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The Python FuzzyWuzzy module uses Levenshtein edit distance to implement fuzzy string matching. FuzzyWuzzy’s matching tools return results on a scale from 0 to 100. The simplest matching tool FuzzyWuzzy offers is the ratio(..) function: The basic ratio function works well for simple string matching. However if you’re trying to fuzzy match a single word […]
Reader comments on an old post about the ijson parser prompted me to check out the project’s more recent releases. The latest pre-release (v3.0rc1) added a coroutine interface, which allow users to supply their own file readers and have more control over when the parser is called. It looked like a fun feature to explore, […]
Figuring out how similar two strings are and then making that similarity a quantitative measurement is a basic problem in text analysis, text mining and natural language processing. There are a number of efficient methods to solve this problem. This survey looks at Python implementations of a simple but widely used method: Levenshtein distance as […]
The KubernetesPodOperator handles communicating XCom values differently than other operators. The basics are described in the operator documentation under the xcom_push parameter. I’ve written up a more detailed example that expands on that documentation. An Airflow task instance described by the KubernetesPodOperator can write a dict to the file /airflow/xcom/return.json (always the same file) that […]
This article and code is applicable to Airflow 1.10.13. Hopefully the REST API will mature as Airflow is developed further, and the authentication methods will be easier. The experimental REST API does not use the Airflow role-based users. Instead, it currently requires a SQLAlchemy models.User object whose data is saved in the database. The code […]
KubernetesExecutor The KubernetesExecutor sets up Airflow to run on a Kubernetes cluster. This executor runs task instances in pods created from the same Airflow Docker image used by the KubernetesExecutor itself, unless configured otherwise (more on that at the end). Getting Airflow deployed with the KubernetesExecutor to a cluster is not a trivial task. I […]
Creating AWS SageMaker Lifecycle configuration scripts to customize notebook instances beats installing packages and making other environment changes in notebook instances. One advantage is that the customization code doesn’t need to be copied from notebook to notebook. Another is that the lifecycle configurations are managed outside of and separately from notebook instances, and can be […]
I’m starting to experiment with the Yellowbrick machine learning visualizer tools to learn how to visualize models more effectively. The documentation is good, and getting started with the tools is pretty straightforward. I started to get bored with the default color palette after playing with some of the basic visualization examples in the documentation (more […]
The AWS SageMaker ntm_20newsgroups_topic_model example notebook is a simple to follow introduction to SageMaker’s pre-packaged Natural Language Processing (NLP) tools. The notebook demonstrates how to use the Neural Topic Model (NTM) algorithm to extract a set of topics from a sample usenet newsgroups dataset and visualize as word clouds. It also contains code demonstrating how […]
This question came up recently on a project where Pandas data needed to be fed to a TensorFlow classifier. In this case, we wanted to divide the dataframe using a random sampling. Frameworks like scikit-learn may have utilities to split data sets into training, test and cross-validation sets. For example, sklearn.model_selection.train_test_split split numpy arrays or […]