I am wondering whether I can perform any kind of Cross-Validation or GridSearchCV for unsupervised learning. The thing is that I have the ground truth labels (but since it is unsupervised I just drop them for training and then reuse them for measuring accuracy, auc, aucpr, f1-score over the test set). Is there any way to do this?
I have a problem where I want to identify Vendors with unusual high amount invoices. What would be the best way to identify such invoices? I am trying to use Isolation Forest but having trouble in grouping by the result by Vendor. Any help will be appreciated. Data is in below format . Vendor ID Amount 1 456 2 1000 1 489 3 896 2 4576
ARIMA is widely used for anomaly detection on time-series data e.g. stock price prediction. ARIMA assumes that future value of a variable (stock price in our case) is dependent on its previous values. When we do root cause analysis of a detected anomaly, there can be numerous reasons e.g. russia-ukraine war. I have 2 questions: Isn't the assumption of ARIMA invalidated because stock price is also dependent on other factors such as war Which models can I use to do …
I'm trying to use the sklearn implementation of the Isolation Forest algorithm to detect anomalies in my time series data. However, even with a very low contamination parameter (0.0001), it is detecting things that should not be outliers in my opinion, as shown in the picture below: While this is the highest peak of the data, it doesn't really seem anomalous to me. How can I configure an Isolation forest to only detect samples that are drastically different from the …
I am working on a project where I train anomaly detection algorithms Isolation Forest and Auto-Encoder. My data is labelled so I have the ground truth but the nature of the problem requires unsupervised/semi-supervised anomaly detection approach rather than simple classification. Thus I will use the labels for validation only. Since I will not train the model with the labels, unlike supervised learning where I would have X_train, X_test, y_train and y_test, what is the right approach for model validation …
One of the major arguments I had recently is if we can save an unsupervised learning model to disk and use it later on incoming data. Isolation forest is one of the models that I use a lot for unsupervised anomaly detection and I always save it to a disk to use on future incoming data. Is it theoretically wrong to do this?
My code is as follows: import joblib as jl _data = pd.read_csv('ifile.csv') contamination = input(":") labelEncoder(_data) model = IsolationForest(contamination=float(contamination), n_estimators=1000, verbose=1) model.fit(_data) jl.dump(model, 'file.joblib') this trains the model and dumps it to joblib file. After that i use the joblib to test data further as follows: _data = pd.read_csv(ifile.csv) model = jl.load('file.joblib') predictions = model.predict(_data) _data[anomaly] = pd.Series(model.predict(_data)) predictions = np.where(predictions == 1, 0, 1) #Mapping 1->0 and -1->1 acc = accuracy_score(_data, predictions) This however return the following error: raise …
I am planning to use Isolation Forests in R (solitude package) to identify outlier medical claims in my data. Each row of my data represents the group of drugs that each provider has administered in the last 12 months. There are approximately 700+ unique drugs in my dataset and using one-hot encoding with a variety of numerical features will blow out the number of columns in my data. As an alternative to one-hot encoding I've reading about using word2vec to …
I am trying to use the Isolation Forest algorithm in the Solitude package to identify anomalous rows in my data. I'm using the examples in the documentation to learn about the algorithm, this example uses the Pima Indians Diabetes dataset. At the end of the example it provides a dataframe of ids, average_depth and anomaly_score sorted from highest score to lowest. How can I tie back the results of the model to the original dataset to see the rows with …
I am using Isolation Forests for Anomaly Detection. Say, my set has 10 variables, var1, var2, ..., var10, and I found an anomaly. Can I rank the 10 variables var1, var2, ..., var10 in such a way I can say that I have an anomaly and the main reason is, say, var6. For example, if I had var1, var2, var3 only, and my set were: 5 25 109 7 26 111 6 23 108 6 26 109 6 978 108 …
Trying to understand when to use zscore and when to use isolation forest for determining outliers in the data. I know that zscore is only applicable if data is normally distributed whereas isolationforest doesn't require data to follow any distribution. However let's say if data is following a normal distribution then will there be any benefit to using isolation forest? Also ,aside from normal distribution , what would be some other reasons to choose one over the other ? thanks
import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns import matplotlib from sklearn.ensemble import IsolationForest from pyod.models.copod import COPOD from pyod.models.hbos import HBOS from pyod.models.cblof import CBLOF from pyod.models.iforest import IForest clf = COPOD() xx, yy = np.meshgrid(np.linspace(0, 1, 100), np.linspace(0, 1, 100)) clf = IForest(contamination=outliers_fraction, random_state=0) clf.fit(df['Profit'].values.reshape(-1, 1)) y_pred = clf.predict(df['Profit'].values.reshape(-1, 1)) Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()]) * -1 Anyone knows how to fix this. I am trying to get the result from …
I've been using isolation forest for anomaly detection, and reviewing its parameters at scikit-learn (link). Looking at "bootstrap", I'm not quite clear what using bootstrap would cause. For supervised learning, this should reduce overfitting, but I'm not clear what the effect on anomaly detection should be. I think it would require the trees to achieve more "consensus" about what the anomaly is, therefore, reducing the effect of any single feature. I.e, an anomalous observation would probably need to be anomalous …
I'm trying to understand and experiment with how the SHAP value can explain behaviour for each outlier events (rows) and how it can be related to shap.force_plot(). I already created a simple synthetic dataset with 7 outliers. I didn't get how 4.85 calculated as output model. outliers_df = temp_dataset.loc[synth_data_df[outdet.label_feature] == 1] outliers_df #Here is the frame of 7 outlier cases I input to SHAP: +----+-------------+--------------+---------------+ | id | NF1 | CF1 |outlier_scores | +----+-------------+--------------+---------------+ | 1 | 904 | 2 …
I'm trying to detect anomalies based on geolocation from IP addresses on a server access log file. I have created two features country and geo_velocity, using the IP address and the timestamp of each request. However, since all the requests are from stationary clients and all the clients are from one country in the log file I have, my dataset ends up looking something like this. | Country | geo_velocity| | ----------- | ----------- | | USA | 0 | …
I am looking at various implementations of the Isolation Forest in python and R. Both sklearn in python and solitude in R use a y variable with the ExtraTrees regressor. Since, Isolation Forest is unsupervised, I am wondering why it is being turned into a supervised problem? Wouldnt this be an issue when scoring on previously unseen data sets? For example sklearn (python) line 248 has this. And in solitude line 144 as well.
I am trying to implement Anomaly Detection over a multivariate dataset having nominal and numerical predictors. Dataset has following pattern: If we consider the below sample records, category_id, currency, and product_id are nominal predictors, whereas price is a numerical variable. My model is able to identify the anomaly in the price for '_id=4' because the price range for different products for the particular combination of category_id-currency-product_id is between 10-500EUR. But it is not able to identify anomalies for product_id=1, product_id=2 …
I have a payment transaction dataset with a large number of predictor variables. I am trying to build a model for anomaly detection and I have evaluated various algorithms/approaches for the same like Isolation Forest, kNN, Autoencoders, and One-class SVM. I am able to identify if a payment record is an anomaly or not but I am not able to pin-point the predictor variable that is causing the anomaly. e.g.: Account || Currency || Beneficiary || Amount || isAnomaly(target) I …
Isolation Forest is widely used when dealing with outlier/anomaly detection when we have no labels. The theory behind is that making random split at random points and counting how many splits you do to isolate a feature will help you determine if an instance is or not an outlier. I have categorical features and I am not sure how to deal with them: Label Encoding: Will misrepresent the data in euclidean space. One Hot Encoding: Will give me more features …