Rival

Config-driven ML benchmarking CLI. Compare multiple machine learning models on the same dataset with a single command. Deterministic, minimal, no nonsense.

Installation

pip install rivalml

For XGBoost support:

pip install rivalml[xgb]

Usage

YAML Config (recommended)

# experiment.yaml
dataset:
  path: data.csv
  target: target_column

models:
  - logistic_regression
  - random_forest
  - xgboost

metrics:
  - accuracy
  - precision
  - recall
  - f1

training:
  test_size: 0.2
  random_seed: 42

rival run --config experiment.yaml

CLI Args (legacy, still supported)

rival run --dataset data.csv --target target_column --models lr,rf,xgb

Options

Flag	Default	Description
--config / -c	(none)	Path to YAML experiment config
--dataset / -d	(none)	Path to CSV file
--target / -t	(none)	Target column name
--models / -m	lr,rf	Comma-separated: lr, rf, xgb
--missing	fill_mean	Missing value strategy: fill_mean or drop
--seed / -s	42	Random seed for reproducibility
--test-size	0.2	Test split ratio
--output / -o	(none)	Export results as JSON

Example Output

------------------------------------------------------------
MODEL      ACCURACY   PRECISION   RECALL     F1         LATENCY
------------------------------------------------------------
LR         0.8523     0.8461      0.8300     0.8380     4.2ms
RF         0.8912     0.8850      0.8720     0.8784     11.8ms
XGB        0.9076     0.9031      0.8900     0.8965     17.3ms
------------------------------------------------------------

Supported Models

Alias	Full name	Backend
lr	logistic_regression	scikit-learn
rf	random_forest	scikit-learn
xgb	xgboost	XGBoost (optional)

Both short (lr) and full (logistic_regression) names work everywhere.

Motivation

Compare ML models easily. No boilerplate, no notebooks, no scattered scripts. One command (or one YAML file), clean output, deterministic results.