def hastings_scores(opqr, pscale=None):
pred, succ, prop, revp = opqr.o, opqr.p, opqr.q, opqr.r
message = "No valid scalar probability distribution found"
assert succ is not None, message
assert isscalar(succ.prob), message
if pred is None:
return None
assert isscalar(pred.prob), "Preceding probability non-scalar"
if prop is None:
return None
else:
assert isscalar(prop.prob), "Proposal probability non-scalar"
prop = rescale(prop.prob, pscale, 1.)
if prop <= 0.:
return None
if revp is None:
return min(
1., div_prob(succ.prob, pred.prob, pscale, pscale, pscale=1.))
else:
assert isscalar(
revp.prob), "Reverse proposal probability non-scalar"
revp = rescale(revp.prob, pscale, 1.)
if revp <= 0.:
return 1.
return min(
1.,
div_prob(succ.prob * prop,
pred.prob * revp,
pscale,
pscale,
pscale=1.))
def set_func(self, func=None, *args, **kwds):
""" Set the Func instance's function object.
:param func: an uncallable object, callable function, or tuple of functions
:param *args: arguments to pass onto callables
:param **kwds: keywords to pass onto callables
Note that the following two reserved keywords are disallowed:
'order': which instead denotes a dictionary of remappings.
'delta': which instead denotes a mapping of differences.
A special case is made for func instances that are Scipy multivariate objects.
"""
self._func = func
self._args = tuple(args)
self._kwds = dict(kwds)
self.__order = None
self.__delta = None
self.__callable = None
self.__scipyobj = None
self.__isscipy = False
# Sanity check func
if self._func is None:
assert not args and not kwds, "No optional args without a function"
self.__ismulti = isinstance(self._func, tuple)
self.__isscalar = False
if not self.__ismulti:
self.__callable = callable(self._func)
if not self.__callable:
assert not args and not kwds, "No optional args with uncallable function"
self.__isscalar = isscalar(self._func)
else:
func_callable = [callable(func) for func in self._func]
func_isscalar = [isscalar(func) for func in self._func]
assert len(set(func_callable)) < 2, \
"Cannot mix callable and uncallable functions"
assert len(set(func_isscalar)) < 2, \
"Cannot mix scalars and nonscalars"
if len(func_callable):
self.__callable = func_callable[0]
self.__isscalar = func_isscalar[0]
if not self.__callable:
assert not args and not kwds, "No optional args with uncallable function"
if is_scipy_stats_mvar(self._func):
self.__isscipy = True
self.__scipyobj = self._func(*args, **kwds)
self.__scipycalls = {
0: self.__scipyobj.pdf,
1: self.__scipyobj.logpdf,
2: self.__scipyobj.cdf,
3: self.__scipyobj.logcdf,
4: self.__scipyobj.rvs,
}
if 'order' in self._kwds:
self.set_order(self._kwds.pop('order'))
if 'delta' in self._kwds:
self.set_delta(self._kwds.pop('delta'))
def metropolis_scores(opqr, pscale=None):
pred, succ = opqr.o, opqr.p
message = "No valid scalar probability distribution found"
assert succ is not None, message
assert isscalar(succ.prob), message
if pred is None:
return None
assert isscalar(pred.prob), "Preceding probability distribution non-scalar"
return min(1., div_prob(succ.prob, pred.prob, pscale, pscale, pscale=1.))
def matrix_cond_sample(pred_vals, succ_vals, prob, vset=None):
""" Returns succ_vals with sampling """
if not isunitset(succ_vals):
return succ_vals
assert isscalar(pred_vals), \
"Can only cumulatively sample from a single predecessor"
assert prob.ndim==2 and len(set(prob.shape)) == 1, \
"Transition matrix must be a square"
support = prob.shape[0]
if vset is None:
vset = set(range(support))
else:
assert len(vset) == support, \
"Transition matrix size {} incommensurate with set support {}".\
format(support, len(vset))
vset = sorted(vset)
pred_idx = vset.index(pred_vals)
cmf = np.cumsum(prob[:, pred_idx], axis=0)
succ_cmf = list(succ_vals)[0]
if type(succ_cmf) in VTYPES[int]:
succ_cmf = uniform(0., 1., succ_cmf)
else:
succ_cmf = np.atleast_1d(succ_cmf)
succ_idx = np.maximum(0, np.minimum(support - 1,
np.digitize(succ_cmf, cmf)))
return vset[succ_idx], pred_idx, succ_idx
def __call__(self, values=None):
""" Return a probability distribution for the quantities in values. """
dist_name = self.eval_dist_name(values)
vals = self.evaluate(values)
prob = self.eval_prob(vals)
dims = {self._name: None} if isscalar(vals[self.name]) else {self._name: 0}
return PD(dist_name, vals, dims=dims, prob=prob, pscale=self._pscale)
def lookup_square_matrix(col_vals,
row_vals,
sq_matrix,
vset=None,
col_idx=None,
row_idx=None):
assert sq_matrix.ndim==2 and len(set(sq_matrix.shape)) == 1, \
"Transition matrix must be a square"
support = sq_matrix.shape[0]
if vset is None:
vset = list(range(support))
else:
assert len(vset) == support, \
"Transition matrix size {} incommensurate with set support {}".\
format(support, len(vset))
vset = sorted(vset)
rc_scalar = False
if row_idx is None:
if isscalar(row_vals):
rc_scalar = True
row_idx = vset.index(row_vals)
else:
if isinstance(row_vals, np.ndarray):
row_vals = np.ravel(row_vals).tolist()
row_idx = [vset.index(row_val) for row_val in row_vals]
elif isscalar(row_idx):
rc_scalar = True
if col_idx is None:
if isscalar(col_vals):
rc_scalar = True
col_idx = vset.index(col_vals)
else:
if isinstance(col_vals, np.ndarray):
col_vals = np.ravel(col_vals).tolist()
col_idx = [vset.index(col_val) for col_val in col_vals]
elif isscalar(col_idx):
rc_scalar = True
if rc_scalar:
return sq_matrix[row_idx, col_idx]
mat = np.empty([len(row_idx), len(col_idx)], dtype=sq_matrix.dtype)
for i, row in enumerate(row_idx):
mat[i] = sq_matrix[row][col_idx]
return mat
def dims_from_vals(vals_dict):
if not isinstance(vals_dict, dict):
raise TypeError("Dictionary type expected")
dims = collections.OrderedDict()
run_dim = 0
for key, val in vals_dict.items():
if isscalar(val):
dims.update({key: None})
else:
assert val.size == np.product(vals.shape), \
"Multiple non-singleton dimensions: {}".format(val.size)
if val.size > 1:
run_dim = np.argmax(val.shape)
dims.update({key: run_dim})
run_dim += 1
def eval_step(self, pred_vals, succ_vals, reverse=False):
""" Returns adjusted succ_vals """
# Evaluate deltas if required
if succ_vals is None:
if self._delta is None:
pred_values = list(pred_vals.values())
if all([isscalar(pred_value) for pred_value in pred_values]):
raise ValueError(
"Stochastic step sampling not supported for Field; use RF"
)
else:
succ_vals = pred_vals
else:
succ_vals = self.eval_delta()
elif isinstance(succ_vals, Expression) or \
isinstance(succ_vals, (tuple, self._delta_type)):
succ_vals = self.eval_delta(succ_vals)
# Apply deltas
cond = None
if isinstance(succ_vals, self._delta_type):
succ_vals = self.apply_delta(pred_vals, succ_vals)
elif isunitsetint(succ_vals):
raise ValueError(
"Stochastic step sampling not supported for Field; use RF")
# Initialise outputs with predecessor values
dims = {}
kwargs = {'reverse': reverse}
if cond is not None:
kwargs = {'cond': cond}
vals = collections.OrderedDict()
for key in self._keylist:
vals.update({key: pred_vals[key]})
if succ_vals is None:
return vals, dims, kwargs
# If stepping, add successor values
for key in self._keylist:
mod_key = key + "'"
succ_key = key if mod_key not in succ_vals else mod_key
vals.update({key + "'": succ_vals[succ_key]})
return vals, dims, kwargs
def eval_step(self, pred_vals, succ_vals, reverse=False):
""" Returns adjusted succ_vals """
if succ_vals is None:
if self._delta is None:
if all([isscalar(pred_value) for pred_value in pred_vals]):
succ_vals = {0}
# If not sampling succeeding values, use deterministic call
if not isunitsetint(succ_vals):
return super().eval_step(pred_vals, succ_vals, reverse=reverse)
if self._tfun is not None and self._tfun.callable:
succ_vals = self.eval_tfun(pred_vals)
elif self._nvars == 1:
var = self._varlist[0]
tran = var.tran
tfun = var.tfun
if (tran is not None and not tran.callable) or \
(tfun is not None and tfun.callable):
vals, dims, kwargs = var.eval_step(pred_vals[var.name],
succ_vals,
reverse=reverse)
return vals, dims, kwargs
raise ValueError("Transitional CDF calling requires callable tfun")
else:
raise ValueError("Transitional CDF calling requires callable tfun")
# Initialise outputs with predecessor values
dims = {}
kwargs = {'reverse': reverse}
vals = collections.OrderedDict()
for key in self._keylist:
vals.update({key: pred_vals[key]})
if succ_vals is None and self._tran is None:
return vals, dims, kwargs
# If stepping or have a transition function, add successor values
for key in self._keylist:
mod_key = key + "'"
succ_key = key if mod_key not in succ_vals else mod_key
vals.update({key + "'": succ_vals[succ_key]})
return vals, dims, kwargs
def step(self, *args, **kwds):
""" Returns a proposal distribution p(args[1]) given args[0], depending on
whether using self._prop, that denotes a simple proposal distribution,
or self._tran, that denotes a transitional distirbution. """
reverse = False if 'reverse' not in kwds else kwds.pop('reverse')
pred_vals, succ_vals = None, None
if len(args) == 1:
if isinstance(args[0], (list, tuple)) and len(args[0]) == 2:
pred_vals, succ_vals = args[0][0], args[0][1]
else:
pred_vals = args[0]
elif len(args) == 2:
pred_vals, succ_vals = args[0], args[1]
# Evaluate predecessor values
if not isinstance(pred_vals, dict):
pred_vals = {key: pred_vals for key in self._keyset}
pred_vals = self.parse_args(pred_vals, pass_all=True)
dist_pred_name = self.eval_dist_name(pred_vals)
pred_vals, pred_dims = self.evaluate(pred_vals)
# Default successor values if None and delta is None
if succ_vals is None and self._delta is None:
pred_values = list(pred_vals.values())
if all([isscalar(pred_value) for pred_value in pred_values]):
succ_vals = {0}
else:
succ_vals = pred_vals
# Evaluate successor evaluates
vals, dims, kwargs = self.eval_step(pred_vals,
succ_vals,
reverse=reverse)
succ_vals = {
key[:-1]: val
for key, val in vals.items() if key[-1] == "'"
}
cond = self.eval_tran(vals, **kwargs)
dist_succ_name = self.eval_dist_name(succ_vals, "'")
dist_name = '|'.join([dist_succ_name, dist_pred_name])
return PD(dist_name, vals, dims=dims, prob=cond, pscale=self._pscale)
def uniform_prob(*args, prob=None, inside=None, pscale=1.):
""" Uniform probability function for discrete and continuous vtypes. """
# Detect ptype, default to prob if no values, otherwise detect vtype
assert len(args) >= 1, "Minimum of a single positional argument"
pscale = eval_pscale(pscale)
use_logs = iscomplex(pscale)
if prob is None:
prob = 0. if use_logs else 1.
vals = args[0]
if vals is None:
return prob
vtype = eval_vtype(vals) if callable(inside) else eval_vtype(inside)
# Set inside function by vtype if not specified
if not callable(inside):
if vtype in VTYPES[float]:
inside = lambda x: np.logical_and(x >= min(inside), x <= max(inside
))
else:
inside = lambda x: np.isin(x, inside)
# If scalar, check within variable set
p_zero = NEARLY_NEGATIVE_INF if use_logs else 0.
if isscalar(vals):
prob = prob if inside(vals) else p_zero
# Otherwise treat as uniform within range
else:
p_true = prob
prob = np.tile(p_zero, vals.shape)
prob[inside(vals)] = p_true
# This section below is there just to play nicely with conditionals
if len(args) > 1:
for arg in args[1:]:
if use_logs:
prob = prob + uniform_prob(arg, inside=inside, pscale=0.j)
else:
prob = prob * uniform_prob(arg, inside=inside)
return prob
def eval_delta(self, delta=None):
""" Evaluates the value(s) of a delta operation without applying them.
:param delta: delta value(s) to offset (see Variable.apply_delta).
:return: the evaluated delta offset values.
:rtype Variable.delta()
If delta is not entered, then the default set by Variable.set_delta() is used.
"""
delta = delta or self._delta
if delta is None:
return None
if isinstance(delta, Expression):
if delta.ret_callable():
return delta
delta = delta()
if isinstance(delta, self._Delta):
delta = delta[0]
orand = isinstance(delta, tuple)
urand = isinstance(delta, list)
if orand:
assert len(delta) == 1, "Tuple delta must contain one element"
delta = delta[0]
if self._vtype not in VTYPES[bool]:
delta = delta if np.random.uniform() > 0.5 else -delta
elif urand:
assert len(delta) == 1, "List delta must contain one element"
delta = delta[0]
if self._vtype in VTYPES[bool]:
pass
elif self._vtype in VTYPES[int]:
delta = np.random.randint(-delta, delta)
else:
delta = np.random.uniform(-delta, delta)
assert isscalar(delta), "Unrecognised delta type: {}".format(delta)
if delta == self._delta and self._delta_kwds['scale']:
assert np.isfinite(self._length), "Cannot scale by infinite length"
delta *= self._length
return self._Delta(delta)
def evaluate(self, *args, _skip_parsing=False, min_dim=0, **kwds):
"""
Keep args and kwds since could be called externally. This ignores self._prob.
"""
values = self.parse_args(*args, **
kwds) if not _skip_parsing else args[0]
dims = collections.OrderedDict()
# Don't reshape if all scalars (and therefore by definition no shared keys)
if all([np.isscalar(value)
for value in values.values()]): # use np.scalar
return values, dims
# Create reference mapping for shared keys across vars
values_ref = collections.OrderedDict(
{key: [key, None]
for key in self._keylist})
for key in values.keys():
if ',' in key:
subkeys = key.split(',')
for i, subkey in enumerate(subkeys):
values_ref[subkey] = [key, i]
# Share dimensions for joint variables and do not dimensionalise scalars
ndim = min_dim
dims = collections.OrderedDict({key: None for key in self._keylist})
seen_keys = set()
for i, key in enumerate(self._keylist):
new_dim = False
if values_ref[key][1] is None: # i.e. not shared
if not isdimensionless(values[key]):
dims[key] = ndim
new_dim = True
seen_keys.add(key)
elif key not in seen_keys:
val_ref = values_ref[key]
subkeys = val_ref[0].split(',')
for subkey in subkeys:
dims[subkey] = ndim
seen_keys.add(subkey)
if not isdimensionless(values[val_ref[0]][val_ref[1]]):
new_dim = True
if new_dim:
ndim += 1
# Reshape
vdims = [dim for dim in dims.values() if dim is not None]
ndims = max(vdims) + 1 if len(vdims) else 0
ones_ndims = np.ones(ndims, dtype=int)
vals = collections.OrderedDict()
for i, var in enumerate(self._varlist):
key = var.name
reshape = True
if key in values.keys():
vals.update({key: values[key]})
reshape = not np.isscalar(vals[key])
if vals[key] is None or isinstance(vals[key], set):
vals.update(var.evaluate(vals[key]))
else:
val_ref = values_ref[key]
vals_val = values[val_ref[0]][val_ref[1]]
if vals_val is None or isinstance(vals_val, set):
vals.update(var.evaluate(vals_val))
else:
vals.update({key: vals_val})
if reshape and not isscalar(vals[key]):
re_shape = np.copy(ones_ndims)
re_dim = dims[key]
re_shape[re_dim] = vals[key].size
vals[key] = vals[key].reshape(re_shape)
# Remove dimensionality for singletons
for key in self._keylist:
if issingleton(vals[key]):
dims[key] = None
return vals, dims
def set_expr(self, expr=None, *args, **kwds):
""" Set the Func instance's function object.
:param expr: an uncallable object, callable function, or tuple of functions
:param *args: arguments to pass onto callables
:param **kwds: keywords to pass onto callables
Note that the following two reserved keywords are disallowed:
'order': which instead denotes a dictionary of remappings.
'delta': which instead denotes a mapping of differences.
"""
self._expr = expr
self._args = tuple(args)
self._kwds = dict(kwds)
self._callable = None
self._islambda = None
self._ismulti = None
self._isscalar = None
self._isiconic = None
self.__order = None
self.__delta = None
self.__inverse = None
self.__invexpr = None
self.__invderi = None
self.__invertible = False if 'invertible' not in kwds else \
self._kwds.pop('invertible')
self._exprs = collections.OrderedDict()
self._symbols = collections.OrderedDict()
# Sanity check func
if self._expr is None:
assert not args and not kwds, "No optional args without a function"
self._isscalar = isscalar(self._expr)
self._isiconic = isiconic(self._expr)
self._ismulti = isinstance(self._expr, (dict, tuple))
if self.__invertible:
assert not self._ismulti,\
"Cannot invert for multiple expressions"
# Non-multi iconic
if self._isiconic:
self._exprs.update(
{None: Expr(self._expr, *self._args, **self._kwds)})
self._symbols.update(self._exprs[None].symbols)
self._ismulti = self.__invertible and \
len(self._exprs[None].symbols) == 1
self._callable = True
self._islambda = False
# Unitary
elif not self._ismulti:
self._callable = callable(self._expr)
self._islambda = islambda(self._expr)
if not self._callable:
assert not args and not kwds, \
"No optional arguments with uncallable expressions"
self._isscalar = isscalar(self._expr)
# Multi
else:
exprs = self._expr if isinstance(self._expr, tuple) else \
self._expr.values()
self._callable = False
self._islambda = False
self._isscalar = False
self._isiconic = False
each_callable = [callable(expr) for expr in exprs]
each_islambda = [islambda(expr) for expr in exprs]
each_isscalar = [isscalar(expr) for expr in exprs]
each_isiconic = [isiconic(expr) for expr in exprs]
assert len(set(each_callable)) < 2, \
"Cannot mix callable and uncallable expressions"
assert len(set(each_islambda)) < 2, \
"Cannot mix lambda and non-lambda expressions"
assert len(set(each_isscalar)) < 2, \
"Cannot mix scalars and nonscalars"
assert len(set(each_isiconic)) < 2, \
"Cannot mix iconics and non-iconics"
if len(self._expr):
self._callable = each_callable[0]
self._islambda = each_islambda[0]
self._isscalar = each_isscalar[0]
self._isiconic = each_isiconic[0]
if not self._callable:
assert not args and not kwds, "No optional args with uncallable function"
if self._isiconic:
assert not args and not kwds, "No optional args with iconic function"
if self._isiconic:
if isinstance(self._expr, tuple):
for i, expr in enumerate(self._expr):
self._exprs.update(
{i: Expr(expr, *self._args, **self._kwds)})
self._symbols.update(self._exprs[i].symbols)
elif isinstance(self._expr, dict):
for key, val in self._expr.items():
self._exprs.update(
{key: Expr(val, *self._args, **self._kwds)})
self._symbols.update(self._exprs[key].symbols)
if 'order' in self._kwds:
self.set_order(self._kwds.pop('order'))
if 'delta' in self._kwds:
self.set_delta(self._kwds.pop('delta'))
self._set_partials()
self._keys = list(self._partials.keys())
def set_prob(self, prob=None, *args, **kwds):
""" Sets the probability and pscale with optional arguments and keywords.
:param prob: may be a scalar, array, or callable function.
:param *args: optional arguments to pass if prob is callable.
:param **kwds: optional keywords to pass if prob is callable.
'pscale' is a reserved keyword. See set_pscale() for explanation of how
pscale is used.
"""
pscale = None if 'pscale' not in kwds else kwds.pop('pscale')
self.pscale = pscale or self._pscale
self.__isscipy = is_scipy_stats_dist(prob)
self.__issympy = is_sympy_stats_dist(prob)
self.__issmvar = is_scipy_stats_mvar(prob)
# Probabilities can be defined as regular expressions, but iconise scalars
if not self.__isscipy and not self.__issympy:
prob_scalar = isscalar(prob)
self._prob = prob if not prob_scalar else sympy.Float(prob)
self.set_expr(self._prob, *args, **kwds)
# Set probability callers to expression
if not prob_scalar:
self._prob = self._expr
return # Expression() takes care of all partials
# Scipy/SymPy expressions (self._expr set by _set_partials() later)
if self.__issmvar or self.__issympy:
self._expr = prob
self._ismulti = True
self._callable = True
self._islambda = False
self._isscalar = False
else:
self.set_expr(prob, *args, **kwds)
self._args = tuple(args)
self._kwds = dict(kwds)
self._prob = self._expr
if 'order' in self._kwds:
self.set_order(self._kwds.pop('order'))
if 'delta' in self._kwds:
self.set_delta(self._kwds.pop('delta'))
self._set_partials()
# Scipy dist - set pfun and sfun calls - self._prob is updated but not used
if self.__isscipy:
self._ismulti = True
self._prob = self._partials['logp'] if self._logp \
else self._partials['prob']
if 'cdf' in self._keys and 'ppf' in self._keys:
self.set_pfun((self._expr.cdf, self._expr.ppf), *self._args,
**self._kwds)
if 'rvs' in self._keys and hasattr(self._expr, 'rvs'):
self.set_sfun(self._expr.rvs, *self._args, **self._kwds)
# Sympy dist - set pfun and sfun calls - self._prob is updated but not used
elif self.__issympy:
self._prob = self._partials['logp'].expr if self._logp \
else self._partials['prob'].expr
if 'cdf' in self._keys and 'icdf' in self._keys:
self.set_pfun((self._partials['cdf'], self._partials['icdf']))
if 'sfun' in self._keys:
self._partials.update({
'sfun':
functools.partial(sympy_sfun, self._distr, dtype=float)
})
self.set_sfun(sympy_sfun, self._expr)
def eval_step(self, pred_vals, succ_vals, reverse=False):
""" Evaluates a successive values from previous values with an optional
direction reversal flag, outputting a three-length tuple that includes the
successive values in the first argument.
:param pred_vals: predecessor values (NumPy array).
:param succ_vals: succecessor values (see step()).
:param reverse: boolean flag (default False) to reverse direction.
:return vals: a dictionary including both predecessor and successor values.
:return dims: a dictionary with dimension indices for the values in vals.
:return kwargs: a dictionary that includes optional keywords for eval_tran()
"""
if succ_vals is None:
assert self._tran is not None, "No transitional function specified"
if isinstance(pred_vals, dict):
pred_vals = pred_vals[self.name]
kwargs = dict() # to pass over to eval_tran()
if succ_vals is None:
if self._delta is None:
succ_vals = {0} if isscalar(pred_vals) else pred_vals
else:
delta = self.eval_delta()
succ_vals = self.apply_delta(pred_vals, delta)
#---------------------------------------------------------------------------
def _reshape_vals(pred, succ):
dims = {}
ndim = 0
# Now reshape the values according to succ > prev dimensionality
if issingleton(succ):
dims.update({self._name+"'": None})
else:
dims.update({self._name+"'": ndim})
ndim += 1
if issingleton(pred):
dims.update({self._name: None})
else:
dims.update({self._name: ndim})
ndim += 1
if ndim == 2: # pred_vals distributed along inner dimension:
pred = pred.reshape([1, pred.size])
succ = succ.reshape([succ.size, 1])
return pred, succ, dims
#---------------------------------------------------------------------------
# Scalar treatment is the most trivial and ignores reverse
if self._tran is None or self._tran.isscalar:
if isunitsetint(succ_vals):
succ_vals = self.evaluate(succ_vals, use_pfun=False)[self._name]
elif isunitsetfloat(succ_vals):
assert self._vtype in VTYPES[float], \
"Inverse CDF sampling for scalar probabilities unavailable for " + \
"{} data type".format(self._vtype)
cdf_val = list(succ_vals)[0]
lo, hi = min(self._limits), max(self._limits)
succ_val = lo*(1.-cdf_val) + hi*cdf_val
if self._ufun is not None:
succ_val = self.ufun[-1](succ_val)
prob = self._tran() if self._tran is not None else None
pred_vals, succ_vals, dims = _reshape_vals(pred_vals, succ_vals)
# Handle discrete non-callables
elif not self._tran.callable:
if reverse and not self._tran.ismulti and not self.__sym_tran:
warnings.warn("Reverse direction called from asymmetric transitional")
prob = self._tran() if not self._tran.ismulti else \
self._tran[int(reverse)]()
if isunitset(succ_vals):
succ_vals, pred_idx, succ_idx = matrix_cond_sample(pred_vals,
succ_vals,
prob=prob,
vset=self._vset)
kwargs.update({'pred_idx': pred_idx, 'succ_idx': succ_idx})
pred_vals, succ_vals, dims = _reshape_vals(pred_vals, succ_vals)
# That just leaves callables
else:
kwds = {self._name: pred_vals}
if isunitset(succ_vals):
assert self._tfun is not None, \
"Conditional sampling requires setting CDF and ICDF " + \
"conditional functions using rv.set.tfun()"
assert isscalar(pred_vals), \
"Successor sampling only possible with scalar predecessors"
succ_vals = list(succ_vals)[0]
if type(succ_vals) in VTYPES[int] or type(succ_vals) in VTYPES[np.uint]:
lo, hi = min(self._ulims), max(self._ulims)
kwds.update({self._name+"'": np.array([lo, hi], dtype=float)})
lohi = self._tfun[0](**kwds)
lo, hi = float(min(lohi)), float(max(lohi))
succ_vals = uniform(lo, hi, succ_vals,
isinstance(self._vset[0], tuple),
isinstance(self._vset[1], tuple))
else:
succ_vals = np.atleast_1d(succ_vals)
kwds.update({self._name: pred_vals,
self._name+"'": succ_vals})
succ_vals = self._tfun[1](**kwds)
elif not isscalar(succ_vals):
succ_vals = np.atleast_1d(succ_vals)
pred_vals, succ_vals, dims = _reshape_vals(pred_vals, succ_vals)
vals = collections.OrderedDict({self._name+"'": succ_vals,
self._name: pred_vals})
kwargs.update({'reverse': reverse})
return vals, dims, kwargs
def slice_by_keyvals(spec, vals, prob, vals_dims=None, spec_dims=None):
""" Slices prob by values of spec in vals.
:param spec: dictionary of {key:val} to match with vals.
:param vals: dictionary of {key:val} describing prob.
:param prob: a multidimensional NumPy array to slice.
:param vals_dims: dictionary of dimensional decription for vals.
:param spec_dims: dictionary of dimensional decription for spec.
If vals_dims and/or spec_dims are not entered, they are 'guessed' from vals
and spec respectively, but correct guessing is not assured. Non-none dimensions
for vals_dims and spec_dims must be mutually ordered monotically by key.
"""
# Check for consistent keys
keys = list(spec.keys())
assert set(keys) == set(vals.keys()), "Keys for spec and vals unmatched"
# Function to default dimensions if not given
def dims_from_vals(vals_dict):
if not isinstance(vals_dict, dict):
raise TypeError("Dictionary type expected")
dims = collections.OrderedDict()
run_dim = 0
for key, val in vals_dict.items():
if isscalar(val):
dims.update({key: None})
else:
assert val.size == np.product(vals.shape), \
"Multiple non-singleton dimensions: {}".format(val.size)
if val.size > 1:
run_dim = np.argmax(val.shape)
dims.update({key: run_dim})
run_dim += 1
# Default spec_dims and vals_dims if not given
if spec_dims is None:
spec_dims = dims_from_vals(spec)
else:
assert set(spec.keys()) == set(spec_dims.keys()), \
"Keys for spec and spec_dims unmatched"
if vals_dims is None:
vals_dims = dims_from_vals(vals)
else:
assert set(vals.keys()) == set(vals_dims.keys()), \
"Keys for spec and spec_dims unmatched"
# Determine maximum dimensionality of input
vals_ndim = 0
for dim in vals_dims.values():
if dim:
vals_ndim = max(vals_ndim, dim)
# Determine maximum dimensionality of output from spec and spec_dims
spec_ndim = 0
for key, dim in spec_dims.items():
if dim:
spec_ndim = max(spec_ndim, dim)
if not isscalar(spec[key]):
spec_ndim = max(spec[key].ndim, dim)
# Check for monotonic ordering of dimensions
dims = [dim for dim in vals_dims.values() if dim is not None]
if len(dims) > 1:
assert np.min(
np.diff(dims)) > 0, "Dimensionality not monotically ordered"
dims = [dim for dim in spec_dims.values() if dim is not None]
if len(dims) > 1:
assert np.min(
np.diff(dims)) > 0, "Dimensionality not monotically ordered"
# Evaluate reshape and slices from matches between spec and vals
reshape = [1] * spec_ndim
slices = [slice(None) for _ in range(vals_ndim + 1)]
for i, key in enumerate(keys):
if vals_dims[key] is None: # If source is scalar
if not isscalar(spec[key]):
assert np.all(spec[key] == vals[key]), \
"Cannot slice by multiple values"
elif spec[key] != vals[key]:
return np.empty(np.zeros(len(dims), dtype=int), dtype=float)
else:
pass
if spec_dims[key] is None: # If target is scalar
dim = vals_dims[key]
match = np.ravel(vals[key]) == spec[key]
n_matches = match.sum()
if n_matches == 0:
slices[dim] = slice(0, 0)
elif n_matches == 1:
slices[dim] = np.nonzero(match)[0]
else:
raise ValueError("Non-unique matches found")
else:
assert np.all(np.ravel(vals[key]) == np.ravel(spec[key])), \
"Ambiguous specification with values mismatch"
dim = spec_dims[key]
reshape[dim] = vals[key].size
return prob[tuple(slices)].reshape(reshape)
def apply_delta(self, values, delta=None, bound=None):
""" Applies delta operation to values optionally contrained by bounds.
:param values: Numpy array values to apply.
:param delta: delta value(s) to offset to the values
:param bound: optional argument to contrain outputs.
:return: Returns the values following the delta operation.
If delta is not entered, then the default set by Variable.set_delta() is used.
Delta may be a scalar or a single scalar value contained in a tuple or list.
1. A scalar value: is summated to values (transformed if ufun is specified).
2. A tuple: the polarity of the scalar value is randomised for the delta.
3. A list: the delta is uniformly sampled in the range [0, scalar].
"""
# If dictionary input type, values are keyed by variable name
if isinstance(values, dict):
values = values[self.name]
# Call eval_delta() if values is a list and return values if delta is None
delta = delta or self._delta
if isinstance(delta, Expression):
if delta.ret_callable():
return delta(values)
delta = delta()
elif self._vtype not in VTYPES[bool]:
if isinstance(delta, (list, tuple)):
delta = self.eval_delta(delta)
if isinstance(delta, self._Delta):
delta = delta[0]
if delta is None:
return values
# Apply the delta, treating bool as a special case
if self._vtype in VTYPES[bool]:
orand = isinstance(delta, tuple)
urand = isinstance(delta, list)
if orand or urand:
assert len(
delta) == 1, "Tuple/list delta must contain one element"
delta = delta[0]
if isscalar(values) or orand:
vals = values if delta > np.random.uniform() > 0.5 \
else np.logical_not(values)
else:
flip = delta > np.random.uniform(size=values.shape)
vals = np.copy(values)
vals[flip] = np.logical_not(vals[flip])
else:
vals = np.array(values, dtype=int) + np.array(delta, dtype=int)
vals = np.array(np.mod(vals, 2), dtype=bool)
elif self._ufun is None or self.__no_ucov:
vals = values + delta
else:
transformed_vals = self.ufun[0](values) + delta
vals = self.ufun[1](transformed_vals)
vals = revtype(vals, self._vtype)
# Apply bounds
if bound is None:
bound = False if 'bound' not in self._delta_kwds \
else self._delta_kwds['bound']
if not bound:
return vals
maybe_bounce = [False] if self._vtype not in VTYPES[float] else \
[isinstance(self._vset[0], tuple),
isinstance(self._vset[1], tuple)]
if not any(maybe_bounce):
return np.maximum(self._vlims[0], np.minimum(self._vlims[1], vals))
# Bouncing scalars and arrays without and with boolean indexing respectively
if isscalar(vals):
if all(maybe_bounce):
if not self._inside(vals):
vals = values
elif maybe_bounce[0]:
if vals < self._vlims[0]:
vals = values
else:
vals = np.minimum(self._vlims[1], vals)
else:
if vals > self._vlims[1]:
vals = values
else:
vals = np.maximum(self._vlims[0], vals)
else:
if all(maybe_bounce):
outside = np.logical_not(self._inside(vals))
vals[outside] = values[outside]
elif maybe_bounce[0]:
outside = vals <= self._vlims[0]
vals[outside] = values[outside]
vals = np.minimum(self._vlims[1], vals)
else:
outside = vals >= self._vlims[1]
vals[outside] = values[outside]
vals = np.maximum(self._vlims[0], vals)
return vals
def product(*args, **kwds):
""" Multiplies two or more PDs subject to the following:
1. They must not share the same marginal variables.
2. Conditional variables must be identical unless contained as marginal from
another distribution.
"""
from probayes.pd import PD
# Check pscales, scalars, possible fasttrack
if not len(args):
return None
kwds = dict(kwds)
pscales = [arg.pscale for arg in args]
pscale = kwds.get('pscale', None) or prod_pscale(pscales)
aresingleton = [arg.issingleton for arg in args]
maybe_fasttrack = all(aresingleton) and \
np.all(pscale == np.array(pscales)) and \
pscale in [0, 1.]
# Collate vals, probs, marg_names, and cond_names as lists
vals = [collections.OrderedDict(arg) for arg in args]
probs = [arg.prob for arg in args]
marg_names = [list(arg.marg.values()) for arg in args]
cond_names = [list(arg.cond.values()) for arg in args]
# Detect uniqueness in marginal keys and identical conditionals
all_marg_keys = []
for arg in args:
all_marg_keys.extend(list(arg.marg.keys()))
marg_sets = None
if len(all_marg_keys) != len(set(all_marg_keys)):
marg_keys, cond_keys, marg_sets, = None, None, None
for arg in args:
if marg_keys is None:
marg_keys = list(arg.marg.keys())
elif marg_keys != list(arg.marg.keys()):
marg_keys = None
break
if cond_keys is None:
cond_keys = list(arg.cond.keys())
elif cond_keys != list(arg.cond.keys()):
marg_keys = None
break
if marg_keys:
are_marg_sets = np.array([isunitsetint(arg[marg_key]) for
marg_key in marg_keys])
if marg_sets is None:
if np.any(are_marg_sets):
marg_sets = are_marg_sets
else:
marg_keys = None
break
elif not np.all(marg_sets == are_marg_sets):
marg_keys = None
break
assert marg_keys is not None and marg_sets is not None, \
"Non-unique marginal variables for currently not supported: {}".\
format(all_marg_keys)
maybe_fasttrack = True
# Maybe fast-track identical conditionals
if maybe_fasttrack:
marg_same = True
cond_same = True
if marg_sets is None: # no need to recheck if not None (I think)
marg_same = True
for name in marg_names[1:]:
if marg_names[0] != name:
marg_same = False
break
cond_same = not any(cond_names)
if not cond_same:
cond_same = True
for name in cond_names[1:]:
if cond_names[0] != name:
cond_same = False
break
if marg_same and cond_same:
marg_names = marg_names[0]
cond_names = cond_names[0]
prod_marg_name = ','.join(marg_names)
prod_cond_name = ','.join(cond_names)
prod_name = '|'.join([prod_marg_name, prod_cond_name])
prod_vals = collections.OrderedDict()
for i, val in enumerate(vals):
areunitsetints = np.array([isunitsetint(_val)
for _val in val.values()])
if not np.any(areunitsetints):
prod_vals.update(val)
else:
assert marg_sets is not None, "Variable mismatch"
assert np.all(marg_sets == areunitsetints[:len(marg_sets)]), \
"Variable mismatch"
if not len(prod_vals):
prod_vals.update(collections.OrderedDict(val))
else:
for j, key in enumerate(prod_vals.keys()):
if areunitsetints[j]:
prod_vals.update({key: {list(prod_vals[key])[0] + \
list(val[key])[0]}})
if marg_sets is not None:
prob, pscale = prod_rule(*tuple(probs), pscales=pscales, pscale=pscale)
return PD(prod_name, prod_vals, dims=args[0].dims, prob=prob, pscale=pscale)
else:
prod_prob = float(sum(probs)) if iscomplex(pscale) else float(np.prod(probs))
return PD(prod_name, prod_vals, prob=prod_prob, pscale=pscale)
# Check cond->marg accounts for all differences between conditionals
prod_marg = [name for dist_marg_names in marg_names \
for name in dist_marg_names]
prod_marg_name = ','.join(prod_marg)
flat_cond_names = [name for dist_cond_names in cond_names \
for name in dist_cond_names]
cond2marg = [cond_name for cond_name in flat_cond_names \
if cond_name in prod_marg]
prod_cond = [cond_name for cond_name in flat_cond_names \
if cond_name not in cond2marg]
cond2marg_set = set(cond2marg)
# Check conditionals compatible
prod_cond_set = set(prod_cond)
cond2marg_dict = {name: None for name in cond2marg}
for i, arg in enumerate(args):
cond_set = set(cond_names[i]) - cond2marg_set
if cond_set:
assert prod_cond_set == cond_set, \
"Incompatible product conditional {} for conditional set {}: ".format(
prod_cond_set, cond_set)
for name in cond2marg:
if name in arg.keys():
values = arg[name]
if not isscalar(values):
values = np.ravel(values)
if cond2marg_dict[name] is None:
cond2marg_dict[name] = values
elif not np.allclose(cond2marg_dict[name], values):
raise ValueError("Mismatch in values for condition {}".format(name))
# Establish product name, values, and dimensions
prod_keys = str2key(prod_marg + prod_cond)
prod_nkeys = len(prod_keys)
prod_aresingleton = np.zeros(prod_nkeys, dtype=bool)
prod_areunitsetints = np.zeros(prod_nkeys, dtype=bool)
prod_cond_name = ','.join(prod_cond)
prod_name = prod_marg_name if not len(prod_cond_name) \
else '|'.join([prod_marg_name, prod_cond_name])
prod_vals = collections.OrderedDict()
for i, key in enumerate(prod_keys):
values = None
for val in vals:
if key in val.keys():
values = val[key]
prod_areunitsetints[i] = isunitsetint(val[key])
if prod_areunitsetints[i]:
values = {0}
break
assert values is not None, "Values for key {} not found".format(key)
prod_aresingleton[i] = issingleton(values)
prod_vals.update({key: values})
if np.any(prod_areunitsetints):
for i, key in enumerate(prod_keys):
if prod_areunitsetints[i]:
for val in vals:
if key in val:
assert isunitsetint(val[key]), "Mismatch in variables {} vs {}".\
format(prod_vals, val)
prod_vals.update({key: {list(prod_vals[key])[0] + list(val[key])[0]}})
prod_newdims = np.array(np.logical_not(prod_aresingleton))
dims_shared = False
for arg in args:
argdims = [dim for dim in arg.dims.values() if dim is not None]
if len(argdims) != len(set(argdims)):
dims_shared = True
# Shared dimensions limit product dimensionality
if dims_shared:
seen_keys = set()
for i, key in enumerate(prod_keys):
if prod_newdims[i] and key not in seen_keys:
for arg in args:
if key in arg.dims:
dim = arg.dims[key]
seen_keys.add(key)
for argkey, argdim in arg.dims.items():
seen_keys.add(argkey)
if argkey != key and argdim is not None:
if dim == argdim:
index = prod_keys.index(argkey)
prod_newdims[index] = False
prod_cdims = np.cumsum(prod_newdims)
prod_ndims = prod_cdims[-1]
# Fast-track scalar products
if maybe_fasttrack and prod_ndims == 0:
prob = float(sum(probs)) if iscomplex(pscale) else float(np.prod(probs))
return PD(prod_name, prod_vals, prob=prob, pscale=pscale)
# Reshape values - they require no axes swapping
ones_ndims = np.ones(prod_ndims, dtype=int)
prod_shape = np.ones(prod_ndims, dtype=int)
scalarset = set()
prod_dims = collections.OrderedDict()
for i, key in enumerate(prod_keys):
if prod_aresingleton[i]:
scalarset.add(key)
else:
values = prod_vals[key]
re_shape = np.copy(ones_ndims)
dim = prod_cdims[i]-1
prod_dims.update({key: dim})
re_shape[dim] = values.size
prod_shape[dim] = values.size
prod_vals.update({key: values.reshape(re_shape)})
# Match probability axes and shapes with axes swapping then reshaping
for i in range(len(args)):
prob = probs[i]
if not isscalar(prob):
dims = collections.OrderedDict()
for key, val in args[i].dims.items():
if val is not None:
dims.update({val: prod_dims[key]})
old_dims = []
new_dims = []
for key, val in dims.items():
if key not in old_dims:
old_dims.append(key)
new_dims.append(val)
if len(old_dims) > 1 and not old_dims == new_dims:
max_dims_inc = max(new_dims) + 1
while prob.ndim < max_dims_inc:
prob = np.expand_dims(prob, -1)
prob = np.moveaxis(prob, old_dims, new_dims)
re_shape = np.copy(ones_ndims)
for dim in new_dims:
re_shape[dim] = prod_shape[dim]
probs[i] = prob.reshape(re_shape)
# Multiply the probabilities and output the result as a distribution instance
prob, pscale = prod_rule(*tuple(probs), pscales=pscales, pscale=pscale)
return PD(prod_name, prod_vals, dims=prod_dims, prob=prob, pscale=pscale)
