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sharanry/graph_explosion_ed2_tfp.ipynb

Created September 13, 2018 13:27
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graph_explosion_ed2_tfp.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Source: https://github.com/tensorflow/probability/blob/master/discussion/higher_level_modeling_api_demo.ipynb"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
}
},
"colab_type": "code",
"id": "YGPK0s6nGPd5"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/sharan/anaconda3/envs/pymc3/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
" from ._conv import register_converters as _register_converters\n"
]
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import seaborn as sns\n",
"\n",
"import tensorflow as tf\n",
"import tensorflow_probability as tfp\n",
"from tensorflow_probability import edward2 as ed\n",
"tfd = tfp.distributions\n",
"# tf.enable_eager_execution()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"cellView": "both",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
}
},
"colab_type": "code",
"id": "OIx9qQwG4bwp"
},
"outputs": [],
"source": [
"#Model Class Source\n",
"import copy\n",
"import collections\n",
"\n",
"\n",
"class MetaModel(type):\n",
"\n",
" def __call__(cls, *args, **kwargs):\n",
" obj = type.__call__(cls, *args, **kwargs)\n",
" obj._load_observed()\n",
" obj._load_unobserved()\n",
" return obj\n",
"\n",
"\n",
"class BaseModel(object):\n",
" __metaclass__ = MetaModel\n",
"\n",
" def _load_unobserved(self):\n",
" unobserved_fun = self._unobserved_vars()\n",
" self.unobserved = unobserved_fun()\n",
"\n",
" def _load_observed(self):\n",
" self.observed = copy.copy(vars(self))\n",
"\n",
" def _unobserved_vars(self):\n",
"\n",
" def unobserved_fn(*args, **kwargs):\n",
" unobserved_vars = collections.OrderedDict()\n",
"\n",
" def interceptor(f, *args, **kwargs):\n",
" name = kwargs.get(\"name\")\n",
" rv = f(*args, **kwargs)\n",
" if name not in self.observed:\n",
" unobserved_vars[name] = rv.shape\n",
" return rv\n",
"\n",
" with ed.interception(interceptor):\n",
" self.__call__()\n",
" return unobserved_vars\n",
"\n",
" return unobserved_fn\n",
" \n",
"# def observe(self, states):\n",
"# for name, value in states.iteritems():\n",
"# setattr(self, name, value)\n",
"\n",
" def target_log_prob_fn(self, *args, **kwargs):\n",
" \"\"\"Unnormalized target density as a function of unobserved states.\"\"\"\n",
"\n",
" def log_joint_fn(*args, **kwargs):\n",
" states = dict(zip(self.unobserved, args))\n",
" states.update(self.observed)\n",
" log_probs = []\n",
"\n",
" def interceptor(f, *args, **kwargs):\n",
" name = kwargs.get(\"name\")\n",
" for name, value in states.items():\n",
" if kwargs.get(\"name\") == name:\n",
" kwargs[\"value\"] = value\n",
" rv = f(*args, **kwargs)\n",
" log_prob = tf.reduce_sum(rv.distribution.log_prob(rv.value))\n",
" log_probs.append(log_prob)\n",
" return rv\n",
"\n",
" with ed.interception(interceptor):\n",
" self.__call__()\n",
" log_prob = sum(log_probs)\n",
" return log_prob\n",
"\n",
" return log_joint_fn\n",
"\n",
" def get_posterior_fn(self, states={}, *args, **kwargs):\n",
" \"\"\"Get the log joint prob given arbitrary values for vars\"\"\"\n",
"\n",
" def posterior_fn(*args, **kwargs):\n",
"\n",
" def interceptor(f, *args, **kwargs):\n",
" name = kwargs.get(\"name\")\n",
" for name, value in states.iteritems():\n",
" if kwargs.get(\"name\") == name:\n",
" kwargs[\"value\"] = value\n",
" rv = f(*args, **kwargs)\n",
" return rv\n",
"\n",
" with ed.interception(interceptor):\n",
" return self.__call__()\n",
"\n",
" return posterior_fn\n",
"\n",
" def __call__(self):\n",
" return self.call()\n",
"\n",
" def call(self, *args, **kwargs):\n",
" raise NotImplementedError\n",
"\n",
"\n",
"# This is a really quick / hacky sample function.\n",
"# Ideally the user could choose the kernel or inference method\n",
"# e.g., I could imagine a user defining a variational approximation in the model\n",
"# and then using VI as a sample option here where the sample method looks for\n",
"# model.q()\n",
"# Also, it's relatively straightforward to see how one could return arbitrary\n",
"# diagnostics given the model.\n",
"# Todo: Add diagnostics, multiple chains, more automatic inference.\n",
"def sample(model,\n",
" num_results=5000,\n",
" num_burnin_steps=3000,\n",
" step_size=.4,\n",
" num_leapfrog_steps=3,\n",
" numpy=True):\n",
" initial_state = []\n",
" for name, shape in model.unobserved.items():\n",
" initial_state.append(.5 * tf.ones(shape, name=\"init_{}\".format(name)))\n",
"\n",
" states, kernel_results = tfp.mcmc.sample_chain(\n",
" num_results=num_results,\n",
" num_burnin_steps=num_burnin_steps,\n",
" current_state=initial_state,\n",
" kernel=tfp.mcmc.HamiltonianMonteCarlo(\n",
" target_log_prob_fn=model.target_log_prob_fn(),\n",
" step_size=step_size,\n",
" num_leapfrog_steps=num_leapfrog_steps))\n",
"\n",
" if numpy:\n",
" with tf.Session() as sess:\n",
" states, is_accepted_ = sess.run([states, kernel_results.is_accepted])\n",
" accepted = np.sum(is_accepted_)\n",
" print(\"Acceptance rate: {}\".format(accepted / num_results))\n",
" return dict(zip(model.unobserved.keys(), states))"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
},
"height": 524
},
"colab_type": "code",
"executionInfo": {
"elapsed": 422,
"status": "ok",
"timestamp": 1527538550919,
"user": {
"displayName": "Joshua Safyan",
"photoUrl": "https://lh3.googleusercontent.com/a/default-user=s128",
"userId": "109065868024101668323"
},
"user_tz": 420
},
"id": "lPNSPo5YIXkp",
"outputId": "c1020b16-7944-47b3-f308-2cc33b5c4c49"
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 720x576 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"num_schools = 8 # number of schools\n",
"treatment_effects = np.array(\n",
" [28, 8, -3, 7, -1, 1, 18, 12], dtype=np.float32) # treatment effects\n",
"treatment_stddevs = np.array(\n",
" [15, 10, 16, 11, 9, 11, 10, 18], dtype=np.float32) # treatment SE\n",
"\n",
"fig, ax = plt.subplots()\n",
"plt.bar(range(num_schools), treatment_effects, yerr=treatment_stddevs)\n",
"plt.title(\"8 Schools treatment effects\")\n",
"plt.xlabel(\"School\")\n",
"plt.ylabel(\"Treatment effect\")\n",
"fig.set_size_inches(10, 8)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
}
},
"colab_type": "code",
"id": "FwjRpRPAsG-L"
},
"outputs": [],
"source": [
"class Model(BaseModel):\n",
"\n",
" def __init__(self, num_schools, treatment_stddevs, treatment_effects):\n",
" super(BaseModel, self).__init__()\n",
" self.num_schools = num_schools\n",
" self.treatment_stddevs = treatment_stddevs\n",
" self.treatment_effects = treatment_effects\n",
"\n",
" def call(self):\n",
" avg_effect = ed.Normal(loc=0., scale=10., name=\"avg_effect\") # `mu` above\n",
" avg_stddev = ed.Normal(\n",
" loc=5., scale=1., name=\"avg_stddev\") # `log(tau)` above\n",
" school_effects_standard = ed.Normal(\n",
" loc=tf.zeros(self.num_schools),\n",
" scale=tf.ones(self.num_schools),\n",
" name=\"school_effects_standard\") # `theta_prime` above\n",
" school_effects = avg_effect + tf.exp(\n",
" avg_stddev) * school_effects_standard # `theta` above\n",
" treatment_effects = ed.Normal(\n",
" loc=school_effects,\n",
" scale=self.treatment_stddevs,\n",
" name=\"treatment_effects\") # `y` above\n",
" return treatment_effects\n",
"\n",
"\n",
"model = Model(num_schools, treatment_stddevs, treatment_effects)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "4iGGOwGy8oZ_"
},
"source": [
"We can see that calling the model runs the generative process like we'd expect."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
},
"height": 51
},
"colab_type": "code",
"executionInfo": {
"elapsed": 829,
"status": "ok",
"timestamp": 1527538556361,
"user": {
"displayName": "Joshua Safyan",
"photoUrl": "https://lh3.googleusercontent.com/a/default-user=s128",
"userId": "109065868024101668323"
},
"user_tz": 420
},
"id": "_apyvy547Zsn",
"outputId": "5e7f2205-f6fa-4f5e-f304-1af6541246b1"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[-26.546001 -31.98491 -46.235744 -3.6238365 4.4316955 -29.370924\n",
" 3.5245728 -38.116684 ]\n"
]
}
],
"source": [
"with tf.Session() as sess:\n",
" print(model().eval())"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "J0t28r84AhYe"
},
"source": [
"We can simply feed `model` to a function to do inference because `model` has knowledge of which variables are observed and unobserved. The example below just uses HMC, but NUTS or some other method could be applied instead (auto-tuning and diagnostics / progress indicators would be advantageous here)."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"model._load_observed()\n",
"model._load_unobserved()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"odict_items([('avg_effect', TensorShape([])), ('avg_stddev', TensorShape([])), ('school_effects_standard', TensorShape([Dimension(8)]))])"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"shape = model.unobserved[\"avg_effect\"]\n",
"model.unobserved.items()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
},
"height": 68
},
"colab_type": "code",
"executionInfo": {
"elapsed": 6062,
"status": "ok",
"timestamp": 1527538564162,
"user": {
"displayName": "Joshua Safyan",
"photoUrl": "https://lh3.googleusercontent.com/a/default-user=s128",
"userId": "109065868024101668323"
},
"user_tz": 420
},
"id": "BNStZe-v8zoT",
"outputId": "ff09b501-71a0-4f75-f57d-05337fe58fef"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Acceptance rate: 0.6\n",
"CPU times: user 9.76 s, sys: 142 ms, total: 9.9 s\n",
"Wall time: 9.83 s\n"
]
}
],
"source": [
"%%time\n",
"trace = sample(model, num_results=10, num_burnin_steps=0)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"# tf.get_default_graph().as_graph_def()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"from graphviz import Digraph\n",
"def tf_to_dot(graph):\n",
" dot = Digraph(format=\"png\")\n",
"\n",
" for n in graph.as_graph_def().node:\n",
" dot.node(n.name, label=n.name)\n",
"\n",
" for i in n.input:\n",
" dot.edge(i, n.name)\n",
" \n",
" return dot"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"image/svg+xml": [
"<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
"<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
" \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
"<!-- Generated by graphviz version 2.40.1 (20161225.0304)\n",
" -->\n",
"<!-- Title: %3 Pages: 1 -->\n",
"<svg width=\"108073pt\" height=\"9692pt\"\n",
" viewBox=\"0.00 0.00 108073.38 9692.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
"<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 9688)\">\n",
"<title>%3</title>\n",
"<polygon fill=\"#ffffff\" stroke=\"transparent\" points=\"-4,4 -4,-9688 108069.3772,-9688 108069.3772,4 -4,4\"/>\n",
"<!-- avg_effect/loc/input -->\n",
"<g id=\"node1\" class=\"node\">\n",
"<title>avg_effect/loc/input</title>\n",
"<ellipse fill=\"none\" stroke=\"#000000\" cx=\"34911\" cy=\"-9234\" rx=\"81.4863\" ry=\"18\"/>\n",
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