Dewen Zeng
I am a 4th year PhD student in the Computer Science and Engineering Department of University of Notre Dame. My advisor is Dr. Yiyu Shi. Here is our lab website SCL. Currently, my research focuses on Deep Learning for Medical Image Applications, and Self-supervised Learning. Before coming to University of Notre Dame, I received my master’s and bachelor’s degrees at Huazhong University of Science & Technology.
GitHub /
Google Scholar /
CV /
LinkedIn
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Experience
Student Researcher, Google, May 2022 - Mar. 2023. (hosted by Tao Wang, Nan Du)
Intern, Allen Institute for Cell Science, May 2021 - Aug. 2021. (hosted by Jianxu Chen)
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Synthetic Data Can Also Teach: Synthesizing Effective Data for Unsupervised Visual Representation Learning
Yawen Wu, Zhepeng Wang, Dewen Zeng, Yiyu Shi, Jingtong Hu
AAAI, 2023
arxiv
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FairPrune: Achieving Fairness Through Pruning for Dermatological Disease Diagnosis
Yawen Wu*, Dewen Zeng* (equal contribution), Xiaowei Xu, Yiyu Shi and Jingtong Hu.
MICCAI, 2022
arxiv
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Decentralized Unsupervised Learning of Visual Representations
Yawen Wu, Zhepeng Wang, Dewen Zeng, Meng Li, Yiyu Shi and Jingtong Hu.
IJCAI, 2022
arxiv
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Positional Contrastive Learning for Volumetric Medical Image Segmentation
Dewen Zeng, Yawen Wu, Xinrong Hu, Xiaowei Xu, Haiyun Yuan, Meiping Huang, Jian Zhuang, Jingtong Hu, and Yiyu Shi
MICCAI, 2021
arxiv
/ code
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Segmentation with Multiple Acceptable Annotations: A Case Study of Myocardial Segmentation in Contrast Echocardiography
Dewen Zeng, Mingqi Li, Yukun Ding, Xiaowei Xu, Qiu Xie, Ruixue Xu, Hongwen Fei, Meiping Huang, Jian Zhuang, and Yiyu Shi.
IPMI, 2021
arxiv
/ code
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Contrastive Learning with Temporal Correlated Medical Images: A Case Study using Lung Segmentation in Chest X-Rays
Dewen Zeng, John N Kheir, Peng Zeng and Yiyu Shi.
ICCAD (invited), 2021
arxiv
/ code
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Semi-supervised Contrastive Learning for Label-efficient Medical Image Segmentation
Xinrong Hu, Dewen Zeng, Xiaowei Xu and Yiyu Shi,
MICCAI, 2021
arxiv
/ code
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Towards Efficient Human-Machine Collaboration: Real-Time Correction Effort Prediction for Ultrasound Data Acquisition
Yukun Ding, Dewen Zeng, Mingqi Li, Hongwen Fei, Haiyun Yuan, Meiping Huang, Jian Zhuang and Yiyu Shi
MICCAI, 2021
arxiv
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Federated Contrastive Learning for Volumetric Medical Image Segmentation
Yawen Wu, Dewen Zeng, Zhepeng Wang, Yiyu Shi and Jingtong Hu
MICCAI, 2021
arxiv
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Hardware-aware Real-time Myocardial Segmentation Quality Control in Contrast Echocardiography
Dewen Zeng, Yukun Ding, Xiaowei Xu, Haiyun Yuan, Hongwen Fei, Meiping Huang, Jian Zhuang, Jingtong Hu and Yiyu Shi.
DAC (invited), 2021
arxiv
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Federated Contrastive Learning for Dermatological Disease Diagnosis via On-device Learning
Yawen Wu, Dewen Zeng, Zhepang Wang, Yi Sheng, Lei Yang, and Alaina J James, Yiyu Shi, Jingtong Hu
ICCAD (invited), 2021
arxiv
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Enabling On-Device Self-supervised Contrastive Learning with Selective Data Contrast
Yawen Wu, Zhepeng Wang, Dewen Zeng, Yiyu Shi and Jingtong Hu
DAC, 2021
arxiv
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A deep learning approach with temporal consistency for automatic myocardial segmentation of quantitative myocardial contrast echocardiography
Mingqi Li, Dewen Zeng, Qiu Xie, Ruixue Xu, Yu Wang, Dunliang Ma, Yiyu Shi, Xiaowei Xu, Meiping Huang, Hongwen Fei
The International Journal of Cardiovascular Imaging, 2021
arxiv
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Towards Cardiac Intervention Assistance: Hardware-Aware Neural Architecture Exploration for Real-Time 3D Cardiac Cine MRI Segmentation
Dewen Zeng, Weiwen Jiang, Tianchen Wang, Xiaowei Xu, Haiyun Yuan, Meiping Huang, Jian Zhuang, Jingtong Hu and Yiyu Shi.
ICCAD (invited), 2020
arxiv
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MDA: A Reconfigurable Memristor-based Distance Accelerator for Time Series Mining on Data Centers
Xiaowei Xu, Yu Hu, Dewen Zeng, Wenyao Xu, Feng Lin, Xinwei Yao and Yiyu Shi
TCAD, 2019
arxiv
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An Efficient Memristor-Based Distance Accelerator for Time Series Data Mining on Data Centers
Xiaowei Xu, Dewen Zeng, Wenyao Xu, Yiyu Shi, Yu Hu
DAC, 2017
arxiv
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Awards
Outstanding RA nominatation, 2023.
Top Winning Award, IEEE SERVICES Hackathon, 2020.
Outstanding undergraduate, Qiming College, Huazhong University of Science and Technology, 2016.
Second prize, National Undergraduate Electronics Design Contest (NUEDC), 2015.
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This is an example of data-theme="b"
applied to the same element as data-role="page"
, showing how the theme is inherited by widgets throughout the page.
I'm the collapsible content in a set so this feels like an accordion. I'm visible by default because I have the data-collapsed="false"
attribute; to collapse me, either click my header or expand another header in my set.
I'm the collapsible content in a set so this feels like an accordion. I'm hidden by default because I have the "collapsed" state; you need to expand the header to see me.
I'm the collapsible content in a set so this feels like an accordion. I'm hidden by default because I have the "collapsed" state; you need to expand the header to see me.


Valentine cake ideas Creative valentines cake decorating ideas ...
Valentine cake ideas free valentine's day cake decorating ideas. . a romantic cake. Here are some interesting ideas and pictures to inspire you. . Want us to create a celebration masterpiece for you? Birthday Click here to find out more.
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Treatment for Herniated or Bulging Discs
Valentine Day Cakes - Monginis
Valentine Day Cakes, Valentines Day Gifts to India - Monginis Online Cake Shop - Monginis . Saint Valentine's Day (popularly known as Valentine's Day) is held on February 14 every year . Chocolate Birthday Cakes . Picture Cakes .
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Tiffanys Bakery offers beautiful , expertly-designed adult\childrens birthday cakes , tier wedding strawberry shortcake, valentine and gourmet cupcakes with .
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Matches 1 - 60 of 137743 . cake Search and download from millions of HD stock photos, royalty free images, cliparts, vectors and illustrations. . Cake : Birthday cake with football player doll in the dark . Cake : Valentine cake Stock Photo .
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Valentine/Birthday cake a | Flickr - Photo Sharing!
Feb 13, 2010 . This photo belongs to. Grizzly D's photostream (199) · Because; pageB&W; Valentine/Birthday cake a; ? Oldest photo .
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Official Impairment Listing for Spinal Disorders
Coolest Homemade Valentine Cakes and How-To Tips
Take a look at the coolest Valentine cakes. You'll also find the most amazing photo gallery of homemade homemade holiday and birthday cakes, how-to tips and .
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Create and share your own photos on Cake Central! Upload Photos . Birthday Cake Photos. 285,333 . 2010 Valentine's Day Cake Contest. 720 photos .
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Create and share your own photos on Cake Central! Upload Photos . Add Photos · Birthday Cake Photos . 2010 Valentine's Day Cake Contest. 720 photos .
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Each design comes with cake pictures and decorating tips. . are categorized according to the occasion for which the cakes are made i.e., birthday cakes, wedding cakes, holiday cakes and graduation cakes. . More Valentine's Day cakes.
http://www.creative-cake-decorating.com/cakes-gallery.html
Cake Decorating Ideas {Valentines Day Edition} | i am baker
Jan 12, 2011 . P.S. I posted a couple additional photos over at my flickr page. . Whether for V- Day or a girl's birthday party, these pink cakes would be perfect .
http://iambaker.net/cake-decorating-ideas-valentines-day-edition/
Medical-Vocational Allowance for Herniated Disc
Valentine Heart Cake Review Photos - YouTube
Jan 4, 2010 . http://www.CakeReviews.com The ultimate cake lover's web page. . Valentine Heart Cake Review Photos . Pretty 60th Birthday Cake decorated with pink sugarpaste flowersby The4manxies136,045 views. Loading more .
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Birthday Cakes | Valentine Cakes House Limited
At Valentine Cake House, we believe that every birthday is a milestone that must be celebrated in a special way . You can view birthday cake images here.
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Valentine Cake Pictures Gallery
Oct 26, 2009 . Birthday Cake Pictures ... 12. Christmas Cake Pictures Gallery 13. Retirement Cake Pictures 14. Halloween Cake Pictures 15. Valentine Cake .
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Firebug Releases
The latest stable release is available on the front page.
Firebug 1.2 Betas
Firebug 1.2 focuses on performance improvements and support for Firefox 3. The 1.2 features are complete and we are actively fixing bugs. Please read the 1.2 release notes before installing. Recommended for Firefox 3.
Firebug 1.2X is the same code as Firebug 1.2 but with internal debug tracing available for Firebug extension developers.
Firebug 1.1 Betas
Firebug 1.1.0 has had many beta tests and is the version recommended for Firefox 2
Firebug 1.0.5
Firebug 1.0.5 (aka 1.05) was released in May 2007. This version has been widely used. Before reporting bugs, please try Firebug 1.2
ChromeBug Alphas
ChromeBug is Firebug for Firefox extensions.
- Requires Firebug 1.2.
- Works with Chromelist.
- Works on Firefox 3.0 and later
- To run chromebug:
firefox.exe -chrome chrome://chromebug/content/chromebug.xul -firefox
- Currently unstable, testing install and bugs in FF3, and comments welcome.
Firebug Source
Firebug open source has a BSD-style license. See the readme.txt for build instructions.


A single zip file will be available for download. It will include all result files along the the original data file. All result data files will be CSV format.
The zip file will included the base name of the original file along with the date and time.
The individual files will include the same basename along with a suffix to represent each step of the calculation process.
As an example if the input file was “xxx.csv” the result files will look like those in the table below.
| Output file | Description |
|---|---|
| xxx | Original input file |
| xxx_taxatrans_0fileversions | Provides documentation of the Operational Taxonomic Unit (OTU) and source files that were used |
| xxx_taxatrans_1attributes | Regional attribute table (same as the ‘ORWA_Attributes_20230112’ csv that can be downloaded from the GitHub link on the webpage) |
| xxx_taxatrans_1metadata | Metadata for the regional attribute table (same as the ‘ORWA_Attributes_Metadata_20230110’ that can be downloaded from the GitHub link on the webpage) |
| xxx_taxatrans_1official | Regional taxa translator table (same as the ‘ORWA_TaxaTranslator_20230112’ table that can be downloaded from the GitHub link on the webpage) |
| xxx_taxatrans_2taxamatch | Lists the taxa that occur in the input file, plus: 1) original TaxonID; 2) ‘translated’ TaxonID (OTU that will be used for the calculation); 3) whether the original TaxonID occurs in the regional taxa translator table (TRUE/FALSE); 4) whether the original TaxonID was changed during the ‘translation’ process (YES/NO); 5) total count in the input file (# individuals summed across samples) |
| xxx_taxatrans_3nonmatch | Lists taxa in the input file that don’t occur in the regional taxa translator table (if the file is blank, all are ‘matches’) |
| xxx_taxatrans_MERGED | Final output, with ‘translated’ TaxonID, NonTarget designations, attributes and phylogeny; the file is almost ready to be run through the BCG calculator (just needs Index_Name and Index_Class; see Assign Index Name and Class function) |
Last updated 2023-02-08


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Component Class
Description here
Copyright (c) 2016 ItsAsbreuk - http://itsasbreuk.nl
New BSD License - http://choosealicense.com/licenses/bsd-3-clause/
Item Index
Methods
_saveHTML
(
private
-
String
Returns a save string
Parameters:
-
String
Objecthtml the text that should be removed from any html-entities
Returns:
String
focus
(
chainable
-
[transitionTime]
Sets the focus on the Component.
Parameters:
-
[transitionTime]
Number optionaltransition-time to focus the element into the view
render
()
React render-method --> renderes the Component.
Returns:
ReactComponent


nips nips2007 nips2007-212 nips2007-212-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Geoffrey E. Hinton, Ruslan Salakhutdinov
Abstract: We show how to use unlabeled data and a deep belief net (DBN) to learn a good covariance kernel for a Gaussian process. We ﬁrst learn a deep generative model of the unlabeled data using the fast, greedy algorithm introduced by [7]. If the data is high-dimensional and highly-structured, a Gaussian kernel applied to the top layer of features in the DBN works much better than a similar kernel applied to the raw input. Performance at both regression and classiﬁcation can then be further improved by using backpropagation through the DBN to discriminatively ﬁne-tune the covariance kernel.


Did you know that bakers are really chemists? That iconic white uniform is really a lab coat, because chemical reactions are taking place all day long in their kitchens! Cakes, cookies, brownies and all your other favorite sweet treats are just a chemistry experiment in your own kitchen.
If you've ever spent any time in the kitchen, baking any recipe, you've probably noticed that most recipes include a lot of the same ingredients. Now, since baking is chemisty, there are a lot of reactions that are happening to take that soft squishy dough and turn it into something that holds its shape and has a wonderful fluffy texture. Here are the main players:


Class ChainedTemporaryCredentials
ChainedTemporaryCredentials class.
Functions
| aws:ChainableTemporaryCredentials (opt) | Constructor, inherits from Credentials. |
Functions
- aws:ChainableTemporaryCredentials (opt)
-
Constructor, inherits from Credentials.
Parameters:
- opt options table, additional fields to the Credentials class:
- params
params table for the
assumeRole
function, or array of those tables in case of a chain of roles to assume. - aws AWS instance, required when creating a chain.
- masterCredentials
Credentials instance to use when assuming the
role. Defaults to
sts.config.credentials
oraws.config.credentials
in that order. - sts
the
STS
service instance to use for fetching the credentials. Defaults to a new instance created asaws:STS()
.
- params
params table for the
Usage:
-- creating a chain of assumed roles local aws = AWS() -- provides the masterCredentials local role1 = { ... } -- parameters to assume role1, from the masterCredentials local role2 = { ... } -- parameters to assume role2, from the role1 credentials local role3 = { ... } -- parameters to assume role3, from the role2 credentials local creds = aws:ChainableTemporaryCredentials { params = { role1, role2, role3 }, } -- Get credentials for role3 local success, id, key, token, expiretime = creds:get() if not success then return nil, id end
- opt options table, additional fields to the Credentials class:


T
- the type of popup
public class Popup<T extends com.badlogic.gdx.scenes.scene2d.Actor>
extends java.lang.Object
implements com.badlogic.gdx.scenes.scene2d.EventListener
Popup is an EventListener with a popup Actor that can be shown
and hidden
and is controlled via its Behavior
.
Behavior is an interface for actual show and hide implementations which are triggered by the Reaction
returned by its handle
method.
Usually a Behavior overrides either show
and hide
or handle
so that its purpose is clearly distinct from other Behaviors.
Those distinct Behaviors are then combined using a BehaviorMultiplexer
. You can of course override all methods in your own implementation though.
Have a look at the VisibilityBehavior
and FadeBehavior
for examples of Behaviors that only override show and hide.
The PositionBehavior
just positions the popup in show. The actual position is applied by its Position
which can be combined using PositionMultiplexers
.
new Popup<>(menu, new MenuBehavior(), new PositionBehavior(new AlignPosition(Align.bottomLeft, Align.topLeft)), new VisibilityBehavior())
new Popup<>(contextMenu, new MenuBehavior(Buttons.RIGHT), new PositionBehavior(new AlignPosition(Align.topRight, Align.topLeft)), new VisibilityBehavior())
new Popup<>(tooltip, new TooltipBehavior(), new PositionBehavior(new PointerPosition(), new AlignedOffsetPosition(Align.topLeft)), new VisibilityBehavior())
Some Behaviors (like the Popup.MenuBehavior
and Popup.TooltipBehavior
) can only function properly if they receive events of Actors other than their listener Actor
(for example to hide when other Actors are clicked).
Those Behaviors usually state so in their documentation. Add the Popups using such Behaviors to an EventMultiplexer
high in the hierarchy or directly on the stage.
A Behavior can distinguish between an Event on its listener Actor by checking if it has its Popup added as listener.
Be warned that in return Behaviors not designed for this may not function properly if you forward events from other Actors to them.
| Modifier and Type | Class and Description |
|---|---|
static class |
Popup.AddToStageBehavior
adds the popup to the
Event's Stage in Popup.AddToStageBehavior.show(Event, Popup) if it is on no or another Stage |
static interface |
Popup.Behavior
what to do in the Popup methods
|
static class |
Popup.BehaviorMultiplexer
a Multiplexer for Behaviors
|
static class |
Popup.FadeBehavior
fades in/out in
Popup.FadeBehavior.show(Event, Popup) /Popup.FadeBehavior.hide(Event, Popup) using AlphaActions |
static class |
Popup.MenuBehavior
The behavior of a menu such as a menu bar or context menu.
|
static class |
Popup.PositionBehavior
sets the position of the popup in
Popup.PositionBehavior.show(Event, Popup) |
static class |
Popup.Reaction |
static class |
Popup.TooltipBehavior
The Behavior of a classic tooltip.
|
static class |
Popup.VisibilityBehavior
shows and hides the popup by settings its visibility
|
| Constructor and Description |
|---|
Popup(T popup,
Popup.Behavior... behaviors) |
Popup(T popup,
Popup.Behavior behavior) |
| Modifier and Type | Method and Description |
|---|---|
Popup.Behavior |
getBehavior() |
T |
getPopup() |
boolean |
handle(com.badlogic.gdx.scenes.scene2d.Event event) |
boolean |
hide() |
boolean |
hide(com.badlogic.gdx.scenes.scene2d.Event event)
Makes the
popup invisible . |
boolean |
isAscendantOf(com.badlogic.gdx.scenes.scene2d.Actor child) |
void |
setBehavior(Popup.Behavior behavior) |
void |
setPopup(T popup) |
boolean |
show() |
boolean |
show(com.badlogic.gdx.scenes.scene2d.Event event)
|
public Popup(T popup, Popup.Behavior behavior)
public Popup(T popup, Popup.Behavior... behaviors)
behaviors
- the Behaviors to create a Popup.BehaviorMultiplexer
forPopup(Actor, Behavior)
public boolean show()
show(Event)
public boolean show(com.badlogic.gdx.scenes.scene2d.Event event)
public boolean hide()
hide(Event)
public boolean hide(com.badlogic.gdx.scenes.scene2d.Event event)
popup
invisible
. Override this for custom behavior.public boolean handle(com.badlogic.gdx.scenes.scene2d.Event event)
handle
in interface com.badlogic.gdx.scenes.scene2d.EventListener
Popup.Behavior.handle(Event, Popup)
public boolean isAscendantOf(com.badlogic.gdx.scenes.scene2d.Actor child)
public Popup.Behavior getBehavior()
behavior
public void setBehavior(Popup.Behavior behavior)
behavior
- the behavior
to set


Date received
01/01/2018 | Digital application
Address
9 Hotspur street,
Townford,
NE1 0LD
Date of birth
02/04/1945 (Age 73)
Telephone
0191 268 1234
Warning Text relay service needed
<EMAIL_ADDRESS>Third party contact
Name
Andrew Green
Telephone
07729 100 000
| Date | Agent | Comments |
|---|---|---|
| 08/01/2018 13.30 | Tim | Email received with 4 most recent payslips. |
| 07/01/2018 13.00 | Joanne | Cust rang to ask what evidence to send. Advice of last 4 payslips given. |
| 06/01/2018 09.03 | Tim | Cust sent in P45. Rang to clarify income from work. Sent email to request payslip. |
- Income
- Capital
- Housing costs
- Others living in home
| State Pension amount | £122.30 |
| State Pension frequency | Paid every 4 weeks |
| Private or Workplace pension | NHS |
| Pension amount | £68.20 |
| Pension frequency | Paid every month |
| Pension Credit Savings Credit | Yes |
| Attendance Allowance | Yes |
| Benefit amount | £220.40 |
| Benefit frequency | Paid every 4 weeks |
| Has a job | No |
| State Pension amount | £122.30 |
| State Pension frequency | Paid every 4 weeks |
| State Pension amount | £122.30 |
| State Pension frequency | Paid every 4 weeks |
| Capital over £6,000 | No |
| Type of property | Rented accommodation |
| Getting housing benefit | Yes |
| How much rent is paid by housing benefit | Some |
| Rent amount after housing benefit | £220.20 |
| Rent frequency | Monthly |
| Are meals or energy bills included in your rent? | No |
| Do you pay service charges? | No |
| Do you pay ground rent? | No |
| Do you pay council tax? | Yes |
| Yearly council tax amount | £1626.12 |
| Partner | No |
| Dependant children | No |
| Anyone aged 18 or over | Yes |
| Name | Anthony Green |
| Border or lodger | No |
| Full time student | No |
| Benefits | No |
| Work more than 16 hours | Yes |
| Income | Over £140 a week |
Uploaded documents
History
3 minutes ago
Change: Job income
If you’re not sure about your nationality, try to find out from an official document like a passport or national ID card.
5 minutes ago
Added document
If you’re not sure about your nationality, try to find out from an official document like a passport or national ID card.
01/01/2018


What is activeAnswers?
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Compaq is one of the most proven, trusted platforms for many of the industry's leading enterprise applications - so we have a deeper understanding of those applications than just about anyone. That knowledge is worth sharing, so we put it at your fingertips on activeAnswers.
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The industry leaders in enterprise solutions are participating and supporting activeAnswers. Click here to see the industry leaders' views on activeAnswers.


class: center, middle ### W4995 Applied Machine Learning # Preprocessing and Feature Transformations 02/05/20 Andreas C. Müller ??? Today we’ll talk about preprocessing and feature engineering. What we’re talking about today mostly applies to linear models, and not to tree-based models, but it also applies to neural nets and kernel SVMs. FIXME making column encoding consistent in pandas the way kaggle says FIXME target encoder be more clear on how to transform test set FIXME add column transformer full syntax FIXME add grid-search column-transformer example FIXME add Max number of columns --- class: center, middle Coming up with features is difficult, time-consuming, requires expert knowledge. "Applied machine learning" is basically feature engineering. .quote_author[Andrew Ng] --- class: middle ![:scale 90%](images/house_price_scatter.png) ??? --- class: center, middle #Scaling ??? N/A --- class: center, middle .center[ ![:scale 70%](images/house_price_boxplot.png) ] ??? Let’s start with the different scales. Many model want data that is on the same scale. KNearestNeighbors: If the distance in TAX is between 300 and 400 then the distance difference in CHArS doesn’t matter! Linear models: the different scales mean different penalty. L2 is the same for all! We can also see non-gaussian distributions here btw! --- # Scaling and Distances ![:scale 90%](images/knn_scaling.png) ??? Here is an example of the importance of scaling using a distance-based algorithm, K nearest neighbors. My favorite toy dataset with two classes in two dimensions. The scatter plots look identical, but on the left hand side, the two axes have very different scales. The x axis has much larger values than the y axis. On the right hand side, I used standard scaler and so both features have zero mean and unit variance. So what do you think will happen if I use k nearest neighbors here? Let's see --- # Scaling and Distances ![:scale 90%](images/knn_scaling2.png) ??? As you can see, the difference is quite dramatic. Because the X axis has such a larger magnitude on the left-hand side, only distances along the x axis matter. However, the important feature for this task is the y axis. So the important feature gets entirely ignored because of the different scales. And usually the scales don't have any meaning - it could be a matter of changing meters to kilometers. Linear models: the different scales mean different penalty. L2 is the same for all! --- class: center # Ways to Scale Data
![:scale 80%](images/scaler_comparison_scatter.png) ??? Here's an illustration of four of the most common ways. One of the most common ones that we already saw before is the Standard Scaler. StandardScaler subtracts the mean and divides by standard deviation. Making all the features have a zero mean and a standard deviation of one. One thing about this is that it won't guarantee any minimum or maximum values. The range can be arbitrarily large. MinMaxScaler subtracts minimum and divides by range. Scales between 0 and 1. So all the features will have an exact minimum at zero and exact maximum at one. This mostly makes sense if there are actually minimum and maximum values in your data set. If it's actually Gaussian distribution, the scaling might not make a lot of sense, because if you add one more data point, it's very far away and it will cram all the other data points more together. This will make sense if you have a grayscale value between 0 and 255 or something else that has like clearly defined boundaries. Another alternative is the RobustScaler. It’s the robust version of the StandardScaler. It computes the median and the quartiles. This cannot be skewed by outliers. The StandardScaler uses mean and standard deviation. So if you have a point that’s very far away, it can have unlimited influence on the mean. The RobustScaler uses robust statistics, so it’s not skewed be outliers. The final one is the Normalizer. This projects things either on the L1 or L0, meaning it makes sure to vectors have length one either in L1 norm or L2 norm. If you do this for L2 norm, it means you don't care about the length you project on a circle. More commonly used in L1 norm, it projects onto the diamond. What that does is basically it means you make sure the sum of all the entries is one. That's often used if you have histograms or if you have counts of things. If you want to make sure that you have frequency features instead of count features, you can use the L1 normalizer. --- class: spacious # Sparse Data - Data with many zeros – only store non-zero entries. - Subtracting anything will make the data “dense” (no more zeros) and blow the RAM. - Only scale, don’t center (use MaxAbsScaler) ??? FIXME ugly slide There’s another one that's important for sparse data. It’s called the MaxAbsScaler in scikit-learn. So sparse data is the kind of data that happens actually quite frequently in practice where most of the features are zero most of the time. So if you have tens of thousands of features, but they're always nearly zero. For example, you can think about these being particular user actions and at any time, any user makes only very few actions. But there are many, many possibilities. So if you have data like that, you only want to store the non-zero values, you don't want to store all the zeros. If you would store all the zeros, the data wouldn't even fit into the RAM often. So you have this very large sparse data set and you want to scale it, but if you subtract anything from it, if you want to make it zero mean then all the entries will be non-zero because the chance that the mean is actually zero is small. So then if you subtract something from everything, you're going to move all those zeros away from zero and then you can't store the data in sparse format and your RAM will blow up. Basically, you can't subtract anything from sparse matrix because that won't be sparse anymore. But we can still scale it because any number times zero is still zero. So if we scale it, then it will have the same structure again. The MaxAbsScaler sets the maximum absolute values to one. Basically, it looks at the maximum absolute values in the whole dataset, or for each feature and makes sure that this is one, just by scaling by one divided by the maximum absolute value. --- # Standard Scaler Example ```python from sklearn.linear_model import Ridge # Back to King Country house prices X_train, X_test, y_train, y_test = train_test_split( X, y, random_state=0) scaler = StandardScaler() scaler.fit(X_train) X_train_scaled = scaler.transform(X_train) ridge = Ridge().fit(X_train_scaled, y_train) X_test_scaled = scaler.transform(X_test) ridge.score(X_test_scaled, y_test) ``` ``` 0.684 ``` ??? Here’s how you do the scaling with StandardScaler in scikit-learn. Similar interface to models, but“transform” instead of “predict”. “transform” is always used when you want a new representation of the data. Fit on training set, transform training set, fit ridge on scaled data, transform test data, score scaled test data. The fit computes mean and standard deviation on the training set, transform subtracts the mean and the standard deviation. We fit on the training set and apply transform on both the training and the test set. That means the training set mean gets subtracted from the test set, not the test-set mean. That’s quite important. --- class: center, middle .center[ ![:scale 100%](images/no_separate_scaling.png) ] ??? Here’s an illustration why this is important using the min-max scaler. Left is the original data. Center is what happens when we fit on the training set and then transform the training and test set using this transformer. The data looks exactly the same, but the ticks changed. Now the data has a minimum of zero and a maximum of one on the training set. That’s not true for the test set, though. No particular range is ensured for the test-set. It could even be outside of 0 and 1. But the transformation is consistent with the transformation on the training set, so the data looks the same. On the right you see what happens when you use the test-set minimum and maximum for scaling the test set. That’s what would happen if you’d fit again on the test set. Now the test set also has minimum at 0 and maximum at 1, but the data is totally distorted from what it was before. So don’t do that. --- class: center, middle # Sckit-Learn API Summary ![:scale 90%](images/api-table.png) Efficient shortcuts: ```python est.fit_transform(X) == est.fit(X).transform(X) # mostly est.fit_predict(X) == est.fit(X).predict(X) # mostly ``` ??? Here’s a summary of the scikit-learn methods. All models have a fit method which takes the training data X_train. If the model is supervised, such as our classification and regression models, they also take a y_train parameter. The scalers don’t use a y_train because they don’t use the labels at all – you could say they are unsupervised methods, but arguably they are not really learning methods at all. Models (also known as estimators in scikit-learn) to make a prediction of a target variable, you use the predict method, as in classification and regression. If you want to create a new representation of the data, a new kind of X, then you use the transform method, as we did with scaling. The transform method is also used for preprocessing, feature extraction and feature selection, which we’ll see later. All of these change X into some new form. There’s two important shortcuts. To fit an estimator and immediately transform the training data, you can use fit_transform. That’s often more efficient then using first fit and then transform. The same goes for fit_predict. --- class: smaller, compact ```python from sklearn.model_selection import cross_val_score from sklearn.linear_model import RidgeCV scores = cross_val_score(RidgeCV(), X_train, y_train, cv=10) np.mean(scores), np.std(scores) ``` ``` (0.694, 0.027) ``` ```python scores = cross_val_score(RidgeCV(), X_train_scaled, y_train, cv=10) np.mean(scores), np.std(scores) ``` ``` (0.694, 0.027) ``` ```python from sklearn.neighbors import KNeighborsRegressor scores = cross_val_score(KNeighborsRegressor(), X_train, y_train, cv=10) np.mean(scores), np.std(scores) ``` ``` (0.500, 0.039) ``` ```python from sklearn.neighbors import KNeighborsRegressor scores = cross_val_score(KNeighborsRegressor(), X_train_scaled, y_train, cv=10) np.mean(scores), np.std(scores) ``` ``` (0.786, 0.030) ``` ??? Let’s apply the scaler to the King Country housing data. First I used the StandardScaler to scale the training data. Then I applied ten-fold cross-validation to evaluate the Ridge model on the data with and without scaling. I used RidgeCV which automatically picks alpha for me. With and without scaling we get an R^2 of about .72, so no difference. Often there is a difference for Ridge, but not in this case. If we use KneighborsRegressor instead, we see a big difference. Without scaling R^2 is about .5, and with scaling it’s .75. That makes sense since we saw that for distance calculations basically all features are dominated by the TAX feature. However, there is a bit of a problem with the analysis we did here. Can you see it? --- class: center, middle # A note on preprocessing # (and pipelines) ??? I want to talk a bit more about preprocessing and cross-validation here, and introduce pipelines. --- class: smallest, compact # A common errror ```python print(X.shape) ``` ``` (100, 10000) ``` ```python # select most informative 5% of features from sklearn.feature_selection import SelectPercentile, f_regression select = SelectPercentile(score_func=f_regression, percentile=5) select.fit(X, y) X_selected = select.transform(X) print(X_selected.shape) ``` ``` (100, 500) ``` ```python from sklearn.model_selection import cross_val_score from sklearn.linear_model import Ridge np.mean(cross_val_score(Ridge(), X_selected, y)) ``` ``` 0.90 ``` ```python ridge = Ridge().fit(X_selected, y) X_test_selected = select.transform(X_test) ridge.score(X_test_selected, y_test) ``` ``` -0.18 ``` --- class: smallest #Leaking Information .left-column[ ```python # BAD! select.fit(X, y) # includes the cv test parts! X_sel = select.transform(X) scores = [] for train, test in cv.split(X, y): ridge = Ridge().fit(X_sel[train], y[train]) score = ridge.score(X_sel[test], y[test]) scores.append(score) ``` ] .right-column[ ```python # GOOD! scores = [] for train, test in cv.split(X, y): select.fit(X[train], y[train]) X_sel_train = select.transform(X[train]) ridge = Ridge().fit(X_sel_train, y[train]) X_sel_test = select.transform(X[test]) score = ridge.score(X_sel_test, y[test]) scores.append(score) ``` ] .reset-column[ Need to include preprocessing in cross-validation ! ] ??? What we did was we trained the scaler on the training data, and then applied cross-validation to the scaled data. Tha’s what’s show on the left. The problem is that we use the information of all of the training data for scaling, so in particular the information in the test fold. This is also known as information leakage. If we apply our model to new data, this data will not have been used to do the scaling, so our cross-validation will give us a biased result that might be too optimistic. On the right you can see how we should do it: we should only use the training part of the data to find the mean and standard deviation, even in cross-validation. That means that for each split in the cross-validation, we need to scale the data a bit differently. This basically means the scaling should happen inside the cross-validation loop, not outside. In practice, estimating mean and standard deviation is quite robust and you will not see a big difference between the two methods. But for other preprocessing steps that we’ll see later, this might make a huge difference. So we should get it right from the start. --- class: smaller ```python # Housing data example from sklearn.linear_model import Ridge X, y = df, target scaler = StandardScaler() scaler.fit(X_train) X_train_scaled = scaler.transform(X_train) ridge = Ridge().fit(X_train_scaled, y_train) X_test_scaled = scaler.transform(X_test) ridge.score(X_test_scaled, y_test) ``` ``` 0.684 ``` ```python from sklearn.pipeline import make_pipeline pipe = make_pipeline(StandardScaler(), Ridge()) pipe.fit(X_train, y_train) pipe.score(X_test, y_test) ``` ``` 0.684 ``` ??? Now I want to show you how to do preprocessing and crossvalidation right with scikit-learn. At the top here you see the workflow for scaling the data and then applying ridge again. Fit the scaler on the training set, transform on the training set, fit ridge on the training set, transform the test set, and evaluate the model. Because this is such a common pattern, scikit-learn has a tool to make this easier, the pipeline. The pipeline is an estimator that allows you to chain multiple transformations of the data before you apply a final model. You can build a pipeline using the make_pipeline function. Just provide as parameters all the estimators. All but the last one need to have a transform method. Here we only have two steps: the standard scaler and ridge. make_pipeline returns an estimator that does both steps at once. We can call fit on it to fit first the scaler and then ridge on the scaled data, and when we call score, it transforms the data and then evaluates the model. The code below is exactly equivalent to the code above, only shorter. --- class: left, middle .center[ ![:scale 70%](images/pipeline.png) ] ??? Let’s dive a bit more into the pipeline. Here is an illustration of what happens with three steps, T1, T2 and Classifier. Imagine T1 to be a scaler and T2 to be any other transformation of the data. If we call fit on this pipeline, it will first call fit on the first step with the input X. Then it will transform the input X to X1, and use X1 to fit the second step, T2. Then it will use T2 to transform the data from X1 to X2. Then it will fit the classifier on X2. If we call predict on some data X’, say the test set, it will call transform on T1, creating X’1. Then it will use T2 to transform X’1 into X’2, and call the predict method of the classifier on X’2. This sounds a bit complicated, but it’s really just doing “the right thing”to apply multiple transformation steps. --- class: smallest # Undoing our feature selection mistake .left-column[ ```python # BAD! select.fit(X, y) # includes the cv test parts! X_sel = select.transform(X) scores = [] for train, test in cv.split(X, y): ridge = Ridge().fit(X_sel[train], y[train]) score = ridge.score(X_sel[test], y[test]) scores.append(score) ``` Same as: ```python select.fit(X, y) X_selected = select.transform(X, y) np.mean(cross_val_score(Ridge(), X_selected, y)) ``` ``` 0.90 ``` ] .right-column[ ```python # GOOD! scores = [] for train, test in cv.split(X, y): select.fit(X[train], y[train]) X_sel_train = select.transform(X[train]) ridge = Ridge().fit(X_sel_train, y[train]) X_sel_test = select.transform(X[test]) score = ridge.score(X_sel_test, y[test]) scores.append(score) ``` Same as: ```python pipe = make_pipeline(select, Ridge()) np.mean(cross_val_score(pipe, X, y)) ``` ``` -0.079 ``` ] ??? How does that help with the cross-validation problem? Because now all steps are contain in pipeline, we can simply pass the whole pipeline to crossvalidation, and all processing will happen inside the cross-validation loop. That solve the data leakage problem. Here you can see how we can build a pipeline using a standard scaler and kneighborsregressor and pass it to cross-validation. --- # Naming Steps ```python from sklearn.pipeline import make_pipeline knn_pipe = make_pipeline(StandardScaler(), KNeighborsRegressor()) print(knn_pipe.steps) ``` ``` [('standardscaler', StandardScaler()), ('kneighborsregressor', KNeighborsRegressor())] ``` ```python from sklearn.pipeline import Pipeline pipe = Pipeline([("scaler", StandardScaler()), ("regressor", KNeighborsRegressor())]) ``` ??? But let’s talk a bit more about pipelines, because they are great. The pipeline has an attribute called steps, which --- contains its steps. Steps is a list of tuples, where the first entry is a string and the second is an estimator (model). The string is the “name” that is assigned to this step in the pipeline. You can see here that our first step is called “standardscaler” in all lower case letters, and the second is called kneighborsregressor, also all lower case letters. By default, step names are just lowercased classnames. You can also name the steps yourself using the Pipeline class directly. Then you can specify the steps as tuples of name and estimator. make_pipeline is just a shortcut to generate the names automatically. --- # Pipeline and GridSearchCV .small-padding-top[ ```python from sklearn.model_selection import GridSearchCV knn_pipe = make_pipeline(StandardScaler(), KNeighborsRegressor()) param_grid = {'kneighborsregressor__n_neighbors': range(1, 10)} grid = GridSearchCV(knn_pipe, param_grid, cv=10) grid.fit(X_train, y_train) print(grid.best_params_) print(grid.score(X_test, y_test)) ``` ``` {'kneighborsregressor__n_neighbors': 7} 0.60 ``` ] ??? These names are important for using pipelines with gridsearch. Recall that for using GridSearchCV you need to specify a parameter grid as a dictionary, where the keys are the parameter names. If you are using a pipeline inside GridSearchCV, you need to specify not only the parameter name, but also the step name – because multiple steps could have a parameter with the same name. The way to do this is to use the stepname, then two underscores, and then the parameter name, as the key for the param_grid dictionary. You can see that the best_params_ will have this same format. This way you can tune the parameters of all steps in a pipeline at once! And you don’t have to worry about leaking information, since all transformations are contained in the pipeline. You should always use pipelines for preprocessing. Not only does it make your code shorter, it also makes it less likely that you have bugs. --- # Going wild with Pipelines ```python from sklearn.datasets import load_diabetes diabetes = load_diabetes() X_train, X_test, y_train, y_test = train_test_split( diabetes.data, diabetes.target, random_state=0) from sklearn.preprocessing import PolynomialFeatures pipe = make_pipeline( StandardScaler(), PolynomialFeatures(), Ridge()) param_grid = {'polynomialfeatures__degree': [1, 2, 3], 'ridge__alpha': [0.001, 0.01, 0.1, 1, 10, 100]} grid = GridSearchCV(pipe, param_grid=param_grid, n_jobs=-1, return_train_score=True) grid.fit(X_train, y_train) ``` --- # Going wilder with Pipelines ```python pipe = Pipeline([('scaler', StandardScaler()), ('regressor', Ridge())]) param_grid = {'scaler': [StandardScaler(), MinMaxScaler(), 'passthrough'], 'regressor': [Ridge(), Lasso()], 'regressor__alpha': np.logspace(-3, 3, 7)} grid = GridSearchCV(pipe, param_grid) grid.fit(X_train, y_train) grid.score(X_test, y_test) ``` --- # Going wildest with Pipelines ```python from sklearn.tree import DecisionTreeRegressor pipe = Pipeline([('scaler', StandardScaler()), ('regressor', Ridge())]) # check out searchgrid for more convenience param_grid = [{'regressor': [DecisionTreeRegressor()], 'regressor__max_depth': [2, 3, 4], 'scaler': ['passthrough']}, {'regressor': [Ridge()], 'regressor__alpha': [0.1, 1], 'scaler': [StandardScaler(), MinMaxScaler(), 'passthrough']} ] grid = GridSearchCV(pipe, param_grid) grid.fit(X_train, y_train) grid.score(X_test, y_test) ``` --- class: left, middle # Categorical Variables --- # Categorical Variables .smaller[ ```python import pandas as pd df = pd.DataFrame({ 'boro': ['Manhattan', 'Queens', 'Manhattan', 'Brooklyn', 'Brooklyn', 'Bronx'], 'salary': [103, 89, 142, 54, 63, 219], 'vegan': ['No', 'No','No','Yes', 'Yes', 'No']}) ``` ]
boro
salary
vegan
0
Manhattan
103
No
1
Queens
89
No
2
Manhattan
142
No
3
Brooklyn
54
Yes
4
Brooklyn
63
Yes
5
Bronx
219
No
??? Before we can apply a machine learning algorithm, we first need to think about how we represent our data. Earlier, I said x \in R^n. That’s not how you usually get data. Often data has units, possibly different units for different sensors, it has a mixture of continuous values and discrete values, and different measurements might be on totally different scales. First, let me explain how to deal with discrete input variables, also known as categorical features. They come up in nearly all applications. Scikit-learn requires you to explicitly handle these, and assumes in general that all your input is continuous numbers. This is different from how many libraries in R do it, which deal with categorical variables implicitly. --- # Ordinal encoding .smaller[ ```python df['boro_ordinal'] = df.boro.astype("category").cat.codes df ``` ] .left-column[
boro
salary
vegan
0
2
103
No
1
3
89
No
2
2
142
No
3
1
54
Yes
4
1
63
Yes
5
0
219
No
] -- .right-column[ ![:scale 100%](images/boro_ordinal.png) ] ??? If you encode all three values using the same feature, then you are imposing a linear relation between them, and in particular you define an order between the categories. Usually, there is no semantic ordering of the categories, and so we shouldn’t introduce one in our representation of the data. --- # Ordinal encoding .smaller[ ```python df['boro_ordinal'] = df.boro.astype("category").cat.codes df ``` ] .left-column[
boro_ordinal
salary
vegan
0
2
103
No
1
3
89
No
2
2
142
No
3
1
54
Yes
4
1
63
Yes
5
0
219
No
] .right-column[ ![:scale 100%](images/boro_ordinal_classification.png) ] --- # One-Hot (Dummy) Encoding .narrow-left-column[
boro
salary
vegan
0
Manhattan
103
No
1
Queens
89
No
2
Manhattan
142
No
3
Brooklyn
54
Yes
4
Brooklyn
63
Yes
5
Bronx
219
No
] .wide-right-column[ .tiny[ ```python pd.get_dummies(df) ```
salary
boro_Bronx
boro_Brooklyn
boro_Manhattan
boro_Queens
vegan_No
vegan_Yes
0
103
0
0
1
0
1
0
1
89
0
0
0
1
1
0
2
142
0
0
1
0
1
0
3
54
0
1
0
0
0
1
4
63
0
1
0
0
0
1
5
219
1
0
0
0
1
0
] ] ??? Instead, we add one new feature for each category, And that feature encodes whether a sample belongs to this category or not. That’s called a one-hot encoding, because only one of the three features in this example is active at a time. You could actually get away with n-1 features, but in machine learning that usually doesn’t matter One way to do is with Pandas. Here I have an example of a data frame where I have the boroughs of New York as a categorical variable and variable saying whether they are vegan. One to get the dummies is to get dummies on this data frame. This will create new columns, it will actually replace borough column by four columns that correspond to the four different values. The get_dummies applies transformation to all columns that have a dtype that's either object or categorical. In this case we didn't actually want to transform the target variable vegan. --- # One-Hot (Dummy) Encoding .narrow-left-column[
boro
salary
vegan
0
Manhattan
103
No
1
Queens
89
No
2
Manhattan
142
No
3
Brooklyn
54
Yes
4
Brooklyn
63
Yes
5
Bronx
219
No
] .wide-right-column[ .tiny[ ```python pd.get_dummies(df, columns=['boro']) ```
salary
vegan
boro_Bronx
boro_Brooklyn
boro_Manhattan
boro_Queens
0
103
No
0
0
1
0
1
89
No
0
0
0
1
2
142
No
0
0
1
0
3
54
Yes
0
1
0
0
4
63
Yes
0
1
0
0
5
219
No
1
0
0
0
] ] ??? We can specify selectively which columns to apply the encoding to. --- # One-Hot (Dummy) Encoding .narrow-left-column[
boro
salary
vegan
0
2
103
No
1
3
89
No
2
2
142
No
3
1
54
Yes
4
1
63
Yes
5
0
219
No
] .wide-right-column[ .tiny[ ```python pd.get_dummies(df_ordinal, columns=['boro']) ```
salary
vegan
boro_0
boro_1
boro_2
boro_3
0
103
No
0
0
1
0
1
89
No
0
0
0
1
2
142
No
0
0
1
0
3
54
Yes
0
1
0
0
4
63
Yes
0
1
0
0
5
219
No
1
0
0
0
] ] ??? This also helps if the variable was already encoded using integers. Sometimes, someone has already encoded the categorical variables to integers like here. So here this is exactly the same information only except instead of strings you have them numbered. If you call the get_dummies on this nothing happens because none of them are object data types or categorical data types. If you want to look at the One Hot Encoding, you can explicitly pass columns equal and this will transform into boro_1, boro_2, boro_3. In this case get_dummies usually wouldn't do anything, but we can tell it which variables are categorical and it will dummy encode those for us. --- .tiny[ .left-column[ ```python df = pd.DataFrame({ 'boro': ['Manhattan', 'Queens', 'Manhattan', 'Brooklyn', 'Brooklyn', 'Bronx'], 'salary': [103, 89, 142, 54, 63, 219], 'vegan': ['No', 'No','No','Yes', 'Yes', 'No']}) df_dummies = pd.get_dummies(df, columns=['boro'] ```
salary
vegan
boro_Bronx
boro_Brooklyn
boro_Manhattan
boro_Queens
0
103
No
0
0
1
0
1
89
No
0
0
0
1
2
142
No
0
0
1
0
3
54
Yes
0
1
0
0
4
63
Yes
0
1
0
0
5
219
No
1
0
0
0
] .right-column[ ```python df = pd.DataFrame({ 'boro': ['Brooklyn', 'Manhattan', 'Brooklyn', 'Queens', 'Brooklyn', 'Staten Island'], 'salary': [61, 146, 142, 212, 98, 47], 'vegan': ['Yes', 'No','Yes','No', 'Yes', 'No']}) df_dummies = pd.get_dummies(df, columns=['boro']) ```
salary
vegan
boro_Brooklyn
boro_Manhattan
boro_Queens
boro_Staten Island
0
61
Yes
1
0
0
0
1
146
No
0
1
0
0
2
142
Yes
1
0
0
0
3
212
No
0
0
1
0
4
98
Yes
1
0
0
0
5
47
No
0
0
0
1
]] ??? If someone else gives you a new data set and in this new data set there is Staten Island, Manhattan, Bronx and Brooklyn. So new dataset doesn't have anyone from Queens. So now you transform this with get_dummies, you get something that has the same shape as the original data but actually, the last column means something completely different. Because now the last column is Staten Island, not Queens. If someone gives you separate training and test data sets, if you call get_dummies, you don't know that the columns correspond actually to the same thing. Unless you take care of the names, unfortunately, scikit-learn completely ignores column names. --- class: smaller #Pandas Categorical Columns .smaller[ ```python df = pd.DataFrame({ 'boro': ['Manhattan', 'Queens', 'Manhattan', 'Brooklyn', 'Brooklyn', 'Bronx'], 'salary': [103, 89, 142, 54, 63, 219], 'vegan': ['No', 'No','No','Yes', 'Yes', 'No']}) df['boro'] = pd.Categorical(df.boro, categories=['Manhattan', 'Queens', 'Brooklyn', 'Bronx', 'Staten Island']) pd.get_dummies(df, columns=['boro']) ``` ] .tiny[
salary
vegan
boro_Manhattan
boro_Queens
boro_Brooklyn
boro_Bronx
boro_Staten Island
0
103
No
1
0
0
0
0
1
89
No
0
1
0
0
0
2
142
No
1
0
0
0
0
3
54
Yes
0
0
1
0
0
4
63
Yes
0
0
1
0
0
5
219
No
0
0
0
1
0
] ??? The way to fix this is by using Pandas categorical types. Since we know what the boroughs of Manhattan are, we can create Pandas categorical dtype, we can create this categorical dtype with the categories Manhattan, Queens, Brooklyn, Bronx, and Staten Island. So now I have my column here and I'm going to convert it to a categorical dtype. So now it will not actually store the strings. It will just internally store zero to four, and it will also store what are the possible values. If a call get_dummies it will use all the possible values and for each of the possible values it will create a column. Even though Staten Island has not appeared in my dataset, it will still make a column for Staten Island. If I fix this categorical dtype I can apply it to the training and test data set and that'll make sure all the columns are always the same no matter what are the values are actually in the data set. --- # OneHotEncoder ```python import pandas as pd df = pd.DataFrame({'salary': [103, 89, 142, 54, 63, 219], 'boro': ['Manhattan', 'Queens', 'Manhattan', 'Brooklyn', 'Brooklyn', 'Bronx']}) ce = OneHotEncoder().fit(df) ce.transform(df).toarray() ``` ``` array([[ 0., 0., 1., 0., 0., 0., 0., 1., 0., 0.], [ 0., 0., 0., 1., 0., 0., 1., 0., 0., 0.], [ 0., 0., 1., 0., 0., 0., 0., 0., 1., 0.], [ 0., 1., 0., 0., 1., 0., 0., 0., 0., 0.], [ 0., 1., 0., 0., 0., 1., 0., 0., 0., 0.], [ 1., 0., 0., 0., 0., 0., 0., 0., 0., 1.]]) ``` - Always transforms all columns ??? - Fit-transform paradigm ensures train and test-set categories correspond. --- # OneHotEncoder + ColumnTransformer ```python categorical = df.dtypes == object preprocess = make_column_transformer( (StandardScaler(), ~categorical), (OneHotEncoder(), categorical)) model = make_pipeline(preprocess, LogisticRegression()) ``` ??? The way to use this with mixed type data is column transformer, which allows you to transforms only some of the columns. For example, you can call categorical encoder only on the categorical columns and call StandardScaler on the non-categorical columns, and then use that to preprocess your data. Right now using Pandas, make sure your column names match up, make everything to an integer, or use column transformer and everything is awesome. In contrast to basically all other estimators in sklearn, this uses the column information in pandas and allows you to slice out different columns based on column names, integer indices or boolean masks. In this example I'm constructing a boolean mask --- class:center, middle ![:scale 100%](images/column_transformer_schematic.png) ??? Here's a schematic of the column transformer. Most commonly you might want to separate continuous and categorical columns, but you can select any subsets of columns you like. They can also overlap. Or you can apply multiple transformations to the same set of columns. Let's say I want a scaled version of the data, but I also want to extract principal components. I can use the same column as inputs to multiple transformers, and the results will be concatenated. FIXME add code --- class: some-space # Dummy variables and colinearity - One-hot is redundant (last one is 1 – sum of others) - Can introduce co-linearity - Can drop one - Choice which one matters for penalized models - Keeping all can make the model more interpretable ??? N/A --- class: some-space #Models Supporting Discrete Features - In principle: - All tree-based models, naive Bayes - In scikit-learn: - Some Naive Bayes classifiers. - In scikit-learn "soon": - Decision trees, random forests, gradient boosting ??? In principle all tree-based models support categorical features, in scikit-learn none of them do, hopefully, soon they will. So what you can do is either you do the One Hot Encoder or you just encode this as integers and treat it as a continuous. If you have very high categorical variables with many levels, maybe it keeping it as an integer might make more sense. --- # Target Encoding (Impact Encoding) ![:scale 100%](images/zip_code_prices.png) --- class: some-space # Target Encoding (Impact Encoding) - For high cardinality categorical features - Instead of 70 one-hot variables, one “response encoded” variable. - For regression: - "average price in zip code” - Binary classification: – “building in this zip code have a likelihood p for class 1” - Multiclass: – One feature per class: probability distribution ??? So there's also another way to encode categorical variables that is often used, I like to call it target-Based Encoding. It's basically for very high cardinality categorical features. For example, if you have categorical feature it's all US states and you don't have a lot of samples or if you have categorical features that's all US zip codes, if you have all different things, you don't want to do One Hot Encoding. So you get 50 new features, which if you don't have a lot of data would be a lot of features. So instead, you can use one single variable, it basically encodes the response. So for regression, it would be people in this state have an average response of that. Obviously you don't want to do this on the test set basically or you want to do this on the whole dataset for each level of the categorical variable, you want to find out what is the mean response and just use this as the future value. So you get one single future. For binary classification, you can just use the fraction of people that are classified as Class One. For multi-class, you usually do the percentage or fraction of people in each of the classes. So in multi-class, you get one new feature per class and you count for each state how many people in this state are classified for each of them. --- class: center, middle # More encodings for categorical features: ## http://contrib.scikit-learn.org/categorical-encoding/ --- # Load data, include ZIP code ```python data = fetch_openml("house_sales", as_frame=True) X = data.frame.drop(['date', 'price'], axis=1) X_train, X_test, y_train, y_test = train_test_split(X, target) X_train.columns ``` ``` Index(['bedrooms', 'bathrooms', 'sqft_living', 'sqft_lot', 'floors', 'waterfront', 'view', 'condition', 'grade', 'sqft_above', 'sqft_basement', 'yr_built', 'yr_renovated', 'zipcode', 'lat', 'long', 'sqft_living15', 'sqft_lot15'], dtype='object') ``` --- class: compact .tiny[ ```python X_train.head() ```
bedrooms
bathrooms
sqft_living
sqft_lot
floors
...
zipcode
lat
long
sqft_living15
sqft_lot15
10666
4.0
2.50
2160.0
7000.0
2.0
...
98029.0
47.566
-122.013
2300.0
7440.0
19108
4.0
4.25
3250.0
11780.0
2.0
...
98004.0
47.632
-122.203
1800.0
9000.0
20132
3.0
2.50
1280.0
1920.0
3.0
...
98105.0
47.662
-122.324
1450.0
1900.0
16169
4.0
1.50
1220.0
9600.0
1.0
...
98014.0
47.646
-121.909
1180.0
9000.0
16890
3.0
1.50
2120.0
6290.0
1.0
...
98108.0
47.566
-122.318
1620.0
5400.0
```python te = TargetEncoder(cols='zipcode').fit(X_train, y_train) te.transform(X_train).head() ```
bedrooms
bathrooms
sqft_living
sqft_lot
floors
...
zipcode
lat
long
sqft_living15
sqft_lot15
10666
4.0
2.50
2160.0
7000.0
2.0
...
6.164e+05
47.566
-122.013
2300.0
7440.0
19108
4.0
4.25
3250.0
11780.0
2.0
...
1.357e+06
47.632
-122.203
1800.0
9000.0
20132
3.0
2.50
1280.0
1920.0
3.0
...
8.503e+05
47.662
-122.324
1450.0
1900.0
16169
4.0
1.50
1220.0
9600.0
1.0
...
4.464e+05
47.646
-121.909
1180.0
9000.0
16890
3.0
1.50
2120.0
6290.0
1.0
...
3.604e+05
47.566
-122.318
1620.0
5400.0
```python y_train.groupby(X_train.zipcode).mean()[X_train.head().zipcode]) ```
zipcode
98029.0
98004.0
98105.0
98014.0
98108.0
price
616356.941
1.357e+06
850306.816
446448.065
360416.811
] --- class: smallest ```python X = data.frame.drop(['date', 'price', 'zipcode'], axis=1) scores = cross_val_score(Ridge(), X, target) np.mean(scores) ``` ``` 0.69 ``` -- ```python from sklearn.compose import make_column_transformer from sklearn.preprocessing import OneHotEncoder X = data.frame.drop(['date', 'price'], axis=1) ct = make_column_transformer((OneHotEncoder(), ['zipcode']), remainder='passthrough') pipe_ohe = make_pipeline(ct, Ridge()) scores = cross_val_score(pipe_ohe, X, target) np.mean(scores) ``` ``` 0.52 ``` -- ```python from category_encoders import TargetEncoder X = data.frame.drop(['date', 'price'], axis=1) pipe_target = make_pipeline(TargetEncoder(cols='zipcode'), Ridge()) scores = cross_val_score(pipe_target, X, target) np.mean(scores) ``` ``` 0.78 ``` --- class: center, middle # Questions?


Integrated Clinical Platform
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Budget Tracking
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WITNESS LIST
Finance-S/C on Fiscal Matters
May 6, 2013 9:00 AM
ON:
Sampson, Chance Legislative director (Comptroller), Austin, TX
Registering, but not testifying:
ON:
Currah, Tom Senior Advisor and Data Analysis Director (Comptroller of Public Accounts),
Austin, TX
FOR:
Bennett, Tony President (also providing written testimony) (Texas Assn of
Manufacturers), Austin, TX
Blaylock, William VP & Senior Tax Counsel Texas Instruments (also providing written
testimony) (Texas Instruments), Dallas, TX
Chase, Brian VP, Government Affairs (Bell Helicopter), Hurst, TX
Craymer, Dale President (Texas Taxpayers & Research Assoc), Austin, TX
AGAINST:
Borel, Dennis Executive Director (Coalition of Texans with Disabilities and Texas
Forward), Austin
Lavine, Dick Fiscal Analyst (also providing written testimony) (Center For Public Policy
Priorities), Austin
ON:
Hockenyos, Jon economist (Texans for Innovation), Austin, TX
Registering, but not testifying:
FOR:
Ashley, Price Consultant (Texas Instruments), Austin, TX
Borskey, Mark Consultant (General Electric), Austin, TX
Brown, Sabrina consultant (Dow Chemical), Austin, TX
Calcote, ML Consultant (United Technologies Corporation), Austin, TX
Calcote, ML Consultant (Pepsico), Austin, TX
Ellmer, Mindy consultant (North Texas Commission), Austin, TX
Jones, Dawn External Affairs (Intel Corporation), Austin, TX
Kowalski, Tom Trade Assoc. (Texas Healthcare and Bioscience Institute (THBI)), Austin,
TX
Reilly, Wendy Director, State Government Affairs (The Technology Association of
America- Tech America), Austin, TX
Shannon, Fred Consultant (Hewlett Packard)
Shelley, Dan Attorney (Lockheed Martin)
Shields, Chris Lobbyist (The Greater San Antonio Chamber of Commerce), Austin, TX
Simpson, Stephanie Consultant (MetroPlay Technology Business Council)
Womack, Daniel (Texas Chemical Council), Austin
Wurzel, Geoff Executive Director (TechNet)
WITNESS LIST
Finance-S/C on Fiscal Matters
May 6, 2013 9:00 AM
AGAINST:
Milam, Ph.D., Susan Govt. Relations Director (Natl. Association of Social Workers/ Texas
Chapter), Austin, TX
Providing written testimony:
AGAINST:
Quinzi, Patty Legislative Counsel (TX- AFT (American Federation of Teachers)), Austin,
TX
FOR:
Vane, Mark Lawyer (Walmart)
FOR:
Haass, Chip Market Development (Chesapeake Energy)
Hubbell, Mike Vice President, Maintenance (Dallas Area Rapid Transit (DART)), Dallas,
TX
Registering, but not testifying:
FOR:
Acevedo, Adrian Mgr., Govt. Relations (Anadarko Petroleum Corp), Austin, TX
Dow, Jim Consultant (Pioneer Natural Resources)
Findeisen, Les Director of Policy (Texas Motor Transportation Association), Austin, TX
Skabowski, Andrew Public Transportation (Houston METRO)
FOR:
Love, Roland Attorney (Texas Land Title Association), Dallas
Registering, but not testifying:
FOR:
Barton, Karey Consultant (Texas Apartment Association), Austin, TX


Marc Steen, Menno Manschot, Nicole de Koning
Already found before Facemooc, but provided as part of the Facemooc course reading materials.
I have a suspicion that the (mainstream field) of Service Design could have a lot in common with Firmware from the point of view of having difficulties materialising the field of work. That is to say, the holistic asynchronous flow of events which combine together to deliver a service are equally hard to concretise, although we would expect users to be able to describe parts accurately, drawing on their common sense, in a way which is perhaps impossible with firmware, because humans can be deployed to deliver the service.
"The design of a system can be considered as a user-centred design only if it meets the aims, requirements and mental models of users."


This feature is still experimental; some aspects may not work fully or at all just yet. Proceed with caution!
It is possible to pull in dependencies from separate repositories using Please. These are called subrepos and are defined in BUILD files.
They're heavily based on Bazel's similar concept and a certain amount of interoperability is possible.
Subrepos are defined using builtins like http_archive or
new_http_archive. These download a remote file and
extract it, and make the contents available to other rules. The main difference is that
new_http_archive
needs to be given a BUILD file but http_archive
uses one existing within the file.
For example (as seen at the root of the Please repo):
http_archive(
name = "pleasings",
hashes = ["388baebf9381c619f13507915f16d0165a5dc13e"],
strip_prefix = "pleasings-f0c549b375067802400699247106e4907de917c2",
urls = ["https://github.com/thought-machine/pleasings/archive/f0c549b375067802400699247106e4907de917c2.zip"],
)
Currently, subrepo rules only work when placed at the repo root. In future we intend to lift this restriction. In Bazel compatibility mode they can also be used within the WORKSPACE file.
Rules within subrepos can be referenced using an @
prefix on rules, anywhere
where a build rule would normally be accepted. For example:
subinclude("@pleasings//go:go_bindata")
cc_test(
name = "my_test",
...
deps = [
"@pleasings//eggs:spam",
],
)
In this case pleasings
corresponds to the name of the subrepo as declared in
the http_archive
rule above.
Subrepos also underpin cross-compiling and share the same syntax; you can use that to reference architectures as well. There is currently some ambiguity here and so it is best not to define subrepo names that match cross-compile architectures.
If the subrepo and the package names are the same, for example, @unittest_cpp//:unittest_cpp
,
the build label reference to the target can be abbreviated, like so:
cc_test(
name = "my_test",
...
deps = [
"@unittest_cpp",
],
)


I love the platform because the derivery system that is always on time and on point.
this is, the main reason why my first choice always when it comes to food derivery
is JUST EAT 👍 4.9
Paccy Smith
Just the name tells you everything about the platform, I have to admit the name is a catch.
as I hangout with my friends, we always use the platform to order food because it is easier
to use and very user friendly. which is one of the best features I love on the platform 👍 4.8
Jacob White
Just the name tells you everything about the platform,On this platform,
I have to admit the name is a catch. as I hangout with my friends,
we always use the platform to order food so as to enjoy our time. 👍 4.8


{{ .Title }}
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Savi Plastic Pvt. Ltd. brings to you a wide range of portable fans, that will cool you down with a gentle breeze ideal for the scorching summer months. Our simple designs and excellent build quality makes the fans a perfect summer accessory for your home, your shop, your classroom or to carry around outdoors.
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SPACE JAM A film review by Craig Good Copyright 1996 Craig Good
Here I was, all pumped up in my best Gomez Addams "Let's watch a train wreck" mood, and all "Space Jam" could deliver was a fender bender. No, it couldn't deliver the kind of down-in-flames badfilm experience of a "Star Trek V", "Hudson Hawk" or "1941". Instead it's just a humorless, overdesigned and underscripted bowl of soggy Wheaties, breakfast of champions. Imagine a movie in which Bill Murray isn't funny.
Well, that's not quite true. Bill did get me to laugh once with his line right after Michael Jordan gets sucked down a golf hole. But by the end of the movie I couldn't remember the line anymore.
 As an actor I'm no better, and if someone dangles that kind of pay check in front of me I'll do "Space Jam II". Besides, even though pro athletes are professional entertainers, we wouldn't be too hard on Placido Domingo or even Kenny G. if it turned out they couldn't play basketball in the NBA. And asking Jordan to carry a movie is every bit as absurd. No, Michael walks away from this pretty unscathed.
It's the Warner Bros studio which needs to hang its corporate head in shame. If there were laws in the world of art, what they did to their own legacy would be a felony. Someone really needs to send them a telegram and let them know that Mel Blanc is dead. None of the voices, except Tweedy and a couple of mice, came anywhere within the ballpark of the old classics. Worse, none of the animation can hold a candle to them.
The character design of the new characters is so unreadable and indistinct that it makes Don Bluth's work look like the Renaissance. The style of the staging is straight TV generation garbage: Extreme angles and manic movement with absolutely no sense of character. Not once did I think that any of the animated characters was thinking. Rather, they were yanked around by a vacuous script which could only have been written by a committee of ten year olds. At several points in the show we glimpse clips of the old Warner Brothers shorts.
Not only did they not understand their own short films, but in their headlong rush to throw money on the screen they lost the whole conceit of their "Who Framed Roger Rabbit" -- uh -- homage. When we get into Loony Tune Land or whatever the heck it was called, Bugs and the gang are running around with fancy pseudo-3D tone mattes on them. Shouldn't they only look like that in the "3D woild" as Bugs calls it? Oh, well. This is just the tip of a frustrating iceberg which waits anybody who sets sail in a sea of thought anywhere near this movie.
And speaking of money, where *did* it go? I know that animation is expensive, but for half the price of the rumored budget of $140 million they could have done some good computer graphics instead of those inflated balloon guy effects which can surely be done by teenagers on home computers these days.
So if you were hoping for either a good movie or one that was so bad it was good, save your money and see "The English Patient" or rent "Star Trek V", respectively.
--Craig<EMAIL_ADDRESS>The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
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Related links: index of all rec.arts.movies.reviews reviews


Kaggle Notebooks
Jupyter notebooks (downloaded Kaggle kernels) here contain personal solution code submitted to Kaggle competitions. Otherwise, the Jupyter notebook and dataset links are used for exploratory data analysis purposes.
RECENT
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Preface
1
Parameter Estimation
1.1
Distributions
1.1.1
Bernoulli
1.1.2
Beta Distribution
1.2
Inference: The Building Blocks
1.3
Maximum Likelihood
1.4
Maximum a Posteriori (MAP)
1.5
Bayesian Inference
1.5.1
Conjugate Priors
1.5.2
Gibbs Sampling
1.5.3
Bias of Two Coins
1.5.4
When was the coin swapped out
1.6
Summary
2
Multinomial Distribution
2.1
Relationship to Bernoulli
2.2
Conjugate Prior: Dirichlet
2.3
Gibbs Sampling Example
3
LDA
4
Components
5
Generative Model ….
6
Inference
6.1
High Level Overview
References
LDA Tutorial
4
Components
Words Documents Topics Alphas Beta


Configure Keys and Certificates
Cloud resources and repository servers require credentials to serve as offloading destinations. Keys and certificates for your offloading resources can be added and edited through the Keys and Certificates pane. Then when utilizing a feature in IBM Spectrum Protect Plus that requires credentials to access an offloading destination, select Use existing key or Use existing certificate, then select the associated key or certificate.
Add a key
- Create your access key and secret key through the resource's interface. Make note of the access and secret keys, which will be used later in this procedure.
- In IBM Spectrum Protect Plus, expand System Configuration, then click Keys and Certificates.
- On the Keys and Certificates pane, click Add in the Key section. The Key Properties pane displays.
- In the Name field, enter a meaningful name to help identify the key.
- In the Access Key field, enter the access key created in Step 1.
- In the Secret Key field, enter the secret key created in Step 1.
- Click Save. The key displays in the Keys table and can be selected when utilizing a feature that requires a key to access a resource.
Add a certificate
- Export a certificate from your Amazon Web Services, IBM Spectrum Protect, or IBM Cloud Object Storage environment.
- In IBM Spectrum Protect Plus, expand System Configuration, then click Keys and Certificates.
- On the Keys and Certificates pane, click Add in the Certificates section. The Certificate Properties pane displays.
- In the Type field, select a certificate type.
- In the Certificate field, select a method to add the certificate:
- Upload: Select to browse for the certificate locally.
- Copy and paste: Select to enter the name of the certificate, then copy and paste the contents of the certificate.
- Click Save. The credential displays in the Credentials table and can be selected when utilizing a feature that requires credentials to access a resource.
IBM Spectrum Protect Plus 10.1.2
Licensed Material - Property of IBM Corp. © IBM Corporation and other(s) 2018. IBM is a registered trademark of the IBM Corporation in the United States, other countries, or both. | 8/2/2018


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ADx Healthcare contracts with PWNHealth, an independent provider for both physician authorization and genetic counseling for our Alzheimer’s ApoE Genetic Test. A physician from PWNHealth will review your health history questions to ensure the test is right for you. Someone from PWNHealth may reach out to you if they have any additional questions.
Once an independent physician from PWNHealth authorizes your test, you will receive a sample kit in the mail. Collect your saliva sample and send the kit back to us for processing, in the prepaid packaging provided.
Obtain your results privately and securely online. We’ll help you schedule your free genetic counseling session if you would like to review your results with a genetic counselor. You can also print a copy of your results if you want to share them with family or your primary healthcare provider.
Take action and reduce your risk for late-onset Alzheimer's disease or delay its onset.


Make sure that you added config with your parameters to firebase.ts file
Also you need change FIREBASE_FUNCTION_URL variable in src/models/UserModel.ts file


-
Create new project
POSThttp://example.com/projects/Returns
ProjectDTO as JSON
application/jsonjavax.ws.rs.core.Response
-
Import project
POSThttp://example.com/projects/importReturns
ProjectDTO as JSON
no typejavax.ws.rs.core.Response
-
Get unique upload url from GAE Blobstore API
GEThttp://example.com/projects/uploadurlReturns
String containing the unique upload URL for the BlobStore API
text/plainjavax.ws.rs.core.Response
-
Delete project
DELETEhttp://example.com/projects/projectIdPath Parameters
projectId number LongReturns
no content
application/jsonjavax.ws.rs.core.Response
-
Download project
GEThttp://example.com/projects/projectId/downloadPath Parameters
projectId number LongReturns
ZIP with the project files
no typejavax.ws.rs.core.Response
-
Add new file
POSThttp://example.com/projects/projectId/filesPath Parameters
projectId number LongMultiPart Parameters
file File the actual file (not a ProjectFileDTO)Returns
ProjectFileDTO as JSON
no typejavax.ws.rs.core.Response
-
Import files
POSThttp://example.com/projects/projectId/files/importReturns
ProjectFileDTOs as JSON
no typejavax.ws.rs.core.Response
-
Get file
GEThttp://example.com/projects/projectId/files/fileIdPath Parameters
projectId number LongfileId number LongReturns
the project file (not DTO, the actual file)
no typejavax.ws.rs.core.Response
-
Update file
PUThttp://example.com/projects/projectId/files/fileIdPath Parameters
projectId number LongfileId number LongMultiPart Parameters
file File the actual file (not a ProjectFileDTO)Returns
JSON with Status OK, Bad Request if Project was not found, Not Found if previous Blob couldn't be deleted
application/jsonjavax.ws.rs.core.Response
-
Delete file
DELETEhttp://example.com/projects/projectId/files/fileIdPath Parameters
projectId number LongfileId number LongReturns
JSON with Status No Content on success, Bad Request if Project was not found, Not Found if Blob couldn't be found in BlobStore
application/jsonjavax.ws.rs.core.Response
-
Update the content of a file with given string
POSThttp://example.com/projects/projectId/files/fileId/contentPath Parameters
projectId number LongfileId number LongForm Parameters
content string The new file content as plain-textReturns
JSON with Status OK on success, Bad Request if Project was not found, Not Found if Blob couldn't be found in BlobStore, Internal Server Error if new Blob couldn't be stored in BlobStore
application/jsonjavax.ws.rs.core.Response
-
Get the meta data of a file
GEThttp://example.com/projects/projectId/files/fileId/metaPath Parameters
projectId number LongfileId number LongReturns
ProjectFileDTO as JSON
application/jsonjavax.ws.rs.core.Response
-
Patch file with single string from diff_match_patch library
POSThttp://example.com/projects/projectId/files/fileId/patchPath Parameters
projectId number LongfileId number LongForm Parameters
patch string Single text patch from diff_match_patch libraryReturns
JSON with Status OK on success, Bad Request if Project was not found, Not Found if Blob couldn't be found in BlobStore, Internal Server Error if new Blob couldn't be stored in BlobStore
application/jsonjavax.ws.rs.core.Response
-
Rename file
POSThttp://example.com/projects/projectId/files/fileId/renamePath Parameters
projectId number LongfileId number LongForm Parameters
value string New nameReturns
updated ProjectFileDTO as JSON
application/jsonjavax.ws.rs.core.Response
-
Set file as main tex
POSThttp://example.com/projects/projectId/files/fileId/setmaintexPath Parameters
projectId number LongfileId number LongForm Parameters
mainTex boolean true|falseReturns
updated ProjectFileDTO as JSON
application/jsonjavax.ws.rs.core.Response
-
Rename project
POSThttp://example.com/projects/projectId/renamePath Parameters
projectId number LongForm Parameters
value string New nameReturns
updated ProjectDTO as JSON
application/jsonjavax.ws.rs.core.Response
-
Search all files for a given query
POSThttp://example.com/search/Form Parameters
query string StringReturns
search results as JSON
application/jsonjavax.ws.rs.core.Response
-
Get unique Channel API Token for Client
GEThttp://example.com/session/channel/tokenReturns
Token as String
text/plainjavax.ws.rs.core.Response
-
Register client for open document
POSThttp://example.com/session/document/fileId/registerPath Parameters
fileId number LongReturns
JSON with Status OK on success
application/jsonjavax.ws.rs.core.Response
-
Unregister client for opened document
POSThttp://example.com/session/document/fileId/unregisterPath Parameters
fileId number LongReturns
JSON with Status OK on success, Not Modified if Client wasn't registered
application/jsonjavax.ws.rs.core.Response
2014-05-14 18:35:45 - REST documentation generated by MireDot version 1.4


SCHINKEL, Nathan William
Date of death: November 15, 2019
Date of birth: July 16, 1986
Age: 33
Visitation: Friday, November 22, 2019, at the Community Christian Reformed Church: 1275 Bleams Rd. from 3:30 - 6:30 PM
Service: Friday, November 22, 2019, at the Community Christian Reformed Church, at 6:30 PM.


Extensions methods for Http related objects.
Converts the IFormFile into a BinaryAttachment.
public BinaryAttachment BinaryAttachment(file)
A BinaryAttachment to store the IFormFile.
| Name | Type | Summary |
|---|---|---|
| file | IFormFile | The IFormFile to convert. |
Check an IFormFile is actually an image.
public Boolean IsImage(file)
true if it's an image, otherwise false.
| Name | Type | Summary |
|---|---|---|
| file | IFormFile | An IFormFile. |
Checks whether the request is has the X-Requested-With header setted to XMLHttpRequest.
public Boolean IsXHR(request)
true if the header is present otherwise false.
| Name | Type | Summary |
|---|---|---|
| request | HttpRequest | The HttpRequest to test. |
In this document


{% extends 'layout-bsa.html' %}
{% block pageTitle %}
Do you get money from any of these? - LIS training - NHSBSA
{% endblock %}
{% block outerContent %}
{% endblock %}
{% block content %}
People who live with you
What you need
In this section, we will ask you:
if you have any children living with you
if anyone else lives with you, and whether they work or get benefits


You've stopped payments for Harvey Dent
No changes made
Change requested
The apprentice's training provider has asked you to stop this apprentice and then add them to a new cohort using updated training details.
Status changed
{% include "includes/account-name-number.html" %}Harvey Dent
| Status | Live | Stopped |
Overview |
|---|
| Training provider | Hackney Skills Solutions Ltd |
| Employer | Acme Coventry Ltd |
| Cohort reference | 98HXGFD |
Apprentice |
|
|---|
| Name | Harvey Dent | |
| Unique learner number |<PHONE_NUMBER> | |
| Date of birth | 28 February 1997 | Apprenticeship training course | Level 2: Aeronautical engineering |
| Training start date | July 2018 | |
| Training end date | July 2020 | |
| Total cost of training | £6,555 | |
| Reference | Banbury site |


<IP_ADDRESS>. Forward Propagation Methods¶
The forward propagation of uncertainty is concerned with the quantification of uncertainties in the output propagated from uncertain inputs. Currently there are five options available: Monte Carlo Sampling (MCS), Latin Hypercube Sampling (LHS), Importance Sampling (IS), and sampling based on surrogate models, including Gaussian Process Regression (GPR) and Polynomial Chaos Expansion (PCE). Depending on the option selected, the user must specifies the appropriate input parameters for each. For instance, for MCS, the number of samples specifies the number of simulations to be performed, and providing a random seed allows the user to reproduce the same set of samples from the random variables multiple times.
The the user selects the sampling method to use from the sampling methods dropdown menu. Additional information regarding sampling methods offered in Dakota can be found here.
Monte Carlo Sampling (MCS)¶
MCS is based on random sampling and is among the most robust approaches for problems where other uncertainty propagation schemes fail. Moreover, the convergence rate of MCS methods is independent of the problem dimensionality, albeit the convergence rate of such MCS methods is relatively slow at \(N^{-1/2}\). In MCS, a sample drawn at any step is independent of all previous samples.
Fig. 3.1.2 shows the input panel corresponding to the Monte Carlo Sampling setting. Two input parameters need to be specified, the number of samples to be executed, as well as the seed used in generating the random samples.
Latin Hypercube Sampling (LHS)¶
LHS represents a pseudo-random, stratified sampling approach for uncertainty propagation. With a better convergence rate than MCS, LHS pseudo-sampling take into account all previous sample points before drawing a new sample point.
Fig. 3.1.3 shows the input panel corresponding to the Latin Hypercube Sampling (LHS) scheme. Two input parameters also need to be specified, the number of samples to be executed, as well as the seed used in generating the LHS samples.
Importance Sampling¶
For problems where one is interested in the tail events rather than the distribution itself, such as rare earthquake or storm surge events, conventional sampling methods do not perform well, or might need a high number of samples to obtain an accurate estimation of tail distribution. For such problems, Importance Sampling provide a bypass to conventional sampling (LHS or MCS), whereby an alternative distribution is constructed with more weight on the tail part so that samples are concentrated there and this more accurate estimates of tail events are achieved.
For rare event analysis, Fig. 3.1.4 shows the input panel for Importance Sampling (IS) scheme. Similar to MCS and LHS, the IS requires both the number of samples to be executed and the corresponding seed for generating such random samples. In addition, the Importance Sampling algorithm can performed via three different approaches, as specified by the third input method. The latter includes
Basic Sampling: a sampling density is constructed in the failure region based on an initial LHS sampling, followed by generation of importance samples and weights and evaluation of the Cumulative Distribution Function.
Adaptive Sampling: similar to based sampling, with the exception that the basic sampling procedure is repeated iteratively until a convergence in failure probability is achieved.
Multimodal Adaptive Sampling: a multimodal sampling density is constructed based on samples in the failure region, from which, the adaptive sampling procedure is used.
For more information on each, please refer to the Dakota manual.
Gaussian Process Regression (GPR)¶
One of methods for uncertainty propagation with surrogates is Gaussian Process Regression (GPR). Fig. 3.1.5 shows the input panel for the GPR model, with input panels for training and sampling.
For problems where conventional sampling schemes such as LHS, MCS, or other fail, surrogates can be leveraged to obtain an approximation of the response surface, and than sample from that surface accordingly.
Gaussian Process Regression, also known as Kriging, represent a well-established method for interpolation and response surface construction based on Gaussian process models with covariance matrices.
For uncertainty propagation with Gaussian Process Regression (GPR), Fig. 3.1.5 shows the input panel for the PCE model, with input panels for training and sampling as well. The first set of input parameters in the surrogate training data specify the dataset used for training the surrogate model, while the second set of input parameters in the surrogate sampling data relate to the dataset used for sampling the surrogate. Care must be taken in specifying the training dataset to results in an accurate response surface approximation.
Polynomial Chaos Expansion (PCE)¶
Another response surface approximation scheme using surrogates is based on Polynomial Chaos Expansion (PCE). For uncertainty propagation with Polynomial Chaos Expansion (PCE), Fig. 3.1.6 shows the input panel for the PCE model, with input panels for training and sampling as well, similar to the input GPR panel. The first set of input parameters in the surrogate training data specify the dataset used for training the surrogate model, while the second set of input parameters in the surrogate sampling data relate to the dataset used for sampling the surrogate. Extreme care must be taken in specifying the parameters of the training dataset to results in an accurate response surface approximation.
If you are not sure about the training parameters of the surrogates, please refrain from using the surrogates (PCE in particular) for forward propagation and use instead conventional sampling such as MCS and LHS as discussed above, even at a higher computational cost.


Ninja is a full stack web framework for Java. Rock solid, fast and super productive.
Ninja makes sure that technology will not be the blocker but the enabler in your next project. Ninja helps you as product owner to deliver business value faster - in stunning high quality. We offer long term commercial support to guarantee your success.
Doesn't matter if you build huge enterprise apps or small RESTful Json microservices. Ninja provides everything you need to get productive at once. Development, testing, deployment, refactoring and maintenance.
Ninja does not reinvent the wheel - instead we are standing on the shoulders of giants. Ninja is built using the most mature and solid technologies in the industry. Servlets, Guice, JPA, Flyway migrations and Maven - just to name a few.
Ninja uses the share nothing approach. Servers do not share any common session information. That means you can scale Ninja horizontally with ease.
You get all goodies that make Java great: Excellent IDE support. A statically typed language. Huge and mature eco-system. First class testing support and much much more. Ninja is licensed under Apache 2.0 Open Source license - our codebase will be open forever.
Generate a new project via our Maven archetype, import it into your favorite IDE with one click and start coding. Ninja's SuperDevMode will pick up all your code changes immediately. Features? All there: Html, Xml, Json, JPA, caching and much more.
Ninja is fully equipped with an extensive set of test tools. Run mocked tests or integration-test your complete application on a real server. Running selenium tests? All included. Ninja tests run locally and on any continuous integration server.
Package your Ninja application as war and run it on your application server (Tomcat, Jetty...). Or create a a standalone application, copy it to your server and start it in production. Ninja is cloud ready and runs on Heroku, App Engine and many other providers.


City
Day (Time)
Weather Condition
Weather Description
90
Cloudiness:
40%
Humidity:
84%
Pressure:
5 mph
Wind Speed:
40%
Wind Direction:
84%
Ground Level:
5 mph
Sea Level:
5 mph
Mon
85°
80°
Tue
85°
80°
Wed
85°
80°
Thu
85°
80°
Fri
85°
80°
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Petrolicious Gift Guide Has Ideal Items For The Petrolista On Your List
by Petrolicious ProductionsEvery year there is always one person who is hard to buy for, the one who is never happy. In the past, that person was probably the car enthusiast in the group. This year you can rest easy, Petrolicious Shop is here to help you out. This year you can give the gift that keeps on giving, the Petrolicious Membership, along with items you won’t find anywhere else. Only the most premium automotive accessories, toys, and gear appear in our shop. No matter your age, or level of automotive devotion, there is something for you. And if you’re buying for someone else you’ll instantly upgrade your enthusiast street cred.
Membership
An exclusive way to experience Petrolicious through brand-new film series, a collectible quarterly print publication, special event access, and more perks for members who share our enthusiasm for Driving Tastefully. Choose from Standard or Premium Membership and gift it for a loved one here.
Petrolicious Originals
Petrolicious Originals are perfect for a Sunday morning drive, working on the car, or relaxing in between. If you know somebody who checks our pages daily and has an affinity for cars cut from a certain cloth, these make for awesome, and most importantly, relevant gifts.
See more from Petrolicious Originals here.
Bart Kuykens Volume 1 – 4
“Sultry and sexy, mean and moody, this is automotive photo porn at its best: Bart’s eye for a stylish shot has captured the characters and the cars absolutely, and his black-and-white subjects brim with charisma and innuendo in mysterious and enigmatic locations, featuring classic 911s festooned with inner meaning, their keepers and models effecting surly postures, contriving fantastic atmosphere and ambiance. It’s the gothic Porsche photo album par excellence.” – Johnny Tipler
See more from Bart Kuykens here.
Candy Lab Toys
Candylab Toys are unique and perfectly engineered modern vintage toy. Made with real waxed canvas, a magnetic surfboard accessory, and carefully crafted woodwork – all lovingly packed in an old school, string tie-down collectors box. Perfect for your inner five year old or your actual one.
See more from Candylab here.
Remove Before
We’ve always been big fans of the well-crafted minimalist artwork from Remove Before, and besides having chosen a group of very poster-worthy cars that includes Countachs and CSLs, they offer a clean and uncluttered boost to the otherwise bland aesthetics of a white wall. If you know somebody with a glaring blankness on their walls and a penchant for badass German classics, look no further. It’s not all Teutonic though, as you’ll find Ferraris and more in the full collection.
See more from Remove Before here.


Current Applications
| First Name |
Last Name |
Address |
City |
Zip |
Phone |
Email |
Called? |
App |
W9 |
ICA |
Liscense |
Badge |
EPA |
| Corin |
Griffin |
1786 Winfield Lane |
Auburn |
30011 |
7705402345 |
<EMAIL_ADDRESS>|
Yes |
Pending |
Complete |
Waiting |
Complete |
Waiting |
Waiting |
| Wendy |
Charles |
5851 Conner Road |
Auburn |
30011 |
7705401123 |
<EMAIL_ADDRESS>|
Yes |
Sent |
Complete |
Sent |
Waiting |
Waiting |
Waiting |
fdsa
Name
Number
Hometown
Email
Called?
App
W9
ICA
License
Badge
Name
Number
Hometown
Email


{% extends "get_together/emails/base.html" %} {% block content %}
You have an event coming up!
Name: {{ event.name|striptags }} Time: {{event.local_start_time}} Location: {{ event.place|striptags }} Go to the event page.


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