initial push
This commit is contained in:
Simon Klüttermann
commit
ce067dbbf2
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<frame >
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<titlepage>
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</frame>
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<frame title="Problem">
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<list>
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<e>Paper with Benedikt</e>
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<e>require multiple very specific datasets</e>
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<l2st>
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<e>many but not to many features</e>
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<e>at least some samples (for the NN)</e>
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<e>Only numerical attributes best</e>
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<e>specific quality</e>
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<e>unrelated datasets</e>
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</l2st>
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<e>Requires you to search for many datasets and filter them</e>
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</list>
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</frame>
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@ -0,0 +1,9 @@
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<frame title="Students">
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<list>
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<e>Not clear what you can use</e>
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<e>Many different formats</e>
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<e>train/test splits</e>
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<e>So for Students I just do this work and send them archives directly</e>
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<e>->Not a good solution</e>
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</list>
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</frame>
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@ -0,0 +1,12 @@
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<frame title="yano">
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<list>
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<e>So I have been packaging all my scripts</e>
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<e>I had surprisingly much fun doing this</e>
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<l2st>
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<e>More than just standard functions</e>
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<e>A couple of weird decisions</e>
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<e>And this will likely grow further</e>
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</l2st>
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<e>->So I would like to discuss some parts with you and maybe you even have more features you might want</e>
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</list>
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</frame>
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@ -0,0 +1,17 @@
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<frame title="yano">
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<split>
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<que>
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<list>
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<e>Simply install it over pip</e>
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<e>Contains 187 real-World Datasets</e>
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<e>->biggest library of datasets explicitely for anomaly detection</e>
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<e>not yet happy with this</e>
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<e>especially only mostly contains numerical and nominal attributes</e>
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<e>->few categorical and no time-series attributes</e>
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</list>
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</que>
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<que>
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<i f="../prep/04yano/a.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 +1,17 @@
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<section Basics>
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<frame title="selector">
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<code>
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import yano
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from yano.symbols import *
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condition= (number_of_features>5) &
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(number_of_features<100) &
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(number_of_samples>100) &
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(number_of_samples<10000) &
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(number_of_samples>2*number_of_features) &
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~index
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print(len(condition), "Datasets found")
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</code>
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->33 Datasets found
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</frame>
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@ -0,0 +1,29 @@
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<frame title="selectors">
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<list>
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<e>Lots of symbols like this</e>
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<l2st>
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<e>name</e>
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<e>number\_of\_features</e>
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<e>number\_of\_samples</e>
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<e>index (correlated datasets)</e>
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</l2st>
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<e>Feature types</e>
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<l2st>
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<e>numeric</e>
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<e>nominal</e>
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<e>categorical</e>
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<e>(textual)</e>
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</l2st>
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<e>Count based</e>
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<l2st>
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<e>number\_anomalies</e>
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<e>number\_normals</e>
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<e>fraction\_anomalies</e>
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</l2st>
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<e>Specific ones</e>
|
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<l2st>
|
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<e>image\_based</e>
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<e>(linearly\_seperable)</e>
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</l2st>
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</list>
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</frame>
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<frame title="iterating">
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<code>
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for dataset in condition:
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print(condition)
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</code>
|
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<l2st>
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<e>\[annthyroid\]</e>
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<e>\[breastw\]</e>
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<e>\[cardio\]</e>
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<e>\[...\]</e>
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<e>\[Housing\_low\]</e>
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</l2st>
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</frame>
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<frame title="iterating">
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|
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<code>
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for dataset in condition:
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x=dataset.getx()
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y=dataset.gety()
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</code>
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</frame>
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<frame title="pipeline">
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|
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<code>
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from yano.iter import *
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for dataset, x,tx,ty in pipeline(condition,
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split,
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shuffle,
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normalize("minmax")):
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...
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</code>
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</frame>
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<frame title="pipeline">
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<list>
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<e>Again there are a couple modifiers possible</e>
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<l2st>
|
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<e>nonconst->remove constant features</e>
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<e>shuffle</e>
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<e>normalize('zscore'/'minmax')</e>
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<e>cut(10)->at most 10 datasets</e>
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<e>split->train test split, all anomalies in test set</e>
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<e>crossval(5)->similar to split, but do multiple times (crossvalidation)</e>
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</l2st>
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<e>modifiers interact with each other</e>
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<e>For example: normalize('minmax'), split</e>
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<e>->train set always below 1, but no guarantees for the test set</e>
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</list>
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</frame>
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<frame title="CrossValidation">
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<list>
|
||||
<e>Learned from DMC: Crossvalidation is important</e>
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<e>Rarely found in Anomaly Detection, why?</e>
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<e>A bit more complicated (not all samples are equal), but no reason why not</e>
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<e>->So I implemented it into yano</e>
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<l2st>
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<e>folding only on normal data</e>
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<e>How to handle anomalies?</e>
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<e>If not folding them, cross-validation less useful</e>
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<e>if folding them, often rare anomalies even more rare</e>
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<e>->test set always 50\% anomalous</e>
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<e>->Also improves simple evaluation metrics (accuracy)</e>
|
||||
</l2st>
|
||||
<e>Do you know a reason why Cross Validation is not common in AD?</e>
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<e>Are there Problems with the way I fold my Anomalies?</e>
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||||
</list>
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||||
</frame>
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<frame title="Logging">
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|
||||
<code>
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from yano.logging import Logger
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||||
from pyod.models.iforest import IForest
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from extended_iforest import train_extended_ifor
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l=Logger({"IFor":IForest(n_estimators=100),
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"eIFor":train_extended_ifor})
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for dataset, folds in pipeline(condition,
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crossval(5),
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normalize("minmax"),
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shuffle):
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l.run_cross(dataset, folds)
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latex=l.to_latex()
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</code>
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|
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</frame>
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<frame title="Seeding">
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||||
<list>
|
||||
<e>If you dont do anything, everything is seeded.</e>
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<e>Makes rerunning a Model until the performance is good quite obvious.</e>
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||||
<e>But as every Run is seeded itself, this might induce bias.</e>
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||||
<e>Do you think this is worth it?</e>
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||||
<e>Are there any Problems with this?</e>
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||||
</list>
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||||
</frame>
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<frame >
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\begin{tabular}{lll}
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\hline
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Dataset & eIFor & IFor \\
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\hline
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$pc3$ & $\textbf{0.7231} \pm 0.0153$ & $\textbf{0.7223} \pm 0.0178$ \\
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$pima$ & $\textbf{0.7405} \pm 0.0110$ & $\textbf{0.7347} \pm 0.0126$ \\
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$Diabetes\_present$ & $\textbf{0.7414} \pm 0.0195$ & $\textbf{0.7344} \pm 0.0242$ \\
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$waveform-5000$ & $\textbf{0.7687} \pm 0.0123$ & $\textbf{0.7592} \pm 0.0206$ \\
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$vowels$ & $\textbf{0.7843} \pm 0.0298$ & $\textbf{0.7753} \pm 0.0334$ \\
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$Vowel\_0$ & $\textbf{0.8425} \pm 0.0698$ & $0.7193 \pm 0.0817$ \\
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$Abalone\_1\_8$ & $\textbf{0.8525} \pm 0.0263$ & $0.8452 \pm 0.0257$ \\
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$annthyroid$ & $0.8399 \pm 0.0135$ & $\textbf{0.9087} \pm 0.0090$ \\
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$Vehicle\_van$ & $\textbf{0.8792} \pm 0.0265$ & $\textbf{0.8697} \pm 0.0383$ \\
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$ionosphere$ & $\textbf{0.9320} \pm 0.0069$ & $0.9086 \pm 0.0142$ \\
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$breastw$ & $\textbf{0.9948} \pm 0.0031$ & $\textbf{0.9952} \pm 0.0033$ \\
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$segment$ & $\textbf{1.0}$ & $\textbf{0.9993} \pm 0.0015$ \\
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$$ & $$ & $$ \\
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$Average$ & $\textbf{0.8005}$ & $\textbf{0.7957}$ \\
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\hline
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\end{tabular}
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||||
</frame>
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<frame title="statistics">
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<list>
|
||||
<e>Friedman test to see if there is a difference between models</e>
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||||
<e>Nemenyi test to see which models are equal, mark those equal to the maximum</e>
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||||
<e>For 2 models, Friedman not defined -> use Wilcoxon test</e>
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||||
<e>Does this match your expectation from the table?</e>
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||||
<e>Two models are 'equal' if their probability of being from the same distribution is #LessThan(p_b,p)#, what value should #Eq(p_b,0.1)# have?</e>
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||||
<e>Do I need to correct for p hacking (n experiments, so increase the difficulty for each, or is that clear from the table)</e>
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||||
</list>
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||||
</frame>
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||||
|
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@ -0,0 +1,24 @@
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|||
<section Experiments 1>
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||||
<repeat w="['ifor','eifor','qual']">
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||||
<frame title="Extended Isolation Forests">
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<split>
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||||
<que>
|
||||
<list>
|
||||
<e>Isolation Forests are one algorithm for AD</e>
|
||||
<e>Tries to isolate abnormal (rare) points instead of modelling normal ones</e>
|
||||
<e>Creative approach->fairly successful (3000 Citations)</e>
|
||||
<e>Many follow up papers</e>
|
||||
<e>Extended Isolation Forest (Hariri et. al. 2018, 140 Citations)</e>
|
||||
<e>Remove bias from the Isolation Forests</e>
|
||||
<e>Also claim to improve their anomaly detection quality</e>
|
||||
</list>
|
||||
</que>
|
||||
|
||||
<que>
|
||||
<i f="???" wmode="True"></i>
|
||||
</que>
|
||||
</split>
|
||||
|
||||
</frame>
|
||||
|
||||
</repeat>
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||||
|
|
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|
|||
<frame >
|
||||
|
||||
\begin{tabular}{lll}
|
||||
\hline
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||||
Dataset & eIFor & IFor \\
|
||||
\hline
|
||||
$Delft\_pump\_5x3\_noisy$ & $\textbf{0.3893} \pm 0.0345$ & $\textbf{0.4272} \pm 0.0680$ \\
|
||||
$vertebral$ & $\textbf{0.4260} \pm 0.0111$ & $\textbf{0.4554} \pm 0.0416$ \\
|
||||
$Liver\_1$ & $0.5367 \pm 0.0508$ & $\textbf{0.5474} \pm 0.0541$ \\
|
||||
$Sonar\_mines$ & $\textbf{0.6882} \pm 0.1264$ & $0.6189 \pm 0.1301$ \\
|
||||
$letter$ & $\textbf{0.6756} \pm 0.0119$ & $0.6471 \pm 0.0111$ \\
|
||||
$Glass\_building\_float$ & $\textbf{0.6480} \pm 0.1012$ & $\textbf{0.6755} \pm 0.1117$ \\
|
||||
$pc3$ & $\textbf{0.7231} \pm 0.0153$ & $\textbf{0.7223} \pm 0.0178$ \\
|
||||
$pima$ & $\textbf{0.7405} \pm 0.0110$ & $\textbf{0.7347} \pm 0.0126$ \\
|
||||
$Diabetes\_present$ & $\textbf{0.7414} \pm 0.0195$ & $\textbf{0.7344} \pm 0.0242$ \\
|
||||
$waveform-5000$ & $\textbf{0.7687} \pm 0.0123$ & $\textbf{0.7592} \pm 0.0206$ \\
|
||||
$steel-plates-fault$ & $\textbf{0.7735} \pm 0.0351$ & $\textbf{0.7682} \pm 0.0402$ \\
|
||||
$vowels$ & $\textbf{0.7843} \pm 0.0298$ & $\textbf{0.7753} \pm 0.0334$ \\
|
||||
\hline
|
||||
\end{tabular}
|
||||
|
||||
</frame>
|
||||
|
|
@ -0,0 +1,22 @@
|
|||
<frame >
|
||||
|
||||
\begin{tabular}{lll}
|
||||
\hline
|
||||
Dataset & eIFor & IFor \\
|
||||
\hline
|
||||
$Vowel\_0$ & $\textbf{0.8425} \pm 0.0698$ & $0.7193 \pm 0.0817$ \\
|
||||
$Housing\_low$ & $\textbf{0.7807} \pm 0.0333$ & $\textbf{0.7862} \pm 0.0336$ \\
|
||||
$ozone-level-8hr$ & $\textbf{0.7904} \pm 0.0207$ & $\textbf{0.7768} \pm 0.0118$ \\
|
||||
$Spectf\_0$ & $\textbf{0.8155} \pm 0.0255$ & $0.7535 \pm 0.0239$ \\
|
||||
$HeartC$ & $0.7795 \pm 0.0258$ & $\textbf{0.8079} \pm 0.0255$ \\
|
||||
$satellite$ & $\textbf{0.8125} \pm 0.0170$ & $\textbf{0.8103} \pm 0.0061$ \\
|
||||
$optdigits$ & $\textbf{0.8099} \pm 0.0310$ & $\textbf{0.8142} \pm 0.0267$ \\
|
||||
$spambase$ & $\textbf{0.8085} \pm 0.0110$ & $\textbf{0.8202} \pm 0.0042$ \\
|
||||
$Abalone\_1\_8$ & $\textbf{0.8525} \pm 0.0263$ & $0.8452 \pm 0.0257$ \\
|
||||
$qsar-biodeg$ & $\textbf{0.8584} \pm 0.0119$ & $\textbf{0.8628} \pm 0.0135$ \\
|
||||
$annthyroid$ & $0.8399 \pm 0.0135$ & $\textbf{0.9087} \pm 0.0090$ \\
|
||||
$Vehicle\_van$ & $\textbf{0.8792} \pm 0.0265$ & $\textbf{0.8697} \pm 0.0383$ \\
|
||||
\hline
|
||||
\end{tabular}
|
||||
|
||||
</frame>
|
||||
|
|
@ -0,0 +1,21 @@
|
|||
<frame >
|
||||
|
||||
\begin{tabular}{lll}
|
||||
\hline
|
||||
Dataset & eIFor & IFor \\
|
||||
\hline
|
||||
$ionosphere$ & $\textbf{0.9320} \pm 0.0069$ & $0.9086 \pm 0.0142$ \\
|
||||
$page-blocks$ & $0.9189 \pm 0.0061$ & $\textbf{0.9299} \pm 0.0016$ \\
|
||||
$Ecoli$ & $\textbf{0.9418} \pm 0.0292$ & $0.9192 \pm 0.0332$ \\
|
||||
$cardio$ & $\textbf{0.9564} \pm 0.0043$ & $\textbf{0.9535} \pm 0.0036$ \\
|
||||
$wbc$ & $\textbf{0.9611} \pm 0.0121$ & $\textbf{0.9607} \pm 0.0107$ \\
|
||||
$pendigits$ & $\textbf{0.9641} \pm 0.0097$ & $\textbf{0.9652} \pm 0.0076$ \\
|
||||
$thyroid$ & $0.9818 \pm 0.0024$ & $\textbf{0.9871} \pm 0.0025$ \\
|
||||
$breastw$ & $\textbf{0.9948} \pm 0.0031$ & $\textbf{0.9952} \pm 0.0033$ \\
|
||||
$segment$ & $\textbf{1.0}$ & $\textbf{0.9993} \pm 0.0015$ \\
|
||||
$$ & $$ & $$ \\
|
||||
$Average$ & $\textbf{0.8005} \pm 0.1458$ & $\textbf{0.7957} \pm 0.1431$ \\
|
||||
\hline
|
||||
\end{tabular}
|
||||
|
||||
</frame>
|
||||
|
|
@ -0,0 +1,4 @@
|
|||
<section Experiments 2>
|
||||
<frame title="highdim">
|
||||
<i f="../prep/19highdim/a.png" wmode="True"></i>
|
||||
</frame>
|
||||
|
|
@ -0,0 +1,13 @@
|
|||
<frame title="New Condition">
|
||||
|
||||
<code>
|
||||
condition= (number_of_samples>200) &
|
||||
(number_of_samples<10000) &
|
||||
(number_of_features>50) &
|
||||
(number_of_features<500) &
|
||||
~index
|
||||
print(len(condition),"Datasets found")
|
||||
</code>
|
||||
->13 Datasets found
|
||||
|
||||
</frame>
|
||||
|
|
@ -0,0 +1,12 @@
|
|||
<frame title="New Models">
|
||||
|
||||
<code>
|
||||
from pyod.models.iforest import IForest
|
||||
from pyod.models.knn import KNN
|
||||
from pyod.models.lof import LOF
|
||||
l=Logger({"IFor":Iforest(n_estimators=100),
|
||||
"Lof":LOF(),
|
||||
"Knn": KNN()}, addfeat=True)
|
||||
</code>
|
||||
|
||||
</frame>
|
||||
|
|
@ -0,0 +1,25 @@
|
|||
<frame >
|
||||
|
||||
\begin{tabular}{llll}
|
||||
\hline
|
||||
Dataset & Knn & Lof & IFor \\
|
||||
\hline
|
||||
$Delft\_pump\_5x3\_noisy(64)$ & $0.3800 \pm 0.0475$ & $0.3462 \pm 0.0327$ & $\textbf{0.4272} \pm 0.0680$ \\
|
||||
$hill-valley(100)$ & $0.4744 \pm 0.0269$ & $\textbf{0.5060} \pm 0.0327$ & $0.4720 \pm 0.0288$ \\
|
||||
$speech(400)$ & $0.4903 \pm 0.0103$ & $\textbf{0.5104} \pm 0.0115$ & $0.4872 \pm 0.0184$ \\
|
||||
$Sonar\_mines(60)$ & $\textbf{0.7284} \pm 0.0939$ & $0.6769 \pm 0.0933$ & $0.6189 \pm 0.1301$ \\
|
||||
$ozone-level-8hr(72)$ & $\textbf{0.8051} \pm 0.0288$ & $0.7738 \pm 0.0292$ & $\textbf{0.7768} \pm 0.0118$ \\
|
||||
$spambase(57)$ & $0.8038 \pm 0.0125$ & $0.7712 \pm 0.0055$ & $\textbf{0.8202} \pm 0.0042$ \\
|
||||
$arrhythmia(274)$ & $\textbf{0.8137} \pm 0.0185$ & $0.8042 \pm 0.0186$ & $\textbf{0.8086} \pm 0.0099$ \\
|
||||
$mnist(100)$ & $0.9345 \pm 0.0039$ & $\textbf{0.9548} \pm 0.0037$ & $0.8732 \pm 0.0069$ \\
|
||||
$Concordia3\_32(256)$ & $0.9246 \pm 0.0107$ & $\textbf{0.9486} \pm 0.0099$ & $\textbf{0.9322} \pm 0.0178$ \\
|
||||
$optdigits(64)$ & $0.9966 \pm 0.0012$ & $\textbf{0.9975} \pm 0.0012$ & $0.8142 \pm 0.0267$ \\
|
||||
$gas-drift(128)$ & $\textbf{0.9790} \pm 0.0018$ & $0.9585 \pm 0.0055$ & $0.8764 \pm 0.0166$ \\
|
||||
$Delft\_pump\_AR(160)$ & $\textbf{0.9965}$ & $\textbf{0.9953} \pm 0.0019$ & $0.9665 \pm 0.0096$ \\
|
||||
$musk(166)$ & $\textbf{1.0}$ & $\textbf{1.0}$ & $0.9808 \pm 0.0117$ \\
|
||||
$$ & $$ & $$ & $$ \\
|
||||
$Average$ & $\textbf{0.7944}$ & $\textbf{0.7879}$ & $0.7580$ \\
|
||||
\hline
|
||||
\end{tabular}
|
||||
|
||||
</frame>
|
||||
|
|
@ -0,0 +1,11 @@
|
|||
<frame >
|
||||
|
||||
<l2st>
|
||||
<e>Hypothesis: Isolation Forests are better when there are numerical and nominal attributes</e>
|
||||
<e>Easy to test</e>
|
||||
</l2st>
|
||||
<code>
|
||||
condition=condition & (numeric & nominal)
|
||||
</code>
|
||||
|
||||
</frame>
|
||||
|
|
@ -0,0 +1,20 @@
|
|||
<frame >
|
||||
|
||||
\begin{tabular}{llll}
|
||||
\hline
|
||||
Dataset & Knn & IFor & Lof \\
|
||||
\hline
|
||||
$ozone-level-8hr(72)$ & $\textbf{0.8051} \pm 0.0288$ & $\textbf{0.7768} \pm 0.0118$ & $0.7738 \pm 0.0292$ \\
|
||||
$spambase(57)$ & $0.8038 \pm 0.0125$ & $\textbf{0.8202} \pm 0.0042$ & $0.7712 \pm 0.0055$ \\
|
||||
$arrhythmia(274)$ & $\textbf{0.8137} \pm 0.0185$ & $\textbf{0.8086} \pm 0.0099$ & $0.8042 \pm 0.0186$ \\
|
||||
$musk(166)$ & $\textbf{1.0}$ & $0.9808 \pm 0.0117$ & $\textbf{1.0}$ \\
|
||||
$$ & $$ & $$ & $$ \\
|
||||
$Average$ & $\textbf{0.8556}$ & $\textbf{0.8466}$ & $\textbf{0.8373}$ \\
|
||||
\hline
|
||||
\end{tabular}
|
||||
<l2st>
|
||||
<e>Only 4 datasets, so not clear at all</e>
|
||||
<e>->More datasets</e>
|
||||
</l2st>
|
||||
|
||||
</frame>
|
||||
|
|
@ -0,0 +1,12 @@
|
|||
<section Experiments 3>
|
||||
<frame title="Unsupervised Optimization">
|
||||
<list>
|
||||
<e>There are analysis that are only possible with many datasets</e>
|
||||
<e>Here: unsupervised optimization</e>
|
||||
<e>Given multiple AD models, find which is best:</e>
|
||||
<e>Use AUC score? Requires Anomalies->Overfitting</e>
|
||||
<e>Can you find an unsupervised Method?</e>
|
||||
<e>In general very complicated, so here only focus on very small differences in the model.</e>
|
||||
<e>So each model is an autoencoder, trained on the same dataset, where the difference is only in the initialisation</e>
|
||||
</list>
|
||||
</frame>
|
||||
|
|
@ -0,0 +1,20 @@
|
|||
<repeat w="['page-blocks','pima']">
|
||||
<frame title="Loss Optimization">
|
||||
<split>
|
||||
<que>
|
||||
<list>
|
||||
<e>First guess Loss of the Model on the training Data</e>
|
||||
<e>How to evaluate this?</e>
|
||||
<e>Train many models, look at the average AUC score.</e>
|
||||
<e>For the alternative, take groups of 20 models, and look at the AUC score of the best model.</e>
|
||||
<e>Is there a meaningfull difference between results? Give result as z\_score (#(m_1-m_2)/sqrt(s_1**2+s_2**2)#)</e>
|
||||
<e>This difference depends a lot on the dataset</e>
|
||||
<e>->even #LessThan(30,z)# does not mean much</e>
|
||||
</list>
|
||||
</que>
|
||||
<que>
|
||||
<i f="histone_???" wmode="True"></i>
|
||||
</que>
|
||||
</split>
|
||||
</frame>
|
||||
</repeat>
|
||||
|
|
@ -0,0 +1,12 @@
|
|||
<frame title="loss">
|
||||
<split>
|
||||
<que>
|
||||
<list>
|
||||
<e>Pick the Model with the lowest l2\-loss</e>
|
||||
</list>
|
||||
</que>
|
||||
<que>
|
||||
<i f="../prep/27loss/z_loss.pdf" wmode="True"></i>
|
||||
</que>
|
||||
</split>
|
||||
</frame>
|
||||
|
|
@ -0,0 +1,14 @@
|
|||
<frame title="Robustness">
|
||||
<split>
|
||||
<que>
|
||||
<list>
|
||||
<e>Pick points with 1\% width difference in input space around each point.</e>
|
||||
<e>for each point, find the maximum difference in output space.</e>
|
||||
<e>average this difference</e>
|
||||
</list>
|
||||
</que>
|
||||
<que>
|
||||
<i f="../prep/28Robustness/z_robu.pdf" wmode="True"></i>
|
||||
</que>
|
||||
</split>
|
||||
</frame>
|
||||
|
|
@ -0,0 +1,14 @@
|
|||
<frame title="Distance Correlation">
|
||||
<split>
|
||||
<que>
|
||||
<list>
|
||||
<e>Pick random points in the input space.</e>
|
||||
<e>measure the distance in input and output space</e>
|
||||
<e>a low correlation is a good model</e>
|
||||
</list>
|
||||
</que>
|
||||
<que>
|
||||
<i f="../prep/29Distance_Correlation/z_dist.pdf" wmode="True"></i>
|
||||
</que>
|
||||
</split>
|
||||
</frame>
|
||||
|
|
@ -0,0 +1,12 @@
|
|||
<section Conclusion>
|
||||
<frame title="Other">
|
||||
<list>
|
||||
<e>Things I still want to add:</e>
|
||||
<l2st>
|
||||
<e>Ensemble Methods</e>
|
||||
<e>Visualisation options</e>
|
||||
<e>Alternative Evaluations</e>
|
||||
<e>Hyperparameter optimisation (with crossvalidation)</e>
|
||||
</l2st>
|
||||
</list>
|
||||
</frame>
|
||||
|
|
@ -0,0 +1,7 @@
|
|||
<frame title="Feedback">
|
||||
<list>
|
||||
<e>What do you think about this?</e>
|
||||
<e>Is there something I should also add?</e>
|
||||
<e>What would you need for you to actually use this?</e>
|
||||
</list>
|
||||
</frame>
|
||||
|
|
@ -0,0 +1,12 @@
|
|||
<plt>
|
||||
|
||||
<name Current experiment status>
|
||||
<title pip install yano>
|
||||
<stitle yano>
|
||||
|
||||
<institute ls9 tu Dortmund>
|
||||
|
||||
<theme CambridgeUS>
|
||||
<colo dolphin>
|
||||
|
||||
</plt>
|
||||
|
|
@ -0,0 +1 @@
|
|||
Subproject commit 62ffd6ae589d7983791feea9d44d7658534d54a0
|
||||
|
|
@ -0,0 +1,3 @@
|
|||
pdflatex main.tex
|
||||
pdflatex main.tex
|
||||
|
||||
|
|
@ -0,0 +1,3 @@
|
|||
pdflatex main.tex
|
||||
pdflatex main.tex
|
||||
|
||||
|
|
@ -0,0 +1,127 @@
|
|||
[
|
||||
{
|
||||
"typ": "img",
|
||||
"files": [
|
||||
"../prep/04yano/a.png"
|
||||
],
|
||||
"label": "prep04yanoapng",
|
||||
"caption": "",
|
||||
"where": "../yano//data/004yano.txt"
|
||||
},
|
||||
{
|
||||
"typ": "section",
|
||||
"title": "Basics",
|
||||
"label": "Basics",
|
||||
"file": "../yano//data/005selector.txt",
|
||||
"issec": true
|
||||
},
|
||||
{
|
||||
"typ": "section",
|
||||
"title": "Experiments 1",
|
||||
"label": "Experiments 1",
|
||||
"file": "../yano//data/016Extended Isolation Forests.txt",
|
||||
"issec": true
|
||||
},
|
||||
{
|
||||
"typ": "img",
|
||||
"files": [
|
||||
"../imgs/ifor"
|
||||
],
|
||||
"label": "ifor",
|
||||
"caption": "",
|
||||
"where": "../yano//data/016Extended Isolation Forests.txt"
|
||||
},
|
||||
{
|
||||
"typ": "img",
|
||||
"files": [
|
||||
"../imgs/eifor"
|
||||
],
|
||||
"label": "eifor",
|
||||
"caption": "",
|
||||
"where": "../yano//data/016Extended Isolation Forests.txt"
|
||||
},
|
||||
{
|
||||
"typ": "img",
|
||||
"files": [
|
||||
"../imgs/qual"
|
||||
],
|
||||
"label": "qual",
|
||||
"caption": "",
|
||||
"where": "../yano//data/016Extended Isolation Forests.txt"
|
||||
},
|
||||
{
|
||||
"typ": "section",
|
||||
"title": "Experiments 2",
|
||||
"label": "Experiments 2",
|
||||
"file": "../yano//data/020highdim.txt",
|
||||
"issec": true
|
||||
},
|
||||
{
|
||||
"typ": "img",
|
||||
"files": [
|
||||
"../prep/19highdim/a.png"
|
||||
],
|
||||
"label": "prep19highdimapng",
|
||||
"caption": "",
|
||||
"where": "../yano//data/020highdim.txt"
|
||||
},
|
||||
{
|
||||
"typ": "section",
|
||||
"title": "Experiments 3",
|
||||
"label": "Experiments 3",
|
||||
"file": "../yano//data/026Unsupervised Optimization.txt",
|
||||
"issec": true
|
||||
},
|
||||
{
|
||||
"typ": "img",
|
||||
"files": [
|
||||
"../imgs/histone_page-blocks"
|
||||
],
|
||||
"label": "histone_page-blocks",
|
||||
"caption": "",
|
||||
"where": "../yano//data/027Loss Optimization.txt"
|
||||
},
|
||||
{
|
||||
"typ": "img",
|
||||
"files": [
|
||||
"../imgs/histone_pima"
|
||||
],
|
||||
"label": "histone_pima",
|
||||
"caption": "",
|
||||
"where": "../yano//data/027Loss Optimization.txt"
|
||||
},
|
||||
{
|
||||
"typ": "img",
|
||||
"files": [
|
||||
"../prep/27loss/z_loss.pdf"
|
||||
],
|
||||
"label": "prep27lossz_losspdf",
|
||||
"caption": "",
|
||||
"where": "../yano//data/028loss.txt"
|
||||
},
|
||||
{
|
||||
"typ": "img",
|
||||
"files": [
|
||||
"../prep/28Robustness/z_robu.pdf"
|
||||
],
|
||||
"label": "prep28Robustnessz_robupdf",
|
||||
"caption": "",
|
||||
"where": "../yano//data/029Robustness.txt"
|
||||
},
|
||||
{
|
||||
"typ": "img",
|
||||
"files": [
|
||||
"../prep/29Distance_Correlation/z_dist.pdf"
|
||||
],
|
||||
"label": "prep29Distance_Correlationz_distpdf",
|
||||
"caption": "",
|
||||
"where": "../yano//data/030Distance Correlation.txt"
|
||||
},
|
||||
{
|
||||
"typ": "section",
|
||||
"title": "Conclusion",
|
||||
"label": "Conclusion",
|
||||
"file": "../yano//data/031Other.txt",
|
||||
"issec": true
|
||||
}
|
||||
]
|
||||
|
|
@ -0,0 +1,258 @@
|
|||
\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}}}
|
||||
\@writefile{nav}{\headcommand {\beamer@framepages {1}{1}}}
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||||
\newlabel{Problem<1>}{{2}{2}{}{Doc-Start}{}}
|
||||
\@writefile{snm}{\beamer@slide {Problem<1>}{2}}
|
||||
\newlabel{Problem}{{2}{2}{}{Doc-Start}{}}
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||||
\@writefile{snm}{\beamer@slide {Problem}{2}}
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\@writefile{nav}{\headcommand {\slideentry {0}{0}{2}{2/2}{}{0}}}
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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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|
||||
\beamer@slide {statistics<1>}{16}
|
||||
\beamer@slide {statistics}{16}
|
||||
\beamer@slide {sec:Experiments 1}{17}
|
||||
\beamer@slide {Extended Isolation Forests<1>}{17}
|
||||
\beamer@slide {Extended Isolation Forests}{17}
|
||||
\beamer@slide {fig:ifor}{17}
|
||||
\beamer@slide {Extended Isolation Forests<1>}{18}
|
||||
\beamer@slide {Extended Isolation Forests}{18}
|
||||
\beamer@slide {fig:eifor}{18}
|
||||
\beamer@slide {Extended Isolation Forests<1>}{19}
|
||||
\beamer@slide {Extended Isolation Forests}{19}
|
||||
\beamer@slide {fig:qual}{19}
|
||||
\beamer@slide {sec:Experiments 2}{23}
|
||||
\beamer@slide {highdim<1>}{23}
|
||||
\beamer@slide {highdim}{23}
|
||||
\beamer@slide {fig:prep19highdimapng}{23}
|
||||
\beamer@slide {New Condition<1>}{24}
|
||||
\beamer@slide {New Condition}{24}
|
||||
\beamer@slide {New Models<1>}{25}
|
||||
\beamer@slide {New Models}{25}
|
||||
\beamer@slide {sec:Experiments 3}{29}
|
||||
\beamer@slide {Unsupervised Optimization<1>}{29}
|
||||
\beamer@slide {Unsupervised Optimization}{29}
|
||||
\beamer@slide {Loss Optimization<1>}{30}
|
||||
\beamer@slide {Loss Optimization}{30}
|
||||
\beamer@slide {fig:histone_page-blocks}{30}
|
||||
\beamer@slide {Loss Optimization<1>}{31}
|
||||
\beamer@slide {Loss Optimization}{31}
|
||||
\beamer@slide {fig:histone_pima}{31}
|
||||
\beamer@slide {loss<1>}{32}
|
||||
\beamer@slide {loss}{32}
|
||||
\beamer@slide {fig:prep27lossz_losspdf}{32}
|
||||
\beamer@slide {Robustness<1>}{33}
|
||||
\beamer@slide {Robustness}{33}
|
||||
\beamer@slide {fig:prep28Robustnessz_robupdf}{33}
|
||||
\beamer@slide {Distance Correlation<1>}{34}
|
||||
\beamer@slide {Distance Correlation}{34}
|
||||
\beamer@slide {fig:prep29Distance_Correlationz_distpdf}{34}
|
||||
\beamer@slide {sec:Conclusion}{35}
|
||||
\beamer@slide {Other<1>}{35}
|
||||
\beamer@slide {Other}{35}
|
||||
\beamer@slide {Feedback<1>}{36}
|
||||
\beamer@slide {Feedback}{36}
|
||||
File diff suppressed because it is too large
Load Diff
|
|
@ -0,0 +1,5 @@
|
|||
\beamer@sectionintoc {1}{Basics}{6}{0}{1}
|
||||
\beamer@sectionintoc {2}{Experiments 1}{17}{0}{2}
|
||||
\beamer@sectionintoc {3}{Experiments 2}{23}{0}{3}
|
||||
\beamer@sectionintoc {4}{Experiments 3}{29}{0}{4}
|
||||
\beamer@sectionintoc {5}{Conclusion}{35}{0}{5}
|
||||
|
|
@ -0,0 +1 @@
|
|||
<titlepage>
|
||||
|
|
@ -0,0 +1,10 @@
|
|||
Paper with Benedikt
|
||||
require multiple very specific datasets
|
||||
<l2st>
|
||||
many but not to many features
|
||||
at least some samples (for the NN)
|
||||
Only numerical attributes best
|
||||
specific quality
|
||||
unrelated datasets
|
||||
</l2st>
|
||||
Requires you to search for many datasets and filter them
|
||||
|
|
@ -0,0 +1,6 @@
|
|||
Not clear what you can use
|
||||
Many different formats
|
||||
train/test splits
|
||||
So for Students I just do this work and send them archives directly
|
||||
->Not a good solution
|
||||
|
||||
|
|
@ -0,0 +1,8 @@
|
|||
So I have been packaging all my scripts
|
||||
I had surprisingly much fun doing this
|
||||
<l2st>
|
||||
More than just standard functions
|
||||
A couple of weird decisions
|
||||
And this will likely grow further
|
||||
</l2st>
|
||||
->So I would like to discuss some parts with you and maybe you even have more features you might want
|
||||
Binary file not shown.
|
After Width: | Height: | Size: 16 KiB |
|
|
@ -0,0 +1,6 @@
|
|||
Simply install it over pip
|
||||
Contains 187 real-World Datasets
|
||||
->biggest library of datasets explicitely for anomaly detection
|
||||
not yet happy with this
|
||||
especially only mostly contains numerical and nominal attributes
|
||||
->few categorical and no time-series attributes
|
||||
|
|
@ -0,0 +1,17 @@
|
|||
<code>
|
||||
import yano
|
||||
from yano.symbols import *
|
||||
|
||||
|
||||
condition= (number_of_features>5) &
|
||||
(number_of_features<100) &
|
||||
(number_of_samples>100) &
|
||||
(number_of_samples<10000) &
|
||||
(number_of_samples>2*number_of_features) &
|
||||
~index
|
||||
|
||||
print(len(condition), "Datasets found")
|
||||
|
||||
|
||||
</code>
|
||||
->33 Datasets found
|
||||
|
|
@ -0,0 +1,26 @@
|
|||
Lots of symbols like this
|
||||
<l2st>
|
||||
name
|
||||
number\_of\_features
|
||||
number\_of\_samples
|
||||
index (correlated datasets)
|
||||
</l2st>
|
||||
Feature types
|
||||
<l2st>
|
||||
numeric
|
||||
nominal
|
||||
categorical
|
||||
(textual)
|
||||
</l2st>
|
||||
Count based
|
||||
<l2st>
|
||||
number\_anomalies
|
||||
number\_normals
|
||||
fraction\_anomalies
|
||||
</l2st>
|
||||
Specific ones
|
||||
<l2st>
|
||||
image\_based
|
||||
(linearly\_seperable)
|
||||
</l2st>
|
||||
|
||||
|
|
@ -0,0 +1,14 @@
|
|||
<code>
|
||||
for dataset in condition:
|
||||
print(condition)
|
||||
|
||||
|
||||
</code>
|
||||
<l2st>
|
||||
<e>\[annthyroid\]</e>
|
||||
<e>\[breastw\]</e>
|
||||
<e>\[cardio\]</e>
|
||||
<e>\[...\]</e>
|
||||
<e>\[Housing\_low\]</e>
|
||||
</l2st>
|
||||
|
||||
|
|
@ -0,0 +1,8 @@
|
|||
<code>
|
||||
|
||||
for dataset in condition:
|
||||
x=dataset.getx()
|
||||
y=dataset.gety()
|
||||
|
||||
|
||||
</code>
|
||||
|
|
@ -0,0 +1,15 @@
|
|||
<code>
|
||||
|
||||
from yano.iter import *
|
||||
|
||||
for dataset, x,tx,ty in pipeline(condition,
|
||||
split,
|
||||
shuffle,
|
||||
normalize("minmax")):
|
||||
...
|
||||
|
||||
|
||||
|
||||
|
||||
</code>
|
||||
|
||||
|
|
@ -0,0 +1,12 @@
|
|||
Again there are a couple modifiers possible
|
||||
<l2st>
|
||||
nonconst->remove constant features
|
||||
shuffle
|
||||
normalize('zscore'/'minmax')
|
||||
cut(10)->at most 10 datasets
|
||||
split->train test split, all anomalies in test set
|
||||
crossval(5)->similar to split, but do multiple times (crossvalidation)
|
||||
</l2st>
|
||||
modifiers interact with each other
|
||||
For example: normalize('minmax'), split
|
||||
->train set always below 1, but no guarantees for the test set
|
||||
|
|
@ -0,0 +1,14 @@
|
|||
Learned from DMC: Crossvalidation is important
|
||||
Rarely found in Anomaly Detection, why?
|
||||
A bit more complicated (not all samples are equal), but no reason why not
|
||||
->So I implemented it into yano
|
||||
<l2st>
|
||||
folding only on normal data
|
||||
How to handle anomalies?
|
||||
If not folding them, cross-validation less useful
|
||||
if folding them, often rare anomalies even more rare
|
||||
->test set always 50\% anomalous
|
||||
->Also improves simple evaluation metrics (accuracy)
|
||||
</l2st>
|
||||
Do you know a reason why Cross Validation is not common in AD?
|
||||
Are there Problems with the way I fold my Anomalies?
|
||||
|
|
@ -0,0 +1,21 @@
|
|||
<code>
|
||||
from yano.logging import Logger
|
||||
from pyod.models.iforest import IForest
|
||||
from extended_iforest import train_extended_ifor
|
||||
|
||||
l=Logger({"IFor":IForest(n_estimators=100),
|
||||
"eIFor":train_extended_ifor})
|
||||
|
||||
for dataset, folds in pipeline(condition,
|
||||
crossval(5),
|
||||
normalize("minmax"),
|
||||
shuffle):
|
||||
l.run_cross(dataset, folds)
|
||||
|
||||
latex=l.to_latex()
|
||||
|
||||
|
||||
</code>
|
||||
|
||||
|
||||
|
||||
|
|
@ -0,0 +1,5 @@
|
|||
If you dont do anything, everything is seeded.
|
||||
Makes rerunning a Model until the performance is good quite obvious.
|
||||
But as every Run is seeded itself, this might induce bias.
|
||||
Do you think this is worth it?
|
||||
Are there any Problems with this?
|
||||
|
|
@ -0,0 +1,21 @@
|
|||
\begin{tabular}{lll}
|
||||
\hline
|
||||
Dataset & eIFor & IFor \\
|
||||
\hline
|
||||
$pc3$ & $\textbf{0.7231} \pm 0.0153$ & $\textbf{0.7223} \pm 0.0178$ \\
|
||||
$pima$ & $\textbf{0.7405} \pm 0.0110$ & $\textbf{0.7347} \pm 0.0126$ \\
|
||||
$Diabetes\_present$ & $\textbf{0.7414} \pm 0.0195$ & $\textbf{0.7344} \pm 0.0242$ \\
|
||||
$waveform-5000$ & $\textbf{0.7687} \pm 0.0123$ & $\textbf{0.7592} \pm 0.0206$ \\
|
||||
$vowels$ & $\textbf{0.7843} \pm 0.0298$ & $\textbf{0.7753} \pm 0.0334$ \\
|
||||
$Vowel\_0$ & $\textbf{0.8425} \pm 0.0698$ & $0.7193 \pm 0.0817$ \\
|
||||
$Abalone\_1\_8$ & $\textbf{0.8525} \pm 0.0263$ & $0.8452 \pm 0.0257$ \\
|
||||
$annthyroid$ & $0.8399 \pm 0.0135$ & $\textbf{0.9087} \pm 0.0090$ \\
|
||||
$Vehicle\_van$ & $\textbf{0.8792} \pm 0.0265$ & $\textbf{0.8697} \pm 0.0383$ \\
|
||||
$ionosphere$ & $\textbf{0.9320} \pm 0.0069$ & $0.9086 \pm 0.0142$ \\
|
||||
$breastw$ & $\textbf{0.9948} \pm 0.0031$ & $\textbf{0.9952} \pm 0.0033$ \\
|
||||
$segment$ & $\textbf{1.0}$ & $\textbf{0.9993} \pm 0.0015$ \\
|
||||
$$ & $$ & $$ \\
|
||||
$Average$ & $\textbf{0.8005}$ & $\textbf{0.7957}$ \\
|
||||
\hline
|
||||
\end{tabular}
|
||||
|
||||
|
|
@ -0,0 +1,7 @@
|
|||
Friedman test to see if there is a difference between models
|
||||
Nemenyi test to see which models are equal, mark those equal to the maximum
|
||||
For 2 models, Friedman not defined -> use Wilcoxon test
|
||||
|
||||
Does this match your expectation from the table?
|
||||
Two models are 'equal' if their probability of being from the same distribution is #LessThan(p_b,p)#, what value should #Eq(p_b,0.1)# have?
|
||||
Do I need to correct for p hacking (n experiments, so increase the difficulty for each, or is that clear from the table)
|
||||
|
|
@ -0,0 +1,9 @@
|
|||
Isolation Forests are one algorithm for AD
|
||||
Tries to isolate abnormal (rare) points instead of modelling normal ones
|
||||
Creative approach->fairly successful (3000 Citations)
|
||||
Many follow up papers
|
||||
Extended Isolation Forest (Hariri et. al. 2018, 140 Citations)
|
||||
Remove bias from the Isolation Forests
|
||||
Also claim to improve their anomaly detection quality
|
||||
(repeat with both cuts and ad quality)
|
||||
|
||||
|
|
@ -0,0 +1,19 @@
|
|||
\begin{tabular}{lll}
|
||||
\hline
|
||||
Dataset & eIFor & IFor \\
|
||||
\hline
|
||||
$Delft\_pump\_5x3\_noisy$ & $\textbf{0.3893} \pm 0.0345$ & $\textbf{0.4272} \pm 0.0680$ \\
|
||||
$vertebral$ & $\textbf{0.4260} \pm 0.0111$ & $\textbf{0.4554} \pm 0.0416$ \\
|
||||
$Liver\_1$ & $0.5367 \pm 0.0508$ & $\textbf{0.5474} \pm 0.0541$ \\
|
||||
$Sonar\_mines$ & $\textbf{0.6882} \pm 0.1264$ & $0.6189 \pm 0.1301$ \\
|
||||
$letter$ & $\textbf{0.6756} \pm 0.0119$ & $0.6471 \pm 0.0111$ \\
|
||||
$Glass\_building\_float$ & $\textbf{0.6480} \pm 0.1012$ & $\textbf{0.6755} \pm 0.1117$ \\
|
||||
$pc3$ & $\textbf{0.7231} \pm 0.0153$ & $\textbf{0.7223} \pm 0.0178$ \\
|
||||
$pima$ & $\textbf{0.7405} \pm 0.0110$ & $\textbf{0.7347} \pm 0.0126$ \\
|
||||
$Diabetes\_present$ & $\textbf{0.7414} \pm 0.0195$ & $\textbf{0.7344} \pm 0.0242$ \\
|
||||
$waveform-5000$ & $\textbf{0.7687} \pm 0.0123$ & $\textbf{0.7592} \pm 0.0206$ \\
|
||||
$steel-plates-fault$ & $\textbf{0.7735} \pm 0.0351$ & $\textbf{0.7682} \pm 0.0402$ \\
|
||||
$vowels$ & $\textbf{0.7843} \pm 0.0298$ & $\textbf{0.7753} \pm 0.0334$ \\
|
||||
\hline
|
||||
\end{tabular}
|
||||
|
||||
|
|
@ -0,0 +1,19 @@
|
|||
\begin{tabular}{lll}
|
||||
\hline
|
||||
Dataset & eIFor & IFor \\
|
||||
\hline
|
||||
$Vowel\_0$ & $\textbf{0.8425} \pm 0.0698$ & $0.7193 \pm 0.0817$ \\
|
||||
$Housing\_low$ & $\textbf{0.7807} \pm 0.0333$ & $\textbf{0.7862} \pm 0.0336$ \\
|
||||
$ozone-level-8hr$ & $\textbf{0.7904} \pm 0.0207$ & $\textbf{0.7768} \pm 0.0118$ \\
|
||||
$Spectf\_0$ & $\textbf{0.8155} \pm 0.0255$ & $0.7535 \pm 0.0239$ \\
|
||||
$HeartC$ & $0.7795 \pm 0.0258$ & $\textbf{0.8079} \pm 0.0255$ \\
|
||||
$satellite$ & $\textbf{0.8125} \pm 0.0170$ & $\textbf{0.8103} \pm 0.0061$ \\
|
||||
$optdigits$ & $\textbf{0.8099} \pm 0.0310$ & $\textbf{0.8142} \pm 0.0267$ \\
|
||||
$spambase$ & $\textbf{0.8085} \pm 0.0110$ & $\textbf{0.8202} \pm 0.0042$ \\
|
||||
$Abalone\_1\_8$ & $\textbf{0.8525} \pm 0.0263$ & $0.8452 \pm 0.0257$ \\
|
||||
$qsar-biodeg$ & $\textbf{0.8584} \pm 0.0119$ & $\textbf{0.8628} \pm 0.0135$ \\
|
||||
$annthyroid$ & $0.8399 \pm 0.0135$ & $\textbf{0.9087} \pm 0.0090$ \\
|
||||
$Vehicle\_van$ & $\textbf{0.8792} \pm 0.0265$ & $\textbf{0.8697} \pm 0.0383$ \\
|
||||
\hline
|
||||
\end{tabular}
|
||||
|
||||
|
|
@ -0,0 +1,18 @@
|
|||
\begin{tabular}{lll}
|
||||
\hline
|
||||
Dataset & eIFor & IFor \\
|
||||
\hline
|
||||
$ionosphere$ & $\textbf{0.9320} \pm 0.0069$ & $0.9086 \pm 0.0142$ \\
|
||||
$page-blocks$ & $0.9189 \pm 0.0061$ & $\textbf{0.9299} \pm 0.0016$ \\
|
||||
$Ecoli$ & $\textbf{0.9418} \pm 0.0292$ & $0.9192 \pm 0.0332$ \\
|
||||
$cardio$ & $\textbf{0.9564} \pm 0.0043$ & $\textbf{0.9535} \pm 0.0036$ \\
|
||||
$wbc$ & $\textbf{0.9611} \pm 0.0121$ & $\textbf{0.9607} \pm 0.0107$ \\
|
||||
$pendigits$ & $\textbf{0.9641} \pm 0.0097$ & $\textbf{0.9652} \pm 0.0076$ \\
|
||||
$thyroid$ & $0.9818 \pm 0.0024$ & $\textbf{0.9871} \pm 0.0025$ \\
|
||||
$breastw$ & $\textbf{0.9948} \pm 0.0031$ & $\textbf{0.9952} \pm 0.0033$ \\
|
||||
$segment$ & $\textbf{1.0}$ & $\textbf{0.9993} \pm 0.0015$ \\
|
||||
$$ & $$ & $$ \\
|
||||
$Average$ & $\textbf{0.8005} \pm 0.1458$ & $\textbf{0.7957} \pm 0.1431$ \\
|
||||
\hline
|
||||
\end{tabular}
|
||||
|
||||
Binary file not shown.
|
After Width: | Height: | Size: 421 KiB |
|
|
@ -0,0 +1,13 @@
|
|||
<code>
|
||||
|
||||
condition= (number_of_samples>200) &
|
||||
(number_of_samples<10000) &
|
||||
(number_of_features>50) &
|
||||
(number_of_features<500) &
|
||||
~index
|
||||
|
||||
print(len(condition),"Datasets found")
|
||||
|
||||
|
||||
</code>
|
||||
->13 Datasets found
|
||||
|
|
@ -0,0 +1,13 @@
|
|||
<code>
|
||||
from pyod.models.iforest import IForest
|
||||
from pyod.models.knn import KNN
|
||||
from pyod.models.lof import LOF
|
||||
|
||||
|
||||
l=Logger({"IFor":Iforest(n_estimators=100),
|
||||
"Lof":LOF(),
|
||||
"Knn": KNN()}, addfeat=True)
|
||||
|
||||
|
||||
|
||||
</code>
|
||||
|
|
@ -0,0 +1,21 @@
|
|||
\begin{tabular}{llll}
|
||||
\hline
|
||||
Dataset & Knn & Lof & IFor \\
|
||||
\hline
|
||||
$Delft\_pump\_5x3\_noisy(64)$ & $0.3800 \pm 0.0475$ & $0.3462 \pm 0.0327$ & $\textbf{0.4272} \pm 0.0680$ \\
|
||||
$hill-valley(100)$ & $0.4744 \pm 0.0269$ & $\textbf{0.5060} \pm 0.0327$ & $0.4720 \pm 0.0288$ \\
|
||||
$speech(400)$ & $0.4903 \pm 0.0103$ & $\textbf{0.5104} \pm 0.0115$ & $0.4872 \pm 0.0184$ \\
|
||||
$Sonar\_mines(60)$ & $\textbf{0.7284} \pm 0.0939$ & $0.6769 \pm 0.0933$ & $0.6189 \pm 0.1301$ \\
|
||||
$ozone-level-8hr(72)$ & $\textbf{0.8051} \pm 0.0288$ & $0.7738 \pm 0.0292$ & $\textbf{0.7768} \pm 0.0118$ \\
|
||||
$spambase(57)$ & $0.8038 \pm 0.0125$ & $0.7712 \pm 0.0055$ & $\textbf{0.8202} \pm 0.0042$ \\
|
||||
$arrhythmia(274)$ & $\textbf{0.8137} \pm 0.0185$ & $0.8042 \pm 0.0186$ & $\textbf{0.8086} \pm 0.0099$ \\
|
||||
$mnist(100)$ & $0.9345 \pm 0.0039$ & $\textbf{0.9548} \pm 0.0037$ & $0.8732 \pm 0.0069$ \\
|
||||
$Concordia3\_32(256)$ & $0.9246 \pm 0.0107$ & $\textbf{0.9486} \pm 0.0099$ & $\textbf{0.9322} \pm 0.0178$ \\
|
||||
$optdigits(64)$ & $0.9966 \pm 0.0012$ & $\textbf{0.9975} \pm 0.0012$ & $0.8142 \pm 0.0267$ \\
|
||||
$gas-drift(128)$ & $\textbf{0.9790} \pm 0.0018$ & $0.9585 \pm 0.0055$ & $0.8764 \pm 0.0166$ \\
|
||||
$Delft\_pump\_AR(160)$ & $\textbf{0.9965}$ & $\textbf{0.9953} \pm 0.0019$ & $0.9665 \pm 0.0096$ \\
|
||||
$musk(166)$ & $\textbf{1.0}$ & $\textbf{1.0}$ & $0.9808 \pm 0.0117$ \\
|
||||
$$ & $$ & $$ & $$ \\
|
||||
$Average$ & $\textbf{0.7944}$ & $\textbf{0.7879}$ & $0.7580$ \\
|
||||
\hline
|
||||
\end{tabular}
|
||||
|
|
@ -0,0 +1,7 @@
|
|||
<l2st>
|
||||
<e>Hypothesis: Isolation Forests are better when there are numerical and nominal attributes</e>
|
||||
<e>Easy to test</e>
|
||||
</l2st>
|
||||
<code>
|
||||
condition=condition & (numeric & nominal)
|
||||
</code>
|
||||
|
|
@ -0,0 +1,19 @@
|
|||
\begin{tabular}{llll}
|
||||
\hline
|
||||
Dataset & Knn & IFor & Lof \\
|
||||
\hline
|
||||
$ozone-level-8hr(72)$ & $\textbf{0.8051} \pm 0.0288$ & $\textbf{0.7768} \pm 0.0118$ & $0.7738 \pm 0.0292$ \\
|
||||
$spambase(57)$ & $0.8038 \pm 0.0125$ & $\textbf{0.8202} \pm 0.0042$ & $0.7712 \pm 0.0055$ \\
|
||||
$arrhythmia(274)$ & $\textbf{0.8137} \pm 0.0185$ & $\textbf{0.8086} \pm 0.0099$ & $0.8042 \pm 0.0186$ \\
|
||||
$musk(166)$ & $\textbf{1.0}$ & $0.9808 \pm 0.0117$ & $\textbf{1.0}$ \\
|
||||
$$ & $$ & $$ & $$ \\
|
||||
$Average$ & $\textbf{0.8556}$ & $\textbf{0.8466}$ & $\textbf{0.8373}$ \\
|
||||
\hline
|
||||
\end{tabular}
|
||||
|
||||
<l2st>
|
||||
<e>Only 4 datasets, so not clear at all</e>
|
||||
<e>->More datasets</e>
|
||||
|
||||
</l2st>
|
||||
|
||||
|
|
@ -0,0 +1,7 @@
|
|||
There are analysis that are only possible with many datasets
|
||||
Here: unsupervised optimization
|
||||
Given multiple AD models, find which is best:
|
||||
Use AUC score? Requires Anomalies->Overfitting
|
||||
Can you find an unsupervised Method?
|
||||
In general very complicated, so here only focus on very small differences in the model.
|
||||
So each model is an autoencoder, trained on the same dataset, where the difference is only in the initialisation
|
||||
|
|
@ -0,0 +1,8 @@
|
|||
First guess Loss of the Model on the training Data
|
||||
How to evaluate this?
|
||||
Train many models, look at the average AUC score.
|
||||
For the alternative, take groups of 20 models, and look at the AUC score of the best model.
|
||||
Is there a meaningfull difference between results? Give result as z\_score (#(m_1-m_2)/sqrt(s_1**2+s_2**2)#)
|
||||
This difference depends a lot on the dataset
|
||||
->even a really good z\_score does not mean much (sometimes #LessThan(30,z)#)
|
||||
(repeat with two histones)
|
||||
|
|
@ -0,0 +1 @@
|
|||
Pick the Model with the lowest l2\-loss
|
||||
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|
|
@ -0,0 +1,3 @@
|
|||
Pick points with 1\% width difference in input space around each point.
|
||||
for each point, find the maximum difference in output space.
|
||||
average this difference
|
||||
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|
|
@ -0,0 +1,3 @@
|
|||
Pick random points in the input space.
|
||||
measure the distance in input and output space
|
||||
a low correlation is a good model
|
||||
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|
|
@ -0,0 +1,9 @@
|
|||
Things I still want to add:
|
||||
<l2st>
|
||||
Ensemble Methods
|
||||
Visualisation options
|
||||
Alternative Evaluations
|
||||
Hyperparameter optimisation (with crossvalidation)
|
||||
|
||||
|
||||
</l2st>
|
||||
|
|
@ -0,0 +1,3 @@
|
|||
What do you think about this?
|
||||
Is there something I should also add?
|
||||
What would you need for you to actually use this?
|
||||
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Reference in New Issue