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Using pymc3's automatic missing-inference to extrapolate a random walk
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pymc3 as pm\n",
"import seaborn as sns\n",
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x11e8e3b50>]"
]
},
"execution_count": 2,
"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"
}
],
"source": [
"# generate a random walk\n",
"sd = .1\n",
"N = 200\n",
"deltas = np.random.normal(scale=sd, size=N)\n",
"y = np.cumsum(deltas)\n",
"x = np.arange(N)\n",
"fig, ax = plt.subplots()\n",
"ax.plot(x, y)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"df = pd.DataFrame({'y': y})\n",
"df = df.reindex(np.arange(210)) # the unseen future"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/danielweitzenfeld/Envs/dw_intro/lib/python3.7/site-packages/pymc3/model.py:1331: UserWarning: Data in obs contains missing values and will be automatically imputed from the sampling distribution.\n",
" warnings.warn(impute_message, UserWarning)\n"
]
}
],
"source": [
"with pm.Model() as model:\n",
" sd = pm.HalfNormal('sd', sd=.5)\n",
" obs = pm.GaussianRandomWalk('obs', sd=sd, observed=df.y)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"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: [obs_missing, sd]\n",
"Sampling 4 chains: 100%|██████████| 4000/4000 [00:03<00:00, 1185.04draws/s]\n",
"The number of effective samples is smaller than 25% for some parameters.\n"
]
}
],
"source": [
"with model:\n",
" trace = pm.sample()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 2000/2000 [00:00<00:00, 3993.93it/s]\n"
]
}
],
"source": [
"with model:\n",
" ppc = pm.sample_posterior_predictive(trace)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots()\n",
"for index in np.random.choice(ppc['obs'].shape[0], replace=False, size=100):\n",
" y_ = ppc['obs'][index]\n",
" ax.plot(df.index.values, y_, alpha=.02)\n",
"ax.plot(df.index.values, df.y);"
]
}
],
"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": "3.7.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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