inference.py 13.6 KB
Newer Older
Chris Jewell's avatar
Chris Jewell committed
1
"""MCMC Test Rig for COVID-19 UK model"""
2
3
4
# pylint: disable=E402

import argparse
Chris Jewell's avatar
Chris Jewell committed
5
import os
6
7
8

# Uncomment to block GPU use

Chris Jewell's avatar
Chris Jewell committed
9
# os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
10

11
from time import perf_counter
Chris Jewell's avatar
Chris Jewell committed
12

13
14
import tqdm
import yaml
Chris Jewell's avatar
Chris Jewell committed
15
import h5py
16
import numpy as np
17
18
import tensorflow as tf
import tensorflow_probability as tfp
Chris Jewell's avatar
Chris Jewell committed
19

Chris Jewell's avatar
Chris Jewell committed
20
from covid.impl.util import compute_state
21
from covid.impl.mcmc import UncalibratedLogRandomWalk, random_walk_mvnorm_fn
22
from covid.impl.event_time_mh import UncalibratedEventTimesUpdate
23
from covid.impl.occult_events_mh import UncalibratedOccultUpdate, TransitionTopology
24
25
from covid.impl.gibbs import DeterministicScanKernel, GibbsStep, flatten_results
from covid.impl.multi_scan_kernel import MultiScanKernel
26
from covid.data import read_phe_cases
Chris Jewell's avatar
Chris Jewell committed
27
from covid.cli_arg_parse import cli_args
28

29
import model_spec
30

31
32
33
34
35
if tf.test.gpu_device_name():
    print("Using GPU")
else:
    print("Using CPU")

36

37
38
tfd = tfp.distributions
tfb = tfp.bijectors
39
DTYPE = model_spec.DTYPE
40
41
42
43
44


if __name__ == "__main__":

    # Read in settings
Chris Jewell's avatar
Chris Jewell committed
45
    args = cli_args()
46

47
    with open(args.config, "r") as f:
48
49
        config = yaml.load(f, Loader=yaml.FullLoader)

50
51
52
53
54
55
56
    inference_period = [
        np.datetime64(x) for x in config["settings"]["inference_period"]
    ]

    covar_data = model_spec.read_covariates(
        config["data"], date_low=inference_period[0], date_high=inference_period[1],
    )
57
58
59

    # We load in cases and impute missing infections first, since this sets the
    # time epoch which we are analysing.
60
61
62
63
    cases = read_phe_cases(
        config["data"]["reported_cases"],
        date_low=inference_period[0],
        date_high=inference_period[1],
Chris Jewell's avatar
Chris Jewell committed
64
65
        date_type=config["data"]["case_date_type"],
        pillar=config["data"]["pillar"],
66
    ).astype(DTYPE)
67
68

    # Impute censored events, return cases
69
    events = model_spec.impute_censored_events(cases)
70
71
72
73
74
75
76
77
78

    # Initial conditions are calculated by calculating the state
    # at the beginning of the inference period
    #
    # Imputed censored events that pre-date the first I-R events
    # in the cases dataset are discarded.  They are only used to
    # to set up a sensible initial state.
    state = compute_state(
        initial_state=tf.concat(
79
            [covar_data["N"][:, tf.newaxis], tf.zeros_like(events[:, 0, :])], axis=-1
80
        ),
81
        events=events,
82
        stoichiometry=model_spec.STOICHIOMETRY,
83
    )
84
85
86
87
88
89
90
91
92
93
94
95
96
    start_time = state.shape[1] - cases.shape[1]
    initial_state = state[:, start_time, :]
    events = events[:, start_time:, :]
    num_metapop = covar_data["N"].shape[0]

    ########################################################
    # Build the model, and then construct the MCMC kernels #
    ########################################################
    model = model_spec.CovidUK(
        covariates=covar_data,
        initial_state=initial_state,
        initial_step=0,
        num_steps=events.shape[1],
97
    )
98

99
100
101
102
    # Full joint log posterior distribution
    # $\pi(\theta, \xi, y^{se}, y^{ei} | y^{ir})$
    def logp(theta, xi, events):
        return model.log_prob(
Chris Jewell's avatar
Chris Jewell committed
103
            dict(beta1=theta[0], beta2=theta[1], gamma=theta[2], xi=xi, seir=events,)
104
        )
105

106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
    # Build Metropolis within Gibbs sampler
    #
    # Kernels are:
    #     Q(\theta, \theta^\prime)
    #     Q(\xi, \xi^\prime)
    #     Q(Z^{se}, Z^{se\prime}) (partially-censored)
    #     Q(Z^{ei}, Z^{ei\prime}) (partially-censored)
    #     Q(Z^{se}, Z^{se\prime}) (occult)
    #     Q(Z^{ei}, Z^{ei\prime}) (occult)
    def make_theta_kernel(scale, bounded_convergence, name):
        return GibbsStep(
            0,
            tfp.mcmc.MetropolisHastings(
                inner_kernel=UncalibratedLogRandomWalk(
                    target_log_prob_fn=logp,
                    new_state_fn=random_walk_mvnorm_fn(scale, p_u=bounded_convergence),
                )
            ),
            name=name,
        )
126

127
128
129
130
131
132
133
134
135
    def make_xi_kernel(scale, bounded_convergence, name):
        return GibbsStep(
            1,
            tfp.mcmc.RandomWalkMetropolis(
                target_log_prob_fn=logp,
                new_state_fn=random_walk_mvnorm_fn(scale, p_u=bounded_convergence),
            ),
            name=name,
        )
Chris Jewell's avatar
Chris Jewell committed
136

137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
    def make_partially_observed_step(
        target_event_id, prev_event_id=None, next_event_id=None, name=None
    ):
        return GibbsStep(
            2,
            tfp.mcmc.MetropolisHastings(
                inner_kernel=UncalibratedEventTimesUpdate(
                    target_log_prob_fn=logp,
                    target_event_id=target_event_id,
                    prev_event_id=prev_event_id,
                    next_event_id=next_event_id,
                    initial_state=initial_state,
                    dmax=config["mcmc"]["dmax"],
                    mmax=config["mcmc"]["m"],
                    nmax=config["mcmc"]["nmax"],
                )
            ),
            name=name,
        )
156

157
158
159
160
161
162
163
164
    def make_occults_step(prev_event_id, target_event_id, next_event_id, name):
        return GibbsStep(
            2,
            tfp.mcmc.MetropolisHastings(
                inner_kernel=UncalibratedOccultUpdate(
                    target_log_prob_fn=logp,
                    topology=TransitionTopology(
                        prev_event_id, target_event_id, next_event_id
165
                    ),
166
167
168
169
170
171
172
                    cumulative_event_offset=initial_state,
                    nmax=config["mcmc"]["occult_nmax"],
                    t_range=(events.shape[1] - 21, events.shape[1]),
                    name=name,
                )
            ),
            name=name,
173
174
        )

175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
    # MCMC tracing functions
    def trace_results_fn(_, results):
        """Returns log_prob, accepted, q_ratio"""

        def is_accepted(result):
            if hasattr(result, "is_accepted"):
                return tf.cast(result.is_accepted, DTYPE)
            return is_accepted(result.inner_results)

        def f(result):
            log_prob = result.proposed_results.target_log_prob
            accepted = is_accepted(result)
            q_ratio = result.proposed_results.log_acceptance_correction
            if hasattr(result.proposed_results, "extra"):
                proposed = tf.cast(result.proposed_results.extra, log_prob.dtype)
                return tf.concat([[log_prob], [accepted], [q_ratio], proposed], axis=0)
            return tf.concat([[log_prob], [accepted], [q_ratio]], axis=0)

        def recurse(f, list_or_atom):
            if isinstance(list_or_atom, list):
                return [recurse(f, x) for x in list_or_atom]
            return f(list_or_atom)

        return recurse(f, results)

    # Build MCMC algorithm here.  This will be run in bursts for memory economy
    @tf.function(autograph=False, experimental_compile=True)
    def sample(n_samples, init_state, sigma_theta, sigma_xi):
        with tf.name_scope("main_mcmc_sample_loop"):

            init_state = init_state.copy()

            kernel = DeterministicScanKernel(
                [
                    make_theta_kernel(sigma_theta, 1.0, "theta_kernel"),
                    make_xi_kernel(sigma_xi, 1.0, "xi_kernel"),
                    MultiScanKernel(
                        config["mcmc"]["num_event_time_updates"],
                        DeterministicScanKernel(
                            [
                                make_partially_observed_step(0, None, 1, "se_events"),
                                make_partially_observed_step(1, 0, 2, "ei_events"),
                                make_occults_step(None, 0, 1, "se_occults"),
                                make_occults_step(0, 1, 2, "ei_occults"),
                            ]
                        ),
                    ),
                ],
                name="gibbs_kernel",
            )
225

226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
            samples, results = tfp.mcmc.sample_chain(
                n_samples, init_state, kernel=kernel, trace_fn=trace_results_fn
            )

            return samples, results

    ####################################
    # Construct bursted MCMC loop here #
    ####################################

    # MCMC Control
    NUM_BURSTS = config["mcmc"]["num_bursts"]
    NUM_BURST_SAMPLES = config["mcmc"]["num_burst_samples"]
    NUM_EVENT_TIME_UPDATES = config["mcmc"]["num_event_time_updates"]
    THIN_BURST_SAMPLES = NUM_BURST_SAMPLES // config["mcmc"]["thin"]
    NUM_SAVED_SAMPLES = THIN_BURST_SAMPLES * NUM_BURSTS

    # RNG stuff
    tf.random.set_seed(2)

    current_state = [
        np.array([0.85, 0.3, 0.25], dtype=DTYPE),
Chris Jewell's avatar
Chris Jewell committed
248
        np.zeros(model.model["xi"]().event_shape[-1], dtype=DTYPE),
249
250
251
252
253
        events,
    ]

    # Output Files
    posterior = h5py.File(
Chris Jewell's avatar
Chris Jewell committed
254
255
256
257
        os.path.join(
            os.path.expandvars(config["output"]["results_dir"]),
            config["output"]["posterior"],
        ),
258
259
260
261
        "w",
        rdcc_nbytes=1024 ** 2 * 400,
        rdcc_nslots=100000,
        libver="latest",
262
    )
263
264
265
    event_size = [NUM_SAVED_SAMPLES] + list(current_state[2].shape)

    posterior.create_dataset("initial_state", data=initial_state)
Chris Jewell's avatar
Chris Jewell committed
266
267
268
269

    # Ideally we insert the inference period into the posterior file
    # as this allows us to post-attribute it to the data.  Maybe better
    # to simply save the data into it as well.
270
    posterior.create_dataset("config", data=yaml.dump(config))
271
272
273
274
    theta_samples = posterior.create_dataset(
        "samples/theta",
        [NUM_SAVED_SAMPLES, current_state[0].shape[0]],
        dtype=np.float64,
275
    )
276
277
    xi_samples = posterior.create_dataset(
        "samples/xi", [NUM_SAVED_SAMPLES, current_state[1].shape[0]], dtype=np.float64,
278
    )
279
280
281
282
283
284
285
    event_samples = posterior.create_dataset(
        "samples/events",
        event_size,
        dtype=DTYPE,
        chunks=(32, 32, 32, 1),
        compression="szip",
        compression_opts=("nn", 16),
286
    )
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307

    output_results = [
        posterior.create_dataset("results/theta", (NUM_SAVED_SAMPLES, 3), dtype=DTYPE,),
        posterior.create_dataset("results/xi", (NUM_SAVED_SAMPLES, 3), dtype=DTYPE,),
        posterior.create_dataset(
            "results/move/S->E", (NUM_SAVED_SAMPLES, 3 + num_metapop), dtype=DTYPE,
        ),
        posterior.create_dataset(
            "results/move/E->I", (NUM_SAVED_SAMPLES, 3 + num_metapop), dtype=DTYPE,
        ),
        posterior.create_dataset(
            "results/occult/S->E", (NUM_SAVED_SAMPLES, 6), dtype=DTYPE
        ),
        posterior.create_dataset(
            "results/occult/E->I", (NUM_SAVED_SAMPLES, 6), dtype=DTYPE
        ),
    ]
    posterior.swmr_mode = True

    print("Initial logpi:", logp(*current_state))

Chris Jewell's avatar
Chris Jewell committed
308
309
310
311
312
313
314
315
    # theta_scale = tf.constant(
    #     [
    #         [1.12e-3, 1.67e-4, 1.61e-4],
    #         [1.67e-4, 7.41e-4, 4.68e-5],
    #         [1.61e-4, 4.68e-5, 1.28e-4],
    #     ],
    #     dtype=DTYPE,
    # )
316
317
    theta_scale = tf.constant(
        [
Chris Jewell's avatar
Chris Jewell committed
318
319
320
            [2.21e-05, -5.33e-05, 4.21e-06],
            [-5.33e-05, 3.66e-04, 1.45e-05],
            [4.21e-06, 1.45e-05, 1.52e-05],
321
322
        ],
        dtype=DTYPE,
Chris Jewell's avatar
Chris Jewell committed
323
324
        )
    theta_scale = theta_scale * 1.0 / theta_scale.shape[0]
325
326

    xi_scale = tf.eye(current_state[1].shape[0], dtype=DTYPE)
Chris Jewell's avatar
Chris Jewell committed
327
    xi_scale = xi_scale * 0.0002 / xi_scale.shape[0]
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385

    # We loop over successive calls to sample because we have to dump results
    #   to disc, or else end OOM (even on a 32GB system).
    # with tf.profiler.experimental.Profile("/tmp/tf_logdir"):
    for i in tqdm.tqdm(range(NUM_BURSTS), unit_scale=NUM_BURST_SAMPLES):
        samples, results = sample(
            NUM_BURST_SAMPLES,
            init_state=current_state,
            sigma_theta=theta_scale,
            sigma_xi=xi_scale,
        )
        current_state = [s[-1] for s in samples]
        s = slice(i * THIN_BURST_SAMPLES, i * THIN_BURST_SAMPLES + THIN_BURST_SAMPLES)
        idx = tf.constant(range(0, NUM_BURST_SAMPLES, config["mcmc"]["thin"]))
        theta_samples[s, ...] = tf.gather(samples[0], idx)
        xi_samples[s, ...] = tf.gather(samples[1], idx)
        # cov = np.cov(
        #     np.log(theta_samples[: (i * NUM_BURST_SAMPLES + NUM_BURST_SAMPLES), ...]),
        #     rowvar=False,
        # )
        print(current_state[0].numpy(), flush=True)
        # print(cov, flush=True)
        # if (i * NUM_BURST_SAMPLES) > 1000 and np.all(np.isfinite(cov)):
        #     theta_scale = 2.38 ** 2 * cov / 2.0

        start = perf_counter()
        event_samples[s, ...] = tf.gather(samples[2], idx)
        end = perf_counter()

        flat_results = flatten_results(results)
        for i, ro in enumerate(output_results):
            ro[s, ...] = tf.gather(flat_results[i], idx)

        posterior.flush()
        print("Storage time:", end - start, "seconds")
        print(
            "Acceptance theta:",
            tf.reduce_mean(tf.cast(flat_results[0][:, 1], tf.float32)),
        )
        print(
            "Acceptance xi:", tf.reduce_mean(tf.cast(flat_results[1][:, 1], tf.float32))
        )
        print(
            "Acceptance move S->E:",
            tf.reduce_mean(tf.cast(flat_results[2][:, 1], tf.float32)),
        )
        print(
            "Acceptance move E->I:",
            tf.reduce_mean(tf.cast(flat_results[3][:, 1], tf.float32)),
        )
        print(
            "Acceptance occult S->E:",
            tf.reduce_mean(tf.cast(flat_results[4][:, 1], tf.float32)),
        )
        print(
            "Acceptance occult E->I:",
            tf.reduce_mean(tf.cast(flat_results[5][:, 1], tf.float32)),
        )
Chris Jewell's avatar
Chris Jewell committed
386

387
388
389
390
391
392
    print(f"Acceptance theta: {output_results[0][:, 1].mean()}")
    print(f"Acceptance xi: {output_results[1][:, 1].mean()}")
    print(f"Acceptance move S->E: {output_results[2][:, 1].mean()}")
    print(f"Acceptance move E->I: {output_results[3][:, 1].mean()}")
    print(f"Acceptance occult S->E: {output_results[4][:, 1].mean()}")
    print(f"Acceptance occult E->I: {output_results[5][:, 1].mean()}")
Chris Jewell's avatar
Chris Jewell committed
393

394
    posterior.close()