filter_sample

If you request a number of rows greater than the dataframe length, it will return the original dataframe instead.

Usage

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

Examples

Example 1
Example 2
Example 3
Signature

This draws a sample of 12.000 random rows from the original dataset:

filter_sample(ds, {"n_samples": 12000}) -> (ds_sampled)

In the next example we keep only a random half of the dataset:

filter_sample(ds, {"n_samples": 0.5}) -> (ds_sampled)

And this draws a sample of 500 rows from each department identified in the original dataset (or the maximum if there are fewer than 500 rows for a particular department):

filter_sample(ds, {"n_samples": 500, "by": "department"}) -> (ds_sampled)

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.

filter_sample(ds_in: dataset, {
    "param": value,
    ...
}) -> (ds_out: dataset)

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

dataset

required

An input dataset to filter.

Outputs

dataset

required

A new dataset containing a random sample of the original rows.

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

[number, integer]

required

Number of rows to sample. How many random rows to pick from the original dataset (without replacement). If the value is greater than 1, it will be interpreted as a count of desired rows. If it is smaller than 1, it will be interpreted as a proportion of the entire dataset.

Options

number
integer

number

number.Values must be in the following range:

0 < {_} < 1

integer

integer.Values must be in the following range:

1 ≤ {_} < inf

string (ds_in.column)

Sample independently in these groups. If a column is specified here, the sampling will be applied separately within each group defined by the unique values in this column. Combining this with a count of rows to pick (rather than a proportion), allows this step to balance the dataset, leading to an (approximately) equal number of rows within each group.

[number, null]

A value used to initialize the random number generator, making it deterministic (reproducible).

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Usage

Inputs & Outputs

Configuration

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​Inputs & Outputs

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Usage

Inputs & Outputs

Configuration