I am using the Wikipedia Dataset to perform mapreduce. The dataset am using is(Wikipedia Wiki namespace) from here. The data in the bz2 file is like this
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REVISION 724 431753 Wikipedia:Adding_Wikipedia_articles_to_Nupedia 2002-05-19T17:36:09Z Eclecticology 372
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Basically I want to transform each revision into one row so that one set of revision with all the other details are in a single row. I tried following something similar to this but its not working. Could someone guide me as to how to go about it?
The easiest (probably not the most elegant) way to preprocess the data. Based on your link we're talking about 18GB that's doable.
And anyway you have to separate the data from the schema (it seems the data contains the filed names too).
A nicer solution to write your own loader for this type of data.
Here you'll find some example project and a tutorial http://help.mortardata.com/technologies/pig/write_your_own
Related
I would like to download and load the pre-trained word2vec for analyzing Korean text.
I download the pre-trained word2vec here: https://drive.google.com/file/d/0B0ZXk88koS2KbDhXdWg1Q2RydlU/view?resourcekey=0-Dq9yyzwZxAqT3J02qvnFwg
from the Github Pre-trained word vectors of 30+ languages: https://github.com/Kyubyong/wordvectors
My gensim version is 4.1.0, thus I used:
KeyedVectors.load_word2vec_format('./ko.bin', binary=False) to load the model. But there was an error that :
UnicodeDecodeError: 'utf-8' codec can't decode byte 0x80 in position 0: invalid start byte
I already tried many options including in stackoverflow and Github, but it still not work well.
Would you mind letting me the suitable solution?
Thanks,
While the page at https://github.com/Kyubyong/wordvectors isn't clear about the formats this author has chosen, by looking at their source code at...
https://github.com/Kyubyong/wordvectors/blob/master/make_wordvectors.py#L61
...shows it using the Gensim model .save() method.
Such saved models should be reloaded using the .load() class method of the same model class. For example, if a Word2Vec model was saved with...
model.save('language.bin')
...then it could be reloaded with...
loaded_model = Word2Vec.load('language.bin')
Note, through, that:
Models saved this way are often split over multiple files that should be kept together (and all start with the same root name) - but I don't see those here.
This work appears to be ~5 years old, based on a pre-1.0 version of Gensim – so there might be issues loading the models directly into the latest Gensim. If you do run into such issues, & absolutely need to make these vectors work, you might need to temporarily use a prior version of Gensim to .load() the model. Then, you could save the plain vectors out with .save_word2vec_format() for later reloading across any version. (Or, using the latest interim version that can load the model, re-save the model as .save(), then repeat the process with the latest version that can read that model, until you reach the current Gensim.)
But, you also might want to find a more recent & better-documented set of pretrained word-vectors.
For example, Facebook makes FastText pretrained vectors available in both a 'text' format and a 'bin' format for many languages at https://fasttext.cc/docs/en/pretrained-vectors.html (trained on Wikipedia only) or https://fasttext.cc/docs/en/crawl-vectors.html (trained on Wikipedia plus web crawl data).
The 'text' format should in fact be loadable with KeyedVectors.load_word2vec_format(filename, binary=False), but will only include full-word vectors. (It will also be relatively easy to view as text, or write simply code to massage into other formats.)
The 'bin' format is Facebook's own native FastText model format, and should be loadable with either the load_facebook_model() or load_facebook_vectors() utility methods. Then, the loaded model (or vectors) will be able to create the FastText algorithm's substring-based guesstimate vectors even for many words that weren't in the model or training data.
Im using a pre-trained image classifier to evaluate input data treatments. I downloaded the ImageNet ILSVRC2014 CLS-LOC validation dataset to use as base. I need to know the actual classes of the images to evaluate my treatments (need to detect correct classifications). In the 2014 toolkit there is ILSVRC2014_clsloc_validation_ground_truth.txt file that according to the readme is supposed to contain class labels (in form of ID:s) for the 50 000 images in the data set. There are 50 000 entries/lines in the file so this far all seems good but i also want the corresponding semantic class labels/names.
I found these in a couple of places online and they seem to be coherent (1000 classes). But then i looked at the first image which is a snake, the ground truth for the first pic is 490, the 490:th row in the semantic name list is "chain". That's weird but still kind of close. The second image is two people skiing, the derived class "polecat". I tried many more with similar results.
I must have misunderstood something. Isn't the ground truth supposed to be the "correct" answers for the validation set? Have i missed something in the translation between ID:s and semantic labels?
The readme in the 2014 imagenet dev-kit states:
" There are a total of 50,000 validation images. They are named as
ILSVRC2012_val_00000001.JPEG
ILSVRC2012_val_00000002.JPEG
...
ILSVRC2012_val_00049999.JPEG
ILSVRC2012_val_00050000.JPEG
There are 50 validation images for each synset.
The classification ground truth of the validation images is in
data/ILSVRC2014_clsloc_validation_ground_truth.txt,
where each line contains one ILSVRC2014_ID for one image, in the
ascending alphabetical order of the image file names.
The localization ground truth for the validation images can be downloaded
in xml format. "
Im doing this as part of my bachelor thesis and really want to get it right.
Thanks in advance
This problem is now solved. In the ILSVRC2017 development kit there is a map_clsloc.txt file with the correct mappings.
In my web application I am going to use OpenLayers.Strategy.AnimatedCluster strategy due to the fact that I need to visualize a great amount of point features. Here is a very good example of what it looks like. In both examples in above mentioned example the data (point features) are generated of taken from the GeoJSON file.
So, can anybody provide me with a file containing 100 000+ (better is even 500 000+) features (world cities, for instance), or explain how I can generate them so that they will be located all over the world (not like in Spain in the first example in above mentioned link).
use a geolocation database to supply you the data you need. GeoLite, for example
If 400K+ locations is ok, use download their CSV CITY LIST
If you want more, then you might want to give the Nominatim downloads, but they are quite bulky (more than 25GB) and parsing data is not as simple as a csv file.
The St. Louis Federal Reserve Bank has a great set of data available on a variety of their web pages, such as:
http://research.stlouisfed.org/fred2/series/OILPRICE/downloaddata?cid=32217
http://www.federalreserve.gov/releases/h10/summary/default.htm
http://research.stlouisfed.org/fred2/series/DGS20
The data sets get updated, some as often as daily. I tend to have an interest in the daily data (see the above settings on the URLS)
I'd like to import these kinds of price or rate data streams (accessible as CSV or Excel files at the above URLs) directly into Mathematica.
I've looked at the documentation on Importing[] but I find scant documentation (actually none) on how to go about something like this.
It looks like I need to navigate to the pages, send some data to select specific files and formats, trigger the download, then access the downloaded data from my own machine. Even better if I could access the data directly from the sites.
I had hoped Wolfram Alpha might make this sort thing easy, but I haven't had any success.
FinancialData[] would seem natural for this sort of thing, but I don't see anyway to do it. Financial data has lots of features, but I don't see a way yo get this sort of thing.
Does anyone have any experience with this or can someone point me in the right direction?
You can Import directly from a URL. For example, the data from federalreserve.gov can be obtained and visualized as follows.
url = "http://www.federalreserve.gov/datadownload/Output.aspx?";
url = url<>"rel=H10&series=a660e724c705cea4b7bd1d1b85789862&lastObs=&";
url = url<>"from=&to=&filetype=csv&label=include&layout=seriescolumn";
data = Import[url, "CSV"];
DateListPlot[data[[7 ;;]], Joined -> True]
I broke up url for convenience, since it's so long. I had to examine the contents of data before I knew exactly how to plot it - a step that is typically necessary. I'm sure that the data from stlouisfed.org can be obtained in a similar way, but it requires the use of an API with key to access it.
As Mark said, you can get the data directly from a URL. Your oil data can be imported from a different URL than you had:
http://research.stlouisfed.org/fred2/data/OILPRICE.txt
With that URL, you can do this:
oil = Import["http://research.stlouisfed.org/fred2/data/OILPRICE.txt",
"Table", "HeaderLines" -> 12, "DateStringFormat" -> {"Year", "Month", "Day"}];
DateListPlot[oil, Joined -> True, PlotRange -> All]
Note that "HeaderLines"->12 option strips off the header text in the first 12 lines (you have to count the header lines to know how many to remove). I've also specified the date format.
To find that URL, do as you did before, but click on a data series and then choose View Data from the menu on the left when you see the chart.
The documentation has a short example on extracting data out of a webpage:
http://reference.wolfram.com/mathematica/howto/CleanUpDataImportedFromAWebsite.html
Of course, what actually needs to be done will vary significantly from page to page.
discussion on how to do this with your API key here:
http://library.wolfram.com/infocenter/MathSource/7583/
the function is based on the API documentation. I haven't looked at the code for a couple of years and from memory I put it together rather quickly but I have used it regularly for over 2 years without problems. Here is an example for monthly non seasonally adjusted retail sales from early 1992 to now:
wolfram alpha also uses FRED data so you could use that as an alternative to direct import but it is more tricky to get the query right. I prefer to use FRED directly. Also from memory the data is only available on alpha the day after the release, which is not what you would typically want.
I would like to represent data that gives an overview but allows them to drill down in an inline fashion - so if you had a grouping of say 6 objects the user could expand the data and it would show the 6 objects immeadiately below it before any more high level data.
It would appear that MSHFlexgrid gives this ability but I can't find any information about actually using it, or what it's limitations are (can you have differing number of fields and/or can they have different spacing, what about column headers, indentation at for the start, etc).
I found this site, but the images are broken (in ie8 and ff3.5). Google searches show people just using the flat data representation but nothing using the hierarchical properties). Does anyone know any good tutorials or forums with a good discussion about pitfalls?
Due to lack of information about using it, I am thinking of coding my own version but if anyone has done work in this area I haven't found it - I would of thought it would be a natural wish for data representation. If someone has coded a version of this (any language) then I wouldn't mind reading about it - maybe my idea of how to do it wouldn't be the best way.
You might want to check out vbAccelerator. He has a Multi-Column Treeview control that sounds like what you may be looking for. He gives you the source and has some pretty decent samples.
The MSHFlexGrid reference pages and the "using the MSHFlexGrid" topic in the Visual Basic manual?
Sorry if you've already looked at these!