Rule: Sklearn-Rule
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Name: Sklearn-Rule

Version: 2

Discussion Thread

There is a newer version of this Rule.

Creator: brendan.garvey@queensu.ca

This Rule is part is triggered when a csv file has missing columns that will be predicted by Sklearn.
This Rule reacts to sklearn_network and creates and presents an Option.
That option allows the user to select parameters for the Program [[program:SklearnPredict]] and executes it.

ID: 583

Created: Oct. 14, 2019, 2:18 p.m.

full definition:

The below is the JSON description of this object.

It is annotated with links to the documentation of each component.

You can hide fields with default values to make things clearer, and copy it to a clipboard to make creating similar Rules and Options easier.

Rule

{ "name" : "Sklearn-Rule", "description" : "This Rule is part is triggered when a csv file has missing columns that will be predicted by Sklearn.
This Rule reacts to sklearn_network and creates and presents an Option.
That option allows the user to select parameters for the Program [[program:SklearnPredict]] and executes it."
, "dependencies" : [

Dependency

{ "symbol" : "sklearn_network" }
], "threshold" : 1.0, "trigger" :

Trigger

{ }
, "actions" : [

Create an Option

{ "type" : "create_option", "var" : "sklearn-network", "name" : "sklearn-network", "description" : "This Option will ask the user for parameters, then execute [[program:SklearnPredict]] with those parameters.", "confidence" : 1000.0, "trigger" :

Trigger

{ }
, "display" :

Option Display

{ "must_always_be_shown" : true, "parameter_file_name" : "userParametersFile", "message_components" : [

Text message component

{ "type" : "text", "text" : "Please upload a nearly complete csv file. The file may contain an undefinied number of columns and rows. Elody will ask you to name a column number to predict(an unfinished column that can be classified) and which columns to exclude when making a prediction(any other unfinished columns or a column you dont want included, like an ID# column). The remaining columns should all be complete and floats or integers. Elody will return the csv file with the unfinished values in the predict column filled in." }
,

Text message component

{ "type" : "text", "text" : "Please select parameters for the prediction." }
,

Option Parameter Selector

{ "type" : "option_parameter_selector", "name" : "PredictColumn", "title" : "Which Column number should be the one predicted", "description" : "Insert an integer representing the number of the column that is unfinished and you want to be predicted(starting from 1)", "value" :

Integer Parameter

{ "type" : "int", "default" : 1 }
}
,

Option Parameter Selector

{ "type" : "option_parameter_selector", "name" : "ExcludeColumn", "title" : "Which columns should be excluded from the prediction process", "description" : "Insert a number or multiple number separated by commas which represent the column numbers of the columns you do not want included in the prediction process(string columns, id # columns, unfinished columns, etc)", "value" :

String Parameter

{ "type" : "string", "default" : "1" }
}
,

Option Parameter Selector

{ "type" : "option_parameter_selector", "name" : "TitleRowIncluded", "title" : "Is there a title row in your CSV?", "description" : "If selected, the program will account for a title row", "optional" : true, "value" :

Boolean Parameter

{ "type" : "bool" }
}
], "buttons" : [

Option Display Submit Button

{ "text" : "Perform the prediction", "style" : "cta" }
] }
, "actions" : [

Execute a Program

{ "type" : "execute_program", "program" : "SklearnPredict", "arguments" : { "user_parameters_file" :

Variable

{ "type" : "variable", "var" : "userParametersFile" }
} }
] }
], "existing_variables" : { "sklearn-network" :

Variable

{ "type" : "option" }
} }

Rule

{ "name" : "Sklearn-Rule", "description" : "This Rule is part is triggered when a csv file has missing columns that will be predicted by Sklearn.
This Rule reacts to sklearn_network and creates and presents an Option.
That option allows the user to select parameters for the Program [[program:SklearnPredict]] and executes it."
, "dependencies" : [

Dependency

{ "symbol" : "sklearn_network", "weight" : 1.0, "comment_filter" : null }
], "threshold" : 1.0, "trigger" :

Trigger

{ "repeat" : "never", "arguments" : [], "deactivate_if" : {} }
, "actions" : [

Create an Option

{ "type" : "create_option", "var" : "sklearn-network", "name" : "sklearn-network", "description" : "This Option will ask the user for parameters, then execute [[program:SklearnPredict]] with those parameters.", "confidence" : 1000.0, "trigger" :

Trigger

{ "repeat" : "never", "arguments" : [], "deactivate_if" : {} }
, "display" :

Option Display

{ "must_always_be_shown" : true, "parameter_file_name" : "userParametersFile", "message_components" : [

Text message component

{ "type" : "text", "text" : "Please upload a nearly complete csv file. The file may contain an undefinied number of columns and rows. Elody will ask you to name a column number to predict(an unfinished column that can be classified) and which columns to exclude when making a prediction(any other unfinished columns or a column you dont want included, like an ID# column). The remaining columns should all be complete and floats or integers. Elody will return the csv file with the unfinished values in the predict column filled in." }
,

Text message component

{ "type" : "text", "text" : "Please select parameters for the prediction." }
,

Option Parameter Selector

{ "type" : "option_parameter_selector", "name" : "PredictColumn", "title" : "Which Column number should be the one predicted", "description" : "Insert an integer representing the number of the column that is unfinished and you want to be predicted(starting from 1)", "optional" : false, "value" :

Integer Parameter

{ "type" : "int", "default" : 1, "min" : null, "max" : null }
}
,

Option Parameter Selector

{ "type" : "option_parameter_selector", "name" : "ExcludeColumn", "title" : "Which columns should be excluded from the prediction process", "description" : "Insert a number or multiple number separated by commas which represent the column numbers of the columns you do not want included in the prediction process(string columns, id # columns, unfinished columns, etc)", "optional" : false, "value" :

String Parameter

{ "type" : "string", "default" : "1", "multiline" : false }
}
,

Option Parameter Selector

{ "type" : "option_parameter_selector", "name" : "TitleRowIncluded", "title" : "Is there a title row in your CSV?", "description" : "If selected, the program will account for a title row", "optional" : true, "value" :

Boolean Parameter

{ "type" : "bool", "default" : false }
}
], "buttons" : [

Option Display Submit Button

{ "text" : "Perform the prediction", "style" : "cta", "replace_normal_actions" : false, "is_the_default_button" : false, "actions" : [] }
] }
, "actions" : [

Execute a Program

{ "type" : "execute_program", "var" : null, "program" : "SklearnPredict", "arguments" : { "user_parameters_file" :

Variable

{ "type" : "variable", "nullable" : false, "var" : "userParametersFile" }
}, "argument_lists" : {} }
] }
], "existing_variables" : { "sklearn-network" :

Variable

{ "type" : "option", "value" : null }
} }

all versions of this Rule:

Version 3

Version 2

Version 1