The python package auton-survival is repository of reusable utilities for projects
involving censored Time-to-Event Data. auton-survival provides a flexible APIs
allowing rapid experimentation including dataset preprocessing, regression,
counterfactual estimation, clustering and phenotyping and propensity adjusted evaluation.
For complete details on auton-survival see:
Survival Analysis involves estimating when an event of interest, ( T ) would take places given some features or covariates ( X ). In statistics and ML these scenarious are modelled as regression to estimate the conditional survival distribution, ( \mathbb{P}(T>t|X) ). As compared to typical regression problems, Survival Analysis differs in two major ways:
- The Event distribution, ( T ) has positive support ie. ( T \in [0, \infty) ).
- There is presence of censoring ie. a large number of instances of data are lost to follow up.
Training a Deep Cox Proportional Hazards Model with auton-survival
from auton_survival import datasets, preprocessing, models
# Load the SUPPORT Dataset
outcomes, features = datasets.load_dataset("SUPPORT")
# Preprocess (Impute and Scale) the features
features = preprocessing.Preprocessor().fit_transform(features)
# Train a Deep Cox Proportional Hazards (DCPH) model
model = models.cph.DeepCoxPH(layers=[100])
model.fit(features, outcomes.time, outcomes.event)
# Predict risk at specific time horizons.
predictions = model.predict_risk(features, t=[8, 12, 16])This module provides a wrapper auton_survival.estimators.SurvivalModel to model
survival datasets with standard survival (time-to-event) analysis methods.
The use of the wrapper allows a simple standard interface for multiple different
survival regression methods.
auton_survival.estimators also provides convenient wrappers around other popular
python survival analysis packages to experiment with Random Survival Forests and
Weibull Accelerated Failure Time regression models.
from auton_survival import estimators
# Train a Deep Survival Machines model using the SurvivalModel class.
model = estimators.SurvivalModel(model='dsm')
model.fit(features, outcomes)
# Predict risk at time horizons.
predictions = model.predict_risk(features, times=[8, 12, 16])Modules to perform standard survival analysis experiments. This module
provides a top-level interface to run auton-survival style experiments
of survival analysis, involving cross-validation style experiments with
multiple different survival analysis models
# auton-survival Style Cross Validation Experiment.
from auton_survival.experiments import SurvivalRegressionCV
# Define the Hyperparameter grid to perform Cross Validation
hyperparam_grid = {'n_estimators' : [50, 100], 'max_depth' : [3, 5],
'max_features' : ['sqrt', 'log2']}
# Train a RSF model with cross-validation using the SurvivalRegressionCV class
model = SurvivalRegressionCV(model='rsf', cv_folds=5, hyperparam_grid=hyperparam_grid)
model.fit(features, outcomes)auton_survival.phenotyping allows extraction of latent clusters or subgroups
of patients that demonstrate similar outcomes. In the context of this package,
we refer to this task as phenotyping. auton_survival.phenotyping allows:
- Unsupervised Phenotyping: Involves first performing dimensionality reduction on the inpute covariates ( x ) followed by the use of a clustering algorithm on this representation.
from auton_survival.phenotyping import ClusteringPhenotyper
# Dimensionality reduction using Principal Component Analysis (PCA) to 8 dimensions.
dim_red_method, = 'pca', 8
# We use a Gaussian Mixture Model (GMM) with 3 components and diagonal covariance.
clustering_method, n_clusters = 'gmm', 3
# Initialize the phenotyper with the above hyperparameters.
phenotyper = ClusteringPhenotyper(clustering_method=clustering_method,
dim_red_method=dim_red_method,
n_components=n_components,
n_clusters=n_clusters)
# Fit and infer the phenogroups.
phenotypes = phenotyper.fit_phenotype(features)
# Plot the phenogroup specific Kaplan-Meier survival estimate.
auton_survival.reporting.plot_kaplanmeier(outcomes, phenotypes)-
Factual Phenotyping: Involves the use of structured latent variable models,
auton_survival.models.dcm.DeepCoxMixturesorauton_survival.models.dsm.DeepSurvivalMachinesto recover phenogroups that demonstrate differential observed survival rates. -
Counterfactual Phenotyping: Involves learning phenotypes that demonstrate heterogenous treatment effects. That is, the learnt phenogroups have differential response to a specific intervention. Relies on the specially designed
auton_survival.models.cmhe.DeepCoxMixturesHeterogenousEffectslatent variable model.
Helper functions to load and prerocsss various time-to-event data like the
popular SUPPORT, FRAMINGHAM and PBC dataset for survival analysis.
# Load the SUPPORT Dataset
from auton_survival import dataset
features, outcomes = datasets.load_dataset('SUPPORT')This module provides a flexible API to perform imputation and data
normalization for downstream machine learning models. The module has
3 distinct classes, Scaler, Imputer and Preprocessor. The Preprocessor
class is a composite transform that does both Imputing and Scaling with
a single function call.
# Preprocessing loaded Datasets
from auton_survival import datasets
features, outcomes = datasets.load_topcat()
from auton_survival.preprocessing import Preprocessing
features = Preprocessor().fit_transform(features,
cat_feats=['GENDER', 'ETHNICITY', 'SMOKE'],
num_feats=['height', 'weight'])
# The `cat_feats` and `num_feats` lists would contain all the categorical and
# numerical features in the dataset.Helper functions to generate standard reports for common Survival Analysis tasks.
Please cite the following if you use auton-survival:
@article{nagpal2022autonsurvival,
url = {https://arxiv.org/abs/2204.07276},
author = {Nagpal, Chirag and Potosnak, Willa and Dubrawski, Artur},
title = {auton-survival: an Open-Source Package for Regression,
Counterfactual Estimation, Evaluation and Phenotyping with
Censored Time-to-Event Data},
publisher = {arXiv},
year = {2022},
}
Additionally, models and methods in auton_survival come from the following papers.
Please cite the individual papers if you employ them in your research:
@article{nagpal2021dsm,
title={Deep survival machines: Fully parametric survival regression and representation learning for censored data with competing risks},
author={Nagpal, Chirag and Li, Xinyu and Dubrawski, Artur},
journal={IEEE Journal of Biomedical and Health Informatics},
volume={25},
number={8},
pages={3163--3175},
year={2021},
publisher={IEEE}
}
[2] Deep Parametric Time-to-Event Regression with Time-Varying Covariates. AAAI Spring Symposium (2021)
@InProceedings{pmlr-v146-nagpal21a,
title={Deep Parametric Time-to-Event Regression with Time-Varying Covariates},
author={Nagpal, Chirag and Jeanselme, Vincent and Dubrawski, Artur},
booktitle={Proceedings of AAAI Spring Symposium on Survival Prediction - Algorithms, Challenges, and Applications 2021},
series={Proceedings of Machine Learning Research},
publisher={PMLR},
}
[3] Deep Cox Mixtures for Survival Regression. Conference on Machine Learning for Healthcare (2021)
@inproceedings{nagpal2021dcm,
title={Deep Cox mixtures for survival regression},
author={Nagpal, Chirag and Yadlowsky, Steve and Rostamzadeh, Negar and Heller, Katherine},
booktitle={Machine Learning for Healthcare Conference},
pages={674--708},
year={2021},
organization={PMLR}
}
[4] Counterfactual Phenotyping with Censored Time-to-Events (2022)
@article{nagpal2022counterfactual,
title={Counterfactual Phenotyping with Censored Time-to-Events},
author={Nagpal, Chirag and Goswami, Mononito and Dufendach, Keith and Dubrawski, Artur},
journal={arXiv preprint arXiv:2202.11089},
year={2022}
}
foo@bar:~$ git clone https://github.com/autonlab/auton_survival
foo@bar:~$ pip install -r requirements.txtauton-survival requires python 3.5+ and pytorch 1.1+.
To evaluate performance using standard metrics
auton-survival requires scikit-survival.
auton-survival is on GitHub. Bug reports and pull requests are welcome.
MIT License
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