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Chris Jewell
covid19uk
Commits
3ef67e70
Commit
3ef67e70
authored
Mar 10, 2021
by
Chris Jewell
Browse files
Make NGM and prediction (almost) model agnostic
parent
6274bc57
Changes
2
Hide whitespace changes
Inline
Side-by-side
covid/tasks/next_generation_matrix.py
View file @
3ef67e70
...
...
@@ -22,36 +22,23 @@ def calc_posterior_ngm(samples, covar_data):
"""
def
r_fn
(
args
):
beta1_
,
beta2_
,
beta3_
,
sigma_
,
xi_
,
gamma0_
,
events_
=
args
par
=
tf
.
nest
.
pack_sequence_as
(
samples
,
args
)
t
=
events_
.
shape
[
-
2
]
-
1
state
=
compute_state
(
samples
[
"init_state"
],
events_
,
model_spec
.
STOICHIOMETRY
samples
[
"init_state"
],
par
[
'seir'
]
,
model_spec
.
STOICHIOMETRY
)
state
=
tf
.
gather
(
state
,
t
,
axis
=-
2
)
# State on final inference day
par
=
dict
(
beta1
=
beta1_
,
beta2
=
beta2_
,
beta3
=
beta3_
,
sigma
=
sigma_
,
gamma0
=
gamma0_
,
xi
=
xi_
,
)
del
par
[
'seir'
]
ngm_fn
=
model_spec
.
next_generation_matrix_fn
(
covar_data
,
par
)
ngm
=
ngm_fn
(
t
,
state
)
return
ngm
return
tf
.
vectorized_map
(
r_fn
,
elems
=
(
samples
[
"beta1"
],
samples
[
"beta2"
],
samples
[
"beta3"
],
samples
[
"sigma"
],
samples
[
"xi"
],
samples
[
"gamma0"
],
samples
[
"seir"
],
),
elems
=
tf
.
nest
.
flatten
(
samples
),
)
...
...
covid/tasks/predict.py
View file @
3ef67e70
...
...
@@ -9,7 +9,7 @@ from covid import model_spec
from
covid.util
import
copy_nc_attrs
from
gemlib.util
import
compute_state
@
tf
.
function
def
predicted_incidence
(
posterior_samples
,
covar_data
,
init_step
,
num_steps
):
"""Runs the simulation forward in time from `init_state` at time `init_time`
for `num_steps`.
...
...
@@ -21,18 +21,19 @@ def predicted_incidence(posterior_samples, covar_data, init_step, num_steps):
transitions
"""
@
tf
.
function
posterior_state
=
compute_state
(
posterior_samples
[
"init_state"
],
posterior_samples
[
"seir"
],
model_spec
.
STOICHIOMETRY
,
)
posterior_samples
[
'init_state_'
]
=
posterior_state
[...,
init_step
,
:]
del
posterior_samples
[
'seir'
]
def
sim_fn
(
args
):
beta1_
,
beta2_
,
sigma_
,
xi_
,
gamma0_
,
gamma1_
,
init_
=
args
par
=
dict
(
beta1
=
beta1_
,
beta2
=
beta2_
,
sigma
=
sigma_
,
xi
=
xi_
,
gamma0
=
gamma0_
,
gamma1
=
gamma1_
,
)
par
=
tf
.
nest
.
pack_sequence_as
(
posterior_samples
,
args
)
init_
=
par
[
'init_state_'
]
del
par
[
'init_state_'
]
model
=
model_spec
.
CovidUK
(
covar_data
,
initial_state
=
init_
,
...
...
@@ -42,24 +43,9 @@ def predicted_incidence(posterior_samples, covar_data, init_step, num_steps):
sim
=
model
.
sample
(
**
par
)
return
sim
[
"seir"
]
posterior_state
=
compute_state
(
posterior_samples
[
"init_state"
],
posterior_samples
[
"seir"
],
model_spec
.
STOICHIOMETRY
,
)
init_state
=
posterior_state
[...,
init_step
,
:]
events
=
tf
.
map_fn
(
sim_fn
,
elems
=
(
posterior_samples
[
"beta1"
],
posterior_samples
[
"beta2"
],
posterior_samples
[
"sigma"
],
posterior_samples
[
"xi"
],
posterior_samples
[
"gamma0"
],
posterior_samples
[
"gamma1"
],
init_state
,
),
elems
=
tf
.
nest
.
flatten
(
posterior_samples
),
fn_output_signature
=
(
tf
.
float64
),
)
return
init_state
,
events
...
...
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