Model generation device, model generation method and program

Abstract

To provide a model generation apparatus capable of easily improving accuracy of a model.SOLUTION: A model generation apparatus 100 for generating models which represent relationships between one or more objective variables and one or more explanatory variables includes: a receiving unit 130 which receives a dataset Ds including three or more variables; a first variable specifying unit 121 and a second variable specifying unit 126 which specify one or more objective variables and one or more explanatory variables from the dataset Ds, and specify, as layer-specific variables, one or more variables from among variables other than the specified objective variable and explanatory variable, out of the three or more variables included in the dataset Ds; a layering unit 127 which classifies the dataset Ds into a plurality of layers based on the trend of the relationship between the layer-specific variables and the objective variables; and a generation unit 128 which generates models for each of the layers.SELECTED DRAWING: Figure 6

Description

[Technical field]

[0001] The present disclosure relates to a model generation device that generates a model indicating a relationship between data. [Background technology]

[0002] A model generating device has been proposed that generates a model for estimating data. For example, the model generating device generates a model by selecting a response variable and an explanatory variable from a data set including data of each of a plurality of variables, and deriving a correlation coefficient between the variables or a regression model using the variables. The data set includes, for example, a plurality of manufacturing data related to manufacturing. The response variable indicates, for example, the quality characteristics of a manufactured product as data, and the explanatory variable indicates data of parameters used in the manufacturing process. Therefore, by using the generated model, it is possible to estimate the quality characteristics of the product from the manufacturing process.

[0003] Also, an association analysis device that creates a local quality model has been proposed (for example, see Patent Document 1). This association analysis device can be said to be a model generation device because it creates a local quality model as the above-mentioned model. Also, this association analysis device divides the space of operation factors corresponding to explanatory variables into multiple local regions, and creates a local quality model for each of the local regions. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Patent No. 4653547 Summary of the Invention [Problem to be solved by the invention]

[0005] However, the model generation device, which is the related analysis device of Patent Document 1, has a problem in that it is difficult to improve the accuracy of the model.

[0006] Therefore, the present disclosure provides a model generation device and the like that can easily improve the accuracy of a model. [Means for solving the problem]

[0007] A model generation device according to one embodiment of the present disclosure is a model generation device that generates a model indicating a relationship between one or more objective variables and one or more explanatory variables, and includes: a receiving means for receiving a dataset including three or more variables; a variable identifying means for identifying one or more objective variables and one or more explanatory variables from the dataset, and identifying one or more variables as stratification variables from among the three or more variables included in the dataset other than the identified objective variables and the explanatory variables; a stratification means for classifying the dataset into a plurality of strata based on a tendency of a relationship between the stratification variables and the objective variables; and a generating means for generating the model for each of the plurality of strata.

[0008] These comprehensive or specific aspects may be realized by a system, a method, an integrated circuit, a computer program, or a computer-readable recording medium such as a CD-ROM, or may be realized by any combination of the system, the method, the integrated circuit, the computer program, and the recording medium. The recording medium may be a non-transitory recording medium. Effect of the Invention

[0009] The model generation device of the present disclosure can easily improve the accuracy of the model.

[0010] Further advantages and benefits of certain aspects of the present disclosure will become apparent from the specification and drawings, in which such advantages and / or benefits are provided by some of the embodiments and features described in the specification and drawings, respectively, but not necessarily all of which are provided to obtain one or more identical features. [Brief description of the drawings]

[0011] [Figure 1]FIG. 1 is a diagram illustrating an example of a model generation system according to an embodiment. [Diagram 2] FIG. 2 is a diagram showing a configuration of a model generating device according to an embodiment. [Figure 3A] FIG. 3A is a diagram showing an example of a data set according to the embodiment. [Figure 3B] FIG. 3B shows the first and second rows of the data set of FIG. 3A. [Figure 3C] FIG. 3C is a diagram showing a response variable and explanatory variables selected from a dataset in the embodiment. [Figure 4] FIG. 4 is a diagram showing another example of the data set according to the embodiment. [Diagram 5] FIG. 5 is a diagram showing simplified variable names of a data set in the embodiment. [Figure 6] FIG. 6 is a block diagram showing a functional configuration of the model generating device according to the embodiment. [Figure 7] FIG. 7 is a diagram showing an example of a stratified data set according to the embodiment. [Figure 8] FIG. 8 is a diagram showing the distribution of coordinate points indicated by each record included in each stratified data set in the embodiment. [Figure 9] FIG. 9 is a flowchart showing an example of the overall processing operation of the model generating device in the embodiment. [Figure 10] FIG. 10 is a flowchart showing a specific example of the process of calculating the influence degree of the qualitative variables in step S8 of FIG. [Figure 11] FIG. 11 is a flowchart showing a specific example of the process of calculating the influence of the quantitative variables in step S9 of FIG. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0012] (Findings on which this disclosure is based) The present inventors have found that the model generating device of Patent Document 1 described in the "Background Art" section has the following problems.

[0013] In the above Patent Document 1, the space of the operation factors corresponding to the explanatory variables is divided into a plurality of local regions, and a local quality model is constructed for each of the local regions. Therefore, a plurality of models are generated from a data set. However, in constructing these models, only the objective variables and explanatory variables included in the data set are used, and variables other than the objective variables and explanatory variables are not used. Specifically, the data set is divided into a plurality of local regions based only on the distribution of data indicated by the explanatory variables included in the data set, and models are generated for these local regions. That is, in the above Patent Document 1, since the influence of the explanatory variables and variables other than the objective variables on the correlation between the explanatory variables and the objective variables is unknown, the explanatory variables and variables other than the objective variables are not used in constructing the model. Therefore, in cases where the data of the explanatory variables and variables other than the objective variables affect the correlation between the explanatory variables and the objective variables, it is difficult to generate a model with high accuracy.

[0014] In view of this, a model generation device according to an embodiment of the present disclosure is a model generation device that generates a model showing a relationship between one or more objective variables and one or more explanatory variables, and includes a receiving means for receiving a dataset including three or more variables, a variable specifying means for identifying one or more objective variables and one or more explanatory variables from the dataset, and identifying one or more variables as stratification variables from among the three or more variables included in the dataset other than the identified objective variables and the explanatory variables, a stratification means for classifying the dataset into a plurality of strata based on a tendency of a relationship between the stratification variables and the objective variables, and a generating means for generating the model for each of the plurality of strata. For example, the stratification means may classify data of the stratification variables into a plurality of groups based on the identity or similarity of data of the stratification variables for each of the stratification variables, and classify the dataset for each combination of the plurality of groups.

[0015] As a result, stratification is performed according to the stratification variables, which are variables other than the objective variables and the explanatory variables, and a model is generated for each of the multiple strata. That is, each data of the stratification variables is classified into multiple groups according to the commonality or similarity between the data. Then, the dataset is stratified into strata corresponding to the groups. By this stratification, each strata includes one or more records each having data of the stratification variables belonging to the same group. Note that a group is a set of data having commonality or similarity, and this group includes a category, which is a set of common data, and a cluster, which is a set of similar numerical data. In addition, the stratification variables are non-utilized variables that are not adopted as variables included in the model, but are used to generate the model. In this way, in the model generation device according to one aspect of the present disclosure, stratification is performed on the dataset by non-utilized variables, which are variables other than the explanatory variables and the objective variables, among the variables included in the dataset, so that even if the non-utilized variables affect the correlation between the explanatory variables and the objective variables, a highly accurate model based on the non-utilized variables can be generated. In other words, the accuracy of the model can be easily improved.

[0016] For example, a first stratification variable and a second stratification variable, each of which is an unexploited variable, are identified. Then, two or more data of the first stratification variable are classified into, for example, a first group and a second group, and two or more data of the second stratification variable are classified into, for example, a third group and a fourth group. All data included in these groups have commonality or high similarity. In this case, for example, a first layer, a second layer, a third layer and a fourth layer are determined as multiple layers. The first layer corresponds to a combination of a first group of the first stratification variable and a third group of the second stratification variable. The second layer corresponds to a combination of a first group of the first stratification variable and a fourth group of the second stratification variable. The third layer corresponds to a combination of a second group of the first stratification variable and a third group of the second stratification variable. The fourth layer corresponds to a combination of a second group of the first stratification variable and a fourth group of the second stratification variable. In this way, multiple layers are determined according to the combination of each group of N stratification variables. Thus, in stratified classification, a record containing data belonging to the first group of the first stratification variable and data belonging to the third group of the second stratification variable is classified into the first stratum. A record containing data belonging to the first group of the first stratification variable and data belonging to the fourth group of the second stratification variable is classified into the second stratum. A record containing data belonging to the second group of the first stratification variable and data belonging to the third group of the second stratification variable is classified into the third stratum. A record containing data belonging to the second group of the first stratification variable and data belonging to the fourth group of the second stratification variable is classified into the fourth stratum.

[0017] In this way, even if there are two or more stratification variables, optimal stratification classification can be performed for the data set, and a highly accurate model can be generated for each of the multiple strata according to the data of those stratification variables, i.e., each of the N unutilized variables.

[0018] The data set may also include qualitative variables representing data that includes text and quantitative variables representing data that includes numbers.

[0019] This makes it possible to specify a plurality of stratification variables including not only one of qualitative variables and quantitative variables but both, thereby increasing the degree of freedom in the variable types of the specified stratification variables.

[0020] The method may further include an influence calculation means for calculating an influence of each of the three or more variables included in the data set, other than the identified objective variable and the explanatory variables, on the objective variable, and the variable identification means may identify the stratification variables based on the influence.

[0021] This makes it possible to identify stratification variables that have a large influence on the target variable data. As a result, by using the stratification variables, more optimal stratification classification can be performed for the data set. As a result, a more accurate model can be generated for each of the multiple strata.

[0022] The data set may further include an influence calculation means for calculating an influence of each of the three or more variables included in the data set, the influence being on the objective variable, for each of the variables other than the identified objective variable and the identified explanatory variables, wherein the receiving means receives priority information indicating whether the qualitative variables or the quantitative variables are to be prioritized, and the variable identification means identifies the stratification variables based on the influence and the priority information.

[0023] In this way, for example, if the priority information is received by the receiving means in response to an input operation by a user, the priority of the qualitative variables and the quantitative variables can be set arbitrarily according to the user's intention, and many of the variables with higher priorities can be identified as stratification variables. As a result, appropriate stratification can be performed depending on whether the data set is analyzed qualitatively or quantitatively, and a model can be generated according to the analysis mode.

[0024] Moreover, the influence calculation means may calculate the influence of the qualitative variable by using a random forest or a gradient boosting decision tree.

[0025] This makes it possible to calculate an appropriate influence of a qualitative variable, for example, by calculating a value corresponding to the Gini coefficient of a random forest as the influence.

[0026] In addition, the influence calculation means may use a Gaussian mixture model or a k-means method to perform clustering on two or more data of the quantitative variables included in the dataset, and calculate the influence of the quantitative variables by simple regression analysis between each cluster obtained by the clustering and the objective variable.

[0027] This makes it possible to calculate, for example, the influence of each of one or more quantitative variables for each cluster by simple regression analysis, and to appropriately calculate the influence of the quantitative variable from the influence of each cluster.

[0028] In addition, the generation means may further calculate an index indicating the likelihood of each of the generated models, and the model generation device may further include a result output means for outputting the index calculated for each of the multiple models.

[0029] As a result, for example, the degree of freedom-adjusted coefficient of determination for each of the multiple models is calculated and output as an index indicating the likelihood of the model. Therefore, the user can easily determine whether or not to use the generated model based on the index.

[0030] The generating means may generate the model indicating a relationship between data of each of the two or more explanatory variables and data of the response variable as a multiple regression equation.

[0031] This makes it possible to generate an appropriate model regardless of the number of explanatory variables.

[0032] Hereinafter, the embodiment will be specifically described with reference to the drawings.

[0033] The embodiments described below are all comprehensive or specific examples. The numerical values, shapes, materials, components, component arrangement and connection forms, steps, and order of steps shown in the following embodiments are merely examples and are not intended to limit the present disclosure. Furthermore, among the components in the following embodiments, components that are not described in an independent claim showing a top concept are described as optional components.

[0034] In addition, each drawing is a schematic diagram and is not necessarily a precise illustration. In addition, in each drawing, the same components are denoted by the same reference numerals.

[0035] (Embodiment 1) [Hardware configuration] FIG. 1 is a diagram showing an example of a model generation system according to the present embodiment.

[0036] The model generation system 1 in this embodiment includes a model generation device 100 and a manufacturing management device 500.

[0037] The manufacturing management device 500 is installed in, for example, a manufacturing factory, and manages a manufacturing system that manufactures products. The manufacturing management device 500 transmits a data set Ds obtained in the manufacturing system to the model generating device 100 via a network such as the Internet. Details of the data set Ds will be described later with reference to FIGS. 3A to 5.

[0038] The model generating device 100 is configured with a personal computer or the like, and receives a data set Ds from the above-mentioned manufacturing management device 500. Then, the model generating device 100 in this embodiment generates a plurality of models indicating the relationship between the data of explanatory variables and the data of objective variables, based on the data set Ds.

[0039] FIG. 2 is a diagram showing a configuration of model generation device 100 according to this embodiment.

[0040] The model generating device 100 includes an input unit 101 , an arithmetic circuit 102 , a memory 103 , an output unit 104 , a storage unit 105 , a database 106 , and a communication unit 107 .

[0041] The communication unit 107 communicates with devices external to the model generating device 100. The communication may be wireless communication or wired communication. The wireless communication method may be Wi-Fi (registered trademark), Bluetooth (registered trademark), ZigBee (registered trademark), or other methods. For example, the communication unit 107 communicates with a manufacturing management device 500 and receives a data set Ds from the manufacturing management device 500.

[0042] The input unit 101 functions as an HMI (Human Machine Interface) that accepts input operations by a user, and includes, for example, a keyboard, a mouse, a touch sensor, a touch pad, and the like.

[0043] The output unit 104 has a display for displaying images or characters, and the display is, for example, a liquid crystal display, a plasma display, an organic EL (Electro-Luminescence) display, etc. The output unit 104 may have a printer for printing images or characters, and may have a function for storing data output from the arithmetic circuit 102 in the storage unit 105 in a file format.

[0044] The storage unit 105 stores a program (i.e., a computer program) 105a in which each instruction to the arithmetic circuit 102 is written. The storage unit 105 may also store each temporary data 105b temporarily generated by the processing of the arithmetic circuit 102. The storage unit 105 is a non-volatile recording medium, for example, a magnetic storage device such as a hard disk, an optical disk, or a semiconductor memory. The program 105a is provided to the model generating device 100 via, for example, a removable medium or a network, and is stored in the storage unit 105. The removable medium is, for example, a CD-ROM (Compact Disc Read Only Memory), a flash memory, or the like. For this reason, the communication unit 107 may be provided with an interface for reading the program 105a from the removable medium.

[0045] A program 105a that is read and expanded by the arithmetic circuit 102 is temporarily stored in the memory 103. Such a memory 103 is, for example, a volatile RAM (Random Access Memory).

[0046] The arithmetic circuit 102 is a circuit that executes the program 105a deployed in the memory 103, and is, for example, a CPU (Central Processing Unit) or a GPU (Graphics Processing Unit). The arithmetic circuit 102 may use each temporary data 105b stored in the storage unit 105 when executing the program 105a.

[0047] The database 106 is a non-volatile recording medium, similar to the storage unit 105, and is, for example, a magnetic storage device such as a hard disk, an optical disk, a semiconductor memory, etc. For example, the arithmetic circuit 102 acquires a data set Ds from the manufacturing management apparatus 500 via the network and the communication unit 107, and stores the data set Ds in the database 106.

[0048] In this embodiment, the storage unit 105 and the database 106 are separate recording media, but the storage unit 105 and the database 106 may be configured as a single recording medium that includes them.

[0049] [Dataset] Fig. 3A is a diagram showing an example of a data set Ds in this embodiment, and Fig. 3B is a diagram showing the first row and the second row of the data set Ds.

[0050] The data set Ds is a raw data set transmitted from the manufacturing management device 500, and is a structured data set consisting of a plurality of manufacturing data indicating, for example, the manufacturing process in the above-mentioned manufacturing system and the quality of the product manufactured by the manufacturing process. As shown in FIG. 3A, such a data set Ds indicates the variable names of a plurality of variables and the data of the variables. Note that the data may be any data as long as it indicates at least one of letters and numbers. The first row of the data set Ds has the variable names of the plurality of variables arranged, and the second row and each subsequent row of the data set Ds has the data of the plurality of variables arranged. Each of the second and subsequent rows is treated as a record including the data of the plurality of variables. In addition, the leftmost column of the data set Ds is a linking information column, and an ID, which is identification information for identifying the records, is indicated. The ID links the data of each variable included in the record.

[0051] Specifically, as shown in FIG. 3B, the first row of the data set Ds contains the variable names "voltage", "speed", "resistance", "operator", "equipment number", "material mix", "material temperature difference", "auxiliary voltage", and "jig temperature". The second row of records contains data d1 to d9 of variables identified by those variable names. Data d1 is data of a variable identified by the variable name "voltage", for example, "5.488135". Data d2 is data of a variable identified by the variable name "speed", for example, "7.151894". Data d3 is data of a variable identified by the variable name "resistance", for example, "44.69831". Data d4 is data of a variable identified by the variable name "operator", for example, "Suzuki". Data d5 is data of a variable identified by the variable name "equipment number", for example, "Unit C". Data d6 is data of a variable identified by the variable name "material composition", for example "0". Data d7 is data of a variable identified by the variable name "material temperature difference", for example "8.815673". Data d8 is data of a variable identified by the variable name "auxiliary voltage", for example "3". Data d9 is data of a variable identified by the variable name "jig temperature", for example "9.298481". A record including data d1 to d9 of each of these variables is identified by an ID "ID200901". In other words, the ID "ID200901" links the data d1 to d9 included in the record identified by that ID.

[0052] The data set Ds includes a plurality of such records as shown in Fig. 3A. For example, the data set Ds includes a record identified by the above-mentioned ID "ID200901", a record identified by the ID "ID200902", and a record identified by the ID "ID200903". In this manner, the data set Ds in this embodiment includes two or more records each having data for a plurality of variables.

[0053] Also, as shown in FIG. 3A, the second column from the left of the data set Ds shows data for each record of the variable identified by the variable name "Voltage". For example, the data are "5.488135", "6.027634", and "4.236548". Similarly, the third column from the left of the data set Ds shows data for each record of the variable identified by the variable name "Speed". The fourth column from the left of the data set Ds shows data for each record of the variable identified by the variable name "Resistance". The fifth column from the left of the data set Ds shows data for each record of the variable identified by the variable name "Worker". The sixth column from the left of the data set Ds shows data for each record of the variable identified by the variable name "Equipment No." The seventh column from the left of the data set Ds shows data for each record of the variable identified by the variable name "Material Mixture". The eighth column from the left of the data set Ds shows data for each record of the variable identified by the variable name "Material Temperature Difference". The ninth column from the left of the data set Ds shows data for each record of the variable identified by the variable name "Auxiliary Voltage". The tenth column from the left of the data set Ds shows data for each record of the variable identified by the variable name "Fixture Temperature".

[0054] Here, the types of variables include quantitative variables and qualitative variables. Each data of a quantitative variable is data represented only by numbers, and each data of a qualitative variable is data represented including characters. In the example of FIG. 3A and FIG. 3B, the variables identified by the variable names "worker" and "equipment number" are qualitative variables. For example, as shown in FIG. 3B, the data d4 of the variable identified by the variable name "worker" is "Suzuki" and includes characters. Therefore, the variable identified by the variable name "worker" is a qualitative variable. Also, in the example of FIG. 3A and FIG. 3B, the variables identified by the variable names "material composition", "material temperature difference", "auxiliary voltage", and "jig temperature" are quantitative variables. For example, as shown in FIG. 3B, the data d6 of the variable identified by the variable name "material composition" is "0" and is represented only by numbers. Therefore, the variable identified by the variable name "material composition" is a quantitative variable.

[0055] The variables are not limited to the examples shown in FIG. 3A and FIG. 3B, and may be any variables. The variables are, for example, variables related to people, variables related to materials, variables related to equipment, and the like. The variables related to people may be variables such as "worker" or "work crew". The variables related to materials may be variables such as "source material lot" or "intermediate process material lot". The variables related to equipment may be variables such as "production equipment type, generation", "production equipment number", "by lane in equipment, by spindle", "mold", "jig", "mold temperature", "drying temperature", "before and after equipment maintenance", and the like. The variables may also be variables such as "ambient temperature", "ambient humidity", "production period, time", and the like. The variables may also be variables such as "production type, part number" or "product size" of the product.

[0056] FIG. 3C is a diagram showing the response variable and explanatory variables selected from the data set Ds.

[0057] Each variable shown in the data set Ds is classified into an utilized variable and a non-utilized variable according to an input operation by the user, and each utilized variable is classified into an explanatory variable and a target variable. The utilized variable is a variable adopted in the model, and the non-utilized variable is a variable not adopted in the model. Conventionally, the non-utilized variable is a variable that is not adopted in the model because all variables other than the target variable included in the data set are not adopted as explanatory variables, and only variables that contribute greatly to the target variable are adopted as explanatory variables. Furthermore, the utilized variables include explanatory variables and target variables. In the example shown in FIG. 3C, the user selects a variable with the variable name "resistance value" as the target variable, and selects a variable with the variable name "voltage" and a variable with the variable name "speed" as the explanatory variables. As a result, the variables with the variable names "operator", "equipment number", "material composition", "material temperature difference", "auxiliary voltage", and "jig temperature" are determined as non-utilized variables. In addition, these non-utilized variables include the above-mentioned qualitative variables and quantitative variables. Therefore, in this embodiment, the M non-utilized variables shown in the data set Ds include one or more qualitative variables each representing data containing characters and one or more quantitative variables each representing data consisting of numbers. Note that the number M is the number of non-utilized variables included in the data set Ds, which is six in the above example.

[0058] FIG. 4 is a diagram showing another example of the data set Ds in this embodiment.

[0059] The arithmetic circuit 102 replaces the qualitative variables included in the data set Ds with dummy variables. That is, the arithmetic circuit 102 performs One Hot Encoding to convert the data of the qualitative variable with the variable name "Worker" shown in Figs. 3A to 3C into a flag string consisting of data of three variables, namely, the variable names "Worker Suzuki", "Worker Sato", and "Worker Takahashi". For example, the variable data "Suzuki" of the variable name "Worker" shown in Fig. 3A is converted into a flag string consisting of the variable data "1" of the variable name "Worker Suzuki", the variable data "0" of the variable name "Worker Sato", and the variable data "0" of the variable name "Worker Takahashi". Also, the data of the variable "Sato" with the variable name "Worker" shown in FIG. 3A is converted into a flag string consisting of the data of the variable "Worker Suzuki" with the variable name "Worker Sato", the data of the variable "Worker Takahashi", and the data of the variable "Worker Takahashi". Similarly, the arithmetic circuit 102 converts the data of the qualitative variable with the variable name "Facility No. " shown in FIG. 3A to FIG. 3C into a flag string consisting of the data of three variables with the variable names "Facility No. C", "Facility No. D", and "Facility No. E". For example, the data of the variable "Facility No. C" with the variable name "Facility No. " shown in FIG. 3A is converted into a flag string consisting of the data of the variable name "Facility No. C", the data of the variable name "Facility No. D", and the data of the variable name "Facility No. E". 3A is converted into a flag string consisting of variable data "0" of variable name "Facility No. C," variable data "1" of variable name "Facility No. D," and variable data "0" of variable name "Facility No. E." When handling qualitative variables in machine learning such as random forest described below, the arithmetic circuit 102 replaces the qualitative variables with dummy variables.

[0060] FIG. 5 is a diagram showing a simplified representation of variable names and the like of a data set Ds in this embodiment.

[0061] For ease of understanding, the variable names "Voltage", "Speed", "Resistance", "Worker", "Facility No.", "Material Mixture", "Material Temperature Difference", "Auxiliary Voltage", and "Jig Temperature" in the data set Ds shown in Figures 3A to 3C are replaced with the variable names "X0", "X1", "Y", "Z0", "Z1", "D1", "D2", "D3", and "D4" as shown in Figure 5. In addition, the variable data "Suzuki" and "Sato" identified by the variable name "Worker" are replaced with "A" and "B", and the variable data "Facility No. C" and "Facility No. D" identified by the variable name "Facility No." are replaced with "C" and "D".

[0062] In the following, when it is necessary to identify each variable, the variable is identified by adding a variable name after the variable, such as variable X1. In the example shown in Figure 5, variables X0, X1, and Y are quantitative variables. Variables Z0 and Z1 are qualitative variables, and variables D1, D2, D3, and D4 are quantitative variables.

[0063] [Function configuration] FIG. 6 is a block diagram showing the functional configuration of the arithmetic circuit 102. As shown in FIG.

[0064] The arithmetic circuit 102 executes the program 105a to realize a plurality of functions for generating a model. Specifically, the arithmetic circuit 102 includes a receiving unit (receiving means) 130, a first variable specifying unit (variable specifying means) 121, a stratification condition setting unit 122, a non-utilized variable extracting unit 123, a variable type determining unit 124, an influence calculating unit (influence calculating means) 125, a second variable specifying unit (variable specifying means) 126, a stratification unit (stratification means) 127, a generating unit (generating means) 128, and a result output unit (result output means) 129. The influence calculating unit 125 includes a qualitative influence calculating unit 125a and a quantitative influence calculating unit 125b. These components are realized by the arithmetic circuit 102 executing the program 105a.

[0065] The receiving unit 130 receives a data set Ds including three or more variables. For example, the receiving unit 130 acquires the data set Ds by reading the data set Ds from the database 106. Then, the user performs an input operation on the input unit 101 to select an explanatory variable and a target variable from among the multiple variables of the data set Ds shown in FIG. 5. The first variable specifying unit 121 specifies, for example, a variable X0 and a variable X1 as explanatory variables from among the multiple variables of the data set Ds shown in FIG. 5 in response to the user's input operation accepted by the input unit 101. Furthermore, the first variable specifying unit 121 specifies, for example, a variable Y as a target variable from among the multiple variables. As a result, two variables are set as explanatory variables, and one variable is set as a target variable.

[0066] In this manner, the first variable specification unit 121 in this embodiment specifies one or more objective variables and one or more explanatory variables from the data set Ds. In this embodiment, two explanatory variables are set and one objective variable is set, but the respective numbers of the explanatory variables and objective variables are not limited to these examples and may be any number. For example, the first variable specification unit 121 may set one variable of the multiple variables of the data set Ds as the explanatory variable, or may set each of three or more variables as the explanatory variable. In addition, the first variable specification unit 121 may specify each of two or more variables of the multiple variables of the data set Ds as the objective variable.

[0067] Furthermore, the first variable identification unit 121 stores in the memory 103 or the storage unit 105 first setting information indicating that the variables set as the explanatory variables are the variable X0 and the variable X1 and that the variable set as the objective variable is the variable Y. When the first setting information is stored in the storage unit 105, the first setting information may be stored as temporary data 105b. Furthermore, the first variable identification unit 121 may output the first setting information to the non-utilized variable extraction unit 123.

[0068] The stratification condition setting unit 122 sets the total number of non-utilized variables used for stratification of the data set Ds to N (N is an integer of 2 or more) in response to the input operation of the user received by the input unit 101. Note that N is the total number of non-utilized variables used for stratification, but in this embodiment, an example in which the total number N is 2 or more will be described. Also, the total number N is not limited to 2 or more as in this embodiment, and may be 1. Also, in this embodiment, as a specific example, the total number N is set to N=2. Note that the non-utilized variables used for stratification are also referred to as stratification variables hereinafter. That is, the stratification condition setting unit 122 in this embodiment sets the total number of stratification variables in response to the input operation by the user. Note that, for the above-mentioned stratification, each data of the stratification variables included in the data set Ds needs to be classified into not only one group but multiple groups according to the commonality or similarity between those data.

[0069] Furthermore, the stratification condition setting unit 122 determines the priority order to be set for the stratification variables for each of the qualitative variables and the quantitative variables in response to the user's input operation accepted by the input unit 101. For example, the stratification condition setting unit 122 determines the priority order of the qualitative variables and the quantitative variables in that order. In this case, the qualitative variables have a higher priority than the quantitative variables. In this manner, the stratification condition setting unit 122 in this embodiment sets the priority order of the qualitative variables and the quantitative variables in response to the user's input operation. In other words, the stratification condition setting unit 122 receives priority information indicating which of the qualitative variables or the quantitative variables is to be prioritized in response to the user's input operation accepted by the input unit 101. In this embodiment, the stratification condition setting unit 122 receives the priority information, but the receiving unit 130 may receive the priority information. Hereinafter, of the qualitative variables and the quantitative variables, the variables with a higher priority are also called higher-order variables, and the variables with a lower priority are also called lower-order variables. The stratification condition setting unit 122 stores second setting information indicating the total number N of stratification variables and the priority order in the memory 103 or the storage unit 105. When the second setting information is stored in the storage unit 105, the second setting information may be stored as temporary data 105b. In addition, the stratification condition setting unit 122 may output the second setting information to the second variable identification unit 126.

[0070] The non-utilized variable extraction unit 123 extracts M non-utilized variables from the multiple variables of the data set Ds shown in FIG. 5 read by the first variable specification unit 121. Specifically, the non-utilized variable extraction unit 123 acquires the first setting information from the first variable specification unit 121, the memory 103, or the storage unit 105. Then, the non-utilized variable extraction unit 123 extracts all variables other than the explanatory variables and the objective variable indicated by the first setting information as non-utilized variables from the multiple variables. For example, the non-utilized variable extraction unit 123 extracts the variables Z0, Z1, D1, D2, D3, and D4 as non-utilized variables from the multiple variables of the data set Ds. As a result, in this embodiment, M non-utilized variables are extracted from the multiple variables indicated by the data set Ds. Then, the non-utilized variable extraction unit 123 stores extraction information indicating the extracted M non-utilized variables in the memory 103 or the storage unit 105. When the extracted information is stored in the storage unit 105, the extracted information may be stored as temporary data 105b. Furthermore, the non-utilized variable extraction unit 123 may output the extracted information to the variable type determination unit .

[0071] The variable type determination unit 124 acquires the extraction information from the non-utilized variable extraction unit 123, the memory 103, or the storage unit 105, and sequentially determines the variable type of each of the M non-utilized variables indicated by the extraction information. There are the above-mentioned qualitative variable type and quantitative variable type for the variable type. That is, the variable type determination unit 124 determines whether the non-utilized variable is a qualitative variable or a quantitative variable based on the data of the non-utilized variable. Specifically, if the data of the non-utilized variable contains characters, the variable type determination unit 124 determines that the non-utilized variable is a qualitative variable. On the other hand, if the data of the non-utilized variable does not contain characters but contains only numbers, the variable type determination unit 124 determines that the non-utilized variable is a quantitative variable. As a result, each of the M non-utilized variables is classified into a qualitative variable and a quantitative variable. For example, in this embodiment, the variable type determination unit 124 determines that the non-utilized variables Z0 and Z1 are qualitative variables, and determines that the non-utilized variables D1, D2, D3, and D4 are quantitative variables. Then, the variable type determination unit 124 stores variable type information indicating the variable type of each of the M non-utilized variables in the memory 103 or the storage unit 105. When the variable type information is stored in the storage unit 105, the variable type information may be stored as temporary data 105b. Furthermore, the variable type determination unit 124 may output the variable type information to the impact degree calculation unit 125.

[0072] The influence calculation unit 125 calculates the influence of each of the three or more variables included in the data set Ds, other than the identified objective variable and explanatory variables, on the objective variable. In other words, the influence calculation unit 125 calculates the influence of each of the M non-utilized variables. That is, the influence calculation unit 125 calculates the influence of each of the M non-utilized variables, other than the identified objective variable and explanatory variables, included in the three or more variables of the data set Ds, on the objective variable data, that is, the influence of the data of the non-utilized variables on the objective variable data. The influence is a numerical value indicating the magnitude of the influence that the data of the non-utilized variables has on the objective variable data, and the greater the influence, the larger the value indicated.

[0073] Specifically, the influence calculation unit 125 includes the qualitative influence calculation unit 125a and the quantitative influence calculation unit 125b as described above. The qualitative influence calculation unit 125a calculates the influence of the non-utilized variable which is a qualitative variable, and the quantitative influence calculation unit 125b calculates the influence of the non-utilized variable which is a quantitative variable. More specifically, the qualitative influence calculation unit 125a acquires variable type information from the variable type determination unit 124, the memory 103, or the storage unit 105, and identifies the variable type of the non-utilized variable indicated in the variable type information. Then, if the variable type of the non-utilized variable is a qualitative variable, the qualitative influence calculation unit 125a calculates the influence of the non-utilized variable by the influence calculation process corresponding to the qualitative variable. On the other hand, the quantitative influence calculation unit 125b identifies the variable type of the non-utilized variable indicated in the variable type information. If the variable type of the non-utilized variable is a quantitative variable, the quantitative influence calculation unit 125b calculates the influence of the non-utilized variable by an influence calculation process corresponding to a quantitative variable.

[0074] Then, the impact calculation unit 125 stores impact information indicating the impact calculated for each of the M non-utilized variables in the memory 103 or the storage unit 105. When the impact information is stored in the storage unit 105, the impact information may be stored as temporary data 105b. Furthermore, the impact calculation unit 125 may output the impact information to the second variable identification unit 126.

[0075] The second variable identification unit 126 acquires the influence information of each of the M non-utilized variables from the influence calculation unit 125, the memory 103, or the storage unit 105. Furthermore, the second variable identification unit 126 acquires the second setting information from the stratification condition setting unit 122, the memory 103, or the storage unit 105. Then, the second variable identification unit 126 uses the influence information and the second setting information to identify N non-utilized variables as stratification variables from among the M non-utilized variables indicated by the data set Ds. The stratification variables are variables used for stratifying the records of the data set Ds. For example, the second variable identification unit 126 attempts to identify N top variables in order of the greatest influence from all the top variables in the data set Ds. If the N top variables are identified, the second variable identification unit 126 treats the N top variables as N stratification variables. On the other hand, if the number of all upper variables in the data set Ds is less than N and the N upper variables cannot be identified, the second variable identification unit 126 identifies the remaining number of lower variables from all the lower variables in the data set Ds in descending order of influence. This identifies N stratification variables, which are the combination of the less than N upper variables and the remaining number of lower variables.

[0076] In this manner, the second variable specification unit 126 in this embodiment specifies, from among a plurality of variables, a non-utilized variable that is different from the objective variable and the explanatory variable and is not utilized in the model, as a stratification variable used for stratifying records. That is, the second variable specification unit 126 specifies one or a plurality of variables as a stratification variable from among three or more variables included in the data set Ds, other than the specified objective variable and explanatory variable. In this embodiment, the second variable specification unit 126 specifies N stratification variables (N is an integer of 2 or more) from among a plurality of variables. That is, the second variable specification unit 126 specifies only N stratification variables, which is the total number set by the stratification condition setting unit 122. At this time, the second variable specification unit 126 specifies the stratification variables based on the influence. Specifically, the second variable specification unit 126 specifies N stratification variables from the M non-utilized variables based on the influence of each of the M non-utilized variables calculated by the influence calculation unit 125. More specifically, the second variable identification unit 126 identifies stratification variables based on the influence and priority information. That is, the second variable identification unit 126 treats the qualitative and quantitative variables with higher priority as higher-order variables, and treats the variables with lower priority as lower-order variables. Then, the second variable identification unit 126 identifies the higher-order variables as stratification variables in descending order of influence from the M non-utilized variables until all the higher-order variables are identified as K (K is an integer less than N) or until N higher-order variables are identified. Then, when the number of identified higher-order variables is K, the second variable identification unit 126 identifies (NK) lower-order variables as stratification variables in descending order of influence from the M non-utilized variables.

[0077] Then, the second variable specification unit 126 stores stratification variable information indicating the specified N stratification variables in the memory 103 or the storage unit 105. When the stratification variable information is stored in the storage unit 105, the stratification variable information may be stored as temporary data 105b. In addition, the second variable specification unit 126 may output the stratification variable information to the stratification unit 127.

[0078] The stratification unit 127 performs stratification based on commonality or similarity between two or more data of the stratification variables included in the data set Ds. In this stratification, the stratification unit 127 performs stratification, which is a process of including one or more records in each of the multiple strata by classifying two or more records included in the data set Ds into multiple strata. That is, the stratification unit 127 classifies the data set Ds into multiple strata based on the tendency of the relationship between the stratification variables and the objective variable. Specifically, the stratification unit 127 classifies the data of the stratification variables into multiple groups for each stratification variable based on the identity or similarity of the data of the stratification variable, and classifies the data set Ds for each combination of the multiple groups. Here, the stratification variables are indicated by the above-mentioned stratification variable information. Therefore, the stratification unit 127 acquires the stratification variable information from the second variable identification unit 126, the memory 103, or the storage unit 105. Then, the stratification unit 127 performs stratification for the data set Ds based on the stratification variable information.

[0079] Specifically, for each of the N stratification variables indicated by the stratification variable information, the stratification unit 127 classifies two or more data of the stratification variables included in the data set Ds into a plurality of groups based on commonality or similarity between two or more data of the stratification variables. Then, the stratification unit 127 determines a plurality of strata according to a combination of the groups of the N stratification variables, and classifies two or more records included in the data set Ds into the determined plurality of strata. As a result, a stratification data set consisting of one or more records is generated for each of the plurality of strata. The stratification data set for each of the plurality of strata includes one or more records classified from the data set Ds. Each of the one or more records includes data belonging to the same group of each of the N stratification variables. Furthermore, each of the one or more records includes data of each of the objective variables and explanatory variables. Details of this stratification data set will be described later with reference to FIG. 7.

[0080] The generating unit 128 generates a model showing the relationship between one or more objective variables and one or more explanatory variables for each of the multiple layers. That is, the generating unit 128 generates a model showing the relationship between the explanatory variable data and the objective variable data for each of the multiple layers, using one or more records included in the layer, that is, a stratified data set. Here, in the above example, the variable X0 and the variable X1 are each an explanatory variable, but the number of explanatory variables may be one or more. Therefore, in this case, the generating unit 128 generates a model showing the relationship between the data of each of the two or more explanatory variables and the objective variable data as a multiple regression equation. For example, the generating unit 128 generates a model showing the relationship between the data of each of the explanatory variables X0 and the explanatory variable X1 and the objective variable Y by performing a multiple regression analysis on the explanatory variables X0 and the explanatory variables X1 and the objective variable Y.

[0081] The result output unit 129 outputs the generated multiple models. That is, the result output unit 129 acquires the models generated for each layer by the generation unit 128 from the generation unit 128 and outputs them to the output unit 104.

[0082] [Stratified Dataset] FIG. 7 is a diagram showing an example of a stratified data set in this embodiment.

[0083] For example, the second variable identification unit 126 identifies the non-utilized variable Z0 and the non-utilized variable Z1, which are qualitative variables, as stratification variables. The data of the stratification variable Z0 included in each record of the data set Ds shown in FIG. 5 indicates "A" or "B". The data of the stratification variable Z1 included in each record of the data set Ds indicates "C" or "D". Therefore, as shown in (a) of FIG. 7, the stratification unit 127 classifies each record including the data "A" of the stratification variable Z0 and the data "C" of the stratification variable Z1 into the first layer. This generates a stratification data set Ds1. The stratification data set Ds1 consists of a record identified by an ID "ID200901", a record identified by an ID "ID200902", and a record identified by an ID "ID200903".

[0084] Similarly, as shown in (b) of FIG. 7, the stratification unit 127 classifies each record including data "B" of the stratification variable Z0 and data "C" of the stratification variable Z1 into the second tier. This generates a stratification dataset Ds2. The stratification dataset Ds2 includes a record identified by an ID "ID200904", a record identified by an ID "ID200905", and a record identified by an ID "ID200906".

[0085] Similarly, as shown in (c) of Fig. 7, the stratification unit 127 classifies each record including data "A" of the stratification variable Z0 and data "D" of the stratification variable Z1 into the third tier. This generates a stratification dataset Ds3. The stratification dataset Ds3 consists of a record identified by an ID "ID200907", a record identified by an ID "ID200908", and a record identified by an ID "ID200909".

[0086] Similarly, as shown in (d) of Fig. 7, the stratification unit 127 classifies each record including data "B" of the stratification variable Z0 and data "D" of the stratification variable Z1 into the fourth tier. This generates a stratification dataset Ds4. The stratification dataset Ds4 includes a record identified by an ID "ID200910", a record identified by an ID "ID200911", and a record identified by an ID "ID200912".

[0087] In other words, two or more data of the stratification variable Z0 are classified into groups "A" and "B", and two or more data of the stratification variable Z1 are classified into groups "C" and "D". The first stratum corresponds to the combination of group "A" of the stratification variable Z0 and group "C" of the stratification variable Z1. The second stratum corresponds to the combination of group "B" of the stratification variable Z0 and group "C" of the stratification variable Z1. The third stratum corresponds to the combination of group "A" of the stratification variable Z0 and group "D" of the stratification variable Z1. The fourth stratum corresponds to the combination of group "B" of the stratification variable Z0 and group "D" of the stratification variable Z1. In this way, multiple strata are determined according to the combination of each group of the stratification variable Z0 and the stratification variable Z1. Therefore, in the stratification classification, a record including data belonging to group "A" of the stratification variable Z0 and data belonging to group "C" of the stratification variable Z1 is classified into the first stratum. Records containing data belonging to group "B" of stratification variable Z0 and data belonging to group "C" of stratification variable Z1 are classified into the second stratum. Records containing data belonging to group "A" of stratification variable Z0 and data belonging to group "D" of stratification variable Z1 are classified into the third stratum. Records containing data belonging to group "B" of stratification variable Z0 and data belonging to group "D" of stratification variable Z1 are classified into the fourth stratum.

[0088] In addition, the records classified into each stratum may contain data on non-utilized variables other than the stratification variables, or may contain only data on the utilization variables and the stratification variables, as in the example shown in Figure 7.

[0089] FIG. 8 is a diagram showing the distribution of coordinate points indicated by each record included in each of the stratified data sets Ds1 to Ds4.

[0090] Each of the stratified data sets Ds1 to Ds4 includes a plurality of records. Each of the plurality of records includes data of explanatory variable X0, data of explanatory variable X1, and data of objective variable Y, and is represented as a coordinate point (X0, X1, Y). That is, the record is represented as a coordinate point in a three-dimensional coordinate system consisting of explanatory variable X0, explanatory variable X1, and objective variable Y.

[0091] It is difficult to find correlations between records from the coordinate points of all the records included in the data set Ds. However, as shown in Fig. 8, in each of the stratification data sets Ds1 to Ds4, the coordinate points of a plurality of records included in the stratification data set are mutually correlated. Therefore, in each of the stratification data sets Ds1 to Ds4, it is possible to find correlations between the records from the coordinate points of all the records included in the stratification data set.

[0092] The generation unit 128 generates a model indicating the relationship between the data of the explanatory variable X0 and the explanatory variable X1 and the data of the objective variable Y, for each of these stratified data sets Ds1 to Ds4, by using one or more records included in the stratified data set.

[0093] In this manner, in this embodiment, the stratification variables are non-utilized variables, and stratification classification according to the non-utilized variables is performed, and a model is generated for each of the multiple strata. Therefore, the stratification classification for the data set Ds can be optimally performed by the non-utilized variables, which are variables other than the explanatory variables. As a result, even if the correlation between the explanatory variables and the objective variable changes according to the non-utilized variables, a model with high accuracy according to the group of the non-utilized variables can be generated. In other words, the accuracy of the model can be easily improved. Furthermore, in this embodiment, even if there are two or more stratification variables, optimal stratification classification can be performed for the data set Ds, and a model with high accuracy according to the data of the stratification variables, i.e., the N non-utilized variables, can be generated for each of the multiple strata.

[0094] [Processing Operation] FIG. 9 is a flowchart showing an example of the overall processing operation of the model generation device 100 in this embodiment.

[0095] First, the receiving unit 130 of the model generating device 100 performs a data receiving process (step S1). In this data receiving process, the first variable identifying unit 121 receives the data set Ds by reading the data set Ds from the database 106. Then, the first variable identifying unit 121 identifies an explanatory variable and a response variable from a plurality of variables indicated by the data set Ds (step S2). As a result, the explanatory variable and the response variable are set. For example, as described above, the variables X0 and X1 are set as explanatory variables, respectively, and the variable Y is set as the response variable.

[0096] Next, the stratification condition setting unit 122 sets the total number N of stratification variables in response to an input operation by the user (step S3). For example, the total number N is set to N=2. Then, the stratification condition setting unit 122 determines the priority of the variable types of the stratification variables (step S4). That is, the priority of the qualitative variables and the quantitative variables is determined. For example, the priority of the variables is determined in the order of qualitative variables, then quantitative variables.

[0097] Next, the non-utilized variable extraction unit 123 extracts variables other than the explanatory variables and the objective variable as non-utilized variables from the multiple variables of the data set Ds (step S5).

[0098] After that, the model generation device 100 executes a first loop process including steps S6 to S9 for each of all the non-utilized variables extracted in step S5 in turn. That is, the first loop process is executed for each of the M non-utilized variables shown in the data set Ds in turn.

[0099] Specifically, first, the variable type determination unit 124 determines the variable type of the non-utilized variable to be processed (step S6). Then, the variable type determination unit 124 determines whether the variable type is a qualitative variable type (step S7). That is, the variable type determination unit 124 determines whether the non-utilized variable to be processed is a qualitative variable. Then, when the variable type determination unit 124 determines that the non-utilized variable to be processed is a qualitative variable (Yes in step S7), the qualitative influence calculation unit 125a calculates the influence of the non-utilized variable, which is a qualitative variable, on the objective variable (step S8). That is, the qualitative influence calculation unit 125a calculates the influence of the non-utilized variable on the objective variable by the influence calculation process of the qualitative variable. On the other hand, when the variable type determination unit 124 determines that the non-utilized variable to be processed is not a qualitative variable (No in step S7), the quantitative influence calculation unit 125b calculates the influence of the non-utilized variable, which is a quantitative variable, on the objective variable (step S9). That is, the quantitative influence calculation unit 125b calculates the influence of the non-utilized variable on the objective variable through the influence calculation process of the quantitative variable.

[0100] The first loop process including steps S6 to S9 is executed in order for each of all non-utilized variables, thereby calculating the degree of influence of each of all the non-utilized variables.

[0101] Then, the second variable identification unit 126 sorts all non-utilized variables belonging to each variable type in order of influence based on the influence of each of the non-utilized variables (step S10). That is, the second variable identification unit 126 rearranges all the extracted qualitative variables in order of their influence. Specifically, the second variable identification unit 126 rearranges the qualitative variables so that the greater the influence of the qualitative variable, the earlier the qualitative variable is placed. Similarly, the second variable identification unit 126 rearranges all the extracted quantitative variables in order of their influence. Specifically, the second variable identification unit 126 rearranges the quantitative variables so that the greater the influence of the quantitative variable, the earlier the quantitative variable is placed.

[0102] Next, the second variable identification unit 126 identifies N non-utilized variables from the sorted M non-utilized variables as stratification variables based on the total number N of stratification variables set in step S3 and the priority order determined in step S4 (step S11). If the qualitative variables have a higher priority than the quantitative variables, the second variable identification unit 126 attempts to identify up to N qualitative variables in descending order of influence from the sorted multiple qualitative variables. If the number of extracted qualitative variables is less than N, the second variable identification unit 126 identifies quantitative variables in descending order of influence from the sorted multiple quantitative variables. The number of quantitative variables identified at this time is the number obtained by subtracting the number of previously identified qualitative variables from N. As a result, a total of N stratification variables are identified. In the above example, N=2, and the qualitative variables have a higher priority than the quantitative variables. Therefore, in this case, the qualitative variable Z0 and the qualitative variable Z1 are identified as stratification variables.

[0103] Next, the stratification unit 127 generates a plurality of stratification data sets by performing stratification classification on the data set Ds using the identified N stratification variables. For example, as shown in FIG. 7, stratification data sets Ds1 to Ds4 are generated. Then, the generation unit 128 calculates a multiple regression equation by performing a multiple regression analysis on the explanatory variables and the response variables for each stratification data set (step S12). As a result, a model consisting of the multiple regression equation is generated for each stratification data set.

[0104] The generating unit 128 further calculates the coefficient of determination after adjusting the degrees of freedom of the explanatory variables for the multiple regression equation calculated for each of the multiple stratified data sets (Step S13).

[0105] The result output unit 129 outputs each multiple regression equation calculated in step S12 and each determination coefficient calculated in step S13 to the output unit 104. As a result, the output unit 104 displays each multiple regression equation and each determination coefficient on a display, prints them on paper, or stores a file indicating them in the storage unit 105 (step S14).

[0106] Fig. 10 is a flow chart showing a specific example of the qualitative variable influence degree calculation process in step S8 in Fig. 9. Note that the non-utilized variables to be processed in this influence degree calculation process are qualitative variables.

[0107] The qualitative influence degree calculation part 125a judges whether the number of categories of the non-utilized variable to be processed is equal to or less than a first threshold (step S81). The first threshold of the number of categories is, for example, 20. The number of categories is the number of groups consisting of a plurality of identical data represented by the non-utilized variable to be processed, which is included in the data set Ds. For example, in the data set Ds shown in FIG. 5, the plurality of data represented by the non-utilized variable Z0, which is a qualitative variable, includes data representing "A" and data representing "B". Therefore, the number of categories of the non-utilized variable Z0 is 2. Similarly, in the data set Ds shown in FIG. 5, the plurality of data represented by the non-utilized variable Z1, which is a qualitative variable, includes data representing "C" and data representing "D". Therefore, the number of categories of the non-utilized variable Z1 is 2.

[0108] Next, when the qualitative influence calculation unit 125a determines that the number of categories of the non-utilized variable to be processed is not equal to or less than the first threshold (No in step S81), the qualitative influence calculation unit 125a excludes the non-utilized variable from the calculation of the influence (step S82). For example, if a non-utilized variable with a relatively large number of categories is used as a stratification variable, many stratification data sets are generated. As a result, it is assumed that it is difficult to expect improvement in the accuracy of each model and improvement in the ease of use of the models due to the generation of many models. Therefore, in step S82, by excluding such a non-utilized variable with a large number of categories from the calculation of the influence, it is possible to suppress the non-utilized variable from being used as a stratification variable.

[0109] On the other hand, when the quality influence calculation unit 125a determines that the number of categories of the non-utilized variable to be processed is equal to or less than the first threshold (Yes in step S81), it calculates the influence of the non-utilized variable to be processed by supervised machine learning (step S83). The supervised machine learning is, for example, learning using a random forest. The random forest is a method using a plurality of decision trees. For example, the quality influence calculation unit 125a executes machine learning of a random forest using each data of the objective variable included in the data set Ds and each data of the non-utilized variable to be processed included in the data set Ds as teacher data. This random forest is, for example, a learning model for estimating the data of the non-utilized variable to be processed from the data of the objective variable. More specifically, the non-utilized variable to be processed is the non-utilized variable Z0. In this case, the quality influence calculation unit 125a executes machine learning so that the data of the objective variable is input to the random forest, and the data of the non-utilized variable Z0 corresponding to the data of the objective variable is output from the random forest. At this time, the data of the non-utilized variable Z0 output from the random forest is "A" or "B".

[0110] The qualitative influence calculation unit 125a calculates the influence of the non-utilized variable of the processing target based on the Gini coefficient G, which is an index representing the impurity of multiple decision trees included in the random forest. The Gini coefficient G is defined by the formula (1) for each node of the decision tree.

[0111]

number

[0112] Here, in formula (1), C is the number of categories. Also, Pi is the number of data belonging to category i divided by the total number of data. In other words, Pi is the quotient obtained by dividing the number of data of category i classified in the node corresponding to the Gini coefficient G by the total number of data classified in that node. For example, if two data indicating "A" and one data indicating "B" are classified in that node, G=1-(2 / 3)2 -(1 / 3) 2 It is.

[0113] The quality influence calculation unit 125a performs learning so that the Gini coefficient in the decision tree is as small as possible. The quality influence calculation unit 125a then calculates the influence that indicates a larger value as the average value of all the Gini coefficients of the multiple decision trees used in the random forest is smaller. For example, the quality influence calculation unit 125a calculates the inverse of the average value as the influence.

[0114] In this way, the qualitative influence degree calculation unit 125a in the present embodiment calculates the influence degree of each of one or more qualitative variables by using the random forest. Therefore, even if the non-utilized variable is a qualitative variable, the influence degree of the non-utilized variable on the objective variable can be appropriately calculated.

[0115] Fig. 11 is a flow chart showing a specific example of the process of calculating the influence of quantitative variables in step S9 in Fig. 9. Note that the non-utilized variables to be processed in this influence calculation process are quantitative variables.

[0116] The quantitative influence degree calculation unit 125b performs clustering on each data of the non-utilized variables to be processed that are included in the data set Ds by unsupervised machine learning (step S91). The unsupervised machine learning is, for example, a Gaussian Mixture Model (GMM).

[0117] The Gaussian mixture model is a method for approximating a given probability distribution by a linear combination of multiple Gaussian functions (i.e., normal distributions). In the linear combination, multiple Gaussian functions are combined using a weight πk. The weight πk is the weight of the k-th Gaussian function and is also called a mixing coefficient. When considered in two dimensions of (a, b), the k-th Gaussian function has an average value μa_k of a, an average value μb_k of b, a variance Σa_k of a, a variance Σb_k of b, and a covariance Σab_k of a and b. The magnitude of each normal distribution can be simply handled as Σb_k+Σa_k. In this embodiment, (a, b) are (objective variables, non-utilized variables that are quantitative variables).

[0118] The quantitative influence calculation unit 125b analyzes the Gaussian mixture model while changing the number of clusters, which is a hyperparameter in the Gaussian mixture model. Then, the quantitative influence calculation unit 125b adopts the number of clusters that minimizes, for example, Akaike's Information Criterion (AIC) or Bayesian Information Criterion (BIC). As a result, clusters are generated by the number of clusters. The number of clusters is one or more. Also, the clusters correspond to the above-mentioned categories or groups.

[0119] Thereafter, the quantitative influence calculation unit 125b executes a second loop process including steps S92 to S94 for each of all the clusters generated in step S91 in order.

[0120] Specifically, the quantitative influence calculation unit 125b first determines whether the number of data in the cluster to be processed is equal to or greater than the second threshold (step S92). Here, when the quantitative influence calculation unit 125b determines that the number of data is equal to or greater than the second threshold (Yes in step S92), it calculates the influence of the data in the cluster on the data of the objective variable as the cluster influence (step S93). At this time, the quantitative influence calculation unit 125b adopts the cluster as a cluster for which calculation processing has been completed. For example, the quantitative influence calculation unit 125b calculates the regression coefficient of the simple regression equation as the cluster influence by performing a simple regression analysis on the cluster to be processed and the objective variable. On the other hand, when the quantitative influence calculation unit 125b determines that the number of data is less than the second threshold (No in step S92), it excludes the cluster from the calculation target of the cluster influence (step S94). This makes it possible to prevent the influence of the non-utilized variable to be processed from being calculated using a cluster with low reliability.

[0121] Such a second loop process including steps S92 to S94 is executed in order for each of all the clusters generated in step 91. As a result, the cluster influence of each of the remaining clusters other than the cluster excluded in step S94 is calculated among all the clusters.

[0122] Then, the quantitative influence calculation unit 125b judges whether the number of clusters for which the cluster influences have been calculated in step S93, that is, the number of clusters for which the calculation process has been completed, is two or more (step S95). Here, if the quantitative influence calculation unit 125b judges that the number of clusters is two or more (Yes in step S95), it selects the maximum influence of the two or more influences calculated in the second loop process (step S97). That is, the quantitative influence calculation unit 125b selects the maximum influence as the influence of the non-utilized variable to be processed on the objective variable. On the other hand, if the quantitative influence calculation unit 125b judges that the number of clusters for which the calculation process has been completed is not two or more (No in step S95), it excludes the non-utilized variable from the calculation target of the influence (step S96). This makes it possible to prevent a non-utilized variable with a low correlation with the objective variable from being specified as a stratification variable.

[0123] In this manner, the quantitative influence calculation unit 125b in this embodiment performs clustering on two or more data of one or more quantitative variables included in the data set Ds using a Gaussian mixture model, and calculates the influence of the quantitative variable by a simple regression analysis between each cluster obtained by the clustering and the objective variable. Therefore, even if the non-utilized variable is a quantitative variable, the influence of the non-utilized variable on the objective variable can be appropriately calculated.

[0124] As described above, in this embodiment, when the stratification variables are qualitative variables, identical data are grouped together, and when the stratification variables are quantitative variables, identical or similar data are grouped together.

[0125] The stratification unit 127 in this embodiment classifies the data of each stratification variable into a plurality of groups based on the identity or similarity of the data of the stratification variable, and classifies the data set Ds for each combination of the plurality of groups. Here, when the stratification variables are quantitative variables, the data of the quantitative variables being similar means that the tendency of the influence of the quantitative variables on the objective variable is similar. Therefore, it can be said that the stratification unit 127 classifies the data set Ds into a plurality of strata based on the tendency of the relationship between the stratification variables and the objective variable.

[0126] In addition, in the Gaussian mixture model, each piece of data has a probability of belonging to each group, and belongs to the group with the highest probability among the multiple groups. When the k-means method described later is used instead of the Gaussian mixture model, each piece of data has a distance from the center of gravity of each group, and belongs to the group with the closest distance among the multiple groups. Therefore, specifically, data of quantitative variables being similar means that the probability corresponding to the data is a certain value or more, or the distance from the center of gravity of the group to the position corresponding to the data is a certain value or less.

[0127] [Model example] As described above, in this embodiment, stratified classification is performed on the data set Ds. For example, when the non-utilized variable Z0 and the non-utilized variable Z1 are identified as stratification variables, four stratified data sets Ds1 to Ds4 are generated as shown in Fig. 7. Then, a model is generated from each of the four stratified data sets Ds1 to Ds4. This can improve the accuracy of the model.

[0128] Specifically, when stratified classification is not performed, the model generated from the data set Ds is expressed as shown in the following formula (2). In formula (2), x0 and x1 correspond to the explanatory variables X0 and X1, respectively, and f corresponds to the objective variable Y.

[0129]

number

[0130] On the other hand, in this embodiment, since stratified classification is performed as described above, the four models shown in the following formulas (3) to (6) are generated as multiple regression equations. In formulas (3) to (6), x0 and x1 correspond to the explanatory variables X0 and X1 described above, respectively, and f 00 , f 01 , f 10 , and f 11 Each of these corresponds to the above-mentioned objective variable Y. Specifically, the formula (3) is a model generated from the stratified data set Ds4 shown in FIG. 7(d) and FIG. 8, and the stratified data set Ds4 includes each record including the data "B" of the stratification variable Z0 and the data "D" of the stratification variable Z1. 00 corresponds to the objective variable Y of this stratified data set Ds4. Formula (4) is a model generated from the stratified data set Ds3 shown in FIG. 7(c) and FIG. 8, and the stratified data set Ds3 includes records including data "A" of the stratification variable Z0 and data "D" of the stratification variable Z1. 01 corresponds to the objective variable Y of this stratified data set Ds3. Formula (5) is a model generated from the stratified data set Ds2 shown in FIG. 7(b) and FIG. 8, and the stratified data set Ds2 includes records including the data "B" of the stratification variable Z0 and the data "C" of the stratification variable Z1. 10 corresponds to the objective variable Y of this stratified data set Ds2. Formula (6) is a model generated from the stratified data set Ds1 shown in FIG. 7(a) and FIG. 8, and the stratified data set Ds1 includes records including data "A" of the stratification variable Z0 and data "C" of the stratification variable Z1. 11 corresponds to the objective variable Y of this stratified dataset Ds1.

[0131] In this embodiment, two stratification variables Z0 and Z1 are specified, each data of the stratification variable Z0 is classified into two groups, and each data of the stratification variable Z1 is classified into two groups. Therefore, the number of group combinations is four, and four models are generated. Here, when three stratification variables are specified and each data of the stratification variables is classified into two groups, the number of group combinations is eight, and eight models are generated. Also, when two stratification variables are specified and each data of the stratification variables is classified into three groups, the number of group combinations is nine, and nine models are generated.

[0132]

number

[0133] Here, R 2* is the coefficient of determination after the degrees of freedom adjustment. This coefficient of determination after the degrees of freedom adjustment is an index indicating the reliability of the model. In this embodiment, as shown in the above formulas (2) to (6), the coefficient of determination after the degrees of freedom adjustment can be increased from 0.273 to a range of 0.503 to 0.969, thereby improving the accuracy of the model. Then, each model and each coefficient of determination after the degrees of freedom adjustment are output by the result output unit 129.

[0134] In this manner, the generating unit 128 in this embodiment calculates an index indicating the likelihood of each of the multiple models generated. Then, the result output unit 129 outputs the calculated index for each of the multiple models. Therefore, the user can easily determine whether or not to use the generated model based on the index.

[0135] [Effects, etc.] As described above, in this embodiment, stratification is performed according to the stratification variables, which are variables other than the objective variable and the explanatory variables, and a model is generated for each of the multiple strata. In addition, the stratification variables are non-utilized variables that are not adopted as variables included in the model but are used to generate the model. Therefore, the non-utilized variables can optimally perform stratification for the data set Ds. As a result, even if the correlation between the explanatory variable and the objective variable changes depending on the non-utilized variables, a highly accurate model can be generated according to the data of the non-utilized variables. In other words, the accuracy of the model can be easily improved.

[0136] In addition, in this embodiment, effective variables are automatically identified as stratification variables from among the M non-utilized variables of the data set Ds. Therefore, it is possible to utilize the intention of a user, such as a factory expert, in selecting the utilized variables (i.e., the objective variable and the explanatory variable), and it is possible to generate a model that is easy for the user to understand and improve the accuracy of the model at the same time.

[0137] In this embodiment, the second variable identification unit 126 identifies N stratification variables (N is an integer equal to or greater than 2) from the multiple variables. Then, the stratification unit 127 classifies two or more data of the stratification variables included in the data set Ds into multiple groups based on commonality or similarity between two or more data of the stratification variables for each of the N stratification variables. Furthermore, the stratification unit 127 determines multiple strata according to the combination of the groups of the N stratification variables, and classifies two or more records included in the data set Ds into the determined multiple strata. When the stratification variables are qualitative variables, the groups correspond to the above-mentioned categories, and when the stratification variables are quantitative variables, they correspond to the above-mentioned clusters.

[0138] As a result, even if there are two or more stratification variables, optimal stratification classification can be performed for the dataset Ds, and a highly accurate model can be generated for each of the multiple strata according to the data of those stratification variables, i.e., the N non-utilized variables.

[0139] Furthermore, in this embodiment, the influence calculation unit 125 calculates the influence of each of the non-utilized variables, and the second variable identification unit 126 identifies N stratification variables from the non-utilized variables based on the influence of each of the non-utilized variables.

[0140] This makes it possible to identify N stratification variables that have a large influence on the target variable data. As a result, by using these stratification variables, more optimal stratification classification can be performed for the data set Ds. As a result, a more accurate model can be generated for each of the multiple strata.

[0141] In this embodiment, the stratification condition setting unit 122 sets the total number of stratification variables in response to an input operation by the user, and the second variable identification unit 126 identifies only N stratification variables, which is the set total number.

[0142] This allows the total number of stratification variables to be identified to be set arbitrarily as intended by the user, and allows the number or accuracy of models to be generated to be adjusted.

[0143] In this embodiment, the M unexploited variables include one or more qualitative variables each indicating data including characters, and one or more quantitative variables each indicating data consisting of numbers.

[0144] This makes it possible to specify N stratification variables including not only one but both of qualitative and quantitative variables, and increases the degree of freedom in the variable types of the specified stratification variables.

[0145] In this embodiment, the stratification condition setting unit 122 sets the priority order of the qualitative variables and the quantitative variables in response to an input operation by the user. The second variable identification unit 126 identifies the top variables in descending order of influence as stratification variables from the M non-utilized variables until all top variables are identified as K (K is an integer less than N) or until N top variables are identified. Then, when the number of identified top variables is K, (NK) lower variables are identified as stratification variables in descending order of influence from the M non-utilized variables.

[0146] This allows the user to arbitrarily set the priority of qualitative and quantitative variables as intended, and many of the variables with higher priorities can be identified as stratification variables. As a result, appropriate stratification can be performed depending on whether the data set Ds is analyzed qualitatively or quantitatively, and a model can be generated according to the analysis mode.

[0147] (Variations, etc.) Although the model generating device according to one aspect of the present disclosure has been described based on the above embodiment, the present disclosure is not limited to the embodiment. As long as it does not deviate from the spirit of the present disclosure, various modifications conceivable by a person skilled in the art to the above embodiment may also be included in the present disclosure.

[0148] For example, in the present embodiment, a random forest is used as an example of supervised machine learning in the influence degree calculation process of the qualitative variables, but the supervised machine learning is not limited to the random forest, and other supervised machine learning may be used. For example, a gradient boosting decision tree (GBDT) may be used instead of a random forest. When the gradient boosting decision tree is used, machine learning is performed so that the error or loss coefficient becomes smaller. Then, the qualitative influence degree calculation unit 125a calculates an influence degree that indicates a larger value as the error or loss coefficient becomes smaller. In addition, a random forest and a gradient boosting decision tree may be combined. For example, the influence degree of the first non-utilized variable, which is a qualitative variable, may be calculated using a random forest, and the influence degree of the second non-utilized variable, which is a qualitative variable, may be calculated using a gradient boosting decision tree. At this time, the influence degrees calculated by the two different machine learning methods may be normalized to make those influence degrees comparable.

[0149] In addition, in this embodiment, a Gaussian mixture model is used as an example of unsupervised machine learning in the influence degree calculation process of the quantitative variable, but the unsupervised machine learning is not limited to the Gaussian mixture model, and other unsupervised machine learning may be used. For example, instead of the Gaussian mixture model, a k-means method may be used. In this case, each data indicated by the non-utilized variable to be processed is clustered by the k-means method. In addition, the Gaussian mixture model and the k-means method may be combined. For example, the clustering of the first non-utilized variable, which is a quantitative variable, may be performed using the Gaussian mixture model, and the clustering of the second non-utilized variable, which is a quantitative variable, may be performed using the k-means method.

[0150] In addition, in the present embodiment, a multiple regression equation is generated as a model, but a simple regression equation may be generated as a model, or a model other than a regression equation may be generated. For example, a neural network may be generated as a model.

[0151] In addition, the data set Ds in this embodiment indicates manufacturing-related variables and data on those variables, but is not limited to those related to manufacturing, and may indicate variables and data on those variables in fields other than manufacturing.

[0152] In addition, the data included in the data set Ds in this embodiment may be separated into operation data and quality data. For example, the operation data is data related to the manufacturing process, and may be data on variables X0, X1, Z0, Z1, D1, D2, D3, and D4 shown in Fig. 5. For example, the quality data is data related to the quality of the product, and may be variable Y shown in Fig. 5.

[0153] In this embodiment, the total number N of stratification variables and the priority order of variable types are set, but items other than these may be set. For example, the total number of qualitative variables and the total number of quantitative variables may be set separately. In this embodiment, the total number N is an integer of 2 or more, but it may be 1.

[0154] Also, in this embodiment, the data set Ds contains data on variables belonging to each of two variable types, but the number of variable types is not limited to two, and may be just one, or may be three or more.

[0155] Furthermore, in this embodiment, the data set Ds is transmitted from the manufacturing management apparatus 500 via a network and stored in the database 106, but the data set Ds may be output from another apparatus or a recording medium to be stored in the database 106. Furthermore, the data set Ds may be stored in the database 106 without going through a network.

[0156] The following cases are also included in this disclosure:

[0157] (1) The at least one device is specifically a computer system consisting of a microprocessor, a ROM (Read Only Memory), a RAM (Random Access Memory), a hard disk unit, a display unit, a keyboard, a mouse, etc. A computer program is stored in the RAM or the hard disk unit. The at least one device achieves its functions by the microprocessor operating in accordance with the computer program. Here, the computer program is composed of a combination of multiple instruction codes that indicate commands for a computer to achieve a specified function.

[0158] (2) Some or all of the components constituting at least one of the above devices may be composed of one system LSI (Large Scale Integration). The system LSI is an ultra-multifunctional LSI manufactured by integrating multiple components on one chip, and specifically, is a computer system including a microprocessor, ROM, RAM, etc. A computer program is stored in the RAM. The system LSI achieves its functions by the microprocessor operating in accordance with the computer program.

[0159] (3) A part or all of the components constituting at least one of the above devices may be composed of an IC card or a standalone module that is detachable from the device. The IC card or module is a computer system composed of a microprocessor, ROM, RAM, etc. The IC card or module may include the above-mentioned ultra-multifunction LSI. The IC card or module achieves its functions by the microprocessor operating according to a computer program. This IC card or module may be tamper-resistant.

[0160] (4) The present disclosure may be embodied as any of the methods described above, a computer program for implementing these methods on a computer, or a digital signal comprising a computer program.

[0161] The present disclosure may also be a computer program or a digital signal recorded on a computer-readable recording medium, such as a flexible disk, a hard disk, a CD (Compact Disc)-ROM, a DVD, a DVD-ROM, a DVD-RAM, a BD (Blu-ray (registered trademark) Disc), a semiconductor memory, etc. Also, the present disclosure may be a digital signal recorded on such a recording medium.

[0162] The present disclosure may also involve transmitting a computer program or a digital signal via a telecommunications line, a wireless or wired communication line, a network such as the Internet, data broadcasting, or the like.

[0163] Furthermore, the program or digital signal may be recorded on a recording medium and transferred, or the program or digital signal may be transferred via a network or the like, so that the program or digital signal may be implemented by another independent computer system. [Industrial Applicability]

[0164] The present disclosure has the effect of easily improving the accuracy of a model, and can be applied, for example, to an apparatus or system that generates a model that estimates the quality of a product manufactured in a manufacturing process from data on variables used in the manufacturing process. [Explanation of symbols]

[0165] 1. Model Generation System 100 Model Generator 101 Input section 102 Arithmetic circuit 103 Memory 104 Output section 105 Storage section 105a Program 105b Temporary Data 106 Database 121 First variable identification section 122 Stratification Condition Setting Section 123 Unused Variable Extraction Unit 124 Variable Type Judgment Section 125 Impact calculation part 125a Qualitative impact calculation section 125b Quantitative impact calculation part 126 Second variable specification section 127 Stratification Department 128 Generation part 129 Result output section 130 Receiving unit 500 Manufacturing control equipment Ds dataset Ds1~Ds4 Stratified Data Set

Claims

1. A model generation device that generates a model indicating a relationship between one or more objective variables and one or more explanatory variables, receiving means for receiving a data set including three or more variables; a variable specifying means for specifying one or more dependent variables and one or more explanatory variables from the data set, and specifying one or more variables as stratification variables from among the three or more variables included in the data set, other than the determined dependent variables and the determined explanatory variables; A stratification means for classifying a data set into a plurality of strata based on a tendency of a relationship between the stratification variable and the objective variable; A generation means for generating the model for each of the plurality of layers; A model generating device comprising:

2. The stratification means classifies the data of the stratification variables into a plurality of groups based on identity or similarity of the data of the stratification variables, and classifies the data set for each combination of the plurality of groups. The model generating device according to claim 1 .

3. The data set includes qualitative variables representing data including characters and quantitative variables representing data consisting of numbers.

3. The model generating device according to claim 1 or 2.

4. The method further includes an influence calculation means for calculating an influence of each of the three or more variables included in the data set on the objective variable, the influence being calculated for each of the variables other than the objective variable and the explanatory variable, The variable identification means identifies the stratification variables based on the influence degree. The model generating device according to any one of claims 1 to 3.

5. The method further includes an influence calculation means for calculating an influence of each of the three or more variables included in the data set on the objective variable, the influence being calculated for each of the variables other than the objective variable and the explanatory variable, the receiving means receives priority information indicating whether the qualitative variables or the quantitative variables are to be prioritized; The variable identification means identifies the stratification variables based on the influence degree and the priority information.

4. The model generating device according to claim 3.

6. The influence degree calculation means Calculating the influence of the qualitative variables using a random forest or a gradient boosting decision tree.

6. The model generating device according to claim 5.

7. The influence degree calculation means Using a Gaussian mixture model or a k-means method, clustering is performed on two or more pieces of data of the quantitative variables included in the data set, and the influence degree of the quantitative variables is calculated by a simple regression analysis between each cluster obtained by the clustering and the objective variable.

7. A model generating device according to claim 5 or 6.

8. The generating means further Calculating an index indicating the likelihood of each of the generated models; The model generating device further comprises: A result output means for outputting the index calculated for each of the plurality of models is provided. A model generating device according to any one of claims 1 to 7.

9. The generating means includes: generating the model indicating a relationship between data of each of the two or more explanatory variables and data of the objective variable as a multiple regression equation; A model generating device according to any one of claims 1 to 8.

10. A model generation method in which a computer generates a model showing a relationship between one or more objective variables and one or more explanatory variables, comprising: receiving a dataset containing three or more variables; Identifying one or more dependent variables and one or more explanatory variables from the data set, and identifying one or more variables as stratification variables from among the three or more variables included in the data set, other than the identified dependent variables and the explanatory variables; Classifying the data set into a plurality of strata based on a trend of a relationship between the stratification variables and the objective variable; generating the model for each of the plurality of layers; Model generation method.

11. A program for causing a computer to generate a model showing a relationship between one or more objective variables and one or more explanatory variables, receiving a dataset containing three or more variables; Identifying one or more dependent variables and one or more explanatory variables from the data set, and identifying one or more variables as stratification variables from among the three or more variables included in the data set, other than the identified dependent variables and the explanatory variables; Classifying the data set into a plurality of strata based on a trend of a relationship between the stratification variables and the objective variable; generating the model for each of the plurality of layers; A program that causes a computer to do something.

Images