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Command-line options

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
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 persistecnce backend like Redis.
  • train: train the ML model with XGBoost/LightGBM.
  • serve: run the inference API to do realtime reranking
  • standalone: run import, train and serve tasks at once.
  • validate: validates data nd configuration files.
  • sort: pre-sorts the dataset by timestamp.
  • autofeature: automatically generates feature configuration based on yourr data.
  • export: export the training dataset for further hyperparam optimization.

Validation

Metarank CLI provides validate command to validate both your data and configuration file.
You will need to provide both data and configuration files
java -jar metarank-x.x.x.jar validate --config config.yml --data events.jsonl.gz
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 signle files and folders with multiple files. In case of folders, sort command will merge all data into one sorted file.
Sorting one file is a simple as
java -jar metarank.jar sort --data unosrted_file.jsonl.gz --out sorted_file.jsonl.gz
You can do sorting with a folder as well
java -jar metarank.jar sort --data /my_folder --out sorted_file.jsonl.gz

Auto feature generation

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.
Simply run
java -jar metarank.jar autofeature --data /path/to/events.json --out /path/to/config.yaml
Check out more about autofeature sub-command in our Automatic feature engineering guide.

Training the model

You can train the underlying ML ranking model:
java -jar metarank.jar train --config /path/to/config.yaml
  • 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:
java -jar metarank.jar export --config /path/to/config.yaml --model <modelname> --out /export/dir
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:
1 qid:1 0:1.0 1:0.2
0 qid:1 0:0.5 1:0.3
1 qid:2 0:0.1 1:1.0
  • 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:
label,group,f1,f2
1.0,1,1.0,0.2
0.0,1,0.5,0.3
1.0,2,0.1,1.0
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.
For XGBoost:
$> ls -l
total 35760
-rw-r--r-- 1 shutty shutty 5852217 Nov 2 12:40 test.svm
-rw-r--r-- 1 shutty shutty 23830680 Nov 2 12:40 train.svm
-rw-r--r-- 1 shutty shutty 171 Nov 2 12:59 xgboost.conf
$> cat xgboost.conf
eta=0.1
max_depth=8
subsample=0.8
num_round=5
objective=rank:pairwise
eval_metric=[email protected]
seed=0
data=train.svm
test:data=test.svm
eval[train=train.svm
eval[test]=test.svm
$> xgboost xgboost.conf
[13:00:09] [0] [email protected]:0.58637041553234925 [email protected]:0.54517512609937979
[13:00:09] [1] [email protected]:0.60275740110564990 [email protected]:0.55988540039586532
[13:00:09] [2] [email protected]:0.61207514143863639 [email protected]:0.56497256071656654
[13:00:10] [3] [email protected]:0.61507850860846780 [email protected]:0.56668620039370876
[13:00:10] [4] [email protected]:0.61723381498663543 [email protected]:0.56796994649060073
For LightGBM:
$> 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 [email protected] : 0.561739
[LightGBM] [Info] 0.009204 seconds elapsed, finished iteration 1
[LightGBM] [Info] Iteration:2, valid_1 [email protected] : 0.572802
[LightGBM] [Info] 0.017980 seconds elapsed, finished iteration 2
[LightGBM] [Info] Iteration:3, valid_1 [email protected] : 0.576812
[LightGBM] [Info] 0.032011 seconds elapsed, finished iteration 3
[LightGBM] [Info] Iteration:4, valid_1 [email protected] : 0.582428
[LightGBM] [Info] 0.045455 seconds elapsed, finished iteration 4
[LightGBM] [Info] Iteration:5, valid_1 [email protected] : 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.

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