def posterior_to_xarray(self):
"""Convert the posterior to an xarray dataset."""
var_names = get_default_varnames(self.trace.varnames,
include_transformed=False)
data = {}
data_warmup = {}
for var_name in var_names:
if self.warmup_trace:
data_warmup[var_name] = np.array(
self.warmup_trace.get_values(var_name,
combine=False,
squeeze=False))
if self.posterior_trace:
data[var_name] = np.array(
self.posterior_trace.get_values(var_name,
combine=False,
squeeze=False))
return (
dict_to_dataset(
data,
library=pymc3,
coords=self.coords,
dims=self.dims,
attrs=self.attrs,
index_origin=self.index_origin,
),
dict_to_dataset(
data_warmup,
library=pymc3,
coords=self.coords,
dims=self.dims,
attrs=self.attrs,
index_origin=self.index_origin,
),
)
def get_point(wrapper, x):
vars = get_default_varnames(wrapper.model.unobserved_RVs, True)
return {
var.name: value
for var, value in zip(vars,
wrapper.model.fastfn(vars)(wrapper.bij.rmap(x)))
}
def __init__(self, model):
self.model = model
self.var_names = get_default_varnames(self.model.named_vars,
include_transformed=False)
self.var_list = self.model.named_vars.values()
self.transform_map = {
v.transformed: v.name
for v in self.var_list if hasattr(v, "transformed")
}
self._deterministics = None
def __init__(self, model=None):
# Get the model
self.model = pm.modelcontext(model)
# Get the variables
self.varnames = get_default_varnames(self.model.unobserved_RVs, False)
# Get the starting point
self.start = Point(self.model.test_point, model=self.model)
self.ndim = len(self.start)
self.mean = None
self.cov = None
# Compile the log probability function
self.vars = inputvars(self.model.cont_vars)
self.bij = DictToArrayBijection(ArrayOrdering(self.vars), self.start)
self.func = get_theano_function_for_var(
self.model.logpt, model=self.model
)
def trace_to_dataframe(trace,
chains=None,
varnames=None,
include_transformed=False):
"""Convert trace to pandas DataFrame.
Parameters
----------
trace: NDarray trace
chains: int or list of ints
Chains to include. If None, all chains are used. A single
chain value can also be given.
varnames: list of variable names
Variables to be included in the DataFrame, if None all variable are
included.
include_transformed: boolean
If true transformed variables will be included in the resulting
DataFrame.
"""
warnings.warn(
"The `trace_to_dataframe` function will soon be removed. "
"Please use ArviZ to save traces. "
"If you have good reasons for using the `trace_to_dataframe` function, file an issue and tell us about them. ",
DeprecationWarning,
)
var_shapes = trace._straces[0].var_shapes
if varnames is None:
varnames = get_default_varnames(
var_shapes.keys(), include_transformed=include_transformed)
flat_names = {v: create_flat_names(v, var_shapes[v]) for v in varnames}
var_dfs = []
for v in varnames:
vals = trace.get_values(v, combine=True, chains=chains)
flat_vals = vals.reshape(vals.shape[0], -1)
var_dfs.append(pd.DataFrame(flat_vals, columns=flat_names[v]))
return pd.concat(var_dfs, axis=1)
def optimize(start=None,
vars=None,
model=None,
return_info=False,
verbose=True,
**kwargs):
"""Maximize the log prob of a PyMC3 model using scipy
All extra arguments are passed directly to the ``scipy.optimize.minimize``
function.
Args:
start: The PyMC3 coordinate dictionary of the starting position
vars: The variables to optimize
model: The PyMC3 model
return_info: Return both the coordinate dictionary and the result of
``scipy.optimize.minimize``
verbose: Print the success flag and log probability to the screen
"""
from scipy.optimize import minimize
model = pm.modelcontext(model)
# Work out the full starting coordinates
if start is None:
start = model.test_point
else:
update_start_vals(start, model.test_point, model)
# Fit all the parameters by default
if vars is None:
vars = model.cont_vars
vars = inputvars(vars)
allinmodel(vars, model)
# Work out the relevant bijection map
start = Point(start, model=model)
bij = DictToArrayBijection(ArrayOrdering(vars), start)
# Pre-compile the theano model and gradient
nlp = -model.logpt
grad = theano.grad(nlp, vars, disconnected_inputs="ignore")
func = get_theano_function_for_var([nlp] + grad, model=model)
if verbose:
names = [
get_untransformed_name(v.name)
if is_transformed_name(v.name) else v.name for v in vars
]
sys.stderr.write("optimizing logp for variables: [{0}]\n".format(
", ".join(names)))
bar = tqdm.tqdm()
# This returns the objective function and its derivatives
def objective(vec):
res = func(*get_args_for_theano_function(bij.rmap(vec), model=model))
d = dict(zip((v.name for v in vars), res[1:]))
g = bij.map(d)
if verbose:
bar.set_postfix(logp="{0:e}".format(-res[0]))
bar.update()
return res[0], g
# Optimize using scipy.optimize
x0 = bij.map(start)
initial = objective(x0)[0]
kwargs["jac"] = True
info = minimize(objective, x0, **kwargs)
# Only accept the output if it is better than it was
x = info.x if (np.isfinite(info.fun) and info.fun < initial) else x0
# Coerce the output into the right format
vars = get_default_varnames(model.unobserved_RVs, True)
point = {
var.name: value
for var, value in zip(vars,
model.fastfn(vars)(bij.rmap(x)))
}
if verbose:
bar.close()
sys.stderr.write("message: {0}\n".format(info.message))
sys.stderr.write("logp: {0} -> {1}\n".format(-initial, -info.fun))
if not np.isfinite(info.fun):
logger.warning("final logp not finite, returning initial point")
logger.warning(
"this suggests that something is wrong with the model")
logger.debug("{0}".format(info))
if return_info:
return point, info
return point
def find_MAP(start=None,
vars=None,
method="L-BFGS-B",
return_raw=False,
include_transformed=True,
progressbar=True,
maxeval=5000,
model=None,
*args,
**kwargs):
"""
Finds the local maximum a posteriori point given a model.
find_MAP should not be used to initialize the NUTS sampler. Simply call pymc3.sample() and it will automatically initialize NUTS in a better way.
Parameters
----------
start: `dict` of parameter values (Defaults to `model.test_point`)
vars: list
List of variables to optimize and set to optimum (Defaults to all continuous).
method: string or callable
Optimization algorithm (Defaults to 'L-BFGS-B' unless
discrete variables are specified in `vars`, then
`Powell` which will perform better). For instructions on use of a callable,
refer to SciPy's documentation of `optimize.minimize`.
return_raw: bool
Whether to return the full output of scipy.optimize.minimize (Defaults to `False`)
include_transformed: bool, optional defaults to True
Flag for reporting automatically transformed variables in addition
to original variables.
progressbar: bool, optional defaults to True
Whether or not to display a progress bar in the command line.
maxeval: int, optional, defaults to 5000
The maximum number of times the posterior distribution is evaluated.
model: Model (optional if in `with` context)
*args, **kwargs
Extra args passed to scipy.optimize.minimize
Notes
-----
Older code examples used find_MAP() to initialize the NUTS sampler,
but this is not an effective way of choosing starting values for sampling.
As a result, we have greatly enhanced the initialization of NUTS and
wrapped it inside pymc3.sample() and you should thus avoid this method.
"""
model = modelcontext(model)
if start is None:
start = model.test_point
else:
update_start_vals(start, model.test_point, model)
check_start_vals(start, model)
if vars is None:
vars = model.cont_vars
vars = inputvars(vars)
disc_vars = list(typefilter(vars, discrete_types))
allinmodel(vars, model)
start = Point(start, model=model)
bij = DictToArrayBijection(ArrayOrdering(vars), start)
logp_func = bij.mapf(model.fastlogp_nojac)
x0 = bij.map(start)
try:
dlogp_func = bij.mapf(model.fastdlogp_nojac(vars))
compute_gradient = True
except (AttributeError, NotImplementedError, tg.NullTypeGradError):
compute_gradient = False
if disc_vars or not compute_gradient:
pm._log.warning(
"Warning: gradient not available." +
"(E.g. vars contains discrete variables). MAP " +
"estimates may not be accurate for the default " +
"parameters. Defaulting to non-gradient minimization " +
"'Powell'.")
method = "Powell"
if "fmin" in kwargs:
fmin = kwargs.pop("fmin")
warnings.warn(
"In future versions, set the optimization algorithm with a string. "
'For example, use `method="L-BFGS-B"` instead of '
'`fmin=sp.optimize.fmin_l_bfgs_b"`.')
cost_func = CostFuncWrapper(maxeval, progressbar, logp_func)
# Check to see if minimization function actually uses the gradient
if "fprime" in getargspec(fmin).args:
def grad_logp(point):
return nan_to_num(-dlogp_func(point))
opt_result = fmin(cost_func, x0, fprime=grad_logp, *args, **kwargs)
else:
# Check to see if minimization function uses a starting value
if "x0" in getargspec(fmin).args:
opt_result = fmin(cost_func, x0, *args, **kwargs)
else:
opt_result = fmin(cost_func, *args, **kwargs)
if isinstance(opt_result, tuple):
mx0 = opt_result[0]
else:
mx0 = opt_result
else:
# remove 'if' part, keep just this 'else' block after version change
if compute_gradient:
cost_func = CostFuncWrapper(maxeval, progressbar, logp_func,
dlogp_func)
else:
cost_func = CostFuncWrapper(maxeval, progressbar, logp_func)
try:
opt_result = minimize(cost_func,
x0,
method=method,
jac=compute_gradient,
*args,
**kwargs)
mx0 = opt_result["x"] # r -> opt_result
except (KeyboardInterrupt, StopIteration) as e:
mx0, opt_result = cost_func.previous_x, None
if isinstance(e, StopIteration):
pm._log.info(e)
finally:
last_v = cost_func.n_eval
if progressbar:
assert isinstance(cost_func.progress, ProgressBar)
cost_func.progress.total = last_v
cost_func.progress.update(last_v)
print()
vars = get_default_varnames(model.unobserved_RVs, include_transformed)
mx = {
var.name: value
for var, value in zip(vars,
model.fastfn(vars)(bij.rmap(mx0)))
}
if return_raw:
return mx, opt_result
else:
return mx
def find_MAP(start=None,
vars=None,
method="L-BFGS-B",
return_raw=False,
include_transformed=True,
progressbar=True,
maxeval=5000,
model=None,
*args,
**kwargs):
"""Finds the local maximum a posteriori point given a model.
`find_MAP` should not be used to initialize the NUTS sampler. Simply call
``pymc3.sample()`` and it will automatically initialize NUTS in a better
way.
Parameters
----------
start: `dict` of parameter values (Defaults to `model.initial_point`)
vars: list
List of variables to optimize and set to optimum (Defaults to all continuous).
method: string or callable
Optimization algorithm (Defaults to 'L-BFGS-B' unless
discrete variables are specified in `vars`, then
`Powell` which will perform better). For instructions on use of a callable,
refer to SciPy's documentation of `optimize.minimize`.
return_raw: bool
Whether to return the full output of scipy.optimize.minimize (Defaults to `False`)
include_transformed: bool, optional defaults to True
Flag for reporting automatically transformed variables in addition
to original variables.
progressbar: bool, optional defaults to True
Whether or not to display a progress bar in the command line.
maxeval: int, optional, defaults to 5000
The maximum number of times the posterior distribution is evaluated.
model: Model (optional if in `with` context)
*args, **kwargs
Extra args passed to scipy.optimize.minimize
Notes
-----
Older code examples used `find_MAP` to initialize the NUTS sampler,
but this is not an effective way of choosing starting values for sampling.
As a result, we have greatly enhanced the initialization of NUTS and
wrapped it inside ``pymc3.sample()`` and you should thus avoid this method.
"""
model = modelcontext(model)
if vars is None:
vars = model.cont_vars
if not vars:
raise ValueError("Model has no unobserved continuous variables.")
vars = inputvars(vars)
disc_vars = list(typefilter(vars, discrete_types))
allinmodel(vars, model)
start = copy.deepcopy(start)
if start is None:
start = model.initial_point
else:
model.update_start_vals(start, model.initial_point)
model.check_start_vals(start)
start = Point(start, model=model)
x0 = DictToArrayBijection.map(start)
# TODO: If the mapping is fixed, we can simply create graphs for the
# mapping and avoid all this bijection overhead
def logp_func(x):
return DictToArrayBijection.mapf(model.fastlogp_nojac)(RaveledVars(
x, x0.point_map_info))
try:
# This might be needed for calls to `dlogp_func`
# start_map_info = tuple((v.name, v.shape, v.dtype) for v in vars)
def dlogp_func(x):
return DictToArrayBijection.mapf(model.fastdlogp_nojac(vars))(
RaveledVars(x, x0.point_map_info))
compute_gradient = True
except (AttributeError, NotImplementedError, tg.NullTypeGradError):
compute_gradient = False
if disc_vars or not compute_gradient:
pm._log.warning(
"Warning: gradient not available." +
"(E.g. vars contains discrete variables). MAP " +
"estimates may not be accurate for the default " +
"parameters. Defaulting to non-gradient minimization " +
"'Powell'.")
method = "Powell"
if compute_gradient:
cost_func = CostFuncWrapper(maxeval, progressbar, logp_func,
dlogp_func)
else:
cost_func = CostFuncWrapper(maxeval, progressbar, logp_func)
try:
opt_result = minimize(cost_func,
x0.data,
method=method,
jac=compute_gradient,
*args,
**kwargs)
mx0 = opt_result["x"] # r -> opt_result
except (KeyboardInterrupt, StopIteration) as e:
mx0, opt_result = cost_func.previous_x, None
if isinstance(e, StopIteration):
pm._log.info(e)
finally:
last_v = cost_func.n_eval
if progressbar:
assert isinstance(cost_func.progress, ProgressBar)
cost_func.progress.total = last_v
cost_func.progress.update(last_v)
print()
mx0 = RaveledVars(mx0, x0.point_map_info)
vars = get_default_varnames(model.unobserved_value_vars,
include_transformed)
mx = {
var.name: value
for var, value in zip(
vars,
model.fastfn(vars)(DictToArrayBijection.rmap(mx0)))
}
if return_raw:
return mx, opt_result
else:
return mx