From e97479ab5ab2e5b73247b8323f95b8ada6924714 Mon Sep 17 00:00:00 2001 From: Francisco Bischoff Date: Tue, 6 Jun 2023 03:03:06 +0100 Subject: [PATCH] flowchart --- R/contrast_data.R | 31 +++ _contrast_profile/meta/meta | 381 +++++++++----------------- flowcharts/contrast_profile.mmd | 101 ++++--- scripts/_contrast_profile.R | 35 ++- scripts/classification/pan_contrast.R | 18 +- 5 files changed, 240 insertions(+), 326 deletions(-) diff --git a/R/contrast_data.R b/R/contrast_data.R index 0209f97..cacaa6f 100644 --- a/R/contrast_data.R +++ b/R/contrast_data.R @@ -26,6 +26,37 @@ register_contrast_model <- function() { # ) # ) + # contrasts + # coverages -> creates cov_sum and cov_idxs + # platos + # thresholds + # cov_counts # never used + # num_segments + + # c_total + # c_median + # c_mean + # c_sd + # cov_con_mean + # k_mean + # cov_mean + # coverage + # cov_percent + # redundancy + # samples + + # data { + # window + # k*** + # plato + # contrast*** + # threshold + # cov_sum (same as cov_counts) *** + # cov_idxs*** + # cov_con*** + # } + + parsnip::set_fit( model = "contrast_model", eng = "contrast_profile", diff --git a/_contrast_profile/meta/meta b/_contrast_profile/meta/meta index 41a134a..49ad189 100644 --- a/_contrast_profile/meta/meta +++ b/_contrast_profile/meta/meta @@ -1,8 +1,43 @@ name|type|data|command|depend|seed|path|time|size|bytes|format|repository|iteration|parent|children|seconds|warnings|error +.Random.seed|object|ea5142439dcdec95||||||||||||||| +"%|||%"|function|031bda8ec980931b||||||||||||||| +"%||NA%"|function|ef771f7e0b2b61dc||||||||||||||| +activity|function|514ba81c8efb42b6||||||||||||||| +ampl|function|22bb8917cc3362d0||||||||||||||| analysis_split|stem|6d6280eeeeaeaa09|3c9d74acfb239c7d|7d9b918f93b2e8de|1188165946||t19502.5658555796s|c1c66b69f95c736a|19205966|rds|local|group||analysis_split_7829d712*analysis_split_96143f68|0.207|| assessment_split|stem|5367848bd2900085|1b41fd63223669d1|7d9b918f93b2e8de|-264140407||t19499.436301588s|54691e044ebfb63d|4805832|rds|local|group||assessment_split_46147acb*assessment_split_ac706e39|0.112|| classify_topk|function|1c48f3523ef2e30a||||||||||||||| +clean_pred|function|2f000150c7903a2e||||||||||||||| +clean_splits_data|function|62dff47d87f498a6||||||||||||||| +clean_truth|function|566b7692f08c8733||||||||||||||| +compl|function|1cc0810c2c8fe26b||||||||||||||| +complexity|function|5fe702a01cef2a6e||||||||||||||| +compute_arcs|function|1a4a6dc48008b78a||||||||||||||| +compute_companion_stats|function|76613610273412a9||||||||||||||| +compute_filters|function|7329a063b58bdd91||||||||||||||| +compute_floss|function|2b4de81bfe11e55d||||||||||||||| +compute_metrics_topk|function|a6523b2f4a20d346||||||||||||||| +compute_overall_metric|function|ac70b78164855ebc||||||||||||||| +compute_s_profile_with_stats|function|2d5c844d8e0e655f||||||||||||||| +compute_score_regimes|function|d6926f3b87fe56cf||||||||||||||| +compute_streaming_profile|function|358c55ef8991349d||||||||||||||| +const_classes|object|4471c27e60bf977b||||||||||||||| +const_sample_freq|object|71e695e858e0833c||||||||||||||| +const_signals|object|90e8be76eb583b77||||||||||||||| +contrast_profiles|pattern|dba37ba3893ad349|334467772a0e266e||-1929489330||||1459865387|rds|local|list||contrast_profiles_28bbc7c8*contrast_profiles_aa674ba5|38693.3|| +contrast_profiles_28bbc7c8|branch|549474b61d1e5197|334467772a0e266e|52a72899b7f3ff59|1564113902||t19512.3166066137s|44d66c1a1e2b30ff|730322387|rds|local|list|contrast_profiles||19615.227|self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.| +contrast_profiles_aa674ba5|branch|e4e55c6fde27c29e|334467772a0e266e|d84878c776a7072a|1334957813||t19512.5379892077s|a61d5f88b23a31ab|729543000|rds|local|list|contrast_profiles||19078.073|self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. 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This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.| -contrast_profiles_aa674ba5|branch|e4e55c6fde27c29e|334467772a0e266e|d84878c776a7072a|1334957813||t19512.5379892077s|a61d5f88b23a31ab|729543000|rds|local|list|contrast_profiles||19078.073|self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. This may happen for small windows.. self_mp contains non finite values. 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+var_window_size|object|762e507151ccadf5||||||||||||||| +win_complex|function|b8c72aa4c85a0679||||||||||||||| +zero_cross_rate|function|135d3ba35c769dc1||||||||||||||| diff --git a/flowcharts/contrast_profile.mmd b/flowcharts/contrast_profile.mmd index 3e7bb7b..8713f54 100644 --- a/flowcharts/contrast_profile.mmd +++ b/flowcharts/contrast_profile.mmd @@ -8,7 +8,8 @@ title: Contrast Profile Classifier "theme": "dark", "fontFamily": "Fira Code Medium, Trebuchet MS, Verdana, Arial, Sans-Serif", "flowchart": { - "diagramPadding": 10 + "rankSpacing": 70, + "nodeSpacing": 70 } } }%% @@ -23,41 +24,32 @@ classDiagram %% -- Link (Solid) (Association without arrows) %% .. Link (Dashed) (Association without arrows and not navigable) - Data "many * classes" <.. "1 * window_sizes" Contrast + + Data "*" <.. "1" Contrast + Data "*" o-- "1" ShapeletMeta Contrast "1" <.. "1" Shapelet Shapelet "1" <.. "1" ShapeletMeta - Data <.. ShapeletMeta -%% Shapelet "1..window_sizes" *-- "1" PanContrast_TopK -%% Contrast "1..window_sizes" *-- "1" PanContrast_TopK - -%% class PanContrast_TopK { -%% Contrast contrasts -%% Shapelet shapelets -%% } - class Data { List~Factor~ classes - List~Numeric~ ts + List~float[]~ ts List~int~ ids } - %% class is the positive class class Contrast { - List~int~ window_sizes* Factor class* - List~Numeric~ contrast_profiles + List~int~ window_sizes* + List~float[]~ contrast_profiles } class Shapelet { - List~int~ window_sizes* Factor class* - List~Numeric~ platos - List~int~ platos_indices - List~Numeric~ platos_twin - List~int~ platos_twin_indices - List~float~ plato_nary_contrasts + int num_platos* + List~int~ window_sizes* + List~int[num_platos]~ platos_indices + List~int[num_platos]~ platos_twin_indices + List~float[num_platos]~ plato_nary_contrasts } %% all Lists have dim m,n where m == num_of_shapelets(k) and n == length(window_sizes) @@ -66,42 +58,49 @@ classDiagram %% TODO: this need to be reshaped %% TODO: num_segments reflect the number of positive samples class ShapeletMeta { - List~int~ window_sizes* Factor class* - List~Numeric~ thresholds - List~Numeric~ overall_contrasts - List~bool~ coverages - List~int~ coverages_counts - int num_segments + int num_segments* + int num_platos* + List~int~ window_sizes* + List~float[num_platos]~ joint_platos + List~float[num_platos]~ thresholds + List~float[num_platos]~ contrasts + List~bool[num_platos]~ coverages } - ShapeletMeta *-- Fitted - Fitted *-- Model - Terms *-- Model - ShapeletMeta <-- Terms : optimizes + ShapeletMeta "n" <|.. "1" Candidate : Optimize + Data "*" o-- "1" Score + Score "1" <|.. "1" Candidate : Optimize + Candidate <|-- Fitted : select - class Fitted { - Factor class* - ShapeletMeta best_shapelets - List~Numeric~ platos - List~Numeric~ thresholds + class Score { + float accuracy + float f1 + float precision + float recall } - class Terms { - float contrast_total - float contrast_median - float contrast_mean - fload contrast_std - fload cov_con_ratio_mean - float k_mean - float cov_mean - fload coverage - fload cov_percent - int redundancy - int num_shapelets + class Fitted { + Factor class* + int num_platos* + Candidate best_score_canditate* + - List~float[num_platos]~ joint_platos + - List~float[num_platos]~ thresholds } - class Model { - Fitted fitted_values - Terms terms + class Candidate { + Factor class* + Score score* + List~ShapeletMeta~ shapelets* + float contrast_total + float contrast_median + float contrast_mean + float contrast_std + float cov_con_ratio_mean + float k_mean + float cov_mean + float coverage + float cov_percent + int redundancy + int num_shapelets } diff --git a/scripts/_contrast_profile.R b/scripts/_contrast_profile.R index b07b28c..bd585b5 100644 --- a/scripts/_contrast_profile.R +++ b/scripts/_contrast_profile.R @@ -284,11 +284,13 @@ list( # iteration = "list" # thus the objects keep their attributes # ), tar_target( + #### Pipeline: score_by_segment - Preparation of the data: the model's data is the shapelets with metadata ---- score_by_segment, { res <- list() for (i in seq_len(var_vfolds)) { cli::cli_alert_info("Scores by segment, fold {i}.") + # These parameter may be tuned on `recipes` tune1 <- 0.1 tune2 <- 1 / 3 score <- score_by_segment_window(contrast_profiles[[i]]$positive, @@ -303,8 +305,15 @@ list( iteration = "list" ), tar_target( + #### Pipeline: find_shapelets - This is the model fit. ---- find_shapelets, { + # Here we can try: fitting all possible solutions and later score them and finally try + # to find which metadata is the best to filter the solutions + # Or, we can try to use some heuristics to find the best metadata for the solutions + # These parameters are tuned on `parsnip`/`tune` + # Currently the parameter `n` draws randomically 1 to `n` samples from the pan contrast profile + # We can try to use a fixed number of samples during the parameter optimization res <- list() for (i in seq_len(var_vfolds)) { cli::cli_alert_info("Finding solutions, fold {i}.") @@ -373,8 +382,11 @@ list( # iteration = "list" # ), tar_target( + #### Pipeline: test_classifiers_self - This is the current score function. ---- test_classifiers_self, { + # With the results of this step, plus the fitted solutions, we need to find which + # metadata is the best to filter the solutions class(analysis_split) <- c("manual_rset", "rset", class(analysis_split)) res <- list() @@ -384,6 +396,9 @@ list( res[[i]] <- list() shapelets <- find_shapelets[[i]] + # the `compute_metrics_topk` function may need testing on the `TRUE` criteria + # currently, if `ANY` shapelet matches, it is considered a positive + # as alternative we can try to use `ALL`, `HALF` or other criteria res[[i]] <- compute_metrics_topk(fold, shapelets, var_future_workers, TRUE) } @@ -416,34 +431,24 @@ list( tar_target( test_classifiers, { - shapelet_sizes <- var_shapelet_sizes - + # Here we test the solutions we chose on the assessment split + # The final `model` we need is the shapelet class(assessment_split) <- c("manual_rset", "rset", class(assessment_split)) res <- list() for (i in seq_len(var_vfolds)) { fold <- rsample::get_rsplit(assessment_split, i) - shapelets <- best_shapelets[[i]] - contrast <- contrast_profiles[[i]] + best_shapelets <- purrr::pluck(find_shapelets, i)[1, ] - res[[i]] <- compute_metrics_topk(fold, shapelets, contrast) + res[[i]] <- compute_metrics_topk(fold, best_shapelets, var_future_workers, TRUE) } - res overall <- compute_overall_metric(res) list(fold = res, overall = overall) }, - pattern = map(best_shapelets, assessment_split, contrast_profiles), + pattern = map(assessment_split, find_shapelets), iteration = "list" ) # tar_target( - # best_shapelets, - # { - # # algorithm for selecting the best shapelet - # }, - # pattern = map(contrast_profiles), - # iteration = "list" - # ), - # tar_target( # train_classifier, # { # # train a classifier based on the best shapelets diff --git a/scripts/classification/pan_contrast.R b/scripts/classification/pan_contrast.R index a38e5f6..6a4c77d 100644 --- a/scripts/classification/pan_contrast.R +++ b/scripts/classification/pan_contrast.R @@ -134,7 +134,7 @@ score_by_segment_window <- function(true_data, false_data, contrast_profiles, qu } # here we compute the total number of segments that each plato could classify - total_counts <- as.matrix(purrr::map_dfr(segs, function(x) apply(x, 1, sum))) + total_counts <- as.matrix(purrr::map_dfr(segs, function(x) apply(x, 1, sum))) # never used colnames(cont) <- w_sizes # set the column names on the overall contrast matrix colnames(thlds) <- w_sizes # set the column names on the overall contrast matrix @@ -148,9 +148,13 @@ score_by_segment_window <- function(true_data, false_data, contrast_profiles, qu coverage = segs, # segs == coverage of each plato (~sensitivity) platos = shapes, thresholds = thlds, # thlds == threshold of each plato - cov_counts = total_counts, # sum of segs == 1. Best is sum == num_segments + cov_counts = total_counts, # sum of segs == 1. Best is sum == num_segments # never used num_segments = (length(segments) - 1) ) + + + # score <- score_candidates(score) + # return(score) } score_candidates <- function(score) { @@ -686,11 +690,11 @@ compute_overall_metric <- function(all_folds) { tp <- fp <- tn <- fn <- acc <- ff <- 0 for (fold in all_folds) { - tp <- tp + fold$tp - fp <- fp + fold$fp - tn <- tn + fold$tn - fn <- fn + fold$fn - ff <- ff + fold$f1 + tp <- tp + fold[[1]]$tp + fp <- fp + fold[[1]]$fp + tn <- tn + fold[[1]]$tn + fn <- fn + fold[[1]]$fn + ff <- ff + fold[[1]]$f1 } tm <- (2 * tp) / (2 * tp + fp + fn)