Run

Run stores the relevance scores estimated by the model under evaluation. There is no constraint on the score values, i.e., zero and negative scores are not removed. The preferred way for creating a Run instance is converting a Python dictionary as follows:

from ranx import Run

run_dict = {
    "q_1": {
        "d_1": 1.5,
        "d_2": 2.6,
    },
    "q_2": {
        "d_3": 2.8,
        "d_2": 1.2,
        "d_5": 3.1,
    },
}

run = Run(run_dict, name="bm25")

Runs can also be loaded from TREC-style and JSON files, and from Pandas DataFrames.

Load from Files

Parse a run file into ranx.Run.
Supported formats are JSON, TREC run, gzipped TREC run, and LZ4.
Correct import behavior is inferred from the file extension: .json -> json, .trec -> trec, .txt -> trec, .gz -> trec, .lz4 -> lz4.
Use the argument kind to override the default behavior. Use the argument name to set the name of the run. Default is None.

run = Run.from_file("path/to/run.json")  # JSON file
run = Run.from_file("path/to/run.trec")  # TREC-Style file
run = Run.from_file("path/to/run.txt")   # TREC-Style file with txt extension
run = Run.from_file("path/to/run.gz")    # Gzipped TREC-Style file
run = Run.from_file("path/to/run.lz4")    # lz4 file produced by saving a ranx.Run as lz4
run = Run.from_file("path/to/run.custom", kind="json")  # Loaded as JSON file

Load from Pandas DataFrames

ranx can load runs from Pandas DataFrames.
The argument name is used to set the name of the run. Default is None.

from pandas import DataFrame

run_df = DataFrame.from_dict({
    "q_id":   [ "q_1",  "q_1",  "q_2",  "q_2"  ],
    "doc_id": [ "d_12", "d_25", "d_11", "d_22" ],
    "score":  [  0.5,    0.3,    0.6,    0.1   ],
})

run = Run.from_df(
    df=run_df,
    q_id_col="q_id",
    doc_id_col="doc_id",
    score_col="score",
    name="my_run",
)

Load from Parquet files

ranx can load runs from Parquet files, even from remote sources.
You can control the behavior of the underlying pandas.read_parquet function by passing additional arguments through the pd_kwargs argument (see https://pandas.pydata.org/docs/reference/api/pandas.read_parquet.html).
The argument name is used to set the name of the run. Default is None.

run = Run.from_parquet(
    path="/path/to/parquet/file""",
    q_id_col="q_id",
    doc_id_col="doc_id",
    score_col="score",
    pd_kwargs=None,
    name="my_run",
)

Save

Write run to path as JSON file, TREC run, LZ4 file, or Parquet file.
File type is automatically inferred form the filename extension: .json -> json, .trec -> trec, .txt -> trec, and .lz4 -> lz4, .parq -> parquet, .parquet -> parquet.
Use the kind argument to override this behavior.

run.save("path/to/run.json")     # Save as JSON file
run.save("path/to/run.trec")     # Save as TREC-Style file
run.save("path/to/run.txt")      # Save as TREC-Style file with txt extension
run.save("path/to/run.lz4")      # Save as lz4 file
run.save("path/to/run.parq")     # Save as Parquet file
run.save("path/to/run.parquet")  # Save as Parquet file
run.save("path/to/run.custom", kind="json")  # Save as JSON file

Make comparable

It adds empty results for queries missing from the run and removes those not appearing in qrels.

run.make_comparable(qrels)