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Edit File: test_array_object.py
import operator from numpy.testing import assert_raises import numpy as np import pytest from .. import ones, asarray, reshape, result_type, all, equal from .._array_object import Array from .._dtypes import ( _all_dtypes, _boolean_dtypes, _floating_dtypes, _integer_dtypes, _integer_or_boolean_dtypes, _numeric_dtypes, int8, int16, int32, int64, uint64, bool as bool_, ) def test_validate_index(): # The indexing tests in the official array API test suite test that the # array object correctly handles the subset of indices that are required # by the spec. But the NumPy array API implementation specifically # disallows any index not required by the spec, via Array._validate_index. # This test focuses on testing that non-valid indices are correctly # rejected. See # https://data-apis.org/array-api/latest/API_specification/indexing.html # and the docstring of Array._validate_index for the exact indexing # behavior that should be allowed. This does not test indices that are # already invalid in NumPy itself because Array will generally just pass # such indices directly to the underlying np.ndarray. a = ones((3, 4)) # Out of bounds slices are not allowed assert_raises(IndexError, lambda: a[:4]) assert_raises(IndexError, lambda: a[:-4]) assert_raises(IndexError, lambda: a[:3:-1]) assert_raises(IndexError, lambda: a[:-5:-1]) assert_raises(IndexError, lambda: a[4:]) assert_raises(IndexError, lambda: a[-4:]) assert_raises(IndexError, lambda: a[4::-1]) assert_raises(IndexError, lambda: a[-4::-1]) assert_raises(IndexError, lambda: a[...,:5]) assert_raises(IndexError, lambda: a[...,:-5]) assert_raises(IndexError, lambda: a[...,:5:-1]) assert_raises(IndexError, lambda: a[...,:-6:-1]) assert_raises(IndexError, lambda: a[...,5:]) assert_raises(IndexError, lambda: a[...,-5:]) assert_raises(IndexError, lambda: a[...,5::-1]) assert_raises(IndexError, lambda: a[...,-5::-1]) # Boolean indices cannot be part of a larger tuple index assert_raises(IndexError, lambda: a[a[:,0]==1,0]) assert_raises(IndexError, lambda: a[a[:,0]==1,...]) assert_raises(IndexError, lambda: a[..., a[0]==1]) assert_raises(IndexError, lambda: a[[True, True, True]]) assert_raises(IndexError, lambda: a[(True, True, True),]) # Integer array indices are not allowed (except for 0-D) idx = asarray([[0, 1]]) assert_raises(IndexError, lambda: a[idx]) assert_raises(IndexError, lambda: a[idx,]) assert_raises(IndexError, lambda: a[[0, 1]]) assert_raises(IndexError, lambda: a[(0, 1), (0, 1)]) assert_raises(IndexError, lambda: a[[0, 1]]) assert_raises(IndexError, lambda: a[np.array([[0, 1]])]) # Multiaxis indices must contain exactly as many indices as dimensions assert_raises(IndexError, lambda: a[()]) assert_raises(IndexError, lambda: a[0,]) assert_raises(IndexError, lambda: a[0]) assert_raises(IndexError, lambda: a[:]) def test_operators(): # For every operator, we test that it works for the required type # combinations and raises TypeError otherwise binary_op_dtypes = { "__add__": "numeric", "__and__": "integer_or_boolean", "__eq__": "all", "__floordiv__": "numeric", "__ge__": "numeric", "__gt__": "numeric", "__le__": "numeric", "__lshift__": "integer", "__lt__": "numeric", "__mod__": "numeric", "__mul__": "numeric", "__ne__": "all", "__or__": "integer_or_boolean", "__pow__": "numeric", "__rshift__": "integer", "__sub__": "numeric", "__truediv__": "floating", "__xor__": "integer_or_boolean", } # Recompute each time because of in-place ops def _array_vals(): for d in _integer_dtypes: yield asarray(1, dtype=d) for d in _boolean_dtypes: yield asarray(False, dtype=d) for d in _floating_dtypes: yield asarray(1.0, dtype=d) for op, dtypes in binary_op_dtypes.items(): ops = [op] if op not in ["__eq__", "__ne__", "__le__", "__ge__", "__lt__", "__gt__"]: rop = "__r" + op[2:] iop = "__i" + op[2:] ops += [rop, iop] for s in [1, 1.0, False]: for _op in ops: for a in _array_vals(): # Test array op scalar. From the spec, the following combinations # are supported: # - Python bool for a bool array dtype, # - a Python int within the bounds of the given dtype for integer array dtypes, # - a Python int or float for floating-point array dtypes # We do not do bounds checking for int scalars, but rather use the default # NumPy behavior for casting in that case. if ((dtypes == "all" or dtypes == "numeric" and a.dtype in _numeric_dtypes or dtypes == "integer" and a.dtype in _integer_dtypes or dtypes == "integer_or_boolean" and a.dtype in _integer_or_boolean_dtypes or dtypes == "boolean" and a.dtype in _boolean_dtypes or dtypes == "floating" and a.dtype in _floating_dtypes ) # bool is a subtype of int, which is why we avoid # isinstance here. and (a.dtype in _boolean_dtypes and type(s) == bool or a.dtype in _integer_dtypes and type(s) == int or a.dtype in _floating_dtypes and type(s) in [float, int] )): # Only test for no error getattr(a, _op)(s) else: assert_raises(TypeError, lambda: getattr(a, _op)(s)) # Test array op array. for _op in ops: for x in _array_vals(): for y in _array_vals(): # See the promotion table in NEP 47 or the array # API spec page on type promotion. Mixed kind # promotion is not defined. if (x.dtype == uint64 and y.dtype in [int8, int16, int32, int64] or y.dtype == uint64 and x.dtype in [int8, int16, int32, int64] or x.dtype in _integer_dtypes and y.dtype not in _integer_dtypes or y.dtype in _integer_dtypes and x.dtype not in _integer_dtypes or x.dtype in _boolean_dtypes and y.dtype not in _boolean_dtypes or y.dtype in _boolean_dtypes and x.dtype not in _boolean_dtypes or x.dtype in _floating_dtypes and y.dtype not in _floating_dtypes or y.dtype in _floating_dtypes and x.dtype not in _floating_dtypes ): assert_raises(TypeError, lambda: getattr(x, _op)(y)) # Ensure in-place operators only promote to the same dtype as the left operand. elif ( _op.startswith("__i") and result_type(x.dtype, y.dtype) != x.dtype ): assert_raises(TypeError, lambda: getattr(x, _op)(y)) # Ensure only those dtypes that are required for every operator are allowed. elif (dtypes == "all" and (x.dtype in _boolean_dtypes and y.dtype in _boolean_dtypes or x.dtype in _numeric_dtypes and y.dtype in _numeric_dtypes) or (dtypes == "numeric" and x.dtype in _numeric_dtypes and y.dtype in _numeric_dtypes) or dtypes == "integer" and x.dtype in _integer_dtypes and y.dtype in _numeric_dtypes or dtypes == "integer_or_boolean" and (x.dtype in _integer_dtypes and y.dtype in _integer_dtypes or x.dtype in _boolean_dtypes and y.dtype in _boolean_dtypes) or dtypes == "boolean" and x.dtype in _boolean_dtypes and y.dtype in _boolean_dtypes or dtypes == "floating" and x.dtype in _floating_dtypes and y.dtype in _floating_dtypes ): getattr(x, _op)(y) else: assert_raises(TypeError, lambda: getattr(x, _op)(y)) unary_op_dtypes = { "__abs__": "numeric", "__invert__": "integer_or_boolean", "__neg__": "numeric", "__pos__": "numeric", } for op, dtypes in unary_op_dtypes.items(): for a in _array_vals(): if ( dtypes == "numeric" and a.dtype in _numeric_dtypes or dtypes == "integer_or_boolean" and a.dtype in _integer_or_boolean_dtypes ): # Only test for no error getattr(a, op)() else: assert_raises(TypeError, lambda: getattr(a, op)()) # Finally, matmul() must be tested separately, because it works a bit # different from the other operations. def _matmul_array_vals(): for a in _array_vals(): yield a for d in _all_dtypes: yield ones((3, 4), dtype=d) yield ones((4, 2), dtype=d) yield ones((4, 4), dtype=d) # Scalars always error for _op in ["__matmul__", "__rmatmul__", "__imatmul__"]: for s in [1, 1.0, False]: for a in _matmul_array_vals(): if (type(s) in [float, int] and a.dtype in _floating_dtypes or type(s) == int and a.dtype in _integer_dtypes): # Type promotion is valid, but @ is not allowed on 0-D # inputs, so the error is a ValueError assert_raises(ValueError, lambda: getattr(a, _op)(s)) else: assert_raises(TypeError, lambda: getattr(a, _op)(s)) for x in _matmul_array_vals(): for y in _matmul_array_vals(): if (x.dtype == uint64 and y.dtype in [int8, int16, int32, int64] or y.dtype == uint64 and x.dtype in [int8, int16, int32, int64] or x.dtype in _integer_dtypes and y.dtype not in _integer_dtypes or y.dtype in _integer_dtypes and x.dtype not in _integer_dtypes or x.dtype in _floating_dtypes and y.dtype not in _floating_dtypes or y.dtype in _floating_dtypes and x.dtype not in _floating_dtypes or x.dtype in _boolean_dtypes or y.dtype in _boolean_dtypes ): assert_raises(TypeError, lambda: x.__matmul__(y)) assert_raises(TypeError, lambda: y.__rmatmul__(x)) assert_raises(TypeError, lambda: x.__imatmul__(y)) elif x.shape == () or y.shape == () or x.shape[1] != y.shape[0]: assert_raises(ValueError, lambda: x.__matmul__(y)) assert_raises(ValueError, lambda: y.__rmatmul__(x)) if result_type(x.dtype, y.dtype) != x.dtype: assert_raises(TypeError, lambda: x.__imatmul__(y)) else: assert_raises(ValueError, lambda: x.__imatmul__(y)) else: x.__matmul__(y) y.__rmatmul__(x) if result_type(x.dtype, y.dtype) != x.dtype: assert_raises(TypeError, lambda: x.__imatmul__(y)) elif y.shape[0] != y.shape[1]: # This one fails because x @ y has a different shape from x assert_raises(ValueError, lambda: x.__imatmul__(y)) else: x.__imatmul__(y) def test_python_scalar_construtors(): b = asarray(False) i = asarray(0) f = asarray(0.0) assert bool(b) == False assert int(i) == 0 assert float(f) == 0.0 assert operator.index(i) == 0 # bool/int/float should only be allowed on 0-D arrays. assert_raises(TypeError, lambda: bool(asarray([False]))) assert_raises(TypeError, lambda: int(asarray([0]))) assert_raises(TypeError, lambda: float(asarray([0.0]))) assert_raises(TypeError, lambda: operator.index(asarray([0]))) # bool/int/float should only be allowed on arrays of the corresponding # dtype assert_raises(ValueError, lambda: bool(i)) assert_raises(ValueError, lambda: bool(f)) assert_raises(ValueError, lambda: int(b)) assert_raises(ValueError, lambda: int(f)) assert_raises(ValueError, lambda: float(b)) assert_raises(ValueError, lambda: float(i)) assert_raises(TypeError, lambda: operator.index(b)) assert_raises(TypeError, lambda: operator.index(f)) def test_device_property(): a = ones((3, 4)) assert a.device == 'cpu' assert all(equal(a.to_device('cpu'), a)) assert_raises(ValueError, lambda: a.to_device('gpu')) assert all(equal(asarray(a, device='cpu'), a)) assert_raises(ValueError, lambda: asarray(a, device='gpu')) def test_array_properties(): a = ones((1, 2, 3)) b = ones((2, 3)) assert_raises(ValueError, lambda: a.T) assert isinstance(b.T, Array) assert b.T.shape == (3, 2) assert isinstance(a.mT, Array) assert a.mT.shape == (1, 3, 2) assert isinstance(b.mT, Array) assert b.mT.shape == (3, 2) def test___array__(): a = ones((2, 3), dtype=int16) assert np.asarray(a) is a._array b = np.asarray(a, dtype=np.float64) assert np.all(np.equal(b, np.ones((2, 3), dtype=np.float64))) assert b.dtype == np.float64 def test_allow_newaxis(): a = ones(5) indexed_a = a[None, :] assert indexed_a.shape == (1, 5) def test_disallow_flat_indexing_with_newaxis(): a = ones((3, 3, 3)) with pytest.raises(IndexError): a[None, 0, 0] def test_disallow_mask_with_newaxis(): a = ones((3, 3, 3)) with pytest.raises(IndexError): a[None, asarray(True)] @pytest.mark.parametrize("shape", [(), (5,), (3, 3, 3)]) @pytest.mark.parametrize("index", ["string", False, True]) def test_error_on_invalid_index(shape, index): a = ones(shape) with pytest.raises(IndexError): a[index] def test_mask_0d_array_without_errors(): a = ones(()) a[asarray(True)] @pytest.mark.parametrize( "i", [slice(5), slice(5, 0), asarray(True), asarray([0, 1])] ) def test_error_on_invalid_index_with_ellipsis(i): a = ones((3, 3, 3)) with pytest.raises(IndexError): a[..., i] with pytest.raises(IndexError): a[i, ...] def test_array_keys_use_private_array(): """ Indexing operations convert array keys before indexing the internal array Fails when array_api array keys are not converted into NumPy-proper arrays in __getitem__(). This is achieved by passing array_api arrays with 0-sized dimensions, which NumPy-proper treats erroneously - not sure why! TODO: Find and use appropriate __setitem__() case. """ a = ones((0, 0), dtype=bool_) assert a[a].shape == (0,) a = ones((0,), dtype=bool_) key = ones((0, 0), dtype=bool_) with pytest.raises(IndexError): a[key]