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conoratoWilliam Harveymateobelanger
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Web/performance (#98)
* updated 78yo anon prediction for clf, electro, physionet * added results on test set * added performance metrics on test set * added openbci dataset intro * completed electro vs clf * added further dev section * added intro alex vs sleepedf * finish * Update web/src/views/performance/index.js Co-authored-by: William Harvey <william.harvey@polymtl.ca> * oups * updated youtube link!!! * Apply suggestions from code review Co-authored-by: Mathieu Bélanger <mbelanger.poly@gmail.com> * update link to embed * Apply suggestions from code review Co-authored-by: Mathieu Bélanger <mbelanger.poly@gmail.com> Co-authored-by: William Harvey <william.harvey@polymtl.ca> Co-authored-by: Mathieu Bélanger <mbelanger.poly@gmail.com>
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‎ai/models/RF_HMM_smaller.ipynb

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},
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},
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"text": [
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"CPU times: user 3min 41s, sys: 2.12 s, total: 3min 43s\n",
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"Wall time: 1min 13s\n"
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"CPU times: user 3min 41s, sys: 2.2 s, total: 3min 44s\n",
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"Wall time: 1min 21s\n"
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{
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"print(\"Agreement score (Cohen Kappa): \", cohen_kappa_score(y_test, y_test_pred))"
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]
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"cell_type": "code",
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"execution_count": 14,
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"metadata": {},
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"outputs": [
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"name": "stdout",
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"text": [
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"[[1457 66 41 5 55]\n",
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" [ 186 259 318 3 217]\n",
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" [ 6 43 3195 245 114]\n",
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" [ 0 0 34 577 0]\n",
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" [ 7 10 146 0 1139]]\n",
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" precision recall f1-score support\n",
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"\n",
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" W 0.88 0.90 0.89 1624\n",
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" N1 0.69 0.26 0.38 983\n",
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" N2 0.86 0.89 0.87 3603\n",
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" N3 0.70 0.94 0.80 611\n",
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" REM 0.75 0.87 0.81 1302\n",
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"\n",
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" accuracy 0.82 8123\n",
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" macro avg 0.77 0.77 0.75 8123\n",
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"weighted avg 0.81 0.82 0.80 8123\n",
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"\n",
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"Agreement score (Cohen Kappa): 0.7413141526163325\n"
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]
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}
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],
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"source": [
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"hmm_model = MultinomialHMM(n_components=N_STAGES)\n",
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"\n",
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"MODELS_DIR = \"../trained_model\"\n",
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"hmm_model.emissionprob_ = np.load(f\"{MODELS_DIR}/HMM_emissionprob.npy\", allow_pickle=True)\n",
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"hmm_model.startprob_ = np.load(f\"{MODELS_DIR}/HMM_startprob.npy\", allow_pickle=True)\n",
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"hmm_model.transmat_ = np.load(f\"{MODELS_DIR}/HMM_transmat.npy\", allow_pickle=True)\n",
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"\n",
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"y_test_pred = hmm_model.predict(y_test_pred.reshape(-1, 1))\n",
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"\n",
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"print(confusion_matrix(y_test, y_test_pred))\n",
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"print(classification_report(y_test, y_test_pred, target_names=SLEEP_STAGES_VALUES.keys()))\n",
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"print(\"Agreement score (Cohen Kappa): \", cohen_kappa_score(y_test, y_test_pred))"
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]
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},
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‎ai/models/model_utils.py

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[85,110] # 32 recordings
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age_categories = np.unique(X[:, AGE_CATEGORY_COL_IDX])
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assert subjects_test is None or len(subjects_test) == len(age_categories), "If subjects are specified, they must be specified for all age groups"
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if subjects_test is None:
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unique_subject_with_age = np.array([

‎ai/prediction_anonymous.ipynb

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"text": [
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"The autoreload extension is already loaded. To reload it, use:\n",
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" %reload_ext autoreload\n"
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]
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}
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],
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"outputs": [],
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"%load_ext autoreload\n",
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"%autoreload 2"
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"%matplotlib inline\n",
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"\n",
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"import mne\n",
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"import json\n",
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"import pandas as pd\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"1 25620.0 1989-04-25 14:50:00 609519000.0 "
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"print(\"Agreement score (Cohen Kappa): \", cohen_kappa_score(y_physionet, y_electrophysiologist))"
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"source": [
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"electro_hypno = {\n",
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" 'timestamps': [\n",
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" int((datetime.fromtimestamp(615514680) + timedelta(seconds=index*EPOCH_DURATION)).timestamp())\n",
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" for index, stage in enumerate(y_electrophysiologist)\n",
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" ],\n",
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" 'stages': [\n",
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" str(list(SLEEP_STAGES_VALUES.keys())[list(SLEEP_STAGES_VALUES.values()).index(stage)])\n",
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" for index, stage in enumerate(y_electrophysiologist)\n",
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" ]\n",
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"}\n",
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"\n",
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"with open('data/electrophysiologist_woman78yo_sleepedf.json', 'w') as fp:\n",
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" json.dump(dict(epochs=electro_hypno), fp)"
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]
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},
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"sleepedf_hypno = {\n",
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" 'timestamps': [\n",
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" int((datetime.fromtimestamp(615514680) + timedelta(seconds=index*EPOCH_DURATION)).timestamp())\n",
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" for index, stage in enumerate(y_physionet)\n",
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" ],\n",
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" 'stages': [\n",
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" str(list(SLEEP_STAGES_VALUES.keys())[list(SLEEP_STAGES_VALUES.values()).index(stage)])\n",
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" for index, stage in enumerate(y_physionet)\n",
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" ]\n",
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"}\n",
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"\n",
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"with open('data/physionet_woman78yo_sleepedf.json', 'w') as fp:\n",
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" json.dump(dict(epochs=sleepedf_hypno), fp)"
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]
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},
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