Difference between revisions of "Orange: Feature Constructor"

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(Created page with "Sumber: https://docs.biolab.si//3/visual-programming/widgets/data/featureconstructor.html Add new features to your dataset. Inputs Data: input dataset Outputs Dat...")
 
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The Feature Constructor allows you to manually add features (columns) into your dataset. The new feature can be a computation of an existing one or a combination of several (addition, subtraction, etc.). You can choose what type of feature it will be (discrete, continuous or string) and what its parameters are (name, value, expression). For continuous variables you only have to construct an expression in Python.
 
The Feature Constructor allows you to manually add features (columns) into your dataset. The new feature can be a computation of an existing one or a combination of several (addition, subtraction, etc.). You can choose what type of feature it will be (discrete, continuous or string) and what its parameters are (name, value, expression). For continuous variables you only have to construct an expression in Python.
  
../../_images/feature-constructor1-stamped.png
+
[[File:Feature-constructor1-stamped.png|center|200px|thumb]]
  
 
     List of constructed variables
 
     List of constructed variables
  
 
     Add or remove variables
 
     Add or remove variables
 
 
     New feature name
 
     New feature name
 
 
     Expression in Python
 
     Expression in Python
 
 
     Select a feature
 
     Select a feature
 
 
     Select a function
 
     Select a function
 
 
     Produce a report
 
     Produce a report
 
 
     Press Send to communicate changes
 
     Press Send to communicate changes
  
 
For discrete variables, however, there’s a bit more work. First add or remove the values you want for the new feature. Then select the base value and the expression. In the example below, we have constructed an expression with ‘if lower than’ and defined three conditions; the program ascribes 0 (which we renamed to lower) if the original value is lower than 6, 1 (mid) if it is lower than 7 and 2 (higher) for all the other values. Notice that we use an underscore for the feature name (e.g. petal_length).
 
For discrete variables, however, there’s a bit more work. First add or remove the values you want for the new feature. Then select the base value and the expression. In the example below, we have constructed an expression with ‘if lower than’ and defined three conditions; the program ascribes 0 (which we renamed to lower) if the original value is lower than 6, 1 (mid) if it is lower than 7 and 2 (higher) for all the other values. Notice that we use an underscore for the feature name (e.g. petal_length).
  
../../_images/feature-constructor2-stamped.png
+
[[File:Feature-constructor2-stamped.png|center|200px|thumb]]
  
 
     List of variable definitions
 
     List of variable definitions
  
 
     Add or remove variables
 
     Add or remove variables
 
 
     New feature name
 
     New feature name
 
 
     Expression in Python
 
     Expression in Python
 
 
     Select a feature
 
     Select a feature
 
 
     Select a function
 
     Select a function
 
 
     Assign values
 
     Assign values
 
 
     Produce a report
 
     Produce a report
 
 
     Press Send to communicate changes
 
     Press Send to communicate changes
  
Example
+
==Contoh==
  
 
With the Feature Constructor you can easily adjust or combine existing features into new ones. Below, we added one new discrete feature to the Titanic dataset. We created a new attribute called Financial status and set the values to be rich if the person belongs to the first class (status = first) and not rich for everybody else. We can see the new dataset with Data Table widget.
 
With the Feature Constructor you can easily adjust or combine existing features into new ones. Below, we added one new discrete feature to the Titanic dataset. We created a new attribute called Financial status and set the values to be rich if the person belongs to the first class (status = first) and not rich for everybody else. We can see the new dataset with Data Table widget.
  
../../_images/FeatureConstructor-Example.png
+
[[File:FeatureConstructor-Example.png|center|200px|thumb]]
Hints
 
  
If you are unfamiliar with Python math language, here’s a quick introduction.
 
  
    +, - to add, subtract
+
==Hints==
  
    * to multiply
+
If you are unfamiliar with Python math language, here’s a quick introduction.
 
 
    / to divide
 
 
 
    % to divide and return the remainder
 
 
 
    ** for exponent (for square root square by 0.5)
 
 
 
    // for floor division
 
 
 
    <, >, <=, >= less than, greater than, less or equal, greater or equal
 
 
 
    == for equal
 
  
    != for not equal
+
  +, - to add, subtract
 +
  * to multiply
 +
  / to divide
 +
  % to divide and return the remainder
 +
  ** for exponent (for square root square by 0.5)
 +
  // for floor division
 +
  <, >, <=, >= less than, greater than, less or equal, greater or equal
 +
  == for equal
 +
  != for not equal
  
 
As in the example: (value) if (feature name) < (value), else (value) if (feature name) < (value), else (value)
 
As in the example: (value) if (feature name) < (value), else (value) if (feature name) < (value), else (value)

Revision as of 13:25, 21 January 2020

Sumber: https://docs.biolab.si//3/visual-programming/widgets/data/featureconstructor.html

Add new features to your dataset.

Inputs

   Data: input dataset

Outputs

   Data: dataset with additional features

The Feature Constructor allows you to manually add features (columns) into your dataset. The new feature can be a computation of an existing one or a combination of several (addition, subtraction, etc.). You can choose what type of feature it will be (discrete, continuous or string) and what its parameters are (name, value, expression). For continuous variables you only have to construct an expression in Python.

Feature-constructor1-stamped.png
   List of constructed variables
   Add or remove variables
   New feature name
   Expression in Python
   Select a feature
   Select a function
   Produce a report
   Press Send to communicate changes

For discrete variables, however, there’s a bit more work. First add or remove the values you want for the new feature. Then select the base value and the expression. In the example below, we have constructed an expression with ‘if lower than’ and defined three conditions; the program ascribes 0 (which we renamed to lower) if the original value is lower than 6, 1 (mid) if it is lower than 7 and 2 (higher) for all the other values. Notice that we use an underscore for the feature name (e.g. petal_length).

Feature-constructor2-stamped.png
   List of variable definitions
   Add or remove variables
   New feature name
   Expression in Python
   Select a feature
   Select a function
   Assign values
   Produce a report
   Press Send to communicate changes

Contoh

With the Feature Constructor you can easily adjust or combine existing features into new ones. Below, we added one new discrete feature to the Titanic dataset. We created a new attribute called Financial status and set the values to be rich if the person belongs to the first class (status = first) and not rich for everybody else. We can see the new dataset with Data Table widget.

FeatureConstructor-Example.png


Hints

If you are unfamiliar with Python math language, here’s a quick introduction.

 +, - to add, subtract
 * to multiply
 / to divide
 % to divide and return the remainder
 ** for exponent (for square root square by 0.5)
 // for floor division
 <, >, <=, >= less than, greater than, less or equal, greater or equal
 == for equal
 != for not equal

As in the example: (value) if (feature name) < (value), else (value) if (feature name) < (value), else (value)

[Use value 1 if feature is less than specified value, else use value 2 if feature is less than specified value 2, else use value 3.]

See more here.



Referensi

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