"""
Posterior statistics and diagnostics.
"""
from __future__ import annotations
import typing
from collections.abc import Sequence
from typing import Any, NamedTuple
import arviz as az
import jax.numpy as jnp
import jaxlib.xla_extension
import numpy as np
import pandas as pd
import xarray
from deprecated.sphinx import deprecated
from liesel.goose.engine import ErrorLog, SamplingResults
from liesel.goose.pytree import slice_leaves, stack_leaves
from liesel.goose.types import Position
from liesel.option import Option
__docformat__ = "numpy"
[docs]def raise_chain_indices_error(
chain_indices: Sequence[int], num_original_chains: int
) -> None:
"""Display informative error message with valid ``chain_indices`` inputs."""
if any(
chain_index not in range(num_original_chains) for chain_index in chain_indices
):
raise ValueError(
f"All chain indices must be between 0 and {num_original_chains-1} "
"(bounds inclusive)."
)
[docs]def validate_chain_indices(
chain_indices: int | Sequence[int] | None,
num_original_chains: int,
) -> Sequence[int]:
"""
Convert ``int`` or ``None`` input of ``chain_indices`` to sequence of integers.
"""
if chain_indices is None:
return list(range(num_original_chains))
if isinstance(chain_indices, int):
chain_indices = [chain_indices]
raise_chain_indices_error(chain_indices, num_original_chains)
return chain_indices
[docs]def numpy_to_arviz(subparam_chains: np.ndarray) -> xarray.DataArray:
"""Convert data structure of ``arviz`` package to numpy array."""
arviz_data = az.convert_to_inference_data(subparam_chains)
return arviz_data["posterior"]["x"]
[docs]def add_rhat(arviz_array: xarray.DataArray, round_digits: int) -> np.ndarray:
"""Compute a single Rhat value for multiple chains of the same subparameter."""
return np.round(az.rhat(arviz_array)["x"].values, decimals=round_digits)
[docs]def combine_chains(subparam_chains: np.ndarray) -> tuple[np.ndarray, xarray.DataArray]:
"""Concatenate all separate chains to a single chain for multi-chain sampling."""
subparam_chains = subparam_chains.reshape(1, -1)
arviz_array = numpy_to_arviz(subparam_chains)
return subparam_chains, arviz_array
[docs]def add_num_effective(arviz_array: xarray.DataArray, round_digits: int) -> np.ndarray:
"""
Compute the effective sample size of one specific subparameter (e.g. ``beta_0``).
"""
return np.array(
[az.ess(arviz_array[[i]])["x"].values for i in range(arviz_array.shape[0])]
).round(decimals=round_digits)
[docs]def compute_quantiles(
subparam_chains: np.ndarray, quantiles: Sequence[float], round_digits: int
) -> np.ndarray:
"""
Compute posterior quantiles of one specific subparameter (e.g. ``beta_0``).
"""
# i'th row contains i'th input quantile for all chains
# j'th column contains all input quantiles for j'th chain
return np.quantile(subparam_chains, q=quantiles, axis=1).round(round_digits)
[docs]def add_quantiles(
subparam_chains: np.ndarray,
subparam_stats: dict[str, int | float | np.ndarray],
quantiles: Sequence[float],
round_digits: int,
) -> dict[str, int | float | np.ndarray]:
"""
Add posterior quantiles to dictionary which collects all summary statistics and
diagnostics for one specific subparameter (e.g. ``beta_0``).
"""
# number of rows equals length of quantiles input
quantiles_array = compute_quantiles(subparam_chains, quantiles, round_digits)
for ind, q in enumerate(quantiles):
subparam_stats[f"q_{100 * q:.5g}"] = quantiles_array[ind]
return subparam_stats
[docs]def compute_single_hdi(
arviz_subarray: xarray.DataArray, hdi_prob: float, round_digits: int
) -> np.ndarray:
"""Compute Highest-Density Interval for a single chain."""
# compute separate hdi for each chain to stay consistent with credible intervals
# from `add_quantile()`
return np.round(
az.hdi(arviz_subarray, hdi_prob=hdi_prob)["x"].values, decimals=round_digits
)
[docs]def compute_hdi(
arviz_array: xarray.DataArray, hdi_prob: float, round_digits: int
) -> tuple[np.ndarray, np.ndarray]:
"""
Compute Highest-Density Intervals for all chains. Returns two numpy arrays of lower
and upper bounds of Highest-Density Intervals.
"""
# numpy array where i'th row contains lower and upper hdi bound of i'th chain
hdi = np.vstack(
[
# subset with list to keep first dimension, arviz needs 2d arrays
compute_single_hdi(arviz_array[[i]], hdi_prob, round_digits)
for i in range(arviz_array.shape[0])
]
)
return hdi[:, 0], hdi[:, 1]
[docs]def add_hdi(
arviz_array: xarray.DataArray,
subparam_stats: dict[str, int | float | np.ndarray],
hdi_prob: float,
round_digits: int,
) -> dict[str, int | float | np.ndarray]:
"""
Add Highest Density Intervals to dictionary which collects all summary statistics
and diagnostics for one specific subparameter (e.g. ``beta_0``).
"""
hdi_lower, hdi_upper = compute_hdi(arviz_array, hdi_prob, round_digits)
subparam_stats[f"hdi_{100 * hdi_prob:.5g}_low"] = hdi_lower
subparam_stats[f"hdi_{100 * hdi_prob:.5g}_high"] = hdi_upper
return subparam_stats
[docs]def subparam_stats_to_df(
subparam_stats: dict[str, int | float | np.ndarray], per_chain: bool
) -> pd.DataFrame:
"""
Convert dictionary which collects all summary statistics and diagnostics for one
specific subparameter (e.g. ``beta_0``) to a :class:`~pandas.DataFrame`.
"""
if not per_chain:
# Must be dropped BEFORE conversion to data frame due to unequal lengths
del subparam_stats["chain_index"]
return pd.DataFrame(subparam_stats).drop(columns="num_chains")
[docs]def get_subparam_stats(
subparam_chains: np.ndarray,
per_chain: bool,
chain_indices: int | Sequence[int] | None,
quantiles: Sequence[float],
hdi_prob: float,
round_digits: int,
param_index: int,
as_dataframe: bool,
) -> dict[str, int | float | np.ndarray] | pd.DataFrame:
"""
Collect all summary statistics and diagnostics for one specific subparameter (e.g.
``beta_0``) into one dictionary or :class:`~pandas.DataFrame`.
"""
num_original_chains = subparam_chains.shape[0]
chain_indices = validate_chain_indices(chain_indices, num_original_chains)
subparam_chains = subparam_chains[chain_indices]
num_filtered_chains = subparam_chains.shape[0]
num_samples: int | list[int] = [subparam_chains.shape[1]] * num_filtered_chains
arviz_array = numpy_to_arviz(subparam_chains)
rhat = add_rhat(arviz_array, round_digits)
if not per_chain:
subparam_chains, arviz_array = combine_chains(subparam_chains)
num_samples = sum(num_samples) # type: ignore
num_effective = add_num_effective(arviz_array, round_digits)
posterior_means = subparam_chains.mean(axis=1).round(round_digits)
posterior_sds = subparam_chains.std(axis=1).round(round_digits)
subparam_stats = {
"param_index": param_index,
"num_chains": num_filtered_chains,
"chain_index": chain_indices,
"num_samples": num_samples,
"num_effective": num_effective,
"mean": posterior_means,
"sd": posterior_sds,
"rhat": rhat,
}
subparam_stats = add_quantiles(
subparam_chains, subparam_stats, quantiles, round_digits
)
subparam_stats = add_hdi(arviz_array, subparam_stats, hdi_prob, round_digits)
if not as_dataframe:
return subparam_stats
return subparam_stats_to_df(subparam_stats, per_chain)
[docs]def raise_dimension_error(param: str, num_dim: int) -> None:
"""Check for correct array dimensions of posterior samples."""
if num_dim not in (2, 3):
raise ValueError(
f"Array of posterior samples for {param} has the wrong number of"
f"dimensions.\nExpected 2 or 3, got {num_dim}."
)
[docs]def adjust_dimensions(param_chains: np.ndarray, num_dim: int) -> np.ndarray:
"""
Make shape of posterior samples for one dimensional parameters (e.g. ``log_sigma``)
consistent with multi-dimensional parameters.
"""
if num_dim == 2:
param_chains = np.expand_dims(param_chains, axis=-1)
return param_chains
[docs]def raise_param_indices_error(
param_indices: Sequence[int], num_original_subparams: int, param: str
) -> None:
"""
Display informative error message with valid ``param_indices`` inputs for this
specific ``param``.
"""
if any(
param_index not in range(num_original_subparams)
for param_index in param_indices
):
raise ValueError(
f"All param indices for {param} must be between "
f"0 and {num_original_subparams-1} (bounds inclusive)."
)
[docs]def validate_param_indices(
param_indices: int | Sequence[int] | None, num_original_subparams: int, param: str
) -> Sequence[int]:
"""
Convert ``int`` or ``None`` input of ``param_indices`` to sequence of integers.
"""
if param_indices is None:
return list(range(num_original_subparams))
if isinstance(param_indices, int):
param_indices = [param_indices]
raise_param_indices_error(param_indices, num_original_subparams, param)
return param_indices
[docs]def collect_subparam_dicts(
param_chains: np.ndarray,
per_chain: bool,
param_indices: Sequence[int],
chain_indices: int | Sequence[int] | None,
quantiles: Sequence[float],
hdi_prob: float,
round_digits: int,
) -> list[dict]:
"""
Combine dictionaries with summary statistics and diagnostics within one parameter
vector (e.g. ``beta_0`` and ``beta_1``) into a list.
"""
return [
get_subparam_stats(
param_chains[..., i],
per_chain,
chain_indices,
quantiles,
hdi_prob,
round_digits,
param_index=i,
as_dataframe=False,
)
for i in param_indices
]
[docs]def move_col_first(df: pd.DataFrame, colname: str) -> pd.DataFrame:
"""Move last column of a :class:`~pandas.DataFrame` to the first column."""
return df[[colname] + [col for col in df.columns if col != colname]]
[docs]def collect_subparam_dfs(
param_chains: np.ndarray,
per_chain: bool,
param_indices: Sequence[int],
chain_indices: int | Sequence[int] | None,
quantiles: Sequence[float],
hdi_prob: float,
round_digits: int,
) -> pd.DataFrame:
"""
Combine data frames with summary statistics and diagnostics within one parameter
vector (e.g. ``beta_0`` and ``beta_1``) into a single :class:`~pandas.DataFrame`.
"""
param_df = pd.concat(
[
get_subparam_stats(
param_chains[..., i],
per_chain,
chain_indices,
quantiles,
hdi_prob,
round_digits,
param_index=i,
as_dataframe=True,
)
for i in param_indices
]
).reset_index(drop=True)
return move_col_first(param_df, "param_index")
[docs]def get_param_stats(
param: str,
posterior_samples: dict[str, jnp.DeviceArray],
per_chain: bool,
param_indices: int | Sequence[int] | None,
chain_indices: int | Sequence[int] | None,
quantiles: Sequence[float],
hdi_prob: float,
round_digits: int,
as_dataframe: bool,
) -> list[dict] | pd.DataFrame:
"""
Collect all summary statistics and diagnostics for one specific parameter vector
(e.g. ``"beta"``) into one dictionary or :class:`~pandas.DataFrame`.
"""
# arviz package requires numpy arrays instead of jax numpy arrays
param_chains = np.array(posterior_samples[param])
num_dim = param_chains.ndim
# check that each param_chain has either two (scalar) or three (vector) dimension
raise_dimension_error(param, num_dim)
# add last dimension if original parameter is a scalar instead of a vector
param_chains = adjust_dimensions(param_chains, num_dim)
num_original_subparams = param_chains.shape[-1]
param_indices = validate_param_indices(param_indices, num_original_subparams, param)
if not as_dataframe:
return collect_subparam_dicts(
param_chains,
per_chain,
param_indices,
chain_indices,
quantiles,
hdi_prob,
round_digits,
)
return collect_subparam_dfs(
param_chains,
per_chain,
param_indices,
chain_indices,
quantiles,
hdi_prob,
round_digits,
)
[docs]def validate_params(
posterior_samples: dict[str, jnp.DeviceArray], params: str | list[str] | None
) -> list[str]:
"""Convert ``str`` or ``None`` input of ``params`` to sequence of strings."""
posterior_keys = list(posterior_samples.keys())
if params is None:
return posterior_keys
if isinstance(params, str):
params = [params]
if any(param not in posterior_keys for param in params):
raise KeyError(f"All params must be in {posterior_keys}.")
return params
[docs]def collect_param_dicts(
posterior_samples: dict[str, jnp.DeviceArray],
per_chain: bool,
params: list[str],
param_indices: int | Sequence[int] | None,
chain_indices: int | Sequence[int] | None,
quantiles: Sequence[float],
hdi_prob: float,
round_digits: int,
) -> dict[str, list[dict]]:
"""
Combine dictionaries with summary statistics and diagnostics of all model parameters
into a single dictionary.
"""
return {
param: get_param_stats(
param,
posterior_samples,
per_chain,
param_indices,
chain_indices,
quantiles,
hdi_prob,
round_digits,
as_dataframe=False,
)
for param in params
}
[docs]def collect_param_dfs(
posterior_samples: dict[str, jnp.DeviceArray],
per_chain: bool,
params: list[str],
param_indices: int | Sequence[int] | None,
chain_indices: int | Sequence[int] | None,
quantiles: Sequence[float],
hdi_prob: float,
round_digits: int,
) -> pd.DataFrame:
"""
Combine data frames with summary statistics and diagnostics of all model parameters
into a single :class:`~pandas.DataFrame`.
"""
param_dfs: list[pd.DataFrame] = []
for param in params:
param_df: pd.DataFrame = get_param_stats(
param,
posterior_samples,
per_chain,
param_indices,
chain_indices,
quantiles,
hdi_prob,
round_digits,
as_dataframe=True,
)
param_df = param_df.assign(param=param).set_index("param")
param_df.index.name = None
param_dfs.append(param_df)
return pd.concat(param_dfs)
[docs]def summary(
results: SamplingResults,
per_chain: bool = True,
params: str | list[str] | None = None,
param_indices: int | Sequence[int] | None = None,
chain_indices: int | Sequence[int] | None = None,
quantiles: Sequence[float] = (0.05, 0.5, 0.95),
hdi_prob: float = 0.9,
round_digits: int = 3,
as_dataframe: bool = True,
) -> dict[str, list[dict]] | pd.DataFrame:
"""
Compute summary statistics and diagnostic measures of posterior samples.
Parameters
----------
results
Result object of the sampling process. Must have a method
``get_posterior_samples()`` which extracts all samples from the posterior
distribution.
per_chain
If ``True``, all statistics and diagnostics (except the Rhat value) are
computed for each chain separately. If ``False``, one metric is computed for
each subparameter after concatenating all chains.
params
Names of the model parameters that are contained in the summary output. Must
coincide with the dictionary keys of the ``Position`` with the posterior
samples. If ``None``, all parameters are included.
param_indices
Indices of each model parameter that are contained in the summary output.
Selects e.g. ``beta_0`` out of a ``beta`` parameter vector.A single index can
be specified as an integer or a sequence containing one integer. If ``None``,
all subparameters are included.
chain_indices
Indices of chains for each model subparameter that are contained in the summary
output. Selects e.g. chain 0 and chain 2 out of multiple chains. A single index
can be specified as an integer or a sequence containing one integer. If
``None``, all chains are included.
quantiles
Quantiles of the posterior distribution that are contained in the summary
output.
hdi_prob
Coverage level of the Highest Density Interval of the posterior distribution.
Summary output contains lower and upper bound of this interval.
round_digits
Number of decimals for each float value within the summary output.
as_dataframe
If ``True``, all statistics and diagnostics are embedded into a ``pandas`` data
frame. If ``False``, a dictionary with the same keys as ``Position`` with the
posterior samples is returned.
Returns
-------
Dictionary if ``as_dataframe`` is False, :class:`.pandas.DataFrame` if
``as_dataframe`` is True.
"""
# NOTE: Docstring Duplications
# The entries `results`, `params`, `param_indices` and `chain_indices` are shared
# with all user plotting functions.
posterior_samples = results.get_posterior_samples()
params = validate_params(posterior_samples, params)
if not as_dataframe:
return collect_param_dicts(
posterior_samples,
per_chain,
params,
param_indices,
chain_indices,
quantiles,
hdi_prob,
round_digits,
)
return collect_param_dfs(
posterior_samples,
per_chain,
params,
param_indices,
chain_indices,
quantiles,
hdi_prob,
round_digits,
)
[docs]class ErrorSummaryForOneCode(NamedTuple):
error_code: int
error_msg: str
count_per_chain: np.ndarray
count_per_chain_posterior: None
ErrorSummary = dict[str, dict[int, ErrorSummaryForOneCode]]
"""
See docstring of ``_make_error_summary``.
"""
def _make_error_summary(
error_log: ErrorLog,
posterior_error_log: Option[ErrorLog],
) -> ErrorSummary:
"""
Creates an error summary from the error log.
The returned value looks like this::
{
kernel_identifier: {
error_code: (error_code, error_msg, count, count_in_posterior),
error_code: (error_code, error_msg, count, count_in_posterior),
...
},
...
}
The ``error_msg`` is the empty string if the kernel class is not supplied in the
``error_log``.
"""
error_summary = {}
for kel in error_log.values():
counter_dict: dict[int, np.ndarray] = {}
# calculate the overall counts
ec_unique = np.unique(kel.error_codes)
for ec in ec_unique:
if ec == 0:
continue
occurences_per_chain = np.sum(kel.error_codes == ec, axis=1)
counter_dict[ec] = occurences_per_chain
krnl_summary: dict[int, ErrorSummaryForOneCode] = {}
for key, count in counter_dict.items():
ec = key
# type ignore is ok since the type must implement the kernel protocol.
error_msg = kel.kernel_cls.map_or(
"", lambda krn_cls: krn_cls.error_book[ec] # type: ignore
)
krnl_summary[ec] = ErrorSummaryForOneCode(ec, error_msg, count, None)
# calculate the counts in the posterior
if posterior_error_log.is_some():
posterior_error_log_unwrapped = posterior_error_log.unwrap()
kel_post = posterior_error_log_unwrapped[kel.kernel_ident]
for ec in ec_unique:
if ec == 0:
continue
occurences_per_chain = np.sum(kel_post.error_codes == ec, axis=1)
krnl_summary[ec] = krnl_summary[ec]._replace(
count_per_chain_posterior=occurences_per_chain
)
error_summary[kel.kernel_ident] = krnl_summary
return error_summary
[docs]class Summary:
"""
A summary object.
Allows easy programmatic access via ``quantities[quantity_name][var_name]``.
The array has a similar shape as the parameter with ``var_name``. However,
if ``per_chain`` is ``True``, and additionally for the quantities ``hdi`` and
``quantile``, the dimensions are different. Please refer to the documentation
of the attribute :attr:`.quantities` for details.
The summary object can be turned into a :class:`~pandas.DataFrame` using
:meth:`.to_dataframe`.
Parameters
----------
results
The sampling results to summarize.
additional_chain
can be supplied to add more parameters to the summary output. Must be a position
chain which matches chain and time dimension of the posterior chain as returned
by :meth:`.SamplingResults.get_posterior_samples`.
hdi_prob
Level on which to return posterior highest density intervals.
selected, deselected
Allow to get a summary only for a subset of the position keys.
per_chain
If *True*, the summary is calculated on a per-chain basis. Certain measures like
``rhat`` are not available if ``per_chain`` is *True*.
Notes
-----
This class is still considered experimental. The API may still undergo larger
changes.
"""
per_chain: bool
quantities: dict[str, dict[str, np.ndarray]]
"""
Dict of summarizing quantities.
Built up in hierarchies as. Let ``summary`` be a :class:`.Summary` instance. The
hierarchy is::
q = summary.quantities["quantity_name"]["parameter_name"]
The extracted object is an ``np.ndarray``. If ``per_chain=True``, the arrays for
the ``"quantile"`` and ``"hdi"`` quantities have the following dimensions:
1. First index refers to the chain
2. Second index refers to the quantile/interval
3. Third and subsequent indices refer to individual parameters.
If ``per_chain=True``, the arrays for the other quantiles have the dimensions:
1. First index refers to the chain
2. Second and subsequent indices refer to individual parameters.
If ``per_chain=False``, the first index is removed for all quantities.
"""
config: dict
sample_info: dict
error_summary: ErrorSummary
kernels_by_pos_key: dict[str, str]
def __init__(
self,
results: SamplingResults,
additional_chain: Position | None = None,
quantiles: Sequence[float] = (0.05, 0.5, 0.95),
hdi_prob: float = 0.9,
selected: list[str] | None = None,
deselected: list[str] | None = None,
per_chain: bool = False,
):
posterior_chain = results.get_posterior_samples()
if additional_chain:
for k, v in additional_chain.items():
posterior_chain[k] = v
if selected:
posterior_chain = Position(
{
key: value
for key, value in posterior_chain.items()
if key in selected
}
)
if deselected is not None:
for key in deselected:
del posterior_chain[key]
# get some general infos on the sampling
param_chain = next(iter(posterior_chain.values()))
epochs = results.positions.get_epochs()
warmup_size = np.sum(
[epoch.duration for epoch in epochs if epoch.type.is_warmup(epoch.type)]
)
sample_info = {
"num_chains": param_chain.shape[0],
"sample_size_per_chain": param_chain.shape[1],
"warmup_size_per_chain": warmup_size,
}
# convert everything to numpy array
for key in posterior_chain:
posterior_chain[key] = np.asarray(posterior_chain[key])
# calculate quantiles either per chain and merge the results or all at once
single_chain_summaries = []
if per_chain:
for chain_idx in range(sample_info["num_chains"]):
single_chain = slice_leaves(
posterior_chain, jnp.s_[None, chain_idx, ...]
)
qdict = _create_quantity_dict(single_chain, quantiles, hdi_prob)
single_chain_summaries.append(qdict)
quantities = stack_leaves(single_chain_summaries, axis=0)
else:
quantities = _create_quantity_dict(posterior_chain, quantiles, hdi_prob)
config = {
"quantiles": quantiles,
"hdi_prob": hdi_prob,
"chains_merged": not per_chain,
}
error_summary = _make_error_summary(
results.get_error_log(False).unwrap(), results.get_error_log(True)
)
self.per_chain = per_chain
self.quantities = quantities
self.config = config
self.sample_info = sample_info
self.error_summary = error_summary
self.kernels_by_pos_key = results.get_kernels_by_pos_key()
[docs] def to_dataframe(self) -> pd.DataFrame:
"""Turns Summary object into a :class:`~pandas.DataFrame` object."""
# don't change the original data
quants = self.quantities.copy()
# make new entries for the quantiles
if self.per_chain:
for i, q in enumerate(self.config["quantiles"]):
quants[f"q_{q}"] = {
k: v[:, i, ...] for k, v in quants["quantile"].items()
}
quants["hdi_low"] = {k: v[:, 0, ...] for k, v in quants["hdi"].items()}
quants["hdi_high"] = {k: v[:, 1, ...] for k, v in quants["hdi"].items()}
else:
for i, q in enumerate(self.config["quantiles"]):
quants[f"q_{q}"] = {k: v[i, ...] for k, v in quants["quantile"].items()}
quants["hdi_low"] = {k: v[0, ...] for k, v in quants["hdi"].items()}
quants["hdi_high"] = {k: v[1, ...] for k, v in quants["hdi"].items()}
# remove the old entries
del quants["quantile"]
del quants["hdi"]
# create one row per entry
df_dict = {}
for var in quants["mean"].keys():
it = np.nditer(quants["mean"][var], flags=["multi_index"])
for _ in it:
var_fqn = (
var if len(it.multi_index) == 0 else f"{var}{list(it.multi_index)}"
)
quant_per_elem: dict[str, Any] = {}
quant_per_elem["variable"] = var
quant_per_elem["kernel"] = self.kernels_by_pos_key.get(var, "-")
if self.config["chains_merged"]:
quant_per_elem["var_index"] = it.multi_index
quant_per_elem["sample_size"] = (
self.sample_info["sample_size_per_chain"]
* self.sample_info["num_chains"]
)
else:
quant_per_elem["chain_index"] = it.multi_index[0]
quant_per_elem["var_index"] = it.multi_index[1:]
quant_per_elem["sample_size"] = self.sample_info[
"sample_size_per_chain"
]
for quant_name, quant_dict in quants.items():
quant_per_elem[quant_name] = quant_dict[var][it.multi_index]
# convert DeviceArrays (scalar) to floats so that
# pandas treats them correctly
for key, val in quant_per_elem.items():
if type(val) == jaxlib.xla_extension.DeviceArray:
# make mypy happy
val = typing.cast(jnp.ndarray, val)
# value should be a scalar
assert val.shape == ()
# convert to float32
val = np.atleast_1d(np.asarray(val))[0]
quant_per_elem[key] = val
df_dict[var_fqn] = quant_per_elem
# convert to dataframe and use varname as index
df = pd.DataFrame.from_dict(df_dict, orient="index")
df = df.reset_index()
df = df.rename(columns={"index": "var_fqn"})
df = df.set_index("variable")
return df
def _param_df(self):
df = self.to_dataframe()
df.index.name = "parameter"
df = df.rename(columns={"var_index": "index"})
df = df.set_index("index", append=True)
qtls = [f"q_{qtl}" for qtl in self.config["quantiles"]]
cols = (
["kernel", "mean", "sd"]
+ qtls
+ ["sample_size", "ess_bulk", "ess_tail", "rhat"]
)
cols = [col for col in cols if col in df.columns]
df = df[cols]
return df
def _error_df(self, per_chain=False):
# fmt: off
df = pd.concat({
kernel: pd.DataFrame.from_dict(code_summary, orient="index")
for kernel, code_summary in self.error_summary.items()
})
# fmt: on
if df.empty:
return df
df = df.reset_index(level=1, drop=True)
df["error_code"] = df["error_code"].astype(int)
df = df.set_index(["error_code", "error_msg"], append=True)
df.index.names = ["kernel", "error_code", "error_msg"]
# fmt: off
df = df.rename(columns={
"count_per_chain": "total",
"count_per_chain_posterior": "posterior",
})
# fmt: on
df = df.explode(["total", "posterior"])
df["warmup"] = df["total"] - df["posterior"]
df = df.drop(columns="total")
df = df.melt(
value_vars=["warmup", "posterior"],
var_name="phase",
value_name="count",
ignore_index=False,
)
df["phase"] = pd.Categorical(df["phase"], categories=["warmup", "posterior"])
df = df.set_index("phase", append=True)
df["chain"] = df.groupby(level=[0, 1, 2, 3]).cumcount()
df = df.set_index("chain", append=True)
df = df.sort_index()
df["sample_size"] = None
warmup_size = self.sample_info["warmup_size_per_chain"]
posterior_size = self.sample_info["sample_size_per_chain"]
df.loc[pd.IndexSlice[:, :, :, "warmup"], "sample_size"] = warmup_size
df.loc[pd.IndexSlice[:, :, :, "posterior"], "sample_size"] = posterior_size
df["relative"] = df["count"] / df["sample_size"]
df = df.drop(columns="sample_size")
if not per_chain:
df = df.groupby(level=[0, 1, 2, 3], observed=True)
df = df.aggregate({"count": "sum", "relative": "mean"})
df = df.sort_index()
return df
def __repr__(self):
param_df = self._param_df()
error_df = self._error_df()
txt = "Parameter summary:\n\n" + repr(param_df)
if not error_df.empty:
txt += "\n\nError summary:\n\n" + repr(error_df)
return txt
def _repr_html_(self):
param_df = self._param_df()
error_df = self._error_df()
html = "\n<p><strong>Parameter summary:</strong></p>\n" + param_df.to_html()
if not error_df.empty:
html += "\n<p><strong>Error summary:</strong></p>\n" + error_df.to_html()
html += "\n"
return html
def _repr_markdown_(self):
param_df = self._param_df()
error_df = self._error_df()
try:
param_md = param_df.to_markdown()
error_md = error_df.to_markdown()
except ImportError:
param_md = f"```\n{repr(param_df)}\n```"
error_md = f"```\n{repr(error_df)}\n```"
md = "\n\n**Parameter summary:**\n\n" + param_md
if not error_df.empty:
md += "\n\n**Error summary:**\n\n" + error_md
md += "\n\n"
return md
def __str__(self):
return str(self.to_dataframe())
[docs] @classmethod
@deprecated(reason="Functionality moved directly to the __init__.", version="0.1.4")
def from_result(
cls,
result: SamplingResults,
additional_chain: Position | None = None,
quantiles: Sequence[float] = (0.05, 0.5, 0.95),
hdi_prob: float = 0.9,
selected: list[str] | None = None,
deselected: list[str] | None = None,
per_chain=False,
) -> Summary:
"""
Alias for :meth:`.from_results` for backwards compatibility.
In addition to the name, there are two further subtle differences to
:meth:`.from_results`.
- The argument ``result`` is in singular. The method :meth:`.from_results` uses
the plural instead.
- The argument ``result`` is of type :class:`.SamplingResult`, which itself is
an alias for :meth:`.SamplingResults`.
"""
return cls.from_results(
results=result,
additional_chain=additional_chain,
quantiles=quantiles,
hdi_prob=hdi_prob,
selected=selected,
deselected=deselected,
per_chain=per_chain,
)
[docs] @staticmethod
@deprecated(reason="Functionality moved directly to the __init__.", version="0.1.4")
def from_results(
results: SamplingResults,
additional_chain: Position | None = None,
quantiles: Sequence[float] = (0.05, 0.5, 0.95),
hdi_prob: float = 0.9,
selected: list[str] | None = None,
deselected: list[str] | None = None,
per_chain=False,
) -> Summary:
"""
Creates a :class:`.Summary` object from a results object.
"""
return Summary(
results=results,
additional_chain=additional_chain,
quantiles=quantiles,
hdi_prob=hdi_prob,
selected=selected,
deselected=deselected,
per_chain=per_chain,
)
def _create_quantity_dict(
chain: Position, quantiles: Sequence[float], hdi_prob: float
) -> dict[str, dict[str, np.ndarray]]:
azchain = az.convert_to_inference_data(chain).posterior
# calculate quantities
mean = azchain.mean(dim=["chain", "draw"])
var = azchain.var(dim=["chain", "draw"])
sd = azchain.std(dim=["chain", "draw"])
quantile = azchain.quantile(q=quantiles, dim=["chain", "draw"])
hdi = az.hdi(azchain, hdi_prob=hdi_prob)
ess_bulk = az.ess(azchain, method="bulk")
ess_tail = az.ess(azchain, method="tail")
mcse_mean = az.mcse(azchain, method="mean")
mcse_sd = az.mcse(azchain, method="sd")
# place quantities in dict
quantities = {
"mean": mean,
"var": var,
"sd": sd,
"quantile": quantile,
"hdi": hdi,
"rhat": None,
"ess_bulk": ess_bulk,
"ess_tail": ess_tail,
"mcse_mean": mcse_mean,
"mcse_sd": mcse_sd,
}
if azchain.chain.size > 1:
quantities["rhat"] = az.rhat(azchain)
else:
del quantities["rhat"]
# convert to simple dict[str, np.ndarray]
for key, val in quantities.items():
quantities[key] = {k: v.values for k, v in val.data_vars.items()}
# hdi shape BEFORE
# VarIDX --- HDI
# special treatment for hdi since the function uses the last axis to refer
# to the quantile
for k, v in quantities["hdi"].items():
quantities["hdi"][k] = np.moveaxis(v, -1, 0)
# hdi shape AFTER
# HDI --- VarIDX
return quantities
