moving namedtuple() calls to module level in stats to facilitate pick…

…ling/multiprocessing (#3268)

* moving namedtuple() calls to module level in stats

* restoring extra newlines between functions

pymc3/stats.py

@@ -170,6 +170,8 @@ def logp_vals_point(pt):
             points.close()
 
 
+WAIC_r_pointwise = namedtuple('WAIC_r_pointwise', 'WAIC, WAIC_se, p_WAIC, var_warn, WAIC_i')
+WAIC_r = namedtuple('WAIC_r', 'WAIC, WAIC_se, p_WAIC, var_warn')
 def waic(trace, model=None, pointwise=False, progressbar=False):
     """Calculate the widely available information criterion, its standard error
     and the effective number of parameters of the samples in trace from model.
@@ -230,13 +232,13 @@ def waic(trace, model=None, pointwise=False, progressbar=False):
             please double check the Observed RV in your model to make sure it
             returns element-wise logp.
             """)
-        WAIC_r = namedtuple('WAIC_r', 'WAIC, WAIC_se, p_WAIC, var_warn, WAIC_i')
-        return WAIC_r(waic, waic_se, p_waic, warn_mg, waic_i)
+        return WAIC_r_pointwise(waic, waic_se, p_waic, warn_mg, waic_i)
     else:
-        WAIC_r = namedtuple('WAIC_r', 'WAIC, WAIC_se, p_WAIC, var_warn')
         return WAIC_r(waic, waic_se, p_waic, warn_mg)
 
 
+LOO_r_pointwise = namedtuple('LOO_r_pointwise', 'LOO, LOO_se, p_LOO, shape_warn, LOO_i')
+LOO_r = namedtuple('LOO_r', 'LOO, LOO_se, p_LOO, shape_warn')
 def loo(trace, model=None, pointwise=False, reff=None, progressbar=False):
     """Calculates leave-one-out (LOO) cross-validation for out of sample
     predictive model fit, following Vehtari et al. (2015). Cross-validation is
@@ -309,10 +311,8 @@ def loo(trace, model=None, pointwise=False, reff=None, progressbar=False):
             please double check the Observed RV in your model to make sure it
             returns element-wise logp.
             """)
-        LOO_r = namedtuple('LOO_r', 'LOO, LOO_se, p_LOO, shape_warn, LOO_i')
-        return LOO_r(loo_lppd, loo_lppd_se, p_loo, warn_mg, loo_lppd_i)
+        return LOO_r_pointwise(loo_lppd, loo_lppd_se, p_loo, warn_mg, loo_lppd_i)
     else:
-        LOO_r = namedtuple('LOO_r', 'LOO, LOO_se, p_LOO, shape_warn')
         return LOO_r(loo_lppd, loo_lppd_se, p_loo, warn_mg)
 
 
@@ -1068,6 +1068,7 @@ def bfmi(trace):
     return np.square(np.diff(energy)).mean() / np.var(energy)
 
 
+r2_r = namedtuple('r2_r', 'r2_median, r2_mean, r2_std')
 def r2_score(y_true, y_pred, round_to=2):
     R"""R-squared for Bayesian regression models. Only valid for linear models.
     http://www.stat.columbia.edu/%7Egelman/research/unpublished/bayes_R2.pdf
@@ -1099,6 +1100,5 @@ def r2_score(y_true, y_pred, round_to=2):
     r2_median = np.around(np.median(r2), round_to)
     r2_mean = np.around(np.mean(r2), round_to)
     r2_std = np.around(np.std(r2), round_to)
-    r2_r = namedtuple('r2_r', 'r2_median, r2_mean, r2_std')
     return r2_r(r2_median, r2_mean, r2_std)