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
                __                              __    
  _____   _____/  |______ ____________    ____ |  | __
 /     \_/ __ \   __\__  \\_  __ \__  \  /    \|  |/ /
|  Y Y  \  ___/|  |  / __ \|  | \// __ \|   |  \    < 
|__|_|  /\___  >__| (____  /__|  (____  /___|  /__|_ \
      \/     \/          \/           \/     \/     \/ 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.

  • 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 your data.

  • export: export the training dataset for further hyperparam optimization.

  • termfreq: compute term frequency dictionary for BM25 field_match extractor

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 single 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 unsorted_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=ndcg@10
seed=0
data=train.svm
test:data=test.svm
eval[train=train.svm
eval[test]=test.svm

$> xgboost xgboost.conf
[13:00:09] [0] train-ndcg@10:0.58637041553234925      test-ndcg@10:0.54517512609937979
[13:00:09] [1] train-ndcg@10:0.60275740110564990      test-ndcg@10:0.55988540039586532
[13:00:09] [2] train-ndcg@10:0.61207514143863639      test-ndcg@10:0.56497256071656654
[13:00:10] [3] train-ndcg@10:0.61507850860846780      test-ndcg@10:0.56668620039370876
[13:00:10] [4] train-ndcg@10:0.61723381498663543      test-ndcg@10:0.56796994649060073

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 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:

  - name: title_match
    type: field_match
    rankingField: ranking.query
    itemField: item.title
    method:
      type: bm25
      language: english
      termFreq: "/path/to/term-freq.json"

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

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