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Web/performance #98

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merged 16 commits into from
Nov 23, 2020
Merged

Web/performance #98

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07fe843
updated 78yo anon prediction for clf, electro, physionet
conorato Nov 22, 2020
cb0eb23
added results on test set
conorato Nov 23, 2020
592b065
added performance metrics on test set
conorato Nov 23, 2020
23f0d1a
Merge branch 'master' into web/performance
conorato Nov 23, 2020
cd9b7ac
added openbci dataset intro
conorato Nov 23, 2020
6367be0
completed electro vs clf
conorato Nov 23, 2020
7460530
added further dev section
conorato Nov 23, 2020
a2bd630
added intro alex vs sleepedf
conorato Nov 23, 2020
49c6fe1
finish
conorato Nov 23, 2020
9d81435
Merge branch 'master' of github.com:PolyCortex/polydodo into web/perf…
conorato Nov 23, 2020
bc7589d
Update web/src/views/performance/index.js
conorato Nov 23, 2020
f364bd9
oups
conorato Nov 23, 2020
e5cfba3
updated youtube link!!!
conorato Nov 23, 2020
5fe696d
Apply suggestions from code review
conorato Nov 23, 2020
45de3b6
update link to embed
conorato Nov 23, 2020
7bc6900
Apply suggestions from code review
conorato Nov 23, 2020
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63 changes: 54 additions & 9 deletions ai/models/RF_HMM_smaller.ipynb
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Expand Up @@ -30,7 +30,7 @@
},
{
"cell_type": "code",
"execution_count": 18,
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
Expand Down Expand Up @@ -164,7 +164,7 @@
},
{
"cell_type": "code",
"execution_count": 11,
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -188,7 +188,7 @@
},
{
"cell_type": "code",
"execution_count": 12,
"execution_count": 8,
"metadata": {},
"outputs": [
{
Expand Down Expand Up @@ -225,7 +225,7 @@
},
{
"cell_type": "code",
"execution_count": 13,
"execution_count": 9,
"metadata": {},
"outputs": [
{
Expand Down Expand Up @@ -259,7 +259,7 @@
},
{
"cell_type": "code",
"execution_count": 14,
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
Expand Down Expand Up @@ -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"
]
}
],
Expand Down Expand Up @@ -620,7 +620,7 @@
},
{
"cell_type": "code",
"execution_count": 22,
"execution_count": 12,
"metadata": {},
"outputs": [
{
Expand Down Expand Up @@ -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": {},
Expand Down
1 change: 0 additions & 1 deletion ai/models/model_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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([
Expand Down
74 changes: 54 additions & 20 deletions ai/prediction_anonymous.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -18,32 +18,24 @@
},
{
"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"
]
},
{
"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",
Expand All @@ -65,7 +57,7 @@
},
{
"cell_type": "code",
"execution_count": 8,
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -81,7 +73,7 @@
},
{
"cell_type": "code",
"execution_count": 9,
"execution_count": 4,
"metadata": {},
"outputs": [
{
Expand Down Expand Up @@ -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"
}
Expand All @@ -175,7 +167,7 @@
},
{
"cell_type": "code",
"execution_count": 10,
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
Expand Down Expand Up @@ -231,7 +223,7 @@
},
{
"cell_type": "code",
"execution_count": 11,
"execution_count": 6,
"metadata": {},
"outputs": [
{
Expand All @@ -257,7 +249,7 @@
},
{
"cell_type": "code",
"execution_count": 12,
"execution_count": 7,
"metadata": {},
"outputs": [
{
Expand All @@ -282,7 +274,7 @@
},
{
"cell_type": "code",
"execution_count": 13,
"execution_count": 8,
"metadata": {},
"outputs": [
{
Expand Down Expand Up @@ -310,7 +302,7 @@
},
{
"cell_type": "code",
"execution_count": 14,
"execution_count": 9,
"metadata": {},
"outputs": [
{
Expand Down Expand Up @@ -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,
Expand Down
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