Remove numpy-based random hint

src/pykeen/datasets/base.py

@@ -20,7 +20,7 @@
 
 from ..constants import PYKEEN_DATASETS
 from ..triples import TriplesFactory
-from ..typing import RandomHint
+from ..typing import TorchRandomHint
 from ..utils import normalize_string
 
 __all__ = [
@@ -520,7 +520,7 @@ def __init__(
         eager: bool = False,
         create_inverse_triples: bool = False,
         delimiter: Optional[str] = None,
-        random_state: RandomHint = None,
+        random_state: TorchRandomHint = None,
         randomize_cleanup: bool = False,
     ):
         """Initialize dataset.
@@ -596,7 +596,7 @@ def __init__(
         cache_root: Optional[str] = None,
         eager: bool = False,
         create_inverse_triples: bool = False,
-        random_state: RandomHint = None,
+        random_state: TorchRandomHint = None,
     ):
         """Initialize dataset.
 
@@ -654,7 +654,7 @@ def __init__(
         cache_root: Optional[str] = None,
         eager: bool = False,
         create_inverse_triples: bool = False,
-        random_state: RandomHint = None,
+        random_state: TorchRandomHint = None,
         read_csv_kwargs: Optional[Mapping[str, Any]] = None,
     ):
         """Initialize dataset.

src/pykeen/datasets/ckg.py

@@ -11,7 +11,7 @@
 import pandas as pd
 
 from .base import TabbedDataset
-from ..typing import RandomHint
+from ..typing import TorchRandomHint
 
 __all__ = [
     'CKG',
@@ -34,7 +34,7 @@ def __init__(
         self,
         eager: bool = False,
         create_inverse_triples: bool = False,
-        random_state: RandomHint = 0,
+        random_state: TorchRandomHint = 0,
         cache_root: Optional[str] = None,
     ):
         super().__init__(

src/pykeen/datasets/conceptnet.py

@@ -9,7 +9,7 @@
 from more_click import verbose_option
 
 from .base import SingleTabbedDataset
-from ..typing import RandomHint
+from ..typing import TorchRandomHint
 
 URL = 'https://s3.amazonaws.com/conceptnet/downloads/2019/edges/conceptnet-assertions-5.7.0.csv.gz'
 
@@ -28,7 +28,7 @@ class ConceptNet(SingleTabbedDataset):
     def __init__(
         self,
         create_inverse_triples: bool = False,
-        random_state: RandomHint = 0,
+        random_state: TorchRandomHint = 0,
         **kwargs,
     ):
         super().__init__(

src/pykeen/datasets/drkg.py

@@ -8,7 +8,7 @@
 import logging
 
 from .base import TarFileSingleDataset
-from ..typing import RandomHint
+from ..typing import TorchRandomHint
 
 __all__ = [
     'DRKG',
@@ -29,7 +29,7 @@ class DRKG(TarFileSingleDataset):
     def __init__(
         self,
         create_inverse_triples: bool = False,
-        random_state: RandomHint = 0,
+        random_state: TorchRandomHint = 0,
         **kwargs,
     ):
         super().__init__(

src/pykeen/datasets/hetionet.py

@@ -10,7 +10,7 @@
 import click
 
 from .base import SingleTabbedDataset
-from ..typing import RandomHint
+from ..typing import TorchRandomHint
 
 __all__ = [
     'Hetionet',
@@ -40,7 +40,7 @@ def __init__(
         self,
         create_inverse_triples: bool = False,
         eager: bool = False,
-        random_state: RandomHint = 0,
+        random_state: TorchRandomHint = 0,
     ):
         super().__init__(
             url=URL,

src/pykeen/typing.py

@@ -17,7 +17,6 @@
     'Constrainer',
     'InteractionFunction',
     'DeviceHint',
-    'RandomHint',
     'TorchRandomHint',
 ]
 
@@ -34,5 +33,4 @@
 Constrainer = Callable[[TensorType], TensorType]
 
 DeviceHint = Union[None, str, torch.device]
-RandomHint = Union[None, int, np.random.RandomState]
 TorchRandomHint = Union[None, int, torch.Generator]

src/pykeen/utils.py

@@ -21,7 +21,7 @@
 import torch.nn.modules.batchnorm
 
 from .constants import PYKEEN_BENCHMARKS
-from .typing import DeviceHint, RandomHint, TorchRandomHint
+from .typing import DeviceHint, TorchRandomHint
 from .version import get_git_hash
 
 __all__ = [
@@ -48,7 +48,6 @@
     'NoRandomSeedNecessary',
     'Result',
     'fix_dataclass_init_docs',
-    'ensure_random_state',
     'get_benchmark',
 ]
 
@@ -406,18 +405,6 @@ def random_non_negative_int() -> int:
     return int(sq.generate_state(1)[0])
 
 
-def ensure_random_state(random_state: RandomHint) -> np.random.RandomState:
-    """Prepare a random state."""
-    if random_state is None:
-        random_state = random_non_negative_int()
-        logger.warning(f'using automatically assigned random_state={random_state}')
-    if isinstance(random_state, int):
-        random_state = np.random.RandomState(random_state)
-    if not isinstance(random_state, np.random.RandomState):
-        raise TypeError
-    return random_state
-
-
 def ensure_torch_random_state(random_state: TorchRandomHint) -> torch.Generator:
     """Prepare a random state for PyTorch."""
     if random_state is None: