PANDA | METRIC

PANDA | METRIC is a framework for processing arbitrary data.

Overview

PANDA | METRIC extends the capabilities of machine learning algorithms for variable structured data types and enables statements to be made about the relationships between the data in the context of artificial intelligence. For this purpose, the recorded data is first modeled using a metric in a metric space. These spaces can now be related to each other and simplified without loss of information. This allows essential information to be extracted and accessible to the user. In various modules, the framework offers a collection of algorithms that are optimized for metric spaces and accessible via a standardized API.

PANDA | METRIC is located in the area of machine learning and artificial intelligence, but offers more than a loose collection of optimized and high class algorithms, because PANDA | METRIC combines these different algorithms seamless. Data Science is no magic, it is all about Information, statistics and optimization and PANDA | METRIC provides all you need to generate data-driven added values. All the algorithms in data science seems like a huge loosely connected family. This framework provides a universal approach that makes it easy to combine these techniques. To do so it brings all these algorithm under one roof together and guides, how to combine them.

PANDA | METRIC is programmed in modern and template based C++, which allows a comfortable use with optimal performance at the same time. Compared to the approach of neural networks, the concept of metric spaces offers significant advantages for industrial applications.

Check the whitepaper for more info: https://github.com/panda-official/metric/blob/master/whitepaper_PANDA_METRIC_EN_07102019.pdf

Modules

PANDA | METRIC is organized in several submodules.

METRIC | DISTANCE provide a extensive collection of metrics, including factory functions for configuring complex metrics.
They are organized into severals levels of complexity and aprio knowledge about the data. Basically the user give a priori information, how the data should be connected for reason, like a picuture is a 2d array of pixels. A metric type is basically a function, which compares two samples of data and gives back the numeric distance between them.

METRIC | SPACE stores and organizes distances. It provides classes to connect and relate data that are kind of the same observations in principle. And it include basic operations such as the search for neighboring elements. If you can compare two entries all entries are automatically related through the d-representations in a metric space.

METRIC | MAPPING contains various algorithms that can ‘calculate’ metric spaces or map them into equivalent metric spaces. In general, you can regard all of the algorithms in METRIC | MAPPING as mapper from one metric space into a new one, usually with the goal to reduce complexity (like clustering or classifying) or to fill missing information (like predicting). Also values can be bidirectionally reconstructed from reduced space using the reverse decoder. In addition, unwanted features can be removed from the space for further evaluation. In this way, the user brings in his a priori knowledge and understandings and on the other hand his a priori influence causes instead of causing a loss of information by an autonomous programming of this knowledge.

METRIC | TRANSFORM provides deterministic algorithms that transfer data element by element into another metric space, e.g. from the time to the frequency domain. This is often useful for complexity reduction as preprocessing step. A distinction can be made between lossy compression and completely reversible methods. In general, however, these are proven, deterministic methods such as wavelets.

METRIC | CORRELATION offers functions to calculate a correlation coefficient of two metric spaces and thus to determine the dependency of two arbitrary data sets. When METRIC | MAPPING is used to quantify and measure relations in data, METRIC | CORRELATION is used to find relations between metric spaces at all.

METRIC | UTILS contains algorithms which are not metric either, but which can be easily combined. On the one hand, there is a high-performance in-memory crossfilter. This allows a piecewise, UI supported and interactive filtering of the patterns from the results of the operations in real time. On the other hand, the METRIC | UTILS module also contains a nonlinear and nonparametric signifcance test for independent features (PMQ) of a metric space that were obtained by mapping.