CN114386615A - Machine learning analysis method, system, device and medium based on visual dragging - Google Patents
Machine learning analysis method, system, device and medium based on visual dragging Download PDFInfo
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Abstract
The application discloses a machine learning analysis method, a system, equipment and a medium based on visual dragging, wherein the method responds to the dragging operation of a user on a function node icon and displays a machine learning flow chart on a front-end page; updating the node configuration information in response to a first operation of a user on the function node icon; the node configuration information comprises at least one of a data source, parameters and model operator types of a machine learning model; and transmitting the machine learning flow chart to a background for processing, calling a model operator corresponding to the machine learning flow chart to build a machine learning model, and training and storing the model operator. The method can modularize a mainstream machine learning algorithm to obtain a plurality of machine learning model operators, and assist a user to efficiently complete complex business modeling, thereby reducing the application threshold and cost of the machine learning model. The method and the device can be widely applied to the technical field of artificial intelligence.
Description
Technical Field
The application relates to the technical field of artificial intelligence, in particular to a machine learning analysis method, a machine learning analysis system, machine learning analysis equipment and a machine learning analysis medium based on visual dragging.
Background
In recent years, with the development of artificial intelligence technology, machine learning has attracted much attention. Machine learning is a multi-field cross subject, relates to a plurality of subjects such as probability theory, statistics, approximation theory, convex analysis, algorithm complexity theory and the like, and is used for specially researching how a computer simulates or realizes human learning behaviors to acquire new knowledge or skills and reorganize an existing knowledge structure to continuously improve the performance of the computer.
In the related technology, the traditional machine learning needs to spend a great deal of manpower and energy to deeply learn, covers probability theory knowledge, statistical knowledge, approximate theory knowledge and complex algorithm knowledge, has a high use threshold, is limited by the limitations and disadvantages of the machine learning algorithms, causes difficulty in large-scale popularization of the machine learning application, has a great deal of repeated work in the related machine learning application, and has low development efficiency.
In summary, the problems of the related art need to be solved.
Disclosure of Invention
The present application aims to solve at least one of the technical problems in the related art to some extent.
To this end, an object of the embodiments of the present application is to provide a machine learning analysis method, system, device and medium based on visual dragging.
In order to achieve the technical purpose, the technical scheme adopted by the embodiment of the application comprises the following steps:
in one aspect, an embodiment of the present application provides a machine learning analysis method based on visual dragging, including the following steps:
responding to the dragging operation of a user on the function node icon, and displaying a machine learning flow chart on a front-end page;
updating the node configuration information in response to a first operation of a user on the function node icon; the node configuration information comprises at least one of a data source, parameters and model operator types of a machine learning model;
and transmitting the machine learning flow chart to a background for processing, calling a model operator corresponding to the machine learning flow chart to build a machine learning model, and training and storing the model operator.
In addition, the machine learning analysis method based on visual dragging according to the above embodiments of the present application may further have the following additional technical features:
further, in an embodiment of the present application, the displaying a machine learning flowchart on a front-end page in response to a drag operation of a user on a function node icon includes:
updating the coordinate position of each functional node icon in response to the dragging operation of the functional node icon by the user;
responding to a second operation of the user on the function node icons, and performing flow connection on each function node icon;
and responding to the saving operation of the user, and displaying the machine learning flow chart on the front-end page according to the current connection relation between each function node icon and the flow.
Further, in an embodiment of the present application, after the step of performing the process connection on each function node icon, the method further includes the following steps:
and carrying out non-closed loop verification on the flow connection relation among the function node icons.
Further, in an embodiment of the present application, the model operator includes a data import dimension operator, a data preprocessing operator, a data feature selection operator, a data tuning operator, and a data export operator.
Further, in an embodiment of the present application, the transmitting the machine learning flowchart to a background process includes:
analyzing the machine learning flow chart to obtain a workflow json string;
determining the relationship between each function node icon and a process connecting line in the machine learning process diagram according to the workflow json string;
and according to the flow connection relation, performing data processing on the node where each functional node icon is located.
Further, in an embodiment of the present application, the performing data processing on the node where each of the function node icons is located includes:
and processing the input data of the node where the functional node icon is located according to the node configuration information corresponding to the functional node icon.
In another aspect, an embodiment of the present application provides a machine learning analysis system based on visual tractor, where the system includes:
the first response module is used for responding to the dragging operation of a user on the function node icon and displaying the machine learning flow chart on the front-end page;
the second response module is used for responding to the first operation of the user on the function node icon and updating the node configuration information; the node configuration information comprises at least one of a data source, parameters and model operator types of a machine learning model;
and the processing module is used for transmitting the machine learning flow chart to a background for processing, calling a model operator corresponding to the machine learning flow chart to build a machine learning model, and training and storing the model operator.
In another aspect, an embodiment of the present application provides a computer device, including:
at least one processor;
at least one memory for storing at least one program;
the at least one program, when executed by the at least one processor, causes the at least one processor to implement the visual-tractor-based machine learning analysis method described above.
In another aspect, an embodiment of the present application further provides a computer-readable storage medium, in which a processor-executable program is stored, where the processor-executable program is used to implement the above-mentioned machine learning analysis method based on visual dragging when being executed by a processor.
Advantages and benefits of the present application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the present application:
the machine learning analysis method based on visual dragging disclosed by the embodiment of the application responds to the dragging operation of a user on a function node icon, and displays a machine learning flow chart on a front-end page; updating the node configuration information in response to a first operation of a user on the function node icon; the node configuration information comprises at least one of a data source, parameters and model operator types of a machine learning model; and transmitting the machine learning flow chart to a background for processing, calling a model operator corresponding to the machine learning flow chart to build a machine learning model, and training and storing the model operator. The method can modularize a mainstream machine learning algorithm to obtain a plurality of machine learning model operators, covers the complete algorithm modeling process from data access, data preprocessing, feature engineering, model training to evaluation and derivation, and assists a user to efficiently complete complex business modeling, thereby reducing the application threshold and the cost of the machine learning model.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings of the embodiments of the present application or the related technical solutions in the prior art are described below, it should be understood that the drawings in the following description are only for convenience and clarity of describing some embodiments of the technical solutions of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a schematic diagram of an implementation environment of a machine learning analysis system based on visual tractor according to an embodiment of the present application;
fig. 2 is a schematic flowchart of a machine learning analysis method based on visual dragging provided in an embodiment of the present application;
FIG. 3 is a schematic structural diagram of a machine learning analysis system based on visual tractor provided in an embodiment of the present application;
fig. 4 is a schematic structural diagram of a computer device provided in an embodiment of the present application.
Detailed Description
The present application is further described with reference to the following figures and specific examples. The described embodiments should not be considered as limiting the present application, and all other embodiments obtained by a person skilled in the art without making any inventive step are within the scope of protection of the present application.
In the following description, reference is made to "some embodiments" which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of the present application only and is not intended to be limiting of the application.
In recent years, with the development of artificial intelligence technology, machine learning has attracted much attention. Machine learning is a multi-field cross subject, relates to a plurality of subjects such as probability theory, statistics, approximation theory, convex analysis, algorithm complexity theory and the like, and is used for specially researching how a computer simulates or realizes human learning behaviors to acquire new knowledge or skills and reorganize an existing knowledge structure to continuously improve the performance of the computer.
In the related technology, the traditional machine learning needs to spend a great deal of manpower and energy to deeply learn, covers probability theory knowledge, statistical knowledge, approximate theory knowledge and complex algorithm knowledge, has a high use threshold, is limited by the limitations and disadvantages of the machine learning algorithms, causes difficulty in large-scale popularization of the machine learning application, has a great deal of repeated work in the related machine learning application, and has low development efficiency.
In view of this, an embodiment of the present application provides a machine learning analysis method based on visual dragging, including: responding to the dragging operation of a user on the function node icon, and displaying a machine learning flow chart on a front-end page; updating the node configuration information in response to a first operation of a user on the function node icon; the node configuration information comprises at least one of a data source, parameters and model operator types of a machine learning model; and transmitting the machine learning flow chart to a background for processing, calling a model operator corresponding to the machine learning flow chart to build a machine learning model, and training and storing the model operator. The method can modularize a mainstream machine learning algorithm to obtain a plurality of machine learning model operators, covers the complete algorithm modeling process from data access, data preprocessing, feature engineering, model training to evaluation and derivation, and assists a user to efficiently complete complex business modeling, thereby reducing the application threshold and the cost of the machine learning model.
Fig. 1 is a schematic diagram of an implementation environment of a machine learning analysis method based on visual dragging according to an embodiment of the present application. Referring to fig. 1, the software and hardware main body of the implementation environment mainly includes an operation terminal 101 and a server 102, and the operation terminal 101 is connected to the server 102 in a communication manner. The machine learning analysis method based on visual dragging may be configured to be executed by the operation terminal 101 alone, or may be configured to be executed by the server 102 alone, or may be executed based on interaction between the operation terminal 101 and the server 102, which may be selected appropriately according to actual application conditions, and this embodiment is not limited in particular. In addition, the operation terminal 101 and the server 102 may be nodes in a block chain, which is not particularly limited in this embodiment.
Specifically, the operation terminal 101 in the present application may include, but is not limited to, any one or more of a smart watch, a smart phone, a computer, a Personal Digital Assistant (PDA), an intelligent voice interaction device, an intelligent household appliance, or a vehicle-mounted terminal. The server 102 may be an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, or a cloud server providing basic cloud computing services such as a cloud service, a cloud database, cloud computing, a cloud function, cloud storage, a Network service, cloud communication, a middleware service, a domain name service, a security service, a CDN (Content Delivery Network), a big data and artificial intelligence platform. The operation terminal 101 and the server 102 may establish a communication connection through a wireless Network or a wired Network, which uses standard communication technologies and/or protocols, and the Network may be set as the internet, or may be any other Network, such as, but not limited to, a Local Area Network (LAN), a Metropolitan Area Network (MAN), a Wide Area Network (WAN), a mobile, wired, or wireless Network, a private Network, or any combination of virtual private networks.
Referring to fig. 2, fig. 2 is a schematic diagram of a machine learning analysis method based on a visual tractor according to an embodiment of the present application, where the machine learning analysis method based on a visual tractor may be configured in at least one of an operation terminal or a server. Referring to fig. 2, the machine learning analysis method based on visual drag includes but is not limited to:
and step 130, transmitting the machine learning flow chart to a background for processing, calling a model operator corresponding to the machine learning flow chart to build a machine learning model, and training and storing the model operator.
In the embodiment of the application, aiming at the problem that the traditional machine learning threshold is high, a machine learning analysis method based on visual dragging is provided, the usability of machine learning can be improved, and the use threshold is reduced. In the embodiment of the application, the mainstream machine learning algorithm can be modularized to obtain a plurality of machine learning model operators, the complete algorithm modeling process from data access, data preprocessing, feature engineering, model training to evaluation and derivation is covered, and a user is assisted to efficiently complete complex business modeling.
In order to optimize the construction cost of the algorithm, the embodiment of the application provides a method for performing machine learning analysis based on the machine learning model operator. Specifically, the method can be realized by a large-scale machine learning platform constructed based on Spark technology, the platform can be compatible with Hadoop clusters, mass data can be flexibly and efficiently processed by utilizing the existing computing resources, and the method has good expandability and can meet the requirements of various machine learning task scenes.
In the embodiment of the application, the machine learning algorithm can be packaged into nodes based on the machine learning model operator, so that a user does not need to pay attention to the bottom level details of the machine learning algorithm, the use threshold is reduced, and the machine learning algorithm can be conveniently applied in a dragging mode. For example, machine learning model operators can be combined under a Spark framework, a VUE front-end technology is used, the machine learning operators are modularized, and a user can connect different data together in a dragging mode, so that the use cost is reduced, the calculation efficiency is greatly improved, and the time cost is greatly reduced.
Of course, it should be noted that the method in the embodiment of the present application is not limited to the Spark framework described above, and for example, the method may also be implemented by combining frames such as PySpark, Spark, Pycaffe, pytorreh, and tensflo, and may meet the use requirements and habits of different developers.
Specifically, in the embodiment of the application, at the front end of the platform, various interactive operations of the user are mainly received, and the selection of a data source, parameters, model operator types and the like of the machine learning model is completed. For example, picture display can be realized based on canvas and dragable attributes of HTML5, algorithms and data related to each machine learning model can be displayed in the form of function node icons, and a user drags the function node icons to be placed in a canvas, so that a node in a machine learning flowchart can be formed. Updating the coordinate position of the function node icon every time the user drags; when the user has configured the function node icons in a mobile manner, the connection operation can be performed on the function node icons, which is recorded as a second operation, and the process connection is performed on each function node icon in response to the second operation performed on the function node icon by the user, and the process connection is used for representing the sequence of execution of the data processing tasks corresponding to each function node icon. After the user finishes the machine learning flowchart, the user can click the related storage key to store the currently set relationship between each function node icon and the flow connection line.
In this embodiment of the application, the user may further perform a first operation on the functional node icon to update node configuration information at the functional node icon, where the node configuration information includes at least one of a data source, a parameter, and a model operator type of the machine learning model. Of course, the configuration information of each node may also be configured with a default value in advance, and the user may also choose to use the default value without changing it.
The established machine learning flow chart can be transmitted to a background for data processing, specifically, the background calls a model operator corresponding to the machine learning flow chart to establish a machine learning model, and trains and stores the model according to output data, so that a user can directly execute a corresponding task by using the trained machine learning model from a platform, and can also directly output a related model file to be transferred to other systems for use, which is not limited in the present application.
In the data processing logic of the back end, for each node in the machine learning flowchart, namely the function node icon, the corresponding data processing step is executed according to the specific type of the function node icon. For each node, it obtains the data source from the previous node having the flow connection relation with the node, processes the data according to the node configuration information corresponding to itself, and then transmits the processed data to the next node having the flow connection relation with the node, and so on.
In some embodiments, said transmitting said machine learning flow graph to a background process comprises:
analyzing the machine learning flow chart to obtain a workflow json string;
determining the relationship between each function node icon and a process connecting line in the machine learning process diagram according to the workflow json string;
and according to the flow connection relation, performing data processing on the node where each functional node icon is located.
In the embodiment of the application, for the machine learning flow chart transmitted by the front end, the background can analyze the machine learning flow chart to obtain the workflow json string, the node id of the functional node icon can be recorded in the workflow json string, and the flow connection relation between the functional node icons can be represented by the key value pair of the node. Therefore, the background can determine the relation between each function node icon and the process connection line in the machine learning flowchart according to the workflow json string, and further can determine the transmission process of data according to the process connection line relation, so that the data processing is performed on the node where each function node icon is located. Specifically, as described above, at the node where each functional node icon is located, the input data of the node where the functional node icon is located may be processed according to the node configuration information corresponding to the functional node icon, and the output data may be transmitted to the next node.
In some embodiments, the model operators of the present application may include a data import dimension operator, a data preprocessing operator, a data feature selection operator, a data tuning operator, and a data export operator.
Specifically, the data import dimension operator may be used to adjust the number of feature dimensions; the data preprocessing operator can be used for configuring the content of data preprocessing, for example, the input data can be selected to be subjected to proportional sampling, sampling according to the number of samples, data segmentation, repeated row removal, ID column generation, missing value filling, column name modification, automatic data preprocessing, data type conversion and the like; the data feature selection operator may be configured to select a data feature, for example, may be configured to select any one of variance-based feature selection, tree-based feature selection, information-based feature selection, and the like, or may select a feature in a weighted manner; similarly, the data tuning operator is used to configure the type of the relevant algorithm for data tuning, and the data export operator is used to configure the manner of data output, which is not described in detail herein.
It can be understood that the machine learning analysis method based on visual dragging provided in the embodiment of the present application has at least the following advantages:
(1) the dragging type task flow adopted by the embodiment of the application has good interaction experience and easy functional design, and can greatly reduce the technical threshold of machine learning;
(2) the method of the present application adapts to multiple learning frameworks: the system comprises Pyspark, Spark, Pycaffe, PyTorch, Tensorflow and the like, and meets the use requirements and habits of different developers;
(3) the method can provide rich algorithm support: the operator based on the machine model can be internally provided with rich algorithms, and various segmentation scenes and application directions are met from the traditional machine learning algorithm to deep learning, image classification, target detection and NLP.
(4) Convenient effect is visual: powerful visual interactive data analysis of the data is realized, so that a user can efficiently and intuitively know the full appearance of the data;
(5) full-automatic modeling: the user only needs to drag the automatic modeling component and input parameters to automatically complete the full modeling process, and a basic beginner can complete the whole training process without obstacles, so that the parameter adjusting efficiency of an engineer can be greatly improved;
(6) complete closed loop of model training: the method provides one-stop machine learning platform experience for users, and covers the whole working process from data preprocessing, model construction, model training to model evaluation to form a complete closed loop of machine learning training.
(7) Flexible resource scheduling: various CPU/GPU resources are supported, the scene requirements of the user on the differential computing power are met, and the cost reduction and the efficiency improvement of the user are facilitated.
Referring to fig. 3, in an embodiment of the present application, there is further provided a machine learning analysis system based on visual tractor, including:
a first response module 201, configured to respond to a drag operation of a user on a function node icon, and display a machine learning flowchart on a front-end page;
a second response module 202, configured to update the node configuration information in response to a first operation of the function node icon by the user; the node configuration information comprises at least one of a data source, parameters and model operator types of a machine learning model;
and the processing module 203 is configured to transmit the machine learning flowchart to a background for processing, call a model operator corresponding to the machine learning flowchart to build a machine learning model, and train and store the model operator.
It can be understood that the contents in the embodiment of the machine learning analysis method based on visual dragging shown in fig. 2 are all applicable to the embodiment of the machine learning analysis system based on visual dragging, the functions implemented in the embodiment of the machine learning analysis system based on visual dragging are the same as those in the embodiment of the machine learning analysis method based on visual dragging shown in fig. 2, and the beneficial effects achieved by the embodiment of the machine learning analysis method based on visual dragging shown in fig. 2 are also the same as those achieved by the embodiment of the machine learning analysis method based on visual dragging shown in fig. 2.
Referring to fig. 4, an embodiment of the present application further discloses a computer device, including:
at least one processor 301;
at least one memory 302 for storing at least one program;
when executed by the at least one processor 301, the at least one program causes the at least one processor 301 to implement the machine learning analysis method embodiment based on visual drag as illustrated in fig. 2.
It can be understood that, the contents in the embodiment of the machine learning analysis method based on visual drag shown in fig. 2 are all applicable to the embodiment of the computer device, the functions implemented by the embodiment of the computer device are the same as those in the embodiment of the machine learning analysis method based on visual drag shown in fig. 2, and the beneficial effects achieved by the embodiment of the machine learning analysis method based on visual drag shown in fig. 2 are also the same as those achieved by the embodiment of the machine learning analysis method based on visual drag shown in fig. 2.
Also disclosed in an embodiment of the present application is a computer-readable storage medium, in which a program executable by a processor is stored, and when the program executable by the processor is executed by the processor, the embodiment of the machine learning analysis method based on visual dragging is implemented as shown in fig. 2.
It is understood that, the contents in the embodiment of the machine learning analysis method based on visual drag shown in fig. 2 are all applicable to the embodiment of the computer readable storage medium, the function implemented by the embodiment of the computer readable storage medium is the same as that in the embodiment of the machine learning analysis method based on visual drag shown in fig. 2, and the beneficial effects achieved by the embodiment of the machine learning analysis method based on visual drag shown in fig. 2 are also the same as those achieved by the embodiment of the machine learning analysis method based on visual drag shown in fig. 2.
In alternative embodiments, the functions/acts noted in the block diagrams may occur out of the order noted in the operational illustrations. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality/acts involved. Furthermore, the embodiments presented and described in the flowcharts of the present application are provided by way of example in order to provide a more thorough understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented herein. Alternative embodiments are contemplated in which the order of various operations is changed and in which sub-operations described as part of larger operations are performed independently.
Furthermore, although the present application is described in the context of functional modules, it should be understood that, unless otherwise stated to the contrary, one or more of the functions and/or features may be integrated in a single physical device and/or software module, or one or more functions and/or features may be implemented in separate physical devices or software modules. It will also be appreciated that a detailed discussion regarding the actual implementation of each module is not necessary for an understanding of the present application. Rather, the actual implementation of the various functional modules in the apparatus disclosed herein will be understood within the ordinary skill of an engineer, given the nature, function, and internal relationship of the modules. Accordingly, those skilled in the art can, using ordinary skill, practice the present application as set forth in the claims without undue experimentation. It is also to be understood that the specific concepts disclosed are merely illustrative of and not intended to limit the scope of the application, which is defined by the appended claims and their full scope of equivalents.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
In the foregoing description of the specification, reference to the description of "one embodiment/example," "another embodiment/example," or "certain embodiments/examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the present application have been shown and described, it will be understood by those of ordinary skill in the art that: numerous changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the application, the scope of which is defined by the claims and their equivalents.
While the present application has been described with reference to the preferred embodiments, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims
In the description herein, references to the description of the term "one embodiment," "another embodiment," or "certain embodiments," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the present application have been shown and described, it will be understood by those of ordinary skill in the art that: numerous changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the application, the scope of which is defined by the claims and their equivalents.
Claims (9)
1. A machine learning analysis method based on visual dragging is characterized by comprising the following steps:
responding to the dragging operation of a user on the function node icon, and displaying a machine learning flow chart on a front-end page;
updating the node configuration information in response to a first operation of a user on the function node icon; the node configuration information comprises at least one of a data source, parameters and model operator types of a machine learning model;
and transmitting the machine learning flow chart to a background for processing, calling a model operator corresponding to the machine learning flow chart to build a machine learning model, and training and storing the model operator.
2. The machine learning analysis method based on visual dragging according to claim 1, wherein the displaying of the machine learning flowchart on the front-end page in response to the dragging operation of the function node icon by the user comprises:
updating the coordinate position of each functional node icon in response to the dragging operation of the functional node icon by the user;
responding to a second operation of the user on the function node icons, and performing flow connection on each function node icon;
and responding to the saving operation of the user, and displaying the machine learning flow chart on the front-end page according to the current connection relation between each function node icon and the flow.
3. The machine learning analysis method based on visual dragging according to claim 2, wherein the step of connecting the flow lines of the function node icons further comprises the following steps:
and carrying out non-closed loop verification on the flow connection relation among the function node icons.
4. The machine learning analysis method based on visual drag as claimed in claim 1, wherein the model operators comprise a data import dimension operator, a data preprocessing operator, a data feature selection operator, a data tuning operator and a data export operator.
5. The visual-tractor-based machine learning analysis method according to claim 1, wherein the transferring the machine learning flowchart to a background process comprises:
analyzing the machine learning flow chart to obtain a workflow json string;
determining the relationship between each function node icon and a process connecting line in the machine learning process diagram according to the workflow json string;
and according to the flow connection relation, performing data processing on the node where each functional node icon is located.
6. The machine learning analysis method based on visual dragging according to claim 5, wherein the data processing of the node where each of the function node icons is located comprises:
and processing the input data of the node where the functional node icon is located according to the node configuration information corresponding to the functional node icon.
7. A machine learning analysis system based on visual pull, comprising:
the first response module is used for responding to the dragging operation of a user on the function node icon and displaying the machine learning flow chart on the front-end page;
the second response module is used for responding to the first operation of the user on the function node icon and updating the node configuration information; the node configuration information comprises at least one of a data source, parameters and model operator types of a machine learning model;
and the processing module is used for transmitting the machine learning flow chart to a background for processing, calling a model operator corresponding to the machine learning flow chart to build a machine learning model, and training and storing the model operator.
8. A computer device, comprising:
at least one processor;
at least one memory for storing at least one program;
when executed by the at least one processor, cause the at least one processor to implement the visual-tractor-based machine learning analysis method of any one of claims 1-6.
9. A computer-readable storage medium in which a program executable by a processor is stored, characterized in that: the processor executable program when executed by a processor is for implementing a visual pull based machine learning analysis method according to any of claims 1-6.
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