Supported Python packages

The following Python packages are pre-installed in Jori, allowing users to import and utilize them in their code:

1. PySpark (version 3.3.1):

   • PySpark is the Python API for Apache Spark, a distributed computing framework for big data processing.

   • It provides a simple and expressive programming model for processing large datasets in parallel across clusters.

   • PySpark supports various data processing tasks, including data manipulation, machine learning, and graph processing.

2. Matplotlib:

   • Matplotlib is a plotting library for creating static, animated, and interactive visualizations in Python.

   • It provides a wide range of plotting functions for creating line plots, scatter plots, bar charts, histograms, and more.

   • Matplotlib allows customization of plot elements, such as colors, labels, titles, and axes.

3. Pydeck:

   • Pydeck is a Python library for creating interactive visualizations using deck.gl, a WebGL-powered framework for visual exploratory data analysis.

   • It allows users to create interactive maps, 3D visualizations, and layered visualizations using a declarative API.

4. Scikit-learn:

   • Scikit-learn is a machine learning library for Python, providing a wide range of supervised and unsupervised learning algorithms.

   • It includes tools for data preprocessing, model selection, evaluation metrics, and feature extraction.

   • Scikit-learn is widely used for tasks such as classification, regression, clustering, and dimensionality reduction.

5. PyTorch:

   • PyTorch is an open-source machine learning library for Python, primarily used for developing and training deep neural networks.

   • It provides a dynamic computational graph and supports both eager execution and graph-based execution.

   • PyTorch is known for its flexibility, ease of use, and strong GPU acceleration.

6. TensorFlow:

   • TensorFlow is an open-source machine learning framework developed by Google.

   • It provides a comprehensive ecosystem for building and deploying machine learning models, with a focus on deep learning.

   • TensorFlow offers high-level APIs, such as Keras, for easy model building and supports distributed training across multiple devices.

7. Plotly:

   • Plotly is a web-based plotting library for creating interactive and publication-quality visualizations.

   • It supports a wide range of chart types, including line charts, scatter plots, heatmaps, 3D plots, and more.

   • Plotly allows users to create interactive and responsive visualizations that can be easily shared and embedded in web applications.

8. DeepSurv:

   • DeepSurv is a Python package for survival analysis using deep learning.

   • It implements deep survival models, such as DeepSurv and DeepHit, for modeling time-to-event data.

   • DeepSurv allows for the incorporation of complex non-linear relationships and high-dimensional covariates in survival analysis.

9. Autoprognosis:

   • Autoprognosis is a Python package for automated machine learning (AutoML) in prognostic modeling.

   • It provides a high-level API for automatically building and evaluating prognostic models, such as survival models and time-to-event models.

   • Autoprognosis simplifies the process of model selection, hyperparameter tuning, and model evaluation in prognostic modeling tasks.

10. Scikit-survival:

    • Scikit-survival is a Python module for survival analysis built on top of scikit-learn.

    • It provides a set of tools and algorithms for analyzing time-to-event data, including survival estimators, evaluation metrics, and regression models.

    • Scikit-survival integrates well with scikit-learn and offers a consistent API for survival analysis tasks.

Users can import these packages in their Python code and leverage their functionalities for various data processing, machine learning, visualization, and survival analysis tasks.