Antanas Kaminskas
Duomenų Gavyba 5-tieji namų darbai
rm(list = ls())library(mlr)## Įkeliamas reikalingas paketas: ParamHelpers## Warning message: 'mlr' is in 'maintenance-only' mode since July 2019.
## Future development will only happen in 'mlr3'
## (<https://mlr3.mlr-org.com>). Due to the focus on 'mlr3' there might be
## uncaught bugs meanwhile in {mlr} - please consider switching.library(tidyverse)## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --## v ggplot2 3.3.5 v purrr 0.3.4
## v tibble 3.1.6 v dplyr 1.0.8
## v tidyr 1.2.0 v stringr 1.4.0
## v readr 2.1.2 v forcats 0.5.1## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()Duomenų rinkinių nuskaitymas iš failo
getwd()## [1] "C:/Users/antanas.kaminskas/Desktop"setwd("C:/Users/antanas.kaminskas/Desktop")dt1 <- read.csv("C:/Users/antanas.kaminskas/Desktop/DG_FF1.csv",
header = TRUE,
sep = ";",
dec = ".") dt2 <- read.csv("C:/Users/antanas.kaminskas/Desktop/DG_OE.csv",
header = TRUE,
sep = ";",
dec = ".") Duomenų išvedimas : Pirmajam rinkiniui
head(dt1)## Y day DC ISI RH wind rain area
## 1 5 fri 94.3 5.1 51 6.7 0.0 0
## 2 4 tue 669.1 6.7 33 0.9 0.0 0
## 3 4 sat 686.9 6.7 33 1.3 0.0 0
## 4 6 fri 77.5 9.0 97 4.0 0.2 0
## 5 6 sun 102.2 9.6 99 1.8 0.0 0
## 6 6 sun 488.0 14.7 29 5.4 0.0 0Antrajam rinkiniui
head(dt2)## Date Time S1_Temp S2_Temp S3_Temp S4_Temp S1_Light S2_Light
## 1 2017-12-22 10:49:41 24.94 24.75 24.56 25.38 121 34
## 2 2017-12-22 10:50:12 24.94 24.75 24.56 25.44 121 33
## 3 2017-12-22 10:50:42 25.00 24.75 24.50 25.44 121 34
## 4 2017-12-22 10:51:13 25.00 24.75 24.56 25.44 121 34
## 5 2017-12-22 10:51:44 25.00 24.75 24.56 25.44 121 34
## 6 2017-12-22 10:52:14 25.00 24.81 24.56 25.44 121 34
## S3_Light S4_Light S1_Sound S2_Sound S3_Sound S4_Sound S5_CO2 S5_CO2_Slope
## 1 53 40 0.08 0.19 0.06 0.06 390 0.7692308
## 2 53 40 0.93 0.05 0.06 0.06 390 0.6461538
## 3 53 40 0.43 0.11 0.08 0.06 390 0.5192308
## 4 53 40 0.41 0.10 0.10 0.09 390 0.3884615
## 5 54 40 0.18 0.06 0.06 0.06 390 0.2538462
## 6 54 40 0.13 0.06 0.06 0.07 390 0.1653846
## S6_PIR S7_PIR Room_Occupancy_Count
## 1 0 0 1
## 2 0 0 1
## 3 0 0 1
## 4 0 0 1
## 5 0 0 1
## 6 0 0 1#Duomenų klasifikavimas
dt1Tib <- as_tibble(dt1)
dt1Tib## # A tibble: 517 x 8
## Y day DC ISI RH wind rain area
## <int> <chr> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1 5 fri 94.3 5.1 51 6.7 0 0
## 2 4 tue 669. 6.7 33 0.9 0 0
## 3 4 sat 687. 6.7 33 1.3 0 0
## 4 6 fri 77.5 9 97 4 0.2 0
## 5 6 sun 102. 9.6 99 1.8 0 0
## 6 6 sun 488 14.7 29 5.4 0 0
## 7 6 mon 496. 8.5 27 3.1 0 0
## 8 6 mon 608. 10.7 86 2.2 0 0
## 9 6 tue 693. 7 63 5.4 0 0
## 10 5 sat 699. 7.1 40 4 0 0
## # ... with 507 more rowsPasirenkame teksitinių kintamųjų stulpelius, kurie bus faktoriai
dt1 <- mutate_at(dt1Tib, vars(day), as.factor)
dt1## # A tibble: 517 x 8
## Y day DC ISI RH wind rain area
## <int> <fct> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1 5 fri 94.3 5.1 51 6.7 0 0
## 2 4 tue 669. 6.7 33 0.9 0 0
## 3 4 sat 687. 6.7 33 1.3 0 0
## 4 6 fri 77.5 9 97 4 0.2 0
## 5 6 sun 102. 9.6 99 1.8 0 0
## 6 6 sun 488 14.7 29 5.4 0 0
## 7 6 mon 496. 8.5 27 3.1 0 0
## 8 6 mon 608. 10.7 86 2.2 0 0
## 9 6 tue 693. 7 63 5.4 0 0
## 10 5 sat 699. 7.1 40 4 0 0
## # ... with 507 more rowsSudarome naujus kintamųjų vardus
names(dt1Tib) <- c( "Y","day",
"DC", "ISI", "RH", "wind", "rain",
"area")Braižome grafikus
dt1Untidy <- gather(dt1Tib, "Variable", "Value", -day)
ggplot(dt1Untidy, aes(day, Value)) +
facet_wrap(~ Variable, scales = "free_y") +
geom_boxplot() +
theme_bw()
# Sudarome LDA modelį
dt1Task <- makeClassifTask(data = dt1Tib, target = "day")## Warning in makeTask(type = type, data = data, weights = weights, blocking =
## blocking, : Provided data is not a pure data.frame but from class tbl_df, hence
## it will be converted.lda <- makeLearner("classif.lda")
ldaModel <- train(lda, dt1Task)Sudarome mokymosi modelį
ldaModelData <- getLearnerModel(ldaModel)
ldaPreds <- predict(ldaModelData)$x
head(ldaPreds)## LD1 LD2 LD3 LD4 LD5 LD6
## 1 -0.3321926 -1.0591736 0.04096479 -0.32194372 -0.4446304 1.3934040
## 2 1.4368780 0.2804332 0.32857382 -0.05326468 0.5562428 -0.2206089
## 3 1.3098352 0.2801697 0.35097700 -0.18466649 0.4402101 -0.3009957
## 4 -1.2639520 -2.1803125 -1.01123724 1.76403871 1.9955097 1.0120663
## 5 -0.5897332 -2.2699209 -1.76121095 2.27039479 2.3764933 0.9421201
## 6 -0.2657164 1.4719503 -0.95082560 -0.43295397 0.2159202 0.5768254Braižome klasių grafikus
dt1Tib %>%
mutate(LD1 = ldaPreds[, 1],
LD2 = ldaPreds[, 2]) %>%
ggplot(aes(LD1, LD2, col = day)) +
geom_point() +
stat_ellipse() +
theme_bw()
Sudarome QDA modelį
qda <- makeLearner("classif.qda")
qda## Learner classif.qda from package MASS
## Type: classif
## Name: Quadratic Discriminant Analysis; Short name: qda
## Class: classif.qda
## Properties: twoclass,multiclass,numerics,factors,prob
## Predict-Type: response
## Hyperparameters:Apmokome QDA modelį, PASTABA: NEVEIKIA
NEVEIKIA
#qdaModel <- train(qda, task = dt1Task)
#qdaModelkFold <- makeResampleDesc(method = "RepCV", folds = 10, reps = 50,
stratify = TRUE)ldaCV <- resample(learner = lda, task = dt1Task, resampling = kFold,
measures = list(mmce, acc))## Resampling: repeated cross-validation## Measures: mmce acc## [Resample] iter 1: 0.6730769 0.3269231## [Resample] iter 2: 0.8653846 0.1346154## [Resample] iter 3: 0.7692308 0.2307692## [Resample] iter 4: 0.7058824 0.2941176## [Resample] iter 5: 0.8627451 0.1372549## [Resample] iter 6: 0.8269231 0.1730769## [Resample] iter 7: 0.8846154 0.1153846## [Resample] iter 8: 0.9245283 0.0754717## [Resample] iter 9: 0.8076923 0.1923077## [Resample] iter 10: 0.8000000 0.2000000## [Resample] iter 11: 0.7000000 0.3000000## [Resample] iter 12: 0.8627451 0.1372549## [Resample] iter 13: 0.7962963 0.2037037## [Resample] iter 14: 0.7884615 0.2115385## [Resample] iter 15: 0.7800000 0.2200000## [Resample] iter 16: 0.8076923 0.1923077## [Resample] iter 17: 0.7692308 0.2307692## [Resample] iter 18: 0.7735849 0.2264151## [Resample] iter 19: 0.8301887 0.1698113## [Resample] iter 20: 0.8600000 0.1400000## [Resample] iter 21: 0.7647059 0.2352941## [Resample] iter 22: 0.8800000 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iter 489: 0.7777778 0.2222222## [Resample] iter 490: 0.8269231 0.1730769## [Resample] iter 491: 0.8431373 0.1568627## [Resample] iter 492: 0.8301887 0.1698113## [Resample] iter 493: 0.8235294 0.1764706## [Resample] iter 494: 0.8400000 0.1600000## [Resample] iter 495: 0.8461538 0.1538462## [Resample] iter 496: 0.8200000 0.1800000## [Resample] iter 497: 0.8653846 0.1346154## [Resample] iter 498: 0.8148148 0.1851852## [Resample] iter 499: 0.7884615 0.2115385## [Resample] iter 500: 0.7884615 0.2115385## ## Aggregated Result: mmce.test.mean=0.8023247,acc.test.mean=0.1976753## NEVEIKIA
#qdaCV <- resample(learner = qda, task = td1Task, resampling = kFold,
# measures = list(mmce, acc))ldaCV$aggr## mmce.test.mean acc.test.mean
## 0.8023247 0.1976753NEVEIKIA
#qdaCV$aggrcalculateConfusionMatrix(ldaCV$pred, relative = TRUE)## Relative confusion matrix (normalized by row/column):
## predicted
## true fri mon sat sun thu tue
## fri 0.250/0.20 0.096/0.13 0.162/0.15 0.407/0.22 0.008/0.01 0.018/0.20
## mon 0.155/0.11 0.149/0.18 0.248/0.20 0.300/0.14 0.090/0.13 0.001/0.01
## sat 0.149/0.12 0.116/0.16 0.202/0.18 0.312/0.16 0.176/0.30 0.005/0.05
## sun 0.272/0.24 0.137/0.21 0.132/0.13 0.306/0.18 0.083/0.16 0.011/0.14
## thu 0.157/0.09 0.082/0.08 0.228/0.15 0.297/0.11 0.157/0.19 0.009/0.07
## tue 0.239/0.14 0.157/0.16 0.138/0.09 0.268/0.11 0.062/0.08 0.052/0.43
## wed 0.181/0.09 0.096/0.08 0.167/0.10 0.226/0.08 0.116/0.13 0.011/0.08
## -err.- 0.80 0.82 0.82 0.82 0.81 0.57
## predicted
## true wed -err.-
## fri 0.059/0.13 0.75
## mon 0.057/0.11 0.85
## sat 0.040/0.09 0.80
## sun 0.059/0.14 0.69
## thu 0.069/0.11 0.84
## tue 0.085/0.14 0.95
## wed 0.203/0.28 0.80
## -err.- 0.72 0.80
##
##
## Absolute confusion matrix:
## predicted
## true fri mon sat sun thu tue wed -err.-
## fri 1063 410 690 1728 32 78 249 3187
## mon 574 552 917 1109 333 5 210 3148
## sat 624 488 849 1311 738 21 169 3351
## sun 1292 651 627 1453 395 54 278 3297
## thu 480 250 695 906 480 28 211 2570
## tue 765 502 440 859 198 165 271 3035
## wed 488 259 451 611 312 31 548 2152
## -err.- 4223 2560 3820 6524 2008 217 1388 20740NEVEIKIA
#calculateConfusionMatrix(qdaCV$pred, relative = TRUE)reiksmes <- tibble(X = 13, Y = 2, day = 2.2, FFMC = 19, DMC = 100,
DC = 2.3, ISI = 2.5, temp = 0.35, RH = 1.7,
wind = 4, rain = 1.1, area = 3)NEVEIKIA
#predict(qdaModel, newdata = reiksmes)