Unsupervised Anomaly Detection | Kaggle Explore and run machine learning code with Kaggle Notebooks | Using data from Numenta Anomaly Benchmark (NAB) Comments (22) Run. Calculate number_of_outliers using outliers_fraction. In time series analysis, it is important that the data is stationary and have no autocorrelation. The data set contains sensor readings from 53 sensors installed on a pump to measure various behaviors of the pump. Anomaly detection involves identifying the differences, deviations, and exceptions from the norm in a dataset. Manufacturing industry is considered a heavy industry in which they tend to utilize various types of heavy machinery such as giant motors, pumps, pipes, furnaces, conveyor belts, haul trucks, dozers, graders, and electric shovels etc. I will perform the following steps using the Pipeline library. After loading dataset and model then run cell by cell in kaggle. It appears that the first two principal components are the most important as per the features extracted by the PCA in above importance plot. As the next step, I will visually inspect the stationarity of each feature in the data set and the following code will do just that. I suggest the following next steps in which the first 3 steps are focused on improving the model and the last two are about making things real: I will continue improving the model besides implementing the above mentioned steps and I will plan to share the outcomes in another post in the future. So both of the principal components are stationary which is what I wanted. This provides the groundwork for the Anomaly Detection framework which we will discuss in Part 2. However, as a starting figure, I estimate outliers_fraction=0.13 (13% of df are outliers as depicted). Models are automatically onboarded into the profiling framework. Or you can use the test images which is already given in the dataset. Is IQR mostly detecting the anomalies that are closer together while the other two models are detecting the anomalies spread across different time periods? Sep 26, 2020 -- 5 Anomaly detection involves identifying the differences, deviations, and exceptions from the norm in a dataset. As seen above, the anomalies are detected right before the pump breaks down. Anomaly Detection in Time Series Sensor Data The profile generation jobs use the raw model inference logs containing the features and predictions. The prime reason why they care so much about these assets is that the failure of these equipment often results in production loss that could consequently lead to loss of hundreds of thousands of dollars if not millions depending on the size and scale of the operations. Final Year project of anomaly detection in surveillance images, gifs. If noticed from the first 10 rows of the tidy data, the magnitude of the values from each feature is not consistent. Now that we have cleaned our data, we can start exploring to acquaint with the data set. So far, we have done anomaly detection with three different methods. Anomaly Detection with Auto-Encoders | Kaggle In addition, we will inspect the autocorrelation of the features before feeding them into the clustering algorithms to detect anomalies. Visualize anomalies with Time Series view. It is very hard to find a public data from a manufacturing industry for this particular use case but I was able to find one that is not perfect. Input. Implement other learning algorithms such as SVM, DBSCAN etc. Filter the data points that fall outside the upper and lower bounds and flag them as outliers. Its sometimes referred to as outlier detection. Looking at the readings from one of the sensors, sensor_17 in this case, notice that the data actually looks pretty stationary where the rolling mean and standard deviation dont seem to change over time except during the downtime of the pump which is expected. On the other hand, autocorrelation refers to the behavior of the data where the data is correlated with itself in a different time period. The production loss from unplanned downtime, the cost of unnecessary maintenance and having excess or shortage of critical components translate into serious magnitudes in terms of dollar amount. On top of some quantitative EDA, I performed additional graphical EDA to look for trends and any odd behaviors. Logs. Download image and gif from (Resource folder from github) and upload in kaggle and put there path links in places where images and gif extesion used in code. This data set can be found here. It is pretty computationally expensive to train models with all of the 52 sensors/features and it is not efficient. Enjoy detecting anomalies and lets connect on LinkedIn. The following code plots the mentioned graph for each of the sensors, but lets take a look at that for the sensor_00. Thus, I will reject the Null Hypothesis and say the data is stationary. For example, I couldnt properly train SVM on this data as it was taking a very long time to train the model with no success. This could be a very valuable information for an operator to see and be able to shut down the pump properly before it actually goes down hard. You can also upload any random image and gif of (Abuse Arrest,Robbery,Explosion,Fighting,Shooting only) from internet. Calculate the distance between each point and its nearest centroid. Therefore, the ability to detect anomalies in advance and be able to mitigate risks is a very valuable capability which further allows preventing an unplanned downtime, unnecessary maintenance (condition based vs mandatory maintenance) and will also enable more effective way of managing critical components for these assets. To do that, Ill write a function that calculates the percentage of missing values so I can use the same function multiple times throughout the notebook. How do we define accuracy? Then train the model (in case if you don't want to train model then take pretrained model[download it with pretrained_model_link.txt] which is uploaded on github). Calculate upper and lower bounds for the outlier. However, the following tables show, on the contrary, that IQR is detecting far more anomalies than that of K-Means and Isolation Forest. Why do you think that is? First, I will download the data using the following code and Kaggle API. The biggest distances are considered as anomaly. To run the code of anomaly detection please follow these steps. Building a large scale unsupervised model anomaly detection system - Medium Therefore, I will employ Principal Component Analysis (PCA) technique to extract new features to be used for the modeling. In order to properly apply PCA, the data must be scaled and standardized. Is IQR more scientific approach than the other two? Finally, plot the outliers on top of the time series data (the readings from sensor_11 in this case). This was the case for most of the sensors in this data set but it may not always be the case in which situations various transformation methods must be applied to make the data stationary before training the data. These are often considered as the most critical assets for their operations. We can already see that the data requires some cleaning, there are missing values, an empty column and a timestamp with an incorrect data type. Calculate IQR which is the difference between 75th (Q3)and 25th (Q1) percentiles. Anomaly Detection with Time Series Forecasting | Kaggle Therefore, the integrity and reliability of these equipment is often the core focus of their Asset Management programs. Once downloaded, read the CSV file into the pandas DataFrame with the following code and check out the details of the data. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Acknowledgements Are you sure you want to create this branch? The anomaly result of anomaly1 contains the above method Cluster (0:normal, 1:anomaly). Lets see if we detect similar pattern in anomalies from the next two algorithms. Load the dataset of UCF-crime on Kaggle which is already present . Prajwal007-supernova-sonic/Anomaly-Detection - GitHub Jupyter notebook can be found on Github for details. The following code does just that. In this step, I will perform the following learning algorithms to detect anomalies. That way, we can clearly see when the pump breaks down and how that reflects in the sensor readings. I will use the latter in this case to quickly visually verify that there is no autocorrelation. So this is a pretty serious deal for a Maintenance Manager of a manufacturing plant to run a robust Asset Management framework with highly skilled Reliability Engineers to ensure the reliability and availability of these critical assets. May 22, 2021 -- In my previous article ( https://medium.com/analytics-vidhya/anomaly-detection-in-python-part-1-basics-code-and-standard-algorithms-37d022cdbcff) we discussed the basics of Anomaly detection, the types of problems and types of methods used. Next, lets handle the missing values and for that lets first see the columns that have missing values and see what percentage of the data is missing. Explore and run machine learning code with Kaggle Notebooks | Using data from multiple data sources Anomaly Detection with Auto-Encoders. The tidy data set has 52 sensors, machine status column that contains three classes (NORMAL, BROKEN, RECOVERING) which represent normal operating, broken and recovering conditions of the pump respectively and then the datetime column which represents the timestamp. Running the Dickey Fuller test on the 1st principal component, I got a p-value of 5.4536849418486247e-05 which is very small number (much smaller than 0.05). Given my new features from PCA are stationary and not autocorrelated, I am ready for modeling. After some analysis, I decided to impute some of the missing values with their mean and drop the rest. I performed the same on the 2nd component and got a similar result. Situations may vary from data set to data set. Anomaly detection is not a new concept or technique, it has been around for a number of years and is a common application of Machine Learning. Anomaly Detection in Python - Towards Data Science In doing so, we went through most of the steps of the commonly applied Data Science Process which includes the following steps: One of the challenges I faced during this project is that training anomaly detection models with unsupervised learning algorithms with such a large data set can be computationally very expensive. Now, I will check again the stationarity and autocorrelation of these two principal components just to be sure they are stationary and not autocorrelated. Output. Notebook. The biggest distances are considered as anomaly, distance = getDistanceByPoint(principalDf, kmeans), # number of observations that equate to the 13% of the entire data set, number_of_outliers = int(outliers_fraction*len(distance)), # Take the minimum of the largest 13% of the distances as the threshold, threshold = distance.nlargest(number_of_outliers).min(), # anomaly1 contain the anomaly result of the above method Cluster (0:normal, 1:anomaly), principalDf['anomaly1'] = (distance >= threshold).astype(int), from sklearn.ensemble import IsolationForest, model = IsolationForest(contamination=outliers_fraction), Detecting stationarity in time series data, A Gentle Introduction to Autocorrelation and Partial Autocorrelation, A One-Stop Shop for Principal Component Analysis, Convert data types to the correct data type, Perform PCA and look at the most important principal components based on inertia, Benchmark model: Interquartile Range (IQR). Users do not need to take any action when new models are introduced. The real world examples of its use cases include (but not limited to) detecting fraud transactions, fraudulent insurance claims, cyber attacks to detecting abnormal equipment behaviors. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Predict the machine status with the best model given a test set BAM. So as the next step, I will perform PCA with 2 components which will be my features to be used in the training of the models. Anomaly detection is not a new concept or technique, it has been around for a number of years and is a common application of Machine Learning. 388.6s . After data wrangling process, my final tidy data looks as follows and is ready for the next step which is Exploratory Data Analysis. To run the code of anomaly detection please follow these steps. In particular, it is interesting to see the sensor readings plotted over time with the machine status of BROKEN marked up on the same graph in red color. For now, let me leave you with these questions to think about. It is interesting to see that all three models detected a lot of the similar anomalies. Feature selection with advanced Feature engineering technique. Create new kaggle notebook. Anomaly-Detection. It can be done one of the two ways; either with the pandas autocorr() method or ACF plot. Now, lets check for autocorrelation in both of these principal components. check results and if it is not matching properly / not able to run code then contact me via email (My email. So I will apply the following steps to tidy up the data set. Copy the code cell by cell given in (Anomaly_detection.ipynb) file and paste in kaggle environment. Copy the code cell by cell given in (Anomaly_detection.ipynb) file and paste in kaggle environment. Set threshold as the minimum distance of these outliers. As seen clearly from the above plot, the red marks, which represent the broken state of the pump, perfectly overlaps with the observed disturbances of the sensor reading. Later, we will also perform the Dickey Fuller test to quantitatively verify the observed stationarity. !kaggle datasets download -d nphantawee/pump-sensor-data, # Function that calculates the percentage of missing values, # Let's use above function to look at top ten columns with NaNs, # Extract the readings from the BROKEN state of the pump, # Resample the entire dataset by daily average, # Standardize/scale the dataset and apply PCA, # Plot the principal components against their inertia, from statsmodels.tsa.stattools import adfuller, # Calculate IQR for the 1st principal component (pc1), q1_pc1, q3_pc1 = df['pc1'].quantile([0.25, 0.75]), # Calculate upper and lower bounds for outlier for pc1, df['anomaly_pc1'] = ((df['pc1']>upper_pc1) | (df['pc1']upper_pc2) | (df['pc2']Unsupervised Anomaly Detection | Kaggle It's sometimes referred to as outlier detection. You signed in with another tab or window. Just by visually looking at the above graphs, one could easily conclude that the Isolation Forest might be detecting a lot more anomalies than the other two. Final Year project of anomaly detection in surveillance images, gifs. I will write more about the model evaluation in more detail in my future posts. In this article, I will focus on the application of anomaly detection in the Manufacturing industry which I believe is an industry that lagged far behind in the area of effectively taking advantage of Machine Learning techniques compared to other industries. Lets get started! Stationarity refers to the behavior where the mean and standard deviation of the data changes over time, the data with such behavior is considered not stationary. We use outliers_fraction to provide information to the algorithm about the proportion of the outliers present in our data set. In this post, I will implement different anomaly detection techniques in Python with Scikit-learn (aka sklearn) and our goal is going to be to search for anomalies in the time series sensor readings from a pump with unsupervised learning algorithms. Now we have a pretty good intuition about how each of the sensor reading behaves when the pump is broken vs operating normally.