changes

docs/ml-survival-regression.md

@@ -4,93 +4,5 @@ title: Survival Regression - spark.ml
 displayTitle: Survival Regression - spark.ml
 ---
 
-
-`\[
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-
-
-In `spark.ml`, we implement the [Accelerated failure time (AFT)](https://en.wikipedia.org/wiki/Accelerated_failure_time_model) 
-model which is a parametric survival regression model for censored data. 
-It describes a model for the log of survival time, so it's often called 
-log-linear model for survival analysis. Different from 
-[Proportional hazards](https://en.wikipedia.org/wiki/Proportional_hazards_model) model
-designed for the same purpose, the AFT model is more easily to parallelize 
-because each instance contribute to the objective function independently.
-
-Given the values of the covariates $x^{'}$, for random lifetime $t_{i}$ of 
-subjects i = 1, ..., n, with possible right-censoring, 
-the likelihood function under the AFT model is given as:
-`\[
-L(\beta,\sigma)=\prod_{i=1}^n[\frac{1}{\sigma}f_{0}(\frac{\log{t_{i}}-x^{'}\beta}{\sigma})]^{\delta_{i}}S_{0}(\frac{\log{t_{i}}-x^{'}\beta}{\sigma})^{1-\delta_{i}}
-\]`
-Where $\delta_{i}$ is the indicator of the event has occurred i.e. uncensored or not.
-Using $\epsilon_{i}=\frac{\log{t_{i}}-x^{'}\beta}{\sigma}$, the log-likelihood function
-assumes the form:
-`\[
-\iota(\beta,\sigma)=\sum_{i=1}^{n}[-\delta_{i}\log\sigma+\delta_{i}\log{f_{0}}(\epsilon_{i})+(1-\delta_{i})\log{S_{0}(\epsilon_{i})}]
-\]`
-Where $S_{0}(\epsilon_{i})$ is the baseline survivor function,
-and $f_{0}(\epsilon_{i})$ is corresponding density function.
-
-The most commonly used AFT model is based on the Weibull distribution of the survival time. 
-The Weibull distribution for lifetime corresponding to extreme value distribution for 
-log of the lifetime, and the $S_{0}(\epsilon)$ function is:
-`\[   
-S_{0}(\epsilon_{i})=\exp(-e^{\epsilon_{i}})
-\]`
-the $f_{0}(\epsilon_{i})$ function is:
-`\[
-f_{0}(\epsilon_{i})=e^{\epsilon_{i}}\exp(-e^{\epsilon_{i}})
-\]`
-The log-likelihood function for AFT model with Weibull distribution of lifetime is:
-`\[
-\iota(\beta,\sigma)= -\sum_{i=1}^n[\delta_{i}\log\sigma-\delta_{i}\epsilon_{i}+e^{\epsilon_{i}}]
-\]`
-Due to minimizing the negative log-likelihood equivalent to maximum a posteriori probability,
-the loss function we use to optimize is $-\iota(\beta,\sigma)$.
-The gradient functions for $\beta$ and $\log\sigma$ respectively are:
-`\[   
-\frac{\partial (-\iota)}{\partial \beta}=\sum_{1=1}^{n}[\delta_{i}-e^{\epsilon_{i}}]\frac{x_{i}}{\sigma}
-\]`
-`\[ 
-\frac{\partial (-\iota)}{\partial (\log\sigma)}=\sum_{i=1}^{n}[\delta_{i}+(\delta_{i}-e^{\epsilon_{i}})\epsilon_{i}]
-\]`
-
-The AFT model can be formulated as a convex optimization problem, 
-i.e. the task of finding a minimizer of a convex function $-\iota(\beta,\sigma)$ 
-that depends coefficients vector $\beta$ and the log of scale parameter $\log\sigma$.
-The optimization algorithm underlying the implementation is L-BFGS.
-The implementation matches the result from R's survival function 
-[survreg](https://stat.ethz.ch/R-manual/R-devel/library/survival/html/survreg.html)
-
-## Example:
-
-<div class="codetabs">
-
-<div data-lang="scala" markdown="1">
-{% include_example scala/org/apache/spark/examples/ml/AFTSurvivalRegressionExample.scala %}
-</div>
-
-<div data-lang="java" markdown="1">
-{% include_example java/org/apache/spark/examples/ml/JavaAFTSurvivalRegressionExample.java %}
-</div>
-
-<div data-lang="python" markdown="1">
-{% include_example python/ml/aft_survival_regression.py %}
-</div>
-
-</div>
+  > This section has been moved into the
+   [classification and regression section](ml-classification-regression.html#survival-regression).