rankerEval 0.2.0

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"])

# 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

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