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"metadata": {},
"outputs": [],
"source": [
"%config InlineBackend.figure_formats = ['svg']"
]
},
{
"cell_type": "code",
"metadata": {},
"outputs": [],
"source": [
"import random\n",
"import numpy as np\n",
"import pandas as pd\n",
"from joblib import dump, load\n",
"np.random.seed(42)\n",
"random.seed(42)"
]
},
{
"cell_type": "code",
"metadata": {},
"outputs": [],
"source": [
"data_dir = './data/'\n",
"with np.load('data/Xy.npz') as f:\n",
" X = f['X']\n",
" y = f['y']\n",
" y34 = f['y34']"
]
},
{
"cell_type": "code",
"metadata": {},
"outputs": [],
"source": [
"# Perform temporal split of data into train/test sets\n",
"pop = pd.read_csv(data_dir + 'population/d10_with_vitals.csv').set_index('BMT_ID')\n",
"\n",
"split_date = 201701001\n",
"split_idx = -85\n",
"\n",
"assert (pop[:split_idx].index < split_date).all()\n",
"assert (pop[split_idx:].index >= split_date).all()"
]
},
{
"cell_type": "code",
"metadata": {},
"outputs": [],
"source": [
"from sklearn import preprocessing, model_selection, metrics, utils\n",
"from sklearn.linear_model import LogisticRegression\n",
"from tqdm import tqdm\n",
"from joblib import Parallel, delayed\n",
"from sklearn.base import clone"
]
},
{
"cell_type": "code",
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"metadata": {},
"outputs": [],
"source": [
"# Specify hyperparameters and cv parameters\n",
"base_estimator = LogisticRegression(penalty='l2', class_weight='balanced', solver='liblinear')\n",
"param_grid = {\n",
" 'C': [10. ** n for n in range(-6, 7)],\n",
" 'penalty': ['l2'],\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Alternative label definition\n",
"{0,1,2} -> negative, {3,4} -> positive"
]
},
{
"cell_type": "code",
"execution_count": 472,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(0.31790123456790126, 0.13580246913580246)"
]
},
"execution_count": 472,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y.mean(), y34.mean()"
]
},
{
"cell_type": "code",
"execution_count": 376,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/data4/tangsp/venv/lib/python3.7/site-packages/sklearn/model_selection/_search.py:814: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
" DeprecationWarning)\n"
]
}
],
"source": [
"Xtr, Xte = X[:split_idx], X[split_idx:]\n",
"ytr, yte = y34[:split_idx], y34[split_idx:]\n",
"\n",
"cv_splits, cv_repeat = 5, 20\n",
"cv = model_selection.RepeatedStratifiedKFold(cv_splits, cv_repeat, random_state=0)\n",
"clf = model_selection.GridSearchCV(\n",
" clone(base_estimator), param_grid, \n",
" cv=cv, scoring='roc_auc', n_jobs=5,\n",
")\n",
"clf.fit(Xtr, ytr)\n",
"test_score = metrics.roc_auc_score(yte, clf.decision_function(Xte))"
]
},
{
"cell_type": "code",
"execution_count": 377,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
" \r"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Test AUC: 0.596 (0.423, 0.761)\n"
]
}
],
"source": [
"y_true = yte\n",
"y_score = clf.decision_function(Xte)\n",
"\n",
"def boostrap_func(i, y_true, y_score):\n",
" yte_true_b, yte_pred_b = utils.resample(y_true, y_score, replace=True, random_state=i)\n",
" return metrics.roc_curve(yte_true_b, yte_pred_b), metrics.roc_auc_score(yte_true_b, yte_pred_b)\n",
"\n",
"roc_curves, auc_scores = zip(*Parallel(n_jobs=4)(delayed(boostrap_func)(i, y_true, y_score) for i in tqdm(range(1000), leave=False)))\n",
"print('Test AUC: {:.3f} ({:.3f}, {:.3f})'.format(np.median(auc_scores), np.percentile(auc_scores, 2.5), np.percentile(auc_scores, 97.5)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Random Forest model"
]
},
{
"cell_type": "code",
"execution_count": 378,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.model_selection import RandomizedSearchCV\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"import scipy"
]
},
{
"cell_type": "code",
"execution_count": 379,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"RandomizedSearchCV(cv=<sklearn.model_selection._split.RepeatedStratifiedKFold object at 0x7f9b8ffacc50>,\n",
" error_score='raise-deprecating',\n",
" estimator=RandomForestClassifier(bootstrap=True,\n",
" class_weight=None,\n",
" criterion='gini',\n",
" max_depth=None,\n",
" max_features='auto',\n",
" max_leaf_nodes=None,\n",
" min_impurity_decrease=0.0,\n",
" min_impurity_split=None,\n",
" min_samples_leaf=1,\n",
" min_sample...\n",
" 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7f9b8ffad050>,\n",
" 'min_samples_split': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7f9b8ffadc10>,\n",
" 'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7f9b8ffad490>},\n",
" pre_dispatch='2*n_jobs', random_state=None, refit=True,\n",
" return_train_score=False, scoring='roc_auc', verbose=0)"
]
},
"execution_count": 379,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Xtr, Xte = X[:split_idx], X[split_idx:]\n",
"ytr, yte = y[:split_idx], y[split_idx:]\n",
"\n",
"cv_splits, cv_repeat = 5, 20\n",
"cv = model_selection.RepeatedStratifiedKFold(cv_splits, cv_repeat, random_state=0)\n",
"clf = RandomizedSearchCV(\n",
" RandomForestClassifier(), \n",
" {\n",
" \"criterion\": [\"gini\", \"entropy\"],\n",
" \"max_depth\": [4, 8, 16, 32, None],\n",
" \"max_features\": scipy.stats.randint(1, 100),\n",
" \"min_samples_split\": scipy.stats.randint(2, 11),\n",
" \"min_samples_leaf\": scipy.stats.randint(1, 11),\n",
" \"n_estimators\": scipy.stats.randint(50,500),\n",
" \"bootstrap\": [True],\n",
" },\n",
" n_iter=10,\n",
" cv=cv,\n",
" scoring='roc_auc',\n",
" n_jobs=5,\n",
" )\n",
"\n",
"clf.fit(Xtr, ytr)"
]
},
{
"cell_type": "code",
"execution_count": 380,
"metadata": {},
"outputs": [],
"source": [
"test_score = metrics.roc_auc_score(yte, clf.predict_proba(Xte)[:,1])"
]
},
{
"cell_type": "code",
"execution_count": 381,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
" \r"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Test AUC: 0.651 (0.525, 0.768)\n"
]
}
],
"source": [
"y_true = yte\n",
"y_score = clf.predict_proba(Xte)[:,1]\n",
"\n",
"def boostrap_func(i, y_true, y_score):\n",
" yte_true_b, yte_pred_b = utils.resample(y_true, y_score, replace=True, random_state=i)\n",
" return metrics.roc_curve(yte_true_b, yte_pred_b), metrics.roc_auc_score(yte_true_b, yte_pred_b)\n",
"\n",
"_, auc_scores_rf = zip(*Parallel(n_jobs=4)(delayed(boostrap_func)(i, y_true, y_score) for i in tqdm(range(1000), leave=False)))\n",
"print('Test AUC: {:.3f} ({:.3f}, {:.3f})'.format(np.median(auc_scores_rf), np.percentile(auc_scores_rf, 2.5), np.percentile(auc_scores_rf, 97.5)))"
]
},
{
"cell_type": "code",
"execution_count": 382,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
" \r"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Test AUC: 0.658 (0.536, 0.784)\n"
]
}
],
"source": [
"clf_main = load('data/model_combined.joblib')\n",
"y_score = clf_main.predict_proba(Xte)[:,1]\n",
"_, auc_scores_main = zip(*Parallel(n_jobs=4)(delayed(boostrap_func)(i, y_true, y_score) for i in tqdm(range(1000), leave=False)))\n",
"print('Test AUC: {:.3f} ({:.3f}, {:.3f})'.format(\n",
" np.median(auc_scores_main), np.percentile(auc_scores_main, 2.5), np.percentile(auc_scores_main, 97.5)))"
]
},
{
"cell_type": "code",
"execution_count": 383,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.782"
]
},
"execution_count": 383,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# H0: proposed == baseline\n",
"# H1: proposed != baseline\n",
"# resampling test, two-sided p-value\n",
"2 * min(\n",
" (np.array(auc_scores_main) < np.array(auc_scores_rf)).mean(),\n",
" (np.array(auc_scores_main) > np.array(auc_scores_rf)).mean()\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Kaplan Meier plot"
]
},
{
"cell_type": "code",
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"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"from sklearn import utils"
]
},
{
"cell_type": "code",
"execution_count": 482,
"metadata": {},
"outputs": [],
"source": [
"Xtr, Xte = X[:split_idx], X[split_idx:]\n",
"ytr, yte = y[:split_idx], y[split_idx:]"
]
},
{
"cell_type": "code",
"execution_count": 483,
"metadata": {},
"outputs": [],
"source": [
"pop = pd.read_csv(data_dir + 'population/d10_with_vitals.csv').set_index('BMT_ID')\n",
"labels = pd.read_csv(data_dir + 'prep/label.csv').set_index('BMT_ID')\n",
"IDs = pop.index[split_idx:]\n",
"onset = labels.loc[IDs, 'GVHD_onset_day'].replace(np.nan, np.inf)"
]
},
{
"cell_type": "code",
"execution_count": 484,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"IntervalIndex([(0.234, 0.539], (0.539, 0.74]],\n",
" closed='right',\n",
" dtype='interval[float64]')\n"
]
}
],
"source": [
"clf = load('data/model_combined.joblib')\n",
"threshold = np.percentile(clf.predict_proba(Xtr)[:,1], [0, 70, 100])\n",
"y_prob = clf.predict_proba(Xte)[:,1]\n",
"y_group = pd.cut(y_prob, threshold)\n",
"print(y_group.categories)\n",
"y_group = y_group.codes"
]
},
{
"cell_type": "code",
"execution_count": 485,
"metadata": {},
"outputs": [],
"source": [
"# Standard errors calculated via the Greenwood's formula\n",
"# http://sphweb.bumc.bu.edu/otlt/MPH-Modules/BS/BS704_Survival/BS704_Survival4.html\n",
"def calculate_survival_curve_se(onset_m, times):\n",
" surv_probs = [(onset_m > 0).mean()]\n",
" stderrs = [0]\n",
" quotients = []\n",
" for day in sorted(times):\n",
" if np.isfinite(day):\n",
" Nt, Dt = (onset_m >= day).sum(), (onset_m == day).sum()\n",
" St = (onset_m > day).mean()\n",
" quotients.append(Dt / (Nt * (Nt - Dt)))\n",
" surv_probs.append(St)\n",
" stderrs.append(St * np.sqrt(np.sum(quotients)))\n",
" return np.array(surv_probs), np.array(stderrs)"
]
},
{
"cell_type": "code",
"execution_count": 486,
"metadata": {},
"outputs": [
{
"data": {
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"<!-- Created with matplotlib (https://matplotlib.org/) -->\n",
"<svg height=\"262.19625pt\" version=\"1.1\" viewBox=\"0 0 395.325 262.19625\" width=\"395.325pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
" <defs>\n",
" <style type=\"text/css\">\n",
"*{stroke-linecap:butt;stroke-linejoin:round;}\n",
" </style>\n",
" </defs>\n",
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