arviz.InferenceData.map — ArviZ dev documentation (original) (raw)
InferenceData.map(fun, groups=None, filter_groups=None, inplace=False, args=None, **kwargs)[source]#
Apply a function to multiple groups.
Applies fun
groupwise to the selected InferenceData
groups and overwrites the group with the result of the function.
Parameters:
funcallable()
Function to be applied to each group. Assumes the function is called asfun(dataset, *args, **kwargs)
.
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 var_names as the real variables names. If “like”, interpret var_names as substrings of the real variables names. If “regex”, interpret var_names as regular expressions on the real variables names. A lapandas.filter
.
inplacebool, optional
If True
, modify the InferenceData object inplace, otherwise, return the modified copy.
argsarray_like, optional
Positional arguments passed to fun
.
**kwargsmapping
, optional
Keyword arguments passed to fun
.
Returns:
A new InferenceData object by default. When inplace==True
perform selection in place and return None
Examples
Shift observed_data, prior_predictive and posterior_predictive.
import arviz as az import numpy as np idata = az.load_arviz_data("non_centered_eight") idata_shifted_obs = idata.map(lambda x: x + 3, groups="observed_vars") idata_shifted_obs
- posterior
<xarray.Dataset> Size: 293kB
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_t (chain, draw, school) float64 128kB ...
tau (chain, draw) float64 16kB ...
theta (chain, draw, school) float64 128kB ...
Attributes:
created_at: 2022-10-13T14:37:26.351883
arviz_version: 0.13.0.dev0
inference_library: pymc
inference_library_version: 4.2.2
sampling_time: 4.738754749298096
tuning_steps: 1000- Dimensions:
* chain: 4
* draw: 500
* school: 8 - 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], shape=(500,))
* school
(school)
<U16
'Choate' ... 'Mt. Hermon'
array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',
'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype='<U16') - Data variables: (4)
* mu
(chain, draw)
float64
...
[2000 values with dtype=float64]
* theta_t
(chain, draw, school)
float64
...
[16000 values with dtype=float64]
* tau
(chain, draw)
float64
...
[2000 values with dtype=float64]
* theta
(chain, draw, school)
float64
...
[16000 values with dtype=float64] - Indexes: (3)
* PandasIndex
PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))
* PandasIndex
PandasIndex(Index([ 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(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',
'Hotchkiss', 'Lawrenceville', 'St. Paul's', 'Mt. Hermon'],
dtype='object', name='school')) - Attributes: (6)
created_at :
2022-10-13T14:37:26.351883
arviz_version :
0.13.0.dev0
inference_library :
pymc
inference_library_version :
4.2.2
sampling_time :
4.738754749298096
tuning_steps :
1000
- Dimensions:
- posterior_predictive
<xarray.Dataset> Size: 132kB
Dimensions: (chain: 4, draw: 500, obs_dim_0: 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
- obs_dim_0 (obs_dim_0) int64 64B 0 1 2 3 4 5 6 7
Data variables:
obs (chain, draw, obs_dim_0) float64 128kB -8.912 0.4851 ... 27.95
Attributes:
arviz_version: 0.13.0.dev0
created_at: 2022-10-13T14:37:34.333731
inference_library: pymc
inference_library_version: 4.2.2- Dimensions:
* chain: 4
* draw: 500
* obs_dim_0: 8 - 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], shape=(500,))
* obs_dim_0
(obs_dim_0)
int64
0 1 2 3 4 5 6 7
array([0, 1, 2, 3, 4, 5, 6, 7]) - Data variables: (1)
* obs
(chain, draw, obs_dim_0)
float64
-8.912 0.4851 -4.71 ... 13.55 27.95
array([[[ -8.91164858, 0.48513691, -4.70995568, ..., 25.54688492,
13.94575018, 9.80299148],
[ 37.4801699 , -10.77370835, -0.5719224 , ..., 0.43335956,
27.32805956, 14.09089747],
[ 17.70959145, -11.12171465, -21.98355095, ..., 19.10421895,
8.85410192, -8.10962076],
...,
[ 27.27407986, 7.61924923, 26.24515535, ..., -0.3350811 ,
11.69606961, 39.63282956],
[ 4.79252722, 0.43572688, -9.33809594, ..., 9.92145935,
-5.72201279, 4.97166968],
[ 8.34887428, 8.83802449, 4.87002072, ..., 9.48416992,
-5.19603508, -1.88720116]],
[[ 14.660454 , 14.56500642, 30.54658322, ..., 0.820571 ,
17.58317036, 11.41523916],
[ 12.23886235, 17.66178128, 10.9959666 , ..., 19.48767939,
31.47333474, 14.07945178],
[ 33.47381555, 23.8333277 , -0.67617714, ..., 7.95021076,
8.66776442, 36.76402444],
...
[ 45.14657727, 19.31827266, -13.77595216, ..., 21.35397137,
12.08007358, 19.71077765],
[ 14.1760222 , -4.54869822, 8.69720246, ..., 7.5198413 ,
-9.63781631, -15.25261963],
[ 9.0665652 , -24.09127382, -5.36918153, ..., 27.94605585,
-5.79860404, -35.2355531 ]],
[[ 27.4155747 , 27.81444338, 22.10513881, ..., 12.74755666,
14.5126698 , 14.57236846],
[ -3.76124287, -20.82045621, 31.33247223, ..., 13.29821842,
1.41832433, 16.34246786],
[ -7.00712196, -7.93492977, 9.95458309, ..., -11.32537975,
21.94623035, 10.70530684],
...,
[ 13.80440934, 15.44637947, -10.90413704, ..., 10.01640909,
16.47727939, 31.34665798],
[-11.99095788, 5.75164919, 28.06463307, ..., 3.68615864,
-12.96358662, -10.4495535 ],
[ 12.81063159, 14.01071347, 17.28607254, ..., 7.91079619,
13.55415258, 27.94927617]]], shape=(4, 500, 8)) - Indexes: (3)
* PandasIndex
PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))
* PandasIndex
PandasIndex(Index([ 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([0, 1, 2, 3, 4, 5, 6, 7], dtype='int64', name='obs_dim_0')) - Attributes: (4)
arviz_version :
0.13.0.dev0
created_at :
2022-10-13T14:37:34.333731
inference_library :
pymc
inference_library_version :
4.2.2
- Dimensions:
- log_likelihood
<xarray.Dataset> Size: 132kB
Dimensions: (chain: 4, draw: 500, obs_dim_0: 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
- obs_dim_0 (obs_dim_0) int64 64B 0 1 2 3 4 5 6 7
Data variables:
obs (chain, draw, obs_dim_0) float64 128kB ...
Attributes:
arviz_version: 0.13.0.dev0
created_at: 2022-10-13T14:37:26.571887
inference_library: pymc
inference_library_version: 4.2.2- Dimensions:
* chain: 4
* draw: 500
* obs_dim_0: 8 - 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], shape=(500,))
* obs_dim_0
(obs_dim_0)
int64
0 1 2 3 4 5 6 7
array([0, 1, 2, 3, 4, 5, 6, 7]) - Data variables: (1)
* obs
(chain, draw, obs_dim_0)
float64
...
[16000 values with dtype=float64] - Indexes: (3)
* PandasIndex
PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))
* PandasIndex
PandasIndex(Index([ 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([0, 1, 2, 3, 4, 5, 6, 7], dtype='int64', name='obs_dim_0')) - Attributes: (4)
arviz_version :
0.13.0.dev0
created_at :
2022-10-13T14:37:26.571887
inference_library :
pymc
inference_library_version :
4.2.2
- Dimensions:
- sample_stats
<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)
lp (chain, draw) float64 16kB ...
largest_eigval (chain, draw) float64 16kB ...
perf_counter_start (chain, draw) float64 16kB ...
perf_counter_diff (chain, draw) float64 16kB ...
step_size (chain, draw) float64 16kB ...
diverging (chain, draw) bool 2kB ...
... ...
max_energy_error (chain, draw) float64 16kB ...
n_steps (chain, draw) float64 16kB ...
step_size_bar (chain, draw) float64 16kB ...
energy_error (chain, draw) float64 16kB ...
smallest_eigval (chain, draw) float64 16kB ...
index_in_trajectory (chain, draw) int64 16kB ...
Attributes:
arviz_version: 0.13.0.dev0
created_at: 2022-10-13T14:37:26.362154
inference_library: pymc
inference_library_version: 4.2.2
sampling_time: 4.738754749298096
tuning_steps: 1000- 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], shape=(500,)) - Data variables: (16)
* lp
(chain, draw)
float64
...
[2000 values with dtype=float64]
* largest_eigval
(chain, draw)
float64
...
[2000 values with dtype=float64]
* perf_counter_start
(chain, draw)
float64
...
[2000 values with dtype=float64]
* perf_counter_diff
(chain, draw)
float64
...
[2000 values with dtype=float64]
* step_size
(chain, draw)
float64
...
[2000 values with dtype=float64]
* diverging
(chain, draw)
bool
...
[2000 values with dtype=bool]
* energy
(chain, draw)
float64
...
[2000 values with dtype=float64]
* process_time_diff
(chain, draw)
float64
...
[2000 values with dtype=float64]
* tree_depth
(chain, draw)
int64
...
[2000 values with dtype=int64]
* acceptance_rate
(chain, draw)
float64
...
[2000 values with dtype=float64]
* max_energy_error
(chain, draw)
float64
...
[2000 values with dtype=float64]
* n_steps
(chain, draw)
float64
...
[2000 values with dtype=float64]
* step_size_bar
(chain, draw)
float64
...
[2000 values with dtype=float64]
* energy_error
(chain, draw)
float64
...
[2000 values with dtype=float64]
* smallest_eigval
(chain, draw)
float64
...
[2000 values with dtype=float64]
* index_in_trajectory
(chain, draw)
int64
...
[2000 values with dtype=int64] - Indexes: (2)
* PandasIndex
PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))
* PandasIndex
PandasIndex(Index([ 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: (6)
arviz_version :
0.13.0.dev0
created_at :
2022-10-13T14:37:26.362154
inference_library :
pymc
inference_library_version :
4.2.2
sampling_time :
4.738754749298096
tuning_steps :
1000
- Dimensions:
- prior
<xarray.Dataset> Size: 77kB
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:
mu (chain, draw) float64 4kB ...
theta_t (chain, draw, school) float64 32kB ...
theta (chain, draw, school) float64 32kB ...
tau (chain, draw) float64 4kB ...
Attributes:
arviz_version: 0.13.0.dev0
created_at: 2022-10-13T14:37:18.108887
inference_library: pymc
inference_library_version: 4.2.2- Dimensions:
* chain: 1
* draw: 500
* school: 8 - Coordinates: (3)
* 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], shape=(500,))
* school
(school)
<U16
'Choate' ... 'Mt. Hermon'
array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',
'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype='<U16') - Data variables: (4)
* mu
(chain, draw)
float64
...
[500 values with dtype=float64]
* theta_t
(chain, draw, school)
float64
...
[4000 values with dtype=float64]
* theta
(chain, draw, school)
float64
...
[4000 values with dtype=float64]
* tau
(chain, draw)
float64
...
[500 values with dtype=float64] - Indexes: (3)
* PandasIndex
PandasIndex(Index([0], dtype='int64', name='chain'))
* PandasIndex
PandasIndex(Index([ 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(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',
'Hotchkiss', 'Lawrenceville', 'St. Paul's', 'Mt. Hermon'],
dtype='object', name='school')) - Attributes: (4)
arviz_version :
0.13.0.dev0
created_at :
2022-10-13T14:37:18.108887
inference_library :
pymc
inference_library_version :
4.2.2
- Dimensions:
- prior_predictive
<xarray.Dataset> Size: 36kB
Dimensions: (chain: 1, draw: 500, obs_dim_0: 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
- obs_dim_0 (obs_dim_0) int64 64B 0 1 2 3 4 5 6 7
Data variables:
obs (chain, draw, obs_dim_0) float64 32kB 25.87 -10.84 ... 17.32
Attributes:
arviz_version: 0.13.0.dev0
created_at: 2022-10-13T14:37:18.111951
inference_library: pymc
inference_library_version: 4.2.2- Dimensions:
* chain: 1
* draw: 500
* obs_dim_0: 8 - Coordinates: (3)
* 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], shape=(500,))
* obs_dim_0
(obs_dim_0)
int64
0 1 2 3 4 5 6 7
array([0, 1, 2, 3, 4, 5, 6, 7]) - Data variables: (1)
* obs
(chain, draw, obs_dim_0)
float64
25.87 -10.84 64.31 ... -4.658 17.32
array([[[ 25.86573102, -10.83562762, 64.30603396, ...,
34.12327354, -31.15539197, 17.40041595],
[ 21.2308377 , -15.94389655, 26.47959507, ...,
27.48371569, 12.54871934, 12.21388191],
[ -14.10920978, 30.7429682 , -13.36386214, ...,
17.25866094, -20.52897454, -14.60687912],
...,
[ -26.10606168, 13.11608297, 15.12723183, ...,
17.87300987, 7.58948329, 12.374558 ],
[ -38.39050122, -45.43587181, -47.4225284 , ...,
6.77697971, -168.96059997, 1.26974321],
[ 28.73909128, 20.70475211, 3.50257694, ...,
20.58279319, -4.65758653, 17.32397509]]], shape=(1, 500, 8)) - Indexes: (3)
* PandasIndex
PandasIndex(Index([0], dtype='int64', name='chain'))
* PandasIndex
PandasIndex(Index([ 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([0, 1, 2, 3, 4, 5, 6, 7], dtype='int64', name='obs_dim_0')) - Attributes: (4)
arviz_version :
0.13.0.dev0
created_at :
2022-10-13T14:37:18.111951
inference_library :
pymc
inference_library_version :
4.2.2
- Dimensions:
- observed_data
<xarray.Dataset> Size: 128B
Dimensions: (obs_dim_0: 8)
Coordinates:- obs_dim_0 (obs_dim_0) int64 64B 0 1 2 3 4 5 6 7
Data variables:
obs (obs_dim_0) float64 64B 31.0 11.0 0.0 10.0 2.0 4.0 21.0 15.0
Attributes:
arviz_version: 0.13.0.dev0
created_at: 2022-10-13T14:37:18.113060
inference_library: pymc
inference_library_version: 4.2.2- Dimensions:
* obs_dim_0: 8 - Coordinates: (1)
* obs_dim_0
(obs_dim_0)
int64
0 1 2 3 4 5 6 7
array([0, 1, 2, 3, 4, 5, 6, 7]) - Data variables: (1)
* obs
(obs_dim_0)
float64
31.0 11.0 0.0 ... 4.0 21.0 15.0
array([31., 11., 0., 10., 2., 4., 21., 15.]) - Indexes: (1)
* PandasIndex
PandasIndex(Index([0, 1, 2, 3, 4, 5, 6, 7], dtype='int64', name='obs_dim_0')) - Attributes: (4)
arviz_version :
0.13.0.dev0
created_at :
2022-10-13T14:37:18.113060
inference_library :
pymc
inference_library_version :
4.2.2
- Dimensions:
- obs_dim_0 (obs_dim_0) int64 64B 0 1 2 3 4 5 6 7
- constant_data
<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: 2022-10-13T14:37:18.114126
inference_library: pymc
inference_library_version: 4.2.2- Dimensions:
* school: 8 - Coordinates: (1)
* school
(school)
<U16
'Choate' ... 'Mt. Hermon'
array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',
'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype='<U16') - Data variables: (1)
* scores
(school)
float64
...
[8 values with dtype=float64] - Indexes: (1)
* PandasIndex
PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',
'Hotchkiss', 'Lawrenceville', 'St. Paul's', 'Mt. Hermon'],
dtype='object', name='school')) - Attributes: (4)
arviz_version :
0.13.0.dev0
created_at :
2022-10-13T14:37:18.114126
inference_library :
pymc
inference_library_version :
4.2.2
- Dimensions:
- school (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'
Rename and update the coordinate values in both posterior and prior groups.
idata = az.load_arviz_data("radon") idata = idata.map( lambda ds: ds.rename({"g_coef": "uranium_coefs"}).assign( uranium_coefs=["intercept", "u_slope"] ), groups=["posterior", "prior"] ) idata
- posterior
<xarray.Dataset> Size: 4MB
Dimensions: (chain: 4, draw: 500, uranium_coefs: 2, County: 85)
Coordinates:- chain (chain) int64 32B 0 1 2 3
- draw (draw) int64 4kB 0 1 2 3 4 5 6 ... 494 495 496 497 498 499
- County (County) <U17 6kB 'AITKIN' 'ANOKA' ... 'YELLOW MEDICINE'
- uranium_coefs (uranium_coefs) <U9 72B 'intercept' 'u_slope'
Data variables:
g (chain, draw, uranium_coefs) float64 32kB ...
za_county (chain, draw, County) float64 1MB ...
b (chain, draw) float64 16kB ...
sigma_a (chain, draw) float64 16kB ...
a (chain, draw, County) float64 1MB ...
a_county (chain, draw, County) float64 1MB ...
sigma (chain, draw) float64 16kB ...
Attributes:
created_at: 2020-07-24T18:15:12.191355
arviz_version: 0.9.0
inference_library: pymc3
inference_library_version: 3.9.2
sampling_time: 18.096983432769775
tuning_steps: 1000- Dimensions:
* chain: 4
* draw: 500
* uranium_coefs: 2
* County: 85 - Coordinates: (4)
* 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], shape=(500,))
* County
(County)
<U17
'AITKIN' ... 'YELLOW MEDICINE'
array(['AITKIN', 'ANOKA', 'BECKER', 'BELTRAMI', 'BENTON', 'BIG STONE',
'BLUE EARTH', 'BROWN', 'CARLTON', 'CARVER', 'CASS', 'CHIPPEWA',
'CHISAGO', 'CLAY', 'CLEARWATER', 'COOK', 'COTTONWOOD', 'CROW WING',
'DAKOTA', 'DODGE', 'DOUGLAS', 'FARIBAULT', 'FILLMORE', 'FREEBORN',
'GOODHUE', 'HENNEPIN', 'HOUSTON', 'HUBBARD', 'ISANTI', 'ITASCA',
'JACKSON', 'KANABEC', 'KANDIYOHI', 'KITTSON', 'KOOCHICHING',
'LAC QUI PARLE', 'LAKE', 'LAKE OF THE WOODS', 'LE SUEUR', 'LINCOLN',
'LYON', 'MAHNOMEN', 'MARSHALL', 'MARTIN', 'MCLEOD', 'MEEKER',
'MILLE LACS', 'MORRISON', 'MOWER', 'MURRAY', 'NICOLLET', 'NOBLES',
'NORMAN', 'OLMSTED', 'OTTER TAIL', 'PENNINGTON', 'PINE', 'PIPESTONE',
'POLK', 'POPE', 'RAMSEY', 'REDWOOD', 'RENVILLE', 'RICE', 'ROCK',
'ROSEAU', 'SCOTT', 'SHERBURNE', 'SIBLEY', 'ST LOUIS', 'STEARNS',
'STEELE', 'STEVENS', 'SWIFT', 'TODD', 'TRAVERSE', 'WABASHA', 'WADENA',
'WASECA', 'WASHINGTON', 'WATONWAN', 'WILKIN', 'WINONA', 'WRIGHT',
'YELLOW MEDICINE'], dtype='<U17')
* uranium_coefs
(uranium_coefs)
<U9
'intercept' 'u_slope'
array(['intercept', 'u_slope'], dtype='<U9') - Data variables: (7)
* g
(chain, draw, uranium_coefs)
float64
...
[4000 values with dtype=float64]
* za_county
(chain, draw, County)
float64
...
[170000 values with dtype=float64]
* b
(chain, draw)
float64
...
[2000 values with dtype=float64]
* sigma_a
(chain, draw)
float64
...
[2000 values with dtype=float64]
* a
(chain, draw, County)
float64
...
[170000 values with dtype=float64]
* a_county
(chain, draw, County)
float64
...
[170000 values with dtype=float64]
* sigma
(chain, draw)
float64
...
[2000 values with dtype=float64] - Indexes: (4)
* PandasIndex
PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))
* PandasIndex
PandasIndex(Index([ 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(['AITKIN', 'ANOKA', 'BECKER', 'BELTRAMI', 'BENTON', 'BIG STONE',
'BLUE EARTH', 'BROWN', 'CARLTON', 'CARVER', 'CASS', 'CHIPPEWA',
'CHISAGO', 'CLAY', 'CLEARWATER', 'COOK', 'COTTONWOOD', 'CROW WING',
'DAKOTA', 'DODGE', 'DOUGLAS', 'FARIBAULT', 'FILLMORE', 'FREEBORN',
'GOODHUE', 'HENNEPIN', 'HOUSTON', 'HUBBARD', 'ISANTI', 'ITASCA',
'JACKSON', 'KANABEC', 'KANDIYOHI', 'KITTSON', 'KOOCHICHING',
'LAC QUI PARLE', 'LAKE', 'LAKE OF THE WOODS', 'LE SUEUR', 'LINCOLN',
'LYON', 'MAHNOMEN', 'MARSHALL', 'MARTIN', 'MCLEOD', 'MEEKER',
'MILLE LACS', 'MORRISON', 'MOWER', 'MURRAY', 'NICOLLET', 'NOBLES',
'NORMAN', 'OLMSTED', 'OTTER TAIL', 'PENNINGTON', 'PINE', 'PIPESTONE',
'POLK', 'POPE', 'RAMSEY', 'REDWOOD', 'RENVILLE', 'RICE', 'ROCK',
'ROSEAU', 'SCOTT', 'SHERBURNE', 'SIBLEY', 'ST LOUIS', 'STEARNS',
'STEELE', 'STEVENS', 'SWIFT', 'TODD', 'TRAVERSE', 'WABASHA', 'WADENA',
'WASECA', 'WASHINGTON', 'WATONWAN', 'WILKIN', 'WINONA', 'WRIGHT',
'YELLOW MEDICINE'],
dtype='object', name='County'))
* PandasIndex
PandasIndex(Index(['intercept', 'u_slope'], dtype='object', name='uranium_coefs')) - Attributes: (6)
created_at :
2020-07-24T18:15:12.191355
arviz_version :
0.9.0
inference_library :
pymc3
inference_library_version :
3.9.2
sampling_time :
18.096983432769775
tuning_steps :
1000
- Dimensions:
- posterior_predictive
<xarray.Dataset> Size: 15MB
Dimensions: (chain: 4, draw: 500, obs_id: 919)
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
- obs_id (obs_id) int64 7kB 0 1 2 3 4 5 6 7 ... 912 913 914 915 916 917 918
Data variables:
y (chain, draw, obs_id) float64 15MB ...
Attributes:
created_at: 2020-07-24T18:15:12.449843
arviz_version: 0.9.0
inference_library: pymc3
inference_library_version: 3.9.2- Dimensions:
* chain: 4
* draw: 500
* obs_id: 919 - 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], shape=(500,))
* obs_id
(obs_id)
int64
0 1 2 3 4 5 ... 914 915 916 917 918
array([ 0, 1, 2, ..., 916, 917, 918], shape=(919,)) - Data variables: (1)
* y
(chain, draw, obs_id)
float64
...
[1838000 values with dtype=float64] - Indexes: (3)
* PandasIndex
PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))
* PandasIndex
PandasIndex(Index([ 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([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
...
909, 910, 911, 912, 913, 914, 915, 916, 917, 918],
dtype='int64', name='obs_id', length=919)) - Attributes: (4)
created_at :
2020-07-24T18:15:12.449843
arviz_version :
0.9.0
inference_library :
pymc3
inference_library_version :
3.9.2
- Dimensions:
- log_likelihood
<xarray.Dataset> Size: 15MB
Dimensions: (chain: 4, draw: 500, obs_id: 919)
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
- obs_id (obs_id) int64 7kB 0 1 2 3 4 5 6 7 ... 912 913 914 915 916 917 918
Data variables:
y (chain, draw, obs_id) float64 15MB ...
Attributes:
created_at: 2020-07-24T18:15:12.448264
arviz_version: 0.9.0
inference_library: pymc3
inference_library_version: 3.9.2- Dimensions:
* chain: 4
* draw: 500
* obs_id: 919 - 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], shape=(500,))
* obs_id
(obs_id)
int64
0 1 2 3 4 5 ... 914 915 916 917 918
array([ 0, 1, 2, ..., 916, 917, 918], shape=(919,)) - Data variables: (1)
* y
(chain, draw, obs_id)
float64
...
[1838000 values with dtype=float64] - Indexes: (3)
* PandasIndex
PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))
* PandasIndex
PandasIndex(Index([ 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([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
...
909, 910, 911, 912, 913, 914, 915, 916, 917, 918],
dtype='int64', name='obs_id', length=919)) - Attributes: (4)
created_at :
2020-07-24T18:15:12.448264
arviz_version :
0.9.0
inference_library :
pymc3
inference_library_version :
3.9.2
- Dimensions:
- sample_stats
<xarray.Dataset> Size: 150kB
Dimensions: (chain: 4, draw: 500)
Coordinates:- chain (chain) int64 32B 0 1 2 3
- draw (draw) int64 4kB 0 1 2 3 4 5 6 ... 494 495 496 497 498 499
Data variables:
step_size_bar (chain, draw) float64 16kB ...
diverging (chain, draw) bool 2kB ...
energy (chain, draw) float64 16kB ...
tree_size (chain, draw) float64 16kB ...
mean_tree_accept (chain, draw) float64 16kB ...
step_size (chain, draw) float64 16kB ...
depth (chain, draw) int64 16kB ...
energy_error (chain, draw) float64 16kB ...
lp (chain, draw) float64 16kB ...
max_energy_error (chain, draw) float64 16kB ...
Attributes:
created_at: 2020-07-24T18:15:12.197697
arviz_version: 0.9.0
inference_library: pymc3
inference_library_version: 3.9.2
sampling_time: 18.096983432769775
tuning_steps: 1000- 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], shape=(500,)) - Data variables: (10)
* step_size_bar
(chain, draw)
float64
...
[2000 values with dtype=float64]
* diverging
(chain, draw)
bool
...
[2000 values with dtype=bool]
* energy
(chain, draw)
float64
...
[2000 values with dtype=float64]
* tree_size
(chain, draw)
float64
...
[2000 values with dtype=float64]
* mean_tree_accept
(chain, draw)
float64
...
[2000 values with dtype=float64]
* step_size
(chain, draw)
float64
...
[2000 values with dtype=float64]
* depth
(chain, draw)
int64
...
[2000 values with dtype=int64]
* energy_error
(chain, draw)
float64
...
[2000 values with dtype=float64]
* lp
(chain, draw)
float64
...
[2000 values with dtype=float64]
* max_energy_error
(chain, draw)
float64
...
[2000 values with dtype=float64] - Indexes: (2)
* PandasIndex
PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))
* PandasIndex
PandasIndex(Index([ 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: (6)
created_at :
2020-07-24T18:15:12.197697
arviz_version :
0.9.0
inference_library :
pymc3
inference_library_version :
3.9.2
sampling_time :
18.096983432769775
tuning_steps :
1000
- Dimensions:
- prior
<xarray.Dataset> Size: 1MB
Dimensions: (chain: 1, draw: 500, County: 85, uranium_coefs: 2)
Coordinates:- chain (chain) int64 8B 0
- draw (draw) int64 4kB 0 1 2 3 4 5 6 ... 494 495 496 497 498 499
- County (County) <U17 6kB 'AITKIN' 'ANOKA' ... 'YELLOW MEDICINE'
- uranium_coefs (uranium_coefs) <U9 72B 'intercept' 'u_slope'
Data variables:
a_county (chain, draw, County) float64 340kB ...
sigma_log__ (chain, draw) float64 4kB ...
sigma_a (chain, draw) float64 4kB ...
a (chain, draw, County) float64 340kB ...
b (chain, draw) float64 4kB ...
za_county (chain, draw, County) float64 340kB ...
sigma (chain, draw) float64 4kB ...
g (chain, draw, uranium_coefs) float64 8kB ...
sigma_a_log__ (chain, draw) float64 4kB ...
Attributes:
created_at: 2020-07-24T18:15:12.454586
arviz_version: 0.9.0
inference_library: pymc3
inference_library_version: 3.9.2- Dimensions:
* chain: 1
* draw: 500
* County: 85
* uranium_coefs: 2 - 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], shape=(500,))
* County
(County)
<U17
'AITKIN' ... 'YELLOW MEDICINE'
array(['AITKIN', 'ANOKA', 'BECKER', 'BELTRAMI', 'BENTON', 'BIG STONE',
'BLUE EARTH', 'BROWN', 'CARLTON', 'CARVER', 'CASS', 'CHIPPEWA',
'CHISAGO', 'CLAY', 'CLEARWATER', 'COOK', 'COTTONWOOD', 'CROW WING',
'DAKOTA', 'DODGE', 'DOUGLAS', 'FARIBAULT', 'FILLMORE', 'FREEBORN',
'GOODHUE', 'HENNEPIN', 'HOUSTON', 'HUBBARD', 'ISANTI', 'ITASCA',
'JACKSON', 'KANABEC', 'KANDIYOHI', 'KITTSON', 'KOOCHICHING',
'LAC QUI PARLE', 'LAKE', 'LAKE OF THE WOODS', 'LE SUEUR', 'LINCOLN',
'LYON', 'MAHNOMEN', 'MARSHALL', 'MARTIN', 'MCLEOD', 'MEEKER',
'MILLE LACS', 'MORRISON', 'MOWER', 'MURRAY', 'NICOLLET', 'NOBLES',
'NORMAN', 'OLMSTED', 'OTTER TAIL', 'PENNINGTON', 'PINE', 'PIPESTONE',
'POLK', 'POPE', 'RAMSEY', 'REDWOOD', 'RENVILLE', 'RICE', 'ROCK',
'ROSEAU', 'SCOTT', 'SHERBURNE', 'SIBLEY', 'ST LOUIS', 'STEARNS',
'STEELE', 'STEVENS', 'SWIFT', 'TODD', 'TRAVERSE', 'WABASHA', 'WADENA',
'WASECA', 'WASHINGTON', 'WATONWAN', 'WILKIN', 'WINONA', 'WRIGHT',
'YELLOW MEDICINE'], dtype='<U17')
* uranium_coefs
(uranium_coefs)
<U9
'intercept' 'u_slope'
array(['intercept', 'u_slope'], dtype='<U9') - Data variables: (9)
* a_county
(chain, draw, County)
float64
...
[42500 values with dtype=float64]
* sigma_log__
(chain, draw)
float64
...
[500 values with dtype=float64]
* sigma_a
(chain, draw)
float64
...
[500 values with dtype=float64]
* a
(chain, draw, County)
float64
...
[42500 values with dtype=float64]
* b
(chain, draw)
float64
...
[500 values with dtype=float64]
* za_county
(chain, draw, County)
float64
...
[42500 values with dtype=float64]
* sigma
(chain, draw)
float64
...
[500 values with dtype=float64]
* g
(chain, draw, uranium_coefs)
float64
...
[1000 values with dtype=float64]
* sigma_a_log__
(chain, draw)
float64
...
[500 values with dtype=float64] - Indexes: (4)
* PandasIndex
PandasIndex(Index([0], dtype='int64', name='chain'))
* PandasIndex
PandasIndex(Index([ 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(['AITKIN', 'ANOKA', 'BECKER', 'BELTRAMI', 'BENTON', 'BIG STONE',
'BLUE EARTH', 'BROWN', 'CARLTON', 'CARVER', 'CASS', 'CHIPPEWA',
'CHISAGO', 'CLAY', 'CLEARWATER', 'COOK', 'COTTONWOOD', 'CROW WING',
'DAKOTA', 'DODGE', 'DOUGLAS', 'FARIBAULT', 'FILLMORE', 'FREEBORN',
'GOODHUE', 'HENNEPIN', 'HOUSTON', 'HUBBARD', 'ISANTI', 'ITASCA',
'JACKSON', 'KANABEC', 'KANDIYOHI', 'KITTSON', 'KOOCHICHING',
'LAC QUI PARLE', 'LAKE', 'LAKE OF THE WOODS', 'LE SUEUR', 'LINCOLN',
'LYON', 'MAHNOMEN', 'MARSHALL', 'MARTIN', 'MCLEOD', 'MEEKER',
'MILLE LACS', 'MORRISON', 'MOWER', 'MURRAY', 'NICOLLET', 'NOBLES',
'NORMAN', 'OLMSTED', 'OTTER TAIL', 'PENNINGTON', 'PINE', 'PIPESTONE',
'POLK', 'POPE', 'RAMSEY', 'REDWOOD', 'RENVILLE', 'RICE', 'ROCK',
'ROSEAU', 'SCOTT', 'SHERBURNE', 'SIBLEY', 'ST LOUIS', 'STEARNS',
'STEELE', 'STEVENS', 'SWIFT', 'TODD', 'TRAVERSE', 'WABASHA', 'WADENA',
'WASECA', 'WASHINGTON', 'WATONWAN', 'WILKIN', 'WINONA', 'WRIGHT',
'YELLOW MEDICINE'],
dtype='object', name='County'))
* PandasIndex
PandasIndex(Index(['intercept', 'u_slope'], dtype='object', name='uranium_coefs')) - Attributes: (4)
created_at :
2020-07-24T18:15:12.454586
arviz_version :
0.9.0
inference_library :
pymc3
inference_library_version :
3.9.2
- Dimensions:
- prior_predictive
<xarray.Dataset> Size: 4MB
Dimensions: (chain: 1, draw: 500, obs_id: 919)
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
- obs_id (obs_id) int64 7kB 0 1 2 3 4 5 6 7 ... 912 913 914 915 916 917 918
Data variables:
y (chain, draw, obs_id) float64 4MB ...
Attributes:
created_at: 2020-07-24T18:15:12.457652
arviz_version: 0.9.0
inference_library: pymc3
inference_library_version: 3.9.2- Dimensions:
* chain: 1
* draw: 500
* obs_id: 919 - Coordinates: (3)
* 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], shape=(500,))
* obs_id
(obs_id)
int64
0 1 2 3 4 5 ... 914 915 916 917 918
array([ 0, 1, 2, ..., 916, 917, 918], shape=(919,)) - Data variables: (1)
* y
(chain, draw, obs_id)
float64
...
[459500 values with dtype=float64] - Indexes: (3)
* PandasIndex
PandasIndex(Index([0], dtype='int64', name='chain'))
* PandasIndex
PandasIndex(Index([ 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([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
...
909, 910, 911, 912, 913, 914, 915, 916, 917, 918],
dtype='int64', name='obs_id', length=919)) - Attributes: (4)
created_at :
2020-07-24T18:15:12.457652
arviz_version :
0.9.0
inference_library :
pymc3
inference_library_version :
3.9.2
- Dimensions:
- observed_data
<xarray.Dataset> Size: 15kB
Dimensions: (obs_id: 919)
Coordinates:- obs_id (obs_id) int64 7kB 0 1 2 3 4 5 6 7 ... 912 913 914 915 916 917 918
Data variables:
y (obs_id) float64 7kB ...
Attributes:
created_at: 2020-07-24T18:15:12.458415
arviz_version: 0.9.0
inference_library: pymc3
inference_library_version: 3.9.2- Dimensions:
* obs_id: 919 - Coordinates: (1)
* obs_id
(obs_id)
int64
0 1 2 3 4 5 ... 914 915 916 917 918
array([ 0, 1, 2, ..., 916, 917, 918], shape=(919,)) - Data variables: (1)
* y
(obs_id)
float64
...
[919 values with dtype=float64] - Indexes: (1)
* PandasIndex
PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
...
909, 910, 911, 912, 913, 914, 915, 916, 917, 918],
dtype='int64', name='obs_id', length=919)) - Attributes: (4)
created_at :
2020-07-24T18:15:12.458415
arviz_version :
0.9.0
inference_library :
pymc3
inference_library_version :
3.9.2
- Dimensions:
- obs_id (obs_id) int64 7kB 0 1 2 3 4 5 6 7 ... 912 913 914 915 916 917 918
- constant_data
<xarray.Dataset> Size: 21kB
Dimensions: (obs_id: 919, County: 85)
Coordinates:- obs_id (obs_id) int64 7kB 0 1 2 3 4 5 6 ... 912 913 914 915 916 917 918
- County (County) <U17 6kB 'AITKIN' 'ANOKA' ... 'YELLOW MEDICINE'
Data variables:
floor_idx (obs_id) int32 4kB ...
county_idx (obs_id) int32 4kB ...
uranium (County) float64 680B ...
Attributes:
created_at: 2020-07-24T18:15:12.459832
arviz_version: 0.9.0
inference_library: pymc3
inference_library_version: 3.9.2- Dimensions:
* obs_id: 919
* County: 85 - Coordinates: (2)
* obs_id
(obs_id)
int64
0 1 2 3 4 5 ... 914 915 916 917 918
array([ 0, 1, 2, ..., 916, 917, 918], shape=(919,))
* County
(County)
<U17
'AITKIN' ... 'YELLOW MEDICINE'
array(['AITKIN', 'ANOKA', 'BECKER', 'BELTRAMI', 'BENTON', 'BIG STONE',
'BLUE EARTH', 'BROWN', 'CARLTON', 'CARVER', 'CASS', 'CHIPPEWA',
'CHISAGO', 'CLAY', 'CLEARWATER', 'COOK', 'COTTONWOOD', 'CROW WING',
'DAKOTA', 'DODGE', 'DOUGLAS', 'FARIBAULT', 'FILLMORE', 'FREEBORN',
'GOODHUE', 'HENNEPIN', 'HOUSTON', 'HUBBARD', 'ISANTI', 'ITASCA',
'JACKSON', 'KANABEC', 'KANDIYOHI', 'KITTSON', 'KOOCHICHING',
'LAC QUI PARLE', 'LAKE', 'LAKE OF THE WOODS', 'LE SUEUR', 'LINCOLN',
'LYON', 'MAHNOMEN', 'MARSHALL', 'MARTIN', 'MCLEOD', 'MEEKER',
'MILLE LACS', 'MORRISON', 'MOWER', 'MURRAY', 'NICOLLET', 'NOBLES',
'NORMAN', 'OLMSTED', 'OTTER TAIL', 'PENNINGTON', 'PINE', 'PIPESTONE',
'POLK', 'POPE', 'RAMSEY', 'REDWOOD', 'RENVILLE', 'RICE', 'ROCK',
'ROSEAU', 'SCOTT', 'SHERBURNE', 'SIBLEY', 'ST LOUIS', 'STEARNS',
'STEELE', 'STEVENS', 'SWIFT', 'TODD', 'TRAVERSE', 'WABASHA', 'WADENA',
'WASECA', 'WASHINGTON', 'WATONWAN', 'WILKIN', 'WINONA', 'WRIGHT',
'YELLOW MEDICINE'], dtype='<U17') - Data variables: (3)
* floor_idx
(obs_id)
int32
...
[919 values with dtype=int32]
* county_idx
(obs_id)
int32
...
[919 values with dtype=int32]
* uranium
(County)
float64
...
[85 values with dtype=float64] - Indexes: (2)
* PandasIndex
PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
...
909, 910, 911, 912, 913, 914, 915, 916, 917, 918],
dtype='int64', name='obs_id', length=919))
* PandasIndex
PandasIndex(Index(['AITKIN', 'ANOKA', 'BECKER', 'BELTRAMI', 'BENTON', 'BIG STONE',
'BLUE EARTH', 'BROWN', 'CARLTON', 'CARVER', 'CASS', 'CHIPPEWA',
'CHISAGO', 'CLAY', 'CLEARWATER', 'COOK', 'COTTONWOOD', 'CROW WING',
'DAKOTA', 'DODGE', 'DOUGLAS', 'FARIBAULT', 'FILLMORE', 'FREEBORN',
'GOODHUE', 'HENNEPIN', 'HOUSTON', 'HUBBARD', 'ISANTI', 'ITASCA',
'JACKSON', 'KANABEC', 'KANDIYOHI', 'KITTSON', 'KOOCHICHING',
'LAC QUI PARLE', 'LAKE', 'LAKE OF THE WOODS', 'LE SUEUR', 'LINCOLN',
'LYON', 'MAHNOMEN', 'MARSHALL', 'MARTIN', 'MCLEOD', 'MEEKER',
'MILLE LACS', 'MORRISON', 'MOWER', 'MURRAY', 'NICOLLET', 'NOBLES',
'NORMAN', 'OLMSTED', 'OTTER TAIL', 'PENNINGTON', 'PINE', 'PIPESTONE',
'POLK', 'POPE', 'RAMSEY', 'REDWOOD', 'RENVILLE', 'RICE', 'ROCK',
'ROSEAU', 'SCOTT', 'SHERBURNE', 'SIBLEY', 'ST LOUIS', 'STEARNS',
'STEELE', 'STEVENS', 'SWIFT', 'TODD', 'TRAVERSE', 'WABASHA', 'WADENA',
'WASECA', 'WASHINGTON', 'WATONWAN', 'WILKIN', 'WINONA', 'WRIGHT',
'YELLOW MEDICINE'],
dtype='object', name='County')) - Attributes: (4)
created_at :
2020-07-24T18:15:12.459832
arviz_version :
0.9.0
inference_library :
pymc3
inference_library_version :
3.9.2
- Dimensions:
Add extra coordinates to all groups containing observed variables
idata = az.load_arviz_data("rugby") home_team, away_team = np.array([ m.split() for m in idata.observed_data.match.values ]).T idata = idata.map( lambda ds, **kwargs: ds.assign_coords(**kwargs), groups="observed_vars", home_team=("match", home_team), away_team=("match", away_team), ) idata
- posterior
<xarray.Dataset> Size: 452kB
Dimensions: (chain: 4, draw: 500, team: 6)
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
- team (team) <U8 192B 'Wales' 'France' 'Ireland' ... 'Italy' 'England'
Data variables:
home (chain, draw) float64 16kB ...
intercept (chain, draw) float64 16kB ...
atts_star (chain, draw, team) float64 96kB ...
defs_star (chain, draw, team) float64 96kB ...
sd_att (chain, draw) float64 16kB ...
sd_def (chain, draw) float64 16kB ...
atts (chain, draw, team) float64 96kB ...
defs (chain, draw, team) float64 96kB ...
Attributes:
created_at: 2024-03-06T20:46:23.841916
arviz_version: 0.17.0
inference_library: pymc
inference_library_version: 5.10.4+7.g34d2a5d9
sampling_time: 8.503105401992798
tuning_steps: 1000- 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], shape=(500,))
* team
(team)
<U8
'Wales' 'France' ... 'England'
array(['Wales', 'France', 'Ireland', 'Scotland', 'Italy', 'England'],
dtype='<U8') - 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(Index([0, 1, 2, 3], dtype='int64', name='chain'))
* PandasIndex
PandasIndex(Index([ 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: (6)
created_at :
2024-03-06T20:46:23.841916
arviz_version :
0.17.0
inference_library :
pymc
inference_library_version :
5.10.4+7.g34d2a5d9
sampling_time :
8.503105401992798
tuning_steps :
1000
- Dimensions:
- posterior_predictive
<xarray.Dataset> Size: 2MB
Dimensions: (chain: 4, draw: 500, match: 60)
Coordinates:- chain (chain) int64 32B 0 1 2 3
- draw (draw) int64 4kB 0 1 2 3 4 5 6 ... 493 494 495 496 497 498 499
- match (match) <U16 4kB 'Wales Italy' ... 'Ireland England'
home_team (match) <U8 2kB 'Wales' 'France' ... 'France' 'Ireland'
away_team (match) <U8 2kB 'Italy' 'England' ... 'Wales' 'England'
Data variables:
home_points (chain, draw, match) int64 960kB ...
away_points (chain, draw, match) int64 960kB ...
Attributes:
created_at: 2024-03-06T20:46:25.689246
arviz_version: 0.17.0
inference_library: pymc
inference_library_version: 5.10.4+7.g34d2a5d9- Dimensions:
* chain: 4
* draw: 500
* match: 60 - Coordinates: (5)
* 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], shape=(500,))
* match
(match)
<U16
'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='<U16')
* home_team
(match)
<U8
'Wales' 'France' ... 'Ireland'
array(['Wales', 'France', 'Ireland', 'Ireland', 'Scotland', 'France',
'Wales', 'Italy', 'England', 'Ireland', 'Scotland', 'England',
'Italy', 'Wales', 'France', 'Wales', 'Italy', 'France', 'England',
'Ireland', 'Scotland', 'Scotland', 'France', 'Ireland', 'Wales',
'England', 'Italy', 'Italy', 'Scotland', 'England', 'France',
'Scotland', 'Ireland', 'France', 'Wales', 'Italy', 'Wales',
'Italy', 'England', 'Ireland', 'England', 'Scotland', 'Wales',
'Ireland', 'France', 'Scotland', 'England', 'Italy', 'Italy',
'Wales', 'France', 'Scotland', 'Ireland', 'England', 'Wales',
'Italy', 'England', 'Scotland', 'France', 'Ireland'], dtype='<U8')
* away_team
(match)
<U8
'Italy' 'England' ... 'England'
array(['Italy', 'England', 'Scotland', 'Wales', 'England', 'Italy',
'France', 'Scotland', 'Ireland', 'Italy', 'France', 'Wales',
'England', 'Scotland', 'Ireland', 'England', 'Ireland', 'Scotland',
'Italy', 'France', 'Wales', 'Italy', 'Wales', 'England', 'Ireland',
'Scotland', 'France', 'Wales', 'Ireland', 'France', 'Italy',
'England', 'Wales', 'Ireland', 'Scotland', 'England', 'France',
'Scotland', 'Ireland', 'Italy', 'Wales', 'France', 'Italy',
'Scotland', 'England', 'Ireland', 'France', 'Wales', 'Ireland',
'England', 'Scotland', 'Wales', 'France', 'Italy', 'Ireland',
'France', 'Scotland', 'Italy', 'Wales', 'England'], dtype='<U8') - 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(Index([0, 1, 2, 3], dtype='int64', name='chain'))
* PandasIndex
PandasIndex(Index([ 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: (4)
created_at :
2024-03-06T20:46:25.689246
arviz_version :
0.17.0
inference_library :
pymc
inference_library_version :
5.10.4+7.g34d2a5d9
- Dimensions:
- log_likelihood
<xarray.Dataset> Size: 2MB
Dimensions: (chain: 4, draw: 500, match: 60)
Coordinates:- chain (chain) int64 32B 0 1 2 3
- draw (draw) int64 4kB 0 1 2 3 4 5 6 ... 493 494 495 496 497 498 499
- match (match) <U16 4kB 'Wales Italy' ... 'Ireland England'
home_team (match) <U8 2kB ...
away_team (match) <U8 2kB ...
Data variables:
home_points (chain, draw, match) float64 960kB ...
away_points (chain, draw, match) float64 960kB ...
Attributes:
created_at: 2024-03-06T20:46:24.120642
arviz_version: 0.17.0
inference_library: pymc
inference_library_version: 5.10.4+7.g34d2a5d9- Dimensions:
* chain: 4
* draw: 500
* match: 60 - Coordinates: (5)
* 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], shape=(500,))
* match
(match)
<U16
'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='<U16')
* home_team
(match)
<U8
...
[60 values with dtype=<U8]
* away_team
(match)
<U8
...
[60 values with dtype=<U8] - Data variables: (2)
* home_points
(chain, draw, match)
float64
...
[120000 values with dtype=float64]
* away_points
(chain, draw, match)
float64
...
[120000 values with dtype=float64] - Indexes: (3)
* PandasIndex
PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))
* PandasIndex
PandasIndex(Index([ 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: (4)
created_at :
2024-03-06T20:46:24.120642
arviz_version :
0.17.0
inference_library :
pymc
inference_library_version :
5.10.4+7.g34d2a5d9
- Dimensions:
- log_prior
<xarray.Dataset> Size: 260kB
Dimensions: (chain: 4, draw: 500, team: 6)
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
- team (team) <U8 192B 'Wales' 'France' 'Ireland' ... 'Italy' 'England'
Data variables:
home (chain, draw) float64 16kB ...
sd_att (chain, draw) float64 16kB ...
sd_def (chain, draw) float64 16kB ...
intercept (chain, draw) float64 16kB ...
atts_star (chain, draw, team) float64 96kB ...
defs_star (chain, draw, team) float64 96kB ...
Attributes:
created_at: 2024-03-06T20:46:24.377610
arviz_version: 0.17.0
inference_library: pymc
inference_library_version: 5.10.4+7.g34d2a5d9- 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], shape=(500,))
* team
(team)
<U8
'Wales' 'France' ... 'England'
array(['Wales', 'France', 'Ireland', 'Scotland', 'Italy', 'England'],
dtype='<U8') - Data variables: (6)
* home
(chain, draw)
float64
...
[2000 values with dtype=float64]
* sd_att
(chain, draw)
float64
...
[2000 values with dtype=float64]
* sd_def
(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] - Indexes: (3)
* PandasIndex
PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))
* PandasIndex
PandasIndex(Index([ 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: (4)
created_at :
2024-03-06T20:46:24.377610
arviz_version :
0.17.0
inference_library :
pymc
inference_library_version :
5.10.4+7.g34d2a5d9
- Dimensions:
- sample_stats
<xarray.Dataset> Size: 248kB
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: (12/17)
max_energy_error (chain, draw) float64 16kB ...
index_in_trajectory (chain, draw) int64 16kB ...
smallest_eigval (chain, draw) float64 16kB ...
perf_counter_start (chain, draw) float64 16kB ...
largest_eigval (chain, draw) float64 16kB ...
step_size (chain, draw) float64 16kB ...
... ...
reached_max_treedepth (chain, draw) bool 2kB ...
perf_counter_diff (chain, draw) float64 16kB ...
tree_depth (chain, draw) int64 16kB ...
process_time_diff (chain, draw) float64 16kB ...
step_size_bar (chain, draw) float64 16kB ...
energy (chain, draw) float64 16kB ...
Attributes:
created_at: 2024-03-06T20:46:23.854033
arviz_version: 0.17.0
inference_library: pymc
inference_library_version: 5.10.4+7.g34d2a5d9
sampling_time: 8.503105401992798
tuning_steps: 1000- 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], shape=(500,)) - Data variables: (17)
* max_energy_error
(chain, draw)
float64
...
[2000 values with dtype=float64]
* index_in_trajectory
(chain, draw)
int64
...
[2000 values with dtype=int64]
* smallest_eigval
(chain, draw)
float64
...
[2000 values with dtype=float64]
* perf_counter_start
(chain, draw)
float64
...
[2000 values with dtype=float64]
* largest_eigval
(chain, draw)
float64
...
[2000 values with dtype=float64]
* step_size
(chain, draw)
float64
...
[2000 values with dtype=float64]
* n_steps
(chain, draw)
float64
...
[2000 values with dtype=float64]
* lp
(chain, draw)
float64
...
[2000 values with dtype=float64]
* diverging
(chain, draw)
bool
...
[2000 values with dtype=bool]
* energy_error
(chain, draw)
float64
...
[2000 values with dtype=float64]
* acceptance_rate
(chain, draw)
float64
...
[2000 values with dtype=float64]
* reached_max_treedepth
(chain, draw)
bool
...
[2000 values with dtype=bool]
* perf_counter_diff
(chain, draw)
float64
...
[2000 values with dtype=float64]
* tree_depth
(chain, draw)
int64
...
[2000 values with dtype=int64]
* process_time_diff
(chain, draw)
float64
...
[2000 values with dtype=float64]
* step_size_bar
(chain, draw)
float64
...
[2000 values with dtype=float64]
* energy
(chain, draw)
float64
...
[2000 values with dtype=float64] - Indexes: (2)
* PandasIndex
PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))
* PandasIndex
PandasIndex(Index([ 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: (6)
created_at :
2024-03-06T20:46:23.854033
arviz_version :
0.17.0
inference_library :
pymc
inference_library_version :
5.10.4+7.g34d2a5d9
sampling_time :
8.503105401992798
tuning_steps :
1000
- Dimensions:
- prior
<xarray.Dataset> Size: 116kB
Dimensions: (chain: 1, draw: 500, team: 6)
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
- team (team) <U8 192B 'Wales' 'France' 'Ireland' ... 'Italy' 'England'
Data variables:
atts_star (chain, draw, team) float64 24kB ...
sd_att (chain, draw) float64 4kB ...
atts (chain, draw, team) float64 24kB ...
sd_def (chain, draw) float64 4kB ...
defs (chain, draw, team) float64 24kB ...
intercept (chain, draw) float64 4kB ...
home (chain, draw) float64 4kB ...
defs_star (chain, draw, team) float64 24kB ...
Attributes:
created_at: 2024-03-06T20:46:09.475945
arviz_version: 0.17.0
inference_library: pymc
inference_library_version: 5.10.4+7.g34d2a5d9- Dimensions:
* chain: 1
* draw: 500
* team: 6 - Coordinates: (3)
* 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], shape=(500,))
* team
(team)
<U8
'Wales' 'France' ... 'England'
array(['Wales', 'France', 'Ireland', 'Scotland', 'Italy', 'England'],
dtype='<U8') - Data variables: (8)
* atts_star
(chain, draw, team)
float64
...
[3000 values with dtype=float64]
* sd_att
(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]
* defs
(chain, draw, team)
float64
...
[3000 values with dtype=float64]
* intercept
(chain, draw)
float64
...
[500 values with dtype=float64]
* home
(chain, draw)
float64
...
[500 values with dtype=float64]
* defs_star
(chain, draw, team)
float64
...
[3000 values with dtype=float64] - Indexes: (3)
* PandasIndex
PandasIndex(Index([0], dtype='int64', name='chain'))
* PandasIndex
PandasIndex(Index([ 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: (4)
created_at :
2024-03-06T20:46:09.475945
arviz_version :
0.17.0
inference_library :
pymc
inference_library_version :
5.10.4+7.g34d2a5d9
- Dimensions:
- prior_predictive
<xarray.Dataset> Size: 492kB
Dimensions: (chain: 1, draw: 500, match: 60)
Coordinates:- chain (chain) int64 8B 0
- draw (draw) int64 4kB 0 1 2 3 4 5 6 ... 493 494 495 496 497 498 499
- match (match) <U16 4kB 'Wales Italy' ... 'Ireland England'
home_team (match) <U8 2kB 'Wales' 'France' ... 'France' 'Ireland'
away_team (match) <U8 2kB 'Italy' 'England' ... 'Wales' 'England'
Data variables:
away_points (chain, draw, match) int64 240kB ...
home_points (chain, draw, match) int64 240kB ...
Attributes:
created_at: 2024-03-06T20:46:09.479330
arviz_version: 0.17.0
inference_library: pymc
inference_library_version: 5.10.4+7.g34d2a5d9- Dimensions:
* chain: 1
* draw: 500
* match: 60 - Coordinates: (5)
* 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], shape=(500,))
* match
(match)
<U16
'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='<U16')
* home_team
(match)
<U8
'Wales' 'France' ... 'Ireland'
array(['Wales', 'France', 'Ireland', 'Ireland', 'Scotland', 'France',
'Wales', 'Italy', 'England', 'Ireland', 'Scotland', 'England',
'Italy', 'Wales', 'France', 'Wales', 'Italy', 'France', 'England',
'Ireland', 'Scotland', 'Scotland', 'France', 'Ireland', 'Wales',
'England', 'Italy', 'Italy', 'Scotland', 'England', 'France',
'Scotland', 'Ireland', 'France', 'Wales', 'Italy', 'Wales',
'Italy', 'England', 'Ireland', 'England', 'Scotland', 'Wales',
'Ireland', 'France', 'Scotland', 'England', 'Italy', 'Italy',
'Wales', 'France', 'Scotland', 'Ireland', 'England', 'Wales',
'Italy', 'England', 'Scotland', 'France', 'Ireland'], dtype='<U8')
* away_team
(match)
<U8
'Italy' 'England' ... 'England'
array(['Italy', 'England', 'Scotland', 'Wales', 'England', 'Italy',
'France', 'Scotland', 'Ireland', 'Italy', 'France', 'Wales',
'England', 'Scotland', 'Ireland', 'England', 'Ireland', 'Scotland',
'Italy', 'France', 'Wales', 'Italy', 'Wales', 'England', 'Ireland',
'Scotland', 'France', 'Wales', 'Ireland', 'France', 'Italy',
'England', 'Wales', 'Ireland', 'Scotland', 'England', 'France',
'Scotland', 'Ireland', 'Italy', 'Wales', 'France', 'Italy',
'Scotland', 'England', 'Ireland', 'France', 'Wales', 'Ireland',
'England', 'Scotland', 'Wales', 'France', 'Italy', 'Ireland',
'France', 'Scotland', 'Italy', 'Wales', 'England'], dtype='<U8') - Data variables: (2)
* away_points
(chain, draw, match)
int64
...
[30000 values with dtype=int64]
* home_points
(chain, draw, match)
int64
...
[30000 values with dtype=int64] - Indexes: (3)
* PandasIndex
PandasIndex(Index([0], dtype='int64', name='chain'))
* PandasIndex
PandasIndex(Index([ 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: (4)
created_at :
2024-03-06T20:46:09.479330
arviz_version :
0.17.0
inference_library :
pymc
inference_library_version :
5.10.4+7.g34d2a5d9
- Dimensions:
- observed_data
<xarray.Dataset> Size: 9kB
Dimensions: (match: 60)
Coordinates:- match (match) <U16 4kB 'Wales Italy' ... 'Ireland England'
home_team (match) <U8 2kB 'Wales' 'France' ... 'France' 'Ireland'
away_team (match) <U8 2kB 'Italy' 'England' ... 'Wales' 'England'
Data variables:
home_points (match) int64 480B ...
away_points (match) int64 480B ...
Attributes:
created_at: 2024-03-06T20:46:09.480812
arviz_version: 0.17.0
inference_library: pymc
inference_library_version: 5.10.4+7.g34d2a5d9- Dimensions:
* match: 60 - Coordinates: (3)
* match
(match)
<U16
'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='<U16')
* home_team
(match)
<U8
'Wales' 'France' ... 'Ireland'
array(['Wales', 'France', 'Ireland', 'Ireland', 'Scotland', 'France',
'Wales', 'Italy', 'England', 'Ireland', 'Scotland', 'England',
'Italy', 'Wales', 'France', 'Wales', 'Italy', 'France', 'England',
'Ireland', 'Scotland', 'Scotland', 'France', 'Ireland', 'Wales',
'England', 'Italy', 'Italy', 'Scotland', 'England', 'France',
'Scotland', 'Ireland', 'France', 'Wales', 'Italy', 'Wales',
'Italy', 'England', 'Ireland', 'England', 'Scotland', 'Wales',
'Ireland', 'France', 'Scotland', 'England', 'Italy', 'Italy',
'Wales', 'France', 'Scotland', 'Ireland', 'England', 'Wales',
'Italy', 'England', 'Scotland', 'France', 'Ireland'], dtype='<U8')
* away_team
(match)
<U8
'Italy' 'England' ... 'England'
array(['Italy', 'England', 'Scotland', 'Wales', 'England', 'Italy',
'France', 'Scotland', 'Ireland', 'Italy', 'France', 'Wales',
'England', 'Scotland', 'Ireland', 'England', 'Ireland', 'Scotland',
'Italy', 'France', 'Wales', 'Italy', 'Wales', 'England', 'Ireland',
'Scotland', 'France', 'Wales', 'Ireland', 'France', 'Italy',
'England', 'Wales', 'Ireland', 'Scotland', 'England', 'France',
'Scotland', 'Ireland', 'Italy', 'Wales', 'France', 'Italy',
'Scotland', 'England', 'Ireland', 'France', 'Wales', 'Ireland',
'England', 'Scotland', 'Wales', 'France', 'Italy', 'Ireland',
'France', 'Scotland', 'Italy', 'Wales', 'England'], dtype='<U8') - Data variables: (2)
* home_points
(match)
int64
...
[60 values with dtype=int64]
* away_points
(match)
int64
...
[60 values with dtype=int64] - 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: (4)
created_at :
2024-03-06T20:46:09.480812
arviz_version :
0.17.0
inference_library :
pymc
inference_library_version :
5.10.4+7.g34d2a5d9
- Dimensions:
- match (match) <U16 4kB 'Wales Italy' ... 'Ireland England'
- unconstrained_posterior
<xarray.Dataset> Size: 36kB
Dimensions: (chain: 4, draw: 500)
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
Data variables:
sd_att (chain, draw) float64 16kB ...
sd_def (chain, draw) float64 16kB ...
Attributes:
sd_att: pymc.logprob.transforms.LogTransform
sd_def: pymc.logprob.transforms.LogTransform- 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], shape=(500,)) - Data variables: (2)
* sd_att
(chain, draw)
float64
...
[2000 values with dtype=float64]
* sd_def
(chain, draw)
float64
...
[2000 values with dtype=float64] - Indexes: (2)
* PandasIndex
PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))
* PandasIndex
PandasIndex(Index([ 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: (2)
sd_att :
pymc.logprob.transforms.LogTransform
sd_def :
pymc.logprob.transforms.LogTransform
- Dimensions: