I'm using caret to compare models for a classification problem with nested CV. Vfold in the outer loop and bootstrap (500 replicates) in the inner loop. I get this error after training knn:
Warning: There were missing values in resampled performance measures.
Which I believe comes from the fact that some resamples have zero items of the class of interest in the holdout sample, yielding NA for Sensitivity and ROC. My question is: Is there any way to ensure that items from this class are present in every bootstrap resample? Kind of what the CreateDataPartition function does (I believe this is also called stratified bootstrap?).
If not, how should we proceed with this? (In terms of comparing model performance on the same resamples)
Thanks!
So I couldn't find a way to do this within caret but here is a workaround using rsample package. The point is to compute the resamples before and feed this information to trainControl function via index and indexOut arguments, previous conversion to caret format.
indices=bootstraps(train,times=50,strata="class_of_interest")
indices=rsample2caret(indices)
train_control <- trainControl(method="boot",number=50,index=indices$index,indexOut = indices$indexOut)
Hope this helps.
Related
I want to use the read-only version of Gensim's FastText Embedding to save some RAM compared to the full model.
After loading the KeyVectors version, I get the following Error when fetching a vector:
IndexError: index 878080 is out of bounds for axis 0 with size 761210
The error occurs when using words that should be out-of-vocabulary e.g. "lawyerxy" instead of "lawyer". The full model returns a vector for both.
from gensim.models import KeyedVectors
model = KeyedVectors.load("model.kv")
model .wv.__getitem__("lawyerxy")
So, my assumption is that the KeyedVectors do not offer FastText's out of vacabulary function - a key feature for my usecase. This limitation is not given in the documentation:
https://radimrehurek.com/gensim/models/word2vec.html
Can anyone prove that assumption and/or name a fix to allow vectors for "lawyerxy" etc. ?
The KeyedVectors name is (as of gensim-3.8.0) just an alias for class Word2VecKeyedVectors, which only maintains a simple word (as key) to vector (as value) mapping.
You shouldn't expect FastText's advanced ability to synthesize vectors for out-of-vocabulary words to appear in any model/representation that doesn't explicitly claim to offer that ability.
(I would expect a lookup of an out-of-vocabulary word to give a clearer KeyError rather than the IndexError you've reported. But, you'd need to show exactly what code created the file you're loading, and triggered the error, and the full error stack, to further guess what's going wrong in your case.)
Depending on how your model.kv file was saved, you might be able to load it, with retained OOV-vector functionality, by using the class FastTextKeyedVectors instead of plain KeyedVectors.
I'm using Gensim with Fasttext Word vectors for return similar words.
This is my code:
import gensim
model = gensim.models.KeyedVectors.load_word2vec_format('cc.it.300.vec')
words = model.most_similar(positive=['sole'],topn=10)
print(words)
This will return:
[('sole.', 0.6860659122467041), ('sole.Ma', 0.6750558614730835), ('sole.Il', 0.6727924942970276), ('sole.E', 0.6680260896682739), ('sole.A', 0.6419174075126648), ('sole.È', 0.6401025652885437), ('splende', 0.6336565613746643), ('sole.La', 0.6049465537071228), ('sole.I', 0.5922051668167114), ('sole.Un', 0.5904430150985718)]
The problem is that "sole" ("sun", in english) return a series of words with a dot in it (like sole., sole.Ma, ecc...). Where is the problem? Why most_similar return this meaningless word?
EDIT
I tried with english word vector and the word "sun" return this:
[('sunlight', 0.6970556974411011), ('sunshine', 0.6911839246749878), ('sun.', 0.6835992336273193), ('sun-', 0.6780728101730347), ('suns', 0.6730450391769409), ('moon', 0.6499731540679932), ('solar', 0.6437565088272095), ('rays', 0.6423950791358948), ('shade', 0.6366724371910095), ('sunrays', 0.6306195259094238)]
Is it impossible to reproduce results like relatedwords.org?
Perhaps the bigger question is: why does the Facebook FastText cc.it.300.vec model include so many meaningless words? (I haven't noticed that before – is there any chance you've downloaded a peculiar model that has decorated words with extra analytical markup?)
To gain the unique benefits of FastText – including the ability to synthesize plausible (better-than-nothing) vectors for out-of-vocabulary words – you may not want to use the general load_word2vec_format() on the plain-text .vec file, but rather a Facebook-FastText specific load method on the .bin file. See:
https://radimrehurek.com/gensim/models/fasttext.html#gensim.models.fasttext.load_facebook_vectors
(I'm not sure that will help with these results, but if choosing to use FastText, you may be interesting it using it "fully".)
Finally, given the source of this training – common-crawl text from the open web, which may contain lots of typos/junk – these might be legimate word-like tokens, essentially typos of sole, that appear often enough in the training data to get word-vectors. (And because they really are typo-synonyms for 'sole', they're not necessarily bad results for all purposes, just for your desired purpose of only seeing "real-ish" words.)
You might find it helpful to try using the restrict_vocab argument of most_similar(), to only receive results from the leading (most-frequent) part of all known word-vectors. For example, to only get results from among the top 50000 words:
words = model.most_similar(positive=['sole'], topn=10, restrict_vocab=50000)
Picking the right value for restrict_vocab might help in practice to leave out long-tail 'junk' words, while still providing the real/common similar words you seek.
I am trying to reduce the size of my data and I cannot make it work. I have data points taken every minute over 1 month. I want to reduce this data to have one sample for every hour. The problem is: Some of my runs have "NA" value, so I delete these rows. There is not exactly 60 points for every hour - it varies.
I have a 'Timestamp' column. I have used this to make a 'datehour' column which has the same value if the data set has the same date and hour. I want to average all the values with the same 'datehour' value.
How can I do this? I have tried using the if and for loop below, but it takes so long to run.
Thanks for all your help! I am new to Julia and come from a Matlab background.
======= CODE ==========
uniquedatehour=unique(datehour,1)
index=[]
avedata=reshape([],0,length(alldata[1,:]))
for j in uniquedatehour
for i in 1:length(datehour)
if datehour[i]==j
index=vcat(index,i)
else
rows=alldata[index,:]
rows=convert(Array{Float64,2},rows)
avehour=mean(rows,1)
avedata=vcat(avedata,avehour)
index=[]
continue
end
end
end
There are several layers to optimizing this code. I am assuming that your data is sorted on datehour (your code assumes this).
Layer one: general recommendation
Wrap your code in a function. Executing code in global scope in Julia is much slower than within a function. By wrapping it make sure to either pass data to your function as arguments or if data is in global scope it should be qualified with const;
Layer two: recommendations to your algorithm
Statement like [] creates an array of type Any which is slow, you should use type qualifier like index=Int[] to make it fast;
Using vcat like index=vcat(index,i) is inefficient, it is better to do push!(index, i) in place;
It is better to preallocate avedata with e.g. fill(NA, length(uniquedatehour), size(alldata, 2)) and assign values to an existing matrix than to do vcat on it;
Your code will produce incorrect results if I am not mistaken as it will not catch the last entry of uniquedatehour vector (assume it has only one element and check what happens - avedata will have zero rows)
Line rows=convert(Array{Float64,2},rows) is probably not needed at all. If alldata is not Matrix{Float64} it is better to convert it at the beginning with Matrix{Float64}(alldata);
You can change line rows=alldata[index,:] to a view like view(alldata, index, :) to avoid allocation;
In general you can avoid creation of index vector as it is enough that you remember start s and end e position of the range of the same values and then use range s:e to select rows you want.
If you correct those things please post your updated code and maybe I can help further as there is still room for improvement but requires a bit different algorithmic approach (but maybe you will prefer option below for simplicity).
Layer three: how I would do it
I would use DataFrames package to handle this problem like this:
using DataFrames
df = DataFrame(alldata) # assuming alldata is Matrix{Float64}, otherwise convert it here
df[:grouping] = datehour
agg = aggregate(df, :grouping, mean) # maybe this is all what you need if DataFrame is OK for you
Matrix(agg[2:end]) # here is how you can convert DataFrame back to a matrix
This is not the fastest solution (as it converts to a DataFrame and back but it is much simpler for me).
I am working on an untrained classifier model. I am working in Python 2.7. I have a loop. It looks like this:
features = [0 for i in xrange(len(dictionary))]
for bgrm in new_scored:
for i in xrange(len(dictionary)):
if bgrm[0] == dictionary[i]:
features[i] = int(bgrm[1])
break
I have a "dictionary" of bigrams that I have collected from a data set containing customer reviews and I would like to construct feature arrays of each review corresponding to the dictionary I have created. It would contain the frequencies of the bigrams found within the review of the features in the dictionary (I hope that makes sense). new_scored is a list of tuples which contains the bigrams found within a particular review paired with their relative frequency of occurrence in that review. The final feature arrays will be the same length as the original dictionary with few non zero entries.
The above works fine but I am looking at a data set of 13000 reviews, for each review to loop through this code is going to take for eeever (if my computer doesnt run out of RAM first). I have been sitting with it for a while and cannot see how I can condense it.
I am very new to python so I was hoping a more experienced could help with condensing it or perhaps point me in the right direction towards a library that will contain the function I need.
Thank you in advance!
Consider making dictionary an actual dict object (or some fancier subclass of dict if it better suits your needs), as opposed to an iterable (list or tuple seems like what it is now). dictionary could map bigrams as keys to an integer identifier that would identify a feature position.
If you refactor dictionary that way, then the loop can be rewritten as:
features = [0 for key in dictionary]
for bgram in new_scored:
try:
features[dictionary[bgram[0]]] = int(bgrm[1])
except KeyError:
# do something if the bigram is not in the dictionary for some reason
This should convert what was an O(n) traversal through dictionary into a hash lookup.
Hope this helps.
Which package is best for a heatmap/image with sorting on rows only, but don't show any dendrogram or other visual clutter (just a 2D colored grid with automatic named labels on both axes). I don't need fancy clustering beyond basic numeric sorting. The data is a 39x10 table of numerics in the range (0,0.21) which I want to visualize.
I searched SO (see this) and the R sites, and tried a few out. Check out R Graphical Manual to see an excellent searchable list of screenshots and corresponding packages.
The range of packages is confusing - which one is the preferred heatmap (like ggplot2 is for most other plotting)? Here is what I found out so far:
base::image - bad, no name labels on axes, no sorting/clustering
base::heatmap - options are far less intelligible than the following:
pheatmap::pheatmap - fantastic but can't seem to turn off the
dendrograms? (any hacks?)
ggplot2 people use geom_tile, as Andrie points out
gplots::heatmap.2 , ref - seems
to be favored by biotech people, but way overkill for my purposes. (no
relation to ggplot* or Prof Wickham)
plotrix::color2D.matplot also exists
base::heatmap is annoying, even with args heatmap(..., Colv=NA, keep.dendro=FALSE) it still plots the unwanted dendrogram on rows.
For now I'm going with pheatmap(..., cluster_cols=FALSE, cluster_rows=FALSE) and manually presorting my table, like this guy: Order of rows in heatmap?
Addendum: to display the value inside each cell, see: display a matrix, including the values, as a heatmap . I didn't need that but it's nice-to-have.
With pheatmap you can use options treeheight_row and treeheight_col and set these to 0.
just another option you have not mentioned...package bipartite as it is as simple as you say
library(bipartite)
mat<-matrix(c(1,2,3,1,2,3,1,2,3),byrow=TRUE,nrow=3)
rownames(mat)<-c("a","b","c")
colnames(mat)<-c("a","b","c")
visweb(mat,type="nested")