def test_check_symmetric(): arr_sym = np.array([[0, 1], [1, 2]]) arr_bad = np.ones(2) arr_asym = np.array([[0, 2], [0, 2]]) test_arrays = {'dense': arr_asym, 'dok': sp.dok_matrix(arr_asym), 'csr': sp.csr_matrix(arr_asym), 'csc': sp.csc_matrix(arr_asym), 'coo': sp.coo_matrix(arr_asym), 'lil': sp.lil_matrix(arr_asym), 'bsr': sp.bsr_matrix(arr_asym)} # check error for bad inputs assert_raises(ValueError, check_symmetric, arr_bad) # check that asymmetric arrays are properly symmetrized for arr_format, arr in test_arrays.items(): # Check for warnings and errors assert_warns(UserWarning, check_symmetric, arr) assert_raises(ValueError, check_symmetric, arr, raise_exception=True) output = check_symmetric(arr, raise_warning=False) if sp.issparse(output): assert_equal(output.format, arr_format) assert_array_equal(output.toarray(), arr_sym) else: assert_array_equal(output, arr_sym)
def test_check_array_dtype_warning(): X_int_list = [[1, 2, 3], [4, 5, 6], [7, 8, 9]] X_float64 = np.asarray(X_int_list, dtype=np.float64) X_float32 = np.asarray(X_int_list, dtype=np.float32) X_int64 = np.asarray(X_int_list, dtype=np.int64) X_csr_float64 = sp.csr_matrix(X_float64) X_csr_float32 = sp.csr_matrix(X_float32) X_csc_float32 = sp.csc_matrix(X_float32) X_csc_int32 = sp.csc_matrix(X_int64, dtype=np.int32) y = [0, 0, 1] integer_data = [X_int64, X_csc_int32] float64_data = [X_float64, X_csr_float64] float32_data = [X_float32, X_csr_float32, X_csc_float32] for X in integer_data: X_checked = assert_no_warnings(check_array, X, dtype=np.float64, accept_sparse=True) assert_equal(X_checked.dtype, np.float64) X_checked = assert_warns(DataConversionWarning, check_array, X, dtype=np.float64, accept_sparse=True, warn_on_dtype=True) assert_equal(X_checked.dtype, np.float64) for X in float64_data: X_checked = assert_no_warnings(check_array, X, dtype=np.float64, accept_sparse=True, warn_on_dtype=True) assert_equal(X_checked.dtype, np.float64) X_checked = assert_no_warnings(check_array, X, dtype=np.float64, accept_sparse=True, warn_on_dtype=False) assert_equal(X_checked.dtype, np.float64) for X in float32_data: X_checked = assert_no_warnings(check_array, X, dtype=[np.float64, np.float32], accept_sparse=True) assert_equal(X_checked.dtype, np.float32) assert_true(X_checked is X) X_checked = assert_no_warnings(check_array, X, dtype=[np.float64, np.float32], accept_sparse=['csr', 'dok'], copy=True) assert_equal(X_checked.dtype, np.float32) assert_false(X_checked is X) X_checked = assert_no_warnings(check_array, X_csc_float32, dtype=[np.float64, np.float32], accept_sparse=['csr', 'dok'], copy=False) assert_equal(X_checked.dtype, np.float32) assert_false(X_checked is X_csc_float32) assert_equal(X_checked.format, 'csr')
def test_check_array_min_samples_and_features_messages(): # empty list is considered 2D by default: msg = "0 feature(s) (shape=(1, 0)) while a minimum of 1 is required." assert_raise_message(ValueError, msg, check_array, [[]]) # If considered a 1D collection when ensure_2d=False, then the minimum # number of samples will break: msg = "0 sample(s) (shape=(0,)) while a minimum of 1 is required." assert_raise_message(ValueError, msg, check_array, [], ensure_2d=False) # Invalid edge case when checking the default minimum sample of a scalar msg = "Singleton array array(42) cannot be considered a valid collection." assert_raise_message(TypeError, msg, check_array, 42, ensure_2d=False) # But this works if the input data is forced to look like a 2 array with # one sample and one feature: X_checked = assert_warns(DeprecationWarning, check_array, [42], ensure_2d=True) assert_array_equal(np.array([[42]]), X_checked)
def test_check_array(): # accept_sparse == None # raise error on sparse inputs X = [[1, 2], [3, 4]] X_csr = sp.csr_matrix(X) assert_raises(TypeError, check_array, X_csr) # ensure_2d assert_warns(DeprecationWarning, check_array, [0, 1, 2]) X_array = check_array([0, 1, 2]) assert_equal(X_array.ndim, 2) X_array = check_array([0, 1, 2], ensure_2d=False) assert_equal(X_array.ndim, 1) # don't allow ndim > 3 X_ndim = np.arange(8).reshape(2, 2, 2) assert_raises(ValueError, check_array, X_ndim) check_array(X_ndim, allow_nd=True) # doesn't raise # force_all_finite X_inf = np.arange(4).reshape(2, 2).astype(np.float) X_inf[0, 0] = np.inf assert_raises(ValueError, check_array, X_inf) check_array(X_inf, force_all_finite=False) # no raise # nan check X_nan = np.arange(4).reshape(2, 2).astype(np.float) X_nan[0, 0] = np.nan assert_raises(ValueError, check_array, X_nan) check_array(X_inf, force_all_finite=False) # no raise # dtype and order enforcement. X_C = np.arange(4).reshape(2, 2).copy("C") X_F = X_C.copy("F") X_int = X_C.astype(np.int) X_float = X_C.astype(np.float) Xs = [X_C, X_F, X_int, X_float] dtypes = [np.int32, np.int, np.float, np.float32, None, np.bool, object] orders = ['C', 'F', None] copys = [True, False] for X, dtype, order, copy in product(Xs, dtypes, orders, copys): X_checked = check_array(X, dtype=dtype, order=order, copy=copy) if dtype is not None: assert_equal(X_checked.dtype, dtype) else: assert_equal(X_checked.dtype, X.dtype) if order == 'C': assert_true(X_checked.flags['C_CONTIGUOUS']) assert_false(X_checked.flags['F_CONTIGUOUS']) elif order == 'F': assert_true(X_checked.flags['F_CONTIGUOUS']) assert_false(X_checked.flags['C_CONTIGUOUS']) if copy: assert_false(X is X_checked) else: # doesn't copy if it was already good if (X.dtype == X_checked.dtype and X_checked.flags['C_CONTIGUOUS'] == X.flags['C_CONTIGUOUS'] and X_checked.flags['F_CONTIGUOUS'] == X.flags['F_CONTIGUOUS']): assert_true(X is X_checked) # allowed sparse != None X_csc = sp.csc_matrix(X_C) X_coo = X_csc.tocoo() X_dok = X_csc.todok() X_int = X_csc.astype(np.int) X_float = X_csc.astype(np.float) Xs = [X_csc, X_coo, X_dok, X_int, X_float] accept_sparses = [['csr', 'coo'], ['coo', 'dok']] for X, dtype, accept_sparse, copy in product(Xs, dtypes, accept_sparses, copys): with warnings.catch_warnings(record=True) as w: X_checked = check_array(X, dtype=dtype, accept_sparse=accept_sparse, copy=copy) if (dtype is object or sp.isspmatrix_dok(X)) and len(w): message = str(w[0].message) messages = ["object dtype is not supported by sparse matrices", "Can't check dok sparse matrix for nan or inf."] assert_true(message in messages) else: assert_equal(len(w), 0) if dtype is not None: assert_equal(X_checked.dtype, dtype) else: assert_equal(X_checked.dtype, X.dtype) if X.format in accept_sparse: # no change if allowed assert_equal(X.format, X_checked.format) else: # got converted assert_equal(X_checked.format, accept_sparse[0]) if copy: assert_false(X is X_checked) else: # doesn't copy if it was already good if (X.dtype == X_checked.dtype and X.format == X_checked.format): assert_true(X is X_checked) # other input formats # convert lists to arrays X_dense = check_array([[1, 2], [3, 4]]) assert_true(isinstance(X_dense, np.ndarray)) # raise on too deep lists assert_raises(ValueError, check_array, X_ndim.tolist()) check_array(X_ndim.tolist(), allow_nd=True) # doesn't raise