synthetic-data-tutorial | tutorial showing how to use Python | Dataset library

by theodi Python Version: Current License: MIT

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kandi X-RAY | synthetic-data-tutorial Summary

synthetic-data-tutorial is a Python library typically used in Artificial Intelligence, Dataset, Deep Learning, Generative adversarial networks applications. synthetic-data-tutorial has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.

In this tutorial you are aiming to create a safe version of accident and emergency (A&E) admissions data, collected from multiple hospitals. This data contains some sensitive personal information about people's health and can't be openly shared. By removing and altering certain identifying information in the data we can greatly reduce the risk that patients can be re-identified and therefore hope to release the data. Just to be clear, we're not using actual A&E data but are creating our own simple, mock, version of it.

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synthetic-data-tutorial has a low active ecosystem.

It has 54 star(s) with 19 fork(s). There are 9 watchers for this library.

It had no major release in the last 6 months.

There are 2 open issues and 1 have been closed. On average issues are closed in 16 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of synthetic-data-tutorial is current.

Quality

synthetic-data-tutorial has 0 bugs and 0 code smells.

Security

synthetic-data-tutorial has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

synthetic-data-tutorial code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

synthetic-data-tutorial is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

synthetic-data-tutorial releases are not available. You will need to build from source code and install.

Build file is available. You can build the component from source.

Installation instructions, examples and code snippets are available.

synthetic-data-tutorial saves you 568 person hours of effort in developing the same functionality from scratch.

It has 1322 lines of code, 104 functions and 22 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed synthetic-data-tutorial and discovered the below as its top functions. This is intended to give you an instant insight into synthetic-data-tutorial implemented functionality, and help decide if they suit your requirements.

Helper function to describe a single dataset in an independent attribute mode
Creates a description of the given dataset in random mode
Helper method to describe a dataset in random mode
Return a JSON representation of the distribution
Creates a dataDescriber for synthetic data
Helper function to describe a single dataset in an independent feature mode
Compares the model_histograms for the given model
Compares two histograms
Convert Lsoa data into a pandas dataframe
Generate a set of ages that are inage
Convert correlation matrix to covariance matrix
Compute the mutual information for pairwise mutual information
Generate synthetic data
Generate a synthetic dataset in a csv format
Put hotel time in 4 hours
Generate arrival times
Generate a dataset in correlation mode
Replace hosts with a random number
Generate random treatment codes
Convert postcode to lsoa
Add age brackets to a dataframe
Generate a list of hosts
Compute the mutual information pairs
Infer the distribution of the histogram
Generate random gender codes
Generate random health service id numbers
Returns a DataFrame containing only non - male or Females

Get all kandi verified functions for this library.

synthetic-data-tutorial Key Features

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synthetic-data-tutorial Examples and Code Snippets

No Code Snippets are available at this moment for synthetic-data-tutorial.

Community Discussions

Trending Discussions on Dataset

Replacing dataframe value given multiple condition from another dataframe with R

Does Hub support integrations for MinIO, AWS, and GCP? If so, how does it work?

Custom Sampler correct use in Pytorch

C++ what is the best sorting container and approach for large datasets (millions of lines)

How to create a dataset for tensorflow from a txt file containing paths and labels?

Converting 0-1 values in dataset with the name of the column if the value of the cell is 1

How can i get person class and segmentation from MSCOCO dataset?

R - If column contains a string from vector, append flag into another column

How to divide a large image dataset into groups of pictures and save them inside subfolders using python?

Proper way of cleaning csv file

QUESTION

Replacing dataframe value given multiple condition from another dataframe with R

Asked 2022-Apr-14 at 16:16

I have two dataframes one with the dates (converted in months) of multiple survey replicates for a given grid cell and the other one with snow data for each month for the same grid cell, they have a matching ID column to identify the cells. What I would like to do is to replace in the first dataframe, the one with months of survey replicates, the month value with the snow value for that month considering the grid cell ID. Thank you

...

ANSWER

Answered 2022-Apr-14 at 14:50

df3 <- df1
df3[!is.na(df1)] <- df2[!is.na(df1)]
#   CellID sampl1 sampl2 sampl3
# 1      1    0.1    0.4    0.6
# 2      2    0.1    0.5    0.7
# 3      3    0.1    0.4    0.8
# 4      4    0.1      
# 5      5         
# 6      6

Source https://stackoverflow.com/questions/71873315

QUESTION

Does Hub support integrations for MinIO, AWS, and GCP? If so, how does it work?

Asked 2022-Mar-19 at 16:28

I was taking a look at Hub—the dataset format for AI—and noticed that hub integrates with GCP and AWS. I was wondering if it also supported integrations with MinIO.

I know that Hub allows you to directly stream datasets from cloud storage to ML workflows but I’m not sure which ML workflows it integrates with.

I would like to use MinIO over S3 since my team has a self-hosted MinIO instance (aka it's free).

...

ANSWER

Answered 2022-Mar-19 at 16:28

Hub allows you to load data from anywhere. Hub works locally, on Google Cloud, MinIO, AWS as well as Activeloop storage (no servers needed!). So, it allows you to load data and directly stream datasets from cloud storage to ML workflows.

You can find more information about storage authentication in the Hub docs.

Then, Hub allows you to stream data to PyTorch or TensorFlow with simple dataset integrations as if the data were local since you can connect Hub datasets to ML frameworks.

Source https://stackoverflow.com/questions/71539946

QUESTION

Custom Sampler correct use in Pytorch

Asked 2022-Mar-17 at 19:22

I have a map-stype dataset, which is used for instance segmentation tasks. The dataset is very imbalanced, in the sense that some images have only 10 objects while others have up to 1200.

How can I limit the number of objects per batch?

A minimal reproducible example is:

...

ANSWER

Answered 2022-Mar-17 at 19:22

If what you are trying to solve really is:

Source https://stackoverflow.com/questions/71500629

QUESTION

C++ what is the best sorting container and approach for large datasets (millions of lines)

Asked 2022-Mar-08 at 11:24

I'm tackling a exercise which is supposed to exactly benchmark the time complexity of such code.

The data I'm handling is made up of pairs of strings like this hbFvMF,PZLmRb, each string is present two times in the dataset, once on position 1 and once on position 2 . so the first string would point to zvEcqe,hbFvMF for example and the list goes on....

example dataset of 50k pairs

I've been able to produce code which doesn't have much problem sorting these datasets up to 50k pairs, where it takes about 4-5 minutes. 10k gets sorted in a matter of seconds.

The problem is that my code is supposed to handle datasets of up to 5 million pairs. So I'm trying to see what more I can do. I will post my two best attempts, initial one with vectors, which I thought I could upgrade by replacing vector with unsorted_map because of the better time complexity when searching, but to my surprise, there was almost no difference between the two containers when I tested it. I'm not sure if my approach to the problem or the containers I'm choosing are causing the steep sorting times...

Attempt with vectors:

...

ANSWER

Answered 2022-Feb-22 at 07:13

You can use a trie data structure, here's a paper that explains an algorithm to do that: https://people.eng.unimelb.edu.au/jzobel/fulltext/acsc03sz.pdf

But you have to implement the trie from scratch because as far as I know there is no default trie implementation in c++.

Source https://stackoverflow.com/questions/71215478

QUESTION

How to create a dataset for tensorflow from a txt file containing paths and labels?

Asked 2022-Feb-09 at 08:09

I'm trying to load the DomainNet dataset into a tensorflow dataset. Each of the domains contain two .txt files for the training and test data respectively, which is structured as follows:

...

ANSWER

Answered 2022-Feb-09 at 08:09

You can use tf.data.TextLineDataset to load and process multiple txt files at a time:

Source https://stackoverflow.com/questions/71045309

QUESTION

Converting 0-1 values in dataset with the name of the column if the value of the cell is 1

Asked 2022-Feb-02 at 07:02

I have a csv dataset with the values 0-1 for the features of the elements. I want to iterate each cell and replace the values 1 with the name of its column. There are more than 500 thousand rows and 200 columns and, because the table is exported from another annotation tool which I update often, I want to find a way in Python to do it automatically. This is not the table, but a sample test which I was using while trying to write a code I tried some, but without success. I would really appreciate it if you can share your knowledge with me. It will be a huge help. The final result I want to have is of the type: (abonojnë, token_pos_verb). If you know any method that I can do this in Excel without the help of Python, it would be even better. Thank you, Brikena

...

ANSWER

Answered 2022-Jan-31 at 10:08

Using pandas, this is quite easy:

Source https://stackoverflow.com/questions/70923533

QUESTION

How can i get person class and segmentation from MSCOCO dataset?

Asked 2022-Jan-06 at 05:04

I want to download only person class and binary segmentation from COCO dataset. How can I do it?

...

ANSWER

Answered 2022-Jan-06 at 05:04

use pycocotools .

import library

Source https://stackoverflow.com/questions/70531408

QUESTION

R - If column contains a string from vector, append flag into another column

Asked 2021-Dec-16 at 23:33

My Data

I have a vector of words, like the below. This is an oversimplification, my real vector is over 600 words:

...

ANSWER

Answered 2021-Dec-16 at 23:33

Update: If a list is preferred: Using str_extract_all:

Source https://stackoverflow.com/questions/70386370

QUESTION

How to divide a large image dataset into groups of pictures and save them inside subfolders using python?

Asked 2021-Dec-08 at 15:13

I have an image dataset that looks like this: Dataset

The timestep of each image is 15 minutes (as you can see, the timestamp is in the filename).

Now I would like to group those images in 3hrs long sequences and save those sequences inside subfolders that would contain respectively 12 images(=3hrs). The result would ideally look like this: Sequences

I have tried using os.walk and loop inside the folder where the image dataset is saved, then I created a dataframe using pandas because I thought I could handle the files more easily but I think I am totally off target here.

...

ANSWER

Answered 2021-Dec-08 at 15:10

The timestep of each image is 15 minutes (as you can see, the timestamp is in the filename).

Now I would like to group those images in 3hrs long sequences and save those sequences inside subfolders that would contain respectively 12 images(=3hrs)

I suggest exploiting datetime built-in libary to get desired result, for each file you have

get substring which is holding timestamp
parse it into datetime.datetime instance using datetime.datetime.strptime
convert said instance into seconds since epoch using .timestamp method
compute number of seconds integer division (//) 10800 (number of seconds inside 3hr)
convert value you got into str and use it as target subfolder name

Source https://stackoverflow.com/questions/70276989

QUESTION

Proper way of cleaning csv file

Asked 2021-Nov-15 at 22:58

I've got a huge CSV file, which looks like this:

...

ANSWER

Answered 2021-Nov-15 at 21:33

You can use a regular expression for this:

Source https://stackoverflow.com/questions/69981109

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install synthetic-data-tutorial

First, make sure you have Python3 installed. Minimum Python 3.6. Download this repository either as a zip or clone using Git. Install required dependent libraries. You can do that, for example, with a virtualenv. Next we'll go through how to create, de-identify and synthesise the code. We'll show this using code snippets but the full code is contained within the /tutorial directory. There's small differences between the code presented here and what's in the Python scripts but it's mostly down to variable naming. I'd encourage you to run, edit and play with the code locally.

Support

A hands-on tutorial showing how to use Python to create synthetic data. It's data that is created by an automated process which contains many of the statistical patterns of an original dataset. It is also sometimes used as a way to release data that has no personal information in it, even if the original did contain lots of data that could identify people. This means programmers and data scientists can crack on with building software and algorithms that they know will work similarly on the real data. For any person who programs who wants to learn about data anonymisation in general or more specifically about synthetic data. Non-programmers. Although we think this tutorial is still worth a browse to get some of the main ideas in what goes in to anonymising a dataset. However, if you're looking for info on how to create synthetic data using the latest and greatest deep learning techniques, this is not the tutorial for you. We're the Open Data Institute. We work with companies and governments to build an open, trustworthy data ecosystem. Anonymisation and synthetic data are some of the many, many ways we can responsibly increase access to data. If you want to learn more, check out our site. We have an R&D program that has a number of projects looking in to how to support innovation, improve data infrastructure and encourage ethical data sharing. One of our projects is about managing the risks of re-identification in shared and open data. As you can see in the Key outputs section, we have other material from the project, but we thought it'd be good to have something specifically aimed at programmers who are interested in learning by doing.

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