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train | TfpConverter.posterior_to_xarray | Convert the posterior to an xarray dataset. | arviz/data/io_tfp.py | def posterior_to_xarray(self):
"""Convert the posterior to an xarray dataset."""
data = {}
for i, var_name in enumerate(self.var_names):
data[var_name] = np.expand_dims(self.posterior[i], axis=0)
return dict_to_dataset(data, library=self.tfp, coords=self.coords, dims=sel... | def posterior_to_xarray(self):
"""Convert the posterior to an xarray dataset."""
data = {}
for i, var_name in enumerate(self.var_names):
data[var_name] = np.expand_dims(self.posterior[i], axis=0)
return dict_to_dataset(data, library=self.tfp, coords=self.coords, dims=sel... | [
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train | TfpConverter.observed_data_to_xarray | Convert observed data to xarray. | arviz/data/io_tfp.py | def observed_data_to_xarray(self):
"""Convert observed data to xarray."""
if self.observed is None:
return None
observed_data = {}
if isinstance(self.observed, self.tf.Tensor):
with self.tf.Session() as sess:
vals = sess.run(self.observed,... | def observed_data_to_xarray(self):
"""Convert observed data to xarray."""
if self.observed is None:
return None
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train | TfpConverter.posterior_predictive_to_xarray | Convert posterior_predictive samples to xarray. | arviz/data/io_tfp.py | def posterior_predictive_to_xarray(self):
"""Convert posterior_predictive samples to xarray."""
if self.model_fn is None:
return None
posterior_preds = []
sample_size = self.posterior[0].shape[0]
for i in np.arange(0, sample_size, int(sample_size / self.post... | def posterior_predictive_to_xarray(self):
"""Convert posterior_predictive samples to xarray."""
if self.model_fn is None:
return None
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sample_size = self.posterior[0].shape[0]
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train | TfpConverter.sample_stats_to_xarray | Extract sample_stats from tfp trace. | arviz/data/io_tfp.py | def sample_stats_to_xarray(self):
"""Extract sample_stats from tfp trace."""
if self.model_fn is None or self.observed is None:
return None
log_likelihood = []
sample_size = self.posterior[0].shape[0]
for i in range(sample_size):
variables = {}
... | def sample_stats_to_xarray(self):
"""Extract sample_stats from tfp trace."""
if self.model_fn is None or self.observed is None:
return None
log_likelihood = []
sample_size = self.posterior[0].shape[0]
for i in range(sample_size):
variables = {}
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train | TfpConverter.to_inference_data | Convert all available data to an InferenceData object.
Note that if groups can not be created (i.e., there is no `trace`, so
the `posterior` and `sample_stats` can not be extracted), then the InferenceData
will not have those groups. | arviz/data/io_tfp.py | def to_inference_data(self):
"""Convert all available data to an InferenceData object.
Note that if groups can not be created (i.e., there is no `trace`, so
the `posterior` and `sample_stats` can not be extracted), then the InferenceData
will not have those groups.
"""
... | def to_inference_data(self):
"""Convert all available data to an InferenceData object.
Note that if groups can not be created (i.e., there is no `trace`, so
the `posterior` and `sample_stats` can not be extracted), then the InferenceData
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train | from_pymc3 | Convert pymc3 data into an InferenceData object. | arviz/data/io_pymc3.py | def from_pymc3(trace=None, *, prior=None, posterior_predictive=None, coords=None, dims=None):
"""Convert pymc3 data into an InferenceData object."""
return PyMC3Converter(
trace=trace,
prior=prior,
posterior_predictive=posterior_predictive,
coords=coords,
dims=dims,
)... | def from_pymc3(trace=None, *, prior=None, posterior_predictive=None, coords=None, dims=None):
"""Convert pymc3 data into an InferenceData object."""
return PyMC3Converter(
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train | PyMC3Converter._extract_log_likelihood | Compute log likelihood of each observation.
Return None if there is not exactly 1 observed random variable. | arviz/data/io_pymc3.py | def _extract_log_likelihood(self):
"""Compute log likelihood of each observation.
Return None if there is not exactly 1 observed random variable.
"""
# This next line is brittle and may not work forever, but is a secret
# way to access the model from the trace.
model = s... | def _extract_log_likelihood(self):
"""Compute log likelihood of each observation.
Return None if there is not exactly 1 observed random variable.
"""
# This next line is brittle and may not work forever, but is a secret
# way to access the model from the trace.
model = s... | [
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train | PyMC3Converter.posterior_to_xarray | Convert the posterior to an xarray dataset. | arviz/data/io_pymc3.py | def posterior_to_xarray(self):
"""Convert the posterior to an xarray dataset."""
var_names = self.pymc3.util.get_default_varnames( # pylint: disable=no-member
self.trace.varnames, include_transformed=False
)
data = {}
for var_name in var_names:
data[var_n... | def posterior_to_xarray(self):
"""Convert the posterior to an xarray dataset."""
var_names = self.pymc3.util.get_default_varnames( # pylint: disable=no-member
self.trace.varnames, include_transformed=False
)
data = {}
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train | PyMC3Converter.sample_stats_to_xarray | Extract sample_stats from PyMC3 trace. | arviz/data/io_pymc3.py | def sample_stats_to_xarray(self):
"""Extract sample_stats from PyMC3 trace."""
rename_key = {"model_logp": "lp"}
data = {}
for stat in self.trace.stat_names:
name = rename_key.get(stat, stat)
data[name] = np.array(self.trace.get_sampler_stats(stat, combine=False))... | def sample_stats_to_xarray(self):
"""Extract sample_stats from PyMC3 trace."""
rename_key = {"model_logp": "lp"}
data = {}
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name = rename_key.get(stat, stat)
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train | PyMC3Converter.posterior_predictive_to_xarray | Convert posterior_predictive samples to xarray. | arviz/data/io_pymc3.py | def posterior_predictive_to_xarray(self):
"""Convert posterior_predictive samples to xarray."""
data = {k: np.expand_dims(v, 0) for k, v in self.posterior_predictive.items()}
return dict_to_dataset(data, library=self.pymc3, coords=self.coords, dims=self.dims) | def posterior_predictive_to_xarray(self):
"""Convert posterior_predictive samples to xarray."""
data = {k: np.expand_dims(v, 0) for k, v in self.posterior_predictive.items()}
return dict_to_dataset(data, library=self.pymc3, coords=self.coords, dims=self.dims) | [
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train | PyMC3Converter.prior_to_xarray | Convert prior samples to xarray. | arviz/data/io_pymc3.py | def prior_to_xarray(self):
"""Convert prior samples to xarray."""
return dict_to_dataset(
{k: np.expand_dims(v, 0) for k, v in self.prior.items()},
library=self.pymc3,
coords=self.coords,
dims=self.dims,
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"""Convert prior samples to xarray."""
return dict_to_dataset(
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library=self.pymc3,
coords=self.coords,
dims=self.dims,
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train | PyMC3Converter.observed_data_to_xarray | Convert observed data to xarray. | arviz/data/io_pymc3.py | def observed_data_to_xarray(self):
"""Convert observed data to xarray."""
# This next line is brittle and may not work forever, but is a secret
# way to access the model from the trace.
model = self.trace._straces[0].model # pylint: disable=protected-access
observations = {obs.... | def observed_data_to_xarray(self):
"""Convert observed data to xarray."""
# This next line is brittle and may not work forever, but is a secret
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model = self.trace._straces[0].model # pylint: disable=protected-access
observations = {obs.... | [
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train | plot_posterior | Plot Posterior densities in the style of John K. Kruschke's book.
Parameters
----------
data : obj
Any object that can be converted to an az.InferenceData object
Refer to documentation of az.convert_to_dataset for details
var_names : list of variable names
Variables to be plotte... | arviz/plots/posteriorplot.py | def plot_posterior(
data,
var_names=None,
coords=None,
figsize=None,
textsize=None,
credible_interval=0.94,
round_to=1,
point_estimate="mean",
rope=None,
ref_val=None,
kind="kde",
bw=4.5,
bins=None,
ax=None,
**kwargs
):
"""Plot Posterior densities in the s... | def plot_posterior(
data,
var_names=None,
coords=None,
figsize=None,
textsize=None,
credible_interval=0.94,
round_to=1,
point_estimate="mean",
rope=None,
ref_val=None,
kind="kde",
bw=4.5,
bins=None,
ax=None,
**kwargs
):
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train | _plot_posterior_op | Artist to draw posterior. | arviz/plots/posteriorplot.py | def _plot_posterior_op(
values,
var_name,
selection,
ax,
bw,
linewidth,
bins,
kind,
point_estimate,
round_to,
credible_interval,
ref_val,
rope,
ax_labelsize,
xt_labelsize,
**kwargs
): # noqa: D202
"""Artist to draw posterior."""
def format_as_per... | def _plot_posterior_op(
values,
var_name,
selection,
ax,
bw,
linewidth,
bins,
kind,
point_estimate,
round_to,
credible_interval,
ref_val,
rope,
ax_labelsize,
xt_labelsize,
**kwargs
): # noqa: D202
"""Artist to draw posterior."""
def format_as_per... | [
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train | bfmi | r"""Calculate the estimated Bayesian fraction of missing information (BFMI).
BFMI quantifies how well momentum resampling matches the marginal energy distribution. For more
information on BFMI, see https://arxiv.org/pdf/1604.00695v1.pdf. The current advice is that
values smaller than 0.3 indicate poor samp... | arviz/stats/stats.py | def bfmi(energy):
r"""Calculate the estimated Bayesian fraction of missing information (BFMI).
BFMI quantifies how well momentum resampling matches the marginal energy distribution. For more
information on BFMI, see https://arxiv.org/pdf/1604.00695v1.pdf. The current advice is that
values smaller than ... | def bfmi(energy):
r"""Calculate the estimated Bayesian fraction of missing information (BFMI).
BFMI quantifies how well momentum resampling matches the marginal energy distribution. For more
information on BFMI, see https://arxiv.org/pdf/1604.00695v1.pdf. The current advice is that
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train | compare | r"""Compare models based on WAIC or LOO cross validation.
WAIC is Widely applicable information criterion, and LOO is leave-one-out
(LOO) cross-validation. Read more theory here - in a paper by some of the
leading authorities on model selection - dx.doi.org/10.1111/1467-9868.00353
Parameters
-----... | arviz/stats/stats.py | def compare(
dataset_dict,
ic="waic",
method="BB-pseudo-BMA",
b_samples=1000,
alpha=1,
seed=None,
scale="deviance",
):
r"""Compare models based on WAIC or LOO cross validation.
WAIC is Widely applicable information criterion, and LOO is leave-one-out
(LOO) cross-validation. Read... | def compare(
dataset_dict,
ic="waic",
method="BB-pseudo-BMA",
b_samples=1000,
alpha=1,
seed=None,
scale="deviance",
):
r"""Compare models based on WAIC or LOO cross validation.
WAIC is Widely applicable information criterion, and LOO is leave-one-out
(LOO) cross-validation. Read... | [
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train | _ic_matrix | Store the previously computed pointwise predictive accuracy values (ics) in a 2D matrix. | arviz/stats/stats.py | def _ic_matrix(ics, ic_i):
"""Store the previously computed pointwise predictive accuracy values (ics) in a 2D matrix."""
cols, _ = ics.shape
rows = len(ics[ic_i].iloc[0])
ic_i_val = np.zeros((rows, cols))
for idx, val in enumerate(ics.index):
ic = ics.loc[val][ic_i]
if len(ic) != ... | def _ic_matrix(ics, ic_i):
"""Store the previously computed pointwise predictive accuracy values (ics) in a 2D matrix."""
cols, _ = ics.shape
rows = len(ics[ic_i].iloc[0])
ic_i_val = np.zeros((rows, cols))
for idx, val in enumerate(ics.index):
ic = ics.loc[val][ic_i]
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train | hpd | Calculate highest posterior density (HPD) of array for given credible_interval.
The HPD is the minimum width Bayesian credible interval (BCI). This implementation works only
for unimodal distributions.
Parameters
----------
x : Numpy array
An array containing posterior samples
credible... | arviz/stats/stats.py | def hpd(x, credible_interval=0.94, circular=False):
"""
Calculate highest posterior density (HPD) of array for given credible_interval.
The HPD is the minimum width Bayesian credible interval (BCI). This implementation works only
for unimodal distributions.
Parameters
----------
x : Numpy ... | def hpd(x, credible_interval=0.94, circular=False):
"""
Calculate highest posterior density (HPD) of array for given credible_interval.
The HPD is the minimum width Bayesian credible interval (BCI). This implementation works only
for unimodal distributions.
Parameters
----------
x : Numpy ... | [
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train | _logsumexp | Stable logsumexp when b >= 0 and b is scalar.
b_inv overwrites b unless b_inv is None. | arviz/stats/stats.py | def _logsumexp(ary, *, b=None, b_inv=None, axis=None, keepdims=False, out=None, copy=True):
"""Stable logsumexp when b >= 0 and b is scalar.
b_inv overwrites b unless b_inv is None.
"""
# check dimensions for result arrays
ary = np.asarray(ary)
if ary.dtype.kind == "i":
ary = ary.astype... | def _logsumexp(ary, *, b=None, b_inv=None, axis=None, keepdims=False, out=None, copy=True):
"""Stable logsumexp when b >= 0 and b is scalar.
b_inv overwrites b unless b_inv is None.
"""
# check dimensions for result arrays
ary = np.asarray(ary)
if ary.dtype.kind == "i":
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train | loo | Pareto-smoothed importance sampling leave-one-out cross-validation.
Calculates leave-one-out (LOO) cross-validation for out of sample predictive model fit,
following Vehtari et al. (2017). Cross-validation is computed using Pareto-smoothed
importance sampling (PSIS).
Parameters
----------
data... | arviz/stats/stats.py | def loo(data, pointwise=False, reff=None, scale="deviance"):
"""Pareto-smoothed importance sampling leave-one-out cross-validation.
Calculates leave-one-out (LOO) cross-validation for out of sample predictive model fit,
following Vehtari et al. (2017). Cross-validation is computed using Pareto-smoothed
... | def loo(data, pointwise=False, reff=None, scale="deviance"):
"""Pareto-smoothed importance sampling leave-one-out cross-validation.
Calculates leave-one-out (LOO) cross-validation for out of sample predictive model fit,
following Vehtari et al. (2017). Cross-validation is computed using Pareto-smoothed
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train | psislw | Pareto smoothed importance sampling (PSIS).
Parameters
----------
log_weights : array
Array of size (n_samples, n_observations)
reff : float
relative MCMC efficiency, `ess / n`
Returns
-------
lw_out : array
Smoothed log weights
kss : array
Pareto tail i... | arviz/stats/stats.py | def psislw(log_weights, reff=1.0):
"""
Pareto smoothed importance sampling (PSIS).
Parameters
----------
log_weights : array
Array of size (n_samples, n_observations)
reff : float
relative MCMC efficiency, `ess / n`
Returns
-------
lw_out : array
Smoothed lo... | def psislw(log_weights, reff=1.0):
"""
Pareto smoothed importance sampling (PSIS).
Parameters
----------
log_weights : array
Array of size (n_samples, n_observations)
reff : float
relative MCMC efficiency, `ess / n`
Returns
-------
lw_out : array
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train | _gpdfit | Estimate the parameters for the Generalized Pareto Distribution (GPD).
Empirical Bayes estimate for the parameters of the generalized Pareto
distribution given the data.
Parameters
----------
x : array
sorted 1D data array
Returns
-------
k : float
estimated shape para... | arviz/stats/stats.py | def _gpdfit(x):
"""Estimate the parameters for the Generalized Pareto Distribution (GPD).
Empirical Bayes estimate for the parameters of the generalized Pareto
distribution given the data.
Parameters
----------
x : array
sorted 1D data array
Returns
-------
k : float
... | def _gpdfit(x):
"""Estimate the parameters for the Generalized Pareto Distribution (GPD).
Empirical Bayes estimate for the parameters of the generalized Pareto
distribution given the data.
Parameters
----------
x : array
sorted 1D data array
Returns
-------
k : float
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train | _gpinv | Inverse Generalized Pareto distribution function. | arviz/stats/stats.py | def _gpinv(probs, kappa, sigma):
"""Inverse Generalized Pareto distribution function."""
x = np.full_like(probs, np.nan)
if sigma <= 0:
return x
ok = (probs > 0) & (probs < 1)
if np.all(ok):
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x = -np.log1p(-probs) # pylint: disable=... | def _gpinv(probs, kappa, sigma):
"""Inverse Generalized Pareto distribution function."""
x = np.full_like(probs, np.nan)
if sigma <= 0:
return x
ok = (probs > 0) & (probs < 1)
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x = -np.log1p(-probs) # pylint: disable=... | [
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train | r2_score | R² for Bayesian regression models. Only valid for linear models.
Parameters
----------
y_true: : array-like of shape = (n_samples) or (n_samples, n_outputs)
Ground truth (correct) target values.
y_pred : array-like of shape = (n_samples) or (n_samples, n_outputs)
Estimated target values... | arviz/stats/stats.py | def r2_score(y_true, y_pred):
"""R² for Bayesian regression models. Only valid for linear models.
Parameters
----------
y_true: : array-like of shape = (n_samples) or (n_samples, n_outputs)
Ground truth (correct) target values.
y_pred : array-like of shape = (n_samples) or (n_samples, n_out... | def r2_score(y_true, y_pred):
"""R² for Bayesian regression models. Only valid for linear models.
Parameters
----------
y_true: : array-like of shape = (n_samples) or (n_samples, n_outputs)
Ground truth (correct) target values.
y_pred : array-like of shape = (n_samples) or (n_samples, n_out... | [
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train | summary | Create a data frame with summary statistics.
Parameters
----------
data : obj
Any object that can be converted to an az.InferenceData object
Refer to documentation of az.convert_to_dataset for details
var_names : list
Names of variables to include in summary
include_circ : b... | arviz/stats/stats.py | def summary(
data,
var_names=None,
fmt="wide",
round_to=2,
include_circ=None,
stat_funcs=None,
extend=True,
credible_interval=0.94,
order="C",
):
"""Create a data frame with summary statistics.
Parameters
----------
data : obj
Any object that can be converted... | def summary(
data,
var_names=None,
fmt="wide",
round_to=2,
include_circ=None,
stat_funcs=None,
extend=True,
credible_interval=0.94,
order="C",
):
"""Create a data frame with summary statistics.
Parameters
----------
data : obj
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train | _make_ufunc | Make ufunc from function. | arviz/stats/stats.py | def _make_ufunc(func, index=Ellipsis, **kwargs): # noqa: D202
"""Make ufunc from function."""
def _ufunc(ary):
target = np.empty(ary.shape[:-2])
for idx in np.ndindex(target.shape):
target[idx] = np.asarray(func(ary[idx].ravel(), **kwargs))[index]
return target
return ... | def _make_ufunc(func, index=Ellipsis, **kwargs): # noqa: D202
"""Make ufunc from function."""
def _ufunc(ary):
target = np.empty(ary.shape[:-2])
for idx in np.ndindex(target.shape):
target[idx] = np.asarray(func(ary[idx].ravel(), **kwargs))[index]
return target
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train | _mc_error | Calculate the simulation standard error, accounting for non-independent samples.
The trace is divided into batches, and the standard deviation of the batch
means is calculated.
Parameters
----------
x : Numpy array
An array containing MCMC samples
batches : integer
Number of ba... | arviz/stats/stats.py | def _mc_error(x, batches=5, circular=False):
"""Calculate the simulation standard error, accounting for non-independent samples.
The trace is divided into batches, and the standard deviation of the batch
means is calculated.
Parameters
----------
x : Numpy array
An array containing MCM... | def _mc_error(x, batches=5, circular=False):
"""Calculate the simulation standard error, accounting for non-independent samples.
The trace is divided into batches, and the standard deviation of the batch
means is calculated.
Parameters
----------
x : Numpy array
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train | waic | Calculate the widely available information criterion.
Also calculates the WAIC's standard error and the effective number of
parameters of the samples in trace from model. Read more theory here - in
a paper by some of the leading authorities on model selection
dx.doi.org/10.1111/1467-9868.00353
Par... | arviz/stats/stats.py | def waic(data, pointwise=False, scale="deviance"):
"""Calculate the widely available information criterion.
Also calculates the WAIC's standard error and the effective number of
parameters of the samples in trace from model. Read more theory here - in
a paper by some of the leading authorities on model... | def waic(data, pointwise=False, scale="deviance"):
"""Calculate the widely available information criterion.
Also calculates the WAIC's standard error and the effective number of
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] | arviz-devs/arviz | python | https://github.com/arviz-devs/arviz/blob/d04d8da07f029fd2931f48d2f7f324cf393e5277/arviz/stats/stats.py#L957-L1047 | [
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train | plot_violin | Plot posterior of traces as violin plot.
Notes
-----
If multiple chains are provided for a variable they will be combined
Parameters
----------
data : obj
Any object that can be converted to an az.InferenceData object
Refer to documentation of az.convert_to_dataset for details
... | arviz/plots/violinplot.py | def plot_violin(
data,
var_names=None,
quartiles=True,
credible_interval=0.94,
shade=0.35,
bw=4.5,
sharey=True,
figsize=None,
textsize=None,
ax=None,
kwargs_shade=None,
):
"""Plot posterior of traces as violin plot.
Notes
-----
If multiple chains are provided... | def plot_violin(
data,
var_names=None,
quartiles=True,
credible_interval=0.94,
shade=0.35,
bw=4.5,
sharey=True,
figsize=None,
textsize=None,
ax=None,
kwargs_shade=None,
):
"""Plot posterior of traces as violin plot.
Notes
-----
If multiple chains are provided... | [
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] | arviz-devs/arviz | python | https://github.com/arviz-devs/arviz/blob/d04d8da07f029fd2931f48d2f7f324cf393e5277/arviz/plots/violinplot.py#L12-L113 | [
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train | _violinplot | Auxiliary function to plot violinplots. | arviz/plots/violinplot.py | def _violinplot(val, shade, bw, ax, **kwargs_shade):
"""Auxiliary function to plot violinplots."""
density, low_b, up_b = _fast_kde(val, bw=bw)
x = np.linspace(low_b, up_b, len(density))
x = np.concatenate([x, x[::-1]])
density = np.concatenate([-density, density[::-1]])
ax.fill_betweenx(x, de... | def _violinplot(val, shade, bw, ax, **kwargs_shade):
"""Auxiliary function to plot violinplots."""
density, low_b, up_b = _fast_kde(val, bw=bw)
x = np.linspace(low_b, up_b, len(density))
x = np.concatenate([x, x[::-1]])
density = np.concatenate([-density, density[::-1]])
ax.fill_betweenx(x, de... | [
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] | arviz-devs/arviz | python | https://github.com/arviz-devs/arviz/blob/d04d8da07f029fd2931f48d2f7f324cf393e5277/arviz/plots/violinplot.py#L116-L124 | [
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train | cat_hist | Auxiliary function to plot discrete-violinplots. | arviz/plots/violinplot.py | def cat_hist(val, shade, ax, **kwargs_shade):
"""Auxiliary function to plot discrete-violinplots."""
bins = get_bins(val)
binned_d, _ = np.histogram(val, bins=bins, normed=True)
bin_edges = np.linspace(np.min(val), np.max(val), len(bins))
centers = 0.5 * (bin_edges + np.roll(bin_edges, 1))[:-1]
... | def cat_hist(val, shade, ax, **kwargs_shade):
"""Auxiliary function to plot discrete-violinplots."""
bins = get_bins(val)
binned_d, _ = np.histogram(val, bins=bins, normed=True)
bin_edges = np.linspace(np.min(val), np.max(val), len(bins))
centers = 0.5 * (bin_edges + np.roll(bin_edges, 1))[:-1]
... | [
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train | plot_joint | Plot a scatter or hexbin of two variables with their respective marginals distributions.
Parameters
----------
data : obj
Any object that can be converted to an az.InferenceData object
Refer to documentation of az.convert_to_dataset for details
var_names : Iter of 2 e.g. (var_1, var_2)
... | arviz/plots/jointplot.py | def plot_joint(
data,
var_names=None,
coords=None,
figsize=None,
textsize=None,
kind="scatter",
gridsize="auto",
contour=True,
fill_last=True,
joint_kwargs=None,
marginal_kwargs=None,
):
"""
Plot a scatter or hexbin of two variables with their respective marginals dis... | def plot_joint(
data,
var_names=None,
coords=None,
figsize=None,
textsize=None,
kind="scatter",
gridsize="auto",
contour=True,
fill_last=True,
joint_kwargs=None,
marginal_kwargs=None,
):
"""
Plot a scatter or hexbin of two variables with their respective marginals dis... | [
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train | plot_hpd | Plot hpd intervals for regression data.
Parameters
----------
x : array-like
Values to plot
y : array-like
values from which to compute the hpd
credible_interval : float, optional
Credible interval to plot. Defaults to 0.94.
color : str
Color used for the limit... | arviz/plots/hpdplot.py | def plot_hpd(
x,
y,
credible_interval=0.94,
color="C1",
circular=False,
smooth=True,
smooth_kwargs=None,
fill_kwargs=None,
plot_kwargs=None,
ax=None,
):
"""
Plot hpd intervals for regression data.
Parameters
----------
x : array-like
Values to plot
... | def plot_hpd(
x,
y,
credible_interval=0.94,
color="C1",
circular=False,
smooth=True,
smooth_kwargs=None,
fill_kwargs=None,
plot_kwargs=None,
ax=None,
):
"""
Plot hpd intervals for regression data.
Parameters
----------
x : array-like
Values to plot
... | [
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] | arviz-devs/arviz | python | https://github.com/arviz-devs/arviz/blob/d04d8da07f029fd2931f48d2f7f324cf393e5277/arviz/plots/hpdplot.py#L10-L86 | [
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train | effective_sample_size | r"""Calculate estimate of the effective sample size.
Parameters
----------
data : obj
Any object that can be converted to an az.InferenceData object
Refer to documentation of az.convert_to_dataset for details
At least 2 posterior chains are needed to compute this diagnostic of one o... | arviz/stats/diagnostics.py | def effective_sample_size(data, *, var_names=None):
r"""Calculate estimate of the effective sample size.
Parameters
----------
data : obj
Any object that can be converted to an az.InferenceData object
Refer to documentation of az.convert_to_dataset for details
At least 2 posteri... | def effective_sample_size(data, *, var_names=None):
r"""Calculate estimate of the effective sample size.
Parameters
----------
data : obj
Any object that can be converted to an az.InferenceData object
Refer to documentation of az.convert_to_dataset for details
At least 2 posteri... | [
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train | _ess_ufunc | Ufunc for computing effective sample size.
This can be used on an xarray Dataset, using
`xr.apply_ufunc(_ess_ufunc, ..., input_core_dims=(('chain', 'draw'),)) | arviz/stats/diagnostics.py | def _ess_ufunc(ary):
"""Ufunc for computing effective sample size.
This can be used on an xarray Dataset, using
`xr.apply_ufunc(_ess_ufunc, ..., input_core_dims=(('chain', 'draw'),))
"""
target = np.empty(ary.shape[:-2])
for idx in np.ndindex(target.shape):
target[idx] = _get_ess(ary[id... | def _ess_ufunc(ary):
"""Ufunc for computing effective sample size.
This can be used on an xarray Dataset, using
`xr.apply_ufunc(_ess_ufunc, ..., input_core_dims=(('chain', 'draw'),))
"""
target = np.empty(ary.shape[:-2])
for idx in np.ndindex(target.shape):
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train | _get_ess | Compute the effective sample size for a 2D array. | arviz/stats/diagnostics.py | def _get_ess(sample_array):
"""Compute the effective sample size for a 2D array."""
shape = sample_array.shape
if len(shape) != 2:
raise TypeError("Effective sample size calculation requires 2 dimensional arrays.")
n_chain, n_draws = shape
if n_chain <= 1:
raise TypeError("Effective ... | def _get_ess(sample_array):
"""Compute the effective sample size for a 2D array."""
shape = sample_array.shape
if len(shape) != 2:
raise TypeError("Effective sample size calculation requires 2 dimensional arrays.")
n_chain, n_draws = shape
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train | rhat | r"""Compute estimate of Split R-hat for a set of traces.
The Split R-hat diagnostic tests for lack of convergence by comparing the variance between
multiple chains to the variance within each chain. If convergence has been achieved, the
between-chain and within-chain variances should be identical. To be mo... | arviz/stats/diagnostics.py | def rhat(data, var_names=None):
r"""Compute estimate of Split R-hat for a set of traces.
The Split R-hat diagnostic tests for lack of convergence by comparing the variance between
multiple chains to the variance within each chain. If convergence has been achieved, the
between-chain and within-chain var... | def rhat(data, var_names=None):
r"""Compute estimate of Split R-hat for a set of traces.
The Split R-hat diagnostic tests for lack of convergence by comparing the variance between
multiple chains to the variance within each chain. If convergence has been achieved, the
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train | _rhat_ufunc | Ufunc for computing effective sample size.
This can be used on an xarray Dataset, using
`xr.apply_ufunc(_neff_ufunc, ..., input_core_dims=(('chain', 'draw'),)) | arviz/stats/diagnostics.py | def _rhat_ufunc(ary):
"""Ufunc for computing effective sample size.
This can be used on an xarray Dataset, using
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"""
target = np.empty(ary.shape[:-2])
for idx in np.ndindex(target.shape):
target[idx] = _get_split_rh... | def _rhat_ufunc(ary):
"""Ufunc for computing effective sample size.
This can be used on an xarray Dataset, using
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train | _get_split_rhat | Compute the split-rhat for a 2d array. | arviz/stats/diagnostics.py | def _get_split_rhat(values, round_to=2):
"""Compute the split-rhat for a 2d array."""
shape = values.shape
if len(shape) != 2:
raise TypeError("Effective sample size calculation requires 2 dimensional arrays.")
_, num_samples = shape
num_split = num_samples // 2
# Calculate split chain m... | def _get_split_rhat(values, round_to=2):
"""Compute the split-rhat for a 2d array."""
shape = values.shape
if len(shape) != 2:
raise TypeError("Effective sample size calculation requires 2 dimensional arrays.")
_, num_samples = shape
num_split = num_samples // 2
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train | geweke | r"""Compute z-scores for convergence diagnostics.
Compare the mean of the first % of series with the mean of the last % of series. x is divided
into a number of segments for which this difference is computed. If the series is converged,
this score should oscillate between -1 and 1.
Parameters
----... | arviz/stats/diagnostics.py | def geweke(values, first=0.1, last=0.5, intervals=20):
r"""Compute z-scores for convergence diagnostics.
Compare the mean of the first % of series with the mean of the last % of series. x is divided
into a number of segments for which this difference is computed. If the series is converged,
this score ... | def geweke(values, first=0.1, last=0.5, intervals=20):
r"""Compute z-scores for convergence diagnostics.
Compare the mean of the first % of series with the mean of the last % of series. x is divided
into a number of segments for which this difference is computed. If the series is converged,
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train | ks_summary | Display a summary of Pareto tail indices.
Parameters
----------
pareto_tail_indices : array
Pareto tail indices.
Returns
-------
df_k : dataframe
Dataframe containing k diagnostic values. | arviz/stats/diagnostics.py | def ks_summary(pareto_tail_indices):
"""Display a summary of Pareto tail indices.
Parameters
----------
pareto_tail_indices : array
Pareto tail indices.
Returns
-------
df_k : dataframe
Dataframe containing k diagnostic values.
"""
kcounts, _ = np.histogram(pareto_tail_... | def ks_summary(pareto_tail_indices):
"""Display a summary of Pareto tail indices.
Parameters
----------
pareto_tail_indices : array
Pareto tail indices.
Returns
-------
df_k : dataframe
Dataframe containing k diagnostic values.
"""
kcounts, _ = np.histogram(pareto_tail_... | [
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train | plot_trace | Plot distribution (histogram or kernel density estimates) and sampled values.
If `divergences` data is available in `sample_stats`, will plot the location of divergences as
dashed vertical lines.
Parameters
----------
data : obj
Any object that can be converted to an az.InferenceData objec... | arviz/plots/traceplot.py | def plot_trace(
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coords=None,
divergences="bottom",
figsize=None,
textsize=None,
lines=None,
combined=False,
plot_kwargs=None,
fill_kwargs=None,
rug_kwargs=None,
hist_kwargs=None,
trace_kwargs=None,
):
"""Plot distribution (histogram or kernel... | def plot_trace(
data,
var_names=None,
coords=None,
divergences="bottom",
figsize=None,
textsize=None,
lines=None,
combined=False,
plot_kwargs=None,
fill_kwargs=None,
rug_kwargs=None,
hist_kwargs=None,
trace_kwargs=None,
):
"""Plot distribution (histogram or kernel... | [
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train | _histplot_op | Add a histogram for the data to the axes. | arviz/plots/traceplot.py | def _histplot_op(ax, data, **kwargs):
"""Add a histogram for the data to the axes."""
bins = get_bins(data)
ax.hist(data, bins=bins, align="left", density=True, **kwargs)
return ax | def _histplot_op(ax, data, **kwargs):
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ax.hist(data, bins=bins, align="left", density=True, **kwargs)
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train | concat | Concatenate InferenceData objects on a group level.
Supports only concatenating with independent unique groups.
Parameters
----------
*args : InferenceData
Variable length InferenceData list or
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copy : bool
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Supports only concatenating with independent unique groups.
Parameters
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*args : InferenceData
Variable length InferenceData list or
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Supports only concatenating with independent unique groups.
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Variable length InferenceData list or
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Expects that the file will have groups, each of which can be loaded by xarray.
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filename : str
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location of netcdf file
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location of netcdf file
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filename : str
Location to write to
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train | from_dict | Convert Dictionary data into an InferenceData object.
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----------
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train | DictConverter.posterior_to_xarray | Convert posterior samples to xarray. | arviz/data/io_dict.py | def posterior_to_xarray(self):
"""Convert posterior samples to xarray."""
data = self.posterior
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if "log_likelihood" in data:
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train | DictConverter.sample_stats_to_xarray | Convert sample_stats samples to xarray. | arviz/data/io_dict.py | def sample_stats_to_xarray(self):
"""Convert sample_stats samples to xarray."""
data = self.sample_stats
if not isinstance(data, dict):
raise TypeError("DictConverter.sample_stats is not a dictionary")
return dict_to_dataset(data, library=None, coords=self.coords, dims=self.... | def sample_stats_to_xarray(self):
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train | DictConverter.posterior_predictive_to_xarray | Convert posterior_predictive samples to xarray. | arviz/data/io_dict.py | def posterior_predictive_to_xarray(self):
"""Convert posterior_predictive samples to xarray."""
data = self.posterior_predictive
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train | DictConverter.prior_to_xarray | Convert prior samples to xarray. | arviz/data/io_dict.py | def prior_to_xarray(self):
"""Convert prior samples to xarray."""
data = self.prior
if not isinstance(data, dict):
raise TypeError("DictConverter.prior is not a dictionary")
return dict_to_dataset(data, library=None, coords=self.coords, dims=self.dims) | def prior_to_xarray(self):
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train | DictConverter.sample_stats_prior_to_xarray | Convert sample_stats_prior samples to xarray. | arviz/data/io_dict.py | def sample_stats_prior_to_xarray(self):
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train | DictConverter.prior_predictive_to_xarray | Convert prior_predictive samples to xarray. | arviz/data/io_dict.py | def prior_predictive_to_xarray(self):
"""Convert prior_predictive samples to xarray."""
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train | DictConverter.observed_data_to_xarray | Convert observed_data to xarray. | arviz/data/io_dict.py | def observed_data_to_xarray(self):
"""Convert observed_data to xarray."""
data = self.observed_data
if not isinstance(data, dict):
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dims = {}
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train | plot_compare | Summary plot for model comparison.
This plot is in the style of the one used in the book Statistical Rethinking (Chapter 6)
by Richard McElreath.
Notes
-----
Defaults to comparing Widely Accepted Information Criterion (WAIC) if present in comp_df column,
otherwise compares Leave-one-ou... | arviz/plots/compareplot.py | def plot_compare(
comp_df,
insample_dev=True,
plot_standard_error=True,
plot_ic_diff=True,
figsize=None,
textsize=None,
plot_kwargs=None,
ax=None,
):
"""
Summary plot for model comparison.
This plot is in the style of the one used in the book Statistical Rethink... | def plot_compare(
comp_df,
insample_dev=True,
plot_standard_error=True,
plot_ic_diff=True,
figsize=None,
textsize=None,
plot_kwargs=None,
ax=None,
):
"""
Summary plot for model comparison.
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train | plot_parallel | Plot parallel coordinates plot showing posterior points with and without divergences.
Described by https://arxiv.org/abs/1709.01449, suggested by Ari Hartikainen
Parameters
----------
data : obj
Any object that can be converted to an az.InferenceData object
Refer to documentation of az... | arviz/plots/parallelplot.py | def plot_parallel(
data,
var_names=None,
coords=None,
figsize=None,
textsize=None,
legend=True,
colornd="k",
colord="C1",
shadend=0.025,
ax=None,
norm_method=None,
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"""
Plot parallel coordinates plot showing posterior points with and without divergences.
Descri... | def plot_parallel(
data,
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coords=None,
figsize=None,
textsize=None,
legend=True,
colornd="k",
colord="C1",
shadend=0.025,
ax=None,
norm_method=None,
):
"""
Plot parallel coordinates plot showing posterior points with and without divergences.
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train | _process_configuration | Extract sampling information. | arviz/data/io_cmdstan.py | def _process_configuration(comments):
"""Extract sampling information."""
num_samples = None
num_warmup = None
save_warmup = None
for comment in comments:
comment = comment.strip("#").strip()
if comment.startswith("num_samples"):
num_samples = int(comment.strip("num_sampl... | def _process_configuration(comments):
"""Extract sampling information."""
num_samples = None
num_warmup = None
save_warmup = None
for comment in comments:
comment = comment.strip("#").strip()
if comment.startswith("num_samples"):
num_samples = int(comment.strip("num_sampl... | [
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"""Read CmdStan output.csv.
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path : str
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List[DataFrame, DataFrame, List[str], List[str], List[str]]
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Sample data
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Sample stats
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"""Read CmdStan output.csv.
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path : str
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Sample stats
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train | _process_data_var | Transform datastring to key, values pair.
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string : str
Returns
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train | _read_data | Read Rdump output and transform to Python dictionary.
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path : str
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train | _unpack_dataframes | Transform a list of pandas.DataFrames to dictionary containing ndarrays.
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dfs : List[pandas.DataFrame]
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key, values pairs. Values are formatted to shape = (nchain, ndraws, *shape) | arviz/data/io_cmdstan.py | def _unpack_dataframes(dfs):
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Parameters
----------
dfs : List[pandas.DataFrame]
Returns
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key, values pairs. Values are formatted to shape = (nchain, ndraws, *shape)
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train | from_cmdstan | Convert CmdStan data into an InferenceData object.
Parameters
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posterior : List[str]
List of paths to output.csv files.
CSV file can be stacked csv containing all the chains
cat output*.csv > combined_output.csv
posterior_predictive : str, List[Str]
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prior=None,
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observed_data_var=None,
log_likelihood=None,
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):
"""Convert CmdStan data into an InferenceData object.
Parameters
----------
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prior=None,
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observed_data=None,
observed_data_var=None,
log_likelihood=None,
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"""Convert CmdStan data into an InferenceData object.
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----------
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train | CmdStanConverter._parse_posterior | Read csv paths to list of dataframes. | arviz/data/io_cmdstan.py | def _parse_posterior(self):
"""Read csv paths to list of dataframes."""
paths = self.posterior_
if isinstance(paths, str):
paths = [paths]
chain_data = []
for path in paths:
parsed_output = _read_output(path)
for sample, sample_stats, config, a... | def _parse_posterior(self):
"""Read csv paths to list of dataframes."""
paths = self.posterior_
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paths = [paths]
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parsed_output = _read_output(path)
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train | CmdStanConverter._parse_prior | Read csv paths to list of dataframes. | arviz/data/io_cmdstan.py | def _parse_prior(self):
"""Read csv paths to list of dataframes."""
paths = self.prior_
if isinstance(paths, str):
paths = [paths]
chain_data = []
for path in paths:
parsed_output = _read_output(path)
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paths = self.prior_
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train | CmdStanConverter.posterior_to_xarray | Extract posterior samples from output csv. | arviz/data/io_cmdstan.py | def posterior_to_xarray(self):
"""Extract posterior samples from output csv."""
columns = self.posterior[0].columns
# filter posterior_predictive and log_likelihood
posterior_predictive = self.posterior_predictive
if posterior_predictive is None or (
isinstance(poste... | def posterior_to_xarray(self):
"""Extract posterior samples from output csv."""
columns = self.posterior[0].columns
# filter posterior_predictive and log_likelihood
posterior_predictive = self.posterior_predictive
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train | CmdStanConverter.sample_stats_to_xarray | Extract sample_stats from fit. | arviz/data/io_cmdstan.py | def sample_stats_to_xarray(self):
"""Extract sample_stats from fit."""
dtypes = {"divergent__": bool, "n_leapfrog__": np.int64, "treedepth__": np.int64}
# copy dims and coords
dims = deepcopy(self.dims) if self.dims is not None else {}
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train | CmdStanConverter.posterior_predictive_to_xarray | Convert posterior_predictive samples to xarray. | arviz/data/io_cmdstan.py | def posterior_predictive_to_xarray(self):
"""Convert posterior_predictive samples to xarray."""
posterior_predictive = self.posterior_predictive
columns = self.posterior[0].columns
if (
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train | CmdStanConverter.prior_to_xarray | Convert prior samples to xarray. | arviz/data/io_cmdstan.py | def prior_to_xarray(self):
"""Convert prior samples to xarray."""
# filter prior_predictive
prior_predictive = self.prior_predictive
columns = self.prior[0].columns
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train | CmdStanConverter.sample_stats_prior_to_xarray | Extract sample_stats from fit. | arviz/data/io_cmdstan.py | def sample_stats_prior_to_xarray(self):
"""Extract sample_stats from fit."""
dtypes = {"divergent__": bool, "n_leapfrog__": np.int64, "treedepth__": np.int64}
# copy dims and coords
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train | CmdStanConverter.prior_predictive_to_xarray | Convert prior_predictive samples to xarray. | arviz/data/io_cmdstan.py | def prior_predictive_to_xarray(self):
"""Convert prior_predictive samples to xarray."""
prior_predictive = self.prior_predictive
if (
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"""Convert prior_predictive samples to xarray."""
prior_predictive = self.prior_predictive
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train | CmdStanConverter.observed_data_to_xarray | Convert observed data to xarray. | arviz/data/io_cmdstan.py | def observed_data_to_xarray(self):
"""Convert observed data to xarray."""
observed_data_raw = _read_data(self.observed_data)
variables = self.observed_data_var
if isinstance(variables, str):
variables = [variables]
observed_data = {}
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"""Convert observed data to xarray."""
observed_data_raw = _read_data(self.observed_data)
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train | get_draws | Extract draws from PyStan fit. | arviz/data/io_pystan.py | def get_draws(fit, variables=None, ignore=None):
"""Extract draws from PyStan fit."""
if ignore is None:
ignore = []
if fit.mode == 1:
msg = "Model in mode 'test_grad'. Sampling is not conducted."
raise AttributeError(msg)
if fit.mode == 2 or fit.sim.get("samples") is No... | def get_draws(fit, variables=None, ignore=None):
"""Extract draws from PyStan fit."""
if ignore is None:
ignore = []
if fit.mode == 1:
msg = "Model in mode 'test_grad'. Sampling is not conducted."
raise AttributeError(msg)
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train | get_sample_stats | Extract sample stats from PyStan fit. | arviz/data/io_pystan.py | def get_sample_stats(fit, log_likelihood=None):
"""Extract sample stats from PyStan fit."""
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"""Extract sample stats from PyStan fit."""
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train | get_draws_stan3 | Extract draws from PyStan3 fit. | arviz/data/io_pystan.py | def get_draws_stan3(fit, model=None, variables=None, ignore=None):
"""Extract draws from PyStan3 fit."""
if ignore is None:
ignore = []
dtypes = {}
if model is not None:
dtypes = infer_dtypes(fit, model)
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variables = fit.param_names
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"""Extract draws from PyStan3 fit."""
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dtypes = {}
if model is not None:
dtypes = infer_dtypes(fit, model)
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train | get_sample_stats_stan3 | Extract sample stats from PyStan3 fit. | arviz/data/io_pystan.py | def get_sample_stats_stan3(fit, model=None, log_likelihood=None):
"""Extract sample stats from PyStan3 fit."""
dtypes = {"divergent__": bool, "n_leapfrog__": np.int64, "treedepth__": np.int64}
data = OrderedDict()
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new_shape = -1, fit.num_chai... | def get_sample_stats_stan3(fit, model=None, log_likelihood=None):
"""Extract sample stats from PyStan3 fit."""
dtypes = {"divergent__": bool, "n_leapfrog__": np.int64, "treedepth__": np.int64}
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train | infer_dtypes | Infer dtypes from Stan model code.
Function strips out generated quantities block and searchs for `int`
dtypes after stripping out comments inside the block. | arviz/data/io_pystan.py | def infer_dtypes(fit, model=None):
"""Infer dtypes from Stan model code.
Function strips out generated quantities block and searchs for `int`
dtypes after stripping out comments inside the block.
"""
pattern_remove_comments = re.compile(
r'//.*?$|/\*.*?\*/|\'(?:\\.|[^\\\'])*\'|"(?:\\... | def infer_dtypes(fit, model=None):
"""Infer dtypes from Stan model code.
Function strips out generated quantities block and searchs for `int`
dtypes after stripping out comments inside the block.
"""
pattern_remove_comments = re.compile(
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train | from_pystan | Convert PyStan data into an InferenceData object.
Parameters
----------
posterior : StanFit4Model or stan.fit.Fit
PyStan fit object for posterior.
posterior_predictive : str, a list of str
Posterior predictive samples for the posterior.
prior : StanFit4Model or stan.fit.Fit
... | arviz/data/io_pystan.py | def from_pystan(
posterior=None,
*,
posterior_predictive=None,
prior=None,
prior_predictive=None,
observed_data=None,
log_likelihood=None,
coords=None,
dims=None,
posterior_model=None,
prior_model=None
):
"""Convert PyStan data into an InferenceData object.
... | def from_pystan(
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*,
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log_likelihood=None,
coords=None,
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"""Convert PyStan data into an InferenceData object.
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train | PyStanConverter.posterior_to_xarray | Extract posterior samples from fit. | arviz/data/io_pystan.py | def posterior_to_xarray(self):
"""Extract posterior samples from fit."""
posterior = self.posterior
# filter posterior_predictive and log_likelihood
posterior_predictive = self.posterior_predictive
if posterior_predictive is None:
posterior_predictive = []
... | def posterior_to_xarray(self):
"""Extract posterior samples from fit."""
posterior = self.posterior
# filter posterior_predictive and log_likelihood
posterior_predictive = self.posterior_predictive
if posterior_predictive is None:
posterior_predictive = []
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train | PyStanConverter.sample_stats_to_xarray | Extract sample_stats from posterior. | arviz/data/io_pystan.py | def sample_stats_to_xarray(self):
"""Extract sample_stats from posterior."""
posterior = self.posterior
# copy dims and coords
dims = deepcopy(self.dims) if self.dims is not None else {}
coords = deepcopy(self.coords) if self.coords is not None else {}
# log_lik... | def sample_stats_to_xarray(self):
"""Extract sample_stats from posterior."""
posterior = self.posterior
# copy dims and coords
dims = deepcopy(self.dims) if self.dims is not None else {}
coords = deepcopy(self.coords) if self.coords is not None else {}
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train | PyStanConverter.posterior_predictive_to_xarray | Convert posterior_predictive samples to xarray. | arviz/data/io_pystan.py | def posterior_predictive_to_xarray(self):
"""Convert posterior_predictive samples to xarray."""
posterior = self.posterior
posterior_predictive = self.posterior_predictive
data = get_draws(posterior, variables=posterior_predictive)
return dict_to_dataset(data, library=self.p... | def posterior_predictive_to_xarray(self):
"""Convert posterior_predictive samples to xarray."""
posterior = self.posterior
posterior_predictive = self.posterior_predictive
data = get_draws(posterior, variables=posterior_predictive)
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train | PyStanConverter.prior_to_xarray | Convert prior samples to xarray. | arviz/data/io_pystan.py | def prior_to_xarray(self):
"""Convert prior samples to xarray."""
prior = self.prior
# filter posterior_predictive and log_likelihood
prior_predictive = self.prior_predictive
if prior_predictive is None:
prior_predictive = []
elif isinstance(prior_predi... | def prior_to_xarray(self):
"""Convert prior samples to xarray."""
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# filter posterior_predictive and log_likelihood
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prior_predictive = []
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train | PyStanConverter.sample_stats_prior_to_xarray | Extract sample_stats_prior from prior. | arviz/data/io_pystan.py | def sample_stats_prior_to_xarray(self):
"""Extract sample_stats_prior from prior."""
prior = self.prior
data = get_sample_stats(prior)
return dict_to_dataset(data, library=self.pystan, coords=self.coords, dims=self.dims) | def sample_stats_prior_to_xarray(self):
"""Extract sample_stats_prior from prior."""
prior = self.prior
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train | PyStanConverter.prior_predictive_to_xarray | Convert prior_predictive samples to xarray. | arviz/data/io_pystan.py | def prior_predictive_to_xarray(self):
"""Convert prior_predictive samples to xarray."""
prior = self.prior
prior_predictive = self.prior_predictive
data = get_draws(prior, variables=prior_predictive)
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"""Convert prior_predictive samples to xarray."""
prior = self.prior
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train | PyStan3Converter.posterior_to_xarray | Extract posterior samples from fit. | arviz/data/io_pystan.py | def posterior_to_xarray(self):
"""Extract posterior samples from fit."""
posterior = self.posterior
posterior_model = self.posterior_model
# filter posterior_predictive and log_likelihood
posterior_predictive = self.posterior_predictive
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train | PyStan3Converter.sample_stats_to_xarray | Extract sample_stats from posterior. | arviz/data/io_pystan.py | def sample_stats_to_xarray(self):
"""Extract sample_stats from posterior."""
posterior = self.posterior
posterior_model = self.posterior_model
# copy dims and coords
dims = deepcopy(self.dims) if self.dims is not None else {}
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posterior_model = self.posterior_model
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train | PyStan3Converter.posterior_predictive_to_xarray | Convert posterior_predictive samples to xarray. | arviz/data/io_pystan.py | def posterior_predictive_to_xarray(self):
"""Convert posterior_predictive samples to xarray."""
posterior = self.posterior
posterior_model = self.posterior_model
posterior_predictive = self.posterior_predictive
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posterior = self.posterior
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train | PyStan3Converter.prior_to_xarray | Convert prior samples to xarray. | arviz/data/io_pystan.py | def prior_to_xarray(self):
"""Convert prior samples to xarray."""
prior = self.prior
prior_model = self.prior_model
# filter posterior_predictive and log_likelihood
prior_predictive = self.prior_predictive
if prior_predictive is None:
prior_predictive =... | def prior_to_xarray(self):
"""Convert prior samples to xarray."""
prior = self.prior
prior_model = self.prior_model
# filter posterior_predictive and log_likelihood
prior_predictive = self.prior_predictive
if prior_predictive is None:
prior_predictive =... | [
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train | PyStan3Converter.sample_stats_prior_to_xarray | Extract sample_stats_prior from prior. | arviz/data/io_pystan.py | def sample_stats_prior_to_xarray(self):
"""Extract sample_stats_prior from prior."""
prior = self.prior
prior_model = self.prior_model
data = get_sample_stats_stan3(prior, model=prior_model)
return dict_to_dataset(data, library=self.stan, coords=self.coords, dims=self.dims) | def sample_stats_prior_to_xarray(self):
"""Extract sample_stats_prior from prior."""
prior = self.prior
prior_model = self.prior_model
data = get_sample_stats_stan3(prior, model=prior_model)
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train | PyStan3Converter.prior_predictive_to_xarray | Convert prior_predictive samples to xarray. | arviz/data/io_pystan.py | def prior_predictive_to_xarray(self):
"""Convert prior_predictive samples to xarray."""
prior = self.prior
prior_model = self.prior_model
prior_predictive = self.prior_predictive
data = get_draws_stan3(prior, model=prior_model, variables=prior_predictive)
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"""Convert prior_predictive samples to xarray."""
prior = self.prior
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train | PyStan3Converter.observed_data_to_xarray | Convert observed data to xarray. | arviz/data/io_pystan.py | def observed_data_to_xarray(self):
"""Convert observed data to xarray."""
posterior_model = self.posterior_model
if self.dims is None:
dims = {}
else:
dims = self.dims
observed_names = self.observed_data
if isinstance(observed_names, str):
... | def observed_data_to_xarray(self):
"""Convert observed data to xarray."""
posterior_model = self.posterior_model
if self.dims is None:
dims = {}
else:
dims = self.dims
observed_names = self.observed_data
if isinstance(observed_names, str):
... | [
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train | _get_var_names | Extract latent and observed variable names from pyro.MCMC.
Parameters
----------
posterior : pyro.MCMC
Fitted MCMC object from Pyro
Returns
-------
list[str], list[str]
observed and latent variable names from the MCMC trace. | arviz/data/io_pyro.py | def _get_var_names(posterior):
"""Extract latent and observed variable names from pyro.MCMC.
Parameters
----------
posterior : pyro.MCMC
Fitted MCMC object from Pyro
Returns
-------
list[str], list[str]
observed and latent variable names from the MCMC trace.
"""
sample_... | def _get_var_names(posterior):
"""Extract latent and observed variable names from pyro.MCMC.
Parameters
----------
posterior : pyro.MCMC
Fitted MCMC object from Pyro
Returns
-------
list[str], list[str]
observed and latent variable names from the MCMC trace.
"""
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train | from_pyro | Convert pyro data into an InferenceData object.
Parameters
----------
posterior : pyro.MCMC
Fitted MCMC object from Pyro
coords : dict[str] -> list[str]
Map of dimensions to coordinates
dims : dict[str] -> list[str]
Map variable names to their coordinates | arviz/data/io_pyro.py | def from_pyro(posterior=None, *, coords=None, dims=None):
"""Convert pyro data into an InferenceData object.
Parameters
----------
posterior : pyro.MCMC
Fitted MCMC object from Pyro
coords : dict[str] -> list[str]
Map of dimensions to coordinates
dims : dict[str] -> list[str]
... | def from_pyro(posterior=None, *, coords=None, dims=None):
"""Convert pyro data into an InferenceData object.
Parameters
----------
posterior : pyro.MCMC
Fitted MCMC object from Pyro
coords : dict[str] -> list[str]
Map of dimensions to coordinates
dims : dict[str] -> list[str]
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train | PyroConverter.posterior_to_xarray | Convert the posterior to an xarray dataset. | arviz/data/io_pyro.py | def posterior_to_xarray(self):
"""Convert the posterior to an xarray dataset."""
# Do not make pyro a requirement
from pyro.infer import EmpiricalMarginal
try: # Try pyro>=0.3 release syntax
data = {
name: np.expand_dims(samples.enumerate_support().squeeze()... | def posterior_to_xarray(self):
"""Convert the posterior to an xarray dataset."""
# Do not make pyro a requirement
from pyro.infer import EmpiricalMarginal
try: # Try pyro>=0.3 release syntax
data = {
name: np.expand_dims(samples.enumerate_support().squeeze()... | [
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train | plot_autocorr | Bar plot of the autocorrelation function for a sequence of data.
Useful in particular for posteriors from MCMC samples which may display correlation.
Parameters
----------
data : obj
Any object that can be converted to an az.InferenceData object
Refer to documentation of az.convert_to_... | arviz/plots/autocorrplot.py | def plot_autocorr(data, var_names=None, max_lag=100, combined=False, figsize=None, textsize=None):
"""Bar plot of the autocorrelation function for a sequence of data.
Useful in particular for posteriors from MCMC samples which may display correlation.
Parameters
----------
data : obj
Any o... | def plot_autocorr(data, var_names=None, max_lag=100, combined=False, figsize=None, textsize=None):
"""Bar plot of the autocorrelation function for a sequence of data.
Useful in particular for posteriors from MCMC samples which may display correlation.
Parameters
----------
data : obj
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train | plot_energy | Plot energy transition distribution and marginal energy distribution in HMC algorithms.
This may help to diagnose poor exploration by gradient-based algorithms like HMC or NUTS.
Parameters
----------
data : xarray dataset, or object that can be converted (must represent
`sample_stats` and h... | arviz/plots/energyplot.py | def plot_energy(
data,
kind="kde",
bfmi=True,
figsize=None,
legend=True,
fill_alpha=(1, 0.75),
fill_color=("C0", "C5"),
bw=4.5,
textsize=None,
fill_kwargs=None,
plot_kwargs=None,
ax=None,
):
"""Plot energy transition distribution and marginal energy distribution in HM... | def plot_energy(
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kind="kde",
bfmi=True,
figsize=None,
legend=True,
fill_alpha=(1, 0.75),
fill_color=("C0", "C5"),
bw=4.5,
textsize=None,
fill_kwargs=None,
plot_kwargs=None,
ax=None,
):
"""Plot energy transition distribution and marginal energy distribution in HM... | [
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train | pairwise | From itertools cookbook. [a, b, c, ...] -> (a, b), (b, c), ... | arviz/plots/forestplot.py | def pairwise(iterable):
"""From itertools cookbook. [a, b, c, ...] -> (a, b), (b, c), ..."""
first, second = tee(iterable)
next(second, None)
return zip(first, second) | def pairwise(iterable):
"""From itertools cookbook. [a, b, c, ...] -> (a, b), (b, c), ..."""
first, second = tee(iterable)
next(second, None)
return zip(first, second) | [
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train | plot_forest | Forest plot to compare credible intervals from a number of distributions.
Generates a forest plot of 100*(credible_interval)% credible intervals from
a trace or list of traces.
Parameters
----------
data : obj or list[obj]
Any object that can be converted to an az.InferenceData object
... | arviz/plots/forestplot.py | def plot_forest(
data,
kind="forestplot",
model_names=None,
var_names=None,
combined=False,
credible_interval=0.94,
rope=None,
quartiles=True,
ess=False,
r_hat=False,
colors="cycle",
textsize=None,
linewidth=None,
markersize=None,
ridgeplot_alpha=None,
rid... | def plot_forest(
data,
kind="forestplot",
model_names=None,
var_names=None,
combined=False,
credible_interval=0.94,
rope=None,
quartiles=True,
ess=False,
r_hat=False,
colors="cycle",
textsize=None,
linewidth=None,
markersize=None,
ridgeplot_alpha=None,
rid... | [
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train | PlotHandler.make_plotters | Initialize an object for each variable to be plotted. | arviz/plots/forestplot.py | def make_plotters(self):
"""Initialize an object for each variable to be plotted."""
plotters, y = {}, 0
for var_name in self.var_names:
plotters[var_name] = VarHandler(
var_name,
self.data,
y,
model_names=self.model_nam... | def make_plotters(self):
"""Initialize an object for each variable to be plotted."""
plotters, y = {}, 0
for var_name in self.var_names:
plotters[var_name] = VarHandler(
var_name,
self.data,
y,
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train | PlotHandler.labels_and_ticks | Collect labels and ticks from plotters. | arviz/plots/forestplot.py | def labels_and_ticks(self):
"""Collect labels and ticks from plotters."""
labels, idxs = [], []
for plotter in self.plotters.values():
sub_labels, sub_idxs, _, _ = plotter.labels_ticks_and_vals()
labels.append(sub_labels)
idxs.append(sub_idxs)
return n... | def labels_and_ticks(self):
"""Collect labels and ticks from plotters."""
labels, idxs = [], []
for plotter in self.plotters.values():
sub_labels, sub_idxs, _, _ = plotter.labels_ticks_and_vals()
labels.append(sub_labels)
idxs.append(sub_idxs)
return n... | [
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train | PlotHandler.display_multiple_ropes | Display ROPE when more than one interval is provided. | arviz/plots/forestplot.py | def display_multiple_ropes(self, rope, ax, y, linewidth, rope_var):
"""Display ROPE when more than one interval is provided."""
vals = dict(rope[rope_var][0])["rope"]
ax.plot(
vals,
(y + 0.05, y + 0.05),
lw=linewidth * 2,
color="C2",
so... | def display_multiple_ropes(self, rope, ax, y, linewidth, rope_var):
"""Display ROPE when more than one interval is provided."""
vals = dict(rope[rope_var][0])["rope"]
ax.plot(
vals,
(y + 0.05, y + 0.05),
lw=linewidth * 2,
color="C2",
so... | [
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] | arviz-devs/arviz | python | https://github.com/arviz-devs/arviz/blob/d04d8da07f029fd2931f48d2f7f324cf393e5277/arviz/plots/forestplot.py#L294-L306 | [
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