arviz.InferenceData.sel#
- InferenceData.sel(groups=None, filter_groups=None, inplace=False, chain_prior=None, **kwargs)[source]#
Perform an xarray selection on all groups.
Loops groups to perform Dataset.sel(key=item) for every kwarg if key is a dimension of the dataset. One example could be performing a burn in cut on the InferenceData object or discarding a chain. The selection is performed on all relevant groups (like posterior, prior, sample stats) while non relevant groups like observed data are omitted. See
xarray.Dataset.sel
- Parameters
- groups
str
orlist
ofstr
, optional Groups where the selection is to be applied. Can either be group names or metagroup names.
- filter_groups{
None
, “like”, “regex”}, optional, default=None If
None
(default), interpret groups as the real group or metagroup names. If “like”, interpret groups as substrings of the real group or metagroup names. If “regex”, interpret groups as regular expressions on the real group or metagroup names. A lapandas.filter
.- inplacebool, optional
If
True
, modify the InferenceData object inplace, otherwise, return the modified copy.- chain_priorbool, optional,
deprecated
If
False
, do not select prior related groups usingchain
dim. Otherwise, use selection onchain
if present. Default=False- kwargs
dict
, optional It must be accepted by Dataset.sel().
- groups
- Returns
InferenceData
A new InferenceData object by default. When
inplace==True
perform selection in-place and returnNone
See also
xarray.Dataset.sel
Returns a new dataset with each array indexed by tick labels along the specified dimension(s).
isel
Returns a new dataset with each array indexed along the specified dimension(s).
Examples
Use
sel
to discard one chain of the InferenceData object. We first check the dimensions of the original object:import arviz as az idata = az.load_arviz_data("centered_eight") idata
arviz.InferenceData-
<xarray.Dataset> Dimensions: (chain: 4, draw: 500, school: 8) Coordinates: * chain (chain) int64 0 1 2 3 * draw (draw) int64 0 1 2 3 4 5 6 7 8 ... 492 493 494 495 496 497 498 499 * school (school) object 'Choate' 'Deerfield' ... "St. Paul's" 'Mt. Hermon' Data variables: mu (chain, draw) float64 ... theta (chain, draw, school) float64 ... tau (chain, draw) float64 ... Attributes: created_at: 2022-10-13T14:37:37.315398 arviz_version: 0.13.0.dev0 inference_library: pymc inference_library_version: 4.2.2 sampling_time: 7.480114936828613 tuning_steps: 1000
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<xarray.Dataset> Dimensions: (chain: 4, draw: 500, school: 8) Coordinates: * chain (chain) int64 0 1 2 3 * draw (draw) int64 0 1 2 3 4 5 6 7 8 ... 492 493 494 495 496 497 498 499 * school (school) object 'Choate' 'Deerfield' ... "St. Paul's" 'Mt. Hermon' Data variables: obs (chain, draw, school) float64 ... Attributes: arviz_version: 0.13.0.dev0 created_at: 2022-10-13T14:37:41.460544 inference_library: pymc inference_library_version: 4.2.2
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<xarray.Dataset> Dimensions: (chain: 4, draw: 500, school: 8) Coordinates: * chain (chain) int64 0 1 2 3 * draw (draw) int64 0 1 2 3 4 5 6 7 8 ... 492 493 494 495 496 497 498 499 * school (school) object 'Choate' 'Deerfield' ... "St. Paul's" 'Mt. Hermon' Data variables: obs (chain, draw, school) float64 ... Attributes: arviz_version: 0.13.0.dev0 created_at: 2022-10-13T14:37:37.487399 inference_library: pymc inference_library_version: 4.2.2
-
<xarray.Dataset> Dimensions: (chain: 4, draw: 500) Coordinates: * chain (chain) int64 0 1 2 3 * draw (draw) int64 0 1 2 3 4 5 6 ... 494 495 496 497 498 499 Data variables: (12/16) max_energy_error (chain, draw) float64 ... energy_error (chain, draw) float64 ... lp (chain, draw) float64 ... index_in_trajectory (chain, draw) int64 ... acceptance_rate (chain, draw) float64 ... diverging (chain, draw) bool ... ... ... smallest_eigval (chain, draw) float64 ... step_size_bar (chain, draw) float64 ... step_size (chain, draw) float64 ... energy (chain, draw) float64 ... tree_depth (chain, draw) int64 ... perf_counter_diff (chain, draw) float64 ... Attributes: arviz_version: 0.13.0.dev0 created_at: 2022-10-13T14:37:37.324929 inference_library: pymc inference_library_version: 4.2.2 sampling_time: 7.480114936828613 tuning_steps: 1000
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<xarray.Dataset> Dimensions: (chain: 1, draw: 500, school: 8) Coordinates: * chain (chain) int64 0 * draw (draw) int64 0 1 2 3 4 5 6 7 8 ... 492 493 494 495 496 497 498 499 * school (school) object 'Choate' 'Deerfield' ... "St. Paul's" 'Mt. Hermon' Data variables: tau (chain, draw) float64 ... theta (chain, draw, school) float64 ... mu (chain, draw) float64 ... Attributes: arviz_version: 0.13.0.dev0 created_at: 2022-10-13T14:37:26.602116 inference_library: pymc inference_library_version: 4.2.2
-
<xarray.Dataset> Dimensions: (chain: 1, draw: 500, school: 8) Coordinates: * chain (chain) int64 0 * draw (draw) int64 0 1 2 3 4 5 6 7 8 ... 492 493 494 495 496 497 498 499 * school (school) object 'Choate' 'Deerfield' ... "St. Paul's" 'Mt. Hermon' Data variables: obs (chain, draw, school) float64 ... Attributes: arviz_version: 0.13.0.dev0 created_at: 2022-10-13T14:37:26.604969 inference_library: pymc inference_library_version: 4.2.2
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<xarray.Dataset> Dimensions: (school: 8) Coordinates: * school (school) object 'Choate' 'Deerfield' ... "St. Paul's" 'Mt. Hermon' Data variables: obs (school) float64 ... Attributes: arviz_version: 0.13.0.dev0 created_at: 2022-10-13T14:37:26.606375 inference_library: pymc inference_library_version: 4.2.2
-
<xarray.Dataset> Dimensions: (school: 8) Coordinates: * school (school) object 'Choate' 'Deerfield' ... "St. Paul's" 'Mt. Hermon' Data variables: scores (school) float64 ... Attributes: arviz_version: 0.13.0.dev0 created_at: 2022-10-13T14:37:26.607471 inference_library: pymc inference_library_version: 4.2.2
In order to remove the third chain:
idata_subset = idata.sel(chain=[0, 1, 3], groups="posterior_groups") idata_subset
arviz.InferenceData-
<xarray.Dataset> Dimensions: (chain: 3, draw: 500, school: 8) Coordinates: * chain (chain) int64 0 1 3 * draw (draw) int64 0 1 2 3 4 5 6 7 8 ... 492 493 494 495 496 497 498 499 * school (school) object 'Choate' 'Deerfield' ... "St. Paul's" 'Mt. Hermon' Data variables: mu (chain, draw) float64 ... theta (chain, draw, school) float64 ... tau (chain, draw) float64 ... Attributes: created_at: 2022-10-13T14:37:37.315398 arviz_version: 0.13.0.dev0 inference_library: pymc inference_library_version: 4.2.2 sampling_time: 7.480114936828613 tuning_steps: 1000
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<xarray.Dataset> Dimensions: (chain: 3, draw: 500, school: 8) Coordinates: * chain (chain) int64 0 1 3 * draw (draw) int64 0 1 2 3 4 5 6 7 8 ... 492 493 494 495 496 497 498 499 * school (school) object 'Choate' 'Deerfield' ... "St. Paul's" 'Mt. Hermon' Data variables: obs (chain, draw, school) float64 ... Attributes: arviz_version: 0.13.0.dev0 created_at: 2022-10-13T14:37:41.460544 inference_library: pymc inference_library_version: 4.2.2
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<xarray.Dataset> Dimensions: (chain: 3, draw: 500, school: 8) Coordinates: * chain (chain) int64 0 1 3 * draw (draw) int64 0 1 2 3 4 5 6 7 8 ... 492 493 494 495 496 497 498 499 * school (school) object 'Choate' 'Deerfield' ... "St. Paul's" 'Mt. Hermon' Data variables: obs (chain, draw, school) float64 ... Attributes: arviz_version: 0.13.0.dev0 created_at: 2022-10-13T14:37:37.487399 inference_library: pymc inference_library_version: 4.2.2
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<xarray.Dataset> Dimensions: (chain: 3, draw: 500) Coordinates: * chain (chain) int64 0 1 3 * draw (draw) int64 0 1 2 3 4 5 6 ... 494 495 496 497 498 499 Data variables: (12/16) max_energy_error (chain, draw) float64 ... energy_error (chain, draw) float64 ... lp (chain, draw) float64 ... index_in_trajectory (chain, draw) int64 ... acceptance_rate (chain, draw) float64 ... diverging (chain, draw) bool ... ... ... smallest_eigval (chain, draw) float64 ... step_size_bar (chain, draw) float64 ... step_size (chain, draw) float64 ... energy (chain, draw) float64 ... tree_depth (chain, draw) int64 ... perf_counter_diff (chain, draw) float64 ... Attributes: arviz_version: 0.13.0.dev0 created_at: 2022-10-13T14:37:37.324929 inference_library: pymc inference_library_version: 4.2.2 sampling_time: 7.480114936828613 tuning_steps: 1000
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<xarray.Dataset> Dimensions: (chain: 1, draw: 500, school: 8) Coordinates: * chain (chain) int64 0 * draw (draw) int64 0 1 2 3 4 5 6 7 8 ... 492 493 494 495 496 497 498 499 * school (school) object 'Choate' 'Deerfield' ... "St. Paul's" 'Mt. Hermon' Data variables: tau (chain, draw) float64 ... theta (chain, draw, school) float64 ... mu (chain, draw) float64 ... Attributes: arviz_version: 0.13.0.dev0 created_at: 2022-10-13T14:37:26.602116 inference_library: pymc inference_library_version: 4.2.2
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<xarray.Dataset> Dimensions: (chain: 1, draw: 500, school: 8) Coordinates: * chain (chain) int64 0 * draw (draw) int64 0 1 2 3 4 5 6 7 8 ... 492 493 494 495 496 497 498 499 * school (school) object 'Choate' 'Deerfield' ... "St. Paul's" 'Mt. Hermon' Data variables: obs (chain, draw, school) float64 ... Attributes: arviz_version: 0.13.0.dev0 created_at: 2022-10-13T14:37:26.604969 inference_library: pymc inference_library_version: 4.2.2
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<xarray.Dataset> Dimensions: (school: 8) Coordinates: * school (school) object 'Choate' 'Deerfield' ... "St. Paul's" 'Mt. Hermon' Data variables: obs (school) float64 ... Attributes: arviz_version: 0.13.0.dev0 created_at: 2022-10-13T14:37:26.606375 inference_library: pymc inference_library_version: 4.2.2
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<xarray.Dataset> Dimensions: (school: 8) Coordinates: * school (school) object 'Choate' 'Deerfield' ... "St. Paul's" 'Mt. Hermon' Data variables: scores (school) float64 ... Attributes: arviz_version: 0.13.0.dev0 created_at: 2022-10-13T14:37:26.607471 inference_library: pymc inference_library_version: 4.2.2