I have a dataset with zipcode column. They have some significance in output and I want to use it as a feature. I am using random forest model.
I need a suggestions on best way to use zipcode column as a feature. (For example should I get lat/long for that zipcode rather than directly feeding zipcodes etc.)
Thanks in advance !!
A common way of handling zip codes or any high cardinality categorical column is called "target encoding" or "impact encoding". In H2O, you can apply target encoding to any categorical columns. As of H2O 3.20, this is only available in R, but in the next stable release, 3.22, it will be available in all clients (JIRA ticket here).
If you are using R, my advice is to try both target encoding and also the GLRM method mentioned by Lauren and compare the results. If you're in Python or another language, then try GLRM for now and give target encoding a try when H2O 3.22 is released.
I'd 2nd what Erin LeDell says about target encoding.
Here are some other options and not all of them may apply:
Reduce the granularity of zip Code to the first 1,2,3 or 4 digits. So
zip code 90210 becomes 902 (902XX) and would represent Los Angeles
County. 902 zipcodes
Can you group zip codes by MSA or CBSA?
Is there a feature about zip codes that can be appended i.e. city/urban/rural etc.
Can you pull in some zip code demographics,population size or income
Distance to/from a key location (airport, city center, etc.)
Target encode but then group into very high, high, medium and low (or whatever makes sense) example this will help prevent over training your models.
Related
We want to identify the address fields from a document. For Identifying the address fields we converted the document to OCR files using Tesseract. From the tesseract output we want to check a string contains the address field or not . Which is the right strategy to resolve this problem ?
Its not possible to solve this problem using the regex because address fields are different for various documents and countries
Tried NLTK for classifying the words but not works perfectly for address field.
Required output
I am staying at 234 23 Philadelphia - Contains address files <234 23 Philadelphia>
I am looking for a place to stay - Not contains address
Provide your suggestions to solve this problem .
As in many ML problems, there are mutiple posible solutions, and the important part(and the one commonly has greater impact) is not which algorithm or model you use, but feature engineering ,data preprocessing and standarization ,and things like that. The first solution comes to my mind(and its just an idea, i would test it and see how it performs) its:
Get your training set examples and list the "N" most commonly used words in all examples(thats your vocabulary), this list will contain every one of the "N" most used words , every word would be represented by a number(the list index)
Transform your training examples: read every training example and change its representation replacing every word by the number of the word in the vocabolary.
Finally, for every training example create a feature vector of the same size as the vocabulary, and for every word in the vocabulary your feature vector will be 0(the corresponding word doesnt exists in your example) or 1(it exists) , or the count of how many times the word appears(again ,this is feature engineering)
Train multiple classifiers ,varing algorithms,parameters, training set sizes, etc, and do cross validation to choose your best model.
And from there keep the standard ML workflow...
If you are interested in just checking YES or NO and not extraction of complete address, One simple solution can be NER.
You can try to check if Text contains Location or not.
For Example :
import nltk
def check_location(text):
for chunk in nltk.ne_chunk(nltk.pos_tag(nltk.word_tokenize(text))):
if hasattr(chunk, "label"):
if chunk.label() == "GPE" or chunk.label() == "GSP":
return "True"
return "False"
text="I am staying at 234 23 Philadelphia."
print(text+" - "+check_location(text))
text="I am looking for a place to stay."
print(text+" - "+check_location(text))
Output:
# I am staying at 234 23 Philadelphia. - True
# I am looking for a place to stay. - False
If you want to extract complete address as well, you will need to train your own model.
You can check: NER with NLTK , CRF++.
You're right. Using regex to find an address in a string is messy.
There are APIs that will attempt to extract addresses for you. These APIs are not always guaranteed to extract addresses from strings, but they will do their best. One example of an street address extract API is from SmartyStreets. Documentation here and demo here.
Something to consider is that even your example (I am staying at 234 23 Philadelphia) doesn't contain a full address. It's missing a state or ZIP code field. This makes is very difficult to programmatically determine if there is an address. Once there is a state or ZIP code added to that sample string (I am staying at 234 23 Philadelphia PA) it becomes much easier to programmatically determine if there is an address contained in the string.
Disclaimer: I work for SmartyStreets
A better method to do this task could be as followed below:
Train your own custom NER model (extending pre-trained SpaCy's model or building your own CRF++ / CRF-biLSTM model, if you have annotated data) or using a pre-trained models like SpaCy's large model or geopandas, etc.
Define a weighted score mechanism based on your problem statement.
For example - Let's assume every address have 3 important components - an address, a telephone number and an email id.
Text that would have all three of them would get a score of 33.33% + 33.33% + 33.33% = 100 %
For identifying if it's an address field or not you may take into account - the per% of SpaCy's location tags (GPE, FAC, LOC, etc) out of total tokens in text which gives a good estimate of how many location tags are present in text. Then run a regex for postal codes, and match the found city names with the 3-4 words just before the found postal code, if there's an overlap, you have correctly identified a postal code and hence an address field - (got your 33.33% score!).
For telephone numbers - certain checks and regex could do it but an important criteria would be that it performs these phone checks only if an address field is located in above text.
For emails/web address again you could perform nomial regex checks and finally add all these 3 scores to a cumulative value.
An ideal address would get 100 score while missing fields wile yield 66% etc. The rest of the text would get a score of 0.
Hope it helped! :)
Why do you say regular expressions won't work?
Basically, define all the different forms of address you might encounter in the form of regular expressions. Then, just match the expressions.
In naive byes classifier i want to find out the accuracy from my train and test. But my train set is like
Happy: absolution abundance abundant accolade accompaniment accomplish accomplished achieve achievement acrobat admirable admiration adorable adoration adore advance advent advocacy aesthetics affection affluence alive allure aloha
Sad: abandon abandoned abandonment abduction abortion abortive abscess absence absent absentee abuse abysmal abyss accident accursed ache aching adder adrift adultery adverse adversity afflict affliction affront aftermath aggravating
Angry: abandoned abandonment abhor abhorrent abolish abomination abuse accursed accusation accused accuser accusing actionable adder adversary adverse adversity advocacy affront aftermath aggravated aggravating aggravation aggression aggressive aggressor agitated agitation agony alcoholism alienate alienation
For test set
data: Dec 7, 2014 ... This well-known nursery rhyme helps children practice emotions, like happy, sad, scared, tired and angry. If You're Happy and You Know It is ...
Now the problem is how do i convert them into arff file
Your training set is not appropriate for training a model for Weka however these information can be used in feature extraction.
Your Test set can be converted into an arff file. From every message extract these basics features like
1. Any form of the word 'Happy' is present or not
2. Any form of the word 'Sad' is present or not
3. Any form of the word 'Angry' is present or not
4. TF-IDF
etc.
then for some messages (say 70%) you should assign one class {Happy, Sad, Angry} manually and for remaining 30% you can test through your model.
More about arff file is given here:
http://www.cs.waikato.ac.nz/ml/weka/arff.html
Where to start ;).
As written before your "training data" is not real training data. Training data should be texts similar to the data you are using for Testing. However, in your example it is merely a list of words. My gut feeling is that you would be better of to avoid using weka, count the number of occurrence in each category, and the take the one with most matches.
In case you want use Weka I'd recommend to use the toolbox https://www.knime.org which nicely integrates with weka.
You should then convert your data into a bag of words representation. This is basically you have the number of times each word occurs in each of the texts as features.
Also for this Knime has nice package. http://www.tech.knime.org/files/KNIME-TextProcessing-HowTo.pdf
I have a very small question which has been baffling me for a while. I have a dataset with interesting features, but some of them are dimensionless quantities (I've tried using z-scores) on them but they've made things worse. These are:
Timestamps (Like YYYYMMDDHHMMSSMis) I am getting the last 9 chars from this.
User IDs (Like in a Hash form) How do I extract meaning from them?
IP Addresses (You know what those are). I only extract the first 3 chars.
City (Has an ID like 1,15,72) How do I extract meaning from this?
Region (Same as city) Should I extract meaning from this or just leave it?
The rest of the things are prices, widths and heights which understand. Any help or insight would be much appreciated. Thank you.
Timestamps can be transformed into Unix Timestamps, which are reasonable natural numbers
User IF/Cities/Regions are nominal values, which has to be encoded somehow. The most common approach is to create as much "dummy" dimensions as the number of possible values. So if you have 100 ciries, than you create 100 dimensions and give "1" only on the one representing a particular city (and 0 on the others)
IPs should rather be removed, or transformed into some small group of them (based on the DNS-network identification and nominal to dummy transformation as above)
I'm look at an example in the Mahout in Action book. It uses the StaticWordValueEncoder to encoder a text in the feature hashing manner.
When encode "text to magically vectorize" with a standard analyser and probe = 1, the vector is {12:1.0, 54:1.0, 78:1.0}. However, I can't figure out which word the hash index refers to.
Is there any method to get the [hash, original word] as a pair? e.g. hash 12 refers to the word "text"?
if you have read Mahout in Action paragraph:
"The value of a continuous
variable gets added directly to one or more locations that are allocated for the storage
of the value. The location or locations are determined by the name of the feature.
This hashed feature approach has the distinct advantage of requiring less memory
and one less pass through the training data, but it can make it much harder to reverse engineer
vectors to determine which original feature mapped to a vector location."
-----I am not sure how the reverse engineering can be done(which certainly a difficult task as Author has put) Perhaps some one might put some light on this.
I'd like to write a spam filter program with SVM and I choose libsvm as the tool.
I got 1000 good mails and 1000 spam mails, then I classify them into :
700 good_train mails 700 spam_train mails
300 good_test mails 300 spam_test mails
Then I wrote a program to count the time of each words occur in each file, got result like:
good_train_1.txt:
today 3
hello 7
help 5
...
I learned that libsvm needs format like:
1 1:3 2:1 3:0
2 1:3 2:3 3:1
1 1:7 3:9
as its input. I know that 1, 2, 1 is the label, but what does 1:3 mean?
How could I transfer what I've got to this format?
Likely, the format is
classLabel attribute1:count1 ... attributeN:countN
N is the total number of different words in your text corpus. You will have to check the documentation for the tool you are using(or its sources), to see if you can use a sparser format by not including the attributes having count 0.
How could I transfer what I've got to this format?
Here's how I would do this. I would use the script you've got to compute the count of words for each mail in the training set. Then, use another script and transfer that data into the LIBSVM format that you've shown earlier. (This can be done in a variety of ways, but it should be reasonable to write with an easy input/output language like Python) I would batch all "good-mail" data into one file, and label that class as "1". Then, I would do the same process with the "spam-mail" data and label that class "-1". As nologin said, LIBSVM requires the class label to precede the features, but the features themselves can be any number as long as they are in ascending order, e.g. 2:5 3:6 5:9 is allowed, but not 3:23 1:3 7:343.
If you're concerned that your data is not in the correct format, use their script
checkdata.py
before training and it should report any possible errors.
Once you have two separate files with data in the correct format, you can call
cat file_good file_spam > file_training
and generate a training file that contains data on both good and spam mail. Then, do the same process with the testing set. One psychological advantage with forming the data this way is that you know the top 700 (or 300) mail in the training (or testing) set is good mail, and the remaining are spam mail. This makes it easier to create other scripts you may want to act on the data, such as a precision/recall code.
If you have other questions, the FAQ at http://www.csie.ntu.edu.tw/~cjlin/libsvm/faq.html should be able to answer a few, as well as the various README files that come with installation. (I personally found the READMEs in the "Tools" and "Python" directories to be a great boon.) Sadly, the FAQ does not touch much on what nologin said, about data being in a sparse format.
On a final note, I doubt that you need to keep counts of every possible word that could appear in mail. I would recommend counting only the most common words you would suspect to appear in spam mail. Other potential features include total word count, average word length, average sentence length, and other possible data that you feel may be helpful.