[SPARK-32320][PYSPARK] Remove mutable default arguments

dev/sparktestsupport/modules.py

@@ -31,9 +31,10 @@ class Module(object):
     files have changed.
     """
 
-    def __init__(self, name, dependencies, source_file_regexes, build_profile_flags=(), environ={},
-                 sbt_test_goals=(), python_test_goals=(), excluded_python_implementations=(),
-                 test_tags=(), should_run_r_tests=False, should_run_build_tests=False):
+    def __init__(self, name, dependencies, source_file_regexes, build_profile_flags=(),
+                 environ=None, sbt_test_goals=(), python_test_goals=(),
+                 excluded_python_implementations=(), test_tags=(), should_run_r_tests=False,
+                 should_run_build_tests=False):
         """
         Define a new module.
 
@@ -62,7 +63,7 @@ def __init__(self, name, dependencies, source_file_regexes, build_profile_flags=
         self.source_file_prefixes = source_file_regexes
         self.sbt_test_goals = sbt_test_goals
         self.build_profile_flags = build_profile_flags
-        self.environ = environ
+        self.environ = environ or {}
         self.python_test_goals = python_test_goals
         self.excluded_python_implementations = excluded_python_implementations
         self.test_tags = test_tags

dev/tox.ini

@@ -19,6 +19,6 @@ max-line-length=100
 exclude=python/pyspark/cloudpickle/*.py,shared.py,python/docs/source/conf.py,work/*/*.py,python/.eggs/*,dist/*,.git/*
 
 [flake8]
-select = E901,E999,F821,F822,F823,F401,F405
+select = E901,E999,F821,F822,F823,F401,F405,B006
 exclude = python/pyspark/cloudpickle/*.py,shared.py*,python/docs/source/conf.py,work/*/*.py,python/.eggs/*,dist/*,.git/*,python/out,python/pyspark/sql/pandas/functions.pyi,python/pyspark/sql/column.pyi,python/pyspark/worker.pyi,python/pyspark/java_gateway.pyi
 max-line-length = 100

python/mypy.ini

@@ -17,6 +17,8 @@
 
 [mypy]
 
+no_implicit_optional = True
+
 [mypy-pyspark.cloudpickle.*]
 ignore_errors = True
 

python/pyspark/ml/regression.py

@@ -1801,7 +1801,7 @@ class AFTSurvivalRegression(_JavaRegressor, _AFTSurvivalRegressionParams,
     @keyword_only
     def __init__(self, *, featuresCol="features", labelCol="label", predictionCol="prediction",
                  fitIntercept=True, maxIter=100, tol=1E-6, censorCol="censor",
-                 quantileProbabilities=list([0.01, 0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95, 0.99]),
+                 quantileProbabilities=list([0.01, 0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95, 0.99]),  # noqa: B005
                  quantilesCol=None, aggregationDepth=2, maxBlockSizeInMB=0.0):
         """
         __init__(self, \\*, featuresCol="features", labelCol="label", predictionCol="prediction", \
@@ -1819,7 +1819,7 @@ def __init__(self, *, featuresCol="features", labelCol="label", predictionCol="p
     @since("1.6.0")
     def setParams(self, *, featuresCol="features", labelCol="label", predictionCol="prediction",
                   fitIntercept=True, maxIter=100, tol=1E-6, censorCol="censor",
-                  quantileProbabilities=list([0.01, 0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95, 0.99]),
+                  quantileProbabilities=list([0.01, 0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95, 0.99]),  # noqa: B005
                   quantilesCol=None, aggregationDepth=2, maxBlockSizeInMB=0.0):
         """
         setParams(self, \\*, featuresCol="features", labelCol="label", predictionCol="prediction", \

python/pyspark/ml/tuning.py

@@ -536,13 +536,13 @@ class CrossValidatorModel(Model, _CrossValidatorParams, MLReadable, MLWritable):
     .. versionadded:: 1.4.0
     """
 
-    def __init__(self, bestModel, avgMetrics=[], subModels=None):
+    def __init__(self, bestModel, avgMetrics=None, subModels=None):
         super(CrossValidatorModel, self).__init__()
         #: best model from cross validation
         self.bestModel = bestModel
         #: Average cross-validation metrics for each paramMap in
         #: CrossValidator.estimatorParamMaps, in the corresponding order.
-        self.avgMetrics = avgMetrics
+        self.avgMetrics = avgMetrics or []
         #: sub model list from cross validation
         self.subModels = subModels
 
@@ -920,12 +920,12 @@ class TrainValidationSplitModel(Model, _TrainValidationSplitParams, MLReadable,
     .. versionadded:: 2.0.0
     """
 
-    def __init__(self, bestModel, validationMetrics=[], subModels=None):
+    def __init__(self, bestModel, validationMetrics=None, subModels=None):
         super(TrainValidationSplitModel, self).__init__()
         #: best model from train validation split
         self.bestModel = bestModel
         #: evaluated validation metrics
-        self.validationMetrics = validationMetrics
+        self.validationMetrics = validationMetrics or []
         #: sub models from train validation split
         self.subModels = subModels
 

python/pyspark/ml/tuning.pyi

@@ -104,7 +104,7 @@ class CrossValidatorModel(
     def __init__(
         self,
         bestModel: Model,
-        avgMetrics: List[float] = ...,
+        avgMetrics: Optional[List[float]] = ...,
         subModels: Optional[List[List[Model]]] = ...,
     ) -> None: ...
     def copy(self, extra: Optional[ParamMap] = ...) -> CrossValidatorModel: ...
@@ -171,7 +171,7 @@ class TrainValidationSplitModel(
     def __init__(
         self,
         bestModel: Model,
-        validationMetrics: List[float] = ...,
+        validationMetrics: Optional[List[float]] = ...,
         subModels: Optional[List[Model]] = ...,
     ) -> None: ...
     def setEstimator(self, value: Estimator) -> TrainValidationSplitModel: ...

python/pyspark/resource/profile.py

@@ -34,13 +34,13 @@ class ResourceProfile(object):
     This API is evolving.
     """
 
-    def __init__(self, _java_resource_profile=None, _exec_req={}, _task_req={}):
+    def __init__(self, _java_resource_profile=None, _exec_req=None, _task_req=None):
         if _java_resource_profile is not None:
             self._java_resource_profile = _java_resource_profile
         else:
             self._java_resource_profile = None
-            self._executor_resource_requests = _exec_req
-            self._task_resource_requests = _task_req
+            self._executor_resource_requests = _exec_req or {}
+            self._task_resource_requests = _task_req or {}
 
     @property
     def id(self):

python/pyspark/sql/avro/functions.py

@@ -25,7 +25,7 @@
 from pyspark.util import _print_missing_jar
 
 
-def from_avro(data, jsonFormatSchema, options={}):
+def from_avro(data, jsonFormatSchema, options=None):
     """
     Converts a binary column of Avro format into its corresponding catalyst value.
     The specified schema must match the read data, otherwise the behavior is undefined:
@@ -70,7 +70,7 @@ def from_avro(data, jsonFormatSchema, options={}):
     sc = SparkContext._active_spark_context
     try:
         jc = sc._jvm.org.apache.spark.sql.avro.functions.from_avro(
-            _to_java_column(data), jsonFormatSchema, options)
+            _to_java_column(data), jsonFormatSchema, options or {})
     except TypeError as e:
         if str(e) == "'JavaPackage' object is not callable":
             _print_missing_jar("Avro", "avro", "avro", sc.version)

python/pyspark/sql/avro/functions.pyi

@@ -16,12 +16,12 @@
 # specific language governing permissions and limitations
 # under the License.
 
-from typing import Dict
+from typing import Dict, Optional
 
 from pyspark.sql._typing import ColumnOrName
 from pyspark.sql.column import Column
 
 def from_avro(
-    data: ColumnOrName, jsonFormatSchema: str, options: Dict[str, str] = ...
+    data: ColumnOrName, jsonFormatSchema: str, options: Optional[Dict[str, str]] = ...
 ) -> Column: ...
 def to_avro(data: ColumnOrName, jsonFormatSchema: str = ...) -> Column: ...

python/pyspark/sql/functions.py

@@ -80,8 +80,10 @@ def _invoke_binary_math_function(name, col1, col2):
     )
 
 
-def _options_to_str(options):
-    return {key: to_str(value) for (key, value) in options.items()}
+def _options_to_str(options=None):
+    if options:
+        return {key: to_str(value) for (key, value) in options.items()}
+    return {}
 
 
 def lit(col):
@@ -3415,7 +3417,7 @@ def json_tuple(col, *fields):
     return Column(jc)
 
 
-def from_json(col, schema, options={}):
+def from_json(col, schema, options=None):
     """
     Parses a column containing a JSON string into a :class:`MapType` with :class:`StringType`
     as keys type, :class:`StructType` or :class:`ArrayType` with
@@ -3471,7 +3473,7 @@ def from_json(col, schema, options={}):
     return Column(jc)
 
 
-def to_json(col, options={}):
+def to_json(col, options=None):
     """
     Converts a column containing a :class:`StructType`, :class:`ArrayType` or a :class:`MapType`
     into a JSON string. Throws an exception, in the case of an unsupported type.
@@ -3518,7 +3520,7 @@ def to_json(col, options={}):
     return Column(jc)
 
 
-def schema_of_json(json, options={}):
+def schema_of_json(json, options=None):
     """
     Parses a JSON string and infers its schema in DDL format.
 
@@ -3555,7 +3557,7 @@ def schema_of_json(json, options={}):
     return Column(jc)
 
 
-def schema_of_csv(csv, options={}):
+def schema_of_csv(csv, options=None):
     """
     Parses a CSV string and infers its schema in DDL format.
 
@@ -3588,7 +3590,7 @@ def schema_of_csv(csv, options={}):
     return Column(jc)
 
 
-def to_csv(col, options={}):
+def to_csv(col, options=None):
     """
     Converts a column containing a :class:`StructType` into a CSV string.
     Throws an exception, in the case of an unsupported type.
@@ -3999,7 +4001,7 @@ def sequence(start, stop, step=None):
             _to_java_column(start), _to_java_column(stop), _to_java_column(step)))
 
 
-def from_csv(col, schema, options={}):
+def from_csv(col, schema, options=None):
     """
     Parses a column containing a CSV string to a row with the specified schema.
     Returns `null`, in the case of an unparseable string.

python/pyspark/sql/functions.pyi

@@ -194,7 +194,7 @@ def json_tuple(col: ColumnOrName, *fields: str) -> Column: ...
 def from_json(
     col: ColumnOrName,
     schema: Union[ArrayType, StructType, Column, str],
-    options: Dict[str, str] = ...,
+    options: Optional[Dict[str, str]] = ...,
 ) -> Column: ...
 def to_json(col: ColumnOrName, options: Dict[str, str] = ...) -> Column: ...
 def schema_of_json(json: ColumnOrName, options: Dict[str, str] = ...) -> Column: ...