InferenceData
schema in PyMC3The description of the InferenceData
structure can be found here.
In [1]:
import arviz as az
import pymc3 as pm
import pandas as pd
import numpy as np
import xarray
xarray.set_options(display_style="html");
In [2]:
#read data
data = pd.read_csv("linear_regression_data.csv", index_col=0)
time = data.time.values
slack_comments = data.comments.values
github_commits = data.commits.values
names = data.index.values
N = len(names)
data
Out[2]:
comments
commits
time
Alice
7500
25
4.5
Bob
10100
32
6.0
Cole
18600
49
7.0
Danielle
25200
66
12.0
Erika
27500
96
18.0
In [3]:
# data for out of sample predictions
candidate_devs = ["Francis", "Gerard"]
candidate_devs_time = np.array([3.6, 5.1])
In [4]:
dims={
"slack_comments": ["developer"],
"github_commits": ["developer"],
"time_since_joined": ["developer"],
}
with pm.Model() as model:
time_since_joined = pm.Data("time_since_joined", time)
b_sigma = pm.HalfNormal('b_sigma', sd=300)
c_sigma = pm.HalfNormal('c_sigma', sd=6)
b0 = pm.Normal("b0", mu=0, sd=200)
b1 = pm.Normal("b1", mu=0, sd=200)
c0 = pm.Normal("c0", mu=0, sd=10)
c1 = pm.Normal("c1", mu=0, sd=10)
pm.Normal("slack_comments", mu=b0 + b1 * time_since_joined, sigma=b_sigma, observed=slack_comments)
pm.Normal("github_commits", mu=c0 + c1 * time_since_joined, sigma=c_sigma, observed=github_commits)
trace = pm.sample(400, chains=4)
posterior_predictive = pm.sample_posterior_predictive(trace)
prior = pm.sample_prior_predictive(150)
idata_pymc3 = az.from_pymc3(
trace,
prior=prior,
posterior_predictive=posterior_predictive,
coords={"developer": names},
dims=dims
)
Only 400 samples in chain.
Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
Multiprocess sampling (4 chains in 4 jobs)
NUTS: [c1, c0, b1, b0, c_sigma, b_sigma]
100.00% [3600/3600 00:04<00:00 Sampling 4 chains, 2 divergences]
There was 1 divergence after tuning. Increase `target_accept` or reparameterize.
There was 1 divergence after tuning. Increase `target_accept` or reparameterize.
100.00% [1600/1600 00:03<00:00]
In [5]:
dims_pred={
"slack_comments": ["candidate developer"],
"github_commits": ["candidate developer"],
"time_since_joined": ["candidate developer"],
}
with model:
pm.set_data({"time_since_joined": candidate_devs_time})
predictions = pm.sample_posterior_predictive(trace)
az.from_pymc3_predictions(
predictions,
idata_orig=idata_pymc3,
inplace=True,
coords={"candidate developer": candidate_devs},
dims=dims_pred,
)
100.00% [1600/1600 00:07<00:00]
In [6]:
idata_pymc3
Out[6]:
Inference data with groups:
> posterior
> posterior_predictive
> sample_stats
> prior
> prior_predictive
> observed_data
> log_likelihood
> constant_data
> predictions
> predictions_constant_data
In this example, each variable has as dimension a combination of the following 3: chain
, draw
and developer
. Moreover, each dimension has specific coordinate values. In the case of chain
and draw
it is an integer identifier starting at 0
; in the case of developer
dimension, its coordinate values are the following strings: ["Alice", "Bob", "Cole", "Danielle", "Erika"]
.
In [7]:
idata_pymc3.posterior
Out[7]:
xarray.Dataset- chain: 4
- draw: 400
- chain(chain)int640 1 2 3
array([0, 1, 2, 3])
- draw(draw)int640 1 2 3 4 5 ... 395 396 397 398 399
array([ 0, 1, 2, ..., 397, 398, 399])
- b0(chain, draw)float64619.3 313.6 88.4 ... 273.1 185.8
array([[ 619.30334334, 313.57972958, 88.40069813, ..., 86.50877829,
209.76527942, 259.75718 ],
[ -11.02038872, 33.40805828, -113.50897834, ..., 446.11783706,
-116.16682146, 108.30910847],
[-100.02157877, 329.40951249, 411.80180413, ..., 304.66719455,
25.6214615 , 66.7679782 ],
[ -23.04302795, 228.32265612, -25.77693619, ..., 325.8748248 ,
273.05245828, 185.78243209]])
- b1(chain, draw)float641.516e+03 1.525e+03 ... 1.485e+03
array([[1516.03232735, 1524.74272889, 1490.03337057, ..., 1572.29989631,
1625.3059752 , 1571.74424293],
[1636.72804519, 1626.22427857, 1690.18786572, ..., 1676.93356261,
1641.80643617, 1496.03948842],
[1639.96161765, 1607.09120127, 1604.45613865, ..., 1449.73855792,
1722.48243714, 1686.04136914],
[1555.04708013, 1540.12021417, 1705.40462246, ..., 1588.25824197,
1620.52009524, 1484.82316904]])
- c0(chain, draw)float64-15.82 -10.81 -15.5 ... 4.305 2.761
array([[-15.81906054, -10.81011788, -15.50198374, ..., 6.56434381,
-0.50750007, 2.69776545],
[ -1.94953744, -0.30238737, 3.10205678, ..., -4.25143102,
7.74451248, -9.45069889],
[ 9.79674617, 11.40941896, -0.97428464, ..., 6.58785802,
7.03364269, 8.99532889],
[ -3.28067461, 24.78784126, 16.31078974, ..., 6.43170307,
4.30476907, 2.76097108]])
- c1(chain, draw)float646.507 7.465 6.517 ... 5.07 5.189
array([[6.50678863, 7.46454211, 6.51747415, ..., 5.00148126, 5.53430053,
5.76080182],
[5.45672499, 5.65062742, 4.91592914, ..., 5.91086493, 4.77355821,
6.46433539],
[4.61091412, 4.61919367, 5.90586605, ..., 5.00453555, 4.86307639,
4.87946413],
[5.68466235, 2.54628608, 4.60572306, ..., 5.16815823, 5.0701375 ,
5.18895417]])
- b_sigma(chain, draw)float641.512e+03 1.815e+03 ... 1.8e+03
array([[1512.47768934, 1815.2198992 , 1812.51271589, ..., 1553.58941483,
1778.91986664, 1834.98805985],
[1834.58934978, 1690.02900839, 1809.19366886, ..., 1290.21915743,
1513.45623476, 1713.82028877],
[1719.00931232, 1586.87256375, 1388.81172907, ..., 1681.10548734,
1718.17237199, 1680.09581278],
[1564.86739502, 1499.60743638, 1674.69654847, ..., 1705.90383399,
1412.25273998, 1800.39424699]])
- c_sigma(chain, draw)float647.493 15.52 8.19 ... 4.379 5.154
array([[ 7.49348452, 15.51931696, 8.18986939, ..., 3.85834311,
6.14802584, 6.53386705],
[ 9.24187281, 7.17949865, 5.47055063, ..., 3.10000522,
12.17010895, 7.13077749],
[ 5.30566752, 3.81042318, 7.31160479, ..., 4.72557784,
4.26385976, 4.28738777],
[ 5.39497422, 9.61525159, 10.67059339, ..., 4.68448828,
4.37858258, 5.15357695]])
- created_at :
- 2020-01-27T22:36:59.510665
- inference_library :
- pymc3
- inference_library_version :
- 3.8
In [8]:
idata_pymc3.sample_stats
Out[8]:
xarray.Dataset- chain: 4
- draw: 400
- chain(chain)int640 1 2 3
array([0, 1, 2, 3])
- draw(draw)int640 1 2 3 4 5 ... 395 396 397 398 399
array([ 0, 1, 2, ..., 397, 398, 399])
- energy_error(chain, draw)float640.1398 -0.232 ... 0.3555 -0.02345
array([[ 0.13983636, -0.23195565, 0.02042407, ..., 0.00255139,
0.09591954, 0.3563224 ],
[ 0.24171132, -0.12041609, 0.14018251, ..., 0.11161431,
-0.32508047, -0.10969865],
[-0.12160326, -0.32012348, 0.43671993, ..., -0.23224975,
0.06337494, 0.08542817],
[ 0.01980747, 0.4951588 , 0.13668189, ..., -0.09915747,
0.3554587 , -0.02345344]])
- energy(chain, draw)float64149.4 151.4 150.4 ... 142.6 140.7
array([[149.38408699, 151.44592656, 150.4231448 , ..., 139.86512224,
140.2389336 , 144.83722516],
[147.9169999 , 142.96251245, 144.23751706, ..., 151.43118902,
150.18209199, 148.19097069],
[142.04807622, 145.87775499, 146.40052954, ..., 141.93543187,
142.15818289, 142.23989061],
[142.39359546, 152.51942216, 152.50938944, ..., 144.12266928,
142.56036496, 140.74251769]])
- step_size_bar(chain, draw)float640.3863 0.3863 ... 0.4529 0.4529
array([[0.38626878, 0.38626878, 0.38626878, ..., 0.38626878, 0.38626878,
0.38626878],
[0.34886932, 0.34886932, 0.34886932, ..., 0.34886932, 0.34886932,
0.34886932],
[0.40656223, 0.40656223, 0.40656223, ..., 0.40656223, 0.40656223,
0.40656223],
[0.45290133, 0.45290133, 0.45290133, ..., 0.45290133, 0.45290133,
0.45290133]])
- tree_size(chain, draw)float6415.0 15.0 7.0 7.0 ... 7.0 7.0 15.0
array([[15., 15., 7., ..., 7., 15., 7.],
[ 7., 7., 7., ..., 15., 7., 15.],
[ 7., 7., 15., ..., 7., 7., 7.],
[15., 15., 15., ..., 7., 7., 15.]])
- max_energy_error(chain, draw)float64-0.1656 -0.6437 ... 1.534 -0.06507
array([[-0.16557173, -0.64374494, 0.08244838, ..., -0.03327802,
0.10929876, 0.58329817],
[ 0.87332475, -0.12041609, 0.35134843, ..., -0.2688141 ,
-0.52489134, -0.16164011],
[ 0.45200611, 1.1549677 , 0.73168338, ..., -0.23224975,
0.13261637, 0.17179817],
[-0.13627861, 0.91811481, 0.58884787, ..., -0.33052978,
1.53362732, -0.06507489]])
- diverging(chain, draw)boolFalse False False ... False False
array([[False, False, False, ..., False, False, False],
[False, False, False, ..., False, False, False],
[False, False, False, ..., False, False, False],
[False, False, False, ..., False, False, False]])
- lp(chain, draw)float64-148.2 -147.3 ... -139.5 -139.2
array([[-148.24152428, -147.32486112, -145.53044042, ..., -138.90737003,
-139.85764801, -141.04208534],
[-142.33067926, -139.91236047, -142.55350999, ..., -148.06902828,
-143.84193462, -142.65061779],
[-140.56882223, -140.61070755, -141.89125629, ..., -140.01146968,
-141.19380329, -140.43253954],
[-140.88991242, -149.1931938 , -145.39972337, ..., -139.10882302,
-139.50844213, -139.23783667]])
- depth(chain, draw)int644 4 3 3 3 3 3 3 ... 4 4 4 3 4 3 3 4
array([[4, 4, 3, ..., 3, 4, 3],
[3, 3, 3, ..., 4, 3, 4],
[3, 3, 4, ..., 3, 3, 3],
[4, 4, 4, ..., 3, 3, 4]])
- tune(chain, draw)boolTrue False False ... False False
array([[ True, False, False, ..., False, False, False],
[ True, False, False, ..., False, False, False],
[ True, False, False, ..., False, False, False],
[ True, False, False, ..., False, False, False]])
- mean_tree_accept(chain, draw)float640.9897 0.9751 ... 0.713 0.9966
array([[0.98967279, 0.97509465, 0.97234547, ..., 0.99874907, 0.93778425,
0.68093223],
[0.79969486, 0.99914181, 0.83092996, ..., 0.9894557 , 1. ,
0.991907 ],
[0.8565305 , 0.72621462, 0.58558934, ..., 0.99390142, 0.94692235,
0.95435264],
[0.99729841, 0.63255491, 0.90015449, ..., 0.98522739, 0.71304518,
0.9966133 ]])
- step_size(chain, draw)float640.3952 0.3952 ... 0.4748 0.4748
array([[0.3951527 , 0.3951527 , 0.3951527 , ..., 0.3951527 , 0.3951527 ,
0.3951527 ],
[0.36115452, 0.36115452, 0.36115452, ..., 0.36115452, 0.36115452,
0.36115452],
[0.50326903, 0.50326903, 0.50326903, ..., 0.50326903, 0.50326903,
0.50326903],
[0.47478076, 0.47478076, 0.47478076, ..., 0.47478076, 0.47478076,
0.47478076]])
- created_at :
- 2020-01-27T22:36:59.515483
- inference_library :
- pymc3
- inference_library_version :
- 3.8
In [9]:
idata_pymc3.log_likelihood
Out[9]:
xarray.Dataset- chain: 4
- developer: 5
- draw: 400
- chain(chain)int640 1 2 3
array([0, 1, 2, 3])
- draw(draw)int640 1 2 3 4 5 ... 395 396 397 398 399
array([ 0, 1, 2, ..., 397, 398, 399])
- developer(developer)object'Alice' 'Bob' ... 'Erika'
array(['Alice', 'Bob', 'Cole', 'Danielle', 'Erika'], dtype=object)
- slack_comments(chain, draw, developer)float64-8.241 -8.273 ... -16.4 -8.468
array([[[ -8.24119229, -8.27275698, -20.10760119, -17.16042219,
-8.27680667],
[ -8.43893608, -8.48465992, -17.21815491, -15.01186896,
-8.43307555],
[ -8.49736526, -8.59611498, -18.36120233, -16.37985789,
-8.47456743],
...,
[ -8.29094799, -8.33687506, -19.94276691, -16.34865995,
-8.43057918],
[ -8.40278849, -8.40572655, -16.17369137, -13.15887684,
-9.01294336],
[ -8.43789264, -8.45866622, -16.42954894, -13.9217099 ,
-8.59780431]],
[[ -8.43667003, -8.44606435, -16.03644324, -13.04294126,
-8.99845028],
[ -8.35530423, -8.36818086, -17.38369218, -13.94348527,
-8.92206379],
[ -8.41958404, -8.42037538, -15.65482765, -12.28638187,
-9.62564682],
...,
[ -8.15430668, -8.13143634, -20.44335297, -14.52219689,
-11.02584292],
[ -8.25244101, -8.27022351, -19.63120825, -15.12207825,
-9.05954993],
[ -8.43946219, -8.54095224, -19.31316609, -17.04183972,
-8.40190796]],
[[ -8.37664596, -8.39040164, -17.18954425, -13.71360176,
-8.99173694],
[ -8.28920555, -8.29171431, -18.07609741, -14.48300382,
-8.9014508 ],
[ -8.15964917, -8.15612156, -20.70179605, -16.09614375,
-8.98760587],
...,
[ -8.4259236 , -8.55901583, -20.08951889, -18.29390778,
-8.56023525],
[ -8.38093109, -8.37944987, -15.56132134, -11.80469706,
-10.47882297],
[ -8.34974304, -8.34676539, -16.37070387, -12.59980876,
-9.85122143]],
[[ -8.33084326, -8.40281627, -20.49925113, -17.0677698 ,
-8.31917803],
[ -8.25777161, -8.3204114 , -21.04322172, -17.59749809,
-8.27701815],
[ -8.34625955, -8.3443536 , -16.31645164, -12.38324148,
-10.13552651],
...,
[ -8.3609138 , -8.37106639, -17.15992655, -14.17063028,
-8.70456924],
[ -8.17295196, -8.17458243, -20.39740179, -15.70228308,
-9.11774422],
[ -8.47641212, -8.57058566, -18.33746722, -16.40306748,
-8.46792298]]])
- github_commits(chain, draw, developer)float64-4.118 -3.619 ... -2.576 -2.559
array([[[-4.11847356, -3.61913349, -6.23998027, -3.05736297,
-3.18339169],
[-3.67125237, -3.6691392 , -3.77962158, -3.99926387,
-5.23689158],
[-3.95247895, -3.54746476, -5.67891635, -3.10263684,
-3.27369091],
...,
[-2.82581397, -2.97162515, -4.12097966, -2.28055767,
-2.28090786],
[-2.73988179, -2.74151996, -4.26869758, -2.7351912 ,
-2.86300597],
[-2.94953215, -3.12029671, -3.21428865, -3.19365727,
-4.06080275]],
[[-3.17624124, -3.15124237, -4.0946857 , -3.17836392,
-3.14311463],
[-2.89032071, -2.91504343, -3.81191651, -2.91214347,
-3.17396042],
[-2.61915416, -2.62428507, -4.8226587 , -2.87332289,
-2.94342389],
...,
[-2.41641614, -2.08250537, -9.38770408, -2.07432615,
-4.01445785],
[-3.4781971 , -3.4828582 , -3.62544975, -3.42111601,
-3.43627111],
[-3.16598901, -2.9531802 , -4.5967921 , -2.92760857,
-4.05321854]],
[[-3.13400972, -3.11765821, -3.43995455, -2.60122882,
-2.77036998],
[-4.03979891, -4.00468204, -3.20809717, -2.28096257,
-2.32859327],
[-2.9117921 , -2.96504306, -3.60549386, -3.05037457,
-3.72278542],
...,
[-2.84982908, -2.94881704, -3.69153326, -2.48116505,
-2.48196433],
[-2.79117818, -2.85704807, -4.09631589, -2.37932806,
-2.42542944],
[-3.33854328, -3.44468648, -3.30499948, -2.43987366,
-2.39316051]],
[[-2.72961097, -2.62803101, -5.2834494 , -2.6238809 ,
-2.76350477],
[-3.86628702, -3.53410664, -3.40298776, -3.79646994,
-6.66564641],
[-3.92263428, -3.91300726, -3.28731609, -3.42313326,
-3.33178591],
...,
[-2.96403383, -3.13764195, -3.39389381, -2.59991667,
-2.73573763],
[-2.51291917, -2.58940626, -4.60509979, -2.41466529,
-2.40054774],
[-2.58187767, -2.62621164, -4.40984152, -2.57640043,
-2.55912451]]])
- created_at :
- 2020-01-27T22:37:00.122554
- inference_library :
- pymc3
- inference_library_version :
- 3.8
In [10]:
idata_pymc3.posterior_predictive
Out[10]:
xarray.Dataset- chain: 4
- developer: 5
- draw: 400
- chain(chain)int640 1 2 3
array([0, 1, 2, 3])
- draw(draw)int640 1 2 3 4 5 ... 395 396 397 398 399
array([ 0, 1, 2, ..., 397, 398, 399])
- developer(developer)object'Alice' 'Bob' ... 'Erika'
array(['Alice', 'Bob', 'Cole', 'Danielle', 'Erika'], dtype=object)
- slack_comments(chain, draw, developer)float646.758e+03 9.359e+03 ... 2.689e+04
array([[[ 6758.20107593, 9359.32381273, 8989.16457557, 19264.85125442,
28597.00621649],
[ 5922.99115335, 9480.83903632, 15654.3641546 , 17751.42362953,
28652.11060436],
[ 7835.94130901, 8434.12646419, 8368.19511593, 16966.67724994,
28459.01987462],
...,
[ 8301.931907 , 10211.89652582, 11072.85899136, 16459.05834074,
30031.56174545],
[ 8161.61554339, 15458.96403853, 13314.08461967, 20231.15447864,
30248.29941464],
[ 7328.10680985, 8769.20253545, 8934.92834002, 20792.02162123,
28943.26841822]],
[[ 8501.62673199, 10301.34310757, 11844.64623926, 19399.87985706,
31642.23509482],
[ 6634.40023629, 6356.45820678, 12977.75466452, 20381.82629607,
27891.44692092],
[ 5712.65533159, 13194.10518098, 13465.19929795, 20269.27518977,
29777.16680212],
...,
[ 6935.98341575, 10785.6526626 , 12347.96724621, 19990.99669045,
32611.34120678],
[ 8982.24431527, 11380.6087484 , 11075.42432976, 18822.64383171,
28721.20657001],
[ 5679.16616732, 9097.02283442, 10330.52453437, 19423.36555902,
24916.47904853]],
[[ 5523.61430428, 8182.61239811, 8175.87750135, 19071.33176908,
27457.70394603],
[ 8503.20872353, 12758.92506465, 10271.76956401, 18777.29056953,
31535.09640007],
[ 7385.79055082, 11480.56277465, 9652.29148086, 20125.09089213,
31265.37289228],
...,
[ 9346.35275189, 6581.81467968, 10724.80531469, 13866.13916387,
25487.13793426],
[ 8481.56029119, 11740.02410192, 13069.61463719, 19941.44909395,
30577.91446128],
[ 6917.64816966, 12175.30056678, 9816.96118712, 20822.917858 ,
30614.48355752]],
[[ 2825.89133452, 6804.57491013, 9462.8239833 , 18736.94452561,
26549.33167684],
[ 8648.66455765, 10270.26242749, 11521.11794697, 19940.99649291,
27022.53490645],
[ 7312.43191078, 10283.95424841, 13136.48677076, 18343.28865168,
26028.71278359],
...,
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- github_commits(chain, draw, developer)float642.162 21.91 32.69 ... 70.43 88.62
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- created_at :
- 2020-01-27T22:37:00.125917
- inference_library :
- pymc3
- inference_library_version :
- 3.8
In [11]:
idata_pymc3.observed_data
Out[11]:
xarray.Dataset- developer: 5
- developer(developer)object'Alice' 'Bob' ... 'Erika'
array(['Alice', 'Bob', 'Cole', 'Danielle', 'Erika'], dtype=object)
- slack_comments(developer)float647.5e+03 1.01e+04 ... 2.75e+04
array([ 7500., 10100., 18600., 25200., 27500.])
- github_commits(developer)float6425.0 32.0 49.0 66.0 96.0
array([25., 32., 49., 66., 96.])
- created_at :
- 2020-01-27T22:37:00.136543
- inference_library :
- pymc3
- inference_library_version :
- 3.8
In [12]:
idata_pymc3.constant_data
Out[12]:
xarray.Dataset- developer: 5
- developer(developer)object'Alice' 'Bob' ... 'Erika'
array(['Alice', 'Bob', 'Cole', 'Danielle', 'Erika'], dtype=object)
- time_since_joined(developer)float644.5 6.0 7.0 12.0 18.0
array([ 4.5, 6. , 7. , 12. , 18. ])
- created_at :
- 2020-01-27T22:37:00.137808
- inference_library :
- pymc3
- inference_library_version :
- 3.8
In [13]:
idata_pymc3.prior
Out[13]:
xarray.Dataset- chain: 1
- draw: 150
- chain(chain)int640
array([0])
- draw(draw)int640 1 2 3 4 5 ... 145 146 147 148 149
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- c0(chain, draw)float642.135 8.599 2.809 ... 5.763 -7.103
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464.55395243, 236.49226296, 86.85654363, 334.27895894,
281.16609105, 120.481762 , 596.78715426, 292.9086711 ,
224.1940135 , 20.8407661 , 235.65985307, 158.96100385,
27.14461422, 177.46275128, 250.8062993 , 6.71784792,
266.05461672, 234.92368838, 39.60407026, 603.3210229 ,
492.96677453, 433.59720963, 166.49457764, 408.53946048,
75.26601595, 302.62450885, 626.04386448, 437.78817987,
79.02220934, 25.60639164, 68.06282505, 286.39408996,
340.23401355, 30.58215066, 198.48595284, 70.36108505,
80.88363944, 341.26887539, 346.17023573, 210.08709645,
217.27165867, 298.6070234 , 311.41733674, 384.98746132,
334.92406324, 278.37659785, 118.77833039, 125.96874673,
275.79564008, 121.62080613, 156.20188375, 751.32508029,
9.23604036, 361.43334425, 131.61006904, 145.84172743,
36.8953005 , 127.26144803]])
- c_sigma(chain, draw)float641.223 3.522 0.4642 ... 11.57 3.553
array([[ 1.22288242, 3.52177051, 0.46418876, 2.83556405, 9.19033784,
5.06973944, 3.52176873, 5.34188805, 2.50066861, 10.0792298 ,
7.05368432, 2.8236868 , 9.67798122, 2.29045641, 10.85754571,
4.73993163, 10.25762458, 5.0975449 , 3.40716321, 12.0106394 ,
1.69596564, 7.0401133 , 5.51475558, 6.72170262, 8.58369956,
5.28702372, 0.37238539, 2.13258426, 11.14890632, 3.13277051,
7.44895483, 3.16033315, 0.50942229, 3.39408482, 1.72141798,
5.00988403, 2.79183503, 5.22671002, 4.14428493, 2.54943697,
1.0078325 , 1.84550021, 4.08505805, 2.44611783, 14.41595369,
7.01485955, 2.394333 , 0.46508681, 6.80360716, 1.23487565,
2.780502 , 1.18903398, 2.98888774, 2.87178392, 11.34392974,
2.14561424, 4.52951564, 4.26729775, 2.82855423, 11.60518545,
3.23140055, 6.35398408, 8.37997815, 5.28183324, 2.11935491,
2.02880854, 1.46893345, 5.3705682 , 8.01733528, 16.39729143,
4.2694474 , 0.60322704, 0.36513581, 10.40615156, 8.13881496,
9.14459563, 1.65744818, 4.92854531, 8.00161904, 1.06456732,
9.43851453, 9.77272135, 0.90701218, 1.43784307, 0.37635436,
9.12341376, 0.25743942, 1.61466584, 8.99924824, 0.29209885,
8.89984758, 4.38987134, 0.99458862, 4.23206713, 5.10350836,
2.73214447, 6.67390874, 2.10256921, 10.91001773, 2.96318235,
6.22078312, 6.34208617, 10.11836996, 5.07509994, 2.17094107,
3.53481404, 4.65513714, 7.52415867, 13.04754615, 1.70876137,
14.98184438, 4.22013446, 0.98008103, 5.93933025, 5.35291029,
3.34925548, 1.31070019, 2.08140542, 0.52935791, 1.54308953,
7.86524041, 1.87109161, 3.00546464, 10.88846951, 1.0064474 ,
2.4326204 , 12.07798049, 7.20319921, 6.99296112, 1.15043009,
5.12628494, 3.8169647 , 2.42048592, 3.78302462, 0.36618393,
4.58352435, 0.7190233 , 3.2937264 , 0.69941209, 3.27909063,
2.50473517, 1.96875606, 4.85985417, 6.12652268, 12.87658619,
7.41032989, 0.91677403, 2.64947695, 11.57481653, 3.55299734]])
- created_at :
- 2020-01-27T22:37:00.129095
- inference_library :
- pymc3
- inference_library_version :
- 3.8
In [14]:
idata_pymc3.predictions
Out[14]:
xarray.Dataset- candidate developer: 2
- chain: 1
- draw: 1600
- chain(chain)int640
array([0])
- draw(draw)int640 1 2 3 4 ... 1596 1597 1598 1599
array([ 0, 1, 2, ..., 1597, 1598, 1599])
- candidate developer(candidate developer)<U7'Francis' 'Gerard'
array(['Francis', 'Gerard'], dtype='<U7')
- slack_comments(chain, draw, candidate developer)float643.537e+03 1.095e+04 ... 8.227e+03
array([[[ 3536.93469044, 10946.35353244],
[ 3283.41196721, 8804.09750257],
[ 3519.01368551, 5445.588128 ],
...,
[ 6475.17499188, 11071.60735234],
[ 7801.48611356, 11197.82909995],
[ 8070.86988497, 8226.97278995]]])
- github_commits(chain, draw, candidate developer)float644.627 22.14 16.92 ... 29.06 35.58
array([[[ 4.62683704, 22.14496593],
[16.91542147, 33.60558345],
[17.91251374, 19.19516229],
...,
[23.85175217, 38.4484609 ],
[17.8965216 , 31.59160004],
[29.06334661, 35.58299212]]])
- created_at :
- 2020-01-27T22:37:07.840756
- inference_library :
- pymc3
- inference_library_version :
- 3.8
In [15]:
idata_pymc3.predictions_constant_data
Out[15]:
xarray.Dataset- candidate developer: 2
- candidate developer(candidate developer)<U7'Francis' 'Gerard'
array(['Francis', 'Gerard'], dtype='<U7')
- time_since_joined(candidate developer)float643.6 5.1
array([3.6, 5.1])
- created_at :
- 2020-01-27T22:37:07.842636
- inference_library :
- pymc3
- inference_library_version :
- 3.8
In [ ]:
Content source: mcmcplotlib/mcmcplotlib
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