diff --git a/dbms/src/AggregateFunctions/AggregateFunctionMLMethod.h b/dbms/src/AggregateFunctions/AggregateFunctionMLMethod.h index 0dc0999da3f..286e19eee50 100644 --- a/dbms/src/AggregateFunctions/AggregateFunctionMLMethod.h +++ b/dbms/src/AggregateFunctions/AggregateFunctionMLMethod.h @@ -178,12 +178,28 @@ public: } void predict_for_all(ColumnVector::Container & container, Block & block, const ColumnNumbers & arguments, const std::vector & weights, Float64 bias) const override { - // TODO - std::ignore = container; - std::ignore = block; - std::ignore = arguments; - std::ignore = weights; - std::ignore = bias; + size_t rows_num = block.rows(); + std::vector results(rows_num, bias); + + for (size_t i = 1; i < arguments.size(); ++i) + { + 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(); + } + } + 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 & weights, Float64 & bias, const std::vector & batch_gradient) override { /// 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) { - 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) { - 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 { - const auto & momentum_rhs = dynamic_cast(rhs); - for (size_t i = 0; i < hk_.size(); ++i) + auto & momentum_rhs = static_cast(rhs); + 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: Float64 alpha_{0.1}; - std::vector hk_; + std::vector accumulated_gradient; }; class LinearModelData diff --git a/dbms/tests/queries/0_stateless/00951_ml_test.reference b/dbms/tests/queries/0_stateless/00951_ml_test.reference new file mode 100644 index 00000000000..b636d351b84 --- /dev/null +++ b/dbms/tests/queries/0_stateless/00951_ml_test.reference @@ -0,0 +1,92 @@ 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a/dbms/tests/queries/0_stateless/00951_ml_test.sql b/dbms/tests/queries/0_stateless/00951_ml_test.sql new file mode 100644 index 00000000000..ca9c2f99505 --- /dev/null +++ b/dbms/tests/queries/0_stateless/00951_ml_test.sql @@ -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 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+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; diff --git a/dbms/tests/queries/0_stateless/00952_ml_test.good_reference b/dbms/tests/queries/0_stateless/00952_ml_test.good_reference new file mode 100644 index 00000000000..5ad7a9176a4 --- /dev/null +++ b/dbms/tests/queries/0_stateless/00952_ml_test.good_reference @@ -0,0 +1,370 @@ +0.72 +0.72 +0.72 +0.72 +0.72 +0.72 +0.72 +0.72 +0.72 +0.72 +0.72 +0.72 +0.72 +0.89 +0.89 +0.89 +0.89 +0.89 +0.89 +0.89 +0.89 +0.89 +0.89 +0.89 +0.89 +0.95 +0.95 +0.95 +0.95 +0.95 +0.95 +0.95 +0.95 +0.95 +0.95 +0.95 +0.95 +0.79 +0.79 +0.79 +0.79 +0.79 +0.79 +0.79 +0.79 +0.79 +0.79 +0.79 +0.79 +0.79 +0.39 +0.39 +0.39 +0.39 +0.39 +0.39 +0.39 +0.39 +0.39 +0.39 +0.39 +0.39 +0.38 +0.38 +0.38 +0.38 +0.38 +0.38 +0.38 +0.38 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@@ -0,0 +1,25 @@ +CREATE DATABASE IF NOT EXISTS test; +DROP TABLE IF EXISTS test.defaults; +CREATE TABLE IF NOT EXISTS test.defaults +( + param1 Float64, + param2 Float64, + param3 Float64, + param4 Float64, + param5 Float64, + param6 Float64, + param7 Float64, + target Float64, + predict1 Float64, + predict2 Float64, + predict3 Float64, + predict4 Float64, + predict5 Float64, + predict6 Float64, + predict7 Float64 + +) ENGINE = Memory; +insert into test.defaults values 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+DROP TABLE IF EXISTS test.model; +create table test.model engine = Memory as select LinearRegressionState(0.1, 5, 1.0)(target, param1, param2, param3, param4, param5, param6, param7) as state from test.defaults; +with (select state from test.model) as model select evalMLMethod(model, predict1, predict2, predict3, predict4, predict5, predict6, predict7) from test.defaults; diff --git a/dbms/tests/queries/0_stateless/00953_ml_test.good_reference b/dbms/tests/queries/0_stateless/00953_ml_test.good_reference new file mode 100644 index 00000000000..ff9c61e403b --- /dev/null +++ b/dbms/tests/queries/0_stateless/00953_ml_test.good_reference @@ -0,0 +1,580 @@ +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +0.0 +0.0 +0.0 +0.0 +0.0 +0.0 +0.0 +0.0 +0.0 +0.0 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+CREATE TABLE IF NOT EXISTS test.defaults +( + param1 Float64, + param2 Float64, + param3 Float64, + param4 Float64, + param5 Float64, + param6 Float64, + param7 Float64, + target Float64, + predict1 Float64, + predict2 Float64, + predict3 Float64, + predict4 Float64, + predict5 Float64, + predict6 Float64, + predict7 Float64 + +) ENGINE = Memory; +insert into test.defaults values 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+DROP TABLE IF EXISTS test.model; +create table test.model engine = Memory as select LogisticRegressionState(400, 2, 2.0)(target, param1, param2, param3, param4, param5, param6, param7) as state from test.defaults; +with (select state from test.model) as model select evalMLMethod(model, predict1, predict2, predict3, predict4, predict5, predict6, predict7) from test.defaults;