mirror of
https://github.com/ClickHouse/ClickHouse.git
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232 lines
9.5 KiB
C++
232 lines
9.5 KiB
C++
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#include <Common/typeid_cast.h>
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#include <Columns/IColumn.h>
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#include <Columns/ColumnNullable.h>
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#include <Columns/ColumnTuple.h>
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#include <Columns/ColumnString.h>
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#include <Columns/ColumnsNumber.h>
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#include <DataTypes/DataTypeTuple.h>
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#include <DataTypes/DataTypesNumber.h>
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#include <Functions/castTypeToEither.h>
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#include <Functions/IFunction.h>
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#include <Functions/FunctionFactory.h>
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#include <Functions/FunctionHelpers.h>
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#include <Interpreters/castColumn.h>
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#include <boost/math/distributions/normal.hpp>
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namespace DB
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{
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namespace ErrorCodes
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{
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extern const int ILLEGAL_TYPE_OF_ARGUMENT;
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extern const int BAD_ARGUMENTS;
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}
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class FunctionTwoSampleProportionsZTest : public IFunction
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{
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public:
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static constexpr auto POOLED = "pooled";
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static constexpr auto UNPOOLED = "unpooled";
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static constexpr auto name = "proportionsZTest";
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static FunctionPtr create(ContextPtr)
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{
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return std::make_shared<FunctionTwoSampleProportionsZTest>();
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}
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String getName() const override
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{
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return name;
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}
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size_t getNumberOfArguments() const override { return 6; }
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ColumnNumbers getArgumentsThatAreAlwaysConstant() const override { return {5}; }
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bool useDefaultImplementationForNulls() const override { return false; }
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bool useDefaultImplementationForConstants() const override { return true; }
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bool isSuitableForShortCircuitArgumentsExecution(const DataTypesWithConstInfo & /*arguments*/) const override { return false; }
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static DataTypePtr getReturnType()
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{
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auto float_data_type = std::make_shared<DataTypeNumber<Float64>>();
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DataTypes types(4, float_data_type);
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Strings names
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{
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"z_statistic",
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"p_value",
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"confidence_interval_low",
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"confidence_interval_high"
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};
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return std::make_shared<DataTypeTuple>(
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std::move(types),
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std::move(names)
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);
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}
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DataTypePtr getReturnTypeImpl(const ColumnsWithTypeAndName & arguments) const override
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{
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for (size_t i = 0; i < 4; ++i)
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{
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if (!isUnsignedInteger(arguments[i].type))
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{
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throw Exception(ErrorCodes::ILLEGAL_TYPE_OF_ARGUMENT,
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"The {}th Argument of function {} must be an unsigned integer.", i + 1, getName());
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}
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}
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if (!isFloat(arguments[4].type))
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{
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throw Exception{ErrorCodes::ILLEGAL_TYPE_OF_ARGUMENT,
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"The fifth argument {} of function {} should be a float,", arguments[4].type->getName(), getName()};
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}
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/// There is an additional check for constancy in ExecuteImpl
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if (!isString(arguments[5].type) || !arguments[5].column)
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{
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throw Exception{ErrorCodes::ILLEGAL_TYPE_OF_ARGUMENT,
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"The sixth argument {} of function {} should be a constant string", arguments[5].type->getName(), getName()};
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}
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return getReturnType();
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}
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ColumnPtr executeImpl(const ColumnsWithTypeAndName & const_arguments, const DataTypePtr &, size_t input_rows_count) const override
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{
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auto arguments = const_arguments;
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/// Only last argument have to be constant
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for (size_t i = 0; i < 5; ++i)
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arguments[i].column = arguments[i].column->convertToFullColumnIfConst();
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static const auto uint64_data_type = std::make_shared<DataTypeNumber<UInt64>>();
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auto column_successes_x = castColumnAccurate(arguments[0], uint64_data_type);
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const auto & data_successes_x = checkAndGetColumn<ColumnVector<UInt64>>(column_successes_x.get())->getData();
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auto column_successes_y = castColumnAccurate(arguments[1], uint64_data_type);
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const auto & data_successes_y = checkAndGetColumn<ColumnVector<UInt64>>(column_successes_y.get())->getData();
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auto column_trials_x = castColumnAccurate(arguments[2], uint64_data_type);
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const auto & data_trials_x = checkAndGetColumn<ColumnVector<UInt64>>(column_trials_x.get())->getData();
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auto column_trials_y = castColumnAccurate(arguments[3], uint64_data_type);
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const auto & data_trials_y = checkAndGetColumn<ColumnVector<UInt64>>(column_trials_y.get())->getData();
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static const auto float64_data_type = std::make_shared<DataTypeNumber<Float64>>();
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auto column_confidence_level = castColumnAccurate(arguments[4], float64_data_type);
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const auto & data_confidence_level = checkAndGetColumn<ColumnVector<Float64>>(column_confidence_level.get())->getData();
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String usevar = checkAndGetColumnConst<ColumnString>(arguments[5].column.get())->getValue<String>();
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if (usevar != UNPOOLED && usevar != POOLED)
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throw Exception{ErrorCodes::BAD_ARGUMENTS,
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"The sixth argument {} of function {} must be equal to `pooled` or `unpooled`", arguments[5].type->getName(), getName()};
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const bool is_unpooled = (usevar == UNPOOLED);
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auto res_z_statistic = ColumnFloat64::create();
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auto & data_z_statistic = res_z_statistic->getData();
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data_z_statistic.reserve(input_rows_count);
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auto res_p_value = ColumnFloat64::create();
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auto & data_p_value = res_p_value->getData();
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data_p_value.reserve(input_rows_count);
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auto res_ci_lower = ColumnFloat64::create();
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auto & data_ci_lower = res_ci_lower->getData();
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data_ci_lower.reserve(input_rows_count);
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auto res_ci_upper = ColumnFloat64::create();
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auto & data_ci_upper = res_ci_upper->getData();
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data_ci_upper.reserve(input_rows_count);
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auto insert_values_into_result = [&data_z_statistic, &data_p_value, &data_ci_lower, &data_ci_upper](Float64 z_stat, Float64 p_value, Float64 lower, Float64 upper)
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{
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data_z_statistic.emplace_back(z_stat);
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data_p_value.emplace_back(p_value);
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data_ci_lower.emplace_back(lower);
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data_ci_upper.emplace_back(upper);
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};
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static constexpr Float64 nan = std::numeric_limits<Float64>::quiet_NaN();
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boost::math::normal_distribution<> nd(0.0, 1.0);
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for (size_t row_num = 0; row_num < input_rows_count; ++row_num)
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{
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const UInt64 successes_x = data_successes_x[row_num];
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const UInt64 successes_y = data_successes_y[row_num];
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const UInt64 trials_x = data_trials_x[row_num];
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const UInt64 trials_y = data_trials_y[row_num];
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const Float64 confidence_level = data_confidence_level[row_num];
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const Float64 props_x = static_cast<Float64>(successes_x) / trials_x;
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const Float64 props_y = static_cast<Float64>(successes_y) / trials_y;
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const Float64 diff = props_x - props_y;
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const UInt64 trials_total = trials_x + trials_y;
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if (successes_x == 0 || successes_y == 0
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|| successes_x > trials_x || successes_y > trials_y
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|| trials_total == 0
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|| !std::isfinite(confidence_level) || confidence_level < 0.0 || confidence_level > 1.0)
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{
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insert_values_into_result(nan, nan, nan, nan);
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continue;
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}
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Float64 se = std::sqrt(props_x * (1.0 - props_x) / trials_x + props_y * (1.0 - props_y) / trials_y);
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/// z-statistics
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/// z = \frac{ \bar{p_{1}} - \bar{p_{2}} }{ \sqrt{ \frac{ \bar{p_{1}} \left ( 1 - \bar{p_{1}} \right ) }{ n_{1} } \frac{ \bar{p_{2}} \left ( 1 - \bar{p_{2}} \right ) }{ n_{2} } } }
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Float64 zstat;
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if (is_unpooled)
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{
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zstat = (props_x - props_y) / se;
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}
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else
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{
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UInt64 successes_total = successes_x + successes_y;
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Float64 p_pooled = static_cast<Float64>(successes_total) / trials_total;
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Float64 trials_fact = 1.0 / trials_x + 1.0 / trials_y;
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zstat = diff / std::sqrt(p_pooled * (1.0 - p_pooled) * trials_fact);
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}
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if (!std::isfinite(zstat))
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{
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insert_values_into_result(nan, nan, nan, nan);
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continue;
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}
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// pvalue
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Float64 pvalue = 0;
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Float64 one_side = 1 - boost::math::cdf(nd, std::abs(zstat));
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pvalue = one_side * 2;
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// Confidence intervals
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Float64 d = props_x - props_y;
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Float64 z = -boost::math::quantile(nd, (1.0 - confidence_level) / 2.0);
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Float64 dist = z * se;
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Float64 ci_low = d - dist;
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Float64 ci_high = d + dist;
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insert_values_into_result(zstat, pvalue, ci_low, ci_high);
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}
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return ColumnTuple::create(Columns{std::move(res_z_statistic), std::move(res_p_value), std::move(res_ci_lower), std::move(res_ci_upper)});
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}
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};
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void registerFunctionZTest(FunctionFactory & factory)
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{
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factory.registerFunction<FunctionTwoSampleProportionsZTest>();
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}
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}
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