rankerEval 0.2.0

A fast implementation of ranking metrics for information retrieval and recommendation.

A fast numpy/numba-based implementation of ranking metrics for information retrieval and recommendation. Coded with efficiency in mind and support for edge cases.

Find the full documentation here.

Features

• Wide array of evaluation metrics for information retrieval and top-N recommender systems:

  • Binary labels: Recall, Precision, MAP, HitRate, MRR, MeanRanks, F1

  • Numeric and binary labels: DCG, nDCG

• Minimal dependencies: Numpy and Numba (required), SciPy (optional)

• Flexible input formats: Supports arrays, lists and sparse matrices

• Built-in support for confidence intervals via bootstrapping

Usage

from rankereval import BinaryLabels, Rankings, Recall

y_true = BinaryLabels.from_positive_indices([[0,2], [0,1,2]])
y_pred = Rankings.from_ranked_indices([[2,1], [1]])

recall_at_3 = Recall(3).mean(y_true, y_pred)
print(recall_at_3["score"])

To get confidence intervals (95% by default), specify conf_interval=True:

recall_at_3 = Recall(3).mean(y_true, y_pred, conf_interval=True)
print(recall_at_3["conf_interval"])

Input formats

RankerEval allows for a variety of input formats, e.g.,

# specify all labels as lists
y_true = BinaryLabels.from_dense([[1,0,1], [1,1,1]])

# specify labels as numpy array
y_true = BinaryLabels.from_dense(np.asarray([[1,0,1], [1,1,1]]))

# or use a sparse matrix
import scipy.sparse as sp
y_true = BinaryLabels.from_sparse(sp.coo_matrix([[1,0,1], [1,1,1]]))

Installation

To install (requires Numpy 1.18 or newer):

pip install rankereval

Licence

This project is licensed under MIT.

Authors

RankerEval was written by Tobias Schnabel.

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

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