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Given a distribution name with a scale and loc parameters, the step optionally applies another scaling to it either based on the standard deviation of the column or a proportionally to each point through the relative parameter in order to preserve the underlying structure of the data. Then the computation is carried as follows: new value = original value + relative scaling factor * random sample from the distribution. If this relative parameter is not given or is set to abs, then the relative scaling factor is 1.

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Usage

The following examples show how the step can be used in a recipe.

Examples

  • Example 1
  • Example 2
  • Signature
Add white noise to a column of embeddings
add_noise(ds.embeddings) -> (ds.embeddings_with_noise)

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Inputs & Outputs

The following are the inputs expected by the step and the outputs it produces. These are generally columns (ds.first_name), datasets (ds or ds[["first_name", "last_name"]]) or models (referenced by name e.g. "churn-clf").
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input_column
column[number|list[number]]
required
The original column.
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result
column
required
The result of applying noise to it.

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Configuration

The following parameters can be used to configure the behaviour of the step by including them in a json object as the last “input” to the step, i.e. step(..., {"param": "value", ...}) -> (output).

Parameters

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relative
[number, string]
default:"abs"
Mode to use. Either set to “std” to use the standard deviation, or use a number to scale the sampling.
  • number
  • string
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{_}
number
number.
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dist_name
string
default:"normal"
Distribution Function that noise is sampled from.Values must be one of the following:
  • gumbel
  • laplace
  • logistic
  • normal
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loc
number
default:"0.0"
Mean (“centre”) of the chosen distribution.
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scale
number
default:"1.0"
Standard deviation (spread or “width”) of the distribution.
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seed
[number, null]
The seed to use for the random distribution, if you wish to get reproducibility in your results.