Package 'triplot' reference manual

Title:	Explaining Correlated Features in Machine Learning Models
Description:	Tools for exploring effects of correlated features in predictive models. The predict_triplot() function delivers instance-level explanations that calculate the importance of the groups of explanatory variables. The model_triplot() function delivers data-level explanations. The generic plot function visualises in a concise way importance of hierarchical groups of predictors. All of the the tools are model agnostic, therefore works for any predictive machine learning models. Find more details in Biecek (2018) <arXiv:1806.08915>.
Authors:	Katarzyna Pekala [aut, cre], Przemyslaw Biecek [aut]
Maintainer:	Katarzyna Pekala <[email protected]>
License:	GPL-3
Version:	1.3.1
Built:	2024-11-21 03:20:12 UTC
Source:	https://github.com/modeloriented/triplot

Calculates importance of variable groups (called aspects) for a selected observation

Description

Predict aspects function takes a sample from a given dataset and modifies it. Modification is made by replacing part of its aspects by values from the observation. Then function is calculating the difference between the prediction made on modified sample and the original sample. Finally, it measures the impact of aspects on the change of prediction by using the linear model or lasso.

Usage

aspect_importance(x, ...)

## S3 method for class 'explainer'
aspect_importance(
  x,
  new_observation,
  variable_groups,
  N = 1000,
  n_var = 0,
  sample_method = "default",
  f = 2,
  ...
)

## Default S3 method:
aspect_importance(
  x,
  data,
  predict_function = predict,
  label = class(x)[1],
  new_observation,
  variable_groups,
  N = 100,
  n_var = 0,
  sample_method = "default",
  f = 2,
  ...
)

lime(x, ...)

predict_aspects(x, ...)
aspect_importance(x, ...)

## S3 method for class 'explainer'
aspect_importance(
  x,
  new_observation,
  variable_groups,
  N = 1000,
  n_var = 0,
  sample_method = "default",
  f = 2,
  ...
)

## Default S3 method:
aspect_importance(
  x,
  data,
  predict_function = predict,
  label = class(x)[1],
  new_observation,
  variable_groups,
  N = 100,
  n_var = 0,
  sample_method = "default",
  f = 2,
  ...
)

lime(x, ...)

predict_aspects(x, ...)

Arguments

`x`	an explainer created with the `DALEX::explain()` function or a model to be explained.
`...`	other parameters
`new_observation`	selected observation with columns that corresponds to variables used in the model
`variable_groups`	list containing grouping of features into aspects
`N`	number of observations to be sampled (with replacement) from data NOTE: Small `N` may cause unstable results.
`n_var`	maximum number of non-zero coefficients after lasso fitting, if zero than linear regression is used
`sample_method`	sampling method in `get_sample`
`f`	frequency in `get_sample`
`data`	dataset, it will be extracted from `x` if it's an explainer NOTE: It is best when target variable is not present in the `data`
`predict_function`	predict function, it will be extracted from `x` if it's an explainer
`label`	name of the model. By default it's extracted from the 'class' attribute of the model.

Value

An object of the class aspect_importance. Contains data frame that describes aspects' importance.

Examples

library("DALEX")

model_titanic_glm <- glm(survived == 1 ~
                         class+gender+age+sibsp+parch+fare+embarked,
                         data = titanic_imputed,
                         family = "binomial")

explain_titanic_glm <- explain(model_titanic_glm,
                               data = titanic_imputed[,-8],
                               y = titanic_imputed$survived == 1,
                               verbose = FALSE)

aspects <- list(wealth = c("class", "fare"),
                family = c("sibsp", "parch"),
                personal = c("gender", "age"),
                embarked = "embarked")

predict_aspects(explain_titanic_glm,
                  new_observation = titanic_imputed[1,],
                  variable_groups = aspects)


library("randomForest")
library("DALEX")
model_titanic_rf <-
 randomForest(factor(survived) ~ class + gender + age + sibsp +
                parch + fare + embarked,
              data = titanic_imputed)

explain_titanic_rf <- explain(model_titanic_rf,
                              data = titanic_imputed[,-8],
                              y = titanic_imputed$survived == 1,
                              verbose = FALSE)

predict_aspects(explain_titanic_rf,
                  new_observation = titanic_imputed[1,],
                  variable_groups = aspects)



library("DALEX")

model_titanic_glm <- glm(survived == 1 ~
                         class+gender+age+sibsp+parch+fare+embarked,
                         data = titanic_imputed,
                         family = "binomial")

explain_titanic_glm <- explain(model_titanic_glm,
                               data = titanic_imputed[,-8],
                               y = titanic_imputed$survived == 1,
                               verbose = FALSE)

aspects <- list(wealth = c("class", "fare"),
                family = c("sibsp", "parch"),
                personal = c("gender", "age"),
                embarked = "embarked")

predict_aspects(explain_titanic_glm,
                  new_observation = titanic_imputed[1,],
                  variable_groups = aspects)


library("randomForest")
library("DALEX")
model_titanic_rf <-
 randomForest(factor(survived) ~ class + gender + age + sibsp +
                parch + fare + embarked,
              data = titanic_imputed)

explain_titanic_rf <- explain(model_titanic_rf,
                              data = titanic_imputed[,-8],
                              y = titanic_imputed$survived == 1,
                              verbose = FALSE)

predict_aspects(explain_titanic_rf,
                  new_observation = titanic_imputed[1,],
                  variable_groups = aspects)

Aspects importance for single aspects

Description

Calculates aspect_importance for single aspects (every aspect contains only one feature).

Usage

aspect_importance_single(x, ...)

## S3 method for class 'explainer'
aspect_importance_single(
  x,
  new_observation,
  N = 1000,
  n_var = 0,
  sample_method = "default",
  f = 2,
  ...
)

## Default S3 method:
aspect_importance_single(
  x,
  data,
  predict_function = predict,
  label = class(x)[1],
  new_observation,
  N = 1000,
  n_var = 0,
  sample_method = "default",
  f = 2,
  ...
)
aspect_importance_single(x, ...)

## S3 method for class 'explainer'
aspect_importance_single(
  x,
  new_observation,
  N = 1000,
  n_var = 0,
  sample_method = "default",
  f = 2,
  ...
)

## Default S3 method:
aspect_importance_single(
  x,
  data,
  predict_function = predict,
  label = class(x)[1],
  new_observation,
  N = 1000,
  n_var = 0,
  sample_method = "default",
  f = 2,
  ...
)

Arguments

`x`	an explainer created with the `DALEX::explain()` function or a model to be explained.
`...`	other parameters
`new_observation`	selected observation with columns that corresponds to variables used in the model, should be without target variable
`N`	number of observations to be sampled (with replacement) from data NOTE: Small `N` may cause unstable results.
`n_var`	how many non-zero coefficients for lasso fitting, if zero than linear regression is used
`sample_method`	sampling method in `get_sample`
`f`	frequency in in `get_sample`
`data`	dataset, it will be extracted from `x` if it's an explainer NOTE: Target variable shouldn't be present in the `data`
`predict_function`	predict function, it will be extracted from `x` if it's an explainer
`label`	name of the model. By default it's extracted from the 'class' attribute of the model.

Value

An object of the class 'aspect_importance'. Contains dataframe that describes aspects' importance.

Examples

library("DALEX")
model_titanic_glm <- glm(survived == 1 ~ class + gender + age +
                         sibsp + parch + fare + embarked,
                         data = titanic_imputed,
                         family = "binomial")

explainer_titanic <- explain(model_titanic_glm,
                             data = titanic_imputed[,-8],
                             verbose = FALSE)
aspect_importance_single(explainer_titanic,
                         new_observation = titanic_imputed[1,-8])

library("DALEX")
model_titanic_glm <- glm(survived == 1 ~ class + gender + age +
                         sibsp + parch + fare + embarked,
                         data = titanic_imputed,
                         family = "binomial")

explainer_titanic <- explain(model_titanic_glm,
                             data = titanic_imputed[,-8],
                             verbose = FALSE)
aspect_importance_single(explainer_titanic,
                         new_observation = titanic_imputed[1,-8])

Calculate triplot that sums up automatic aspect/feature importance grouping

Description

This function shows:

plot for the importance of single variables,
tree that shows importance for every newly expanded group of variables,
clustering tree.

Usage

calculate_triplot(x, ...)

## S3 method for class 'explainer'
calculate_triplot(
  x,
  type = c("predict", "model"),
  new_observation = NULL,
  N = 1000,
  loss_function = DALEX::loss_root_mean_square,
  B = 10,
  fi_type = c("raw", "ratio", "difference"),
  clust_method = "complete",
  cor_method = "spearman",
  ...
)

## Default S3 method:
calculate_triplot(
  x,
  data,
  y = NULL,
  predict_function = predict,
  label = class(x)[1],
  type = c("predict", "model"),
  new_observation = NULL,
  N = 1000,
  loss_function = DALEX::loss_root_mean_square,
  B = 10,
  fi_type = c("raw", "ratio", "difference"),
  clust_method = "complete",
  cor_method = "spearman",
  ...
)

## S3 method for class 'triplot'
print(x, ...)

model_triplot(x, ...)

predict_triplot(x, ...)
calculate_triplot(x, ...)

## S3 method for class 'explainer'
calculate_triplot(
  x,
  type = c("predict", "model"),
  new_observation = NULL,
  N = 1000,
  loss_function = DALEX::loss_root_mean_square,
  B = 10,
  fi_type = c("raw", "ratio", "difference"),
  clust_method = "complete",
  cor_method = "spearman",
  ...
)

## Default S3 method:
calculate_triplot(
  x,
  data,
  y = NULL,
  predict_function = predict,
  label = class(x)[1],
  type = c("predict", "model"),
  new_observation = NULL,
  N = 1000,
  loss_function = DALEX::loss_root_mean_square,
  B = 10,
  fi_type = c("raw", "ratio", "difference"),
  clust_method = "complete",
  cor_method = "spearman",
  ...
)

## S3 method for class 'triplot'
print(x, ...)

model_triplot(x, ...)

predict_triplot(x, ...)

Arguments

`x`	an explainer created with the `DALEX::explain()` function or a model to be explained.
`...`	other parameters
`type`	if `predict` then aspect_importance is used, if `model` than feature_importance is calculated
`new_observation`	selected observation with columns that corresponds to variables used in the model, should be without target variable
`N`	number of rows to be sampled from data NOTE: Small `N` may cause unstable results.
`loss_function`	a function that will be used to assess variable importance, if `type = model`
`B`	integer, number of permutation rounds to perform on each variable in feature importance calculation, if `type = model`
`fi_type`	character, type of transformation that should be applied for dropout loss, if `type = model`. "raw" results raw drop losses, "ratio" returns `drop_loss/drop_loss_full_model`.
`clust_method`	the agglomeration method to be used, see `hclust` methods
`cor_method`	the correlation method to be used see `cor` methods
`data`	dataset, it will be extracted from `x` if it's an explainer NOTE: Target variable shouldn't be present in the `data`
`y`	true labels for `data`, will be extracted from `x` if it's an explainer
`predict_function`	predict function, it will be extracted from `x` if it's an explainer
`label`	name of the model. By default it's extracted from the 'class' attribute of the model.

Value

triplot object

Examples


library(DALEX)
set.seed(123)
apartments_num <- apartments[,unlist(lapply(apartments, is.numeric))]
apartments_num_lm_model <- lm(m2.price ~ ., data = apartments_num)
apartments_num_new_observation <- apartments_num[30, ]
explainer_apartments <- explain(model = apartments_num_lm_model,
                                data = apartments_num[,-1],
                                y = apartments_num[, 1],
                                verbose = FALSE)
apartments_tri <- calculate_triplot(x = explainer_apartments,
                                    new_observation =
                                      apartments_num_new_observation[-1])
apartments_tri
                                   

library(DALEX)
set.seed(123)
apartments_num <- apartments[,unlist(lapply(apartments, is.numeric))]
apartments_num_lm_model <- lm(m2.price ~ ., data = apartments_num)
apartments_num_new_observation <- apartments_num[30, ]
explainer_apartments <- explain(model = apartments_num_lm_model,
                                data = apartments_num[,-1],
                                y = apartments_num[, 1],
                                verbose = FALSE)
apartments_tri <- calculate_triplot(x = explainer_apartments,
                                    new_observation =
                                      apartments_num_new_observation[-1])
apartments_tri

Creates a cluster tree from numeric features

Description

Creates a cluster tree from numeric features and their correlations.

Usage

cluster_variables(x, ...)

## Default S3 method:
cluster_variables(x, clust_method = "complete", cor_method = "spearman", ...)
cluster_variables(x, ...)

## Default S3 method:
cluster_variables(x, clust_method = "complete", cor_method = "spearman", ...)

Arguments

`x`	dataframe with only numeric columns
`...`	other parameters
`clust_method`	the agglomeration method to be used see `hclust` methods
`cor_method`	the correlation method to be used see `cor` methods

Value

an hclust object

Examples

library("DALEX")
dragons_data <- dragons[,c(2,3,4,7,8)]
cluster_variables(dragons_data, clust_method = "complete")

library("DALEX")
dragons_data <- dragons[,c(2,3,4,7,8)]
cluster_variables(dragons_data, clust_method = "complete")

Function for getting binary matrix

Description

Function creates binary matrix, to be used in aspect_importance method. It starts with a zero matrix. Then it replaces some zeros with ones. If sample_method = "default" it randomly replaces one or two zeros per row. If sample_method = "binom" it replaces random number of zeros per row - average number of replaced zeros can be controlled by parameter sample_method = "f". Function doesn't allow the returned matrix to have rows with only zeros.

Usage

get_sample(n, p, sample_method = c("default", "binom"), f = 2)
get_sample(n, p, sample_method = c("default", "binom"), f = 2)

Arguments

`n`	number of rows
`p`	number of columns
`sample_method`	sampling method
`f`	frequency for binomial sampling

Value

a binary matrix

Examples

 get_sample(100,6,"binom",3)

get_sample(100,6,"binom",3)

Helper function that combines clustering variables and creating aspect list

Description

Divides correlated features into groups, called aspects. Division is based on correlation cutoff level.

Usage

group_variables(x, h, clust_method = "complete", cor_method = "spearman")
group_variables(x, h, clust_method = "complete", cor_method = "spearman")

Arguments

`x`	hclust object
`h`	correlation value for tree cutting
`clust_method`	the agglomeration method to be used see `hclust` methods
`cor_method`	the correlation method to be used see `cor` methods

Value

list with aspect

Examples

library("DALEX")
dragons_data <- dragons[,c(2,3,4,7,8)]
group_variables(dragons_data, h = 0.5, clust_method = "complete")

library("DALEX")
dragons_data <- dragons[,c(2,3,4,7,8)]
group_variables(dragons_data, h = 0.5, clust_method = "complete")

Calculates importance of hierarchically grouped aspects

Description

This function creates a tree that shows order of feature grouping and calculates importance of every newly created aspect.

Usage

hierarchical_importance(
  x,
  data,
  y = NULL,
  predict_function = predict,
  type = "predict",
  new_observation = NULL,
  N = 1000,
  loss_function = DALEX::loss_root_mean_square,
  B = 10,
  fi_type = c("raw", "ratio", "difference"),
  clust_method = "complete",
  cor_method = "spearman",
  ...
)

## S3 method for class 'hierarchical_importance'
plot(
  x,
  absolute_value = FALSE,
  show_labels = TRUE,
  add_last_group = TRUE,
  axis_lab_size = 10,
  text_size = 3,
  ...
)
hierarchical_importance(
  x,
  data,
  y = NULL,
  predict_function = predict,
  type = "predict",
  new_observation = NULL,
  N = 1000,
  loss_function = DALEX::loss_root_mean_square,
  B = 10,
  fi_type = c("raw", "ratio", "difference"),
  clust_method = "complete",
  cor_method = "spearman",
  ...
)

## S3 method for class 'hierarchical_importance'
plot(
  x,
  absolute_value = FALSE,
  show_labels = TRUE,
  add_last_group = TRUE,
  axis_lab_size = 10,
  text_size = 3,
  ...
)

Arguments

`x`	a model to be explained.
`data`	dataset NOTE: Target variable shouldn't be present in the `data`
`y`	true labels for `data`
`predict_function`	predict function
`type`	if `predict` then aspect_importance is used, if `model` than feature_importance is calculated
`new_observation`	selected observation with columns that corresponds to variables used in the model, should be without target variable
`N`	number of rows to be sampled from data NOTE: Small `N` may cause unstable results.
`loss_function`	a function that will be used to assess variable importance, if `type = model`
`B`	integer, number of permutation rounds to perform on each variable in feature importance calculation, if `type = model`
`fi_type`	character, type of transformation that should be applied for dropout loss, if `type = model`. "raw" results raw drop losses, "ratio" returns `drop_loss/drop_loss_full_model`.
`clust_method`	the agglomeration method to be used, see `hclust` methods
`cor_method`	the correlation method to be used see `cor` methods
`...`	other parameters
`absolute_value`	if TRUE, aspects importance values will be drawn as absolute values
`show_labels`	if TRUE, plot will have annotated axis Y
`add_last_group`	if TRUE, plot will draw connecting line between last two groups
`axis_lab_size`	size of labels on axis Y, if applicable
`text_size`	size of labels annotating values of aspects importance

Value

ggplot

Examples

library(DALEX)
apartments_num <- apartments[,unlist(lapply(apartments, is.numeric))]
apartments_num_lm_model <- lm(m2.price ~ ., data = apartments_num)
hi <- hierarchical_importance(x = apartments_num_lm_model,
 data = apartments_num[,-1],
 y = apartments_num[,1],
 type = "model")
plot(hi, add_last_group = TRUE, absolute_value = TRUE)

library(DALEX)
apartments_num <- apartments[,unlist(lapply(apartments, is.numeric))]
apartments_num_lm_model <- lm(m2.price ~ ., data = apartments_num)
hi <- hierarchical_importance(x = apartments_num_lm_model,
 data = apartments_num[,-1],
 y = apartments_num[,1],
 type = "model")
plot(hi, add_last_group = TRUE, absolute_value = TRUE)

Cuts tree at custom height and returns a list

Description

This function creates aspect list after cutting a cluster tree of features at a given height.

Usage

list_variables(x, h)
list_variables(x, h)

Arguments

`x`	hclust object
`h`	correlation value for tree cutting

Value

list of aspects

Examples

library("DALEX")
dragons_data <- dragons[,c(2,3,4,7,8)]
cv <- cluster_variables(dragons_data, clust_method = "complete")
list_variables(cv, h = 0.5)

library("DALEX")
dragons_data <- dragons[,c(2,3,4,7,8)]
cv <- cluster_variables(dragons_data, clust_method = "complete")
list_variables(cv, h = 0.5)

Function for plotting aspect_importance results

Description

This function plots the results of aspect_importance.

Usage

## S3 method for class 'aspect_importance'
plot(
  x,
  ...,
  bar_width = 10,
  show_features = aspects_on_axis,
  aspects_on_axis = TRUE,
  add_importance = FALSE,
  digits_to_round = 2,
  text_size = 3
)
## S3 method for class 'aspect_importance'
plot(
  x,
  ...,
  bar_width = 10,
  show_features = aspects_on_axis,
  aspects_on_axis = TRUE,
  add_importance = FALSE,
  digits_to_round = 2,
  text_size = 3
)

Arguments

`x`	object of aspect_importance class
`...`	other parameters
`bar_width`	bar width
`show_features`	if TRUE, labels on axis Y show aspect names, otherwise they show features names
`aspects_on_axis`	alias for `show_features` held for backwards compatibility
`add_importance`	if TRUE, plot is annotated with values of aspects importance
`digits_to_round`	integer indicating the number of decimal places used for rounding values of aspects importance shown on the plot
`text_size`	size of labels annotating values of aspects importance, if applicable

Value

a ggplot2 object

Examples

library("DALEX")

model_titanic_glm <- glm(survived == 1 ~
                         class+gender+age+sibsp+parch+fare+embarked,
                         data = titanic_imputed,
                         family = "binomial")

explain_titanic_glm <- explain(model_titanic_glm,
                               data = titanic_imputed[,-8],
                               y = titanic_imputed$survived == 1,
                               verbose = FALSE)

aspects <- list(wealth = c("class", "fare"),
                family = c("sibsp", "parch"),
                personal = c("gender", "age"),
                embarked = "embarked")

titanic_ai <- predict_aspects(explain_titanic_glm,
                  new_observation = titanic_imputed[1,],
                  variable_groups = aspects)
plot(titanic_ai)

library("DALEX")

model_titanic_glm <- glm(survived == 1 ~
                         class+gender+age+sibsp+parch+fare+embarked,
                         data = titanic_imputed,
                         family = "binomial")

explain_titanic_glm <- explain(model_titanic_glm,
                               data = titanic_imputed[,-8],
                               y = titanic_imputed$survived == 1,
                               verbose = FALSE)

aspects <- list(wealth = c("class", "fare"),
                family = c("sibsp", "parch"),
                personal = c("gender", "age"),
                embarked = "embarked")

titanic_ai <- predict_aspects(explain_titanic_glm,
                  new_observation = titanic_imputed[1,],
                  variable_groups = aspects)
plot(titanic_ai)

Plots tree with correlation values

Description

Plots tree that illustrates the results of cluster_variables function.

Usage

## S3 method for class 'cluster_variables'
plot(x, p = NULL, show_labels = TRUE, axis_lab_size = 10, text_size = 3, ...)
## S3 method for class 'cluster_variables'
plot(x, p = NULL, show_labels = TRUE, axis_lab_size = 10, text_size = 3, ...)

Arguments

`x`	`cluster_variables` or `hclust` object
`p`	correlation value for cutoff level, if not NULL, cutoff line will be drawn
`show_labels`	if TRUE, plot will have annotated axis Y
`axis_lab_size`	size of labels on axis Y, if applicable
`text_size`	size of labels annotating values of correlations
`...`	other parameters

Value

plot

Examples

library("DALEX")
dragons_data <- dragons[,c(2,3,4,7,8)]
cv <- cluster_variables(dragons_data, clust_method = "complete")
plot(cv, p = 0.7)

library("DALEX")
dragons_data <- dragons[,c(2,3,4,7,8)]
cv <- cluster_variables(dragons_data, clust_method = "complete")
plot(cv, p = 0.7)

Plots triplot

Description

Plots triplot that sum up automatic aspect/feature importance grouping

Usage

## S3 method for class 'triplot'
plot(
  x,
  absolute_value = FALSE,
  add_importance_labels = FALSE,
  show_model_label = FALSE,
  abbrev_labels = 0,
  add_last_group = TRUE,
  axis_lab_size = 10,
  text_size = 3,
  bar_width = 5,
  margin_mid = 0.3,
  ...
)
## S3 method for class 'triplot'
plot(
  x,
  absolute_value = FALSE,
  add_importance_labels = FALSE,
  show_model_label = FALSE,
  abbrev_labels = 0,
  add_last_group = TRUE,
  axis_lab_size = 10,
  text_size = 3,
  bar_width = 5,
  margin_mid = 0.3,
  ...
)

Arguments

`x`	triplot object
`absolute_value`	if TRUE, aspect importance values will be drawn as absolute values
`add_importance_labels`	if TRUE, first plot is annotated with values of aspects importance on the bars
`show_model_label`	if TRUE, adds subtitle with model label
`abbrev_labels`	if greater than 0, labels for axis Y in single aspect importance plot will be abbreviated according to this parameter
`add_last_group`	if TRUE and `type = predict`, plot will draw connecting line between last two groups at the level of 105 biggest importance value, for `model` this line is always drawn at the baseline value
`axis_lab_size`	size of labels on axis
`text_size`	size of labels annotating values of aspects importance and correlations
`bar_width`	bar width in the first plot
`margin_mid`	size of a right margin of a middle plot
`...`	other parameters

Value

plot

Examples

library(DALEX)
set.seed(123)
apartments_num <- apartments[,unlist(lapply(apartments, is.numeric))]
apartments_num_lm_model <- lm(m2.price ~ ., data = apartments_num)
apartments_num_new_observation <- apartments_num[30, ]
explainer_apartments <- explain(model = apartments_num_lm_model,
                                data = apartments_num[,-1],
                                y = apartments_num[, 1],
                                verbose = FALSE)
apartments_tri <- calculate_triplot(x = explainer_apartments,
 new_observation = apartments_num_new_observation[-1])
plot(apartments_tri)

library(DALEX)
set.seed(123)
apartments_num <- apartments[,unlist(lapply(apartments, is.numeric))]
apartments_num_lm_model <- lm(m2.price ~ ., data = apartments_num)
apartments_num_new_observation <- apartments_num[30, ]
explainer_apartments <- explain(model = apartments_num_lm_model,
                                data = apartments_num[,-1],
                                y = apartments_num[, 1],
                                verbose = FALSE)
apartments_tri <- calculate_triplot(x = explainer_apartments,
 new_observation = apartments_num_new_observation[-1])
plot(apartments_tri)

Function for printing aspect_importance results

Description

This function prints the results of aspect_importance.

Usage

## S3 method for class 'aspect_importance'
print(x, show_features = FALSE, show_corr = FALSE, ...)
## S3 method for class 'aspect_importance'
print(x, show_features = FALSE, show_corr = FALSE, ...)

Arguments

`x`	object of aspect_importance class
`show_features`	show list of features for every aspect
`show_corr`	show if all features in aspect are pairwise positively correlated (for numeric features only)
`...`	other parameters

Examples

library("DALEX")

model_titanic_glm <- glm(survived == 1 ~
                         class+gender+age+sibsp+parch+fare+embarked,
                         data = titanic_imputed,
                         family = "binomial")

explain_titanic_glm <- explain(model_titanic_glm,
                               data = titanic_imputed[,-8],
                               y = titanic_imputed$survived == 1,
                               verbose = FALSE)

aspects <- list(wealth = c("class", "fare"),
                family = c("sibsp", "parch"),
                personal = c("gender", "age"),
                embarked = "embarked")

titanic_ai <- predict_aspects(explain_titanic_glm,
                  new_observation = titanic_imputed[1,],
                  variable_groups = aspects)
print(titanic_ai)

library("DALEX")

model_titanic_glm <- glm(survived == 1 ~
                         class+gender+age+sibsp+parch+fare+embarked,
                         data = titanic_imputed,
                         family = "binomial")

explain_titanic_glm <- explain(model_titanic_glm,
                               data = titanic_imputed[,-8],
                               y = titanic_imputed$survived == 1,
                               verbose = FALSE)

aspects <- list(wealth = c("class", "fare"),
                family = c("sibsp", "parch"),
                personal = c("gender", "age"),
                embarked = "embarked")

titanic_ai <- predict_aspects(explain_titanic_glm,
                  new_observation = titanic_imputed[1,],
                  variable_groups = aspects)
print(titanic_ai)

Package 'triplot'

Help Index

Calculates importance of variable groups (called aspects) for a selected observation

Description

Usage

Arguments

Value

Examples

Aspects importance for single aspects

Description

Usage

Arguments

Value

Examples

Calculate triplot that sums up automatic aspect/feature importance grouping

Description

Usage

Arguments

Value

Examples

Creates a cluster tree from numeric features

Description

Usage

Arguments

Value

Examples

Function for getting binary matrix

Description

Usage

Arguments

Value

Examples

Helper function that combines clustering variables and creating aspect list

Description

Usage

Arguments

Value

Examples

Calculates importance of hierarchically grouped aspects

Description

Usage

Arguments

Value

Examples

Cuts tree at custom height and returns a list

Description

Usage

Arguments

Value

Examples

Function for plotting aspect_importance results

Description

Usage

Arguments

Value

Examples

Plots tree with correlation values

Description

Usage

Arguments

Value

Examples

Plots triplot

Description

Usage

Arguments

Value

Examples

Function for printing aspect_importance results

Description

Usage

Arguments

Examples