Learn how you can use Reveal’s R & Python scripting capability to bring advanced data preparation, deeper analytics, and richer visualizations to your users!
Scalable tabular (SFrame, SArray) and graph (SGraph) data-structures built for out-of-core data analysis.
The SFrame package provides the complete implementation of:
SFrame
SArray
SGraph
The C++ SDK surface area (gl_sframe, gl_sarray, gl_sgraph)
I am shubham sharma graduated from Acropolis Institute of technology in Computer Science and Engineering. I have spent around 2 years in field of Machine learning. I am currently working as Data Scientist in Reliance industries private limited Mumbai. Mainly focused on problems related to data handing, data analysis, modeling, forecasting, statistics and machine learning, Deep learning, Computer Vision, Natural language processing etc. Area of interests are Data Analytics, Machine Learning, Machine learning, Time Series Forecasting, web information retrieval, algorithms, Data structures, design patterns, OOAD.
Scalable tabular (SFrame, SArray) and graph (SGraph) data-structures built for out-of-core data analysis.
The SFrame package provides the complete implementation of:
SFrame
SArray
SGraph
The C++ SDK surface area (gl_sframe, gl_sarray, gl_sgraph)
I am shubham sharma graduated from Acropolis Institute of technology in Computer Science and Engineering. I have spent around 2 years in field of Machine learning. I am currently working as Data Scientist in Reliance industries private limited Mumbai. Mainly focused on problems related to data handing, data analysis, modeling, forecasting, statistics and machine learning, Deep learning, Computer Vision, Natural language processing etc. Area of interests are Data Analytics, Machine Learning, Machine learning, Time Series Forecasting, web information retrieval, algorithms, Data structures, design patterns, OOAD.
Video and slides synchronized, mp3 and slide download available at URL http://bit.ly/2nwSwEh.
Marco Bonzanini discusses the process of building data pipelines, e.g. extraction, cleaning, integration, pre-processing of data; in general, all the steps necessary to prepare data for a data-driven product. In particular, he focuses on data plumbing and on the practice of going from prototype to production. Filmed at qconlondon.com.
Marco Bonzanini is Data Scientist and co-organizer of PyData London Meetup.
Data Scientists and Machine Learning practitioners, nowadays, seem to be churning out models by the dozen and they continuously experiment to find ways to improve their accuracies. They also use a variety of ML and DL frameworks & languages , and a typical organization may find that this results in a heterogenous, complicated bunch of assets that require different types of runtimes, resources and sometimes even specialized compute to operate efficiently.
But what does it mean for an enterprise to actually take these models to "production" ? How does an organization scale inference engines out & make them available for real-time applications without significant latencies ? There needs to be different techniques for batch (offline) inferences and instant, online scoring. Data needs to be accessed from various sources and cleansing, transformations of data needs to be enabled prior to any predictions. In many cases, there maybe no substitute for customized data handling with scripting either.
Enterprises also require additional auditing and authorizations built in, approval processes and still support a "continuous delivery" paradigm whereby a data scientist can enable insights faster. Not all models are created equal, nor are consumers of a model - so enterprises require both metering and allocation of compute resources for SLAs.
In this session, we will take a look at how machine learning is operationalized in IBM Data Science Experience (DSX), a Kubernetes based offering for the Private Cloud and optimized for the HortonWorks Hadoop Data Platform. DSX essentially brings in typical software engineering development practices to Data Science, organizing the dev->test->production for machine learning assets in much the same way as typical software deployments. We will also see what it means to deploy, monitor accuracies and even rollback models & custom scorers as well as how API based techniques enable consuming business processes and applications to remain relatively stable amidst all the chaos.
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Piotr Mierzejewski, Program Director Development IBM DSX Local, IBM
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Video and slides synchronized, mp3 and slide download available at URL http://bit.ly/2nwSwEh.
Marco Bonzanini discusses the process of building data pipelines, e.g. extraction, cleaning, integration, pre-processing of data; in general, all the steps necessary to prepare data for a data-driven product. In particular, he focuses on data plumbing and on the practice of going from prototype to production. Filmed at qconlondon.com.
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Data Scientists and Machine Learning practitioners, nowadays, seem to be churning out models by the dozen and they continuously experiment to find ways to improve their accuracies. They also use a variety of ML and DL frameworks & languages , and a typical organization may find that this results in a heterogenous, complicated bunch of assets that require different types of runtimes, resources and sometimes even specialized compute to operate efficiently.
But what does it mean for an enterprise to actually take these models to "production" ? How does an organization scale inference engines out & make them available for real-time applications without significant latencies ? There needs to be different techniques for batch (offline) inferences and instant, online scoring. Data needs to be accessed from various sources and cleansing, transformations of data needs to be enabled prior to any predictions. In many cases, there maybe no substitute for customized data handling with scripting either.
Enterprises also require additional auditing and authorizations built in, approval processes and still support a "continuous delivery" paradigm whereby a data scientist can enable insights faster. Not all models are created equal, nor are consumers of a model - so enterprises require both metering and allocation of compute resources for SLAs.
In this session, we will take a look at how machine learning is operationalized in IBM Data Science Experience (DSX), a Kubernetes based offering for the Private Cloud and optimized for the HortonWorks Hadoop Data Platform. DSX essentially brings in typical software engineering development practices to Data Science, organizing the dev->test->production for machine learning assets in much the same way as typical software deployments. We will also see what it means to deploy, monitor accuracies and even rollback models & custom scorers as well as how API based techniques enable consuming business processes and applications to remain relatively stable amidst all the chaos.
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3. Today’s Agenda
• Advanced Scripting in Reveal
• Pre-Requisites for R & Python
• Visualization Customizations
• Live Demo
• Wrap Up
House Keeping
• Recording and slides will be available after the webinar. We’ll send a follow-up email
• Please ask questions in the Questions Window
4. Summer of BI Series …
• June 10th: Dashboard Best Practices, Do’s and Don’ts
• June 24th: Building a Data Driven Org
• July 8th: Advanced Analytics: Using R & Python
• July 22nd: Advanced Analytics: Machine Learning with Reveal
• Aug 5th: Embedded Analytics: 5 Steps to App Modernization
https://www.infragistics.com/webinars
6. Using R & Python in Custom Visualizations
• Using Reveal, you are not limited to “what’s in the box”
in terms of chart types and visualizations
• We’ve enabled widely used scripting / programming
languages like R and Python which are common
amongst data scientists
• You and your power-user co-workers are only limited by
your imagination (and what custom libraries will offer) in
terms of data visualizations
• Both R & Python can do more than data visualizations –
you can perform any action on your available data
7. Common Scenarios for R & Python Visualizations
1. There is a visualization you require that we do not
ship in Reveal
2. You require advanced scripting or data preparation
tasks to be applied to the data you are working with
3. You are a citizen data scientist or developer and you
have created visualizations that you want to re-use
4. You are using a different visualization product and
want to create mashups with existing Reveal
dashboards
8. Reveal Data
Visualizations
40 Data Visualizations
in 7 Categories
Compare Data
Part to Whole
Data Distribution
Data Trend Analysis
Data Relationships
KPI’s and Gauges
Geospatial Data
9. Custom
Visualizations
Endless options … consider
that Python’s most popular
library is Matplotlib, and it
has many extensions …
• Biggle, Chac, DISLIN,
GNU, Octave, Gnuplot-
py, Plplot, PyCha,
PyPlotter, SageMath,
SciPy, wxPython, Plotly,
Bokeh …. and more!
11. System Requirements
• Advanced Scripting is supported in the following
platforms:
• WPF Desktop Client
• WPF Desktop SDK
• Python 2.7 or higher (3.8 or higher preferred)
• R 3.x or higher (4.0 or higher preferred)
17. Matplotlib
• The most popular open
source graphics library for
Python
• Thousands of examples
online to inspire you to
extend what is possible with
Reveal
• Explore the gallery at
https://matplotlib.org/3.1.0/g
allery/index.html
20. Creating a Custom Visualization
• Connect to your data
source as if you were
using a built in Reveal
Chart and drag / drop
the fields you require for
your visualization
21. Creating a Custom Visualization
• Select Python from the
Change Visualization
dropdown
22. Setting Up Your Script
• Switch the Settings Tab,
click the Edit Script
button
• Note the Libraries and
Fields that are available
by default
• Your data is referenced
in the data object
23. Setting Up Your Script
• Switch the Settings Tab,
click the Edit Script
button
• Note the Libraries and
Fields that are available
by default
• Your data is referenced
in the data object
24. Watch the Magic Happen!
• By default, Python with
Matplotlib renders as an
Image in the Reveal
Visualization canvas
25. Clean Up the Visualization
• Apply a Data Filter
• All Settings, Filters, etc in
Reveal work across any
custom visualization
27. Area Chart
ax = plt.gca()
data.plot(kind='area',x='Date',y='Sum of Spend',ax=ax)
data.plot(kind='area',x='Date',y='Sum of Budget', color='green', ax=ax)
29. Heatmap Chart
from plotly import data
campaignid = np.unique(np.array(data['CampaignID']))
territory = np.unique(np.array(data['Territory']))
spend = np.array(data['Sum of Spend']).reshape((7, 5))
fig, ax = plt.subplots(figsize=(5.5, 6.5))
im = ax.imshow(spend)
# Show all ticks...
ax.set_xticks(np.arange(len(territory)))
ax.set_yticks(np.arange(len(campaignid)))
# ... and label them with the respective list entries
ax.set_xticklabels(territory)
ax.set_yticklabels(campaignid)
# Loop over data dimensions and create text annotations.
for i in range(len(campaignid)):
for j in range(len(territory)):
text = ax.text(j, i, spend[i, j],
ha="center", va="center", color="w")
ax.set_title("Campaign Spend (dollars)")
fig.tight_layout()
32. Reveal – Simple and Beautiful Visualizations
• Using Reveal, you are not limited to “what’s in the box”
in terms of chart types and visualizations
• Don’t limit the experience you can deliver to your
customer
• Use R & Python to super-charge your dashboards with
custom visualizations
• Learn R:
https://www.tutorialspoint.com/r/r_boxplots.htm
• Learn Python:
https://matplotlib.org/devdocs/gallery/index.html
33. Use Reveal to Enable Advanced Scripting
and Custom Data Visualizations
Try Today at revealbi.io
Contact Us for a Personalized Demo! sales@infragistics.com
34. Email Us with Questions!
Jason Beres
Senior VP, Developer Tools
jasonb@Infragistics.com
Casey McGuigan
Product Manager, Reveal
cmcguigan@Infragistics.com
revealbi.io
https://www.codespeedy.com/how-to-change-line-color-in-matplotlib/
https://matplotlib.org/3.1.0/gallery/color/named_colors.html
Color Demo - https://matplotlib.org/3.1.0/gallery/color/color_demo.html
https://matplotlib.org/3.1.0/gallery/lines_bars_and_markers/linestyles.html#sphx-glr-gallery-lines-bars-and-markers-linestyles-py
an RGB or RGBA (red, green, blue, alpha) tuple of float values in [0, 1] (e.g., (0.1, 0.2, 0.5) or (0.1, 0.2, 0.5, 0.3));
a hex RGB or RGBA string (e.g., '#0f0f0f' or '#0f0f0f80'; case-insensitive);
a string representation of a float value in [0, 1] inclusive for gray level (e.g., '0.5');
one of {'b', 'g', 'r', 'c', 'm', 'y', 'k', 'w'};
a X11/CSS4 color name (case-insensitive);
a name from the xkcd color survey, prefixed with 'xkcd:' (e.g., 'xkcd:sky blue'; case insensitive);
one of the Tableau Colors from the 'T10' categorical palette (the default color cycle): {'tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple', 'tab:brown', 'tab:pink', 'tab:gray', 'tab:olive', 'tab:cyan'} (case-insensitive);
a "CN" color spec, i.e. 'C' followed by a number, which is an index into the default property cycle (matplotlib.rcParams['axes.prop_cycle']); the indexing is intended to occur at rendering time, and defaults to black if the cycle does not include color.
