Good afternoon
I am currently trying to pull some data from pushshift but I am maxing out at 100 posts. Below is the code for pulling one day that works great.
testdata1<-getPushshiftData(postType = "submission", size = 1000, before = "1546300800", after= "1546200800", subreddit = "mysubreddit", nest_level = 1)
I have a list of Universal Time Codes for the beginning and ending of each day for a month. What I would like to do is get the syntax to replace the "after" and "before" values for each day and for each day to be added to the end of the pulled data. Even if it placed the data to a bunch of separate smaller datasets I could work with it.
Here is my (feeble) attempt. "links" is the data frame with the UTCs
mydata<- lapply(1:30, function(x) getPushshiftData(postType = "submission", size = 1000, after= links$utcstart[,x],before = links$utcendstart[,x], subreddit = "mysubreddit", nest_level = 1))
Here is the error message I get: Error in links$utcstart[, x] : incorrect number of dimensions
I've also tried without the "function (x)" argument and get the following message:
Error in ifelse(is.null(after), "", sprintf("&after=%s", after)) :
object 'x' not found
Can anyone help with this?
Related
I'm working on a network using triplet mining for training. In order to make it work properly, I need my batches to contain several images of the same class. The problem I'm currently facing is that I have 751 classes, for a total of 12,937 pictures, and a batch size of 48 pictures. When shuffling the dataset using the command below, the odds to get pictures from the same class are really low, making the triplet mining inefficient.
dataset = dataset.shuffle(12937)
What I would need instead is a way of generating batches that contain a specific number of pictures for every class represented in this batch. As an example, let's say here that I want 12 classes per batch, there would be 4 pictures for each of them.
Another problem I'm facing is how would I shuffle this dataset at the end of every epoch so that I can have different batches that still follow the condition fixed above, that is 12 classes, 4 pictures for each one of them?
Is there any proper way to do it? I can't really find one. Please let me know if I'm unclear, and if you need further details.
================ EDIT ================
I've been trying a few things, and came up with something that would do what I want. The function would be the following:
counter = 0.
# Assuming a format such as (data, label)
def predicate(data, label):
global counter
allowed_labels = tf.constant([counter])
isallowed = tf.equal(allowed_labels, tf.cast(label, tf.float32))
reduced = tf.reduce_sum(tf.cast(isallowed, tf.float32))
counter += 1
return tf.greater(reduced, tf.constant(0.))
##tf.function
def custom_shuffle(train_dataset, batch_size, samples_per_class = 4, iterations_in_epoch = 100, database='market'):
assert batch_size%samples_per_class==0, F'batch size must be a {samples_per_class} multiple.'
if database == 'market':
class_nbr = 751
else:
raise Exception('Unsuported database yet')
all_datasets = [train_dataset.filter(predicate) for _ in range(class_nbr)] # Every element of this array is a dataset of one class
for i in range(iterations_in_epoch):
choice = tf.random.uniform(
shape=(batch_size//samples_per_class,),
minval=0,
maxval=class_nbr,
dtype=tf.dtypes.int64,
) # Which classes will be in batch
choice = tf.data.Dataset.from_tensor_slices(tf.concat([choice for _ in range(4)], axis=0)) # Exactly 4 picture from each class in the batch
batch = tf.data.experimental.choose_from_datasets(all_datasets, choice)
if i==0:
all_batches = batch
else:
all_batches = all_batches.concatenate(batch)
all_batches = all_batches.batch(batch_size)
return all_batches
It does what I want, however the returned dataset is extremely slow to iterate, making modele learning impossible. As per this thread, I understood that I needed to decorate custom_shuffle with #tf.function, as the one commented out. However, when doing so, it raises the following error:
Traceback (most recent call last):
File "training.py", line 137, in <module>
main()
File "training.py", line 80, in main
train_dataset = get_dataset(TRAINING_FILENAMES, IMG_SIZE, BATCH_SIZE, database=database, func_type='train')
File "E:\Morgan\TransReID_TF\tfr_to_dataset.py", line 260, in get_dataset
dataset = custom_shuffle(dataset, batch_size)
File "D:\Programs\Anaconda3\envs\AlignedReID_TF\lib\site-packages\tensorflow\python\eager\def_function.py", line 780, in __call__
result = self._call(*args, **kwds)
File "D:\Programs\Anaconda3\envs\AlignedReID_TF\lib\site-packages\tensorflow\python\eager\def_function.py", line 846, in _call
return self._concrete_stateful_fn._filtered_call(canon_args, canon_kwds) # pylint: disable=protected-access
File "D:\Programs\Anaconda3\envs\AlignedReID_TF\lib\site-packages\tensorflow\python\eager\function.py", line 1843, in _filtered_call
return self._call_flat(
File "D:\Programs\Anaconda3\envs\AlignedReID_TF\lib\site-packages\tensorflow\python\eager\function.py", line 1923, in _call_flat
return self._build_call_outputs(self._inference_function.call(
File "D:\Programs\Anaconda3\envs\AlignedReID_TF\lib\site-packages\tensorflow\python\eager\function.py", line 545, in call
outputs = execute.execute(
File "D:\Programs\Anaconda3\envs\AlignedReID_TF\lib\site-packages\tensorflow\python\eager\execute.py", line 59, in quick_execute
tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
tensorflow.python.framework.errors_impl.InternalError: No unary variant device copy function found for direction: 1 and Variant type_index: class tensorflow::data::`anonymous namespace'::DatasetVariantWrapper
[[{{node BatchDatasetV2/_206}}]] [Op:__inference_custom_shuffle_11485]
Function call stack:
custom_shuffle
Which I don't understand, and don't see how to fix.
Is there something I'm doing wrong?
PS: I'm aware the lack of minimal code to reproduce this behavior makes it hard to debug, I'll try to provide some as soon as possible.
Looking for some kind of solution to this issue:
trying to create a tensor from an array of timestamps
[
1612892067115,
],
but here is what happens
tf.tensor([1612892067115]).arraySync()
> [ 1612892078080 ]
as you can see, the result is incorrect.
Somebody pointed out, I may need to use the datatype int64, but this doesn't seem to exist in tfjs ðŸ˜
I have also tried to divide my timestamp to a small float, but I get a similar result
tf.tensor([1.612892067115, 1.612892068341]).arraySync()
[ 1.6128920316696167, 1.6128920316696167 ]
If you know a way to work around using timestamps in a tensor, please help :)
:edit:
As an attempted workaround, I tried to remove my year, month, and date from my timestamp
Here are my subsequent input values:
[
56969701,
56969685,
56969669,
56969646,
56969607,
56969602
]
and their outputs:
[
56969700,
56969684,
56969668,
56969648,
56969608,
56969600
]
as you can see, they are still incorrect, and should be well within the acceptable range
found a solution that worked for me:
Since I only require a subset of the timestamp (just the date / hour / minute / second / ms) for my purposes, I simply truncate out the year / month:
export const subts = (ts: number) => {
// a sub timestamp which can be used over the period of a month
const yearMonth = +new Date(new Date().getFullYear(), new Date().getMonth())
return ts - yearMonth
}
then I can use this with:
subTimestamps = timestamps.map(ts => subts(ts))
const x_vals = tf.tensor(subTimestamps, [subTimestamps.length], 'int32')
now all my results work as expected.
Currently only int32 is supported with tensorflow.js, your data has gone out of the range supported by int32.
Until int64 is supported, this can be solved by using a relative timestamp. Currently a timestamp in js uses the number of ms that elapsed since 1 January 1970. A relative timestamp can be used by using another origin and compute the difference of ms that has elapsed since that date. That way, we will have a lower number that can be represented using int32. The best origin to take will be the starting date of the records
const a = Date.now() // computing a tensor out of it will give an accurate result since the number is out of range
const origin = new Date("02/01/2021").now()
const relative = a - origin
const tensor = tf.tensor(relative, undefined, 'int32')
// get back the data
const data = tensor.dataSync()[0]
// get the initial date
const initial date = new Date(data + origin)
In other scenarios, if using the ms is not of interest, using the number of s that has elapsed since the start would be better. It is called the unix time
I'm creating a shiny app and i'm letting the user choose what data that should be displayed in a plot and a table. This choice is done through 3 different input variables that contain 14, 4 and two choices respectivly.
ui <- dashboardPage(
dashboardHeader(),
dashboardSidebar(
selectInput(inputId = "DataSource", label = "Data source", choices =
c("Restoration plots", "all semi natural grasslands")),
selectInput(inputId = "Variabel", label = "Variable", choices =
choicesVariables)),
#choicesVariables definition is omitted here, because it's very long but it
#contains 14 string values
selectInput(inputId = "Factor", label = "Factor", choices = c("Company
type", "Region and type of application", "Approved or not approved
applications", "Age group" ))
),
dashboardBody(
plotOutput("thePlot"),
tableOutput("theTable")
))
This adds up to 73 choices (yes, i know the math doesn't add up there, but some choices are invalid). I would like to do this using a lookup table so a created one with every valid combination of choices like this:
rad1<-c(rep("Company type",20), rep("Region and type of application",20),
rep("Approved or not approved applications", 13), rep("Age group", 20))
rad2<-choicesVariable[c(1:14,1,4,5,9,10,11, 1:14,1,4,5,9,10,11, 1:7,9:14,
1:14,1,4,5,9,10,11)]
rad3<-c(rep("Restoration plots",14),rep("all semi natural grasslands",6),
rep("Restoration plots",14), rep("all semi natural grasslands",6),
rep("Restoration plots",27), rep("all semi natural grasslands",6))
rad4<-1:73
letaLista<-data.frame(rad1,rad2,rad3, rad4)
colnames(letaLista) <- c("Factor", "Variabel", "rest_alla", "id")
Now its easy to use subset to only get the choice that the user made. But how do i use this information to plot the plot and table without using a 73 line long ifelse statment?
I tried to create some sort of multidimensional array that could hold all the tables (and one for the plots) but i couldn't make it work. My experience with these kind of arrays is limited and this might be a simple issue, but any hints would be helpful!
My dataset that is the foundation for the plots and table consists of dataframe with 23 variables, factors and numerical. The plots and tabels are then created using the following code for all 73 combinations
s_A1 <- summarySE(Samlad_info, measurevar="Dist_brukcentrum",
groupvars="Companytype")
s_A1 <- s_A1[2:6,]
p_A1=ggplot(s_A1, aes(x=Companytype,
y=Dist_brukcentrum))+geom_bar(position=position_dodge(), stat="identity") +
geom_errorbar(aes(ymin=Dist_brukcentrum-se,
ymax=Dist_brukcentrum+se),width=.2,position=position_dodge(.9))+
scale_y_continuous(name = "") + scale_x_discrete(name = "")
where summarySE is the following function, burrowed from cookbook for R
summarySE <- function(data=NULL, measurevar, groupvars=NULL, na.rm=TRUE,
conf.interval=.95, .drop=TRUE) {
# New version of length which can handle NA's: if na.rm==T, don't count them
length2 <- function (x, na.rm=FALSE) {
if (na.rm) sum(!is.na(x))
else length(x)
}
# This does the summary. For each group's data frame, return a vector with
# N, mean, and sd
datac <- ddply(data, groupvars, .drop=.drop,
.fun = function(xx, col) {
c(N = length2(xx[[col]], na.rm=na.rm),
mean = mean (xx[[col]], na.rm=na.rm),
sd = sd (xx[[col]], na.rm=na.rm)
)
},
measurevar
)
# Rename the "mean" column
datac <- rename(datac, c("mean" = measurevar))
datac$se <- datac$sd / sqrt(datac$N) # Calculate standard error of the mean
# Confidence interval multiplier for standard error
# Calculate t-statistic for confidence interval:
# e.g., if conf.interval is .95, use .975 (above/below), and use df=N-1
ciMult <- qt(conf.interval/2 + .5, datac$N-1)
datac$ci <- datac$se * ciMult
return(datac)
}
The code in it's entirety is a bit to large but i hope this may clarify what i'm trying to do.
Well, thanks to florian's comment i think i might have found a solution my self. I'll present it here but leave the question open as there is probably far neater ways of doing it.
I rigged up the plots (that was created as lists by ggplot) into a list
plotList <- list(p_A1, p_A2, p_A3...)
tableList <- list(s_A1, s_A2, s_A3...)
I then used subset on my lookup table to get the matching id of the list to select the right plot and table.
output$thePlot <-renderPlot({
plotValue<-subset(letaLista, letaLista$Factor==input$Factor &
letaLista$Variabel== input$Variabel & letaLista$rest_alla==input$DataSource)
plotList[as.integer(plotValue[1,4])]
})
output$theTable <-renderTable({
plotValue<-subset(letaLista, letaLista$Factor==input$Factor &
letaLista$Variabel== input$Variabel & letaLista$rest_alla==input$DataSource)
skriva <- tableList[as.integer(plotValue[4])]
print(skriva)
})
I have this table named BondData which contains the following:
Settlement Maturity Price Coupon
8/27/2016 1/12/2017 106.901 9.250
8/27/2019 1/27/2017 104.79 7.000
8/28/2016 3/30/2017 106.144 7.500
8/28/2016 4/27/2017 105.847 7.000
8/29/2016 9/4/2017 110.779 9.125
For each day in this table, I am about to perform a certain task which is to assign several values to a variable and perform necessary computations. The logic is like:
do while Settlement is the same
m_settle=current_row_settlement_value
m_maturity=current_row_maturity_value
and so on...
my_computation_here...
end
It's like I wanted to loop through my settlement dates and perform task for as long as the date is the same.
EDIT: Just to clarify my issue, I am implementing Yield Curve fitting using Nelson-Siegel and Svensson models.Here are my codes so far:
function NS_SV_Models()
load bondsdata
BondData=table(Settlement,Maturity,Price,Coupon);
BondData.Settlement = categorical(BondData.Settlement);
Settlements = categories(BondData.Settlement); % get all unique Settlement
for k = 1:numel(Settlements)
rows = BondData.Settlement==Settlements(k);
Bonds.Settle = Settlements(k); % current_row_settlement_value
Bonds.Maturity = BondData.Maturity(rows); % current_row_maturity_value
Bonds.Prices=BondData.Price(rows);
Bonds.Coupon=BondData.Coupon(rows);
Settle = Bonds.Settle;
Maturity = Bonds.Maturity;
CleanPrice = Bonds.Prices;
CouponRate = Bonds.Coupon;
Instruments = [Settle Maturity CleanPrice CouponRate];
Yield = bndyield(CleanPrice,CouponRate,Settle,Maturity);
NSModel = IRFunctionCurve.fitNelsonSiegel('Zero',Settlements(k),Instruments);
SVModel = IRFunctionCurve.fitSvensson('Zero',Settlements(k),Instruments);
NSModel.Parameters
SVModel.Parameters
end
end
Again, my main objective is to get each model's parameters (beta0, beta1, beta2, etc.) on a per day basis. I am getting an error in Instruments = [Settle Maturity CleanPrice CouponRate]; because Settle contains only one record (8/27/2016), it's suppose to have two since there are two rows for this date. Also, I noticed that Maturity, CleanPrice and CouponRate contains all records. They should only contain respective data for each day.
Hope I made my issue clearer now. By the way, I am using MATLAB R2015a.
Use categorical array. Here is your function (without its' headline, and all rows I can't run are commented):
BondData = table(datetime(Settlement),datetime(Maturity),Price,Coupon,...
'VariableNames',{'Settlement','Maturity','Price','Coupon'});
BondData.Settlement = categorical(BondData.Settlement);
Settlements = categories(BondData.Settlement); % get all unique Settlement
for k = 1:numel(Settlements)
rows = BondData.Settlement==Settlements(k);
Settle = BondData.Settlement(rows); % current_row_settlement_value
Mature = BondData.Maturity(rows); % current_row_maturity_value
CleanPrice = BondData.Price(rows);
CouponRate = BondData.Coupon(rows);
Instruments = [datenum(char(Settle)) datenum(char(Mature))...
CleanPrice CouponRate];
% Yield = bndyield(CleanPrice,CouponRate,Settle,Mature);
%
% NSModel = IRFunctionCurve.fitNelsonSiegel('Zero',Settlements(k),Instruments);
% SVModel = IRFunctionCurve.fitSvensson('Zero',Settlements(k),Instruments);
%
% NSModel.Parameters
% SVModel.Parameters
end
Keep in mind the following:
You cannot concat different types of variables as you try to do in: Instruments = [Settle Maturity CleanPrice CouponRate];
There is no need in the structure Bond, you don't use it (e.g. Settle = Bonds.Settle;).
Use the relevant functions to convert between a datetime object and string or numbers. For instance, in the code above: datenum(char(Settle)). I don't know what kind of input you need to pass to the following functions.
I need help with matlab using 'strtok' to find an ID in a text file and then read in or manipulate the rest of the row that is contained where that ID is. I also need this function to find (using strtok preferably) all occurrences of that same ID and group them in some way so that I can find averages. On to the sample code:
ID list being input:
(This is the KOIName variable)
010447529
010468501
010481335
010529637
010603247......etc.
File with data format:
(This is the StarData variable)
ID>>>>Values
002141865 3.867144e-03 742.000000 0.001121 16.155089 6.297494 0.001677
002141865 5.429278e-03 1940.000000 0.000477 16.583748 11.945627 0.001622
002141865 4.360715e-03 1897.000000 0.000667 16.863406 13.438383 0.001460
002141865 3.972467e-03 2127.000000 0.000459 16.103060 21.966853 0.001196
002141865 8.542932e-03 2094.000000 0.000421 17.452007 18.067214 0.002490
Do not be mislead by the examples I posted, that first number is repeated for about 15 lines then the ID changes and that goes for an entire set of different ID's, then they are repeated as a whole group again, think [1,2,3],[1,2,3], the main difference is the values trailing the ID which I need to average out in matlab.
My current code is:
function Avg_Koi
N = evalin('base', 'KOIName');
file_1 = evalin('base', 'StarData');
global result;
for i=1:size(N)
[id, values] = strtok(file_1);
result = result(id);
result = result(values)
end
end
Thanks for any assistance.
You let us guess a lot, so I guess you want something like this:
load StarData.txt
IDs = { 010447529;
010468501;
010481335;
010529637;
010603247;
002141865}
L = numel(IDs);
values = cell(L,1);
% Iteration through all arrays and creating an cell array with matrices for every ID
for ii=1:L;
ID = IDs{ii};
ID_first = find(StarData(:,1) == ID,1,'first');
ID_last = find(StarData(:,1) == ID,1,'last');
values{ii} = StarData( ID_first:ID_last , 2:end );
end
When you now access the index ii=6 adressing the ID = 002141865
MatrixOfCertainID6 = values{6};
you get:
0.0038671440 742 0.001121 16.155089 6.2974940 0.001677
0.0054292780 1940 0.000477 16.583748 11.945627 0.001622
0.0043607150 1897 0.000667 16.863406 13.438383 0.001460
0.0039724670 2127 0.000459 16.103060 21.966853 0.001196
0.0085429320 2094 0.000421 17.452007 18.067214 0.002490
... for further calculations.