🐛 Resolve outdated PyMC3 or Pandas functionality to current form

Author: CloudChaoszero

Diff for: examples/case_studies/disaster_model.py

@@ -151,5 +151,5 @@
     # Initial values for stochastic nodes
     start = {"early_mean": 2.0, "late_mean": 3.0}
 
-    tr = pm.sample(1000, tune=500, start=start)
+    tr = pm.sample(1000, tune=500, start=start, cores=1)
     az.plot_trace(tr)

Diff for: examples/case_studies/disaster_model_theano_op.py

@@ -165,5 +165,5 @@ def rate_(switchpoint, early_mean, late_mean):
     # Initial values for stochastic nodes
     start = {"early_mean": 2.0, "late_mean": 3.0}
 
-    tr = pm.sample(1000, tune=500, start=start, step=[step1, step2], cores=2)
+    tr = pm.sample(1000, tune=500, start=start, step=[step1, step2], cores=1)
     az.plot_trace(tr)

Diff for: examples/pymc3_howto/LKJ_correlation.py

@@ -54,7 +54,7 @@ def run(n=1000):
     with model:
         trace = pm.sample(n)
     az.plot_trace(
-        trace, var_names=["mu", "r"], lines={"mu": mu_r, "r": corr_r[np.triu_indices(n_var, k=1)]}
+        trace, var_names=["mu", "r"]
     )
 
 

Diff for: examples/pymc3_howto/rankdata_ordered.py

@@ -1,3 +1,4 @@
+import arviz as az
 import numpy as np
 import theano.tensor as tt
 

Diff for: examples/samplers/samplers_mvnormal.py

@@ -47,7 +47,7 @@ def run(steppers, p):
         for step_cls in steppers:
             name = step_cls.__name__
             t_start = time.time()
-            mt = pm.sample(draws=10000, chains=16, parallelize=False, step=step_cls(), start=start)
+            mt = pm.sample(draws=10000, chains=16, step=step_cls(), start=start)
             runtimes[name] = time.time() - t_start
             print("{} samples across {} chains".format(len(mt) * mt.nchains, mt.nchains))
             traces[name] = mt
@@ -75,9 +75,9 @@ def run(steppers, p):
     for p in df_effectiven.index:
         trace, rate, runtime = run(methods, p)
         for name in names:
-            df_effectiven.set_value(p, name, rate[name])
-            df_runtime.set_value(p, name, runtime[name])
-            df_performance.set_value(p, name, rate[name] / runtime[name])
+            df_effectiven.at[p, name] = rate[name]
+            df_runtime.at[p, name] = runtime[name]
+            df_performance.at[p, name] =  rate[name] / runtime[name]
 
     print("\r\nEffective sample size [0...1]")
     print(df_effectiven.T.to_string(float_format="{:.3f}".format))