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Add aggregate function for exp smoothing
This commit is contained in:
Alexey Milovidov
parent
a4153e5629
commit
0745631bf7
@ -0,0 +1,100 @@
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#include <AggregateFunctions/IAggregateFunction.h>
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#include <AggregateFunctions/AggregateFunctionFactory.h>
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#include <AggregateFunctions/FactoryHelpers.h>
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#include <Common/ExponentiallySmoothedCounter.h>
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#include <Common/FieldVisitorConvertToNumber.h>
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#include <DataTypes/DataTypesNumber.h>
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#include <IO/ReadHelpers.h>
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#include <IO/WriteHelpers.h>
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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 NUMBER_OF_ARGUMENTS_DOESNT_MATCH;
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extern const int ILLEGAL_TYPE_OF_ARGUMENT;
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}
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/** See the comments in ExponentiallySmoothedCounter.h
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* Caveats:
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* - if aggregated data is unordered, the calculation is non-deterministic;
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* - so, multithreaded and distributed aggregation is also non-deterministic;
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* - it highly depends on what value will be first and last;
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* Nevertheless it is useful in window functions and aggregate functions over ordered data.
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*/
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class AggregateFunctionExponentialMovingAverage final
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: public IAggregateFunctionDataHelper<ExponentiallySmoothedCounter, AggregateFunctionExponentialMovingAverage>
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{
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private:
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Float64 half_decay;
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public:
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AggregateFunctionExponentialMovingAverage(const DataTypes & argument_types_, const Array & params)
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: IAggregateFunctionDataHelper<ExponentiallySmoothedCounter, AggregateFunctionExponentialMovingAverage>(argument_types_, params)
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{
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if (params.size() != 1)
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throw Exception{"Aggregate function " + getName() + " requires exactly one parameter: half decay time.",
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ErrorCodes::NUMBER_OF_ARGUMENTS_DOESNT_MATCH};
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half_decay = applyVisitor(FieldVisitorConvertToNumber<Float64>(), params[0]);
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}
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String getName() const override { return "exponentialMovingAverage"; }
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DataTypePtr getReturnType() const override
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{
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return std::make_shared<DataTypeNumber<Float64>>();
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}
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bool allocatesMemoryInArena() const override { return false; }
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void add(AggregateDataPtr __restrict place, const IColumn ** columns, size_t row_num, Arena *) const override
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{
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const auto & value = columns[0]->getFloat64(row_num);
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const auto & time = columns[1]->getFloat64(row_num);
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this->data(place).add(value, time, half_decay);
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}
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void merge(AggregateDataPtr __restrict place, ConstAggregateDataPtr rhs, Arena *) const override
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{
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this->data(place).merge(this->data(rhs), half_decay);
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}
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void serialize(ConstAggregateDataPtr __restrict place, WriteBuffer & buf) const override
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{
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writeBinary(this->data(place).value, buf);
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writeBinary(this->data(place).update_time, buf);
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}
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void deserialize(AggregateDataPtr __restrict place, ReadBuffer & buf, Arena *) const override
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{
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readBinary(this->data(place).value, buf);
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readBinary(this->data(place).update_time, buf);
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}
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void insertResultInto(AggregateDataPtr __restrict place, IColumn & to, Arena *) const override
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{
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auto & column = assert_cast<ColumnVector<Float64> &>(to);
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column.getData().push_back(this->data(place).value);
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}
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};
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void registerAggregateFunctionExponentialMovingAverage(AggregateFunctionFactory & factory)
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{
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factory.registerFunction("exponentialMovingAverage",
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[](const std::string & name, const DataTypes & argument_types, const Array & params, const Settings *) -> AggregateFunctionPtr
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{
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assertBinary(name, argument_types);
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for (const auto & type : argument_types)
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if (!isNumber(*type))
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throw Exception(ErrorCodes::ILLEGAL_TYPE_OF_ARGUMENT,
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"Both arguments for aggregate function {} must have numeric type, got {}", name, type->getName());
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return std::make_shared<AggregateFunctionExponentialMovingAverage>(argument_types, params);
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});
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}
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}
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@ -50,6 +50,7 @@ void registerAggregateFunctionWelchTTest(AggregateFunctionFactory &);
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void registerAggregateFunctionStudentTTest(AggregateFunctionFactory &);
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void registerAggregateFunctionSingleValueOrNull(AggregateFunctionFactory &);
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void registerAggregateFunctionSequenceNextNode(AggregateFunctionFactory &);
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void registerAggregateFunctionExponentialMovingAverage(AggregateFunctionFactory &);
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class AggregateFunctionCombinatorFactory;
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void registerAggregateFunctionCombinatorIf(AggregateFunctionCombinatorFactory &);
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@ -115,10 +116,10 @@ void registerAggregateFunctions()
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registerAggregateFunctionWelchTTest(factory);
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registerAggregateFunctionStudentTTest(factory);
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registerAggregateFunctionSingleValueOrNull(factory);
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registerAggregateFunctionIntervalLengthSum(factory);
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registerAggregateFunctionExponentialMovingAverage(factory);
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registerWindowFunctions(factory);
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registerAggregateFunctionIntervalLengthSum(factory);
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}
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{
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130
tests/queries/0_stateless/2020_exponential_smoothing.reference
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130
tests/queries/0_stateless/2020_exponential_smoothing.reference
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@ -0,0 +1,130 @@
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1 0 1
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0 1 0.5
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0 2 0.25
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0 3 0.125
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0 4 0.0625
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0 5 0.03125
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0 6 0.015625
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0 7 0.0078125
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0 8 0.00390625
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0 9 0.001953125
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1 0 1
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0 1 0.933
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0 2 0.871
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0 3 0.812
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0 4 0.758
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0 5 0.707
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0 6 0.66
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0 7 0.616
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0 8 0.574
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0 9 0.536
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0 0 0
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1 1 0.5
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2 2 1.25
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3 3 2.125
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4 4 3.0625
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5 5 4.03125
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6 6 5.015625
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7 7 6.0078125
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8 8 7.00390625
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9 9 8.001953125
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1 0 1 ██████████████████████████████████████████████████
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0 1 0.933 ██████████████████████████████████████████████▋
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0 2 0.871 ███████████████████████████████████████████▌
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0 3 0.812 ████████████████████████████████████████▌
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0 4 0.758 █████████████████████████████████████▊
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0 5 0.707 ███████████████████████████████████▎
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0 6 0.66 ████████████████████████████████▊
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0 7 0.616 ██████████████████████████████▋
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0 8 0.574 ████████████████████████████▋
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0 9 0.536 ██████████████████████████▋
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0 10 0.5 █████████████████████████
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0 11 0.467 ███████████████████████▎
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0 12 0.435 █████████████████████▋
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0 13 0.406 ████████████████████▎
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0 14 0.379 ██████████████████▊
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0 15 0.354 █████████████████▋
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0 16 0.33 ████████████████▍
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0 17 0.308 ███████████████▍
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0 18 0.287 ██████████████▎
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0 19 0.268 █████████████▍
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0 20 0.25 ████████████▌
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0 21 0.233 ███████████▋
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0 22 0.218 ██████████▊
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0 23 0.203 ██████████▏
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0 24 0.189 █████████▍
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1 25 0.244 ████████████▏
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1 26 0.294 ██████████████▋
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1 27 0.342 █████████████████
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1 28 0.386 ███████████████████▎
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1 29 0.427 █████████████████████▎
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1 30 0.465 ███████████████████████▎
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1 31 0.501 █████████████████████████
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1 32 0.534 ██████████████████████████▋
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1 33 0.566 ████████████████████████████▎
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1 34 0.595 █████████████████████████████▋
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1 35 0.622 ███████████████████████████████
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1 36 0.647 ████████████████████████████████▎
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1 37 0.671 █████████████████████████████████▌
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1 38 0.693 ██████████████████████████████████▋
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1 39 0.713 ███████████████████████████████████▋
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1 40 0.733 ████████████████████████████████████▋
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1 41 0.751 █████████████████████████████████████▌
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1 42 0.767 ██████████████████████████████████████▎
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1 43 0.783 ███████████████████████████████████████▏
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1 44 0.797 ███████████████████████████████████████▋
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1 45 0.811 ████████████████████████████████████████▌
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1 46 0.824 █████████████████████████████████████████▏
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1 47 0.835 █████████████████████████████████████████▋
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1 48 0.846 ██████████████████████████████████████████▎
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1 49 0.857 ██████████████████████████████████████████▋
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1 0 1 ██████████████████████████████████████████████████
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0 1 0.5 █████████████████████████
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0 2 0.25 ████████████▌
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0 3 0.125 ██████▎
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0 4 0.062 ███
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1 5 0.531 ██████████████████████████▌
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0 6 0.266 █████████████▎
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0 7 0.133 ██████▋
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0 8 0.066 ███▎
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0 9 0.033 █▋
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1 10 0.517 █████████████████████████▋
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0 11 0.258 ████████████▊
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0 12 0.129 ██████▍
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0 13 0.065 ███▏
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0 14 0.032 █▌
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1 15 0.516 █████████████████████████▋
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0 16 0.258 ████████████▊
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0 17 0.129 ██████▍
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0 18 0.065 ███▏
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0 19 0.032 █▌
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1 20 0.516 █████████████████████████▋
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0 21 0.258 ████████████▊
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0 22 0.129 ██████▍
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0 23 0.065 ███▏
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0 24 0.032 █▌
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1 25 0.516 █████████████████████████▋
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0 26 0.258 ████████████▊
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0 27 0.129 ██████▍
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0 28 0.065 ███▏
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0 29 0.032 █▌
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1 30 0.516 █████████████████████████▋
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0 31 0.258 ████████████▊
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0 32 0.129 ██████▍
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0 33 0.065 ███▏
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0 34 0.032 █▌
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1 35 0.516 █████████████████████████▋
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0 36 0.258 ████████████▊
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0 37 0.129 ██████▍
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0 38 0.065 ███▏
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0 39 0.032 █▌
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1 40 0.516 █████████████████████████▋
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0 41 0.258 ████████████▊
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0 42 0.129 ██████▍
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0 43 0.065 ███▏
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0 44 0.032 █▌
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1 45 0.516 █████████████████████████▋
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0 46 0.258 ████████████▊
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0 47 0.129 ██████▍
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0 48 0.065 ███▏
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0 49 0.032 █▌
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32
tests/queries/0_stateless/2020_exponential_smoothing.sql
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32
tests/queries/0_stateless/2020_exponential_smoothing.sql
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@ -0,0 +1,32 @@
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SELECT number = 0 AS value, number AS time, exponentialMovingAverage(1)(value, time) OVER (ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS exp_smooth FROM numbers(10);
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SELECT value, time, round(exp_smooth, 3) FROM (SELECT number = 0 AS value, number AS time, exponentialMovingAverage(10)(value, time) OVER (ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS exp_smooth FROM numbers(10));
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SELECT number AS value, number AS time, exponentialMovingAverage(1)(value, time) OVER (ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS exp_smooth FROM numbers(10);
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SELECT
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value,
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time,
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round(exp_smooth, 3),
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bar(exp_smooth, 0, 1, 50) AS bar
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FROM
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(
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SELECT
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(number = 0) OR (number >= 25) AS value,
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number AS time,
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exponentialMovingAverage(10)(value, time) OVER (ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS exp_smooth
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FROM numbers(50)
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);
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SELECT
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value,
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time,
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round(exp_smooth, 3),
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bar(exp_smooth, 0, 1, 50) AS bar
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FROM
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(
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SELECT
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(number % 5) = 0 AS value,
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number AS time,
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exponentialMovingAverage(1)(value, time) OVER (ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS exp_smooth
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FROM numbers(50)
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);
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