Some linting while re-reading library (#219)

README.md

@@ -1,5 +1,5 @@
-Bambi
-------
+# Bambi
+
 BAyesian Model-Building Interface in Python
 
 [![Build Status](https://travis-ci.org/bambinos/bambi.svg?branch=master)](https://travis-ci.org/bambinos/bambi)
@@ -26,7 +26,6 @@ Alternatively, if you want the bleeding edge version of the package you can inst
 
 Bambi requires working versions of numpy, pandas, matplotlib, patsy, pymc3, and theano. Dependencies are listed in `requirements.txt`, and should all be installed by the Bambi installer; no further action should be required.
 
-
 ## Documentation
 
 The Bambi documentation can be found in the [official docs](https://bambinos.github.io/bambi/index.html)

bambi/backends/__init__.py

@@ -1,6 +1,4 @@
 from .pymc import PyMC3BackEnd
 
 
-__all__ = [
-    'PyMC3BackEnd'
-]
+__all__ = ["PyMC3BackEnd"]

bambi/backends/base.py

@@ -2,10 +2,8 @@
 
 
 class BackEnd:
+    """Base class for BackEnd hierarchy."""
 
-    '''
-    Base class for BackEnd hierarchy.
-    '''
     __metaclass__ = ABCMeta
 
     @abstractmethod

bambi/backends/pymc.py

@@ -1,17 +1,15 @@
 import numpy as np
+import pymc3 as pm
 import theano
 from arviz import from_pymc3
-import pymc3 as pm
+
 from bambi.priors import Prior
 
 from .base import BackEnd
 
 
 class PyMC3BackEnd(BackEnd):
-
-    """
-    PyMC3 model-fitting back-end.
-    """
+    """PyMC3 model-fitting back-end."""
 
     # Available link functions
     links = {
@@ -25,7 +23,6 @@ class PyMC3BackEnd(BackEnd):
     dists = {"HalfFlat": pm.Bound(pm.Flat, lower=0)}
 
     def __init__(self):
-
         self.reset()
 
         # Attributes defined elsewhere
@@ -35,15 +32,13 @@ def __init__(self):
         self.advi_params = None  # build()
 
     def reset(self):
-        """
-        Reset PyMC3 model and all tracked distributions and parameters.
-        """
+        """Reset PyMC3 model and all tracked distributions and parameters."""
         self.model = pm.Model()
         self.mu = None
         self.par_groups = {}
 
     def _build_dist(self, spec, label, dist, **kwargs):
-        """ Build and return a PyMC3 Distribution. """
+        """Build and return a PyMC3 Distribution."""
         if isinstance(dist, str):
             if hasattr(pm, dist):
                 dist = getattr(pm, dist)
@@ -77,8 +72,7 @@ def _expand_args(key, value, label):
         return dist(label, **kwargs)
 
     def build(self, spec, reset=True):  # pylint: disable=arguments-differ
-        """
-        Compile the PyMC3 model from an abstract model specification.
+        """Compile the PyMC3 model from an abstract model specification.
 
         Parameters
         ----------
@@ -122,8 +116,7 @@ def build(self, spec, reset=True):  # pylint: disable=arguments-differ
 
     # pylint: disable=arguments-differ, inconsistent-return-statements
     def run(self, start=None, method="mcmc", init="auto", n_init=50000, **kwargs):
-        """
-        Run the PyMC3 MCMC sampler.
+        """Run the PyMC3 MCMC sampler.
 
         Parameters
         ----------
@@ -168,9 +161,9 @@ def run(self, start=None, method="mcmc", init="auto", n_init=50000, **kwargs):
 
 
 def _laplace(model):
-    """
-    Fit a model using a laplace approximation. Mainly for pedagogical use. ``mcmc`` and ``advi``
-    are better approximations
+    """Fit a model using a laplace approximation.
+
+    Mainly for pedagogical use. ``mcmc`` and ``advi`` are better approximations.
 
     Parameters
     ----------

bambi/external/patsy.py

@@ -1,14 +1,16 @@
-import numpy as np
 import re
-from patsy.util import safe_scalar_isnan
+
+import numpy as np
+
 from patsy import PatsyError
+from patsy.util import safe_scalar_isnan
 
 
 class Custom_NA:
-    """
-    Custom patsy.missing.NAAction class. Similar to patsy drop/raise defaults,
-    but changes the raised message and logs which rows (if any) are dropped.
-    See Patsy code/API for NAAction documentation.
+    """Custom patsy.missing.NAAction class.
+
+    Similar to patsy drop/raise defaults, but changes the raised message and logs which rows (if
+    any) are dropped. See Patsy code/API for NAAction documentation.
     """
 
     def __init__(self, dropna=False, NA_types=["None", "NaN"]):

bambi/models.py

@@ -4,23 +4,21 @@
 from collections import OrderedDict
 from copy import deepcopy
 
-import pandas as pd
 import numpy as np
-from patsy import dmatrices, dmatrix
+import pandas as pd
+import pymc3 as pm
 import statsmodels.api as sm
 from arviz.plots import plot_posterior
-import pymc3 as pm
-
+from patsy import dmatrices, dmatrix
 
+from .backends import PyMC3BackEnd
 from .external.patsy import Custom_NA
-from .priors import PriorFactory, PriorScaler, Prior
+from .priors import Prior, PriorFactory, PriorScaler
 from .utils import listify
-from .backends import PyMC3BackEnd
 
 
 class Model:
-    """
-    Specification of model class
+    """Specification of model class.
 
     Parameters
     ----------
@@ -104,9 +102,7 @@ def __init__(
         self.built = False  # build()
 
     def reset(self):
-        """
-        Reset list of terms and y-variable.
-        """
+        """Reset list of terms and y-variable."""
         self.terms = OrderedDict()
         self.y = None
         self.backend = None
@@ -116,7 +112,6 @@ def reset(self):
         self.clean_data = None
 
     def _set_backend(self, backend):
-
         backend = backend.lower()
 
         if backend.startswith("pymc"):
@@ -140,7 +135,6 @@ def build(self, backend=None):
             supported. If None, assume that `fit()` has already been called (possibly without
             building) and look in self._backend_name.
         """
-
         # retain only the complete cases
         n_total = len(self.data.index)
         if self.completes:
@@ -316,7 +310,6 @@ def fit(
         backend : str
             The name of the BackEnd to use. Currently only 'pymc' backen is supported.
         """
-
         if fixed is not None or random is not None:
             self.add(
                 fixed=fixed,
@@ -332,7 +325,6 @@ def fit(
         if backend is None:
             backend = "pymc" if self._backend_name is None else self._backend_name
 
-
         if run:
             if not self.built or backend != self._backend_name:
                 self.build(backend)
@@ -351,8 +343,7 @@ def add(
         categorical=None,
         append=True,
     ):
-        """
-        Adds one or more terms to the model via an R-like formula syntax.
+        """Add one or more terms to the model via an R-like formula syntax.
 
         Parameters
         ----------
@@ -385,7 +376,6 @@ def add(
             If True, terms are appended to the existing model rather than replacing any
             existing terms. This allows formula-based specification of the model in stages.
         """
-
         data = self.data
 
         # Primitive values (floats, strs) can be overwritten with Prior objects
@@ -446,7 +436,6 @@ def _add(
 
         Runs during Model.build()
         """
-
         # use cleaned data with NAs removed (if user requested)
         data = self.clean_data
         # alter this pandas flag to avoid false positive SettingWithCopyWarnings
@@ -613,13 +602,11 @@ def _add_y(self, variable, prior=None, family="gaussian", link=None, *args, **kw
         self.built = False
 
     def _match_derived_terms(self, name):
-        """
-        Returns all (random) terms whose named are derived from the
-        specified string. For example, 'condition|subject' should match the
-        terms with names '1|subject', 'condition[T.1]|subject', and so on.
-        Only works for strings with grouping operator ('|').
-        """
+        """Return all (random) terms whose named are derived from the specified string.
 
+        For example, 'condition|subject' should match the terms with names '1|subject',
+        'condition[T.1]|subject', and so on. Only works for strings with grouping operator ('|').
+        """
         if "|" not in name:
             return None
 
@@ -679,7 +666,6 @@ def _set_priors(self, priors=None, fixed=None, random=None, match_derived_names=
 
         Runs during Model.build().
         """
-
         targets = {}
 
         if fixed is not None:
@@ -705,16 +691,15 @@ def _set_priors(self, priors=None, fixed=None, random=None, match_derived_names=
         for name, prior in targets.items():
             self.terms[name].prior = prior
 
-    # helper function to correctly set default priors, auto_scaling, etc.
     def _prepare_prior(self, prior, _type):
-        """
+        """Helper function to correctly set default priors, auto_scaling, etc.
+
         Parameters
         ----------
         prior : Prior object, or float, or None.
         _type : string
             accepted values are: 'intercept, 'fixed', or 'random'.
         """
-
         if prior is None and not self.auto_scale:
             prior = self.default_priors.get(term=_type + "_flat")
 
@@ -736,8 +721,7 @@ def plot_priors(self, var_names=None):
             raise ValueError("Cannot plot priors until model is built!")
 
         with pm.Model():
-            # get priors for fixed fx, separately for each level of each
-            # predictor
+            # get priors for fixed fx, separately for each level of each predictor
             dists = []
             for fixed_term in self.fixed_terms.values():
                 if var_names is not None and fixed_term.name not in var_names:
@@ -811,7 +795,6 @@ class Term:
     random = False
 
     def __init__(self, name, data, categorical=False, prior=None, constant=None):
-
         self.name = name
         self.categorical = categorical
         self._reduced_data = None

bambi/priors.py

@@ -71,7 +71,6 @@ def update(self, **kwargs):
             kwargs : dict
                 Optional keyword arguments to add to prior args.
         """
-
         # Backends expect numpy arrays, so make sure all numeric values are represented as such.
         kwargs = {k: (np.array(v) if isinstance(v, (int, float)) else v) for k, v in kwargs.items()}
         self.args.update(kwargs)
@@ -114,7 +113,6 @@ class PriorFactory:
     """
 
     def __init__(self, defaults=None, dists=None, terms=None, families=None):
-
         if defaults is None:
             defaults = join(dirname(__file__), "config", "priors.json")
 
@@ -138,7 +136,6 @@ def __init__(self, defaults=None, dists=None, terms=None, families=None):
         self.families = defaults["families"]
 
     def _get_prior(self, spec, **kwargs):
-
         if isinstance(spec, str):
             spec = re.sub(r"^\#", "", spec)
             return self._get_prior(self.dists[spec])
@@ -217,10 +214,13 @@ def __init__(self, model, taylor):
 
     def _get_slope_stats(self, exog, predictor, sigma_corr, full_mod=None, points=4):
         """
-        Args:
-            full_mod: statsmodels GLM to replace MLE model. For when 'predictor'
-                is not in the fixed part of the model.
-            points: number of points to use for LL approximation.
+        Parameters
+        ----------
+            full_mod : statsmodels.genmod.generalized_linear_model.GLM
+                Statsmodels GLM to replace MLE model. For when 'predictor' is not in the fixed part
+                of the model.
+            points : int
+                Number of points to use for LL approximation.
         """
 
         if full_mod is None:

bambi/utils.py

@@ -1,6 +1,8 @@
 def listify(obj):
-    """ Wraps all non-list or tuple objects in a list; provides a simple
-    way to accept flexible arguments. """
+    """Wrap all non-list or tuple objects in a list.
+
+    Provides a simple way to accept flexible arguments.
+    """
     if obj is None:
         return []
     else: