Merge pull request #513 from ICB-DCM/develop

Release 0.11.6

Author: yannikschaelte

.readthedocs.yml

@@ -11,7 +11,7 @@ sphinx:
 
 # python requirements
 python:
-  version: 3.7
+  version: 3.8
   install:
     - method: pip
       path: .

CHANGELOG.rst

@@ -8,6 +8,15 @@ Release Notes
 ...........
 
 
+0.11.6 (2021-11-05)
+-------------------
+
+* Unfix sphinx version for documentation (#509)
+* Streamline function wrapper objects (#511)
+* Remove rpy2 warning upon import of `pyabc.external` (#512)
+* Move ot distance to scipy due to bug in pot 0.8.0 (#512)
+
+
 0.11.5 (2021-10-29)
 -------------------
 

pyabc/__init__.py

@@ -27,8 +27,8 @@
 from . import settings, visualization
 from .acceptor import (
     Acceptor,
+    FunctionAcceptor,
     ScaledPDFNorm,
-    SimpleFunctionAcceptor,
     StochasticAcceptor,
     UniformAcceptor,
     pdf_norm_from_kernel,
@@ -42,6 +42,8 @@
     BinomialKernel,
     Distance,
     DistanceWithMeasureList,
+    FunctionDistance,
+    FunctionKernel,
     IndependentLaplaceKernel,
     IndependentNormalKernel,
     InfoWeightedPNormDistance,
@@ -54,7 +56,6 @@
     PNormDistance,
     PoissonKernel,
     RangeEstimatorDistance,
-    SimpleFunctionDistance,
     SlicedWassersteinDistance,
     StochasticKernel,
     WassersteinDistance,
@@ -80,7 +81,7 @@
     TemperatureScheme,
 )
 from .inference import ABCSMC
-from .model import IntegratedModel, Model, ModelResult, SimpleModel
+from .model import FunctionModel, IntegratedModel, Model, ModelResult
 from .parameters import Parameter
 from .population import Particle, Population, Sample
 from .populationstrategy import AdaptivePopulationSize, ConstantPopulationSize

pyabc/acceptor/__init__.py

@@ -10,7 +10,7 @@
 from .acceptor import (
     Acceptor,
     AcceptorResult,
-    SimpleFunctionAcceptor,
+    FunctionAcceptor,
     StochasticAcceptor,
     UniformAcceptor,
 )

pyabc/acceptor/acceptor.py

@@ -206,7 +206,7 @@ def get_epsilon_config(self, t: int) -> dict:
         return {}
 
 
-class SimpleFunctionAcceptor(Acceptor):
+class FunctionAcceptor(Acceptor):
     """
     Initialize from function.
 
@@ -224,7 +224,7 @@ def __call__(self, distance_function, eps, x, x_0, t, par):
         return self.fun(distance_function, eps, x, x_0, t, par)
 
     @staticmethod
-    def assert_acceptor(maybe_acceptor: Union[Acceptor, Callable]):
+    def to_acceptor(maybe_acceptor: Union[Acceptor, Callable]) -> Acceptor:
         """
         Create an acceptor object from input.
 
@@ -242,7 +242,7 @@ def assert_acceptor(maybe_acceptor: Union[Acceptor, Callable]):
         if isinstance(maybe_acceptor, Acceptor):
             return maybe_acceptor
         else:
-            return SimpleFunctionAcceptor(maybe_acceptor)
+            return FunctionAcceptor(maybe_acceptor)
 
 
 def accept_use_current_time(distance_function, eps, x, x_0, t, par):

pyabc/distance/__init__.py

@@ -11,13 +11,7 @@
 """
 
 from .aggregate import AdaptiveAggregatedDistance, AggregatedDistance
-from .base import (
-    AcceptAllDistance,
-    Distance,
-    NoDistance,
-    SimpleFunctionDistance,
-    to_distance,
-)
+from .base import AcceptAllDistance, Distance, FunctionDistance, NoDistance
 from .distance import (
     DistanceWithMeasureList,
     MinMaxDistance,
@@ -30,12 +24,12 @@
     SCALE_LIN,
     SCALE_LOG,
     BinomialKernel,
+    FunctionKernel,
     IndependentLaplaceKernel,
     IndependentNormalKernel,
     NegativeBinomialKernel,
     NormalKernel,
     PoissonKernel,
-    SimpleFunctionKernel,
     StochasticKernel,
 )
 from .ot import SlicedWassersteinDistance, WassersteinDistance

pyabc/distance/aggregate.py

@@ -7,7 +7,7 @@
 
 from ..population import Sample
 from ..storage import save_dict_to_json
-from .base import Distance, to_distance
+from .base import Distance, FunctionDistance
 from .scale import span
 
 logger = logging.getLogger("ABC.Distance")
@@ -25,7 +25,7 @@ class AggregatedDistance(Distance):
 
     def __init__(
         self,
-        distances: List[Distance],
+        distances: List[Union[Distance, Callable]],
         weights: Union[List, dict] = None,
         factors: Union[List, dict] = None,
     ):
@@ -52,10 +52,10 @@ def __init__(
         """
         super().__init__()
 
-        if not isinstance(distances, list):
+        if isinstance(distances, (Distance, Callable)):
             distances = [distances]
         self.distances: List[Distance] = [
-            to_distance(distance) for distance in distances
+            FunctionDistance.to_distance(distance) for distance in distances
         ]
 
         self.weights = weights

pyabc/distance/base.py

@@ -2,7 +2,7 @@
 
 import json
 from abc import ABC, abstractmethod
-from typing import Callable
+from typing import Callable, Union
 
 from ..population import Sample
 
@@ -209,7 +209,7 @@ def __call__(
         return -1
 
 
-class SimpleFunctionDistance(Distance):
+class FunctionDistance(Distance):
     """
     This is a wrapper around a simple function which calculates the distance.
     If a function/callable is passed to the ABCSMC class, which is not
@@ -249,22 +249,22 @@ def get_config(self):
                 pass
         return conf
 
+    @staticmethod
+    def to_distance(maybe_distance: Union[Callable, Distance]) -> Distance:
+        """
+        Parameters
+        ----------
+        maybe_distance: either a Callable as in FunctionDistance, or a
+        pyabc.Distance object.
 
-def to_distance(maybe_distance):
-    """
-    Parameters
-    ----------
-    maybe_distance: either a Callable as in SimpleFunctionDistance, or a
-    pyabc.Distance object.
-
-    Returns
-    -------
-    A Distance instance.
-    """
-    if maybe_distance is None:
-        return NoDistance()
+        Returns
+        -------
+        A Distance instance.
+        """
+        if maybe_distance is None:
+            return NoDistance()
 
-    if isinstance(maybe_distance, Distance):
-        return maybe_distance
+        if isinstance(maybe_distance, Distance):
+            return maybe_distance
 
-    return SimpleFunctionDistance(maybe_distance)
+        return FunctionDistance(maybe_distance)

pyabc/distance/kernel.py

@@ -1,5 +1,5 @@
 """Stochastic kernels."""
-
+from abc import ABC
 from typing import Callable, List, Sequence, Union
 
 import numpy as np
@@ -13,7 +13,7 @@
 SCALES = [SCALE_LIN, SCALE_LOG]
 
 
-class StochasticKernel(Distance):
+class StochasticKernel(Distance, ABC):
     """
     A stochastic kernel assesses the similarity between observed and
     simulated summary statistics or data via a probability measure.
@@ -79,7 +79,7 @@ def initialize_keys(self, x):
         self.keys = sorted(x)
 
 
-class SimpleFunctionKernel(StochasticKernel):
+class FunctionKernel(StochasticKernel):
     """
     This is a wrapper around a simple function which calculates the
     probability density.

pyabc/distance/ot.py

@@ -6,6 +6,7 @@
 
 import numpy as np
 import scipy.linalg as la
+import scipy.spatial as spat
 
 from ..population import Sample
 from ..sumstat import Sumstat
@@ -106,7 +107,7 @@ def __init__(
                 # of course, we could permit arbitrary norms here if needed
                 raise ValueError(f"Cannot translate p={p} into a distance.")
         if isinstance(dist, str):
-            dist = partial(ot.dist, metric=dist)
+            dist = partial(spat.distance.cdist, metric=dist)
         self.dist: Callable = dist
 
         if emd_args is None:
@@ -164,7 +165,7 @@ def __call__(
         n, n0 = s.shape[0], s0.shape[0]
 
         # pairwise cost matrix, shape (n, n0)
-        m = self.dist(x1=s, x2=s0)
+        m = self.dist(XA=s, XB=s0)
 
         # weights (could also be passed/learned?)
         w, w0 = np.ones((n,)) / n, np.ones((n0,)) / n0

pyabc/external/r_rpy2.py

@@ -35,8 +35,9 @@
         pandas2ri,
         r,
     )
-except ImportError:  # in Python 3.6 ModuleNotFoundError can be used
-    logger.error("Install rpy2 to enable simple support for the R language.")
+except ImportError:
+    ListVector = conversion = r = None
+    default_converter = numpy2ri = pandas2ri = None
 
 
 __all__ = ["R"]
@@ -84,6 +85,8 @@ class R:
     """
 
     def __init__(self, source_file: str):
+        if r is None:
+            raise ImportError("Install rpy2, e.g. via `pip install pyabc[R]`")
         warnings.warn("The support of R via rpy2 is considered experimental.")
         self.source_file = source_file
         self._read_source()

pyabc/inference/smc.py

@@ -9,15 +9,15 @@
 
 from pyabc.acceptor import (
     Acceptor,
-    SimpleFunctionAcceptor,
+    FunctionAcceptor,
     StochasticAcceptor,
     UniformAcceptor,
 )
 from pyabc.distance import (
     Distance,
+    FunctionDistance,
     PNormDistance,
     StochasticKernel,
-    to_distance,
 )
 from pyabc.epsilon import Epsilon, MedianEpsilon, TemperatureBase
 from pyabc.inference_util import (
@@ -29,7 +29,7 @@
     create_transition_pdf,
     termination_criteria_fulfilled,
 )
-from pyabc.model import Model, SimpleModel
+from pyabc.model import FunctionModel, Model
 from pyabc.platform_factory import DefaultSampler
 from pyabc.population import Population, Sample
 from pyabc.populationstrategy import ConstantPopulationSize, PopulationStrategy
@@ -185,7 +185,7 @@ def __init__(
     ):
         if not isinstance(models, list):
             models = [models]
-        models = list(map(SimpleModel.assert_model, models))
+        models = list(map(FunctionModel.to_model, models))
         self.models = models
 
         if not isinstance(parameter_priors, list):
@@ -200,7 +200,9 @@ def __init__(
 
         if distance_function is None:
             distance_function = PNormDistance()
-        self.distance_function = to_distance(distance_function)
+        self.distance_function = FunctionDistance.to_distance(
+            distance_function,
+        )
 
         self.summary_statistics = summary_statistics
 
@@ -234,7 +236,7 @@ def __init__(
 
         if acceptor is None:
             acceptor = UniformAcceptor()
-        self.acceptor = SimpleFunctionAcceptor.assert_acceptor(acceptor)
+        self.acceptor = FunctionAcceptor.to_acceptor(acceptor)
 
         self.stop_if_only_single_model_alive = stop_if_only_single_model_alive
         self.max_nr_recorded_particles = max_nr_recorded_particles

pyabc/model.py

@@ -5,7 +5,7 @@
 A model defines how input parameters relate to output simulated data.
 """
 
-from typing import Any, Callable
+from typing import Any, Callable, Union
 
 from .acceptor import Acceptor
 from .distance import Distance
@@ -218,7 +218,7 @@ def accept(
         return result
 
 
-class SimpleModel(Model):
+class FunctionModel(Model):
     """
     A model which is initialized with a function which generates the samples.
     For most cases this class will be adequate.
@@ -247,27 +247,26 @@ def sample(self, pars: Parameter):
         return self.sample_function(pars)
 
     @staticmethod
-    def assert_model(model_or_function):
+    def to_model(maybe_model: Union[Callable, Model]) -> Model:
         """
         Alternative constructor. Accepts either a Model instance or a
         function and returns always a Model instance.
 
         Parameters
         ----------
-        model_or_function: Model, function
+        maybe_model:
             Constructs a SimpleModel instance if a function is passed.
             If a Model instance is passed, the Model instance itself is
             returned.
 
         Returns
         -------
-        model: SimpleModel or Model
-
+        model: A valid model instance
         """
-        if isinstance(model_or_function, Model):
-            return model_or_function
+        if isinstance(maybe_model, Model):
+            return maybe_model
         else:
-            return SimpleModel(model_or_function)
+            return FunctionModel(maybe_model)
 
 
 class IntegratedModel(Model):