I recently started to use Google’s deep learning framework TensorFlow. Since version 1.3, TensorFlow includes a high-level interface inspired by scikit-learn. Unfortunately, as of version 1.4, only 3 different classification and 3 different regression models implementing the Estimator interface are included. To better understand the Estimator interface, Dataset API, and components in tf-slim, I started to implement a simple Autoencoder and applied it to the well-known MNIST dataset of handwritten digits. This post is about my journey and is split in the following sections:
Custom Estimators Autoencoder network architecture Autoencoder as TensorFlow Estimator Using the Dataset API Denoising Autocendoer I will assume that you are familiar with TensorFlow basics. The full code is available at https://github.com/sebp/tf_autoencoder. A second part on Convolutional Autoencoders is available too.
Today, I released a new version of scikit-survival. This release adds support for the latest version of scikit-learn (0.19) and pandas (0.21). In turn, support for Python 3.4, scikit-learn 0.18 and pandas 0.18 has been dropped.
I’m pleased to announce that scikit-survival version 0.4 has been released.
This release adds CoxnetSurvivalAnalysis, which implements an efficient algorithm to fit Cox’s proportional hazards model with LASSO, ridge, and elastic net penalty. This allows fitting a Cox model to high-dimensional data and perform feature selection. Moreover, it includes support for Windows with Python 3.5 and later by making the cvxopt package optional.
Today, I released a new version of scikit-survival, a Python module for survival analysis built on top of scikit-learn.
This release adds predict_survival_function and predict_cumulative_hazard_function to sksurv.linear_model.CoxPHSurvivalAnalysis, which return the survival function and cumulative hazard function using Breslow’s estimator.
Moreover, it fixes a build error on Windows (#3) and adds the sksurv.preprocessing.OneHotEncoder class, which can be used in a scikit-learn pipeline.