Wsrtjtyk

I'm a begginer with isolation forest and i started with the tutorial in this link :

https://www.kaggle.com/rgaddati/unsupervised-fraud-detection-isolation-forest

I didn't understand the first plot of path length. what does the brown color means ?

Thank you for your help

##All General Import Statements

import pandas as pd

import numpy as np

import math

import random

%matplotlib inline

import random

from matplotlib import pyplot

import os

print(os.listdir("../input"))

class ExNode:

    def __init__(self,size):

        self.size=size



class InNode:

    def __init__(self,left,right,splitAtt,splitVal):

        self.left=left

        self.right=right

        self.splitAtt=splitAtt

        self.splitVal=splitVal



def iForest(X,noOfTrees,sampleSize):

    forest=

    hlim=math.ceil(math.log(sampleSize,2))

    for i in range(noOfTrees):

        X_train=df_data.sample(sampleSize)

        forest.append(iTree(X_train,0,hlim))

    return forest



def pathLength(x,Tree,currHeight):

    if isinstance(Tree,ExNode):

        return currHeight

    a=Tree.splitAtt

    if x[a]<Tree.splitVal:

        return pathLength(x,Tree.left,currHeight+1)

    else:

        return pathLength(x,Tree.right,currHeight+1)



df=pd.read_csv("../input/creditcard.csv")

y_true=df['Class']

df_data=df.drop('Class',1)



sampleSize=10000

ifor=iForest(df_data.sample(100000),10,sampleSize) ##Forest of 10 trees



posLenLst=

negLenLst=



for sim in range(1000):

    ind=random.choice(df_data[y_true==1].index)

    for tree in ifor:

        posLenLst.append(pathLength(df_data.iloc[ind],tree,0))



    ind=random.choice(df_data[y_true==0].index)

    for tree in ifor:

        negLenLst.append(pathLength(df_data.iloc[ind],tree,0))



bins = np.linspace(0,math.ceil(math.log(sampleSize,2)), math.ceil(math.log(sampleSize,2)))



pyplot.figure(figsize=(12,8))

pyplot.hist(posLenLst, bins, alpha=0.5, label='Anomaly')

pyplot.hist(negLenLst, bins, alpha=0.5, label='Normal')

pyplot.xlabel('Path Length')

pyplot.ylabel('Frequency')

pyplot.legend(loc='upper left')

The brown look likes a mix of the tan and blue. So I think thats where they overlap. This is evident because the alpha of each is .5

Further Explained: The colors are mixing where they overlap. Its plotting the blue then if the tan is also there it plots it on top. Since the colors have alpha of .5 which alpha is transparency the colors are mixing. Resulting in a brown where they overlap. Try plotting each separately and you will see that they will overlap.