Enhance plotting functions and documentation#8
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Updated documentation for plotPRC, plotROC, plotCM, plotDensity, and plotTopFeatsVI functions. Added new functions for plotting ROC curves, confusion matrices, and density of predicted class probabilities.
epbrenner
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epbrenner
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These work, but I'd strongly suggest that the import functions for the plots to natively include the tsv reading rather than requiring it.
Add functions to plot drug phenotype distribution, model performance, and cross-drug generalization heatmaps.
I have modified the function to input tsv. |
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@AbhirupaGhosh @jananiravi @amcim |
| #' @return A heatmap (`ggplot2` object) showing the confusion matrix. | ||
| #' | ||
| #' @export | ||
| plotCM <- function(test_data_plus_predictions_file) { |
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plotConfusion will seem odd?
| #' @param shuffled_label_results Output of `runMLPipeline(shuffle_labels = TRUE)` | ||
| #' | ||
| #' @importFrom graphics barplot | ||
| #' @return A base R barplot comparing balanced accuracy across models. |
| beside = TRUE, | ||
| legend.text = TRUE, col = c("skyblue", "lightpink"), | ||
| ylab = "Balanced accuracy", xlab = "Antibiotic" | ||
| ylab = "Balanced Accuracy" |
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| ylab = "Balanced Accuracy" | |
| ylab = "Balanced accuracy" |
| "#0F2A5A" # very dark for ~1 | ||
| ), | ||
| values = scales::rescale(c(-1, 0, 0.85, 1)), | ||
| name = "Best MCC" |
| feat_pal <- c( | ||
| "args" = "#56B4E9", # sky blue | ||
| "cogs" = "#E69F00", # orange | ||
| "genes" = "#009E73", # bluish green | ||
| "domains" = "#F0E442", # yellow | ||
| "proteins" = "#CC79A7", # reddish purple | ||
| "struct" = "#D55E00" # vermillion | ||
| ) |
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not defined separately in a function -- so it's the same colors across plots/panels?
| final_plot | ||
| } | ||
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| #' Plot cross-drug generalization heatmap |
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unless I'm missing something
| #' Plot cross-drug generalization heatmap | |
| #' Plot cross-drug prediction as a heatmap |
| cross_drug <- arrow::read_parquet(file.path(cross_test_performance_path, "cross_drug_perf.parquet")) | ||
| performance <- arrow::read_parquet(file.path(drug_performance_path, "all_performance.parquet")) | ||
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| ###################### CROSS DRUG Testing ############################# |
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| ###################### CROSS DRUG Testing ############################# |
| tibble::column_to_rownames("drug_or_class") |> | ||
| as.matrix() | ||
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| row_order <- heatmap_df |> |
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drugs ordered by their classes?
| ggplot2::labs( | ||
| title = if (year_or_country == "year") { | ||
| "Temporal performance by drug" | ||
| } else { | ||
| "Geographical performance by drug" |
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| ggplot2::labs( | |
| title = if (year_or_country == "year") { | |
| "Temporal performance by drug" | |
| } else { | |
| "Geographical performance by drug" | |
| ggplot2::labs( | |
| title = if (year_or_country == "year") { | |
| "Performance of AMR drug models with temporal holdouts" | |
| } else { | |
| "Performance of AMR drug models with geographical holdouts" |
| #' ) | ||
| plotTopClusters <- function(top_feat_path = ".", cluster_feature_path = ".", | ||
| protein_names_path = ".", top_n = 10) { | ||
| ################### Top features ######################### |
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| ################### Top features ######################### |
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Updated documentation for plotPRC, plotROC, plotCM, plotDensity, and plotTopFeatsVI functions. Added new functions for plotting ROC curves, confusion matrices, and density of predicted class probabilities.
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