On same layer in QGIS I have highways (primary, secondary etc.) and cities boundaries (place=city). I want to show/label only primary roads crossing cities. I try something like
"highway" = 'tertiary' AND "place" = 'city'
but got no items, when
"highway" = 'tertiary'
alone returns 6 elements.
Or maybe another way? How can I find crossing elements?
Since both your target variables are in the same layer it seems to be a query problem.
Have you checked if the condition that you are looking for really exists in your dataset?
If so, like many things in QGIS, Rule-based Labeling uses Structured Query Language (SQL), which is strongly typed, to fetch the data you are looking for. Thereby, it's important to be sure about the data types of your dataset and be aware of the double-quote pattern for "column" and single quote for the 'values', if they are varchar or string, exactly as you already did in your example.
Furthermore, check if the values that you are trying to match, in your Rule-based Labeling, are written exactly as they appear in your data attribute table. Anyway, if your dataset is huge, you can use this approach as a workaround:
"highway" ilike '%tertiary% and "place" ilike '%city%'
The ilike operator will make no difference from the upper or lower case in your query, which means that if you have a value 'Tertiary' it would be possible to fetch the data anyway.
Besides, as I put it, the wildcard character % finds any values that have the strings 'tertiary' and 'city' in any position, which can avoid lots of headaches due to whitespaces before or after the string.
Related
I'm trying to match everything except a specific string in R, and I've seen a bunch of posts on this suggesting a negative lookaround, but I haven't gotten that to work.
I have a dataset looking at crime incidents in SF, and I want to sort cases that have a resolution or do not. In the resolution field, cases have things listed like arrest booked, arrest cited, juvenile booked, etc., or none. I want to relabel all the specific resolutions like the different arrests to "RESOLVED" and keep the instances with "NONE" as such. So, I thought I could gsub or grep for not "NONE".
Based on what I've read on finding all strings except one specific string, I would have thought this would work:
resolution_vector = grep("^(?!NONE$).*", trainData$Resolution, fixed=TRUE)
Where I make a vector that searches through my training dataset, specifically the resolution column, and finds the terms that aren't "NONE". But, I just get an empty vector.
Does anyone have suggestions, or know why this might not be working in R? Or, even if there was a way to just use gsub, how do I say "not NONE" for my regex in R?
trainData$Resolution = gsub("!NONE", RESOLVED, trainData$Resolution) << what's the way to negate the string here?
Based on your explanation, it seems as though you don't need regular expressions (i.e. gsub()) at all. You can use != since you are looking for all non-matches of an exact string. Perhaps you want
within(trainData, {
## next line only necessary if you have a factor column
Resolution <- as.character(Resolution)
Resolution[Resolution != "NONE"] <- "RESOLVED"
})
resolution_vector = grep("^(?!NONE$).*", trainData$Resolution, fixed=TRUE,perl=TRUE)
You need to use option perl=TRUE.
I am asking this here because I couldn't find the answer I am looking for elsewhere and I don't know where else I could ask this. I hope someone can reply without saying that the question is irrelevant to the forum. I have a biology background and I am currently using bioinformatics. I need to understand in lay language hash tables and suffix trees. Something simple, I don't get the O(n) concepts and all that stuff, I think they are both kind of the same: a way to store string data? But I would like to understand better the differences. This will help enormously to other people like me. We are a lot in this field now!
Thanks in advance.
OK, lets use bioinformatics to help illustrate the differences.
Let's say you have several DNA sequences that are pretty long. If we want to store these sequences in a datastructure.
If we want to use a hashtable
A Hashtable is a useful way to store a bunch of objects but very quickly search the datastructure to see if we already contain a particular object.
One bioinformatics usecase that we can solve with a hashtable is de-duping a large sequence set. Let's say we have a huge dataset of next-gen sequenced data and we want to de-duplicate it before we assemble. We can use a hashtable to store the unique sequences. Before inserting any sequences into the hashtable, we can first check to see if it already exists in the hashtable and if it does we skip that read. Only if it is not yet in the hashtable do we add it. Then when we are done the elements in the hash will be the unique sequences.
Hashtables are basically an array of LinkedLists. Each cell in the array we will call a "bin". When we insert or search for something in the hashtable, we have to first know what bin it is in. The way we determine which bin to use is by a hash algorithm.
We have to come up with a hash algorithm. Something that will convert our sequence into a number. A requirement of this equation is the same sequence must always evaluate to the same number. It's OK if different sequences evaluate to the same number (which is called as hash collision) since there are an infinite number of possible sequences and we will only have a limited range of possible number values in our hash.
A simple hash algorithm is to assign a value to each base A =1 G =2 C = 3 T =4 (assume no ambiguities) then we can just sum up the bases in our sequence. This would mean that any sequences with the same number of As, Cs Gs and Ts will have the same hash value. If we wanted, we could also have a more complicated algorithm that also takes position into account so to get the same number we would have to also have the same sequence in the same order.
Once we have our hash algorithm. We can make a hash table by binning the sequences by their hash values. The more bins we have in our table, the fewer hash values per bin. Hashtables are often implemented by an array of LinkedLists. This is a very fast lookup because to see if a sequence is in our hashtable or to add a new sequence to our hash table, we just compute the hash value for the sequence to see what bin it is in, then we only have to look at the values inside that bin. We can ignore the rest of the bins.
suffix tree
A Suffix Tree is a different datastructure which is a graph where each node is (in this case) a residue in our sequence. Edges in the graph will point to the next node etc. So for example if our sequence was ACGT the path in the graph will be A->C->G->T->$. If we had another sequence ACTT the path will be A->C->T->T->$.
We can combine consecutive nodes if there is only 1 path so in the previous example since both sequence start with AC then the paths will be AC->G->T->$and AC->T->T->$.
In bioinformatics this is really useful for substring matching (like finding repetitive regions or primer binding sites etc) since we can easily see where there are subpaths in our graph that match our motif.
Hope that helps
I'm attempting to clean up a database that, over the years, had acquired many duplicate records, with slightly different names. For example, in the companies table, there are names like "Some Company Limited" and "SOME COMPANY LTD!".
My plan was to export the offending tables into R, convert names to lower case, replace common synonyms (like "limited" -> "ltd"), strip out non-alphabetic characters and then use agrep to see what looks similar.
My first problem is that agrep only accepts a single pattern to match, and looping over every company name to match against the others is slow. (Some tables to be cleaned will have tens, possibly hundreds of thousands of names to check.)
I've very briefly looked at the tm package (JSS article), and it seems very powerful but geared towards analysing big chunks of text, rather than just names.
I have a few related questions:
Is the tm package appropriate for this sort of task?
Is there a faster alternative to agrep? (Said function uses the
Levenshtein edit distance which is anecdotally slow.)
Are there other suitable tools in R, apart from agrep and tm?
Should I even be doing this in R, or should this sort of thing be
done directly in the database? (It's an Access database, so I'd
rather avoid touching it if possible.)
If you're just doing small batches that are relatively well-formed, then the compare.linkage() or compare.dedup() functions in the RecordLinkage package should be a great starting point. But if you have big batches, then you might have to do some more tinkering.
I use the functions jarowinkler(), levenshteinSim(), and soundex() in RecordLinkage to write my own function that use my own weighting scheme (also, as it is, you can't use soundex() for big data sets with RecordLinkage).
If I have two lists of names that I want to match ("record link"), then I typically convert both to lower case and remove all punctuation. To take care of "Limited" versus "LTD" I typically create another vector of the first word from each list, which allows extra weighting on the first word. If I think that one list may contain acronyms (maybe ATT or IBM) then I'll acronym-ize the other list. For each list I end up with a data frame of strings that I would like to compare that I write as separate tables in a MySQL database.
So that I don't end up with too many candidates, I LEFT OUTER JOIN these two tables on something that has to match between the two lists (maybe that's the first three letters in each list or the first three letters and the first three letters in the acronym). Then I calculate match scores using the above functions.
You still have to do a lot of manual inspection, but you can sort on the score to quickly rule out non-matches.
Maybe google refine could help. It looks maybe more fitted if you have lots of exceptions and you don't know them all yet.
What you're doing is called record linkage, and it's been a huge field of research over many decades already. Luckily for you, there's a whole bunch of tools out there that are ready-made for this sort of thing. Basically, you can point them at your database, set up some cleaning and comparators (like Levenshtein or Jaro-Winkler or ...), and they'll go off and do the job for you.
These tools generally have features in place to solve the performance issues, so that even though Levenshtein is slow they can run fast because most record pairs never get compared at all.
The Wikipedia link above has links to a number of record linkage tools you can use. I've personally written one called Duke in Java, which I've used successfully for exactly this. If you want something big and expensive you can buy a Master Data Management tool.
In your case probably something like edit-distance calculation would work, but if you need to find near duplicates in larger text based documents, you can try
http://www.softcorporation.com/products/neardup/
I'm attempting to clean up a database that, over the years, had acquired many duplicate records, with slightly different names. For example, in the companies table, there are names like "Some Company Limited" and "SOME COMPANY LTD!".
My plan was to export the offending tables into R, convert names to lower case, replace common synonyms (like "limited" -> "ltd"), strip out non-alphabetic characters and then use agrep to see what looks similar.
My first problem is that agrep only accepts a single pattern to match, and looping over every company name to match against the others is slow. (Some tables to be cleaned will have tens, possibly hundreds of thousands of names to check.)
I've very briefly looked at the tm package (JSS article), and it seems very powerful but geared towards analysing big chunks of text, rather than just names.
I have a few related questions:
Is the tm package appropriate for this sort of task?
Is there a faster alternative to agrep? (Said function uses the
Levenshtein edit distance which is anecdotally slow.)
Are there other suitable tools in R, apart from agrep and tm?
Should I even be doing this in R, or should this sort of thing be
done directly in the database? (It's an Access database, so I'd
rather avoid touching it if possible.)
If you're just doing small batches that are relatively well-formed, then the compare.linkage() or compare.dedup() functions in the RecordLinkage package should be a great starting point. But if you have big batches, then you might have to do some more tinkering.
I use the functions jarowinkler(), levenshteinSim(), and soundex() in RecordLinkage to write my own function that use my own weighting scheme (also, as it is, you can't use soundex() for big data sets with RecordLinkage).
If I have two lists of names that I want to match ("record link"), then I typically convert both to lower case and remove all punctuation. To take care of "Limited" versus "LTD" I typically create another vector of the first word from each list, which allows extra weighting on the first word. If I think that one list may contain acronyms (maybe ATT or IBM) then I'll acronym-ize the other list. For each list I end up with a data frame of strings that I would like to compare that I write as separate tables in a MySQL database.
So that I don't end up with too many candidates, I LEFT OUTER JOIN these two tables on something that has to match between the two lists (maybe that's the first three letters in each list or the first three letters and the first three letters in the acronym). Then I calculate match scores using the above functions.
You still have to do a lot of manual inspection, but you can sort on the score to quickly rule out non-matches.
Maybe google refine could help. It looks maybe more fitted if you have lots of exceptions and you don't know them all yet.
What you're doing is called record linkage, and it's been a huge field of research over many decades already. Luckily for you, there's a whole bunch of tools out there that are ready-made for this sort of thing. Basically, you can point them at your database, set up some cleaning and comparators (like Levenshtein or Jaro-Winkler or ...), and they'll go off and do the job for you.
These tools generally have features in place to solve the performance issues, so that even though Levenshtein is slow they can run fast because most record pairs never get compared at all.
The Wikipedia link above has links to a number of record linkage tools you can use. I've personally written one called Duke in Java, which I've used successfully for exactly this. If you want something big and expensive you can buy a Master Data Management tool.
In your case probably something like edit-distance calculation would work, but if you need to find near duplicates in larger text based documents, you can try
http://www.softcorporation.com/products/neardup/
I need to automatically match product names (cameras, laptops, tv-s etc) that come from different sources to a canonical name in the database.
For example "Canon PowerShot a20IS", "NEW powershot A20 IS from Canon" and "Digital Camera Canon PS A20IS"
should all match "Canon PowerShot A20 IS". I've worked with levenshtein distance with some added heuristics (removing obvious common words, assigning higher cost to number changes etc), which works to some extent, but not well enough unfortunately.
The main problem is that even single-letter changes in relevant keywords can make a huge difference, but it's not easy to detect which are the relevant keywords. Consider for example three product names:
Lenovo T400
Lenovo R400
New Lenovo T-400, Core 2 Duo
The first two are ridiculously similar strings by any standard (ok, soundex might help to disinguish the T and R in this case, but the names might as well be 400T and 400R), the first and the third are quite far from each other as strings, but are the same product.
Obviously, the matching algorithm cannot be a 100% precise, my goal is to automatically match around 80% of the names with a high confidence.
Any ideas or references is much appreciated
I think this will boil down to distinguishing key words such as Lenovo from chaff such as New.
I would run some analysis over the database of names to identify key words. You could use code similar to that used to generate a word cloud.
Then I would hand-edit the list to remove anything obviously chaff, like maybe New is actually common but not key.
Then you will have a list of key words that can be used to help identify similarities. You would associate the "raw" name with its keywords, and use those keywords when comparing two or more raw names for similarities (literally, percentage of shared keywords).
Not a perfect solution by any stretch, but I don't think you are expecting one?
The key understanding here is that you do have a proper distance metric. That is in fact not your problem at all. Your problem is in classification.
Let me give you an example. Say you have 20 entries for the Foo X1 and 20 for the Foo Y1. You can safely assume they are two groups. On the other hand, if you have 39 entries for the Bar X1 and 1 for the Bar Y1, you should treat them as a single group.
Now, the distance X1 <-> Y1 is the same in both examples, so why is there a difference in the classification? That is because Bar Y1 is an outlier, whereas Foo Y1 isn't.
The funny part is that you do not actually need to do a whole lot of work to determine these groups up front. You simply do an recursive classification. You start out with node per group, and then add the a supernode for the two closest nodes. In the supernode, store the best assumption, the size of its subtree and the variation in it. As many of your strings will be identical, you'll soon get large subtrees with identical entries. Recursion ends with the supernode containing at the root of the tree.
Now map the canonical names against this tree. You'll quickly see that each will match an entire subtree. Now, use the distances between these trees to pick the distance cutoff for that entry. If you have both Foo X1 and Foo Y1 products in the database, the cut-off distance will need to be lower to reflect that.
edg's answer is in the right direction, I think - you need to distinguish key words from fluff.
Context matters. To take your example, Core 2 Duo is fluff when looking at two instances of a T400, but not when looking at a a CPU OEM package.
If you can mark in your database which parts of the canonical form of a product name are more important and must appear in one form or another to identify a product, you should do that. Maybe through the use of some sort of semantic markup? Can you afford to have a human mark up the database?
You can try to define equivalency classes for things like "T-400", "T400", "T 400" etc. Maybe a set of rules that say "numbers bind more strongly than letters attached to those numbers."
Breaking down into cases based on manufacturer, model number, etc. might be a good approach. I would recommend that you look at techniques for term spotting to try and accomplish that: http://www.worldcat.org/isbn/9780262100854
Designing everything in a flexible framework that's mostly rule driven, where the rules can be modified based on your needs and emerging bad patterns (read: things that break your algorithm) would be a good idea, as well. This way you'd be able to improve the system's performance based on real world data.
You might be able to make use of a trigram search for this. I must admit I've never seen the algorithm to implement an index, but have seen it working in pharmaceutical applications, where it copes very well indeed with badly misspelt drug names. You might be able to apply the same kind of logic to this problem.
This is a problem of record linkage. The dedupe python library provides a complete implementation, but even if you don't use python, the documentation has a good overview of how to approach this problem.
Briefly, within the standard paradigm, this task is broken into three stages
Compare the fields, in this case just the name. You can use one or more comparator for this, for example an edit distance like the Levenshtein distance or something like the cosine distance that compares the number of common words.
Turn an array fo distance scores into a probability that a pair of records are truly about the same thing
Cluster those pairwise probability scores into groups of records that likely all refer to the same thing.
You might want to create logic that ignores the letter/number combination of model numbers (since they're nigh always extremely similar).
Not having any experience with this type of problem, but I think a very naive implementation would be to tokenize the search term, and search for matches that happen to contain any of the tokens.
"Canon PowerShot A20 IS", for example, tokenizes into:
Canon
Powershot
A20
IS
which would match each of the other items you want to show up in the results. Of course, this strategy will likely produce a whole lot of false matches as well.
Another strategy would be to store "keywords" with each item, such as "camera", "canon", "digital camera", and searching based on items that have matching keywords. In addition, if you stored other attributes such as Maker, Brand, etc., you could search on each of these.
Spell checking algorithms come to mind.
Although I could not find a good sample implementation, I believe you can modify a basic spell checking algorithm to comes up with satisfactory results. i.e. working with words as a unit instead of a character.
The bits and pieces left in my memory:
Strip out all common words (a, an, the, new). What is "common" depends on context.
Take the first letter of each word and its length and make that an word key.
When a suspect word comes up, looks for words with the same or similar word key.
It might not solve your problems directly... but you say you were looking for ideas, right?
:-)
That is exactly the problem I'm working on in my spare time. What I came up with is:
based on keywords narrow down the scope of search:
in this case you could have some hierarchy:
type --> company --> model
so that you'd match
"Digital Camera" for a type
"Canon" for company and there you'd be left with much narrower scope to search.
You could work this down even further by introducing product lines etc.
But the main point is, this probably has to be done iteratively.
We can use the Datadecision service for matching products.
It will allow you to automatically match your product data using statistical algorithms. This operation is done after defining a threshold score of confidence.
All data that cannot be automatically matched will have to be manually reviewed through a dedicated user interface.
The online service uses lookup tables to store synonyms as well as your manual matching history. This allows you to improve the data matching automation next time you import new data.
I worked on the exact same thing in the past. What I have done is using an NLP method; TF-IDF Vectorizer to assign weights to each word. For example in your case:
Canon PowerShot a20IS
Canon --> weight = 0.05 (not a very distinguishing word)
PowerShot --> weight = 0.37 (can be distinguishing)
a20IS --> weight = 0.96 (very distinguishing)
This will tell your model which words to care and which words to not. I had quite good matches thanks to TF-IDF.
But note this: a20IS cannot be recognized as a20 IS, you may consider to use some kind of regex to filter such cases.
After that, you can use a numeric calculation like cosine similarity.