{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GLM: Model Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running on PyMC3 v3.3\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import pymc3 as pm\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from collections import OrderedDict\n",
    "from ipywidgets import interactive, fixed\n",
    "\n",
    "plt.style.use('seaborn-darkgrid')\n",
    "print('Running on PyMC3 v{}'.format(pm.__version__))\n",
    "rndst = np.random.RandomState(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A fairly minimal reproducable example of Model Selection using WAIC, and LOO as currently implemented in PyMC3. \n",
    "\n",
    "This example creates two toy datasets under linear and quadratic models, and then tests the fit of a range of polynomial linear models upon those datasets by using Widely Applicable Information Criterion (WAIC), and leave-one-out (LOO) cross-validation using Pareto-smoothed importance sampling (PSIS). \n",
    "\n",
    "The example was inspired by Jake Vanderplas' [blogpost](https://jakevdp.github.io/blog/2015/08/07/frequentism-and-bayesianism-5-model-selection/) on model selection, although Cross-Validation and Bayes Factor comparison are not implemented. The datasets are tiny and generated within this Notebook. They contain errors in the measured value (y) only.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Local Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def generate_data(n=20, p=0, a=1, b=1, c=0, latent_sigma_y=20):\n",
    "    ''' \n",
    "    Create a toy dataset based on a very simple model that we might\n",
    "    imagine is a noisy physical process:\n",
    "        1. random x values within a range\n",
    "        2. latent error aka inherent noise in y\n",
    "        3. optionally create labelled outliers with larger noise\n",
    "\n",
    "    Model form: y ~ a + bx + cx^2 + e\n",
    "    \n",
    "    NOTE: latent_sigma_y is used to create a normally distributed,\n",
    "    'latent error' aka 'inherent noise' in the 'physical' generating\n",
    "    process, rather than experimental measurement error. \n",
    "    Please don't use the returned `latent_error` values in inferential \n",
    "    models, it's returned in the dataframe for interest only.\n",
    "    '''\n",
    "    \n",
    "    df = pd.DataFrame({'x':rndst.choice(np.arange(100), n, replace=False)})\n",
    "                \n",
    "    ## create linear or quadratic model\n",
    "    df['y'] = a + b*(df['x']) + c*(df['x'])**2 \n",
    "\n",
    "    ## create latent noise and marked outliers\n",
    "    df['latent_error'] = rndst.normal(0, latent_sigma_y, n)\n",
    "    df['outlier_error'] = rndst.normal(0, latent_sigma_y*10, n)\n",
    "    df['outlier'] = rndst.binomial(1, p, n)\n",
    "    \n",
    "    ## add noise, with extreme noise for marked outliers\n",
    "    df['y'] += ((1-df['outlier']) * df['latent_error'])\n",
    "    df['y'] += (df['outlier'] * df['outlier_error'])\n",
    "   \n",
    "    ## round\n",
    "    for col in ['y','latent_error','outlier_error','x']:\n",
    "        df[col] = np.round(df[col],3)\n",
    "       \n",
    "    ## add label\n",
    "    df['source'] = 'linear' if c == 0 else 'quadratic'\n",
    "    \n",
    "    ## create simple linspace for plotting true model\n",
    "    plotx = np.linspace(df['x'].min() - np.ptp(df['x'])*.1, \n",
    "                        df['x'].max() + np.ptp(df['x'])*.1, 100)\n",
    "\n",
    "    ploty = a + b * plotx + c * plotx ** 2\n",
    "    dfp = pd.DataFrame({'x':plotx, 'y':ploty})\n",
    "        \n",
    "    return df, dfp\n",
    "    \n",
    "\n",
    "def interact_dataset(n=20, p=0, a=-30, b=5, c=0, latent_sigma_y=20):\n",
    "    ''' \n",
    "    Convenience function:\n",
    "    Interactively generate dataset and plot\n",
    "    '''\n",
    "    \n",
    "    df, dfp = generate_data(n, p, a, b, c, latent_sigma_y)\n",
    "\n",
    "    g = sns.FacetGrid(df, size=8, hue='outlier', hue_order=[True,False]\n",
    "                    ,palette=sns.color_palette('Set1'), legend_out=False)\n",
    "\n",
    "    g.map(plt.errorbar, 'x', 'y', 'latent_error', marker=\"o\",\n",
    "          ms=10, mec='w', mew=2, ls='', elinewidth=0.7).add_legend()\n",
    "\n",
    "    plt.plot(dfp['x'], dfp['y'], '--', alpha=0.8)\n",
    "\n",
    "    plt.subplots_adjust(top=0.92)\n",
    "    g.fig.suptitle('Sketch of Data Generation ({})'.format(df['source'][0]), fontsize=16)\n",
    "        \n",
    "\n",
    "def plot_datasets(df_lin, df_quad, dfp_lin, dfp_quad):\n",
    "    '''\n",
    "    Convenience function:\n",
    "    Plot the two generated datasets in facets with generative model\n",
    "    '''\n",
    "    \n",
    "    df = pd.concat((df_lin, df_quad), axis=0)\n",
    "   \n",
    "    g = sns.FacetGrid(col='source', hue='source', data=df, size=6,\n",
    "                      sharey=False, legend_out=False)\n",
    "\n",
    "    g.map(plt.scatter, 'x', 'y', alpha=0.7, s=100, lw=2, edgecolor='w')\n",
    "\n",
    "    g.axes[0][0].plot(dfp_lin['x'], dfp_lin['y'], '--', alpha=0.6)\n",
    "    g.axes[0][1].plot(dfp_quad['x'], dfp_quad['y'], '--', alpha=0.6)\n",
    "                \n",
    "        \n",
    "def plot_traces(traces, retain=1000):\n",
    "    ''' \n",
    "    Convenience function:\n",
    "    Plot traces with overlaid means and values\n",
    "    '''\n",
    "    \n",
    "    ax = pm.traceplot(traces[-retain:], figsize=(12,len(traces.varnames)*1.5),\n",
    "        lines={k: v['mean'] for k, v in pm.summary(traces[-retain:]).iterrows()})\n",
    "\n",
    "    for i, mn in enumerate(pm.summary(traces[-retain:])['mean']):\n",
    "        ax[i,0].annotate('{:.2f}'.format(mn), xy=(mn,0), xycoords='data',\n",
    "                         xytext=(5,10), textcoords='offset points', rotation=90,\n",
    "                         va='bottom', fontsize='large', color='#AA0022')\n",
    "    \n",
    "    \n",
    "def create_poly_modelspec(k=1):\n",
    "    ''' \n",
    "    Convenience function:\n",
    "    Create a polynomial modelspec string for patsy\n",
    "    '''\n",
    "    return ('y ~ 1 + x ' + ' '.join(['+ np.power(x,{})'.format(j) \n",
    "                                     for j in range(2, k+1)])).strip()\n",
    "\n",
    "\n",
    "def run_models(df, upper_order=5):\n",
    "    ''' \n",
    "    Convenience function:\n",
    "    Fit a range of pymc3 models of increasing polynomial complexity. \n",
    "    Suggest limit to max order 5 since calculation time is exponential.\n",
    "    '''\n",
    "    \n",
    "    models, traces = OrderedDict(), OrderedDict()\n",
    "\n",
    "    for k in range(1, upper_order+1):\n",
    "\n",
    "        nm = 'k{}'.format(k)\n",
    "        fml = create_poly_modelspec(k)\n",
    "\n",
    "        with pm.Model() as models[nm]:\n",
    "\n",
    "            print('\\nRunning: {}'.format(nm))\n",
    "            pm.glm.GLM.from_formula(fml, df,\n",
    "                                    priors={'Intercept':pm.Normal.dist(mu=0, sd=100)},\n",
    "                                    family=pm.glm.families.Normal())\n",
    "\n",
    "            traces[nm] = pm.sample(2000)\n",
    "            \n",
    "    return models, traces\n",
    "\n",
    "\n",
    "def plot_posterior_cr(models, traces, rawdata, xlims,\n",
    "                      datamodelnm='linear', modelnm='k1'):\n",
    "    '''\n",
    "    Convenience function:\n",
    "    Plot posterior predictions with credible regions shown as filled areas.\n",
    "    '''\n",
    "    \n",
    "    ## Get traces and calc posterior prediction for npoints in x\n",
    "    npoints = 100\n",
    "    mdl = models[modelnm]\n",
    "    trc = pm.trace_to_dataframe(traces[modelnm][-1000:])\n",
    "    trc = trc[[str(v) for v in mdl.cont_vars[:-1]]]\n",
    "\n",
    "    ordr = int(modelnm[-1:])\n",
    "    x = np.linspace(xlims[0], xlims[1], npoints).reshape((npoints,1))\n",
    "    pwrs = np.ones((npoints,ordr+1)) * np.arange(ordr+1)\n",
    "    X = x ** pwrs\n",
    "    cr = np.dot(X, trc.T)\n",
    "\n",
    "    ## Calculate credible regions and plot over the datapoints\n",
    "    dfp = pd.DataFrame(np.percentile(cr,[2.5, 25, 50, 75, 97.5], axis=1).T,\n",
    "                       columns=['025','250','500','750','975'])\n",
    "    dfp['x'] = x\n",
    "\n",
    "    pal = sns.color_palette('Greens')\n",
    "    f, ax1d = plt.subplots(1,1, figsize=(7,7))\n",
    "    f.suptitle('Posterior Predictive Fit -- Data: {} -- Model: {}'.format(datamodelnm,\n",
    "                                                                          modelnm), fontsize=16)\n",
    "    plt.subplots_adjust(top=0.95)\n",
    "\n",
    "    ax1d.fill_between(dfp['x'], dfp['025'], dfp['975'], alpha=0.5,\n",
    "                      color=pal[1], label='CR 95%')\n",
    "    ax1d.fill_between(dfp['x'], dfp['250'], dfp['750'], alpha=0.5,\n",
    "                      color=pal[4], label='CR 50%')\n",
    "    ax1d.plot(dfp['x'], dfp['500'], alpha=0.6, color=pal[5], label='Median')\n",
    "    \n",
    "    plt.legend()\n",
    "    ax1d.set_xlim(xlims)\n",
    "    sns.regplot(x='x', y='y', data=rawdata, fit_reg=False,\n",
    "                scatter_kws={'alpha':0.7,'s':100, 'lw':2,'edgecolor':'w'}, ax=ax1d)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate toy datasets\n",
    "\n",
    "### Interactively draft data\n",
    "\n",
    "Throughout the rest of the Notebook, we'll use two toy datasets created by a linear and a quadratic model respectively, so that we can better evaluate the fit of the model selection.\n",
    "\n",
    "Right now, lets use an interactive session to play around with the data generation function in this Notebook, and get a feel for the possibilities of data we could generate.\n",
    "\n",
    "\n",
    "$$y_{i} = a + bx_{i} + cx_{i}^{2} + \\epsilon_{i}$$\n",
    "\n",
    "where:  \n",
    "$i \\in n$ datapoints\n",
    "\n",
    "$\\epsilon \\sim \\mathcal{N}(0,latent\\_sigma\\_y)$\n",
    "\n",
    "\n",
    "**NOTE on outliers:** \n",
    "\n",
    "+ We can use value `p` to set the (approximate) proportion of 'outliers' under a bernoulli distribution.\n",
    "+ These outliers have a 10x larger `latent_sigma_y`\n",
    "+ These outliers are labelled in the returned datasets and may be useful for other modelling, see another example Notebook `GLM-robust-with-outlier-detection.ipynb`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c3bc69576a1f47c9991de88fb7a0f579",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "interactive(interact_dataset, n=[5,50,5], p=[0,.5,.05], a=[-50,50],\n",
    "            b=[-10,10], c=[-3,3], latent_sigma_y=[0,1000,50])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observe:**\n",
    "\n",
    "+ I've shown the `latent_error` in errorbars, but this is for interest only, since this shows the _inherent noise_ in whatever 'physical process' we imagine created the data.\n",
    "+ There is no _measurement error_.\n",
    "+ Datapoints created as outliers are shown in **red**, again for interest only."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create datasets for modelling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use the above interactive plot to get a feel for the effect of the params. Now we'll create 2 fixed datasets to use for the remainder of the Notebook. \n",
    "\n",
    "1. For a start, we'll create a linear model with small noise. Keep it simple.\n",
    "2. Secondly, a quadratic model with small noise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "n = 12\n",
    "df_lin, dfp_lin = generate_data(n=n, p=0, a=-30, b=5, c=0, latent_sigma_y=40)\n",
    "df_quad, dfp_quad = generate_data(n=n, p=0, a=-200, b=2, c=3, latent_sigma_y=500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scatterplot against model line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ikvMMw4B4DCvYfcTrrMFuvA6on1RMJBIfo+o+Pk3mFxHJMO91DZK0LLxuO5+bUZ4xByeK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\nERERkQny/wEGuV+3RrWFjwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbe51e81518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_datasets(df_lin, df_quad, dfp_lin, dfp_quad)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observe:**\n",
    "\n",
    "+ We now have two datasets `df_lin` and `df_quad` created by a linear model and quadratic model respectively.\n",
    "+ You can see this raw data, the ideal model fit and the effect of the latent noise in the scatterplots above\n",
    "+ In the folowing plots in this Notebook, the linear-generated data will be shown in Blue and the quadratic in Green.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Standardize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dfs_lin = df_lin.copy()\n",
    "dfs_lin['x'] = (df_lin['x'] - df_lin['x'].mean()) / df_lin['x'].std()\n",
    "\n",
    "dfs_quad = df_quad.copy()\n",
    "dfs_quad['x'] = (df_quad['x'] - df_quad['x'].mean()) / df_quad['x'].std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create ranges for later ylim xim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dfs_lin_xlims = (dfs_lin['x'].min() - np.ptp(dfs_lin['x'])/10,\n",
    "                 dfs_lin['x'].max() + np.ptp(dfs_lin['x'])/10)\n",
    "\n",
    "dfs_lin_ylims = (dfs_lin['y'].min() - np.ptp(dfs_lin['y'])/10,\n",
    "                 dfs_lin['y'].max() + np.ptp(dfs_lin['y'])/10)\n",
    "\n",
    "dfs_quad_ylims = (dfs_quad['y'].min() - np.ptp(dfs_quad['y'])/10,\n",
    "                  dfs_quad['y'].max() + np.ptp(dfs_quad['y'])/10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Demonstrate simple linear model\n",
    "\n",
    "This *linear model* is really simple and conventional, an OLS with L2 constraints (Ridge Regression):\n",
    "\n",
    "$$y = a + bx + \\epsilon$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define model using explicit PyMC3 method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (2 chains in 2 jobs)\n",
      "NUTS: [sigma_y_log__, b1, b0]\n",
      "100%|██████████| 2500/2500 [00:03<00:00, 826.32it/s]\n"
     ]
    }
   ],
   "source": [
    "with pm.Model() as mdl_ols:  \n",
    "    ## define Normal priors to give Ridge regression\n",
    "    b0 = pm.Normal('b0', mu=0, sd=100)\n",
    "    b1 = pm.Normal('b1', mu=0, sd=100)\n",
    " \n",
    "    ## define Linear model\n",
    "    yest = b0 + b1 * df_lin['x']\n",
    "\n",
    "    ## define Normal likelihood with HalfCauchy noise (fat tails, equiv to HalfT 1DoF)\n",
    "    sigma_y = pm.HalfCauchy('sigma_y', beta=10)\n",
    "    likelihood = pm.Normal('likelihood', mu=yest, sd=sigma_y, observed=df_lin['y'])\n",
    "\n",
    "    traces_ols = pm.sample(2000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RLVWxqS9PKCRYsmS0s3DojErB+vnPf84111zDbbfdRihU/TAvWrSI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JaFBQYszgc+Px6zGYjGhtRux2C0p1IQpzzePsg4P/Vx7HW2zE7AC9RofZYghrs5o46zI4zFgt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sLNsJkAIegOzRt3q9IUTls3kRKMwnlfaPp6mI9BIbVzIo7BmPceteNKWikpbzWaYRXXo+CM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\nRCuKgIz8LRDI/I/j/pYSC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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbe27f4ac18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_traces(traces_ols, retain=1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observe:**\n",
    "\n",
    "+ This simple OLS manages to make fairly good guesses on the model parameters - the data has been generated fairly simply after all - but it does appear to have been fooled slightly by the inherent noise.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define model using PyMC3 GLM method\n",
    "\n",
    "PyMC3 has a module - `glm` - for defining models using a `patsy`-style formula syntax. This seems really useful, especially for defining simple regression models in fewer lines of code. \n",
    "\n",
    "Here's the same OLS model as above, defined using `glm`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (2 chains in 2 jobs)\n",
      "NUTS: [sd_log__, x, Intercept]\n",
      "100%|██████████| 2500/2500 [00:03<00:00, 788.53it/s]\n",
      "There were 1 divergences after tuning. Increase `target_accept` or reparameterize.\n"
     ]
    }
   ],
   "source": [
    "with pm.Model() as mdl_ols_glm:\n",
    "    # setup model with Normal likelihood (which uses HalfCauchy for error prior)\n",
    "    pm.glm.GLM.from_formula('y ~ 1 + x', df_lin, family=pm.glm.families.Normal())\n",
    "    \n",
    "    traces_ols_glm = pm.sample(2000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Mm3cRkXPvwjjiS0MtqkSy3/Lpp59y3XXXcfjhhwMwceJErrnmGm666SYsy6KpqYlf//rX\neL3ewRGgzI1SrPR2xY3B6Xc/QzFiCD0Ayu7PJ5eaEwerfZHN5YS1Hw3A+o44xwcH6Tu4j6B1r0Xr\n2YDQg1jDjhxqcSRZpAdLIumFNW2OcRWoZFx1r6Xx+ZkIzUvo4iex68cNoaTlKIrCN04fz83nHMW7\nG7v59rzldDVOIfR/XsYcPpGGV64luOg3cl2WRLKbJBIJpk+fzqOPPsqjjz7Kz372M+6++25mz57N\n448/ztixY5k3b97eE6hE6V28NbT3+t5XsS28m19Hb1tSbCRlkng3vwGZZHXtiNKP+4+BMZDkTHrb\ndl2/EGDECwWv9wa2hRrevMv3VypjsaEzzt/Wde4duYYINR0GQOj+IZZE4mZQDaw77riDmTNnMmvW\nLJYtW1a077333uOKK65g5syZ3Hvvvfnta9euZdq0afzpT3/Kb2tpaeHqq69m9uzZfPe738Uw5Myc\nZHBZ0xbj+nmOcfXfpcZVzwYan5uJUDTCF/8Zu3HCEEraN1ecNIY7ZxzHyrYo35j7CS1iOKFL55E8\ndiY1i39P3Rvfk2EFEsluEI+Xp+tetGgR55xzDgDnnHMO77///uB0bhlgWyUbD06lv0+Ek+1PTbQX\nbdYim1GMKFpkc7+aG1Xnq+q4DZ1x3tnY1a+29weUrNfUdBlYnm1v4dv8Gt4tf9mtNjOWjdnPpBl6\n2xI8bUtRkn2Pcc7TdsAn5bCz7/A98NLuk9j7d7bOQQsR/OCDD9iyZQtz585l/fr1/PjHP+app57K\n77/99tv54x//yKhRo5g9ezbTp09nzJgx3HbbbZx22mlFbeVmBb/0pS/xq1/9innz5jF79uzBEl1y\nkLOmLcZ185YRrGRcda+j4YVZgCB88dz9wh1/zsQmGvwefvD8Cr7+xFLuufxEJpz9G+yGw6hZ9GuU\nVA+R6feBJ7jrxiQSCeB4sD766COuueYakskkN9xwA8lkMh8S2NTUREdHR8Vza2t96Lq2230rK15B\nVRWCNQWFP1AfgEDhOxysKYRFNTbuO99tIQSWLdC1gVMGNU2tfI0ZBaXGB5oPX0MwPyZ1jQ0oKR8E\nVEQVY2N5DII1cRobAkRtQX19MF9YvhI7t4ZA14Zs3Hsbj6Rh4dNVVHX3snTUdSUxFIWaOn++fWVH\nErLPYWA3rvfFZTsBmDF5TNXnKJ0G1PgI1Puhtvc+1WSGYI0XVe3l+aimr925tlQEZeu7iCPOBr06\no3y3CG0Ff4Mjo+pD1Puhvm85e/2u7GuYaZQ1ryCaJ0HTsYPWzWCOx6AZWO+//z7Tpk0D4KijjiIS\niRCLxaitrWXbtm00NDQwevRoAKZOncr777/PVVddxQMPPMADDzxQ1NaiRYu49dZbAWdW8KGHHpIG\nlmRQcBtX982czNiGgnGlty6h4aV/AtVD6OInsYZPHEJJ+8ephzVy/5VT+M4zy7n2yaX856UncOKp\n38UOjKD2rZ/Q+Pwsp3bXEGVAlEgGktbWVu69917C4TB33303L7/8MieddBJjx44dsD6OPfZYrr/+\nes455xw2bdrE1772NUyzMOPaV9a6WCy9R3374mmCNT4S8UI7RjiBSHvzfSdca7BCoX0nk+C6jhgb\nOxOcM3HkgBlZjY3BytdoxPHF0wiPRjKUyI9JJG7jiaex1AhmcNdjE04YJOIGca9GIm4QDiewUr2r\nT7l+hmrcK41HZ9zgo60hjmmuZfyI3VMoE/E0iViK9MYPCR96LMJbh8/1DKZ74v1Osbg7Y+WNpVCM\nNEY4iTB7Py+czJCIGwRrvLt9L3LXl+7H+XrrJ2iRHjI7N2HXH7Zb/VaDb+07ANj+YaipNJlIEtvu\nW85evyv7GEqqB288jd2ykYxn8MZwIMajqalyWZ5B8yd2dnYybFhBWRsxYkR+Nq+jo4Phw4fn940c\nOZKOjg50XcfvL48hrXZWUCLZE1a3RXs1rjxb33TWXPka6Ln8OawRgzejMlgcM6qWP151EnV+nW89\ntYx3N3aTmjSHyPT70DtX0PjMZaixnUMtpkSyx/z0pz9l2rRpdHd3AzB8+HB+9KMfDWgfRx55ZD4c\ncMKECYwcOZJIJEIqlQKgra2N5ubmvpoYYAoGnTuEa19je8gZn70ho0I2NKw0dEp1vIeKVd1yg5yt\nXI3pkDAKoZvWPnQf0qYjV2QPiiQLBLXJHfjCm9BCG511UG7svbt8o1LmTDelyThaI6kDu0jyvvO4\n7Tm59XXK7nv6h5pBM7BKZ++EEPn43Uoze0ofsx7ufQNZy0IiybG6Lcr185ZT4y03rnzrnqfh5a9h\nNUyg57JnsRsOH0JJ94xxjQH+MOskxg8PcuNzn7JgVTvGkV8mPONR1Hgrjc9cjhrZOtRiSiR7hG3b\nTJ06Nf/uOO200wb83TFv3jweeeQRwJk07Orq4rLLLmPBggUALFy4kLPOOmtA+6yWUsV+d5RKw7RZ\nsKqdlkhqoMQCBk8HVGM78W5aWJz4IP93L6q4KF3H1jfVOGc2dxdmw/clQ3egHn9VWNmxVPC0LS3a\np+zt9by7SHLhHn7DtPlkR4Sl28ODLFSBag343aLo2kXJ/0OLGm/Dt/a56pPIVCK3xnQ/Xlc2aJKP\nGjWKzs5C5pb29nZGjhxZcV9bWxtNTU29thUIBIZwVlByoFNkXF05pWBcCUHgkz9Qt/DbZA45mdCl\nTyFq9v9nb0SNl/uunMzksQ38bP5qXljeSmbs6YQvfhLFiND47OVooY1DLaZEstt4PB7ef/99bNum\ns7OTJ554Ap9vYNdCnHvuubz99tt85Stf4brrruOWW27he9/7Hs899xyzZ88mFApxySWXDGifQO+a\nsujdg/Xmuk5Cif4pv9Gsp2Nrzx4oSRWoOMFqRCsk7egfettSlEzCSQCS78xRQkWZkiaK9u+KnMQ5\n+6ovY8VtzO5LHqwcezLRkDtVQGXjVNiosRa0ng273cdAkhv/5s5FeHa8B0Ays2fPWVVkB0rv+BRw\nsg4riQHOZOj+vuxjjgct69lU0z273YaSe77UPfBgCdupD2YO7CRRtQyagXXGGWfkZ/JWrlxJc3Mz\ntbW1AIwbN45YLMb27dsxTZO//vWvnHHGGb22dfrpp+8Ts4KSA49S42pMQzZE1TKoffMmat+5BeOI\n6YRn/AnhaxhSWQeSWp/O3ZedwOfGD+O2hWuZu2QHZvMUQpc8hWIZNDx7BVr32qEWUyLZLW6//XZe\neuklenp6uOaaa1i1ahV33nnngPbR0NDAAw88wGOPPcZTTz3F1KlTaW5u5sEHH+Txxx/nN7/5DR6P\nZ0D7BPrwuhSUrEqK/aItlZUdd0ibm5yR5tF2LyFCSyRV0ajLiZb7X0mF8G5+A61n3W71A4CVcXkL\nXNduu5Q0lxL6Vi5tdwXFVI21oCS7McxsdjvLQM+GTvcVaZPD2EcNLPelal2rUFL9V34FEEi3O0Za\nLwaxZ+ciR6mtgGLEUMNb+t1veUN5U7fPw3LGZMDoRivJJFkVA2S46J0r8W5/Z0DaylPRwN13njdg\nzxxq+cmR3Tew1HgbWs+GXp/HwWbQklycfPLJTJo0iVmzZqEoCj//+c955plnqKur49xzz+WWW27h\nxhtvBODLX/4yEyZM4NNPP+WXv/wlO3bsQNd1FixYwD333MMNN9zAzTffzNy5cxkzZszgzApKDjp6\nM66UZBf1r3wDb8si4qd+l8Q/3Lhfu6l7w+/R+O3Fk/jpy6v4zV83YFg2V3/2eEKXzKPh+Vk0PnsF\noYuewGqaNNSiSiT9wjRNrr/+eqAQnu5OQLFf01vqYpdyZQlBc89HeM0Y25um9tpUV9xg8dYQk0bX\nFWVLhUJqa12t8rfPiIPmBc0xKpftcOoiTT+u4PW3bJGvoWRn5VXSTr0uJVOe9t65LhstvBmrYXyv\nv8PK6hdcx7trNOWMnV7Oq6ABenYuAuBV63RUVeHLwdUEelpRtdNQcJJD9FUHy73uJ2dgRVMmy1si\nHNNcy4ia/he9DSUyBLwaPn0AiiULgd61BrrWkJ7YP11KM+MEjG6E34ciLNKmjUDgz2XEtPv2knq2\nvYViZUjXH9bvZBgV2YUCn/fk7nZX+5jB4sYVQqcasezG/smbzFhYtqDW139TwLQFsbRJY6B8EklQ\nnQHcJ7nfuT3RvbLnDmqoZh9UNaq2baNW+yPr4gc/+EHR52OPLSQG+OxnP8vcuXOL9p9wwgk8+uij\nFdt68MEH+92/RNIbq9qiXP/Ucmp9xcaV1rWKhpe/hproIHLuf/X7BbS/4dVV7pxxPD+fv5q7/7YJ\nj6Yy6+SjCV06j8bnZ9L4/JWEZzyGOeqkoRZVIqmaG264Ie9tyGQybNu2jeOPP76ovuJ+S6+hdMWK\nfTC962RQ8az3KpIqN9pyoW5VebDMFL7Nr2HVH4p5yCkVD4mmTDZ2FYyoaifbtdBGJ9RKiKrKYij5\nNUKuMCOl1JTKxbr1IYQQ2DYoRiR/rppfR97Haa6/c8bW+5u7EcIp/uw2OKtl0ZYefLpKU62X7aEU\n045pQutnqvWcZ00Ruz/RoOSNewG2yZo2R7GfPLYeADXR+zPnW/tc4YNtgFZdyO7OcIq4YeJRVQ4f\nHijxIu7KgwWKncGyBdvDKehvEMoAe4T09mWYzZMHpK38s11FmGtLJMWyHRGmHVO8FOdv6506Yrvz\nTH6yI0xnzODsiSPxlGYDHRDjufokF6Yt2NAR56immqLvRd77VWUo8EBTlYF13nnn8YUvfIEZM2Yw\nZcqUwZZJIhlUcsZVnU/jv13GlXfTQupeuwHhqSV06dMHjVGhqwq3fukYMrbgt3/dgFdTuGzKBEKX\nPk3j8zNpeH4W4RmPYo7+7FCLKpFUxdNPP130uaOjg7vuumuIpBlgequrU8FzUi2Vjs4ZWH3pmEq8\nHe+O9zCbTgRATXZXPC6aMnlvU/E+UWLkqPE2sNLlindu9nkX3pFCwxWSXJTMgiu9rmMrTRygoNiu\nbf3UG/OpBwZAT0+bdj4D4+JtIT53ePUlNTZ3JdjQ6Ri3yh4Ub82F3AlBZaW1SkVWsQxElQbW8p2R\n/N8+j8ro+kKmaU/rR9ihjZgjjkUEy9fxW0KgZg2RaNIsMrDCyQw1Xq3vUgH9UMy39iRpqvUS8JQY\nBK7x1kIb0UIbB2bitmKocOUHbV2Hc+9TZuGccLLv71PGslnZGuW4UXV4K3hOw9lJmUo/NVp0R1ac\n3X/wlV6+u5XY0p1gc3cCXVM4cmSNq5HsubmxyiRB9w+MAVgFVbml5s+fz1lnncXTTz/NV77yFe66\n6y42bNg3FjFKJP2honElBIGP/ov6+V/HGnYUof/z0kFjXOXQNZVfXHAsZx4xnDtfX88Ln7Zi1x9K\n6NJ52DXNNL7wFTzb3x1qMSWS3aKpqYnVq1cPtRgDg6pj1Y0r2+z20VSbva4vNSNnYNl9KEladLsj\nUrLvBfxpq1xRLURvZUMFLQPPzg8rCNlfq6bQl2I4yrlQe1sLV3JtrlCi/Hi6FNmKgU+2idazviQD\nRO7A7LXtpj6XylhEK3gX+5uwpM1dd21PkokIl9G9qzVAfRknuxmyZVol90vYqMkuvL29m4RLUXdh\nC8HfN/ewdEekwkklDVRBxrJZ1Rrlw62h8vOqnRioFjOJko70YuBWljfn1HHPFazv7CUkN8vWniSt\nkTRbevpZI6ro+doTz1HuB6L66LnyiaXs98+2IJPAt2kBWvfeew9U5cHyer1MnTqVM888k/feey9f\nuHHcuHH8+Mc/5uijjx5sOSWSPWZla5RvzysxrswUdX/9If61z5I6+mKiZ/8G9MCuGzsA8Wgq/zHj\neH7w3Ap+sXAtwwIezjpyDKFL5tH4wlU0vPRPhC94kMyh/zjUokokfXL55ZcXlQXp7u7m85///BBL\nNYDsyboEIco1fiFQUj2osRaskccDhZCyvr1hJemhe1GqK0WzVcwmmImVH9hfXDKouUQOur+y7lmy\nUXMnYBACFMjYImtQiIqGkt61Gq1nPUIPYNeNLVqflQ9VVJTdyt73VjaEK8ewoIeeRIaRtf1cx+Xq\nek9CBN13W6lgqCkuhVrvWI7ZXDniSbHSu7U6p9C/UpUzUZTIlCM3eRDdZU2wKicq4u2Mb11Iy+hz\nyvftaer6TCLrdXG+876NTsI3Y+zpFQ6uLG9utDIuC0sdIC+OEAKtey1650rSR365+LdpF8Z8RyxN\nayTNiWPqKzScNearuNO9hu6g/D0SAAAgAElEQVTmJz0sFMuZZFBjrVgjjttlmwNBVQbW3//+d+bP\nn8+SJUs444wzuOWWW5g0aRKbNm3ixhtv5JlnnhlsOSWSPcJtXN03cwqj6/0o8XYa5v8znvalxD93\nE4lTbthrruN9FZ+u8quLj+df5n7CT15axf/MmsJxo5oJXfKUEy748tcIf+kPZA7/4lCLKpH0yt13\n353/W1EUamtrqa+v8BLfX6n0O+UyLHqziZREB97t72IcNpUd6UBR0Vnv1recc+vGgqJhZ5W10mKt\nxX1mZ4jzi+x7MbAqKEmVZAylQc1Y+EvDrPpDUYhgb/WBKm9Xst4Gx0hy9q1qiTJumB8FUbgO92nZ\nELCcAlfkxMn+rSq9jIwQ2GYS1RPc1VVl23H616p8T6UyFlt7kkX3UN2DEMHcTROAXckL5epHC23q\n1cCqZAfEss9iXwkXqjFSw8kMuqZQ49Ud72uFc9IZ5254+woPBPQqM+nq2bImPqMHGFu8c088WLaJ\nb9PCimsbK1Z262V8co9LptQD2AdVH2mm0DtXOv1k4ghPbaHfXYRYLtnm1CSrbGDlPFjVGFjO/5u7\nExwzqta1x+WFzq7H2pVMA0lVBtaTTz7JpZdeys9//nM0rfDDN2HCBK688spBE04iGQgqGVdaz3oa\nXrwaNdlJ+EsPYBzxpaEWc58h4NH43aUn8M+Pf8z3nl3Bg7NPYnT9cEKX/JmG52fRMP/rRL70AMb4\n8tk6iWQo+eUvf9lnKu2bbrppL0oziFT0YDnKhN6+DNUeVfE0LZtuXE31sLwlU3Kmg3fLX51tHsdT\nXcmD1RZNY5g2EygxsHpTXirZg/k/RL6f9V0pkoEInxtfvL4oY9ko4W3oZmqX4duKsCsqh+5thfC/\n0pAzi7RpsaYtjtJcOKclnIIRgoJ95TpPzapR2dn6ogjB7P+9Gbyt6z8kvH01R545E92z6zVJXXHH\nqLERLFjVzshaL6cc2tjr8X/b0FV2iUo/iyu7ETkzUQhEJc9M1cprVigzSU1yJ/HAGN7d6KzR6yvh\nQjVK/9839+TbcTxY5debW4u0qwQuVdfzyoagqhWMqYoZ7IRdnRc6awyr8bbKbewCxYgiFC1vgGQs\n9ySMM5qVxiCVsXa5Riv3YAn39dkWRVMJJc+a1rUaFI3MsKPy2UT76GAX+wtU9MZZGdRUqCBr7pg9\neP77S1VxBtdffz2ffPJJ3ri67bbbWLfOqVkxa9aswZNOItlDVrWVG1f6zg9ofPpiFDNJ6NJ50riq\nwMgaL/956QmkTYvvPvMp0ZSJ8A8jfPGTmCOOpf6Va/Fuem2oxZRIipg4cSJHH310xX8TJkwYavEG\njt6Us0wSLbSR2rZFhW1uRSxnBFSRmauvBA1Lt4dZ2RqtYKBUVooqeR6Ey7skEKRMG1vRSZkWvrXP\noXWtyR+7qjXGhpa2fAHTvgWvfm1K7irV8GY8294B23LVsRJollNkOTfxX0mRE7lCqNnQu2IPlij8\n79oRS5us64jRvdNR4DOZDHrrR/kU8btCTTtrhzpjrjVjRtQJJ8uSyli9hEW6PFiZykWk06bNG2s7\nypTsomVmlcK/qjWwsnW0vNvfpSm8rCjxhmHabO5KoCQ68a19DtV2F46urnn38ZUSmuTWKPaWiTHV\nz2LEdrY0gZobW3ef/RinnoTB+5u7XWsoy704thBYtqhY1qDUq+Xd/Aa+TQsBqEtsRZiFtXg5ESvZ\nOX/b0FX0bGUsm1i4C627vFade7JBEVav6/CUZBd612r0zhV8vD3MG2sL6zYreyarv9mV7CvPjvfQ\nO1cU2nKFC+4tqjKwbrnlFk4/vRDvefnll3PrrbcOmlASyUDgTmiRM66861+i8YWrsAMj6Ln8+d5D\nGCQcObKGX180iW09SW56YQUZy84aWU9gjjyO+le/gXfjq0MtpkSS59JLL83/O+GEExg3bhzjxo2j\nubmZRx55ZKjFG0B6cwnlQrgKyolupRgRXp5VfnJpy4sNrN70m0C6HV+yMHu+viPOpy2FxAClCl1v\n4TdF3iM7w2Htr6PGncKvthAs3xFlU1ccW/XkayrpXasAaIk4SmHacNruiKVZtLmnUCC5zMirFCJY\nKlCxAutpW+ok6hBWoSSNEIzq+cjZn53lr1zdJ1trJ2e8FhlSTq0o3Ywzvm0BwZQzlh9uDbGxM5H3\nJpmWhRbZhhprqSyvi0C6nZFtb1OT3Fm03a1MQ++hnW5jxrvtbxWP6YobmJZgc3dJggNXkgv3dUZS\nOUOsOqVYb1+Kb/2Lec+n5jKilrdEWNMeI9Xq3H9fJpTfV9k32TsCUdFjlzNgKhnMnXGDt9Z30RZN\nl+3DtlAjW1GjxWMv8uFnlYyp6g2szd1JIkmT9lzf+cstyLm2Pc6KlqhTuqBKmlrfZERkJcGOjwsi\nZBuvlMTGvUlB4YOtIbZ/PN8xWEqM8qLThUXxM+AyvlzGndt4yx3l3fIX9JbFvTTcf9TeCmnvSZKX\n/spQzUGWZXHqqafmPx9//PG7tWBTItlbrM56rmqzCS1G1/sJfPIH6hd8C7PpREKXP4/dcPhQi7nP\nc+phjfxs+kQWbwtz+8K1CCEQvgbCFz2B2XQi9Qu+iXfDy0MtpkRSxL/9279x66238t3vfpc//vGP\n3HzzzVx++eVDLdaAIXoJEcwpz271bXh0FXXJHdSkWgrKdRWzuLYQjOpZwvCuj/LbNnTG2ZFNFV5Z\nsHLFcen2cJGu5DVjqLaJr8fJ5mVlw5ZsGyzVh64W6xabSrKdbe1OEEpmCkp9ybUkDYO44arXlP2/\nWGfJzWYXy6sUZQwUqMLpQ1cVlJIkF2p4c1bZLG7DrbBu6Izz9oYuPNnkHbXJ7dhC5L1karZ/sx9K\nn8eMYQuB14xWfU4eIYrWBClmZQ+WbsaoTW6vkDQgFwYpiq5zW0/SCSV1bbNrKoepQnmq+HGdBUMv\nF8Ymcoar6x7lmu+MG+XGXwWcPCXFF2FYdr5+l2HaLuPQIfc5VOK907pW4Vv/Ip7WJXhaPijviMoG\nVmWjy/XMZJKOpy6ynWB27WHei1ohVXnBw1o9mumMlTtcMe/QcQ3Pa2s68un83cRSZt4T6Nu0oPhS\nitYj2iX2lduT13vIoS1ASUfyWUl7FbAEJdntGJol3x/DtIsmGBQrk/d+70mIbH+pysCaPHky3/nO\nd3jooYf43//9X/7lX/6FyZMHpliaRDLQrGmLcf285QQ92SLC9T6CH/yO2nduwThiOqGLn0D4q68h\ncrDz5eNH8S+nH878le08/ME2AISvnvBFj2E2n0T9guvwrn9piKWUSAqsX7+eP/3pTxx55JHcd999\nPPXUUwdWaZGKSS5EwdgQhf1KBWOiVMmopMKM3flKdp+AXhTxyoZeMW3RdOUQweysvFVi5HRFk8Vr\nnErwJ1uLZS45f/GWHt7ZkKu51Xs7W3uSfLI9XLzRtlx9i5LrK7QlrAyetqV4d7zryn3t7C9NkW/Z\ngsZaJzOthsmnLeWGkdmb0ixsgqlW6uOb8puUPq/KdWqFg8Z0v0ddeE35jhLqd/6NkeFPC4lObOGk\nhnfFCK5pKyjilg0rWqIks4at0ANVSllOPhGDUu4vFABWhta2NiLJXSfrEFR41l1ihZIZ3t9U7OlQ\nKiUyoSTDZGk/IlfEubraYKtbI2wPOd8pNVtKQItuLTuukAGxyuRbJTdd6P6iz7YQENoKJVkchXAm\nIGxbsL6j2MCKpCsbRoWEGe6QwFIPFtnspDv7NmwqZRTNXnupgWzaBc+qp30pWs961HSo6Ji/rutk\nbXvxdWjZRCQDXTy6L6oysH7yk59w1VVXYZomqqpy7bXXcvPNNw+2bBJJv1nTHuP6ecsIeDTumzmZ\nMfU+at69jZoPf0fq2CuJTL/voE3Dvid8/fOHcd4xTfy/dzbz9gYndbDw1hGe8Scyh5xK/cLr8a17\nYYillEgcLMsiFnNmqbu7uxk9evRerYN1xx13MHPmTGbNmsWyZcsGoYdeklzYFl1xg9Zo2qWTZWfX\nIT8TLnbhMXF7KATuLIG7h9vm8GQ9L2o65KzLyGYgs1QPCgIFQSpTMoPvYlj3x9k2K6+pUCqFCJYo\nVQq5WlLl4YXFh2bTP2c/uUPKBAJhpgqKo7Cxhaiov2nZbHVBzaYzXp70wDRdYXtb/uIkA7AyjOl6\nj+bQUoZHC0ZRwKNWpSNWSk7izUT7NF5z5Irz5mzHRVt6WL3yI2pijqHXWwtJI6uMq3rJWpzqldpC\nIoacB8tlYAmB3r607Bzb11C2bX1nHAQ0hT+pum9wElU0hZZmjRBBV9xwnrVea6mR96DknoXidPXl\noXwt3WFWVDC0y+5N3oNVbXbjkvNLJkBUM4Wy40On3pzr0I+2hfOp60vpiLq8XtmT3LXZomkrnzSk\nfA2WwLv1LTw7PwBXUhSPGQNh0xhbh2KblZPA9PLMrGmPsaYtRkcsna+nJipkxjRcGRM7Y0aZR3Jv\nUJWB1drayrp160in08RiMf7+97/zX//1X4Mtm0TSLzZ1Jbj+qWX4dJX7rpzM2DovtW/eTPCT/yFx\n4tecGldqVYkzJSUoisLPpk/kmOZafjZ/NRu7nNkh4a0lfOEjZEafSt1rN0hPlmSf4Oqrr+aVV15h\nzpw5zJgxg6lTp3LUUUftlb4/+OADtmzZwty5c7n99tu57bbbBr6TigqXQBEmO0IpxyhSYPLYekYE\ntfz+nEdGK1H6Sj1MLeE0llXBwBJ28Ux0lUkt3OFkIyMr8215t71dcAApCh7VUdJyx9trX6nYPrgd\nR32tA+tbsVdtC23DqwXF1jbzsjuKvch3oVAIERRCsKYtzrIdEZfXUOSNmkPqSzICZoVNp9Po8VZ8\nRg/BVKtrtys0MR1B71qNd8vreM2CYZtLIe/zaNii4AHsjV4LRLv1X60PowFnLZZlmgRaFjEsuhZP\ndl1Lb013xh2jRChqieHbHwMr58HKGlgUhwjmM8PltumBvPHjNio3dMTJ2HbFJBd94YtvoybVSjCy\njrhhsSOUYkc4BVaFNVl5IcxiWXeR7GNM1/t97veH16F1rXK1s3vlY4RtsTOSyhtPImcAmsVe4q64\nQWt2raMmMhXlF67fnIQrCciq1ihrc95MYRfdryLvk22ytSdJZ9xgbOc7jAotoTG2gcb4BpZsL76n\nubayfxQ2CUE6a8w59zq7NtLqe8JoZzjF1u7KXvjBpCoD65vf/CYdHR00NjYybNiw/D+JZF+hNZLi\nhqeXo6kK9105hXF1OnWvf4fAyseJn/Id4mf9+54V55Tg92j8+uLj8ekqP3huRSF23VtD+IJHMA85\nhfqF18s1WZIhp7a2lksuuYTp06fz5ptv8vzzz3PnnXfulb7ff/99pk2bBsBRRx1FJBLJe9MGjoKy\nYzWMd/7IZmbL7c8dUavlZpdtQCGUzDgKYx/0uGd7RcFoGdf5Nw5vK2QPVRAkK2Rcy6lEHjPG+NZX\n0RLtZcfkdOFcGJpAxe9x1jrlUjgb6d7X2dh5Q6hUGaxcB0uLtTC+9VV0M4FbaVu5tT0fTlSc4l0g\ncCcDKZg0CqJ8nYyw8wp+0FuSpdEl46ieJYzuXkRjfEPF/TncSQEAdNu5Z7pCXn7NctbvKIlCRjbf\n2uey3rSyJp2uBAhPDQB2sHyd1PZQkk1dBWV0Q2s7wXRHcRuVmyaSMOiOZ0DVUJPdrhP6H5YlCtZs\n3/2qWn5PqVFp9qPuU76P7CSsYmfy36JoyuzTaBJZA9o2y7MIhlOZXr1DxY0UzgmEVqN3rUFv7937\nVtETWXL9hmnRGTXyA5ef+FDKq2jlPh/e8QbNoY+phG3D9nCKpGGVnwjOGFVa64hTJy6UyLAzu4bT\nazqhkYqwCGfDPTOW7RqrYu+zGmthw9t/oivk8vzlPPLCVXJCiHwymaGmqun8xsZGbrzxxsGWRSLZ\nLULJDN95+lNiaZP7Z07h0DqF+le/gW/za8RO+zHJk68fahEPGA6p9/Ori47nm39exk9eWsXvLzsR\nXVUcI+vCR2h4cQ71C68nMl2V6e8lQ8aCBQu44447mDJlCtOnT+cf//Ef91rfnZ2dTJo0Kf95xIgR\ndHR0UFtbKIBZW+tD1/egmK4VRE0rBGt8iHETUcwWRL0fUPD6dVRdx297CNb4mFDnwcr4Mfw6dbUe\n1m12lJE6n4nHjJHyN1Nb6ydIwevi9RaK03p1lbo6HzQGCXpM8OgEa7wARC2bnRGDiU011AUcL0Kg\nMYhlC4I1XmpiUbx+ncbUBoI1n3Ha8ztqRyDgyJdRszJrXuprfXQrGl7LS7DGi6Eo+FUPXtM5J1jj\nw5/IUOczGRVaROPYsyDgZ9XyKJoCYxoCBAI6Zo2XxsYgSqcXdB/U+vEkduD169T7MuiajtcoqD8i\n2zaKiREM4I1nCAY96Jk6vOkUXl0lEPTS0BBkxM5N1AcPJeLXsRUdn1/Da/mg1ouoCxCs8TKsIUhz\nxzqCiZ20N59BUNMRfh1N09Esp1/hC+JNO4ZMXUAlKHyYlu38nmaNi9xYAdT4FQyfl3rFTzqiEwjo\n6HqUYI2PoN0KNYX7p1pJauv8+fuUv69+HZ9Xo6a+BkQQfF5EY6HI8fIdYTZH0ozL9hsMerBVC5+/\nWFX0+XQqVHdC+DQUj0atGocaH6F4O4u7vJw8ro5hNZXrfOWusVTWmho/Qd1HwNSxs/tqa/3U4HOd\n4wNfAFQd0RgklbGK2vEHvXj9Ooqi4An4yvoIxrdhq14aGg7P18+LhetI+XVqfAoBvHj96aw8XrCd\n7+yOngSdsQyTxzmhickOHa9fx/Kr4NXx+XVaQ4JxDT5WdyVQyPCZwwpOCa/f+Q41NgbBzKDU+KDG\nT53XTzBt4RcevD4PnbFuDmkIgM9PMHufCt8fH2pJmnmR/Z7mSAU8Rc+Q3+9BVRVqanzU4MNUC5PO\ngRofwRqDgN+LbvcQq/GiWmmaOhfROeJUvH4PhqoQM222x438WHoMHW9cJwkMr/VCfcC5HkDU+lAM\n52/TJ4pkMTU/umXjDzjf9aDmY8nWHrbvjDBj8hiUuBdMH9T6EI1BWjevR/NoNHjTpP11NDQE0OMe\nFNVDTUAnqDjyNNDNuNRy8GnO81H0jDmfA64x0jTVuQ+DQFUG1uc//3kee+wxTjnlFHS9cMreCrmQ\nSHojmbH4/rOfsiOc5O7LT+TYRqh/6f/i3fEu0X/8BakT/+9Qi3jAMWVsAz+edjS3LVzL3W9t5Ptf\nPBLIhgvOeNQxshZ8i8j5/4Mx4bwhllZyMHLnnXdi2zZLlizhjTfe4P777+ewww7jt7/97aD3XRoe\nJ4QoK34ci/URblQFZneCJluQiKcxomm88TSZcALViGKkTAzdCdVJxNOgGKi2jbd9BdHGYzGy6yeG\nbf0LqrDpPuR8YpriHJslnc7kQ/CErhKNJLD0RP5cf8tSuuuOpdNKkDYEPZEUWm4Wf/mrpJpPJhE3\n8MTjGCmTdE8HCb+jkhtpEwQkEhkSqkI8mcFImWR0i0zKIGWnidtpfAjiiQwpw6BZgUjKJBFPk05l\n8KVWkPJ007l9K0pgOEnDxEiZxJMGAaOHLnsEoVAj3ngaJZ3G0pJk4uHsMTa+TCZ/LTly1x9mOEaq\nm2QiTSCZRjVMhK6QiUaIdXVR1/IhJlsxUia2Kvh4Qzsn1qWx1CRdWpxE3CAZTxNsW+60W2MQV1Po\nKRNTs9Atp9+o6kPJyhCNxImmUqzYGWV4rZdxDX4ytiiSMR1PkjQNkloaI5UhqZhkMhkSahpLS6O5\n7l8gnaCnG0ZufInOhskk/Ic4Y58yUWybWMJ2sroZCTKhgpdwzfYQlqvfRNwgoiZoKBmrpBAY6XLP\nZdo2SFkZEl5HlsXtLST8o3l3VSszPJWfeXdfbqIYeFJp0ok0CcXZF/EliSdS+XPi8RTCCgAmmVCC\nhGEVtdNj29SZPmr0NCnDzu9TVYU6zaK21fHUdPdMzNfEikbTGCmTRDxBQk2TTlsoQpCIpfNhj9s6\n4mzWehgb1NFUhUw06XzvRJrO7jgtO3roCaXY0VHwtuSeL1uIbPsG4dadeLcuzm5PEfWkSMQNEskM\nq8MpYikT3Rb46/z5+1S49nRZHS8zksTyFO6nHU8XPUMJTwbb9hKPG0RFmmQiRU26lVhgHD3hBMlY\nknjCGaNMqIuA0YmIhQhkPiGTNkgmVIyUSTKWzKem92YMjJTJuh0Rjm6Isa2rg6PjKRQULD2Rfy6T\nqXCRLIZu4zVNkpozFgktnR+XUCiBHk2hxdNYnhRmKMH2zihWyiSZzJCyDMLhJBvWdxEQCfxjEySM\n7DPSHXE9U+miMdvcFkUF6sYWxqixMUgotOuMlH3R1FRXcXtVBta7774LwKuvFmreKIpygNUVkexv\nmJbNj15cyYrWKP8x43hObRI0vHAVevs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6XDi+ZA1Hqch55dMdZkjO5QO4QgQBj+4cV+uq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mfn3nvmzp2Ze+acew7e0+9BrZjV36JLesFVV13FggUL8Hg8TJ0azWdjsViYPXs2l1xySY/qLCsr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\n/fWsWrWK97///dx334Gdb7OpP83u3j183FyNOu8E1EW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Sd8U1VmBkMwDhHfcCjKuMgivk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QuPVfmdZasKhQlsgm5jWFvTSbn5VdW9l2Ci16720GrkOrnnkfUlK9YJuMfNa6Xz9KedCYRa\nnrBD9VcwtrB2Tj7A0br1ztjdnK+gz8uydVsmeGsKn495qgrghJpq7tcuG19L1MvyplCxqFpfaFnZ\n91zpweoLLSnbS+0crDfrr5jyvprQA8s1VuOBqdNiipenlAgpaYlW3i/VhnDhOB11tXVqKXQyZsT9\nnZt8vMsaQjPOk9MMV64z7Fn2dlAG1gnwes8od/2/vaxpCfM76+bP9nDOCo4//gQvXP1xxl4/iOb1\nMPDPu7GzWQae3c0vL/8gA2dJo+EZIzTG1t7I4DXfwkh18YlXf4+fXPJvfHpDK786OsRn/uZXfPSR\nF9jx4jHSudMX+6tQKEp89KMf5S//8i+577772L59OwCWZbF27Vr+8A//cJZHd+JIs1JRlZpeNBAK\n6Jog5NXxGBqxgM8NEZQ4BtYiDtVfgZmr3SuqFrYwOB5bR3/iQvpCy4r9vqBSYSoP8XEMp0k8T0KQ\nLRhYaEi0YkRILaWvlhE5GWPeRg40bOVw4tJiiFh7zIfH0vCZjgurWuEsKPC2ZhQ1xIBHpznqRTdM\n0mW5OhO8MpoB7nkKIVhQ5wMknuwAtqZPMJjKjQVdE3gsZ9uc7qU7esEEBbRaZdQ1wfHYhQBEQxON\nMec7qNwq5Uk6RoK3qaYDT6IR6NxE2nQjLaryXWqprR2JoLttaVyFMDZNTCwlcSSxecJRTUOjJeqd\n1kNplBkqHfObi8baVAY8gK17OJ7cyGh8dfGYBTqTARJufp7QoHHFbzn/V4+y2MS3tvK+oimEP+IY\ncLrrGZGahe0vGVFp16jNGEEO1l/JqLc8vUTgNXQOJTeTq5o8ybvn6TiwxKSGZuF6LkykpDzlReJE\nTaeSrZkzz20UjhcQSiF+tXI53aMVxyyFVnFsKQQInRXNIaJxRz6FUESPoVXkS5a2qQi8ndFw7Rq/\nIUfrNvBm/TucN4XJBGVgzT59Yxn+8Psv4bd07nzXspnNpijYv/2vuPCpb7H47tvo/MJNrH78QVL7\nD7H0z+9g1Xe+yt5b7pztIZ4Wsq2b6b/uKbJN60g8+8f8l94/4v9+tI3Pb1lE3pb82VOv8a7/uYv7\n/2k/B2dYJUehULx9WlpaKkLSAT74wQ+ivc2Gv7OJrFLCNMOqHZXjKi6dEfBl+tDtNFKI4oxyf6h2\nm4yIr2TMjHob6IquIWcEyHgTnL90KZd21oGmE3GLEIybVe0phKN01lKG2uoKCqPG/HiAgNdVdITm\n5JZIiayRU5Y2IwwEnbL4BcWurc5HpxtJMlamUEqhgxDkjADd0fPBDOJtX8+SZJCiU6VKYAVFu1zh\n1IQgEbDQ9arwOV9d5eB0i7QbziRwFEwBeLP9TrijEET8JZnqmigaFJoQeI1Kj4CwcwS9Bo0Rp/BE\n9WRcuf7h9VXO+g8G2umPrpi62MQkHkLpTzLsbyuuP27VkXENrsqMocr9iKIBUlKGBbWNsnISfgNT\nc6ojlhvQPZHziv93RdfQF1rM+LxSpU8/Gd6KX8KRxEY2rVqGaUxSwMEtepG1IkUPY7mXK2DpJIIW\nPeHlyEg7sUSze161i1zImqap832G3XvB645FGFbFfob9zqS8bmfdnMLyyosCr6mR130VHjWAvOGv\nGkVtVjQ5hnbBwLKFXvRIykkMLOfYkxu25R5IAWQpGFiFyYjanqx0w4XkQy3kjWDtyZJAA7oQaJbz\nO5SfJqqn/J4sN+qSIYvGyMRQ2ETIKp6Xx9ToTPpZmAyQ1zwlj2DhvGuEVJ4qzr4nyywwmslx09++\nSNdIhnvevZxE8O31TZmLCNPA09RQfG8l60jtd5IQA0s6yY/Vbjh4LmAHmxm85tsMX/ZlzKO/oPGx\nLfx78SO++zvn87UPrmR1c5hv7z7Etf9rN5/67r/yDy8eI5U9fTe7QqE4N5BGpVKh60aF9rUg4Shl\n2Ran75fIOb+zhZyg81xlbDDQwYHG3+Z47MKiMjXia6rI8TFFngULnPA/TYDX1PFbOgiDkMfADCcq\nFMmsEUAThTyhiVpd0DJojnjpSATwerx01gWckD0EflNzw/dqqCZCI9a2moGOa+iOrMbQBRGvifQ6\n+VrjVh1Zbwx8ldlo41YdLLwKOzwf25dAYBc9eRUy9Sc5lHQ8LIVPCmJoCjvyttyZ+3zdMvqDi0rb\nWkFs1ygMuN6oqM9Az4+T1R0DqDFUHVborGdooiI8EUCXWQxNkAxaaBo0hCp1jvLvp3D+4PTr6g8t\nxda9E86v0kQSVM8rSKFhSxjxttATOY8h19AaCHQ4n+s1Qgbd63DzwiTLGoNu9T1Bd2QVQ43rOdL8\n2xWrBzyVynxLpHRe5Yp+2iiFMI55GxgKdGAGShUNNZlz8r+MEELTi9XsBBJDFyxrDLKoKcbhMo9Z\nKV++2jsoGPG3MhhbOfH8XPJuhcWQx0BoUB8u+z4E2J4IS+qDXDA/wrKGEE0RL7FA5XcQ9Dv3pJC5\nCgMbYGEiUPadVjU31t0uS1MYIaYuSIZ9xAImdb7iDELpGDWsq6N1zm9DMuzFY9Y2BRaWhewJARnp\nrFf09gqT5U0Tc8tzwRZyTesYaLkcEGhCkC0zFO1QC5nWzeSDjkGbKguXzPgcfTFcNslTLqvyMwl7\nTeqDFsNVIcENQU/xnGN+k4BlOP2Jy/ZT8MadTg/WGS9ycbYxMJblxr97kde6RvjKe1awukVVhDsR\nIutW89KnbqHxw++GfJ4j33iU6HrHpb/vi/fhnV877vicQQjGV36c7PxLCT5zG6F/+iO8rzzKhs13\nsu6959MzkuaJl7vY8eIx/vSp17j3H19ny9Ik16xoYGVzWBVRUSgUEzGqPVhmcbY0G20n5OkjH56P\ndD0tdqABKBSzmPib0tTSRvOxV3ijN0VO96G568SDFvWLlpEKFGZ9y7Z1Z8dFVZ+hlCdZzKkoGEpe\nS2PcTfTXNMdblAt7yUaWoQ/uRx+Iksnk8Jk6Ub/FoSoDqz7kIRm0mBf18dJbQ/RLG1MX5EMtpONr\neEvrYnhcZ8Pyi3i9ZxS6K/tbFTwsCIHu9qeq9q4JXSev+zBkpvhJQentSARpSxqlMDUhGAq0Exnb\nT19oKUvKztUXiIA9RtDSGLOzxZyb6sIUdpkHy/D4GQh2MuJ1FM68MDGE4wcrGMPVpDxJuiOriMQX\nw4EXJ3wuhY5paGRd71dlOJeYmF8jdGwp2dARZ+f+0ropK0HKijMW64Cs0y/pSGIjQkqWuPu0TB1T\n17B0zelH5G3A9gYR2XHAZjDQTsYIs641xlCvRVfKVdADjTDsFE3oDy4uG0tBzqXxBTwmxe5b5aW1\nRelcljYE8GpuwQTySKmXr+ZuO7kXqkDEZ9JTdgllDMeIMHSNxRs/xPBAH/zaqdioC2ciQxOCZNCD\nPuIUochqle0QbNdA1ewsXlNj1A6B6YXsOIJ8jbvSIWdVGjkFEkGL45bOZZ2uNzXVx/yoj0Fd52Bf\nqWy5DrTX+dlTdU/ZwiTg0akP+5mXdYy4Xx2pLHhTfc1mbB0PYLjWuS0MDK0kzxFfE7YwKUypL0oG\nOZ5qJpIZJjMWwMyNsTDherC9MRqCA+A3qI+FkONjCDvHWLAdug8RtIxi3mKxPL0mENJG0wp5W5JM\n6+X05it7vemaM/kyMpqp0KEK+7m4PQaFcdt5pijCelIoA2sKXj0+zOd/8IrjuXrPCicsQnFCLLrz\nVg78twfZf+dXQQjim9fT9p//IwPP7sZKxFj+4PbZHuIZIR/tYPA938WzbweBn/8p0cevIb3k/Wjr\n/hMfv6idj62bx78eGWLHi8d46pUuvv/rYzSGPFy1tJ6tS5MsSgZKSoJCoZjT2Gaw4r2uWwgEttAY\n888H+pCekmcj27weuXcfosywCHh0RtOOx3xRMgjRqzm8t5fQ6EE0V7Hqab6SZDQBrme9XEVzGvZS\nEW7WGPZwbChNxiiMzzVQ6gKksnn8ll5WklmA6SOfWI450kcqq5EzQgTaFyG7y44kYE1ZqwtNE6Q8\nSaSMkq9biqVpThiVcJSsKYONhKhZac40NETh7IRWVOxT8RVk5rcjDGuCsiSFzpv1V5Z6/hTkEEiS\nJ48+8gZQyh0TgQSkeiu2B0cZNDWNgTKP2GCwE/JDwCRNgoXOkvogAc+yCsXcZxl0JPw01AXxJ8A4\n4OffjheKSZXLVNAa95HK5ukeLshNI2DphL0mYV+ZcquZHI9fRNRMQxaQkqxR7bVwRtES8bI3XTxE\nURHvDzmhuR5DI7/wnRw56owp37C6aGClqkM8y2gIedzKi6XzL6cp4uX48DheXasI+fqtRQn2do/Q\nl7XLjJPK7z/rer+8ZmmfTWEvTZ4Q/zTYQMaMIDWj9LnhRRolb56micoJj6IBV2XAu6FpmrQpVB/P\nh1vRe1/Dtu3ifdEY9NDkhV5vG6/0ZKlzPVi2e79l2q/AOvATmiNe6sp10qoqgk7+o3PthbyW890B\nbXE/e48OIXWDTR11iMwIhV68CxJ+9vfUTlkQCNJSJ0R5iKBBccdAT2S1KwLnBA1N0NK+nGx6Hq3B\nboI9gwQsA/cSQTc9tMX85LwBsvWb0UaPw0ghh80oNmO2hU7QY9AS9WIOa4iswfB4jpwtMTwRKJne\nZfe389f2JyhMLhWuK1MXoJvk6ldBuBkm7zd+UigDqwYDY1m++ctDfPeFI8T9Jn957UrluXqbZPsH\naPkP19Hyex8q/gBk+wZ46ZM3c+GPvo2dTk+zh3MIIUgveg+Ztnfg3/0/8P36f+HZ+33Gl17H2Nob\nWDOvhTXzIvzXd3Ty0329PPVqF9/efYj//ctDLKjzs3Vpkq1L65kX9U1/LIVCcc4iA/X0x89naDRH\neOwA/lCcgdRijot68raP9MKtYJSHMQlyut/pY+X+Dl/UGuPpvWXl001fMT+hMHFd0BU9hkbUb9KZ\nKMv3KXiuygysohJaVazC0AQhT7W6UVJAWyI+hlLD2AuuIGTqWAMlhanimDgeH1szybRejrSCTFbr\nriZCpzxKrS+0lIviI3gyg2RMA9LQHPEWK+ALw0J6anuQ5sd8HOov6xvlNmzVNa3Sw+IaNrnm9eTH\n+5GeEGbXHupjHQwM6k5p6qomsFLoEGiAXO2ePlLTaXcbs/aMlJ6hnXV+jGSQaNTPYNdIhSlR7rGr\nD3mIjpmMlYWkS6HR6IZC1oqcKPcCToapa6xti/Nct6Ah5GEgVaNKpW4Vw7OE0MjM2+R4Q/eX1q02\nsJrcPJtCf7JM22/B6yVDIBm0SAYtqo0ny9BcI88uemULF3WubgnaaBcpTwKGM8XQTnBDKBGs6piP\nFm7G0LVKc8kTZjDQTmT0QIVhBo5Crw8fRlqOEVoIKzU9JSOsENLYngiSSltEfTr9xVLlzv2iR9sY\nSuWxhU6mdTNHciM0I5BG7ee/NJ3rQR8v6+lVVnjFazj/+9zzFK5HrVzWIY/BgoSf8WztAlyxoB96\nwdAFSxuD2IEYlLVuSIYsuoczE4qBSE+Y+kgKc6QyZ8sONpNtXIsdagahkbdCaCOHsHEnkdxJk5SV\nZGOHk+dpDfgYHM4xPJ7Dnr9p0uvRawpGAMP0IuW4Y2y60YCF6zsf7QDTD5yePHhlYLn8494edu7v\n49BAin89MoRtS965ooGbNncQ9U1dklIxOc9d+E7MeBQ94Idi3Lsk2z/Iv773E4Bg/e4nZneQZxhp\nhRi95I9Irf4k/ufvx/vSt/G+8l0yHVtJrfxdAs0beOfyBt65vIH+sQw/ea2HH73axQP/fJAH/vkg\nKxpDXLU0yZWLk9SHVD6gQjEXkdFWxvKjjHkbWOO3eNXfQd4tc405UQlLm2G3UbAb1uV6BKrzYgrK\nJZT68QohuLgtVrFesQpXmYJmTjAU3NwizUC4uQ62P4k21k25gdUa89Ec9hRD8DZ01KFn/ORsScDS\nKZ+Gmx/14TG0iommjkSAjBvGWN4guBqRGSEgR1nuzXIw7YT5mfFR9K49WJbFlUuS6DLHWz1B0mYW\nX6Bh0n0tqQ9WGFiF4gROoY6yY7rNhtFNZMDJNck2r6cJaEoW5DYxB8YSU+Tjlsk8L6E3vIy6oVeq\ninFUyaE8ulNAtmU9KXkYhp1+ROV5b7VKXec9UfJmB7loJ5saPJWRdmXrBzwm71icmPS8EkEPdQGL\npQ1BhBBIf8L9xFHUW6Je8nkTxiBX5aldWh/ElhLbCrK8Ua/hi6xRpt0daClE0A2ZtMJk65Yx35bY\nYozWWHk/LGeboNfENifGj4W8JpG2C+js7sZvaRV+RjvSTjrQAK4hVBcwMQ2D8ViY44V13DGZhkEs\n4iWvAwWbxh2nVvASSYn0xsjp7nU0WVl13dEF/JZGPOghG/ORGyhNeKxqiZAd8hYNvpDXLH5Wjr9h\nMaGBN2oeYlljCD0dRCDQ6pdipCqbWJ/XGOaob5yof6LObHud3w9ZFVJsh+dVvG9OJhjvsgi2LEfv\n+kcA0lZZXqXME/WZhJsNsjEnxSTmNyv61gE0xmPEPWDEGshEN5BKZ2G/0wD6TMUCKQPL5af7eth1\ncIBkwOKjF87j6hX1k9bkV8ycld/6C9740v00fOhdzP/MNoT7AHj2vCtZv/uHszy62cUONDBy2ZcY\nW/P7+H7913hf/j94Xv8hufgSxhe/j/SidxMLt3Lt+c1ce34zx4bG+X//1s1Tr3bz3595g//+zBus\naAyxeWEdmxfWsSDuV2GECsUcYf2COE+PpRlK5Yj5zaLS0DlJf8a85sygr2wuRWNcvigxsWktFAsg\nTFXcqxB+lLFL2xeUN83OAhYdyShmOEIm9C48r/29s50nAmPdExS78jLcHkNDn3cBga5fTTiu39Jp\nj/srlq1ojjAw4M5CT/ETaFsh9MwwMZ/BwUHXgCl423SPKwuTrvpLOTaUJmFNHi1QGH7EVVSFqyFL\n3YOWSxVV/bzupTU+ddSBx9BY1xbllwcHistktAPZ21U0TMspL2wR9OjkdGf/ulbS06VmVIjYNAwi\nQYueEScvxQ40UrcgwfBwP9rwW2T1kjFTuCZWtYTZ2z1KKpNHE5oTUgVMqRmVGQC+GsaJoQnWtk7W\nFrtUgCWd+236jo7AaMmbomsCHUEax4NYINt0kdO6wM5hHa5sRJtzvUWFcyqFkInieKq9pLYVRk8P\nYZsTCziAY0QvaQiiedeSqxWUWuZlyjT65+kAABchSURBVLVcgtcKkbNLcskXx1SoZGcXr/+8rx7y\nh10PU449R4awDA0ppfN9TvGMz8cWovfvY17UT7IpTC4TY6x3FEPTkR4f3o4NGMMvsiDhRyuE3VZ5\nC/PBJnTXwMrMvwyO/KCYRyg8EQx/lEzDGqQ3hnbw6YptLUMrelYnoFvO/qbRUXxeH74LPwzA8vlJ\nXj7UTVYvq6ToJtzJWEdx2drWaDHssoD01aE1riXveqDFLFSLVQaWy5/8u6XTr6Q4Yeq2XEZ000Uc\n+MoDPL91G4vuupXIutXT3mRzCTvUwuglf8ToRf8Zz94d+F7+DsHn7ib43N1kG9aQad9CZt5GGutX\n8zvr5vM76+ZzoHeMp/f18NN9vXzt5wf42s8P0BrzsbnTMbZUgQyF4tzGa+psaI+7ilepDHN9jT4y\nUPKwBExZiJSpzGvBCRvsP+xFc4sQTGlguUpkQXGJ+U1aIl6GxnO0Z5xl8xrqiqGK2aaLkLqJVshD\nmqZ6Vz7agTZ0CDt8Yj0nO+v8eA2NV93co/KfwVzDGkR2FC09SHPES1NbFNuXIJfPkI92FtdrDDvn\nUWjoWgvN9eoVPIDLlp4PxwX56AK0o78g4jXYm53P6hWrCE4Ij5xI3G+xsSNO31iW17pG8ITiZGLv\nwujagzZ8BJHPYPuT5GMLscsMrIBlsK41Ss72omta0cDC8JGuXwPHfuYuKF0jhRw7n2WwbO0Wntnz\nKr5IyVtX8ETm7bLAwpk+TsoE3hb3IQQkAhYj6ROs1mZ4aU/odI31EyuLIsrXuB7sUHPx/0KOUnF9\nu5QP5OBe1FOcT65+JflYJ1hTT7LbkbZpTqJQYAasnE1XdDU5PVC8r3SjVCq8LebDb+pEgxeQtleg\nYVEIXSs3vAFyieXY/iTV2Faool6D17IIBC1s9zuxw/Nh+EVCHoN0YUKjrLm0HWxEuvuVph/pi3Ow\nYQsSjWWdcdBNMm3vKK0fno/WPTitDApIX3z6lcrPZ956eob2k9cnTlDkw63F/7Vik/Kq400S3num\nJqKVgaU47eg+L51fuImRl/fy2s1fJrCkE1mrVftcx/CRXnYd6WXXoQ0dwrNvB559PyCw6x4Cu5wf\nz2zjWnL1q1nccD4d553H7110Pl0jGf7p9V5+uq+X77xwhEd2HybuN9nQHmNDe5yL22I1XfYKheLs\np6AsLG0I8dJbw/it2o/1YghbbvLWGFG/SV1dgNEjzvupfqULOR/tEZPuvMHqlghCCJY3hrDGY5AZ\nhrJ+XUUlOO0knNfyzFSTba1uTDs9hq7RHPEWDawKD51uko8vgYHXqYvXkfe7YYXxyn5gDSHPhNLo\ntSj/XQ2HghDa6LzRNDyGxkXnLceegXFVIOgxCHqMinC1XP1qqF/tVDsTWs3JyYDHxAxaxabKBcxQ\nPX6Pzlg6jxRaMfQzl6/8ZtcvX1ThQSyE9mXydtHYmrlKWlpTE6LobZzOyLywNTrhGFG/yVVLy5r1\nLnrPtJOzhdynAjnbBkTp/ArWzWShdgC6B6mf2vB7QxeMeZ2QtqjPZCCVRYiSB8t0r1vn+D5EbuLd\nVzj16uu1NO6q57w20YMo453k9eGynZb1mDIdgzLT9lvFVhAXL0g44XfV+8b1mA28QWu8Rq7dKUB6\nYxCVBMsnSRLL0QcPFMc6YUyh+WhjXeSqZFSeF6ZCBBXnHMHli7jgB3/N0W8+Tvqt49NvMIexw/NJ\nXXA9qQuuR6R6sQ4/i3n455jHdmMd+inCjSO3rRDRaAed0Q4+1tbJyPI2nh+O8aNjGk+/0csTL3ch\ngOWNIda3x9jQHmNFU1g1ylYozjESAYvNCyevdJs1HIVEeqYp2CSceno53Ut+ChVB+pPYvjo88QVs\nDFTOTGfmXYLIjtVUhkv9Z06PUgaOgXD5ogT7ukcmFAWyQ80VHo/TQS5xHpr3KLYvMf3KM6WGslzA\n9teTS55HPlzpURGaTlvMxyvHRsjrXuIBi+PDaUbSlfld1YUaCqGe2ZwsGlvTRUTkox3og/sn9Gib\nKVN5C4vM0POQq19VNJBytgRNFJ95ueR5GNI+td/NDCiX34XzI4znbEgfcxbIicZUeTrjvJiPw/2p\nKT3KALKqSTG1+mA1nU/OV1nUQRo+RC5FwfQo/42I+EwiU9QhsH11RLPO/k5HybJCcYsC+Vin412c\nDN0k27x+wuJyledMqT/KwFKccZo/fi3NH792todx1iB9daQXXUN60TXOgswoRs9LGD0vYgy8jt7/\nBubRXXhf+zsCwDvdl22FGY20cEQ08FIqzr/8Mso3f1FPr9lM8/xOVs9PcMG8CAuTARVOqFCc42TM\nKEfr1rMk3j7lenawiWyoj758AM8UGp20gmTnX1r7Q8M3abWzwky4OI0GFjjhjysm6SF1upGecDH3\n44wgBPnYwonLdQtpeOmOOPkqzWEPrxwbntBkuJqw11ENfZZOtpDzMs0QcvWryCVX/kaE/+ejpfyc\nRMDiWCpXzAeTVojsvI2zMq5kyMK2HS9rUNeQON62Qr+6cgxd47zmED5TZyyT5zCl4hiTUfDqFMIo\nRdoJ35v0XixsZwURuVTtBt/TkKs/Hy3Vhz3dxM0s4zWd1gaaJiq8tacTZWApFGcbVoBc80Xkmi+q\nXJ4dQx88gD70pvs6iDX4JguH3mRJ9lk+YJZqHeUPa7x1KM5+u5En9XbGY0uxGs+jrvU8ljTHiftP\nqPixQqE4C8iYUdCnvrelFYKWtYyN91F/GiroykIYW/4Ec3IUJ47QGF/w24zu7cHCUdpXt4SnrYxc\nF7DYsCBGyGNwsM9R6odnkkP1G2BcVbMwEWBV0EtqdPZbwlwwr7K4h/RESHdsrSiKUU5LxFketGxG\n43nmxabxDpo+0p1XFycxbF8dWqqXXGL5lJvlYwuRuoUdbJrhmZSh6WQWbDnx7WaBSQtwnCaUgaVQ\nnCuYfvKJ5eRr/ZhKG230GPrQIbShN9EHDxLo3c+y7te4ePRHWP0/gH7Ivqzzumzmeb2N/uAiUtGl\nkFxOvL6VlpiP+qAHvzV5qIpCoTj7CVgGGzviFb2BThWF8KNyL4PizFHodTUdYbc6YnPEy5GhcVrP\n0v6LQgg8pk5q+lVnh2m8S+BU51vSEJx2PaAiVypft9S5z6bJJ7MDDcViHIpThzKwFIq5gNCwg83Y\nwWZovrjio0E7hz54gNyxFxk+tAer+2UuGv434sM/h2HgEAzIAK/KVv7RbmW/1sqQp5mUv4V8sAmf\n10/I6yRnh9xX0KOX3nsNgpaB39JrloRWKM5W7rnnHp5//nlyuRyf/vSn2bVrF//yL/9CIOCE6nzi\nE5/g8ssvn91Bvk1mUvnubaFbpBe/9/TsWzEBj6HRGvcxL/L2DCTL0Ni44MSqvyl+QxAavM2cOMXJ\nowwshWKuoxnkYwsRsYWElzmKTx7oGR9A732VzLEXyR57iY6+V7lg5GdY9hhkgUHn1U2Mo7KObjvE\nIEEGZJCDMsAgAYaln2H8jOBjWPrJmwHyZgjhCeH1+hxDzDLc6lmOURawSv9Xfx6wDGWkKX4jeO65\n59i7dy+PPvoo/f39vO9972PDhg18+ctfZtmyZbM9vAlctrCOdFZVb52LLGuo3c9JoVCcPs64gXXn\nnXeyZ88ehBB8/vOfZ9WqVcXPnn32We677z50Xeeyyy7j+uuvn3YbhUJxepDeKLmW9Wgt6ykEGAxK\nG234CPrwIffvYYLDh1k2fJTl4wMwvh8tPYCeHZl8x3lgDDJjFqPCxwh+hqWPQdvHkPQx7L4/KEP0\nEaJPhugj7PyVYdJmBJ/HIuBxPGNhr0EyaFEf9FAfsqgPeUgGPTQEPQQ9umq+rDgtrFu3rvgsikQi\npFIphoaGZnlUk+Mz9ZqNXxUKhUJx6jmjBtYvfvELDh48yKOPPsq+ffu47bbbeOyxx4qff+lLX+Lh\nhx+moaGBj3zkI2zdupW+vr4pt1EoFGcQoWGH50/f/DOfRaQHEZlhtMwwovgaQWSG0Ny/3swIvsww\n9e56pIchfRSRGcbIDk+6+1E7xFA6wmAmRN9wmGNvBTmWC/CWDPGyDDGORRYDTTfx+3wEfV7iPp06\nryDmFcQsScQjiFgSv27jIetUNcuNI/IZRD4N+TQin4FcGpFPl5bJPCCQCDepW7i9RARSt8DwIHWv\nU67YcP467z1OdTXTjzT8SMOHNH3kNB+jtsWwtBjOWwzmTUYykpFMjpF0npF0juF0jtF03l3mLG+P\n+7jjdDRIl7ZTQjufc2RiF/7mwc46/YtkDmzbkYXMu9vkEdhkk6vAPLPJxLOBruv4/c55PvbYY1x2\n2WX09fXx1a9+laGhIRoaGrj99tuJRqMTtg0GPRjGyRk7uq4RjZ77cp4JShaVKHlUouRRQsmiktMp\njzNqYO3cuZMrr7wSgIULFzI0NMTIyAjBYJBDhw4RiURoanKqmGzevJmdO3fS19c36TYKheI3FN1E\n+hNIf2LKZqVTks8ixvvRxnvRUn1oqT5E4f/xXmKpfupSvSwc70WkDqCN99ZuXppxXzNvOE8Wg5yw\nyAqTnLDIaRZ5YZLXPCAK7TolIBHS/YtEtzMYMoMpMxh2GtNOYzB99a1k1fu0NBnDwxgexqXFuPCS\nFl5ymhd0A10IQrZB+InCg6HMSydthMw5VdrsaiMpVzKS7CzCNZqcz91lNXqynAip8z7GyOY7T2of\nZxM//vGPefzxx/nGN77Bc889x8KFC1mwYAF/9Vd/xf33388f//EfT9hmZOTkK5pFo34GBsamX3EO\noGRRiZJHJUoeJZQsKjkV8kgma4fgnlEDq6enhxUrVhTf19XV0d3dTTAYpLu7m3i8lEiZSCQ4dOgQ\n/f39k25TzmQnqDiD/PpXwERlUaF4+8SBKZoKniZM9zVbdbM87is2S8c/GXzMntzOND/72c944IEH\neOihhwiFQmzZUipXvGXLFu64446a252q55V67pVQsqhEyaMSJY8SShaVnC55nJluWy6yqkmalLKY\nH1H9GTjlNafaRqFQKBSK2WB4eJh77rmHBx98sBgG+JnPfIajR48CsGvXLhYtWjSbQ1QoFArFLHFG\nPVgNDQ309PQU33d1dZFIJGp+dvz4cZLJJIZhTLqNQqFQKBSzwQ9/+EP6+/u56aabiss+8IEP8Ad/\n8Af4/X58Ph933XXXLI5QoVAoFLPFGTWwNm7cyP3338+HP/xhXn75Zerr64uhfvPmzWNkZITDhw/T\n2NjI008/zb333kt/f/+k2ygUCoVCMRtcd911XHfddROWv/e9qseTQqFQzHWErBWbdxq599572b17\nN0IIvvjFL/Lyyy8XY9d/+ctfcu+99wJw1VVX8YlPfKLmNkuXnobKWQqFQqFQKBQKhUJxkpxxA0tx\n8oyPj3P11Vdz/fXXs2HDBm6++Wby+TzJZJKvfOUrWJY120OcdXbs2MFDDz2EYRjceOONLF68WMmp\nitHRUW655RYGBwfJZrNcf/31JJPJYmL+kiVL+JM/+ZPZHeQs89prr/HZz36W3/3d32Xbtm289dZb\nNa+jHTt28M1vfhNN07juuuu49tprZ3voZ5RacrrtttvI5XIYhsFXvvIVksnknJfTqWCu9oW85557\neP7558nlcnz6059m5cqVc/5enIkuMFfkMdNn/rkujxN5rj/00EM8+eSTCCH43Oc+x+bNm2dx5Kee\nk3l+Z7NZbr31Vo4ePYqu69x1113Mnz9Ne5pqpOKs47777pPvf//75fe+9z156623yh/+8IdSSim3\nb98uv/3tb8/y6Gafvr4+edVVV8nh4WF5/Phxefvttys51eCRRx6R9957r5RSymPHjsmtW7fKbdu2\nyT179kgppbzhhhvkM888M5tDnFVGR0fltm3b5O233y4feeQRKaWseR2Njo7Kq666Sg4NDclUKiW3\nbt0q+/v7Z3PoZ5Racrr55pvlE088IaWU8lvf+pbcvn37nJfTqWDXrl3yU5/6lJRSyr1798prr712\nlkd0Zti5c6f85Cc/KaV0ft83b96s7kU5vS4wV+Qx02f+XJDHTJ/rb775pnzf+94n0+m07O3tlVu2\nbJG5XG42h35KOdnn99/+7d/KO+64Q0op5TPPPCNvvPHGEx7DGa0iqDh5Xn/9dfbt28fll18OOJWq\nrrjiCgCuuOIKdu7cOYuj+81g586dbNiwgWAwSH19PX/2Z3+m5FSDWCzGwMAAAENDQ0SjUY4cOVKc\nEZ/rcrIsi69//evU19cXl9W6jvbs2cPKlSsJhUJ4vV7Wrl3LCy+8MFvDPuPUktMXv/hFtm7dCpSu\ns7kup1PBZL0kz3XWrVvHX/zFXwAQiURIpVJz/l6ciS4wV+Qx02f+XJDHTJ/ru3bt4tJLL8WyLOLx\nOC0tLezbt282h35KOdnn986dO4ttNzZt2sTzzz9/wmNQBtZZxvbt27n11luL71OpVDHULZlM0t3d\nPVtD+43h8OHDSCm56aab+MhHPsLOnTuVnGpw9dVXc/ToUbZs2cK2bdu4+eabCYfDxc/nupwMw8Dr\n9VYsq3Ud9fT0TOjhN5fkVktOfr8fXdfJ5/N85zvf4ZprrpnzcjoV9PT0EIuVuqMV+kKe6+i6jt/v\nNNV+7LHHuOyyy+b8vTgTXWCuyGOmz/y5II+ZPtfPdVmc7PO7fLmu62iaRiaTObExnOQ5KM4gf//3\nf8/5559fEQda3hNMqnS6IsePH+erX/0qR48e5WMf+5iSUw2+//3v09zczMMPP8yrr77KDTfcUFRi\nQMmpFrWuo2o5SdWrD4B8Ps/NN9/M+vXr2bBhAzt27Kj4XMnpxJnr19qPf/xjHn/8cb7xjW8UPaQw\n9+7FmeoCc0UeMLNn/lyQx0yf63NBFtWcyDVxKuSjPFhnEc888ww/+clP+NCHPsRjjz3G1772NXw+\nH+Pj44DzA1PuDp2r1NXVsWbNGgzDoLW1lUAgoORUgxdeeIFNmzYBsHTpUsbGxib0olNyqqTWdVSr\nv18ymZytIf7GcNttt9HW1sbnPvc5oHYfRCWnE2OqXpLnOj/72c944IEH+PrXv04oFJrT9+JMdYG5\nIo+ZPvPngjxm+lyfrPfsucyJXBMNDQ1Fj142m0VKiWmaJ3Q8ZWCdRfz5n/853/ve9/ibv/kbPvjB\nD/LZz36WSy65hKeeegqAH/3oR1x66aWzPMrZZ9OmTTz33HPYtk1fXx9jY2NKTjVoa2tjz549ABw5\ncoRAIMDixYvZvXs3oORUi1rX0erVq/n1r3/N0NAQo6OjvPDCC6xdu3aWRzq77NixA9M0ueGGG4rL\nlJxOno0bNxavv7nUF3J4eJh77rmHBx98kGg0Cszte3GmusBckcdMn/lzQR4zfa6vX7+eZ555hkwm\nw/Hjx+nq6mLhwoWzOfTTzolcExs3buTJJ58E4Omnn+biiy8+4eOpMu1nKffffz8tLS1s2rSJW265\nhXQ6TXNzM3fdddcJW9nnIt/97nd54oknSKVS/P7v/z4rV65UcqpidHSUz3/+8/T29pLL5bjxxhtJ\nJpN84QtfwLZtVq9ezW233Tbbw5w1XnzxRbZv386RI0cwDIOGhgbuvfdebr311gnX0ZNPPsnDDz+M\nEIJt27bx7ne/e7aHf8aoJafe3l48Hk9R+e/s7OSOO+6Y03I6VczFvpCPPvoo999/PwsWLCguu/vu\nu7n99tvn/L04nS4wV+Qx02f+uS6PE3muP/LII/zDP/wDQghuuukmNmzYMMujP3Wc7PM7n89z++23\nc+DAASzL4u6776apqemExqAMLIVCoVAoFAqFQqE4RagQQYVCoVAoFAqFQqE4RSgDS6FQKBQKhUKh\nUChOEcrAUigUCoVCoVAoFIpThDKwFAqFQqFQKBQKheIUoQwshUKhUCgUCoVCoThFKANLoVAoFAqF\nQqFQKE4RysBSKBQKhUKhUCgUilPE/wfZI/T60vSfQwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbe27d4ed30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_traces(traces_ols_glm, retain=1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observe:**\n",
    "\n",
    "+ The output parameters are of course named differently to the custom naming before. Now we have:\n",
    "\n",
    "    `b0 == Intercept`  \n",
    "    `b1 == x`  \n",
    "    `sigma_y == sd`  \n",
    "    \n",
    "    \n",
    "+ However, naming aside, this `glm`-defined model appears to behave in a very similar way, and finds the same parameter values as the conventionally-defined model - any differences are due to the random nature of the sampling.\n",
    "+ We can quite happily use the `glm` syntax for further models below, since it allows us to create a small model factory very easily."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create higher-order linear models\n",
    "\n",
    "Back to the real purpose of this Notebook, to demonstrate model selection.\n",
    "\n",
    "First, let's create and run a set of polynomial models on each of our toy datasets. By default this is for models of order 1 to 5.\n",
    "\n",
    "### Create and run polynomial models\n",
    "\n",
    "Please see `run_models()` above for details. Generally, we're creating 5 polynomial models and fitting each to the chosen dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Running: k1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (2 chains in 2 jobs)\n",
      "NUTS: [sd_log__, x, Intercept]\n",
      "100%|██████████| 2500/2500 [00:01<00:00, 1251.87it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Running: k2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (2 chains in 2 jobs)\n",
      "NUTS: [sd_log__, np.power(x, 2), x, Intercept]\n",
      "100%|██████████| 2500/2500 [00:02<00:00, 1036.36it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Running: k3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (2 chains in 2 jobs)\n",
      "NUTS: [sd_log__, np.power(x, 3), np.power(x, 2), x, Intercept]\n",
      "100%|██████████| 2500/2500 [00:04<00:00, 530.45it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Running: k4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (2 chains in 2 jobs)\n",
      "NUTS: [sd_log__, np.power(x, 4), np.power(x, 3), np.power(x, 2), x, Intercept]\n",
      "100%|██████████| 2500/2500 [00:07<00:00, 347.64it/s]\n",
      "There were 2 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "There were 3 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The number of effective samples is smaller than 25% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Running: k5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (2 chains in 2 jobs)\n",
      "NUTS: [sd_log__, np.power(x, 5), np.power(x, 4), np.power(x, 3), np.power(x, 2), x, Intercept]\n",
      "100%|██████████| 2500/2500 [00:24<00:00, 101.41it/s]\n",
      "There were 33 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "There were 28 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The number of effective samples is smaller than 25% for some parameters.\n"
     ]
    }
   ],
   "source": [
    "models_lin, traces_lin = run_models(dfs_lin, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Running: k1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (2 chains in 2 jobs)\n",
      "NUTS: [sd_log__, x, Intercept]\n",
      "100%|██████████| 2500/2500 [00:02<00:00, 875.37it/s]\n",
      "The acceptance probability does not match the target. It is 0.993112884126, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Running: k2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (2 chains in 2 jobs)\n",
      "NUTS: [sd_log__, np.power(x, 2), x, Intercept]\n",
      "100%|██████████| 2500/2500 [00:04<00:00, 578.24it/s]\n",
      "The acceptance probability does not match the target. It is 0.883398371777, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "The acceptance probability does not match the target. It is 0.999997671953, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "The chain reached the maximum tree depth. Increase max_treedepth, increase target_accept or reparameterize.\n",
      "The gelman-rubin statistic is larger than 1.2 for some parameters.\n",
      "The estimated number of effective samples is smaller than 200 for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Running: k3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (2 chains in 2 jobs)\n",
      "NUTS: [sd_log__, np.power(x, 3), np.power(x, 2), x, Intercept]\n",
      "100%|██████████| 2500/2500 [03:07<00:00, 13.32it/s] \n",
      "The acceptance probability does not match the target. It is 0.999381198882, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "The acceptance probability does not match the target. It is 0.899455530613, but should be close to 0.8. Try to increase the number of tuning steps.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Running: k4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (2 chains in 2 jobs)\n",
      "NUTS: [sd_log__, np.power(x, 4), np.power(x, 3), np.power(x, 2), x, Intercept]\n",
      "100%|██████████| 2500/2500 [00:05<00:00, 430.11it/s]\n",
      "The acceptance probability does not match the target. It is 0.920287453662, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "The acceptance probability does not match the target. It is 0.993538143406, but should be close to 0.8. Try to increase the number of tuning steps.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Running: k5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (2 chains in 2 jobs)\n",
      "NUTS: [sd_log__, np.power(x, 5), np.power(x, 4), np.power(x, 3), np.power(x, 2), x, Intercept]\n",
      "100%|██████████| 2500/2500 [00:06<00:00, 407.53it/s]\n",
      "The acceptance probability does not match the target. It is 0.924822538129, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "The acceptance probability does not match the target. It is 0.986333585677, but should be close to 0.8. Try to increase the number of tuning steps.\n"
     ]
    }
   ],
   "source": [
    "models_quad, traces_quad = run_models(dfs_quad, 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A really bad method for model selection: compare likelihoods\n",
    "\n",
    "Evaluate log likelihoods straight from model.logp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dfll = pd.DataFrame(index=['k1','k2','k3','k4','k5'], columns=['lin','quad'])\n",
    "dfll.index.name = 'model'\n",
    "\n",
    "for nm in dfll.index:\n",
    "    dfll.loc[nm,'lin'] = - models_lin[nm].logp(pm.summary(traces_lin[nm],\n",
    "                                                          traces_lin[nm].varnames)['mean'].to_dict())\n",
    "    \n",
    "    dfll.loc[nm,'quad'] = - models_quad[nm].logp(pm.summary(traces_quad[nm],\n",
    "                                                            traces_quad[nm].varnames)['mean'].to_dict())\n",
    "\n",
    "dfll = pd.melt(dfll.reset_index(), id_vars=['model'],\n",
    "               var_name='poly', value_name='log_likelihood')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot log-likelihoods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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IWAAAAAAQIQQsAAAAAIgQAhYAAAAARAgBCwAAAAAihIAFAAAAABFCwAIAAACA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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbe1e9e4da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.factorplot(x='model', y='log_likelihood', col='poly',\n",
    "                   hue='poly', data=dfll, size=6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observe:**\n",
    "\n",
    "+ Again we're showing the linear-generated data at left (Blue) and the quadratic-generated data on the right (Green)\n",
    "+ For both datasets, as the models get more complex, the likelhood increases monotonically\n",
    "+ This is expected, since the models are more flexible and thus able to (over)fit more easily.\n",
    "+ This overfitting makes it a terrible idea to simply use the likelihood to evaluate the model fits."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### View posterior predictive fit\n",
    "\n",
    "Just for the linear, generated data, lets take an interactive look at the posterior predictive fit for the models k1 through k5.\n",
    "\n",
    "As indicated by the likelhood plots above, the higher-order polynomial models exhibit some quite wild swings in the function in order to (over)fit the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bdaedc89aca5423ba4e3a82c6a1d2694",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "interactive(plot_posterior_cr, models=fixed(models_lin), traces=fixed(traces_lin),\n",
    "            rawdata=fixed(dfs_lin), xlims=fixed(dfs_lin_xlims), datamodelnm=fixed('linear'),\n",
    "            modelnm = ['k1','k2','k3','k4','k5'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compare models using WAIC\n",
    "\n",
    "The Widely Applicable Information Criterion (WAIC) can be used to calculate the goodness-of-fit of a model using numerical techniques. See [(Watanabe 2013)](http://www.jmlr.org/papers/volume14/watanabe13a/watanabe13a.pdf) for details."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observe:**\n",
    "\n",
    "+ We get three different measurements: \n",
    "    - waic: widely available information criterion\n",
    "    - waic_se: standard error of waic\n",
    "    - p_waic: effective number parameters\n",
    "    \n",
    "In this case we are interested in the WAIC score. We also plot error bars for the standard error of the estimated scores. This gives us a more accurate view of how much they might differ.\n",
    "\n",
    "Now loop through all the models and calculate the WAIC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/osvaldo/proyectos/00_PyMC3/pymc3/pymc3/stats.py:194: UserWarning: For one or more samples the posterior variance of the\n",
      "        log predictive densities exceeds 0.4. This could be indication of\n",
      "        WAIC starting to fail see http://arxiv.org/abs/1507.04544 for details\n",
      "        \n",
      "  \"\"\")\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbe174bd780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfwaic = pd.DataFrame(index=['k1','k2','k3','k4','k5'], columns=['lin','quad'])\n",
    "dfwaic.index.name = 'model'\n",
    "\n",
    "for nm in dfwaic.index:\n",
    "    dfwaic.loc[nm, 'lin'] = pm.waic(traces_lin[nm], models_lin[nm])[:2]\n",
    "    dfwaic.loc[nm, 'quad'] = pm.waic(traces_quad[nm], models_quad[nm])[:2]\n",
    "dfwaic = pd.melt(dfwaic.reset_index(), id_vars=['model'], var_name='poly', value_name='waic_')\n",
    "dfwaic[['waic', 'waic_se']] = dfwaic['waic_'].apply(pd.Series)\n",
    "\n",
    "# Define a wrapper function for plt.errorbar\n",
    "def errorbar(x, y, se, order, color, **kws):\n",
    "    xnum = [order.index(x_i) for x_i in x]\n",
    "    plt.errorbar(xnum, y, yerr=se, color='k', ls='None')\n",
    "    \n",
    "g = sns.factorplot(x='model', y='waic', col='poly', hue='poly', data=dfwaic, size=6)\n",
    "order = sns.utils.categorical_order(dfwaic['model'])\n",
    "g.map(errorbar,'model', 'waic', 'waic_se', order=order);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observe**\n",
    "\n",
    "+ We should prefer the model(s) with lower WAIC\n",
    "\n",
    "\n",
    "+ Linear-generated data (lhs):\n",
    "    + The WAIC seems quite flat across models\n",
    "    + The WAIC seems best (lowest) for simpler models.\n",
    "\n",
    "\n",
    "+ Quadratic-generated data (rhs):\n",
    "    + The WAIC is also quite flat across the models\n",
    "    + The lowest WAIC is model **k4**, but **k3** - **k5** are more or less the same. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compare leave-one-out Cross-Validation [LOO]\n",
    "\n",
    "Leave-One-Out Cross-Validation or K-fold Cross-Validation is another quite universal approach for model selection. However, to implement K-fold cross-validation we need to paritition the data repeatly and fit the model on every partition. It can be very time consumming (computation time increase roughly as a factor of K). Here we are applying the numerical approach using the posterier trace as suggested in Vehtari et al 2015."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/osvaldo/proyectos/00_PyMC3/pymc3/pymc3/stats.py:270: UserWarning: Estimated shape parameter of Pareto distribution is\n",
      "        greater than 0.7 for one or more samples.\n",
      "        You should consider using a more robust model, this is because\n",
      "        importance sampling is less likely to work well if the marginal\n",
      "        posterior and LOO posterior are very different. This is more likely to\n",
      "        happen with a non-robust model and highly influential observations.\n",
      "  happen with a non-robust model and highly influential observations.\"\"\")\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbe16dc5e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfloo = pd.DataFrame(index=['k1','k2','k3','k4','k5'], columns=['lin','quad'])\n",
    "dfloo.index.name = 'model'\n",
    "\n",
    "for nm in dfloo.index:\n",
    "    dfloo.loc[nm, 'lin'] = pm.loo(traces_lin[nm], models_lin[nm])[:2]\n",
    "    dfloo.loc[nm, 'quad'] = pm.loo(traces_quad[nm], models_quad[nm])[:2]\n",
    "dfloo = pd.melt(dfloo.reset_index(), id_vars=['model'], var_name='poly', value_name='loo_')\n",
    "dfloo[['loo', 'loo_se']] = dfloo['loo_'].apply(pd.Series)\n",
    "    \n",
    "g = sns.factorplot(x='model', y='loo', col='poly', hue='poly', data=dfloo, size=6)\n",
    "order = sns.utils.categorical_order(dfloo['model'])\n",
    "g.map(errorbar,'model', 'loo', 'loo_se', order=order);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observe**\n",
    "\n",
    "+ We should prefer the model(s) with lower LOO. You can see that LOO is nearly identical with WAIC. That's because WAIC is asymptotically equal to LOO. However, PSIS-LOO is supposedly more robust than WAIC in the finite case (under weak priors or influential observation). \n",
    "\n",
    "\n",
    "+ Linear-generated data (lhs):\n",
    "    + The LOO is also quite flat across models\n",
    "    + The LOO is also seems best (lowest) for simpler models.\n",
    "\n",
    "\n",
    "+ Quadratic-generated data (rhs):\n",
    "    + The same pattern as the WAIC\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Final remarks and tips\n",
    "\n",
    "It is important to keep in mind that, with more data points, the real underlying model (one that we used to generate the data) should outperform other models. \n",
    "\n",
    "There is some agreement that PSIS-LOO offers the best indication of a model's quality. To quote from [avehtari's comment](https://github.com/pymc-devs/pymc3/issues/938#issuecomment-313425552): \"I also recommend using PSIS-LOO instead of WAIC, because it's more reliable and has better diagnostics as discussed in http://link.springer.com/article/10.1007/s11222-016-9696-4 (preprint https://arxiv.org/abs/1507.04544), but if you insist to have one information criterion then leave WAIC\". \n",
    "\n",
    "Alternatively, Watanabe [says](http://watanabe-www.math.dis.titech.ac.jp/users/swatanab/index.html) \"WAIC is a better approximator of the generalization error than the pareto smoothing importance sampling cross validation. The Pareto smoothing cross validation may be the better approximator of the cross validation than WAIC, however, it is not of the generalization error\"."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reference\n",
    "\n",
    "\n",
    "For more information on Model Selection in PyMC3, and about Bayesian model selection, you could start with:\n",
    "\n",
    "+ Thomas Wiecki's [detailed response](https://stats.stackexchange.com/questions/161082/bayesian-model-selection-in-pymc3/166383#166383) to a question on Cross Validated\n",
    "+ The Deviance Information Criterion: 12 Years On [(Speigelhalter et al 2014)](http://onlinelibrary.wiley.com/doi/10.1111/rssb.12062/abstract)\n",
    "+ Bayesian predictive information criterion for the evaluation of hierarchical Bayesian and empirical Bayes models [(Ando 2007)](https://doi.org/10.1093/biomet/asm017)\n",
    "+ A Widely Applicable Bayesian Information Criterion [(Watanabe 2013)](http://www.jmlr.org/papers/volume14/watanabe13a/watanabe13a.pdf)\n",
    "+ Efficient Implementation of Leave-One-Out Cross-Validation and WAIC for Evaluating Fitted Bayesian Models [(Vehtari et al 2015)](http://arxiv.org/abs/1507.04544)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Example originally contributed by Jonathan Sedar 2016-01-09 [github.com/jonsedar](https://github.com/jonsedar). Edited by Junpeng Lao 2017-07-6 [github.com/junpenglao](https://github.com/junpenglao)"
   ]
  }
 ],
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