CN111461349A - Modeling method and system - Google Patents
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Abstract
The invention provides a modeling method and a modeling system. The modeling method comprises the following steps: acquiring a modeling file and calling a model method in the modeling file; acquiring packaged component information according to a flow ID acquired in advance through a model method; analyzing the component information by a model method to obtain parameters of a data processing method; and calling the data processing method in the modeling file according to the data processing method parameter to perform modeling. The invention can automatically establish the model and improve the service realization efficiency.
Description
Technical Field
The invention relates to the technical field of model creation, in particular to a modeling method and a modeling system.
Background
At present, in the prior art, a modeling process is realized by writing codes, so that business personnel without a development foundation cannot quickly understand the codes, and the business realization efficiency is reduced.
Disclosure of Invention
The embodiment of the invention mainly aims to provide a modeling method and a modeling system, so as to automatically establish a model and improve the service implementation efficiency.
In order to achieve the above object, an embodiment of the present invention provides a modeling method, including:
acquiring a modeling file and calling a model method in the modeling file;
acquiring packaged component information according to a flow ID acquired in advance through a model method;
analyzing the component information by a model method to obtain parameters of a data processing method;
and calling the data processing method in the modeling file according to the data processing method parameter to perform modeling.
An embodiment of the present invention further provides a modeling system, including:
the calling unit is used for acquiring the modeling file and calling the model method in the modeling file;
a component information acquisition unit for acquiring the packaged component information according to the flow ID acquired in advance by the model method;
the analysis unit is used for analyzing the component information by a model method to obtain parameters of a data processing method;
and the modeling unit is used for calling the data processing method in the modeling file according to the data processing method parameter so as to perform modeling.
The embodiment of the present invention further provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and running on the processor, and the steps of the modeling method are implemented when the processor executes the computer program.
Embodiments of the present invention further provide a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of the modeling method.
The modeling method and the modeling system of the embodiment of the invention firstly call the model method in the modeling file, then obtain the packaged component information according to the process ID through the modeling method, then analyze the component information through the model method to obtain the parameters of the data processing method, and finally call the data processing method in the modeling file according to the parameters of the data processing method to model, so that the model can be automatically established, and the service realization efficiency is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained based on these drawings without creative efforts.
FIG. 1 is a flow chart of a modeling method in an embodiment of the invention;
FIG. 2 is a block diagram of the architecture of a modeling system in an embodiment of the invention;
fig. 3 is a block diagram of a computer device in an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As will be appreciated by one skilled in the art, embodiments of the present invention may be embodied as a system, apparatus, device, method, or computer program product. Accordingly, the present disclosure may be embodied in the form of: entirely hardware, entirely software (including firmware, resident software, micro-code, etc.), or a combination of hardware and software.
In view of the fact that in the prior art, a business person without a development foundation cannot quickly understand codes due to the fact that a modeling process is realized by writing the codes, and business realization efficiency is reduced, embodiments of the present invention provide a modeling method to automatically establish a model and improve business realization efficiency. The present invention will be described in detail below with reference to the accompanying drawings.
FIG. 1 is a flow chart of a modeling method in an embodiment of the invention. As shown in fig. 1, the modeling method includes:
s101: and acquiring a modeling file and calling a model method in the modeling file.
When the operating environment is spark, the modeling file is jar file, and the model method is main method. When the operating environment is tensorflow, the modeling file is a python file, and the model method is an entry method.
S102: and acquiring the packaged component information according to the flow ID acquired in advance through a model method.
In specific implementation, S102 includes: the model method comprises the steps of firstly obtaining a component json string according to a process ID, then converting the component json string into single component information, and putting the component information into a set.
S103: and analyzing the component information by a model method to obtain parameters of a data processing method.
In specific implementation, S103 includes: and analyzing the component information in the set according to the sequence corresponding to the process ID by calling the model method, sequentially acquiring the attribute information corresponding to the model method in the component information, and taking the attribute information as a data processing method parameter.
When the operating environment is spark, for the component information with the context, spark will return the generation result of the former component information as a parameter to the latter component information.
When the runtime environment is tensorflow, the high-level property in the component information depends on the generation result of the previous component information of the component information.
S104: and calling the data processing method in the modeling file according to the data processing method parameter to perform modeling.
Wherein, when the running environment is spark. The established model is a machine learning model. When the running environment is tensorflow, the built model is a deep learning model.
In specific implementation, a data processing method in the modeling file can be called according to the reflection principle. When the component information includes a model saving component, the model calling method may further save a model File generated after modeling in an HDFS (Hadoop Distributed File System).
The execution subject of the modeling method shown in fig. 1 may be a computer. The invention relates to a dragging type visual modeling method, wherein the flow in the modeling method can be stored and recorded in a log, and a user can check the log record or the historical record in real time, so that the user can conveniently communicate with personnel related to a project, and the communication efficiency is improved. As can be seen from the process shown in fig. 1, the modeling method according to the embodiment of the present invention first calls a model method in a modeling file, then obtains encapsulated component information according to a process ID by the model method, then analyzes the component information by the model method to obtain a data processing method parameter, and finally calls a data processing method in the modeling file according to the data processing method parameter to perform modeling, so that a model can be automatically established, and the efficiency of implementing a service is improved.
In an embodiment, when the execution environment is spark, before executing S101, the method further includes: and when a machine learning instruction sent by a user is received, obtaining the modeling file according to a pre-obtained modeling file storage path through a spark calculation engine.
When the runtime environment is tensorflow, before executing S101, the method further includes: and obtaining a modeling file according to the pre-obtained IP address of the server. In specific implementation, the deep learning instruction is sent to the corresponding GPU server according to the IP address of the server, and then the GPU server calls the entry method in the python file according to the deep learning instruction.
In addition, a user can acquire all component information from the component table of the front-end page, drag or click the component information, set parameters of a data processing method in the component information, send a machine learning instruction or a deep learning instruction, store a json string of the component to the component table, and the like.
The specific process of the embodiment of the invention is as follows:
1. and when the operating environment is spark, receiving a machine learning instruction sent by a user, and acquiring a modeling file according to a pre-acquired modeling file storage path through a spark calculation engine. And when the operating environment is tensorflow, sending the deep learning instruction to the corresponding GPU server according to the IP address of the server, and enabling the GPU server to call the modeling file according to the deep learning instruction.
2. And calling a model method in the modeling file, and acquiring the packaged component information according to the flow ID acquired in advance through the model method.
3. And analyzing the component information in the set according to the sequence corresponding to the process ID by calling the model method, sequentially acquiring the attribute information corresponding to the model method in the component information, and taking the attribute information as a data processing method parameter.
4. And calling a data processing method in the modeling file according to the reflection principle to perform modeling.
To sum up, the modeling method of the embodiment of the present invention first calls the model method in the modeling file, then obtains the encapsulated component information according to the process ID through the modeling method, then analyzes the component information through the model method to obtain the data processing method parameters, and finally calls the data processing method in the modeling file according to the data processing method parameters to perform modeling, so that the model can be automatically established, and the business implementation efficiency can be improved. Compared with the prior art, the method and the device can package each module code of the modeling process, a user can quickly construct the model only by adopting a dragging mode without paying attention to the principle and the code behind the system, and the extended modeling and the model tuning are convenient to carry out.
Based on the same inventive concept, the embodiment of the invention also provides a modeling system, and as the principle of solving the problems of the system is similar to that of the modeling method, the implementation of the system can refer to the implementation of the method, and repeated parts are not repeated.
FIG. 2 is a block diagram of a modeling system in an embodiment of the invention. As shown in fig. 2, the modeling system includes:
the calling unit is used for acquiring the modeling file and calling the model method in the modeling file;
a component information acquisition unit for acquiring the packaged component information according to the flow ID acquired in advance by the model method;
the analysis unit is used for analyzing the component information by a model method to obtain parameters of a data processing method;
and the modeling unit is used for calling the data processing method in the modeling file according to the data processing method parameter so as to perform modeling.
In one embodiment, the method further comprises the following steps:
and the first modeling file acquisition unit is used for acquiring the modeling file according to the pre-acquired modeling file storage path through the calculation engine.
In one embodiment, the method further comprises the following steps:
and the second modeling file acquisition unit is used for acquiring the modeling file according to the pre-acquired server IP address.
In one embodiment, the modeling file is a jar file or a python file.
To sum up, the modeling system of the embodiment of the present invention first calls the model method in the modeling file, then obtains the encapsulated component information according to the process ID by the model method, then analyzes the component information by the model method to obtain the data processing method parameter, and finally calls the data processing method in the modeling file according to the data processing method parameter to perform modeling, so that the model can be automatically established, and the business implementation efficiency can be improved. Compared with the prior art, the method and the device can package each module code of the modeling process, a user can quickly construct the model only by adopting a dragging mode without paying attention to the principle and the code behind the system, and the extended modeling and the model tuning are convenient to carry out.
The embodiment of the invention also provides a specific implementation mode of computer equipment capable of realizing all the steps in the modeling method in the embodiment. Fig. 3 is a block diagram of a computer device in an embodiment of the present invention, and referring to fig. 3, the computer device specifically includes the following:
a processor (processor)301 and a memory (memory) 302.
The processor 301 is configured to call the computer program in the memory 302, and the processor implements all the steps of the modeling method in the above embodiments when executing the computer program, for example, the processor implements the following steps when executing the computer program:
acquiring a modeling file and calling a model method in the modeling file;
acquiring packaged component information according to a flow ID acquired in advance through a model method;
analyzing the component information by a model method to obtain parameters of a data processing method;
and calling the data processing method in the modeling file according to the data processing method parameter to perform modeling.
To sum up, the computer device of the embodiment of the present invention first calls the model method in the modeling file, then obtains the encapsulated component information according to the process ID by the model method, then analyzes the component information by the model method to obtain the data processing method parameter, and finally calls the data processing method in the modeling file according to the data processing method parameter to perform modeling, so that the model can be automatically established, and the business implementation efficiency can be improved. Compared with the prior art, the method and the device can package each module code of the modeling process, a user can quickly construct the model only by adopting a dragging mode without paying attention to the principle and the code behind the system, and the extended modeling and the model tuning are convenient to carry out.
An embodiment of the present invention further provides a computer-readable storage medium capable of implementing all the steps in the modeling method in the foregoing embodiment, where the computer-readable storage medium stores a computer program, and the computer program implements all the steps in the modeling method in the foregoing embodiment when executed by a processor, for example, the processor implements the following steps when executing the computer program:
acquiring a modeling file and calling a model method in the modeling file;
acquiring packaged component information according to a flow ID acquired in advance through a model method;
analyzing the component information by a model method to obtain parameters of a data processing method;
and calling the data processing method in the modeling file according to the data processing method parameter to perform modeling.
To sum up, the computer-readable storage medium of the embodiment of the present invention first calls a model method in a modeling file, then obtains encapsulated component information according to a process ID by the model method, then analyzes the component information by the model method to obtain a data processing method parameter, and finally calls a data processing method in the modeling file according to the data processing method parameter to perform modeling, so that a model can be automatically established, and the efficiency of implementing a service is improved. Compared with the prior art, the method and the device can package each module code of the modeling process, a user can quickly construct the model only by adopting a dragging mode without paying attention to the principle and the code behind the system, and the extended modeling and the model tuning are convenient to carry out.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are only exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Those of skill in the art will further appreciate that the various illustrative logical blocks, units, and steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate the interchangeability of hardware and software, various illustrative components, elements, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design requirements of the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present embodiments.
The various illustrative logical blocks, or elements, or devices described in connection with the embodiments disclosed herein may be implemented or performed with a general purpose processor, a digital signal processor, an Application Specific Integrated Circuit (ASIC), a field programmable gate array or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general-purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a digital signal processor and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a digital signal processor core, or any other similar configuration.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may be stored in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. For example, a storage medium may be coupled to the processor such the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC, which may be located in a user terminal. In the alternative, the processor and the storage medium may reside in different components in a user terminal.
In one or more exemplary designs, the functions described in the embodiments of this invention may be implemented in hardware, software, firmware, or any combination thereof, if implemented in software, these functions may be stored on a computer-readable medium or transmitted as one or more instructions or code on a computer-readable medium including a computer storage medium and a communications medium that facilitates transfer of a computer program from one place to another.
Claims (10)
1. A modeling method, comprising:
obtaining a modeling file and calling a model method in the modeling file;
acquiring packaged component information according to a pre-acquired process ID through the model method;
analyzing the component information by the model method to obtain data processing method parameters;
and calling a data processing method in the modeling file according to the data processing method parameter to perform modeling.
2. The modeling method of claim 1, further comprising:
and acquiring the modeling file according to a pre-acquired modeling file storage path through a computing engine.
3. The modeling method of claim 1, further comprising:
and obtaining the modeling file according to the pre-obtained IP address of the server.
4. A modeling method in accordance with claim 1, wherein the modeling file is a jar file or a python file.
5. A modeling system, comprising:
the calling unit is used for acquiring a modeling file and calling a model method in the modeling file;
a component information acquisition unit for acquiring the packaged component information according to a flow ID acquired in advance by the model method;
the analysis unit is used for analyzing the component information through the model method to obtain data processing method parameters;
and the modeling unit is used for calling the data processing method in the modeling file according to the data processing method parameter so as to perform modeling.
6. The modeling system of claim 5, further comprising:
and the first modeling file acquisition unit is used for acquiring the modeling file according to a pre-acquired modeling file storage path through a calculation engine.
7. The modeling system of claim 5, further comprising:
and the second modeling file acquisition unit is used for acquiring the modeling file according to the pre-acquired server IP address.
8. The modeling system of claim 5, wherein the modeling file is a jar file or a python file.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and running on the processor, characterized in that the steps of the modeling method of any one of claims 1 to 4 are implemented by the processor when executing the computer program.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the modeling method according to any one of claims 1 to 4.
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