The goal of bagofpatternsr is to provide a simple implementation of the ‘Bag of Patterns’ time series classification algorithm as described by Lin et al (2012). It’s based on the description at timeseriesclassification.com - there are other implementations of it in R, but some are built in Java which can be tricky to run.
It uses the seewave:: implementation of Symbolic Aggregate eXPressions
(SAX) for the patterns, data.table:: for data munging and FNN:: for
fast K-Nearest Neighbours matching.
You can install the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("mattsq/bagofpatternsr")Using it is easy! Models are fit like so:
library(bagofpatternsr)
data("FaceAll_TRAIN")
data("FaceAll_TEST")
model <- bagofpatternsr::bagofpatterns_knn(FaceAll_TRAIN,
target = "target",
window_size = 20,
verbose = FALSE,
normalize = TRUE,
alphabet_size = 2,
word_size = 4,
k = 1)
print(model)
#> A trained Bag Of Patterns object with:
#> A time series of length 131 converted into a word histogram with 17 entries, predicting class: target
#> The object has the following hyperparameters:
#> Window Size: 20
#> Alphabet Size: 2
#> Word Size: 4
#> SAX breakpoint method: quantiles
#> Term weighting method: term frequency
#> Sparsity constraint: 1.00
#> Trained with sparse windows:
#> Windows Z-normalized before creating words: TRUE
#>
#> Examples of words in dictionary include: aaab, aaba, aabb, abaa, abab
#> K-Nearest Neighbours to be used: 1Predictions on new datasets can be retrieved like so:
new_preds <- predict(model,
newdata = FaceAll_TEST,
verbose = FALSE)
table(new_preds, FaceAll_TEST$target)
#>
#> new_preds 1 10 11 12 13 14 2 3 4 5 6 7 8 9
#> 1 44 2 0 0 17 0 2 6 5 5 4 6 11 5
#> 10 3 26 0 3 37 3 3 18 3 11 6 3 59 8
#> 11 2 3 3 1 55 1 6 11 8 2 8 4 9 0
#> 12 0 4 0 31 10 1 12 9 6 7 13 3 24 5
#> 13 4 6 1 0 51 4 9 10 1 4 2 3 10 1
#> 14 1 3 2 1 2 3 18 1 6 4 5 7 3 15
#> 2 0 3 0 5 5 3 20 0 9 3 8 4 5 5
#> 3 3 16 0 12 80 1 7 62 1 12 2 1 34 1
#> 4 2 1 0 1 1 1 9 2 26 1 15 2 4 2
#> 5 6 9 0 0 5 3 5 6 4 55 13 5 15 10
#> 6 0 0 0 1 1 4 10 0 18 5 29 10 5 3
#> 7 4 3 1 4 5 6 15 1 18 7 20 50 5 8
#> 8 0 8 0 6 10 2 12 10 9 17 14 5 36 1
#> 9 3 11 1 1 8 0 10 0 17 3 8 6 13 36
mean(new_preds == FaceAll_TEST$target)
#> [1] 0.2792899There’s support for the entirely atheoretical idea of ‘sparse windows’ -
essentially, rather than generating a dictionary out of every single
window, we take inspiration from Time Series Forest by taking sqrt(m)
random windows from each vector. It speed up training dramatically, and
seems to improve generalization - compare:
library(tsforest)
data("FreezerRegularTrain_TRAIN")
model <- bagofpatterns_knn(FreezerRegularTrain_TRAIN,
window_size = 20,
sparse_windows = FALSE,
normalize = TRUE,
verbose = FALSE,
alphabet_size = 3,
word_size = 8,
k = 1)
preds <- predict(model, FreezerRegularTrain_TEST, verbose = FALSE)
table(preds, FreezerRegularTrain_TEST$target)
#>
#> preds 1 2
#> 1 1006 510
#> 2 419 915
mean(preds == FreezerRegularTrain_TEST$target)
#> [1] 0.6740351With:
data("FreezerRegularTrain_TRAIN")
model <- bagofpatterns_knn(FreezerRegularTrain_TRAIN,
window_size = 20,
sparse_windows = TRUE,
verbose = FALSE,
alphabet_size = 3,
word_size = 8,
k = 1)
preds <- predict(model, FreezerRegularTrain_TEST, verbose = FALSE)
table(preds, FreezerRegularTrain_TEST$target)
#>
#> preds 1 2
#> 1 994 880
#> 2 431 545
mean(preds == FreezerRegularTrain_TEST$target)
#> [1] 0.54You can also use fit_bagofpatterns and bake_bagofpatterns to use the
underlying Bag Of Patterns representation for other models. Here’s a
simple example using logistic regression instead of K-Nearest Neighbors:
data("FreezerRegularTrain_TRAIN")
data("FreezerRegularTrain_TEST")
bop_obj <- fit_bagofpatterns(FreezerRegularTrain_TRAIN,
window_size = 20,
sparse_windows = FALSE,
verbose = FALSE,
alphabet_size = 2,
word_size = 3)
FreezerRegularTrain_TRAIN_conv <- bake_bagofpatterns(bop_obj)
glm_model <- glm(target ~ ., data = FreezerRegularTrain_TRAIN_conv, family = "binomial")
summary(glm_model)
#>
#> Call:
#> glm(formula = target ~ ., family = "binomial", data = FreezerRegularTrain_TRAIN_conv)
#>
#> Deviance Residuals:
#> Min 1Q Median 3Q Max
#> -1.89414 -1.01502 0.06302 1.04366 2.05352
#>
#> Coefficients:
#> Estimate Std. Error z value Pr(>|z|)
#> (Intercept) 5.69392 2.02425 2.813 0.00491 **
#> aaa -0.02959 0.07189 -0.412 0.68063
#> aab -0.07574 0.09457 -0.801 0.42320
#> aba 0.01853 0.09922 0.187 0.85187
#> abb 0.05246 0.09540 0.550 0.58240
#> baa -0.01049 0.07977 -0.132 0.89533
#> bab -0.20666 0.10068 -2.053 0.04010 *
#> bba -0.23673 0.07919 -2.989 0.00280 **
#> bbb 0.11444 0.07018 1.631 0.10298
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> (Dispersion parameter for binomial family taken to be 1)
#>
#> Null deviance: 207.94 on 149 degrees of freedom
#> Residual deviance: 186.61 on 141 degrees of freedom
#> AIC: 204.61
#>
#> Number of Fisher Scoring iterations: 4We can generate predictions by calling bake_bagofpatterns on a new
dataset:
FreezerRegularTrain_TEST_conv <- bake_bagofpatterns(bop_obj, FreezerRegularTrain_TEST)
preds <- predict(glm_model, FreezerRegularTrain_TEST_conv, type = "response")
preds <- as.numeric(preds>=0.5) + 1
table(preds, FreezerRegularTrain_TEST$target)
#>
#> preds 1 2
#> 1 877 452
#> 2 548 973