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#pragma once
#include <AggregateFunctions/IAggregateFunction.h>
#include <AggregateFunctions/StatCommon.h>
#include <Columns/ColumnVector.h>
#include <Columns/ColumnTuple.h>
#include <Common/assert_cast.h>
#include <Core/Types.h>
#include <DataTypes/DataTypesNumber.h>
#include <DataTypes/DataTypeTuple.h>
#include <cmath>
namespace DB
{
struct Settings;
class ReadBuffer;
class WriteBuffer;
namespace ErrorCodes
{
extern const int BAD_ARGUMENTS;
}
/// Returns tuple of (z-statistic, p-value, confidence-interval-low, confidence-interval-high)
template <typename Data>
class AggregateFunctionMeanZTest :
public IAggregateFunctionDataHelper<Data, AggregateFunctionMeanZTest<Data>>
{
private:
Float64 pop_var_x;
Float64 pop_var_y;
Float64 confidence_level;
public:
AggregateFunctionMeanZTest(const DataTypes & arguments, const Array & params)
: IAggregateFunctionDataHelper<Data, AggregateFunctionMeanZTest<Data>>({arguments}, params, createResultType())
{
pop_var_x = params.at(0).safeGet<Float64>();
pop_var_y = params.at(1).safeGet<Float64>();
confidence_level = params.at(2).safeGet<Float64>();
if (!std::isfinite(pop_var_x) || !std::isfinite(pop_var_y) || !std::isfinite(confidence_level))
{
throw Exception(ErrorCodes::BAD_ARGUMENTS, "Aggregate function {} requires finite parameter values.", Data::name);
}
if (pop_var_x < 0.0 || pop_var_y < 0.0)
{
throw Exception(ErrorCodes::BAD_ARGUMENTS,
"Population variance parameters must be larger than or equal to zero "
"in aggregate function {}.", Data::name);
}
if (confidence_level <= 0.0 || confidence_level >= 1.0)
{
throw Exception(ErrorCodes::BAD_ARGUMENTS, "Confidence level parameter must be between 0 and 1 in aggregate function {}.", Data::name);
}
}
String getName() const override
{
return Data::name;
}
static DataTypePtr createResultType()
{
DataTypes types
{
std::make_shared<DataTypeNumber<Float64>>(),
std::make_shared<DataTypeNumber<Float64>>(),
std::make_shared<DataTypeNumber<Float64>>(),
std::make_shared<DataTypeNumber<Float64>>(),
};
Strings names
{
"z_statistic",
"p_value",
"confidence_interval_low",
"confidence_interval_high"
};
return std::make_shared<DataTypeTuple>(
std::move(types),
std::move(names)
);
}
bool allocatesMemoryInArena() const override { return false; }
void add(AggregateDataPtr __restrict place, const IColumn ** columns, size_t row_num, Arena *) const override
{
Float64 value = columns[0]->getFloat64(row_num);
UInt8 is_second = columns[1]->getUInt(row_num);
if (is_second)
this->data(place).addY(value);
else
this->data(place).addX(value);
}
void merge(AggregateDataPtr __restrict place, ConstAggregateDataPtr rhs, Arena *) const override
{
this->data(place).merge(this->data(rhs));
}
void serialize(ConstAggregateDataPtr __restrict place, WriteBuffer & buf, std::optional<size_t> /* version */) const override
{
this->data(place).write(buf);
}
void deserialize(AggregateDataPtr __restrict place, ReadBuffer & buf, std::optional<size_t> /* version */, Arena *) const override
{
this->data(place).read(buf);
}
void insertResultInto(AggregateDataPtr __restrict place, IColumn & to, Arena *) const override
{
auto [z_stat, p_value] = this->data(place).getResult(pop_var_x, pop_var_y);
auto [ci_low, ci_high] = this->data(place).getConfidenceIntervals(pop_var_x, pop_var_y, confidence_level);
/// Because p-value is a probability.
p_value = std::min(1.0, std::max(0.0, p_value));
auto & column_tuple = assert_cast<ColumnTuple &>(to);
auto & column_stat = assert_cast<ColumnVector<Float64> &>(column_tuple.getColumn(0));
auto & column_value = assert_cast<ColumnVector<Float64> &>(column_tuple.getColumn(1));
auto & column_ci_low = assert_cast<ColumnVector<Float64> &>(column_tuple.getColumn(2));
auto & column_ci_high = assert_cast<ColumnVector<Float64> &>(column_tuple.getColumn(3));
column_stat.getData().push_back(z_stat);
column_value.getData().push_back(p_value);
column_ci_low.getData().push_back(ci_low);
column_ci_high.getData().push_back(ci_high);
}
};
}
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