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More variable renamings

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Robert Schulze 2024-12-17 12:01:37 +00:00
parent 8d200605dc
commit b11c7ab4d2
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GPG Key ID: 26703B55FB13728A
1 changed files with 78 additions and 86 deletions
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164
src/Functions/array/arrayNormalizedGini.cpp
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@ -31,13 +31,13 @@ struct NormalizedGiniImpl
template <typename T1, typename T2>
static void vectorArrayConstArrayNormalizedGini(
const PaddedPODArray<T1> & array_datas1,
const ColumnArray::Offsets & offsets1,
const ColumnArray::Offsets & array_predicted_offsets,
const PaddedPODArray<T2> & const_array,
PaddedPODArray<Float64> & pred_gini_col,
PaddedPODArray<Float64> & label_gini_col,
PaddedPODArray<Float64> & norm_gini_col)
PaddedPODArray<Float64> & col_gini_predicted,
PaddedPODArray<Float64> & col_gini_labels,
PaddedPODArray<Float64> & col_gini_normalized)
{
size_t size = pred_gini_col.size();
size_t size = col_gini_predicted.size();
size_t array_size = const_array.size();
if (array_size > MAX_ARRAY_SIZE)
@ -49,35 +49,33 @@ struct NormalizedGiniImpl
for (size_t i = 0; i < size; ++i)
{
size_t array1_size = offsets1[i] - offsets1[i - 1];
size_t array1_size = array_predicted_offsets[i] - array_predicted_offsets[i - 1];
if (array1_size != array_size)
{
throw Exception(ErrorCodes::ILLEGAL_COLUMN, "All array in function arrayNormalizedGini should have same 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 arrayNormalizedGiniImpl.
PODArrayWithStackMemory<T2, 1024> array2(const_array.begin(), const_array.end());
auto [pred_gini, label_gini, norm_gini] = arrayNormalizedGiniImpl(array_datas1, offsets1[i - 1], array2, array_size);
auto [gini_predicted, gini_labels, gini_normalized] = arrayNormalizedGiniImpl(array_datas1, array_predicted_offsets[i - 1], array2, array_size);
pred_gini_col[i] = pred_gini;
label_gini_col[i] = label_gini;
norm_gini_col[i] = norm_gini;
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 vectorArrayVectorArrayNormalizedGini(
const PaddedPODArray<T1> & array_datas1,
const ColumnArray::Offsets & offsets1,
const ColumnArray::Offsets & array_predicted_offsets,
const PaddedPODArray<T2> & array_datas2,
const ColumnArray::Offsets & offsets2,
PaddedPODArray<Float64> & pred_gini_col,
PaddedPODArray<Float64> & label_gini_col,
PaddedPODArray<Float64> & norm_gini_col)
PaddedPODArray<Float64> & col_gini_predicted,
PaddedPODArray<Float64> & col_gini_labels,
PaddedPODArray<Float64> & col_gini_normalized)
{
size_t size = pred_gini_col.size();
size_t array_size = size > 0 ? offsets1[0] - offsets1[-1] : 0;
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(
@ -85,20 +83,18 @@ struct NormalizedGiniImpl
for (size_t i = 0; i < size; ++i)
{
size_t array1_size = offsets1[i] - offsets1[i - 1];
size_t array1_size = array_predicted_offsets[i] - array_predicted_offsets[i - 1];
size_t array2_size = offsets2[i] - offsets2[i - 1];
if (array1_size != array_size || array2_size != array_size)
{
throw Exception(ErrorCodes::ILLEGAL_COLUMN, "All array in function arrayNormalizedGini should have same size");
}
throw Exception(ErrorCodes::ILLEGAL_COLUMN, "All arrays in function arrayNormalizedGini should have same size");
PODArrayWithStackMemory<T2, 1024> array2(array_datas2.data() + offsets2[i - 1], array_datas2.data() + offsets2[i]);
auto [pred_gini, label_gini, norm_gini] = arrayNormalizedGiniImpl(array_datas1, offsets1[i - 1], array2, array_size);
auto [gini_predicted, gini_labels, gini_normalized] = arrayNormalizedGiniImpl(array_datas1, array_predicted_offsets[i - 1], array2, array_size);
pred_gini_col[i] = pred_gini;
label_gini_col[i] = label_gini;
norm_gini_col[i] = norm_gini;
col_gini_predicted[i] = gini_predicted;
col_gini_labels[i] = gini_labels;
col_gini_normalized[i] = gini_normalized;
}
}
@ -107,11 +103,11 @@ struct NormalizedGiniImpl
const PaddedPODArray<T1> & const_array,
const PaddedPODArray<T2> & array_datas1,
const ColumnArray::Offsets & offsets1,
PaddedPODArray<Float64> & pred_gini_col,
PaddedPODArray<Float64> & label_gini_col,
PaddedPODArray<Float64> & norm_gini_col)
PaddedPODArray<Float64> & col_gini_predicted,
PaddedPODArray<Float64> & col_gini_labels,
PaddedPODArray<Float64> & col_gini_normalized)
{
size_t size = pred_gini_col.size();
size_t size = col_gini_predicted.size();
size_t array_size = const_array.size();
if (array_size > MAX_ARRAY_SIZE)
@ -122,32 +118,29 @@ struct NormalizedGiniImpl
{
size_t array1_size = offsets1[i] - offsets1[i - 1];
if (array1_size != array_size)
{
throw Exception(ErrorCodes::ILLEGAL_COLUMN, "All array in function arrayNormalizedGini should have same size");
}
throw Exception(ErrorCodes::ILLEGAL_COLUMN, "All arrays in function arrayNormalizedGini should have same size");
PODArrayWithStackMemory<T2, 1024> array2(array_datas1.data() + offsets1[i - 1], array_datas1.data() + offsets1[i]);
auto [pred_gini, label_gini, norm_gini] = arrayNormalizedGiniImpl(const_array, 0, array2, array_size);
auto [gini_predicted, gini_labels, gini_normalized] = arrayNormalizedGiniImpl(const_array, 0, array2, array_size);
pred_gini_col[i] = pred_gini;
label_gini_col[i] = label_gini;
norm_gini_col[i] = norm_gini;
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>
arrayNormalizedGiniImpl(const PaddedPODArray<T1> & array1, size_t offset, PODArrayWithStackMemory<T2, 1024> & array2, size_t array_size)
static std::tuple<Float64, Float64, Float64> arrayNormalizedGiniImpl(
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);
@ -175,9 +168,9 @@ private:
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 label_gini = (random_gain_cumsum_ratio - accumulate_ltv_ratio) / array_size;
Float64 gini_labels = (random_gain_cumsum_ratio - accumulate_ltv_ratio) / array_size;
return std::make_tuple(pred_gini, label_gini, pred_gini / label_gini);
return std::make_tuple(pred_gini, gini_labels, pred_gini / gini_labels);
}
template <typename T>
@ -243,17 +236,17 @@ public:
if (const ColumnArray * array_predicted = checkAndGetColumn<ColumnArray>(col_predicted.get()))
{
const auto & offsets1 = array_predicted->getOffsets();
const auto & array_arg_type1 = typeid_cast<const DataTypeArray *>(arguments[0].type.get())->getNestedType();
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 = checkAndGetColumn<ColumnArray>(array_labels_const->getDataColumnPtr().get());
const auto & array_arg_type2 = typeid_cast<const DataTypeArray *>(arguments[1].type.get())->getNestedType();
const auto & array_labels_type = typeid_cast<const DataTypeArray *>(arguments[1].type.get())->getNestedType();
if (castBothTypes(
array_arg_type1.get(),
array_arg_type2.get(),
array_predicted_type.get(),
array_labels_type.get(),
[&](const auto & array_column_type1, const auto & array_column_type2)
{
using ArrayDataType1 = std::decay_t<decltype(array_column_type1)>;
@ -264,22 +257,21 @@ public:
using T2 = typename ArrayDataType2::FieldType;
const ColumnVector<T2> * array_column_vector2 = checkAndGetColumn<ColumnVector<T2>>(column_array_const->getDataPtr().get());
auto pred_gini_col = ColumnFloat64::create(input_rows_count);
auto label_gini_col = ColumnFloat64::create(input_rows_count);
auto norm_gini_col = ColumnFloat64::create(input_rows_count);
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);
NormalizedGiniImpl::vectorArrayConstArrayNormalizedGini(
array_column_vector1->getData(),
offsets1,
array_predicted_offsets,
array_column_vector2->getData(),
pred_gini_col->getData(),
label_gini_col->getData(),
norm_gini_col->getData());
col_gini_predicted->getData(),
col_gini_labels->getData(),
col_gini_normalized->getData());
result[0] = std::move(pred_gini_col);
result[1] = std::move(label_gini_col);
result[2] = std::move(norm_gini_col);
result[0] = std::move(col_gini_predicted);
result[1] = std::move(col_gini_labels);
result[2] = std::move(col_gini_normalized);
return true;
}))
@ -291,11 +283,11 @@ public:
{
const ColumnArray * array_labels = checkAndGetColumn<ColumnArray>(col_labels.get());
const auto & offsets2 = array_labels->getOffsets();
const auto & array_arg_type2 = typeid_cast<const DataTypeArray *>(arguments[1].type.get())->getNestedType();
const auto & array_labels_type = typeid_cast<const DataTypeArray *>(arguments[1].type.get())->getNestedType();
if (castBothTypes(
array_arg_type1.get(),
array_arg_type2.get(),
array_predicted_type.get(),
array_labels_type.get(),
[&](const auto & array_column_type1, const auto & array_column_type2)
{
using ArrayDataType1 = std::decay_t<decltype(array_column_type1)>;
@ -306,23 +298,23 @@ public:
using T2 = typename ArrayDataType2::FieldType;
const ColumnVector<T2> * array_column_vector2 = checkAndGetColumn<ColumnVector<T2>>(array_labels->getDataPtr().get());
auto pred_gini_col = ColumnFloat64::create(input_rows_count);
auto label_gini_col = ColumnFloat64::create(input_rows_count);
auto norm_gini_col = ColumnFloat64::create(input_rows_count);
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);
NormalizedGiniImpl::vectorArrayVectorArrayNormalizedGini(
array_column_vector1->getData(),
offsets1,
array_predicted_offsets,
array_column_vector2->getData(),
offsets2,
pred_gini_col->getData(),
label_gini_col->getData(),
norm_gini_col->getData());
col_gini_predicted->getData(),
col_gini_labels->getData(),
col_gini_normalized->getData());
result[0] = std::move(pred_gini_col);
result[1] = std::move(label_gini_col);
result[2] = std::move(norm_gini_col);
result[0] = std::move(col_gini_predicted);
result[1] = std::move(col_gini_labels);
result[2] = std::move(col_gini_normalized);
return true;
}))
@ -336,15 +328,15 @@ public:
/// Note that const-const case is handled by useDefaultImplementationForConstants.
const ColumnArray * column_array_const = checkAndGetColumn<ColumnArray>(array_predicted_const->getDataColumnPtr().get());
const auto & array_arg_type1 = typeid_cast<const DataTypeArray *>(arguments[0].type.get())->getNestedType();
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 & offsets2 = array_labels->getOffsets();
const auto & array_arg_type2 = typeid_cast<const DataTypeArray *>(arguments[1].type.get())->getNestedType();
const auto & array_labels_type = typeid_cast<const DataTypeArray *>(arguments[1].type.get())->getNestedType();
if (castBothTypes(
array_arg_type1.get(),
array_arg_type2.get(),
array_predicted_type.get(),
array_labels_type.get(),
[&](const auto & array_column_type1, const auto & array_column_type2)
{
using ArrayDataType1 = std::decay_t<decltype(array_column_type1)>;
@ -355,22 +347,22 @@ public:
using T2 = typename ArrayDataType2::FieldType;
const ColumnVector<T2> * array_column_vector2 = checkAndGetColumn<ColumnVector<T2>>(array_labels->getDataPtr().get());
auto pred_gini_col = ColumnFloat64::create(input_rows_count);
auto label_gini_col = ColumnFloat64::create(input_rows_count);
auto norm_gini_col = ColumnFloat64::create(input_rows_count);
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);
NormalizedGiniImpl::constArrayVectorArrayNormalizedGini(
array_column_vector1->getData(),
array_column_vector2->getData(),
offsets2,
pred_gini_col->getData(),
label_gini_col->getData(),
norm_gini_col->getData());
col_gini_predicted->getData(),
col_gini_labels->getData(),
col_gini_normalized->getData());
result[0] = std::move(pred_gini_col);
result[1] = std::move(label_gini_col);
result[2] = std::move(norm_gini_col);
result[0] = std::move(col_gini_predicted);
result[1] = std::move(col_gini_labels);
result[2] = std::move(col_gini_normalized);
return true;
}))