Read on for four such highlights. H/T R-Bloggers.
Let’s illustrate the use of DBScan (Density Based Spatial Clustering of Applications with Noise), using the scikit-learn Python package, for a “manufactured” dataset. This example will illustrate how this density based algorithm works (See my other blog post which compares different Clustering algorithms for this same dataset). DBSCAN is better suited for datasets that have disproportional cluster sizes (or densities), and whose data can be separated in a non-linear fashion.
Click through for an interesting read on a dataset which is historically difficult to cluster (unless you know the general shape and translate everything to polar coordinates).
The article explains the algorithm behind the recently introduced Python package named PyHard, based on the concept of Instance Space Analysis. It helps in assessing the quality of a dataset and identifying what are the instances which are hard/easy to classify. With the help of this algorithm we can separate out noisy instances. It also provides an interactive visualization tool to deep dive into the instance space.
Click through for the details. I’m going to wait for PyHard 2: PyHarder. Or maybe PyHardWithAVengeance. But it’ll all go downhill by the time we get to PyHard 5.
If you’re really lucky, there will be a docstring for this function that outlines the structure of the parameter user, saving you from having to dig through the function and identify the possible keys that exist in parameter user.
The problem here is twofold:
1. Dictionaries in python are mutable and can have arbitrary schemas.
a. This in itself isn’t a problem and can be a good thing, depending on your needs. Its usage, however, is really only enabled by the quality of the second point, which is:
2. You must rely on the documentation to know the structure, and the documentation must stay updated as the structure evolves.
Read on to see how the
dataclass library can create a wrapper around dictionary objects.
TensorFlow is an open-source end-to-end machine learning library. It is for preprocessing data, modeling data, and serving models (getting them into the hands of others).
It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art in ML. And developers easily build and deploy ML-powered applications.
Read on for basic setup instructions and a primer on tensors.
We’re thrilled to announce the pandas API as part of the upcoming Apache Spark™ 3.2 release. pandas is a powerful, flexible library and has grown rapidly to become one of the standard data science libraries. Now pandas users can leverage the pandas API on their existing Spark clusters.
A few years ago, we launched Koalas, an open source project that implements the pandas DataFrame API on top of Spark, which became widely adopted among data scientists. Recently, Koalas was officially merged into PySpark by SPIP: Support pandas API layer on PySpark as part of Project Zen (see also Project Zen: Making Data Science Easier in PySpark from Data + AI Summit 2021).
pandas users can now scale their workloads with one simple line change in the upcoming Spark 3.2 release:
Click through to see more details on the change.
A Type 2 SCD is probably one of the most common examples to easily preserve history in a dimension table and is commonly used throughout any Data Warehousing/Modelling architecture. Active rows can be indicated with a boolean flag or a start and end date. In this example from the table above, all active rows can be displayed simply by returning a query where the end date is null.
Read on to see how you can implement this pattern using Delta Lake’s capabilities.
First and foremost – what is Pandas?
Pandas is a popular Python library that allows users to easily analyse and manipulate data. It offers powerful and flexible data structures and is vastly popular among data scientists and analysts. As with any other library to be able to use Pandas you have to import the library.
Click through to learn more.
Machine learning teams require the ability to reproduce and explain their results–whether for regulatory, debugging or other purposes. This means every production model must have a record of its lineage and performance characteristics. While some ML practitioners diligently version their source code, hyperparameters and performance metrics, others find it cumbersome or distracting from their rapid prototyping. As a result, data teams encounter three primary challenges when recording this information: (1) standardizing machine learning artifacts tracked across ML teams, (2) ensuring reproducibility and auditability across a diverse set of ML problems and (3) maintaining readable code across many logging calls.
Read on to see how Databricks Autologging can satisfy these issues.
The first question I wanted to model out was a bigger issue with on-premises databases – when are we going to run out of storage?
Back in the day I’d cheat with msdb backups, comparing compressed sized to actuals, and moving on. However I don’t have a historical reference for Stack Overflow… so what can I do?
Taking a look at the tables we see a commonality in many tables – CreationDate! It looks like the rows faithfully are stamped when they are created.
Constantine does at the end hit on something we tend to forget: most operations in life aren’t quite linear. We often get lucky in that certain stretches are close enough to be linear that we can model them that way, but even in this dataset, you can see the effects of polynomial growth slowly build up. Still, this is a good way of taking us through what an analysis and projection can look like.