Renascence使用方法

2021-10-17 23:19:39

Renascence使用方法

下层库的适配

类型

下层库所有向Renascence架构提供的函数，其输入输出都必须给一个对应的继承于IStatusType的类，用于读取、保存、映射、释放该类型。

/*Basic API*/
class IStatusType
{
public:
    IStatusType(const std::string name):mName(name){}
    virtual ~IStatusType(){}
    inline std::string name() const {return mName;}

    /*GPStream是输入流的抽象，输入可能来源于文件、内存、网络等等*/
    virtual void* vLoad(GPStream* input) const = 0;

    /*GPWStream是输出流的抽象，可能输出到文件、内存、网络等等*/
    virtual void vSave(void* contents, GPWStream* output) const = 0;
    virtual void vFree(void* contents) const = 0;
    /* map
     * Modify contents by values.
     * return the number of parameters it needed.
     * If value is NULL, just return the number of parameters.
     * If *content is NULL and value is not NULL, alloc a new one.
     */
    virtual int vMap(void** content, double* value) const = 0;
    /* Check(Optional)
     * For Continue Data (Stream), Check if the data is completed, content must be not null
     */
    virtual bool vCheckCompleted(void* content) const {return NULL!=content;}
    /* Merge(Optional)
     * For Continue Data (Stream), Merge the src data to dst, dst and src must be not null
     * Normally, dst and src will be freed after calling this api
     * return NULL means can't merge
     */
    virtual void* vMerge(void* dst, void* src) const {return NULL;}

private:
    std::string mName;
};

函数

下层库需要提供如下形式的标准函数：

typedef GPContents*(*computeFunction)(GPContents* inputs);

GPContents 中包含一组带类型定义的数据：

struct GPContents
{
    struct GP_Unit
    {
        void* content;
        const IStatusType* type;
    };
    std::vector<GP_Unit> contents;
};

鉴于直接写这种类型的函数比较复杂，可以按如下方式，在需要导出的函数（函数本身需要保证单输出，不改输入，不改变全局、静态变量）前加 /*GP FUNCTION*/，然后执行 tools/makeGPFunction.py，自动生成对应的函数。
下面是机器学习库中的导出例子：

/*GP FUNCTION*/ALFloatMatrix* ALPackageValidateMatrix(ALIMatrixPredictor* l, ALFloatMatrix* m);
/*GP FUNCTION*/ALFloatMatrix* ALPackageValidateChain(ALFloatPredictor* l, ALLabeldData* c);
/*GP FUNCTION*/ALIMatrixPredictor* ALPackageSuperLearning(ALISuperviseLearner* l, ALFloatMatrix* m);
/*GP FUNCTION*/ALIMatrixPredictor* ALPackageUnSuperLearning(ALIUnSuperLearner* l, ALFloatMatrix* m);
/*GP FUNCTION*/ALFloatPredictor* ALPackageLearn(ALIChainLearner* l, ALLabeldData* d);
/*GP FUNCTION*/ALIChainLearner* ALPackageCreateDivider(ALIChainLearner* l, ALIChainLearner* r, ALDividerParameter* p/*S*/);
/*GP FUNCTION*/ALIChainLearner* ALPackageCreateCGP(ALCGPParameter* p/*S*/);
/*GP FUNCTION*/ALISuperviseLearner* ALPackageCreateRegress();
/*GP FUNCTION*/ALLabeldData* ALPackageLabled(ALFloatDataChain* c, double delay);
/*GP FUNCTION*/double ALPackageCrossValidate(ALIChainLearner* l, ALLabeldData* c);
/*GP FUNCTION*/ALIChainLearner* ALPackageCombine(ALARStructure* ar/*S*/, ALISuperviseLearner* l);
/*GP FUNCTION*/ALClassifierCreator* ALPackageCreateSVM(ALSVMParameter* p/*S*/);
/*GP FUNCTION*/ALClassifierCreator* ALPackageCreateGMM();
/*GP FUNCTION*/ALClassifierCreator* ALPackageCreateLogicalRegress();
/*GP FUNCTION*/ALClassifierCreator* ALPackageCreateDecisionTree(ALDecisionTreeParameter* p/*S*/);
/*GP FUNCTION*/ALFloatMatrix* ALPackageMatrixMerge(ALFloatMatrix* A, ALFloatMatrix* B, double aleft, double aright, double bleft, double bright);
/*GP FUNCTION*/ALFloatMatrix* ALPackageMatrixCrop(ALFloatMatrix* A, double aleft, double aright);

最终需要提供一个函数表，这个函数表可以在运行makeGPFunction.py时自动生成。
如上面的例子：

<libAbstract_learning>
    <ALPackageCreateClassify_GPpackage>
        <shortName>CreateClassify</shortName>
        <output>ALClassifier</output>
        <status></status>
        <input>ALClassifierCreator ALFloatMatrix </input>
        <inputNeedComplete>False False </inputNeedComplete>
    </ALPackageCreateClassify_GPpackage>

    <ALPackageRamdomForest_GPpackage>
        <shortName>RamdomForest</shortName>
        <output>ALClassifier</output>
        <status></status>
        <input>ALFloatMatrix </input>
        <inputNeedComplete>False </inputNeedComplete>
    </ALPackageRamdomForest_GPpackage>

    <ALPackageClassify_GPpackage>
        <shortName>Classify</shortName>
        <output>ALFloatMatrix</output>
        <status></status>
        <input>ALClassifier ALFloatMatrix </input>
        <inputNeedComplete>False False </inputNeedComplete>
    </ALPackageClassify_GPpackage>

    <ALPackageClassifyProb_GPpackage>
        <shortName>ClassifyProb</shortName>
        <output>ALFloatMatrix</output>
        <status></status>
        <input>ALClassifier ALFloatMatrix </input>
        <inputNeedComplete>False False </inputNeedComplete>
    </ALPackageClassifyProb_GPpackage>

    <ALPackageClassifyProbValues_GPpackage>
        <shortName>ClassifyProbValues</shortName>
        <output>ALFloatMatrix</output>
        <status></status>
        <input>ALClassifier </input>
        <inputNeedComplete>False </inputNeedComplete>
    </ALPackageClassifyProbValues_GPpackage>

    <ALPackageCrossValidateClassify_GPpackage>
        <shortName>CrossValidateClassify</shortName>
        <output>double</output>
        <status></status>
        <input>ALClassifierCreator ALFloatMatrix </input>
        <inputNeedComplete>False False </inputNeedComplete>
    </ALPackageCrossValidateClassify_GPpackage>

    <ALPackageValidateMatrix_GPpackage>
        <shortName>ValidateMatrix</shortName>
        <output>ALFloatMatrix</output>
        <status></status>
        <input>ALIMatrixPredictor ALFloatMatrix </input>
        <inputNeedComplete>False False </inputNeedComplete>
    </ALPackageValidateMatrix_GPpackage>

    <ALPackageValidateChain_GPpackage>
        <shortName>ValidateChain</shortName>
        <output>ALFloatMatrix</output>
        <status></status>
        <input>ALFloatPredictor ALLabeldData </input>
        <inputNeedComplete>False False </inputNeedComplete>
    </ALPackageValidateChain_GPpackage>

    <ALPackageSuperLearning_GPpackage>
        <shortName>SuperLearning</shortName>
        <output>ALIMatrixPredictor</output>
        <status></status>
        <input>ALISuperviseLearner ALFloatMatrix </input>
        <inputNeedComplete>False False </inputNeedComplete>
    </ALPackageSuperLearning_GPpackage>

    <ALPackageUnSuperLearning_GPpackage>
        <shortName>UnSuperLearning</shortName>
        <output>ALIMatrixPredictor</output>
        <status></status>
        <input>ALIUnSuperLearner ALFloatMatrix </input>
        <inputNeedComplete>False False </inputNeedComplete>
    </ALPackageUnSuperLearning_GPpackage>

    <ALPackageLearn_GPpackage>
        <shortName>Learn</shortName>
        <output>ALFloatPredictor</output>
        <status></status>
        <input>ALIChainLearner ALLabeldData </input>
        <inputNeedComplete>False False </inputNeedComplete>
    </ALPackageLearn_GPpackage>

    <ALPackageCreateDivider_GPpackage>
        <shortName>CreateDivider</shortName>
        <output>ALIChainLearner</output>
        <status>ALDividerParameter </status>
        <input>ALIChainLearner ALIChainLearner </input>
        <inputNeedComplete>False False </inputNeedComplete>
    </ALPackageCreateDivider_GPpackage>

    <ALPackageCreateCGP_GPpackage>
        <shortName>CreateCGP</shortName>
        <output>ALIChainLearner</output>
        <status>ALCGPParameter </status>
        <input></input>
        <inputNeedComplete></inputNeedComplete>
    </ALPackageCreateCGP_GPpackage>

    <ALPackageCreateRegress_GPpackage>
        <shortName>CreateRegress</shortName>
        <output>ALISuperviseLearner</output>
        <status></status>
        <input></input>
        <inputNeedComplete></inputNeedComplete>
    </ALPackageCreateRegress_GPpackage>

    <ALPackageLabled_GPpackage>
        <shortName>Labled</shortName>
        <output>ALLabeldData</output>
        <status></status>
        <input>ALFloatDataChain double </input>
        <inputNeedComplete>False False </inputNeedComplete>
    </ALPackageLabled_GPpackage>

    <ALPackageCrossValidate_GPpackage>
        <shortName>CrossValidate</shortName>
        <output>double</output>
        <status></status>
        <input>ALIChainLearner ALLabeldData </input>
        <inputNeedComplete>False False </inputNeedComplete>
    </ALPackageCrossValidate_GPpackage>

    <ALPackageCombine_GPpackage>
        <shortName>Combine</shortName>
        <output>ALIChainLearner</output>
        <status>ALARStructure </status>
        <input>ALISuperviseLearner </input>
        <inputNeedComplete>False </inputNeedComplete>
    </ALPackageCombine_GPpackage>

    <ALPackageCreateSVM_GPpackage>
        <shortName>CreateSVM</shortName>
        <output>ALClassifierCreator</output>
        <status>ALSVMParameter </status>
        <input></input>
        <inputNeedComplete></inputNeedComplete>
    </ALPackageCreateSVM_GPpackage>

    <ALPackageCreateGMM_GPpackage>
        <shortName>CreateGMM</shortName>
        <output>ALClassifierCreator</output>
        <status></status>
        <input></input>
        <inputNeedComplete></inputNeedComplete>
    </ALPackageCreateGMM_GPpackage>

    <ALPackageCreateLogicalRegress_GPpackage>
        <shortName>CreateLogicalRegress</shortName>
        <output>ALClassifierCreator</output>
        <status></status>
        <input></input>
        <inputNeedComplete></inputNeedComplete>
    </ALPackageCreateLogicalRegress_GPpackage>

    <ALPackageCreateDecisionTree_GPpackage>
        <shortName>CreateDecisionTree</shortName>
        <output>ALClassifierCreator</output>
        <status>ALDecisionTreeParameter </status>
        <input></input>
        <inputNeedComplete></inputNeedComplete>
    </ALPackageCreateDecisionTree_GPpackage>

    <ALPackageMatrixMerge_GPpackage>
        <shortName>MatrixMerge</shortName>
        <output>ALFloatMatrix</output>
        <status></status>
        <input>ALFloatMatrix ALFloatMatrix double double double double </input>
        <inputNeedComplete>False False False False False False </inputNeedComplete>
    </ALPackageMatrixMerge_GPpackage>

    <ALPackageMatrixCrop_GPpackage>
        <shortName>MatrixCrop</shortName>
        <output>ALFloatMatrix</output>
        <status></status>
        <input>ALFloatMatrix double double </input>
        <inputNeedComplete>False False False </inputNeedComplete>
    </ALPackageMatrixCrop_GPpackage>

</libAbstract_learning>

GP公式语法

见 doc/formula.txt
1、基本形式：
f(x0, g(x1, h(x2), f(x2, x3)));
f(x0, ADF[NAME, x0,x1,f(x0,x3)])
2、符号说明：
f、g、h：函数的简写或全写，在xml格式的metaData中提供
x0-xn：输入变量名，必须从x0开始，中间不允许间断，此函数构建完成后的ADF，必须按由小到大的顺序组织输入变量
ADF：自动生成标志
NAME：ADF别名，必须取一个独特的名字，以便后续使用
f(x0,x3)：此表示ADF的一个输入为固定的函数
3、自动生成
自动生成是将某一个输入变量改由指定了输入的子函数替代，子函数由GP库去搜索得出
格式为 ADF[NAME, x1, x2, x3, f(x0,x1), …]
自动生成函数的输入由[]中的内容描述，按类型:输入变量编号来排，输出类型就是该位置上的输入所应有的类型
自动生成的函数确定用完所有输入，但有可能会重复利用

调用方法

C层级调用

就当做一般的C++库使用，对外接口均封装在

include/user/GPAPI.h

Python层级调用

先安装 swig，然后进入python-renascence/module，运行 ./build_source.sh && sudo python setup.py install，这样安装好。

下面这个例子使用 Renascence架构，调用机器学习库作一次时间序列的预测：

#!/usr/bin/python
import Renascence

producer = Renascence.init(["./libAbstract_learning.xml"])
print producer.listAllFunctions()
print producer.listAllTypes()
x0 = producer.load('ALFloatDataChain', './bao.txt')
formula = 'CrossValidate(ADF(GodTrain), Labled(x0, x1))'
#formula = 'CrossValidate(CreateCGP(), Labled(x0, x1))'
#[trainedformula, bestValue] = producer.train(formula, producer.merge(x0, 1.0))
[trainedformula, bestValue] = producer.train(formula, producer.merge(x0, 1.0), times=100000, cacheFile='temp.txt')
print bestValue
print trainedformula.ADF('GodTrain')
#print trainedformula.parameters()
p = trainedformula.parameters()
with open('temp_parameter.txt', 'w') as f:
    f.write(p)
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