aggregate_list_items
Group a dataset by elements in a column of lists and aggregate remaining columns using one or more predefined functions.
This is essentially aggregate after “exploding” a column of lists such that each list item has its
own row. By default the step produces one row per unique list item, and two columns: the count of how many
times each list item was encountered, and a column rows recording the row numbers of the lists in which the
element was found ([1,3,7] would mean an item was present in the lists of rows 1, 3 and 7). In addition,
predefined functions can be used to add further aggregations of the grouped input dataset.
For example, if a dataset contains texts already separated into lists of individual words, this step will create a new dataset containing one row per word, a column containing each word’s frequency (count) across all texts, and another column of lists indicating in which rows the word was found.
Optionally, if a grouping column is specified using the "by" parameter, otherwise identical items belonging to
different groups will be counted separately. If the dataset contains texts in different languages, for example, one
may not want to group all occurences of the same word together, irrespective of language. The word “angel”
in German signifies a fishing rod, for example, “any” in Catalan means “year”, and the Italian word “burro” means
“butter” while in Spanish it refers to “donkey”. Using language as the grouping column would preserve the word in each
language as a separate group.
Usage
The following example shows how the step can be used in a recipe.
The following example performs a simple word-count, returning a new dataset with one row per word and each word’s frequency in the “count” column. The aggregation will be performed separately for each language. Also, for each word, a custom aggregation collects the dates of the texts in which the word was mentioned:
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").
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).
Name of column containing the lists to be split and grouped.
Optional grouping column to use for item counting and aggregation.
Count unique occurences only. Whether to collect in the output column “rows” only the unique rows each item appeared in, or all rows (duplicate row IDs if item appeared more than once in a single row).
Row IDs as strings. Output occurrence of items in rows as lists of strings (categorical) rather than lists of row numbers.
Definition of additional 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,sizeorcount: calculate number of rows in groupsum: sum total of valuesmean: take mean of valuesmax: take max of valuesmin: take min of valuesfirst: take first item foundlast: take last item foundunique: collect a list of unique valuesn_unique: count the number of unique valueslist: collect a list of all valuesconcatenate: convert all values to text and concatenate them into one long textconcat_lists: concatenate lists in all rows into a single larger listcount_where: number of rows in which the column matches a value, needs parametervaluewith the value that you want to countpercent_where: percentage of the column where the column matches a value, needs parametervaluewith 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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