README.md

In gloverd2/codeBase: Contains standard functions for Admiral Advance Analytics

codeBase

Introduction

What functions are included in the package?

Currently the master branch contains the following functions however we have many others as pull request

ls("package:codeBase")
#>  [1] "check_all_identical"            "create_html_pack"              
#>  [3] "encode_freq"                    "expl_categorical"              
#>  [5] "expl_na"                        "expl_summary"                  
#>  [7] "feature_importance_permutation" "hh_test"                       
#>  [9] "hh_train"                       "metric_confusion_matrix"       
#> [11] "metric_deviance"                "metric_F1"                     
#> [13] "metric_Fbeta"                   "metric_gini"                   
#> [15] "metric_mae"                     "metric_nloglik"                
#> [17] "metric_pode"                    "metric_pove"                   
#> [19] "metric_precision"               "metric_PrecisionRecall"        
#> [21] "metric_recall"                  "metric_rmse"                   
#> [23] "metric_ROC"                     "null_gini"                     
#> [25] "null_PrecisionRecall"           "null_ROC"                      
#> [27] "plot_2way_comparison"           "plot_ALE"                      
#> [29] "plot_feature"                   "plot_feature_predictions"      
#> [31] "plot_gini"                      "plot_lift_curve"               
#> [33] "plot_lift_curve_relative"       "plot_magic_carpet"             
#> [35] "plot_PDP"                       "plot_PrecisionRecall"          
#> [37] "plot_ROC"                       "plotting_numerical_buckets"    
#> [39] "prep_char_num_sort"             "prep_num_band"                 
#> [41] "prep_num_bin"                   "prep_numeric_caps"

Using the Package

Import ‘Predicting Household Sale Prices’ dataset

Importing the packages inbuilt testing datasets

data(hh_train)
data(hh_test)

This data is the Californian house prices dataset and has a target of SalePrice

Data Exploration

expl_na

For each variable, find the number or percentage of NA’s in a dataset

na_vars <- codeBase::expl_na(df = hh_train, na.strings = NULL, ignore.case = FALSE) 

We can now see that we have some factors which are useless as they are mostly unknown:

head(dplyr::arrange(na_vars, -na)) %>% kable()

| var | na | perc | |:------------|-----:|---------:| | PoolQC | 1453 | 99.52055 | | MiscFeature | 1406 | 96.30137 | | Alley | 1369 | 93.76712 | | Fence | 1179 | 80.75342 | | FireplaceQu | 690 | 47.26027 | | LotFrontage | 259 | 17.73973 |

expl_categorical

For each categorical variable, find the number of / percentage of each level

codeBase::expl_categorical(df = hh_train, char.level = 3, num.level =2)  %>% kable()

| var | level | class | n | perc | |:-----------|:-------|:----------|-----:|-----------:| | Alley | Grvl | character | 50 | 3.4246575 | | Alley | Pave | character | 41 | 2.8082192 | | Alley | NA | character | 1369 | 93.7671233 | | CentralAir | N | character | 95 | 6.5068493 | | CentralAir | Y | character | 1365 | 93.4931507 | | LandSlope | Gtl | character | 1382 | 94.6575342 | | LandSlope | Mod | character | 65 | 4.4520548 | | LandSlope | Sev | character | 13 | 0.8904110 | | PavedDrive | N | character | 90 | 6.1643836 | | PavedDrive | P | character | 30 | 2.0547945 | | PavedDrive | Y | character | 1340 | 91.7808219 | | Street | Grvl | character | 6 | 0.4109589 | | Street | Pave | character | 1454 | 99.5890411 | | Utilities | AllPub | character | 1459 | 99.9315068 | | Utilities | NoSeWa | character | 1 | 0.0684932 |

This format can be easily plotted to show factor distributions for each factor (either in a loop or using ggplot2 facet functions)

expl_summary

Find the mean, standard deviation, length, and NA count of numeric and integer variables, with the option to group by other variables

codeBase::expl_summary(hh_train, summarise_vars = "SalePrice", group_vars = "YrSold") %>% kable()

| YrSold | mean | sd | n | na_count | |-------:|---------:|---------:|----:|----------:| | 2006 | 182549.5 | 79426.84 | 314 | 0 | | 2007 | 186063.2 | 85768.17 | 329 | 0 | | 2008 | 177360.8 | 69735.61 | 304 | 0 | | 2009 | 179432.1 | 80879.24 | 338 | 0 | | 2010 | 177393.7 | 80451.28 | 175 | 0 |

encode_freq

Apply frequency encoding with the option to group the rarest levels

# Add some unknowns to show what it looks like
hh_train$Neighborhood[5:10] <- NA

From the below output we can see that the factor Neighborhood has more levels than can usefully be modelled

hh_train %>% group_by(Neighborhood) %>% summarise(Volume = n()) %>% kable()

| Neighborhood | Volume | |:-------------|-------:| | Blmngtn | 17 | | Blueste | 2 | | BrDale | 16 | | BrkSide | 57 | | ClearCr | 28 | | CollgCr | 150 | | Crawfor | 51 | | Edwards | 100 | | Gilbert | 79 | | IDOTRR | 37 | | MeadowV | 17 | | Mitchel | 48 | | NAmes | 225 | | NoRidge | 40 | | NPkVill | 9 | | NridgHt | 77 | | NWAmes | 72 | | OldTown | 112 | | Sawyer | 74 | | SawyerW | 59 | | Somerst | 85 | | StoneBr | 25 | | SWISU | 25 | | Timber | 38 | | Veenker | 11 | | NA | 6 |

We decide to frequency encode the factor

nhood_freq <- codeBase::encode_freq(data = hh_train$Neighborhood, 
                                n_levels = 5, 
                                min_level_count = NULL, 
                                unknown_levels = NULL,
                                unknown_treatment_method = 1)

which gives us with the transformed factor

hh_train$nhood_freq <- factor(x = nhood_freq$data, labels = nhood_freq$levels)
hh_train %>% group_by(nhood_freq) %>% summarise(Volume = n()) %>% kable()

| nhood_freq | Volume | |:------------|-------:| | Unknown | 6 | | NAmes | 225 | | CollgCr | 150 | | OldTown | 112 | | Edwards | 100 | | Somerst | 85 | | Other | 782 |

Build a Model

We build a model using standard R functions

hh_train %<>% mutate(GarageYrBlt = coalesce(GarageYrBlt, YearBuilt))
model_vars <- c('GarageYrBlt', 'nhood_freq', 'OverallCond', 'YearBuilt', 'YrSold', target)
hh_train %<>% select(all_of(model_vars))

dataset <- sample(1:10, nrow(hh_train), replace = TRUE)
dTrn <- hh_train[dataset[dataset<8],]
dVal <- hh_train[dataset[dataset>7],]

mTrn <- model.matrix(~., select(dTrn, -SalePrice))
mVal <- model.matrix(~., select(dVal, -SalePrice))

xTrn <- xgb.DMatrix(mTrn, label = dTrn$SalePrice)
xVal <- xgb.DMatrix(mVal, label = dVal$SalePrice)

params_xgb <- list(nthread = 4
                   ,alpha = 0
                   ,lambda = 1
                   ,booster = 'gbtree'
                   ,objective = "reg:squarederror" ,eval_metric = 'rmse' ,maximize = FALSE
)

model_xgb <- xgb.train(data = xTrn
                       ,params = params_xgb  
                       ,nrounds = 100
                       ,early_stopping_rounds = 250
                       ,print_every_n = 20
                       ,watchlist = list(train = xTrn, eval = xVal))
#> [09:27:47] WARNING: amalgamation/../src/learner.cc:573: 
#> Parameters: { "maximize" } might not be used.
#> 
#>   This may not be accurate due to some parameters are only used in language bindings but
#>   passed down to XGBoost core.  Or some parameters are not used but slip through this
#>   verification. Please open an issue if you find above cases.
#> 
#> 
#> [1]  train-rmse:150514.562500    eval-rmse:98545.468750 
#> Multiple eval metrics are present. Will use eval_rmse for early stopping.
#> Will train until eval_rmse hasn't improved in 250 rounds.
#> 
#> [21] train-rmse:127.156044   eval-rmse:49563.132812 
#> [41] train-rmse:0.108875 eval-rmse:49639.621094 
#> [61] train-rmse:0.018694 eval-rmse:49639.707031 
#> [81] train-rmse:0.018694 eval-rmse:49639.707031 
#> [100]    train-rmse:0.018694 eval-rmse:49639.707031


dTrn$pred <- predict(object = model_xgb, newdata = xTrn)
dVal$pred <- predict(object = model_xgb, newdata = xVal)

xgboost::xgb.importance(feature_names = colnames(mTrn),
                                                          model = model_xgb)
#>              Feature         Gain       Cover   Frequency
#> 1:       GarageYrBlt 9.866174e-01 0.877731160 0.872964169
#> 2: nhood_freqCollgCr 1.338263e-02 0.117529988 0.123778502
#> 3:         YearBuilt 9.971814e-15 0.004738852 0.003257329

Model Evaluation

plot_PDP

We can now plot PDP plots for any factor

codeBase::plot_PDP(data = as.data.frame(mTrn),
               model = model_xgb,
               explain_col = 'GarageYrBlt',
               n_bins = 20,
               use_plotly = FALSE)

plot_lift_curve

We can also plot useful global model outputs such as lift curves

codeBase::plot_lift_curve(actual = dTrn$SalePrice,
                      predicted = dTrn$pred,
                      use_plotly = FALSE)

plotly vs ggplot2

Some users may require plotly and others ggplot2 so all plots work with both engines. This can be controlled with the use_plotly parameter of the plotting functions.

Metrics

We have many metrics all of which have the same construction of arguments meaning they can be used by other functions or in loops

codeBase::metric_rmse(actual = dTrn$SalePrice, predicted = dTrn$pred)
#> [1] 36530.87
codeBase::metric_mae(actual = dTrn$SalePrice, predicted = dTrn$pred)
#> [1] 35177.08

codeBase::metric_deviance(actual = dTrn$SalePrice, predicted = dTrn$pred, family = "gaussian")
#> [1] 1334504222
codeBase::metric_nloglik(actual = dTrn$SalePrice, predicted = dTrn
                         $pred, family = "gaussian")
#> [1] 12.06549

gloverd2/codeBase documentation built on Dec. 20, 2021, 11:48 a.m.