load A dataset with minute-slices already generated

library(caret)
library(readr)
library(dplyr)
library(reshape2)
library(tidyr)
library(catboost)
library(doMC)
suppressPackageStartupMessages(library("optparse"))
registerDoMC(cores=8)
set.seed(01092020)
#set.seed(21092022)
caprino_data<-readr::read_csv("data/caprino_data_sliced.csv",col_types = 
                                cols(
                                  imu_ts = col_double(),
                                  accX = col_double(),
                                  accY = col_double(),
                                  accZ = col_double(),
                                  gyroX = col_double(),
                                  gyroY = col_double(),
                                  gyroZ = col_double(),
                                  gps_hour = col_time(format = ""),
                                  gps_ts = col_double(),
                                  lat = col_double(),
                                  long = col_double(),
                                  alt = col_double(),
                                  activity = col_character(),
                                  id = col_character()
                                )
)
caprino_data

MAIN

Bag of features using kmeans

cluster_features<-c('accX','accY','accZ','gyroX','gyroY','gyroZ')

  minute_slice <-
    as.vector(t(
      caprino_data %>% group_by(id) %>% 
        summarise(n = n()) %>% select(id)
    ))

  trainidx <- sample(1:length(minute_slice), 
                     (length(minute_slice) * 70) / 100.0)
  data_train <- caprino_data %>% 
    filter(id %in% minute_slice[trainidx])
  data_test <- caprino_data %>% 
    filter(id %in% (minute_slice[-trainidx]))
  
  cluster_results <-
    kmeans(
      x = data_train %>% select(cluster_features),
      centers = 24,
      nstart = 5
    )
did not converge in 10 iterationsQuick-TRANSfer stage steps exceeded maximum (= 8097600)Quick-TRANSfer stage steps exceeded maximum (= 8097600)Quick-TRANSfer stage steps exceeded maximum (= 8097600)
  data_train_cluster <-
    cbind(data_train, cluster = cluster_results$cluster) %>% 
    group_by(activity, cluster)
  
  cluster_results_test <-
    kmeans(x = data_test %>% select(cluster_features),
           centers = cluster_results$centers)
Quick-TRANSfer stage steps exceeded maximum (= 3490700)
  data_test_cluster <-
    cbind(data_test, cluster = cluster_results_test$cluster) %>% 
    group_by(activity, cluster)
  
  
  data_train_cluster_labeled <- data_train_cluster   %>%
    group_by(id, cluster) %>% summarise(n = n()) %>%
    reshape2::dcast(id ~ cluster, fill = 0) %>%
    inner_join(data_train_cluster %>% ungroup() %>%
                 select(id, activity)  %>%
                 unique()  ,
               by = "id") %>% filter(!is.na(activity))
Using n as value column: use value.var to override.
  data_test_cluster_labeled <- data_test_cluster   %>% 
    group_by(id, cluster) %>% summarise(n = n()) %>%
    reshape2::dcast(id ~ cluster, fill = 0) %>%
    inner_join(data_test_cluster %>%  ungroup() %>%
                 select(id, activity)  %>%
                 unique() ,
               by = "id")  %>% filter(!is.na(activity))
Using n as value column: use value.var to override.
  
  data_eval_cluster_labeled_idx <- sample(1:nrow(data_train_cluster_labeled), 
                     (length(data_train_cluster_labeled) * 70) / 100.0)
  data_eval_cluster_labeled <- data_train_cluster_labeled[data_eval_cluster_labeled_idx,]
  data_train_cluster_labeled <- data_train_cluster_labeled[-data_eval_cluster_labeled_idx,]

create pool objects as required by catboost R package

pool_train = catboost.load_pool( data_train_cluster_labeled %>% select(-id,-activity), label = as.numeric(as.factor(data_train_cluster_labeled$activity))-1)
pool_test = catboost.load_pool( data_test_cluster_labeled %>% select(-id,-activity), label = as.numeric(as.factor(data_test_cluster_labeled$activity))-1)
pool_valid = catboost.load_pool( data_eval_cluster_labeled %>% select(-id,-activity), label = as.numeric(as.factor(data_eval_cluster_labeled$activity))-1)

Analysis of the CV results

The idea of these analisys was to check overfitting.


cv_results <- catboost.cv(pool_train,fold_count = 3,early_stopping_rounds=40)
Training catboost with default parameters! See help(catboost.train).
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Stopped by overfitting detector  (40 iterations wait)
311:    learn: 0.3525582    test: 0.6624124 best: 0.6614705 (271)   total: 13.1s    remaining: 29s
cv_results %>% select(test.RMSE.mean, train.RMSE.mean) %>% tibble::add_column(id=seq(1:nrow(cv_results))) %>% reshape2::melt(id.var="id") %>%
ggplot(aes(x=id,y=value))+
   geom_line(aes(color=variable,linetype=variable))+
   ggdark::dark_theme_gray()

Training the model

A large iteration but we use early stoping

fit_params <- list(iterations = 1000,
                   loss_function = 'MultiClass',
                   #border_count = 32,
                   depth = 3,
                   #learning_rate = 0.03,
                   #l2_leaf_reg = 3.5,
                   task_type = 'CPU',
                   verbose = 10,
                   early_stopping_rounds=40
                   )
model <- catboost.train(pool_train, pool_valid, params = fit_params)
Learning rate set to 0.106203
0:  learn: 1.3009113    test: 1.2966790 best: 1.2966790 (0) total: 1.66ms   remaining: 1.66s
10: learn: 0.8909315    test: 0.7817814 best: 0.7817814 (10)    total: 13.2ms   remaining: 1.19s
20: learn: 0.7336459    test: 0.6398310 best: 0.6398310 (20)    total: 21.6ms   remaining: 1.01s
30: learn: 0.6483809    test: 0.5784817 best: 0.5784817 (30)    total: 31.9ms   remaining: 996ms
40: learn: 0.5982220    test: 0.5468035 best: 0.5468035 (40)    total: 40.7ms   remaining: 951ms
50: learn: 0.5606395    test: 0.5278374 best: 0.5278374 (50)    total: 52.8ms   remaining: 982ms
60: learn: 0.5342883    test: 0.5173544 best: 0.5173544 (60)    total: 64.2ms   remaining: 988ms
70: learn: 0.5129109    test: 0.4895823 best: 0.4893849 (69)    total: 78.3ms   remaining: 1.02s
80: learn: 0.4932392    test: 0.4867612 best: 0.4867612 (80)    total: 87.2ms   remaining: 989ms
90: learn: 0.4754259    test: 0.4780816 best: 0.4780816 (90)    total: 97.6ms   remaining: 975ms
100:    learn: 0.4601673    test: 0.4660612 best: 0.4660612 (100)   total: 106ms    remaining: 939ms
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160:    learn: 0.3864554    test: 0.4297152 best: 0.4275898 (125)   total: 169ms    remaining: 881ms
170:    learn: 0.3751811    test: 0.4194522 best: 0.4181774 (168)   total: 178ms    remaining: 864ms
180:    learn: 0.3663357    test: 0.4180910 best: 0.4164844 (178)   total: 187ms    remaining: 847ms
190:    learn: 0.3585514    test: 0.4063765 best: 0.4060892 (189)   total: 196ms    remaining: 832ms
200:    learn: 0.3530526    test: 0.4031559 best: 0.4031559 (200)   total: 207ms    remaining: 825ms
210:    learn: 0.3465621    test: 0.4017483 best: 0.4014957 (209)   total: 219ms    remaining: 819ms
220:    learn: 0.3424457    test: 0.4019696 best: 0.3995103 (215)   total: 229ms    remaining: 807ms
230:    learn: 0.3363199    test: 0.3966621 best: 0.3965031 (229)   total: 238ms    remaining: 793ms
240:    learn: 0.3275563    test: 0.3929800 best: 0.3929800 (240)   total: 249ms    remaining: 783ms
250:    learn: 0.3198824    test: 0.3939049 best: 0.3914459 (249)   total: 259ms    remaining: 773ms
260:    learn: 0.3160815    test: 0.3924385 best: 0.3914459 (249)   total: 272ms    remaining: 771ms
270:    learn: 0.3116538    test: 0.3892632 best: 0.3889103 (269)   total: 282ms    remaining: 758ms
280:    learn: 0.3064914    test: 0.3859660 best: 0.3859660 (280)   total: 300ms    remaining: 767ms
290:    learn: 0.2999560    test: 0.3867871 best: 0.3852553 (284)   total: 308ms    remaining: 751ms
300:    learn: 0.2953233    test: 0.3860416 best: 0.3852553 (284)   total: 318ms    remaining: 739ms
310:    learn: 0.2921905    test: 0.3848209 best: 0.3848209 (310)   total: 328ms    remaining: 727ms
320:    learn: 0.2844603    test: 0.3932209 best: 0.3847087 (313)   total: 340ms    remaining: 719ms
330:    learn: 0.2793579    test: 0.3912462 best: 0.3847087 (313)   total: 349ms    remaining: 706ms
340:    learn: 0.2739184    test: 0.3900825 best: 0.3847087 (313)   total: 360ms    remaining: 696ms
350:    learn: 0.2682450    test: 0.3920053 best: 0.3847087 (313)   total: 370ms    remaining: 685ms
Stopped by overfitting detector  (40 iterations wait)

bestTest = 0.3847086679
bestIteration = 313

Shrink model to first 314 iterations.

Check the relation between the labels used in RF and the labels used in catboost

as.factor(data_test_cluster_labeled$activity)
  [1] RP RP RP RP RP RP RP RP RP RP RP RP RP RP RP RP RP RP RP RP RP RP RP RP RP RP RP RP RP RP RP W  W  W  W  W  W  W 
 [39] W  W  W  W  W  W  W  G  G  G  G  G  G  G  G  RF RF RF RF RF RF RF RF RF RF RF RF RF RF RF RF RF RF RF RF RF RF RF
 [77] RF RF RF RF RF RF RF RF RF RF RF RF RF RF RF RF RF RF RF RF RF RF RF RF RF RF RF RF RF RF RF RF RF RF RF RF RF RF
[115] RF RF RF RF RF RF RF RF RF RF RF RF G  G  G  G  G  G  G  G  G  G  G  G  G  G  G  G  G  G  G  G  G  G  G  G  G  G 
[153] G  G  G  G  G  G  G  G  G  G  G  G  G  G  G  G  G  G  G  G  G  G  G  G  G  G  G  W  W  W  W  W  W  W  W  W  W  W 
[191] W  W  RP RP RP RP RP RP
Levels: G RF RP W
as.factor(as.numeric(as.factor(data_test_cluster_labeled$activity))-1)
  [1] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 0 0 0 0 0 0 0 0 1 1 1 1
 [58] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[115] 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[172] 0 0 0 0 0 0 0 0 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2
Levels: 0 1 2 3

Confusion Matrix and macro and micro metrics

  data_test_prediction <-catboost.predict(model, pool_test,prediction_type='Class')
  cm <-
    caret::confusionMatrix(as.factor(data_test_prediction),as.factor(as.numeric(as.factor(data_test_cluster_labeled$activity))-1),
                           mode = 'everything')

cm
Confusion Matrix and Statistics

          Reference
Prediction  0  1  2  3
         0 49 20  6  1
         1 10 49  1  0
         2  0  4 29  0
         3  2  0  1 26

Overall Statistics
                                         
               Accuracy : 0.7727         
                 95% CI : (0.708, 0.8291)
    No Information Rate : 0.3687         
    P-Value [Acc > NIR] : < 2.2e-16      
                                         
                  Kappa : 0.6839         
                                         
 Mcnemar's Test P-Value : NA             

Statistics by Class:

                     Class: 0 Class: 1 Class: 2 Class: 3
Sensitivity            0.8033   0.6712   0.7838   0.9630
Specificity            0.8029   0.9120   0.9752   0.9825
Pos Pred Value         0.6447   0.8167   0.8788   0.8966
Neg Pred Value         0.9016   0.8261   0.9515   0.9941
Precision              0.6447   0.8167   0.8788   0.8966
Recall                 0.8033   0.6712   0.7838   0.9630
F1                     0.7153   0.7368   0.8286   0.9286
Prevalence             0.3081   0.3687   0.1869   0.1364
Detection Rate         0.2475   0.2475   0.1465   0.1313
Detection Prevalence   0.3838   0.3030   0.1667   0.1465
Balanced Accuracy      0.8031   0.7916   0.8795   0.9727
  macro_metrics <- (apply(cm$byClass, 2, sum) / 4) [c(5, 6, 7)]
  s <-
    table(data_test_prediction,
          as.factor(data_test_cluster_labeled$activity))
  
  Precision_micro <-  (sum(diag(s)) / sum(apply(s, 1, sum)))
  Recall_micro <- (sum(diag(s)) / sum(apply(s, 2, sum)))
  
  micro_metrics <- c((sum(diag(s)) / sum(apply(s, 1, sum))),
                     (sum(diag(s)) / sum(apply(s, 2, sum))),
                     2 * ((Precision_micro * Recall_micro) / (Precision_micro + Recall_micro)
                     ))
  
  mm <- cbind(c(macro_metrics), c(micro_metrics))
mm
               [,1]      [,2]
Precision 0.8091858 0.7727273
Recall    0.8053146 0.7727273
F1        0.8023284 0.7727273

Confusion Matrix (plot)

len_data_test_prediction = length(data_test_prediction)
reshape2::melt(table(data_test_prediction,as.factor(data_test_cluster_labeled$activity))) %>% mutate(value=(value)) %>%
  ggplot(aes(x=data_test_prediction,y=Var2))+
  geom_tile(aes(fill=value), colour = "white") + 
   geom_text(aes(label = sprintf("%1.2f", value)), vjust = 1)+
  scale_fill_gradient(low = "white", high = "red")+
  xlab(" Predicted Activity ")+ylab(" Actual Activity")+
  theme_bw()+ theme(legend.position = "none")

sessionInfo()
R version 3.6.3 (2020-02-29)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 16.04.6 LTS

Matrix products: default
BLAS:   /usr/lib/atlas-base/atlas/libblas.so.3.0
LAPACK: /usr/lib/atlas-base/atlas/liblapack.so.3.0

locale:
 [1] LC_CTYPE=es_AR.UTF-8       LC_NUMERIC=C               LC_TIME=es_AR.UTF-8        LC_COLLATE=es_AR.UTF-8    
 [5] LC_MONETARY=es_AR.UTF-8    LC_MESSAGES=es_AR.UTF-8    LC_PAPER=es_AR.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C             LC_MEASUREMENT=es_AR.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] parallel  stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] optparse_1.6.4   doMC_1.3.6       iterators_1.0.12 foreach_1.4.7    catboost_0.24.1  tidyr_1.0.0     
 [7] reshape2_1.4.3   dplyr_0.8.3      readr_1.3.1      caret_6.0-84     ggplot2_3.2.1    lattice_0.20-38 
[13] reticulate_1.16 

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.3          lubridate_1.7.4     class_7.3-17        assertthat_0.2.1    digest_0.6.23      
 [6] ipred_0.9-9         R6_2.4.1            plyr_1.8.4          ggdark_0.2.1        backports_1.1.5    
[11] stats4_3.6.3        evaluate_0.14       e1071_1.7-2         pillar_1.4.6        rlang_0.4.7        
[16] lazyeval_0.2.2      rstudioapi_0.10     data.table_1.12.4   rpart_4.1-15        Matrix_1.2-17      
[21] rmarkdown_2.3       labeling_0.3        splines_3.6.3       gower_0.2.1         stringr_1.4.0      
[26] tinytex_0.16        munsell_0.5.0       broom_0.7.0         compiler_3.6.3      xfun_0.10          
[31] pkgconfig_2.0.3     base64enc_0.1-3     htmltools_0.4.0     nnet_7.3-14         tidyselect_0.2.5   
[36] gridExtra_2.3       tibble_3.0.3        prodlim_2018.04.18  codetools_0.2-16    randomForest_4.6-14
[41] fansi_0.4.0         crayon_1.3.4        withr_2.1.2         MASS_7.3-51.6       recipes_0.1.7      
[46] rappdirs_0.3.1      ModelMetrics_1.2.2  grid_3.6.3          nlme_3.1-147        jsonlite_1.6       
[51] gtable_0.3.0        lifecycle_0.2.0     magrittr_1.5        scales_1.1.0        cli_2.0.0          
[56] stringi_1.4.3       farver_2.0.1        getopt_1.20.3       timeDate_3043.102   ellipsis_0.3.0     
[61] generics_0.0.2      vctrs_0.3.2         lava_1.6.6          tools_3.6.3         glue_1.3.1         
[66] purrr_0.3.3         hms_0.5.1           rsconnect_0.8.15    survival_3.1-12     yaml_2.2.0         
[71] colorspace_1.4-1    knitr_1.25         
---
title: "Testing catboost for Goat Behavior classification"
output: html_notebook
---
### load A dataset with minute-slices already generated
```{r}
library(caret)
library(readr)
library(dplyr)
library(reshape2)
library(tidyr)
library(catboost)
library(doMC)
suppressPackageStartupMessages(library("optparse"))
registerDoMC(cores=8)
set.seed(01092020)
#set.seed(21092022)
caprino_data<-readr::read_csv("data/caprino_data_sliced.csv",col_types = 
                                cols(
                                  imu_ts = col_double(),
                                  accX = col_double(),
                                  accY = col_double(),
                                  accZ = col_double(),
                                  gyroX = col_double(),
                                  gyroY = col_double(),
                                  gyroZ = col_double(),
                                  gps_hour = col_time(format = ""),
                                  gps_ts = col_double(),
                                  lat = col_double(),
                                  long = col_double(),
                                  alt = col_double(),
                                  activity = col_character(),
                                  id = col_character()
                                )
)

```

```{r}
caprino_data
```

### MAIN
#### Bag of features using kmeans
```{r}
cluster_features<-c('accX','accY','accZ','gyroX','gyroY','gyroZ')

  minute_slice <-
    as.vector(t(
      caprino_data %>% group_by(id) %>% 
        summarise(n = n()) %>% select(id)
    ))

  trainidx <- sample(1:length(minute_slice), 
                     (length(minute_slice) * 70) / 100.0)
  data_train <- caprino_data %>% 
    filter(id %in% minute_slice[trainidx])
  data_test <- caprino_data %>% 
    filter(id %in% (minute_slice[-trainidx]))
  
  cluster_results <-
    kmeans(
      x = data_train %>% select(cluster_features),
      centers = 24,
      nstart = 5
    )
  data_train_cluster <-
    cbind(data_train, cluster = cluster_results$cluster) %>% 
    group_by(activity, cluster)
  
  cluster_results_test <-
    kmeans(x = data_test %>% select(cluster_features),
           centers = cluster_results$centers)
  data_test_cluster <-
    cbind(data_test, cluster = cluster_results_test$cluster) %>% 
    group_by(activity, cluster)
  
  
  data_train_cluster_labeled <- data_train_cluster   %>%
    group_by(id, cluster) %>% summarise(n = n()) %>%
    reshape2::dcast(id ~ cluster, fill = 0) %>%
    inner_join(data_train_cluster %>% ungroup() %>%
                 select(id, activity)  %>%
                 unique()  ,
               by = "id") %>% filter(!is.na(activity))
  data_test_cluster_labeled <- data_test_cluster   %>% 
    group_by(id, cluster) %>% summarise(n = n()) %>%
    reshape2::dcast(id ~ cluster, fill = 0) %>%
    inner_join(data_test_cluster %>%  ungroup() %>%
                 select(id, activity)  %>%
                 unique() ,
               by = "id")  %>% filter(!is.na(activity))
  
  data_eval_cluster_labeled_idx <- sample(1:nrow(data_train_cluster_labeled), 
                     (length(data_train_cluster_labeled) * 70) / 100.0)
  data_eval_cluster_labeled <- data_train_cluster_labeled[data_eval_cluster_labeled_idx,]
  data_train_cluster_labeled <- data_train_cluster_labeled[-data_eval_cluster_labeled_idx,]

```

#### create pool objects as required by catboost R package
```{r}
pool_train = catboost.load_pool( data_train_cluster_labeled %>% select(-id,-activity), label = as.numeric(as.factor(data_train_cluster_labeled$activity))-1)
pool_test = catboost.load_pool( data_test_cluster_labeled %>% select(-id,-activity), label = as.numeric(as.factor(data_test_cluster_labeled$activity))-1)
pool_valid = catboost.load_pool( data_eval_cluster_labeled %>% select(-id,-activity), label = as.numeric(as.factor(data_eval_cluster_labeled$activity))-1)
```

#### Analysis of the CV results
The idea of these analisys was to check overfitting.

```{r}

cv_results <- catboost.cv(pool_train,fold_count = 3,early_stopping_rounds=40)
cv_results %>% select(test.RMSE.mean, train.RMSE.mean) %>% tibble::add_column(id=seq(1:nrow(cv_results))) %>% reshape2::melt(id.var="id") %>%
ggplot(aes(x=id,y=value))+
   geom_line(aes(color=variable,linetype=variable))+
   ggdark::dark_theme_gray()
```

#### Training the model
A large iteration but we use early stoping

```{r}
fit_params <- list(iterations = 1000,
                   loss_function = 'MultiClass',
                   #border_count = 32,
                   depth = 3,
                   #learning_rate = 0.03,
                   #l2_leaf_reg = 3.5,
                   task_type = 'CPU',
                   verbose = 10,
                   early_stopping_rounds=40
                   )
model <- catboost.train(pool_train, pool_valid, params = fit_params)
```

#### Check the relation between the labels used in RF and the labels used in catboost

```{r}
as.factor(data_test_cluster_labeled$activity)
as.factor(as.numeric(as.factor(data_test_cluster_labeled$activity))-1)
```
  
#### Confusion Matrix and macro and micro metrics
```{r}
  data_test_prediction <-catboost.predict(model, pool_test,prediction_type='Class')
  cm <-
    caret::confusionMatrix(as.factor(data_test_prediction),as.factor(as.numeric(as.factor(data_test_cluster_labeled$activity))-1),
                           mode = 'everything')

cm

```

```{r}
  macro_metrics <- (apply(cm$byClass, 2, sum) / 4) [c(5, 6, 7)]
  s <-
    table(data_test_prediction,
          as.factor(data_test_cluster_labeled$activity))
  
  Precision_micro <-  (sum(diag(s)) / sum(apply(s, 1, sum)))
  Recall_micro <- (sum(diag(s)) / sum(apply(s, 2, sum)))
  
  micro_metrics <- c((sum(diag(s)) / sum(apply(s, 1, sum))),
                     (sum(diag(s)) / sum(apply(s, 2, sum))),
                     2 * ((Precision_micro * Recall_micro) / (Precision_micro + Recall_micro)
                     ))
  
  mm <- cbind(c(macro_metrics), c(micro_metrics))
mm
```

## Confusion  Matrix (plot)
```{r}
len_data_test_prediction = length(data_test_prediction)
reshape2::melt(table(data_test_prediction,as.factor(data_test_cluster_labeled$activity))) %>% mutate(value=(value)) %>%
  ggplot(aes(x=data_test_prediction,y=Var2))+
  geom_tile(aes(fill=value), colour = "white") + 
   geom_text(aes(label = sprintf("%1.2f", value)), vjust = 1)+
  scale_fill_gradient(low = "white", high = "red")+
  xlab(" Predicted Activity ")+ylab(" Actual Activity")+
  theme_bw()+ theme(legend.position = "none")
```
```{r}
sessionInfo()
```

