so
-        it only supports int, float and str. Pass in None to turn off type conversion.
-        strip_before_parsing: call strip() on strings before casting to either str, int or float. It is
-            only intended to be used with these three data types. It defaults to True for str. For all other
-            types, it defaults to False
-        parser: a custom parser for the field. When called, `data_points[idx][src_name]` is passed in as input,
-            it is expected to return the parsed result. If parser is not None, `strip_before_parsing` and dtype
-            will not have any effect
+        dtype: data type of the field in the loaded data. It is only intended for simple
+            type conversion so it only supports int, float and str. Pass in None to turn
+            off type conversion.
+        strip_before_parsing: call strip() on strings before casting to either str, int
+            or float. It is only intended to be used with these three data types.
+            It defaults to True for str. For all other types, it defaults to False
+        parser: a custom parser for the field. When called, `data_points[idx][src_name]`
+            is passed in as input, it is expected to return the parsed result.
+            If parser is not None, `strip_before_parsing` and dtype will not have any
+            effect.
     """
 
     src_name: Union[int, str]
@@ -94,7 +97,8 @@ def generate_id(self, parsed_data_point: dict, sample_idx: int):
         elif self._id_field_name:
             if self._id_field_name not in parsed_data_point:
                 raise ValueError(
-                    f"The {sample_idx} data point in system outputs file does not have field {self._id_field_name}"
+                    f"The {sample_idx} data point in system outputs file does not have "
+                    f"field {self._id_field_name}"
                 )
             parsed_data_point["id"] = str(parsed_data_point[self._id_field_name])
 
@@ -104,16 +108,17 @@ def load_raw(cls, data: str, source: Source) -> Iterable:
         fields information to parse the data points.
 
         Args:
-            data (str): base64 encoded system output content or a path for the system output file
-                source: source of data
+            data (str): base64 encoded system output content or a path for the system
+                output file
+            source: source of data
         """
         raise NotImplementedError(
             "load_raw() is not implemented for the base FileLoader"
         )
 
     def load(self, data: str, source: Source) -> Iterable[dict]:
-        """Load data from source, parse data points with fields information and return an
-        iterable of data points.
+        """Load data from source, parse data points with fields information and return
+        an iterable of data points.
         """
         raw_data = self.load_raw(data, source)
         parsed_data_points: list[dict] = []

explainaboard/loaders/kg_link_tail_prediction.py

@@ -13,7 +13,7 @@
 class KgLinkTailPredictionLoader(Loader):
     """
     Validate and Reformat system output file with json format:
-    "head \t relation \t trueTail": [predTail1, predTail2, predTail3, predTail4, predTail5],
+    "head \t relation \t trueTail": [predTail1, predTail2, ..., predTail5],
 
     usage:
         please refer to `test_loaders.py`
@@ -24,14 +24,16 @@ class KgLinkTailPredictionLoader(Loader):
 
     def load(self) -> Iterable[dict]:
         """
-        :param path_system_output: the path of system output file with following format:
-        "head \t relation \t trueTail": [predTail1, predTail2, predTail3, predTail4, predTail5],
+        :param path_system_output:
+            the path of system output file with following format:
+            "head \t relation \t trueTail": [predTail1, predTail2, ..., predTail5],
 
         :return: class object
         """
         data: list[dict] = []
 
-        # TODO(odashi): Avoid potential bug: load_raw returns Iterable[Any] which is not a dict.
+        # TODO(odashi):
+        # Avoid potential bug: load_raw returns Iterable[Any] which is not a dict.
         raw_data: dict[str, dict[str, str]] = self.file_loaders[  # type: ignore
             unwrap(self._file_type)
         ].load_raw(self._data, self._source)

explainaboard/loaders/loader.py

@@ -21,8 +21,8 @@ class Loader:
         data: base64 encoded system output content or a path for the system output file
         source: source of data
         file type: tsv, json, conll, etc.
-        file_loaders: a dict of file loaders. To customize the loading process, either implement
-            a custome FileLoader or override `load()`
+        file_loaders: a dict of file loaders. To customize the loading process, either
+            implement a custome FileLoader or override `load()`
     """
 
     _default_source = Source.local_filesystem
@@ -49,7 +49,8 @@ def __init__(
 
         if self._file_type not in self.file_loaders:
             raise NotImplementedError(
-                f"A file loader for {self._file_type} is not provided. please add it to the file_loaders."
+                f"A file loader for {self._file_type} is not provided. "
+                "please add it to the file_loaders."
             )
 
         self._user_defined_features_configs: dict = (
@@ -60,7 +61,8 @@ def __init__(
     def user_defined_features_configs(self) -> dict:
         if self._user_defined_features_configs is None:
             raise Exception(
-                "User defined features configs are not available (data has not been loaded))"
+                "User defined features configs are not available "
+                "(data has not been loaded))"
             )
         return self._user_defined_features_configs
 

explainaboard/loaders/qa_multiple_choice.py

@@ -13,7 +13,7 @@
 class QAMultipleChoiceLoader(Loader):
     """
     Validate and Reformat system output file with json format:
-    "head \t relation \t trueTail": [predTail1, predTail2, predTail3, predTail4, predTail5],
+    "head \t relation \t trueTail": [predTail1, predTail2, ..., predTail5],
 
     usage:
         please refer to `test_loaders.py`
@@ -26,8 +26,9 @@ class QAMultipleChoiceLoader(Loader):
 
     def load(self) -> Iterable[dict]:
         """
-        :param path_system_output: the path of system output file with following format:
-        "head \t relation \t trueTail": [predTail1, predTail2, predTail3, predTail4, predTail5],
+        :param path_system_output:
+            the path of system output file with following format:
+            "head \t relation \t trueTail": [predTail1, predTail2, ..., predTail5],
 
         :return: class object
         """