Plot a confusion matrix
lifecycle::badge("experimental")
Creates a ggplot2 object representing a confusion matrix with counts, overall percentages, row percentages and column percentages. An extra row and column with sum tiles and the total count can be added.
The confusion matrix can be created with evaluate(). See Examples.
While this function is intended to be very flexible (hence the large number of arguments), the defaults should work in most cases for most users. See the Examples.
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allows using this function without code. Select from multiple design templates or make your own.
plot_confusion_matrix( conf_matrix, target_col = "Target", prediction_col = "Prediction", counts_col = "N", sub_col = NULL, class_order = NULL, add_sums = FALSE, add_counts = TRUE, add_normalized = TRUE, add_row_percentages = TRUE, add_col_percentages = TRUE, diag_percentages_only = FALSE, rm_zero_percentages = TRUE, rm_zero_text = TRUE, add_zero_shading = TRUE, amount_3d_effect = 1, add_arrows = TRUE, counts_on_top = FALSE, palette = "Blues", intensity_by = "counts", intensity_lims = NULL, intensity_beyond_lims = "truncate", theme_fn = ggplot2::theme_minimal, place_x_axis_above = TRUE, rotate_y_text = TRUE, digits = 1, font_counts = font(), font_normalized = font(), font_row_percentages = font(), font_col_percentages = font(), arrow_size = 0.048, arrow_nudge_from_text = 0.065, tile_border_color = NA, tile_border_size = 0.1, tile_border_linetype = "solid", sums_settings = sum_tile_settings(), darkness = 0.8 )
conf_matrix: Confusion matrix tibble with each combination of targets and predictions along with their counts.
E.g. for a binary classification:
| Target | Prediction | N |
| class_1 | class_1 | 5 |
| class_1 | class_2 | 9 |
| class_2 | class_1 | 3 |
| class_2 | class_2 | 2 |
As created with the various evaluation functions in cvms, like evaluate().
An additional sub_col column (character) can be specified as well. Its content will replace the bottom text (counts by default or normalized when counts_on_top is enabled).
Note : If you supply the results from evaluate()
or confusion_matrix() directly, the confusion matrix tibble is extracted automatically, if possible.
target_col: Name of column with target levels.
prediction_col: Name of column with prediction levels.
counts_col: Name of column with a count for each combination of the target and prediction levels.
sub_col: Name of column with text to replace the bottom text (counts by default or normalized when counts_on_top is enabled).
It simply replaces the text, so all settings will still be called e.g. font_counts etc. When other settings make it so, that no bottom text is displayed (e.g. ```add_counts` = FALSE``), this text is not displayed either.
class_order: Names of the classes in conf_matrix in the desired order. When NULL, the classes are ordered alphabetically. Note that the entire set of unique classes from both target_col
and prediction_col must be specified.
add_sums: Add tiles with the row/column sums. Also adds a total count tile. (Logical)
The appearance of these tiles can be specified in sums_settings.
Note: Adding the sum tiles with a palette requires the ggnewscale package.
add_counts: Add the counts to the middle of the tiles. (Logical)
add_normalized: Normalize the counts to percentages and add to the middle of the tiles. (Logical)
add_row_percentages: Add the row percentages, i.e. how big a part of its row the tile makes up. (Logical)
By default, the row percentage is placed to the right of the tile, rotated 90 degrees.
add_col_percentages: Add the column percentages, i.e. how big a part of its column the tile makes up. (Logical)
By default, the row percentage is placed at the bottom of the tile.
diag_percentages_only: Whether to only have row and column percentages in the diagonal tiles. (Logical)
rm_zero_percentages: Whether to remove row and column percentages when the count is 0. (Logical)
rm_zero_text: Whether to remove counts and normalized percentages when the count is 0. (Logical)
add_zero_shading: Add image of skewed lines to zero-tiles. (Logical)
Note: Adding the zero-shading requires the rsvg and ggimage packages.
amount_3d_effect: Amount of 3D effect (tile overlay) to add. Passed as whole number from 0 (no effect) up to 6 (biggest effect). This helps separate tiles with the same intensities.
Note: The overlay may not fit the tiles in many-class cases that haven't been tested. If the boxes do not overlap properly, simply turn it off.
add_arrows: Add the arrows to the row and col percentages. (Logical)
Note: Adding the arrows requires the rsvg and ggimage packages.
counts_on_top: Switch the counts and normalized counts, such that the counts are on top. (Logical)
palette: Color scheme. Passed directly to palette in ggplot2::scale_fill_distiller.
Try these palettes: "Greens", "Oranges", "Greys", "Purples", "Reds", as well as the default "Blues".
Alternatively, pass a named list with limits of a custom gradient as e.g. list("low"="#B1F9E8", "high"="#239895"). These are passed to ggplot2::scale_fill_gradient.
intensity_by: The measure that should control the color intensity of the tiles. Either counts, normalized, row_percentages, col_percentages, or one of log counts`, `log2 counts`, `log10 counts`, `arcsinh counts.
For normalized, row_percentages, and col_percentages, the color limits become 0-100 (except when intensity_lims are specified), why the intensities can better be compared across plots.
Note : When add_sums=TRUE, the row_percentages and col_percentages options are only available for the main tiles. A separate intensity metric must be specified for the sum tiles (e.g., via sums_settings = sum_tile_settings(intensity_by='normalized')).
For the log* and arcsinh versions, the log/arcsinh transformed counts are used.
Note : In log* transformed counts, 0-counts are set to 0, why they won't be distinguishable from 1-counts.
intensity_lims: A specific range of values for the color intensity of the tiles. Given as a numeric vector with c(min, max).
This allows having the same intensity scale across plots for better comparison of prediction sets.
intensity_beyond_lims: What to do with values beyond the intensity_lims. One of "truncate", "grey".
theme_fn: The ggplot2 theme function to apply.
place_x_axis_above: Move the x-axis text to the top and reverse the levels such that the "correct" diagonal goes from top left to bottom right. (Logical)
rotate_y_text: Whether to rotate the y-axis text to be vertical instead of horizontal. (Logical)
digits: Number of digits to round to (percentages only). Set to a negative number for no rounding.
Can be set for each font individually via the font_* arguments.
font_counts: list of font settings for the counts. Can be provided with font().
font_normalized: list of font settings for the normalized counts. Can be provided with font().
font_row_percentages: list of font settings for the row percentages. Can be provided with font().
font_col_percentages: list of font settings for the column percentages. Can be provided with font().
arrow_size: Size of arrow icons. (Numeric)
Is divided by sqrt(nrow(conf_matrix)) and passed on to ggimage::geom_icon().
arrow_nudge_from_text: Distance from the percentage text to the arrow. (Numeric)
tile_border_color: Color of the tile borders. Passed as colour to ggplot2::geom_tile.
tile_border_size: Size of the tile borders. Passed as size to ggplot2::geom_tile.
tile_border_linetype: Linetype for the tile borders. Passed as linetype to ggplot2::geom_tile.
sums_settings: A list of settings for the appearance of the sum tiles. Can be provided with sum_tile_settings().
darkness: How dark the darkest colors should be, between 0 and 1, where 1 is darkest.
Technically, a lower value increases the upper limit in ggplot2::scale_fill_distiller.
A ggplot2 object representing a confusion matrix. Color intensity depends on either the counts (default) or the overall percentages.
By default, each tile has the normalized count (overall percentage) and count in the middle, the column percentage at the bottom, and the row percentage to the right and rotated 90 degrees.
In the "correct" diagonal (upper left to bottom right, by default), the column percentages are the class-level sensitivity scores, while the row percentages are the class-level positive predictive values.
Inspired by Antoine Sachet's answer at https://stackoverflow.com/a/53612391/11832955
# Attach cvms library(cvms) library(ggplot2) # Two classes # Create targets and predictions data frame data <- data.frame( "target" = c("A", "B", "A", "B", "A", "B", "A", "B", "A", "B", "A", "B", "A", "B", "A", "A"), "prediction" = c("B", "B", "A", "A", "A", "B", "B", "B", "B", "B", "A", "B", "A", "A", "A", "A"), stringsAsFactors = FALSE ) # Evaluate predictions and create confusion matrix evaluation <- evaluate( data = data, target_col = "target", prediction_cols = "prediction", type = "binomial" ) # Inspect confusion matrix tibble evaluation[["Confusion Matrix"]][[1]] # Plot confusion matrix # Supply confusion matrix tibble directly plot_confusion_matrix(evaluation[["Confusion Matrix"]][[1]]) # Plot first confusion matrix in evaluate() output plot_confusion_matrix(evaluation) ## Not run: # Add sum tiles plot_confusion_matrix(evaluation, add_sums = TRUE) ## End(Not run) # Add labels to diagonal row and column percentages # This example assumes "B" is the positive class # but you could write anything as prefix to the percentages plot_confusion_matrix( evaluation, font_row_percentages = font(prefix=c("NPV = ", "", "", "PPV = ")), font_col_percentages = font(prefix=c("Spec = ", "", "", "Sens = ")) ) # Three (or more) classes # Create targets and predictions data frame data <- data.frame( "target" = c("A", "B", "C", "B", "A", "B", "C", "B", "A", "B", "C", "B", "A"), "prediction" = c("C", "B", "A", "C", "A", "B", "B", "C", "A", "B", "C", "A", "C"), stringsAsFactors = FALSE ) # Evaluate predictions and create confusion matrix evaluation <- evaluate( data = data, target_col = "target", prediction_cols = "prediction", type = "multinomial" ) # Inspect confusion matrix tibble evaluation[["Confusion Matrix"]][[1]] # Plot confusion matrix # Supply confusion matrix tibble directly plot_confusion_matrix(evaluation[["Confusion Matrix"]][[1]]) # Plot first confusion matrix in evaluate() output plot_confusion_matrix(evaluation) ## Not run: # Add sum tiles plot_confusion_matrix(evaluation, add_sums = TRUE) ## End(Not run) # Counts only plot_confusion_matrix( evaluation[["Confusion Matrix"]][[1]], add_normalized = FALSE, add_row_percentages = FALSE, add_col_percentages = FALSE ) # Change color palette to green # Change theme to `theme_light`. plot_confusion_matrix( evaluation[["Confusion Matrix"]][[1]], palette = "Greens", theme_fn = ggplot2::theme_light ) ## Not run: # Change colors palette to custom gradient # with a different gradient for sum tiles plot_confusion_matrix( evaluation[["Confusion Matrix"]][[1]], palette = list("low" = "#B1F9E8", "high" = "#239895"), sums_settings = sum_tile_settings( palette = list("low" = "#e9e1fc", "high" = "#BE94E6") ), add_sums = TRUE ) ## End(Not run) # The output is a ggplot2 object # that you can add layers to # Here we change the axis labels plot_confusion_matrix(evaluation[["Confusion Matrix"]][[1]]) + ggplot2::labs(x = "True", y = "Guess") # Replace the bottom tile text # with some information # First extract confusion matrix # Then add new column with text cm <- evaluation[["Confusion Matrix"]][[1]] cm[["Trials"]] <- c( "(8/9)", "(3/9)", "(1/9)", "(3/9)", "(7/9)", "(4/9)", "(1/9)", "(2/9)", "(8/9)" ) # Now plot with the `sub_col` argument specified plot_confusion_matrix(cm, sub_col="Trials")
Other plotting functions: font(), plot_metric_density(), plot_probabilities(), plot_probabilities_ecdf(), sum_tile_settings()
Ludvig Renbo Olsen, r-pkgs@ludvigolsen.dk