Remove inheritance from object (#5240)

Signed-off-by: harupy <hkawamura0130@gmail.com>

examples/flower_classifier/image_pyfunc.py

@@ -32,7 +32,7 @@ def decode_and_resize_image(raw_bytes, size):
     return np.asarray(Image.open(BytesIO(raw_bytes)).resize(size), dtype=np.float32)
 
 
-class KerasImageClassifierPyfunc(object):
+class KerasImageClassifierPyfunc:
     """
     Image classification model with embedded pre-processing.
 

mlflow/entities/_mlflow_object.py

@@ -2,7 +2,7 @@
 import pprint
 
 
-class _MLflowObject(object):
+class _MLflowObject:
     def __iter__(self):
         # Iterate through list of properties and yield as key -> value
         for prop in self._properties():
@@ -38,7 +38,7 @@ def get_classname(obj):
     return type(obj).__name__
 
 
-class _MLflowObjectPrinter(object):
+class _MLflowObjectPrinter:
     def __init__(self):
         super().__init__()
         self.printer = pprint.PrettyPrinter()

mlflow/entities/lifecycle_stage.py

@@ -2,7 +2,7 @@
 from mlflow.exceptions import MlflowException
 
 
-class LifecycleStage(object):
+class LifecycleStage:
     ACTIVE = "active"
     DELETED = "deleted"
     _VALID_STAGES = set([ACTIVE, DELETED])

mlflow/entities/model_registry/model_version_status.py

@@ -1,7 +1,7 @@
 from mlflow.protos.model_registry_pb2 import ModelVersionStatus as ProtoModelVersionStatus
 
 
-class ModelVersionStatus(object):
+class ModelVersionStatus:
     """Enum for status of an :py:class:`mlflow.entities.model_registry.ModelVersion`."""
 
     PENDING_REGISTRATION = ProtoModelVersionStatus.Value("PENDING_REGISTRATION")

mlflow/entities/run_status.py

@@ -1,7 +1,7 @@
 from mlflow.protos.service_pb2 import RunStatus as ProtoRunStatus
 
 
-class RunStatus(object):
+class RunStatus:
     """Enum for status of an :py:class:`mlflow.entities.Run`."""
 
     RUNNING = ProtoRunStatus.Value("RUNNING")

mlflow/entities/source_type.py

@@ -1,4 +1,4 @@
-class SourceType(object):
+class SourceType:
     """Enum for originating source of a :py:class:`mlflow.entities.Run`."""
 
     NOTEBOOK, JOB, PROJECT, LOCAL, UNKNOWN = range(1, 6)

mlflow/entities/view_type.py

@@ -1,7 +1,7 @@
 from mlflow.protos import service_pb2
 
 
-class ViewType(object):
+class ViewType:
     """Enum to filter requested experiment types."""
 
     ACTIVE_ONLY, DELETED_ONLY, ALL = range(1, 4)

mlflow/models/flavor_backend.py

@@ -1,7 +1,7 @@
 from abc import ABCMeta, abstractmethod
 
 
-class FlavorBackend(object):
+class FlavorBackend:
     """
     Abstract class for Flavor Backend.
     This class defines the API interface for local model deployment of MLflow model flavors.

mlflow/models/model.py

@@ -28,7 +28,7 @@
 )
 
 
-class Model(object):
+class Model:
     """
     An MLflow Model that can support multiple model flavors. Provides APIs for implementing
     new Model flavors.

mlflow/models/signature.py

@@ -24,7 +24,7 @@
         MlflowInferableDataset = Union[pd.DataFrame, np.ndarray, Dict[str, np.ndarray]]
 
 
-class ModelSignature(object):
+class ModelSignature:
     """
     ModelSignature specifies schema of model's inputs and outputs.
 

mlflow/models/utils.py

@@ -14,7 +14,7 @@
 ModelInputExample = Union[pd.DataFrame, np.ndarray, dict, list, csr_matrix, csc_matrix]
 
 
-class _Example(object):
+class _Example:
     """
     Represents an input example for MLflow model.
 

mlflow/paddle/__init__.py

@@ -419,7 +419,7 @@ def _load_pyfunc(path):
     return _PaddleWrapper(load_model(path))
 
 
-class _PaddleWrapper(object):
+class _PaddleWrapper:
     """
     Wrapper class that creates a predict function such that
     predict(data: pd.DataFrame) -> model's output as pd.DataFrame (pandas DataFrame)

mlflow/projects/_project_spec.py

@@ -107,7 +107,7 @@ def load_project(directory):
     )
 
 
-class Project(object):
+class Project:
     """A project specification loaded from an MLproject file in the passed-in directory."""
 
     def __init__(self, conda_env_path, entry_points, docker_env, name):
@@ -136,7 +136,7 @@ def get_entry_point(self, entry_point):
         )
 
 
-class EntryPoint(object):
+class EntryPoint:
     """An entry point in an MLproject specification."""
 
     def __init__(self, name, parameters, command):
@@ -199,7 +199,7 @@ def _sanitize_param_dict(param_dict):
         return {str(key): quote(str(value)) for key, value in param_dict.items()}
 
 
-class Parameter(object):
+class Parameter:
     """A parameter in an MLproject entry point."""
 
     def __init__(self, name, yaml_obj):

mlflow/projects/databricks.py

@@ -69,7 +69,7 @@ def before_run_validations(tracking_uri, backend_config):
         )
 
 
-class DatabricksJobRunner(object):
+class DatabricksJobRunner:
     """
     Helper class for running an MLflow project as a Databricks Job.
     :param databricks_profile: Optional Databricks CLI profile to use to fetch hostname &

mlflow/projects/submitted_run.py

@@ -9,7 +9,7 @@
 _logger = logging.getLogger(__name__)
 
 
-class SubmittedRun(object):
+class SubmittedRun:
     """
     Wrapper around an MLflow project run (e.g. a subprocess running an entry point
     command or a Databricks job run) and exposing methods for waiting on and cancelling the run.

mlflow/pyfunc/__init__.py

@@ -563,7 +563,7 @@ def _enforce_schema(pfInput: PyFuncInput, input_schema: Schema):
     )
 
 
-class PyFuncModel(object):
+class PyFuncModel:
     """
     MLflow 'python function' model.
 

mlflow/pyfunc/model.py

@@ -57,7 +57,7 @@ def get_default_conda_env():
     return _mlflow_conda_env(additional_pip_deps=get_default_pip_requirements())
 
 
-class PythonModel(object):
+class PythonModel:
     """
     Represents a generic Python model that evaluates inputs and produces API-compatible outputs.
     By subclassing :class:`~PythonModel`, users can create customized MLflow models with the
@@ -94,7 +94,7 @@ def predict(self, context, model_input):
         """
 
 
-class PythonModelContext(object):
+class PythonModelContext:
     """
     A collection of artifacts that a :class:`~PythonModel` can use when performing inference.
     :class:`~PythonModelContext` objects are created *implicitly* by the
@@ -281,7 +281,7 @@ def _load_pyfunc(model_path):
     return _PythonModelPyfuncWrapper(python_model=python_model, context=context)
 
 
-class _PythonModelPyfuncWrapper(object):
+class _PythonModelPyfuncWrapper:
     """
     Wrapper class that creates a predict function such that
     predict(model_input: pd.DataFrame) -> model's output as pd.DataFrame (pandas DataFrame)

mlflow/pyfunc/spark_model_cache.py

@@ -6,7 +6,7 @@
 from pyspark.files import SparkFiles
 
 
-class SparkModelCache(object):
+class SparkModelCache:
     """Caches models in memory on Spark Executors, to avoid continually reloading from disk.
 
     This class has to be part of a different module than the one that _uses_ it. This is

mlflow/pytorch/__init__.py

@@ -730,7 +730,7 @@ def _load_pyfunc(path, **kwargs):
     return _PyTorchWrapper(_load_model(path, **kwargs))
 
 
-class _PyTorchWrapper(object):
+class _PyTorchWrapper:
     """
     Wrapper class that creates a predict function such that
     predict(data: pd.DataFrame) -> model's output as pd.DataFrame (pandas DataFrame)

mlflow/spark.py

@@ -708,7 +708,7 @@ def _load_pyfunc(path):
     return _PyFuncModelWrapper(spark, _load_model(model_uri=path))
 
 
-class _PyFuncModelWrapper(object):
+class _PyFuncModelWrapper:
     """
     Wrapper around Spark MLlib PipelineModel providing interface for scoring pandas DataFrame.
     """

mlflow/tensorflow/__init__.py

@@ -455,7 +455,7 @@ def _load_pyfunc(path):
     return _TF2Wrapper(model=loaded_model, infer=loaded_model.signatures[tf_signature_def_key])
 
 
-class _TF2Wrapper(object):
+class _TF2Wrapper:
     """
     Wrapper class that exposes a TensorFlow model for inference via a ``predict`` function such that
     ``predict(data: pandas.DataFrame) -> pandas.DataFrame``. For TensorFlow versions >= 2.0.0.

mlflow/tracking/_model_registry/client.py

@@ -19,7 +19,7 @@
 AWAIT_MODEL_VERSION_CREATE_SLEEP_DURATION_SECONDS = 3
 
 
-class ModelRegistryClient(object):
+class ModelRegistryClient:
     """
     Client of an MLflow Model Registry Server that creates and manages registered
     models and model versions.

mlflow/tracking/_tracking_service/client.py

@@ -29,7 +29,7 @@
 from collections import OrderedDict
 
 
-class TrackingServiceClient(object):
+class TrackingServiceClient:
     """
     Client of an MLflow Tracking Server that creates and manages experiments and runs.
     """

mlflow/tracking/client.py

@@ -47,7 +47,7 @@
 _logger = logging.getLogger(__name__)
 
 
-class MlflowClient(object):
+class MlflowClient:
     """
     Client of an MLflow Tracking Server that creates and manages experiments and runs, and of an
     MLflow Registry Server that creates and manages registered models and model versions. It's a

mlflow/tracking/context/abstract_context.py

@@ -1,7 +1,7 @@
 from abc import ABCMeta, abstractmethod
 
 
-class RunContextProvider(object):
+class RunContextProvider:
     """
     Abstract base class for context provider objects specifying custom tags at run-creation time
     (e.g. tags specifying the git repo with which the run is associated).

mlflow/tracking/context/registry.py

@@ -13,7 +13,7 @@
 _logger = logging.getLogger(__name__)
 
 
-class RunContextProviderRegistry(object):
+class RunContextProviderRegistry:
     """Registry for run context provider implementations
 
     This class allows the registration of a run context provider which can be used to infer meta

mlflow/tracking/request_header/abstract_request_header_provider.py

@@ -1,7 +1,7 @@
 from abc import ABCMeta, abstractmethod
 
 
-class RequestHeaderProvider(object):
+class RequestHeaderProvider:
     """
     Abstract base class for specifying custom request headers to add to outgoing requests
     (e.g. request headers specifying the environment from which mlflow is running).

mlflow/tracking/request_header/registry.py

@@ -9,7 +9,7 @@
 _logger = logging.getLogger(__name__)
 
 
-class RequestHeaderProviderRegistry(object):
+class RequestHeaderProviderRegistry:
     def __init__(self):
         self._registry = []
 

mlflow/types/schema.py

@@ -68,7 +68,7 @@ def to_spark(self):
         return getattr(pyspark.sql.types, self._spark_type)()
 
 
-class ColSpec(object):
+class ColSpec:
     """
     Specification of name and type of a single column in a dataset.
     """
@@ -119,7 +119,7 @@ def __repr__(self) -> str:
             return "{name}: {type}".format(name=repr(self.name), type=repr(self.type))
 
 
-class TensorInfo(object):
+class TensorInfo:
     """
     Representation of the shape and type of a Tensor.
     """
@@ -181,7 +181,7 @@ def __repr__(self) -> str:
         return "Tensor({type}, {shape})".format(type=repr(self.dtype.name), shape=repr(self.shape))
 
 
-class TensorSpec(object):
+class TensorSpec:
     """
     Specification used to represent a dataset stored as a Tensor.
     """
@@ -249,7 +249,7 @@ def __repr__(self) -> str:
             return "{name}: {info}".format(name=repr(self.name), info=repr(self._tensorInfo))
 
 
-class Schema(object):
+class Schema:
     """
     Specification of a dataset.
 

mlflow/types/utils.py

@@ -212,7 +212,7 @@ def _infer_pandas_column(col: pd.Series) -> DataType:
     if len(col.values.shape) > 1:
         raise MlflowException("Expected 1d array, got array with shape {}".format(col.shape))
 
-    class IsInstanceOrNone(object):
+    class IsInstanceOrNone:
         def __init__(self, *args):
             self.classes = args
             self.seen_instances = 0

mlflow/utils/autologging_utils/__init__.py

@@ -505,7 +505,7 @@ def _get_new_training_session_class():
     # 1. We don't currently have any use cases for allow_children=True.
     # 2. The list append & pop operations are thread-safe, so we will always clear the session stack
     #    once all _TrainingSessions exit.
-    class _TrainingSession(object):
+    class _TrainingSession:
         _session_stack = []
 
         def __init__(self, clazz, allow_children=True):

mlflow/utils/file_utils.py

@@ -181,7 +181,7 @@ def read_yaml(root, file_name):
         raise e
 
 
-class TempDir(object):
+class TempDir:
     def __init__(self, chdr=False, remove_on_exit=True):
         self._dir = None
         self._path = None

mlflow/utils/gorilla.py

@@ -92,7 +92,7 @@ def default_filter(name, obj):
     return not (isinstance(obj, types.ModuleType) or name.startswith("_"))
 
 
-class DecoratorData(object):
+class DecoratorData:
 
     """Decorator data.
 
@@ -115,7 +115,7 @@ def __init__(self):
         self.filter = None
 
 
-class Settings(object):
+class Settings:
 
     """Define the patching behaviour.
 
@@ -171,7 +171,7 @@ def _update(self, **kwargs):
         self.__dict__.update(**kwargs)
 
 
-class Patch(object):
+class Patch:
 
     """Describe all the information required to apply a patch.
 

mlflow/utils/rest_utils.py

@@ -291,7 +291,7 @@ def cloud_storage_http_request(
         raise MlflowException("API request failed with exception %s" % e)
 
 
-class MlflowHostCreds(object):
+class MlflowHostCreds:
     """
     Provides a hostname and optional authentication for talking to an MLflow tracking server.
     :param host: Hostname (e.g., http://localhost:5000) to MLflow server. Required.