A typical problem: to write a Metarank with event to feature mapping, you need to read the docs on and well-understand your click-through input dataset:
Which fields do items have? Which values does each field have?
Do these values look like categories?
How many unique values are there per field?
Nobody likes reading docs and writing YAML, so Metarank has an generator of typical feature extractors based on the you already have.
Running the autofeature generator
Use the autofeature sub-command from the main binary:
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Usage: metarank <subcommand> <options>
Subcommand: autofeature - generate reference config based on existing data
-d, --data <arg> path to a directory with input files
-f, --format <arg> input file format: json, snowplow, snowplow:tsv,
snowplow:json (optional, default=json)
-o, --offset <arg> offset: earliest, latest, ts=1663161962, last=1h
(optional, default=earliest)
--out <arg> path to an output config file
-r, --ruleset <arg> set of rules to generate config: stable, all (optional,
default=stable, values: [stable, all])
-c, --cat-threshold <arg> min threshold of category frequency, when its
considered a catergory (optional, default=0.003)
-h, --help Show help message
For all other tricks, consult the docs on https://docs.metarank.ai
An example minimal command to generate the config file for your dataset:
15:32:11.284 INFO a.metarank.main.command.AutoFeature$ - Generating config file
15:32:11.524 INFO ai.metarank.source.FileEventSource - path=/home/shutty/code/metarank/src/test/resources/ranklens/events/events.jsonl.gz is a file
15:32:11.537 INFO ai.metarank.source.FileEventSource - file /home/shutty/code/metarank/src/test/resources/ranklens/events/events.jsonl.gz selected=true (timeMatch=true formatMatch=true)
15:32:11.538 INFO ai.metarank.source.FileEventSource - reading file /home/shutty/code/metarank/src/test/resources/ranklens/events/events.jsonl.gz (with gzip decompressor)
15:32:15.686 INFO a.metarank.main.command.AutoFeature$ - Event model statistics collected
15:32:15.691 INFO a.m.m.c.a.r.InteractionFeatureRule$ - generated interacted_with feature for interaction 'click' over field 'writer'
15:32:15.692 INFO a.m.m.c.a.r.InteractionFeatureRule$ - generated interacted_with feature for interaction 'click' over field 'tags'
15:32:15.692 INFO a.m.m.c.a.r.InteractionFeatureRule$ - generated interacted_with feature for interaction 'click' over field 'director'
15:32:15.693 INFO a.m.m.c.a.r.InteractionFeatureRule$ - generated interacted_with feature for interaction 'click' over field 'title'
15:32:15.693 INFO a.m.m.c.a.r.InteractionFeatureRule$ - generated interacted_with feature for interaction 'click' over field 'genres'
15:32:15.693 INFO a.m.m.c.a.r.InteractionFeatureRule$ - generated interacted_with feature for interaction 'click' over field 'actors'
15:32:15.700 INFO a.m.m.c.a.r.NumericalFeatureRule$ - generated `number` feature for item field popularity in the range 0.6..967.351
15:32:15.704 INFO a.m.m.c.a.r.NumericalFeatureRule$ - generated `number` feature for item field vote_avg in the range 3.2..8.7
15:32:15.705 INFO a.m.m.c.a.r.NumericalFeatureRule$ - generated `number` feature for item field release_date in the range 3.18816E8..1.56168E9
15:32:15.707 INFO a.m.m.c.a.r.NumericalFeatureRule$ - generated `number` feature for item field budget in the range 0.0..3.8E8
15:32:15.708 INFO a.m.m.c.a.r.NumericalFeatureRule$ - generated `number` feature for item field vote_cnt in the range 15.0..30232.0
15:32:15.709 INFO a.m.m.c.a.r.NumericalFeatureRule$ - generated `number` feature for item field runtime in the range 3.0..242.0
15:32:15.719 INFO a.m.m.c.a.rules.StringFeatureRule - field writer is not looking like a categorial value, skipping
15:32:15.726 INFO a.m.m.c.a.rules.StringFeatureRule - field tags is not looking like a categorial value, skipping
15:32:15.728 INFO a.m.m.c.a.rules.StringFeatureRule - field director is not looking like a categorial value, skipping
15:32:15.730 INFO a.m.m.c.a.rules.StringFeatureRule - field title is not looking like a categorial value, skipping
15:32:15.731 INFO a.m.m.c.a.rules.StringFeatureRule - item field genres has 19 distinct values, generated 'string' feature with index encoding for top 11 items
15:32:15.734 INFO a.m.m.c.a.rules.StringFeatureRule - field actors is not looking like a categorial value, skipping
15:32:15.735 INFO a.m.m.c.a.rules.RelevancyRule$ - skipped generating relevancy feature: non_zero=0 min=Some(0.0) max=Some(0.0)
15:32:15.851 INFO ai.metarank.main.Main$ - My job is done, exiting.
Process finished with exit code 0
Supported heuristics
Metarank has multiple sets of heuristics to generate feature configuration, toggled by the --ruleset CLI option:
stable: a default one, ruleset with less agressive heuristics, proven to be safe in production use.
all: generates all features it can, even the problematic ones (like CTR, which may introduce biases).
The following stable heuristics are supported:
- source: item.budget
type: number
name: budget
scope: item
- name: genres
type: string
source: item.genres
scope: item
encode: index
values:
- drama
- comedy
- thriller
- action
- adventure
- romance
- crime
- science fiction
- fantasy
- family
- horror
If you have a lot of distinct categories, and Metarank does not pick them up (e.g. decides that a category is too infrequent, and you get much less possible categories than expected), you can lower the category frequency threshold with a --cat-threshold flag.
The default --cat-threshold value of 0.003 means that only categories with frequencies above 0.3% are included.
Vector: all numerical vectors are transformed into statically-sized features. Vectors of static size are passed through as-is, and variable-length vectors are reduced into a quadruplets of [min, max, size, avg] values:
- name: embedding
type: vector
field: item.embedding // must be a singular number or a list of numbers
scope: item
# which reducers to use. optional. Default: [min, max, size, avg]
reduce: [vector16]
The all ruleset contains all stable heuristics with an addition of a couple of extra ones:
The main difference between two rulesets is the lack of rate/window_count features, which is made deliberately:
rate/window_count features usually introduce a popularity bias to the final ranking: as people tend to click more on popular items, ML model may attempt to always put popular items on top just because they're popular.
this behavior may be not that bad from the business KPI standpoint, but may make your ranking more static and less affected by past visitor actions.
For a dataset, for example, it will emit the following:
Numeric: all numerical item fields are encoded as a feature. So for a numeric field budget describing a movie budget in dollars, it will generate the following feature extractor defitition:
String: string item fields with low-cardinality are encoded as a feature. So movie genres field is a good candidate for this type of heuristic due to its low cardinality:
InteractedWith: all interaction over low-cardinality fields are translated to feature. So if a user clicked on an item with horror genre, other horror movies may get extra points:
Relevancy: if rankings with non-zero relevancy are present, then a feature is built:
Rate: For all interaction types a feature is generated over multiple typical time windows:
InteractionCount: all interaction types are wrapped into feature.
