commit ce067dbbf2db1ac173193aceb0c980a4b6300d16 Author: Simon Klüttermann Date: Mon May 23 18:49:15 2022 +0200 initial push diff --git a/data/000.txt b/data/000.txt new file mode 100644 index 0000000..75cfe48 --- /dev/null +++ b/data/000.txt @@ -0,0 +1,5 @@ + + + + + diff --git a/data/001Problem.txt b/data/001Problem.txt new file mode 100644 index 0000000..b8e3674 --- /dev/null +++ b/data/001Problem.txt @@ -0,0 +1,14 @@ + + + Paper with Benedikt +require multiple very specific datasets + +many but not to many features +at least some samples (for the NN) +Only numerical attributes best +specific quality +unrelated datasets + +Requires you to search for many datasets and filter them + + \ No newline at end of file diff --git a/data/002Students.txt b/data/002Students.txt new file mode 100644 index 0000000..02e7f40 --- /dev/null +++ b/data/002Students.txt @@ -0,0 +1,9 @@ + + + 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 + + \ No newline at end of file diff --git a/data/003yano.txt b/data/003yano.txt new file mode 100644 index 0000000..ecb3a86 --- /dev/null +++ b/data/003yano.txt @@ -0,0 +1,12 @@ + + + So I have been packaging all my scripts +I had surprisingly much fun doing this + +More than just standard functions +A couple of weird decisions +And this will likely grow further + +->So I would like to discuss some parts with you and maybe you even have more features you might want + + \ No newline at end of file diff --git a/data/004yano.txt b/data/004yano.txt new file mode 100644 index 0000000..d6eaaac --- /dev/null +++ b/data/004yano.txt @@ -0,0 +1,17 @@ + + + + + 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 + + + + + + + \ No newline at end of file diff --git a/data/005selector.txt b/data/005selector.txt new file mode 100644 index 0000000..ea5677f --- /dev/null +++ b/data/005selector.txt @@ -0,0 +1,17 @@ +
+ + + +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") + +->33 Datasets found + + diff --git a/data/006selectors.txt b/data/006selectors.txt new file mode 100644 index 0000000..da912eb --- /dev/null +++ b/data/006selectors.txt @@ -0,0 +1,29 @@ + + + Lots of symbols like this + +name +number\_of\_features +number\_of\_samples +index (correlated datasets) + +Feature types + +numeric +nominal +categorical +(textual) + +Count based + +number\_anomalies +number\_normals +fraction\_anomalies + +Specific ones + +image\_based +(linearly\_seperable) + + + \ No newline at end of file diff --git a/data/007iterating.txt b/data/007iterating.txt new file mode 100644 index 0000000..1bbbf1a --- /dev/null +++ b/data/007iterating.txt @@ -0,0 +1,15 @@ + + + +for dataset in condition: + print(condition) + + +\[annthyroid\] +\[breastw\] +\[cardio\] +\[...\] +\[Housing\_low\] + + + \ No newline at end of file diff --git a/data/008iterating.txt b/data/008iterating.txt new file mode 100644 index 0000000..6abde00 --- /dev/null +++ b/data/008iterating.txt @@ -0,0 +1,9 @@ + + + +for dataset in condition: + x=dataset.getx() + y=dataset.gety() + + + \ No newline at end of file diff --git a/data/009pipeline.txt b/data/009pipeline.txt new file mode 100644 index 0000000..f7befa7 --- /dev/null +++ b/data/009pipeline.txt @@ -0,0 +1,12 @@ + + + +from yano.iter import * +for dataset, x,tx,ty in pipeline(condition, + split, + shuffle, + normalize("minmax")): + ... + + + \ No newline at end of file diff --git a/data/010pipeline.txt b/data/010pipeline.txt new file mode 100644 index 0000000..b10ce46 --- /dev/null +++ b/data/010pipeline.txt @@ -0,0 +1,16 @@ + + + Again there are a couple modifiers possible + +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) + +modifiers interact with each other +For example: normalize('minmax'), split +->train set always below 1, but no guarantees for the test set + + \ No newline at end of file diff --git a/data/011CrossValidation.txt b/data/011CrossValidation.txt new file mode 100644 index 0000000..1b30baa --- /dev/null +++ b/data/011CrossValidation.txt @@ -0,0 +1,18 @@ + + + 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 + +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) + +Do you know a reason why Cross Validation is not common in AD? +Are there Problems with the way I fold my Anomalies? + + \ No newline at end of file diff --git a/data/012Logging.txt b/data/012Logging.txt new file mode 100644 index 0000000..b8cf3d0 --- /dev/null +++ b/data/012Logging.txt @@ -0,0 +1,17 @@ + + + +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() + + + \ No newline at end of file diff --git a/data/013Seeding.txt b/data/013Seeding.txt new file mode 100644 index 0000000..78b4c15 --- /dev/null +++ b/data/013Seeding.txt @@ -0,0 +1,9 @@ + + + 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? + + \ No newline at end of file diff --git a/data/014.txt b/data/014.txt new file mode 100644 index 0000000..5662658 --- /dev/null +++ b/data/014.txt @@ -0,0 +1,24 @@ + + + \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} + + \ No newline at end of file diff --git a/data/015statistics.txt b/data/015statistics.txt new file mode 100644 index 0000000..419f814 --- /dev/null +++ b/data/015statistics.txt @@ -0,0 +1,10 @@ + + + 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) + + \ No newline at end of file diff --git a/data/016Extended Isolation Forests.txt b/data/016Extended Isolation Forests.txt new file mode 100644 index 0000000..7d36ffd --- /dev/null +++ b/data/016Extended Isolation Forests.txt @@ -0,0 +1,24 @@ +
+ + + + + + 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 + + + + + + + + + + + diff --git a/data/017.txt b/data/017.txt new file mode 100644 index 0000000..9dd85cd --- /dev/null +++ b/data/017.txt @@ -0,0 +1,22 @@ + + + \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} + + \ No newline at end of file diff --git a/data/018.txt b/data/018.txt new file mode 100644 index 0000000..32bc470 --- /dev/null +++ b/data/018.txt @@ -0,0 +1,22 @@ + + + \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} + + \ No newline at end of file diff --git a/data/019.txt b/data/019.txt new file mode 100644 index 0000000..0d9c7c0 --- /dev/null +++ b/data/019.txt @@ -0,0 +1,21 @@ + + + \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} + + \ No newline at end of file diff --git a/data/020highdim.txt b/data/020highdim.txt new file mode 100644 index 0000000..222d64f --- /dev/null +++ b/data/020highdim.txt @@ -0,0 +1,4 @@ +
+ + + diff --git a/data/021New Condition.txt b/data/021New Condition.txt new file mode 100644 index 0000000..814fdb2 --- /dev/null +++ b/data/021New Condition.txt @@ -0,0 +1,13 @@ + + + +condition= (number_of_samples>200) & + (number_of_samples<10000) & + (number_of_features>50) & + (number_of_features<500) & + ~index +print(len(condition),"Datasets found") + +->13 Datasets found + + \ No newline at end of file diff --git a/data/022New Models.txt b/data/022New Models.txt new file mode 100644 index 0000000..c531812 --- /dev/null +++ b/data/022New Models.txt @@ -0,0 +1,12 @@ + + + +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) + + + \ No newline at end of file diff --git a/data/023.txt b/data/023.txt new file mode 100644 index 0000000..959d66e --- /dev/null +++ b/data/023.txt @@ -0,0 +1,25 @@ + + + \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} + + \ No newline at end of file diff --git a/data/024.txt b/data/024.txt new file mode 100644 index 0000000..bbb77dc --- /dev/null +++ b/data/024.txt @@ -0,0 +1,11 @@ + + + +Hypothesis: Isolation Forests are better when there are numerical and nominal attributes +Easy to test + + +condition=condition & (numeric & nominal) + + + \ No newline at end of file diff --git a/data/025.txt b/data/025.txt new file mode 100644 index 0000000..df8b327 --- /dev/null +++ b/data/025.txt @@ -0,0 +1,20 @@ + + + \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} + +Only 4 datasets, so not clear at all +->More datasets + + + \ No newline at end of file diff --git a/data/026Unsupervised Optimization.txt b/data/026Unsupervised Optimization.txt new file mode 100644 index 0000000..ca1886c --- /dev/null +++ b/data/026Unsupervised Optimization.txt @@ -0,0 +1,12 @@ +
+ + + 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 + + diff --git a/data/027Loss Optimization.txt b/data/027Loss Optimization.txt new file mode 100644 index 0000000..ad597f3 --- /dev/null +++ b/data/027Loss Optimization.txt @@ -0,0 +1,20 @@ + + + + + + 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 #LessThan(30,z)# does not mean much + + + + + + + + diff --git a/data/028loss.txt b/data/028loss.txt new file mode 100644 index 0000000..5ab001b --- /dev/null +++ b/data/028loss.txt @@ -0,0 +1,12 @@ + + + + + Pick the Model with the lowest l2\-loss + + + + + + + \ No newline at end of file diff --git a/data/029Robustness.txt b/data/029Robustness.txt new file mode 100644 index 0000000..13153e4 --- /dev/null +++ b/data/029Robustness.txt @@ -0,0 +1,14 @@ + + + + + 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 + + + + + + + \ No newline at end of file diff --git a/data/030Distance Correlation.txt b/data/030Distance Correlation.txt new file mode 100644 index 0000000..bfe10b6 --- /dev/null +++ b/data/030Distance Correlation.txt @@ -0,0 +1,14 @@ + + + + + Pick random points in the input space. +measure the distance in input and output space +a low correlation is a good model + + + + + + + \ No newline at end of file diff --git a/data/031Other.txt b/data/031Other.txt new file mode 100644 index 0000000..1cbcb0f --- /dev/null +++ b/data/031Other.txt @@ -0,0 +1,12 @@ +
+ + + Things I still want to add: + +Ensemble Methods +Visualisation options +Alternative Evaluations +Hyperparameter optimisation (with crossvalidation) + + + diff --git a/data/032Feedback.txt b/data/032Feedback.txt new file mode 100644 index 0000000..301027b --- /dev/null +++ b/data/032Feedback.txt @@ -0,0 +1,7 @@ + + + What do you think about this? +Is there something I should also add? +What would you need for you to actually use this? + + \ No newline at end of file diff --git a/general.txt b/general.txt new file mode 100644 index 0000000..4ec46bc --- /dev/null +++ b/general.txt @@ -0,0 +1,12 @@ + + + + +<stitle yano> + +<institute ls9 tu Dortmund> + +<theme CambridgeUS> +<colo dolphin> + +</plt> diff --git a/imgs b/imgs new file mode 160000 index 0000000..62ffd6a --- /dev/null +++ b/imgs @@ -0,0 +1 @@ +Subproject commit 62ffd6ae589d7983791feea9d44d7658534d54a0 diff --git a/out/compile.bat b/out/compile.bat new file mode 100644 index 0000000..4024411 --- /dev/null +++ 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+\beamer@slide {Feedback}{36} diff --git a/out/main.tex b/out/main.tex new file mode 100644 index 0000000..810dd70 --- /dev/null +++ b/out/main.tex @@ -0,0 +1,1116 @@ +\UseRawInputEncoding +%\documentclass[hyperref={pdfpagelabels=false}]{beamer} +\documentclass[hyperref={pdfpagelabels=false},aspectratio=169]{beamer} +% Die Hyperref Option hyperref={pdfpagelabels=false} verhindert die Warnung: +% Package hyperref Warning: Option `pdfpagelabels' is turned off +% (hyperref) because \thepage is undefined. +% Hyperref stopped early +% + +\usepackage{lmodern} +% Das Paket lmodern erspart die folgenden Warnungen: +% LaTeX Font Warning: Font shape `OT1/cmss/m/n' in size <4> not available +% (Font) size <5> substituted on input line 22. +% LaTeX Font Warning: Size substitutions with differences +% (Font) up to 1.0pt have occurred. +% + +% Wenn \titel{\ldots} \author{\ldots} erst nach \begin{document} kommen, +% kommt folgende Warnung: +% Package hyperref Warning: Option `pdfauthor' has already been used, +% (hyperref) ... +% Daher steht es hier vor \begin{document} + +\title[yano]{pip install yano} +\author{Simon Kluettermann} +\date{\today} + + + \institute{ls9 tu Dortmund} + + +% Dadurch wird verhindert, dass die Navigationsleiste angezeigt wird. +\setbeamertemplate{navigation symbols}{} + +% zusaetzlich ist das usepackage{beamerthemeshadow} eingebunden +\usepackage{beamerthemeshadow} + +\hypersetup{pdfstartview={Fit}} % fits the presentation to the window when first displayed + +\usepackage{appendixnumberbeamer} +\usepackage{listings} + + +\usetheme{CambridgeUS} +\usepackage{ngerman} +\usecolortheme{dolphin} + + +% \beamersetuncovermixins{\opaqueness<1>{25}}{\opaqueness<2$\Rightarrow${15}} +% sorgt dafuer das die Elemente die erst noch (zukuenftig) kommen +% nur schwach angedeutet erscheinen +%\beamersetuncovermixins{\opaqueness<1>{25}}{\opaqueness<2$\Rightarrow${15}}%here disabled +% klappt auch bei Tabellen, wenn teTeX verwendet wird\ldots +\renewcommand{\figurename}{} + +\setbeamertemplate{footline} +{ + \leavevmode% + \hbox{% + \begin{beamercolorbox}[wd=.4\paperwidth,ht=2.25ex,dp=1ex,center]{author in head/foot}% + \usebeamerfont{author in head/foot}\insertshorttitle + \end{beamercolorbox}% + \begin{beamercolorbox}[wd=.25\paperwidth,ht=2.25ex,dp=1ex,center]{title in head/foot}% + \usebeamerfont{title in head/foot}\insertsection + \end{beamercolorbox}% + \begin{beamercolorbox}[wd=.3499\paperwidth,ht=2.25ex,dp=1ex,right]{date in head/foot}% + \usebeamerfont{date in head/foot}\insertshortdate{}\hspace*{2em} + \hyperlink{toc}{\insertframenumber{} / \inserttotalframenumber\hspace*{2ex}} + \end{beamercolorbox}}% + \vskip0pt% +} + +\usepackage[absolute,overlay]{textpos} +\usepackage{graphicx} + +\newcommand{\source}[1]{\begin{textblock*}{9cm}(0.1cm,8.9cm) + \begin{beamercolorbox}[ht=0.5cm,left]{framesource} + \usebeamerfont{framesource}\usebeamercolor[fg!66]{framesource} Source: {#1} + \end{beamercolorbox} +\end{textblock*}} + + +\begin{document} + + + +%from file ../yano//data/000.txt +\begin{frame}[label=] +\frametitle{} +\begin{titlepage} + + \centering + {\huge\bfseries \par} + \vspace{2cm} + {\LARGE\itshape Simon Kluettermann\par} + \vspace{1.5cm} + {\scshape\Large Master Thesis in Physics\par} + \vspace{0.2cm} + {\Large submitted to the \par} + \vspace{0.2cm} + {\scshape\Large Faculty of Mathematics Computer Science and Natural Sciences \par} + \vspace{0.2cm} + {\Large \par} + \vspace{0.2cm} + {\scshape\Large RWTH Aachen University} + \vspace{1cm} + + \vfill + {\scshape\Large Department of Physics\par} + \vspace{0.2cm} + {\scshape\Large Insitute for theoretical Particle Physics and Cosmology\par} + \vspace{0.2cm} + { \Large\par} + \vspace{0.2cm} + {\Large First Referee: Prof. Dr. Michael Kraemer \par} + {\Large Second Referee: Prof. Dr. Felix Kahlhoefer} + + \vfill + +% Bottom of the page + {\large November 2020 \par} +\end{titlepage} +\pagenumbering{roman} +\thispagestyle{empty} +\null +\newpage +\setcounter{page}{1} +\pagenumbering{arabic} +\end{frame} + + + +%from file ../yano//data/001Problem.txt +\begin{frame}[label=Problem] +\frametitle{Problem} +\begin{itemize} + + \item Paper with Benedikt + + \item require multiple very specific datasets + +\begin{itemize} + + \item many but not to many features + + \item at least some samples (for the NN) + + \item Only numerical attributes best + + \item specific quality + + \item unrelated datasets + + +\end{itemize} + \item Requires you to search for many datasets and filter them + + +\end{itemize} +\end{frame} + + +%from file ../yano//data/002Students.txt +\begin{frame}[label=Students] +\frametitle{Students} +\begin{itemize} + + \item Not clear what you can use + + \item Many different formats + + \item train/test splits + + \item So for Students I just do this work and send them archives directly + + \item $\Rightarrow$Not a good solution + + +\end{itemize} +\end{frame} + + +%from file ../yano//data/003yano.txt +\begin{frame}[label=yano] +\frametitle{yano} +\begin{itemize} + + \item So I have been packaging all my scripts + + \item I had surprisingly much fun doing this + +\begin{itemize} + + \item More than just standard functions + + \item A couple of weird decisions + + \item And this will likely grow further + + +\end{itemize} + \item $\Rightarrow$So I would like to discuss some parts with you and maybe you even have more features you might want + + +\end{itemize} +\end{frame} + + +%from file ../yano//data/004yano.txt +\begin{frame}[label=yano] +\frametitle{yano} +\begin{columns}[c] % align columns +\begin{column}{0.48\textwidth}%.48 +\begin{itemize} + + \item Simply install it over pip + + \item Contains 187 real-World Datasets + + \item $\Rightarrow$biggest library of datasets explicitely for anomaly detection + + \item not yet happy with this + + \item especially only mostly contains numerical and nominal attributes + + \item $\Rightarrow$few categorical and no time-series attributes + + +\end{itemize} +\end{column}% +\hfill% + \begin{column}{0.48\textwidth}%.48 +\begin{figure}[H] + \centering +\includegraphics[width=0.9\textwidth]{../prep/04yano/a.png} +\label{fig:prep04yanoapng} + \end{figure} + + +\end{column}% +\hfill% +\end{columns} + +\end{frame} + + +%from file ../yano//data/005selector.txt +\newpage +\section{Basics}\label{sec:Basics} +%{{{for_Basics}}} + +\begin{frame}[label=selector,containsverbatim] +\frametitle{selector} +\begin{lstlisting}[language=Python] +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") +\end{lstlisting} + +$\Rightarrow$33 Datasets found +\end{frame} + + + +%from file ../yano//data/006selectors.txt +\begin{frame}[label=selectors] +\frametitle{selectors} +\begin{itemize} + + \item Lots of symbols like this + +\begin{itemize} + + \item name + + \item number\_of\_features + + \item number\_of\_samples + + \item index (correlated datasets) + + +\end{itemize} + \item Feature types + +\begin{itemize} + + \item numeric + + \item nominal + + \item categorical + + \item (textual) + + +\end{itemize} + \item Count based + +\begin{itemize} + + \item number\_anomalies + + \item number\_normals + + \item fraction\_anomalies + + +\end{itemize} + \item Specific ones + +\begin{itemize} + + \item image\_based + + \item (linearly\_seperable) + + +\end{itemize} + +\end{itemize} +\end{frame} + + +%from file ../yano//data/007iterating.txt +\begin{frame}[label=iterating,containsverbatim] +\frametitle{iterating} +\begin{lstlisting}[language=Python] +for dataset in condition: + print(condition) +\end{lstlisting} + +\begin{itemize} + + \item \[annthyroid\] + + \item \[breastw\] + + \item \[cardio\] + + \item \[...\] + + \item \[Housing\_low\] + + +\end{itemize} +\end{frame} + + +%from file ../yano//data/008iterating.txt +\begin{frame}[label=iterating,containsverbatim] +\frametitle{iterating} +\begin{lstlisting}[language=Python] +for dataset in condition: + x=dataset.getx() + y=dataset.gety() +\end{lstlisting} + +\end{frame} + + +%from file ../yano//data/009pipeline.txt +\begin{frame}[label=pipeline,containsverbatim] +\frametitle{pipeline} +\begin{lstlisting}[language=Python] +from yano.iter import * +for dataset, x,tx,ty in pipeline(condition, + split, + shuffle, + normalize("minmax")): + ... +\end{lstlisting} + +\end{frame} + + +%from file ../yano//data/010pipeline.txt +\begin{frame}[label=pipeline] +\frametitle{pipeline} +\begin{itemize} + + \item Again there are a couple modifiers possible + +\begin{itemize} + + \item nonconst$\Rightarrow$remove constant features + + \item shuffle + + \item normalize('zscore'/'minmax') + + \item cut(10)$\Rightarrow$at most 10 datasets + + \item split$\Rightarrow$train test split, all anomalies in test set + + \item crossval(5)$\Rightarrow$similar to split, but do multiple times (crossvalidation) + + +\end{itemize} + \item modifiers interact with each other + + \item For example: normalize('minmax'), split + + \item $\Rightarrow$train set always below 1, but no guarantees for the test set + + +\end{itemize} +\end{frame} + + +%from file ../yano//data/011CrossValidation.txt +\begin{frame}[label=CrossValidation] +\frametitle{CrossValidation} +\begin{itemize} + + \item Learned from DMC: Crossvalidation is important + + \item Rarely found in Anomaly Detection, why? + + \item A bit more complicated (not all samples are equal), but no reason why not + + \item $\Rightarrow$So I implemented it into yano + +\begin{itemize} + + \item folding only on normal data + + \item How to handle anomalies? + + \item If not folding them, cross-validation less useful + + \item if folding them, often rare anomalies even more rare + + \item $\Rightarrow$test set always 50\% anomalous + + \item $\Rightarrow$Also improves simple evaluation metrics (accuracy) + + +\end{itemize} + \item Do you know a reason why Cross Validation is not common in AD? + + \item Are there Problems with the way I fold my Anomalies? + + +\end{itemize} +\end{frame} + + +%from file ../yano//data/012Logging.txt +\begin{frame}[label=Logging,containsverbatim] +\frametitle{Logging} +\begin{lstlisting}[language=Python] +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() +\end{lstlisting} + +\end{frame} + + +%from file ../yano//data/013Seeding.txt +\begin{frame}[label=Seeding] +\frametitle{Seeding} +\begin{itemize} + + \item If you dont do anything, everything is seeded. + + \item Makes rerunning a Model until the performance is good quite obvious. + + \item But as every Run is seeded itself, this might induce bias. + + \item Do you think this is worth it? + + \item Are there any Problems with this? + + +\end{itemize} +\end{frame} + + +%from file ../yano//data/014.txt +\begin{frame}[label=] +\frametitle{} +\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} +\end{frame} + + +%from file ../yano//data/015statistics.txt +\begin{frame}[label=statistics] +\frametitle{statistics} +\begin{itemize} + + \item Friedman test to see if there is a difference between models + + \item Nemenyi test to see which models are equal, mark those equal to the maximum + + \item For 2 models, Friedman not defined $\Rightarrow$ use Wilcoxon test + + \item Does this match your expectation from the table? + + \item Two models are 'equal' if their probability of being from the same distribution is $p_{b} \leq p$, what value should $p_{b} = 0.1$ have? + + \item Do I need to correct for p hacking (n experiments, so increase the difficulty for each, or is that clear from the table) + + +\end{itemize} +\end{frame} + + +%from file ../yano//data/016Extended Isolation Forests.txt +\newpage +\section{Experiments 1}\label{sec:Experiments 1} +%{{{for_Experiments 1}}} + +\begin{frame}[label=Extended Isolation Forests] +\frametitle{Extended Isolation Forests} +\begin{columns}[c] % align columns +\begin{column}{0.48\textwidth}%.48 +\begin{itemize} + + \item Isolation Forests are one algorithm for AD + + \item Tries to isolate abnormal (rare) points instead of modelling normal ones + + \item Creative approach$\Rightarrow$fairly successful (3000 Citations) + + \item Many follow up papers + + \item Extended Isolation Forest (Hariri et. al. 2018, 140 Citations) + + \item Remove bias from the Isolation Forests + + \item Also claim to improve their anomaly detection quality + + +\end{itemize} +\end{column}% +\hfill% + +\begin{column}{0.48\textwidth}%.48 +\begin{figure}[H] + \centering +\includegraphics[width=0.9\textwidth]{../imgs/ifor} +\label{fig:ifor} + \end{figure} + + +\end{column}% +\hfill% +\end{columns} + +\end{frame} + + +\begin{frame}[label=Extended Isolation Forests] +\frametitle{Extended Isolation Forests} +\begin{columns}[c] % align columns +\begin{column}{0.48\textwidth}%.48 +\begin{itemize} + + \item Isolation Forests are one algorithm for AD + + \item Tries to isolate abnormal (rare) points instead of modelling normal ones + + \item Creative approach$\Rightarrow$fairly successful (3000 Citations) + + \item Many follow up papers + + \item Extended Isolation Forest (Hariri et. al. 2018, 140 Citations) + + \item Remove bias from the Isolation Forests + + \item Also claim to improve their anomaly detection quality + + +\end{itemize} +\end{column}% +\hfill% + +\begin{column}{0.48\textwidth}%.48 +\begin{figure}[H] + \centering +\includegraphics[width=0.9\textwidth]{../imgs/eifor} +\label{fig:eifor} + \end{figure} + + +\end{column}% +\hfill% +\end{columns} + +\end{frame} + + +\begin{frame}[label=Extended Isolation Forests] +\frametitle{Extended Isolation Forests} +\begin{columns}[c] % align columns +\begin{column}{0.48\textwidth}%.48 +\begin{itemize} + + \item Isolation Forests are one algorithm for AD + + \item Tries to isolate abnormal (rare) points instead of modelling normal ones + + \item Creative approach$\Rightarrow$fairly successful (3000 Citations) + + \item Many follow up papers + + \item Extended Isolation Forest (Hariri et. al. 2018, 140 Citations) + + \item Remove bias from the Isolation Forests + + \item Also claim to improve their anomaly detection quality + + +\end{itemize} +\end{column}% +\hfill% + +\begin{column}{0.48\textwidth}%.48 +\begin{figure}[H] + \centering +\includegraphics[width=0.9\textwidth]{../imgs/qual} +\label{fig:qual} + \end{figure} + + +\end{column}% +\hfill% +\end{columns} + +\end{frame} + + + +%from file ../yano//data/017.txt +\begin{frame}[label=] +\frametitle{} +\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} +\end{frame} + + +%from file ../yano//data/018.txt +\begin{frame}[label=] +\frametitle{} +\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} +\end{frame} + + +%from file ../yano//data/019.txt +\begin{frame}[label=] +\frametitle{} +\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} +\end{frame} + + +%from file ../yano//data/020highdim.txt +\newpage +\section{Experiments 2}\label{sec:Experiments 2} +%{{{for_Experiments 2}}} + +\begin{frame}[label=highdim] +\frametitle{highdim} +\begin{figure}[H] + \centering +\includegraphics[width=0.9\textwidth]{../prep/19highdim/a.png} +\label{fig:prep19highdimapng} + \end{figure} + + +\end{frame} + + + +%from file ../yano//data/021New Condition.txt +\begin{frame}[label=New Condition,containsverbatim] +\frametitle{New Condition} +\begin{lstlisting}[language=Python] +condition= (number_of_samples>200) & + (number_of_samples<10000) & + (number_of_features>50) & + (number_of_features<500) & + ~index +print(len(condition),"Datasets found") +\end{lstlisting} + +$\Rightarrow$13 Datasets found +\end{frame} + + +%from file ../yano//data/022New Models.txt +\begin{frame}[label=New Models,containsverbatim] +\frametitle{New Models} +\begin{lstlisting}[language=Python] +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) +\end{lstlisting} + +\end{frame} + + +%from file ../yano//data/023.txt +\begin{frame}[label=] +\frametitle{} +\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} +\end{frame} + + +%from file ../yano//data/024.txt +\begin{frame}[label=,containsverbatim] +\frametitle{} +\begin{itemize} + + \item Hypothesis: Isolation Forests are better when there are numerical and nominal attributes + + \item Easy to test + + +\end{itemize} +\begin{lstlisting}[language=Python] +condition=condition & (numeric & nominal) +\end{lstlisting} + +\end{frame} + + +%from file ../yano//data/025.txt +\begin{frame}[label=] +\frametitle{} +\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} +\begin{itemize} + + \item Only 4 datasets, so not clear at all + + \item $\Rightarrow$More datasets + + +\end{itemize} +\end{frame} + + +%from file ../yano//data/026Unsupervised Optimization.txt +\newpage +\section{Experiments 3}\label{sec:Experiments 3} +%{{{for_Experiments 3}}} + +\begin{frame}[label=Unsupervised Optimization] +\frametitle{Unsupervised Optimization} +\begin{itemize} + + \item There are analysis that are only possible with many datasets + + \item Here: unsupervised optimization + + \item Given multiple AD models, find which is best: + + \item Use AUC score? Requires Anomalies$\Rightarrow$Overfitting + + \item Can you find an unsupervised Method? + + \item In general very complicated, so here only focus on very small differences in the model. + + \item So each model is an autoencoder, trained on the same dataset, where the difference is only in the initialisation + + +\end{itemize} +\end{frame} + + + +%from file ../yano//data/027Loss Optimization.txt +\begin{frame}[label=Loss Optimization] +\frametitle{Loss Optimization} +\begin{columns}[c] % align columns +\begin{column}{0.48\textwidth}%.48 +\begin{itemize} + + \item First guess Loss of the Model on the training Data + + \item How to evaluate this? + + \item Train many models, look at the average AUC score. + + \item For the alternative, take groups of 20 models, and look at the AUC score of the best model. + + \item Is there a meaningfull difference between results? Give result as z\_score ($\frac{m_{1} - m_{2}}{\sqrt{s_{1}^{2} + s_{2}^{2}}}$) + + \item This difference depends a lot on the dataset + + \item $\Rightarrow$even $30 \leq z$ does not mean much + + +\end{itemize} +\end{column}% +\hfill% + \begin{column}{0.48\textwidth}%.48 +\begin{figure}[H] + \centering +\includegraphics[width=0.9\textwidth]{../imgs/histone_page-blocks} +\label{fig:histone_page-blocks} + \end{figure} + + +\end{column}% +\hfill% +\end{columns} + +\end{frame} + + +\begin{frame}[label=Loss Optimization] +\frametitle{Loss Optimization} +\begin{columns}[c] % align columns +\begin{column}{0.48\textwidth}%.48 +\begin{itemize} + + \item First guess Loss of the Model on the training Data + + \item How to evaluate this? + + \item Train many models, look at the average AUC score. + + \item For the alternative, take groups of 20 models, and look at the AUC score of the best model. + + \item Is there a meaningfull difference between results? Give result as z\_score ($\frac{m_{1} - m_{2}}{\sqrt{s_{1}^{2} + s_{2}^{2}}}$) + + \item This difference depends a lot on the dataset + + \item $\Rightarrow$even $30 \leq z$ does not mean much + + +\end{itemize} +\end{column}% +\hfill% + \begin{column}{0.48\textwidth}%.48 +\begin{figure}[H] + \centering +\includegraphics[width=0.9\textwidth]{../imgs/histone_pima} +\label{fig:histone_pima} + \end{figure} + + +\end{column}% +\hfill% +\end{columns} + +\end{frame} + + + +%from file ../yano//data/028loss.txt +\begin{frame}[label=loss] +\frametitle{loss} +\begin{columns}[c] % align columns +\begin{column}{0.48\textwidth}%.48 +\begin{itemize} + + \item Pick the Model with the lowest l2\-loss + + +\end{itemize} +\end{column}% +\hfill% + \begin{column}{0.48\textwidth}%.48 +\begin{figure}[H] + \centering +\includegraphics[width=0.9\textwidth]{../prep/27loss/z_loss.pdf} +\label{fig:prep27lossz_losspdf} + \end{figure} + + +\end{column}% +\hfill% +\end{columns} + +\end{frame} + + +%from file ../yano//data/029Robustness.txt +\begin{frame}[label=Robustness] +\frametitle{Robustness} +\begin{columns}[c] % align columns +\begin{column}{0.48\textwidth}%.48 +\begin{itemize} + + \item Pick points with 1\% width difference in input space around each point. + + \item for each point, find the maximum difference in output space. + + \item average this difference + + +\end{itemize} +\end{column}% +\hfill% + \begin{column}{0.48\textwidth}%.48 +\begin{figure}[H] + \centering +\includegraphics[width=0.9\textwidth]{../prep/28Robustness/z_robu.pdf} +\label{fig:prep28Robustnessz_robupdf} + \end{figure} + + +\end{column}% +\hfill% +\end{columns} + +\end{frame} + + +%from file ../yano//data/030Distance Correlation.txt +\begin{frame}[label=Distance Correlation] +\frametitle{Distance Correlation} +\begin{columns}[c] % align columns +\begin{column}{0.48\textwidth}%.48 +\begin{itemize} + + \item Pick random points in the input space. + + \item measure the distance in input and output space + + \item a low correlation is a good model + + +\end{itemize} +\end{column}% +\hfill% + \begin{column}{0.48\textwidth}%.48 +\begin{figure}[H] + \centering +\includegraphics[width=0.9\textwidth]{../prep/29Distance_Correlation/z_dist.pdf} +\label{fig:prep29Distance_Correlationz_distpdf} + \end{figure} + + +\end{column}% +\hfill% +\end{columns} + +\end{frame} + + +%from file ../yano//data/031Other.txt +\newpage +\section{Conclusion}\label{sec:Conclusion} +%{{{for_Conclusion}}} + +\begin{frame}[label=Other] +\frametitle{Other} +\begin{itemize} + + \item Things I still want to add: + +\begin{itemize} + + \item Ensemble Methods + + \item Visualisation options + + \item Alternative Evaluations + + \item Hyperparameter optimisation (with crossvalidation) + + +\end{itemize} + +\end{itemize} +\end{frame} + + + +%from file ../yano//data/032Feedback.txt +\begin{frame}[label=Feedback] +\frametitle{Feedback} +\begin{itemize} + + \item What do you think about this? + + \item Is there something I should also add? + + \item What would you need for you to actually use this? + + +\end{itemize} +\end{frame} + + +%from folder ../yano//data/Forests.txt + + +%from folder ../yano//data/Isolation + + +%from folder ../yano//data/Optimization.txt + + + +\end{document} diff --git a/out/main.toc b/out/main.toc new file mode 100644 index 0000000..861c1a4 --- /dev/null +++ b/out/main.toc @@ -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} diff --git a/prep/000/nonl b/prep/000/nonl new file mode 100644 index 0000000..e69de29 diff --git a/prep/000/q b/prep/000/q new file mode 100644 index 0000000..97c72ec --- /dev/null +++ b/prep/000/q @@ -0,0 +1 @@ +<titlepage> diff --git a/prep/01Problem/q b/prep/01Problem/q new file mode 100644 index 0000000..4430ae6 --- /dev/null +++ b/prep/01Problem/q @@ -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 diff --git a/prep/02Students/q b/prep/02Students/q new file mode 100644 index 0000000..194801d --- /dev/null +++ b/prep/02Students/q @@ -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 + diff --git a/prep/03yano/q b/prep/03yano/q new file mode 100644 index 0000000..4727f40 --- /dev/null +++ b/prep/03yano/q @@ -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 diff --git a/prep/04yano/a.png b/prep/04yano/a.png new file mode 100644 index 0000000..73526e5 Binary files /dev/null and b/prep/04yano/a.png differ diff --git a/prep/04yano/q b/prep/04yano/q new file mode 100644 index 0000000..a693bff --- /dev/null +++ b/prep/04yano/q @@ -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 diff --git a/prep/05selector/nonl b/prep/05selector/nonl new file mode 100644 index 0000000..e69de29 diff --git a/prep/05selector/q b/prep/05selector/q new file mode 100644 index 0000000..7c36d1f --- /dev/null +++ b/prep/05selector/q @@ -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 diff --git a/prep/06selectors/q b/prep/06selectors/q new file mode 100644 index 0000000..701d63b --- /dev/null +++ b/prep/06selectors/q @@ -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> + diff --git a/prep/07iterating/nonl b/prep/07iterating/nonl new file mode 100644 index 0000000..e69de29 diff --git a/prep/07iterating/q b/prep/07iterating/q new file mode 100644 index 0000000..6fd8d86 --- /dev/null +++ b/prep/07iterating/q @@ -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> + diff --git a/prep/08iterating/nonl b/prep/08iterating/nonl new file mode 100644 index 0000000..e69de29 diff --git a/prep/08iterating/q b/prep/08iterating/q new file mode 100644 index 0000000..32c2810 --- /dev/null +++ b/prep/08iterating/q @@ -0,0 +1,8 @@ +<code> + +for dataset in condition: + x=dataset.getx() + y=dataset.gety() + + +</code> diff --git a/prep/09pipeline/nonl b/prep/09pipeline/nonl new file mode 100644 index 0000000..e69de29 diff --git a/prep/09pipeline/q b/prep/09pipeline/q new file mode 100644 index 0000000..f9a7088 --- /dev/null +++ b/prep/09pipeline/q @@ -0,0 +1,15 @@ +<code> + +from yano.iter import * + +for dataset, x,tx,ty in pipeline(condition, + split, + shuffle, + normalize("minmax")): + ... + + + + +</code> + diff --git a/prep/10pipeline/q b/prep/10pipeline/q new file mode 100644 index 0000000..e9c617c --- /dev/null +++ b/prep/10pipeline/q @@ -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 diff --git a/prep/11CrossValidation/q b/prep/11CrossValidation/q new file mode 100644 index 0000000..1c43912 --- /dev/null +++ b/prep/11CrossValidation/q @@ -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? diff --git a/prep/12Logging/nonl b/prep/12Logging/nonl new file mode 100644 index 0000000..e69de29 diff --git a/prep/12Logging/q b/prep/12Logging/q new file mode 100644 index 0000000..b010f14 --- /dev/null +++ b/prep/12Logging/q @@ -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> + + + diff --git a/prep/12Seeding/q b/prep/12Seeding/q new file mode 100644 index 0000000..cfa9304 --- /dev/null +++ b/prep/12Seeding/q @@ -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? diff --git a/prep/13/nonl b/prep/13/nonl new file mode 100644 index 0000000..e69de29 diff --git a/prep/13/q b/prep/13/q new file mode 100644 index 0000000..7b68788 --- /dev/null +++ b/prep/13/q @@ -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} + diff --git a/prep/14statistics/q b/prep/14statistics/q new file mode 100644 index 0000000..0433fbf --- /dev/null +++ b/prep/14statistics/q @@ -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) diff --git a/prep/15Extended_Isolation_Forests/q b/prep/15Extended_Isolation_Forests/q new file mode 100644 index 0000000..f68a59e --- /dev/null +++ b/prep/15Extended_Isolation_Forests/q @@ -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) + diff --git a/prep/16/nonl b/prep/16/nonl new file mode 100644 index 0000000..e69de29 diff --git a/prep/16/q b/prep/16/q new file mode 100644 index 0000000..921cb56 --- /dev/null +++ b/prep/16/q @@ -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} + diff --git a/prep/17/nonl b/prep/17/nonl new file mode 100644 index 0000000..e69de29 diff --git a/prep/17/q b/prep/17/q new file mode 100644 index 0000000..cd7b21f --- /dev/null +++ b/prep/17/q @@ -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} + diff --git a/prep/18/nonl b/prep/18/nonl new file mode 100644 index 0000000..e69de29 diff --git a/prep/18/q b/prep/18/q new file mode 100644 index 0000000..aba2ef5 --- /dev/null +++ b/prep/18/q @@ -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} + diff --git a/prep/19highdim/a.png b/prep/19highdim/a.png new file mode 100644 index 0000000..c87d092 Binary files /dev/null and b/prep/19highdim/a.png differ diff --git a/prep/20New_Condition/nonl b/prep/20New_Condition/nonl new file mode 100644 index 0000000..e69de29 diff --git a/prep/20New_Condition/q b/prep/20New_Condition/q new file mode 100644 index 0000000..f2dd730 --- /dev/null +++ b/prep/20New_Condition/q @@ -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 diff --git a/prep/21New_Models/nonl b/prep/21New_Models/nonl new file mode 100644 index 0000000..e69de29 diff --git a/prep/21New_Models/q b/prep/21New_Models/q new file mode 100644 index 0000000..074e556 --- /dev/null +++ b/prep/21New_Models/q @@ -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> diff --git a/prep/22/nonl b/prep/22/nonl new file mode 100644 index 0000000..e69de29 diff --git a/prep/22/q b/prep/22/q new file mode 100644 index 0000000..38bb9b4 --- /dev/null +++ b/prep/22/q @@ -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} diff --git a/prep/23/nonl b/prep/23/nonl new file mode 100644 index 0000000..e69de29 diff --git a/prep/23/q b/prep/23/q new file mode 100644 index 0000000..820c1c6 --- /dev/null +++ b/prep/23/q @@ -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> diff --git a/prep/24/nonl b/prep/24/nonl new file mode 100644 index 0000000..e69de29 diff --git a/prep/24/q b/prep/24/q new file mode 100644 index 0000000..08ebc35 --- /dev/null +++ b/prep/24/q @@ -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> + diff --git a/prep/25Unsupervised_Optimization/q b/prep/25Unsupervised_Optimization/q new file mode 100644 index 0000000..dcd0011 --- /dev/null +++ b/prep/25Unsupervised_Optimization/q @@ -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 diff --git a/prep/26Loss_Optimization/q b/prep/26Loss_Optimization/q new file mode 100644 index 0000000..920fb92 --- /dev/null +++ b/prep/26Loss_Optimization/q @@ -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) diff --git a/prep/27loss/q b/prep/27loss/q new file mode 100644 index 0000000..8b9b7a3 --- /dev/null +++ b/prep/27loss/q @@ -0,0 +1 @@ +Pick the Model with the lowest l2\-loss diff --git a/prep/27loss/z_loss.pdf b/prep/27loss/z_loss.pdf new file mode 100644 index 0000000..d0e8b80 Binary files /dev/null and b/prep/27loss/z_loss.pdf differ diff --git a/prep/28Robustness/q b/prep/28Robustness/q new file mode 100644 index 0000000..250dac6 --- /dev/null +++ b/prep/28Robustness/q @@ -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 diff --git a/prep/28Robustness/z_robu.pdf b/prep/28Robustness/z_robu.pdf new file mode 100644 index 0000000..5839ccd Binary files /dev/null and b/prep/28Robustness/z_robu.pdf differ diff --git a/prep/29Distance_Correlation/q b/prep/29Distance_Correlation/q new file mode 100644 index 0000000..d792ea2 --- /dev/null +++ b/prep/29Distance_Correlation/q @@ -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 diff --git a/prep/29Distance_Correlation/z_dist.pdf b/prep/29Distance_Correlation/z_dist.pdf new file mode 100644 index 0000000..64b0ba3 Binary files /dev/null and b/prep/29Distance_Correlation/z_dist.pdf differ diff --git a/prep/30Other/q b/prep/30Other/q new file mode 100644 index 0000000..cc8547e --- /dev/null +++ b/prep/30Other/q @@ -0,0 +1,9 @@ +Things I still want to add: +<l2st> +Ensemble Methods +Visualisation options +Alternative Evaluations +Hyperparameter optimisation (with crossvalidation) + + +</l2st> diff --git a/prep/31Feedback/q b/prep/31Feedback/q new file mode 100644 index 0000000..b7ac220 --- /dev/null +++ b/prep/31Feedback/q @@ -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? diff --git a/touse/Ethereum-Price-Prediction.webp b/touse/Ethereum-Price-Prediction.webp new file mode 100644 index 0000000..eb94164 Binary files /dev/null and b/touse/Ethereum-Price-Prediction.webp differ diff --git a/touse/TIM220328_Buterin.Cover_.FINAL2_.jpg b/touse/TIM220328_Buterin.Cover_.FINAL2_.jpg new file mode 100644 index 0000000..baa037c Binary files /dev/null and b/touse/TIM220328_Buterin.Cover_.FINAL2_.jpg differ diff --git a/touse/TIM220328_Buterin.Cover_.FINAL2_.webp b/touse/TIM220328_Buterin.Cover_.FINAL2_.webp new file mode 100644 index 0000000..e0fa6f9 Binary files /dev/null and b/touse/TIM220328_Buterin.Cover_.FINAL2_.webp differ