I have collected data from a survey in order to perform a choice based conjoint analysis. I have preprocessed and clean data. However, when I apply the function mlogit on the dataset I get the following error:
Error in model.frame.default(terms(formula, lhs = lhs, rhs = rhs, data = data, : variable lengths differ (found for 'OSTAN1')
while I omitted 'OSTAN1'variable, the same error happened for the next variable and so on.
I really do not understand why. Can please some one help me to understand what I am doing wrong ?
library("mlogit")
data("DATA3", package = "mlogit")
Electr <- dfidx(DATA3, idx = list(c("chid", "ID")),
choice = "NOE_TASADOF", varying = 2:10, sep = "")
Elec.mxl <- mlogit(NOE_TASADOF ~ TIME + WEEKEND + NOR + HAVA + GHANON +
KHASTEGI + GOVAHINAME + TAHSILAT+ OSTAN1 + OSTAN2+ OSTAN3+ AGE1+ AGE2+AGE3+ FASL1+
FASL2+FASL3+FASL4|0,
rpar=c(OSTAN1 = 'n', OSTAN2 = 'n',
OSTAN3='n', AGE1='n', AGE2='n',
AGE3='n', TIME='n',WEEKEND='n',
NOR='n',HAVA='n', GHANON='n',KHASTEGI='n',
GOVAHINAME='n', TAHSILAT='n') ,R = 100, correlation = FALSE,halton = NA, Electr , panel = TRUE)
Related
I am trying to plot the Pareto front of a TuneMultiCritResult object, tuned with a control object of class TuneMultiCritControlMBO:
# multi-criteria optimization of (tpr, fpr) with MBO
lrn = makeLearner("classif.ksvm")
rdesc = makeResampleDesc("Holdout")
ps = makeParamSet(
makeNumericParam("C", lower = -12, upper = 12, trafo = function(x) 2^x),
makeNumericParam("sigma", lower = -12, upper = 12, trafo = function(x) 2^x)
)
ctrl = makeTuneMultiCritControlMBO()
res = tuneParamsMultiCrit(lrn, sonar.task, rdesc, par.set = ps,
measures = list(tpr, fpr), control = ctrl)
Printing the object res gives the following:
> res
Tune multicrit result:
Points on front: 14
> res$ind
[1] 1 2 4 5 6 7 9 11 12 14 15 16 17 18
But the length of the optimization path saved in res$opt.path only has 10 points, the ones proposed by MBO I guess.
> res$opt.path
Optimization path
Dimensions: x = 2/2, y = 2
Length: 10
Add x values transformed: FALSE
Error messages: TRUE. Errors: 0 / 10.
Exec times: TRUE. Range: 0.031 - 0.041. 0 NAs.
Since the function plotTuneMultiCritResult relies on the objects res$ind and res$opt.path to print the front, it shows weird results.
I think that the correct way to go is to copy the optimization path of the object res$mbo.result$opt.path into res$opt.path, but my question is: What's the point of having different optimization paths in res$opt.path and res$mbo.result$opt.path?
Thanks!!
Víctor
Using mlr_2.13 and mlrMBO_1.1.3 and the following code everything works like expected. I suggeset that you use the MBO Control object to specify how much iterations your optimization should have. Otherwise a default (4*d evaluations for the initial design + 10 iterations) will be used.
set.seed(1)
library(mlr)
library(mlrMBO)
# multi-criteria optimization of (tpr, fpr) with MBO
lrn = makeLearner("classif.ksvm")
rdesc = makeResampleDesc("Holdout")
ps = makeParamSet(
makeNumericParam("C", lower = -12, upper = 12, trafo = function(x) 2^x),
makeNumericParam("sigma", lower = -12, upper = 12, trafo = function(x) 2^x)
)
mbo.ctrl = makeMBOControl(n.objectives = 2)
mbo.ctrl = setMBOControlTermination(mbo.ctrl, iters = 20)
ctrl = makeTuneMultiCritControlMBO(n.objectives = 2)
res = tuneParamsMultiCrit(lrn, sonar.task, rdesc, par.set = ps,
measures = list(tpr, fpr), control = ctrl)
plotTuneMultiCritResult(res = res, path = FALSE) # path = FALSE would only shows the Pareto Front
I'm trying to calculate the precision and recall using the nltk.metrics.score (http://www.nltk.org/_modules/nltk/metrics/scores.html) with my NLTK.NaiveBayesClassifier.
However, I stumble upon the error:
"unsupported operand type(s) for +: 'int' and 'NoneType".
which I suspect is from my 10-fold cross-validation where in some reference sets, there are zero negative (the data set is a bit imbalanced where 87% of it is positive).
According to nltk.metrics.score,
def precision(reference, test):
"Given a set of reference values and a set of test values, return
the fraction of test values that appear in the reference set.
In particular, return card(``reference`` intersection
``test``)/card(``test``).
If ``test`` is empty, then return None."
It seems that some of my 10-fold set is returning recall as None since there are no Negative in the reference set. Any idea on how to approach this problem?
My full code is as follow:
trainfeats = negfeats + posfeats
n = 10 # 5-fold cross-validation
subset_size = len(trainfeats) // n
accuracy = []
pos_precision = []
pos_recall = []
neg_precision = []
neg_recall = []
pos_fmeasure = []
neg_fmeasure = []
cv_count = 1
for i in range(n):
testing_this_round = trainfeats[i*subset_size:][:subset_size]
training_this_round = trainfeats[:i*subset_size] + trainfeats[(i+1)*subset_size:]
classifier = NaiveBayesClassifier.train(training_this_round)
refsets = collections.defaultdict(set)
testsets = collections.defaultdict(set)
for i, (feats, label) in enumerate(testing_this_round):
refsets[label].add(i)
observed = classifier.classify(feats)
testsets[observed].add(i)
cv_accuracy = nltk.classify.util.accuracy(classifier, testing_this_round)
cv_pos_precision = precision(refsets['Positive'], testsets['Positive'])
cv_pos_recall = recall(refsets['Positive'], testsets['Positive'])
cv_pos_fmeasure = f_measure(refsets['Positive'], testsets['Positive'])
cv_neg_precision = precision(refsets['Negative'], testsets['Negative'])
cv_neg_recall = recall(refsets['Negative'], testsets['Negative'])
cv_neg_fmeasure = f_measure(refsets['Negative'], testsets['Negative'])
accuracy.append(cv_accuracy)
pos_precision.append(cv_pos_precision)
pos_recall.append(cv_pos_recall)
neg_precision.append(cv_neg_precision)
neg_recall.append(cv_neg_recall)
pos_fmeasure.append(cv_pos_fmeasure)
neg_fmeasure.append(cv_neg_fmeasure)
cv_count += 1
print('---------------------------------------')
print('N-FOLD CROSS VALIDATION RESULT ' + '(' + 'Naive Bayes' + ')')
print('---------------------------------------')
print('accuracy:', sum(accuracy) / n)
print('precision', (sum(pos_precision)/n + sum(neg_precision)/n) / 2)
print('recall', (sum(pos_recall)/n + sum(neg_recall)/n) / 2)
print('f-measure', (sum(pos_fmeasure)/n + sum(neg_fmeasure)/n) / 2)
print('')
Perhaps not the most elegant, but guess the most simple fix would be setting it to 0 and the actual value if not None, e.g.:
cv_pos_precision = 0
if precision(refsets['Positive'], testsets['Positive']):
cv_pos_precision = precision(refsets['Positive'], testsets['Positive'])
And for the others as well, of course.
I am using R to extract tweets and analyse their sentiment, however when I get to the lines below I get an error saying "Object of type 'closure' is not subsettable"
scores$drink = factor(rep(c("east"), nd))
scores$very.pos = as.numeric(scores$score >= 2)
scores$very.neg = as.numeric(scores$score <= -2)
Full code pasted below
load("twitCred.Rdata")
east_tweets <- filterStream("tweetselnd.json", locations = c(-0.10444, 51.408699, 0.33403, 51.64661),timeout = 120, oauth = twitCred)
tweets.df <- parseTweets("tweetselnd.json", verbose = FALSE)
##function score.sentiment
score.sentiment = function(sentences, pos.words, neg.words, .progress='none')
{
# Parameters
# sentences: vector of text to score
# pos.words: vector of words of postive sentiment
# neg.words: vector of words of negative sentiment
# .progress: passed to laply() to control of progress bar
scores = laply(sentences,
function(sentence, pos.words, neg.words)
{
# remove punctuation
sentence = gsub("[[:punct:]]", "", sentence)
# remove control characters
sentence = gsub("[[:cntrl:]]", "", sentence)
# remove digits?
sentence = gsub('\\d+', '', sentence)
# define error handling function when trying tolower
tryTolower = function(x)
{
# create missing value
y = NA
# tryCatch error
try_error = tryCatch(tolower(x), error=function(e) e)
# if not an error
if (!inherits(try_error, "error"))
y = tolower(x)
# result
return(y)
}
# use tryTolower with sapply
sentence = sapply(sentence, tryTolower)
# split sentence into words with str_split (stringr package)
word.list = str_split(sentence, "\\s+")
words = unlist(word.list)
# compare words to the dictionaries of positive & negative terms
pos.matches = match(words, pos.words)
neg.matches = match(words, neg.words)
# get the position of the matched term or NA
# we just want a TRUE/FALSE
pos.matches = !is.na(pos.matches)
neg.matches = !is.na(neg.matches)
# final score
score = sum(pos.matches) - sum(neg.matches)
return(score)
}, pos.words, neg.words, .progress=.progress )
# data frame with scores for each sentence
scores.df = data.frame(text=sentences, score=scores)
return(scores.df)
}
pos = readLines(file.choose())
neg = readLines(file.choose())
east_text = sapply(east_tweets, function(x) x$getText())
scores = score.sentiment(tweetseldn.json, pos, neg, .progress='text')
scores()$drink = factor(rep(c("east"), nd))
scores()$very.pos = as.numeric(scores()$score >= 2)
scores$very.neg = as.numeric(scores$score <= -2)
# how many very positives and very negatives
numpos = sum(scores$very.pos)
numneg = sum(scores$very.neg)
# global score
global_score = round( 100 * numpos / (numpos + numneg) )
If anyone could help with as to why I'm getting this error it will be much appreciated. Also I've seen other answeres about adding '()' when referring to the variable 'scores' such as scores()$.... but it hasn't worked for me. Thank you.
The changes below got rid of the error:
x <- scores
x$drink = factor(rep(c("east"), nd))
x$very.pos = as.numeric(x$score >= 2)
x$very.neg = as.numeric(x$score <= -2)
I'm learning programming in computercraft (minecraft) and having some trouble with reading some storage cells.
The function that I'm working on shall go through all cells and add together the storage capacity to a variable in a for loop.
This is what I got so far
local cell1 = peripheral.wrap("tile_thermalexpansion_cell_reinforced_name_2")
local cell2 = peripheral.wrap("tile_thermalexpansion_cell_reinforced_name_3")
local cell3 = peripheral.wrap("tile_thermalexpansion_cell_reinforced_name_4")
local cell4 = peripheral.wrap("tile_thermalexpansion_cell_reinforced_name_5")
local cell5 = peripheral.wrap("tile_thermalexpansion_cell_reinforced_name_6")
local cell6 = peripheral.wrap("tile_thermalexpansion_cell_reinforced_name_7")
cells = {"cell1", "cell2", "cell3", "cell4", "cell5", "cell6"}
local totStorage
function getTotStorage(table)
for key = 1,6 do
x = table[key]
totStorage = totStorage + x.getMaxEnergyStored()
end
print(totStorage)
end
I get an error on this line
totStorage = totStorage + x.getMaxEnergyStored()
saying "Attempt to call nil".
Any suggestions?
cells = {"cell1", "cell2", "cell3", "cell4", "cell5", "cell6"}
Is shorthand for:
cells = {
-- key value
[1] = "cell1",
[2] = "cell2",
[3] = "cell3",
[4] = "cell4",
[5] = "cell5",
[6] = "cell6"
}
Assuming that your getTotStorage call is similar to below (you didn't post it)
getTotStorage(cells)
In the loop you try to call a method on x which is a string value:
for key = 1,6 do
x = table[key]
totStorage = totStorage + x.getMaxEnergyStored()
end
This is essentially trying to do:
totStorage = totStorage + ("cell1").getMaxEnergyStored()
What you could do is rearrange your code so that the values are the objects returned by peripheral.wrap:
local cells = {
peripheral.wrap("tile_thermalexpansion_cell_reinforced_name_2"),
peripheral.wrap("tile_thermalexpansion_cell_reinforced_name_3"),
peripheral.wrap("tile_thermalexpansion_cell_reinforced_name_4"),
peripheral.wrap("tile_thermalexpansion_cell_reinforced_name_5"),
peripheral.wrap("tile_thermalexpansion_cell_reinforced_name_6"),
peripheral.wrap("tile_thermalexpansion_cell_reinforced_name_7"),
}
function getTotStorage(t)
local totStorage = 0
for i,v in ipairs(t) do
totStorage = totStorage + v.getMaxEnergyStored()
end
print(totStorage)
end
A few observations:
Use local when you can (i.e. totStorage) to limit the scope of a variable (no need to have a loop counter as a global)
Don't name variables that collide with the standard Lua library (i.e. string, table, math)
ipairs is a better way to loop through a sequence
I'm trying to plot a function that contains a definite integral. My code uses all anonymous functions. When I run the file, it gives me an error. My code is below:
%%% List of Parameters %%%
gamma_sp = 1;
cap_gamma = 15;
gamma_ph = 0;
omega_0 = -750;
d_omega_0 = 400;
omega_inh = 100;
d_omega_inh = 1000;
%%% Formulae %%%
gamma_t = gamma_sp/2 + cap_gamma/2 + gamma_ph;
G = #(x) exp(-(x-omega_inh).^2./(2*d_omega_inh.^2))./(sqrt(2*pi)*d_omega_inh);
F = #(x) exp(-(x-omega_0).^2./(2*d_omega_0.^2))./(sqrt(2*pi)*d_omega_0);
A_integral = #(x,y) G(x)./(y - x + 1i*gamma_t);
Q_integral = #(x,y) F(x)./(y - x + 1i*gamma_t);
A = #(y) integral(#(x)A_integral(x,y),-1000,1000);
Q = #(y) integral(#(x)Q_integral(x,y),-3000,0);
P1 = #(y) -1./(1i.*(gamma_sp + cap_gamma)).*(1./(y + 2.*1i.*gamma_t)*(A(y)-conj(A(0)))-1./y.*(A(y)-A(0))+cap_gamma./gamma_sp.*Q(y).*(A(0)-conj(A(0))));
P2 = #(y) conj(P1(y));
P = #(y) P1(y) - P2(y);
sig = #(y) abs(P(y)).^2;
rng = -2000:0.05:1000;
plot(rng,sig(rng))
It seems to me that when the program runs the plot command, it should put each value of rng into sig(y), and that value will be used as the y value in A_integral and Q_integral. However, matlab throws an error when I try to run the program.
Error using -
Matrix dimensions must agree.
Error in #(x,y)G(x)./(y-x+1i*gamma_t)
Error in #(x)A_integral(x,y)
Error in integralCalc/iterateScalarValued (line 314)
fx = FUN(t);
Error in integralCalc/vadapt (line 133)
[q,errbnd] = iterateScalarValued(u,tinterval,pathlen);
Error in integralCalc (line 76)
[q,errbnd] = vadapt(#AtoBInvTransform,interval);
Error in integral (line 89)
Q = integralCalc(fun,a,b,opstruct);
Error in #(y)integral(#(x)A_integral(x,y),-1000,1000)
Error in
#(y)-1./(1i.*(gamma_sp+cap_gamma)).*(1./(y+2.*1i.*gamma_t)*(A(y)-conj(A(0)))-1. /y.*(A(y)-A(0))+cap_gamma./gamma_sp.*Q(y).*(A(0)-conj(A(0))))
Error in #(y)P1(y)-P2(y)
Error in #(y)abs(P(y)).^2
Error in fwm_spec_diff_paper_eqn (line 26)
plot(rng,sig(rng))
Any ideas about what I'm doing wrong?
You have
>> rng = -2000:0.05:1000;
>> numel(rng)
ans =
60001
all 60001 elements get passed down to
A = #(y) integral(#(x)A_integral(x,y),-1000,1000);
which calls
A_integral = #(x,y) G(x)./(y - x + 1i*gamma_t);
(similar for Q). The thing is, integral is an adaptive quadrature method, meaning (roughly) that the amount of x's it will insert into A_integral varies with how A_integral behaves at certain x.
Therefore, the amount of elements in y will generally be different from the elements in x in the call to A_integral. This is why y-x +1i*gamma_t fails.
Considering the complexity of what you're trying to do, I think it is best to re-define all anonymous functions as proper functions, and integrate a few of them into single functions. Look into the documentation of bsxfun to see if that can help (e.g., bsxfun(#minus, y.', x) instead of y-x could perhaps fix a few of these issues), otherwise, vectorize only in x and loop over y.
Thanks Rody, that made sense to me. I keep trying to use matlab like mathematica and I forget how matlab does things. I modified the code a bit, and it produces the right result. The integrals are evaluated very roughly, but it should be easy to fix that. I've posted my modified code below.
%%% List of Parameters %%%
gamma_sp = 1;
cap_gamma = 15;
gamma_ph = 0;
omega_0 = -750;
d_omega_0 = 400;
omega_inh = 100;
d_omega_inh = 1000;
%%% Formulae %%%
gamma_t = gamma_sp/2 + cap_gamma/2 + gamma_ph;
G = #(x) exp(-(x-omega_inh).^2./(2*d_omega_inh.^2))./(sqrt(2*pi)*d_omega_inh);
F = #(x) exp(-(x-omega_0).^2./(2*d_omega_0.^2))./(sqrt(2*pi)*d_omega_0);
A_integral = #(x,y) G(x)./(y - x + 1i*gamma_t);
Q_integral = #(x,y) F(x)./(y - x + 1i*gamma_t);
w = -2000:0.05:1000;
sigplot = zeros(size(w));
P1plot = zeros(size(w));
P2plot = zeros(size(w));
Pplot = zeros(size(w));
aInt_range = -1000:0.1:1200;
qInt_range = -2000:0.1:100;
A_0 = sum(A_integral(aInt_range,0).*0.1);
for k=1:size(w,2)
P1plot(k) = -1./(1i*(gamma_sp + cap_gamma)).*(1./(w(k)+2.*1i.*gamma_t).*(sum(A_integral(aInt_range,w(k)).*0.1)-conj(A_0))-1./w(k).*(sum(A_integral(aInt_range,w(k)).*0.1)-A_0)+cap_gamma./gamma_sp.*sum(Q_integral(qInt_range,w(k)).*0.1).*(A_0-conj(A_0)));
P2plot(k) = conj(P1plot(k));
Pplot(k) = P1plot(k) - P2plot(k);
sigplot(k) = abs(Pplot(k)).^2;
end
plot(w,sigplot)