| Title: | Ceteris Paribus Profiles |
| Version: | 0.6 |
| Description: | Ceteris Paribus Profiles (What-If Plots) are designed to present model responses around selected points in a feature space. For example around a single prediction for an interesting observation. Plots are designed to work in a model-agnostic fashion, they are working for any predictive Machine Learning model and allow for model comparisons. Ceteris Paribus Plots supplement the Break Down Plots from 'breakDown' package. |
| Depends: | R (≥ 3.3), ggplot2, gower |
| Suggests: | randomForest, ggiraph, e1071, testthat, rpart |
| Imports: | DALEX |
| License: | GPL-2 |
| Encoding: | UTF-8 |
| URL: | https://pbiecek.github.io/ceterisParibus/ |
| BugReports: | https://github.com/pbiecek/ceterisParibus/issues |
| RoxygenNote: | 7.3.2 |
| NeedsCompilation: | no |
| Packaged: | 2024-11-21 10:02:52 UTC; pbiecek |
| Author: | Przemyslaw Biecek 
[aut, cre] |
| Maintainer: | Przemyslaw Biecek <przemyslaw.biecek@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2024-11-21 10:20:02 UTC |
Add More Layers to a Ceteris Paribus Plot
Description
Add More Layers to a Ceteris Paribus Plot
Usage
## S3 method for class 'plot_ceteris_paribus_explainer'
e1 + e2
Arguments
e1 |
An object of class 'plot_ceteris_paribus_explainer'. |
e2 |
A plot component |
Calculate Oscillations for Ceteris Paribus Explainer
Description
Calculate Oscillations for Ceteris Paribus Explainer
Usage
calculate_oscillations(x, sort = TRUE, ...)
Arguments
x |
a ceteris_paribus explainer produced with the 'ceteris_paribus()' function |
sort |
a logical value. If TRUE then rows are sorted along the oscillations |
... |
other arguments |
Examples
library("DALEX")
## Not run:
library("randomForest")
set.seed(59)
apartments_rf_model <- randomForest(m2.price ~ construction.year + surface + floor +
no.rooms + district, data = apartments)
explainer_rf <- explain(apartments_rf_model,
data = apartmentsTest, y = apartmentsTest$m2.price)
apartment <- apartmentsTest[1,]
cp_rf <- ceteris_paribus(explainer_rf, apartment)
calculate_oscillations(cp_rf)
## End(Not run)
Calculate Ceteris Paribus Profiles
Description
This function calculates ceteris paribus profiles, i.e. series of predictions from a model calculated for observations with altered single coordinate.
Usage
calculate_profiles(
data,
variable_splits,
model,
predict_function = predict,
...
)
Arguments
data |
set of observations. Profile will be calculated for every observation (every row) |
variable_splits |
named list of vectors. Elements of the list are vectors with points in which profiles should be calculated. See an example for more details. |
model |
a model that will be passed to the |
predict_function |
function that takes data and model and returns numeric predictions. Note that the ... arguments will be passed to this function. |
... |
other parameters that will be passed to the |
Details
Note that calculate_profiles function is S3 generic.
If you want to work on non standard data sources (like H2O ddf, external databases)
you should overload it.
Value
a data frame with profiles for selected variables and selected observations
Examples
library("DALEX")
## Not run:
library("randomForest")
set.seed(59)
apartments_rf_model <- randomForest(m2.price ~ construction.year + surface + floor +
no.rooms + district, data = apartments)
vars <- c("construction.year", "surface", "floor", "no.rooms", "district")
variable_splits <- calculate_variable_splits(apartments, vars)
new_apartment <- apartmentsTest[1:10, ]
profiles <- calculate_profiles(new_apartment, variable_splits,
apartments_rf_model)
profiles
# only subset of observations
small_apartments <- select_sample(apartmentsTest, n = 10)
small_apartments
small_profiles <- calculate_profiles(small_apartments, variable_splits,
apartments_rf_model)
small_profiles
# neighbors for a selected observation
new_apartment <- apartments[1, 2:6]
small_apartments <- select_neighbours(apartmentsTest, new_apartment, n = 10)
small_apartments
small_profiles <- calculate_profiles(small_apartments, variable_splits,
apartments_rf_model)
new_apartment
small_profiles
## End(Not run)
Calculate Local Conditional Expectation profiles
Description
This function Local Conditional Expectation profiles
Usage
calculate_profiles_lce(
data,
variable_splits,
model,
dataset,
predict_function = predict,
...
)
Arguments
data |
set of observations. Profile will be calculated for every observation (every row) |
variable_splits |
named list of vectors. Elements of the list are vectors with points in which profiles should be calculated. See an example for more details. |
model |
a model that will be passed to the |
dataset |
a data.frame, usually training data of a model, used for calculation of LCE profiles |
predict_function |
function that takes data and model and returns numeric predictions. Note that the ... arguments will be passed to this function. |
... |
other parameters that will be passed to the |
Details
Note that calculate_profiles_lce function is S3 generic.
If you want to work on non standard data sources (like H2O ddf, external databases)
you should overload it.
Value
a data frame with profiles for selected variables and selected observations
Examples
library("DALEX")
## Not run:
library("randomForest")
set.seed(59)
apartments_rf_model <- randomForest(m2.price ~ construction.year + surface + floor +
no.rooms + district, data = apartments)
explainer_rf <- explain(apartments_rf_model,
data = apartments[,2:6], y = apartments$m2.price)
vars <- c("construction.year", "surface", "floor", "no.rooms", "district")
variable_splits <- calculate_variable_splits(apartments, vars)
new_apartment <- apartments[1, ]
profiles <- calculate_profiles_lce(new_apartment, variable_splits,
apartments_rf_model, explainer_rf$data)
profiles
## End(Not run)
Calculate Split Points for Selected Variables
Description
This function calculate candidate splits for each selected variable. For numerical variables splits are calculated as percentiles (in general uniform quantiles of the length grid_points). For all other variables splits are calculated as unique values.
Usage
calculate_variable_splits(
data,
variables = colnames(data),
grid_points = 101,
variable_splits_type = "quantiles"
)
Arguments
data |
validation dataset. Is used to determine distribution of observations. |
variables |
names of variables for which splits shall be calculated |
grid_points |
number of points used for response path |
variable_splits_type |
how variable grids shall be calculated? Use "quantiles" (default) for percentiles or "uniform" to get uniform grid of points |
Details
Note that calculate_variable_splits function is S3 generic.
If you want to work on non standard data sources (like H2O ddf, external databases)
you should overload it.
Value
A named list with splits for selected variables
Examples
library("DALEX")
## Not run:
library("randomForest")
set.seed(59)
apartments_rf_model <- randomForest(m2.price ~ construction.year + surface + floor +
no.rooms + district, data = apartments)
vars <- c("construction.year", "surface", "floor", "no.rooms", "district")
calculate_variable_splits(apartments, vars)
## End(Not run)
Ceteris Paribus Explainer
Description
This function calculate ceteris paribus profiles for selected data points.
Usage
ceteris_paribus(
explainer,
observations,
y = NULL,
variable_splits = NULL,
variable_splits_type = "quantiles",
variables = NULL,
grid_points = 101
)
Arguments
explainer |
a model to be explained, preprocessed by function 'DALEX::explain()'. |
observations |
set of observarvation for which profiles are to be calculated |
y |
true labels for 'observations'. If specified then will be added to ceteris paribus plots. |
variable_splits |
named list of splits for variables, in most cases created with 'calculate_variable_splits()'. If NULL then it will be calculated based on validation data avaliable in the 'explainer'. |
variable_splits_type |
how variable grids shall be calculated? Use "quantiles" (default) for percentiles or "uniform" to get uniform grid of points |
variables |
names of variables for which profiles shall be calculated. Will be passed to 'calculate_variable_splits()'. If NULL then all variables from the validation data will be used. |
grid_points |
number of points for profile. Will be passed to 'calculate_variable_splits()'. |
Value
An object of the class 'ceteris_paribus_explainer'. It's a data frame with calculated average responses.
Examples
library("DALEX")
## Not run:
library("randomForest")
set.seed(59)
apartments_rf_model <- randomForest(m2.price ~ construction.year + surface + floor +
no.rooms + district, data = apartments)
explainer_rf <- explain(apartments_rf_model,
data = apartmentsTest[,2:6], y = apartmentsTest$m2.price)
apartments_small <- select_sample(apartmentsTest, 10)
cp_rf <- ceteris_paribus(explainer_rf, apartments_small)
cp_rf
cp_rf <- ceteris_paribus(explainer_rf, apartments_small, y = apartments_small$m2.price)
cp_rf
## End(Not run)
Add Layer to the Ceteris Paribus Plot
Description
Function 'ceteris_paribus_layer()' adds a layer to a plot created with 'plot.ceteris_paribus_explainer()' plots. Various parameters help to decide what should be plotted, profiles, aggregated profiles, points or rugs.
Usage
ceteris_paribus_layer(
x,
...,
size = 1,
alpha = 0.3,
color = "black",
size_points = 2,
alpha_points = 1,
color_points = color,
size_rugs = 0.5,
alpha_rugs = 1,
color_rugs = color,
size_residuals = 1,
alpha_residuals = 1,
color_residuals = color,
only_numerical = TRUE,
show_profiles = TRUE,
show_observations = TRUE,
show_rugs = FALSE,
show_residuals = FALSE,
aggregate_profiles = NULL,
as.gg = FALSE,
facet_ncol = NULL,
selected_variables = NULL,
init_plot = FALSE
)
Arguments
x |
a ceteris paribus explainer produced with function 'ceteris_paribus()' |
... |
other explainers that shall be plotted together |
size |
a numeric. Size of lines to be plotted |
alpha |
a numeric between 0 and 1. Opacity of lines |
color |
a character. Either name of a color or name of a variable that should be used for coloring |
size_points |
a numeric. Size of points to be plotted |
alpha_points |
a numeric between 0 and 1. Opacity of points |
color_points |
a character. Either name of a color or name of a variable that should be used for coloring |
size_rugs |
a numeric. Size of rugs to be plotted |
alpha_rugs |
a numeric between 0 and 1. Opacity of rugs |
color_rugs |
a character. Either name of a color or name of a variable that should be used for coloring |
size_residuals |
a numeric. Size of line and points to be plotted for residuals |
alpha_residuals |
a numeric between 0 and 1. Opacity of points and lines for residuals |
color_residuals |
a character. Either name of a color or name of a variable that should be used for coloring for residuals |
only_numerical |
a logical. If TRUE then only numerical variables will be plotted. If FALSE then only categorical variables will be plotted. |
show_profiles |
a logical. If TRUE then profiles will be plotted. Either individual or aggregate (see 'aggregate_profiles') |
show_observations |
a logical. If TRUE then individual observations will be marked as points |
show_rugs |
a logical. If TRUE then individual observations will be marked as rugs |
show_residuals |
a logical. If TRUE then residuals will be plotted as a line ended with a point |
aggregate_profiles |
function. If NULL (default) then individual profiles will be plotted. If a function (e.g. mean or median) then profiles will be aggregated and only the aggregate profile will be plotted |
as.gg |
if TRUE then returning plot will have gg class |
facet_ncol |
number of columns for the 'facet_wrap()'. |
selected_variables |
if not NULL then only 'selected_variables' will be presented |
init_plot |
technical parameter, do not use. |
Value
a ggplot2 object
Examples
library("DALEX")
## Not run:
library("randomForest")
set.seed(59)
apartments_rf_model <- randomForest(m2.price ~ construction.year + surface + floor +
no.rooms + district, data = apartments)
explainer_rf <- explain(apartments_rf_model,
data = apartmentsTest[,2:6], y = apartmentsTest$m2.price)
apartments_small_1 <- apartmentsTest[1,]
apartments_small_2 <- select_sample(apartmentsTest, n = 20)
apartments_small_3 <- select_neighbours(apartmentsTest, apartments_small_1, n = 20)
cp_rf_y1 <- ceteris_paribus(explainer_rf, apartments_small_1, y = apartments_small_1$m2.price)
cp_rf_y2 <- ceteris_paribus(explainer_rf, apartments_small_2, y = apartments_small_2$m2.price)
cp_rf_y3 <- ceteris_paribus(explainer_rf, apartments_small_3, y = apartments_small_3$m2.price)
tmp <- plot(cp_rf_y3, show_profiles = TRUE, show_observations = TRUE,
show_residuals = TRUE, color = "black",
alpha = 0.2, color_residuals = "darkred",
selected_variables = c("construction.year", "surface"))
tmp <- plot(cp_rf_y3, show_profiles = TRUE, show_observations = TRUE,
show_residuals = TRUE, color = "black",
alpha = 0.2, color_residuals = "darkred")
tmp
tmp +
ceteris_paribus_layer(cp_rf_y2, show_profiles = TRUE, show_observations = TRUE,
alpha = 0.2, color = "darkblue")
tmp +
ceteris_paribus_layer(cp_rf_y2, show_profiles = TRUE, show_observations = TRUE,
alpha = 0.2, color = "darkblue") +
ceteris_paribus_layer(cp_rf_y2, show_profiles = TRUE, show_observations = FALSE,
alpha = 1, size = 2, color = "blue", aggregate_profiles = mean) +
ceteris_paribus_layer(cp_rf_y1, show_profiles = TRUE, show_observations = FALSE,
alpha = 1, size = 2, color = "red", aggregate_profiles = mean)
## End(Not run)
Local Conditional Expectation Explainer
Description
This explainer works for individual observations. For each observation it calculates Local Conditional Expectation (LCE) profiles for selected variables.
Usage
local_conditional_expectations(
explainer,
observations,
y = NULL,
variable_splits = NULL,
variables = NULL,
grid_points = 101
)
Arguments
explainer |
a model to be explained, preprocessed by function 'DALEX::explain()'. |
observations |
set of observarvation for which profiles are to be calculated |
y |
true labels for 'observations'. If specified then will be added to local conditional expectations plots. |
variable_splits |
named list of splits for variables, in most cases created with 'calculate_variable_splits()'. If NULL then it will be calculated based on validation data avaliable in the 'explainer'. |
variables |
names of variables for which profiles shall be calculated. Will be passed to 'calculate_variable_splits()'. If NULL then all variables from the validation data will be used. |
grid_points |
number of points for profile. Will be passed to 'calculate_variable_splits()'. |
Value
An object of the class 'ceteris_paribus_explainer'. A data frame with calculated LCE profiles.
Examples
library("DALEX")
## Not run:
library("randomForest")
set.seed(59)
apartments_rf_model <- randomForest(m2.price ~ construction.year + surface + floor +
no.rooms + district, data = apartments)
explainer_rf <- explain(apartments_rf_model,
data = apartments[,2:6], y = apartments$m2.price)
new_apartment <- apartments[1, ]
cp_rf <- ceteris_paribus(explainer_rf, new_apartment)
lce_rf <- local_conditional_expectations(explainer_rf, new_apartment)
lce_rf
lce_rf <- local_conditional_expectations(explainer_rf, new_apartment, y = new_apartment$m2.price)
lce_rf
# Plot LCE
sel_vars <- c("surface", "no.rooms")
plot(lce_rf, selected_variables = sel_vars)
# Compare ceteris paribus profiles with LCE profiles
plot(cp_rf, selected_variables = sel_vars) +
ceteris_paribus_layer(lce_rf, selected_variables = sel_vars, color = "red")
## End(Not run)
Local Fit / Wangkardu Explanations
Description
Local Fit / Wangkardu Explanations
Usage
local_fit(
explainer,
observation,
selected_variable,
grid_points = 101,
select_points = 0.1
)
Arguments
explainer |
a model to be explained, preprocessed by the 'DALEX::explain' function |
observation |
a new observation for which predictions need to be explained |
selected_variable |
variable to be presented in the local fit plot |
grid_points |
number of points used for response path |
select_points |
fraction of points from validation data to be presented in local fit plots |
Value
An object of the class 'local_fit_explainer'. It's a data frame with calculated average responses.
Examples
library("DALEX")
## Not run:
library("randomForest")
set.seed(59)
apartments_rf_model <- randomForest(m2.price ~ construction.year + surface + floor +
no.rooms + district, data = apartments)
explainer_rf <- explain(apartments_rf_model,
data = apartmentsTest[,2:6], y = apartmentsTest$m2.price)
new_apartment <- apartmentsTest[1, ]
new_apartment
cr_rf <- local_fit(explainer_rf, observation = new_apartment,
select_points = 0.002, selected_variable = "surface")
cr_rf
## End(Not run)
Plot Ceteris Paribus Explanations
Description
Function 'plot.ceteris_paribus_explainer' plots Ceteris Paribus Plots for selected observations. Various parameters help to decide what should be plotted, profiles, aggregated profiles, points or rugs.
Usage
## S3 method for class 'ceteris_paribus_explainer'
plot(
x,
...,
size = 1,
alpha = 0.3,
color = "black",
size_points = 2,
alpha_points = 1,
color_points = color,
size_rugs = 0.5,
alpha_rugs = 1,
color_rugs = color,
size_residuals = 1,
alpha_residuals = 1,
color_residuals = color,
only_numerical = TRUE,
show_profiles = TRUE,
show_observations = TRUE,
show_rugs = FALSE,
show_residuals = FALSE,
aggregate_profiles = NULL,
as.gg = FALSE,
facet_ncol = NULL,
selected_variables = NULL
)
Arguments
x |
a ceteris paribus explainer produced with function 'ceteris_paribus()' |
... |
other explainers that shall be plotted together |
size |
a numeric. Size of lines to be plotted |
alpha |
a numeric between 0 and 1. Opacity of lines |
color |
a character. Either name of a color or name of a variable that should be used for coloring |
size_points |
a numeric. Size of points to be plotted |
alpha_points |
a numeric between 0 and 1. Opacity of points |
color_points |
a character. Either name of a color or name of a variable that should be used for coloring |
size_rugs |
a numeric. Size of rugs to be plotted |
alpha_rugs |
a numeric between 0 and 1. Opacity of rugs |
color_rugs |
a character. Either name of a color or name of a variable that should be used for coloring |
size_residuals |
a numeric. Size of line and points to be plotted for residuals |
alpha_residuals |
a numeric between 0 and 1. Opacity of points and lines for residuals |
color_residuals |
a character. Either name of a color or name of a variable that should be used for coloring for residuals |
only_numerical |
a logical. If TRUE then only numerical variables will be plotted. If FALSE then only categorical variables will be plotted. |
show_profiles |
a logical. If TRUE then profiles will be plotted. Either individual or aggregate (see 'aggregate_profiles') |
show_observations |
a logical. If TRUE then individual observations will be marked as points |
show_rugs |
a logical. If TRUE then individual observations will be marked as rugs |
show_residuals |
a logical. If TRUE then residuals will be plotted as a line ended with a point |
aggregate_profiles |
function. If NULL (default) then individual profiles will be plotted. If a function (e.g. mean or median) then profiles will be aggregated and only the aggregate profile will be plotted |
as.gg |
if TRUE then returning plot will have gg class |
facet_ncol |
number of columns for the 'facet_wrap()' |
selected_variables |
if not NULL then only 'selected_variables' will be presented |
Value
a ggplot2 object
Examples
library("DALEX")
## Not run:
library("randomForest")
set.seed(59)
apartments_rf_model <- randomForest(m2.price ~ construction.year + surface + floor +
no.rooms + district, data = apartments)
explainer_rf <- explain(apartments_rf_model,
data = apartmentsTest[,2:6], y = apartmentsTest$m2.price)
apartments_small <- apartmentsTest[1:20,]
apartments_small_1 <- apartmentsTest[1,]
apartments_small_2 <- select_sample(apartmentsTest, n = 20)
apartments_small_3 <- select_neighbours(apartmentsTest, apartments_small_1, n = 20)
cp_rf <- ceteris_paribus(explainer_rf, apartments_small)
cp_rf_1 <- ceteris_paribus(explainer_rf, apartments_small_1)
cp_rf_2 <- ceteris_paribus(explainer_rf, apartments_small_2)
cp_rf_3 <- ceteris_paribus(explainer_rf, apartments_small_3)
cp_rf
cp_rf_y <- ceteris_paribus(explainer_rf, apartments_small, y = apartments_small$m2.price)
cp_rf_y1 <- ceteris_paribus(explainer_rf, apartments_small_1, y = apartments_small_1$m2.price)
cp_rf_y2 <- ceteris_paribus(explainer_rf, apartments_small_2, y = apartments_small_2$m2.price)
cp_rf_y3 <- ceteris_paribus(explainer_rf, apartments_small_3, y = apartments_small_3$m2.price)
plot(cp_rf_y, show_profiles = TRUE, show_observations = TRUE,
show_residuals = TRUE, color = "black",
alpha = 0.3, alpha_points = 1, alpha_residuals = 0.5,
size_points = 2, size_rugs = 0.5)
plot(cp_rf_y, show_profiles = TRUE, show_observations = TRUE,
show_residuals = TRUE, color = "black",
selected_variables = c("construction.year", "surface"),
alpha = 0.3, alpha_points = 1, alpha_residuals = 0.5,
size_points = 2, size_rugs = 0.5)
plot(cp_rf_y1, show_profiles = TRUE, show_observations = TRUE, show_rugs = TRUE,
show_residuals = TRUE, alpha = 0.5, size_points = 3,
alpha_points = 1, size_rugs = 0.5)
plot(cp_rf_y2, show_profiles = TRUE, show_observations = TRUE, show_rugs = TRUE,
alpha = 0.2, alpha_points = 1, size_rugs = 0.5)
plot(cp_rf_y3, show_profiles = TRUE, show_rugs = TRUE,
show_residuals = TRUE, alpha = 0.2, color_residuals = "orange", size_rugs = 0.5)
plot(cp_rf_y, show_profiles = TRUE, show_observations = TRUE, show_rugs = TRUE, size_rugs = 0.5,
show_residuals = TRUE, alpha = 0.5, color = "surface", as.gg = TRUE) +
scale_color_gradient(low = "darkblue", high = "darkred")
plot(cp_rf_y1, show_profiles = TRUE, show_observations = TRUE, show_rugs = TRUE,
show_residuals = TRUE, alpha = 0.5, color = "surface", size_points = 3)
plot(cp_rf_y2, show_profiles = TRUE, show_observations = TRUE, show_rugs = TRUE,
size = 0.5, alpha = 0.5, color = "surface")
plot(cp_rf_y, show_profiles = TRUE, show_rugs = TRUE, size_rugs = 0.5,
show_residuals = FALSE, aggregate_profiles = mean, color = "darkblue")
## End(Not run)
Plot Ceteris Paribus Oscillations
Description
Function 'plot.ceteris_paribus_oscillations' plots variable importance plots.
Usage
## S3 method for class 'ceteris_paribus_oscillations'
plot(x, ...)
Arguments
x |
a ceteris paribus oscillation explainer produced with function 'calculate_oscillations()' |
... |
other explainers that shall be plotted together |
Value
a ggplot2 object
Examples
library("DALEX")
## Not run:
library("randomForest")
set.seed(59)
apartments_rf_model <- randomForest(m2.price ~ construction.year + surface + floor +
no.rooms + district, data = apartments)
explainer_rf <- explain(apartments_rf_model,
data = apartmentsTest, y = apartmentsTest$m2.price)
apartment <- apartmentsTest[1:2,]
cp_rf <- ceteris_paribus(explainer_rf, apartment)
plot(cp_rf, color = "_ids_")
vips <- calculate_oscillations(cp_rf)
vips
plot(vips)
## End(Not run)
Local Fit Plots / Wangkardu Explanations
Description
Function 'plot.local_fit_explainer' plots Local Fit Plots for a single prediction / observation.
Usage
## S3 method for class 'local_fit_explainer'
plot(x, ..., plot_residuals = TRUE, palette = "default")
Arguments
x |
a local fir explainer produced with the 'local_fit' function |
... |
other explainers that shall be plotted together |
plot_residuals |
if TRUE (default) then residuals are plotted as red/blue bars |
palette |
color palette. Currently the choice is limited to 'wangkardu' and 'default' |
Value
a ggplot2 object
Examples
library("DALEX")
## Not run:
library("randomForest")
set.seed(59)
apartments_rf_model <- randomForest(m2.price ~ construction.year + surface + floor +
no.rooms + district, data = apartments)
explainer_rf <- explain(apartments_rf_model,
data = apartmentsTest[,2:6], y = apartmentsTest$m2.price)
new_apartment <- apartmentsTest[1, ]
new_apartment
cr_rf <- local_fit(explainer_rf, observation = new_apartment,
select_points = 0.002, selected_variable = "surface")
plot(cr_rf, plot_residuals = FALSE)
plot(cr_rf)
cr_rf <- local_fit(explainer_rf, observation = new_apartment,
select_points = 0.002, selected_variable = "surface")
plot(cr_rf, plot_residuals = FALSE, palette = "wangkardu")
plot(cr_rf, palette = "wangkardu")
new_apartment <- apartmentsTest[10, ]
cr_rf <- local_fit(explainer_rf, observation = new_apartment,
select_points = 0.002, selected_variable = "surface")
plot(cr_rf, plot_residuals = FALSE)
plot(cr_rf)
new_apartment <- apartmentsTest[302, ]
cr_rf <- local_fit(explainer_rf, observation = new_apartment,
select_points = 0.002, selected_variable = "surface")
plot(cr_rf, plot_residuals = FALSE)
plot(cr_rf)
new_apartment <- apartmentsTest[720, ]
cr_rf <- local_fit(explainer_rf, observation = new_apartment,
select_points = 0.002, selected_variable = "surface")
plot(cr_rf, plot_residuals = FALSE)
plot(cr_rf)
## End(Not run)
Plot What If 2D Explanations
Description
Function 'plot.what_if_2d_explainer' plots What-If Plots for a single prediction / observation.
Usage
## S3 method for class 'what_if_2d_explainer'
plot(
x,
...,
split_ncol = NULL,
add_raster = TRUE,
add_contour = TRUE,
add_observation = TRUE,
bins = 3
)
Arguments
x |
a ceteris paribus explainer produced with the 'what_if_2d' function |
... |
currently will be ignored |
split_ncol |
number of columns for the 'facet_wrap' |
add_raster |
if TRUE then 'geom_raster' will be added to present levels with diverging colors |
add_contour |
if TRUE then 'geom_contour' will be added to present contours |
add_observation |
if TRUE then 'geom_point' will be added to present observation that is explained |
bins |
number of contours to be added |
Value
a ggplot2 object
Examples
library("DALEX")
## Not run:
library("randomForest")
set.seed(59)
apartments_rf_model <- randomForest(m2.price ~ construction.year + surface + floor +
no.rooms + district, data = apartments)
explainer_rf <- explain(apartments_rf_model,
data = apartmentsTest[,2:6], y = apartmentsTest$m2.price)
new_apartment <- apartmentsTest[1, ]
new_apartment
wi_rf_2d <- what_if_2d(explainer_rf, observation = new_apartment)
wi_rf_2d
plot(wi_rf_2d)
plot(wi_rf_2d, add_contour = FALSE)
plot(wi_rf_2d, add_observation = FALSE)
plot(wi_rf_2d, add_raster = FALSE)
# HR data
model <- randomForest(status ~ gender + age + hours + evaluation + salary, data = HR)
pred1 <- function(m, x) predict(m, x, type = "prob")[,1]
explainer_rf_fired <- explain(model, data = HR[,1:5],
y = HR$status == "fired",
predict_function = pred1, label = "fired")
new_emp <- HR[1, ]
new_emp
wi_rf_2d <- what_if_2d(explainer_rf_fired, observation = new_emp)
wi_rf_2d
plot(wi_rf_2d)
## End(Not run)
Plot What If Explanations
Description
Function 'plot.what_if_explainer' plots What-If Plots for a single prediction / observation.
Usage
## S3 method for class 'what_if_explainer'
plot(
x,
...,
quantiles = TRUE,
split = "models",
split_ncol = NULL,
color = "variables"
)
Arguments
x |
a ceteris paribus explainer produced with the 'what_if' function |
... |
other explainers that shall be plotted together |
quantiles |
if TRUE (default) then quantiles will be presented on OX axis. If FALSE then original values will be presented on OX axis |
split |
a character, either 'models' or 'variables'. Sets the variable for faceting |
split_ncol |
number of columns for the 'facet_wrap' |
color |
a character, either 'models' or 'variables'. Sets the variable for coloring |
Value
a ggplot2 object
Examples
library("DALEX")
## Not run:
library("randomForest")
set.seed(59)
apartments_rf_model <- randomForest(m2.price ~ construction.year + surface + floor +
no.rooms + district, data = apartments)
explainer_rf <- explain(apartments_rf_model,
data = apartmentsTest[,2:6], y = apartmentsTest$m2.price)
new_apartment <- apartmentsTest[1, ]
new_apartment
wi_rf <- what_if(explainer_rf, observation = new_apartment)
wi_rf
plot(wi_rf, split = "variables", color = "variables")
plot(wi_rf)
## End(Not run)
Print Ceteris Paribus Explainer Summary
Description
Print Ceteris Paribus Explainer Summary
Usage
## S3 method for class 'ceteris_paribus_explainer'
print(x, ...)
Arguments
x |
a ceteris_paribus explainer produced with the 'ceteris_paribus()' function |
... |
other arguments that will be passed to 'head()' |
Examples
library("DALEX")
## Not run:
library("randomForest")
set.seed(59)
apartments_rf_model <- randomForest(m2.price ~ construction.year + surface + floor +
no.rooms + district, data = apartments)
explainer_rf <- explain(apartments_rf_model,
data = apartmentsTest[,2:6], y = apartmentsTest$m2.price)
apartments_small <- select_sample(apartmentsTest, 10)
cp_rf <- ceteris_paribus(explainer_rf, apartments_small)
cp_rf
## End(Not run)
Print Ceteris Paribus Profiles
Description
Print Ceteris Paribus Profiles
Usage
## S3 method for class 'ceteris_paribus_profile'
print(x, ...)
Arguments
x |
a ceteris paribus profile produced with the 'calculate_profiles' function |
... |
other arguments that will be passed to head() |
Examples
library("DALEX")
## Not run:
library("randomForest")
set.seed(59)
apartments_rf_model <- randomForest(m2.price ~ construction.year + surface + floor +
no.rooms + district, data = apartments)
vars <- c("construction.year", "surface", "floor", "no.rooms", "district")
variable_splits <- calculate_variable_splits(apartments, vars)
new_apartment <- apartmentsTest[1:10, ]
profiles <- calculate_profiles(new_apartment, variable_splits,
apartments_rf_model)
profiles
# only subset of observations
small_apartments <- select_sample(apartmentsTest, n = 10)
small_apartments
small_profiles <- calculate_profiles(small_apartments, variable_splits,
apartments_rf_model)
small_profiles
# neighbors for a selected observation
new_apartment <- apartments[1, 2:6]
small_apartments <- select_neighbours(apartmentsTest, new_apartment, n = 10)
small_apartments
small_profiles <- calculate_profiles(small_apartments, variable_splits,
apartments_rf_model)
new_apartment
small_profiles
## End(Not run)
Prints Local Fit / Wangkardu Summary
Description
Prints Local Fit / Wangkardu Summary
Usage
## S3 method for class 'local_fit_explainer'
print(x, ...)
Arguments
x |
a local fit explainer produced with the 'local_fit' function |
... |
other arguments that will be passed to 'head' function |
Examples
library("DALEX")
## Not run:
library("randomForest")
apartments_rf_model <- randomForest(m2.price ~ construction.year + surface + floor +
no.rooms + district, data = apartments)
explainer_rf <- explain(apartments_rf_model,
data = apartmentsTest[,2:6], y = apartmentsTest$m2.price)
new_apartment <- apartmentsTest[1, ]
new_apartment
cr_rf <- local_fit(explainer_rf, observation = new_apartment,
select_points = 0.002, selected_variable = "surface")
cr_rf
## End(Not run)
Print Ceteris Paribus Explainer Summary
Description
See more examples in the ceteris_paribus_layer function
Usage
## S3 method for class 'plot_ceteris_paribus_explainer'
print(x, ...)
Arguments
x |
a plot_ceteris_paribus_explainer object to plot |
... |
other arguments that will be passed to 'print.ggplot()' |
Print What If 2D Explainer Summary
Description
Print What If 2D Explainer Summary
Usage
## S3 method for class 'what_if_2d_explainer'
print(x, ...)
Arguments
x |
a what_if_2d explainer produced with the 'what_if_2d' function |
... |
other arguments that will be passed to head() |
Examples
library("DALEX")
## Not run:
library("randomForest")
apartments_rf_model <- randomForest(m2.price ~ construction.year + surface + floor +
no.rooms + district, data = apartments)
explainer_rf <- explain(apartments_rf_model,
data = apartmentsTest[,2:6], y = apartmentsTest$m2.price)
new_apartment <- apartmentsTest[1, ]
new_apartment
## End(Not run)
Print What If Explainer Summary
Description
Print What If Explainer Summary
Usage
## S3 method for class 'what_if_explainer'
print(x, ...)
Arguments
x |
a what_if explainer produced with the 'what_if' function |
... |
other arguments that will be passed to head() |
Examples
library("DALEX")
## Not run:
library("randomForest")
apartments_rf_model <- randomForest(m2.price ~ construction.year + surface + floor +
no.rooms + district, data = apartments)
explainer_rf <- explain(apartments_rf_model,
data = apartmentsTest[,2:6], y = apartmentsTest$m2.price)
new_apartment <- apartmentsTest[1, ]
new_apartment
## End(Not run)
Select Subset of Rows Closest to a Specified Observation
Description
This function selects subset of rows from data set. This is useful if data is large and we need just a sample to calculate profiles.
Usage
select_neighbours(
data,
observation,
variables = NULL,
distance = gower::gower_dist,
n = 20,
frac = NULL
)
Arguments
data |
set of observations |
observation |
single observation |
variables |
variables that shall be used for calculation of distance. By default these are all variables present in 'data' and 'observation' |
distance |
distance function, by default the 'gower_dist' function. |
n |
number of neighbors to select |
frac |
if 'n' is not specified (NULL), then will be calculated as 'frac' * number of rows in 'data'. Either 'n' or 'frac' need to be specified. |
Details
Note that select_neighbours function is S3 generic.
If you want to work on non standard data sources (like H2O ddf, external databases)
you should overload it.
Value
a data frame with selected rows
Examples
library("DALEX")
new_apartment <- apartments[1, 2:6]
small_apartments <- select_neighbours(apartmentsTest, new_apartment, n = 10)
new_apartment
small_apartments
Select Subset of Rows
Description
This function selects subset of rows from data set. This is useful if data is large and we need just a sample to calculate profiles.
Usage
select_sample(data, n = 100, seed = 1313)
Arguments
data |
set of observations. Profile will be calculated for every observation (every row) |
n |
named list of vectors. Elements of the list are vectors with points in which profiles should be calculated. See an example for more details. |
seed |
seed for random number generator. |
Details
Note that select_subsample function is S3 generic.
If you want to work on non standard data sources (like H2O ddf, external databases)
you should overload it.
Value
a data frame with selected rows
Examples
library("DALEX")
small_apartments <- select_sample(apartmentsTest)
head(small_apartments)
What-If Plot
Description
What-If Plot
Usage
what_if(explainer, observation, grid_points = 101, selected_variables = NULL)
Arguments
explainer |
a model to be explained, preprocessed by the 'DALEX::explain' function |
observation |
a new observarvation for which predictions need to be explained |
grid_points |
number of points used for response path |
selected_variables |
if specified, then only these variables will be explained |
Value
An object of the class 'what_if_explainer'. It's a data frame with calculated average responses.
Examples
library("DALEX")
## Not run:
library("randomForest")
set.seed(59)
apartments_rf_model <- randomForest(m2.price ~ construction.year + surface + floor +
no.rooms + district, data = apartments)
explainer_rf <- explain(apartments_rf_model,
data = apartmentsTest[,2:6], y = apartmentsTest$m2.price)
new_apartment <- apartmentsTest[1, ]
new_apartment
wi_rf <- what_if(explainer_rf, observation = new_apartment)
wi_rf
wi_rf <- what_if(explainer_rf, observation = new_apartment,
selected_variables = c("surface", "floor", "no.rooms"))
wi_rf
## End(Not run)
What-If 2D Plot
Description
This function calculates what if scores for grid of values spanned by two variables.
Usage
what_if_2d(
explainer,
observation,
grid_points = 101,
selected_variables = NULL
)
Arguments
explainer |
a model to be explained, preprocessed by the 'DALEX::explain' function |
observation |
a new observarvation for which predictions need to be explained |
grid_points |
number of points used for response path. Will be used for both variables |
selected_variables |
if specified, then only these variables will be explained |
Value
An object of the class 'what_if_2d_explainer'. It's a data frame with calculated average responses.
Examples
library("DALEX")
## Not run:
library("randomForest")
set.seed(59)
apartments_rf_model <- randomForest(m2.price ~ construction.year + surface + floor +
no.rooms + district, data = apartments)
explainer_rf <- explain(apartments_rf_model,
data = apartmentsTest[,2:6], y = apartmentsTest$m2.price)
new_apartment <- apartmentsTest[1, ]
new_apartment
wi_rf_2d <- what_if_2d(explainer_rf, observation = new_apartment,
selected_variables = c("surface", "floor", "no.rooms"))
wi_rf_2d
plot(wi_rf_2d)
## End(Not run)