survmetr
Advanced predictive metrics for survival analysis, focused on Antolini's time-dependent concordance index.
Installation
pip install survmetr
For numba-accelerated computation:
pip install survmetr[numba]
Quick Start
import numpy as np
from sksurv.util import Surv
from survmetr import c_index_antolini
# Create survival data
y = Surv.from_arrays(
event=[True, True, False, True, False],
time=[1.0, 3.0, 4.0, 5.0, 6.0],
)
n_events = y['event'].sum()
# Prediction matrix: (n_samples, n_events) failure probabilities
estimate = np.random.rand(len(y), n_events)
# Compute Antolini's time-dependent C-index
c = c_index_antolini(estimate, y)
print(f"C-index: {c:.3f}")
# Get detailed statistics
result = c_index_antolini(estimate, y, return_all=True)
print(result)
# {'c-index': ..., 'concordant': ..., 'comparable': ...,
# 'tied_risk': ..., 'discordant': ..., 'numerator': ...}
Available Metrics
| Function | Description |
|---|---|
c_index_antolini |
Antolini's time-dependent C-index (alias for vector impl) |
c_index_antolini_vector |
Vectorized NumPy implementation |
c_index_antolini_sksurv |
Implementation using scikit-survival internals |
c_index_antolini_pycox |
Numba-JIT parallel implementation |
make_survival_scorer |
Factory for time-dependent classification scorers |
harrel_c_index_scorer |
Harrell's standard C-index scorer |
AntoliniCIndexVecScorer |
Dataclass scorer for the vector implementation |
split_y |
Extract event/time from structured arrays |
Tutorial
See Tutorial.ipynb for a detailed walkthrough.
License
This project is licensed under the GNU Lesser General Public License v3.0 - see the LICENSE file for details.
1 maintainer
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