From b19fd9ef7c0bb3c068fedb52ce67ddb1b70207c9 Mon Sep 17 00:00:00 2001 From: rvandewater Date: Wed, 30 Oct 2024 15:05:32 +0100 Subject: [PATCH 01/82] bring back cass specifics --- experiments/benchmark_cass.yml | 76 ++++++++++++++++++++++++++ experiments/charhpc_wandb_sweep.sh | 14 +++++ experiments/charhpc_wandb_sweep_cpu.sh | 15 +++++ 3 files changed, 105 insertions(+) create mode 100644 experiments/benchmark_cass.yml create mode 100644 experiments/charhpc_wandb_sweep.sh create mode 100644 experiments/charhpc_wandb_sweep_cpu.sh diff --git a/experiments/benchmark_cass.yml b/experiments/benchmark_cass.yml new file mode 100644 index 00000000..4389deb8 --- /dev/null +++ b/experiments/benchmark_cass.yml @@ -0,0 +1,76 @@ +command: + - ${env} + - ${program} + - train + - -d + - ../data/ + - -t +# - BinaryClassification + - CassClassification + - --log-dir + - /sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs + - --tune + - --wandb-sweep + - -tn + - SSI +# - Mortality +# - --verbose +# - --hp-checkpoint +# - /sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/activity_notes/SSI/XGBClassifier/2024-07-25T14-32-55/hyperparameter_tuning_logs.db + - --modalities + - "all" +# - --verbose +# - /sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/cass/SSI/XGBClassifier/2024-06-28T17-28-04/hyperparameter_tuning_logs.db +# - /sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/cass/SSI/Transformer/2024-06-26T15-30-53/hyperparameter_tuning_logs.db +# - -gc +# - -lc +method: grid +name: yaib_classification_benchmark +parameters: + data_dir: + values: + - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/dataset_segment_1.0_horizon_6:00:00_transfer_3_2024-10-07T23:26:22" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/dataset_segment_1.0_horizon_6:00:00_transfer_full_2024-09-27T16:14:05_complication" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/dataset_segment_1.0_horizon_6:00:00_transfer_full_2024-09-26T16:26:24" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/dataset_x_segment_duration_x_transfer_2024-09-19T13:57:36" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/dataset_baseline_dynamic_6h_2024-09-18T11:24:23" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/dataset_baseline_flat_2024-09-17T17:25:20" +# - /home/vandewrp/projects/YAIB/data/YAIB_Datasets/data/mortality24/miiv +# - /sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/activity_notes_fixed +# - /sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/19-08-2024_all_wards +# - /sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/dataset_x_segment_duration_x_transfer_2024-08-26 13:43:20 +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/dataset_x_segment_duration_x_transfer_2024-09-12T16:22:01" + model: + values: +# - LogisticRegression +# - TimesNet +# - LGBMClassifier + - XGBClassifier +# - CBClassifier +# - BRFClassifier +# - RUSBClassifier +# - RFClassifier + - GRU +# - LSTM +# - TCN + - Transformer + modalities: + values: + - "all" +# - [copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, cat_med_embeddings_map, cat_clinical_notes, wearable_activity, wearable_core, static] +# - [copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, cat_med_embeddings_map, wearable_activity, wearable_core, static] +# - [copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, static] # without wearable data +# - [copra_observations, copra_scores, copra_fluid_balance, cat_med_embeddings_map, wearable_activity, wearable_core, static] # without ishmed lab data and clinical notes +# - [copra_observations, copra_scores, copra_fluid_balance, cat_med_embeddings_map, static ] # without wearable data and ishmed lab data, clinical notes +## - [copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, cat_med_embeddings_map, wearable_activity, wearable_core, static] # without clinical notes +# - [copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, cat_clinical_notes, wearable_activity, wearable_core, static] # without med embeddings +# - [wearable_activity, wearable_core, static] +# - [ishmed_lab_numeric, static] +# - [static] + seed: + values: + - 1111 + use_pretrained_imputation: + values: + - None +program: icu-benchmarks \ No newline at end of file diff --git a/experiments/charhpc_wandb_sweep.sh b/experiments/charhpc_wandb_sweep.sh new file mode 100644 index 00000000..3426d5fd --- /dev/null +++ b/experiments/charhpc_wandb_sweep.sh @@ -0,0 +1,14 @@ +#!/bin/bash +#SBATCH --job-name=yaib_experiment +#SBATCH --partition=pgpu # -p +#SBATCH --cpus-per-task=8 # -c +#SBATCH --mem=200gb +#SBATCH --output=logs/classification_%a_%j.log # %j is job id +#SBATCH --gpus=1 +#SBATCH --time=24:00:00 + +source /etc/profile.d/conda.sh + +eval "$(conda shell.bash hook)" +conda activate yaib_req_pl +wandb agent --count 1 cassandra_hpi/cassandra/"$1" \ No newline at end of file diff --git a/experiments/charhpc_wandb_sweep_cpu.sh b/experiments/charhpc_wandb_sweep_cpu.sh new file mode 100644 index 00000000..de81d07e --- /dev/null +++ b/experiments/charhpc_wandb_sweep_cpu.sh @@ -0,0 +1,15 @@ +#!/bin/bash +#SBATCH --job-name=yaib_experiment +#SBATCH --partition=pgpu # -p +#SBATCH --cpus-per-task=16 # -c +#SBATCH --mem=100gb +#SBATCH --output=logs/classification_%a_%j.log # %j is job id +#SBATCH --time=24:00:00 + +source /etc/profile.d/conda.sh + +eval "$(conda shell.bash hook)" +conda activate yaib_req_pl +wandb agent --count 1 cassandra_hpi/cassandra/"$1" + +# Debug instance: srun -p gpu --pty -t 5:00:00 --gres=gpu:1 --cpus-per-task=16 --mem=100GB bash \ No newline at end of file From 943d32bc3a07358bd15848793702012226767f7c Mon Sep 17 00:00:00 2001 From: rvandewater Date: Wed, 30 Oct 2024 15:05:49 +0100 Subject: [PATCH 02/82] sort indicators --- icu_benchmarks/data/loader.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/icu_benchmarks/data/loader.py b/icu_benchmarks/data/loader.py index b227dbf2..14f4156b 100644 --- a/icu_benchmarks/data/loader.py +++ b/icu_benchmarks/data/loader.py @@ -48,6 +48,7 @@ def __init__( self.row_indicators = data[split][Segment.features][self.vars["GROUP"]] self.features_df = data[split][Segment.features] # calculate basic info for the data + self.row_indicators = self.row_indicators.sort([self.vars["GROUP"], self.vars["SEQUENCE"]]) self.num_stays = self.grouping_df[self.vars["GROUP"]].unique().shape[0] self.maxlen = self.features_df.group_by([self.vars["GROUP"]]).len().max().item(0, 1) self.mps = mps @@ -169,6 +170,7 @@ def get_data_and_labels(self) -> Tuple[np.array, np.array, np.array]: else: # Adding segment count for each stay id and timestep. rep = rep.with_columns(pl.col(self.vars["GROUP"]).cum_count().over(self.vars["GROUP"]).alias("counter")) + # rep = rep.sort([self.vars["GROUP"], "counter"]) rep = rep.to_numpy().astype(float) logging.debug(f"rep shape: {rep.shape}") logging.debug(f"labels shape: {labels.shape}") From 1aea731d60fb4ad99fea2042da08a6a813e1877c Mon Sep 17 00:00:00 2001 From: rvandewater Date: Wed, 30 Oct 2024 15:24:17 +0100 Subject: [PATCH 03/82] dependencies update --- requirements.txt | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/requirements.txt b/requirements.txt index e48f4d06..2e8495f5 100644 --- a/requirements.txt +++ b/requirements.txt @@ -4,7 +4,7 @@ coverage==7.2.3 flake8>=7.0.0 matplotlib==3.7.1 gin-config==0.5.0 -pytorch-ignite==0.5.0.post2 +pytorch-ignite==0.5.1 # Note: versioning of Pytorch might be dependent on compatible CUDA version. # Please check yourself if your Pytorch installation supports cuda (for gpu acceleration) torch==2.4 @@ -26,7 +26,7 @@ einops==0.6.1 hydra-core==1.3 optuna==4.0.0 optuna-integration==4.0.0 -wandb==0.17.3 +wandb==0.18.5 recipies==1.0 #Fixed version because of NumPy incompatibility and stale development status. scikit-optimize-fix==0.9.1 From 57815a56183de21f1a653ecc628049dc3f6d5d8a Mon Sep 17 00:00:00 2001 From: rvandewater Date: Fri, 15 Nov 2024 13:28:52 +0100 Subject: [PATCH 04/82] alarm toy --- alarm_toy.ipynb | 791 ++++++++++++++++++ configs/prediction_models/XGBClassifier.gin | 10 +- .../mimic_demo_static/attrition.csv | 3 + experiments/benchmark_cass.yml | 20 +- experiments/charhpc_wandb_sweep_cpu.sh | 2 +- experiments/slurm_base_char_sc.sh | 44 + icu_benchmarks/models/alarm_metrics.py | 72 ++ icu_benchmarks/models/constants.py | 6 + icu_benchmarks/models/custom_metrics.py | 76 +- icu_benchmarks/models/ml_models/xgboost.py | 5 +- icu_benchmarks/models/wrappers.py | 61 +- icu_benchmarks/run_utils.py | 6 +- 12 files changed, 1057 insertions(+), 39 deletions(-) create mode 100644 alarm_toy.ipynb create mode 100644 demo_data/mortality24/mimic_demo_static/attrition.csv create mode 100644 experiments/slurm_base_char_sc.sh create mode 100644 icu_benchmarks/models/alarm_metrics.py diff --git a/alarm_toy.ipynb b/alarm_toy.ipynb new file mode 100644 index 00000000..730ad65b --- /dev/null +++ b/alarm_toy.ipynb @@ -0,0 +1,791 @@ +{ + "cells": [ + { + "cell_type": "code", + "id": "initial_id", + "metadata": { + "collapsed": true, + "ExecuteTime": { + "end_time": "2024-11-14T12:22:55.485296Z", + "start_time": "2024-11-14T12:22:55.401283Z" + } + }, + "source": [ + "import numpy as np\n", + "\n", + "# Sample data\n", + "data = np.array([\n", + " [1, 10, 100],\n", + " [2, 20, 200],\n", + " [1, 30, 300],\n", + " [2, 40, 400],\n", + " [3, 50, 500]\n", + "])\n", + "\n", + "# Column index for IDs\n", + "id_column_index = 0\n", + "\n", + "# Get unique IDs\n", + "unique_ids = np.unique(data[:, id_column_index])\n", + "\n", + "# Dictionary to store separated arrays\n", + "separated_arrays = {}\n", + "\n", + "# Separate arrays based on IDs\n", + "for unique_id in unique_ids:\n", + " separated_arrays[unique_id] = data[data[:, id_column_index] == unique_id]\n", + "\n", + "# Print separated arrays\n", + "for unique_id, array in separated_arrays.items():\n", + " print(f\"ID {unique_id}:\\n{array}\\n\")" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ID 1:\n", + "[[ 1 10 100]\n", + " [ 1 30 300]]\n", + "\n", + "ID 2:\n", + "[[ 2 20 200]\n", + " [ 2 40 400]]\n", + "\n", + "ID 3:\n", + "[[ 3 50 500]]\n", + "\n" + ] + } + ], + "execution_count": 1 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-11-14T12:22:55.576737Z", + "start_time": "2024-11-14T12:22:55.567569Z" + } + }, + "cell_type": "code", + "source": "", + "id": "cae5c1b3b95093db", + "outputs": [], + "execution_count": null + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-11-14T12:22:55.870267Z", + "start_time": "2024-11-14T12:22:55.654565Z" + } + }, + "cell_type": "code", + "source": [ + "import polars as pl\n", + "preds = pl.read_csv(r\"C:\\Users\\Robin\\Downloads\\pred_indicators_only_complications.csv\")" + ], + "id": "67be5cf6c6ac18f", + "outputs": [], + "execution_count": 2 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-11-14T12:22:55.901519Z", + "start_time": "2024-11-14T12:22:55.888514Z" + } + }, + "cell_type": "code", + "source": "\n", + "id": "a0abef17da834506", + "outputs": [], + "execution_count": 2 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-11-14T12:22:56.458349Z", + "start_time": "2024-11-14T12:22:55.919572Z" + } + }, + "cell_type": "code", + "source": "predictions", + "id": "e988983945c055e1", + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'predictions' is not defined", + "output_type": "error", + "traceback": [ + "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", + "\u001B[1;31mNameError\u001B[0m Traceback (most recent call last)", + "Cell \u001B[1;32mIn[3], line 1\u001B[0m\n\u001B[1;32m----> 1\u001B[0m \u001B[43mpredictions\u001B[49m\n", + "\u001B[1;31mNameError\u001B[0m: name 'predictions' is not defined" + ] + } + ], + "execution_count": 3 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-11-14T12:22:56.539577400Z", + "start_time": "2024-11-14T12:19:50.586084Z" + } + }, + "cell_type": "code", + "source": "", + "id": "cf593fd5e8a42f4d", + "outputs": [], + "execution_count": null + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-11-14T12:28:12.916074Z", + "start_time": "2024-11-14T12:28:02.509602Z" + } + }, + "cell_type": "code", + "source": [ + "from numpy import genfromtxt\n", + "from icu_benchmarks.models.alarm_metrics import silence_positives, fill_gaps, convert_to_alarm\n", + "import numpy as np\n", + "\n", + "predictions_no_threshold = genfromtxt(r\"C:\\Users\\Robin\\Downloads\\pred_indicators_only_complications.csv\", delimiter=',')\n", + "predictions = predictions_no_threshold.copy()\n", + "# Define the threshold\n", + "threshold = 0.5\n", + "\n", + "# Round predictions to 0 and 1 according to the threshold\n", + "predictions = np.where(predictions >= threshold, 1, 0)\n", + "\n", + "# Column index for grouping\n", + "group_column_index = 0\n", + "\n", + "# Get unique group values\n", + "unique_groups = np.unique(predictions[:, group_column_index])\n", + "# Dictionary to store grouped arrays\n", + "grouped_arrays = {}\n", + "\n", + "unique_groups = np.unique(predictions[:, group_column_index])\n", + "\n", + "modified_arrays = []\n", + "# print(predictions)\n", + "# Group arrays based on unique values in the group column\n", + "for group in unique_groups:\n", + " group_array = predictions[predictions[:, group_column_index] == group]\n", + " # print(f\"Group {group} before modification:\\n{group_array}\\n\")\n", + " ground_truth = group_array[:, 2]\n", + " predictions = group_array[:, 4]\n", + " group_array[:, 4] = silence_positives(ground_truth, predictions)\n", + " group_array[:, 4], group_array[:, 2] = fill_gaps(predictions, ground_truth)\n", + " # print(f\"Group {group} after modification:\\n{group_array}\\n\")\n", + " modified_arrays.append(group_array)\n", + "\n", + "# Combine the modified arrays back into a single array\n", + "combined_array = np.vstack(modified_arrays)\n", + "# combined_array = np.delete(combined_array, 3, axis=1)\n", + "\n", + "# print(\"Combined array:\")\n", + "# print(combined_array)\n", + "# \n", + "# print(f\"ROC AUC: {roc_auc_score(predictions[:, 2], predictions[:, 4])}\")\n", + "# print(f\"ROC AUC: {roc_auc_score(combined_array[:, 2], combined_array[:, 4])}\")\n", + "# print(f\"Average precision: {average_precision_score(predictions[:, 2], predictions[:, 4])}\")\n", + "# print(f\"Average precision: {average_precision_score(combined_array[:, 2], combined_array[:, 4])}\")" + ], + "id": "4781ed7dd0de97e4", + "outputs": [], + "execution_count": 4 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-11-14T12:22:56.539577400Z", + "start_time": "2024-11-14T12:19:50.648226Z" + } + }, + "cell_type": "code", + "source": "predictions_no_threshold", + "id": "a5a491f898213828", + "outputs": [ + { + "data": { + "text/plain": [ + "array([[4.200e+01, 0.000e+00, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [4.200e+01, 1.000e+00, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [4.200e+01, 2.000e+00, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " ...,\n", + " [1.044e+03, 1.730e+02, 1.000e+00, 4.100e-02, 9.590e-01],\n", + " [1.044e+03, 1.740e+02, 1.000e+00, 2.700e-02, 9.730e-01],\n", + " [1.044e+03, 1.750e+02, 1.000e+00, 2.000e-02, 9.800e-01]])" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 29 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-11-14T12:22:56.539577400Z", + "start_time": "2024-11-14T12:19:50.662767Z" + } + }, + "cell_type": "code", + "source": "predictions", + "id": "e3b2aedf47bce384", + "outputs": [ + { + "data": { + "text/plain": [ + "array([0, 0, 0, ..., 1, 1, 1])" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 30 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-11-14T12:28:13.430436Z", + "start_time": "2024-11-14T12:28:12.932442Z" + } + }, + "cell_type": "code", + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.metrics import PrecisionRecallDisplay, RocCurveDisplay, average_precision_score, roc_auc_score\n", + "\n", + "# Assuming predictions, combined_array, and predictions_no_threshold are already defined\n", + "# predictions[:, 2] is the ground truth\n", + "# predictions[:, 4] is the original predictions\n", + "# combined_array[:, 4] is the modified predictions\n", + "# predictions_no_threshold[:, 4] is the no_threshold predictions\n", + "\n", + "# Calculate metrics\n", + "roc_auc_original = roc_auc_score(predictions[:, 2], predictions[:, 4])\n", + "roc_auc_modified = roc_auc_score(combined_array[:, 2], combined_array[:, 4])\n", + "roc_auc_no_threshold = roc_auc_score(predictions[:, 2], predictions_no_threshold[:, 4])\n", + "average_precision_original = average_precision_score(predictions[:, 2], predictions[:, 4])\n", + "average_precision_modified = average_precision_score(combined_array[:, 2], combined_array[:, 4])\n", + "average_precision_no_threshold = average_precision_score(predictions[:, 2], predictions_no_threshold[:, 4])\n", + "\n", + "# Plot ROC curves\n", + "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 6))\n", + "\n", + "RocCurveDisplay.from_predictions(predictions[:, 2], predictions[:, 4], ax=ax1, name='Original')\n", + "RocCurveDisplay.from_predictions(combined_array[:, 2], combined_array[:, 4], ax=ax1, name='Modified')\n", + "RocCurveDisplay.from_predictions(predictions[:, 2], predictions_no_threshold[:, 4], ax=ax1, name='No Threshold')\n", + "ax1.set_title(f'ROC Curve\\nOriginal AUC: {roc_auc_original:.2f}, Modified AUC: {roc_auc_modified:.2f}, No Threshold AUC: {roc_auc_no_threshold:.2f}')\n", + "\n", + "# Plot PRC curves\n", + "PrecisionRecallDisplay.from_predictions(predictions[:, 2], predictions[:, 4], ax=ax2, name='Original')\n", + "PrecisionRecallDisplay.from_predictions(combined_array[:, 2], combined_array[:, 4], ax=ax2, name='Modified')\n", + "PrecisionRecallDisplay.from_predictions(predictions[:, 2], predictions_no_threshold[:, 4], ax=ax2, name='No Threshold')\n", + "ax2.set_title(f'Precision-Recall Curve\\nOriginal AP: {average_precision_original:.2f}, Modified AP: {average_precision_modified:.2f}, No Threshold AP: {average_precision_no_threshold:.2f}')\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ], + "id": "4937d5d3f28a8a46", + "outputs": [ + { + "ename": "IndexError", + "evalue": "too many indices for array: array is 1-dimensional, but 2 were indexed", + "output_type": "error", + "traceback": [ + "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", + "\u001B[1;31mIndexError\u001B[0m Traceback (most recent call last)", + "Cell \u001B[1;32mIn[5], line 12\u001B[0m\n\u001B[0;32m 3\u001B[0m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01msklearn\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mmetrics\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m PrecisionRecallDisplay, RocCurveDisplay, average_precision_score, roc_auc_score\n\u001B[0;32m 5\u001B[0m \u001B[38;5;66;03m# Assuming predictions, combined_array, and predictions_no_threshold are already defined\u001B[39;00m\n\u001B[0;32m 6\u001B[0m \u001B[38;5;66;03m# predictions[:, 2] is the ground truth\u001B[39;00m\n\u001B[0;32m 7\u001B[0m \u001B[38;5;66;03m# predictions[:, 4] is the original predictions\u001B[39;00m\n\u001B[1;32m (...)\u001B[0m\n\u001B[0;32m 10\u001B[0m \n\u001B[0;32m 11\u001B[0m \u001B[38;5;66;03m# Calculate metrics\u001B[39;00m\n\u001B[1;32m---> 12\u001B[0m roc_auc_original \u001B[38;5;241m=\u001B[39m roc_auc_score(\u001B[43mpredictions\u001B[49m\u001B[43m[\u001B[49m\u001B[43m:\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m2\u001B[39;49m\u001B[43m]\u001B[49m, predictions[:, \u001B[38;5;241m4\u001B[39m])\n\u001B[0;32m 13\u001B[0m roc_auc_modified \u001B[38;5;241m=\u001B[39m roc_auc_score(combined_array[:, \u001B[38;5;241m2\u001B[39m], combined_array[:, \u001B[38;5;241m4\u001B[39m])\n\u001B[0;32m 14\u001B[0m roc_auc_no_threshold \u001B[38;5;241m=\u001B[39m roc_auc_score(predictions[:, \u001B[38;5;241m2\u001B[39m], predictions_no_threshold[:, \u001B[38;5;241m4\u001B[39m])\n", + "\u001B[1;31mIndexError\u001B[0m: too many indices for array: array is 1-dimensional, but 2 were indexed" + ] + } + ], + "execution_count": 5 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-11-14T12:22:56.539577400Z", + "start_time": "2024-11-12T17:04:57.239395Z" + } + }, + "cell_type": "code", + "source": [ + "from sklearn.metrics import PrecisionRecallDisplay, RocCurveDisplay\n", + "\n" + ], + "id": "6586e44915be74e3", + "outputs": [ + { + "ename": "ImportError", + "evalue": "cannot import name 'PrecisionRecallDisplay' from 'sklearn' (C:\\Users\\Robin\\miniconda\\envs\\yaib_19\\lib\\site-packages\\sklearn\\__init__.py)", + "output_type": "error", + "traceback": [ + "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", + "\u001B[1;31mImportError\u001B[0m Traceback (most recent call last)", + "Cell \u001B[1;32mIn[58], line 1\u001B[0m\n\u001B[1;32m----> 1\u001B[0m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01msklearn\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m PrecisionRecallDisplay, RocCurveDisplay\n", + "\u001B[1;31mImportError\u001B[0m: cannot import name 'PrecisionRecallDisplay' from 'sklearn' (C:\\Users\\Robin\\miniconda\\envs\\yaib_19\\lib\\site-packages\\sklearn\\__init__.py)" + ] + } + ], + "execution_count": 58 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-11-14T12:22:56.539577400Z", + "start_time": "2024-11-12T16:57:47.562930Z" + } + }, + "cell_type": "code", + "source": "predictions", + "id": "f81b74431b6e391a", + "outputs": [ + { + "data": { + "text/plain": [ + "array([[4.200e+01, 0.000e+00, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [4.200e+01, 1.000e+00, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [4.200e+01, 2.000e+00, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " ...,\n", + " [1.044e+03, 1.730e+02, 1.000e+00, 4.100e-02, 9.590e-01],\n", + " [1.044e+03, 1.740e+02, 1.000e+00, 2.700e-02, 9.730e-01],\n", + " [1.044e+03, 1.750e+02, 1.000e+00, 2.000e-02, 9.800e-01]])" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 34 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-11-14T12:22:56.540541100Z", + "start_time": "2024-11-12T16:54:27.852976Z" + } + }, + "cell_type": "code", + "source": "combined_array", + "id": "20480d22741f38a5", + "outputs": [ + { + "data": { + "text/plain": [ + "array([[4.200e+01, 0.000e+00, 0.000e+00, 0.000e+00],\n", + " [4.200e+01, 1.000e+00, 0.000e+00, 0.000e+00],\n", + " [4.200e+01, 2.000e+00, 0.000e+00, 0.000e+00],\n", + " ...,\n", + " [1.044e+03, 1.730e+02, 1.000e+00, 1.000e+00],\n", + " [1.044e+03, 1.740e+02, 1.000e+00, 1.000e+00],\n", + " [1.044e+03, 1.750e+02, 1.000e+00, 1.000e+00]])" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 28 + }, + { + "metadata": {}, + "cell_type": "code", + "outputs": [], + "execution_count": null, + "source": "", + "id": "ed297983230629bd" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-11-14T12:22:56.540541100Z", + "start_time": "2024-11-12T16:50:26.843347Z" + } + }, + "cell_type": "code", + "source": "group_array", + "id": "518b670047396a91", + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1.044e+03, 0.000e+00, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 1.000e+00, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 2.000e+00, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 3.000e+00, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 4.000e+00, 0.000e+00, 9.990e-01, 1.000e-03],\n", + " [1.044e+03, 5.000e+00, 0.000e+00, 9.990e-01, 1.000e-03],\n", + " [1.044e+03, 6.000e+00, 0.000e+00, 9.990e-01, 1.000e-03],\n", + " [1.044e+03, 7.000e+00, 0.000e+00, 9.990e-01, 1.000e-03],\n", + " [1.044e+03, 8.000e+00, 0.000e+00, 9.990e-01, 1.000e-03],\n", + " [1.044e+03, 9.000e+00, 0.000e+00, 9.990e-01, 1.000e-03],\n", + " [1.044e+03, 1.000e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 1.100e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 1.200e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 1.300e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 1.400e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 1.500e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 1.600e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 1.700e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 1.800e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 1.900e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 2.000e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 2.100e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 2.200e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 2.300e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 2.400e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 2.500e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 2.600e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 2.700e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 2.800e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 2.900e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 3.000e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 3.100e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 3.200e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 3.300e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 3.400e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 3.500e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 3.600e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 3.700e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 3.800e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 3.900e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 4.000e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 4.100e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 4.200e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 4.300e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 4.400e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 4.500e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 4.600e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 4.700e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 4.800e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 4.900e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 5.000e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 5.100e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 5.200e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 5.300e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 5.400e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 5.500e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 5.600e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 5.700e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 5.800e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 5.900e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 6.000e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 6.100e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 6.200e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 6.300e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 6.400e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 6.500e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 6.600e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 6.700e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 6.800e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 6.900e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 7.000e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 7.100e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 7.200e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 7.300e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 7.400e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 7.500e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 7.600e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 7.700e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 7.800e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 7.900e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 8.000e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 8.100e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 8.200e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 8.300e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 8.400e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 8.500e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 8.600e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 8.700e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 8.800e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 8.900e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 9.000e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 9.100e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 9.200e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 9.300e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 9.400e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 9.500e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 9.600e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 9.700e+01, 0.000e+00, 9.990e-01, 1.000e-03],\n", + " [1.044e+03, 9.800e+01, 0.000e+00, 9.990e-01, 1.000e-03],\n", + " [1.044e+03, 9.900e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 1.000e+02, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 1.010e+02, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 1.020e+02, 0.000e+00, 9.990e-01, 1.000e-03],\n", + " [1.044e+03, 1.030e+02, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 1.040e+02, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 1.050e+02, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 1.060e+02, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 1.070e+02, 0.000e+00, 9.990e-01, 1.000e-03],\n", + " [1.044e+03, 1.080e+02, 0.000e+00, 9.990e-01, 1.000e-03],\n", + " [1.044e+03, 1.090e+02, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 1.100e+02, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 1.110e+02, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 1.120e+02, 0.000e+00, 9.990e-01, 1.000e-03],\n", + " [1.044e+03, 1.130e+02, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 1.140e+02, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 1.150e+02, 0.000e+00, 9.990e-01, 1.000e-03],\n", + " [1.044e+03, 1.160e+02, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 1.170e+02, 0.000e+00, 9.980e-01, 2.000e-03],\n", + " [1.044e+03, 1.180e+02, 0.000e+00, 9.980e-01, 2.000e-03],\n", + " [1.044e+03, 1.190e+02, 0.000e+00, 9.990e-01, 1.000e-03],\n", + " [1.044e+03, 1.200e+02, 0.000e+00, 9.990e-01, 1.000e-03],\n", + " [1.044e+03, 1.210e+02, 0.000e+00, 9.990e-01, 1.000e-03],\n", + " [1.044e+03, 1.220e+02, 0.000e+00, 9.990e-01, 1.000e-03],\n", + " [1.044e+03, 1.230e+02, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 1.240e+02, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 1.250e+02, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 1.260e+02, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 1.270e+02, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 1.280e+02, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 1.290e+02, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 1.300e+02, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 1.310e+02, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 1.320e+02, 0.000e+00, 9.990e-01, 1.000e-03],\n", + " [1.044e+03, 1.330e+02, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 1.340e+02, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 1.350e+02, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 1.360e+02, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 1.370e+02, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 1.380e+02, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 1.390e+02, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 1.400e+02, 0.000e+00, 9.830e-01, 1.700e-02],\n", + " [1.044e+03, 1.410e+02, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 1.420e+02, 0.000e+00, 9.990e-01, 1.000e-03],\n", + " [1.044e+03, 1.430e+02, 0.000e+00, 9.970e-01, 3.000e-03],\n", + " [1.044e+03, 1.440e+02, 0.000e+00, 9.980e-01, 2.000e-03],\n", + " [1.044e+03, 1.450e+02, 0.000e+00, 9.990e-01, 1.000e-03],\n", + " [1.044e+03, 1.460e+02, 0.000e+00, 9.970e-01, 3.000e-03],\n", + " [1.044e+03, 1.470e+02, 0.000e+00, 9.990e-01, 1.000e-03],\n", + " [1.044e+03, 1.480e+02, 0.000e+00, 9.990e-01, 1.000e-03],\n", + " [1.044e+03, 1.490e+02, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 1.500e+02, 0.000e+00, 9.990e-01, 1.000e-03],\n", + " [1.044e+03, 1.510e+02, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 1.520e+02, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 1.530e+02, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 1.540e+02, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 1.550e+02, 0.000e+00, 9.980e-01, 2.000e-03],\n", + " [1.044e+03, 1.560e+02, 0.000e+00, 9.990e-01, 1.000e-03],\n", + " [1.044e+03, 1.570e+02, 0.000e+00, 1.000e+00, 0.000e+00],\n", + " [1.044e+03, 1.580e+02, 0.000e+00, 9.990e-01, 1.000e-03],\n", + " [1.044e+03, 1.590e+02, 0.000e+00, 9.990e-01, 1.000e-03],\n", + " [1.044e+03, 1.600e+02, 0.000e+00, 9.980e-01, 2.000e-03],\n", + " [1.044e+03, 1.610e+02, 0.000e+00, 9.980e-01, 2.000e-03],\n", + " [1.044e+03, 1.620e+02, 0.000e+00, 9.970e-01, 3.000e-03],\n", + " [1.044e+03, 1.630e+02, 0.000e+00, 9.960e-01, 4.000e-03],\n", + " [1.044e+03, 1.640e+02, 0.000e+00, 9.840e-01, 1.600e-02],\n", + " [1.044e+03, 1.650e+02, 0.000e+00, 9.970e-01, 3.000e-03],\n", + " [1.044e+03, 1.660e+02, 0.000e+00, 9.960e-01, 4.000e-03],\n", + " [1.044e+03, 1.670e+02, 0.000e+00, 9.950e-01, 5.000e-03],\n", + " [1.044e+03, 1.680e+02, 0.000e+00, 9.970e-01, 3.000e-03],\n", + " [1.044e+03, 1.690e+02, 0.000e+00, 2.900e-02, 9.710e-01],\n", + " [1.044e+03, 1.700e+02, 1.000e+00, 2.000e-03, 9.980e-01],\n", + " [1.044e+03, 1.710e+02, 1.000e+00, 6.000e-03, 9.940e-01],\n", + " [1.044e+03, 1.720e+02, 1.000e+00, 7.000e-03, 9.930e-01],\n", + " [1.044e+03, 1.730e+02, 1.000e+00, 4.100e-02, 9.590e-01],\n", + " [1.044e+03, 1.740e+02, 1.000e+00, 2.700e-02, 9.730e-01],\n", + " [1.044e+03, 1.750e+02, 1.000e+00, 2.000e-02, 9.800e-01]])" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 21 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-11-14T12:22:56.541541800Z", + "start_time": "2024-11-12T16:41:43.152975Z" + } + }, + "cell_type": "code", + "source": [ + "pred_len = 24\n", + "label_horizon = 6\n", + "ground_truth = np.zeros(pred_len, dtype=int)\n", + "ground_truth[-label_horizon:] = 1\n", + "print(f\"Ground truth: {ground_truth}\")\n", + "\n", + "# Simulate the model predictions (for example, using random values between 0 and 1)\n", + "predictions = np.random.rand(pred_len)\n", + "threshold = 0.5\n", + "predictions = np.where(predictions >= threshold, 1, 0)\n", + "print(f\"Unaltered predictions: {predictions}\")\n", + "\n", + "# grace_horizon > label_horizon that we want to consider (i.e., the number of hours before the actual complication when the model is allowed but not required to predict 1)\n", + "grace_horizon = label_horizon * 2\n", + "# For how long should the alarm be silenced?\n", + "silencing_length = label_horizon\n", + "predictions = silence_positives(ground_truth, predictions, grace_horizon=grace_horizon, silencing_length=silencing_length)\n", + "print(f\"Silenced predictions: {predictions}\")\n", + "\n", + "# Fill gaps in the predictions: we also want to consider early predictions with the grace_horizon.\n", + "predictions = fill_gaps(predictions)\n", + "print(f\"Filled + silenced predictions: {predictions}\")" + ], + "id": "39448152d16e0217", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ground truth: [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1]\n", + "Unaltered predictions: [0 0 1 0 1 0 0 0 0 1 1 0 1 0 1 1 0 1 1 0 1 0 0 0]\n", + "Silenced predictions: [0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 1 0 1 0 0 0]\n", + "Filled + silenced predictions: [0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 1 1 1 1 1 1 1 1 1]\n" + ] + } + ], + "execution_count": 3 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-11-14T12:22:56.541541800Z", + "start_time": "2024-11-12T16:41:45.201Z" + } + }, + "cell_type": "code", + "source": "", + "id": "a46d26884039d6d1", + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'p' is not defined", + "output_type": "error", + "traceback": [ + "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", + "\u001B[1;31mNameError\u001B[0m Traceback (most recent call last)", + "Cell \u001B[1;32mIn[4], line 1\u001B[0m\n\u001B[1;32m----> 1\u001B[0m preds[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mprediction_0\u001B[39m\u001B[38;5;124m\"\u001B[39m] \u001B[38;5;241m=\u001B[39m \u001B[43mp\u001B[49m\n", + "\u001B[1;31mNameError\u001B[0m: name 'p' is not defined" + ] + } + ], + "execution_count": 4 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-11-14T12:22:56.541541800Z", + "start_time": "2024-11-12T16:43:19.523441Z" + } + }, + "cell_type": "code", + "source": "preds.group_by(\"# id\").map_groups(lambda group_df: pl.DataFrame(group_df[\"time\"],convert_to_alarm(group_df[\"ground_truth\"], group_df[\"prediction_1\"])))", + "id": "27c9b104e3a52992", + "outputs": [ + { + "ename": "PanicException", + "evalue": "UDF failed: data does not match the number of columns", + "output_type": "error", + "traceback": [ + "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", + "\u001B[1;31mPanicException\u001B[0m Traceback (most recent call last)", + "Cell \u001B[1;32mIn[7], line 1\u001B[0m\n\u001B[1;32m----> 1\u001B[0m \u001B[43mpreds\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mgroup_by\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43m# id\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mmap_groups\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43;01mlambda\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[43mgroup_df\u001B[49m\u001B[43m:\u001B[49m\u001B[43m \u001B[49m\u001B[43mpl\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mDataFrame\u001B[49m\u001B[43m(\u001B[49m\u001B[43mgroup_df\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mtime\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m]\u001B[49m\u001B[43m,\u001B[49m\u001B[43mconvert_to_alarm\u001B[49m\u001B[43m(\u001B[49m\u001B[43mgroup_df\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mground_truth\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m]\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mgroup_df\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mprediction_1\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m]\u001B[49m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m\n", + "File \u001B[1;32m~\\miniconda\\envs\\yaib_19\\lib\\site-packages\\polars\\dataframe\\group_by.py:302\u001B[0m, in \u001B[0;36mGroupBy.map_groups\u001B[1;34m(self, function)\u001B[0m\n\u001B[0;32m 298\u001B[0m msg \u001B[38;5;241m=\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mcannot call `map_groups` when grouping by an expression\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m 299\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mTypeError\u001B[39;00m(msg)\n\u001B[0;32m 301\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mdf\u001B[38;5;241m.\u001B[39m\u001B[38;5;18m__class__\u001B[39m\u001B[38;5;241m.\u001B[39m_from_pydf(\n\u001B[1;32m--> 302\u001B[0m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mdf\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_df\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mgroup_by_map_groups\u001B[49m\u001B[43m(\u001B[49m\n\u001B[0;32m 303\u001B[0m \u001B[43m \u001B[49m\u001B[38;5;28;43mlist\u001B[39;49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mby\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mfunction\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mmaintain_order\u001B[49m\n\u001B[0;32m 304\u001B[0m \u001B[43m \u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 305\u001B[0m )\n", + "\u001B[1;31mPanicException\u001B[0m: UDF failed: data does not match the number of columns" + ] + } + ], + "execution_count": 7 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-11-14T12:22:56.541541800Z", + "start_time": "2024-11-12T16:42:52.394724Z" + } + }, + "cell_type": "code", + "source": "preds", + "id": "f1b7d2a0fabeeb70", + "outputs": [ + { + "data": { + "text/plain": [ + "shape: (3_536, 5)\n", + "┌──────┬──────┬──────────────┬──────────────┬──────────────┐\n", + "│ # id ┆ time ┆ ground_truth ┆ prediction_0 ┆ prediction_1 │\n", + "│ --- ┆ --- ┆ --- ┆ --- ┆ --- │\n", + "│ i64 ┆ i64 ┆ f64 ┆ f64 ┆ f64 │\n", + "╞══════╪══════╪══════════════╪══════════════╪══════════════╡\n", + "│ 42 ┆ 0 ┆ 0.0 ┆ 1.0 ┆ 0.0 │\n", + "│ 42 ┆ 1 ┆ 0.0 ┆ 1.0 ┆ 0.0 │\n", + "│ 42 ┆ 2 ┆ 0.0 ┆ 1.0 ┆ 0.0 │\n", + "│ 42 ┆ 3 ┆ 0.0 ┆ 1.0 ┆ 0.0 │\n", + "│ 42 ┆ 4 ┆ 0.0 ┆ 1.0 ┆ 0.0 │\n", + "│ … ┆ … ┆ … ┆ … ┆ … │\n", + "│ 1044 ┆ 171 ┆ 1.0 ┆ 0.006 ┆ 0.994 │\n", + "│ 1044 ┆ 172 ┆ 1.0 ┆ 0.007 ┆ 0.993 │\n", + "│ 1044 ┆ 173 ┆ 1.0 ┆ 0.041 ┆ 0.959 │\n", + "│ 1044 ┆ 174 ┆ 1.0 ┆ 0.027 ┆ 0.973 │\n", + "│ 1044 ┆ 175 ┆ 1.0 ┆ 0.02 ┆ 0.98 │\n", + "└──────┴──────┴──────────────┴──────────────┴──────────────┘" + ], + "text/html": [ + "
\n", + "shape: (3_536, 5)
# idtimeground_truthprediction_0prediction_1
i64i64f64f64f64
4200.01.00.0
4210.01.00.0
4220.01.00.0
4230.01.00.0
4240.01.00.0
……………
10441711.00.0060.994
10441721.00.0070.993
10441731.00.0410.959
10441741.00.0270.973
10441751.00.020.98
" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 6 + }, + { + "metadata": {}, + "cell_type": "code", + "outputs": [], + "execution_count": null, + "source": "", + "id": "77488f552d1480d0" + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/configs/prediction_models/XGBClassifier.gin b/configs/prediction_models/XGBClassifier.gin index f1070672..f5d1a5a9 100644 --- a/configs/prediction_models/XGBClassifier.gin +++ b/configs/prediction_models/XGBClassifier.gin @@ -8,10 +8,14 @@ train_common.model = @XGBClassifier model/hyperparameter.class_to_tune = @XGBClassifier model/hyperparameter.learning_rate = (0.01, 0.1, "log") -model/hyperparameter.n_estimators = [50, 100, 250, 500, 750, 1000,1500,2000] +model/hyperparameter.n_estimators = [50, 100, 250, 500, 750, 1000,1500,2000, 2500, 3000, 3500, 4000, 4500, 5000] model/hyperparameter.max_depth = [3, 5, 10, 15] model/hyperparameter.scale_pos_weight = [1, 5, 10, 15, 20, 25, 30, 35, 40, 50, 75, 99, 100, 1000] -model/hyperparameter.min_child_weight = [1, 0.5] +model/hyperparameter.min_child_weight = [0.1, 0.5, 1, 2, 5, 10] model/hyperparameter.max_delta_step = [0, 1, 2, 3, 4, 5, 10] model/hyperparameter.colsample_bytree = [0.1, 0.25, 0.5, 0.75, 1.0] -model/hyperparameter.eval_metric = "aucpr" \ No newline at end of file +# model/hyperparameter.eval_metric = "aucpr" +model/hyperparameter.gamma = [0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 2.0] +model/hyperparameter.early_stopping_rounds = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100] +model/hyperparameter.reg_lambda = [0, 0.01, 0.1, 1, 10, 100] +model/hyperparameter.reg_alpha = [0, 0.01, 0.1, 1, 10, 100] \ No newline at end of file diff --git a/demo_data/mortality24/mimic_demo_static/attrition.csv b/demo_data/mortality24/mimic_demo_static/attrition.csv new file mode 100644 index 00000000..2ef75d4e --- /dev/null +++ b/demo_data/mortality24/mimic_demo_static/attrition.csv @@ -0,0 +1,3 @@ +incl_n,excl_n_total,excl_n +125,10,7 +99,34,26 diff --git a/experiments/benchmark_cass.yml b/experiments/benchmark_cass.yml index 4389deb8..557d22ee 100644 --- a/experiments/benchmark_cass.yml +++ b/experiments/benchmark_cass.yml @@ -15,8 +15,8 @@ command: - SSI # - Mortality # - --verbose -# - --hp-checkpoint -# - /sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/activity_notes/SSI/XGBClassifier/2024-07-25T14-32-55/hyperparameter_tuning_logs.db + - --hp-checkpoint + - /sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/activity_notes/SSI/XGBClassifier/2024-07-25T14-32-55/hyperparameter_tuning_logs.db - --modalities - "all" # - --verbose @@ -29,7 +29,15 @@ name: yaib_classification_benchmark parameters: data_dir: values: - - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/dataset_segment_1.0_horizon_6:00:00_transfer_3_2024-10-07T23:26:22" + - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/dataset_segment_1.0_horizon_6:00:00_transfer_3_2024-11-11T11:17:09" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/cohort_separation/1/" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/cohort_separation/2/" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/cohort_separation/3/" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/cohort_separation/4/" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/dataset_segment_1.0_horizon_6:00:00_transfer_3_2024-11-08T12:45:05/" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/dataset_segment_1.0_horizon_6:00:00_transfer_3_2024-10-07T23:26:22" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/dataset_icu_segment_1.0_horizon_6:00:00_transfer_3_2024-10-30T14:51:38/" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/dataset_segment_1.0_horizon_6:00:00_transfer_3_2024-10-07T23:26:22" # - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/dataset_segment_1.0_horizon_6:00:00_transfer_full_2024-09-27T16:14:05_complication" # - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/dataset_segment_1.0_horizon_6:00:00_transfer_full_2024-09-26T16:26:24" # - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/dataset_x_segment_duration_x_transfer_2024-09-19T13:57:36" @@ -50,16 +58,16 @@ parameters: # - BRFClassifier # - RUSBClassifier # - RFClassifier - - GRU +# - GRU # - LSTM # - TCN - - Transformer +# - Transformer modalities: values: - "all" # - [copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, cat_med_embeddings_map, cat_clinical_notes, wearable_activity, wearable_core, static] # - [copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, cat_med_embeddings_map, wearable_activity, wearable_core, static] -# - [copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, static] # without wearable data +# - [copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, static] #, wearable_activity, wearable_core] # without wearable data # - [copra_observations, copra_scores, copra_fluid_balance, cat_med_embeddings_map, wearable_activity, wearable_core, static] # without ishmed lab data and clinical notes # - [copra_observations, copra_scores, copra_fluid_balance, cat_med_embeddings_map, static ] # without wearable data and ishmed lab data, clinical notes ## - [copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, cat_med_embeddings_map, wearable_activity, wearable_core, static] # without clinical notes diff --git a/experiments/charhpc_wandb_sweep_cpu.sh b/experiments/charhpc_wandb_sweep_cpu.sh index de81d07e..a08cb35f 100644 --- a/experiments/charhpc_wandb_sweep_cpu.sh +++ b/experiments/charhpc_wandb_sweep_cpu.sh @@ -1,6 +1,6 @@ #!/bin/bash #SBATCH --job-name=yaib_experiment -#SBATCH --partition=pgpu # -p +#SBATCH --partition=compute # -p #SBATCH --cpus-per-task=16 # -c #SBATCH --mem=100gb #SBATCH --output=logs/classification_%a_%j.log # %j is job id diff --git a/experiments/slurm_base_char_sc.sh b/experiments/slurm_base_char_sc.sh new file mode 100644 index 00000000..6693b339 --- /dev/null +++ b/experiments/slurm_base_char_sc.sh @@ -0,0 +1,44 @@ +#!/bin/bash +#SBATCH --job-name=default +#SBATCH --mail-type=ALL +#SBATCH --mail-user=[INSERT:EMAIL] +#SBATCH --partition=gpu # -p +#SBATCH --cpus-per-task=4 # -c +#SBATCH --mem=48gb +#SBATCH --gpus=1 +#SBATCH --output=%x_%a_%j.log # %x is job-name, %j is job id, %a is array id +#SBATCH --array=0-3 + +# Submit with e.g. --export=TASK_NAME=mortality24,MODEL_NAME=LGBMClassifier +# Basic experiment variables, please exchange [INSERT] for your experiment parameters + +TASK=BinaryClassification # BinaryClassification +YAIB_PATH=/home/vandewrp/projects/YAIB #/dhc/home/robin.vandewater/projects/yaib +EXPERIMENT_PATH=../yaib_logs/${TASK_NAME}_experiment +DATASET_ROOT_PATH= /sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format #data/YAIB_Datasets/data + +echo "This is a SLURM job named" $SLURM_JOB_NAME "with array id" $SLURM_ARRAY_TASK_ID "and job id" $SLURM_JOB_ID +echo "Resources allocated: " $SLURM_CPUS_PER_TASK "CPUs, " $SLURM_MEM_PER_NODE "GB RAM, " $SLURM_GPUS_PER_NODE "GPUs" +echi "Task type:" ${TASK} +echo "Task: " ${TASK_NAME} +echo "Model: "${MODEL_NAME} +echo "Dataset: "${DATASETS[$SLURM_ARRAY_TASK_ID]} +echo "Experiment path: "${EXPERIMENT_PATH} + +cd ${YAIB_PATH} + +eval "$(conda shell.bash hook)" +conda activate yaib + + + +icu-benchmarks train \ + -d ${DATASET_ROOT_PATH} \ + -n ${DATASETS} \ + -t ${TASK} \ + -tn ${TASK_NAME} \ + -m ${MODEL_NAME} \ + -c \ + -s 1111 \ + -l ${EXPERIMENT_PATH} \ + --tune \ No newline at end of file diff --git a/icu_benchmarks/models/alarm_metrics.py b/icu_benchmarks/models/alarm_metrics.py new file mode 100644 index 00000000..cdc0dcb2 --- /dev/null +++ b/icu_benchmarks/models/alarm_metrics.py @@ -0,0 +1,72 @@ +import numpy as np +from numpy import ndarray +import torch +from sklearn.metrics import precision_score + +# def convert_to_alarm(y_true: ndarray, y_pred: ndarray, normalize=False) -> torch.tensor: +# y_pred = np.rint(y_pred).astype(int) +# confusion = sk_confusion_matrix(y_true, y_pred) +# +# return y_pred + + +def convert_to_alarm(ground_truth, predictions, grace_horizon=12, silencing_length=6): + """Convert the predictions to alarm predictions based on the grace horizon and silencing length.""" + # Silence positive predictions + if len(ground_truth) != len(predictions): + raise ValueError("Ground truth and predictions must have the same length.") + if len(ground_truth) < grace_horizon: + raise ValueError("Length of the ground truth must be greater than the grace horizon.") + if not isinstance(ground_truth, np.ndarray): + ground_truth = np.array(ground_truth) + if not isinstance(predictions, np.ndarray): + predictions = np.array(predictions) + threshold = 0.5 + predictions = np.where(predictions >= threshold, 1, 0) + silenced_predictions = silence_positives(ground_truth, predictions, grace_horizon, silencing_length) + # Fill gaps in the predictions + filled_predictions = fill_gaps(silenced_predictions, grace_horizon) + return filled_predictions, ground_truth + +def silence_positives(ground_truth, predictions, grace_horizon=12, silencing_length=6): + """Silence positive predictions in the predictions array based on the grace horizon and silencing length.""" + # Find all positive indices in the rounded predictions + positive_indices = np.where(predictions == 1)[0] + positive_indices = positive_indices[positive_indices < len(ground_truth) - grace_horizon] + # print(positive_indices) + # Create the silence array + silence_array = np.ones_like(ground_truth) + + while len(positive_indices) > 0: + positive_index = positive_indices[0] + if silence_array[positive_indices[0]] == 0: + # print(f"Already silenced: {positive_index}") + positive_indices = positive_indices[1:] + continue + silence_array[positive_index + 1:positive_index + silencing_length] = 0 + positive_indices = positive_indices[1:] + # print(predictions) + # print(silence_array) + silenced_predictions = predictions * silence_array + # print(silenced_predictions) + return silenced_predictions + + +def fill_gaps(predictions, ground_truth, grace_horizon=12): + """ Fill gaps in the predictions by taking the maximum value between ground truth and predictions.""" + if grace_horizon > len(predictions): + grace_horizon = len(predictions) + # Take the last grace_horizon values + grace_values = predictions[-grace_horizon:] + # print(grace_values) + # Find the first occurrence of 1 in the grace_values + first_one_index = np.where(grace_values == 1) + if first_one_index[0].size > 0: + # Make all subsequent values after the first 1 positive + first_one_index = first_one_index[0][0] + grace_values[first_one_index:] = 1 + # Update the original prediction array + # print(grace_values) + predictions[-grace_horizon:] = grace_values + ground_truth[-grace_horizon:] = np.maximum(predictions[-grace_horizon:], ground_truth[-grace_horizon:]) + return predictions, ground_truth \ No newline at end of file diff --git a/icu_benchmarks/models/constants.py b/icu_benchmarks/models/constants.py index 43843db8..a931c9fd 100644 --- a/icu_benchmarks/models/constants.py +++ b/icu_benchmarks/models/constants.py @@ -27,6 +27,7 @@ JSD, BinaryFairnessWrapper, confusion_matrix, + sensitivity, specificity, positive_predictive_value ) @@ -38,6 +39,11 @@ class MLMetrics: "PR_Curve": precision_recall_curve, "RO_Curve": roc_curve, "Confusion_Matrix": confusion_matrix, + "Sensitivity": sensitivity, + # "Precision": precision_score, + # "Recall": recall_score, + "Specificity": specificity, + "PPV": positive_predictive_value } MULTICLASS_CLASSIFICATION = { diff --git a/icu_benchmarks/models/custom_metrics.py b/icu_benchmarks/models/custom_metrics.py index eb0a5d23..3059b4b4 100644 --- a/icu_benchmarks/models/custom_metrics.py +++ b/icu_benchmarks/models/custom_metrics.py @@ -1,3 +1,4 @@ +import numpy import torch from typing import Callable import numpy as np @@ -6,7 +7,7 @@ from sklearn.metrics import balanced_accuracy_score, mean_absolute_error, confusion_matrix as sk_confusion_matrix from sklearn.calibration import calibration_curve from scipy.spatial.distance import jensenshannon -from torchmetrics.classification import BinaryFairness +from torchmetrics.classification import BinaryFairness, Specificity """" This file contains custom metrics that can be added to YAIB. @@ -67,10 +68,10 @@ def __init__( check_compute_fn=check_compute_fn, ) - def mae_with_invert_compute_fn(y_preds: torch.Tensor, y_targets: torch.Tensor, invert_fn=Callable) -> float: - y_true = invert_fn(y_targets.numpy().reshape(-1, 1))[:, 0] - y_pred = invert_fn(y_preds.numpy().reshape(-1, 1))[:, 0] - return mean_absolute_error(y_true, y_pred) + def mae_with_invert_compute_fn(y_preds: torch.Tensor, y_targets: torch.Tensor, invert_fn=Callable) -> float: + y_true = invert_fn(y_targets.numpy().reshape(-1, 1))[:, 0] + y_pred = invert_fn(y_preds.numpy().reshape(-1, 1))[:, 0] + return mean_absolute_error(y_true, y_pred) class JSD(EpochMetric): @@ -143,3 +144,68 @@ def confusion_matrix(y_true: ndarray, y_pred: ndarray, normalize=False) -> torch for j in range(confusion.shape[1]): confusion_dict[f"class_{i}_pred_{j}"] = confusion[i][j] return confusion_dict + + +class Sensitivity(EpochMetric): + def __init__(self, output_transform: Callable = lambda x: x, check_compute_fn: bool = False) -> None: + super(Sensitivity, self).__init__( + self.sensitivity_compute, output_transform=output_transform, check_compute_fn=check_compute_fn + ) + +def sensitivity(y_preds: torch.Tensor | ndarray, y_targets: torch.Tensor | ndarray) -> float: + if isinstance(y_preds, torch.Tensor): + y_preds = y_preds.numpy() + if isinstance(y_targets, torch.Tensor): + y_targets = y_targets.numpy() + y_true = np.rint(y_targets).astype(int) + y_pred = np.rint(y_preds).astype(int) + tn, fp, fn, tp = sk_confusion_matrix(y_true, y_pred).ravel() + return tp / (tp + fn) + +class Specificity(EpochMetric): + def __init__(self, output_transform: Callable = lambda x: x, check_compute_fn: bool = False) -> None: + super(Specificity, self).__init__( + self.specificity_compute, output_transform=output_transform, check_compute_fn=check_compute_fn + ) + +def specificity(y_preds: torch.Tensor, y_targets: torch.Tensor) -> float: + if isinstance(y_preds, torch.Tensor): + y_preds = y_preds.numpy() + if isinstance(y_targets, torch.Tensor): + y_targets = y_targets.numpy() + y_true = np.rint(y_targets).astype(int) + y_pred = np.rint(y_preds).astype(int) + tn, fp, fn, tp = sk_confusion_matrix(y_true, y_pred).ravel() + return tn / (tn + fp) + +def positive_predictive_value(y_preds: torch.Tensor | np.ndarray, y_targets: torch.Tensor | np.ndarray) -> float: + if isinstance(y_preds, torch.Tensor): + y_preds = y_preds.numpy() + if isinstance(y_targets, torch.Tensor): + y_targets = y_targets.numpy() + y_true = np.rint(y_targets).astype(int) + y_pred = np.rint(y_preds).astype(int) + tn, fp, fn, tp = sk_confusion_matrix(y_true, y_pred).ravel() + return tp / (tp + fp) + +# from torchmetrics.classification import Specificity as TorchMetricsSpecificity +# +# class Specificity(EpochMetric): +# def __init__(self, task="binary", output_transform: Callable = lambda x: x, check_compute_fn: bool = False) -> None: +# super(Specificity, self).__init__( +# self.specificity_compute, output_transform=output_transform, check_compute_fn=check_compute_fn +# ) +# if isinstance(task, np.ndarray): +# task = task.item() +# self.metric = TorchMetricsSpecificity(task=task) +# +# def specificity_compute(self, y_preds: torch.Tensor, y_targets: torch.Tensor) -> float: +# if isinstance(y_preds, np.ndarray): +# y_preds = torch.tensor(y_preds) +# if isinstance(y_targets, np.ndarray): +# y_targets = torch.tensor(y_targets) +# self.metric.update(y_preds, y_targets) +# return self.metric.compute().item() + + + diff --git a/icu_benchmarks/models/ml_models/xgboost.py b/icu_benchmarks/models/ml_models/xgboost.py index 5ca738ac..93272051 100644 --- a/icu_benchmarks/models/ml_models/xgboost.py +++ b/icu_benchmarks/models/ml_models/xgboost.py @@ -8,6 +8,7 @@ import xgboost as xgb from xgboost.callback import EarlyStopping from wandb.integration.xgboost import wandb_callback as wandb_xgb +from sklearn.metrics import log_loss from icu_benchmarks.constants import RunMode from icu_benchmarks.models.wrappers import MLWrapper @@ -23,7 +24,7 @@ class XGBClassifier(MLWrapper): _explain_values = False def __init__(self, *args, **kwargs): - self.model = self.set_model_args(xgb.XGBClassifier, *args, **kwargs, device="cpu") + self.model = self.set_model_args(xgb.XGBClassifier, *args, **kwargs, eval_metric=log_loss, device="cpu") super().__init__(*args, **kwargs) def predict(self, features): @@ -46,7 +47,7 @@ def fit_model(self, train_data, train_labels, val_data, val_labels): callbacks.append(wandb_xgb()) logging.info(f"train_data: {train_data.shape}, train_labels: {train_labels.shape}") logging.info(train_labels) - self.model.fit(train_data, train_labels, eval_set=[(val_data, val_labels)], verbose=False) + self.model.fit(train_data, train_labels, eval_set=[(val_data, val_labels)]) if self._explain_values: self.explainer = shap.TreeExplainer(self.model) self.train_shap_values = self.explainer(train_data) diff --git a/icu_benchmarks/models/wrappers.py b/icu_benchmarks/models/wrappers.py index 310f9fe6..2298303e 100644 --- a/icu_benchmarks/models/wrappers.py +++ b/icu_benchmarks/models/wrappers.py @@ -3,6 +3,7 @@ from typing import Dict, Any, List, Optional, Union from pathlib import Path import torchmetrics +from ignite.metrics import EpochMetric from sklearn.metrics import log_loss, mean_squared_error, average_precision_score, roc_auc_score import torch @@ -46,7 +47,7 @@ class BaseModule(LightningModule): trained_columns = None # Type of run mode run_mode = None - debug = False + debug = True explain_features = False def forward(self, *args, **kwargs): @@ -423,7 +424,7 @@ def fit(self, train_dataset, val_dataset): logging.debug(f"Train loss: {self.loss(train_label, train_pred)}") self.log("val/loss", val_loss, sync_dist=True) logging.debug(f"Val loss: {val_loss}") - self.log_metrics(train_label, train_pred, "train") + self.log_metrics(train_label, train_pred, "train", row_indicators) def fit_model(self, train_data, train_labels, val_data, val_labels): """Fit the model to the training data (default SKlearn syntax)""" @@ -457,10 +458,10 @@ def test_step(self, dataset, _): self.explain_model(test_rep, test_label) if self.mps: self.log("test/loss", np.float32(self.loss(test_label, test_pred)), sync_dist=True) - self.log_metrics(np.float32(test_label), np.float32(test_pred), "test") + self.log_metrics(np.float32(test_label), np.float32(test_pred), "test", pred_indicators) else: self.log("test/loss", self.loss(test_label, test_pred), sync_dist=True) - self.log_metrics(test_label, test_pred, "test") + self.log_metrics(test_label, test_pred, "test", pred_indicators) logging.debug(f"Test loss: {self.loss(test_label, test_pred)}") def predict(self, features): @@ -469,21 +470,42 @@ def predict(self, features): else: # Classification: return probabilities return self.model.predict_proba(features) - def log_metrics(self, label, pred, metric_type): + def log_metrics(self, label, pred, metric_type, pred_indicators): """Log metrics to the PL logs.""" - if "Confusion_Matrix" in self.metrics: - self.log_dict(confusion_matrix(self.label_transform(label), self.output_transform(pred)), sync_dist=True) - self.log_dict( - { - f"{metric_type}/{name}": (metric(self.label_transform(label), self.output_transform(pred))) - # For every metric - for name, metric in self.metrics.items() - # Filter out metrics that return a tuple (e.g. precision_recall_curve) - if not isinstance(metric(self.label_transform(label), self.output_transform(pred)), tuple) - and name != "Confusion_Matrix" - }, - sync_dist=True, - ) + if pred_indicators is None: + if "Confusion_Matrix" in self.metrics: + self.log_dict(confusion_matrix(self.label_transform(label), self.output_transform(pred)), sync_dist=True) + self.log_dict( + { + f"{metric_type}/{name}": (metric(self.label_transform(label), self.output_transform(pred))) + # For every metric + for name, metric in self.metrics.items() + # Filter out metrics that return a tuple (e.g. precision_recall_curve) + if not isinstance(metric(self.label_transform(label), self.output_transform(pred)), tuple) + and name != "Confusion_Matrix" + }, + sync_dist=True, + ) + else: + if len(pred_indicators.shape) > 1 and len(pred.shape) > 1 and pred_indicators.shape[1] == pred.shape[1]: + pred_indicators = np.hstack((pred_indicators, label.reshape(-1, 1))) + pred_indicators = np.hstack((pred_indicators, pred)) + # Format: id, time (hours), ground truth, prediction 0, prediction 1 + + # TODO: Implement alarm metrics using row indicators + if "Confusion_Matrix" in self.metrics: + self.log_dict(confusion_matrix(self.label_transform(label), self.output_transform(pred)), sync_dist=True) + self.log_dict( + { + f"{metric_type}/{name}": (metric(self.label_transform(label), self.output_transform(pred))) + # For every metric + for name, metric in self.metrics.items() + # Filter out metrics that return a tuple (e.g. precision_recall_curve) + if not isinstance(metric(self.label_transform(label), self.output_transform(pred)), tuple) + and name != "Confusion_Matrix" + }, + sync_dist=True, + ) def _explain_model(self, test_rep, test_label): if self.explainer is not None: @@ -496,7 +518,8 @@ def _save_model_outputs(self, pred_indicators, test_pred, test_label): pred_indicators = np.hstack((pred_indicators, test_label.reshape(-1, 1))) pred_indicators = np.hstack((pred_indicators, test_pred)) # Save as: id, time (hours), ground truth, prediction 0, prediction 1 - np.savetxt(Path(self.logger.save_dir) / "pred_indicators.csv", pred_indicators, delimiter=",") + np.savetxt(Path(self.logger.save_dir) / "pred_indicators.csv", pred_indicators, delimiter=",", + header="id,time,ground_truth,prediction_0,prediction_1", fmt='%d,%d,%.3f,%.3f,%.3f') logging.debug(f"Saved row indicators to {Path(self.logger.save_dir) / f'row_indicators.csv'}") else: logging.warning("Could not save row indicators.") diff --git a/icu_benchmarks/run_utils.py b/icu_benchmarks/run_utils.py index 97b50f8c..9dcf274f 100644 --- a/icu_benchmarks/run_utils.py +++ b/icu_benchmarks/run_utils.py @@ -16,7 +16,7 @@ from icu_benchmarks.models.utils import JsonResultLoggingEncoder from icu_benchmarks.wandb_utils import wandb_log import polars as pl - +import random def build_parser() -> ArgumentParser: """Builds an ArgumentParser for the command line. @@ -76,9 +76,9 @@ def create_run_dir(log_dir: Path, randomly_searched_params: str = None) -> Path: Returns: Path to the created run log directory. """ - log_dir_run = log_dir / str(datetime.now().strftime("%Y-%m-%dT%H-%M-%S")) + log_dir_run = log_dir / str(datetime.now().strftime("%Y-%m-%dT%H-%M-%S.%f")) while log_dir_run.exists(): - log_dir_run = log_dir / str(datetime.now().strftime("%Y-%m-%dT%H-%M-%S.%f")) + log_dir_run = log_dir_run.with_name(log_dir_run.name + random.randint(1, 10)) log_dir_run.mkdir(parents=True) if randomly_searched_params: (log_dir_run / randomly_searched_params).touch() From 92ed52d86f3b42f7868b122281b9207b679a1d35 Mon Sep 17 00:00:00 2001 From: Robin van de Water Date: Mon, 2 Dec 2024 10:59:42 +0100 Subject: [PATCH 05/82] preprocessing exclusion added --- icu_benchmarks/data/split_process_data.py | 104 ++++++++-------------- 1 file changed, 38 insertions(+), 66 deletions(-) diff --git a/icu_benchmarks/data/split_process_data.py b/icu_benchmarks/data/split_process_data.py index a47c9729..1ff12a9f 100644 --- a/icu_benchmarks/data/split_process_data.py +++ b/icu_benchmarks/data/split_process_data.py @@ -1,12 +1,13 @@ import copy import logging import os - +import numpy as np import gin import json import hashlib import pandas as pd import polars as pl +import polars.selectors as cs from pathlib import Path import pickle from timeit import default_timer as timer @@ -15,6 +16,7 @@ from icu_benchmarks.constants import RunMode from icu_benchmarks.run_utils import check_required_keys from .constants import DataSplit as Split, DataSegment as Segment, VarType as Var +from .utils import check_sanitize_data, modality_selection @gin.configurable("preprocess") @@ -26,6 +28,7 @@ def preprocess_data( vars: dict[str] = gin.REQUIRED, modality_mapping: dict[str] = {}, selected_modalities: list[str] = "all", + exclude_preproc: list[str] = None, seed: int = 42, debug: bool = False, cv_repetitions: int = 5, @@ -52,6 +55,9 @@ def preprocess_data( data_dir: Path to the directory holding the data. file_names: Contains the parquet file names in data_dir. vars: Contains the names of columns in the data. + modality_mapping: Mapping of modalities to column names. + selected_modalities: List of selected modalities to use. + exclude_preproc: List of modalities to exclude from preprocessing. seed: Random seed. debug: Load less data if true. cv_repetitions: Number of times to repeat cross validation. @@ -89,19 +95,22 @@ def preprocess_data( cache_filename = f"s_{seed}_r_{repetition_index}_f_{fold_index}_t_{train_size}_d_{debug}" logging.log(logging.INFO, f"Using preprocessor: {preprocessor.__name__}") - vars_to_exclude = ( - modality_mapping.get("cat_clinical_notes") + modality_mapping.get("cat_med_embeddings_map") - if ( - modality_mapping.get("cat_clinical_notes") is not None - and modality_mapping.get("cat_med_embeddings_map") is not None - ) - else None - ) + + excluded_vars = [] + if exclude_preproc is not None: + # Exclude variables from preprocessing based on modality: + # useful if modality has already undergone extensive preprocessing. + for modality in exclude_preproc: + if modality in modality_mapping: + excluded_vars.extend(modality_mapping.get(modality)) + else: + logging.warning(f"Modality '{modality}' not found in modality mapping.") + logging.info(f"Excluding vars from preprocessing: {excluded_vars}") preprocessor = preprocessor( use_static_features=use_static, save_cache=data_dir / "preproc" / (cache_filename + "_recipe"), - vars_to_exclude=vars_to_exclude, + vars_to_exclude=excluded_vars, ) if isinstance(preprocessor, PandasClassificationPreprocessor): preprocessor.set_imputation_model(pretrained_imputation_model) @@ -180,6 +189,19 @@ def preprocess_data( logging.debug("Dropping columns with nulls") sel = dict[key].select(pl.all().has_nulls()) logging.debug(sel.select(col.name for col in sel if col.item(0))) + logging.info("Checking for infinite values.") + for col in val.select(cs.numeric()).columns: + if val[col].is_infinite().any(): + logging.info(f"Column '{col}' contains infinite values. Datatype: {val[col].dtype}") + + max_float64 = np.finfo(np.float64).max / 100 + # Replace infinite values with the maximum value for float64 + val = val.with_columns([ + pl.when(pl.col(col).is_infinite()).then(max_float64).otherwise(pl.col(col)).alias(col) + for col in val.columns if val[col].dtype == pl.Float64 + ]) + dict[key] = val + # Generate cache if generate_cache: @@ -192,59 +214,6 @@ def preprocess_data( return data -def check_sanitize_data(data, vars): - """Check for duplicates in the loaded data and remove them.""" - group = vars[Var.group] if Var.group in vars.keys() else None - sequence = vars[Var.sequence] if Var.sequence in vars.keys() else None - keep = "last" - if Segment.static in data.keys(): - old_len = len(data[Segment.static]) - data[Segment.static] = data[Segment.static].unique(subset=group, keep=keep, maintain_order=True) - logging.warning(f"Removed {old_len - len(data[Segment.static])} duplicates from static data.") - if Segment.dynamic in data.keys(): - old_len = len(data[Segment.dynamic]) - data[Segment.dynamic] = data[Segment.dynamic].unique(subset=[group, sequence], keep=keep, maintain_order=True) - logging.warning(f"Removed {old_len - len(data[Segment.dynamic])} duplicates from dynamic data.") - if Segment.outcome in data.keys(): - old_len = len(data[Segment.outcome]) - if sequence in data[Segment.outcome].columns: - # We have a dynamic outcome with group and sequence - data[Segment.outcome] = data[Segment.outcome].unique(subset=[group, sequence], keep=keep, maintain_order=True) - else: - data[Segment.outcome] = data[Segment.outcome].unique(subset=[group], keep=keep, maintain_order=True) - logging.warning(f"Removed {old_len - len(data[Segment.outcome])} duplicates from outcome data.") - return data - - -def modality_selection( - data: dict[pl.DataFrame], modality_mapping: dict[str], selected_modalities: list[str], vars -) -> dict[pl.DataFrame]: - logging.info(f"Selected modalities: {selected_modalities}") - selected_columns = [modality_mapping[cols] for cols in selected_modalities if cols in modality_mapping.keys()] - if not any(col in modality_mapping.keys() for col in selected_modalities): - raise ValueError("None of the selected modalities found in modality mapping.") - if selected_columns == []: - logging.info("No columns selected. Using all columns.") - return data, vars - selected_columns = sum(selected_columns, []) - selected_columns.extend([vars[Var.group], vars[Var.label], vars[Var.sequence]]) - old_columns = [] - # Update vars dict - for key, value in vars.items(): - if key not in [Var.group, Var.label, Var.sequence]: - old_columns.extend(value) - vars[key] = [col for col in value if col in selected_columns] - # -3 because of standard columns - logging.info(f"Selected columns: {len(selected_columns) - 3}, old columns: {len(old_columns)}") - logging.debug(f"Difference: {set(old_columns) - set(selected_columns)}") - # Update data dict - for key in data.keys(): - sel_col = [col for col in data[key].columns if col in selected_columns] - data[key] = data[key].select(sel_col) - logging.debug(f"Selected columns in {key}: {len(data[key].columns)}") - return data, vars - - def make_train_val( data: dict[pd.DataFrame], vars: dict[str], @@ -347,7 +316,7 @@ def make_single_split( repetition_index: int, cv_folds: int, fold_index: int, - train_size: int = None, + train_size: int = 0.80, seed: int = 42, debug: bool = False, runmode: RunMode = RunMode.classification, @@ -408,8 +377,10 @@ def make_single_split( outer_cv = StratifiedShuffleSplit(cv_repetitions, train_size=train_size) else: outer_cv = StratifiedKFold(cv_repetitions, shuffle=True, random_state=seed) - inner_cv = StratifiedKFold(cv_folds, shuffle=True, random_state=seed) - + if cv_folds > 2: + inner_cv = StratifiedKFold(cv_folds, shuffle=True, random_state=seed) + else: + inner_cv = StratifiedShuffleSplit(cv_folds, train_size=0.75, random_state=seed) dev, test = list(outer_cv.split(stays, labels))[repetition_index] if polars: dev_stays = stays[dev] @@ -423,6 +394,7 @@ def make_single_split( outer_cv = ShuffleSplit(cv_repetitions, train_size=train_size) else: outer_cv = KFold(cv_repetitions, shuffle=True, random_state=seed) + inner_cv = KFold(cv_folds, shuffle=True, random_state=seed) dev, test = list(outer_cv.split(stays))[repetition_index] From e16cdb7d6d20162428075a5cc6316385314ec402 Mon Sep 17 00:00:00 2001 From: Robin van de Water Date: Fri, 20 Dec 2024 12:00:17 +0100 Subject: [PATCH 06/82] hpc --- experiments/charhpc_wandb_sweep_cpu.sh | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/experiments/charhpc_wandb_sweep_cpu.sh b/experiments/charhpc_wandb_sweep_cpu.sh index a08cb35f..b4268548 100644 --- a/experiments/charhpc_wandb_sweep_cpu.sh +++ b/experiments/charhpc_wandb_sweep_cpu.sh @@ -2,9 +2,9 @@ #SBATCH --job-name=yaib_experiment #SBATCH --partition=compute # -p #SBATCH --cpus-per-task=16 # -c -#SBATCH --mem=100gb +#SBATCH --mem=250gb #SBATCH --output=logs/classification_%a_%j.log # %j is job id -#SBATCH --time=24:00:00 +#SBATCH --time=48:00:00 source /etc/profile.d/conda.sh From 60fb35168fe8f75834adceefab522a71448fe8df Mon Sep 17 00:00:00 2001 From: Robin van de Water Date: Fri, 20 Dec 2024 12:00:46 +0100 Subject: [PATCH 07/82] specifying 0 hyperparameter calls --- icu_benchmarks/tuning/hyperparameters.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/icu_benchmarks/tuning/hyperparameters.py b/icu_benchmarks/tuning/hyperparameters.py index c879bd62..925171e1 100644 --- a/icu_benchmarks/tuning/hyperparameters.py +++ b/icu_benchmarks/tuning/hyperparameters.py @@ -222,6 +222,9 @@ def choose_and_bind_hyperparameters_optuna( ValueError: If checkpoint is not None and the checkpoint does not exist. """ hyperparams = {} + if n_calls <= 0: + logging.info(f"Initialized with n_calls: {n_calls} , skipping tuning.") + return if len(scopes) == 0 or folds_to_tune_on is None: logging.warning("No scopes and/or folds to tune on, skipping tuning.") From 64b38edbd5d622f4ea5122e3d5acd12e0095d7d1 Mon Sep 17 00:00:00 2001 From: Robin van de Water Date: Fri, 20 Dec 2024 12:02:00 +0100 Subject: [PATCH 08/82] logging --- icu_benchmarks/data/preprocessor.py | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/icu_benchmarks/data/preprocessor.py b/icu_benchmarks/data/preprocessor.py index 9f6103dd..3c2056aa 100644 --- a/icu_benchmarks/data/preprocessor.py +++ b/icu_benchmarks/data/preprocessor.py @@ -1,6 +1,7 @@ import copy import pickle +import recipys.constants import torch import logging @@ -177,14 +178,18 @@ def _model_impute(self, data, group=None): return data def _process_dynamic(self, data, vars): + # self.vars_to_exclude = ["hr"] + old_columns = data[Split.train][Segment.dynamic].columns dyn_rec = Recipe(data[Split.train][Segment.dynamic], [], vars[Segment.dynamic], vars["GROUP"], vars["SEQUENCE"]) if self.scaling: - dyn_rec.add_step(StepScale()) + dyn_rec.add_step(StepScale(sel=all_numeric_predictors(backend=recipys.constants.Backend.POLARS))) if self.imputation_model is not None: dyn_rec.add_step(StepImputeModel(model=self.model_impute, sel=all_of(vars[Segment.dynamic]))) if self.vars_to_exclude is not None: # Exclude vars_to_exclude from missing indicator/ feature generation + # logging.info(f"Excluding {len(self.vars_to_exclude)} : {self.vars_to_exclude}") vars_to_apply = list(set(vars[Segment.dynamic]) - set(self.vars_to_exclude)) + # logging.info(f"Applying to {len(vars_to_apply)}") else: vars_to_apply = vars[Segment.dynamic] dyn_rec.add_step(StepSklearn(MissingIndicator(features="all"), sel=all_of(vars_to_apply), in_place=False)) @@ -195,6 +200,7 @@ def _process_dynamic(self, data, vars): if self.generate_features: dyn_rec = self._dynamic_feature_generation(dyn_rec, all_of(vars_to_apply)) data = apply_recipe_to_splits(dyn_rec, data, Segment.dynamic, self.save_cache, self.load_cache) + # logging.info(f"Data columns: {len(data[Split.train][Segment.dynamic].columns)} -> old columns: {len(old_columns)}, added columns: {set(data[Split.train][Segment.dynamic].columns) - set(old_columns)}") return data def _dynamic_feature_generation(self, data, dynamic_vars): From d943d03cf76fca055f2bcb39413c72d7f879d5d7 Mon Sep 17 00:00:00 2001 From: Robin van de Water Date: Fri, 20 Dec 2024 12:02:15 +0100 Subject: [PATCH 09/82] add shap and update optuna --- requirements.txt | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 2e8495f5..62dd1bbe 100644 --- a/requirements.txt +++ b/requirements.txt @@ -24,7 +24,7 @@ tensorboard==2.12.2 tqdm==4.66.3 einops==0.6.1 hydra-core==1.3 -optuna==4.0.0 +optuna==4.1.0 optuna-integration==4.0.0 wandb==0.18.5 recipies==1.0 @@ -32,4 +32,5 @@ recipies==1.0 scikit-optimize-fix==0.9.1 hydra-submitit-launcher==1.2.0 pytest-runner==6.0.1 +shap==0.46.0 From ff0358b3fbf316b740219136b2fedb03bfe68310 Mon Sep 17 00:00:00 2001 From: Robin van de Water Date: Fri, 20 Dec 2024 12:03:01 +0100 Subject: [PATCH 10/82] modality mapping failsafe --- icu_benchmarks/data/split_process_data.py | 21 ++++++++++++--------- 1 file changed, 12 insertions(+), 9 deletions(-) diff --git a/icu_benchmarks/data/split_process_data.py b/icu_benchmarks/data/split_process_data.py index 1ff12a9f..e372c102 100644 --- a/icu_benchmarks/data/split_process_data.py +++ b/icu_benchmarks/data/split_process_data.py @@ -100,13 +100,15 @@ def preprocess_data( if exclude_preproc is not None: # Exclude variables from preprocessing based on modality: # useful if modality has already undergone extensive preprocessing. - for modality in exclude_preproc: - if modality in modality_mapping: - excluded_vars.extend(modality_mapping.get(modality)) - else: - logging.warning(f"Modality '{modality}' not found in modality mapping.") - logging.info(f"Excluding vars from preprocessing: {excluded_vars}") - + if modality_mapping is not None and len(modality_mapping) > 0: + for modality in exclude_preproc: + if modality in modality_mapping: + excluded_vars.extend(modality_mapping.get(modality)) + else: + logging.warning(f"Modality '{modality}' not found in modality mapping.") + logging.info(f"Excluding vars from preprocessing: {excluded_vars}") + else: + logging.warning("No modality mapping provided. Excluding variables from preprocessing will have no effect.") preprocessor = preprocessor( use_static_features=use_static, save_cache=data_dir / "preproc" / (cache_filename + "_recipe"), @@ -133,7 +135,7 @@ def preprocess_data( f: pl.read_parquet(data_dir / file_names[f]) for f in file_names.keys() if os.path.exists(data_dir / file_names[f]) } logging.info(f"Loaded data: {list(data.keys())}") - data = check_sanitize_data(data, vars) + data, vars = check_sanitize_data(data, vars) if not (Segment.dynamic in data.keys()): logging.warning("No dynamic data found, using only static data.") @@ -194,13 +196,14 @@ def preprocess_data( if val[col].is_infinite().any(): logging.info(f"Column '{col}' contains infinite values. Datatype: {val[col].dtype}") - max_float64 = np.finfo(np.float64).max / 100 + max_float64 = 0 # Replace infinite values with the maximum value for float64 val = val.with_columns([ pl.when(pl.col(col).is_infinite()).then(max_float64).otherwise(pl.col(col)).alias(col) for col in val.columns if val[col].dtype == pl.Float64 ]) dict[key] = val + logging.info(f"Amount of columns: {len(val.columns)}") # Generate cache From d89433246e0dc5c86b7541b4a0a5ab84a64a539b Mon Sep 17 00:00:00 2001 From: Robin van de Water Date: Fri, 20 Dec 2024 12:03:38 +0100 Subject: [PATCH 11/82] move to utils --- icu_benchmarks/data/utils.py | 77 ++++++++++++++++++++++++++++++++++++ 1 file changed, 77 insertions(+) create mode 100644 icu_benchmarks/data/utils.py diff --git a/icu_benchmarks/data/utils.py b/icu_benchmarks/data/utils.py new file mode 100644 index 00000000..ec87bece --- /dev/null +++ b/icu_benchmarks/data/utils.py @@ -0,0 +1,77 @@ +import logging +import polars as pl +import polars.selectors as cs +import numpy as np + +from icu_benchmarks.data.constants import VarType as Var, DataSegment as Segment + + +def infinite_removal(val): + for col in val.select(cs.numeric()).columns: + if val[col].is_infinite().any(): + logging.info(f"Column '{col}' contains infinite values. Datatype: {val[col].dtype}") + + max_float64 = np.finfo(np.float64).max / 100 + # Replace infinite values with the maximum value for float64 + val = val.with_columns([ + pl.when(pl.col(col).is_infinite()).then(max_float64).otherwise(pl.col(col)).alias(col) + for col in val.columns if val[col].dtype == pl.Float64 + ]) + return val + + +def check_sanitize_data(data, vars): + """Check for duplicates in the loaded data and remove them.""" + group = vars[Var.group] if Var.group in vars.keys() else None + sequence = vars[Var.sequence] if Var.sequence in vars.keys() else None + keep = "last" + logging.info(data.keys()) + if Segment.static in data.keys(): + old_len = len(data[Segment.static]) + data[Segment.static] = data[Segment.static].unique(subset=group, keep=keep, maintain_order=True) + logging.warning(f"Removed {old_len - len(data[Segment.static])} duplicates from static data.") + if Segment.dynamic in data.keys(): + old_len = len(data[Segment.dynamic]) + data[Segment.dynamic] = data[Segment.dynamic].unique(subset=[group, sequence], keep=keep, maintain_order=True) + data[Segment.dynamic] = infinite_removal(data[Segment.dynamic]) + data[Segment.dynamic] = data[Segment.dynamic].select(~cs.starts_with("wearable_ppgfeature_HRV_SampEn")) + vars[Segment.dynamic] = [col for col in vars[Segment.dynamic] if col in data[Segment.dynamic].columns] + logging.warning(f"Removed {old_len - len(data[Segment.dynamic])} duplicates from dynamic data.") + if Segment.outcome in data.keys(): + old_len = len(data[Segment.outcome]) + if sequence in data[Segment.outcome].columns: + # We have a dynamic outcome with group and sequence + data[Segment.outcome] = data[Segment.outcome].unique(subset=[group, sequence], keep=keep, maintain_order=True) + else: + data[Segment.outcome] = data[Segment.outcome].unique(subset=[group], keep=keep, maintain_order=True) + logging.warning(f"Removed {old_len - len(data[Segment.outcome])} duplicates from outcome data.") + return data, vars + + +def modality_selection( + data: dict[pl.DataFrame], modality_mapping: dict[str], selected_modalities: list[str], vars +) -> dict[pl.DataFrame]: + logging.info(f"Selected modalities: {selected_modalities}") + selected_columns = [modality_mapping[cols] for cols in selected_modalities if cols in modality_mapping.keys()] + if not any(col in modality_mapping.keys() for col in selected_modalities): + raise ValueError("None of the selected modalities found in modality mapping.") + if selected_columns == []: + logging.info("No columns selected. Using all columns.") + return data, vars + selected_columns = sum(selected_columns, []) + selected_columns.extend([vars[Var.group], vars[Var.label], vars[Var.sequence]]) + old_columns = [] + # Update vars dict + for key, value in vars.items(): + if key not in [Var.group, Var.label, Var.sequence]: + old_columns.extend(value) + vars[key] = [col for col in value if col in selected_columns] + # -3 because of standard columns + logging.info(f"Selected columns: {len(selected_columns) - 3}, old columns: {len(old_columns)}") + logging.debug(f"Difference: {set(old_columns) - set(selected_columns)}") + # Update data dict + for key in data.keys(): + sel_col = [col for col in data[key].columns if col in selected_columns] + data[key] = data[key].select(sel_col) + logging.debug(f"Selected columns in {key}: {len(data[key].columns)}") + return data, vars From ab313fb26847055145380ce5ae6191e07d8ad47c Mon Sep 17 00:00:00 2001 From: Robin van de Water Date: Fri, 20 Dec 2024 12:03:57 +0100 Subject: [PATCH 12/82] explain features --- icu_benchmarks/models/ml_models/xgboost.py | 13 +++++++++--- icu_benchmarks/models/train.py | 24 +++++++++++++--------- icu_benchmarks/models/wrappers.py | 19 +++++++++++------ icu_benchmarks/run.py | 1 + icu_benchmarks/run_utils.py | 16 +++++++-------- 5 files changed, 46 insertions(+), 27 deletions(-) diff --git a/icu_benchmarks/models/ml_models/xgboost.py b/icu_benchmarks/models/ml_models/xgboost.py index 93272051..c8e773cd 100644 --- a/icu_benchmarks/models/ml_models/xgboost.py +++ b/icu_benchmarks/models/ml_models/xgboost.py @@ -24,7 +24,7 @@ class XGBClassifier(MLWrapper): _explain_values = False def __init__(self, *args, **kwargs): - self.model = self.set_model_args(xgb.XGBClassifier, *args, **kwargs, eval_metric=log_loss, device="cpu") + self.model = self.set_model_args(xgb.XGBClassifier, *args, **kwargs, eval_metric=log_loss, device="cpu", missing='inf') super().__init__(*args, **kwargs) def predict(self, features): @@ -48,7 +48,9 @@ def fit_model(self, train_data, train_labels, val_data, val_labels): logging.info(f"train_data: {train_data.shape}, train_labels: {train_labels.shape}") logging.info(train_labels) self.model.fit(train_data, train_labels, eval_set=[(val_data, val_labels)]) - if self._explain_values: + self.explain_features = True + if self.explain_features: + logging.info("Explaining features") self.explainer = shap.TreeExplainer(self.model) self.train_shap_values = self.explainer(train_data) # shap.summary_plot(shap_values, X_test, feature_names=features) @@ -69,7 +71,12 @@ def set_model_args(self, model, *args, **kwargs): logging.debug(f"Creating model with: {valid_kwargs}.") return model(**valid_kwargs) - def get_feature_importance(self): + def explain_model(self, reps, labels): + if not hasattr(self.model, "feature_importances_"): + raise ValueError("Model has not been fit yet. Call fit_model() before getting feature importances.") + return self.model.feature_importances_ + + def explainer(self, reps): if not hasattr(self.model, "feature_importances_"): raise ValueError("Model has not been fit yet. Call fit_model() before getting feature importances.") return self.model.feature_importances_ diff --git a/icu_benchmarks/models/train.py b/icu_benchmarks/models/train.py index 7bc8e45a..725d39cd 100644 --- a/icu_benchmarks/models/train.py +++ b/icu_benchmarks/models/train.py @@ -53,6 +53,7 @@ def train_common( num_workers: int = min(cpu_core_count, torch.cuda.device_count() * 8 * int(torch.cuda.is_available()), 32), polars=True, persistent_workers=None, + explain_features=False, ): """Common wrapper to train all benchmarked models. @@ -173,6 +174,8 @@ def train_common( return 0 test_dataset = dataset_class(data, split=test_on, name=dataset_names["test"], ram_cache=ram_cache) test_dataset = assure_minimum_length(test_dataset) + # Set explainer for testing + model.set_explain_features(explain_features) logging.info(f"Testing on {test_dataset.name} with {len(test_dataset)} samples.") test_loader = ( DataLoader( @@ -190,7 +193,8 @@ def train_common( model.set_weight("balanced", train_dataset) test_loss = trainer.test(model, dataloaders=test_loader, verbose=verbose)[0]["test/loss"] - persist_shap_data(trainer, log_dir) + if explain_features: + persist_shap_data(trainer, log_dir) save_config_file(log_dir) return test_loss @@ -203,16 +207,16 @@ def persist_shap_data(trainer: Trainer, log_dir: Path): log_dir: Log directory """ try: - if trainer.lightning_module.test_shap_values is not None: - shap_values = trainer.lightning_module.test_shap_values - shaps_test = pl.DataFrame(schema=trainer.lightning_module.trained_columns, data=np.transpose(shap_values.values)) - with (log_dir / "shap_values_test.parquet").open("wb") as f: + if trainer.lightning_module.explainer_values_test is not None: + explainer_values = trainer.lightning_module.explainer_values_test + shaps_test = pl.DataFrame(schema=trainer.lightning_module.trained_columns, data=np.transpose(explainer_values.values)) + with (log_dir / "explainer_values_test.parquet").open("wb") as f: shaps_test.write_parquet(f) - logging.info(f"Saved shap values to {log_dir / 'test_shap_values.parquet'}") - if trainer.lightning_module.train_shap_values is not None: - shap_values = trainer.lightning_module.train_shap_values - shaps_train = pl.DataFrame(schema=trainer.lightning_module.trained_columns, data=np.transpose(shap_values.values)) - with (log_dir / "shap_values_train.parquet").open("wb") as f: + logging.info(f"Saved explainer values to {log_dir / 'explainer_values_test.parquet'}") + if trainer.lightning_module.explainer_values_train is not None: + explainer_values = trainer.lightning_module.explainer_values_train + shaps_train = pl.DataFrame(schema=trainer.lightning_module.trained_columns, data=np.transpose(explainer_values.values)) + with (log_dir / "explainer_values_train.parquet").open("wb") as f: shaps_train.write_parquet(f) except Exception as e: diff --git a/icu_benchmarks/models/wrappers.py b/icu_benchmarks/models/wrappers.py index 2298303e..06e7911d 100644 --- a/icu_benchmarks/models/wrappers.py +++ b/icu_benchmarks/models/wrappers.py @@ -105,6 +105,8 @@ def check_supported_runmode(self, runmode: RunMode): raise ValueError(f"Runmode {runmode} not supported for {self.__class__.__name__}") return True + def set_explain_features(self, set_explain_features: bool): + self.explain_features = set_explain_features @gin.configurable("DLWrapper") class DLWrapper(BaseModule, ABC): @@ -222,6 +224,8 @@ def save_model(self, save_path, file_name, file_extension=".ckpt"): logging.error(f"Cannot save model to path {str(path.resolve())}: {e}.") + + @gin.configurable("DLPredictionWrapper") class DLPredictionWrapper(DLWrapper): """Interface for Deep Learning models.""" @@ -428,8 +432,8 @@ def fit(self, train_dataset, val_dataset): def fit_model(self, train_data, train_labels, val_data, val_labels): """Fit the model to the training data (default SKlearn syntax)""" - self.model.fit(train_data, train_labels) - val_loss = 0.0 + val_loss = self.model.fit(train_data, train_labels) + # val_loss = 0.0 return val_loss def validation_step(self, val_dataset, _): @@ -455,7 +459,8 @@ def test_step(self, dataset, _): if self.debug: self._save_model_outputs(pred_indicators, test_pred, test_label) if self.explain_features: - self.explain_model(test_rep, test_label) + # self.explain_model(test_rep, test_label) + self._explain_model(test_rep, test_label) if self.mps: self.log("test/loss", np.float32(self.loss(test_label, test_pred)), sync_dist=True) self.log_metrics(np.float32(test_label), np.float32(test_pred), "test", pred_indicators) @@ -487,7 +492,8 @@ def log_metrics(self, label, pred, metric_type, pred_indicators): sync_dist=True, ) else: - if len(pred_indicators.shape) > 1 and len(pred.shape) > 1 and pred_indicators.shape[1] == pred.shape[1]: + if (len(pred_indicators.shape) > 1 and len(pred.shape) > 1 and pred_indicators.shape[1] == pred.shape[1] + and pred_indicators.shape[0] == pred.shape[0]): pred_indicators = np.hstack((pred_indicators, label.reshape(-1, 1))) pred_indicators = np.hstack((pred_indicators, pred)) # Format: id, time (hours), ground truth, prediction 0, prediction 1 @@ -509,12 +515,13 @@ def log_metrics(self, label, pred, metric_type, pred_indicators): def _explain_model(self, test_rep, test_label): if self.explainer is not None: - self.test_shap_values = self.explainer(test_rep) + self.explainer_values_test = self.explainer(test_rep) else: logging.warning("No explainer or explain_features values set.") def _save_model_outputs(self, pred_indicators, test_pred, test_label): - if len(pred_indicators.shape) > 1 and len(test_pred.shape) > 1 and pred_indicators.shape[1] == test_pred.shape[1]: + if (len(pred_indicators.shape) > 1 and len(test_pred.shape) > 1 + and pred_indicators.shape[1] == test_pred.shape[1] and pred_indicators.shape[0] == test_pred.shape[0]): pred_indicators = np.hstack((pred_indicators, test_label.reshape(-1, 1))) pred_indicators = np.hstack((pred_indicators, test_pred)) # Save as: id, time (hours), ground truth, prediction 0, prediction 1 diff --git a/icu_benchmarks/run.py b/icu_benchmarks/run.py index 1c5479a0..8d968e13 100644 --- a/icu_benchmarks/run.py +++ b/icu_benchmarks/run.py @@ -184,6 +184,7 @@ def main(my_args=tuple(sys.argv[1:])): cpu=args.cpu, wandb=args.wandb_sweep, complete_train=args.complete_train, + explain_features=True, ) log_full_line("FINISHED TRAINING", level=logging.INFO, char="=", num_newlines=3) diff --git a/icu_benchmarks/run_utils.py b/icu_benchmarks/run_utils.py index 9dcf274f..931e1ea2 100644 --- a/icu_benchmarks/run_utils.py +++ b/icu_benchmarks/run_utils.py @@ -106,7 +106,7 @@ def aggregate_results(log_dir: Path, execution_time: timedelta = None): execution_time: Overall execution time. """ aggregated = {} - shap_values_test = [] + explainer_values_test = [] for repetition in log_dir.iterdir(): if repetition.is_dir(): aggregated[repetition.name] = {} @@ -125,16 +125,16 @@ def aggregate_results(log_dir: Path, execution_time: timedelta = None): with open(fold_iter / "durations.json", "r") as f: result = json.load(f) aggregated[repetition.name][fold_iter.name].update(result) - if (fold_iter / "test_shap_values.parquet").is_file(): - shap_values_test.append(pl.read_parquet(fold_iter / "test_shap_values.parquet")) + if (fold_iter / "explainer_values_test.parquet").is_file(): + explainer_values_test.append(pl.read_parquet(fold_iter / "explainer_values_test.parquet")) - if shap_values_test: - shap_values = pl.concat(shap_values_test) - shap_values.write_parquet(log_dir / "aggregated_shap_values.parquet") + if explainer_values_test: + shap_values = pl.concat(explainer_values_test) + shap_values.write_parquet(log_dir / "aggregated_explainer_values.parquet") try: - shap_values = pl.concat(shap_values_test) - shap_values.write_parquet(log_dir / "aggregated_shap_values.parquet") + shap_values = pl.concat(explainer_values_test) + shap_values.write_parquet(log_dir / "aggregated_explainer_values.parquet") except Exception as e: logging.error(f"Error aggregating or writing SHAP values: {e}") # Aggregate results per metric From 0d5fa059be4c70b01e09384e31d13c6736c37c18 Mon Sep 17 00:00:00 2001 From: Robin van de Water Date: Fri, 20 Dec 2024 12:04:29 +0100 Subject: [PATCH 13/82] explain features and tuning folds mismatch --- icu_benchmarks/cross_validation.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/icu_benchmarks/cross_validation.py b/icu_benchmarks/cross_validation.py index e1563f9e..9ccd9409 100644 --- a/icu_benchmarks/cross_validation.py +++ b/icu_benchmarks/cross_validation.py @@ -38,6 +38,7 @@ def execute_repeated_cv( verbose: bool = False, wandb: bool = False, complete_train: bool = False, + explain_features: bool = False, ) -> float: """Preprocesses data and trains a model for each fold. @@ -72,6 +73,9 @@ def execute_repeated_cv( cv_repetitions_to_train = cv_repetitions if not cv_folds_to_train: cv_folds_to_train = cv_folds + elif cv_folds_to_train > cv_folds: + raise ValueError(f"cv_folds_to_train is {cv_folds_to_train}, cv_folds is {cv_folds}. " + f" This is likely due to a hyperparameter tuning settings mismatch.") agg_loss = 0 seed_everything(seed, reproducible) if complete_train: @@ -119,6 +123,7 @@ def execute_repeated_cv( verbose=verbose, use_wandb=wandb, train_only=complete_train, + explain_features=explain_features, ) train_time = datetime.now() - start_time From baaba5b3da61ed459afe8829238af9a81084ae86 Mon Sep 17 00:00:00 2001 From: Robin van de Water Date: Fri, 20 Dec 2024 12:05:32 +0100 Subject: [PATCH 14/82] remove grouping value from features --- icu_benchmarks/data/loader.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/icu_benchmarks/data/loader.py b/icu_benchmarks/data/loader.py index 14f4156b..cc9ad544 100644 --- a/icu_benchmarks/data/loader.py +++ b/icu_benchmarks/data/loader.py @@ -69,7 +69,7 @@ def __len__(self) -> int: return self.num_stays def get_feature_names(self) -> List[str]: - return self.features_df.columns + return [col for col in self.features_df.columns if col != self.vars["GROUP"]] def to_tensor(self) -> List[Tensor]: values = [] @@ -172,6 +172,8 @@ def get_data_and_labels(self) -> Tuple[np.array, np.array, np.array]: rep = rep.with_columns(pl.col(self.vars["GROUP"]).cum_count().over(self.vars["GROUP"]).alias("counter")) # rep = rep.sort([self.vars["GROUP"], "counter"]) rep = rep.to_numpy().astype(float) + # Remove the first column from the rep array (group column) + rep = rep[:, 1:] logging.debug(f"rep shape: {rep.shape}") logging.debug(f"labels shape: {labels.shape}") return rep, labels, self.row_indicators.to_numpy() @@ -179,7 +181,7 @@ def get_data_and_labels(self) -> Tuple[np.array, np.array, np.array]: def to_tensor(self) -> Tuple[Tensor, Tensor, Tensor]: data, labels, row_indicators = self.get_data_and_labels() if self.mps: - return from_numpy(data).to(float32), from_numpy(labels).to(float32) + return from_numpy(data).to(float32), from_numpy(labels).to(float32), row_indicators else: return from_numpy(data), from_numpy(labels), row_indicators From 0d25e4382f66f6f0e0168fe943560bf6c4c40669 Mon Sep 17 00:00:00 2001 From: Robin van de Water Date: Mon, 20 Jan 2025 15:35:34 +0100 Subject: [PATCH 15/82] explanation features --- alarm_toy.ipynb | 132 +++++++++++++++--- configs/prediction_models/common/MLTuning.gin | 2 +- configs/tasks/common/Dataloader.gin | 1 + experiments/benchmark_cass.yml | 18 +-- experiments/charhpc_wandb_sweep_cpu.sh | 1 + icu_benchmarks/data/loader.py | 2 +- icu_benchmarks/models/ml_models/xgboost.py | 18 ++- icu_benchmarks/models/train.py | 16 ++- icu_benchmarks/models/wrappers.py | 14 +- setup.py | 2 +- 10 files changed, 158 insertions(+), 48 deletions(-) diff --git a/alarm_toy.ipynb b/alarm_toy.ipynb index 730ad65b..26816595 100644 --- a/alarm_toy.ipynb +++ b/alarm_toy.ipynb @@ -6,8 +6,8 @@ "metadata": { "collapsed": true, "ExecuteTime": { - "end_time": "2024-11-14T12:22:55.485296Z", - "start_time": "2024-11-14T12:22:55.401283Z" + "end_time": "2024-11-18T10:34:56.395941Z", + "start_time": "2024-11-18T10:34:55.939403Z" } }, "source": [ @@ -41,20 +41,14 @@ ], "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "ID 1:\n", - "[[ 1 10 100]\n", - " [ 1 30 300]]\n", - "\n", - "ID 2:\n", - "[[ 2 20 200]\n", - " [ 2 40 400]]\n", - "\n", - "ID 3:\n", - "[[ 3 50 500]]\n", - "\n" + "ename": "ModuleNotFoundError", + "evalue": "No module named 'numpy'", + "output_type": "error", + "traceback": [ + "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m", + "\u001B[0;31mModuleNotFoundError\u001B[0m Traceback (most recent call last)", + "Cell \u001B[0;32mIn[1], line 1\u001B[0m\n\u001B[0;32m----> 1\u001B[0m \u001B[38;5;28;01mimport\u001B[39;00m \u001B[38;5;21;01mnumpy\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m \u001B[38;5;21;01mnp\u001B[39;00m\n\u001B[1;32m 3\u001B[0m \u001B[38;5;66;03m# Sample data\u001B[39;00m\n\u001B[1;32m 4\u001B[0m data \u001B[38;5;241m=\u001B[39m np\u001B[38;5;241m.\u001B[39marray([\n\u001B[1;32m 5\u001B[0m [\u001B[38;5;241m1\u001B[39m, \u001B[38;5;241m10\u001B[39m, \u001B[38;5;241m100\u001B[39m],\n\u001B[1;32m 6\u001B[0m [\u001B[38;5;241m2\u001B[39m, \u001B[38;5;241m20\u001B[39m, \u001B[38;5;241m200\u001B[39m],\n\u001B[0;32m (...)\u001B[0m\n\u001B[1;32m 9\u001B[0m [\u001B[38;5;241m3\u001B[39m, \u001B[38;5;241m50\u001B[39m, \u001B[38;5;241m500\u001B[39m]\n\u001B[1;32m 10\u001B[0m ])\n", + "\u001B[0;31mModuleNotFoundError\u001B[0m: No module named 'numpy'" ] } ], @@ -759,12 +753,110 @@ "execution_count": 6 }, { - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2025-01-09T11:35:07.132903Z", + "start_time": "2025-01-09T11:35:07.129738Z" + } + }, "cell_type": "code", + "source": [ + "import polars as pl\n", + "values = pl.read_parquet(\"/Users/robin/Documents/git/yaib_logs/mimic_demo/Mortality24/XGBClassifier/2025-01-09T12-33-43.607662/repetition_4/fold_4/explainer_values_test.parquet\")" + ], + "id": "77488f552d1480d0", "outputs": [], - "execution_count": null, - "source": "", - "id": "77488f552d1480d0" + "execution_count": 5 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-01-09T11:35:09.020311Z", + "start_time": "2025-01-09T11:35:09.016349Z" + } + }, + "cell_type": "code", + "source": "values.sum()", + "id": "7dbd2435574a6215", + "outputs": [ + { + "data": { + "text/plain": [ + "shape: (1, 296)\n", + "┌─────┬──────────┬─────┬─────┬───┬────────────────┬────────────────┬───────────────┬───────────────┐\n", + "│ alb ┆ alp ┆ alt ┆ ast ┆ … ┆ MissingIndicat ┆ MissingIndicat ┆ MissingIndica ┆ MissingIndica │\n", + "│ --- ┆ --- ┆ --- ┆ --- ┆ ┆ or_age ┆ or_sex ┆ tor_height ┆ tor_weight │\n", + "│ f32 ┆ f32 ┆ f32 ┆ f32 ┆ ┆ --- ┆ --- ┆ --- ┆ --- │\n", + "│ ┆ ┆ ┆ ┆ ┆ f32 ┆ f32 ┆ f32 ┆ f32 │\n", + "╞═════╪══════════╪═════╪═════╪═══╪════════════════╪════════════════╪═══════════════╪═══════════════╡\n", + "│ 0.0 ┆ 0.078583 ┆ 0.0 ┆ 0.0 ┆ … ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 0.0 │\n", + "└─────┴──────────┴─────┴─────┴───┴────────────────┴────────────────┴───────────────┴───────────────┘" + ], + "text/html": [ + "
\n", + "shape: (1, 296)
albalpaltastbebicarbilibili_dirbndbuncacaickckmbclcreacrpdbpfgnfio2gluhgbhrinr_ptklactlymphmapmchmchcmcvmethbmgnaneuto2satpco2…fio2_mean_histglu_mean_histhgb_mean_histhr_mean_histinr_pt_mean_histk_mean_histlact_mean_histlymph_mean_histmap_mean_histmch_mean_histmchc_mean_histmcv_mean_histmethb_mean_histmg_mean_histna_mean_histneut_mean_histo2sat_mean_histpco2_mean_histph_mean_histphos_mean_histplt_mean_histpo2_mean_histptt_mean_histresp_mean_histsbp_mean_histtemp_mean_histtnt_mean_histurine_mean_histwbc_mean_histagesexheightweightMissingIndicator_ageMissingIndicator_sexMissingIndicator_heightMissingIndicator_weight
f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32…f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32
0.00.0785830.00.0-0.258690.00.00.00.00.3978972.458770.00.00.00.00.00.00.00.00.00.00.00.0-0.2626310.00.00.0-3.5106580.116930.00.00.0-0.7752580.00.00.00.643269…0.0-0.565497-0.2239260.00.00.00.00.00.2006590.00.00.00.00.22341-0.4922790.00.00.00.00.00.0-5.3272160.00.00.02.4498270.0-0.5833121.3199810.00.00.00.7442510.00.00.00.0
" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 6 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-01-09T11:35:13.397032Z", + "start_time": "2025-01-09T11:35:12.567741Z" + } + }, + "cell_type": "code", + "source": [ + "import matplotlib.pyplot as plt\n", + "# Sum the values for aggregation\n", + "aggregated_data = values.sum()\n", + "\n", + "# Convert to Pandas DataFrame for plotting\n", + "aggregated_data_pd = aggregated_data.to_pandas()\n", + "\n", + "# Plot the bar plot\n", + "plt.figure(figsize=(10, 6))\n", + "aggregated_data_pd.plot(kind='bar')\n", + "plt.title('Aggregated Data')\n", + "plt.xlabel('Columns')\n", + "plt.ylabel('Sum')\n", + "plt.show()" + ], + "id": "cac7ef7572cda8b7", + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ], + "image/png": "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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 7 } ], "metadata": { diff --git a/configs/prediction_models/common/MLTuning.gin b/configs/prediction_models/common/MLTuning.gin index 9df38c47..2334321b 100644 --- a/configs/prediction_models/common/MLTuning.gin +++ b/configs/prediction_models/common/MLTuning.gin @@ -1,5 +1,5 @@ # Hyperparameter tuner settings for classical Machine Learning. tune_hyperparameters.scopes = ["model"] tune_hyperparameters.n_initial_points = 5 -tune_hyperparameters.n_calls = 30 +tune_hyperparameters.n_calls = 0 tune_hyperparameters.folds_to_tune_on = 5 \ No newline at end of file diff --git a/configs/tasks/common/Dataloader.gin b/configs/tasks/common/Dataloader.gin index 6bed1b7e..5b8b0b63 100644 --- a/configs/tasks/common/Dataloader.gin +++ b/configs/tasks/common/Dataloader.gin @@ -3,6 +3,7 @@ PredictionPandasDataset.vars = %vars PredictionPandasDataset.ram_cache = True PredictionPolarsDataset.vars = %vars PredictionPolarsDataset.ram_cache = True +PredictionPolarsDataset.mps = True # Imputation ImputationPandasDataset.vars = %vars ImputationPandasDataset.ram_cache = True \ No newline at end of file diff --git a/experiments/benchmark_cass.yml b/experiments/benchmark_cass.yml index 557d22ee..11d89a89 100644 --- a/experiments/benchmark_cass.yml +++ b/experiments/benchmark_cass.yml @@ -15,8 +15,6 @@ command: - SSI # - Mortality # - --verbose - - --hp-checkpoint - - /sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/activity_notes/SSI/XGBClassifier/2024-07-25T14-32-55/hyperparameter_tuning_logs.db - --modalities - "all" # - --verbose @@ -29,7 +27,14 @@ name: yaib_classification_benchmark parameters: data_dir: values: - - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/dataset_segment_1.0_horizon_6:00:00_transfer_3_2024-11-11T11:17:09" + - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/selected_ids_2025-01-15_17:38:58" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/selected_ids_2025-01-15_11:50:05" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/selected_ids_2024-12-13_16:24:38" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/SSI-3_POPF_heamatoma_Galleleck_BDA_segment_1.0_horizon_6:00:00_transfer_full_2024-11-28T14:02:51" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/combined_endpoints_segment_1.0_horizon_6:00:00_transfer_3_2024-11-25T17:56:08" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/dataset_segment_1.0_horizon_6:00:00_transfer_3_2024-11-22T13:19:53" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/dataset_segment_1.0_horizon_6:00:00_transfer_3_2024-11-11T16:03:12" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/dataset_segment_1.0_horizon_6:00:00_transfer_3_2024-11-11T11:17:09/" # - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/cohort_separation/1/" # - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/cohort_separation/2/" # - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/cohort_separation/3/" @@ -43,11 +48,6 @@ parameters: # - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/dataset_x_segment_duration_x_transfer_2024-09-19T13:57:36" # - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/dataset_baseline_dynamic_6h_2024-09-18T11:24:23" # - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/dataset_baseline_flat_2024-09-17T17:25:20" -# - /home/vandewrp/projects/YAIB/data/YAIB_Datasets/data/mortality24/miiv -# - /sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/activity_notes_fixed -# - /sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/19-08-2024_all_wards -# - /sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/dataset_x_segment_duration_x_transfer_2024-08-26 13:43:20 -# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/dataset_x_segment_duration_x_transfer_2024-09-12T16:22:01" model: values: # - LogisticRegression @@ -65,7 +65,7 @@ parameters: modalities: values: - "all" -# - [copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, cat_med_embeddings_map, cat_clinical_notes, wearable_activity, wearable_core, static] + - [copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, cat_med_embeddings_map, cat_clinical_notes, static] # - [copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, cat_med_embeddings_map, wearable_activity, wearable_core, static] # - [copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, static] #, wearable_activity, wearable_core] # without wearable data # - [copra_observations, copra_scores, copra_fluid_balance, cat_med_embeddings_map, wearable_activity, wearable_core, static] # without ishmed lab data and clinical notes diff --git a/experiments/charhpc_wandb_sweep_cpu.sh b/experiments/charhpc_wandb_sweep_cpu.sh index b4268548..45407de3 100644 --- a/experiments/charhpc_wandb_sweep_cpu.sh +++ b/experiments/charhpc_wandb_sweep_cpu.sh @@ -6,6 +6,7 @@ #SBATCH --output=logs/classification_%a_%j.log # %j is job id #SBATCH --time=48:00:00 + source /etc/profile.d/conda.sh eval "$(conda shell.bash hook)" diff --git a/icu_benchmarks/data/loader.py b/icu_benchmarks/data/loader.py index cc9ad544..65966f2f 100644 --- a/icu_benchmarks/data/loader.py +++ b/icu_benchmarks/data/loader.py @@ -69,7 +69,7 @@ def __len__(self) -> int: return self.num_stays def get_feature_names(self) -> List[str]: - return [col for col in self.features_df.columns if col != self.vars["GROUP"]] + return [col for col in self.features_df.columns ]#if col != self.vars["GROUP"] and col != self.vars["SEQUENCE"]] def to_tensor(self) -> List[Tensor]: values = [] diff --git a/icu_benchmarks/models/ml_models/xgboost.py b/icu_benchmarks/models/ml_models/xgboost.py index c8e773cd..88c770b8 100644 --- a/icu_benchmarks/models/ml_models/xgboost.py +++ b/icu_benchmarks/models/ml_models/xgboost.py @@ -3,6 +3,7 @@ from statistics import mean import gin +import numpy as np import shap import wandb import xgboost as xgb @@ -71,12 +72,15 @@ def set_model_args(self, model, *args, **kwargs): logging.debug(f"Creating model with: {valid_kwargs}.") return model(**valid_kwargs) - def explain_model(self, reps, labels): + def _explain_model(self, reps, labels): if not hasattr(self.model, "feature_importances_"): raise ValueError("Model has not been fit yet. Call fit_model() before getting feature importances.") - return self.model.feature_importances_ - - def explainer(self, reps): - if not hasattr(self.model, "feature_importances_"): - raise ValueError("Model has not been fit yet. Call fit_model() before getting feature importances.") - return self.model.feature_importances_ + feature_importances = self.model.feature_importances_ + shap_values = self.explainer.shap_values(reps) + # feature_importances = np.abs(shap_values).mean(axis=1) + return shap_values + + # def explainer(self, reps): + # if not hasattr(self.model, "feature_importances_"): + # raise ValueError("Model has not been fit yet. Call fit_model() before getting feature importances.") + # return self.model.feature_importances_ diff --git a/icu_benchmarks/models/train.py b/icu_benchmarks/models/train.py index 725d39cd..58a7d171 100644 --- a/icu_benchmarks/models/train.py +++ b/icu_benchmarks/models/train.py @@ -206,21 +206,31 @@ def persist_shap_data(trainer: Trainer, log_dir: Path): trainer: Pytorch lightning trainer object log_dir: Log directory """ + logging.info("Persisting explainer values to disk.") try: + trained_columns = trainer.lightning_module.trained_columns + + if "stay_id" in trainer.lightning_module.trained_columns: + trained_columns.remove("stay_id") + logging.info(f"Saving SHAPS") if trainer.lightning_module.explainer_values_test is not None: + # todo: abs values explainer_values = trainer.lightning_module.explainer_values_test - shaps_test = pl.DataFrame(schema=trainer.lightning_module.trained_columns, data=np.transpose(explainer_values.values)) + # logging.info(f"{explainer_values.values.shape}") + # shaps_test = pl.DataFrame(schema=trainer.lightning_module.trained_columns, data=np.transpose(explainer_values.values)) + shaps_test = pl.DataFrame(schema=trained_columns, data=explainer_values) with (log_dir / "explainer_values_test.parquet").open("wb") as f: shaps_test.write_parquet(f) logging.info(f"Saved explainer values to {log_dir / 'explainer_values_test.parquet'}") if trainer.lightning_module.explainer_values_train is not None: explainer_values = trainer.lightning_module.explainer_values_train - shaps_train = pl.DataFrame(schema=trainer.lightning_module.trained_columns, data=np.transpose(explainer_values.values)) + # shaps_train = pl.DataFrame(schema=trainer.lightning_module.trained_columns, data=np.transpose(explainer_values.values)) + shaps_train = pl.DataFrame(schema=trained_columns, data=explainer_values.values) with (log_dir / "explainer_values_train.parquet").open("wb") as f: shaps_train.write_parquet(f) except Exception as e: - logging.error(f"Failed to save shap values: {e}") + logging.error(f"Failed to save explainer values: {e}") def load_model(model, source_dir, pl_model=True): diff --git a/icu_benchmarks/models/wrappers.py b/icu_benchmarks/models/wrappers.py index 06e7911d..ec14a52a 100644 --- a/icu_benchmarks/models/wrappers.py +++ b/icu_benchmarks/models/wrappers.py @@ -63,6 +63,7 @@ def set_metrics(self, *args, **kwargs): self.metrics = {} def set_trained_columns(self, columns: List[str]): + logging.info(f"Setting trained columns: {columns}") self.trained_columns = columns def set_weight(self, weight, dataset): @@ -460,7 +461,7 @@ def test_step(self, dataset, _): self._save_model_outputs(pred_indicators, test_pred, test_label) if self.explain_features: # self.explain_model(test_rep, test_label) - self._explain_model(test_rep, test_label) + self.explainer_values_test = self._explain_model(test_rep, test_label) if self.mps: self.log("test/loss", np.float32(self.loss(test_label, test_pred)), sync_dist=True) self.log_metrics(np.float32(test_label), np.float32(test_pred), "test", pred_indicators) @@ -513,11 +514,12 @@ def log_metrics(self, label, pred, metric_type, pred_indicators): sync_dist=True, ) - def _explain_model(self, test_rep, test_label): - if self.explainer is not None: - self.explainer_values_test = self.explainer(test_rep) - else: - logging.warning("No explainer or explain_features values set.") + # def _explain_model(self, test_rep, test_label): + # if self.explainer is not None: + # logging.info(f"Explaining features for {self}") + # self.explainer_values_test = self.explainer(test_rep) + # else: + # logging.warning("No explainer or explain_features values set.") def _save_model_outputs(self, pred_indicators, test_pred, test_label): if (len(pred_indicators.shape) > 1 and len(test_pred.shape) > 1 diff --git a/setup.py b/setup.py index 384562ea..f5f6c4ee 100644 --- a/setup.py +++ b/setup.py @@ -76,6 +76,6 @@ def parse_environment_yml(): test_suite="tests", tests_require=[], url="https://github.com/rvandewater/YAIB", - version="0.3.1", + version="1.1.0", zip_safe=False, ) From af984aa0aafcd98a10a75b9d9f7b5fa3c8d443ed Mon Sep 17 00:00:00 2001 From: Robin van de Water Date: Wed, 26 Feb 2025 15:40:32 +0100 Subject: [PATCH 16/82] Support for static prediction --- icu_benchmarks/data/loader.py | 5 ++- icu_benchmarks/data/preprocessor.py | 19 ++++++---- icu_benchmarks/data/split_process_data.py | 4 +- icu_benchmarks/models/train.py | 6 ++- icu_benchmarks/models/wrappers.py | 46 +++++++++++++++++++---- 5 files changed, 60 insertions(+), 20 deletions(-) diff --git a/icu_benchmarks/data/loader.py b/icu_benchmarks/data/loader.py index 65966f2f..e3857721 100644 --- a/icu_benchmarks/data/loader.py +++ b/icu_benchmarks/data/loader.py @@ -42,13 +42,16 @@ def __init__( self.features_df = data[split][Segment.features] self.features_df = self.features_df.sort([self.vars["GROUP"], self.vars["SEQUENCE"]]) self.features_df = self.features_df.drop(self.vars["SEQUENCE"]) + self.row_indicators = self.row_indicators.sort([self.vars["GROUP"], self.vars["SEQUENCE"]]) + else: # We have a static dataset logging.info("Using static dataset") self.row_indicators = data[split][Segment.features][self.vars["GROUP"]] self.features_df = data[split][Segment.features] + # Series with unique values + self.row_indicators = self.row_indicators.sort() # calculate basic info for the data - self.row_indicators = self.row_indicators.sort([self.vars["GROUP"], self.vars["SEQUENCE"]]) self.num_stays = self.grouping_df[self.vars["GROUP"]].unique().shape[0] self.maxlen = self.features_df.group_by([self.vars["GROUP"]]).len().max().item(0, 1) self.mps = mps diff --git a/icu_benchmarks/data/preprocessor.py b/icu_benchmarks/data/preprocessor.py index 3c2056aa..4fcb6645 100644 --- a/icu_benchmarks/data/preprocessor.py +++ b/icu_benchmarks/data/preprocessor.py @@ -128,14 +128,17 @@ def apply(self, data, vars) -> dict[dict[pl.DataFrame]]: logging.debug("Data head") logging.debug(data[Split.train][Segment.features].head()) logging.debug(data[Split.train][Segment.outcome]) - for split in [Split.train, Split.val, Split.test]: - if vars["SEQUENCE"] in data[split][Segment.outcome] and len(data[split][Segment.features]) != len( - data[split][Segment.outcome] - ): - raise Exception( - f"Data and outcome length mismatch in {split} split: " - f"features: {len(data[split][Segment.features])}, outcome: {len(data[split][Segment.outcome])}" - ) + # If sequence is in data + if "SEQUENCE" in vars: + for split in [Split.train, Split.val, Split.test]: + # Check if we have sequence in the outcome and the data and outcome length match + if vars["SEQUENCE"] in data[split][Segment.outcome] and len(data[split][Segment.features]) != len( + data[split][Segment.outcome] + ): + raise Exception( + f"Data and outcome length mismatch in {split} split: " + f"features: {len(data[split][Segment.features])}, outcome: {len(data[split][Segment.outcome])}" + ) data[Split.train][Segment.features] = data[Split.train][Segment.features].unique() data[Split.val][Segment.features] = data[Split.val][Segment.features].unique() data[Split.test][Segment.features] = data[Split.test][Segment.features].unique() diff --git a/icu_benchmarks/data/split_process_data.py b/icu_benchmarks/data/split_process_data.py index 182dee98..d2edb34e 100644 --- a/icu_benchmarks/data/split_process_data.py +++ b/icu_benchmarks/data/split_process_data.py @@ -26,7 +26,7 @@ def preprocess_data( preprocessor: Preprocessor = PolarsClassificationPreprocessor, use_static: bool = True, vars: dict[str] = gin.REQUIRED, - modality_mapping: dict[str] = {}, + modality_mapping: dict[str] = None, selected_modalities: list[str] = "all", exclude_preproc: list[str] = None, seed: int = 42, @@ -42,7 +42,7 @@ def preprocess_data( complete_train: bool = False, runmode: RunMode = RunMode.classification, label: str = None, - required_var_types=["GROUP", "SEQUENCE", "LABEL"], + required_var_types=["GROUP", "LABEL"], required_segments=[Segment.static, Segment.dynamic, Segment.outcome], ) -> dict[dict[pl.DataFrame]] or dict[dict[pd.DataFrame]]: """Perform loading, splitting, imputing and normalising of task data. diff --git a/icu_benchmarks/models/train.py b/icu_benchmarks/models/train.py index 58a7d171..4de53532 100644 --- a/icu_benchmarks/models/train.py +++ b/icu_benchmarks/models/train.py @@ -210,7 +210,8 @@ def persist_shap_data(trainer: Trainer, log_dir: Path): try: trained_columns = trainer.lightning_module.trained_columns - if "stay_id" in trainer.lightning_module.trained_columns: + if ("stay_id" in trainer.lightning_module.trained_columns and + len(trainer.lightning_module.trained_columns) != trainer.lightning_module.explainer_values_test.shape[1]): trained_columns.remove("stay_id") logging.info(f"Saving SHAPS") if trainer.lightning_module.explainer_values_test is not None: @@ -219,9 +220,10 @@ def persist_shap_data(trainer: Trainer, log_dir: Path): # logging.info(f"{explainer_values.values.shape}") # shaps_test = pl.DataFrame(schema=trainer.lightning_module.trained_columns, data=np.transpose(explainer_values.values)) shaps_test = pl.DataFrame(schema=trained_columns, data=explainer_values) + shaps_test = shaps_test.select(pl.all().mean()) with (log_dir / "explainer_values_test.parquet").open("wb") as f: shaps_test.write_parquet(f) - logging.info(f"Saved explainer values to {log_dir / 'explainer_values_test.parquet'}") + logging.debug(f"Saved explainer values to {log_dir / 'explainer_values_test.parquet'}") if trainer.lightning_module.explainer_values_train is not None: explainer_values = trainer.lightning_module.explainer_values_train # shaps_train = pl.DataFrame(schema=trainer.lightning_module.trained_columns, data=np.transpose(explainer_values.values)) diff --git a/icu_benchmarks/models/wrappers.py b/icu_benchmarks/models/wrappers.py index ec14a52a..e5788630 100644 --- a/icu_benchmarks/models/wrappers.py +++ b/icu_benchmarks/models/wrappers.py @@ -458,7 +458,10 @@ def test_step(self, dataset, _): self.set_metrics(test_label) test_pred = self.predict(test_rep) if self.debug: + # try: self._save_model_outputs(pred_indicators, test_pred, test_label) + # except Exception as e: + # logging.warning(f"Could not save model outputs: {e}") if self.explain_features: # self.explain_model(test_rep, test_label) self.explainer_values_test = self._explain_model(test_rep, test_label) @@ -522,16 +525,45 @@ def log_metrics(self, label, pred, metric_type, pred_indicators): # logging.warning("No explainer or explain_features values set.") def _save_model_outputs(self, pred_indicators, test_pred, test_label): - if (len(pred_indicators.shape) > 1 and len(test_pred.shape) > 1 - and pred_indicators.shape[1] == test_pred.shape[1] and pred_indicators.shape[0] == test_pred.shape[0]): - pred_indicators = np.hstack((pred_indicators, test_label.reshape(-1, 1))) + if len(pred_indicators.shape) > 1 and len(test_pred.shape) > 1: + # Temporal dataset + if pred_indicators.shape[1] == test_pred.shape[1] and pred_indicators.shape[0] == test_pred.shape[0]: + # One outcome per dataset + pred_indicators = np.hstack((pred_indicators, test_label.reshape(-1, 1))) + pred_indicators = np.hstack((pred_indicators, test_pred)) + # Save as: id, time (hours), ground truth, prediction 0, prediction 1 + # if pred_indicators.shape(1) == 5: + np.savetxt(Path(self.logger.save_dir) / "pred_indicators.csv", pred_indicators, delimiter=",", + header="id,time,ground_truth,prediction_0,prediction_1", fmt='%d,%d,%.3f,%.3f,%.3f') + logging.debug(f"Saved row indicators to {Path(self.logger.save_dir) / f'row_indicators.csv'}") + # else: + # # Flat/static dataset + # np.savetxt(Path(self.logger.save_dir) / "pred_indicators.csv", pred_indicators, delimiter=",", + # header="id,ground_truth,prediction_0,prediction_1", fmt='%d,%d,%.3f,%.3f,%.3f') + # np.savetxt(Path(self.logger.save_dir) / "pred_indicators.csv", pred_indicators, delimiter=",", + # header="id,time,ground_truth,prediction_0,prediction_1", fmt='%d,%d,%.3f,%.3f,%.3f') + # logging.debug(f"Saved row indicators to {Path(self.logger.save_dir) / f'row_indicators.csv'}") + else: + logging.info(np.unique(pred_indicators[:, 0])) + pred_indicators = np.unique(pred_indicators[:, 0]) + pred_indicators = np.hstack((pred_indicators.reshape(-1,1), test_label.reshape(-1,1))) + pred_indicators = np.hstack((pred_indicators, test_pred)) + + np.savetxt(Path(self.logger.save_dir) / "pred_indicators.csv", pred_indicators, delimiter=",", + header="id,ground_truth,prediction_0,prediction_1", fmt='%d,%d,%0.3f,%0.3f') + logging.debug(f"Saved row indicators to {Path(self.logger.save_dir) / f'row_indicators.csv'}") + + # logging.warning("Could not save row indicators; no support for temporal dataset with single outcomes yet.") + else: + pred_indicators = np.hstack((pred_indicators.reshape(-1,1), test_label.reshape(-1,1))) + logging.info(pred_indicators.shape) pred_indicators = np.hstack((pred_indicators, test_pred)) - # Save as: id, time (hours), ground truth, prediction 0, prediction 1 np.savetxt(Path(self.logger.save_dir) / "pred_indicators.csv", pred_indicators, delimiter=",", - header="id,time,ground_truth,prediction_0,prediction_1", fmt='%d,%d,%.3f,%.3f,%.3f') + header="id,ground_truth,prediction_0,prediction_1", fmt='%d,%d,%0.3f,%0.3f') logging.debug(f"Saved row indicators to {Path(self.logger.save_dir) / f'row_indicators.csv'}") - else: - logging.warning("Could not save row indicators.") + + # else: + # logging.warning("Could not save row indicators.") def configure_optimizers(self): return None From 179c09865500c22a6c02038ad5fcbcc6d8f89981 Mon Sep 17 00:00:00 2001 From: Robin van de Water Date: Wed, 26 Feb 2025 15:49:37 +0100 Subject: [PATCH 17/82] hyperparameter checkpoint finding fix --- icu_benchmarks/tuning/hyperparameters.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/icu_benchmarks/tuning/hyperparameters.py b/icu_benchmarks/tuning/hyperparameters.py index 925171e1..d7bc40c6 100644 --- a/icu_benchmarks/tuning/hyperparameters.py +++ b/icu_benchmarks/tuning/hyperparameters.py @@ -83,7 +83,7 @@ def choose_and_bind_hyperparameters_scikit_optimize( configuration, evaluation = None, None if checkpoint: checkpoint_path = checkpoint / checkpoint_file - if not checkpoint_path.exists(): + if not checkpoint_path.isfile(): logging.warning(f"Hyperparameter checkpoint {checkpoint_path} does not exist.") logging.info("Attempting to find latest checkpoint file.") checkpoint_path = find_checkpoint(log_dir.parent, checkpoint_file) From ee186e8a6a3fe9084bdbc25fd51d84ccadd12367 Mon Sep 17 00:00:00 2001 From: Robin van de Water Date: Fri, 4 Apr 2025 09:57:19 +0200 Subject: [PATCH 18/82] failsafe for computing scores --- icu_benchmarks/run_utils.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/icu_benchmarks/run_utils.py b/icu_benchmarks/run_utils.py index 931e1ea2..46fb5b3c 100644 --- a/icu_benchmarks/run_utils.py +++ b/icu_benchmarks/run_utils.py @@ -1,4 +1,5 @@ import importlib +import math import sys import warnings from math import sqrt @@ -143,6 +144,9 @@ def aggregate_results(log_dir: Path, execution_time: timedelta = None): for fold, result in folds.items(): for metric, score in result.items(): if isinstance(score, (float, int)): + if math.isnan(score): + logging.warning(f"Score for metric {metric} is NaN, adding 0 instead.") + score = 0 list_scores[metric] = list_scores.setdefault(metric, []) list_scores[metric].append(score) From 63ea7becf5d5572d7fce95ee2a88ed5fa4fdd2c3 Mon Sep 17 00:00:00 2001 From: Robin van de Water Date: Fri, 4 Apr 2025 09:57:52 +0200 Subject: [PATCH 19/82] explainer corrections --- icu_benchmarks/models/ml_models/xgboost.py | 13 +++++++------ icu_benchmarks/models/wrappers.py | 2 +- icu_benchmarks/tuning/hyperparameters.py | 1 + 3 files changed, 9 insertions(+), 7 deletions(-) diff --git a/icu_benchmarks/models/ml_models/xgboost.py b/icu_benchmarks/models/ml_models/xgboost.py index 88c770b8..b9039fcb 100644 --- a/icu_benchmarks/models/ml_models/xgboost.py +++ b/icu_benchmarks/models/ml_models/xgboost.py @@ -25,7 +25,7 @@ class XGBClassifier(MLWrapper): _explain_values = False def __init__(self, *args, **kwargs): - self.model = self.set_model_args(xgb.XGBClassifier, *args, **kwargs, eval_metric=log_loss, device="cpu", missing='inf') + self.model = self.set_model_args(xgb.XGBClassifier, *args, **kwargs, eval_metric=log_loss, device="cpu", missing='inf', verbosity=0) super().__init__(*args, **kwargs) def predict(self, features): @@ -48,12 +48,13 @@ def fit_model(self, train_data, train_labels, val_data, val_labels): callbacks.append(wandb_xgb()) logging.info(f"train_data: {train_data.shape}, train_labels: {train_labels.shape}") logging.info(train_labels) - self.model.fit(train_data, train_labels, eval_set=[(val_data, val_labels)]) - self.explain_features = True + self.model.fit(train_data, train_labels, eval_set=[(val_data, val_labels)], verbose=0) + # if self.explain_features: + self.explainer = shap.TreeExplainer(self.model, train_data, feature_perturbation="interventional", + model_output="probability") if self.explain_features: logging.info("Explaining features") - self.explainer = shap.TreeExplainer(self.model) - self.train_shap_values = self.explainer(train_data) + self.train_shap_values = self.explainer.shap_values(train_data) # shap.summary_plot(shap_values, X_test, feature_names=features) # logging.info(self.model.get_booster().get_score(importance_type='weight')) # self.log_dict(self.model.get_booster().get_score(importance_type='weight')) @@ -75,7 +76,7 @@ def set_model_args(self, model, *args, **kwargs): def _explain_model(self, reps, labels): if not hasattr(self.model, "feature_importances_"): raise ValueError("Model has not been fit yet. Call fit_model() before getting feature importances.") - feature_importances = self.model.feature_importances_ + # feature_importances = self.model.feature_importances_ shap_values = self.explainer.shap_values(reps) # feature_importances = np.abs(shap_values).mean(axis=1) return shap_values diff --git a/icu_benchmarks/models/wrappers.py b/icu_benchmarks/models/wrappers.py index e5788630..dfda2e0b 100644 --- a/icu_benchmarks/models/wrappers.py +++ b/icu_benchmarks/models/wrappers.py @@ -63,7 +63,7 @@ def set_metrics(self, *args, **kwargs): self.metrics = {} def set_trained_columns(self, columns: List[str]): - logging.info(f"Setting trained columns: {columns}") + logging.info(f"Setting trained columns: {len(columns)}") self.trained_columns = columns def set_weight(self, weight, dataset): diff --git a/icu_benchmarks/tuning/hyperparameters.py b/icu_benchmarks/tuning/hyperparameters.py index d7bc40c6..a379d4a6 100644 --- a/icu_benchmarks/tuning/hyperparameters.py +++ b/icu_benchmarks/tuning/hyperparameters.py @@ -317,6 +317,7 @@ def bind_params_and_train(hyperparams): debug=debug, verbose=verbose, wandb=wandb, + explain_features=False ) logging.info(f"Score: {score}") return score From 0ecdb592d1c88e9dbe00d9f38087654f4608c507 Mon Sep 17 00:00:00 2001 From: Robin van de Water Date: Fri, 4 Apr 2025 09:58:28 +0200 Subject: [PATCH 20/82] modality exclusions and explainability --- icu_benchmarks/data/split_process_data.py | 4 ++-- icu_benchmarks/models/train.py | 6 +++--- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/icu_benchmarks/data/split_process_data.py b/icu_benchmarks/data/split_process_data.py index d2edb34e..327d9f44 100644 --- a/icu_benchmarks/data/split_process_data.py +++ b/icu_benchmarks/data/split_process_data.py @@ -106,7 +106,7 @@ def preprocess_data( excluded_vars.extend(modality_mapping.get(modality)) else: logging.warning(f"Modality '{modality}' not found in modality mapping.") - logging.info(f"Excluding vars from preprocessing: {excluded_vars}") + logging.info(f"Excluding modalities in {exclude_preproc}. Total vars excluded from preprocessing: {len(excluded_vars)}") else: logging.warning("No modality mapping provided. Excluding variables from preprocessing will have no effect.") preprocessor = preprocessor( @@ -434,7 +434,7 @@ def make_single_split( data_split[fold] = { data_type: data[data_type].merge(split[fold], on=id, how="right", sort=True) for data_type in data.keys() } - logging.info(f"Data split: {data_split}") + # logging.info(f"Data split: {data_split}") return data_split diff --git a/icu_benchmarks/models/train.py b/icu_benchmarks/models/train.py index 4de53532..ce2a2b02 100644 --- a/icu_benchmarks/models/train.py +++ b/icu_benchmarks/models/train.py @@ -210,9 +210,9 @@ def persist_shap_data(trainer: Trainer, log_dir: Path): try: trained_columns = trainer.lightning_module.trained_columns - if ("stay_id" in trainer.lightning_module.trained_columns and - len(trainer.lightning_module.trained_columns) != trainer.lightning_module.explainer_values_test.shape[1]): - trained_columns.remove("stay_id") + if (any(x in ["stay_id", "id"] for x in trained_columns) and + len(trained_columns) != trainer.lightning_module.explainer_values_test.shape[1]): + trained_columns.remove("stay_id" if "stay_id" in trained_columns else "id") logging.info(f"Saving SHAPS") if trainer.lightning_module.explainer_values_test is not None: # todo: abs values From 320007a183e7a77aecc60440c2d25cd585ef2953 Mon Sep 17 00:00:00 2001 From: Robin van de Water Date: Fri, 4 Apr 2025 09:58:43 +0200 Subject: [PATCH 21/82] configs --- configs/prediction_models/common/MLTuning.gin | 2 +- configs/tasks/BinaryClassification.gin | 6 ++++++ experiments/benchmark_cass.yml | 19 ++++++++++++++++++- 3 files changed, 25 insertions(+), 2 deletions(-) diff --git a/configs/prediction_models/common/MLTuning.gin b/configs/prediction_models/common/MLTuning.gin index 2334321b..1334ebd7 100644 --- a/configs/prediction_models/common/MLTuning.gin +++ b/configs/prediction_models/common/MLTuning.gin @@ -1,5 +1,5 @@ # Hyperparameter tuner settings for classical Machine Learning. tune_hyperparameters.scopes = ["model"] tune_hyperparameters.n_initial_points = 5 -tune_hyperparameters.n_calls = 0 +tune_hyperparameters.n_calls = 10 tune_hyperparameters.folds_to_tune_on = 5 \ No newline at end of file diff --git a/configs/tasks/BinaryClassification.gin b/configs/tasks/BinaryClassification.gin index f86436a4..7f10c214 100644 --- a/configs/tasks/BinaryClassification.gin +++ b/configs/tasks/BinaryClassification.gin @@ -22,6 +22,12 @@ preprocess.preprocessor = @base_classification_preprocessor preprocess.modality_mapping = %modality_mapping preprocess.vars = %vars preprocess.use_static = True +preprocess.required_segments = ["OUTCOME", "STATIC"] +preprocess.file_names = { +# "DYNAMIC": "dyn.parquet", + "OUTCOME": "outc.parquet", + "STATIC": "sta.parquet", +} # SELECTING DATASET include "configs/tasks/common/Dataloader.gin" diff --git a/experiments/benchmark_cass.yml b/experiments/benchmark_cass.yml index 11d89a89..9e7cb2d1 100644 --- a/experiments/benchmark_cass.yml +++ b/experiments/benchmark_cass.yml @@ -13,6 +13,12 @@ command: - --wandb-sweep - -tn - SSI +# - --hp-checkpoint +# - /sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/dataset_baseline_flat_2025-02-20T10:40:52/SSI/XGBClassifier/2025-02-20T11-29-27.323418/hyperparameter_tuning_logs.db +# - /sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/dataset_baseline_retro_2025-02-25/SSI/XGBClassifier/2025-02-25T10-52-44.899424/hyperparameter_tuning_logs.db +# - /sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/selected_ids_2025-01-15_17:38:58/SSI/XGBClassifier/2025-01-15T19-24-30.343814/hyperparameter_tuning_logs.db +# - /sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/selected_ids_2025-01-15_17:38:58/SSI/XGBClassifier/2025-01-15T19-24-33.047304/hyperparameter_tuning_logs.db +# - /sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/selected_ids_2025-01-15_17:38:58/SSI/XGBClassifier/2025-01-15T19-24-30.343814/hyperparameter_tuning_logs.db # - Mortality # - --verbose - --modalities @@ -27,7 +33,18 @@ name: yaib_classification_benchmark parameters: data_dir: values: - - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/selected_ids_2025-01-15_17:38:58" + - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/normal_ward_SSI-3_POPF_heamatoma_Galleleck_BDA_segment_1.0_horizon_6:00:00_transfer_full_2025-03-13T14:53:05" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/static_2025-03-11T09:47:15" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/dataset_baseline_flat_2025-03-10T16:17:59" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/normal_ward_SSI-3_POPF_heamatoma_Galleleck_BDA_segment_1.0_horizon_6:00:00_transfer_full_2025-03-07T12:51:24_wearable_cut_off_0.75" +# - /sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/normal_ward_SSI-3_POPF_heamatoma_Galleleck_BDA_segment_1.0_horizon_6:00:00_transfer_full_2025-03-06T19:55:44_wearable_cut_off_0.75 +# - /sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/dataset_baseline_retro_2025-02-25 +# - /sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/dataset_baseline_flat_2025-02-20T10:40:52 +# - /sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/ICU_SSI-3_POPF_heamatoma_Galleleck_BDA_segment_1.0_horizon_6:00:00_transfer_full_2025-02-14T17:21:43 +# - /sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/SSI-3_POPF_heamatoma_Galleleck_BDA_segment_1.0_horizon_6:00:00_transfer_full_2025-01-26T20:14:47_wearable_cut_off_0.75 +# - /sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/SSI-3_POPF_heamatoma_Galleleck_BDA_segment_1.0_horizon_6:00:00_transfer_full_2025-01-21T18:56:10_wearable_cut_off_0.75 +# - /sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/SSI-3_POPF_heamatoma_Galleleck_BDA_segment_1.0_horizon_6:00:00_transfer_full_2025-01-21T18:44:35_wearable_cut_off_0.75 +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/selected_ids_2025-01-15_17:38:58" # - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/selected_ids_2025-01-15_11:50:05" # - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/selected_ids_2024-12-13_16:24:38" # - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/SSI-3_POPF_heamatoma_Galleleck_BDA_segment_1.0_horizon_6:00:00_transfer_full_2024-11-28T14:02:51" From de09ec8d3244ccacf7384614731b00348722d34d Mon Sep 17 00:00:00 2001 From: Robin van de Water Date: Fri, 4 Apr 2025 10:48:26 +0200 Subject: [PATCH 22/82] return vars --- icu_benchmarks/data/split_process_data.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/icu_benchmarks/data/split_process_data.py b/icu_benchmarks/data/split_process_data.py index 53b859df..ac5539d9 100644 --- a/icu_benchmarks/data/split_process_data.py +++ b/icu_benchmarks/data/split_process_data.py @@ -241,7 +241,7 @@ def check_sanitize_data(data, vars): data[Segment.outcome] = data[Segment.outcome].unique(subset=[group], keep=keep, maintain_order=True) if old_len != len(data[Segment.outcome]): logging.warning(f"Removed {old_len - len(data[Segment.outcome])} duplicates from outcome data.") - return data + return data, vars def modality_selection( From 31ff2e62b7bc8f187b20a6a14709c72e9b9effaa Mon Sep 17 00:00:00 2001 From: Robin van de Water Date: Fri, 4 Apr 2025 11:12:31 +0200 Subject: [PATCH 23/82] linting --- configs/tasks/common/Dataloader.gin | 3 +- icu_benchmarks/cross_validation.py | 6 +- icu_benchmarks/data/loader.py | 71 +++++++++++----------- icu_benchmarks/data/split_process_data.py | 16 +++-- icu_benchmarks/data/utils.py | 11 ++-- icu_benchmarks/models/alarm_metrics.py | 7 ++- icu_benchmarks/models/constants.py | 6 +- icu_benchmarks/models/custom_metrics.py | 10 +-- icu_benchmarks/models/ml_models/xgboost.py | 9 ++- icu_benchmarks/models/train.py | 6 +- icu_benchmarks/models/wrappers.py | 70 +++++++++++++-------- icu_benchmarks/run_utils.py | 1 + icu_benchmarks/tuning/hyperparameters.py | 2 +- 13 files changed, 128 insertions(+), 90 deletions(-) diff --git a/configs/tasks/common/Dataloader.gin b/configs/tasks/common/Dataloader.gin index 5b8b0b63..8e0a14a2 100644 --- a/configs/tasks/common/Dataloader.gin +++ b/configs/tasks/common/Dataloader.gin @@ -6,4 +6,5 @@ PredictionPolarsDataset.ram_cache = True PredictionPolarsDataset.mps = True # Imputation ImputationPandasDataset.vars = %vars -ImputationPandasDataset.ram_cache = True \ No newline at end of file +ImputationPandasDataset.ram_cache = True +PredictionPolarsDataset.mps = True \ No newline at end of file diff --git a/icu_benchmarks/cross_validation.py b/icu_benchmarks/cross_validation.py index 9ccd9409..483d5746 100644 --- a/icu_benchmarks/cross_validation.py +++ b/icu_benchmarks/cross_validation.py @@ -74,8 +74,10 @@ def execute_repeated_cv( if not cv_folds_to_train: cv_folds_to_train = cv_folds elif cv_folds_to_train > cv_folds: - raise ValueError(f"cv_folds_to_train is {cv_folds_to_train}, cv_folds is {cv_folds}. " - f" This is likely due to a hyperparameter tuning settings mismatch.") + raise ValueError( + f"cv_folds_to_train is {cv_folds_to_train}, cv_folds is {cv_folds}. " + f" This is likely due to a hyperparameter tuning settings mismatch." + ) agg_loss = 0 seed_everything(seed, reproducible) if complete_train: diff --git a/icu_benchmarks/data/loader.py b/icu_benchmarks/data/loader.py index 2c7b5205..4d45fd94 100644 --- a/icu_benchmarks/data/loader.py +++ b/icu_benchmarks/data/loader.py @@ -16,15 +16,15 @@ @gin.configurable("CommonPolarsDataset") class CommonPolarsDataset(Dataset): def __init__( - self, - data: dict, - split: str = Split.train, - vars: Dict[str, str] = gin.REQUIRED, - grouping_segment: str = Segment.outcome, - mps: bool = False, - name: str = "", - *args, - **kwargs, + self, + data: dict, + split: str = Split.train, + vars: Dict[str, str] = gin.REQUIRED, + grouping_segment: str = Segment.outcome, + mps: bool = False, + name: str = "", + *args, + **kwargs, ): # super().__init__(*args, **kwargs) self.split = split @@ -72,7 +72,7 @@ def __len__(self) -> int: return self.num_stays def get_feature_names(self) -> List[str]: - return [col for col in self.features_df.columns ]#if col != self.vars["GROUP"] and col != self.vars["SEQUENCE"]] + return [col for col in self.features_df.columns] # if col != self.vars["GROUP"] and col != self.vars["SEQUENCE"]] def to_tensor(self) -> List[Tensor]: values = [] @@ -116,10 +116,10 @@ def __getitem__(self, idx: int) -> Tuple[Tensor, Tensor, Tensor]: # slice to make sure to always return a DF # window = self.features_df.loc[stay_id:stay_id].to_numpy() # labels = self.outcome_df.loc[stay_id:stay_id][self.vars["LABEL"]].to_numpy(dtype=float) - window = self.features_df.filter(pl.col(self.vars["GROUP"]) == stay_id).select( - pl.exclude(self.vars["GROUP"])).to_numpy() - labels = self.outcome_df.filter(pl.col(self.vars["GROUP"]) == stay_id)[self.vars["LABEL"]].to_numpy().astype( - float) + window = ( + self.features_df.filter(pl.col(self.vars["GROUP"]) == stay_id).select(pl.exclude(self.vars["GROUP"])).to_numpy() + ) + labels = self.outcome_df.filter(pl.col(self.vars["GROUP"]) == stay_id)[self.vars["LABEL"]].to_numpy().astype(float) if len(labels) == 1: # only one label per stay, align with window @@ -202,16 +202,15 @@ class CommonPandasDataset(Dataset): """ def __init__( - self, - data: dict, - split: str = Split.train, - vars: Dict[str, str] = gin.REQUIRED, - grouping_segment: str = Segment.outcome, - mps: bool = False, - name: str = "", + self, + data: dict, + split: str = Split.train, + vars: Dict[str, str] = gin.REQUIRED, + grouping_segment: str = Segment.outcome, + mps: bool = False, + name: str = "", ): - warnings.warn("CommonPandasDataset is deprecated. Use CommonPolarsDataset instead.", DeprecationWarning, - stacklevel=2) + warnings.warn("CommonPandasDataset is deprecated. Use CommonPolarsDataset instead.", DeprecationWarning, stacklevel=2) self.split = split self.vars = vars self.grouping_df = data[split][grouping_segment].set_index(self.vars["GROUP"]) @@ -350,14 +349,14 @@ class ImputationPandasDataset(CommonPandasDataset): """Subclass of Common Dataset that contains data for imputation models.""" def __init__( - self, - data: Dict[str, DataFrame], - split: str = Split.train, - vars: Dict[str, str] = gin.REQUIRED, - mask_proportion=0.3, - mask_method="MCAR", - mask_observation_proportion=0.3, - ram_cache: bool = True, + self, + data: Dict[str, DataFrame], + split: str = Split.train, + vars: Dict[str, str] = gin.REQUIRED, + mask_proportion=0.3, + mask_method="MCAR", + mask_observation_proportion=0.3, + ram_cache: bool = True, ): """ Args: @@ -424,11 +423,11 @@ class ImputationPredictionDataset(Dataset): """ def __init__( - self, - data: DataFrame, - grouping_column: str = "stay_id", - select_columns: List[str] = None, - ram_cache: bool = True, + self, + data: DataFrame, + grouping_column: str = "stay_id", + select_columns: List[str] = None, + ram_cache: bool = True, ): self.dyn_df = data diff --git a/icu_benchmarks/data/split_process_data.py b/icu_benchmarks/data/split_process_data.py index ac5539d9..eb3577f1 100644 --- a/icu_benchmarks/data/split_process_data.py +++ b/icu_benchmarks/data/split_process_data.py @@ -106,7 +106,9 @@ def preprocess_data( excluded_vars.extend(modality_mapping.get(modality)) else: logging.warning(f"Modality '{modality}' not found in modality mapping.") - logging.info(f"Excluding modalities in {exclude_preproc}. Total vars excluded from preprocessing: {len(excluded_vars)}") + logging.info( + f"Excluding modalities in {exclude_preproc}. Total vars excluded from preprocessing: {len(excluded_vars)}" + ) else: logging.warning("No modality mapping provided. Excluding variables from preprocessing will have no effect.") preprocessor = preprocessor( @@ -198,14 +200,16 @@ def preprocess_data( max_float64 = 0 # Replace infinite values with the maximum value for float64 - val = val.with_columns([ - pl.when(pl.col(col).is_infinite()).then(max_float64).otherwise(pl.col(col)).alias(col) - for col in val.columns if val[col].dtype == pl.Float64 - ]) + val = val.with_columns( + [ + pl.when(pl.col(col).is_infinite()).then(max_float64).otherwise(pl.col(col)).alias(col) + for col in val.columns + if val[col].dtype == pl.Float64 + ] + ) dict[key] = val logging.info(f"Amount of columns: {len(val.columns)}") - # Generate cache if generate_cache: caching(cache_dir, cache_file, data, load_cache) diff --git a/icu_benchmarks/data/utils.py b/icu_benchmarks/data/utils.py index ec87bece..5b95e9d6 100644 --- a/icu_benchmarks/data/utils.py +++ b/icu_benchmarks/data/utils.py @@ -13,10 +13,13 @@ def infinite_removal(val): max_float64 = np.finfo(np.float64).max / 100 # Replace infinite values with the maximum value for float64 - val = val.with_columns([ - pl.when(pl.col(col).is_infinite()).then(max_float64).otherwise(pl.col(col)).alias(col) - for col in val.columns if val[col].dtype == pl.Float64 - ]) + val = val.with_columns( + [ + pl.when(pl.col(col).is_infinite()).then(max_float64).otherwise(pl.col(col)).alias(col) + for col in val.columns + if val[col].dtype == pl.Float64 + ] + ) return val diff --git a/icu_benchmarks/models/alarm_metrics.py b/icu_benchmarks/models/alarm_metrics.py index cdc0dcb2..c892abdc 100644 --- a/icu_benchmarks/models/alarm_metrics.py +++ b/icu_benchmarks/models/alarm_metrics.py @@ -28,6 +28,7 @@ def convert_to_alarm(ground_truth, predictions, grace_horizon=12, silencing_leng filled_predictions = fill_gaps(silenced_predictions, grace_horizon) return filled_predictions, ground_truth + def silence_positives(ground_truth, predictions, grace_horizon=12, silencing_length=6): """Silence positive predictions in the predictions array based on the grace horizon and silencing length.""" # Find all positive indices in the rounded predictions @@ -43,7 +44,7 @@ def silence_positives(ground_truth, predictions, grace_horizon=12, silencing_len # print(f"Already silenced: {positive_index}") positive_indices = positive_indices[1:] continue - silence_array[positive_index + 1:positive_index + silencing_length] = 0 + silence_array[positive_index + 1 : positive_index + silencing_length] = 0 positive_indices = positive_indices[1:] # print(predictions) # print(silence_array) @@ -53,7 +54,7 @@ def silence_positives(ground_truth, predictions, grace_horizon=12, silencing_len def fill_gaps(predictions, ground_truth, grace_horizon=12): - """ Fill gaps in the predictions by taking the maximum value between ground truth and predictions.""" + """Fill gaps in the predictions by taking the maximum value between ground truth and predictions.""" if grace_horizon > len(predictions): grace_horizon = len(predictions) # Take the last grace_horizon values @@ -69,4 +70,4 @@ def fill_gaps(predictions, ground_truth, grace_horizon=12): # print(grace_values) predictions[-grace_horizon:] = grace_values ground_truth[-grace_horizon:] = np.maximum(predictions[-grace_horizon:], ground_truth[-grace_horizon:]) - return predictions, ground_truth \ No newline at end of file + return predictions, ground_truth diff --git a/icu_benchmarks/models/constants.py b/icu_benchmarks/models/constants.py index a931c9fd..21f751ee 100644 --- a/icu_benchmarks/models/constants.py +++ b/icu_benchmarks/models/constants.py @@ -27,7 +27,9 @@ JSD, BinaryFairnessWrapper, confusion_matrix, - sensitivity, specificity, positive_predictive_value + sensitivity, + specificity, + positive_predictive_value, ) @@ -43,7 +45,7 @@ class MLMetrics: # "Precision": precision_score, # "Recall": recall_score, "Specificity": specificity, - "PPV": positive_predictive_value + "PPV": positive_predictive_value, } MULTICLASS_CLASSIFICATION = { diff --git a/icu_benchmarks/models/custom_metrics.py b/icu_benchmarks/models/custom_metrics.py index 3059b4b4..0669024b 100644 --- a/icu_benchmarks/models/custom_metrics.py +++ b/icu_benchmarks/models/custom_metrics.py @@ -63,7 +63,7 @@ def __init__( invert_transform: Callable = lambda x: x, ) -> None: super(MAE, self).__init__( - lambda x, y: mae_with_invert_compute_fn(x, y, invert_transform), + lambda x, y: self.mae_with_invert_compute_fn(x, y, invert_transform), output_transform=output_transform, check_compute_fn=check_compute_fn, ) @@ -152,6 +152,7 @@ def __init__(self, output_transform: Callable = lambda x: x, check_compute_fn: b self.sensitivity_compute, output_transform=output_transform, check_compute_fn=check_compute_fn ) + def sensitivity(y_preds: torch.Tensor | ndarray, y_targets: torch.Tensor | ndarray) -> float: if isinstance(y_preds, torch.Tensor): y_preds = y_preds.numpy() @@ -162,12 +163,14 @@ def sensitivity(y_preds: torch.Tensor | ndarray, y_targets: torch.Tensor | ndarr tn, fp, fn, tp = sk_confusion_matrix(y_true, y_pred).ravel() return tp / (tp + fn) + class Specificity(EpochMetric): def __init__(self, output_transform: Callable = lambda x: x, check_compute_fn: bool = False) -> None: super(Specificity, self).__init__( self.specificity_compute, output_transform=output_transform, check_compute_fn=check_compute_fn ) + def specificity(y_preds: torch.Tensor, y_targets: torch.Tensor) -> float: if isinstance(y_preds, torch.Tensor): y_preds = y_preds.numpy() @@ -178,6 +181,7 @@ def specificity(y_preds: torch.Tensor, y_targets: torch.Tensor) -> float: tn, fp, fn, tp = sk_confusion_matrix(y_true, y_pred).ravel() return tn / (tn + fp) + def positive_predictive_value(y_preds: torch.Tensor | np.ndarray, y_targets: torch.Tensor | np.ndarray) -> float: if isinstance(y_preds, torch.Tensor): y_preds = y_preds.numpy() @@ -188,6 +192,7 @@ def positive_predictive_value(y_preds: torch.Tensor | np.ndarray, y_targets: tor tn, fp, fn, tp = sk_confusion_matrix(y_true, y_pred).ravel() return tp / (tp + fp) + # from torchmetrics.classification import Specificity as TorchMetricsSpecificity # # class Specificity(EpochMetric): @@ -206,6 +211,3 @@ def positive_predictive_value(y_preds: torch.Tensor | np.ndarray, y_targets: tor # y_targets = torch.tensor(y_targets) # self.metric.update(y_preds, y_targets) # return self.metric.compute().item() - - - diff --git a/icu_benchmarks/models/ml_models/xgboost.py b/icu_benchmarks/models/ml_models/xgboost.py index b9039fcb..5565ddb6 100644 --- a/icu_benchmarks/models/ml_models/xgboost.py +++ b/icu_benchmarks/models/ml_models/xgboost.py @@ -25,7 +25,9 @@ class XGBClassifier(MLWrapper): _explain_values = False def __init__(self, *args, **kwargs): - self.model = self.set_model_args(xgb.XGBClassifier, *args, **kwargs, eval_metric=log_loss, device="cpu", missing='inf', verbosity=0) + self.model = self.set_model_args( + xgb.XGBClassifier, *args, **kwargs, eval_metric=log_loss, device="cpu", missing="inf", verbosity=0 + ) super().__init__(*args, **kwargs) def predict(self, features): @@ -50,8 +52,9 @@ def fit_model(self, train_data, train_labels, val_data, val_labels): logging.info(train_labels) self.model.fit(train_data, train_labels, eval_set=[(val_data, val_labels)], verbose=0) # if self.explain_features: - self.explainer = shap.TreeExplainer(self.model, train_data, feature_perturbation="interventional", - model_output="probability") + self.explainer = shap.TreeExplainer( + self.model, train_data, feature_perturbation="interventional", model_output="probability" + ) if self.explain_features: logging.info("Explaining features") self.train_shap_values = self.explainer.shap_values(train_data) diff --git a/icu_benchmarks/models/train.py b/icu_benchmarks/models/train.py index ce2a2b02..1d5b225a 100644 --- a/icu_benchmarks/models/train.py +++ b/icu_benchmarks/models/train.py @@ -210,8 +210,10 @@ def persist_shap_data(trainer: Trainer, log_dir: Path): try: trained_columns = trainer.lightning_module.trained_columns - if (any(x in ["stay_id", "id"] for x in trained_columns) and - len(trained_columns) != trainer.lightning_module.explainer_values_test.shape[1]): + if ( + any(x in ["stay_id", "id"] for x in trained_columns) + and len(trained_columns) != trainer.lightning_module.explainer_values_test.shape[1] + ): trained_columns.remove("stay_id" if "stay_id" in trained_columns else "id") logging.info(f"Saving SHAPS") if trainer.lightning_module.explainer_values_test is not None: diff --git a/icu_benchmarks/models/wrappers.py b/icu_benchmarks/models/wrappers.py index dfda2e0b..e4559fb2 100644 --- a/icu_benchmarks/models/wrappers.py +++ b/icu_benchmarks/models/wrappers.py @@ -109,6 +109,7 @@ def check_supported_runmode(self, runmode: RunMode): def set_explain_features(self, set_explain_features: bool): self.explain_features = set_explain_features + @gin.configurable("DLWrapper") class DLWrapper(BaseModule, ABC): requires_backprop = True @@ -225,8 +226,6 @@ def save_model(self, save_path, file_name, file_extension=".ckpt"): logging.error(f"Cannot save model to path {str(path.resolve())}: {e}.") - - @gin.configurable("DLPredictionWrapper") class DLPredictionWrapper(DLWrapper): """Interface for Deep Learning models.""" @@ -496,8 +495,12 @@ def log_metrics(self, label, pred, metric_type, pred_indicators): sync_dist=True, ) else: - if (len(pred_indicators.shape) > 1 and len(pred.shape) > 1 and pred_indicators.shape[1] == pred.shape[1] - and pred_indicators.shape[0] == pred.shape[0]): + if ( + len(pred_indicators.shape) > 1 + and len(pred.shape) > 1 + and pred_indicators.shape[1] == pred.shape[1] + and pred_indicators.shape[0] == pred.shape[0] + ): pred_indicators = np.hstack((pred_indicators, label.reshape(-1, 1))) pred_indicators = np.hstack((pred_indicators, pred)) # Format: id, time (hours), ground truth, prediction 0, prediction 1 @@ -526,40 +529,55 @@ def log_metrics(self, label, pred, metric_type, pred_indicators): def _save_model_outputs(self, pred_indicators, test_pred, test_label): if len(pred_indicators.shape) > 1 and len(test_pred.shape) > 1: - # Temporal dataset + # Temporal dataset if pred_indicators.shape[1] == test_pred.shape[1] and pred_indicators.shape[0] == test_pred.shape[0]: - # One outcome per dataset - pred_indicators = np.hstack((pred_indicators, test_label.reshape(-1, 1))) - pred_indicators = np.hstack((pred_indicators, test_pred)) - # Save as: id, time (hours), ground truth, prediction 0, prediction 1 - # if pred_indicators.shape(1) == 5: - np.savetxt(Path(self.logger.save_dir) / "pred_indicators.csv", pred_indicators, delimiter=",", - header="id,time,ground_truth,prediction_0,prediction_1", fmt='%d,%d,%.3f,%.3f,%.3f') - logging.debug(f"Saved row indicators to {Path(self.logger.save_dir) / f'row_indicators.csv'}") - # else: - # # Flat/static dataset - # np.savetxt(Path(self.logger.save_dir) / "pred_indicators.csv", pred_indicators, delimiter=",", - # header="id,ground_truth,prediction_0,prediction_1", fmt='%d,%d,%.3f,%.3f,%.3f') - # np.savetxt(Path(self.logger.save_dir) / "pred_indicators.csv", pred_indicators, delimiter=",", - # header="id,time,ground_truth,prediction_0,prediction_1", fmt='%d,%d,%.3f,%.3f,%.3f') - # logging.debug(f"Saved row indicators to {Path(self.logger.save_dir) / f'row_indicators.csv'}") + # One outcome per dataset + pred_indicators = np.hstack((pred_indicators, test_label.reshape(-1, 1))) + pred_indicators = np.hstack((pred_indicators, test_pred)) + # Save as: id, time (hours), ground truth, prediction 0, prediction 1 + # if pred_indicators.shape(1) == 5: + np.savetxt( + Path(self.logger.save_dir) / "pred_indicators.csv", + pred_indicators, + delimiter=",", + header="id,time,ground_truth,prediction_0,prediction_1", + fmt="%d,%d,%.3f,%.3f,%.3f", + ) + logging.debug(f"Saved row indicators to {Path(self.logger.save_dir) / f'row_indicators.csv'}") + # else: + # # Flat/static dataset + # np.savetxt(Path(self.logger.save_dir) / "pred_indicators.csv", pred_indicators, delimiter=",", + # header="id,ground_truth,prediction_0,prediction_1", fmt='%d,%d,%.3f,%.3f,%.3f') + # np.savetxt(Path(self.logger.save_dir) / "pred_indicators.csv", pred_indicators, delimiter=",", + # header="id,time,ground_truth,prediction_0,prediction_1", fmt='%d,%d,%.3f,%.3f,%.3f') + # logging.debug(f"Saved row indicators to {Path(self.logger.save_dir) / f'row_indicators.csv'}") else: logging.info(np.unique(pred_indicators[:, 0])) pred_indicators = np.unique(pred_indicators[:, 0]) - pred_indicators = np.hstack((pred_indicators.reshape(-1,1), test_label.reshape(-1,1))) + pred_indicators = np.hstack((pred_indicators.reshape(-1, 1), test_label.reshape(-1, 1))) pred_indicators = np.hstack((pred_indicators, test_pred)) - np.savetxt(Path(self.logger.save_dir) / "pred_indicators.csv", pred_indicators, delimiter=",", - header="id,ground_truth,prediction_0,prediction_1", fmt='%d,%d,%0.3f,%0.3f') + np.savetxt( + Path(self.logger.save_dir) / "pred_indicators.csv", + pred_indicators, + delimiter=",", + header="id,ground_truth,prediction_0,prediction_1", + fmt="%d,%d,%0.3f,%0.3f", + ) logging.debug(f"Saved row indicators to {Path(self.logger.save_dir) / f'row_indicators.csv'}") # logging.warning("Could not save row indicators; no support for temporal dataset with single outcomes yet.") else: - pred_indicators = np.hstack((pred_indicators.reshape(-1,1), test_label.reshape(-1,1))) + pred_indicators = np.hstack((pred_indicators.reshape(-1, 1), test_label.reshape(-1, 1))) logging.info(pred_indicators.shape) pred_indicators = np.hstack((pred_indicators, test_pred)) - np.savetxt(Path(self.logger.save_dir) / "pred_indicators.csv", pred_indicators, delimiter=",", - header="id,ground_truth,prediction_0,prediction_1", fmt='%d,%d,%0.3f,%0.3f') + np.savetxt( + Path(self.logger.save_dir) / "pred_indicators.csv", + pred_indicators, + delimiter=",", + header="id,ground_truth,prediction_0,prediction_1", + fmt="%d,%d,%0.3f,%0.3f", + ) logging.debug(f"Saved row indicators to {Path(self.logger.save_dir) / f'row_indicators.csv'}") # else: diff --git a/icu_benchmarks/run_utils.py b/icu_benchmarks/run_utils.py index 46fb5b3c..69712143 100644 --- a/icu_benchmarks/run_utils.py +++ b/icu_benchmarks/run_utils.py @@ -19,6 +19,7 @@ import polars as pl import random + def build_parser() -> ArgumentParser: """Builds an ArgumentParser for the command line. diff --git a/icu_benchmarks/tuning/hyperparameters.py b/icu_benchmarks/tuning/hyperparameters.py index a379d4a6..0800e0e1 100644 --- a/icu_benchmarks/tuning/hyperparameters.py +++ b/icu_benchmarks/tuning/hyperparameters.py @@ -317,7 +317,7 @@ def bind_params_and_train(hyperparams): debug=debug, verbose=verbose, wandb=wandb, - explain_features=False + explain_features=False, ) logging.info(f"Score: {score}") return score From cf29a5a2ae248bbb2a166629c38967fdc762fe3c Mon Sep 17 00:00:00 2001 From: Robin van de Water Date: Tue, 15 Apr 2025 11:37:38 +0200 Subject: [PATCH 24/82] file_names option --- configs/tasks/BinaryClassification.gin | 2 +- icu_benchmarks/run.py | 15 +++++++++--- icu_benchmarks/run_utils.py | 33 +++++++++++++++++++++----- 3 files changed, 40 insertions(+), 10 deletions(-) diff --git a/configs/tasks/BinaryClassification.gin b/configs/tasks/BinaryClassification.gin index 7f10c214..3bd32779 100644 --- a/configs/tasks/BinaryClassification.gin +++ b/configs/tasks/BinaryClassification.gin @@ -24,7 +24,7 @@ preprocess.vars = %vars preprocess.use_static = True preprocess.required_segments = ["OUTCOME", "STATIC"] preprocess.file_names = { -# "DYNAMIC": "dyn.parquet", + "DYNAMIC": "dyn.parquet", "OUTCOME": "outc.parquet", "STATIC": "sta.parquet", } diff --git a/icu_benchmarks/run.py b/icu_benchmarks/run.py index 8d968e13..18b677c9 100644 --- a/icu_benchmarks/run.py +++ b/icu_benchmarks/run.py @@ -102,7 +102,8 @@ def main(my_args=tuple(sys.argv[1:])): log_full_line(f"Available GPU {name}: {torch.cuda.get_device_name(name)}", level=logging.INFO) else: log_full_line( - "No GPUs available: please check your device and Torch,Cuda installation if unintended.", level=logging.WARNING + "No GPUs available: please check your device and Torch,Cuda installation if unintended.", + level=logging.WARNING ) if args.preprocessor: @@ -119,7 +120,8 @@ def main(my_args=tuple(sys.argv[1:])): name_datasets(args.name, args.name, args.name) run_dir = create_run_dir(log_dir) source_dir = args.source_dir - logging.info(f"Will load weights from {source_dir} and bind train gin-config. Note: this might override your config.") + logging.info( + f"Will load weights from {source_dir} and bind train gin-config. Note: this might override your config.") gin.parse_config_file(source_dir / "train_config.gin") elif args.samples and args.source_dir is not None: # Train model with limited samples and bind existing config logging.info("Binding train gin-config. Note: this might override your config.") @@ -132,16 +134,23 @@ def main(my_args=tuple(sys.argv[1:])): name_datasets(args.name, args.name, args.name) hp_checkpoint = log_dir / args.hp_checkpoint if args.hp_checkpoint else None model_path = ( - Path("configs") / ("imputation_models" if mode == RunMode.imputation else "prediction_models") / f"{model}.gin" + Path("configs") / ( + "imputation_models" if mode == RunMode.imputation else "prediction_models") / f"{model}.gin" ) gin_config_files = ( [Path(f"configs/experiments/{args.experiment}.gin")] if args.experiment else [model_path, Path(f"configs/tasks/{task}.gin")] ) + gin.parse_config_files_and_bindings(gin_config_files, args.hyperparams, finalize_config=False) log_full_line(f"Data directory: {data_dir.resolve()}", level=logging.INFO) run_dir = create_run_dir(log_dir) + + # manually bind dataset files + if args.file_names: + gin.bind_parameter("preprocess.file_names", args.file_names) + choose_and_bind_hyperparameters_optuna( do_tune=args.tune, data_dir=data_dir, diff --git a/icu_benchmarks/run_utils.py b/icu_benchmarks/run_utils.py index 69712143..f17a175a 100644 --- a/icu_benchmarks/run_utils.py +++ b/icu_benchmarks/run_utils.py @@ -1,3 +1,4 @@ +import argparse import importlib import math import sys @@ -34,9 +35,11 @@ def build_parser() -> ArgumentParser: parser.add_argument("-tn", "--task-name", help="Name of the task, used for naming experiments.") parser.add_argument("-m", "--model", default="LGBMClassifier", help="Name of the model gin.") parser.add_argument("-e", "--experiment", help="Name of the experiment gin.") - parser.add_argument("-l", "--log-dir", default=Path("../yaib_logs/"), type=Path, help="Log directory for model weights.") + parser.add_argument("-l", "--log-dir", default=Path("../yaib_logs/"), type=Path, + help="Log directory for model weights.") parser.add_argument("-s", "--seed", default=1234, type=int, help="Random seed for processing, tuning and training.") - parser.add_argument("-v", "--verbose", default=False, action=BOA, help="Set to log verbosly. Disable for clean logs.") + parser.add_argument("-v", "--verbose", default=False, action=BOA, + help="Set to log verbosly. Disable for clean logs.") parser.add_argument("--cpu", default=False, action=BOA, help="Set to use CPU.") parser.add_argument("-db", "--debug", default=False, action=BOA, help="Set to load less data.") parser.add_argument("--reproducible", default=True, action=BOA, help="Make torch reproducible.") @@ -50,7 +53,8 @@ def build_parser() -> ArgumentParser: parser.add_argument("--tune", default=False, action=BOA, help="Find best hyperparameters.") parser.add_argument("--hp-checkpoint", type=Path, help="Use previous hyperparameter checkpoint.") parser.add_argument("--eval", default=False, action=BOA, help="Only evaluate model, skip training.") - parser.add_argument("--complete-train", default=False, action=BOA, help="Use all data to train model, skip testing.") + parser.add_argument("--complete-train", default=False, action=BOA, + help="Use all data to train model, skip testing.") parser.add_argument("-ft", "--fine-tune", default=None, type=int, help="Finetune model with amount of train data.") parser.add_argument("-sn", "--source-name", type=Path, help="Name of the source dataset.") parser.add_argument("--source-dir", type=Path, help="Directory containing gin and model weights.") @@ -61,10 +65,25 @@ def build_parser() -> ArgumentParser: nargs="+", help="Optional modality selection to use. Specify multiple modalities separated by spaces.", ) - parser.add_argument("--label", type=str, help="Label to use for evaluation in case of multiple labels.", default=None) + parser.add_argument("--label", type=str, help="Label to use for evaluation in case of multiple labels.", + default=None) + parser.add_argument( + "--file_names", + type=parse_dict, + help="Dictionary of file names to use in data_dir (e.g., 'DYNAMIC:dyno.parquet,OUTCOME:outco.parquet,STATIC:sta.parquet')", + default=None, + ) return parser +def parse_dict(arg): + """Parses a string in the format 'key1:value1,key2:value2' into a dictionary.""" + try: + return dict(item.split(":") for item in arg.split(",")) + except ValueError: + raise argparse.ArgumentTypeError("Invalid dictionary format. Use 'key1:value1,key2:value2'.") + + def create_run_dir(log_dir: Path, randomly_searched_params: str = None) -> Path: """Creates a log directory with the current time as name. @@ -199,7 +218,8 @@ def log_full_line(msg: str, level: int = logging.INFO, char: str = "-", num_newl reserved_chars = len(logging.getLevelName(level)) + 28 logging.log( level, - "{0:{char}^{width}}{1}".format(msg, "\n" * num_newlines, char=char, width=terminal_size.columns - reserved_chars), + "{0:{char}^{width}}{1}".format(msg, "\n" * num_newlines, char=char, + width=terminal_size.columns - reserved_chars), ) @@ -218,7 +238,8 @@ def load_pretrained_imputation_model(use_pretrained_imputation): pretrained_imputation_model_checkpoint = torch.load(use_pretrained_imputation, map_location=torch.device("cpu")) if isinstance(pretrained_imputation_model_checkpoint, dict): imputation_model_class = pretrained_imputation_model_checkpoint["class"] - pretrained_imputation_model = imputation_model_class(**pretrained_imputation_model_checkpoint["hyper_parameters"]) + pretrained_imputation_model = imputation_model_class( + **pretrained_imputation_model_checkpoint["hyper_parameters"]) pretrained_imputation_model.set_trained_columns(pretrained_imputation_model_checkpoint["trained_columns"]) pretrained_imputation_model.load_state_dict(pretrained_imputation_model_checkpoint["state_dict"]) else: From 46716261e927a3a615fe0efb43ca943963bc2f87 Mon Sep 17 00:00:00 2001 From: Robin van de Water Date: Tue, 15 Apr 2025 15:12:37 +0200 Subject: [PATCH 25/82] cleanup and loader fix --- experiments/benchmark_cass.yml | 48 +++++++++------------------------- icu_benchmarks/data/loader.py | 2 ++ icu_benchmarks/run.py | 13 +++++++-- icu_benchmarks/run_utils.py | 27 +++++++++++++------ icu_benchmarks/wandb_utils.py | 5 ++++ 5 files changed, 50 insertions(+), 45 deletions(-) diff --git a/experiments/benchmark_cass.yml b/experiments/benchmark_cass.yml index 9e7cb2d1..4c9c21b4 100644 --- a/experiments/benchmark_cass.yml +++ b/experiments/benchmark_cass.yml @@ -15,17 +15,13 @@ command: - SSI # - --hp-checkpoint # - /sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/dataset_baseline_flat_2025-02-20T10:40:52/SSI/XGBClassifier/2025-02-20T11-29-27.323418/hyperparameter_tuning_logs.db -# - /sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/dataset_baseline_retro_2025-02-25/SSI/XGBClassifier/2025-02-25T10-52-44.899424/hyperparameter_tuning_logs.db -# - /sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/selected_ids_2025-01-15_17:38:58/SSI/XGBClassifier/2025-01-15T19-24-30.343814/hyperparameter_tuning_logs.db -# - /sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/selected_ids_2025-01-15_17:38:58/SSI/XGBClassifier/2025-01-15T19-24-33.047304/hyperparameter_tuning_logs.db -# - /sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/selected_ids_2025-01-15_17:38:58/SSI/XGBClassifier/2025-01-15T19-24-30.343814/hyperparameter_tuning_logs.db # - Mortality # - --verbose - --modalities - "all" + - --file-names + - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc.parquet","STATIC":"sta.parquet"' # - --verbose -# - /sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/cass/SSI/XGBClassifier/2024-06-28T17-28-04/hyperparameter_tuning_logs.db -# - /sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/cass/SSI/Transformer/2024-06-26T15-30-53/hyperparameter_tuning_logs.db # - -gc # - -lc method: grid @@ -33,38 +29,12 @@ name: yaib_classification_benchmark parameters: data_dir: values: - - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/normal_ward_SSI-3_POPF_heamatoma_Galleleck_BDA_segment_1.0_horizon_6:00:00_transfer_full_2025-03-13T14:53:05" -# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/static_2025-03-11T09:47:15" -# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/dataset_baseline_flat_2025-03-10T16:17:59" -# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/normal_ward_SSI-3_POPF_heamatoma_Galleleck_BDA_segment_1.0_horizon_6:00:00_transfer_full_2025-03-07T12:51:24_wearable_cut_off_0.75" -# - /sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/normal_ward_SSI-3_POPF_heamatoma_Galleleck_BDA_segment_1.0_horizon_6:00:00_transfer_full_2025-03-06T19:55:44_wearable_cut_off_0.75 -# - /sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/dataset_baseline_retro_2025-02-25 -# - /sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/dataset_baseline_flat_2025-02-20T10:40:52 -# - /sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/ICU_SSI-3_POPF_heamatoma_Galleleck_BDA_segment_1.0_horizon_6:00:00_transfer_full_2025-02-14T17:21:43 -# - /sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/SSI-3_POPF_heamatoma_Galleleck_BDA_segment_1.0_horizon_6:00:00_transfer_full_2025-01-26T20:14:47_wearable_cut_off_0.75 -# - /sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/SSI-3_POPF_heamatoma_Galleleck_BDA_segment_1.0_horizon_6:00:00_transfer_full_2025-01-21T18:56:10_wearable_cut_off_0.75 -# - /sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/SSI-3_POPF_heamatoma_Galleleck_BDA_segment_1.0_horizon_6:00:00_transfer_full_2025-01-21T18:44:35_wearable_cut_off_0.75 -# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/selected_ids_2025-01-15_17:38:58" -# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/selected_ids_2025-01-15_11:50:05" -# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/selected_ids_2024-12-13_16:24:38" -# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/SSI-3_POPF_heamatoma_Galleleck_BDA_segment_1.0_horizon_6:00:00_transfer_full_2024-11-28T14:02:51" -# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/combined_endpoints_segment_1.0_horizon_6:00:00_transfer_3_2024-11-25T17:56:08" -# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/dataset_segment_1.0_horizon_6:00:00_transfer_3_2024-11-22T13:19:53" -# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/dataset_segment_1.0_horizon_6:00:00_transfer_3_2024-11-11T16:03:12" -# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/dataset_segment_1.0_horizon_6:00:00_transfer_3_2024-11-11T11:17:09/" + - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/ICU_and_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_1.0_horizon_6:00:00_transfer_full_2025-04-15T12:42:04" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/normal_ward_SSI-3_POPF_heamatoma_Galleleck_BDA_segment_1.0_horizon_6:00:00_transfer_full_2025-04-07T15:35:16" # - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/cohort_separation/1/" # - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/cohort_separation/2/" # - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/cohort_separation/3/" # - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/cohort_separation/4/" -# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/dataset_segment_1.0_horizon_6:00:00_transfer_3_2024-11-08T12:45:05/" -# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/dataset_segment_1.0_horizon_6:00:00_transfer_3_2024-10-07T23:26:22" -# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/dataset_icu_segment_1.0_horizon_6:00:00_transfer_3_2024-10-30T14:51:38/" -# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/dataset_segment_1.0_horizon_6:00:00_transfer_3_2024-10-07T23:26:22" -# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/dataset_segment_1.0_horizon_6:00:00_transfer_full_2024-09-27T16:14:05_complication" -# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/dataset_segment_1.0_horizon_6:00:00_transfer_full_2024-09-26T16:26:24" -# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/dataset_x_segment_duration_x_transfer_2024-09-19T13:57:36" -# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/dataset_baseline_dynamic_6h_2024-09-18T11:24:23" -# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/dataset_baseline_flat_2024-09-17T17:25:20" model: values: # - LogisticRegression @@ -82,7 +52,7 @@ parameters: modalities: values: - "all" - - [copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, cat_med_embeddings_map, cat_clinical_notes, static] +# - [copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, cat_med_embeddings_map, cat_clinical_notes, static] # - [copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, cat_med_embeddings_map, wearable_activity, wearable_core, static] # - [copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, static] #, wearable_activity, wearable_core] # without wearable data # - [copra_observations, copra_scores, copra_fluid_balance, cat_med_embeddings_map, wearable_activity, wearable_core, static] # without ishmed lab data and clinical notes @@ -92,6 +62,14 @@ parameters: # - [wearable_activity, wearable_core, static] # - [ishmed_lab_numeric, static] # - [static] + file_names: + values: + - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_3.parquet","STATIC":"sta.parquet"' + - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_6.parquet","STATIC":"sta.parquet"' + - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_9.parquet","STATIC":"sta.parquet"' + - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_12.parquet","STATIC":"sta.parquet"' + - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_18.parquet","STATIC":"sta.parquet"' + - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_24.parquet","STATIC":"sta.parquet"' seed: values: - 1111 diff --git a/icu_benchmarks/data/loader.py b/icu_benchmarks/data/loader.py index 4d45fd94..892bf5df 100644 --- a/icu_benchmarks/data/loader.py +++ b/icu_benchmarks/data/loader.py @@ -182,6 +182,8 @@ def get_data_and_labels(self) -> Tuple[np.array, np.array, np.array]: # needs to still be in there? logging.debug(f"rep shape: {rep.shape}") logging.debug(f"labels shape: {labels.shape}") + rep = rep.to_numpy().astype(float) + return rep, labels, self.row_indicators.to_numpy() def to_tensor(self) -> Tuple[Tensor, Tensor, Tensor]: diff --git a/icu_benchmarks/run.py b/icu_benchmarks/run.py index 18b677c9..10e5a05f 100644 --- a/icu_benchmarks/run.py +++ b/icu_benchmarks/run.py @@ -18,7 +18,7 @@ setup_logging, import_preprocessor, name_datasets, - get_config_files, + get_config_files, parse_dict, ) from icu_benchmarks.constants import RunMode @@ -149,7 +149,16 @@ def main(my_args=tuple(sys.argv[1:])): # manually bind dataset files if args.file_names: - gin.bind_parameter("preprocess.file_names", args.file_names) + if isinstance(args.file_names, dict): + logging.info(f"Will load data from {args.file_names}") + gin.bind_parameter("preprocess.file_names", args.file_names) + elif isinstance(args.file_names, str): + file_names = parse_dict(args.file_names) + logging.info(f"Will load data from {args.file_names}") + gin.bind_parameter("preprocess.file_names", file_names) + else: + return ValueError(f"Please provide a dictionary type for the file names, got {args.file_names}, " + f"type: {type(args.file_names)}") choose_and_bind_hyperparameters_optuna( do_tune=args.tune, diff --git a/icu_benchmarks/run_utils.py b/icu_benchmarks/run_utils.py index f17a175a..5009626c 100644 --- a/icu_benchmarks/run_utils.py +++ b/icu_benchmarks/run_utils.py @@ -4,7 +4,7 @@ import sys import warnings from math import sqrt - +import re import gin import torch import json @@ -16,7 +16,6 @@ import shutil from statistics import mean, pstdev from icu_benchmarks.models.utils import JsonResultLoggingEncoder -from icu_benchmarks.wandb_utils import wandb_log import polars as pl import random @@ -70,19 +69,31 @@ def build_parser() -> ArgumentParser: parser.add_argument( "--file_names", type=parse_dict, - help="Dictionary of file names to use in data_dir (e.g., 'DYNAMIC:dyno.parquet,OUTCOME:outco.parquet,STATIC:sta.parquet')", + help="Dictionary of file names to use in data_dir " + "(e.g., 'DYNAMIC:dyno.parquet,OUTCOME:outco.parquet,STATIC:sta.parquet')", default=None, ) return parser def parse_dict(arg): - """Parses a string in the format 'key1:value1,key2:value2' into a dictionary.""" + """ + Parses a string into a dictionary. Handles both: + - Unquoted format: 'key1:value1,key2:value2' + - JSON-like quoted format: '"key1":"value1","key2":"value2"' + """ try: - return dict(item.split(":") for item in arg.split(",")) - except ValueError: - raise argparse.ArgumentTypeError("Invalid dictionary format. Use 'key1:value1,key2:value2'.") - + # Check if the input is in JSON-like format + if ":" in arg and '"' in arg: + # Wrap in curly braces to make it valid JSON + json_string = f"{{{arg}}}" + return json.loads(json_string) + else: + # Handle unquoted format + pairs = arg.split(',') + return {key.strip(): value.strip() for key, value in (pair.split(':', 1) for pair in pairs)} + except Exception as e: + raise argparse.ArgumentTypeError(f"Invalid dictionary format: {e}") def create_run_dir(log_dir: Path, randomly_searched_params: str = None) -> Path: """Creates a log directory with the current time as name. diff --git a/icu_benchmarks/wandb_utils.py b/icu_benchmarks/wandb_utils.py index 2ea06b57..1d79d191 100644 --- a/icu_benchmarks/wandb_utils.py +++ b/icu_benchmarks/wandb_utils.py @@ -4,6 +4,8 @@ import wandb +from icu_benchmarks.run_utils import parse_dict + def wandb_running() -> bool: """Check if wandb is running.""" @@ -72,6 +74,9 @@ def set_wandb_experiment_name(args, mode): run_name += f"_train_size_{args.samples}_samples" elif args.complete_train: run_name += "_complete_training" + elif args.file_names: + file_names = parse_dict(args.file_names) + run_name += f"_outcome_{file_names['OUTCOME'].removesuffix('.parquet')}" if wandb_running(): wandb.config.update({"run-name": run_name}) From 2bb47aeeeb5073c7ef5072877bc8ce535cb06443 Mon Sep 17 00:00:00 2001 From: Robin van de Water Date: Wed, 16 Apr 2025 08:02:04 +0200 Subject: [PATCH 26/82] fix xgboost hyperparam binding --- icu_benchmarks/models/ml_models/xgboost.py | 9 +++++---- icu_benchmarks/run.py | 1 + 2 files changed, 6 insertions(+), 4 deletions(-) diff --git a/icu_benchmarks/models/ml_models/xgboost.py b/icu_benchmarks/models/ml_models/xgboost.py index 5565ddb6..ec41d407 100644 --- a/icu_benchmarks/models/ml_models/xgboost.py +++ b/icu_benchmarks/models/ml_models/xgboost.py @@ -67,12 +67,13 @@ def fit_model(self, train_data, train_labels, val_data, val_labels): def set_model_args(self, model, *args, **kwargs): """XGBoost signature does not include the hyperparams so we need to pass them manually.""" - signature = inspect.signature(model.__init__).parameters - valid_params = signature.keys() - + # signature = inspect.signature(model.__init__).parameters + # valid_params = signature.keys() + valid_params = model().get_params().keys() # Filter out invalid arguments valid_kwargs = {k: v for k, v in kwargs.items() if k in valid_params} - + if len(valid_kwargs) == 0: + logging.warning("No valid arguments passed to XGBoost") logging.debug(f"Creating model with: {valid_kwargs}.") return model(**valid_kwargs) diff --git a/icu_benchmarks/run.py b/icu_benchmarks/run.py index 10e5a05f..a04b6539 100644 --- a/icu_benchmarks/run.py +++ b/icu_benchmarks/run.py @@ -149,6 +149,7 @@ def main(my_args=tuple(sys.argv[1:])): # manually bind dataset files if args.file_names: + logging.info(f"Attempting to bind dataset files: {args.file_names}, type: {type(args.file_names)}") if isinstance(args.file_names, dict): logging.info(f"Will load data from {args.file_names}") gin.bind_parameter("preprocess.file_names", args.file_names) From d585996e51f950e1291631191fc090824808c3ba Mon Sep 17 00:00:00 2001 From: Robin van de Water Date: Wed, 16 Apr 2025 09:55:19 +0200 Subject: [PATCH 27/82] fix xgboost unsupported argument --- icu_benchmarks/models/ml_models/xgboost.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/icu_benchmarks/models/ml_models/xgboost.py b/icu_benchmarks/models/ml_models/xgboost.py index ec41d407..9e0f3b5c 100644 --- a/icu_benchmarks/models/ml_models/xgboost.py +++ b/icu_benchmarks/models/ml_models/xgboost.py @@ -26,7 +26,7 @@ class XGBClassifier(MLWrapper): def __init__(self, *args, **kwargs): self.model = self.set_model_args( - xgb.XGBClassifier, *args, **kwargs, eval_metric=log_loss, device="cpu", missing="inf", verbosity=0 + xgb.XGBClassifier, *args, **kwargs, eval_metric=log_loss, device="cpu", verbosity=0 ) super().__init__(*args, **kwargs) From 623ea09bfc6ccd435b0bfac0059235df397a6325 Mon Sep 17 00:00:00 2001 From: Robin van de Water Date: Wed, 14 May 2025 17:28:24 +0200 Subject: [PATCH 28/82] fix xgboost hyperparam binding --- icu_benchmarks/run_utils.py | 1 + 1 file changed, 1 insertion(+) diff --git a/icu_benchmarks/run_utils.py b/icu_benchmarks/run_utils.py index 5009626c..4e0ee84a 100644 --- a/icu_benchmarks/run_utils.py +++ b/icu_benchmarks/run_utils.py @@ -18,6 +18,7 @@ from icu_benchmarks.models.utils import JsonResultLoggingEncoder import polars as pl import random +from icu_benchmarks.wandb_utils import wandb_log def build_parser() -> ArgumentParser: From b01cf29f8d2b68f100216ec040dc73ee4b29627e Mon Sep 17 00:00:00 2001 From: Robin van de Water Date: Wed, 14 May 2025 17:28:56 +0200 Subject: [PATCH 29/82] outcome time experiment --- experiments/benchmark_cass.yml | 20 ++--- experiments/benchmark_cass_outcome_time.yml | 81 +++++++++++++++++++++ 2 files changed, 92 insertions(+), 9 deletions(-) create mode 100644 experiments/benchmark_cass_outcome_time.yml diff --git a/experiments/benchmark_cass.yml b/experiments/benchmark_cass.yml index 4c9c21b4..8a003f65 100644 --- a/experiments/benchmark_cass.yml +++ b/experiments/benchmark_cass.yml @@ -14,13 +14,13 @@ command: - -tn - SSI # - --hp-checkpoint -# - /sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/dataset_baseline_flat_2025-02-20T10:40:52/SSI/XGBClassifier/2025-02-20T11-29-27.323418/hyperparameter_tuning_logs.db +# - /sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/normal_ward_SSI-3_segment_1.0_horizon_6:00:00_transfer_full_2025-04-24T21:24:01_wearable_cut_off_0.75/SSI/XGBClassifier/2025-04-25T10-06-24.947526/hyperparameter_tuning_logs.db # - Mortality # - --verbose - --modalities - "all" - --file-names - - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc.parquet","STATIC":"sta.parquet"' + - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_6.parquet","STATIC":"sta.parquet"' # - --verbose # - -gc # - -lc @@ -29,7 +29,9 @@ name: yaib_classification_benchmark parameters: data_dir: values: - - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/ICU_and_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_1.0_horizon_6:00:00_transfer_full_2025-04-15T12:42:04" + - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/normal_ward_SSI-3_segment_1.0_horizon_6:00:00_transfer_full_2025-05-09T19:19:33_wearable_cut_off_0.75" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/ICU_and_normal_ward_SSI-3_segment_1.0_horizon_6:00:00_transfer_full_2025-04-22T21:19:32" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/ICU_and_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_1.0_horizon_6:00:00_transfer_full_2025-04-15T12:42:04" # - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/normal_ward_SSI-3_POPF_heamatoma_Galleleck_BDA_segment_1.0_horizon_6:00:00_transfer_full_2025-04-07T15:35:16" # - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/cohort_separation/1/" # - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/cohort_separation/2/" @@ -52,7 +54,7 @@ parameters: modalities: values: - "all" -# - [copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, cat_med_embeddings_map, cat_clinical_notes, static] + - [copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, cat_med_embeddings_map, cat_clinical_notes, static] # - [copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, cat_med_embeddings_map, wearable_activity, wearable_core, static] # - [copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, static] #, wearable_activity, wearable_core] # without wearable data # - [copra_observations, copra_scores, copra_fluid_balance, cat_med_embeddings_map, wearable_activity, wearable_core, static] # without ishmed lab data and clinical notes @@ -64,12 +66,12 @@ parameters: # - [static] file_names: values: - - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_3.parquet","STATIC":"sta.parquet"' +# - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_3.parquet","STATIC":"sta.parquet"' - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_6.parquet","STATIC":"sta.parquet"' - - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_9.parquet","STATIC":"sta.parquet"' - - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_12.parquet","STATIC":"sta.parquet"' - - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_18.parquet","STATIC":"sta.parquet"' - - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_24.parquet","STATIC":"sta.parquet"' +# - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_9.parquet","STATIC":"sta.parquet"' +# - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_12.parquet","STATIC":"sta.parquet"' +# - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_18.parquet","STATIC":"sta.parquet"' +# - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_24.parquet","STATIC":"sta.parquet"' seed: values: - 1111 diff --git a/experiments/benchmark_cass_outcome_time.yml b/experiments/benchmark_cass_outcome_time.yml new file mode 100644 index 00000000..30dcebf2 --- /dev/null +++ b/experiments/benchmark_cass_outcome_time.yml @@ -0,0 +1,81 @@ +command: + - ${env} + - ${program} + - train + - -d + - ../data/ + - -t +# - BinaryClassification + - CassClassification + - --log-dir + - /sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs + - --tune + - --wandb-sweep + - -tn + - SSI + - --hp-checkpoint + - /sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/normal_ward_SSI-3_segment_1.0_horizon_6:00:00_transfer_full_2025-04-24T21:24:01_wearable_cut_off_0.75/SSI/XGBClassifier/2025-04-25T10-06-24.947526/hyperparameter_tuning_logs.db +# - Mortality +# - --verbose + - --modalities + - "all" + - --file-names + - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_6.parquet","STATIC":"sta.parquet"' +# - --verbose +# - -gc +# - -lc +method: grid +name: yaib_classification_benchmark +parameters: + data_dir: + values: + - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/normal_ward_SSI-3_segment_1.0_horizon_6:00:00_transfer_full_2025-04-24T21:24:01_wearable_cut_off_0.75" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/ICU_and_normal_ward_SSI-3_segment_1.0_horizon_6:00:00_transfer_full_2025-04-22T21:19:32" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/ICU_and_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_1.0_horizon_6:00:00_transfer_full_2025-04-15T12:42:04" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/normal_ward_SSI-3_POPF_heamatoma_Galleleck_BDA_segment_1.0_horizon_6:00:00_transfer_full_2025-04-07T15:35:16" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/cohort_separation/1/" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/cohort_separation/2/" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/cohort_separation/3/" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/cohort_separation/4/" + model: + values: +# - LogisticRegression +# - TimesNet +# - LGBMClassifier + - XGBClassifier +# - CBClassifier +# - BRFClassifier +# - RUSBClassifier +# - RFClassifier +# - GRU +# - LSTM +# - TCN +# - Transformer + modalities: + values: + - "all" +# - [copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, cat_med_embeddings_map, cat_clinical_notes, static] +# - [copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, cat_med_embeddings_map, wearable_activity, wearable_core, static] +# - [copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, static] #, wearable_activity, wearable_core] # without wearable data +# - [copra_observations, copra_scores, copra_fluid_balance, cat_med_embeddings_map, wearable_activity, wearable_core, static] # without ishmed lab data and clinical notes +# - [copra_observations, copra_scores, copra_fluid_balance, cat_med_embeddings_map, static ] # without wearable data and ishmed lab data, clinical notes +## - [copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, cat_med_embeddings_map, wearable_activity, wearable_core, static] # without clinical notes +# - [copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, cat_clinical_notes, wearable_activity, wearable_core, static] # without med embeddings +# - [wearable_activity, wearable_core, static] +# - [ishmed_lab_numeric, static] +# - [static] + file_names: + values: + - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_3.parquet","STATIC":"sta.parquet"' +# - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_6.parquet","STATIC":"sta.parquet"' + - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_9.parquet","STATIC":"sta.parquet"' + - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_12.parquet","STATIC":"sta.parquet"' +# - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_18.parquet","STATIC":"sta.parquet"' + - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_24.parquet","STATIC":"sta.parquet"' + seed: + values: + - 1111 + use_pretrained_imputation: + values: + - None +program: icu-benchmarks \ No newline at end of file From 381a9687ab8af956144ae9a6d85f3c0cce162f88 Mon Sep 17 00:00:00 2001 From: Robin van de Water Date: Wed, 4 Jun 2025 09:55:33 +0200 Subject: [PATCH 30/82] delete ids (for explainer) --- icu_benchmarks/data/loader.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/icu_benchmarks/data/loader.py b/icu_benchmarks/data/loader.py index 892bf5df..83694dc7 100644 --- a/icu_benchmarks/data/loader.py +++ b/icu_benchmarks/data/loader.py @@ -178,11 +178,11 @@ def get_data_and_labels(self) -> Tuple[np.array, np.array, np.array]: # rep = rep.sort([self.vars["GROUP"], "counter"]) # rep = rep.to_numpy().astype(float) # Remove the first column from the rep array (group column) - # rep = rep[:, 1:] # needs to still be in there? logging.debug(f"rep shape: {rep.shape}") logging.debug(f"labels shape: {labels.shape}") rep = rep.to_numpy().astype(float) + rep = rep[:, 1:] return rep, labels, self.row_indicators.to_numpy() From 118e1cfbaaeeb6f8e78009e8c358c1721d724699 Mon Sep 17 00:00:00 2001 From: Robin van de Water Date: Wed, 4 Jun 2025 09:57:05 +0200 Subject: [PATCH 31/82] xgb gpu (not fully working yet) --- configs/prediction_models/XGBClassifier.gin | 2 +- .../prediction_models/XGBClassifierGPU.gin | 21 ++++++ icu_benchmarks/models/ml_models/xgboost.py | 67 +++++++++++++++++++ 3 files changed, 89 insertions(+), 1 deletion(-) create mode 100644 configs/prediction_models/XGBClassifierGPU.gin diff --git a/configs/prediction_models/XGBClassifier.gin b/configs/prediction_models/XGBClassifier.gin index f5d1a5a9..74099f09 100644 --- a/configs/prediction_models/XGBClassifier.gin +++ b/configs/prediction_models/XGBClassifier.gin @@ -16,6 +16,6 @@ model/hyperparameter.max_delta_step = [0, 1, 2, 3, 4, 5, 10] model/hyperparameter.colsample_bytree = [0.1, 0.25, 0.5, 0.75, 1.0] # model/hyperparameter.eval_metric = "aucpr" model/hyperparameter.gamma = [0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 2.0] -model/hyperparameter.early_stopping_rounds = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100] +# model/hyperparameter.early_stopping_rounds = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100] model/hyperparameter.reg_lambda = [0, 0.01, 0.1, 1, 10, 100] model/hyperparameter.reg_alpha = [0, 0.01, 0.1, 1, 10, 100] \ No newline at end of file diff --git a/configs/prediction_models/XGBClassifierGPU.gin b/configs/prediction_models/XGBClassifierGPU.gin new file mode 100644 index 00000000..482f9972 --- /dev/null +++ b/configs/prediction_models/XGBClassifierGPU.gin @@ -0,0 +1,21 @@ +# Settings for XGBoost classifier. + +# Common settings for ML models +include "configs/prediction_models/common/MLCommon.gin" + +# Train params +train_common.model = @XGBClassifierGPU + +model/hyperparameter.class_to_tune = @XGBClassifierGPU +model/hyperparameter.learning_rate = (0.01, 0.1, "log") +model/hyperparameter.n_estimators = [50, 100, 250, 500, 750, 1000,1500,2000, 2500, 3000, 3500, 4000, 4500, 5000] +model/hyperparameter.max_depth = [3, 5, 10, 15] +model/hyperparameter.scale_pos_weight = [1, 5, 10, 15, 20, 25, 30, 35, 40, 50, 75, 99, 100, 1000] +model/hyperparameter.min_child_weight = [0.1, 0.5, 1, 2, 5, 10] +model/hyperparameter.max_delta_step = [0, 1, 2, 3, 4, 5, 10] +model/hyperparameter.colsample_bytree = [0.1, 0.25, 0.5, 0.75, 1.0] +# model/hyperparameter.eval_metric = "aucpr" +model/hyperparameter.gamma = [0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 2.0] +# model/hyperparameter.early_stopping_rounds = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100] +model/hyperparameter.reg_lambda = [0, 0.01, 0.1, 1, 10, 100] +model/hyperparameter.reg_alpha = [0, 0.01, 0.1, 1, 10, 100] \ No newline at end of file diff --git a/icu_benchmarks/models/ml_models/xgboost.py b/icu_benchmarks/models/ml_models/xgboost.py index 9e0f3b5c..a8e9c020 100644 --- a/icu_benchmarks/models/ml_models/xgboost.py +++ b/icu_benchmarks/models/ml_models/xgboost.py @@ -89,3 +89,70 @@ def _explain_model(self, reps, labels): # if not hasattr(self.model, "feature_importances_"): # raise ValueError("Model has not been fit yet. Call fit_model() before getting feature importances.") # return self.model.feature_importances_ + +@gin.configurable +class XGBClassifierGPU(MLWrapper): + _supported_run_modes = [RunMode.classification] + _explain_values = False + + def __init__(self, *args, **kwargs): + # self.model = self.set_model_args( + # xgb, *args, **kwargs, eval_metric="logloss", tree_method="hist", device="cuda", verbosity=0 + # ) + self.model = xgb + self.params = { + "eval_metric": "logloss", + "tree_method": "hist", + "device": "cuda", + "verbosity": 0, + **kwargs, + } + super().__init__(*args, **kwargs) + + def predict(self, features): + """ + Predicts class probabilities for the given features. + + Args: + features: Input features for prediction. + + Returns: + numpy.ndarray: Predicted probabilities for each class. + """ + return self.model.predict(xgb.DMatrix(features)) + + def fit_model(self, train_data, train_labels, val_data, val_labels): + """ + Train the model using the XGBoost `train` method. + + Args: + train_data: Training features. + train_labels: Training labels. + val_data: Validation features. + val_labels: Validation labels. + + Returns: + float: Evaluation score on the validation set. + """ + dtrain = xgb.DMatrix(train_data, label=train_labels) + dval = xgb.DMatrix(val_data, label=val_labels) + evals = [(dtrain, "train"), (dval, "validation")] + + callbacks = [EarlyStopping(self.hparams.patience)] + + if wandb.run is not None: + callbacks.append(wandb_xgb()) + self.model.train( self.params,train_data=dtrain,evals=evals, callbacks=callbacks) + # self.model.fit(train_data, train_labels, eval_set=[(val_data, val_labels)], verbose=0) + + + shap_interaction_values = self.model.predict(dtrain) + # self.explainer = shap.TreeExplainer( + # self.model, dtrain, feature_perturbation="interventional", model_output="probability" + # ) + # if self.explain_features: + # logging.info("Explaining features") + # self.train_shap_values = self.explainer.shap_values(dtrain) + + eval_score = mean(next(iter(self.model.evals_result()["validation"].values()))) + return eval_score From 042ceb71453bef595facdd7797cee476ab4730f3 Mon Sep 17 00:00:00 2001 From: Robin van de Water Date: Wed, 4 Jun 2025 09:57:20 +0200 Subject: [PATCH 32/82] utils --- icu_benchmarks/utils.py | 22 ++++++++++++++++++++++ icu_benchmarks/wandb_utils.py | 2 +- 2 files changed, 23 insertions(+), 1 deletion(-) create mode 100644 icu_benchmarks/utils.py diff --git a/icu_benchmarks/utils.py b/icu_benchmarks/utils.py new file mode 100644 index 00000000..500afb53 --- /dev/null +++ b/icu_benchmarks/utils.py @@ -0,0 +1,22 @@ +import argparse +import json + + +def parse_dict(arg): + """ + Parses a string into a dictionary. Handles both: + - Unquoted format: 'key1:value1,key2:value2' + - JSON-like quoted format: '"key1":"value1","key2":"value2"' + """ + try: + # Check if the input is in JSON-like format + if ":" in arg and '"' in arg: + # Wrap in curly braces to make it valid JSON + json_string = f"{{{arg}}}" + return json.loads(json_string) + else: + # Handle unquoted format + pairs = arg.split(',') + return {key.strip(): value.strip() for key, value in (pair.split(':', 1) for pair in pairs)} + except Exception as e: + raise argparse.ArgumentTypeError(f"Invalid dictionary format: {e}") diff --git a/icu_benchmarks/wandb_utils.py b/icu_benchmarks/wandb_utils.py index 1d79d191..eae6d06f 100644 --- a/icu_benchmarks/wandb_utils.py +++ b/icu_benchmarks/wandb_utils.py @@ -4,7 +4,7 @@ import wandb -from icu_benchmarks.run_utils import parse_dict +from .utils import parse_dict def wandb_running() -> bool: From 2e419aad106967ed17f0da94f67f1db312edf344 Mon Sep 17 00:00:00 2001 From: Robin van de Water Date: Wed, 4 Jun 2025 10:12:07 +0200 Subject: [PATCH 33/82] explainer fixes --- icu_benchmarks/models/ml_models/xgboost.py | 6 +++--- icu_benchmarks/models/train.py | 12 ++++++++---- icu_benchmarks/models/wrappers.py | 4 ++++ 3 files changed, 15 insertions(+), 7 deletions(-) diff --git a/icu_benchmarks/models/ml_models/xgboost.py b/icu_benchmarks/models/ml_models/xgboost.py index a8e9c020..0292ee81 100644 --- a/icu_benchmarks/models/ml_models/xgboost.py +++ b/icu_benchmarks/models/ml_models/xgboost.py @@ -55,9 +55,9 @@ def fit_model(self, train_data, train_labels, val_data, val_labels): self.explainer = shap.TreeExplainer( self.model, train_data, feature_perturbation="interventional", model_output="probability" ) - if self.explain_features: - logging.info("Explaining features") - self.train_shap_values = self.explainer.shap_values(train_data) + # if self.explain_features: + # logging.info("Explaining features") + # self.train_shap_values = self.explainer.shap_values(train_data) # shap.summary_plot(shap_values, X_test, feature_names=features) # logging.info(self.model.get_booster().get_score(importance_type='weight')) # self.log_dict(self.model.get_booster().get_score(importance_type='weight')) diff --git a/icu_benchmarks/models/train.py b/icu_benchmarks/models/train.py index 1d5b225a..65f6a888 100644 --- a/icu_benchmarks/models/train.py +++ b/icu_benchmarks/models/train.py @@ -216,7 +216,7 @@ def persist_shap_data(trainer: Trainer, log_dir: Path): ): trained_columns.remove("stay_id" if "stay_id" in trained_columns else "id") logging.info(f"Saving SHAPS") - if trainer.lightning_module.explainer_values_test is not None: + if hasattr(trainer.lightning_module, "explainer_values_test"): # todo: abs values explainer_values = trainer.lightning_module.explainer_values_test # logging.info(f"{explainer_values.values.shape}") @@ -225,13 +225,17 @@ def persist_shap_data(trainer: Trainer, log_dir: Path): shaps_test = shaps_test.select(pl.all().mean()) with (log_dir / "explainer_values_test.parquet").open("wb") as f: shaps_test.write_parquet(f) - logging.debug(f"Saved explainer values to {log_dir / 'explainer_values_test.parquet'}") - if trainer.lightning_module.explainer_values_train is not None: + logging.debug(f"Saved test explainer values to {log_dir / 'explainer_values_test.parquet'}") + else: + logging.warning("No explainer values for test set found. Skipping saving.") + if hasattr(trainer.lightning_module, "explainer_values_train"): explainer_values = trainer.lightning_module.explainer_values_train # shaps_train = pl.DataFrame(schema=trainer.lightning_module.trained_columns, data=np.transpose(explainer_values.values)) - shaps_train = pl.DataFrame(schema=trained_columns, data=explainer_values.values) + shaps_train = pl.DataFrame(schema=trained_columns, data=explainer_values) with (log_dir / "explainer_values_train.parquet").open("wb") as f: shaps_train.write_parquet(f) + else: + logging.warning("No explainer values for train set found. Skipping saving.") except Exception as e: logging.error(f"Failed to save explainer values: {e}") diff --git a/icu_benchmarks/models/wrappers.py b/icu_benchmarks/models/wrappers.py index e4559fb2..c4b76062 100644 --- a/icu_benchmarks/models/wrappers.py +++ b/icu_benchmarks/models/wrappers.py @@ -48,6 +48,7 @@ class BaseModule(LightningModule): # Type of run mode run_mode = None debug = True + # We do not want to explain features by default as it is expensive (and not needed during hp tuning) explain_features = False def forward(self, *args, **kwargs): @@ -420,6 +421,9 @@ def fit(self, train_dataset, val_dataset): val_loss = self.fit_model(train_rep, train_label, val_rep, val_label) + if self.explain_features: + self.explainer_values_train = self._explain_model(train_rep, train_label) + train_pred = self.predict(train_rep) logging.debug(f"Model:{self.model}") From 1270d5ef601bdf869f60933eeafceb4c8d0a9d3d Mon Sep 17 00:00:00 2001 From: Robin van de Water Date: Wed, 4 Jun 2025 10:12:20 +0200 Subject: [PATCH 34/82] utils fix --- icu_benchmarks/run.py | 6 +++--- icu_benchmarks/run_utils.py | 25 +++---------------------- 2 files changed, 6 insertions(+), 25 deletions(-) diff --git a/icu_benchmarks/run.py b/icu_benchmarks/run.py index a04b6539..dbb12a35 100644 --- a/icu_benchmarks/run.py +++ b/icu_benchmarks/run.py @@ -18,8 +18,8 @@ setup_logging, import_preprocessor, name_datasets, - get_config_files, parse_dict, -) + get_config_files, ) +from icu_benchmarks.utils import parse_dict from icu_benchmarks.constants import RunMode @@ -134,7 +134,7 @@ def main(my_args=tuple(sys.argv[1:])): name_datasets(args.name, args.name, args.name) hp_checkpoint = log_dir / args.hp_checkpoint if args.hp_checkpoint else None model_path = ( - Path("configs") / ( + Path("configs") / ( "imputation_models" if mode == RunMode.imputation else "prediction_models") / f"{model}.gin" ) gin_config_files = ( diff --git a/icu_benchmarks/run_utils.py b/icu_benchmarks/run_utils.py index 4e0ee84a..6b6ab2bd 100644 --- a/icu_benchmarks/run_utils.py +++ b/icu_benchmarks/run_utils.py @@ -1,10 +1,8 @@ -import argparse import importlib import math import sys import warnings from math import sqrt -import re import gin import torch import json @@ -18,7 +16,9 @@ from icu_benchmarks.models.utils import JsonResultLoggingEncoder import polars as pl import random -from icu_benchmarks.wandb_utils import wandb_log + +from .utils import parse_dict +from .wandb_utils import wandb_log def build_parser() -> ArgumentParser: @@ -77,25 +77,6 @@ def build_parser() -> ArgumentParser: return parser -def parse_dict(arg): - """ - Parses a string into a dictionary. Handles both: - - Unquoted format: 'key1:value1,key2:value2' - - JSON-like quoted format: '"key1":"value1","key2":"value2"' - """ - try: - # Check if the input is in JSON-like format - if ":" in arg and '"' in arg: - # Wrap in curly braces to make it valid JSON - json_string = f"{{{arg}}}" - return json.loads(json_string) - else: - # Handle unquoted format - pairs = arg.split(',') - return {key.strip(): value.strip() for key, value in (pair.split(':', 1) for pair in pairs)} - except Exception as e: - raise argparse.ArgumentTypeError(f"Invalid dictionary format: {e}") - def create_run_dir(log_dir: Path, randomly_searched_params: str = None) -> Path: """Creates a log directory with the current time as name. From 4194538fc4030366d28f001b5470b8fbf96802a4 Mon Sep 17 00:00:00 2001 From: Robin van de Water Date: Wed, 4 Jun 2025 10:12:35 +0200 Subject: [PATCH 35/82] copy checkpoint --- icu_benchmarks/tuning/hyperparameters.py | 12 ++++++++---- 1 file changed, 8 insertions(+), 4 deletions(-) diff --git a/icu_benchmarks/tuning/hyperparameters.py b/icu_benchmarks/tuning/hyperparameters.py index 0800e0e1..f3a34514 100644 --- a/icu_benchmarks/tuning/hyperparameters.py +++ b/icu_benchmarks/tuning/hyperparameters.py @@ -1,4 +1,6 @@ import json +import shutil + import gin import logging from logging import NOTSET @@ -330,12 +332,14 @@ def bind_params_and_train(hyperparams): # Optuna study # Attempt checkpoint loading if checkpoint and checkpoint.exists(): - logging.warning(f"Hyperparameter checkpoint {checkpoint} does not exist.") + # logging.warning(f"Hyperparameter checkpoint {checkpoint} does not exist.") # logging.info("Attempting to find latest checkpoint file.") # checkpoint_path = find_checkpoint(log_dir.parent, checkpoint_file) - # Check if we found a checkpoint file - logging.info(f"Loading checkpoint at {checkpoint}") - study = optuna.load_study(study_name="tuning", storage="sqlite:///" + str(checkpoint), sampler=sampler, pruner=pruner) + # Check if we found a checkpoint file and copy it. + logging.info(f"Copying checkpoint and loading checkpoint at {checkpoint}") + local_path = log_dir / checkpoint_file + shutil.copy(str(checkpoint), local_path) + study = optuna.load_study(study_name="tuning", storage="sqlite:///" + str(local_path), sampler=sampler, pruner=pruner) n_calls = n_calls - len(study.trials) else: if checkpoint: From f1e7973993df659b562e5b0423818e2333295b14 Mon Sep 17 00:00:00 2001 From: Robin van de Water Date: Wed, 4 Jun 2025 10:13:01 +0200 Subject: [PATCH 36/82] matthews correlation (not implemented yet; thresholding needed) --- icu_benchmarks/models/constants.py | 7 +++++++ icu_benchmarks/models/custom_metrics.py | 18 +++++++++++++++++- 2 files changed, 24 insertions(+), 1 deletion(-) diff --git a/icu_benchmarks/models/constants.py b/icu_benchmarks/models/constants.py index 21f751ee..20ca2622 100644 --- a/icu_benchmarks/models/constants.py +++ b/icu_benchmarks/models/constants.py @@ -11,13 +11,16 @@ roc_curve, r2_score, mean_squared_error, + matthews_corrcoef ) +from torchmetrics import MatthewsCorrCoef from torchmetrics.classification import ( AUROC, AveragePrecision as TorchMetricsAveragePrecision, PrecisionRecallCurve as TorchMetricsPrecisionRecallCurve, CalibrationError, F1Score, + MatthewsCorrCoef ) from enum import Enum from icu_benchmarks.models.custom_metrics import ( @@ -30,6 +33,7 @@ sensitivity, specificity, positive_predictive_value, + binary_incidence ) @@ -46,6 +50,8 @@ class MLMetrics: # "Recall": recall_score, "Specificity": specificity, "PPV": positive_predictive_value, + # "MCC": matthews_corrcoef, + "Incidence": binary_incidence, } MULTICLASS_CLASSIFICATION = { @@ -71,6 +77,7 @@ class DLMetrics: "PR": AveragePrecision, "PR_Curve": PrecisionRecallCurve, "RO_Curve": RocCurve, + # "MCC": MatthewsCorrCoef } BINARY_CLASSIFICATION_TORCHMETRICS = { diff --git a/icu_benchmarks/models/custom_metrics.py b/icu_benchmarks/models/custom_metrics.py index 0669024b..0a1a27d9 100644 --- a/icu_benchmarks/models/custom_metrics.py +++ b/icu_benchmarks/models/custom_metrics.py @@ -4,7 +4,8 @@ import numpy as np from ignite.metrics import EpochMetric from numpy import ndarray -from sklearn.metrics import balanced_accuracy_score, mean_absolute_error, confusion_matrix as sk_confusion_matrix +from sklearn.metrics import (balanced_accuracy_score, mean_absolute_error, confusion_matrix as sk_confusion_matrix, + matthews_corrcoef) from sklearn.calibration import calibration_curve from scipy.spatial.distance import jensenshannon from torchmetrics.classification import BinaryFairness, Specificity @@ -145,6 +146,10 @@ def confusion_matrix(y_true: ndarray, y_pred: ndarray, normalize=False) -> torch confusion_dict[f"class_{i}_pred_{j}"] = confusion[i][j] return confusion_dict +def matthews_correlation_coefficient(y_true: ndarray, y_pred:ndarray, normalize=False) -> float: + if y_pred.ndim == 2: + y_pred = np.argmax(y_pred, axis=-1) + return matthews_corrcoef() class Sensitivity(EpochMetric): def __init__(self, output_transform: Callable = lambda x: x, check_compute_fn: bool = False) -> None: @@ -192,7 +197,18 @@ def positive_predictive_value(y_preds: torch.Tensor | np.ndarray, y_targets: tor tn, fp, fn, tp = sk_confusion_matrix(y_true, y_pred).ravel() return tp / (tp + fp) +def binary_incidence(y_preds, y_targets): + """ + Computes the binary incidence (proportion of positive labels). + + Args: + y_true (numpy.ndarray): Ground truth binary labels (0 or 1). + Returns: + float: Proportion of positive labels. + """ + y_true = np.rint(y_targets).astype(int) # Ensure binary labels + return np.sum(y_true) # from torchmetrics.classification import Specificity as TorchMetricsSpecificity # # class Specificity(EpochMetric): From c1385f96394b5d572a7527e5e9b927a012d79fd1 Mon Sep 17 00:00:00 2001 From: Robin van de Water Date: Wed, 4 Jun 2025 10:13:54 +0200 Subject: [PATCH 37/82] experiments --- experiments/benchmark_cass.yml | 29 ++++++++++++++++----- experiments/benchmark_cass_outcome_time.yml | 10 +++---- 2 files changed, 27 insertions(+), 12 deletions(-) diff --git a/experiments/benchmark_cass.yml b/experiments/benchmark_cass.yml index 8a003f65..c91d8840 100644 --- a/experiments/benchmark_cass.yml +++ b/experiments/benchmark_cass.yml @@ -14,6 +14,10 @@ command: - -tn - SSI # - --hp-checkpoint +# - /sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/normal_ward_SSI-3_segment_1.0_horizon_6:00:00_transfer_full_2025-05-27T23:34:00/SSI/XGBClassifier/2025-05-28T18-35-20.907325/hyperparameter_tuning_logs.db +# - /sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/normal_ward_SSI-3_segment_1.0_horizon_6:00:00_transfer_full_2025-05-09T19:19:33_wearable_cut_off_0.75/SSI/XGBClassifier/2025-05-12T17-24-38.032040/hyperparameter_tuning_logs.db +# - /sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/normal_ward_SSI-3_segment_1.0_horizon_6:00:00_transfer_full_2025-05-09T19:19:33_wearable_cut_off_0.75/SSI/XGBClassifier/2025-05-16T18-15-39.225575/hyperparameter_tuning_logs.db +# - /sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/normal_ward_SSI-3_segment_1.0_horizon_6:00:00_transfer_full_2025-05-09T19:19:33_wearable_cut_off_0.75/SSI/XGBClassifier/2025-05-16T18-15-39.225575/ # - /sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/normal_ward_SSI-3_segment_1.0_horizon_6:00:00_transfer_full_2025-04-24T21:24:01_wearable_cut_off_0.75/SSI/XGBClassifier/2025-04-25T10-06-24.947526/hyperparameter_tuning_logs.db # - Mortality # - --verbose @@ -29,7 +33,9 @@ name: yaib_classification_benchmark parameters: data_dir: values: - - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/normal_ward_SSI-3_segment_1.0_horizon_6:00:00_transfer_full_2025-05-09T19:19:33_wearable_cut_off_0.75" + - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/real_life_normal_ward_SSI-3_segment_1.0_horizon_6:00:00_transfer_full_2025-06-03T16:05:00" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/normal_ward_SSI-3_segment_1.0_horizon_6:00:00_transfer_full_2025-05-27T23:34:00" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/normal_ward_SSI-3_segment_1.0_horizon_6:00:00_transfer_full_2025-05-09T19:19:33_wearable_cut_off_0.75" # - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/ICU_and_normal_ward_SSI-3_segment_1.0_horizon_6:00:00_transfer_full_2025-04-22T21:19:32" # - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/ICU_and_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_1.0_horizon_6:00:00_transfer_full_2025-04-15T12:42:04" # - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/normal_ward_SSI-3_POPF_heamatoma_Galleleck_BDA_segment_1.0_horizon_6:00:00_transfer_full_2025-04-07T15:35:16" @@ -53,17 +59,26 @@ parameters: # - Transformer modalities: values: - - "all" - - [copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, cat_med_embeddings_map, cat_clinical_notes, static] -# - [copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, cat_med_embeddings_map, wearable_activity, wearable_core, static] +# - "all" +# - [copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, cat_clinical_notes, cat_clinical_notes, static] +# - [copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, cat_clinical_notes, wearable_activity, wearable_core, static] # - [copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, static] #, wearable_activity, wearable_core] # without wearable data -# - [copra_observations, copra_scores, copra_fluid_balance, cat_med_embeddings_map, wearable_activity, wearable_core, static] # without ishmed lab data and clinical notes -# - [copra_observations, copra_scores, copra_fluid_balance, cat_med_embeddings_map, static ] # without wearable data and ishmed lab data, clinical notes -## - [copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, cat_med_embeddings_map, wearable_activity, wearable_core, static] # without clinical notes +# - [copra_observations, copra_scores, copra_fluid_balance, cat_clinical_notes, wearable_activity, wearable_core, static] # without ishmed lab data and clinical notes +# - [copra_observations, copra_scores, copra_fluid_balance, cat_clinical_notes, static ] # without wearable data and ishmed lab data, clinical notes +# - [copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, cat_clinical_notes, wearable_activity, wearable_core, static] # without clinical notes # - [copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, cat_clinical_notes, wearable_activity, wearable_core, static] # without med embeddings # - [wearable_activity, wearable_core, static] # - [ishmed_lab_numeric, static] # - [static] + # 1 missing modality + - "all" + - [ copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, cat_medications, cat_clinical_notes, wearable_activity, wearable_ppgfeature, wearable_ppgembedding, wearable_core, static ] # not missing any + - [ copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, cat_clinical_notes, wearable_core, static ] # missing: wearable (activity, ppgfeature, ppgembedding) + - [ ishmed_lab_numeric, cat_clinical_notes, wearable_activity, wearable_ppgfeature, wearable_ppgembedding, wearable_core, static ] # missing: copra + - [ copra_observations, copra_scores, copra_fluid_balance, cat_clinical_notes, wearable_activity, wearable_ppgfeature, wearable_ppgembedding, wearable_core, static ] # 1 missing: ishmed (lab) + - [ copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, wearable_activity, wearable_ppgfeature, wearable_ppgembedding, wearable_core, static ] # missing cat (notes, med embeddings) + - [ copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, cat_clinical_notes, wearable_activity, wearable_ppgfeature, wearable_ppgembedding, static ] # 1 missing: wearable_core + - [ copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, cat_clinical_notes, wearable_activity, wearable_ppgfeature, wearable_ppgembedding, wearable_core ] # 1 missing: static file_names: values: # - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_3.parquet","STATIC":"sta.parquet"' diff --git a/experiments/benchmark_cass_outcome_time.yml b/experiments/benchmark_cass_outcome_time.yml index 30dcebf2..6074f42d 100644 --- a/experiments/benchmark_cass_outcome_time.yml +++ b/experiments/benchmark_cass_outcome_time.yml @@ -14,7 +14,7 @@ command: - -tn - SSI - --hp-checkpoint - - /sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/normal_ward_SSI-3_segment_1.0_horizon_6:00:00_transfer_full_2025-04-24T21:24:01_wearable_cut_off_0.75/SSI/XGBClassifier/2025-04-25T10-06-24.947526/hyperparameter_tuning_logs.db + - /sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/normal_ward_SSI-3_segment_1.0_horizon_6:00:00_transfer_full_2025-05-09T19:19:33_wearable_cut_off_0.75/SSI/XGBClassifier/2025-05-12T17-24-38.032040/hyperparameter_tuning_logs.db # - Mortality # - --verbose - --modalities @@ -29,7 +29,7 @@ name: yaib_classification_benchmark parameters: data_dir: values: - - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/normal_ward_SSI-3_segment_1.0_horizon_6:00:00_transfer_full_2025-04-24T21:24:01_wearable_cut_off_0.75" + - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/normal_ward_SSI-3_segment_1.0_horizon_6:00:00_transfer_full_2025-05-09T19:19:33_wearable_cut_off_0.75" # - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/ICU_and_normal_ward_SSI-3_segment_1.0_horizon_6:00:00_transfer_full_2025-04-22T21:19:32" # - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/ICU_and_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_1.0_horizon_6:00:00_transfer_full_2025-04-15T12:42:04" # - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/normal_ward_SSI-3_POPF_heamatoma_Galleleck_BDA_segment_1.0_horizon_6:00:00_transfer_full_2025-04-07T15:35:16" @@ -66,11 +66,11 @@ parameters: # - [static] file_names: values: - - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_3.parquet","STATIC":"sta.parquet"' -# - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_6.parquet","STATIC":"sta.parquet"' +# - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_3.parquet","STATIC":"sta.parquet"' + - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_6.parquet","STATIC":"sta.parquet"' - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_9.parquet","STATIC":"sta.parquet"' - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_12.parquet","STATIC":"sta.parquet"' -# - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_18.parquet","STATIC":"sta.parquet"' + - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_18.parquet","STATIC":"sta.parquet"' - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_24.parquet","STATIC":"sta.parquet"' seed: values: From 9d6ed4c0d8795697e6ef6242e5d01f24f0fd7c92 Mon Sep 17 00:00:00 2001 From: Robin van de Water Date: Wed, 4 Jun 2025 10:17:46 +0200 Subject: [PATCH 38/82] experiments --- alarm_toy.ipynb | 35 +++++++++++++++++++++++++++++++++++ 1 file changed, 35 insertions(+) diff --git a/alarm_toy.ipynb b/alarm_toy.ipynb index 26816595..8330987d 100644 --- a/alarm_toy.ipynb +++ b/alarm_toy.ipynb @@ -54,6 +54,41 @@ ], "execution_count": 1 }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-05-08T15:53:22.829692Z", + "start_time": "2025-05-08T15:53:22.801754Z" + } + }, + "cell_type": "code", + "source": [ + "from sklearn.metrics import matthews_corrcoef\n", + "import numpy as np\n", + "\n", + "ground_truth = np.array([1, 0, 1, 0, ])\n", + "predictions = np.array([1, 0, 0, 0, 0.9])\n", + "matthews_corrcoef(ground_truth, predictions)" + ], + "id": "22cb35f00419d404", + "outputs": [ + { + "ename": "ValueError", + "evalue": "continuous is not supported", + "output_type": "error", + "traceback": [ + "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m", + "\u001B[0;31mValueError\u001B[0m Traceback (most recent call last)", + "Cell \u001B[0;32mIn[3], line 5\u001B[0m\n\u001B[1;32m 3\u001B[0m ground_truth \u001B[38;5;241m=\u001B[39m np\u001B[38;5;241m.\u001B[39marray([\u001B[38;5;241m1\u001B[39m, \u001B[38;5;241m0\u001B[39m, \u001B[38;5;241m1\u001B[39m, \u001B[38;5;241m0\u001B[39m, \u001B[38;5;241m0.1\u001B[39m])\n\u001B[1;32m 4\u001B[0m predictions \u001B[38;5;241m=\u001B[39m np\u001B[38;5;241m.\u001B[39marray([\u001B[38;5;241m1\u001B[39m, \u001B[38;5;241m0\u001B[39m, \u001B[38;5;241m0\u001B[39m, \u001B[38;5;241m0\u001B[39m, \u001B[38;5;241m0.9\u001B[39m])\n\u001B[0;32m----> 5\u001B[0m \u001B[43mmatthews_corrcoef\u001B[49m\u001B[43m(\u001B[49m\u001B[43mground_truth\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mpredictions\u001B[49m\u001B[43m)\u001B[49m\n", + "File \u001B[0;32m/opt/miniconda3/envs/yaib/lib/python3.10/site-packages/sklearn/utils/_param_validation.py:213\u001B[0m, in \u001B[0;36mvalidate_params..decorator..wrapper\u001B[0;34m(*args, **kwargs)\u001B[0m\n\u001B[1;32m 207\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m 208\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m config_context(\n\u001B[1;32m 209\u001B[0m skip_parameter_validation\u001B[38;5;241m=\u001B[39m(\n\u001B[1;32m 210\u001B[0m prefer_skip_nested_validation \u001B[38;5;129;01mor\u001B[39;00m global_skip_validation\n\u001B[1;32m 211\u001B[0m )\n\u001B[1;32m 212\u001B[0m ):\n\u001B[0;32m--> 213\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mfunc\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 214\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m InvalidParameterError \u001B[38;5;28;01mas\u001B[39;00m e:\n\u001B[1;32m 215\u001B[0m \u001B[38;5;66;03m# When the function is just a wrapper around an estimator, we allow\u001B[39;00m\n\u001B[1;32m 216\u001B[0m \u001B[38;5;66;03m# the function to delegate validation to the estimator, but we replace\u001B[39;00m\n\u001B[1;32m 217\u001B[0m \u001B[38;5;66;03m# the name of the estimator by the name of the function in the error\u001B[39;00m\n\u001B[1;32m 218\u001B[0m \u001B[38;5;66;03m# message to avoid confusion.\u001B[39;00m\n\u001B[1;32m 219\u001B[0m msg \u001B[38;5;241m=\u001B[39m re\u001B[38;5;241m.\u001B[39msub(\n\u001B[1;32m 220\u001B[0m \u001B[38;5;124mr\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mparameter of \u001B[39m\u001B[38;5;124m\\\u001B[39m\u001B[38;5;124mw+ must be\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[1;32m 221\u001B[0m \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mparameter of \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mfunc\u001B[38;5;241m.\u001B[39m\u001B[38;5;18m__qualname__\u001B[39m\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m must be\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[1;32m 222\u001B[0m \u001B[38;5;28mstr\u001B[39m(e),\n\u001B[1;32m 223\u001B[0m )\n", + "File \u001B[0;32m/opt/miniconda3/envs/yaib/lib/python3.10/site-packages/sklearn/metrics/_classification.py:989\u001B[0m, in \u001B[0;36mmatthews_corrcoef\u001B[0;34m(y_true, y_pred, sample_weight)\u001B[0m\n\u001B[1;32m 918\u001B[0m \u001B[38;5;129m@validate_params\u001B[39m(\n\u001B[1;32m 919\u001B[0m {\n\u001B[1;32m 920\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124my_true\u001B[39m\u001B[38;5;124m\"\u001B[39m: [\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124marray-like\u001B[39m\u001B[38;5;124m\"\u001B[39m],\n\u001B[0;32m (...)\u001B[0m\n\u001B[1;32m 925\u001B[0m )\n\u001B[1;32m 926\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mmatthews_corrcoef\u001B[39m(y_true, y_pred, \u001B[38;5;241m*\u001B[39m, sample_weight\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m):\n\u001B[1;32m 927\u001B[0m \u001B[38;5;250m \u001B[39m\u001B[38;5;124;03m\"\"\"Compute the Matthews correlation coefficient (MCC).\u001B[39;00m\n\u001B[1;32m 928\u001B[0m \n\u001B[1;32m 929\u001B[0m \u001B[38;5;124;03m The Matthews correlation coefficient is used in machine learning as a\u001B[39;00m\n\u001B[0;32m (...)\u001B[0m\n\u001B[1;32m 987\u001B[0m \u001B[38;5;124;03m -0.33...\u001B[39;00m\n\u001B[1;32m 988\u001B[0m \u001B[38;5;124;03m \"\"\"\u001B[39;00m\n\u001B[0;32m--> 989\u001B[0m y_type, y_true, y_pred \u001B[38;5;241m=\u001B[39m \u001B[43m_check_targets\u001B[49m\u001B[43m(\u001B[49m\u001B[43my_true\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43my_pred\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 990\u001B[0m check_consistent_length(y_true, y_pred, sample_weight)\n\u001B[1;32m 991\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m y_type \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;129;01min\u001B[39;00m {\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mbinary\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mmulticlass\u001B[39m\u001B[38;5;124m\"\u001B[39m}:\n", + "File \u001B[0;32m/opt/miniconda3/envs/yaib/lib/python3.10/site-packages/sklearn/metrics/_classification.py:119\u001B[0m, in \u001B[0;36m_check_targets\u001B[0;34m(y_true, y_pred)\u001B[0m\n\u001B[1;32m 117\u001B[0m \u001B[38;5;66;03m# No metrics support \"multiclass-multioutput\" format\u001B[39;00m\n\u001B[1;32m 118\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m y_type \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;129;01min\u001B[39;00m [\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mbinary\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mmulticlass\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mmultilabel-indicator\u001B[39m\u001B[38;5;124m\"\u001B[39m]:\n\u001B[0;32m--> 119\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;132;01m{0}\u001B[39;00m\u001B[38;5;124m is not supported\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;241m.\u001B[39mformat(y_type))\n\u001B[1;32m 121\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m y_type \u001B[38;5;129;01min\u001B[39;00m [\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mbinary\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mmulticlass\u001B[39m\u001B[38;5;124m\"\u001B[39m]:\n\u001B[1;32m 122\u001B[0m xp, _ \u001B[38;5;241m=\u001B[39m get_namespace(y_true, y_pred)\n", + "\u001B[0;31mValueError\u001B[0m: continuous is not supported" + ] + } + ], + "execution_count": 3 + }, { "metadata": { "ExecuteTime": { From 5efa7d7e3b93f3810c84409418ffbaef04634084 Mon Sep 17 00:00:00 2001 From: Robin van de Water Date: Wed, 4 Jun 2025 10:51:01 +0200 Subject: [PATCH 39/82] ruff --- alarm_toy.ipynb | 473 +++++++++++---------- icu_benchmarks/data/split_process_data.py | 2 +- icu_benchmarks/models/constants.py | 6 +- icu_benchmarks/models/custom_metrics.py | 15 +- icu_benchmarks/models/ml_models/xgboost.py | 8 +- icu_benchmarks/models/wrappers.py | 36 +- icu_benchmarks/run.py | 18 +- icu_benchmarks/run_utils.py | 20 +- icu_benchmarks/utils.py | 4 +- 9 files changed, 306 insertions(+), 276 deletions(-) diff --git a/alarm_toy.ipynb b/alarm_toy.ipynb index 8330987d..fc796f29 100644 --- a/alarm_toy.ipynb +++ b/alarm_toy.ipynb @@ -2,25 +2,33 @@ "cells": [ { "cell_type": "code", + "execution_count": 1, "id": "initial_id", "metadata": { - "collapsed": true, "ExecuteTime": { "end_time": "2024-11-18T10:34:56.395941Z", "start_time": "2024-11-18T10:34:55.939403Z" - } + }, + "collapsed": true }, + "outputs": [ + { + "ename": "ModuleNotFoundError", + "evalue": "No module named 'numpy'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;66;03m# Sample data\u001b[39;00m\n\u001b[1;32m 4\u001b[0m data \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray([\n\u001b[1;32m 5\u001b[0m [\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m10\u001b[39m, \u001b[38;5;241m100\u001b[39m],\n\u001b[1;32m 6\u001b[0m [\u001b[38;5;241m2\u001b[39m, \u001b[38;5;241m20\u001b[39m, \u001b[38;5;241m200\u001b[39m],\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 9\u001b[0m [\u001b[38;5;241m3\u001b[39m, \u001b[38;5;241m50\u001b[39m, \u001b[38;5;241m500\u001b[39m]\n\u001b[1;32m 10\u001b[0m ])\n", + "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'numpy'" + ] + } + ], "source": [ "import numpy as np\n", "\n", "# Sample data\n", - "data = np.array([\n", - " [1, 10, 100],\n", - " [2, 20, 200],\n", - " [1, 30, 300],\n", - " [2, 40, 400],\n", - " [3, 50, 500]\n", - "])\n", + "data = np.array([[1, 10, 100], [2, 20, 200], [1, 30, 300], [2, 40, 400], [3, 50, 500]])\n", "\n", "# Column index for IDs\n", "id_column_index = 0\n", @@ -38,151 +46,150 @@ "# Print separated arrays\n", "for unique_id, array in separated_arrays.items():\n", " print(f\"ID {unique_id}:\\n{array}\\n\")" - ], - "outputs": [ - { - "ename": "ModuleNotFoundError", - "evalue": "No module named 'numpy'", - "output_type": "error", - "traceback": [ - "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m", - "\u001B[0;31mModuleNotFoundError\u001B[0m Traceback (most recent call last)", - "Cell \u001B[0;32mIn[1], line 1\u001B[0m\n\u001B[0;32m----> 1\u001B[0m \u001B[38;5;28;01mimport\u001B[39;00m \u001B[38;5;21;01mnumpy\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m \u001B[38;5;21;01mnp\u001B[39;00m\n\u001B[1;32m 3\u001B[0m \u001B[38;5;66;03m# Sample data\u001B[39;00m\n\u001B[1;32m 4\u001B[0m data \u001B[38;5;241m=\u001B[39m np\u001B[38;5;241m.\u001B[39marray([\n\u001B[1;32m 5\u001B[0m [\u001B[38;5;241m1\u001B[39m, \u001B[38;5;241m10\u001B[39m, \u001B[38;5;241m100\u001B[39m],\n\u001B[1;32m 6\u001B[0m [\u001B[38;5;241m2\u001B[39m, \u001B[38;5;241m20\u001B[39m, \u001B[38;5;241m200\u001B[39m],\n\u001B[0;32m (...)\u001B[0m\n\u001B[1;32m 9\u001B[0m [\u001B[38;5;241m3\u001B[39m, \u001B[38;5;241m50\u001B[39m, \u001B[38;5;241m500\u001B[39m]\n\u001B[1;32m 10\u001B[0m ])\n", - "\u001B[0;31mModuleNotFoundError\u001B[0m: No module named 'numpy'" - ] - } - ], - "execution_count": 1 + ] }, { + "cell_type": "code", + "execution_count": 3, + "id": "22cb35f00419d404", "metadata": { "ExecuteTime": { "end_time": "2025-05-08T15:53:22.829692Z", "start_time": "2025-05-08T15:53:22.801754Z" } }, - "cell_type": "code", - "source": [ - "from sklearn.metrics import matthews_corrcoef\n", - "import numpy as np\n", - "\n", - "ground_truth = np.array([1, 0, 1, 0, ])\n", - "predictions = np.array([1, 0, 0, 0, 0.9])\n", - "matthews_corrcoef(ground_truth, predictions)" - ], - "id": "22cb35f00419d404", "outputs": [ { "ename": "ValueError", "evalue": "continuous is not supported", "output_type": "error", "traceback": [ - "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m", - "\u001B[0;31mValueError\u001B[0m Traceback (most recent call last)", - "Cell \u001B[0;32mIn[3], line 5\u001B[0m\n\u001B[1;32m 3\u001B[0m ground_truth \u001B[38;5;241m=\u001B[39m np\u001B[38;5;241m.\u001B[39marray([\u001B[38;5;241m1\u001B[39m, \u001B[38;5;241m0\u001B[39m, \u001B[38;5;241m1\u001B[39m, \u001B[38;5;241m0\u001B[39m, \u001B[38;5;241m0.1\u001B[39m])\n\u001B[1;32m 4\u001B[0m predictions \u001B[38;5;241m=\u001B[39m np\u001B[38;5;241m.\u001B[39marray([\u001B[38;5;241m1\u001B[39m, \u001B[38;5;241m0\u001B[39m, \u001B[38;5;241m0\u001B[39m, \u001B[38;5;241m0\u001B[39m, \u001B[38;5;241m0.9\u001B[39m])\n\u001B[0;32m----> 5\u001B[0m \u001B[43mmatthews_corrcoef\u001B[49m\u001B[43m(\u001B[49m\u001B[43mground_truth\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mpredictions\u001B[49m\u001B[43m)\u001B[49m\n", - "File \u001B[0;32m/opt/miniconda3/envs/yaib/lib/python3.10/site-packages/sklearn/utils/_param_validation.py:213\u001B[0m, in \u001B[0;36mvalidate_params..decorator..wrapper\u001B[0;34m(*args, **kwargs)\u001B[0m\n\u001B[1;32m 207\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m 208\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m config_context(\n\u001B[1;32m 209\u001B[0m skip_parameter_validation\u001B[38;5;241m=\u001B[39m(\n\u001B[1;32m 210\u001B[0m prefer_skip_nested_validation \u001B[38;5;129;01mor\u001B[39;00m global_skip_validation\n\u001B[1;32m 211\u001B[0m )\n\u001B[1;32m 212\u001B[0m ):\n\u001B[0;32m--> 213\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mfunc\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 214\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m InvalidParameterError \u001B[38;5;28;01mas\u001B[39;00m e:\n\u001B[1;32m 215\u001B[0m \u001B[38;5;66;03m# When the function is just a wrapper around an estimator, we allow\u001B[39;00m\n\u001B[1;32m 216\u001B[0m \u001B[38;5;66;03m# the function to delegate validation to the estimator, but we replace\u001B[39;00m\n\u001B[1;32m 217\u001B[0m \u001B[38;5;66;03m# the name of the estimator by the name of the function in the error\u001B[39;00m\n\u001B[1;32m 218\u001B[0m \u001B[38;5;66;03m# message to avoid confusion.\u001B[39;00m\n\u001B[1;32m 219\u001B[0m msg \u001B[38;5;241m=\u001B[39m re\u001B[38;5;241m.\u001B[39msub(\n\u001B[1;32m 220\u001B[0m \u001B[38;5;124mr\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mparameter of \u001B[39m\u001B[38;5;124m\\\u001B[39m\u001B[38;5;124mw+ must be\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[1;32m 221\u001B[0m \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mparameter of \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mfunc\u001B[38;5;241m.\u001B[39m\u001B[38;5;18m__qualname__\u001B[39m\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m must be\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[1;32m 222\u001B[0m \u001B[38;5;28mstr\u001B[39m(e),\n\u001B[1;32m 223\u001B[0m )\n", - "File \u001B[0;32m/opt/miniconda3/envs/yaib/lib/python3.10/site-packages/sklearn/metrics/_classification.py:989\u001B[0m, in \u001B[0;36mmatthews_corrcoef\u001B[0;34m(y_true, y_pred, sample_weight)\u001B[0m\n\u001B[1;32m 918\u001B[0m \u001B[38;5;129m@validate_params\u001B[39m(\n\u001B[1;32m 919\u001B[0m {\n\u001B[1;32m 920\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124my_true\u001B[39m\u001B[38;5;124m\"\u001B[39m: [\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124marray-like\u001B[39m\u001B[38;5;124m\"\u001B[39m],\n\u001B[0;32m (...)\u001B[0m\n\u001B[1;32m 925\u001B[0m )\n\u001B[1;32m 926\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mmatthews_corrcoef\u001B[39m(y_true, y_pred, \u001B[38;5;241m*\u001B[39m, sample_weight\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m):\n\u001B[1;32m 927\u001B[0m \u001B[38;5;250m \u001B[39m\u001B[38;5;124;03m\"\"\"Compute the Matthews correlation coefficient (MCC).\u001B[39;00m\n\u001B[1;32m 928\u001B[0m \n\u001B[1;32m 929\u001B[0m \u001B[38;5;124;03m The Matthews correlation coefficient is used in machine learning as a\u001B[39;00m\n\u001B[0;32m (...)\u001B[0m\n\u001B[1;32m 987\u001B[0m \u001B[38;5;124;03m -0.33...\u001B[39;00m\n\u001B[1;32m 988\u001B[0m \u001B[38;5;124;03m \"\"\"\u001B[39;00m\n\u001B[0;32m--> 989\u001B[0m y_type, y_true, y_pred \u001B[38;5;241m=\u001B[39m \u001B[43m_check_targets\u001B[49m\u001B[43m(\u001B[49m\u001B[43my_true\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43my_pred\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 990\u001B[0m check_consistent_length(y_true, y_pred, sample_weight)\n\u001B[1;32m 991\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m y_type \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;129;01min\u001B[39;00m {\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mbinary\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mmulticlass\u001B[39m\u001B[38;5;124m\"\u001B[39m}:\n", - "File \u001B[0;32m/opt/miniconda3/envs/yaib/lib/python3.10/site-packages/sklearn/metrics/_classification.py:119\u001B[0m, in \u001B[0;36m_check_targets\u001B[0;34m(y_true, y_pred)\u001B[0m\n\u001B[1;32m 117\u001B[0m \u001B[38;5;66;03m# No metrics support \"multiclass-multioutput\" format\u001B[39;00m\n\u001B[1;32m 118\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m y_type \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;129;01min\u001B[39;00m [\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mbinary\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mmulticlass\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mmultilabel-indicator\u001B[39m\u001B[38;5;124m\"\u001B[39m]:\n\u001B[0;32m--> 119\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;132;01m{0}\u001B[39;00m\u001B[38;5;124m is not supported\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;241m.\u001B[39mformat(y_type))\n\u001B[1;32m 121\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m y_type \u001B[38;5;129;01min\u001B[39;00m [\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mbinary\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mmulticlass\u001B[39m\u001B[38;5;124m\"\u001B[39m]:\n\u001B[1;32m 122\u001B[0m xp, _ \u001B[38;5;241m=\u001B[39m get_namespace(y_true, y_pred)\n", - "\u001B[0;31mValueError\u001B[0m: continuous is not supported" + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[3], line 5\u001b[0m\n\u001b[1;32m 3\u001b[0m ground_truth \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray([\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m0.1\u001b[39m])\n\u001b[1;32m 4\u001b[0m predictions \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray([\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m0.9\u001b[39m])\n\u001b[0;32m----> 5\u001b[0m \u001b[43mmatthews_corrcoef\u001b[49m\u001b[43m(\u001b[49m\u001b[43mground_truth\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpredictions\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/opt/miniconda3/envs/yaib/lib/python3.10/site-packages/sklearn/utils/_param_validation.py:213\u001b[0m, in \u001b[0;36mvalidate_params..decorator..wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 207\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 208\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[1;32m 209\u001b[0m skip_parameter_validation\u001b[38;5;241m=\u001b[39m(\n\u001b[1;32m 210\u001b[0m prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[1;32m 211\u001b[0m )\n\u001b[1;32m 212\u001b[0m ):\n\u001b[0;32m--> 213\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 214\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m InvalidParameterError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 215\u001b[0m \u001b[38;5;66;03m# When the function is just a wrapper around an estimator, we allow\u001b[39;00m\n\u001b[1;32m 216\u001b[0m \u001b[38;5;66;03m# the function to delegate validation to the estimator, but we replace\u001b[39;00m\n\u001b[1;32m 217\u001b[0m \u001b[38;5;66;03m# the name of the estimator by the name of the function in the error\u001b[39;00m\n\u001b[1;32m 218\u001b[0m \u001b[38;5;66;03m# message to avoid confusion.\u001b[39;00m\n\u001b[1;32m 219\u001b[0m msg \u001b[38;5;241m=\u001b[39m re\u001b[38;5;241m.\u001b[39msub(\n\u001b[1;32m 220\u001b[0m \u001b[38;5;124mr\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparameter of \u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mw+ must be\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 221\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparameter of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__qualname__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m must be\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 222\u001b[0m \u001b[38;5;28mstr\u001b[39m(e),\n\u001b[1;32m 223\u001b[0m )\n", + "File \u001b[0;32m/opt/miniconda3/envs/yaib/lib/python3.10/site-packages/sklearn/metrics/_classification.py:989\u001b[0m, in \u001b[0;36mmatthews_corrcoef\u001b[0;34m(y_true, y_pred, sample_weight)\u001b[0m\n\u001b[1;32m 918\u001b[0m \u001b[38;5;129m@validate_params\u001b[39m(\n\u001b[1;32m 919\u001b[0m {\n\u001b[1;32m 920\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124my_true\u001b[39m\u001b[38;5;124m\"\u001b[39m: [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124marray-like\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 925\u001b[0m )\n\u001b[1;32m 926\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mmatthews_corrcoef\u001b[39m(y_true, y_pred, \u001b[38;5;241m*\u001b[39m, sample_weight\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 927\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Compute the Matthews correlation coefficient (MCC).\u001b[39;00m\n\u001b[1;32m 928\u001b[0m \n\u001b[1;32m 929\u001b[0m \u001b[38;5;124;03m The Matthews correlation coefficient is used in machine learning as a\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 987\u001b[0m \u001b[38;5;124;03m -0.33...\u001b[39;00m\n\u001b[1;32m 988\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 989\u001b[0m y_type, y_true, y_pred \u001b[38;5;241m=\u001b[39m \u001b[43m_check_targets\u001b[49m\u001b[43m(\u001b[49m\u001b[43my_true\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_pred\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 990\u001b[0m check_consistent_length(y_true, y_pred, sample_weight)\n\u001b[1;32m 991\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m y_type \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbinary\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmulticlass\u001b[39m\u001b[38;5;124m\"\u001b[39m}:\n", + "File \u001b[0;32m/opt/miniconda3/envs/yaib/lib/python3.10/site-packages/sklearn/metrics/_classification.py:119\u001b[0m, in \u001b[0;36m_check_targets\u001b[0;34m(y_true, y_pred)\u001b[0m\n\u001b[1;32m 117\u001b[0m \u001b[38;5;66;03m# No metrics support \"multiclass-multioutput\" format\u001b[39;00m\n\u001b[1;32m 118\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m y_type \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbinary\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmulticlass\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmultilabel-indicator\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n\u001b[0;32m--> 119\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{0}\u001b[39;00m\u001b[38;5;124m is not supported\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(y_type))\n\u001b[1;32m 121\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m y_type \u001b[38;5;129;01min\u001b[39;00m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbinary\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmulticlass\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n\u001b[1;32m 122\u001b[0m xp, _ \u001b[38;5;241m=\u001b[39m get_namespace(y_true, y_pred)\n", + "\u001b[0;31mValueError\u001b[0m: continuous is not supported" ] } ], - "execution_count": 3 + "source": [ + "from sklearn.metrics import matthews_corrcoef\n", + "import numpy as np\n", + "\n", + "ground_truth = np.array(\n", + " [\n", + " 1,\n", + " 0,\n", + " 1,\n", + " 0,\n", + " ]\n", + ")\n", + "predictions = np.array([1, 0, 0, 0, 0.9])\n", + "matthews_corrcoef(ground_truth, predictions)" + ] }, { + "cell_type": "code", + "execution_count": null, + "id": "cae5c1b3b95093db", "metadata": { "ExecuteTime": { "end_time": "2024-11-14T12:22:55.576737Z", "start_time": "2024-11-14T12:22:55.567569Z" } }, - "cell_type": "code", - "source": "", - "id": "cae5c1b3b95093db", "outputs": [], - "execution_count": null + "source": [] }, { + "cell_type": "code", + "execution_count": 2, + "id": "67be5cf6c6ac18f", "metadata": { "ExecuteTime": { "end_time": "2024-11-14T12:22:55.870267Z", "start_time": "2024-11-14T12:22:55.654565Z" } }, - "cell_type": "code", + "outputs": [], "source": [ "import polars as pl\n", + "\n", "preds = pl.read_csv(r\"C:\\Users\\Robin\\Downloads\\pred_indicators_only_complications.csv\")" - ], - "id": "67be5cf6c6ac18f", - "outputs": [], - "execution_count": 2 + ] }, { + "cell_type": "code", + "execution_count": 2, + "id": "a0abef17da834506", "metadata": { "ExecuteTime": { "end_time": "2024-11-14T12:22:55.901519Z", "start_time": "2024-11-14T12:22:55.888514Z" } }, - "cell_type": "code", - "source": "\n", - "id": "a0abef17da834506", "outputs": [], - "execution_count": 2 + "source": [] }, { + "cell_type": "code", + "execution_count": 3, + "id": "e988983945c055e1", "metadata": { "ExecuteTime": { "end_time": "2024-11-14T12:22:56.458349Z", "start_time": "2024-11-14T12:22:55.919572Z" } }, - "cell_type": "code", - "source": "predictions", - "id": "e988983945c055e1", "outputs": [ { "ename": "NameError", "evalue": "name 'predictions' is not defined", "output_type": "error", "traceback": [ - "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", - "\u001B[1;31mNameError\u001B[0m Traceback (most recent call last)", - "Cell \u001B[1;32mIn[3], line 1\u001B[0m\n\u001B[1;32m----> 1\u001B[0m \u001B[43mpredictions\u001B[49m\n", - "\u001B[1;31mNameError\u001B[0m: name 'predictions' is not defined" + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[3], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mpredictions\u001b[49m\n", + "\u001b[1;31mNameError\u001b[0m: name 'predictions' is not defined" ] } ], - "execution_count": 3 + "source": [ + "predictions" + ] }, { + "cell_type": "code", + "execution_count": null, + "id": "cf593fd5e8a42f4d", "metadata": { "ExecuteTime": { "end_time": "2024-11-14T12:22:56.539577400Z", "start_time": "2024-11-14T12:19:50.586084Z" } }, - "cell_type": "code", - "source": "", - "id": "cf593fd5e8a42f4d", "outputs": [], - "execution_count": null + "source": [] }, { + "cell_type": "code", + "execution_count": 4, + "id": "4781ed7dd0de97e4", "metadata": { "ExecuteTime": { "end_time": "2024-11-14T12:28:12.916074Z", "start_time": "2024-11-14T12:28:02.509602Z" } }, - "cell_type": "code", + "outputs": [], "source": [ "from numpy import genfromtxt\n", "from icu_benchmarks.models.alarm_metrics import silence_positives, fill_gaps, convert_to_alarm\n", "import numpy as np\n", "\n", - "predictions_no_threshold = genfromtxt(r\"C:\\Users\\Robin\\Downloads\\pred_indicators_only_complications.csv\", delimiter=',')\n", + "predictions_no_threshold = genfromtxt(r\"C:\\Users\\Robin\\Downloads\\pred_indicators_only_complications.csv\", delimiter=\",\")\n", "predictions = predictions_no_threshold.copy()\n", "# Define the threshold\n", "threshold = 0.5\n", @@ -219,26 +226,23 @@ "\n", "# print(\"Combined array:\")\n", "# print(combined_array)\n", - "# \n", + "#\n", "# print(f\"ROC AUC: {roc_auc_score(predictions[:, 2], predictions[:, 4])}\")\n", "# print(f\"ROC AUC: {roc_auc_score(combined_array[:, 2], combined_array[:, 4])}\")\n", "# print(f\"Average precision: {average_precision_score(predictions[:, 2], predictions[:, 4])}\")\n", "# print(f\"Average precision: {average_precision_score(combined_array[:, 2], combined_array[:, 4])}\")" - ], - "id": "4781ed7dd0de97e4", - "outputs": [], - "execution_count": 4 + ] }, { + "cell_type": "code", + "execution_count": 29, + "id": "a5a491f898213828", "metadata": { "ExecuteTime": { "end_time": "2024-11-14T12:22:56.539577400Z", "start_time": "2024-11-14T12:19:50.648226Z" } }, - "cell_type": "code", - "source": "predictions_no_threshold", - "id": "a5a491f898213828", "outputs": [ { "data": { @@ -257,18 +261,20 @@ "output_type": "execute_result" } ], - "execution_count": 29 + "source": [ + "predictions_no_threshold" + ] }, { + "cell_type": "code", + "execution_count": 30, + "id": "e3b2aedf47bce384", "metadata": { "ExecuteTime": { "end_time": "2024-11-14T12:22:56.539577400Z", "start_time": "2024-11-14T12:19:50.662767Z" } }, - "cell_type": "code", - "source": "predictions", - "id": "e3b2aedf47bce384", "outputs": [ { "data": { @@ -281,16 +287,33 @@ "output_type": "execute_result" } ], - "execution_count": 30 + "source": [ + "predictions" + ] }, { + "cell_type": "code", + "execution_count": 5, + "id": "4937d5d3f28a8a46", "metadata": { "ExecuteTime": { "end_time": "2024-11-14T12:28:13.430436Z", "start_time": "2024-11-14T12:28:12.932442Z" } }, - "cell_type": "code", + "outputs": [ + { + "ename": "IndexError", + "evalue": "too many indices for array: array is 1-dimensional, but 2 were indexed", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mIndexError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[5], line 12\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmetrics\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m PrecisionRecallDisplay, RocCurveDisplay, average_precision_score, roc_auc_score\n\u001b[0;32m 5\u001b[0m \u001b[38;5;66;03m# Assuming predictions, combined_array, and predictions_no_threshold are already defined\u001b[39;00m\n\u001b[0;32m 6\u001b[0m \u001b[38;5;66;03m# predictions[:, 2] is the ground truth\u001b[39;00m\n\u001b[0;32m 7\u001b[0m \u001b[38;5;66;03m# predictions[:, 4] is the original predictions\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 10\u001b[0m \n\u001b[0;32m 11\u001b[0m \u001b[38;5;66;03m# Calculate metrics\u001b[39;00m\n\u001b[1;32m---> 12\u001b[0m roc_auc_original \u001b[38;5;241m=\u001b[39m roc_auc_score(\u001b[43mpredictions\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m]\u001b[49m, predictions[:, \u001b[38;5;241m4\u001b[39m])\n\u001b[0;32m 13\u001b[0m roc_auc_modified \u001b[38;5;241m=\u001b[39m roc_auc_score(combined_array[:, \u001b[38;5;241m2\u001b[39m], combined_array[:, \u001b[38;5;241m4\u001b[39m])\n\u001b[0;32m 14\u001b[0m roc_auc_no_threshold \u001b[38;5;241m=\u001b[39m roc_auc_score(predictions[:, \u001b[38;5;241m2\u001b[39m], predictions_no_threshold[:, \u001b[38;5;241m4\u001b[39m])\n", + "\u001b[1;31mIndexError\u001b[0m: too many indices for array: array is 1-dimensional, but 2 were indexed" + ] + } + ], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", @@ -313,74 +336,62 @@ "# Plot ROC curves\n", "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 6))\n", "\n", - "RocCurveDisplay.from_predictions(predictions[:, 2], predictions[:, 4], ax=ax1, name='Original')\n", - "RocCurveDisplay.from_predictions(combined_array[:, 2], combined_array[:, 4], ax=ax1, name='Modified')\n", - "RocCurveDisplay.from_predictions(predictions[:, 2], predictions_no_threshold[:, 4], ax=ax1, name='No Threshold')\n", - "ax1.set_title(f'ROC Curve\\nOriginal AUC: {roc_auc_original:.2f}, Modified AUC: {roc_auc_modified:.2f}, No Threshold AUC: {roc_auc_no_threshold:.2f}')\n", + "RocCurveDisplay.from_predictions(predictions[:, 2], predictions[:, 4], ax=ax1, name=\"Original\")\n", + "RocCurveDisplay.from_predictions(combined_array[:, 2], combined_array[:, 4], ax=ax1, name=\"Modified\")\n", + "RocCurveDisplay.from_predictions(predictions[:, 2], predictions_no_threshold[:, 4], ax=ax1, name=\"No Threshold\")\n", + "ax1.set_title(\n", + " f\"ROC Curve\\nOriginal AUC: {roc_auc_original:.2f}, Modified AUC: {roc_auc_modified:.2f}, No Threshold AUC: {roc_auc_no_threshold:.2f}\"\n", + ")\n", "\n", "# Plot PRC curves\n", - "PrecisionRecallDisplay.from_predictions(predictions[:, 2], predictions[:, 4], ax=ax2, name='Original')\n", - "PrecisionRecallDisplay.from_predictions(combined_array[:, 2], combined_array[:, 4], ax=ax2, name='Modified')\n", - "PrecisionRecallDisplay.from_predictions(predictions[:, 2], predictions_no_threshold[:, 4], ax=ax2, name='No Threshold')\n", - "ax2.set_title(f'Precision-Recall Curve\\nOriginal AP: {average_precision_original:.2f}, Modified AP: {average_precision_modified:.2f}, No Threshold AP: {average_precision_no_threshold:.2f}')\n", + "PrecisionRecallDisplay.from_predictions(predictions[:, 2], predictions[:, 4], ax=ax2, name=\"Original\")\n", + "PrecisionRecallDisplay.from_predictions(combined_array[:, 2], combined_array[:, 4], ax=ax2, name=\"Modified\")\n", + "PrecisionRecallDisplay.from_predictions(predictions[:, 2], predictions_no_threshold[:, 4], ax=ax2, name=\"No Threshold\")\n", + "ax2.set_title(\n", + " f\"Precision-Recall Curve\\nOriginal AP: {average_precision_original:.2f}, Modified AP: {average_precision_modified:.2f}, No Threshold AP: {average_precision_no_threshold:.2f}\"\n", + ")\n", "\n", "plt.tight_layout()\n", "plt.show()" - ], - "id": "4937d5d3f28a8a46", - "outputs": [ - { - "ename": "IndexError", - "evalue": "too many indices for array: array is 1-dimensional, but 2 were indexed", - "output_type": "error", - "traceback": [ - "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", - "\u001B[1;31mIndexError\u001B[0m Traceback (most recent call last)", - "Cell \u001B[1;32mIn[5], line 12\u001B[0m\n\u001B[0;32m 3\u001B[0m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01msklearn\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mmetrics\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m PrecisionRecallDisplay, RocCurveDisplay, average_precision_score, roc_auc_score\n\u001B[0;32m 5\u001B[0m \u001B[38;5;66;03m# Assuming predictions, combined_array, and predictions_no_threshold are already defined\u001B[39;00m\n\u001B[0;32m 6\u001B[0m \u001B[38;5;66;03m# predictions[:, 2] is the ground truth\u001B[39;00m\n\u001B[0;32m 7\u001B[0m \u001B[38;5;66;03m# predictions[:, 4] is the original predictions\u001B[39;00m\n\u001B[1;32m (...)\u001B[0m\n\u001B[0;32m 10\u001B[0m \n\u001B[0;32m 11\u001B[0m \u001B[38;5;66;03m# Calculate metrics\u001B[39;00m\n\u001B[1;32m---> 12\u001B[0m roc_auc_original \u001B[38;5;241m=\u001B[39m roc_auc_score(\u001B[43mpredictions\u001B[49m\u001B[43m[\u001B[49m\u001B[43m:\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m2\u001B[39;49m\u001B[43m]\u001B[49m, predictions[:, \u001B[38;5;241m4\u001B[39m])\n\u001B[0;32m 13\u001B[0m roc_auc_modified \u001B[38;5;241m=\u001B[39m roc_auc_score(combined_array[:, \u001B[38;5;241m2\u001B[39m], combined_array[:, \u001B[38;5;241m4\u001B[39m])\n\u001B[0;32m 14\u001B[0m roc_auc_no_threshold \u001B[38;5;241m=\u001B[39m roc_auc_score(predictions[:, \u001B[38;5;241m2\u001B[39m], predictions_no_threshold[:, \u001B[38;5;241m4\u001B[39m])\n", - "\u001B[1;31mIndexError\u001B[0m: too many indices for array: array is 1-dimensional, but 2 were indexed" - ] - } - ], - "execution_count": 5 + ] }, { + "cell_type": "code", + "execution_count": 58, + "id": "6586e44915be74e3", "metadata": { "ExecuteTime": { "end_time": "2024-11-14T12:22:56.539577400Z", "start_time": "2024-11-12T17:04:57.239395Z" } }, - "cell_type": "code", - "source": [ - "from sklearn.metrics import PrecisionRecallDisplay, RocCurveDisplay\n", - "\n" - ], - "id": "6586e44915be74e3", "outputs": [ { "ename": "ImportError", "evalue": "cannot import name 'PrecisionRecallDisplay' from 'sklearn' (C:\\Users\\Robin\\miniconda\\envs\\yaib_19\\lib\\site-packages\\sklearn\\__init__.py)", "output_type": "error", "traceback": [ - "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", - "\u001B[1;31mImportError\u001B[0m Traceback (most recent call last)", - "Cell \u001B[1;32mIn[58], line 1\u001B[0m\n\u001B[1;32m----> 1\u001B[0m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01msklearn\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m PrecisionRecallDisplay, RocCurveDisplay\n", - "\u001B[1;31mImportError\u001B[0m: cannot import name 'PrecisionRecallDisplay' from 'sklearn' (C:\\Users\\Robin\\miniconda\\envs\\yaib_19\\lib\\site-packages\\sklearn\\__init__.py)" + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mImportError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[58], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m PrecisionRecallDisplay, RocCurveDisplay\n", + "\u001b[1;31mImportError\u001b[0m: cannot import name 'PrecisionRecallDisplay' from 'sklearn' (C:\\Users\\Robin\\miniconda\\envs\\yaib_19\\lib\\site-packages\\sklearn\\__init__.py)" ] } ], - "execution_count": 58 + "source": [ + "from sklearn.metrics import PrecisionRecallDisplay, RocCurveDisplay" + ] }, { + "cell_type": "code", + "execution_count": 34, + "id": "f81b74431b6e391a", "metadata": { "ExecuteTime": { "end_time": "2024-11-14T12:22:56.539577400Z", "start_time": "2024-11-12T16:57:47.562930Z" } }, - "cell_type": "code", - "source": "predictions", - "id": "f81b74431b6e391a", "outputs": [ { "data": { @@ -399,18 +410,20 @@ "output_type": "execute_result" } ], - "execution_count": 34 + "source": [ + "predictions" + ] }, { + "cell_type": "code", + "execution_count": 28, + "id": "20480d22741f38a5", "metadata": { "ExecuteTime": { "end_time": "2024-11-14T12:22:56.540541100Z", "start_time": "2024-11-12T16:54:27.852976Z" } }, - "cell_type": "code", - "source": "combined_array", - "id": "20480d22741f38a5", "outputs": [ { "data": { @@ -429,26 +442,28 @@ "output_type": "execute_result" } ], - "execution_count": 28 + "source": [ + "combined_array" + ] }, { - "metadata": {}, "cell_type": "code", - "outputs": [], "execution_count": null, - "source": "", - "id": "ed297983230629bd" + "id": "ed297983230629bd", + "metadata": {}, + "outputs": [], + "source": [] }, { + "cell_type": "code", + "execution_count": 21, + "id": "518b670047396a91", "metadata": { "ExecuteTime": { "end_time": "2024-11-14T12:22:56.540541100Z", "start_time": "2024-11-12T16:50:26.843347Z" } }, - "cell_type": "code", - "source": "group_array", - "id": "518b670047396a91", "outputs": [ { "data": { @@ -636,16 +651,32 @@ "output_type": "execute_result" } ], - "execution_count": 21 + "source": [ + "group_array" + ] }, { + "cell_type": "code", + "execution_count": 3, + "id": "39448152d16e0217", "metadata": { "ExecuteTime": { "end_time": "2024-11-14T12:22:56.541541800Z", "start_time": "2024-11-12T16:41:43.152975Z" } }, - "cell_type": "code", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ground truth: [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1]\n", + "Unaltered predictions: [0 0 1 0 1 0 0 0 0 1 1 0 1 0 1 1 0 1 1 0 1 0 0 0]\n", + "Silenced predictions: [0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 1 0 1 0 0 0]\n", + "Filled + silenced predictions: [0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 1 1 1 1 1 1 1 1 1]\n" + ] + } + ], "source": [ "pred_len = 24\n", "label_horizon = 6\n", @@ -669,86 +700,86 @@ "# Fill gaps in the predictions: we also want to consider early predictions with the grace_horizon.\n", "predictions = fill_gaps(predictions)\n", "print(f\"Filled + silenced predictions: {predictions}\")" - ], - "id": "39448152d16e0217", - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ground truth: [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1]\n", - "Unaltered predictions: [0 0 1 0 1 0 0 0 0 1 1 0 1 0 1 1 0 1 1 0 1 0 0 0]\n", - "Silenced predictions: [0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 1 0 1 0 0 0]\n", - "Filled + silenced predictions: [0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 1 1 1 1 1 1 1 1 1]\n" - ] - } - ], - "execution_count": 3 + ] }, { + "cell_type": "code", + "execution_count": 4, + "id": "a46d26884039d6d1", "metadata": { "ExecuteTime": { "end_time": "2024-11-14T12:22:56.541541800Z", "start_time": "2024-11-12T16:41:45.201Z" } }, - "cell_type": "code", - "source": "", - "id": "a46d26884039d6d1", "outputs": [ { "ename": "NameError", "evalue": "name 'p' is not defined", "output_type": "error", "traceback": [ - "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", - "\u001B[1;31mNameError\u001B[0m Traceback (most recent call last)", - "Cell \u001B[1;32mIn[4], line 1\u001B[0m\n\u001B[1;32m----> 1\u001B[0m preds[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mprediction_0\u001B[39m\u001B[38;5;124m\"\u001B[39m] \u001B[38;5;241m=\u001B[39m \u001B[43mp\u001B[49m\n", - "\u001B[1;31mNameError\u001B[0m: name 'p' is not defined" + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[4], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m preds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mprediction_0\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[43mp\u001b[49m\n", + "\u001b[1;31mNameError\u001b[0m: name 'p' is not defined" ] } ], - "execution_count": 4 + "source": [] }, { + "cell_type": "code", + "execution_count": 7, + "id": "27c9b104e3a52992", "metadata": { "ExecuteTime": { "end_time": "2024-11-14T12:22:56.541541800Z", "start_time": "2024-11-12T16:43:19.523441Z" } }, - "cell_type": "code", - "source": "preds.group_by(\"# id\").map_groups(lambda group_df: pl.DataFrame(group_df[\"time\"],convert_to_alarm(group_df[\"ground_truth\"], group_df[\"prediction_1\"])))", - "id": "27c9b104e3a52992", "outputs": [ { "ename": "PanicException", "evalue": "UDF failed: data does not match the number of columns", "output_type": "error", "traceback": [ - "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", - "\u001B[1;31mPanicException\u001B[0m Traceback (most recent call last)", - "Cell \u001B[1;32mIn[7], line 1\u001B[0m\n\u001B[1;32m----> 1\u001B[0m \u001B[43mpreds\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mgroup_by\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43m# id\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mmap_groups\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43;01mlambda\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[43mgroup_df\u001B[49m\u001B[43m:\u001B[49m\u001B[43m \u001B[49m\u001B[43mpl\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mDataFrame\u001B[49m\u001B[43m(\u001B[49m\u001B[43mgroup_df\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mtime\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m]\u001B[49m\u001B[43m,\u001B[49m\u001B[43mconvert_to_alarm\u001B[49m\u001B[43m(\u001B[49m\u001B[43mgroup_df\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mground_truth\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m]\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mgroup_df\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mprediction_1\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m]\u001B[49m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m\n", - "File \u001B[1;32m~\\miniconda\\envs\\yaib_19\\lib\\site-packages\\polars\\dataframe\\group_by.py:302\u001B[0m, in \u001B[0;36mGroupBy.map_groups\u001B[1;34m(self, function)\u001B[0m\n\u001B[0;32m 298\u001B[0m msg \u001B[38;5;241m=\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mcannot call `map_groups` when grouping by an expression\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m 299\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mTypeError\u001B[39;00m(msg)\n\u001B[0;32m 301\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mdf\u001B[38;5;241m.\u001B[39m\u001B[38;5;18m__class__\u001B[39m\u001B[38;5;241m.\u001B[39m_from_pydf(\n\u001B[1;32m--> 302\u001B[0m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mdf\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_df\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mgroup_by_map_groups\u001B[49m\u001B[43m(\u001B[49m\n\u001B[0;32m 303\u001B[0m \u001B[43m \u001B[49m\u001B[38;5;28;43mlist\u001B[39;49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mby\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mfunction\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mmaintain_order\u001B[49m\n\u001B[0;32m 304\u001B[0m \u001B[43m \u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 305\u001B[0m )\n", - "\u001B[1;31mPanicException\u001B[0m: UDF failed: data does not match the number of columns" + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mPanicException\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[7], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mpreds\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgroup_by\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m# id\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmap_groups\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mgroup_df\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mpl\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mDataFrame\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgroup_df\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtime\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43mconvert_to_alarm\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgroup_df\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mground_truth\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgroup_df\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mprediction_1\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32m~\\miniconda\\envs\\yaib_19\\lib\\site-packages\\polars\\dataframe\\group_by.py:302\u001b[0m, in \u001b[0;36mGroupBy.map_groups\u001b[1;34m(self, function)\u001b[0m\n\u001b[0;32m 298\u001b[0m msg \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcannot call `map_groups` when grouping by an expression\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 299\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(msg)\n\u001b[0;32m 301\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdf\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m_from_pydf(\n\u001b[1;32m--> 302\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_df\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgroup_by_map_groups\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 303\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mby\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfunction\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmaintain_order\u001b[49m\n\u001b[0;32m 304\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 305\u001b[0m )\n", + "\u001b[1;31mPanicException\u001b[0m: UDF failed: data does not match the number of columns" ] } ], - "execution_count": 7 + "source": [ + "preds.group_by(\"# id\").map_groups(\n", + " lambda group_df: pl.DataFrame(group_df[\"time\"], convert_to_alarm(group_df[\"ground_truth\"], group_df[\"prediction_1\"]))\n", + ")" + ] }, { + "cell_type": "code", + "execution_count": 6, + "id": "f1b7d2a0fabeeb70", "metadata": { "ExecuteTime": { "end_time": "2024-11-14T12:22:56.541541800Z", "start_time": "2024-11-12T16:42:52.394724Z" } }, - "cell_type": "code", - "source": "preds", - "id": "f1b7d2a0fabeeb70", "outputs": [ { "data": { + "text/html": [ + "
\n", + "shape: (3_536, 5)
# idtimeground_truthprediction_0prediction_1
i64i64f64f64f64
4200.01.00.0
4210.01.00.0
4220.01.00.0
4230.01.00.0
4240.01.00.0
……………
10441711.00.0060.994
10441721.00.0070.993
10441731.00.0410.959
10441741.00.0270.973
10441751.00.020.98
" + ], "text/plain": [ "shape: (3_536, 5)\n", "┌──────┬──────┬──────────────┬──────────────┬──────────────┐\n", @@ -768,16 +799,6 @@ "│ 1044 ┆ 174 ┆ 1.0 ┆ 0.027 ┆ 0.973 │\n", "│ 1044 ┆ 175 ┆ 1.0 ┆ 0.02 ┆ 0.98 │\n", "└──────┴──────┴──────────────┴──────────────┴──────────────┘" - ], - "text/html": [ - "
\n", - "shape: (3_536, 5)
# idtimeground_truthprediction_0prediction_1
i64i64f64f64f64
4200.01.00.0
4210.01.00.0
4220.01.00.0
4230.01.00.0
4240.01.00.0
……………
10441711.00.0060.994
10441721.00.0070.993
10441731.00.0410.959
10441741.00.0270.973
10441751.00.020.98
" ] }, "execution_count": 6, @@ -785,37 +806,52 @@ "output_type": "execute_result" } ], - "execution_count": 6 + "source": [ + "preds" + ] }, { + "cell_type": "code", + "execution_count": 5, + "id": "77488f552d1480d0", "metadata": { "ExecuteTime": { "end_time": "2025-01-09T11:35:07.132903Z", "start_time": "2025-01-09T11:35:07.129738Z" } }, - "cell_type": "code", + "outputs": [], "source": [ "import polars as pl\n", - "values = pl.read_parquet(\"/Users/robin/Documents/git/yaib_logs/mimic_demo/Mortality24/XGBClassifier/2025-01-09T12-33-43.607662/repetition_4/fold_4/explainer_values_test.parquet\")" - ], - "id": "77488f552d1480d0", - "outputs": [], - "execution_count": 5 + "\n", + "values = pl.read_parquet(\n", + " \"/Users/robin/Documents/git/yaib_logs/mimic_demo/Mortality24/XGBClassifier/2025-01-09T12-33-43.607662/repetition_4/fold_4/explainer_values_test.parquet\"\n", + ")" + ] }, { + "cell_type": "code", + "execution_count": 6, + "id": "7dbd2435574a6215", "metadata": { "ExecuteTime": { "end_time": "2025-01-09T11:35:09.020311Z", "start_time": "2025-01-09T11:35:09.016349Z" } }, - "cell_type": "code", - "source": "values.sum()", - "id": "7dbd2435574a6215", "outputs": [ { "data": { + "text/html": [ + "
\n", + "shape: (1, 296)
albalpaltastbebicarbilibili_dirbndbuncacaickckmbclcreacrpdbpfgnfio2gluhgbhrinr_ptklactlymphmapmchmchcmcvmethbmgnaneuto2satpco2…fio2_mean_histglu_mean_histhgb_mean_histhr_mean_histinr_pt_mean_histk_mean_histlact_mean_histlymph_mean_histmap_mean_histmch_mean_histmchc_mean_histmcv_mean_histmethb_mean_histmg_mean_histna_mean_histneut_mean_histo2sat_mean_histpco2_mean_histph_mean_histphos_mean_histplt_mean_histpo2_mean_histptt_mean_histresp_mean_histsbp_mean_histtemp_mean_histtnt_mean_histurine_mean_histwbc_mean_histagesexheightweightMissingIndicator_ageMissingIndicator_sexMissingIndicator_heightMissingIndicator_weight
f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32…f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32
0.00.0785830.00.0-0.258690.00.00.00.00.3978972.458770.00.00.00.00.00.00.00.00.00.00.00.0-0.2626310.00.00.0-3.5106580.116930.00.00.0-0.7752580.00.00.00.643269…0.0-0.565497-0.2239260.00.00.00.00.00.2006590.00.00.00.00.22341-0.4922790.00.00.00.00.00.0-5.3272160.00.00.02.4498270.0-0.5833121.3199810.00.00.00.7442510.00.00.00.0
" + ], "text/plain": [ "shape: (1, 296)\n", "┌─────┬──────────┬─────┬─────┬───┬────────────────┬────────────────┬───────────────┬───────────────┐\n", @@ -826,16 +862,6 @@ "╞═════╪══════════╪═════╪═════╪═══╪════════════════╪════════════════╪═══════════════╪═══════════════╡\n", "│ 0.0 ┆ 0.078583 ┆ 0.0 ┆ 0.0 ┆ … ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 0.0 │\n", "└─────┴──────────┴─────┴─────┴───┴────────────────┴────────────────┴───────────────┴───────────────┘" - ], - "text/html": [ - "
\n", - "shape: (1, 296)
albalpaltastbebicarbilibili_dirbndbuncacaickckmbclcreacrpdbpfgnfio2gluhgbhrinr_ptklactlymphmapmchmchcmcvmethbmgnaneuto2satpco2…fio2_mean_histglu_mean_histhgb_mean_histhr_mean_histinr_pt_mean_histk_mean_histlact_mean_histlymph_mean_histmap_mean_histmch_mean_histmchc_mean_histmcv_mean_histmethb_mean_histmg_mean_histna_mean_histneut_mean_histo2sat_mean_histpco2_mean_histph_mean_histphos_mean_histplt_mean_histpo2_mean_histptt_mean_histresp_mean_histsbp_mean_histtemp_mean_histtnt_mean_histurine_mean_histwbc_mean_histagesexheightweightMissingIndicator_ageMissingIndicator_sexMissingIndicator_heightMissingIndicator_weight
f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32…f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32
0.00.0785830.00.0-0.258690.00.00.00.00.3978972.458770.00.00.00.00.00.00.00.00.00.00.00.0-0.2626310.00.00.0-3.5106580.116930.00.00.0-0.7752580.00.00.00.643269…0.0-0.565497-0.2239260.00.00.00.00.00.2006590.00.00.00.00.22341-0.4922790.00.00.00.00.00.0-5.3272160.00.00.02.4498270.0-0.5833121.3199810.00.00.00.7442510.00.00.00.0
" ] }, "execution_count": 6, @@ -843,33 +869,20 @@ "output_type": "execute_result" } ], - "execution_count": 6 + "source": [ + "values.sum()" + ] }, { + "cell_type": "code", + "execution_count": 7, + "id": "cac7ef7572cda8b7", "metadata": { "ExecuteTime": { "end_time": "2025-01-09T11:35:13.397032Z", "start_time": "2025-01-09T11:35:12.567741Z" } }, - "cell_type": "code", - "source": [ - "import matplotlib.pyplot as plt\n", - "# Sum the values for aggregation\n", - "aggregated_data = values.sum()\n", - "\n", - "# Convert to Pandas DataFrame for plotting\n", - "aggregated_data_pd = aggregated_data.to_pandas()\n", - "\n", - "# Plot the bar plot\n", - "plt.figure(figsize=(10, 6))\n", - "aggregated_data_pd.plot(kind='bar')\n", - "plt.title('Aggregated Data')\n", - "plt.xlabel('Columns')\n", - "plt.ylabel('Sum')\n", - "plt.show()" - ], - "id": "cac7ef7572cda8b7", "outputs": [ { "data": { @@ -882,16 +895,32 @@ }, { "data": { + "image/png": "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", "text/plain": [ "
" - ], - "image/png": "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" + ] }, "metadata": {}, "output_type": "display_data" } ], - "execution_count": 7 + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "# Sum the values for aggregation\n", + "aggregated_data = values.sum()\n", + "\n", + "# Convert to Pandas DataFrame for plotting\n", + "aggregated_data_pd = aggregated_data.to_pandas()\n", + "\n", + "# Plot the bar plot\n", + "plt.figure(figsize=(10, 6))\n", + "aggregated_data_pd.plot(kind=\"bar\")\n", + "plt.title(\"Aggregated Data\")\n", + "plt.xlabel(\"Columns\")\n", + "plt.ylabel(\"Sum\")\n", + "plt.show()" + ] } ], "metadata": { diff --git a/icu_benchmarks/data/split_process_data.py b/icu_benchmarks/data/split_process_data.py index ad6ef614..48633eff 100644 --- a/icu_benchmarks/data/split_process_data.py +++ b/icu_benchmarks/data/split_process_data.py @@ -139,7 +139,7 @@ def preprocess_data( logging.info(f"Loaded data: {list(data.keys())}") data, vars = check_sanitize_data(data, vars) - if (Segment.dynamic not in data.keys()): + if Segment.dynamic not in data.keys(): logging.warning("No dynamic data found, using only static data.") logging.debug(f"Modality mapping: {modality_mapping}") diff --git a/icu_benchmarks/models/constants.py b/icu_benchmarks/models/constants.py index 20ca2622..8c192239 100644 --- a/icu_benchmarks/models/constants.py +++ b/icu_benchmarks/models/constants.py @@ -11,7 +11,7 @@ roc_curve, r2_score, mean_squared_error, - matthews_corrcoef + matthews_corrcoef, ) from torchmetrics import MatthewsCorrCoef from torchmetrics.classification import ( @@ -20,7 +20,7 @@ PrecisionRecallCurve as TorchMetricsPrecisionRecallCurve, CalibrationError, F1Score, - MatthewsCorrCoef + MatthewsCorrCoef, ) from enum import Enum from icu_benchmarks.models.custom_metrics import ( @@ -33,7 +33,7 @@ sensitivity, specificity, positive_predictive_value, - binary_incidence + binary_incidence, ) diff --git a/icu_benchmarks/models/custom_metrics.py b/icu_benchmarks/models/custom_metrics.py index 0a1a27d9..6b1ea344 100644 --- a/icu_benchmarks/models/custom_metrics.py +++ b/icu_benchmarks/models/custom_metrics.py @@ -4,8 +4,12 @@ import numpy as np from ignite.metrics import EpochMetric from numpy import ndarray -from sklearn.metrics import (balanced_accuracy_score, mean_absolute_error, confusion_matrix as sk_confusion_matrix, - matthews_corrcoef) +from sklearn.metrics import ( + balanced_accuracy_score, + mean_absolute_error, + confusion_matrix as sk_confusion_matrix, + matthews_corrcoef, +) from sklearn.calibration import calibration_curve from scipy.spatial.distance import jensenshannon from torchmetrics.classification import BinaryFairness, Specificity @@ -146,11 +150,13 @@ def confusion_matrix(y_true: ndarray, y_pred: ndarray, normalize=False) -> torch confusion_dict[f"class_{i}_pred_{j}"] = confusion[i][j] return confusion_dict -def matthews_correlation_coefficient(y_true: ndarray, y_pred:ndarray, normalize=False) -> float: + +def matthews_correlation_coefficient(y_true: ndarray, y_pred: ndarray, normalize=False) -> float: if y_pred.ndim == 2: y_pred = np.argmax(y_pred, axis=-1) return matthews_corrcoef() + class Sensitivity(EpochMetric): def __init__(self, output_transform: Callable = lambda x: x, check_compute_fn: bool = False) -> None: super(Sensitivity, self).__init__( @@ -197,6 +203,7 @@ def positive_predictive_value(y_preds: torch.Tensor | np.ndarray, y_targets: tor tn, fp, fn, tp = sk_confusion_matrix(y_true, y_pred).ravel() return tp / (tp + fp) + def binary_incidence(y_preds, y_targets): """ Computes the binary incidence (proportion of positive labels). @@ -209,6 +216,8 @@ def binary_incidence(y_preds, y_targets): """ y_true = np.rint(y_targets).astype(int) # Ensure binary labels return np.sum(y_true) + + # from torchmetrics.classification import Specificity as TorchMetricsSpecificity # # class Specificity(EpochMetric): diff --git a/icu_benchmarks/models/ml_models/xgboost.py b/icu_benchmarks/models/ml_models/xgboost.py index 0292ee81..805fb61c 100644 --- a/icu_benchmarks/models/ml_models/xgboost.py +++ b/icu_benchmarks/models/ml_models/xgboost.py @@ -25,9 +25,7 @@ class XGBClassifier(MLWrapper): _explain_values = False def __init__(self, *args, **kwargs): - self.model = self.set_model_args( - xgb.XGBClassifier, *args, **kwargs, eval_metric=log_loss, device="cpu", verbosity=0 - ) + self.model = self.set_model_args(xgb.XGBClassifier, *args, **kwargs, eval_metric=log_loss, device="cpu", verbosity=0) super().__init__(*args, **kwargs) def predict(self, features): @@ -90,6 +88,7 @@ def _explain_model(self, reps, labels): # raise ValueError("Model has not been fit yet. Call fit_model() before getting feature importances.") # return self.model.feature_importances_ + @gin.configurable class XGBClassifierGPU(MLWrapper): _supported_run_modes = [RunMode.classification] @@ -142,10 +141,9 @@ def fit_model(self, train_data, train_labels, val_data, val_labels): if wandb.run is not None: callbacks.append(wandb_xgb()) - self.model.train( self.params,train_data=dtrain,evals=evals, callbacks=callbacks) + self.model.train(self.params, train_data=dtrain, evals=evals, callbacks=callbacks) # self.model.fit(train_data, train_labels, eval_set=[(val_data, val_labels)], verbose=0) - shap_interaction_values = self.model.predict(dtrain) # self.explainer = shap.TreeExplainer( # self.model, dtrain, feature_perturbation="interventional", model_output="probability" diff --git a/icu_benchmarks/models/wrappers.py b/icu_benchmarks/models/wrappers.py index ddd54c08..b63e0c72 100644 --- a/icu_benchmarks/models/wrappers.py +++ b/icu_benchmarks/models/wrappers.py @@ -571,24 +571,24 @@ def _save_model_outputs(self, pred_indicators, test_pred, test_label): logging.debug(f"Saved row indicators to {Path(self.logger.save_dir) / f'row_indicators.csv'}") # logging.warning("Could not save row indicators; no support for temporal dataset with single outcomes yet.") - if len(pred_indicators.shape) > 1 and len(test_pred.shape) > 1 and pred_indicators.shape[1] == test_pred.shape[1]: - pred_indicators = np.hstack((pred_indicators, test_label.reshape(-1, 1))) - pred_indicators = np.hstack((pred_indicators, test_pred)) - # Save as: id, time (hours), ground truth, prediction 0, prediction 1 - np.savetxt(Path(self.logger.save_dir) / "pred_indicators.csv", pred_indicators, delimiter=",") - logging.debug(f"Saved row indicators to {Path(self.logger.save_dir) / 'row_indicators.csv'}") - else: - pred_indicators = np.hstack((pred_indicators.reshape(-1, 1), test_label.reshape(-1, 1))) - logging.info(pred_indicators.shape) - pred_indicators = np.hstack((pred_indicators, test_pred)) - np.savetxt( - Path(self.logger.save_dir) / "pred_indicators.csv", - pred_indicators, - delimiter=",", - header="id,ground_truth,prediction_0,prediction_1", - fmt="%d,%d,%0.3f,%0.3f", - ) - logging.debug(f"Saved row indicators to {Path(self.logger.save_dir) / f'row_indicators.csv'}") + # if len(pred_indicators.shape) > 1 and len(test_pred.shape) > 1 and pred_indicators.shape[1] == test_pred.shape[1]: + # pred_indicators = np.hstack((pred_indicators, test_label.reshape(-1, 1))) + # pred_indicators = np.hstack((pred_indicators, test_pred)) + # # Save as: id, time (hours), ground truth, prediction 0, prediction 1 + # np.savetxt(Path(self.logger.save_dir) / "pred_indicators.csv", pred_indicators, delimiter=",") + # logging.debug(f"Saved row indicators to {Path(self.logger.save_dir) / 'row_indicators.csv'}") + # else: + # pred_indicators = np.hstack((pred_indicators.reshape(-1, 1), test_label.reshape(-1, 1))) + # logging.info(pred_indicators.shape) + # pred_indicators = np.hstack((pred_indicators, test_pred)) + # np.savetxt( + # Path(self.logger.save_dir) / "pred_indicators.csv", + # pred_indicators, + # delimiter=",", + # header="id,ground_truth,prediction_0,prediction_1", + # fmt="%d,%d,%0.3f,%0.3f", + # ) + # logging.debug(f"Saved row indicators to {Path(self.logger.save_dir) / f'row_indicators.csv'}") # else: # logging.warning("Could not save row indicators.") diff --git a/icu_benchmarks/run.py b/icu_benchmarks/run.py index dbb12a35..d1167ec4 100644 --- a/icu_benchmarks/run.py +++ b/icu_benchmarks/run.py @@ -18,7 +18,8 @@ setup_logging, import_preprocessor, name_datasets, - get_config_files, ) + get_config_files, +) from icu_benchmarks.utils import parse_dict from icu_benchmarks.constants import RunMode @@ -102,8 +103,7 @@ def main(my_args=tuple(sys.argv[1:])): log_full_line(f"Available GPU {name}: {torch.cuda.get_device_name(name)}", level=logging.INFO) else: log_full_line( - "No GPUs available: please check your device and Torch,Cuda installation if unintended.", - level=logging.WARNING + "No GPUs available: please check your device and Torch,Cuda installation if unintended.", level=logging.WARNING ) if args.preprocessor: @@ -120,8 +120,7 @@ def main(my_args=tuple(sys.argv[1:])): name_datasets(args.name, args.name, args.name) run_dir = create_run_dir(log_dir) source_dir = args.source_dir - logging.info( - f"Will load weights from {source_dir} and bind train gin-config. Note: this might override your config.") + logging.info(f"Will load weights from {source_dir} and bind train gin-config. Note: this might override your config.") gin.parse_config_file(source_dir / "train_config.gin") elif args.samples and args.source_dir is not None: # Train model with limited samples and bind existing config logging.info("Binding train gin-config. Note: this might override your config.") @@ -134,8 +133,7 @@ def main(my_args=tuple(sys.argv[1:])): name_datasets(args.name, args.name, args.name) hp_checkpoint = log_dir / args.hp_checkpoint if args.hp_checkpoint else None model_path = ( - Path("configs") / ( - "imputation_models" if mode == RunMode.imputation else "prediction_models") / f"{model}.gin" + Path("configs") / ("imputation_models" if mode == RunMode.imputation else "prediction_models") / f"{model}.gin" ) gin_config_files = ( [Path(f"configs/experiments/{args.experiment}.gin")] @@ -158,8 +156,10 @@ def main(my_args=tuple(sys.argv[1:])): logging.info(f"Will load data from {args.file_names}") gin.bind_parameter("preprocess.file_names", file_names) else: - return ValueError(f"Please provide a dictionary type for the file names, got {args.file_names}, " - f"type: {type(args.file_names)}") + return ValueError( + f"Please provide a dictionary type for the file names, got {args.file_names}, " + f"type: {type(args.file_names)}" + ) choose_and_bind_hyperparameters_optuna( do_tune=args.tune, diff --git a/icu_benchmarks/run_utils.py b/icu_benchmarks/run_utils.py index 6b6ab2bd..8faa515d 100644 --- a/icu_benchmarks/run_utils.py +++ b/icu_benchmarks/run_utils.py @@ -35,11 +35,9 @@ def build_parser() -> ArgumentParser: parser.add_argument("-tn", "--task-name", help="Name of the task, used for naming experiments.") parser.add_argument("-m", "--model", default="LGBMClassifier", help="Name of the model gin.") parser.add_argument("-e", "--experiment", help="Name of the experiment gin.") - parser.add_argument("-l", "--log-dir", default=Path("../yaib_logs/"), type=Path, - help="Log directory for model weights.") + parser.add_argument("-l", "--log-dir", default=Path("../yaib_logs/"), type=Path, help="Log directory for model weights.") parser.add_argument("-s", "--seed", default=1234, type=int, help="Random seed for processing, tuning and training.") - parser.add_argument("-v", "--verbose", default=False, action=BOA, - help="Set to log verbosly. Disable for clean logs.") + parser.add_argument("-v", "--verbose", default=False, action=BOA, help="Set to log verbosly. Disable for clean logs.") parser.add_argument("--cpu", default=False, action=BOA, help="Set to use CPU.") parser.add_argument("-db", "--debug", default=False, action=BOA, help="Set to load less data.") parser.add_argument("--reproducible", default=True, action=BOA, help="Make torch reproducible.") @@ -53,8 +51,7 @@ def build_parser() -> ArgumentParser: parser.add_argument("--tune", default=False, action=BOA, help="Find best hyperparameters.") parser.add_argument("--hp-checkpoint", type=Path, help="Use previous hyperparameter checkpoint.") parser.add_argument("--eval", default=False, action=BOA, help="Only evaluate model, skip training.") - parser.add_argument("--complete-train", default=False, action=BOA, - help="Use all data to train model, skip testing.") + parser.add_argument("--complete-train", default=False, action=BOA, help="Use all data to train model, skip testing.") parser.add_argument("-ft", "--fine-tune", default=None, type=int, help="Finetune model with amount of train data.") parser.add_argument("-sn", "--source-name", type=Path, help="Name of the source dataset.") parser.add_argument("--source-dir", type=Path, help="Directory containing gin and model weights.") @@ -65,13 +62,12 @@ def build_parser() -> ArgumentParser: nargs="+", help="Optional modality selection to use. Specify multiple modalities separated by spaces.", ) - parser.add_argument("--label", type=str, help="Label to use for evaluation in case of multiple labels.", - default=None) + parser.add_argument("--label", type=str, help="Label to use for evaluation in case of multiple labels.", default=None) parser.add_argument( "--file_names", type=parse_dict, help="Dictionary of file names to use in data_dir " - "(e.g., 'DYNAMIC:dyno.parquet,OUTCOME:outco.parquet,STATIC:sta.parquet')", + "(e.g., 'DYNAMIC:dyno.parquet,OUTCOME:outco.parquet,STATIC:sta.parquet')", default=None, ) return parser @@ -211,8 +207,7 @@ def log_full_line(msg: str, level: int = logging.INFO, char: str = "-", num_newl reserved_chars = len(logging.getLevelName(level)) + 28 logging.log( level, - "{0:{char}^{width}}{1}".format(msg, "\n" * num_newlines, char=char, - width=terminal_size.columns - reserved_chars), + "{0:{char}^{width}}{1}".format(msg, "\n" * num_newlines, char=char, width=terminal_size.columns - reserved_chars), ) @@ -231,8 +226,7 @@ def load_pretrained_imputation_model(use_pretrained_imputation): pretrained_imputation_model_checkpoint = torch.load(use_pretrained_imputation, map_location=torch.device("cpu")) if isinstance(pretrained_imputation_model_checkpoint, dict): imputation_model_class = pretrained_imputation_model_checkpoint["class"] - pretrained_imputation_model = imputation_model_class( - **pretrained_imputation_model_checkpoint["hyper_parameters"]) + pretrained_imputation_model = imputation_model_class(**pretrained_imputation_model_checkpoint["hyper_parameters"]) pretrained_imputation_model.set_trained_columns(pretrained_imputation_model_checkpoint["trained_columns"]) pretrained_imputation_model.load_state_dict(pretrained_imputation_model_checkpoint["state_dict"]) else: diff --git a/icu_benchmarks/utils.py b/icu_benchmarks/utils.py index 500afb53..60148d59 100644 --- a/icu_benchmarks/utils.py +++ b/icu_benchmarks/utils.py @@ -16,7 +16,7 @@ def parse_dict(arg): return json.loads(json_string) else: # Handle unquoted format - pairs = arg.split(',') - return {key.strip(): value.strip() for key, value in (pair.split(':', 1) for pair in pairs)} + pairs = arg.split(",") + return {key.strip(): value.strip() for key, value in (pair.split(":", 1) for pair in pairs)} except Exception as e: raise argparse.ArgumentTypeError(f"Invalid dictionary format: {e}") From ff4b47d448135dd88d4f5468c5a3fd2d27a778e9 Mon Sep 17 00:00:00 2001 From: Robin van de Water Date: Wed, 4 Jun 2025 11:03:58 +0200 Subject: [PATCH 40/82] ruff --- icu_benchmarks/data/preprocessor.py | 2 +- icu_benchmarks/data/split_process_data.py | 7 +++---- icu_benchmarks/models/alarm_metrics.py | 3 --- icu_benchmarks/models/constants.py | 3 --- icu_benchmarks/models/custom_metrics.py | 13 ++++++------- icu_benchmarks/models/ml_models/xgboost.py | 4 +--- icu_benchmarks/models/train.py | 3 +-- icu_benchmarks/models/wrappers.py | 5 ++--- 8 files changed, 14 insertions(+), 26 deletions(-) diff --git a/icu_benchmarks/data/preprocessor.py b/icu_benchmarks/data/preprocessor.py index 4fcb6645..085b4b7f 100644 --- a/icu_benchmarks/data/preprocessor.py +++ b/icu_benchmarks/data/preprocessor.py @@ -182,7 +182,7 @@ def _model_impute(self, data, group=None): def _process_dynamic(self, data, vars): # self.vars_to_exclude = ["hr"] - old_columns = data[Split.train][Segment.dynamic].columns + # old_columns = data[Split.train][Segment.dynamic].columns dyn_rec = Recipe(data[Split.train][Segment.dynamic], [], vars[Segment.dynamic], vars["GROUP"], vars["SEQUENCE"]) if self.scaling: dyn_rec.add_step(StepScale(sel=all_numeric_predictors(backend=recipys.constants.Backend.POLARS))) diff --git a/icu_benchmarks/data/split_process_data.py b/icu_benchmarks/data/split_process_data.py index 48633eff..d8c643d5 100644 --- a/icu_benchmarks/data/split_process_data.py +++ b/icu_benchmarks/data/split_process_data.py @@ -1,7 +1,6 @@ import copy import logging import os -import numpy as np import gin import json import hashlib @@ -221,7 +220,7 @@ def preprocess_data( return data -def check_sanitize_data(data, vars): +def check_sanitize_data(data, vars): # noqa: F811 """Check for duplicates in the loaded data and remove them.""" group = vars[Var.group] if Var.group in vars.keys() else None sequence = vars[Var.sequence] if Var.sequence in vars.keys() else None @@ -248,8 +247,8 @@ def check_sanitize_data(data, vars): return data, vars -def modality_selection( - data: dict[pl.DataFrame], modality_mapping: dict[str], selected_modalities: list[str], vars +def modality_selection( # noqa: F811 + data: dict[pl.DataFrame], modality_mapping: dict[str], selected_modalities: list[str], vars ) -> dict[pl.DataFrame]: logging.info(f"Selected modalities: {selected_modalities}") selected_columns = [modality_mapping[cols] for cols in selected_modalities if cols in modality_mapping.keys()] diff --git a/icu_benchmarks/models/alarm_metrics.py b/icu_benchmarks/models/alarm_metrics.py index c892abdc..68c8c21b 100644 --- a/icu_benchmarks/models/alarm_metrics.py +++ b/icu_benchmarks/models/alarm_metrics.py @@ -1,7 +1,4 @@ import numpy as np -from numpy import ndarray -import torch -from sklearn.metrics import precision_score # def convert_to_alarm(y_true: ndarray, y_pred: ndarray, normalize=False) -> torch.tensor: # y_pred = np.rint(y_pred).astype(int) diff --git a/icu_benchmarks/models/constants.py b/icu_benchmarks/models/constants.py index 8c192239..088584ab 100644 --- a/icu_benchmarks/models/constants.py +++ b/icu_benchmarks/models/constants.py @@ -11,16 +11,13 @@ roc_curve, r2_score, mean_squared_error, - matthews_corrcoef, ) -from torchmetrics import MatthewsCorrCoef from torchmetrics.classification import ( AUROC, AveragePrecision as TorchMetricsAveragePrecision, PrecisionRecallCurve as TorchMetricsPrecisionRecallCurve, CalibrationError, F1Score, - MatthewsCorrCoef, ) from enum import Enum from icu_benchmarks.models.custom_metrics import ( diff --git a/icu_benchmarks/models/custom_metrics.py b/icu_benchmarks/models/custom_metrics.py index 6b1ea344..e5b3e963 100644 --- a/icu_benchmarks/models/custom_metrics.py +++ b/icu_benchmarks/models/custom_metrics.py @@ -1,4 +1,3 @@ -import numpy import torch from typing import Callable import numpy as np @@ -12,7 +11,7 @@ ) from sklearn.calibration import calibration_curve from scipy.spatial.distance import jensenshannon -from torchmetrics.classification import BinaryFairness, Specificity +from torchmetrics.classification import BinaryFairness """" This file contains custom metrics that can be added to YAIB. @@ -175,11 +174,11 @@ def sensitivity(y_preds: torch.Tensor | ndarray, y_targets: torch.Tensor | ndarr return tp / (tp + fn) -class Specificity(EpochMetric): - def __init__(self, output_transform: Callable = lambda x: x, check_compute_fn: bool = False) -> None: - super(Specificity, self).__init__( - self.specificity_compute, output_transform=output_transform, check_compute_fn=check_compute_fn - ) +# class Specificity(EpochMetric): +# def __init__(self, output_transform: Callable = lambda x: x, check_compute_fn: bool = False) -> None: +# super(Specificity, self).__init__( +# self.specificity_compute, output_transform=output_transform, check_compute_fn=check_compute_fn +# ) def specificity(y_preds: torch.Tensor, y_targets: torch.Tensor) -> float: diff --git a/icu_benchmarks/models/ml_models/xgboost.py b/icu_benchmarks/models/ml_models/xgboost.py index 805fb61c..b9ab2249 100644 --- a/icu_benchmarks/models/ml_models/xgboost.py +++ b/icu_benchmarks/models/ml_models/xgboost.py @@ -1,9 +1,7 @@ -import inspect import logging from statistics import mean import gin -import numpy as np import shap import wandb import xgboost as xgb @@ -144,7 +142,7 @@ def fit_model(self, train_data, train_labels, val_data, val_labels): self.model.train(self.params, train_data=dtrain, evals=evals, callbacks=callbacks) # self.model.fit(train_data, train_labels, eval_set=[(val_data, val_labels)], verbose=0) - shap_interaction_values = self.model.predict(dtrain) + # shap_interaction_values = self.model.predict(dtrain) # self.explainer = shap.TreeExplainer( # self.model, dtrain, feature_perturbation="interventional", model_output="probability" # ) diff --git a/icu_benchmarks/models/train.py b/icu_benchmarks/models/train.py index 65f6a888..d962a986 100644 --- a/icu_benchmarks/models/train.py +++ b/icu_benchmarks/models/train.py @@ -1,6 +1,5 @@ import os import gin -import numpy as np import torch import logging import polars as pl @@ -215,7 +214,7 @@ def persist_shap_data(trainer: Trainer, log_dir: Path): and len(trained_columns) != trainer.lightning_module.explainer_values_test.shape[1] ): trained_columns.remove("stay_id" if "stay_id" in trained_columns else "id") - logging.info(f"Saving SHAPS") + logging.info("Saving SHAPS") if hasattr(trainer.lightning_module, "explainer_values_test"): # todo: abs values explainer_values = trainer.lightning_module.explainer_values_test diff --git a/icu_benchmarks/models/wrappers.py b/icu_benchmarks/models/wrappers.py index b63e0c72..3b5783c1 100644 --- a/icu_benchmarks/models/wrappers.py +++ b/icu_benchmarks/models/wrappers.py @@ -3,7 +3,6 @@ from typing import Dict, Any, List, Optional, Union from pathlib import Path import torchmetrics -from ignite.metrics import EpochMetric from sklearn.metrics import log_loss, mean_squared_error, average_precision_score, roc_auc_score import torch @@ -547,7 +546,7 @@ def _save_model_outputs(self, pred_indicators, test_pred, test_label): header="id,time,ground_truth,prediction_0,prediction_1", fmt="%d,%d,%.3f,%.3f,%.3f", ) - logging.debug(f"Saved row indicators to {Path(self.logger.save_dir) / f'row_indicators.csv'}") + logging.debug(f"Saved row indicators to {Path(self.logger.save_dir) / 'row_indicators.csv'}") # else: # # Flat/static dataset # np.savetxt(Path(self.logger.save_dir) / "pred_indicators.csv", pred_indicators, delimiter=",", @@ -568,7 +567,7 @@ def _save_model_outputs(self, pred_indicators, test_pred, test_label): header="id,ground_truth,prediction_0,prediction_1", fmt="%d,%d,%0.3f,%0.3f", ) - logging.debug(f"Saved row indicators to {Path(self.logger.save_dir) / f'row_indicators.csv'}") + logging.debug(f"Saved row indicators to {Path(self.logger.save_dir) / 'row_indicators.csv'}") # logging.warning("Could not save row indicators; no support for temporal dataset with single outcomes yet.") # if len(pred_indicators.shape) > 1 and len(test_pred.shape) > 1 and pred_indicators.shape[1] == test_pred.shape[1]: From 212604671c8bbc77622857e4040b4aac2aaa8f4b Mon Sep 17 00:00:00 2001 From: Robin van de Water Date: Wed, 4 Jun 2025 14:52:44 +0200 Subject: [PATCH 41/82] fixes --- icu_benchmarks/data/split_process_data.py | 8 +++----- icu_benchmarks/models/train.py | 2 -- 2 files changed, 3 insertions(+), 7 deletions(-) diff --git a/icu_benchmarks/data/split_process_data.py b/icu_benchmarks/data/split_process_data.py index 715db3db..7ff03e31 100644 --- a/icu_benchmarks/data/split_process_data.py +++ b/icu_benchmarks/data/split_process_data.py @@ -27,8 +27,6 @@ ) from .constants import DataSegment, DataSplit, VarType -from icu_benchmarks.run_utils import check_required_keys -from .constants import DataSplit as Split, DataSegment as Segment, VarType as Var from .utils import check_sanitize_data, modality_selection @@ -173,7 +171,7 @@ def preprocess_data( } logging.info(f"Loaded data: {list(data.keys())}") - sanitized_data = check_sanitize_data(data, vars) + sanitized_data, vars = check_sanitize_data(data, vars) if DataSegment.dynamic not in sanitized_data.keys(): logging.warning("No dynamic data found, using only static data.") @@ -263,7 +261,7 @@ def flatten_column_names(*args: object) -> list[str]: return result -def check_sanitize_data(data: dict[str, pl.DataFrame], vars: dict[str, str | list[str]]) -> dict[str, pl.DataFrame]: +def check_sanitize_data(data: dict[str, pl.DataFrame], vars: dict[str, str | list[str]]) -> dict[str, pl.DataFrame]: # noqa: F811 """Check for duplicates in the loaded data and remove them.""" group: Optional[Union[str, list[str]]] = vars.get(VarType.group) sequence: Optional[Union[str, list[str]]] = vars.get(VarType.sequence) @@ -295,7 +293,7 @@ def check_sanitize_data(data: dict[str, pl.DataFrame], vars: dict[str, str | lis return data, vars -def modality_selection( # noqa: F811 +def modality_selection( # noqa: F811 data: dict[str, pl.DataFrame], modality_mapping: dict[str, list[str]], selected_modalities: list[str], diff --git a/icu_benchmarks/models/train.py b/icu_benchmarks/models/train.py index 332e4331..a73cbc96 100644 --- a/icu_benchmarks/models/train.py +++ b/icu_benchmarks/models/train.py @@ -4,7 +4,6 @@ from typing import Literal, Optional import gin -import numpy as np import polars as pl import torch from joblib import load @@ -59,7 +58,6 @@ def train_common( polars: bool = True, persistent_workers: bool = False, explain_features: bool = False, - ): """Common wrapper to train all benchmarked models. From cb25cfb60e986cab00ca30eb06e6571f8d8d65dc Mon Sep 17 00:00:00 2001 From: Robin van de Water Date: Wed, 4 Jun 2025 14:53:31 +0200 Subject: [PATCH 42/82] fixes --- icu_benchmarks/data/split_process_data.py | 8 +------- 1 file changed, 1 insertion(+), 7 deletions(-) diff --git a/icu_benchmarks/data/split_process_data.py b/icu_benchmarks/data/split_process_data.py index 7ff03e31..5b967f78 100644 --- a/icu_benchmarks/data/split_process_data.py +++ b/icu_benchmarks/data/split_process_data.py @@ -3,21 +3,15 @@ import json import logging import os -import pickle -from pathlib import Path from timeit import default_timer as timer from typing import Any, Iterable, Optional, Union - import gin import pandas as pd import polars as pl from sklearn.model_selection import KFold, ShuffleSplit, StratifiedKFold, StratifiedShuffleSplit - import polars.selectors as cs from pathlib import Path import pickle -from timeit import default_timer as timer -from sklearn.model_selection import StratifiedKFold, KFold, StratifiedShuffleSplit, ShuffleSplit from icu_benchmarks.constants import RunMode from icu_benchmarks.data.preprocessor import ( PandasClassificationPreprocessor, @@ -261,7 +255,7 @@ def flatten_column_names(*args: object) -> list[str]: return result -def check_sanitize_data(data: dict[str, pl.DataFrame], vars: dict[str, str | list[str]]) -> dict[str, pl.DataFrame]: # noqa: F811 +def check_sanitize_data(data: dict[str, pl.DataFrame], vars: dict[str, str | list[str]]) -> dict[str, pl.DataFrame]: # noqa: F811 """Check for duplicates in the loaded data and remove them.""" group: Optional[Union[str, list[str]]] = vars.get(VarType.group) sequence: Optional[Union[str, list[str]]] = vars.get(VarType.sequence) From ec6bc0d279f6754d1afeb9cf2c1368ade99b509e Mon Sep 17 00:00:00 2001 From: Robin van de Water Date: Wed, 4 Jun 2025 14:57:25 +0200 Subject: [PATCH 43/82] fixes --- icu_benchmarks/data/split_process_data.py | 22 +++++++++++----------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/icu_benchmarks/data/split_process_data.py b/icu_benchmarks/data/split_process_data.py index 5b967f78..fa869f7a 100644 --- a/icu_benchmarks/data/split_process_data.py +++ b/icu_benchmarks/data/split_process_data.py @@ -204,7 +204,7 @@ def preprocess_data( logging.info(f"Preprocessing took {end - start:.2f} seconds.") logging.info(f"Checking for NaNs and nulls in {data.keys()}.") for _dict in sanitized_data.values(): - for key, val in _dict.items(): + for key, dataframe in _dict.items(): logging.debug(f"Data type: {key}") logging.debug("Is NaN:") sel = _dict[key].select(pl.selectors.numeric().is_nan().max()) @@ -212,27 +212,27 @@ def preprocess_data( logging.debug("Has nulls:") sel = _dict[key].select(pl.all().has_nulls()) logging.debug(sel.select(col.name for col in sel if col.item(0))) - _dict[key] = val.fill_null(strategy="zero") - _dict[key] = val.fill_nan(0) + _dict[key] = dataframe.fill_null(strategy="zero") + _dict[key] = dataframe.fill_nan(0) logging.debug("Dropping columns with nulls") sel = _dict[key].select(pl.all().has_nulls()) logging.debug(sel.select(col.name for col in sel if col.item(0))) logging.info("Checking for infinite values.") - for col in val.select(cs.numeric()).columns: - if val[col].is_infinite().any(): - logging.info(f"Column '{col}' contains infinite values. Datatype: {val[col].dtype}") + for col in dataframe.select(cs.numeric()).columns: + if dataframe[col].is_infinite().any(): + logging.info(f"Column '{col}' contains infinite values. Datatype: {dataframe[col].dtype}") max_float64 = 0 # Replace infinite values with the maximum value for float64 - val = val.with_columns( + dataframe = dataframe.with_columns( [ pl.when(pl.col(col).is_infinite()).then(max_float64).otherwise(pl.col(col)).alias(col) - for col in val.columns - if val[col].dtype == pl.Float64 + for col in dataframe.columns + if dataframe[col].dtype == pl.Float64 ] ) - dict[key] = val - logging.info(f"Amount of columns: {len(val.columns)}") + _dict[key] = dataframe + logging.info(f"Amount of columns: {len(dataframe.columns)}") # Generate cache if generate_cache: From cbe9a646d0ad9b200a3a3b68137120f37d5c75b8 Mon Sep 17 00:00:00 2001 From: Robin van de Water Date: Wed, 4 Jun 2025 17:01:51 +0200 Subject: [PATCH 44/82] preprocessor type hint --- icu_benchmarks/data/split_process_data.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/icu_benchmarks/data/split_process_data.py b/icu_benchmarks/data/split_process_data.py index fa869f7a..543e0e6f 100644 --- a/icu_benchmarks/data/split_process_data.py +++ b/icu_benchmarks/data/split_process_data.py @@ -28,7 +28,7 @@ def preprocess_data( data_dir: Path, file_names: dict[str, str] | Any = gin.REQUIRED, - preprocessor: type[PolarsClassificationPreprocessor | PolarsRegressionPreprocessor] = PolarsClassificationPreprocessor, + preprocessor: type[Preprocessor] = PolarsClassificationPreprocessor, use_static: bool = True, vars: dict[str, str | list[str]] | Any = gin.REQUIRED, modality_mapping: Optional[dict[str, list[str]]] = None, From ff7dcec94de96269113453b9e666188468c2cc65 Mon Sep 17 00:00:00 2001 From: Robin van de Water Date: Wed, 4 Jun 2025 17:05:20 +0200 Subject: [PATCH 45/82] restore exclusion --- icu_benchmarks/data/split_process_data.py | 15 +++------------ 1 file changed, 3 insertions(+), 12 deletions(-) diff --git a/icu_benchmarks/data/split_process_data.py b/icu_benchmarks/data/split_process_data.py index 543e0e6f..d6b8f42d 100644 --- a/icu_benchmarks/data/split_process_data.py +++ b/icu_benchmarks/data/split_process_data.py @@ -16,7 +16,6 @@ from icu_benchmarks.data.preprocessor import ( PandasClassificationPreprocessor, PolarsClassificationPreprocessor, - PolarsRegressionPreprocessor, Preprocessor, ) @@ -110,31 +109,23 @@ def preprocess_data( dumped_vars = json.dumps(vars, sort_keys=True) logging.info(f"Using preprocessor: {preprocessor.__name__}") - - cat_clinical_notes = modality_mapping.get("cat_clinical_notes") - cat_med_embeddings_map = modality_mapping.get("cat_med_embeddings_map") - if cat_clinical_notes is not None and cat_med_embeddings_map is not None: - vars_to_exclude = cat_clinical_notes + cat_med_embeddings_map - else: - vars_to_exclude = None - cache_dir = data_dir / "cache" cache_filename = f"s_{seed}_r_{repetition_index}_f_{fold_index}_t_{train_size}_d_{debug}" logging.log(logging.INFO, f"Using preprocessor: {preprocessor.__name__}") - excluded_vars = [] + vars_to_exclude = [] if exclude_preproc is not None: # Exclude variables from preprocessing based on modality: # useful if modality has already undergone extensive preprocessing. if modality_mapping is not None and len(modality_mapping) > 0: for modality in exclude_preproc: if modality in modality_mapping: - excluded_vars.extend(modality_mapping.get(modality)) + vars_to_exclude.extend(modality_mapping.get(modality)) else: logging.warning(f"Modality '{modality}' not found in modality mapping.") logging.info( - f"Excluding modalities in {exclude_preproc}. Total vars excluded from preprocessing: {len(excluded_vars)}" + f"Excluding modalities in {exclude_preproc}. Total vars excluded from preprocessing: {len(vars_to_exclude)}" ) else: logging.warning("No modality mapping provided. Excluding variables from preprocessing will have no effect.") From fba0bce500512750191ce660af5ebf64f8ee1b8d Mon Sep 17 00:00:00 2001 From: Robin van de Water Date: Tue, 10 Jun 2025 13:55:57 +0200 Subject: [PATCH 46/82] add explain_features flag --- icu_benchmarks/run.py | 2 +- icu_benchmarks/run_utils.py | 1 + 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/icu_benchmarks/run.py b/icu_benchmarks/run.py index d1167ec4..0a2268d3 100644 --- a/icu_benchmarks/run.py +++ b/icu_benchmarks/run.py @@ -203,7 +203,7 @@ def main(my_args=tuple(sys.argv[1:])): cpu=args.cpu, wandb=args.wandb_sweep, complete_train=args.complete_train, - explain_features=True, + explain_features=args.explain_features, ) log_full_line("FINISHED TRAINING", level=logging.INFO, char="=", num_newlines=3) diff --git a/icu_benchmarks/run_utils.py b/icu_benchmarks/run_utils.py index e053e7d7..ca4133d1 100644 --- a/icu_benchmarks/run_utils.py +++ b/icu_benchmarks/run_utils.py @@ -70,6 +70,7 @@ def build_parser() -> ArgumentParser: "(e.g., 'DYNAMIC:dyno.parquet,OUTCOME:outco.parquet,STATIC:sta.parquet')", default=None, ) + parser.add_argument("--explain_features", default=False, action=BOA, help="Enable feature explanation.") return parser From c58574f9561b7ee52944bc15ea0050c179f04182 Mon Sep 17 00:00:00 2001 From: Robin van de Water Date: Wed, 11 Jun 2025 11:48:33 +0200 Subject: [PATCH 47/82] explain features --- icu_benchmarks/run_utils.py | 19 ++++++++++--------- 1 file changed, 10 insertions(+), 9 deletions(-) diff --git a/icu_benchmarks/run_utils.py b/icu_benchmarks/run_utils.py index ca4133d1..10a545b8 100644 --- a/icu_benchmarks/run_utils.py +++ b/icu_benchmarks/run_utils.py @@ -109,7 +109,7 @@ def import_preprocessor(preprocessor_path: str): logging.error(f"Could not import custom preprocessor from {preprocessor_path}: {e}") -def aggregate_results(log_dir: Path, execution_time: timedelta = None): +def aggregate_results(log_dir: Path, execution_time: timedelta = None, explain_features: bool = False): """Aggregates results from all folds and writes to JSON file. Args: @@ -139,15 +139,16 @@ def aggregate_results(log_dir: Path, execution_time: timedelta = None): if (fold_iter / "explainer_values_test.parquet").is_file(): explainer_values_test.append(pl.read_parquet(fold_iter / "explainer_values_test.parquet")) - if explainer_values_test: - shap_values = pl.concat(explainer_values_test) - shap_values.write_parquet(log_dir / "aggregated_explainer_values.parquet") - try: - shap_values = pl.concat(explainer_values_test) - shap_values.write_parquet(log_dir / "aggregated_explainer_values.parquet") - except Exception as e: - logging.error(f"Error aggregating or writing SHAP values: {e}") + if explain_features: + if explainer_values_test: + shap_values = pl.concat(explainer_values_test) + shap_values.write_parquet(log_dir / "aggregated_explainer_values.parquet") + try: + shap_values = pl.concat(explainer_values_test) + shap_values.write_parquet(log_dir / "aggregated_explainer_values.parquet") + except Exception as e: + logging.error(f"Error aggregating or writing SHAP values: {e}") # Aggregate results per metric list_scores = {} for repetition, folds in aggregated.items(): From 22587e70f492385efcbdc6248e7a2c3c0b5a6c4e Mon Sep 17 00:00:00 2001 From: Robin van de Water Date: Wed, 11 Jun 2025 11:53:13 +0200 Subject: [PATCH 48/82] explicit conversion to amount of hours for row indicators --- icu_benchmarks/data/loader.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/icu_benchmarks/data/loader.py b/icu_benchmarks/data/loader.py index d25e2265..b540ef90 100644 --- a/icu_benchmarks/data/loader.py +++ b/icu_benchmarks/data/loader.py @@ -178,7 +178,10 @@ def get_data_and_labels(self) -> tuple[np.ndarray, np.ndarray, np.ndarray]: logging.debug(f"labels shape: {labels.shape}") rep = rep.to_numpy().astype(float) rep = rep[:, 1:] - + if self.vars["SEQUENCE"] in self.row_indicators: + self.row_indicators = self.row_indicators.with_columns( + pl.col(self.vars["SEQUENCE"]).dt.total_hours() + ) return rep, labels, self.row_indicators.to_numpy() def to_tensor(self) -> tuple[Union[Tensor, np.ndarray], Union[Tensor, np.ndarray], Union[Tensor, np.ndarray]]: From 347b45341d55fe7b5b0a89c525124110261c31af Mon Sep 17 00:00:00 2001 From: Robin van de Water Date: Fri, 13 Jun 2025 12:44:38 +0200 Subject: [PATCH 49/82] ruff and incidence computation --- icu_benchmarks/cross_validation.py | 2 +- icu_benchmarks/data/loader.py | 4 +--- icu_benchmarks/models/custom_metrics.py | 2 +- icu_benchmarks/run_utils.py | 1 - 4 files changed, 3 insertions(+), 6 deletions(-) diff --git a/icu_benchmarks/cross_validation.py b/icu_benchmarks/cross_validation.py index 6fba8873..f62d7f0e 100644 --- a/icu_benchmarks/cross_validation.py +++ b/icu_benchmarks/cross_validation.py @@ -142,7 +142,7 @@ def execute_repeated_cv( wandb_log({"Iteration": repetition * cv_folds_to_train + fold_index}) if repetition * cv_folds_to_train + fold_index > 1: try: - aggregate_results(log_dir) + aggregate_results(log_dir, explain_features=explain_features) except Exception as e: logging.error(f"Failed to aggregate results: {e}") log_full_line(f"FINISHED CV REPETITION {repetition}", level=logging.INFO, char="=", num_newlines=3) diff --git a/icu_benchmarks/data/loader.py b/icu_benchmarks/data/loader.py index b540ef90..39ff59ca 100644 --- a/icu_benchmarks/data/loader.py +++ b/icu_benchmarks/data/loader.py @@ -179,9 +179,7 @@ def get_data_and_labels(self) -> tuple[np.ndarray, np.ndarray, np.ndarray]: rep = rep.to_numpy().astype(float) rep = rep[:, 1:] if self.vars["SEQUENCE"] in self.row_indicators: - self.row_indicators = self.row_indicators.with_columns( - pl.col(self.vars["SEQUENCE"]).dt.total_hours() - ) + self.row_indicators = self.row_indicators.with_columns(pl.col(self.vars["SEQUENCE"]).dt.total_hours()) return rep, labels, self.row_indicators.to_numpy() def to_tensor(self) -> tuple[Union[Tensor, np.ndarray], Union[Tensor, np.ndarray], Union[Tensor, np.ndarray]]: diff --git a/icu_benchmarks/models/custom_metrics.py b/icu_benchmarks/models/custom_metrics.py index e5b3e963..5c621da3 100644 --- a/icu_benchmarks/models/custom_metrics.py +++ b/icu_benchmarks/models/custom_metrics.py @@ -214,7 +214,7 @@ def binary_incidence(y_preds, y_targets): float: Proportion of positive labels. """ y_true = np.rint(y_targets).astype(int) # Ensure binary labels - return np.sum(y_true) + return np.sum(y_true) / len(y_true) # from torchmetrics.classification import Specificity as TorchMetricsSpecificity diff --git a/icu_benchmarks/run_utils.py b/icu_benchmarks/run_utils.py index 10a545b8..9b534d8e 100644 --- a/icu_benchmarks/run_utils.py +++ b/icu_benchmarks/run_utils.py @@ -139,7 +139,6 @@ def aggregate_results(log_dir: Path, execution_time: timedelta = None, explain_f if (fold_iter / "explainer_values_test.parquet").is_file(): explainer_values_test.append(pl.read_parquet(fold_iter / "explainer_values_test.parquet")) - if explain_features: if explainer_values_test: shap_values = pl.concat(explainer_values_test) From 18165c2bd132b4335bc5c5e44e72ac2fa8c88425 Mon Sep 17 00:00:00 2001 From: Robin van de Water Date: Wed, 18 Jun 2025 15:23:05 +0200 Subject: [PATCH 50/82] update experiments --- experiments/benchmark_cass.yml | 19 +++-- experiments/benchmark_cass_outcome_time.yml | 27 ++----- experiments/benchmark_organsystems.yml | 81 +++++++++++++++++++++ 3 files changed, 97 insertions(+), 30 deletions(-) create mode 100644 experiments/benchmark_organsystems.yml diff --git a/experiments/benchmark_cass.yml b/experiments/benchmark_cass.yml index c91d8840..9eecefdf 100644 --- a/experiments/benchmark_cass.yml +++ b/experiments/benchmark_cass.yml @@ -14,26 +14,23 @@ command: - -tn - SSI # - --hp-checkpoint -# - /sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/normal_ward_SSI-3_segment_1.0_horizon_6:00:00_transfer_full_2025-05-27T23:34:00/SSI/XGBClassifier/2025-05-28T18-35-20.907325/hyperparameter_tuning_logs.db -# - /sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/normal_ward_SSI-3_segment_1.0_horizon_6:00:00_transfer_full_2025-05-09T19:19:33_wearable_cut_off_0.75/SSI/XGBClassifier/2025-05-12T17-24-38.032040/hyperparameter_tuning_logs.db -# - /sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/normal_ward_SSI-3_segment_1.0_horizon_6:00:00_transfer_full_2025-05-09T19:19:33_wearable_cut_off_0.75/SSI/XGBClassifier/2025-05-16T18-15-39.225575/hyperparameter_tuning_logs.db -# - /sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/normal_ward_SSI-3_segment_1.0_horizon_6:00:00_transfer_full_2025-05-09T19:19:33_wearable_cut_off_0.75/SSI/XGBClassifier/2025-05-16T18-15-39.225575/ -# - /sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/normal_ward_SSI-3_segment_1.0_horizon_6:00:00_transfer_full_2025-04-24T21:24:01_wearable_cut_off_0.75/SSI/XGBClassifier/2025-04-25T10-06-24.947526/hyperparameter_tuning_logs.db -# - Mortality +# - /sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/real_life_normal_ward_SSI-3_segment_1.0_horizon_6:00:00_transfer_full_2025-06-03T16:05:00/SSI/XGBClassifier/2025-06-04T17-09-19.640244/hyperparameter_tuning_logs.db +## - Mortality # - --verbose - --modalities - "all" - --file-names - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_6.parquet","STATIC":"sta.parquet"' +# - --explain_features # - --verbose -# - -gc -# - -lc method: grid name: yaib_classification_benchmark parameters: data_dir: values: - - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/real_life_normal_ward_SSI-3_segment_1.0_horizon_6:00:00_transfer_full_2025-06-03T16:05:00" + - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/lab_set_ICU_SSI-3_segment_1.0_horizon_6:00:00_transfer_full_2025-06-10T20:57:18" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/lab_set_ICU_and_normal_ward_SSI-3_segment_1.0_horizon_6:00:00_transfer_full_2025-06-10T16:12:39" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/real_life_normal_ward_SSI-3_segment_1.0_horizon_6:00:00_transfer_full_2025-06-03T16:05:00" # - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/normal_ward_SSI-3_segment_1.0_horizon_6:00:00_transfer_full_2025-05-27T23:34:00" # - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/normal_ward_SSI-3_segment_1.0_horizon_6:00:00_transfer_full_2025-05-09T19:19:33_wearable_cut_off_0.75" # - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/ICU_and_normal_ward_SSI-3_segment_1.0_horizon_6:00:00_transfer_full_2025-04-22T21:19:32" @@ -72,7 +69,9 @@ parameters: # - [static] # 1 missing modality - "all" - - [ copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, cat_medications, cat_clinical_notes, wearable_activity, wearable_ppgfeature, wearable_ppgembedding, wearable_core, static ] # not missing any + - [ copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, cat_medications, cat_clinical_notes, + wearable_activity, wearable_ppgfeature, wearable_ppgembedding, wearable_core, static_retro_icu, + static_retro_pre_intra, static ] # not missing any - [ copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, cat_clinical_notes, wearable_core, static ] # missing: wearable (activity, ppgfeature, ppgembedding) - [ ishmed_lab_numeric, cat_clinical_notes, wearable_activity, wearable_ppgfeature, wearable_ppgembedding, wearable_core, static ] # missing: copra - [ copra_observations, copra_scores, copra_fluid_balance, cat_clinical_notes, wearable_activity, wearable_ppgfeature, wearable_ppgembedding, wearable_core, static ] # 1 missing: ishmed (lab) diff --git a/experiments/benchmark_cass_outcome_time.yml b/experiments/benchmark_cass_outcome_time.yml index 6074f42d..24b0df1e 100644 --- a/experiments/benchmark_cass_outcome_time.yml +++ b/experiments/benchmark_cass_outcome_time.yml @@ -14,14 +14,13 @@ command: - -tn - SSI - --hp-checkpoint - - /sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/normal_ward_SSI-3_segment_1.0_horizon_6:00:00_transfer_full_2025-05-09T19:19:33_wearable_cut_off_0.75/SSI/XGBClassifier/2025-05-12T17-24-38.032040/hyperparameter_tuning_logs.db + - /sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/normal_ward_SSI-3_segment_1.0_horizon_6:00:00_transfer_full_2025-05-27T23:34:00/SSI/XGBClassifier/2025-06-02T12-50-42.447250/hyperparameter_tuning_logs.db # - Mortality # - --verbose - --modalities - "all" - --file-names - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_6.parquet","STATIC":"sta.parquet"' -# - --verbose # - -gc # - -lc method: grid @@ -29,14 +28,7 @@ name: yaib_classification_benchmark parameters: data_dir: values: - - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/normal_ward_SSI-3_segment_1.0_horizon_6:00:00_transfer_full_2025-05-09T19:19:33_wearable_cut_off_0.75" -# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/ICU_and_normal_ward_SSI-3_segment_1.0_horizon_6:00:00_transfer_full_2025-04-22T21:19:32" -# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/ICU_and_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_1.0_horizon_6:00:00_transfer_full_2025-04-15T12:42:04" -# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/normal_ward_SSI-3_POPF_heamatoma_Galleleck_BDA_segment_1.0_horizon_6:00:00_transfer_full_2025-04-07T15:35:16" -# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/cohort_separation/1/" -# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/cohort_separation/2/" -# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/cohort_separation/3/" -# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/cohort_separation/4/" + - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/normal_ward_SSI-3_segment_1.0_horizon_6:00:00_transfer_full_2025-05-27T23:34:00" model: values: # - LogisticRegression @@ -54,20 +46,15 @@ parameters: modalities: values: - "all" -# - [copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, cat_med_embeddings_map, cat_clinical_notes, static] -# - [copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, cat_med_embeddings_map, wearable_activity, wearable_core, static] # - [copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, static] #, wearable_activity, wearable_core] # without wearable data -# - [copra_observations, copra_scores, copra_fluid_balance, cat_med_embeddings_map, wearable_activity, wearable_core, static] # without ishmed lab data and clinical notes -# - [copra_observations, copra_scores, copra_fluid_balance, cat_med_embeddings_map, static ] # without wearable data and ishmed lab data, clinical notes -## - [copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, cat_med_embeddings_map, wearable_activity, wearable_core, static] # without clinical notes -# - [copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, cat_clinical_notes, wearable_activity, wearable_core, static] # without med embeddings -# - [wearable_activity, wearable_core, static] -# - [ishmed_lab_numeric, static] -# - [static] + file_names: values: -# - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_3.parquet","STATIC":"sta.parquet"' + - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_1.parquet","STATIC":"sta.parquet"' + - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_3.parquet","STATIC":"sta.parquet"' + - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_4.parquet","STATIC":"sta.parquet"' - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_6.parquet","STATIC":"sta.parquet"' + - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_8.parquet","STATIC":"sta.parquet"' - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_9.parquet","STATIC":"sta.parquet"' - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_12.parquet","STATIC":"sta.parquet"' - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_18.parquet","STATIC":"sta.parquet"' diff --git a/experiments/benchmark_organsystems.yml b/experiments/benchmark_organsystems.yml new file mode 100644 index 00000000..06f712e5 --- /dev/null +++ b/experiments/benchmark_organsystems.yml @@ -0,0 +1,81 @@ +command: + - ${env} + - ${program} + - train + - -d + - ../data/ + - -t +# - BinaryClassification + - CassClassification + - --log-dir + - /sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs + - --tune + - --wandb-sweep + - -tn + - SSI +# - --hp-checkpoint +# - /sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/real_life_normal_ward_SSI-3_segment_1.0_horizon_6:00:00_transfer_full_2025-06-03T16:05:00/SSI/XGBClassifier/2025-06-04T17-09-19.640244/hyperparameter_tuning_logs.db +# - /sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/normal_ward_SSI-3_segment_1.0_horizon_6:00:00_transfer_full_2025-05-27T23:34:00/SSI/XGBClassifier/2025-05-28T18-35-20.907325/hyperparameter_tuning_logs.db +# - /sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/normal_ward_SSI-3_segment_1.0_horizon_6:00:00_transfer_full_2025-05-09T19:19:33_wearable_cut_off_0.75/SSI/XGBClassifier/2025-05-12T17-24-38.032040/hyperparameter_tuning_logs.db +# - /sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/normal_ward_SSI-3_segment_1.0_horizon_6:00:00_transfer_full_2025-05-09T19:19:33_wearable_cut_off_0.75/SSI/XGBClassifier/2025-05-16T18-15-39.225575/hyperparameter_tuning_logs.db +# - /sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/normal_ward_SSI-3_segment_1.0_horizon_6:00:00_transfer_full_2025-05-09T19:19:33_wearable_cut_off_0.75/SSI/XGBClassifier/2025-05-16T18-15-39.225575/ +# - /sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/normal_ward_SSI-3_segment_1.0_horizon_6:00:00_transfer_full_2025-04-24T21:24:01_wearable_cut_off_0.75/SSI/XGBClassifier/2025-04-25T10-06-24.947526/hyperparameter_tuning_logs.db +# - Mortality +# - --verbose + - --modalities + - "all" + - --file-names + - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_6.parquet","STATIC":"sta.parquet"' +# - --verbose +# - -gc +# - -lc +method: grid +name: yaib_classification_benchmark +parameters: + data_dir: + values: + - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/organ_cohorts_real_life_normal_ward_SSI-3_segment_1.0_horizon_6:00:00_transfer_full_2025-06-03T16:05:00/Pancreas" + - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/organ_cohorts_real_life_normal_ward_SSI-3_segment_1.0_horizon_6:00:00_transfer_full_2025-06-03T16:05:00/Liver" + - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/organ_cohorts_real_life_normal_ward_SSI-3_segment_1.0_horizon_6:00:00_transfer_full_2025-06-03T16:05:00/Upper_GI" + - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/organ_cohorts_real_life_normal_ward_SSI-3_segment_1.0_horizon_6:00:00_transfer_full_2025-06-03T16:05:00/Colorectal" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/normal_ward_SSI-3_segment_1.0_horizon_6:00:00_transfer_full_2025-05-27T23:34:00" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/normal_ward_SSI-3_segment_1.0_horizon_6:00:00_transfer_full_2025-05-09T19:19:33_wearable_cut_off_0.75" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/ICU_and_normal_ward_SSI-3_segment_1.0_horizon_6:00:00_transfer_full_2025-04-22T21:19:32" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/ICU_and_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_1.0_horizon_6:00:00_transfer_full_2025-04-15T12:42:04" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/normal_ward_SSI-3_POPF_heamatoma_Galleleck_BDA_segment_1.0_horizon_6:00:00_transfer_full_2025-04-07T15:35:16" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/cohort_separation/1/" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/cohort_separation/2/" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/cohort_separation/3/" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/cohort_separation/4/" + model: + values: +# - LogisticRegression +# - TimesNet +# - LGBMClassifier + - XGBClassifier +# - CBClassifier +# - BRFClassifier +# - RUSBClassifier +# - RFClassifier +# - GRU +# - LSTM +# - TCN +# - Transformer + modalities: + values: + - "all" + file_names: + values: +# - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_3.parquet","STATIC":"sta.parquet"' + - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_6.parquet","STATIC":"sta.parquet"' +# - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_9.parquet","STATIC":"sta.parquet"' +# - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_12.parquet","STATIC":"sta.parquet"' +# - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_18.parquet","STATIC":"sta.parquet"' +# - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_24.parquet","STATIC":"sta.parquet"' + seed: + values: + - 1111 + use_pretrained_imputation: + values: + - None +program: icu-benchmarks \ No newline at end of file From 80f33cbed4eec6dfeda1d25189f2b18ed1c8021c Mon Sep 17 00:00:00 2001 From: Robin van de Water Date: Mon, 23 Jun 2025 09:31:51 +0200 Subject: [PATCH 51/82] checkpoint dir failsafe --- icu_benchmarks/tuning/hyperparameters.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/icu_benchmarks/tuning/hyperparameters.py b/icu_benchmarks/tuning/hyperparameters.py index 82342795..c9232dc2 100644 --- a/icu_benchmarks/tuning/hyperparameters.py +++ b/icu_benchmarks/tuning/hyperparameters.py @@ -338,6 +338,9 @@ def bind_params_and_train(hyperparams): # Check if we found a checkpoint file and copy it. logging.info(f"Copying checkpoint and loading checkpoint at {checkpoint}") local_path = log_dir / checkpoint_file + if not str(local_path).endswith('.db'): + local_path = local_path / "hyperparameter_tuning_logs.db" + logging.warning(f"Checkpoint file {checkpoint_file} does not end with .db, trying {local_path} instead.") shutil.copy(str(checkpoint), local_path) study = optuna.load_study(study_name="tuning", storage="sqlite:///" + str(local_path), sampler=sampler, pruner=pruner) n_calls = n_calls - len(study.trials) From 15e37ea9f1cb3571241d5a03e439876370db5faf Mon Sep 17 00:00:00 2001 From: Robin van de Water Date: Mon, 23 Jun 2025 11:57:09 +0200 Subject: [PATCH 52/82] add option to append data vars automatically --- icu_benchmarks/run.py | 12 ++++++++++++ icu_benchmarks/run_utils.py | 23 ++++++++++++++++------- 2 files changed, 28 insertions(+), 7 deletions(-) diff --git a/icu_benchmarks/run.py b/icu_benchmarks/run.py index 0a2268d3..d73f4e7e 100644 --- a/icu_benchmarks/run.py +++ b/icu_benchmarks/run.py @@ -52,6 +52,8 @@ def main(my_args=tuple(sys.argv[1:])): experiment = args.experiment source_dir = args.source_dir modalities = args.modalities + load_data_vars = args.load_data_vars + if modalities: logging.debug(f"Binding modalities: {modalities}") gin.bind_parameter("preprocess.selected_modalities", modalities) @@ -65,6 +67,16 @@ def main(my_args=tuple(sys.argv[1:])): f"Model: {model} {'not ' if model not in models else ''}found." ) # Load task config + if load_data_vars: + logging.info(f"Loading variables from {task} from {data_dir} configuration") + if (data_dir / "vars.gin").exists(): + # Open the task config file in append mode and add a line + with open(f"configs/tasks/{task}.gin", "a") as config_file: + config_file.write("\n# Added automatically by run.py\n") + config_file.write(f"# Timestamp: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n") + config_file.write(f'include "{data_dir}/vars.gin"\n') + else: + logging.warning(f"No vars.gin file found in {data_dir}. Please ensure the file exists.") gin.parse_config_file(f"configs/tasks/{task}.gin") mode = get_mode() diff --git a/icu_benchmarks/run_utils.py b/icu_benchmarks/run_utils.py index 9b534d8e..68a89a39 100644 --- a/icu_benchmarks/run_utils.py +++ b/icu_benchmarks/run_utils.py @@ -35,9 +35,11 @@ def build_parser() -> ArgumentParser: parser.add_argument("-tn", "--task-name", help="Name of the task, used for naming experiments.") parser.add_argument("-m", "--model", default="LGBMClassifier", help="Name of the model gin.") parser.add_argument("-e", "--experiment", help="Name of the experiment gin.") - parser.add_argument("-l", "--log-dir", default=Path("../yaib_logs/"), type=Path, help="Log directory for model weights.") + parser.add_argument("-l", "--log-dir", default=Path("../yaib_logs/"), type=Path, + help="Log directory for model weights.") parser.add_argument("-s", "--seed", default=1234, type=int, help="Random seed for processing, tuning and training.") - parser.add_argument("-v", "--verbose", default=False, action=BOA, help="Set to log verbosly. Disable for clean logs.") + parser.add_argument("-v", "--verbose", default=False, action=BOA, + help="Set to log verbosly. Disable for clean logs.") parser.add_argument("--cpu", default=False, action=BOA, help="Set to use CPU.") parser.add_argument("-db", "--debug", default=False, action=BOA, help="Set to load less data.") parser.add_argument("--reproducible", default=True, action=BOA, help="Make torch reproducible.") @@ -51,7 +53,8 @@ def build_parser() -> ArgumentParser: parser.add_argument("--tune", default=False, action=BOA, help="Find best hyperparameters.") parser.add_argument("--hp-checkpoint", type=Path, help="Use previous hyperparameter checkpoint.") parser.add_argument("--eval", default=False, action=BOA, help="Only evaluate model, skip training.") - parser.add_argument("--complete-train", default=False, action=BOA, help="Use all data to train model, skip testing.") + parser.add_argument("--complete-train", default=False, action=BOA, + help="Use all data to train model, skip testing.") parser.add_argument("-ft", "--fine-tune", default=None, type=int, help="Finetune model with amount of train data.") parser.add_argument("-sn", "--source-name", type=Path, help="Name of the source dataset.") parser.add_argument("--source-dir", type=Path, help="Directory containing gin and model weights.") @@ -62,15 +65,19 @@ def build_parser() -> ArgumentParser: nargs="+", help="Optional modality selection to use. Specify multiple modalities separated by spaces.", ) - parser.add_argument("--label", type=str, help="Label to use for evaluation in case of multiple labels.", default=None) + parser.add_argument("--label", type=str, help="Label to use for evaluation in case of multiple labels.", + default=None) parser.add_argument( "--file_names", type=parse_dict, help="Dictionary of file names to use in data_dir " - "(e.g., 'DYNAMIC:dyno.parquet,OUTCOME:outco.parquet,STATIC:sta.parquet')", + "(e.g., 'DYNAMIC:dyno.parquet,OUTCOME:outco.parquet,STATIC:sta.parquet')", default=None, ) parser.add_argument("--explain_features", default=False, action=BOA, help="Enable feature explanation.") + parser.add_argument("--load_data_vars", default=False, action=BOA, + help="Load data variables from the dataset directory. Avoids having to manually add " + "the path in the task.gin") return parser @@ -208,7 +215,8 @@ def log_full_line(msg: str, level: int = logging.INFO, char: str = "-", num_newl reserved_chars = len(logging.getLevelName(level)) + 28 logging.log( level, - "{0:{char}^{width}}{1}".format(msg, "\n" * num_newlines, char=char, width=terminal_size.columns - reserved_chars), + "{0:{char}^{width}}{1}".format(msg, "\n" * num_newlines, char=char, + width=terminal_size.columns - reserved_chars), ) @@ -227,7 +235,8 @@ def load_pretrained_imputation_model(use_pretrained_imputation): pretrained_imputation_model_checkpoint = torch.load(use_pretrained_imputation, map_location=torch.device("cpu")) if isinstance(pretrained_imputation_model_checkpoint, dict): imputation_model_class = pretrained_imputation_model_checkpoint["class"] - pretrained_imputation_model = imputation_model_class(**pretrained_imputation_model_checkpoint["hyper_parameters"]) + pretrained_imputation_model = imputation_model_class( + **pretrained_imputation_model_checkpoint["hyper_parameters"]) pretrained_imputation_model.set_trained_columns(pretrained_imputation_model_checkpoint["trained_columns"]) pretrained_imputation_model.load_state_dict(pretrained_imputation_model_checkpoint["state_dict"]) else: From 08ff68fd0b6a8b019a2941c7783bc268df8cfe53 Mon Sep 17 00:00:00 2001 From: Robin van de Water Date: Mon, 23 Jun 2025 11:57:29 +0200 Subject: [PATCH 53/82] logging curves --- icu_benchmarks/models/wrappers.py | 16 +++++++++++++++- 1 file changed, 15 insertions(+), 1 deletion(-) diff --git a/icu_benchmarks/models/wrappers.py b/icu_benchmarks/models/wrappers.py index 3b5783c1..48fdbe07 100644 --- a/icu_benchmarks/models/wrappers.py +++ b/icu_benchmarks/models/wrappers.py @@ -17,7 +17,7 @@ from ignite.exceptions import NotComputableError from icu_benchmarks.models.constants import ImputationInit from icu_benchmarks.models.custom_metrics import confusion_matrix -from icu_benchmarks.models.utils import create_optimizer, create_scheduler +from icu_benchmarks.models.utils import create_optimizer, create_scheduler, log_single_metric_to_file from joblib import dump from pytorch_lightning import LightningModule @@ -467,6 +467,7 @@ def test_step(self, dataset, _): if self.explain_features: # self.explain_model(test_rep, test_label) self.explainer_values_test = self._explain_model(test_rep, test_label) + self.log_curves(test_label, test_pred, "test", pred_indicators) if self.mps: self.log("test/loss", np.float32(self.loss(test_label, test_pred)), sync_dist=True) self.log_metrics(np.float32(test_label), np.float32(test_pred), "test", pred_indicators) @@ -481,6 +482,17 @@ def predict(self, features): else: # Classification: return probabilities return self.model.predict_proba(features) + def log_curves(self, label, pred, metric_type, pred_indicators): + for name, metric in self.metrics.items(): + result = metric(self.label_transform(label), self.output_transform(pred)) + if isinstance(result, tuple): + # Vertical stacking for saving to file + # result = tuple(arr.reshape(-1, 1) for arr in result) + log_single_metric_to_file(metric_name=name, + data_points=result, + output_file=Path(self.logger.save_dir) / f"{metric_type}_metrics_{name}.csv", + ) + def log_metrics(self, label, pred, metric_type, pred_indicators): """Log metrics to the PL logs.""" if pred_indicators is None: @@ -523,6 +535,8 @@ def log_metrics(self, label, pred, metric_type, pred_indicators): sync_dist=True, ) + + # def _explain_model(self, test_rep, test_label): # if self.explainer is not None: # logging.info(f"Explaining features for {self}") From 754c44142ded1a2889f44f08b54e060c26337f95 Mon Sep 17 00:00:00 2001 From: Robin van de Water Date: Mon, 23 Jun 2025 11:57:47 +0200 Subject: [PATCH 54/82] make logging more informative/cleaner --- icu_benchmarks/data/split_process_data.py | 31 +++++++++++++++++------ 1 file changed, 23 insertions(+), 8 deletions(-) diff --git a/icu_benchmarks/data/split_process_data.py b/icu_benchmarks/data/split_process_data.py index d6b8f42d..972c8145 100644 --- a/icu_benchmarks/data/split_process_data.py +++ b/icu_benchmarks/data/split_process_data.py @@ -112,8 +112,6 @@ def preprocess_data( cache_dir = data_dir / "cache" cache_filename = f"s_{seed}_r_{repetition_index}_f_{fold_index}_t_{train_size}_d_{debug}" - logging.log(logging.INFO, f"Using preprocessor: {preprocessor.__name__}") - vars_to_exclude = [] if exclude_preproc is not None: # Exclude variables from preprocessing based on modality: @@ -155,7 +153,7 @@ def preprocess_data( f: pl.read_parquet(data_dir / file_names[f]) for f in file_names.keys() if os.path.exists(data_dir / file_names[f]) } - logging.info(f"Loaded data: {list(data.keys())}") + logging.info(f"Loaded datasets: {list(data.keys())}") sanitized_data, vars = check_sanitize_data(data, vars) if DataSegment.dynamic not in sanitized_data.keys(): @@ -193,7 +191,7 @@ def preprocess_data( sanitized_data = preprocessor_instance.apply(sanitized_data, vars) end = timer() logging.info(f"Preprocessing took {end - start:.2f} seconds.") - logging.info(f"Checking for NaNs and nulls in {data.keys()}.") + logging.debug(f"Checking for NaNs and nulls in {data.keys()}.") for _dict in sanitized_data.values(): for key, dataframe in _dict.items(): logging.debug(f"Data type: {key}") @@ -208,10 +206,10 @@ def preprocess_data( logging.debug("Dropping columns with nulls") sel = _dict[key].select(pl.all().has_nulls()) logging.debug(sel.select(col.name for col in sel if col.item(0))) - logging.info("Checking for infinite values.") + logging.debug("Checking for infinite values.") for col in dataframe.select(cs.numeric()).columns: if dataframe[col].is_infinite().any(): - logging.info(f"Column '{col}' contains infinite values. Datatype: {dataframe[col].dtype}") + logging.warning(f"Column '{col}' contains infinite values. Datatype: {dataframe[col].dtype}") max_float64 = 0 # Replace infinite values with the maximum value for float64 @@ -223,8 +221,25 @@ def preprocess_data( ] ) _dict[key] = dataframe - logging.info(f"Amount of columns: {len(dataframe.columns)}") - + logging.debug(f"Amount of columns: {len(dataframe.columns)}") + logging.info(f"{len(sanitized_data[DataSplit.train][DataSegment.features].columns)} columns in dynamic data.") + train_samples = len(sanitized_data[DataSplit.train][DataSegment.outcome]) + val_samples = len(sanitized_data[DataSplit.val][DataSegment.outcome]) + test_samples = len(sanitized_data[DataSplit.test][DataSegment.outcome]) + train_incidence = sanitized_data[DataSplit.test][DataSegment.outcome][vars[VarType.label]] + val_incidence = sanitized_data[DataSplit.val][DataSegment.outcome][vars[VarType.label]] + test_incidence = sanitized_data[DataSplit.test][DataSegment.outcome][vars[VarType.label]] + total_samples = train_samples + val_samples + test_samples + logging.info(f"Train segments: {train_samples} ({train_samples/total_samples:.1%}), " + f"Val segments: {val_samples} ({val_samples/total_samples:.1%}), " + f"Test segments: {test_samples} ({test_samples/total_samples:.1%})") + # Define the number of decimal places for rounding + decimal_places = 4 # + logging.info(f"Train incidence: {train_incidence.mean():.{decimal_places}f}, STD {train_incidence.std():.{decimal_places}f}| " + f"Val incidence: {val_incidence.mean():.{decimal_places}f}, STD {val_incidence.std():.{decimal_places}f}| " + f"Test incidence: {test_incidence.mean():.{decimal_places}f}, STD {test_incidence.std():.{decimal_places}f}") + + # logging.info(f"{len(ou)}") # Generate cache if generate_cache: caching(cache_dir, cache_file, data, load_cache) From 9ce70b053b2ac171ffcb8c231ef655066b83fbe4 Mon Sep 17 00:00:00 2001 From: Robin van de Water Date: Mon, 23 Jun 2025 11:57:58 +0200 Subject: [PATCH 55/82] clean logging --- icu_benchmarks/data/preprocessor.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/icu_benchmarks/data/preprocessor.py b/icu_benchmarks/data/preprocessor.py index ca2b2f57..dbb5b09b 100644 --- a/icu_benchmarks/data/preprocessor.py +++ b/icu_benchmarks/data/preprocessor.py @@ -174,7 +174,7 @@ def apply( data[DataSplit.val][DataSegment.features] = data[DataSplit.val][DataSegment.features].unique() data[DataSplit.test][DataSegment.features] = data[DataSplit.test][DataSegment.features].unique() - logging.info(f"Generate features: {self.generate_features}") + logging.debug(f"Generate features in preprocessing: {self.generate_features}") return data def _process_static(self, data: dict[str, dict[str, pl.DataFrame]], vars: dict[str, Union[str, list[str]]]): @@ -402,7 +402,7 @@ def apply( logging.debug("Data head") logging.debug(data[DataSplit.train][DataSegment.features].head()) logging.debug(data[DataSplit.train][DataSegment.outcome].head()) - logging.info(f"Generate features: {self.generate_features}") + logging.debug(f"Generate features: {self.generate_features}") return data def _process_static( From 05eb6dccd4aeb794428eb6fd06d9d28be44902f6 Mon Sep 17 00:00:00 2001 From: Robin van de Water Date: Mon, 23 Jun 2025 11:58:24 +0200 Subject: [PATCH 56/82] logging metrics to file for curves --- icu_benchmarks/models/utils.py | 31 ++++++++++++++++++++++++++++++- 1 file changed, 30 insertions(+), 1 deletion(-) diff --git a/icu_benchmarks/models/utils.py b/icu_benchmarks/models/utils.py index fc5b5506..3a877fc5 100644 --- a/icu_benchmarks/models/utils.py +++ b/icu_benchmarks/models/utils.py @@ -150,7 +150,7 @@ def __init__(self, output_dir=None, **kwargs): super().__init__(**kwargs) if output_dir is None: output_dir = Path.cwd() / "metrics" - logging.info(f"logging metrics to file: {str(output_dir.resolve())}") + logging.info(f"Logging metrics to file: {str(output_dir.resolve())}") self.output_dir = output_dir self.output_dir.mkdir(parents=True, exist_ok=True) @@ -282,3 +282,32 @@ def get_smoothed_labels( return np.array( list(map(lambda x: smoothing_fn(x, h_true=h_true, h_min=h_min, h_max=h_max, delta_h=delta_h, gamma=gamma), dte)) ) + + +import csv +def log_single_metric_to_file(metric_name: str, data_points: tuple[np.ndarray], output_file: Path) -> None: + """ + Logs a single metric to a file, where the input is a tuple of numpy arrays. + + Args: + metric_name (str): Name of the metric. + data_points (tuple[np.ndarray]): Tuple of numpy arrays to log. + output_file (Path): Path to the file where the metric will be saved. + + Raises: + ValueError: If data_points is not a tuple of numpy arrays or output_file is not a valid Path. + """ + if not isinstance(data_points, tuple) or not all(isinstance(arr, np.ndarray) for arr in data_points): + raise ValueError("data_points must be a tuple of numpy arrays.") + if not isinstance(output_file, Path): + raise ValueError("output_file must be a Path object.") + + output_file.parent.mkdir(parents=True, exist_ok=True) # Ensure the directory exists + + with output_file.open(mode="w", newline="") as file: + writer = csv.writer(file) + # Write header + writer.writerow(["Row"] + [f"Column {i}" for i in range(data_points[0].size)]) + # Write each numpy array as a row + for index, array in enumerate(data_points): + writer.writerow([index] + array.tolist()) \ No newline at end of file From ae77b6d7d97c3c2d47559a1ecbe02a826e61a140 Mon Sep 17 00:00:00 2001 From: Robin van de Water Date: Mon, 23 Jun 2025 12:11:59 +0200 Subject: [PATCH 57/82] ruff --- alarm_toy.ipynb | 417 ++++++++++++++++++++-- icu_benchmarks/data/split_process_data.py | 18 +- icu_benchmarks/models/utils.py | 4 +- icu_benchmarks/models/wrappers.py | 11 +- icu_benchmarks/run_utils.py | 29 +- icu_benchmarks/tuning/hyperparameters.py | 2 +- 6 files changed, 428 insertions(+), 53 deletions(-) diff --git a/alarm_toy.ipynb b/alarm_toy.ipynb index fc796f29..d11b18cc 100644 --- a/alarm_toy.ipynb +++ b/alarm_toy.ipynb @@ -2,28 +2,10 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, - "id": "initial_id", - "metadata": { - "ExecuteTime": { - "end_time": "2024-11-18T10:34:56.395941Z", - "start_time": "2024-11-18T10:34:55.939403Z" - }, - "collapsed": true - }, - "outputs": [ - { - "ename": "ModuleNotFoundError", - "evalue": "No module named 'numpy'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;66;03m# Sample data\u001b[39;00m\n\u001b[1;32m 4\u001b[0m data \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray([\n\u001b[1;32m 5\u001b[0m [\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m10\u001b[39m, \u001b[38;5;241m100\u001b[39m],\n\u001b[1;32m 6\u001b[0m [\u001b[38;5;241m2\u001b[39m, \u001b[38;5;241m20\u001b[39m, \u001b[38;5;241m200\u001b[39m],\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 9\u001b[0m [\u001b[38;5;241m3\u001b[39m, \u001b[38;5;241m50\u001b[39m, \u001b[38;5;241m500\u001b[39m]\n\u001b[1;32m 10\u001b[0m ])\n", - "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'numpy'" - ] - } - ], + "execution_count": null, + "id": "392ddb72e535edb4", + "metadata": {}, + "outputs": [], "source": [ "import numpy as np\n", "\n", @@ -48,6 +30,397 @@ " print(f\"ID {unique_id}:\\n{array}\\n\")" ] }, + { + "cell_type": "code", + "execution_count": 3, + "id": "16e091727d83764d", + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-20T13:52:23.207626Z", + "start_time": "2025-06-20T13:52:23.091745Z" + } + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import numpy as np\n", + "from sklearn.metrics import PrecisionRecallDisplay, RocCurveDisplay\n", + "\n", + "\n", + "def plot_curves_from_csv(csv_path: str) -> None:\n", + " \"\"\"\n", + " Plots AUPRC and AUROC curves from data logged in a CSV file.\n", + "\n", + " Args:\n", + " csv_path (str): Path to the CSV file containing ground truth and predictions.\n", + " \"\"\"\n", + " # Load data from CSV\n", + " data = pd.read_csv(csv_path, delimiter=\",\", encoding=\"utf-8\")\n", + "\n", + " # print(data.head())\n", + " # data = data.T\n", + "\n", + " # Preprocess data to handle infinity and large values\n", + " # Replace infinities with NaN\n", + " data = data.replace([np.inf, -np.inf], np.nan)\n", + "\n", + " # Fill NaN values with a default value (e.g., 0) instead of dropping rows\n", + " data = data.fillna(0)\n", + "\n", + " # Clip values to the float64 range\n", + " data = data.clip(-np.finfo(np.float64).max, np.finfo(np.float64).max)\n", + " # print(data.head())\n", + " # Extract precision, recall, and thresholds\n", + " precision = data.iloc[0, 1:].values # Precision values (excluding the first column)\n", + " recall = data.iloc[1, 1:].values # Recall values (excluding the first column)\n", + " thresholds = data.iloc[2, 1:].values # Thresholds (excluding the first column)\n", + " # Ensure recall and precision are sorted correctly\n", + " # sorted_indices = np.argsort(recall)\n", + " # recall = recall[sorted_indices]\n", + " # precision = precision[sorted_indices]\n", + " # Plot Precision-Recall curve\n", + " plt.figure(figsize=(12, 6))\n", + " plt.plot(recall, precision, label=\"Precision-Recall Curve\")\n", + " plt.xlabel(\"Recall\")\n", + " plt.ylabel(\"Precision\")\n", + " plt.title(\"Precision-Recall Curve\")\n", + " plt.legend()\n", + " plt.grid()\n", + "\n", + " # Plot thresholds\n", + " # plt.figure(figsize=(12, 6))\n", + " # plt.plot(thresholds, precision[:-1], label=\"Precision vs Threshold\")\n", + " # plt.plot(thresholds, recall[:-1], label=\"Recall vs Threshold\")\n", + " # plt.xlabel(\"Thresholds\")\n", + " # plt.ylabel(\"Values\")\n", + " # plt.title(\"Precision and Recall vs Thresholds\")\n", + " # plt.legend()\n", + " # plt.grid()\n", + "\n", + " # Show plots\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + "\n", + "csv_path = \"/Users/robin/Documents/git/yaib_logs/mimic_demo/aki/XGBClassifier/2025-06-18T17-11-27.152583/repetition_0/fold_1/test_metrics_RO_Curve.csv\"\n", + "plot_curves_from_csv(csv_path)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f6ec20105259df69", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7fbebb12c68e59fe", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "35fe0bf84155fdef", + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-20T13:55:55.553437Z", + "start_time": "2025-06-20T13:55:55.544915Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['# id', 'time', 'ground_truth', 'prediction_0', 'prediction_1'], dtype='object')" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "13be64a0f98bf02d", + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-20T13:59:20.113873Z", + "start_time": "2025-06-20T13:59:19.971315Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "from sklearn.metrics import PrecisionRecallDisplay, average_precision_score\n", + "from sklearn.metrics import RocCurveDisplay, roc_auc_score\n", + "\n", + "\n", + "def plot_auprc(df):\n", + " \"\"\"\n", + " Plots the AUPRC curve based on predictions in a DataFrame.\n", + "\n", + " Args:\n", + " df (pd.DataFrame): DataFrame containing columns '# id', 'time', 'ground_truth', 'prediction_0', 'prediction_1'.\n", + " \"\"\"\n", + " # Extract ground truth and predictions\n", + " ground_truth = df[\"ground_truth\"]\n", + " predictions = df[\"prediction_1\"]\n", + "\n", + " # Calculate average precision score\n", + " average_precision = average_precision_score(ground_truth, predictions)\n", + "\n", + " # Calculate chance level (mean of ground truth)\n", + " chance_level = ground_truth.mean()\n", + "\n", + " # Plot Precision-Recall curve\n", + " plt.figure(figsize=(8, 6))\n", + " PrecisionRecallDisplay.from_predictions(ground_truth, predictions)\n", + " plt.axhline(y=chance_level, color=\"red\", linestyle=\"--\", label=f\"Chance Level ({chance_level:.2f})\")\n", + " plt.title(f\"Precision-Recall Curve (AP: {average_precision:.2f})\")\n", + " plt.legend()\n", + " plt.grid()\n", + " plt.show()\n", + "\n", + "\n", + "def plot_auroc(df):\n", + " \"\"\"\n", + " Plots the AUROC curve based on predictions in a DataFrame.\n", + "\n", + " Args:\n", + " df (pd.DataFrame): DataFrame containing columns '# id', 'time', 'ground_truth', 'prediction_0', 'prediction_1'.\n", + " \"\"\"\n", + " # Extract ground truth and predictions\n", + " ground_truth = df[\"ground_truth\"]\n", + " predictions = df[\"prediction_1\"]\n", + "\n", + " # Calculate AUROC score\n", + " roc_auc = roc_auc_score(ground_truth, predictions)\n", + "\n", + " # Plot ROC curve\n", + " plt.figure(figsize=(8, 6))\n", + " RocCurveDisplay.from_predictions(ground_truth, predictions)\n", + " plt.title(f\"ROC Curve (AUC: {roc_auc:.2f})\")\n", + " plt.grid()\n", + " plt.show()\n", + "\n", + "\n", + "preds_path = \"/Users/robin/Documents/git/yaib_logs/mimic_demo/aki/XGBClassifier/2025-06-18T17-11-27.152583/repetition_0/fold_0/pred_indicators.csv\"\n", + "preds = pd.read_csv(preds_path)\n", + "plot_auprc(preds)\n", + "plot_auroc(preds)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "e88ff2e71f32ce9a", + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-20T14:06:03.507937Z", + "start_time": "2025-06-20T14:06:03.263979Z" + } + }, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAArMAAAIjCAYAAAAQgZNYAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAACcUklEQVR4nOzdd3xTVeMG8Cc76aZ0MQoFygZBQBCQoSBFEMQBiMhU0VdAeRH3AH4OHKi4EAfjVUGWgAvQgiAIKMoSZY9SVhfQmZ17fn+kuW1oOmmbpH2+n08/bW7Ovffc3DR5cnLuOQohhAARERERkR9SersCREREREQVxTBLRERERH6LYZaIiIiI/BbDLBERERH5LYZZIiIiIvJbDLNERERE5LcYZomIiIjIbzHMEhEREZHfYpglIiIiIr/FMEtE5CVJSUlQKBRYsmRJudbr27cv+vbtWyV18ndbt26FQqHA1q1b5WXjx49HXFyc1+pERFWLYZaoBpo/fz4UCgW6devm7ar4nLi4OCgUCvknKioKvXr1wtq1a71dNb8wfvx4t8dPp9OhRYsWeOmll2A2m71dvUqxdu1a3HbbbYiIiIBWq0X9+vUxYsQI/PLLL96uGhF5oPZ2BYio8i1duhRxcXHYvXs3Tpw4gfj4eG9Xyad07NgRTzzxBADgwoUL+OSTT3DXXXfh448/xiOPPFJt9WjcuDFMJhM0Gk251vv555+rqEZlo9Pp8PnnnwMAsrKy8O233+Lll1/GyZMnsXTpUq/W7VoIITBx4kQsWbIE119/PaZPn46YmBhcvHgRa9euRb9+/bBjxw706NHD21UlokIYZolqmNOnT2Pnzp1Ys2YNHn74YSxduhQzZ86s1jpIkgSr1Qq9Xl+t+y2rBg0a4P7775dvjx07FvHx8Xj33XeLDbN2ux2SJEGr1VZaPRQKRYUeo8qsQ0Wo1Wq3x+/RRx9Fjx498PXXX+Odd95BdHS0F2tXcW+//TaWLFmCadOm4Z133oFCoZDve/755/Hll19Crb72t00hBMxmMwwGwzVvi4jYzYCoxlm6dCnq1KmDwYMH45577nFrKbPZbAgPD8eECROKrJednQ29Xo8ZM2bIyywWC2bOnIn4+HjodDrExsbiqaeegsVicVtXoVBgypQpWLp0Kdq2bQudToeNGzcCAObOnYsePXqgbt26MBgM6Ny5M1avXl1k/yaTCY899hgiIiIQHByMoUOH4vz581AoFJg1a5Zb2fPnz2PixImIjo6GTqdD27ZtsWjRogo/ZjExMWjdujVOnz4NoKAv69y5czFv3jw0a9YMOp0Ohw4dAgAcOXIE99xzD8LDw6HX69GlSxd89913RbabmZmJ//73v4iLi4NOp0PDhg0xduxYZGRkuO2ncJ/ZlJQUTJgwAQ0bNoROp0O9evVwxx13ICkpSS7jqc9sWloaHnjgAURHR0Ov16NDhw743//+51am8HF9+umn8nHdcMMN+PPPPyv8+CkUCtx0000QQuDUqVNu923YsAG9evVCYGAggoODMXjwYPz7779FtnHkyBGMGDECkZGRMBgMaNmyJZ5//nn5/jNnzuDRRx9Fy5YtYTAYULduXQwfPtztcbkWJpMJc+bMQatWrTB37ly3IOsyZswYdO3aFQAwa9Ysj2WWLFkChULhVq+4uDjcfvvt+Omnn9ClSxcYDAZ88sknaNeuHW6++eYi25AkCQ0aNMA999zjtmzevHlo27Yt9Ho9oqOj8fDDD+PKlSuVcPRE/o0ts0Q1zNKlS3HXXXdBq9Vi1KhR+Pjjj/Hnn3/ihhtugEajwZ133ok1a9bgk08+cWvhW7duHSwWC+69914AzjfPoUOH4rfffsOkSZPQunVrHDx4EO+++y6OHTuGdevWue33l19+wcqVKzFlyhRERETIF9y89957GDp0KEaPHg2r1Yrly5dj+PDh+OGHHzB48GB5/fHjx2PlypUYM2YMbrzxRvz6669u97ukpqbixhtvlAN0ZGQkNmzYgAceeADZ2dmYNm1auR8zm82Gs2fPom7dum7LFy9eDLPZjEmTJkGn0yE8PBz//vsvevbsiQYNGuCZZ55BYGAgVq5ciWHDhuGbb77BnXfeCQDIzc1Fr169cPjwYUycOBGdOnVCRkYGvvvuO5w7dw4REREe63L33Xfj33//xdSpUxEXF4e0tDQkJiYiOTm52IuYTCYT+vbtixMnTmDKlClo0qQJVq1ahfHjxyMzMxOPP/64W/lly5YhJycHDz/8MBQKBd58803cddddOHXqVLm7PLi4wludOnXkZV9++SXGjRuHhIQEvPHGGzAajfj4449x0003Yd++ffLx/P333+jVqxc0Gg0mTZqEuLg4nDx5Et9//z1effVVAMCff/6JnTt34t5770XDhg2RlJSEjz/+GH379sWhQ4cQEBBQoXq7/Pbbb7h8+TKmTZsGlUp1Tdvy5OjRoxg1ahQefvhhPPTQQ2jZsiVGjhyJWbNmISUlBTExMW51uXDhgvy/CAAPP/wwlixZggkTJuCxxx7D6dOn8eGHH2Lfvn3YsWNHhc8bUY0giKjG+OuvvwQAkZiYKIQQQpIk0bBhQ/H444/LZX766ScBQHz//fdu6w4aNEg0bdpUvv3ll18KpVIptm/f7lZuwYIFAoDYsWOHvAyAUCqV4t9//y1SJ6PR6HbbarWKdu3aiVtuuUVetmfPHgFATJs2za3s+PHjBQAxc+ZMedkDDzwg6tWrJzIyMtzK3nvvvSI0NLTI/q7WuHFjMWDAAJGeni7S09PFgQMHxL333isAiKlTpwohhDh9+rQAIEJCQkRaWprb+v369RPt27cXZrNZXiZJkujRo4do3ry5vOyll14SAMSaNWuK1EGSJLf9LF68WAghxJUrVwQA8dZbb5V4DH369BF9+vSRb8+bN08AEF999ZW8zGq1iu7du4ugoCCRnZ3ttr+6deuKy5cvy2W//fZbj88JT8aNGycCAwPlx+/EiRNi7ty5QqFQiHbt2snHlpOTI8LCwsRDDz3ktn5KSooIDQ11W967d28RHBwszpw54/FxEqLo80gIIXbt2iUAiC+++EJetmXLFgFAbNmyxa3OjRs3LvG43nvvPQFArF27trSHQAghxMyZM4Wnt9DFixcLAOL06dPyssaNGwsAYuPGjW5ljx49KgCIDz74wG35o48+KoKCguRj3r59uwAgli5d6lZu48aNHpcT1TbsZkBUgyxduhTR0dHyV5cKhQIjR47E8uXL4XA4AAC33HILIiIisGLFCnm9K1euIDExESNHjpSXrVq1Cq1bt0arVq2QkZEh/9xyyy0AgC1btrjtu0+fPmjTpk2ROhXuF3jlyhVkZWWhV69e2Lt3r7zc1SXh0UcfdVt36tSpbreFEPjmm28wZMgQCCHc6pWQkICsrCy37Rbn559/RmRkJCIjI9GhQwesWrUKY8aMwRtvvOFW7u6770ZkZKR8+/Lly/jll18wYsQI5OTkyPu+dOkSEhIScPz4cZw/fx4A8M0336BDhw5yS21hnr6edj1WWq0WW7duLdfXx+vXr0dMTAxGjRolL9NoNHjssceQm5uLX3/91a38yJEj3VpQe/XqBQBFuggUJy8vT3784uPjMWPGDPTs2RPffvutfGyJiYnIzMzEqFGj3M6TSqVCt27d5OdPeno6tm3bhokTJ6JRo0Zu+yn8OBV+HtlsNly6dAnx8fEICwsr0zkvTXZ2NgAgODj4mrflSZMmTZCQkOC2rEWLFujYsaPb/6LD4cDq1asxZMgQ+ZhXrVqF0NBQ3HrrrW6PZefOnREUFFTkf5GotmE3A6IawuFwYPny5bj55pvlvp8A0K1bN7z99tvYvHkzBgwYALVajbvvvhvLli2DxWKBTqfDmjVrYLPZ3MLs8ePHcfjwYbcwV1haWprb7SZNmngs98MPP+CVV17B/v373fraFg4qZ86cgVKpLLKNq0dhSE9PR2ZmJj799FN8+umnZaqXJ926dcMrr7wChUKBgIAAtG7dGmFhYUXKXV2fEydOQAiBF198ES+++GKx+2/QoAFOnjyJu+++u9S6FKbT6fDGG2/giSeeQHR0NG688UbcfvvtGDt2rNvX0Fc7c+YMmjdvDqXSvX2idevW8v2FXR0aXcHWFaBNJhOysrLcyhTev16vx/fffw8AOHfuHN58802kpaW5Bc7jx48DgPzh52ohISEACgJ0u3btij0+V53mzJmDxYsX4/z58xBCyPddXdeKcNUnJyfnmrflSXH/HyNHjsRzzz2H8+fPo0GDBti6dSvS0tKK/C9mZWUhKirK4zbK8pwnqskYZolqiF9++QUXL17E8uXLsXz58iL3L126FAMGDAAA3Hvvvfjkk0+wYcMGDBs2DCtXrkSrVq3QoUMHubwkSWjfvj3eeecdj/uLjY11u+3pyuzt27dj6NCh6N27N+bPn4969epBo9Fg8eLFWLZsWbmPUZIkAMD999+PcePGeSxz3XXXlbqdiIgI9O/fv9RyVx+Ta/8zZswo0srmcq3DoE2bNg1DhgzBunXr8NNPP+HFF1/EnDlz8Msvv+D666+/pm27FNcn1BUQV6xYUeQiwcLhUaVSuT1+CQkJaNWqFR5++GH5QjjXY/Xll196DOLlHRVg6tSpWLx4MaZNm4bu3bsjNDQUCoUC9957r7yva9GqVSsAwMGDBzFs2LBSyxfXuu76BuRqxY1cMHLkSDz77LNYtWoVpk2bhpUrVyI0NBQDBw6Uy0iShKioqGKHPSvuAydRbcEwS1RDLF26FFFRUfjoo4+K3LdmzRqsXbsWCxYsgMFgQO/evVGvXj2sWLECN910E3755Re3K8cBoFmzZjhw4AD69etX7Bt3ab755hvo9Xr89NNP0Ol08vLFixe7lWvcuDEkScLp06fRvHlzefmJEyfcykVGRiI4OBgOh6NMYbSyNW3aFIDzK/zS9t+sWTP8888/FdpPs2bN8MQTT+CJJ57A8ePH0bFjR7z99tv46quvPJZv3Lgx/v77b0iS5NY6e+TIEfn+8khISEBiYmKZy9erVw///e9/MXv2bPz++++48cYb0axZMwBAVFRUiY+V6zEt7bFavXo1xo0bh7ffflteZjabkZmZWeZ6luSmm25CnTp18PXXX+O5554r9SIwV2t2ZmamW6v+1a3gpWnSpAm6du2KFStWYMqUKVizZg2GDRvm9v/SrFkzbNq0CT179uRwXkQesM8sUQ1gMpmwZs0a3H777bjnnnuK/EyZMgU5OTlyq5lSqcQ999yD77//Hl9++SXsdrvb15oAMGLECJw/fx6fffaZx/3l5eWVWi+VSgWFQuHWWpWUlFRkJARXK+f8+fPdln/wwQdFtnf33Xfjm2++8Rh+0tPTS63TtYiKikLfvn3xySef4OLFiyXu/+6778aBAwc8zixWuJWzMKPRWGQWrWbNmiE4OLjIcGiFDRo0CCkpKW59L+12Oz744AMEBQWhT58+pR5bYfXq1UP//v3dfkozdepUBAQE4PXXXwfgPKchISF47bXXYLPZipR3PVaRkZHo3bs3Fi1ahOTkZLcyV7cGX/24ffDBB8W2hJZXQEAAnn76aRw+fBhPP/20x3P01VdfYffu3QAgh/Vt27bJ9+fl5RUZDq0sRo4cid9//x2LFi1CRkaGx/9Fh8OBl19+uci6dru90gI9kb9iyyxRDfDdd98hJycHQ4cO9Xj/jTfeiMjISCxdulR+oxw5ciQ++OADzJw5E+3bt5f7V7qMGTMGK1euxCOPPIItW7agZ8+ecDgcOHLkCFauXCmPmVmSwYMH45133sHAgQNx3333IS0tDR999BHi4+Px999/y+U6d+6Mu+++G/PmzcOlS5fkobmOHTsGwP0r3ddffx1btmxBt27d8NBDD6FNmza4fPky9u7di02bNuHy5csVegzL6qOPPsJNN92E9u3b46GHHkLTpk2RmpqKXbt24dy5czhw4AAA4Mknn8Tq1asxfPhwTJw4EZ07d8bly5fx3XffYcGCBW5dOlyOHTuGfv36YcSIEWjTpg3UajXWrl2L1NRUt2GarjZp0iR88sknGD9+PPbs2YO4uDisXr0aO3bswLx586rsoqbC6tatiwkTJmD+/Pk4fPgwWrdujY8//hhjxoxBp06dcO+99yIyMhLJycn48ccf0bNnT3z44YcAgPfffx833XQTOnXqhEmTJqFJkyZISkrCjz/+iP379wMAbr/9dnz55ZcIDQ1FmzZtsGvXLmzatKnIcGrX4sknn8S///6Lt99+G1u2bME999yDmJgYpKSkYN26ddi9ezd27twJABgwYAAaNWqEBx54AE8++SRUKhUWLVokH2N5jBgxAjNmzMCMGTMQHh5e5MNDnz598PDDD2POnDnYv38/BgwYAI1Gg+PHj2PVqlV477333MakJap1vDWMAhFVniFDhgi9Xi/y8vKKLTN+/Hih0WjkIa0kSRKxsbECgHjllVc8rmO1WsUbb7wh2rZtK3Q6nahTp47o3LmzmD17tsjKypLLARCTJ0/2uI2FCxeK5s2bC51OJ1q1aiUWL17scVijvLw8MXnyZBEeHi6CgoLEsGHD5KGLXn/9dbeyqampYvLkySI2NlZoNBoRExMj+vXrJz799NNSH6vGjRuLwYMHl1jGNYRVcUNknTx5UowdO1bExMQIjUYjGjRoIG6//XaxevVqt3KXLl0SU6ZMEQ0aNBBarVY0bNhQjBs3Tj4HVw/NlZGRISZPnixatWolAgMDRWhoqOjWrZtYuXKl23avHprL9ZhMmDBBRERECK1WK9q3by9vtyzHhauGQCuOa2iu4h4XlUolxo0bJy/bsmWLSEhIEKGhoUKv14tmzZqJ8ePHi7/++stt3X/++UfceeedIiwsTOj1etGyZUvx4osvyvdfuXJFPr6goCCRkJAgjhw5Iho3blxkf6jA0FyFrV69WgwYMECEh4cLtVot6tWrJ0aOHCm2bt3qVm7Pnj2iW7duQqvVikaNGol33nmn2KG5SnvO9ezZUwAQDz74YLFlPv30U9G5c2dhMBhEcHCwaN++vXjqqafEhQsXynxsRDWRQohivu8iIvKy/fv34/rrr8dXX32F0aNHe7s6RETkg9hnloh8gslkKrJs3rx5UCqV6N27txdqRERE/oB9ZonIJ7z55pvYs2cPbr75ZqjVamzYsAEbNmzApEmTigwDRkRE5MJuBkTkExITEzF79mwcOnQIubm5aNSoEcaMGYPnn3++3GOSEhFR7cEwS0RERER+i31miYiIiMhvMcwSERERkd+qdR3RJEnChQsXEBwcXOEpOomIiIio6gghkJOTg/r167tN0+1JrQuzFy5c4JXRRERERH7g7NmzaNiwYYllal2YdU3rePbsWYSEhFT5/mw2G37++Wd5+kHyPzyH/o/n0P/xHPo3nj//V93nMDs7G7GxsWWajrvWhVlX14KQkJBqC7MBAQEICQnhP7Cf4jn0fzyH/o/n0L/x/Pk/b53DsnQJ5QVgREREROS3GGaJiIiIyG8xzBIRERGR36p1fWaJyPuEELDb7XA4HNWyP5vNBrVaDbPZXG37pMrFc+jfeP78X1WcQ41GA5VKdc3bYZglompltVpx8eJFGI3GatunEAIxMTE4e/Ysx5f2UzyH/o3nz/9VxTlUKBRo2LAhgoKCrmk7DLNEVG0kScLp06ehUqlQv359aLXaanljkyQJubm5CAoKKnXwbfJNPIf+jefP/1X2ORRCID09HefOnUPz5s2vqYWWYZaIqo3VaoUkSYiNjUVAQEC17VeSJFitVuj1er6R+imeQ//G8+f/quIcRkZGIikpCTab7ZrCLJ9RRFTt+GZGRESV9c0c31GIiIiIyG8xzBIRERGR32KYJSIin7JkyRKEhYVVetmaYPz48Rg2bJh8u2/fvpg2bZrX6lOVFi5ciAEDBni7GlRBVqsVcXFx+Ouvv6p8XwyzRETlsGvXLqhUKgwePNjbVakWCoVC/gkNDUXPnj3xyy+/VOk+R44ciWPHjlV62WuxdetWt8ciMjISgwYNwsGDB6t839fKarXizTffRIcOHRAQEICIiAj07NkTixcvhs1m83b1PDKbzXjxxRcxc+bMIvedO3cOWq0W7dq187huZT1nP/roI8TFxUGv16Nbt27YvXt3qeusWrUKrVq1gl6vR/v27bF+/foiZQ4fPoyhQ4ciNDQUgYGBuOGGG5CcnFzu+pVHWep1ta1bt6JTp07Q6XSIj4/HkiVLii37+uuvQ6FQuH2w0mq1mDFjBp5++ulKOIKSMcwSEZXDwoULMXXqVGzbtg0XLlyo0n25JpfwtsWLF+PixYvYsWMHIiIicPvtt+PUqVMey1ZGODIYDIiKiqr0spXh6NGjuHjxIn766SdYLBYMHjwYVqu12vZfXlarFQkJCXj99dcxadIk7Ny5E7t378bkyZPxwQcf4N9//63wtqsyCK9evRohISHo2bNnkfuWLFmCESNGIDs7G3/88YfH9cvznPVkxYoVmD59OmbOnIm9e/eiQ4cOSEhIQFpaWrHr7Ny5E6NGjcIDDzyAffv2YdiwYRg2bBj++ecfuczJkydx0003oVWrVti6dSv+/vtvvPjii9Dr9WWu2/jx4zFr1qwyly9Lva52+vRpDB48GDfffDP279+PadOmYdKkSdi8eXORsn/++Sc++eQTXHfddUXuGz16NH777bdrep6ViahlsrKyBACRlZVVLfuzWq1i3bp1wmq1Vsv+qPLxHFYek8kkDh06JEwmk7xMkiSRZ7FV6U+OySIupGaIHJPFbbkkSeWqf05OjggKChJHjhwRI0eOFK+++qp836hRo8SIESPcylutVlG3bl3xv//9TwghhMPhEK+99pqIi4sTer1eXHfddWLVqlVy+S1btggAYv369aJTp05Co9GILVu2iBMnToihQ4eKqKgoERgYKLp06SISExPd9nXhwgUxaNAgodfrRVxcnFi6dKlo3LixePfdd+UyV65cEQ888ICIiIgQwcHB4uabbxb79+8v8ZgBiLVr18q3z58/LwCIBQsWyPfPnz9fDBkyRAQEBIiZM2cKIYRYt26duP7664VOpxNNmjQRs2bNEjabza0ukyZNElFRUUKn04m2bduK77//XgghxOLFi0VoaKhcdv/+/aJv374iKChIBAcHi06dOok///zTY1khhJg/f75o2rSp0Gg0okWLFuKLL74ockyfffaZGDZsmDAYDCI+Pl58++23JT4OrnNz5coVedl3330nAIgDBw7Iy7Zv3y5uuukmodfrRcOGDcXUqVNFbm6ufL/ZbBZPPfWUaNiwodBqtaJZs2bi888/F0IIYbfbxcSJE+XnR4sWLcS8efPc6jFu3Dhxxx13yLf79OkjHn/88WLr/cYbbwilUin27t1b5D6r1SrX7ernihBCdOjQQT6fQhQ91y+++KJo0KCBmD9/vtt6e/fuFQqFQiQlJQkhnOd64sSJom7dumV+3g0ePFjMmDGjyHJJkkTTpk3Fxo0bxdNPPy0eeuihImVKe86WRdeuXcXkyZPl2w6HQ9SvX1/MmTOn2HVGjBghBg8e7LasW7du4uGHH5Zvjxw5Utx///1lrocn48aNczsvpSlLva721FNPibZt2xbZTr9+/YTD4ZCX5eTkiObNm4vExMRin4s333yzeOGFFzzux9N7gkt58hrHmSUirzLZHGjz0k9e2feh/0tAgLbsL4MrV65Eq1at0LJlS9x///2YNm0ann32WSgUCowePRrDhw+XBxUHgJ9++glGoxF33nknAGDOnDn46quvsGDBAjRv3hzbtm3D/fffj8jISPTp00fezzPPPIO5c+eiadOmqFOnDs6ePYtBgwbh1VdfhU6nwxdffIEhQ4bg6NGjaNSoEQBg7NixyMjIwNatW6HRaDB9+vQirUjDhw+HwWDAhg0bEBoaik8++QT9+vXDsWPHEB4eXqbHwGAwAIBba+SsWbPw+uuvY968eVCr1di+fTvGjh2L999/H7169cLJkycxadIkAMDMmTMhSRJuu+025OTk4KuvvkKzZs1w6NChYseZHD16NDp27Ig33ngDoaGh+Pvvv6HRaDyWXbt2LR5//HHMmzcP/fv3xw8//IAJEyagYcOGuPnmm+Vys2fPxptvvom33noLH3zwAUaPHo0zZ86U+XHIysrC8uXLATi/TgWcrW4DBw7EK6+8gkWLFiE9PR1TpkzBlClTsHjxYgDO87Rr1y68//776NChA06fPo2MjAwAznE8GzZsiFWrVqFu3brYuXMnJk2ahHr16mHEiBFlqtfVli5div79++P6668vcp9Goyn2cSzO1efaZDJh2bJl+M9//uO2z549e6Jx48YAnM87vV6PVatWoX79+vjss89Kfd799ttvGDNmTJHlW7ZsgdFoRP/+/dGgQQP06NED7777LgIDA4ut89XP2SVLlmDChAkQQngsb7VasWfPHjz77LPyMqVSif79+2PXrl3F7mfXrl2YPn2627KEhASsW7cOgPP8/vjjj3jqqaeQkJCAffv2oUmTJnj22Wfd+kFXttLqVdw6/fv3d1s2YMAA/Pe//3VbNnnyZAwePBj9+/fHK6+84nFbXbt2xfbt2ytW+TJimCUiKqOFCxfi/vvvBwAMHDgQWVlZ+PXXX9G3b18kJCQgMDAQa9euld+Ely1bhqFDhyI4OBgWiwWvvfYaNm3ahO7duwMAmjZtit9++w2ffPKJW5j9v//7P9x6663y7fDwcHTo0EG+/fLLL2Pt2rX47rvvMGXKFBw5cgSbNm3Cn3/+iS5dugAAPv/8czRv3lxe57fffsPu3buRlpYGnU4HAJg7dy7WrVuH1atXy2GzJEajES+88AJUKpVbfe+77z5MmDBBvj1x4kQ888wzGDdunHycL7/8Mp566inMnDkTmzZtwu7du3H48GG0aNFCLlOc5ORkPPHEE2jRogVCQkLQsmXLYsvOnTsX48ePx6OPPgoAmD59On7//XfMnTvXLcyOHz8eo0aNAgC89tpreP/997F7924MHDiwxMegYcOGAIC8vDwAwNChQ9GqVSsAzg8ro0ePlvsNNm/eHO+//z769OmDjz/+GMnJyVi5ciUSExPloFD4uDUaDWbPni3fbtKkCXbt2oWVK1dWOMweP34cffv2rdC6nlx9rkePHo23334bycnJaNSoESRJwvLly/HCCy8AKHjepaSkwGKxICQkpNTnXWZmJrKyslC/fv0i9y1cuBD33nsvVCoV2rVrh6ZNm2LVqlUYP368x/p6es6GhoaW+BzKyMiAw+FAdHS02/Lo6GgcOXKk2PVSUlI8rpOSkgIASEtLQ25uLl5//XW88soreOONN7Bx40bcdddd2LJli9v/VGFLly7Fww8/LN+2WCxQKBSYO3euvGzDhg3o1atXhepVnnVycnJgMpkQGBiI5cuXY+/evfjzzz+L3Q4A1K9fH2fOnCmxzLVimCUirzJoVDj0fwlVug9JkpCTnYPgkGC3CRsMmrLPOHP06FHs3r0ba9euBQCo1WqMHDkSCxcuRN++faFWqzFixAgsXboUY8aMQV5eHr799lu59e7EiRMwGo1uIRVwtgJd3WrmCqQuubm5mDVrFn788UdcvHgRdrsdJpNJvmjk6NGjUKvV6NSpk7xOfHw86tSpI98+cOAAcnNzUbduXbdtm0wmnDx5ssRjHzVqFFQqFUwmEyIjI7Fw4UK3/nFX1/fAgQPYsWMHXn31VXmZw+GA2WyG0WjE/v370bBhQznIlmb69OmYNGkS/ve//yEhIQEjRoxAs2bNPJY9fPhwkYDUs2dPvPfee27LCtc/MDAQISEhckt227Zt5TffXr16YcOGDXLZ7du3IyAgAL///jtee+01LFiwwO24//77byxdulReJoSQp3E+ePBgkQ8CV/voo4+waNEiJCcnw2QywWq1omPHjqU8QsUrrvWxoq4+1x07dkTr1q2xbNkyPPPMM/j111+RlpaG4cOHAyh43kVGRrqtV9LzzmQyAUCRfqSZmZlYs2YNfvvtN3nZ/fffj4ULFxYJsyU9Z++8807525LqJEkSAOCOO+6QWzg7duyInTt3YsGCBcU+L4YOHYpu3brJt59++mk0aNAAjz32mLysQYMGVVjzos6ePYvHH38ciYmJpfb3NRgMMBqNVVofhlkvkSTnC4xSWfXz0hP5MoVCUa6v+itCkiTYtSoEaNUVnn1s4cKFsNvtbq1FQgjodDp8+OGHCA0NxejRo9GnTx+kpaUhMTERBoNBbunLzc0FAPz4449F3nhcLaUuV39lOmPGDCQmJmLu3LmIj4+HwWDAPffcU64Lj3Jzc1GvXj1s3bq1yH2lDW317rvvon///ggNDS0SSjzVNzc3F7Nnz8Zdd91VpKxer5e/9i2rWbNm4d5778WaNWvwyy+/YNasWVi+fPk1BZKrv15XKBRy2Fi/fr18cdPVdW3SpAnCwsLQsmVLpKWlYeTIkdi2bRsA53E//PDDbiHDpVGjRjhx4kSJdVq+fDlmzJiBt99+G927d0dwcDDeeuutYi9yKosWLVqU2JroolQqiwRfTxd4efo6f/To0XKYXbZsGQYOHCh/aHI973755Re5C47rf7C4513dunWhUChw5coVt+XLli2D2Wx2C3auDwvHjh1z+3BU2nO2JBEREVCpVEhNTXVbnpqaipiYmGLXi4mJKXGdiIgIqNVqtGnTxq1M69at3QL61YKDgxEcHOx2Ozw8HPHx8WU6ntLqVZ51goODYTAYsGfPHqSlpbl9gHY4HNi2bRs+/PBDWCwWudvQ5cuXy30OyoujGVQzhySQlm3GkZQcmGwOb1eHiMrAbrfjiy++wNtvv439+/fLPwcOHED9+vXx9ddfAwB69OiB2NhYrFixAkuXLsXw4cPl0NSmTRvodDokJycjPj7e7Sc2NrbE/e/YsQPjx4/HnXfeifbt2yMmJgZJSUny/S1btoTdbse+ffvkZSdOnHALA506dUJKSgrUanWR/UdERJS4/5iYGMTHx5f5DalTp044evRokf3Ex8dDqVTiuuuuw7lz58o1pFaLFi3w6KOP4qeffsJdd90l90G9WuvWrbFjxw63ZTt27CgSIErSuHFjub4ltXhNnjwZ//zzj9xa36lTJxw6dMjjcWu1WrRv3x6SJOHXX3/1uL0dO3agR48eePTRR3H99dcjPj6+1Fbz0tx3333YtGmT23PDxWazyd0lIiMjcfHiRfm+7OxsnD59usz7+Oeff7Bnzx6sXr0ao0ePlu8r/Lxr2rRpmZ53Wq0Wbdq0waFDh9yWL1y4EE888USR/8FevXph0aJFbmXL+5y9ev+dO3d2u3JfkiRs3rxZ7iLkSffu3Ytc7Z+YmCivo9VqccMNN+Do0aNuZY4dOyb3L64KpdWrrOts2rQJXbt2BQD069cPBw8edDsXXbp0wejRo7F//363/u///POPxz7blarUS8RqGG+NZpBnMosLmUZx8Fym+Pus8+dyrqVa6kDXhqMZVJ6SrlytSg6HQ1y5csXtKtzyWLt2rdBqtSIzM7PIfU899ZTo0qWLfPv5558Xbdq0EWq1Wmzfvt2t7PPPPy/q1q0rlixZIk6cOCH27Nkj3n//fbFkyRIhhOcr5oUQ4s477xQdO3YU+/btE/v37xdDhgwRwcHBblcO9+/fX3Tq1En88ccfYu/eveLmm28WBoNBvhpekiRx0003iQ4dOoiffvpJnD59WuzYsUM899xz8sgAnuCqK8PLcv/GjRuFWq0Ws2bNEv/88484dOiQ+Prrr8Xzzz8vl+nbt69o166d+Pnnn8WpU6fE+vXrxYYNG4QQ7iMUGI1GMXnyZLF582Zx4MABsW3bNtGsWTPx1FNPFSkrhPNcaTQaMX/+fHHs2DHx9ttvC5VKJbZs2VJinUNDQ8XixYuLPc7izs1TTz0l2rdvLyRJEgcOHBAGg0FMnjxZ7Nu3Txw7dkysW7fO7ar48ePHi9jYWLF27Vpx6tQpsWXLFrFixQohhBDvvfeeCAkJERs3bhRHjx4VL7zwgggJCREdOnSQ1y/vaAZms1n06tVL1KlTR3z44Ydi//794uTJk2LFihWiU6dOYt++fUIIIZ555hkRExMjtm3bJv7++28xbNgwERQUVGQ0g+KeCz179hQdOnQQwcHBwmg0yssLP++++eYbcfLkyTI976ZPny7uvvtu+fa+ffsEAHH48OEiZefPny9iYmLk0TJKe86uWbNGtGzZstj7hRBi+fLlQqfTiSVLlohDhw6JSZMmibCwMJGSkiKXGTNmjHjmmWfk2zt27BBqtVrMnTtXHD58WMycOVNoNBpx8OBBt31rNBrx6aefiuPHj4sPPvhAqFSqIq8VhRmNRnHx4sUSfyyW4vNEWer1zDPPiDFjxsi3T506JQICAsSTTz4pDh8+LD766COhUqnE6tWri30dLe652Lhx4yIjirhU1mgGDLNVzBWEDpzJkEOs6yc1q3rf0KliGGYrj7+G2dtvv10MGjTI431//PGH2/BMhw4dEgBE48aNiwz9JUmSmDdvnmjZsqXQaDQiMjJSJCQkiF9//VUIUXxgOn36tBxOY2NjxYcffljkjePChQvitttuEzqdTjRu3FgsW7ZMREVFuQ1HlJ2dLaZOnSrq168vNBqNiI2NFaNHjxbJycnFHntFwqwQzkDbo0cPYTAYREhIiOjatav49NNP5fsvXbokJkyYIOrWrSv0er1o166d+OGHH4QQ7gHVYrGIe++9V8TGxgqtVivq168vpkyZIj+HKjo0V2WF2eTkZKFWq+VAunv3bnHrrbeKoKAgERgYKK677jq3IdxMJpP473//K+rVqye0Wq2Ij48XixYtEkI4g+f48eNFaGioCAsLE//5z3/EM888c01h1rXdOXPmiPbt2wu9Xi/Cw8NFz549xZIlS+QAmJWVJUaOHClCQkJEbGysWLJkicehuYp7LsyfP18AEGPHji1yX3Z2tpgyZYqoV69emZ93//77rzAYDPIHyClTpog2bdp4LHvx4kWhVCrl4dVKe84uXrxYlKUt74MPPhCNGjUSWq1WdO3aVfz+++9u9/fp00eMGzfObdnKlStFixYthFarFW3bthU//vhjke0uXLhQxMfHC71eLzp06CDWrVtXYj1c9S3pp/CHNU9Kq9e4ceNEnz593JZt2bJFdOzYUWi1WtG0aVOxcOHCEl9HPT0Xd+7cKcLCwtw+4BRWWWFWIUQl9w73cdnZ2QgNDUVWVhZCQkKqfH82mw3r169HXMeboFS59wusE6hBwzoBVV4Hujauczho0KByD2ND7sxmM06fPo0mTZqUa5DwayVJErKzsxESElLhPrP+5ty5c4iNjcWmTZvQr18/b1fnmtXGc1iTVOT8DR8+HJ06dXIbIou8pyLncOTIkejQoQOee+45j/eX9J5QnrzGVwQvsjlq1ecIIqpCv/zyC7777jucPn0aO3fuxL333ou4uDj07t3b21UjqpC33npLHrOZ/I/VakX79u2LjE1bFTiagRfZHJK3q0BENYTNZsNzzz2HU6dOITg4GD169MDSpUv5bQL5rbi4OEydOtXb1aAK0mq18njDVY1h1ousdoZZIqocCQkJSEio2vF6iYh8EbsZeJEQbJ0lIiIiuhYMs17GMEtERERUcQyzXmaz8yIwIiIioopimPUyK1tmiYiIiCqMYdbL2M2AiIiIqOIYZr2MIxoQERERVRyH5vIytswSOR08l1Vl25aEhLy8PATmCCgVzs/w7RuGVtn+rpUQAg8//DBWr16NK1euYN++fZg2bRo6duyIefPmFbteXFwcpk2bhmnTplVbXWsrPtZEvoMts17GPrNE/iMlJQVTp05F06ZNodPpEBsbiyFDhmDz5s2Vup+NGzdiyZIl+OGHH3Dx4kW0a9cOa9aswcsvv1yp+/GGpKQkKBQK7N+/v8zrzJo1C506daq6ShGRX2PLrJdJEuCQBFRKhberQkQlSEpKQs+ePREWFoa33noL7du3h81mw08//YTJkyfjyJEjlbavkydPol69eujRo4e8LDw8vNK2X1tZrVZotVpvV4OIKhlbZn0AuxoQ+b5HH30UCoUCu3fvxt13340WLVqgbdu2mD59On7//Xe5XHJyMu644w4EBQUhJCQEI0aMQGpqqnz/rFmz0LFjR3z55ZeIi4tDaGgo7r33XuTk5AAAxo8fj6lTpyI5ORkKhQJxcXEAgL59+7p9pZ2WloYhQ4bAYDCgSZMmWLp0aZE6Z2Zm4sEHH0RkZCRCQkJwyy234MCBA2WuCwBIkoQ333wT8fHx0Ol0aNSoEV599VX5/rNnz2LEiBEICwtDeHg47rjjDiQlJZX5cd26dSsUCgU2b96MLl26ICAgAD169MDRo0cBAEuWLMHs2bNx4MAB1KlTByqVCkuWLCnX8X3++edo0qQJ9Ho9Pv30U9SvXx+S5P66e8cdd2DixIkAnB8m7rjjDkRHRyMoKAg33HADNm3aVOZjIqLqxTDrA9jVgMi3Xb58GRs3bsTkyZMRGBhY5P6wsDAAzuB3xx134PLly/j111+RmJiIU6dOYeTIkW7lT548iXXr1uGHH37ADz/8gF9//RWvv/46AOC9997D//3f/6Fhw4a4ePEi/vzzT491Gj9+PM6ePYstW7Zg9erVmD9/PtLS0tzKDB8+HGlpadiwYQP27NmDTp06oV+/frh8+XKZ6gIAzz77LF5//XW8+OKLOHToEJYtW4bo6GgAgM1mQ0JCAoKDg7F9+3bs2LEDQUFBGDhwIKxWa7ke4+effx5vv/02/vrrL6jVajlYjhw5Ek888QTatm2LI0eO4Pz58/LjWZbjO3HiBL755husWbMG+/fvx/Dhw3Hp0iVs2bJFLuM6v6NHjwYA5ObmYtCgQdi8eTP27duHgQMHYsiQIUhOTi7XMRFR9WA3Ax9g44gGRD7txIkTEEKgVatWJZbbvHkzDh48iNOnTyM2NhYA8MUXX6Bt27b4888/ccMNNwBwht4lS5YgODgYADBmzBhs3rwZr776KkJDQxEcHAyVSoWYmBiP+zl27Bg2bNiA3bt3y9tcuHAhWrduLZf57bffsHv3bqSlpUGn0wEA5s6di3Xr1mH16tWYNGlSqXXJycnBe++9hw8//BDjxo0DADRr1gw33XQTAGDFihWQJAmff/45FApnV6nFixcjLCwMW7duxYABA8r8GL/66qvo06cPAOCZZ57B4MGDYTabYTAYEBQUBLVajejoaISEhECpVJb5+KxWK7744gtERkbK+7rtttuwbNky9OvXDwCwevVqRERE4OabbwYAdOjQAR06dJDLv/zyy1i7di2+++47TJkypczHRETVgy2zPoAts0S+TYiyzdR3+PBhxMbGykEWANq0aYOwsDAcPnxYXhYXFyeHRwCoV69ekVbV0vajVqvRuXNneVmrVq3kFmIAOHDgAHJzc1G3bl0EBQXJP6dPn8bJkyfLVJfDhw/DYrHIoe9qBw4cwIkTJxAcHCxvPzw8HGaz2W0fZXHddde51QFAiY9JWY+vcePGbkEWAEaPHo1vvvkGFosFALB06VLce++9UCqdb4m5ubmYMWMGWrdujbCwMAQFBeHw4cNsmSXyUWyZ9QGc0pbItzVv3hwKhaLSLvLSaDRutxUKRZE+nNcqNzcX9erVw9atW4vcVzj0llQXg8FQ6j46d+7ssb/u1QGyNIXr4WrlLekxKevxeeoWMmTIEAgh8OOPP+KGG27A9u3b8e6778r3z5gxA4mJiZg7dy7i4+NhMBhwzz33lLvrBBFVD4ZZH8CWWSLfFh4ejoSEBHz00Ud47LHHigSkzMxMhIWFoXXr1jh79izOnj0rt84eOnQImZmZaNOmTaXVp1WrVrDb7dizZ4/czeDo0aPIzMyUy3Tq1AkpKSlQq9XyRWTl1bx5cxgMBmzevBkPPvhgkfs7deqEFStWICoqCiEhIRXaR1lotVo4HI4i+67o8en1etx1111YunQpTpw4gZYtW7oN/bVjxw6MHz8ed955JwBncC7PRW1EVL3YzcAHcDQDIt/30UcfweFwoGvXrvjmm29w/PhxHD58GO+//z66d+8OAOjfvz/at2+P0aNHY+/evdi9ezfGjh2LPn36oEuXLpVWl5YtW2LgwIF4+OGH8ccff2DPnj148MEH3VpS+/fvj+7du2PYsGH4+eefkZSUhJ07d+L555/HX3/9Vab96PV6PP3003jqqafwxRdf4OTJk/j999+xcOFCAM6v6yMiInDHHXdg+/btOH36NLZu3YrHHnsM586dq7TjjYuLw+nTp3Hw4EFkZGTAYrFc8/GNHj0aP/74IxYtWiRf+OXSvHlz+YKxAwcO4L777qv0lnMiqjxsmfUBdoeAEEL+ao2oNqrKGbkkSUJ2tkK+eKgimjZtir179+LVV1/FE088gYsXLyIyMhKdO3fGxx9/DMD59fi3336LqVOnonfv3lAqlRg4cCA++OCDyjwcAM4LrR588EH06dMH0dHReOWVV/Diiy/K9ysUCqxfvx7PP/88JkyYgPT0dMTExKB3797yaARl8eKLL0KtVuOll17ChQsXUK9ePTzyyCMAgICAAGzbtg1PP/007rrrLuTk5KBBgwbo169fpbbU3n333fjmm28wZMgQZGVlYfHixRg/fvw1Hd8tt9yC8PBwHD16FPfdd5/bfe+88w4mTpyIHj16ICIiAk8//TSys7Mr7XiIqHIpRFmvbKghsrOzERoaiqysrCr9WszFZrNh/fr1iOt4E5Sq4j87tIgJgk6tqvL6UPm5zuGgQYOK9C+k8jGbzTh9+rQ85md1cYbZ7GsKs+RdPIf+jefP/1XFOSzpPaE8eY3PKB9h5fBcREREROXGMOsjbI5a1UBOREREVCkYZn0ELwIjIiIiKj+GWR/BbgZERERE5ccw6yPYMktERERUfgyzPoITJxARERGVH8Osj3CNNUtEREREZccw6yOE4IgGREREROXFMOtD2G+WiIiIqHwYZn0IwyyR/1MoFFi3bp23q+GXxo8fj2HDhpVabsyYMXjttdeqvkK1yKFDh9CwYUPk5eV5uypE5cYw60N4ERiRb0tJScHUqVPRtGlT6HQ6xMbGYsiQIdi8ebO3q3bNtm7dCoVCgczMTG9XpUQHDhzA+vXr8dhjj8nLhBB46aWXUK9ePRgMBvTv3x/Hjx8vdVsfffQR4uLioNfr0a1bN+zevdvtfrPZjMmTJ6Nu3boICgrC3XffjdTU1Eo/psK8dSxt2rTBjTfeiHfeeafSj4moqjHM+hCONUvku5KSktC5c2f88ssveOutt3Dw4EFs3LgRN998MyZPnuzt6tUaH374IYYPH46goCB52Ztvvon3338fCxYswB9//IHAwEAkJCTAbDYXu50VK1Zg+vTpmDlzJvbu3YsOHTogISEBaWlpcpn//ve/+P7777Fq1Sr8+uuvuHDhAu66665y1TcuLg5bt24tc3lvHsuECRPw8ccfw263l+sYibxOeNmHH34oGjduLHQ6nejatav4448/Siz/7rvvihYtWgi9Xi8aNmwopk2bJkwmU5n3l5WVJQCIrKysa616mVitVrFu3TqxPylD/H02s8SfU+m51VInKh/XObRard6uit8zmUzi0KFDnv9nc3OL/7m6fElljcYiZR3Z2eLKuXPCkZ1dUK6cbrvtNtGgQQOR62HdK1euyH8DEJ999pkYNmyYMBgMIj4+Xnz77bfy/Xa7XUycOFHExcUJvV4vWrRoIebNm+e2vXHjxok77rhDvPXWWyImJkaEh4eLRx991O05aDabxVNPPSUaNmwotFqtaNasmfj888/l+w8ePCgGDhwoAgMDRVRUlLj//vtFenp6sce3ZcsWAcDtWAozm83iiSeeEPXr1xcBAQGia9euYsuWLUII5+uqXq8X69evd1tnzZo1IigoSOTl5QkhhEhOThbDhw8XoaGhok6dOmLo0KHi9OnTRY7bE4fDITIyMkRoaKj44Ycf5OWSJImYmBjx1ltvycsyMzOFTqcTX3/9dbHH27VrVzF58mS37devX1/MmTNH3oZGoxGrVq2Syxw+fFgAELt27Sp2u1dr3Lix/DiVxtvHYrFYhE6nE5s2bSrr4ZWZw+EQV65cEQ6Ho9K3TdWjKs5hSe8J5clrXm2ZLcunycKWLVuGZ555BjNnzsThw4excOFCrFixAs8991w117xqsM8s1WpBQcX/3H23e9moqOLL3nabe9m4OChDQhDWsCGUISEF5crh8uXL2LhxIyZPnozAwMAi94eFhbndnj17NkaMGIG///4bgwYNwujRo3H58mUAgCRJaNiwIVatWoVDhw7hpZdewnPPPYeVK1e6bWPLli04efIktmzZgv/9739YsmQJlixZIt8/duxYfP3113j//fdx+PBhfPLJJ3JrZWZmJm655RZcf/31+Ouvv7Bx40akpqZixIgR5TruwqZMmYJdu3Zh+fLl+PvvvzF8+HAMHDgQx48fR0hICG6//XYsW7bMbZ2lS5di2LBhCAgIgM1mQ0JCAoKDg7F9+3bs2LEDQUFBGDhwIKxWa5nq8O+//yIrKwtdunSRl50+fRopKSno37+/vCw0NBTdunXDrl27PG7HarViz549busolUr0799fXmfPnj2w2WxuZVq1aoVGjRoVu91r5e1j0Wq16NixI7Zv317Zh0ZUpdTe3Pk777yDhx56CBMmTAAALFiwAD/++CMWLVqEZ555pkj5nTt3omfPnrjvvvsAOL++GTVqFP74449qrXdVYTcDIt904sQJCCHQqlWrMpUfP348Ro0aBQB47bXX8P7772P37t0YOHAgNBoNZs+eLZdt0qQJdu3ahZUrV7qFzTp16uDDDz+ESqVCq1atMHjwYGzevBkPPfQQjh07hpUrVyIxMVEOKE2bNpXX/fDDD3H99de7XSS1aNEixMbG4tixY2jRokW5jj85ORmLFy9GcnIy6tevDwCYMWMGNm7ciMWLF+O1117D6NGjMWbMGBiNRgQEBCA7Oxs//vgj1q5dC8DZeCFJEj7//HMoFAoAwOLFixEWFoatW7diwIABpdbj7NmzUKlUiIqKkpelpKQAAKKjo93KRkdHy/ddLSMjAw6Hw+M6R44ckber1WqLfFApabsA8Mgjj+Crr76SbxuNRtx2221QqVTystzcXI/r+sKx1K9fH2fOnCn2+Ih8kdfCrOvT5LPPPisvu/rT5NV69OiBr776Crt370bXrl1x6tQprF+/HmPGjCl2PxaLBRaLRb6dnZ0NALDZbLDZbJV0NMVz7UNylK0PkslsgVrFrsy+xHUOq+P5UtPZbDYIISBJEiTpqg9v+f+bHqlUQOHyJYQJKJXuZU+dghACOTk5CA4OloMUrt5/CRwOR/4qHurtQbt27eRyBoMBISEhSElJkZfNnz9fDocmkwlWqxUdO3aU7xdCoE2bNlAoFPKymJgY/PPPP5AkCXv37oVKpUKvXr081mf//v3YsmWLW79Sl+PHjyM+Pr7Ictd2PB3jgQMH4HA4ioRgi8WC8PBwSJIkB/V169bh3nvvxapVqxASEoJbbrkFkiRh//79OHHiBIKDg922YTabcfz4cfTv3x9CCPn5cTUhBEwmE3Q6nVyupHpffX9ZjrXwOoXLeKpLcc+DWbNmYfr06fLtW265BXPmzEG3bt2K7L8i9arqY9Hr9cjLyyvT87w8XPUp6bEj31YV51CSJAghYLPZ3D7wAeV7z/VamC3Lp8mr3XfffcjIyMBNN90EIQTsdjseeeSRErsZzJkzx60VxOXnn39GQEDAtR1EOSQf/L1M5ZKqthp0DRITE71dBb+nVqsRExOD3NzcMn+1DABwOICylnc4AE8vgoGByCn8AlxSeL5KTEwMFAoFDhw4gH79+pVa3m63yx+cXYxGI7Kzs/HNN9/gySefxMsvv4yuXbsiKCgI77//Pvbs2eP2YVuhULhtw2azwWq1Ijs7W35Tyc7OhkajKbL/zMxMDBw4ELNmzSpyX3R0dJG6ueoHADk5OVAq3T9Qp6enQ6VSYcuWLUXecAIDA+XtDR06FF9++SUGDRqEr776CsOGDZO3e/nyZXTs2BGffvppkX3XrVsX2dnZsNlsHh+7wuWMRiMyMjKg1WoBQA7sp06dcusCcuHCBbRv397jtrRaLVQqFZKSktC2bVt5+blz5+S6hISEwGq14uzZswgNDZXLXLx4EWFhYcXWUa/XQ6/Xy7eVSiXq1Knj1ppc3Lq+cCxpaWlo0qRJsXW8Vjk5OVWyXao+lXkOrVYrTCYTtm3bVuTCQ9drR1l4tZtBeW3duhWvvfYa5s+fj27duuHEiRN4/PHH8fLLL+PFF1/0uM6zzz7r9ik5OzsbsbGxGDBgAEJCQqq8zjabDYmJiWjU/kYoVaU/3A3DDQjRF31zIu9xncNbb73VY3CgsjObzTh79iyCgoLc3vCrmseW2XIICQnBgAEDsGjRIjz55JNF+s1mZma6fYXrao11USgU0Ov1CAkJwb59+9CjRw+316Vz585BpVLJ62g0GqjVardtaLVaeVm3bt0gSRL27dvn1g/SpWvXrlizZg3atWsHtbpsL/OuD/fBwcFFXht79OgBh8MBo9GIXr16FbuNcePGISEhAWfPnsW2bdvw2muvydvq1q0b1q1bh6ZNmxb72uvpuF2EEGjfvj0A5+PVsWNHAED79u0RExODP/74Az179gTgfJ3fs2cPJk+eXOy+OnfujF27dsndQSRJwvbt2+V1evXqBY1Gg927d+Pu/D7bR48exblz59C3b98yv38olUoEBASUqbwvHMvRo0cxcuTISn9/vNb/QfK+qjiHZrMZBoMBvXv3LvKeUJ4PVF4LsxEREVCpVEXG7EtNTUVMTIzHdV588UWMGTMGDz74IADnP35eXh4mTZqE559/vkhrAgDodDrodLoiyzUaTbUGE6VKXaYwK0HFwOSjqvs5UxM5HA4oFAoolUqP/69VxfWVmGvfFTF//nz07NkTN954I/7v//4P1113Hex2OxITE/Hxxx/j8OHDcllPx+da1qJFC3z55ZdITExEkyZN8OWXX+LPP/9EkyZN5HUUCkWRurrePJRKJZo2bYpx48bhwQcfxPvvv48OHTrgzJkzSEtLw4gRIzBlyhR8/vnnGD16NJ566imEh4fjxIkTWL58OT7//PMirauu7QLOi6wKdwVQKBTo0KEDRo8ejfHjx+Ptt9/G9ddfj/T0dGzevBnXXXcdBg8eDADo27cvYmJiMGbMGDRp0gTdu3eXtzNmzBi8/fbbuPPOO/F///d/aNiwIc6cOYM1a9bgqaeeQsOGDT0et4skSYiIiECnTp2wc+dOdOrUSb5v2rRpePXVV9GiRQs0adIEL774IurXr4+77rpL3la/fv1w5513YsqUKQCA6dOnY9y4cbjhhhvQtWtXzJs3D3l5eZg4caLcmvrAAw9gxowZiIiIQEhICKZOnYru3bujR48exT5PsrKyYDKZ5Nu//+78Vq7whc3Fvcd5+1iSkpJw/vx5DBgwoNL/Pyvjf5C8qyrOoVKphEKh8Pj+Wp73W6+FWa1Wi86dO2Pz5s3yjC+SJGHz5s3yP+jVjEZjkQfQ9aLs+trN33FEAyLf1LRpU+zduxevvvoqnnjiCVy8eBGRkZHo3LkzPv744zJv5+GHH8a+ffswcuRIKBQKjBo1Co8++ig2bNhQrvp8/PHHeO655/Doo4/i0qVLaNSokdzlqn79+tixYweefvppDBgwABaLBY0bN8bAgQNLfRPq3bu3222VSgW73Y7FixfjlVdewRNPPIHz588jIiICN954I26//Xa5rOt43nzzTbz00ktu2wkICMC2bdvw9NNP46677kJOTg4aNGiAfv36lasVcOLEifjqq6/c3ieeeuopuWEjMzMTN910EzZu3OjW0nPy5ElkZGTIt0eOHIn09HS89NJLSElJQceOHbFx40a3rm/vvvsulEol7r77blgsFiQkJGD+/Pkl1u/xxx/H//73vxLLlPR+5c1j+frrrzFgwAA0bty4xPoT+RqF8GIKXLFiBcaNG4dPPvlE/jS5cuVKHDlyBNHR0Rg7diwaNGiAOXPmAHB2rH/nnXfw6aefyt0M/vOf/6Bz585YsWJFmfaZnZ2N0NBQZGVlVVs3g/Xr1yOu401lapkNMajRuG7RoX/Ie1zncNCgQWyZvUZmsxmnT59GkyZNqrWbgSRJct9Btgr5J9c51Gg0aN26NVasWOHW8kvXxmq1onnz5li2bJncxaEy8X/Q/1XFOSzpPaE8ec2rfWZL+zSZnJzs9oC98MILUCgUeOGFF3D+/HlERkZiyJAhePXVV711CJWOw3MRERXPYDDgiy++cGuZpGuXnJyM5557rkqCLFFV8/oFYFOmTCm2W8HVUwCq1WrMnDkTM2fOrIaaeYfZJiHTaEVYgNbbVSEi8kl9+/b1dhVqnPj4eI9DthH5A7b1+6DzmSZY7A5vV4OIiIjI5zHM+iBJAs5eNtaYi9qIiIiIqgrDrI8yWSVczDJ7uxpEVYIf1IiIqLLeCxhmfdilXCuyTJxClWoO12gQ5ZnZhYiIaibXTJCexr4uD69fAEYlO3/FBINGBa2anzvI/6lUKoSFhckDyAcEBFTLbECSJMFqtcJsNnNYID/Fc+jfeP78X2WfQ0mSkJ6ejoCAgDLPVFgchlkf55AEzl4xomlEIKcApBrBNftR4RmRqpoQAiaTCQaDgf9Hforn0L/x/Pm/qjiHSqUSjRo1uubtMcz6AaPFgfRcC6KCq2+QeaKqolAoUK9ePURFRcFmq55uNDabDdu2bUPv3r058YWf4jn0bzx//q8qzqFWq62UVl6GWT+Ra7YjKrj0ckT+QqVSXXM/qfLsy263Q6/X843UT/Ec+jeeP//ny+eQHVf8hMnGcWeJiIiIrsYw6yckCTAz0BIRERG5YZj1IwyzRERERO4YZv2I0cowS0RERFQYw6wfYb9ZIiIiIncMs37EZHVwGlAiIiKiQhhm/YgQgMUuebsaRERERD6DYdbPmNhvloiIiEjGMOtnjOw3S0RERCRjmPUzbJklIiIiKsAw62fMNl4ERkREROTCMOtnhADMNl4ERkRERAQwzPolo9Xu7SoQERER+QSGWT/EyROIiIiInBhm/RAvAiMiIiJyYpj1Qxa7BEniRWBEREREDLN+SAh2NSAiIiICGGb9FsMsEREREcOs32K/WSIiIiKGWb/FllkiIiIihlm/ZbFJcPAiMCIiIqrlGGb9GFtniYiIqLZjmPVj7DdLREREtR3DrB9jmCUiIqLajmHWj7GbAREREdV2DLN+zGqXYHdI3q4GERERkdcwzPq5PHY1ICIiolqMYdbPZZts3q4CERERkdcwzPq5bLMNQnC8WSIiIqqdGGb9nCQBORa7t6tBRERE5BUMszVAlpFdDYiIiKh2YpitAXLMdnY1ICIiolqJYbYGcEgCuexqQERERLUQw2wNkcVRDYiIiKgWYpitIbJN7GpAREREtQ/DbA3hkAQnUCAiIqJah2G2BmFXAyIiIqptGGZrEM4GRkRERLUNw2wNYncI5HFUAyIiIqpFGGZrGHY1ICIiotqEYbaGyTYzzBIREVHtwTBbw9jsAkYruxoQERFR7cAwWwOxqwERERHVFgyzNVC2iS2zREREVDswzNZAVrsEu0PydjWIiIiIqhzDbA1lsTPMEhERUc3HMFtDmW2c2paIiIhqPobZGsrMllkiIiKqBRhmayi2zBIREVFtwDBbQ1lsbJklIiKimo9htoZySAI2jmhARERENRzDbA3GrgZERERU0zHM1mBmdjUgIiKiGo5htgZjyywRERHVdAyzNZjFzjBLRERENRvDbA3GbgZERERU0zHM1mBCsHWWiIiIajaG2RqOrbNERERUkzHM1nAWXgRGRERENRjDbA1nsbNlloiIiGouhtkajsNzERERUU3GMFvDWewShBDergYRERFRlWCYreGcIxqwqwERERHVTAyztYCFIxoQERFRDcUwWwuYOdYsERER1VAMs7UALwIjIiKimsrrYfajjz5CXFwc9Ho9unXrht27d5dYPjMzE5MnT0a9evWg0+nQokULrF+/vppq6584cQIRERHVVGpv7nzFihWYPn06FixYgG7dumHevHlISEjA0aNHERUVVaS81WrFrbfeiqioKKxevRoNGjTAmTNnEBYWVv2V9yNWuwRJElAqFd6uChEREVGl8mqYfeedd/DQQw9hwoQJAIAFCxbgxx9/xKJFi/DMM88UKb9o0SJcvnwZO3fuhEajAQDExcVVZ5X9lsUuwaBVebsaRERERJXKa2HWarViz549ePbZZ+VlSqUS/fv3x65duzyu891336F79+6YPHkyvv32W0RGRuK+++7D008/DZXKc1CzWCywWCzy7ezsbACAzWaDzWarxCPyzLUPyWGv8n2VJNdkhlqh9Wod/JXrHFbH84WqBs+h/+M59G88f/6vus9hefbjtTCbkZEBh8OB6Ohot+XR0dE4cuSIx3VOnTqFX375BaNHj8b69etx4sQJPProo7DZbJg5c6bHdebMmYPZs2cXWf7zzz8jICDg2g+kjJIP/l5t+/Ikyat7rxkSExO9XQW6RjyH/o/n0L/x/Pm/6jqHRqOxzGW92s2gvCRJQlRUFD799FOoVCp07twZ58+fx1tvvVVsmH322Wcxffp0+XZ2djZiY2MxYMAAhISEVHmdbTYbEhMT0aj9jVCqvPdwB+rUaFy3+sJ7TeI6h7feeqvcvYX8C8+h/+M59G88f/6vus+h65v0svBauoqIiIBKpUJqaqrb8tTUVMTExHhcp169etBoNG5dClq3bo2UlBRYrVZotUW/RtfpdNDpdEWWazSaav2HUqrUXg2zdij4AnKNqvs5Q5WP59D/8Rz6N54//1dd57A8+/Da0FxarRadO3fG5s2b5WWSJGHz5s3o3r27x3V69uyJEydOQJIKhpo6duwY6tWr5zHIUgGbXcAhiSLLzTYHx6ElIiIiv+XVcWanT5+Ozz77DP/73/9w+PBh/Oc//0FeXp48usHYsWPdLhD7z3/+g8uXL+Pxxx/HsWPH8OOPP+K1117D5MmTvXUIfqVwaHVIAhezTDiRlos8i3cvTiMiIiKqKK/2mR05ciTS09Px0ksvISUlBR07dsTGjRvli8KSk5OhVBbk7djYWPz000/473//i+uuuw4NGjTA448/jqefftpbh+BXzDYHAnVqXMmzIiXbDLvD2VJr99BiS0REROQPvH4B2JQpUzBlyhSP923durXIsu7du+P33707MoC/yjHbkWmywWhx71Zgc3CGMCIiIvJPXg+zVH1yzJ67E7haaImIiIj8jVf7zJJvsEtsmSUiIiL/xDBLsLFlloiIiPwUwyx5HLKLiIiIyB8wzBKE4EVgRERE5J8YZgkALwIjIiIi/8QwSwAAGy8CIyIiIj/EMEsA2DJLRERE/olhlgBweC4iIiLyTwyzBIAts0REROSfGGYJAMMsERER+SeGWQLAC8CIiIjIPzHMEgC2zBIREZF/YpglAJw0gYiIiPwTwywBcM4CxmltiYiIyN8wzJKMrbNERETkbxhmSWZnyywRERH5GYZZktnZMktERER+hmGWZDaOaEBERER+hmGWZJzSloiIiPwNwyzJONYsERER+RuGWZLxAjAiIiLyNwyzJOMFYERERORvGGZJxgvAiIiIyN8wzJLMIQkIwUBLRERE/oNhltywdZaIiIj8CcMsueHwXERERORPGGbJDVtmiYiIyJ8wzJIbjmhARERE/oRhltxwrFkiIiLyJwyz5MbGllkiIiLyIwyz5IZT2hIREZE/YZglNxzNgIiIiPwJwyy5YZ9ZIiIi8icMs+SG3QyIiIjInzDMkhshODwXERER+Q+GWSqCXQ2IiIjIXzDMUhEcnouIiIj8BcMsFcF+s0REROQvGGapCBuH5yIiIiI/wTBLRbBlloiIiPwFwywVwTBLRERE/oJhlopgNwMiIiLyFwyzVARbZomIiMhfMMxSERyai4iIiPwFwywVIQQgceIEIiIi8gMMs+QR+80SERGRP2CYJY/Yb5aIiIj8AcMsecQwS0RERP6AYZY8YjcDIiIi8gfqiqzkcDiwZMkSbN68GWlpaZCuCj6//PJLpVSOvIcts0REROQPKhRmH3/8cSxZsgSDBw9Gu3btoFAoKrte5GUcnouIiIj8QYXC7PLly7Fy5UoMGjSosutDPsLOobmIiIjID1Soz6xWq0V8fHxl14V8iJ0ts0REROQHKhRmn3jiCbz33nsQgq13NZWNfWaJiIjID1Som8Fvv/2GLVu2YMOGDWjbti00Go3b/WvWrKmUypH3OCQBIQT7QxMREZFPq1CYDQsLw5133lnZdSEf45AE1CqGWSIiIvJdFQqzixcvrux6kA9yCFGxJwgRERFRNbmmrJKeno6jR48CAFq2bInIyMhKqRT5Bs6bQERERL6uQheA5eXlYeLEiahXrx569+6N3r17o379+njggQdgNBoru47kJQ5e4EdEREQ+rkJhdvr06fj111/x/fffIzMzE5mZmfj222/x66+/4oknnqjsOpKXODjWLBEREfm4CnUz+Oabb7B69Wr07dtXXjZo0CAYDAaMGDECH3/8cWXVj7yIYZaIiIh8XYVaZo1GI6Kjo4ssj4qKYjeDGoRhloiIiHxdhcJs9+7dMXPmTJjNZnmZyWTC7Nmz0b1790qrHHmXxD6zRERE5OMq1M3gvffeQ0JCAho2bIgOHToAAA4cOAC9Xo+ffvqpUitYU+1NvoJzV0wYcl09n52YgC2zRERE5OsqFGbbtWuH48ePY+nSpThy5AgAYNSoURg9ejQMBkOlVrAmyjHb8Nr6w7DYJTSPCkLreiHerpJHDLNERETk6yo8zmxAQAAeeuihyqxLrbHhnxRY7M5BXI+kZPtsmGU3AyIiIvJ1ZQ6z3333HW677TZoNBp89913JZYdOnToNVesprI5JPzw9wX59tHUXC/WpmR2tswSERGRjytzmB02bBhSUlIQFRWFYcOGFVtOoVDA4XBURt1qpF+PpeOK0QaVUgGHJHAsNcfbVSqWxDBLREREPq7MoxlIkoSoqCj57+J+GGSLJ4TAun3nAQD3dGoIBYD0HAuu5Fm9W7FicAYwIiIi8nUVGprLk8zMzMraVI21LzkTZy4bYdCoMOz6BogNDwAAHPXR1lleAEZERES+rkJh9o033sCKFSvk28OHD0d4eDgaNGiAAwcOVFrlapq1+52tsre2iUaQTo2W0cEA4LNdDSTJ2zUgIiIiKlmFwuyCBQsQGxsLAEhMTMSmTZuwceNG3HbbbXjyyScrtYI1RdKlPOw/mwmlAhjaoT4AoEV+mPXVllmArbNERETk2yo0NFdKSoocZn/44QeMGDECAwYMQFxcHLp161apFawpvj1wEQDQo1kEokP0AICWMUEAgOOpuXBIAiql702eYJckqJQqb1eDiIiIyKMKtczWqVMHZ8+eBQBs3LgR/fv3B+C8wKkiF4B99NFHiIuLg16vR7du3bB79+4yrbd8+XIoFIoSR1fwBVlWYNvxSwCAO69vIC9vFB4InVoJk82Bc1eM3qpeidjVgIiIiHxZhcLsXXfdhfvuuw+33norLl26hNtuuw0AsG/fPsTHx5drWytWrMD06dMxc+ZM7N27Fx06dEBCQgLS0tJKXC8pKQkzZsxAr169KnII1WrbRSXskkDb+iFy1wIAUCkViI9yts76ar9ZjmhAREREvqxCYfbdd9/FlClT0KZNGyQmJiIoyBnILl68iEcffbRc23rnnXfw0EMPYcKECWjTpg0WLFiAgIAALFq0qNh1HA4HRo8ejdmzZ6Np06YVOYRqk2exY0eqs/vAsI4NitzfUu4365uTJ7DPLBEREfmyCvWZ1Wg0mDFjRpHl//3vf8u1HavVij179uDZZ5+VlymVSvTv3x+7du0qdr3/+7//Q1RUFB544AFs3769xH1YLBZYLBb5dnZ2NgDAZrPBZrOVq74VsfKvszA5FKgXqkOXRiGQHHa3+5tHOofnOpaSXeQ+X2C1WmGr8KTHNYPreVIdzxeqGjyH/o/n0L/x/Pm/6j6H5dmPV6ezzcjIgMPhQHR0tNvy6OhoHDlyxOM6v/32GxYuXIj9+/eXaR9z5szB7Nmziyz/+eefERAQUKZtXIsNx5QAlLgp3IjkAzuK3B9gAQA1ki7l4eie36DzsWutkrxdAR+SmJjo7SrQNeI59H88h/6N58//Vdc5NBrLfi2RX01nm5OTgzFjxuCzzz5DREREmdZ59tlnMX36dPl2dnY2YmNjMWDAAISEhFRJPQvr39+KT9ZsQo9uN8Cg03ksE350Dy7n2WCvdx1a1q/6OpVHRLAOUcGe611b2Gw2JCYm4tZbb4VGo/F2dagCeA79H8+hf+P583/VfQ5d36SXRZnDrFTosnapki5xj4iIgEqlQmpqqtvy1NRUxMTEFCl/8uRJJCUlYciQIUXqolarcfToUTRr1sxtHZ1OB52HEKnRaKrtH6pZCGDQ6aBUeX64W0aHYNepSziebkT72PBqqVNZKZQqvvDkq87nDFUNnkP/x3Po33j+/F91ncPy7KPSprOtCK1Wi86dO2Pz5s3yMkmSsHnzZnTv3r1I+VatWuHgwYPYv3+//DN06FDcfPPN2L9/vzz2rb/x5ckTeAEYERER+bIKXdrz2GOPIT4+Ho899pjb8g8//BAnTpzAvHnzyryt6dOnY9y4cejSpQu6du2KefPmIS8vDxMmTAAAjB07Fg0aNMCcOXOg1+vRrl07t/XDwsIAoMhyf9Iy2neH55I4NBcRERH5sAq1zH7zzTfo2bNnkeU9evTA6tWry7WtkSNHYu7cuXjppZfQsWNH7N+/Hxs3bpQvCktOTsbFixcrUk2/ER8VDKUCyMi14lKupfQVqhFbZomIiMiXVahl9tKlSwgNDS2yPCQkBBkZGeXe3pQpUzBlyhSP923durXEdZcsWVLu/fkag1aFRuEBSLpkxLHUHHQPcu/ju+VoGj759SQe6tUU/VpHF7OVqsGWWSIiIvJlFWqZjY+Px8aNG4ss37Bhg89PYuCrWhQzecKhi9l4f/Nx5FkdWPjbaeSaq3csWjtbZomIiMiHVahldvr06ZgyZQrS09Nxyy23AAA2b96Mt99+u1z9ZalAi+hg/Hwo1a3fbFqOGXPWH4ZdElAAyLHYsXLPWUzs2aTa6sVuBkREROTLKhRmJ06cCIvFgldffRUvv/wyACAuLg4ff/wxxo4dW6kVrC1c09qeSMuFQxKwOSS8+uNhZJpsaBoRiBFdYvH6xiP4/sAFDG5fD9Eh+mqpVyWNwkZERERUJSo8NNd//vMfnDt3DqmpqcjOzsapU6cYZK9BbHgADBoVTDYHki8bMW/TMZzKyEOoQYPnB7dGj2Z10TE2DHZJ4ItdZ6q1bmydJSIiIl9V4TBrt9uxadMmrFmzBiL/IqELFy4gNze3lDXJE5VSgfgo5xBd8zYfw46Tl6BWKvDcoNaICtZDoVBgQo84KABsO55ercN4McwSERGRr6pQmD1z5gzat2+PO+64A5MnT0Z6ejoA4I033sCMGTMqtYK1iesisFPpeQCAR/s2Q5t6BdPbNo0Mws2togAAi3aclj9EVDWOaEBERES+qkJh9vHHH0eXLl1w5coVGAwGefmdd97pNpsXlY9r8gQAGNqhPm5tU3RK3zE3NoZWpcS/F7Lxx+nL1VIvjmhAREREvqpCYXb79u144YUXoNVq3ZbHxcXh/PnzlVKx2qhDbBji6gagd/PIYkcsiAjS4Y6O9QEAS3Ymwe6o+iu02M2AiIiIfFWFwqwkSXA4HEWWnzt3DsHBwddcqdoqQKvGB6M64cmEllApFcWWu6dzQ4QaNDifacJPh1KrvF4SwywRERH5qAqF2QEDBriNJ6tQKJCbm4uZM2di0KBBlVU3KkaAVo1RXRsBAJb9cQZGa9VOpOBgn1kiIiLyURUKs3PnzsWOHTvQpk0bmM1m3HfffXIXgzfeeKOy60geJLSJRr1QPbLNduyu4r6zbJklIiIiX1WhSRNiY2Nx4MABrFixAgcOHEBubi4eeOABjB492u2CMKo6apUSNzati7X7zuPv81no2zKqyvbFllkiIiLyVeUOszabDa1atcIPP/yA0aNHY/To0VVRLyqD6xqEYu2+8zh4LqtK92N3MMwSERGRbyp3NwONRgOz2VwVdaFyalM/BEoFkJJtRlp21Z0TjjNLREREvqpCfWYnT56MN954A3Z71V54RCUL0KrRPMo5esTf56uudZZDcxEREZGvqlCf2T///BObN2/Gzz//jPbt2yMwMNDt/jVr1lRK5ah01zUMxdHUHBw8l4X+raOrZB9smSUiIiJfVaEwGxYWhrvvvruy60IVcF3DMKzacw5/n8+EEAIKRfHj01ZUNczLQERERFQh5QqzkiThrbfewrFjx2C1WnHLLbdg1qxZHMHAi1rFBEOtVCAj14qLWWbUD6v8c8FuBkREROSrytVn9tVXX8Vzzz2HoKAgNGjQAO+//z4mT55cVXWjMtBrVGgZ4+w3e7CK+s2ymwERERH5qnKF2S+++ALz58/HTz/9hHXr1uH777/H0qVLIUn8HtqbrmsQCgD4+1xmlWxfCLbOEhERkW8qV5hNTk52m662f//+UCgUuHDhQqVXjMqufcMwAM4RDUQVtaIyzBIREZEvKleYtdvt0Ov1bss0Gg1sNlulVorKp1VMMLQqJTKNNpy7YqqSfbCrAREREfmicl0AJoTA+PHjodPp5GVmsxmPPPKI2/BcHJqremlUSrSuF4wD57Lw97lMxIYHVPo+2DJLREREvqhcYXbcuHFFlt1///2VVhmquPYNw5xh9nwWBl9Xv9K372DLLBEREfmgcoXZxYsXV1U96Bp1aBCKr+Ac0UASAspKHm/W4WCYJSIiIt9ToelsyffERwVBr1Eix2zHmUt5lb59tswSERGRL2KYrSHUKiXa1HMN0VX5481K7DNLREREPohhtgbp0NAZZqti8gS2zBIREZEvYpitQdrnT57wz/msSh99gKMZEBERkS9imK1BmkYGIVCrQp7VgVPpuZW6bU7yRkRERL6IYbYGUSkVaNegaroa2JlmiYiIyAcxzNYwrq4GByr5IjDOAEZERES+iGG2hrmuYRgAZ7/Z9BxLpW3XwYZZIiIi8kEMszVMXN0AtK4XAqtDwuKdpyttu7wAjIiIiHwRw2wNo1Ao8EjvplAqgO3HM3DwXGalbJfdDIiIiMgXMczWQE0jg5DQNgYA8On2U5XSqioEJ04gIiIi38MwW0Pd360xgnVqJF0yYv3Bi5WyTU6cQERERL6GYbaGCjFoMKZ7YwDA0j/OINNoveZtst8sERER+RqG2RpsQJsYNIsMRJ7VgS9+P3PN22OYJSIiIl/DMFuDqZQKPNy7GQBg06FUHEvNuabtsZsBERER+RqG2Rqudb0Q3NwyEgLAJ9tOXtOoBLwAjIiIiHwNw2wtML5HExg0KhxLzcXSP5Jhq+AMCOxmQERERL6GYbYWCA/UYnS3RgCAlX+dxZRle/H7qUsQ5Wyl9UaYlSQBq53TjxEREZFnam9XgKrH0A71YdCq8OXvZ3Ahy4xX1x/GdQ1D8eBNTdEkIrBM26juPrMWuwPJl4xQKIBmkUFQKBTVun8iIiLyfWyZrSUUCgUGtInBJ/d3xvDODaFRKfD3uSxMW7EP87eeKFOra3W2zOaYbTiZlgezTYLJKuFilrna9k1ERET+g2G2lgnQqjG2exw+Ht0ZN8VHQBLAhn9SsPlIaqnrStX0bX96jgVnLhndwvOlXCuyTLbqqQARERH5DYbZWio6RI+nB7bCyBtiAQB/Jl0udZ2q7mYgSQJnLxuRkmWGp12dv2Ji/1kiIiJywzBby93YpC4A4MDZrFJHOajKbgZ2h4RTGXnINBbf+uqQBM5eMZb7wjUiIiKquRhma7mmkYGoE6CByebAoQvZJZatqjBrsTtwMj0PJquj1LJGiwNpOZYqqQcRERH5H4bZWk6pUKBTozoAgL/OXCmxbFWEWZPVgZNpeeXqPpCWbUGuxV7pdSEiIiL/wzBL6BIXDgDYc6bkfrPXMnuYJzlmG06m51YoJF/INFVqXYiIiMg/McwSOsaGQakAzl4xISW7+CGwhKi8KW2v5Flx5pLR44VeZcELwYiIiAhgmCUAQTo1WtcLAQDsKa2rQSW0zqZmm3HuiqnCQRZwBmt7BaflJSIiopqDYZYAAJ0bO/vNltbV4Fr6zTokgdMZeUjLrpwLuOxemF6XiIiIfAvDLAEAujR29ps9cC6rxK/wKxpmTVYHTqTlItdceRdulTaUGBEREdV8DLMEAIirG4C6gVpY7RL+OZ9VbLmKdDO4kmfFyfTcSu/nanewZZaIiKi2Y5glAIBCoSjoapBcfL/Z4i4Ay7PYkWWyIdvs/MnJ/zmfabrm/rHFsVXX/LpERETks9TergD5ji6N6+DnQ6n4K+kyHurV1GMZT90M0nLMSM2q/okM2DJLREREbJklWYfYMKiUClzIMhc7juvV3QxSsrwTZAH2mSUiIiKGWSokQKtG2/whuoqbDazwN/sXMk1I9+LUsja2zBIREdV6DLPkpmCILs9h1p6fZs9dMeJSrrXa6lVSXYiIiKj2YpglN64we/B8Jsw2R5H7HZLA2ctGXMmzVXfVimCfWSIiImKYJTeNwgMQGayDzSFw0MMQXdkmOzKN3g+yAGcBIyIiIoZZuopCoUCXUroa+BLOAkZERFS7McxSEa6uBn8mXYaoigFiKxFHNCAiIqrdGGapiOsahAEA0nIsyK7E6WerAvvNEhER1W4Ms1SEQatCeIAWAJCWbfZybUrGWcCIiIhqN4ZZ8igyWAfA2Trry9gyS0REVLsxzJJHUSHOMOvNSRHKgn1miYiIajeGWfIoSm6Z9fFuBmyZJSIiqtUYZsmjyGA9AD/oZsA+s0RERLUawyx55GqZ9fVuBuwzS0REVLsxzJJHUX5yARhnASMiIqrdGGbJI9doBrkWO4xWHx9rlrOAERER1Vo+EWY/+ugjxMXFQa/Xo1u3bti9e3exZT/77DP06tULderUQZ06ddC/f/8Sy1PFBGjVCNKpAfh+VwOOaEBERFR7eT3MrlixAtOnT8fMmTOxd+9edOjQAQkJCUhLS/NYfuvWrRg1ahS2bNmCXbt2ITY2FgMGDMD58+erueY1n790NWC/WSIiotrL62H2nXfewUMPPYQJEyagTZs2WLBgAQICArBo0SKP5ZcuXYpHH30UHTt2RKtWrfD5559DkiRs3ry5mmte8/nLxAmcBYyIiKj2Untz51arFXv27MGzzz4rL1Mqlejfvz927dpVpm0YjUbYbDaEh4d7vN9iscBiKQhj2dnZAACbzQabzXYNtS8b1z4kh2/3O/UkMih/Stsso0/X32xRwqZXVdn2XeewOp4vVDV4Dv0fz6F/4/nzf9V9DsuzH6+G2YyMDDgcDkRHR7stj46OxpEjR8q0jaeffhr169dH//79Pd4/Z84czJ49u8jyn3/+GQEBAeWvdAUlH/y92vZVWdTZCgAqnD57HkmGs96uTrGSAOyrhv0kJiZWw16oKvEc+j+eQ//G8+f/quscGo3GMpf1api9Vq+//jqWL1+OrVu3Qq/Xeyzz7LPPYvr06fLt7OxsuZ9tSEhIldfRZrMhMTERjdrfCKXKvx7ulsGXgDPHYVSHIK5jO29Xp1h6jQpNIwOrbPuuc3jrrbdCo9FU2X6o6vAc+j+eQ//G8+f/qvscur5JLwuvpquIiAioVCqkpqa6LU9NTUVMTEyJ686dOxevv/46Nm3ahOuuu67YcjqdDjqdrshyjUZTrf9QSpXa78JsdKiz5To9x+rTdRdKRbWcy+p+zlDl4zn0fzyH/o3nz/9V1zkszz68egGYVqtF586d3S7ecl3M1b1792LXe/PNN/Hyyy9j48aN6NKlS3VUtVaKCnG2dl82Wn16+CuOZkBERFR7eX00g+nTp+Ozzz7D//73Pxw+fBj/+c9/kJeXhwkTJgAAxo4d63aB2BtvvIEXX3wRixYtQlxcHFJSUpCSkoLc3FxvHUKNFaJXQ6t2PkV8eaxZzgJGRERUe3n9u+ORI0ciPT0dL730ElJSUtCxY0ds3LhRvigsOTkZSmVB5v74449htVpxzz33uG1n5syZmDVrVnVWvcZTKBSICtbh3BUT0nMsqB9m8HaVimWXBNRVN6ABERER+Sivh1kAmDJlCqZMmeLxvq1bt7rdTkpKqvoKkcwVZtNyzN6uSolsDgl6DdMsERFRbeP1bgbk2yKDnf1mU324mwHAfrNERES1FcMslcg1pW16tm+HWc4CRkREVDsxzFKJouQpbX29mwFbZomIiGojhlkqUaQcZn27ZZajGRAREdVODLNUoqj8PrOX8qxwSL7b+smWWSIiotqJYZZKFB6ohUqpgEMSuJxn9XZ1imVnn1kiIqJaiWGWSqRSKlA3UAvAt/vNcjQDIiKi2olhlkolj2jgw/1mOQsYERFR7cQwS6Vy9Zv1+YvAfLhPLxEREVUNhlkqVWSIf4xoYGPLLBERUa3DMEulKuhm4Lt9ZgH2myUiIqqNGGapVP7SzYCzgBEREdU+DLNUqqhCEycI4butnxxrloiIqPZhmKVSRQQ5w6zVLiHbbPdybYrH0QyIiIhqH4ZZKpVWrUSdAA0AIC3bd/vNsmWWiIio9mGYpTLxh36znAWMiIio9mGYpTKJ9IOJEziaARERUe3DMEtlUnARmO92M+AsYERERLUPwyyVSeERDXwZZwEjIiKqXRhmqUwi8/vM+nI3A4CzgBEREdU2am9XgPyD37TMVqDfrN0hwWKXYLVLsDqcvw1aFUL0GmjV/LxHRETkyxhmqUyiQpxhNtdih9FqR4C2ap86doeEfWczsfVoOix2B6bcHI+wAG2p613dMuuQhPxjdUiw5YdVW/7fFrsET4MgZBptuAgz9BolDGoFAEAI53ak/N9CAA6Rf9shYHftSwgIIdAgzACFQlEpjwcRERF5xjBLZRKgVSNQp0KexYH0HAsa1638p44QAkdTcrD1WDq2H093m6AhNfsfvDKsPUINmhK3kZFrxWWj1Rk6K6HHgdkmwZhfj8MXc6BUlf24NSolokP0114JIiIiKhbDLJVZVLAepy15SMuxoHHdQHm5EAKSAFTK8rdCCiFwPC0Xu05ewo6TGbiYVTBaQphBg5viI7Dz5CUkXTLipW//wSvD2iFYX3ygdbbClrsaVSI9x4JgvbrKW7GJiIhqM77LUplFBetwOiNP7jdrtNqx9Wg6NvxzEUmXjOjUKAyD2tdDl8bhJQZbSQgcvpiNnScvYdepS24Xlek1StzYtC5ubhGFDrFhUCkVGHRdPTy39iBOZeThpW//xcvD2iFI5/tPXSGAc1dMiI8MgrICQZ+IiIhK5/uJgHyGa+KEg+ezkJSRh1+PpcNkc8j3703OxN7kTEQG63Bb2xjc2iYaYQFa5FrsOJaag+OpOTiamoOjKTluXQj0GiU6Nw5Hz2Z1cUNcOPQaldt+Y+sE4JU72uH5df/gRHouZn33L/7vjrZ+0eJpsUm4mG1GgzCDt6tCRERUI/l+GiCf4RrRYMeJDHlZgzADbmsXg/YNQvHrsXQkHkpFeo4FX/x+Bst2JyMyWOfWdcAlUKtC1ybh6NEsAtc3CoNOrSpSprDGdQPx8h3t8PzagziamoNZ3x/C7CFtYdCWvJ4vuJxrRYheXWL3CCIiIqoYhlkqs5YxIQAAtVKB7s3q4ra2MWjXIFS+Yr9pZBDu69YIvx3PwPp/LuJYaq4cZOuF6tE8KhgtY4LQIjoYzSKDoFGVb9irJhGBeHlYOzy/7iAOX8zG3J+P4sXb21TuQVaR85kmNI9SV6hfMRERERWPYZbKrE29EHw46nqEGjTFDpOlU6vQr3U0+rWOxqn0XGQabWgWFVTqKARl1SwyCP83tB2eXH0Au5Mu42KWCfVCff8rfJtd4EKmCbHhAd6uChERUY3CEeGpXBrXDSzTeK+As6W2U+M6lRZkXVpEB6NDwzAAwNaj6ZW67aqUabQhLdsMo9UOidPuEhERVQqGWfJLfVtGAQC2Hk2DEP4TDFOzLTiZlod/L2TjSEo2kjLycDHLBHOhC+mIiIio7BhmyS91b1oXOrUSF7LMOJ6W6+3qVIjNLpBjtiMjx4oTabnIyPXtqYKJiIh8EcMs+SWDVoVuTeoCcLbO+jshgIuZZpzOyCsyJS8REREVj2GW/NbNLSMBANuOZ8BeQwJgrtmO46m5yDbbvF0VIiIiv8DRDMhvdYwNQ6hBgyyTDfvPZaJL43BvV6lSOCSBMxlGhAdpYdCoIAkBSQgI4WzBdd2WpEJ/CwAQABRQKAClQgEFnL/rBmkR6AczphEREVUE3+HIb6lVSvRqHoEf/r6IrUfTa0yYdbmca62U7WSbbYgM1iEqWCePCUxERFRTsJsB+bWb80c1+P3UJZisHBHAEyGAtGwLTqbnctQEIiKqcRhmya81jwpCvVA9LHYJv5++5O3q+DSTVcKJtFxc4qgJRERUgzDMkl9TKBRy62xNGNWgqgkBXMg041R6LrKMNk7eQEREfo9hlvxenxbOUQ32n83ElbzK6Wda0+VZHEi+bMShi9k4e9mIbLPNryafICIicuEFYOT36ocZ0DI6GEdTc7DteDru6NigwtuyOyRkmWwIMWigUdX8z3pCOKfZzTTaoFQCgVq1cySE/OvEFApn67daqYBGpYRWrYRGpYBWpeTFZERE5BMYZqlG6NsyEkdTc7D1aPFhVhICV/KsSMuxIDXbjPQcC9JzLbiUa8WlPAsu5VmRZbRBAFAqgHqhBjSsY0CDMD0M2Qpkn8+CXShgtkkwWx0w2Rww2xwwWh3Is9qdvy125FkdsNgd6NyoDoZ3joVBq6reB6OCJAnIMdvLVFahALRqJWJC9QjRa6q4ZkRERMVjmKUaoVfzSHy2/RROpOdi69E02B0C6bkWpOdYkJZjRlqO8297GfuISgI4n2nC+UxT/hIVcPJwuep0Kj0PvxxJwwM3NcFN8RE1qiVTCMBik3Amw4iwAA3qhxmgUtac4yMiIv/BMEs1QqhBg06N6uCvM1fwduKxYsspFUBEkHPM1agQPSKDdKgbpEXdQNdvLYL1GlwxWnHuignnrhiRfCkPJ85ehFEZAL1GBb1GBYPbbyUCdWoEatUI0KkQqFXDYndg2e5kpGZb8OZPR/HTvyl4uHczxIYHVOOjUj0yjTbkWuxoUMfAVloiIqp2DLNUY9zTuSGSLhmh1ygRmR9YI10/Qc7wWjdQC3UZ+sJGBOkQEaRDx9gwSA47kvafR1zHjlCqyv4v0zM+Amv2nsfqPedw4FwWpi7fh8Ht66F9g1BEBju3H6JX14gWW7tDyK20MaF6qJWKGnFcRETk+xhmqcZoWz8Ui8ff4O1qyHRqFUZ1bYSbW0Xh8+2n8Mfpy/juwAV8d+CCXEardgbvRuEBuLFpXXRtEo4gP5561nUxGQB5Wl2l0vlbpVRA5fqtdF5UplIqoFY5LypTK52/GYKJiKg8/Pddk8hPxITo8cLgNvgr6TI2HU5Fao4FGbkWZBptsNoluW/urlOXoFYq0CE2DD2b1UW3JnURYvDfr+2FABxCwCEBQNmH/VKrFHKoVSkURQJx4WCsVCI/GDMIExHVVgyzRNWkS1w4usSFy7etdgmX8ixIy7Hg3/NZ2HHyEpIvG7HnzBXsOXMFSsUJhAVo3VoxVQoFVKr8gOdq+cwfSss1fJZrCC2tSgm1SoH6oQa0axCKJhGBfnGRlt0hYHeUf8xbhcIZhLWFHgNNfquvQuI0vkRENRXDLJGXaNVK1As1oF6oAR0ahuG+bo1x9rIRO09mYOfJSziVkYfLlTgJRJBOjbb1Q3Bdw1C0bxCGRuEBfhFuy0oIwGYXsNkdANzDq+RwDjl2JCUHBp1GDv2Fg79GpShTf2oiIvItDLNEPiQ2PAAjwxth5A2NkJZjRo7ZDock4JAE7JKA5PotXD+AlH/bIQnYHBJsDgGrQ4LNIcFik3AyPRf/XshGrsWOP05fxh+nLwMAtColGoUHIC4iAI3rBqJJ3UDUDzPIEyNoVMoadyGXJAmYrBJMkDze7xo/19Wi67GVtwY9HkRENQHDLJGPigrWIyq4crblkAROpOXi7/OZOHguC4dTsmG2STiRnosT6bklrqtWKhCkVyMiyDkqhHMkBi2igvXoEBvm1xesXc01fq7FVnzYVecHfa2qIOBq1AW3a1JrNxGRP6g570JEVCyVUoGWMcFoGROM4Z1j4ZAEUrPNSLqUh6SMPCRdMiLpUh5Ss824el4JuyTkUQpOpLkH30CtCnd0bIChHeojsAaF2uIU7spghOd+uEolCoJufsjVqpTQqAv6NBMRUeWp+e8+RFSESqlA/TAD6ocZ0KNZhNt9zi4Nzu4KtvzuCtkmOzJyLfJPeo4VJ9NzcT7ThGW7k/HtgfO48/qGGHJdPQRoa/fLiiQBZkmCuYTWXblF96ouDK4WXyVbd4mIyqx2v+sQURHOcWBVKNzQGhUMxEcFuZWThMCOExn4encyzl4x4avfz+Db/ecx5Lr6aBIRiPBALcIDtQgzaHhhVSFCOEeysNqBqy9Uc9Gonf11dRpV/m8l9GoVtGo+jkREV2OYJaIKUSoU6NU8Ej2aRWD78XQs//Os3FJbmAJAaIAGdQOd/Wwjg/OnEw7WITJYj3qh+lrRRaE8XF0Z8izuYVelVMCgdU6jbNConCFXo/JSLYmIfAPfQYjomqiUCvRtGYVezSPx67E07Dp1CZfzrPKPJApmBjuZnudxG2EBGjTI7/bQIMyA+qF6RAbrERGkRahBwxEE8jkkgVyzHblmu7xMoQAMWhX0moKQq9co+ZgRUa3BMEtElUKlVOCWVtG4pVW0vEwSAtkmGy7lWXEp1zlBhOsnPceMtGwLMk02Oez+eyG7yHY1KgUignSICNKhToAWgToVgnRqBOrUCNSqEahTIVivQbBejWC9GiF6DXTq2hPmhACMFgeMhVpxFQpAp3a22urzw61Bo2J3DyKqkRhmiajKKBUKhAVoERagRbPIII9ljFY7LmSace6KERcyTTifacbFLJM85a/NIXAxy4yLWeYy71ejUiBYr5FDb5BWCeQpEZWbJAffIJ3zfuffagTl/64Jow0IAZhtrovQbPJytUqR33LrDLh6japWBX8iqpkYZonIqwK0asRHBRW5wAwAbA4Jl/Os+SMoOFtxjRY78qwO5JrtyLPakWuxI8dsR47ZhhyzHXZJwOYQcjeHAkogI6XU+ug1Sme4zf9xheIgvRrBhUKvfF/+7QCtCkofD4V2h0COw/l4uSgUzmPWqd1Dbk0I9URUOzDMEpHP0qiUiA7RIzpEX6byQgiYbRJyzDZkm+3Is+SHXZMVZ08fhzYiFnlWSQ7AuRZn/9Mciw1GiwMCrhZNKzJyyzeVsFIBBGrzQ68ceDUFIVh39X0FgdiboxQIAeesaFb3VlyVUiFfYKZXO0dW0KuV7KpARD6HYZaIagyFIv9qf60KUSEFyyWHHUnWY4jr2AhKleeXPUkIGC0O5Fhs+QHXGXRzLQWtv3kWu3x/4UBssUuQBJBjca53Mat89daqlHILr6vfb4jeGXRDDM7foQbnT5hBgxCDpspHMXBIokhfXKAg5OrUztbcwn8TEXkDwyxRNdGqnRfhSELAIQQkyfnb7nBOueUc31UBpcL5W6VQQEDALgk4Cv0IUcqOSlC4z6TrG3HX9gSc25aE87cQzmWSABz5kyi46loTKRXOaXuD9GogtHzrWu1SodBrc4bekgKx2YYci/NvSQDW/O4U7t0iSqbXKOWAG5IfdsMCCv4OzQ+9rr8rK/y6h9yCllyFwvkc16mV+b9V8kQQKqUCaqWCfXOJqEowzBJVEbVKIX+VHKhTV9pXyZLkDLiSEPmzdRUEY2cIdQZQVxDV5AfYyriaXQiR3ydVgs0ukG22Idtsg+R5sqtaQ6tWIlztnCSiPIQQMNkcV3V5sCPb5Hxcc8wFf2eZnD+ZRhvsksjvDmFBaralzHV0Bl+1HHSvDr6u+0INGgTq1OXqAywEYLFJsBQz8xngnOpXo1K6fWBTKAo+yCkUgCr/PqXrg51CAaUScDhq+ZOMiIrFMEu1lmtaUZUSUCmVzjdRlfPNEyhoqbTblUiCcyxUjUYDZf6bq1KhyP+B2xuv6825qi6gUSoV0HppulOFQiG3tkHrnAxBkgRyzHZkmqzIMduvqeW4tlEoFAjQqhGgVSO69OIACgJwprEg4GaZbMg22ZCZ/ztLDsDOMGx1SLDaJaTnOC+kKwulAgjRaxBs0CBUr5ZbeUPyuz44f7vfLq31V5IASwU/+UgO50Vrhy9mQ63W5AdgyMFYDshXfbuhVMLDMrYQE9UkDLNUo6mUCueV2hrnV546lUqeKrSsrZQ2mw37ANQPM0Cj0VRthf2QUqlAaIAGoQEaeVB/i90Bi12Cxe6A2SYx4FaiwgG4fpih1PKui+KyzAVBN8t01d9mG7JNdrkF2Gh1OCe7yA/IZ8tYN51a6dbCG6zXIECrQoBWjUCtyvm3Ti1fVOYaGuzq3yX9bwoBuctNRSkUKBR+yxaI1UqlMxgrXK3IDMREvoJhlmoUg1aJUIOWwwt5iSo/2ALuod9aKNiabc6ga7Y5GHKrQeGL4mLKOCqEzSHld2+w5wfdwi2+Bcvk2yZn1wdLOVt/i6OWLzJzD73CpERYylHotWro1QXDiBUOx4UnitCpC2ZEcy1XKRXOQCyuPRCX1BLsagGWfxe+n4GYqFIxzFKNEhGkQ1hA+fotUtXT5l8UFHxVlioccE1WB0w2R42+yMxfaFRK1A3SoW6QrkzlXV0fsk32Qi29zj6/RqtzXGCj1Q6j1QGj1XmuXa33ZpvzOWCxO1uDAcAuCdgtDuRdNZICoASyrlzjsSncQ2/+hZm6UoKxXu2aMtj5TY9rHdcFlRW5wK28gVh9dVl2lyACwDBLNUyAlk9pf6JTq6BTqxBqKGjJtTkkmGwOZwtuoZZctuL6rsJdH2JCy9b6ezXXxYWFW+7l3zYJJqsN504eRlD9eFgdQv4QZLZLMFsdMNsdcjA22x35y1xhuSAo2xwCtqsmjqgMKqXCLQS7gq6zdbggAMutxFdNUuEWmrXuLdIlhWRPwdd1Ad3VLcFu9zMQUw3Cd36qMdQqhVcHn6fKoVEpoVEpEaIvCLhCOL/CtjokOBxC/orY9SMPJ4aCocWAgmUOhfN5oVYpoFTlX+AnCi7yc90m7yl8cWGwh/slhx1J2QJxbaOLHSu4OK6gbLI6CgKwzflNgCU/AJtsztZik03KX1YQjAuvZ7EVlDHZHLDnp2SHJJBndSDPenVr8rVRAIUmr/AQfl2txPmh2Rmk8wO0WuXW6mzID8cGrbO8K8hWNBDzgjryFQyzVGMEslW2xlIoCr4argibzYZjAFpEB5d6EZ8oFIyFEPm/3YMvrlp2dVmUdB+c90G+r2hZwHO4dl24pFAACrh+A3Atz3+sFHDehgJymcLLXQ198u9C23KVc7u/jF+fF6771cct5d/n+rAhue6TCv4uGOPYfXg5IQTsNmcdgnRqKFSqgu0LFJSVCj2WhR4/OSgbnBeoVSa7QyoUch2FWozzW5TzA7PJVtBKXLjF2HzVOoX7lQMoNCud+wxtlUGrUnoMx0VDc+GuF0W7Y7j6ZAfqVAjSqeWgXLj1V3I4Q362yQatgyNMUOXiuz/VGAYtZyCia6coFPYAvsGWhyv0VsXjZ7PZcABAo7oBZR5VRJIKQrKnyUBcoRnCPVBL+Wn86kDtWt89fCsRVIZAXV6SEHI3m8IhV25NdgvEBV1zCvdBLhygLa7uF4W6XFgdzm87siu5y4XrAj5Xi7Gz+4QSkkmJlWkHoNeoPbYuG7QqBGrVCNApnb+1KgTmj9MdpFPLY2VzyDW6GsOsH3PNuFPSIOW1SaCOYZaICrjCjcqLH0oKB+riWqivPVCXrYUacG7D6nC29Lq1JtuvaiUu3B/5qkDt1je50DJXl4sSL+DLvFzhx1IBFNs67FpuyB8KzqBR5vfjdg4HF+gKxtqCcByoUyFY7+zrrVUrGYj9GMOsH4utEwCTzYF027UNg1MTKBSAoYrnqiciKi9fD9QltVAX1+WjuEBttUvO0SpszumOTa7+xlYHjBYbzp06guB6zWB2CLcuGFe3MhcOzyabA9ZCXS5M+QG80rtcqJ2txwats5uFq+uEq8U4QKMqFI5VBa3G2oJZHl0zPgYb1AjQqBGg4/CQ1YVh1k9FBGud43mavF0T32DQqjhmIxGRB74QqC0WKzbmHsat/eOhUquL7fIhCgVuV1B2OIRzWDdb/vBuFudQb6b8od6M+UHYucwZgI1Wu1vXDPdW5YLbroZrq905S15ld7koPBRcgMY5UkWAKxzr1Ahwda9wtRrrnV0tgvMDsqtlOcC1Xn6gLm2Ui9qm9obZvDxA5aElT6UC9Hr3csVRKgGDoeSyNhtUZjMUZhMQWHCNrsJkLL5DlUIBYQgotmyQTo0YlQrIy4PhqjE5FSaT84qKYoiAwIqVNZsBqfirdMtV1hAgd6pTWCyAo/gXj7KWDVRqASnAeU4AwGoFbCV8cjcYyl628POktLJ6fUF5m81Zvjg6HaBWl7+s3Q5YSmiN12oBV5/C8pR1OACzufiyGo2zfHnLShJgKuFTV3nKqtXOxwJw/k8YjWUuqzKbnf+jnvpbluf/vrJfI4orayz5NQIBARUrazI5H+fiBAZWrKzZ7HxeVEbZgIL/e1gszudx/mtpkXPoqWxxyvN/X56yhf/v+Rrhsawy//xpzCZnn+fS/u8VhX7rNIBrtrsKvkYUDslyS7NSBbNSg1yLHblmK8xZOTBanEE3L398ZJPVAZPVjjwHkKNQO1ucrXY4cnMLRrnID8zOlmaBPIeAUel8zGwOAbUpF7kAcj1UV1IoYNEUjOdssBb/+BYuq1QAYbAhIL9vsiG/5diQH34NWhW0wcEI0jlbiEMkGwwaJQJ1KhjUahi0zr/1Gmdo1ocFI1Dr7JestJg9/9+7/gcLq8rXiJJeL68mfMCHH34oGjduLHQ6nejatav4448/Siy/cuVK0bJlS6HT6US7du3Ejz/+WOZ9ZWVlCQAiCwUfDN1+Bg1yXyEgwHM5QIg+fdzLRkQUWzavfQfx96l0+cfSILbYsqbmLd3Kmpq3LL4OjRuLQ2cy5LJ57TsWW9YWXtdtuzndehRb1mEIEFnZeeL0hSvi71PpIqtv/+LrALhtN/O2ISWWPfhPklz28t0jSyz775+H5bIZ908osaw4dkwIq9X5M316yWX37Sso+8ILJZa1bdsm1q1bJ6x5eULMmVPydhMTC7b73nsll123rqDs55+XXHbZsoKyy5aVXPbzzwvKrltXctn33isom5hYctk5cwrK7txZctkXXigou29fyWWnTy8oe+xYyWUfeaSg7PnzJZcdM0Yua01LK7nsXXcVbNdqLbnsbbe5ly3pNaJ3b/eyJbxGiM6d3cs2blx82dat3cu2bl182caN3ct27lx82YgI97K9exdfNiDAvextt5X8uBUue9ddJZe9cqWg7JgxJZc9f76g7COPlFy2il4jxM6dBWX5GuGXrxHiypWSy5bzNcJiNIvMrDxxPj1bOAzFv0acbX+DePW7g+L5b/aL/369R2QHhxVb9u96zUXjp3+Qf86GRBVb9mjdRm5lj9ZtVHwdQqLcyv5dr3mxZTMDQ0RyRnZB7unTp/jHISDAPSMNGlTy41bYPfeILDgHLcnKyio123m9ZXbFihWYPn06FixYgG7dumHevHlISEjA0aNHERUVVaT8zp07MWrUKMyZMwe33347li1bhmHDhmHv3r1o166dF46gbBRWK7Rnkwpul9AaqbBdVdZWwqdxux3BF88iz2LP308Jn7AdDrftKi0lfMIWEkIunkMInF+/iFL65bptt6TWMgDac8kQ+S1QyjxPn1ULlb1wFg6js4wyN6fEsjh71vkvAQCZmSWXPX++oLWqtLIXLzo/6Z85A1wu5eKFixeB06edf1+6VHLZ1NSCsunpJZdNSysom5ZWctn09IKyqakll710qaDsxYsll718uaDs+fMll83MrFjZc+dKLpudXVC2tHORm1tQNqeU505eXkHZ0hiN7mVdzzlPzGb3siW1Slgs7mVLamG02dzLltQSaLe7ly2pFc7hcC9bUiucEO5lS/m/dytbWotLUlLB/2duya8RSE4uKJOdXXLZqnqNOH8eqFvX+TdfI5x/+9trRGnP33K+RmjPnoEWQCgAoPjXiIYGBZ5rWWjWSnXxXQfa19Xh5Ph4GO0STHaBOl+ogWKe8hEGFR7vGI48mwSjTUKwtvi+u65h+Vy1LGmWZ7sEWC2V21+5MiiEKOmVuOp169YNN9xwAz788EMAgCRJiI2NxdSpU/HMM88UKT9y5Ejk5eXhhx9+kJfdeOON6NixIxYsWFCkvMVigaXQi3d2djZiY2ORceYMQkJCilaokr9CtJnN+OXXXxEXFg2lvlDXAbO5+DdBhQKiUB1cZaNCdAjRaYqUvexQ4ZLReYwKi7nEZ6IoVN+SysaE6REUWujxsVgg7HbkWh2w2gRsDgesDgk2h+T86OS2XUuJXzsIvb6g64DVWuIbfFnKqpVKxNUNcJ63wl8LlhQGylHWplYjMSkJtzZrBo3DUfJ2dTr3rxBLChlarftXiGUta7eX/HWj6yu88pZ1OEoOOmq1+1eIZS0rSSWHooqWFaL0rxvzy9rsdvxy6BBuadIEmuK6F+kKTd1a0htbecoqle6vJ+UpazKV+Brh9tpTnrLmYr5CdCncJaE8ZS2W0rsOlLWswVDQdSD//9PmcOCX06eLnkMPZYtVRa8Rbv/35Slbi14jipw/H3uNKLVsDX+NEHo9zA4Bo03AlGuE0SbBlB+aTXYJRrtArtWOv1Mz8OSYAQgLCSzYbhV1M8i+cgURjRsjKyvLc14rfAjeDLNWqxUBAQFYvXo1hg0bJi8fN24cMjMz8e233xZZp1GjRpg+fTqmTZsmL5s5cybWrVuHAwcOFCk/a9YszJ49u8jyZcuWIaDwiysRERER+QSj0Yj77ruvTGHWq90MMjIy4HA4EB0d7bY8OjoaR44c8bhOSkqKx/IpKSkeyz/77LOYPn26fNvVMjtgwIBSH5zKYLPZkJiYiEbtbyz3FIyFNaprQNDVrbL57A4Jx1JL+SquHAJ1ajSuW/6gb7E5cOayEXZH9X4+ignVIzxQW3rBCnKdw1tvvbXMg7WTb+E59H88h/6N58//Vfc5zC6t21AhXu8zW9V0Oh10hZv782k0mmr9h1Kq1BUOswoFEBpgKHYwZ40G0Ous8lh81yosSF+hx0aj0aClTovky0YPg2VXndBAPTTVMMZsdT9nqPLxHPo/nkP/xvPn/6rrHJZnH14dzTciIgIqlQqpV3VAT01NRUxMjMd1YmJiylW+JjBoVaXOShJQiVO5Busr/hlHrVKiSUQgwoOqrqW0MKXSOSMMERER1U5eDbNarRadO3fG5s2b5WWSJGHz5s3o3r27x3W6d+/uVh4AEhMTiy1fEwTrSg+XlRXodBoldOpr25ZCoUCDMAPqh+lR1WM6B2hr/JcLREREVAKvJ4Hp06dj3Lhx6NKlC7p27Yp58+YhLy8PEyZMAACMHTsWDRo0wJw5cwAAjz/+OPr06YO3334bgwcPxvLly/HXX3/h008/9eZhVKnAMoTZymqZvZZW2avVDdIhLECLLJMNmUZrlXQ9qMwWaSIiIvI/Xg+zI0eORHp6Ol566SWkpKSgY8eO2Lhxo3yRV3JyMpTKggbkHj16YNmyZXjhhRfw3HPPoXnz5li3bp1PjzF7LZTKsgU2g0YFhaLkIS9Lux8AgvWV2w9GpVQgPFCL8EAtrHYJWSYbskxWmKyV07+XYZaIiKh283qYBYApU6ZgypQp/9/encZGVf1hHH/ubLcLXVC2FitSFjGsIoEUxAZTgkFQEgMYkJS4oAHeQAANaGpEAQlxCbLLJiE2SoAYICiLxbC5AE0QEISC4FIMRtP+qXSb83+BrRamhWk7d3rp95P0Bbfndn6XXwaenjn33JDfy8vLu+nY6NGjNXr06AhX1TTEB3y39fxlj8eS7fPoWnntIbF9cqwKi67VutuAxyPFRzAcBnwetU6w1TrBVnllUCWllfpfWYWullaotI6668IyAwAAmjeSQBPXIoyP/WMD3lrDrM9rKTnu+qzrz3+G3hi6hX17wbkx+L0eJcV5lPRPTRWVQV0tq1RFZVAVQaPyyqAqg0bllUbXyitDzijH+D3y3uLGOAAAcGcjzDZxLW5jvWyVuIBPf14N/YSYu+MDsixLLeMD+uNqaciP+Rt7iUE4fF6PkmJD349YVhHUz3/evN1XXBh/NwAA4M4U1d0MUDef1wprl4La1o9altTyPw8VSE2ODTmuMW/+akwBn0fprVuoXVLN3RHi2JILAIBmjzDbhIUzKytJts8TciusxBi//N5/Wx0X8FUvOagSG/DUGNMUtU6w1blNC8UGrtcZZxNmAQBo7pp2emnmwg2zlmUpNsTsbKgHGNw4yxnNJQbhiPF71emfWdqG7ocLAADcjzDbhN3O/rI3ir3ho3fb7wkZiv1ej9ok/PuY36a6xCAUy7LUOuHmRxQDAIDmhzDbRNl+jwK+8Ntz47rZu+Jrf6xsqxa2Ar7rOwKwxRUAAHAjwmwTVZ9ZWUk1lhlYltQyrvYw6/FYapcY46pZWQAAgP8izEZRq4Tag2a462Wr2D5v9d6ryXH+W+7DmhTn5yN7AADgWoTZKIkNeJSSFBsySFpW/cPs9Z99fXb27vjbC6nhbP8FAADQlPD5cpRULSNolxSjoDH6439l1d+L8Xsb9GSruIBXlUETcmcDAACAOwlhNkr+uyY2NTlWQWOqn97VkFlZ6XoYDjTxPWMBAAAaA2E2SuJv2D3gnpZxMqZEf5WUq0UDb8iKD3jlCfX0BAAAgDsMYTYKYgOekMsI7ml5/TGz8Q1cHuBjVhYAADQThNkoqG3bLcuylHZXnMPVAAAAuBdTeFFQ3z1kAQAAUBNh1mGWdfN6WQAAANQPYdZhDd12CwAAAP8izDos3mbvVwAAgMZCmHUY62UBAAAaD2HWQayXBQAAaFyEWQexXhYAAKBxEWYd1NDH1AIAAKAmwqyDuPkLAACgcRFmHcJ6WQAAgMZHmHWI7fPKw3pZAACARkWYdQhbcgEAADQ+wqxD4m3+qgEAABobCcshcX5mZgEAABobYdYhrJcFAABofIRZAAAAuBZhFgAAAK5FmAUAAIBrEWYBAADgWoRZAAAAuBZhFgAAAK5FmAUAAIBrEWYBAADgWoRZAAAAuBZhFgAAAK5FmAUAAIBrEWYBAADgWoRZAAAAuBZhFgAAAK5FmAUAAIBrEWYBAADgWoRZAAAAuBZhFgAAAK5FmAUAAIBrEWYBAADgWoRZAAAAuJYv2gU4zRgjSSoqKnLk9crLy1VSUqKioiL5/X5HXhONix66Hz10P3robvTP/ZzuYVVOq8ptdWl2Yba4uFiSlJaWFuVKAAAAUJfi4mIlJSXVOcYytxN57yDBYFC//vqrEhISZFlWxF+vqKhIaWlpunTpkhITEyP+emh89ND96KH70UN3o3/u53QPjTEqLi5WamqqPJ66V8U2u5lZj8eje+65x/HXTUxM5A3scvTQ/eih+9FDd6N/7udkD281I1uFG8AAAADgWoRZAAAAuBZhNsJs21ZOTo5s2452Kagneuh+9ND96KG70T/3a8o9bHY3gAEAAODOwcwsAAAAXIswCwAAANcizAIAAMC1CLMAAABwLcJsI1iyZInuu+8+xcTEaMCAAfrmm2/qHP/pp5+qW7duiomJUc+ePbVjxw6HKkVtwunhqlWrNHjwYLVs2VItW7ZUVlbWLXuOyAv3fVglNzdXlmVp1KhRkS0QtxRuD//66y9NmTJFKSkpsm1bXbt25d/TKAq3f++9957uv/9+xcbGKi0tTdOmTdO1a9ccqhY3+uqrrzRy5EilpqbKsixt3br1lufk5eWpb9++sm1bnTt31rp16yJeZ0gGDZKbm2sCgYBZs2aNOXHihHnhhRdMcnKyuXz5csjxBw4cMF6v1yxcuNCcPHnSvPrqq8bv95vjx487XDmqhNvDcePGmSVLlphjx46ZU6dOmYkTJ5qkpCTz888/O1w5qoTbwyrnz5837du3N4MHDzZPPvmkM8UipHB7WFpaavr162eGDx9u9u/fb86fP2/y8vJMfn6+w5XDmPD7t3HjRmPbttm4caM5f/68+fzzz01KSoqZNm2aw5Wjyo4dO8ycOXPM5s2bjSSzZcuWOscXFBSYuLg4M336dHPy5EmzePFi4/V6zc6dO50p+D8Isw3Uv39/M2XKlOo/V1ZWmtTUVDN//vyQ48eMGWMef/zxGscGDBhgXnzxxYjWidqF28MbVVRUmISEBLN+/fpIlYhbqE8PKyoqzMCBA82HH35osrOzCbNRFm4Ply1bZtLT001ZWZlTJaIO4fZvypQp5tFHH61xbPr06WbQoEERrRO353bC7KxZs0z37t1rHBs7dqwZNmxYBCsLjWUGDVBWVqYjR44oKyur+pjH41FWVpYOHToU8pxDhw7VGC9Jw4YNq3U8Iqs+PbxRSUmJysvLddddd0WqTNShvj1844031KZNGz333HNOlIk61KeHn332mTIyMjRlyhS1bdtWPXr00Lx581RZWelU2fhHffo3cOBAHTlypHopQkFBgXbs2KHhw4c7UjMarinlGZ/jr3gHuXLliiorK9W2bdsax9u2basffvgh5DmFhYUhxxcWFkasTtSuPj280csvv6zU1NSb3tRwRn16uH//fq1evVr5+fkOVIhbqU8PCwoKtHfvXo0fP147duzQ2bNnNXnyZJWXlysnJ8eJsvGP+vRv3LhxunLlih5++GEZY1RRUaGXXnpJs2fPdqJkNILa8kxRUZH+/vtvxcbGOlYLM7NAAyxYsEC5ubnasmWLYmJiol0ObkNxcbEmTJigVatWqVWrVtEuB/UUDAbVpk0brVy5Ug899JDGjh2rOXPmaPny5dEuDbchLy9P8+bN09KlS3X06FFt3rxZ27dv19y5c6NdGlyImdkGaNWqlbxery5fvlzj+OXLl9WuXbuQ57Rr1y6s8Yis+vSwyqJFi7RgwQLt3r1bvXr1imSZqEO4PTx37pwuXLigkSNHVh8LBoOSJJ/Pp9OnT6tTp06RLRo11Od9mJKSIr/fL6/XW33sgQceUGFhocrKyhQIBCJaM/5Vn/699tprmjBhgp5//nlJUs+ePXX16lVNmjRJc+bMkcfDXFtTV1ueSUxMdHRWVmJmtkECgYAeeugh7dmzp/pYMBjUnj17lJGREfKcjIyMGuMladeuXbWOR2TVp4eStHDhQs2dO1c7d+5Uv379nCgVtQi3h926ddPx48eVn59f/fXEE09oyJAhys/PV1pampPlQ/V7Hw4aNEhnz56t/kVEks6cOaOUlBSCrMPq07+SkpKbAmvVLybGmMgVi0bTpPKM47ec3WFyc3ONbdtm3bp15uTJk2bSpEkmOTnZFBYWGmOMmTBhgnnllVeqxx84cMD4fD6zaNEic+rUKZOTk8PWXFEWbg8XLFhgAoGA2bRpk/ntt9+qv4qLi6N1Cc1euD28EbsZRF+4Pbx48aJJSEgwU6dONadPnzbbtm0zbdq0MW+++Wa0LqFZC7d/OTk5JiEhwXz88cemoKDAfPHFF6ZTp05mzJgx0bqEZq+4uNgcO3bMHDt2zEgy77zzjjl27Jj56aefjDHGvPLKK2bChAnV46u25po5c6Y5deqUWbJkCVtzudnixYvNvffeawKBgOnfv785fPhw9fcyMzNNdnZ2jfGffPKJ6dq1qwkEAqZ79+5m+/btDleMG4XTww4dOhhJN33l5OQ4Xziqhfs+/C/CbNMQbg8PHjxoBgwYYGzbNunp6eatt94yFRUVDleNKuH0r7y83Lz++uumU6dOJiYmxqSlpZnJkyebP//80/nCYYwx5ssvvwz5f1tV37Kzs01mZuZN5/Tp08cEAgGTnp5u1q5d63jdxhhjGcN8PgAAANyJNbMAAABwLcIsAAAAXIswCwAAANcizAIAAMC1CLMAAABwLcIsAAAAXIswCwAAANcizAIAAMC1CLMA0IxZlqWtW7dKki5cuCDLspSfnx/VmgAgHIRZAIiSiRMnyrIsWZYlv9+vjh07atasWbp27Vq0SwMA1/BFuwAAaM4ee+wxrV27VuXl5Tpy5Iiys7NlWZbefvvtaJcGAK7AzCwARJFt22rXrp3S0tI0atQoZWVladeuXZKkYDCo+fPnq2PHjoqNjVXv3r21adOmGuefOHFCI0aMUGJiohISEjR48GCdO3dOkvTtt99q6NChatWqlZKSkpSZmamjR486fo0AEEmEWQBoIr7//nsdPHhQgUBAkjR//nx99NFHWr58uU6cOKFp06bpmWee0b59+yRJv/zyix555BHZtq29e/fqyJEjevbZZ1VRUSFJKi4uVnZ2tvbv36/Dhw+rS5cuGj58uIqLi6N2jQDQ2FhmAABRtG3bNrVo0UIVFRUqLS2Vx+PRBx98oNLSUs2bN0+7d+9WRkaGJCk9PV379+/XihUrlJmZqSVLligpKUm5ubny+/2SpK5du1b/7EcffbTGa61cuVLJycnat2+fRowY4dxFAkAEEWYBIIqGDBmiZcuW6erVq3r33Xfl8/n01FNP6cSJEyopKdHQoUNrjC8rK9ODDz4oScrPz9fgwYOrg+yNLl++rFdffVV5eXn6/fffVVlZqZKSEl28eDHi1wUATiHMAkAUxcfHq3PnzpKkNWvWqHfv3lq9erV69OghSdq+fbvat29f4xzbtiVJsbGxdf7s7Oxs/fHHH3r//ffVoUMH2batjIwMlZWVReBKACA6CLMA0ER4PB7Nnj1b06dP15kzZ2Tbti5evKjMzMyQ43v16qX169ervLw85OzsgQMHtHTpUg0fPlySdOnSJV25ciWi1wAATuMGMABoQkaPHi2v16sVK1ZoxowZmjZtmtavX69z587p6NGjWrx4sdavXy9Jmjp1qoqKivT000/ru+++048//qgNGzbo9OnTkqQuXbpow4YNOnXqlL7++muNHz/+lrO5AOA2zMwCQBPi8/k0depULVy4UOfPn1fr1q01f/58FRQUKDk5WX379tXs2bMlSXfffbf27t2rmTNnKjMzU16vV3369NGgQYMkSatXr9akSZPUt29fpaWlad68eZoxY0Y0Lw8AGp1ljDHRLgIAAACoD5YZAAAAwLUIswAAAHAtwiwAAABcizALAAAA1yLMAgAAwLUIswAAAHAtwiwAAABcizALAAAA1yLMAgAAwLUIswAAAHAtwiwAAABc6/96sP+D6CUKHAAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import os\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.metrics import precision_recall_curve, roc_curve\n", + "import os\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.metrics import precision_recall_curve, roc_curve, roc_auc_score, average_precision_score\n", + "\n", + "\n", + "def plot_average_curves(directory: str):\n", + " \"\"\"\n", + " Plots average AUPRC and AUROC curves with confidence intervals from multiple experiments.\n", + "\n", + " Args:\n", + " directory (str): Path to the directory containing prediction DataFrames named 'pred_indicators.csv'.\n", + " \"\"\"\n", + " # Initialize lists to store interpolated precision, recall, and ROC values\n", + " all_precisions = []\n", + " all_recalls = []\n", + " all_roc_aucs = []\n", + " all_pr_aucs = []\n", + " all_roc_fprs = []\n", + " all_roc_tprs = []\n", + " chance_levels = []\n", + "\n", + " # Define a common recall and FPR range for interpolation\n", + " common_recall = np.linspace(0, 1, 100)\n", + " common_fpr = np.linspace(0, 1, 100)\n", + "\n", + " # Iterate through the directory and load all 'pred_indicators.csv' files\n", + " for root, _, files in os.walk(directory):\n", + " for file in files:\n", + " if file == \"pred_indicators.csv\":\n", + " file_path = os.path.join(root, file)\n", + " df = pd.read_csv(file_path)\n", + "\n", + " # Extract ground truth and predictions\n", + " ground_truth = df[\"ground_truth\"]\n", + " predictions = df[\"prediction_1\"]\n", + "\n", + " # Compute precision-recall curve\n", + " precision, recall, _ = precision_recall_curve(ground_truth, predictions)\n", + " interpolated_precision = np.interp(common_recall, recall[::-1], precision[::-1])\n", + " all_precisions.append(interpolated_precision)\n", + "\n", + " # Compute ROC curve\n", + " fpr, tpr, _ = roc_curve(ground_truth, predictions)\n", + " interpolated_tpr = np.interp(common_fpr, fpr, tpr)\n", + " all_roc_fprs.append(common_fpr)\n", + " all_roc_tprs.append(interpolated_tpr)\n", + "\n", + " # Compute AUCs\n", + " roc_auc = roc_auc_score(ground_truth, predictions)\n", + " pr_auc = average_precision_score(ground_truth, predictions)\n", + " all_roc_aucs.append(roc_auc)\n", + " all_pr_aucs.append(pr_auc)\n", + "\n", + " # Compute chance level\n", + " chance_levels.append(ground_truth.mean())\n", + "\n", + " # Compute averages and standard deviations\n", + " avg_precision = np.mean(all_precisions, axis=0)\n", + " std_precision = np.std(all_precisions, axis=0)\n", + " avg_tpr = np.mean(all_roc_tprs, axis=0)\n", + " std_tpr = np.std(all_roc_tprs, axis=0)\n", + " avg_roc_auc = np.mean(all_roc_aucs)\n", + " std_roc_auc = np.std(all_roc_aucs)\n", + " avg_pr_auc = np.mean(all_pr_aucs)\n", + " std_pr_auc = np.std(all_pr_aucs)\n", + " avg_chance_level = np.mean(chance_levels)\n", + " std_chance_level = np.std(chance_levels)\n", + "\n", + " # Plot Precision-Recall curve\n", + " plt.figure(figsize=(8, 6))\n", + " plt.plot(common_recall, avg_precision, label=f\"Average Precision-Recall Curve (AP: {avg_pr_auc:.2f} ± {std_pr_auc:.2f})\")\n", + " plt.fill_between(\n", + " common_recall, avg_precision - std_precision, avg_precision + std_precision, alpha=0.2, label=\"Confidence Interval\"\n", + " )\n", + " plt.axhline(\n", + " y=avg_chance_level,\n", + " color=\"red\",\n", + " linestyle=\"--\",\n", + " label=f\"Chance Level ({avg_chance_level:.2f} ± {std_chance_level:.2f})\",\n", + " )\n", + " plt.fill_between(\n", + " common_recall, avg_chance_level - std_chance_level, avg_chance_level + std_chance_level, color=\"red\", alpha=0.1\n", + " )\n", + " plt.xlabel(\"Recall\")\n", + " plt.ylabel(\"Precision\")\n", + " plt.title(\"Average Precision-Recall Curve\")\n", + " plt.legend()\n", + " plt.grid()\n", + "\n", + " # Plot ROC curve\n", + " plt.figure(figsize=(8, 6))\n", + " plt.plot(common_fpr, avg_tpr, label=f\"Average ROC Curve (AUC: {avg_roc_auc:.2f} ± {std_roc_auc:.2f})\")\n", + " plt.fill_between(common_fpr, avg_tpr - std_tpr, avg_tpr + std_tpr, alpha=0.2, label=\"Confidence Interval\")\n", + " # plt.axhline(y=avg_chance_level, color='red', linestyle='--', label=f\"Chance Level ({avg_chance_level:.2f} ± {std_chance_level:.2f})\")\n", + " plt.fill_between(\n", + " common_fpr, avg_chance_level - std_chance_level, avg_chance_level + std_chance_level, color=\"red\", alpha=0.1\n", + " )\n", + " plt.xlabel(\"False Positive Rate\")\n", + " plt.ylabel(\"True Positive Rate\")\n", + " plt.title(\"Average ROC Curve\")\n", + " plt.legend()\n", + " plt.grid()\n", + "\n", + " plt.show()\n", + "\n", + "\n", + "# Example usage\n", + "\n", + "# Example usage\n", + "plot_average_curves(\"/Users/robin/Downloads/cassandra_logs/2024-12-09T11-46-13.259943\")" + ] + }, { "cell_type": "code", "execution_count": 3, diff --git a/icu_benchmarks/data/split_process_data.py b/icu_benchmarks/data/split_process_data.py index 972c8145..ccdb62b5 100644 --- a/icu_benchmarks/data/split_process_data.py +++ b/icu_benchmarks/data/split_process_data.py @@ -230,14 +230,18 @@ def preprocess_data( val_incidence = sanitized_data[DataSplit.val][DataSegment.outcome][vars[VarType.label]] test_incidence = sanitized_data[DataSplit.test][DataSegment.outcome][vars[VarType.label]] total_samples = train_samples + val_samples + test_samples - logging.info(f"Train segments: {train_samples} ({train_samples/total_samples:.1%}), " - f"Val segments: {val_samples} ({val_samples/total_samples:.1%}), " - f"Test segments: {test_samples} ({test_samples/total_samples:.1%})") - # Define the number of decimal places for rounding + logging.info( + f"Train segments: {train_samples} ({train_samples / total_samples:.1%}), " + f"Val segments: {val_samples} ({val_samples / total_samples:.1%}), " + f"Test segments: {test_samples} ({test_samples / total_samples:.1%})" + ) + # Define the number of decimal places for rounding decimal_places = 4 # - logging.info(f"Train incidence: {train_incidence.mean():.{decimal_places}f}, STD {train_incidence.std():.{decimal_places}f}| " - f"Val incidence: {val_incidence.mean():.{decimal_places}f}, STD {val_incidence.std():.{decimal_places}f}| " - f"Test incidence: {test_incidence.mean():.{decimal_places}f}, STD {test_incidence.std():.{decimal_places}f}") + logging.info( + f"Train incidence: {train_incidence.mean():.{decimal_places}f}, STD {train_incidence.std():.{decimal_places}f}| " + f"Val incidence: {val_incidence.mean():.{decimal_places}f}, STD {val_incidence.std():.{decimal_places}f}| " + f"Test incidence: {test_incidence.mean():.{decimal_places}f}, STD {test_incidence.std():.{decimal_places}f}" + ) # logging.info(f"{len(ou)}") # Generate cache diff --git a/icu_benchmarks/models/utils.py b/icu_benchmarks/models/utils.py index 3a877fc5..9eddee20 100644 --- a/icu_benchmarks/models/utils.py +++ b/icu_benchmarks/models/utils.py @@ -285,6 +285,8 @@ def get_smoothed_labels( import csv + + def log_single_metric_to_file(metric_name: str, data_points: tuple[np.ndarray], output_file: Path) -> None: """ Logs a single metric to a file, where the input is a tuple of numpy arrays. @@ -310,4 +312,4 @@ def log_single_metric_to_file(metric_name: str, data_points: tuple[np.ndarray], writer.writerow(["Row"] + [f"Column {i}" for i in range(data_points[0].size)]) # Write each numpy array as a row for index, array in enumerate(data_points): - writer.writerow([index] + array.tolist()) \ No newline at end of file + writer.writerow([index] + array.tolist()) diff --git a/icu_benchmarks/models/wrappers.py b/icu_benchmarks/models/wrappers.py index 48fdbe07..8664b5f4 100644 --- a/icu_benchmarks/models/wrappers.py +++ b/icu_benchmarks/models/wrappers.py @@ -488,10 +488,11 @@ def log_curves(self, label, pred, metric_type, pred_indicators): if isinstance(result, tuple): # Vertical stacking for saving to file # result = tuple(arr.reshape(-1, 1) for arr in result) - log_single_metric_to_file(metric_name=name, - data_points=result, - output_file=Path(self.logger.save_dir) / f"{metric_type}_metrics_{name}.csv", - ) + log_single_metric_to_file( + metric_name=name, + data_points=result, + output_file=Path(self.logger.save_dir) / f"{metric_type}_metrics_{name}.csv", + ) def log_metrics(self, label, pred, metric_type, pred_indicators): """Log metrics to the PL logs.""" @@ -535,8 +536,6 @@ def log_metrics(self, label, pred, metric_type, pred_indicators): sync_dist=True, ) - - # def _explain_model(self, test_rep, test_label): # if self.explainer is not None: # logging.info(f"Explaining features for {self}") diff --git a/icu_benchmarks/run_utils.py b/icu_benchmarks/run_utils.py index 68a89a39..fc97c3c1 100644 --- a/icu_benchmarks/run_utils.py +++ b/icu_benchmarks/run_utils.py @@ -35,11 +35,9 @@ def build_parser() -> ArgumentParser: parser.add_argument("-tn", "--task-name", help="Name of the task, used for naming experiments.") parser.add_argument("-m", "--model", default="LGBMClassifier", help="Name of the model gin.") parser.add_argument("-e", "--experiment", help="Name of the experiment gin.") - parser.add_argument("-l", "--log-dir", default=Path("../yaib_logs/"), type=Path, - help="Log directory for model weights.") + parser.add_argument("-l", "--log-dir", default=Path("../yaib_logs/"), type=Path, help="Log directory for model weights.") parser.add_argument("-s", "--seed", default=1234, type=int, help="Random seed for processing, tuning and training.") - parser.add_argument("-v", "--verbose", default=False, action=BOA, - help="Set to log verbosly. Disable for clean logs.") + parser.add_argument("-v", "--verbose", default=False, action=BOA, help="Set to log verbosly. Disable for clean logs.") parser.add_argument("--cpu", default=False, action=BOA, help="Set to use CPU.") parser.add_argument("-db", "--debug", default=False, action=BOA, help="Set to load less data.") parser.add_argument("--reproducible", default=True, action=BOA, help="Make torch reproducible.") @@ -53,8 +51,7 @@ def build_parser() -> ArgumentParser: parser.add_argument("--tune", default=False, action=BOA, help="Find best hyperparameters.") parser.add_argument("--hp-checkpoint", type=Path, help="Use previous hyperparameter checkpoint.") parser.add_argument("--eval", default=False, action=BOA, help="Only evaluate model, skip training.") - parser.add_argument("--complete-train", default=False, action=BOA, - help="Use all data to train model, skip testing.") + parser.add_argument("--complete-train", default=False, action=BOA, help="Use all data to train model, skip testing.") parser.add_argument("-ft", "--fine-tune", default=None, type=int, help="Finetune model with amount of train data.") parser.add_argument("-sn", "--source-name", type=Path, help="Name of the source dataset.") parser.add_argument("--source-dir", type=Path, help="Directory containing gin and model weights.") @@ -65,19 +62,21 @@ def build_parser() -> ArgumentParser: nargs="+", help="Optional modality selection to use. Specify multiple modalities separated by spaces.", ) - parser.add_argument("--label", type=str, help="Label to use for evaluation in case of multiple labels.", - default=None) + parser.add_argument("--label", type=str, help="Label to use for evaluation in case of multiple labels.", default=None) parser.add_argument( "--file_names", type=parse_dict, help="Dictionary of file names to use in data_dir " - "(e.g., 'DYNAMIC:dyno.parquet,OUTCOME:outco.parquet,STATIC:sta.parquet')", + "(e.g., 'DYNAMIC:dyno.parquet,OUTCOME:outco.parquet,STATIC:sta.parquet')", default=None, ) parser.add_argument("--explain_features", default=False, action=BOA, help="Enable feature explanation.") - parser.add_argument("--load_data_vars", default=False, action=BOA, - help="Load data variables from the dataset directory. Avoids having to manually add " - "the path in the task.gin") + parser.add_argument( + "--load_data_vars", + default=False, + action=BOA, + help="Load data variables from the dataset directory. Avoids having to manually add the path in the task.gin", + ) return parser @@ -215,8 +214,7 @@ def log_full_line(msg: str, level: int = logging.INFO, char: str = "-", num_newl reserved_chars = len(logging.getLevelName(level)) + 28 logging.log( level, - "{0:{char}^{width}}{1}".format(msg, "\n" * num_newlines, char=char, - width=terminal_size.columns - reserved_chars), + "{0:{char}^{width}}{1}".format(msg, "\n" * num_newlines, char=char, width=terminal_size.columns - reserved_chars), ) @@ -235,8 +233,7 @@ def load_pretrained_imputation_model(use_pretrained_imputation): pretrained_imputation_model_checkpoint = torch.load(use_pretrained_imputation, map_location=torch.device("cpu")) if isinstance(pretrained_imputation_model_checkpoint, dict): imputation_model_class = pretrained_imputation_model_checkpoint["class"] - pretrained_imputation_model = imputation_model_class( - **pretrained_imputation_model_checkpoint["hyper_parameters"]) + pretrained_imputation_model = imputation_model_class(**pretrained_imputation_model_checkpoint["hyper_parameters"]) pretrained_imputation_model.set_trained_columns(pretrained_imputation_model_checkpoint["trained_columns"]) pretrained_imputation_model.load_state_dict(pretrained_imputation_model_checkpoint["state_dict"]) else: diff --git a/icu_benchmarks/tuning/hyperparameters.py b/icu_benchmarks/tuning/hyperparameters.py index c9232dc2..7b800579 100644 --- a/icu_benchmarks/tuning/hyperparameters.py +++ b/icu_benchmarks/tuning/hyperparameters.py @@ -338,7 +338,7 @@ def bind_params_and_train(hyperparams): # Check if we found a checkpoint file and copy it. logging.info(f"Copying checkpoint and loading checkpoint at {checkpoint}") local_path = log_dir / checkpoint_file - if not str(local_path).endswith('.db'): + if not str(local_path).endswith(".db"): local_path = local_path / "hyperparameter_tuning_logs.db" logging.warning(f"Checkpoint file {checkpoint_file} does not end with .db, trying {local_path} instead.") shutil.copy(str(checkpoint), local_path) From 60081ed5a9370e8177f1d00c76f667d6f51ce023 Mon Sep 17 00:00:00 2001 From: Robin van de Water Date: Mon, 23 Jun 2025 14:17:51 +0200 Subject: [PATCH 58/82] no alarm toy notebook --- alarm_toy.ipynb | 1320 ----------------------------------------------- 1 file changed, 1320 deletions(-) delete mode 100644 alarm_toy.ipynb diff --git a/alarm_toy.ipynb b/alarm_toy.ipynb deleted file mode 100644 index d11b18cc..00000000 --- a/alarm_toy.ipynb +++ /dev/null @@ -1,1320 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "id": "392ddb72e535edb4", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "\n", - "# Sample data\n", - "data = np.array([[1, 10, 100], [2, 20, 200], [1, 30, 300], [2, 40, 400], [3, 50, 500]])\n", - "\n", - "# Column index for IDs\n", - "id_column_index = 0\n", - "\n", - "# Get unique IDs\n", - "unique_ids = np.unique(data[:, id_column_index])\n", - "\n", - "# Dictionary to store separated arrays\n", - "separated_arrays = {}\n", - "\n", - "# Separate arrays based on IDs\n", - "for unique_id in unique_ids:\n", - " separated_arrays[unique_id] = data[data[:, id_column_index] == unique_id]\n", - "\n", - "# Print separated arrays\n", - "for unique_id, array in separated_arrays.items():\n", - " print(f\"ID {unique_id}:\\n{array}\\n\")" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "16e091727d83764d", - "metadata": { - "ExecuteTime": { - "end_time": "2025-06-20T13:52:23.207626Z", - "start_time": "2025-06-20T13:52:23.091745Z" - } - }, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "import pandas as pd\n", - "import numpy as np\n", - "from sklearn.metrics import PrecisionRecallDisplay, RocCurveDisplay\n", - "\n", - "\n", - "def plot_curves_from_csv(csv_path: str) -> None:\n", - " \"\"\"\n", - " Plots AUPRC and AUROC curves from data logged in a CSV file.\n", - "\n", - " Args:\n", - " csv_path (str): Path to the CSV file containing ground truth and predictions.\n", - " \"\"\"\n", - " # Load data from CSV\n", - " data = pd.read_csv(csv_path, delimiter=\",\", encoding=\"utf-8\")\n", - "\n", - " # print(data.head())\n", - " # data = data.T\n", - "\n", - " # Preprocess data to handle infinity and large values\n", - " # Replace infinities with NaN\n", - " data = data.replace([np.inf, -np.inf], np.nan)\n", - "\n", - " # Fill NaN values with a default value (e.g., 0) instead of dropping rows\n", - " data = data.fillna(0)\n", - "\n", - " # Clip values to the float64 range\n", - " data = data.clip(-np.finfo(np.float64).max, np.finfo(np.float64).max)\n", - " # print(data.head())\n", - " # Extract precision, recall, and thresholds\n", - " precision = data.iloc[0, 1:].values # Precision values (excluding the first column)\n", - " recall = data.iloc[1, 1:].values # Recall values (excluding the first column)\n", - " thresholds = data.iloc[2, 1:].values # Thresholds (excluding the first column)\n", - " # Ensure recall and precision are sorted correctly\n", - " # sorted_indices = np.argsort(recall)\n", - " # recall = recall[sorted_indices]\n", - " # precision = precision[sorted_indices]\n", - " # Plot Precision-Recall curve\n", - " plt.figure(figsize=(12, 6))\n", - " plt.plot(recall, precision, label=\"Precision-Recall Curve\")\n", - " plt.xlabel(\"Recall\")\n", - " plt.ylabel(\"Precision\")\n", - " plt.title(\"Precision-Recall Curve\")\n", - " plt.legend()\n", - " plt.grid()\n", - "\n", - " # Plot thresholds\n", - " # plt.figure(figsize=(12, 6))\n", - " # plt.plot(thresholds, precision[:-1], label=\"Precision vs Threshold\")\n", - " # plt.plot(thresholds, recall[:-1], label=\"Recall vs Threshold\")\n", - " # plt.xlabel(\"Thresholds\")\n", - " # plt.ylabel(\"Values\")\n", - " # plt.title(\"Precision and Recall vs Thresholds\")\n", - " # plt.legend()\n", - " # plt.grid()\n", - "\n", - " # Show plots\n", - " plt.tight_layout()\n", - " plt.show()\n", - "\n", - "\n", - "csv_path = \"/Users/robin/Documents/git/yaib_logs/mimic_demo/aki/XGBClassifier/2025-06-18T17-11-27.152583/repetition_0/fold_1/test_metrics_RO_Curve.csv\"\n", - "plot_curves_from_csv(csv_path)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f6ec20105259df69", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7fbebb12c68e59fe", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "35fe0bf84155fdef", - "metadata": { - "ExecuteTime": { - "end_time": "2025-06-20T13:55:55.553437Z", - "start_time": "2025-06-20T13:55:55.544915Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['# id', 'time', 'ground_truth', 'prediction_0', 'prediction_1'], dtype='object')" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "13be64a0f98bf02d", - "metadata": { - "ExecuteTime": { - "end_time": "2025-06-20T13:59:20.113873Z", - "start_time": "2025-06-20T13:59:19.971315Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAcAAAAHHCAYAAAAoIIjLAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABwFklEQVR4nO3dd1hT1xsH8G8SSNhLZIriwK3gwoqDqihW625FcaCtWgdqpc46cNRVrdW2jrpHtThaW391Iu5RF2JVEAdTBRUB2SQk5/cH5daYAAkkBMj7eZ48kpM73ryGvNx7z7mHxxhjIIQQQvQMX9cBEEIIIbpABZAQQoheogJICCFEL1EBJIQQopeoABJCCNFLVAAJIYToJSqAhBBC9BIVQEIIIXqJCiAhhBC9RAWQkGpMJpOhefPmWLZsma5DqZY2b96M2rVrIz8/X9ehkDKgAki0ZteuXeDxeNzDwMAAzs7OGD16NJ4/f650HcYY9u7diy5dusDKygomJiZo0aIFlixZguzs7GL3deTIEXz00UewtbWFUCiEk5MThgwZgrNnz6oUa15eHr7//nu0b98elpaWMDIyQsOGDREYGIhHjx6V6f1XBr/++isSExMRGBio9PWNGzeCx+Ohffv2Sl+Pi4sDj8fDmjVrlL6+Zs0a8Hg8xMXFKbxW3v8TZWQyGb799lvUrVsXRkZGaNmyJX799VeV1n3/8/juIzk5WW7ZAwcOYMSIEXBzcwOPx8OHH36odJujR4+GWCzGzz//XOb3RHTHQNcBkOpvyZIlqFu3LvLy8vD3339j165duHz5Mu7fvw8jIyNuOalUCn9/fxw8eBCdO3fGokWLYGJigkuXLmHx4sU4dOgQzpw5A3t7e24dxhg+++wz7Nq1C61atUJQUBAcHByQlJSEI0eOoHv37rhy5Qq8vLyKjS8lJQW9evXC7du38fHHH8Pf3x9mZmaIjo5GSEgItmzZArFYrNUcacvq1asxdOhQWFpaKn193759cHV1xY0bN/DkyRM0aNCg3PvUxP9JcebNm4eVK1di3LhxaNeuHf7880/4+/uDx+Nh6NChKm2j6PP4LisrK7nnmzZtwu3bt9GuXTu8efOm2G0ZGRkhICAAa9euxZQpU8Dj8dR+T0SHGCFasnPnTgaA3bx5U6599uzZDAA7cOCAXPvy5csZADZjxgyFbR09epTx+XzWq1cvufbVq1czAOzLL79kMplMYb09e/aw69evlxhnnz59GJ/PZ4cPH1Z4LS8vj3311Vclrq8qiUTC8vPzNbItVYSHhzMA7MyZM0pfj4mJYQDY77//zmrWrMkWLVqksExsbCwDwFavXq10G0X5j42NVWgrz/+JMs+ePWOGhoZs8uTJXJtMJmOdO3dmtWrVYgUFBSWuX9znUZmEhAQmlUoZY4w1a9aMeXt7F7vsrVu3GAAWFham2hshlQYVQKI1xX3h/PXXXwwAW758OdeWk5PDrK2tWcOGDZlEIlG6vTFjxjAA7Nq1a9w6NjY2rHHjxqV++RXn77//ZgDYuHHjVFre29tb6ZdhQEAAq1OnDvf83cLx/fffs3r16jE+n8/+/vtvJhAIlBabhw8fMgDsxx9/5NrS0tLYtGnTWK1atZhQKGT169dnK1eu5L6cS7Jw4UImFAqZWCxW+vrSpUuZtbU1y8/PZxMnTmRubm4Ky6hbAMvyf/LkyRP25MmTUpfbsGEDA8AePHgg175//34GgF26dKnE9d/9PGZkZKgcX2kFkDHGbGxs2NSpU1XaHqk86BogqXBF14usra25tsuXLyMtLQ3+/v4wMFB+Zn7UqFEAgL/++otbJzU1Ff7+/hAIBGWK5ejRowCAkSNHlmn90uzcuRM//vgjxo8fj++++w6Ojo7w9vbGwYMHFZY9cOAABAIBPv30UwBATk4OvL298csvv2DUqFH44Ycf0LFjR8ydOxdBQUGl7vvq1ato3rw5DA0Nlb6+b98+DBo0CEKhEMOGDcPjx49x8+bNcr3fsvyfdO/eHd27dy91uTt37sDU1BRNmjSRa/f09OReV0XXrl1hYWEBExMT9OvXD48fP1ZpvZK0bt0aV65cKfd2SMWia4BE696+fYuUlBTk5eXh+vXrWLx4MUQiET7++GNumcjISACAu7t7sdspei0qKkru3xYtWpQ5Nk1soyTPnj3DkydPULNmTa7Nz88PX3zxBe7fv4/mzZtz7QcOHIC3tzd3jXPt2rV4+vQp7ty5Azc3NwDAF198AScnJ6xevRpfffUVXFxcit33w4cPi+3ccvv2bTx8+BA//vgjAKBTp06oVasW9u3bh3bt2pX5/Wozn0lJSbC3t1e4zubo6AgAePHiRYnrm5iYYPTo0VwBvH37NtauXQsvLy+Eh4eXmMvS1KtXD3v37i3z+kQ36AiQaJ2Pjw9q1qwJFxcXfPLJJzA1NcXRo0dRq1YtbpnMzEwAgLm5ebHbKXotIyND7t+S1imNJrZRksGDB8sVPwAYNGgQDAwMcODAAa7t/v37iIyMhJ+fH9d26NAhdO7cGdbW1khJSeEePj4+kEqluHjxYon7fvPmjdxR9rv27dsHe3t7dO3aFQDA4/Hg5+eHkJAQSKXSsr7dMuUzLi5OaS/S9+Xm5kIkEim0F3Wkys3NLXH9IUOGYOfOnRg1ahQGDBiApUuX4tSpU3jz5k25h4lYW1sjNzcXOTk55doOqVhUAInWbdiwAaGhoTh8+DB69+6NlJQUhS+yoi/MokKozPtF0sLCotR1SqOJbZTk/d6GAGBra4vu3bvLnQY9cOAADAwMMGjQIK7t8ePHOHnyJGrWrCn38PHxAQC8evWq1P0zxhTapFIpQkJC0LVrV8TGxuLJkyd48uQJ2rdvj5cvXyIsLEzt91l0VKbNfBobGysdb5eXl8e9rq5OnTqhffv2OHPmTLliK8oz9QKtWqgAEq3z9PSEj48PBg8ejKNHj6J58+bw9/dHVlYWt0zRdZ1//vmn2O0Uvda0aVMAQOPGjQEA9+7dK3Ns6m6juC+44o6aivtSHjp0KB49eoSIiAgAwMGDB9G9e3fY2tpyy8hkMvTo0QOhoaFKH4MHDy4x1ho1aiAtLU2h/ezZs0hKSkJISAjc3Ny4x5AhQwAUHh0WKe3oquiIp2g5TfyfFMfR0RHJyckKRT0pKQkA4OTkVKbturi4IDU1tVyxpaWlwcTEpExFmOgOFUBSoQQCAVasWIEXL17gp59+4to7deoEKysr7N+/v9hismfPHgDgrh126tQJ1tbW+PXXX8t82q5v374AgF9++UWl5a2trZGenq7QHh8fr9Z+BwwYAKFQiAMHDiAiIgKPHj1SGMdWv359ZGVlwcfHR+mjdu3aJe6jcePGiI2NVWjft28f7OzscOjQIYXHsGHDcOTIEa7g1axZEyYmJoiOjla6j+joaJiYmHCFWxP/J8Xx8PBATk4Od52xyPXr17nXyyImJkbhNLW6YmNjFTrnkCpAx71QSTVW0rgrT09PZm9vz3Jzc7m2b775hgFgs2fPVlj+r7/+Ynw+n/n6+sq1r1y5kgFgX331ldIxZ3v37i11zFmvXr0Yn89nR44cUXgtPz9fbhzgjBkzmEgkYq9eveLaIiIiGJ/PL3YYRHH69u3L6tWrx2bPns2EQiFLS0uTe33RokUMADt58qTCumlpacUOFymyYMECZmhoyPLy8ri2nJwcZm5uzj777DOl61y5coUBYCEhIVzbgAEDmIWFBYuPj5dbNj4+npmbm7MBAwbItav7f6LqMIjExMRixwE6OzvLDWt48eIFi4qKkhsC8u7/WZFjx44xACUOYVB1GMSUKVNKfQ+kcqECSLSmpAJ46NAhBoBt2rSJaysoKGCDBw9mAFiXLl3Y+vXr2ZYtW9ioUaMYn89nzZo1Y8nJyXLbkUqlbOTIkQwAa926NVu+fDnbsWMHW758OfP09GQA2NWrV0uM89WrV8zDw4PxeDzWr18/tn79erZt2zY2e/ZsVqdOHSYUCrllIyMjGZ/PZ61atWI//fQTW7hwIbOzs2MtWrRQuwD+8ssvDAAzNzdnffv2VXg9OzubtW7dmhkYGLCxY8eyTZs2sTVr1rCAgABmamrKXr9+XeL7KhqgferUKa4tJCSEAWB//PGH0nWkUimrWbOmXDyRkZHMwsKC1ahRg82dO5f9/PPPbO7cuaxGjRrMwsKCRUZGKmxDnf+TOnXqyOWuJDNnzmQA2Pjx49nWrVtZnz59GAC2b98+ueUCAgIUBug3aNCAffrpp2zVqlVs8+bNbPz48czAwIC5uLgofK4uXLjAli5dypYuXcrs7OyYq6sr9/zChQtK81zcDQdI5UUFkGhNSQVQKpWy+vXrs/r168v95S6VStnOnTtZx44dmYWFBTMyMmLNmjVjixcvZllZWcXu6/Dhw6xnz57MxsaGGRgYMEdHR+bn58fOnz+vUqw5OTlszZo1rF27dszMzIwJhULm5ubGpkyZonB08ssvv7B69eoxoVDIPDw82KlTp0ocCF+cjIwMZmxszACwX375RekymZmZbO7cuaxBgwZMKBQyW1tb5uXlxdasWVPsAPd3tWzZkn3++efc8759+zIjIyOWnZ1d7DqjR49mhoaGLCUlhWuLiopifn5+zM7OjhkYGDA7Ozs2dOhQFhUVVex2VP0/UacASqVStnz5cu4Pk2bNminNnbICOG/ePObh4cEsLS2ZoaEhq127Nps4caJC8WOMseDgYAZA6SM4OFhu2dmzZ7PatWsrPdollRuPMSXdxAgh1cLevXsxefJkJCQkKNzvkpRffn4+XF1dMWfOHEybNk3X4RA1UScYQqqx4cOHo3bt2tiwYYOuQ6mWdu7cCUNDQ0yYMEHXoZAyoCNAQggheomOAAkhhOglKoCEEEL0EhVAQggheokKICGEEL2k0+mQLl68iNWrV+P27dtISkrCkSNHMGDAgBLXOX/+PIKCgvDgwQO4uLhg/vz5GD16tMr7lMlkePHiBczNzenGtYQQUgUxxpCZmQknJyfw+WU/jtNpAczOzoa7uzs+++wzubvgFyc2NhZ9+vTBhAkTsG/fPoSFhWHs2LFwdHSEr6+vSvt88eJFueb9IoQQUjkkJibKTaumrkozDILH45V6BDh79mwcO3YM9+/f59qGDh2K9PR0nDx5UqX9vH37FlZWVkhMTISFhQUkEglOnz6Nnj17Fjtztj6j/JSOclQyyk/pKEclez8/GRkZcHFxQXp6OiwtLcu83So1I/y1a9e4udCK+Pr64ssvv1R5G+/OW1ZUAE1MTGBhYUEfPCUoP6WjHJWM8lO6qpgjqYzhVlwqsvILtL6vAqkUMfmmEPNFqGFhxrWX9zJWlSqAycnJsLe3l2uzt7dHRkYGcnNzlc7FlZ+fLzeJZtGM1RKJhHsUPSeKKD+loxyVjPJTuqqWI4lUhqkhd3Hm4esK3KsArTxSYWsm0lieqlQBLIsVK1Zg8eLFCu2nT5+GiYkJ9zw0NLQiw6pyKD+loxyVjPJTuqqQIykD9jziIyKVDwMeg5NJ6etoysN7dyCJv8NNxFxeVaoAOjg44OXLl3JtL1++hIWFRbEzMc+dOxdBQUHc86Jzxz179uROgYaGhqJHjx5V5tRDRaL8lI5yVDLKT+mqSo6kMoaZv91DRGoyDAU8bPJvBe+G5ZtMWBXv56foTF55VakC2KFDBxw/flyuLTQ0FB06dCh2HZFIBJFIpNBuaGgo90F7/zmRR/kpHeWoZJSf0lX2HO25HIv//ZMMAz4PG4e3gU9T+9JX0qCi/GgqRzodCJ+VlYWIiAhEREQAKBzmEBERgYSEBACFR2+jRo3ilp8wYQJiYmIwa9YsPHz4EBs3bsTBgwcxffp0XYRPCCF6JSyq8AzcDN9G6FHBxU8bdFoAb926hVatWqFVq1YAgKCgILRq1QoLFy4EACQlJXHFEADq1q2LY8eOITQ0FO7u7vjuu++wbds2lccAEkIIKRtxgQzhCWkAgO6N7XQcjWbo9BTohx9+iJKGIe7atUvpOnfu3NFiVIQQQt537/lb5ElksDEVooGdWekrVAF0L1BCCCGluhGbCgBo52pdbW4jSQWQEEJIqW7EvgEAeNatoeNINIcKICGEkBIV3vWl8Ppf+7o2Oo5Gc6gAEkIIKVFyRh4y8wtgKOChiaOFrsPRGCqAhBBCSvQ8LRcA4GhpDAG/elz/A6gAEkIIKcWztMJbjzlbKb/jVlVFBZAQQkiJio4Ana2pABJCCNEjz9P/LYB0BEgIIUSfFBXAWnQESAghRJ/QKVBCCCF6hzH23xGgVQVO/lcBqAASQggpVkqWGPkFMvB4gIOlka7D0SgqgIQQQopVdPRnb24EoUH1KhnV690QQgjRqKIxgNWtAwxABZAQQkgJqmsHGIAKICGEkBIkvc0DADhVszGAABVAQgghJXiZUVgA7c1FOo5E86gAEkIIKdbrzHwAgJ1F9eoBClABJIQQUoJXRQWQjgAJIYToC8YYXmUWngK1M6cjQEIIIXoiM78AeRIZAKAmHQESQgjRF68yCk9/mosMYCwU6DgazaMCSAghRKmi0581Larf0R9ABZAQQkgxXlfjDjAAFUBCCCHFKDoFWh07wABUAAkhhBTjvx6gdARICCFEj3BjAOkaICGEEH3y3zVAOgVKCCFEj1Tnu8AAVAAJIYQU49W/N8KujoPgASqAhBBClMgVS5GRVwCAToESQgjRI/eevwUA2JqJYGFsoONotKN6vitCCCFlwhhDfoEMV5+mAADa17MBj8fTcVTaQQWQEEIIAKBAKsOAjVdw/3kG19a+ro0OI9IuOgVKCCEEAJCQmiNX/KxNDOHTxF6HEWkXHQESQggBADxPzwUA1K9pij8DO8HIgA8DQfU9TqICSAgheioxNQeBv95BzOssAIBEWjj3n4uNCcxE1b88VP93SAghREFGngSf7bqJx6+yFF7rUK+GDiKqeFQACSFEz0ikMkzeF47Hr7JgbyHCtlHtYGZUWA5EBnw4WRnrOMKKQQWQEEL0zPLjUbj0OAXGhgJsD2iH5s6Wug5JJ6rv1U1CCCEK4t9kY/fVOADAuqEeelv8ACqAhBCiVzadfwoZAz5sVBO+zRx0HY5O0SlQQgippmJeZ2H58Shk50u5tptxqQCAKd3cdBVWpUEFkBBCqqntl2NxJuqVQntnN1u0qWOtg4gqFyqAhBBSTd2ILTzam9y1Pho7WAAABHweOta31WVYlQYVQEIIqcL2/h2PW/+e1nwXY+DG+I3tVA/WpsKKDq3SowJICCFVVMzrLCz4436JyzR3tqDiVwwqgIQQUkUdufMcANCyliX6ezgrvM7nAV0b2VV0WFUGFUBCCKmCZDKG38MLC+C4zvXQ191JxxFVPTQOkBBCqqCbcal4np4Lc5EBejStvlMWaRMVQEIIqYKKjv56t3CEkaFAx9FUTXQKlBBCKlBKVj6ORrxAgUzGtUmlMjx8wcOLy3EQqDj/3vF7SQCAga0Vr/0R1ZSpACYkJCA+Ph45OTmoWbMmmjVrBpFIpOnYCCGkWmGMYdK+cG58njwB/ox/pNb2nK2M4elqo5ng9JDKBTAuLg6bNm1CSEgInj17BsYY95pQKETnzp0xfvx4DB48GHw+nVklhJD3HbnzHDdiU2FkyEfv5o5cu0wmw/Pnz+Hs7Kzy9yePx8OQtrXA5/O0FW61p1IBnDp1Knbv3g1fX19888038PT0hJOTE4yNjZGamor79+/j0qVLWLhwIRYvXoydO3eiXbt22o6dEEIqtRfpudzRHgPD8uNRAArvwzm5awNuOYlEguPHE9G7dwsYGhrqJFZ9pFIBNDU1RUxMDGrUUJwl2M7ODt26dUO3bt0QHByMkydPIjExkQogIUTvjdl5E9EvM+Xa6tc0xbjO9XQUEXmXSgVwxYoVKm+wV69eZQ6GEEKqC3GBDI9eFRa/DvVqwEDAg1DAx5c+DSE0oMtElQH1AiWEkPcUSGW4+ywd+RJZ6QsXIyVbDMYAkQEf+8e1B49H1+oqG40VwKioKPTp0wcxMTGa2iQhhOjED2GP8cPZJxrZlrOVMRW/SkpjBVAsFiM+Pl5TmyOEEJ0J/XcOPRcbY5gYlv1rkscDPutUV1NhEQ1T+X82KCioxNdfv35d7mAIIUTX3uZI8DA5AwDw20Qv2Jkb6Tgioi0qF8D169fDw8MDFhYWSl/PysrSWFCEEKIrt+JTwRhQz9aUil81p3JXpAYNGmD69Ok4d+6c0sfWrVvLFMCGDRvg6uoKIyMjtG/fHjdu3Chx+XXr1qFRo0YwNjaGi4sLpk+fjry8vDLtmxBC3nf/eeHRn0dtK90GQrRO5QLYtm1b3L59u9jXeTye3N1hVHHgwAEEBQUhODgY4eHhcHd3h6+vL169eqV0+f3792POnDkIDg5GVFQUtm/fjgMHDuDrr79Wa7+EEFKcqKTCAtjUUfnZLlJ9qHwK9LvvvkN+fn6xr7u7u0MmU6/L8Nq1azFu3DiMGTMGALB582YcO3YMO3bswJw5cxSWv3r1Kjp27Ah/f38AgKurK4YNG4br16+rtV9CCClO1L/X/xo7UAGs7lQugA4ODhrdsVgsxu3btzF37lyujc/nw8fHB9euXVO6jpeXF3755RfcuHEDnp6eiImJwfHjxzFy5Mhi95Ofny9XuDMyCj/cEomEexQ9J4ooP6WjHJWsKuUnO78A8W9yAAANahpXWMxVKUe68H5+NJUnnQ2ET0lJgVQqhb29/ESO9vb2ePjwodJ1/P39kZKSgk6dOoExhoKCAkyYMKHEU6ArVqzA4sWLFdpPnz4NExMT7nloaGgZ34l+oPyUjnJUsqqQn9hMADCAhSHD9QtnKnz/VSFHulSUn5ycHI1sr0rdCeb8+fNYvnw5Nm7ciPbt2+PJkyeYNm0ali5digULFihdZ+7cuXJDODIyMuDi4oKePXvCwsICEokEoaGh6NGjB92EVgnKT+koRyWrKvmZEnIXZ6JeAWDwcLVF795tKmzfVSVHuvJ+forO5JWXzgqgra0tBAIBXr58Kdf+8uXLYk+3LliwACNHjsTYsWMBAC1atEB2djbGjx+PefPmKZ1GRCQSKZ2r0NDQUO6D9v5zIo/yUzrKUckqc34y8iQ4+eC/76IeTR10EmtlzlFlUJQfTeVIZ3dkFQqFaNOmDcLCwrg2mUyGsLAwdOjQQek6OTk5CkVOIBAAgNo9UAkhpMjztFwAgJWJIW7N98HIDq66DYhUCJ2eAg0KCkJAQADatm0LT09PrFu3DtnZ2Vyv0FGjRsHZ2ZmbjaJv375Yu3YtWrVqxZ0CXbBgAfr27csVQkIIUVdRAaxlbQxbM8UzRqR6KlMBvHjxIkxMTNC2bVuu7datW8jJyUGXLl1U3o6fnx9ev36NhQsXIjk5GR4eHjh58iTXMSYhIUHuiG/+/Png8XiYP38+nj9/jpo1a6Jv375YtmxZWd4GIYQA+G/sn2sNUx1HQipSmQrghx9+iMaNGyMyMpJrGzlyJB49egSpVKrWtgIDAxEYGKj0tfPnz8s9NzAwQHBwMIKDg9WOmRBCinMjrnDWds+6NjqOhFSkMhXA2NhYhYuQYWFhNIaFEFJlnH6QjK2XYiCVMe72Z1QA9UuZCmCdOnUU2pycnModDCGEVJSfL8bgdnwa99zJ0ggN7cx1GBGpaFVqHCAhhGhKeo4YADDTtxHc7MzQspYV+HyauFafqFQAra2tVZ7RODU1tVwBEUJIRcjMKwAAfNioJpo5Weo4GqILKhXAdevWaTkMQgipWBl5hX0WLIxo4Lm+UqkABgQEaDsOQgipMOICGfIkhbPXUAHUX2W6Bvj06VPs3LkTT58+xfr162FnZ4cTJ06gdu3aaNasmaZjJISQcsmTSLH7ahyepxcOeBcX/Dd1m5kRdYXQV2r/z1+4cAEfffQROnbsiIsXL2LZsmWws7PD3bt3sX37dhw+fFgbcRJCSJnkF0gx8ZfbOBf9WuG1muYiCKjji95SuwDOmTMH33zzDYKCgmBu/l+X4W7duuGnn37SaHCEEFIeEqkMgfvv4Fz0axgZ8jGmY10YCv67u5R3Q1sdRkd0Te0CeO/ePezfv1+h3c7ODikpKRoJihBCykImYzgc/gyvMvIAALfi03A++jWEBnxsG9UOndyo4JH/qF0ArayskJSUhLp168q137lzB87OzhoLjBBC1HX5SQpmHf5Hrk0o4OPnkW2o+BEFahfAoUOHYvbs2Th06BB4PB5kMhmuXLmCGTNmYNSoUdqIkRBCVPLkVRYAwLWGCTrUrwE+j4cBrZzRzpVucUYUqV0Aly9fjsmTJ8PFxQVSqRRNmzaFVCqFv78/5s+fr40YCSFEJUW9PHs0tce8Pk11HA2p7NQugEKhEFu3bsWCBQtw//59ZGVloVWrVnBzc9NGfIQQUqr8Ail2XolDyI0EAEBtGxMdR0SqgjIPgKlduzZcXFwAQOXbpBFCiKade/gKS/6KRGxKNgCgdW0rDGpdS8dRkaqgTAVw+/bt+P777/H48WMAgJubG7788kuMHTtWo8ERQvQDYwzhCWnIyC1QeR2pjGH/jQScffgKAGBrJsLcjxpjYCtnuqk1UYnaBXDhwoVYu3YtpkyZgg4dOgAArl27hunTpyMhIQFLlizReJCEkOpt3/UEzP/jfpnWNeDz8FmnupjSrQHM6bZmRA1qF8BNmzZh69atGDZsGNfWr18/tGzZElOmTKECSAhRy+vMfKw6+RAA0MDODMaGApXXrV3DBNN9GqKBnZm2wiPVmNoFUCKRoG3btgrtbdq0QUGB6qcvCCH6522OBIlpOXJtmy88RWZeAZo7W+DPyZ3o1mSkwqhdAEeOHIlNmzZh7dq1cu1btmzB8OHDNRYYIaR6uR2fhs/3hCNbLFV4jccDvhnQgoofqVAqFcCgoCDuZx6Ph23btuH06dP44IMPAADXr19HQkICDYQnhCiVkgcs2h+BbLEUViaGMDL47zQnjwcM86wNDxcr3QVI9JJKBfDOnTtyz9u0aQOgcFokALC1tYWtrS0ePHig4fAIIZVZnkTKTSxbnKzcfGx5KEBargQtnC1x4IsPYCKkKYiI7qn0KTx37py24yCEVDEv0nPRa91FZOSpcu2fBwcLEbYFtKXiRyoN+iQSQsrk0K1nXPEr7dKdjYjh5xGtYG9hVAGREaKaMhXAW7du4eDBg0hISIBYLJZ77ffff9dIYISQyosxht/vPAMArB3iXuKdVyQSCY4fP46mjhYVFR4hKlG7AIaEhGDUqFHw9fXF6dOn0bNnTzx69AgvX77EwIEDtREjIUQLbsenYvSOm8jML/vwJROhAL7NHDQYFSEVh1/6IvKWL1+O77//Hv/73/8gFAqxfv16PHz4EEOGDEHt2rW1ESMhRAtWnYguV/EDAH/P2jAV0ZUUUjWp/cl9+vQp+vTpA6BwZojs7GzweDxMnz4d3bp1w+LFizUeJCFEs/6OeYMbcakQCvj4a2on2JgK1d6GgMeDdRnWI6SyULsAWltbIzMzEwDg7OyM+/fvo0WLFkhPT0dOTk4paxNCKhpjDIG/3sHN2FSuLevfI79P29ZCQ3tzXYVGiE6pXQC7dOmC0NBQtGjRAp9++immTZuGs2fPIjQ0FN27d9dGjISQcnienotj/yQptJsKBZj4YX0dRERI5aB2Afzpp5+Ql5cHAJg3bx4MDQ1x9epVDB48mGaEJ6QSep5WOEu6s5Uxto767z6+9hYi1DAT6SosQnRO7QJoY2PD/czn8zFnzhyNBkQI0axn/xbAOjVM0NSJhiIQUkSlApiRkaHyBi0s6BeMkMokLadwrK4tHe0RIkelAmhlZQUer+RbPTDGwOPxIJUq3umdEKJ7NNECIfLoXqCEEEL0kkoF0NvbW9txEEIIIRVK7TvBEEIIIdUBFUBCqrlrT9/oOgRCKiUqgIRUYwVSGcIevgIAmNA9OwmRQwWQkGrs8pMU7uep3dx0GAkhlU+ZCmBBQQHOnDmDn3/+mbsv6IsXL5CVlaXR4AghZSeVMXx18C4AIKBDHThY0mS0hLxL7XMi8fHx6NWrFxISEpCfn48ePXrA3Nwcq1atQn5+PjZv3qyNOAkhajr38BXeZBcOgh9YwoS1hOgrtY8Ap02bhrZt2yItLQ3GxsZc+8CBAxEWFqbR4AghZff0deEZGQGfBw8XK90GQ0glpPYR4KVLl3D16lUIhfLzgLm6uuL58+caC4wQUj7P0wvvAfpFl3o6joSQykntI0CZTKb0dmfPnj2DuTnNK0ZIZcHNAmFtXMqShOgntQtgz549sW7dOu45j8dDVlYWgoOD0bt3b03GRggph2fvTINECFGk9inQ7777Dr6+vmjatCny8vLg7++Px48fw9bWFr/++qs2YiSEqOl1Zj6iXxb20K5FR4CEKKV2AaxVqxbu3r2LkJAQ/PPPP8jKysLnn3+O4cOHy3WKIYToRkaeBP1/usw9d6IjQEKUUrsA5uXlwcjICCNGjNBGPISQcmCMYd6R+3jxNg8AMNO3EUyEdAcYQpRR+xqgnZ0dAgICEBoaCplMpo2YCCFqysovQHhCGrZeisH/7r6AgM/DbxO9MLlrA12HRkilpfafhrt378b+/fvRv39/WFpaws/PDyNGjEDbtm21ER8hpBSMMfT78TJiUrK5tqAeDdGmjrUOoyKk8lP7CHDgwIE4dOgQXr58ieXLlyMyMhIffPABGjZsiCVLlmgjRkJICd7mSrjiV9vGBJ+2qYWJ3vV1HBUhlV+Zb4Ztbm6OMWPG4PTp0/jnn39gamqKxYsXazI2QogKioY72JoJcXFWV6z+1B18Pk/HURFS+ZW5AObl5eHgwYMYMGAAWrdujdTUVMycOVOTsRFCVFB0xxca70eIetS+Bnjq1Cns378ff/zxBwwMDPDJJ5/g9OnT6NKlizbiI4SUgu74QkjZqF0ABw4ciI8//hh79uxB7969YWhoqI24CCEqKjoFWsvaRMeREFK1qF0AX758Sff8JKQSeZ6eA4BOgRKiLpUKYEZGBiwsLAAUdrnOyMgodtmi5QghFYOuARJSNioVQGtrayQlJcHOzg5WVlbg8RR7mDHGwOPxlM4UQQjRHroGSEjZqFQAz549CxsbGwDAuXPntBoQIUR1OeICpOVIAFABJERdKhVAb29v7ue6devCxcVF4SiQMYbExETNRkcIKVHR0Z+FkQEsjKhDGiHqUHscYN26dfH69WuF9tTUVNStW1ftADZs2ABXV1cYGRmhffv2uHHjRonLp6enY/LkyXB0dIRIJELDhg1x/PhxtfdLSHXAzflHPUAJUZvavUCLrvW9LysrC0ZGRmpt68CBAwgKCsLmzZvRvn17rFu3Dr6+voiOjoadnZ3C8mKxGD169ICdnR0OHz4MZ2dnxMfHw8rKSt23QUi18Iw6wBBSZioXwKCgIACFM8AvWLAAJib//cUplUpx/fp1eHh4qLXztWvXYty4cRgzZgwAYPPmzTh27Bh27NiBOXPmKCy/Y8cOpKam4urVq9z4Q1dXV7X2SUh1kp4tBlB4GzRCiHpULoB37twBUHgEeO/ePQiF//3CCYVCuLu7Y8aMGSrvWCwW4/bt25g7dy7Xxufz4ePjg2vXrild5+jRo+jQoQMmT56MP//8EzVr1oS/vz9mz54NgUCg8r4JqW6UnZUhhJRM5QJY1PtzzJgxWL9+fbnH+6WkpEAqlcLe3l6u3d7eHg8fPlS6TkxMDM6ePYvhw4fj+PHjePLkCSZNmgSJRILg4GCl6+Tn5yM/P597XjSGUSKRcI+i50QR5ad0FZ0jxhhWn36MBy8ykPjvNUCZTFZp/4/oM1Q6ylHJ3s+PpvLEY4wxjWxJTS9evICzszOuXr2KDh06cO2zZs3ChQsXcP36dYV1GjZsiLy8PMTGxnJHfGvXrsXq1auRlJSkdD+LFi1SOkvF/v375U7jElJVvMoFlkXI/+3aq5YMH7nQBNVEP+Tk5MDf3x9v374t18GYSkeAgwYNwq5du2BhYYFBgwaVuOzvv/+u0o5tbW0hEAjw8uVLufaXL1/CwcFB6TqOjo4wNDSUO93ZpEkTJCcnQywWy52WLTJ37lzu+iVQeATo4uKCnj17wsLCAhKJBKGhoejRowfd11QJyk/pKjpHlx6nABHhcLYywlc93GBsKEDnBjUgMqyclwHoM1Q6ylHJ3s9PSXcjU4dKBdDS0pK7xmBpaamRHQuFQrRp0wZhYWEYMGAAgMLTOGFhYQgMDFS6TseOHbF//37IZDLw+YUjOB49egRHR0elxQ8ARCIRRCKRQruhoaHcB+3950Qe5ad0FZGjJ6+yMHZvOACgkYMFBrWprdX9aRJ9hkpHOSpZUX40lSOVCuDOnTuV/lxeQUFBCAgIQNu2beHp6Yl169YhOzub6xU6atQoODs7Y8WKFQCAiRMn4qeffsK0adMwZcoUPH78GMuXL8fUqVM1FhMhldmGc08g+/eihZudmW6DIaSKU3scYG5uLhhj3PWz+Ph4HDlyBE2bNkXPnj3V2pafnx9ev36NhQsXIjk5GR4eHjh58iTXMSYhIYE70gMAFxcXnDp1CtOnT0fLli3h7OyMadOmYfbs2eq+DUKqpPg32QCAjg1qYFLXBjqOhpCqTe0C2L9/fwwaNAgTJkxAeno6PD09IRQKkZKSgrVr12LixIlqbS8wMLDYU57nz59XaOvQoQP+/vtvdcMmpMp7/DIT4QnpAIBZvo1haUynyggpD7VvhRYeHo7OnTsDAA4fPgwHBwfEx8djz549+OGHHzQeICGk0PLjUdzPtW2oBzMh5aV2AczJyeEmxD19+jQGDRoEPp+PDz74APHx8RoPkBBSKDu/cKqxvu5OsDalO78QUl5qF8AGDRrgjz/+QGJiIk6dOsVd93v16hVNhkuIFt2ISwUA9G6ufJgQIUQ9ahfAhQsXYsaMGXB1dYWnpyc3iP306dNo1aqVxgMkhBRe/ysiMlT715YQooTanWA++eQTdOrUCUlJSXB3d+fau3fvjoEDB2o0OEJIod/CnwMA+DzAq76tjqMhpHpQuwACgIODAxwcHPDs2TMAQK1ateDp6anRwAghhaQyhj/uFBbAjcNbw6iS3vGFkKpG7XMpMpkMS5YsgaWlJerUqYM6derAysoKS5cuhUxG9yIkRNMiEtOQnJEHCyMDdG2sOE8mIaRs1D4CnDdvHrZv346VK1eiY8eOAIDLly9j0aJFyMvLw7JlyzQeJCH6LOZ14eB3dxcriAzo6I8QTVG7AO7evRvbtm1Dv379uLaiu7JMmjSJCiAhGpQnkeLPiBcAaNZ3QjRN7VOgqampaNy4sUJ748aNkZqaqpGgCCGFZhy6i8tPUgBQASRE09QugO7u7vjpp58U2n/66Se5XqGEkPK7+ywdAGDA56Gvu5NugyGkmlH7FOi3336LPn364MyZM9wYwGvXriExMRHHjx/XeICEVGevMvOQmVeg9DXGgKT0PADAxVld4URHgIRolNoF0NvbG48ePcLGjRsRFVV4b8JBgwZh0qRJcHKiv1AJUdXFR68xaseNUpcz4PNgb2FUARERol/UKoBxcXEIDQ2FWCzG0KFD0bx5c23FRUi1dz32DQBAaMCHcQlj+wZ4OEHA51VUWIToDZUL4Llz5/Dxxx8jNze3cEUDA+zYsQMjRozQWnCEVEfZ+QWQMYb4NzkAgKAeDTHBu76OoyJE/6jcCWbBggXo0aMHnj9/jjdv3mDcuHGYNWuWNmMjpNpZ/L8HaBZ8Ci0WncZf/yQBoN6dhOiKygXw/v37WL58ORwdHWFtbY3Vq1fj1atXePPmjTbjI6RaufpE/vfF1kwEz7o2OoqGEP2m8inQjIwM2Nr+dxNeExMTGBsb4+3bt6hRo4ZWgiOkuto1ph061K8BQz4ffLq+R4hOqNUJ5tSpU7C0tOSey2QyhIWF4f79+1zbu3eIIYTIY2AAAKGAT7c1I0TH1CqAAQEBCm1ffPEF9zOPx4NUKi1/VIRUQwVSGZ6nFXYiszKhGd0J0TWVCyDN9EBI+UQmZSBbLIW5kQEaOZjrOhxC9F6Z5gMkhCiKTs7EnD8eICNX+Z1dcsSF7Z6uNjSuj5BKQKUC+Pfff+ODDz5QaYM5OTmIjY1Fs2bNyhUYIVXNmYevcf95RqnL+TS1r4BoCCGlUakAjhw5EvXq1cPYsWPRu3dvmJqaKiwTGRmJX375BTt37sSqVauoABK9w1hhB5ceTe2LHdhuKhKgkT2d/iSkMlCpAEZGRmLTpk2YP38+/P390bBhQzg5OcHIyAhpaWl4+PAhsrKyMHDgQJw+fRotWrTQdtyEVCqxmcD6a08BFI7ta1PHWscREUJKo1IBNDQ0xNSpUzF16lTcunULly9fRnx8PHJzc+Hu7o7p06eja9eusLGhAb1EP51+9t89JRzoxtWEVAlqd4Jp27Yt2rZtq41YCKmSpDKGmMzCTi0zfRthXOd6Oo6IEKIK6gVKSBlk5Enw7cmHSM0WIzu/AHlSHkxFAnzRpR4MBGrPM00I0QEqgISUwcn7yfjl7wS5Nq96Naj4EVKFUAEkpAzSssUAgFa1rdC/pQOiIh/gy35NdBwVIUQd9OcqIWWQkScBALjXssLw9rXhZc9gaybScVSEEHWUqwDm5eVpKg5CqpTMvMK7ulgY0UkUQqoqtQugTCbD0qVL4ezsDDMzM8TExAAonDB3+/btGg+QkMooI7fwCNDcyFDHkRBCykrtAvjNN99g165d+PbbbyEU/ndH++bNm2Pbtm0aDY6QyurE/WQAgIUxHQESUlWpXQD37NmDLVu2YPjw4RAI/pvPzN3dHQ8fPtRocIRURm+y8pFfUDg7Sg1Tuu5HSFWldgF8/vw5GjRooNAuk8kgkUg0EhQhlVlW/n+zPXRuaKvDSAgh5aF2AWzatCkuXbqk0H748GG0atVKI0ERUhWYiQxoVndCqjC1L2AsXLgQAQEBeP78OWQyGX7//XdER0djz549+Ouvv7QRIyGEEKJxah8B9u/fH//73/9w5swZmJqaYuHChYiKisL//vc/9OjRQxsxEkIIIRpXpi5snTt3RmhoqKZjIYQQQiqM2keA9erVw5s3bxTa09PTUa8e3QWfEEJI1aB2AYyLi4NUKlVoz8/Px/PnzzUSFCGEEKJtKp8CPXr0KPfzqVOnYGlpyT2XSqUICwuDq6urRoMjhBBCtEXlAjhgwAAAAI/HQ0BAgNxrhoaGcHV1xXfffafR4AghhBBtUbkAymSFd76oW7cubt68CVtbGgBMCCGk6lK7F2hsbKw24iCkyii6DRohpGor0zCI7OxsXLhwAQkJCRCLxXKvTZ06VSOBEVIZvcrIQ8/vL+o6DEKIBqhdAO/cuYPevXsjJycH2dnZsLGxQUpKCkxMTGBnZ0cFkFRrj15mcT/3bGavw0gIIeWl9jCI6dOno2/fvkhLS4OxsTH+/vtvxMfHo02bNlizZo02YiSk0mnsYI61Qzx0HQYhpBzUPgKMiIjAzz//DD6fD4FAgPz8fNSrVw/ffvstAgICMGjQIG3ESYjO/Hb7Geb9cQ/iAhmYroMhhGiM2keAhoaG4PMLV7Ozs0NCQgIAwNLSEomJiZqNjpBK4Fz0K+RJZJAxgP1bAdvUsdZtUISQclP7CLBVq1a4efMm3Nzc4O3tjYULFyIlJQV79+5F8+bNtREjIZXCjJ4NMaSdC/g8HmzNaCJcQqo6tY8Aly9fDkdHRwDAsmXLYG1tjYkTJ+L169f4+eefNR4gIZWFmcgAduZGVPwIqSbUPgJs27Yt97OdnR1Onjyp0YAIIYSQiqD2EWBxwsPD8fHHH2tqc4QQQohWqVUAT506hRkzZuDrr79GTEwMAODhw4cYMGAA2rVrx90ujZDq4q9/XuCvf5J0HQYhRAtUPgW6fft2jBs3DjY2NkhLS8O2bduwdu1aTJkyBX5+frh//z6aNGmizVgJqXDbL/936z8nK2MdRkII0TSVjwDXr1+PVatWISUlBQcPHkRKSgo2btyIe/fuYfPmzVT8SLUkkxWOe5jarQF6NKU7vxBSnah8BPj06VN8+umnAIBBgwbBwMAAq1evRq1atbQWHCG6cjcxHZsvPEVMSjYAwN3FCjweT8dREUI0SeUCmJubCxMTEwCFcwKKRCJuOAQh1c32y7E4cT+Ze16Dhj4QUu2oNQxi27ZtMDMzAwAUFBRg165dCvMC0s2wSXUgkRZ26Orv4YTBrWvBvZaljiMihGiaygWwdu3a2Lp1K/fcwcEBe/fulVuGx+NRASTVSts61ujSsKauwyCEaIHKnWDi4uIQGxtb4qNoaIS6NmzYAFdXVxgZGaF9+/a4ceOGSuuFhISAx+NhwIABZdovIcUR/zvprYCvsaGyhJBKRue/3QcOHEBQUBCCg4MRHh4Od3d3+Pr64tWrVyWuFxcXhxkzZqBz584VFCnRJy/e5gEAHCzp2h8h1ZXOC+DatWsxbtw4jBkzBk2bNsXmzZthYmKCHTt2FLuOVCrF8OHDsXjxYtSrV68CoyX64nlaDgDA2cpEx5EQQrRF7XuBapJYLMbt27cxd+5cro3P58PHxwfXrl0rdr0lS5bAzs4On3/+OS5dulTiPvLz85Gfn889z8jIAABIJBLuUfScKNLH/GTmFSAjrwAAYGdmUOp718ccqYPyUzrKUcnez4+m8qTTApiSkgKpVAp7e/kBxvb29nj48KHSdS5fvozt27cjIiJCpX2sWLECixcvVmg/ffo0N6wDAEJDQ1UPXA/pU35eZAOAAUwMGC6GnVZ5PX3KUVlQfkpHOSpZUX5ycnI0sj2dFkB1ZWZmYuTIkdi6davC8IvizJ07F0FBQdzzjIwMuLi4oGfPnrCwsIBEIkFoaCh69OgBQ0NDbYVeZelLfq7HpuLp68JB70ksG0ACXGtaoHfvDqWuqy85KivKT+koRyV7Pz9FZ/LKq0wF8OnTp9i5cyeePn2K9evXw87ODidOnEDt2rXRrFkzlbdja2sLgUCAly9fyrW/fPkSDg4OSvcbFxeHvn37cm1FN+A2MDBAdHQ06tevL7eOSCSCSKTYkcHQ0FDug/b+cyKvOufneXouRuy4pdBep4apWu+5OudIEyg/paMclawoP5rKkdqdYC5cuIAWLVrg+vXr+P3335GVlQUAuHv3LoKDg9XallAoRJs2bRAWFsa1yWQyhIWFoUMHxb+8GzdujHv37iEiIoJ79OvXD127dkVERARcXFzUfTuEIC1bDAAQGvDRq5kDejVzQD93J0zu2kDHkRFCtEntI8A5c+bgm2++QVBQEMzNzbn2bt264aefflI7gKCgIAQEBKBt27bw9PTEunXrkJ2djTFjxgAARo0aBWdnZ6xYsQJGRkZo3ry53PpWVlYAoNBOiLqsTQyxeWQbXYdBCKkgahfAe/fuYf/+/QrtdnZ2SElJUTsAPz8/vH79GgsXLkRycjI8PDxw8uRJrmNMQkIC+DQYmRBCiIapXQCtrKyQlJSEunXryrXfuXMHzs7OZQoiMDAQgYGBSl87f/58ievu2rWrTPskhBCi39Q+tBo6dChmz56N5ORk8Hg8yGQyXLlyBTNmzMCoUaO0ESMhhBCicWoXwOXLl6Nx48ZwcXFBVlYWmjZtii5dusDLywvz58/XRoyEaNXv4c8BAP9OAEEI0RNqnwIVCoXYunUrFixYgPv37yMrKwutWrWCm5ubNuIjROtkrHDW9wIZVUBC9InaBfDy5cvo1KkTateujdq1a2sjJkJ0YkT7OroOgRBSgdQ+BdqtWzfUrVsXX3/9NSIjI7UREyGEEKJ1ahfAFy9e4KuvvsKFCxfQvHlzeHh4YPXq1Xj27Jk24iOEEEK0Qu0CaGtri8DAQFy5cgVPnz7Fp59+it27d8PV1RXdunXTRoyEaI1UxrhrgIQQ/VKum2HXrVsXc+bMgbu7OxYsWIALFy5oKi5CtO5GbCpG77yBHLFU16EQQnSgzLdYuXLlCiZNmgRHR0f4+/ujefPmOHbsmCZjI0Srjtx5zhU/oYCP1nWsdBsQIaRCqX0EOHfuXISEhODFixfo0aMH1q9fj/79+8vNrUdIVXA99g0A4IdhrdCjiT2MhQIdR0QIqUhqF8CLFy9i5syZGDJkiMpz8hFSmRy8lYjVp6LxOjMfPB7g7VaTih8hekjtAnjlyhVtxEFIhTlwMxGvM/MBAF71a8DShOZfI0QfqVQAjx49io8++giGhoY4evRoicv269dPI4ERoi3P03IBABuHt0bPpvY6joYQoisqFcABAwYgOTkZdnZ2GDBgQLHL8Xg8SKXUo45UXnuvxSE5Iw8A0M7VBgYCmmqLEH2lUgGUvXOPRBndL5FUYbuvxXM/25oJdRgJIUTX1P7zd8+ePcjPz1doF4vF2LNnj0aCIkRb2L+D3jePaA0ej6fjaAghuqR2ARwzZgzevn2r0J6ZmYkxY8ZoJChCtCEqKQNPX2cDAKxN6OiPEH2ndgFkjCn9y/nZs2ewtLTUSFCEaEPIjQTu55rmIh1GQgipDFQeBtGqVSvweDzweDx0794dBgb/rSqVShEbG4tevXppJUhCNEEiKzz9+WGjmqhX00zH0RBCdE3lAljU+zMiIgK+vr4wM/vvC0QoFMLV1RWDBw/WeICEaFrr2ta6DoEQUgmoXACDg4MBAK6urvDz84ORkZHWgiKEEEK0Te07wQQEBGgjDkIIIaRCqVQAbWxs8OjRI9ja2sLa2rrE7uOpqakaC44QQgjRFpUK4Pfffw9zc3PuZxo/Raqi7PwCAICAT59fQoiKBfDd056jR4/WViyEaFV4QhoAoLkzDdchhJRhHGB4eDju3bvHPf/zzz8xYMAAfP311xCLxRoNjhBNeZGei8TUXAj4PLSpQ71ACSFlKIBffPEFHj16BACIiYmBn58fTExMcOjQIcyaNUvjARKiCf88SwcANHE0h5lI7b5fhJBqSO0C+OjRI3h4eAAADh06BG9vb+zfvx+7du3Cb7/9pun4CNGI/ILCm7hbGtPcf4SQQmW6FVrRjBBnzpxB7969AQAuLi5ISUnRbHSEEEKIlqhdANu2bYtvvvkGe/fuxYULF9CnTx8AQGxsLOztaXJRQgghVYPaBXDdunUIDw9HYGAg5s2bhwYNGgAADh8+DC8vL40HSIgmZP07BMJESNf/CCGF1P42aNmypVwv0CKrV6+GQCDQSFCEaFpmXmEBNDeiAkgIKVTmb4Pbt28jKioKANC0aVO0bt1aY0ERomkZuRIAgIURdYIhhBRSuwC+evUKfn5+uHDhAqysrAAA6enp6Nq1K0JCQlCzZk1Nx0hIuRUdAVrQESAh5F9qXwOcMmUKsrKy8ODBA6SmpiI1NRX3799HRkYGpk6dqo0YCSm3V5l5AAALGgZBCPmX2n8Onzx5EmfOnEGTJk24tqZNm2LDhg3o2bOnRoMjRBPEBTKcevASAF0DJIT8R+0jQJlMBkNDxb+iDQ0NufGBhFQmaTn/3aLPq76tDiMhhFQmahfAbt26Ydq0aXjx4gXX9vz5c0yfPh3du3fXaHCEqEsmY8jOL5B75IilAApngXCxMdFxhISQykLt80E//fQT+vXrB1dXV7i4uAAAEhMT0bx5c/zyyy8aD5AQVYkLZOj9wyU8eZWl61AIIVWA2gXQxcUF4eHhCAsL44ZBNGnSBD4+PhoPjhB1vMzIK7H4eTekHsqEkP+oVQAPHDiAo0ePQiwWo3v37pgyZYq24iKkzIwM+YhYqNghS2Sg9hl/Qkg1pnIB3LRpEyZPngw3NzcYGxvj999/x9OnT7F69WptxkeI2njgwciQ7kpECCmZyn8S//TTTwgODkZ0dDQiIiKwe/dubNy4UZuxEUIIIVqjcgGMiYlBQEAA99zf3x8FBQVISkrSSmCEEEKINqlcAPPz82Fqavrfinw+hEIhcnNztRIYIYQQok1qdYJZsGABTEz+G0clFouxbNkyWFpacm1r167VXHSEqKFoyiMjQ+rsQggpncoFsEuXLoiOjpZr8/LyQkxMDPecx+NpLjJC1PQ8rfBshJOVsY4jIYRUBSoXwPPnz2sxDELK505CGsbuuQUAqGVNBZAQUjo6V0Sqhf3XE7ifmzpalrAkIYQUogJIqgWpjAEAvOrXwIQP6+k4GkJIVUAFkFQLf94tvDl710Z2EBnQIHhCSOmoAJIqTyZj3BEg9QAlhKiKvi1Ilcfe+blnMwedxUEIqVrKVAAvXbqEESNGoEOHDnj+/DkAYO/evbh8+bJGgyOkNKnZYqw784h7LhTQ33SEENWo/W3x22+/wdfXF8bGxrhz5w7y8/MBAG/fvsXy5cs1HiAhJQm5mYAfzz4BABgKeBDRKVBCiIrU/rb45ptvsHnzZmzduhWGhoZce8eOHREeHq7R4AgpTU5+4WzvLWtZYvOINjARqj3FJSFET6n9bREdHY0uXbootFtaWiI9PV0TMRFSqnMPX+FhciZux6cBAFrXtkb3JvY6jooQUpWoXQAdHBzw5MkTuLq6yrVfvnwZ9erR+Cuifa8y8/DZ7ptg7/R+ofn/CCHqUrsAjhs3DtOmTcOOHTvA4/Hw4sULXLt2DTNmzMCCBQu0ESMhcjLzCsBY4TW/AR7OMBUZYGSHOroOixBSxahdAOfMmQOZTIbu3bsjJycHXbp0gUgkwowZMzBlyhRtxEiIUiZCA6z+1F3XYRBCqii1CyCPx8O8efMwc+ZMPHnyBFlZWWjatCnMzMy0ER8hhBCiFWXuMy4UCtG0aVN4enqWu/ht2LABrq6uMDIyQvv27XHjxo1il926dSs6d+4Ma2trWFtbw8fHp8TlCSGEEGXUPgLs2rVrifP+nT17Vq3tHThwAEFBQdi8eTPat2+PdevWwdfXF9HR0bCzs1NY/vz58xg2bBi8vLxgZGSEVatWoWfPnnjw4AGcnZ3VfTukCmLv9n4hhJAyUvsI0MPDA+7u7tyjadOmEIvFCA8PR4sWLdQOYO3atRg3bhzGjBmDpk2bYvPmzTAxMcGOHTuULr9v3z5MmjQJHh4eaNy4MbZt2waZTIawsDC1902qpiN3Cu8+RIWQEFIeah8Bfv/990rbFy1ahKysLLW2JRaLcfv2bcydO5dr4/P58PHxwbVr11TaRk5ODiQSCWxsbNTaN6m6MvMKAADGQhr6QAgpO43dNmPEiBHw9PTEmjVrVF4nJSUFUqkU9vbyA5jt7e3x8OFDlbYxe/ZsODk5wcfHR+nr+fn53O3aACAjIwMAIJFIuEfRc6KoMuYnPVsMABjjVadSxFUZc1SZUH5KRzkq2fv50VSeNFYAr127BiMjI01tTiUrV65ESEgIzp8/X+y+V6xYgcWLFyu0nz59GiYmJtzz0NBQrcVZHVSm/DxJ4APgI+5RFI6/jdR1OJzKlKPKiPJTOspRyYryk5OTo5HtqV0ABw0aJPecMYakpCTcunVL7YHwtra2EAgEePnypVz7y5cv4eBQ8rQ2a9aswcqVK3HmzBm0bNmy2OXmzp2LoKAg7nlGRgZcXFzQs2dPWFhYQCKRIDQ0FD169JC7tykpVBnzs/fFDSA9HV7tWuGj5rqf/qgy5qgyofyUjnJUsvfzU3Qmr7zULoCWlpZyz/l8Pho1aoQlS5agZ8+eam1LKBSiTZs2CAsLw4ABAwCA69ASGBhY7Hrffvstli1bhlOnTqFt27Yl7kMkEkEkEim0Gxoayn3Q3n9O5FWG/EikMmTmFSA9t/AaoI2Zsc5jeldlyFFlRvkpHeWoZEX50VSO1CqAUqkUY8aMQYsWLWBtba2RAIKCghAQEIC2bdvC09MT69atQ3Z2NsaMGQMAGDVqFJydnbFixQoAwKpVq7Bw4ULs378frq6uSE5OBgCYmZnRYPxqLEdcgO7fXUDS2zyuzdyIZn4ghJSdWt8gAoEAPXv2RFRUlMYKoJ+fH16/fo2FCxciOTkZHh4eOHnyJNcxJiEhAXz+f6M1Nm3aBLFYjE8++URuO8HBwVi0aJFGYiKVz7O0XLni18DODA3tzXUYESGkqlP7T+jmzZsjJiYGdevW1VgQgYGBxZ7yPH/+vNzzuLg4je2XVD3WJoYIX9ADAEq8IQMhhJSmTBPizpgxA3/99ReSkpKQkZEh9yBEm3g8HvcghJDyUPkIcMmSJfjqq6/Qu3dvAEC/fv3kvoQYY+DxeJBKpZqPkhBCCNEwlQvg4sWLMWHCBJw7d06b8RBCCCEVQuUCWHTfRW9vb60FQwghhFQUta4B0nUXQggh1YVavUAbNmxYahFMTU0tV0CEEEJIRVCrAC5evFjhTjCEEEJIVaRWARw6dKjSSWoJIYSQqkbla4B0/Y8QQkh1onIBpNm3CSGEVCcqnwKVyWTajIMQQgipUGrfCo0QQgipDqgAEkII0UtUAAkhhOglKoCEEEL0EhVAQggheokKICGEEL1EBZAQQoheogJICCFEL1EBJIQQopeoABJCCNFLVABJlXA3MV3XIRBCqhkqgKTSu/o0BTMP/wMA4NOsJIQQDaECSCq9AzcTuZ+ndW+gw0gIIdWJWhPiElLRElNz8GfECwDAH5M7wsPFSrcBEUKqDToCJJVWeo4Ynb89BwCoZ2sK91qWOo6IEFKdUAEklVbS2zzu5696NgKPrv8RQjSICiCp9Gqai9CnpaOuwyCEVDNUAAkhhOglKoCEEEL0EhVAQggheokKICGEEL1EBZAQQoheogJICCFEL9GdYIhWZeZJMGLbdTxLy1V73QIZ00JEhBBSiAog0arrMam4++xtubbR2MFcQ9EQQsh/qAASrXqWlgMA6Oxmi/l9mqq9Po9XeBs0QgjRNCqARCsYY5h1+B+ci34FAGhob45GengkJ5VKIZFIdB2GTkkkEhgYGCAvLw9SqVTX4VRKlCN5AoEABgYGWr/9IRVAohXP03Nx6PYz7nlLPbyRdVZWFp49ewbG9PtaJmMMDg4OSExMpPu5FoNypMjExASOjo4QCoVa2wcVQKIVRd/5QgM+Dk/ogBbO+lUApVIpnj17BhMTE9SsWVOvv9RkMhmysrJgZmYGPp86nitDOfoPYwxisRivX79GbGws3NzctLYvKoBEqwz4PLSsZaXrMCqcRCIBYww1a9aEsbGxrsPRKZlMBrFYDCMjI73/ci8O5UiesbExDA0NER8fD7FYDIFAoJX9UKYJ0SJ9PvIjpDwq4g8BKoCEEEL0EhVAQggheokKICFEbTweD3/88YfW93P+/HnweDykp6dzbX/88QcaNGgAgUCAL7/8Ert27YKVlZXWYoiOjoaDgwMyMzO1tg9988EHH+C3337TdRhUAAkh8pKTkzFlyhTUq1cPIpEILi4u6Nu3L8LCwio8Fi8vLyQlJcHS8r9exF988QU++eQTJCYmYunSpfDz88OjR4+0FsPcuXMxZcoUmJsrjmNt3LgxRCIRkpOTFV5zdXXFunXrFNoXLVoEDw8Pubbk5GTMmjULDRo0qNCcHzp0CI0bN4aRkRFatGiB48ePl7h80R8k7z/eff8XL15E37594eTkVOwfSvPnz8ecOXMgk8k0/ZbUQr1ASamkDPjlegJeZ6k+oDsjT78Hf1dVcXFx6NixI6ysrLB69Wq0aNECEokEp06dwuTJk/Hw4cMKjUcoFMLBwYF7npWVhVevXsHX1xdOTk5ce3l72kokEhgaGiq0JyQk4K+//sKPP/6o8Nrly5eRm5uLTz75BLt378bs2bPLtO+inFtYWGDVqlVwd3evkJxfvXoVw4YNw4oVK/Dxxx9j//79GDBgAMLDw9G8efMS142OjoaFhQX33M7Ojvs5Ozsb7u7u+OyzzzBo0CCl63/00UcYO3YsTpw4gT59+mjmDZUBFUBSqicZPGz8u2y/hCZC+ogBhWObciW6ucOHsaFA5d6okyZNAo/Hw40bN2Bq+t8t6Jo1a4bPPvus2PVmz56NI0eO4NmzZ3BwcMDw4cOxcOFCrqjcu3cPCxcuxK1bt8Dj8eDm5oaff/4Zbdu2RXx8PAIDA3H58mWIxWK4urpi9erV6N27N86fP4+uXbsiLS0NERER6Nq1KwCgW7duAIBz584hLi4OX375pdxp0j///BOLFy9GZGQknJycEBAQgHnz5sHAoPDzyOPxsHHjRpw4cQJhYWGYOXMmFi1apPC+Dh48CHd3dzg7Oyu8tn37dvj7+8Pb2xvTpk0rcwEsyvmZM2fg6OjI9X4sLefltX79evTq1QszZ84EACxduhShoaH46aefsHnz5hLXtbOzK/a080cffYSPPvqoxPUFAgF69+6NkJAQKoCkcsv/93vbzlyEj1s6lbzwe7o1tit9IT2QK5Gi6cJTOtl35BJflf4QSU1NxcmTJ7Fs2TK54lekpOts5ubm2LVrF5ycnHDv3j2MGzcO5ubmmDVrFgBg/PjxaNOmDTZt2gSBQICIiAiuOE6ePBlisRgXL16EqakpIiMjYWZmprAPLy8vREdHo1GjRvjtt9/g5eUFGxsbxMXFyS136dIljBo1Cj/88AM6d+6Mp0+fYvz48QCA4OBgbrlFixZh5cqVWLduHVcY33fp0iW0bdtWoT0zMxOHDh3C9evX0bhxY7x9+xaXLl1C586di82RMkU5/+abb9TO+b59+/DFF1+UuP0TJ04UG9O1a9cQFBQk1+br66vStV0PDw/k5+ejefPmWLRoETp27FjqOu/z9PTEypUr1V5Pk6gAEqVux6fhn2fpkEqleJBWePTgYmOChX3Vv6E1qRqePHkCxhgaN26s9rrz58/nfnZ1dcWMGTMQEhLCFcDnz59j1qxZ3LbfvbtHQkICBg8ejBYtWgAA6tWrp3QfQqGQO9VmY2Mjd2r0XYsXL8acOXMQEBDAbW/p0qWYNWuWXAH09/fHmDFjSnxf8fHxSgtgSEgI3Nzc0KxZMwDA0KFDsX37drULYFHOGzVqpNZ6ANCvXz+0b9++xGWUHbkWSU5Ohr29vVybvb290uuZRRwdHbF582a0bdsW+fn52LZtGz788ENcv34drVu3Vit+JycnJCYmQiaT6WzwPxVAoiBHXAD/rX8jv6DoAnXhh1MooD5TZWVsKEDkEl+d7VsV5bln6YEDB/DDDz/g6dOnyMrKQkFBgdw1okmTJmH8+PHYt28ffHx88Omnn6J+/foAgKlTp2LixIk4ffo0fHx8MHjwYLRs2bLMsdy9exdXrlzBsmXLuDapVIq8vDzk5OTAxMQEAJQWtvfl5ubCyMhIoX3Hjh0YMWIE93zEiBHw9vbGjz/+qLSzTHHKk3Nzc3O19qUJjRo1kivWXl5eePr0Kb7//nvs3btXrW0ZGxtDJpMhPz9fZ3dLom80oiBXLOWKX+/m9mhVQ4a+LR0Q1LOhjiOrung8HkyEBjp5qHr9z83NDTweT+1OF9euXcPw4cPRu3dv/PXXX7hz5w7mzZsHsVjMLTNnzhzcu3cPffr0wdmzZ9G0aVMcOXIEADB27FjExMRg5MiRuHfvHtq2bau004mqsrKysHjxYkRERHCPe/fu4fHjx3LFTNkpx/fZ2toiLS1Nri0yMhJ///03Zs2aBQMDAxgYGOCDDz5ATk4OQkJCuOUsLCzw9q3iXJjp6elcr9ainEdHR6v9Pvft2wczM7MSH5cuXSp2fQcHB7x8+VKu7eXLl8UeWRfH09MTT548UTv+1NRUmJqa6vRWgXQESEq0bkhLnDjxHL17t1TaS45UHzY2NvD19cWGDRswdepUhQKRnp6u9JrU1atXUadOHcybN49ri4+PV1iuYcOGaNy4MaZPn45hw4Zh586dGDhwIADAxcUFEyZMwIQJEzB37lxs3boVU6ZMKdP7aN26NaKjo9GgQYMyrf+uVq1aITIyUq5t+/bt6NKlCzZs2CDXvnPnTmzfvh3jxo0DUHi0dPv2bYVthoeHc0dRRTnfuHEjAgIC5I6ageJzDpT/FGiHDh0QFhaGL7/8kmsLDQ1Fhw4dStzm+yIiIuDo6KjWOgBw//59tGrVSu31NIkKICGEs2HDBnTs2BGenp5YsmQJWrZsiYKCAoSGhmLTpk2IiopSWMfNzQ0JCQkICQlBu3btcOzYMe7oDig8jThz5kwMGzYM9evXx7Nnz3Dz5k0MHjwYAPDll1/io48+QsOGDZGWloZz586hSZMmZX4PCxcuxMcff4zatWvjk08+AZ/Px927d3H//n188803am3L19cXY8eOhVQqhUAggEQiwd69e7FkyRKFoQJjx47F2rVr8eDBAzRr1gzTp09H586dsWzZMgwaNAhSqRS//vorrl27ho0bN3LrFeXcx8cHS5YsgYeHR6k5B8p/CnTatGnw9vbGd999hz59+iAkJAS3bt3Cli1buGXmzp2L58+fY8+ePQCAdevWoW7dumjWrBny8vKwbds2nD17FqdPn+bWycrKkjsijI2NRUREBGxsbFC7dm2u/dKlS+jZs2eZ49cIpmfevn3LALC3b98yxhgTi8Xsjz/+YGKxWMeRVR4pmXmszuy/WJ3Zf7H8/HzKTymUfYZyc3NZZGQky83N1WFkZfPixQs2efJkVqdOHSYUCpmzszPr168fO3fuHLcMAHbkyBHu+cyZM1mNGjWYmZkZ8/PzY99//z2ztLRkjBXmYtCgQczFxYUJhULm5OTEAgMDudwEBgay+vXrM5FIxGrWrMlGjhzJUlJSGGOMnTt3jgFgaWlpjDHG0tLSGAC5WHbu3Mntq8jJkyeZl5cXMzY2ZhYWFszT05Nt2bKl2PiLI5FImJOTEzt58iRjjLHDhw8zPp/PkpOTlS7fpEkTNn36dO75qVOnWMeOHZm1tTWrUaMG+/DDD9mFCxcU1nv27BkbO3ZsiTnXhoMHD7KGDRsyoVDImjVrxo4dOyb3ekBAAPP29uaer1q1itWvX58ZGRkxGxsb9uGHH7KzZ8/KrVP0f/b+IyAggFvm2bNnzNDQkCUmJhYb27u/Q+//jr3/PV5WPMb0a7bOjIwMWFpa4u3bt7CwsIBEIsHx48fRu3fvKn2KTypjuPf8LSTS8t9Z4W2OBGP33AIAPFrSAydOnKjy+dEmZZ+hvLw8xMbGom7duko7UegTmUyGjIwMWFhYVMmpfjZs2ICjR4/i1CntDWOp6jlS1+zZs5GWliZ3tPm+d3+HBAKB3O/Y+9/jZUWnQKuJlSeisPVSrK7DIKTa+eKLL5Ceno7MzMwK73VZXdnZ2SmMQdQFKoDVRERiOgDA3kIEUw3dfcWnqT3NZ0f0noGBgVwHH1J+X331la5DAEAFsFpgjOFmXGFX7U0j2qB1bWuNbVsioXt6EkKqp+p/slkPzDj0D/dzLWvdjakhhJCqhApgNfB3zBsAQJ0aJqhpJtJxNORdetbHjBCNqYjfHToFqiOMMeRJyt9js0AmQ9LbXADAoS860DW7SkIgKLz9mFgs1umdLgipqnJycgAAhoaGWps3sFIUwA0bNmD16tVITk6Gu7s7fvzxR3h6eha7/KFDh7BgwQLExcXBzc0Nq1atQu/evSsw4vJhjGHolr9xPTZVY9sUCviwpaO/SsPAwAAmJiZ4/fo1DA0N9aJre3FkMhnEYjHy8vL0Og8loRz9hzGGnJwcvHr1ClZWVhAIBNW3AB44cABBQUHYvHkz2rdvj3Xr1sHX1xfR0dFykywWKc8kjpVFWo5Eo8UPAHo2swefT0d/lQWPx4OjoyNiY2OV3hZMnzDGkJubC2NjYzpDUQzKkSIrKyu170uqLp0XwLVr12LcuHHctCSbN2/GsWPHsGPHDsyZM0dh+fJM4lhZPEsrPLS3NRPh4qwPNbJNmni28hEKhXBzc5O7KbQ+kkgkuHjxIrp06UI3UygG5UieoaEhdxlBm3T6rSkWi3H79m3MnTuXa+Pz+fDx8cG1a9eUrqPuJI75+fnIz8/nnmdkZAAo/MA9ffkW/TdeQ0GBAPPCwwBUzF9e0n8P52tZG8GQp5kLvdoarlC0XRoOUbzSclQRv8iVmUwmQ0FBAQQCgd7nojiUI3kymUzutOf7v2Oa+j7SaQFMSUmBVCpVOiljcVOyqDuJ44oVK7B48WKF9tOnTyObZ4KsfAMAPEAqLdubKAdbWRqOHz9e4fsti9DQUF2HUOlRjkpG+Skd5ahkRfkp6iBTXtX+vNncuXPljhgzMjLg4uKCnj17wsjEDB07ZeLK5Svo2KkjDA0q7tSDgYAHJ0ujSn++XyKRIDQ0FD169KBTM8WgHJWM8lM6ylHJ3s9P0Zm88tJpAbS1tYVAIFBrUkZ1J3EUiUQQiRR7RxoaGsLUWIQG9nw8MgYa2FvSB68EhoaGlJ9SUI5KRvkpHeWoZEX50VSOdFoAhUIh2rRpg7CwMAwYMABA4bnfsLAwBAYGKl2nvJM4Fg2ufPdaYE5ODjIyMuiDpwTlp3SUo5JRfkpHOSrZ+/kp+v4u92D5ck2mpAEhISFMJBKxXbt2scjISDZ+/HhmZWXFzbc1cuRINmfOHG75K1euMAMDA7ZmzRoWFRXFgoODmaGhIbt3755K+0tMTFQ6VxU96EEPetCjaj1Kmk9QFTq/Bujn54fXr19j4cKFSE5OhoeHB06ePMl1dElISJAbGOrl5YX9+/dj/vz5+Prrr+Hm5oY//vhD5TGATk5OSExMhLm5OXg8HndNMDExsVzzSlVXlJ/SUY5KRvkpHeWoZO/nhzGGzMxMODk5lWu7ejch7vs0NbFidUX5KR3lqGSUn9JRjkqmrfzo9z13CCGE6C0qgIQQQvSS3hdAkUiE4OBgpUMlCOVHFZSjklF+Skc5Kpm28qP31wAJIYToJ70/AiSEEKKfqAASQgjRS1QACSGE6CUqgIQQQvSSXhTADRs2wNXVFUZGRmjfvj1u3LhR4vKHDh1C48aNYWRkhBYtWlSZKYvKSp38bN26FZ07d4a1tTWsra3h4+NTaj6rA3U/Q0VCQkLA4/G4e91WV+rmJz09HZMnT4ajoyNEIhEaNmxIv2fvWbduHRo1agRjY2O4uLhg+vTpyMvLq6BoK9bFixfRt29fODk5gcfjFTu/67vOnz+P1q1bQyQSoUGDBti1a5f6Oy7XjdSqgJCQECYUCtmOHTvYgwcP2Lhx45iVlRV7+fKl0uWvXLnCBAIB+/bbb1lkZCSbP3++WvcarWrUzY+/vz/bsGEDu3PnDouKimKjR49mlpaW7NmzZxUcecVRN0dFYmNjmbOzM+vcuTPr379/xQSrA+rmJz8/n7Vt25b17t2bXb58mcXGxrLz58+ziIiICo684qibo3379jGRSMT27dvHYmNj2alTp5ijoyObPn16BUdeMY4fP87mzZvHfv/9dwaAHTlypMTlY2JimImJCQsKCmKRkZHsxx9/ZAKBgJ08eVKt/Vb7Aujp6ckmT57MPZdKpczJyYmtWLFC6fJDhgxhffr0kWtr3749++KLL7Qap66om5/3FRQUMHNzc7Z7925thahzZclRQUEB8/LyYtu2bWMBAQHVugCqm59NmzaxevXqMbFYXFEh6py6OZo8eTLr1q2bXFtQUBDr2LGjVuOsDFQpgLNmzWLNmjWTa/Pz82O+vr5q7atanwIVi8W4ffs2fHx8uDY+nw8fHx9cu3ZN6TrXrl2TWx4AfH19i12+KitLft6Xk5MDiUQCGxsbbYWpU2XN0ZIlS2BnZ4fPP/+8IsLUmbLk5+jRo+jQoQMmT54Me3t7NG/eHMuXL4dUKq2osCtUWXLk5eWF27dvc6dJY2JicPz4cfTu3btCYq7sNPU9rfPZILQpJSUFUqmUm1miiL29PR4+fKh0neTkZKXLJycnay1OXSlLft43e/ZsODk5KXwYq4uy5Ojy5cvYvn07IiIiKiBC3SpLfmJiYnD27FkMHz4cx48fx5MnTzBp0iRIJBIEBwdXRNgVqiw58vf3R0pKCjp16gTGGAoKCjBhwgR8/fXXFRFypVfc93RGRgZyc3NhbGys0naq9REg0a6VK1ciJCQER44cgZGRka7DqRQyMzMxcuRIbN26Fba2troOp1KSyWSws7PDli1b0KZNG/j5+WHevHnYvHmzrkOrNM6fP4/ly5dj48aNCA8Px++//45jx45h6dKlug6tWqnWR4C2trYQCAR4+fKlXPvLly/h4OCgdB0HBwe1lq/KypKfImvWrMHKlStx5swZtGzZUpth6pS6OXr69Cni4uLQt29frk0mkwEADAwMEB0djfr162s36ApUls+Qo6MjDA0NIRAIuLYmTZogOTkZYrEYQqFQqzFXtLLkaMGCBRg5ciTGjh0LAGjRogWys7Mxfvx4zJs3T26OVH1U3Pe0hYWFykd/QDU/AhQKhWjTpg3CwsK4NplMhrCwMHTo0EHpOh06dJBbHgBCQ0OLXb4qK0t+AODbb7/F0qVLcfLkSbRt27YiQtUZdXPUuHFj3Lt3DxEREdyjX79+6Nq1KyIiIuDi4lKR4WtdWT5DHTt2xJMnT7g/DADg0aNHcHR0rHbFDyhbjnJychSKXNEfDIxu36y572n1+udUPSEhIUwkErFdu3axyMhINn78eGZlZcWSk5MZY4yNHDmSzZkzh1v+ypUrzMDAgK1Zs4ZFRUWx4ODgaj8MQp38rFy5kgmFQnb48GGWlJTEPTIzM3X1FrRO3Ry9r7r3AlU3PwkJCczc3JwFBgay6Oho9tdffzE7Ozv2zTff6OotaJ26OQoODmbm5ubs119/ZTExMez06dOsfv36bMiQIbp6C1qVmZnJ7ty5w+7cucMAsLVr17I7d+6w+Ph4xhhjc+bMYSNHjuSWLxoGMXPmTBYVFcU2bNhAwyCK8+OPP7LatWszoVDIPD092d9//8295u3tzQICAuSWP3jwIGvYsCETCoWsWbNm7NixYxUcccVSJz916tRhABQewcHBFR94BVL3M/Su6l4AGVM/P1evXmXt27dnIpGI1atXjy1btowVFBRUcNQVS50cSSQStmjRIla/fn1mZGTEXFxc2KRJk1haWlrFB14Bzp07p/R7pSgnAQEBzNvbW2EdDw8PJhQKWb169djOnTvV3i9Nh0QIIUQvVetrgIQQQkhxqAASQgjRS1QACSGE6CUqgIQQQvQSFUBCCCF6iQogIYQQvUQFkBBCiF6iAkgIIUQvUQEkxdq1axesrKx0HUaZ8Xg8/PHHHyUuM3r0aAwYMKBC4qlsFixYgPHjx1fIvs6fPw8ej4f09PQSl3N1dcW6deu0Gou6+9DU74Eqn0d1RUZGolatWsjOztbodvUFFcBqbvTo0eDxeAqPJ0+e6Do07Nq1i4uHz+ejVq1aGDNmDF69eqWR7SclJeGjjz4CAMTFxYHH4ynM0bd+/Xrs2rVLI/srzqJFi7j3KRAI4OLigvHjxyM1NVWt7WiyWCcnJ2P9+vWYN2+e3PaL4hQKhWjQoAGWLFmCgoKCcu/Py8sLSUlJsLS0BFB8Ubl582aFFeWqYNmyZfDy8oKJiYnSfDVt2hQffPAB1q5dW/HBVQNUAPVAr169kJSUJPeoW7eursMCAFhYWCApKQnPnj3D1q1bceLECYwcOVIj23ZwcIBIJCpxGUtLywo5ym3WrBmSkpKQkJCAnTt34uTJk5g4caLW91ucbdu2wcvLC3Xq1JFrL/qsPH78GF999RUWLVqE1atXl3t/QqEQDg4O4PF4JS5Xs2ZNmJiYlHt/1YVYLMann35a4mdlzJgx2LRpk0b+UNE3VAD1gEgkgoODg9xDIBBg7dq1aNGiBUxNTeHi4oJJkyYhKyur2O3cvXsXXbt2hbm5OSwsLNCmTRvcunWLe/3y5cvo3LkzjI2N4eLigqlTp5Z6aobH48HBwQFOTk746KOPMHXqVJw5cwa5ubmQyWRYsmQJatWqBZFIBA8PD5w8eZJbVywWIzAwEI6OjjAyMkKdOnWwYsUKuW0XnXIqKvitWrUCj8fDhx9+CED+qGrLli1wcnKSm6YHAPr374/PPvuMe/7nn3+idevWMDIyQr169bB48eJSv3wMDAzg4OAAZ2dn+Pj44NNPP0VoaCj3ulQqxeeff466devC2NgYjRo1wvr167nXFy1ahN27d+PPP//kjtLOnz8PAEhMTMSQIUNgZWUFGxsb9O/fH3FxcSXGExISIjdnYZGiz0qdOnUwceJE+Pj44OjRowCAtLQ0jBo1CtbW1jAxMcFHH32Ex48fc+vGx8ejb9++sLa2hqmpKZo1a4bjx48DkD8Fev78eYwZMwZv377l3suiRYsAyJ+e9Pf3h5+fn1x8EokEtra22LNnD4DCaYVWrFjB5c3d3R2HDx8u8b2/T9Xfgz/++ANubm4wMjKCr68vEhMT5V4vy+eiNIsXL8b06dPRokWLYpfp0aMHUlNTceHChXLtSx9RAdRjfD4fP/zwAx48eIDdu3fj7NmzmDVrVrHLDx8+HLVq1cLNmzdx+/ZtzJkzB4aGhgAKJ4Lt1asXBg8ejH/++QcHDhzA5cuXERgYqFZMxsbGkMlkKCgowPr16/Hdd99hzZo1+Oeff+Dr64t+/fpxX7o//PADjh49ioMHDyI6Ohr79u2Dq6ur0u3euHEDAHDmzBkkJSXh999/V1jm008/xZs3b3Du3DmuLTU1FSdPnsTw4cMBAJcuXcKoUaMwbdo0REZG4ueff8auXbuwbNkyld9jXFwcTp06JTf3nUwmQ61atXDo0CFERkZi4cKF+Prrr3Hw4EEAwIwZMzBkyBC5o3kvLy9IJBL4+vrC3Nwcly5dwpUrV2BmZoZevXpBLBYr3X9qaioiIyNVmsvR2NiY287o0aNx69YtHD16FNeuXQNjDL1794ZEIgEATJ48Gfn5+bh48SLu3buHVatWwczMTGGbXl5eWLduHXf0n5SUhBkzZigsN3z4cPzvf/+TK0anTp1CTk4OBg4cCABYsWIF9uzZg82bN+PBgweYPn06RowYoVYxUOX3ICcnB8uWLcOePXtw5coVpKenY+jQodzrZflcfPjhhxg9erTKcRZHKBTCw8MDly5dKve29E45Z7EglVxAQAATCATM1NSUe3zyySdKlz106BCrUaMG93znzp3M0tKSe25ubs527dqldN3PP/+cjR8/Xq7t0qVLjM/ns9zcXKXrvL/9R48esYYNG7K2bdsyxhhzcnJiy5Ytk1unXbt2bNKkSYwxxqZMmcK6devGZDKZ0u0DYEeOHGGMMRYbG8sAsDt37sgt8/5URf3792efffYZ9/znn39mTk5OTCqVMsYY6969O1u+fLncNvbu3cscHR2VxsBY4dxufD6fmZqaMiMjI26ql7Vr1xa7DmOMTZ48mQ0ePLjYWIv23ahRI7kc5OfnM2NjY3bq1Cml2y2acy0hIUGu/d3ty2QyFhoaykQiEZsxYwZ79OgRA8CuXLnCLZ+SksKMjY3ZwYMHGWOMtWjRgi1atEjpPoumuymazuf9//siderUYd9//z1jrHBKIFtbW7Znzx7u9WHDhjE/Pz/GGGN5eXnMxMSEXb16VW4bn3/+ORs2bJjSON7fhzLKfg8AyE1fFBUVxQCw69evM8ZU+1y8+3lkrPR5JN9VXL6KDBw4kI0ePVqlbZH/GOiq8JKK07VrV2zatIl7bmpqCqDwaGjFihV4+PAhMjIyUFBQgLy8POTk5Ci9DhMUFISxY8di79693Gm8+vXrAyg8PfrPP/9g37593PKMMchkMsTGxqJJkyZKY3v79i3MzMwgk8mQl5eHTp06Ydu2bcjIyMCLFy/QsWNHueU7duyIu3fvAig8IunRowcaNWqEXr164eOPP0bPnj3Llavhw4dj3Lhx2LhxI0QiEfbt24ehQ4dys3PfvXsXV65ckfvLXiqVlpg3AGjUqBGOHj2KvLw8/PLLL4iIiMCUKVPkltmwYQN27NiBhIQE5ObmQiwWw8PDo8R47969iydPnsDc3FyuPS8vD0+fPlW6Tm5uLgDAyMhI4bW//voLZmZmkEgkkMlk8Pf3x6JFixAWFgYDAwO0b9+eW7ZGjRpo1KgRoqKiAABTp07FxIkTcfr0afj4+GDw4MFo2bJlifGXxMDAAEOGDMG+ffswcuRIZGdn488//0RISAgA4MmTJ8jJyUGPHj3k1hOLxWjVqpXK+1Hl98DAwADt2rXj1mncuDGsrKwQFRUFT0/PMn0uik7jaoKxsTFycnI0tj19QQVQD5iamqJBgwZybXFxcfj4448xceJELFu2DDY2Nrh8+TI+//xziMVipb+wixYtgr+/P44dO4YTJ04gODgYISEhGDhwILKysvDFF19g6tSpCuvVrl272NjMzc0RHh4OPp8PR0dHGBsbAwAyMjJKfV+tW7dGbGwsTpw4gTNnzmDIkCHw8fFR+xrQu/r27QvGGI4dO4Z27drh0qVL+P7777nXs7KysHjxYgwaNEhhXWUFpUhRr0oAWLlyJfr06YPFixdj6dKlAAqvyc2YMQPfffcdOnToAHNzc6xevRrXr18vMd6srCy0adNG7g+PIjVr1lS6jq2tLYDCa3rvL1P0x5JQKISTkxMMDFT/ihg7dix8fX1x7NgxnD59GitWrMB3332nUOjVMXz4cHh7e+PVq1cIDQ2FsbExevXqBQDcqdFjx47B2dlZbr3SOj8VKcvvgTJl/VxoSmpqKvfHKFEdFUA9dfv2bchkMnz33Xfc0U3R9aaSNGzYEA0bNsT06dMxbNgw7Ny5EwMHDkTr1q0RGRmpUGhLw+fzla5jYWEBJycnXLlyBd7e3lz7lStX4OnpKbecn58f/Pz88Mknn6BXr15ITU2FjY2N3PaKrrdJpdIS4zEyMsKgQYOwb98+PHnyBI0aNULr1q2511u3bo3o6Gi13+f75s+fj27dumHixInc+/Ty8sKkSZO4Zd4/ghMKhQrxt27dGgcOHICdnR0sLCxU2nf9+vVhYWGByMhINGzYUO41ZX8sAUCTJk1QUFCA69evw8vLCwDw5s0bREdHo2nTptxyLi4umDBhAiZMmIC5c+di69atSgugsveijJeXF1xcXHDgwAGcOHECn376KXfduWnTphCJREhISJD7jKhD1d+DgoIC3Lp1i/vsRUdHIz09nTuzoanPRVndv38fn3zyiU72XZVRJxg91aBBA0gkEvz444+IiYnB3r17sXnz5mKXz83NRWBgIM6fP4/4+HhcuXIFN2/e5L4AZs+ejatXryIwMBARERF4/Pgx/vzzT7U7wbxr5syZWLVqFQ4cOIDo6GjMmTMHERERmDZtGoDC3nu//vorHj58iEePHuHQoUNwcHBQOqzBzs4OxsbGOHnyJF6+fIm3b98Wu9/hw4fj2LFj2LFjB9f5pcjChQuxZ88eLF68GA8ePEBUVBRCQkIwf/58td5bhw4d0LJlSyxfvhwA4Obmhlu3buHUqVN49OgRFixYgJs3b8qt4+rqin/++QfR0dFISUmBRCLB8OHDYWtri/79++PSpUuIjY3F+fPnMXXqVDx79kzpvvl8Pnx8fHD58mWV43Vzc0P//v0xbtw4XL58GXfv3sWIESPg7OyM/v37AwC+/PJLnDp1CrGxsQgPD8e5c+eKPfXt6uqKrKwshIWFISUlpcTTd/7+/ti8eTNCQ0Pl/j/Mzc0xY8YMTJ8+Hbt378bTp08RHh6OH3/8Ebt371bpfan6e2BoaIgpU6bg+vXruH37NkaPHo0PPviAK4hl+VyMGjUKc+fOLTG+hIQEREREICEhAVKpFBEREYiIiJDrGBQXF4fnz5/Dx8dHpfdM3qHri5BEu5R1nCiydu1a5ujoyIyNjZmvry/bs2dPsR0V8vPz2dChQ5mLiwsTCoXMycmJBQYGynVwuXHjBuvRowczMzNjpqamrGXLlgqdWN5V2oV9qVTKFi1axJydnZmhoSFzd3dnJ06c4F7fsmUL8/DwYKampszCwoJ1796dhYeHc6/jvU4HW7duZS4uLozP5zNvb+9i8yOVSpmjoyMDwJ4+faoQ18mTJ5mXlxczNjZmFhYWzNPTk23ZsqXY9xEcHMzc3d0V2n/99VcmEolYQkICy8vLY6NHj2aWlpbMysqKTZw4kc2ZM0duvVevXnH5BcDOnTvHGGMsKSmJjRo1itna2jKRSMTq1avHxo0bx96+fVtsTMePH2fOzs5c557icvGu1NRUNnLkSGZpacl9Zh49esS9HhgYyOrXr89EIhGrWbMmGzlyJEtJSWGMKXaCYYyxCRMmsBo1ajAALDg4mDGmvINKZGQkA8Dq1Kmj0OFJJpOxdevWsUaNGjFDQ0NWs2ZN5uvryy5cuFDs+3h/H6r+Hvz222+sXr16TCQSMR8fHxYfHy+33dI+F+9/Hr29vVlAQECxcTJW+H+CfztNvfso+r9njLHly5czX1/fErdDlOMxxpguCi8hRHcYY2jfvj13KptUTWKxGG5ubti/f79ChzFSOjoFSoge4vF42LJlC909pIpLSEjA119/TcWvjOgIkBBCiF6iI0BCCCF6iQogIYQQvUQFkBBCiF6iAkgIIUQvUQEkhBCil6gAEkII0UtUAAkhhOglKoCEEEL0EhVAQggheun/g5T60fObbNIAAAAASUVORK5CYII=", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "from sklearn.metrics import PrecisionRecallDisplay, average_precision_score\n", - "from sklearn.metrics import RocCurveDisplay, roc_auc_score\n", - "\n", - "\n", - "def plot_auprc(df):\n", - " \"\"\"\n", - " Plots the AUPRC curve based on predictions in a DataFrame.\n", - "\n", - " Args:\n", - " df (pd.DataFrame): DataFrame containing columns '# id', 'time', 'ground_truth', 'prediction_0', 'prediction_1'.\n", - " \"\"\"\n", - " # Extract ground truth and predictions\n", - " ground_truth = df[\"ground_truth\"]\n", - " predictions = df[\"prediction_1\"]\n", - "\n", - " # Calculate average precision score\n", - " average_precision = average_precision_score(ground_truth, predictions)\n", - "\n", - " # Calculate chance level (mean of ground truth)\n", - " chance_level = ground_truth.mean()\n", - "\n", - " # Plot Precision-Recall curve\n", - " plt.figure(figsize=(8, 6))\n", - " PrecisionRecallDisplay.from_predictions(ground_truth, predictions)\n", - " plt.axhline(y=chance_level, color=\"red\", linestyle=\"--\", label=f\"Chance Level ({chance_level:.2f})\")\n", - " plt.title(f\"Precision-Recall Curve (AP: {average_precision:.2f})\")\n", - " plt.legend()\n", - " plt.grid()\n", - " plt.show()\n", - "\n", - "\n", - "def plot_auroc(df):\n", - " \"\"\"\n", - " Plots the AUROC curve based on predictions in a DataFrame.\n", - "\n", - " Args:\n", - " df (pd.DataFrame): DataFrame containing columns '# id', 'time', 'ground_truth', 'prediction_0', 'prediction_1'.\n", - " \"\"\"\n", - " # Extract ground truth and predictions\n", - " ground_truth = df[\"ground_truth\"]\n", - " predictions = df[\"prediction_1\"]\n", - "\n", - " # Calculate AUROC score\n", - " roc_auc = roc_auc_score(ground_truth, predictions)\n", - "\n", - " # Plot ROC curve\n", - " plt.figure(figsize=(8, 6))\n", - " RocCurveDisplay.from_predictions(ground_truth, predictions)\n", - " plt.title(f\"ROC Curve (AUC: {roc_auc:.2f})\")\n", - " plt.grid()\n", - " plt.show()\n", - "\n", - "\n", - "preds_path = \"/Users/robin/Documents/git/yaib_logs/mimic_demo/aki/XGBClassifier/2025-06-18T17-11-27.152583/repetition_0/fold_0/pred_indicators.csv\"\n", - "preds = pd.read_csv(preds_path)\n", - "plot_auprc(preds)\n", - "plot_auroc(preds)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "e88ff2e71f32ce9a", - "metadata": { - "ExecuteTime": { - "end_time": "2025-06-20T14:06:03.507937Z", - "start_time": "2025-06-20T14:06:03.263979Z" - } - }, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import os\n", - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from sklearn.metrics import precision_recall_curve, roc_curve\n", - "import os\n", - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from sklearn.metrics import precision_recall_curve, roc_curve, roc_auc_score, average_precision_score\n", - "\n", - "\n", - "def plot_average_curves(directory: str):\n", - " \"\"\"\n", - " Plots average AUPRC and AUROC curves with confidence intervals from multiple experiments.\n", - "\n", - " Args:\n", - " directory (str): Path to the directory containing prediction DataFrames named 'pred_indicators.csv'.\n", - " \"\"\"\n", - " # Initialize lists to store interpolated precision, recall, and ROC values\n", - " all_precisions = []\n", - " all_recalls = []\n", - " all_roc_aucs = []\n", - " all_pr_aucs = []\n", - " all_roc_fprs = []\n", - " all_roc_tprs = []\n", - " chance_levels = []\n", - "\n", - " # Define a common recall and FPR range for interpolation\n", - " common_recall = np.linspace(0, 1, 100)\n", - " common_fpr = np.linspace(0, 1, 100)\n", - "\n", - " # Iterate through the directory and load all 'pred_indicators.csv' files\n", - " for root, _, files in os.walk(directory):\n", - " for file in files:\n", - " if file == \"pred_indicators.csv\":\n", - " file_path = os.path.join(root, file)\n", - " df = pd.read_csv(file_path)\n", - "\n", - " # Extract ground truth and predictions\n", - " ground_truth = df[\"ground_truth\"]\n", - " predictions = df[\"prediction_1\"]\n", - "\n", - " # Compute precision-recall curve\n", - " precision, recall, _ = precision_recall_curve(ground_truth, predictions)\n", - " interpolated_precision = np.interp(common_recall, recall[::-1], precision[::-1])\n", - " all_precisions.append(interpolated_precision)\n", - "\n", - " # Compute ROC curve\n", - " fpr, tpr, _ = roc_curve(ground_truth, predictions)\n", - " interpolated_tpr = np.interp(common_fpr, fpr, tpr)\n", - " all_roc_fprs.append(common_fpr)\n", - " all_roc_tprs.append(interpolated_tpr)\n", - "\n", - " # Compute AUCs\n", - " roc_auc = roc_auc_score(ground_truth, predictions)\n", - " pr_auc = average_precision_score(ground_truth, predictions)\n", - " all_roc_aucs.append(roc_auc)\n", - " all_pr_aucs.append(pr_auc)\n", - "\n", - " # Compute chance level\n", - " chance_levels.append(ground_truth.mean())\n", - "\n", - " # Compute averages and standard deviations\n", - " avg_precision = np.mean(all_precisions, axis=0)\n", - " std_precision = np.std(all_precisions, axis=0)\n", - " avg_tpr = np.mean(all_roc_tprs, axis=0)\n", - " std_tpr = np.std(all_roc_tprs, axis=0)\n", - " avg_roc_auc = np.mean(all_roc_aucs)\n", - " std_roc_auc = np.std(all_roc_aucs)\n", - " avg_pr_auc = np.mean(all_pr_aucs)\n", - " std_pr_auc = np.std(all_pr_aucs)\n", - " avg_chance_level = np.mean(chance_levels)\n", - " std_chance_level = np.std(chance_levels)\n", - "\n", - " # Plot Precision-Recall curve\n", - " plt.figure(figsize=(8, 6))\n", - " plt.plot(common_recall, avg_precision, label=f\"Average Precision-Recall Curve (AP: {avg_pr_auc:.2f} ± {std_pr_auc:.2f})\")\n", - " plt.fill_between(\n", - " common_recall, avg_precision - std_precision, avg_precision + std_precision, alpha=0.2, label=\"Confidence Interval\"\n", - " )\n", - " plt.axhline(\n", - " y=avg_chance_level,\n", - " color=\"red\",\n", - " linestyle=\"--\",\n", - " label=f\"Chance Level ({avg_chance_level:.2f} ± {std_chance_level:.2f})\",\n", - " )\n", - " plt.fill_between(\n", - " common_recall, avg_chance_level - std_chance_level, avg_chance_level + std_chance_level, color=\"red\", alpha=0.1\n", - " )\n", - " plt.xlabel(\"Recall\")\n", - " plt.ylabel(\"Precision\")\n", - " plt.title(\"Average Precision-Recall Curve\")\n", - " plt.legend()\n", - " plt.grid()\n", - "\n", - " # Plot ROC curve\n", - " plt.figure(figsize=(8, 6))\n", - " plt.plot(common_fpr, avg_tpr, label=f\"Average ROC Curve (AUC: {avg_roc_auc:.2f} ± {std_roc_auc:.2f})\")\n", - " plt.fill_between(common_fpr, avg_tpr - std_tpr, avg_tpr + std_tpr, alpha=0.2, label=\"Confidence Interval\")\n", - " # plt.axhline(y=avg_chance_level, color='red', linestyle='--', label=f\"Chance Level ({avg_chance_level:.2f} ± {std_chance_level:.2f})\")\n", - " plt.fill_between(\n", - " common_fpr, avg_chance_level - std_chance_level, avg_chance_level + std_chance_level, color=\"red\", alpha=0.1\n", - " )\n", - " plt.xlabel(\"False Positive Rate\")\n", - " plt.ylabel(\"True Positive Rate\")\n", - " plt.title(\"Average ROC Curve\")\n", - " plt.legend()\n", - " plt.grid()\n", - "\n", - " plt.show()\n", - "\n", - "\n", - "# Example usage\n", - "\n", - "# Example usage\n", - "plot_average_curves(\"/Users/robin/Downloads/cassandra_logs/2024-12-09T11-46-13.259943\")" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "22cb35f00419d404", - "metadata": { - "ExecuteTime": { - "end_time": "2025-05-08T15:53:22.829692Z", - "start_time": "2025-05-08T15:53:22.801754Z" - } - }, - "outputs": [ - { - "ename": "ValueError", - "evalue": "continuous is not supported", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[3], line 5\u001b[0m\n\u001b[1;32m 3\u001b[0m ground_truth \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray([\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m0.1\u001b[39m])\n\u001b[1;32m 4\u001b[0m predictions \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray([\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m0.9\u001b[39m])\n\u001b[0;32m----> 5\u001b[0m \u001b[43mmatthews_corrcoef\u001b[49m\u001b[43m(\u001b[49m\u001b[43mground_truth\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpredictions\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m/opt/miniconda3/envs/yaib/lib/python3.10/site-packages/sklearn/utils/_param_validation.py:213\u001b[0m, in \u001b[0;36mvalidate_params..decorator..wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 207\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 208\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[1;32m 209\u001b[0m skip_parameter_validation\u001b[38;5;241m=\u001b[39m(\n\u001b[1;32m 210\u001b[0m prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[1;32m 211\u001b[0m )\n\u001b[1;32m 212\u001b[0m ):\n\u001b[0;32m--> 213\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 214\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m InvalidParameterError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 215\u001b[0m \u001b[38;5;66;03m# When the function is just a wrapper around an estimator, we allow\u001b[39;00m\n\u001b[1;32m 216\u001b[0m \u001b[38;5;66;03m# the function to delegate validation to the estimator, but we replace\u001b[39;00m\n\u001b[1;32m 217\u001b[0m \u001b[38;5;66;03m# the name of the estimator by the name of the function in the error\u001b[39;00m\n\u001b[1;32m 218\u001b[0m \u001b[38;5;66;03m# message to avoid confusion.\u001b[39;00m\n\u001b[1;32m 219\u001b[0m msg \u001b[38;5;241m=\u001b[39m re\u001b[38;5;241m.\u001b[39msub(\n\u001b[1;32m 220\u001b[0m \u001b[38;5;124mr\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparameter of \u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mw+ must be\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 221\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparameter of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__qualname__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m must be\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 222\u001b[0m \u001b[38;5;28mstr\u001b[39m(e),\n\u001b[1;32m 223\u001b[0m )\n", - "File \u001b[0;32m/opt/miniconda3/envs/yaib/lib/python3.10/site-packages/sklearn/metrics/_classification.py:989\u001b[0m, in \u001b[0;36mmatthews_corrcoef\u001b[0;34m(y_true, y_pred, sample_weight)\u001b[0m\n\u001b[1;32m 918\u001b[0m \u001b[38;5;129m@validate_params\u001b[39m(\n\u001b[1;32m 919\u001b[0m {\n\u001b[1;32m 920\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124my_true\u001b[39m\u001b[38;5;124m\"\u001b[39m: [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124marray-like\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 925\u001b[0m )\n\u001b[1;32m 926\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mmatthews_corrcoef\u001b[39m(y_true, y_pred, \u001b[38;5;241m*\u001b[39m, sample_weight\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 927\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Compute the Matthews correlation coefficient (MCC).\u001b[39;00m\n\u001b[1;32m 928\u001b[0m \n\u001b[1;32m 929\u001b[0m \u001b[38;5;124;03m The Matthews correlation coefficient is used in machine learning as a\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 987\u001b[0m \u001b[38;5;124;03m -0.33...\u001b[39;00m\n\u001b[1;32m 988\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 989\u001b[0m y_type, y_true, y_pred \u001b[38;5;241m=\u001b[39m \u001b[43m_check_targets\u001b[49m\u001b[43m(\u001b[49m\u001b[43my_true\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_pred\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 990\u001b[0m check_consistent_length(y_true, y_pred, sample_weight)\n\u001b[1;32m 991\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m y_type \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbinary\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmulticlass\u001b[39m\u001b[38;5;124m\"\u001b[39m}:\n", - "File \u001b[0;32m/opt/miniconda3/envs/yaib/lib/python3.10/site-packages/sklearn/metrics/_classification.py:119\u001b[0m, in \u001b[0;36m_check_targets\u001b[0;34m(y_true, y_pred)\u001b[0m\n\u001b[1;32m 117\u001b[0m \u001b[38;5;66;03m# No metrics support \"multiclass-multioutput\" format\u001b[39;00m\n\u001b[1;32m 118\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m y_type \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbinary\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmulticlass\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmultilabel-indicator\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n\u001b[0;32m--> 119\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{0}\u001b[39;00m\u001b[38;5;124m is not supported\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(y_type))\n\u001b[1;32m 121\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m y_type \u001b[38;5;129;01min\u001b[39;00m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbinary\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmulticlass\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n\u001b[1;32m 122\u001b[0m xp, _ \u001b[38;5;241m=\u001b[39m get_namespace(y_true, y_pred)\n", - "\u001b[0;31mValueError\u001b[0m: continuous is not supported" - ] - } - ], - "source": [ - "from sklearn.metrics import matthews_corrcoef\n", - "import numpy as np\n", - "\n", - "ground_truth = np.array(\n", - " [\n", - " 1,\n", - " 0,\n", - " 1,\n", - " 0,\n", - " ]\n", - ")\n", - "predictions = np.array([1, 0, 0, 0, 0.9])\n", - "matthews_corrcoef(ground_truth, predictions)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cae5c1b3b95093db", - "metadata": { - "ExecuteTime": { - "end_time": "2024-11-14T12:22:55.576737Z", - "start_time": "2024-11-14T12:22:55.567569Z" - } - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "67be5cf6c6ac18f", - "metadata": { - "ExecuteTime": { - "end_time": "2024-11-14T12:22:55.870267Z", - "start_time": "2024-11-14T12:22:55.654565Z" - } - }, - "outputs": [], - "source": [ - "import polars as pl\n", - "\n", - "preds = pl.read_csv(r\"C:\\Users\\Robin\\Downloads\\pred_indicators_only_complications.csv\")" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "a0abef17da834506", - "metadata": { - "ExecuteTime": { - "end_time": "2024-11-14T12:22:55.901519Z", - "start_time": "2024-11-14T12:22:55.888514Z" - } - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "e988983945c055e1", - "metadata": { - "ExecuteTime": { - "end_time": "2024-11-14T12:22:56.458349Z", - "start_time": "2024-11-14T12:22:55.919572Z" - } - }, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'predictions' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[3], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mpredictions\u001b[49m\n", - "\u001b[1;31mNameError\u001b[0m: name 'predictions' is not defined" - ] - } - ], - "source": [ - "predictions" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cf593fd5e8a42f4d", - "metadata": { - "ExecuteTime": { - "end_time": "2024-11-14T12:22:56.539577400Z", - "start_time": "2024-11-14T12:19:50.586084Z" - } - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "4781ed7dd0de97e4", - "metadata": { - "ExecuteTime": { - "end_time": "2024-11-14T12:28:12.916074Z", - "start_time": "2024-11-14T12:28:02.509602Z" - } - }, - "outputs": [], - "source": [ - "from numpy import genfromtxt\n", - "from icu_benchmarks.models.alarm_metrics import silence_positives, fill_gaps, convert_to_alarm\n", - "import numpy as np\n", - "\n", - "predictions_no_threshold = genfromtxt(r\"C:\\Users\\Robin\\Downloads\\pred_indicators_only_complications.csv\", delimiter=\",\")\n", - "predictions = predictions_no_threshold.copy()\n", - "# Define the threshold\n", - "threshold = 0.5\n", - "\n", - "# Round predictions to 0 and 1 according to the threshold\n", - "predictions = np.where(predictions >= threshold, 1, 0)\n", - "\n", - "# Column index for grouping\n", - "group_column_index = 0\n", - "\n", - "# Get unique group values\n", - "unique_groups = np.unique(predictions[:, group_column_index])\n", - "# Dictionary to store grouped arrays\n", - "grouped_arrays = {}\n", - "\n", - "unique_groups = np.unique(predictions[:, group_column_index])\n", - "\n", - "modified_arrays = []\n", - "# print(predictions)\n", - "# Group arrays based on unique values in the group column\n", - "for group in unique_groups:\n", - " group_array = predictions[predictions[:, group_column_index] == group]\n", - " # print(f\"Group {group} before modification:\\n{group_array}\\n\")\n", - " ground_truth = group_array[:, 2]\n", - " predictions = group_array[:, 4]\n", - " group_array[:, 4] = silence_positives(ground_truth, predictions)\n", - " group_array[:, 4], group_array[:, 2] = fill_gaps(predictions, ground_truth)\n", - " # print(f\"Group {group} after modification:\\n{group_array}\\n\")\n", - " modified_arrays.append(group_array)\n", - "\n", - "# Combine the modified arrays back into a single array\n", - "combined_array = np.vstack(modified_arrays)\n", - "# combined_array = np.delete(combined_array, 3, axis=1)\n", - "\n", - "# print(\"Combined array:\")\n", - "# print(combined_array)\n", - "#\n", - "# print(f\"ROC AUC: {roc_auc_score(predictions[:, 2], predictions[:, 4])}\")\n", - "# print(f\"ROC AUC: {roc_auc_score(combined_array[:, 2], combined_array[:, 4])}\")\n", - "# print(f\"Average precision: {average_precision_score(predictions[:, 2], predictions[:, 4])}\")\n", - "# print(f\"Average precision: {average_precision_score(combined_array[:, 2], combined_array[:, 4])}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "a5a491f898213828", - "metadata": { - "ExecuteTime": { - "end_time": "2024-11-14T12:22:56.539577400Z", - "start_time": "2024-11-14T12:19:50.648226Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[4.200e+01, 0.000e+00, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [4.200e+01, 1.000e+00, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [4.200e+01, 2.000e+00, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " ...,\n", - " [1.044e+03, 1.730e+02, 1.000e+00, 4.100e-02, 9.590e-01],\n", - " [1.044e+03, 1.740e+02, 1.000e+00, 2.700e-02, 9.730e-01],\n", - " [1.044e+03, 1.750e+02, 1.000e+00, 2.000e-02, 9.800e-01]])" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "predictions_no_threshold" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "e3b2aedf47bce384", - "metadata": { - "ExecuteTime": { - "end_time": "2024-11-14T12:22:56.539577400Z", - "start_time": "2024-11-14T12:19:50.662767Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0, 0, 0, ..., 1, 1, 1])" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "predictions" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "4937d5d3f28a8a46", - "metadata": { - "ExecuteTime": { - "end_time": "2024-11-14T12:28:13.430436Z", - "start_time": "2024-11-14T12:28:12.932442Z" - } - }, - "outputs": [ - { - "ename": "IndexError", - "evalue": "too many indices for array: array is 1-dimensional, but 2 were indexed", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mIndexError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[5], line 12\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmetrics\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m PrecisionRecallDisplay, RocCurveDisplay, average_precision_score, roc_auc_score\n\u001b[0;32m 5\u001b[0m \u001b[38;5;66;03m# Assuming predictions, combined_array, and predictions_no_threshold are already defined\u001b[39;00m\n\u001b[0;32m 6\u001b[0m \u001b[38;5;66;03m# predictions[:, 2] is the ground truth\u001b[39;00m\n\u001b[0;32m 7\u001b[0m \u001b[38;5;66;03m# predictions[:, 4] is the original predictions\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 10\u001b[0m \n\u001b[0;32m 11\u001b[0m \u001b[38;5;66;03m# Calculate metrics\u001b[39;00m\n\u001b[1;32m---> 12\u001b[0m roc_auc_original \u001b[38;5;241m=\u001b[39m roc_auc_score(\u001b[43mpredictions\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m]\u001b[49m, predictions[:, \u001b[38;5;241m4\u001b[39m])\n\u001b[0;32m 13\u001b[0m roc_auc_modified \u001b[38;5;241m=\u001b[39m roc_auc_score(combined_array[:, \u001b[38;5;241m2\u001b[39m], combined_array[:, \u001b[38;5;241m4\u001b[39m])\n\u001b[0;32m 14\u001b[0m roc_auc_no_threshold \u001b[38;5;241m=\u001b[39m roc_auc_score(predictions[:, \u001b[38;5;241m2\u001b[39m], predictions_no_threshold[:, \u001b[38;5;241m4\u001b[39m])\n", - "\u001b[1;31mIndexError\u001b[0m: too many indices for array: array is 1-dimensional, but 2 were indexed" - ] - } - ], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from sklearn.metrics import PrecisionRecallDisplay, RocCurveDisplay, average_precision_score, roc_auc_score\n", - "\n", - "# Assuming predictions, combined_array, and predictions_no_threshold are already defined\n", - "# predictions[:, 2] is the ground truth\n", - "# predictions[:, 4] is the original predictions\n", - "# combined_array[:, 4] is the modified predictions\n", - "# predictions_no_threshold[:, 4] is the no_threshold predictions\n", - "\n", - "# Calculate metrics\n", - "roc_auc_original = roc_auc_score(predictions[:, 2], predictions[:, 4])\n", - "roc_auc_modified = roc_auc_score(combined_array[:, 2], combined_array[:, 4])\n", - "roc_auc_no_threshold = roc_auc_score(predictions[:, 2], predictions_no_threshold[:, 4])\n", - "average_precision_original = average_precision_score(predictions[:, 2], predictions[:, 4])\n", - "average_precision_modified = average_precision_score(combined_array[:, 2], combined_array[:, 4])\n", - "average_precision_no_threshold = average_precision_score(predictions[:, 2], predictions_no_threshold[:, 4])\n", - "\n", - "# Plot ROC curves\n", - "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 6))\n", - "\n", - "RocCurveDisplay.from_predictions(predictions[:, 2], predictions[:, 4], ax=ax1, name=\"Original\")\n", - "RocCurveDisplay.from_predictions(combined_array[:, 2], combined_array[:, 4], ax=ax1, name=\"Modified\")\n", - "RocCurveDisplay.from_predictions(predictions[:, 2], predictions_no_threshold[:, 4], ax=ax1, name=\"No Threshold\")\n", - "ax1.set_title(\n", - " f\"ROC Curve\\nOriginal AUC: {roc_auc_original:.2f}, Modified AUC: {roc_auc_modified:.2f}, No Threshold AUC: {roc_auc_no_threshold:.2f}\"\n", - ")\n", - "\n", - "# Plot PRC curves\n", - "PrecisionRecallDisplay.from_predictions(predictions[:, 2], predictions[:, 4], ax=ax2, name=\"Original\")\n", - "PrecisionRecallDisplay.from_predictions(combined_array[:, 2], combined_array[:, 4], ax=ax2, name=\"Modified\")\n", - "PrecisionRecallDisplay.from_predictions(predictions[:, 2], predictions_no_threshold[:, 4], ax=ax2, name=\"No Threshold\")\n", - "ax2.set_title(\n", - " f\"Precision-Recall Curve\\nOriginal AP: {average_precision_original:.2f}, Modified AP: {average_precision_modified:.2f}, No Threshold AP: {average_precision_no_threshold:.2f}\"\n", - ")\n", - "\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "id": "6586e44915be74e3", - "metadata": { - "ExecuteTime": { - "end_time": "2024-11-14T12:22:56.539577400Z", - "start_time": "2024-11-12T17:04:57.239395Z" - } - }, - "outputs": [ - { - "ename": "ImportError", - "evalue": "cannot import name 'PrecisionRecallDisplay' from 'sklearn' (C:\\Users\\Robin\\miniconda\\envs\\yaib_19\\lib\\site-packages\\sklearn\\__init__.py)", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mImportError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[58], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m PrecisionRecallDisplay, RocCurveDisplay\n", - "\u001b[1;31mImportError\u001b[0m: cannot import name 'PrecisionRecallDisplay' from 'sklearn' (C:\\Users\\Robin\\miniconda\\envs\\yaib_19\\lib\\site-packages\\sklearn\\__init__.py)" - ] - } - ], - "source": [ - "from sklearn.metrics import PrecisionRecallDisplay, RocCurveDisplay" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "f81b74431b6e391a", - "metadata": { - "ExecuteTime": { - "end_time": "2024-11-14T12:22:56.539577400Z", - "start_time": "2024-11-12T16:57:47.562930Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[4.200e+01, 0.000e+00, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [4.200e+01, 1.000e+00, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [4.200e+01, 2.000e+00, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " ...,\n", - " [1.044e+03, 1.730e+02, 1.000e+00, 4.100e-02, 9.590e-01],\n", - " [1.044e+03, 1.740e+02, 1.000e+00, 2.700e-02, 9.730e-01],\n", - " [1.044e+03, 1.750e+02, 1.000e+00, 2.000e-02, 9.800e-01]])" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "predictions" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "20480d22741f38a5", - "metadata": { - "ExecuteTime": { - "end_time": "2024-11-14T12:22:56.540541100Z", - "start_time": "2024-11-12T16:54:27.852976Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[4.200e+01, 0.000e+00, 0.000e+00, 0.000e+00],\n", - " [4.200e+01, 1.000e+00, 0.000e+00, 0.000e+00],\n", - " [4.200e+01, 2.000e+00, 0.000e+00, 0.000e+00],\n", - " ...,\n", - " [1.044e+03, 1.730e+02, 1.000e+00, 1.000e+00],\n", - " [1.044e+03, 1.740e+02, 1.000e+00, 1.000e+00],\n", - " [1.044e+03, 1.750e+02, 1.000e+00, 1.000e+00]])" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "combined_array" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ed297983230629bd", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "518b670047396a91", - "metadata": { - "ExecuteTime": { - "end_time": "2024-11-14T12:22:56.540541100Z", - "start_time": "2024-11-12T16:50:26.843347Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[1.044e+03, 0.000e+00, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 1.000e+00, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 2.000e+00, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 3.000e+00, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 4.000e+00, 0.000e+00, 9.990e-01, 1.000e-03],\n", - " [1.044e+03, 5.000e+00, 0.000e+00, 9.990e-01, 1.000e-03],\n", - " [1.044e+03, 6.000e+00, 0.000e+00, 9.990e-01, 1.000e-03],\n", - " [1.044e+03, 7.000e+00, 0.000e+00, 9.990e-01, 1.000e-03],\n", - " [1.044e+03, 8.000e+00, 0.000e+00, 9.990e-01, 1.000e-03],\n", - " [1.044e+03, 9.000e+00, 0.000e+00, 9.990e-01, 1.000e-03],\n", - " [1.044e+03, 1.000e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 1.100e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 1.200e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 1.300e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 1.400e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 1.500e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 1.600e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 1.700e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 1.800e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 1.900e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 2.000e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 2.100e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 2.200e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 2.300e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 2.400e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 2.500e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 2.600e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 2.700e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 2.800e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 2.900e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 3.000e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 3.100e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 3.200e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 3.300e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 3.400e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 3.500e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 3.600e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 3.700e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 3.800e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 3.900e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 4.000e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 4.100e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 4.200e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 4.300e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 4.400e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 4.500e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 4.600e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 4.700e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 4.800e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 4.900e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 5.000e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 5.100e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 5.200e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 5.300e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 5.400e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 5.500e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 5.600e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 5.700e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 5.800e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 5.900e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 6.000e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 6.100e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 6.200e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 6.300e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 6.400e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 6.500e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 6.600e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 6.700e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 6.800e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 6.900e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 7.000e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 7.100e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 7.200e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 7.300e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 7.400e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 7.500e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 7.600e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 7.700e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 7.800e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 7.900e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 8.000e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 8.100e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 8.200e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 8.300e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 8.400e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 8.500e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 8.600e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 8.700e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 8.800e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 8.900e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 9.000e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 9.100e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 9.200e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 9.300e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 9.400e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 9.500e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 9.600e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 9.700e+01, 0.000e+00, 9.990e-01, 1.000e-03],\n", - " [1.044e+03, 9.800e+01, 0.000e+00, 9.990e-01, 1.000e-03],\n", - " [1.044e+03, 9.900e+01, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 1.000e+02, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 1.010e+02, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 1.020e+02, 0.000e+00, 9.990e-01, 1.000e-03],\n", - " [1.044e+03, 1.030e+02, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 1.040e+02, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 1.050e+02, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 1.060e+02, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 1.070e+02, 0.000e+00, 9.990e-01, 1.000e-03],\n", - " [1.044e+03, 1.080e+02, 0.000e+00, 9.990e-01, 1.000e-03],\n", - " [1.044e+03, 1.090e+02, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 1.100e+02, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 1.110e+02, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 1.120e+02, 0.000e+00, 9.990e-01, 1.000e-03],\n", - " [1.044e+03, 1.130e+02, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 1.140e+02, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 1.150e+02, 0.000e+00, 9.990e-01, 1.000e-03],\n", - " [1.044e+03, 1.160e+02, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 1.170e+02, 0.000e+00, 9.980e-01, 2.000e-03],\n", - " [1.044e+03, 1.180e+02, 0.000e+00, 9.980e-01, 2.000e-03],\n", - " [1.044e+03, 1.190e+02, 0.000e+00, 9.990e-01, 1.000e-03],\n", - " [1.044e+03, 1.200e+02, 0.000e+00, 9.990e-01, 1.000e-03],\n", - " [1.044e+03, 1.210e+02, 0.000e+00, 9.990e-01, 1.000e-03],\n", - " [1.044e+03, 1.220e+02, 0.000e+00, 9.990e-01, 1.000e-03],\n", - " [1.044e+03, 1.230e+02, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 1.240e+02, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 1.250e+02, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 1.260e+02, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 1.270e+02, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 1.280e+02, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 1.290e+02, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 1.300e+02, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 1.310e+02, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 1.320e+02, 0.000e+00, 9.990e-01, 1.000e-03],\n", - " [1.044e+03, 1.330e+02, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 1.340e+02, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 1.350e+02, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 1.360e+02, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 1.370e+02, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 1.380e+02, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 1.390e+02, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 1.400e+02, 0.000e+00, 9.830e-01, 1.700e-02],\n", - " [1.044e+03, 1.410e+02, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 1.420e+02, 0.000e+00, 9.990e-01, 1.000e-03],\n", - " [1.044e+03, 1.430e+02, 0.000e+00, 9.970e-01, 3.000e-03],\n", - " [1.044e+03, 1.440e+02, 0.000e+00, 9.980e-01, 2.000e-03],\n", - " [1.044e+03, 1.450e+02, 0.000e+00, 9.990e-01, 1.000e-03],\n", - " [1.044e+03, 1.460e+02, 0.000e+00, 9.970e-01, 3.000e-03],\n", - " [1.044e+03, 1.470e+02, 0.000e+00, 9.990e-01, 1.000e-03],\n", - " [1.044e+03, 1.480e+02, 0.000e+00, 9.990e-01, 1.000e-03],\n", - " [1.044e+03, 1.490e+02, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 1.500e+02, 0.000e+00, 9.990e-01, 1.000e-03],\n", - " [1.044e+03, 1.510e+02, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 1.520e+02, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 1.530e+02, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 1.540e+02, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 1.550e+02, 0.000e+00, 9.980e-01, 2.000e-03],\n", - " [1.044e+03, 1.560e+02, 0.000e+00, 9.990e-01, 1.000e-03],\n", - " [1.044e+03, 1.570e+02, 0.000e+00, 1.000e+00, 0.000e+00],\n", - " [1.044e+03, 1.580e+02, 0.000e+00, 9.990e-01, 1.000e-03],\n", - " [1.044e+03, 1.590e+02, 0.000e+00, 9.990e-01, 1.000e-03],\n", - " [1.044e+03, 1.600e+02, 0.000e+00, 9.980e-01, 2.000e-03],\n", - " [1.044e+03, 1.610e+02, 0.000e+00, 9.980e-01, 2.000e-03],\n", - " [1.044e+03, 1.620e+02, 0.000e+00, 9.970e-01, 3.000e-03],\n", - " [1.044e+03, 1.630e+02, 0.000e+00, 9.960e-01, 4.000e-03],\n", - " [1.044e+03, 1.640e+02, 0.000e+00, 9.840e-01, 1.600e-02],\n", - " [1.044e+03, 1.650e+02, 0.000e+00, 9.970e-01, 3.000e-03],\n", - " [1.044e+03, 1.660e+02, 0.000e+00, 9.960e-01, 4.000e-03],\n", - " [1.044e+03, 1.670e+02, 0.000e+00, 9.950e-01, 5.000e-03],\n", - " [1.044e+03, 1.680e+02, 0.000e+00, 9.970e-01, 3.000e-03],\n", - " [1.044e+03, 1.690e+02, 0.000e+00, 2.900e-02, 9.710e-01],\n", - " [1.044e+03, 1.700e+02, 1.000e+00, 2.000e-03, 9.980e-01],\n", - " [1.044e+03, 1.710e+02, 1.000e+00, 6.000e-03, 9.940e-01],\n", - " [1.044e+03, 1.720e+02, 1.000e+00, 7.000e-03, 9.930e-01],\n", - " [1.044e+03, 1.730e+02, 1.000e+00, 4.100e-02, 9.590e-01],\n", - " [1.044e+03, 1.740e+02, 1.000e+00, 2.700e-02, 9.730e-01],\n", - " [1.044e+03, 1.750e+02, 1.000e+00, 2.000e-02, 9.800e-01]])" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "group_array" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "39448152d16e0217", - "metadata": { - "ExecuteTime": { - "end_time": "2024-11-14T12:22:56.541541800Z", - "start_time": "2024-11-12T16:41:43.152975Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ground truth: [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1]\n", - "Unaltered predictions: [0 0 1 0 1 0 0 0 0 1 1 0 1 0 1 1 0 1 1 0 1 0 0 0]\n", - "Silenced predictions: [0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 1 0 1 0 0 0]\n", - "Filled + silenced predictions: [0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 1 1 1 1 1 1 1 1 1]\n" - ] - } - ], - "source": [ - "pred_len = 24\n", - "label_horizon = 6\n", - "ground_truth = np.zeros(pred_len, dtype=int)\n", - "ground_truth[-label_horizon:] = 1\n", - "print(f\"Ground truth: {ground_truth}\")\n", - "\n", - "# Simulate the model predictions (for example, using random values between 0 and 1)\n", - "predictions = np.random.rand(pred_len)\n", - "threshold = 0.5\n", - "predictions = np.where(predictions >= threshold, 1, 0)\n", - "print(f\"Unaltered predictions: {predictions}\")\n", - "\n", - "# grace_horizon > label_horizon that we want to consider (i.e., the number of hours before the actual complication when the model is allowed but not required to predict 1)\n", - "grace_horizon = label_horizon * 2\n", - "# For how long should the alarm be silenced?\n", - "silencing_length = label_horizon\n", - "predictions = silence_positives(ground_truth, predictions, grace_horizon=grace_horizon, silencing_length=silencing_length)\n", - "print(f\"Silenced predictions: {predictions}\")\n", - "\n", - "# Fill gaps in the predictions: we also want to consider early predictions with the grace_horizon.\n", - "predictions = fill_gaps(predictions)\n", - "print(f\"Filled + silenced predictions: {predictions}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "a46d26884039d6d1", - "metadata": { - "ExecuteTime": { - "end_time": "2024-11-14T12:22:56.541541800Z", - "start_time": "2024-11-12T16:41:45.201Z" - } - }, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'p' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[4], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m preds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mprediction_0\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[43mp\u001b[49m\n", - "\u001b[1;31mNameError\u001b[0m: name 'p' is not defined" - ] - } - ], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "27c9b104e3a52992", - "metadata": { - "ExecuteTime": { - "end_time": "2024-11-14T12:22:56.541541800Z", - "start_time": "2024-11-12T16:43:19.523441Z" - } - }, - "outputs": [ - { - "ename": "PanicException", - "evalue": "UDF failed: data does not match the number of columns", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mPanicException\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[7], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mpreds\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgroup_by\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m# id\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmap_groups\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mgroup_df\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mpl\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mDataFrame\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgroup_df\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtime\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43mconvert_to_alarm\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgroup_df\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mground_truth\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgroup_df\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mprediction_1\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[1;32m~\\miniconda\\envs\\yaib_19\\lib\\site-packages\\polars\\dataframe\\group_by.py:302\u001b[0m, in \u001b[0;36mGroupBy.map_groups\u001b[1;34m(self, function)\u001b[0m\n\u001b[0;32m 298\u001b[0m msg \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcannot call `map_groups` when grouping by an expression\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 299\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(msg)\n\u001b[0;32m 301\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdf\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m_from_pydf(\n\u001b[1;32m--> 302\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_df\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgroup_by_map_groups\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 303\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mby\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfunction\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmaintain_order\u001b[49m\n\u001b[0;32m 304\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 305\u001b[0m )\n", - "\u001b[1;31mPanicException\u001b[0m: UDF failed: data does not match the number of columns" - ] - } - ], - "source": [ - "preds.group_by(\"# id\").map_groups(\n", - " lambda group_df: pl.DataFrame(group_df[\"time\"], convert_to_alarm(group_df[\"ground_truth\"], group_df[\"prediction_1\"]))\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "f1b7d2a0fabeeb70", - "metadata": { - "ExecuteTime": { - "end_time": "2024-11-14T12:22:56.541541800Z", - "start_time": "2024-11-12T16:42:52.394724Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "shape: (3_536, 5)
# idtimeground_truthprediction_0prediction_1
i64i64f64f64f64
4200.01.00.0
4210.01.00.0
4220.01.00.0
4230.01.00.0
4240.01.00.0
……………
10441711.00.0060.994
10441721.00.0070.993
10441731.00.0410.959
10441741.00.0270.973
10441751.00.020.98
" - ], - "text/plain": [ - "shape: (3_536, 5)\n", - "┌──────┬──────┬──────────────┬──────────────┬──────────────┐\n", - "│ # id ┆ time ┆ ground_truth ┆ prediction_0 ┆ prediction_1 │\n", - "│ --- ┆ --- ┆ --- ┆ --- ┆ --- │\n", - "│ i64 ┆ i64 ┆ f64 ┆ f64 ┆ f64 │\n", - "╞══════╪══════╪══════════════╪══════════════╪══════════════╡\n", - "│ 42 ┆ 0 ┆ 0.0 ┆ 1.0 ┆ 0.0 │\n", - "│ 42 ┆ 1 ┆ 0.0 ┆ 1.0 ┆ 0.0 │\n", - "│ 42 ┆ 2 ┆ 0.0 ┆ 1.0 ┆ 0.0 │\n", - "│ 42 ┆ 3 ┆ 0.0 ┆ 1.0 ┆ 0.0 │\n", - "│ 42 ┆ 4 ┆ 0.0 ┆ 1.0 ┆ 0.0 │\n", - "│ … ┆ … ┆ … ┆ … ┆ … │\n", - "│ 1044 ┆ 171 ┆ 1.0 ┆ 0.006 ┆ 0.994 │\n", - "│ 1044 ┆ 172 ┆ 1.0 ┆ 0.007 ┆ 0.993 │\n", - "│ 1044 ┆ 173 ┆ 1.0 ┆ 0.041 ┆ 0.959 │\n", - "│ 1044 ┆ 174 ┆ 1.0 ┆ 0.027 ┆ 0.973 │\n", - "│ 1044 ┆ 175 ┆ 1.0 ┆ 0.02 ┆ 0.98 │\n", - "└──────┴──────┴──────────────┴──────────────┴──────────────┘" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "preds" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "77488f552d1480d0", - "metadata": { - "ExecuteTime": { - "end_time": "2025-01-09T11:35:07.132903Z", - "start_time": "2025-01-09T11:35:07.129738Z" - } - }, - "outputs": [], - "source": [ - "import polars as pl\n", - "\n", - "values = pl.read_parquet(\n", - " \"/Users/robin/Documents/git/yaib_logs/mimic_demo/Mortality24/XGBClassifier/2025-01-09T12-33-43.607662/repetition_4/fold_4/explainer_values_test.parquet\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "7dbd2435574a6215", - "metadata": { - "ExecuteTime": { - "end_time": "2025-01-09T11:35:09.020311Z", - "start_time": "2025-01-09T11:35:09.016349Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "shape: (1, 296)
albalpaltastbebicarbilibili_dirbndbuncacaickckmbclcreacrpdbpfgnfio2gluhgbhrinr_ptklactlymphmapmchmchcmcvmethbmgnaneuto2satpco2…fio2_mean_histglu_mean_histhgb_mean_histhr_mean_histinr_pt_mean_histk_mean_histlact_mean_histlymph_mean_histmap_mean_histmch_mean_histmchc_mean_histmcv_mean_histmethb_mean_histmg_mean_histna_mean_histneut_mean_histo2sat_mean_histpco2_mean_histph_mean_histphos_mean_histplt_mean_histpo2_mean_histptt_mean_histresp_mean_histsbp_mean_histtemp_mean_histtnt_mean_histurine_mean_histwbc_mean_histagesexheightweightMissingIndicator_ageMissingIndicator_sexMissingIndicator_heightMissingIndicator_weight
f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32…f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32f32
0.00.0785830.00.0-0.258690.00.00.00.00.3978972.458770.00.00.00.00.00.00.00.00.00.00.00.0-0.2626310.00.00.0-3.5106580.116930.00.00.0-0.7752580.00.00.00.643269…0.0-0.565497-0.2239260.00.00.00.00.00.2006590.00.00.00.00.22341-0.4922790.00.00.00.00.00.0-5.3272160.00.00.02.4498270.0-0.5833121.3199810.00.00.00.7442510.00.00.00.0
" - ], - "text/plain": [ - "shape: (1, 296)\n", - "┌─────┬──────────┬─────┬─────┬───┬────────────────┬────────────────┬───────────────┬───────────────┐\n", - "│ alb ┆ alp ┆ alt ┆ ast ┆ … ┆ MissingIndicat ┆ MissingIndicat ┆ MissingIndica ┆ MissingIndica │\n", - "│ --- ┆ --- ┆ --- ┆ --- ┆ ┆ or_age ┆ or_sex ┆ tor_height ┆ tor_weight │\n", - "│ f32 ┆ f32 ┆ f32 ┆ f32 ┆ ┆ --- ┆ --- ┆ --- ┆ --- │\n", - "│ ┆ ┆ ┆ ┆ ┆ f32 ┆ f32 ┆ f32 ┆ f32 │\n", - "╞═════╪══════════╪═════╪═════╪═══╪════════════════╪════════════════╪═══════════════╪═══════════════╡\n", - "│ 0.0 ┆ 0.078583 ┆ 0.0 ┆ 0.0 ┆ … ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 0.0 │\n", - "└─────┴──────────┴─────┴─────┴───┴────────────────┴────────────────┴───────────────┴───────────────┘" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "values.sum()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "cac7ef7572cda8b7", - "metadata": { - "ExecuteTime": { - "end_time": "2025-01-09T11:35:13.397032Z", - "start_time": "2025-01-09T11:35:12.567741Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "# Sum the values for aggregation\n", - "aggregated_data = values.sum()\n", - "\n", - "# Convert to Pandas DataFrame for plotting\n", - "aggregated_data_pd = aggregated_data.to_pandas()\n", - "\n", - "# Plot the bar plot\n", - "plt.figure(figsize=(10, 6))\n", - "aggregated_data_pd.plot(kind=\"bar\")\n", - "plt.title(\"Aggregated Data\")\n", - "plt.xlabel(\"Columns\")\n", - "plt.ylabel(\"Sum\")\n", - "plt.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} From 0aa66c70d3556b9e1627d9f687c585fa0b1937b6 Mon Sep 17 00:00:00 2001 From: Robin van de Water Date: Mon, 23 Jun 2025 14:18:01 +0200 Subject: [PATCH 59/82] utils refactor --- icu_benchmarks/models/utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/icu_benchmarks/models/utils.py b/icu_benchmarks/models/utils.py index 9eddee20..4cf35980 100644 --- a/icu_benchmarks/models/utils.py +++ b/icu_benchmarks/models/utils.py @@ -8,6 +8,7 @@ import logging import numpy as np import torch +import csv from pytorch_lightning.loggers.logger import Logger from pytorch_lightning.utilities import rank_zero_only @@ -284,7 +285,6 @@ def get_smoothed_labels( ) -import csv def log_single_metric_to_file(metric_name: str, data_points: tuple[np.ndarray], output_file: Path) -> None: From 4a0f7256057d4c7a957a3f9fa75469b56dadaf58 Mon Sep 17 00:00:00 2001 From: Robin van de Water Date: Thu, 10 Jul 2025 11:18:32 +0200 Subject: [PATCH 60/82] data dir check --- experiments/benchmark_cass.yml | 58 ++++++++++++++++++----- icu_benchmarks/data/split_process_data.py | 2 + 2 files changed, 48 insertions(+), 12 deletions(-) diff --git a/experiments/benchmark_cass.yml b/experiments/benchmark_cass.yml index 9eecefdf..dd7148a8 100644 --- a/experiments/benchmark_cass.yml +++ b/experiments/benchmark_cass.yml @@ -9,11 +9,16 @@ command: - CassClassification - --log-dir - /sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs - - --tune +# - --tune - --wandb-sweep - -tn - SSI -# - --hp-checkpoint + - --hp-checkpoint +# - "/sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/real_life_set_ICU_and_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_1.0_horizon_6:00:00_transfer_full_2025-06-27T14:38:43/SSI/XGBClassifier/2025-06-27T17-13-28.076070/hyperparameter_tuning_logs.db" +# - "/sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/lab_set_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_0.5_horizon_6:00:00_transfer_full_2025-06-27T13:36:02/SSI/XGBClassifier/2025-06-27T17-12-03.184584/hyperparameter_tuning_logs.db" +# - "/sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/lab_set_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_1.0_horizon_6:00:00_transfer_full_2025-06-27T14:45:50/SSI/XGBClassifier/2025-06-27T17-08-55.389121/hyperparameter_tuning_logs.db" +# - "/sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/lab_set_normal_ward_SSI-3_segment_1.0_horizon_6:00:00_transfer_full_2025-06-19T19:11:23/SSI/XGBClassifier/2025-06-20T12-14-27.191590/hyperparameter_tuning_logs.db" +# - "/sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/BDA_segment_1.0_horizon_6:00:00_transfer_full_2025-06-20T16:37:14/SSI/XGBClassifier/2025-06-20T17-36-53.795313/hyperparameter_tuning_logs.db" # - /sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/real_life_normal_ward_SSI-3_segment_1.0_horizon_6:00:00_transfer_full_2025-06-03T16:05:00/SSI/XGBClassifier/2025-06-04T17-09-19.640244/hyperparameter_tuning_logs.db ## - Mortality # - --verbose @@ -23,12 +28,36 @@ command: - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_6.parquet","STATIC":"sta.parquet"' # - --explain_features # - --verbose + - --load_data_vars method: grid name: yaib_classification_benchmark parameters: data_dir: values: - - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/lab_set_ICU_SSI-3_segment_1.0_horizon_6:00:00_transfer_full_2025-06-10T20:57:18" + - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/lab_set_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_0.5_horizon_6:00:00_transfer_full_2025-07-09T16:09:03" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/lab_set_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_1.0_horizon_6:00:00_transfer_full_2025-07-08T15:24:29" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/lab_set_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_1.0_horizon_6:00:00_transfer_full_2025-07-08T12:12:15" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/lab_set_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_1.0_horizon_6:00:00_transfer_full_2025-07-07T19:04:46" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/lab_set_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_0.5_horizon_6:00:00_transfer_full_2025-07-06T15:14:29" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/lab_set_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_1.0_horizon_6:00:00_transfer_full_2025-07-06T15:06:43" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/lab_set_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_1.0_horizon_6:00:00_transfer_full_2025-07-04T13:11:42" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/lab_set_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_0.5_horizon_6:00:00_transfer_full_2025-07-04T13:30:20" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/real_life_set_ICU_and_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_1.0_horizon_6:00:00_transfer_full_2025-07-02T14:52:41" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/lab_set_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_0.5_horizon_6:00:00_transfer_full_2025-06-27T13:36:02" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/lab_set_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_1.0_horizon_6:00:00_transfer_full_2025-06-27T14:45:50" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/real_life_set_ICU_and_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_1.0_horizon_6:00:00_transfer_full_2025-06-27T14:38:43" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/real_life_set_ICU_and_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_1.0_horizon_6:00:00_transfer_full_2025-06-26T21:23:03" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/lab_set_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_1.0_horizon_6:00:00_transfer_full_2025-06-25T19:27:55" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/lab_set_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_0.5_horizon_6:00:00_transfer_full_2025-06-25T16:49:25" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/lab_set_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_0.5_horizon_6:00:00_transfer_full_2025-06-23T17:45:52" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/lab_set_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_1.0_horizon_6:00:00_transfer_full_2025-06-23T12:50:14" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/lab_set_ICU_and_normal_ward_SSI-3_POPF_Galleleck/BDA_segment_1.0_horizon_6:00:00_transfer_full_2025-06-20T16:37:14" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/lab_set_ICU_SSI-3_POPF_Galleleck/BDA_segment_1.0_horizon_6:00:00_transfer_full_2025-06-20T14:51:44" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/lab_set_normal_ward_SSI-3_POPF_Galleleck/BDA_segment_1.0_horizon_6:00:00_transfer_full_2025-06-20T15:17:09" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/lab_set_normal_ward_SSI-3_segment_1.0_horizon_6:00:00_transfer_full_2025-06-19T15:30:35" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/lab_set_ICU_SSI-3_segment_1.0_horizon_6:00:00_transfer_full_2025-06-19T12:07:34" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/lab_set_ICU_and_normal_ward_SSI-3_segment_1.0_horizon_6:00:00_transfer_full_2025-06-19T13:53:54" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/lab_set_ICU_SSI-3_segment_1.0_horizon_6:00:00_transfer_full_2025-06-10T20:57:18" # - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/lab_set_ICU_and_normal_ward_SSI-3_segment_1.0_horizon_6:00:00_transfer_full_2025-06-10T16:12:39" # - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/real_life_normal_ward_SSI-3_segment_1.0_horizon_6:00:00_transfer_full_2025-06-03T16:05:00" # - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/normal_ward_SSI-3_segment_1.0_horizon_6:00:00_transfer_full_2025-05-27T23:34:00" @@ -69,15 +98,20 @@ parameters: # - [static] # 1 missing modality - "all" - - [ copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, cat_medications, cat_clinical_notes, - wearable_activity, wearable_ppgfeature, wearable_ppgembedding, wearable_core, static_retro_icu, - static_retro_pre_intra, static ] # not missing any - - [ copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, cat_clinical_notes, wearable_core, static ] # missing: wearable (activity, ppgfeature, ppgembedding) - - [ ishmed_lab_numeric, cat_clinical_notes, wearable_activity, wearable_ppgfeature, wearable_ppgembedding, wearable_core, static ] # missing: copra - - [ copra_observations, copra_scores, copra_fluid_balance, cat_clinical_notes, wearable_activity, wearable_ppgfeature, wearable_ppgembedding, wearable_core, static ] # 1 missing: ishmed (lab) - - [ copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, wearable_activity, wearable_ppgfeature, wearable_ppgembedding, wearable_core, static ] # missing cat (notes, med embeddings) - - [ copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, cat_clinical_notes, wearable_activity, wearable_ppgfeature, wearable_ppgembedding, static ] # 1 missing: wearable_core - - [ copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, cat_clinical_notes, wearable_activity, wearable_ppgfeature, wearable_ppgembedding, wearable_core ] # 1 missing: static +# - [ wearable_ppgembedding, static ] +# - [ wearable_activity, static ] +# - [ wearable_ppgfeature, static ] +# - [ wearable_core, static] +# - [ copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, cat_medications, cat_clinical_notes, static, static_retro_icu, static_retro_pre_intra ] # missing: wearable (activity, ppgfeature, ppgembedding) +# - [ copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, cat_medications, cat_clinical_notes, +# wearable_activity, wearable_ppgfeature, wearable_ppgembedding, wearable_core, static_retro_icu, +# static_retro_pre_intra, static ] # not missing any +# - [ copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, cat_clinical_notes, wearable_core, static ] # missing: wearable (activity, ppgfeature, ppgembedding) +# - [ ishmed_lab_numeric, cat_clinical_notes, wearable_activity, wearable_ppgfeature, wearable_ppgembedding, wearable_core, static ] # missing: copra +# - [ copra_observations, copra_scores, copra_fluid_balance, cat_clinical_notes, wearable_activity, wearable_ppgfeature, wearable_ppgembedding, wearable_core, static ] # 1 missing: ishmed (lab) +# - [ copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, wearable_activity, wearable_ppgfeature, wearable_ppgembedding, wearable_core, static ] # missing cat (notes, med embeddings) +# - [ copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, cat_clinical_notes, wearable_activity, wearable_ppgfeature, wearable_ppgembedding, static ] # 1 missing: wearable_core +# - [ copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, cat_clinical_notes, cat_medications, wearable_activity, wearable_ppgfeature, wearable_ppgembedding, wearable_core, static_retro_icu, static_retro_pre_intra] # 1 missing: static file_names: values: # - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_3.parquet","STATIC":"sta.parquet"' diff --git a/icu_benchmarks/data/split_process_data.py b/icu_benchmarks/data/split_process_data.py index ccdb62b5..fc1ff38a 100644 --- a/icu_benchmarks/data/split_process_data.py +++ b/icu_benchmarks/data/split_process_data.py @@ -149,6 +149,8 @@ def preprocess_data( # Read parquet files into dataframes and remove the parquet file from memory logging.info(f"Loading data from directory {data_dir.absolute()}") + if not data_dir.exists(): + raise FileNotFoundError(f"Data directory {data_dir} does not exist. Please check the path.") data: dict[str, pl.DataFrame] = { f: pl.read_parquet(data_dir / file_names[f]) for f in file_names.keys() if os.path.exists(data_dir / file_names[f]) } From 2546b2747dc64e5e0aea26cc915e2e4287b87c57 Mon Sep 17 00:00:00 2001 From: Robin van de Water Date: Thu, 10 Jul 2025 11:22:04 +0200 Subject: [PATCH 61/82] allow preprocessing to pass if excluding modalities and failsafe for row indicator --- icu_benchmarks/data/loader.py | 2 +- icu_benchmarks/data/preprocessor.py | 43 +++++++++++++++++++++++------ 2 files changed, 36 insertions(+), 9 deletions(-) diff --git a/icu_benchmarks/data/loader.py b/icu_benchmarks/data/loader.py index 39ff59ca..0023506c 100644 --- a/icu_benchmarks/data/loader.py +++ b/icu_benchmarks/data/loader.py @@ -178,7 +178,7 @@ def get_data_and_labels(self) -> tuple[np.ndarray, np.ndarray, np.ndarray]: logging.debug(f"labels shape: {labels.shape}") rep = rep.to_numpy().astype(float) rep = rep[:, 1:] - if self.vars["SEQUENCE"] in self.row_indicators: + if self.vars["SEQUENCE"] in self.row_indicators and self.row_indicators[self.vars["SEQUENCE"]].dtype == pl.Duration: self.row_indicators = self.row_indicators.with_columns(pl.col(self.vars["SEQUENCE"]).dt.total_hours()) return rep, labels, self.row_indicators.to_numpy() diff --git a/icu_benchmarks/data/preprocessor.py b/icu_benchmarks/data/preprocessor.py index dbb5b09b..7ea35bd1 100644 --- a/icu_benchmarks/data/preprocessor.py +++ b/icu_benchmarks/data/preprocessor.py @@ -67,6 +67,24 @@ def set_imputation_model(self, imputation_model): update_wandb_config({"imputation_model": self.imputation_model.__class__.__name__}) + def vars_selection(self, input_variables, segment: str) -> Union[str, list[str]]: + if input_variables[segment] is None or len(input_variables[segment]) == 0: + logging.warning("No dynamic variables provided. Skipping dynamic preprocessing.") + return [] # Return empty list if no variables are provided + vars_to_apply: Union[str, list[str]] + if self.vars_to_exclude is not None: + # Exclude vars_to_exclude from missing indicator/ feature generation + vars_to_apply = list(set(input_variables[segment]) - set(self.vars_to_exclude)) + logging.info(f"Excluding {len(self.vars_to_exclude)} : " + f"{self.vars_to_exclude if len(self.vars_to_exclude) < 10 else f'{self.vars_to_exclude[:10]}...'}...") + if len(vars_to_apply) == 0: + logging.warning( + f"No variables left after excluding vars_to_exclude: {self.vars_to_exclude} for segment {segment}. " + "Skipping preprocessing for this segment." + ) + else: + vars_to_apply = input_variables[segment] + return vars_to_apply @gin.configurable("base_classification_preprocessor") class PolarsClassificationPreprocessor(Preprocessor): @@ -178,6 +196,9 @@ def apply( return data def _process_static(self, data: dict[str, dict[str, pl.DataFrame]], vars: dict[str, Union[str, list[str]]]): + vars_to_apply = self.vars_selection(input_variables=vars, segment=DataSegment.dynamic) + if len(vars_to_apply) == 0: + return data sta_rec = Recipe(data[DataSplit.train][DataSegment.static], [], vars[DataSegment.static]) sta_rec.add_step(StepSklearn(MissingIndicator(features="all"), sel=all_of(vars[DataSegment.static]), in_place=False)) if self.scaling: @@ -211,6 +232,9 @@ def _model_impute(self, data: pd.DataFrame, group: Optional[str] = None): return data def _process_dynamic(self, data: dict[str, dict[str, pl.DataFrame]], vars: dict[str, Union[str, list[str]]]): + vars_to_apply = self.vars_selection(input_variables=vars, segment=DataSegment.dynamic) + if len(vars_to_apply) == 0: + return data dyn_rec = Recipe( data[DataSplit.train][DataSegment.dynamic], [], vars[DataSegment.dynamic], vars["GROUP"], vars["SEQUENCE"] ) @@ -218,14 +242,6 @@ def _process_dynamic(self, data: dict[str, dict[str, pl.DataFrame]], vars: dict[ dyn_rec.add_step(StepScale(sel=all_numeric_predictors(backend=recipys.constants.Backend.POLARS))) if self.imputation_model is not None: dyn_rec.add_step(StepImputeModel(model=self.model_impute, sel=all_of(vars[DataSegment.dynamic]))) - - vars_to_apply: Union[str, list[str]] - if self.vars_to_exclude is not None: - # Exclude vars_to_exclude from missing indicator/ feature generation - vars_to_apply = list(set(vars[DataSegment.dynamic]) - set(self.vars_to_exclude)) - # logging.info(f"Excluding {len(self.vars_to_exclude)} : {self.vars_to_exclude}") - else: - vars_to_apply = vars[DataSegment.dynamic] dyn_rec.add_step(StepSklearn(MissingIndicator(features="all"), sel=all_of(vars_to_apply), in_place=False)) dyn_rec.add_step(StepImputeFill(strategy="forward")) dyn_rec.add_step(StepImputeFill(strategy="zero")) @@ -235,6 +251,8 @@ def _process_dynamic(self, data: dict[str, dict[str, pl.DataFrame]], vars: dict[ # logging.info(f"Data columns: {len(data[Split.train][Segment.dynamic].columns)} -> old columns: {len(old_columns)}, added columns: {set(data[Split.train][Segment.dynamic].columns) - set(old_columns)}") return data + + def _dynamic_feature_generation(self, data, dynamic_vars): logging.debug("Adding dynamic feature generation.") data.add_step(StepHistorical(sel=dynamic_vars, fun=Accumulator.MIN, suffix="min_hist")) @@ -408,6 +426,9 @@ def apply( def _process_static( self, data: dict[str, dict[str, pd.DataFrame]], vars: dict[str, Union[str, list[str]]] ) -> dict[str, dict[str, pd.DataFrame]]: + vars_to_apply = self.vars_selection(input_variables=vars, segment=DataSegment.static) + if len(vars_to_apply) == 0: + return data sta_rec = Recipe(data[DataSplit.train][DataSegment.static], [], vars[DataSegment.static]) if self.scaling: sta_rec.add_step(StepScale()) @@ -443,9 +464,15 @@ def _model_impute(self, data: pd.DataFrame, group: Optional[str] = None) -> pd.D def _process_dynamic( self, data: dict[str, dict[str, pd.DataFrame]], vars: dict[str, Union[str, list[str]]] ) -> dict[str, dict[str, pd.DataFrame]]: + vars_to_apply = self.vars_selection(input_variables=vars, segment=DataSegment.dynamic) + if len(vars_to_apply) == 0: + return data dyn_rec = Recipe( data[DataSplit.train][DataSegment.dynamic], [], vars[DataSegment.dynamic], vars["GROUP"], vars["SEQUENCE"] ) + vars_to_apply = self.vars_selection(input_variables=vars, segment=DataSegment.dynamic) + if len(vars_to_apply) == 0: + return data if self.scaling: dyn_rec.add_step(StepScale()) if self.imputation_model is not None: From 95ff49d8a3bf5ef50403e881e69205989a265be3 Mon Sep 17 00:00:00 2001 From: Robin van de Water Date: Tue, 15 Jul 2025 14:27:08 +0200 Subject: [PATCH 62/82] adding website --- .github/workflows/ci.yml | 33 -- configs/prediction_models/common/MLTuning.gin | 5 +- docs/Makefile | 20 + docs/make.bat | 35 ++ docs/requirements.txt | 4 + docs/source/adding_models.rst | 226 +++++++++++ docs/source/api.rst | 122 ++++++ docs/source/conf.py | 72 ++++ docs/source/development.rst | 55 +++ docs/source/imputation_methods.rst | 100 +++++ docs/source/index.rst | 372 ++++++++++++++++++ icu_benchmarks/data/preprocessor.py | 2 +- 12 files changed, 1010 insertions(+), 36 deletions(-) delete mode 100644 .github/workflows/ci.yml create mode 100644 docs/Makefile create mode 100644 docs/make.bat create mode 100644 docs/requirements.txt create mode 100644 docs/source/adding_models.rst create mode 100644 docs/source/api.rst create mode 100644 docs/source/conf.py create mode 100644 docs/source/development.rst create mode 100644 docs/source/imputation_methods.rst create mode 100644 docs/source/index.rst diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml deleted file mode 100644 index 959ba588..00000000 --- a/.github/workflows/ci.yml +++ /dev/null @@ -1,33 +0,0 @@ -name: Ruff CI - -on: - push: - branches: - - main - - development - pull_request: - branches: - - main - - development - -jobs: - lint: - runs-on: ubuntu-latest - - steps: - - name: Checkout code - uses: actions/checkout@v3 - - - name: Set up Python - uses: actions/setup-python@v4 - with: - python-version: "3.10.17" - - - name: Install dependencies - run: | - python -m pip install --upgrade pip - pip install ruff - - - name: Run Ruff - run: | - ruff check --line-length 127 --statistics diff --git a/configs/prediction_models/common/MLTuning.gin b/configs/prediction_models/common/MLTuning.gin index 1334ebd7..51361f39 100644 --- a/configs/prediction_models/common/MLTuning.gin +++ b/configs/prediction_models/common/MLTuning.gin @@ -1,5 +1,6 @@ # Hyperparameter tuner settings for classical Machine Learning. tune_hyperparameters.scopes = ["model"] tune_hyperparameters.n_initial_points = 5 -tune_hyperparameters.n_calls = 10 -tune_hyperparameters.folds_to_tune_on = 5 \ No newline at end of file +tune_hyperparameters.n_calls = 100 +tune_hyperparameters.folds_to_tune_on = 1 +tune_hyperparameters.repetitions_to_tune_on = 5 \ No newline at end of file diff --git a/docs/Makefile b/docs/Makefile new file mode 100644 index 00000000..d0c3cbf1 --- /dev/null +++ b/docs/Makefile @@ -0,0 +1,20 @@ +# Minimal makefile for Sphinx documentation +# + +# You can set these variables from the command line, and also +# from the environment for the first two. +SPHINXOPTS ?= +SPHINXBUILD ?= sphinx-build +SOURCEDIR = source +BUILDDIR = build + +# Put it first so that "make" without argument is like "make help". +help: + @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + +.PHONY: help Makefile + +# Catch-all target: route all unknown targets to Sphinx using the new +# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). +%: Makefile + @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) diff --git a/docs/make.bat b/docs/make.bat new file mode 100644 index 00000000..dc1312ab --- /dev/null +++ b/docs/make.bat @@ -0,0 +1,35 @@ +@ECHO OFF + +pushd %~dp0 + +REM Command file for Sphinx documentation + +if "%SPHINXBUILD%" == "" ( + set SPHINXBUILD=sphinx-build +) +set SOURCEDIR=source +set BUILDDIR=build + +%SPHINXBUILD% >NUL 2>NUL +if errorlevel 9009 ( + echo. + echo.The 'sphinx-build' command was not found. Make sure you have Sphinx + echo.installed, then set the SPHINXBUILD environment variable to point + echo.to the full path of the 'sphinx-build' executable. Alternatively you + echo.may add the Sphinx directory to PATH. + echo. + echo.If you don't have Sphinx installed, grab it from + echo.https://www.sphinx-doc.org/ + exit /b 1 +) + +if "%1" == "" goto help + +%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% +goto end + +:help +%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% + +:end +popd diff --git a/docs/requirements.txt b/docs/requirements.txt new file mode 100644 index 00000000..a4401b59 --- /dev/null +++ b/docs/requirements.txt @@ -0,0 +1,4 @@ +sphinx +sphinx-rtd-theme +sphinx-autoapi +sphinx-autobuild \ No newline at end of file diff --git a/docs/source/adding_models.rst b/docs/source/adding_models.rst new file mode 100644 index 00000000..e445a574 --- /dev/null +++ b/docs/source/adding_models.rst @@ -0,0 +1,226 @@ +Adding new models to YAIB +========================== + +Example +------- + +We refer to the page `adding a new model `_ for detailed instructions on adding new models. +We allow prediction models to be easily added and integrated into a Pytorch Lightning module. This +incorporates advanced logging and debugging capabilities, as well as +built-in parallelism. Our interface derives from the `BaseModule `_. + +Adding a model consists of three steps: + +1. Add a model through the existing ``MLPredictionWrapper`` or ``DLPredictionWrapper``. +2. Add a GIN config file to bind hyperparameters. +3. Execute YAIB using a simple command. + +This folder contains everything you need to add a model to YAIB. +Putting the ``RNN.gin`` file in ``configs/prediction_models`` and the ``rnn.py`` file into ``icu_benchmarks/models`` allows you to run the model fully. + +.. code-block:: bash + + icu-benchmarks train \ + -d demo_data/mortality24/mimic_demo \ # Insert cohort dataset here + -n mimic_demo \ + -t BinaryClassification \ # Insert task name here + -tn Mortality24 \ + --log-dir ../yaib_logs/ \ + -m RNN \ # Insert model here + -s 2222 \ + -l ../yaib_logs/ \ + --tune + +Adding more models +================== + +Regular ML +---------- + +For standard Scikit-Learn type models (e.g., LGBM), one can +simply wrap ``MLPredictionWrapper`` the function with minimal code +overhead. Many ML (and some DL) models can be incorporated this way, requiring minimal code additions. See below. + +.. code-block:: python + :caption: Example ML model definition + + @gin.configurable + class RFClassifier(MLWrapper): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self.model = self.model_args() + + @gin.configurable(module="RFClassifier") + def model_args(self, *args, **kwargs): + return RandomForestClassifier(*args, **kwargs) + +Adding DL models +---------------- + +It is relatively straightforward to add new Pytorch models to YAIB. We first provide a standard RNN-model which needs no extra components. Then, we show the implementation of the Temporal Fusion Transformer model. + +Standard RNN-model +~~~~~~~~~~~~~~~~~~ + +The definition of dl models can be done by creating a subclass from the +``DLPredictionWrapper``, inherits the standard methods needed for +training dl learning models. Pytorch Lightning significantly reduces the code +overhead. + +.. code-block:: python + :caption: Example DL model definition + + @gin.configurable + class RNNet(DLPredictionWrapper): + """Torch standard RNN model""" + + def __init__(self, input_size, hidden_dim, layer_dim, num_classes, *args, **kwargs): + super().__init__( + input_size=input_size, hidden_dim=hidden_dim, layer_dim=layer_dim, num_classes=num_classes, *args, **kwargs + ) + self.hidden_dim = hidden_dim + self.layer_dim = layer_dim + self.rnn = nn.RNN(input_size[2], hidden_dim, layer_dim, batch_first=True) + self.logit = nn.Linear(hidden_dim, num_classes) + + def init_hidden(self, x): + h0 = x.new_zeros(self.layer_dim, x.size(0), self.hidden_dim) + return h0 + + def forward(self, x): + h0 = self.init_hidden(x) + out, hn = self.rnn(x, h0) + pred = self.logit(out) + return pred + +Adding a SOTA model: Temporal Fusion Transformer +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +There are two main questions when you want to add a more complex model: + +* *Do you want to manually define the model or use an existing library?* This might require adapting the ``DLPredictionWrapper``. +* *Does the model expect the data to be in a certain format?* This might require adapting the ``PredictionDataset``. + +By adapting, we mean creating a new subclass that inherits most functionality to avoid code duplication, is future-proof, and follows good coding practices. + +First, you can add modules to ``models/layers.py`` to use them for your model. + +.. code-block:: python + :caption: Example building block + + class StaticCovariateEncoder(nn.Module): + """ + Network to produce 4 context vectors to enrich static variables + Variable selection Network --> GRNs + """ + + def __init__(self, num_static_vars, hidden, dropout): + super().__init__() + self.vsn = VariableSelectionNetwork(hidden, dropout, num_static_vars) + self.context_grns = nn.ModuleList([GRN(hidden, hidden, dropout=dropout) for _ in range(4)]) + + def forward(self, x: Tensor) -> Tuple[Tensor, Tensor, Tensor, Tensor]: + variable_ctx, sparse_weights = self.vsn(x) + + # Context vectors: + # variable selection context + # enrichment context + # state_c context + # state_h context + cs, ce, ch, cc = [m(variable_ctx) for m in self.context_grns] + + return cs, ce, ch, cc + +Note that we can create modules out of modules as well. + +Adapting the ``DLPredictionWrapper`` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The next step is to use the building blocks defined in ``layers.py`` or modules from an existing library to add to the model in ``models/dl_models.py``. In this case, we use the Pytorch-forecasting library (https://github.com/jdb78/pytorch-forecasting): + +.. code-block:: python + :caption: Example DL model definition + + class TFTpytorch(DLPredictionWrapper): + + supported_run_modes = [RunMode.classification, RunMode.regression] + + def __init__(self, dataset, hidden, dropout, n_heads, dropout_att, lr, optimizer, num_classes, *args, **kwargs): + super().__init__(lr=lr, optimizer=optimizer, *args, **kwargs) + self.model = TemporalFusionTransformer.from_dataset( + dataset=dataset) + self.logit = nn.Linear(7, num_classes) + + + def forward(self, x): + out = self.model(x) + pred = self.logit(out["prediction"]) + return pred + +Adapting the ``PredictionDataset`` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Some models require an adjusted dataloader to facilitate, for example, explainability methods. In this case, changes need to be made to the ``data/loader.py`` file to ensure the data loader returns the data in the correct format. +This can be done by creating a class that inherits from ``PredictionDataset`` and editing the ``get_item`` method. + +.. code-block:: python + :caption: Example custom dataset definition + + @gin.configurable("PredictionDatasetTFT") + class PredictionDatasetTFT(PredictionDataset): + def __init__(self, *args, ram_cache: bool = True, **kwargs): + super().__init__(*args, ram_cache=True, **kwargs) + + def __getitem__(self, idx: int) -> Tuple[Tensor, Tensor, Tensor]: + """Function to sample from the data split of choice. Used for TFT. + The data needs to be given to the model in the following order + [static categorical, static continuous,known categorical,known continuous, observed categorical, observed continuous,target,id] + +Then, you must check ``models/wrapper.py``, particularly the ``step_fn`` method, to ensure the data is correctly transferred to the device. + +Adding the model config GIN file +================================= + +To define hyperparameters for each model in a standardized manner, we use GIN-config. We need to specify a GIN file to bind the parameters to train and optimize this model from a choice of hyperparameters. Note that we can use modifiers for the optimizer (e.g, Adam optimizer) and ranges that we can specify in rounded brackets "()" . Square brackets, "[]", result in a random choice where the variable is uniformly sampled. + +.. code-block:: gin + + # Hyperparameters for TFT model. + + # Common settings for DL models + include "configs/prediction_models/common/DLCommon.gin" + + # Optimizer params + train_common.model = @TFT + + optimizer/hyperparameter.class_to_tune = @Adam + optimizer/hyperparameter.weight_decay = 1e-6 + optimizer/hyperparameter.lr = (1e-5, 3e-4) + + # Encoder params + model/hyperparameter.class_to_tune = @TFT + model/hyperparameter.encoder_length = 24 + model/hyperparameter.hidden = 256 + model/hyperparameter.num_classes = %NUM_CLASSES + model/hyperparameter.dropout = (0.0, 0.4) + model/hyperparameter.dropout_att = (0.0, 0.4) + model/hyperparameter.n_heads =4 + model/hyperparameter.example_length=25 + +Training the model +================== + +After these steps, your model should be trainable with the following command: + +.. code-block:: bash + + icu-benchmarks train \ + -d demo_data/mortality24/mimic_demo \ # Insert cohort dataset here + -n mimic_demo \ + -t BinaryClassification \ # Insert task name here + -tn Mortality24 \ + --log-dir ../yaib_logs/ \ + -m TFT \ # Insert model here + -s 2222 \ + -l ../yaib_logs/ \ + --tune \ No newline at end of file diff --git a/docs/source/api.rst b/docs/source/api.rst new file mode 100644 index 00000000..45c57ae3 --- /dev/null +++ b/docs/source/api.rst @@ -0,0 +1,122 @@ +API Reference +============= + +This page contains the API reference for YAIB (Yet Another ICU Benchmark). + +Core Modules +------------ + +.. automodule:: icu_benchmarks + :members: + :undoc-members: + :show-inheritance: + +Run Utilities +~~~~~~~~~~~~~ + +.. automodule:: icu_benchmarks.run + :members: + :undoc-members: + :show-inheritance: + +.. automodule:: icu_benchmarks.run_utils + :members: + :undoc-members: + :show-inheritance: + +Utilities +~~~~~~~~~ + +.. automodule:: icu_benchmarks.utils + :members: + :undoc-members: + :show-inheritance: + +.. automodule:: icu_benchmarks.wandb_utils + :members: + :undoc-members: + :show-inheritance: + +Cross Validation +~~~~~~~~~~~~~~~~ + +.. automodule:: icu_benchmarks.cross_validation + :members: + :undoc-members: + :show-inheritance: + +Constants +~~~~~~~~~ + +.. automodule:: icu_benchmarks.constants + :members: + :undoc-members: + :show-inheritance: + +Data Processing +--------------- + +.. automodule:: icu_benchmarks.data + :members: + :undoc-members: + :show-inheritance: + +Models +------ + +.. automodule:: icu_benchmarks.models + :members: + :undoc-members: + :show-inheritance: + +Imputation +---------- + +.. automodule:: icu_benchmarks.imputation + :members: + :undoc-members: + :show-inheritance: + +Hyperparameter Tuning +---------------------- + +.. automodule:: icu_benchmarks.tuning + :members: + :undoc-members: + :show-inheritance: + +Scripts +------- + +Evaluation Scripts +~~~~~~~~~~~~~~~~~~ + +.. automodule:: scripts.evaluate_results + :members: + :undoc-members: + :show-inheritance: + +Plotting Scripts +~~~~~~~~~~~~~~~~ + +.. automodule:: scripts.plotting + :members: + :undoc-members: + :show-inheritance: + +Sample Usage +~~~~~~~~~~~~ + +.. automodule:: scripts.sample_usage + :members: + :undoc-members: + :show-inheritance: + +Sweep Configurations +~~~~~~~~~~~~~~~~~~~~ + +.. automodule:: scripts.sweep_configs + :members: + :undoc-members: + :show-inheritance: + diff --git a/docs/source/conf.py b/docs/source/conf.py new file mode 100644 index 00000000..b2534226 --- /dev/null +++ b/docs/source/conf.py @@ -0,0 +1,72 @@ +import os +import sys +sys.path.insert(0, os.path.abspath('../../')) # Add your project root to Python path +# Configuration file for the Sphinx documentation builder. +# +# For the full list of built-in configuration values, see the documentation: +# https://www.sphinx-doc.org/en/master/usage/configuration.html + +# -- Project information ----------------------------------------------------- +# https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information + +project = 'Yet Another ICU Benchmark' +copyright = '2025, Robin P. van de Water, Hendrik Schmidt, Patrick Rockenschaub, MIT License' +author = 'Robin P. van de Water, Hendrik Schmidt, Patrick Rockenschaub' +release = '1.0' + +# -- General configuration --------------------------------------------------- +# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration + +extensions = ['sphinx.ext.autodoc'] + +templates_path = ['_templates'] +exclude_patterns = [] + + + +# -- Options for HTML output ------------------------------------------------- +# https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output + +# Theme configuration +html_theme = 'sphinx_rtd_theme' +# Logo configuration +html_logo = '../figures/yaib_logo.png' # Path to your logo file +html_favicon = '../figures/yaib_logo.png' # Optional: favicon +# Theme options +html_theme_options = { + 'canonical_url': '', + 'analytics_id': '', + 'logo_only': False, + 'display_version': True, + 'prev_next_buttons_location': 'bottom', + 'style_external_links': False, + 'vcs_pageview_mode': '', + 'style_nav_header_background': 'white', + # Toc options + 'collapse_navigation': True, + 'sticky_navigation': True, + 'navigation_depth': 4, + 'includehidden': True, + 'titles_only': False +} + +html_static_path = ['_static'] + +extensions = [ + 'sphinx.ext.autodoc', + 'sphinx.ext.autosummary', + 'sphinx.ext.viewcode', + 'sphinx.ext.napoleon', # For Google/NumPy style docstrings +] + +# Autodoc settings +autodoc_default_options = { + 'members': True, + 'member-order': 'bysource', + 'special-members': '__init__', + 'undoc-members': True, + 'exclude-members': '__weakref__' +} + +# Generate autosummary even if no references +autosummary_generate = True \ No newline at end of file diff --git a/docs/source/development.rst b/docs/source/development.rst new file mode 100644 index 00000000..37f4d52a --- /dev/null +++ b/docs/source/development.rst @@ -0,0 +1,55 @@ +Development +=========== + +YAIB is in active development. The following sections could be relevant for adding new code to our repository. + +Libraries +--------- + +The following libraries are important to the operation of YAIB: + +Core Dependencies +~~~~~~~~~~~~~~~~~ + +- `Pandas `_: Popular data structure framework. +- `ReciPys `_: A modular preprocessing package for Pandas dataframes. +- `Pytorch `_: An open source machine learning framework for deep learning applications. +- `Pytorch Lightning `_: A lightweight Pytorch wrapper for AI research. +- `Pytorch Ignite `_: Library for training and evaluating neural networks in Pytorch. +- `Cuda Toolkit `_: GPU acceleration used for deep learning models. +- `Scikit-learn `_: Machine learning library. +- `Scikit-optimize `_: Used for Bayesian optimization. +- `LightGBM `_: Gradient boosting framework. +- `GIN `_: Provides a lightweight configuration framework for Python. +- `Wandb `_: A tool for visualizing and tracking machine learning experiments. +- `Pytest `_: A testing framework for Python. + +Imputation Libraries +~~~~~~~~~~~~~~~~~~~~ + +- `HyperImpute `_: Imputation library for MissForest and GAIN. +- `PyPOTS `_: Imputation library. + +Running Tests +------------- + +To run the test suite: + +.. code-block:: bash + + python -m pytest ./tests/recipes + coverage run -m pytest ./tests/recipes + + # then use either of the following + coverage report + coverage html + +Code Formatting and Linting +---------------------------- + +For development purposes, we use the ``Black`` package to autoformat our code and a ``Flake8`` linting/CI check: + +.. code-block:: bash + + black . -l 127 + flake8 . --count --max-complexity=14 --max-line-length=127 --statistics \ No newline at end of file diff --git a/docs/source/imputation_methods.rst b/docs/source/imputation_methods.rst new file mode 100644 index 00000000..4b04c553 --- /dev/null +++ b/docs/source/imputation_methods.rst @@ -0,0 +1,100 @@ +=========================== +Adding New Imputation Models +============================ + +To add another imputation model, you have to create a class that inherits from ``ImputationWrapper`` in ``icu_benchmarks.models.wrappers``. Your model class should look like this: + +.. code-block:: python + + from icu_benchmarks.models.wrappers import ImputationWrapper + import gin + + + @gin.configurable("newmethod") + class New_Method(ImputationWrapper): + # adjust this accordingly + # if true, the method is trained iteratively (like a deep learning model). + # If false it receives the complete training data to perform a fit on + requires_backprop = False + + def __init__(self, *args, model_arg1, model_arg2, **kwargs): + super().__init__(*args, **kwargs) + # define your new model here + self.model = ... + + # the following method has to be implemented for all methods + def forward(self, amputated_values, amputation_mask): + imputated_values = amputated_values + ... + return imputated_values + + # implement this, if needs_fit is true, otherwise you can leave it out. + # this method receives the complete input training data to perform a fit on. + def fit(self, train_data): + ... + +Configuration File +------------------ + +You also need to create a gin configuration file in the `configs/imputation` directory, +named `newmethod.gin` after the name that was entered into the ``gin.configurable`` decorator call. + +Your ``.gin`` file should look like this: + +.. code-block:: python + + import gin.torch.external_configurables + import icu_benchmarks.models.wrappers + import icu_benchmarks.models.dl_models + import icu_benchmarks.models.utils + import icu_benchmarks.data.split_process_data + # import here the file you created your New_Method class in + import icu_benchmarks.imputation.new_model + + # Train params + train_common.model = + + + @newmethod # change this into the name of the gin configuration file + + # here you can set some training parameters + + + train_common.epochs = 1000 + train_common.batch_size = 64 + train_common.patience = 10 + train_common.min_delta = 1e-4 + train_common.use_wandb = True + + ImputationWrapper.optimizer = + + + @Adam + + + ImputationWrapper.lr_scheduler = "cosine" + + # Optimizer params + Adam.lr = 3e-4 + Adam.weight_decay = 1e-6 + + # here you can set the model parameters you want to configure + newmethod.model_arg1 = 20 + newmethod.model_arg2 = 15 + +Running Training +---------------- + +You can find further configurations in the ``Dataset_Imputation.gin`` file in the `configs/tasks/` directory. +To start a training of an imputation method with the newly created imputation method, use the following command: + +.. code-block:: bash + + python run.py train -d path/to/preprocessed/data/files -n dataset_name -t Dataset_Imputation -m newmethod + +For the dataset path please enter the path to the directory where the preprocessed `dyn.parquet`, `outc.parquet` and `sta.parquet` are stored. The ``dataset_name`` is only for logging purposes and breaks nothing if not set correctly. Keep in mind to use the name of the ``.gin`` config file created for the imputation method as model name for the ``-m`` parameter. + +Examples and References +----------------------- + +For reference for a deep learning based imputation method you can take a look at how the ``MLPImputation`` method is implemented in `icu_benchmarks/imputation/mlp.py` with its `MLP.gin` configuration file. For reference regarding methods with ``needs_fit=True``, take a look at the `icu_benchmarks/imputation/baselines.py` file with several baseline implementations and their corresponding config files in `configs/imputation/`. \ No newline at end of file diff --git a/docs/source/index.rst b/docs/source/index.rst new file mode 100644 index 00000000..c786a319 --- /dev/null +++ b/docs/source/index.rst @@ -0,0 +1,372 @@ +.. image:: https://github.com/rvandewater/YAIB/blob/development/docs/figures/yaib_logo.png?raw=true + :alt: YAIB logo + +🧪 Yet Another ICU Benchmark +============================ + +.. image:: https://github.com/rvandewater/YAIB/actions/workflows/ci.yml/badge.svg?branch=development + :target: https://github.com/rvandewater/YAIB/actions/workflows/ci.yml + :alt: CI + +.. image:: https://img.shields.io/badge/code%20style-black-000000.svg + :target: https://github.com/psf/black + :alt: Black + +.. image:: https://img.shields.io/badge/platform-linux--64%20|%20win--64%20|%20osx--64-lightgrey + :alt: Platform + +.. image:: https://img.shields.io/badge/arXiv-2306.05109-b31b1b.svg + :target: http://arxiv.org/abs/2306.05109 + :alt: arXiv + +.. image:: https://img.shields.io/pypi/v/yaib.svg + :target: https://pypi.python.org/pypi/yaib/ + :alt: PyPI version shields.io + +.. image:: https://img.shields.io/badge/-Python_3.10-blue?logo=python&logoColor=white + :target: https://www.python.org/downloads/release/python-3100/ + :alt: python + +.. image:: https://img.shields.io/badge/PyTorch_2.0+-ee4c2c?logo=pytorch&logoColor=white + :target: https://pytorch.org/get-started/locally/ + :alt: pytorch + +.. image:: https://img.shields.io/badge/-Lightning_2.0+-792ee5?logo=pytorchlightning&logoColor=white + :target: https://pytorchlightning.ai/ + :alt: lightning + +.. image:: https://img.shields.io/badge/license-MIT-green.svg + :target: LICENSE + :alt: License + +Yet another ICU benchmark (YAIB) provides a framework for doing clinical machine learning experiments on Intensive Care Unit +(ICU) EHR data. + + ++----------------+---------------------------------------------------------+--------------------------------------------------------+--------------------------------------------------------+-----------------------------------------------------+ +| **Dataset** | `MIMIC-III `__ | `eICU-CRD `__ | `HiRID `__ | `AUMCdb `__ | +| | / `IV `__ | | | | ++================+=========================================================+========================================================+========================================================+=====================================================+ +| **Admissions** | 40k / 73k | 200k | 33k | 23k | ++----------------+---------------------------------------------------------+--------------------------------------------------------+--------------------------------------------------------+-----------------------------------------------------+ +| **Version** | v1.4 / v2.2 | v2.0 | v1.1.1 | v1.0.2 | ++----------------+---------------------------------------------------------+--------------------------------------------------------+--------------------------------------------------------+-----------------------------------------------------+ +| **Frequency** | 1 hour | 5 minutes | 2 / 5 minutes | up to 1 minute | +| (time-series) | | | | | ++----------------+---------------------------------------------------------+--------------------------------------------------------+--------------------------------------------------------+-----------------------------------------------------+ +| **Originally | 2015 / 2020 | 2017 | 2020 | 2019 | +| published** | | | | | ++----------------+---------------------------------------------------------+--------------------------------------------------------+--------------------------------------------------------+-----------------------------------------------------+ +| **Origin** | USA | USA | Switzerland | Netherlands | ++----------------+---------------------------------------------------------+--------------------------------------------------------+--------------------------------------------------------+-----------------------------------------------------+ + +New datasets can also be added. We are currently working on a package to +make this process as smooth as possible. The benchmark is designed for +operating on preprocessed parquet files. + +We provide five common tasks for clinical prediction by default: + ++----+----------------------+----------------------+-------------------+ +| No | Task | Frequency | Type | ++====+======================+======================+===================+ +| 1 | ICU Mortality | Once per Stay (after | Binary | +| | | 24H) | Classification | ++----+----------------------+----------------------+-------------------+ +| 2 | Acute Kidney Injury | Hourly (within 6H) | Binary | +| | (AKI) | | Classification | ++----+----------------------+----------------------+-------------------+ +| 3 | Sepsis | Hourly (within 6H) | Binary | +| | | | Classification | ++----+----------------------+----------------------+-------------------+ +| 4 | Kidney Function(KF) | Once per stay | Regression | ++----+----------------------+----------------------+-------------------+ +| 5 | Length of Stay (LoS) | Hourly (within 7D) | Regression | ++----+----------------------+----------------------+-------------------+ + +New tasks can be easily added. +To get started right away, we include the eICU and MIMIC-III demo datasets in our repository. + +The following repositories may be relevant as well: + +- `YAIB-cohorts `_: Cohort generation for YAIB. +- `YAIB-models `_: Pretrained models for YAIB. +- `ReciPys `_: Preprocessing package for YAIB pipelines. + +For all YAIB-related repositories, please see: https://github.com/stars/rvandewater/lists/yaib. + +📄Paper +======= + +To reproduce the benchmarks in our paper, we refer to the `ML reproducibility document `_. +If you use this code in your research, please cite the following publication: + +.. code-block:: bibtex + + @inproceedings{vandewaterYetAnotherICUBenchmark2024, + title = {Yet Another ICU Benchmark: A Flexible Multi-Center Framework for Clinical ML}, + shorttitle = {Yet Another ICU Benchmark}, + booktitle = {The Twelfth International Conference on Learning Representations}, + author = {van de Water, Robin and Schmidt, Hendrik Nils Aurel and Elbers, Paul and Thoral, Patrick and Arnrich, Bert and Rockenschaub, Patrick}, + year = {2024}, + month = oct, + urldate = {2024-02-19}, + langid = {english}, + } + +This paper can also be found on arxiv `2306.05109 `_ + +💿Installation +============== + +YAIB is currently ideally installed from source, however we also offer it an early PyPi release. + +Installation from source +------------------------- + +First, we clone this repository using git: + +.. code-block:: bash + + git clone https://github.com/rvandewater/YAIB.git + +Please note the branch. The newest features and fixes are available at the development branch: + +.. code-block:: bash + + git checkout development + +YAIB can be installed using a conda environment (preferred) or pip. Below are the three CLI commands to install YAIB +using **conda**. + +The first command will install an environment based on Python 3.10. + +.. code-block:: bash + + conda env update -f environment.yml + +.. note:: + Use ``environment.yml`` on x86 hardware. Please note that this installs Pytorch as well. + +.. note:: + For mps, one needs to comment out *pytorch-cuda*, see the `PyTorch install guide `_. + +We then activate the environment and install a package called ``icu-benchmarks``, after which YAIB should be operational. + +.. code-block:: bash + + conda activate yaib + pip install -e . + +After installation, please check if your Pytorch version works with CUDA (in case available) to ensure the best performance. +YAIB will automatically list available processors at initialization in its log files. + +👩‍💻Usage +========= + +Please refer to `our wiki `_ for detailed information on how to use YAIB. + +Quickstart 🚀 (demo data) +-------------------------- + +The authors of MIMIC-III and eICU have made a small demo dataset available to demonstrate their use. They can be found on Physionet: `MIMIC-III Clinical Database Demo `_ and `eICU Collaborative Research Database Demo `_. These datasets are published under the `Open Data Commons Open Database License v1.0 `_ and can be used without credentialing procedure. We have created demo cohorts processed **solely from these datasets** for each of our currently supported task endpoints. To the best of our knowledge, this complies with the license and the respective dataset author's instructions. Usage of the task cohorts and the dataset is only permitted with the above license. +We **strongly recommend** completing a human subject research training to ensure you properly handle human subject research data. + +In the folder ``demo_data`` we provide processed publicly available demo datasets from eICU and MIMIC with the necessary labels +for ``Mortality at 24h``, ``Sepsis``, ``Akute Kidney Injury``, ``Kidney Function``, and ``Length of Stay``. + +If you do not yet have access to the ICU datasets, you can run the following command to train models for the included demo +cohorts: + +.. code-block:: bash + + wandb sweep --verbose experiments/demo_benchmark_classification.yml + wandb sweep --verbose experiments/demo_benchmark_regression.yml + +.. code-block:: bash + + wandb agent + +.. tip:: + You can choose to run each of the configurations on a SLURM cluster instance by ``wandb agent --count 1 `` + +.. note:: + You will need to have a wandb account and be logged in to run the above commands. + +Getting the datasets +--------------------- + +HiRID, eICU, and MIMIC IV can be accessed through `PhysioNet `_. A guide to this process can be +found `here `_. +AUMCdb can be accessed through a separate access `procedure `_. We do not have +involvement in the access procedure and can not answer to any requests for data access. + +Cohort creation +--------------- + +Since the datasets were created independently of each other, they do not share the same data structure or data identifiers. In +order to make them interoperable, use the preprocessing utilities +provided by the `ricu package `_. +Ricu pre-defines a large number of clinical concepts and how to load them from a given dataset, providing a common interface to +the data, that is used in this +benchmark. Please refer to our `cohort definition `_ code for generating the cohorts +using our python interface for ricu. +After this, you can run the benchmark once you have gained access to the datasets. + +👟 Running YAIB +=============== + +Preprocessing and Training +--------------------------- + +The following command will run training and evaluation on the MIMIC demo dataset for (Binary) mortality prediction at 24h with +the +LGBMClassifier. Child samples are reduced due to the small amount of training data. We load available cache and, if available, +load +existing cache files. + +.. code-block:: bash + + icu-benchmarks \ + -d demo_data/mortality24/mimic_demo \ + -n mimic_demo \ + -t BinaryClassification \ + -tn Mortality24 \ + -m LGBMClassifier \ + -hp LGBMClassifier.min_child_samples=10 \ + --generate_cache \ + --load_cache \ + --seed 2222 \ + -l ../yaib_logs/ \ + --tune + +.. tip:: + For a list of available flags, run ``icu-benchmarks train -h``. + +.. tip:: + Run with ``PYTORCH_ENABLE_MPS_FALLBACK=1`` on Macs with Metal Performance Shaders. + +.. note:: + For Windows based systems, the next line character (\\) needs to be replaced by (^) (Command Prompt) or (\`) (Powershell) + respectively. + +Alternatively, the easiest method to train all the models in the paper is to run these commands from the directory root: + +.. code-block:: bash + + wandb sweep --verbose experiments/benchmark_classification.yml + wandb sweep --verbose experiments/benchmark_regression.yml + +This will create two hyperparameter sweeps for WandB for the classification and regression tasks. +This configuration will train all the models in the paper. You can then run the following command to train the models: + +.. code-block:: bash + + wandb agent + +.. tip:: + You can choose to run each of the configurations on a SLURM cluster instance by ``wandb agent --count 1 `` + +.. note:: + You will need to have a wandb account and be logged in to run the above commands. + +Evaluate or Finetune +-------------------- + +It is possible to evaluate a model trained on another dataset and no additional training is done. +In this case, the source dataset is the demo data from MIMIC and the target is the eICU demo: + +.. code-block:: bash + + icu-benchmarks \ + --eval \ + -d demo_data/mortality24/eicu_demo \ + -n eicu_demo \ + -t BinaryClassification \ + -tn Mortality24 \ + -m LGBMClassifier \ + --generate_cache \ + --load_cache \ + -s 2222 \ + -l ../yaib_logs \ + -sn mimic \ + --source-dir ../yaib_logs/mimic_demo/Mortality24/LGBMClassifier/2022-12-12T15-24-46/repetition_0/fold_0 + +.. note:: + A similar syntax is used for finetuning, where a model is loaded and then retrained. To run finetuning, replace ``--eval`` with ``-ft``. + +Models +------ + +We provide several existing machine learning models that are commonly used for multivariate time-series data. +``pytorch`` is used for the deep learning models, ``lightgbm`` for the boosted tree approaches, and ``sklearn`` for other classical +machine learning models. +The benchmark provides (among others) the following built-in models: + +- `Logistic Regression `_: + Standard regression approach. +- `Elastic Net `_: Linear regression with + combined L1 and L2 priors as regularizer. +- `LightGBM `_: Efficient gradient + boosting trees. +- `Long Short-term Memory (LSTM) `_: The most commonly used type of Recurrent Neural + Networks for long sequences. +- `Gated Recurrent Unit (GRU) `_ : A extension to LSTM which showed + improvements (`paper `_). +- `Temporal Convolutional Networks (TCN) `_ : 1D convolution approach to sequence data. By + using dilated convolution to extend the receptive field of the network it has shown great performance on long-term + dependencies. +- `Transformers `_: The most common Attention + based approach. + +🛠️ Development +=============== + +To adapt YAIB to your own use case, you can use +the `development information `_ page as a reference. +We appreciate contributions to the project. Please read the `contribution guidelines `_ before submitting a pull +request. + +Acknowledgements +================ + +This project has been developed partially under the funding of "Gemeinsamer Bundesausschuss (G-BA) Innovationsausschuss" in the framework of "CASSANDRA - Clinical ASSist AND aleRt Algorithms". +(project number 01VSF20015). We would like to acknowledge the work of Alisher Turubayev, Anna Shopova, Fabian Lange, Mahmut Kamalak, Paul Mattes, and Victoria Ayvasky for adding Pytorch Lightning, Weights and Biases compatibility, and several optional imputation methods to a later version of the benchmark repository. + +We do not own any of the datasets used in this benchmark. This project uses heavily adapted components of +the `HiRID benchmark `_. We thank the authors for providing this codebase and +encourage further development to benefit the scientific community. The demo datasets have been released under +an `Open Data Commons Open Database License (ODbL) `_. + +License +======= + +This source code is released under the MIT license, included `here `_. We do not own any of the datasets used or +included in this repository. + + .. toctree:: + :maxdepth: 2 + + api + Example-usage + adding_models + development + imputation_methods + Adding-datasets + Defining-a-new-clinical-concept + Generating-Cohorts + Adding-tasks + Preprocessing + Adding-a-new-model + Adding-evaluation-metrics + Creating-Pooled-datasets + Getting-access-to-ICU-EHR-datasets + Reproducing-Paper-Results + Weights-and-Biases + + + Indices and tables + ================== + + * :ref:`genindex` + * :ref:`modindex` + * :ref:`search` \ No newline at end of file diff --git a/icu_benchmarks/data/preprocessor.py b/icu_benchmarks/data/preprocessor.py index 7ea35bd1..0da8c35c 100644 --- a/icu_benchmarks/data/preprocessor.py +++ b/icu_benchmarks/data/preprocessor.py @@ -68,7 +68,7 @@ def set_imputation_model(self, imputation_model): update_wandb_config({"imputation_model": self.imputation_model.__class__.__name__}) def vars_selection(self, input_variables, segment: str) -> Union[str, list[str]]: - if input_variables[segment] is None or len(input_variables[segment]) == 0: + if input_variables.get(segment, None) is None or len(input_variables[segment]) == 0: logging.warning("No dynamic variables provided. Skipping dynamic preprocessing.") return [] # Return empty list if no variables are provided vars_to_apply: Union[str, list[str]] From de49bc6fb3e973f706829b00ded2898c3b266164 Mon Sep 17 00:00:00 2001 From: Robin van de Water Date: Tue, 15 Jul 2025 16:03:40 +0200 Subject: [PATCH 63/82] docs --- icu_benchmarks/data/preprocessor.py | 2 +- icu_benchmarks/data/utils.py | 5 +++-- 2 files changed, 4 insertions(+), 3 deletions(-) diff --git a/icu_benchmarks/data/preprocessor.py b/icu_benchmarks/data/preprocessor.py index 0da8c35c..ef126839 100644 --- a/icu_benchmarks/data/preprocessor.py +++ b/icu_benchmarks/data/preprocessor.py @@ -75,7 +75,7 @@ def vars_selection(self, input_variables, segment: str) -> Union[str, list[str]] if self.vars_to_exclude is not None: # Exclude vars_to_exclude from missing indicator/ feature generation vars_to_apply = list(set(input_variables[segment]) - set(self.vars_to_exclude)) - logging.info(f"Excluding {len(self.vars_to_exclude)} : " + logging.info(f"Excluding features: {len(self.vars_to_exclude)} : " f"{self.vars_to_exclude if len(self.vars_to_exclude) < 10 else f'{self.vars_to_exclude[:10]}...'}...") if len(vars_to_apply) == 0: logging.warning( diff --git a/icu_benchmarks/data/utils.py b/icu_benchmarks/data/utils.py index 5b95e9d6..30afcae7 100644 --- a/icu_benchmarks/data/utils.py +++ b/icu_benchmarks/data/utils.py @@ -70,8 +70,9 @@ def modality_selection( old_columns.extend(value) vars[key] = [col for col in value if col in selected_columns] # -3 because of standard columns - logging.info(f"Selected columns: {len(selected_columns) - 3}, old columns: {len(old_columns)}") - logging.debug(f"Difference: {set(old_columns) - set(selected_columns)}") + logging.info(f"Selected columns: {len(selected_columns) - 3}, original columns: {len(old_columns)}, " + f"not using: {len(set(old_columns) - set(selected_columns))} columns") + logging.debug(f"Not using columns: {set(old_columns) - set(selected_columns)}") # Update data dict for key in data.keys(): sel_col = [col for col in data[key].columns if col in selected_columns] From 5b71c644190f70b069e82fd6f3a6298030fb7842 Mon Sep 17 00:00:00 2001 From: Robin van de Water Date: Tue, 15 Jul 2025 16:04:28 +0200 Subject: [PATCH 64/82] fixes for just using static data. Added repetitions to tune on. --- icu_benchmarks/data/loader.py | 18 ++++++++++++------ icu_benchmarks/tuning/hyperparameters.py | 4 +++- 2 files changed, 15 insertions(+), 7 deletions(-) diff --git a/icu_benchmarks/data/loader.py b/icu_benchmarks/data/loader.py index 0023506c..ce9e50cf 100644 --- a/icu_benchmarks/data/loader.py +++ b/icu_benchmarks/data/loader.py @@ -32,9 +32,10 @@ def __init__( self.vars = vars self.grouping_df = data[split][grouping_segment] # Get the row indicators for the data to be able to match predicted labels - if not isinstance(vars["SEQUENCE"], str): - raise ValueError(f'Expected key "SEQUENCE" to be of type str, got {type(vars["SEQUENCE"])} instead') + if "SEQUENCE" in self.vars and self.vars["SEQUENCE"] in data[split][DataSegment.features].columns: + if not isinstance(vars["SEQUENCE"], str): + raise ValueError(f'Expected key "SEQUENCE" to be of type str, got {type(vars["SEQUENCE"])} instead') # We have a time series dataset self.row_indicators = data[split][DataSegment.features][self.vars["GROUP"], self.vars["SEQUENCE"]] @@ -167,19 +168,24 @@ def get_data_and_labels(self) -> tuple[np.ndarray, np.ndarray, np.ndarray]: if len(labels) == self.num_stays: # order of groups could be random, we make sure not to change it rep = rep.group_by(self.vars["GROUP"]).last() - else: - # Adding segment count for each stay id and timestep. - rep = rep.with_columns(pl.col(self.vars["GROUP"]).cum_count().over(self.vars["GROUP"]).alias("counter")) + # else: + # # Adding segment count for each stay id and timestep. + # rep = rep.with_columns(pl.col(self.vars["GROUP"]).cum_count().over(self.vars["GROUP"]).alias("counter")) # rep = rep.sort([self.vars["GROUP"], "counter"]) # rep = rep.to_numpy().astype(float) # Remove the first column from the rep array (group column) # needs to still be in there? logging.debug(f"rep shape: {rep.shape}") logging.debug(f"labels shape: {labels.shape}") + # if self.vars["SEQUENCE"] in rep.columns: + # rep = rep.select(pl.exclude(self.vars["SEQUENCE"])) + # logging.info(f"Removed sequence column {self.vars['SEQUENCE']} from features_df.") rep = rep.to_numpy().astype(float) rep = rep[:, 1:] - if self.vars["SEQUENCE"] in self.row_indicators and self.row_indicators[self.vars["SEQUENCE"]].dtype == pl.Duration: + if ("SEQUENCE" in self.vars and self.vars["SEQUENCE"] in self.row_indicators + and self.row_indicators[self.vars["SEQUENCE"]].dtype == pl.Duration): self.row_indicators = self.row_indicators.with_columns(pl.col(self.vars["SEQUENCE"]).dt.total_hours()) + # Todo: check if row indicators introduce information loss return rep, labels, self.row_indicators.to_numpy() def to_tensor(self) -> tuple[Union[Tensor, np.ndarray], Union[Tensor, np.ndarray], Union[Tensor, np.ndarray]]: diff --git a/icu_benchmarks/tuning/hyperparameters.py b/icu_benchmarks/tuning/hyperparameters.py index 7b800579..30b220b1 100644 --- a/icu_benchmarks/tuning/hyperparameters.py +++ b/icu_benchmarks/tuning/hyperparameters.py @@ -190,6 +190,7 @@ def choose_and_bind_hyperparameters_optuna( n_calls: int = 20, sampler=optuna.samplers.GPSampler, folds_to_tune_on: int = None, + repetitions_to_tune_on: int = 1, checkpoint_file: str = "hyperparameter_tuning_logs.db", generate_cache: bool = False, load_cache: bool = False, @@ -201,6 +202,7 @@ def choose_and_bind_hyperparameters_optuna( """Choose hyperparameters to tune and bind them to gin. Uses Optuna for hyperparameter optimization. Args: + repetitions_to_tune_on: Repetitions to tune on. If None, 1 repetitions are trained on. plot: Whether to plot hyperparameter importances. sampler: The sampler to use for hyperparameter optimization. wandb: Whether we use wandb or not. @@ -311,7 +313,7 @@ def bind_params_and_train(hyperparams): Path(temp_dir), seed, mode=run_mode, - cv_repetitions_to_train=1, + cv_repetitions_to_train=repetitions_to_tune_on, cv_folds_to_train=folds_to_tune_on, generate_cache=generate_cache, load_cache=load_cache, From 187cb2c7d34dec57ebf4706c4f2b377a0bdb266d Mon Sep 17 00:00:00 2001 From: Robin van de Water Date: Fri, 1 Aug 2025 10:42:08 +0200 Subject: [PATCH 65/82] add append predictions --- icu_benchmarks/run.py | 4 ++- icu_benchmarks/run_utils.py | 64 +++++++++++++++++++++++++++++++++++++ 2 files changed, 67 insertions(+), 1 deletion(-) diff --git a/icu_benchmarks/run.py b/icu_benchmarks/run.py index d73f4e7e..dfb9d91e 100644 --- a/icu_benchmarks/run.py +++ b/icu_benchmarks/run.py @@ -18,7 +18,7 @@ setup_logging, import_preprocessor, name_datasets, - get_config_files, + get_config_files, append_predictions_foldwise, ) from icu_benchmarks.utils import parse_dict from icu_benchmarks.constants import RunMode @@ -223,6 +223,8 @@ def main(my_args=tuple(sys.argv[1:])): log_full_line(f"DURATION: {execution_time}", level=logging.INFO, char="") try: aggregate_results(run_dir, execution_time) + append_predictions_foldwise(run_dir, "pred_indicators.csv") + except Exception as e: logging.error(f"Failed to aggregate results: {e}") logging.debug("Error details:", exc_info=True) diff --git a/icu_benchmarks/run_utils.py b/icu_benchmarks/run_utils.py index fc97c3c1..9c2ef180 100644 --- a/icu_benchmarks/run_utils.py +++ b/icu_benchmarks/run_utils.py @@ -115,6 +115,70 @@ def import_preprocessor(preprocessor_path: str): logging.error(f"Could not import custom preprocessor from {preprocessor_path}: {e}") +def append_predictions_foldwise(directory: str, filename: str, max_id: int = 1300) -> pl.DataFrame: + """ + Load all prediction CSV files with the specified filename in the given directory and its subdirectories, + and append them vertically into a single Polars DataFrame. + Files are processed fold by fold across all iterations. + + Parameters: + directory (str): The root directory to search for CSV files. + filename (str): The specific filename to look for. + max_id (int): The maximum ID value to offset the IDs in each fold to prevent clashes. + + Returns: + pl.DataFrame: A single DataFrame containing all the appended CSV files. + + Example usage: + directory = 'home' + filename = 'pred_indicators.csv' + combined_df = load_and_append_csv_files(directory, filename) + """ + dataframes = [] + id_column = "# id" + counter = 0 + + # Get all iteration directories sorted + iterations = sorted([d for d in Path(directory).iterdir() if d.is_dir()]) + + # Get all unique fold names across all iterations + all_folds = set() + for iteration in iterations: + for fold_dir in iteration.iterdir(): + if fold_dir.is_dir(): + all_folds.add(fold_dir.name) + + # Process fold by fold across all iterations + for fold_name in sorted(all_folds): + for iteration in iterations: + fold_path = iteration / fold_name + if fold_path.exists(): + for file_path in sorted(fold_path.rglob(filename)): + print(f"Loading file: {file_path}") + + # Load the CSV file + df = pl.read_csv(file_path) + + # Check original ID range + original_ids = df.select(pl.col(id_column)).to_numpy().flatten() + print(f" Original ID range: {original_ids.min()} - {original_ids.max()}") + + # Apply offset to prevent clashes + df = df.with_columns(pl.col(id_column) + counter * max_id) + + # Check modified ID range + modified_ids = df.select(pl.col(id_column)).to_numpy().flatten() + print(f" Modified ID range: {modified_ids.min()} - {modified_ids.max()}") + print(f" Counter: {counter}") + print() + dataframes.append(df) + counter += 1 + + # Concatenate all DataFrames vertically + combined_df = pl.concat(dataframes, how="vertical") + + return combined_df + def aggregate_results(log_dir: Path, execution_time: timedelta = None, explain_features: bool = False): """Aggregates results from all folds and writes to JSON file. From aac4d189fdd299ddd7106400a473a381e2a42b13 Mon Sep 17 00:00:00 2001 From: Robin van de Water Date: Wed, 13 Aug 2025 10:40:30 +0200 Subject: [PATCH 66/82] logging run dir --- icu_benchmarks/run.py | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/icu_benchmarks/run.py b/icu_benchmarks/run.py index dfb9d91e..3a129d8f 100644 --- a/icu_benchmarks/run.py +++ b/icu_benchmarks/run.py @@ -168,11 +168,17 @@ def main(my_args=tuple(sys.argv[1:])): logging.info(f"Will load data from {args.file_names}") gin.bind_parameter("preprocess.file_names", file_names) else: - return ValueError( + raise ValueError( f"Please provide a dictionary type for the file names, got {args.file_names}, " f"type: {type(args.file_names)}" ) + update_wandb_config({ + "data_dir": data_dir.resolve(), + "task": task, + "run_dir": run_dir.resolve(), + }) + choose_and_bind_hyperparameters_optuna( do_tune=args.tune, data_dir=data_dir, From 5e93636842e31cae2f72590849ead5f54fe73dcf Mon Sep 17 00:00:00 2001 From: Robin van de Water Date: Wed, 13 Aug 2025 21:18:41 +0200 Subject: [PATCH 67/82] added preds for neural networks --- icu_benchmarks/data/loader.py | 17 +++- icu_benchmarks/models/wrappers.py | 149 +++++++++++++++++------------- 2 files changed, 97 insertions(+), 69 deletions(-) diff --git a/icu_benchmarks/data/loader.py b/icu_benchmarks/data/loader.py index ce9e50cf..b3808d7e 100644 --- a/icu_benchmarks/data/loader.py +++ b/icu_benchmarks/data/loader.py @@ -94,6 +94,14 @@ class PredictionPolarsDataset(CommonPolarsDataset): def __init__(self, *args, ram_cache: bool = True, **kwargs): super().__init__(*args, **kwargs) + if "SEQUENCE" in self.vars: + if self.row_indicators[self.vars["SEQUENCE"]].dtype.is_temporal(): + self.row_indicators = self.row_indicators.with_columns(pl.col(self.vars["SEQUENCE"]).dt.total_hours()) + else: + self.row_indicators = self.row_indicators.with_columns(pl.col(self.vars["SEQUENCE"])) + else: + logging.info("Using static dataset") + self.row_indicators = self.grouping_df[self.vars["GROUP"]].to_frame(self.vars["GROUP"]) self.outcome_df = self.grouping_df self.ram_cache(ram_cache) @@ -121,6 +129,7 @@ def __getitem__(self, idx: int) -> tuple[Tensor, Tensor, Tensor]: if len(labels) == 1: # only one label per stay, align with window labels = np.concatenate([np.empty(window.shape[0] - 1) * np.nan, labels], axis=0) + row_inds = self.row_indicators.filter(pl.col(self.vars["GROUP"]) == stay_id).to_numpy() length_diff = self.maxlen - window.shape[0] pad_mask = np.ones(window.shape[0]) @@ -131,7 +140,9 @@ def __getitem__(self, idx: int) -> tuple[Tensor, Tensor, Tensor]: window = np.concatenate([window, np.ones((length_diff, window.shape[1])) * pad_value], axis=0) labels = np.concatenate([labels, np.ones(length_diff) * pad_value], axis=0) pad_mask = np.concatenate([pad_mask, np.zeros(length_diff)], axis=0) - + # row_inds = np.concatenate([row_inds, np.ones(length_diff, row_inds.shape[1]) * pad_value], axis=0) + row_inds = np.concatenate([row_inds, np.ones((length_diff, row_inds.shape[1])) * pad_value], axis=0) + row_inds = row_inds.astype(np.float32) not_labeled = np.argwhere(np.isnan(labels)) if len(not_labeled) > 0: labels[not_labeled] = -1 @@ -140,8 +151,8 @@ def __getitem__(self, idx: int) -> tuple[Tensor, Tensor, Tensor]: pad_mask = pad_mask.astype(bool) labels = labels.astype(np.float32) data = window.astype(np.float32) - - return from_numpy(data), from_numpy(labels), from_numpy(pad_mask) + # if self.vars"SEQUENCE" in self.vars: + return from_numpy(data), from_numpy(labels), from_numpy(pad_mask), from_numpy(row_inds) def get_balance(self) -> list: """Return the weight balance for the split of interest. diff --git a/icu_benchmarks/models/wrappers.py b/icu_benchmarks/models/wrappers.py index 8664b5f4..50318d6c 100644 --- a/icu_benchmarks/models/wrappers.py +++ b/icu_benchmarks/models/wrappers.py @@ -109,6 +109,67 @@ def check_supported_runmode(self, runmode: RunMode): def set_explain_features(self, set_explain_features: bool): self.explain_features = set_explain_features + def _save_model_outputs(self, pred_indicators, test_pred, test_label): + if len(pred_indicators.shape) > 1 and len(test_pred.shape) > 1: + # Temporal dataset + if pred_indicators.shape[1] == test_pred.shape[1] and pred_indicators.shape[0] == test_pred.shape[0]: + # One outcome per dataset + pred_indicators = np.hstack((pred_indicators, test_label.reshape(-1, 1))) + pred_indicators = np.hstack((pred_indicators, test_pred)) + # Save as: id, time (hours), ground truth, prediction 0, prediction 1 + # if pred_indicators.shape(1) == 5: + np.savetxt( + Path(self.logger.save_dir) / "pred_indicators.csv", + pred_indicators, + delimiter=",", + header="id,time,ground_truth,prediction_0,prediction_1", + fmt="%d,%d,%.3f,%.3f,%.3f", + ) + logging.debug(f"Saved row indicators to {Path(self.logger.save_dir) / 'row_indicators.csv'}") + # else: + # # Flat/static dataset + # np.savetxt(Path(self.logger.save_dir) / "pred_indicators.csv", pred_indicators, delimiter=",", + # header="id,ground_truth,prediction_0,prediction_1", fmt='%d,%d,%.3f,%.3f,%.3f') + # np.savetxt(Path(self.logger.save_dir) / "pred_indicators.csv", pred_indicators, delimiter=",", + # header="id,time,ground_truth,prediction_0,prediction_1", fmt='%d,%d,%.3f,%.3f,%.3f') + # logging.debug(f"Saved row indicators to {Path(self.logger.save_dir) / f'row_indicators.csv'}") + else: + logging.info(np.unique(pred_indicators[:, 0])) + pred_indicators = np.unique(pred_indicators[:, 0]) + pred_indicators = np.hstack((pred_indicators.reshape(-1, 1), test_label.reshape(-1, 1))) + pred_indicators = np.hstack((pred_indicators, test_pred)) + + np.savetxt( + Path(self.logger.save_dir) / "pred_indicators.csv", + pred_indicators, + delimiter=",", + header="id,ground_truth,prediction_0,prediction_1", + fmt="%d,%d,%0.3f,%0.3f", + ) + logging.debug(f"Saved row indicators to {Path(self.logger.save_dir) / 'row_indicators.csv'}") + + # logging.warning("Could not save row indicators; no support for temporal dataset with single outcomes yet.") + # if len(pred_indicators.shape) > 1 and len(test_pred.shape) > 1 and pred_indicators.shape[1] == test_pred.shape[1]: + # pred_indicators = np.hstack((pred_indicators, test_label.reshape(-1, 1))) + # pred_indicators = np.hstack((pred_indicators, test_pred)) + # # Save as: id, time (hours), ground truth, prediction 0, prediction 1 + # np.savetxt(Path(self.logger.save_dir) / "pred_indicators.csv", pred_indicators, delimiter=",") + # logging.debug(f"Saved row indicators to {Path(self.logger.save_dir) / 'row_indicators.csv'}") + else: + pred_indicators = np.hstack((pred_indicators.reshape(-1, 1), test_label.reshape(-1, 1))) + logging.info(pred_indicators.shape) + pred_indicators = np.hstack((pred_indicators, test_pred)) + np.savetxt( + Path(self.logger.save_dir) / "pred_indicators.csv", + pred_indicators, + delimiter=",", + header="id,ground_truth,prediction_0,prediction_1", + fmt="%d,%d,%0.3f,%0.3f", + ) + logging.debug(f"Saved row indicators to {Path(self.logger.save_dir) / f'row_indicators.csv'}") + + # else: + # logging.warning("Could not save row indicators.") @gin.configurable("DLWrapper") class DLWrapper(BaseModule, ABC): @@ -311,8 +372,8 @@ def step_fn(self, element, step_prefix=""): step_prefix (str): Step type, by default: test, train, val. """ - if len(element) == 2: - data, labels = element[0], element[1].to(self.device) + if len(element) == 3: + data, labels, indicators = element[0], element[1].to(self.device), element[2] if isinstance(data, list): for i in range(len(data)): data[i] = data[i].float().to(self.device) @@ -320,8 +381,8 @@ def step_fn(self, element, step_prefix=""): data = data.float().to(self.device) mask = torch.ones_like(labels).bool() - elif len(element) == 3: - data, labels, mask = element[0], element[1].to(self.device), element[2].to(self.device) + elif len(element) == 4: + data, labels, mask, indicators = element[0], element[1].to(self.device), element[2].to(self.device), element[3] if isinstance(data, list): for i in range(len(data)): data[i] = data[i].float().to(self.device) @@ -340,6 +401,18 @@ def step_fn(self, element, step_prefix=""): prediction = torch.masked_select(out, mask.unsqueeze(-1)).reshape(-1, out.shape[-1]).to(self.device) target = torch.masked_select(labels, mask).to(self.device) + valid_idx = mask.detach().cpu().numpy().astype(bool) + + # valid_idx is (batch, seq_len) boolean mask + valid_idx = mask.detach().cpu().numpy().astype(bool) + + # indicators is (batch, seq_len, ...) or (batch, seq_len, 2) + indicators_np = indicators.detach().cpu().numpy()[valid_idx] + predictors = prediction.detach().cpu() + target_np = target.detach().cpu().numpy() + transformed_predictors = torch.softmax(predictors, dim=1) + transformed_predictors = transformed_predictors.numpy() + if prediction.shape[-1] > 1 and self.run_mode == RunMode.classification: # Classification task loss = self.loss(prediction, target.long(), weight=self.loss_weights.to(self.device)) + aux_loss @@ -351,6 +424,9 @@ def step_fn(self, element, step_prefix=""): raise ValueError(f"Run mode {self.run_mode} not yet supported. Please implement it.") transformed_output = self.output_transform((prediction, target)) + # Save predictions to file + self._save_model_outputs(indicators_np, transformed_predictors, target_np) + for key, value in self.metrics[step_prefix].items(): if isinstance(value, torchmetrics.Metric): if key == "Binary_Fairness": @@ -435,8 +511,9 @@ def fit(self, train_dataset, val_dataset): def fit_model(self, train_data, train_labels, val_data, val_labels): """Fit the model to the training data (default SKlearn syntax)""" - val_loss = self.model.fit(train_data, train_labels) - # val_loss = 0.0 + self.model.fit(train_data, train_labels) + val_pred = self.predict(val_data) + val_loss = self.loss(val_labels, val_pred) return val_loss def validation_step(self, val_dataset, _): @@ -543,67 +620,7 @@ def log_metrics(self, label, pred, metric_type, pred_indicators): # else: # logging.warning("No explainer or explain_features values set.") - def _save_model_outputs(self, pred_indicators, test_pred, test_label): - if len(pred_indicators.shape) > 1 and len(test_pred.shape) > 1: - # Temporal dataset - if pred_indicators.shape[1] == test_pred.shape[1] and pred_indicators.shape[0] == test_pred.shape[0]: - # One outcome per dataset - pred_indicators = np.hstack((pred_indicators, test_label.reshape(-1, 1))) - pred_indicators = np.hstack((pred_indicators, test_pred)) - # Save as: id, time (hours), ground truth, prediction 0, prediction 1 - # if pred_indicators.shape(1) == 5: - np.savetxt( - Path(self.logger.save_dir) / "pred_indicators.csv", - pred_indicators, - delimiter=",", - header="id,time,ground_truth,prediction_0,prediction_1", - fmt="%d,%d,%.3f,%.3f,%.3f", - ) - logging.debug(f"Saved row indicators to {Path(self.logger.save_dir) / 'row_indicators.csv'}") - # else: - # # Flat/static dataset - # np.savetxt(Path(self.logger.save_dir) / "pred_indicators.csv", pred_indicators, delimiter=",", - # header="id,ground_truth,prediction_0,prediction_1", fmt='%d,%d,%.3f,%.3f,%.3f') - # np.savetxt(Path(self.logger.save_dir) / "pred_indicators.csv", pred_indicators, delimiter=",", - # header="id,time,ground_truth,prediction_0,prediction_1", fmt='%d,%d,%.3f,%.3f,%.3f') - # logging.debug(f"Saved row indicators to {Path(self.logger.save_dir) / f'row_indicators.csv'}") - else: - logging.info(np.unique(pred_indicators[:, 0])) - pred_indicators = np.unique(pred_indicators[:, 0]) - pred_indicators = np.hstack((pred_indicators.reshape(-1, 1), test_label.reshape(-1, 1))) - pred_indicators = np.hstack((pred_indicators, test_pred)) - - np.savetxt( - Path(self.logger.save_dir) / "pred_indicators.csv", - pred_indicators, - delimiter=",", - header="id,ground_truth,prediction_0,prediction_1", - fmt="%d,%d,%0.3f,%0.3f", - ) - logging.debug(f"Saved row indicators to {Path(self.logger.save_dir) / 'row_indicators.csv'}") - - # logging.warning("Could not save row indicators; no support for temporal dataset with single outcomes yet.") - # if len(pred_indicators.shape) > 1 and len(test_pred.shape) > 1 and pred_indicators.shape[1] == test_pred.shape[1]: - # pred_indicators = np.hstack((pred_indicators, test_label.reshape(-1, 1))) - # pred_indicators = np.hstack((pred_indicators, test_pred)) - # # Save as: id, time (hours), ground truth, prediction 0, prediction 1 - # np.savetxt(Path(self.logger.save_dir) / "pred_indicators.csv", pred_indicators, delimiter=",") - # logging.debug(f"Saved row indicators to {Path(self.logger.save_dir) / 'row_indicators.csv'}") - # else: - # pred_indicators = np.hstack((pred_indicators.reshape(-1, 1), test_label.reshape(-1, 1))) - # logging.info(pred_indicators.shape) - # pred_indicators = np.hstack((pred_indicators, test_pred)) - # np.savetxt( - # Path(self.logger.save_dir) / "pred_indicators.csv", - # pred_indicators, - # delimiter=",", - # header="id,ground_truth,prediction_0,prediction_1", - # fmt="%d,%d,%0.3f,%0.3f", - # ) - # logging.debug(f"Saved row indicators to {Path(self.logger.save_dir) / f'row_indicators.csv'}") - # else: - # logging.warning("Could not save row indicators.") def configure_optimizers(self): return None From 7559c5f077e49254657e9262bc0640e181140f25 Mon Sep 17 00:00:00 2001 From: Robin van de Water Date: Mon, 25 Aug 2025 11:37:24 +0200 Subject: [PATCH 68/82] Add more hyperparameter ranges lgbm and remove callback that crashed run --- configs/prediction_models/LGBMClassifier.gin | 57 +++++++++++++++++--- icu_benchmarks/models/ml_models/lgbm.py | 2 - 2 files changed, 50 insertions(+), 9 deletions(-) diff --git a/configs/prediction_models/LGBMClassifier.gin b/configs/prediction_models/LGBMClassifier.gin index f29f40cc..0ea5d245 100644 --- a/configs/prediction_models/LGBMClassifier.gin +++ b/configs/prediction_models/LGBMClassifier.gin @@ -6,11 +6,54 @@ include "configs/prediction_models/common/MLCommon.gin" # Train params train_common.model = @LGBMClassifier +# Hyperparameter tuning configuration model/hyperparameter.class_to_tune = @LGBMClassifier -model/hyperparameter.colsample_bytree = (0.33, 1.0) -model/hyperparameter.max_depth = (3, 7) -model/hyperparameter.min_child_samples = 1000 -model/hyperparameter.n_estimators = 100000 -model/hyperparameter.num_leaves = (8, 128, "log", 2) -model/hyperparameter.subsample = (0.33, 1.0) -model/hyperparameter.subsample_freq = 1 + +# Core tree parameters +model/hyperparameter.n_estimators = (500, 2000, 5000, 10000, 100000) +model/hyperparameter.max_depth = (3, 5, 7, 10, 15) +model/hyperparameter.num_leaves = (8, 16, 31, 64, 128, 256, "log", 2) +model/hyperparameter.min_child_samples = (10, 20, 50, 100, 500, 1000) +model/hyperparameter.min_child_weight = (1e-3, 10.0, "log") + +# Learning rate and regularization +model/hyperparameter.learning_rate = (0.01, 0.3, "log") +model/hyperparameter.reg_alpha = (1e-6, 1.0, "log") +model/hyperparameter.reg_lambda = (1e-6, 1.0, "log") + +# Sampling parameters +model/hyperparameter.subsample = (0.4, 1.0) +model/hyperparameter.subsample_freq = (0, 1, 5, 10) +model/hyperparameter.colsample_bytree = (0.4, 1.0) +model/hyperparameter.colsample_bynode = (0.4, 1.0) + +# Boosting parameters +model/hyperparameter.boosting_type = ["gbdt", "dart"] + +# Advanced DART parameters (active when boosting_type="dart") +model/hyperparameter.drop_rate = (0.1, 0.5) +model/hyperparameter.max_drop = (10, 50) +model/hyperparameter.skip_drop = (0.1, 0.9) + +# GOSS parameters (active when boosting_type="goss") +model/hyperparameter.top_rate = (0.1, 0.5) +model/hyperparameter.other_rate = (0.05, 0.2) + +# Performance and stability +model/hyperparameter.feature_fraction = (0.4, 1.0) +model/hyperparameter.bagging_fraction = (0.4, 1.0) +model/hyperparameter.bagging_freq = (0, 1, 5, 10) +model/hyperparameter.min_split_gain = (1e-6, 1.0, "log") + +# Categorical handling +model/hyperparameter.cat_smooth = (1.0, 100.0, "log") +model/hyperparameter.cat_l2 = (1.0, 100.0, "log") + +# Early stopping and validation +model/hyperparameter.early_stopping_rounds = 100 +model/hyperparameter.eval_metric = ["binary_logloss", "auc", "binary_error"] + +# Class imbalance handling +model/hyperparameter.is_unbalance = [True, False] +model/hyperparameter.scale_pos_weight = (0.1, 10.0, "log") + diff --git a/icu_benchmarks/models/ml_models/lgbm.py b/icu_benchmarks/models/ml_models/lgbm.py index c2207555..12124a69 100644 --- a/icu_benchmarks/models/ml_models/lgbm.py +++ b/icu_benchmarks/models/ml_models/lgbm.py @@ -14,8 +14,6 @@ def fit_model(self, train_data, train_labels, val_data, val_labels): self.model.set_params(random_state=np.random.get_state()[1][0]) callbacks = [lgbm.early_stopping(self.hparams.patience, verbose=True), lgbm.log_evaluation(period=-1)] - if wandb.run is not None: - callbacks.append(wandb_lgbm()) self.model = self.model.fit( train_data, From 02889e221ab3ad54f4378da2cfdee5cf225cec66 Mon Sep 17 00:00:00 2001 From: Robin van de Water Date: Mon, 25 Aug 2025 11:37:44 +0200 Subject: [PATCH 69/82] sphinx config --- docs/source/conf.py | 54 ++++++++++++++++++++++++--------------------- 1 file changed, 29 insertions(+), 25 deletions(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index b2534226..88cd230d 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -13,11 +13,16 @@ copyright = '2025, Robin P. van de Water, Hendrik Schmidt, Patrick Rockenschaub, MIT License' author = 'Robin P. van de Water, Hendrik Schmidt, Patrick Rockenschaub' release = '1.0' - +extensions = [ + 'sphinx.ext.autodoc', + 'sphinx.ext.autosummary', + 'sphinx.ext.viewcode', + 'sphinx.ext.napoleon', # For Google/NumPy style docstrings + 'sphinx_immaterial' +] # -- General configuration --------------------------------------------------- # https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration -extensions = ['sphinx.ext.autodoc'] templates_path = ['_templates'] exclude_patterns = [] @@ -28,36 +33,35 @@ # https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output # Theme configuration -html_theme = 'sphinx_rtd_theme' +html_theme = 'sphinx_immaterial' # Logo configuration html_logo = '../figures/yaib_logo.png' # Path to your logo file html_favicon = '../figures/yaib_logo.png' # Optional: favicon # Theme options -html_theme_options = { - 'canonical_url': '', - 'analytics_id': '', - 'logo_only': False, - 'display_version': True, - 'prev_next_buttons_location': 'bottom', - 'style_external_links': False, - 'vcs_pageview_mode': '', - 'style_nav_header_background': 'white', - # Toc options - 'collapse_navigation': True, - 'sticky_navigation': True, - 'navigation_depth': 4, - 'includehidden': True, - 'titles_only': False -} +# html_theme_options = { +# 'canonical_url': '', +# 'analytics_id': '', +# 'logo_only': False, +# 'display_version': True, +# 'prev_next_buttons_location': 'bottom', +# 'style_external_links': False, +# 'vcs_pageview_mode': '', +# 'style_nav_header_background': 'white', +# # Toc options +# 'collapse_navigation': True, +# 'sticky_navigation': True, +# 'navigation_depth': 4, +# 'includehidden': True, +# 'titles_only': False, +# 'body_max_width': 'none', +# 'page_width': 'auto', +# } +# sphinx_immaterial theme options + html_static_path = ['_static'] -extensions = [ - 'sphinx.ext.autodoc', - 'sphinx.ext.autosummary', - 'sphinx.ext.viewcode', - 'sphinx.ext.napoleon', # For Google/NumPy style docstrings -] + # Autodoc settings autodoc_default_options = { From d01674e71827b212b0f20f051cd9d6980c25ba1d Mon Sep 17 00:00:00 2001 From: Robin van de Water Date: Mon, 25 Aug 2025 11:37:57 +0200 Subject: [PATCH 70/82] allow val change --- icu_benchmarks/wandb_utils.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/icu_benchmarks/wandb_utils.py b/icu_benchmarks/wandb_utils.py index eae6d06f..1e9559a7 100644 --- a/icu_benchmarks/wandb_utils.py +++ b/icu_benchmarks/wandb_utils.py @@ -32,7 +32,8 @@ def apply_wandb_sweep(args: Namespace) -> Namespace: Returns: Namespace: arguments with sweep configuration applied (some are applied via hyperparams) """ - wandb.init(allow_val_change=True, dir=args.log_dir) + wandb.init(allow_val_change=True, dir=args.log_dir, config={"allow_val_change": True}) + wandb.config.allow_val_change = True sweep_config = wandb.config args.__dict__.update(sweep_config) if args.hyperparams is None: From 1a564e49538103e3847392c960430ecd8d0a819f Mon Sep 17 00:00:00 2001 From: Robin van de Water Date: Mon, 25 Aug 2025 11:38:15 +0200 Subject: [PATCH 71/82] experiment with imblearn ensemble --- icu_benchmarks/models/ml_models/imblearn.py | 43 +++++++++++++++++++-- 1 file changed, 39 insertions(+), 4 deletions(-) diff --git a/icu_benchmarks/models/ml_models/imblearn.py b/icu_benchmarks/models/ml_models/imblearn.py index d1db0703..42916da8 100644 --- a/icu_benchmarks/models/ml_models/imblearn.py +++ b/icu_benchmarks/models/ml_models/imblearn.py @@ -1,8 +1,12 @@ +import logging + from imblearn.ensemble import BalancedRandomForestClassifier, RUSBoostClassifier from icu_benchmarks.constants import RunMode from icu_benchmarks.models.wrappers import MLWrapper import gin - +from sklearn.tree import DecisionTreeClassifier +from imblearn.ensemble import RUSBoostClassifier, BalancedRandomForestClassifier, EasyEnsembleClassifier +import xgboost as xgb @gin.configurable class BRFClassifier(MLWrapper): @@ -14,9 +18,40 @@ def __init__(self, *args, **kwargs): @gin.configurable -class RUSBClassifier(MLWrapper): +class XGBEnsembleClassifier(MLWrapper): _supported_run_modes = [RunMode.classification] - + individual_model = xgb.XGBClassifier( + learning_rate=0.1, + n_estimators=5000, + max_depth=10, + scale_pos_weight=30, + min_child_weight=1, + max_delta_step=3, + colsample_bytree=0.25, + gamma=0.9, + reg_lambda=0.1, + reg_alpha=100, + random_state=42, + eval_metric='logloss' + ) def __init__(self, *args, **kwargs): - self.model = self.set_model_args(RUSBoostClassifier, *args, **kwargs) + self.model = self.set_model_args(EasyEnsembleClassifier, *args, **kwargs, estimator=self.individual_model) super().__init__(*args, **kwargs) + + def fit_model(self, train_data, train_labels, val_data, val_labels): + import xgboost as xgb + from sklearn.metrics import log_loss + + # Try XGBoost first with provided config + try: + self.model.fit(train_data, train_labels,) + + val_pred_proba = self.model.predict_proba(val_data) + val_loss = log_loss(val_labels, val_pred_proba) + logging.info(f"XGBoost model trained successfully. Validation loss: {val_loss:.4f}") + return val_loss + + except Exception as e: + logging.warning(f"XGBoost failed: {e}") + + return val_loss From 8af76b17a184a8f6756ead9b9995cc27c521afd8 Mon Sep 17 00:00:00 2001 From: Robin van de Water Date: Mon, 25 Aug 2025 11:38:47 +0200 Subject: [PATCH 72/82] more time for running experiments --- experiments/charhpc_wandb_sweep_cpu.sh | 2 +- .../{charhpc_wandb_sweep.sh => charhpc_wandb_sweep_gpu.sh} | 6 +++--- 2 files changed, 4 insertions(+), 4 deletions(-) rename experiments/{charhpc_wandb_sweep.sh => charhpc_wandb_sweep_gpu.sh} (77%) diff --git a/experiments/charhpc_wandb_sweep_cpu.sh b/experiments/charhpc_wandb_sweep_cpu.sh index 45407de3..da98bda8 100644 --- a/experiments/charhpc_wandb_sweep_cpu.sh +++ b/experiments/charhpc_wandb_sweep_cpu.sh @@ -2,7 +2,7 @@ #SBATCH --job-name=yaib_experiment #SBATCH --partition=compute # -p #SBATCH --cpus-per-task=16 # -c -#SBATCH --mem=250gb +#SBATCH --mem=300gb #SBATCH --output=logs/classification_%a_%j.log # %j is job id #SBATCH --time=48:00:00 diff --git a/experiments/charhpc_wandb_sweep.sh b/experiments/charhpc_wandb_sweep_gpu.sh similarity index 77% rename from experiments/charhpc_wandb_sweep.sh rename to experiments/charhpc_wandb_sweep_gpu.sh index 3426d5fd..1181ee2e 100644 --- a/experiments/charhpc_wandb_sweep.sh +++ b/experiments/charhpc_wandb_sweep_gpu.sh @@ -1,11 +1,11 @@ #!/bin/bash #SBATCH --job-name=yaib_experiment -#SBATCH --partition=pgpu # -p -#SBATCH --cpus-per-task=8 # -c +#SBATCH --partition=gpu # -p +#SBATCH --cpus-per-task=16 # -c #SBATCH --mem=200gb #SBATCH --output=logs/classification_%a_%j.log # %j is job id #SBATCH --gpus=1 -#SBATCH --time=24:00:00 +#SBATCH --time=48:00:00 source /etc/profile.d/conda.sh From 9a2aa2ab6b466530a7a31f6a2400dd04880202d7 Mon Sep 17 00:00:00 2001 From: Robin van de Water Date: Mon, 25 Aug 2025 11:44:05 +0200 Subject: [PATCH 73/82] persist all shaps, reps, and labels for shap value/heatmap plots --- icu_benchmarks/models/ml_models/xgboost.py | 8 +------- icu_benchmarks/models/train.py | 17 +++++++++++++++-- icu_benchmarks/models/wrappers.py | 20 ++++++++++---------- 3 files changed, 26 insertions(+), 19 deletions(-) diff --git a/icu_benchmarks/models/ml_models/xgboost.py b/icu_benchmarks/models/ml_models/xgboost.py index b9ab2249..dedbfbb4 100644 --- a/icu_benchmarks/models/ml_models/xgboost.py +++ b/icu_benchmarks/models/ml_models/xgboost.py @@ -77,16 +77,10 @@ def _explain_model(self, reps, labels): if not hasattr(self.model, "feature_importances_"): raise ValueError("Model has not been fit yet. Call fit_model() before getting feature importances.") # feature_importances = self.model.feature_importances_ - shap_values = self.explainer.shap_values(reps) + shap_values = self.explainer.shap_values(reps, labels) # feature_importances = np.abs(shap_values).mean(axis=1) return shap_values - # def explainer(self, reps): - # if not hasattr(self.model, "feature_importances_"): - # raise ValueError("Model has not been fit yet. Call fit_model() before getting feature importances.") - # return self.model.feature_importances_ - - @gin.configurable class XGBClassifierGPU(MLWrapper): _supported_run_modes = [RunMode.classification] diff --git a/icu_benchmarks/models/train.py b/icu_benchmarks/models/train.py index a73cbc96..21593117 100644 --- a/icu_benchmarks/models/train.py +++ b/icu_benchmarks/models/train.py @@ -207,7 +207,7 @@ def train_common( return test_loss -def persist_shap_data(trainer: Trainer, log_dir: Path): +def persist_shap_data(trainer: Trainer, log_dir: Path, save_full_valuesets=True) -> None: """ Persist shap values to disk. Args: @@ -227,9 +227,22 @@ def persist_shap_data(trainer: Trainer, log_dir: Path): if hasattr(trainer.lightning_module, "explainer_values_test"): # todo: abs values explainer_values = trainer.lightning_module.explainer_values_test + shaps_test = pl.DataFrame(schema=trained_columns, data=explainer_values) + # reps_test = pl.DataFrame(schema=trained_columns, data=trainer.lightning_module.representations_test) + if save_full_valuesets: + with (log_dir / "full_explainer_values_test.parquet").open("wb") as f: + shaps_test.write_parquet(f) + # with (log_dir / "explainer_reps_test.parquet").open("wb") as f: + # reps_test.write_parquet(f) + if hasattr(trainer.lightning_module, "rep_test"): + reps_test = pl.DataFrame(schema=trained_columns, data=trainer.lightning_module.rep_test) + with (log_dir / "explainer_rep_test.parquet").open("wb") as f: + reps_test.write_parquet(f) + labels = pl.DataFrame(schema=["label"], data=trainer.lightning_module.label_test) + with (log_dir / "explainer_label_test.parquet").open("wb") as f: + labels.write_parquet(f) # logging.info(f"{explainer_values.values.shape}") # shaps_test = pl.DataFrame(schema=trainer.lightning_module.trained_columns, data=np.transpose(explainer_values.values)) - shaps_test = pl.DataFrame(schema=trained_columns, data=explainer_values) shaps_test = shaps_test.select(pl.all().mean()) with (log_dir / "explainer_values_test.parquet").open("wb") as f: shaps_test.write_parquet(f) diff --git a/icu_benchmarks/models/wrappers.py b/icu_benchmarks/models/wrappers.py index 50318d6c..8b267063 100644 --- a/icu_benchmarks/models/wrappers.py +++ b/icu_benchmarks/models/wrappers.py @@ -108,8 +108,13 @@ def check_supported_runmode(self, runmode: RunMode): def set_explain_features(self, set_explain_features: bool): self.explain_features = set_explain_features + self.persist_reps = set_explain_features + + def _explain_model(self, reps, labels): + raise NotImplementedError(f"Model {self.__class__.__name__} does not currently support feature explanation.") def _save_model_outputs(self, pred_indicators, test_pred, test_label): + "Save model outputs to CSV file for further post-hoc analysis" if len(pred_indicators.shape) > 1 and len(test_pred.shape) > 1: # Temporal dataset if pred_indicators.shape[1] == test_pred.shape[1] and pred_indicators.shape[0] == test_pred.shape[0]: @@ -499,6 +504,7 @@ def fit(self, train_dataset, val_dataset): if self.explain_features: self.explainer_values_train = self._explain_model(train_rep, train_label) + train_pred = self.predict(train_rep) logging.debug(f"Model:{self.model}") @@ -536,15 +542,15 @@ def test_step(self, dataset, _): ) self.set_metrics(test_label) test_pred = self.predict(test_rep) + self.log_curves(test_label, test_pred, "test", pred_indicators) if self.debug: - # try: self._save_model_outputs(pred_indicators, test_pred, test_label) - # except Exception as e: - # logging.warning(f"Could not save model outputs: {e}") if self.explain_features: # self.explain_model(test_rep, test_label) self.explainer_values_test = self._explain_model(test_rep, test_label) - self.log_curves(test_label, test_pred, "test", pred_indicators) + if self.persist_reps: + self.rep_test = test_rep + self.label_test = test_label if self.mps: self.log("test/loss", np.float32(self.loss(test_label, test_pred)), sync_dist=True) self.log_metrics(np.float32(test_label), np.float32(test_pred), "test", pred_indicators) @@ -613,12 +619,6 @@ def log_metrics(self, label, pred, metric_type, pred_indicators): sync_dist=True, ) - # def _explain_model(self, test_rep, test_label): - # if self.explainer is not None: - # logging.info(f"Explaining features for {self}") - # self.explainer_values_test = self.explainer(test_rep) - # else: - # logging.warning("No explainer or explain_features values set.") From 26322284c59e0a82c459538dc2615cfabf64117c Mon Sep 17 00:00:00 2001 From: Robin van de Water Date: Wed, 27 Aug 2025 11:48:21 +0200 Subject: [PATCH 74/82] reduce complexity for shaps --- icu_benchmarks/models/ml_models/xgboost.py | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/icu_benchmarks/models/ml_models/xgboost.py b/icu_benchmarks/models/ml_models/xgboost.py index dedbfbb4..c6a7ef18 100644 --- a/icu_benchmarks/models/ml_models/xgboost.py +++ b/icu_benchmarks/models/ml_models/xgboost.py @@ -5,6 +5,7 @@ import shap import wandb import xgboost as xgb +import numpy as np from xgboost.callback import EarlyStopping from wandb.integration.xgboost import wandb_callback as wandb_xgb from sklearn.metrics import log_loss @@ -20,7 +21,6 @@ @gin.configurable class XGBClassifier(MLWrapper): _supported_run_modes = [RunMode.classification] - _explain_values = False def __init__(self, *args, **kwargs): self.model = self.set_model_args(xgb.XGBClassifier, *args, **kwargs, eval_metric=log_loss, device="cpu", verbosity=0) @@ -47,9 +47,11 @@ def fit_model(self, train_data, train_labels, val_data, val_labels): logging.info(f"train_data: {train_data.shape}, train_labels: {train_labels.shape}") logging.info(train_labels) self.model.fit(train_data, train_labels, eval_set=[(val_data, val_labels)], verbose=0) - # if self.explain_features: + n_samples = min(1000, len(train_data)) + indices = np.random.choice(len(train_data), size=n_samples, replace=False) + background_sample = train_data[indices] self.explainer = shap.TreeExplainer( - self.model, train_data, feature_perturbation="interventional", model_output="probability" + self.model, background_sample, feature_perturbation="interventional", model_output="probability" ) # if self.explain_features: # logging.info("Explaining features") From b9443ded717e629279bc186a08ead6659925eec0 Mon Sep 17 00:00:00 2001 From: Robin van de Water Date: Tue, 2 Sep 2025 20:59:03 +0200 Subject: [PATCH 75/82] Added calibaration capabilities --- icu_benchmarks/models/ml_models/xgboost.py | 25 ++++---- icu_benchmarks/models/wrappers.py | 68 +++++++++++++++++++--- 2 files changed, 73 insertions(+), 20 deletions(-) diff --git a/icu_benchmarks/models/ml_models/xgboost.py b/icu_benchmarks/models/ml_models/xgboost.py index c6a7ef18..064d4b8e 100644 --- a/icu_benchmarks/models/ml_models/xgboost.py +++ b/icu_benchmarks/models/ml_models/xgboost.py @@ -26,17 +26,17 @@ def __init__(self, *args, **kwargs): self.model = self.set_model_args(xgb.XGBClassifier, *args, **kwargs, eval_metric=log_loss, device="cpu", verbosity=0) super().__init__(*args, **kwargs) - def predict(self, features): - """ - Predicts class probabilities for the given features. - - Args: - features: Input features for prediction. - - Returns: - numpy.ndarray: Predicted probabilities for each class. - """ - return self.model.predict_proba(features) + # def predict(self, features): + # """ + # Predicts class probabilities for the given features. + # + # Args: + # features: Input features for prediction. + # + # Returns: + # numpy.ndarray: Predicted probabilities for each class. + # """ + # return self.model.predict_proba(features) def fit_model(self, train_data, train_labels, val_data, val_labels): """Fit the model to the training data (default SKlearn syntax)""" @@ -47,6 +47,7 @@ def fit_model(self, train_data, train_labels, val_data, val_labels): logging.info(f"train_data: {train_data.shape}, train_labels: {train_labels.shape}") logging.info(train_labels) self.model.fit(train_data, train_labels, eval_set=[(val_data, val_labels)], verbose=0) + n_samples = min(1000, len(train_data)) indices = np.random.choice(len(train_data), size=n_samples, replace=False) background_sample = train_data[indices] @@ -77,7 +78,7 @@ def set_model_args(self, model, *args, **kwargs): def _explain_model(self, reps, labels): if not hasattr(self.model, "feature_importances_"): - raise ValueError("Model has not been fit yet. Call fit_model() before getting feature importances.") + raise ValueError("Model has not been fit yet. Call fit_model() before getting feature importances.") # feature_importances = self.model.feature_importances_ shap_values = self.explainer.shap_values(reps, labels) # feature_importances = np.abs(shap_values).mean(axis=1) diff --git a/icu_benchmarks/models/wrappers.py b/icu_benchmarks/models/wrappers.py index 8b267063..1a560b07 100644 --- a/icu_benchmarks/models/wrappers.py +++ b/icu_benchmarks/models/wrappers.py @@ -4,6 +4,7 @@ from pathlib import Path import torchmetrics from sklearn.metrics import log_loss, mean_squared_error, average_precision_score, roc_auc_score +from sklearn.calibration import CalibratedClassifierCV import torch from torch.nn import MSELoss, CrossEntropyLoss @@ -489,6 +490,42 @@ def set_metrics(self, labels): self.label_transform = lambda x: x self.metrics = MLMetrics.REGRESSION + def setup_calibration(self, val_data, val_labels, method='isotonic'): + """ + Setup model calibration using validation data for better probability estimates. + + Args: + val_data: Validation features for calibration + val_labels: Validation labels for calibration + method: 'isotonic' or 'sigmoid' calibration method + + Returns: + float: Validation loss after calibration + """ + if self.run_mode != RunMode.classification: + logging.warning("Calibration only supported for classification tasks") + return None + + logging.info(f"Applying {method} calibration using validation data") + + # Create calibrated version using validation data as holdout set + self.calibrated_model = CalibratedClassifierCV( + self.model, + method=method, + cv='prefit' # Use prefit model with holdout validation set + ) + self.calibrated_model.fit(val_data, val_labels) + + # Calculate calibrated validation score + cal_val_pred = self.calibrated_model.predict_proba(val_data) + cal_val_loss = self.loss(val_labels, cal_val_pred) + + logging.info(f"Calibration complete. Original val loss: {self.loss(val_labels, self.predict(val_data)):.4f}, " + f"Calibrated val loss: {cal_val_loss:.4f}") + + return cal_val_loss + + def fit(self, train_dataset, val_dataset): """Fit the model to the training data.""" train_rep, train_label, row_indicators = train_dataset.get_data_and_labels() @@ -500,6 +537,13 @@ def fit(self, train_dataset, val_dataset): self.model.set_params(class_weight=self.weight) val_loss = self.fit_model(train_rep, train_label, val_rep, val_label) + calibrate = True + method = "isotonic" + # Apply calibration if desired + calibrate = True + if calibrate and self.run_mode == RunMode.classification: + cal_val_loss = self.setup_calibration(val_rep, val_label, method='isotonic') + logging.info(f"Model calibrated. val loss {val_loss} Calibrated val loss: {cal_val_loss}") if self.explain_features: self.explainer_values_train = self._explain_model(train_rep, train_label) @@ -542,6 +586,7 @@ def test_step(self, dataset, _): ) self.set_metrics(test_label) test_pred = self.predict(test_rep) + test_pred_uncalibrated = self.predict(test_rep, use_calibrated=False) self.log_curves(test_label, test_pred, "test", pred_indicators) if self.debug: self._save_model_outputs(pred_indicators, test_pred, test_label) @@ -557,14 +602,21 @@ def test_step(self, dataset, _): else: self.log("test/loss", self.loss(test_label, test_pred), sync_dist=True) self.log_metrics(test_label, test_pred, "test", pred_indicators) - logging.debug(f"Test loss: {self.loss(test_label, test_pred)}") - - def predict(self, features): - if self.run_mode == RunMode.regression: - return self.model.predict(features) - else: # Classification: return probabilities - return self.model.predict_proba(features) - + logging.info(f"Test loss: {self.loss(test_label, test_pred)}, " + f"uncalibrated {self.loss(test_label,test_pred_uncalibrated)}") + + def predict(self, features, use_calibrated=True): + """Predict using calibrated model if available, otherwise use base model.""" + if hasattr(self, 'calibrated_model') and use_calibrated: + if self.run_mode == RunMode.regression: + return self.calibrated_model.predict(features) + else: + return self.calibrated_model.predict_proba(features) + else: + if self.run_mode == RunMode.regression: + return self.model.predict(features) + else: + return self.model.predict_proba(features) def log_curves(self, label, pred, metric_type, pred_indicators): for name, metric in self.metrics.items(): result = metric(self.label_transform(label), self.output_transform(pred)) From 52895ac59199b57bb88aa2663a7d7218857e4203 Mon Sep 17 00:00:00 2001 From: Robin van de Water Date: Tue, 2 Sep 2025 20:59:29 +0200 Subject: [PATCH 76/82] new experiments --- experiments/benchmark_cass.yml | 65 +++++++++----- experiments/benchmark_cass_baselines.yml | 64 ++++++++++++++ .../benchmark_cass_explain_modalities.yml | 69 +++++++++++++++ experiments/benchmark_cass_modalities.yml | 68 ++++++++++++++ .../benchmark_cass_model_architecture.yml | 74 ++++++++++++++++ experiments/benchmark_cass_segment_length.yml | 79 +++++++++++++++++ .../benchmark_cass_wearable_modalities.yml | 73 +++++++++++++++ experiments/benchmark_sex.yml | 60 +++++++++++++ experiments/top_features_benchmark_cass.yml | 88 +++++++++++++++++++ 9 files changed, 619 insertions(+), 21 deletions(-) create mode 100644 experiments/benchmark_cass_baselines.yml create mode 100644 experiments/benchmark_cass_explain_modalities.yml create mode 100644 experiments/benchmark_cass_modalities.yml create mode 100644 experiments/benchmark_cass_model_architecture.yml create mode 100644 experiments/benchmark_cass_segment_length.yml create mode 100644 experiments/benchmark_cass_wearable_modalities.yml create mode 100644 experiments/benchmark_sex.yml create mode 100644 experiments/top_features_benchmark_cass.yml diff --git a/experiments/benchmark_cass.yml b/experiments/benchmark_cass.yml index dd7148a8..0dd22e51 100644 --- a/experiments/benchmark_cass.yml +++ b/experiments/benchmark_cass.yml @@ -9,11 +9,20 @@ command: - CassClassification - --log-dir - /sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs -# - --tune + - --tune - --wandb-sweep - -tn - SSI - --hp-checkpoint + - "/sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/lab_set_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_1.0_horizon_6:00:00_transfer_full_2025-07-14T13:20:08/SSI/XGBClassifier/2025-07-20T18-29-31.571508/hyperparameter_tuning_logs.db" +# - "/sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/lab_set_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_1.0_horizon_6:00:00_transfer_full_2025-07-14T13:20:08/SSI/XGBClassifier/2025-07-23T09-42-04.806320/hyperparameter_tuning_logs.db" +# - "/sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/lab_set_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_1.0_horizon_6:00:00_transfer_full_2025-07-14T13:20:08/SSI/XGBClassifier/2025-07-20T18-29-31.571508/hyperparameter_tuning_logs.db" +# - "/sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/lab_set_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_0.5_horizon_6:00:00_transfer_full_2025-07-16T14:02:27/SSI/XGBClassifier/2025-07-20T18-32-19.553702/hyperparameter_tuning_logs.db" +# - "/sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/real_life_set_ICU_and_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_1.0_horizon_6:00:00_transfer_full_2025-07-16T14:58:54/SSI/XGBClassifier/2025-07-16T19-51-45.484723/hyperparameter_tuning_logs.db" +# - "/sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/lab_set_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_0.5_horizon_6:00:00_transfer_full_2025-07-16T14:02:27/SSI/XGBClassifier/2025-07-16T19-41-44.887611/hyperparameter_tuning_logs.db" +# - "/sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/lab_set_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_1.0_horizon_6:00:00_transfer_full_2025-07-14T13:20:08/SSI/XGBClassifier/2025-07-16T19-39-58.063736/hyperparameter_tuning_logs.db" +# - "/sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/lab_set_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_1.0_horizon_6:00:00_transfer_full_2025-07-14T13:20:08/SSI/XGBClassifier/2025-07-14T17-02-09.569252/hyperparameter_tuning_logs.db" +# - "/sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/lab_set_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_1.0_horizon_6:00:00_transfer_full_2025-07-14T13:20:08/SSI/XGBClassifier/2025-07-14T13-49-47.629185/hyperparameter_tuning_logs.db" # - "/sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/real_life_set_ICU_and_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_1.0_horizon_6:00:00_transfer_full_2025-06-27T14:38:43/SSI/XGBClassifier/2025-06-27T17-13-28.076070/hyperparameter_tuning_logs.db" # - "/sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/lab_set_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_0.5_horizon_6:00:00_transfer_full_2025-06-27T13:36:02/SSI/XGBClassifier/2025-06-27T17-12-03.184584/hyperparameter_tuning_logs.db" # - "/sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/lab_set_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_1.0_horizon_6:00:00_transfer_full_2025-06-27T14:45:50/SSI/XGBClassifier/2025-06-27T17-08-55.389121/hyperparameter_tuning_logs.db" @@ -26,7 +35,7 @@ command: - "all" - --file-names - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_6.parquet","STATIC":"sta.parquet"' -# - --explain_features + - --explain_features # - --verbose - --load_data_vars method: grid @@ -34,7 +43,26 @@ name: yaib_classification_benchmark parameters: data_dir: values: - - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/lab_set_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_0.5_horizon_6:00:00_transfer_full_2025-07-09T16:09:03" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/lab_set_ICU_and_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_0.5_horizon_6:00:00_transfer_full_2025-08-21T17:08:54" + - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/real_life_set_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_0.5_horizon_6:00:00_transfer_full_2025-08-09T13:30:00" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/lab_set_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_0.5_horizon_6:00:00_transfer_full_2025-08-05T12:24:09" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/real_life_set_ICU_SSI-3_POPF_Galleleck_BDA_segment_0.5_horizon_6:00:00_transfer_full_2025-08-06T10:34:13" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/lab_set_ICU_and_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_0.5_horizon_6:00:00_transfer_full_2025-08-01T12:13:31" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/lab_set_ICU_SSI-3_POPF_Galleleck_BDA_segment_0.5_horizon_6:00:00_transfer_full_2025-08-05T12:05:19" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/real_life_set_ICU_and_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_0.5_horizon_6:00:00_transfer_full_2025-08-05T17:02:14" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/lab_set_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_0.5_horizon_6:00:00_transfer_full_2025-08-05T12:24:09" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/lab_set_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_0.5_horizon_6:00:00_transfer_full_2025-07-31T11:05:22" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/lab_set_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_0.5_horizon_6:00:00_transfer_full_2025-07-30T10:54:08" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/lab_set_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_1.0_horizon_6:00:00_transfer_full_2025-07-29T12:10:11" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/lab_set_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_1.0_horizon_6:00:00_transfer_full_2025-07-27T13:50:45" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/lab_set_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_1.0_horizon_6:00:00_transfer_full_2025-07-25T17:15:01" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/real_life_set_ICU_and_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_1.0_horizon_6:00:00_transfer_full_2025-07-16T14:58:54" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/lab_set_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_0.5_horizon_6:00:00_transfer_full_2025-07-16T14:02:27" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/lab_set_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_1.0_horizon_6:00:00_transfer_full_2025-07-14T13:20:08" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/lab_set_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_1.0_horizon_6:00:00_transfer_full_2025-07-12T16:49:25" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/lab_set_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_1.0_horizon_6:00:00_transfer_full_2025-07-10T21:42:32" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/lab_set_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_0.5_horizon_6:00:00_transfer_full_2025-07-10T14:16:29" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/lab_set_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_0.5_horizon_6:00:00_transfer_full_2025-07-09T16:09:03" # - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/lab_set_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_1.0_horizon_6:00:00_transfer_full_2025-07-08T15:24:29" # - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/lab_set_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_1.0_horizon_6:00:00_transfer_full_2025-07-08T12:12:15" # - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/lab_set_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_1.0_horizon_6:00:00_transfer_full_2025-07-07T19:04:46" @@ -75,9 +103,9 @@ parameters: # - TimesNet # - LGBMClassifier - XGBClassifier +# - RUSBClassifier # - CBClassifier # - BRFClassifier -# - RUSBClassifier # - RFClassifier # - GRU # - LSTM @@ -85,8 +113,10 @@ parameters: # - Transformer modalities: values: -# - "all" -# - [copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, cat_clinical_notes, cat_clinical_notes, static] + - "all" +# - [ top_features ] +# - [copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, cat_medications, cat_clinical_notes, wearable_activity, wearable_core, wearable_ppgfeature, wearable_ppgembedding, static, static_retro_icu, static_retro_pre_intra ] # missing:hour + # - [copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, cat_clinical_notes, cat_clinical_notes, static] # - [copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, cat_clinical_notes, wearable_activity, wearable_core, static] # - [copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, static] #, wearable_activity, wearable_core] # without wearable data # - [copra_observations, copra_scores, copra_fluid_balance, cat_clinical_notes, wearable_activity, wearable_core, static] # without ishmed lab data and clinical notes @@ -97,26 +127,19 @@ parameters: # - [ishmed_lab_numeric, static] # - [static] # 1 missing modality - - "all" -# - [ wearable_ppgembedding, static ] +# - [ wearable_ppgembedding, core, wearable_ppgfeature, static ] # - [ wearable_activity, static ] # - [ wearable_ppgfeature, static ] # - [ wearable_core, static] -# - [ copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, cat_medications, cat_clinical_notes, static, static_retro_icu, static_retro_pre_intra ] # missing: wearable (activity, ppgfeature, ppgembedding) -# - [ copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, cat_medications, cat_clinical_notes, -# wearable_activity, wearable_ppgfeature, wearable_ppgembedding, wearable_core, static_retro_icu, -# static_retro_pre_intra, static ] # not missing any -# - [ copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, cat_clinical_notes, wearable_core, static ] # missing: wearable (activity, ppgfeature, ppgembedding) -# - [ ishmed_lab_numeric, cat_clinical_notes, wearable_activity, wearable_ppgfeature, wearable_ppgembedding, wearable_core, static ] # missing: copra -# - [ copra_observations, copra_scores, copra_fluid_balance, cat_clinical_notes, wearable_activity, wearable_ppgfeature, wearable_ppgembedding, wearable_core, static ] # 1 missing: ishmed (lab) -# - [ copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, wearable_activity, wearable_ppgfeature, wearable_ppgembedding, wearable_core, static ] # missing cat (notes, med embeddings) -# - [ copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, cat_clinical_notes, wearable_activity, wearable_ppgfeature, wearable_ppgembedding, static ] # 1 missing: wearable_core -# - [ copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, cat_clinical_notes, cat_medications, wearable_activity, wearable_ppgfeature, wearable_ppgembedding, wearable_core, static_retro_icu, static_retro_pre_intra] # 1 missing: static +# - [ copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, static, static_retro_icu, static_retro_pre_intra, wearable_activity, wearable_core, wearable_ppgfeature, wearable_ppgembedding, cat_medications, cat_clinical_notes] #hour ] # missing:notes, medications +# - [ copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, static, static_retro_icu, static_retro_pre_intra, wearable_activity, wearable_core, wearable_ppgfeature, wearable_ppgembedding, hour ] # missing:notes, medications +# - [ copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, cat_medications, cat_clinical_notes, static, static_retro_icu, static_retro_pre_intra, hour ] # missing: wearable (activity, ppgfeature, ppgembedding) + - [static, hour, cat_clinical_notes] file_names: values: # - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_3.parquet","STATIC":"sta.parquet"' - - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_6.parquet","STATIC":"sta.parquet"' -# - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_9.parquet","STATIC":"sta.parquet"' +# - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_6.parquet","STATIC":"sta.parquet"' + - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_9.parquet","STATIC":"sta.parquet"' # - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_12.parquet","STATIC":"sta.parquet"' # - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_18.parquet","STATIC":"sta.parquet"' # - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_24.parquet","STATIC":"sta.parquet"' @@ -126,4 +149,4 @@ parameters: use_pretrained_imputation: values: - None -program: icu-benchmarks \ No newline at end of file +program: icu-benchmarks diff --git a/experiments/benchmark_cass_baselines.yml b/experiments/benchmark_cass_baselines.yml new file mode 100644 index 00000000..00069b90 --- /dev/null +++ b/experiments/benchmark_cass_baselines.yml @@ -0,0 +1,64 @@ +command: + - ${env} + - ${program} + - train + - -d + - ../data/ + - -t +# - BinaryClassification + - CassClassification + - --log-dir + - /sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs + - --tune + - --wandb-sweep + - -tn + - SSI + - --hp-checkpoint + - "/sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/dataset_baseline_cohort_post-operative_static_2025-08-01T11:00:26/SSI/XGBClassifier/2025-08-01T11-08-08.547141/hyperparameter_tuning_logs.db" + - --modalities + - "all" + - --file-names + - '"DYNAMIC":"dyn.parquet","STATIC":"sta.parquet"' + - --explain_features +# - --verbose + - --load_data_vars +method: grid +name: yaib_classification_benchmark +parameters: + data_dir: + values: +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/dataset_baseline_cohort_pre-operative_2025-08-01T10:57:46" + - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/dataset_baseline_cohort_intra-operative_static_2025-08-01T10:59:15" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/dataset_baseline_cohort_post-operative_static_2025-08-01T11:00:26" + model: + values: +# - LogisticRegression +# - TimesNet +# - LGBMClassifier + - XGBClassifier +# - CBClassifier +# - BRFClassifier +# - RUSBClassifier +# - RFClassifier +# - GRU +# - LSTM +# - TCN +# - Transformer + modalities: + values: + - "all" + file_names: + values: + - '"OUTCOME":"outc.parquet","STATIC":"sta.parquet"' +# - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_6.parquet","STATIC":"sta.parquet"' +# - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_9.parquet","STATIC":"sta.parquet"' +# - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_12.parquet","STATIC":"sta.parquet"' +# - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_18.parquet","STATIC":"sta.parquet"' +# - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_24.parquet","STATIC":"sta.parquet"' + seed: + values: + - 1111 + use_pretrained_imputation: + values: + - None +program: icu-benchmarks \ No newline at end of file diff --git a/experiments/benchmark_cass_explain_modalities.yml b/experiments/benchmark_cass_explain_modalities.yml new file mode 100644 index 00000000..6b4f1586 --- /dev/null +++ b/experiments/benchmark_cass_explain_modalities.yml @@ -0,0 +1,69 @@ +command: + - ${env} + - ${program} + - train + - -d + - ../data/ + - -t +# - BinaryClassification + - CassClassification + - --log-dir + - /sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs +# - --tune + - --wandb-sweep + - -tn + - SSI + - --hp-checkpoint + - "/sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/lab_set_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_1.0_horizon_6:00:00_transfer_full_2025-07-14T13:20:08/SSI/XGBClassifier/2025-07-20T18-29-31.571508/hyperparameter_tuning_logs.db" +# - --verbose + - --modalities + - "all" + - --file-names + - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_6.parquet","STATIC":"sta.parquet"' + - --explain_features +# - --verbose + - --load_data_vars +method: grid +name: yaib_classification_benchmark +parameters: + data_dir: + values: + - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/lab_set_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_0.5_horizon_6:00:00_transfer_full_2025-08-05T12:24:09" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/lab_set_ICU_and_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_0.5_horizon_6:00:00_transfer_full_2025-08-01T12:13:31" + model: + values: +# - LogisticRegression +# - TimesNet +# - LGBMClassifier + - XGBClassifier +# - CBClassifier +# - BRFClassifier +# - RUSBClassifier +# - RFClassifier +# - GRU +# - LSTM +# - TCN +# - Transformer + modalities: + values: +# - "all" +# - [ copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, cat_medications, cat_clinical_notes, wearable_activity, wearable_core, wearable_ppgfeature, wearable_ppgembedding, static, static_retro_icu, static_retro_pre_intra, hour ] +# - [ copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, cat_medications, cat_clinical_notes, static, static_retro_icu, static_retro_pre_intra, hour ] # missing:wearable_activity, wearable_core, wearable_ppgfeature, wearable_ppgembedding +# - [ copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, cat_medications, cat_clinical_notes, static, static_retro_icu, static_retro_pre_intra, hour, wearable_core ] # missing:wearable_activity, wearable_core, wearable_ppgfeature, wearable_ppgembedding +# - [ copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, cat_medications, cat_clinical_notes, static, static_retro_icu, static_retro_pre_intra, hour, wearable_ppgfeature ] # missing:wearable_activity, wearable_core, wearable_ppgfeature, wearable_ppgembedding +# - [ copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, cat_medications, cat_clinical_notes, static, static_retro_icu, static_retro_pre_intra, hour, wearable_activity ] # missing:wearable_activity, wearable_core, wearable_ppgfeature, wearable_ppgembedding +# - [ copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, cat_medications, cat_clinical_notes, static, static_retro_icu, static_retro_pre_intra, hour, wearable_ppgembedding ] # missing:wearable_activity, wearable_core, wearable_ppgfeature, wearable_ppgembedding + - [ copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, cat_medications, cat_clinical_notes, wearable_activity, wearable_core, wearable_ppgfeature, wearable_ppgembedding, static, static_retro_icu, static_retro_pre_intra ] # missing:hour +# - [ copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, wearable_activity, wearable_core, wearable_ppgfeature, wearable_ppgembedding, static, static_retro_icu, static_retro_pre_intra, hour ] # missing:cat_medications, cat_clinical_notes +# - [ copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, cat_medications, cat_clinical_notes, wearable_activity, wearable_core, wearable_ppgfeature, wearable_ppgembedding, static, hour ] # missing:static_retro_icu, static_retro_pre_intra + file_names: + values: +# - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_6.parquet","STATIC":"sta.parquet"' + - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_9.parquet","STATIC":"sta.parquet"' + seed: + values: + - 1111 + use_pretrained_imputation: + values: + - None +program: icu-benchmarks \ No newline at end of file diff --git a/experiments/benchmark_cass_modalities.yml b/experiments/benchmark_cass_modalities.yml new file mode 100644 index 00000000..efc8b893 --- /dev/null +++ b/experiments/benchmark_cass_modalities.yml @@ -0,0 +1,68 @@ +command: + - ${env} + - ${program} + - train + - -d + - ../data/ + - -t +# - BinaryClassification + - CassClassification + - --log-dir + - /sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs +# - --tune + - --wandb-sweep + - -tn + - SSI + - --hp-checkpoint + - "/sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/lab_set_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_1.0_horizon_6:00:00_transfer_full_2025-07-14T13:20:08/SSI/XGBClassifier/2025-07-20T18-29-31.571508/hyperparameter_tuning_logs.db" +# - --verbose + - --modalities + - "all" + - --file-names + - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_6.parquet","STATIC":"sta.parquet"' +# - --explain_features +# - --verbose + - --load_data_vars +method: grid +name: yaib_classification_benchmark +parameters: + data_dir: + values: + - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/lab_set_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_0.5_horizon_6:00:00_transfer_full_2025-08-05T12:24:09" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/lab_set_ICU_and_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_0.5_horizon_6:00:00_transfer_full_2025-08-01T12:13:31" + model: + values: +# - LogisticRegression +# - TimesNet +# - LGBMClassifier + - XGBClassifier +# - CBClassifier +# - BRFClassifier +# - RUSBClassifier +# - RFClassifier +# - GRU +# - LSTM +# - TCN +# - Transformer + modalities: + values: +# - "all" + - [ copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, cat_medications, cat_clinical_notes, wearable_activity, wearable_core, wearable_ppgfeature, wearable_ppgembedding, static, static_retro_icu, static_retro_pre_intra, hour ] + - [ copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, cat_medications, cat_clinical_notes, static, static_retro_icu, static_retro_pre_intra, hour ] # missing:wearable_activity, wearable_core, wearable_ppgfeature, wearable_ppgembedding +# - [ ishmed_lab_numeric, cat_medications, cat_clinical_notes, wearable_activity, wearable_core, wearable_ppgfeature, wearable_ppgembedding, static, static_retro_icu, static_retro_pre_intra, hour ] # missing:copra_observations, copra_scores, copra_fluid_balance +# - [ copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, cat_medications, cat_clinical_notes, wearable_activity, wearable_core, wearable_ppgfeature, wearable_ppgembedding, hour ] # missing:static, static_retro_icu, static_retro_pre_intra + - [ copra_observations, copra_scores, copra_fluid_balance, cat_medications, cat_clinical_notes, wearable_activity, wearable_core, wearable_ppgfeature, wearable_ppgembedding, static, static_retro_icu, static_retro_pre_intra, hour ] # missing:ishmed_lab_numeric +# - [ copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, cat_medications, cat_clinical_notes, wearable_activity, wearable_core, wearable_ppgfeature, wearable_ppgembedding, static, static_retro_icu, static_retro_pre_intra ] # missing:hour +# - [ copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, wearable_activity, wearable_core, wearable_ppgfeature, wearable_ppgembedding, static, static_retro_icu, static_retro_pre_intra, hour ] # missing:cat_medications, cat_clinical_notes +# - [ copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, cat_medications, cat_clinical_notes, wearable_activity, wearable_core, wearable_ppgfeature, wearable_ppgembedding, static, hour ] # missing:static_retro_icu, static_retro_pre_intra + file_names: + values: +# - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_6.parquet","STATIC":"sta.parquet"' + - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_9.parquet","STATIC":"sta.parquet"' + seed: + values: + - 1111 + use_pretrained_imputation: + values: + - None +program: icu-benchmarks \ No newline at end of file diff --git a/experiments/benchmark_cass_model_architecture.yml b/experiments/benchmark_cass_model_architecture.yml new file mode 100644 index 00000000..47db893f --- /dev/null +++ b/experiments/benchmark_cass_model_architecture.yml @@ -0,0 +1,74 @@ +command: + - ${env} + - ${program} + - train + - -d + - ../data/ + - -t +# - BinaryClassification + - CassClassification + - --log-dir + - /sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs + - --tune + - --wandb-sweep + - -tn + - SSI + - --hp-checkpoint + - "/sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/lab_set_ICU_and_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_0.5_horizon_6:00:00_transfer_full_2025-08-21T17:08:54/SSI/GRU/2025-08-28T10-30-02.002119/hyperparameter_tuning_logs.db" +# - "/sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/lab_set_ICU_and_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_0.5_horizon_6:00:00_transfer_full_2025-08-01T12:13:31/SSI/CBClassifier/2025-08-13T11-36-55.269201/hyperparameter_tuning_logs.db" +# - "/sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/lab_set_ICU_and_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_0.5_horizon_6:00:00_transfer_full_2025-08-01T12:13:31/SSI/GRU/2025-08-10T10-57-05.448548/hyperparameter_tuning_logs.db" +# - "/sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/lab_set_ICU_and_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_0.5_horizon_6:00:00_transfer_full_2025-08-01T12:13:31/SSI/Transformer/2025-08-10T10-59-28.332900/hyperparameter_tuning_logs.db" +# - "/sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/lab_set_ICU_and_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_0.5_horizon_6:00:00_transfer_full_2025-08-01T12:13:31/SSI/RFClassifier/2025-08-11T10-10-03.033462/hyperparameter_tuning_logs.db" +# - "/sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/lab_set_ICU_and_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_0.5_horizon_6:00:00_transfer_full_2025-08-01T12:13:31/SSI/CBClassifier/2025-08-10T10-53-33.604092/hyperparameter_tuning_logs.db" +# - "/sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/lab_set_ICU_and_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_0.5_horizon_6:00:00_transfer_full_2025-08-01T12:13:31/SSI/LGBMClassifier/2025-08-11T10-09-52.484304/hyperparameter_tuning_logs.db" +# - "/sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/lab_set_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_1.0_horizon_6:00:00_transfer_full_2025-07-14T13:20:08/SSI/XGBClassifier/2025-07-20T18-29-31.571508/hyperparameter_tuning_logs.db" + - --modalities + - "all" + - --file-names + - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_6.parquet","STATIC":"sta.parquet"' +# - --explain_features +# - --verbose + - --load_data_vars +method: grid +name: yaib_classification_benchmark +parameters: + data_dir: + values: + - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/lab_set_ICU_and_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_0.5_horizon_6:00:00_transfer_full_2025-08-21T17:08:54" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/lab_set_ICU_and_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_0.5_horizon_6:00:00_transfer_full_2025-08-01T12:13:31" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/lab_set_ICU_SSI-3_POPF_Galleleck_BDA_segment_0.5_horizon_6:00:00_transfer_full_2025-08-05T12:05:19" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/lab_set_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_0.5_horizon_6:00:00_transfer_full_2025-08-05T12:24:09" + model: + values: +# - LogisticRegression +## - TimesNet +# - LGBMClassifier +## - XGBClassifier +# - CBClassifier +# - BRFClassifier +# - XGBEnsembleClassifier +# - RUSBClassifier +# - RFClassifier + - GRU +# - LSTM +# - TCN +# - Transformer + modalities: + values: +# - "all" + - [ top_500_features ] + file_names: + values: +# - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_3.parquet","STATIC":"sta.parquet"' +# - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_6.parquet","STATIC":"sta.parquet"' + - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_9.parquet","STATIC":"sta.parquet"' +# - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_12.parquet","STATIC":"sta.parquet"' +# - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_18.parquet","STATIC":"sta.parquet"' +# - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_24.parquet","STATIC":"sta.parquet"' + seed: + values: + - 1111 + use_pretrained_imputation: + values: + - None +program: icu-benchmarks \ No newline at end of file diff --git a/experiments/benchmark_cass_segment_length.yml b/experiments/benchmark_cass_segment_length.yml new file mode 100644 index 00000000..753690a1 --- /dev/null +++ b/experiments/benchmark_cass_segment_length.yml @@ -0,0 +1,79 @@ +command: + - ${env} + - ${program} + - train + - -d + - ../data/ + - -t +# - BinaryClassification + - CassClassification + - --log-dir + - /sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs +# - --tune + - --wandb-sweep + - -tn + - SSI + - --hp-checkpoint + - "/sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/lab_set_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_1.0_horizon_6:00:00_transfer_full_2025-07-14T13:20:08/SSI/XGBClassifier/2025-07-20T18-29-31.571508/hyperparameter_tuning_logs.db" +# - "/sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/lab_set_ICU_and_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_0.25_horizon_6:00:00_transfer_full_2025-08-07T20:09:44/SSI/XGBClassifier/2025-08-08T10-30-09.905452/hyperparameter_tuning_logs.db" +# - "/sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/lab_set_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_1.0_horizon_6:00:00_transfer_full_2025-07-14T13:20:08/SSI/XGBClassifier/2025-07-20T18-29-31.571508/hyperparameter_tuning_logs.db" +## - Mortality +# - --verbose + - --modalities + - "all" + - --file-names + - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_6.parquet","STATIC":"sta.parquet"' +# - --explain_features +# - --verbose + - --load_data_vars +method: grid +name: cass_segmentation_length +parameters: + data_dir: + values: + - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/lab_set_ICU_and_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_0.125_full_2025-08-22T12:28:04" + - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/lab_set_ICU_and_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_0.25_horizon_6:00:00_transfer_full_2025-08-21T17:01:27" + - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/lab_set_ICU_and_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_0.5_horizon_6:00:00_transfer_full_2025-08-21T17:08:54" + - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/lab_set_ICU_and_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_1.0_full_2025-08-21T16:46:25" + - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/lab_set_ICU_and_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_3.0_full_2025-08-21T16:44:31" + - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/lab_set_ICU_and_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_6.0_full_2025-08-22T11:38:16" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/lab_set_ICU_and_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_9.0_full_2025-08-21T16:46:46" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/lab_set_ICU_and_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_12.0_full_2025-08-21T16:46:09" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/lab_set_ICU_and_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_0.25_horizon_6:00:00_transfer_full_2025-08-07T20:09:44" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/lab_set_ICU_and_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_0.5_horizon_6:00:00_transfer_full_2025-08-01T12:13:31" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/lab_set_ICU_and_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_1.0_horizon_6:00:00_transfer_full_2025-08-07T20:36:31" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/lab_set_ICU_and_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_2.0_horizon_6:00:00_transfer_full_2025-08-07T20:02:59" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/lab_set_ICU_and_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_4.0_horizon_6:00:00_transfer_full_2025-08-07T20:11:16" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/lab_set_ICU_and_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_6.0_horizon_6:00:00_transfer_full_2025-08-07T20:03:41" + model: + values: +# - LogisticRegression +# - TimesNet +# - LGBMClassifier + - XGBClassifier +# - CBClassifier +# - BRFClassifier +# - RUSBClassifier +# - RFClassifier +# - GRU +# - LSTM +# - TCN +# - Transformer + modalities: + values: + - "all" + file_names: + values: +# - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_3.parquet","STATIC":"sta.parquet"' + - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_6.parquet","STATIC":"sta.parquet"' +# - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_9.parquet","STATIC":"sta.parquet"' +# - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_12.parquet","STATIC":"sta.parquet"' +# - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_18.parquet","STATIC":"sta.parquet"' +# - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_24.parquet","STATIC":"sta.parquet"' + seed: + values: + - 1111 + use_pretrained_imputation: + values: + - None +program: icu-benchmarks \ No newline at end of file diff --git a/experiments/benchmark_cass_wearable_modalities.yml b/experiments/benchmark_cass_wearable_modalities.yml new file mode 100644 index 00000000..87c0ba64 --- /dev/null +++ b/experiments/benchmark_cass_wearable_modalities.yml @@ -0,0 +1,73 @@ +command: + - ${env} + - ${program} + - train + - -d + - ../data/ + - -t +# - BinaryClassification + - CassClassification + - --log-dir + - /sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs +# - --tune + - --wandb-sweep + - -tn + - SSI + - --hp-checkpoint + - "/sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/lab_set_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_1.0_horizon_6:00:00_transfer_full_2025-07-14T13:20:08/SSI/XGBClassifier/2025-07-20T18-29-31.571508/hyperparameter_tuning_logs.db" +# - --verbose + - --modalities + - "all" + - --file-names + - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_6.parquet","STATIC":"sta.parquet"' +# - --explain_features +# - --verbose + - --load_data_vars +method: grid +name: yaib_classification_benchmark +parameters: + data_dir: + values: + - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/lab_set_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_0.5_horizon_6:00:00_transfer_full_2025-08-05T12:24:09" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/lab_set_ICU_and_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_0.5_horizon_6:00:00_transfer_full_2025-08-01T12:13:31" + model: + values: +# - LogisticRegression +# - TimesNet +# - LGBMClassifier + - XGBClassifier +# - CBClassifier +# - BRFClassifier +# - RUSBClassifier +# - RFClassifier +# - GRU +# - LSTM +# - TCN +# - Transformer + modalities: + values: +# - "all" + - [ copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, cat_medications, cat_clinical_notes, wearable_activity, wearable_core, wearable_ppgfeature, wearable_ppgembedding, static, static_retro_icu, static_retro_pre_intra, hour ] + - [ copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, cat_medications, cat_clinical_notes, wearable_activity, wearable_core, wearable_ppgfeature, static, static_retro_icu, static_retro_pre_intra, hour ] # missing:wearable_ppgembedding + - [ copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, cat_medications, cat_clinical_notes, wearable_activity, wearable_ppgfeature, wearable_ppgembedding, static, static_retro_icu, static_retro_pre_intra, hour ] # missing:wearable_core + - [ copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, cat_medications, cat_clinical_notes, wearable_activity, wearable_core, wearable_ppgembedding, static, static_retro_icu, static_retro_pre_intra, hour ] # missing:wearable_ppgfeature + - [ copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, cat_medications, cat_clinical_notes, wearable_core, wearable_ppgfeature, wearable_ppgembedding, static, static_retro_icu, static_retro_pre_intra, hour ] # missing:wearable_activity +# - [ copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, cat_medications, cat_clinical_notes, static, static_retro_icu, static_retro_pre_intra, hour ] # missing:wearable_activity, wearable_core, wearable_ppgfeature, wearable_ppgembedding +# - [ copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, cat_medications, cat_clinical_notes, static, static_retro_icu, static_retro_pre_intra, hour, wearable_core ] # missing:wearable_activity, wearable_core, wearable_ppgfeature, wearable_ppgembedding +# - [ copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, cat_medications, cat_clinical_notes, static, static_retro_icu, static_retro_pre_intra, hour, wearable_ppgfeature ] # missing:wearable_activity, wearable_core, wearable_ppgfeature, wearable_ppgembedding +# - [ copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, cat_medications, cat_clinical_notes, static, static_retro_icu, static_retro_pre_intra, hour, wearable_activity ] # missing:wearable_activity, wearable_core, wearable_ppgfeature, wearable_ppgembedding +# - [ copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, cat_medications, cat_clinical_notes, static, static_retro_icu, static_retro_pre_intra, hour, wearable_ppgembedding ] # missing:wearable_activity, wearable_core, wearable_ppgfeature, wearable_ppgembedding +# - [ copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, cat_medications, cat_clinical_notes, wearable_activity, wearable_core, wearable_ppgfeature, wearable_ppgembedding, static, static_retro_icu, static_retro_pre_intra ] # missing:hour +# - [ copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, wearable_activity, wearable_core, wearable_ppgfeature, wearable_ppgembedding, static, static_retro_icu, static_retro_pre_intra, hour ] # missing:cat_medications, cat_clinical_notes +# - [ copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, cat_medications, cat_clinical_notes, wearable_activity, wearable_core, wearable_ppgfeature, wearable_ppgembedding, static, hour ] # missing:static_retro_icu, static_retro_pre_intra + file_names: + values: +# - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_6.parquet","STATIC":"sta.parquet"' + - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_9.parquet","STATIC":"sta.parquet"' + seed: + values: + - 1111 + use_pretrained_imputation: + values: + - None +program: icu-benchmarks \ No newline at end of file diff --git a/experiments/benchmark_sex.yml b/experiments/benchmark_sex.yml new file mode 100644 index 00000000..5daf9f92 --- /dev/null +++ b/experiments/benchmark_sex.yml @@ -0,0 +1,60 @@ +command: + - ${env} + - ${program} + - train + - -d + - ../data/ + - -t +# - BinaryClassification + - CassClassification + - --log-dir + - /sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs +# - --tune + - --wandb-sweep + - -tn + - SSI + - --hp-checkpoint + - "/sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/lab_set_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_1.0_horizon_6:00:00_transfer_full_2025-07-14T13:20:08/SSI/XGBClassifier/2025-07-20T18-29-31.571508/hyperparameter_tuning_logs.db" +# - --verbose + - --modalities + - "all" + - --file-names + - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_6.parquet","STATIC":"sta.parquet"' +# - --explain_features +# - --verbose + - --load_data_vars +method: grid +name: organ_system_benchmark +parameters: + data_dir: + values: +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/sex_cohorts_lab_set_ICU_and_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_0.5_horizon_6:00:00_transfer_full_2025-08-01T12:13:31/female" + - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/sex_cohorts_lab_set_ICU_and_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_0.5_horizon_6:00:00_transfer_full_2025-08-01T12:13:31/male" + model: + values: +# - LogisticRegression +# - TimesNet +# - LGBMClassifier + - XGBClassifier +# - CBClassifier +# - BRFClassifier +# - RUSBClassifier +# - RFClassifier +# - GRU +# - LSTM +# - TCN +# - Transformer + modalities: + values: + - "all" + file_names: + values: +# - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_6.parquet","STATIC":"sta.parquet"' + - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_9.parquet","STATIC":"sta.parquet"' + seed: + values: + - 1111 + use_pretrained_imputation: + values: + - None +program: icu-benchmarks \ No newline at end of file diff --git a/experiments/top_features_benchmark_cass.yml b/experiments/top_features_benchmark_cass.yml new file mode 100644 index 00000000..aa03153e --- /dev/null +++ b/experiments/top_features_benchmark_cass.yml @@ -0,0 +1,88 @@ +command: + - ${env} + - ${program} + - train + - -d + - ../data/ + - -t +# - BinaryClassification + - CassClassification + - --log-dir + - /sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs + - --tune + - --wandb-sweep + - -tn + - SSI + - --hp-checkpoint + - "/sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/lab_set_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_1.0_horizon_6:00:00_transfer_full_2025-07-14T13:20:08/SSI/XGBClassifier/2025-07-20T18-29-31.571508/hyperparameter_tuning_logs.db" + - --modalities + - "all" + - --file-names + - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_6.parquet","STATIC":"sta.parquet"' +# - --explain_features +# - --verbose + - --load_data_vars +method: grid +name: yaib_classification_benchmark +parameters: + data_dir: + values: + - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/lab_set_ICU_and_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_0.5_horizon_6:00:00_transfer_full_2025-08-21T17:08:54" + model: + values: +# - LogisticRegression +# - TimesNet +# - LGBMClassifier + - XGBClassifier +# - RUSBClassifier +# - CBClassifier +# - BRFClassifier +# - RFClassifier +# - GRU +# - LSTM +# - TCN +# - Transformer + modalities: + values: +# - "all" + - [ top_50_features ] +# - [ top_100_features ] + - [ top_250_features ] + - [ top_500_features ] + - [ top_1000_features ] + - [ top_2000_features ] +# - [copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, cat_medications, cat_clinical_notes, wearable_activity, wearable_core, wearable_ppgfeature, wearable_ppgembedding, static, static_retro_icu, static_retro_pre_intra ] # missing:hour + # - [copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, cat_clinical_notes, cat_clinical_notes, static] +# - [copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, cat_clinical_notes, wearable_activity, wearable_core, static] +# - [copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, static] #, wearable_activity, wearable_core] # without wearable data +# - [copra_observations, copra_scores, copra_fluid_balance, cat_clinical_notes, wearable_activity, wearable_core, static] # without ishmed lab data and clinical notes +# - [copra_observations, copra_scores, copra_fluid_balance, cat_clinical_notes, static ] # without wearable data and ishmed lab data, clinical notes +# - [copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, cat_clinical_notes, wearable_activity, wearable_core, static] # without clinical notes +# - [copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, cat_clinical_notes, wearable_activity, wearable_core, static] # without med embeddings +# - [wearable_activity, wearable_core, static] +# - [ishmed_lab_numeric, static] +# - [static] + # 1 missing modality +# - [ wearable_ppgembedding, core, wearable_ppgfeature, static ] +# - [ wearable_activity, static ] +# - [ wearable_ppgfeature, static ] +# - [ wearable_core, static] +# - [ copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, static, static_retro_icu, static_retro_pre_intra, wearable_activity, wearable_core, wearable_ppgfeature, wearable_ppgembedding, cat_medications, cat_clinical_notes] #hour ] # missing:notes, medications +# - [ copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, static, static_retro_icu, static_retro_pre_intra, wearable_activity, wearable_core, wearable_ppgfeature, wearable_ppgembedding, hour ] # missing:notes, medications +# - [ copra_observations, copra_scores, copra_fluid_balance, ishmed_lab_numeric, cat_medications, cat_clinical_notes, static, static_retro_icu, static_retro_pre_intra, hour ] # missing: wearable (activity, ppgfeature, ppgembedding) +# - [static, cat_clinical_notes] + file_names: + values: +# - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_3.parquet","STATIC":"sta.parquet"' +# - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_6.parquet","STATIC":"sta.parquet"' + - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_9.parquet","STATIC":"sta.parquet"' +# - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_12.parquet","STATIC":"sta.parquet"' +# - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_18.parquet","STATIC":"sta.parquet"' +# - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_24.parquet","STATIC":"sta.parquet"' + seed: + values: + - 1111 + use_pretrained_imputation: + values: + - None +program: icu-benchmarks From ead4582ff4eb88598df98da9ae5198130420172a Mon Sep 17 00:00:00 2001 From: Robin van de Water Date: Tue, 2 Sep 2025 21:08:33 +0200 Subject: [PATCH 77/82] cleanup --- configs/prediction_models/RUSBClassifier.gin | 22 ++++++++-- experiments/benchmark_cass_outcome_time.yml | 42 ++++++++++++------- experiments/benchmark_organsystems.yml | 43 ++++++-------------- 3 files changed, 58 insertions(+), 49 deletions(-) diff --git a/configs/prediction_models/RUSBClassifier.gin b/configs/prediction_models/RUSBClassifier.gin index e8f17722..2516fb44 100644 --- a/configs/prediction_models/RUSBClassifier.gin +++ b/configs/prediction_models/RUSBClassifier.gin @@ -1,4 +1,4 @@ -# Settings for ImbLearn Balanced Random Forest Classifier. +# Settings for ImbLearn RUSBoost Classifier (Random Under-sampling with Boosting) # Common settings for ML models include "configs/prediction_models/common/MLCommon.gin" @@ -6,9 +6,23 @@ include "configs/prediction_models/common/MLCommon.gin" # Train params train_common.model = @RUSBClassifier +# Hyperparameter tuning configuration model/hyperparameter.class_to_tune = @RUSBClassifier -model/hyperparameter.n_estimators = (10, 50, 100, 200, 500) -model/hyperparameter.learning_rate = (0.005, 1, "log") -model/hyperparameter.sampling_strategy = "auto" +# Number of estimators (boosting rounds) +model/hyperparameter.n_estimators = (50, 100, 200, 300, 500) + +# Learning rate for boosting +model/hyperparameter.learning_rate = (0.01, 2.0, "log") + +# Sampling strategy for random under-sampling +model/hyperparameter.sampling_strategy = ["auto", "majority", "not minority"] + +# Base estimator parameters (typically DecisionTreeClassifier) +model/hyperparameter.base_estimator__max_depth = [1, 2, 3, 4, 5, 6] +model/hyperparameter.base_estimator__min_samples_split = [2, 5, 10, 20] +model/hyperparameter.base_estimator__min_samples_leaf = [1, 2, 5, 10] + +# Replacement strategy for under-sampling +model/hyperparameter.replacement = [True, False] diff --git a/experiments/benchmark_cass_outcome_time.yml b/experiments/benchmark_cass_outcome_time.yml index 24b0df1e..8a5d57a4 100644 --- a/experiments/benchmark_cass_outcome_time.yml +++ b/experiments/benchmark_cass_outcome_time.yml @@ -9,26 +9,41 @@ command: - CassClassification - --log-dir - /sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs - - --tune +# - --tune - --wandb-sweep - -tn - SSI - --hp-checkpoint - - /sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/normal_ward_SSI-3_segment_1.0_horizon_6:00:00_transfer_full_2025-05-27T23:34:00/SSI/XGBClassifier/2025-06-02T12-50-42.447250/hyperparameter_tuning_logs.db -# - Mortality + - "/sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/lab_set_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_1.0_horizon_6:00:00_transfer_full_2025-07-14T13:20:08/SSI/XGBClassifier/2025-07-20T18-29-31.571508/hyperparameter_tuning_logs.db" +# - "/sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/lab_set_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_1.0_horizon_6:00:00_transfer_full_2025-07-14T13:20:08/SSI/XGBClassifier/2025-07-23T09-42-04.806320/hyperparameter_tuning_logs.db" +# - "/sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/lab_set_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_1.0_horizon_6:00:00_transfer_full_2025-07-14T13:20:08/SSI/XGBClassifier/2025-07-20T18-29-31.571508/hyperparameter_tuning_logs.db" +# - "/sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/lab_set_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_0.5_horizon_6:00:00_transfer_full_2025-07-16T14:02:27/SSI/XGBClassifier/2025-07-20T18-32-19.553702/hyperparameter_tuning_logs.db" +# - "/sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/real_life_set_ICU_and_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_1.0_horizon_6:00:00_transfer_full_2025-07-16T14:58:54/SSI/XGBClassifier/2025-07-16T19-51-45.484723/hyperparameter_tuning_logs.db" +# - "/sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/lab_set_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_0.5_horizon_6:00:00_transfer_full_2025-07-16T14:02:27/SSI/XGBClassifier/2025-07-16T19-41-44.887611/hyperparameter_tuning_logs.db" +# - "/sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/lab_set_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_1.0_horizon_6:00:00_transfer_full_2025-07-14T13:20:08/SSI/XGBClassifier/2025-07-16T19-39-58.063736/hyperparameter_tuning_logs.db" +# - "/sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/lab_set_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_1.0_horizon_6:00:00_transfer_full_2025-07-14T13:20:08/SSI/XGBClassifier/2025-07-14T17-02-09.569252/hyperparameter_tuning_logs.db" +# - "/sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/lab_set_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_1.0_horizon_6:00:00_transfer_full_2025-07-14T13:20:08/SSI/XGBClassifier/2025-07-14T13-49-47.629185/hyperparameter_tuning_logs.db" +# - "/sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/real_life_set_ICU_and_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_1.0_horizon_6:00:00_transfer_full_2025-06-27T14:38:43/SSI/XGBClassifier/2025-06-27T17-13-28.076070/hyperparameter_tuning_logs.db" +# - "/sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/lab_set_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_0.5_horizon_6:00:00_transfer_full_2025-06-27T13:36:02/SSI/XGBClassifier/2025-06-27T17-12-03.184584/hyperparameter_tuning_logs.db" +# - "/sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/lab_set_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_1.0_horizon_6:00:00_transfer_full_2025-06-27T14:45:50/SSI/XGBClassifier/2025-06-27T17-08-55.389121/hyperparameter_tuning_logs.db" +# - "/sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/lab_set_normal_ward_SSI-3_segment_1.0_horizon_6:00:00_transfer_full_2025-06-19T19:11:23/SSI/XGBClassifier/2025-06-20T12-14-27.191590/hyperparameter_tuning_logs.db" +# - "/sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/BDA_segment_1.0_horizon_6:00:00_transfer_full_2025-06-20T16:37:14/SSI/XGBClassifier/2025-06-20T17-36-53.795313/hyperparameter_tuning_logs.db" +# - /sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/real_life_normal_ward_SSI-3_segment_1.0_horizon_6:00:00_transfer_full_2025-06-03T16:05:00/SSI/XGBClassifier/2025-06-04T17-09-19.640244/hyperparameter_tuning_logs.db +## - Mortality # - --verbose - --modalities - "all" - --file-names - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_6.parquet","STATIC":"sta.parquet"' -# - -gc -# - -lc +# - --explain_features +# - --verbose + - --load_data_vars method: grid name: yaib_classification_benchmark parameters: data_dir: values: - - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/normal_ward_SSI-3_segment_1.0_horizon_6:00:00_transfer_full_2025-05-27T23:34:00" + - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/lab_set_ICU_and_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_0.5_horizon_6:00:00_transfer_full_2025-08-01T12:13:31" model: values: # - LogisticRegression @@ -51,14 +66,13 @@ parameters: file_names: values: - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_1.parquet","STATIC":"sta.parquet"' - - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_3.parquet","STATIC":"sta.parquet"' - - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_4.parquet","STATIC":"sta.parquet"' - - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_6.parquet","STATIC":"sta.parquet"' - - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_8.parquet","STATIC":"sta.parquet"' - - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_9.parquet","STATIC":"sta.parquet"' - - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_12.parquet","STATIC":"sta.parquet"' - - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_18.parquet","STATIC":"sta.parquet"' - - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_24.parquet","STATIC":"sta.parquet"' + - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_2.parquet","STATIC":"sta.parquet"' +# - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_3.parquet","STATIC":"sta.parquet"' +# - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_6.parquet","STATIC":"sta.parquet"' +# - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_9.parquet","STATIC":"sta.parquet"' +# - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_12.parquet","STATIC":"sta.parquet"' +# - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_18.parquet","STATIC":"sta.parquet"' +# - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_24.parquet","STATIC":"sta.parquet"' seed: values: - 1111 diff --git a/experiments/benchmark_organsystems.yml b/experiments/benchmark_organsystems.yml index 06f712e5..4dc17890 100644 --- a/experiments/benchmark_organsystems.yml +++ b/experiments/benchmark_organsystems.yml @@ -9,44 +9,29 @@ command: - CassClassification - --log-dir - /sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs - - --tune +# - --tune - --wandb-sweep - -tn - SSI -# - --hp-checkpoint -# - /sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/real_life_normal_ward_SSI-3_segment_1.0_horizon_6:00:00_transfer_full_2025-06-03T16:05:00/SSI/XGBClassifier/2025-06-04T17-09-19.640244/hyperparameter_tuning_logs.db -# - /sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/normal_ward_SSI-3_segment_1.0_horizon_6:00:00_transfer_full_2025-05-27T23:34:00/SSI/XGBClassifier/2025-05-28T18-35-20.907325/hyperparameter_tuning_logs.db -# - /sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/normal_ward_SSI-3_segment_1.0_horizon_6:00:00_transfer_full_2025-05-09T19:19:33_wearable_cut_off_0.75/SSI/XGBClassifier/2025-05-12T17-24-38.032040/hyperparameter_tuning_logs.db -# - /sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/normal_ward_SSI-3_segment_1.0_horizon_6:00:00_transfer_full_2025-05-09T19:19:33_wearable_cut_off_0.75/SSI/XGBClassifier/2025-05-16T18-15-39.225575/hyperparameter_tuning_logs.db -# - /sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/normal_ward_SSI-3_segment_1.0_horizon_6:00:00_transfer_full_2025-05-09T19:19:33_wearable_cut_off_0.75/SSI/XGBClassifier/2025-05-16T18-15-39.225575/ -# - /sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/normal_ward_SSI-3_segment_1.0_horizon_6:00:00_transfer_full_2025-04-24T21:24:01_wearable_cut_off_0.75/SSI/XGBClassifier/2025-04-25T10-06-24.947526/hyperparameter_tuning_logs.db -# - Mortality + - --hp-checkpoint + - "/sc-projects/sc-proj-cc08-cassandra/RW_Prospective/yaib_logs/lab_set_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_1.0_horizon_6:00:00_transfer_full_2025-07-14T13:20:08/SSI/XGBClassifier/2025-07-20T18-29-31.571508/hyperparameter_tuning_logs.db" # - --verbose - --modalities - "all" - --file-names - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_6.parquet","STATIC":"sta.parquet"' +# - --explain_features # - --verbose -# - -gc -# - -lc + - --load_data_vars method: grid -name: yaib_classification_benchmark +name: organ_system_benchmark parameters: data_dir: values: - - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/organ_cohorts_real_life_normal_ward_SSI-3_segment_1.0_horizon_6:00:00_transfer_full_2025-06-03T16:05:00/Pancreas" - - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/organ_cohorts_real_life_normal_ward_SSI-3_segment_1.0_horizon_6:00:00_transfer_full_2025-06-03T16:05:00/Liver" - - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/organ_cohorts_real_life_normal_ward_SSI-3_segment_1.0_horizon_6:00:00_transfer_full_2025-06-03T16:05:00/Upper_GI" - - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/organ_cohorts_real_life_normal_ward_SSI-3_segment_1.0_horizon_6:00:00_transfer_full_2025-06-03T16:05:00/Colorectal" -# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/normal_ward_SSI-3_segment_1.0_horizon_6:00:00_transfer_full_2025-05-27T23:34:00" -# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/normal_ward_SSI-3_segment_1.0_horizon_6:00:00_transfer_full_2025-05-09T19:19:33_wearable_cut_off_0.75" -# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/ICU_and_normal_ward_SSI-3_segment_1.0_horizon_6:00:00_transfer_full_2025-04-22T21:19:32" -# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/ICU_and_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_1.0_horizon_6:00:00_transfer_full_2025-04-15T12:42:04" -# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/normal_ward_SSI-3_POPF_heamatoma_Galleleck_BDA_segment_1.0_horizon_6:00:00_transfer_full_2025-04-07T15:35:16" -# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/cohort_separation/1/" -# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/cohort_separation/2/" -# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/cohort_separation/3/" -# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/cohort_separation/4/" + - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/organ_cohorts_lab_set_ICU_and_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_0.5_horizon_6:00:00_transfer_full_2025-08-01T12:13:31/Pancreas" + - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/organ_cohorts_lab_set_ICU_and_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_0.5_horizon_6:00:00_transfer_full_2025-08-01T12:13:31/Liver" +# - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/organ_cohorts_lab_set_ICU_and_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_0.5_horizon_6:00:00_transfer_full_2025-08-01T12:13:31/Upper_GI" + - "/sc-projects/sc-proj-cc08-cassandra/Prospective_Preprocessed/yaib_format/organ_cohorts_lab_set_ICU_and_normal_ward_SSI-3_POPF_Galleleck_BDA_segment_0.5_horizon_6:00:00_transfer_full_2025-08-01T12:13:31/Colorectal" model: values: # - LogisticRegression @@ -66,12 +51,8 @@ parameters: - "all" file_names: values: -# - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_3.parquet","STATIC":"sta.parquet"' - - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_6.parquet","STATIC":"sta.parquet"' -# - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_9.parquet","STATIC":"sta.parquet"' -# - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_12.parquet","STATIC":"sta.parquet"' -# - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_18.parquet","STATIC":"sta.parquet"' -# - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_24.parquet","STATIC":"sta.parquet"' +# - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_6.parquet","STATIC":"sta.parquet"' + - '"DYNAMIC":"dyn.parquet","OUTCOME":"outc_9.parquet","STATIC":"sta.parquet"' seed: values: - 1111 From ba742cdcfb359a28e3f72e43ccae441cebf2e078 Mon Sep 17 00:00:00 2001 From: Robin van de Water Date: Tue, 2 Sep 2025 21:08:57 +0200 Subject: [PATCH 78/82] init --- icu_benchmarks/models/__init__.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/icu_benchmarks/models/__init__.py b/icu_benchmarks/models/__init__.py index ffe34641..9821d2fe 100644 --- a/icu_benchmarks/models/__init__.py +++ b/icu_benchmarks/models/__init__.py @@ -4,7 +4,7 @@ from icu_benchmarks.models.dl_models.tcn import TemporalConvNet from icu_benchmarks.models.dl_models.transformer import BaseTransformer, LocalTransformer, Transformer from icu_benchmarks.models.ml_models.catboost import CBClassifier -from icu_benchmarks.models.ml_models.imblearn import BRFClassifier, RUSBClassifier +from icu_benchmarks.models.ml_models.imblearn import BRFClassifier, XGBEnsembleClassifier from icu_benchmarks.models.ml_models.lgbm import LGBMClassifier, LGBMRegressor from icu_benchmarks.models.ml_models.sklearn import ( ElasticNet, @@ -31,7 +31,7 @@ MLModelClassifier = Union[ XGBClassifier, LGBMClassifier, - RUSBClassifier, + XGBEnsembleClassifier, BRFClassifier, CBClassifier, LogisticRegression, @@ -57,7 +57,7 @@ "Transformer", "LocalTransformer", "CBClassifier", - "RUSBClassifier", + "XGBEnsembleClassifier", "BRFClassifier", "LGBMClassifier", "LGBMRegressor", From cd25d4ecfe5150bfd9355d8506db51f9f3511f0a Mon Sep 17 00:00:00 2001 From: Robin van de Water Date: Mon, 8 Sep 2025 09:58:06 +0200 Subject: [PATCH 79/82] adding functionality to reduce stay steps --- icu_benchmarks/data/split_process_data.py | 151 +++++++++++++++++++++- 1 file changed, 150 insertions(+), 1 deletion(-) diff --git a/icu_benchmarks/data/split_process_data.py b/icu_benchmarks/data/split_process_data.py index fc1ff38a..78ed446b 100644 --- a/icu_benchmarks/data/split_process_data.py +++ b/icu_benchmarks/data/split_process_data.py @@ -48,6 +48,9 @@ def preprocess_data( label: Optional[str] = None, required_var_types: Optional[list[str]] = None, required_segments: Optional[list[str]] = None, + reduce_sequence_steps: int = 0, + remove_short_stays: bool = True, + min_remaining_steps: int = 1, ) -> dict[str, dict[str, pl.DataFrame]]: """ Perform loading, splitting, imputing and normalising of task data. @@ -73,7 +76,9 @@ def preprocess_data( generate_cache: Generate cached preprocessed data if true. fold_index: Index of the fold to return. pretrained_imputation_model: pretrained imputation model to use. if None, standard imputation is used. - + reduce_sequence_steps: Number of steps to reduce sequence length. + remove_short_stays: Whether to remove stays that are shorter than reduce_sequence_steps. + min_remaining_steps: Minimum number of remaining steps after reduction to keep a stay. Returns: Preprocessed data as DataFrame in a hierarchical dict with features type (STATIC) / DYNAMIC/ OUTCOME nested within split (train/val/test). @@ -169,6 +174,16 @@ def preprocess_data( else: logging.info("Selecting all modalities.") + # Reduce stays by sequence steps if requested + if reduce_sequence_steps > 0: + logging.info(f"Reducing stays by {reduce_sequence_steps} sequence steps") + sanitized_data = reduce_stays_by_steps( + sanitized_data, + vars, + reduce_sequence_steps, + remove_short_stays, + min_remaining_steps + ) # Generate the splits logging.info("Generating splits.") if not complete_train: @@ -719,3 +734,137 @@ def check_required_keys(vars, required_keys): missing_keys = [key for key in required_keys if key not in vars] if missing_keys: raise KeyError(f"Missing required keys in vars: {', '.join(missing_keys)}") + + +def reduce_stays_by_steps( + data: dict[str, pl.DataFrame], + vars: dict[str, Union[str, list[str]]], + steps_to_remove: int, + remove_short_stays: bool = True, + min_remaining_steps: int = 1 +) -> dict[str, pl.DataFrame]: + """ + Reduce stays by removing the last x sequence steps from each stay. + + Args: + data: Dictionary containing DataFrames for different segments (DYNAMIC, STATIC, OUTCOME) + vars: Dictionary containing variable names including GROUP and SEQUENCE + steps_to_remove: Number of sequence steps to remove from the end of each stay + remove_short_stays: Whether to remove stays that have fewer steps than steps_to_remove + min_remaining_steps: Minimum number of steps that must remain after reduction + + Returns: + Modified data dictionary with reduced stays + """ + if steps_to_remove <= 0: + logging.warning("steps_to_remove must be positive. No changes made.") + return data + + group_var = vars[VarType.group] + sequence_var = vars[VarType.sequence] + + if not isinstance(group_var, str) or not isinstance(sequence_var, str): + raise TypeError(f"GROUP and SEQUENCE variables must be strings, got {type(group_var)} and {type(sequence_var)}") + + # Check if we have dynamic data with sequences + if DataSegment.dynamic not in data: + logging.warning("No dynamic data found. Cannot reduce stays by sequence steps.") + return data + + dynamic_data = data[DataSegment.dynamic] + + # Get stay lengths from dynamic data + stay_lengths = dynamic_data.group_by(group_var).len().sort(group_var) + total_stays = stay_lengths.height + + stays_to_remove = [] + stays_modified = 0 + + # Identify stays that are too short + short_stays = stay_lengths.filter(pl.col("len") <= steps_to_remove) + if short_stays.height > 0: + short_stay_ids = short_stays[group_var].to_list() + if remove_short_stays: + stays_to_remove.extend(short_stay_ids) + logging.info(f"Removing {len(short_stay_ids)} stays with <= {steps_to_remove} steps: {short_stay_ids}") + else: + logging.warning( + f"Found {len(short_stay_ids)} stays with <= {steps_to_remove} steps. Keeping them unchanged.") + + # Identify stays that would have too few remaining steps + insufficient_stays = stay_lengths.filter( + (pl.col("len") > steps_to_remove) & + (pl.col("len") - steps_to_remove < min_remaining_steps) + ) + if insufficient_stays.height > 0: + insufficient_stay_ids = insufficient_stays[group_var].to_list() + if remove_short_stays: + stays_to_remove.extend(insufficient_stay_ids) + logging.info( + f"Removing {len(insufficient_stay_ids)} stays that would have < {min_remaining_steps} steps after reduction: {insufficient_stay_ids}") + else: + logging.warning( + f"Found {len(insufficient_stay_ids)} stays that would have < {min_remaining_steps} steps after reduction. Keeping them unchanged.") + + # Remove identified stays from all data segments + if stays_to_remove: + for segment in data: + original_length = data[segment].height + data[segment] = data[segment].filter(~pl.col(group_var).is_in(stays_to_remove)) + removed_count = original_length - data[segment].height + logging.info(f"Removed {removed_count} rows from {segment} segment") + + # Reduce remaining stays by removing last steps from dynamic data + valid_stays = stay_lengths.filter( + (pl.col("len") > steps_to_remove) & + (pl.col("len") - steps_to_remove >= min_remaining_steps) & + (~pl.col(group_var).is_in(stays_to_remove)) + )[group_var].to_list() + + if valid_stays: + # For dynamic data: keep only the first (total_length - steps_to_remove) rows per stay + reduced_dynamic_parts = [] + unchanged_dynamic = data[DataSegment.dynamic].filter(~pl.col(group_var).is_in(valid_stays + stays_to_remove)) + + for stay_id in valid_stays: + stay_data = data[DataSegment.dynamic].filter(pl.col(group_var) == stay_id) + current_length = stay_data.height + keep_length = current_length - steps_to_remove + + # Sort by sequence and keep first keep_length rows + reduced_stay = stay_data.sort(sequence_var).head(keep_length) + reduced_dynamic_parts.append(reduced_stay) + stays_modified += 1 + + # Reconstruct dynamic data + if reduced_dynamic_parts: + data[DataSegment.dynamic] = pl.concat([unchanged_dynamic] + reduced_dynamic_parts).sort( + [group_var, sequence_var]) + + # Handle outcome data if it has sequence information (sequence-to-sequence case) + if DataSegment.outcome in data and sequence_var in data[DataSegment.outcome].columns: + logging.info("Outcome data has sequence information. Reducing outcome sequences to match dynamic data.") + + reduced_outcome_parts = [] + unchanged_outcome = data[DataSegment.outcome].filter( + ~pl.col(group_var).is_in(valid_stays + stays_to_remove)) + + for stay_id in valid_stays: + stay_outcome = data[DataSegment.outcome].filter(pl.col(group_var) == stay_id) + current_length = stay_outcome.height + keep_length = current_length - steps_to_remove + + # Sort by sequence and keep first keep_length rows + reduced_outcome = stay_outcome.sort(sequence_var).head(keep_length) + reduced_outcome_parts.append(reduced_outcome) + + # Reconstruct outcome data + if reduced_outcome_parts: + data[DataSegment.outcome] = pl.concat([unchanged_outcome] + reduced_outcome_parts).sort( + [group_var, sequence_var]) + + logging.info(f"Stay reduction complete: {stays_modified} stays modified, " + f"{len(stays_to_remove)} stays removed, " + f"{total_stays - len(stays_to_remove)} stays remaining") + + return data \ No newline at end of file From 235c02f76b73bb957eaabab86b1abc7807756f8d Mon Sep 17 00:00:00 2001 From: Robin van de Water Date: Mon, 8 Sep 2025 09:58:31 +0200 Subject: [PATCH 80/82] caliration update --- icu_benchmarks/models/wrappers.py | 250 ++++++++++++++++++++++++++---- 1 file changed, 224 insertions(+), 26 deletions(-) diff --git a/icu_benchmarks/models/wrappers.py b/icu_benchmarks/models/wrappers.py index 1a560b07..b7ae2512 100644 --- a/icu_benchmarks/models/wrappers.py +++ b/icu_benchmarks/models/wrappers.py @@ -47,7 +47,7 @@ class BaseModule(LightningModule): trained_columns = None # Type of run mode run_mode = None - debug = True + debug = False # We do not want to explain features by default as it is expensive (and not needed during hp tuning) explain_features = False @@ -153,14 +153,6 @@ def _save_model_outputs(self, pred_indicators, test_pred, test_label): fmt="%d,%d,%0.3f,%0.3f", ) logging.debug(f"Saved row indicators to {Path(self.logger.save_dir) / 'row_indicators.csv'}") - - # logging.warning("Could not save row indicators; no support for temporal dataset with single outcomes yet.") - # if len(pred_indicators.shape) > 1 and len(test_pred.shape) > 1 and pred_indicators.shape[1] == test_pred.shape[1]: - # pred_indicators = np.hstack((pred_indicators, test_label.reshape(-1, 1))) - # pred_indicators = np.hstack((pred_indicators, test_pred)) - # # Save as: id, time (hours), ground truth, prediction 0, prediction 1 - # np.savetxt(Path(self.logger.save_dir) / "pred_indicators.csv", pred_indicators, delimiter=",") - # logging.debug(f"Saved row indicators to {Path(self.logger.save_dir) / 'row_indicators.csv'}") else: pred_indicators = np.hstack((pred_indicators.reshape(-1, 1), test_label.reshape(-1, 1))) logging.info(pred_indicators.shape) @@ -174,9 +166,6 @@ def _save_model_outputs(self, pred_indicators, test_pred, test_label): ) logging.debug(f"Saved row indicators to {Path(self.logger.save_dir) / f'row_indicators.csv'}") - # else: - # logging.warning("Could not save row indicators.") - @gin.configurable("DLWrapper") class DLWrapper(BaseModule, ABC): requires_backprop = True @@ -282,6 +271,10 @@ def on_test_epoch_start(self) -> None: step_name: {metric_name: metric() for metric_name, metric in self.set_metrics().items()} for step_name in ["train", "val", "test"] } + if hasattr(self.trainer, 'val_dataloaders') and self.trainer.val_dataloaders: + val_loader = self.trainer.val_dataloaders[0] if isinstance(self.trainer.val_dataloaders, + list) else self.trainer.val_dataloaders + self.setup_calibration(val_loader) return super().on_test_epoch_start() def save_model(self, save_path, file_name, file_extension=".ckpt"): @@ -332,6 +325,8 @@ def __init__( ) self.output_transform = None self.loss_weights = None + self.calibrated_model = None + def set_metrics(self, *args): """Set the evaluation metrics for the prediction model.""" @@ -407,8 +402,6 @@ def step_fn(self, element, step_prefix=""): prediction = torch.masked_select(out, mask.unsqueeze(-1)).reshape(-1, out.shape[-1]).to(self.device) target = torch.masked_select(labels, mask).to(self.device) - valid_idx = mask.detach().cpu().numpy().astype(bool) - # valid_idx is (batch, seq_len) boolean mask valid_idx = mask.detach().cpu().numpy().astype(bool) @@ -416,13 +409,37 @@ def step_fn(self, element, step_prefix=""): indicators_np = indicators.detach().cpu().numpy()[valid_idx] predictors = prediction.detach().cpu() target_np = target.detach().cpu().numpy() - transformed_predictors = torch.softmax(predictors, dim=1) - transformed_predictors = transformed_predictors.numpy() - + # transformed_predictors = torch.softmax(predictors, dim=1) + # transformed_predictors = transformed_predictors.numpy() + # Apply calibration for test step if available + if (step_prefix == "test" and hasattr(self, 'calibrated_model') and + self.calibrated_model is not None and self.run_mode == RunMode.classification): + # Get uncalibrated probabilities + uncalibrated_probs = torch.softmax(prediction, dim=1).cpu().numpy() + # Apply calibration + calibrated_probs = self.calibrated_model.predict_proba(uncalibrated_probs) + # Convert back to tensor for loss calculation (keeping original logits for loss) + calibrated_tensor = torch.tensor(calibrated_probs, device=self.device) + transformed_predictors = calibrated_probs + logging.info("Using calibrated predictions for test step") + else: + # Use uncalibrated predictions + predictors = prediction.detach().cpu() + transformed_predictors = torch.softmax(predictors, dim=1) + transformed_predictors = transformed_predictors.numpy() + + # if prediction.shape[-1] > 1 and self.run_mode == RunMode.classification: + # # Classification task + # loss = self.loss(prediction, target.long(), weight=self.loss_weights.to(self.device)) + aux_loss + # # Returns torch.long because negative log likelihood loss if prediction.shape[-1] > 1 and self.run_mode == RunMode.classification: # Classification task loss = self.loss(prediction, target.long(), weight=self.loss_weights.to(self.device)) + aux_loss - # Returns torch.long because negative log likelihood loss + # Use calibrated probabilities for output transform in test step + if step_prefix == "test" and hasattr(self, 'calibrated_model') and self.calibrated_model is not None: + transformed_output = self.output_transform((calibrated_tensor, target)) + else: + transformed_output = self.output_transform((prediction, target)) elif self.run_mode == RunMode.regression: # Regression task loss = self.loss(prediction[:, 0], target.float()) + aux_loss @@ -445,6 +462,141 @@ def step_fn(self, element, step_prefix=""): self.log(f"{step_prefix}/loss", loss, on_step=False, on_epoch=True, sync_dist=True) return loss + def setup_calibration(self, val_loader, method='isotonic'): + """ + Setup model calibration using validation data for better probability estimates. + + Args: + val_loader: Validation dataloader for calibration + method: 'isotonic' or 'sigmoid' calibration method + + Returns: + float: Validation loss after calibration + """ + if self.run_mode != RunMode.classification: + logging.warning("Calibration only supported for classification tasks") + return None + + logging.info(f"Applying {method} calibration using validation data") + + # Collect validation predictions and labels + self.eval() + val_probs = [] + val_labels = [] + + with torch.no_grad(): + for batch in val_loader: + if len(batch) == 3: + data, labels, _ = batch + mask = torch.ones_like(labels).bool() + elif len(batch) == 4: + data, labels, mask, _ = batch + else: + raise Exception("Loader should return either (data, label) or (data, label, mask)") + + # Move to device + if isinstance(data, list): + data = [d.float().to(self.device) for d in data] + else: + data = data.float().to(self.device) + labels = labels.to(self.device) + mask = mask.to(self.device) + + # Get model predictions + out = self(data) + if len(out) == 2 and isinstance(out, tuple): + out, _ = out + + # Apply mask and get probabilities + prediction = torch.masked_select(out, mask.unsqueeze(-1)).reshape(-1, out.shape[-1]) + target = torch.masked_select(labels, mask) + + # Convert to probabilities + probs = torch.softmax(prediction, dim=1) + + val_probs.append(probs.cpu().numpy()) + val_labels.append(target.cpu().numpy()) + + # Concatenate all validation data + val_probs = np.vstack(val_probs) + val_labels = np.concatenate(val_labels) + + # Create sklearn-compatible wrapper for the PyTorch model + class PytorchModelWrapper: + def __init__(self, model, device): + self.model = model + self.device = device + self.classes_ = np.arange(val_probs.shape[1]) + + def predict_proba(self, X): + # X should be the raw features, but we'll use pre-computed probabilities + # This is a simplified approach - in practice you'd need to handle the full pipeline + return X # X is already probabilities in this case + + # Use pre-computed probabilities for calibration + wrapper = PytorchModelWrapper(self, self.device) + + # Create calibrated version + self.calibrated_model = CalibratedClassifierCV( + wrapper, + method=method, + cv='prefit' + ) + + # Fit calibration on validation probabilities + self.calibrated_model.fit(val_probs, val_labels) + + # Calculate calibrated validation score + cal_val_pred = self.calibrated_model.predict_proba(val_probs) + cal_val_loss = self.loss_fn_numpy(val_labels, cal_val_pred) + orig_val_loss = self.loss_fn_numpy(val_labels, val_probs) + + logging.info(f"Calibration complete. " + f"Original val loss: {orig_val_loss:.4f}, " + f"Calibrated val loss: {cal_val_loss:.4f}") + + return cal_val_loss + + + def loss_fn_numpy(self, labels, probs): + """Convert tensor loss to numpy equivalent for calibration evaluation.""" + if self.run_mode == RunMode.classification: + return log_loss(labels, probs) + else: + return mean_squared_error(labels, probs) + + + def predict_calibrated(self, features): + """Get calibrated predictions if calibration is available.""" + if self.calibrated_model is None: + logging.warning("Calibration not set up. Using base model predictions.") + return self.predict_uncalibrated(features) + + # Get base model probabilities + base_probs = self.predict_uncalibrated(features) + + # Apply calibration + if isinstance(base_probs, torch.Tensor): + base_probs = base_probs.cpu().numpy() + + cal_probs = self.calibrated_model.predict_proba(base_probs) + return torch.tensor(cal_probs, device=self.device) + + + def predict_uncalibrated(self, features): + """Get raw model predictions without calibration.""" + self.eval() + with torch.no_grad(): + if isinstance(features, list): + features = [f.float().to(self.device) for f in features] + else: + features = features.float().to(self.device) + + out = self(features) + if len(out) == 2 and isinstance(out, tuple): + out, _ = out + + return torch.softmax(out, dim=1) @gin.configurable("MLWrapper") class MLWrapper(BaseModule, ABC): @@ -463,6 +615,7 @@ def __init__(self, *args, run_mode=RunMode.classification, loss=log_loss, patien self.patience = patience self.mps = mps self.loss_weight = None + self.save_model_outputs = True def set_metrics(self, labels): if self.run_mode == RunMode.classification: @@ -490,7 +643,7 @@ def set_metrics(self, labels): self.label_transform = lambda x: x self.metrics = MLMetrics.REGRESSION - def setup_calibration(self, val_data, val_labels, method='isotonic'): + def setup_calibration(self, val_data, val_labels, method='sigmoid'): """ Setup model calibration using validation data for better probability estimates. @@ -520,7 +673,8 @@ def setup_calibration(self, val_data, val_labels, method='isotonic'): cal_val_pred = self.calibrated_model.predict_proba(val_data) cal_val_loss = self.loss(val_labels, cal_val_pred) - logging.info(f"Calibration complete. Original val loss: {self.loss(val_labels, self.predict(val_data)):.4f}, " + logging.info(f"Calibration complete. " + f"Original val loss: {self.loss(val_labels, self.model.predict_proba(val_data)):.4f}, " f"Calibrated val loss: {cal_val_loss:.4f}") return cal_val_loss @@ -538,11 +692,11 @@ def fit(self, train_dataset, val_dataset): val_loss = self.fit_model(train_rep, train_label, val_rep, val_label) calibrate = True - method = "isotonic" + method = "sigmoid" # Apply calibration if desired calibrate = True if calibrate and self.run_mode == RunMode.classification: - cal_val_loss = self.setup_calibration(val_rep, val_label, method='isotonic') + cal_val_loss = self.setup_calibration(val_rep, val_label, method=method) logging.info(f"Model calibrated. val loss {val_loss} Calibrated val loss: {cal_val_loss}") if self.explain_features: @@ -577,6 +731,28 @@ def validation_step(self, val_dataset, _): logging.info(f"Val loss: {self.loss(val_label, val_pred)}") self.log_metrics(val_label, val_pred, "val") + def compare_rankings(self, features, labels): + """Compare rankings between base and calibrated models.""" + base_probs = self.model.predict_proba(features)[:, 1] + cal_probs = self.calibrated_model.predict_proba(features)[:, 1] + + # Check if rankings are identical + base_ranking = np.argsort(base_probs) + cal_ranking = np.argsort(cal_probs) + + ranking_identical = np.array_equal(base_ranking, cal_ranking) + + # Calculate metrics for both + from sklearn.metrics import roc_auc_score, average_precision_score + base_auroc = roc_auc_score(labels, base_probs) + cal_auroc = roc_auc_score(labels, cal_probs) + base_auprc = average_precision_score(labels, base_probs) + cal_auprc = average_precision_score(labels, cal_probs) + logging.info(f"ranking identical: {ranking_identical}") + logging.info(f"Base AUROC: {base_auroc}, Calibrated AUROC: {cal_auroc}, ") + logging.info(f"Base AUPRC: {base_auprc}, Calibrated AUPRC: {cal_auprc}, ") + + def test_step(self, dataset, _): test_rep, test_label, pred_indicators = dataset test_rep, test_label, pred_indicators = ( @@ -586,9 +762,11 @@ def test_step(self, dataset, _): ) self.set_metrics(test_label) test_pred = self.predict(test_rep) - test_pred_uncalibrated = self.predict(test_rep, use_calibrated=False) + # test_pred_uncalibrated = self.predict(test_rep, use_calibrated=False)False + self.compare_rankings(test_rep, test_label) self.log_curves(test_label, test_pred, "test", pred_indicators) - if self.debug: + # if self.debug: + if self.save_model_outputs: self._save_model_outputs(pred_indicators, test_pred, test_label) if self.explain_features: # self.explain_model(test_rep, test_label) @@ -602,8 +780,8 @@ def test_step(self, dataset, _): else: self.log("test/loss", self.loss(test_label, test_pred), sync_dist=True) self.log_metrics(test_label, test_pred, "test", pred_indicators) - logging.info(f"Test loss: {self.loss(test_label, test_pred)}, " - f"uncalibrated {self.loss(test_label,test_pred_uncalibrated)}") + # logging.info(f"Test loss: {self.loss(test_label, test_pred)}, " + # f"uncalibrated {self.loss(test_label,test_pred_uncalibrated)}") def predict(self, features, use_calibrated=True): """Predict using calibrated model if available, otherwise use base model.""" @@ -617,6 +795,26 @@ def predict(self, features, use_calibrated=True): return self.model.predict(features) else: return self.model.predict_proba(features) + + + # def predict_with_preserved_ranking(self, features): + # """Get calibrated probabilities while preserving base model ranking.""" + # base_probs = self.model.predict_proba(features)[:, 1] + # cal_probs = self.calibrated_model.predict_proba(features)[:, 1] + # + # # Get ranking from base model + # ranking_indices = np.argsort(base_probs) + # + # # Sort calibrated probabilities to match base model ranking + # sorted_cal_probs = np.sort(cal_probs) + # + # # Create output that preserves base ranking but uses calibrated values + # result = np.zeros_like(cal_probs) + # result[ranking_indices] = sorted_cal_probs + # + # # Convert back to two-column format + # return np.column_stack([1 - result, result]) + def log_curves(self, label, pred, metric_type, pred_indicators): for name, metric in self.metrics.items(): result = metric(self.label_transform(label), self.output_transform(pred)) From 7701b004c90635ce09e1451f2337ff87b293fdeb Mon Sep 17 00:00:00 2001 From: Robin van de Water Date: Mon, 20 Oct 2025 09:53:14 +0200 Subject: [PATCH 81/82] reduce stay steps option --- icu_benchmarks/data/split_process_data.py | 2 ++ icu_benchmarks/run_utils.py | 6 ++++++ 2 files changed, 8 insertions(+) diff --git a/icu_benchmarks/data/split_process_data.py b/icu_benchmarks/data/split_process_data.py index 78ed446b..39a1a9f2 100644 --- a/icu_benchmarks/data/split_process_data.py +++ b/icu_benchmarks/data/split_process_data.py @@ -177,6 +177,8 @@ def preprocess_data( # Reduce stays by sequence steps if requested if reduce_sequence_steps > 0: logging.info(f"Reducing stays by {reduce_sequence_steps} sequence steps") + if remove_short_stays: + logging.info(f"Removing stays with less than {min_remaining_steps} remaining steps after reduction.") sanitized_data = reduce_stays_by_steps( sanitized_data, vars, diff --git a/icu_benchmarks/run_utils.py b/icu_benchmarks/run_utils.py index 9c2ef180..58019be5 100644 --- a/icu_benchmarks/run_utils.py +++ b/icu_benchmarks/run_utils.py @@ -77,6 +77,12 @@ def build_parser() -> ArgumentParser: action=BOA, help="Load data variables from the dataset directory. Avoids having to manually add the path in the task.gin", ) + parser.add_argument( + "--reduce_stay_steps", + default=None, + type=int, + help="Reduce the stay length dynamically", + ) return parser From 88b63e75dbc417ba0486889c40ca930c7eb742d0 Mon Sep 17 00:00:00 2001 From: Robin van de Water Date: Mon, 20 Oct 2025 09:53:33 +0200 Subject: [PATCH 82/82] lgbm explainer --- icu_benchmarks/models/ml_models/lgbm.py | 53 ++++++++++++++++++++----- 1 file changed, 44 insertions(+), 9 deletions(-) diff --git a/icu_benchmarks/models/ml_models/lgbm.py b/icu_benchmarks/models/ml_models/lgbm.py index 12124a69..6d262b13 100644 --- a/icu_benchmarks/models/ml_models/lgbm.py +++ b/icu_benchmarks/models/ml_models/lgbm.py @@ -1,6 +1,10 @@ import gin import lightgbm as lgbm import numpy as np +import shap +import sys +from io import StringIO +import os import wandb from wandb.integration.lightgbm import wandb_callback as wandb_lgbm @@ -12,14 +16,36 @@ class LGBMWrapper(MLWrapper): def fit_model(self, train_data, train_labels, val_data, val_labels): """Fitting function for LGBM models.""" self.model.set_params(random_state=np.random.get_state()[1][0]) - callbacks = [lgbm.early_stopping(self.hparams.patience, verbose=True), lgbm.log_evaluation(period=-1)] - - - self.model = self.model.fit( - train_data, - train_labels, - eval_set=(val_data, val_labels), - callbacks=callbacks, + # callbacks = [lgbm.early_stopping(self.hparams.patience, verbose=False)] + callbacks = [ + lgbm.log_evaluation(period=0), # Disable default logging + ] + # callbacks = [] + # Redirect stderr and stdout to suppress warnings + stderr_backup = sys.stderr + stdout_backup = sys.stdout + sys.stderr = StringIO() + sys.stdout = StringIO() + try: + self.model = self.model.fit( + train_data, + train_labels, + eval_set=(val_data, val_labels), + callbacks=callbacks, + ) + finally: + sys.stderr = stderr_backup + # to surpress: [Warning] No further splits with positive gain, best gain: -inf with the current config + sys.stdout = stdout_backup + reduce_samples = False + if reduce_samples: + n_samples = min(1000, len(train_data)) + indices = np.random.choice(len(train_data), size=n_samples, replace=False) + else: + indices = np.arange(len(train_data)) + background_sample = train_data[indices] + self.explainer = shap.TreeExplainer( + self.model, background_sample, feature_perturbation="interventional", model_output="probability" ) val_loss = list(self.model.best_score_["valid_0"].values())[0] return val_loss @@ -30,7 +56,8 @@ class LGBMClassifier(LGBMWrapper): _supported_run_modes = [RunMode.classification] def __init__(self, *args, **kwargs): - self.model = self.set_model_args(lgbm.LGBMClassifier, *args, **kwargs) + kwargs.setdefault("verbosity", -1) + self.model = self.set_model_args(lgbm.LGBMClassifier, *args, **kwargs, verbose=-1) super().__init__(*args, **kwargs) def predict(self, features): @@ -45,6 +72,14 @@ def predict(self, features): """ return self.model.predict_proba(features) + def _explain_model(self, reps, labels): + if not hasattr(self.model, "feature_importances_"): + raise ValueError("Model has not been fit yet. Call fit_model() before getting feature importances.") + # feature_importances = self.model.feature_importances_ + shap_values = self.explainer.shap_values(reps, labels) + # feature_importances = np.abs(shap_values).mean(axis=1) + return shap_values + @gin.configurable class LGBMRegressor(LGBMWrapper):