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Using the link columns in the provided dataset (including at least a targets columns containing lists of target row numbers that each row connects to), for each row calculate requested aggregations over all its direct (first-degree) neighbours. Will use the first set of link columns encountered in the datasets metadata.

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Usage

The following example shows how the step can be used in a recipe.

Examples

  • Example 1
  • Signature
Assuming a dataset products where each row represents a supermarket product (having at least a price and aisle column), and containing a targets column dataset representing connections between similar products, the following example calculates for each product
  • the average price of similar products
  • the percentage of similar products assigned to aisles “produce”, “deli” and “drinks”
aggregate_neighbours(products, {
  "aggregations": {
    "price": {
      "similar_price_avg": {"func": "mean"}
    },
    "aisle": {
      "similar_pct_produce": {"func": "percent_where", "value": "produce"},
      "similar_pct_deli": {"func": "percent_where", "value": "deli"},
      "similar_pct_drinks": {"func": "percent_where", "value": "drinks"}
    }
  }
}) -> (products_agg)

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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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ds_in
dataset
required
A dataset containing the nodes (rows) to group and aggregate, and its corresponding links.
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ds_out
dataset
required
The original dataset plus newly aggregated columns. Will have one column per specified aggregation function (more than one aggregation can be specified for each original input column).

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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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presort
object
Pre-aggregation row sorting. Sort the dataset rows before aggregating, e.g. when in a particular aggregation function (such as list) the encountered order is important.
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columns
[null, string, array]
The sort column name(s). These column(s) will be used to sort the dataset before aggregating (if multiple, in specified order). E.g. to first sort links by their weight, and if the weight column is called “gx_weight”, use "gx_weight"
  • No sorting
  • Single column
  • Multiple columns
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{_}
null
When null, no sorting is applied.
  • date_added
  • [‘lastname’, ‘firstname’]
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ascending
boolean
default:"true"
Whether to sort in ascending order (or in descending order if false).
  • For example, to sort first by price, then dimension, and in descending order:
{
"columns": ["price", "dimension"],
"ascending": false
}
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aggregations
object
required
Definition of desired aggregations. A dictionary mapping original columns to new aggregated columns, specifying an aggregation function for each. Aggregations are functions that reduce all the values in a particular column of a single group to a single summary value of that group. E.g. a sum aggregation of column A calculates a single total by adding up all the values in A belonging to each group.Possible aggregations functions accepted as func parameters are:
  • n, size or count: calculate number of rows in group
  • sum: sum total of values
  • mean: take mean of values
  • max: take max of values
  • min: take min of values
  • mode: find most frequent value (returns first mode if multiple exist)
  • first: take first item found
  • last: take last item found
  • unique: collect a list of unique values
  • n_unique: count the number of unique values
  • list: collect a list of all values
  • concatenate: convert all values to text and concatenate them into one long text
  • concat_lists: concatenate lists in all rows into a single larger list
  • count_where: number of rows in which the column matches a value, needs parameter value with the value that you want to count
  • percent_where: percentage of the column where the column matches a value, needs parameter value with the value that you want to count
Note that in the case of count_where and percent_where an additional value parameter is required.
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input_aggregations
object
One item per input column. Each key should be the name of an input column, and each value an object defining one or more aggregations for that column. An individual aggregation consists of the name of a desired output column, mapped to a specific aggregation function. For example:
{
"input_col": {
"output_col": {"func": "sum"}
}
}
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aggregation_func
object
Object defining how to aggregate a single output column. Needs at least the "func" parameter. If the aggregation function accepts further arguments, like the "value" parameter in case of count_where and percent_where, these need to be provided also. For example:
{
"output_col": {"func": "count_where", "value": 2}
}
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func
string
Aggregation function.Values must be one of the following:n size count sum mean n_unique count_where percent_where concatenate max min first last mode concat_lists unique list
  • Including an aggregation function with additional parameters:
{
"product_id": {
"products": {"func": "list"},
"size": {"func": "count"}
},
"item_total": {
"total": {"func": "sum"},
},
"item_category": {
"num_food_items": {"func": "count_where", "value": "food"}
}
}
​
directed
boolean
default:"false"
Whether the links provided should be interpreted as being directed. Directed here meaning that the link A→B (from node A to B) may be different from the link B→A (i.e. they may have different weight attributes for example). When "directed": false, in contrast, i.e. links are undirected, it is assumed that the link A→B is always identical to B→A (i.e. A↔B always). This is usually the case when links represent a similarity between nodes.