def make_dataset(
self,
dag: FugueWorkflow,
dataset: Any,
df: Any = None,
df_name: str = TUNE_DATASET_DF_DEFAULT_NAME,
test_df: Any = None,
test_df_name: str = TUNE_DATASET_VALIDATION_DF_DEFAULT_NAME,
partition_keys: Optional[List[str]] = None,
temp_path: str = "",
) -> TuneDataset:
assert_or_throw(dataset is not None,
TuneCompileError("dataset can't be None"))
if isinstance(dataset, TuneDataset):
assert_or_throw(
df is None,
TuneCompileError("can't set df when dataset is TuneDataset"))
return dataset
if isinstance(dataset, Space):
path = self.get_path_or_temp(temp_path)
builder = TuneDatasetBuilder(dataset, path)
if df is not None:
wdf = dag.df(df)
if partition_keys is not None and len(partition_keys) > 0:
wdf = wdf.partition_by(*partition_keys)
builder.add_df(df_name, wdf)
if test_df is not None:
wdf = dag.df(test_df)
how = "cross"
if partition_keys is not None and len(partition_keys) > 0:
wdf = wdf.partition_by(*partition_keys)
how = "inner"
builder.add_df(test_df_name, wdf, how=how)
return builder.build(dag, batch_size=1, shuffle=True)
raise TuneCompileError(f"{dataset} can't be converted to TuneDataset")
def _object_to_iterative_objective(self,
obj: Any) -> IterativeObjectiveFunc:
assert_or_throw(obj is not None,
TuneCompileError("objective can't be None"))
if isinstance(obj, IterativeObjectiveFunc):
return obj
raise TuneCompileError(
f"{obj} can't be converted to iterative objective function")
def make_noniterative_objective(self,
obj: Any) -> NonIterativeObjectiveFunc:
assert_or_throw(obj is not None,
TuneCompileError("objective can't be None"))
if isinstance(obj, NonIterativeObjectiveFunc):
return obj
return self._noniterative_objective_converter(obj)
def _object_to_stopper(self, obj: Any) -> Optional[NonIterativeStopper]:
if isinstance(obj, NonIterativeStopper):
return obj
if obj is None:
return None
raise TuneCompileError(
f"{obj} can't be converted to NonIterativeStopper")
def _object_to_noniterative_local_optimizer(
self, obj: Any) -> NonIterativeObjectiveLocalOptimizer:
if isinstance(obj, NonIterativeObjectiveLocalOptimizer):
return obj
if obj is None:
return NonIterativeObjectiveLocalOptimizer()
raise TuneCompileError(
f"{obj} can't be converted to non iterative objective optimizer")
def add_df(self,
name: str,
df: WorkflowDataFrame,
how: str = "") -> "TuneDatasetBuilder":
"""Add a dataframe to the dataset
:param name: name of the dataframe, it will also create a
``__tune_df__<name>`` column in the dataset dataframe
:param df: the dataframe to add.
:param how: join type, can accept ``semi``, ``left_semi``,
``anti``, ``left_anti``, ``inner``, ``left_outer``,
``right_outer``, ``full_outer``, ``cross``
:returns: the builder itself
.. note::
For the first dataframe you add, ``how`` should be empty.
From the second dataframe you add, ``how`` must be set.
.. note::
If ``df`` is prepartitioned, the partition key will be used to
join with the added dataframes. Read
:ref:`TuneDataset Tutorial </notebooks/tune_dataset.ipynb>`
for more details
"""
assert_or_throw(
not any(r[0] == name for r in self._dfs_spec),
TuneCompileError(name + " already exists"),
)
if len(self._dfs_spec) == 0:
assert_or_throw(
how == "",
TuneCompileError("first dataframe can't specify how to join"))
else:
assert_or_throw(
how != "",
TuneCompileError(
"must specify how to join after first dataframe"),
)
self._dfs_spec.append((name, df, how))
return self
def add_df(self,
name: str,
df: WorkflowDataFrame,
how: str = "") -> "TuneDatasetBuilder":
assert_or_throw(
not any(r[0] == name for r in self._dfs_spec),
TuneCompileError(name + " already exists"),
)
if len(self._dfs_spec) == 0:
assert_or_throw(
how == "",
TuneCompileError("first dataframe can't specify how to join"))
else:
assert_or_throw(
how != "",
TuneCompileError(
"must specify how to join after first dataframe"),
)
self._dfs_spec.append((name, df, how))
return self
def _get_distributed(self, distributed: Optional[bool]) -> bool:
if distributed is None:
return self._optimizer.distributable
if distributed:
assert_or_throw(
self._optimizer.distributable,
TuneCompileError(
f"can't distribute non-distributable optimizer {self._optimizer}"
),
)
return True
return False
def suggest_by_hyperband(
objective: Any,
space: Space,
plans: List[List[Tuple[float, int]]],
train_df: Any = None,
temp_path: str = "",
partition_keys: Optional[List[str]] = None,
top_n: int = 1,
monitor: Any = None,
distributed: Optional[bool] = None,
execution_engine: Any = None,
execution_engine_conf: Any = None,
) -> List[TrialReport]:
assert_or_throw(
not space.has_random_parameter,
TuneCompileError("space can't contain random parameters, "
"use sample method before calling this function"),
)
dag = FugueWorkflow()
dataset = TUNE_OBJECT_FACTORY.make_dataset(
dag,
space,
df=train_df,
partition_keys=partition_keys,
temp_path=temp_path,
)
study = optimize_by_hyperband(
objective=objective,
dataset=dataset,
plans=plans,
checkpoint_path=temp_path,
distributed=distributed,
monitor=monitor,
)
study.result(top_n).yield_dataframe_as("result")
rows = list(
dag.run(
execution_engine,
conf=execution_engine_conf,
)["result"].as_dict_iterable())
return [
TrialReport.from_jsondict(json.loads(r[TUNE_REPORT]))
for r in sorted(rows, key=lambda r: r[TUNE_REPORT_METRIC])
]
def to_noniterative_objective(
obj: Any,
min_better: bool = True,
global_vars: Optional[Dict[str, Any]] = None,
local_vars: Optional[Dict[str, Any]] = None,
) -> NonIterativeObjectiveFunc:
if isinstance(obj, NonIterativeObjectiveFunc):
return copy.copy(obj)
global_vars, local_vars = get_caller_global_local_vars(global_vars, local_vars)
try:
f = to_function(obj, global_vars=global_vars, local_vars=local_vars)
# this is for string expression of function with decorator
if isinstance(f, NonIterativeObjectiveFunc):
return copy.copy(f)
# this is for functions without decorator
return _NonIterativeObjectiveFuncWrapper.from_func(f, min_better)
except Exception as e:
exp = e
raise TuneCompileError(f"{obj} is not a valid tunable function", exp)
def suggest_by_sha(
objective: Any,
space: Space,
plan: List[Tuple[float, int]],
train_df: Any = None,
temp_path: str = "",
partition_keys: Optional[List[str]] = None,
top_n: int = 1,
monitor: Any = None,
distributed: Optional[bool] = None,
execution_engine: Any = None,
execution_engine_conf: Any = None,
) -> List[TrialReport]:
assert_or_throw(
not space.has_stochastic,
TuneCompileError("space can't contain random parameters, "
"use sample method before calling this function"),
)
dag = FugueWorkflow()
dataset = TUNE_OBJECT_FACTORY.make_dataset(
dag,
space,
df=train_df,
partition_keys=partition_keys,
temp_path=temp_path,
)
study = optimize_by_sha(
objective=objective,
dataset=dataset,
plan=plan,
checkpoint_path=temp_path,
distributed=distributed,
monitor=monitor,
)
study.result(top_n).yield_dataframe_as("result")
return _run(
dag=dag,
execution_engine=execution_engine,
execution_engine_conf=execution_engine_conf,
)
def _object_to_monitor(self, obj: Any) -> Optional[Monitor]:
if obj is None:
return None
raise TuneCompileError(f"{obj} can't be converted to Monitor")
def _object_to_noniterative_objective_runner(
self, obj: Any) -> NonIterativeObjectiveRunner:
if obj is None:
return NonIterativeObjectiveRunner()
raise TuneCompileError(
f"{obj} can't be converted to non iterative objective runner")
def _object_to_noniterative_objective(
self, obj: Any) -> NonIterativeObjectiveFunc:
raise TuneCompileError(
f"{obj} can't be converted to non iterative objective function")
def get_path_or_temp(self, path: str) -> str:
if path is None or path == "":
path = self._tmp
assert_or_throw(path != "",
TuneCompileError("path or temp path must be set"))
return path
