Category: Sklearn isolation forest

18.04.2021

Sklearn isolation forest

By Grozuru

Anomaly, also known as an outlier is a data point which is so far away from the other data points that suspicions arise over the authenticity or the truthfulness of the dataset.

Hawkins defines outliers as:. Depending upon the feature space, outliers can be of two kinds: Univariate and Multivariate. The univariate outliers are the outliers generated by manipulating the value of a single feature.

Univariate outliers are visible to the naked eye when plotted on one dimensional or two-dimensional feature space.

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The multivariate outliers are generated by manipulating values of multiple features. In addition to categorizing outlier by feature space, we can also group outliers by their type. There are three major types of outliers:. Observation or data point that is too far from other data points in n-dimensional feature space.

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These are the simplest type of outlier. Contextual outliers are the type of outliers that depend upon the context. Hence these outliers depend upon the context. Collective outliers are a group of data points that occur together closely but are far away from the mean of the rest of the data points. Presence of outliers in the dataset, can be attributed to several reasons. Some of them have been enlisted below:.

Outlier detection is important for two reasons. Outliers correspond to the aberrations in the dataset, outlier detection can help detect fraudulent bank transactions. Consider the scenario where most of the bank transactions of a particular customer take place from a certain geographical location. Now if a transaction of that particular customer takes place through another geographical location, the transaction will be detected as an outlier. In such cases, further checks such as one-time-pin for cell phones can be used to ensure that the actual user is executing the transaction.

Outlier detection is also important because it highly impacts the mean and standard deviation of the dataset which can result in increased classification or regression error. To train a prediction algorithm that generalizes well on the unseen data, the outliers are often removed from the training data. In this section, we will see how outlier detection can be performed using Isolation Forestwhich is one of the most widely used algorithms for outlier detection.

Jan van der Vegt: A walk through the isolation forest - PyData Amsterdam 2019

We will first see a very simple and intuitive example of isolation forest before moving to a more advanced example where we will see how isolation forest can be used for predicting fraudulent transactions. Next, we need to create a two-dimensional array that will contain our dummy dataset. Execute the following script:.A sudden spike or dip in a metric is an anomalous behavior and both the cases needs attention.

Detection of anomaly can be solved by supervised learning algorithms if we have information on anomalous behavior before modeling, but initially without feedback its difficult to identify that points. Here we are identifying anomalies using isolation forest. The data here is for a use case eg revenue, traffic etc is at a day level with 12 metrics.

We have to identify first if there is an anomaly at a use case level. Then for better actionability, we drill down to individual metrics and identify anomalies in them. Now do a pivot on the dataframe to create a dataframe with all metrics at a date level. Level the multi-index pivot dataframe and treat na with 0. Isolation forest tries to separate each point in the data.

In case of 2D it randomly creates a line and tries to single out a point. Here an anomalous point could be separated in a few steps while normal points which are closer could take significantly more steps to be segregated.

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I am not going deep into each parameter. Contamination is an important parameter here and I have arrived at its value based on trial and error on validating its results with outliers in 2D plot.

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It stands for percentage of outlier points in the data. Now here we have 12 metrics on which we have classified anomalies based on isolation forest. We will try to visualize the results and check if the classification makes sense. Normalize and fit the metrics to a PCA to reduce the number of dimensions and then plot them in 3D highlighting the anomalies.

Now as we see the 3D point the anomaly points are mostly wide from the cluster of normal points,but a 2D point will help us to even judge better. Lets try plotting the same fed to a PCA reduced to 2 dimensions. So a 2D plot gives us a clear picture that the algorithm classifies anomalies points in the use case rightly. Anomalies are highlighted as red edges and normal points are indicated with green points in the plot. Here the contamination parameter plays a great factor.

Our idea here is to capture all the anomalous point in the system.

sklearn isolation forest

So its better to identify few points which might be normal as anomalous false positives ,but not to miss out catching an anomaly true negative. Now we have figured the anomalous behavior at a use case level. But to be actionable on the anomaly its important to identify and provide information on which metrics are anomalous in it individually.

So creating a good visualization is equally important in this process. This function creates actuals plot on a time series with anomaly points highlighted on it.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service. The dark mode beta is finally here.

Change your preferences any time. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. I am currently working on detecting outliers in my dataset using Isolation Forest in Python and I did not completely understand the example and explanation given in scikit-learn documentation.

Is it possible to use Isolation Forest to detect outliers in my dataset that has rows and 10 columns? Do I need a separate dataset to train the model? If yes, is it necessary to have that training dataset free from outliers?

Using Isolation Forest for Outlier Detection In Python

IsolationForest is an unsupervised learning algorithm that's intended to clean your data from outliers see docs for more. In usual machine learning settings, you would run it to clean your training dataset. As far as your toy example concerned:. As specified by contamination param, the fraction of outliers is 0. Learn more. Isolation Forest in Python Ask Question. Asked 1 year, 1 month ago. Active 1 year, 1 month ago. Viewed 3k times.

I am currently working on detecting outliers in my dataset using Isolation Forest in Python and I did not completely understand the example and explanation given in scikit-learn documentation Is it possible to use Isolation Forest to detect outliers in my dataset that has rows and 10 columns? Nnn Nnn 1 1 silver badge 8 8 bronze badges. Your code is working for your toy example with minor corrections. Do you have ground truth labels for your "outliers"?

SergeyBushmanov I understand that ground truth labels are not needed in order to use IsolationForest however if OP has such labels, then you could use this information to tune hyperparameters or score IsolationForest on test data for comparison with other models. Active Oldest Votes. Short answer is "No". You train and predict outliers on the same data. Sergey Bushmanov Sergey Bushmanov 8, 2 2 gold badges 27 27 silver badges 43 43 bronze badges.

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Feedback on Q2 Community Roadmap. Technical site integration observational experiment live on Stack Overflow. Triage needs to be fixed urgently, and users need to be notified upon….After half a year since my first article on anomaly detection, one of its readers has brought to my attention the fact that there is a recent improvement to the Isolation Forest algorithm, namely Extended Isolation Forest EIFwhich addresses major drawbacks of the original method.

In this article, I give a quick reminder of the original IF algorithms, describe the potential problem with it and how EIF handles it. In the end, I will present a Python example of how to use both algorithms and compare their performance. The forest is built on the basis of decision trees, each of the trees having access to a sub-sample of the training data. In order to create a branch in the tree, first, a random feature is selected. Afterward, a random split value between min and max value is chosen for that feature.

If the given observation has a lower value of this feature then the one selected it follows the left branch, otherwise the right one. This process is continued until a single point is isolated or specified maximum depth is reached.

In principle, outliers are less frequent than regular observations and are different from them in terms of values they lie further away from the regular observations in the feature space. That is why by using such random partitioning they should be identified closer to the root of the tree shorter average path length, i. The anomaly score is created on the basis of all trees in the forest and the depth the point reaches in these trees.

I believe the best way to understand the issue is to see it as an example. In the left picture, we can see data sampled from the multivariate normal distribution. Intuitively, we would assume that the anomaly score assigned to the observations would increase radially from the central point of the distribution [0, 0]. However, this is clearly not the case, as seen in the right image.

What is more, there are also rectangular artifacts of a lower score, such as the vertical one between point 0 and 1 on the x-axis. Here we see two blobs centered at points [0, 10] and [10, 0].

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By inspecting the right figure we see not only the artifacts that were present before, but also two ghost clusters approximately at [0, 0] and [10, 10].

The reason for this peculiar behavior originates from the fact that the decision boundaries of the Isolation Forest are either vertical or horizontal random value of a random featureas seen in the picture below, where the authors plot branch cuts generated by the IF during the training phase. We see that the branches tend to cluster where the majority of the points are located. But as the lines can only be parallel to the axes, there are regions that contain many branch cuts and only a few or single observations, which results in improper anomaly scores for some of the observations.

Isolation Forest in Python using Scikit learn

An example might be points around [3, 0] many branch cuts and [3, 3] few cuts. Extended Random Forest addresses that issue by approaching the problem a bit differently.

sklearn isolation forest

Instead of selecting a random feature and then random value within the range of data it selects:. Extended Random Forest generalizes well into higher dimensions, where instead of straight lines we are dealing with hyperplanes. For a deeper dive into N-dimensional generalization, please refer to [1] for a very approachable explanation.

An extra feature captured by the EIF is the higher anomaly score region directly in-between the two clusters where they kind of link. For this short exercise, I use the Forest Cover dataset downloaded from here. The dataset contains observations and 10 features.Update: Part 2 describing the Extended Isolation Forest is available here.

During a recent project, I was working on a clustering problem with data collected from users of a mobile app. The goal was to classify the users in terms of their behavior, potentially with the use of K-means clustering. However, after inspecting the data it turned out that some users represented abnormal behavior — they were outliers.

A lot of machine learning algorithms suffer in terms of their performance when outliers are not taken care of. In order to avoid this kind of problem you could, for example, drop them from your sample, cap the values at some reasonable point based on domain knowledge or transform the data.

However, in this article, I would like to focus on identifying them and leave the possible solutions for another time. As in my case, I took a lot of features into consideration, I ideally wanted to have an algorithm that would identify the outliers in a multidimensional space. That is when I came across Isolation Forest, a method which in principle is similar to the well-known and popular Random Forest.

In this article, I will focus on the Isolation Forest, without describing in detail the ideas behind decision trees and ensembles, as there is already a plethora of good sources available. The main idea, which is different from other popular outlier detection methods, is that Isolation Forest explicitly identifies anomalies instead of profiling normal data points.

Isolation Forest, like any tree ensemble method, is built on the basis of decision trees. In these trees, partitions are created by first randomly selecting a feature and then selecting a random split value between the minimum and maximum value of the selected feature.

In principle, outliers are less frequent than regular observations and are different from them in terms of values they lie further away from the regular observations in the feature space. That is why by using such random partitioning they should be identified closer to the root of the tree shorter average path length, i.

The idea of identifying a normal vs. A normal point on the left requires more partitions to be identified than an abnormal point right. As with other outlier detection methods, an anomaly score is required for decision making. In the case of Isolation Forest, it is defined as:. More on the anomaly score and its components can be read in [1].

Each observation is given an anomaly score and the following decision can be made on its basis:. For simplicity, I will work on an artificial, 2-dimensional dataset. This way we can monitor the outlier identification process on a plot.

sklearn isolation forest

First, I need to generate observations. The second group is new observations, coming from the same distribution as the training ones. Lastly, I generate outliers. Figure 2 presents the generated dataset. Now I need to train the Isolation Forest on the training set.Please cite us if you use the software.

Since recursive partitioning can be represented by a tree structure, the number of splittings required to isolate a sample is equivalent to the path length from the root node to the terminating node.

This path length, averaged over a forest of such random trees, is a measure of normality and our decision function. Random partitioning produces noticeably shorter paths for anomalies. Hence, when a forest of random trees collectively produce shorter path lengths for particular samples, they are highly likely to be anomalies. Read more in the User Guide. The amount of contamination of the data set, i.

Used when fitting to define the threshold on the scores of the samples. Changed in version 0. If True, individual trees are fit on random subsets of the training data sampled with replacement. If False, sampling without replacement is performed. The number of jobs to run in parallel for both fit and predict.

None means 1 unless in a joblib. See Glossary for more details. New in version 0. Deprecated since version 0. When set to Truereuse the solution of the previous call to fit and add more estimators to the ensemble, otherwise, just fit a whole new forest. See the Glossary. Offset used to define the decision function from the raw scores. Unsupervised Outlier Detection. Estimate the support of a high-dimensional distribution.

The implementation is based on libsvm. The implementation is based on an ensemble of ExtraTreeRegressor. The anomaly score of an input sample is computed as the mean anomaly score of the trees in the forest. The measure of normality of an observation given a tree is the depth of the leaf containing this observation, which is equivalent to the number of splittings required to isolate this point.

Anomaly Detection with Isolation Forest & Visualization

The input samples. The anomaly score of the input samples. The lower, the more abnormal. Negative scores represent outliers, positive scores represent inliers.Please cite us if you use the software. Many applications require being able to decide whether a new observation belongs to the same distribution as existing observations it is an inlieror should be considered as different it is an outlier.

Often, this ability is used to clean real data sets. Two important distinctions must be made:. The training data contains outliers which are defined as observations that are far from the others. Outlier detection estimators thus try to fit the regions where the training data is the most concentrated, ignoring the deviant observations.

The training data is not polluted by outliers and we are interested in detecting whether a new observation is an outlier. In this context an outlier is also called a novelty. Outlier detection and novelty detection are both used for anomaly detection, where one is interested in detecting abnormal or unusual observations. Outlier detection is then also known as unsupervised anomaly detection and novelty detection as semi-supervised anomaly detection.

The scikit-learn project provides a set of machine learning tools that can be used both for novelty or outlier detection. This strategy is implemented with objects learning in an unsupervised way from the data:. Inliers are labeled 1, while outliers are labeled The predict method makes use of a threshold on the raw scoring function computed by the estimator. Note that neighbors. If you really want to use neighbors. LocalOutlierFactor for novelty detection, i.

The behavior of neighbors. LocalOutlierFactor is summarized in the following table. A comparison of the outlier detection algorithms in scikit-learn. Local Outlier Factor LOF does not show a decision boundary in black as it has no predict method to be applied on new data when it is used for outlier detection.

IsolationForest and neighbors. LocalOutlierFactor perform reasonably well on the data sets considered here. The svm. OneClassSVM is known to be sensitive to outliers and thus does not perform very well for outlier detection.