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tests/testthat/_snaps/discretize_xgb.md
In embed: Extra Recipes for Encoding Predictors
run_xgboost for classification
Code
xgboost
Output
##### xgb.Booster
raw: 69.1 Kb
call:
xgboost::xgb.train(params = .params, data = .train, nrounds = 100,
watchlist = list(train = .train, test = .test), verbose = 0,
early_stopping_rounds = 10, tree_method = "hist", objective = .objective,
nthread = 1)
params (as set within xgb.train):
eta = "0.3", max_bin = "10", max_depth = "1", min_child_weight = "5", tree_method = "hist", objective = "binary:logistic", nthread = "1", validate_parameters = "TRUE"
xgb.attributes:
best_iteration, best_msg, best_ntreelimit, best_score, niter
callbacks:
cb.evaluation.log()
cb.early.stop(stopping_rounds = early_stopping_rounds, maximize = maximize,
verbose = verbose)
# of features: 13
niter: 89
best_iteration : 79
best_ntreelimit : 79
best_score : 0.4442069
best_msg : [79] train-logloss:0.420000 test-logloss:0.444207
nfeatures : 13
evaluation_log:
iter train_logloss test_logloss
1 0.6279229 0.6303495
2 0.5869984 0.5894989
---
88 0.4180034 0.4449478
89 0.4178084 0.4446590
run_xgboost for multi-classification
Code
xgboost
Output
##### xgb.Booster
raw: 149.7 Kb
call:
xgboost::xgb.train(params = .params, data = .train, nrounds = 100,
watchlist = list(train = .train, test = .test), verbose = 0,
early_stopping_rounds = 10, tree_method = "hist", objective = .objective,
nthread = 1)
params (as set within xgb.train):
eta = "0.3", max_bin = "10", max_depth = "1", min_child_weight = "5", num_class = "6", tree_method = "hist", objective = "multi:softprob", nthread = "1", validate_parameters = "TRUE"
xgb.attributes:
best_iteration, best_msg, best_ntreelimit, best_score, niter
callbacks:
cb.evaluation.log()
cb.early.stop(stopping_rounds = early_stopping_rounds, maximize = maximize,
verbose = verbose)
# of features: 30
niter: 33
best_iteration : 23
best_ntreelimit : 23
best_score : 1.246428
best_msg : [23] train-mlogloss:1.178121 test-mlogloss:1.246428
nfeatures : 30
evaluation_log:
iter train_mlogloss test_mlogloss
1 1.623174 1.631783
2 1.515108 1.531188
---
32 1.159813 1.249701
33 1.158088 1.250462
run_xgboost for regression
Code
xgboost
Output
##### xgb.Booster
raw: 40.2 Kb
call:
xgboost::xgb.train(params = .params, data = .train, nrounds = 100,
watchlist = list(train = .train, test = .test), verbose = 0,
early_stopping_rounds = 10, tree_method = "hist", objective = .objective,
nthread = 1)
params (as set within xgb.train):
eta = "0.3", max_bin = "10", max_depth = "1", min_child_weight = "5", tree_method = "hist", objective = "reg:squarederror", nthread = "1", validate_parameters = "TRUE"
xgb.attributes:
best_iteration, best_msg, best_ntreelimit, best_score, niter
callbacks:
cb.evaluation.log()
cb.early.stop(stopping_rounds = early_stopping_rounds, maximize = maximize,
verbose = verbose)
# of features: 73
niter: 50
best_iteration : 40
best_ntreelimit : 40
best_score : 0.1165337
best_msg : [40] train-rmse:0.064010 test-rmse:0.116534
nfeatures : 73
evaluation_log:
iter train_rmse test_rmse
1 3.31007782 3.3068878
2 2.31969213 2.3262197
---
49 0.06207940 0.1175223
50 0.06191289 0.1188113
xgb_binning for classification
Code
xgb_binning
Output
[1] 1 2 3 5 6 9 12 15 20
xgb_binning for multi-classification
Code
xgb_binning
Output
[1] 26 31 35 38
Code
embed:::xgb_binning(attrition_data_small, "EducationField", "Age", sample_val = 0.3,
learn_rate = 0.3, num_breaks = 10, tree_depth = 1, min_n = 5)
Output
numeric(0)
xgb_binning for regression
Code
xgb_binning
Output
[1] 42.01972 42.02510 42.03122 42.03462 42.03840 42.04638 42.05236 42.05917
Code
embed:::xgb_binning(ames_data_small, "Sale_Price", "Latitude", sample_val = 0.3,
learn_rate = 0.3, num_breaks = 10, tree_depth = 1, min_n = 5)
Output
numeric(0)
step_discretize_xgb for classification
Code
xgb_train_bins[1:10, ]
Output
# A tibble: 10 x 3
x z class
<fct> <fct> <fct>
1 [0.6808, Inf] [0.4208,0.7999) a
2 [0.5749,0.6808) [0.4208,0.7999) b
3 [0.3687,0.5749) [-Inf,0.3327) b
4 [0.5749,0.6808) [0.4208,0.7999) b
5 [0.6808, Inf] [0.4208,0.7999) a
6 [0.5749,0.6808) [0.7999, Inf] a
7 [0.5749,0.6808) [-Inf,0.3327) b
8 [0.5749,0.6808) [-Inf,0.3327) a
9 [0.2779,0.3687) [0.4208,0.7999) a
10 [0.3687,0.5749) [0.4208,0.7999) b
Code
xgb_test_bins[1:10, ]
Output
# A tibble: 10 x 3
x z class
<fct> <fct> <fct>
1 [0.5749,0.6808) [-Inf,0.3327) b
2 [0.5749,0.6808) [0.4208,0.7999) b
3 [0.3687,0.5749) [-Inf,0.3327) b
4 [0.2779,0.3687) [0.4208,0.7999) b
5 [0.6808, Inf] [0.7999, Inf] a
6 [0.3687,0.5749) [-Inf,0.3327) b
7 [0.2779,0.3687) [0.4208,0.7999) a
8 [0.6808, Inf] [0.7999, Inf] a
9 [0.3687,0.5749) [0.7999, Inf] b
10 [0.3687,0.5749) [0.4208,0.7999) b
Code
recipe(class ~ ., data = sim_tr_cls[1:9, ]) %>% step_discretize_xgb(
all_predictors(), outcome = "class") %>% prep()
Condition
Error in `step_discretize_xgb()`:
Caused by error in `prep()`:
! Too few observations in the early stopping validation set.Consider increasing the `sample_val` parameter.
Code
set.seed(1)
recipe(Status ~ ., data = credit_data_train) %>% step_discretize_xgb(Time,
outcome = "Status") %>% prep(retain = TRUE)
Condition
Warning:
More than 20 unique training set values are required. Predictors 'Time' were not processed; their original values will be used.
Message
-- Recipe ----------------------------------------------------------------------
-- Inputs
Number of variables by role
outcome: 1
predictor: 13
-- Training information
Training data contained 3340 data points and 301 incomplete rows.
-- Operations
* Discretizing variables using xgboost: <none> | Trained
step_discretize_xgb for multi-classification
Code
xgb_train_bins[1:10, ]
Output
# A tibble: 10 x 3
x z class
<fct> <fct> <fct>
1 [0.6879, Inf] [0.3274, Inf] c
2 [0.5863,0.6879) [0.3274, Inf] b
3 [0.3821,0.5863) [-Inf,0.3274) b
4 [0.5863,0.6879) [0.3274, Inf] b
5 [0.6879, Inf] [0.3274, Inf] c
6 [0.5863,0.6879) [0.3274, Inf] c
7 [0.5863,0.6879) [-Inf,0.3274) b
8 [0.5863,0.6879) [-Inf,0.3274) c
9 [-Inf,0.2887) [0.3274, Inf] a
10 [0.3821,0.5863) [0.3274, Inf] b
Code
xgb_test_bins[1:10, ]
Output
# A tibble: 10 x 3
x z class
<fct> <fct> <fct>
1 [0.5863,0.6879) [-Inf,0.3274) b
2 [0.5863,0.6879) [0.3274, Inf] b
3 [0.3821,0.5863) [-Inf,0.3274) b
4 [0.2887,0.3821) [0.3274, Inf] b
5 [0.6879, Inf] [0.3274, Inf] c
6 [0.3821,0.5863) [-Inf,0.3274) b
7 [0.2887,0.3821) [0.3274, Inf] a
8 [0.6879, Inf] [0.3274, Inf] c
9 [0.3821,0.5863) [0.3274, Inf] b
10 [0.3821,0.5863) [0.3274, Inf] b
Code
recipe(class ~ ., data = sim_tr_mcls[1:9, ]) %>% step_discretize_xgb(
all_predictors(), outcome = "class") %>% prep()
Condition
Error in `step_discretize_xgb()`:
Caused by error in `prep()`:
! Too few observations in the early stopping validation set.Consider increasing the `sample_val` parameter.
xgb_binning() errors if only one class in outcome
Code
embed:::xgb_binning(const_outcome, "outcome", "predictor", sample_val = 0.2,
learn_rate = 0.3, num_breaks = 10, tree_depth = 1, min_n = 5)
Condition
Error:
! Outcome variable only has less than 2 levels. Doesn't conform to regresion or classification task.
case weights step_discretize_xgb
Code
xgb_rec_cw
Message
-- Recipe ----------------------------------------------------------------------
-- Inputs
Number of variables by role
outcome: 1
predictor: 2
case_weights: 1
-- Training information
Training data contained 1000 data points and no incomplete rows.
-- Operations
* Discretizing variables using xgboost: x, z | Trained, weighted
empty printing
Code
rec
Message
-- Recipe ----------------------------------------------------------------------
-- Inputs
Number of variables by role
outcome: 1
predictor: 10
-- Operations
* Discretizing variables using xgboost: <none>
Code
rec
Message
-- Recipe ----------------------------------------------------------------------
-- Inputs
Number of variables by role
outcome: 1
predictor: 10
-- Training information
Training data contained 32 data points and no incomplete rows.
-- Operations
* Discretizing variables using xgboost: <none> | Trained
printing
Code
print(rec)
Message
-- Recipe ----------------------------------------------------------------------
-- Inputs
Number of variables by role
outcome: 1
predictor: 2
-- Operations
* Discretizing variables using xgboost: all_predictors()
Code
prep(rec)
Message
-- Recipe ----------------------------------------------------------------------
-- Inputs
Number of variables by role
outcome: 1
predictor: 2
-- Training information
Training data contained 1000 data points and no incomplete rows.
-- Operations
* Discretizing variables using xgboost: x, z | Trained
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run_xgboost for classification
Code
xgboost
Output
##### xgb.Booster
raw: 69.1 Kb
call:
xgboost::xgb.train(params = .params, data = .train, nrounds = 100,
watchlist = list(train = .train, test = .test), verbose = 0,
early_stopping_rounds = 10, tree_method = "hist", objective = .objective,
nthread = 1)
params (as set within xgb.train):
eta = "0.3", max_bin = "10", max_depth = "1", min_child_weight = "5", tree_method = "hist", objective = "binary:logistic", nthread = "1", validate_parameters = "TRUE"
xgb.attributes:
best_iteration, best_msg, best_ntreelimit, best_score, niter
callbacks:
cb.evaluation.log()
cb.early.stop(stopping_rounds = early_stopping_rounds, maximize = maximize,
verbose = verbose)
# of features: 13
niter: 89
best_iteration : 79
best_ntreelimit : 79
best_score : 0.4442069
best_msg : [79] train-logloss:0.420000 test-logloss:0.444207
nfeatures : 13
evaluation_log:
iter train_logloss test_logloss
1 0.6279229 0.6303495
2 0.5869984 0.5894989
---
88 0.4180034 0.4449478
89 0.4178084 0.4446590
run_xgboost for multi-classification
Code
xgboost
Output
##### xgb.Booster
raw: 149.7 Kb
call:
xgboost::xgb.train(params = .params, data = .train, nrounds = 100,
watchlist = list(train = .train, test = .test), verbose = 0,
early_stopping_rounds = 10, tree_method = "hist", objective = .objective,
nthread = 1)
params (as set within xgb.train):
eta = "0.3", max_bin = "10", max_depth = "1", min_child_weight = "5", num_class = "6", tree_method = "hist", objective = "multi:softprob", nthread = "1", validate_parameters = "TRUE"
xgb.attributes:
best_iteration, best_msg, best_ntreelimit, best_score, niter
callbacks:
cb.evaluation.log()
cb.early.stop(stopping_rounds = early_stopping_rounds, maximize = maximize,
verbose = verbose)
# of features: 30
niter: 33
best_iteration : 23
best_ntreelimit : 23
best_score : 1.246428
best_msg : [23] train-mlogloss:1.178121 test-mlogloss:1.246428
nfeatures : 30
evaluation_log:
iter train_mlogloss test_mlogloss
1 1.623174 1.631783
2 1.515108 1.531188
---
32 1.159813 1.249701
33 1.158088 1.250462
run_xgboost for regression
Code
xgboost
Output
##### xgb.Booster
raw: 40.2 Kb
call:
xgboost::xgb.train(params = .params, data = .train, nrounds = 100,
watchlist = list(train = .train, test = .test), verbose = 0,
early_stopping_rounds = 10, tree_method = "hist", objective = .objective,
nthread = 1)
params (as set within xgb.train):
eta = "0.3", max_bin = "10", max_depth = "1", min_child_weight = "5", tree_method = "hist", objective = "reg:squarederror", nthread = "1", validate_parameters = "TRUE"
xgb.attributes:
best_iteration, best_msg, best_ntreelimit, best_score, niter
callbacks:
cb.evaluation.log()
cb.early.stop(stopping_rounds = early_stopping_rounds, maximize = maximize,
verbose = verbose)
# of features: 73
niter: 50
best_iteration : 40
best_ntreelimit : 40
best_score : 0.1165337
best_msg : [40] train-rmse:0.064010 test-rmse:0.116534
nfeatures : 73
evaluation_log:
iter train_rmse test_rmse
1 3.31007782 3.3068878
2 2.31969213 2.3262197
---
49 0.06207940 0.1175223
50 0.06191289 0.1188113
xgb_binning for classification
Code
xgb_binning
Output
[1] 1 2 3 5 6 9 12 15 20
xgb_binning for multi-classification
Code
xgb_binning
Output
[1] 26 31 35 38
Code
embed:::xgb_binning(attrition_data_small, "EducationField", "Age", sample_val = 0.3,
learn_rate = 0.3, num_breaks = 10, tree_depth = 1, min_n = 5)
Output
numeric(0)
xgb_binning for regression
Code
xgb_binning
Output
[1] 42.01972 42.02510 42.03122 42.03462 42.03840 42.04638 42.05236 42.05917
Code
embed:::xgb_binning(ames_data_small, "Sale_Price", "Latitude", sample_val = 0.3,
learn_rate = 0.3, num_breaks = 10, tree_depth = 1, min_n = 5)
Output
numeric(0)
step_discretize_xgb for classification
Code
xgb_train_bins[1:10, ]
Output
# A tibble: 10 x 3
x z class
<fct> <fct> <fct>
1 [0.6808, Inf] [0.4208,0.7999) a
2 [0.5749,0.6808) [0.4208,0.7999) b
3 [0.3687,0.5749) [-Inf,0.3327) b
4 [0.5749,0.6808) [0.4208,0.7999) b
5 [0.6808, Inf] [0.4208,0.7999) a
6 [0.5749,0.6808) [0.7999, Inf] a
7 [0.5749,0.6808) [-Inf,0.3327) b
8 [0.5749,0.6808) [-Inf,0.3327) a
9 [0.2779,0.3687) [0.4208,0.7999) a
10 [0.3687,0.5749) [0.4208,0.7999) b
Code
xgb_test_bins[1:10, ]
Output
# A tibble: 10 x 3
x z class
<fct> <fct> <fct>
1 [0.5749,0.6808) [-Inf,0.3327) b
2 [0.5749,0.6808) [0.4208,0.7999) b
3 [0.3687,0.5749) [-Inf,0.3327) b
4 [0.2779,0.3687) [0.4208,0.7999) b
5 [0.6808, Inf] [0.7999, Inf] a
6 [0.3687,0.5749) [-Inf,0.3327) b
7 [0.2779,0.3687) [0.4208,0.7999) a
8 [0.6808, Inf] [0.7999, Inf] a
9 [0.3687,0.5749) [0.7999, Inf] b
10 [0.3687,0.5749) [0.4208,0.7999) b
Code
recipe(class ~ ., data = sim_tr_cls[1:9, ]) %>% step_discretize_xgb(
all_predictors(), outcome = "class") %>% prep()
Condition
Error in `step_discretize_xgb()`:
Caused by error in `prep()`:
! Too few observations in the early stopping validation set.Consider increasing the `sample_val` parameter.
Code
set.seed(1)
recipe(Status ~ ., data = credit_data_train) %>% step_discretize_xgb(Time,
outcome = "Status") %>% prep(retain = TRUE)
Condition
Warning:
More than 20 unique training set values are required. Predictors 'Time' were not processed; their original values will be used.
Message
-- Recipe ----------------------------------------------------------------------
-- Inputs
Number of variables by role
outcome: 1
predictor: 13
-- Training information
Training data contained 3340 data points and 301 incomplete rows.
-- Operations
* Discretizing variables using xgboost: <none> | Trained
step_discretize_xgb for multi-classification
Code
xgb_train_bins[1:10, ]
Output
# A tibble: 10 x 3
x z class
<fct> <fct> <fct>
1 [0.6879, Inf] [0.3274, Inf] c
2 [0.5863,0.6879) [0.3274, Inf] b
3 [0.3821,0.5863) [-Inf,0.3274) b
4 [0.5863,0.6879) [0.3274, Inf] b
5 [0.6879, Inf] [0.3274, Inf] c
6 [0.5863,0.6879) [0.3274, Inf] c
7 [0.5863,0.6879) [-Inf,0.3274) b
8 [0.5863,0.6879) [-Inf,0.3274) c
9 [-Inf,0.2887) [0.3274, Inf] a
10 [0.3821,0.5863) [0.3274, Inf] b
Code
xgb_test_bins[1:10, ]
Output
# A tibble: 10 x 3
x z class
<fct> <fct> <fct>
1 [0.5863,0.6879) [-Inf,0.3274) b
2 [0.5863,0.6879) [0.3274, Inf] b
3 [0.3821,0.5863) [-Inf,0.3274) b
4 [0.2887,0.3821) [0.3274, Inf] b
5 [0.6879, Inf] [0.3274, Inf] c
6 [0.3821,0.5863) [-Inf,0.3274) b
7 [0.2887,0.3821) [0.3274, Inf] a
8 [0.6879, Inf] [0.3274, Inf] c
9 [0.3821,0.5863) [0.3274, Inf] b
10 [0.3821,0.5863) [0.3274, Inf] b
Code
recipe(class ~ ., data = sim_tr_mcls[1:9, ]) %>% step_discretize_xgb(
all_predictors(), outcome = "class") %>% prep()
Condition
Error in `step_discretize_xgb()`:
Caused by error in `prep()`:
! Too few observations in the early stopping validation set.Consider increasing the `sample_val` parameter.
xgb_binning() errors if only one class in outcome
Code
embed:::xgb_binning(const_outcome, "outcome", "predictor", sample_val = 0.2,
learn_rate = 0.3, num_breaks = 10, tree_depth = 1, min_n = 5)
Condition
Error:
! Outcome variable only has less than 2 levels. Doesn't conform to regresion or classification task.
case weights step_discretize_xgb
Code
xgb_rec_cw
Message
-- Recipe ----------------------------------------------------------------------
-- Inputs
Number of variables by role
outcome: 1
predictor: 2
case_weights: 1
-- Training information
Training data contained 1000 data points and no incomplete rows.
-- Operations
* Discretizing variables using xgboost: x, z | Trained, weighted
empty printing
Code
rec
Message
-- Recipe ----------------------------------------------------------------------
-- Inputs
Number of variables by role
outcome: 1
predictor: 10
-- Operations
* Discretizing variables using xgboost: <none>
Code
rec
Message
-- Recipe ----------------------------------------------------------------------
-- Inputs
Number of variables by role
outcome: 1
predictor: 10
-- Training information
Training data contained 32 data points and no incomplete rows.
-- Operations
* Discretizing variables using xgboost: <none> | Trained
printing
Code
print(rec)
Message
-- Recipe ----------------------------------------------------------------------
-- Inputs
Number of variables by role
outcome: 1
predictor: 2
-- Operations
* Discretizing variables using xgboost: all_predictors()
Code
prep(rec)
Message
-- Recipe ----------------------------------------------------------------------
-- Inputs
Number of variables by role
outcome: 1
predictor: 2
-- Training information
Training data contained 1000 data points and no incomplete rows.
-- Operations
* Discretizing variables using xgboost: x, z | Trained
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