From 07fe8434b8cc08e36ee06f88b06ba2a9b1f8fe08 Mon Sep 17 00:00:00 2001 From: Claudia Onorato Date: Sun, 22 Nov 2020 18:47:07 -0500 Subject: [PATCH 01/14] updated 78yo anon prediction for clf, electro, physionet --- ai/models/model_utils.py | 1 - ai/prediction_anonymous.ipynb | 74 +- ai/prediction_anonymous_classifier.ipynb | 444 ++++ ...lectrophysiologist_woman78yo_sleepedf.json | 2065 +---------------- .../data/physionet_woman78yo_sleepedf.json | 2065 +---------------- .../data/predicted_woman78yo_sleepedf.json | 2065 +---------------- web/src/views/performance/index.js | 5 +- web/src/views/performance/text.json | 2 +- 8 files changed, 504 insertions(+), 6217 deletions(-) create mode 100644 ai/prediction_anonymous_classifier.ipynb diff --git a/ai/models/model_utils.py b/ai/models/model_utils.py index f959a987..2dd8fb3f 100644 --- a/ai/models/model_utils.py +++ b/ai/models/model_utils.py @@ -114,7 +114,6 @@ def train_test_split_according_to_age(X, y, use_continuous_age, subjects_test=No [85,110] # 32 recordings ] age_categories = np.unique(X[:, AGE_CATEGORY_COL_IDX]) - assert subjects_test is None or len(subjects_test) == len(age_categories), "If subjects are specified, they must be specified for all age groups" if subjects_test is None: unique_subject_with_age = np.array([ diff --git a/ai/prediction_anonymous.ipynb b/ai/prediction_anonymous.ipynb index 028b0bb1..0d817dc9 100644 --- a/ai/prediction_anonymous.ipynb +++ b/ai/prediction_anonymous.ipynb @@ -18,18 +18,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 1, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The autoreload extension is already loaded. To reload it, use:\n", - " %reload_ext autoreload\n" - ] - } - ], + "outputs": [], "source": [ "%load_ext autoreload\n", "%autoreload 2" @@ -37,13 +28,14 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "import mne\n", + "import json\n", "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", @@ -65,7 +57,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -81,7 +73,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -155,7 +147,7 @@ "1 25620.0 1989-04-25 14:50:00 609519000.0 " ] }, - "execution_count": 9, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -175,7 +167,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -231,7 +223,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -257,7 +249,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -282,7 +274,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -310,7 +302,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -357,6 +349,48 @@ "print(\"Agreement score (Cohen Kappa): \", cohen_kappa_score(y_physionet, y_electrophysiologist))" ] }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "electro_hypno = {\n", + " 'timestamps': [\n", + " int((datetime.fromtimestamp(615514680) + timedelta(seconds=index*EPOCH_DURATION)).timestamp())\n", + " for index, stage in enumerate(y_electrophysiologist)\n", + " ],\n", + " 'stages': [\n", + " str(list(SLEEP_STAGES_VALUES.keys())[list(SLEEP_STAGES_VALUES.values()).index(stage)])\n", + " for index, stage in enumerate(y_electrophysiologist)\n", + " ]\n", + "}\n", + "\n", + "with open('data/electrophysiologist_woman78yo_sleepedf.json', 'w') as fp:\n", + " json.dump(dict(epochs=electro_hypno), fp)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "sleepedf_hypno = {\n", + " 'timestamps': [\n", + " int((datetime.fromtimestamp(615514680) + timedelta(seconds=index*EPOCH_DURATION)).timestamp())\n", + " for index, stage in enumerate(y_physionet)\n", + " ],\n", + " 'stages': [\n", + " str(list(SLEEP_STAGES_VALUES.keys())[list(SLEEP_STAGES_VALUES.values()).index(stage)])\n", + " for index, stage in enumerate(y_physionet)\n", + " ]\n", + "}\n", + "\n", + "with open('data/physionet_woman78yo_sleepedf.json', 'w') as fp:\n", + " json.dump(dict(epochs=sleepedf_hypno), fp)" + ] + }, { "cell_type": "code", "execution_count": null, diff --git a/ai/prediction_anonymous_classifier.ipynb b/ai/prediction_anonymous_classifier.ipynb new file mode 100644 index 00000000..174e814b --- /dev/null +++ b/ai/prediction_anonymous_classifier.ipynb @@ -0,0 +1,444 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Prediction on anonymous by our classifier\n", + "___\n", + "\n", + "This notebook aims to try on our dataset, without including the anonymous subject, and then to see what is the classification accuracy.\n", + "\n", + "The recording that was randomly selected is `SC4441EC-Hypnogram.edf`. It corresponds to an 74 year old woman, and the subject index is then 44." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "\n", + "import numpy as np\n", + "import json\n", + "\n", + "from datetime import datetime, timedelta\n", + "\n", + "from sklearn.pipeline import Pipeline\n", + "from sklearn.compose import ColumnTransformer\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.neighbors import KNeighborsClassifier\n", + "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n", + "from sklearn.ensemble import (RandomForestClassifier,\n", + " VotingClassifier)\n", + "from sklearn.metrics import (confusion_matrix,\n", + " classification_report,\n", + " cohen_kappa_score)\n", + "from hmmlearn.hmm import MultinomialHMM\n", + "\n", + "from models.model_utils import (train_test_split_according_to_age)\n", + "from constants import (SLEEP_STAGES_VALUES, EPOCH_DURATION,N_STAGES)\n", + "from models.model_utils import (print_hypnogram)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generate trained pipeline\n", + "____" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "SUBJECT_IDX = 0 \n", + "NIGHT_IDX = 1\n", + "USE_CONTINUOUS_AGE = False\n", + "DOWNSIZE_SET = False\n", + "TEST_SET_SUBJECTS = [44]\n", + "MODELS_DIR = \"trained_model\"\n", + "\n", + "NIGHT = [1]" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(168954, 50)\n", + "(168954,)\n", + "Number of subjects: 78\n", + "Number of nights: 153\n", + "Subjects available: [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17.\n", + " 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 35.\n", + " 36. 37. 38. 40. 41. 42. 43. 44. 45. 46. 47. 48. 49. 50. 51. 52. 53. 54.\n", + " 55. 56. 57. 58. 59. 60. 61. 62. 63. 64. 65. 66. 67. 70. 71. 72. 73. 74.\n", + " 75. 76. 77. 80. 81. 82.]\n", + "Selected subjects for the test set are: [44]\n", + "(2223, 50) (166731, 50) (2223,) (166731,)\n" + ] + } + ], + "source": [ + "def load_features():\n", + " if USE_CONTINUOUS_AGE:\n", + " X_file_name = \"data/x_features-age-continuous.npy\"\n", + " y_file_name = \"data/y_observations-age-continuous.npy\"\n", + " else:\n", + " X_file_name = \"data/x_features.npy\"\n", + " y_file_name = \"data/y_observations.npy\"\n", + "\n", + " X_init = np.load(X_file_name, allow_pickle=True)\n", + " y_init = np.load(y_file_name, allow_pickle=True)\n", + "\n", + " X_init = np.vstack(X_init)\n", + " y_init = np.hstack(y_init)\n", + "\n", + " print(X_init.shape)\n", + " print(y_init.shape)\n", + " print(\"Number of subjects: \", np.unique(X_init[:,SUBJECT_IDX]).shape[0]) # Some subject indexes are skipped, thus total number is below 83 (as we can see in https://physionet.org/content/sleep-edfx/1.0.0/)\n", + " print(\"Number of nights: \", len(np.unique([f\"{int(x[0])}-{int(x[1])}\" for x in X_init[:,SUBJECT_IDX:NIGHT_IDX+1]])))\n", + " print(\"Subjects available: \", np.unique(X_init[:,SUBJECT_IDX]))\n", + " \n", + " return X_init, y_init\n", + "\n", + "def split_data(X_init, y_init):\n", + " X_test, X_train_valid, y_test, y_train_valid = train_test_split_according_to_age(\n", + " X_init,\n", + " y_init,\n", + " use_continuous_age=USE_CONTINUOUS_AGE,\n", + " subjects_test=TEST_SET_SUBJECTS)\n", + " \n", + " print(X_test.shape, X_train_valid.shape, y_test.shape, y_train_valid.shape)\n", + " \n", + " return X_test, X_train_valid, y_test, y_train_valid\n", + "\n", + "X_init, y_init = load_features()\n", + "X_test, X_train_valid, y_test, y_train_valid = split_data(X_init, y_init)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "hmm_model = MultinomialHMM(n_components=N_STAGES)\n", + "\n", + "\n", + "hmm_model.emissionprob_ = np.load(f\"{MODELS_DIR}/HMM_emissionprob.npy\", allow_pickle=True)\n", + "hmm_model.startprob_ = np.load(f\"{MODELS_DIR}/HMM_startprob.npy\", allow_pickle=True)\n", + "hmm_model.transmat_ = np.load(f\"{MODELS_DIR}/HMM_transmat.npy\", allow_pickle=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Pipeline(memory=None,\n", + " steps=[('scaling',\n", + " ColumnTransformer(n_jobs=None, remainder='drop',\n", + " sparse_threshold=0.3,\n", + " transformer_weights=None,\n", + " transformers=[('pass-through-categorical',\n", + " 'passthrough', [0, 1]),\n", + " ('scaling-continuous',\n", + " StandardScaler(copy=False,\n", + " with_mean=True,\n", + " with_std=True),\n", + " [2, 3, 4, 5, 6, 7, 8, 9, 10,\n", + " 11, 12, 13, 14, 15, 16, 17,\n", + " 18, 19, 20, 21, 22, 23, 24,\n", + " 25, 26, 27, 28, 29,...\n", + " LinearDiscriminantAnalysis(n_components=None,\n", + " priors=None,\n", + " shrinkage=None,\n", + " solver='svd',\n", + " store_covariance=False,\n", + " tol=0.0001)),\n", + " ('knn_clf',\n", + " KNeighborsClassifier(algorithm='auto',\n", + " leaf_size=100,\n", + " metric='chebyshev',\n", + " metric_params=None,\n", + " n_jobs=-1,\n", + " n_neighbors=300,\n", + " p=2,\n", + " weights='uniform'))],\n", + " verbose=False))],\n", + " flatten_transform=False, n_jobs=-1,\n", + " voting='soft',\n", + " weights=array([0.83756205, 0.16243795])))],\n", + " verbose=False)" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def get_voting_classifier_pipeline():\n", + " NB_CATEGORICAL_FEATURES = 2\n", + " NB_FEATURES = 48\n", + "\n", + " estimator_list = [\n", + " ('random_forest', RandomForestClassifier(\n", + " random_state=42, # enables deterministic behaviour\n", + " n_jobs=-1\n", + " )),\n", + " ('knn', Pipeline([\n", + " ('knn_dim_red', LinearDiscriminantAnalysis()),\n", + " ('knn_clf', KNeighborsClassifier(\n", + " weights='uniform',\n", + " n_neighbors=300,\n", + " leaf_size=100,\n", + " metric='chebyshev',\n", + " n_jobs=-1\n", + " ))\n", + " ])),\n", + " ]\n", + " \n", + " return Pipeline([\n", + " ('scaling', ColumnTransformer([\n", + " ('pass-through-categorical', 'passthrough', list(range(NB_CATEGORICAL_FEATURES))),\n", + " ('scaling-continuous', StandardScaler(copy=False), list(range(NB_CATEGORICAL_FEATURES,NB_FEATURES)))\n", + " ])),\n", + " ('voting_clf', VotingClassifier(\n", + " estimators=estimator_list,\n", + " voting='soft',\n", + " weights=np.array([0.83756205, 0.16243795]),\n", + " flatten_transform=False,\n", + " n_jobs=-1,\n", + " ))\n", + " ])\n", + "\n", + "vtg_testing_pipeline = get_voting_classifier_pipeline()\n", + "vtg_testing_pipeline.fit(X_train_valid[:, 2:], y_train_valid)" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[394 11 2 0 2]\n", + " [ 37 66 52 0 28]\n", + " [ 5 24 321 1 54]\n", + " [ 0 0 52 39 0]\n", + " [ 0 0 0 0 100]]\n", + " precision recall f1-score support\n", + "\n", + " W 0.90 0.96 0.93 409\n", + " N1 0.65 0.36 0.46 183\n", + " N2 0.75 0.79 0.77 405\n", + " N3 0.97 0.43 0.60 91\n", + " REM 0.54 1.00 0.70 100\n", + "\n", + " accuracy 0.77 1188\n", + " macro avg 0.77 0.71 0.69 1188\n", + "weighted avg 0.79 0.77 0.76 1188\n", + "\n", + "Agreement score (Cohen Kappa): 0.687724728854155\n" + ] + } + ], + "source": [ + "X_test_selected_night = X_test[X_test[:,1] == NIGHT[0]]\n", + "y_test_selected_night = y_test[X_test[:,1] == NIGHT[0]]\n", + "\n", + "voting_y_test_pred = vtg_testing_pipeline.predict(X_test_selected_night[:,2:])\n", + "voting_y_test_pred = hmm_model.predict(voting_y_test_pred.reshape(-1, 1))\n", + "\n", + "print(confusion_matrix(y_test_selected_night, voting_y_test_pred))\n", + "print(classification_report(y_test_selected_night, voting_y_test_pred, target_names=SLEEP_STAGES_VALUES.keys()))\n", + "print(\"Agreement score (Cohen Kappa): \", cohen_kappa_score(y_test_selected_night, voting_y_test_pred))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generate RF trained pipeline\n", + "___" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 3min 51s, sys: 2.08 s, total: 3min 53s\n", + "Wall time: 1min 25s\n" + ] + } + ], + "source": [ + "%%time\n", + "\n", + "def get_random_forest_model():\n", + " NB_CATEGORICAL_FEATURES = 2\n", + " NB_FEATURES = 48\n", + " \n", + " return Pipeline([\n", + " ('scaling', ColumnTransformer([\n", + " ('pass-through-categorical', 'passthrough', list(range(NB_CATEGORICAL_FEATURES))),\n", + " ('scaling-continuous', StandardScaler(copy=False), list(range(NB_CATEGORICAL_FEATURES,NB_FEATURES)))\n", + " ])),\n", + " ('classifier', RandomForestClassifier(\n", + " n_estimators=100,\n", + " max_depth=24,\n", + " random_state=42, # enables deterministic behaviour\n", + " n_jobs=-1\n", + " ))\n", + " ])\n", + "\n", + "rf_testing_pipeline = get_random_forest_model()\n", + "rf_testing_pipeline.fit(X_train_valid[:, 2:], y_train_valid)" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[398 8 1 0 2]\n", + " [ 48 64 45 0 26]\n", + " [ 4 26 313 0 62]\n", + " [ 0 1 59 31 0]\n", + " [ 0 0 0 0 100]]\n", + " precision recall f1-score support\n", + "\n", + " W 0.88 0.97 0.93 409\n", + " N1 0.65 0.35 0.45 183\n", + " N2 0.75 0.77 0.76 405\n", + " N3 1.00 0.34 0.51 91\n", + " REM 0.53 1.00 0.69 100\n", + "\n", + " accuracy 0.76 1188\n", + " macro avg 0.76 0.69 0.67 1188\n", + "weighted avg 0.78 0.76 0.75 1188\n", + "\n", + "Agreement score (Cohen Kappa): 0.6709289967448082\n" + ] + } + ], + "source": [ + "X_test_selected_night = X_test[X_test[:,1] == NIGHT[0]]\n", + "y_test_selected_night = y_test[X_test[:,1] == NIGHT[0]]\n", + "\n", + "rf_y_test_pred = rf_testing_pipeline.predict(X_test_selected_night[:,2:])\n", + "rf_y_test_pred = hmm_model.predict(rf_y_test_pred.reshape(-1, 1))\n", + "\n", + "print(confusion_matrix(y_test_selected_night, rf_y_test_pred))\n", + "print(classification_report(y_test_selected_night, rf_y_test_pred, target_names=SLEEP_STAGES_VALUES.keys()))\n", + "print(\"Agreement score (Cohen Kappa): \", cohen_kappa_score(y_test_selected_night, rf_y_test_pred))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Saving to display in performance page\n", + "___\n", + "\n", + "She went to bed at 1989-07-04 00:18:00, so in UTC its 615514680." + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [], + "source": [ + "rf_hypnogram = {\n", + " 'timestamps': [\n", + " int((datetime.fromtimestamp(615514680) + timedelta(seconds=index*EPOCH_DURATION)).timestamp())\n", + " for index, stage in enumerate(rf_y_test_pred)\n", + " ],\n", + " 'stages': [\n", + " str(list(SLEEP_STAGES_VALUES.keys())[list(SLEEP_STAGES_VALUES.values()).index(stage)])\n", + " for index, stage in enumerate(rf_y_test_pred)\n", + " ]\n", + "}\n", + "\n", + "with open('data/predicted_woman78yo_sleepedf.json', 'w') as fp:\n", + " json.dump(dict(epochs=rf_hypnogram), fp)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "voting_hypnogram = {\n", + " 'timestamps': [int((datetime.fromtimestamp(615514680) + timedelta(seconds=index*EPOCH_DURATION)).timestamp()) for index, stage in enumerate(voting_y_test_pred)],\n", + " 'stages': [str(list(SLEEP_STAGES_VALUES.keys())[list(SLEEP_STAGES_VALUES.values()).index(stage)]) for index, stage in enumerate(voting_y_test_pred)]\n", + "}\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/web/src/assets/data/electrophysiologist_woman78yo_sleepedf.json b/web/src/assets/data/electrophysiologist_woman78yo_sleepedf.json index 386081e2..a353def7 100644 --- a/web/src/assets/data/electrophysiologist_woman78yo_sleepedf.json +++ 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027d4e27..3171cb45 100644 --- a/web/src/views/performance/index.js +++ b/web/src/views/performance/index.js @@ -57,9 +57,8 @@ const Performance = () => { />

- Ever wonder what is the value of this application? This page aims to illustrate the relative performance of - our sleep scoring compared to clinical hypnogram scoring (which is usually considered the state-of-the-art - technique). + This page aims to illustrate the relative performance of our sleep scoring compared to clinical hypnogram + scoring (which is usually considered the state-of-the-art technique).

Here is the plan: diff --git a/web/src/views/performance/text.json b/web/src/views/performance/text.json index 2d865b90..f8e4812f 100644 --- a/web/src/views/performance/text.json +++ b/web/src/views/performance/text.json @@ -1,5 +1,5 @@ { "header_title": "Performances", - "header_subtitle": "of our classifier and the state of the art", + "header_subtitle": "of our classifier compared to manual scoring", "header_description": "Let's see the performances of Polydodo and nuance its output" } From cb0eb23642ca97222bbf3a38f60f0e461eed8434 Mon Sep 17 00:00:00 2001 From: Claudia Onorato Date: Sun, 22 Nov 2020 19:24:31 -0500 Subject: [PATCH 02/14] added results on test set --- ai/models/RF_HMM_smaller.ipynb | 63 +++++++++++++++++++++++---- web/src/views/performance/index.js | 69 ++++++++++++++++++++---------- 2 files changed, 100 insertions(+), 32 deletions(-) diff --git a/ai/models/RF_HMM_smaller.ipynb b/ai/models/RF_HMM_smaller.ipynb index 7e6f6838..a1c29d41 100644 --- a/ai/models/RF_HMM_smaller.ipynb +++ b/ai/models/RF_HMM_smaller.ipynb @@ -30,7 +30,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -164,7 +164,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -188,7 +188,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -225,7 +225,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -259,7 +259,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -533,15 +533,15 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 3min 41s, sys: 2.12 s, total: 3min 43s\n", - "Wall time: 1min 13s\n" + "CPU times: user 3min 41s, sys: 2.2 s, total: 3min 44s\n", + "Wall time: 1min 21s\n" ] } ], @@ -620,7 +620,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -658,6 +658,51 @@ "print(\"Agreement score (Cohen Kappa): \", cohen_kappa_score(y_test, y_test_pred))" ] }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[1457 66 41 5 55]\n", + " [ 186 259 318 3 217]\n", + " [ 6 43 3195 245 114]\n", + " [ 0 0 34 577 0]\n", + " [ 7 10 146 0 1139]]\n", + " precision recall f1-score support\n", + "\n", + " W 0.88 0.90 0.89 1624\n", + " N1 0.69 0.26 0.38 983\n", + " N2 0.86 0.89 0.87 3603\n", + " N3 0.70 0.94 0.80 611\n", + " REM 0.75 0.87 0.81 1302\n", + "\n", + " accuracy 0.82 8123\n", + " macro avg 0.77 0.77 0.75 8123\n", + "weighted avg 0.81 0.82 0.80 8123\n", + "\n", + "Agreement score (Cohen Kappa): 0.7413141526163325\n" + ] + } + ], + "source": [ + "hmm_model = MultinomialHMM(n_components=N_STAGES)\n", + "\n", + "MODELS_DIR = \"../trained_model\"\n", + "hmm_model.emissionprob_ = np.load(f\"{MODELS_DIR}/HMM_emissionprob.npy\", allow_pickle=True)\n", + "hmm_model.startprob_ = np.load(f\"{MODELS_DIR}/HMM_startprob.npy\", allow_pickle=True)\n", + "hmm_model.transmat_ = np.load(f\"{MODELS_DIR}/HMM_transmat.npy\", allow_pickle=True)\n", + "\n", + "y_test_pred = hmm_model.predict(y_test_pred.reshape(-1, 1))\n", + "\n", + "print(confusion_matrix(y_test, y_test_pred))\n", + "print(classification_report(y_test, y_test_pred, target_names=SLEEP_STAGES_VALUES.keys()))\n", + "print(\"Agreement score (Cohen Kappa): \", cohen_kappa_score(y_test, y_test_pred))" + ] + }, { "cell_type": "markdown", "metadata": {}, diff --git a/web/src/views/performance/index.js b/web/src/views/performance/index.js index 3171cb45..08104ec0 100644 --- a/web/src/views/performance/index.js +++ b/web/src/views/performance/index.js @@ -56,9 +56,17 @@ const Performance = () => { description={text['header_description']} /> +

+ This page aims to illustrate the performance of our sleep scoring compared to clinical hypnogram scoring + (which is usually considered the state-of-the-art technique). If you want to learn more about how we've + defined our sleep scoring algorithm, please either refer to our presentation video in our home page, or to our{' '} + + wiki page + + . +

- This page aims to illustrate the relative performance of our sleep scoring compared to clinical hypnogram - scoring (which is usually considered the state-of-the-art technique). +

Here is the plan: @@ -81,49 +89,64 @@ const Performance = () => { results and maybe get an idea of the usual disagreement level between professional scorers. -

Classifier's accuracy according to Sleep-EDF

+

Classifier's accuracy according to Sleep-EDF (kappa:0.6709)

createComparativeHypnogram(svg, data, ['Classifier', 'Sleep-EDF'])} data={[predictedWoman78yoSleepEDF.epochs, physionetWoman78yoSleepEDF.epochs]} /> -

Classifier's accuracy according to the electrophysiologist

+

Classifier's accuracy according to the electrophysiologist (kappa;0.8310)

createComparativeHypnogram(svg, data, ['Classifier', 'Electrophysiologist'])} data={[predictedWilliamCyton.epochs, electrophysiologistWilliamCyton.epochs]} /> -

Electrophysiologist and Sleep-EDF's agreement

+

Electrophysiologist and Sleep-EDF's agreement (kappa:0.6315)

createComparativeHypnogram(svg, data, ['Electrophysiologist', 'Sleep-EDF'])} data={[electrophysiologistWoman78yoSleepEDF.epochs, physionetWoman78yoSleepEDF.epochs]} /> +

Classifier's accuracy according to Sleep-EDF testing set (kappa:0.74131)

+ createComparativeHypnogram(svg, data, ['Classifier', 'Sleep-EDF'])} + data={[predictedWoman78yoSleepEDF.epochs, physionetWoman78yoSleepEDF.epochs]} + /> + From 592b0654c9f4d984aefe4bfd84b37b543d1a38a2 Mon Sep 17 00:00:00 2001 From: Claudia Onorato Date: Sun, 22 Nov 2020 20:35:36 -0500 Subject: [PATCH 03/14] added performance metrics on test set --- web/src/views/performance/index.js | 78 ++++++++++++++++++++++-------- 1 file changed, 59 insertions(+), 19 deletions(-) diff --git a/web/src/views/performance/index.js b/web/src/views/performance/index.js index 08104ec0..b50b4221 100644 --- a/web/src/views/performance/index.js +++ b/web/src/views/performance/index.js @@ -57,20 +57,75 @@ const Performance = () => { />

- This page aims to illustrate the performance of our sleep scoring compared to clinical hypnogram scoring - (which is usually considered the state-of-the-art technique). If you want to learn more about how we've - defined our sleep scoring algorithm, please either refer to our presentation video in our home page, or to our{' '} + This page aims to illustrate the performance of our sleep scoring compared to clinical hypnogram scoring, + which is usually considered the gold standard in sleep stage scoring. If you want to learn more about how + we've defined our sleep scoring algorithm, please either refer to our presentation video in our home page, or + to our{' '} wiki page .

+

Model training and selection

+ In order to train our model to predict sleep stages based on EEG data, we used an open source dataset, + +  Physionet SleepEDF expanded + + , which is composed of 153 nights of sleep coming from 82 subjects, of different ages and sex. The labelled + data has been produced by well-trained technicians according to the 1968 Rechtschaffen and Kales manual, + adapted to the used EEG montage. In order to compare and validate the prototyped models, we divided the data + into a training and testing set, and we made sure that both sets had different subjects. Indeed, we didn't + want our test metrics to be biaised, as we'd have already trained on data coming from a same subject. +

+

+ In order to select the most accurate model, we looked at the{' '} + + Cohen's Kappa agreement score + + , as it is often used in the automatic sleep scoring litterature. We then chose the model with the highest + agreement score, which is a voting classifier, composed of a K Nearest Neighbour and a Random Forest + classifiers. Unfortunatly, the model couldn't be exported to be used in the server, as we included, inside the + voting classifier, a pipeline to reduce the dimension of the inputed features. We then decided to use our + second best model, which is a Random Forest. It is the one we will evaluate the performances, as it is the one + currently used to classify sleep stages in our server.

- Here is the plan: + Let's first look at the performances of the classifier over the selected test set composed of subjects from + the SleepEDF dataset. The resulted Cohen's Kappa agreement score is{' '} + + 0.741 + + . Please note that the classes are imbalanced, and thus impacting the metrics displayed.

+

Classifier's accuracy according to Sleep-EDF testing set

+
+ +
+

+ The test set, on which these metrics were calculated, is composed of subjects from different ages groups (33 + year old female, 54 year old female, 67 year old female, 88 year old male), so that the obtained score is the + most representative of our ability to classify sleep, no matter the age. In another side, we could compare the + results obtained to the ones found in the litterature. To do so, we had to find a paper that uses the same + dataset, the same metric and that splits their dataset in a similar fashion as ours.TODO. +

+

+ Although we obtain good results, it didn't quite validate that our classifier could accurately score OpenBCI + data into sleep stages. After all, we only validated on data coming from the same acquisition hardware than + the data we trained on, which is not the case when we analyze data submitted in the application. We then had + to make our own polysomnographic dataset based on the hardware we use, so using an OpenBCI board. +

+