A Graph Learning library for Humans

Overview

A Graph Learning library for Humans

These novel algorithms include but are not limited to:

  • A graph construction and graph searching class can be found here (NodeGraph). It was developed and invented as a faster alternative for hierarchical DAG construction and searching.
  • A fast DBSCAN method utilizing my connectivity code as invented during my PhD.
  • A NLP pattern matching algorithm useful for sequence alignment clustering.
  • High dimensional alignment code for aligning models to data.
  • An SVD based variant of the Distance Geometry algorithm. For going from relative to absolute coordinates.

License DOI Downloads

Visit the active code via : https://github.com/richardtjornhammar/graphtastic

Pip installation with :

pip install graphtastic

Version controlled installation of the Graphtastic library

The Graphtastic library

In order to run these code snippets we recommend that you download the nix package manager. Nix package manager links from Februari 2022:

https://nixos.org/download.html

$ curl -L https://nixos.org/nix/install | sh

If you cannot install it using your Wintendo then please consider installing Windows Subsystem for Linux first:

https://docs.microsoft.com/en-us/windows/wsl/install-win10

In order to run the code in this notebook you must enter a sensible working environment. Don't worry! We have created one for you. It's version controlled against python3.9 (and experimental python3.10 support) and you can get the file here:

https://github.com/richardtjornhammar/graphtastic/blob/master/env/env39.nix

Since you have installed Nix as well as WSL, or use a Linux (NixOS) or bsd like system, you should be able to execute the following command in a termnial:

$ nix-shell env39.nix

Now you should be able to start your jupyter notebook locally:

$ jupyter-notebook graphhaxxor.ipynb

and that's it.

EXAMPLE 0

Running

import graphtastic.graphs as gg
import graphtastic.clustering as gl
import graphtastic.fit as gf
import graphtastic.convert as gc

Should work if the install was succesful

Example 1 : Absolute and relative coordinates

In this example, we will use the SVD based distance geometry method to go between absolute coordinates, relative coordinate distances and back to ordered absolute coordinates. Absolute coordinates are float values describing the position of something in space. If you have several of these then the same information can be conveyed via the pairwise distance graph. Going from absolute coordinates to pairwise distances is simple and only requires you to calculate all the pairwise distances between your absolute coordinates. Going back to mutually orthogonal ordered coordinates from the pariwise distances is trickier, but a solved problem. The distance geometry can be obtained with SVD and it is implemented in the graphtastic.fit module under the name distance_matrix_to_absolute_coordinates. We start by defining coordinates afterwhich we can calculate the pair distance matrix and transforming it back by using the code below

import numpy as np

coordinates = np.array([[-23.7100 ,  24.1000 ,  85.4400],
  [-22.5600 ,  23.7600 ,  85.6500],
  [-21.5500 ,  24.6200 ,  85.3800],
  [-22.2600 ,  22.4200 ,  86.1900],
  [-23.2900 ,  21.5300 ,  86.4800],
  [-20.9300 ,  22.0300 ,  86.4300],
  [-20.7100 ,  20.7600 ,  86.9400],
  [-21.7900 ,  19.9300 ,  87.1900],
  [-23.0300 ,  20.3300 ,  86.9600],
  [-24.1300 ,  19.4200 ,  87.2500],
  [-23.7400 ,  18.0500 ,  87.0000],
  [-24.4900 ,  19.4600 ,  88.7500],
  [-23.3700 ,  19.8900 ,  89.5200],
  [-24.8500 ,  18.0000 ,  89.0900],
  [-23.9600 ,  17.4800 ,  90.0800],
  [-24.6600 ,  17.2400 ,  87.7500],
  [-24.0800 ,  15.8500 ,  88.0100],
  [-23.9600 ,  15.1600 ,  86.7600],
  [-23.3400 ,  13.7100 ,  87.1000],
  [-21.9600 ,  13.8700 ,  87.6300],
  [-24.1800 ,  13.0300 ,  88.1100],
  [-23.2900 ,  12.8200 ,  85.7600],
  [-23.1900 ,  11.2800 ,  86.2200],
  [-21.8100 ,  11.0000 ,  86.7000],
  [-24.1500 ,  11.0300 ,  87.3200],
  [-23.5300 ,  10.3200 ,  84.9800],
  [-23.5400 ,   8.9800 ,  85.4800],
  [-23.8600 ,   8.0100 ,  84.3400],
  [-23.9800 ,   6.5760 ,  84.8900],
  [-23.2800 ,   6.4460 ,  86.1300],
  [-23.3000 ,   5.7330 ,  83.7800],
  [-22.7300 ,   4.5360 ,  84.3100],
  [-22.2000 ,   6.7130 ,  83.3000],
  [-22.7900 ,   8.0170 ,  83.3800],
  [-21.8100 ,   6.4120 ,  81.9200],
  [-20.8500 ,   5.5220 ,  81.5200],
  [-20.8300 ,   5.5670 ,  80.1200],
  [-21.7700 ,   6.4720 ,  79.7400],
  [-22.3400 ,   6.9680 ,  80.8000],
  [-20.0100 ,   4.6970 ,  82.1500],
  [-19.1800 ,   3.9390 ,  81.4700] ]);

if __name__=='__main__':

    import graphtastic.fit as gf

    distance_matrix = gf.absolute_coordinates_to_distance_matrix( coordinates )
    ordered_coordinates = gf.distance_matrix_to_absolute_coordinates( distance_matrix , n_dimensions=3 )

    print ( ordered_coordinates )

You will notice that the largest variation is now aligned with the X axis, the second most variation aligned with the Y axis and the third most, aligned with the Z axis while the graph topology remained unchanged.

Example 2 : Deterministic DBSCAN

DBSCAN is a clustering algorithm that can be seen as a way of rejecting points, from any cluster, that are positioned in low dense regions of a point cloud. This introduces holes and may result in a larger segment, that would otherwise be connected via a non dense link to become disconnected and form two segments, or clusters. The rejection criterion is simple. The central concern is to evaluate a distance matrix with an applied cutoff this turns the distances into true or false values depending on if a pair distance between point i and j is within the distance cutoff. This new binary Neighbour matrix tells you wether or not two points are neighbours (including itself). The DBSCAN criterion states that a point is not part of any cluster if it has fewer than minPts neighbors. Once you've calculated the distance matrix you can immediately evaluate the number of neighbors each point has and the rejection criterion, via . If the rejection vector R value of a point is True then all the pairwise distances in the distance matrix of that point is set to a value larger than epsilon. This ensures that a distance matrix search will reject those points as neighbours of any other for the choosen epsilon. By tracing out all points that are neighbors and assessing the connectivity (search for connectivity) you can find all the clusters.

import numpy as np
from graphtastic.clustering import dbscan, reformat_dbscan_results
from graphtastic.fit import absolute_coordinates_to_distance_matrix

N   = 100
N05 = int ( np.floor(0.5*N) )
R   = 0.25*np.random.randn(N).reshape(N05,2) + 1.5
P   = 0.50*np.random.randn(N).reshape(N05,2)

coordinates = np.array([*P,*R])

results = dbscan ( distance_matrix = absolute_coordinates_to_distance_matrix(coordinates,bInvPow=True) , eps=0.45 , minPts=4 )
clusters = reformat_dbscan_results(results)
print ( clusters )

Example 3 : NodeGraph, distance matrix to DAG

Here we demonstrate how to convert the graph coordinates into a hierarchy. The leaf nodes will correspond to the coordinate positions.

import numpy as np

coordinates = np.array([[-23.7100 ,  24.1000 ,  85.4400],
  [-22.5600 ,  23.7600 ,  85.6500],
  [-21.5500 ,  24.6200 ,  85.3800],
  [-22.2600 ,  22.4200 ,  86.1900],
  [-23.2900 ,  21.5300 ,  86.4800],
  [-20.9300 ,  22.0300 ,  86.4300],
  [-20.7100 ,  20.7600 ,  86.9400],
  [-21.7900 ,  19.9300 ,  87.1900],
  [-23.0300 ,  20.3300 ,  86.9600],
  [-24.1300 ,  19.4200 ,  87.2500],
  [-23.7400 ,  18.0500 ,  87.0000],
  [-24.4900 ,  19.4600 ,  88.7500],
  [-23.3700 ,  19.8900 ,  89.5200],
  [-24.8500 ,  18.0000 ,  89.0900],
  [-23.9600 ,  17.4800 ,  90.0800],
  [-24.6600 ,  17.2400 ,  87.7500],
  [-24.0800 ,  15.8500 ,  88.0100],
  [-23.9600 ,  15.1600 ,  86.7600],
  [-23.3400 ,  13.7100 ,  87.1000],
  [-21.9600 ,  13.8700 ,  87.6300],
  [-24.1800 ,  13.0300 ,  88.1100],
  [-23.2900 ,  12.8200 ,  85.7600],
  [-23.1900 ,  11.2800 ,  86.2200],
  [-21.8100 ,  11.0000 ,  86.7000],
  [-24.1500 ,  11.0300 ,  87.3200],
  [-23.5300 ,  10.3200 ,  84.9800],
  [-23.5400 ,   8.9800 ,  85.4800],
  [-23.8600 ,   8.0100 ,  84.3400],
  [-23.9800 ,   6.5760 ,  84.8900],
  [-23.2800 ,   6.4460 ,  86.1300],
  [-23.3000 ,   5.7330 ,  83.7800],
  [-22.7300 ,   4.5360 ,  84.3100],
  [-22.2000 ,   6.7130 ,  83.3000],
  [-22.7900 ,   8.0170 ,  83.3800],
  [-21.8100 ,   6.4120 ,  81.9200],
  [-20.8500 ,   5.5220 ,  81.5200],
  [-20.8300 ,   5.5670 ,  80.1200],
  [-21.7700 ,   6.4720 ,  79.7400],
  [-22.3400 ,   6.9680 ,  80.8000],
  [-20.0100 ,   4.6970 ,  82.1500],
  [-19.1800 ,   3.9390 ,  81.4700] ]);


if __name__=='__main__':

    import graphtastic.graphs as gg
    import graphtastic.fit as gf
    GN = gg.NodeGraph()
    #
    # bInvPow refers to the distance type. If True then R distances are returned
    # instead of R2 (R**2) distances. That is also computing the square root if True
    #
    distm = gf.absolute_coordinates_to_distance_matrix( coordinates , bInvPow=True )
    #
    # Now a Graph DAG is constructed from the pairwise distances
    GN.distance_matrix_to_graph_dag( distm )
    #
    # And write it to a json file so that we may employ JS visualisations
    # such as D3 or other nice packages to view our hierarchy
    GN.write_json( jsonfile='./graph_hierarchy.json' )

Manually updated code backups for this library :

GitLab | https://gitlab.com/richardtjornhammar/graphtastic

CSDN | https://codechina.csdn.net/m0_52121311/graphtastic

You might also like...
Fastest Gephi's ForceAtlas2 graph layout algorithm implemented for Python and NetworkX
Fastest Gephi's ForceAtlas2 graph layout algorithm implemented for Python and NetworkX

ForceAtlas2 for Python A port of Gephi's Force Atlas 2 layout algorithm to Python 2 and Python 3 (with a wrapper for NetworkX and igraph). This is the

🐍PyNode Next allows you to easily create beautiful graph visualisations and animations
🐍PyNode Next allows you to easily create beautiful graph visualisations and animations

PyNode Next A complete rewrite of PyNode for the modern era. Up to five times faster than the original PyNode. PyNode Next allows you to easily create

LabGraph is a a Python-first framework used to build sophisticated research systems with real-time streaming, graph API, and parallelism.
LabGraph is a a Python-first framework used to build sophisticated research systems with real-time streaming, graph API, and parallelism.

LabGraph is a a Python-first framework used to build sophisticated research systems with real-time streaming, graph API, and parallelism.

Automatization of BoxPlot graph usin Python MatPlotLib and Excel

BoxPlotGraphAutomation Automatization of BoxPlot graph usin Python / Excel. This file is an automation of BoxPlot-Graph using python graph library mat

Library for exploring and validating machine learning data

TensorFlow Data Validation TensorFlow Data Validation (TFDV) is a library for exploring and validating machine learning data. It is designed to be hig

Library for exploring and validating machine learning data

TensorFlow Data Validation TensorFlow Data Validation (TFDV) is a library for exploring and validating machine learning data. It is designed to be hig

Declarative statistical visualization library for Python
Declarative statistical visualization library for Python

Altair http://altair-viz.github.io Altair is a declarative statistical visualization library for Python. With Altair, you can spend more time understa

Plotting library for IPython/Jupyter notebooks
Plotting library for IPython/Jupyter notebooks

bqplot 2-D plotting library for Project Jupyter Introduction bqplot is a 2-D visualization system for Jupyter, based on the constructs of the Grammar

Cartopy - a cartographic python library with matplotlib support
Cartopy - a cartographic python library with matplotlib support

Cartopy is a Python package designed to make drawing maps for data analysis and visualisation easy. Table of contents Overview Get in touch License an

Releases(v0.12.0)
Owner
Richard Tjörnhammar
PhD in Biological physics https://richardtjornhammar.github.io
Richard Tjörnhammar
Comparing USD and GBP Exchange Rates

Currency Data Visualization Comparing USD and GBP Exchange Rates This is a bar graph comparing GBP and USD exchange rates. I chose blue for the UK bec

5 Oct 28, 2021
A simple interpreted language for creating basic mathematical graphs.

graphr Introduction graphr is a small language written to create basic mathematical graphs. It is an interpreted language written in python and essent

2 Dec 26, 2021
An interactive dashboard for visualisation, integration and classification of data using Active Learning.

AstronomicAL An interactive dashboard for visualisation, integration and classification of data using Active Learning. AstronomicAL is a human-in-the-

45 Nov 28, 2022
Realtime Web Apps and Dashboards for Python and R

H2O Wave Realtime Web Apps and Dashboards for Python and R New! R Language API Build and control Wave dashboards using R! New! Easily integrate AI/ML

H2O.ai 3.4k Jan 06, 2023
Functions for easily making publication-quality figures with matplotlib.

Data-viz utils 📈 Functions for data visualization in matplotlib 📚 API Can be installed using pip install dvu and then imported with import dvu. You

Chandan Singh 16 Sep 15, 2022
China and India Population and GDP Visualization

China and India Population and GDP Visualization Historical Population Comparison between India and China This graph shows the population data of Indi

Nicolas De Mello 10 Oct 27, 2021
Project coded in Python using Pandas to look at changes in chase% for batters facing a pitcher first time through the order vs. thrid time

Project coded in Python using Pandas to look at changes in chase% for batters facing a pitcher first time through the order vs. thrid time

Jason Kraynak 1 Jan 07, 2022
Statistics and Visualization of acceptance rate, main keyword of CVPR 2021 accepted papers for the main Computer Vision conference (CVPR)

Statistics and Visualization of acceptance rate, main keyword of CVPR 2021 accepted papers for the main Computer Vision conference (CVPR)

Hoseong Lee 78 Aug 23, 2022
a simple REPL display lib for circuitpython

Circuitpython-termio-lib a simple REPL display lib for circuitpython Fonctions cls clear terminal screen and set cursor on top left : coords 0,0 usage

BeBoXoS 1 Nov 17, 2021
Pyan3 - Offline call graph generator for Python 3

Pyan takes one or more Python source files, performs a (rather superficial) static analysis, and constructs a directed graph of the objects in the combined source, and how they define or use each oth

Juha Jeronen 235 Jan 02, 2023
The open-source tool for building high-quality datasets and computer vision models

The open-source tool for building high-quality datasets and computer vision models. Website • Docs • Try it Now • Tutorials • Examples • Blog • Commun

Voxel51 2.4k Jan 07, 2023
Visualize large time-series data in plotly

plotly_resampler enables visualizing large sequential data by adding resampling functionality to Plotly figures. In this Plotly-Resampler demo over 11

PreDiCT.IDLab 604 Dec 28, 2022
Movie recommendation using RASA, TigerGraph

Demo run: The below video will highlight the runtime of this setup and some sample real-time conversations using the power of RASA + TigerGraph, Steps

Sudha Vijayakumar 3 Sep 10, 2022
The plottify package is makes matplotlib plots more legible

plottify The plottify package is makes matplotlib plots more legible. It's a thin wrapper around matplotlib that automatically adjusts font sizes, sca

Andy Jones 97 Nov 04, 2022
GD-UltraHack - A Mod Menu for Geometry Dash. Specifically a MegahackV5 clone in Python. Only for Windows

GD UltraHack: The Mod Menu that Nobody asked for. This is a mod menu for the gam

zeo 1 Jan 05, 2022
在原神中使用围栏绘图

yuanshen_draw 在原神中使用围栏绘图 文件说明 toLines.py 将一张图片转换为对应的线条集合,视频可以按帧转换。 draw.py 在原神家园里绘制一张线条图。 draw_video.py 在原神家园里绘制视频(自动按帧摆放,截图(win)并回收) cat_to_video.py

14 Oct 08, 2022
Visualize the training curve from the *.csv file (tensorboard format).

Training-Curve-Vis Visualize the training curve from the *.csv file (tensorboard format). Feature Custom labels Curve smoothing Support for multiple c

Luckky 7 Feb 23, 2022
A Python library for plotting hockey rinks with Matplotlib.

Hockey Rink A Python library for plotting hockey rinks with Matplotlib. Installation pip install hockey_rink Current Rinks The following shows the cus

24 Jan 02, 2023
Type-safe YAML parser and validator.

StrictYAML StrictYAML is a type-safe YAML parser that parses and validates a restricted subset of the YAML specification. Priorities: Beautiful API Re

Colm O'Connor 1.2k Jan 04, 2023
Fractals plotted on MatPlotLib in Python.

About The Project Learning more about fractals through the process of visualization. Built With Matplotlib Numpy License This project is licensed unde

Akeel Ather Medina 2 Aug 30, 2022