curate | data curation Ruby on Rails engine built | Awesome List library
kandi X-RAY | curate Summary
kandi X-RAY | curate Summary
Curate is a Rails engine leveraging ProjectHydra and ProjectBlacklight components to deliver a foundation for an Institutional Repositories. It is released under the Apache 2 License.
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Top functions reviewed by kandi - BETA
- Creates a new user
- Checks if a user subscribes or edit accessor
- Check if a user is read or not read permission
- Creates a set of controls for the form .
- Append the query to the current repository
- Updates the content of the resource
- Authorization of the resource
- Renders a single field .
- Create a layout for the theme
- Logs a user in the current session .
curate Key Features
curate Examples and Code Snippets
Community Discussions
Trending Discussions on curate
QUESTION
I have recently sourced and curated a lot of reddit data from Google Bigquery.
The dataset looks like this:
Before passing this data to word2vec to create a vocabulary and be trained, it is required that I properly tokenize the 'body_cleaned' column.
I have attempted the tokenization with both manually created functions and NLTK's word_tokenize, but for now I'll keep it focused on using word_tokenize.
Because my dataset is rather large, close to 12 million rows, it is impossible for me to open and perform functions on the dataset in one go. Pandas tries to load everything to RAM and as you can understand it crashes, even on a system with 24GB of ram.
I am facing the following issue:
- When I tokenize the dataset (using NTLK word_tokenize), if I perform the function on the dataset as a whole, it correctly tokenizes and word2vec accepts that input and learns/outputs words correctly in its vocabulary.
- When I tokenize the dataset by first batching the dataframe and iterating through it, the resulting token column is not what word2vec prefers; although word2vec trains its model on the data gathered for over 4 hours, the resulting vocabulary it has learnt consists of single characters in several encodings, as well as emojis - not words.
To troubleshoot this, I created a tiny subset of my data and tried to perform the tokenization on that data in two different ways:
- Knowing that my computer can handle performing the action on the dataset, I simply did:
ANSWER
Answered 2021-May-27 at 18:28First & foremost, beyond a certain size of data, & especially when working with raw text or tokenized text, you probably don't want to be using Pandas dataframes for every interim result.
They add extra overhead & complication that isn't fully 'Pythonic'. This is particularly the case for:
- Python
list
objects where each word is a separate string: once you've tokenized raw strings into this format, as for example to feed such texts to Gensim'sWord2Vec
model, trying to put those into Pandas just leads to confusing list-representation issues (as with your columns where the same text might be shown as either['yessir', 'shit', 'is', 'real']
– which is a true Python list literal – or[yessir, shit, is, real]
– which is some other mess likely to break if any tokens have challenging characters). - the raw word-vectors (or later, text-vectors): these are more compact & natural/efficient to work with in raw Numpy arrays than Dataframes
So, by all means, if Pandas helps for loading or other non-text fields, use it there. But then use more fundamntal Python or Numpy datatypes for tokenized text & vectors - perhaps using some field (like a unique ID) in your Dataframe to correlate the two.
Especially for large text corpuses, it's more typical to get away from CSV and instead use large text files, with one text per newline-separated line, and any each line being pre-tokenized so that spaces can be fully trusted as token-separated.
That is: even if your initial text data has more complicated punctuation-sensative tokenization, or other preprocessing that combines/changes/splits other tokens, try to do that just once (especially if it involves costly regexes), writing the results to a single simple text file which then fits the simple rules: read one text per line, split each line only by spaces.
Lots of algorithms, like Gensim's Word2Vec
or FastText
, can either stream such files directly or via very low-overhead iterable-wrappers - so the text is never completely in memory, only read as needed, repeatedly, for multiple training iterations.
For more details on this efficient way to work with large bodies of text, see this artice: https://rare-technologies.com/data-streaming-in-python-generators-iterators-iterables/
QUESTION
This will be a long question, sorry about that but im new to hmtl/css/js and still trying to learn, im trying to implement lightbox for my photos that i am fetching from pexels with an API, I wanna implement lightbox so when i click the pictures it shows bigger etc, I would really appreciate the help I hope its not too much, code is coming here, if you need anything else just let me know:
...ANSWER
Answered 2021-May-20 at 13:48QUESTION
I am trying to manually curate a dataset by going through each set and choosing whether or not to keep it or reject it. To do this, I want to plot a dataset, use the click module (https://click.palletsprojects.com/en/7.x/) to choose whether to keep it, then plot the next dataset and repeat. Currently, I am able to plot each set and choose whether or not to save it, but the problem is each graph remains above the next one. I need to go through thousands of datasets so it isn't viable to have them all plotted simultaneously.
...ANSWER
Answered 2021-May-07 at 15:53A solution would have to clear the current figure and draw a new figure on the cleared canvas. There are multiple approaches that would work, which are pretty well described in
Since you are showing two graphs, I am first providing a solution that works on a single graph. Then, I am going to show a solution that works with multiple rows or columns.
Solution SingleThe following solution and test code was used to update a single graph, and combine the data that you want to save.
QUESTION
I have a mammography image dataset (mini DDSM). These images show letter artifacts indicating left or right mamma and other useless information for my ML model, so I want to curate this dataset before training the model.
In this paper, Preprocessing of Digital Mammogram Image Based on Otsu’s Threshold, they use Otsu's binarization and opening on the mammography to clean the image (page 5 of 10):
So far, I have coded this:
...ANSWER
Answered 2021-Apr-25 at 18:41Here is one way to process your image in Python/OpenCV.
QUESTION
Some background of what I am attempting to do is that, my backend /content route returns an array with id data in it such as:
...ANSWER
Answered 2021-Apr-19 at 15:39You want to do something like this :
(I also made a proposition for your usage of data, I am using setMyArray
to update data
when the request returns)
QUESTION
I'm trying to process a folder full of data files of various formats and encodings. I no that building a generic process will require plenty of extreme cases to cover, but I do want to be able to address most files and folders.
To address unknown encodings, I ran file -I *.csv
on 4-5 sample folders to curate a list of the most common encodings. I then iterate the list in order to "guess" the right encoding for a certain file. Below is my code:
ANSWER
Answered 2021-Mar-27 at 03:09You could try using chardet
to detect the file encodings, something like:
QUESTION
Hi Im trying to build a scraper (in Python) for the website ReelGood.com.
now I got this topic to and I figured out how to scrape the url from the movie page. but what I can't seem t figure out why this script won't work:
...ANSWER
Answered 2021-Mar-23 at 17:38I would use a combination of attribute = value selectors to target the elements which have the full url in the content attribute
QUESTION
I am trying to extract linkedin url that is written in this format,
...ANSWER
Answered 2021-Mar-23 at 16:16You can use
QUESTION
I have a table containing a user id, purchase amount, and date. I need to calculate the sum over several periods, for the same user. For example, given:
UserId Date Amount 1 2021-01-03 10 1 2021-01-10 20 1 2021-02-07 30From an API, for the same user, I might need to sum and return all of the following ranges:
Description Range Expected Result The first week of Jan 1st (Friday) to the 3rd (Sunday) 10 The month of Jan 1st to the 31st 30 The month of Feb 1st to the 28th 30Some restrictions:
- I don't know the dates up front (the consumer either needs calendar periods, or rolling periods), so I don't think I can create curated queries using
SUM
andCASE
? I saw an example of this here. - I also need to use a stored procedure when interacting with the DB, so I wouldn't be able to dynamically create the
SELECT
statement from the API.
I don't have a lot of experiences writing SQL, but I imagine it's better to have a single query that calculates all of the totals than calculating the sums individually? Any suggestions?
...ANSWER
Answered 2021-Mar-05 at 16:09Thank you @Larnu, this seems to work, where Range
is passed in as a table, and Purchases
is the table listed in the question:
QUESTION
I am trying to send a stylized email to a new customer once they submit a payment on my website. I read somewhere to try the render to string method which I did. The email triggers correctly, the only problem is that it doesn't seem to be using the email template. It's a basic plain text email.
Please see my code below. Any help on how to make this work would be great.
Django view code
...ANSWER
Answered 2021-Mar-04 at 23:16You have to provide html_message
to send_mail()
(Django Docs), and also add the message as context to render_to_string()
to use the message in template:
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