Time-to-event prediction with PyTorch
Get Started • Methods • Evaluation Criteria • Datasets • Installation • References
pycox is a python package for survival analysis and time-to-event prediction with PyTorch.
The package contains implementations of various survival models, some useful evaluation metrics, and a collection of event-time datasets.
In addition, some useful preprocessing tools are available in the pycox.preprocessing
module.
To get started you first need to install the package.
We then recommend to start with THIS INTRODUCTION NOTEBOOK, which explains the general usage of the package in terms of preprocessing, creation of neural networks, model training, and evaluation procedure.
The notebook use the LogisticHazard
method for illustration, but most of the principles generalize to the other methods.
Alternatively, there are many examples listed in the examples folder.
The following methods are available in the pycox.methods
module.
Method | Description | Example |
---|---|---|
CoxTime | Cox-Time is a relative risk model that extends Cox regression beyond the proportional hazards [1]. | notebook |
CoxCC | Cox-CC is a proportional version of the Cox-Time model [1]. | notebook |
CoxPH (DeepSurv) | CoxPH is a Cox proportional hazards model also referred to as DeepSurv [2]. | notebook |
PCHazard | The Piecewise Constant Hazard (PC-Hazard) model [12] assumes that the continuous-time hazard function is constant in predefined intervals. It is similar to the Piecewise Exponential Models [11] and PEANN [14], but with a softplus activation instead of the exponential function. | notebook |
Method | Description | Example |
---|---|---|
LogisticHazard (Nnet-survival) | The Logistic-Hazard method parametrize the discrete hazards and optimize the survival likelihood [12] [7]. It is also called Partial Logistic Regression [13] and Nnet-survival [8]. | notebook |
PMF | The PMF method parametrize the probability mass function (PMF) and optimize the survival likelihood [12]. It is the foundation of methods such as DeepHit and MTLR. | notebook |
DeepHit, DeepHitSingle | DeepHit is a PMF method with a loss for improved ranking that can handle competing risks [3]. | single competing |
MTLR (N-MTLR) | The (Neural) Multi-Task Logistic Regression is a PMF methods proposed by [9] and [10]. | notebook |
The following evaluation metrics are available with pycox.evalutation.EvalSurv
.
Metric | Description |
---|---|
concordance_td | The time-dependent concordance index evaluated at the event times [4]. |
brier_score | The IPCW Brier score (inverse probability of censoring weighted Brier score) [5][6]. |
nbll | The IPCW (negative) binomial log-likelihood [5][1]. I.e., this is minus the binomial log-likelihood and should not be confused with the negative binomial distribution. |
integrated_brier_score | The integrated IPCW Brier score. Numerical integration of the `brier_score` [5][6]. |
integrated_nbll | The integrated IPCW (negative) binomial log-likelihood. Numerical integration of the `nbll` [5][1]. |
A collection of datasets are available through the pycox.datasets
module.
For example, the following code will download the metabric
dataset and load it in the form of a pandas dataframe
from pycox import datasets
df = datasets.metabric.read_df()
Dataset | Size | Dataset | Data source |
---|---|---|---|
flchain | 6,524 | The Assay of Serum Free Light Chain (FLCHAIN) dataset. See [1] for preprocessing. | source |
gbsg | 2,232 | The Rotterdam & German Breast Cancer Study Group. See [2] for details. | source |
kkbox_v1 | 2,646,746 | A survival dataset created from the WSDM - KKBox's Churn Prediction Challenge 2017. See [1] for details. Note: You need Kaggle credentials to access the dataset. | source |
metabric | 1,904 | The Molecular Taxonomy of Breast Cancer International Consortium (METABRIC). See [2] for details. | source |
nwtco | 4,028 | Data from the National Wilm's Tumor (NWTCO). | source |
support | 8,873 | Study to Understand Prognoses Preferences Outcomes and Risks of Treatment (SUPPORT). See [2] for details. | source |
Dataset | Size | Dataset | Data source |
---|---|---|---|
rr_nl_nph | 25,000 | Dataset from simulation study in [1]. This is a continuous-time simulation study with event times drawn from a relative risk non-linear non-proportional hazards model (RRNLNPH). | SimStudyNonLinearNonPH |
sac3 | 100,000 | Dataset from simulation study in [12]. This is a discrete time dataset with 1000 possible event-times. | SimStudySACCensorConst |
Note: This package is still in its early stages of development, so please don't hesitate to report any problems you may experience.
The package only works for python 3.6+.
Before installing pycox, please install PyTorch (version >= 1.1). You can then run the following command to install the package:
pip install git+git://github.com/havakv/pycox.git git+git://github.com/havakv/torchtuples.git
Installation from source depends on PyTorch, in addition to torchtuples which can be installed with
pip install git+git://github.com/havakv/torchtuples.git
Next, clone and install with
git clone https://github.com/havakv/pycox.git
cd pycox
python setup.py install
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