initial push
This commit is contained in:
Simon Klüttermann
commit
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<frame >
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<titlepage>
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</frame>
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<frame title="Anomaly Detection">
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||||||
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<list>
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||||||
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<e>Find strange (unexpected) samples.</e>
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<e>->If a traffic light is constantly yellow, probably something broke</e>
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<e>But this could happen in a lot of different ways</e>
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<e>->Most likely the traffic light is just off. But it could also fluctuate quickly or start smoking</e>
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<e>How to cover all possible anomalies?</e>
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<e>->Unsupervised Machine Learning</e>
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</list>
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</frame>
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<frame title="Unsupervised Machine Learning">
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<list>
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<e>Normal machine learning: Input - Label</e>
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<e>Here: Only Input.</e>
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<e>->Instead of classifying different types, try to understand your given dataset</e>
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<e>Deviations from this understanding are anomalies</e>
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<l2st>
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<e>x: training samples</e>
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<e>tx: test samples</e>
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<e>ty: test labels (is a certain sample an anomaly or not)</e>
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</l2st>
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<e>Useful: \emph{peak /global/cardio.npz}</e>
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</list>
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</frame>
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<frame title="kNN">
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<split>
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<que>
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||||||
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<list>
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<e>How to do this? Here one algorithm: kNN</e>
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<e>Goal: Generate an anomaly score (high value->highly anomalous)</e>
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<e>Here: The anomaly score is the distance to the kth closest samples</e>
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</list>
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||||||
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</que>
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||||||
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<que>
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||||||
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<i f="..//prep/03kNN/yanghuang 08.png" wmode="True"></i>
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</que>
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</split>
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</frame>
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<frame title="kNN">
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||||||
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<split>
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||||||
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<que>
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||||||
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<list>
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||||||
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<e>How to do this? Here one algorithm: kNN</e>
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<e>Goal: Generate an anomaly score (high value->highly anomalous)</e>
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<e>Here: The anomaly score is the distance to the kth closest samples</e>
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||||||
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</list>
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||||||
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</que>
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||||||
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<que>
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||||||
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<i f="..//prep/04kNN/dist0.pdf" wmode="True"></i>
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</que>
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</split>
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</frame>
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<frame >
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<i f="..//prep/05/dist0.pdf" wmode="True"></i>
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</frame>
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<frame title="AUC Score">
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<split>
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<que w="0.47619047619047616"><i f="..//prep/06AUC_Score/02confusion.png" wmode="True"></i></que>
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||||||
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<que w="0.47619047619047616"><i f="..//prep/06AUC_Score/01dist0.pdf" wmode="True"></i></que>
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||||||
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</split>
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||||||
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</frame>
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@ -0,0 +1,22 @@
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||||||
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<frame title="AUC Score">
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||||||
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<split>
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||||||
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<que>
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||||||
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<list>
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||||||
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<e>Iterate every threshold</e>
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<e>Plot fpr vs tpr</e>
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<e>False Positive Rate</e>
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||||||
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<l2st>
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||||||
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<e>$\frac{FP}{FP+TN}$</e>
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</l2st>
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||||||
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<e>True Positive Rate</e>
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<l2st>
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||||||
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<e>$\frac{TP}{TP+FN}$</e>
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||||||
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</l2st>
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||||||
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<e>ROC-AUC: Integral of this curve!</e>
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||||||
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</list>
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||||||
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</que>
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||||||
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<que>
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||||||
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<i f="..//prep/07AUC_Score/roc.pdf" wmode="True"></i>
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</que>
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||||||
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</split>
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</frame>
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<frame title="AUC Score">
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||||||
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<list>
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||||||
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<e>calculcate with \emph{sklearn.metrics.roc\_auc\_score}</e>
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||||||
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<e>Higher AUC score->better</e>
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<e>$AUC=1.0$->Perfect seperation</e>
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||||||
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<e>$AUC=0.5$->Random model</e>
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||||||
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<e>$AUC=0.0$->Inverse seperation (every anomaly is normal, and every normal sample is anomalous)</e>
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||||||
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</list>
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||||||
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</frame>
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||||||
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<frame title="AUC Scores">
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||||||
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<i f="..//prep/09AUC_Scores/students.png" wmode="True"></i>
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||||||
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</frame>
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@ -0,0 +1,11 @@
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<frame title="AutoML">
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||||||
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<list>
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||||||
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<e>But: We can beat this!</e>
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<e>How? Hyperparameter</e>
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<l2st>
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<e>Every algorithm has hyperparameter that control how it works</e>
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<e>For example: k in kNN (number of close points considered)</e>
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</l2st>
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<e>Lets take the worst algorithm (kNN: $0.927$) and try to improve it</e>
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||||||
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</list>
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||||||
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</frame>
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||||||
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@ -0,0 +1,3 @@
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||||||
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<frame title="Optimize">
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||||||
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<i f="..//prep/11Optimize/baseline.png" wmode="True"></i>
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||||||
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</frame>
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<frame title="Optimize">
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||||||
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<i f="..//prep/12Optimize/optimize.png" wmode="True"></i>
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</frame>
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@ -0,0 +1,13 @@
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<frame title="flaml">
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<split>
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<que>
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<list>
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||||||
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<e>\emph{source folder/bin/activate}</e>
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||||||
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<e>\emph{pip install flaml}</e>
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||||||
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</list>
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||||||
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</que>
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||||||
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<que>
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||||||
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<i f="..//prep/15flaml/forflaml.png" wmode="True"></i>
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||||||
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</que>
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||||||
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</split>
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||||||
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</frame>
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@ -0,0 +1,3 @@
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<frame title="flaml">
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||||||
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<i f="..//prep/16flaml/flaml.png" wmode="True"></i>
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||||||
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</frame>
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||||||
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<frame >
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<i f="..//prep/17/hist.pdf" wmode="True"></i>
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</frame>
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@ -0,0 +1,8 @@
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<frame title="Your Turn">
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||||||
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<list>
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||||||
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<e>Remember your last algorithm</e>
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<e>Find its hyperparameters (Tip: pyod website)</e>
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||||||
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<e>Optimize your algorithm and give me a new AUC!</e>
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||||||
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<e>Bonus Question: Is there a problem with what we are doing?</e>
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||||||
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</list>
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||||||
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</frame>
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<plt>
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<name Current experiment status>
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<title Anomaly Detection and AutoML>
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<stitle Anomaly Detection and AutoML>
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||||||
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<institute ls9 tu Dortmund>
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<theme CambridgeUS>
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<colo dolphin>
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</plt>
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Two distributions
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<l2st>
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One known (=normal)
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One unknown (=anomalies)
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</l2st>
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Seperate them
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Two distributions
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<l2st>
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One known (=normal)
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One unknown (=anomalies)
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</l2st>
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Seperate them
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Problem: few anomalies
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Anomalies are rare, so often only a few datapoints known (e.g. Machine Failure in an Aircraft)
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In practice, anomalies might appear that are not known during testing
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->So train the model only on normal samples
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Unsupervised Machine Learning
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<l2st>
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What can we say without knowing anomalies?
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''Understand you dataset''
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</l2st>
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Anomalies are rare, so often only a few datapoints known (e.g. Machine Failure in an Aircraft)
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In practice, anomalies might appear that are not known during testing
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||||||
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->So train the model only on normal samples
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Unsupervised Machine Learning
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||||||
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<l2st>
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||||||
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What can we say without knowing anomalies?
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''Understand you dataset''
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</l2st>
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Seems easy? Now do this
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<l2st>
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in thousands of dimensions
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with complicated distributions
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and overlap between anomalies and normal points
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</l2st>
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Most machine learning requires Hyperparameter Optimisation
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(Find model parameters that result in the best results)
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->AutoML: Do this automatically as fast as possible
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So lets combine both (Auto Anomaly Detection)
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->Problem
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<l2st>
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AutoMl requires Evaluation (loss, accuracy, AUC) to optimize
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AD can only be evaluated with regards to the anomalies
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->no longer unsupervised
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</l2st>
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So most Anomaly Detection is ''unoptimized''
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So how to solve this?
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One option: Think of some function to evaluate only the normal points
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->A bit hard to do in a case study
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@ -0,0 +1,5 @@
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So how to solve this?
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One option: ''Just find the best solution directly''
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->Zero Shot AutoML
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||||||
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Find best practices for hyperparameters
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Requires optimisation for each model seperately -> matches the case study structure quite well!
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@ -0,0 +1,12 @@
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Basics of Scientific Computing
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Basics of AD
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Basics of AutoML
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||||||
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Build groups for each algorithm
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||||||
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<l2st>
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||||||
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Choose a set of Hyperparameters
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||||||
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Find ''best practice`s'' for them
|
||||||
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Maybe consider more complicated Transformations (Preprocessing, Ensemble)
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||||||
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</l2st>
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||||||
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Compare between groups (best algorithm for current situation)
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Evaluate on new datasets
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Write a report/Present your work
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After Width: | Height: | Size: 80 KiB |
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@ -0,0 +1,7 @@
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Requirements:
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||||||
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<l2st>
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||||||
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MD Req 1->MD Req 8
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||||||
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Basic Python/Math Knowledge
|
||||||
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Motivation to learn something new;)
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||||||
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</l2st>
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||||||
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Registration till Saturday, by Email to Simon.Kluettermann@cs.tu-dortmund.de
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pdflatex main.tex
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pdflatex main.tex
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||||||
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@ -0,0 +1,3 @@
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pdflatex main.tex
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pdflatex main.tex
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@ -0,0 +1,110 @@
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[
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{
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"typ": "img",
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"files": [
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"..//prep/03kNN/yanghuang 08.png"
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],
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"label": "prep03kNNyanghuang 08png",
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"caption": "",
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"where": "../case2/data/003kNN.txt"
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},
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{
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"typ": "img",
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"files": [
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"..//prep/04kNN/dist0.pdf"
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],
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"label": "prep04kNNdist0pdf",
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"caption": "",
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"where": "../case2/data/004kNN.txt"
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},
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{
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"typ": "img",
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"files": [
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"..//prep/05/dist0.pdf"
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],
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"label": "prep05dist0pdf",
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"caption": "",
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"where": "../case2/data/005.txt"
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},
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||||||
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{
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"typ": "img",
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"files": [
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"..//prep/06AUC_Score/02confusion.png"
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||||||
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],
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||||||
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"label": "prep06AUC_Score02confusionpng",
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"caption": "",
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"where": "../case2/data/006AUC Score.txt"
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},
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{
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"typ": "img",
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"files": [
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"..//prep/06AUC_Score/01dist0.pdf"
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],
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"label": "prep06AUC_Score01dist0pdf",
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"caption": "",
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"where": "../case2/data/006AUC Score.txt"
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},
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{
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"typ": "img",
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"files": [
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"..//prep/07AUC_Score/roc.pdf"
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],
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"label": "prep07AUC_Scorerocpdf",
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"caption": "",
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"where": "../case2/data/007AUC Score.txt"
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},
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{
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"typ": "img",
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"files": [
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"..//prep/09AUC_Scores/students.png"
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],
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"label": "prep09AUC_Scoresstudentspng",
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"caption": "",
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"where": "../case2/data/009AUC Scores.txt"
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},
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{
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"typ": "img",
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"files": [
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"..//prep/11Optimize/baseline.png"
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],
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"label": "prep11Optimizebaselinepng",
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"caption": "",
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"where": "../case2/data/011Optimize.txt"
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},
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{
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"typ": "img",
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"files": [
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"..//prep/12Optimize/optimize.png"
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||||||
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],
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||||||
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"label": "prep12Optimizeoptimizepng",
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"caption": "",
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||||||
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"where": "../case2/data/012Optimize.txt"
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},
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||||||
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{
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||||||
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"typ": "img",
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"files": [
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"..//prep/15flaml/forflaml.png"
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||||||
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],
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||||||
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"label": "prep15flamlforflamlpng",
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"caption": "",
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"where": "../case2/data/013flaml.txt"
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},
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{
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"typ": "img",
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"files": [
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"..//prep/16flaml/flaml.png"
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||||||
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],
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||||||
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"label": "prep16flamlflamlpng",
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"caption": "",
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||||||
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"where": "../case2/data/014flaml.txt"
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},
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||||||
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{
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||||||
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"typ": "img",
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||||||
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"files": [
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"..//prep/17/hist.pdf"
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||||||
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],
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||||||
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"label": "prep17histpdf",
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"caption": "",
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"where": "../case2/data/015.txt"
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||||||
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}
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||||||
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]
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@ -0,0 +1,138 @@
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\relax
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% Die Hyperref Option hyperref={pdfpagelabels=false} verhindert die Warnung:
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|
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|
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|
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|
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|
||||||
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|
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|
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|
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|
||||||
|
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|
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|
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|
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|
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|
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|
||||||
|
% Daher steht es hier vor \begin{document}
|
||||||
|
|
||||||
|
\title[Anomaly Detection and AutoML]{Anomaly Detection and AutoML}
|
||||||
|
\author{Simon Kluettermann}
|
||||||
|
\date{\today}
|
||||||
|
|
||||||
|
|
||||||
|
\institute{ls9 tu Dortmund}
|
||||||
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|
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|
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|
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|
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|
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|
||||||
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|
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|
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|
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|
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|
||||||
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|
||||||
|
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|
||||||
|
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||||||
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|
||||||
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|
||||||
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|
||||||
|
% \beamersetuncovermixins{\opaqueness<1>{25}}{\opaqueness<2$\Rightarrow${15}}
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% sorgt dafuer das die Elemente die erst noch (zukuenftig) kommen
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|
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|
||||||
|
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|
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|
% klappt auch bei Tabellen, wenn teTeX verwendet wird\ldots
|
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|
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|
||||||
|
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|
||||||
|
{
|
||||||
|
\leavevmode%
|
||||||
|
\hbox{%
|
||||||
|
\begin{beamercolorbox}[wd=.4\paperwidth,ht=2.25ex,dp=1ex,center]{author in head/foot}%
|
||||||
|
\usebeamerfont{author in head/foot}\insertshorttitle
|
||||||
|
\end{beamercolorbox}%
|
||||||
|
\begin{beamercolorbox}[wd=.25\paperwidth,ht=2.25ex,dp=1ex,center]{title in head/foot}%
|
||||||
|
\usebeamerfont{title in head/foot}\insertsection
|
||||||
|
\end{beamercolorbox}%
|
||||||
|
\begin{beamercolorbox}[wd=.3499\paperwidth,ht=2.25ex,dp=1ex,right]{date in head/foot}%
|
||||||
|
\usebeamerfont{date in head/foot}\insertshortdate{}\hspace*{2em}
|
||||||
|
\hyperlink{toc}{\insertframenumber{} / \inserttotalframenumber\hspace*{2ex}}
|
||||||
|
\end{beamercolorbox}}%
|
||||||
|
\vskip0pt%
|
||||||
|
}
|
||||||
|
|
||||||
|
\usepackage[absolute,overlay]{textpos}
|
||||||
|
\usepackage{graphicx}
|
||||||
|
|
||||||
|
\newcommand{\source}[1]{\begin{textblock*}{9cm}(0.1cm,8.9cm)
|
||||||
|
\begin{beamercolorbox}[ht=0.5cm,left]{framesource}
|
||||||
|
\usebeamerfont{framesource}\usebeamercolor[fg!66]{framesource} Source: {#1}
|
||||||
|
\end{beamercolorbox}
|
||||||
|
\end{textblock*}}
|
||||||
|
|
||||||
|
|
||||||
|
\begin{document}
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
%from file ../case2/data/000.txt
|
||||||
|
\begin{frame}[label=]
|
||||||
|
\frametitle{}
|
||||||
|
\begin{titlepage}
|
||||||
|
|
||||||
|
\centering
|
||||||
|
{\huge\bfseries \par}
|
||||||
|
\vspace{2cm}
|
||||||
|
{\LARGE\itshape Simon Kluettermann\par}
|
||||||
|
\vspace{1.5cm}
|
||||||
|
{\scshape\Large Master Thesis in Physics\par}
|
||||||
|
\vspace{0.2cm}
|
||||||
|
{\Large submitted to the \par}
|
||||||
|
\vspace{0.2cm}
|
||||||
|
{\scshape\Large Faculty of Mathematics Computer Science and Natural Sciences \par}
|
||||||
|
\vspace{0.2cm}
|
||||||
|
{\Large \par}
|
||||||
|
\vspace{0.2cm}
|
||||||
|
{\scshape\Large RWTH Aachen University}
|
||||||
|
\vspace{1cm}
|
||||||
|
|
||||||
|
\vfill
|
||||||
|
{\scshape\Large Department of Physics\par}
|
||||||
|
\vspace{0.2cm}
|
||||||
|
{\scshape\Large Insitute for theoretical Particle Physics and Cosmology\par}
|
||||||
|
\vspace{0.2cm}
|
||||||
|
{ \Large\par}
|
||||||
|
\vspace{0.2cm}
|
||||||
|
{\Large First Referee: Prof. Dr. Michael Kraemer \par}
|
||||||
|
{\Large Second Referee: Prof. Dr. Felix Kahlhoefer}
|
||||||
|
|
||||||
|
\vfill
|
||||||
|
|
||||||
|
% Bottom of the page
|
||||||
|
{\large November 2020 \par}
|
||||||
|
\end{titlepage}
|
||||||
|
\pagenumbering{roman}
|
||||||
|
\thispagestyle{empty}
|
||||||
|
\null
|
||||||
|
\newpage
|
||||||
|
\setcounter{page}{1}
|
||||||
|
\pagenumbering{arabic}
|
||||||
|
\end{frame}
|
||||||
|
|
||||||
|
|
||||||
|
%from file ../case2/data/001Anomaly Detection.txt
|
||||||
|
\begin{frame}[label=Anomaly Detection]
|
||||||
|
\frametitle{Anomaly Detection}
|
||||||
|
\begin{itemize}
|
||||||
|
|
||||||
|
\item Find strange (unexpected) samples.
|
||||||
|
|
||||||
|
\item $\Rightarrow$If a traffic light is constantly yellow, probably something broke
|
||||||
|
|
||||||
|
\item But this could happen in a lot of different ways
|
||||||
|
|
||||||
|
\item $\Rightarrow$Most likely the traffic light is just off. But it could also fluctuate quickly or start smoking
|
||||||
|
|
||||||
|
\item How to cover all possible anomalies?
|
||||||
|
|
||||||
|
\item $\Rightarrow$Unsupervised Machine Learning
|
||||||
|
|
||||||
|
|
||||||
|
\end{itemize}
|
||||||
|
\end{frame}
|
||||||
|
|
||||||
|
|
||||||
|
%from file ../case2/data/002Unsupervised Machine Learning.txt
|
||||||
|
\begin{frame}[label=Unsupervised Machine Learning]
|
||||||
|
\frametitle{Unsupervised Machine Learning}
|
||||||
|
\begin{itemize}
|
||||||
|
|
||||||
|
\item Normal machine learning: Input - Label
|
||||||
|
|
||||||
|
\item Here: Only Input.
|
||||||
|
|
||||||
|
\item $\Rightarrow$Instead of classifying different types, try to understand your given dataset
|
||||||
|
|
||||||
|
\item Deviations from this understanding are anomalies
|
||||||
|
|
||||||
|
\begin{itemize}
|
||||||
|
|
||||||
|
\item x: training samples
|
||||||
|
|
||||||
|
\item tx: test samples
|
||||||
|
|
||||||
|
\item ty: test labels (is a certain sample an anomaly or not)
|
||||||
|
|
||||||
|
|
||||||
|
\end{itemize}
|
||||||
|
\item Useful: \emph{peak /global/cardio.npz}
|
||||||
|
|
||||||
|
|
||||||
|
\end{itemize}
|
||||||
|
\end{frame}
|
||||||
|
|
||||||
|
|
||||||
|
%from file ../case2/data/003kNN.txt
|
||||||
|
\begin{frame}[label=kNN]
|
||||||
|
\frametitle{kNN}
|
||||||
|
\begin{columns}[c] % align columns
|
||||||
|
\begin{column}{0.48\textwidth}%.48
|
||||||
|
\begin{itemize}
|
||||||
|
|
||||||
|
\item How to do this? Here one algorithm: kNN
|
||||||
|
|
||||||
|
\item Goal: Generate an anomaly score (high value$\Rightarrow$highly anomalous)
|
||||||
|
|
||||||
|
\item Here: The anomaly score is the distance to the kth closest samples
|
||||||
|
|
||||||
|
|
||||||
|
\end{itemize}
|
||||||
|
\end{column}%
|
||||||
|
\hfill%
|
||||||
|
\begin{column}{0.48\textwidth}%.48
|
||||||
|
\begin{figure}[H]
|
||||||
|
\centering
|
||||||
|
\includegraphics[width=0.9\textwidth]{..//prep/03kNN/yanghuang 08.png}
|
||||||
|
\label{fig:prep03kNNyanghuang 08png}
|
||||||
|
\caption{[Yang, Huang 08]}
|
||||||
|
\end{figure}
|
||||||
|
|
||||||
|
|
||||||
|
\end{column}%
|
||||||
|
\hfill%
|
||||||
|
\end{columns}
|
||||||
|
|
||||||
|
\end{frame}
|
||||||
|
|
||||||
|
|
||||||
|
%from file ../case2/data/004kNN.txt
|
||||||
|
\begin{frame}[label=kNN]
|
||||||
|
\frametitle{kNN}
|
||||||
|
\begin{columns}[c] % align columns
|
||||||
|
\begin{column}{0.48\textwidth}%.48
|
||||||
|
\begin{itemize}
|
||||||
|
|
||||||
|
\item How to do this? Here one algorithm: kNN
|
||||||
|
|
||||||
|
\item Goal: Generate an anomaly score (high value$\Rightarrow$highly anomalous)
|
||||||
|
|
||||||
|
\item Here: The anomaly score is the distance to the kth closest samples
|
||||||
|
|
||||||
|
|
||||||
|
\end{itemize}
|
||||||
|
\end{column}%
|
||||||
|
\hfill%
|
||||||
|
\begin{column}{0.48\textwidth}%.48
|
||||||
|
\begin{figure}[H]
|
||||||
|
\centering
|
||||||
|
\includegraphics[width=0.9\textwidth]{..//prep/04kNN/dist0.pdf}
|
||||||
|
\label{fig:prep04kNNdist0pdf}
|
||||||
|
\end{figure}
|
||||||
|
|
||||||
|
|
||||||
|
\end{column}%
|
||||||
|
\hfill%
|
||||||
|
\end{columns}
|
||||||
|
|
||||||
|
\end{frame}
|
||||||
|
|
||||||
|
|
||||||
|
%from file ../case2/data/005.txt
|
||||||
|
\begin{frame}[label=]
|
||||||
|
\frametitle{}
|
||||||
|
\begin{figure}[H]
|
||||||
|
\centering
|
||||||
|
\includegraphics[width=0.8\textwidth]{..//prep/05/dist0.pdf}
|
||||||
|
\label{fig:prep05dist0pdf}
|
||||||
|
\end{figure}
|
||||||
|
|
||||||
|
|
||||||
|
\end{frame}
|
||||||
|
|
||||||
|
|
||||||
|
%from file ../case2/data/006AUC Score.txt
|
||||||
|
\begin{frame}[label=AUC Score]
|
||||||
|
\frametitle{AUC Score}
|
||||||
|
\begin{columns}[c] % align columns
|
||||||
|
\begin{column}{0.47619047619047616\textwidth}%.48
|
||||||
|
\begin{figure}[H]
|
||||||
|
\centering
|
||||||
|
\includegraphics[width=0.9\textwidth]{..//prep/06AUC_Score/02confusion.png}
|
||||||
|
\label{fig:prep06AUC_Score02confusionpng}
|
||||||
|
\end{figure}
|
||||||
|
|
||||||
|
|
||||||
|
\end{column}%
|
||||||
|
\hfill%
|
||||||
|
\begin{column}{0.47619047619047616\textwidth}%.48
|
||||||
|
\begin{figure}[H]
|
||||||
|
\centering
|
||||||
|
\includegraphics[width=0.9\textwidth]{..//prep/06AUC_Score/01dist0.pdf}
|
||||||
|
\label{fig:prep06AUC_Score01dist0pdf}
|
||||||
|
\end{figure}
|
||||||
|
|
||||||
|
|
||||||
|
\end{column}%
|
||||||
|
\hfill%
|
||||||
|
\end{columns}
|
||||||
|
|
||||||
|
\end{frame}
|
||||||
|
|
||||||
|
|
||||||
|
%from file ../case2/data/007AUC Score.txt
|
||||||
|
\begin{frame}[label=AUC Score]
|
||||||
|
\frametitle{AUC Score}
|
||||||
|
\begin{columns}[c] % align columns
|
||||||
|
\begin{column}{0.48\textwidth}%.48
|
||||||
|
\begin{itemize}
|
||||||
|
|
||||||
|
\item Iterate every threshold
|
||||||
|
|
||||||
|
\item Plot fpr vs tpr
|
||||||
|
|
||||||
|
\item False Positive Rate
|
||||||
|
|
||||||
|
\begin{itemize}
|
||||||
|
|
||||||
|
\item $\frac{FP}{FP+TN}$
|
||||||
|
|
||||||
|
|
||||||
|
\end{itemize}
|
||||||
|
\item True Positive Rate
|
||||||
|
|
||||||
|
\begin{itemize}
|
||||||
|
|
||||||
|
\item $\frac{TP}{TP+FN}$
|
||||||
|
|
||||||
|
|
||||||
|
\end{itemize}
|
||||||
|
\item ROC-AUC: Integral of this curve!
|
||||||
|
|
||||||
|
|
||||||
|
\end{itemize}
|
||||||
|
\end{column}%
|
||||||
|
\hfill%
|
||||||
|
\begin{column}{0.48\textwidth}%.48
|
||||||
|
\begin{figure}[H]
|
||||||
|
\centering
|
||||||
|
\includegraphics[width=0.8\textwidth]{..//prep/07AUC_Score/roc.pdf}
|
||||||
|
\label{fig:prep07AUC_Scorerocpdf}
|
||||||
|
\end{figure}
|
||||||
|
|
||||||
|
|
||||||
|
\end{column}%
|
||||||
|
\hfill%
|
||||||
|
\end{columns}
|
||||||
|
|
||||||
|
\end{frame}
|
||||||
|
|
||||||
|
|
||||||
|
%from file ../case2/data/008AUC Score.txt
|
||||||
|
\begin{frame}[label=AUC Score]
|
||||||
|
\frametitle{AUC Score}
|
||||||
|
\begin{itemize}
|
||||||
|
|
||||||
|
\item calculcate with \emph{sklearn.metrics.roc\_auc\_score}
|
||||||
|
|
||||||
|
\item Higher AUC score$\Rightarrow$better
|
||||||
|
|
||||||
|
\item $AUC=1.0$$\Rightarrow$Perfect seperation
|
||||||
|
|
||||||
|
\item $AUC=0.5$$\Rightarrow$Random model
|
||||||
|
|
||||||
|
\item $AUC=0.0$$\Rightarrow$Inverse seperation (every anomaly is normal, and every normal sample is anomalous)
|
||||||
|
|
||||||
|
|
||||||
|
\end{itemize}
|
||||||
|
\end{frame}
|
||||||
|
|
||||||
|
|
||||||
|
%from file ../case2/data/009AUC Scores.txt
|
||||||
|
\begin{frame}[label=AUC Scores]
|
||||||
|
\frametitle{AUC Scores}
|
||||||
|
\begin{figure}[H]
|
||||||
|
\centering
|
||||||
|
\includegraphics[width=0.9\textwidth]{..//prep/09AUC_Scores/students.png}
|
||||||
|
\label{fig:prep09AUC_Scoresstudentspng}
|
||||||
|
\end{figure}
|
||||||
|
|
||||||
|
|
||||||
|
\end{frame}
|
||||||
|
|
||||||
|
|
||||||
|
%from file ../case2/data/010AutoML.txt
|
||||||
|
\begin{frame}[label=AutoML]
|
||||||
|
\frametitle{AutoML}
|
||||||
|
\begin{itemize}
|
||||||
|
|
||||||
|
\item But: We can beat this!
|
||||||
|
|
||||||
|
\item How? Hyperparameter
|
||||||
|
|
||||||
|
\begin{itemize}
|
||||||
|
|
||||||
|
\item Every algorithm has hyperparameter that control how it works
|
||||||
|
|
||||||
|
\item For example: k in kNN (number of close points considered)
|
||||||
|
|
||||||
|
|
||||||
|
\end{itemize}
|
||||||
|
\item Lets take the worst algorithm (kNN: $0.927$) and try to improve it
|
||||||
|
|
||||||
|
|
||||||
|
\end{itemize}
|
||||||
|
\end{frame}
|
||||||
|
|
||||||
|
|
||||||
|
%from file ../case2/data/011Optimize.txt
|
||||||
|
\begin{frame}[label=Optimize]
|
||||||
|
\frametitle{Optimize}
|
||||||
|
\begin{figure}[H]
|
||||||
|
\centering
|
||||||
|
\includegraphics[width=0.9\textwidth]{..//prep/11Optimize/baseline.png}
|
||||||
|
\label{fig:prep11Optimizebaselinepng}
|
||||||
|
\end{figure}
|
||||||
|
|
||||||
|
|
||||||
|
\end{frame}
|
||||||
|
|
||||||
|
|
||||||
|
%from file ../case2/data/012Optimize.txt
|
||||||
|
\begin{frame}[label=Optimize]
|
||||||
|
\frametitle{Optimize}
|
||||||
|
\begin{figure}[H]
|
||||||
|
\centering
|
||||||
|
\includegraphics[width=0.7\textwidth]{..//prep/12Optimize/optimize.png}
|
||||||
|
\label{fig:prep12Optimizeoptimizepng}
|
||||||
|
\end{figure}
|
||||||
|
|
||||||
|
|
||||||
|
\end{frame}
|
||||||
|
|
||||||
|
|
||||||
|
%from file ../case2/data/013flaml.txt
|
||||||
|
\begin{frame}[label=flaml]
|
||||||
|
\frametitle{flaml}
|
||||||
|
\begin{columns}[c] % align columns
|
||||||
|
\begin{column}{0.48\textwidth}%.48
|
||||||
|
\begin{itemize}
|
||||||
|
|
||||||
|
\item \emph{source folder/bin/activate}
|
||||||
|
|
||||||
|
\item \emph{pip install flaml}
|
||||||
|
|
||||||
|
|
||||||
|
\end{itemize}
|
||||||
|
\end{column}%
|
||||||
|
\hfill%
|
||||||
|
\begin{column}{0.48\textwidth}%.48
|
||||||
|
\begin{figure}[H]
|
||||||
|
\centering
|
||||||
|
\includegraphics[width=0.9\textwidth]{..//prep/15flaml/forflaml.png}
|
||||||
|
\label{fig:prep15flamlforflamlpng}
|
||||||
|
\end{figure}
|
||||||
|
|
||||||
|
|
||||||
|
\end{column}%
|
||||||
|
\hfill%
|
||||||
|
\end{columns}
|
||||||
|
|
||||||
|
\end{frame}
|
||||||
|
|
||||||
|
|
||||||
|
%from file ../case2/data/014flaml.txt
|
||||||
|
\begin{frame}[label=flaml]
|
||||||
|
\frametitle{flaml}
|
||||||
|
\begin{figure}[H]
|
||||||
|
\centering
|
||||||
|
\includegraphics[width=0.9\textwidth]{..//prep/16flaml/flaml.png}
|
||||||
|
\label{fig:prep16flamlflamlpng}
|
||||||
|
\end{figure}
|
||||||
|
|
||||||
|
|
||||||
|
\end{frame}
|
||||||
|
|
||||||
|
|
||||||
|
%from file ../case2/data/015.txt
|
||||||
|
\begin{frame}[label=]
|
||||||
|
\frametitle{}
|
||||||
|
\begin{figure}[H]
|
||||||
|
\centering
|
||||||
|
\includegraphics[width=0.7\textwidth]{..//prep/17/hist.pdf}
|
||||||
|
\label{fig:prep17histpdf}
|
||||||
|
\end{figure}
|
||||||
|
|
||||||
|
|
||||||
|
\end{frame}
|
||||||
|
|
||||||
|
|
||||||
|
%from file ../case2/data/016Your Turn.txt
|
||||||
|
\begin{frame}[label=Your Turn]
|
||||||
|
\frametitle{Your Turn}
|
||||||
|
\begin{itemize}
|
||||||
|
|
||||||
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\item Remember your last algorithm
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\item Find its hyperparameters (Tip: pyod website)
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\item Optimize your algorithm and give me a new AUC!
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\item Bonus Question: Is there a problem with what we are doing?
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\end{itemize}
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\end{frame}
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\end{document}
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<titlepage>
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Find strange (unexpected) samples.
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->If a traffic light is constantly yellow, probably something broke
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But this could happen in a lot of different ways
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->Most likely the traffic light is just off. But it could also fluctuate quickly or start smoking
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How to cover all possible anomalies?
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->Unsupervised Machine Learning
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Normal machine learning: Input - Label
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Here: Only Input.
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->Instead of classifying different types, try to understand your given dataset
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Deviations from this understanding are anomalies
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<l2st>
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x: training samples
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tx: test samples
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ty: test labels (is a certain sample an anomaly or not)
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</l2st>
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Useful: \emph{peak /global/cardio.npz}
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How to do this? Here one algorithm: kNN
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Goal: Generate an anomaly score (high value->highly anomalous)
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Here: The anomaly score is the distance to the kth closest samples
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How to do this? Here one algorithm: kNN
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Goal: Generate an anomaly score (high value->highly anomalous)
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Here: The anomaly score is the distance to the kth closest samples
|
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Iterate every threshold
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Plot fpr vs tpr
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|
False Positive Rate
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|
<l2st>
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|
$\frac{FP}{FP+TN}$
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|
</l2st>
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|
True Positive Rate
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|
<l2st>
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|
$\frac{TP}{TP+FN}$
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|
</l2st>
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|
ROC-AUC: Integral of this curve!
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calculcate with \emph{sklearn.metrics.roc\_auc\_score}
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|
Higher AUC score->better
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|
$AUC=1.0$->Perfect seperation
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|
$AUC=0.5$->Random model
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$AUC=0.0$->Inverse seperation (every anomaly is normal, and every normal sample is anomalous)
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But: We can beat this!
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|
How? Hyperparameter
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|
<l2st>
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|
Every algorithm has hyperparameter that control how it works
|
||||||
|
For example: k in kNN (number of close points considered)
|
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|
</l2st>
|
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|
Lets take the worst algorithm (kNN: $0.927$) and try to improve it
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\emph{source folder/bin/activate}
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|
\emph{pip install flaml}
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@ -0,0 +1,4 @@
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||||||
|
Remember your last algorithm
|
||||||
|
Find its hyperparameters (Tip: pyod website)
|
||||||
|
Optimize your algorithm and give me a new AUC!
|
||||||
|
Bonus Question: Is there a problem with what we are doing?
|
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Reference in New Issue