ENH: stats.monte_carlo_test: add array API support

.github/workflows/array_api.yml

@@ -98,3 +98,4 @@ jobs:
         python dev.py --no-build test -b all -t scipy._lib.tests.test__util -- --durations 3 --timeout=60
         python dev.py --no-build test -b all -t scipy.stats.tests.test_stats -- --durations 3 --timeout=60
         python dev.py --no-build test -b all -t scipy.stats.tests.test_morestats -- --durations 3 --timeout=60
+        python dev.py --no-build test -b all -t scipy.stats.tests.test_resampling -- --durations 3 --timeout=60

scipy/_lib/_array_api.py

@@ -398,4 +398,24 @@ def xp_minimum(x1, x2):
     res = xp.asarray(x1, copy=True, dtype=dtype)
     i = (x2 < x1) | xp.isnan(x2)
     res[i] = x2[i]
-    return res
+    return res[()] if res.ndim == 0 else res
+
+
+# temporary substitute for xp.clip, which is not yet in all backends
+# or covered by array_api_compat.
+def xp_clip(x, a, b, xp=None):
+    xp = array_namespace(xp) if xp is None else xp
+    y = xp.asarray(x, copy=True)
+    y[y < a] = a
+    y[y > b] = b
+    return y[()] if y.ndim == 0 else y
+
+
+# temporary substitute for xp.moveaxis, which is not yet in all backends
+# or covered by array_api_compat.
+def _move_axis_to_end(x, source, xp=None):
+    xp = array_namespace(xp) if xp is None else xp
+    axes = list(range(x.ndim))
+    temp = axes.pop(source)
+    axes = axes + [temp]
+    return xp.permute_dims(x, axes)

scipy/stats/_axis_nan_policy.py

@@ -12,25 +12,20 @@
 import inspect
 
 
-def _broadcast_arrays(arrays, axis=None):
+def _broadcast_arrays(arrays, axis=None, xp=None):
     """
     Broadcast shapes of arrays, ignoring incompatibility of specified axes
     """
-    new_shapes = _broadcast_array_shapes(arrays, axis=axis)
+    xp = array_namespace(*arrays) if xp is None else xp
+    arrays = [xp.asarray(arr) for arr in arrays]
+    shapes = [arr.shape for arr in arrays]
+    new_shapes = _broadcast_shapes(shapes, axis)
     if axis is None:
         new_shapes = [new_shapes]*len(arrays)
-    return [np.broadcast_to(array, new_shape)
+    return [xp.broadcast_to(array, new_shape)
             for array, new_shape in zip(arrays, new_shapes)]
 
 
-def _broadcast_array_shapes(arrays, axis=None):
-    """
-    Broadcast shapes of arrays, ignoring incompatibility of specified axes
-    """
-    shapes = [np.asarray(arr).shape for arr in arrays]
-    return _broadcast_shapes(shapes, axis)
-
-
 def _broadcast_shapes(shapes, axis=None):
     """
     Broadcast shapes, ignoring incompatibility of specified axes
@@ -103,10 +98,10 @@ def _broadcast_array_shapes_remove_axis(arrays, axis=None):
     Examples
     --------
     >>> import numpy as np
-    >>> from scipy.stats._axis_nan_policy import _broadcast_array_shapes
+    >>> from scipy.stats._axis_nan_policy import _broadcast_array_shapes_remove_axis
     >>> a = np.zeros((5, 2, 1))
     >>> b = np.zeros((9, 3))
-    >>> _broadcast_array_shapes((a, b), 1)
+    >>> _broadcast_array_shapes_remove_axis((a, b), 1)
     (5, 3)
     """
     # Note that here, `axis=None` means do not consume/drop any axes - _not_
@@ -119,7 +114,7 @@ def _broadcast_shapes_remove_axis(shapes, axis=None):
     """
     Broadcast shapes, dropping specified axes
 
-    Same as _broadcast_array_shapes, but given a sequence
+    Same as _broadcast_array_shapes_remove_axis, but given a sequence
     of array shapes `shapes` instead of the arrays themselves.
     """
     shapes = _broadcast_shapes(shapes, axis)

scipy/stats/_resampling.py

@@ -4,12 +4,14 @@
 import numpy as np
 from itertools import combinations, permutations, product
 from collections.abc import Sequence
+from dataclasses import dataclass
 import inspect
 
-from scipy._lib._util import check_random_state, _rename_parameter
+from scipy._lib._util import check_random_state, _rename_parameter, rng_integers
+from scipy._lib._array_api import (array_namespace, is_numpy, xp_minimum,
+                                   xp_clip, _move_axis_to_end)
 from scipy.special import ndtr, ndtri, comb, factorial
-from scipy._lib._util import rng_integers
-from dataclasses import dataclass
+
 from ._common import ConfidenceInterval
 from ._axis_nan_policy import _broadcast_concatenate, _broadcast_arrays
 from ._warnings_errors import DegenerateDataWarning
@@ -662,7 +664,6 @@ def percentile_fun(a, q):
 def _monte_carlo_test_iv(data, rvs, statistic, vectorized, n_resamples,
                          batch, alternative, axis):
     """Input validation for `monte_carlo_test`."""
-
     axis_int = int(axis)
     if axis != axis_int:
         raise ValueError("`axis` must be an integer.")
@@ -677,26 +678,45 @@ def _monte_carlo_test_iv(data, rvs, statistic, vectorized, n_resamples,
         if not callable(rvs_i):
             raise TypeError("`rvs` must be callable or sequence of callables.")
 
+    # At this point, `data` should be a sequence
+    # If it isn't, the user passed a sequence for `rvs` but not `data`
+    message = "If `rvs` is a sequence, `len(rvs)` must equal `len(data)`."
+    try:
+        len(data)
+    except TypeError as e:
+        raise ValueError(message) from e
     if not len(rvs) == len(data):
-        message = "If `rvs` is a sequence, `len(rvs)` must equal `len(data)`."
         raise ValueError(message)
 
     if not callable(statistic):
         raise TypeError("`statistic` must be callable.")
 
     if vectorized is None:
-        vectorized = 'axis' in inspect.signature(statistic).parameters
+        try:
+            signature = inspect.signature(statistic).parameters
+        except ValueError as e:
+            message = (f"Signature inspection of {statistic=} failed; "
+                       "pass `vectorize` explicitly.")
+            raise ValueError(message) from e
+        vectorized = 'axis' in signature
+
+    xp = array_namespace(*data)
 
     if not vectorized:
-        statistic_vectorized = _vectorize_statistic(statistic)
+        if is_numpy(xp):
+            statistic_vectorized = _vectorize_statistic(statistic)
+        else:
+            message = ("`statistic` must be vectorized (i.e. support an `axis` "
+                       f"argument) when `data` contains {xp.__name__} arrays.")
+            raise ValueError(message)
     else:
         statistic_vectorized = statistic
 
-    data = _broadcast_arrays(data, axis)
+    data = _broadcast_arrays(data, axis, xp=xp)
     data_iv = []
     for sample in data:
-        sample = np.atleast_1d(sample)
-        sample = np.moveaxis(sample, axis_int, -1)
+        sample = xp.broadcast_to(sample, (1,)) if sample.ndim == 0 else sample
+        sample = _move_axis_to_end(sample, axis_int, xp=xp)
         data_iv.append(sample)
 
     n_resamples_int = int(n_resamples)
@@ -715,8 +735,12 @@ def _monte_carlo_test_iv(data, rvs, statistic, vectorized, n_resamples,
     if alternative not in alternatives:
         raise ValueError(f"`alternative` must be in {alternatives}")
 
+    # Infer the desired p-value dtype based on the input types
+    min_float = getattr(xp, 'float16', xp.float32)
+    dtype = xp.result_type(*data_iv, min_float)
+
     return (data_iv, rvs, statistic_vectorized, vectorized, n_resamples_int,
-            batch_iv, alternative, axis_int)
+            batch_iv, alternative, axis_int, dtype, xp)
 
 
 @dataclass
@@ -908,11 +932,12 @@ def monte_carlo_test(data, rvs, statistic, *, vectorized=None,
     """
     args = _monte_carlo_test_iv(data, rvs, statistic, vectorized,
                                 n_resamples, batch, alternative, axis)
-    (data, rvs, statistic, vectorized,
-     n_resamples, batch, alternative, axis) = args
+    (data, rvs, statistic, vectorized, n_resamples,
+     batch, alternative, axis, dtype, xp) = args
 
     # Some statistics return plain floats; ensure they're at least a NumPy float
-    observed = np.asarray(statistic(*data, axis=-1))[()]
+    observed = xp.asarray(statistic(*data, axis=-1))
+    observed = observed[()] if observed.ndim == 0 else observed
 
     n_observations = [sample.shape[-1] for sample in data]
     batch_nominal = batch or n_resamples
@@ -922,37 +947,39 @@ def monte_carlo_test(data, rvs, statistic, *, vectorized=None,
         resamples = [rvs_i(size=(batch_actual, n_observations_i))
                      for rvs_i, n_observations_i in zip(rvs, n_observations)]
         null_distribution.append(statistic(*resamples, axis=-1))
-    null_distribution = np.concatenate(null_distribution)
-    null_distribution = null_distribution.reshape([-1] + [1]*observed.ndim)
+    null_distribution = xp.concat(null_distribution)
+    null_distribution = xp.reshape(null_distribution, [-1] + [1]*observed.ndim)
 
     # relative tolerance for detecting numerically distinct but
     # theoretically equal values in the null distribution
-    eps =  (0 if not np.issubdtype(observed.dtype, np.inexact)
-            else np.finfo(observed.dtype).eps*100)
-    gamma = np.abs(eps * observed)
+    eps =  (0 if not xp.isdtype(observed.dtype, ('real floating'))
+            else xp.finfo(observed.dtype).eps*100)
+    gamma = xp.abs(eps * observed)
 
     def less(null_distribution, observed):
         cmps = null_distribution <= observed + gamma
-        pvalues = (cmps.sum(axis=0) + 1) / (n_resamples + 1)  # see [1]
+        cmps = xp.asarray(cmps, dtype=dtype)
+        pvalues = (xp.sum(cmps, axis=0, dtype=dtype) + 1.) / (n_resamples + 1.)
         return pvalues
 
     def greater(null_distribution, observed):
         cmps = null_distribution >= observed - gamma
-        pvalues = (cmps.sum(axis=0) + 1) / (n_resamples + 1)  # see [1]
+        cmps = xp.asarray(cmps, dtype=dtype)
+        pvalues = (xp.sum(cmps, axis=0, dtype=dtype) + 1.) / (n_resamples + 1.)
         return pvalues
 
     def two_sided(null_distribution, observed):
         pvalues_less = less(null_distribution, observed)
         pvalues_greater = greater(null_distribution, observed)
-        pvalues = np.minimum(pvalues_less, pvalues_greater) * 2
+        pvalues = xp_minimum(pvalues_less, pvalues_greater) * 2
         return pvalues
 
     compare = {"less": less,
                "greater": greater,
                "two-sided": two_sided}
 
     pvalues = compare[alternative](null_distribution, observed)
-    pvalues = np.clip(pvalues, 0, 1)
+    pvalues = xp_clip(pvalues, 0., 1., xp=xp)
 
     return MonteCarloTestResult(observed, pvalues, null_distribution)
 

scipy/stats/_stats_py.py

@@ -67,7 +67,8 @@
 from scipy import stats
 from scipy.optimize import root_scalar
 from scipy._lib._util import normalize_axis_index
-from scipy._lib._array_api import array_namespace, is_numpy, atleast_nd
+from scipy._lib._array_api import (array_namespace, is_numpy, atleast_nd,
+                                   xp_clip, _move_axis_to_end)
 from scipy._lib.array_api_compat import size as xp_size
 
 # In __all__ but deprecated for removal in SciPy 1.13.0
@@ -4535,20 +4536,6 @@ def confidence_interval(self, confidence_level=0.95, method=None):
         return ci
 
 
-def _move_axis_to_end(x, source, xp):
-    axes = list(range(x.ndim))
-    temp = axes.pop(source)
-    axes = axes + [temp]
-    return xp.permute_dims(x, axes)
-
-
-def _clip(x, a, b, xp):
-    y = xp.asarray(x, copy=True)
-    y[y < a] = a
-    y[y > b] = b
-    return y
-
-
 def pearsonr(x, y, *, alternative='two-sided', method=None, axis=0):
     r"""
     Pearson correlation coefficient and p-value for testing non-correlation.
@@ -4934,7 +4921,7 @@ def statistic(x, y, axis):
     one = xp.asarray(1, dtype=dtype)
     # `clip` only recently added to array API, so it's not yet available in
     # array_api_strict. Replace with e.g. `xp.clip(r, -one, one)` when available.
-    r = xp.asarray(_clip(r, -one, one, xp))
+    r = xp.asarray(xp_clip(r, -one, one, xp))
     r[const_xy] = xp.nan
 
     # As explained in the docstring, the distribution of `r` under the null