"""Compute weighted expectations and predictive metrics using PSIS-LOO-CV."""
import numpy as np
import xarray as xr
from arviz_base import convert_to_datatree, extract, rcParams
from xarray import apply_ufunc
from arviz_stats.loo.helper_loo import _get_r_eff, _warn_pareto_k
from arviz_stats.metrics import _metrics, _summary_r2
from arviz_stats.utils import get_log_likelihood_dataset
[docs]
def loo_expectations(
data,
var_name=None,
kind="mean",
probs=None,
log_weights=None,
pareto_k=None,
):
"""Compute weighted expectations using the PSIS-LOO-CV method.
The expectations assume that the PSIS approximation is working well.
The PSIS-LOO-CV method is described in [1]_ and [2]_.
Parameters
----------
data: DataTree or InferenceData
It should contain the groups `posterior_predictive` and `log_likelihood`.
var_name: str, optional
The name of the variable in log_likelihood groups storing the pointwise log
likelihood data to use for loo computation.
kind: str, optional
The kind of expectation to compute. Available options are:
- 'mean'. Default.
- 'median'.
- 'var'.
- 'sd'.
- 'quantile'.
- 'circular_mean'.
- 'circular_var'.
- 'circular_sd'.
probs: float or list of float, optional
The quantile(s) to compute when kind is 'quantile'.
log_weights : DataArray, optional
Pre-computed smoothed log weights from PSIS. Must be provided together with pareto_k.
If not provided, PSIS will be computed internally.
pareto_k : DataArray, optional
Pre-computed Pareto k-hat diagnostic values. Must be provided together with log_weights.
Returns
-------
loo_expec : DataArray
The weighted expectations.
khat : DataArray
Function-specific Pareto k-hat diagnostics for each observation.
Examples
--------
Calculate predictive 0.25 and 0.75 quantiles and the function-specific Pareto k-hat diagnostics
.. ipython::
In [1]: from arviz_stats import loo_expectations
...: from arviz_base import load_arviz_data
...: dt = load_arviz_data("radon")
...: loo_expec, khat = loo_expectations(dt, kind="quantile", probs=[0.25, 0.75])
...: loo_expec
.. ipython::
In [2]: khat
References
----------
.. [1] Vehtari et al. *Practical Bayesian model evaluation using leave-one-out cross-validation
and WAIC*. Statistics and Computing. 27(5) (2017) https://doi.org/10.1007/s11222-016-9696-4
arXiv preprint https://arxiv.org/abs/1507.04544.
.. [2] Vehtari et al. *Pareto Smoothed Importance Sampling*.
Journal of Machine Learning Research, 25(72) (2024) https://jmlr.org/papers/v25/19-556.html
arXiv preprint https://arxiv.org/abs/1507.02646
"""
if kind not in [
"mean",
"median",
"var",
"sd",
"quantile",
"circular_mean",
"circular_var",
"circular_sd",
]:
raise ValueError(
"""kind must be either 'mean', 'median', 'var', 'sd', 'quantile',
'circular_mean', 'circular_var', or 'circular_sd'"""
)
if kind == "quantile" and probs is None:
raise ValueError("probs must be provided when kind is 'quantile'")
dims = ("chain", "draw")
if var_name is None:
var_name = list(data.observed_data.data_vars.keys())[0]
data = convert_to_datatree(data)
log_likelihood = get_log_likelihood_dataset(data, var_names=var_name)
n_samples = log_likelihood[var_name].sizes["chain"] * log_likelihood[var_name].sizes["draw"]
r_eff = _get_r_eff(data, n_samples)
posterior_predictive = extract(
data, group="posterior_predictive", var_names=var_name, combined=False
)
sample_dims = list(dims)
if log_weights is not None and pareto_k is not None:
if kind in ("mean", "median", "var", "sd", "circular_mean", "circular_var", "circular_sd"):
loo_expec, _ = posterior_predictive.azstats.loo_expectation(
log_weights=log_weights,
pareto_k=pareto_k,
kind=kind,
r_eff=r_eff,
sample_dims=sample_dims,
)
else:
loo_expec, _ = posterior_predictive.azstats.loo_quantile(
log_weights=log_weights,
pareto_k=pareto_k,
probs=probs,
r_eff=r_eff,
sample_dims=sample_dims,
)
log_ratios_for_khat = log_weights
else:
log_ratios = -log_likelihood[var_name]
if kind in ("mean", "median", "var", "sd", "circular_mean", "circular_var", "circular_sd"):
loo_expec, _ = posterior_predictive.azstats.loo_expectation(
log_ratios=log_ratios,
kind=kind,
r_eff=r_eff,
sample_dims=sample_dims,
)
else:
loo_expec, _ = posterior_predictive.azstats.loo_quantile(
log_ratios=log_ratios,
probs=probs,
r_eff=r_eff,
sample_dims=sample_dims,
)
log_ratios_for_khat = log_ratios
khat = apply_ufunc(
_get_function_khat,
posterior_predictive,
log_ratios_for_khat,
input_core_dims=[dims, dims],
output_core_dims=[[]],
exclude_dims=set(dims),
kwargs={"kind": kind},
vectorize=True,
dask="parallelized",
output_dtypes=[float],
)
_warn_pareto_k(khat.values, n_samples)
return loo_expec, khat
[docs]
def loo_metrics(data, kind="rmse", var_name=None, round_to=None):
"""Compute predictive metrics using the PSIS-LOO-CV method.
Currently supported metrics are mean absolute error, mean squared error and
root mean squared error. For classification problems, accuracy and balanced
accuracy are also supported.
The PSIS-LOO-CV method is described in [1]_ and [2]_.
Parameters
----------
data: DataTree or InferenceData
It should contain groups `observed_data`, `posterior_predictive` and `log_likelihood`.
kind: str
The kind of metric to compute. Available options are:
- 'mae': mean absolute error.
- 'mse': mean squared error.
- 'rmse': root mean squared error. Default.
- 'acc': classification accuracy.
- 'acc_balanced': balanced classification accuracy.
var_name: str, optional
The name of the variable in log_likelihood groups storing the pointwise log
likelihood data to use for loo computation.
round_to: int or str or None, optional
If integer, number of decimal places to round the result. If string of the
form '2g' number of significant digits to round the result. Defaults to '2g'.
Returns
-------
estimate: namedtuple
A namedtuple with the mean of the computed metric and its standard error.
Examples
--------
Calculate predictive root mean squared error
.. ipython::
In [1]: from arviz_stats import loo_metrics
...: from arviz_base import load_arviz_data
...: dt = load_arviz_data("radon")
...: loo_metrics(dt, kind="rmse")
Calculate accuracy of a logistic regression model
.. ipython::
In [1]: dt = load_arviz_data("anes")
...: loo_metrics(dt, kind="acc")
References
----------
.. [1] Vehtari et al. *Practical Bayesian model evaluation using leave-one-out cross-validation
and WAIC*. Statistics and Computing. 27(5) (2017) https://doi.org/10.1007/s11222-016-9696-4
arXiv preprint https://arxiv.org/abs/1507.04544.
.. [2] Vehtari et al. *Pareto Smoothed Importance Sampling*.
Journal of Machine Learning Research, 25(72) (2024) https://jmlr.org/papers/v25/19-556.html
arXiv preprint https://arxiv.org/abs/1507.02646
"""
if round_to is None:
round_to = rcParams["stats.round_to"]
if var_name is None:
var_name = list(data.observed_data.data_vars.keys())[0]
observed = data.observed_data[var_name]
predicted, _ = loo_expectations(data, kind="mean", var_name=var_name)
return _metrics(observed, predicted, kind, round_to)
[docs]
def loo_r2(
data,
var_name,
n_simulations=4000,
summary=True,
point_estimate=None,
ci_kind=None,
ci_prob=None,
circular=False,
round_to=None,
):
"""Compute LOO-adjusted :math:`R^2`.
Parameters
----------
data: DataTree or InferenceData
It should contain groups `observed_data`, `posterior_predictive` and `log_likelihood`.
var_name : str
Name of the observed variable
n_simulations : int, default 4000
Number of Dirichlet-weighted bootstrap samples for variance estimation.
circular : bool, default False
Whether the variable is circular (angles in radians, range [-π, π]).
summary: bool
Whether to return a summary (default) or an array of :math:`R^2` samples.
The summary is a named tuple with a point estimate and a credible interval
point_estimate: str
The point estimate to compute. If None, the default value is used.
Defaults values are defined in rcParams["stats.point_estimate"]. Ignored if
summary is False.
ci_kind: str
The kind of credible interval to compute. If None, the default value is used.
Defaults values are defined in rcParams["stats.ci_kind"]. Ignored if
summary is False.
ci_prob: float
The probability for the credible interval. If None, the default value is used.
Defaults values are defined in rcParams["stats.ci_prob"]. Ignored if
summary is False.
circular: bool
Whether to compute the Bayesian :math:`R^2` for circular data. Defaults to False.
It's assumed that the circular data is in radians and ranges from -π to π.
We use the same definition of :math:`R^2` for circular data as in the linear case,
but using circular variance instead of regular variance.
round_to: int or str or None, optional
If integer, number of decimal places to round the result. Integers can be negative.
If string of the form '2g' number of significant digits to round the result.
Defaults to rcParams["stats.round_to"] if None. Use the string "None" or "none" to
return raw numbers.
Returns
-------
Namedtuple or array
See Also
--------
arviz_stats.bayesian_r2 : Bayesian :math:`R^2`.
arviz_stats.residual_r2 : Residual :math:`R^2`.
Examples
--------
Calculate LOO-adjusted :math:`R^2` for Bayesian logistic regression:
.. ipython::
:okwarning:
In [1]: from arviz_stats import loo_r2
...: from arviz_base import load_arviz_data
...: data = load_arviz_data('anes')
...: loo_r2(data, var_name="vote")
Calculate LOO-adjusted :math:`R^2` for circular regression:
.. ipython::
:okwarning:
In [1]: data = load_arviz_data('periwinkles')
...: loo_r2(data, var_name='direction', circular=True)
"""
if point_estimate is None:
point_estimate = rcParams["stats.point_estimate"]
if ci_kind is None:
ci_kind = rcParams["stats.ci_kind"]
if ci_prob is None:
ci_prob = rcParams["stats.ci_prob"]
if round_to is None:
round_to = rcParams["stats.round_to"]
y_obs = data.observed_data[var_name]
if circular:
kind = "circular_mean"
else:
kind = "mean"
ypred_loo = loo_expectations(data, var_name=var_name, kind=kind)[0]
loo_r_squared = y_obs.azstats.loo_r2(
ypred_loo=ypred_loo,
n_simulations=n_simulations,
circular=circular,
random_state=42,
)
if summary:
return _summary_r2("loo", loo_r_squared, point_estimate, ci_kind, ci_prob, round_to)
return loo_r_squared
def _get_function_khat(
values,
log_weights,
kind,
):
"""Compute function-specific k-hat diagnostics for LOO expectations.
Parameters
----------
values : ndarray
Values of the posterior predictive distribution, raveled across sample dimensions.
log_weights : ndarray
Raw log weights from PSIS, raveled across sample dimensions.
kind : str
Type of expectation being computed ('mean', 'median', 'var', 'sd', 'quantile').
Returns
-------
khat : float
Function-specific k-hat estimate.
"""
r_theta_da = xr.DataArray(
np.exp(log_weights.ravel() - np.max(log_weights.ravel())), dims=["sample"]
)
# Get right tail khat
try:
khat_r_da = r_theta_da.azstats.pareto_khat(
sample_dims="sample", tail="right", log_weights=False
)
khat_r = khat_r_da.item()
except ValueError:
khat_r = np.nan
# For quantile/median, only need khat_r
if kind in ["quantile", "median"]:
return khat_r
h_theta_values = values.ravel() if kind == "mean" else values.ravel() ** 2
unique_h = np.unique(h_theta_values[np.isfinite(h_theta_values)])
if len(unique_h) <= 1:
return khat_r
if len(unique_h) == 2:
return khat_r
if np.any(~np.isfinite(h_theta_values)):
return khat_r
hr_theta_da = xr.DataArray(h_theta_values * r_theta_da.values, dims=["sample"])
try:
khat_hr_da = hr_theta_da.azstats.pareto_khat(
sample_dims="sample", tail="both", log_weights=False
)
khat_hr = khat_hr_da.item()
except ValueError:
khat_hr = np.nan
if np.isnan(khat_hr) and np.isnan(khat_r):
return np.nan
return np.nanmax([khat_hr, khat_r])
