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Nifty 50 November Futures (11,958)
by Yoganand D, BL Research BureauThe Sensex and the Nifty commenced the session on a positive note and continue to hover in green, gaining about 0.5 per cent each in today's session. Asian markets are up, the Nikkei 225 index has gained 0.7 per cent to 23,303 and the Hang Seng index has marginally advanced by 0.17 per cent to 26,367 levels.
The market breadth of the Nifty is biased towards advances while the India VIX has slumped 3 per cent to 15.15 levels. Although large cap indices have surged about 0.4 per cent, the small-cap index hovers in the negative territory, down by 0.28 per cent. The Nifty mid-cap index has gained 0.3 per cent. Buying interest is seen the Nifty PSU bank index which has jumped 4 per cent.
The Nifty futures November contract started the session with a gap-up open at 11,913 and extended the up-move. But after recording an intra-day high at 12,008, the contract began to decline due to key resistance at 12,000 and on the back of profit taking. Key support in the band between 11,930 and 11,940 can provide base for the contract. As long as the contract trades above this band, the near-term outlook stays positive. However, a strong fall below 11,930 can drag the contract down to 11,913 and then to 11,900. A further decline below this base can pull the contract lower to 11,875 and 11,850 levels. On the upside, the immediate resistance at 11,975 and 12,000 can retested.
Strategy: Go long with a fixed stop-loss at 11,930 levels.
Supports: 11,930 and 11,913
Resistances: 11,975 and 12,000
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{% extends "dreamsapp/base.html" %} {% block body %}
Perceive your sleep as a unique musical experience!
Musicalization turns data into a genuine piece of composed music.
Sleep musicalization is a novel way of perceiving and experiencing sleep measurement data. The goal is to help users understand and analyze their sleeping patterns and eventually improve their sleep.
The musicalization process follows musicological principles when composing a melody, designing the rhythm and changes in tempo, arranging the accompaniment, and playing out the music at different levels of volume. These aspects are inspired but not dictated by the data. The result of musicalization of eight hours of sleep is an origianal piece of couple of minutes of music.
Musicalization of data provides a whole new way to experience data as a music. Music has a unique capability to invoke emotions, giving users a novel opportunity to perceive their data also as innate feelings. In the case of sleep measurements, musicalization complements the more informative, no-nonsense visual results with an emotional component towards one's own sleep.
Sleep analysis in this service is based on the Beddit sleep measurement device, which measures sleep with a thin sensor installed under the mattress. Sleep measurements are retrieved from the Beddit service, by the user's authorization, and then musicalized by composing a novel piece of music.
Tulilaulu, A., Paalasmaa, J., Waris, M. & Toivonen, H. 2012. Sleep Musicalization: Automatic Music Composition from Sleep Measurements. In The Eleventh International Symposium on Intelligent Data Analysis (IDA 2012), LNCS 7619, 392-403. Springer 2012. [PDF]
Paalasmaa, J., Waris, M., Toivonen, H., Leppäkorpi, L. & Partinen, M., 2012. Unobtrusive Online Monitoring of Sleep at Home. In 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'12. [PDF]
Python source codes of the musicalization implementation and this website are available.
The musicalization project has been carried out in the Discovery Research Group at the University of Helsinki.
A big thank you to the Algorithmic Data Analysis (Algodan) Centre of Excellence of the Academy of Finland, and to the Department of Computer Science of the University of Helsinki for their financial support. Thanks also to Tomi Jylhä-Ollila for help with the Kunquat system used to render the music!
Data mining and musicalization research: Prof. Hannu Toivonen. Sleep analysis methods and services: Beddit Ltd.


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Studio Movie Grill
Loyalty Retention Manager
I am the Loyalty Retention Manager for Studio Movie Grill’s Home Office. I am responsible for the development and strategy of our loyalty program by using data driven campaigns to elevate our marketing initiatives.
I am currently enrolled into Southern Methodist University's Programming Bootcamp which teaches multiple languages including but not limited to: HTML5, CSS3, JavaScript, jQuery, Node.js, Java, Express.js, React.js, Database Theory, Bookshelf.js, MongoDB, MySQL, Command Line, and Git.
My goal is to leverage my coding skills to further my development as a young professional. I am looking forward to hearing from you.
(918) 931-0767


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Your chance to learn more about vaccinations! Most pertinent concerns selected through topic modeling of various online communities.
More InfoHear what other's are talking about in the community. New text data analysis and topic modeling to provide insights into the public's opinion.
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bool openssl_pkey_export_to_file ( mixed $key , string $outfilename [, string $passphrase [, array $configargs ]] )
openssl_pkey_export_to_file() saves an ascii-armoured (PEM encoded) rendition of key into the file named by outfilename. Note: You need to have a valid openssl.cnf installed for this function to operate correctly. See the notes under the installation section for more information.
Returns TRUE on success or FALSE on failure.


Here is where you show that you’re not all talk. Find examples. Look back through your professional experience, and see what relates to what you are promising. What you’ve done in the past can translate into evidence of how you can deliver solutions. This can require some creative thinking—and looking at your experience in a new way. What you’ve done in one area can translate into meaningful proof of what you can deliver in another.
Marnie wanted to transition from a 20-year career in retail fashion sales to getting a job in the elder care industry, a move that would satisfy her soul. How could she be a credible job candidate when she hadn’t done any professional work in the industry in decades? What was her evidence?
Her initial goal was a sales job with companies providing home care for seniors. To show her interest in elder care, she volunteered as a pet therapist with Alzheimer’s patients, joined elder care networking groups and worked as a caregiver for an elderly patient. For evidence of her sales and service capability, she highlighted her Customer Service All-Star award for a leading retailer and promoted her excellent sales record of achievement. Finally, she demonstrated professional knowledge by talking about her early career experience as a social worker for elder care facilities and her college degrees in psychology and counseling. Her caring nature came through as she discussed how she had taken care of her mother, a longtime Parkinson’s patient, in her mom’s final years.
To use the famous rubber-band analogy, create your goals far enough above what you are doing now to keep the rubber band taut. If it’s slack, your aspirations may be too low—you may not be growing as you should. But don’t set your sights so far away from your present reality that the rubber band breaks. Likewise, it’s okay to stretch how skills in one area can apply to evidence in another. Just don’t break the rubber band. If you stretch too much, you won’t be credible.
If you are light on evidence, you now know where to start. What can you do here and now—from your current position, or lack of one—to show you can deliver solutions to your target audience? If you want to be known as an innovator, what are you doing now as proof that is the case? Don’t have the opportunity to show that potential where you are now? Think outside the cubicle. Volunteer. Teach. Get involved in community groups where you can bring out the characteristics you want to shine. These quickly become evidence of what you can offer in a professional context.
After watching the video above, enter values for one or more evidence fields:


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[CHAPTER 481L]
MOTOR VEHICLE LEASE DISCLOSURE ACT
Section
481L-1 Definitions
481L-2 Disclosures
481L-3 Trade-in vehicle
481L-4 Civil remedies
Cross References
Terminal rental adjustment clause; vehicle leases, see §286-52.4.


--- layout: post title: Episode 034 | Michael Crump - Family First With Passion for Tech link: http://traffic.libsyn.com/developeronfire/DeveloperOnFire-34-MichaelCrump.mp3 length: 46137344 speaker: Dave Rael duration: '48:48' subtitle: 'Developer On Fire: Michael Crump' guest_name: Michael Crump image: /assets/images/MichaelCrump.png guest_blog: http://michaelcrump.net/ guest_twitter: mbcrump disqus_url: http://developeronfire.com/Podcast/Episodes/michael-crump-family-first-with-passion-for-tech episode_number: 34 book_recommendations: - { url: "http://www.amazon.com/dp/0804137382/?tag=devonfir-20", title: "Essentialism: The Disciplined Pursuit of Less", author: "Greg McKeown"} tags: - { url: "http://www.amazon.com/dp/0804137382/?tag=devonfir-20", title: "Essentialism: The Disciplined Pursuit of Less", author: "Greg McKeown"} redirect_from: - "/34/" - "/034/" - "/0034/" - "/michael-crump-family-first-with-passion-for-tech/" - "/Michael-Crump/" - "/MichaelCrump/" - "/Podcast/Episodes/34/" - "/Podcast/Episodes/034/" - "/Podcast/Episodes/0034/" - "/Podcast/Episodes/michael-crump-family-first-with-passion-for-tech/" - "/Podcast/Episodes/Michael-Crump/" - "/Podcast/Episodes/MichaelCrump/" guid: http://developeronfire.com:80/Podcast/Episodes/michael-crump-family-first-with-passion-for-tech ---
Michael Crump works at Telerik and is a Microsoft MVP, coder, blogger and speaker of various software development topics. He has a passion for a wide range of technology stacks that involve web and mobile. In his free time, he likes to experiment with wearables and is a big fan of IoT. Michael can be found on twitter at @mbcrump or by visiting michaelcrump.net.
1. Family first
2. Work with people smarter than you
3. Step out of your comfort zone


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Which videos get most number of views?
Which videos get most number of likes?
Which videos get most number of dislikes?
Which videos get most number of comments?
How many trending videos does each category have?
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What's the average number of views of a single video in each category?

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This is a big question to answer but in essence you can think docker containers as virtual machines with applications but without the bulky OS part, or applications with stripped down OSes. Docker containers are much more lightweight than virtual machines because all docker containers share the same core OS and related containers (e.g. different applications derived from the same CentOS or Ubuntu OS) share the same base container. Please refer to the docker website for details about docker. I have found it helpful to watch a few youtube videos on docker.
The reason why docker is very helpful in building (bioinformatics) workflows are that
Applications are encapsulated in docker containers so that they do not interfere with the underlying OS, and with other applications. For example, we can run a workflow with applications that based on different versions of Python2 and Python 3 without having to install them locally and calling the correct version of Python, because all applications use the specific version of Python and required libraries and tools inside their own containers.
Workflows will be more stable and reproducible because unlike, for example, a local installation of Python that can be affected by other software and upgrades of python, Docker containers are stable and will not change.
The same docker containers can be executed on different OS (e.g. various version of Linux, MacOSX etc) so your workflow built on a Mac OS workstation can be executed on a cluster environment.
There are of course some complexity in the use of docker but SoS has made it extremely easy to use docker in your workflows.
Docker is relatively new and is evolving very fast. It is crucial for you to install the latest version from docker website. This website provides very detailed step by step instruction and you should have no problem installing docker on your machine.
After installation, you should be able to start a docker terminal and run command
$ docker run hello-world
as suggested by the documentation. Depending on the different versions of docker (e.g. docker under windows), docker might be run under a virtual machine. It is very important to understand that the configuration (e.g. RAM, CPU) of docker machines are different from the host machines so your docker machine might be restricuted to, for example, 1 CPU, 1G of RAM, which is insufficient for any serious work. You will most likely need to re-configure your docker virtual machine (e.g. from VirtualBox app locate a machine named default
).
Running a docker-based workflow is easy because SoS will automatically download docker images and execute scripts inside docker container. Anyway, before you start any workflow running docker, it is a good idea to check if your docker daemon is running by
!docker ps
Now, suppose you do not have ruby installed locally and would like to run a ruby script, you can execute it inside a ruby
container. Here we set option to -v2
to demonstrate the actual command executed by SoS.
%run -v2
ruby: docker_image='ruby'
line1 = "Cats are smarter than dogs";
line2 = "Dogs also like meat";
if ( line1 =~ /Cats(.*)/ )
puts "Line1 contains Cats"
end
if ( line2 =~ /Cats(.*)/ )
puts "Line2 contains Dogs"
end
Building a docker image is usually done outside of SoS if you are maintaining a collection of docker containers to be shared by your workflows, your groups, or everyone. However, if you need to create a docker image on-the-fly or would like to embed the Dockerfile inside a SoS script, you can use the docker_build
action to build a docker container.
For example, you can build a container for MISO as follows:
[miso_build]
# building miso from a Dockerfile
docker_build: tag='mdabioinfo/miso:latest'
############################################################
# Dockerfile to build MISO container images
# Based on Anaconda python
############################################################
# Set the base image to anaconda Python 2.7 (miso does not support python 3)
FROM continuumio/anaconda
# File Author / Maintainer
MAINTAINER Bo Peng<EMAIL_ADDRESS># Update the repository sources list
RUN apt-get update
# Install compiler and python stuff, samtools and git
RUN apt-get install --yes \
build-essential \
gcc-multilib \
gfortran \
apt-utils \
libblas3 \
liblapack3 \
libc6 \
cython \
samtools \
libbam-dev \
bedtools \
wget \
zlib1g-dev \
tar \
gzip
WORKDIR /usr/local
RUN pip install misopy
Command
sos run script miso_build
would build a docker image mdabioinfo/miso:latest
that can be used by other SoS steps.
Writing a workflow with docker support is a bit more complicated because you will need to understand a few concepts of docker, so reading through the docker run manual should be helpful. The most important concept is Volumes, whch is how the host directories are mounted to a docker container so that the command executed inside the container can access (and change) files on the host machine. SoS helps the use of docker by
/tmp
to /tmp
/Users
to /Users
under MacOS X/var/lib/sos/xxxxx
so that step input
and output
are almost always identical inside and outside of docker.
To use existing public docker container, you will need to specify its tag using option docker_image
. For example, to use compbio/ngseasy-fastqc
container to run fastqc
, instead of installing fastqc
locally, you can do
[MISO_1]
run: docker_image='compbio/ngseasy-fastqc:1.0-r001'
fastqc ${input} -o /tmp
(More to follow)
ln -s
inside a docker machine under Mac will cause a strange error message Read-only file system
.


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Karnataka government releases public holidays list for 2020
Karnataka holiday list for 2020: The list, which was released on Friday, has 18 public holidays for staff working in government offices and banks.
by Express Web DeskThe Karnataka government has announced a list of public holidays for the year 2020 under the Negotiable Instruments Act, 1881. The list, which was released on Friday, has 18 public holidays for staff working in government offices and banks. A long weekend is expected in the year 2020 since the list has 10 holidays that fall either on a Monday or a Friday.
According to the notification by government, the list does not include holidays such as Republic Day (January 26), Basava Jayanti (April 26), Moharram (August 8), Mahanavami (October 26), Ayudha Pooja (October 26) and Kannada Rajyotsava (November 1) which falls on Sundays and Naraka Chaturdashi (November 14) which falls on second Saturday and Varasiddhi Vinayaka Vratha (August 22) which falls on the fourth Saturday.
The Karnataka government has also declared a local holiday for the Kodagu district for Kail Muhurtha (September 3), Tula Sankramana(October 17) and Huthri (December 1).
The separate list of government holidays for schools and colleges will be released by the commissioner of public instruction, the government said in the notification.
Here is the list of holidays for Karnataka:
January 15 (Wednesday) – Makara Sankranti
February 21 (Friday) – Shivaratri
March 21 (Wednesday) – Ugadi
April 6 (Monday) – Mahaveer Jayanti
April 10 (Friday) – Good Friday
April 14 (Tuesday) – Ambedkar Jayanti
May 1 (Friday) – May Day
May 25 (Monday) – Khutba-e-Ramzan
August 1 (Saturday) – Bakrid
Aug 15 (Saturday) – Independence Day
September 15 (Thursday) – Mahalaya Amavasya
October 2 (Friday) – Gandhi Jayanti
October 26 (Monday) – Vijayadashami
October 30 (Friday) – Eid Milad
October 31 (Saturday) – Valmiki Jayanti
November 16 (Monday) – Deepavali
December 3 (Thursday) – Kanakadasa Jayanti
December 25 (Friday) – Christmas


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Dr. Muhammad Yunus
pioneering the concepts of microcredit and microfinance
- Muhammad Yunus was born in Chittagong as the third of 14 children. His father, Hazi Dula Mia Shoudagar was a successful goldsmith and his mother Sufia Khatun was a kind lady who always helped the poor. Five of his siblings died in infancy.
- As a child he attended the village school, later shifting to Lamabazar Primary School, Chittagong. He passed his matriculation examination from Chittagong Collegiate School.
- He attended Chittagong College where he actively participated in cultural activities. He later joined Dhaka University from where he completed his B.A and M.A.
- After receiving a Fulbright Scholarship to study in the U.S, he enrolled in Vanderbilt University and obtained his PhD in economics in 1971.
- While pursuing his PhD in the U.S, he worked as Assistant Professor of Economics at Middle Tennessee State University, Tennessee from 1969 to 1972. Upon his return to Bangladesh, he was appointed as the Deputy Chief of the General Economics Division in the Government’s Planning Commission in July 1972. However, he found the job boring and resigned within few months in September, the same year.
- He joined the Chittagong University as Associate Professor of Economics, and later became Head of the Department of Economics. In 1975, he was made Director of the Rural Economics Programme, a role he held till 1989.
- While working at the Chittagong University, he also started his work on establishing the Grameen Bank. In 1976, he personally started lending tiny amounts to the poor, and in 1983 what had begun as a pilot project started operating as a full-fledged bank, the Grameen bank with Yunus as the Managing Director.
- He has also authored several books including ‘Banker to the Poor’ and ‘Creating a World without Poverty.’
- The idea for Grameen Bank originated in Yunus’s mind while conducting a research programme in the 1970’s for designing a banking system that would help the poor in overcoming their poverty.
- Initially he started by personally lending small amounts of money to the poor in Jobra village 1976. Then he sought support from the Central Bangladesh Bank to extend micro-credit facilities to other places as well. The project was immensely successful.
- In 1983, the Grameen Bank was officially incorporated by a Bangladeshi government ordinance. The bank also received help in form of a grant from the Ford Foundation.
- Yunus’s purpose of setting up the bank was to make available small loans to the poor at reasonable rates in order to encourage self-employment opportunities that would help the poor in utilizing their skills and making economic gains. It has been estimated that 97% of the borrowers from Grameen Bank are women.
- Over the years, the Grameen Bank has grown to encompass over two dozen enterprises, all dedicated to the betterment of society. These include: Grameen Trust, Grameen Fund, Grameen Telecom, Grameen Shakti, etc.
- Yunus won the Ramon Magsaysay Award in 1984 in the field of ‘Community Leadership’ for "Enabling the neediest rural men and women to make themselves productive with sound group managed credit."
- The Independence Day Award, the highest civilian national award of Bangladesh was bestowed upon him in 1987 for the outstanding contribution he had made to rural development.
- He received the 1993 Humanitarian Award by CARE, U.S, for his role in providing a “uniquely pragmatic and effective method of empowering poor women and men to embark on income generating activities.”
- He was awarded World Food Prize in 1994 by World Food Prize Foundation, U.S.A for “his original approach to promoting the economic and social empowerment of the poorest citizens of Bangladesh, specifically women and children.”
- Yunus and Grameen Bank were jointly awarded the Nobel Peace Prize in 2006 "for their efforts to create economic and social development from below."
Here's a biography of Dr. Muhammad Yunus:
Childhood & Early Life
Career
Major Works
Awards & Achievements
--Nobel Prize Winner Muhammad Yunus: The Key To Super-Happiness--"Making money is a happiness. And that's a great incentive. Making other people happy is a super-happiness."


I'll just start with a little background info. I am a 22-year old engineering student. As early as six months ago, I believed that all illegal drugs are evil. Then I tried some weed with my brother, and my life has been different ever since. Now, before I determine my opinions about substances, I think first, but I really do think before I do things and I always do sufficient research in order to protect myself and the people I love. Except the day I tried meth.
I was over at my friendís house just hanging out with a couple of his friends, getting nice and baked. Through the conversation, it became evident that one of his friends had some crystal, and we started talking about it. He eventually asked me if I wanted to try it, and I stated that I may want to later after I had a chance to research the downsides. I was then informed of the remarkably limited availability of the substance in my area, so I proceeded to purchase a quarter gram that I could try later.
 When we got there, the other two decided to fire up a light bulb.  They taught me how to hit the bulb and eventually I had taken a few solid hits.
Meth is an incredible drug.  My mind would jump all over the place at 100 mph, so I found it difficult to hold a coherent thought for more than ten or so seconds. This also proved to be frustrating when other people would talk to me, because I would either completely lose track of what they were saying, or I would have to focus extremely hard. I know that other experiences state that they were able to concentrate better, but not me.
It actually kicked some ass, in a way.   I had to keep talking and talking at a remarkable speed.
So I kept on speeding all through the night and into the next day. And so did the other two. We did a lot of shit, and while I was alone in my apartment, I cleaned like a motherfucker. Then around 2 pm or so, some extreme paranoia set in. Not the paranoia I expected, which was that people I saw in public would know I was tweaking or that I would think everyone was a cop. It was paranoia about real problems in my life such as some troubles I was having with my classes or my financial difficulties. I obsessed over these things until around 8 pm, at which point I smoked about three more bulbs in a row, with the help of my newfound friends, and I started to speed hardcore again, and I canít remember what happened from then until about 1 am the next day.
 I do not think that this is a normal experience, because at least one other person that used meth from the same batch had an uncharacteristic, fucked-up experience. Also, pretty much everyone was confused by what happened when we smoked it: it melted into a greenish, aqua pool instead of a clear pool. I have no idea what that would mean, but regardless, I donít think that the following events were normal.
At some point during our sporadic conversations, one of them said that they wanted the Lortabs I had, and I donít like them, so I gave them away. Then we smoked the rest of the crystal and then the other two went home. I was still tweaking pretty hard, so I decided to do some more cleaning.
No, I have no idea how this would even make sense because itís not like the crushed up Lortab would have melted and then vaporized, but I guess my brain didnít care. Somehow that led into me thinking that the interactions between the two was probably toxic, and then I started to imagine that my heart was slowing down and skipping beats. After worrying about that for a couple of hours, I then imagined that my heart was stopping. Now get this: for some reason I thought that I could keep my heart from stopping (or restart it if it stopped) by running!
Anyways, I ended up running nearly continuously for about five hours from one side of my apartment to the other before I decided to call an ambulance. I know that sounds insane, but I seriously thought that I was dying. So when the ambulance got there, they pretty much reassured me that I wasnít going to die and that I was just tweaked out of my mind, and they convinced me to go to the hospital, which for some reason I thought was a good idea at the time. It was kind of good, though, because after not eating or drinking for over two days, I was pretty dehydrated, and IVs are faster and better than drinking water. Though mostly it sucked because I had to call a ride and now I owe the hospital well over $800 that I canít claim on my insurance because itís through my parentsí still. Lame.
I was released from the hospital around 2 pm on the third day that I was awake, and I was completely unable to fall asleep until around 8 pm. Then began the two days from hell. The comedown took two full days, and all I could really do was get up to force food down and watch TV whenever I wasnít passed out. It sucked balls. I ended up neglecting a number of things that I needed to do, and that wasnít good either.
Though most might say that my experience would convince them to never try it again, I am not so convinced. For one, there was something weird about the batch that I bought my quarter from. And second, just because doing it until your mind decides to freak out may be a bad idea, I still think that I would like to try it again over smaller periods of time, like maybe over two days instead of three.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


| Sr | Fieldname | Type | Label | Options |
|---|---|---|---|---|
| 1 | get_items_from |
Select | Get Items From |
Sales Order Material Request |
| 2 | filters |
Section Break | Filters | |
| 3 | fg_item |
Link | Item | Item |
| 4 | customer |
Link | Customer | Customer |
| 5 | warehouse |
Link | Warehouse | Warehouse |
| 6 | company |
Link | Company | Company |
| 7 | column_break1 |
Column Break | ||
| 8 | from_date |
Date | From Date | |
| 9 | to_date |
Date | To Date | |
| 10 | project |
Link | Project | Project |
| 11 | section_break1 |
Section Break | ||
| 12 | get_sales_orders |
Button |
Get Sales Orders
Pull sales orders (pending to deliver) based on the above criteria |
|
| 13 | sales_orders |
Table | Sales Orders | Production Plan Sales Order |
| 14 | section_break_16 |
Section Break | ||
| 15 | get_material_request |
Button |
Get Material Request
Pull Material Request of type Manufacture based on the above criteria |
|
| 16 | material_requests |
Table | Material Requests | Production Plan Material Request |
| 17 | items_for_production |
Section Break |
Select Items
Enter items and planned qty for which you want to raise production orders or download raw materials for analysis. |
|
| 18 | get_items |
Button | Get Items | |
| 19 | items |
Table | Items | Production Plan Item |
| 20 | create_production_orders |
Section Break | Production Orders | |
| 21 | create_production_order |
Button |
Create Production Orders
Separate production order will be created for each finished good item. |
|
| 22 | sb5 |
Section Break | Material Requirement | |
| 23 | purchase_request_for_warehouse |
Link | Material Request For Warehouse | Warehouse |
| 24 | create_material_requests_for_all_required_qty |
Check | Create for full quantity, ignoring quantity already on order | |
| 25 | create_material_requests |
Button | Create Material Requests | |
| 26 | download_materials_required |
Button |
Download Materials Required
Download a report containing all raw materials with their latest inventory status |
|
| 27 | column_break_2 |
Column Break | ||
| 28 | create_material_requests_non_stock_request |
Check |
Include non-stock items
If checked, Will include non-stock items in the Material Requests. |
|
| 29 | use_multi_level_bom |
Check |
Use Multi-Level BOM
If checked, all the children of each production item will be included in the Material Requests. |
|
| 30 | only_raw_materials |
Check |
Only Obtain Raw Materials
If checked, only Purchase material requests for final raw materials will be included in the Material Requests. Otherwise, Material Requests for parent items will be created |
|
| 31 | include_subcontracted |
Check |
Include sub-contracted raw materials
If checked, raw materials for items that are sub-contracted will be included in the Material Requests |
Inherits from frappe.model.document.Document
__init__ (self, arg1, arg2=None)
No docs
No docs
add_mr_in_table (self, pending_mr)
Add Material Requests in the table
add_so_in_table (self, open_so)
Add sales orders in the table
clear_table (self, table_name)
No docs
create_material_request (self)
No docs
create_production_order (self, item_dict)
Create production order. Called from Production Planning Tool
Create csv data for required raw material to produce finished goods
No docs
get_item_projected_qty (self, item)
No docs
No docs
No docs
Pull sales orders which are pending to deliver based on criteria selected
get_pending_material_requests (self)
Pull Material Requests that are pending based on criteria selected
No docs
No docs
get_raw_materials (self, bom_dict, non_stock_item=0)
Get raw materials considering sub-assembly items { "itemcode": [qtyrequired, description, stockuom, minorder_qty] }
No docs
No docs
get_so_wise_planned_qty (self)
bom_dict {
bom_no: ['sales_order', 'qty']
}
get_subitems (self, bom_wise_item_details, bom, parent_qty, include_sublevel, only_raw, supply_subs, non_stock_item=0)
No docs
make_items_dict (self, item_list)
No docs
raise_material_requests (self)
Raise Material Request if projected qty is less than qty required
Requested qty should be shortage qty considering minimum order qty
raise_production_orders (self)
It will raise production order (Draft) for all distinct FG items
No docs
No docs


Example Menu
First item
Sub First item
Sub Second item
Sub Third item
Sub Fourth item
Sub Fifth item
Sub Sixth item
Second item
Third item
Fourth item
Fifth item
Sixth item


Blood and Milk
"Women who are prickly, sharpwitted, and highstrung, who tend to quip about their emotional pain and to act selfdestructively, are the protagonists of the 11 fine short stories in Solwitz's first collection. . . . Most achieve insights the hard wayafter independent, even rash actions that sometimes bring pain."
Publishers Weekly
"Solwitz's debut collection is trenchant and unnerving. She specializes in dislocation, dysfunction, and distress as she writes about the imperfect emotional fit between men and women, cultural displacement, and madness both personal and societal. . . . Indeed, as Solwitz traces the jagged edges of our collective soul, she reminds us that even home can become a foreign country."
Starred Review in Booklist
"A flair for dark comedy and the ability to turn on a dime are prized qualities for these unpredictable characters; time and again, their intrepid investigations lead them into uncharted territory where bizarre dramatic action seems to be the only possible move. Solwitz's finetoothed examinations of complex emotional states are dead on. . . . "
The New York Times Book Review
"Sharon Solwitz has been winning short story prizes for yearsit's about time we get to see all her characters gathered between covers. They are a lively and challenging crowd, full of intelligence, charm, and tendencies toward selfdestruction and selfrealization that make them stunningly recognizable. Their complex lives pose emotional and moral quandaries hard to dismiss and hard to put behind us at stories' end. Solwitz has wit, passion, and the remarkable ventriloquial skill to throw interior voices we might never otherwise hear."
Rosellen Brown, author of Before and After
Bloody Mary
After her debut with the widely praised stories in Blood and Milk, Sharon Solwitz offers up her first, darkly radiant, fulllength novel. Bloody Mary, which takes its title from the childhood game, tells the story of socially adept, 12yearold Hadley and her protective mother. They live a privileged life in the Chicago neighborhood of Lakeview, but soon find themselves in a state of chaos and flux.
Starred review in Booklist


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Events (\(\omega\)) are possible outcomes of a random experiment.
An event space (\(\Omega\)) is the set of all possible outcomes.
\(\Omega=\{\omega_0, \omega_1, \dots, \omega_n\}\)
Random variables are functions from events to real numbers: \[X: \Omega \rightarrow \mathbb{R}\]
Probability measure is in reference to a subset of event outcomes occurring.
A function from subsets to \([0,1]\)
Notation:
\(P(X)\) often means \(P(X=x)\)
\(P(A)\) can mean \(P(\omega \in A)\), where \(A\subseteq\Omega\)
Axioms
\(P(\Omega)=1\)
\(P(\emptyset)=0\)
If \(A\) disjoint from B, \(P(A \cup B)=P(A)+P(B)\)
Discrete: probability mass function (pmf) gives \(P(\{\omega_i\})\) for each outcome.
Continuous:
Cumulative distribution function (cdf), denoted \(F(t)\), gives \(P(X \leq t)\) for \(t\in\mathbb{R}\), \(F(-\infty)=0, F(\infty)=1\).
If there exists \(f\), such that \(\int_{-\infty}^{t}f(x)dx=P(X\leq t)\), then \(f\) is called the probability density function (pdf).
Cumulative distribution function applies to both discrete and continuous event spaces.
Median: \(t:F(t)=0.5, P(X\leq t)=P(X>t)\)
Mode: point where pmf or pdf is maximum.
Mean (expected value): weighted average of \(X(\omega)\), where the weights are given by the probability measure.
pmf: \(E[X]=\sum_i{x_iP(X=x_i)}\)
pdf: \(E[g(X)]=\int {g(x)f(x)dx}\)
Variance: \(E[(X-E[X])^2]\), how far do values tend to be from the mean, measure of dispersion.
Multidimensional event space, consider an event to be an outcome for all of the variables jointly. \[P_{XY}(X,Y)\]
pmf: \(P_X(X)=\sum_y{P_{XY}(X,Y=y)}\)
pdf: \(f_X=\int_y{f_{XY}(x,y)dy}\)
\(X,Y\) are independent iff \(\forall x,y, P_{XY}(X,Y)=P_X(X)P_Y(Y)\)
Mean: if \(Z=(X,Y)\), then \(\mathbb{E}[Z]=(\mathbb{E}[X],\mathbb{E}[Y])\) and \(\mathbb{E}[g(Z)]=\int_x\int_y g((x,y))f_{XY}(x,y)dxdy\)
Covariance: \(\mathbb{E}[(Z-\mathbb{E}[Z])(Z-\mathbb{E}[Z])^T]\)
\[P(X|Y)=\frac{P(X,Y)}{P(Y)}=\frac{P(Y|X)P(X)}{P(Y)}=\frac{P(Y|X)P(X)}{\sum_x P(Y|X)P(X)}\]
\[P(\Theta|D)=\frac{P(D|\Theta)P(\Theta)}{P(D)}\]
Prior: \(P(\Theta)\), the probability of parameters \(\theta\).
Likelihood: \(P(D|\Theta)\), the conditional probability of observing a feature value of \(d\) given that the parameters \(\theta\).
Posterior: \(P(\Theta|D)\), the conditional probability of correct parameters being \(\theta\), given that feature value \(d\) has been observed.
Bernoulli distribution (binary distribution)
\(Bern(x|\mu)=\mu^x(1-\mu)^{1-x}\), where \(x\in\{0,1\}, 0\leq\mu\leq1\)
\(\mathbb{E}[x]=\mu\)
\(var[x]=\mu(1-\mu)\)
Beta distribution
\(Beta(\mu|a,b)=\frac{\Gamma(a+b)}{\Gamma(a)\Gamma(b)}\mu^{a-1}(1-\mu)^{b-1}\), where \(\Gamma(x)=\int_{0}^{\infty}u^{x-1}e^{-u}du\)
\(\mathbb{E}[x]=\frac{a}{a+b}\)
\(var[x]=\frac{ab}{(a+b)^2(a+b+1)}\)
Multinomial distribution
\(p(\vec{x}|\vec{\mu})=\prod_{k=1}^{K}\mu_k^{x_k}\), where \(\vec{x}\) is encoded with \(\{0,1\}\), and \(\sum_kx_k=1\), \(\sum_k\mu_k=1\)
\(\mathbb{E}[\vec{x}|\vec{\mu}]=\vec{\mu}\)
Dirichlet distribution
\(Dir(\vec{\mu}|\vec{\alpha})=\frac{\Gamma(\alpha_0)}{\Gamma(\alpha_1)\dots\Gamma(\alpha_K)}\prod_{k=1}^{K}\mu_k^{\alpha_k-1}\), where \(\alpha_0=\sum_{k=1}^{K}\alpha_k\)
Gaussian distribution
\(\mathcal{N}(\vec{x}|\vec{\mu},\Sigma)=\frac{1}{(2\pi)^{D/2}|\Sigma|^{1/2}}\exp\{ -\frac{1}{2} (\vec{x}-\vec{\mu})^T \Sigma^{-1} (\vec{x}-\vec{\mu}) \}\)


¶ Basic exports |
|
|
Re-export interfaces so clients can import from this single module. |
import U from './Upw/Obj';
import T from './Upw/Txt';
import E from './Upw/Elt';
import F from './Upw/Tem';
import C from './Upw/Fun';
import UpCount from './Upw/Cnt';
import UpStyle from './Upw/Css';
import './Upw/Arr';
export {U, T, E, F, C, UpCount, UpStyle};
export {P, H1, H2, H3, H4, H5, H6, B, I, LI, LABEL, A, BUTTON} from './Upw/Tag';
|


class: center, middle ### W4995 Applied Machine Learning # Boosting, Stacking, Calibration 02/21/18 Andreas C. Müller ??? We'll continue tree-based models, talking about boosting. Another ensemble technique called stacking. Finally, a technique called calibration that looks somewhat similar to ensembles but has the goal of obtaining good probability estimates from any classifier. FIXME: XGBOOST!! FIXME early stopping FIXME GBRT: better explain log-loss FIXME: add example of calibrated and inaccurate vs accurate but not calibrated! FIXME: calibration: what are the estimators fit to, and why does fitting it to 0 and 1 create probabilities in between Partial dependence plot. how is that different from feature importances. calibration curve plotting: bins are different for hist and calibration curve? slide 27: maybe put probabilities on x axis? calibration curve: blue histogram is number of total points, y axis not labeled --- class: centre,middle # Gradient Boosting ??? Gradient boosting is one of the most successfull supervised machine learning methods in practice. It's often used in kaggle to win competition, it's used for credit scoring, it's one of the standard tools of the trade. It's one of the best of-the-shelf models A standard implementation that people use is XGBoost, but there's also an implementation in scikit-learn, and we'll talk about both of them. Last time we talked about Random forests, which builds many trees independently, each randomized in a different way, and then averages their predictions. Gradient boosting on the other hand builds trees one by one in a sequential manner, with each tree requiring the results of previous trees. Often, Gradient boosting is done with very small trees, or even decision stumps, which is trees of depth one, so a single split. --- class: center, middle # Boosting (in General) ??? - “Meta-algorithm” to create strong learners from weak learners. - AdaBoost, GentleBoost, … - Trees or stumps work best - Gradient Boosting often the best of the bunch - Many specialized algorithms (ranking etc) This is an instance of a more general family of models, called boosting models, which all iteratively try to improve a model built up from weak learners. Gradient boosting is this particular technique where we are trying to fit the residuals, and it's been found to work very well in practice, in particular if you're using shallow trees as the weak learners. In principle, you could use any model as a weak learner, but trees just work really well. --- # Gradient Boosting Algorithm `$$ f_{1}(x) \approx y $$` `$$ f_{2}(x) \approx y - f_{1}(x) $$` `$$ f_{3}(x) \approx y - f_{1}(x) - f_{2}(x)$$` -- $y \approx$ ![:scale 22.5%](images/grad_boost_term_1.png) + ![:scale 22.5%](images/grad_boost_term_2.png) + ![:scale 20%](images/grad_boost_term_3.png) + ... ??? Let's look at the regression case first. We start by building a single tree f1 to try to predict the output y. But we strongly restrict f1, so it will be rather bad at predicting y. Next, we'll look at the residual of this first model, so y - f1(x). We now train a new model f2 to try and predict this residual, in other words to correct the mistakes made by f1. Again, this will be a very simple model, so it will still not be able to fix all errors. Then, we look at the residual of both of the models together, so y - f1(x) - f2(x), so the mistakes that could not be fixed by f2, and we build f3 to fix that, and so on. This is natural for regression. For classification this is not as clear. For binary classification you use log-loss, or rather you apply the logistic function to get a binary prediction, for multi-class you can use 1 vs rest. So we're sequentially building up a model using what's called "weak learners", small trees, and create a more powerful composite model. --- # Gradient Boosting Algorithm `$$ f_{1}(x) \approx y $$` `$$ f_{2}(x) \approx y - \alpha f_{1}(x) $$` `$$ f_{3}(x) \approx y - \alpha f_{1}(x) - \alpha f_{2}(x)$$` $y \approx \alpha$ ![:scale 22.5%](images/grad_boost_term_1.png) + $\alpha$ ![:scale 22.5%](images/grad_boost_term_2.png) + $\alpha$ ![:scale 20%](images/grad_boost_term_3.png) + ...
Learning rate $\alpha, i.e. 0.1$ ??? - Iteratively add regression trees to model - Use log loss for classification - Discount update by learning rate FIXME plot for regression models Come back to this --- #GradientBoostingRegressor .center[ ![:scale 50%](images/grad_boost_regression_steps.png) ] ??? --- #GradientBoostingClassifier .center[ ![:scale 70%](images/grad_boost_depth2.png) ] ??? --- class:spacious # Gradient Boosting Advantages - Slower to train than RF (serial), but much faster to predict - Small model size - Typically more accurate than Random Forests ??? --- class:spacious # Tuning Gradient Boosting - Pick n_estimators, tune learning rate - Can also tune max_features - Typically strong pruning via max_depth ??? --- # Partial Dependence Plots - Marginal dependence of prediction on one or two features .smaller[ ```python from sklearn.ensemble.partial_dependence import plot_partial_dependence boston = load_boston() X_train, X_test, y_train, y_test = train_test_split( boston.data, boston.target,random_state=0) gbrt = GradientBoostingRegressor().fit(X_train, y_train) fig, axs = plot_partial_dependence( gbrt, X_train, np.argsort(gbrt.feature_importances_)[-6:], feature_names=boston.feature_names, n_jobs=3, grid_resolution=50) ``` ] ??? --- # Partial Dependence Plots .center[ ![:scale 70%](images/feat_impo_part_dep.png) ] ??? --- # Bivariate Partial Dependence Plots .smaller[ ```python plot_partial_dependence( gbrt, X_train, [np.argsort(gbrt.feature_importances_)[-2:]], feature_names=boston.feature_names, n_jobs=3, grid_resolution=50) ``` ] .center[ ![:scale 80%](images/feature_importance.png) ] ??? --- # Partial Dependence for Classification .tiny-code[ ```python from sklearn.ensemble.partial_dependence import plot_partial_dependence for i in range(3): fig, axs = plot_partial_dependence(gbrt, X_train, range(4), n_cols=4, feature_names=iris.feature_names, grid_resolution=50, label=i) ``` ] .center[ ![:scale 50%](images/feat_impo_part_dep_class.png) ] ??? --- # XGBoost `conda install -c conda-forge xgboost` ```python from xgboost import XGBClassifier xgb = XGBClassifier() xgb.fit(X_train, y_train) xgb.score(X_test, y_test)) ``` - supports missing values - supports multi-core ??? - Efficient implementation of gradient boosting (5x sklearn) - Improvements on original algorithm - https://arxiv.org/abs/1603.02754 - Adds l1 and l2 penalty on leaf-weights - Fast approximate split finding - Scikit-learn compatible interface --- class: spacious .center[ ![:scale 50%](images/xgboost_sklearn_bench.png) ] - Exact splits ~5x faster on single core - Appoximate splits, multi-core -> more speed! - Also check lightGBM (even faster?) - Both have GPU support - https://github.com/szilard/GBM-perf ??? --- # Early stopping - Adding trees can lead to overfitting - Stop adding trees when validation accuracy stops increasing - Optional in XGBoost and sklearn >= 0.20 (development) ??? --- class:spacious # When to use tree-based models - Model non-linear relationships - Doesn’t care about scaling, no need for feature engineering - Single tree: very interpretable (if small) - Random forests very robust, good benchmark - Gradient boosting often best performance with careful tuning ??? --- class: centre,middle # More ensembles: Stacking ??? --- # Simplified Stacking .tiny-code[ ```python from sklearn.neighbors import KNeighborsClassifier X, y = make_moons(noise=.4, random_state=16, n_samples=300) X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, random_state=0) voting = VotingClassifier( [('logreg', LogisticRegression(C=100)), ('tree', DecisionTreeClassifier(max_depth=3)), ('knn', KNeighborsClassifier(n_neighbors=3))], voting='soft') voting.fit(X_train, y_train) lr, tree, knn = voting.estimators_ ``` ] .center[ ![:scale 90%](images/average_voting.png) ] ??? --- # Simplified Stacking .tiny-code[ ```python stacking = make_pipeline(voting, LogisticRegression(C=100)) stacking.fit(X_train, y_train) print(stacking.score(X_train, y_train)) print(stacking.score(X_test, y_test)) ``` ``` 0.90 0.77 ``` ```python stacking.named_steps.logisticregression.coef_ ``` ``` array([[ 0.487, 1.519, 3.686]]) ``` ] .left-column[
![:scale 90%](images/average_voting.png) ] .right-column[ ![:scale 90%](images/simple_stacking_result.png) ] ??? --- class:centre,middle ## Problem : Overfitting! ??? --- class:spacious # Stacking - Use cross-validation (even LOO!) to produce probability estimates on training set. - Train second step estimator on held-out estimates - No overfitting of second step! - For testing: as usual ??? --- # Hold-out estimates of Probabilities ![:scale 100%](images/holdout_estimate.png) - Split 1 produces probabilities for Fold 1, split2 for Fold 2 etc. - Get a probability estimate for each data point! - Unbiased estimates (like on the test set) for the whole training set! - Without it: The best estimator is the one that memorized the training set. ??? --- .tiny-code[ ```python from sklearn.model_selection import cross_val_predict # take only probabilities of positive classes for more interpretable coefficients first_stage = make_pipeline(voting, FunctionTransformer(lambda X: X[:, 1::2])) transform_cv = cross_val_predict(first_stage, X_train, y_train, cv=10, method="transform") second_stage = LogisticRegression(C=100).fit(transform_cv, y_train) print(second_stage.coef_) ``` ``` [[ 2.192 1.402 1.012]] ``` ```python print(second_stage.score(transform_cv, y_train)) print(second_stage.score(first_stage.transform(X_test), y_test)) ``` ``` 0.813 0.800 ``` ] .center[ ![:scale 40%](images/simple_stacking_result.png) ![:scale 40%](images/stacking_result.png) ] ??? --- class:spacious #Calibration .center[
Source
] - Probabilities can be much more informative than labels: - “The model predicted you don’t have cancer” vs “The model predicted you’re 40% likely to have cancer” ??? --- class: spacious #Calibration curve (Reliability diagram) .left-column[ ![:scale 80%](images/prob_table.png) ] .right-column[ ![:scale 80%](images/calib_curve.png) ] ??? - For binary classification only - you can be calibrated and inaccurate! - Given a predicted ranking or probability from a supervised classifier, bin predictions. - Plot fraction of data that’s positive in each bin. - Doesn’t require ground truth probabilities! --- class: split-40 # calibration_curve with sklearn Using subsample of covertype dataset .left-column[ .tiny-code[ ```python from sklearn.linear_model import LogisticRegressionCV print(X_train.shape) print(np.bincount(y_train)) lr = LogisticRegressionCV().fit(X_train, y_train) (52292, 54) [19036 33256]``` ```python lr.C_ array([ 2.783]) ``` ```python print(lr.predict_proba(X_test)[:10]) print(y_test[:10]) [[ 0.681 0.319] [ 0.049 0.951] [ 0.706 0.294] [ 0.537 0.463] [ 0.819 0.181] [ 0. 1. ] [ 0.794 0.206] [ 0.676 0.324] [ 0.727 0.273] [ 0.597 0.403]] [0 1 0 1 1 1 0 0 0 1] ``` ] ] .right-column[ .tiny-code[ ```python from sklearn.calibration import calibration_curve probs = lr.predict_proba(X_test)[:, 1] prob_true, prob_pred = calibration_curve(y_test, probs, n_bins=5) print(prob_true) print(prob_pred) [ 0.2 0.303 0.458 0.709 0.934] [ 0.138 0.306 0.498 0.701 0.926] ``` ] .center[![:scale 70%](images/predprob_positive.png) ] ] ??? --- class:spacious #Influence of number of bins .center[![:scale 100%](images/influence_bins.png)] ??? - Works here because dataset is big - Might become very noisy for larger datasets --- class:spacious # Comparing Models .center[![:scale 90%](images/calib_curve_models.png)] ??? --- # Brier Score (for binary classification) - “mean squared error of probability estimate” `$$ BS = \frac{\sum_{i=1}^{n} (\widehat{p} (y_i)-y_i)^{2}}{n}$$` .center[![:scale 70%](images/models_bscore.png)] ??? --- # Fixing it: Calibrating a classifier - Build another model, mapping classifier probabilities to better probabilities! - 1d model! (or more for multi-class) `$$ f_{callib}(s(x)) \approx p(y)$$` - s(x) is score given by model, usually - Can also work with models that don’t even provide probabilities! Need model for $f_{callib}$, need to decide what data to train it on. - Can train on training set → Overfit - Can train using cross-validation → use data, slower ??? --- # Platt Scaling - Use a logistic sigmoid for $f_{callib}$ - Basically learning a 1d logistic regression - (+ some tricks) - Works well for SVMs `$$f_{platt} = \frac{1}{1 + exp(-ws(x))}$$` ??? --- # Isotonic Regression - Very flexible way to specify $f_{callib}$ - Learns arbitrary monotonically increasing step-functions in 1d. - Groups data into constant parts, steps in between. - Optimum monotone function on training data (wrt mse). .center[![:scale 40%](images/isotonic_regression.png)] ??? --- class:spacious # Building the model - Using the training set is bad - Either use hold-out set or cross-validation - Cross-validation can be use as in stacking to make unbiased probability predictions, use that as training set. ??? --- # CalibratedClassifierCV .tiny-code[ ```python from sklearn.calibration import CalibratedClassifierCV X_train_sub, X_val, y_train_sub, y_val = train_test_split(X_train, y_train, stratify=y_train, random_state=0) rf = RandomForestClassifier(n_estimators=100).fit(X_train_sub, y_train_sub) scores = rf.predict_proba(X_test)[:, 1] plot_calibration_curve(y_test, scores, n_bins=20) ``` .center[![:scale 30%](images/random_forest.png)] ] ??? --- # Calibration on Random Forest .smaller[ ```python cal_rf = CalibratedClassifierCV(rf, cv="prefit", method='sigmoid') cal_rf.fit(X_val, y_val) scores_sigm = cal_rf.predict_proba(X_test)[:, 1] cal_rf_iso = CalibratedClassifierCV(rf, cv="prefit", method='isotonic') cal_rf_iso.fit(X_val, y_val) scores_iso = cal_rf_iso.predict_proba(X_test)[:, 1] ```] .center[![:scale 90%](images/types_callib.png)] ??? --- # Cross-validated Calibration ```python cal_rf_iso_cv = CalibratedClassifierCV(rf, method='isotonic') cal_rf_iso_cv.fit(X_train, y_train) scores_iso_cv = cal_rf_iso_cv.predict_proba(X_test)[:, 1] ``` .center[![:scale 90%](images/types_callib_cv.png)] ??? - kinda cheating, we have more trees now lol - we use all the data, get good probabilities. just time-consuming --- # Multi-Class Calibration .center[![:scale 100%](images/multi_class_calibration.png)] ??? - per-class calibratoin - renormalization --- class: center, middle # Questions ?


Use a modular scale to help you decide on the font-size of your elements. Modular scale refers to a series of harmonious numbers that relate to one another in a meaningful way.
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November 9, 2020 - Okhtyrka Association of Veterans of of the Russian-Ukrainian war “Unstoppable”. Gifts from the Lozowy Fund: Flags and chevrons for members.
May 18, 2020 - Lebedyn City Joint Military Commissariat. Gift from the Lozowy Fund: New boiler.
October 31, 2019 - Velyka Pysarivka Organization of Veterans of the Anti-terrorist Operation. Gifts from the Lozowy Fund: First aid kits.
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abstract val processorInfo: Flow<ProcessorInfo>
A flow of processor details, updated every time blocks are processed to include the latest block height, blocks downloaded and blocks scanned. Similar to the progress flow but with a lot more detail.