Plot predicted probabilities
lifecycle::badge("experimental")
Creates a ggplot2 line plot object with the probabilities of either the target classes or the predicted classes.
The observations are ordered by the highest probability.
TODO line geom: average probability per observation
TODO points geom: actual probabilities per observation
The meaning of the horizontal lines depend on the settings. These are either recall scores, precision scores, or accuracy scores, depending on the probability_of
and apply_facet arguments.
plot_probabilities( data, target_col, probability_cols, predicted_class_col = NULL, obs_id_col = NULL, group_col = NULL, probability_of = "target", positive = 2, order = "centered", theme_fn = ggplot2::theme_minimal, color_scale = ggplot2::scale_colour_brewer(palette = "Dark2"), apply_facet = length(probability_cols) > 1, smoothe = FALSE, add_points = !is.null(obs_id_col), add_hlines = TRUE, add_caption = TRUE, show_x_scale = FALSE, line_settings = list(), smoothe_settings = list(), point_settings = list(), hline_settings = list(), facet_settings = list(), ylim = c(0, 1) )
data: data.frame with probabilities, target classes and (optional) predicted classes. Can also include observation identifiers and a grouping variable.
Example for binary classification:
| Classifier | Observation | Probability | Target | Prediction |
| SVM | 1 | 0.3 | cl_1 | cl_1 |
| SVM | 2 | 0.7 | cl_1 | cl_2 |
| NB | 1 | 0.2 | cl_2 | cl_1 |
| NB | 2 | 0.8 | cl_2 | cl_2 |
| ... | ... | ... | ... | ... |
Example for multiclass classification:
| Classifier | Observation | cl_1 | cl_2 | cl_3 | Target | Prediction |
| SVM | 1 | 0.2 | 0.1 | 0.7 | cl_1 | cl_3 |
| SVM | 2 | 0.3 | 0.5 | 0.2 | cl_1 | cl_2 |
| NB | 1 | 0.8 | 0.1 | 0.1 | cl_2 | cl_1 |
| NB | 2 | 0.1 | 0.6 | 0.3 | cl_3 | cl_2 |
| ... | ... | ... | ... | ... | ... | ... |
You can have multiple rows per observation ID per group. If, for instance, we have run repeated cross-validation of 3 classifiers, we would have one predicted probability per fold column per classifier.
As created with the various validation functions in cvms, like
cross_validate_fn().
target_col: Name of column with target levels.
probability_cols: Name of columns with predicted probabilities.
For binary classification, this should be one column with the probability of the second class (alphabetically).
For multiclass classification, this should be one column per class . These probabilities must sum to 1 row-wise.
predicted_class_col: Name of column with predicted classes.
This is required when probability_of = "prediction" and/or add_hlines = TRUE.
obs_id_col: Name of column with observation identifiers for grouping the x-axis . When NULL, each row is an observation.
Use case: when you have multiple predicted probabilities per observation by a classifier (e.g. from repeated cross-validation).
Can also be a grouping variable that you wish to aggregate.
group_col: Name of column with groups. The plot elements are split by these groups and can be identified by their color.
E.g. the classifier responsible for the prediction.
N.B. With more than ‘8’ groups, the default color_scale might run out of colors.
probability_of: Whether to plot the probabilities of the target classes ("target") or the predicted classes ("prediction").
For each row, we extract the probability of either the target class or the predicted class. Both are useful to plot, as they show the behavior of the classifier in a way a confusion matrix doesn't. One classifier might be very certain in its predictions (whether wrong or right), whereas another might be less certain.
positive: TODO
order: How to order of the the probabilities. (Character)
One of: "descending", "ascending", and "centered".
theme_fn: The ggplot2 theme function to apply.
color_scale: ggplot2 color scale object for adding discrete colors to the plot.
E.g. the output of ggplot2::scale_colour_brewer() or ggplot2::scale_colour_viridis_d().
N.B. The number of colors in the object's palette should be at least the same as the number of groups in the group_col column.
apply_facet: Whether to use ggplot2::facet_wrap(). (Logical)
By default, faceting is applied when there are more than one probability column (multiclass).
smoothe: Whether to use ggplot2::geom_smooth() instead of ggplot2::geom_line(). This also adds a 95% confidence interval by default.
Settings can be passed via the smoothe_settings argument.
add_points: Add a point for each predicted probability. These are grouped on the x-axis by the obs_id_col column. (Logical)
add_hlines: Add horizontal lines. (Logical)
The meaning of these lines depends on the probability_of
and apply_facet arguments:
| ‘apply_facet’ | ‘probability_of’ | Metric |
FALSE | "target" | Accuracy |
FALSE | "prediction" | Accuracy |
TRUE | "target" | Recall / Sensitivity |
TRUE | "prediction" | Precision / PPV |
add_caption: Whether to add a caption explaining the plot. This is dynamically generated and intended as a starting point. (Logical)
You can overwrite the text with ggplot2::labs(caption = "...").
show_x_scale: TODO
line_settings: Named list of arguments for ggplot2::geom_line().
The mapping argument is set separately.
Any argument not in the list will use its default value.
Default: list(size = 0.5)
N.B. Ignored when smoothe = TRUE.
smoothe_settings: Named list of arguments for ggplot2::geom_smooth().
The mapping argument is set separately.
Any argument not in the list will use its default value.
Default: list(size = 0.5, alpha = 0.18, level = 0.95, se = TRUE)
N.B. Only used when smoothe = TRUE.
point_settings: Named list of arguments for ggplot2::geom_point().
The mapping argument is set separately.
Any argument not in the list will use its default value.
Default: list(size = 0.1, alpha = 0.4)
hline_settings: Named list of arguments for ggplot2::geom_hline().
The mapping argument is set separately.
Any argument not in the list will use its default value.
Default: list(size = 0.35, alpha = 0.5)
facet_settings: Named list of arguments for ggplot2::facet_wrap().
The facets argument is set separately.
Any argument not in the list will use its default value.
Commonly set arguments are nrow and ncol.
ylim: Limits for the y-scale.
A ggplot2 object with a faceted line plot. TODO
TODO
# Attach cvms library(cvms) library(ggplot2) library(dplyr) # # Multiclass # # Plot probabilities of target classes # From repeated cross-validation of three classifiers # plot_probabilities( # data = predicted.musicians, # target_col = "Target", # probability_cols = c("A", "B", "C", "D"), # predicted_class_col = "Predicted Class", # group_col = "Classifier", # obs_id_col = "ID", # probability_of = "target" # ) # Plot probabilities of predicted classes # From repeated cross-validation of three classifiers # plot_probabilities( # data = predicted.musicians, # target_col = "Target", # probability_cols = c("A", "B", "C", "D"), # predicted_class_col = "Predicted Class", # group_col = "Classifier", # obs_id_col = "ID", # probability_of = "prediction" # ) # Center probabilities # plot_probabilities( # data = predicted.musicians, # target_col = "Target", # probability_cols = c("A", "B", "C", "D"), # predicted_class_col = "Predicted Class", # group_col = "Classifier", # obs_id_col = "ID", # probability_of = "prediction", # order = "centered" # ) # # Binary # # Filter the predicted.musicians dataset # binom_data <- predicted.musicians %>% # dplyr::filter( # Target %in% c("A", "B") # ) %>% # # "B" is the second class alphabetically # dplyr::rename(Probability = B) %>% # dplyr::mutate(`Predicted Class` = ifelse( # Probability > 0.5, "B", "A")) %>% # dplyr::select(-dplyr::all_of(c("A","C","D"))) # Plot probabilities of predicted classes # From repeated cross-validation of three classifiers # plot_probabilities( # data = binom_data, # target_col = "Target", # probability_cols = "Probability", # predicted_class_col = "Predicted Class", # group_col = "Classifier", # obs_id_col = "ID", # probability_of = "target" # ) # plot_probabilities( # data = binom_data, # target_col = "Target", # probability_cols = "Probability", # predicted_class_col = "Predicted Class", # group_col = "Classifier", # obs_id_col = "ID", # probability_of = "prediction", # ylim = c(0.5, 1) # )
Other plotting functions: font(), plot_confusion_matrix(), plot_metric_density(), plot_probabilities_ecdf(), sum_tile_settings()
Ludvig Renbo Olsen, r-pkgs@ludvigolsen.dk