Remane regression methods.

dbms/src/AggregateFunctions/AggregateFunctionMLMethod.cpp

@@ -110,8 +110,8 @@ namespace
 
 void registerAggregateFunctionMLMethod(AggregateFunctionFactory & factory)
 {
-    factory.registerFunction("LinearRegression", createAggregateFunctionMLMethod<FuncLinearRegression>);
-    factory.registerFunction("LogisticRegression", createAggregateFunctionMLMethod<FuncLogisticRegression>);
+    factory.registerFunction("linearRegression", createAggregateFunctionMLMethod<FuncLinearRegression>);
+    factory.registerFunction("logisticRegression", createAggregateFunctionMLMethod<FuncLogisticRegression>);
 }
 
 LinearModelData::LinearModelData(

dbms/src/AggregateFunctions/AggregateFunctionMLMethod.h

@@ -321,10 +321,10 @@ private:
 
 struct NameLinearRegression
 {
-    static constexpr auto name = "LinearRegression";
+    static constexpr auto name = "linearRegression";
 };
 struct NameLogisticRegression
 {
-    static constexpr auto name = "LogisticRegression";
+    static constexpr auto name = "logisticRegression";
 };
 }

dbms/src/AggregateFunctions/AggregateFunctionSimpleLinearRegression.cpp

@@ -1,8 +1,9 @@
-#include <AggregateFunctions/AggregateFunctionLeastSqr.h>
+#include <AggregateFunctions/AggregateFunctionSimpleLinearRegression.h>
 
 #include <AggregateFunctions/AggregateFunctionFactory.h>
 #include <AggregateFunctions/FactoryHelpers.h>
 
+#include <Core/TypeListNumber.h>
 
 namespace DB
 {
@@ -10,7 +11,7 @@ namespace DB
 namespace
 {
 
-AggregateFunctionPtr createAggregateFunctionLeastSqr(
+AggregateFunctionPtr createAggregateFunctionSimpleLinearRegression(
     const String & name,
     const DataTypes & arguments,
     const Array & params
@@ -20,16 +21,11 @@ AggregateFunctionPtr createAggregateFunctionLeastSqr(
     assertBinary(name, arguments);
 
     const IDataType * x_arg = arguments.front().get();
-
-    WhichDataType which_x {
-        x_arg
-    };
+    WhichDataType which_x = x_arg;
 
     const IDataType * y_arg = arguments.back().get();
+    WhichDataType which_y = y_arg;
 
-    WhichDataType which_y {
-        y_arg
-    };
 
     #define FOR_LEASTSQR_TYPES_2(M, T) \
         M(T, UInt8) \
@@ -55,7 +51,7 @@ AggregateFunctionPtr createAggregateFunctionLeastSqr(
         FOR_LEASTSQR_TYPES_2(M, Float64)
     #define DISPATCH(T1, T2) \
         if (which_x.idx == TypeIndex::T1 && which_y.idx == TypeIndex::T2) \
-            return std::make_shared<AggregateFunctionLeastSqr<T1, T2>>( \
+            return std::make_shared<AggregateFunctionSimpleLinearRegression<T1, T2>>( \
                 arguments, \
                 params \
             );
@@ -77,9 +73,9 @@ AggregateFunctionPtr createAggregateFunctionLeastSqr(
 
 }
 
-void registerAggregateFunctionLeastSqr(AggregateFunctionFactory & factory)
+void registerAggregateFunctionSimpleLinearRegression(AggregateFunctionFactory & factory)
 {
-    factory.registerFunction("leastSqr", createAggregateFunctionLeastSqr);
+    factory.registerFunction("simpleLinearRegression", createAggregateFunctionSimpleLinearRegression);
 }
 
 }

dbms/src/AggregateFunctions/AggregateFunctionSimpleLinearRegression.h

@@ -19,7 +19,7 @@ namespace ErrorCodes
 }
 
 template <typename X, typename Y, typename Ret>
-struct AggregateFunctionLeastSqrData final
+struct AggregateFunctionSimpleLinearRegressionData final
 {
     size_t count = 0;
     Ret sum_x = 0;
@@ -36,7 +36,7 @@ struct AggregateFunctionLeastSqrData final
         sum_xy += x * y;
     }
 
-    void merge(const AggregateFunctionLeastSqrData & other)
+    void merge(const AggregateFunctionSimpleLinearRegressionData & other)
     {
         count += other.count;
         sum_x += other.sum_x;
@@ -85,19 +85,19 @@ struct AggregateFunctionLeastSqrData final
 /// Calculates simple linear regression parameters.
 /// Result is a tuple (k, b) for y = k * x + b equation, solved by least squares approximation.
 template <typename X, typename Y, typename Ret = Float64>
-class AggregateFunctionLeastSqr final : public IAggregateFunctionDataHelper<
-    AggregateFunctionLeastSqrData<X, Y, Ret>,
-    AggregateFunctionLeastSqr<X, Y, Ret>
+class AggregateFunctionSimpleLinearRegression final : public IAggregateFunctionDataHelper<
+    AggregateFunctionSimpleLinearRegressionData<X, Y, Ret>,
+    AggregateFunctionSimpleLinearRegression<X, Y, Ret>
 >
 {
 public:
-    AggregateFunctionLeastSqr(
+    AggregateFunctionSimpleLinearRegression(
         const DataTypes & arguments,
         const Array & params
     ):
         IAggregateFunctionDataHelper<
-            AggregateFunctionLeastSqrData<X, Y, Ret>,
-            AggregateFunctionLeastSqr<X, Y, Ret>
+            AggregateFunctionSimpleLinearRegressionData<X, Y, Ret>,
+            AggregateFunctionSimpleLinearRegression<X, Y, Ret>
         > {arguments, params}
     {
         // notice: arguments has been checked before
@@ -105,7 +105,7 @@ public:
 
     String getName() const override
     {
-        return "leastSqr";
+        return "simpleLinearRegression";
     }
 
     const char * getHeaderFilePath() const override
@@ -120,12 +120,8 @@ public:
         Arena *
     ) const override
     {
-        auto col_x {
-            static_cast<const ColumnVector<X> *>(columns[0])
-        };
-        auto col_y {
-            static_cast<const ColumnVector<Y> *>(columns[1])
-        };
+        auto col_x = static_cast<const ColumnVector<X> *>(columns[0]);
+        auto col_y = static_cast<const ColumnVector<Y> *>(columns[1]);
 
         X x = col_x->getData()[row_num];
         Y y = col_y->getData()[row_num];
@@ -159,12 +155,14 @@ public:
 
     DataTypePtr getReturnType() const override
     {
-        DataTypes types {
+        DataTypes types
+        {
             std::make_shared<DataTypeNumber<Ret>>(),
             std::make_shared<DataTypeNumber<Ret>>(),
         };
 
-        Strings names {
+        Strings names
+        {
             "k",
             "b",
         };

dbms/src/AggregateFunctions/registerAggregateFunctions.cpp

@@ -30,7 +30,7 @@ void registerAggregateFunctionsBitmap(AggregateFunctionFactory &);
 void registerAggregateFunctionsMaxIntersections(AggregateFunctionFactory &);
 void registerAggregateFunctionMLMethod(AggregateFunctionFactory &);
 void registerAggregateFunctionEntropy(AggregateFunctionFactory &);
-void registerAggregateFunctionLeastSqr(AggregateFunctionFactory &);
+void registerAggregateFunctionSimpleLinearRegression(AggregateFunctionFactory &);
 
 void registerAggregateFunctionCombinatorIf(AggregateFunctionCombinatorFactory &);
 void registerAggregateFunctionCombinatorArray(AggregateFunctionCombinatorFactory &);
@@ -73,7 +73,7 @@ void registerAggregateFunctions()
         registerAggregateFunctionTSgroupSum(factory);
         registerAggregateFunctionMLMethod(factory);
         registerAggregateFunctionEntropy(factory);
-        registerAggregateFunctionLeastSqr(factory);
+        registerAggregateFunctionSimpleLinearRegression(factory);
     }
 
     {

dbms/tests/queries/0_stateless/00917_least_sqr.sql

@@ -1,9 +1,9 @@
-select arrayReduce('leastSqr', [1, 2, 3, 4], [100, 110, 120, 130]);
-select arrayReduce('leastSqr', [1, 2, 3, 4], [100, 110, 120, 131]);
-select arrayReduce('leastSqr', [-1, -2, -3, -4], [-100, -110, -120, -130]);
-select arrayReduce('leastSqr', [5, 5.1], [6, 6.1]);
-select arrayReduce('leastSqr', [0], [0]);
-select arrayReduce('leastSqr', [3, 4], [3, 3]);
-select arrayReduce('leastSqr', [3, 3], [3, 4]);
-select arrayReduce('leastSqr', emptyArrayUInt8(), emptyArrayUInt8());
+select arrayReduce('simpleLinearRegression', [1, 2, 3, 4], [100, 110, 120, 130]);
+select arrayReduce('simpleLinearRegression', [1, 2, 3, 4], [100, 110, 120, 131]);
+select arrayReduce('simpleLinearRegression', [-1, -2, -3, -4], [-100, -110, -120, -130]);
+select arrayReduce('simpleLinearRegression', [5, 5.1], [6, 6.1]);
+select arrayReduce('simpleLinearRegression', [0], [0]);
+select arrayReduce('simpleLinearRegression', [3, 4], [3, 3]);
+select arrayReduce('simpleLinearRegression', [3, 3], [3, 4]);
+select arrayReduce('simpleLinearRegression', emptyArrayUInt8(), emptyArrayUInt8());
 

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

@@ -11,7 +11,7 @@ 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 linearRegressionState(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));

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 logisticRegressionState(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);