arviz.InferenceData.isel#

InferenceData.isel(groups=None, filter_groups=None, inplace=False, **kwargs)[source]#

Perform an xarray selection on all groups.

Loops groups to perform Dataset.isel(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.isel()

Parameters:
groupsstr or list of str, optional

Groups where the selection is to be applied. Can either be group names or metagroup names.

filter_groups{None, “like”, “regex”}, optional

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 la pandas.filter.

inplacebool, optional

If True, modify the InferenceData object inplace, otherwise, return the modified copy.

kwargsdict, optional

It must be accepted by xarray.Dataset.isel().

Returns:
InferenceData

A new InferenceData object by default. When inplace==True perform selection in-place and return None

See also

xarray.Dataset.isel

Returns a new dataset with each array indexed along the specified dimension(s).

sel

Returns a new dataset with each array indexed by tick labels along the specified dimension(s).

Examples

Use isel 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> Size: 165kB
      Dimensions:  (chain: 4, draw: 500, school: 8)
      Coordinates:
        * chain    (chain) int64 32B 0 1 2 3
        * draw     (draw) int64 4kB 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499
        * school   (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'
      Data variables:
          mu       (chain, draw) float64 16kB ...
          theta    (chain, draw, school) float64 128kB ...
          tau      (chain, draw) float64 16kB ...
      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

    • <xarray.Dataset> Size: 133kB
      Dimensions:  (chain: 4, draw: 500, school: 8)
      Coordinates:
        * chain    (chain) int64 32B 0 1 2 3
        * draw     (draw) int64 4kB 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499
        * school   (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'
      Data variables:
          obs      (chain, draw, school) float64 128kB ...
      Attributes:
          arviz_version:              0.13.0.dev0
          created_at:                 2022-10-13T14:37:41.460544
          inference_library:          pymc
          inference_library_version:  4.2.2

    • <xarray.Dataset> Size: 133kB
      Dimensions:  (chain: 4, draw: 500, school: 8)
      Coordinates:
        * chain    (chain) int64 32B 0 1 2 3
        * draw     (draw) int64 4kB 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499
        * school   (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'
      Data variables:
          obs      (chain, draw, school) float64 128kB ...
      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> Size: 246kB
      Dimensions:              (chain: 4, draw: 500)
      Coordinates:
        * chain                (chain) int64 32B 0 1 2 3
        * draw                 (draw) int64 4kB 0 1 2 3 4 5 ... 495 496 497 498 499
      Data variables: (12/16)
          max_energy_error     (chain, draw) float64 16kB ...
          energy_error         (chain, draw) float64 16kB ...
          lp                   (chain, draw) float64 16kB ...
          index_in_trajectory  (chain, draw) int64 16kB ...
          acceptance_rate      (chain, draw) float64 16kB ...
          diverging            (chain, draw) bool 2kB ...
          ...                   ...
          smallest_eigval      (chain, draw) float64 16kB ...
          step_size_bar        (chain, draw) float64 16kB ...
          step_size            (chain, draw) float64 16kB ...
          energy               (chain, draw) float64 16kB ...
          tree_depth           (chain, draw) int64 16kB ...
          perf_counter_diff    (chain, draw) float64 16kB ...
      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

    • <xarray.Dataset> Size: 45kB
      Dimensions:  (chain: 1, draw: 500, school: 8)
      Coordinates:
        * chain    (chain) int64 8B 0
        * draw     (draw) int64 4kB 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499
        * school   (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'
      Data variables:
          tau      (chain, draw) float64 4kB ...
          theta    (chain, draw, school) float64 32kB ...
          mu       (chain, draw) float64 4kB ...
      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> Size: 37kB
      Dimensions:  (chain: 1, draw: 500, school: 8)
      Coordinates:
        * chain    (chain) int64 8B 0
        * draw     (draw) int64 4kB 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499
        * school   (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'
      Data variables:
          obs      (chain, draw, school) float64 32kB ...
      Attributes:
          arviz_version:              0.13.0.dev0
          created_at:                 2022-10-13T14:37:26.604969
          inference_library:          pymc
          inference_library_version:  4.2.2

    • <xarray.Dataset> Size: 576B
      Dimensions:  (school: 8)
      Coordinates:
        * school   (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'
      Data variables:
          obs      (school) float64 64B ...
      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> Size: 576B
      Dimensions:  (school: 8)
      Coordinates:
        * school   (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'
      Data variables:
          scores   (school) float64 64B ...
      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.isel(chain=[0, 1, 3], groups="posterior_groups")
idata_subset
arviz.InferenceData
    • <xarray.Dataset> Size: 125kB
      Dimensions:  (chain: 3, draw: 500, school: 8)
      Coordinates:
        * chain    (chain) int64 24B 0 1 3
        * draw     (draw) int64 4kB 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499
        * school   (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'
      Data variables:
          mu       (chain, draw) float64 12kB ...
          theta    (chain, draw, school) float64 96kB ...
          tau      (chain, draw) float64 12kB ...
      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

    • <xarray.Dataset> Size: 101kB
      Dimensions:  (chain: 3, draw: 500, school: 8)
      Coordinates:
        * chain    (chain) int64 24B 0 1 3
        * draw     (draw) int64 4kB 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499
        * school   (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'
      Data variables:
          obs      (chain, draw, school) float64 96kB ...
      Attributes:
          arviz_version:              0.13.0.dev0
          created_at:                 2022-10-13T14:37:41.460544
          inference_library:          pymc
          inference_library_version:  4.2.2

    • <xarray.Dataset> Size: 101kB
      Dimensions:  (chain: 3, draw: 500, school: 8)
      Coordinates:
        * chain    (chain) int64 24B 0 1 3
        * draw     (draw) int64 4kB 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499
        * school   (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'
      Data variables:
          obs      (chain, draw, school) float64 96kB ...
      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> Size: 186kB
      Dimensions:              (chain: 3, draw: 500)
      Coordinates:
        * chain                (chain) int64 24B 0 1 3
        * draw                 (draw) int64 4kB 0 1 2 3 4 5 ... 495 496 497 498 499
      Data variables: (12/16)
          max_energy_error     (chain, draw) float64 12kB ...
          energy_error         (chain, draw) float64 12kB ...
          lp                   (chain, draw) float64 12kB ...
          index_in_trajectory  (chain, draw) int64 12kB ...
          acceptance_rate      (chain, draw) float64 12kB ...
          diverging            (chain, draw) bool 2kB ...
          ...                   ...
          smallest_eigval      (chain, draw) float64 12kB ...
          step_size_bar        (chain, draw) float64 12kB ...
          step_size            (chain, draw) float64 12kB ...
          energy               (chain, draw) float64 12kB ...
          tree_depth           (chain, draw) int64 12kB ...
          perf_counter_diff    (chain, draw) float64 12kB ...
      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

    • <xarray.Dataset> Size: 45kB
      Dimensions:  (chain: 1, draw: 500, school: 8)
      Coordinates:
        * chain    (chain) int64 8B 0
        * draw     (draw) int64 4kB 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499
        * school   (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'
      Data variables:
          tau      (chain, draw) float64 4kB ...
          theta    (chain, draw, school) float64 32kB ...
          mu       (chain, draw) float64 4kB ...
      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> Size: 37kB
      Dimensions:  (chain: 1, draw: 500, school: 8)
      Coordinates:
        * chain    (chain) int64 8B 0
        * draw     (draw) int64 4kB 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499
        * school   (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'
      Data variables:
          obs      (chain, draw, school) float64 32kB ...
      Attributes:
          arviz_version:              0.13.0.dev0
          created_at:                 2022-10-13T14:37:26.604969
          inference_library:          pymc
          inference_library_version:  4.2.2

    • <xarray.Dataset> Size: 576B
      Dimensions:  (school: 8)
      Coordinates:
        * school   (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'
      Data variables:
          obs      (school) float64 64B ...
      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> Size: 576B
      Dimensions:  (school: 8)
      Coordinates:
        * school   (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'
      Data variables:
          scores   (school) float64 64B ...
      Attributes:
          arviz_version:              0.13.0.dev0
          created_at:                 2022-10-13T14:37:26.607471
          inference_library:          pymc
          inference_library_version:  4.2.2

You can expand the groups and coords in each group to see how now only the chains 0, 1 and 3 are present.