Fix comments and typos.

src/pydvl/utils/functional.py

@@ -2,28 +2,35 @@
 
 import inspect
 from functools import partial
-from typing import Callable, Set, Tuple
+from typing import Callable, Set, Tuple, Union
 
-__all__ = ["unroll_partial_fn_args"]
+__all__ = ["get_free_args_fn"]
 
 
-def unroll_partial_fn_args(fun: Callable) -> Set[str]:
+def get_free_args_fn(fun: Union[Callable, partial]) -> Set[str]:
     """
-    Unroll a function that was set by functools.partial.
+    Accept a function or partial definition and return the set of arguments that are
+    free. An argument is free if it is not set by the partial and is a parameter of the
+    function.
 
     Args:
-        fun: Either or a function to unroll.
+        fun: A partial or a function to unroll.
 
     Returns:
-        A tuple of the unrolled function and a set of the parameters that were set by
-        functools.partial.
+        A set of arguments that were set by the partial.
     """
     args_set_by_partial: Set[str] = set()
 
-    def _rec_unroll_partial_function(g: Callable):
+    def _rec_unroll_partial_function(g: Union[Callable, partial]) -> Callable:
         """
         Store arguments and recursively call itself if the function is a partial. In the
-        end, return the original function.
+        end, return the initial wrapped function.
+
+        Args:
+            g: A partial or a function to unroll.
+
+        Returns:
+            Initial wrapped function.
         """
         nonlocal args_set_by_partial
 

src/pydvl/utils/numeric.py

@@ -122,8 +122,6 @@ def random_powerset(
         ValueError: if the element sampling probability is not in [0,1]
 
     """
-    if not isinstance(s, np.ndarray):
-        raise TypeError("Set must be an NDArray")
     if q < 0 or q > 1:
         raise ValueError("Element sampling probability must be in [0,1]")
 

src/pydvl/utils/types.py

@@ -5,13 +5,12 @@
 
 import functools
 from abc import ABCMeta
-from copy import deepcopy
-from typing import Any, Callable, Optional, Protocol, Tuple, TypeVar, Union, cast
+from typing import Any, Callable, Optional, Protocol, TypeVar, Union, cast
 
 from numpy.random import Generator, SeedSequence
 from numpy.typing import NDArray
 
-from pydvl.utils.functional import unroll_partial_fn_args
+from pydvl.utils.functional import get_free_args_fn
 
 __all__ = ["SupervisedModel", "MapFunction", "ReduceFunction", "NoPublicConstructor"]
 
@@ -83,7 +82,7 @@ def maybe_add_argument(fun: Callable, new_arg: str) -> Callable:
     Returns:
         A new function accepting one more keyword argument.
     """
-    if new_arg in unroll_partial_fn_args(fun):
+    if new_arg in get_free_args_fn(fun):
         return fun
 
     return functools.partial(call_fun_remove_arg, fun=fun, arg=new_arg)
@@ -137,22 +136,3 @@ def ensure_seed_sequence(
         return cast(SeedSequence, seed.bit_generator.seed_seq)  # type: ignore
     else:
         return SeedSequence(seed)
-
-
-def call_fn_multiple_seeds(
-    fn: Callable, *args, seeds: Tuple[Seed, ...], **kwargs
-) -> Tuple:
-    """
-    Execute a function multiple times with different seeds. It copies the arguments
-    and keyword arguments before passing them to the function.
-
-    Args:
-        fn: The function to execute.
-        args: The arguments to pass to the function.
-        seeds: The seeds to use.
-        kwargs: The keyword arguments to pass to the function.
-
-    Returns:
-        A tuple of the results of the function.
-    """
-    return tuple(fn(*deepcopy(args), **deepcopy(kwargs), seed=seed) for seed in seeds)

src/pydvl/value/result.py

@@ -695,8 +695,7 @@ def from_random(
                 ("efficiency" property of Shapley values).
             kwargs: Additional options to pass to the constructor of
                 [ValuationResult][pydvl.value.result.ValuationResult]. Use to override status, names, etc.
-            seed: ither an instance of a numpy random number generator or a seed for
-            it.
+
         Returns:
             A valuation result with its status set to
             [Status.Converged][pydvl.utils.status.Status] by default.

src/pydvl/value/shapley/common.py

@@ -90,7 +90,7 @@ def compute_shapley_values(
             others require specific subtypes.
         n_jobs: Number of parallel jobs (available only to some methods)
         seed: Either an instance of a numpy random number generator or a seed
-        for it.
+            for it.
         mode: Choose which shapley algorithm to use. See
             [ShapleyMode][pydvl.value.shapley.ShapleyMode] for a list of allowed value.
 

src/pydvl/value/shapley/gt.py

@@ -141,7 +141,7 @@ def _group_testing_shapley(
         progress: Whether to display progress bars for each job.
         job_id: id to use for reporting progress (e.g. to place progres bars)
         seed: Either an instance of a numpy random number generator or a seed
-        for it.
+            for it.
     Returns:
 
     """
@@ -203,7 +203,7 @@ def group_testing_shapley(
             address, number of cpus, etc.
         progress: Whether to display progress bars for each job.
         seed: Either an instance of a numpy random number generator or a seed
-        for it.
+            for it.
         options: Additional options to pass to
             [cvxpy.Problem.solve()](https://www.cvxpy.org/tutorial/advanced/index.html#solve-method-options).
             E.g. to change the solver (which defaults to `cvxpy.SCS`) pass

tests/value/shapley/test_montecarlo.py

@@ -15,14 +15,15 @@
 )
 from pydvl.utils.numeric import num_samples_permutation_hoeffding
 from pydvl.utils.score import Scorer, squashed_r2
-from pydvl.utils.types import Seed, call_fn_multiple_seeds
+from pydvl.utils.types import Seed
 from pydvl.value import compute_shapley_values
 from pydvl.value.shapley import ShapleyMode
 from pydvl.value.shapley.naive import combinatorial_exact_shapley
 from pydvl.value.stopping import MaxChecks, MaxUpdates
 
 from .. import check_rank_correlation, check_total_value, check_values
 from ..conftest import polynomial_dataset
+from ..utils import call_fn_multiple_seeds
 
 log = logging.getLogger(__name__)
 

tests/value/utils.py

@@ -0,0 +1,25 @@
+from __future__ import annotations
+
+from copy import deepcopy
+from typing import Callable, Tuple
+
+from pydvl.utils.types import Seed
+
+
+def call_fn_multiple_seeds(
+    fn: Callable, *args, seeds: Tuple[Seed, ...], **kwargs
+) -> Tuple:
+    """
+    Execute a function multiple times with different seeds. It copies the arguments
+    and keyword arguments before passing them to the function.
+
+    Args:
+        fn: The function to execute.
+        args: The arguments to pass to the function.
+        seeds: The seeds to use.
+        kwargs: The keyword arguments to pass to the function.
+
+    Returns:
+        A tuple of the results of the function.
+    """
+    return tuple(fn(*deepcopy(args), **deepcopy(kwargs), seed=seed) for seed in seeds)