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Logistic Regression and new tests

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This commit is contained in:
Masha 2019-02-15 22:47:56 +00:00
parent 2feee7ebe5
commit d5753636bd
11 changed files with 2091 additions and 15 deletions
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46
dbms/src/AggregateFunctions/AggregateFunctionMLMethod.h
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@ -178,12 +178,28 @@ public:
} }
void predict_for_all(ColumnVector<Float64>::Container & container, Block & block, const ColumnNumbers & arguments, const std::vector<Float64> & weights, Float64 bias) const override void predict_for_all(ColumnVector<Float64>::Container & container, Block & block, const ColumnNumbers & arguments, const std::vector<Float64> & weights, Float64 bias) const override
{ {
// TODO size_t rows_num = block.rows();
std::ignore = container; std::vector<Float64> results(rows_num, bias);
std::ignore = block;
std::ignore = arguments; for (size_t i = 1; i < arguments.size(); ++i)
std::ignore = weights; {
std::ignore = bias; ColumnPtr cur_col = block.getByPosition(arguments[i]).column;
for (size_t row_num = 0; row_num != rows_num; ++row_num)
{
const auto &element = (*cur_col)[row_num];
if (element.getType() != Field::Types::Float64)
throw Exception("Prediction arguments must be values of type Float",
ErrorCodes::BAD_ARGUMENTS);
results[row_num] += weights[i - 1] * element.get<Float64>();
}
}
for (size_t row_num = 0; row_num != rows_num; ++row_num)
{
results[row_num] = 1 / (1 + exp(-results[row_num]));
container.emplace_back(results[row_num]);
}
} }
}; };
@ -218,31 +234,31 @@ public:
void update(UInt32 batch_size, std::vector<Float64> & weights, Float64 & bias, const std::vector<Float64> & batch_gradient) override { void update(UInt32 batch_size, std::vector<Float64> & weights, Float64 & bias, const std::vector<Float64> & batch_gradient) override {
/// batch_size is already checked to be greater than 0 /// batch_size is already checked to be greater than 0
if (hk_.size() == 0) if (accumulated_gradient.size() == 0)
{ {
hk_.resize(batch_gradient.size(), Float64{0.0}); accumulated_gradient.resize(batch_gradient.size(), Float64{0.0});
} }
for (size_t i = 0; i < batch_gradient.size(); ++i) for (size_t i = 0; i < batch_gradient.size(); ++i)
{ {
hk_[i] = hk_[i] * alpha_ + batch_gradient[i]; accumulated_gradient[i] = accumulated_gradient[i] * alpha_ + batch_gradient[i];
} }
for (size_t i = 0; i < weights.size(); ++i) for (size_t i = 0; i < weights.size(); ++i)
{ {
weights[i] += hk_[i] / batch_size; weights[i] += accumulated_gradient[i] / batch_size;
} }
bias += hk_[weights.size()] / batch_size; bias += accumulated_gradient[weights.size()] / batch_size;
} }
virtual void merge(const IWeightsUpdater & rhs, Float64 frac, Float64 rhs_frac) override { virtual void merge(const IWeightsUpdater & rhs, Float64 frac, Float64 rhs_frac) override {
const auto & momentum_rhs = dynamic_cast<const Momentum &>(rhs); auto & momentum_rhs = static_cast<const Momentum &>(rhs);
for (size_t i = 0; i < hk_.size(); ++i) for (size_t i = 0; i < accumulated_gradient.size(); ++i)
{ {
hk_[i] = hk_[i] * frac + momentum_rhs.hk_[i] * rhs_frac; accumulated_gradient[i] = accumulated_gradient[i] * frac + momentum_rhs.accumulated_gradient[i] * rhs_frac;
} }
} }
private: private:
Float64 alpha_{0.1}; Float64 alpha_{0.1};
std::vector<Float64> hk_; std::vector<Float64> accumulated_gradient;
}; };
class LinearModelData class LinearModelData

92
dbms/tests/queries/0_stateless/00951_ml_test.reference Normal file
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16
dbms/tests/queries/0_stateless/00951_ml_test.sql Normal file
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@ -0,0 +1,16 @@
CREATE DATABASE IF NOT EXISTS test;
DROP TABLE IF EXISTS test.defaults;
CREATE TABLE IF NOT EXISTS test.defaults
(
param1 Float64,
param2 Float64,
target Float64,
predict1 Float64,
predict2 Float64
) ENGINE = Memory;
insert into test.defaults values (1,2,1,-1,-2),(-1,-2,0,1,2),(1,2,1,-1,-2),(-1,-2,0,1,2),(1,2,1,-1,-2),(-1,-2,0,1,2),(1,2,1,-1,-2),(-1,-2,0,1,2),(1,2,1,-1,-2),(-1,-2,0,1,2),(1,2,1,-1,-2),(-1,-2,0,1,2),(1,2,1,-1,-2),(-1,-2,0,1,2),(1,2,1,-1,-2),(-1,-2,0,1,2),(1,2,1,-1,-2),(-1,-2,0,1,2),(1,2,1,-1,-2),(-1,-2,0,1,2),(1,2,1,-1,-2),(-1,-2,0,1,2),(1,2,1,-1,-2),(-1,-2,0,1,2),(1,2,1,-1,-2),(-1,-2,0,1,2),(1,2,1,-1,-2),(-1,-2,0,1,2),(1,2,1,-1,-2),(-1,-2,0,1,2),(1,2,1,-1,-2),(-1,-2,0,1,2),(1,2,1,-1,-2),(-1,-2,0,1,2),(1,2,1,-1,-2),(-1,-2,0,1,2),(1,2,1,-1,-2),(-1,-2,0,1,2),(1,2,1,-1,-2),(-1,-2,0,1,2),(1,2,1,-1,-2),(-1,-2,0,1,2),(1,2,1,-1,-2),(-1,-2,0,1,2),(1,2,1,-1,-2),(-1,-2,0,1,2),(1,2,1,-1,-2),(-1,-2,0,1,2),(1,2,1,-1,-2),(-1,-2,0,1,2),(1,2,1,-1,-2),(-1,-2,0,1,2),(1,2,1,-1,-2),(-1,-2,0,1,2),(1,2,1,-1,-2),(-1,-2,0,1,2),(1,2,1,-1,-2),(-1,-2,0,1,2),(1,2,1,-1,-2),(-1,-2,0,1,2),(1,2,1,-1,-2),(-1,-2,0,1,2),(1,2,1,-1,-2),(-1,-2,0,1,2),(1,2,1,-1,-2),(-1,-2,0,1,2),(1,2,1,-1,-2),(-1,-2,0,1,2),(1,2,1,-1,-2),(-1,-2,0,1,2),(1,2,1,-1,-2),(-1,-2,0,1,2),(1,2,1,-1,-2),(-1,-2,0,1,2),(1,2,1,-1,-2),(-1,-2,0,1,2),(1,2,1,-1,-2),(-1,-2,0,1,2),(1,2,1,-1,-2),(-1,-2,0,1,2),(1,2,1,-1,-2),(-1,-2,0,1,2),(1,2,1,-1,-2),(-1,-2,0,1,2),(1,2,1,-1,-2),(-1,-2,0,1,2),(1,2,1,-1,-2),(-1,-2,0,1,2),(1,2,1,-1,-2),(-1,-2,0,1,2),(1,2,1,-1,-2),(-1,-2,0,1,2)
DROP TABLE IF EXISTS test.model;
create table test.model engine = Memory as select LogisticRegressionState(0.1, 5, 1.0)(target, param1, param2) as state from test.defaults;
with (select state from test.model) as model select evalMLMethod(model, predict1, predict2) from test.defaults;

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dbms/tests/queries/0_stateless/00952_ml_test.good_reference Normal file
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dbms/tests/queries/0_stateless/00952_ml_test.norm_reference Normal file
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