docs/source/notebooks/model_averaging.ipynb

{
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   "source": [
    "import arviz as az\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pymc3 as pm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext watermark\n",
    "az.style.use('arviz-darkgrid')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model averaging\n",
    "\n",
    "When confronted with more than one model we have several options. One of them is to perform model selection, using for example a given Information Criterion as exemplified [in this notebook](model_comparison.ipynb) and this other [example](GLM-model-selection.ipynb). Model selection is appealing for its simplicity, but we are discarding information about the uncertainty in our models. This is somehow similar to computing the full posterior and then just keep a point-estimate like the posterior mean; we may become overconfident of what we really know.\n",
    "\n",
    "One alternative is to perform model selection but discuss all the different models together with the computed values of a given Information Criterion. It is important to put all these numbers and tests in the context of our problem so that we and our audience can have a better feeling of the possible limitations and shortcomings of our methods. If you are in the academic world you can use this approach to add elements to the discussion section of a paper, presentation, thesis, and so on.\n",
    "\n",
    "Yet another approach is to perform model averaging. The idea now is to generate a meta-model (and meta-predictions) using a weighted average of the models. There are several ways to do this and PyMC3 includes 3 of them that we are going to briefly discuss, you will find a more thorough explanation in the work by [Yuling Yao et. al.](https://arxiv.org/abs/1704.02030)\n",
    "\n",
    "## Pseudo Bayesian model averaging\n",
    "\n",
    "Bayesian models can be weighted by their marginal likelihood, this is known as Bayesian Model Averaging. While this is theoretically appealing, is problematic in practice: on the one hand the marginal likelihood is highly sensible to the specification of the prior, in a way that parameter estimation is not, and on the other computing the marginal likelihood is usually a challenging task. An alternative route is to use the values of WAIC (Widely Applicable Information Criterion) or LOO (pareto-smoothed importance sampling Leave-One-Out cross-validation), which we will call generically IC, to estimate weights. We can do this by using the following formula:\n",
    "\n",
    "$$w_i = \\frac {e^{ - \\frac{1}{2} dIC_i }} {\\sum_j^M e^{ - \\frac{1}{2} dIC_j }}$$\n",
    "\n",
    "Where $dIC_i$ is the difference between the i-esim information criterion value and the lowest one. Remember that the lowest the value of the IC, the better. We can use any information criterion we want to compute a set of weights, but, of course, we cannot mix them. \n",
    "\n",
    "This approach is called pseudo Bayesian model averaging, or Akaike-like weighting and is an heuristic way to compute the relative probability of each model (given a fixed set of models) from the information criteria values. Look how the denominator is just a normalization term to ensure that the weights sum up to one.\n",
    "\n",
    "## Pseudo Bayesian model averaging with Bayesian Bootstrapping\n",
    "\n",
    "The above formula for computing weights is a very nice and simple approach, but with one major caveat it does not take into account the uncertainty in the computation of the IC. We could compute the standard error of the IC (assuming a Gaussian approximation) and modify the above formula accordingly. Or we can do something more robust, like using a [Bayesian Bootstrapping](http://www.sumsar.net/blog/2015/04/the-non-parametric-bootstrap-as-a-bayesian-model/) to estimate, and incorporate this uncertainty.\n",
    "\n",
    "## Stacking\n",
    "\n",
    "The third approach implemented in PyMC3 is know as _stacking of predictive distributions_ and it has been recently [proposed](https://arxiv.org/abs/1704.02030). We want to combine several models in a metamodel in order to minimize the diverge between the meta-model and the _true_ generating model, when using a logarithmic scoring rule this is equivalently to:\n",
    "\n",
    "$$\\max_{n} \\frac{1}{n} \\sum_{i=1}^{n}log\\sum_{k=1}^{K} w_k p(y_i|y_{-i}, M_k)$$\n",
    "\n",
    "Where $n$ is the number of data points and $K$ the number of models. To enforce a solution we constrain $w$ to be $w_k \\ge 0$ and  $\\sum_{k=1}^{K} w_k = 1$. \n",
    "\n",
    "The quantity $p(y_i|y_{-i}, M_k)$ is the leave-one-out predictive distribution for the $M_k$ model. Computing it requires fitting each model $n$ times, each time leaving out one data point. Fortunately we can approximate the exact leave-one-out predictive distribution using LOO (or even WAIC), and that is what we do in practice.\n",
    "\n",
    "## Weighted posterior predictive samples\n",
    "\n",
    "Once we have computed the weights, using any of the above 3 methods,  we can use them to get a weighted posterior predictive samples. PyMC3 offers functions to perform these steps in a simple way, so let see them in action using an example.\n",
    "\n",
    "The following example is taken from the superb book [Statistical Rethinking](http://xcelab.net/rm/statistical-rethinking/) by Richard McElreath. You will find more PyMC3 examples from this book in this [repository](https://github.com/aloctavodia/Statistical-Rethinking-with-Python-and-PyMC3). We are going to explore a simplified version of it. Check the book for the whole example and a more thorough discussion of both, the biological motivation for this problem and a theoretical/practical discussion of using Information Criteria to compare, select and average models.\n",
    "\n",
    "Briefly, our problem is as follows: We want to explore the composition of milk across several primate species, it is hypothesized that females from species of primates with larger brains produce more _nutritious_ milk (loosely speaking this is done _in order to_ support the development of such big brains). This is an important question for evolutionary biologists and try to give and answer we will use 3 variables, two predictor variables: the proportion of neocortex compare to the total mass of the brain and the logarithm of the body mass of the mothers. And for predicted variable, the kilocalories per gram of milk. With these variables we are going to build 3 different linear models:\n",
    " \n",
    "1. A model using only the neocortex variable\n",
    "2. A model using only the logarithm of the mass variable\n",
    "3. A model using both variables\n",
    "\n",
    "Let start by uploading the data and centering the `neocortex` and `log mass` variables, for better sampling."
   ]
  },
  {
   "cell_type": "code",
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    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>kcal.per.g</th>\n",
       "      <th>neocortex</th>\n",
       "      <th>log_mass</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.49</td>\n",
       "      <td>-0.123706</td>\n",
       "      <td>-0.831353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.47</td>\n",
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       "      <td>0.158647</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.56</td>\n",
       "      <td>-0.030706</td>\n",
       "      <td>0.181647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.89</td>\n",
       "      <td>0.000294</td>\n",
       "      <td>-0.579353</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.92</td>\n",
       "      <td>0.012294</td>\n",
       "      <td>-1.885353</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "   kcal.per.g  neocortex  log_mass\n",
       "0        0.49  -0.123706 -0.831353\n",
       "1        0.47  -0.030706  0.158647\n",
       "2        0.56  -0.030706  0.181647\n",
       "3        0.89   0.000294 -0.579353\n",
       "4        0.92   0.012294 -1.885353"
      ]
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     "execution_count": 4,
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   "source": [
    "d = pd.read_csv('../data/milk.csv')\n",
    "d.iloc[:,1:] = d.iloc[:,1:] - d.iloc[:,1:].mean()\n",
    "d.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have the data we are going to build our first model using only the `neocortex`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
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     "name": "stderr",
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     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (2 chains in 2 jobs)\n",
      "NUTS: [sigma, beta, alpha]\n"
     ]
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       "      100.00% [6000/6000 00:03<00:00 Sampling 2 chains, 0 divergences]\n",
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     "text": [
      "Sampling 2 chains for 1_000 tune and 2_000 draw iterations (2_000 + 4_000 draws total) took 4 seconds.\n"
     ]
    }
   ],
   "source": [
    "with pm.Model() as model_0:\n",
    "    alpha = pm.Normal('alpha', mu=0, sigma=10)\n",
    "    beta = pm.Normal('beta', mu=0, sigma=10)\n",
    "    sigma = pm.HalfNormal('sigma', 10)\n",
    "    \n",
    "    mu = alpha + beta * d['neocortex']\n",
    "    \n",
    "    kcal = pm.Normal('kcal', mu=mu, sigma=sigma, observed=d['kcal.per.g'])\n",
    "    trace_0 = pm.sample(2000, return_inferencedata=True)"
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The second model is exactly the same as the first one, except we now use the logarithm of the mass"
   ]
  },
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   "execution_count": 6,
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     "name": "stderr",
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     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (2 chains in 2 jobs)\n",
      "NUTS: [sigma, beta, alpha]\n"
     ]
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       "      100.00% [6000/6000 00:03<00:00 Sampling 2 chains, 0 divergences]\n",
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     "text": [
      "Sampling 2 chains for 1_000 tune and 2_000 draw iterations (2_000 + 4_000 draws total) took 3 seconds.\n"
     ]
    }
   ],
   "source": [
    "with pm.Model() as model_1:\n",
    "    alpha = pm.Normal('alpha', mu=0, sigma=10)\n",
    "    beta = pm.Normal('beta', mu=0, sigma=1)\n",
    "    sigma = pm.HalfNormal('sigma', 10)\n",
    "    \n",
    "    mu = alpha + beta * d['log_mass']\n",
    "    \n",
    "    kcal = pm.Normal('kcal', mu=mu, sigma=sigma, observed=d['kcal.per.g'])\n",
    "    \n",
    "    trace_1 = pm.sample(2000, return_inferencedata=True)"
   ]
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And finally the third model using the `neocortex` and `log_mass` variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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, beta, alpha]\n"
     ]
    },
    {
     "data": {
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       "\n",
       "    <div>\n",
       "        <style>\n",
       "            /* Turns off some styling */\n",
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       "                /* gets rid of default border in Firefox and Opera. */\n",
       "                border: none;\n",
       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
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       "      <progress value='6000' class='' max='6000', style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      100.00% [6000/6000 00:03<00:00 Sampling 2 chains, 0 divergences]\n",
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     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling 2 chains for 1_000 tune and 2_000 draw iterations (2_000 + 4_000 draws total) took 4 seconds.\n"
     ]
    }
   ],
   "source": [
    "with pm.Model() as model_2:\n",
    "    alpha = pm.Normal('alpha', mu=0, sigma=10)\n",
    "    beta = pm.Normal('beta', mu=0, sigma=1, shape=2)\n",
    "    sigma = pm.HalfNormal('sigma', 10)\n",
    "\n",
    "    mu = alpha + pm.math.dot(beta, d[['neocortex','log_mass']].T)\n",
    "\n",
    "    kcal = pm.Normal('kcal', mu=mu, sigma=sigma, observed=d['kcal.per.g'])\n",
    "\n",
    "    trace_2 = pm.sample(2000, return_inferencedata=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have sampled the posterior for the 3 models, we are going to compare them visually. One option is to use the `forestplot` function that supports plotting more than one trace."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "traces = [trace_0, trace_1, trace_2]\n",
    "az.plot_forest(traces, figsize=(10, 5));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another option is to plot several traces in a same plot is to use `densityplot`. This plot is somehow similar to a forestplot, but we get truncated KDE plots (by default 95% credible intervals) grouped by variable names together with a point estimate (by default the mean)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1656x552 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "az.plot_density(traces, var_names=['alpha', 'sigma']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have sampled the posterior for the 3 models, we are going to use WAIC (Widely applicable information criterion) to compare the 3 models. We can do this using the `compare` function included with PyMC3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/osvaldo/proyectos/00_BM/arviz/arviz/stats/stats.py:150: UserWarning: \n",
      "The scale is now log by default. Use 'scale' argument or 'stats.ic_scale' rcParam if you rely on a specific value.\n",
      "A higher log-score (or a lower deviance) indicates a model with better predictive accuracy.\n",
      "  \"\\nThe scale is now log by default. Use 'scale' argument or \"\n"
     ]
    },
    {
     "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>rank</th>\n",
       "      <th>loo</th>\n",
       "      <th>p_loo</th>\n",
       "      <th>d_loo</th>\n",
       "      <th>weight</th>\n",
       "      <th>se</th>\n",
       "      <th>dse</th>\n",
       "      <th>warning</th>\n",
       "      <th>loo_scale</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>model_2</th>\n",
       "      <td>0</td>\n",
       "      <td>7.54742</td>\n",
       "      <td>2.69847</td>\n",
       "      <td>0</td>\n",
       "      <td>0.881161</td>\n",
       "      <td>1.56652</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>log</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_1</th>\n",
       "      <td>1</td>\n",
       "      <td>4.41805</td>\n",
       "      <td>2.07408</td>\n",
       "      <td>3.12936</td>\n",
       "      <td>0.0513577</td>\n",
       "      <td>2.04406</td>\n",
       "      <td>1.05462</td>\n",
       "      <td>False</td>\n",
       "      <td>log</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_0</th>\n",
       "      <td>2</td>\n",
       "      <td>3.5033</td>\n",
       "      <td>1.98011</td>\n",
       "      <td>4.04412</td>\n",
       "      <td>0.0674812</td>\n",
       "      <td>2.41377</td>\n",
       "      <td>2.05646</td>\n",
       "      <td>False</td>\n",
       "      <td>log</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        rank      loo    p_loo    d_loo     weight       se      dse warning  \\\n",
       "model_2    0  7.54742  2.69847        0   0.881161  1.56652        0   False   \n",
       "model_1    1  4.41805  2.07408  3.12936  0.0513577  2.04406  1.05462   False   \n",
       "model_0    2   3.5033  1.98011  4.04412  0.0674812  2.41377  2.05646   False   \n",
       "\n",
       "        loo_scale  \n",
       "model_2       log  \n",
       "model_1       log  \n",
       "model_0       log  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_dict = dict(zip([\"model_0\", \"model_1\", \"model_2\"], traces))\n",
    "comp = az.compare(model_dict)\n",
    "comp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the best model is `model_2`, the one with both predictor variables. Notice the DataFrame is ordered from lowest to highest WAIC (_i.e_ from _better_ to _worst_ model). Check [this notebook](model_comparison.ipynb) for a more detailed discussing on model comparison.\n",
    "\n",
    "We can also see that we get a column with the relative `weight` for each model (according to the first equation at the beginning of this notebook). This weights can be _vaguely_ interpreted as the probability that each model will make the correct predictions on future data. Of course this interpretation is conditional on the models used to compute the weights, if we add or remove models the weights will change. And also is dependent on the assumptions behind WAIC (or any other Information Criterion used). So try to do not overinterpret these `weights`. \n",
    "\n",
    "Now we are going to use copmuted `weights` to generate predictions based not on a single model but on the weighted set of models. This is one way to perform model averaging. Using PyMC3 we can call the `sample_posterior_predictive_w` function as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "ppc_w = pm.sample_posterior_predictive_w(traces, 1000, [model_0, model_1, model_2],\n",
    "                        weights=comp.weight.sort_index(ascending=True),\n",
    "                        progressbar=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that we are passing the weights ordered by their index. We are doing this because we pass `traces` and `models` ordered from model 0 to 2, but the computed weights are ordered from lowest to highest WAIC (or equivalently from larger to lowest weight). In summary, we must be sure that we are correctly pairing the weights and models.\n",
    "\n",
    "We are also going to compute PPCs for the lowest-WAIC model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/osvaldo/proyectos/00_BM/pymc3/pymc3/sampling.py:1679: UserWarning: samples parameter is smaller than nchains times ndraws, some draws and/or chains may not be represented in the returned posterior predictive sample\n",
      "  \"samples parameter is smaller than nchains times ndraws, some draws \"\n"
     ]
    }
   ],
   "source": [
    "ppc_2 = pm.sample_posterior_predictive(trace_2, 1000, model_2, progressbar=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A simple way to compare both kind of predictions is to plot their mean and hpd interval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/osvaldo/proyectos/00_BM/arviz/arviz/stats/stats.py:485: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n",
      "  FutureWarning,\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean_w = ppc_w['kcal'].mean()\n",
    "hpd_w = az.hdi(ppc_w['kcal']).mean(0)\n",
    "\n",
    "mean = ppc_2['kcal'].mean()\n",
    "hpd = az.hdi(ppc_2['kcal']).mean(0)\n",
    "\n",
    "plt.plot(mean_w, 1, 'C0o', label='weighted models')\n",
    "plt.hlines(1, *hpd_w, \"C0\")\n",
    "plt.plot(mean, 0, 'C1o', label='model 2')\n",
    "plt.hlines(0, *hpd, \"C1\")\n",
    "\n",
    "plt.yticks([])\n",
    "plt.ylim(-1, 2)\n",
    "plt.xlabel('kcal per g')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see the mean value is almost the same for both predictions but the uncertainty in the weighted model is larger. We have effectively propagated the uncertainty about which model we should select to the posterior predictive samples. You can now try with the other two methods for computing weights `stacking` (the default and recommended method) and `pseudo-BMA`.\n",
    "\n",
    "**Final notes:** \n",
    "\n",
    "There are other ways to average models such as, for example, explicitly building a meta-model that includes all the models we have. We then perform parameter inference while jumping between the models. One problem with this approach is that jumping between models could hamper the proper sampling of the posterior.\n",
    "\n",
    "Besides averaging discrete models we can sometimes think of continuous versions of them. A toy example is to imagine that we have a coin and we want to estimated it's degree of bias, a number between 0 and 1 being 0.5 equal chance of head and tails. We could think of two separated models one with a prior biased towards heads and one towards tails. We could fit both separate models and then average them using, for example, IC-derived weights. An alternative, is to build a hierarchical model to estimate the prior distribution, instead of contemplating two discrete models we will be computing a continuous model that includes these the discrete ones as particular cases. Which approach is better? That depends on our concrete problem. Do we have good reasons to think about two discrete models, or is our problem better represented with a continuous bigger model?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "json     2.0.9\n",
      "pymc3    3.9.3\n",
      "numpy    1.18.5\n",
      "autopep8 1.5\n",
      "pandas   1.1.0\n",
      "arviz    0.9.0\n",
      "last updated: Thu Aug 13 2020 \n",
      "\n",
      "CPython 3.7.6\n",
      "IPython 7.17.0\n",
      "watermark 2.0.2\n"
     ]
    }
   ],
   "source": [
    "%watermark -n -u -v -iv -w"
   ]
  }
 ],
 "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}