From a89d800353a95dbca0f80c9927d951eec7553900 Mon Sep 17 00:00:00 2001 From: NotXia <35894453+NotXia@users.noreply.github.com> Date: Sun, 10 Nov 2024 14:19:32 +0100 Subject: [PATCH] Add A3I survival analysis + biomedical data --- .../a3i.tex | 1 + .../img/_biomed_categ_distr.pdf | Bin 0 -> 8112 bytes .../img/_biomed_corr_matrix.pdf | Bin 0 -> 21338 bytes .../img/_biomed_numeric_distr.pdf | Bin 0 -> 13624 bytes .../img/_biomed_target_categ_distr.pdf | Bin 0 -> 7870 bytes .../img/_biomed_target_distr.pdf | Bin 0 -> 6087 bytes .../img/_biomed_target_num_distr.pdf | Bin 0 -> 14025 bytes .../img/_biomed_target_univariate_distr.pdf | Bin 0 -> 7870 bytes .../img/_rul_survival_analysis_classifier.pdf | Bin 0 -> 13987 bytes .../sections/_features_selection.tex | 63 ++++++++ .../sections/_remaining_useful_life.tex | 151 ++++++++++++++---- 11 files changed, 181 insertions(+), 34 deletions(-) create mode 100644 src/year2/artificial-intelligence-in-industry/img/_biomed_categ_distr.pdf create mode 100644 src/year2/artificial-intelligence-in-industry/img/_biomed_corr_matrix.pdf create mode 100644 src/year2/artificial-intelligence-in-industry/img/_biomed_numeric_distr.pdf create mode 100644 src/year2/artificial-intelligence-in-industry/img/_biomed_target_categ_distr.pdf create mode 100644 src/year2/artificial-intelligence-in-industry/img/_biomed_target_distr.pdf create mode 100644 src/year2/artificial-intelligence-in-industry/img/_biomed_target_num_distr.pdf create mode 100644 src/year2/artificial-intelligence-in-industry/img/_biomed_target_univariate_distr.pdf create mode 100644 src/year2/artificial-intelligence-in-industry/img/_rul_survival_analysis_classifier.pdf create mode 100644 src/year2/artificial-intelligence-in-industry/sections/_features_selection.tex diff --git a/src/year2/artificial-intelligence-in-industry/a3i.tex b/src/year2/artificial-intelligence-in-industry/a3i.tex index 34d41a3..95435c1 100644 --- a/src/year2/artificial-intelligence-in-industry/a3i.tex +++ b/src/year2/artificial-intelligence-in-industry/a3i.tex @@ -18,5 +18,6 @@ \include{./sections/_remaining_useful_life.tex} \include{./sections/_wear_anomalies.tex} \include{./sections/_arrivals_predicition.tex} + \include{./sections/_features_selection.tex} \end{document} \ No newline at end of file diff --git a/src/year2/artificial-intelligence-in-industry/img/_biomed_categ_distr.pdf b/src/year2/artificial-intelligence-in-industry/img/_biomed_categ_distr.pdf new file mode 100644 index 0000000000000000000000000000000000000000..99f63a3622a731a45b8c71a7a2cae22ad6d29861 GIT binary patch literal 8112 zcmd5>c_5VE*AFdBLS)O*Q`yDrGh|C7Vq}jm$zU+HVa$;54SnTHglsKjNs*;8Wr@-z zC2L6bBx{ILgyemmLHVWk{r-OM`_GHVbDwkXIrp4%?>*<<&y_UN)=@wzVqlW_T|izL z3<)3rioGjLRTY4n`FoK7xE7H{bf-82a3i8K$p=7z4NL%abr{Lh5o)Nkib03sNdwT# z25`&6_O2uc8h~H=JaC+L(3D1`kpRpR!H7tsk*J;k7W#z2Ov@1G>|cIsEx+}EqpQ?Gt~CJC8`7Ri0<~LGA8tbOq0p%gpgoZO zVE+V?Bbm6Lavbat0sbP9NUS0bhruAQ2pk#*pp}&r5lUcBa4pbAP>>Ixyrik7rzZvc 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zf$<6e3yJ@%gYpAj@E>*jfaw1d4u*Dv|EL2N5dWy-14Q>9eSrtQ-|_MC!r;H-;|0vp zKjEN&cliw;f*-x0_>VCl5I)%N;{r<)z_a`g$BX!VTnLm8*jWBgIDi(wHvU$}2m8mk zynIO5Kj=fk{@E9a0BrMb_>la(|ES{!PAvTn#}7yR&Koa3Kj7qk!zTbo{$2-IXCz=+ z(7(Uu0icfGX@^a%Ns+?acvOgMLZQ Z%!dog$QgAuEg>)%FA~JeETt?B`acLGETjMc literal 0 HcmV?d00001 diff --git a/src/year2/artificial-intelligence-in-industry/sections/_features_selection.tex b/src/year2/artificial-intelligence-in-industry/sections/_features_selection.tex new file mode 100644 index 0000000..9d76f39 --- /dev/null +++ b/src/year2/artificial-intelligence-in-industry/sections/_features_selection.tex @@ -0,0 +1,63 @@ +\chapter{Feature selection and important: Biomedical analysis} + + +\section{Data} + +The dataset contains anonymized biomedical data and is composed of a binary target variable and unknown variables. + +\begin{remark} + In reality, this dataset contains real-world examples. Features have been anonymized for the purpose of feature analysis. +\end{remark} + + +\subsection{Preliminary analysis} + +\begin{description} + \item[Data distribution] + There are both categorical and numerical features. + \begin{figure}[H] + \centering + \begin{subfigure}{0.75\linewidth} + \centering + \includegraphics[width=\linewidth]{./img/_biomed_numeric_distr.pdf} + \caption{Numerical features} + \end{subfigure} + \begin{subfigure}{0.75\linewidth} + \centering + \includegraphics[width=\linewidth]{./img/_biomed_categ_distr.pdf} + \caption{Categorical features} + \end{subfigure} + \begin{subfigure}{0.2\linewidth} + \centering + \includegraphics[width=\linewidth]{./img/_biomed_target_distr.pdf} + \caption{Target} + \end{subfigure} + \caption{Distribution of the dataset} + \end{figure} + + \item[Univariate dependencies] + Determine the fraction of examples with target $Y=1$ (i.e., likelihood that a feature has a specific value while the target is $1$). + + \begin{figure}[H] + \centering + \begin{subfigure}{0.75\linewidth} + \centering + \includegraphics[width=\linewidth]{./img/_biomed_target_num_distr.pdf} + \caption{Numerical features} + \end{subfigure} + \begin{subfigure}{0.75\linewidth} + \centering + \includegraphics[width=\linewidth]{./img/_biomed_target_categ_distr.pdf} + \caption{Categorical features} + \end{subfigure} + \caption{Univariate dependencies with $Y=1$} + \end{figure} + + \item[Linear correlation] + Determine the Pearson's correlation between variables. + + \begin{figure}[H] + \centering + \includegraphics[width=0.8\linewidth]{./img/_biomed_corr_matrix.pdf} + \end{figure} +\end{description} \ No newline at end of file diff --git a/src/year2/artificial-intelligence-in-industry/sections/_remaining_useful_life.tex b/src/year2/artificial-intelligence-in-industry/sections/_remaining_useful_life.tex index cd54441..9b6b141 100644 --- a/src/year2/artificial-intelligence-in-industry/sections/_remaining_useful_life.tex +++ b/src/year2/artificial-intelligence-in-industry/sections/_remaining_useful_life.tex @@ -135,7 +135,7 @@ Predict RUL with a regressor $f$ and set a threshold to trigger maintenance: \end{description} -\subsection{Classifier} +\subsection{Classifier} \label{sec:rul_classifier_naive} Predict RUL with a classifier $f_\varepsilon$ (for a chosen $\varepsilon$) that determines whether a failure will occur in $\varepsilon$ steps: \[ f_\varepsilon(x, \theta) = \begin{cases} @@ -256,7 +256,7 @@ Predict RUL with a classifier $f_\varepsilon$ (for a chosen $\varepsilon$) that \end{description} -\subsection{Survival analysis model} +\subsection{Survival analysis (regression)} \begin{remark} Chronologically, this approach has been presented after \Cref{ch:ap_hospital}. @@ -315,42 +315,125 @@ Predict RUL with a classifier $f_\varepsilon$ (for a chosen $\varepsilon$) that \end{description} -% \subsection{Survival function} +\subsection{Survival analysis (classification)} -% \begin{description} -% \item[Censoring] \marginnote{Censoring} -% Hide from the dataset key events. +\begin{description} + \item[Censoring] \marginnote{Censoring} + Hide key events from the dataset. -% \begin{remark} -% For this dataset, it is more realistic to use partial runs as run-to-failure experiments are expensive to obtain. -% \begin{figure}[H] -% \centering -% \includegraphics[width=0.7\linewidth]{./img/_rul_censoring.pdf} -% \caption{Example of partial runs} -% \end{figure} -% \end{remark} + \begin{remark} + For this dataset, it is more realistic to have more partial runs than run-to-failure experiments as they are expensive to obtain. + \begin{figure}[H] + \centering + \includegraphics[width=0.7\linewidth]{./img/_rul_censoring.pdf} + \caption{Example of partial runs} + \end{figure} + \end{remark} -% \item[Survival function] \marginnote{Survival function} -% Given the random variable $T$ to model survival time, the survival function $S$ is defined as: -% \[ S(\bar{t}) = \prob{T > \bar{t}} \] -% In other words, it is the probability of surviving at least until time $\bar{t}$. + \item[Survival function] \marginnote{Survival function} + Given the random variable $T$ to model survival time, the survival function $S$ is defined as: + \[ S(\bar{t}) = \prob{T > \bar{t}} \] + In other words, it is the probability of surviving at least until time $\bar{t}$. -% For this problem, the survival function can account for past sensor readings: -% \[ S(\bar{t}, X_{\leq \bar{t}}) = \prob{T > \bar{t} \mid X_{\leq \bar{t}}} \] + For this problem, the survival function can account for past sensor readings $X_{\leq \bar{t}}$: + \[ S(\bar{t}, X_{\leq \bar{t}}) = \prob{T > \bar{t} \mid X_{\leq \bar{t}}} \] -% \item[Hazard function] \marginnote{Hazard function} -% Given the random variable $T$ to model survival time, the hazard function $\lambda$ is defined as: -% \[ \lambda(\bar{t}) = \prob{T > \bar{t} \mid T > \bar{t}-1} \] -% In other words, it is the conditional probability of not surviving at time $\bar{t}$ knowing that the entity survived until time $\bar{t}-1$. + \item[Hazard function] \marginnote{Hazard function} + Given the random variable $T$ to model survival time, the hazard function $\lambda$ is defined as: + \[ \lambda(\bar{t}) = \prob{T < \bar{t} \mid T > \bar{t}-1} \] + In other words, it is the conditional probability of not surviving at time $\bar{t}$ knowing that the entity survived until time $\bar{t}-1$. -% With discrete time, the survival function can be factorized using the hazard function: -% \[ S(\bar{t}) = (1-\lambda(\bar{t})) \cdot (1 - \lambda(\bar{t}-1)) \cdot \dots \] + With discrete time, the survival function can be factorized using the hazard function: + \[ S(\bar{t}) \approx (1-\lambda(\bar{t})) \cdot (1 - \lambda(\bar{t}-1)) \cdot \dots \] -% For this problem, the hazard function is the following: -% \[ -% \begin{gathered} -% \lambda(\bar{t}, X_{\bar{t}}) = \prob{T > \bar{t} \mid T > \bar{t}-1, X_{\bar{t}}} \\ -% S(\bar{t}, X_{\bar{t}}) = (1-\lambda(\bar{t}, X_{\bar{t}})) \cdot (1 - \lambda(\bar{t}-1, X_{\bar{t}-1})) \cdot \dots -% \end{gathered} -% \] -% \end{description} \ No newline at end of file + \begin{remark} + The hazard function only depends on one observation. + \end{remark} + + \begin{remark} + The fact that the probability of not surviving is used is for historical reasons. + \end{remark} + + For this problem, the hazard function is the following: + \[ + \begin{gathered} + \lambda(\bar{t}, X_{\bar{t}}) = \prob{T < \bar{t} \mid T > \bar{t}-1, X_{\bar{t}}} \\ + S(\bar{t}, X_{\bar{t}}) \approx (1-\lambda(\bar{t}, X_{\bar{t}})) \cdot (1 - \lambda(\bar{t}-1, X_{\bar{t}-1})) \cdot \dots + \end{gathered} + \] + + \item[Hazard estimator] + We want to train an estimator $\hat{\lambda}_\theta(\bar{t}, x_{\bar{t}})$ for the hazard function. + + Consider the $k$-th experiment that ended at time $e^{(k)}$. The probability of the survival event can be modelled as follows: + \[ + \underbrace{ + \vphantom{\prod_{t=1}^{e^{(k)}-1}} + \hat{\lambda}_\theta(e^{(k)}, x^{(k)}_{e^{(k)}}) + }_{\qquad\mathllap{\text{Not surviving at time $e^{(k)}$}}} + \underbrace{ + \prod_{t=1}^{e^{(k)}-1} (1 - \hat{\lambda}_\theta(t, x^{(k)}_{t})) + }_{\mathrlap{\text{Surviving until time $e^{(k)}-1$}}\qquad} + \] + + \begin{description} + \item[Training] + In likelihood maximization terms, the problem for $m$ experiments is formulated as: + \[ + \arg\max_\theta \prod_{k=1}^{m} \left( + \hat{\lambda}_\theta(e^{(k)}, x^{(k)}_{e^{(k)}}) + \prod_{t=1}^{e^{(k)}-1} (1 - \hat{\lambda}_\theta(t, x^{(k)}_{t})) + \right) + \] + Let $d^{(k)}_{t} = 1 \iff t = e^{(k)}$ (i.e., $1$ when not surviving at time $t$), the problem can be rewritten as: + \[ + \begin{split} + &\arg\max_\theta \prod_{k=1}^{m} \prod_{t=1}^{e^{(k)}} + d^{(k)}_{t} \hat{\lambda}_\theta(t, x^{(k)}_{t}) + (1-d^{(k)}_{t})(1-\hat{\lambda}_\theta(t, x^{(k)}_{t})) \\ + &= \arg\max_\theta \sum_{k=1}^{m} \sum_{t=1}^{e^{(k)}} + \log\left( d^{(k)}_{t} \hat{\lambda}_\theta(t, x^{(k)}_{t}) + (1-d^{(k)}_{t})(1-\hat{\lambda}_\theta(t, x^{(k)}_{t})) \right) \\ + &= \arg\max_\theta \sum_{k=1}^{m} \sum_{t=1}^{e^{(k)}} + d^{(k)}_{t} \log\left(\hat{\lambda}_\theta(t, x^{(k)}_{t})\right) + (1-d^{(k)}_{t}) \log\left(1-\hat{\lambda}_\theta(t, x^{(k)}_{t})\right) \\ + &= \arg\min_\theta -\sum_{k=1}^{m} \sum_{t=1}^{e^{(k)}} + d^{(k)}_{t} \log\left(\hat{\lambda}_\theta(t, x^{(k)}_{t})\right) + (1-d^{(k)}_{t}) \log\left(1-\hat{\lambda}_\theta(t, x^{(k)}_{t})\right) \\ + \end{split} + \] + Which corresponds to a binary cross-entropy minimization problem where $d^{(k)}_{t}$ can be seen as the class. Therefore, $\hat{\lambda}_\theta(t, x^{(k)}_{t})$ can be seen as a classifier. + + \begin{remark} + This shows that the classification approach used in \Cref{sec:rul_classifier_naive} is not strictly wrong. Instead of predicting whether the machine fails in $\varepsilon$ time steps, in probabilistic terms, the correct approach is to consider whether the machine fails at a time step $t$ knowing it survived at time $t-1$. + \end{remark} + + \begin{remark} + Censored data skews the distribution of the data. Sample weights can be used to deal with the issue. + \end{remark} + \end{description} +\end{description} + +\begin{figure}[H] + \centering + \includegraphics[width=0.9\linewidth]{./img/_rul_survival_analysis_classifier.pdf} + \caption{Failure probability of the classifier} +\end{figure} + +\begin{description} + \item[Inference] \phantom{} + Given a threshold $\varepsilon$, maintenance is triggered when: + \[ \hat{\lambda}(t, x_t) \geq \varepsilon \] + + \item[Forecasting] + Determine the probability of surviving $n$ more steps as: + \[ + \begin{split} + \frac{S(t+n)}{S(t)} &= \prod_{h=0}^{n} (1 - \lambda(t+h, X_{t+h})) \\ + &\approx \prod_{h=0}^{n} (1 - \hat{\lambda}(t+h, x^{(k)}_{t+h})) + \end{split} + \] + However, this would require accessing future values of $X_t$. Possible workarounds are: + \begin{itemize} + \item Ignore time-varying inputs (i.e., sort of marginalization) so that $\hat{\lambda}(t+h, x_{t+h}) \approx \hat{\lambda}(t+h, x^{(k)})$ where $x^{(k)}$ only contains stable information. + \item Predict future values of $x^{(k)}_t$ by training another estimator. + \item Assume that all the variables $x^{(k)}_t$ are stable for some time so that: + \[ \frac{S(t+n)}{S(t)} \approx \prod_{h=0}^{n} (1 - \hat{\lambda}(t+h, x^{(k)}_{t})) \] + \end{itemize} +\end{description} \ No newline at end of file