diff --git a/.DS_Store b/.DS_Store index badec83..77d2240 100644 Binary files a/.DS_Store and b/.DS_Store differ diff --git a/README.md b/README.md index 8829538..11af666 100644 --- a/README.md +++ b/README.md @@ -2,79 +2,6 @@ The exploratory project estimates and measures carbon emissions from various Machine Learning and deep learning datasets to quantify and analyze their impact. -
- -- [About](https://github.com/blessinvarkey/EcoCoding#about) -- [Emissions](https://github.com/blessinvarkey/EcoCoding#emissions) -- [Installation](https://github.com/blessinvarkey/Energy-Consumption-In-Machine-Learning/blob/main/README.md#installation) -- [IRIS-Dataset](https://github.com/blessinvarkey/Estimation-of-Energy-Consumption-In-Machine-Learning/blob/main/README.md#1-iris-dataset) -- [ASD-Screening](https://github.com/blessinvarkey/Estimation-of-Energy-Consumption-In-Machine-Learning/blob/main/README.md#2-autism-spectrum-quotient-aq-10--q-chat) -- [Fashion MNIST](https://github.com/blessinvarkey/EcoCoding#3-fashion-mnist) - -## Definitions -- 1 Wh is 3600 Joules - -## Why do we need Sustainable Software? -- A 200g ebook requires about 15kg of raw materials (e.g. rare earth metal) 300L water, and 170 kg of CO2. Ecologically more beneficial than paperback only if you read between 30-60 books -- Energy consumption of ICT - -__Email:__ -- 1 MB Email =≈ 20g of CO2 -- 20 emails on 365 days = car travelling 1000 km - -__Web search:__ -- ≈3.5 Wh, ≈ 1g CO2 equivalent -- 2-3 web searches per day =≈ 10 kg of CO2 per year - -Per capita consumption: equivalent to ≈ 1,400 km by car - -__ICT global demand__ -- 1% to 10% -- 2% of global emissions, equal to aviation industry from fuel - -__Forecast:__ -- Data centres could consume 28% of total energy consumption (est.: 11%, best 4%) -- “In worst-case scenario, ICT electricity usage could contribute upto 23% of the globally released greenhouse gas emissions in 2030” - - -![](https://theshiftproject.org/wp-content/uploads/2019/07/Graphique_EN_Energy-consumption-of-digital-technologies.png) - -Product development/software development teams - -__Organizational__: Product development teams and methods -Examples: _Agile Development_, _DevOps_ - -__Technological__: Technological Advancements -Examples: _cloud infrastructure_, _mobile devices_ - -__Market__: -Examples: _hyperscale cloud providers_, _consumer demands_ - -# GREENSOFT Model -- A wholistic reference model for producing and consuming sustainable software -- "Green and Sustainable Software Engineering is the art of defining and developing software products in a way, so that the negative and positive impacts on sustainable development that result from the software product over its whole life cycle are continously assessed, documented, and used for further optimization of the software product."[[3](https://www.semanticscholar.org/paper/The-GREENSOFT-Model%3A-A-reference-model-for-green-Naumann-Hirsch-Dick/d52109cceee36af7731cdcd6a69c1972e0905a4b)] - -Lifecycle phases in the "Greensoft- Green and Sustainable Software Model"? -- Development -- Usage -- End of Life - - -![](https://ars.els-cdn.com/content/image/1-s2.0-S2210537911000473-gr1.jpg) - - -The following projects in the repo uses code carbon taking into account the computing infrastructure, location, usage and running time. - -# Emissions - -| Algorithm | Dataset/ Link to code | Instance | Accuracy| Emissions Recorded in Joules* (average) |Emissions Recorded in kg* (average) | -| ------------- |------------- | ------------- | ------------- | ------------- | ------------- | -|Logistic Regression | [IRIS Dataset](https://github.com/blessinvarkey/EcoCoding/blob/main/iris-dataset/train.py)|50 | 95.56%|11322593948.720951 joules|1.2598084791716727e-07 kg | -| Logistic Regression | [ASD Screening](https://github.com/blessinvarkey/EcoCoding/blob/main/asd-dataset/train.py) | 761 | 99.53% | 23098647146.30177 joules | 2.570071104209542e-07 kg | -|Support Vector Machines | [IRIS Dataset](https://github.com/blessinvarkey/EcoCoding/blob/main/iris-dataset/train.py)| 50 | 97.78%|11020140817.496267 joules|1.226156029830599e-07 kg| -|K Nearest Neighbors | [IRIS Dataset](https://github.com/blessinvarkey/EcoCoding/blob/main/iris-dataset/train.py)| 50 | 93.33%|11183853335.19536 joules|1.2443715040300566e-07 kg | -| Adam Optimization | [Fashion MNIST](https://github.com/blessinvarkey/EcoCoding/blob/main/fashion-mnist/train.py) | 70,000 | |52381696453822.83 joules (5 epochs)| | - # Installation Download the project along with a code editor. @@ -119,51 +46,23 @@ python train.py # About the Dataset -## 1. Iris Dataset -The IRIS data set contains 3 classes of 50 instances each, where each class refers to a type of iris plant. One class is linearly separable from the other 2; the latter are NOT linearly separable from each other. - -The details on the dataset can be found [here](https://archive.ics.uci.edu/ml/datasets/iris) - - -### Data Visualization: Sepal Length vs Width & Petal Length vs Width -|![Screenshot](images/Figure_1.png)|![Screenshot](images/Figure_2.png)|![Screenshot](images/Figure_3.png)| -| ------------- | ------------- |------------- | - - -## 2. ASD Screening -The Autism-Spectrum Quotient publisehed by Baron-Cohen, Wheelwright, Skinner, Martin, & Clubley was developed to assess how adults with 'normal' intelligence has the traits associated with autism spectrum conditions. Each question allows the subject to indicate "Definitely agree", "Slightly agree", "Slightly disagree" or "Definitely disagree". Approximately half the questions are worded to elicit an "agree" response from normal individuals, and half to elicit a "disagree" response. The subject scores one point for each question which is answered "autistically" either slightly or definitely." The questions cover five different domains associated with the autism spectrum: social skills; communication skills; imagination; attention to detail; and attention switching/tolerance of change. +# Data Visualization -According to the dataset, 'Yes' indicates that the individual is on the Autism Spectum and 'No' is indicated when the final score is less than or equal to 7. You can refer the questions from [here](https://www.nice.org.uk/guidance/cg142/resources/autism-spectrum-quotient-aq10-test-pdf-186582493). -The Dataset can be downloaded from the UCI Repository which is publicly available [here](https://archive.ics.uci.edu/ml/datasets/Autism+Screening+Adult). + # Emissions - Red Wine -### Data Visualization -|![Screenshot](images/asd_dataset/Figure_1.png)|![Screenshot](images/asd_dataset/Figure_2.png)|![Screenshot](images/asd_dataset/Figure_3.png)|![Screenshot](images/asd_dataset/Figure_4.png)| -| ------------- | ------------- |------------- | ------------- | -|![Screenshot](images/asd_dataset/Figure_5.png)|![Screenshot](images/asd_dataset/Figure_6.png)|![Screenshot](images/asd_dataset/Figure_7.png)|![Screenshot](images/asd_dataset/Figure_9.png)| -|![Screenshot](images/asd_dataset/Figure_10.png)|![Screenshot](images/asd_dataset/Figure_11.png)| -| ------------- |------------- | - - -## 3. Fashion MNIST -Fashion MNIST is a dataset comprised of 60,000 small square 28×28 pixel grayscale images of items of 10 types of clothing, such as shoes, t-shirts, dresses, and more. The mapping of all 0-9 integers to class labels is listed below. + + # Emissions -0: T-shirt/top -1: Trouser -2: Pullover -3: Dress -4: Coat -5: Sandal -6: Shirt -7: Sneaker -8: Bag -9: Ankle boot +| Algorithm | Dataset/ Link to code | Instance | Accuracy| Emissions Recorded in Joules* (average) |Emissions Recorded in kg* (average) | +| ------------- |------------- | ------------- | ------------- | ------------- | ------------- | +|Logistic Regression | [IRIS Dataset](https://github.com/blessinvarkey/EcoCoding/blob/main/iris-dataset/train.py)|50 | 95.56%|11322593948.720951 joules|1.2598084791716727e-07 kg | +| Logistic Regression | [ASD Screening](https://github.com/blessinvarkey/EcoCoding/blob/main/asd-dataset/train.py) | 761 | 99.53% | 23098647146.30177 joules | 2.570071104209542e-07 kg | +|Support Vector Machines | [IRIS Dataset](https://github.com/blessinvarkey/EcoCoding/blob/main/iris-dataset/train.py)| 50 | 97.78%|11020140817.496267 joules|1.226156029830599e-07 kg| +|K Nearest Neighbors | [IRIS Dataset](https://github.com/blessinvarkey/EcoCoding/blob/main/iris-dataset/train.py)| 50 | 93.33%|11183853335.19536 joules|1.2443715040300566e-07 kg | +| Adam Optimization | [Fashion MNIST](https://github.com/blessinvarkey/EcoCoding/blob/main/fashion-mnist/train.py) | 70,000 | |52381696453822.83 joules (5 epochs)| | -### Data Visualization -|![Screenshot](images/f1.png)|![Screenshot](images/f2.png)|![Screenshot](images/f3.png)| -|--|--|--| - [↑ Back to top](https://github.com/blessinvarkey/EcoCoding#energy-consumption-in-machine-learning--deep-learning-models) diff --git a/ResNet50/bay.jpg b/ResNet50/bay.jpg deleted file mode 100644 index 8ff82e6..0000000 Binary files a/ResNet50/bay.jpg and /dev/null differ diff --git a/ResNet50/train.py b/ResNet50/train.py deleted file mode 100644 index 101feee..0000000 --- a/ResNet50/train.py +++ /dev/null @@ -1,12 +0,0 @@ -import tensorflow as tf -print("tf. version = ", tf.__version__) -import tensorflow.keras -print("keras version=", tensorflow.keras.__version__) -from keras.applications import resnet50 - -from tensorflow.keras.applications.resnet50 import ResNet50 -#from tensorflow.keras.preprocessing import image -#from tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictions -#import numpy as np - -model = ResNet50(weights='imagenet') diff --git a/_config.yml b/_config.yml deleted file mode 100644 index c741881..0000000 --- a/_config.yml +++ /dev/null @@ -1 +0,0 @@ -theme: jekyll-theme-slate \ No newline at end of file diff --git a/asd-dataset/.DS_Store b/asd-dataset/.DS_Store deleted file mode 100644 index 3a7e7d9..0000000 Binary files a/asd-dataset/.DS_Store and /dev/null differ diff --git a/asd-dataset/Autism_Data.arff b/asd-dataset/Autism_Data.arff deleted file mode 100644 index 52de5aa..0000000 --- a/asd-dataset/Autism_Data.arff +++ /dev/null @@ -1,706 +0,0 @@ -A1_Score,A2_Score,A3_Score,A4_Score,A5_Score,A6_Score,A7_Score,A8_Score,A9_Score,A10_Score,age,gender,ethnicity,jundice,austim,contry_of_res,used_app_before,result,age_desc,relation,Class/ASD - -1,1,1,1,0,0,1,1,0,0,26,f,White-European,no,no,'United States',no,6,'18 and more',Self,NO -1,1,0,1,0,0,0,1,0,1,24,m,Latino,no,yes,Brazil,no,5,'18 and more',Self,NO -1,1,0,1,1,0,1,1,1,1,27,m,Latino,yes,yes,Spain,no,8,'18 and more',Parent,YES -1,1,0,1,0,0,1,1,0,1,35,f,White-European,no,yes,'United States',no,6,'18 and more',Self,NO -1,0,0,0,0,0,0,1,0,0,40,f,?,no,no,Egypt,no,2,'18 and more',?,NO -1,1,1,1,1,0,1,1,1,1,36,m,Others,yes,no,'United States',no,9,'18 and more',Self,YES -0,1,0,0,0,0,0,1,0,0,17,f,Black,no,no,'United States',no,2,'18 and more',Self,NO -1,1,1,1,0,0,0,0,1,0,64,m,White-European,no,no,'New Zealand',no,5,'18 and more',Parent,NO -1,1,0,0,1,0,0,1,1,1,29,m,White-European,no,no,'United States',no,6,'18 and more',Self,NO -1,1,1,1,0,1,1,1,1,0,17,m,Asian,yes,yes,Bahamas,no,8,'18 and more','Health care professional',YES -1,1,1,1,1,1,1,1,1,1,33,m,White-European,no,no,'United States',no,10,'18 and more',Relative,YES -0,1,0,1,1,1,1,0,0,1,18,f,'Middle Eastern ',no,no,Burundi,no,6,'18 and more',Parent,NO -0,1,1,1,1,1,0,0,1,0,17,f,?,no,no,Bahamas,no,6,'18 and more',?,NO -1,0,0,0,0,0,1,1,0,1,17,m,?,no,no,Austria,no,4,'18 and more',?,NO -1,0,0,0,0,0,1,1,0,1,17,f,?,no,no,Argentina,no,4,'18 and more',?,NO -1,1,0,1,1,0,0,1,0,1,18,m,'Middle Eastern ',no,yes,'New Zealand',no,6,'18 and more',Parent,NO -1,0,0,0,0,0,1,1,1,1,31,m,'Middle Eastern ',no,no,Jordan,no,5,'18 and more',Self,NO -0,0,0,0,0,0,0,1,0,1,30,m,White-European,no,no,Ireland,no,2,'18 and more',Self,NO -0,0,1,0,1,1,0,0,0,0,35,f,'Middle Eastern ',no,yes,'United Arab Emirates',no,3,'18 and more',Self,NO -0,0,0,0,0,0,1,1,0,1,34,m,?,yes,no,'United Arab Emirates',no,3,'18 and more',?,NO -0,1,1,1,0,0,0,0,0,0,38,m,?,no,no,'United Arab Emirates',no,3,'18 and more',?,NO -0,0,0,0,0,0,0,0,0,0,27,f,Black,no,no,'United Arab Emirates',no,0,'18 and more',Self,NO -0,0,0,1,0,0,1,1,1,1,27,m,'Middle Eastern ',no,no,Afghanistan,no,5,'18 and more',Self,NO -0,0,0,0,0,0,0,1,0,1,42,m,'Middle Eastern ',yes,no,'United Arab Emirates',no,2,'18 and more',Relative,NO -1,1,1,1,0,0,0,1,0,0,43,m,?,no,no,Lebanon,no,5,'18 and more',?,NO -0,1,1,0,0,0,0,1,0,0,24,f,?,yes,no,Afghanistan,no,3,'18 and more',?,NO -0,0,0,0,0,0,0,1,0,0,40,m,Pasifika,yes,yes,'United Arab Emirates',no,1,'18 and more',Self,NO -0,0,0,0,0,0,0,1,0,0,40,m,'Middle Eastern ',yes,yes,Afghanistan,no,1,'18 and more',Parent,NO -0,0,0,0,0,0,0,1,0,0,48,m,Black,no,no,'New Zealand',no,1,'18 and more',Self,NO -0,1,1,0,0,0,0,0,1,1,31,m,'Middle Eastern ',no,no,'United Kingdom',no,4,'18 and more',Self,NO -0,0,0,0,0,0,0,0,0,0,18,m,White-European,no,no,'United Kingdom',no,0,'18 and more',Self,NO -1,0,0,1,1,1,1,1,0,1,37,f,White-European,no,yes,'United States',no,7,'18 and more',Self,YES -1,1,0,0,0,0,1,0,0,1,55,f,Others,no,no,'New Zealand',no,4,'18 and more',Self,NO -1,1,1,1,1,1,1,1,1,1,18,f,White-European,yes,no,'South Africa',no,10,'18 and more',Self,YES -1,1,1,1,1,1,1,1,1,1,18,f,White-European,no,no,'South Africa',no,10,'18 and more',Self,YES -0,0,1,0,0,0,0,0,0,0,55,m,White-European,no,no,'New Zealand',no,1,'18 and more',Self,NO -0,1,1,0,1,0,0,1,1,1,50,m,'Middle Eastern ',no,no,'United Arab Emirates',no,6,'18 and more',Self,NO -1,0,1,1,1,1,0,0,1,0,34,f,White-European,no,no,'New Zealand',no,6,'18 and more',Self,NO -1,0,0,1,1,1,1,0,1,1,53,f,White-European,no,no,'New Zealand',no,7,'18 and more',Self,YES -1,0,1,1,0,1,1,1,1,1,35,f,White-European,no,yes,'United States',no,8,'18 and more',Self,YES -1,0,1,1,1,0,1,1,0,1,20,f,Latino,yes,no,Italy,no,7,'18 and more',Self,YES -0,0,0,0,1,1,0,0,0,0,28,f,Asian,no,no,Pakistan,no,2,'18 and more',Self,NO -0,0,1,1,0,0,0,0,0,1,34,f,'Middle Eastern ',no,yes,Egypt,no,3,'18 and more',Self,NO -0,1,1,1,0,0,0,0,0,1,36,f,White-European,yes,yes,'United States',no,4,'18 and more',Self,NO -1,1,1,1,1,1,0,1,0,1,27,f,White-European,no,no,'New Zealand',no,8,'18 and more',Self,YES -1,0,1,1,1,1,0,1,1,0,53,f,White-European,no,no,'New Zealand',no,7,'18 and more',Relative,YES -1,1,1,1,0,1,0,0,0,0,24,f,Pasifika,no,no,'New Zealand',no,5,'18 and more',Relative,NO -0,0,1,1,1,0,1,0,0,0,24,m,Pasifika,no,no,'New Zealand',no,4,'18 and more',Relative,NO -0,1,1,0,0,1,0,0,0,0,55,m,White-European,no,no,'New Zealand',no,3,'18 and more',Relative,NO -1,1,0,0,0,1,1,1,0,1,30,f,Asian,no,no,Bangladesh,no,6,'18 and more',Self,NO -1,0,0,1,0,0,0,1,0,0,21,f,Latino,no,no,Chile,no,3,'18 and more',Self,NO -1,1,1,1,1,1,1,1,1,1,35,f,Black,no,no,France,no,10,'18 and more',Parent,YES -1,0,0,0,0,0,0,0,0,0,383,f,Pasifika,no,no,'New Zealand',no,1,'18 and more',Self,NO -1,0,1,1,1,1,1,1,0,1,21,m,Latino,no,yes,Brazil,no,8,'18 and more',Self,YES -1,1,1,1,1,1,1,1,1,1,47,m,White-European,no,no,'United States',no,10,'18 and more',Self,YES -1,1,1,1,1,1,0,1,1,1,30,f,Asian,no,no,China,no,9,'18 and more',Self,YES -1,0,1,1,1,1,0,1,1,1,28,f,White-European,no,no,Australia,no,8,'18 and more',Self,YES -1,1,1,1,1,1,0,1,1,1,43,f,White-European,no,no,Australia,no,9,'18 and more',Self,YES -1,0,0,0,1,0,0,1,0,0,32,f,'South Asian',no,yes,Canada,no,3,'18 and more',Self,NO -1,1,1,1,0,0,0,1,0,0,44,f,White-European,no,no,Australia,no,5,'18 and more',Self,NO -1,0,1,1,1,1,1,1,0,1,20,f,Others,no,no,Canada,no,8,'18 and more',Self,YES -1,0,1,1,0,1,1,1,0,1,20,f,Others,no,no,Canada,yes,7,'18 and more',Self,YES -0,0,0,0,0,0,0,0,0,0,?,m,?,no,no,'Saudi Arabia',no,0,'18 and more',?,NO -1,0,0,1,1,0,0,1,1,0,19,m,White-European,no,no,Australia,no,5,'18 and more',Parent,NO -1,1,1,1,1,1,1,1,0,1,29,f,White-European,no,no,Australia,no,9,'18 and more',Self,YES -1,0,1,0,0,0,0,1,0,0,21,m,'Middle Eastern ',no,no,'United States',no,3,'18 and more',Self,NO -0,1,0,0,0,0,0,1,0,0,27,m,'Middle Eastern ',no,no,France,no,2,'18 and more',Parent,NO -1,0,1,0,1,1,1,1,0,0,21,m,Hispanic,no,no,'United States',no,6,'18 and more',Self,NO -1,0,0,0,1,0,1,0,0,1,35,m,'Middle Eastern ',no,no,'Saudi Arabia',no,4,'18 and more',Self,NO -0,0,0,0,1,0,0,0,0,0,42,m,Black,no,no,'New Zealand',no,1,'18 and more',Self,NO -1,1,0,0,0,0,1,1,0,1,29,f,'South Asian',no,no,'New Zealand',no,5,'18 and more',Self,NO -0,0,1,1,0,0,0,1,0,0,58,m,Asian,no,no,'New Zealand',no,3,'18 and more',Self,NO -1,0,1,1,0,0,1,1,0,1,21,m,Others,no,no,'United States',no,6,'18 and more',Self,NO -1,1,1,0,0,0,0,1,1,1,21,m,Others,no,no,'United States',no,6,'18 and more',Self,NO -0,0,1,1,1,0,0,1,1,1,37,f,White-European,no,yes,'United States',no,6,'18 and more',Self,NO -1,1,0,1,1,1,1,1,0,0,21,m,Hispanic,no,no,'United States',no,7,'18 and more',Self,YES -1,0,0,0,0,0,1,1,0,0,20,m,'Middle Eastern ',no,no,'United States',no,3,'18 and more',Self,NO -1,1,0,1,1,1,1,1,1,0,33,f,Black,no,yes,'United States',no,8,'18 and more',Self,YES -1,0,0,1,1,0,0,0,0,1,20,m,'Middle Eastern ',no,no,'United States',no,4,'18 and more',Self,NO -1,1,0,0,0,0,0,0,0,0,45,f,?,yes,no,Jordan,no,2,'18 and more',?,NO -0,0,1,1,0,0,1,1,0,1,32,m,?,yes,yes,Afghanistan,no,5,'18 and more',?,NO -1,0,0,0,0,0,1,0,0,1,30,m,?,no,no,Jordan,no,3,'18 and more',?,NO -1,1,1,1,1,1,1,1,1,1,33,f,White-European,no,yes,Netherlands,no,10,'18 and more',Self,YES -0,0,1,0,1,0,0,1,0,1,42,f,'Middle Eastern ',no,no,Jordan,no,4,'18 and more',Self,NO -1,1,0,0,0,0,0,0,0,0,17,f,White-European,no,no,'New Zealand',no,2,'18 and more',Self,NO -1,0,0,0,0,0,0,1,0,1,27,f,'Middle Eastern ',no,no,'United States',no,3,'18 and more',Self,NO -1,0,0,1,1,1,1,1,1,1,35,f,'Middle Eastern ',no,yes,Afghanistan,no,8,'18 and more',Parent,YES -1,0,0,0,0,0,0,0,0,0,37,m,White-European,no,no,'United Kingdom',no,1,'18 and more',Self,NO -1,0,0,0,0,0,0,1,1,1,30,f,White-European,no,yes,'United Kingdom',no,4,'18 and more',Self,NO -1,1,1,1,1,1,0,1,1,1,29,f,White-European,no,no,'New Zealand',no,9,'18 and more',Self,YES -1,0,1,1,0,0,0,0,0,1,17,f,White-European,no,yes,Romania,no,4,'18 and more',Self,NO -0,1,0,0,1,0,1,0,0,1,?,f,?,no,no,Jordan,no,4,'18 and more',?,NO -0,0,0,1,1,1,0,1,0,1,22,f,Pasifika,no,no,'New Zealand',no,5,'18 and more',Self,NO -1,1,1,0,1,1,1,0,1,1,19,f,Latino,no,yes,Brazil,no,8,'18 and more',Self,YES -1,1,1,1,1,0,0,1,1,1,42,f,White-European,no,yes,Sweden,no,8,'18 and more',Self,YES -1,1,0,0,0,0,0,1,0,1,37,f,White-European,no,no,'United Kingdom',no,4,'18 and more',Self,NO -1,0,0,0,1,0,1,1,0,0,28,f,White-European,no,no,Australia,no,4,'18 and more',Self,NO -1,0,0,1,1,0,1,0,0,0,22,m,Others,no,no,'New Zealand',no,4,'18 and more',Self,NO -1,1,0,0,0,0,0,1,0,0,26,f,Pasifika,no,no,Tonga,no,3,'18 and more',Self,NO -0,0,0,0,0,0,0,0,1,0,21,m,Pasifika,no,no,Oman,no,1,'18 and more',Self,NO -0,1,1,0,0,0,0,1,0,0,26,f,'South Asian',no,no,India,no,3,'18 and more',Self,NO -1,0,0,0,0,0,0,1,0,0,39,f,Asian,no,no,Philippines,no,2,'18 and more',Self,NO -1,0,0,0,0,0,0,0,1,1,26,m,Asian,no,no,'New Zealand',no,3,'18 and more',Self,NO -1,1,0,0,1,0,1,1,0,0,26,m,Asian,no,no,India,no,5,'18 and more',Self,NO -0,0,0,0,1,1,0,1,0,1,21,m,'South Asian',no,no,India,no,4,'18 and more',Self,NO -0,0,0,0,0,0,0,0,0,0,25,m,Asian,no,no,India,no,0,'18 and more',Self,NO -0,0,1,0,1,1,1,1,0,1,26,m,'South Asian',no,no,'New Zealand',no,6,'18 and more',Self,NO -1,1,0,1,1,0,1,1,0,1,30,f,White-European,no,no,'United States',no,7,'18 and more',Self,YES -1,0,0,1,1,1,1,0,0,0,19,f,Asian,no,no,India,no,5,'18 and more',Parent,NO -1,0,0,0,0,1,1,0,0,0,19,f,Asian,no,no,India,no,3,'18 and more',Parent,NO -1,0,1,1,1,1,1,1,1,1,25,f,White-European,no,yes,'New Zealand',no,9,'18 and more',Self,YES -0,0,1,1,0,1,0,1,1,1,23,m,White-European,no,no,'United Kingdom',no,6,'18 and more',Self,NO -1,0,0,0,1,0,0,0,0,1,31,m,Asian,no,no,'Sri Lanka',no,3,'18 and more',Parent,NO -0,1,0,0,0,0,0,1,1,1,27,m,Asian,no,no,'New Zealand',no,4,'18 and more',Self,NO -1,1,0,1,1,1,1,1,0,1,29,f,'Middle Eastern ',no,no,'United Arab Emirates',no,8,'18 and more',Self,YES -1,0,1,1,1,1,0,1,1,0,38,f,White-European,no,no,'United Kingdom',no,7,'18 and more',Parent,YES -1,1,1,1,1,1,0,1,0,1,23,f,White-European,no,no,'United States',no,8,'18 and more',Self,YES -1,1,1,0,1,0,1,1,0,0,27,m,White-European,no,no,'United States',no,6,'18 and more',Self,NO -1,1,1,1,1,1,0,1,1,1,27,f,White-European,no,no,Canada,no,9,'18 and more',Self,YES -1,1,1,1,1,1,0,0,1,0,42,m,White-European,no,no,'United States',no,7,'18 and more',Self,YES -1,1,1,1,1,1,0,1,1,1,20,m,White-European,no,no,'United Kingdom',no,9,'18 and more',Self,YES -1,0,1,1,1,1,1,1,1,1,17,f,White-European,no,no,'United States',no,9,'18 and more',Self,YES -1,0,0,0,0,0,0,0,0,0,20,f,Black,no,no,Spain,no,1,'18 and more',Self,NO -1,0,0,1,1,1,1,1,0,1,30,m,Asian,yes,no,'Sierra Leone',no,7,'18 and more',Self,YES -1,0,1,1,1,1,1,1,1,1,28,f,White-European,no,yes,Australia,no,9,'18 and more',Self,YES -1,1,0,0,1,1,1,1,0,1,26,f,White-European,no,no,'New Zealand',no,7,'18 and more',Self,YES -0,0,0,0,0,0,0,0,0,0,26,f,White-European,no,no,Australia,no,0,'18 and more',Self,NO -1,0,0,1,1,1,0,1,0,1,31,m,'Middle Eastern ',no,yes,Canada,no,6,'18 and more',Self,NO -1,0,1,1,0,0,1,0,0,1,40,f,White-European,no,no,'United Kingdom',no,5,'18 and more',Self,NO -1,0,0,0,1,0,0,0,1,1,39,f,White-European,no,yes,Ireland,no,4,'18 and more',Relative,NO -1,1,0,0,0,0,0,1,0,0,18,f,White-European,no,no,'United States',no,3,'18 and more',Self,NO -1,0,0,0,0,0,0,1,0,1,24,m,Black,no,no,Ethiopia,no,3,'18 and more',Self,NO -1,1,0,0,0,0,0,1,0,0,23,m,'South Asian',no,no,India,no,3,'18 and more',Self,NO -1,0,1,1,0,0,0,1,0,1,24,m,Asian,no,no,India,no,5,'18 and more',Self,NO -0,1,0,0,1,1,0,1,1,0,24,m,Asian,no,no,'New Zealand',no,5,'18 and more',Self,NO -1,0,1,1,0,0,0,1,1,1,27,m,Asian,no,no,'New Zealand',no,6,'18 and more',Self,NO -0,1,1,0,0,1,0,0,1,1,24,m,Asian,no,no,'New Zealand',no,5,'18 and more',Self,NO -0,0,0,0,1,0,0,0,0,1,25,m,Asian,no,no,India,no,2,'18 and more',Self,NO -1,0,0,0,0,0,1,0,0,1,22,m,Asian,no,no,'New Zealand',no,3,'18 and more',Self,NO -0,1,0,0,0,0,0,0,0,1,40,f,Asian,no,no,'New Zealand',no,2,'18 and more',Self,NO -0,0,1,1,0,0,0,1,0,1,23,m,Asian,no,no,India,no,4,'18 and more',Self,NO -1,0,0,0,0,0,0,1,0,0,29,m,'South Asian',no,no,India,no,2,'18 and more',Self,NO -1,0,1,0,0,0,0,1,0,0,25,m,Asian,no,no,India,no,3,'18 and more',Self,NO -1,0,0,1,1,0,0,1,0,0,27,m,Asian,no,no,India,no,4,'18 and more',Self,NO -0,0,0,0,0,0,0,1,0,0,22,m,'South Asian',no,no,'New Zealand',no,1,'18 and more',Self,NO -1,0,0,1,1,1,1,1,1,1,28,m,White-European,no,no,'United States',no,8,'18 and more',Parent,YES -1,0,1,1,1,1,1,1,1,1,20,m,White-European,no,no,Australia,no,9,'18 and more',Self,YES -1,1,1,1,1,1,1,1,1,1,19,f,White-European,no,no,Romania,no,10,'18 and more',Self,YES -1,1,0,0,1,1,1,1,0,1,37,f,Others,no,no,'United States',no,7,'18 and more',Self,YES -1,1,1,1,1,0,1,1,1,0,35,m,White-European,yes,yes,'United States',no,8,'18 and more',Self,YES -0,0,0,0,1,0,0,1,0,0,26,m,Asian,no,no,'New Zealand',no,2,'18 and more',Self,NO -0,1,1,1,1,0,1,1,0,1,23,m,Asian,no,no,'Viet Nam',no,7,'18 and more',Self,YES -1,1,0,0,1,0,0,0,0,1,32,f,'South Asian',no,no,'New Zealand',no,4,'18 and more',Self,NO -0,0,1,0,1,0,1,0,1,1,30,m,Asian,no,no,'Sri Lanka',no,5,'18 and more',Self,NO -1,0,0,0,1,0,1,0,0,1,31,m,Asian,no,no,'Sri Lanka',no,4,'18 and more',Self,NO -0,0,0,0,0,0,1,1,0,1,28,f,Asian,no,no,'New Zealand',no,3,'18 and more',Self,NO -1,0,0,0,0,1,0,1,0,1,29,f,Asian,no,no,'Sri Lanka',no,4,'18 and more',Self,NO -0,1,0,0,1,0,0,1,0,1,29,f,'South Asian',no,no,'Sri Lanka',no,4,'18 and more',Self,NO -1,0,0,1,0,0,0,0,1,1,29,f,Asian,no,no,India,no,4,'18 and more',Self,NO -1,0,0,0,0,0,1,1,0,0,31,m,Asian,no,no,India,no,3,'18 and more',Self,NO -0,0,0,0,0,0,0,0,0,0,36,m,Asian,no,no,India,no,0,'18 and more',Parent,NO -0,1,0,0,0,0,0,1,0,0,32,f,'South Asian',no,no,India,no,2,'18 and more',Self,NO -0,0,0,0,0,0,0,1,0,0,34,f,'South Asian',no,no,'New Zealand',no,1,'18 and more',Self,NO -1,1,0,0,0,0,0,1,0,0,24,m,Asian,no,no,'Sri Lanka',no,3,'18 and more',Self,NO -1,0,1,0,1,0,1,1,0,1,24,f,Asian,no,no,'New Zealand',no,6,'18 and more',Self,NO -1,0,0,0,1,0,1,1,0,1,42,m,Asian,no,no,'Sri Lanka',no,5,'18 and more',Self,NO -0,0,1,0,0,0,0,1,1,1,26,f,Others,no,no,India,no,4,'18 and more',Self,NO -1,0,0,0,1,0,1,1,0,0,27,f,Asian,no,no,'Sri Lanka',no,4,'18 and more',Self,NO -0,0,1,1,1,0,0,1,1,1,36,f,Latino,no,yes,Brazil,no,6,'18 and more',Self,NO -1,1,1,1,1,0,0,1,1,1,36,f,Latino,no,yes,Brazil,no,8,'18 and more',Self,YES -1,0,1,1,0,0,1,1,0,1,55,m,Hispanic,no,no,'United States',no,6,'18 and more',Self,NO -1,1,0,1,0,0,1,0,0,0,23,f,Asian,no,no,'New Zealand',no,4,'18 and more',Self,NO -0,0,1,1,1,0,0,0,0,1,22,f,Asian,no,no,'Sri Lanka',no,4,'18 and more',Self,NO -1,0,0,0,0,0,0,1,0,0,42,f,'Middle Eastern ',no,no,France,no,2,'18 and more',Parent,NO -1,0,0,1,0,0,0,1,0,0,54,m,White-European,no,no,Canada,no,3,'18 and more',Self,NO -1,1,0,0,0,0,1,1,1,1,43,f,Black,no,no,'United States',no,6,'18 and more',Self,NO -1,1,1,1,1,1,0,1,1,1,43,f,Black,no,no,'United States',yes,9,'18 and more',Self,YES -0,0,1,1,1,0,0,1,1,1,29,m,'Middle Eastern ',no,no,Iran,no,6,'18 and more',Self,NO -1,0,1,0,1,0,0,1,0,1,29,m,'Middle Eastern ',no,no,Iran,no,5,'18 and more',Self,NO -1,0,1,0,1,0,1,0,0,0,37,m,White-European,no,no,Australia,no,4,'18 and more',Self,NO -1,0,0,0,1,0,1,0,0,1,39,f,White-European,no,no,Australia,no,4,'18 and more',Self,NO -1,1,1,1,1,0,0,0,0,0,18,f,Asian,no,no,'Viet Nam',no,5,'18 and more',Self,NO -0,1,0,1,1,1,0,1,0,1,31,m,White-European,no,no,'United States',no,6,'18 and more',Others,NO -0,0,1,1,1,0,0,0,0,1,34,m,Asian,no,no,'New Zealand',no,4,'18 and more',Self,NO -0,0,1,1,1,0,1,0,0,1,22,m,White-European,no,no,'United States',no,5,'18 and more',Self,NO -1,0,1,1,1,1,1,0,1,0,28,m,'South Asian',no,no,India,no,7,'18 and more',Self,YES -1,0,0,0,0,0,1,1,0,1,38,m,Others,no,yes,Netherlands,no,4,'18 and more',Self,NO -1,0,0,0,0,0,1,1,0,1,28,f,White-European,no,no,Australia,no,4,'18 and more',Self,NO -1,1,0,1,1,1,0,1,1,1,53,f,White-European,no,yes,'United States',no,8,'18 and more',Self,YES -1,1,1,0,1,0,1,0,0,0,26,m,White-European,no,no,'United Kingdom',no,5,'18 and more',Self,NO -1,1,1,1,1,1,0,1,1,1,37,m,White-European,no,no,'United Kingdom',no,9,'18 and more',Self,YES -1,1,0,1,0,0,0,1,0,1,42,m,Latino,yes,yes,'Costa Rica',no,5,'18 and more',Parent,NO -1,0,0,0,0,0,0,1,0,1,43,m,Hispanic,no,no,'United States',no,3,'18 and more',Self,NO -1,0,0,0,0,0,0,1,0,0,32,f,Asian,no,no,'United Kingdom',no,2,'18 and more',Self,NO -0,1,0,1,0,0,0,1,0,0,31,m,White-European,no,yes,'United Kingdom',no,3,'18 and more',Self,NO -1,0,1,1,1,1,1,1,1,1,53,f,White-European,no,no,'United States',no,9,'18 and more',Self,YES -1,1,1,1,1,1,1,1,1,1,28,m,White-European,no,no,'United States',no,10,'18 and more',Self,YES -1,0,1,1,1,1,1,1,1,1,38,m,White-European,no,no,Germany,no,9,'18 and more',Self,YES -1,1,1,0,1,1,0,1,1,0,31,m,White-European,no,no,'United States',no,7,'18 and more',Self,YES -1,1,1,1,0,0,0,0,0,0,31,f,Pasifika,no,yes,'United States',no,4,'18 and more',Self,NO -1,1,1,1,1,0,1,1,1,1,21,f,White-European,no,no,Australia,no,9,'18 and more',Self,YES -1,1,0,1,1,0,0,0,1,1,25,f,White-European,no,no,'United States',no,6,'18 and more',Self,NO -1,1,0,1,1,0,0,1,1,1,25,f,White-European,no,no,'United States',no,7,'18 and more',Self,YES -1,1,1,1,1,1,1,0,1,1,60,f,White-European,no,yes,'United States',no,9,'18 and more',Relative,YES -1,0,1,1,0,0,0,0,0,0,39,m,White-European,no,yes,'United States',no,3,'18 and more',Self,NO -1,1,1,1,1,1,1,1,1,1,53,m,White-European,no,no,'United States',no,10,'18 and more',Self,YES -1,1,1,1,1,1,1,1,1,1,20,m,'Middle Eastern ',no,yes,'United States',no,10,'18 and more',Parent,YES -1,1,1,0,1,0,0,1,1,1,25,f,White-European,no,no,'United States',yes,7,'18 and more',Self,YES -1,1,1,1,0,0,1,1,0,0,50,m,White-European,no,no,'United Kingdom',no,6,'18 and more',Self,NO -1,0,0,1,1,1,1,1,1,1,28,f,White-European,no,no,'United States',no,8,'18 and more',Self,YES -1,0,1,1,1,1,1,1,1,1,37,f,White-European,no,yes,'United Kingdom',no,9,'18 and more',Self,YES -0,0,0,1,0,1,0,1,1,1,30,m,Asian,no,yes,India,no,5,'18 and more',Self,NO -1,1,0,0,0,1,1,1,0,1,32,f,White-European,no,no,'United Kingdom',no,6,'18 and more',Self,NO -1,0,1,1,0,0,0,0,0,1,34,f,White-European,no,no,'United States',no,4,'18 and more',Self,NO -1,1,1,1,0,0,0,0,0,1,42,m,White-European,no,no,'United States',no,5,'18 and more',Self,NO -1,1,0,0,1,1,1,1,1,1,22,f,Black,no,no,Spain,no,8,'18 and more',Self,YES -1,0,0,0,0,0,1,1,0,1,37,f,?,yes,no,'United Arab Emirates',no,4,'18 and more',?,NO -1,1,0,0,0,0,0,1,0,1,39,m,White-European,no,yes,'United States',no,4,'18 and more',Parent,NO -1,1,0,1,1,0,1,1,0,1,18,m,White-European,no,no,Bangladesh,no,7,'18 and more',Self,YES -1,0,0,1,0,0,0,0,0,0,54,f,White-European,no,yes,'United States',no,2,'18 and more',Parent,NO -1,1,0,0,0,0,0,1,0,0,42,m,White-European,no,no,'United Kingdom',no,3,'18 and more',Self,NO -0,0,1,0,0,0,0,0,0,0,27,f,?,no,no,Afghanistan,no,1,'18 and more',?,NO -1,1,1,1,1,0,0,1,0,0,22,m,White-European,no,no,'United Kingdom',no,6,'18 and more',Self,NO -1,0,1,1,0,1,1,1,1,1,22,m,White-European,no,no,'United Kingdom',no,8,'18 and more',Self,YES -1,1,1,1,1,1,1,0,1,1,21,f,White-European,no,no,'United Kingdom',no,9,'18 and more',Self,YES -0,1,0,1,0,0,0,1,0,1,28,f,White-European,no,no,'United States',no,4,'18 and more',Self,NO -1,1,1,1,1,1,1,0,1,1,17,m,Asian,yes,no,India,no,9,'18 and more',Relative,YES -0,1,0,0,0,0,0,0,0,0,28,m,White-European,no,no,'United States',no,1,'18 and more',Self,NO -0,0,0,1,1,0,1,1,0,1,21,m,Latino,no,no,Mexico,no,5,'18 and more',Self,NO -1,1,1,1,1,1,1,1,1,1,31,f,Asian,no,no,'New Zealand',no,10,'18 and more',Self,YES -1,0,0,0,0,0,1,1,0,0,20,m,'South Asian',no,no,'New Zealand',no,3,'18 and more',Self,NO -1,0,0,1,0,0,0,0,0,0,21,m,White-European,no,no,'United Kingdom',no,2,'18 and more',Self,NO -1,1,0,0,1,0,0,0,0,0,22,m,White-European,no,no,'New Zealand',no,3,'18 and more',Self,NO -0,1,0,0,0,0,0,1,1,1,21,m,White-European,no,no,'New Zealand',no,4,'18 and more',Self,NO -0,1,1,0,0,0,0,0,0,0,24,m,Black,no,no,'New Zealand',no,2,'18 and more',Self,NO -1,0,0,0,0,0,0,1,0,0,24,m,'South Asian',no,no,India,no,2,'18 and more',Self,NO -1,0,0,0,1,1,1,1,0,1,25,m,Asian,no,no,'New Zealand',no,6,'18 and more',Self,NO -1,0,0,0,0,0,0,0,0,1,17,m,'South Asian',no,no,'New Zealand',no,2,'18 and more',Self,NO -0,1,0,0,0,0,0,1,1,0,21,m,?,no,no,Russia,no,3,'18 and more',?,NO -1,0,0,0,0,0,0,1,0,0,23,m,Pasifika,no,no,'New Zealand',no,2,'18 and more',Self,NO -1,0,0,1,1,0,1,1,0,0,21,m,Asian,no,no,'New Zealand',no,5,'18 and more',Self,NO -1,0,0,0,1,0,1,1,1,1,22,m,Others,no,no,'New Zealand',no,6,'18 and more',Self,NO -1,0,0,0,1,0,0,1,0,1,24,f,Asian,no,no,India,no,4,'18 and more',Self,NO -0,0,1,0,1,1,0,0,1,1,34,m,Others,no,no,'New Zealand',no,5,'18 and more',Self,NO -1,0,1,0,1,0,1,1,0,1,30,m,Asian,no,no,'New Zealand',no,6,'18 and more',Self,NO -1,0,0,0,0,0,1,1,0,1,29,f,Asian,no,no,India,no,4,'18 and more',Self,NO -1,0,0,1,1,0,1,1,0,0,20,m,Pasifika,no,no,'New Zealand',no,5,'18 and more',Self,NO -0,0,1,1,0,1,0,1,1,1,23,m,White-European,no,no,'New Zealand',no,6,'18 and more',Self,NO -0,0,0,0,0,0,0,1,0,1,26,m,Asian,no,no,India,no,2,'18 and more',Self,NO -1,0,0,1,1,1,1,1,1,1,20,m,'South Asian',no,no,'New Zealand',no,8,'18 and more',Self,YES -1,0,0,0,0,0,1,1,0,0,41,m,Asian,no,no,'New Zealand',no,3,'18 and more',Self,NO -1,0,0,1,0,0,1,1,0,0,24,m,Asian,no,no,India,no,4,'18 and more',Self,NO -0,0,0,1,0,0,0,1,0,0,35,m,Asian,no,no,India,no,2,'18 and more',Self,NO -0,0,0,1,0,0,1,0,0,0,23,m,'South Asian',no,no,India,no,2,'18 and more',Self,NO -1,0,1,0,0,0,1,0,0,0,36,m,White-European,no,no,'New Zealand',no,3,'18 and more',Self,NO -1,1,0,0,1,0,0,1,0,0,33,m,Turkish,no,no,Armenia,no,4,'18 and more',Self,NO -1,0,0,0,0,1,0,1,1,1,24,m,'South Asian',no,no,India,no,5,'18 and more',Self,NO -0,1,1,0,1,0,0,1,0,0,25,m,?,no,no,'New Zealand',no,4,'18 and more',?,NO -1,0,0,0,1,1,0,1,0,1,25,m,Asian,no,no,India,no,5,'18 and more',Self,NO -1,0,0,0,0,0,1,1,1,1,25,m,Asian,no,no,India,no,5,'18 and more',Self,NO -0,0,0,0,0,0,1,1,0,1,24,m,Asian,no,no,India,no,3,'18 and more',Self,NO -1,0,1,0,1,0,0,1,0,1,27,f,'South Asian',no,no,India,no,5,'18 and more',Self,NO -1,0,0,0,0,0,1,1,0,0,26,m,Asian,no,no,India,no,3,'18 and more',Self,NO -0,0,0,0,0,0,0,0,0,0,26,m,Others,no,no,'New Zealand',no,0,'18 and more',Self,NO -1,0,0,0,1,0,0,1,1,0,23,f,Asian,no,no,India,no,4,'18 and more',Self,NO -1,1,1,0,1,0,1,1,0,1,30,m,Asian,no,no,'New Zealand',no,7,'18 and more',Self,YES -1,0,0,1,0,0,0,0,1,0,25,m,Asian,no,no,Iceland,no,3,'18 and more',Self,NO -1,0,0,0,0,0,0,1,0,1,23,m,Pasifika,no,no,'New Zealand',no,3,'18 and more',Self,NO -1,0,1,1,1,0,0,1,0,0,34,f,White-European,no,no,'United States',no,5,'18 and more',Self,NO -1,0,0,1,1,0,1,1,0,1,20,f,Asian,no,no,'New Zealand',no,6,'18 and more',Self,NO -1,0,1,1,1,1,0,0,1,1,22,f,?,no,yes,Russia,no,7,'18 and more',?,YES -1,1,1,1,0,0,0,1,1,1,33,f,Black,no,yes,France,no,7,'18 and more',Parent,YES -1,1,1,1,1,1,1,1,1,1,46,m,White-European,no,no,Netherlands,no,10,'18 and more',Self,YES -1,1,0,0,0,0,0,0,0,0,17,m,White-European,no,no,'United Kingdom',no,2,'18 and more',Others,NO -1,1,1,0,0,0,0,1,0,0,45,f,White-European,no,yes,'United States',no,4,'18 and more',Self,NO -0,1,1,1,0,0,0,0,1,0,26,m,Asian,no,no,India,no,4,'18 and more',Others,NO -1,0,0,0,1,0,0,0,1,0,30,m,?,no,no,Jordan,no,3,'18 and more',?,NO -1,1,0,0,0,0,0,0,0,0,32,f,?,no,yes,Jordan,no,2,'18 and more',?,NO -1,0,1,1,1,1,1,1,1,1,38,f,White-European,no,no,'United Kingdom',no,9,'18 and more',Self,YES -0,1,1,0,1,0,0,0,0,0,26,m,Hispanic,yes,no,Nicaragua,no,3,'18 and more',Self,NO -1,0,0,0,0,0,0,0,0,0,29,f,Hispanic,no,yes,'United States',no,1,'18 and more',Self,NO -1,1,1,1,1,0,0,1,1,1,33,m,White-European,no,no,Australia,no,8,'18 and more',Self,YES -0,1,1,0,1,1,1,0,1,1,32,f,Black,no,no,France,no,7,'18 and more',Parent,YES -1,1,1,1,1,1,1,1,1,1,44,f,White-European,no,no,'United States',no,10,'18 and more',Self,YES -0,1,0,0,0,0,1,0,0,1,47,f,White-European,yes,no,'United Kingdom',no,3,'18 and more',Self,NO -0,0,0,1,1,1,1,0,1,1,32,f,?,no,no,'Hong Kong',no,6,'18 and more',?,NO -0,0,0,0,0,0,0,0,0,0,40,m,White-European,no,no,'New Zealand',no,0,'18 and more',Self,NO -1,0,0,1,1,0,0,1,0,1,40,m,White-European,no,no,'United States',no,5,'18 and more',Self,NO -0,0,0,0,0,0,0,0,0,0,44,f,White-European,no,no,'New Zealand',no,0,'18 and more',Self,NO -1,1,1,0,1,1,1,0,1,1,56,m,White-European,yes,no,'United Kingdom',no,8,'18 and more',Self,YES -1,1,1,0,1,1,1,0,1,1,23,f,White-European,no,no,Ireland,no,8,'18 and more',Self,YES -1,1,0,1,1,0,0,1,1,1,32,f,Black,no,no,Canada,no,7,'18 and more',Self,YES -0,1,1,1,0,0,0,1,0,1,27,m,White-European,no,yes,'United Kingdom',no,5,'18 and more',Self,NO -1,0,0,0,1,0,1,0,0,1,28,f,White-European,no,yes,'United Kingdom',no,4,'18 and more',Self,NO -0,0,0,0,0,0,0,0,0,0,40,f,'Middle Eastern ',no,no,Afghanistan,no,0,'18 and more',Parent,NO -1,0,1,1,1,1,0,0,1,1,45,f,White-European,no,no,Canada,no,7,'18 and more',Self,YES -1,1,1,1,1,1,0,0,1,1,19,f,White-European,yes,no,'United Kingdom',no,8,'18 and more',Parent,YES -1,1,1,1,0,1,0,1,1,1,55,m,White-European,no,yes,'United States',no,8,'18 and more',Others,YES -1,0,0,1,0,0,1,1,0,1,27,f,White-European,yes,no,Australia,no,5,'18 and more',Self,NO -1,1,1,1,1,1,1,0,1,1,19,f,White-European,yes,no,'United Kingdom',no,9,'18 and more',Self,YES -1,0,0,1,0,0,0,1,0,0,29,m,White-European,no,no,Ireland,no,3,'18 and more',Self,NO -1,1,1,1,1,1,1,1,1,1,33,f,White-European,yes,no,'United Kingdom',no,10,'18 and more',Self,YES -0,0,1,1,1,1,1,1,0,0,48,f,White-European,no,no,Netherlands,no,6,'18 and more',Self,NO -0,0,0,0,0,0,0,1,1,0,37,f,Others,no,no,'United Kingdom',no,2,'18 and more',Self,NO -1,1,1,1,1,0,0,0,1,0,30,m,Asian,no,no,Afghanistan,no,6,'18 and more',Self,NO -1,1,0,1,0,0,0,0,0,1,37,f,White-European,no,no,'United Kingdom',no,4,'18 and more',Self,NO -1,0,0,1,1,0,1,1,1,1,28,f,?,no,no,'Saudi Arabia',no,7,'18 and more',?,YES -1,0,0,1,1,1,1,1,1,1,20,f,White-European,no,no,Austria,no,8,'18 and more',Self,YES -1,1,1,1,1,1,0,0,1,1,25,f,Others,no,no,'United States',no,8,'18 and more',Self,YES -1,1,1,1,1,1,0,1,0,0,58,f,'Middle Eastern ',no,no,'United Kingdom',no,7,'18 and more',Self,YES -1,0,1,0,0,0,0,0,0,0,36,f,White-European,no,no,'United States',no,2,'18 and more',Self,NO -0,0,1,1,1,0,0,1,0,0,36,m,White-European,yes,no,'United States',no,4,'18 and more',Self,NO -1,0,1,0,1,1,1,1,1,0,19,m,Hispanic,no,no,'United States',no,7,'18 and more',Self,YES -1,0,1,1,1,1,0,1,1,0,19,m,Hispanic,no,no,'United States',yes,7,'18 and more',Self,YES -1,0,0,1,1,0,0,1,0,1,22,f,White-European,no,no,'New Zealand',no,5,'18 and more',Self,NO -1,1,0,0,0,0,0,1,0,0,20,m,?,no,no,'United Arab Emirates',no,3,'18 and more',?,NO -0,1,0,0,0,0,1,1,0,1,24,f,'Middle Eastern ',no,no,'United Arab Emirates',no,4,'18 and more',Self,NO -0,0,0,1,1,0,0,1,1,0,19,m,'Middle Eastern ',no,no,'United Arab Emirates',no,4,'18 and more',Self,NO -1,1,1,0,1,0,0,1,0,1,21,m,Others,no,no,'United Arab Emirates',no,6,'18 and more',Self,NO -0,0,0,0,0,0,0,0,0,0,20,m,'Middle Eastern ',no,no,'United Arab Emirates',no,0,'18 and more',Self,NO -1,1,0,0,0,0,0,0,0,0,18,f,'Middle Eastern ',no,no,'United Arab Emirates',no,2,'18 and more',Self,NO -0,1,1,0,0,0,0,1,0,1,23,f,'Middle Eastern ',no,no,'United Arab Emirates',no,4,'18 and more',Self,NO -1,1,1,0,1,1,1,1,0,1,22,m,'Middle Eastern ',no,no,'United Arab Emirates',no,8,'18 and more',Self,YES -1,1,0,1,1,0,0,0,0,0,18,f,'South Asian',no,yes,Bangladesh,no,4,'18 and more',Self,NO -1,0,0,0,0,0,0,0,0,1,37,f,?,no,no,'United Arab Emirates',no,2,'18 and more',?,NO -1,1,1,0,1,0,0,1,0,0,18,m,'Middle Eastern ',no,no,'United Arab Emirates',no,5,'18 and more',Relative,NO -1,1,1,0,1,0,0,1,0,0,18,m,'Middle Eastern ',no,no,'United Arab Emirates',no,5,'18 and more',Relative,NO -0,0,0,0,1,0,0,1,0,0,18,m,'Middle Eastern ',no,no,'United Arab Emirates',no,2,'18 and more',Relative,NO -1,1,1,0,0,0,0,1,0,0,18,m,'Middle Eastern ',no,no,'United Arab Emirates',no,4,'18 and more',Relative,NO -0,1,0,0,0,0,1,1,0,0,18,m,'Middle Eastern ',no,no,'United Arab Emirates',no,3,'18 and more',Relative,NO -1,1,1,0,0,0,0,1,0,0,18,m,'Middle Eastern ',no,no,'United Arab Emirates',no,4,'18 and more',Relative,NO -1,1,1,1,1,1,1,1,1,1,19,m,White-European,no,no,Austria,no,10,'18 and more',Self,YES -1,0,0,1,1,1,1,0,1,1,55,m,White-European,no,no,'United Kingdom',no,7,'18 and more',Self,YES -1,0,0,0,0,1,1,0,1,1,32,f,Asian,no,no,Canada,no,5,'18 and more',Parent,NO -1,1,0,1,0,0,1,1,0,0,50,m,White-European,yes,no,'United Kingdom',no,5,'18 and more',Self,NO -1,1,1,1,0,0,1,1,0,0,40,m,White-European,no,no,'United Kingdom',no,6,'18 and more',Self,NO -1,0,0,1,1,1,0,0,0,0,47,m,'Middle Eastern ',no,no,Jordan,no,4,'18 and more',Self,NO -1,1,0,0,0,0,0,0,0,1,20,m,?,no,no,Jordan,no,3,'18 and more',?,NO -1,0,1,0,0,0,1,1,0,0,22,f,?,no,no,Jordan,no,4,'18 and more',?,NO -1,0,0,0,0,0,1,0,0,0,21,f,?,no,no,Jordan,no,2,'18 and more',?,NO -1,0,1,1,0,0,1,1,1,0,21,f,?,no,no,Jordan,no,6,'18 and more',?,NO -1,1,1,1,1,0,0,1,0,0,19,m,?,no,no,Jordan,no,6,'18 and more',?,NO -1,0,1,1,0,0,0,0,0,0,21,f,?,no,no,Jordan,no,3,'18 and more',?,NO -1,0,0,1,0,0,0,0,0,0,21,m,?,no,no,Jordan,no,2,'18 and more',?,NO -0,1,0,1,0,1,0,1,0,1,23,f,?,no,no,Jordan,no,5,'18 and more',?,NO -1,0,0,0,0,0,0,0,0,0,21,m,?,no,no,Jordan,no,1,'18 and more',?,NO -1,0,0,0,1,0,1,1,1,0,21,m,?,no,no,Jordan,no,5,'18 and more',?,NO -0,0,0,0,0,0,0,1,0,0,23,m,?,no,no,Jordan,no,1,'18 and more',?,NO -1,0,1,1,0,1,1,1,1,1,20,f,?,no,no,Jordan,no,8,'18 and more',?,YES -1,1,1,0,0,0,0,1,0,1,21,f,?,no,no,Jordan,no,5,'18 and more',?,NO -1,1,1,0,1,0,1,1,1,1,20,m,?,no,no,Argentina,no,8,'18 and more',?,YES -1,1,0,0,0,0,1,1,0,0,19,m,?,no,no,Jordan,no,4,'18 and more',?,NO -0,1,0,0,0,0,0,1,0,0,21,m,?,no,no,Jordan,no,2,'18 and more',?,NO -1,0,1,0,0,0,1,0,0,0,26,m,?,no,yes,Jordan,no,3,'18 and more',?,NO -0,0,0,1,0,0,0,1,0,0,21,m,?,no,no,Jordan,no,2,'18 and more',?,NO -0,0,0,0,1,0,0,1,0,0,19,m,?,no,no,Japan,no,2,'18 and more',?,NO -1,0,0,1,0,0,0,1,0,1,22,m,'Middle Eastern ',no,no,'United Arab Emirates',no,4,'18 and more',Parent,NO -1,0,0,0,0,0,0,1,0,0,19,f,'Middle Eastern ',no,no,'United Arab Emirates',no,2,'18 and more',Self,NO -1,0,0,1,0,0,1,0,0,1,22,m,'Middle Eastern ',no,no,'United Arab Emirates',no,4,'18 and more',Self,NO -0,0,1,0,0,0,0,0,0,1,20,m,'Middle Eastern ',no,no,'United Arab Emirates',no,2,'18 and more',Self,NO -1,0,1,0,1,0,0,1,0,0,23,m,'Middle Eastern ',no,no,'United Arab Emirates',no,4,'18 and more',Self,NO -0,0,0,0,0,1,0,1,0,0,19,f,?,no,no,Ukraine,no,2,'18 and more',?,NO -0,0,0,0,1,0,0,1,0,1,20,m,'Middle Eastern ',no,no,'United Arab Emirates',no,3,'18 and more',Parent,NO -0,1,0,1,0,0,0,0,0,0,24,m,'Middle Eastern ',no,no,'United Arab Emirates',no,2,'18 and more',Self,NO -0,1,1,0,0,0,0,1,0,1,19,m,'Middle Eastern ',no,no,'United Arab Emirates',no,4,'18 and more',Self,NO -1,0,0,0,0,1,0,1,0,1,20,m,?,no,no,'United Arab Emirates',no,4,'18 and more',?,NO -1,0,1,0,1,0,0,1,0,1,23,m,'Middle Eastern ',no,yes,'United Arab Emirates',no,5,'18 and more',Self,NO -0,0,1,1,0,0,1,0,0,1,20,m,Others,no,no,Russia,no,4,'18 and more',Self,NO -1,0,1,0,1,0,0,1,0,1,20,m,'Middle Eastern ',no,no,'United Arab Emirates',no,5,'18 and more',Self,NO -0,0,0,0,0,0,0,1,1,0,20,m,?,yes,no,'United Arab Emirates',no,2,'18 and more',?,NO -1,0,1,0,0,0,0,0,0,1,20,f,?,no,no,'United Arab Emirates',no,3,'18 and more',?,NO -1,0,0,1,1,0,0,1,0,1,21,f,Asian,no,no,Afghanistan,no,5,'18 and more',Self,NO -0,1,0,0,0,0,0,0,0,1,19,m,?,no,no,Kazakhstan,no,2,'18 and more',?,NO -1,0,0,0,0,0,0,1,0,1,20,f,'Middle Eastern ',no,no,'United Arab Emirates',no,3,'18 and more',Self,NO -1,0,0,1,1,0,1,0,0,1,20,m,'Middle Eastern ',no,no,'Saudi Arabia',no,5,'18 and more',Self,NO -1,0,0,0,0,1,0,0,1,0,25,m,'Middle Eastern ',yes,yes,Armenia,yes,3,'18 and more',Relative,NO -1,0,0,0,0,0,0,1,0,0,20,f,'Middle Eastern ',no,no,'United Arab Emirates',no,2,'18 and more',Self,NO -1,0,0,0,0,1,1,1,0,1,22,m,'Middle Eastern ',no,no,AmericanSamoa,no,5,'18 and more',Self,NO -1,0,0,0,0,0,0,1,0,0,20,f,?,no,no,'United Arab Emirates',no,2,'18 and more',?,NO -0,1,0,0,1,1,0,0,0,0,20,m,?,no,no,'United Arab Emirates',no,3,'18 and more',?,NO -0,0,0,1,0,0,1,0,0,0,20,f,?,no,no,Jordan,no,2,'18 and more',?,NO -1,0,0,0,0,0,0,1,0,0,23,f,?,no,no,Jordan,no,2,'18 and more',?,NO -1,1,1,1,1,0,1,0,1,1,21,f,?,no,no,Jordan,no,8,'18 and more',?,YES -1,0,0,0,0,0,1,1,0,1,22,m,?,no,no,Jordan,no,4,'18 and more',?,NO -1,1,0,0,0,0,0,1,0,0,21,f,?,no,no,Jordan,no,3,'18 and more',?,NO -1,0,1,0,0,0,0,0,0,1,20,f,?,no,no,Jordan,no,3,'18 and more',?,NO -1,0,0,1,0,0,0,0,0,0,22,f,?,no,no,Jordan,no,2,'18 and more',?,NO -1,0,1,1,1,0,1,1,0,0,21,f,?,yes,no,Jordan,no,6,'18 and more',?,NO -1,1,0,0,1,0,0,1,0,0,20,f,?,no,no,Jordan,no,4,'18 and more',?,NO -0,1,0,0,0,0,0,1,0,0,20,f,'Middle Eastern ',no,no,'United Arab Emirates',no,2,'18 and more',Self,NO -1,0,0,0,1,0,0,1,0,0,21,m,?,no,no,Jordan,no,3,'18 and more',?,NO -1,0,1,1,1,0,0,0,1,1,21,m,'Middle Eastern ',no,no,'United Arab Emirates',no,6,'18 and more',Self,NO -0,1,0,0,0,1,0,1,0,1,23,f,'Middle Eastern ',no,no,'United Arab Emirates',no,4,'18 and more',Self,NO -1,1,0,0,0,0,0,1,0,0,21,f,Turkish,no,no,'United Arab Emirates',no,3,'18 and more',Self,NO -1,0,0,0,1,1,0,1,0,1,22,f,Black,no,no,'United Arab Emirates',no,5,'18 and more',Self,NO -0,1,1,1,0,1,0,1,0,0,19,f,?,no,no,'United Arab Emirates',no,5,'18 and more',?,NO -1,1,1,0,1,0,0,0,0,0,20,f,Asian,no,no,'United Arab Emirates',no,4,'18 and more',Self,NO -0,1,0,0,0,0,0,1,0,1,21,f,Others,no,no,'United Arab Emirates',no,3,'18 and more',Self,NO -0,1,1,0,0,0,0,1,0,0,26,m,'Middle Eastern ',no,no,'United Arab Emirates',no,3,'18 and more',Self,NO -1,0,0,0,0,0,0,0,0,0,19,f,?,no,no,Kazakhstan,no,1,'18 and more',?,NO -1,0,0,1,0,0,1,1,0,1,20,f,?,no,no,'United Arab Emirates',no,5,'18 and more',?,NO -1,0,0,0,0,0,0,1,0,1,26,f,?,no,no,'United Arab Emirates',no,3,'18 and more',?,NO -1,0,0,1,0,0,1,0,0,1,23,f,Black,no,no,'United Arab Emirates',no,4,'18 and more',Self,NO -1,0,0,1,0,0,1,0,0,1,28,f,?,no,no,'United Arab Emirates',no,4,'18 and more',?,NO -0,1,0,1,1,0,0,0,0,0,19,f,'Middle Eastern ',no,no,'United Arab Emirates',no,3,'18 and more',Self,NO -1,0,0,0,0,0,1,1,0,0,20,f,'Middle Eastern ',no,no,'United Arab Emirates',no,3,'18 and more',Self,NO -0,1,0,0,1,0,1,1,0,1,19,f,Black,no,no,'United Arab Emirates',no,5,'18 and more',Self,NO -1,0,1,1,1,1,1,1,1,0,24,m,Asian,no,no,India,no,8,'18 and more',Self,YES -1,0,0,1,1,0,0,1,0,1,19,f,'South Asian',no,no,'United Arab Emirates',no,5,'18 and more',Self,NO -1,1,0,0,0,0,0,0,0,0,22,f,'Middle Eastern ',no,no,'United Arab Emirates',no,2,'18 and more',Self,NO -1,1,0,1,0,0,0,0,0,1,20,f,'Middle Eastern ',no,no,'United Arab Emirates',no,4,'18 and more',Self,NO -0,1,0,1,0,0,0,1,0,0,21,m,'Middle Eastern ',no,no,'United Arab Emirates',no,3,'18 and more',Self,NO -0,0,1,1,1,0,0,1,0,0,24,f,Others,no,no,'United Arab Emirates',no,4,'18 and more',Self,NO -0,0,1,1,1,0,0,0,0,1,18,f,'Middle Eastern ',no,no,'United Arab Emirates',no,4,'18 and more',Self,NO -1,0,1,1,1,0,1,1,0,1,19,f,'Middle Eastern ',no,no,'United Arab Emirates',no,7,'18 and more',Self,YES -1,0,1,0,0,0,0,0,0,0,19,f,'Middle Eastern ',no,no,'United Arab Emirates',no,2,'18 and more',Self,NO -1,0,0,0,0,0,1,1,0,1,31,m,'Middle Eastern ',yes,no,'United Arab Emirates',no,4,'18 and more',Self,NO -1,1,0,1,0,0,1,1,0,0,19,m,'Middle Eastern ',no,no,'United Arab Emirates',no,5,'18 and more',Self,NO -1,0,0,0,0,0,1,0,1,1,21,f,'Middle Eastern ',no,no,'United Arab Emirates',no,4,'18 and more',Self,NO -1,0,0,0,1,0,1,0,0,1,20,m,'Middle Eastern ',no,no,'United Arab Emirates',no,4,'18 and more',Self,NO -1,0,1,0,1,0,1,1,0,1,26,f,'Middle Eastern ',no,no,'United Arab Emirates',no,6,'18 and more',Self,NO -0,0,0,0,0,0,1,0,0,0,27,m,'Middle Eastern ',no,no,Afghanistan,no,1,'18 and more',Self,NO -1,0,1,0,0,1,0,1,0,0,23,f,'Middle Eastern ',no,no,'United Arab Emirates',no,4,'18 and more',Self,NO -0,1,0,1,0,0,0,0,0,0,29,f,?,no,no,'United Arab Emirates',no,2,'18 and more',?,NO -1,1,1,1,0,0,1,0,0,0,23,m,'Middle Eastern ',no,no,'United Arab Emirates',no,5,'18 and more',Self,NO -1,0,0,0,0,0,1,1,0,1,23,m,Turkish,no,no,'United Arab Emirates',no,4,'18 and more',Others,NO -1,0,1,1,1,1,0,1,1,1,24,m,Asian,no,yes,India,no,8,'18 and more',Self,YES -1,0,0,1,0,0,1,1,0,0,21,m,?,no,no,Jordan,no,4,'18 and more',?,NO -0,0,0,0,1,0,1,0,0,0,20,f,?,no,no,Brazil,yes,2,'18 and more',?,NO -1,0,1,0,1,0,1,1,1,0,20,m,?,no,no,Jordan,no,6,'18 and more',?,NO -1,1,1,1,1,1,1,0,1,1,32,f,Black,no,no,France,no,9,'18 and more',Parent,YES -1,1,1,1,1,1,1,1,1,1,61,m,White-European,yes,yes,Uruguay,no,10,'18 and more',Self,YES -1,0,0,1,1,0,0,0,0,0,19,m,?,no,no,Jordan,no,3,'18 and more',?,NO -1,1,1,1,1,1,0,1,1,0,35,f,White-European,no,no,Italy,no,8,'18 and more',Self,YES -1,0,0,0,0,1,0,0,0,1,34,f,White-European,no,no,Ukraine,yes,3,'18 and more',Self,NO -0,0,1,1,0,0,0,0,0,0,23,f,'Middle Eastern ',no,no,'United Arab Emirates',yes,2,'18 and more',Self,NO -1,1,1,1,0,0,0,0,0,1,19,f,White-European,no,no,Serbia,no,5,'18 and more',Self,NO -1,1,0,0,1,1,0,1,0,1,36,f,Others,no,no,Philippines,no,6,'18 and more',Parent,NO -1,1,0,0,0,0,0,1,0,1,21,f,?,no,no,Jordan,no,4,'18 and more',?,NO -0,1,0,0,0,0,0,1,0,0,23,m,White-European,no,no,'United States',no,2,'18 and more',Self,NO -1,0,1,1,1,0,1,0,0,1,17,m,Black,no,no,'United States',no,6,'18 and more',Relative,NO -1,0,1,0,0,0,0,0,0,0,22,m,White-European,no,no,Portugal,no,2,'18 and more',Self,NO -0,1,1,1,1,0,1,0,0,1,33,m,White-European,no,yes,Australia,no,6,'18 and more',Relative,NO -1,1,1,1,1,0,0,1,0,1,23,f,Asian,no,no,Malaysia,no,7,'18 and more',Self,YES -0,1,1,1,1,1,0,1,0,1,35,f,White-European,no,yes,Sweden,no,7,'18 and more',Self,YES -1,0,0,0,0,0,0,1,0,1,21,m,White-European,no,yes,Australia,no,3,'18 and more',Self,NO -1,0,0,1,1,0,1,1,1,1,20,f,White-European,no,no,Austria,no,7,'18 and more',Self,YES -1,0,0,0,0,0,0,1,0,0,29,m,White-European,no,no,Netherlands,no,2,'18 and more','Health care professional',NO -1,1,1,1,1,1,1,0,1,1,59,m,White-European,no,no,'United States',no,9,'18 and more',Self,YES -1,1,1,0,0,0,0,1,0,1,18,f,White-European,no,no,Netherlands,no,5,'18 and more',Self,NO -1,1,0,0,0,0,0,1,0,0,19,f,'Middle Eastern ',no,no,'United Arab Emirates',no,3,'18 and more',Self,NO -0,0,1,1,0,0,0,1,0,0,24,m,'Middle Eastern ',no,no,'United Arab Emirates',no,3,'18 and more',Self,NO -1,1,1,1,1,0,0,1,1,0,36,f,White-European,no,no,Italy,no,7,'18 and more',Self,YES -0,0,1,1,1,0,0,0,0,1,19,f,?,yes,no,Jordan,no,4,'18 and more',?,NO -1,1,0,0,0,0,1,0,0,0,17,f,Black,no,no,'United Arab Emirates',no,3,'18 and more',Self,NO -1,0,0,0,0,0,0,1,0,1,52,m,White-European,yes,no,'United Kingdom',no,3,'18 and more',Self,NO -1,1,0,1,1,0,0,0,0,0,18,m,'Middle Eastern ',no,no,'United Arab Emirates',no,4,'18 and more',Self,NO -0,0,0,0,0,0,1,0,0,0,30,m,Asian,no,no,Philippines,no,1,'18 and more',Self,NO -0,1,1,1,1,1,0,1,1,1,18,m,'Middle Eastern ',yes,no,'New Zealand',no,8,'18 and more',Self,YES -1,1,1,1,1,0,0,1,0,0,27,m,Latino,no,no,Ecuador,no,6,'18 and more',Self,NO -0,0,0,1,0,0,0,0,0,0,52,f,White-European,no,no,'United Kingdom',no,1,'18 and more',Self,NO -0,0,0,0,0,0,0,1,0,0,45,f,Asian,no,no,'Viet Nam',no,1,'18 and more',Self,NO -0,1,1,1,1,1,0,0,1,0,25,f,Others,no,no,Afghanistan,no,6,'18 and more','Health care professional',NO -0,0,0,1,0,0,1,0,0,1,29,f,White-European,no,no,'United Kingdom',no,3,'18 and more',Self,NO -1,1,0,1,1,1,1,1,0,1,29,f,White-European,yes,no,'United States',no,8,'18 and more',Self,YES -1,0,0,0,0,1,0,0,0,1,18,m,White-European,no,yes,Netherlands,no,3,'18 and more',Self,NO -0,0,1,1,0,1,1,1,1,1,18,m,White-European,no,no,Netherlands,no,7,'18 and more',Relative,YES -1,0,1,1,0,1,0,0,0,1,17,m,White-European,no,no,Netherlands,no,5,'18 and more',Relative,NO -1,1,1,1,1,1,1,1,1,1,27,f,Asian,no,no,'United States',no,10,'18 and more',Self,YES -0,0,0,0,0,0,0,1,0,0,22,f,Asian,yes,no,Malaysia,no,1,'18 and more',Self,NO -0,0,0,0,0,1,0,1,1,0,23,m,Asian,no,no,India,no,3,'18 and more',Self,NO -1,1,1,1,1,0,1,1,0,0,25,m,Latino,no,no,'United Kingdom',no,7,'18 and more',Self,YES -1,0,1,1,1,0,0,0,0,0,23,f,'South Asian',no,no,'New Zealand',no,4,'18 and more',Self,NO -1,0,0,0,0,0,0,1,0,1,29,m,Asian,no,no,India,no,3,'18 and more',Self,NO -1,0,0,1,1,0,1,0,0,0,27,f,Asian,no,no,'Sri Lanka',no,4,'18 and more',Self,NO -1,0,0,0,0,1,0,1,0,0,27,f,Asian,no,no,India,no,3,'18 and more',Self,NO -0,0,0,0,0,0,0,0,0,0,43,f,'South Asian',no,no,India,no,0,'18 and more',Parent,NO -1,0,0,0,0,0,0,1,0,0,26,f,Asian,no,no,India,no,2,'18 and more',Self,NO -1,0,0,0,1,0,0,1,0,1,30,f,Asian,no,no,India,no,4,'18 and more',Self,NO -0,0,0,0,0,0,0,1,0,0,23,m,White-European,no,no,India,no,1,'18 and more',Self,NO -1,0,0,0,1,0,1,1,0,0,29,f,Asian,no,no,'Sri Lanka',no,4,'18 and more',Self,NO -0,0,0,0,0,0,0,1,0,1,37,m,White-European,no,no,'United Kingdom',no,2,'18 and more',Parent,NO -0,0,0,0,0,0,1,0,0,1,28,f,White-European,no,no,'United Kingdom',no,2,'18 and more',Self,NO -1,1,1,1,0,0,1,1,0,0,25,m,'South Asian',no,no,'United States',no,6,'18 and more',Self,NO -1,0,1,1,0,0,1,1,0,0,25,m,Others,no,no,India,no,5,'18 and more',Self,NO -0,1,0,0,0,0,0,1,1,0,21,m,?,no,no,Jordan,no,3,'18 and more',?,NO -1,1,1,1,1,1,1,0,1,1,43,m,White-European,no,no,'United Kingdom',no,9,'18 and more',Self,YES -1,1,0,1,1,1,0,0,1,1,30,f,White-European,no,no,'United States',no,7,'18 and more',Self,YES -0,1,1,0,0,0,1,0,0,1,18,m,Black,no,no,Niger,no,4,'18 and more',Self,NO -1,1,1,1,0,0,0,1,0,0,18,f,White-European,no,no,Romania,no,5,'18 and more',Self,NO -1,1,1,1,0,0,1,1,1,0,30,m,Others,no,no,Canada,no,7,'18 and more',Self,YES -0,0,0,0,0,0,0,0,0,0,31,f,White-European,no,no,Germany,no,0,'18 and more',Self,NO -1,0,0,0,1,0,1,1,0,1,33,m,Latino,no,no,Mexico,no,5,'18 and more',Self,NO -1,1,1,1,1,1,1,1,1,1,33,m,Latino,no,no,Mexico,no,10,'18 and more',Self,YES -1,1,1,1,0,0,1,0,0,1,26,m,White-European,no,no,'United Kingdom',no,6,'18 and more',Self,NO -1,0,1,1,0,0,0,0,0,0,61,f,White-European,no,no,'United States',no,3,'18 and more',Self,NO -0,0,0,1,0,0,0,1,0,1,46,m,Others,no,no,'Viet Nam',no,3,'18 and more',Self,NO -0,1,1,1,1,1,1,0,1,1,33,m,Asian,no,no,India,no,8,'18 and more',Self,YES -1,1,1,1,1,0,1,1,0,1,38,f,Black,no,no,Canada,no,8,'18 and more',Self,YES -1,1,0,0,0,0,0,1,0,0,44,m,Black,no,no,France,no,3,'18 and more',Parent,NO -0,1,1,1,1,0,0,0,1,0,48,f,Black,no,yes,France,no,5,'18 and more',Parent,NO -1,1,0,1,1,1,0,1,1,1,42,f,Latino,yes,no,Mexico,no,8,'18 and more',Self,YES -0,1,0,0,1,0,1,1,0,0,37,m,White-European,no,no,Belgium,no,4,'18 and more',Self,NO -1,0,1,1,1,0,0,1,0,1,23,f,White-European,no,yes,'United Kingdom',no,6,'18 and more',Self,NO -1,0,1,1,1,0,1,1,1,1,24,m,White-European,no,yes,'United Kingdom',no,8,'18 and more',Relative,YES -1,0,1,0,1,0,1,1,0,1,41,m,?,yes,yes,'United Kingdom',no,6,'18 and more',?,NO -1,0,1,0,0,0,1,0,0,1,41,m,Asian,no,no,'United Kingdom',no,4,'18 and more',Self,NO -0,1,1,0,1,0,0,1,0,0,27,m,White-European,no,no,Belgium,no,4,'18 and more',Self,NO -1,1,1,1,1,1,1,1,1,1,46,f,White-European,yes,yes,'United Kingdom',no,10,'18 and more',Self,YES -0,1,0,0,0,0,0,0,0,1,40,f,White-European,yes,no,'United States',no,2,'18 and more',Self,NO -0,1,1,1,1,1,1,1,0,1,22,f,White-European,no,no,'United States',no,8,'18 and more',Self,YES -1,1,1,1,1,1,1,0,1,1,42,m,White-European,no,no,'United Kingdom',no,9,'18 and more',Self,YES -1,1,1,0,0,0,0,0,0,1,18,f,Asian,no,no,'Viet Nam',no,4,'18 and more',Self,NO -1,0,1,1,0,0,0,0,0,0,44,f,White-European,no,no,'United Kingdom',no,3,'18 and more',Self,NO -1,1,1,1,1,0,1,1,1,1,30,f,White-European,no,no,'United States',no,9,'18 and more',Self,YES -1,1,1,1,1,1,0,1,1,1,42,m,White-European,no,no,Australia,no,9,'18 and more',Self,YES -1,1,0,1,0,0,1,0,0,0,35,f,White-European,no,yes,'United States',no,4,'18 and more',Parent,NO -1,0,1,1,1,0,1,0,1,1,40,m,Black,no,no,AmericanSamoa,no,7,'18 and more',Self,YES -0,1,0,0,0,0,0,0,1,1,50,f,?,no,no,'New Zealand',no,3,'18 and more',?,NO -0,0,1,1,0,0,0,0,0,1,38,m,'Middle Eastern ',no,no,Egypt,no,3,'18 and more',Self,NO -1,1,1,1,1,0,0,1,1,1,26,f,White-European,no,yes,Italy,no,8,'18 and more',Self,YES -1,1,1,1,1,0,0,1,0,1,27,f,White-European,no,no,Malaysia,no,7,'18 and more',Self,YES -1,1,1,1,1,0,1,0,1,0,47,f,White-European,yes,yes,'United States',no,7,'18 and more',Self,YES -1,1,0,1,1,1,0,0,0,1,21,m,Black,yes,yes,'United States',no,6,'18 and more',Parent,NO -1,1,1,1,0,1,0,1,1,1,37,m,White-European,no,no,'United Kingdom',no,8,'18 and more',Self,YES -1,0,0,0,0,0,1,1,0,1,19,f,Others,no,no,'United Kingdom',no,4,'18 and more',Self,NO -0,0,0,1,0,0,1,1,0,1,32,m,Asian,no,no,'United Arab Emirates',no,4,'18 and more',Self,NO -0,0,0,0,1,0,0,1,0,1,26,f,?,no,no,Iran,no,3,'18 and more',?,NO -1,1,1,1,1,1,1,1,1,1,46,f,White-European,yes,yes,Australia,no,10,'18 and more',Self,YES -0,1,0,0,0,0,0,1,0,0,29,m,Black,no,no,France,no,2,'18 and more',Self,NO -1,0,0,0,1,0,0,0,0,0,30,f,Black,no,no,France,no,2,'18 and more',Self,NO -1,1,0,0,0,0,1,1,0,0,32,f,Latino,no,no,Bolivia,no,4,'18 and more',Relative,NO -0,1,1,1,0,0,0,0,0,1,35,f,White-European,yes,no,'United States',no,4,'18 and more',Self,NO -0,1,1,1,1,1,1,1,1,1,22,m,White-European,no,no,'United Kingdom',no,9,'18 and more',Self,YES -0,0,0,1,1,0,1,0,0,1,19,f,?,no,no,Iran,no,4,'18 and more',?,NO -0,1,0,0,1,0,0,0,0,1,24,m,?,no,no,Iran,no,3,'18 and more',?,NO -0,1,0,0,1,0,0,0,0,0,52,f,?,no,no,Iran,no,2,'18 and more',?,NO -0,1,0,0,1,1,0,0,1,1,52,m,?,no,no,Iran,no,5,'18 and more',?,NO -1,0,1,0,0,1,0,1,0,0,32,f,White-European,no,no,'United States',no,4,'18 and more',Self,NO -0,1,1,1,1,1,0,0,0,1,24,m,White-European,no,no,'United States',no,6,'18 and more',Relative,NO -1,1,1,1,1,1,0,1,1,1,24,m,White-European,no,no,'United States',no,9,'18 and more',Relative,YES -0,1,0,0,0,0,0,0,0,0,49,f,'Middle Eastern ',yes,no,'New Zealand',no,1,'18 and more',Parent,NO -0,1,1,1,1,1,1,1,1,1,30,f,Asian,no,no,Malaysia,no,9,'18 and more',Self,YES -0,1,1,1,1,1,1,1,1,1,30,f,Asian,no,yes,Malaysia,yes,9,'18 and more',Self,YES -1,0,1,1,1,1,1,1,1,1,35,f,White-European,yes,no,'United Kingdom',no,9,'18 and more',Self,YES -1,0,1,1,1,1,1,0,0,1,35,f,White-European,yes,no,'United Kingdom',no,7,'18 and more',Self,YES -1,0,1,1,0,0,0,0,0,1,37,f,White-European,no,yes,'United Kingdom',no,4,'18 and more',Self,NO -0,1,1,1,0,0,0,1,0,1,43,m,White-European,no,no,'United Kingdom',no,5,'18 and more',Self,NO -1,1,1,1,0,0,1,0,0,1,52,m,White-European,no,no,'United Kingdom',no,6,'18 and more',Self,NO -1,1,1,1,1,1,1,1,1,1,44,m,White-European,no,yes,'United Kingdom',no,10,'18 and more',Self,YES -1,1,0,0,0,0,0,0,0,0,46,f,White-European,no,yes,'United Kingdom',no,2,'18 and more',Self,NO -1,0,0,0,1,0,0,0,0,1,42,f,White-European,no,yes,Australia,no,3,'18 and more',Self,NO -0,0,1,0,0,0,0,0,0,0,20,m,Asian,no,no,Aruba,no,1,'18 and more',Self,NO -1,1,1,0,1,0,1,0,1,1,18,m,'Middle Eastern ',no,no,'New Zealand',no,7,'18 and more',Self,YES -1,1,1,1,1,1,0,1,1,1,38,m,White-European,no,no,Finland,no,9,'18 and more',Relative,YES -1,0,0,1,1,0,0,1,0,0,24,f,Latino,no,yes,Mexico,no,4,'18 and more',Self,NO -1,1,0,0,0,0,0,1,0,0,32,m,?,no,no,Jordan,no,3,'18 and more',?,NO -1,1,0,1,1,0,0,0,0,0,29,m,Turkish,no,no,'United States',no,4,'18 and more',Self,NO -1,1,1,1,1,1,1,1,0,1,22,f,White-European,no,no,'United States',no,9,'18 and more',Self,YES -0,1,1,1,1,1,0,0,0,1,19,f,White-European,yes,no,'United Kingdom',no,6,'18 and more',Self,NO -1,1,1,1,1,1,1,0,1,1,39,f,White-European,no,yes,'United States',no,9,'18 and more',Self,YES -1,0,0,1,1,1,1,1,1,1,17,m,Black,no,no,'United States',no,8,'18 and more',Self,YES -0,1,0,1,0,1,1,0,1,1,19,f,'Middle Eastern ',no,no,Iceland,no,6,'18 and more',Parent,NO -1,0,0,0,0,0,1,1,1,0,18,m,White-European,no,no,Australia,no,4,'18 and more',Parent,NO -0,1,0,0,1,1,0,0,1,0,19,m,?,no,no,Kazakhstan,yes,4,'18 and more',?,NO -0,1,0,0,0,0,0,1,0,0,28,f,Turkish,no,no,Turkey,no,2,'18 and more',Self,NO -0,1,0,1,0,1,0,0,0,1,29,m,Asian,no,no,'New Zealand',no,4,'18 and more',Self,NO -1,1,0,0,0,0,1,1,0,0,48,m,White-European,no,no,Australia,no,4,'18 and more',Self,NO -1,1,1,1,1,1,0,1,1,1,27,f,White-European,yes,yes,Netherlands,no,9,'18 and more',Self,YES -1,0,1,1,0,0,0,0,0,0,34,f,White-European,no,no,'United Kingdom',no,3,'18 and more',Self,NO -1,1,0,1,0,0,0,0,0,1,31,m,White-European,no,no,Australia,no,4,'18 and more',Self,NO -1,0,0,0,0,0,0,1,0,1,47,m,?,no,no,Jordan,no,3,'18 and more',?,NO -1,0,0,1,0,0,0,1,0,0,47,m,?,no,no,Jordan,no,3,'18 and more',?,NO -1,1,1,1,1,1,1,1,1,1,44,m,White-European,no,yes,'United Kingdom',no,10,'18 and more',Self,YES -1,0,0,0,1,0,1,1,0,0,25,f,White-European,no,no,'United States',no,4,'18 and more',Self,NO -1,0,0,0,1,0,1,1,0,0,25,f,White-European,no,no,'United States',no,4,'18 and more',Self,NO -1,1,0,0,1,1,1,1,1,1,20,m,White-European,no,no,'United Kingdom',no,8,'18 and more',Self,YES -1,1,1,1,1,0,1,1,1,1,43,m,White-European,yes,no,'New Zealand',no,9,'18 and more',Self,YES -1,1,1,1,1,0,0,1,1,1,36,f,White-European,no,yes,'United Kingdom',no,8,'18 and more',Self,YES -1,1,0,0,0,0,0,1,0,0,30,f,Asian,no,yes,'United States',no,3,'18 and more',Self,NO -0,0,1,1,0,0,0,0,0,0,44,m,Black,no,no,'United Kingdom',no,2,'18 and more',Self,NO -1,1,0,0,1,0,0,0,0,1,40,f,Black,no,no,'United Kingdom',no,4,'18 and more',Parent,NO -1,1,0,0,1,1,0,1,1,0,40,f,Black,yes,no,'United Kingdom',no,6,'18 and more',Relative,NO -1,1,0,1,1,1,0,1,1,0,40,f,Black,yes,yes,'United Kingdom',no,7,'18 and more',Relative,YES -1,1,1,1,1,0,0,1,0,1,30,m,Others,no,no,'United States',no,7,'18 and more',Self,YES -1,0,1,1,0,0,1,0,0,1,47,f,White-European,no,no,'United Kingdom',no,5,'18 and more',Self,NO -1,1,1,1,1,1,1,1,1,1,21,m,Black,no,no,Brazil,no,10,'18 and more',Self,YES -1,1,1,1,1,1,0,1,1,1,21,m,Black,no,no,Brazil,no,9,'18 and more',Self,YES -1,0,1,1,1,1,1,1,1,1,31,f,?,no,no,'New Zealand',no,9,'18 and more',?,YES -1,1,1,0,0,0,0,0,0,0,21,f,Asian,no,no,'New Zealand',no,3,'18 and more',Self,NO -0,1,1,1,0,0,0,1,1,0,29,f,Asian,no,no,'United States',no,5,'18 and more',Self,NO -0,1,1,1,1,0,1,0,1,0,22,m,'Middle Eastern ',no,no,Afghanistan,no,6,'18 and more',Self,NO -1,1,1,1,1,1,1,1,1,1,51,m,White-European,yes,no,'United Kingdom',no,10,'18 and more',Self,YES -1,0,0,0,0,0,0,0,0,0,23,m,?,no,no,Russia,no,1,'18 and more',?,NO -1,0,0,0,1,0,0,0,1,1,30,m,Asian,no,no,India,no,4,'18 and more',Self,NO -1,1,0,0,0,0,0,1,0,0,22,m,Others,no,no,India,no,3,'18 and more',Self,NO -1,1,0,0,0,0,0,1,0,1,22,m,Asian,no,no,'New Zealand',no,4,'18 and more',Self,NO -1,0,0,0,1,0,1,1,0,1,24,m,Asian,no,no,India,no,5,'18 and more',Self,NO -1,0,0,0,0,0,0,1,0,1,26,f,'South Asian',no,no,India,no,3,'18 and more',Self,NO -1,0,1,1,0,0,0,1,1,1,33,f,Asian,no,no,India,no,6,'18 and more',Parent,NO -1,0,0,0,0,0,0,1,1,0,23,m,Asian,no,no,India,no,3,'18 and more',Self,NO -1,0,1,0,0,0,0,1,0,0,42,f,Asian,no,no,India,no,3,'18 and more',Self,NO -1,0,1,0,0,0,0,0,0,0,26,f,Asian,no,no,India,no,2,'18 and more',Self,NO -1,0,0,0,0,0,0,1,0,0,29,f,Asian,no,no,'New Zealand',no,2,'18 and more',Self,NO -1,0,0,0,0,0,1,0,0,1,35,f,Asian,no,no,India,no,3,'18 and more',Self,NO -1,0,0,0,1,1,0,1,0,0,25,f,'South Asian',no,no,India,no,4,'18 and more',Self,NO -1,0,0,0,0,0,1,1,0,0,25,m,Asian,no,no,'Sri Lanka',no,3,'18 and more',Self,NO -0,0,0,0,0,0,0,1,0,0,28,f,Asian,yes,no,India,no,1,'18 and more',Self,NO -1,0,0,0,0,0,0,0,0,0,33,m,White-European,no,no,Germany,no,1,'18 and more',Self,NO -1,0,0,1,0,0,1,1,0,0,25,m,Asian,no,no,India,no,4,'18 and more',Self,NO -0,0,1,0,1,1,0,1,0,1,25,m,Asian,no,no,India,no,5,'18 and more',Self,NO -1,0,0,0,1,0,1,1,0,0,24,m,Asian,no,no,India,no,4,'18 and more',Self,NO -1,1,1,1,0,0,0,0,0,1,27,f,'South Asian',no,no,India,no,5,'18 and more',Self,NO -1,0,0,0,0,0,0,0,0,0,22,m,'South Asian',no,no,India,no,1,'18 and more',Self,NO -1,1,0,1,0,0,0,1,0,1,21,f,'South Asian',no,no,India,no,5,'18 and more',Self,NO -1,0,1,1,0,1,0,1,0,1,27,f,'South Asian',no,no,India,no,6,'18 and more',Self,NO -1,0,0,0,1,0,1,0,0,0,23,m,'South Asian',no,no,India,no,3,'18 and more',Self,NO -0,0,0,1,1,0,0,1,0,1,22,m,Asian,no,no,India,no,4,'18 and more',Self,NO -0,1,0,0,0,0,0,0,0,1,23,f,'South Asian',no,no,India,no,2,'18 and more',Self,NO -1,0,0,1,0,0,0,1,0,1,30,m,Asian,no,no,India,no,4,'18 and more',Self,NO -0,1,0,0,1,0,1,1,0,0,33,m,Asian,no,no,India,no,4,'18 and more',Self,NO -1,0,1,1,1,0,1,1,1,0,24,f,'South Asian',no,no,India,no,7,'18 and more',Self,YES -1,0,0,0,1,0,1,1,0,0,22,f,Asian,no,no,'New Zealand',no,4,'18 and more',Self,NO -1,0,0,1,1,0,1,1,1,1,22,m,Asian,no,no,'New Zealand',no,7,'18 and more',Self,YES -1,0,0,0,0,0,0,1,0,1,29,f,'South Asian',no,no,India,no,3,'18 and more',Self,NO -1,0,1,0,1,0,1,1,0,1,22,f,Asian,no,no,India,no,6,'18 and more',Self,NO -1,0,1,0,0,0,0,1,0,0,21,m,Asian,yes,no,India,no,3,'18 and more',Self,NO -1,0,0,0,0,0,1,1,0,0,23,m,Asian,no,no,India,no,3,'18 and more',Self,NO -1,0,0,0,1,0,1,1,0,1,23,f,Others,no,no,India,no,5,'18 and more',Self,NO -1,0,0,0,1,0,1,1,1,1,23,f,Asian,no,no,India,no,6,'18 and more',Self,NO -1,1,0,1,1,0,1,1,0,0,32,m,Asian,no,no,'Sri Lanka',no,6,'18 and more',Self,NO -0,0,0,0,0,0,1,0,0,1,27,m,Asian,no,no,'Sri Lanka',no,2,'18 and more',Self,NO -0,0,1,0,0,0,0,1,0,0,28,f,Asian,no,no,India,no,2,'18 and more',Parent,NO -1,1,0,0,1,0,0,1,1,1,25,f,'Middle Eastern ',no,no,India,no,6,'18 and more',Self,NO -1,0,1,0,1,0,1,1,1,1,22,m,Black,no,no,Nepal,no,7,'18 and more',Self,YES -0,1,1,0,0,0,1,0,0,1,36,m,White-European,no,no,'United Kingdom',no,4,'18 and more',Self,NO -0,0,0,0,0,0,0,0,0,0,27,f,?,no,no,Russia,no,0,'18 and more',?,NO -1,1,1,1,1,1,1,1,1,1,21,f,White-European,no,no,Germany,no,10,'18 and more',Self,YES -1,1,0,1,1,0,0,1,0,0,18,m,Black,no,no,'United States',no,5,'18 and more',Self,NO -1,0,0,0,1,1,1,1,1,1,49,m,Black,no,no,Mexico,no,7,'18 and more',Parent,YES -1,1,1,0,0,0,0,1,0,0,18,m,Asian,no,no,Indonesia,no,4,'18 and more',Self,NO -1,0,1,1,1,0,1,1,1,0,29,m,Latino,no,no,'United States',no,7,'18 and more',Self,YES -0,0,0,0,1,0,0,1,0,0,31,f,?,no,no,'New Zealand',no,2,'18 and more',?,NO -1,0,0,0,0,0,0,0,0,0,37,f,'Middle Eastern ',no,no,'New Zealand',no,1,'18 and more',Parent,NO -1,0,1,1,1,0,0,1,0,1,17,m,'Middle Eastern ',yes,no,'New Zealand',no,6,'18 and more',Parent,NO -1,0,1,1,0,0,1,1,1,1,17,m,?,no,yes,'New Zealand',no,7,'18 and more',?,YES -1,1,1,1,0,0,0,0,0,0,38,f,'Middle Eastern ',no,no,Jordan,no,4,'18 and more',Self,NO -1,0,1,0,0,1,0,1,0,1,18,m,White-European,yes,no,Angola,no,5,'18 and more',Parent,NO -1,0,1,1,1,0,0,0,0,0,31,f,'Middle Eastern ',no,no,'United Arab Emirates',no,4,'18 and more',Self,NO -0,1,1,1,0,0,0,0,0,0,33,m,'Middle Eastern ',yes,no,'United Arab Emirates',yes,3,'18 and more',Self,NO -1,1,0,0,0,0,0,0,0,0,32,f,'Middle Eastern ',yes,no,Jordan,no,2,'18 and more',Self,NO -1,0,0,0,0,0,1,0,0,1,30,m,?,yes,no,Jordan,no,3,'18 and more',?,NO -0,0,0,0,0,0,0,0,0,1,33,f,?,no,no,'United States',no,1,'18 and more',?,NO -1,1,1,1,1,1,1,1,1,1,30,f,White-European,no,yes,Canada,no,10,'18 and more',Self,YES -1,1,1,1,1,0,1,1,1,1,30,m,Asian,no,no,'United States',no,9,'18 and more',Self,YES -1,0,0,1,1,0,1,0,0,1,38,f,White-European,no,yes,'United Kingdom',no,5,'18 and more',Self,NO -1,0,0,0,1,0,1,0,1,1,22,m,Asian,no,no,India,no,5,'18 and more',Self,NO -1,1,0,0,1,0,1,0,1,0,36,m,others,no,no,'United States',no,5,'18 and more',Self,NO -0,0,1,1,0,0,1,0,0,0,43,m,?,no,no,Azerbaijan,no,3,'18 and more',?,NO -1,1,1,1,1,1,0,0,1,1,44,m,?,no,no,Pakistan,no,8,'18 and more',?,YES -1,1,1,0,1,1,0,1,1,1,20,f,White-European,no,no,France,no,8,'18 and more',Self,YES -1,1,1,1,1,1,0,1,1,1,40,f,Others,yes,yes,Australia,no,9,'18 and more',Self,YES -1,0,0,1,1,1,0,0,1,0,25,m,White-European,no,no,Italy,no,5,'18 and more',Self,NO -1,0,0,0,0,0,1,1,0,1,28,m,White-European,no,no,Australia,no,4,'18 and more',Self,NO -0,0,0,0,0,0,1,1,0,1,17,m,White-European,no,no,Canada,no,3,'18 and more',Self,NO -1,1,1,1,1,1,1,0,1,1,34,m,White-European,no,no,'United States',no,9,'18 and more',Self,YES -0,0,0,0,0,0,0,1,0,0,56,m,?,no,no,Iraq,no,1,'18 and more',?,NO -0,0,1,0,0,0,1,1,0,0,50,f,'Middle Eastern ',no,no,'New Zealand',no,3,'18 and more',Parent,NO -1,0,0,0,0,0,1,1,0,1,38,m,White-European,no,no,'United States',no,4,'18 and more',Self,NO -1,1,1,1,1,1,1,1,1,1,47,m,White-European,no,no,'United States',no,10,'18 and more',Self,YES -1,1,1,1,1,0,1,1,1,1,30,m,Others,no,no,'United States',no,9,'18 and more',Self,YES -1,1,1,1,1,0,0,1,0,1,21,m,Hispanic,no,no,'United States',no,7,'18 and more',Self,YES -1,1,1,1,1,0,1,1,0,1,21,m,White-European,no,no,Ireland,no,8,'18 and more',Self,YES -1,1,1,0,1,0,1,1,1,1,31,m,Latino,no,no,Mexico,no,8,'18 and more',Self,YES -1,1,0,0,1,1,0,1,0,1,27,f,White-European,yes,no,'Czech Republic',no,6,'18 and more',Self,NO -1,1,1,1,0,0,0,0,1,0,24,f,White-European,yes,no,'United States',no,5,'18 and more',Self,NO -1,1,0,0,0,0,1,0,0,1,35,m,White-European,yes,no,'United Kingdom',no,4,'18 and more',Parent,NO -1,0,0,0,0,0,1,0,0,1,18,m,Black,no,no,Ethiopia,no,3,'18 and more','Health care professional',NO -1,1,1,1,1,0,0,1,1,1,43,f,Black,no,no,'United States',no,8,'18 and more',Self,YES -1,1,1,1,1,1,1,1,1,1,44,m,White-European,no,yes,Afghanistan,no,10,'18 and more',Self,YES -1,1,1,1,1,1,1,1,1,1,40,m,White-European,yes,yes,'United Kingdom',no,10,'18 and more',Parent,YES -1,1,0,1,1,1,1,1,1,1,49,f,Hispanic,no,no,'United States',no,9,'18 and more',Self,YES -1,1,0,0,0,0,0,1,0,0,24,m,Hispanic,no,no,'United States',no,3,'18 and more',Self,NO -1,1,0,0,0,0,0,1,1,0,30,f,White-European,yes,yes,'United States',no,4,'18 and more',Self,NO -1,0,1,0,0,0,0,1,0,1,53,m,Hispanic,no,no,'United States',no,4,'18 and more',Relative,NO -1,0,1,1,1,0,0,1,1,1,38,m,White-European,no,yes,Belgium,no,7,'18 and more',Self,YES -1,0,0,0,1,0,1,1,0,1,28,m,White-European,no,no,'United States',no,5,'18 and more',Self,NO -1,1,1,0,1,1,1,1,1,1,28,m,White-European,no,no,'United States',no,9,'18 and more',Self,YES -1,0,1,1,1,1,1,0,1,1,26,m,Black,no,no,Canada,no,8,'18 and more',Self,YES -1,0,0,0,1,1,1,1,1,1,39,f,White-European,no,no,Canada,no,7,'18 and more',Self,YES -0,0,0,0,1,0,0,0,0,0,31,f,Asian,yes,no,India,no,1,'18 and more',Self,NO -1,0,0,1,0,0,1,1,0,0,24,m,Black,no,no,'United States',no,4,'18 and more',Self,NO -1,1,1,0,1,1,1,1,0,1,28,m,White-European,no,no,'United States',no,8,'18 and more',Self,YES -1,0,0,1,0,0,0,1,0,1,31,f,White-European,no,no,'United Kingdom',no,4,'18 and more',Self,NO -1,1,1,1,1,0,0,1,0,1,27,m,White-European,yes,no,'United States',no,7,'18 and more',Self,YES -1,0,1,1,0,0,1,1,0,0,28,m,Latino,no,no,Brazil,yes,5,'18 and more',Parent,NO -1,1,1,1,1,1,0,1,1,1,31,m,Turkish,no,yes,Australia,no,9,'18 and more',Self,YES -1,1,1,1,1,0,0,0,0,1,46,f,Asian,no,no,Philippines,no,6,'18 and more',Self,NO -1,1,1,1,1,1,1,1,1,1,27,f,Pasifika,no,no,Australia,no,10,'18 and more',Self,YES -0,1,0,1,1,0,1,1,1,1,25,f,White-European,no,no,Russia,no,7,'18 and more',Self,YES -1,0,0,0,0,0,0,1,0,1,34,m,Hispanic,no,no,Mexico,no,3,'18 and more',Parent,NO -1,0,1,1,1,0,1,1,0,1,24,f,?,no,no,Russia,no,7,'18 and more',?,YES -1,0,0,1,1,0,1,0,1,1,35,m,'South Asian',no,no,Pakistan,no,6,'18 and more',Self,NO -1,0,1,1,1,0,1,1,1,1,26,f,White-European,no,no,Cyprus,no,8,'18 and more',Self,YES diff --git a/asd-dataset/emissions.csv b/asd-dataset/emissions.csv deleted file mode 100644 index d67c63a..0000000 --- a/asd-dataset/emissions.csv +++ /dev/null @@ -1,15 +0,0 @@ -timestamp,experiment_id,project_name,duration,emissions,energy_consumed,country_name,country_iso_code,region,on_cloud,cloud_provider,cloud_region -2021-09-05T00:08:35,cf1fb6e0-2372-4387-9d6f-878a76b04fad,codecarbon,4.624737977981567,6.741129161637182e-07,8.084840560397574e-07,India,IND,national capital territory of delhi,N,, -2021-09-05T00:09:01,f5a4e505-6193-4fe5-9265-66eb3bafbf3e,codecarbon,5.6174681186676025,8.066486905344194e-07,9.67438228055015e-07,India,IND,national capital territory of delhi,N,, -2021-09-05T00:09:42,c345a5de-238c-4d74-a0b3-c2d547a93936,codecarbon,3.8135712146759033,1.3354526112054477e-06,1.6016488007686333e-06,India,IND,national capital territory of delhi,N,, -2021-09-05T00:09:58,f9462ef7-d3cb-49ba-b164-d86565b74af3,codecarbon,2.676934003829956,9.912451820076472e-07,1.1888303963082305e-06,India,IND,national capital territory of delhi,N,, -2021-09-05T00:10:08,09128377-9b5f-49b4-9ae5-58153dfaaa30,codecarbon,5.522851943969727,6.578048567071733e-07,7.889253059558726e-07,India,IND,national capital territory of delhi,N,, -2021-09-05T00:10:21,be7d76a1-2115-4f9f-b903-55251ef32107,codecarbon,3.6848490238189697,1.337746539761072e-06,1.6043999788256729e-06,India,IND,national capital territory of delhi,N,, -2021-09-05T00:22:46,d826df12-026b-4619-9568-628aeef58510,codecarbon,9.25957703590393,2.5292295738146114e-06,3.033381701288637e-06,India,IND,national capital territory of delhi,N,, -2021-09-05T00:23:26,1fecb93f-064d-4535-8dbd-b1f0e569b96b,codecarbon,7.564344644546509,1.6159354279987829e-06,1.938040345765315e-06,India,IND,national capital territory of delhi,N,, -2021-09-05T00:24:00,72618c77-96da-4c69-8e44-6c2d8a72f22a,codecarbon,8.502308130264282,1.644396389571211e-06,1.972174439771133e-06,India,IND,national capital territory of delhi,N,, -2021-09-05T00:25:23,7797bf7b-63ba-4606-96bc-d25c1c78d203,codecarbon,7.541054964065552,1.629778451616236e-06,1.954642703639993e-06,India,IND,national capital territory of delhi,N,, -2021-09-05T00:26:06,3dbacdd2-e142-440d-b8f7-21a2f2ff3a78,codecarbon,8.631039142608643,1.71297105943454e-06,2.0544181201744124e-06,India,IND,national capital territory of delhi,N,, -2021-09-05T00:26:33,a5811dbc-8f31-4404-86bd-5730b39c9d47,codecarbon,6.576951026916504,1.7069007790660136e-06,2.0471378489083254e-06,India,IND,national capital territory of delhi,N,, -2021-10-01T16:00:36,5a784f81-f373-41db-b142-b60fcb8867d7,codecarbon,5.574628829956055,2.4069817271309726e-07,2.886766152826262e-07,India,IND,national capital territory of delhi,N,, -2021-10-01T17:02:34,a79d0a53-6f5a-4156-b05e-76d02bec0bb2,codecarbon,4.533468008041382,2.570071104209542e-07,3.082364186799336e-07,India,IND,national capital territory of delhi,N,, diff --git a/asd-dataset/intel_power_gadget_log.csv b/asd-dataset/intel_power_gadget_log.csv deleted file mode 100644 index c310083..0000000 --- a/asd-dataset/intel_power_gadget_log.csv +++ /dev/null @@ -1,28 +0,0 @@ -"System Time","RDTSC","Elapsed Time (sec)","CPU Utilization(%)","CPU Frequency_0(MHz)","CPU Min Frequency_0(MHz)","CPU Max Frequency_0(MHz)","CPU Requsted Frequency_0(MHz)","Processor Power_0(Watt)","Cumulative Processor Energy_0(Joules)","Cumulative Processor Energy_0(mWh)","IA Power_0(Watt)","Cumulative IA Energy_0(Joules)","Cumulative IA Energy_0(mWh)","Package Temperature_0(C)","Package Hot_0","CPU Min Temperature_0(C)","CPU Max Temperature_0(C)","DRAM Power_0(Watt)","Cumulative DRAM Energy_0(Joules)","Cumulative DRAM Energy_0(mWh)","Package Power Limit_0(Watt)","GT Frequency(MHz)","GT Requsted Frequency(MHz)" -"17:02:31:297","14855002237689"," 0.107"," 3.906"," 1688"," 700"," 3200"," 1981"," 0.785"," 0.084"," 0.023"," 0.329"," 0.035"," 0.010"," 56","0"," 53"," 62"," 57.162"," 53.000"," 14.722"," 9.000"," 0"," 0" -"17:02:31:411","14855129988884"," 0.222"," 14.582"," 1187"," 700"," 2000"," 1190"," 1.311"," 0.235"," 0.065"," 0.519"," 0.095"," 0.026"," 55","0"," 52"," 56"," 54.082"," 105.000"," 29.167"," 9.000"," 0"," 0" -"17:02:31:519","14855250087436"," 0.330"," 18.520"," 959"," 700"," 1400"," 970"," 1.442"," 0.390"," 0.108"," 0.493"," 0.148"," 0.041"," 54","0"," 52"," 55"," 53.000"," 157.000"," 43.611"," 9.000"," 0"," 0" -"17:02:31:621","14855363532983"," 0.432"," 14.449"," 887"," 700"," 1400"," 888"," 1.023"," 0.494"," 0.137"," 0.333"," 0.182"," 0.051"," 55","0"," 51"," 53"," 52.403"," 208.000"," 57.778"," 9.000"," 0"," 0" -"17:02:31:728","14855482559104"," 0.539"," 15.939"," 868"," 700"," 1200"," 867"," 1.106"," 0.613"," 0.170"," 0.381"," 0.223"," 0.062"," 54","0"," 51"," 54"," 51.927"," 259.000"," 71.944"," 9.000"," 0"," 0" -"17:02:31:829","14855595353390"," 0.640"," 14.899"," 885"," 700"," 1200"," 890"," 1.109"," 0.725"," 0.201"," 0.346"," 0.258"," 0.072"," 55","0"," 51"," 54"," 51.707"," 310.000"," 86.111"," 9.000"," 0"," 0" -"17:02:31:933","14855710545289"," 0.744"," 17.598"," 873"," 700"," 1300"," 880"," 1.460"," 0.876"," 0.243"," 0.382"," 0.297"," 0.083"," 54","0"," 51"," 53"," 51.975"," 361.000"," 100.278"," 9.000"," 0"," 0" -"17:02:32:042","14855831802918"," 0.852"," 10.997"," 870"," 700"," 1200"," 869"," 0.874"," 0.971"," 0.270"," 0.241"," 0.324"," 0.090"," 54","0"," 50"," 52"," 51.457"," 411.000"," 114.167"," 9.000"," 0"," 0" -"17:02:32:150","14855951911568"," 0.960"," 9.330"," 867"," 700"," 1200"," 873"," 0.830"," 1.061"," 0.295"," 0.266"," 0.352"," 0.098"," 54","0"," 50"," 53"," 51.193"," 461.000"," 128.056"," 9.000"," 0"," 0" -"17:02:32:253","14856066689276"," 1.063"," 8.193"," 887"," 700"," 1200"," 897"," 0.680"," 1.131"," 0.314"," 0.203"," 0.373"," 0.104"," 54","0"," 49"," 52"," 50.851"," 510.000"," 141.667"," 9.000"," 0"," 0" -"17:02:32:361","14856186987115"," 1.171"," 11.524"," 878"," 700"," 1300"," 883"," 0.917"," 1.230"," 0.342"," 0.300"," 0.406"," 0.113"," 53","0"," 49"," 52"," 50.972"," 559.000"," 155.278"," 9.000"," 0"," 0" -"17:02:32:465","14856302702640"," 1.275"," 13.109"," 957"," 700"," 1300"," 977"," 1.094"," 1.343"," 0.373"," 0.337"," 0.441"," 0.122"," 53","0"," 50"," 52"," 50.808"," 609.000"," 169.167"," 9.000"," 0"," 0" - -"Total Elapsed Time (sec) = 1.275368" -"Measured RDTSC Frequency (GHz) = 1.113" - -"Cumulative Package Energy_0 (Joules) = 1.343445" -"Cumulative Package Energy_0 (mWh) = 0.373179" -"Average Package Power_0 (Watt) = 1.053378" - -"Cumulative IA Energy_0 (Joules) = 0.440552" -"Cumulative IA Energy_0 (mWh) = 0.122375" -"Average Package IA_0 (Watt) = 0.345431" - -"Cumulative DRAM Energy_0 (Joules) = 609.000000" -"Cumulative DRAM Energy_0 (mWh) = 169.166667" -"Average Package DRAM_0 (Watt) = 477.509067" diff --git a/asd-dataset/train.py b/asd-dataset/train.py deleted file mode 100644 index b082e35..0000000 --- a/asd-dataset/train.py +++ /dev/null @@ -1,171 +0,0 @@ -#Import python modules -import numpy as np -import pandas as pd - -#Visualisation -import matplotlib.pyplot as plt -#import missingno -import seaborn as sns - -#Model -from sklearn.model_selection import train_test_split -from sklearn.linear_model import LogisticRegression -from sklearn.metrics import accuracy_score, roc_auc_score, roc_curve, classification_report -from sklearn.preprocessing import LabelEncoder - -from sklearn import svm #for Support Vector Machine (SVM) Algorithm -from sklearn import metrics #for checking the model accuracy - - -# split into train/test -def train_test_split(df, train_frac= 0.7, seed=1): - df_matrix = df.values - - # shuffle the data - np.random.seed(seed) - np.random.shuffle(df_matrix) - - # split the data - train_size = int(df_matrix.shape[0] * train_frac) - # features are all except last column - train_features = df_matrix[:train_size, :-1] - # the last column - train_labels = df_matrix[:train_size, -1] - - # test data - test_features = df_matrix[train_size:, :-1] - test_labels = df_matrix[train_size:, -1] - - return (train_features, train_labels), (test_features, test_labels) - -#Energy Usage -from codecarbon import EmissionsTracker - -tracker = EmissionsTracker() -#@track_emissions(project_name="asd screening") - -def main(): - - #load the adult autism dataset - df = pd.read_csv("Autism_Data.arff") - df = df.replace('?', np.nan) - - #column name spell check/ fixes - df = df.rename(columns = {"A1_Score":"A1","A2_Score":"A2", 'A3_Score':'A3', 'A4_Score':'A4', 'A5_Score':'A5', 'A6_Score':'A6', 'A7_Score':'A7', 'A8_Score':'A8', 'A9_Score':'A9', 'A10_Score':'A10', "jundice":"jaundice", "austim":"autism", "contry_of_res": "country", "Class/ASD":"asd_classification"}) - - #Missing data - # missingno.matrix(df, figsize =(30,10)) - # print(df.dtypes) - # print(df.describe()) - print(df.age) - - fig = plt.figure(figsize=(26,8)) - sns.countplot(x="age", data=df) - - #Check how many NaN values exist - df['age'].isnull().sum() - - #Removing the two rows that don't have the age values - df.dropna(subset = ["age"], inplace=True) - - df['age'].max() - mean = df['age'].mean() - df['age']= df['age'].replace(383.0 ,mean) - - #Country: Country of residence of the participants. Maximum participants are from United States, UAE, India, New Zealand and the UK. - fig = plt.figure(figsize=(22,25)) - sns.countplot(y='country', data=df); - df.country.value_counts() - - #Gender Distribution - fig = plt.figure(figsize=(10,6)) - sns.countplot(x="gender", data=df, facecolor=(0, 0, 0, 0), linewidth=5, edgecolor=sns.color_palette("dark", 3)) - - - #Jaundice at birth - fig = plt.figure(figsize=(25,6)) - sns.countplot(y='jaundice', data=df); - df.jaundice.value_counts() - - # If the user has an immediate family member who had a Pervasive Developmental Disorder then, yes( = 1); If the user has an immediate family member who had Pervasive Developmental Disorder then, no( = 0); According to the dataset, 91 individuals had a family member who had PDD out of 704. - new_df= pd.DataFrame({'autism':df.autism.map(dict(yes=1,no=0))}) - df.update(new_df) - df['autism'] = df['autism'].astype('int') - fig = plt.figure(figsize=(25,6)) - sns.countplot(y='autism', data=df); - df.autism.value_counts() - - #ASD Classification - new_df = pd.DataFrame({'asd_classification':df.asd_classification.map(dict(YES=1,NO=0))}) - df.update(new_df) - - df['asd_classification'] = df['asd_classification'].astype('int') - - fig = plt.figure(figsize=(25,6)) - sns.countplot(y='asd_classification', data=df); - df.asd_classification.value_counts() - - #Used App Before Whether the p/w ASD has used a screening app - - new_df = pd.DataFrame({'used_app_before':df.used_app_before.map(dict(yes=1,no=0))}) - df.update(new_df) - df['used_app_before'] = df['used_app_before'].astype('int') - fig = plt.figure(figsize=(25,6)) - sns.countplot(y='used_app_before', data=df); - df.used_app_before.value_counts() - df.age_desc.value_counts() - - plt.figure(figsize =(15,10)) - sns.countplot(x= 'ethnicity',data = df) - df['ethnicity'].value_counts() - - plt.figure(figsize =(15,10)) - sns.countplot(x= 'relation', data = df) - df['relation'].value_counts() - - # Dropped age (since its not an indicator whether an individual has ASD). - # Incomplete data on Ethinicity, Relation (Check missing data plot) - df.drop(['country', 'ethnicity', 'used_app_before' , 'age','age_desc','relation', 'result'],axis=1, inplace=True) - - fig = plt.figure(figsize=(12,10)) - sns.heatmap(df.corr()) -# plt.show() - - le = LabelEncoder() - df.gender = le.fit_transform(df.gender) - df.jaundice = le.fit_transform(df.jaundice) - print(df) - - # get train/test data - (train_features, train_labels), (test_features, test_labels) = train_test_split(df, train_frac=0.7) - - #train the model - lg = LogisticRegression() - - lg.fit(train_features, train_labels) - lgpredict = lg.predict(test_features) - lg.score(test_features, test_labels) - pred_lg = lg.predict(test_features) - log_reg_predict_proba = lg.predict_proba(test_features)[:, 1] - print('\nLogistic Regression Accuracy: {:.2f}%'.format(accuracy_score(test_labels, lgpredict) * 100)) - print('Logistic Regression AUC: {:.2f}%'.format(roc_auc_score(test_labels, lgpredict) * 100)) - print(classification_report(test_labels,pred_lg)) - #SupportVectorMachines - - model = svm.SVC() - model.fit(train_features, train_labels) - prediction=model.predict(test_features) - score = model.score(test_features, test_labels) - print('\nSVM Regression Accuracy: {:.2f}%'.format(accuracy_score(test_labels, prediction) * 100)) - print('SVM Regression AUC: {:.2f}%'.format(roc_auc_score(test_labels, prediction) * 100)) - print(classification_report(test_labels,prediction)) - - - -if __name__ == "__main__": - tracker.start() - main() - emi: float=tracker.stop() - print(f"overall emmisions:{emi} kg") - emi= emi*89875517873681764 - print(f"overall emmisions:{emi} joules") diff --git a/emissions.csv b/emissions.csv deleted file mode 100644 index a430e5e..0000000 --- a/emissions.csv +++ /dev/null @@ -1,12 +0,0 @@ -timestamp,experiment_id,project_name,duration,emissions,energy_consumed,country_name,country_iso_code,region,on_cloud,cloud_provider,cloud_region -2021-08-29T05:16:48,fc635694-9700-413c-a335-59d813a68708,iris,3.027456045150757,1.2433679567456529e-08,1.491208883135319e-08,India,IND,national capital territory of delhi,N,, -2021-08-29T05:27:18,9fff6e86-28b9-4999-918b-8252bc5e9906,codecarbon,2.022529125213623,8.603599338296646e-09,1.0318557503914833e-08,India,IND,national capital territory of delhi,N,, -2021-08-29T05:46:37,d23ad4c3-2874-4f5a-b55d-ca63a83d9380,iris,5.144466876983643,2.6292664093555447e-09,3.153358910762359e-09,India,IND,national capital territory of delhi,N,, -2021-08-29T05:51:41,70dd419d-4935-4adb-b5d9-8ac4ce51b3a2,iris,3.483754873275757,1.1393243740015263e-09,1.3664262602765537e-09,India,IND,national capital territory of delhi,N,, -2021-08-29T05:53:42,869036a2-1b70-42d2-8de5-d56688cc9dd0,mnist,30.999211072921753,5.166184063029413e-05,6.195961159289792e-05,India,IND,national capital territory of delhi,N,, -2021-08-29T05:53:54,00bd6329-38b3-4041-a59b-47970401b01d,iris,2.4305310249328613,3.2053165454528488e-09,3.844233301902335e-09,India,IND,national capital territory of delhi,N,, -2021-08-29T05:54:38,df2b0080-4feb-4149-96bc-f6e125e29b67,mnist,28.767144918441772,4.189902315894638e-05,5.025076864040049e-05,India,IND,national capital territory of delhi,N,, -2021-08-30T12:35:51,1a2f005f-80f9-41c0-af49-916b05577662,iris,4.590723991394043,2.0072288461780173e-09,2.407330404219524e-09,India,IND,national capital territory of delhi,N,, -2021-08-30T12:37:16,c21caf82-34c3-4ab0-844d-2053d010ec9c,iris,3.431018829345703,7.023703392234898e-10,8.423740401370908e-10,India,IND,national capital territory of delhi,N,, - -2021-08-30T16:46:35,74db8b84-50ce-4822-a1cd-d65bdf3b1099,codecarbon,3.8178460597991943,2.18745410150636e-06,2.6234800164504694e-06,India,IND,kerala,N,, diff --git a/energy_pyRAPL.csv b/energy_pyRAPL.csv deleted file mode 100755 index ac504be..0000000 Binary files a/energy_pyRAPL.csv and /dev/null differ diff --git a/fashion-mnist/emissions.csv b/fashion-mnist/emissions.csv deleted file mode 100644 index d275cac..0000000 --- a/fashion-mnist/emissions.csv +++ /dev/null @@ -1,11 +0,0 @@ -timestamp,experiment_id,project_name,duration,emissions,energy_consumed,country_name,country_iso_code,region,on_cloud,cloud_provider,cloud_region -2021-09-28T16:37:25,3b2b88ad-e53c-424d-aa86-f393944342b7,codecarbon,22.133535861968994,3.7505914697371225e-05,4.4981980485666096e-05,India,IND,national capital territory of delhi,N,, -2021-09-28T16:47:47,f1a0aada-d868-4b2f-b652-f00175186b5c,codecarbon,162.69278597831726,0.00029356306968959155,0.00035207908882208867,India,IND,national capital territory of delhi,N,, -2021-09-28T17:01:14,6be716b3-6967-42a8-898a-d82dd3ab73b6,codecarbon,549.6006600856781,0.0010130474845447706,0.0012149785586762372,India,IND,national capital territory of delhi,N,, -2021-09-28T17:14:43,6e6bc042-11c6-4324-8887-73a73643063e,codecarbon,308.59175395965576,0.000582824974955296,0.0006989996607611191,India,IND,national capital territory of delhi,N,, -2021-09-28T18:47:41,8c8eb01b-d030-4eb3-932c-b01386360505,codecarbon,10.2429838180542,1.5848877122647386e-06,1.9008038790762044e-06,India,IND,national capital territory of delhi,N,, -2021-09-28T18:58:10,c81f5aae-059a-40b4-b4c0-9fec04b8d39d,codecarbon,10.744313955307007,1.2640911427325301e-06,1.5160628283112747e-06,India,IND,national capital territory of delhi,N,, -2021-09-28T19:04:32,092757b8-d572-4fd5-a25c-8a8785370188,codecarbon,32.57274913787842,4.5233241145091314e-05,5.424959734776276e-05,India,IND,national capital territory of delhi,N,, -2021-09-28T19:08:02,a12d110d-f310-42c1-b8d0-8612981efb0d,codecarbon,149.9842071533203,0.0002837856315779544,0.0003403527108924685,India,IND,national capital territory of delhi,N,, -2021-09-28T19:18:27,d926e04d-ac79-446a-86ff-d2b6c0f0d403,codecarbon,147.46917724609375,0.00028979694848811725,0.0003475622654954669,India,IND,national capital territory of delhi,N,, -2021-09-28T19:30:04,d7b30acc-930c-4de2-9bc2-be42e12b3193,codecarbon,136.84412097930908,0.0002701375652271687,0.0003239841711778206,India,IND,national capital territory of delhi,N,, diff --git a/fashion-mnist/intel_power_gadget_log.csv b/fashion-mnist/intel_power_gadget_log.csv deleted file mode 100644 index d3fe58f..0000000 --- a/fashion-mnist/intel_power_gadget_log.csv +++ /dev/null @@ -1,28 +0,0 @@ -"System Time","RDTSC","Elapsed Time (sec)","CPU Utilization(%)","CPU Frequency_0(MHz)","CPU Min Frequency_0(MHz)","CPU Max Frequency_0(MHz)","CPU Requsted Frequency_0(MHz)","Processor Power_0(Watt)","Cumulative Processor Energy_0(Joules)","Cumulative Processor Energy_0(mWh)","IA Power_0(Watt)","Cumulative IA Energy_0(Joules)","Cumulative IA Energy_0(mWh)","Package Temperature_0(C)","Package Hot_0","CPU Min Temperature_0(C)","CPU Max Temperature_0(C)","DRAM Power_0(Watt)","Cumulative DRAM Energy_0(Joules)","Cumulative DRAM Energy_0(mWh)","Package Power Limit_0(Watt)","GT Frequency(MHz)","GT Requsted Frequency(MHz)" -"19:30:00:616","41416509087283"," 0.100"," 7.717"," 801"," 700"," 1000"," 800"," 0.695"," 0.070"," 0.019"," 0.245"," 0.025"," 0.007"," 84","0"," 83"," 87"," 84.897"," 83.000"," 23.056"," 9.000"," 0"," 0" -"19:30:00:737","41416643874889"," 0.221"," 12.032"," 958"," 700"," 1400"," 961"," 1.417"," 0.241"," 0.067"," 0.441"," 0.078"," 0.022"," 83","0"," 82"," 86"," 83.656"," 165.000"," 45.833"," 9.000"," 0"," 0" -"19:30:00:839","41416758101185"," 0.324"," 5.695"," 980"," 700"," 1400"," 980"," 1.051"," 0.349"," 0.097"," 0.235"," 0.102"," 0.028"," 82","0"," 81"," 83"," 82.471"," 246.000"," 68.333"," 9.000"," 0"," 0" -"19:30:00:942","41416872561760"," 0.427"," 6.079"," 923"," 700"," 1400"," 927"," 1.033"," 0.455"," 0.126"," 0.277"," 0.130"," 0.036"," 82","0"," 80"," 84"," 81.691"," 326.000"," 90.556"," 9.000"," 0"," 0" -"19:30:01:049","41416991408851"," 0.534"," 5.770"," 972"," 700"," 1400"," 967"," 1.031"," 0.565"," 0.157"," 0.263"," 0.159"," 0.044"," 82","0"," 79"," 82"," 81.000"," 405.000"," 112.500"," 9.000"," 0"," 0" -"19:30:01:153","41417106867463"," 0.637"," 8.801"," 898"," 700"," 1400"," 905"," 1.266"," 0.696"," 0.193"," 0.273"," 0.187"," 0.052"," 80","0"," 78"," 82"," 80.225"," 483.000"," 134.167"," 9.000"," 0"," 0" -"19:30:01:254","41417219001792"," 0.738"," 8.472"," 1115"," 700"," 1400"," 1125"," 1.306"," 0.828"," 0.230"," 0.321"," 0.219"," 0.061"," 80","0"," 78"," 82"," 79.787"," 561.000"," 155.833"," 9.000"," 0"," 0" -"19:30:01:357","41417334438767"," 0.842"," 5.104"," 992"," 700"," 1400"," 991"," 1.050"," 0.937"," 0.260"," 0.262"," 0.246"," 0.068"," 80","0"," 78"," 81"," 79.283"," 639.000"," 177.500"," 9.000"," 0"," 0" -"19:30:01:461","41417449949705"," 0.946"," 5.819"," 923"," 700"," 1400"," 928"," 0.983"," 1.039"," 0.289"," 0.206"," 0.268"," 0.074"," 80","0"," 78"," 81"," 78.868"," 717.000"," 199.167"," 9.000"," 0"," 0" -"19:30:01:566","41417567134829"," 1.051"," 5.546"," 986"," 700"," 1400"," 1000"," 1.031"," 1.147"," 0.319"," 0.282"," 0.297"," 0.083"," 79","0"," 78"," 80"," 78.706"," 795.000"," 220.833"," 9.000"," 0"," 0" -"19:30:01:667","41417679258119"," 1.152"," 7.303"," 921"," 700"," 1400"," 922"," 1.175"," 1.266"," 0.352"," 0.252"," 0.323"," 0.090"," 79","0"," 77"," 80"," 78.328"," 872.000"," 242.222"," 9.000"," 0"," 0" -"19:30:01:773","41417797044905"," 1.258"," 7.443"," 926"," 700"," 1400"," 922"," 1.346"," 1.408"," 0.391"," 0.298"," 0.354"," 0.098"," 80","0"," 77"," 80"," 78.391"," 949.000"," 263.611"," 9.000"," 0"," 0" - -"Total Elapsed Time (sec) = 1.257544" -"Measured RDTSC Frequency (GHz) = 1.113" - -"Cumulative Package Energy_0 (Joules) = 1.408020" -"Cumulative Package Energy_0 (mWh) = 0.391117" -"Average Package Power_0 (Watt) = 1.119658" - -"Cumulative IA Energy_0 (Joules) = 0.354126" -"Cumulative IA Energy_0 (mWh) = 0.098368" -"Average Package IA_0 (Watt) = 0.281601" - -"Cumulative DRAM Energy_0 (Joules) = 949.000000" -"Cumulative DRAM Energy_0 (mWh) = 263.611111" -"Average Package DRAM_0 (Watt) = 754.645396" diff --git a/fashion-mnist/train.py b/fashion-mnist/train.py deleted file mode 100644 index 27e445c..0000000 --- a/fashion-mnist/train.py +++ /dev/null @@ -1,71 +0,0 @@ -import tensorflow as tf -from tensorflow import keras -import matplotlib.pyplot as plt -from codecarbon import EmissionsTracker #Energy Usage - - -#@track_emissions(project_name="asd screening") - -class myCallback(tf.keras.callbacks.Callback): - def on_epoch_end(self, epoch, logs={}): - if(logs.get('loss')<0.1): - print("\n accuracy") - self.model.stop_training = True - -tracker = EmissionsTracker() - -def main(): - mnist = tf.keras.datasets.fashion_mnist - # Load MNIST dataset - (input_train, training_labels), (input_test, test_labels) = mnist.load_data() - - # Set input shape --> - # https://www.machinecurve.com/index.php/question/valueerror-input-0-of-layer-sequential-is-incompatible-with-the-layer-expected-min_ndim4-found-ndim3-full-shape-received-250-28-28/ - sample_shape = input_train[0].shape - img_width, img_height = sample_shape[0], sample_shape[1] - input_shape = (img_width, img_height, 1) - - # Reshape data - training_images = input_train.reshape(len(input_train), input_shape[0], input_shape[1], input_shape[2]) - test_images = input_test.reshape(len(input_test), input_shape[0], input_shape[1], input_shape[2]) - #--> - - #Image in index 0 - #plt.imshow(training_images[0]) - #print(training_labels[0]) - #print(training_images[0]) - - #Image in index 42 - #plt.imshow(training_images[42]) - #print(training_labels[42]) - #print(training_images[42]) - - training_images = training_images/255.0 - test_images = test_images/255.0 - callbacks = myCallback() - - model = tf.keras.Sequential([ - tf.keras.layers.Conv2D(64, (3,3), activation = 'relu', input_shape=(28, 28, 1)), - tf.keras.layers.MaxPooling2D(2,2), - tf.keras.layers.Conv2D(64, (3,3), activation = 'relu'), - tf.keras.layers.MaxPooling2D(2,2), - tf.keras.layers.Flatten(), - tf.keras.layers.Dense(128, activation=tf.nn.relu), - tf.keras.layers.Dense(10, activation=tf.nn.softmax)]) - - # compile - model.compile(optimizer = 'adam', loss='sparse_categorical_crossentropy') - model.summary() - # model training: - model.fit(training_images, training_labels, epochs = 5) - # model testing: - test_loss = model.evaluate(test_images, test_labels) - print("Test Loss: ", test_loss) - -if __name__ == "__main__": - tracker.start() - main() - emi: float=tracker.stop() - print(f"overall emmisions:{emi} kg") - emi= emi*89875517873681764 - print(f"overall emmisions:{emi} joules") diff --git a/images/.DS_Store b/images/.DS_Store deleted file mode 100644 index a9408c2..0000000 Binary files a/images/.DS_Store and /dev/null differ diff --git a/images/Figure_0.png b/images/Figure_0.png deleted file mode 100644 index ad45a72..0000000 Binary files a/images/Figure_0.png and /dev/null differ diff --git a/images/Figure_1.png b/images/Figure_1.png deleted file mode 100644 index d3c3b3e..0000000 Binary files a/images/Figure_1.png and /dev/null differ diff --git a/images/Figure_2.png b/images/Figure_2.png deleted file mode 100644 index 48c03a7..0000000 Binary files a/images/Figure_2.png and /dev/null differ diff --git a/images/Figure_3.png b/images/Figure_3.png deleted file mode 100644 index a03b901..0000000 Binary files a/images/Figure_3.png and /dev/null differ diff --git a/images/asd_dataset/.DS_Store b/images/asd_dataset/.DS_Store deleted file mode 100644 index 0daa16b..0000000 Binary files a/images/asd_dataset/.DS_Store and /dev/null differ diff --git a/images/asd_dataset/Figure_1.png b/images/asd_dataset/Figure_1.png deleted file mode 100644 index 909174c..0000000 Binary files a/images/asd_dataset/Figure_1.png and /dev/null differ diff --git a/images/asd_dataset/Figure_10.png b/images/asd_dataset/Figure_10.png deleted file mode 100644 index b9a5974..0000000 Binary files a/images/asd_dataset/Figure_10.png and /dev/null differ diff --git a/images/asd_dataset/Figure_11.png b/images/asd_dataset/Figure_11.png deleted file mode 100644 index 8e0ac9f..0000000 Binary files a/images/asd_dataset/Figure_11.png and /dev/null differ diff --git a/images/asd_dataset/Figure_2.png b/images/asd_dataset/Figure_2.png deleted file mode 100644 index 51e3f54..0000000 Binary files a/images/asd_dataset/Figure_2.png and /dev/null differ diff --git a/images/asd_dataset/Figure_3.png b/images/asd_dataset/Figure_3.png deleted file mode 100644 index 2a3e0d5..0000000 Binary files a/images/asd_dataset/Figure_3.png and /dev/null differ diff --git a/images/asd_dataset/Figure_4.png b/images/asd_dataset/Figure_4.png deleted file mode 100644 index 7933bee..0000000 Binary files a/images/asd_dataset/Figure_4.png and /dev/null differ diff --git a/images/asd_dataset/Figure_5.png b/images/asd_dataset/Figure_5.png deleted file mode 100644 index 986277b..0000000 Binary files a/images/asd_dataset/Figure_5.png and /dev/null differ diff --git a/images/asd_dataset/Figure_6.png b/images/asd_dataset/Figure_6.png deleted file mode 100644 index 8427cb6..0000000 Binary files a/images/asd_dataset/Figure_6.png and /dev/null differ diff --git a/images/asd_dataset/Figure_7.png b/images/asd_dataset/Figure_7.png deleted file mode 100644 index 229256d..0000000 Binary files a/images/asd_dataset/Figure_7.png and /dev/null differ diff --git a/images/asd_dataset/Figure_9.png b/images/asd_dataset/Figure_9.png deleted file mode 100644 index c8a1b58..0000000 Binary files a/images/asd_dataset/Figure_9.png and /dev/null differ diff --git a/images/f1.png b/images/f1.png deleted file mode 100644 index b7284c4..0000000 Binary files a/images/f1.png and /dev/null differ diff --git a/images/f2.png b/images/f2.png deleted file mode 100644 index 148df1e..0000000 Binary files a/images/f2.png and /dev/null differ diff --git a/images/f3.png b/images/f3.png deleted file mode 100644 index dfc52ef..0000000 Binary files a/images/f3.png and /dev/null differ diff --git a/intel_power_gadget_log.csv b/intel_power_gadget_log.csv deleted file mode 100644 index 88fd8e8..0000000 --- a/intel_power_gadget_log.csv +++ /dev/null @@ -1,28 +0,0 @@ -"System Time","RDTSC","Elapsed Time (sec)","CPU Utilization(%)","CPU Frequency_0(MHz)","CPU Min Frequency_0(MHz)","CPU Max Frequency_0(MHz)","CPU Requsted Frequency_0(MHz)","Processor Power_0(Watt)","Cumulative Processor Energy_0(Joules)","Cumulative Processor Energy_0(mWh)","IA Power_0(Watt)","Cumulative IA Energy_0(Joules)","Cumulative IA Energy_0(mWh)","Package Temperature_0(C)","Package Hot_0","CPU Min Temperature_0(C)","CPU Max Temperature_0(C)","DRAM Power_0(Watt)","Cumulative DRAM Energy_0(Joules)","Cumulative DRAM Energy_0(mWh)","Package Power Limit_0(Watt)","GT Frequency(MHz)","GT Requsted Frequency(MHz)" -"23:23:44:751","7471000118759"," 0.104"," 40.588"," 1460"," 700"," 1800"," 1622"," 4.330"," 0.453"," 0.126"," 2.083"," 0.218"," 0.060"," 73","0"," 69"," 75"," 71.891"," 69.000"," 19.167"," 9.000"," 0"," 0" -"23:23:44:864","7471125741232"," 0.217"," 39.327"," 1373"," 700"," 3200"," 2329"," 5.568"," 1.081"," 0.300"," 3.215"," 0.580"," 0.161"," 72","0"," 70"," 80"," 73.578"," 139.000"," 38.611"," 9.000"," 300"," 300" -"23:23:44:964","7471237648479"," 0.318"," 51.525"," 973"," 700"," 1800"," 1348"," 3.350"," 1.417"," 0.394"," 1.420"," 0.723"," 0.201"," 71","0"," 69"," 73"," 70.753"," 208.000"," 57.778"," 9.000"," 0"," 0" -"23:23:45:066","7471350897412"," 0.419"," 55.345"," 927"," 700"," 1400"," 1075"," 3.085"," 1.731"," 0.481"," 1.251"," 0.850"," 0.236"," 70","0"," 68"," 72"," 70.010"," 276.000"," 76.667"," 9.000"," 0"," 0" -"23:23:45:170","7471466450949"," 0.523"," 53.567"," 1012"," 700"," 1800"," 1203"," 3.480"," 2.092"," 0.581"," 1.368"," 0.992"," 0.276"," 70","0"," 68"," 73"," 70.294"," 344.000"," 95.556"," 9.000"," 0"," 0" -"23:23:45:271","7471579094970"," 0.624"," 55.711"," 876"," 700"," 1400"," 933"," 3.213"," 2.417"," 0.671"," 1.125"," 1.106"," 0.307"," 70","0"," 68"," 71"," 69.321"," 412.000"," 114.444"," 9.000"," 300"," 300" -"23:23:45:372","7471691273443"," 0.725"," 56.376"," 831"," 700"," 1200"," 869"," 2.945"," 2.714"," 0.754"," 0.984"," 1.205"," 0.335"," 70","0"," 67"," 70"," 68.910"," 479.000"," 133.056"," 9.000"," 0"," 0" -"23:23:45:473","7471804229187"," 0.827"," 58.966"," 1202"," 700"," 1800"," 1376"," 3.749"," 3.094"," 0.859"," 1.633"," 1.371"," 0.381"," 72","0"," 67"," 72"," 69.449"," 546.000"," 151.667"," 9.000"," 0"," 0" -"23:23:45:574","7471916288167"," 0.927"," 71.759"," 1760"," 700"," 2400"," 1768"," 6.335"," 3.732"," 1.037"," 3.775"," 1.751"," 0.486"," 77","0"," 69"," 77"," 72.711"," 615.000"," 170.833"," 9.000"," 0"," 0" -"23:23:45:674","7472028448872"," 1.028"," 61.346"," 1834"," 700"," 3200"," 2857"," 9.747"," 4.713"," 1.309"," 7.118"," 2.468"," 0.685"," 74","0"," 74"," 89"," 79.346"," 689.000"," 191.389"," 9.000"," 0"," 0" -"23:23:45:775","7472140839818"," 1.129"," 57.340"," 1046"," 700"," 1800"," 1133"," 3.227"," 5.039"," 1.400"," 1.283"," 2.597"," 0.721"," 71","0"," 70"," 75"," 71.727"," 759.000"," 210.833"," 9.000"," 0"," 0" -"23:23:45:876","7472253242034"," 1.230"," 69.149"," 975"," 700"," 1800"," 1128"," 3.588"," 5.401"," 1.500"," 1.356"," 2.734"," 0.759"," 71","0"," 68"," 73"," 70.684"," 827.000"," 229.722"," 9.000"," 0"," 0" - -"Total Elapsed Time (sec) = 1.229756" -"Measured RDTSC Frequency (GHz) = 1.114" - -"Cumulative Package Energy_0 (Joules) = 5.401123" -"Cumulative Package Energy_0 (mWh) = 1.500312" -"Average Package Power_0 (Watt) = 4.392029" - -"Cumulative IA Energy_0 (Joules) = 2.734009" -"Cumulative IA Energy_0 (mWh) = 0.759447" -"Average Package IA_0 (Watt) = 2.223213" - -"Cumulative DRAM Energy_0 (Joules) = 827.000000" -"Cumulative DRAM Energy_0 (mWh) = 229.722222" -"Average Package DRAM_0 (Watt) = 672.491195" diff --git a/iris-dataset/.DS_Store b/iris-dataset/.DS_Store deleted file mode 100644 index 58d2ea8..0000000 Binary files a/iris-dataset/.DS_Store and /dev/null differ diff --git a/iris-dataset/Iris.csv b/iris-dataset/Iris.csv deleted file mode 100644 index 1bf42f2..0000000 --- a/iris-dataset/Iris.csv +++ /dev/null @@ -1,151 +0,0 @@ -Id,SepalLengthCm,SepalWidthCm,PetalLengthCm,PetalWidthCm,Species -1,5.1,3.5,1.4,0.2,Iris-setosa -2,4.9,3.0,1.4,0.2,Iris-setosa -3,4.7,3.2,1.3,0.2,Iris-setosa -4,4.6,3.1,1.5,0.2,Iris-setosa -5,5.0,3.6,1.4,0.2,Iris-setosa -6,5.4,3.9,1.7,0.4,Iris-setosa -7,4.6,3.4,1.4,0.3,Iris-setosa -8,5.0,3.4,1.5,0.2,Iris-setosa -9,4.4,2.9,1.4,0.2,Iris-setosa -10,4.9,3.1,1.5,0.1,Iris-setosa -11,5.4,3.7,1.5,0.2,Iris-setosa -12,4.8,3.4,1.6,0.2,Iris-setosa -13,4.8,3.0,1.4,0.1,Iris-setosa -14,4.3,3.0,1.1,0.1,Iris-setosa -15,5.8,4.0,1.2,0.2,Iris-setosa -16,5.7,4.4,1.5,0.4,Iris-setosa -17,5.4,3.9,1.3,0.4,Iris-setosa -18,5.1,3.5,1.4,0.3,Iris-setosa -19,5.7,3.8,1.7,0.3,Iris-setosa -20,5.1,3.8,1.5,0.3,Iris-setosa -21,5.4,3.4,1.7,0.2,Iris-setosa -22,5.1,3.7,1.5,0.4,Iris-setosa -23,4.6,3.6,1.0,0.2,Iris-setosa -24,5.1,3.3,1.7,0.5,Iris-setosa -25,4.8,3.4,1.9,0.2,Iris-setosa -26,5.0,3.0,1.6,0.2,Iris-setosa -27,5.0,3.4,1.6,0.4,Iris-setosa -28,5.2,3.5,1.5,0.2,Iris-setosa -29,5.2,3.4,1.4,0.2,Iris-setosa -30,4.7,3.2,1.6,0.2,Iris-setosa -31,4.8,3.1,1.6,0.2,Iris-setosa -32,5.4,3.4,1.5,0.4,Iris-setosa -33,5.2,4.1,1.5,0.1,Iris-setosa -34,5.5,4.2,1.4,0.2,Iris-setosa -35,4.9,3.1,1.5,0.1,Iris-setosa -36,5.0,3.2,1.2,0.2,Iris-setosa -37,5.5,3.5,1.3,0.2,Iris-setosa -38,4.9,3.1,1.5,0.1,Iris-setosa -39,4.4,3.0,1.3,0.2,Iris-setosa -40,5.1,3.4,1.5,0.2,Iris-setosa -41,5.0,3.5,1.3,0.3,Iris-setosa -42,4.5,2.3,1.3,0.3,Iris-setosa -43,4.4,3.2,1.3,0.2,Iris-setosa -44,5.0,3.5,1.6,0.6,Iris-setosa -45,5.1,3.8,1.9,0.4,Iris-setosa -46,4.8,3.0,1.4,0.3,Iris-setosa -47,5.1,3.8,1.6,0.2,Iris-setosa -48,4.6,3.2,1.4,0.2,Iris-setosa -49,5.3,3.7,1.5,0.2,Iris-setosa -50,5.0,3.3,1.4,0.2,Iris-setosa -51,7.0,3.2,4.7,1.4,Iris-versicolor -52,6.4,3.2,4.5,1.5,Iris-versicolor -53,6.9,3.1,4.9,1.5,Iris-versicolor -54,5.5,2.3,4.0,1.3,Iris-versicolor -55,6.5,2.8,4.6,1.5,Iris-versicolor -56,5.7,2.8,4.5,1.3,Iris-versicolor -57,6.3,3.3,4.7,1.6,Iris-versicolor -58,4.9,2.4,3.3,1.0,Iris-versicolor -59,6.6,2.9,4.6,1.3,Iris-versicolor -60,5.2,2.7,3.9,1.4,Iris-versicolor -61,5.0,2.0,3.5,1.0,Iris-versicolor -62,5.9,3.0,4.2,1.5,Iris-versicolor -63,6.0,2.2,4.0,1.0,Iris-versicolor -64,6.1,2.9,4.7,1.4,Iris-versicolor -65,5.6,2.9,3.6,1.3,Iris-versicolor -66,6.7,3.1,4.4,1.4,Iris-versicolor -67,5.6,3.0,4.5,1.5,Iris-versicolor -68,5.8,2.7,4.1,1.0,Iris-versicolor -69,6.2,2.2,4.5,1.5,Iris-versicolor -70,5.6,2.5,3.9,1.1,Iris-versicolor -71,5.9,3.2,4.8,1.8,Iris-versicolor -72,6.1,2.8,4.0,1.3,Iris-versicolor -73,6.3,2.5,4.9,1.5,Iris-versicolor -74,6.1,2.8,4.7,1.2,Iris-versicolor -75,6.4,2.9,4.3,1.3,Iris-versicolor -76,6.6,3.0,4.4,1.4,Iris-versicolor -77,6.8,2.8,4.8,1.4,Iris-versicolor -78,6.7,3.0,5.0,1.7,Iris-versicolor -79,6.0,2.9,4.5,1.5,Iris-versicolor -80,5.7,2.6,3.5,1.0,Iris-versicolor -81,5.5,2.4,3.8,1.1,Iris-versicolor -82,5.5,2.4,3.7,1.0,Iris-versicolor -83,5.8,2.7,3.9,1.2,Iris-versicolor -84,6.0,2.7,5.1,1.6,Iris-versicolor -85,5.4,3.0,4.5,1.5,Iris-versicolor -86,6.0,3.4,4.5,1.6,Iris-versicolor -87,6.7,3.1,4.7,1.5,Iris-versicolor -88,6.3,2.3,4.4,1.3,Iris-versicolor -89,5.6,3.0,4.1,1.3,Iris-versicolor -90,5.5,2.5,4.0,1.3,Iris-versicolor -91,5.5,2.6,4.4,1.2,Iris-versicolor -92,6.1,3.0,4.6,1.4,Iris-versicolor -93,5.8,2.6,4.0,1.2,Iris-versicolor -94,5.0,2.3,3.3,1.0,Iris-versicolor -95,5.6,2.7,4.2,1.3,Iris-versicolor -96,5.7,3.0,4.2,1.2,Iris-versicolor -97,5.7,2.9,4.2,1.3,Iris-versicolor -98,6.2,2.9,4.3,1.3,Iris-versicolor -99,5.1,2.5,3.0,1.1,Iris-versicolor -100,5.7,2.8,4.1,1.3,Iris-versicolor -101,6.3,3.3,6.0,2.5,Iris-virginica -102,5.8,2.7,5.1,1.9,Iris-virginica -103,7.1,3.0,5.9,2.1,Iris-virginica -104,6.3,2.9,5.6,1.8,Iris-virginica -105,6.5,3.0,5.8,2.2,Iris-virginica -106,7.6,3.0,6.6,2.1,Iris-virginica -107,4.9,2.5,4.5,1.7,Iris-virginica -108,7.3,2.9,6.3,1.8,Iris-virginica -109,6.7,2.5,5.8,1.8,Iris-virginica -110,7.2,3.6,6.1,2.5,Iris-virginica -111,6.5,3.2,5.1,2.0,Iris-virginica -112,6.4,2.7,5.3,1.9,Iris-virginica -113,6.8,3.0,5.5,2.1,Iris-virginica -114,5.7,2.5,5.0,2.0,Iris-virginica -115,5.8,2.8,5.1,2.4,Iris-virginica -116,6.4,3.2,5.3,2.3,Iris-virginica -117,6.5,3.0,5.5,1.8,Iris-virginica -118,7.7,3.8,6.7,2.2,Iris-virginica -119,7.7,2.6,6.9,2.3,Iris-virginica -120,6.0,2.2,5.0,1.5,Iris-virginica -121,6.9,3.2,5.7,2.3,Iris-virginica -122,5.6,2.8,4.9,2.0,Iris-virginica -123,7.7,2.8,6.7,2.0,Iris-virginica -124,6.3,2.7,4.9,1.8,Iris-virginica -125,6.7,3.3,5.7,2.1,Iris-virginica -126,7.2,3.2,6.0,1.8,Iris-virginica -127,6.2,2.8,4.8,1.8,Iris-virginica -128,6.1,3.0,4.9,1.8,Iris-virginica -129,6.4,2.8,5.6,2.1,Iris-virginica -130,7.2,3.0,5.8,1.6,Iris-virginica -131,7.4,2.8,6.1,1.9,Iris-virginica -132,7.9,3.8,6.4,2.0,Iris-virginica -133,6.4,2.8,5.6,2.2,Iris-virginica -134,6.3,2.8,5.1,1.5,Iris-virginica -135,6.1,2.6,5.6,1.4,Iris-virginica -136,7.7,3.0,6.1,2.3,Iris-virginica -137,6.3,3.4,5.6,2.4,Iris-virginica -138,6.4,3.1,5.5,1.8,Iris-virginica -139,6.0,3.0,4.8,1.8,Iris-virginica -140,6.9,3.1,5.4,2.1,Iris-virginica -141,6.7,3.1,5.6,2.4,Iris-virginica -142,6.9,3.1,5.1,2.3,Iris-virginica -143,5.8,2.7,5.1,1.9,Iris-virginica -144,6.8,3.2,5.9,2.3,Iris-virginica -145,6.7,3.3,5.7,2.5,Iris-virginica -146,6.7,3.0,5.2,2.3,Iris-virginica -147,6.3,2.5,5.0,1.9,Iris-virginica -148,6.5,3.0,5.2,2.0,Iris-virginica -149,6.2,3.4,5.4,2.3,Iris-virginica -150,5.9,3.0,5.1,1.8,Iris-virginica diff --git a/iris-dataset/emissions.csv b/iris-dataset/emissions.csv deleted file mode 100644 index db3af44..0000000 --- a/iris-dataset/emissions.csv +++ /dev/null @@ -1,9 +0,0 @@ -timestamp,experiment_id,project_name,duration,emissions,energy_consumed,country_name,country_iso_code,region,on_cloud,cloud_provider,cloud_region -2021-09-04T23:39:58,3f72de02-5328-4356-9a8e-c27aad7f977a,codecarbon,4.8744282722473145,2.395008492812252e-07,2.872406289940083e-07,India,IND,national capital territory of delhi,N,, -2021-10-01T16:17:44,fd66afc8-2470-496c-a474-7755518ef51e,codecarbon,3.8585121631622314,1.0290958799910258e-06,1.2342258858408858e-06,India,IND,national capital territory of delhi,N,, -2021-10-01T16:17:55,4d386de7-8b01-40a9-928b-4b4778d2f053,codecarbon,1.9195787906646729,1.6625838296145782e-07,1.9939871879660813e-07,India,IND,national capital territory of delhi,N,, -2021-10-01T16:54:11,de69a4df-3642-4057-bd25-3863748522ec,codecarbon,4.985802888870239,9.584904711039873e-08,1.149546678564764e-07,India,IND,national capital territory of delhi,N,, -2021-10-01T16:54:55,139adaea-47c5-40a9-b9be-71ec22223852,codecarbon,3.0712690353393555,1.0894615713181159e-07,1.3066242894310596e-07,India,IND,national capital territory of delhi,N,, -2021-10-01T16:55:22,42e560eb-ad81-4f3c-8858-8ac23c342ddb,codecarbon,4.9131269454956055,1.2598084791716727e-07,1.5109265000741005e-07,India,IND,national capital territory of delhi,N,, -2021-10-01T16:56:57,2586d40f-dbda-4900-a684-cb54ca5fb5ef,codecarbon,2.9814281463623047,1.226156029830599e-07,1.4705660974077672e-07,India,IND,national capital territory of delhi,N,, -2021-10-01T16:58:33,e9a98011-91cc-4890-a80f-c284a32ea53e,codecarbon,4.921020030975342,1.2443715040300566e-07,1.492412467815968e-07,India,IND,national capital territory of delhi,N,, diff --git a/iris-dataset/intel_power_gadget_log.csv b/iris-dataset/intel_power_gadget_log.csv deleted file mode 100644 index 94d1872..0000000 --- a/iris-dataset/intel_power_gadget_log.csv +++ /dev/null @@ -1,28 +0,0 @@ -"System Time","RDTSC","Elapsed Time (sec)","CPU Utilization(%)","CPU Frequency_0(MHz)","CPU Min Frequency_0(MHz)","CPU Max Frequency_0(MHz)","CPU Requsted Frequency_0(MHz)","Processor Power_0(Watt)","Cumulative Processor Energy_0(Joules)","Cumulative Processor Energy_0(mWh)","IA Power_0(Watt)","Cumulative IA Energy_0(Joules)","Cumulative IA Energy_0(mWh)","Package Temperature_0(C)","Package Hot_0","CPU Min Temperature_0(C)","CPU Max Temperature_0(C)","DRAM Power_0(Watt)","Cumulative DRAM Energy_0(Joules)","Cumulative DRAM Energy_0(mWh)","Package Power Limit_0(Watt)","GT Frequency(MHz)","GT Requsted Frequency(MHz)" -"16:58:29:295","14585555001782"," 0.103"," 6.179"," 909"," 700"," 1200"," 895"," 0.838"," 0.086"," 0.024"," 0.165"," 0.017"," 0.005"," 60","0"," 57"," 62"," 58.945"," 57.000"," 15.833"," 9.000"," 0"," 0" -"16:58:29:412","14585685307235"," 0.220"," 15.959"," 1096"," 700"," 1400"," 1096"," 1.681"," 0.283"," 0.079"," 0.483"," 0.073"," 0.020"," 60","0"," 57"," 60"," 58.652"," 114.000"," 31.667"," 9.000"," 0"," 0" -"16:58:29:519","14585805297041"," 0.328"," 6.006"," 1012"," 700"," 1400"," 1025"," 0.954"," 0.386"," 0.107"," 0.189"," 0.094"," 0.026"," 60","0"," 56"," 58"," 57.193"," 170.000"," 47.222"," 9.000"," 0"," 0" -"16:58:29:627","14585925311654"," 0.435"," 6.796"," 946"," 700"," 1300"," 947"," 0.748"," 0.466"," 0.130"," 0.193"," 0.115"," 0.032"," 59","0"," 56"," 58"," 56.609"," 226.000"," 62.778"," 9.000"," 0"," 0" -"16:58:29:728","14586037615595"," 0.536"," 8.892"," 906"," 700"," 1400"," 914"," 0.851"," 0.552"," 0.153"," 0.249"," 0.140"," 0.039"," 59","0"," 55"," 58"," 56.468"," 281.000"," 78.056"," 9.000"," 0"," 0" -"16:58:29:830","14586151703793"," 0.639"," 15.602"," 933"," 700"," 1400"," 941"," 1.354"," 0.691"," 0.192"," 0.365"," 0.177"," 0.049"," 58","0"," 55"," 58"," 56.157"," 336.000"," 93.333"," 9.000"," 0"," 0" -"16:58:29:932","14586264553819"," 0.740"," 18.812"," 958"," 700"," 1400"," 973"," 1.541"," 0.847"," 0.235"," 0.395"," 0.217"," 0.060"," 58","0"," 55"," 58"," 56.327"," 391.000"," 108.611"," 9.000"," 0"," 0" -"16:58:30:033","14586377696116"," 0.842"," 18.422"," 1074"," 700"," 1800"," 1087"," 1.689"," 1.019"," 0.283"," 0.521"," 0.270"," 0.075"," 58","0"," 55"," 58"," 56.691"," 446.000"," 123.889"," 9.000"," 0"," 0" -"16:58:30:139","14586495439155"," 0.947"," 7.364"," 944"," 700"," 1300"," 950"," 1.049"," 1.130"," 0.314"," 0.280"," 0.300"," 0.083"," 59","0"," 55"," 58"," 56.368"," 501.000"," 139.167"," 9.000"," 0"," 0" -"16:58:30:241","14586608633113"," 1.049"," 10.456"," 872"," 700"," 1300"," 877"," 1.102"," 1.242"," 0.345"," 0.251"," 0.325"," 0.090"," 58","0"," 54"," 57"," 55.785"," 555.000"," 154.167"," 9.000"," 0"," 0" -"16:58:30:346","14586725889873"," 1.154"," 6.267"," 1003"," 700"," 1400"," 1010"," 0.812"," 1.327"," 0.369"," 0.190"," 0.345"," 0.096"," 58","0"," 55"," 56"," 55.483"," 610.000"," 169.444"," 9.000"," 0"," 0" -"16:58:30:451","14586842648466"," 1.259"," 14.609"," 1037"," 700"," 1400"," 1187"," 1.088"," 1.441"," 0.400"," 0.332"," 0.380"," 0.106"," 58","0"," 54"," 57"," 55.696"," 664.000"," 184.444"," 9.000"," 0"," 0" - -"Total Elapsed Time (sec) = 1.259160" -"Measured RDTSC Frequency (GHz) = 1.114" - -"Cumulative Package Energy_0 (Joules) = 1.441162" -"Cumulative Package Energy_0 (mWh) = 0.400323" -"Average Package Power_0 (Watt) = 1.144542" - -"Cumulative IA Energy_0 (Joules) = 0.380066" -"Cumulative IA Energy_0 (mWh) = 0.105574" -"Average Package IA_0 (Watt) = 0.301841" - -"Cumulative DRAM Energy_0 (Joules) = 664.000000" -"Cumulative DRAM Energy_0 (mWh) = 184.444444" -"Average Package DRAM_0 (Watt) = 527.335667" diff --git a/iris-dataset/train.py b/iris-dataset/train.py deleted file mode 100644 index 15c2a23..0000000 --- a/iris-dataset/train.py +++ /dev/null @@ -1,117 +0,0 @@ -#Import python modules - -import numpy as np -import pandas as pd - -#Visualisation -import matplotlib.pyplot as plt - -#Model -from sklearn.model_selection import train_test_split -from sklearn.linear_model import LogisticRegression -from sklearn.neighbors import KNeighborsClassifier -from sklearn.metrics import accuracy_score, roc_auc_score, roc_curve, classification_report -from sklearn.preprocessing import LabelEncoder - -from sklearn import svm #for Support Vector Machine (SVM) Algorithm -from sklearn import metrics #for checking the model accuracy - - - -#Energy Usage -from codecarbon import EmissionsTracker - -tracker = EmissionsTracker() - -# @track_emissions(project_name="iris") -# split into train/test - -def train_test_split(dataset, train_frac= 0.7, seed=1): - df_matrix = dataset.values - - # shuffle the data - np.random.seed(seed) - np.random.shuffle(df_matrix) - - # split the data - train_size = int(df_matrix.shape[0] * train_frac) - # features are all except last column - train_features = df_matrix[:train_size, :-1] - # the last column - train_labels = df_matrix[:train_size, -1] - - # test data - test_features = df_matrix[train_size:, :-1] - test_labels = df_matrix[train_size:, -1] - - return (train_features, train_labels), (test_features, test_labels) - -def main(): - dataset = pd.read_csv("Iris.csv") - #print(dataset.head(5)) - #print(dataset.info()) - dataset.drop('Id', axis=1, inplace=True) - print(dataset.head(15)) - #EDA - - # Sepal length and width - fig = dataset[dataset.Species=='Iris-setosa'].plot(kind='scatter',x='SepalLengthCm',y='SepalWidthCm',color='orange', label='Setosa') - dataset[dataset.Species=='Iris-versicolor'].plot(kind='scatter',x='SepalLengthCm',y='SepalWidthCm',color='blue', label='versicolor',ax=fig) - dataset[dataset.Species=='Iris-virginica'].plot(kind='scatter',x='SepalLengthCm',y='SepalWidthCm',color='green', label='virginica', ax=fig) - fig.set_xlabel("Sepal Length") - fig.set_ylabel("Sepal Width") - fig.set_title("Sepal Length VS Width") - fig=plt.gcf() - fig.set_size_inches(10,6) - #plt.show() - - # Petal length and width - fig = dataset[dataset.Species=='Iris-setosa'].plot.scatter(x='PetalLengthCm',y='PetalWidthCm',color='orange', label='Setosa') - dataset[dataset.Species=='Iris-versicolor'].plot.scatter(x='PetalLengthCm',y='PetalWidthCm',color='blue', label='versicolor',ax=fig) - dataset[dataset.Species=='Iris-virginica'].plot.scatter(x='PetalLengthCm',y='PetalWidthCm',color='green', label='virginica', ax=fig) - fig.set_xlabel("Petal Length") - fig.set_ylabel("Petal Width") - fig.set_title(" Petal Length VS Width") - fig=plt.gcf() - fig.set_size_inches(10,6) - #plt.show() - dataset.hist(edgecolor='black', linewidth=1.2) - fig=plt.gcf() - fig.set_size_inches(12,6) - #plt.show() - print(dataset.shape) - (train_features, train_labels), (test_features, test_labels) = train_test_split(dataset, train_frac=0.7) - - #LogisticRegression - lg = LogisticRegression() - lg.fit(train_features, train_labels) - lgpredict = lg.predict(test_features) - score = lg.score(test_features, test_labels) - #print(score) - pred_lg = lg.predict(test_features) - log_reg_predict_proba = lg.predict_proba(test_features)[:, 1] - print('\nLogistic Regression Accuracy: {:.2f}%'.format(accuracy_score(test_labels, lgpredict) * 100)) - - #SupportVectorMachines - model = svm.SVC() - model.fit(train_features, train_labels) - prediction=model.predict(test_features) - score = model.score(test_features, test_labels) - print('SVM Accuracy: {:.2f}%'.format(accuracy_score(test_labels, prediction) * 100)) - - #KNearestClassifier - model = KNeighborsClassifier() - model.fit(train_features, train_labels) - prediction=model.predict(test_features) - score = model.score(test_features, test_labels) - print('KNN Accuracy: {:.2f}%'.format(accuracy_score(test_labels, prediction) * 100)) - - -if __name__ == "__main__": - tracker.start() - main() - emi: float=tracker.stop() - print(f"overall emmisions:{emi} kg") - emi= emi*89875517873681764 - print(f"overall emmisions:{emi} joules") - # energyusage.evaluate(main,pdf=True) diff --git a/red-wine/train.py b/red-wine/train.py new file mode 100644 index 0000000..f9ec9f3 --- /dev/null +++ b/red-wine/train.py @@ -0,0 +1,24 @@ +#Import python modules +#Visualisation +#Model + + +#Energy Usage +from codecarbon import EmissionsTracker + +tracker = EmissionsTracker() + +def main(): + +##TO DO: + + + +if __name__ == "__main__": + tracker.start() + main() + emi: float=tracker.stop() + print(f"overall emmisions:{emi} kg") + emi= emi*89875517873681764 + print(f"overall emmisions:{emi} joules") + # energyusage.evaluate(main,pdf=True) diff --git a/server.js b/server.js deleted file mode 100644 index 64d9bbe..0000000 --- a/server.js +++ /dev/null @@ -1,13 +0,0 @@ -var express = require('express'), - mds = require('markdown-serve'), - path = require('path'); - -var app = express(); - -app.set('views', path.join(__dirname, 'views')); -app.set('view engine', 'jade'); - -app.use(mds.middleware({ - rootDirectory: path.resolve(__dirname, '/'), - view: 'markdown' -})); diff --git a/vg-dataset/.DS_Store b/vg-dataset/.DS_Store deleted file mode 100644 index a40b30a..0000000 Binary files a/vg-dataset/.DS_Store and /dev/null differ diff --git a/vg-dataset/create_model.py b/vg-dataset/create_model.py deleted file mode 100755 index e06da65..0000000 --- a/vg-dataset/create_model.py +++ /dev/null @@ -1,30 +0,0 @@ -import pandas as pd -import tensorflow as tf -import tensorflow.keras -#from keras.models import Sequential -#from keras.layers import * - -training_data = pd.read_csv("sales_data_training_scaled.csv") - -train_features = training_data.drop('total_earnings', axis=1).values -train_labels = training_data[['total_earnings']].values - -# Define the model -model = tf.keras.Sequential([ - tf.keras.layers.Dense(50, input_dim=9, activation='relu'), - tf.keras.layers.Dense(100, activation='relu'), - tf.keras.layers.Dense(50, activation='relu'), - tf.keras.layers.Dense(1,activation='linear') -]) - -# Training the model -model.compile(optimizer='adam', loss = 'mean_squared_error') -model.fit(train_features, train_labels, epochs=50, shuffle=2, verbose=2) -#model.summary() - -# Test the model -test_data=pd.read_csv("sales_data_test_scaled.csv") -test_features= test_data.drop('total_earnings', axis=1).values -test_labels = test_data['total_earnings'].values -test_error_rate = model.evaluate(test_features, test_labels, verbose=0) -print("The mean squared error for the test data is: {}".format(test_error_rate)) diff --git a/vg-dataset/load_saved_model.py b/vg-dataset/load_saved_model.py deleted file mode 100755 index 229478e..0000000 --- a/vg-dataset/load_saved_model.py +++ /dev/null @@ -1,18 +0,0 @@ -import pandas as pd -from keras.models import load_model - -#model = - -X = pd.read_csv("proposed_new_product.csv").values -#prediction = model.predict(X) - -# Grab just the first element of the first prediction (since we only have one) -prediction = prediction[0][0] - -# Re-scale the data from the 0-to-1 range back to dollars -# These constants are from when the data was originally scaled down to the 0-to-1 range -prediction = prediction + 0.1159 -prediction = prediction / 0.0000036968 - -print("Earnings Prediction for Proposed Product - ${}".format(prediction)) - diff --git a/vg-dataset/predict.py b/vg-dataset/predict.py deleted file mode 100755 index c637b56..0000000 --- a/vg-dataset/predict.py +++ /dev/null @@ -1,50 +0,0 @@ -import pandas as pd -from keras.models import Sequential -from keras.layers import * - -training_data_df = pd.read_csv("sales_data_training_scaled.csv") - -X = training_data_df.drop('total_earnings', axis=1).values -Y = training_data_df[['total_earnings']].values - -# Define the model -model = Sequential() -model.add(Dense(50, input_dim=9, activation='relu')) -model.add(Dense(100, activation='relu')) -model.add(Dense(50, activation='relu')) -model.add(Dense(1, activation='linear')) -model.compile(loss='mean_squared_error', optimizer='adam') - -# Train the model -model.fit( - X, - Y, - epochs=50, - shuffle=True, - verbose=2 -) - -# Load the separate test data set -test_data_df = pd.read_csv("sales_data_test_scaled.csv") - -X_test = test_data_df.drop('total_earnings', axis=1).values -Y_test = test_data_df[['total_earnings']].values - -test_error_rate = model.evaluate(X_test, Y_test, verbose=0) -print("The mean squared error (MSE) for the test data set is: {}".format(test_error_rate)) - -# Load the data we make to use to make a prediction -X = pd.read_csv("proposed_new_product.csv").values - -# Make a prediction with the neural network -prediction = - -# Grab just the first element of the first prediction (since that's the only have one) -prediction = prediction[0][0] - -# Re-scale the data from the 0-to-1 range back to dollars -# These constants are from when the data was originally scaled down to the 0-to-1 range -prediction = prediction + 0.1159 -prediction = prediction / 0.0000036968 - -print("Earnings Prediction for Proposed Product - ${}".format(prediction)) diff --git a/vg-dataset/preprocess_data.py b/vg-dataset/preprocess_data.py deleted file mode 100755 index d0c6d7e..0000000 --- a/vg-dataset/preprocess_data.py +++ /dev/null @@ -1,23 +0,0 @@ -import pandas as pd -from sklearn.preprocessing import MinMaxScaler - -training_data = pd.read_csv("sales_data_training.csv") -test_data =pd.read_csv("sales_data_test.csv") - -# Data needs to be scaled to a small range like 0 to 1 for the neural network to work well. -scaler = MinMaxScaler(feature_range=(0, 1)) - -# Scaling both the training inputs and outputs -scaled_training = scaler.fit_transform(training_data) -scaled_testing = scaler.transform(test_data) - -# Print out the adjustment that the scaler applied to the total_earnings column of data -print("Note: total_earnings values were scaled by multiplying by {:.10f} and adding {:.6f}".format(scaler.scale_[8], scaler.min_[8])) - -# Create new pandas DataFrame objects from the scaled data -scaled_training_df = pd.DataFrame(scaled_training, columns=training_data.columns.values) -scaled_testing_df = pd.DataFrame(scaled_testing, columns=test_data.columns.values) - -# Save scaled data dataframes to new CSV files -scaled_training_df.to_csv("sales_data_training_scaled.csv", index=False) -scaled_testing_df.to_csv("sales_data_test_scaled.csv", index=False) \ No newline at end of file diff --git a/vg-dataset/proposed_new_product.csv b/vg-dataset/proposed_new_product.csv deleted file mode 100755 index cc0b55e..0000000 --- a/vg-dataset/proposed_new_product.csv +++ /dev/null @@ -1,2 +0,0 @@ -critic_rating,is_action,is_exclusive_to_us,is_portable,is_role_playing,is_sequel,is_sports,suitable_for_kids,unit_price -0.7,1.0,1.0,1.0,0.0,1.0,0.0,1.0,0.8 \ No newline at end of file diff --git a/vg-dataset/sales_data_test.csv b/vg-dataset/sales_data_test.csv deleted file mode 100755 index 0ff7875..0000000 --- a/vg-dataset/sales_data_test.csv +++ /dev/null @@ -1,401 +0,0 @@ -critic_rating,is_action,is_exclusive_to_us,is_portable,is_role_playing,is_sequel,is_sports,suitable_for_kids,total_earnings,unit_price -3.5,1,1,1,0,1,0,1,247537,59.99 -2.5,0,0,0,1,1,0,0,73960,59.99 -3.5,0,0,0,0,1,1,0,82671,59.99 -4.0,1,1,0,0,1,0,0,137456,39.99 -2.0,1,0,1,0,1,0,0,89639,59.99 -3.5,1,0,1,0,0,0,1,103734,59.99 -4.0,1,0,0,0,0,0,1,96766,59.99 -3.5,1,1,1,0,1,0,0,223148,59.99 -4.0,1,1,0,0,0,0,0,149979,59.99 -2.5,0,1,0,1,1,0,1,138418,59.99 -5.0,1,0,0,0,1,0,0,96810,39.99 -4.0,1,0,0,0,1,0,0,122264,59.99 -3.5,0,1,0,0,0,1,0,101834,59.99 -4.0,0,0,1,1,1,0,0,126065,59.99 -4.5,0,1,1,0,1,1,0,138090,39.99 -4.0,0,0,0,0,1,1,0,92173,59.99 -3.5,0,0,1,0,1,1,1,109436,59.99 -3.5,0,0,0,1,0,0,0,45607,39.99 -5.0,1,0,0,0,1,0,0,97444,39.99 -2.5,1,0,1,0,1,0,0,105476,59.99 -3.0,0,0,0,1,1,0,0,85838,59.99 -3.5,0,1,0,1,1,0,0,107157,39.99 -2.5,0,1,0,1,1,0,1,141427,59.99 -4.0,0,0,1,1,0,0,0,89481,59.99 -3.5,1,0,1,0,1,0,1,146337,59.99 -3.5,0,0,1,0,1,1,1,108485,59.99 -3.0,1,1,0,0,1,0,0,139760,49.99 -3.5,1,0,0,0,1,0,1,82241,39.99 -3.0,1,0,0,0,0,0,0,70001,59.99 -4.5,1,0,1,0,0,0,1,105182,49.99 -2.5,0,0,0,1,1,0,0,75069,59.99 -3.5,1,1,0,0,1,0,0,189098,59.99 -4.0,1,0,0,0,1,0,0,82347,39.99 -4.5,0,1,0,1,1,0,0,196858,59.99 -5.0,0,0,0,1,1,0,0,82558,39.99 -3.5,0,0,0,1,0,0,0,68575,59.99 -5.0,0,1,0,0,1,1,0,124787,39.99 -2.5,0,0,0,1,1,0,1,68098,49.99 -5.0,0,1,1,0,1,1,1,162900,39.99 -5.0,0,1,0,1,1,0,1,234234,59.99 -2.0,0,0,0,1,1,0,0,42862,39.99 -3.5,0,0,0,1,1,0,0,62816,39.99 -2.5,1,1,0,0,0,0,0,106427,59.99 -3.5,0,0,1,0,0,1,0,58992,49.99 -3.5,0,1,0,0,1,1,0,95332,39.99 -3.0,1,0,1,0,1,0,0,80130,39.99 -2.0,0,0,0,1,0,0,0,46561,59.99 -5.0,1,1,1,0,1,0,0,246394,49.99 -3.5,1,0,0,0,1,0,1,81502,39.99 -4.5,0,0,0,0,1,1,0,101359,59.99 -2.5,0,0,0,1,0,0,0,54005,59.99 -5.0,1,1,1,0,0,0,1,232809,59.99 -2.5,1,0,0,0,1,0,0,87897,59.99 -4.0,1,0,0,0,1,0,0,82347,39.99 -2.5,0,0,1,0,1,1,0,65194,49.99 -4.0,1,0,0,0,1,0,0,120997,59.99 -3.0,1,0,1,0,1,0,1,131925,59.99 -2.0,0,0,0,1,1,0,1,70159,59.99 -3.0,1,1,1,0,1,0,0,202559,59.99 -4.5,1,1,0,0,1,0,0,228216,59.99 -4.0,1,0,0,0,0,0,0,72321,49.99 -4.5,1,1,1,0,1,0,0,270660,59.99 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-4.5,0,0,0,1,1,0,0,116087,59.99 -3.5,0,0,0,0,1,1,0,83779,59.99 -2.0,1,0,0,0,1,0,0,75385,59.99 -4.5,1,0,1,0,1,0,1,148998,49.99 -4.5,0,0,1,0,1,1,0,120680,59.99 -3.5,0,0,0,1,0,0,0,68259,59.99 -4.5,1,0,0,0,1,0,0,134775,59.99 -3.5,0,1,0,1,1,0,1,175319,59.99 -4.0,0,0,0,1,0,0,0,74593,59.99 -4.5,0,0,0,1,1,0,0,114187,59.99 -4.0,0,1,0,1,1,0,1,198442,59.99 -4.0,0,0,0,0,1,1,0,91856,59.99 -4.0,0,1,0,1,1,0,0,149526,49.99 -4.5,0,0,0,0,1,1,0,101042,59.99 -4.0,0,0,0,0,1,1,0,92648,59.99 -3.5,1,0,0,0,0,0,0,78078,59.99 -3.5,0,0,0,1,1,0,0,95499,59.99 -4.0,1,1,0,0,1,0,0,139251,39.99 -2.5,1,0,1,0,0,0,0,50569,39.99 -4.5,1,0,1,0,1,0,1,175477,59.99 -4.0,1,0,1,0,1,0,0,146337,59.99 -4.0,1,0,0,0,1,0,0,80869,39.99 -5.0,0,0,0,0,1,1,1,100827,49.99 -4.0,0,1,0,0,1,1,0,102828,39.99 -3.5,1,0,1,0,1,0,1,145228,59.99 -3.5,0,0,0,1,1,0,0,96132,59.99 -2.5,0,0,0,0,1,1,0,55164,49.99 -4.0,1,1,0,0,1,0,0,138934,39.99 -2.5,1,0,0,0,1,0,0,88214,59.99 -4.5,0,0,1,1,1,0,1,151405,59.99 -2.0,1,0,0,0,1,0,0,50675,39.99 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9875ea3..0000000 --- a/vg-dataset/sales_data_test_scaled.csv +++ /dev/null @@ -1,401 +0,0 @@ -critic_rating,is_action,is_exclusive_to_us,is_portable,is_role_playing,is_sequel,is_sports,suitable_for_kids,total_earnings,unit_price -0.4999999999999999,1.0,1.0,1.0,0.0,1.0,0.0,1.0,0.7991793127668619,1.0 -0.16666666666666663,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.15750170976506905,1.0 -0.4999999999999999,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.18970444169239015,1.0 -0.6666666666666666,1.0,1.0,0.0,0.0,1.0,0.0,0.0,0.39223304559989647,0.0 -0.0,1.0,0.0,1.0,0.0,1.0,0.0,0.0,0.21546366980277626,1.0 -0.4999999999999999,1.0,0.0,1.0,0.0,0.0,0.0,1.0,0.2675699155283636,1.0 -0.6666666666666666,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.2418106874179775,1.0 -0.4999999999999999,1.0,1.0,1.0,0.0,1.0,0.0,0.0,0.7090183175911721,1.0 -0.6666666666666666,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.4385279384854254,1.0 -0.16666666666666663,0.0,1.0,0.0,1.0,1.0,0.0,1.0,0.3957893569434946,1.0 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