library(h2o)
## Warning: package 'h2o' was built under R version 4.3.3
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## ----------------------------------------------------------------------
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## Your next step is to start H2O:
## > h2o.init()
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## For H2O package documentation, ask for help:
## > ??h2o
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## After starting H2O, you can use the Web UI at http://localhost:54321
## For more information visit https://docs.h2o.ai
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## Attaching package: 'h2o'
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library(tidyverse)
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library(tidymodels)
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## • Dig deeper into tidy modeling with R at https://www.tmwr.org
library(tidyquant)
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library(umap)
## Warning: package 'umap' was built under R version 4.3.3
library(timetk)
start_date <- "1989-01-01"
symbols_txt <- c("CTICLAIMS", # Connecticut
"MEICLAIMS", # Maine
"MAICLAIMS", # Massachusetts
"NHICLAIMS", # New Hampshire
"RIICLAIMS", # Rhode Island
"VTICLAIMS") # Vermont
claims_tbl <- tq_get(symbols_txt, get = "economic.data", from = start_date) %>%
mutate(symbol = fct_recode(symbol,
"Connecticut" = "CTICLAIMS",
"Maine" = "MEICLAIMS",
"Massachusetts" = "MAICLAIMS",
"New Hampshire" = "NHICLAIMS",
"Rhode Island" = "RIICLAIMS",
"Vermont" = "VTICLAIMS")) %>%
rename(claims = price)
set.seed(1234)
data_split <- initial_split(claims_tbl, strata = "claims")
train_tbl <- training(data_split)
test_tbl <- testing(data_split)
recipe_obj <- recipe(claims ~ ., data = train_tbl) %>%
# Remove zero variance variables
step_zv(all_predictors())
# Initialize h2o
h2o.init()
## Connection successful!
##
## R is connected to the H2O cluster:
## H2O cluster uptime: 24 minutes 7 seconds
## H2O cluster timezone: America/New_York
## H2O data parsing timezone: UTC
## H2O cluster version: 3.44.0.3
## H2O cluster version age: 4 months and 16 days
## H2O cluster name: H2O_started_from_R_Jstan_fhp551
## H2O cluster total nodes: 1
## H2O cluster total memory: 1.79 GB
## H2O cluster total cores: 8
## H2O cluster allowed cores: 8
## H2O cluster healthy: TRUE
## H2O Connection ip: localhost
## H2O Connection port: 54321
## H2O Connection proxy: NA
## H2O Internal Security: FALSE
## R Version: R version 4.3.1 (2023-06-16 ucrt)
## Warning in h2o.clusterInfo():
## Your H2O cluster version is (4 months and 16 days) old. There may be a newer version available.
## Please download and install the latest version from: https://h2o-release.s3.amazonaws.com/h2o/latest_stable.html
split.h2o <- h2o.splitFrame(as.h2o(train_tbl), ratios = c(0.85), seed = 2345)
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train_h2o <- split.h2o[[1]]
valid_h2o <- split.h2o[[2]]
test_h2o <- as.h2o(test_tbl)
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y <- "claims"
x <- setdiff(names(train_tbl), y)
models_h2o <- h2o.automl(
x = x,
y = y,
training_frame = train_h2o,
validation_frame = valid_h2o,
leaderboard_frame = test_h2o,
max_runtime_secs = 30,
nfolds = 5,
seed = 3456
)
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## 00:59:56.501: User specified a validation frame with cross-validation still enabled. Please note that the models will still be validated using cross-validation only, the validation frame will be used to provide purely informative validation metrics on the trained models.
## 00:59:56.501: AutoML: XGBoost is not available; skipping it.
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models_h2o %>% typeof()
## [1] "S4"
models_h2o %>% slotNames()
## [1] "project_name" "leader" "leaderboard" "event_log"
## [5] "modeling_steps" "training_info"
models_h2o@leaderboard
## model_id rmse mse
## 1 StackedEnsemble_AllModels_3_AutoML_15_20240507_05956 2786.735 7765894
## 2 StackedEnsemble_BestOfFamily_4_AutoML_15_20240507_05956 2865.915 8213469
## 3 GBM_grid_1_AutoML_15_20240507_05956_model_1 2890.723 8356278
## 4 GBM_grid_1_AutoML_15_20240507_05956_model_7 2970.755 8825383
## 5 StackedEnsemble_AllModels_1_AutoML_15_20240507_05956 3043.806 9264756
## 6 StackedEnsemble_BestOfFamily_2_AutoML_15_20240507_05956 3047.181 9285311
## mae rmsle mean_residual_deviance
## 1 1005.3676 NaN 7765894
## 2 1042.8428 NaN 8213469
## 3 954.5562 NaN 8356278
## 4 1048.8784 NaN 8825383
## 5 1102.8636 NaN 9264756
## 6 1096.2601 NaN 9285311
##
## [28 rows x 6 columns]
models_h2o@leader
## Model Details:
## ==============
##
## H2ORegressionModel: stackedensemble
## Model ID: StackedEnsemble_AllModels_3_AutoML_15_20240507_05956
## Model Summary for Stacked Ensemble:
## key value
## 1 Stacking strategy cross_validation
## 2 Number of base models (used / total) 2/21
## 3 # GBM base models (used / total) 2/14
## 4 # DeepLearning base models (used / total) 0/4
## 5 # DRF base models (used / total) 0/2
## 6 # GLM base models (used / total) 0/1
## 7 Metalearner algorithm GLM
## 8 Metalearner fold assignment scheme Random
## 9 Metalearner nfolds 5
## 10 Metalearner fold_column NA
## 11 Custom metalearner hyperparameters None
##
##
## H2ORegressionMetrics: stackedensemble
## ** Reported on training data. **
##
## MSE: 6541424
## RMSE: 2557.621
## MAE: 922.1452
## RMSLE: NaN
## Mean Residual Deviance : 6541424
##
##
## H2ORegressionMetrics: stackedensemble
## ** Reported on validation data. **
##
## MSE: 11245300
## RMSE: 3353.401
## MAE: 951.84
## RMSLE: NaN
## Mean Residual Deviance : 11245300
##
##
## H2ORegressionMetrics: stackedensemble
## ** Reported on cross-validation data. **
## ** 5-fold cross-validation on training data (Metrics computed for combined holdout predictions) **
##
## MSE: 8037346
## RMSE: 2835.021
## MAE: 962.8897
## RMSLE: NaN
## Mean Residual Deviance : 8037346
##
##
## Cross-Validation Metrics Summary:
## mean sd cv_1_valid
## mae 939.484560 125.077545 768.723100
## mean_residual_deviance 7853581.500000 9010445.000000 1970762.600000
## mse 7853581.500000 9010445.000000 1970762.600000
## null_deviance 29319390000.000000 18405093000.000000 15692573000.000000
## r2 0.694418 0.151424 0.818108
## residual_deviance 11174962000.000000 12962821000.000000 2831986000.000000
## rmse 2509.134000 1395.451500 1403.838500
## rmsle NA 0.000000 NA
## cv_2_valid cv_3_valid cv_4_valid
## mae 1064.648900 858.143200 1042.189500
## mean_residual_deviance 7292892.500000 3005789.000000 23563594.000000
## mse 7292892.500000 3005789.000000 23563594.000000
## null_deviance 32341625000.000000 18835726000.000000 60209290000.000000
## r2 0.679760 0.766536 0.438625
## residual_deviance 10348615000.000000 4397469700.000000 33790194000.000000
## rmse 2700.535600 1733.721200 4854.234400
## rmsle NA NA NA
## cv_5_valid
## mae 963.718100
## mean_residual_deviance 3434871.000000
## mse 3434871.000000
## null_deviance 19517730000.000000
## r2 0.769063
## residual_deviance 4506551000.000000
## rmse 1853.340500
## rmsle NA
#h2o.getModel("StackedEnsemble_BestOfFamily_4_AutoML_14_20240507_05625") %>% h2o.saveModel("h2o_models/")
best_model <- h2o.loadModel("h2o_models/StackedEnsemble_BestOfFamily_4_AutoML_14_20240507_05625")
predictions <- h2o.predict(best_model, newdata = test_h2o)
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predictions_tbl <- predictions %>%
as_tibble()
predictions_tbl %>%
bind_cols(test_tbl)
## # A tibble: 2,766 × 4
## predict symbol date claims
## <dbl> <fct> <date> <int>
## 1 4997. Connecticut 1989-01-14 6503
## 2 4997. Connecticut 1989-01-28 4663
## 3 3608. Connecticut 1989-04-08 3610
## 4 3589. Connecticut 1989-04-29 3191
## 5 3574. Connecticut 1989-05-06 3224
## 6 5042. Connecticut 1989-08-12 3704
## 7 4929. Connecticut 1989-08-26 3373
## 8 4929. Connecticut 1989-09-02 2902
## 9 4929. Connecticut 1989-09-09 2856
## 10 4858. Connecticut 1989-09-16 3025
## # ℹ 2,756 more rows
?h2o.performance
## starting httpd help server ... done
performance_h2o <- h2o.performance(best_model, newdata = test_h2o)
typeof(performance_h2o)
## [1] "S4"
slotNames(performance_h2o)
## [1] "algorithm" "on_train" "on_valid" "on_xval" "metrics"
performance_h2o@metrics
## $model
## $model$`__meta`
## $model$`__meta`$schema_version
## [1] 3
##
## $model$`__meta`$schema_name
## [1] "ModelKeyV3"
##
## $model$`__meta`$schema_type
## [1] "Key<Model>"
##
##
## $model$name
## [1] "StackedEnsemble_BestOfFamily_4_AutoML_14_20240507_05625"
##
## $model$type
## [1] "Key<Model>"
##
## $model$URL
## [1] "/3/Models/StackedEnsemble_BestOfFamily_4_AutoML_14_20240507_05625"
##
##
## $model_checksum
## [1] "-4735192276950071808"
##
## $frame
## $frame$name
## [1] "test_tbl_sid_9df6_3"
##
##
## $frame_checksum
## [1] "8320518109852287548"
##
## $description
## NULL
##
## $scoring_time
## [1] 1.715058e+12
##
## $predictions
## NULL
##
## $MSE
## [1] 4502463
##
## $RMSE
## [1] 2121.901
##
## $nobs
## [1] 2766
##
## $custom_metric_name
## NULL
##
## $custom_metric_value
## [1] 0
##
## $r2
## [1] 0.8058291
##
## $mean_residual_deviance
## [1] 4502463
##
## $mae
## [1] 792.3475
##
## $rmsle
## [1] "NaN"
##
## $residual_deviance
## [1] 12453812864
##
## $null_deviance
## [1] 64138698712
##
## $AIC
## [1] 50231.06
##
## $loglikelihood
## [1] 0
##
## $null_degrees_of_freedom
## [1] 2765
##
## $residual_degrees_of_freedom
## [1] 2764
h2o.auc(performance_h2o)
## NULL
h2o.confusionMatrix(performance_h2o)
## Warning in .local(object, ...): No Confusion Matrices for H2ORegressionMetrics
## NULL