Jalmari Tuominen (*1), Antti Roine (2), Taavi Saviauk (3), Anssi Seppo (3), Miika Pihlaja (3), Jani Ovaska (4), Satu-Liisa Pauniaho (4), Niku Oksala (1,5)
(1) Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
(2) Department of Surgery, Faculty of Medicine and Health Technology, Tampere University, Tampere Finland
(3) Unitary Healthcare Ltd, Tampere, Finland
(4) Emergency Division of Pirkanmaa Hospital District, Tampere University Hospital and Tampere University, Tampere, Finland.
(5) Centre for Vascular Surgery and Interventional Radiology, Tampere University Hospital, Tampere Finland
*Corresponding author. jalmari.tuominen@tuni.fi, +358 505 961192, Arvo Ylpön katu 34, 33520 Tampere
import pandas as pd
from pathlib import Path
import joblib
Independent variables:
models = []
for i in list(Path("./results/").glob("*sarima*.pkl")):
model = joblib.load(i)
target, model_name, features = i.stem.upper().split(":")
print(f"""
Target variable : {target}
Model name: {model_name}
Feature set: {features}
""")
print(model.summary())
print("\n \n \n")
Target variable : DPO
Model name: SARIMA
Feature set: NONE
SARIMAX Results
==============================================================================================
Dep. Variable: y No. Observations: 1480
Model: SARIMAX(2, 1, 1)x(1, 0, [1, 2], 7) Log Likelihood -5562.512
Date: Wed, 06 Jan 2021 AIC 11139.024
Time: 14:38:02 BIC 11176.118
Sample: 0 HQIC 11152.853
- 1480
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 0.0959 0.024 3.929 0.000 0.048 0.144
ar.L2 0.0254 0.023 1.115 0.265 -0.019 0.070
ma.L1 -0.8405 0.017 -50.893 0.000 -0.873 -0.808
ar.S.L7 0.9942 0.004 278.438 0.000 0.987 1.001
ma.S.L7 -0.8685 0.025 -35.421 0.000 -0.917 -0.820
ma.S.L14 -0.0709 0.024 -2.947 0.003 -0.118 -0.024
sigma2 107.6419 2.556 42.113 0.000 102.632 112.652
===================================================================================
Ljung-Box (L1) (Q): 0.00 Jarque-Bera (JB): 763.96
Prob(Q): 0.96 Prob(JB): 0.00
Heteroskedasticity (H): 2.13 Skew: 0.49
Prob(H) (two-sided): 0.00 Kurtosis: 6.38
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
Target variable : DPO
Model name: SARIMA
Feature set: CALVARS
SARIMAX Results
==============================================================================================
Dep. Variable: y No. Observations: 1480
Model: SARIMAX(2, 1, 1)x(1, 0, [1, 2], 7) Log Likelihood -5539.606
Date: Wed, 06 Jan 2021 AIC 11131.212
Time: 14:38:02 BIC 11268.989
Sample: 0 HQIC 11182.575
- 1480
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
x1 5.2063 7156.438 0.001 0.999 -1.4e+04 1.4e+04
x2 6.3831 7156.243 0.001 0.999 -1.4e+04 1.4e+04
x3 -4.6173 7156.632 -0.001 0.999 -1.4e+04 1.4e+04
x4 -8.1894 7156.517 -0.001 0.999 -1.4e+04 1.4e+04
x5 0.2047 7156.569 2.86e-05 1.000 -1.4e+04 1.4e+04
x6 0.8065 7156.533 0.000 1.000 -1.4e+04 1.4e+04
x7 0.2063 7156.555 2.88e-05 1.000 -1.4e+04 1.4e+04
x8 -9.8444 983.825 -0.010 0.992 -1938.106 1918.418
x9 4.6452 983.701 0.005 0.996 -1923.373 1932.663
x10 3.8620 983.767 0.004 0.997 -1924.286 1932.010
x11 -4.0468 983.779 -0.004 0.997 -1932.219 1924.125
x12 -1.8801 983.750 -0.002 0.998 -1929.996 1926.235
x13 4.7341 983.769 0.005 0.996 -1923.417 1932.885
x14 5.3250 983.809 0.005 0.996 -1922.905 1933.555
x15 -5.3897 983.800 -0.005 0.996 -1933.602 1922.823
x16 -13.5254 983.815 -0.014 0.989 -1941.767 1914.716
x17 2.3072 983.734 0.002 0.998 -1925.776 1930.390
x18 6.6913 983.710 0.007 0.995 -1921.344 1934.727
x19 7.1216 983.706 0.007 0.994 -1920.906 1935.149
ar.L1 0.0941 0.026 3.570 0.000 0.042 0.146
ar.L2 0.0262 0.025 1.063 0.288 -0.022 0.075
ma.L1 -0.8609 0.016 -52.700 0.000 -0.893 -0.829
ar.S.L7 0.9700 0.015 62.840 0.000 0.940 1.000
ma.S.L7 -0.8331 0.029 -28.312 0.000 -0.891 -0.775
ma.S.L14 -0.0812 0.025 -3.241 0.001 -0.130 -0.032
sigma2 104.7174 2.995 34.963 0.000 98.847 110.588
===================================================================================
Ljung-Box (L1) (Q): 0.00 Jarque-Bera (JB): 226.94
Prob(Q): 0.96 Prob(JB): 0.00
Heteroskedasticity (H): 2.65 Skew: 0.15
Prob(H) (two-sided): 0.00 Kurtosis: 4.90
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
[2] Covariance matrix is singular or near-singular, with condition number 3.78e+18. Standard errors may be unstable.
Target variable : TDA
Model name: SARIMA
Feature set: CALVARS
SARIMAX Results
==============================================================================
Dep. Variable: y No. Observations: 1480
Model: SARIMAX(1, 0, 1) Log Likelihood -6533.697
Date: Wed, 06 Jan 2021 AIC 13111.394
Time: 14:38:02 BIC 13227.989
Sample: 0 HQIC 13154.859
- 1480
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
x1 169.2070 1.616 104.734 0.000 166.041 172.374
x2 173.7345 1.525 113.927 0.000 170.746 176.723
x3 179.5748 1.528 117.486 0.000 176.579 182.571
x4 173.4969 1.365 127.082 0.000 170.821 176.173
x5 150.3921 1.685 89.228 0.000 147.089 153.696
x6 154.3008 1.548 99.672 0.000 151.267 157.335
x7 153.3391 1.514 101.257 0.000 150.371 156.307
x8 94.0609 4.110 22.884 0.000 86.005 102.117
x9 93.6065 4.117 22.737 0.000 85.537 101.676
x10 97.6429 3.652 26.734 0.000 90.484 104.802
x11 100.6548 3.397 29.628 0.000 93.996 107.313
x12 98.2829 2.959 33.219 0.000 92.484 104.082
x13 99.7160 4.098 24.331 0.000 91.684 107.748
x14 100.0284 3.399 29.430 0.000 93.367 106.690
x15 101.1497 3.978 25.426 0.000 93.352 108.947
x16 99.2426 4.128 24.043 0.000 91.152 107.333
x17 88.6450 3.993 22.202 0.000 80.820 96.470
x18 87.3396 4.318 20.225 0.000 78.876 95.804
x19 93.6782 4.138 22.637 0.000 85.567 101.789
ar.L1 0.8928 0.022 40.255 0.000 0.849 0.936
ma.L1 -0.6893 0.036 -18.930 0.000 -0.761 -0.618
sigma2 399.9821 14.328 27.917 0.000 371.901 428.064
===================================================================================
Ljung-Box (L1) (Q): 1.09 Jarque-Bera (JB): 12.46
Prob(Q): 0.30 Prob(JB): 0.00
Heteroskedasticity (H): 1.03 Skew: 0.18
Prob(H) (two-sided): 0.71 Kurtosis: 3.28
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
[2] Covariance matrix is singular or near-singular, with condition number 1.13e+17. Standard errors may be unstable.
Target variable : TDA
Model name: SARIMA
Feature set: NONE
SARIMAX Results
==============================================================================
Dep. Variable: y No. Observations: 1480
Model: SARIMAX(5, 0, 4) Log Likelihood -6588.170
Date: Wed, 06 Jan 2021 AIC 13198.340
Time: 14:38:02 BIC 13256.638
Sample: 0 HQIC 13220.072
- 1480
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
intercept 162.8397 25.372 6.418 0.000 113.111 212.568
ar.L1 0.6869 0.100 6.863 0.000 0.491 0.883
ar.L2 0.2222 0.097 2.301 0.021 0.033 0.411
ar.L3 -1.0036 0.087 -11.502 0.000 -1.175 -0.833
ar.L4 0.2649 0.117 2.265 0.024 0.036 0.494
ar.L5 0.2066 0.041 4.983 0.000 0.125 0.288
ma.L1 -0.4592 0.104 -4.403 0.000 -0.664 -0.255
ma.L2 -0.1291 0.072 -1.792 0.073 -0.270 0.012
ma.L3 0.9343 0.073 12.817 0.000 0.791 1.077
ma.L4 -0.1291 0.105 -1.232 0.218 -0.334 0.076
sigma2 418.2131 14.804 28.250 0.000 389.197 447.229
===================================================================================
Ljung-Box (L1) (Q): 0.12 Jarque-Bera (JB): 8.20
Prob(Q): 0.72 Prob(JB): 0.02
Heteroskedasticity (H): 1.11 Skew: 0.15
Prob(H) (two-sided): 0.25 Kurtosis: 3.19
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
models = []
for i in list(Path("./results/").glob("*glm*.pkl")):
model = joblib.load(i)
target, model_name, features = i.stem.upper().split(":")
print(
f"""
Target variable : {target}
Model name: {model_name}
Feature set: {features}
""")
print(model.summary())
print("\n \n \n")
Target variable : TDA
Model name: GLM
Feature set: CALVARS
Generalized Linear Model Regression Results
==============================================================================
Dep. Variable: tda No. Observations: 976
Model: GLM Df Residuals: 958
Model Family: Poisson Df Model: 17
Link Function: log Scale: 1.0000
Method: IRLS Log-Likelihood: -4382.9
Date: Wed, 06 Jan 2021 Deviance: 1534.4
Time: 14:40:35 Pearson chi2: 1.54e+03
No. Iterations: 4
Covariance Type: nonrobust
===========================================================================================
coef std err z P>|z| [0.025 0.975]
-------------------------------------------------------------------------------------------
Intercept 5.5650 0.009 596.188 0.000 5.547 5.583
C(weekday)[T.Monday] 0.0248 0.007 3.411 0.001 0.011 0.039
C(weekday)[T.Saturday] 0.0423 0.007 5.855 0.000 0.028 0.056
C(weekday)[T.Sunday] 0.0253 0.007 3.491 0.000 0.011 0.040
C(weekday)[T.Thursday] -0.0772 0.007 -10.348 0.000 -0.092 -0.063
C(weekday)[T.Tuesday] -0.0644 0.007 -8.659 0.000 -0.079 -0.050
C(weekday)[T.Wednesday] -0.0694 0.007 -9.318 0.000 -0.084 -0.055
C(month)[T.August] 0.0194 0.010 1.887 0.059 -0.001 0.040
C(month)[T.December] 0.0692 0.010 6.795 0.000 0.049 0.089
C(month)[T.February] 0.0437 0.011 3.834 0.000 0.021 0.066
C(month)[T.January] 0.0305 0.011 2.720 0.007 0.009 0.052
C(month)[T.July] 0.0281 0.010 2.735 0.006 0.008 0.048
C(month)[T.June] 0.0327 0.010 3.170 0.002 0.012 0.053
C(month)[T.March] 0.0252 0.011 2.244 0.025 0.003 0.047
C(month)[T.May] 0.0380 0.010 3.708 0.000 0.018 0.058
C(month)[T.November] 0.0024 0.010 0.228 0.819 -0.018 0.023
C(month)[T.October] -0.0047 0.010 -0.459 0.646 -0.025 0.015
C(month)[T.September] 0.0205 0.010 1.979 0.048 0.000 0.041
===========================================================================================
Target variable : DPO
Model name: GLM
Feature set: CALVARS
Generalized Linear Model Regression Results
==============================================================================
Dep. Variable: dpo No. Observations: 579
Model: GLM Df Residuals: 561
Model Family: Poisson Df Model: 17
Link Function: log Scale: 1.0000
Method: IRLS Log-Likelihood: -2213.0
Date: Wed, 06 Jan 2021 Deviance: 853.09
Time: 14:40:35 Pearson chi2: 852.
No. Iterations: 4
Covariance Type: nonrobust
===========================================================================================
coef std err z P>|z| [0.025 0.975]
-------------------------------------------------------------------------------------------
Intercept 4.4090 0.024 184.409 0.000 4.362 4.456
C(weekday)[T.Monday] 0.0335 0.017 1.992 0.046 0.001 0.066
C(weekday)[T.Saturday] -0.2097 0.018 -11.763 0.000 -0.245 -0.175
C(weekday)[T.Sunday] -0.2638 0.018 -14.567 0.000 -0.299 -0.228
C(weekday)[T.Thursday] -0.0924 0.017 -5.350 0.000 -0.126 -0.059
C(weekday)[T.Tuesday] -0.0686 0.017 -3.978 0.000 -0.102 -0.035
C(weekday)[T.Wednesday] -0.0758 0.017 -4.401 0.000 -0.110 -0.042
C(month)[T.August] -0.0163 0.026 -0.629 0.529 -0.067 0.034
C(month)[T.December] 0.1135 0.025 4.480 0.000 0.064 0.163
C(month)[T.February] 0.0935 0.030 3.147 0.002 0.035 0.152
C(month)[T.January] 0.0524 0.029 1.788 0.074 -0.005 0.110
C(month)[T.July] -0.0201 0.026 -0.778 0.437 -0.071 0.031
C(month)[T.June] -0.0329 0.026 -1.262 0.207 -0.084 0.018
C(month)[T.March] 0.0903 0.029 3.114 0.002 0.033 0.147
C(month)[T.May] 0.0017 0.030 0.056 0.956 -0.056 0.060
C(month)[T.November] 0.0225 0.026 0.872 0.383 -0.028 0.073
C(month)[T.October] 0.0529 0.026 2.069 0.039 0.003 0.103
C(month)[T.September] 0.0312 0.026 1.209 0.227 -0.019 0.082
===========================================================================================
models = []
for i in list(Path("./results/").glob("*prophet*.pkl")):
model = joblib.load(i)
target, model_name, features = i.stem.upper().split(":")
print(
f"""
Target variable : {target}
Model name: {model_name}
Feature set: {features}
""")
print(model.params)
print("\n \n \n")
Target variable : DPO
Model name: PROPHET
Feature set: NONE
{'k': array([[-0.03641323]]), 'm': array([[0.71485214]]), 'delta': array([[-4.69016236e-09, -3.87266768e-09, -1.71370298e-08,
-6.06375008e-09, 3.50205096e-09, -8.11007850e-09,
-3.68794097e-09, 2.66729923e-10, 2.32835579e-10,
2.87814268e-09, -4.50785622e-09, -1.98734354e-10,
2.27866760e-09, -3.42271879e-09, -3.87970698e-09,
-4.12532554e-09, -1.00669608e-08, 4.36934568e-09,
6.03926537e-09, -4.82558365e-07, -7.33165702e-09,
-5.65244196e-10, -1.84960969e-09, 5.06413027e-10,
7.49635150e-10]]), 'sigma_obs': array([[0.09951791]]), 'beta': array([[ 0.02930224, -0.01499343, 0.02098915, 0.01181057, -0.00394915,
0.00405219, -0.01871631, -0.00924216, -0.01661922, 0.0155221 ,
-0.00536672, -0.00245936, -0.00333017, 0.0229835 , -0.00683602,
-0.01892154, -0.00900497, 0.00469705, 0.01115159, 0.00221738,
-0.01632162, 0.02022643, 0.06871486, 0.00814305, -0.03123156,
-0.01940975]]), 'trend': array([[0.71485214, 0.7147521 , 0.71465207, 0.71455203, 0.71445199,
0.71435196, 0.71425192, 0.71415188, 0.71405185, 0.71395181,
0.71385177, 0.71375174, 0.7136517 , 0.71355167, 0.71345163,
0.71335159, 0.71325156, 0.71315152, 0.71305148, 0.71295145,
0.71285141, 0.71275137, 0.71265134, 0.7125513 , 0.71245127,
0.71235123, 0.71225119, 0.71215116, 0.71205112, 0.71195108,
0.71185105, 0.71175101, 0.71165097, 0.71155094, 0.7114509 ,
0.71135087, 0.71125083, 0.71115079, 0.71105076, 0.71095072,
0.71085068, 0.71075065, 0.71065061, 0.71055057, 0.71045054,
0.7103505 , 0.71025046, 0.71015043, 0.71005039, 0.70995036,
0.70985032, 0.70975028, 0.70965025, 0.70955021, 0.70945017,
0.70935014, 0.7092501 , 0.70915006, 0.70905003, 0.70894999,
0.70884995, 0.70874992, 0.70864988, 0.70854985, 0.70844981,
0.70834977, 0.70824974, 0.7081497 , 0.70804966, 0.70794963,
0.70784959, 0.70774955, 0.70764952, 0.70754948, 0.70744944,
0.70734941, 0.70724937, 0.70714934, 0.7070493 , 0.70694926,
0.70684923, 0.70674919, 0.70664915, 0.70654912, 0.70644908,
0.70634904, 0.70624901, 0.70614897, 0.70604893, 0.7059489 ,
0.70584886, 0.70574883, 0.70564879, 0.70554875, 0.70544872,
0.70534868, 0.70524864, 0.70514861, 0.70504857, 0.70494853,
0.7048485 , 0.70474846, 0.70464842, 0.70454839, 0.70444835,
0.70434831, 0.70424828, 0.70414824, 0.70404821, 0.70394817,
0.70384813, 0.7037481 , 0.70364806, 0.70354802, 0.70344799,
0.70334795, 0.70324791, 0.70314788, 0.70304784, 0.7029478 ,
0.70284777, 0.70274773, 0.7026477 , 0.70254766, 0.70244762,
0.70234759, 0.70224755, 0.70214751, 0.70204748, 0.70194744,
0.7018474 , 0.70174737, 0.70164733, 0.70154729, 0.70144726,
0.70134722, 0.70124718, 0.70114715, 0.70104711, 0.70094708,
0.70084704, 0.700747 , 0.70064697, 0.70054693, 0.70044689,
0.70034686, 0.70024682, 0.70014678, 0.70004675, 0.69994671,
0.69984667, 0.69974664, 0.6996466 , 0.69954656, 0.69944653,
0.69934649, 0.69924646, 0.69914642, 0.69904638, 0.69894635,
0.69884631, 0.69874627, 0.69864624, 0.6985462 , 0.69844616,
0.69834613, 0.69824609, 0.69814605, 0.69804602, 0.69794598,
0.69784595, 0.69774591, 0.69764587, 0.69754584, 0.6974458 ,
0.69734576, 0.69724573, 0.69714569, 0.69704565, 0.69694562,
0.69684558, 0.69674554, 0.69664551, 0.69654547, 0.69644543,
0.6963454 , 0.69624536, 0.69614532, 0.69604529, 0.69594525,
0.69584522, 0.69574518, 0.69564514, 0.69554511, 0.69544507,
0.69534503, 0.695245 , 0.69514496, 0.69504492, 0.69494489,
0.69484485, 0.69474481, 0.69464478, 0.69454474, 0.6944447 ,
0.69434467, 0.69424463, 0.69414459, 0.69404456, 0.69394452,
0.69384449, 0.69374445, 0.69364441, 0.69354438, 0.69344434,
0.6933443 , 0.69324427, 0.69314423, 0.69304419, 0.69294416,
0.69284412, 0.69274408, 0.69264405, 0.69254401, 0.69244397,
0.69234394, 0.6922439 , 0.69214386, 0.69204383, 0.69194379,
0.69184376, 0.69174372, 0.69164368, 0.69154365, 0.69144361,
0.69134357, 0.69124353, 0.69114349, 0.69104346, 0.69094342,
0.69084338, 0.69074334, 0.69064331, 0.69054327, 0.69044323,
0.69034319, 0.69024315, 0.69014312, 0.69004308, 0.68994304,
0.689843 , 0.68974297, 0.68964293, 0.68954289, 0.68944285,
0.68934281, 0.68924278, 0.68914274, 0.6890427 , 0.68894266,
0.68884262, 0.68874259, 0.68864255, 0.68854251, 0.68844247,
0.68834244, 0.6882424 , 0.68814236, 0.68804232, 0.68794228,
0.68784225, 0.68774221, 0.68764217, 0.68754213, 0.6874421 ,
0.68734206, 0.68724202, 0.68714198, 0.68704194, 0.68694191,
0.68684187, 0.68674183, 0.68664179, 0.68654175, 0.68644172,
0.68634168, 0.68624164, 0.6861416 , 0.68604157, 0.68594153,
0.68584149, 0.68574145, 0.68564141, 0.68554138, 0.68544134,
0.6853413 , 0.68524126, 0.68514122, 0.68504119, 0.68494115,
0.68484111, 0.68474107, 0.68464104, 0.684541 , 0.68444096,
0.68434092, 0.68424088, 0.68414085, 0.68404081, 0.68394077,
0.68384073, 0.6837407 , 0.68364066, 0.68354062, 0.68344058,
0.68334054, 0.68324051, 0.68314047, 0.68304043, 0.68294039,
0.68284035, 0.68274032, 0.68264028, 0.68254024, 0.6824402 ,
0.68234017, 0.68224013, 0.68214009, 0.68204005, 0.68194001,
0.68183998, 0.68173994, 0.6816399 , 0.68153986, 0.68143983,
0.68133979, 0.68123975, 0.68113971, 0.68103967, 0.68093964,
0.6808396 , 0.68073956, 0.68063952, 0.68053948, 0.68043945,
0.68033941, 0.68023937, 0.68013933, 0.6800393 , 0.67993926,
0.67983922, 0.67973918, 0.67963914, 0.67953911, 0.67943907,
0.67933903, 0.67923899, 0.67913895, 0.67903892, 0.67893888,
0.67883884, 0.6787388 , 0.67863877, 0.67853873, 0.67843869]])}
Target variable : TDA
Model name: PROPHET
Feature set: NONE
{'k': array([[-0.02120485]]), 'm': array([[0.77008795]]), 'delta': array([[ 1.43498226e-09, 1.64559204e-09, -3.42585011e-09,
8.60685587e-04, 1.35149737e-08, 9.30656276e-09,
-1.61629301e-08, 4.46207403e-10, 4.72533695e-09,
-8.28943666e-11, -2.77861754e-09, -6.29942200e-09,
-5.48235380e-09, 3.65347932e-08, -1.20944384e-09,
1.21633792e-08, -4.84979949e-09, -3.62981104e-09,
1.58032271e-09, 1.09447077e-08, -1.50859043e-09,
-6.33929078e-09, -3.30300159e-09, -2.54943722e-08,
1.14367623e-08]]), 'sigma_obs': array([[0.05624163]]), 'beta': array([[ 0.043581 , -0.04219342, 0.01198192, 0.00949269, -0.0106929 ,
-0.00160688, -0.01237742, -0.01422361, -0.02143703, 0.01789368,
-0.00859515, 0.00031261, 0.00238742, 0.02454944, -0.0035721 ,
-0.01242225, -0.00231845, 0.0067845 , 0.00679158, 0.00204918,
0.02644764, -0.01893359, 0.00644417, -0.00688974, -0.00823797,
-0.00839808]]), 'trend': array([[0.77008795, 0.7700297 , 0.76997144, 0.76991319, 0.76985493,
0.76979668, 0.76973842, 0.76968017, 0.76962191, 0.76956366,
0.7695054 , 0.76944715, 0.76938889, 0.76933064, 0.76927238,
0.76921413, 0.76915587, 0.76909762, 0.76903936, 0.76898111,
0.76892285, 0.7688646 , 0.76880634, 0.76874809, 0.76868983,
0.76863158, 0.76857332, 0.76851507, 0.76845681, 0.76839856,
0.7683403 , 0.76828205, 0.76822379, 0.76816554, 0.76810728,
0.76804903, 0.76799077, 0.76793251, 0.76787426, 0.767816 ,
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0.76746647, 0.76740822, 0.76734996, 0.76729407, 0.76723818,
0.76718229, 0.7671264 , 0.76707051, 0.76701462, 0.76695873,
0.76690284, 0.76684695, 0.76679106, 0.76673517, 0.76667928,
0.76662339, 0.7665675 , 0.76651161, 0.76645572, 0.76639982,
0.76634393, 0.76628804, 0.76623215, 0.76617626, 0.76612037,
0.76606448, 0.76600859, 0.7659527 , 0.76589681, 0.76584092,
0.76578503, 0.76572914, 0.76567325, 0.76561736, 0.76556147,
0.76550558, 0.76544969, 0.7653938 , 0.76533791, 0.76528201,
0.76522612, 0.76517023, 0.76511434, 0.76505845, 0.76500256,
0.76494667, 0.76489078, 0.76483489, 0.764779 , 0.76472311,
0.76466722, 0.76461133, 0.76455544, 0.76449955, 0.76444366,
0.76438777, 0.76433188, 0.76427599, 0.76422009, 0.7641642 ,
0.76410831, 0.76405242, 0.76399653, 0.76394064, 0.76388475,
0.76382886, 0.76377297, 0.76371708, 0.76366119, 0.7636053 ,
0.76354941, 0.76349352, 0.76343763, 0.76338174, 0.76332585,
0.76326996, 0.76321407, 0.76315817, 0.76310228, 0.76304639,
0.7629905 , 0.76293461, 0.76287872, 0.76282283, 0.76276694,
0.76271105, 0.76265516, 0.76259927, 0.76254338, 0.76248749,
0.7624316 , 0.76237571, 0.76231982, 0.76226393, 0.76220804,
0.76215215, 0.76209625, 0.76204036, 0.76198447, 0.76192858,
0.76187269, 0.7618168 , 0.76176091, 0.76170502, 0.76164913,
0.76159324, 0.76153735, 0.76148146, 0.76142557, 0.76136968,
0.76131379, 0.7612579 , 0.76120201, 0.76114612, 0.76109022,
0.76103433, 0.76097844, 0.76092255, 0.76086666, 0.76081077,
0.76075488, 0.76069899, 0.7606431 , 0.76058721, 0.76053132,
0.76047543, 0.76041954, 0.76036365, 0.76030776, 0.76025187,
0.76019598, 0.76014009, 0.7600842 , 0.76002831, 0.75997241,
0.75991652, 0.75986063, 0.75980474, 0.75974885, 0.75969296,
0.75963707, 0.75958118, 0.75952529, 0.7594694 , 0.75941351,
0.75935762, 0.75930173, 0.75924584, 0.75918995, 0.75913406,
0.75907817, 0.75902228, 0.75896639, 0.7589105 , 0.75885461,
0.75879872, 0.75874283, 0.75868693, 0.75863104, 0.75857515,
0.75851926, 0.75846337, 0.75840748, 0.75835159, 0.7582957 ,
0.75823981, 0.75818392, 0.75812803, 0.75807214, 0.75801625,
0.75796036, 0.75790447, 0.75784858, 0.75779269, 0.7577368 ,
0.75768091, 0.75762502, 0.75756913, 0.75751324, 0.75745734,
0.75740145, 0.75734556, 0.75728967, 0.75723378, 0.75717789,
0.757122 , 0.75706611, 0.75701022, 0.75695433, 0.75689844,
0.75684255, 0.75678666, 0.75673077, 0.75667488, 0.75661899,
0.7565631 , 0.75650721, 0.75645132, 0.75639543, 0.75633954,
0.75628365, 0.75622776, 0.75617186, 0.75611597, 0.75606008,
0.75600419, 0.7559483 , 0.75589241, 0.75583652, 0.75578063,
0.75572474, 0.75566885, 0.75561296, 0.75555707, 0.75550118,
0.75544529, 0.7553894 , 0.75533351, 0.75527762, 0.75522173,
0.75516584, 0.75510995, 0.75505406, 0.75499817, 0.75494228,
0.75488638, 0.75483049, 0.7547746 , 0.75471871, 0.75466282,
0.75460693, 0.75455104, 0.75449515, 0.75443926, 0.75438337,
0.75432748, 0.75427159, 0.7542157 , 0.75415981, 0.75410392,
0.75404803, 0.75399214, 0.75393625, 0.75388036, 0.75382447,
0.75376857, 0.75371268, 0.75365679, 0.7536009 , 0.75354501,
0.75348912, 0.75343323, 0.75337734, 0.75332145, 0.75326556,
0.75320967, 0.75315378, 0.75309789, 0.753042 , 0.75298611,
0.75293022, 0.75287433, 0.75281844, 0.75276255, 0.75270666,
0.75265077, 0.75259487, 0.75253898, 0.75248309, 0.7524272 ,
0.75237131, 0.75231542, 0.75225953, 0.75220364, 0.75214775,
0.75209186, 0.75203597, 0.75198008, 0.75192419, 0.7518683 ,
0.75181241, 0.75175652, 0.75170063, 0.75164474, 0.75158885,
0.75153296, 0.75147706, 0.75142117, 0.75136528, 0.75130939,
0.7512535 , 0.75119761, 0.75114172, 0.75108583, 0.75102994,
0.75097405, 0.75091816, 0.75086227, 0.75080638, 0.75075049,
0.7506946 , 0.75063871, 0.75058282, 0.75052693, 0.75047104,
0.75041515, 0.75035925, 0.75030336, 0.75024747, 0.75019158,
0.75013569, 0.7500798 , 0.75002391, 0.74996802, 0.74991213,
0.74985624, 0.74980035, 0.74974446, 0.74968857, 0.74963268]])}
Target variable : TDA
Model name: PROPHET
Feature set: CALVARS
{'k': array([[-0.00924766]]), 'm': array([[0.75764358]]), 'delta': array([[ 1.42499142e-09, -1.45906599e-09, 7.55061571e-09,
8.92332453e-09, -3.02442605e-10, -3.74833736e-09,
1.29907545e-06, -6.80366102e-10, 9.65824457e-09,
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7.88455007e-09, -4.37312927e-09, 1.01031692e-08,
8.86817968e-09, 5.82364297e-09, -1.66277028e-09,
-4.81745778e-10, -1.33023154e-08, 6.51965055e-09,
-2.54492192e-09, -5.87061254e-09, -5.41186510e-09,
2.82802834e-09]]), 'sigma_obs': array([[0.05427223]]), 'beta': array([[-1.42841911e-02, -6.15960683e-02, 3.46259482e-02,
4.25776752e-02, 3.37187784e-03, -2.24396773e-02,
-1.49647801e-02, -3.39139577e-02, -1.75634848e-02,
1.56333941e-02, -4.54666016e-03, 1.59329089e-03,
5.46928192e-03, 2.51745730e-02, -8.77823032e-03,
-3.28011335e-03, 5.07458490e-05, 1.57458598e-02,
1.37149432e-02, -1.93977241e-03, 2.08137864e-02,
-1.37687026e-02, 4.46657201e-03, -4.98270273e-03,
-6.28036273e-03, -6.58357867e-03, 8.91948328e-03,
7.48791609e-03, 1.18778326e-02, 7.15406233e-03,
-2.99286110e-03, -1.41596945e-03, -1.31052353e-03,
1.37575752e-01, -6.83205138e-02, 3.13086748e-02,
-1.82352164e-02, 8.83218288e-03, -5.57932828e-02,
-3.87384557e-02, 7.21293351e-02, 4.05061743e-02,
-3.97042046e-02, -9.68980015e-03, -3.01507054e-02]]), 'trend': array([[0.75764358, 0.75761817, 0.75759277, 0.75756736, 0.75754196,
0.75751655, 0.75749115, 0.75746574, 0.75744033, 0.75741493,
0.75738952, 0.75736412, 0.75733871, 0.75731331, 0.7572879 ,
0.7572625 , 0.75723709, 0.75721168, 0.75718628, 0.75716087,
0.75713547, 0.75711006, 0.75708466, 0.75705925, 0.75703384,
0.75700844, 0.75698303, 0.75695763, 0.75693222, 0.75690682,
0.75688141, 0.756856 , 0.7568306 , 0.75680519, 0.75677979,
0.75675438, 0.75672898, 0.75670357, 0.75667817, 0.75665276,
0.75662735, 0.75660195, 0.75657654, 0.75655114, 0.75652573,
0.75650033, 0.75647492, 0.75644951, 0.75642411, 0.7563987 ,
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0.75624627, 0.75622086, 0.75619546, 0.75617005, 0.75614465,
0.75611924, 0.75609384, 0.75606843, 0.75604303, 0.75601762,
0.75599221, 0.75596681, 0.7559414 , 0.755916 , 0.75589059,
0.75586519, 0.75583978, 0.75581437, 0.75578897, 0.75576356,
0.75573816, 0.75571275, 0.75568735, 0.75566194, 0.75563654,
0.75561113, 0.75558572, 0.75556032, 0.75553492, 0.75550952,
0.75548412, 0.75545871, 0.75543331, 0.75540791, 0.75538251,
0.75535711, 0.7553317 , 0.7553063 , 0.7552809 , 0.7552555 ,
0.7552301 , 0.75520469, 0.75517929, 0.75515389, 0.75512849,
0.75510309, 0.75507768, 0.75505228, 0.75502688, 0.75500148,
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0.75484906, 0.75482366, 0.75479826, 0.75477286, 0.75474746,
0.75472205, 0.75469665, 0.75467125, 0.75464585, 0.75462045,
0.75459504, 0.75456964, 0.75454424, 0.75451884, 0.75449344,
0.75446803, 0.75444263, 0.75441723, 0.75439183, 0.75436643,
0.75434102, 0.75431562, 0.75429022, 0.75426482, 0.75423942,
0.75421401, 0.75418861, 0.75416321, 0.75413781, 0.75411241,
0.754087 , 0.7540616 , 0.7540362 , 0.7540108 , 0.7539854 ,
0.75395999, 0.75393459, 0.75390919, 0.75388379, 0.75385839,
0.75383298, 0.75380758, 0.75378218, 0.75375678, 0.75373138,
0.75370597, 0.75368057, 0.75365517, 0.75362977, 0.75360436,
0.75357896, 0.75355356, 0.75352816, 0.75350276, 0.75347735,
0.75345195, 0.75342655, 0.75340115, 0.75337575, 0.75335034,
0.75332494, 0.75329954, 0.75327414, 0.75324874, 0.75322333,
0.75319793, 0.75317253, 0.75314713, 0.75312173, 0.75309632,
0.75307092, 0.75304552, 0.75302012, 0.75299472, 0.75296931,
0.75294391, 0.75291851, 0.75289311, 0.75286771, 0.7528423 ,
0.7528169 , 0.7527915 , 0.7527661 , 0.7527407 , 0.75271529,
0.75268989, 0.75266449, 0.75263909, 0.75261369, 0.75258828,
0.75256288, 0.75253748, 0.75251208, 0.75248668, 0.75246128,
0.75243587, 0.75241047, 0.75238507, 0.75235967, 0.75233427,
0.75230886, 0.75228346, 0.75225806, 0.75223266, 0.75220726,
0.75218185, 0.75215645, 0.75213105, 0.75210565, 0.75208025,
0.75205484, 0.75202944, 0.75200404, 0.75197864, 0.75195324,
0.75192783, 0.75190243, 0.75187703, 0.75185163, 0.75182623,
0.75180082, 0.75177542, 0.75175002, 0.75172462, 0.75169922,
0.75167381, 0.75164841, 0.75162301, 0.75159761, 0.75157221,
0.7515468 , 0.7515214 , 0.751496 , 0.7514706 , 0.7514452 ,
0.75141979, 0.75139439, 0.75136899, 0.75134359, 0.75131819,
0.75129278, 0.75126738, 0.75124198, 0.75121658, 0.75119118,
0.75116577, 0.75114037, 0.75111497, 0.75108957, 0.75106417,
0.75103876, 0.75101336, 0.75098796, 0.75096256, 0.75093716,
0.75091175, 0.75088635, 0.75086095, 0.75083555, 0.75081015,
0.75078474, 0.75075934, 0.75073394, 0.75070854, 0.75068314,
0.75065773, 0.75063233, 0.75060693, 0.75058153, 0.75055613,
0.75053072, 0.75050532, 0.75047992, 0.75045452, 0.75042912,
0.75040371, 0.75037831, 0.75035291, 0.75032751, 0.75030211,
0.7502767 , 0.7502513 , 0.7502259 , 0.7502005 , 0.7501751 ,
0.75014969, 0.75012429, 0.75009889, 0.75007349, 0.75004809,
0.75002268, 0.74999728, 0.74997188, 0.74994648, 0.74992108,
0.74989567, 0.74987027, 0.74984487, 0.74981947, 0.74979407,
0.74976866, 0.74974326, 0.74971786, 0.74969246, 0.74966706,
0.74964165, 0.74961625, 0.74959085, 0.74956545, 0.74954005,
0.74951464, 0.74948924, 0.74946384, 0.74943844, 0.74941304,
0.74938763, 0.74936223, 0.74933683, 0.74931143, 0.74928603,
0.74926062, 0.74923522, 0.74920982, 0.74918442, 0.74915902,
0.74913361, 0.74910821, 0.74908281, 0.74905741, 0.74903201,
0.7490066 , 0.7489812 , 0.7489558 , 0.7489304 , 0.748905 ,
0.74887959, 0.74885419, 0.74882879, 0.74880339, 0.74877799,
0.74875258, 0.74872718, 0.74870178, 0.74867638, 0.74865098,
0.74862557, 0.74860017, 0.74857477, 0.74854937, 0.74852396,
0.74849856, 0.74847316, 0.74844776, 0.74842236, 0.74839695]])}
Target variable : DPO
Model name: PROPHET
Feature set: CALVARS
{'k': array([[-0.02159868]]), 'm': array([[0.69273856]]), 'delta': array([[-3.63383880e-09, -5.41362633e-09, 2.97475446e-09,
3.93428294e-09, 2.44201384e-09, -5.00637649e-09,
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1.60333831e-09, 9.37900273e-09, 2.24516713e-09,
-3.11692601e-10, -1.83986876e-09, -9.03294699e-10,
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-1.97445126e-09, 1.56097271e-09, 2.49524715e-09,
-9.34556571e-10, 9.98325922e-10, 8.73115259e-10,
3.76095650e-10]]), 'sigma_obs': array([[0.09879627]]), 'beta': array([[-0.00346862, -0.04066104, 0.05255951, 0.04090483, 0.01664783,
-0.00906319, -0.0084119 , -0.05081401, -0.01577798, 0.01220966,
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0.00309242, -0.00286723, 0.01254908, 0.02002684, -0.0007309 ,
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0.01154535, 0.0080332 , 0.00664203, 0.11785889, -0.08556843,
0.07594081, -0.07324768, 0.00657578, -0.0400257 , -0.01354649,
0.08091436, -0.01334122, -0.01729966, 0.02195067, 0.00189202]]), 'trend': array([[0.69273856, 0.69267923, 0.69261989, 0.69256055, 0.69250122,
0.69244188, 0.69238254, 0.69232321, 0.69226387, 0.69220453,
0.69214519, 0.69208586, 0.69202652, 0.69196718, 0.69190785,
0.69184851, 0.69178917, 0.69172983, 0.6916705 , 0.69161116,
0.69155182, 0.69149249, 0.69143315, 0.69137381, 0.69131448,
0.69125514, 0.6911958 , 0.69113646, 0.69107713, 0.69101779,
0.69095845, 0.69089912, 0.69083978, 0.69078044, 0.6907211 ,
0.69066177, 0.69060243, 0.69054309, 0.69048376, 0.69042442,
0.69036508, 0.69030575, 0.69024641, 0.69018707, 0.69012773,
0.6900684 , 0.69000906, 0.68994972, 0.68989039, 0.68983105,
0.68977171, 0.68971237, 0.68965304, 0.6895937 , 0.68953436,
0.68947503, 0.68941569, 0.68935635, 0.68929702, 0.68923768,
0.68917834, 0.689119 , 0.68905967, 0.68900033, 0.68894099,
0.68888166, 0.68882232, 0.68876298, 0.68870364, 0.68864431,
0.68858497, 0.68852563, 0.6884663 , 0.68840696, 0.68834762,
0.68828829, 0.68822895, 0.68816961, 0.68811027, 0.68805094,
0.6879916 , 0.68793226, 0.68787293, 0.68781359, 0.68775425,
0.68769491, 0.68763558, 0.68757624, 0.6875169 , 0.68745757,
0.68739823, 0.68733889, 0.68727956, 0.68722022, 0.68716088,
0.68710154, 0.68704221, 0.68698287, 0.68692353, 0.6868642 ,
0.68680486, 0.68674552, 0.68668618, 0.68662685, 0.68656751,
0.68650817, 0.68644884, 0.6863895 , 0.68633016, 0.68627083,
0.68621149, 0.68615215, 0.68609281, 0.68603348, 0.68597414,
0.6859148 , 0.68585547, 0.68579613, 0.68573679, 0.68567745,
0.68561812, 0.68555878, 0.68549944, 0.68544011, 0.68538077,
0.68532143, 0.6852621 , 0.68520276, 0.68514342, 0.68508408,
0.68502475, 0.68496541, 0.68490607, 0.68484674, 0.6847874 ,
0.68472806, 0.68466873, 0.68460939, 0.68455005, 0.68449071,
0.68443138, 0.68437204, 0.6843127 , 0.68425337, 0.68419403,
0.68413469, 0.68407535, 0.68401602, 0.68395668, 0.68389734,
0.68383801, 0.68377867, 0.68371933, 0.68366 , 0.68360066,
0.68354132, 0.68348198, 0.68342265, 0.68336331, 0.68330397,
0.68324464, 0.6831853 , 0.68312596, 0.68306663, 0.68300729,
0.68294795, 0.68288861, 0.68282928, 0.68276994, 0.6827106 ,
0.68265127, 0.68259193, 0.68253259, 0.68247326, 0.68241392,
0.68235458, 0.68229524, 0.68223591, 0.68217657, 0.68211723,
0.6820579 , 0.68199856, 0.68193922, 0.68187988, 0.68182055,
0.68176121, 0.68170187, 0.68164254, 0.6815832 , 0.68152386,
0.68146453, 0.68140519, 0.68134585, 0.68128651, 0.68122718,
0.68116784, 0.6811085 , 0.68104917, 0.68098983, 0.68093049,
0.68087116, 0.68081182, 0.68075248, 0.68069314, 0.68063381,
0.68057447, 0.68051513, 0.6804558 , 0.68039646, 0.68033712,
0.68027778, 0.68021845, 0.68015911, 0.68009977, 0.68004044,
0.6799811 , 0.67992176, 0.67986243, 0.67980309, 0.67974375,
0.67968441, 0.67962508, 0.67956574, 0.6795064 , 0.67944707,
0.67938773, 0.67932839, 0.67926905, 0.67920972, 0.67915038,
0.67909104, 0.67903171, 0.67897237, 0.67891303, 0.6788537 ,
0.67879436, 0.67873502, 0.67867568, 0.67861635, 0.67855701,
0.67849767, 0.67843834, 0.678379 , 0.67831966, 0.67826033,
0.67820099, 0.67814165, 0.67808231, 0.67802298, 0.67796364,
0.6779043 , 0.67784497, 0.67778563, 0.67772629, 0.67766695,
0.67760762, 0.67754828, 0.67748894, 0.67742961, 0.67737027,
0.67731093, 0.6772516 , 0.67719226, 0.67713292, 0.67707358,
0.67701425, 0.67695491, 0.67689557, 0.67683624, 0.6767769 ,
0.67671756, 0.67665823, 0.67659889, 0.67653955, 0.67648021,
0.67642088, 0.67636154, 0.6763022 , 0.67624287, 0.67618353,
0.67612419, 0.67606486, 0.67600552, 0.67594618, 0.67588684,
0.67582751, 0.67576817, 0.67570883, 0.6756495 , 0.67559016,
0.67553082, 0.67547148, 0.67541215, 0.67535281, 0.67529347,
0.67523414, 0.6751748 , 0.67511546, 0.67505613, 0.67499679,
0.67493745, 0.67487811, 0.67481878, 0.67475944, 0.6747001 ,
0.67464077, 0.67458143, 0.67452209, 0.67446276, 0.67440342,
0.67434408, 0.67428474, 0.67422541, 0.67416607, 0.67410673,
0.6740474 , 0.67398806, 0.67392872, 0.67386939, 0.67381005,
0.67375071, 0.67369137, 0.67363204, 0.6735727 , 0.67351336,
0.67345403, 0.67339469, 0.67333535, 0.67327601, 0.67321668,
0.67315734, 0.673098 , 0.67303867, 0.67297933, 0.67291999,
0.67286066, 0.67280132, 0.67274198, 0.67268264, 0.67262331,
0.67256397, 0.67250463, 0.6724453 , 0.67238596, 0.67232662,
0.67226729, 0.67220795, 0.67214861, 0.67208927, 0.67202994,
0.6719706 , 0.67191126, 0.67185193, 0.67179259, 0.67173325,
0.67167391, 0.67161458, 0.67155524, 0.6714959 , 0.67143657,
0.67137723, 0.67131789, 0.67125856, 0.67119922, 0.67113988]])}
!conda list -n covariates
# packages in environment at /Users/jalmarituominen/miniconda3/envs/covariates: # # Name Version Build Channel absl-py 0.11.0 py37hf985489_0 conda-forge adjusttext 0.7.3 pypi_0 pypi altair 4.1.0 py_1 conda-forge ansiwrap 0.8.4 py_0 conda-forge appdirs 1.4.4 pyh9f0ad1d_0 conda-forge appnope 0.1.2 py37hf985489_0 conda-forge argon2-cffi 20.1.0 py37h4b544eb_2 conda-forge arviz 0.10.0 py_0 conda-forge astor 0.8.1 pyh9f0ad1d_0 conda-forge astroid 2.4.2 py37hc8dfbb8_1 conda-forge async_generator 1.10 py_0 conda-forge attrs 20.3.0 pyhd3deb0d_0 conda-forge backcall 0.2.0 pyh9f0ad1d_0 conda-forge backports 1.0 py_2 conda-forge backports.functools_lru_cache 1.6.1 py_0 conda-forge bioinfokit 0.9.6 pypi_0 pypi black 20.8b1 py_1 conda-forge bleach 3.2.1 pyh9f0ad1d_0 conda-forge brotlipy 0.7.0 py37h395d20d_1001 conda-forge bzip2 1.0.8 hc929b4f_4 conda-forge c-ares 1.17.1 hc929b4f_0 conda-forge ca-certificates 2020.12.5 h033912b_0 conda-forge cached-property 1.5.1 py_0 conda-forge cctools_osx-64 949.0.1 h2f0f38f_19 conda-forge certifi 2020.12.5 py37hf985489_0 conda-forge cffi 1.14.4 py37hc5b2277_1 conda-forge cftime 1.3.0 py37h8ba3199_0 conda-forge chardet 4.0.0 py37hf985489_0 conda-forge clang 10.0.1 default_hf57f61e_1 conda-forge clang_osx-64 10.0.1 h05bbb7f_10 conda-forge clangxx 10.0.1 default_hf57f61e_1 conda-forge clangxx_osx-64 10.0.1 h05bbb7f_10 conda-forge click 7.1.2 pyh9f0ad1d_0 conda-forge compiler-rt 10.0.1 he6db49b_0 conda-forge compiler-rt_osx-64 10.0.1 h033240e_0 conda-forge convertdate 2.3.0 pyhd8ed1ab_0 conda-forge cryptography 3.3.1 py37haf76d9e_0 conda-forge curl 7.71.1 hcb81553_8 conda-forge cycler 0.10.0 py_2 conda-forge cython 0.29.17 pypi_0 pypi dataclasses 0.7 pyhb2cacf7_7 conda-forge dbus 1.13.6 h0c50699_1 conda-forge decorator 4.4.2 py_0 conda-forge defusedxml 0.6.0 py_0 conda-forge entrypoints 0.3 pyhd8ed1ab_1003 conda-forge ephem 3.7.7.1 py37h60d8a13_1 conda-forge et_xmlfile 1.0.1 py_1001 conda-forge expat 2.2.9 hb1e8313_2 conda-forge factor_analyzer 0.3.2 pyh39e3cac_0 ets fbprophet 0.7.1 py37hdadc0f0_0 conda-forge fire 0.3.1 pyh9f0ad1d_0 conda-forge freetype 2.10.4 h3f75d11_0 conda-forge gast 0.4.0 pyh9f0ad1d_0 conda-forge gettext 0.19.8.1 h7937167_1005 conda-forge glib 2.66.4 h22858aa_1 conda-forge google-pasta 0.2.0 pyh8c360ce_0 conda-forge grpcio 1.34.0 py37hddd7880_0 conda-forge h5py 3.1.0 nompi_py37h6dbf366_100 conda-forge hdf4 4.2.13 h71d84a9_1004 conda-forge hdf5 1.10.6 nompi_h0f9794f_1112 conda-forge holidays 0.10.3 pyh9f0ad1d_0 conda-forge icu 64.2 h6de7cb9_1 conda-forge idna 2.10 pyh9f0ad1d_0 conda-forge importlib-metadata 3.3.0 py37hf985489_2 conda-forge importlib_metadata 3.3.0 hd8ed1ab_2 conda-forge iniconfig 1.1.1 pyh9f0ad1d_0 conda-forge ipykernel 5.4.2 py37he01cfaa_0 conda-forge ipython 7.19.0 py37he01cfaa_0 conda-forge ipython_genutils 0.2.0 py_1 conda-forge ipywidgets 7.5.1 pyh9f0ad1d_1 conda-forge isort 5.6.4 py_0 conda-forge jdcal 1.4.1 py_0 conda-forge jedi 0.15.2 py37_0 conda-forge jinja2 2.11.2 pyh9f0ad1d_0 conda-forge joblib 1.0.0 pyhd8ed1ab_0 conda-forge jpeg 9d hbcb3906_0 conda-forge json5 0.9.5 pyh9f0ad1d_0 conda-forge jsonschema 3.2.0 py_2 conda-forge jupyter 1.0.0 py_2 conda-forge jupyter_client 6.1.7 py_0 conda-forge jupyter_console 6.2.0 py_0 conda-forge jupyter_contrib_core 0.3.3 py_2 conda-forge jupyter_contrib_nbextensions 0.5.1 py37hc8dfbb8_1 conda-forge jupyter_core 4.7.0 py37hf985489_0 conda-forge jupyter_highlight_selected_word 0.2.0 py37hc8dfbb8_1002 conda-forge jupyter_latex_envs 1.4.6 py37hc8dfbb8_1001 conda-forge jupyter_nbextensions_configurator 0.4.1 py37hc8dfbb8_2 conda-forge jupyterlab 2.2.9 py_0 conda-forge jupyterlab_pygments 0.1.2 pyh9f0ad1d_0 conda-forge jupyterlab_server 1.2.0 py_0 conda-forge keras 2.3.1 py37_0 conda-forge keras-applications 1.0.8 py_1 conda-forge keras-preprocessing 1.1.0 py_0 conda-forge kiwisolver 1.3.1 py37h8ec247f_0 conda-forge korean_lunar_calendar 0.2.1 pyh9f0ad1d_0 conda-forge krb5 1.17.2 h60d9502_0 conda-forge lazy-object-proxy 1.4.3 py37h60d8a13_2 conda-forge lcms2 2.11 h11f7e16_1 conda-forge ld64_osx-64 530 hea264c1_17 conda-forge ldid 2.1.2 h7660a38_2 conda-forge libblas 3.9.0 3_openblas conda-forge libcblas 3.9.0 3_openblas conda-forge libclang-cpp10 10.0.1 default_hf57f61e_1 conda-forge libcurl 7.71.1 h9bf37e3_8 conda-forge libcxx 11.0.0 h4c3b8ed_1 conda-forge libedit 3.1.20191231 h0678c8f_2 conda-forge libev 4.33 haf1e3a3_1 conda-forge libffi 3.3 h046ec9c_2 conda-forge libgfortran 5.0.0 h7cc5361_13 conda-forge libgfortran5 9.3.0 h7cc5361_13 conda-forge libglib 2.66.4 h7424822_1 conda-forge libgpuarray 0.7.6 h1de35cc_1003 conda-forge libiconv 1.16 haf1e3a3_0 conda-forge liblapack 3.9.0 3_openblas conda-forge libllvm10 10.0.1 h009f743_1 conda-forge libllvm11 11.0.0 hf85e3d2_0 conda-forge libnetcdf 4.7.4 nompi_h9d8a93f_107 conda-forge libnghttp2 1.41.0 h7580e61_2 conda-forge libopenblas 0.3.12 openmp_h54245bb_1 conda-forge libpng 1.6.37 h7cec526_2 conda-forge libprotobuf 3.14.0 hfd3ada9_0 conda-forge libsodium 1.0.18 hbcb3906_1 conda-forge libssh2 1.9.0 h8a08a2b_5 conda-forge libtiff 4.1.0 hca7d577_6 conda-forge libuv 1.34.0 h0b31af3_0 conda-forge libwebp-base 1.1.0 hbcb3906_3 conda-forge libxml2 2.9.10 h53d96d6_0 conda-forge libxslt 1.1.33 h320ff13_0 conda-forge llvm-openmp 11.0.0 h73239a0_1 conda-forge llvm-tools 10.0.1 h1341992_1 conda-forge llvmlite 0.35.0 py37hd739bdf_0 conda-forge lunarcalendar 0.0.9 py_0 conda-forge lxml 4.6.2 py37h52eb7ab_0 conda-forge lz4-c 1.9.2 hb1e8313_3 conda-forge mako 1.1.3 pyh9f0ad1d_0 conda-forge markdown 3.3.3 pyh9f0ad1d_0 conda-forge markupsafe 1.1.1 py37h395d20d_2 conda-forge matplotlib 3.3.3 py37hf985489_0 conda-forge matplotlib-base 3.3.3 py37hdacc966_0 conda-forge matplotlib-venn 0.11.5 pypi_0 pypi mccabe 0.6.1 py_1 conda-forge missingno 0.4.2 py_1 conda-forge mistune 0.8.4 py37h4b544eb_1002 conda-forge mlxtend 0.18.0 pyhd3deb0d_0 conda-forge more-itertools 8.6.0 pyhd8ed1ab_0 conda-forge mypy_extensions 0.4.3 py37hf985489_2 conda-forge nano 2.9.8 h1b7e3c9_1001 conda-forge nbclient 0.5.1 py_0 conda-forge nbconvert 6.0.7 py37hf985489_3 conda-forge nbformat 5.0.8 py_0 conda-forge ncurses 6.2 h2e338ed_4 conda-forge nest-asyncio 1.4.3 pyhd8ed1ab_0 conda-forge netcdf4 1.5.5 nompi_py37h40892a9_100 conda-forge nodejs 13.13.0 h38d8c5a_0 conda-forge notebook 6.1.5 py37hf985489_0 conda-forge numba 0.52.0 py37h53bd85b_0 conda-forge numpy 1.19.4 py37hec87de9_2 conda-forge olefile 0.46 pyh9f0ad1d_1 conda-forge openpyxl 3.0.5 py_0 conda-forge openssl 1.1.1i h35c211d_0 conda-forge packaging 20.8 pyhd3deb0d_0 conda-forge pandas 1.1.5 py37h010c265_0 conda-forge pandoc 2.11.3 h35c211d_0 conda-forge pandocfilters 1.4.2 py_1 conda-forge papermill 2.2.2 pyhd8ed1ab_0 conda-forge parso 0.8.1 pyhd8ed1ab_0 conda-forge pathlib 1.0.1 py37hc8dfbb8_3 conda-forge pathspec 0.8.1 pyhd3deb0d_0 conda-forge patsy 0.5.1 py_0 conda-forge pcre 8.44 hb1e8313_0 conda-forge pexpect 4.8.0 pyh9f0ad1d_2 conda-forge pickleshare 0.7.5 py_1003 conda-forge pillow 8.0.1 py37h09f51b7_0 conda-forge pip 20.3.3 pyhd8ed1ab_0 conda-forge plotly 4.14.1 pyhd3deb0d_0 conda-forge pluggy 0.13.1 py37h2987424_3 conda-forge pmdarima 1.7.1 pypi_0 pypi prometheus_client 0.9.0 pyhd3deb0d_0 conda-forge prompt-toolkit 3.0.8 pyha770c72_0 conda-forge prompt_toolkit 3.0.8 hd8ed1ab_0 conda-forge protobuf 3.14.0 py37h54c7649_0 conda-forge ptyprocess 0.6.0 py_1001 conda-forge py 1.10.0 pyhd3deb0d_0 conda-forge pycparser 2.20 pyh9f0ad1d_2 conda-forge pygments 2.7.3 pyhd8ed1ab_0 conda-forge pygpu 0.7.6 py37h57c32b8_1002 conda-forge pylint 2.6.0 py37hc8dfbb8_1 conda-forge pymeeus 0.3.7 pyh9f0ad1d_0 conda-forge pyopenssl 20.0.1 pyhd8ed1ab_0 conda-forge pyparsing 2.4.7 pyh9f0ad1d_0 conda-forge pyprojroot 0.2.0 py_0 conda-forge pyqt 5.9.2 py37h2a560b1_4 conda-forge pyrsistent 0.17.3 py37h4b544eb_1 conda-forge pysocks 1.7.1 py37h2987424_2 conda-forge pystan 2.19.1.1 py37h6d0141a_2 conda-forge pytest 6.2.1 py37hf985489_0 conda-forge python 3.7.9 h6c3b2c9_0_cpython conda-forge python-dateutil 2.8.1 py_0 conda-forge python_abi 3.7 1_cp37m conda-forge pytz 2020.4 pyhd8ed1ab_0 conda-forge pyyaml 5.3.1 py37h395d20d_1 conda-forge pyzmq 20.0.0 py37h47fd9b3_1 conda-forge qt 5.9.7 h8cf7e54_3 conda-forge qtconsole 5.0.1 pyhd8ed1ab_0 conda-forge qtpy 1.9.0 py_0 conda-forge readline 8.0 h0678c8f_2 conda-forge regex 2020.11.13 py37h4b544eb_0 conda-forge requests 2.25.1 pyhd3deb0d_0 conda-forge retrying 1.3.3 py_2 conda-forge rope 0.18.0 pyh9f0ad1d_0 conda-forge scikit-learn 0.23.2 py37ha0250bc_3 conda-forge scipy 1.5.3 py37h821cff1_0 conda-forge seaborn 0.11.0 h694c41f_1 conda-forge seaborn-base 0.11.0 pyhd8ed1ab_1 conda-forge send2trash 1.5.0 py_0 conda-forge setuptools 49.6.0 py37h2987424_2 conda-forge sip 4.19.8 py37h0a44026_0 six 1.15.0 pyh9f0ad1d_0 conda-forge sqlite 3.34.0 h17101e1_0 conda-forge statsmodels 0.12.1 py37h8ba3199_1 conda-forge tabulate 0.8.7 pypi_0 pypi tapi 1100.0.11 h9ce4665_0 conda-forge tenacity 6.3.1 pyhd8ed1ab_0 conda-forge tensorboard 1.14.0 py37_0 conda-forge tensorflow 1.14.0 h3cdfc77_0 conda-forge tensorflow-base 1.14.0 py37hc8dfbb8_0 conda-forge tensorflow-estimator 1.14.0 py37h5ca1d4c_0 conda-forge termcolor 1.1.0 py_2 conda-forge terminado 0.9.1 py37hf985489_1 conda-forge testpath 0.4.4 py_0 conda-forge textwrap3 0.9.2 py_0 conda-forge theano 1.0.5 py37he8cc110_1 conda-forge threadpoolctl 2.1.0 pyh5ca1d4c_0 conda-forge tk 8.6.10 h0419947_1 conda-forge toml 0.10.2 pyhd8ed1ab_0 conda-forge toolz 0.11.1 py_0 conda-forge tornado 6.1 py37h4b544eb_0 conda-forge tqdm 4.54.1 pyhd8ed1ab_0 conda-forge traitlets 5.0.5 py_0 conda-forge typed-ast 1.4.1 py37h4b544eb_1 conda-forge typing_extensions 3.7.4.3 py_0 conda-forge urllib3 1.26.2 pyhd8ed1ab_0 conda-forge vega_datasets 0.9.0 pyhd3deb0d_0 conda-forge wcwidth 0.2.5 pyh9f0ad1d_2 conda-forge webencodings 0.5.1 py_1 conda-forge werkzeug 1.0.1 pyh9f0ad1d_0 conda-forge wheel 0.36.2 pyhd3deb0d_0 conda-forge widgetsnbextension 3.5.1 py37hf985489_4 conda-forge wrapt 1.11.2 py37h60d8a13_1 conda-forge xarray 0.16.2 pyhd8ed1ab_0 conda-forge xlrd 1.2.0 pyh9f0ad1d_1 conda-forge xz 5.2.5 haf1e3a3_1 conda-forge yaml 0.2.5 haf1e3a3_0 conda-forge zeromq 4.3.3 h74dc148_3 conda-forge zipp 3.4.0 py_0 conda-forge zlib 1.2.11 h7795811_1010 conda-forge zstd 1.4.5 h289c70a_2 conda-forge