replace uses of np.ndarray with npt.NDArray in type annotations

Summary:
To facilitate PSS-2 upgrade, this uses `ndt.NDArray` instead of `nd.ndarray` in type annotations. It was backported to Numpy-1.19 (PSS-1) as alias to `nd.ndarray` -- a noop. In Numpy-1.24, `ndt.NDArray` a proper generic type, and without this change uses of `nd.ndarray` generate the following Pyre error:
```counterexample
 Invalid type parameters [24]: Generic type `np.ndarray` expects 2 type parameters.
```

Differential Revision: D63360858

aepsych/benchmark/problem.py

@@ -9,6 +9,7 @@
 
 import aepsych
 import numpy as np
+import numpy.typing as npt
 import torch
 from scipy.stats import bernoulli, norm, pearsonr
 from aepsych.strategy import SequentialStrategy, Strategy
@@ -51,7 +52,7 @@ def metadata(self) -> Dict[str, Any]:
         Benchmark's output dataframe, with its associated value stored in each row."""
         return {"name": self.name}
 
-    def p(self, x: np.ndarray) -> np.ndarray:
+    def p(self, x: npt.NDArray) -> npt.NDArray:
         """Evaluate response probability from test function.
 
         Args:
@@ -62,7 +63,7 @@ def p(self, x: np.ndarray) -> np.ndarray:
         """
         return norm.cdf(self.f(x))
 
-    def sample_y(self, x: np.ndarray) -> np.ndarray:
+    def sample_y(self, x: npt.NDArray) -> npt.NDArray:
         """Sample a response from test function.
 
         Args:
@@ -86,7 +87,7 @@ def f_hat(self, model: aepsych.models.base.ModelProtocol) -> torch.Tensor:
         return f_hat
 
     @cached_property
-    def f_true(self) -> np.ndarray:
+    def f_true(self) -> npt.NDArray:
         """Evaluate true test function over evaluation grid.
 
         Returns:
@@ -238,7 +239,7 @@ def inverse_link(x):
         return inverse_link(self.thresholds).astype(np.float32)
 
     @cached_property
-    def true_below_threshold(self) -> np.ndarray:
+    def true_below_threshold(self) -> npt.NDArray:
         """
         Evaluate whether the true function is below threshold over the eval grid
         (used for proper scoring and threshold missclassification metric).

aepsych/benchmark/test_functions.py

@@ -10,6 +10,7 @@
 from typing import Callable
 
 import numpy as np
+import numpy.typing as npt
 import pandas as pd
 from scipy.interpolate import CubicSpline, interp1d
 from scipy.stats import norm
@@ -53,7 +54,7 @@
 dubno_data = pd.read_csv(io.StringIO(raw))
 
 
-def make_songetal_threshfun(x: np.ndarray, y: np.ndarray) -> Callable[[float], float]:
+def make_songetal_threshfun(x: npt.NDArray, y: npt.NDArray) -> Callable[[float], float]:
     """Generate a synthetic threshold function by interpolation of real data.
 
     Real data is from Dubno et al. 2013, and procedure follows Song et al. 2017, 2018.
@@ -83,7 +84,7 @@ def f_combo(x):
 
 def make_songetal_testfun(
     phenotype: str = "Metabolic", beta: float = 1
-) -> Callable[[np.ndarray, bool], np.ndarray]:
+) -> Callable[[npt.NDArray, bool], npt.NDArray]:
     """Make an audiometric test function following Song et al. 2017.
 
     To do so,we first compute a threshold by interpolation/extrapolation
@@ -129,7 +130,7 @@ def song_testfun(x, cdf=False):
     return song_testfun
 
 
-def novel_discrimination_testfun(x: np.ndarray) -> np.ndarray:
+def novel_discrimination_testfun(x: npt.NDArray) -> npt.NDArray:
     """Evaluate novel discrimination test function from Owen et al.
 
     The threshold is roughly parabolic with context, and the slope

aepsych/config.py

@@ -16,6 +16,7 @@
 import botorch
 import gpytorch
 import numpy as np
+import numpy.typing as npt
 import torch
 
 from aepsych.version import __version__
@@ -175,7 +176,7 @@ def _str_to_list(self, v: str, element_type: _T = float) -> List[_T]:
         else:
             return [v.strip()]
 
-    def _str_to_array(self, v: str) -> np.ndarray:
+    def _str_to_array(self, v: str) -> npt.NDArray:
         v = ast.literal_eval(v)
         return np.array(v, dtype=float)
 

aepsych/generators/manual_generator.py

@@ -9,6 +9,7 @@
 from typing import Optional, Union, Dict
 
 import numpy as np
+import numpy.typing as npt
 import torch
 from aepsych.config import Config
 from aepsych.generators.base import AEPsychGenerator
@@ -24,9 +25,9 @@ class ManualGenerator(AEPsychGenerator):
 
     def __init__(
         self,
-        lb: Union[np.ndarray, torch.Tensor],
-        ub: Union[np.ndarray, torch.Tensor],
-        points: Union[np.ndarray, torch.Tensor],
+        lb: Union[npt.NDArray, torch.Tensor],
+        ub: Union[npt.NDArray, torch.Tensor],
+        points: Union[npt.NDArray, torch.Tensor],
         dim: Optional[int] = None,
         shuffle: bool = True,
         seed: Optional[int] = None,
@@ -101,10 +102,10 @@ class SampleAroundPointsGenerator(ManualGenerator):
 
     def __init__(
         self,
-        lb: Union[np.ndarray, torch.Tensor],
-        ub: Union[np.ndarray, torch.Tensor],
-        window: Union[np.ndarray, torch.Tensor],
-        points: Union[np.ndarray, torch.Tensor],
+        lb: Union[npt.NDArray, torch.Tensor],
+        ub: Union[npt.NDArray, torch.Tensor],
+        window: Union[npt.NDArray, torch.Tensor],
+        points: Union[npt.NDArray, torch.Tensor],
         samples_per_point: int,
         dim: Optional[int] = None,
         shuffle: bool = True,

aepsych/generators/random_generator.py

@@ -8,6 +8,7 @@
 from typing import Dict, Optional, Union
 
 import numpy as np
+import numpy.typing as npt
 import torch
 from aepsych.config import Config
 from aepsych.generators.base import AEPsychGenerator
@@ -22,8 +23,8 @@ class RandomGenerator(AEPsychGenerator):
 
     def __init__(
         self,
-        lb: Union[np.ndarray, torch.Tensor],
-        ub: Union[np.ndarray, torch.Tensor],
+        lb: Union[npt.NDArray, torch.Tensor],
+        ub: Union[npt.NDArray, torch.Tensor],
         dim: Optional[int] = None,
     ):
         """Iniatialize RandomGenerator.

aepsych/generators/sobol_generator.py

@@ -9,6 +9,7 @@
 from typing import Dict, Optional, Union
 
 import numpy as np
+import numpy.typing as npt
 import torch
 from aepsych.config import Config
 from aepsych.generators.base import AEPsychGenerator
@@ -24,8 +25,8 @@ class SobolGenerator(AEPsychGenerator):
 
     def __init__(
         self,
-        lb: Union[np.ndarray, torch.Tensor],
-        ub: Union[np.ndarray, torch.Tensor],
+        lb: Union[npt.NDArray, torch.Tensor],
+        ub: Union[npt.NDArray, torch.Tensor],
         dim: Optional[int] = None,
         seed: Optional[int] = None,
         stimuli_per_trial: int = 1,

aepsych/models/base.py

@@ -14,6 +14,7 @@
 
 import gpytorch
 import numpy as np
+import numpy.typing as npt
 import torch
 
 from aepsych.config import Config, ConfigurableMixin
@@ -91,7 +92,7 @@ def _get_extremum(
         extremum_type: str,
         locked_dims: Optional[Mapping[int, List[float]]],
         n_samples=1000,
-    ) -> Tuple[float, np.ndarray]:
+    ) -> Tuple[float, npt.NDArray]:
         pass
 
     def dim_grid(self, gridsize: int = 30) -> torch.Tensor:
@@ -105,7 +106,7 @@ def update(
     ) -> None:
         pass
 
-    def p_below_threshold(self, x, f_thresh) -> np.ndarray:
+    def p_below_threshold(self, x, f_thresh) -> npt.NDArray:
         pass
 
 
@@ -216,7 +217,7 @@ def inv_query(
 
     def get_jnd(
         self: ModelProtocol,
-        grid: Optional[Union[np.ndarray, torch.Tensor]] = None,
+        grid: Optional[Union[npt.NDArray, torch.Tensor]] = None,
         cred_level: Optional[float] = None,
         intensity_dim: int = -1,
         confsamps: int = 500,
@@ -376,7 +377,7 @@ def _fit_mll(
         )
         return res
 
-    def p_below_threshold(self, x, f_thresh) -> np.ndarray:
+    def p_below_threshold(self, x, f_thresh) -> npt.NDArray:
         f, var = self.predict(x)
         f_thresh = f_thresh.reshape(-1, 1)
         f = f.reshape(1, -1)

aepsych/models/gp_classification.py

@@ -11,6 +11,7 @@
 
 import gpytorch
 import numpy as np
+import numpy.typing as npt
 import torch
 from aepsych.config import Config
 from aepsych.factory.default import default_mean_covar_factory
@@ -47,8 +48,8 @@ class GPClassificationModel(AEPsychMixin, ApproximateGP):
 
     def __init__(
         self,
-        lb: Union[np.ndarray, torch.Tensor],
-        ub: Union[np.ndarray, torch.Tensor],
+        lb: Union[npt.NDArray, torch.Tensor],
+        ub: Union[npt.NDArray, torch.Tensor],
         dim: Optional[int] = None,
         mean_module: Optional[gpytorch.means.Mean] = None,
         covar_module: Optional[gpytorch.kernels.Kernel] = None,
@@ -232,7 +233,7 @@ def fit(
         self._fit_mll(mll, **kwargs)
 
     def sample(
-        self, x: Union[torch.Tensor, np.ndarray], num_samples: int
+        self, x: Union[torch.Tensor, npt.NDArray], num_samples: int
     ) -> torch.Tensor:
         """Sample from underlying model.
 
@@ -247,7 +248,7 @@ def sample(
         return self.posterior(x).rsample(torch.Size([num_samples])).detach().squeeze()
 
     def predict(
-        self, x: Union[torch.Tensor, np.ndarray], probability_space: bool = False
+        self, x: Union[torch.Tensor, npt.NDArray], probability_space: bool = False
     ) -> Tuple[torch.Tensor, torch.Tensor]:
         """Query the model for posterior mean and variance.
 
@@ -288,7 +289,7 @@ def predict(
             return promote_0d(fmean), promote_0d(fvar)
 
     def predict_probability(
-        self, x: Union[torch.Tensor, np.ndarray]
+        self, x: Union[torch.Tensor, npt.NDArray]
     ) -> Tuple[torch.Tensor, torch.Tensor]:
         return self.predict(x, probability_space=True)
 
@@ -304,8 +305,8 @@ class GPBetaRegressionModel(GPClassificationModel):
 
     def __init__(
         self,
-        lb: Union[np.ndarray, torch.Tensor],
-        ub: Union[np.ndarray, torch.Tensor],
+        lb: Union[npt.NDArray, torch.Tensor],
+        ub: Union[npt.NDArray, torch.Tensor],
         dim: Optional[int] = None,
         mean_module: Optional[gpytorch.means.Mean] = None,
         covar_module: Optional[gpytorch.kernels.Kernel] = None,

aepsych/models/gp_regression.py

@@ -11,6 +11,7 @@
 
 import gpytorch
 import numpy as np
+import numpy.typing as npt
 import torch
 from aepsych.config import Config
 from aepsych.factory.default import default_mean_covar_factory
@@ -33,8 +34,8 @@ class GPRegressionModel(AEPsychMixin, ExactGP):
 
     def __init__(
         self,
-        lb: Union[np.ndarray, torch.Tensor],
-        ub: Union[np.ndarray, torch.Tensor],
+        lb: Union[npt.NDArray, torch.Tensor],
+        ub: Union[npt.NDArray, torch.Tensor],
         dim: Optional[int] = None,
         mean_module: Optional[gpytorch.means.Mean] = None,
         covar_module: Optional[gpytorch.kernels.Kernel] = None,
@@ -140,7 +141,7 @@ def fit(self, train_x: torch.Tensor, train_y: torch.Tensor, **kwargs) -> None:
         return self._fit_mll(mll, **kwargs)
 
     def sample(
-        self, x: Union[torch.Tensor, np.ndarray], num_samples: int
+        self, x: Union[torch.Tensor, npt.NDArray], num_samples: int
     ) -> torch.Tensor:
         """Sample from underlying model.
 
@@ -159,7 +160,7 @@ def update(self, train_x: torch.Tensor, train_y: torch.Tensor, **kwargs):
         return self.fit(train_x, train_y, **kwargs)
 
     def predict(
-        self, x: Union[torch.Tensor, np.ndarray], **kwargs
+        self, x: Union[torch.Tensor, npt.NDArray], **kwargs
     ) -> Tuple[torch.Tensor, torch.Tensor]:
         """Query the model for posterior mean and variance.
 

aepsych/models/monotonic_projection_gp.py

@@ -11,6 +11,7 @@
 
 import gpytorch
 import numpy as np
+import numpy.typing as npt
 import torch
 from aepsych.config import Config
 from aepsych.factory.default import default_mean_covar_factory
@@ -92,8 +93,8 @@ class MonotonicProjectionGP(GPClassificationModel):
 
     def __init__(
         self,
-        lb: Union[np.ndarray, torch.Tensor],
-        ub: Union[np.ndarray, torch.Tensor],
+        lb: Union[npt.NDArray, torch.Tensor],
+        ub: Union[npt.NDArray, torch.Tensor],
         monotonic_dims: List[int],
         monotonic_grid_size: int = 20,
         min_f_val: Optional[float] = None,
@@ -135,7 +136,7 @@ def posterior(
         for i, dim in enumerate(self.monotonic_dims):
             # using numpy because torch doesn't support vectorized linspace,
             # pytorch/issues/61292
-            grid: Union[np.ndarray, torch.Tensor] = np.linspace(
+            grid: Union[npt.NDArray, torch.Tensor] = np.linspace(
                 self.lb[dim],
                 X[:, dim].numpy(),
                 s + 1,
@@ -167,7 +168,7 @@ def posterior(
         return GPyTorchPosterior(mvn_proj)
 
     def sample(
-        self, x: Union[torch.Tensor, np.ndarray], num_samples: int
+        self, x: Union[torch.Tensor, npt.NDArray], num_samples: int
     ) -> torch.Tensor:
         samps = super().sample(x=x, num_samples=num_samples)
         if self.min_f_val is not None:

aepsych/models/monotonic_rejection_gp.py

@@ -12,6 +12,7 @@
 
 import gpytorch
 import numpy as np
+import numpy.typing as npt
 import torch
 from aepsych.acquisition.rejection_sampler import RejectionSampler
 from aepsych.config import Config
@@ -52,8 +53,8 @@ class MonotonicRejectionGP(AEPsychMixin, ApproximateGP):
     def __init__(
         self,
         monotonic_idxs: Sequence[int],
-        lb: Union[np.ndarray, torch.Tensor],
-        ub: Union[np.ndarray, torch.Tensor],
+        lb: Union[npt.NDArray, torch.Tensor],
+        ub: Union[npt.NDArray, torch.Tensor],
         dim: Optional[int] = None,
         mean_module: Optional[Mean] = None,
         covar_module: Optional[Kernel] = None,
@@ -280,7 +281,7 @@ def predict(
         return mean, variance
 
     def predict_probability(
-        self, x: Union[torch.Tensor, np.ndarray]
+        self, x: Union[torch.Tensor, npt.NDArray]
     ) -> Tuple[torch.Tensor, torch.Tensor]:
         return self.predict(x, probability_space=True)
 

aepsych/models/pairwise_probit.py

@@ -9,6 +9,7 @@
 
 import gpytorch
 import numpy as np
+import numpy.typing as npt
 import torch
 from aepsych.config import Config
 from aepsych.factory import default_mean_covar_factory
@@ -57,8 +58,8 @@ def _get_index_of_equal_row(arr, x, dim=0):
 
     def __init__(
         self,
-        lb: Union[np.ndarray, torch.Tensor],
-        ub: Union[np.ndarray, torch.Tensor],
+        lb: Union[npt.NDArray, torch.Tensor],
+        ub: Union[npt.NDArray, torch.Tensor],
         dim: Optional[int] = None,
         covar_module: Optional[gpytorch.kernels.Kernel] = None,
         max_fit_time: Optional[float] = None,