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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<list>
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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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@ -0,0 +1,14 @@
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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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@ -0,0 +1,14 @@
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<frame title="kNN">
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<split>
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<que>
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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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</que>
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<que>
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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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@ -0,0 +1,14 @@
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<frame title="kNN">
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<split>
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<que>
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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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</que>
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<que>
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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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@ -0,0 +1,3 @@
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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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@ -0,0 +1,6 @@
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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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<que w="0.47619047619047616"><i f="..//prep/06AUC_Score/01dist0.pdf" wmode="True"></i></que>
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</split>
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</frame>
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@ -0,0 +1,22 @@
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<frame title="AUC Score">
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<split>
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<que>
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<list>
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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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<l2st>
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<e>$\frac{FP}{FP+TN}$</e>
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</l2st>
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<e>True Positive Rate</e>
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<l2st>
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<e>$\frac{TP}{TP+FN}$</e>
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</l2st>
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<e>ROC-AUC: Integral of this curve!</e>
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</list>
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</que>
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<que>
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<i f="..//prep/07AUC_Score/roc.pdf" wmode="True"></i>
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</que>
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</split>
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</frame>
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@ -0,0 +1,9 @@
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<frame title="AUC Score">
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<list>
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<e>calculcate with \emph{sklearn.metrics.roc\_auc\_score}</e>
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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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<e>$AUC=0.5$->Random model</e>
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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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</list>
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</frame>
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@ -0,0 +1,3 @@
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<frame title="AUC Scores">
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<i f="..//prep/09AUC_Scores/students.png" wmode="True"></i>
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</frame>
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<frame title="AutoML">
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<list>
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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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</list>
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||||
</frame>
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@ -0,0 +1,3 @@
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<frame title="Optimize">
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||||
<i f="..//prep/11Optimize/baseline.png" wmode="True"></i>
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||||
</frame>
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||||
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@ -0,0 +1,3 @@
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<frame title="Optimize">
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<i f="..//prep/12Optimize/optimize.png" wmode="True"></i>
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||||
</frame>
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||||
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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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<e>\emph{source folder/bin/activate}</e>
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<e>\emph{pip install flaml}</e>
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</list>
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</que>
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<que>
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<i f="..//prep/15flaml/forflaml.png" wmode="True"></i>
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</que>
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</split>
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</frame>
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@ -0,0 +1,3 @@
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<frame title="flaml">
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<i f="..//prep/16flaml/flaml.png" wmode="True"></i>
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</frame>
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@ -0,0 +1,3 @@
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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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<list>
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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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<e>Optimize your algorithm and give me a new AUC!</e>
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<e>Bonus Question: Is there a problem with what we are doing?</e>
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</list>
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</frame>
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@ -0,0 +1,12 @@
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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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<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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@ -0,0 +1,6 @@
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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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->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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After Width: | Height: | Size: 648 KiB |
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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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After Width: | Height: | Size: 47 KiB |
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@ -0,0 +1,3 @@
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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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@ -0,0 +1,3 @@
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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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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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Build groups for each algorithm
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<l2st>
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Choose a set of Hyperparameters
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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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</l2st>
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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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||||
<l2st>
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||||
MD Req 1->MD Req 8
|
||||
Basic Python/Math Knowledge
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||||
Motivation to learn something new;)
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||||
</l2st>
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||||
Registration till Saturday, by Email to Simon.Kluettermann@cs.tu-dortmund.de
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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 +1,3 @@
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pdflatex main.tex
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||||
pdflatex main.tex
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||||
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||||
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@ -0,0 +1,110 @@
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|||
[
|
||||
{
|
||||
"typ": "img",
|
||||
"files": [
|
||||
"..//prep/03kNN/yanghuang 08.png"
|
||||
],
|
||||
"label": "prep03kNNyanghuang 08png",
|
||||
"caption": "",
|
||||
"where": "../case2/data/003kNN.txt"
|
||||
},
|
||||
{
|
||||
"typ": "img",
|
||||
"files": [
|
||||
"..//prep/04kNN/dist0.pdf"
|
||||
],
|
||||
"label": "prep04kNNdist0pdf",
|
||||
"caption": "",
|
||||
"where": "../case2/data/004kNN.txt"
|
||||
},
|
||||
{
|
||||
"typ": "img",
|
||||
"files": [
|
||||
"..//prep/05/dist0.pdf"
|
||||
],
|
||||
"label": "prep05dist0pdf",
|
||||
"caption": "",
|
||||
"where": "../case2/data/005.txt"
|
||||
},
|
||||
{
|
||||
"typ": "img",
|
||||
"files": [
|
||||
"..//prep/06AUC_Score/02confusion.png"
|
||||
],
|
||||
"label": "prep06AUC_Score02confusionpng",
|
||||
"caption": "",
|
||||
"where": "../case2/data/006AUC Score.txt"
|
||||
},
|
||||
{
|
||||
"typ": "img",
|
||||
"files": [
|
||||
"..//prep/06AUC_Score/01dist0.pdf"
|
||||
],
|
||||
"label": "prep06AUC_Score01dist0pdf",
|
||||
"caption": "",
|
||||
"where": "../case2/data/006AUC Score.txt"
|
||||
},
|
||||
{
|
||||
"typ": "img",
|
||||
"files": [
|
||||
"..//prep/07AUC_Score/roc.pdf"
|
||||
],
|
||||
"label": "prep07AUC_Scorerocpdf",
|
||||
"caption": "",
|
||||
"where": "../case2/data/007AUC Score.txt"
|
||||
},
|
||||
{
|
||||
"typ": "img",
|
||||
"files": [
|
||||
"..//prep/09AUC_Scores/students.png"
|
||||
],
|
||||
"label": "prep09AUC_Scoresstudentspng",
|
||||
"caption": "",
|
||||
"where": "../case2/data/009AUC Scores.txt"
|
||||
},
|
||||
{
|
||||
"typ": "img",
|
||||
"files": [
|
||||
"..//prep/11Optimize/baseline.png"
|
||||
],
|
||||
"label": "prep11Optimizebaselinepng",
|
||||
"caption": "",
|
||||
"where": "../case2/data/011Optimize.txt"
|
||||
},
|
||||
{
|
||||
"typ": "img",
|
||||
"files": [
|
||||
"..//prep/12Optimize/optimize.png"
|
||||
],
|
||||
"label": "prep12Optimizeoptimizepng",
|
||||
"caption": "",
|
||||
"where": "../case2/data/012Optimize.txt"
|
||||
},
|
||||
{
|
||||
"typ": "img",
|
||||
"files": [
|
||||
"..//prep/15flaml/forflaml.png"
|
||||
],
|
||||
"label": "prep15flamlforflamlpng",
|
||||
"caption": "",
|
||||
"where": "../case2/data/013flaml.txt"
|
||||
},
|
||||
{
|
||||
"typ": "img",
|
||||
"files": [
|
||||
"..//prep/16flaml/flaml.png"
|
||||
],
|
||||
"label": "prep16flamlflamlpng",
|
||||
"caption": "",
|
||||
"where": "../case2/data/014flaml.txt"
|
||||
},
|
||||
{
|
||||
"typ": "img",
|
||||
"files": [
|
||||
"..//prep/17/hist.pdf"
|
||||
],
|
||||
"label": "prep17histpdf",
|
||||
"caption": "",
|
||||
"where": "../case2/data/015.txt"
|
||||
}
|
||||
]
|
||||
|
|
@ -0,0 +1,138 @@
|
|||
\relax
|
||||
\providecommand\hyper@newdestlabel[2]{}
|
||||
\providecommand\HyperFirstAtBeginDocument{\AtBeginDocument}
|
||||
\HyperFirstAtBeginDocument{\ifx\hyper@anchor\@undefined
|
||||
\global\let\oldcontentsline\contentsline
|
||||
\gdef\contentsline#1#2#3#4{\oldcontentsline{#1}{#2}{#3}}
|
||||
\global\let\oldnewlabel\newlabel
|
||||
\gdef\newlabel#1#2{\newlabelxx{#1}#2}
|
||||
\gdef\newlabelxx#1#2#3#4#5#6{\oldnewlabel{#1}{{#2}{#3}}}
|
||||
\AtEndDocument{\ifx\hyper@anchor\@undefined
|
||||
\let\contentsline\oldcontentsline
|
||||
\let\newlabel\oldnewlabel
|
||||
\fi}
|
||||
\fi}
|
||||
\global\let\hyper@last\relax
|
||||
\gdef\HyperFirstAtBeginDocument#1{#1}
|
||||
\providecommand\HyField@AuxAddToFields[1]{}
|
||||
\providecommand\HyField@AuxAddToCoFields[2]{}
|
||||
\@writefile{nav}{\headcommand {\slideentry {0}{0}{1}{1/1}{}{0}}}
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||||
\@writefile{nav}{\headcommand {\beamer@framepages {1}{1}}}
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||||
\newlabel{Anomaly Detection<1>}{{2}{2}{}{Doc-Start}{}}
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||||
\@writefile{snm}{\beamer@slide {Anomaly Detection<1>}{2}}
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||||
\newlabel{Anomaly Detection}{{2}{2}{}{Doc-Start}{}}
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||||
\@writefile{snm}{\beamer@slide {Anomaly Detection}{2}}
|
||||
\@writefile{nav}{\headcommand {\slideentry {0}{0}{2}{2/2}{}{0}}}
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||||
\@writefile{nav}{\headcommand {\beamer@framepages {2}{2}}}
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||||
\newlabel{Unsupervised Machine Learning<1>}{{3}{3}{}{Doc-Start}{}}
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||||
\@writefile{snm}{\beamer@slide {Unsupervised Machine Learning<1>}{3}}
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||||
\newlabel{Unsupervised Machine Learning}{{3}{3}{}{Doc-Start}{}}
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||||
\@writefile{snm}{\beamer@slide {Unsupervised Machine Learning}{3}}
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||||
\@writefile{nav}{\headcommand {\slideentry {0}{0}{3}{3/3}{}{0}}}
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\@writefile{nav}{\headcommand {\beamer@framepages {3}{3}}}
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\newlabel{kNN<1>}{{4}{4}{}{Doc-Start}{}}
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\@writefile{snm}{\beamer@slide {kNN<1>}{4}}
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\newlabel{kNN}{{4}{4}{}{Doc-Start}{}}
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\@writefile{snm}{\beamer@slide {kNN}{4}}
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\newlabel{fig:prep03kNNyanghuang 08png}{{4}{4}{}{Doc-Start}{}}
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\@writefile{snm}{\beamer@slide {fig:prep03kNNyanghuang 08png}{4}}
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\@writefile{nav}{\headcommand {\slideentry {0}{0}{4}{4/4}{}{0}}}
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\@writefile{nav}{\headcommand {\beamer@framepages {4}{4}}}
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\newlabel{kNN<1>}{{5}{5}{}{Doc-Start}{}}
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\@writefile{snm}{\beamer@slide {kNN<1>}{5}}
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||||
\newlabel{kNN}{{5}{5}{}{Doc-Start}{}}
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||||
\@writefile{snm}{\beamer@slide {kNN}{5}}
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||||
\newlabel{fig:prep04kNNdist0pdf}{{5}{5}{}{Doc-Start}{}}
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\@writefile{snm}{\beamer@slide {fig:prep04kNNdist0pdf}{5}}
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\@writefile{nav}{\headcommand {\slideentry {0}{0}{5}{5/5}{}{0}}}
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||||
\@writefile{nav}{\headcommand {\beamer@framepages {5}{5}}}
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||||
\newlabel{fig:prep05dist0pdf}{{6}{6}{}{Doc-Start}{}}
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\@writefile{snm}{\beamer@slide {fig:prep05dist0pdf}{6}}
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\@writefile{nav}{\headcommand {\slideentry {0}{0}{6}{6/6}{}{0}}}
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\vspace{1.5cm}
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{\scshape\Large Master Thesis in Physics\par}
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|
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|
||||
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||||
\vfill
|
||||
|
||||
% Bottom of the page
|
||||
{\large November 2020 \par}
|
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|
||||
|
||||
|
||||
%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]
|
||||
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|
||||
\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}
|
||||
|
||||
|
||||
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|
||||
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|
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|
||||
|
||||
\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}
|
||||
|
||||
\item Remember your last algorithm
|
||||
|
||||
\item Find its hyperparameters (Tip: pyod website)
|
||||
|
||||
\item Optimize your algorithm and give me a new AUC!
|
||||
|
||||
\item Bonus Question: Is there a problem with what we are doing?
|
||||
|
||||
|
||||
\end{itemize}
|
||||
\end{frame}
|
||||
|
||||
|
||||
|
||||
\end{document}
|
||||
|
|
@ -0,0 +1 @@
|
|||
<titlepage>
|
||||
|
|
@ -0,0 +1,6 @@
|
|||
Find strange (unexpected) samples.
|
||||
->If a traffic light is constantly yellow, probably something broke
|
||||
But this could happen in a lot of different ways
|
||||
->Most likely the traffic light is just off. But it could also fluctuate quickly or start smoking
|
||||
How to cover all possible anomalies?
|
||||
->Unsupervised Machine Learning
|
||||
|
|
@ -0,0 +1,10 @@
|
|||
Normal machine learning: Input - Label
|
||||
Here: Only Input.
|
||||
->Instead of classifying different types, try to understand your given dataset
|
||||
Deviations from this understanding are anomalies
|
||||
<l2st>
|
||||
x: training samples
|
||||
tx: test samples
|
||||
ty: test labels (is a certain sample an anomaly or not)
|
||||
</l2st>
|
||||
Useful: \emph{peak /global/cardio.npz}
|
||||
|
|
@ -0,0 +1,3 @@
|
|||
How to do this? Here one algorithm: kNN
|
||||
Goal: Generate an anomaly score (high value->highly anomalous)
|
||||
Here: The anomaly score is the distance to the kth closest samples
|
||||
Binary file not shown.
|
After Width: | Height: | Size: 20 KiB |
Binary file not shown.
|
|
@ -0,0 +1,3 @@
|
|||
How to do this? Here one algorithm: kNN
|
||||
Goal: Generate an anomaly score (high value->highly anomalous)
|
||||
Here: The anomaly score is the distance to the kth closest samples
|
||||
Binary file not shown.
Binary file not shown.
Binary file not shown.
|
After Width: | Height: | Size: 28 KiB |
|
|
@ -0,0 +1,13 @@
|
|||
Iterate every threshold
|
||||
Plot fpr vs tpr
|
||||
False Positive Rate
|
||||
<l2st>
|
||||
$\frac{FP}{FP+TN}$
|
||||
</l2st>
|
||||
True Positive Rate
|
||||
<l2st>
|
||||
$\frac{TP}{TP+FN}$
|
||||
</l2st>
|
||||
ROC-AUC: Integral of this curve!
|
||||
|
||||
|
||||
Binary file not shown.
|
|
@ -0,0 +1,5 @@
|
|||
calculcate with \emph{sklearn.metrics.roc\_auc\_score}
|
||||
Higher AUC score->better
|
||||
$AUC=1.0$->Perfect seperation
|
||||
$AUC=0.5$->Random model
|
||||
$AUC=0.0$->Inverse seperation (every anomaly is normal, and every normal sample is anomalous)
|
||||
Binary file not shown.
|
After Width: | Height: | Size: 78 KiB |
|
|
@ -0,0 +1,7 @@
|
|||
But: We can beat this!
|
||||
How? Hyperparameter
|
||||
<l2st>
|
||||
Every algorithm has hyperparameter that control how it works
|
||||
For example: k in kNN (number of close points considered)
|
||||
</l2st>
|
||||
Lets take the worst algorithm (kNN: $0.927$) and try to improve it
|
||||
Binary file not shown.
|
After Width: | Height: | Size: 33 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 56 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 18 KiB |
|
|
@ -0,0 +1,2 @@
|
|||
\emph{source folder/bin/activate}
|
||||
\emph{pip install flaml}
|
||||
Binary file not shown.
|
After Width: | Height: | Size: 26 KiB |
Binary file not shown.
|
|
@ -0,0 +1,4 @@
|
|||
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?
|
||||
Binary file not shown.
|
After Width: | Height: | Size: 20 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 989 KiB |
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|
After Width: | Height: | Size: 265 KiB |
Loading…
Reference in New Issue