Replacing PyMC3 plots w/ Arviz plots & sigma Param change

examples/case_studies/LKJ.ipynb

@@ -40,7 +40,9 @@
    "outputs": [],
    "source": [
     "az.style.use(\"arviz-darkgrid\")\n",
+    "\n",
     "warnings.simplefilter(action=\"ignore\", category=FutureWarning)\n",
+    "\n",
     "RANDOM_SEED = 8924\n",
     "np.random.seed(3264602)  # from random.org"
    ]
@@ -716,7 +718,7 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "pymc3-dev",
+   "display_name": "Python (PyMC3 Dev)",
    "language": "python",
    "name": "pymc3-dev"
   },

examples/case_studies/blackbox_external_likelihood.ipynb

@@ -320,7 +320,7 @@
     "    trace = pm.sample(ndraws, tune=nburn, discard_tuned_samples=True)\n",
     "\n",
     "# plot the traces\n",
-    "_ = pm.traceplot(trace, lines={\"m\": mtrue, \"c\": ctrue})\n",
+    "_ = az.plot_trace(trace, lines={\"m\": mtrue, \"c\": ctrue})\n",
     "\n",
     "# put the chains in an array (for later!)\n",
     "samples_pymc3 = np.vstack((trace[\"m\"], trace[\"c\"])).T"
@@ -616,7 +616,7 @@
     "    trace = pm.sample(ndraws, tune=nburn, discard_tuned_samples=True)\n",
     "\n",
     "# plot the traces\n",
-    "_ = pm.traceplot(trace, lines={\"m\": mtrue, \"c\": ctrue})\n",
+    "_ = az.plot_trace(trace, lines={\"m\": mtrue, \"c\": ctrue})\n",
     "\n",
     "# put the chains in an array (for later!)\n",
     "samples_pymc3_2 = np.vstack((trace[\"m\"], trace[\"c\"])).T"
@@ -644,12 +644,12 @@
     "    theta = tt.as_tensor_variable([m, c])\n",
     "\n",
     "    # use a Normal distribution\n",
-    "    pm.Normal(\"likelihood\", mu=(m * x + c), sd=sigma, observed=data)\n",
+    "    pm.Normal(\"likelihood\", mu=(m * x + c), sigma=sigma, observed=data)\n",
     "\n",
     "    trace = pm.sample(ndraws, tune=nburn, discard_tuned_samples=True)\n",
     "\n",
     "# plot the traces\n",
-    "_ = pm.traceplot(trace, lines={\"m\": mtrue, \"c\": ctrue})\n",
+    "_ = az.plot_trace(trace, lines={\"m\": mtrue, \"c\": ctrue})\n",
     "\n",
     "# put the chains in an array (for later!)\n",
     "samples_pymc3_3 = np.vstack((trace[\"m\"], trace[\"c\"])).T"
@@ -832,7 +832,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.2"
+   "version": "3.8.5"
   }
  },
  "nbformat": 4,

examples/case_studies/conditional-autoregressive-model.ipynb

@@ -122,7 +122,7 @@
     "\n",
     "The classical `WinBUGS` implementation (more information [here](http://glau.ca/?p=340)):\n",
     "\n",
-    "```\n",
+    "```python\n",
     "model\n",
     "{\n",
     "   for (i in 1 : regions) {\n",
@@ -2717,7 +2717,7 @@
     }
    ],
    "source": [
-    "summary2 = pm.summary(infdata2)\n",
+    "summary2 = az.summary(infdata2)\n",
     "summary2[summary2[\"r_hat\"] > 1.05]"
    ]
   },
@@ -3004,7 +3004,7 @@
     "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",
     "\n",
     "For example, a more efficient sparse representation of the CAR in `Stan`:\n",
-    "```\n",
+    "```python\n",
     "functions {\n",
     "  /**\n",
     "  * Return the log probability of a proper conditional autoregressive (CAR) prior \n",
@@ -3040,7 +3040,7 @@
     "                    - tau * (phit_D * phi - alpha * (phit_W * phi)));\n",
     "  }\n",
     "}\n",
-    "```\n",
+    "```python\n",
     "with the data transformed in the model:\n",
     "```\n",
     "transformed data {\n",
@@ -3500,7 +3500,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.3"
+   "version": "3.8.5"
   }
  },
  "nbformat": 4,

examples/case_studies/disaster_model.py

@@ -8,7 +8,7 @@
 
 """
 
-
+import arviz as az
 import theano.tensor as tt
 
 from numpy import arange, array
@@ -152,4 +152,4 @@
     start = {"early_mean": 2.0, "late_mean": 3.0}
 
     tr = pm.sample(1000, tune=500, start=start)
-    pm.traceplot(tr)
+    pm.plot_trace(tr)

examples/case_studies/disaster_model_theano_op.py

@@ -4,7 +4,7 @@
 Note that gradient based samplers will not work.
 """
 
-
+import arviz as az
 import theano.tensor as tt
 
 from numpy import arange, array, empty
@@ -166,4 +166,4 @@ def rate_(switchpoint, early_mean, late_mean):
     start = {"early_mean": 2.0, "late_mean": 3.0}
 
     tr = pm.sample(1000, tune=500, start=start, step=[step1, step2], cores=2)
-    pm.traceplot(tr)
+    az.plot_trace(tr)

examples/case_studies/garch_example.py

@@ -1,7 +1,8 @@
 import numpy as np
 import theano.tensor as tt
 
-from pymc3 import Model, Normal, Uniform, sample, summary
+from arviz import summary
+from pymc3 import Model, Normal, Uniform, sample
 
 """
 Example from Stan - slightly altered

examples/case_studies/gelman_schools.py

@@ -1,6 +1,7 @@
 import numpy as np
 
-from pymc3 import HalfCauchy, Model, Normal, loo, sample
+from arviz import loo
+from pymc3 import HalfCauchy, Model, Normal, sample
 
 """Original Stan model
 

examples/case_studies/lightspeed_example.py

@@ -1,5 +1,5 @@
 import numpy as np
-
+import arviz as az
 import pymc3 as pm
 
 light_speed = np.array(
@@ -92,7 +92,7 @@ def run(n=5000):
     with model_1:
         trace = pm.sample(n)
 
-        pm.summary(trace)
+        az.summary(trace)
 
 
 if __name__ == "__main__":

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

@@ -320,7 +320,7 @@
    "outputs": [],
    "source": [
     "with pm.Model() as lgcp_model:\n",
-    "    mu = pm.Normal(\"mu\", sd=3)\n",
+    "    mu = pm.Normal(\"mu\", sigma=3)\n",
     "    rho = pm.Uniform(\"rho\", lower=25, upper=200)\n",
     "    cov_scale = pm.Exponential(\"cov_scale\", lam=1)\n",
     "\n",
@@ -601,9 +601,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "metadata": {
-    "scrolled": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -773,7 +771,7 @@
     "n_centroids = centroids.shape[0]\n",
     "\n",
     "with pm.Model() as mark_model:\n",
-    "    mu = pm.Normal(\"mu\", sd=3)\n",
+    "    mu = pm.Normal(\"mu\", sigma=3)\n",
     "    rho = pm.Uniform(\"rho\", lower=25, upper=200)\n",
     "\n",
     "    cov_scale = pm.Exponential(\"scale\", lam=1)\n",
@@ -809,14 +807,14 @@
    "outputs": [],
    "source": [
     "with mark_model:\n",
-    "    alpha = pm.Normal(\"alpha\", sd=10.0)\n",
-    "    beta = pm.Normal(\"beta\", sd=5)\n",
+    "    alpha = pm.Normal(\"alpha\", sigma=10.0)\n",
+    "    beta = pm.Normal(\"beta\", sigma=5)\n",
     "    eps_sd = pm.HalfCauchy(\"eps_sd\", beta=1.0)\n",
     "\n",
     "    marks = pm.Normal(\n",
     "        \"marks\",\n",
     "        mu=alpha + beta * intensity[n_centroids::],\n",
-    "        sd=eps_sd,\n",
+    "        sigma=eps_sd,\n",
     "        shape=n,\n",
     "        observed=data[\"marks\"].values,\n",
     "    )"
@@ -1037,7 +1035,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.2"
+   "version": "3.8.5"
   },
   "toc": {
    "base_numbering": 1,
@@ -1054,5 +1052,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

examples/case_studies/multilevel_modeling.ipynb

@@ -76,7 +76,9 @@
     "from theano import tensor as tt\n",
     "\n",
     "print(f\"Running on PyMC3 v{pm.__version__}\")\n",
+    "\n",
     "warnings.simplefilter(action=\"ignore\", category=FutureWarning)\n",
+    "\n",
     "RANDOM_SEED = 8924\n",
     "np.random.seed(286)"
    ]
@@ -8531,13 +8533,6 @@
     "%load_ext watermark\n",
     "%watermark -n -u -v -iv -w"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -8557,7 +8552,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.9"
+   "version": "3.8.5"
   }
  },
  "nbformat": 4,

examples/case_studies/probabilistic_matrix_factorization.ipynb

@@ -1649,7 +1649,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.7"
+   "version": "3.8.5"
   }
  },
  "nbformat": 4,

examples/case_studies/rugby_analytics.ipynb

@@ -1499,9 +1499,9 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "pymc-dev",
+   "display_name": "Python (PyMC3 Dev)",
    "language": "python",
-   "name": "pymc-dev"
+   "name": "pymc3-dev"
   },
   "language_info": {
    "codemirror_mode": {
@@ -1513,7 +1513,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.2"
+   "version": "3.8.5"
   }
  },
  "nbformat": 4,

examples/diagnostics_and_criticism/Bayes_factor.ipynb

@@ -694,7 +694,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6"
+   "version": "3.8.5"
   }
  },
  "nbformat": 4,

examples/diagnostics_and_criticism/Diagnosing_biased_Inference_with_Divergences.ipynb

@@ -462,7 +462,7 @@
     }
    ],
    "source": [
-    "pm.summary(short_trace).round(2)"
+    "az.summary(short_trace).round(2)"
    ]
   },
   {
@@ -495,7 +495,7 @@
    ],
    "source": [
     "# plot the trace of log(tau)\n",
-    "pm.traceplot({\"log(tau)\": short_trace.get_values(varname=\"tau_log__\", combine=False)});"
+    "az.plot_trace({\"log(tau)\": short_trace.get_values(varname=\"tau_log__\", combine=False)});"
    ]
   },
   {
@@ -774,7 +774,7 @@
     "# A small wrapper function for displaying the MCMC sampler diagnostics as above\n",
     "def report_trace(trace):\n",
     "    # plot the trace of log(tau)\n",
-    "    pm.traceplot({\"log(tau)\": trace.get_values(varname=\"tau_log__\", combine=False)})\n",
+    "    az.plot_trace({\"log(tau)\": trace.get_values(varname=\"tau_log__\", combine=False)})\n",
     "\n",
     "    # plot the estimate for the mean of log(τ) cumulating mean\n",
     "    logtau = np.log(trace[\"tau\"])\n",
@@ -1156,7 +1156,7 @@
     }
    ],
    "source": [
-    "pm.summary(longer_trace).round(2)"
+    "az.summary(longer_trace).round(2)"
    ]
   },
   {
@@ -2099,7 +2099,7 @@
     }
    ],
    "source": [
-    "pm.summary(fit_ncp80).round(2)"
+    "az.summary(fit_ncp80).round(2)"
    ]
   },
   {
@@ -2370,7 +2370,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.7"
+   "version": "3.8.5"
   }
  },
  "nbformat": 4,

examples/diagnostics_and_criticism/model_averaging.ipynb

@@ -993,7 +993,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6"
+   "version": "3.8.5"
   }
  },
  "nbformat": 4,

examples/diagnostics_and_criticism/model_comparison.ipynb

@@ -548,7 +548,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6"
+   "version": "3.8.5"
   }
  },
  "nbformat": 4,

examples/diagnostics_and_criticism/posterior_predictive.ipynb

@@ -850,7 +850,7 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "pymc3-dev",
+   "display_name": "Python (PyMC3 Dev)",
    "language": "python",
    "name": "pymc3-dev"
   },

examples/diagnostics_and_criticism/sampler-stats.ipynb

@@ -549,7 +549,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.7"
+   "version": "3.8.5"
   }
  },
  "nbformat": 4,