Another alternative is to use the heatmap function in seaborn to plot the covariance. This example uses the Auto data set from the ISLR package in R (the same as in the example you showed).
import pandas.rpy.common as com
import seaborn as sns
# load the R package ISLR
infert = com.importr("ISLR")
# load the Auto dataset
auto_df = com....
Tensorflow, Keras, MXNet, PyTorch
If the neural network is given as a Tensorflow graph, then you can visualize this graph with TensorBoard.
Here is how the MNIST CNN looks like:
You can add names / scopes (like "dropout", "softmax", "fc1", "conv1", "conv2") yourself.
The following is only about the left graph. I ignore the 4 small graphs ...
I suggest some sort of play on the following:
Using the UCI Abalone data for this example...
import numpy as np
import matplotlib.pyplot as plt
# Read file into a Pandas dataframe
from pandas import DataFrame, read_csv
f = 'https://archive.ics.uci.edu/ml/machine-learning-databases/abalone/abalone.data'
df = read_csv(f)
I also suggest Gephi software (https://gephi.github.io), which seems to be quite powerful. Some additional information on using Gephi with large networks can be found here and, more generally, here. Cytoscape (http://www.cytoscape.org) is an alternative to Gephi, being an another popular platform for complex network analysis and visualization.
If you'd like ...
I think that Shiny is an overkill in this situation and doesn't match your requirement of dashboard reports to be static. I guess, that your use of the term "dashboard" is a bit confusing, as some people might consider that it has more emphasis of interactivity (real-time dashboards), rather than information layout, as is my understanding (confirmed by the "...
See: Graphviz's executables are not found (Python 3.4) and graphviz package doesn't add executable to PATH on windows #1666 and Problem with graphviz #1357 - it's a reoccurring problem (for that program) with the PATH environment variable settings. Installing particular versions, or in a particular order, or manually adding a PATH fixes the problem.
Wow, this was bit challenging but I was able to make one of these plots in python.
The two main components are:
plotting multiple radial axes on a polar plot
remapping radial axes for variables with reversed scales
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns # improves plot aesthetics
def _invert(x, limits):
There is an open source project called Netron
Netron is a viewer for neural network, deep learning and machine learning models.
Netron supports ONNX (.onnx, .pb), Keras (.h5, .keras), CoreML (.mlmodel) and TensorFlow Lite (.tflite). Netron has experimental support for Caffe (.caffemodel), Caffe2 (predict_net.pb), MXNet (-symbol.json), TensorFlow.js (...
Personally going to make a strong argument in favor of Python here. There are a large number of reasons for this, but I'm going to build on some of the points that other people have mentioned here:
Picking a single language: It's definitely possible to mix and match languages, picking d3 for your visualization needs, FORTRAN for your fast matrix multiplies, ...
Fancy animations are cool
I was very impressed when I saw this animation of the discourse git repository. They used Gourse which is specifically for git. But it may give ideas about how to represent the dynamics of growth.
You can create animations with matplotlib
This stackoverflow answer seems to point at a python/networkx/matplotlib solution.
I would add ASCII visualizations using keras-sequential-ascii (disclaimer: I am the author).
A small network for CIFAR-10 (from this tutorial) would be:
OPERATION DATA DIMENSIONS WEIGHTS(N) WEIGHTS(%)
Input ##### 32 32 3
Conv2D \|/ ------------------- 896 2.1%
relu ##### ...
First of all your explanation about the methods are right. The point is that Embedding algorithms are not to only visualize but basically reducing the dimentionality to cope with two main problems in Statistical Data Analysis, namely Curse of Dimentionaliy and Low-Sample Size Problem so that they are not supposed to depict physically understood features and ...
There is a Tablaeu API and you can use Python to use it, but maybe not in the sense that you think. There is a Data Extract API that you could use to import your data into Python and do your visualizations there, so I do not know if this is going to answer your question entirely.
As in the first comment you can use Matplotlib from Matplotlib website, or you ...
The closes thing I know is ConvNetJS:
Demos on this site plot weighs and how do they change with time (bear in mind, its many ...
Looking at the source (seaborn/seaborn/categorical.py, line 2166), we find
def barplot(x=None, y=None, hue=None, data=None, order=None, hue_order=None,
estimator=np.mean, ci=95, n_boot=1000, units=None,
orient=None, color=None, palette=None, saturation=.75,
errcolor=".26", ax=None, **kwargs):
so the default value is, indeed, .95, as ...
Richard Hamming is attributed with the sentence: "The purpose of computing is insight, not numbers." In this 1973 academic paper (see discussion in What is the famous data set that looks totally different but has similar summary stats?), Francis Anscombe argues that "graphs are essential to good statistical analysis." Anscombe's quartet is a long time ...
There is an awesome library called MPLD3 that generates interactive D3 plots.
This code produces an HTML interactive plot of the popular iris dataset that is compatible with Jupyter Notebook. When the paintbrush is selected, it allows you to select a subset of data to be highlighted among all of the plots. When the cross-arrow is selected, it allows you to ...
The keras.utils.vis_utils module provides utility functions to plot a Keras model (using graphviz)
The following shows a network model that the first hidden layer has 50 neurons and expects 104 input variables.
plot_model(model, to_file='model.png', show_shapes=True, show_layer_names=True)
Shiny is a framework for generating HTML-based apps that execute R code dynamically. Shiny apps can stand alone or be built into Markdown documents with knitr, and Shiny development is fully integrated into RStudio. There's even a free service called shinyapps.io for hosting Shiny apps, the shiny package has functions for deploying Shiny apps directly from R,...
Word of warning from a former airline Revenue Management analyst: you might be barking up the wrong tree with this approach. Apologies for the wall of text that follows, but this data is a lot more complex and noisy than might appear at first glance, so wanted to provide a short description of how it's generated; forewarned is forearmed.
Airline fares have ...
I take Natural Language Processing as an example because that's the field that I have more experience in so I encourage others to share their insights in other fields like in Computer Vision, Biostatistics, time series, etc. I'm sure in those fields there are similar examples.
I agree that sometimes model visualizations can be meaningless but I think the ...
The Python package conx can visualize networks with activations with the function net.picture() to produce SVG, PNG, or PIL Images like this:
Conx is built on Keras, and can read in Keras' models. The colormap at each bank can be changed, and it can show all bank types.
More information can be found at: http://conx.readthedocs.io/en/latest/
https://gephi.github.io/ says it can handle a million edges. If your graph has 1000000 vertices and only 50000 edges then most of your vertices won't have any edges anyway.
In fact the Gephi spec is the dual of your example: "Networks up to 50,000 nodes and 1,000,000 edges"
I think, that Gephi could face with lack-of-memory issues, you will need at least 8Gb of RAM. Though number of edges is not extremely huge.
Possibly, more appropriate tool in this case would be GraphViz. It's a command line tool for network visualizations, and presumably would be more tolerant to graph size. Moreover, as I remember, in GraphViz it is ...