Big Data Science

3 types of data anomalies

😱3 types of data anomalies Data analysts and machine learning professionals often detect anomalies in data – detection that is not detected to pattern detection and detection. There are 3 types of anomalies: • point anomaly, when one data point (observation) in the data set is far from the sources of the others and represents an extreme, unevenness or deviation that occurs randomly and is not related to the overall load in the data. The point anomaly also eliminates the global outlier because it is significantly different from the rest of the dataset. • context anomaly, when a particular instance is revealed to be observable from the context. For example, in the case of time series data, such as recording a certain amount over time, the context is temporal. Data points that are very different from other data in the same sense are due to contextual outliers. For example, when the number of cars passing through the checkpoint on the border of the region in March, on average, is 1 thousand over the past 20 years. And in June, when the vacation period begins, this number reaches 8 thousand. If at the beginning of March it is 9 thousand, it will be considered an anomaly, and in the summer it will not be an anomaly. It's common for retail to see shoppers pop up during the holiday season. But a sharp increase in sales outside of holidays or sales can be called a contextual outlier. • Collective anomaly, where a group of correlated, interrelated, or sequential instances is significantly different from the rest of the data, ie these data points are judged to be anomalous. For time series data, this may look like typical peaks and troughs occurring outside of the time period when the seasonal sequence is normal, or as a set of time series that are in outlier conditions. For example, at the same time, along with a large number of companies, there is a drop in sales, although before that there was an upward trend. https://medium.com/datadailyread/types-of-data-anomalies-2f6fb1747eb1

11 августа состоится Alfa Data Science MeetUp#2 📟 Участие бесплатное, необходимо зарегистрироваться на сайте, чтобы получить ссылку на онлайн-трансляцию. Темы и спикеры: 🖲 Развитие клиентской базы: моделирование LTV и прогноз будущих доходов - Сергей Королёв, Middle Data Scientist Альфа-Банк 🖲 Uplift-моделирование в ценообразовании кредитных продуктов - Максим Коматовский, Junior Data Scientist Альфа-Банк 🖲 Совершенный код расчёт - Максим Cтаценко, Team Lead/Senior DWH Developer в Яндекс 🖲 Побеждаем смещение распределения в задаче нейросетевого кредитного скоринга - Алексей Фирстов, Senior Data Scientist Альфа-Банк Митап пройдет в интерактивном формате, вопросы спикерам приветствуются, авторы лучших вопросов получат призы от Alfa Digital.

👀Looking for data to train ML-models? Generate it Yourself: 3 Python Packages for Generating Synthetic Data Synthetic data is an artificially generated, not collected, topic learning dataset for training ML models or practicing analysis techniques. You can create them yourself using possible Python packages: • Faker is a very simple and efficient Python package for creating their data. It's great when you need to load data into a database, create a use of XML documents, prepare for load testing, or anonymize data retrieved from involved services. https://github.com/joke2k/faker • SDV (Synthetic Data Vault) is a synthetic data storage for creating synthetic data based on a given dataset. The generated data can be a single summary, pivot table, or time series, and have the same properties and statistics as the original dataset. SDV uses synthetic data with DL models. Even if the original dataset contains multiple data types and gaps, SDV handles them. https://sdv.dev/SDV/ • Gretel Synthetics - a source code package based on a recurrent neural network for generating structured and unstructured data. The batch approach treats a data set as text data and trains a model based on it. The model will then create synthetic data with text data. Gretel is based on RNN networks, it requires more computing power, so when working with it, it is better to use Google Colab, rather than load a personal computer. https://synthetics.docs.gretel.ai/en/stable/

👍🏻4 utilities for working with JSON files Hadoop and Spark are the most popular big data frameworks for working with big data - large files. But often you need to process many small files, for example, in JSON format, which in Hadoop HDFS are distributed over many data blocks and partitions. The number of partitions determines the number of tasks since 1 task can deal with only 1 partition at a time. This will be a high load for the Application Master and reduction of productivity for the entire cluster. In addition, most of the time is spent only on opening and closing files, and not on reading data. Therefore, it is worth to combine many small files in large one, which Hadoop and Spark can process very quickly. In the case of JSON files, such a union into an array of records can be done using the tools: • jq – is used to filter and process incoming JSON data, great for parsing and processing data streams https://stedolan.github.io/jq/ • jo - creates JSON data structures https://github.com/jpmens/jo • json_pp - displays JSON objects in a more convenient format, as well as convert them between textures https://github.com/deftek/json_pp • jshon - JSON parser with fast evaluation of large amounts of data http://kmkeen.com/jshon/ https://sidk17.medium.com/boss-we-have-a-large-number-of-small-files-now-how-to-process-these-files-ee27f67dc461

🗣Data analysts speak SQL. How can they understand themselfs? Every analyst knows 5 rules of SQL query formatting to make them easy to read: • Place Key Words (SELECT, FROM and WHERE) On New Lines • List Column Names after SELECT On New Lines • Indent Sub-Elements On New Line • Add Subquery Parenthesis On Their Own Lines • Place Case Statement Conditions On New Lines However, not all analysts apply these rules in practice. Of course, specialized IDEs take on formatting functions, for example, Visual Studio Code has built-in document formatting capabilities, as well as the ability to connect external extensions such as SQLTools or SqlBeautifier. If you need to read a very big SQL- query from colleagues, presented in the form of flat text, use online formatters to convert the text of the SQL query to a readable form: • https://codebeautify.org/sqlformatter • https://www.freeformatter.com/sql-formatter.html • https://sqlformat.org/

🌸🌤TOP-10 DS-events in August 2022 all over the World: • Aug 5, Bayesian Modelling Applications Workshop. Eindhoven, The Netherlands + Virtual. http://abnms.org/uai2022-apps-workshop/ • Aug 8-12, Data Matters. Virtual. https://datamatters.org/ • Aug 11, Subsurface Community Meetup: Why Apache Arrow is the industry-standard for columnar data processing and transport. Virtual. https://subsurfacemeetupaugust2022.splashthat.com/ • Aug 14-18, KDD 2022: ACM SIGKDD 2022. Washington, DC, USA. https://kdd.org/kdd2022/ • Aug 15, 1st ACM SIGKDD Workshop on Content Understanding and Generation for E-commerce. Washington, DC, USA. https://content-generation.github.io/workshop/ • Aug 15-17, TDWI Data Literacy Bootcamp. Virtual. https://tdwi.org/events/seminars/august/tdwi-data-literacy-bootcamp/home.aspx • Aug 15-17, Disney Data & Analytics Conference. Orlando, FL, USA. https://disneydataconference.com/ • Aug 16, StateOfTheArt() - Free AI Conference with Top AI/ML Influencers. Virtual. https://www.eventbrite.com/e/stateoftheart-free-ai-conference-with-top-aiml-influencers-tickets-379160628647 • Aug 16-18, Ai4 2022, the industry's leading AI event. Aug 16, Las Vegas, NV, USA. https://ai4.io/usa/application-attendee/ • Aug 23, The data dividend: Mumbai. Mumbai, India. Aug 23-24, Ray Summit. San Francisco, CA, USA. https://events.economist.com/custom-events/the-data-dividend-mumbai

🔥PyMLPipe: A lightweight MLOps Python Package PyMLPipe is a lightweight Python package for MLOps processes. It helps to automate: • Monitoring of models and data schemas • Versioning of ML models and data • Model performance comparison • API deployment in one click This source library supports Scikit-Learn, XGBoost, LightGBM and Pytorch. It has a modular structure, represented by a set of Python functions combined into an API and a visual graphical interface. PyMLPipe is great for working with tabular data. https://neelindresh.github.io/pymlpipe.documentation.io/

👍🏻TOP 4 dbt tips for data analyst and data engineer dbt (data build tool) is an open source code framework for executing, testing and documenting SQL queries, which allows you to process data analysis machine, including structuring and description of arrivals, their search, nested calls, rule triggering, documentation and testing. For example, you can use the dbt CLI or dbt Cloud to work with data collection to consume, transform, and load data into storage by computing a dynamic database on a schedule. To increase the efficiency of using dbt for the selection of schemas, sources and models, it is possible to use data: • The Schema.yml file can only be found in the dbt models folder. The tool allows you to create a unit test that counts the duration of a column for nulls. • dbt data tests have a strict rule that they must return null rows in order to pass the test. Instead of looking for a value such as the number of a particular set of rows, the data test should be written to expect to find null rows if the results do not match the correct set of sums. Therefore, when developing test data, you need to think about how to return 0 rows in the expected key, but at the same time you need to check the number. You can use the != or <= operators to validate data. • To increase the speed of testing increase the number of threads in the project profile, in the profiles.yml file. For example, if there are 30 tests, then there are 40 threads, indicate in the profiles.yml file. Probably 30 data and schema tests in 4 seconds. • The history test needs a meaningful name. Although dbt automatically learns the test names, it is recommended that you label them yourself. dbt doesn't have much control over running small test suites, it needs to be able to see all running projects. In the same way that developers are encouraged to use functions and variables with semantic name definitions, testing should be given tests for meaningful names. Otherwise, it will be difficult to determine which test passes or fails during test execution. When a test error is found in dbt, all schema and data tests are run together. It's not easy to use a single directory in the data tests folder, but you can name them "dbt test - schema" or "dbt test - data" to quickly determine which tests to use. https://corissa-haury.medium.com/4-quick-facts-about-dbt-testing-5c32b487b8cd

Полезная статья на Хабр о промышленном мониторинге качества данных в Feature Store В ней аналитик финтеха Big Data из МТС рассказал о построении автоматизированного контроля за качеством данных в их Feature Store, позволяющем не допустить принятия бизнес-решений на искаженных или ненадежных данных. О том, что такое Feature Store, почему в нем возникла потребность и какое место занимает промышленный мониторинг качества данных, читать здесь.

Computational Complexity of Machine Learning Algorithms

🙌🏼Вычислительная сложность ML-алгоритмов Когда объем данных невелик, почти любой ML-алгоритм дает приемлемую точность и подходит для решения соответствующей задачи. Но когда объем и размер данных становятся большими, нужно выбирать для обучения ML-модели такой алгоритм, который не требует слишком много вычислительных ресурсов. Лучше выбрать простой или менее затратный в вычислительном отношении алгоритм, чем алгоритм, требующий больших вычислительных ресурсов, когда точность прогнозирования и сложность выполнения аналогичны или даже немного хуже. Выбор алгоритма зависит от следующих факторов: • порядок времени (Time Complexity), необходимого для вычисления алгоритма - функция параметров, связанных с данными, самого алгоритма, объема и количества фич • сложность вычислительного пространства (Space Complexity) - порядок пространства, требуемый во время вычисления алгоритма - функция параметров, связанных с алгоритмом, таких как количество фичей, коэффициентов, скрытых слоев нейросети. Сложность пространства включает в себя как размер входных данных, так и вспомогательное пространство (Auxiliary Space), используемое алгоритмом во время выполнения;. Например, сортировка слиянием (Mergesort) имеет вспомогательное пространство 𝑂(𝑛) и пространственную сложность 𝑂(𝑛), а быстрая сортировка (Quicksort) имеет вспомогательное пространство 𝑂(1) и пространственную сложность 𝑂(𝑛). В итоге и сортировка слиянием, и быстрая сортировка имеют временную сложность 𝑂(𝑛log𝑛). https://medium.com/datadailyread/computational-complexity-of-machine-learning-algorithms-16e7ffcafa7d

🙌🏼7 Platforms of Federated ML Federated learning is also referred to as collaborative because ML models are trained on multiple decentralized edge devices or servers containing local data samples without exchanging them. This approach differs from traditional centralized ML methods, where all local datasets are uploaded to a single server, and from more classical decentralized approaches with the same distribution of local data. Today, federated learning is actively used in the defense industry, telecommunications, pharmaceuticals and IoT platforms. Federated Machine Learning ideas were first introduced by Google in 2017 to improve mobile keyboard text prediction using machine learning models trained on data from multiple devices. In federated ML, models are trained on multiple local datasets on local nodes without explicit data exchange, but with periodic exchange of parameters, such as deep neural network weights and biases, between local nodes to create a common global model. Unlike distributed learning, which was originally aimed at parallelizing computations, federated learning is aimed at learning heterogeneous data sets. In federated ML, datasets are usually highly heterogeneous in size. And clients, i.e. end devices where local models are trained can be unreliable and more prone to failure than in distributed learning systems where the nodes are data centers with powerful computing capabilities. Therefore, in order to provide distributed computing and synchronization of its results, federated ML requires frequent data exchange between nodes. Due to its architectural features, federated ML has a number of disadvantages: • heterogeneity between different local datasets - each node has an error in relation to the general population, and sample sizes can vary significantly; • temporal heterogeneity - the distribution of each local dataset changes over time; • it is necessary to ensure the compatibility of the data set on all nodes; • hiding training datasets is fraught with the risk of introducing vulnerabilities into the global model; • lack of access to global training data makes it difficult to identify unwanted biases in training inputs; • there is a risk of losing updates to local ML models due to failures at individual nodes, which may affect the global model. Today, federated ML is supported by the following platforms: • FATE (Federated AI Technology Enabler) https://fate.fedai.org/ • Substra https://www.substra.ai/ • Python libraries PySyft and PyGrid https://github.com/OpenMined/PySyft, https://github.com/OpenMined/PyGrid, https://github.com/OpenMined/pygrid-admin • Open FL https://github.com/intel/openfl • TensorFlow Federated (TFF) https://www.tensorflow.org/federated • IBM Federated Learning https://ibmfl.mybluemix.net/ • NVIDIA CLARA https://developer.nvidia.com/clara

🗣7 speech recognition tools To develop your own ML speech recognition system, you can use the following frameworks and libraries: • wav2letter - an open-course open source toolkit from Facebook AI Research merged with a larger library called Flashlight https://github.com/flashlight/wav2letter • DeepSpeech powered by Baidu DeepSpeech, which will help you decode an audio file using pre-trained models or set up/train a custom dataset https://deepspeech.readthedocs.io/en/r0.9/?badge=latest • TensorFlowASR - Open source package from Tensorflow implements some reference models trained using RNN with CTC https://github.com/TensorSpeech/TensorFlowASR • OpenSeq2Seq - research project by NVIDIA on the problems of converting sequences to sequences https://github.com/NVIDIA/OpenSeq2Seq/blob/master/Streaming-ASR.ipynb • SpeechRecognition - the project provides access to several automatic speech recognition models, including speech API wrappers from Google, Microsoft Azure and IBM https://github.com/Uberi/speech_recognition We also note 2 ready-made services that provide an API for accessing the capabilities of services, from speech recognition to generating "natural" voice data: • SmartSpeech by SberDevices https://sberdevices.ru/smartspeech/ • Yandex SpeechKit by Yandex https://cloud.yandex.ru/services/speechkit

💥Python library for sending PySpark scripts to Spark cluster via Livy REST API Spark-apps developers know that there are two approaches to programmatically submit jobs to an Apache Spark cluster, and each has some limitations to achieve real-time interaction: spark-submit and spark-shell. However, in practice, there are times when you need to submit Spark jobs interactively from a web or mobile app. At the same time, the Apache Spark cluster is hosted in the local infrastructure, but it is necessary that many users simultaneously consume and perform heavy aggregations with data sources from their mobile phones, web or desktop applications. In this case, a service approach, Spark-as-a-Service, will help, including exposing JDBC/ODBC data sources through a Spark standby server or using Apache Livy, a service that allows you to easily interact with an Apache Spark cluster via a REST API. For Livy, the livyc Python package comes in handy - it works well for sending PySpark scripts dynamically and asynchronously to the Apache Livy server, interacting transparently with the Apache Spark cluster. https://github.com/Wittline/livyc

🪢2 libraries for unit testing Python scripts Unit testing allows developers to ensure that their code base is working as intended at an atomic level. The point of unit testing is not that the code is working as a whole, but instead that each individual function is doing what it is supposed to be doing. For writing Python unit tests you can use Pytest and Chispa: • Pytest is framework to write small, readable tests, and can scale to support complex functional testing for applications and libraries. It requires: Python 3.7+ or PyPy3. https://docs.pytest.org/en/7.1.x/ • Chispa provides fast PySpark test helper methods that output descriptive error messages. This library makes it easy to write high quality PySpark code. By the way, chispa means Spark in Spanish. https://github.com/MrPowers/chispa