Source code for scenic.core.distributions

"""Objects representing distributions that can be sampled from."""

import collections
import functools
import itertools
import math
import numbers
import random
import typing
import warnings

import numpy

from scenic.core.errors import InvalidScenarioError, ScenicError
from scenic.core.lazy_eval import (
    LazilyEvaluable,
    dependencies,
    isLazy,
    makeDelayedFunctionCall,
    needsLazyEvaluation,
    needsSampling,
    requiredProperties,
    valueInContext,
)
from scenic.core.utils import (
    DefaultIdentityDict,
    argsToString,
    cached,
    get_type_hints,
    sqrt2,
)

## Misc


[docs]def supportInterval(thing): """Lower and upper bounds on this value, if known.""" if hasattr(thing, "supportInterval"): return thing.supportInterval() elif isinstance(thing, (int, float)): return thing, thing else: return None, None
def supmin(*vals): return None if None in vals else min(vals) def supmax(*vals): return None if None in vals else max(vals) def unionOfSupports(supports): mins, maxes = zip(*supports) return supmin(*mins), supmax(*maxes) def addSupports(sup1, sup2): l1, u1 = sup1 l2, u2 = sup2 lower = None if l1 is None or l2 is None else l1 + l2 upper = None if u1 is None or u2 is None else u1 + u2 return lower, upper
[docs]def underlyingFunction(thing): """Original function underlying a distribution wrapper.""" func = getattr(thing, "__wrapped__", thing) return getattr(func, "__func__", func)
[docs]def canUnpackDistributions(func): """Whether the function supports iterable unpacking of distributions.""" return getattr(func, "_canUnpackDistributions", False)
[docs]def unpacksDistributions(func): """Decorator indicating the function supports iterable unpacking of distributions.""" func._canUnpackDistributions = True return func
[docs]class RejectionException(Exception): """Exception used to signal that the sample currently being generated must be rejected.""" pass
[docs]class RandomControlFlowError(ScenicError): """Exception indicating illegal conditional control flow depending on a random value. This includes trying to iterate over a random value, making a range of random length, etc. """ pass
## Abstract distributions
[docs]class Samplable(LazilyEvaluable): """Abstract class for values which can be sampled, possibly depending on other values. Samplables may specify a proxy object which must have the same distribution as the original after conditioning on the scenario's requirements. This allows transparent conditioning without modifying Samplable fields of immutable objects. Args: dependencies: sequence of values that this value may depend on (formally, objects for which sampled values must be provided to `sampleGiven`). It is legal to include values which are not instances of `Samplable`, e.g. integers. Attributes: _conditioned: proxy object as described above; set using `conditionTo`. _dependencies: tuple of other samplables which must be sampled before this one; set by the initializer and subsequently immutable. """ def __init__(self, dependencies): deps = [] props = set() for dep in dependencies: if isLazy(dep): deps.append(dep) props.update(requiredProperties(dep)) super().__init__(props, deps) self._conditioned = self # version (partially) conditioned on requirements
[docs] @staticmethod def sampleAll(quantities): """Sample all the given Samplables, which may have dependencies in common. Reproducibility note: the order in which the quantities are given can affect the order in which calls to random are made, affecting the final result. """ subsamples = DefaultIdentityDict() for q in quantities: if q not in subsamples: subsamples[q] = q.sample(subsamples) if needsSampling(q) else q return subsamples
[docs] def sample(self, subsamples=None): """Sample this value, optionally given some values already sampled.""" if subsamples is None: subsamples = DefaultIdentityDict() for child in self._conditioned._dependencies: if child not in subsamples: subsamples[child] = child.sample(subsamples) return self._conditioned.sampleGiven(subsamples)
[docs] def sampleGiven(self, value): """Sample this value, given values for all its dependencies. Implemented by subclasses. Args: value (DefaultIdentityDict): dictionary mapping objects to their sampled values. Guaranteed to provide values for all objects given in the set of dependencies when this `Samplable` was created. """ raise NotImplementedError
def serializeValue(self, values, serializer): for child in self._conditioned._dependencies: serializer.writeSamplable(child, values) def deserializeValue(self, serializer, values): for child in self._conditioned._dependencies: serializer.readSamplable(child, values) return self._conditioned.sampleGiven(values)
[docs] def conditionTo(self, value): """Condition this value to another value with the same conditional distribution.""" assert isinstance(value, Samplable) self._conditioned = value
[docs] def evaluateIn(self, context): """See `LazilyEvaluable.evaluateIn`.""" value = super().evaluateIn(context) # Check that all dependencies have been evaluated assert all(not needsLazyEvaluation(dep) for dep in value._dependencies) return value
[docs]class ConstantSamplable(Samplable): """A samplable which always evaluates to a constant value. Only for internal use. """ def __init__(self, value): assert not isLazy(value), value self.value = value super().__init__(()) def sampleGiven(self, value): return self.value
[docs]class Distribution(Samplable): """Abstract class for distributions. .. note:: When called during dynamic simulations (vs. scenario compilation), constructors for distributions return *actual sampled values*, not `Distribution` objects. Args: dependencies: values which this distribution may depend on (see `Samplable`). valueType: **_valueType** to use (see below), or `None` for the default. Attributes: _valueType: type of the values sampled from this distribution, or `object` if the type is not known. """ #: Default valueType for distributions of this class, when not otherwise specified. _defaultValueType = object #: Whether this type of distribution is a deterministic function of its dependencies. #: #: For example, `Options` is implemented as deterministic by using an internal #: `DiscreteRange` to select which of its finitely-many options to choose from: the #: value of the `Options` is then completely determined by the value of the range and #: the values of each of the options. This simplifies serialization because these #: dependencies likely have simpler valueTypes than the `Options` itself (e.g. if we #: had a random choice between a list and a string, encoding the actual sampled value #: would require saving type information). _deterministic = False def __new__(cls, *args, **kwargs): dist = super().__new__(cls) # During a simulation, return a sample from the distribution immediately import scenic.syntax.veneer as veneer if veneer.simulationInProgress(): dist.__init__(*args, **kwargs) sim = veneer.simulation() subsamples = DefaultIdentityDict() # If we're replaying a previous simulation, use the saved value; otherwise sample one if sim.replayCanContinue(): value = sim.replaySampledValue(dist, subsamples) else: value = dist.sample(subsamples) # Save the value for future replay subsamples[dist] = value sim.recordSampledValue(dist, subsamples) return value elif veneer.evaluatingRequirement: raise InvalidScenarioError( "cannot define distributions inside a requirement" " (try moving them outside)" ) else: return dist def __init__(self, *dependencies, valueType=None): super().__init__(dependencies) self._needsSampling = True self._isLazy = True if valueType is None: valueType = self._defaultValueType self._valueType = valueType
[docs] def clone(self): """Construct an independent copy of this Distribution. Optionally implemented by subclasses. """ raise NotImplementedError("clone() not supported by this distribution")
@property @cached def isPrimitive(self): """Whether this is a primitive Distribution.""" try: self.clone() return True except NotImplementedError: return False
[docs] def serializeValue(self, values, serializer): """Serialize the sampled value of this distribution. This method is used internally by `Scenario.sceneToBytes` and related APIs. If you define a new subclass of `Distribution`, you probably don't need to override this method. If your distribution has an unusual **valueType** (i.e. not `float`, `int`, or `Vector`), see the documentation for `Serializer` for instructions on how to support serialization. """ if self._deterministic: # It suffices to just serialize our dependencies; this is preferable to # saving our sampled value directly, which could be arbitrarily complex. super().serializeValue(values, serializer) else: # Need to save our sampled value, which can't be reconstructed from the # values of our dependencies. There is then no need to save the latter. serializer.writeValue(values[self], self._valueType)
def deserializeValue(self, serializer, values): if self._deterministic: return super().deserializeValue(serializer, values) else: return serializer.readValue(self._valueType)
[docs] def bucket(self, buckets=None): """Construct a bucketed approximation of this Distribution. Optionally implemented by subclasses. This function factors a given Distribution into a discrete distribution over buckets together with a distribution for each bucket. The argument *buckets* controls how many buckets the domain of the original Distribution is split into. Since the result is an independent distribution, the original must support `clone`. """ raise NotImplementedError("bucket() not supported by this distribution")
[docs] def supportInterval(self): """Compute lower and upper bounds on the value of this Distribution. By default returns :scenic:`(None, None)` indicating that no lower or upper bounds are known. Subclasses may override this method to provide more accurate results. """ return None, None
def __getattr__(self, name): if name.startswith("__") and name.endswith("__"): # ignore special attributes return object.__getattribute__(self, name) return AttributeDistribution(name, self) def __call__(self, *args, **kwargs): return OperatorDistribution("__call__", self, args, kwargs) def __iter__(self): raise RandomControlFlowError(f"cannot iterate through a random value") def _comparisonError(self, other): raise RandomControlFlowError( "random values cannot be compared " "(and control flow cannot depend on them)" ) __lt__ = _comparisonError __le__ = _comparisonError __gt__ = _comparisonError __ge__ = _comparisonError __eq__ = _comparisonError __ne__ = _comparisonError def __hash__(self): # need to explicitly define since we overrode __eq__ return id(self) def __bool__(self): raise RandomControlFlowError("control flow cannot depend on a random value")
## Derived distributions
[docs]class TupleDistribution(Distribution, collections.abc.Sequence): """Distributions over tuples (or namedtuples, or lists).""" _deterministic = True def __init__(self, *coordinates, builder=tuple): super().__init__(*coordinates) self.coordinates = coordinates self.builder = builder def __len__(self): return len(self.coordinates) def __getitem__(self, index): return self.coordinates[index] def __iter__(self): yield from self.coordinates def sampleGiven(self, value): return self.builder(value[coordinate] for coordinate in self.coordinates) def evaluateInner(self, context): coordinates = (valueInContext(coord, context) for coord in self.coordinates) return TupleDistribution(*coordinates, builder=self.builder) def __repr__(self): coords = ", ".join(repr(c) for c in self.coordinates) if self.builder is tuple: return f"({coords},)" elif self.builder is list: return f"[{coords}]" else: return f"TupleDistribution({coords}, builder={self.builder!r})"
[docs]class SliceDistribution(Distribution): """Distributions over `slice` objects.""" _deterministic = True def __init__(self, start, stop, step): super().__init__(start, stop, step, valueType=slice) self.start = start self.stop = stop self.step = step def sampleGiven(self, value): return slice(value[self.start], value[self.stop], value[self.step]) def evaluateInner(self, context): start = valueInContext(self.start, context) stop = valueInContext(self.stop, context) step = valueInContext(self.step, context) return SliceDistribution(start, stop, step) def __repr__(self): return f"slice({self.start!r}, {self.stop!r}, {self.step!r})"
[docs]def toDistribution(val): """Wrap Python data types with Distributions, if necessary. For example, tuples containing Samplables need to be converted into TupleDistributions in order to keep track of dependencies properly. """ if isinstance(val, (tuple, list)): coords = [toDistribution(c) for c in val] if any(isLazy(c) for c in coords): if isinstance(val, tuple) and hasattr(val, "_fields"): # namedtuple builder = type(val)._make else: builder = type(val) return TupleDistribution(*coords, builder=builder) elif isinstance(val, slice): attrs = (val.start, val.stop, val.step) if any(isLazy(a) for a in attrs): return SliceDistribution(*attrs) return val
[docs]class FunctionDistribution(Distribution): """Distribution resulting from passing distributions to a function""" _deterministic = True def __init__(self, func, args, kwargs, support=None, valueType=None): args = tuple(toDistribution(arg) for arg in args) kwargs = {name: toDistribution(arg) for name, arg in kwargs.items()} if valueType is None: valueType = get_type_hints(func).get("return") super().__init__(*args, *kwargs.values(), valueType=valueType) self.function = func self.arguments = args self.kwargs = kwargs self.support = support def sampleGiven(self, value): args = [] for arg in self.arguments: if isinstance(arg, StarredDistribution): val = value[arg] try: iter(val) except TypeError: # TODO improve backtrace raise TypeError( f"'{type(val).__name__}' object on line {arg.lineno} " "is not iterable" ) from None args.extend(val) else: args.append(value[arg]) kwargs = {name: value[arg] for name, arg in self.kwargs.items()} return self.function(*args, **kwargs) def evaluateInner(self, context): function = valueInContext(self.function, context) arguments = tuple(valueInContext(arg, context) for arg in self.arguments) kwargs = {name: valueInContext(arg, context) for name, arg in self.kwargs.items()} return FunctionDistribution(function, arguments, kwargs) def supportInterval(self): if self.support is None: return None, None subsupports = (supportInterval(arg) for arg in self.arguments) kwss = {name: supportInterval(arg) for name, arg in self.kwargs.items()} return self.support(*subsupports, **kwss) def __repr__(self): return f"{self.function.__name__}({argsToString(self.arguments, self.kwargs)})"
[docs]def distributionFunction(wrapped=None, *, support=None, valueType=None): """Decorator for wrapping a function so that it can take distributions as arguments. This decorator is mainly for internal use, and is not necessary when defining a function in a Scenic file. It is, however, needed when calling external functions which contain control flow or other operations that Scenic distribution objects (representing random values) do not support. """ if wrapped is None: # written without arguments as @distributionFunction return lambda wrapped: distributionFunction( wrapped, support=support, valueType=valueType ) @unpacksDistributions @functools.wraps(wrapped) def helper(*args, **kwargs): args = tuple(toDistribution(arg) for arg in args) kwargs = {name: toDistribution(arg) for name, arg in kwargs.items()} if any(needsSampling(arg) for arg in itertools.chain(args, kwargs.values())): return FunctionDistribution(wrapped, args, kwargs, support, valueType) elif any( needsLazyEvaluation(arg) for arg in itertools.chain(args, kwargs.values()) ): # recursively call this helper (not the original function), since the # delayed arguments may evaluate to distributions, in which case we'll # have to make a FunctionDistribution return makeDelayedFunctionCall(helper, args, kwargs) else: return wrapped(*args, **kwargs) return helper
[docs]def monotonicDistributionFunction(method, valueType=None): """Like distributionFunction, but additionally specifies that the function is monotonic.""" def support(*subsupports, **kwss): mins, maxes = zip(*subsupports) kwmins = {name: interval[0] for name, interval in kwss.items()} kwmaxes = {name: interval[1] for name, interval in kwss.items()} l = None if None in mins or None in kwmins else method(*mins, **kwmins) r = None if None in maxes or None in kwmaxes else method(*maxes, **kwmaxes) return l, r return distributionFunction(method, support=support, valueType=valueType)
[docs]class StarredDistribution(Distribution): """A placeholder for the iterable unpacking operator * applied to a distribution.""" _deterministic = True def __init__(self, value, lineno): assert isinstance(value, Distribution) self.value = value self.lineno = lineno # for error handling when unpacking fails super().__init__(value, valueType=value._valueType) def sampleGiven(self, value): return value[self.value] def evaluateInner(self, context): return StarredDistribution(valueInContext(self.value, context), self.lineno) def __repr__(self): return f"*{self.value!r}"
[docs]class MethodDistribution(Distribution): """Distribution resulting from passing distributions to a method of a fixed object""" _deterministic = True def __init__(self, method, obj, args, kwargs, valueType=None): args = tuple(toDistribution(arg) for arg in args) kwargs = {name: toDistribution(arg) for name, arg in kwargs.items()} if valueType is None: valueType = get_type_hints(method).get("return") super().__init__(*args, *kwargs.values(), valueType=valueType) self.method = method self.object = obj self.arguments = args self.kwargs = kwargs def sampleGiven(self, value): args = [] for arg in self.arguments: if isinstance(arg, StarredDistribution): args.extend(value[arg.value]) else: args.append(value[arg]) kwargs = {name: value[arg] for name, arg in self.kwargs.items()} return self.method(self.object, *args, **kwargs) def evaluateInner(self, context): obj = valueInContext(self.object, context) arguments = tuple(valueInContext(arg, context) for arg in self.arguments) kwargs = {name: valueInContext(arg, context) for name, arg in self.kwargs.items()} return MethodDistribution(self.method, obj, arguments, kwargs) def __repr__(self): args = argsToString(self.arguments, self.kwargs) return f"{self.object!r}.{self.method.__name__}({args})"
[docs]def distributionMethod(method=None, *, identity=None): """Decorator for wrapping a method so that it can take distributions as arguments.""" if method is None: return lambda method: distributionMethod(method, identity=identity) @unpacksDistributions @functools.wraps(method) def helper(self, *args, **kwargs): if identity is not None and self == identity: return toDistribution(args[0]) args = tuple(toDistribution(arg) for arg in args) kwargs = {name: toDistribution(arg) for name, arg in kwargs.items()} if any(needsSampling(arg) for arg in itertools.chain(args, kwargs.values())): return MethodDistribution(method, self, args, kwargs) elif any( needsLazyEvaluation(arg) for arg in itertools.chain(args, kwargs.values()) ): # see analogous comment in distributionFunction return makeDelayedFunctionCall(helper, (self,) + args, kwargs) else: return method(self, *args, **kwargs) return helper
[docs]class AttributeDistribution(Distribution): """Distribution resulting from accessing an attribute of a distribution""" _deterministic = True def __init__(self, attribute, obj, valueType=None): if valueType is None: ty = type_support.underlyingType(obj) valueType = self.inferType(ty, attribute) super().__init__(obj, valueType=valueType) self.attribute = attribute self.object = obj
[docs] @staticmethod def inferType(ty, attribute): """Attempt to infer the type of the given attribute.""" # If the object's type is known, see if we have an attribute type annotation. try: hints = get_type_hints(ty) attrTy = hints.get(attribute) if attrTy: return attrTy except Exception: pass # couldn't get type annotations # Handle unions origin = type_support.get_type_origin(ty) if origin == typing.Union: types = [] for option in type_support.get_type_args(ty): if option is type(None) and not hasattr(None, attribute): # None does not have this attribute; accessing it will raise an # exception, so we can ignore this case for type inference. continue res = AttributeDistribution.inferType(option, attribute) types.append(res) return type_support.unifierOfTypes(types) if types else object # Check for a @property defined on the class with a return type if ( ty is not object and (func := getattr(ty, attribute, None)) and isinstance(func, property) ): return get_type_hints(func.fget).get("return") # We can't tell what the attribute type is. return object
def sampleGiven(self, value): obj = value[self.object] return getattr(obj, self.attribute) def evaluateInner(self, context): obj = valueInContext(self.object, context) return AttributeDistribution(self.attribute, obj) def supportInterval(self): obj = self.object if isinstance(obj, MultiplexerDistribution): attrs = (getattr(opt, self.attribute) for opt in obj.options) return unionOfSupports(supportInterval(attr) for attr in attrs) return None, None def __call__(self, *args, **kwargs): vty = self.object._valueType retTy = None if vty is not object: func = getattr(vty, self.attribute, None) if func: if isinstance(func, property): func = func.fget retTy = get_type_hints(func).get("return") return OperatorDistribution("__call__", self, args, kwargs, valueType=retTy) def __repr__(self): return f"{self.object!r}.{self.attribute}"
[docs]class OperatorDistribution(Distribution): """Distribution resulting from applying an operator to one or more distributions""" _deterministic = True def __init__(self, operator, obj, operands, kwoperands, valueType=None): operands = tuple(toDistribution(arg) for arg in operands) kwoperands = {key: toDistribution(kwarg) for key, kwarg in kwoperands.items()} dependencies = operands + tuple(kwoperands.values()) if valueType is None: ty = type_support.underlyingType(obj) valueType = self.inferType(ty, operator, operands, kwoperands) super().__init__(obj, *dependencies, valueType=valueType) self.operator = operator self.object = obj self.operands = operands self.kwoperands = kwoperands self.symbol = allowedReversibleOperators.get(operator) if self.symbol: if operator[:3] == "__r": self.reverse = "__" + operator[3:] else: self.reverse = "__r" + operator[2:] else: self.reverse = None
[docs] @staticmethod def inferType(ty, operator, operands, kwoperands): """Attempt to infer the result type of the given operator application.""" # If the object's type is known, see if we have a return type annotation. origin = type_support.get_type_origin(ty) op = getattr(origin if origin else ty, operator, None) if op: retTy = get_type_hints(op).get("return") if retTy: return retTy # Handle unions if origin == typing.Union: types = [] for option in type_support.get_type_args(ty): if option is type(None) and not hasattr(None, operator): # None does not support this operator; using it will raise an # exception, so we can ignore this case for type inference. continue res = OperatorDistribution.inferType( option, operator, operands, kwoperands ) types.append(res) return type_support.unifierOfTypes(types) if types else object # The supported arithmetic operations on scalars all return scalars. def scalar(thing): ty = type_support.underlyingType(thing) return type_support.canCoerceType(ty, float) if type_support.canCoerceType(ty, float) and all( scalar(operand) for operand in operands ): return float # Handle __getitem__ for known container types. if operator == "__getitem__" and len(operands) == 1: if origin and isinstance(origin, type): # parametrized ordinary type res = object index = operands[0] ity = type_support.underlyingType(index) isSlice = not hasattr(ity, "__index__") if issubclass(origin, list): res = ty if isSlice else type_support.get_type_args(ty)[0] elif issubclass(origin, tuple): args = type_support.get_type_args(ty) if type_support.is_typing_generic(ty): # If annotation used Tuple instead of tuple, preserve it origin = typing.Tuple if len(args) == 2 and args[1] is ...: # homogeneous variable-length tuple res = ty if isSlice else args[0] elif not needsSampling(index): res = args[index] if isSlice: res = origin[res] else: # can't pin down the index, so cover all possibilities if any(arg is typing.Any for arg in args): return origin if isSlice else object res = typing.Union[args] if isSlice: res = origin[res, ...] elif issubclass(origin, (dict, typing.Mapping, collections.abc.Mapping)): res = type_support.get_type_args(ty)[1] return object if res is typing.Any else res elif not origin: # ordinary type if issubclass(ty, str): return str # We can't tell what the result type is. return object
def sampleGiven(self, value): first = value[self.object] rest = [value[child] for child in self.operands] kwargs = {key: value[child] for key, child in self.kwoperands.items()} op = getattr(first, self.operator) result = op(*rest, **kwargs) if result is NotImplemented and self.reverse: assert len(rest) == 1 and len(kwargs) == 0 rop = getattr(rest[0], self.reverse) result = rop(first) if result is NotImplemented and self.symbol: raise TypeError( f"unsupported operand type(s) for {self.symbol}: " f"'{type(first).__name__}' and '{type(rest[0]).__name__}'" ) return result def evaluateInner(self, context): obj = valueInContext(self.object, context) operands = tuple(valueInContext(arg, context) for arg in self.operands) kwoperands = { key: valueInContext(arg, context) for key, kwarg in self.kwoperands.items() } return OperatorDistribution(self.operator, obj, operands, kwoperands) def supportInterval(self): if self.operator in ( "__add__", "__radd__", "__sub__", "__rsub__", "__mul__", "__rmul__", "__truediv__", "__rtruediv__", ): assert len(self.operands) == 1 and len(self.kwoperands) == 0 l1, r1 = supportInterval(self.object) l2, r2 = supportInterval(self.operands[0]) if l1 is None or l2 is None or r1 is None or r2 is None: return None, None if self.operator == "__add__" or self.operator == "__radd__": l = l1 + l2 r = r1 + r2 elif self.operator == "__sub__": l = l1 - r2 r = r1 - l2 elif self.operator == "__rsub__": l = l2 - r1 r = r2 - l1 elif self.operator in ("__mul__", "__rmul__"): prods = (l1 * l2, l1 * r2, r1 * l2, r1 * r2) l = min(*prods) r = max(*prods) elif self.operator == "__truediv__": if l2 > 0: l = l1 / r2 if l1 >= 0 else l1 / l2 r = r1 / l2 if r1 >= 0 else r1 / r2 else: l, r = None, None # TODO improve elif self.operator == "__rtruediv__": if l1 > 0: l = l2 / r1 if l2 >= 0 else l2 / l1 r = r2 / l1 if r2 >= 0 else r2 / r1 else: l, r = None, None else: raise AssertionError(f"unexpected operator {self.operator}") return l, r elif self.operator in ("__neg__", "__abs__"): assert len(self.operands) == 0 and len(self.kwoperands) == 0 l, r = supportInterval(self.object) if self.operator == "__neg__": return -r, -l elif self.operator == "__abs__": if r < 0: return -r, -l elif l < 0: return 0, max(-l, r) else: return l, r else: raise AssertionError(f"unexpected operator {self.operator}") return None, None def __repr__(self): return f"{self.object!r}.{self.operator}({argsToString(self.operands)})"
# Operators which can be applied to distributions. # Note that we deliberately do not include comparisons and __bool__, # since Scenic does not allow control flow to depend on random variables. allowedOperators = ( "__neg__", "__pos__", "__abs__", "__round__", "__getitem__", ("__len__", int), ) allowedReversibleOperators = { "__add__": "+", "__radd__": "+", "__sub__": "-", "__rsub__": "-", "__mul__": "*", "__rmul__": "*", "__truediv__": "/", "__rtruediv__": "/", "__floordiv__": "//", "__rfloordiv__": "//", "__mod__": "%", "__rmod__": "%", "__divmod__": "divmod()", "__rdivmod__": "divmod()", "__pow__": "**", "__rpow__": "**", } def makeOperatorHandler(op, ty): # Various special cases to simplify the expression forest by removing some # operations that do nothing (such as adding zero to a random number). if op in ("__add__", "__radd__", "__sub__"): def handler(self, arg): if ( not isLazy(arg) and issubclass(self._valueType, numbers.Number) and arg == 0 ): return self return OperatorDistribution(op, self, (arg,), {}, valueType=ty) elif op in ("__mul__", "__rmul__"): def handler(self, arg): if not isLazy(arg): if issubclass(self._valueType, numbers.Number) and arg == 1: return self from scenic.core.vectors import Orientation, globalOrientation if issubclass(self._valueType, Orientation) and arg == globalOrientation: return self return OperatorDistribution(op, self, (arg,), {}, valueType=ty) elif op in ("__truediv__", "__floordiv__", "__pow__"): def handler(self, arg): if ( not isLazy(arg) and issubclass(self._valueType, numbers.Number) and arg == 1 ): return self return OperatorDistribution(op, self, (arg,), {}, valueType=ty) else: # The general case. def handler(self, *args): return OperatorDistribution(op, self, args, {}, valueType=ty) return handler for data in allowedOperators: if isinstance(data, tuple): op, ty = data else: op = data ty = None setattr(Distribution, op, makeOperatorHandler(op, ty)) for op in allowedReversibleOperators: setattr(Distribution, op, makeOperatorHandler(op, None)) import scenic.core.type_support as type_support
[docs]class MultiplexerDistribution(Distribution): """Distribution selecting among values based on another distribution.""" _deterministic = True def __init__(self, index, options): self.index = index self.options = tuple(toDistribution(opt) for opt in options) assert len(self.options) > 0 valueType = type_support.unifyingType(self.options) super().__init__(index, *self.options, valueType=valueType) def sampleGiven(self, value): idx = value[self.index] assert 0 <= idx < len(self.options), (idx, len(self.options)) return value[self.options[idx]] def serializeValue(self, values, serializer): # We override this method to save space: we don't need to serialize all # of our options, only the one we're selecting. serializer.writeSamplable(self.index, values) choice = self.options[values[self.index]] serializer.writeSamplable(choice, values) def deserializeValue(self, serializer, values): serializer.readSamplable(self.index, values) choice = self.options[values[self.index]] serializer.readSamplable(choice, values) return values[choice] def evaluateInner(self, context): return type(self)( valueInContext(self.index, context), (valueInContext(opt, context) for opt in self.options), ) def supportInterval(self): return unionOfSupports(supportInterval(opt) for opt in self.options)
## Simple distributions
[docs]class Range(Distribution): """Uniform distribution over a range""" def __init__(self, low, high): low = type_support.toScalar(low, f"Range endpoint {low} is not a scalar") high = type_support.toScalar(high, f"Range endpoint {high} is not a scalar") super().__init__(low, high, valueType=float) self.low = low self.high = high def clone(self): return type(self)(self.low, self.high) def bucket(self, buckets=None): if buckets is None: buckets = 5 if not isinstance(buckets, int) or buckets < 1: raise ValueError(f"Invalid buckets for Range.bucket: {buckets}") if not isinstance(self.low, float) or not isinstance(self.high, float): raise RuntimeError(f"Cannot bucket Range with non-constant endpoints") endpoints = numpy.linspace(self.low, self.high, buckets + 1) ranges = [] for i, left in enumerate(endpoints[:-1]): right = endpoints[i + 1] ranges.append(Range(left, right)) return Options(ranges) def sampleGiven(self, value): return random.uniform(value[self.low], value[self.high]) def evaluateInner(self, context): low = valueInContext(self.low, context) high = valueInContext(self.high, context) return Range(low, high) def supportInterval(self): return unionOfSupports((supportInterval(self.low), supportInterval(self.high))) def __repr__(self): return f"Range({self.low!r}, {self.high!r})"
[docs]class Normal(Distribution): """Normal distribution""" def __init__(self, mean, stddev): mean = type_support.toScalar(mean, f"Normal mean {mean} is not a scalar") stddev = type_support.toScalar(stddev, f"Normal stddev {stddev} is not a scalar") super().__init__(mean, stddev, valueType=float) self.mean = mean self.stddev = stddev @staticmethod def cdf(mean, stddev, x): return (1 + math.erf((x - mean) / (sqrt2 * stddev))) / 2 @staticmethod def cdfinv(mean, stddev, x): import scipy # slow import not often needed return mean + (sqrt2 * stddev * scipy.special.erfinv(2 * x - 1)) def clone(self): return type(self)(self.mean, self.stddev) def bucket(self, buckets=None): if not isinstance(self.stddev, float): # TODO relax restriction? raise RuntimeError( f"Cannot bucket Normal with non-constant standard deviation" ) if buckets is None: buckets = 5 if isinstance(buckets, int): if buckets < 1: raise ValueError(f"Invalid buckets for Normal.bucket: {buckets}") elif buckets == 1: endpoints = [] elif buckets == 2: endpoints = [0] else: left = self.stddev * (-(buckets - 3) / 2 - 0.5) right = self.stddev * ((buckets - 3) / 2 + 0.5) endpoints = numpy.linspace(left, right, buckets - 1) else: endpoints = tuple(buckets) for i, v in enumerate(endpoints[:-1]): if v >= endpoints[i + 1]: raise ValueError( "Non-increasing bucket endpoints for " f"Normal.bucket: {endpoints}" ) if len(endpoints) == 0: return Options([self.clone()]) buckets = [(-math.inf, endpoints[0])] buckets.extend((v, endpoints[i + 1]) for i, v in enumerate(endpoints[:-1])) buckets.append((endpoints[-1], math.inf)) pieces = [] probs = [] for left, right in buckets: pieces.append(self.mean + TruncatedNormal(0, self.stddev, left, right)) prob = Normal.cdf(0, self.stddev, right) - Normal.cdf(0, self.stddev, left) probs.append(prob) assert math.isclose(math.fsum(probs), 1), probs return Options(dict(zip(pieces, probs))) def sampleGiven(self, value): return random.gauss(value[self.mean], value[self.stddev]) def evaluateInner(self, context): mean = valueInContext(self.mean, context) stddev = valueInContext(self.stddev, context) return Normal(mean, stddev) def __repr__(self): return f"Normal({self.mean!r}, {self.stddev!r})"
[docs]class TruncatedNormal(Normal): """Truncated normal distribution.""" def __init__(self, mean, stddev, low, high): if not isinstance(low, (float, int)) or not isinstance( high, (float, int) ): # TODO relax restriction? raise ValueError("Endpoints of TruncatedNormal must be constant") if low >= high: raise ValueError( "low endpoint of TruncatedNormal must be below high endpoint" ) super().__init__(mean, stddev) self.low = low self.high = high def clone(self): return type(self)(self.mean, self.stddev, self.low, self.high) def bucket(self, buckets=None): if not isinstance(self.stddev, float): # TODO relax restriction? raise RuntimeError( "Cannot bucket TruncatedNormal with " "non-constant standard deviation" ) if buckets is None: buckets = 5 if isinstance(buckets, int): if buckets < 1: raise ValueError(f"Invalid buckets for TruncatedNormal.bucket: {buckets}") endpoints = numpy.linspace(self.low, self.high, buckets + 1) else: endpoints = tuple(buckets) if len(endpoints) < 2: raise ValueError( "Too few bucket endpoints for " f"TruncatedNormal.bucket: {endpoints}" ) if endpoints[0] != self.low or endpoints[-1] != self.high: raise ValueError( f"TruncatedNormal.bucket endpoints {endpoints} " "do not match domain" ) for i, v in enumerate(endpoints[:-1]): if v >= endpoints[i + 1]: raise ValueError( "Non-increasing bucket endpoints for " f"TruncatedNormal.bucket: {endpoints}" ) pieces, probs = [], [] for i, left in enumerate(endpoints[:-1]): right = endpoints[i + 1] pieces.append(TruncatedNormal(self.mean, self.stddev, left, right)) prob = Normal.cdf(self.mean, self.stddev, right) - Normal.cdf( self.mean, self.stddev, left ) probs.append(prob) return Options(dict(zip(pieces, probs))) def sampleGiven(self, value): # TODO switch to method less prone to underflow? mean, stddev = value[self.mean], value[self.stddev] alpha = (self.low - mean) / stddev beta = (self.high - mean) / stddev alpha_cdf = Normal.cdf(0, 1, alpha) beta_cdf = Normal.cdf(0, 1, beta) if beta_cdf - alpha_cdf < 1e-15: warnings.warn("low precision when sampling TruncatedNormal") unif = random.random() p = alpha_cdf + unif * (beta_cdf - alpha_cdf) return mean + (stddev * Normal.cdfinv(0, 1, p)) def evaluateInner(self, context): mean = valueInContext(self.mean, context) stddev = valueInContext(self.stddev, context) return TruncatedNormal(mean, stddev, self.low, self.high) def supportInterval(self): return self.low, self.high def __repr__(self): return f"TruncatedNormal({self.mean!r}, {self.stddev!r}, {self.low!r}, {self.high!r})"
[docs]class DiscreteRange(Distribution): """Distribution over a range of integers.""" def __init__(self, low, high, weights=None, emptyMessage="empty DiscreteRange"): if weights: if not isinstance(low, int): raise TypeError("weighted DiscreteRange left endpoint must be an integer") if not isinstance(high, int): raise TypeError( "weighted DiscreteRange right endpoint must be an integer" ) if not low <= high: raise ValueError( f"DiscreteRange lower bound {low} is above upper bound {high}" ) self.low, self.high = low, high weights = tuple(weights) if len(weights) != high - low + 1: raise ValueError("weighted DiscreteRange with wrong number of weights") self.weights = weights self.cumulativeWeights = tuple(itertools.accumulate(weights)) self.options = tuple(range(low, high + 1)) else: self.low = type_support.toScalar( low, "DiscreteRange left endpoint not a scalar" ) self.high = type_support.toScalar( high, "DiscreteRange right endpoint not a scalar" ) self.weights = None self.emptyMessage = emptyMessage super().__init__(self.low, self.high, valueType=int) def clone(self): return type(self)(self.low, self.high, self.weights, self.emptyMessage) def bucket(self, buckets=None): return self.clone() # already bucketed def sampleGiven(self, value): if self.weights: return random.choices(self.options, cum_weights=self.cumulativeWeights)[0] left, right = math.ceil(value[self.low]), math.floor(value[self.high]) if right < left: raise RejectionException(self.emptyMessage) return random.randint(left, right) def supportInterval(self): ll, lh = supportInterval(self.low) hl, hh = supportInterval(self.high) return ll, hh def __repr__(self): weights = self.weights if all(weight == weights[0] for weight in weights): return f"DiscreteRange({self.low!r}, {self.high!r})" else: return f"DiscreteRange({self.low!r}, {self.high!r}, {self.weights})"
[docs]class Options(MultiplexerDistribution): """Distribution over a finite list of options. Specified by a dict giving probabilities; otherwise uniform over a given iterable. """ def __init__(self, opts): if isinstance(opts, dict): options, weights = [], [] for opt, prob in opts.items(): if not isinstance(prob, (float, int)): raise TypeError( f"discrete distribution weight {prob}" " is not a constant number" ) if prob < 0: raise ValueError(f"discrete distribution weight {prob} is negative") if prob == 0: continue options.append(opt) weights.append(prob) self.optWeights = dict(zip(options, weights)) else: weights = None options = tuple(opts) self.optWeights = None if len(options) == 0: raise RejectionException( "tried to make discrete distribution over empty domain!" ) index = self.makeSelector(len(options) - 1, weights) super().__init__(index, options) @staticmethod def makeSelector(n, weights): return DiscreteRange(0, n, weights) def clone(self): return type(self)(self.optWeights if self.optWeights else self.options) def bucket(self, buckets=None): return self.clone() # already bucketed def evaluateInner(self, context): if self.optWeights is None: return type(self)(valueInContext(opt, context) for opt in self.options) else: return type(self)( {valueInContext(opt, context): wt for opt, wt in self.optWeights.items()} ) def __repr__(self): if self.optWeights is not None: return f"{type(self).__name__}({self.optWeights!r})" else: args = ", ".join(repr(opt) for opt in self.options) return f"{type(self).__name__}({args})"
[docs]@unpacksDistributions def Uniform(*opts): """Uniform distribution over a finite list of options. Implemented as an instance of :obj:`Options` when the set of options is known statically, and an instance of `UniformDistribution` otherwise. """ if any(isinstance(opt, StarredDistribution) for opt in opts): return UniformDistribution(opts) else: return Options(opts)
[docs]class UniformDistribution(Distribution): """Uniform distribution over a variable number of options. See :obj:`Options` for the more common uniform distribution over a fixed number of options. This class is for the special case where iterable unpacking is applied to a distribution, so that the number of options is unknown at compile time. """ _deterministic = True def __init__(self, opts): self.options = opts valueType = type_support.unifyingType(self.options) length = 0 for opt in opts: if isinstance(opt, StarredDistribution): length += opt.__len__() else: length += 1 self.selector = DiscreteRange( 0, length - 1, emptyMessage="uniform distribution over empty domain" ) super().__init__(*self.options, self.selector, valueType=valueType) def clone(self): return type(self)(self.options) def sampleGiven(self, value): index = value[self.selector] opts = [] for opt in self.options: if isinstance(opt, StarredDistribution): opts.extend(value[opt]) else: opts.append(value[opt]) assert 0 <= index < len(opts) return opts[index] def evaluateInner(self, context): opts = tuple(valueInContext(opt, context) for opt in self.options) return UniformDistribution(opts) def __repr__(self): return f"UniformDistribution({self.options!r})"