arviz.InferenceData.rename_vars#

InferenceData.rename_vars(name_dict=None, groups=None, filter_groups=None, inplace=False)[source]#

Perform xarray renaming of variable or coordinate names on all groups.

Loops groups to perform Dataset.rename_vars(name_dict) for every key in name_dict if key is a variable or coordinate names of the dataset. The renaming is performed on all relevant groups (like posterior, prior, sample stats) while non relevant groups like observed data are omitted. See xarray.Dataset.rename_vars()

Parameters
name_dictdict

Dictionary whose keys are current variable or coordinate names and whose values are the desired names.

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.

Returns
InferenceData

A new InferenceData object with renamed variables including coordinates by default. When inplace==True perform renaming in-place and return None

See also

xarray.Dataset.rename_vars

Returns a new object with renamed variables including coordinates.

rename

Perform xarray renaming of variable and dimensions on all groups of an InferenceData object.

rename_dims

Perform xarray renaming of dimensions on all groups of InferenceData object.

Examples

Use rename_vars to renaming of variable and coordinates on all groups of the InferenceData object. We first check the data variables of original object:

import arviz as az
idata = az.load_arviz_data("rugby")
idata
arviz.InferenceData
    • <xarray.Dataset>
      Dimensions:    (chain: 4, draw: 500, team: 6)
      Coordinates:
        * chain      (chain) int64 0 1 2 3
        * draw       (draw) int64 0 1 2 3 4 5 6 7 ... 492 493 494 495 496 497 498 499
        * team       (team) object 'Wales' 'France' 'Ireland' ... 'Italy' 'England'
      Data variables:
          home       (chain, draw) float64 0.1642 0.1162 0.09299 ... 0.148 0.2265
          intercept  (chain, draw) float64 2.893 2.941 2.939 ... 2.951 2.903 2.892
          atts_star  (chain, draw, team) float64 0.1673 0.04184 ... -0.4652 0.02878
          defs_star  (chain, draw, team) float64 -0.03638 -0.04109 ... 0.7136 -0.0649
          sd_att     (chain, draw) float64 0.4854 0.1438 0.2139 ... 0.2883 0.4591
          sd_def     (chain, draw) float64 0.2747 1.033 0.6363 ... 0.5574 0.2849
          atts       (chain, draw, team) float64 0.1063 -0.01913 ... -0.2911 0.2029
          defs       (chain, draw, team) float64 -0.06765 -0.07235 ... 0.5799 -0.1986
      Attributes:
          created_at:                 2019-07-12T20:31:53.545143
          inference_library:          pymc3
          inference_library_version:  3.7

    • <xarray.Dataset>
      Dimensions:      (chain: 4, draw: 500, match: 60)
      Coordinates:
        * chain        (chain) int64 0 1 2 3
        * draw         (draw) int64 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499
        * match        (match) object 'Wales Italy' ... 'Ireland England'
      Data variables:
          home_points  (chain, draw, match) int64 ...
          away_points  (chain, draw, match) int64 ...
      Attributes:
          created_at:                 2019-07-12T20:31:53.563854
          inference_library:          pymc3
          inference_library_version:  3.7

    • <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 ... 493 494 495 496 497 498 499
      Data variables:
          energy_error      (chain, draw) float64 -0.07666 -0.4523 ... 0.115 -0.07691
          energy            (chain, draw) float64 540.2 545.3 542.3 ... 544.0 545.6
          tree_size         (chain, draw) float64 15.0 63.0 31.0 ... 63.0 31.0 31.0
          tune              (chain, draw) bool True False False ... False False False
          mean_tree_accept  (chain, draw) float64 1.0 0.8851 0.8875 ... 0.7791 0.7539
          lp                (chain, draw) float64 -536.4 -536.0 ... -536.1 -536.4
          depth             (chain, draw) int64 4 6 5 4 4 4 5 5 5 ... 6 4 6 5 3 6 5 5
          max_energy_error  (chain, draw) float64 -0.5361 -0.5871 ... 0.7109 1.014
          step_size         (chain, draw) float64 0.2469 0.2469 ... 0.2459 0.2459
          step_size_bar     (chain, draw) float64 0.2313 0.2313 ... 0.2488 0.2488
          diverging         (chain, draw) bool False False False ... False False False
      Attributes:
          created_at:                 2019-07-12T20:31:53.555203
          inference_library:          pymc3
          inference_library_version:  3.7

    • <xarray.Dataset>
      Dimensions:       (chain: 1, draw: 500, team: 6, match: 60)
      Coordinates:
        * chain         (chain) int64 0
        * draw          (draw) int64 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499
        * team          (team) object 'Wales' 'France' 'Ireland' ... 'Italy' 'England'
        * match         (match) object 'Wales Italy' ... 'Ireland England'
      Data variables:
          sd_att_log__  (chain, draw) float64 1.322 -2.014 1.588 ... -0.8585 -0.1922
          intercept     (chain, draw) float64 4.464 3.352 1.567 ... 4.363 4.128 1.049
          atts_star     (chain, draw, team) float64 -2.64 4.172 ... -0.2874 -0.8538
          defs_star     (chain, draw, team) float64 -0.7817 -0.1478 ... 0.1655 0.01067
          away_points   (chain, draw, match) int64 11308 0 11 1 0 21442 ... 11 1 2 2 0
          sd_att        (chain, draw) float64 3.752 0.1334 4.896 ... 0.4238 0.8251
          sd_def_log__  (chain, draw) float64 -0.2662 0.2411 0.6071 ... 1.402 -1.981
          home          (chain, draw) float64 -1.511 -0.001582 ... -0.02416 0.2651
          atts          (chain, draw, team) float64 -4.667 2.145 ... -0.2702 -0.8365
          sd_def        (chain, draw) float64 0.7663 1.273 1.835 ... 3.922 4.063 0.138
          home_points   (chain, draw, match) int64 0 47 11899 3262 1 ... 3 2 1 12 13
          defs          (chain, draw, team) float64 -0.2517 0.3823 ... 0.089 -0.06586
      Attributes:
          created_at:                 2019-07-12T20:31:53.573731
          inference_library:          pymc3
          inference_library_version:  3.7

    • <xarray.Dataset>
      Dimensions:      (match: 60)
      Coordinates:
        * match        (match) object 'Wales Italy' ... 'Ireland England'
      Data variables:
          home_points  (match) float64 23.0 26.0 28.0 26.0 0.0 ... 61.0 29.0 20.0 13.0
          away_points  (match) float64 15.0 24.0 6.0 3.0 20.0 ... 21.0 0.0 18.0 9.0
      Attributes:
          created_at:                 2019-07-12T20:31:53.581293
          inference_library:          pymc3
          inference_library_version:  3.7

In order to rename the data variables, we use:

idata.rename_vars({"home": "home_new"}, inplace=True)
idata
arviz.InferenceData
    • <xarray.Dataset>
      Dimensions:    (chain: 4, draw: 500, team: 6)
      Coordinates:
        * chain      (chain) int64 0 1 2 3
        * draw       (draw) int64 0 1 2 3 4 5 6 7 ... 492 493 494 495 496 497 498 499
        * team       (team) object 'Wales' 'France' 'Ireland' ... 'Italy' 'England'
      Data variables:
          home_new   (chain, draw) float64 0.1642 0.1162 0.09299 ... 0.148 0.2265
          intercept  (chain, draw) float64 2.893 2.941 2.939 ... 2.951 2.903 2.892
          atts_star  (chain, draw, team) float64 0.1673 0.04184 ... -0.4652 0.02878
          defs_star  (chain, draw, team) float64 -0.03638 -0.04109 ... 0.7136 -0.0649
          sd_att     (chain, draw) float64 0.4854 0.1438 0.2139 ... 0.2883 0.4591
          sd_def     (chain, draw) float64 0.2747 1.033 0.6363 ... 0.5574 0.2849
          atts       (chain, draw, team) float64 0.1063 -0.01913 ... -0.2911 0.2029
          defs       (chain, draw, team) float64 -0.06765 -0.07235 ... 0.5799 -0.1986
      Attributes:
          created_at:                 2019-07-12T20:31:53.545143
          inference_library:          pymc3
          inference_library_version:  3.7

    • <xarray.Dataset>
      Dimensions:      (chain: 4, draw: 500, match: 60)
      Coordinates:
        * chain        (chain) int64 0 1 2 3
        * draw         (draw) int64 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499
        * match        (match) object 'Wales Italy' ... 'Ireland England'
      Data variables:
          home_points  (chain, draw, match) int64 ...
          away_points  (chain, draw, match) int64 ...
      Attributes:
          created_at:                 2019-07-12T20:31:53.563854
          inference_library:          pymc3
          inference_library_version:  3.7

    • <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 ... 493 494 495 496 497 498 499
      Data variables:
          energy_error      (chain, draw) float64 -0.07666 -0.4523 ... 0.115 -0.07691
          energy            (chain, draw) float64 540.2 545.3 542.3 ... 544.0 545.6
          tree_size         (chain, draw) float64 15.0 63.0 31.0 ... 63.0 31.0 31.0
          tune              (chain, draw) bool True False False ... False False False
          mean_tree_accept  (chain, draw) float64 1.0 0.8851 0.8875 ... 0.7791 0.7539
          lp                (chain, draw) float64 -536.4 -536.0 ... -536.1 -536.4
          depth             (chain, draw) int64 4 6 5 4 4 4 5 5 5 ... 6 4 6 5 3 6 5 5
          max_energy_error  (chain, draw) float64 -0.5361 -0.5871 ... 0.7109 1.014
          step_size         (chain, draw) float64 0.2469 0.2469 ... 0.2459 0.2459
          step_size_bar     (chain, draw) float64 0.2313 0.2313 ... 0.2488 0.2488
          diverging         (chain, draw) bool False False False ... False False False
      Attributes:
          created_at:                 2019-07-12T20:31:53.555203
          inference_library:          pymc3
          inference_library_version:  3.7

    • <xarray.Dataset>
      Dimensions:       (chain: 1, draw: 500, team: 6, match: 60)
      Coordinates:
        * chain         (chain) int64 0
        * draw          (draw) int64 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499
        * team          (team) object 'Wales' 'France' 'Ireland' ... 'Italy' 'England'
        * match         (match) object 'Wales Italy' ... 'Ireland England'
      Data variables:
          sd_att_log__  (chain, draw) float64 1.322 -2.014 1.588 ... -0.8585 -0.1922
          intercept     (chain, draw) float64 4.464 3.352 1.567 ... 4.363 4.128 1.049
          atts_star     (chain, draw, team) float64 -2.64 4.172 ... -0.2874 -0.8538
          defs_star     (chain, draw, team) float64 -0.7817 -0.1478 ... 0.1655 0.01067
          away_points   (chain, draw, match) int64 11308 0 11 1 0 21442 ... 11 1 2 2 0
          sd_att        (chain, draw) float64 3.752 0.1334 4.896 ... 0.4238 0.8251
          sd_def_log__  (chain, draw) float64 -0.2662 0.2411 0.6071 ... 1.402 -1.981
          home_new      (chain, draw) float64 -1.511 -0.001582 ... -0.02416 0.2651
          atts          (chain, draw, team) float64 -4.667 2.145 ... -0.2702 -0.8365
          sd_def        (chain, draw) float64 0.7663 1.273 1.835 ... 3.922 4.063 0.138
          home_points   (chain, draw, match) int64 0 47 11899 3262 1 ... 3 2 1 12 13
          defs          (chain, draw, team) float64 -0.2517 0.3823 ... 0.089 -0.06586
      Attributes:
          created_at:                 2019-07-12T20:31:53.573731
          inference_library:          pymc3
          inference_library_version:  3.7

    • <xarray.Dataset>
      Dimensions:      (match: 60)
      Coordinates:
        * match        (match) object 'Wales Italy' ... 'Ireland England'
      Data variables:
          home_points  (match) float64 23.0 26.0 28.0 26.0 0.0 ... 61.0 29.0 20.0 13.0
          away_points  (match) float64 15.0 24.0 6.0 3.0 20.0 ... 21.0 0.0 18.0 9.0
      Attributes:
          created_at:                 2019-07-12T20:31:53.581293
          inference_library:          pymc3
          inference_library_version:  3.7