WO2018126936A1 - 组件发布及基于图形化机器学习算法平台的组件构建方法、图形化机器学习算法平台 - Google Patents

组件发布及基于图形化机器学习算法平台的组件构建方法、图形化机器学习算法平台 Download PDF

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Publication number
WO2018126936A1
WO2018126936A1 PCT/CN2017/118433 CN2017118433W WO2018126936A1 WO 2018126936 A1 WO2018126936 A1 WO 2018126936A1 CN 2017118433 W CN2017118433 W CN 2017118433W WO 2018126936 A1 WO2018126936 A1 WO 2018126936A1
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Prior art keywords
component
new component
functional model
machine learning
parameter
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PCT/CN2017/118433
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English (en)
French (fr)
Chinese (zh)
Inventor
雷宗雄
李博
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Alibaba Group Holding Ltd
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Alibaba Group Holding Ltd
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Priority to EP17890410.8A priority Critical patent/EP3567474B1/en
Priority to JP2019534335A priority patent/JP7075933B2/ja
Publication of WO2018126936A1 publication Critical patent/WO2018126936A1/zh
Anticipated expiration legal-status Critical
Priority to US16/505,617 priority patent/US20190332970A1/en
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F8/00—Arrangements for software engineering
    • G06F8/30—Creation or generation of source code
    • G06F8/34—Graphical or visual programming
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/44—Arrangements for executing specific programs
    • G06F9/445—Program loading or initiating
    • G06F9/44505—Configuring for program initiating, e.g. using registry, configuration files
    • G06F9/4451—User profiles; Roaming
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/44—Arrangements for executing specific programs
    • G06F9/445—Program loading or initiating
    • G06F9/44505—Configuring for program initiating, e.g. using registry, configuration files

Definitions

  • the present application relates to the field of electronic information, and in particular, to a component release and a component construction method based on a graphical machine learning algorithm platform, and a graphical machine learning algorithm platform.
  • the graphical machine learning algorithm platform is a user interaction platform that can provide modeling functions to users.
  • the components are the basic units of the graphical machine learning algorithm platform.
  • the user organizes the components into an orderly process to build a model with certain functionality.
  • FIG. 1 shows a model for analyzing the user churn data function established by the user, wherein the elliptical icon represents the component, and the names of the elliptical icons, such as split-1 and random forest, represent algorithms for running the component. Users use these arrows to form an orderly process using arrows to establish a model for analyzing user churn data.
  • the applicant found that if the established functional model can be released or built into a new component in the graphical machine learning algorithm platform, if the function is needed again, the new component can be directly selected without repeated establishment.
  • the functional model In the course of the research, the applicant found that if the established functional model can be released or built into a new component in the graphical machine learning algorithm platform, if the function is needed again, the new component can be directly selected without repeated establishment.
  • the functional model In the course of the research, the applicant found that if the established functional model can be released or built into a new component in the graphical machine learning algorithm platform, if the function is needed again, the new component can be directly selected without repeated establishment.
  • the functional model In the course of the research, the applicant found that if the established functional model can be released or built into a new component in the graphical machine learning algorithm platform, if the function is needed again, the new component can be directly selected without repeated establishment. The functional model.
  • the present application provides a component publishing method and a component building method based on a graphical machine learning algorithm platform, and a graphical machine learning algorithm platform, which aims to solve the problem of how to publish or build a new component in a graphical machine learning algorithm platform.
  • a component publishing method including:
  • the functional model is published as the new component.
  • the determining the unique identifier of the required parameter of the component in the functional model includes:
  • a unique identifier of a mandatory parameter of the component is received through the visualization interface.
  • the visual interface includes:
  • the configuration parameter of the mandatory parameter configuration control of the component is configured to receive a configuration instruction for the required parameter during the running of the new component.
  • the visual interface further includes:
  • An optional parameter configuration control configuration interface wherein the optional parameter configuration control is configured to receive a configuration instruction for the optional parameter during operation of the new component.
  • the publishing the function model as the new component includes:
  • a component creation method based on a graphical machine learning platform comprising:
  • the graphical machine learning platform After receiving the new component creation instruction, the graphical machine learning platform creates a new component according to the established functional model, and the required parameters of each component of the new component have a unique identifier, and the unique identifier is used for the new component.
  • the value of the mandatory parameter is identified during operation.
  • the creating a new component according to the established functional model includes:
  • a graphical machine learning algorithm platform including:
  • An input/output determining module configured to determine an input end and an output end of the new component according to a connection relationship of the components in the functional model after receiving an instruction to publish the function model as a new component;
  • An identifier determining module configured to determine a unique identifier of a required parameter of a component in the functional model, the unique identifier being used by the new component to identify a value of the required parameter during operation;
  • a publishing module for publishing the functional model as the new component.
  • the identifier that is used by the identifier determining module to determine a mandatory parameter of the component in the function model includes:
  • the identifier determining module is configured to: after receiving an instruction to select a component in the functional model, display a visual interface of the component; and receive, by using the visual interface, a unique identifier of a mandatory parameter of the component.
  • the visualizing interface used by the identifier determining module to display the component includes:
  • the identifier determining module is specifically configured to display a configuration interface of a mandatory parameter configuration control of the component, where the mandatory parameter configuration control is configured to receive the required parameter during the running of the new component Configuration instructions.
  • the visual interface further includes:
  • An optional parameter configuration control configuration interface wherein the optional parameter configuration control is configured to receive a configuration instruction for the optional parameter during operation of the new component.
  • the publishing module is configured to publish the function model as the new component, including:
  • the issuing module is specifically configured to: input test data to the new component, and run the new component; input the test data to the functional model, and run the functional model; if the new component is running The outputted data is the same as the data output by the functional model after running, and the functional model is published as the new component.
  • a graphical machine learning algorithm platform including:
  • a component creation module configured to create a new component according to the established function model after receiving the new component creation instruction, wherein a mandatory parameter of each component of the new component has a unique identifier, and the unique identifier is used for the new component
  • the component identifies the value of the required parameter during operation.
  • the component creation module is configured to create a new component according to the established functional model, including:
  • the component creation module is specifically configured to determine a unique identifier of a mandatory parameter of a component in the functional model, and determine an input end and an output end of the new component according to a connection relationship of components in the functional model, Create the new component.
  • the method and the graphical machine learning algorithm platform described in the present application can directly use the new component without releasing the function component by publishing or constructing the function model as a new component. Functional model for user convenience.
  • FIG. 1 is a schematic diagram of a model established by a user to analyze user churn data functions
  • FIG. 2 is a flowchart of a component publishing method according to an embodiment of the present application
  • FIG. 3 is a schematic diagram of an instruction for a graphical machine learning algorithm platform disclosed in an embodiment of the present application to release a function model as a new component;
  • FIG. 4 is a schematic diagram of a configuration process and a running process of a super component disclosed in an embodiment of the present application
  • FIG. 5 is a schematic diagram of a visual interface of a basic component disclosed in an embodiment of the present application.
  • FIG. 6 is a schematic diagram of a configuration interface of a mandatory parameter configuration control disclosed in an embodiment of the present application.
  • FIG. 7 is a schematic diagram of a process of a component publishing method according to an embodiment of the present disclosure.
  • FIG. 8 is a schematic diagram of the use of the super component disclosed in the embodiment of the present application.
  • FIG. 9 is a schematic structural diagram of a graphical machine learning algorithm platform disclosed in an embodiment of the present application.
  • a component publishing or building method provided by the present application can be applied to a graphical machine learning algorithm platform, and aims to publish or construct a functional model constructed by the original components of the graphical machine learning algorithm platform as a new component.
  • the original component of the graphical machine learning algorithm platform is referred to as a basic component
  • the new component that is released or constructed by the basic component is referred to as a super component.
  • the basic component may be a component that implements a single algorithm, or a component that is composed of multiple components that implement a single algorithm.
  • FIG. 2 is a schematic diagram of a component publishing method according to an embodiment of the present application, including the following steps:
  • the graphical machine learning algorithm platform obtains a functional model to be constructed as a super component based on a user's operation instruction.
  • the graphical machine learning algorithm platform receives an instruction to publish the functional model as a new component.
  • Figure 3 shows the user's established process in the graphical machine learning algorithm platform.
  • the selected part is the functional model to be built as a super component.
  • the user can right click on the function model and select “Merge” in the pop-up menu, and the graphical machine learning algorithm platform determines that the function that has released the function model of the frame selection part is released as a new component.
  • the graphical machine learning algorithm platform can also receive a name entered by the user for the super component. For example, after the user selects "Merge", the graphical machine learning algorithm platform pops up a dialog box and receives the name "Logistic Regression & Random Forest Assessment" entered by the user in the dialog box.
  • the graphical machine learning algorithm platform determines the input end and the output end of the super component according to the connection relationship of the components in the functional model.
  • connection relationship is a connection relationship indicated by an arrow in the function model
  • the graphical machine learning algorithm platform uses the connection end of the function model and the upstream component as the input end of the super component, and the connection end of the function model and the downstream component as the super component. The output.
  • connection between the functional model and the upstream component is the port pointed to by the component "missing value fill -1" arrow, and the graphical machine learning algorithm platform uses the port as the input of the super component.
  • the connection between the functional model and the downstream component is the port of the component "two classification evaluation-1" and the component "two classification evaluation-2" connection arrow, respectively, and the graphical machine learning algorithm platform uses these two ports as the output of the super component.
  • the ports connected to the upstream components are all input terminals of the super component.
  • the ports that are connected to the downstream components are all the outputs of the super component.
  • the graphical machine learning algorithm platform determines a unique identifier of a mandatory parameter of a component in the functional model.
  • a unique identifier is used to identify the value of the mandatory parameter during the running of the new component.
  • the graphical machine learning algorithm platform displays a visual interface of the component, and receives a unique identifier of the required parameter of the component through the visual interface. For example, as shown in the configuration process in FIG. 4, after receiving the instruction of the component “random forest” in the function model of the user double-clicking, the graphical machine learning algorithm platform pops up a visual interface of the “random forest” component, and the user can The unique identifier of the required parameter of the component "random forest” is entered on the visualization interface.
  • the visual interface of the basic component includes a configuration interface of a mandatory parameter configuration control and a configuration interface of a configuration parameter of an optional parameter (a configuration interface of a configuration control in which an optional parameter is not shown in FIG. 4) .
  • the mandatory parameter configuration control is used to receive configuration commands for the required parameters during the running of the super component.
  • An optional parameter configuration control is used to receive configuration instructions for optional parameters while the super component is running.
  • the user configures the mandatory parameters through the mandatory parameter configuration control. For example, enter the value of the required parameter.
  • the configuration interface of the mandatory parameter configuration control in Figure 4 is used to configure the mandatory parameter configuration controls.
  • the existing graphical machine learning algorithm platform, the parameter configuration control is automatically set by the system, and the user cannot configure.
  • the configuration interface of the mandatory parameter configuration control includes at least a unique identifier configuration item, and the unique identifier configuration item is used to receive the identifier set by the user for the required parameter.
  • the user can input the identifier set for the mandatory parameter through the identifier configuration item, and the graphical machine learning algorithm platform identifies the super component (including receiving or internally transmitting) the data with the identifier as the value of the mandatory parameter.
  • the graphical machine learning algorithm platform uses the data as the value of the mandatory parameter. Regardless of which of the base components in the super component recognizes this data, it is the value of the required parameter.
  • the configuration interface of the mandatory parameter configuration control may include, but is not limited to, a control type configuration item, a control name configuration item, and a control prompt (including prompts and long prompts) text configuration items.
  • Figure 6 shows the configuration items of the mandatory parameter "training feature column", including:
  • control type in Figure 6, the user selects the control type as "Multi-field selection control (downstream inherits all fields)" by the down option.
  • the prompt text in Figure 6, the user enters "required” as the prompt text of the control.
  • the configuration interface of the optional parameter configuration control includes the name of the optional parameter and the default value set by the graphical machine learning algorithm platform for the parameter.
  • the “concurrent calculation amount” in FIG. 5 is the name of an optional parameter, and the parameter The default is 100. The user can take the default value or modify the default value in the parameter bar.
  • S205 input test data to the super component after completion of configuration, and input the same test data into a functional model corresponding to the super component (ie, construct a functional model of the super component), if the output result of the super component is the same as the output result of the functional model Then, S206 is executed; otherwise, at least one of S203 to S204 is executed.
  • the user drags the base component onto the canvas on the graphical machine learning algorithm platform and uses the arrows to group the base components into a process.
  • the user selects a part from the process, and the user can also right click, select the "Merge” item in the pop-up menu, merge the selected components, called the modeling process subset, and enter the name "Logistic Regression & Random Forest” Evaluation".
  • the graphical machine learning algorithm platform will use the port of the component of the starting base component "missing value padding-1" of the modeling process subset as the input of the super component "Logistic Regression & Random Forest Assessment", and the modeling process will be
  • the end-end components of the set "Two-Class Evaluation-1" and “Two-Class Evaluation-2" connect the ports of the downstream components as the output of the super component "Logistic Regression & Random Forest Assessment”.
  • the user completes the configuration of the parameter configuration control on the visual interface.
  • the graphical machine learning algorithm platform receives the input parameters of the user for completing the set super component, runs the super component, and obtains the output data of the super component.
  • the graphical machine learning algorithm platform receives input parameters of the user's subset of the modeling process, runs a subset of the modeling process, and obtains output data of the subset of the modeling process.
  • the graphical machine learning algorithm platform publishes the super component.
  • the graphical machine learning algorithm platform has released a new super component. If users need to model the function subset, there is no need to build a subset of the modeling process, and directly use the super component.
  • the super component is used in the same way as the basic component, as shown in Figure 8:
  • the user drags the super component "Logistic Regression & Random Forest Assessment" onto the canvas on the graphical machine learning algorithm platform.
  • Other base components and/or super component build processes are also used.
  • the graphical machine learning algorithm platform pops up a parameter configuration control, such as "training feature column configuration control”.
  • the user selects a field in the Training Feature Column Configuration Control to enter data as a training feature column.
  • the data is input and input from the input, and the data includes the values of the required parameters of each component in the super component, and each component identifies from the data.
  • the data is what you need, and the basis for identification is the unique identifier set for the required parameters during the component's release process.
  • the graphical machine learning algorithm platform establishes a Mysql temporary table according to the direction of the arrow in the super component, and records the input component and the output component of each basic component to pass it to each of the basic components. Information about the respective input and output components.
  • the contents of the MySQL temporary table are the four elements of the component: input, output, field settings, parameter settings, and when the component pointed to by the arrow is run, four elements are extracted from the Mysql table.
  • the graphical machine learning algorithm platform clears the Mysql table.
  • the component publishing process shown in FIG. 2 sets a unique identifier for the required parameters of the basic component by configuring the parameter configuration control of the basic component in the functional model, so that the mandatory parameter has a global
  • the nature of the parameter, that is, during the operation of the super component, the underlying component in the super component can identify which data is the value of the required parameter that it needs. Therefore, the super component released in Figure 2 can be reused for user convenience.
  • the embodiment of the present application further discloses a component creation method based on a graphical machine learning platform, including the following steps:
  • the graphical machine learning platform After receiving the new component creation instruction, the graphical machine learning platform creates a new component according to the established functional model, and the required parameters of each component of the new component have a unique identifier, and the unique identifier is used for the new component.
  • the value of the mandatory parameter is identified during operation.
  • the specific manner of creating a new component according to the established function model is: determining a unique identifier of a required parameter of the component in the functional model, and determining the according to a connection relationship of components in the functional model.
  • the input and output of the new component to create the new component can be seen in Figure 2.
  • the graphical machine learning platform can publish new components based on the user's instructions.
  • the graphical machine learning platform has the function of creating new components.
  • FIG. 9 is a schematic diagram of a machine learning algorithm platform according to an embodiment of the present disclosure, comprising: an input and output determination module, an identification determination module, and a release module.
  • the input/output determining module is configured to determine an input end and an output end of the new component according to a connection relationship of components in the functional model after receiving an instruction to publish the function model as a new component.
  • the identity determining module is operative to determine a unique identifier of a mandatory parameter of a component in the functional model, the unique identifier being used by the new component to identify a value of the mandatory parameter during operation.
  • a publishing module is used to publish the functional model as the new component.
  • the graphical machine learning algorithm platform described in this embodiment has the function of publishing the function model as a new component, and thus can be conveniently used by the user.
  • the embodiment of the present application further discloses a graphical machine learning algorithm platform, including a component creation module, configured to create a new component according to an established function model after receiving a new component creation instruction, and each component in the new component
  • the mandatory parameter has a unique identifier for the new component to identify the value of the mandatory parameter during operation.
  • the specific implementation manner of creating a new component according to the established functional model is: determining a unique identifier of a required parameter of a component in the functional model, and determining the new component according to a connection relationship of components in the functional model. The input and output to create the new component.
  • the graphical machine learning algorithm platform described in this embodiment has the function of creating a new component.
  • the functions described in the methods of the embodiments of the present application may be stored in a computing device readable storage medium. Based on such understanding, a portion of the embodiments of the present application that contributes to the prior art or a portion of the technical solution may be embodied in the form of a software product stored in a storage medium, including a plurality of instructions for causing a
  • the computing device (which may be a personal computer, server, mobile computing device, or network device, etc.) performs all or part of the steps of the methods described in various embodiments of the present application.
  • the foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, and the like. .

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PCT/CN2017/118433 2017-01-06 2017-12-26 组件发布及基于图形化机器学习算法平台的组件构建方法、图形化机器学习算法平台 Ceased WO2018126936A1 (zh)

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EP17890410.8A EP3567474B1 (en) 2017-01-06 2017-12-26 Component publishing method, component building method based on graphical machine learning algorithm platform, and graphical machine learning algorithm platform
JP2019534335A JP7075933B2 (ja) 2017-01-06 2017-12-26 コンポーネントリリース方法、グラフィック機械学習アルゴリズムプラットフォームベースのコンポーネント構築方法及びグラフィック機械学習アルゴリズムプラットフォーム
US16/505,617 US20190332970A1 (en) 2017-01-06 2019-07-08 Component releasing method, component creation method, and graphic machine learning algorithm platform

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CN201710011143.6A CN108279890B (zh) 2017-01-06 2017-01-06 组件发布方法、组件构建方法及图形化机器学习算法平台
CN201710011143.6 2017-01-06

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