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Weibull Survival.ipynb
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{
"cells": [
{
"metadata": {
"collapsed": true,
"trusted": true
},
"cell_type": "code",
"source": "%matplotlib inline\nimport pandas as pd\nimport numpy as np\nfrom numpy.ma import masked_values\n\nfrom pymc3 import Model, sample, Metropolis, NUTS, traceplot, forestplot, find_MAP\nfrom pymc3 import Bernoulli, Normal, Uniform, invlogit, log, exp, Deterministic, DensityDist\nfrom pymc3 import Dirichlet, Categorical, Poisson, Lognormal, HalfCauchy, Gamma",
"execution_count": 1,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"scrolled": true,
"trusted": true
},
"cell_type": "code",
"source": "dataset = pd.read_csv(\"rtw.csv\", index_col=0).reset_index(drop=True)\ndataset.head()",
"execution_count": 2,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>age1</th>\n <th>age2</th>\n <th>age3</th>\n <th>bmi1</th>\n <th>bmi2</th>\n <th>bmi3</th>\n <th>gender</th>\n <th>race</th>\n <th>hispanic</th>\n <th>education</th>\n <th>...</th>\n <th>los</th>\n <th>rtw</th>\n <th>day</th>\n <th>practice.factor</th>\n <th>pracind</th>\n <th>practice</th>\n <th>select2</th>\n <th>smoke</th>\n <th>day0b</th>\n <th>day1</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>-1.097827</td>\n <td>0.031377</td>\n <td>0.000000</td>\n <td>1.074295</td>\n <td>1.327160</td>\n <td>0.279160</td>\n <td>0</td>\n <td>2</td>\n <td>-1</td>\n <td>4</td>\n <td>...</td>\n <td>1.0</td>\n <td>1</td>\n <td>51</td>\n <td>AHN</td>\n <td>0</td>\n <td>AHN</td>\n <td>False</td>\n <td>2</td>\n <td>51</td>\n <td>0</td>\n </tr>\n <tr>\n <th>1</th>\n <td>-0.304202</td>\n <td>0.309668</td>\n <td>0.000002</td>\n <td>0.934801</td>\n <td>1.142968</td>\n <td>0.224315</td>\n <td>1</td>\n <td>0</td>\n <td>-1</td>\n <td>3</td>\n <td>...</td>\n <td>1.0</td>\n <td>1</td>\n <td>43</td>\n <td>AHN</td>\n <td>0</td>\n <td>AHN</td>\n <td>False</td>\n <td>2</td>\n <td>43</td>\n <td>0</td>\n </tr>\n <tr>\n <th>2</th>\n <td>-1.160901</td>\n <td>0.023567</td>\n <td>0.000000</td>\n <td>-0.620389</td>\n <td>0.043296</td>\n <td>0.000000</td>\n <td>0</td>\n <td>0</td>\n <td>-1</td>\n <td>2</td>\n <td>...</td>\n <td>1.0</td>\n <td>1</td>\n <td>7</td>\n <td>AHN</td>\n <td>0</td>\n <td>AHN</td>\n <td>False</td>\n <td>2</td>\n <td>7</td>\n <td>0</td>\n </tr>\n <tr>\n <th>3</th>\n <td>-1.188626</td>\n <td>0.020591</td>\n <td>0.000000</td>\n <td>-0.029360</td>\n <td>0.240722</td>\n <td>0.008388</td>\n <td>0</td>\n <td>0</td>\n <td>-1</td>\n <td>1</td>\n <td>...</td>\n <td>1.0</td>\n <td>1</td>\n <td>83</td>\n <td>AHN</td>\n <td>0</td>\n <td>AHN</td>\n <td>False</td>\n <td>2</td>\n <td>83</td>\n <td>0</td>\n </tr>\n <tr>\n <th>4</th>\n <td>-0.988314</td>\n <td>0.048724</td>\n <td>0.000000</td>\n <td>0.093370</td>\n <td>0.312118</td>\n <td>0.017463</td>\n <td>0</td>\n <td>0</td>\n <td>-1</td>\n <td>3</td>\n <td>...</td>\n <td>1.0</td>\n <td>1</td>\n <td>46</td>\n <td>AHN</td>\n <td>0</td>\n <td>AHN</td>\n <td>False</td>\n <td>2</td>\n <td>46</td>\n <td>0</td>\n </tr>\n </tbody>\n</table>\n<p>5 rows × 53 columns</p>\n</div>",
"text/plain": " age1 age2 age3 bmi1 bmi2 bmi3 gender race \\\n0 -1.097827 0.031377 0.000000 1.074295 1.327160 0.279160 0 2 \n1 -0.304202 0.309668 0.000002 0.934801 1.142968 0.224315 1 0 \n2 -1.160901 0.023567 0.000000 -0.620389 0.043296 0.000000 0 0 \n3 -1.188626 0.020591 0.000000 -0.029360 0.240722 0.008388 0 0 \n4 -0.988314 0.048724 0.000000 0.093370 0.312118 0.017463 0 0 \n\n hispanic education ... los rtw day practice.factor pracind \\\n0 -1 4 ... 1.0 1 51 AHN 0 \n1 -1 3 ... 1.0 1 43 AHN 0 \n2 -1 2 ... 1.0 1 7 AHN 0 \n3 -1 1 ... 1.0 1 83 AHN 0 \n4 -1 3 ... 1.0 1 46 AHN 0 \n\n practice select2 smoke day0b day1 \n0 AHN False 2 51 0 \n1 AHN False 2 43 0 \n2 AHN False 2 7 0 \n3 AHN False 2 83 0 \n4 AHN False 2 46 0 \n\n[5 rows x 53 columns]"
},
"metadata": {},
"execution_count": 2
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "failure = (dataset.day1.values==0).astype(int)\nt = dataset.day0b.replace(500, np.nan).combine_first(dataset.day1).values + 1",
"execution_count": 3,
"outputs": []
},
{
"metadata": {
"collapsed": true,
"trusted": true
},
"cell_type": "code",
"source": "beta_names = ['age1', 'age2', 'age3', 'bmi1', 'bmi2', 'bmi3', 'osteoporosis', 'cad', 'gender', \n 'hispanic', 'compensation', 'pastsurgery', 'diabetes', 'anxiety', 'depression', \n 'motordef', 'duration', 'arthrodesis', 'interbody', 'approach']",
"execution_count": 4,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "n_prac = dataset.pracind.unique().shape[0]",
"execution_count": 5,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "assert not np.isnan(t).sum()\nassert not np.isnan(failure).sum()",
"execution_count": 6,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "with Model() as weibull_surv:\n \n mu_int = Normal('mu_int', 0, sd=100)\n sigma_int = HalfCauchy('sigma_int', 5)\n intercept = Normal('intercept', mu_int, sigma_int, shape=n_prac)\n \n beta = Normal('beta', 0, sd=1000, shape=len(beta_names))\n X = dataset[beta_names].values.T\n \n theta = exp(intercept[dataset.pracind.values] + beta.dot(X))\n \n alpha = HalfCauchy(\"alpha\", 2.5)\n \n \n # Weibull survival likelihood, accounting for censoring\n def logp(failure, value):\n hazard = alpha * theta**alpha * value**(alpha-1)\n return (failure * log(hazard) - (theta * value)**alpha).sum()\n\n day = DensityDist('day', logp, observed={'failure':failure, 'value':t})",
"execution_count": 7,
"outputs": [
{
"output_type": "stream",
"text": "Applied log-transform to sigma_int and added transformed sigma_int_log_ to model.\nApplied log-transform to alpha and added transformed alpha_log_ to model.\n",
"name": "stdout"
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "from pymc3.variational import advi, sample_vp\n\nwith weibull_surv:\n \n fit = advi(n=100000)\n trace = sample_vp(fit)",
"execution_count": 13,
"outputs": [
{
"output_type": "stream",
"text": "Iteration 0 [0%]: ELBO = -3.2675045052572436e+18\nIteration 10000 [10%]: Average ELBO = -1.6609694956981292e+208\nIteration 20000 [20%]: Average ELBO = -31807.88\nIteration 30000 [30%]: Average ELBO = -28720.5\nIteration 40000 [40%]: Average ELBO = -28061.28\nIteration 50000 [50%]: Average ELBO = -27823.17\nIteration 60000 [60%]: Average ELBO = -27716.09\nIteration 70000 [70%]: Average ELBO = -27672.36\nIteration 80000 [80%]: Average ELBO = -27652.08\nIteration 90000 [90%]: Average ELBO = -27642.41\nFinished [100%]: Average ELBO = -27637.48\n",
"name": "stdout"
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "forestplot(trace, varnames=['beta'], ylabels=beta_names)",
"execution_count": 15,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": "<matplotlib.gridspec.GridSpec at 0x1264ef780>"
},
"metadata": {},
"execution_count": 15
},
{
"output_type": "display_data",
"data": {
"image/png": 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jQ5KW6fgk9atGPaz+nH1n1hiqek0p30J9HSn5YCgpReEIUizBPsBKwO0R8bVc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\nDHdKZmZWGP8fnEI4Hqck/8oAAAAASUVORK5CYII=\n",
"text/plain": "<matplotlib.figure.Figure at 0x1264ef7f0>"
},
"metadata": {}
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "traceplot(trace, varnames=['mu_int', 'sigma_int'])",
"execution_count": 16,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x126891438>,\n <matplotlib.axes._subplots.AxesSubplot object at 0x1268c9b70>],\n [<matplotlib.axes._subplots.AxesSubplot object at 0x126b17710>,\n <matplotlib.axes._subplots.AxesSubplot object at 0x126b4eef0>]], dtype=object)"
},
"metadata": {},
"execution_count": 16
},
{
"output_type": "display_data",
"data": {
"image/png": 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sLqaAhrFx40YRl8W0WyxBEWr7WQm6TkER2biRLJ/Icgl6Pt1Ilm1n2DWYOlVS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\nbOHjXwB3qOonRKQH0Kee8hr1QxU++1lXkOKaayqrIFgtPXq4ioVHHAF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IWkT2AlZltDc2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"text/plain": "<matplotlib.figure.Figure at 0x1264efb38>"
},
"metadata": {}
}
]
}
],
"metadata": {
"kernelspec": {
"name": "python3",
"display_name": "Python 3",
"language": "python"
},
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