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dr_constant_icml.yaml
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dr_constant_icml.yaml
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# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under a Microsoft Research License.
data:
devices: ['Pcat_Y81C76','RS100S32_Y81C76','RS100S34_Y81C76','R33S32_Y81C76','R33S34_Y81C76','R33S175_Y81C76']
pretty_devices: ['Pcat-Pcat','R100-S32','R100-S34','R33-S32','R33-S34','R33-S175']
groups:
aR: [0, 1, 1, 2, 2, 2] # LuxR RBS
aS: [0, 1, 2, 1, 2, 3] # LasR RBS
default_devices:
aR: 0
aS: 0
files: ['proc140916.csv','proc140930.csv','proc141006.csv','proc141021.csv','proc141023.csv','proc141028.csv']
signals: ["OD", "mRFP1", "EYFP", "ECFP"]
conditions: ["C6","C12"]
separate_conditions: True
params:
model: dr_constant
learning_boundaries: [250,1000]
learning_rates: [0.01,0.002,0.0002]
constant: { init_x: 0.002, init_rfp: 0.0, init_yfp: 0.0, init_cfp: 0.0, init_luxR: 0.0, init_lasR: 0.0 }
# SHARED PARAMETERS
shared:
data_precision_lognormal: { distribution: LogNormal, mu: 8.0, sigma: 2.0 }
data_precision_lognormal_x: { distribution: LogNormal, mu: 8.0, sigma: 2.0 }
auto_precision_lognormal: { distribution: LogNormal, mu: -5.0, sigma: 2.0 }
a_precision_lognormal: { distribution: LogNormal, mu: 1.0, sigma: 2.0 }
dfp_precision_lognormal: { distribution: LogNormal, mu: -2.0, sigma: 1.5 }
global_conditioned:
conditioning: { devices: true, treatments: false }
#aR: { distribution: LogNormal, mu: 1.0, sigma: 3.0}
#aS: { distribution: LogNormal, mu: 1.0, sigma: 3.0}
global:
prec_x: { distribution: data_precision_lognormal_x}
prec_rfp: { distribution: data_precision_lognormal}
prec_yfp: { distribution: data_precision_lognormal}
prec_cfp: { distribution: data_precision_lognormal}
e76: { distribution: LogNormal, mu: -3.0, sigma: 1.0}
e81: { distribution: LogNormal, mu: -3.0, sigma: 1.0}
KGR_76: { distribution: LogNormal, mu: 2.0, sigma: 3.0}
KGR_81: { distribution: LogNormal, mu: -2.0, sigma: 3.0}
KGS_76: { distribution: LogNormal, mu: -2.0, sigma: 3.0}
KGS_81: { distribution: LogNormal, mu: 2.0, sigma: 3.0}
KR6: { distribution: LogNormal, mu: -6.0, sigma: 3.0}
KR12: { distribution: LogNormal, mu: -12.0, sigma: 3.0}
KS6: { distribution: LogNormal, mu: -12.0, sigma: 3.0}
KS12: { distribution: LogNormal, mu: -6.0, sigma: 3.0}
nR: { distribution: LogNormal, mu: 1.0, sigma: 0.25}
nS: { distribution: LogNormal, mu: 1.0, sigma: 0.25}
aYFP: { distribution: LogNormal, mu: 0.0, sigma: 2.0}
aCFP: { distribution: LogNormal, mu: 0.0, sigma: 2.0}
dR: { distribution: LogNormal, mu: -2.0, sigma: 1.0}
dS: { distribution: LogNormal, mu: -2.0, sigma: 1.0}
drfp: { distribution: dfp_precision_lognormal}
dyfp: { distribution: dfp_precision_lognormal}
dcfp: { distribution: dfp_precision_lognormal}
autoY: { distribution: auto_precision_lognormal}
autoC: { distribution: auto_precision_lognormal}
# LOCAL PARAMETERS:
local:
conditioning: { devices: true, treatments: False }
r: {distribution: LogNormal, mu: 0.0, sigma: 0.25}
K: {distribution: LogNormal, mu: 1.0, prec: 2.0}
tlag: {distribution: LogNormal, mu: 0.0, prec: 2.0}
rc: {distribution: LogNormal, mu: 0.0, sigma: 2.0} #blows up on 'prec', why?