linear regression

dbms/src/AggregateFunctions/AggregateFunctionMLMethod.h

@@ -459,10 +459,10 @@ private:
 
 
 template <
-        /// Implemented Machine Learning method
-        typename Data,
-        /// Name of the method
-        typename Name
+    /// Implemented Machine Learning method
+    typename Data,
+    /// Name of the method
+    typename Name
 >
 class AggregateFunctionMLMethod final : public IAggregateFunctionDataHelper<Data, AggregateFunctionMLMethod<Data, Name>>
 {

dbms/src/AggregateFunctions/IAggregateFunction.h

@@ -152,7 +152,7 @@ public:
     IAggregateFunctionDataHelper(const DataTypes & argument_types_, const Array & parameters_)
             : IAggregateFunctionHelper<Derived>(argument_types_, parameters_) {}
 
-    virtual void create(AggregateDataPtr place) const override
+    void create(AggregateDataPtr place) const override
     {
         new (place) Data;
     }

dbms/src/Columns/ColumnAggregateFunction.cpp

@@ -84,16 +84,11 @@ MutableColumnPtr ColumnAggregateFunction::convertToValues() const
         *   AggregateFunction(quantileTiming(0.5), UInt64)
         * into UInt16 - already finished result of `quantileTiming`.
         */
-//    if (const AggregateFunctionState * function_state = typeid_cast<const AggregateFunctionState *>(func.get()))
-//    {
-//        auto res = createView();
-//        res->set(function_state->getNestedFunction());
-//        res->data.assign(data.begin(), data.end());
-//        return res;
-//    }
-//
-//    MutableColumnPtr res = func->getReturnType()->createColumn();
-//    res->reserve(data.size());
+
+    /** Convertion function is used in convertToValues and predictValues
+        * in the similar part of both functions
+        */
+
     MutableColumnPtr res;
     if (convertion(&res))
     {
@@ -114,8 +109,6 @@ MutableColumnPtr ColumnAggregateFunction::predictValues(Block & block, const Col
         return res;
     }
 
-    /// На моих тестах дважды в эту функцию приходит нечтно, имеющее data.size() == 0 однако оно по сути ничего не делает в следующих строках
-
     auto ML_function_Linear = typeid_cast<AggregateFunctionMLMethod<LinearModelData, NameLinearRegression> *>(func.get());
     auto ML_function_Logistic = typeid_cast<AggregateFunctionMLMethod<LinearModelData, NameLogisticRegression> *>(func.get());
     if (ML_function_Linear)

dbms/src/Functions/evalMLMethod.cpp

@@ -25,84 +25,59 @@ namespace DB
 /** finalizeAggregation(agg_state) - get the result from the aggregation state.
 * Takes state of aggregate function. Returns result of aggregation (finalized state).
 */
-    class FunctionEvalMLMethod : public IFunction
+class FunctionEvalMLMethod : public IFunction
+{
+public:
+    static constexpr auto name = "evalMLMethod";
+    static FunctionPtr create(const Context &)
     {
-    public:
-        static constexpr auto name = "evalMLMethod";
-        static FunctionPtr create(const Context &)
-        {
-            return std::make_shared<FunctionEvalMLMethod>();
-        }
-
-        String getName() const override
-        {
-            return name;
-        }
-
-        bool isVariadic() const override {
-            return true;
-        }
-        size_t getNumberOfArguments() const override
-        {
-            return 0;
-        }
-
-        DataTypePtr getReturnTypeImpl(const DataTypes & arguments) const override
-        {
-            const DataTypeAggregateFunction * type = checkAndGetDataType<DataTypeAggregateFunction>(arguments[0].get());
-            if (!type)
-                throw Exception("Argument for function " + getName() + " must have type AggregateFunction - state of aggregate function.",
-                                ErrorCodes::ILLEGAL_TYPE_OF_ARGUMENT);
-
-            return type->getReturnType();
-        }
-
-        void executeImpl(Block & block, const ColumnNumbers & arguments, size_t result, size_t /*input_rows_count*/) override
-        {
-//            const ColumnAggregateFunction * column_with_states =
-//                typeid_cast<const ColumnAggregateFunction *>(static_cast<const ColumnConst *>(&*block.getByPosition(arguments.at(0)).column));
-
-
-            // завести МЛ_аггр_функции как отдельный класс, чтобы тут сразу это проверять, а не делать это внутри predictValues()
-
-//            const ColumnAggregateFunction * column_with_states
-//                    = typeid_cast<const ColumnAggregateFunction *>(&*block.getByPosition(arguments.at(0)).column);
-
-
-            const ColumnConst * column_with_states
-                    = typeid_cast<const ColumnConst *>(&*block.getByPosition(arguments.at(0)).column);
-
-
-            if (!column_with_states)
-                throw Exception("Illegal column " + block.getByPosition(arguments.at(0)).column->getName()
-                                + " of first argument of function "
-                                + getName(),
-                                ErrorCodes::ILLEGAL_COLUMN);
-
-//        const ColumnArray * col_array = checkAndGetColumnConstData<ColumnArray>(block.getByPosition(arguments[1]).column.get());
-//        if (!col_array)
-//            throw std::runtime_error("wtf");
-
-//        const IColumn & array_elements = col_array->getData();
-
-/*
-        std::vector<Float64> predict_features(arguments.size());
-        for (size_t i = 1; i < arguments.size(); ++i)
-        {
-//            predict_features[i] = array_elements[i].get<Float64>();
-            predict_features[i - 1] = typeid_cast<const ColumnConst *>(block.getByPosition(arguments[i]).column.get())->getValue<Float64>();
-        }
-        block.getByPosition(result).column = column_with_states->predictValues(predict_features);
-*/
-            block.getByPosition(result).column =
-                    typeid_cast<const ColumnAggregateFunction *>(&*column_with_states->getDataColumnPtr())->predictValues(block, arguments);
-        }
-
-    };
-
-    void registerFunctionEvalMLMethod(FunctionFactory & factory)
-    {
-        factory.registerFunction<FunctionEvalMLMethod>();
+        return std::make_shared<FunctionEvalMLMethod>();
     }
 
+    String getName() const override
+    {
+        return name;
+    }
+
+    bool isVariadic() const override {
+        return true;
+    }
+    size_t getNumberOfArguments() const override
+    {
+        return 0;
+    }
+
+    DataTypePtr getReturnTypeImpl(const DataTypes & arguments) const override
+    {
+        const DataTypeAggregateFunction * type = checkAndGetDataType<DataTypeAggregateFunction>(arguments[0].get());
+        if (!type)
+            throw Exception("Argument for function " + getName() + " must have type AggregateFunction - state of aggregate function.",
+                            ErrorCodes::ILLEGAL_TYPE_OF_ARGUMENT);
+
+        return type->getReturnType();
+    }
+
+    void executeImpl(Block & block, const ColumnNumbers & arguments, size_t result, size_t /*input_rows_count*/) override
+    {
+        const ColumnConst * column_with_states
+                = typeid_cast<const ColumnConst *>(&*block.getByPosition(arguments.at(0)).column);
+
+
+        if (!column_with_states)
+            throw Exception("Illegal column " + block.getByPosition(arguments.at(0)).column->getName()
+                            + " of first argument of function "
+                            + getName(),
+                            ErrorCodes::ILLEGAL_COLUMN);
+
+        block.getByPosition(result).column =
+                typeid_cast<const ColumnAggregateFunction *>(&*column_with_states->getDataColumnPtr())->predictValues(block, arguments);
+    }
+
+};
+
+void registerFunctionEvalMLMethod(FunctionFactory & factory)
+{
+    factory.registerFunction<FunctionEvalMLMethod>();
+}
+
 }

dbms/tests/queries/0_stateless/00952_ml_test.good_reference

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dbms/tests/queries/0_stateless/00952_ml_test.norm_reference

@@ -1 +0,0 @@
-0.00676015