Merge pull request #3317 from ColCarroll/remove_py_2

Remove Python 2.7 support

Author: twiecki

.travis.yml

@@ -4,7 +4,6 @@ sudo: required
 cache:
   directories:
     - $HOME/.theano
-    - $HOME/miniconda2
     - $HOME/miniconda3
 
 addons:
@@ -22,17 +21,14 @@ install:
   - pip install coveralls travis-sphinx==2.0.0
 
 env:
-  - PYTHON_VERSION=2.7 FLOATX='float32' TESTCMD="--durations=10 --ignore=pymc3/tests/test_examples.py --cov-append --ignore=pymc3/tests/test_distributions_random.py --ignore=pymc3/tests/test_variational_inference.py --ignore=pymc3/tests/test_shared.py --ignore=pymc3/tests/test_smc.py --ignore=pymc3/tests/test_updates.py --ignore=pymc3/tests/test_posteriors.py --ignore=pymc3/tests/test_sampling.py"
-  - PYTHON_VERSION=2.7 FLOATX='float32' RUN_PYLINT="true" TESTCMD="--durations=10 --cov-append pymc3/tests/test_distributions_random.py pymc3/tests/test_shared.py pymc3/tests/test_smc.py pymc3/tests/test_sampling.py"
-  - PYTHON_VERSION=2.7 FLOATX='float32' TESTCMD="--durations=10 --cov-append pymc3/tests/test_examples.py pymc3/tests/test_posteriors.py"
-  - PYTHON_VERSION=2.7 FLOATX='float32' TESTCMD="--durations=10 --cov-append pymc3/tests/test_variational_inference.py pymc3/tests/test_updates.py"
-  - PYTHON_VERSION=2.7 FLOATX='float64' TESTCMD="--durations=10 --ignore=pymc3/tests/test_examples.py --cov-append --ignore=pymc3/tests/test_distributions_random.py --ignore=pymc3/tests/test_variational_inference.py --ignore=pymc3/tests/test_shared.py --ignore=pymc3/tests/test_smc.py --ignore=pymc3/tests/test_updates.py --ignore=pymc3/tests/test_posteriors.py --ignore=pymc3/tests/test_sampling.py"
-  - PYTHON_VERSION=2.7 FLOATX='float64' RUN_PYLINT="true" TESTCMD="--durations=10 --cov-append pymc3/tests/test_distributions_random.py pymc3/tests/test_shared.py pymc3/tests/test_smc.py pymc3/tests/test_sampling.py"
-  - PYTHON_VERSION=2.7 FLOATX='float64' TESTCMD="--durations=10 --cov-append pymc3/tests/test_examples.py pymc3/tests/test_posteriors.py"
-  - PYTHON_VERSION=3.6 FLOATX='float64' TESTCMD="--durations=10 --cov-append --ignore=pymc3/tests/test_examples.py --ignore=pymc3/tests/test_distributions_random.py --ignore=pymc3/tests/test_variational_inference.py --ignore=pymc3/tests/test_shared.py --ignore=pymc3/tests/test_smc.py --ignore=pymc3/tests/test_updates.py --ignore=pymc3/tests/test_posteriors.py --ignore=pymc3/tests/test_sampling.py"
-  - PYTHON_VERSION=3.6 FLOATX='float64' TESTCMD="--durations=10 --cov-append pymc3/tests/test_distributions_random.py pymc3/tests/test_shared.py pymc3/tests/test_smc.py pymc3/tests/test_sampling.py"
-  - PYTHON_VERSION=3.6 FLOATX='float64' BUILD_DOCS="true" TESTCMD="--durations=10 --cov-append pymc3/tests/test_examples.py pymc3/tests/test_posteriors.py"
-  - PYTHON_VERSION=3.6 FLOATX='float64' TESTCMD="--durations=10 --cov-append pymc3/tests/test_variational_inference.py pymc3/tests/test_updates.py"
+  - FLOATX='float32' TESTCMD="--durations=10 --ignore=pymc3/tests/test_examples.py --cov-append --ignore=pymc3/tests/test_distributions_random.py --ignore=pymc3/tests/test_variational_inference.py --ignore=pymc3/tests/test_shared.py --ignore=pymc3/tests/test_smc.py --ignore=pymc3/tests/test_updates.py --ignore=pymc3/tests/test_posteriors.py --ignore=pymc3/tests/test_sampling.py"
+  - FLOATX='float32' RUN_PYLINT="true" TESTCMD="--durations=10 --cov-append pymc3/tests/test_distributions_random.py pymc3/tests/test_shared.py pymc3/tests/test_smc.py pymc3/tests/test_sampling.py"
+  - FLOATX='float32' TESTCMD="--durations=10 --cov-append pymc3/tests/test_examples.py pymc3/tests/test_posteriors.py"
+  - FLOATX='float32' TESTCMD="--durations=10 --cov-append pymc3/tests/test_variational_inference.py pymc3/tests/test_updates.py"
+  - FLOATX='float64' TESTCMD="--durations=10 --cov-append --ignore=pymc3/tests/test_examples.py --ignore=pymc3/tests/test_distributions_random.py --ignore=pymc3/tests/test_variational_inference.py --ignore=pymc3/tests/test_shared.py --ignore=pymc3/tests/test_smc.py --ignore=pymc3/tests/test_updates.py --ignore=pymc3/tests/test_posteriors.py --ignore=pymc3/tests/test_sampling.py"
+  - FLOATX='float64' TESTCMD="--durations=10 --cov-append pymc3/tests/test_distributions_random.py pymc3/tests/test_shared.py pymc3/tests/test_smc.py pymc3/tests/test_sampling.py"
+  - FLOATX='float64' BUILD_DOCS="true" TESTCMD="--durations=10 --cov-append pymc3/tests/test_examples.py pymc3/tests/test_posteriors.py"
+  - FLOATX='float64' TESTCMD="--durations=10 --cov-append pymc3/tests/test_variational_inference.py pymc3/tests/test_updates.py"
 
 script:
   - . ./scripts/test.sh $TESTCMD

README.rst

@@ -104,8 +104,8 @@ Another option is to clone the repository and install PyMC3 using
 Dependencies
 ============
 
-PyMC3 is tested on Python 2.7 and 3.6 and depends on Theano, NumPy,
-SciPy, Pandas, and Matplotlib (see ``requirements.txt`` for version
+PyMC3 is tested on Python 3.6 and depends on Theano, NumPy,
+SciPy, and Pandas (see ``requirements.txt`` for version
 information).
 
 Optional

benchmarks/benchmarks/benchmarks.py

@@ -58,7 +58,7 @@ def mixture_model(random_seed=1234):
     return model, start
 
 
-class OverheadSuite(object):
+class OverheadSuite:
     """
     Just tests how long sampling from a normal distribution takes for various
     samplers
@@ -77,7 +77,7 @@ def time_overhead_sample(self, step):
                       progressbar=False, compute_convergence_checks=False)
 
 
-class ExampleSuite(object):
+class ExampleSuite:
     """Implements examples to keep up with benchmarking them."""
     timeout = 360.0  # give it a few minutes
     timer = timeit.default_timer
@@ -128,7 +128,7 @@ def time_glm_hierarchical(self):
                       progressbar=False, compute_convergence_checks=False)
 
 
-class NUTSInitSuite(object):
+class NUTSInitSuite:
     """Tests initializations for NUTS sampler on models
     """
     timeout = 360.0
@@ -173,7 +173,7 @@ def track_marginal_mixture_model_ess(self, init):
 NUTSInitSuite.track_marginal_mixture_model_ess.unit = 'Effective samples per second'
 
 
-class CompareMetropolisNUTSSuite(object):
+class CompareMetropolisNUTSSuite:
     timeout = 360.0
     # None will be the "sensible default", and include initialization, but should be fastest
     params = (None, pm.NUTS, pm.Metropolis)

docs/source/developer_guide.rst

@@ -25,7 +25,7 @@ A few important points to highlight in the Distribution Class:
 
 .. code:: python
 
-    class Distribution(object):
+    class Distribution:
         """Statistical distribution"""
         def __new__(cls, name, *args, **kwargs):
             ...
@@ -610,7 +610,7 @@ does not edit or rewrite the graph directly.
 
 .. code:: python
 
-    class ValueGradFunction(object):
+    class ValueGradFunction:
         """Create a theano function that computes a value and its gradient.
         ...
         """
@@ -1104,4 +1104,4 @@ While having the samplers be written in Python allows for a lot of
 flexibility and intuitive for experiment (writing e.g. NUTS in Theano is
 also very difficult), it comes at a performance penalty and makes
 sampling on the GPU very inefficient because memory needs to be copied
-for every logp evaluation.
+for every logp evaluation.

docs/source/sphinxext/gallery_generator.py

@@ -63,7 +63,7 @@ def create_thumbnail(infile, width=275, height=275, cx=0.5, cy=0.5, border=4):
     return fig
 
 
-class NotebookGenerator(object):
+class NotebookGenerator:
     """Tools for generating an example page from a file"""
 
     def __init__(self, filename, target_dir):
@@ -121,7 +121,7 @@ def gen_previews(self):
         create_thumbnail(self.png_path)
 
 
-class TableOfContentsJS(object):
+class TableOfContentsJS:
     """Container to load table of contents JS file"""
 
     def load(self, path):

pymc3/backends/base.py

@@ -20,7 +20,7 @@ class BackendError(Exception):
     pass
 
 
-class BaseTrace(object):
+class BaseTrace:
     """Base trace object
 
     Parameters
@@ -218,7 +218,7 @@ def stat_names(self):
             return set()
 
 
-class MultiTrace(object):
+class MultiTrace:
     """Main interface for accessing values from MCMC results
 
     The core method to select values is `get_values`. The method

pymc3/backends/hdf5.py

@@ -37,7 +37,7 @@ def __init__(self, name=None, model=None, vars=None, test_point=None):
         self.hdf5_file = None
         self.draw_idx = 0
         self.draws = None
-        super(HDF5, self).__init__(name, model, vars, test_point)
+        super().__init__(name, model, vars, test_point)
 
     def _get_sampler_stats(self, varname, sampler_idx, burn, thin):
         with self.activate_file:

pymc3/backends/ndarray.py

@@ -78,7 +78,7 @@ def load_trace(directory, model=None):
     return base.MultiTrace(straces)
 
 
-class SerializeNDArray(object):
+class SerializeNDArray:
     metadata_file = 'metadata.json'
     samples_file = 'samples.npz'
 
@@ -157,7 +157,7 @@ class NDArray(base.BaseTrace):
     supports_sampler_stats = True
 
     def __init__(self, name=None, model=None, vars=None, test_point=None):
-        super(NDArray, self).__init__(name, model, vars, test_point)
+        super().__init__(name, model, vars, test_point)
         self.draw_idx = 0
         self.draws = None
         self.samples = {}
@@ -178,7 +178,7 @@ def setup(self, draws, chain, sampler_vars=None):
             Names and dtypes of the variables that are
             exported by the samplers.
         """
-        super(NDArray, self).setup(draws, chain, sampler_vars)
+        super().setup(draws, chain, sampler_vars)
 
         self.chain = chain
         if self.samples:  # Concatenate new array if chain is already present.

pymc3/backends/report.py

@@ -36,7 +36,7 @@ class WarningType(enum.Enum):
 }
 
 
-class SamplerReport(object):
+class SamplerReport:
     def __init__(self):
         self._chain_warnings = {}
         self._global_warnings = []

pymc3/backends/sqlite.py

@@ -75,7 +75,7 @@ class SQLite(base.BaseTrace):
     """
 
     def __init__(self, name, model=None, vars=None, test_point=None):
-        super(SQLite, self).__init__(name, model, vars, test_point)
+        super().__init__(name, model, vars, test_point)
         self._var_cols = {}
         self.var_inserts = {}  # varname -> insert statement
         self.draw_idx = 0
@@ -270,7 +270,7 @@ def point(self, idx):
         return var_values
 
 
-class _SQLiteDB(object):
+class _SQLiteDB:
 
     def __init__(self, name):
         self.name = name
@@ -347,8 +347,7 @@ def _get_var_strs(cursor, varname):
 def _get_chain_list(cursor, varname):
     """Return a list of sorted chains for `varname`."""
     cursor.execute('SELECT DISTINCT chain FROM [{}]'.format(varname))
-    chains = [chain[0] for chain in cursor.fetchall()]
-    chains.sort()
+    chains = sorted([chain[0] for chain in cursor.fetchall()])
     return chains
 
 

pymc3/backends/text.py

@@ -43,7 +43,7 @@ class Text(base.BaseTrace):
     def __init__(self, name, model=None, vars=None, test_point=None):
         if not os.path.exists(name):
             os.mkdir(name)
-        super(Text, self).__init__(name, model, vars, test_point)
+        super().__init__(name, model, vars, test_point)
 
         self.flat_names = {v: ttab.create_flat_names(v, shape)
                            for v, shape in self.var_shapes.items()}

pymc3/blocking.py

@@ -16,7 +16,7 @@
 # TODO Classes and methods need to be fully documented.
 
 
-class ArrayOrdering(object):
+class ArrayOrdering:
     """
     An ordering for an array space
     """
@@ -45,7 +45,7 @@ def __getitem__(self, key):
         return self.by_name[key]
 
 
-class DictToArrayBijection(object):
+class DictToArrayBijection:
     """
     A mapping between a dict space and an array space
     """
@@ -106,7 +106,7 @@ def mapf(self, f):
         return Compose(f, self.rmap)
 
 
-class ListArrayOrdering(object):
+class ListArrayOrdering:
     """
     An ordering for a list to an array space. Takes also non theano.tensors.
     Modified from pymc3 blocking.
@@ -138,7 +138,7 @@ def __init__(self, list_arrays, intype='numpy'):
             self.size += array.size
 
 
-class ListToArrayBijection(object):
+class ListToArrayBijection:
     """
     A mapping between a List of arrays and an array space
 
@@ -217,7 +217,7 @@ def rmap(self, array):
         return a_list
 
 
-class DictToVarBijection(object):
+class DictToVarBijection:
     """
     A mapping between a dict space and the array space for one element within the dict space
     """
@@ -244,7 +244,7 @@ def mapf(self, f):
         return Compose(f, self.rmap)
 
 
-class Compose(object):
+class Compose:
     """
     Compose two functions in a pickleable way
     """

pymc3/data.py

@@ -33,7 +33,7 @@ def get_data(filename):
 
 class GenTensorVariable(tt.TensorVariable):
     def __init__(self, op, type, name=None):
-        super(GenTensorVariable, self).__init__(type=type, name=name)
+        super().__init__(type=type, name=name)
         self.op = op
 
     def set_gen(self, gen):
@@ -48,7 +48,7 @@ def clone(self):
         return cp
 
 
-class GeneratorAdapter(object):
+class GeneratorAdapter:
     """
     Helper class that helps to infer data type of generator with looking
     at the first item, preserving the order of the resulting generator
@@ -99,7 +99,7 @@ class Minibatch(tt.TensorVariable):
     data : :class:`ndarray`
         initial data
     batch_size : `int` or `List[int|tuple(size, random_seed)]`
-        batch size for inference, random seed is needed 
+        batch size for inference, random seed is needed
         for child random generators
     dtype : `str`
         cast data to specific type
@@ -110,10 +110,10 @@ class Minibatch(tt.TensorVariable):
     random_seed : `int`
         random seed that is used by default
     update_shared_f : `callable`
-        returns :class:`ndarray` that will be carefully 
+        returns :class:`ndarray` that will be carefully
         stored to underlying shared variable
-        you can use it to change source of 
-        minibatches programmatically 
+        you can use it to change source of
+        minibatches programmatically
     in_memory_size : `int` or `List[int|slice|Ellipsis]`
         data size for storing in theano.shared
 
@@ -141,7 +141,7 @@ class Minibatch(tt.TensorVariable):
     >>> x = Minibatch(data, batch_size=10)
 
     Note, that your data is cast to `floatX` if it is not integer type
-    But you still can add `dtype` kwarg for :class:`Minibatch` 
+    But you still can add `dtype` kwarg for :class:`Minibatch`
 
     in case we want 10 sampled rows and columns
     `[(size, seed), (size, seed)]` it is
@@ -182,7 +182,7 @@ class Minibatch(tt.TensorVariable):
     >>> x.update_shared()
 
     To be more concrete about how we get minibatch, here is a demo
-    1) create shared variable 
+    1) create shared variable
     >>> shared = theano.shared(data)
 
     2) create random slice of size 10
@@ -191,7 +191,7 @@ class Minibatch(tt.TensorVariable):
     3) take that slice
     >>> minibatch = shared[ridx]
 
-    That's done. Next you can use this minibatch somewhere else. 
+    That's done. Next you can use this minibatch somewhere else.
     You can see that implementation does not require fixed shape
     for shared variable. Feel free to use that if needed.
 
@@ -222,7 +222,7 @@ class Minibatch(tt.TensorVariable):
     We take slice only for the first and last dimension
     >>> assert x.eval().shape == (2, 20, 30, 40, 10)
 
-    2) Skipping particular dimension, `total_size = (10, None, 30)` 
+    2) Skipping particular dimension, `total_size = (10, None, 30)`
     >>> x = Minibatch(moredata, [2, None, 20])
     >>> assert x.eval().shape == (2, 20, 20, 40, 50)
 
@@ -249,8 +249,7 @@ def __init__(self, data, batch_size=128, dtype=None, broadcastable=None, name='M
             broadcastable = (False, ) * minibatch.ndim
         minibatch = tt.patternbroadcast(minibatch, broadcastable)
         self.minibatch = minibatch
-        super(Minibatch, self).__init__(
-            self.minibatch.type, None, None, name=name)
+        super().__init__(self.minibatch.type, None, None, name=name)
         theano.Apply(
             theano.compile.view_op,
             inputs=[self.minibatch], outputs=[self])

pymc3/diagnostics.py

@@ -292,7 +292,7 @@ def generate_neff(trace_values):
             'of the same length.')
 
     if varnames is None:
-        varnames = get_default_varnames(mtrace.varnames,include_transformed=include_transformed)
+        varnames = get_default_varnames(mtrace.varnames, include_transformed=include_transformed)
 
     n_eff = {}