OCI Data Science – Initial Setup and Configuration

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After a very, very, very long wait (18+ months) Oracle OCI Data Science platform is now available.

But before you jump straight into using OCI Data Science, there is a little bit of setup required for your Cloud Tenancy. There is the easy simple approach and then there is the slightly more involved approach. These are

  • Simple approach. Assuming you are just going to use the root tenancy and compartment, you just need to setup a new policy to enable the use of the OCI Data Science services. This assuming you have your VNC configuration complete with NAT etc. This can be done by creating a policy with the following policy statement. After creating this you can proceed with creating your first notebook in OCI Data Science.
allow service datascience to use virtual-network-family in tenancy

Screenshot 2020-02-11 19.46.38

  • Slightly more complicated approach. When you get into having a team based approach you will need to create some additional Oracle Cloud components to manage them and what resources are allocated to them. This involved creating Compartments, allocating users, VNCs, Policies etc. The following instructions brings you through these steps

IMPORTANT: After creating a Compartment or some of the other things listed below, and they are not displayed in the expected drop-down lists etc, then either refresh your screen or log-out and log back in again!

1. Create a Group for your Data Science Team & Add Users

The first step involves creating a Group to ‘group’ the various users who will be using the OCI Data Science services.

Go to Governance and Administration ->Identity and click on Groups.

Enter some basic descriptive information. I called my Group, ‘my-data-scientists’.

Now click on your Group in the list of Groups and add the users to the group.

You may need to create the accounts for the various users.

Screenshot 2020-02-11 12.03.58

2. Create a Compartment for your Data Science work

Now create a new Compartment to own the network resources and the Data Science resources.

Go to Governance and Administration ->Identity and click on Compartments.

Enter some basic descriptive information. I’ve called my compartment, ‘My-DS-Compartment’.

3. Create Network for your Data Science work

Creating and setting up the VNC can be a little bit of fun. You can do it the manual way whereby you setup and configure everything. Or you can use the wizard to do this. I;m going to show the wizard approach below.

But the first thing you need to do is to select the Compartment the VNC will belong to. Select this from the drop-down list on the left hand side of the Virtual Cloud Network page. If your compartment is not listed, then log-out and log-in!

To use the wizard approach click the Networking QuickStart button.

Screenshot 2020-02-11 20.15.28

Select the option ‘VCN with Internet Connectivity and click Start Workflow, as you will want to connect to it and to allow the service to connect to other cloud services.

Screenshot 2020-02-11 20.17.22

I called my VNC ‘My-DS-vnc’ and took the default settings. Then click the Next button.

Screenshot 2020-02-11 20.19.31

The next screen shows a summary of what will be done. Click the Create button, and all of these networking components will be created.

Screenshot 2020-02-11 20.22.39

All done with creating the VNC.

4. Create required Policies enable OCI Data Science for your Compartment

There are three policies needed to allocated the necessary resources to the various components we have just created. To create these go to Governance and Administration ->Identity and click on Policies.

Select your Compartment from the drop-down list. This should be ‘My-DS-Compartment’, then click on Create Policy.

The first policy allocates a group to a compartment for the Data Science services. I called this policy, ‘DS-Manage-Access’.

allow group My-data-scientists to manage data-science-family in compartment My-DS-Compartment

Screenshot 2020-02-11 20.30.10

The next policy is to give the Data Science users access to the network resources. I called this policy, ‘DS-Manage-Network’.

allow group My-data-scientists to use virtual-network-family in compartment My-DS-Compartment

Screenshot 2020-02-11 20.37.47

And the third policy is to give Data Science service access to the network resources. I called this policy, ‘DS-Network-Access’.

allow service datascience to use virtual-network-family in compartment My-DS-Compartment

Screenshot 2020-02-11 20.41.01

Job Done 🙂

You are now setup to run the OCI Data Science service.  Check out my Blog Post on creating your first OCI Data Science Notebook and exploring what is available in this Notebook.

Data Science (The MIT Press Essential Knowledge series) – available in English, Korean and Chinese

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Back in the middle of 2018 MIT Press published my Data Science book, co-written with John Kelleher. It book was published as part of their Essentials Series.

During the few months it was available in 2018 it became a best seller on Amazon, and one of the top best selling books for MIT Press. This happened again in 2019. Yes, two years running it has been a best seller!

2020 kicks off with the book being translated into Korean and Chinese. Here are the covers of these translated books.

The Japanese and Turkish translations will be available in a few months!

Go get the English version of the book on Amazon in print, Kindle and Audio formats.

https://amzn.to/2qC84KN

This book gives a concise introduction to the emerging field of data science, explaining its evolution, relation to machine learning, current uses, data infrastructure issues and ethical challenge the goal of data science is to improve decision making through the analysis of data. Today data science determines the ads we see online, the books and movies that are recommended to us online, which emails are filtered into our spam folders, even how much we pay for health insurance.

Go check it out.

Amazon.com.          Amazon.co.uk

Screenshot 2020-02-05 11.46.03

Scottish Whisky Data Set – Updated

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The Scottish Whiskey data set consist of tasting notes and evaluations from 86 distilleries around Scotland. This data set has been around a long time andwas a promotional site for a book, Whisky Classified: Choosing Single Malts by Flavour. Written by David Wishart of the University of Saint Andrews, the book had its most recent printing in February 2012.

I’ve been using this data set in one of my conference presentations (Planning my Summer Vacation), but to use this data set I need to add 2 new attributes/features to the data set. Each of the attributes are listed below and the last 2 are the attributes I added. These were added to include the converted LAT and LONG comparable with Google Maps and other similar mapping technology.

Attributes include:

  • RowID
  • Distillery
  • Body
  • Sweetness
  • Smoky
  • Medicinal
  • Tobacco
  • Honey
  • Spicy
  • Winey
  • Nutty,
  • Malty,
  • Fruity,
  • Floral,
  • Postcode,
  • Latitude,
  • Longitude
  • lat  — newly added
  • long  — newly added

Here is the link to download and use this updated Scottish Whisky data set.

The original website is no longer available but if you have a look at the Internet Archive you will find the website.

Screenshot 2020-01-23 14.44.53

Python-Connecting to multiple Oracle Autonomous DBs in one program

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More and more people are using the FREE Oracle Autonomous Database for building new new applications, or are migrating to it.

I’ve previously written about connecting to an Oracle Database using Python. Check out that post for details of how to setup Oracle Client and the Oracle Python library cx_Oracle.

In thatblog post I gave examples of connecting to an Oracle Database using the HostName (or IP address), the Service Name or the SID.

But with the Autonomous Oracle Database things are a little bit different. With the Autonomous Oracle Database (ADW or ATP) you will need to use an Oracle Wallet file. This file contains some of the connection details, but you don’t have access to ServiceName/SID, HostName, etc.  Instead you have the name of the Autonomous Database. The Wallet is used to create a secure connection to the Autonomous Database.

You can download the Wallet file from the Database console on Oracle Cloud.

Screenshot 2020-01-10 12.24.10

Most people end up working with multiple database. Sometimes these can be combined into one TNSNAMES file. This can make things simple and easy. To use the download TNSNAME file you will need to set the TNS_ADMIN environment variable. This will allow Python and cx_Oracle library to automatically pick up this file and you can connect to the ATP/ADW Database.

But most people don’t work with just a single database or use a single TNSNAMES file. In most cases you need to switch between different database connections and hence need to use multiple TNSNAMES files.

The question is how can you switch between ATP/ADW Database using different TNSNAMES files while inside one Python program?

Use the os.environ setting in Python. This allows you to reassign the TNS_ADMIN environment variable to point to a new directory containing the TNSNAMES file. This is a temporary assignment and over rides the TNS_ADMIN environment variable.

For example,

import cx_Oracle
import os

os.environ['TNS_ADMIN'] = "/Users/brendan.tierney/Dropbox/wallet_ATP"

p_username = ''p_password = ''p_service = 'atp_high'
con = cx_Oracle.connect(p_username, p_password, p_service)

print(con)
print(con.version)
con.close()

I can now easily switch to another ATP/ADW Database, in the same Python program, by changing the value of os.environ and opening a new connection.

import cx_Oracle
import os

os.environ['TNS_ADMIN'] = "/Users/brendan.tierney/Dropbox/wallet_ATP"
p_username = ''
p_password = ''
p_service = 'atp_high'
con1 = cx_Oracle.connect(p_username, p_password, p_service)
...
con1.close()

...
os.environ['TNS_ADMIN'] = "/Users/brendan.tierney/Dropbox/wallet_ADW2"
p_username = ''
p_password = ''
p_service = 'ADW2_high'
con2 = cx_Oracle.connect(p_username, p_password, p_service)
...
con2.close()

As mentioned previously the setting and resetting of TNS_ADMIN using os.environ, is only temporary, and when your Python program exists or completes the original value for this environment variable will remain.

#GE2020 Comparing Party Manifestos to 2016

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A few days ago I wrote a blog post about using Python to analyze the 2016 general (government) elections manifestos of the four main political parties in Ireland.

Today the two (traditional) largest parties released their #GE2020 manifestos. You can get them by following these links

The following images show the WordClouds generated for the #GE2020 Manifestos. I used the same Python code used in my previous post. If you want to try this out yourself, all the Python code is there.

First let us look at the WordClouds from Fine Gael.

FG2020
2020 Manifesto
FG_2016
2016 Manifesto

Now for the Fianna Fail WordClouds.

FF2020
2020 Manifesto
FF_2016
2016 Manifesto

When you look closely at the differences between the manifestos you will notice there are some common themes across the manifestos from 2016 to those in the 2020 manifestos. It is also interesting to see some new words appearing/disappearing for the 2020 manifestos. Some of these are a little surprising and interesting.

#GE2020 Analysing Party Manifestos using Python

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The general election is underway here in Ireland with polling day set for Saturday 8th February. All the politicians are out campaigning and every day the various parties are looking for publicity on whatever the popular topic is for that day. Each day is it a different topic.

Most of the political parties have not released their manifestos for the #GE2020 election (as of date of this post). I want to use some simple Python code to perform some analyse of their manifestos. As their new manifestos weren’t available (yet) I went looking for their manifestos from the previous general election. Michael Pidgeon has a website with party manifestos dating back to the early 1970s, and also has some from earlier elections. Check out his website.

I decided to look at manifestos from the 4 main political parties from the 2016 general election. Yes there are other manifestos available, and you can use the Python code, given below to analyse those, with only some minor edits required.

The end result of this simple analyse is a WordCloud showing the most commonly used words in their manifestos. This is graphical way to see what some of the main themes and emphasis are for each party, and also allows us to see some commonality between the parties.

Let’s begin with the Python code.

1 – Initial Setup

There are a number of Python Libraries available for processing PDF files. Not all of them worked on all of the Part Manifestos PDFs! It kind of depends on how these files were generated. In my case I used the pdfminer library, as it worked with all four manifestos. The common library PyPDF2 didn’t work with the Fine Gael manifesto document.

import io
import pdfminer
from pprint import pprint
from pdfminer.converter import TextConverter
from pdfminer.pdfinterp import PDFPageInterpreter
from pdfminer.pdfinterp import PDFResourceManager
from pdfminer.pdfpage import PDFPage

#directory were manifestos are located
wkDir = '.../General_Election_Ire/'

#define the names of the Manifesto PDF files & setup party flag
pdfFile = wkDir+'FGManifesto16_2.pdf'
party = 'FG'
#pdfFile = wkDir+'Fianna_Fail_GE_2016.pdf'
#party = 'FF'
#pdfFile = wkDir+'Labour_GE_2016.pdf'
#party = 'LB'
#pdfFile = wkDir+'Sinn_Fein_GE_2016.pdf'
#party = 'SF'

All of the following code will run for a given manifesto. Just comment in or out the manifesto you are interested in. The WordClouds for each are given below.

2 – Load the PDF File into Python

The following code loops through each page in the PDF file and extracts the text from that page.

I added some addition code to ignore pages containing the Irish Language. The Sinn Fein Manifesto contained a number of pages which were the Irish equivalent of the preceding pages in English. I didn’t want to have a mixture of languages in the final output.

SF_IrishPages = [14,15,16,17,18,19,20,21,22,23,24]
text = ""

pageCounter = 0
resource_manager = PDFResourceManager()
fake_file_handle = io.StringIO()
converter = TextConverter(resource_manager, fake_file_handle)
page_interpreter = PDFPageInterpreter(resource_manager, converter)

for page in PDFPage.get_pages(open(pdfFile,'rb'), caching=True, check_extractable=True):
    if (party == 'SF') and (pageCounter in SF_IrishPages):
        print(party+' - Not extracting page - Irish page', pageCounter)
    else:
        print(party+' - Extracting Page text', pageCounter)
        page_interpreter.process_page(page)

        text = fake_file_handle.getvalue()

    pageCounter += 1

print('Finished processing PDF document')
converter.close()
fake_file_handle.close()
FG - Extracting Page text 0
FG - Extracting Page text 1
FG - Extracting Page text 2
FG - Extracting Page text 3
FG - Extracting Page text 4
FG - Extracting Page text 5
...

3 – Tokenize the Words

The next step is to Tokenize the text. This breaks the text into individual words.

from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
tokens = []

tokens = word_tokenize(text)

print('Number of Pages =', pageCounter)
print('Number of Tokens =',len(tokens))
Number of Pages = 140
Number of Tokens = 66975

4 – Filter words, Remove Numbers & Punctuation

There will be a lot of things in the text that we don’t want included in the analyse. We want the text to only contain words. The following extracts the words and ignores numbers, punctuation, etc.

#converts to lower case, and removes punctuation and numbers
wordsFiltered = [tokens.lower() for tokens in tokens if tokens.isalpha()]
print(len(wordsFiltered))
print(wordsFiltered)
58198
['fine', 'gael', 'general', 'election', 'manifesto', 's', 'keep', 'the', 'recovery', 'going', 'gaelgeneral', 'election', 'manifesto', 'foreward', 'from', 'an', 'taoiseach', 'the', 'long', 'term', 'economic', 'three', 'steps', 'to', 'keep', 'the', 'recovery', 'going', 'agriculture', 'and', 'food', 'generational',
...

As you can see the number of tokens has reduced from 66,975 to 58,198.

5 – Setup Stop Words

Stop words are general words in a language that doesn’t contain any meanings and these can be removed from the data set. Python NLTK comes with a set of stop words defined for most languages.

#We initialize the stopwords variable which is a list of words like 
#"The", "I", "and", etc. that don't hold much value as keywords
stop_words = stopwords.words('english')
print(stop_words)
['i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', "you're", "you've", "you'll", "you'd", 'your', 'yours', 'yourself',
....

Additional stop words can be added to this list. I added the words listed below. Some of these you might expect to be in the stop word list, others are to remove certain words that appeared in the various manifestos that don’t have a lot of meaning. I also added the name of the parties  and some Irish words to the stop words list.

#some extra stop words are needed after examining the data and word cloud
#these are added
extra_stop_words = ['ireland','irish','ł','need', 'also', 'set', 'within', 'use', 'order', 'would', 'year', 'per', 'time', 'place', 'must', 'years', 'much', 'take','make','making','manifesto','ð','u','part','needs','next','keep','election', 'fine','gael', 'gaelgeneral', 'fianna', 'fáil','fail','labour', 'sinn', 'fein','féin','atá','go','le','ar','agus','na','ár','ag','haghaidh','téarnamh','bplean','page','two','number','cothromfor']
stop_words.extend(extra_stop_words)
print(stop_words)

Now remove these stop words from the list of tokens.

# remove stop words from tokenised data set
filtered_words = [word for word in wordsFiltered if word not in stop_words]
print(len(filtered_words))
print(filtered_words)
31038
['general', 'recovery', 'going', 'foreward', 'taoiseach', 'long', 'term', 'economic', 'three', 'steps', 'recovery', 'going', 'agriculture', 'food',

The number of tokens is reduced to 31,038

6 – Word Frequency Counts

Now calculate how frequently these words occur in the list of tokens.

#get the frequency of each word
from collections import Counter

# count frequencies
cnt = Counter()
for word in filtered_words:
cnt[word] += 1

print(cnt)
Counter({'new': 340, 'support': 249, 'work': 190, 'public': 186, 'government': 177, 'ensure': 177, 'plan': 176, 'continue': 168, 'local': 150, 
...

7 – WordCloud

We can use the word frequency counts to add emphasis to the WordCloud. The more frequently it occurs the larger it will appear in the WordCloud.

#create a word cloud using frequencies for emphasis 
from wordcloud import WordCloud
import matplotlib.pyplot as plt

wc = WordCloud(max_words=100, margin=9, background_color='white',
scale=3, relative_scaling = 0.5, width=500, height=400,
random_state=1).generate_from_frequencies(cnt)

plt.figure(figsize=(20,10))
plt.imshow(wc)
#plt.axis("off")
plt.show()

#Save the image in the img folder:
wc.to_file(wkDir+party+"_2016.png")

The last line of code saves the WordCloud image as a file in the directory where the manifestos are located.

8 – WordClouds for Each Party

Screenshot 2020-01-21 11.10.25

Remember these WordClouds are for the manifestos from the 2016 general election.

When the parties have released their manifestos for the 2020 general election, I’ll run them through this code and produce the WordClouds for 2020. It will be interesting to see the differences between the 2016 and 2020 manifesto WordClouds.

Responsible AI: Principles & Standards around the World

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During 2019 there was been a increase awareness of AI and the need for Responsible AI. During 2020 (and beyond) we will see more and more on this topic. To get you started on some of the details and some background reading, here are links to various Principles and Standards for Responsible AI from around the World.

Standard/Principles Description
EU AI Ethics Guidelines T​he Ethics Guidelines for Trustworthy Artificial Intelligence developed by EU High-Level Expert Group on AI ​highlights that trustworthy AI should be lawful, ethical and robust. Puts forward seven key requirements for AI systems should meet in order to be deemed trustworthy, including among others ​diversity, non-discrimination, societal and environmental well-being, transparency and accountability.
OECD principles on Artificial Intelligence OECD’s ​member​ countries along with partner countries adopted the first ever set of intergovernmental policy guidelines on ​AI​, agreeing to uphold international standards ​that aim to ensure AI systems are ​designed in a way that respects the rule of law, human rights, democratic values and diversity. They emphasize that AI should benefit people and the planet by driving inclusive growth, sustainable development and well-being.
CoE: Human Rights impacts of Algorithms Council of Europe draft recommendation on the human rights impacts of algorithmic AI systems, released for consultation in August 2019 and to be adopted in early 2020. ​The document explicitly refers to the UN Guiding Principles on Business and Human Rights as a guidance for due diligence process and ​Human ​Rights Impact Assessments.
IEEE Global Initiative: ​Ethically Aligned Design Ethically Aligned Design (EAD) Document is created ​​to ​educate a broader public ​and to inspire ​academics, engineers, policy makers and manufacturers of autonomous and intelligent systems​ to take action ​on prioritiz​ing ethical considerations​​.​ The general principles for AI design, manufacturing and use include: human rights, wellbeing, ​data agency, effectiveness, transparency, accountability, awareness of misuse, competence. ​The unique IEEE P7000 Standards series address specific issues at the intersection of technology and ethics​ and aimed to empower innovation across borders and enable societal benefit.
UN Sustainable Development Goals The UN Sustainable Goals include the annual ​AI for Good Global Summit is the leading UN platform for global and inclusive dialogue on how artificial intelligence could help accelerate progress towards the ​​Global Goals.
UN Business ​and Human Rights The UN Guiding Principles on Business and Human Rights (UNGPs)gives a framework offering a roadmap​ to navigate responsibility-related challenges, rapid ​technological disruption and rising ​inequality, business has a ​unique opportunity ​to implement​ human-centered innovation by taking into account ​social, ethical​ and human rights implications of AI.
EU Collaborative Platforms and Social Learning Several EU countries have ​​articulated their ambitions related to artificial intelligence, it is of paramount importance to find your unique voice, ​​track and join ​essential conversations, strategically engage in collective efforts and leave meaningful digital footprint.​