def posterior_prediction(parameter_vector, parameter_names=parameter_names):
# Make predictions
Mixed_Model.set_parameters(parameter_vector)
dradf = Mixed_Model.doseresponse(times,
'TotalpSTAT',
'Ia',
dose_range,
parameters={'Ib': 0},
scale_factor=sf,
return_type='dataframe',
dataframe_labels='Alpha')
drbdf = Mixed_Model.doseresponse(times,
'TotalpSTAT',
'Ib',
dose_range,
parameters={'Ia': 0},
scale_factor=sf,
return_type='dataframe',
dataframe_labels='Beta')
posterior = IfnData('custom',
df=pd.concat((dradf, drbdf)),
conditions={
'Alpha': {
'Ib': 0
},
'Beta': {
'Ia': 0
}
})
posterior.drop_sigmas()
return posterior
def posterior_prediction(model, parameter_vector, parameter_names, sf,
test_times=[2.5, 5.0, 7.5, 10.0, 20.0, 60.0],
alpha_doses=np.logspace(-1, 5, 15),
beta_doses=np.logspace(-1, 5, 15)):
"""
Produce predictions for IFNa and IFNb using model with parameters given
as input to the function.
"""
# Make predictions
model.set_parameters(parameter_vector)
dradf = model.doseresponse(test_times, 'TotalpSTAT', 'Ia',
alpha_doses, parameters={'Ib': 0},
scale_factor=sf,
return_type='dataframe',
dataframe_labels='Alpha')
drbdf = model.doseresponse(test_times, 'TotalpSTAT', 'Ib',
beta_doses, parameters={'Ia': 0},
scale_factor=sf,
return_type='dataframe',
dataframe_labels='Beta')
posterior = IfnData('custom', df=pd.concat((dradf, drbdf)),
conditions={'Alpha': {'Ib': 0},
'Beta': {'Ia': 0}})
posterior.drop_sigmas()
return posterior
def posterior_prediction(parameter_vector, parameter_names=parameter_names):
# Make predictions
Mixed_Model.update_parameters(parameter_vector)
dradf = Mixed_Model.mixed_dose_response(times, 'TotalpSTAT', 'Ia', list(np.logspace(1, 5.2)),
parameters={'Ib': 0}, sf=scale_factor)
drbdf = Mixed_Model.mixed_dose_response(times, 'TotalpSTAT', 'Ib', list(np.logspace(-1, 4)),
parameters={'Ia': 0}, sf=scale_factor)
posterior = IfnData('custom', df=pd.concat((dradf, drbdf)), conditions={'Alpha': {'Ib': 0}, 'Beta': {'Ia': 0}})
posterior.drop_sigmas()
return posterior
def __posterior_prediction__(self, parameter_dict, test_times,
observable, dose_species, doses,
scale_factor, conditions, dataframe_label):
"""
Produce predictions for IFNa and IFNb using model with parameters given
as input to the function.
"""
# Update parameters
if self.param_dist_flag:
# find all distribution parameters
dist_param_names = []
for key in parameter_dict.keys():
if key.endswith('_mu*'):
dist_param_names.append(key[:-4])
# sample according to mu, std
dist_param_dict = {}
for pname in dist_param_names:
mu = np.log10(parameter_dict[pname + '_mu*'])
std = parameter_dict[pname + '_std*']
sample = 10 ** np.random.normal(loc=mu, scale=std)
dist_param_dict.update({pname: sample})
# remove distribution parameters
parameter_dict.pop(pname + '_mu*')
parameter_dict.pop(pname + '_std*')
# add sample to parameter_dict
parameter_dict.update(dist_param_dict)
parameter_dict.update(conditions)
# Make predictions
df = self.model.doseresponse(test_times, observable, dose_species,
doses, parameters=parameter_dict,
scale_factor=scale_factor,
return_type='dataframe',
dataframe_labels=dataframe_label)
posterior = IfnData('custom', df=df, conditions=conditions)
posterior.drop_sigmas()
return posterior
# Make predictions
times = [2.5, 5.0, 7.5, 10.0, 20.0, 60.0]
test_doses = list(logspace(-1, 5.2))
dradf = Mixed_Model.mixed_dose_response(times, 'TotalpSTAT', 'Ia', test_doses,
parameters={'Ib': 0}, sf=scale_factor)
drbdf = Mixed_Model.mixed_dose_response(times, 'TotalpSTAT', 'Ib', test_doses,
parameters={'Ia': 0}, sf=scale_factor)
dra60 = IfnData('custom', df=dradf, conditions={'Alpha': {'Ib': 0}})
drb60 = IfnData('custom', df=drbdf, conditions={'Beta': {'Ia': 0}})
# -------------------------------------------
# Get comparisons of beta to alpha response
# -------------------------------------------
dra60_noSigma = dra60.drop_sigmas(in_place=False)
drb60_noSigma = drb60.drop_sigmas(in_place=False)
temp1 = drb60_noSigma.data_set.loc['Beta'] / dra60_noSigma.data_set.loc['Alpha']
temp1 = pd.concat([temp1], keys=['Ratio'], names=['Dose_Species'])
ratio_response = IfnData('custom', df=temp1)
ratio_response.add_sigmas()
temp2 = drb60_noSigma.data_set.loc['Beta'] - dra60_noSigma.data_set.loc['Alpha']
temp2 = pd.concat([temp2], keys=['Difference'], names=['Dose_Species'])
difference_response = IfnData('custom', df=temp2)
difference_response.add_sigmas()
# ---------------------------------------------
# Signal integration requires a bit more work
# ---------------------------------------------
def score_params(params):
# --------------------
# Set up Model
# --------------------
Mixed_Model, DR_method = lm.load_model()
scale_factor, DR_KWARGS = lm.SCALE_FACTOR, lm.DR_KWARGS
Mixed_Model.set_parameters(params)
# Make predictions
times = [2.5, 5.0, 7.5, 10.0, 20.0, 60.0]
alpha_doses = [10, 100, 300, 1000, 3000, 10000, 100000]
beta_doses = [0.2, 6, 20, 60, 200, 600, 2000]
dra60 = DR_method(times,
'TotalpSTAT',
'Ia',
alpha_doses,
parameters={'Ib': 0},
sf=scale_factor,
**DR_KWARGS)
drb60 = DR_method(times,
'TotalpSTAT',
'Ib',
beta_doses,
parameters={'Ia': 0},
sf=scale_factor,
**DR_KWARGS)
sim_df = IfnData('custom',
df=pd.concat((dra60.data_set, drb60.data_set)),
conditions={
'Alpha': {
'Ib': 0
},
'Beta': {
'Ia': 0
}
})
# --------------------
# Set up Data
# --------------------
newdata_1 = IfnData("20190108_pSTAT1_IFN_Bcell")
newdata_2 = IfnData("20190119_pSTAT1_IFN_Bcell")
newdata_3 = IfnData("20190121_pSTAT1_IFN_Bcell")
newdata_4 = IfnData("20190214_pSTAT1_IFN_Bcell")
# Aligned data, to get scale factors for each data set
alignment = DataAlignment()
alignment.add_data([newdata_4, newdata_3, newdata_2, newdata_1])
alignment.align()
alignment.get_scaled_data()
mean_data = alignment.summarize_data()
# --------------------
# Score model
# --------------------
sim_df.drop_sigmas()
mean_data.drop_sigmas()
# rmse = RMSE(mean_data.data_set.values, sim_df.data_set.values)
mae = MAE(mean_data.data_set.values, sim_df.data_set.values)
return mae
'Beta': {
'Ia': 0
}
})
# --------------------
# Set up Data
# --------------------
newdata_1 = IfnData("20190108_pSTAT1_IFN_Bcell")
newdata_2 = IfnData("20190119_pSTAT1_IFN_Bcell")
newdata_3 = IfnData("20190121_pSTAT1_IFN_Bcell")
newdata_4 = IfnData("20190214_pSTAT1_IFN_Bcell")
# Aligned data, to get scale factors for each data set
alignment = DataAlignment()
alignment.add_data([newdata_4, newdata_3, newdata_2, newdata_1])
alignment.align()
alignment.get_scaled_data()
mean_data = alignment.summarize_data()
# --------------------
# Score model
# --------------------
sim_df.drop_sigmas()
mean_data.drop_sigmas()
rmse = RMSE(mean_data.data_set.values, sim_df.data_set.values)
mae = MAE(mean_data.data_set.values, sim_df.data_set.values)
print("RMSE = ", rmse)
print("MAE = ", mae)
# Perform simulations
# --------------------
times = [2.5, 5.0, 10.0, 20.0, 30.0, 60.0]
doses = [10, 100, 300, 1000, 3000, 10000, 100000]
df = testModel.doseresponse(times,
'TotalpSTAT',
'Ia',
doses,
parameters={'Ib': 0},
scale_factor=4.1,
return_type='dataframe',
dataframe_labels='Alpha')
print(df)
DR_Simulation = IfnData('custom', df=df, conditions={'Alpha': {'Ib': 0}})
DR_Simulation.drop_sigmas()
# -----------------------------------------------------------
# Load several sets of experimental data and align them
# -----------------------------------------------------------
# These datasets are already prepared for use in /ifndatabase
expdata_1 = IfnData("20190214_pSTAT1_IFN_Bcell")
expdata_2 = IfnData("20190121_pSTAT1_IFN_Bcell")
expdata_3 = IfnData("20190119_pSTAT1_IFN_Bcell")
expdata_4 = IfnData("20190108_pSTAT1_IFN_Bcell")
# Aligned data, to get scale factors for each data set
alignment = DataAlignment()
alignment.add_data([expdata_1, expdata_2, expdata_3, expdata_4])
alignment.align()
