Why does as.ts create new values for the date variable? - time-series
I want to create a Time-Series with as.ts.
The Problem is that after using the function as.ts(), the date variable has different values.
Here you can see the difference of the date variable before and after using as.ts()-function:
[1]: https://i.stack.imgur.com/kc4np.png
[2]: https://i.stack.imgur.com/SKMnY.png
sapply(Oct_Apr, class)
#character to date
Oct_Apr$date <- lubridate::ymd(Oct_Apr$date)
#Klassen überprüfen
sapply(Oct_Apr, class)
Oct_Apr %>% as_tsibble(index = "date")
#################################################################
#Trainings and Test-Set
#Trainings-Set (Okt. 2019 - März 2020)
Oct_Mar <- Oct_Apr %>% slice(01:183)
#Test-Set (Apr. 2020)
Apr <- Oct_Apr %>% slice(184:213)
#################################################################
#################################################################
Oct_Mar <- as.ts(Oct_Mar)
Apr <- as.ts(Apr)```
#################################################################
Subset-data:
structure(list(date = structure(c(18170, 18171, 18172, 18173,
18174, 18175, 18176, 18177, 18178, 18179, 18180, 18181, 18182,
18183, 18184, 18185, 18186, 18187, 18188, 18189, 18190, 18191,
18192, 18193, 18194, 18195, 18196, 18197, 18198, 18199, 18200,
18201, 18202, 18203, 18204, 18205, 18206, 18207, 18208, 18209,
18210, 18211, 18212, 18213, 18214, 18215, 18216, 18217, 18218,
18219, 18220, 18221, 18222, 18223, 18224, 18225, 18226, 18227,
18228, 18229, 18230, 18231, 18232, 18233, 18234, 18235, 18236,
18237, 18238, 18239, 18240, 18241, 18242, 18243, 18244, 18245,
18246, 18247, 18248, 18249, 18250, 18251, 18252, 18253, 18254,
18255, 18256, 18257, 18258, 18259, 18260, 18261, 18262, 18263,
18264, 18265, 18266, 18267, 18268, 18269, 18270, 18271, 18272,
18273, 18274, 18275, 18276, 18277, 18278, 18279, 18280, 18281,
18282, 18283, 18284, 18285, 18286, 18287, 18288, 18289, 18290,
18291, 18292, 18293, 18294, 18295, 18296, 18297, 18298, 18299,
18300, 18301, 18302, 18303, 18304, 18305, 18306, 18307, 18308,
18309, 18310, 18311, 18312, 18313, 18314, 18315, 18316, 18317,
18318, 18319, 18320, 18321, 18322, 18323, 18324, 18325, 18326,
18327, 18328, 18329, 18330, 18331, 18332, 18333, 18334, 18335,
18336, 18337, 18338, 18339, 18340, 18341, 18342, 18343, 18344,
18345, 18346, 18347, 18348, 18349, 18350, 18351, 18352, 18353,
18354, 18355, 18356, 18357, 18358, 18359, 18360, 18361, 18362,
18363, 18364, 18365, 18366, 18367, 18368, 18369, 18370, 18371,
18372, 18373, 18374, 18375, 18376, 18377, 18378, 18379, 18380,
18381, 18382), class = "Date"), price_view = c(35.79, 180.16,
437.57, 10.3, 74.26, 79.8, 89.84, 121.24, 461.95, 142.06, 241.71,
52, 43.24, 41.16, 167.05, 764.06, 91.64, 189.82, 38.59, 152.64,
86.23, 321.33, 411.83, 256.88, 352.39, 76.32, 360.11, 123.53,
43.41, 149.38, 14.16, 489.07, 1661.74, 1253.07, 25.71, 154.42,
990.89, 1645.93, 144.12, 84.43, 240.25, 148.18, 41.13, 262.56,
168.78, 860.85, 239.31, 372.98, 165.64, 134.32, 20.7, 43.73,
765.76, 51.48, 599.49, 893.79, 155.29, 334.37, 46.82, 1814.72,
196.27, 1302.48, 40.16, 1161.68, 381.48, 184.48, 48.91, 221.11,
434.73, 149.27, 77.22, 882.49, 106.05, 669.23, 282.86, 179.67,
12.97, 460.24, 38.59, 278.26, 243.76, 1904.79, 84.93, 32.18,
25.71, 496.54, 29.6, 1466.83, 164.33, 234.76, 19.95, 308.37,
1130.02, 7.47, 79.8, 65.9, 746.45, 1347.78, 1270.82, 69.42, 231.41,
195.6, 715.33, 208.47, 720.46, 414.68, 24.45, 217.82, 434.45,
483.92, 1500.42, 318.15, 339.29, 267.45, 133.85, 9.03, 11.81,
280.57, 916.74, 58.51, 339.78, 33.98, 263.58, 19.31, 239.88,
489.07, 84.92, 344.9, 95.24, 99.1, 142.58, 480.58, 104.74, 14.83,
252, 1039.41, 28.3, 328.97, 341.55, 278.26, 43.73, 91.35, 102.32,
131.25, 155.15, 77.74, 14.67, 132.63, 1185.36, 291.13, 1106.59,
849.42, 117.63, 171.32, 167.31, 252.23, 248.14, 111.15, 257.15,
27.62, 169.86, 101.89, 282.89, 298.57, 86.49, 196.32, 1415.45,
898.35, 334.6, 17.99, 13.62, 566.27, 60.41, 36.34, 62.04, 308.81,
32.95, 127.44, 836.57, 221.34, 360.34, 159.31, 20.57, 230.38,
563.72, 103.71, 509.67, 125.87, 80.27, 37.58, 14.13, 527.14,
5.15, 567.3, 2316.4, 21.85, 141.06, 25.71, 62.16, 328.68, 15.44,
156.99, 15.42, 54.03, 514.56, 561.63, 97.56, 46.31, 41.16, 32.15,
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1747.79, 190.56, 128.28, 252.38, 250.91, 720.48, 33.42, 643,
191.77, 460.11, 408.5, 789.9, 577.94, 49.36, 380.7, 19.56, 994.86,
756.71, 223.66, 437.33, 1684.28, 366.16, 968.34, 1683.07, 550.77,
503.09, 29.09, 179.67, 210.62, 22.66, 131.66, 68.96, 360.06,
494.22, 1023.62, 1569.92, 28.29, 694.97, 127.05, 37.85, 282.89,
178.9, 913.28, 1022.42, 424.7, 573.7, 1029.34, 30.12, 20.82,
17.99, 107.53, 41.19, 85.82, 1002.55, 140.98, 167.03, 231.67,
25.71, 205.64, 30.81, 51.22, 65.9, 7.08, 308.63, 227.79, 16.22,
7.89, 62.52, 48.88, 586.63, 602.07, 1312.26, 128.32, 179.9, 849.42,
100.9, 1284.2, 12.84, 128.42, 59.18, 176.99, 38.02, 48.88, 694.54,
262.3, 1402.84, 1453.18, 3.84, 453.01, 76.93, 7.04, 865.93, 865.4,
40.75, 1423.07, 1534.66, 679.27, 11.25, 102.63, 436.3, 853.93,
694.97, 850.47, 477.49, 1234.97, 10.27, 23.94, 643.23, 89.84,
290.34, 320.99, 6.44, 140.28, 188.89, 56.88, 1326.31, 194.34,
140.28, 771.96, 140.03, 20.21, 1464.39, 59.18, 57.92, 1156.81,
50.43, 300.12, 38.1, 832.71, 57.91, 174.5, 100.36, 248.14, 109.34,
100.7, 242.7, 266.67, 592.01, 242.18, 22.66, 566.04, 38.61, 812.06,
123.92, 168.6, 172.03, 49.91, 16.73, 108.04, 347.47, 97.79, 111.15,
514.79, 126.1, 178.87, 870.03, 529.31, 43.5, 2110.48, 771.94,
15.32, 105.25, 7.14, 312.67, 61.75, 165.51, 48.37, 643.49, 303.48,
35.78, 154.42, 209.71, 76.69, 25.46, 1415.45, 123.53, 602.31,
117.12, 334.35, 455.3, 643.26, 101.16, 245.82, 280.74, 143.89,
114.67, 12.84, 31.89, 32.69, 203.35, 66.9, 208.24, 57.92, 14.32,
400.5, 146.46, 827.35, 30.86, 143.89, 47.29, 426.01, 30.07, 36.28,
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0.0354306787130485, 0.0369234479028471, 0.0313956783546004, 0.0175071227813499,
0.0153811379243536, 0.0149988963637585, 0.0159494386911496, 0.0151254119566314,
0.0151138709885764, 0.0152175253675449, 0.015437458845637, 0.0147726989007463,
0.163043027137275, 0.0264129334017437, 0.0201436139554002, 0.0169325780144314,
0.0214876387664511, 0.0177445890793224, 0.0172606914733451, 0.017133563936406,
0.0185813515317095, 0.0180607614170281, 0.0187505760492009, 0.0169511102933927,
0.0178137558385877, 0.0184785698285323, 0.0201169476464591, 0.0220364679005246,
0.0212945438945016, 0.021660499704709, 0.0203399246100257, 0.021042755155702,
0.0207706911083365, 0.0191080416224264, 0.0188521002714409, 0.0176177277744931,
0.0170654092407268, 0.0167173964870618, 0.0169957130997929, 0.0173290930652611,
0.018723068602435, 0.0190819762151085, 0.0178334964691514, 0.0160768402598991,
0.0172883539665594, 0.0176745227466401, 0.0176229174238593, 0.0192486537177652,
0.0184480409967815, 0.016738809715337, 0.0152409794110955, 0.0164830717865742,
0.0156835503722764, 0.0146390833265517, 0.014437303236394, 0.0152291157019601,
0.0150381488303582, 0.0144744286877027, 0.0154780133881995, 0.0165875912852526,
0.0167586910540363, 0.016434923542878, 0.016619416098569, 0.0156371489941345,
0.0150586881991463, 9.20923636174885e-06, 1.20043248684906e-05,
0.0164035785954663, 0.0163922122870294, 0.0158218503562475, 0.0150495685631071,
0.0147135181693682, 0.0165419629891279, 0.015608161423137, 0.0152906026574993,
0.0157815980810835)), row.names = c(NA, -213L), class = "data.frame")
as.ts() converts a numeric vector to a ts object, but it does not convert from one time series class to another. For that you need the tsbox package. In your example, you can convert your data frame to a tsibble, and then to a ts object (although why you would want to do that, I have no idea).
Oct_Apr |>
tsibble::as_tsibble(index = "date") |>
tsbox::ts_ts()
In general, ts objects are extremely limited, and with daily data, you would be better using tsibble objects and the functions provided in the feasts and fable packages.
Related
str_replace using arguments from another data frame
This is closely related to my question:
str_replace in a data frame?
So I want to solve this problem:
dog_descriptions <- data.frame(breed_primary = c("Pit Bull Terrier",
"Labrador Retriever",
"Border Collie"),
number_of_legs = rep(4, 3))
dog_descriptions2 <-
dog_descriptions %>%
mutate(breed_primary2 = str_replace_all(breed_primary, c("Pit Bull Terrier" = "Pit Bull\nTerrier", "Labrador Retriever" = "Labrador\nRetriever", "Border Collie" = "Border\nCollie")))
But not using a long text string, but rather the data.frame replacement_input:
Is there any possibility in R to use an object (e.g. the data frame named replacement_input) for the replacement of a complex text string.
replacement_input <- data.frame(replace = c("Pit Bull Terrier",
"Labrador Retriever",
"Border Collie"),
replace_with = c("Pit Bull\nTerrier",
"Labrador\nRetriever",
"Border\nCollie" ))
Conversion to this format should help:
c("Pit Bull Terrier" = "Pit Bull\nTerrier", "Labrador Retriever" = "Labrador\nRetriever", "Border Collie" = "Border\nCollie"))
I did not success with combination of paste0 and stringr::str_c to create the "replacment vector"
Maybe it is also the wrong approach.
How can I use Deedle to get every row from some key and below?
I want to return every value up to and including some key. Whilst I could generate every such key and chuck them all into the Get, I suspect this will inefficiently search for the value of every key. Inspired by this answer, I have come up with the following let getAllUpTo key (frame:Frame<'key,'col>) : Frame<'key, 'col> = let endRng = frame.RowIndex.Locate key let startRng = frame.RowIndex.KeyRange |> fst |> frame.RowIndex.Locate let fixedRange = RangeRestriction.Fixed (startRng, endRng) frame.GetAddressRange fixedRange Is there a built in method for doing this efficiently?
If you want to access a sub-range of a data frame with a specified starting/ending key, you can do this using the df.Rows.[ ... ] indexer. Say we have some data indexed by (sorted) dates: let s1 = series [ let rnd = Random() for d in 0 .. 365 -> DateTime(2020, 1, 1).AddDays(float d) => rnd.Next() ] let df = frame [ "S1" => s1 ] To get a part of the data frame starting/ending on a specific date, you can use: // Get all rows from 1 June (inclusive) df.Rows.[DateTime(2020, 6, 1) ..] // Get all rows until 1 June (inclusive) df.Rows.[.. DateTime(2020, 6, 1)] The API you are using is essentially what this does under the cover - but you are using a very low-level operations that you do not typically need to use in user code.
PHPSpreadsheet: getHighestDataRow/getHighestDataColumn after fromArray
I'm using fromArray to load an array of data into a worksheet. This is working fine. After doing so, getHighestDataColumn and getHighestDataRow do not seem to be updated. Is there a way to force PHPSpreadsheet to recalculate these values after calling fromArray? [edit] An update seems to have fixed the issue.
Be aware to not use the github#PHPExcel lib if you're still using them.
They've said to update to its directly successor, which is beig maintained and recently updated at github#PhpSpreadsheet
Recently Microsoft released an update which breaks some of the functionalities when we use the old lib.
Getting this example from their doc, you could try using the fromArray():
To set data from an array:
$arrayData = [
[NULL, 2010, 2011, 2012],
['Q1', 12, 15, 21],
['Q2', 56, 73, 86],
['Q3', 52, 61, 69],
['Q4', 30, 32, 0],
];
$sheet->getActiveSheet()
->fromArray(
$arrayData, // The data to set
NULL, // Array values with this value will not be set
'C3' // Top left coordinate of the worksheet range where
// we want to set these values (default is A1)
);
Then you follow this to extract data from the spreadsheet:
$highestRow = $sheet->getHighestRow();
$highestColumn = $sheet->getHighestColumn();
// Loop for each row
for ($row = 0; $row <= $highestRow; $row++) {
// here you extract the columns for that row
$columns = array_shift(array_values($sheet->rangeToArray('A' . $row . ':' . $highestColumn . $row, NULL, TRUE, FALSE)));
}
How do you apply an object detector to each frame of a given video?
I have posted code on this site before and I learnt that I can't post the whole thing. So, I will only post the code that matters.
So, what I am trying to do is to take an object detector(for images) and applying it to each frame of a given video.
The only thing is that I don't know how to finish it up. That is, once I detect the first frame what do I do with this frame? Do I store it somewhere? What do I do with the other frames? And once I handle these frames how do I recombine these frames into a video ie the output video?
Here is the code:
import numpy as np
import cv2
from numpy import expand_dims
from keras.models import load_model
from keras.preprocessing.image import load_img
from keras.preprocessing.image import img_to_array
from matplotlib import pyplot
from matplotlib.patches import Rectangle
model = load_model('model.h5')
# define the expected input shape for the model
input_w, input_h = 416, 416
# define the anchors
anchors = [[116,90, 156,198, 373,326], [30,61, 62,45, 59,119], [10,13, 16,30, 33,23]]
# define the labels
labels = ["person", "bicycle", "car", "motorbike", "aeroplane", "bus", "train", "truck",
"boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench",
"bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe",
"backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard",
"sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard",
"tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana",
"apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake",
"chair", "sofa", "pottedplant", "bed", "diningtable", "toilet", "tvmonitor", "laptop", "mouse",
"remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator",
"book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"]
vs = cv2.VideoCapture('video.mp4')
class_threshold = 0.6
boxes = list()
while True:
(grabbed, frame) = vs.read()
if not grabbed:
break
if W is None or H is None:
(H, W) = frame.shape[:2]
image, image_w, image_h = load_image_pixels(frame, (input_w, input_h))
yhat = model.predict(image)
for i in range(len(yhat)):
# decode the output of the network
boxes += decode_netout(yhat[i][0], anchors[i], class_threshhold, input_h, input_w)
# correct the sizes of the bounding boxes for the shape of the image
correct_yolo_boxes(boxes, image_h, image_w, input_h, input_w)
# suppress non-maximal boxes
do_nms(boxes, 0.5)
# get the details of the detected objects
v_boxes, v_labels, v_scores = get_boxes(boxes, labels, class_threshold)
# draw what we found
draw_boxes(frame, v_boxes, v_labels, v_scores)
You can use the VideoWriter from opencv to output the frames again as a video.
Some example code on how to use it:
fourcc = cv2.VideoWriter_fourcc(*'XVID')
video_writer = cv2.VideoWriter('test.avi', fourcc, 30, (image_w, image_h))
...
while True:
....
video_writer.write(frame)
....
....
video_writer.release()
For reference openCV video saving in python
Caret - Setting the seeds inside the gafsControl()
I am trying to set the seeds inside the caret's gafsControl(), but I am getting this error:
Error in { : task 1 failed - "supplied seed is not a valid integer"
I understand that seeds for trainControl() is a vector equal to the number of resamples plus one, with the number of combinations of models's tuning parameters (in my case 36, SVM with 6 Sigma and 6 Cost values) in each (resamples) entries. However, I couldn't figure out what I should use for gafsControl(). I've tried iters*popSize (100*10), iters (100), popSize (10), but none has worked.
Thanks in advance.
here is my code (with simulated data):
library(caret)
library(doMC)
library(kernlab)
registerDoMC(cores=32)
set.seed(1234)
train.set <- twoClassSim(300, noiseVars = 100, corrVar = 100, corrValue = 0.75)
mylogGA <- caretGA
mylogGA$fitness_extern <- mnLogLoss
#Index for gafsControl
set.seed(1045481)
ga_index <- createFolds(train.set$Class, k=3)
#Seed for the gafsControl()
set.seed(1056)
ga_seeds <- vector(mode = "list", length = 4)
for(i in 1:3) ga_seeds[[i]] <- sample.int(1500, 1000)
## For the last model:
ga_seeds[[4]] <- sample.int(1000, 1)
#Index for the trainControl()
set.seed(1045481)
tr_index <- createFolds(train.set$Class, k=5)
#Seeds for the trainControl()
set.seed(1056)
tr_seeds <- vector(mode = "list", length = 6)
for(i in 1:5) tr_seeds[[i]] <- sample.int(1000, 36)#
## For the last model:
tr_seeds[[6]] <- sample.int(1000, 1)
gaCtrl <- gafsControl(functions = mylogGA,
method = "cv",
number = 3,
metric = c(internal = "logLoss",
external = "logLoss"),
verbose = TRUE,
maximize = c(internal = FALSE,
external = FALSE),
index = ga_index,
seeds = ga_seeds,
allowParallel = TRUE)
tCtrl = trainControl(method = "cv",
number = 5,
classProbs = TRUE,
summaryFunction = mnLogLoss,
index = tr_index,
seeds = tr_seeds,
allowParallel = FALSE)
svmGrid <- expand.grid(sigma= 2^c(-25, -20, -15,-10, -5, 0), C= 2^c(0:5))
t1 <- Sys.time()
set.seed(1234235)
svmFuser.gafs <- gafs(x = train.set[, names(train.set) != "Class"],
y = train.set$Class,
gafsControl = gaCtrl,
trControl = tCtrl,
popSize = 10,
iters = 100,
method = "svmRadial",
preProc = c("center", "scale"),
tuneGrid = svmGrid,
metric="logLoss",
maximize = FALSE)
t2<- Sys.time()
svmFuser.gafs.time<-difftime(t2,t1)
save(svmFuser.gafs, file ="svmFuser.gafs.rda")
save(svmFuser.gafs.time, file ="svmFuser.gafs.time.rda")
Session Info:
> sessionInfo()
R version 3.2.2 (2015-08-14)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 14.04.3 LTS
locale:
[1] LC_CTYPE=en_CA.UTF-8 LC_NUMERIC=C LC_TIME=en_CA.UTF-8
[4] LC_COLLATE=en_CA.UTF-8 LC_MONETARY=en_CA.UTF-8 LC_MESSAGES=en_CA.UTF-8
[7] LC_PAPER=en_CA.UTF-8 LC_NAME=C LC_ADDRESS=C
[10] LC_TELEPHONE=C LC_MEASUREMENT=en_CA.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] kernlab_0.9-22 doMC_1.3.3 iterators_1.0.7 foreach_1.4.2 caret_6.0-52 ggplot2_1.0.1 lattice_0.20-33
loaded via a namespace (and not attached):
[1] Rcpp_0.12.0 magrittr_1.5 splines_3.2.2 MASS_7.3-43 munsell_0.4.2
[6] colorspace_1.2-6 foreach_1.4.2 minqa_1.2.4 car_2.0-26 stringr_1.0.0
[11] plyr_1.8.3 tools_3.2.2 parallel_3.2.2 pbkrtest_0.4-2 nnet_7.3-10
[16] grid_3.2.2 gtable_0.1.2 nlme_3.1-122 mgcv_1.8-7 quantreg_5.18
[21] MatrixModels_0.4-1 iterators_1.0.7 gtools_3.5.0 lme4_1.1-9 digest_0.6.8
[26] Matrix_1.2-2 nloptr_1.0.4 reshape2_1.4.1 codetools_0.2-11 stringi_0.5-5
[31] compiler_3.2.2 BradleyTerry2_1.0-6 scales_0.3.0 stats4_3.2.2 SparseM_1.7
[36] brglm_0.5-9 proto_0.3-10
>
I am not so familiar with the gafsControl() function that you mention, but I encountered a very similar issue when setting parallel seeds using trainControl(). In the instructions, it describes how to create a list (length = number of resamples + 1), where each item is a list (length = number of parameter combinations to test). I find that doing that does not work (see topepo/caret issue #248 for info). However, if you then turn each item into a vector, e.g. seeds <- lapply(seeds, as.vector) then the seeds seem to work (i.e. models and predictions are entirely reproducible). I should clarify that this is using doMC as the backend. It may be different for other parallel backends. Hope this helps
I was able to figure out my mistake by inspecting gafs.default. The seeds inside gafsControl() takes a vector with length (n_repeats*nresampling)+1 and not a list (as in trainControl$seeds). It is actually stated in the documentation of ?gafsControl that seeds is a vector or integers that can be used to set the seed during each search. The number of seeds must be equal to the number of resamples plus one. I figured it out the hard way, this is a reminder to carefully read the documentation :D.
if (!is.null(gafsControl$seeds)) {
if (length(gafsControl$seeds) < length(gafsControl$index) +
1)
stop(paste("There must be at least", length(gafsControl$index) +
1, "random number seeds passed to gafsControl"))
}
else {
gafsControl$seeds <- sample.int(1e+05, length(gafsControl$index) +
1)
}
So, the proper way to set my ga_seeds is:
#Index for gafsControl
set.seed(1045481)
ga_index <- createFolds(train.set$Class, k=3)
#Seed for the gafsControl()
set.seed(1056)
ga_seeds <- sample.int(1500, 4)
If that way settings seeds you can ensure each run the same feature subset is selected ? I ams asking due randominess of GA