arviz.InferenceData.rename_dims — ArviZ 0.14.0 documentation (original) (raw)
InferenceData.rename_dims(name_dict=None, groups=None, filter_groups=None, inplace=False)[source]#
Perform xarray renaming of dimensions on all groups.
Loops groups to perform Dataset.rename_dims(name_dict) for every key in name_dict if key is a dimension 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_dims()
Parameters
name_dictdict
Dictionary whose keys are current dimension 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 dimension by default. When inplace==True
perform renaming in-place and return None
See also
Returns a new object with renamed dimensions only.
Perform xarray renaming of variable and dimensions on all groups of an InferenceData object.
Perform xarray renaming of variable or coordinate names on all groups of an InferenceData object.
Examples
Use rename_dims
to renaming of dimensions on all groups of the InferenceData object. We first check the dimensions of original object:
import arviz as az idata = az.load_arviz_data("rugby") idata
- posterior
<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 ...
intercept (chain, draw) float64 ...
atts_star (chain, draw, team) float64 ...
defs_star (chain, draw, team) float64 ...
sd_att (chain, draw) float64 ...
sd_def (chain, draw) float64 ...
atts (chain, draw, team) float64 ...
defs (chain, draw, team) float64 ...
Attributes:
created_at: 2019-07-12T20:31:53.545143
inference_library: pymc3
inference_library_version: 3.7- Dimensions:
* chain: 4
* draw: 500
* team: 6 - Coordinates: (3)
* chain
(chain)
int64
0 1 2 3
* draw
(draw)
int64
0 1 2 3 4 5 ... 495 496 497 498 499
array([ 0, 1, 2, ..., 497, 498, 499])
* team
(team)
object
'Wales' 'France' ... 'England'
array(['Wales', 'France', 'Ireland', 'Scotland', 'Italy', 'England'],
dtype=object) - Data variables: (8)
* home
(chain, draw)
float64
...
[2000 values with dtype=float64]
* intercept
(chain, draw)
float64
...
[2000 values with dtype=float64]
* atts_star
(chain, draw, team)
float64
...
[12000 values with dtype=float64]
* defs_star
(chain, draw, team)
float64
...
[12000 values with dtype=float64]
* sd_att
(chain, draw)
float64
...
[2000 values with dtype=float64]
* sd_def
(chain, draw)
float64
...
[2000 values with dtype=float64]
* atts
(chain, draw, team)
float64
...
[12000 values with dtype=float64]
* defs
(chain, draw, team)
float64
...
[12000 values with dtype=float64] - Indexes: (3)
* PandasIndex
PandasIndex(Int64Index([0, 1, 2, 3], dtype='int64', name='chain'))
* PandasIndex
PandasIndex(Int64Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
...
490, 491, 492, 493, 494, 495, 496, 497, 498, 499],
dtype='int64', name='draw', length=500))
* PandasIndex
PandasIndex(Index(['Wales', 'France', 'Ireland', 'Scotland', 'Italy', 'England'], dtype='object', name='team')) - Attributes: (3)
created_at :
2019-07-12T20:31:53.545143
inference_library :
pymc3
inference_library_version :
3.7
- Dimensions:
- posterior_predictive
<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- Dimensions:
* chain: 4
* draw: 500
* match: 60 - Coordinates: (3)
* chain
(chain)
int64
0 1 2 3
* draw
(draw)
int64
0 1 2 3 4 5 ... 495 496 497 498 499
array([ 0, 1, 2, ..., 497, 498, 499])
* match
(match)
object
'Wales Italy' ... 'Ireland England'
array(['Wales Italy', 'France England', 'Ireland Scotland', 'Ireland Wales',
'Scotland England', 'France Italy', 'Wales France', 'Italy Scotland',
'England Ireland', 'Ireland Italy', 'Scotland France', 'England Wales',
'Italy England', 'Wales Scotland', 'France Ireland', 'Wales England',
'Italy Ireland', 'France Scotland', 'England Italy', 'Ireland France',
'Scotland Wales', 'Scotland Italy', 'France Wales', 'Ireland England',
'Wales Ireland', 'England Scotland', 'Italy France', 'Italy Wales',
'Scotland Ireland', 'England France', 'France Italy',
'Scotland England', 'Ireland Wales', 'France Ireland', 'Wales Scotland',
'Italy England', 'Wales France', 'Italy Scotland', 'England Ireland',
'Ireland Italy', 'England Wales', 'Scotland France', 'Wales Italy',
'Ireland Scotland', 'France England', 'Scotland Ireland',
'England France', 'Italy Wales', 'Italy Ireland', 'Wales England',
'France Scotland', 'Scotland Wales', 'Ireland France', 'England Italy',
'Wales Ireland', 'Italy France', 'England Scotland', 'Scotland Italy',
'France Wales', 'Ireland England'], dtype=object) - Data variables: (2)
* home_points
(chain, draw, match)
int64
...
[120000 values with dtype=int64]
* away_points
(chain, draw, match)
int64
...
[120000 values with dtype=int64] - Indexes: (3)
* PandasIndex
PandasIndex(Int64Index([0, 1, 2, 3], dtype='int64', name='chain'))
* PandasIndex
PandasIndex(Int64Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
...
490, 491, 492, 493, 494, 495, 496, 497, 498, 499],
dtype='int64', name='draw', length=500))
* PandasIndex
PandasIndex(Index(['Wales Italy', 'France England', 'Ireland Scotland', 'Ireland Wales',
'Scotland England', 'France Italy', 'Wales France', 'Italy Scotland',
'England Ireland', 'Ireland Italy', 'Scotland France', 'England Wales',
'Italy England', 'Wales Scotland', 'France Ireland', 'Wales England',
'Italy Ireland', 'France Scotland', 'England Italy', 'Ireland France',
'Scotland Wales', 'Scotland Italy', 'France Wales', 'Ireland England',
'Wales Ireland', 'England Scotland', 'Italy France', 'Italy Wales',
'Scotland Ireland', 'England France', 'France Italy',
'Scotland England', 'Ireland Wales', 'France Ireland', 'Wales Scotland',
'Italy England', 'Wales France', 'Italy Scotland', 'England Ireland',
'Ireland Italy', 'England Wales', 'Scotland France', 'Wales Italy',
'Ireland Scotland', 'France England', 'Scotland Ireland',
'England France', 'Italy Wales', 'Italy Ireland', 'Wales England',
'France Scotland', 'Scotland Wales', 'Ireland France', 'England Italy',
'Wales Ireland', 'Italy France', 'England Scotland', 'Scotland Italy',
'France Wales', 'Ireland England'],
dtype='object', name='match')) - Attributes: (3)
created_at :
2019-07-12T20:31:53.563854
inference_library :
pymc3
inference_library_version :
3.7
- Dimensions:
- sample_stats
<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 ...
energy (chain, draw) float64 ...
tree_size (chain, draw) float64 ...
tune (chain, draw) bool ...
mean_tree_accept (chain, draw) float64 ...
lp (chain, draw) float64 ...
depth (chain, draw) int64 ...
max_energy_error (chain, draw) float64 ...
step_size (chain, draw) float64 ...
step_size_bar (chain, draw) float64 ...
diverging (chain, draw) bool ...
Attributes:
created_at: 2019-07-12T20:31:53.555203
inference_library: pymc3
inference_library_version: 3.7- Dimensions:
* chain: 4
* draw: 500 - Coordinates: (2)
* chain
(chain)
int64
0 1 2 3
* draw
(draw)
int64
0 1 2 3 4 5 ... 495 496 497 498 499
array([ 0, 1, 2, ..., 497, 498, 499]) - Data variables: (11)
* energy_error
(chain, draw)
float64
...
[2000 values with dtype=float64]
* energy
(chain, draw)
float64
...
[2000 values with dtype=float64]
* tree_size
(chain, draw)
float64
...
[2000 values with dtype=float64]
* tune
(chain, draw)
bool
...
[2000 values with dtype=bool]
* mean_tree_accept
(chain, draw)
float64
...
[2000 values with dtype=float64]
* lp
(chain, draw)
float64
...
[2000 values with dtype=float64]
* depth
(chain, draw)
int64
...
[2000 values with dtype=int64]
* max_energy_error
(chain, draw)
float64
...
[2000 values with dtype=float64]
* step_size
(chain, draw)
float64
...
[2000 values with dtype=float64]
* step_size_bar
(chain, draw)
float64
...
[2000 values with dtype=float64]
* diverging
(chain, draw)
bool
...
[2000 values with dtype=bool] - Indexes: (2)
* PandasIndex
PandasIndex(Int64Index([0, 1, 2, 3], dtype='int64', name='chain'))
* PandasIndex
PandasIndex(Int64Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
...
490, 491, 492, 493, 494, 495, 496, 497, 498, 499],
dtype='int64', name='draw', length=500)) - Attributes: (3)
created_at :
2019-07-12T20:31:53.555203
inference_library :
pymc3
inference_library_version :
3.7
- Dimensions:
- prior
<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 ...
intercept (chain, draw) float64 ...
atts_star (chain, draw, team) float64 ...
defs_star (chain, draw, team) float64 ...
away_points (chain, draw, match) int64 ...
sd_att (chain, draw) float64 ...
sd_def_log__ (chain, draw) float64 ...
home (chain, draw) float64 ...
atts (chain, draw, team) float64 ...
sd_def (chain, draw) float64 ...
home_points (chain, draw, match) int64 ...
defs (chain, draw, team) float64 ...
Attributes:
created_at: 2019-07-12T20:31:53.573731
inference_library: pymc3
inference_library_version: 3.7- Dimensions:
* chain: 1
* draw: 500
* team: 6
* match: 60 - Coordinates: (4)
* chain
(chain)
int64
0
* draw
(draw)
int64
0 1 2 3 4 5 ... 495 496 497 498 499
array([ 0, 1, 2, ..., 497, 498, 499])
* team
(team)
object
'Wales' 'France' ... 'England'
array(['Wales', 'France', 'Ireland', 'Scotland', 'Italy', 'England'],
dtype=object)
* match
(match)
object
'Wales Italy' ... 'Ireland England'
array(['Wales Italy', 'France England', 'Ireland Scotland', 'Ireland Wales',
'Scotland England', 'France Italy', 'Wales France', 'Italy Scotland',
'England Ireland', 'Ireland Italy', 'Scotland France', 'England Wales',
'Italy England', 'Wales Scotland', 'France Ireland', 'Wales England',
'Italy Ireland', 'France Scotland', 'England Italy', 'Ireland France',
'Scotland Wales', 'Scotland Italy', 'France Wales', 'Ireland England',
'Wales Ireland', 'England Scotland', 'Italy France', 'Italy Wales',
'Scotland Ireland', 'England France', 'France Italy',
'Scotland England', 'Ireland Wales', 'France Ireland', 'Wales Scotland',
'Italy England', 'Wales France', 'Italy Scotland', 'England Ireland',
'Ireland Italy', 'England Wales', 'Scotland France', 'Wales Italy',
'Ireland Scotland', 'France England', 'Scotland Ireland',
'England France', 'Italy Wales', 'Italy Ireland', 'Wales England',
'France Scotland', 'Scotland Wales', 'Ireland France', 'England Italy',
'Wales Ireland', 'Italy France', 'England Scotland', 'Scotland Italy',
'France Wales', 'Ireland England'], dtype=object) - Data variables: (12)
* sd_att_log__
(chain, draw)
float64
...
[500 values with dtype=float64]
* intercept
(chain, draw)
float64
...
[500 values with dtype=float64]
* atts_star
(chain, draw, team)
float64
...
[3000 values with dtype=float64]
* defs_star
(chain, draw, team)
float64
...
[3000 values with dtype=float64]
* away_points
(chain, draw, match)
int64
...
[30000 values with dtype=int64]
* sd_att
(chain, draw)
float64
...
[500 values with dtype=float64]
* sd_def_log__
(chain, draw)
float64
...
[500 values with dtype=float64]
* home
(chain, draw)
float64
...
[500 values with dtype=float64]
* atts
(chain, draw, team)
float64
...
[3000 values with dtype=float64]
* sd_def
(chain, draw)
float64
...
[500 values with dtype=float64]
* home_points
(chain, draw, match)
int64
...
[30000 values with dtype=int64]
* defs
(chain, draw, team)
float64
...
[3000 values with dtype=float64] - Indexes: (4)
* PandasIndex
PandasIndex(Int64Index([0], dtype='int64', name='chain'))
* PandasIndex
PandasIndex(Int64Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
...
490, 491, 492, 493, 494, 495, 496, 497, 498, 499],
dtype='int64', name='draw', length=500))
* PandasIndex
PandasIndex(Index(['Wales', 'France', 'Ireland', 'Scotland', 'Italy', 'England'], dtype='object', name='team'))
* PandasIndex
PandasIndex(Index(['Wales Italy', 'France England', 'Ireland Scotland', 'Ireland Wales',
'Scotland England', 'France Italy', 'Wales France', 'Italy Scotland',
'England Ireland', 'Ireland Italy', 'Scotland France', 'England Wales',
'Italy England', 'Wales Scotland', 'France Ireland', 'Wales England',
'Italy Ireland', 'France Scotland', 'England Italy', 'Ireland France',
'Scotland Wales', 'Scotland Italy', 'France Wales', 'Ireland England',
'Wales Ireland', 'England Scotland', 'Italy France', 'Italy Wales',
'Scotland Ireland', 'England France', 'France Italy',
'Scotland England', 'Ireland Wales', 'France Ireland', 'Wales Scotland',
'Italy England', 'Wales France', 'Italy Scotland', 'England Ireland',
'Ireland Italy', 'England Wales', 'Scotland France', 'Wales Italy',
'Ireland Scotland', 'France England', 'Scotland Ireland',
'England France', 'Italy Wales', 'Italy Ireland', 'Wales England',
'France Scotland', 'Scotland Wales', 'Ireland France', 'England Italy',
'Wales Ireland', 'Italy France', 'England Scotland', 'Scotland Italy',
'France Wales', 'Ireland England'],
dtype='object', name='match')) - Attributes: (3)
created_at :
2019-07-12T20:31:53.573731
inference_library :
pymc3
inference_library_version :
3.7
- Dimensions:
- observed_data
<xarray.Dataset>
Dimensions: (match: 60)
Coordinates:- match (match) object 'Wales Italy' ... 'Ireland England'
Data variables:
home_points (match) float64 ...
away_points (match) float64 ...
Attributes:
created_at: 2019-07-12T20:31:53.581293
inference_library: pymc3
inference_library_version: 3.7- Dimensions:
* match: 60 - Coordinates: (1)
* match
(match)
object
'Wales Italy' ... 'Ireland England'
array(['Wales Italy', 'France England', 'Ireland Scotland', 'Ireland Wales',
'Scotland England', 'France Italy', 'Wales France', 'Italy Scotland',
'England Ireland', 'Ireland Italy', 'Scotland France', 'England Wales',
'Italy England', 'Wales Scotland', 'France Ireland', 'Wales England',
'Italy Ireland', 'France Scotland', 'England Italy', 'Ireland France',
'Scotland Wales', 'Scotland Italy', 'France Wales', 'Ireland England',
'Wales Ireland', 'England Scotland', 'Italy France', 'Italy Wales',
'Scotland Ireland', 'England France', 'France Italy',
'Scotland England', 'Ireland Wales', 'France Ireland', 'Wales Scotland',
'Italy England', 'Wales France', 'Italy Scotland', 'England Ireland',
'Ireland Italy', 'England Wales', 'Scotland France', 'Wales Italy',
'Ireland Scotland', 'France England', 'Scotland Ireland',
'England France', 'Italy Wales', 'Italy Ireland', 'Wales England',
'France Scotland', 'Scotland Wales', 'Ireland France', 'England Italy',
'Wales Ireland', 'Italy France', 'England Scotland', 'Scotland Italy',
'France Wales', 'Ireland England'], dtype=object) - Data variables: (2)
* home_points
(match)
float64
...
[60 values with dtype=float64]
* away_points
(match)
float64
...
[60 values with dtype=float64] - Indexes: (1)
* PandasIndex
PandasIndex(Index(['Wales Italy', 'France England', 'Ireland Scotland', 'Ireland Wales',
'Scotland England', 'France Italy', 'Wales France', 'Italy Scotland',
'England Ireland', 'Ireland Italy', 'Scotland France', 'England Wales',
'Italy England', 'Wales Scotland', 'France Ireland', 'Wales England',
'Italy Ireland', 'France Scotland', 'England Italy', 'Ireland France',
'Scotland Wales', 'Scotland Italy', 'France Wales', 'Ireland England',
'Wales Ireland', 'England Scotland', 'Italy France', 'Italy Wales',
'Scotland Ireland', 'England France', 'France Italy',
'Scotland England', 'Ireland Wales', 'France Ireland', 'Wales Scotland',
'Italy England', 'Wales France', 'Italy Scotland', 'England Ireland',
'Ireland Italy', 'England Wales', 'Scotland France', 'Wales Italy',
'Ireland Scotland', 'France England', 'Scotland Ireland',
'England France', 'Italy Wales', 'Italy Ireland', 'Wales England',
'France Scotland', 'Scotland Wales', 'Ireland France', 'England Italy',
'Wales Ireland', 'Italy France', 'England Scotland', 'Scotland Italy',
'France Wales', 'Ireland England'],
dtype='object', name='match')) - Attributes: (3)
created_at :
2019-07-12T20:31:53.581293
inference_library :
pymc3
inference_library_version :
3.7
- Dimensions:
- match (match) object 'Wales Italy' ... 'Ireland England'
In order to rename the dimensions, we use:
idata.rename_dims({"team": "team_new"}, inplace=True) idata
- posterior
<xarray.Dataset>
Dimensions: (chain: 4, draw: 500, team_new: 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_new) object 'Wales' 'France' ... 'Italy' 'England'
Dimensions without coordinates: team_new
Data variables:
home (chain, draw) float64 ...
intercept (chain, draw) float64 ...
atts_star (chain, draw, team_new) float64 ...
defs_star (chain, draw, team_new) float64 ...
sd_att (chain, draw) float64 ...
sd_def (chain, draw) float64 ...
atts (chain, draw, team_new) float64 ...
defs (chain, draw, team_new) float64 ...
Attributes:
created_at: 2019-07-12T20:31:53.545143
inference_library: pymc3
inference_library_version: 3.7- Dimensions:
* chain: 4
* draw: 500
* team_new: 6 - Coordinates: (3)
* chain
(chain)
int64
0 1 2 3
* draw
(draw)
int64
0 1 2 3 4 5 ... 495 496 497 498 499
array([ 0, 1, 2, ..., 497, 498, 499])
* team
(team_new)
object
'Wales' 'France' ... 'England'
array(['Wales', 'France', 'Ireland', 'Scotland', 'Italy', 'England'],
dtype=object) - Data variables: (8)
* home
(chain, draw)
float64
...
[2000 values with dtype=float64]
* intercept
(chain, draw)
float64
...
[2000 values with dtype=float64]
* atts_star
(chain, draw, team_new)
float64
...
[12000 values with dtype=float64]
* defs_star
(chain, draw, team_new)
float64
...
[12000 values with dtype=float64]
* sd_att
(chain, draw)
float64
...
[2000 values with dtype=float64]
* sd_def
(chain, draw)
float64
...
[2000 values with dtype=float64]
* atts
(chain, draw, team_new)
float64
...
[12000 values with dtype=float64]
* defs
(chain, draw, team_new)
float64
...
[12000 values with dtype=float64] - Indexes: (3)
* PandasIndex
PandasIndex(Int64Index([0, 1, 2, 3], dtype='int64', name='chain'))
* PandasIndex
PandasIndex(Int64Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
...
490, 491, 492, 493, 494, 495, 496, 497, 498, 499],
dtype='int64', name='draw', length=500))
* PandasIndex
PandasIndex(Index(['Wales', 'France', 'Ireland', 'Scotland', 'Italy', 'England'], dtype='object', name='team')) - Attributes: (3)
created_at :
2019-07-12T20:31:53.545143
inference_library :
pymc3
inference_library_version :
3.7
- Dimensions:
- posterior_predictive
<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- Dimensions:
* chain: 4
* draw: 500
* match: 60 - Coordinates: (3)
* chain
(chain)
int64
0 1 2 3
* draw
(draw)
int64
0 1 2 3 4 5 ... 495 496 497 498 499
array([ 0, 1, 2, ..., 497, 498, 499])
* match
(match)
object
'Wales Italy' ... 'Ireland England'
array(['Wales Italy', 'France England', 'Ireland Scotland', 'Ireland Wales',
'Scotland England', 'France Italy', 'Wales France', 'Italy Scotland',
'England Ireland', 'Ireland Italy', 'Scotland France', 'England Wales',
'Italy England', 'Wales Scotland', 'France Ireland', 'Wales England',
'Italy Ireland', 'France Scotland', 'England Italy', 'Ireland France',
'Scotland Wales', 'Scotland Italy', 'France Wales', 'Ireland England',
'Wales Ireland', 'England Scotland', 'Italy France', 'Italy Wales',
'Scotland Ireland', 'England France', 'France Italy',
'Scotland England', 'Ireland Wales', 'France Ireland', 'Wales Scotland',
'Italy England', 'Wales France', 'Italy Scotland', 'England Ireland',
'Ireland Italy', 'England Wales', 'Scotland France', 'Wales Italy',
'Ireland Scotland', 'France England', 'Scotland Ireland',
'England France', 'Italy Wales', 'Italy Ireland', 'Wales England',
'France Scotland', 'Scotland Wales', 'Ireland France', 'England Italy',
'Wales Ireland', 'Italy France', 'England Scotland', 'Scotland Italy',
'France Wales', 'Ireland England'], dtype=object) - Data variables: (2)
* home_points
(chain, draw, match)
int64
...
[120000 values with dtype=int64]
* away_points
(chain, draw, match)
int64
...
[120000 values with dtype=int64] - Indexes: (3)
* PandasIndex
PandasIndex(Int64Index([0, 1, 2, 3], dtype='int64', name='chain'))
* PandasIndex
PandasIndex(Int64Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
...
490, 491, 492, 493, 494, 495, 496, 497, 498, 499],
dtype='int64', name='draw', length=500))
* PandasIndex
PandasIndex(Index(['Wales Italy', 'France England', 'Ireland Scotland', 'Ireland Wales',
'Scotland England', 'France Italy', 'Wales France', 'Italy Scotland',
'England Ireland', 'Ireland Italy', 'Scotland France', 'England Wales',
'Italy England', 'Wales Scotland', 'France Ireland', 'Wales England',
'Italy Ireland', 'France Scotland', 'England Italy', 'Ireland France',
'Scotland Wales', 'Scotland Italy', 'France Wales', 'Ireland England',
'Wales Ireland', 'England Scotland', 'Italy France', 'Italy Wales',
'Scotland Ireland', 'England France', 'France Italy',
'Scotland England', 'Ireland Wales', 'France Ireland', 'Wales Scotland',
'Italy England', 'Wales France', 'Italy Scotland', 'England Ireland',
'Ireland Italy', 'England Wales', 'Scotland France', 'Wales Italy',
'Ireland Scotland', 'France England', 'Scotland Ireland',
'England France', 'Italy Wales', 'Italy Ireland', 'Wales England',
'France Scotland', 'Scotland Wales', 'Ireland France', 'England Italy',
'Wales Ireland', 'Italy France', 'England Scotland', 'Scotland Italy',
'France Wales', 'Ireland England'],
dtype='object', name='match')) - Attributes: (3)
created_at :
2019-07-12T20:31:53.563854
inference_library :
pymc3
inference_library_version :
3.7
- Dimensions:
- sample_stats
<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 ...
energy (chain, draw) float64 ...
tree_size (chain, draw) float64 ...
tune (chain, draw) bool ...
mean_tree_accept (chain, draw) float64 ...
lp (chain, draw) float64 ...
depth (chain, draw) int64 ...
max_energy_error (chain, draw) float64 ...
step_size (chain, draw) float64 ...
step_size_bar (chain, draw) float64 ...
diverging (chain, draw) bool ...
Attributes:
created_at: 2019-07-12T20:31:53.555203
inference_library: pymc3
inference_library_version: 3.7- Dimensions:
* chain: 4
* draw: 500 - Coordinates: (2)
* chain
(chain)
int64
0 1 2 3
* draw
(draw)
int64
0 1 2 3 4 5 ... 495 496 497 498 499
array([ 0, 1, 2, ..., 497, 498, 499]) - Data variables: (11)
* energy_error
(chain, draw)
float64
...
[2000 values with dtype=float64]
* energy
(chain, draw)
float64
...
[2000 values with dtype=float64]
* tree_size
(chain, draw)
float64
...
[2000 values with dtype=float64]
* tune
(chain, draw)
bool
...
[2000 values with dtype=bool]
* mean_tree_accept
(chain, draw)
float64
...
[2000 values with dtype=float64]
* lp
(chain, draw)
float64
...
[2000 values with dtype=float64]
* depth
(chain, draw)
int64
...
[2000 values with dtype=int64]
* max_energy_error
(chain, draw)
float64
...
[2000 values with dtype=float64]
* step_size
(chain, draw)
float64
...
[2000 values with dtype=float64]
* step_size_bar
(chain, draw)
float64
...
[2000 values with dtype=float64]
* diverging
(chain, draw)
bool
...
[2000 values with dtype=bool] - Indexes: (2)
* PandasIndex
PandasIndex(Int64Index([0, 1, 2, 3], dtype='int64', name='chain'))
* PandasIndex
PandasIndex(Int64Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
...
490, 491, 492, 493, 494, 495, 496, 497, 498, 499],
dtype='int64', name='draw', length=500)) - Attributes: (3)
created_at :
2019-07-12T20:31:53.555203
inference_library :
pymc3
inference_library_version :
3.7
- Dimensions:
- prior
<xarray.Dataset>
Dimensions: (chain: 1, draw: 500, team_new: 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_new) object 'Wales' 'France' ... 'Italy' 'England'
- match (match) object 'Wales Italy' ... 'Ireland England'
Dimensions without coordinates: team_new
Data variables:
sd_att_log__ (chain, draw) float64 ...
intercept (chain, draw) float64 ...
atts_star (chain, draw, team_new) float64 ...
defs_star (chain, draw, team_new) float64 ...
away_points (chain, draw, match) int64 ...
sd_att (chain, draw) float64 ...
sd_def_log__ (chain, draw) float64 ...
home (chain, draw) float64 ...
atts (chain, draw, team_new) float64 ...
sd_def (chain, draw) float64 ...
home_points (chain, draw, match) int64 ...
defs (chain, draw, team_new) float64 ...
Attributes:
created_at: 2019-07-12T20:31:53.573731
inference_library: pymc3
inference_library_version: 3.7- Dimensions:
* chain: 1
* draw: 500
* team_new: 6
* match: 60 - Coordinates: (4)
* chain
(chain)
int64
0
* draw
(draw)
int64
0 1 2 3 4 5 ... 495 496 497 498 499
array([ 0, 1, 2, ..., 497, 498, 499])
* team
(team_new)
object
'Wales' 'France' ... 'England'
array(['Wales', 'France', 'Ireland', 'Scotland', 'Italy', 'England'],
dtype=object)
* match
(match)
object
'Wales Italy' ... 'Ireland England'
array(['Wales Italy', 'France England', 'Ireland Scotland', 'Ireland Wales',
'Scotland England', 'France Italy', 'Wales France', 'Italy Scotland',
'England Ireland', 'Ireland Italy', 'Scotland France', 'England Wales',
'Italy England', 'Wales Scotland', 'France Ireland', 'Wales England',
'Italy Ireland', 'France Scotland', 'England Italy', 'Ireland France',
'Scotland Wales', 'Scotland Italy', 'France Wales', 'Ireland England',
'Wales Ireland', 'England Scotland', 'Italy France', 'Italy Wales',
'Scotland Ireland', 'England France', 'France Italy',
'Scotland England', 'Ireland Wales', 'France Ireland', 'Wales Scotland',
'Italy England', 'Wales France', 'Italy Scotland', 'England Ireland',
'Ireland Italy', 'England Wales', 'Scotland France', 'Wales Italy',
'Ireland Scotland', 'France England', 'Scotland Ireland',
'England France', 'Italy Wales', 'Italy Ireland', 'Wales England',
'France Scotland', 'Scotland Wales', 'Ireland France', 'England Italy',
'Wales Ireland', 'Italy France', 'England Scotland', 'Scotland Italy',
'France Wales', 'Ireland England'], dtype=object) - Data variables: (12)
* sd_att_log__
(chain, draw)
float64
...
[500 values with dtype=float64]
* intercept
(chain, draw)
float64
...
[500 values with dtype=float64]
* atts_star
(chain, draw, team_new)
float64
...
[3000 values with dtype=float64]
* defs_star
(chain, draw, team_new)
float64
...
[3000 values with dtype=float64]
* away_points
(chain, draw, match)
int64
...
[30000 values with dtype=int64]
* sd_att
(chain, draw)
float64
...
[500 values with dtype=float64]
* sd_def_log__
(chain, draw)
float64
...
[500 values with dtype=float64]
* home
(chain, draw)
float64
...
[500 values with dtype=float64]
* atts
(chain, draw, team_new)
float64
...
[3000 values with dtype=float64]
* sd_def
(chain, draw)
float64
...
[500 values with dtype=float64]
* home_points
(chain, draw, match)
int64
...
[30000 values with dtype=int64]
* defs
(chain, draw, team_new)
float64
...
[3000 values with dtype=float64] - Indexes: (4)
* PandasIndex
PandasIndex(Int64Index([0], dtype='int64', name='chain'))
* PandasIndex
PandasIndex(Int64Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
...
490, 491, 492, 493, 494, 495, 496, 497, 498, 499],
dtype='int64', name='draw', length=500))
* PandasIndex
PandasIndex(Index(['Wales', 'France', 'Ireland', 'Scotland', 'Italy', 'England'], dtype='object', name='team'))
* PandasIndex
PandasIndex(Index(['Wales Italy', 'France England', 'Ireland Scotland', 'Ireland Wales',
'Scotland England', 'France Italy', 'Wales France', 'Italy Scotland',
'England Ireland', 'Ireland Italy', 'Scotland France', 'England Wales',
'Italy England', 'Wales Scotland', 'France Ireland', 'Wales England',
'Italy Ireland', 'France Scotland', 'England Italy', 'Ireland France',
'Scotland Wales', 'Scotland Italy', 'France Wales', 'Ireland England',
'Wales Ireland', 'England Scotland', 'Italy France', 'Italy Wales',
'Scotland Ireland', 'England France', 'France Italy',
'Scotland England', 'Ireland Wales', 'France Ireland', 'Wales Scotland',
'Italy England', 'Wales France', 'Italy Scotland', 'England Ireland',
'Ireland Italy', 'England Wales', 'Scotland France', 'Wales Italy',
'Ireland Scotland', 'France England', 'Scotland Ireland',
'England France', 'Italy Wales', 'Italy Ireland', 'Wales England',
'France Scotland', 'Scotland Wales', 'Ireland France', 'England Italy',
'Wales Ireland', 'Italy France', 'England Scotland', 'Scotland Italy',
'France Wales', 'Ireland England'],
dtype='object', name='match')) - Attributes: (3)
created_at :
2019-07-12T20:31:53.573731
inference_library :
pymc3
inference_library_version :
3.7
- Dimensions:
- observed_data
<xarray.Dataset>
Dimensions: (match: 60)
Coordinates:- match (match) object 'Wales Italy' ... 'Ireland England'
Data variables:
home_points (match) float64 ...
away_points (match) float64 ...
Attributes:
created_at: 2019-07-12T20:31:53.581293
inference_library: pymc3
inference_library_version: 3.7- Dimensions:
* match: 60 - Coordinates: (1)
* match
(match)
object
'Wales Italy' ... 'Ireland England'
array(['Wales Italy', 'France England', 'Ireland Scotland', 'Ireland Wales',
'Scotland England', 'France Italy', 'Wales France', 'Italy Scotland',
'England Ireland', 'Ireland Italy', 'Scotland France', 'England Wales',
'Italy England', 'Wales Scotland', 'France Ireland', 'Wales England',
'Italy Ireland', 'France Scotland', 'England Italy', 'Ireland France',
'Scotland Wales', 'Scotland Italy', 'France Wales', 'Ireland England',
'Wales Ireland', 'England Scotland', 'Italy France', 'Italy Wales',
'Scotland Ireland', 'England France', 'France Italy',
'Scotland England', 'Ireland Wales', 'France Ireland', 'Wales Scotland',
'Italy England', 'Wales France', 'Italy Scotland', 'England Ireland',
'Ireland Italy', 'England Wales', 'Scotland France', 'Wales Italy',
'Ireland Scotland', 'France England', 'Scotland Ireland',
'England France', 'Italy Wales', 'Italy Ireland', 'Wales England',
'France Scotland', 'Scotland Wales', 'Ireland France', 'England Italy',
'Wales Ireland', 'Italy France', 'England Scotland', 'Scotland Italy',
'France Wales', 'Ireland England'], dtype=object) - Data variables: (2)
* home_points
(match)
float64
...
[60 values with dtype=float64]
* away_points
(match)
float64
...
[60 values with dtype=float64] - Indexes: (1)
* PandasIndex
PandasIndex(Index(['Wales Italy', 'France England', 'Ireland Scotland', 'Ireland Wales',
'Scotland England', 'France Italy', 'Wales France', 'Italy Scotland',
'England Ireland', 'Ireland Italy', 'Scotland France', 'England Wales',
'Italy England', 'Wales Scotland', 'France Ireland', 'Wales England',
'Italy Ireland', 'France Scotland', 'England Italy', 'Ireland France',
'Scotland Wales', 'Scotland Italy', 'France Wales', 'Ireland England',
'Wales Ireland', 'England Scotland', 'Italy France', 'Italy Wales',
'Scotland Ireland', 'England France', 'France Italy',
'Scotland England', 'Ireland Wales', 'France Ireland', 'Wales Scotland',
'Italy England', 'Wales France', 'Italy Scotland', 'England Ireland',
'Ireland Italy', 'England Wales', 'Scotland France', 'Wales Italy',
'Ireland Scotland', 'France England', 'Scotland Ireland',
'England France', 'Italy Wales', 'Italy Ireland', 'Wales England',
'France Scotland', 'Scotland Wales', 'Ireland France', 'England Italy',
'Wales Ireland', 'Italy France', 'England Scotland', 'Scotland Italy',
'France Wales', 'Ireland England'],
dtype='object', name='match')) - Attributes: (3)
created_at :
2019-07-12T20:31:53.581293
inference_library :
pymc3
inference_library_version :
3.7
- Dimensions:
- match (match) object 'Wales Italy' ... 'Ireland England'