code style

dbms/src/AggregateFunctions/AggregateFunctionMLMethod.cpp

@@ -7,34 +7,45 @@
 namespace DB
 {
 
-    namespace
+namespace
+{
+
+using FuncLinearRegression = AggregateFunctionMLMethod<LinearRegressionData, NameLinearRegression>;
+
+template <class Method>
+AggregateFunctionPtr createAggregateFunctionMLMethod(
+        const std::string & name, const DataTypes & argument_types, const Array & parameters)
+{
+    if (parameters.size() > 1)
+        throw Exception("Aggregate function " + name + " requires at most one parameter", ErrorCodes::NUMBER_OF_ARGUMENTS_DOESNT_MATCH);
+
+    for (size_t i = 0; i < argument_types.size(); ++i)
     {
-
-        using FuncLinearRegression = AggregateFunctionMLMethod<LinearRegressionData, NameLinearRegression>;
-
-        template <class Method>
-        AggregateFunctionPtr createAggregateFunctionMLMethod(
-                const std::string & name, const DataTypes & arguments, const Array & parameters)
-        {
-            if (parameters.size() > 1)
-                throw Exception("Aggregate function " + name + " requires at most one parameter", ErrorCodes::NUMBER_OF_ARGUMENTS_DOESNT_MATCH);
-
-            Float64 lr;
-            if (parameters.empty())
-                lr = Float64(0.01);
-            else
-                lr = static_cast<const Float64>(parameters[0].template get<Float64>());
-
-            if (arguments.size() < 2)
-                throw Exception("Aggregate function " + name + " requires at least two arguments", ErrorCodes::NUMBER_OF_ARGUMENTS_DOESNT_MATCH);
-
-            return std::make_shared<Method>(arguments.size() - 1, lr);
-        }
-
+        if (!WhichDataType(argument_types[i]).isFloat64())
+            throw Exception("Illegal type " + argument_types[i]->getName() + " of argument " +
+                                    std::to_string(i) + "for aggregate function " + name,
+                                  ErrorCodes::ILLEGAL_TYPE_OF_ARGUMENT);
     }
 
-    void registerAggregateFunctionMLMethod(AggregateFunctionFactory & factory) {
-        factory.registerFunction("LinearRegression", createAggregateFunctionMLMethod<FuncLinearRegression>);
+    Float64 learning_rate;
+    if (parameters.empty())
+    {
+        learning_rate = Float64(0.01);
+    } else
+    {
+        learning_rate = applyVisitor(FieldVisitorConvertToNumber<Float64>(), parameters[0]);
     }
 
+    if (argument_types.size() < 2)
+        throw Exception("Aggregate function " + name + " requires at least two arguments", ErrorCodes::NUMBER_OF_ARGUMENTS_DOESNT_MATCH);
+
+    return std::make_shared<Method>(argument_types.size() - 1, learning_rate);
+}
+
+}
+
+void registerAggregateFunctionMLMethod(AggregateFunctionFactory & factory) {
+    factory.registerFunction("LinearRegression", createAggregateFunctionMLMethod<FuncLinearRegression>);
+}
+
 }

dbms/src/AggregateFunctions/AggregateFunctionMLMethod.h

@@ -23,35 +23,41 @@
 namespace DB
 {
 
+namespace ErrorCodes
+{
+    extern const int NUMBER_OF_ARGUMENTS_DOESNT_MATCH;
+    extern const int BAD_ARGUMENTS;
+}
+
+
 struct LinearRegressionData
 {
+    LinearRegressionData()
+    {}
+    LinearRegressionData(Float64 learning_rate_, UInt32 param_num_)
+    : learning_rate(learning_rate_) {
+        weights.resize(param_num_);
+    }
+
     Float64 bias{0.0};
-    std::vector<Float64> w1;
-    Float64 learning_rate{0.01};
+    std::vector<Float64> weights;
+    Float64 learning_rate;
     UInt32 iter_num = 0;
-    UInt32 param_num = 0;
 
-
-    void add(Float64 target, std::vector<Float64>& feature, Float64 learning_rate_, UInt32 param_num_)
+    void add(Float64 target, const IColumn ** columns, size_t row_num)
     {
-        if (w1.empty())
-        {
-            learning_rate = learning_rate_;
-            param_num = param_num_;
-            w1.resize(param_num);
-        }
-
         Float64 derivative = (target - bias);
-        for (size_t i = 0; i < param_num; ++i)
+        for (size_t i = 0; i < weights.size(); ++i)
         {
-            derivative -= w1[i] * feature[i];
+            derivative -= weights[i] * static_cast<const ColumnVector<Float64> &>(*columns[i + 1]).getData()[row_num];
+
         }
         derivative *= (2 * learning_rate);
 
         bias += derivative;
-        for (size_t i = 0; i < param_num; ++i)
+        for (size_t i = 0; i < weights.size(); ++i)
         {
-            w1[i] += derivative * feature[i];
+            weights[i] += derivative * static_cast<const ColumnVector<Float64> &>(*columns[i + 1]).getData()[row_num];;
         }
 
         ++iter_num;
@@ -60,20 +66,14 @@ struct LinearRegressionData
     void merge(const LinearRegressionData & rhs)
     {
         if (iter_num == 0 && rhs.iter_num == 0)
-            throw std::runtime_error("Strange...");
-
-        if (param_num == 0)
-        {
-            param_num = rhs.param_num;
-            w1.resize(param_num);
-        }
+            return;
 
         Float64 frac = static_cast<Float64>(iter_num) / (iter_num + rhs.iter_num);
         Float64 rhs_frac = static_cast<Float64>(rhs.iter_num) / (iter_num + rhs.iter_num);
 
-        for (size_t i = 0; i < param_num; ++i)
+        for (size_t i = 0; i < weights.size(); ++i)
         {
-            w1[i] = w1[i] * frac + rhs.w1[i] * rhs_frac;
+            weights[i] = weights[i] * frac + rhs.weights[i] * rhs_frac;
         }
 
         bias = bias * frac + rhs.bias * rhs_frac;
@@ -83,23 +83,23 @@ struct LinearRegressionData
     void write(WriteBuffer & buf) const
     {
         writeBinary(bias, buf);
-        writeBinary(w1, buf);
+        writeBinary(weights, buf);
         writeBinary(iter_num, buf);
     }
 
     void read(ReadBuffer & buf)
     {
         readBinary(bias, buf);
-        readBinary(w1, buf);
+        readBinary(weights, buf);
         readBinary(iter_num, buf);
     }
 
-    Float64 predict(std::vector<Float64>& predict_feature) const
+    Float64 predict(const std::vector<Float64>& predict_feature) const
     {
         Float64 res{0.0};
-        for (size_t i = 0; i < static_cast<size_t>(param_num); ++i)
+        for (size_t i = 0; i < predict_feature.size(); ++i)
         {
-            res += predict_feature[i] * w1[i];
+            res += predict_feature[i] * weights[i];
         }
         res += bias;
 
@@ -128,18 +128,16 @@ public:
         return std::make_shared<DataTypeNumber<Float64>>();
     }
 
+    void create(AggregateDataPtr place) const override
+    {
+        new (place) Data(learning_rate, param_num);
+    }
+
     void add(AggregateDataPtr place, const IColumn ** columns, size_t row_num, Arena *) const override
     {
         const auto & target = static_cast<const ColumnVector<Float64> &>(*columns[0]);
 
-        std::vector<Float64> x(param_num);
-        for (size_t i = 0; i < param_num; ++i)
-        {
-            x[i] = static_cast<const ColumnVector<Float64> &>(*columns[i + 1]).getData()[row_num];
-        }
-
-        this->data(place).add(target.getData()[row_num], x, learning_rate, param_num);
-
+        this->data(place).add(target.getData()[row_num], columns, row_num);
     }
 
     void merge(AggregateDataPtr place, ConstAggregateDataPtr rhs, Arena *) const override
@@ -159,18 +157,23 @@ public:
 
     void predictResultInto(ConstAggregateDataPtr place, IColumn & to, Block & block, size_t row_num, const ColumnNumbers & arguments) const
     {
+        if (arguments.size() != param_num + 1)
+            throw Exception("Predict got incorrect number of arguments. Got: " + std::to_string(arguments.size()) + ". Required: " + std::to_string(param_num + 1),
+                            ErrorCodes::NUMBER_OF_ARGUMENTS_DOESNT_MATCH);
+
         auto &column = dynamic_cast<ColumnVector<Float64> &>(to);
 
         std::vector<Float64> predict_features(arguments.size() - 1);
-//        for (size_t row_num = 0, rows = block.rows(); row_num < rows; ++row_num)
         for (size_t i = 1; i < arguments.size(); ++i)
         {
-//            predict_features[i] = array_elements[i].get<Float64>();
-            predict_features[i - 1] = applyVisitor(FieldVisitorConvertToNumber<Float64>(), (*block.getByPosition(arguments[i]).column)[row_num]);
+            const auto& element = (*block.getByPosition(arguments[i]).column)[row_num];
+            if (element.getType() != Field::Types::Float64)
+                throw Exception("Prediction arguments must be values of type Float",
+                        ErrorCodes::BAD_ARGUMENTS);
+
+            predict_features[i - 1] = element.get<Float64>();
         }
-//            column.getData().push_back(this->data(place).predict(predict_features));
         column.getData().push_back(this->data(place).predict(predict_features));
-//        }
     }
 
     void insertResultInto(ConstAggregateDataPtr place, IColumn & to) const override

dbms/src/AggregateFunctions/IAggregateFunction.h

@@ -137,7 +137,7 @@ protected:
     static const Data & data(ConstAggregateDataPtr place) { return *reinterpret_cast<const Data*>(place); }
 
 public:
-    void create(AggregateDataPtr place) const override
+    virtual void create(AggregateDataPtr place) const override
     {
         new (place) Data;
     }

dbms/src/Columns/ColumnAggregateFunction.cpp

@@ -17,6 +17,7 @@ namespace ErrorCodes
 {
     extern const int PARAMETER_OUT_OF_BOUND;
     extern const int SIZES_OF_COLUMNS_DOESNT_MATCH;
+    extern const int ILLEGAL_TYPE_OF_ARGUMENT;
 }
 
 
@@ -32,6 +33,23 @@ void ColumnAggregateFunction::addArena(ArenaPtr arena_)
     arenas.push_back(arena_);
 }
 
+bool ColumnAggregateFunction::convertion(MutableColumnPtr* res_) const
+{
+    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());
+        *res_ = std::move(res);
+        return true;
+    }
+
+    MutableColumnPtr res = func->getReturnType()->createColumn();
+    res->reserve(data.size());
+    *res_ = std::move(res);
+    return false;
+}
+
 MutableColumnPtr ColumnAggregateFunction::convertToValues() const
 {
     /** If the aggregate function returns an unfinalized/unfinished state,
@@ -64,38 +82,46 @@ 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()))
+//    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());
+    MutableColumnPtr res;
+    if (convertion(&res))
     {
-        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());
-
     for (auto val : data)
         func->insertResultInto(val, *res);
 
     return res;
 }
 
-//MutableColumnPtr ColumnAggregateFunction::predictValues(std::vector<Float64> predict_feature) const
 MutableColumnPtr ColumnAggregateFunction::predictValues(Block & block, const ColumnNumbers & arguments) const
 {
-    if (const AggregateFunctionState * function_state = typeid_cast<const AggregateFunctionState *>(func.get()))
+//    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());
+    MutableColumnPtr res;
+    if (convertion(&res))
     {
-        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());
-
-//    const AggregateFunctionMLMethod * ML_function = typeid_cast<const AggregateFunctionMLMethod *>(func.get());
     auto ML_function = typeid_cast<const AggregateFunctionMLMethod<LinearRegressionData, NameLinearRegression> *>(func.get());
     if (ML_function)
     {
@@ -105,7 +131,8 @@ MutableColumnPtr ColumnAggregateFunction::predictValues(Block & block, const Col
             ++row_num;
         }
     } else {
-
+        throw Exception("Illegal aggregate function is passed",
+                ErrorCodes::ILLEGAL_TYPE_OF_ARGUMENT);
     }
 
     return res;

dbms/src/Columns/ColumnAggregateFunction.h

@@ -116,6 +116,7 @@ public:
     std::string getName() const override { return "AggregateFunction(" + func->getName() + ")"; }
     const char * getFamilyName() const override { return "AggregateFunction"; }
 
+    bool convertion(MutableColumnPtr* res_) const;
     MutableColumnPtr predictValues(Block & block, const ColumnNumbers & arguments) const;
 
     size_t size() const override

dbms/src/Functions/evalMLMethod.cpp

@@ -59,6 +59,7 @@ namespace DB
 
         void executeImpl(Block & block, const ColumnNumbers & arguments, size_t result, size_t /*input_rows_count*/) override
         {
+            // 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);
             if (!column_with_states)