Formatted Next 15 Files (#4150)

* Formatted Next 15 Files

* Run command pre-commit run

* removed quotes

* Update test_examples.py

Changes Done Sir used # fmt: off before and # fmt:on after the array

* Update test_examples.py

Changes updated

Author: Yash1256

pymc3/model.py

@@ -70,9 +70,9 @@ def _str_repr(self, name=None, dist=None, formatting="plain"):
             else:
                 return super().__str__()
 
-        if name is None and hasattr(self, 'name'):
+        if name is None and hasattr(self, "name"):
             name = self.name
-        if dist is None and hasattr(self, 'distribution'):
+        if dist is None and hasattr(self, "distribution"):
             dist = self.distribution
         return self.distribution._str_repr(name=name, dist=dist, formatting=formatting)
 
@@ -123,8 +123,7 @@ def incorporate_methods(source, destination, methods, wrapper=None, override=Fal
     for method in methods:
         if hasattr(destination, method) and not override:
             raise AttributeError(
-                f"Cannot add method {method!r}"
-                + "to destination object as it already exists. "
+                f"Cannot add method {method!r}" + "to destination object as it already exists. "
                 "To prevent this error set 'override=True'."
             )
         if hasattr(source, method):
@@ -172,12 +171,8 @@ def get_named_nodes_and_relations(graph):
     else:
         ancestors = {}
         descendents = {}
-    descendents, ancestors = _get_named_nodes_and_relations(
-        graph, None, ancestors, descendents
-    )
-    leaf_dict = {
-        node.name: node for node, ancestor in ancestors.items() if len(ancestor) == 0
-    }
+    descendents, ancestors = _get_named_nodes_and_relations(graph, None, ancestors, descendents)
+    leaf_dict = {node.name: node for node, ancestor in ancestors.items() if len(ancestor) == 0}
     return leaf_dict, descendents, ancestors
 
 
@@ -529,9 +524,7 @@ def tree_contains(self, item):
 
     def __setitem__(self, key, value):
         raise NotImplementedError(
-            "Method is removed as we are not"
-            " able to determine "
-            "appropriate logic for it"
+            "Method is removed as we are not able to determine appropriate logic for it"
         )
 
     # Added this because mypy didn't like having __imul__ without __mul__
@@ -620,7 +613,7 @@ def __init__(
         dtype=None,
         casting="no",
         compute_grads=True,
-        **kwargs
+        **kwargs,
     ):
         from .distributions import TensorType
 
@@ -695,9 +688,7 @@ def __init__(
 
         inputs = [self._vars_joined]
 
-        self._theano_function = theano.function(
-            inputs, outputs, givens=givens, **kwargs
-        )
+        self._theano_function = theano.function(inputs, outputs, givens=givens, **kwargs)
 
     def set_weights(self, values):
         if values.shape != (self._n_costs - 1,):
@@ -713,10 +704,7 @@ def get_extra_values(self):
         if not self._extra_are_set:
             raise ValueError("Extra values are not set.")
 
-        return {
-            var.name: self._extra_vars_shared[var.name].get_value()
-            for var in self._extra_vars
-        }
+        return {var.name: self._extra_vars_shared[var.name].get_value() for var in self._extra_vars}
 
     def __call__(self, array, grad_out=None, extra_vars=None):
         if extra_vars is not None:
@@ -727,8 +715,7 @@ def __call__(self, array, grad_out=None, extra_vars=None):
 
         if array.shape != (self.size,):
             raise ValueError(
-                "Invalid shape for array. Must be %s but is %s."
-                % ((self.size,), array.shape)
+                "Invalid shape for array. Must be {} but is {}.".format((self.size,), array.shape)
             )
 
         if grad_out is None:
@@ -758,13 +745,10 @@ def dict_to_array(self, point):
     def array_to_dict(self, array):
         """Convert an array to a dictionary containing the grad_vars."""
         if array.shape != (self.size,):
-            raise ValueError(
-                f"Array should have shape ({self.size},) but has {array.shape}"
-            )
+            raise ValueError(f"Array should have shape ({self.size},) but has {array.shape}")
         if array.dtype != self.dtype:
             raise ValueError(
-                "Array has invalid dtype. Should be %s but is %s"
-                % (self._dtype, self.dtype)
+                f"Array has invalid dtype. Should be {self._dtype} but is {self.dtype}"
             )
         point = {}
         for varmap in self._ordering.vmap:
@@ -988,17 +972,15 @@ def logp_dlogp_function(self, grad_vars=None, tempered=False, **kwargs):
             for var in grad_vars:
                 if var.dtype not in continuous_types:
                     raise ValueError(
-                        "Can only compute the gradient of " "continuous types: %s" % var
+                        "Can only compute the gradient of continuous types: %s" % var
                     )
 
         if tempered:
             with self:
-                free_RVs_logp = tt.sum([
-                    tt.sum(var.logpt) for var in self.free_RVs + self.potentials
-                ])
-                observed_RVs_logp = tt.sum([
-                    tt.sum(var.logpt) for var in self.observed_RVs
-                ])
+                free_RVs_logp = tt.sum(
+                    [tt.sum(var.logpt) for var in self.free_RVs + self.potentials]
+                )
+                observed_RVs_logp = tt.sum([tt.sum(var.logpt) for var in self.observed_RVs])
 
             costs = [free_RVs_logp, observed_RVs_logp]
         else:
@@ -1038,7 +1020,7 @@ def logp_nojact(self):
     @property
     def varlogpt(self):
         """Theano scalar of log-probability of the unobserved random variables
-           (excluding deterministic)."""
+        (excluding deterministic)."""
         with self:
             factors = [var.logpt for var in self.free_RVs]
             return tt.sum(factors)
@@ -1110,9 +1092,7 @@ def add_coords(self, coords):
                 )
             if name in self.coords:
                 if not coords[name].equals(self.coords[name]):
-                    raise ValueError(
-                        "Duplicate and incompatiple coordinate: %s." % name
-                    )
+                    raise ValueError("Duplicate and incompatiple coordinate: %s." % name)
             else:
                 self.coords[name] = coords[name]
 
@@ -1141,9 +1121,7 @@ def Var(self, name, dist, data=None, total_size=None, dims=None):
         if data is None:
             if getattr(dist, "transform", None) is None:
                 with self:
-                    var = FreeRV(
-                        name=name, distribution=dist, total_size=total_size, model=self
-                    )
+                    var = FreeRV(name=name, distribution=dist, total_size=total_size, model=self)
                 self.free_RVs.append(var)
             else:
                 with self:
@@ -1218,8 +1196,7 @@ def prefix(self):
         return "%s_" % self.name if self.name else ""
 
     def name_for(self, name):
-        """Checks if name has prefix and adds if needed
-        """
+        """Checks if name has prefix and adds if needed"""
         if self.prefix:
             if not name.startswith(self.prefix):
                 return f"{self.prefix}{name}"
@@ -1229,8 +1206,7 @@ def name_for(self, name):
             return name
 
     def name_of(self, name):
-        """Checks if name has prefix and deletes if needed
-        """
+        """Checks if name has prefix and deletes if needed"""
         if not self.prefix or not name:
             return name
         elif name.startswith(self.prefix):
@@ -1269,7 +1245,7 @@ def makefn(self, outs, mode=None, *args, **kwargs):
                 accept_inplace=True,
                 mode=mode,
                 *args,
-                **kwargs
+                **kwargs,
             )
 
     def fn(self, outs, mode=None, *args, **kwargs):
@@ -1391,10 +1367,7 @@ def check_test_point(self, test_point=None, round_vals=2):
             test_point = self.test_point
 
         return Series(
-            {
-                RV.name: np.round(RV.logp(self.test_point), round_vals)
-                for RV in self.basic_RVs
-            },
+            {RV.name: np.round(RV.logp(self.test_point), round_vals) for RV in self.basic_RVs},
             name="Log-probability of test_point",
         )
 
@@ -1403,23 +1376,31 @@ def _str_repr(self, formatting="plain", **kwargs):
 
         if formatting == "latex":
             rv_reprs = [rv.__latex__() for rv in all_rv]
-            rv_reprs = [rv_repr.replace(r"\sim", r"&\sim &").strip("$")
-                for rv_repr in rv_reprs if rv_repr is not None]
+            rv_reprs = [
+                rv_repr.replace(r"\sim", r"&\sim &").strip("$")
+                for rv_repr in rv_reprs
+                if rv_repr is not None
+            ]
             return r"""$$
                 \begin{{array}}{{rcl}}
                 {}
                 \end{{array}}
                 $$""".format(
-                "\\\\".join(rv_reprs))
+                "\\\\".join(rv_reprs)
+            )
         else:
             rv_reprs = [rv.__str__() for rv in all_rv]
-            rv_reprs = [rv_repr for rv_repr in rv_reprs if not 'TransformedDistribution()' in rv_repr]
+            rv_reprs = [
+                rv_repr for rv_repr in rv_reprs if not "TransformedDistribution()" in rv_repr
+            ]
             # align vars on their ~
-            names = [s[:s.index('~')-1] for s in rv_reprs]
-            distrs = [s[s.index('~')+2:] for s in rv_reprs]
+            names = [s[: s.index("~") - 1] for s in rv_reprs]
+            distrs = [s[s.index("~") + 2 :] for s in rv_reprs]
             maxlen = str(max(len(x) for x in names))
-            rv_reprs = [('{name:>' + maxlen + '} ~ {distr}').format(name=n, distr=d)
-                for n, d in zip(names, distrs)]
+            rv_reprs = [
+                ("{name:>" + maxlen + "} ~ {distr}").format(name=n, distr=d)
+                for n, d in zip(names, distrs)
+            ]
             return "\n".join(rv_reprs)
 
     def __str__(self, **kwargs):
@@ -1537,8 +1518,9 @@ def Point(*args, **kwargs):
     except Exception as e:
         raise TypeError(f"can't turn {args} and {kwargs} into a dict. {e}")
     return {
-        get_var_name(k): np.array(v) for k, v in d.items()
-         if get_var_name(k) in map(get_var_name, model.vars)
+        get_var_name(k): np.array(v)
+        for k, v in d.items()
+        if get_var_name(k) in map(get_var_name, model.vars)
     }
 
 
@@ -1593,11 +1575,7 @@ def _get_scaling(total_size, shape, ndim):
             denom = 1
         coef = floatX(total_size) / floatX(denom)
     elif isinstance(total_size, (list, tuple)):
-        if not all(
-            isinstance(i, int)
-            for i in total_size
-            if (i is not Ellipsis and i is not None)
-        ):
+        if not all(isinstance(i, int) for i in total_size if (i is not Ellipsis and i is not None)):
             raise TypeError(
                 "Unrecognized `total_size` type, expected "
                 "int or list of ints, got %r" % total_size
@@ -1625,16 +1603,13 @@ def _get_scaling(total_size, shape, ndim):
         else:
             shp_end = np.asarray([])
         shp_begin = shape[: len(begin)]
-        begin_coef = [
-            floatX(t) / shp_begin[i] for i, t in enumerate(begin) if t is not None
-        ]
+        begin_coef = [floatX(t) / shp_begin[i] for i, t in enumerate(begin) if t is not None]
         end_coef = [floatX(t) / shp_end[i] for i, t in enumerate(end) if t is not None]
         coefs = begin_coef + end_coef
         coef = tt.prod(coefs)
     else:
         raise TypeError(
-            "Unrecognized `total_size` type, expected "
-            "int or list of ints, got %r" % total_size
+            "Unrecognized `total_size` type, expected int or list of ints, got %r" % total_size
         )
     return tt.as_tensor(floatX(coef))
 
@@ -1753,9 +1728,7 @@ def as_tensor(data, name, model, distribution):
             testval=testval,
             parent_dist=distribution,
         )
-        missing_values = FreeRV(
-            name=name + "_missing", distribution=fakedist, model=model
-        )
+        missing_values = FreeRV(name=name + "_missing", distribution=fakedist, model=model)
         constant = tt.as_tensor_variable(data.filled())
 
         dataTensor = tt.set_subtensor(constant[data.mask.nonzero()], missing_values)
@@ -1854,14 +1827,11 @@ def __init__(self, name, data, distribution, total_size=None, model=None):
         """
         self.name = name
         self.data = {
-            name: as_tensor(data, name, model, distribution)
-            for name, data in data.items()
+            name: as_tensor(data, name, model, distribution) for name, data in data.items()
         }
 
         self.missing_values = [
-            datum.missing_values
-            for datum in self.data.values()
-            if datum.missing_values is not None
+            datum.missing_values for datum in self.data.values() if datum.missing_values is not None
         ]
         self.logp_elemwiset = distribution.logp(**self.data)
         # The logp might need scaling in minibatches.
@@ -1871,9 +1841,7 @@ def __init__(self, name, data, distribution, total_size=None, model=None):
         self.total_size = total_size
         self.model = model
         self.distribution = distribution
-        self.scaling = _get_scaling(
-            total_size, self.logp_elemwiset.shape, self.logp_elemwiset.ndim
-        )
+        self.scaling = _get_scaling(total_size, self.logp_elemwiset.shape, self.logp_elemwiset.ndim)
 
     # Make hashable by id for draw_values
     def __hash__(self):
@@ -1888,7 +1856,7 @@ def __ne__(self, other):
         return not self == other
 
 
-def _walk_up_rv(rv, formatting='plain'):
+def _walk_up_rv(rv, formatting="plain"):
     """Walk up theano graph to get inputs for deterministic RV."""
     all_rvs = []
     parents = list(itertools.chain(*[j.inputs for j in rv.get_parents()]))
@@ -1903,21 +1871,23 @@ def _walk_up_rv(rv, formatting='plain'):
 
 
 class DeterministicWrapper(tt.TensorVariable):
-    def _str_repr(self, formatting='plain'):
-        if formatting == 'latex':
+    def _str_repr(self, formatting="plain"):
+        if formatting == "latex":
             return r"$\text{{{name}}} \sim \text{{Deterministic}}({args})$".format(
-                name=self.name, args=r",~".join(_walk_up_rv(self, formatting=formatting)))
+                name=self.name, args=r",~".join(_walk_up_rv(self, formatting=formatting))
+            )
         else:
             return "{name} ~ Deterministic({args})".format(
-                name=self.name, args=", ".join(_walk_up_rv(self, formatting=formatting)))
+                name=self.name, args=", ".join(_walk_up_rv(self, formatting=formatting))
+            )
 
     def _repr_latex_(self):
-        return self._str_repr(formatting='latex')
+        return self._str_repr(formatting="latex")
 
     __latex__ = _repr_latex_
 
     def __str__(self):
-        return self._str_repr(formatting='plain')
+        return self._str_repr(formatting="plain")
 
 
 def Deterministic(name, var, model=None, dims=None):
@@ -1936,7 +1906,7 @@ def Deterministic(name, var, model=None, dims=None):
     var = var.copy(model.name_for(name))
     model.deterministics.append(var)
     model.add_random_variable(var, dims)
-    var.__class__ = DeterministicWrapper # adds str and latex functionality
+    var.__class__ = DeterministicWrapper  # adds str and latex functionality
 
     return var
 
@@ -2030,7 +2000,7 @@ def as_iterargs(data):
 
 def all_continuous(vars):
     """Check that vars not include discrete variables, excepting
-    ObservedRVs.  """
+    ObservedRVs."""
     vars_ = [var for var in vars if not isinstance(var, pm.model.ObservedRV)]
     if any([var.dtype in pm.discrete_types for var in vars_]):
         return False