CN110008121B - Personalized test system and test method thereof - Google Patents
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- CN110008121B CN110008121B CN201910209593.5A CN201910209593A CN110008121B CN 110008121 B CN110008121 B CN 110008121B CN 201910209593 A CN201910209593 A CN 201910209593A CN 110008121 B CN110008121 B CN 110008121B
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Abstract
The invention discloses an individualized test system and a test method thereof, and the individualized test system comprises a model creating unit, wherein the model creating unit comprises a custom parameter module and a model uploading module, the custom parameter module is communicatively connected with the model uploading module, the model uploading module creates at least one machine learning model, and the custom parameter module edits the mapping relation of the machine learning model; and the model testing unit comprises a personalized display module and a selection module, the personalized display module is in communication connection with the custom parameter module to complete the mapping display of the machine learning model, and the selection module is in communication connection with the personalized display module to select one mapping relation of one machine learning model. The model testing unit can map and display preset parameters, so that a tester can select a desired machine learning model for testing.
Description
Technical Field
The invention relates to the field of communication, in particular to a personalized test system based on a machine learning model and a test method thereof.
Background
With the advent of the information age, many industries have been emerging, and huge data information is generated every day. These data are numerous and cluttered, but each is extremely important, containing valuable information inside. While the traditional data analysis is used for analyzing data by a method arranged in advance to discover valuable information, the big data analysis is not limited by the limitation, and useful information is directly analyzed from huge and structurally complex data, so that the data can exert the maximum value, but the process is quite complex, and information is difficult to obtain quickly, so that the analysis must be completed by a machine learning model. The goal realization of big data technology is inevitably inseparable from the development of machine learning.
Therefore, the design of the machine model is indispensable for the development of the social aspect. Machine learning is the core of artificial intelligence, is the fundamental approach for making computers have intelligence, is applied to all fields of artificial intelligence, and mainly uses induction, synthesis rather than deduction. That is, when a machine learning model is built, the user can obtain a predictable result based on the collection and analysis of certain data.
When a machine learning model is developed, especially when multiple developers have sharing needs. Different model parameters are input to the machine learning model, wherein each machine learning model is different in representation form, and may be pictures, video or audio. Therefore, when a tester inputs a test data, the test result is different, even worse, and the test process becomes very complicated. In addition, since the parameter requirements of the parameter developers of different developers are different, and the developer of the machine learning model cannot be known as the tester, the tester is likely to make mistakes in this case.
Disclosure of Invention
The invention aims to provide a personalized test system, which is used for solving the problems of test stability and test personalization in machine learning models of a plurality of developers.
In order to solve the technical problems, the technical scheme of the invention is as follows:
a personalization test system comprising:
the model creating unit comprises a custom parameter module and a model uploading module, wherein the custom parameter module is communicatively connected with the model uploading module, the model uploading module creates at least one machine learning model, and the custom parameter module edits the mapping relation of the machine learning model; and
the model testing unit comprises a personalized display module and a selection module, wherein the personalized display module is in communication connection with the custom parameter module to complete the mapping display of the machine learning model, and the selection module is in communication connection with the personalized display module to select one mapping relation of one machine learning model.
Preferably, the custom parameter module comprises at least one input element and at least one output element, and the input element and the output element are both communicated with the machine learning module to form a mapping relation.
Preferably, the custom parameter module further comprises an editing element, and the editing element is connected with the input element and the output element in an editable manner to edit the input and output parameter information.
Preferably, the personalized display module further includes a preset parameter element and a display element, wherein the preset parameter element is communicatively connected to the custom parameter module, generates at least one preset output parameter according to the mapping relationship of the machine learning model, and the display element is communicatively connected to the preset parameter element to display information displayed by the preset output parameter.
Preferably, the selection module selects the machine learning model and the input element and the output element corresponding to the mapping relationship after receiving a selected signal, and the input element and the output element are the test input element and the test output element to wait for testing.
Preferably, the model testing unit further comprises a parameter prompt module, the parameter prompt module is communicatively connected to the selection module to prompt the parameter requirement of the selected mapping relationship, and the personalized testing system further comprises a log unit, and the log unit is communicatively connected to the model testing unit and the model creating unit to record the operation process of the personalized testing system.
The invention also provides a system testing method, which comprises the following steps:
(a) setting input and output parameter information characteristics;
(b) receiving a model file and creating at least one machine learning model, and simultaneously connecting the machine learning model with at least one input element and at least one output element in a mapping manner;
(c) displaying display contents of parameter information including at least one output parameter mapped by a preset input parameter, and selecting the input element and the output element corresponding to the mapping relation after one of the display contents is selected; and
(d) the input element and the output element at this time become the current test input element and test output element to wait for the test.
Preferably, in step (b), the machine learning model is mappingly connected to a plurality of input elements and a plurality of output elements, or the machine learning module is mappingly connected to one input element and one output element.
Preferably, wherein step (c) further comprises the steps of:
(c1) outputting a preset output parameter corresponding to a preset input parameter after the system test unit is started; and
(c2) and selecting the input element and the output element corresponding to the corresponding mapping relation of the corresponding machine learning model after one preset output parameter is selected.
Preferably, wherein step (d) further comprises the steps of:
(d1) prompting the selected test input element and the parameter requirement required by the test output element.
By adopting the technical scheme, the model testing unit can map and display the preset parameters, so that a tester can select a desired machine learning model to test, and can prompt the parameter requirements required by the tester in the testing process.
Drawings
FIG. 1 is a system diagram of a personalization test system according to the present invention;
FIG. 2 is a schematic process diagram of the model creation unit of the personalization test system of the present invention;
FIG. 3 is a schematic process diagram of a portion of the model test unit of the personalization test system of the present invention;
FIG. 4 is a schematic process diagram of another part of the model test unit of the personalization test system of the present invention shown in succession to FIG. 3;
fig. 5A is a schematic diagram of one mapping relationship of the machine learning model of the personalized testing system according to the present invention.
Fig. 5B is a schematic diagram of another mapping relationship of the machine learning model of the personalized testing system according to the invention.
Detailed Description
The following description of the embodiments of the present invention will be made with reference to the accompanying drawings. It should be noted that the description of the embodiments is provided to help understanding of the present invention, but the present invention is not limited thereto. In addition, the technical features involved in the embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
As shown in fig. 1, the personalized testing system includes a model creation unit 10 and a model testing unit 20, wherein the model creation unit 10 is used for creating at least one machine learning model 100, and the model testing unit 20 is used for testing the machine learning model 100. Specifically, the model testing unit 20 of the personalized testing system includes a personalized display module 21, the model testing unit 20 further includes a selection module 22 and a parameter prompt module 23, and the selection module 22 is configured to select the information displayed by the preset output parameter displayed by the display element 212. Wherein the personalized test system further comprises a logging unit 30, the logging unit 30 communicatively connecting the model test unit 20 and the model creation unit 10 to record the operation process of the personalized test system.
As shown in fig. 2, the model creating unit 10 includes a custom parameter module 11, the custom parameter module 11 receives parameter names included in a model developed by a user, and the custom parameter module 11 can output a parameter name, a parameter display name and a parameter display type according to the parameter names. When a plurality of developers input their parameters in the custom parameter module 11, the finally output parameter display names and the parameter display types are different. In addition, the personalized display module 21 is communicatively connected to the custom parameter module 11, so that a model conforming to the user's mind can be output by customization of the custom parameter module 11.
Therefore, the model creation unit 10 further comprises a model uploading module 12, the model uploading module 12 receives the model file uploaded by the developer, and the model uploading module 12 is communicatively connected to the custom parameter module 11. The model file comprises a sample library, a sample library classification module, a feature extraction module and a model forming module, and particularly, a primary machine learning model 100 can be created through the model file.
As shown in fig. 3 and 4, the customized parameter module 11 includes at least one input element 111 and at least one output element 112, wherein the input element 111 receives input data from a user, that is, a parameter name, a parameter display name, and a parameter display type, and output data generated by the output element 112, that is, the parameter name, the parameter display name, and the parameter display type, can be set. Specifically, the custom parameter module 11 includes an editing component 113, the editing component 113 is communicatively connected to the input component 111 and the output component 112, and the editing component 113 is connected to the output component 112 in an editable manner, so that the output data can be edited.
It is understood that in the above system, the input data in the custom parameter module 11 can be parameter names required by the model files uploaded by the developer. The output data may be information edited by the developer, that is, the same parameter name, display name and type as the input data, or different parameter names, display names and types.
When the model file is uploaded to the model creating unit 10, the personalized test system can perform a test. That is, the personalization presentation module 21 communicates with the machine learning model 100. Meanwhile, the personalized display module 21 is communicatively connected to the custom parameter module 11, so that the personalized display module 21 displays the output data generated by the output element 112.
In this process, no testing has been performed. But rather, the output data of the machine learning model 100 set by the developer is shown by the personalization display module 21. When the tester selects one of the output data in the test process, the personalized test system can improve the input data requirement required by the corresponding output data to the tester. Thus, the tester can know the test data requirements needed in the current test process.
Specifically, in the first case, the editing component 113 is communicatively connected to the model uploading module 12, so that the editing component 113 can acquire the mapping relationship of the machine learning model 100, and complete the mapping relationship between the input component 111 and the output component 112.
In the second case, the parameter display names and types of the input element 111 and the output element 112 are set by the custom parameter module 11 through the editing element 113. In this case, the display name and type of the parameter output by the output element 112 are defined.
Meanwhile, after the model uploading module 12 receives the uploaded model file, the model uploading module 12 generates a machine learning model 100 according to the model file. A preset input parameter is input into the input element 111, and according to the preset input parameter, the personalized display module 21 obtains the output parameter, thereby forming personalized display information.
The model test unit 20 further comprises a selection module 22, and the selection module 22 is used for selecting one of the personalized display information. When the personalized presentation information is selected, the selection module 22 generates a selection signal. Therefore, the selection module 22 is communicatively connected to the custom parameter module 11 and selects the mapping relationship of the machine learning model 100 corresponding to the preset output parameters, that is, the input element 111 and the output element 112.
According to the system content, the personalized test system comprises a setting process, a creating process and a testing process. In the setting process, the developer sets parameter information of the input element 111 and the output element 112 respectively through the editing element 113, wherein the parameter information comprises a parameter name, a parameter display name and a parameter type. After the setting is completed, the parameter requirements required by the input element 111 and the output element 112 are defined.
During the creation process, the model upload module 12 receives the model file and creates a preliminary machine learning model 100, and the model upload module 12 communicatively connects the input element 111 and the output element 112, so that the machine learning model 100 communicates the input element 111 and the output element 112. Therefore, when a parameter information is input, a learned parameter information is output according to the analysis of the machine learning model 100.
It should be noted that the external features of the parameter information, i.e. the display name and type, are defined by the custom parameter module 11, and the mapping relationship of the parameter information is defined by the machine learning model 100.
In the testing process, a personalized display stage is first performed, in which the personalized display module 21 includes a predicted parameter component 211, and a preset input parameter set by a developer is set in the predicted parameter component 211. Meanwhile, the personalized display module 21 further includes a display element 212, and the display element 212 is used for displaying the output parameters under the condition of the current preset input parameters.
Specifically, the preset parameter component 211 is communicatively connected to the model uploading module 12, and when the model uploading module 12 completes creation of the machine learning model 100, the preset input parameter input by the preset parameter component 211 is input to the machine learning model 100, and at this time, the output component 112 outputs a preset output parameter. The output element 112 is communicatively connected to the display element 212, and the display element 212 is capable of displaying the information displayed by the preset output parameter at the time.
As shown in fig. 5A, it can be appreciated that when multiple developers develop multiple models, the presentation component 212 automatically queues the information displayed by the preset output parameters. Each preset output parameter corresponds to a mapping relationship of the machine learning model 100. The selection module 22 is communicatively connected to the custom parameter module 11 to select a mapping relationship corresponding to a current preset output parameter, that is, a mapping relationship of the machine learning model 100.
As shown in fig. 5B, in another case, when a developer sets a plurality of output mappings in one machine learning model 100, that is, a plurality of mappings of input elements 111 and output elements 112 are set among the editing elements 113. The display element 212 automatically displays the information displayed by the preset output parameters in a queue. Each preset output parameter corresponds to one of the mapping relationships of the machine learning model 100. The selection module 22 is communicatively connected to the custom parameter module 11 to select a mapping relationship corresponding to the preset output parameter, that is, one of the mapping relationships of the machine learning module 100.
Meanwhile, the parameter prompt module 23 prompts the tester that the corresponding parameter requirement needs to be increased.
According to the system content, the invention provides a system testing process, which comprises the following processes:
the first step is as follows: setting input/output parameter information
The editing component 113 receives parameter information set by a developer so that an external feature of the parameter information between the input component 111 and the output component 112 is defined.
The second step is that: creating machine learning models
The model uploading module 12 receives at least one model element uploaded by a developer, and creates a machine learning model according to the model element. Meanwhile, the model uploading module 12 is communicatively connected to the custom parameter module 11, and the machine learning model 100 is in mapping communication with at least one of the input elements 111 and at least one of the output elements 112 to complete the mapping of data.
The third step: personalized display
After the model testing unit 20 is started, the preset parameter element 211 is communicatively connected to the custom parameter module 11, so that a preset input parameter built in the preset parameter element 211 is mapped to output a preset output parameter according to the corresponding machine learning model 100.
The display element 212 obtains at least one preset output parameter and automatically displays parameter information of the preset output parameter.
The fourth step: selecting the type of test required
The selection module 22 selects parameter information of one of the preset output parameters, and the parameter prompt module 23 prompts the tester to input a corresponding input parameter requirement in the input element 111.
Specifically, the selection module 22 receives a selection signal, communicatively connects to the self-defined parameter module 11, and selects the mapping relationship of the machine learning model 100 corresponding to the preset output parameter. At this time, the input element 111 and the output element 112 of the current mapping relationship are selected. Meanwhile, the parameter prompt module 23 prompts the tester of the parameter requirement required for the test.
It is worth mentioning that in the second step, the mapping relationship between the input elements 111 and the output elements 112 and the machine learning model 100 is that one machine learning model 100 corresponds to a plurality of input elements 111 and a plurality of output elements 112. In yet another embodiment, the mapping relationship between the input elements 111 and the output elements 112 and the machine learning model 100 is that one machine learning module 100 corresponds to one input element 111 and one output element 112.
According to the system content and the system flow, the invention further provides a system testing method, which comprises the following steps:
(a) setting input and output parameter information characteristics;
(b) receiving a model file and creating at least one machine learning model, and simultaneously connecting the machine learning model with at least one input element and at least one output element in a mapping manner;
(c) displaying display contents of parameter information including at least one output parameter mapped by a preset input parameter, and selecting the input element and the output element corresponding to the mapping relation after one of the display contents is selected; and (a) and
(d) the input element and the output element at this time become the current test input element and test output element to wait for the test.
The system test method according to the above, wherein in the step (a), the parameter information characteristics include a parameter name, a parameter display name, and a parameter type.
The system testing method as described above, wherein in step (b) the machine learning model is mappingly connected to a plurality of input elements and a plurality of output elements.
The system testing method as described above, wherein in step (b), the machine learning module is mappingly connected to an input element and an output element.
The system testing method as described above, wherein the step (c) further comprises the steps of:
(c1) outputting a preset output parameter corresponding to a preset input parameter after the system test unit is started;
(c2) selecting the input element and the output element corresponding to the corresponding mapping relation of the corresponding machine learning model when one preset output parameter is selected;
the system testing method as described above, wherein the step (d) further comprises the steps of:
(d1) prompting the selected test input element and the parameter requirement required by the test output element.
The embodiments of the present invention have been described in detail above with reference to the accompanying drawings, but the present invention is not limited to the described embodiments. It will be apparent to those skilled in the art that various changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, and the scope of protection is still within the scope of the invention.
Claims (5)
1. A personalization test system, comprising:
the model creating unit comprises a custom parameter module and a model uploading module, wherein the custom parameter module is in communication connection with the model uploading module, the model uploading module creates at least one machine learning model, and the custom parameter module edits the mapping relation of the machine learning model; and
the model testing unit comprises a personalized display module and a selection module, wherein the personalized display module is in communication connection with the custom parameter module to complete mapping display of the machine learning model, and the selection module is in communication connection with the personalized display module to select one mapping relation of one machine learning model;
the self-defining parameter module comprises at least one input element and at least one output element, and the input element and the output element are both communicated with the machine learning module to form a mapping relation;
the user-defined parameter module also comprises an editing element which is connected with the input element and the output element in an editable way to edit the input and output parameter information;
the personalized display module further comprises a preset parameter element and a display element, wherein the preset parameter element is in communication connection with the user-defined parameter module and generates at least one preset output parameter according to the mapping relation of the machine learning model, and the display element is in communication connection with the preset parameter element to display information displayed by the preset output parameter;
the selection module selects the machine learning model, the input element and the output element corresponding to the mapping relationship after receiving a selected signal, and the input element and the output element are the test input element and the test output element to wait for testing;
the model testing unit further comprises a parameter prompting module which is in communication connection with the selection module to prompt the selected parameter requirement of the mapping relation, wherein the personalized testing system further comprises a log unit which is in communication connection with the model testing unit and the model creating unit to record the operation process of the personalized testing system.
2. A system testing method for personalizing a system according to claim 1, comprising the steps of:
(a) setting input and output parameter information characteristics;
(b) receiving a model file and creating at least one machine learning model, and simultaneously connecting the machine learning model with at least one input element and at least one output element in a mapping manner;
(c) displaying display contents of parameter information including at least one output parameter mapped by a preset input parameter, and selecting the input element and the output element corresponding to the mapping relation after one of the display contents is selected; and
(d) the input element and the output element at this time become the current test input element and test output element to wait for the test.
3. The system testing method of claim 2, wherein in step (b), the machine learning model is mappingly connected to a plurality of input elements and a plurality of output elements, or the machine learning module is mappingly connected to one input element and one output element.
4. The method of claim 3, wherein step (c) further comprises the steps of:
(c1) outputting a preset output parameter corresponding to a preset input parameter after the system test unit is started; and
(c2) and selecting the input element and the output element corresponding to the corresponding mapping relation of the corresponding machine learning model when one preset output parameter is selected.
5. The method of claim 4, wherein step (d) further comprises the steps of:
(d1) prompting the selected test input element and the parameter requirement required by the test output element.
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