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@chartl
Last active May 18, 2019 06:54
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>x1</th>\n",
" <th>x2</th>\n",
" <th>y</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>-1.270291</td>\n",
" <td>-0.255200</td>\n",
" <td>-1.302439</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1.090380</td>\n",
" <td>1.258400</td>\n",
" <td>1.361324</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.924211</td>\n",
" <td>0.705971</td>\n",
" <td>-0.028952</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>-0.258931</td>\n",
" <td>1.303901</td>\n",
" <td>1.406818</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.353085</td>\n",
" <td>1.506337</td>\n",
" <td>2.483516</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" x1 x2 y\n",
"0 -1.270291 -0.255200 -1.302439\n",
"1 1.090380 1.258400 1.361324\n",
"2 0.924211 0.705971 -0.028952\n",
"3 -0.258931 1.303901 1.406818\n",
"4 0.353085 1.506337 2.483516"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as np\n",
"import scipy as sp\n",
"import pandas as pd\n",
"import theano\n",
"import theano.tensor as tt\n",
"import seaborn as sbn\n",
"import pymc3 as pm\n",
"import matplotlib\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# generate data\n",
"theta_true = np.array([-1.1, -0.45, 2.])\n",
"fail_rate_true = 0.0825\n",
"def gen_sample():\n",
" x = np.random.normal(size=(2,))\n",
" if np.random.random() < fail_rate_true:\n",
" theta_rn = np.sign(np.random.normal(size=(3,)))*np.random.exponential(8, size=(3,))\n",
" theta_rn[0] = theta_rn[0] + 150\n",
" else:\n",
" theta_rn = theta_true\n",
" y = theta_rn[0] + np.dot(x, theta_rn[1:]) + np.random.normal(0, 0.25)\n",
" return {'x1': x[0], 'x2': x[1], 'y': y}\n",
"\n",
"\n",
"df = pd.DataFrame([gen_sample() for _ in range(300)])\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([-1.76711242, -1.8455394 , -0.91935763])"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x432 with 3 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# plot data\n",
"sbn.jointplot(x='x1', y='y', data=df)\n",
"\n",
"l = pm.Normal.dist(0,1).logp\n",
"l(df.values[:3,2]).eval()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1c374378d0>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# likelihood calculation\n",
"X = df.values[:,:2]\n",
"y = df.values[:,2]\n",
"with pm.Model() as mod:\n",
" alpha = pm.Normal('alpha', 0., 1.)\n",
" beta = pm.Normal('beta', 0., sd=1., shape=(2,))\n",
" err_se = pm.Deterministic('err_se', 0.01 + pm.HalfNormal('err_se__', 1.))\n",
" y_pred = pm.Deterministic('y_pred', alpha + tt.dot(X, beta))\n",
" # data likelihood\n",
" lik_y = pm.Deterministic('lik_y', pm.Normal.dist(y_pred, err_se).logp(y))\n",
" # prior likelihood\n",
" lik_th = pm.Deterministic('lik_th', pm.Normal.dist(0, 1).logp(alpha) +\n",
" pm.Normal.dist(0, 1).logp(beta).sum() +\n",
" pm.HalfNormal.dist(1).logp(err_se - 0.01))\n",
" tr = pm.sample_prior_predictive(50000)\n",
"\n",
"def logSumLog(z):\n",
" mv = np.max(z)\n",
" return np.log(np.sum(np.exp(z-mv)))+mv\n",
"\n",
"# plot convergence\n",
"nv = np.linspace(100, 50000, 25, dtype=np.int32)\n",
"for j in [0, 1, 2, 4, 5]:\n",
" ll = list()\n",
" for n in nv:\n",
" ll.append(logSumLog(tr['lik_y'][:n,j]) - np.log(n))\n",
" matplotlib.pyplot.plot(nv, ll)\n",
" \n",
"\n",
"matplotlib.pyplot.figure()\n",
"sbn.kdeplot(tr['lik_th'])"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Auto-assigning NUTS sampler...\n",
"Initializing NUTS using jitter+adapt_diag...\n",
"Multiprocess sampling (4 chains in 4 jobs)\n",
"NUTS: [err_se__, fail_pr, b, a, beta, alpha]\n",
"Sampling 4 chains: 100%|██████████| 8000/8000 [00:21<00:00, 375.19draws/s]\n"
]
}
],
"source": [
"prior_liks = np.array([logSumLog(tr['lik_y'][:,j]) - np.log(tr['lik_y'].shape[0]) for j in range(y.shape[0])])\n",
"prior_like_mean = np.log(np.mean(np.exp(prior_liks)))\n",
"prior_liks = theano.shared(prior_liks)\n",
"\n",
"# likelihood mixture\n",
"def data_lik_logp(yp, se, lik_vec, r_fail, values):\n",
" l1 = pm.Normal.dist(yp, se).logp(values) + tt.log(1-r_fail)\n",
" l2 = lik_vec + tt.log(r_fail)\n",
" # below is the same as\n",
" # m = max(l1, l2)\n",
" # log(exp(l1-m) + exp(l2-m)) + m\n",
" # which is the same as return tt.log(tt.exp(l1) + tt.exp(l2))\n",
" return tt.switch(tt.lt(l1, l2), tt.log(1 + tt.exp(l1)) + l2, tt.log(1 + tt.exp(l2)) + l1) \n",
" \n",
"# model\n",
"with pm.Model() as full_mod:\n",
" alpha = pm.Normal('alpha', 0., 1.)\n",
" beta = pm.Normal('beta', 0., sd=1., shape=(2,))\n",
" a = pm.Exponential('a', 1.)\n",
" b = pm.Exponential('b', 6.)\n",
" r_fail = pm.Beta('fail_pr', 1 + a, 1 + b, shape=(y.shape[0],)) # prior over each failure\n",
" err_se = pm.Deterministic('err_se', 0.01 + pm.HalfNormal('err_se__', 1.))\n",
" y_pred = pm.Deterministic('y_pred', alpha + tt.dot(X, beta))\n",
" data_lik = pm.DensityDist('lik', data_lik_logp, observed={'yp': y_pred, 'se': err_se, \n",
" 'lik_vec': prior_like_mean, \n",
" 'r_fail': r_fail, 'values': y})\n",
" tr2 = pm.sample(1000, tune=1000)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1c4bc34f28>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# plot posteriors\n",
"colors = ['red', 'green', 'blue', 'orange', 'yellow', 'purple', 'magenta', 'black', 'gray']\n",
"for j in range(40):\n",
" sbn.kdeplot(tr2['fail_pr'][:,j], color=colors[j % len(colors)])\n",
"matplotlib.pyplot.figure()\n",
"sbn.kdeplot(tr2['beta'][:,0])\n",
"sbn.kdeplot(tr2['beta'][:,1], color='red')\n",
"sbn.kdeplot(tr2['alpha'], color='purple')\n",
"matplotlib.pyplot.figure()\n",
"sbn.kdeplot(tr2['err_se'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"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": "3.7.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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