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🎉 Start Replacing PyMC3 plots with arviz plots in examples
* Related to issue pymc-devs/pymc#4371 🎨 Replace Pymc3 plots w/arviz plots and sigma param change 🎨 Update plots to arviz dependency instead of pymc3 dependency 🎨 Update plots to arviz dependency instead of pymc3 dependency 🎉 Finish residual replacement for Pymc3 plots to arviz & sigma param change
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‎examples/case_studies/BEST.ipynb

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‎examples/case_studies/LKJ.ipynb

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"metadata": {
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"anaconda-cloud": {},
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"kernelspec": {
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"display_name": "pymc3-dev",
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"display_name": "Python (PyMC3 Dev)",
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"language": "python",
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"name": "pymc3-dev"
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},

‎examples/case_studies/blackbox_external_likelihood.ipynb

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" trace = pm.sample(ndraws, tune=nburn, discard_tuned_samples=True)\n",
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"\n",
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"# plot the traces\n",
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"_ = pm.traceplot(trace, lines={\"m\": mtrue, \"c\": ctrue})\n",
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"_ = az.plot_trace(trace, lines={\"m\": mtrue, \"c\": ctrue})\n",
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"\n",
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"# put the chains in an array (for later!)\n",
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"samples_pymc3 = np.vstack((trace[\"m\"], trace[\"c\"])).T"
@@ -616,7 +616,7 @@
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" trace = pm.sample(ndraws, tune=nburn, discard_tuned_samples=True)\n",
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"\n",
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"# plot the traces\n",
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"_ = pm.traceplot(trace, lines={\"m\": mtrue, \"c\": ctrue})\n",
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"_ = az.plot_trace(trace, lines={\"m\": mtrue, \"c\": ctrue})\n",
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"\n",
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"# put the chains in an array (for later!)\n",
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"samples_pymc3_2 = np.vstack((trace[\"m\"], trace[\"c\"])).T"
@@ -644,12 +644,12 @@
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" theta = tt.as_tensor_variable([m, c])\n",
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"\n",
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" # use a Normal distribution\n",
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" pm.Normal(\"likelihood\", mu=(m * x + c), sd=sigma, observed=data)\n",
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" pm.Normal(\"likelihood\", mu=(m * x + c), sigma=sigma, observed=data)\n",
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"\n",
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" trace = pm.sample(ndraws, tune=nburn, discard_tuned_samples=True)\n",
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"\n",
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"# plot the traces\n",
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"_ = pm.traceplot(trace, lines={\"m\": mtrue, \"c\": ctrue})\n",
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"_ = az.plot_trace(trace, lines={\"m\": mtrue, \"c\": ctrue})\n",
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"\n",
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"# put the chains in an array (for later!)\n",
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"samples_pymc3_3 = np.vstack((trace[\"m\"], trace[\"c\"])).T"
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.2"
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"version": "3.8.5"
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}
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},
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"nbformat": 4,

‎examples/case_studies/conditional-autoregressive-model.ipynb

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"\n",
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"The classical `WinBUGS` implementation (more information [here](http://glau.ca/?p=340)):\n",
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"\n",
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"```\n",
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"```python\n",
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"model\n",
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"{\n",
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" for (i in 1 : regions) {\n",
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}
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],
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"source": [
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"summary2 = pm.summary(infdata2)\n",
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"summary2 = az.summary(infdata2)\n",
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"summary2[summary2[\"r_hat\"] > 1.05]"
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]
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},
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"Note that in the node $\\phi \\sim \\mathcal{N}(0, [D_\\tau (I - \\alpha B)]^{-1})$, we are computing the log-likelihood for a multivariate Gaussian distribution, which might not scale well in high-dimensions. We can take advantage of the fact that the covariance matrix here $[D_\\tau (I - \\alpha B)]^{-1}$ is **sparse**, and there are faster ways to compute its log-likelihood. \n",
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"\n",
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"For example, a more efficient sparse representation of the CAR in `Stan`:\n",
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"```\n",
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"```python\n",
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"functions {\n",
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" /**\n",
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" * Return the log probability of a proper conditional autoregressive (CAR) prior \n",
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" - tau * (phit_D * phi - alpha * (phit_W * phi)));\n",
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" }\n",
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"}\n",
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"```\n",
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"```python\n",
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"with the data transformed in the model:\n",
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"```\n",
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"transformed data {\n",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.3"
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"version": "3.8.5"
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}
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},
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"nbformat": 4,

‎examples/case_studies/disaster_model.py

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"""
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import arviz as az
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import theano.tensor as tt
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from numpy import arange, array
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start = {"early_mean": 2.0, "late_mean": 3.0}
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tr = pm.sample(1000, tune=500, start=start)
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pm.traceplot(tr)
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pm.plot_trace(tr)

‎examples/case_studies/disaster_model_theano_op.py

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Note that gradient based samplers will not work.
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"""
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import arviz as az
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import theano.tensor as tt
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from numpy import arange, array, empty
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start = {"early_mean": 2.0, "late_mean": 3.0}
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tr = pm.sample(1000, tune=500, start=start, step=[step1, step2], cores=2)
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pm.traceplot(tr)
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az.plot_trace(tr)

‎examples/case_studies/factor_analysis.ipynb

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‎examples/case_studies/garch_example.py

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import numpy as np
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import theano.tensor as tt
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from pymc3 import Model, Normal, Uniform, sample, summary
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from arviz import summary
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from pymc3 import Model, Normal, Uniform, sample
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"""
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Example from Stan - slightly altered

‎examples/case_studies/gelman_schools.py

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import numpy as np
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from pymc3 import HalfCauchy, Model, Normal, loo, sample
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from arviz import loo
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from pymc3 import HalfCauchy, Model, Normal, sample
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"""Original Stan model
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‎examples/case_studies/hierarchical_partial_pooling.ipynb

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‎examples/case_studies/lightspeed_example.py

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import numpy as np
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import arviz as az
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import pymc3 as pm
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light_speed = np.array(
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with model_1:
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trace = pm.sample(n)
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pm.summary(trace)
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az.summary(trace)
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if __name__ == "__main__":

‎examples/case_studies/log-gaussian-cox-process.ipynb

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"outputs": [],
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"source": [
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"with pm.Model() as lgcp_model:\n",
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" mu = pm.Normal(\"mu\", sd=3)\n",
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" mu = pm.Normal(\"mu\", sigma=3)\n",
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" rho = pm.Uniform(\"rho\", lower=25, upper=200)\n",
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" cov_scale = pm.Exponential(\"cov_scale\", lam=1)\n",
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{
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"cell_type": "code",
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"execution_count": 14,
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"metadata": {
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"scrolled": false
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},
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"metadata": {},
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"outputs": [
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"data": {
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"n_centroids = centroids.shape[0]\n",
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"\n",
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"with pm.Model() as mark_model:\n",
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" mu = pm.Normal(\"mu\", sd=3)\n",
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" mu = pm.Normal(\"mu\", sigma=3)\n",
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" rho = pm.Uniform(\"rho\", lower=25, upper=200)\n",
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"\n",
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" cov_scale = pm.Exponential(\"scale\", lam=1)\n",
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"with mark_model:\n",
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" alpha = pm.Normal(\"alpha\", sd=10.0)\n",
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" beta = pm.Normal(\"beta\", sd=5)\n",
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" alpha = pm.Normal(\"alpha\", sigma=10.0)\n",
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" beta = pm.Normal(\"beta\", sigma=5)\n",
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" eps_sd = pm.HalfCauchy(\"eps_sd\", beta=1.0)\n",
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"\n",
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" marks = pm.Normal(\n",
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" \"marks\",\n",
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" mu=alpha + beta * intensity[n_centroids::],\n",
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" sd=eps_sd,\n",
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" sigma=eps_sd,\n",
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" shape=n,\n",
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" observed=data[\"marks\"].values,\n",
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" )"
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"version": "3.7.2"
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"version": "3.8.5"
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"toc": {
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}
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"nbformat": 4,
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‎examples/case_studies/multilevel_modeling.ipynb

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"%load_ext watermark\n",
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"%watermark -n -u -v -iv -w"
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]
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},
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"cell_type": "code",
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‎examples/case_studies/probabilistic_matrix_factorization.ipynb

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‎examples/case_studies/putting_workflow.ipynb

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‎examples/case_studies/rugby_analytics.ipynb

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"metadata": {
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"anaconda-cloud": {},
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"display_name": "Python (PyMC3 Dev)",
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"language": "python",
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‎examples/case_studies/stochastic_volatility.ipynb

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‎examples/diagnostics_and_criticism/Diagnosing_biased_Inference_with_Divergences.ipynb

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}
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"az.summary(short_trace).round(2)"
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"pm.traceplot({\"log(tau)\": short_trace.get_values(varname=\"tau_log__\", combine=False)});"
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"az.plot_trace({\"log(tau)\": short_trace.get_values(varname=\"tau_log__\", combine=False)});"
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{
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"# A small wrapper function for displaying the MCMC sampler diagnostics as above\n",
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"def report_trace(trace):\n",
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" # plot the trace of log(tau)\n",
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" pm.traceplot({\"log(tau)\": trace.get_values(varname=\"tau_log__\", combine=False)})\n",
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" az.plot_trace({\"log(tau)\": trace.get_values(varname=\"tau_log__\", combine=False)})\n",
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"\n",
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" # plot the estimate for the mean of log(τ) cumulating mean\n",
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" logtau = np.log(trace[\"tau\"])\n",
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}
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"source": [
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}
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"source": [
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"name": "python",
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"version": "3.8.5"
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‎examples/diagnostics_and_criticism/model_averaging.ipynb

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"name": "python",
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"version": "3.8.5"
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‎examples/diagnostics_and_criticism/model_comparison.ipynb

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‎examples/diagnostics_and_criticism/posterior_predictive.ipynb

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‎examples/diagnostics_and_criticism/sampler-stats.ipynb

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‎examples/gaussian_processes/GP-Circular.ipynb

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"language": "python",
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"name": "pymc3-dev"
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"version": "3.8.5"
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},
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"nbformat": 4,

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