Metarank CLI has a set of command-line options to control its behavior.
To run the main app, download the latest jar file and run the following command:
java -jar metarank-x.x.x.jar
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\/ \/ \/ \/ \/ \/ Metarank v:unknown
Usage: metarank <subcommand> <options>
Options:
-h, --help Show help message
-v, --version Show version of this program
Subcommand: import - import historical clickthrough data
-c, --config <arg> path to config file
-d, --data <arg> path to an input file
-f, --format <arg> input file format: json, snowplow, snowplow:tsv,
snowplow:json (optional, default=json)
-o, --offset <arg> offset: earliest, latest, ts=1663171036, last=1h
(optional, default=earliest)
-s, --sort-files-by <arg> how should multiple input files be sorted
(optional, default: name, values:
[name,last-modified]
-v, --validation <arg> should input validation be enabled (optional,
default=false)
-h, --help Show help message
Subcommand: train - train the ML model
-c, --config <arg> path to config file
-m, --model <arg> model name to train
-s, --split <arg> train/test splitting strategy (optional, default:
time=80%, options: random=N%,time=N%,hold_last=N%)
-h, --help Show help message
Subcommand: serve - run the inference API
-c, --config <arg> path to config file
-h, --help Show help message
Subcommand: standalone - import, train and serve at once
-c, --config <arg> path to config file
-d, --data <arg> path to an input file
-f, --format <arg> input file format: json, snowplow, snowplow:tsv,
snowplow:json (optional, default=json)
-o, --offset <arg> offset: earliest, latest, ts=1663171036, last=1h
(optional, default=earliest)
-s, --sort-files-by <arg> how should multiple input files be sorted
(optional, default: name, values:
[name,last-modified]
-v, --validation <arg> should input validation be enabled (optional,
default=false)
-h, --help Show help message
Subcommand: validate - run the input data validation suite
-c, --config <arg> path to config file
-d, --data <arg> path to an input file
-f, --format <arg> input file format: json, snowplow, snowplow:tsv,
snowplow:json (optional, default=json)
-o, --offset <arg> offset: earliest, latest, ts=1663171036, last=1h
(optional, default=earliest)
-s, --sort-files-by <arg> how should multiple input files be sorted
(optional, default: name, values:
[name,last-modified]
-v, --validation <arg> should input validation be enabled (optional,
default=false)
-h, --help Show help message
Subcommand: sort - sort the dataset by timestamp
-d, --data <arg> path to a file/directory with input files
-o, --out <arg> path to an output file
-h, --help Show help message
Subcommand: autofeature - generate reference config based on existing data
-c, --cat-threshold <arg> min threshold of category frequency, when its
considered a catergory (optional, default=0.003)
-d, --data <arg> path to an input file
-f, --format <arg> input file format: json, snowplow, snowplow:tsv,
snowplow:json (optional, default=json)
-o, --offset <arg> offset: earliest, latest, ts=1663171036, 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])
-s, --sort-files-by <arg> how should multiple input files be sorted
(optional, default: name, values:
[name,last-modified]
-v, --validation <arg> should input validation be enabled (optional,
default=false)
-h, --help Show help message
Subcommand: export - export training dataset for hyperparameter optimization
-c, --config <arg> path to config file
-m, --model <arg> model name to export data for
-o, --out <arg> a directory to export model training files
--sample <arg> sampling ratio of exported training click-through events
-s, --split <arg> train/test splitting strategy (optional, default:
time=80%, options: random=N%,time=N%,hold_last=N%)
-h, --help Show help message
Subcommand: termfreq - compute term frequencies for the BM25 field_match extractor
-d, --data <arg> path to an input file
-f, --fields <arg> Comma-separated list of text fields
-l, --language <arg> Language to use for tokenization, stemming and
stopwords
-o, --out <arg> an file to write term-freq dict to
-h, --help Show help message
For all other tricks, consult the docs on https://docs.metarank.ai
The command-line argument structure is:
java -jar metarank.jar <command> <args>
Running modes
Metarank CLI has a set of different running modes:
import: import and process historical data, writing state to the chosen persistence backend like Redis.
The above command will output validation checks performed on the files provided and will output information similar to the following:
17:46:45.790 INFO ai.metarank.config.Config$ - api conf block is not defined: using default ApiConfig(Hostname(localhost),Port(8080))
17:46:45.793 INFO ai.metarank.config.Config$ - state conf block is not defined: using default MemoryStateConfig()
17:46:45.798 INFO ai.metarank.config.Config$ - Loaded config file, state=memory, features=[popularity,vote_avg,vote_cnt,budget,release_date,runtime,title_length,genre,ctr,liked_genre,liked_actors,liked_tags,liked_director,visitor_click_count,global_item_click_count,day_item_click_count], models=[xgboost]
17:46:45.874 INFO ai.metarank.FeatureMapping - optimized schema: removed 6 unused features
17:46:46.023 INFO ai.metarank.main.command.Validate$ - Dataset validation is enabled
17:46:46.024 INFO ai.metarank.main.command.Validate$ - Validation loads all events to RAM, so use --validation=false to skip in case of OOM
17:46:46.085 INFO ai.metarank.source.FileEventSource - path=events.jsonl.gz is a file
17:46:46.144 INFO ai.metarank.source.FileEventSource - file events.jsonl.gz selected=true (timeMatch=true formatMatch=true)
17:46:46.146 INFO ai.metarank.source.FileEventSource - reading file events.jsonl.gz (with gzip decompressor)
17:46:55.240 INFO ai.metarank.main.command.Validate$ - Validation done
17:46:55.280 INFO a.m.v.checks.EventOrderValidation$ - Event ordering check = PASS (58437 events sorted by timestamp)
17:46:55.351 INFO a.m.v.checks.EventTypesValidation$ - event types check = PASS (2512 item events, 9800 rankings, 46125 interactions)
17:46:55.441 INFO a.m.v.c.FeatureOverMissingFieldValidation$ - field reference check = PASS (16 features referencing existing 12 event fields)
17:46:55.509 INFO a.m.v.c.InteractionKeyValidation$ - interaction-ranking join key check = PASS (9800 rankings, all interactions reference existing ones)
17:46:55.603 ERROR a.m.v.c.InteractionMetadataValidation$ - Interaction metadata check: FAIL (96 interaction happened on never seen items)
17:46:55.604 ERROR a.m.v.c.InteractionMetadataValidation$ - examples: List(ecc0c55e-8e30-4f7d-8c7e-26f05951300b, 22dda925-8dec-46e0-98d2-bc6a32dcdaec, 40342ea0-1575-417f-8c3d-d3e8dcdfa7f5, 1c0504a5-8e2c-4a0d-be92-b9930df8d041, bb9a79b2-689c-492b-b758-9f1ce0fade09, c2b5d050-3dae-4b24-a7ba-1652d6f4b2e2, 0689b95a-4532-4d05-b87d-e69daed6c910, 5592ff1e-cccd-4157-8c7e-052242c56d0b, d9d1fd91-0629-4623-af2d-1a0682ee62a7, 0d9ef491-ef19-4003-9f20-09d0daea481a)
17:46:55.827 INFO a.m.v.c.InteractionPositionValidation$ - interaction positions check = PASS (int distribution: [2435,2309,2199,2068,2005,2008,1970,2003,1890,1925,1933,1847,1807,1778,1790,1781,1787,1778,1713,1783,1745,1789,1791,1991]
17:46:55.858 INFO a.m.v.c.InteractionTypeValidation$ - interaction type check = PASS (46125 interactions have known types: Set(click))
17:46:55.859 INFO ai.metarank.main.Main$ - My job is done, exiting.
Historical data sorting
Metarank expects your historical data to be ordered by the timestamp in the ascending order. If for any reason, you cannot generate a sorted file, the sort sub-command can do the job for you.
You can sort both single files and folders with multiple files. In case of folders, sort command will merge all data into one sorted file.
If you don't know what features to include in the configuration file, the autofeature sub-command can generate the configuration for you based on the historical data you have.
if the --model <name> option is not given, then Metarank will train all the defined models sequentially.
While training the model, Metarank will split your data into train/validation datasets with the following supported splitting strategies:
random: shuffle all the training samples and take N% as a training part. May result in an implicit model leakage, when information about the future was leaked in the training set.
An example: on Christmas items with Santa are selling much better (and not selling at all afterwards), and leaking this knowledge into your training set will result in better offline scores (as model knows that Christmas is coming). In production, it will behave significantly worse, as there is no way to predict the future out of the training data anymore.
time: sort all training samples by timestamp and pick first N% as training set (the default option).
hold_last: group all samples by user, and sort per-user samples by timestamp. N% first samples within each user are picked into the training dataset.
Has the same issue with future leaking in the model, but optimizes the train dataset to focus on last user click.
The format of split strategy CLI flag is --strategy name=ratio%. For example:
random with 90% ratio: --split random=90%
random with a default 80% ratio: --split random
Dataset export
Metarank can emit CSV/LibSVM formatted datasets and corresponding config files for LightGBM and XGBoost, so you can later perform a hyper-parameter optimization using your favourite tool:
Metarank export format is dependent on model backend type:
For XGBoost, we export a LibSVM-encoded train/test files with embedded qid. This format is not compatible with the LightGBM LibSVM reader implementation (and we were unable to make it work with both). An example:
for LightGBM, we export a CSV-encoded train/test files with header. XGBoost (as for version 1.70) cannot load query information from CSV files, so it cannot be used for LambdaMART. Example:
For both booster implementations Metarank also emits a corresponding config file with all default values filled in. You can just run the lightgbm/xgboost cli tool externally to replicate what's Metarank is doing.
$> ls -l
total 39740
-rw-r--r-- 1 shutty shutty 219 Nov 2 13:01 lightgbm.conf
-rw-r--r-- 1 shutty shutty 6845983 Nov 2 13:01 test.csv
-rw-r--r-- 1 shutty shutty 28453327 Nov 2 13:01 train.csv
$> cat lightgbm.conf
objective=lambdarank
data=train.csv
valid=test.csv
num_iterations=5
learning_rate=0.1
seed=0
max_depth=8
header=true
label_column=name:label
group_column=name:group
lambdarank_truncation_level=10
metric=ndcg
eval_at=10
$> lightgbm config=lightgbm.conf
[LightGBM] [Info] Warning: last line of lightgbm.conf has no end of line, still using this line
[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).
[LightGBM] [Info] Finished loading parameters
[LightGBM] [Info] Using column label as label
[LightGBM] [Info] Using column group as group/query id
[LightGBM] [Info] Construct bin mappers from text data time 0.14 seconds
[LightGBM] [Info] Finished loading data in 0.279901 seconds
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.020266 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1837
[LightGBM] [Info] Number of data points in the train set: 164232, number of used features: 29
[LightGBM] [Info] Finished initializing training
[LightGBM] [Info] Started training...
[LightGBM] [Info] Iteration:1, valid_1 ndcg@10 : 0.561739
[LightGBM] [Info] 0.009204 seconds elapsed, finished iteration 1
[LightGBM] [Info] Iteration:2, valid_1 ndcg@10 : 0.572802
[LightGBM] [Info] 0.017980 seconds elapsed, finished iteration 2
[LightGBM] [Info] Iteration:3, valid_1 ndcg@10 : 0.576812
[LightGBM] [Info] 0.032011 seconds elapsed, finished iteration 3
[LightGBM] [Info] Iteration:4, valid_1 ndcg@10 : 0.582428
[LightGBM] [Info] 0.045455 seconds elapsed, finished iteration 4
[LightGBM] [Info] Iteration:5, valid_1 ndcg@10 : 0.582633
[LightGBM] [Info] 0.052650 seconds elapsed, finished iteration 5
[LightGBM] [Info] Finished training
Metarank supports the same train/test split strategies for export subcommand as for the train one.
BM25 term frequencies dictionary
To use the BM25 score in the field_match, you need to compute a bit of statistics over your textual information.
To do so, run the termfreq subcommand:
$> java -jar meratank.jar termfreq --data <path-to-data>\
--out /term-freq.json --fields title,description --language en
INFO ai.metarank.main.Main$ - Metarank vunknown is starting.
INFO ai.metarank.source.FileEventSource - path=src/test/resources/ranklens/events/events.jsonl.gz is a file
INFO ai.metarank.source.FileEventSource - file src/test/resources/ranklens/events/events.jsonl.gz selected=true (timeMatch=true formatMatch=true)
INFO ai.metarank.source.FileEventSource - reading file src/test/resources/ranklens/events/events.jsonl.gz (with gzip decompressor)
INFO ai.metarank.flow.PrintProgress$ - processed 0 events, perf=0rps GC=9.54% heap=0.82%/7.82G
INFO ai.metarank.flow.PrintProgress$ - processed 2048 events, perf=1557rps GC=2.51% heap=2.52%/7.82G
INFO ai.metarank.flow.PrintProgress$ - processed 14336 events, perf=12130rps GC=0.0% heap=6.44%/7.82G
INFO ai.metarank.flow.PrintProgress$ - processed 37888 events, perf=23296rps GC=0.3% heap=1.71%/7.82G
INFO ai.metarank.main.command.TermFreq$ - built term-freq lang=en fields=[title, description] terms=11560
INFO ai.metarank.main.command.TermFreq$ - writing /tmp/tf.json, size=204 KB
INFO ai.metarank.main.command.TermFreq$ - done
INFO ai.metarank.main.command.TermFreq$ - done
INFO ai.metarank.main.Main$ - My job is done, exiting.
With the resulting term-freq.json file you can configure the BM25 score extractor in the following way:
The same dictionary can be used for multiple field_match extractors, for example when you want to have separate BM25 scores for query-title and query-description matches.
Environment variables
Config file can be passed to the Metarank not only as a command-line argument, but also as an environment variable. This is typically used in docker and k8s-based deployments:
METARANK_CONFIG: path to config file, for example s3://bucket/prefix/config.yml
