From 50638fbae09c717ef02ca3820921da76a5379da7 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Simon=20Kl=C3=BCttermann?= Date: Sat, 29 Jan 2022 13:04:08 +0100 Subject: [PATCH] initial push --- README | 9 +++ __pycache__/choosenext.cpython-39.pyc | Bin 0 -> 1318 bytes __pycache__/data.cpython-39.pyc | Bin 0 -> 651 bytes __pycache__/loss.cpython-39.pyc | Bin 0 -> 819 bytes __pycache__/mu.cpython-39.pyc | Bin 0 -> 1155 bytes __pycache__/n2ulayer.cpython-39.pyc | Bin 0 -> 2104 bytes before.png | Bin 0 -> 117695 bytes choosenext.py | 54 +++++++++++++ data.py | 20 +++++ imgs/0.png | Bin 0 -> 117695 bytes imgs/1.png | Bin 0 -> 109761 bytes imgs/10.png | Bin 0 -> 108107 bytes imgs/11.png | Bin 0 -> 108047 bytes imgs/12.png | Bin 0 -> 98934 bytes imgs/13.png | Bin 0 -> 106533 bytes imgs/14.png | Bin 0 -> 118215 bytes imgs/15.png | Bin 0 -> 107680 bytes imgs/16.png | Bin 0 -> 108575 bytes imgs/17.png | Bin 0 -> 109497 bytes imgs/18.png | Bin 0 -> 108413 bytes imgs/19.png | Bin 0 -> 103705 bytes 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205714 bytes suggestion.png | Bin 0 -> 116976 bytes updated.png | Bin 0 -> 116448 bytes 79 files changed, 469 insertions(+) create mode 100644 README create mode 100644 __pycache__/choosenext.cpython-39.pyc create mode 100644 __pycache__/data.cpython-39.pyc create mode 100644 __pycache__/loss.cpython-39.pyc create mode 100644 __pycache__/mu.cpython-39.pyc create mode 100644 __pycache__/n2ulayer.cpython-39.pyc create mode 100644 before.png create mode 100644 choosenext.py create mode 100644 data.py create mode 100644 imgs/0.png create mode 100644 imgs/1.png create mode 100644 imgs/10.png create mode 100644 imgs/11.png create mode 100644 imgs/12.png create mode 100644 imgs/13.png create mode 100644 imgs/14.png create mode 100644 imgs/15.png create mode 100644 imgs/16.png create mode 100644 imgs/17.png create mode 100644 imgs/18.png create mode 100644 imgs/19.png create mode 100644 imgs/2.png create mode 100644 imgs/3.png create mode 100644 imgs/4.png create mode 100644 imgs/5.png create mode 100644 imgs/6.png create mode 100644 imgs/7.png create mode 100644 imgs/8.png create mode 100644 imgs/9.png create mode 100644 imgs/recombine.png create mode 100644 loss.py create mode 100644 main.py create mode 100644 merged.npz create mode 100644 mu.py create mode 100644 multimodel.py create mode 100644 n2ulayer.py create mode 100644 old/imgs/0.png create mode 100644 old/imgs/1.png create mode 100644 old/imgs/2.png create mode 100644 old/imgs/3.png create mode 100644 old/imgs/4.png create mode 100644 old/imgs/5.png create mode 100644 old/imgs/6.png create mode 100644 old/imgs/7.png create mode 100644 old/imgs/8.png create mode 100644 old/runs/0.npz create mode 100644 old/runs/1.npz create mode 100644 old/runs/2.npz create mode 100644 old/runs/3.npz create mode 100644 old/runs/4.npz create mode 100644 old/runs/5.npz create mode 100644 old/runs/6.npz create mode 100644 old/runs/7.npz create mode 100644 old/runs/8.npz create mode 100644 onemodel.py create mode 100644 recombine.py create mode 100644 requirements.txt create mode 100644 runs/0.npz create mode 100644 runs/1.npz create mode 100644 runs/10.npz create mode 100644 runs/11.npz create mode 100644 runs/12.npz create mode 100644 runs/13.npz create mode 100644 runs/14.npz create mode 100644 runs/15.npz create mode 100644 runs/16.npz create mode 100644 runs/17.npz create mode 100644 runs/18.npz create mode 100644 runs/19.npz create mode 100644 runs/2.npz create mode 100644 runs/3.npz create mode 100644 runs/4.npz create mode 100644 runs/5.npz create mode 100644 runs/6.npz create mode 100644 runs/7.npz create mode 100644 runs/8.npz create mode 100644 runs/9.npz create mode 100644 suggestion.png create mode 100644 updated.png diff --git a/README b/README new file mode 100644 index 0000000..150c06f --- /dev/null +++ b/README @@ -0,0 +1,9 @@ +Instead of trying to find a model that is perfect at finding anomalies, ensembles try to combine multiple (maybe bad) models into one. +To do this, we need an algorithm to combine the predictions of different models. One way (that I commonly use) is to just average them in some way (score=sqrt(score_1**2+score_2**2)). This works only well if you have a huge number of mostly uncorrelated models. +If you have only a few models or correlated ones you can introduce bias this way. Assume we have three models: An isolation forest (iforest), an svm and a kNN algorithm. The iforest has low correlation to the other models (if finds different things anomalous compared to the svm and kNN), bzt the svm and the kNN find basically the same anomalies. If we just average each model, the svm and kNN have a much bigger influence on the result compared to the iforest. There is no real reason why this should be the case. +To solve this, you can add models depending on correlations between them. But instead of relying on the correlation existing between the models themself, this repository uses a special kind of neural network to find uncorrelated parts of the model predictions. + +n2ulayer.py and mu.py define this special kind of neural network. loss.py defines the correlation we want to minimize for use in tensorflow. +onemodel.py generates a quick (and quite random) anomaly detection model for use on the data defined in data.py (just a 2d gaussian). 20 models are generated and their predictions (sorted from most normal (green) to most anomal (red)) drawn in the numbered images in imgs +If you use all 20 models and simply average them this results in imgs/recombine.png. 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Correlations are hard to optimize. So use corvariance and metric keeping properties""" + if len(b.shape)>1: + b=b[:,0] + if len(a.shape)>1: + a=a[:,0] + + return K.abs(K.mean((a-K.mean(a))*(b-K.mean(b)))) + + +if __name__=='__main__': + import numpy as np + x=np.random.uniform(-1,1,size=(1000,2)) + print(numpyloss2d(x[:,0],x[:,1],n=25)) + + + + diff --git a/main.py b/main.py new file mode 100644 index 0000000..409663f --- /dev/null +++ b/main.py @@ -0,0 +1,63 @@ +import numpy as np + +from choosenext import choosenext + +from plt import plt + + +def draw(p,dat): + mp=np.mean(p) + d=np.square(p-mp) + sx=[(xx,dd) for xx,dd in zip(dat,d)] + sx.sort(key=lambda x:x[1]) + sx=[zw[0] for zw in sx] + sx=np.array(sx) + + col1=[1.0,0.0,0.0] + col2=[0.0,1.0,0.0] + col1,col2=np.array(col1),np.array(col2) + ln=len(sx) + cols=[col1*(i/ln)+col2*(1-i/ln) for i in range(ln)] + + plt.scatter(sx[:,0],sx[:,1],c=cols) + + +def combine(a,b): + a=1+(a-np.mean(a))/np.std(a) + b=1+(b-np.mean(b))/np.std(b) + toc=np.concatenate((np.expand_dims(a,axis=1),np.expand_dims(b,axis=1)),axis=1) + toc=np.sqrt(np.mean(toc**2,axis=1)) + return toc + +if __name__=="__main__": + f=np.load("merged.npz") + dat=f["x"] + x=f["ps"] + np.random.shuffle(x) + given=x[0] + possble=x[1:5] + + ac=choosenext(given,possble) + nextbest=ac[:,0] + remainder=ac[:,1:] + + for row in np.transpose(ac): + print(np.corrcoef(given,row)[0,1]) + #as you see: the correlation is the lowest for the first row. + #so lets combine it + updated=combine(given,nextbest) + + + draw(given,dat) + plt.savefig("before.png") + plt.show() + draw(nextbest,dat) + plt.savefig("suggestion.png") + plt.show() + draw(updated,dat) + plt.savefig("updated.png") + plt.show() + + + + diff --git a/merged.npz b/merged.npz new file mode 100644 index 0000000000000000000000000000000000000000..41b4ca2b0ba2781dfaf30cc09a034fa432f49784 GIT binary patch literal 1059606 zcmV(#K;*wrO9KQH000080000X0QN3eG@nlb0Dy4<00jUP0C+BLaCrd$5CDL20ssI2 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range(i+1,dim): + dex.append([i,j]) + np.random.shuffle(dex) + for i,j in dex: + q=ulayer(dim,i,j)(q) + return q +def partu(q,pdim,dim,ulayer): + for i in range(pdim): + for j in range(i+1,dim): + q=ulayer(dim,i,j)(q) + return q +def partr(q,pdim,dim,ulayer): + #this is often just an approximation. But for sqrt runtime.... + dex=[] + for i in range(pdim): + for j in range(i+1,dim): + dex.append([i,j]) + np.random.shuffle(dex) + for i,j in dex: + q=ulayer(dim,i,j)(q) + return q +def cutdown(q,pdim): + return q[:,:pdim] + + diff --git a/multimodel.py b/multimodel.py new file mode 100644 index 0000000..82fd314 --- /dev/null +++ b/multimodel.py @@ -0,0 +1,6 @@ +import os + +#yes I know, not the best way to do this... +for i in range(20): + os.system(f"python3 onemodel.py {i}") + diff --git a/n2ulayer.py b/n2ulayer.py new file mode 100644 index 0000000..5724809 --- /dev/null +++ b/n2ulayer.py @@ -0,0 +1,72 @@ +#use sin cos to get better gradients (than nulayer) +#migth habe better gradients? (seems that way but not sure yet) + +#should rename it, but who cares +#now also able to export the given matrix +from tensorflow.keras.layers import Layer +from tensorflow.keras import backend as K +from tensorflow import keras +import tensorflow as tf + +import numpy as np + + + +class ulayer(Layer): + def __init__(self,siz,dex1,dex2, **kwargs): + self.siz = siz + self.dex1 = dex1 + self.dex2 = dex2 + super(ulayer, self).__init__(**kwargs) + + def build(self, input_shape): + # Create a trainable weight variable for this layer. + self.kernel = self.add_weight(name='kernel', + shape=(1,), + initializer=keras.initializers.RandomUniform(-0.5, 0.5), + trainable=True) + super(ulayer, self).build(input_shape) # Be sure to call this at the end + + def numpify(self): + mat=np.eye(self.siz) + val=self.weights[0].numpy()[0] + sin,cos=np.sin(val),np.cos(val) + mat[self.dex1,self.dex2]=sin + mat[self.dex2,self.dex1]=-sin + mat[self.dex1,self.dex1]=cos + mat[self.dex2,self.dex2]=cos + return mat + + + + def call(self, x): + kernel=self.kernel + sin=K.sin(kernel) + cos=K.cos(kernel) + tan=sin/cos#that should diverge? + rows=[tf.expand_dims(x[:,i],1) for i in range(self.siz)] + #instead of ((1,a),(-a,1)), I want this to be + #((1,a),(-a,1))/sqrt(1+a**2) + #and with trigonometry, I can get the same result by + #a=sin(kernel)? + #multiply to make 1->cos(x) (aka *cos(x)) + #so a actually tan(kernel) + z1=rows[self.dex2]*tan + z2=rows[self.dex1]*tan + rows[self.dex1]+=z1 + rows[self.dex2]-=z2 + rows[self.dex1]*=cos + rows[self.dex2]*=cos + rows=K.concatenate(rows,axis=1) + return rows + + + mat=tf.eye(self.siz) + tf.assign(mat[self.dex1,self.dex2],self.kernel) + #mat[self.dex2,self.dex1]=-self.kernel + return K.dot(x, mat) + + def compute_output_shape(self, input_shape): + return input_shape + + diff --git a/old/imgs/0.png b/old/imgs/0.png new file mode 100644 index 0000000000000000000000000000000000000000..1138c1b0d3ab48dd09f16e5824e28fb644b17a42 GIT binary patch literal 112333 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+q=keras.layers.Dense(1,activation='linear')(q) + +model=keras.models.Model(inputs=inp,outputs=q) + +model.compile(optimizer='adam',loss='mse') + +model.fit(x,np.ones(len(x)), + epochs=500, + batch_size=25, + validation_split=0.2, + verbose=1, + shuffle=True, + callbacks=[keras.callbacks.EarlyStopping(monitor='val_loss',patience=10)]) + + +#Evaluation phase + + +x=X + +p=model.predict(x) +mp=np.mean(p) +d=(p-mp)**2 +d=np.sqrt(np.mean(d,axis=-1)) + +np.savez_compressed(f"runs/{dex}",d=d,x=x,p=p,mp=mp) + + +sx=[(xx,dd) for xx,dd in zip(x,d)] +sx.sort(key=lambda x:x[1]) +print(sx[0],sx[-1]) + +sx=[xx for xx,dd in sx] +sx=np.array(sx) + +from plt import plt + + +col1=[1.0,0.0,0.0] +col2=[0.0,1.0,0.0] + +col1,col2=np.array(col1),np.array(col2) + +ln=len(sx) + +cols=[col1*(i/ln)+col2*(1-i/ln) for i in range(ln)] + + + +plt.scatter(sx[:,0],sx[:,1],c=cols) + +plt.savefig(f"imgs/{dex}.png") + +#plt.plot(sx[:,0],sx[:,1],'.') + +plt.how() + + + + + + + + + + + + + + + + + diff --git a/recombine.py b/recombine.py new file mode 100644 index 0000000..f36ee8d --- /dev/null +++ b/recombine.py @@ -0,0 +1,77 @@ +import tensorflow as tf +from tensorflow import keras + +import numpy as np + +import os +import sys + + +from data import data + +fns=[f"runs/{zw}" for zw in os.listdir("runs")] + + +fs=[np.load(fn) for fn in fns if os.path.isfile(fn)] + +x=fs[0]["x"] +ds=[f["d"] for f in fs] +ps=[f["p"][:,0] for f in fs] + + +ds=np.array(ds) +ps=np.array(ps) + +d=np.sqrt(np.mean(np.square(ds),axis=0)) + +np.savez_compressed("merged",x=x,ds=ds,ps=ps,d=d) + + +print(np.corrcoef(ps)) + + + +sx=[(xx,dd) for xx,dd in zip(x,d)] +sx.sort(key=lambda x:x[1]) + +sx=[xx for xx,dd in sx] +sx=np.array(sx) + +from plt import plt + + +col1=[1.0,0.0,0.0] +col2=[0.0,1.0,0.0] + +col1,col2=np.array(col1),np.array(col2) + +ln=len(sx) + +cols=[col1*(i/ln)+col2*(1-i/ln) for i in range(ln)] + + + +plt.scatter(sx[:,0],sx[:,1],c=cols) + +plt.savefig(f"imgs/recombine.png") + +#plt.plot(sx[:,0],sx[:,1],'.') + +plt.how() + + + + + + + + + + + + + + + + + diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..5816112 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,4 @@ +numpy +tensorflow +keras +matplotlib diff --git a/runs/0.npz b/runs/0.npz new file mode 100644 index 0000000000000000000000000000000000000000..30a747b7ecf24b412b93f2b2e01cbba4604eb35e GIT binary patch literal 198552 zcmW)GbyO3M`?ZRKN~tIvDgr7k9UCG7Dy5=;)P&Kan+2$J%LfId1pyVd(cO&*1A&bm zT?0l8#x};@@9%xidH#9s^Y^{y+Pi#rogmXu)_#2H)5%6q;c z{I=dr*K5~q23T~*#0?2Qe*ZY4{5ja-lRMJ|=6CMr@7x|NXPM%Emk=ZO(BS50+S%`E z8q2IECd7DHO#v{6e|?0t@|qx5<+Pqq=F`)!Mn{TxR$^MC;q(Qg#!>Mxa#rI2_oz4x zL0-_|oF{0&Ldgq=<5lv^PQbz)+7>x_yUjcdMAuv`^q|k!k4YDZ=ri&dW|t|ut}(*K zIzF%Xf4fr80uUf85UuDAT%2Qqjm|KCet-Dc6AKNFcx7S~U4;;JkfZno|3U|7N*tvf z(EiC~7vky9v>3Oq&>N^{VWfFkg1Uas_mj%`wzoG5z@)=Bh@3+FP7z@@Ghz$uy1~$C zYqCU-DltR7PM-5`QCu@>%v2&cH`ihR$pLNfq6k1S0hkr>T5DZv9XKXuixP;F9vcA~ 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