run black on 15 files (#4110)

Author: MarcoGorelli

pymc3/__init__.py

@@ -31,6 +31,7 @@
 def __set_compiler_flags():
     # Workarounds for Theano compiler problems on various platforms
     import theano
+
     current = theano.config.gcc.cxxflags
     theano.config.gcc.cxxflags = f"{current} -Wno-c++11-narrowing"
 

pymc3/backends/__init__.py

@@ -136,9 +136,8 @@
 from ..backends.sqlite import SQLite
 from ..backends.hdf5 import HDF5
 
-_shortcuts = {'text': {'backend': Text,
-                       'name': 'mcmc'},
-              'sqlite': {'backend': SQLite,
-                         'name': 'mcmc.sqlite'},
-              'hdf5': {'backend': HDF5,
-                       'name': 'mcmc.hdf5'}}
+_shortcuts = {
+    "text": {"backend": Text, "name": "mcmc"},
+    "sqlite": {"backend": SQLite, "name": "mcmc.sqlite"},
+    "hdf5": {"backend": HDF5, "name": "mcmc.hdf5"},
+}

pymc3/backends/base.py

@@ -30,7 +30,7 @@
 from .report import SamplerReport, merge_reports
 from ..util import get_var_name
 
-logger = logging.getLogger('pymc3')
+logger = logging.getLogger("pymc3")
 
 
 class BackendError(Exception):
@@ -75,10 +75,8 @@ def __init__(self, name, model=None, vars=None, test_point=None):
             test_point_.update(test_point)
             test_point = test_point_
         var_values = list(zip(self.varnames, self.fn(test_point)))
-        self.var_shapes = {var: value.shape
-                           for var, value in var_values}
-        self.var_dtypes = {var: value.dtype
-                           for var, value in var_values}
+        self.var_shapes = {var: value.shape for var, value in var_values}
+        self.var_dtypes = {var: value.dtype for var, value in var_values}
         self.chain = None
         self._is_base_setup = False
         self.sampler_vars = None
@@ -104,8 +102,7 @@ def _set_sampler_vars(self, sampler_vars):
         for stats in sampler_vars:
             for key, dtype in stats.items():
                 if dtypes.setdefault(key, dtype) != dtype:
-                    raise ValueError("Sampler statistic %s appears with "
-                                     "different types." % key)
+                    raise ValueError("Sampler statistic %s appears with " "different types." % key)
 
         self.sampler_vars = sampler_vars
 
@@ -155,7 +152,7 @@ def __getitem__(self, idx):
         try:
             return self.point(int(idx))
         except (ValueError, TypeError):  # Passed variable or variable name.
-            raise ValueError('Can only index with slice or integer')
+            raise ValueError("Can only index with slice or integer")
 
     def __len__(self):
         raise NotImplementedError
@@ -199,13 +196,13 @@ def get_sampler_stats(self, stat_name, sampler_idx=None, burn=0, thin=1):
         if sampler_idx is not None:
             return self._get_sampler_stats(stat_name, sampler_idx, burn, thin)
 
-        sampler_idxs = [i for i, s in enumerate(self.sampler_vars)
-                        if stat_name in s]
+        sampler_idxs = [i for i, s in enumerate(self.sampler_vars) if stat_name in s]
         if not sampler_idxs:
             raise KeyError("Unknown sampler stat %s" % stat_name)
 
-        vals = np.stack([self._get_sampler_stats(stat_name, i, burn, thin)
-                         for i in sampler_idxs], axis=-1)
+        vals = np.stack(
+            [self._get_sampler_stats(stat_name, i, burn, thin) for i in sampler_idxs], axis=-1
+        )
         if vals.shape[-1] == 1:
             return vals[..., 0]
         else:
@@ -296,13 +293,12 @@ def __init__(self, straces):
 
         self._report = SamplerReport()
         for strace in straces:
-            if hasattr(strace, '_warnings'):
+            if hasattr(strace, "_warnings"):
                 self._report._add_warnings(strace._warnings, strace.chain)
 
     def __repr__(self):
-        template = '<{}: {} chains, {} iterations, {} variables>'
-        return template.format(self.__class__.__name__,
-                               self.nchains, len(self), len(self.varnames))
+        template = "<{}: {} chains, {} iterations, {} variables>"
+        return template.format(self.__class__.__name__, self.nchains, len(self), len(self.varnames))
 
     @property
     def nchains(self):
@@ -339,16 +335,17 @@ def __getitem__(self, idx):
         var = get_var_name(var)
         if var in self.varnames:
             if var in self.stat_names:
-                warnings.warn("Attribute access on a trace object is ambigous. "
-                              "Sampler statistic and model variable share a name. Use "
-                              "trace.get_values or trace.get_sampler_stats.")
+                warnings.warn(
+                    "Attribute access on a trace object is ambigous. "
+                    "Sampler statistic and model variable share a name. Use "
+                    "trace.get_values or trace.get_sampler_stats."
+                )
             return self.get_values(var, burn=burn, thin=thin)
         if var in self.stat_names:
             return self.get_sampler_stats(var, burn=burn, thin=thin)
         raise KeyError("Unknown variable %s" % var)
 
-    _attrs = {'_straces', 'varnames', 'chains', 'stat_names',
-                  'supports_sampler_stats', '_report'}
+    _attrs = {"_straces", "varnames", "chains", "stat_names", "supports_sampler_stats", "_report"}
 
     def __getattr__(self, name):
         # Avoid infinite recursion when called before __init__
@@ -359,14 +356,15 @@ def __getattr__(self, name):
         name = get_var_name(name)
         if name in self.varnames:
             if name in self.stat_names:
-                warnings.warn("Attribute access on a trace object is ambigous. "
-                              "Sampler statistic and model variable share a name. Use "
-                              "trace.get_values or trace.get_sampler_stats.")
+                warnings.warn(
+                    "Attribute access on a trace object is ambigous. "
+                    "Sampler statistic and model variable share a name. Use "
+                    "trace.get_values or trace.get_sampler_stats."
+                )
             return self.get_values(name)
         if name in self.stat_names:
             return self.get_sampler_stats(name)
-        raise AttributeError("'{}' object has no attribute '{}'".format(
-            type(self).__name__, name))
+        raise AttributeError("'{}' object has no attribute '{}'".format(type(self).__name__, name))
 
     def __len__(self):
         chain = self.chains[-1]
@@ -425,10 +423,12 @@ def add_values(self, vals, overwrite=False) -> None:
             l_samples = len(self) * len(self.chains)
             l_v = len(v)
             if l_v != l_samples:
-                warnings.warn("The length of the values you are trying to "
-                              "add ({}) does not match the number ({}) of "
-                              "total samples in the trace "
-                              "(chains * iterations)".format(l_v, l_samples))
+                warnings.warn(
+                    "The length of the values you are trying to "
+                    "add ({}) does not match the number ({}) of "
+                    "total samples in the trace "
+                    "(chains * iterations)".format(l_v, l_samples)
+                )
 
             v = np.squeeze(v.reshape(len(chains), len(self), -1))
 
@@ -457,8 +457,7 @@ def remove_values(self, name):
                     chain.vars.remove(va)
                     del chain.samples[name]
 
-    def get_values(self, varname, burn=0, thin=1, combine=True, chains=None,
-                   squeeze=True):
+    def get_values(self, varname, burn=0, thin=1, combine=True, chains=None, squeeze=True):
         """Get values from traces.
 
         Parameters
@@ -485,14 +484,12 @@ def get_values(self, varname, burn=0, thin=1, combine=True, chains=None,
             chains = self.chains
         varname = get_var_name(varname)
         try:
-            results = [self._straces[chain].get_values(varname, burn, thin)
-                       for chain in chains]
+            results = [self._straces[chain].get_values(varname, burn, thin) for chain in chains]
         except TypeError:  # Single chain passed.
             results = [self._straces[chains].get_values(varname, burn, thin)]
         return _squeeze_cat(results, combine, squeeze)
 
-    def get_sampler_stats(self, stat_name, burn=0, thin=1, combine=True,
-                          chains=None, squeeze=True):
+    def get_sampler_stats(self, stat_name, burn=0, thin=1, combine=True, chains=None, squeeze=True):
         """Get sampler statistics from the trace.
 
         Parameters
@@ -520,8 +517,9 @@ def get_sampler_stats(self, stat_name, burn=0, thin=1, combine=True,
         except TypeError:
             chains = [chains]
 
-        results = [self._straces[chain].get_sampler_stats(stat_name, None, burn, thin)
-                   for chain in chains]
+        results = [
+            self._straces[chain].get_sampler_stats(stat_name, None, burn, thin) for chain in chains
+        ]
         return _squeeze_cat(results, combine, squeeze)
 
     def _slice(self, slice):
@@ -582,7 +580,9 @@ def merge_traces(mtraces: List[MultiTrace]) -> MultiTrace:
     base_mtrace = mtraces[0]
     chain_len = len(base_mtrace)
     # check base trace
-    if any(len(st) != chain_len for _, st in base_mtrace._straces.items()):  # pylint: disable=line-too-long
+    if any(
+        len(st) != chain_len for _, st in base_mtrace._straces.items()
+    ):  # pylint: disable=line-too-long
         raise ValueError("Chains are of different lengths.")
     for new_mtrace in mtraces[1:]:
         for new_chain, strace in new_mtrace._straces.items():

pymc3/backends/hdf5.py

@@ -16,14 +16,15 @@
 import h5py
 from contextlib import contextmanager
 
+
 @contextmanager
 def activator(instance):
     if isinstance(instance.hdf5_file, h5py.File):
         if instance.hdf5_file.id:  # if file is open, keep open
             yield
             return
     # if file is closed/not referenced: open, do job, then close
-    instance.hdf5_file = h5py.File(instance.name, 'a')
+    instance.hdf5_file = h5py.File(instance.name, "a")
     yield
     instance.hdf5_file.close()
     return
@@ -43,7 +44,7 @@ class HDF5(base.BaseTrace):
         `model.unobserved_RVs` is used.
     test_point: dict
         use different test point that might be with changed variables shapes
-        """
+    """
 
     supports_sampler_stats = True
 
@@ -64,21 +65,21 @@ def activate_file(self):
     @property
     def samples(self):
         g = self.hdf5_file.require_group(str(self.chain))
-        if 'name' not in g.attrs:
-            g.attrs['name'] = self.chain
-        return g.require_group('samples')
+        if "name" not in g.attrs:
+            g.attrs["name"] = self.chain
+        return g.require_group("samples")
 
     @property
     def stats(self):
         g = self.hdf5_file.require_group(str(self.chain))
-        if 'name' not in g.attrs:
-            g.attrs['name'] = self.chain
-        return g.require_group('stats')
+        if "name" not in g.attrs:
+            g.attrs["name"] = self.chain
+        return g.require_group("stats")
 
     @property
     def chains(self):
         with self.activate_file:
-            return [v.attrs['name'] for v in self.hdf5_file.values()]
+            return [v.attrs["name"] for v in self.hdf5_file.values()]
 
     @property
     def is_new_file(self):
@@ -98,19 +99,19 @@ def nchains(self):
     @property
     def records_stats(self):
         with self.activate_file:
-            return self.hdf5_file.attrs['records_stats']
+            return self.hdf5_file.attrs["records_stats"]
 
     @records_stats.setter
     def records_stats(self, v):
         with self.activate_file:
-            self.hdf5_file.attrs['records_stats'] = bool(v)
+            self.hdf5_file.attrs["records_stats"] = bool(v)
 
     def _resize(self, n):
         for v in self.samples.values():
             v.resize(n, axis=0)
         for key, group in self.stats.items():
             for statds in group.values():
-                statds.resize((n, ))
+                statds.resize((n,))
 
     @property
     def sampler_vars(self):
@@ -137,10 +138,13 @@ def sampler_vars(self, values):
                 if not data.keys():  # no pre-recorded stats
                     for varname, dtype in sampler.items():
                         if varname not in data:
-                            data.create_dataset(varname, (self.draws,), dtype=dtype, maxshape=(None,))
+                            data.create_dataset(
+                                varname, (self.draws,), dtype=dtype, maxshape=(None,)
+                            )
                 elif data.keys() != sampler.keys():
                     raise ValueError(
-                        f"Sampler vars can't change, names incompatible: {data.keys()} != {sampler.keys()}")
+                        f"Sampler vars can't change, names incompatible: {data.keys()} != {sampler.keys()}"
+                    )
             self.records_stats = True
 
     def setup(self, draws, chain, sampler_vars=None):
@@ -160,16 +164,18 @@ def setup(self, draws, chain, sampler_vars=None):
         with self.activate_file:
             for varname, shape in self.var_shapes.items():
                 if varname not in self.samples:
-                    self.samples.create_dataset(name=varname, shape=(draws, ) + shape,
-                                                dtype=self.var_dtypes[varname],
-                                                maxshape=(None, ) + shape)
+                    self.samples.create_dataset(
+                        name=varname,
+                        shape=(draws,) + shape,
+                        dtype=self.var_dtypes[varname],
+                        maxshape=(None,) + shape,
+                    )
             self.draw_idx = len(self)
             self.draws = self.draw_idx + draws
             self._set_sampler_vars(sampler_vars)
             self._is_base_setup = True
             self._resize(self.draws)
 
-
     def close(self):
         with self.activate_file:
             if self.draw_idx == self.draws:
@@ -204,8 +210,7 @@ def _slice(self, idx):
             start, stop, step = idx.indices(len(self))
             sliced = ndarray.NDArray(model=self.model, vars=self.vars)
             sliced.chain = self.chain
-            sliced.samples = {v: self.samples[v][start:stop:step]
-                              for v in self.varnames}
+            sliced.samples = {v: self.samples[v][start:stop:step] for v in self.varnames}
             sliced.draw_idx = (stop - start) // step
             return sliced