Correct some random errors pylint picks up (#2585)

* Correct some random errors pylint picks up

* Fix broken tests

Author: ColCarroll

pymc3/distributions/multivariate.py

@@ -239,7 +239,7 @@ def random(self, point=None, size=None):
             try:
                 dist = stats.multivariate_normal(
                     mean=mu, cov=cov, allow_singular=True)
-            except ValueError as error:
+            except ValueError:
                 size.append(mu.shape[-1])
                 return np.nan * np.zeros(size)
             return dist.rvs(size)
@@ -473,11 +473,11 @@ class Multinomial(Discrete):
     Parameters
     ----------
     n : int or array
-        Number of trials (n > 0). If n is an array its shape must be (N,) with 
+        Number of trials (n > 0). If n is an array its shape must be (N,) with
         N = p.shape[0]
     p : one- or two-dimensional array
         Probability of each one of the different outcomes. Elements must
-        be non-negative and sum to 1 along the last axis. They will be 
+        be non-negative and sum to 1 along the last axis. They will be
         automatically rescaled otherwise.
     """
 
@@ -532,7 +532,7 @@ def _random(self, n, p, size=None):
             randnum = np.asarray([
                 np.random.multinomial(nn, p.squeeze(), size=size)
                 for nn in n
-            ])   
+            ])
         else:
             p = p / p.sum(axis=1, keepdims=True)
             randnum = np.asarray([
@@ -605,7 +605,7 @@ def perform(self, node, inputs, outputs):
         try:
             z[0] = np.array(posdef(x), dtype='int8')
         except Exception:
-            pm._log.exception('Failed to check if positive definite', x)
+            pm._log.exception('Failed to check if %s positive definite', x)
             raise
 
     def infer_shape(self, node, shapes):
@@ -675,11 +675,11 @@ def __init__(self, nu, V, *args, **kwargs):
                               (nu - p - 1) * V,
                               np.nan)
 
-    def random(self, point=None, size=None): 
-        nu, V = draw_values([self.nu, self.V], point=point) 
+    def random(self, point=None, size=None):
+        nu, V = draw_values([self.nu, self.V], point=point)
         size= 1 if size is None else size
-        return generate_samples(stats.wishart.rvs, np.asscalar(nu), V, 
-                                    broadcast_shape=(size,)) 
+        return generate_samples(stats.wishart.rvs, np.asscalar(nu), V,
+                                    broadcast_shape=(size,))
 
     def logp(self, X):
         nu = self.nu
@@ -1049,7 +1049,7 @@ def __init__(self, eta=None, n=None, p=None, transform='interval', *args, **kwar
         self.tri_index[np.triu_indices(n, k=1)[::-1]] = np.arange(shape)
 
     def _random(self, n, eta, size=None):
-        size = size if isinstance(size, tuple) else (size,) 
+        size = size if isinstance(size, tuple) else (size,)
         # original implementation in R see:
         # https://github.com/rmcelreath/rethinking/blob/master/R/distributions.r
         beta = eta - 1 + n/2

pymc3/examples/disaster_model_theano_op.py

@@ -6,6 +6,7 @@
 
 
 import pymc3 as pm
+from theano.compile.ops import as_op
 import theano.tensor as tt
 from numpy import arange, array, empty
 
@@ -51,9 +52,9 @@ def rate_(switchpoint, early_mean, late_mean):
     step1 = pm.Slice([early_mean, late_mean])
     # Use Metropolis for switchpoint, since it accomodates discrete variables
     step2 = pm.Metropolis([switchpoint])
-    
+
     # Initial values for stochastic nodes
     start = {'early_mean': 2., 'late_mean': 3.}
-    
+
     tr = pm.sample(1000, tune=500, start=start, step=[step1, step2], njobs=2)
     pm.traceplot(tr)

pymc3/examples/garch_example.py

@@ -57,4 +57,4 @@ def run(n=1000):
 
 
 if __name__ == '__main__':
-    print(summary(run()))
+    summary(run())

pymc3/plots/energyplot.py

@@ -1,3 +1,5 @@
+import warnings
+
 import matplotlib.pyplot as plt
 import numpy as np
 from .kdeplot import kdeplot
@@ -36,8 +38,9 @@ def energyplot(trace, kind='kde', figsize=None, ax=None, legend=True,
     try:
         energy = trace['energy']
     except KeyError:
-        print('There is no energy information in the passed trace.')
+        warnings.warn('There is no energy information in the passed trace.')
         return ax
+
     series = [('Marginal energy distribution', energy - energy.mean()),
               ('Energy transition distribution', np.diff(energy))]
 

pymc3/plots/forestplot.py

@@ -101,25 +101,25 @@ def _plot_tree(ax, y, ntiles, show_quartiles, **plot_kwargs):
     """
     if show_quartiles:
         # Plot median
-        ax.plot(ntiles[2], y, color=plot_kwargs.get('color', 'blue'), 
-                        marker=plot_kwargs.get('marker', 'o'), 
+        ax.plot(ntiles[2], y, color=plot_kwargs.get('color', 'blue'),
+                        marker=plot_kwargs.get('marker', 'o'),
                         markersize=plot_kwargs.get('markersize', 4))
         # Plot quartile interval
-        ax.errorbar(x=(ntiles[1], ntiles[3]), y=(y, y), 
-                        linewidth=plot_kwargs.get('linewidth', 2), 
+        ax.errorbar(x=(ntiles[1], ntiles[3]), y=(y, y),
+                        linewidth=plot_kwargs.get('linewidth', 2),
                         color=plot_kwargs.get('color', 'blue'))
 
     else:
         # Plot median
-        ax.plot(ntiles[1], y, marker=plot_kwargs.get('marker', 'o'), 
+        ax.plot(ntiles[1], y, marker=plot_kwargs.get('marker', 'o'),
                             color=plot_kwargs.get('color', 'blue'),
                             markersize=plot_kwargs.get('markersize', 4))
 
     # Plot outer interval
-    ax.errorbar(x=(ntiles[0], ntiles[-1]), y=(y, y), 
-                linewidth=int(plot_kwargs.get('linewidth', 2)/2), 
+    ax.errorbar(x=(ntiles[0], ntiles[-1]), y=(y, y),
+                linewidth=int(plot_kwargs.get('linewidth', 2)/2),
                 color=plot_kwargs.get('color', 'blue'))
-                
+
     return ax
 
 
@@ -227,11 +227,11 @@ def forestplot(trace_obj, varnames=None, transform=identity_transform, alpha=0.0
             quants[-1] = var_hpd[1].T
 
             # Ensure x-axis contains range of current interval
-            if plotrange:
+            if plotrange is None:
+                plotrange = [np.min(quants), np.max(quants)]
+            else:
                 plotrange = [min(plotrange[0], np.min(quants)),
                              max(plotrange[1], np.max(quants))]
-            else:
-                plotrange = [np.min(quants), np.max(quants)]
 
             # Number of elements in current variable
             value = trace_obj.get_values(varname, chains=[chain])[0]
@@ -255,12 +255,10 @@ def forestplot(trace_obj, varnames=None, transform=identity_transform, alpha=0.0
             if k > 1:
                 for q in np.transpose(quants).squeeze():
                     # Multiple y values
-                    interval_plot = _plot_tree(interval_plot, y, q, quartiles,
-                                    **plot_kwargs)
+                    interval_plot = _plot_tree(interval_plot, y, q, quartiles, **plot_kwargs)
                     y -= 1
             else:
-                interval_plot = _plot_tree(interval_plot, y, quants, quartiles,
-                                **plot_kwargs)
+                interval_plot = _plot_tree(interval_plot, y, quants, quartiles, **plot_kwargs)
 
             # Increment index
             var += k

pymc3/stats.py

@@ -9,6 +9,7 @@
 from collections import namedtuple
 from .model import modelcontext
 from .util import get_default_varnames
+import pymc3 as pm
 from pymc3.theanof import floatX
 
 from scipy.misc import logsumexp
@@ -399,7 +400,7 @@ def compare(traces, models, ic='WAIC', method='stacking', b_samples=1000,
         It's always 0 for the top-ranked model.
     weight: Relative weight for each model.
         This can be loosely interpreted as the probability of each model
-        (among the compared model) given the data. By default the uncertainty 
+        (among the compared model) given the data. By default the uncertainty
         in the weights estimation is considered using Bayesian bootstrap.
     SE : Standard error of the IC estimate.
         If method = BB-pseudo-BMA these values are estimated using Bayesian
@@ -718,10 +719,10 @@ def quantiles(x, qlist=(2.5, 25, 50, 75, 97.5), transform=lambda x: x):
         return dict(zip(qlist, quants))
 
     except IndexError:
-        _log.warning("Too few elements for quantile calculation")
+        pm._log.warning("Too few elements for quantile calculation")
 
 
-def df_summary(trace, varnames=None, transform=lambda x: x, stat_funcs=None, 
+def df_summary(trace, varnames=None, transform=lambda x: x, stat_funcs=None,
                extend=False, include_transformed=False,
                alpha=0.05, start=0, batches=None):
     R"""Create a data frame with summary statistics.

pymc3/step_methods/hmc/integration.py

@@ -14,7 +14,7 @@ def __init__(self, potential, logp_dlogp_func):
         self._logp_dlogp_func = logp_dlogp_func
         self._dtype = self._logp_dlogp_func.dtype
         if self._potential.dtype != self._dtype:
-            raise ValueError("dtypes of potential and logp function "
+            raise ValueError("dtypes of potential (%s) and logp function (%s)"
                              "don't match."
                              % (self._potential.dtype, self._dtype))
 

pymc3/tests/test_dist_math.py

@@ -154,10 +154,7 @@ def func(chol_vec, delta):
         chol_vec_val = floatX(np.array([0.5, 1., -0.1]))
 
         delta_val = floatX(np.random.randn(1, 2))
-        try:
-            utt.verify_grad(func, [chol_vec_val, delta_val])
-        except ValueError as e:
-            print(e.args[0])
+        utt.verify_grad(func, [chol_vec_val, delta_val])
 
         delta_val = floatX(np.random.randn(5, 2))
         utt.verify_grad(func, [chol_vec_val, delta_val])

pymc3/tests/test_ndarray_backend.py

@@ -63,20 +63,13 @@ class TestNDArray0dSelection(bf.SelectionTestCase):
     sampler_vars = STATS1
 
 
-class TestNDArray0dSelection(bf.SelectionTestCase):
+class TestNDArray0dSelection2(bf.SelectionTestCase):
     backend = ndarray.NDArray
     name = None
     shape = ()
     sampler_vars = STATS2
 
 
-class TestNDArray0dSelection(bf.SelectionTestCase):
-    backend = ndarray.NDArray
-    name = None
-    shape = ()
-    sampler_vars = STATS1
-
-
 class TestNDArray0dSelectionStats1(bf.SelectionTestCase):
     backend = ndarray.NDArray
     name = None
@@ -136,7 +129,7 @@ def test_add_values(self):
         orig_varnames = list(mtrace.varnames)
         name = 'new_var'
         vals = mtrace[orig_varnames[0]]
-        mtrace.add_values({name : vals})
+        mtrace.add_values({name: vals})
         assert len(orig_varnames) == len(mtrace.varnames) - 1
         assert name in mtrace.varnames
         assert np.all(mtrace[orig_varnames[0]] == mtrace[name])

pymc3/tests/test_plots.py

@@ -33,7 +33,7 @@ def test_plots():
 
 
 def test_energyplot():
-    with asmod.build_model() as model:
+    with asmod.build_model():
         trace = sample()
 
     energyplot(trace)