My original intention to do this is to integrate dplyr with shiny
Prior to 0.3 I have used eval(parse(text=....)), do.call() approach.
In 0.3, I saw two more options, for example:
var <- c('disp','hp')
select_(mtcars,.dots = as.lazy_dots(var))
select(mtcars,one_of(var))
but which one is better? I intended to pass the selectInput values from Shiny app to do data transformations through dplyr.
Another question, what will be the right way to join two different dataset with dynamic but different key column? Is there anything I can leverage in 0.3?
for example
col_a, col_b are key variables to join from datasets a & b
left_join(dataset_a,dataset_b, by=c(col_a=col_b))
Thanks.
After a few attempts, here is my solution for the 2nd question, use a function to create a named vector, and then feed to left_join.
joinCol_a = xxx
joinCol_b = xxx
f <- function(a,b){
vec <- c(b)
names(vec) <- a
return(vec)
}
left_join(dataset_a,dataset_b,by=f(joinCol_a,joinCol_b))
I know it's not the best solution but this is what I can think of so far.
Related
The LDA code generates topics say from 0 to 5 . Is there a standard way (a norm) used to link the generated topics and the documents themselves. Eg: doc1 is of Topic0 , doc5 is of topic Topic1 etc.
One way i can think of is to string search each of geenrated key words in each topic on the docs , is there a generic way or practice followed for this?
Ex LDA code - https://github.com/manhcompany/lda/blob/master/lda.py
I "collected some code", and this worked for me. Assuming you have a term frequency
tf_vectorizer = CountVectorizer("parameters of your choice")
tf = tf_vectorizer.fit_transform("your data)`
lda_model = LatentDirichletAllocation("other parameters of your choice")
lda_model.fit(tf)
create the topic-document matrix (the crucial step), and select the num_topic most important topics
doc_topic = lda_model.transform(tf)
num_most_important_topic = 2
dominant_topic = []
for ind_doc in range(doc_topic.shape[0]):
dominant_topic.append(sorted(range(len(doc_topic[ind_doc])),
key=lambda ind_top: doc_topic[ind_doc][ind_top],
reverse=True)[:num_most_important_topic])
This should give you an array of the num_most_important_topic topics. Good luck!
Assume I have an array of Strings:
['Laptop Apple Macbook Air A1465, Core i7, 8Gb, 256Gb SSD, 15"Retina, MacOS' ... 'another device description']
I'd like to extract from this description features like:
item=Laptop
brand=Apple
model=Macbook Air A1465
cpu=Core i7
...
Should I prepare the pre-defined known features first? Like
brands = ['apple', 'dell', 'hp', 'asus', 'acer', 'lenovo']
cpu = ['core i3', 'core i5', 'core i7', 'intel pdc', 'core m', 'intel pentium', 'intel core duo']
I am not sure that I need to use CountVectorizer and TfidfVectorizer here, it's more appropriate to have DictVictorizer, but how can I make dicts with keys extracting values from the entire string?
is it possible with scikit-learn's Feature Extraction? Or should I make my own .fit(), and .transform() methods?
UPDATE:
#sergzach, please review if I understood you right:
data = ['Laptop Apple Macbook..', 'Laptop Dell Latitude...'...]
for d in data:
for brand in brands:
if brand in d:
# ok brand is found
for model in models:
if model in d:
# ok model is found
So creating N-loops per each feature? This might be working, but not sure if it is right and flexible.
Yes, something like the next.
Excuse me, probably you should correct the code below.
import re
data = ['Laptop Apple Macbook..', 'Laptop Dell Latitude...'...]
features = {
'brand': [r'apple', r'dell', r'hp', r'asus', r'acer', r'lenovo'],
'cpu': [r'core\s+i3', r'core\s+i5', r'core\s+i7', r'intel\s+pdc', r'core\s+m', r'intel\s+pentium', r'intel\s+core\s+duo']
# and other features
}
cat_data = [] # your categories which you should convert into numbers
not_found_columns = []
for line in data:
line_cats = {}
for col, features in features.iteritems():
for i, feature in enumerate(features):
found = False
if re.findall(feature, line.lower(), flags=re.UNICODE) != []:
line_cats[col] = i + 1 # found numeric category in column. For ex., for dell it's 2, for acer it's 5.
found = True
break # current category is determined by a first occurence
# cycle has been end but feature had not been found. Make column value as default not existing feature
if not found:
line_cats[col] = 0
not_found_columns.append((col, line))
cat_data.append(line_cats)
# now we have cat_data where each column is corresponding to a categorial (index+1) if a feature had been determined otherwise 0.
Now you have column names with lines (not_found_columns) which was not found. View them, probably you forgot some features.
We can also write strings (instead of numbers) as categories and then use DV. In result the approaches are equivalent.
Scikit Learn's vectorizers will convert an array of strings to an inverted index matrix (2d array, with a column for each found term/word). Each row (1st dimension) in the original array maps to a row in the output matrix. Each cell will hold a count or a weight, depending on which kind of vectorizer you use and its parameters.
I am not sure this is what you need, based on your code. Could you tell where you intend to use this features you are looking for? Do you intend to train a classifier? To what purpose?
I am working on my project that uses elgamal elliptic curve.
I know when the elgamal ec encrypt by following steps
Represent the message m as a point M in E(Fp).
Select k ∈R [1,n−1].
Compute C1 = kP.
Compute C2 = M +kQ.
Return(C1,C2).
Where Q is the intended recipient’s public key, P is base point.
My qusetion at number one. how represent m as a point. Is point represent one character or represent group of characters.
There's no obvious way to attach m to points in E(Fp). However, you can use variant algorithm of ElGamal such as Menezes-Vanstone Elliptic curve cryptosystem to encode a message
in a point, a good reference here(P.31).
As for java code, I suggest you do some work, and post another question on SO when you encounter a problem you really can't solve by yourself.
I have a set S. It contains N subsets (which in turn contain some sub-subsets of various lengths):
1. [[a,b],[c,d],[*]]
2. [[c],[d],[e,f],[*]]
3. [[d,e],[f],[f,*]]
N. ...
I also have a list L of 'unique' elements that are contained in the set S:
a, b, c, d, e, f, *
I need to find all possible combinations between each sub-subset from each subset so, that each resulting combination has exactly one element from the list L, but any number of occurrences of the element [*] (it is a wildcard element).
So, the result of the needed function working with the above mentioned set S should be (not 100% accurate):
- [a,b],[c],[d,e],[f];
- [a,b],[c],[*],[d,e],[f];
- [a,b],[c],[d,e],[f],[*];
- [a,b],[c],[d,e],[f,*],[*];
So, basically I need an algorithm that does the following:
take a sub-subset from the subset 1,
add one more sub-subset from the subset 2 maintaining the list of 'unique' elements acquired so far (the check on the 'unique' list is skipped if the sub-subset contains the * element);
Repeat 2 until N is reached.
In other words, I need to generate all possible 'chains' (it is pairs, if N == 2, and triples if N==3), but each 'chain' should contain exactly one element from the list L except the wildcard element * that can occur many times in each generated chain.
I know how to do this with N == 2 (it is a simple pair generation), but I do not know how to enhance the algorithm to work with arbitrary values for N.
Maybe Stirling numbers of the second kind could help here, but I do not know how to apply them to get the desired result.
Note: The type of data structure to be used here is not important for me.
Note: This question has grown out from my previous similar question.
These are some pointers (not a complete code) that can take you to right direction probably:
I don't think you will need some advanced data structures here (make use of erlang list comprehensions). You must also explore erlang sets and lists module. Since you are dealing with sets and list of sub-sets, they seems like an ideal fit.
Here is how things with list comprehensions will get solved easily for you: [{X,Y} || X <- [[c],[d],[e,f]], Y <- [[a,b],[c,d]]]. Here i am simply generating a list of {X,Y} 2-tuples but for your use case you will have to put real logic here (including your star case)
Further note that with list comprehensions, you can use output of one generator as input of a later generator e.g. [{X,Y} || X1 <- [[c],[d],[e,f]], X <- X1, Y1 <- [[a,b],[c,d]], Y <- Y1].
Also for removing duplicates from a list of things L = ["a", "b", "a"]., you can anytime simply do sets:to_list(sets:from_list(L)).
With above tools you can easily generate all possible chains and also enforce your logic as these chains get generated.
I am coding a survey that outputs a .csv file. Within this csv I have some entries that are space delimited, which represent multi-select questions (e.g. questions with more than one response). In the end I want to parse these space delimited entries into their own columns and create headers for them so i know where they came from.
For example I may start with this (note that the multiselect columns have an _M after them):
Q1, Q2_M, Q3, Q4_M
6, 1 2 88, 3, 3 5 99
6, , 3, 1 2
and I want to go to this:
Q1, Q2_M_1, Q2_M_2, Q2_M_88, Q3, Q4_M_1, Q4_M_2, Q4_M_3, Q4_M_5, Q4_M_99
6, 1, 1, 1, 3, 0, 0, 1, 1, 1
6,,,,3,1,1,0,0,0
I imagine this is a relatively common issue to deal with but I have not been able to find it in the R section. Any ideas how to do this in R after importing the .csv ? My general thoughts (which often lead to inefficient programs) are that I can:
(1) pull column numbers that have the special suffix with grep()
(2) loop through (or use an apply) each of the entries in these columns and determine the levels of responses and then create columns accordingly
(3) loop through (or use an apply) and place indicators in appropriate columns to indicate presence of selection
I appreciate any help and please let me know if this is not clear.
I agree with ran2 and aL3Xa that you probably want to change the format of your data to have a different column for each possible reponse. However, if you munging your dataset to a better format proves problematic, it is possible to do what you asked.
process_multichoice <- function(x) lapply(strsplit(x, " "), as.numeric)
q2 <- c("1 2 3 NA 4", "2 5")
processed_q2 <- process_multichoice(q2)
[[1]]
[1] 1 2 3 NA 4
[[2]]
[1] 2 5
The reason different columns for different responses are suggested is because it is still quite unpleasant trying to retrieve any statistics from the data in this form. Although you can do things like
# Number of reponses given
sapply(processed_q2, length)
#Frequency of each response
table(unlist(processed_q2), useNA = "ifany")
EDIT: One more piece of advice. Keep the code that processes your data separate from the code that analyses it. If you create any graphs, keep the code for creating them separate again. I've been down the road of mixing things together, and it isn't pretty. (Especially when you come back to the code six months later.)
I am not entirely sure what you trying to do respectively what your reasons are for coding like this. Thus my advice is more general – so just feel to clarify and I will try to give a more concrete response.
1) I say that you are coding the survey on your own, which is great because it means you have influence on your .csv file. I would NEVER use different kinds of separation in the same .csv file. Just do the naming from the very beginning, just like you suggested in the second block.
Otherwise you might geht into trouble with checkboxes for example. Let's say someone checks 3 out of 5 possible answers, the next only checks 1 (i.e. "don't know") . Now it will be much harder to create a spreadsheet (data.frame) type of results view as opposed to having an empty field (which turns out to be an NA in R) that only needs to be recoded.
2) Another important question is whether you intend to do a panel survey(i.e longitudinal study asking the same participants over and over again) . That (among many others) would be a good reason to think about saving your data to a MySQL database instead of .csv . RMySQL can connect directly to the database and access its tables and more important its VIEWS.
Views really help with survey data since you can rearrange the data in different views, conditional on many different needs.
3) Besides all the personal / opinion and experience, here's some (less biased) literature to get started:
Complex Surveys: A Guide to Analysis Using R (Wiley Series in Survey Methodology
The book is comparatively simple and leaves out panel surveys but gives a lot of R Code and examples which should be a practical start.
To prevent re-inventing the wheel you might want to check LimeSurvey, a pretty decent (not speaking of the templates :) ) tool for survey conductors. Besides I TYPO3 CMS extensions pbsurvey and ke_questionnaire (should) work well too (only tested pbsurvey).
Multiple choice items should always be coded as separate variables. That is, if you have 5 alternatives and multiple choice, you should code them as i1, i2, i3, i4, i5, i.e. each one is a binary variable (0-1). I see that you have values 3 5 99 for Q4_M variable in the first example. Does that mean that you have 99 alternatives in an item? Ouch...
First you should go on and create separate variables for each alternative in a multiple choice item. That is, do:
# note that I follow your example with Q4_M variable
dtf_ins <- as.data.frame(matrix(0, nrow = nrow(<initial dataframe>), ncol = 99))
# name vars appropriately
names(dtf_ins) <- paste("Q4_M_", 1:99, sep = "")
now you have a data.frame with 0s, so what you need to do is to get 1s in an appropriate position (this is a bit cumbersome), a function will do the job...
# first you gotta change spaces to commas and convert character variable to a numeric one
y <- paste("c(", gsub(" ", ", ", x), ")", sep = "")
z <- eval(parse(text = y))
# now you assing 1 according to indexes in z variable
dtf_ins[1, z] <- 1
And that's pretty much it... basically, you would like to reconsider creating a data.frame with _M variables, so you can write a function that does this insertion automatically. Avoid for loops!
Or, even better, create a matrix with logicals, and just do dtf[m] <- 1, where dtf is your multiple-choice data.frame, and m is matrix with logicals.
I would like to help you more on this one, but I'm recuperating after a looong night! =) Hope that I've helped a bit! =)
Thanks for all the responses. I agree with most of you that this format is kind of silly but it is what I have to work with (survey is coded and going into use next week). This is what I came up with from all the responses. I am sure this is not the most elegant or efficient way to do it but I think it should work.
colnums <- grep("_M",colnames(dat))
responses <- nrow(dat)
for (i in colnums) {
vec <- as.vector(dat[,i]) #turn into vector
b <- lapply(strsplit(vec," "),as.numeric) #split up and turn into numeric
c <- sort(unique(unlist(b))) #which values were used
newcolnames <- paste(colnames(dat[i]),"_",c,sep="") #column names
e <- matrix(nrow=responses,ncol=length(c)) #create new matrix for indicators
colnames(e) <- newcolnames
#next loop looks for responses and puts indicators in the correct places
for (i in 1:responses) {
e[i,] <- ifelse(c %in% b[[i]],1,0)
}
dat <- cbind(dat,e)
}
Suggestions for improvement are welcome.