add_noise
Add noise to a column with numbers or lists of numbers.
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.
Usage
The following examples show how the step can be used in a recipe.
Examples
Examples
Add white noise to a column of embeddings
Add white noise to a column of embeddings
Add std-dependant noise to a numerical column
General syntax for using the step in a recipe. Shows the inputs and outputs the step is expected to receive and will produce respectively. For futher details see sections below.
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").
Inputs
Inputs
The original column.
Outputs
Outputs
The result of applying noise to it.
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
Parameters
Distribution Function that noise is sampled from.
Values must be one of the following:
gumbellaplacelogisticnormal
Mean (“centre”) of the chosen distribution.
Standard deviation (spread or “width”) of the distribution.
The seed to use for the random distribution, if you wish to get reproducibility in your results.
add_noise
Add noise to a column with numbers or lists of numbers.
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.
Usage
The following examples show how the step can be used in a recipe.
Examples
Examples
Add white noise to a column of embeddings
Add white noise to a column of embeddings
Add std-dependant noise to a numerical column
General syntax for using the step in a recipe. Shows the inputs and outputs the step is expected to receive and will produce respectively. For futher details see sections below.
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").
Inputs
Inputs
The original column.
Outputs
Outputs
The result of applying noise to it.
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
Parameters
Distribution Function that noise is sampled from.
Values must be one of the following:
gumbellaplacelogisticnormal
Mean (“centre”) of the chosen distribution.
Standard deviation (spread or “width”) of the distribution.
The seed to use for the random distribution, if you wish to get reproducibility in your results.