categoricalHash: Machine Learning Categorical HashData Transform

Cikk
02/28/2023

Categorical hash transform that can be performed on data before training a model.

Usage

  categoricalHash(vars, hashBits = 16, seed = 314489979, ordered = TRUE,
    invertHash = 0, outputKind = "Bag", ...)

Arguments

`vars`

A character vector or list of variable names to transform. If named, the names represent the names of new variables to be created.

`hashBits`

An integer specifying the number of bits to hash into. Must be between 1 and 30, inclusive. The default value is 16.

`seed`

An integer specifying the hashing seed. The default value is 314489979.

`ordered`

TRUE to include the position of each term in the hash. Otherwise, FALSE. The default value is TRUE.

`invertHash`

An integer specifying the limit on the number of keys that can be used to generate the slot name. 0 means no invert hashing; -1 means no limit. While a zero value gives better performance, a non-zero value is needed to get meaningful coefficient names. The default value is 0.

`outputKind`

A character string that specifies the kind of output kind.

"ind": Outputs an indicator vector. The input column is a vector of categories, and the output contains one indicator vector per slot in the input column.
"bag": Outputs a multi-set vector. If the input column is a vector of categories, the output contains one vector, where the value in each slot is the number of occurrences of the category in the input vector. If the input column contains a single category, the indicator vector and the bag vector are equivalent
"key": Outputs an index. The output is an integer ID (between 1 and the number of categories in the dictionary) of the category.
The default value is "Bag".

`...`

Additional arguments sent to the compute engine.

Details

categoricalHash converts a categorical value into an indicator array by hashing the value and using the hash as an index in the bag. If the input column is a vector, a single indicator bag is returned for it.

categoricalHash does not currently support handling factor data.

Value

a maml object defining the transform.

Author(s)

Microsoft Corporation Microsoft Technical Support

Examples


 trainReviews <- data.frame(review = c( 
         "This is great",
         "I hate it",
         "Love it",
         "Do not like it",
         "Really like it",
         "I hate it",
         "I like it a lot",
         "I kind of hate it",
         "I do like it",
         "I really hate it",
         "It is very good",
         "I hate it a bunch",
         "I love it a bunch",
         "I hate it",
         "I like it very much",
         "I hate it very much.",
         "I really do love it",
         "I really do hate it",
         "Love it!",
         "Hate it!",
         "I love it",
         "I hate it",
         "I love it",
         "I hate it",
         "I love it"),
      like = c(TRUE, FALSE, TRUE, FALSE, TRUE,
         FALSE, TRUE, FALSE, TRUE, FALSE, TRUE, FALSE, TRUE,
         FALSE, TRUE, FALSE, TRUE, FALSE, TRUE, FALSE, TRUE, 
         FALSE, TRUE, FALSE, TRUE), stringsAsFactors = FALSE
     )

     testReviews <- data.frame(review = c(
         "This is great",
         "I hate it",
         "Love it",
         "Really like it",
         "I hate it",
         "I like it a lot",
         "I love it",
         "I do like it",
         "I really hate it",
         "I love it"), stringsAsFactors = FALSE)


 # Use a categorical hash transform
 outModel2 <- rxLogisticRegression(like~reviewCatHash, data = trainReviews, 
     mlTransforms = list(categoricalHash(vars = c(reviewCatHash = "review"))))
 # Weights are similar to categorical
 summary(outModel2)

 # Use the model to score
 scoreOutDF2 <- rxPredict(outModel2, data = testReviews, 
     extraVarsToWrite = "review")
 scoreOutDF2

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