arviz.summary — ArviZ dev documentation (original) (raw)
arviz.summary(data, var_names=None, filter_vars=None, group=None, fmt='wide', kind='all', round_to=None, circ_var_names=None, stat_focus='mean', stat_funcs=None, extend=True, hdi_prob=None, skipna=False, labeller=None, coords=None, index_origin=None, order=None)[source]#
Create a data frame with summary statistics.
Parameters:
data: obj
Any object that can be converted to an arviz.InferenceData object Refer to documentation of arviz.convert_to_dataset() for details
var_names: list
Names of variables to include in summary. Prefix the variables by ~ when you want to exclude them from the summary: ["~beta"] instead of ["beta"] (see examples below).
filter_vars: {None, “like”, “regex”}, optional, default=None
If None (default), interpret var_names as the real variables names. If “like”, interpret var_names as substrings of the real variables names. If “regex”, interpret var_names as regular expressions on the real variables names. A lapandas.filter.
coords: Dict[str, List[Any]], optional
Coordinate subset for which to calculate the summary.
group: str
Select a group for summary. Defaults to “posterior”, “prior” or first group in that order, depending what groups exists.
fmt: {‘wide’, ‘long’, ‘xarray’}
Return format is either pandas.DataFrame {‘wide’, ‘long’} or xarray.Dataset {‘xarray’}.
kind: {‘all’, ‘stats’, ‘diagnostics’}
Whether to include the stats: mean, sd, hdi_3%, hdi_97%, or the diagnostics:mcse_mean, mcse_sd, ess_bulk, ess_tail, and r_hat. Default to include all of them.
round_to: int
Number of decimals used to round results. Defaults to 2. Use “none” to return raw numbers.
circ_var_names: list
A list of circular variables to compute circular stats for
stat_focusstr, default “mean”
Select the focus for summary.
stat_funcs: dict
A list of functions or a dict of functions with function names as keys used to calculate statistics. By default, the mean, standard deviation, simulation standard error, and highest posterior density intervals are included.
The functions will be given one argument, the samples for a variable as an nD array, The functions should be in the style of a ufunc and return a single number. For example,numpy.mean(), or scipy.stats.var would both work.
extend: boolean
If True, use the statistics returned by stat_funcs in addition to, rather than in place of, the default statistics. This is only meaningful when stat_funcs is not None.
hdi_prob: float, optional
Highest density interval to compute. Defaults to 0.94. This is only meaningful whenstat_funcs is None.
skipna: bool
If true ignores nan values when computing the summary statistics, it does not affect the behaviour of the functions passed to stat_funcs. Defaults to false.
labellerlabeller instance, optional
Class providing the method make_label_flat to generate the labels in the plot titles. For more details on labeller usage see Label guide
credible_interval: float, optional
deprecated: Please see hdi_prob
order
deprecated: order is now ignored.
index_origin
deprecated: index_origin is now ignored, modify the coordinate values to change the value used in summary.
Returns:
pandas.DataFrame or xarray.Dataset
Return type dicated by fmt argument.
Return value will contain summary statistics for each variable. Default statistics depend on the value of stat_focus:
stat_focus="mean": mean, sd, hdi_3%, hdi_97%, mcse_mean, mcse_sd,ess_bulk, ess_tail, and r_hat
stat_focus="median": median, mad, eti_3%, eti_97%, mcse_median, ess_median,ess_tail, and r_hat
r_hat is only computed for traces with 2 or more chains.
See also
Compute the widely applicable information criterion.
Compute Pareto-smoothed importance sampling leave-one-out cross-validation (PSIS-LOO-CV).
Calculate estimate of the effective sample size (ess).
Compute estimate of rank normalized splitR-hat for a set of traces.
Calculate Markov Chain Standard Error statistic.
Examples
In [1]: import arviz as az ...: data = az.load_arviz_data("centered_eight") ...: az.summary(data, var_names=["mu", "tau"]) ...: Out[1]: mean sd hdi_3% hdi_97% ... mcse_sd ess_bulk ess_tail r_hat mu 4.486 3.487 -1.623 10.693 ... 0.114 241.0 659.0 1.02 tau 4.124 3.102 0.896 9.668 ... 0.174 67.0 38.0 1.06
[2 rows x 9 columns]
You can use filter_vars to select variables without having to specify all the exact names. Use filter_vars="like" to select based on partial naming:
In [2]: az.summary(data, var_names=["the"], filter_vars="like") Out[2]: mean sd hdi_3% ... ess_bulk ess_tail r_hat theta[Choate] 6.460 5.868 -4.564 ... 365.0 710.0 1.01 theta[Deerfield] 5.028 4.883 -4.311 ... 427.0 851.0 1.01 theta[Phillips Andover] 3.938 5.688 -7.769 ... 515.0 730.0 1.01 theta[Phillips Exeter] 4.872 5.012 -4.490 ... 337.0 869.0 1.01 theta[Hotchkiss] 3.667 4.956 -6.470 ... 365.0 1034.0 1.01 theta[Lawrenceville] 3.975 5.187 -7.041 ... 521.0 1031.0 1.01 theta[St. Paul's] 6.581 5.105 -3.093 ... 276.0 586.0 1.01 theta[Mt. Hermon] 4.772 5.737 -5.858 ... 452.0 754.0 1.01
[8 rows x 9 columns]
Use filter_vars="regex" to select based on regular expressions, and prefix the variables you want to exclude by ~. Here, we exclude from the summary all the variables starting with the letter t:
In [3]: az.summary(data, var_names=["~^t"], filter_vars="regex") Out[3]: mean sd hdi_3% hdi_97% ... mcse_sd ess_bulk ess_tail r_hat mu 4.486 3.487 -1.623 10.693 ... 0.114 241.0 659.0 1.02
[1 rows x 9 columns]
Other statistics can be calculated by passing a list of functions or a dictionary with key, function pairs.
In [4]: import numpy as np ...: def median_sd(x): ...: median = np.percentile(x, 50) ...: sd = np.sqrt(np.mean((x-median)**2)) ...: return sd ...: ...: func_dict = { ...: "std": np.std, ...: "median_std": median_sd, ...: "5%": lambda x: np.percentile(x, 5), ...: "median": lambda x: np.percentile(x, 50), ...: "95%": lambda x: np.percentile(x, 95), ...: } ...: az.summary( ...: data, ...: var_names=["mu", "tau"], ...: stat_funcs=func_dict, ...: extend=False ...: ) ...: Out[4]: std median_std 5% median 95% mu 3.486 3.486 -1.152 4.548 10.020 tau 3.101 3.217 1.054 3.269 10.106
Use stat_focus to change the focus of summary statistics obatined to median:
In [5]: az.summary(data, stat_focus="median") Out[5]: median mad eti_3% ... ess_median ess_tail r_hat mu 4.548 2.283 -1.968 ... 199.205 659.0 1.02 theta[Choate] 6.082 3.130 -3.511 ... 383.402 710.0 1.01 theta[Deerfield] 5.011 3.347 -4.066 ... 320.345 851.0 1.01 theta[Phillips Andover] 4.227 3.150 -7.771 ... 258.296 730.0 1.01 theta[Phillips Exeter] 5.022 3.251 -4.777 ... 197.764 869.0 1.01 theta[Hotchkiss] 3.892 3.171 -6.187 ... 272.506 1034.0 1.01 theta[Lawrenceville] 4.136 3.133 -6.549 ... 321.125 1031.0 1.01 theta[St. Paul's] 6.065 3.014 -2.254 ... 278.395 586.0 1.01 theta[Mt. Hermon] 4.706 3.340 -6.348 ... 245.548 754.0 1.01 tau 3.269 1.600 0.927 ... 119.695 38.0 1.06
[10 rows x 8 columns]