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@nabeel-oz nabeel-oz released this 13 Jan 00:19
· 25 commits to master since this release
6.3
9e67c33

Maintenance Release

qlik-py-tools-6.3.zip

This zip archive only contains the files needed to deploy the SSE. You can get sample Qlik Sense apps from the docs or download the full source code below.

Tips for a quick update on Windows
If you're on PyTools v.6.0 or above, you can simply copy the files from the zip file above under the core folder and overwrite the files at qlik-py-tools\qlik-py-env\core. Then just restart the SSE.

Change Log v.6.3

  • Ability to provide parameters for Keras optimizers
  • Updates for issues #78 #79
  • Docker image v.6.3 published

Recent Updates

Exciting new capabilities for Deep Learning with Keras and Tensorflow. You can now train and use neural networks for sequence predictions and complex timeseries forecasting.

This release also includes the ability to use Additional Regressors with Prophet, allowing you to model more complex timeseries.

For more information refer to the Usage section.

Capabilities

  • Supervised Machine Learning : Implemented using scikit-learn, the go-to machine learning library for Python. This SSE implements the full machine learning flow from data preparation, model training and evaluation, to making predictions in Qlik. In addition, models can be interpreted using Skater.
  • Unsupervised Machine Learning : Also implemented using scikit-learn. This provides capabilities for dimensionality reduction and clustering.
  • Deep Learning : Implemented using Keras and TensorFlow. This SSE implements the full flow of setting up a neural network, training and evaluating it, and using it to make predictions. Deep Learning models can be used for sequence predictions and complex timeseries forecasting.
  • Named Entity Recognition : Implemented using spaCy, an excellent Natural Language Processing library that comes with pre-trained neural networks. This SSE allows you to use spaCy's models for Named Entity Recognition or retrain them with your data for even better results.
  • Association rules : Implemented using Efficient-Apriori. Association Rules Analysis is a data mining technique to uncover how items are associated to each other. This technique is best known for Market Basket Analysis, but can be used more generally for finding interesting associations between sets of items that occur together, for example, in a transaction, a paragraph, or a diagnosis.
  • Clustering : Implemented using HDBSCAN, a high performance algorithm that is great for exploratory data analysis.
  • Time series forecasting : Implemented using Facebook Prophet, a modern library for easily generating good quality forecasts. Now with the ability to use multiple regressors as input.
  • Seasonality and holiday analysis : Also using Facebook Prophet.
  • Linear correlations : Implemented using Pandas.

For more information refer to the Usage section.

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