[enhancement] add oneDAL finiteness_checker implementation to onedal

onedal/dal.cpp

@@ -79,6 +79,9 @@ namespace oneapi::dal::python {
     #if defined(ONEDAL_VERSION) && ONEDAL_VERSION >= 20240001
     ONEDAL_PY_INIT_MODULE(logistic_regression);
     #endif // defined(ONEDAL_VERSION) && ONEDAL_VERSION >= 20240001
+    #if defined(ONEDAL_VERSION) && ONEDAL_VERSION >= 20240700
+    ONEDAL_PY_INIT_MODULE(finiteness_checker);
+    #endif // defined(ONEDAL_VERSION) && ONEDAL_VERSION >= 20240700
 #endif // ONEDAL_DATA_PARALLEL_SPMD
 
 #ifdef ONEDAL_DATA_PARALLEL_SPMD
@@ -138,6 +141,9 @@ namespace oneapi::dal::python {
     #if defined(ONEDAL_VERSION) && ONEDAL_VERSION >= 20240001
         init_logistic_regression(m);
     #endif // defined(ONEDAL_VERSION) && ONEDAL_VERSION >= 20240001
+    #if defined(ONEDAL_VERSION) && ONEDAL_VERSION >= 20240700
+        init_finiteness_checker(m);
+    #endif // defined(ONEDAL_VERSION) && ONEDAL_VERSION >= 20240700
     }
 #endif // ONEDAL_DATA_PARALLEL_SPMD
 

onedal/datatypes/table.cpp

@@ -72,6 +72,10 @@ ONEDAL_PY_INIT_MODULE(table) {
         const auto column_count = t.get_column_count();
         return py::make_tuple(row_count, column_count);
     });
+    table_obj.def_property_readonly("dtype", [](const table& t){
+        // returns a numpy dtype, even if source was not from numpy
+        return py::dtype(convert_dal_to_npy_type(t.get_metadata().get_data_type(0)));
+    });
 
 #ifdef ONEDAL_DATA_PARALLEL
     define_sycl_usm_array_property(table_obj);

onedal/utils/finiteness_checker.cpp

@@ -0,0 +1,103 @@
+/*******************************************************************************
+* Copyright 2024 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+// fix error with missing headers
+#if defined(ONEDAL_VERSION) && ONEDAL_VERSION >= 20250200
+    #include "oneapi/dal/algo/finiteness_checker.hpp"
+#else
+    #include "oneapi/dal/algo/finiteness_checker/compute.hpp"
+#endif // defined(ONEDAL_VERSION) && ONEDAL_VERSION >= 20250200
+
+#include "onedal/common.hpp"
+#include "onedal/version.hpp"
+
+namespace py = pybind11;
+
+namespace oneapi::dal::python {
+
+template <typename Task, typename Ops>
+struct method2t {
+    method2t(const Task& task, const Ops& ops) : ops(ops) {}
+
+    template <typename Float>
+    auto operator()(const py::dict& params) {
+        using namespace finiteness_checker;
+
+        const auto method = params["method"].cast<std::string>();
+
+        ONEDAL_PARAM_DISPATCH_VALUE(method, "dense", ops, Float, method::dense);
+        ONEDAL_PARAM_DISPATCH_VALUE(method, "by_default", ops, Float, method::by_default);
+        ONEDAL_PARAM_DISPATCH_THROW_INVALID_VALUE(method);
+    }
+
+    Ops ops;
+};
+
+struct params2desc {
+    template <typename Float, typename Method, typename Task>
+    auto operator()(const pybind11::dict& params) {
+        using namespace dal::finiteness_checker;
+
+        auto desc = descriptor<Float, Method, Task>();
+        desc.set_allow_NaN(params["allow_nan"].cast<bool>());
+        return desc;
+    }
+};
+
+template <typename Policy, typename Task>
+void init_compute_ops(py::module_& m) {
+    m.def("compute",
+          [](const Policy& policy,
+             const py::dict& params,
+             const table& data) {
+              using namespace finiteness_checker;
+              using input_t = compute_input<Task>;
+
+              compute_ops ops(policy, input_t{ data }, params2desc{});
+              return fptype2t{ method2t{ Task{}, ops } }(params);
+          });
+}
+
+template <typename Task>
+void init_compute_result(py::module_& m) {
+    using namespace finiteness_checker;
+    using result_t = compute_result<Task>;
+
+    py::class_<result_t>(m, "compute_result")
+        .def(py::init())
+        .DEF_ONEDAL_PY_PROPERTY(finite, result_t);
+}
+
+ONEDAL_PY_TYPE2STR(finiteness_checker::task::compute, "compute");
+
+ONEDAL_PY_DECLARE_INSTANTIATOR(init_compute_ops);
+ONEDAL_PY_DECLARE_INSTANTIATOR(init_compute_result);
+
+ONEDAL_PY_INIT_MODULE(finiteness_checker) {
+    using namespace dal::detail;
+    using namespace finiteness_checker;
+    using namespace dal::finiteness_checker;
+
+    using task_list = types<task::compute>;
+    auto sub = m.def_submodule("finiteness_checker");
+
+    #ifndef ONEDAL_DATA_PARALLEL_SPMD
+        ONEDAL_PY_INSTANTIATE(init_compute_ops, sub, policy_list, task_list);
+        ONEDAL_PY_INSTANTIATE(init_compute_result, sub, task_list);
+    #endif
+}
+
+} // namespace oneapi::dal::python

onedal/utils/tests/test_validation.py

@@ -0,0 +1,149 @@
+# ==============================================================================
+# Copyright 2024 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+import time
+
+import numpy as np
+import numpy.random as rand
+import pytest
+import scipy.sparse as sp
+
+from onedal.tests.utils._dataframes_support import (
+    _convert_to_dataframe,
+    get_dataframes_and_queues,
+)
+from onedal.utils.validation import _assert_all_finite, assert_all_finite
+
+
+@pytest.mark.parametrize("dtype", [np.float32, np.float64])
+@pytest.mark.parametrize(
+    "shape",
+    [
+        [16, 2048],
+        [
+            2**16 + 3,
+        ],
+        [1000, 1000],
+        [
+            3,
+        ],
+    ],
+)
+@pytest.mark.parametrize("allow_nan", [False, True])
+@pytest.mark.parametrize(
+    "dataframe, queue", get_dataframes_and_queues("numpy,dpnp,dpctl")
+)
+def test_sum_infinite_actually_finite(dtype, shape, allow_nan, dataframe, queue):
+    X = np.array(shape, dtype=dtype)
+    X.fill(np.finfo(dtype).max)
+    X = _convert_to_dataframe(X, sycl_queue=queue, target_df=dataframe)
+    _assert_all_finite(X, allow_nan=allow_nan)
+
+
+@pytest.mark.parametrize("dtype", [np.float32, np.float64])
+@pytest.mark.parametrize(
+    "shape",
+    [
+        [16, 2048],
+        [
+            2**16 + 3,
+        ],
+        [1000, 1000],
+        [
+            3,
+        ],
+    ],
+)
+@pytest.mark.parametrize("allow_nan", [False, True])
+@pytest.mark.parametrize("check", ["inf", "NaN", None])
+@pytest.mark.parametrize("seed", [0, int(time.time())])
+@pytest.mark.parametrize(
+    "dataframe, queue", get_dataframes_and_queues("numpy,dpnp,dpctl")
+)
+def test_assert_finite_random_location(
+    dtype, shape, allow_nan, check, seed, dataframe, queue
+):
+    rand.seed(seed)
+    X = rand.uniform(high=np.finfo(dtype).max, size=shape).astype(dtype)
+
+    if check:
+        loc = rand.randint(0, X.size - 1)
+        X.reshape((-1,))[loc] = float(check)
+
+    X = _convert_to_dataframe(X, sycl_queue=queue, target_df=dataframe)
+
+    if check is None or (allow_nan and check == "NaN"):
+        _assert_all_finite(X, allow_nan=allow_nan)
+    else:
+        msg_err = "Input contains " + ("infinity" if allow_nan else "NaN, infinity") + "."
+        with pytest.raises(ValueError, match=msg_err):
+            _assert_all_finite(X, allow_nan=allow_nan)
+
+
+@pytest.mark.parametrize("dtype", [np.float32, np.float64])
+@pytest.mark.parametrize("allow_nan", [False, True])
+@pytest.mark.parametrize("check", ["inf", "NaN", None])
+@pytest.mark.parametrize("seed", [0, int(time.time())])
+@pytest.mark.parametrize(
+    "dataframe, queue", get_dataframes_and_queues("numpy,dpnp,dpctl")
+)
+def test_assert_finite_random_shape_and_location(
+    dtype, allow_nan, check, seed, dataframe, queue
+):
+    lb, ub = 2, 1048576  # lb is a patching condition, ub 2^20
+    rand.seed(seed)
+    X = rand.uniform(high=np.finfo(dtype).max, size=rand.randint(lb, ub)).astype(dtype)
+
+    if check:
+        loc = rand.randint(0, X.size - 1)
+        X[loc] = float(check)
+
+    X = _convert_to_dataframe(X, sycl_queue=queue, target_df=dataframe)
+
+    if check is None or (allow_nan and check == "NaN"):
+        _assert_all_finite(X, allow_nan=allow_nan)
+    else:
+        msg_err = "Input contains " + ("infinity" if allow_nan else "NaN, infinity") + "."
+        with pytest.raises(ValueError, match=msg_err):
+            _assert_all_finite(X, allow_nan=allow_nan)
+
+
+@pytest.mark.parametrize("dtype", [np.float32, np.float64])
+@pytest.mark.parametrize("allow_nan", [False, True])
+@pytest.mark.parametrize("check", ["inf", "NaN", None])
+@pytest.mark.parametrize("seed", [0, int(time.time())])
+def test_assert_finite_sparse(dtype, allow_nan, check, seed):
+    lb, ub = 2, 2056
+    rand.seed(seed)
+    X = sp.random(
+        rand.randint(lb, ub),
+        rand.randint(lb, ub),
+        format="csr",
+        dtype=dtype,
+        random_state=rand.default_rng(seed),
+    )
+
+    if check:
+        locx = rand.randint(0, X.shape[0] - 1)
+        locy = rand.randint(0, X.shape[1] - 1)
+        X[locx, locy] = float(check)
+
+    if check is None or (allow_nan and check == "NaN"):
+        assert_all_finite(X, allow_nan=allow_nan)
+    else:
+        msg_err = "Input contains " + ("infinity" if allow_nan else "NaN, infinity") + "."
+        with pytest.raises(ValueError, match=msg_err):
+            assert_all_finite(X, allow_nan=allow_nan)

onedal/utils/validation.py

@@ -31,7 +31,12 @@
 from sklearn.preprocessing import LabelEncoder
 from sklearn.utils.validation import check_array
 
-from daal4py.sklearn.utils.validation import _assert_all_finite
+from daal4py.sklearn.utils.validation import (
+    _assert_all_finite as _daal4py_assert_all_finite,
+)
+from onedal import _backend
+from onedal.common._policy import _get_policy
+from onedal.datatypes import _convert_to_supported, to_table
 
 
 class DataConversionWarning(UserWarning):
@@ -135,10 +140,10 @@ def _check_array(
     if force_all_finite:
         if sp.issparse(array):
             if hasattr(array, "data"):
-                _assert_all_finite(array.data)
+                _daal4py_assert_all_finite(array.data)
                 force_all_finite = False
         else:
-            _assert_all_finite(array)
+            _daal4py_assert_all_finite(array)
             force_all_finite = False
     array = check_array(
         array=array,
@@ -200,7 +205,7 @@ def _check_X_y(
     if y_numeric and y.dtype.kind == "O":
         y = y.astype(np.float64)
     if force_all_finite:
-        _assert_all_finite(y)
+        _daal4py_assert_all_finite(y)
 
     lengths = [X.shape[0], y.shape[0]]
     uniques = np.unique(lengths)
@@ -285,7 +290,7 @@ def _type_of_target(y):
     # check float and contains non-integer float values
     if y.dtype.kind == "f" and np.any(y != y.astype(int)):
         # [.1, .2, 3] or [[.1, .2, 3]] or [[1., .2]] and not [1., 2., 3.]
-        _assert_all_finite(y)
+        _daal4py_assert_all_finite(y)
         return "continuous" + suffix
 
     if (len(np.unique(y)) > 2) or (y.ndim >= 2 and len(y[0]) > 1):
@@ -438,3 +443,31 @@ def _is_csr(x):
     return isinstance(x, sp.csr_matrix) or (
         hasattr(sp, "csr_array") and isinstance(x, sp.csr_array)
     )
+
+
+def _assert_all_finite(X, allow_nan=False, input_name=""):
+    policy = _get_policy(None, X)
+    X_t = to_table(_convert_to_supported(policy, X))
+    params = {
+        "fptype": "float" if X_t.dtype == np.float32 else "double",
+        "method": "dense",
+        "allow_nan": allow_nan,
+    }
+    if not _backend.finiteness_checker.compute.compute(policy, params, X_t).finite:
+        type_err = "infinity" if allow_nan else "NaN, infinity"
+        padded_input_name = input_name + " " if input_name else ""
+        msg_err = f"Input {padded_input_name}contains {type_err}."
+        raise ValueError(msg_err)
+
+
+def assert_all_finite(
+    X,
+    *,
+    allow_nan=False,
+    input_name="",
+):
+    _assert_all_finite(
+        X.data if sp.issparse(X) else X,
+        allow_nan=allow_nan,
+        input_name=input_name,
+    )