Merge pull request #72823 from ucasfl/norm-gini

Add function `arrayNormalizedGini`
2024-12-19 21:03:51 +00:00 · 2024-12-17 19:02:02 +00:00 · 2024-12-17 19:02:02 +00:00 · 994e606153
commit 994e606153
parent 11c86ed884 4ee4aa08a6
5 changed files with 490 additions and 0 deletions
--- a/docs/en/sql-reference/functions/array-functions.md
+++ b/docs/en/sql-reference/functions/array-functions.md
@ -3222,6 +3222,41 @@ Result:
 └─────────┘
 ```

+## arrayNormalizedGini
+
+Calculates the normalized Gini coefficient.
+
+**Syntax**
+
+```sql
+arrayNormalizedGini(predicted, label)
+```
+
+**Arguments**
+
+- `predicted` — Predicted values ([Array(T)](../data-types/array.md))
+- `label` — Actual values ([Array(T)](../data-types/array.md))
+
+**Returned Value**
+
+- A tuple containing the Gini coefficients of the predicted values, the Gini coefficient of the normalized values, and the normalized Gini coefficient (= the ratio of the former two Gini coefficients).
+
+**Examples**
+
+Query:
+
+```sql
+SELECT arrayNormalizedGini([0.9, 0.3, 0.8, 0.7], [6, 1, 0, 2]);
+```
+
+Result:
+
+```
+┌─arrayNormalizedGini([0.9, 0.3, 0.8, 0.7], [6, 1, 0, 2])──────────┐
+│ (0.18055555555555558,0.2638888888888889,0.6842105263157896) │
+└─────────────────────────────────────────────────────────────┘
+```
+
 ## Distance functions

 All supported functions are described in [distance functions documentation](../../sql-reference/functions/distance-functions.md).
--- a/src/Functions/array/arrayNormalizedGini.cpp
+++ b/src/Functions/array/arrayNormalizedGini.cpp
@ -0,0 +1,415 @@
+#include <Columns/ColumnArray.h>
+#include <Columns/ColumnTuple.h>
+#include <Columns/ColumnVector.h>
+#include <Columns/ColumnsNumber.h>
+#include <DataTypes/DataTypeArray.h>
+#include <DataTypes/DataTypeTuple.h>
+#include <DataTypes/DataTypesNumber.h>
+#include <Functions/FunctionFactory.h>
+#include <Functions/FunctionHelpers.h>
+#include <Functions/IFunction.h>
+#include <Functions/castTypeToEither.h>
+#include <Interpreters/Context_fwd.h>
+
+#include <numeric>
+#include <pdqsort.h>
+
+namespace DB
+{
+static constexpr size_t MAX_ARRAY_SIZE = 1 << 20;
+
+namespace ErrorCodes
+{
+    extern const int ILLEGAL_COLUMN;
+    extern const int NUMBER_OF_ARGUMENTS_DOESNT_MATCH;
+    extern const int ILLEGAL_TYPE_OF_ARGUMENT;
+    extern const int TOO_LARGE_ARRAY_SIZE;
+}
+
+struct Impl
+{
+    template <typename T1, typename T2>
+    static void vectorConst(
+        const PaddedPODArray<T1> & array_predicted_data,
+        const ColumnArray::Offsets & array_predicted_offsets,
+        const PaddedPODArray<T2> & array_labels_const,
+        PaddedPODArray<Float64> & col_gini_predicted,
+        PaddedPODArray<Float64> & col_gini_labels,
+        PaddedPODArray<Float64> & col_gini_normalized)
+    {
+        size_t size = col_gini_predicted.size();
+        size_t array_size = array_labels_const.size();
+
+        if (array_size > MAX_ARRAY_SIZE)
+            throw Exception(
+                ErrorCodes::TOO_LARGE_ARRAY_SIZE,
+                "Too large array size in function arrayNormalizedGini: {}, maximum: {}",
+                array_size,
+                MAX_ARRAY_SIZE);
+
+        for (size_t i = 0; i < size; ++i)
+        {
+            size_t array1_size = array_predicted_offsets[i] - array_predicted_offsets[i - 1];
+            if (array1_size != array_size)
+                throw Exception(ErrorCodes::ILLEGAL_COLUMN, "All arrays in function arrayNormalizedGini should have same size");
+
+            // Why we need to create a new array here every loop, because array2 will be sorted in calculateNormalizedGini.
+            PODArrayWithStackMemory<T2, 1024> array2(array_labels_const.begin(), array_labels_const.end());
+
+            auto [gini_predicted, gini_labels, gini_normalized] = calculateNormalizedGini(array_predicted_data, array_predicted_offsets[i - 1], array2, array_size);
+
+            col_gini_predicted[i] = gini_predicted;
+            col_gini_labels[i] = gini_labels;
+            col_gini_normalized[i] = gini_normalized;
+        }
+    }
+
+    template <typename T1, typename T2>
+    static void vectorVector(
+        const PaddedPODArray<T1> & array_predicted_data,
+        const ColumnArray::Offsets & array_predicted_offsets,
+        const PaddedPODArray<T2> & array_labels_data,
+        const ColumnArray::Offsets & array_labels_offsets,
+        PaddedPODArray<Float64> & col_gini_predicted,
+        PaddedPODArray<Float64> & col_gini_labels,
+        PaddedPODArray<Float64> & col_gini_normalized)
+    {
+        size_t size = col_gini_predicted.size();
+        size_t array_size = size > 0 ? array_predicted_offsets[0] - array_predicted_offsets[-1] : 0;
+
+        if (array_size > MAX_ARRAY_SIZE)
+            throw Exception(
+                ErrorCodes::TOO_LARGE_ARRAY_SIZE, "Too large array size in arrayNormalizedGini: {}, maximum: {}", array_size, MAX_ARRAY_SIZE);
+
+        for (size_t i = 0; i < size; ++i)
+        {
+            size_t array1_size = array_predicted_offsets[i] - array_predicted_offsets[i - 1];
+            size_t array2_size = array_labels_offsets[i] - array_labels_offsets[i - 1];
+            if (array1_size != array_size || array2_size != array_size)
+                throw Exception(ErrorCodes::ILLEGAL_COLUMN, "All arrays in function arrayNormalizedGini should have same size");
+
+            PODArrayWithStackMemory<T2, 1024> array2(array_labels_data.data() + array_labels_offsets[i - 1], array_labels_data.data() + array_labels_offsets[i]);
+
+            auto [gini_predicted, gini_labels, gini_normalized] = calculateNormalizedGini(array_predicted_data, array_predicted_offsets[i - 1], array2, array_size);
+
+            col_gini_predicted[i] = gini_predicted;
+            col_gini_labels[i] = gini_labels;
+            col_gini_normalized[i] = gini_normalized;
+        }
+    }
+
+    template <typename T1, typename T2>
+    static void constVector(
+        const PaddedPODArray<T1> & array_predicted_const,
+        const PaddedPODArray<T2> & array_labels_data,
+        const ColumnArray::Offsets & array_labels_offsets,
+        PaddedPODArray<Float64> & col_gini_predicted,
+        PaddedPODArray<Float64> & col_gini_labels,
+        PaddedPODArray<Float64> & col_gini_normalized)
+    {
+        size_t size = col_gini_predicted.size();
+        size_t array_size = array_predicted_const.size();
+
+        if (array_size > MAX_ARRAY_SIZE)
+            throw Exception(
+                ErrorCodes::TOO_LARGE_ARRAY_SIZE, "Too large array size in arrayNormalizedGini: {}, maximum: {}", array_size, MAX_ARRAY_SIZE);
+
+        for (size_t i = 0; i < size; ++i)
+        {
+            size_t array1_size = array_labels_offsets[i] - array_labels_offsets[i - 1];
+            if (array1_size != array_size)
+                throw Exception(ErrorCodes::ILLEGAL_COLUMN, "All arrays in function arrayNormalizedGini should have same size");
+
+            PODArrayWithStackMemory<T2, 1024> array2(array_labels_data.data() + array_labels_offsets[i - 1], array_labels_data.data() + array_labels_offsets[i]);
+
+            auto [gini_predicted, gini_labels, gini_normalized] = calculateNormalizedGini(array_predicted_const, 0, array2, array_size);
+
+            col_gini_predicted[i] = gini_predicted;
+            col_gini_labels[i] = gini_labels;
+            col_gini_normalized[i] = gini_normalized;
+        }
+    }
+
+private:
+    template <typename T1, typename T2>
+    static std::tuple<Float64, Float64, Float64> calculateNormalizedGini(
+        const PaddedPODArray<T1> & array1, size_t offset,
+        PODArrayWithStackMemory<T2, 1024> & array2, size_t array_size)
+    {
+        auto sort_idx = sortIndexes(array1, offset, array_size);
+
+        PODArrayWithStackMemory<T2, 1024> sorted_array2(array_size);
+        for (size_t i = 0; i < array_size; ++i)
+            sorted_array2[i] = array2[sort_idx[i]];
+
+        Float64 total_sum = std::accumulate(array2.begin(), array2.end(), 0.0);
+
+        PODArrayWithStackMemory<Float64, 1024> pred_cumsum_ratio(array_size);
+
+        Float64 pred_cumsum = 0;
+        for (size_t i = 0; i < array_size; ++i)
+        {
+            pred_cumsum += sorted_array2[i] / total_sum;
+            pred_cumsum_ratio[i] = pred_cumsum;
+        }
+
+        pdqsort(array2.begin(), array2.end());
+        PODArrayWithStackMemory<Float64, 1024> ltv_cumsum_ratio(array_size);
+
+        Float64 ltv_cumsum = 0;
+        for (size_t i = 0; i < array_size; ++i)
+        {
+            ltv_cumsum += array2[i] / total_sum;
+            ltv_cumsum_ratio[i] = ltv_cumsum;
+        }
+
+        Float64 random_gain_cumsum_ratio = 0.5 * (array_size + 1);
+        Float64 accumulate_pred_ratio = std::accumulate(pred_cumsum_ratio.begin(), pred_cumsum_ratio.end(), 0.0);
+        Float64 accumulate_ltv_ratio = std::accumulate(ltv_cumsum_ratio.begin(), ltv_cumsum_ratio.end(), 0.0);
+
+        Float64 pred_gini = (random_gain_cumsum_ratio - accumulate_pred_ratio) / array_size;
+        Float64 gini_labels = (random_gain_cumsum_ratio - accumulate_ltv_ratio) / array_size;
+
+        return std::make_tuple(pred_gini, gini_labels, pred_gini / gini_labels);
+    }
+
+    template <typename T>
+    static PODArrayWithStackMemory<size_t, 1024> sortIndexes(const PaddedPODArray<T> & array, size_t offset, size_t array_size)
+    {
+        PODArrayWithStackMemory<size_t, 1024> idx(array_size);
+        std::iota(idx.begin(), idx.end(), 0);
+        pdqsort(idx.begin(), idx.end(), [&array, offset](size_t i1, size_t i2) { return array[i1 + offset] < array[i2 + offset]; });
+        return idx;
+    }
+};
+
+/**
+ * Calculate the normalized Gini coefficient. See https://arxiv.org/pdf/1912.07753
+ */
+class FunctionArrayNormalizedGini : public IFunction
+{
+public:
+    static constexpr auto name = "arrayNormalizedGini";
+    static FunctionPtr create(ContextPtr) { return std::make_shared<FunctionArrayNormalizedGini>(); }
+
+    String getName() const override { return name; }
+    size_t getNumberOfArguments() const override { return 2; }
+    bool isSuitableForShortCircuitArgumentsExecution(const DataTypesWithConstInfo & /*arguments*/) const override { return true; }
+    bool useDefaultImplementationForConstants() const override { return true; }
+
+    DataTypePtr getReturnTypeImpl(const DataTypes & arguments) const override
+    {
+        if (arguments.size() != 2)
+            throw Exception(ErrorCodes::NUMBER_OF_ARGUMENTS_DOESNT_MATCH,
+                "Number of arguments for function {} doesn't match: passed {}, should be 2",
+                getName(), arguments.size());
+
+        const DataTypeArray * arg1_type = checkAndGetDataType<DataTypeArray>(arguments[0].get());
+        if (arg1_type == nullptr || !isNumber(arg1_type->getNestedType()))
+            throw Exception(
+                ErrorCodes::ILLEGAL_TYPE_OF_ARGUMENT,
+                "First argument for function {} must be an Array of numeric type, got {}",
+                getName(),
+                arguments[0]->getName());
+
+        const DataTypeArray * arg2_type = checkAndGetDataType<DataTypeArray>(arguments[1].get());
+        if (arg2_type == nullptr || !isNumber(arg2_type->getNestedType()))
+            throw Exception(
+                ErrorCodes::ILLEGAL_TYPE_OF_ARGUMENT,
+                "Second argument for function {} must be an Array of numeric typegot {}",
+                getName(),
+                arguments[1]->getName());
+
+        return std::make_shared<DataTypeTuple>(
+            DataTypes{std::make_shared<DataTypeFloat64>(), std::make_shared<DataTypeFloat64>(), std::make_shared<DataTypeFloat64>()});
+    }
+
+    ColumnPtr executeImpl(const ColumnsWithTypeAndName & arguments, const DataTypePtr &, size_t input_rows_count) const override
+    {
+        const auto & col_predicted = arguments[0].column;
+        const auto & col_labels = arguments[1].column;
+
+        Columns result(3);
+        for (size_t i = 0; i < 3; ++i)
+            result[i] = DataTypeFloat64().createColumn();
+
+        if (const ColumnArray * array_predicted = checkAndGetColumn<ColumnArray>(col_predicted.get()))
+        {
+            const auto & array_predicted_offsets = array_predicted->getOffsets();
+            const auto & array_predicted_type = typeid_cast<const DataTypeArray *>(arguments[0].type.get())->getNestedType();
+
+            if (const ColumnConst * array_labels_const = checkAndGetColumn<ColumnConst>(col_labels.get()))
+            {
+                const ColumnArray * column_array_const_internal_array = checkAndGetColumn<ColumnArray>(array_labels_const->getDataColumnPtr().get());
+                const auto & array_labels_type = typeid_cast<const DataTypeArray *>(arguments[1].type.get())->getNestedType();
+
+                if (castBothTypes(
+                        array_predicted_type.get(),
+                        array_labels_type.get(),
+                        [&](const auto & type_predicted, const auto & type_labels)
+                        {
+                            using TypePredicted = typename std::decay_t<decltype(type_predicted)>::FieldType;
+                            const ColumnVector<TypePredicted> * array_predicted_data = checkAndGetColumn<ColumnVector<TypePredicted>>(array_predicted->getDataPtr().get());
+
+                            using TypeLabels = typename std::decay_t<decltype(type_labels)>::FieldType;
+                            const ColumnVector<TypeLabels> * col_labels_data = checkAndGetColumn<ColumnVector<TypeLabels>>(column_array_const_internal_array->getDataPtr().get());
+
+                            auto col_gini_predicted = ColumnFloat64::create(input_rows_count);
+                            auto col_gini_labels = ColumnFloat64::create(input_rows_count);
+                            auto col_gini_normalized = ColumnFloat64::create(input_rows_count);
+
+                            Impl::vectorConst(
+                                array_predicted_data->getData(),
+                                array_predicted_offsets,
+                                col_labels_data->getData(),
+                                col_gini_predicted->getData(),
+                                col_gini_labels->getData(),
+                                col_gini_normalized->getData());
+
+                            result[0] = std::move(col_gini_predicted);
+                            result[1] = std::move(col_gini_labels);
+                            result[2] = std::move(col_gini_normalized);
+
+                            return true;
+                        }))
+                {
+                    return ColumnTuple::create(result);
+                }
+            }
+            else
+            {
+                const ColumnArray * array_labels = checkAndGetColumn<ColumnArray>(col_labels.get());
+                const auto & array_label_offsets = array_labels->getOffsets();
+                const auto & array_labels_type = typeid_cast<const DataTypeArray *>(arguments[1].type.get())->getNestedType();
+
+                if (castBothTypes(
+                        array_predicted_type.get(),
+                        array_labels_type.get(),
+                        [&](const auto & type_predicted, const auto & type_labels)
+                        {
+                            using TypePredicted = typename std::decay_t<decltype(type_predicted)>::FieldType;
+                            const ColumnVector<TypePredicted> * array_predicted_data = checkAndGetColumn<ColumnVector<TypePredicted>>(array_predicted->getDataPtr().get());
+
+                            using TypeLabels = typename std::decay_t<decltype(type_labels)>::FieldType;
+                            const ColumnVector<TypeLabels> * col_labels_data = checkAndGetColumn<ColumnVector<TypeLabels>>(array_labels->getDataPtr().get());
+
+                            auto col_gini_predicted = ColumnFloat64::create(input_rows_count);
+                            auto col_gini_labels = ColumnFloat64::create(input_rows_count);
+                            auto col_gini_normalized = ColumnFloat64::create(input_rows_count);
+
+                            Impl::vectorVector(
+                                array_predicted_data->getData(),
+                                array_predicted_offsets,
+                                col_labels_data->getData(),
+                                array_label_offsets,
+                                col_gini_predicted->getData(),
+                                col_gini_labels->getData(),
+                                col_gini_normalized->getData());
+
+                            result[0] = std::move(col_gini_predicted);
+                            result[1] = std::move(col_gini_labels);
+                            result[2] = std::move(col_gini_normalized);
+
+                            return true;
+                        }))
+                {
+                    return ColumnTuple::create(result);
+                }
+            }
+        }
+        else if (const ColumnConst * array_predicted_const = checkAndGetColumn<ColumnConst>(col_predicted.get()))
+        {
+            /// Note that const-const case is handled by useDefaultImplementationForConstants.
+
+            const ColumnArray * column_array_const = checkAndGetColumn<ColumnArray>(array_predicted_const->getDataColumnPtr().get());
+            const auto & array_predicted_type = typeid_cast<const DataTypeArray *>(arguments[0].type.get())->getNestedType();
+
+            const ColumnArray * array_labels = checkAndGetColumn<ColumnArray>(col_labels.get());
+            const auto & array_label_offsets = array_labels->getOffsets();
+            const auto & array_labels_type = typeid_cast<const DataTypeArray *>(arguments[1].type.get())->getNestedType();
+
+            if (castBothTypes(
+                    array_predicted_type.get(),
+                    array_labels_type.get(),
+                    [&](const auto & type_predicted, const auto & type_labels)
+                    {
+                        using TypePredicted = typename std::decay_t<decltype(type_predicted)>::FieldType;
+                        const ColumnVector<TypePredicted> * array_predicted_data = checkAndGetColumn<ColumnVector<TypePredicted>>(column_array_const->getDataPtr().get());
+
+                        using TypeLabels = typename std::decay_t<decltype(type_labels)>::FieldType;
+                        const ColumnVector<TypeLabels> * col_labels_data = checkAndGetColumn<ColumnVector<TypeLabels>>(array_labels->getDataPtr().get());
+
+                        auto col_gini_predicted = ColumnFloat64::create(input_rows_count);
+                        auto col_gini_labels = ColumnFloat64::create(input_rows_count);
+                        auto col_gini_normalized = ColumnFloat64::create(input_rows_count);
+
+                        Impl::constVector(
+                            array_predicted_data->getData(),
+                            col_labels_data->getData(),
+                            array_label_offsets,
+                            col_gini_predicted->getData(),
+                            col_gini_labels->getData(),
+                            col_gini_normalized->getData());
+
+                        result[0] = std::move(col_gini_predicted);
+                        result[1] = std::move(col_gini_labels);
+                        result[2] = std::move(col_gini_normalized);
+
+                        return true;
+                    }))
+            {
+                return ColumnTuple::create(result);
+            }
+        }
+
+        throw Exception(ErrorCodes::ILLEGAL_COLUMN, "Illegal column of argument of function {}", getName());
+    }
+
+private:
+    template <typename F>
+    static bool castType(const IDataType * type, F && f)
+    {
+        return castTypeToEither<
+            DataTypeInt8,
+            DataTypeInt16,
+            DataTypeInt32,
+            DataTypeInt64,
+            DataTypeUInt8,
+            DataTypeUInt16,
+            DataTypeUInt32,
+            DataTypeUInt64,
+            DataTypeFloat32,
+            DataTypeFloat64>(type, std::forward<F>(f));
+    }
+
+    template <typename F>
+    static bool castBothTypes(const IDataType * left, const IDataType * right, F && f)
+    {
+        return castType(left, [&](const auto & left_)
+        {
+            return castType(right, [&](const auto & right_)
+            {
+                return f(left_, right_);
+            });
+        });
+    }
+
+};
+
+REGISTER_FUNCTION(NormalizedGini)
+{
+    FunctionDocumentation::Description doc_description = "Calculates the normalized Gini coefficient.";
+    FunctionDocumentation::Syntax doc_syntax = "arrayNormalizedGini(predicted, label)";
+    FunctionDocumentation::Arguments doc_arguments = {{"predicted", "Predicted value (Array(T))."}, {"label", "Actual value (Array(T))."}};
+    FunctionDocumentation::ReturnedValue doc_returned_value = "A tuple containing the Gini coefficients of the predicted values, the Gini coefficient of the normalized values, and the normalized Gini coefficient (= the ratio of the former two Gini coefficients).";
+    FunctionDocumentation::Examples doc_examples
+        = {{"Example",
+            "SELECT arrayNormalizedGini([0.9, 0.3, 0.8, 0.7],[6, 1, 0, 2]);",
+            "(0.18055555555555558,0.2638888888888889,0.6842105263157896)"}};
+    FunctionDocumentation::Categories doc_categories = {"Array"};
+
+    factory.registerFunction<FunctionArrayNormalizedGini>(
+        {doc_description, doc_syntax, doc_arguments, doc_returned_value, doc_examples, doc_categories}, FunctionFactory::Case::Sensitive);
+}
+
+}
--- a/tests/queries/0_stateless/03279_array_normalized_gini.reference
+++ b/tests/queries/0_stateless/03279_array_normalized_gini.reference
@ -0,0 +1,14 @@
+(0.18055555555555558,0.2638888888888889,0.6842105263157896)
+(0.18888888888888894,0.3,0.6296296296296299)
+(0.18055555555555558,0.2638888888888889,0.6842105263157896)
+(0.18055555555555558,0.2638888888888889,0.6842105263157896)
+(0.18055555555555558,0.2638888888888889,0.6842105263157896)
+(0.18055555555555558,0.2638888888888889,0.6842105263157896)
+(0.18055555555555558,0.2638888888888889,0.6842105263157896)
+(0.18055555555555558,0.2638888888888889,0.6842105263157896)
+(0.18055555555555558,0.2638888888888889,0.6842105263157896)
+(0.18055555555555558,0.2638888888888889,0.6842105263157896)
+(0.18055555555555558,0.2638888888888889,0.6842105263157896)
+(0.18055555555555558,0.2638888888888889,0.6842105263157896)
+(0.18055555555555558,0.2638888888888889,0.6842105263157896)
+(0.18055555555555558,0.2638888888888889,0.6842105263157896)
--- a/tests/queries/0_stateless/03279_array_normalized_gini.sql
+++ b/tests/queries/0_stateless/03279_array_normalized_gini.sql
@ -0,0 +1,24 @@
+SELECT arrayNormalizedGini([0.9, 0.3, 0.8, 0.7], [6, 1, 0, 2]);
+SELECT arrayNormalizedGini([0.9, 0.3, 0.8, 0.7], [6, 1, 0, 2, 1]); -- { serverError ILLEGAL_COLUMN }
+
+SELECT arrayNormalizedGini([0.9, 0.3, 0.8, 0.75, 0.65, 0.6, 0.78, 0.7, 0.05, 0.4, 0.4, 0.05, 0.5, 0.1, 0.1], [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]);
+
+SELECT arrayNormalizedGini(arrayResize([1], 2000000), arrayResize([1], 2000000)); -- { serverError TOO_LARGE_ARRAY_SIZE }
+
+DROP TABLE IF EXISTS t;
+CREATE TABLE t
+(
+    `a1` Array(Float32),
+    `a2` Array(UInt32)
+)
+ENGINE = MergeTree
+ORDER BY tuple();
+
+INSERT INTO t VALUES ([0.9, 0.3, 0.8, 0.7], [6, 1, 0, 2]), ([0.9, 0.3, 0.8, 0.7], [6, 1, 0, 2]), ([0.9, 0.3, 0.8, 0.7], [6, 1, 0, 2]), ([0.9, 0.3, 0.8, 0.7], [6, 1, 0, 2]);
+
+SELECT arrayNormalizedGini(a1, a2) FROM t;
+
+SELECT arrayNormalizedGini(a1, [6, 1, 0, 2]) FROM t;
+SELECT arrayNormalizedGini([0.9, 0.3, 0.8, 0.7], a2) FROM t;
+
+DROP TABLE t;
--- a/utils/check-style/aspell-ignore/en/aspell-dict.txt
+++ b/utils/check-style/aspell-ignore/en/aspell-dict.txt
@ -50,6 +50,7 @@ Autocompletion
 AvroConfluent
 AzureQueue
 Azurite
+arrayNormalizedGini
 BFloat
 BIGINT
 BIGSERIAL
@ -350,6 +351,7 @@ GetResolution
 GetUnidirectionalEdge
 GetUnidirectionalEdgeBoundary
 GetUnidirectionalEdgesFromHexagon
+Gini
 GitLab
 GlobalThread
 GlobalThreadActive