some changes and fixes on regression methods

dbms/src/AggregateFunctions/AggregateFunctionMLMethod.cpp

@@ -7,6 +7,7 @@
 #include <Columns/ColumnTuple.h>
 #include <Common/FieldVisitors.h>
 #include <Common/typeid_cast.h>
+#include <Interpreters/convertFieldToType.h>
 #include "AggregateFunctionFactory.h"
 #include "FactoryHelpers.h"
 #include "Helpers.h"
@@ -45,11 +46,11 @@ namespace
 
         /// Such default parameters were picked because they did good on some tests,
         /// though it still requires to fit parameters to achieve better result
-        auto learning_rate = Float64(0.01);
-        auto l2_reg_coef = Float64(0.01);
-        UInt32 batch_size = 1;
+        auto learning_rate = Float64(0.00001);
+        auto l2_reg_coef = Float64(0.1);
+        UInt32 batch_size = 15;
 
-        std::shared_ptr<IWeightsUpdater> weights_updater = std::make_shared<StochasticGradientDescent>();
+        std::string weights_updater_name = "\'SGD\'";
         std::shared_ptr<IGradientComputer> gradient_computer;
 
         if (!parameters.empty())
@@ -66,19 +67,8 @@ namespace
         }
         if (parameters.size() > 3)
         {
-            if (applyVisitor(FieldVisitorToString(), parameters[3]) == "\'SGD\'")
-            {
-                weights_updater = std::make_shared<StochasticGradientDescent>();
-            }
-            else if (applyVisitor(FieldVisitorToString(), parameters[3]) == "\'Momentum\'")
-            {
-                weights_updater = std::make_shared<Momentum>();
-            }
-            else if (applyVisitor(FieldVisitorToString(), parameters[3]) == "\'Nesterov\'")
-            {
-                weights_updater = std::make_shared<Nesterov>();
-            }
-            else
+            weights_updater_name = applyVisitor(FieldVisitorToString(), parameters[3]);
+            if (weights_updater_name != "\'SGD\'" && weights_updater_name != "\'Momentum\'" && weights_updater_name != "\'Nesterov\'")
             {
                 throw Exception("Invalid parameter for weights updater", ErrorCodes::ILLEGAL_TYPE_OF_ARGUMENT);
             }
@@ -100,20 +90,19 @@ namespace
         return std::make_shared<Method>(
             argument_types.size() - 1,
             gradient_computer,
-            weights_updater,
+            weights_updater_name,
             learning_rate,
             l2_reg_coef,
             batch_size,
             argument_types,
             parameters);
     }
-
 }
 
 void registerAggregateFunctionMLMethod(AggregateFunctionFactory & factory)
 {
-    factory.registerFunction("linearRegression", createAggregateFunctionMLMethod<FuncLinearRegression>);
-    factory.registerFunction("logisticRegression", createAggregateFunctionMLMethod<FuncLogisticRegression>);
+    factory.registerFunction("stochasticLinearRegression", createAggregateFunctionMLMethod<FuncLinearRegression>);
+    factory.registerFunction("stochasticLogisticRegression", createAggregateFunctionMLMethod<FuncLogisticRegression>);
 }
 
 LinearModelData::LinearModelData(
@@ -165,11 +154,8 @@ void LinearModelData::returnWeights(IColumn & to) const
             = static_cast<ColumnFloat64 &>(arr_to.getData()).getData();
 
     val_to.reserve(old_size + size);
-    size_t i = 0;
-    while (i < weights.size())
-    {
+    for (size_t i = 0; i != weights.size(); ++i) {
         val_to.push_back(weights[i]);
-        i++;
     }
     val_to.push_back(bias);
 }
@@ -217,7 +203,8 @@ void LinearModelData::merge(const DB::LinearModelData & rhs)
 void LinearModelData::add(const IColumn ** columns, size_t row_num)
 {
     /// first column stores target; features start from (columns + 1)
-    const auto target = (*columns[0])[row_num].get<Float64>();
+    Float64 target = (*columns[0]).getFloat64(row_num);
+
     /// Here we have columns + 1 as first column corresponds to target value, and others - to features
     weights_updater->add_to_batch(
         gradient_batch, *gradient_computer, weights, bias, learning_rate, l2_reg_coef, target, columns + 1, row_num);
@@ -410,7 +397,7 @@ void LogisticRegression::compute(
     Float64 derivative = bias;
     for (size_t i = 0; i < weights.size(); ++i)
     {
-        auto value = (*columns[i])[row_num].get<Float64>();
+        auto value = (*columns[i]).getFloat64(row_num);
         derivative += weights[i] * value;
     }
     derivative *= target;
@@ -419,8 +406,8 @@ void LogisticRegression::compute(
     batch_gradient[weights.size()] += learning_rate * target / (derivative + 1);
     for (size_t i = 0; i < weights.size(); ++i)
     {
-        auto value = (*columns[i])[row_num].get<Float64>();
-        batch_gradient[i] += learning_rate * target * value / (derivative + 1) - 2 * l2_reg_coef * weights[i];
+        auto value = (*columns[i]).getFloat64(row_num);
+        batch_gradient[i] += learning_rate * target * value / (derivative + 1) - 2 * learning_rate * l2_reg_coef * weights[i];
     }
 }
 
@@ -483,7 +470,7 @@ void LinearRegression::compute(
     Float64 derivative = (target - bias);
     for (size_t i = 0; i < weights.size(); ++i)
     {
-        auto value = (*columns[i])[row_num].get<Float64>();
+        auto value = (*columns[i]).getFloat64(row_num);
         derivative -= weights[i] * value;
     }
     derivative *= (2 * learning_rate);
@@ -491,8 +478,8 @@ void LinearRegression::compute(
     batch_gradient[weights.size()] += derivative;
     for (size_t i = 0; i < weights.size(); ++i)
     {
-        auto value = (*columns[i])[row_num].get<Float64>();
-        batch_gradient[i] += derivative * value - 2 * l2_reg_coef * weights[i];
+        auto value = (*columns[i]).getFloat64(row_num);
+        batch_gradient[i] += derivative * value - 2 * learning_rate * l2_reg_coef * weights[i];
     }
 }
 

dbms/src/AggregateFunctions/AggregateFunctionMLMethod.h

@@ -86,7 +86,7 @@ public:
         Float64 l2_reg_coef,
         Float64 target,
         const IColumn ** columns,
-        size_t row_num) override;
+        size_t row_num) override ;
 
     void predict(
         ColumnVector<Float64>::Container & container,
@@ -256,7 +256,7 @@ public:
     explicit AggregateFunctionMLMethod(
         UInt32 param_num,
         std::shared_ptr<IGradientComputer> gradient_computer,
-        std::shared_ptr<IWeightsUpdater> weights_updater,
+        std::string weights_updater_name,
         Float64 learning_rate,
         Float64 l2_reg_coef,
         UInt32 batch_size,
@@ -268,15 +268,17 @@ public:
         , l2_reg_coef(l2_reg_coef)
         , batch_size(batch_size)
         , gradient_computer(std::move(gradient_computer))
-        , weights_updater(std::move(weights_updater))
+        , weights_updater_name(std::move(weights_updater_name))
     {
     }
 
+    /// This function is called when select linearRegression(...) is called
     DataTypePtr getReturnType() const override
     {
         return std::make_shared<DataTypeArray>(std::make_shared<DataTypeFloat64>());
     }
 
+    /// This function is called from evalMLMethod function for correct predictValues call
     DataTypePtr getReturnTypeToPredict() const override
     {
         return std::make_shared<DataTypeNumber<Float64>>();
@@ -284,7 +286,21 @@ public:
 
     void create(AggregateDataPtr place) const override
     {
-        new (place) Data(learning_rate, l2_reg_coef, param_num, batch_size, gradient_computer, weights_updater);
+        std::shared_ptr<IWeightsUpdater> new_weights_updater;
+        if (weights_updater_name == "\'SGD\'")
+        {
+            new_weights_updater = std::make_shared<StochasticGradientDescent>();
+        } else if (weights_updater_name == "\'Momentum\'")
+        {
+            new_weights_updater = std::make_shared<Momentum>();
+        } else if (weights_updater_name == "\'Nesterov\'")
+        {
+            new_weights_updater = std::make_shared<Nesterov>();
+        } else
+        {
+            throw Exception("Illegal name of weights updater (should have been checked earlier)", ErrorCodes::LOGICAL_ERROR);
+        }
+        new (place) Data(learning_rate, l2_reg_coef, param_num, batch_size, gradient_computer, new_weights_updater);
     }
 
     void add(AggregateDataPtr place, const IColumn ** columns, size_t row_num, Arena *) const override
@@ -307,6 +323,7 @@ public:
                     + ". Required: " + std::to_string(param_num + 1),
                 ErrorCodes::NUMBER_OF_ARGUMENTS_DOESNT_MATCH);
 
+        /// This cast is correct because column type is based on getReturnTypeToPredict.
         auto & column = dynamic_cast<ColumnVector<Float64> &>(to);
 
         this->data(place).predict(column.getData(), block, arguments, context);
@@ -328,15 +345,15 @@ private:
     Float64 l2_reg_coef;
     UInt32 batch_size;
     std::shared_ptr<IGradientComputer> gradient_computer;
-    std::shared_ptr<IWeightsUpdater> weights_updater;
+    std::string weights_updater_name;
 };
 
 struct NameLinearRegression
 {
-    static constexpr auto name = "linearRegression";
+    static constexpr auto name = "stochasticLinearRegression";
 };
 struct NameLogisticRegression
 {
-    static constexpr auto name = "logisticRegression";
+    static constexpr auto name = "stochasticLogisticRegression";
 };
 }

dbms/src/Columns/ColumnVector.cpp

@@ -215,6 +215,12 @@ UInt64 ColumnVector<T>::get64(size_t n) const
     return ext::bit_cast<UInt64>(data[n]);
 }
 
+template <typename T>
+Float64 ColumnVector<T>::getFloat64(size_t n) const
+{
+    return static_cast<Float64>(data[n]);
+}
+
 template <typename T>
 void ColumnVector<T>::insertRangeFrom(const IColumn & src, size_t start, size_t length)
 {

dbms/src/Columns/ColumnVector.h

@@ -202,6 +202,8 @@ public:
 
     UInt64 get64(size_t n) const override;
 
+    Float64 getFloat64(size_t n) const override;
+
     UInt64 getUInt(size_t n) const override
     {
         return UInt64(data[n]);

dbms/src/Columns/IColumn.h

@@ -91,6 +91,13 @@ public:
         throw Exception("Method get64 is not supported for " + getName(), ErrorCodes::NOT_IMPLEMENTED);
     }
 
+    /// If column stores native numeric type, it returns n-th element casted to Float64
+    /// Is used in regression methods to cast each features into uniform type
+    virtual Float64 getFloat64(size_t /*n*/) const
+    {
+        throw Exception("Method getFloat64 is not supported for " + getName(), ErrorCodes::NOT_IMPLEMENTED);
+    }
+
     /** If column is numeric, return value of n-th element, casted to UInt64.
       * For NULL values of Nullable column it is allowed to return arbitrary value.
       * Otherwise throw an exception.

dbms/tests/performance/linear_regression.xml

@@ -17,22 +17,21 @@
         <min_time/>
     </main_metric>
 
-    <create_query>CREATE TABLE test_model engine = Memory as select linearRegressionState(0.0001)(Age, Income, ParamPrice, Robotness, RefererHash) as state from test.hits</create_query>
+    <create_query>DROP TABLE IF EXISTS test_model</create_query>
+    <create_query>CREATE TABLE test_model engine = Memory as select stochasticLinearRegressionState(0.0001)(Age, Income, ParamPrice, Robotness, RefererHash) as state from test.hits</create_query>
 
 <!--    Check model fit-->
-    <query>with (SELECT linearRegressionState(0.0001, 0, 15)(Age, Income, ParamPrice, Robotness, RefererHash) FROM test.hits) as model select toColumnTypeName(model)</query>
+    <query>WITH (SELECT stochasticLinearRegressionState(0.0001, 0, 15)(Age, Income, ParamPrice, Robotness, RefererHash) FROM test.hits) AS model SELECT 1</query>
+    <query>SELECT stochasticLinearRegression(Age, Income, ParamPrice, Robotness, RefererHash) FROM test.hits</query>
 
 <!--    Check model fit with Momentum-->
-    <query>with (SELECT linearRegressionState(0.0001, 0, 15, 'Momentum')(Age, Income, ParamPrice, Robotness, RefererHash) FROM test.hits) as model select toColumnTypeName(model)</query>
+    <query>WITH (SELECT stochasticLinearRegressionState(0.0001, 0, 15, 'Momentum')(Age, Income, ParamPrice, Robotness, RefererHash) FROM test.hits) AS model SELECT 1</query>
 
 <!--    Check model fit with Nesterov-->
-    <query>with (SELECT linearRegressionState(0.0001, 0, 15, 'Nesterov')(Age, Income, ParamPrice, Robotness, RefererHash) FROM test.hits) as model select toColumnTypeName(model)</query>
+    <query>WITH (SELECT stochasticLinearRegressionState(0.0001, 0, 15, 'Nesterov')(Age, Income, ParamPrice, Robotness, RefererHash) FROM test.hits) AS model SELECT 1</query>
 
 <!--    Check model predict-->
     <query>with (SELECT state FROM test_model) as model select evalMLMethod(model, Income, ParamPrice, Robotness, RefererHash) from test.hits</query>
 
-
-    <drop_query>DROP TABLE IF EXISTS train_dataset</drop_query>
-    <drop_query>DROP TABLE IF EXISTS test_dataset</drop_query>
     <drop_query>DROP TABLE IF EXISTS test_model</drop_query>
 </test>

dbms/tests/queries/0_stateless/00944_ml_test.reference

@@ -1,2 +1,3 @@
 1
 1
+1

dbms/tests/queries/0_stateless/00944_ml_test.sql

@@ -11,7 +11,10 @@ CREATE TABLE IF NOT EXISTS test.defaults
 insert into test.defaults values (-3.273, -1.452, 4.267, 20.0, 40.0), (0.121, -0.615, 4.290, 20.0, 40.0);
 
 DROP TABLE IF EXISTS test.model;
-create table test.model engine = Memory as select linearRegressionState(0.1, 0.0, 2, 'SGD')(target, param1, param2) as state from test.defaults;
+create table test.model engine = Memory as select stochasticLinearRegressionState(0.1, 0.0, 2, 'SGD')(target, param1, param2) as state from test.defaults;
 
 select ans < -61.374 and ans > -61.375 from
 (with (select state from remote('127.0.0.1', test.model)) as model select evalMLMethod(model, predict1, predict2) as ans from remote('127.0.0.1', test.defaults));
+
+SELECT 0 < ans[1] and ans[1] < 0.15 and 0.95 < ans[2] and ans[2] < 1.0 and 0 < ans[3] and ans[3] < 0.05 FROM
+(SELECT stochasticLinearRegression(0.000001, 0.01, 100)(number, rand() % 100, number) AS ans FROM numbers(1000))

dbms/tests/queries/0_stateless/00945_ml_test.sql

@@ -10,7 +10,7 @@ CREATE TABLE IF NOT EXISTS test.defaults
 ) ENGINE = Memory;
 insert into test.defaults values (1,2,1,-1,-2),(-1,-2,-1,1,2),(1,2,1,-1,-2),(-1,-2,-1,1,2),(1,2,1,-1,-2),(-1,-2,-1,1,2),(1,2,1,-1,-2),(-1,-2,-1,1,2),(1,2,1,-1,-2),(-1,-2,-1,1,2),(1,2,1,-1,-2),(-1,-2,-1,1,2),(1,2,1,-1,-2),(-1,-2,-1,1,2),(1,2,1,-1,-2),(-1,-2,-1,1,2),(1,2,1,-1,-2),(-1,-2,-1,1,2),(1,2,1,-1,-2),(-1,-2,-1,1,2),(1,2,1,-1,-2),(-1,-2,-1,1,2),(1,2,1,-1,-2),(-1,-2,-1,1,2),(1,2,1,-1,-2),(-1,-2,-1,1,2),(1,2,1,-1,-2),(-1,-2,-1,1,2),(1,2,1,-1,-2),(-1,-2,-1,1,2),(1,2,1,-1,-2),(-1,-2,-1,1,2),(1,2,1,-1,-2),(-1,-2,-1,1,2),(1,2,1,-1,-2),(-1,-2,-1,1,2),(1,2,1,-1,-2),(-1,-2,-1,1,2),(1,2,1,-1,-2),(-1,-2,-1,1,2),(1,2,1,-1,-2),(-1,-2,-1,1,2),(1,2,1,-1,-2),(-1,-2,-1,1,2),(1,2,1,-1,-2),(-1,-2,-1,1,2),(1,2,1,-1,-2),(-1,-2,-1,1,2),(1,2,1,-1,-2),(-1,-2,-1,1,2),(1,2,1,-1,-2),(-1,-2,-1,1,2),(1,2,1,-1,-2),(-1,-2,-1,1,2),(1,2,1,-1,-2),(-1,-2,-1,1,2),(1,2,1,-1,-2),(-1,-2,-1,1,2),(1,2,1,-1,-2),(-1,-2,-1,1,2),(1,2,1,-1,-2),(-1,-2,-1,1,2),(1,2,1,-1,-2),(-1,-2,-1,1,2),(1,2,1,-1,-2),(-1,-2,-1,1,2),(1,2,1,-1,-2),(-1,-2,-1,1,2),(1,2,1,-1,-2),(-1,-2,-1,1,2),(1,2,1,-1,-2),(-1,-2,-1,1,2),(1,2,1,-1,-2),(-1,-2,-1,1,2),(1,2,1,-1,-2),(-1,-2,-1,1,2),(1,2,1,-1,-2),(-1,-2,-1,1,2),(1,2,1,-1,-2),(-1,-2,-1,1,2),(1,2,1,-1,-2),(-1,-2,-1,1,2),(1,2,1,-1,-2),(-1,-2,-1,1,2),(1,2,1,-1,-2),(-1,-2,-1,1,2),(1,2,1,-1,-2),(-1,-2,-1,1,2),(1,2,1,-1,-2),(-1,-2,-1,1,2),(1,2,1,-1,-2),(-1,-2,-1,1,2)
 DROP TABLE IF EXISTS test.model;
-create table test.model engine = Memory as select logisticRegressionState(0.1, 0.0, 1.0, 'SGD')(target, param1, param2) as state from test.defaults;
+create table test.model engine = Memory as select stochasticLogisticRegressionState(0.1, 0.0, 1.0, 'SGD')(target, param1, param2) as state from test.defaults;
 
 select ans < 1.1 and ans > 0.9 from
 (with (select state from test.model) as model select evalMLMethod(model, predict1, predict2) as ans from test.defaults limit 2);