It is hard to explain this without just showing what I have, where I am, and what I need in terms of data structure:
What structure I had:
Where I have got to with my transformation efforts:
What I need to end up with:
Notes:
I've not given actual names for anything as the data is classed as sensitive, but:
Metrics are things that can be measured- for example, the number of permanent or full-time jobs. The number of metrics is larger than presented in the test data (and the example structure above).
Each metric has many years of data (whilst trying to do the code I have restricted myself to just 3 years. The illustration of the structure is based on this test). The number of years captured will change overtime- generally it will increase.
The number of policies will fluctuate, I've just labelled them policy 1, 2 etc for sensitivity reasons and limited the number whilst testing the code. Again, I have limited the number to make it easier to check the outputs.
The source data comes from a workbook of surveys with a tab for each policy. The initial import creates a list of tibbles consisting of a row for each metric, and 4 columns (the metric names, the values for 2024, the values for 2030, and the values for 2035). I converted this to a dataframe, created a vector to be a column header and used cbind() to put this on top to get the "What structure I had" data.
To get to the "Where I have got to with my transformation efforts" version of the table, I removed all the metric columns, created another vector of metrics and used rbind() to put this as the first column.
The idea in my head was to group the data by policy to get a vector for each metric, then transpose this so that the metric became the column, and the grouped data would become the row. Then expand the data to get the metrics repeated for each year. A friend of mine who does coding (but has never used R) has suggested using loops might be a better way forward. Again, I am not sure of the best approach so welcome advice. On Reddit someone suggested using pivot_wider/pivot_longer but this appears to be a summarise tool and I am not trying to summarise the data rather transform its structure.
Any suggestions on approaches or possible tools/functions to use would be gratefully received. I am learning R whilst trying to pull this data together to create a database that can be used for analysis, so, if my approach sounds weird, feel free to suggest alternatives. Thanks
I could find no relevant answers in either StackOverflow or google. Perhaps one of you has the answer. This is a continuation from a previous question...
In Business Objects Webi, I have two variables. One dimension, one measure. My goal is to create a simple pie chart.
Here's the dimension variable titled "EWFMCodeSelect":
`=If([Code]InList("BRK1"; "BRK2"; "BRK3" )) Then"BREAK"
ElseIf([Code]InList("TEAM"; "MTG"; "MTNG"; "PROJ"; "TRNG";"WCGB")) Then "DISC"
ElseIf([Code]InList("LUNCH")) Then "LUNCH"
ElseIf([Code]InList("LATE";"NOSHOW";"UNPAID";"UPVAC")) Then "MISS"
ElseIf([Code]InList("COACH";"VTO")) Then "NEUTR"
ElseIf([Code]InList("VAC";"LOA";"SICKUP";"SICKPL")) Then "NODISC"
ElseIf([Code]InList("PREP")) Then "OTHER"
ElseIf([Code]InList("OVER")) Then "OVER"
Else("SHIFT")`
This is the measure variable titled EWFMPieChart(%):
=[TimeDiff (ToInt)]
/ NoFilter(( Sum([TimeDiff (ToInt)]
ForAll([EWFMCodeSelect])
Where ([EWFMCodeSelect] = "SHIFT")))ForEach())
The previous advice I received was to filter the value "SHIFT" from the report block. I thought this would be a simple affair but it's proving more difficult than anticipated. I tried creating a Report Block filter in the Analysis tab "EWFMCodeSelect Not Equal To SHIFT"
"EWFMCodeSelect Not In List > SHIFT"
but only ended up with a single row, the dimension field empty, the measure field showing #MULTIVALUE. I tried a variety of other combinations but all had the same effect.
I tried a Column filter:
=[EWFMCodeSelect] Where ([EWFMCodeSelect] <> "SHIFT")
but ended up with a single row, the dimension field showing: "BREAKDISCLUNCH..." as the value and the measure, again, showing #MULTIVALUE.
I'm missing some important clue here. Can anyone educate me either why this approach is incorrect and maybe supply me with a direction to achieve my goal?
Thanks,
mfc
I don't have the correct answer but I did solve the problem by cleaning up all of the TEST variables, deleting all unused and unnecessary errata that collected up to this point and re-running the report as a scheduled item. I also cleared the browser cache (never bad advice).
After re-opening the report, I was able to filter the report block without issue.
I guess the answer is "When in doubt and receiving undocumented results, clean up your work-space and try again".
I am trying to plot a variable that is in a NetCDF file using GrADS and I would like to plot only the values that are smaller than -20 (could be any other number as an example). I can't find a way to do it though. I saw several examples of variable substitution using both maskoutand const (for example define ones = const(const(maskout(p,p-10),1),0,-u), which is here) but I couldn't make that work for my purposes.
I want the variable at a given point not plotted in case its value is below -20, and not this value change to another one in such point.
Solved it using the command maskout(p,p+20). The badly written documentation is what delayed me on this one. Answered here so that the community may benefit.
Thanks.
I have a complex wind turbine model that I'm inserting at 130 different locations. The wind turbine model is actually 8 sub-models (a-h) that simulate blade rotation. I'm using setVisibility in a while-loop with a timeout delay to have one sub-model visble at a time; works great.
Here's my problem: When I insert (using placemark) my 8 sub-models at the 130 locations, I have to insert my while-loop into a huge for-loop to address each of the 1040 models to turn them on and off individually, because they behave as individual entities. This works, but results in very choppy behavior; too much data. Clunky. There has to be a better way.
What I want to learn: Can I insert the 8 sub-models in 130 locations, and have all 130 "a" models turn on with one setVisibility(true) command? That way, my while loop would work and I could get rid of the for loop.
I can post my code, but there's a lot of other peripheral crap in there and my programming skills are embarrassing...
Figured it out...
use multiGeometry.getGeometries().appendChild(model);
attributes can be changed with one command using a multiGeometry. Don't forget to make your variable global if you're working outside of a function.
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.