Optimize utilize intermediate results (#116)

* Add optimize hook, rename calibrate's

* Correction to total_possible_iterations

* removing hook's print

* Add types to rabbitmq messages.

* surround optimize hook in try

* defining progress_hook in exception case.

service/models/operations/calibrate.py

@@ -13,7 +13,7 @@
     fetch_and_convert_static_interventions,
     fetch_and_convert_dynamic_interventions,
 )
-from utils.rabbitmq import gen_rabbitmq_hook
+from utils.rabbitmq import gen_calibrate_rabbitmq_hook
 from utils.tds import fetch_dataset, fetch_model
 
 
@@ -77,7 +77,7 @@ def gen_pyciemss_args(self, job_id):
 
         # TODO: Test RabbitMQ
         try:
-            hook = gen_rabbitmq_hook(job_id)
+            hook = gen_calibrate_rabbitmq_hook(job_id)
         except (socket.gaierror, AMQPConnectionError):
             logging.warning(
                 "%s: Failed to connect to RabbitMQ. Unable to log progress", job_id

service/models/operations/ensemble_calibrate.py

@@ -11,7 +11,7 @@
 
 from models.base import Dataset, OperationRequest, Timespan, ModelConfig
 from models.converters import convert_to_solution_mapping
-from utils.rabbitmq import gen_rabbitmq_hook
+from utils.rabbitmq import gen_calibrate_rabbitmq_hook
 from utils.tds import fetch_dataset, fetch_model
 
 
@@ -52,7 +52,7 @@ def gen_pyciemss_args(self, job_id):
         dataset_path = fetch_dataset(self.dataset.dict(), job_id)
 
         try:
-            hook = gen_rabbitmq_hook(job_id)
+            hook = gen_calibrate_rabbitmq_hook(job_id)
         except (socket.gaierror, AMQPConnectionError):
             logging.warning(
                 "%s: Failed to connect to RabbitMQ. Unable to log progress", job_id

service/models/operations/optimize.py

@@ -2,6 +2,11 @@
 
 from typing import ClassVar, List, Optional
 from enum import Enum
+from utils.rabbitmq import OptimizeHook
+from pika.exceptions import AMQPConnectionError
+import socket
+import logging
+
 
 import numpy as np
 import torch
@@ -151,6 +156,20 @@ def gen_pyciemss_args(self, job_id):
         if step_size is not None and solver_method == "euler":
             solver_options["step_size"] = step_size
 
+        total_possible_iterations = (
+            extra_options.get("maxiter") + 1
+        ) * extra_options.get("maxfeval")
+        try:
+            progress_hook = OptimizeHook(job_id, total_possible_iterations)
+        except (socket.gaierror, AMQPConnectionError):
+            logging.warning(
+                "%s: Failed to connect to RabbitMQ. Unable to log progress", job_id
+            )
+
+            # Log progress hook when unable to connect - for testing purposes.
+            def progress_hook(current_results):
+                logging.info(f"Optimize current results: {current_results.tolist()}")
+
         return {
             "model_path_or_json": amr_path,
             "logging_step_size": self.logging_step_size,
@@ -173,6 +192,7 @@ def gen_pyciemss_args(self, job_id):
             "n_samples_ouu": n_samples_ouu,
             "solver_method": solver_method,
             "solver_options": solver_options,
+            "progress_hook": progress_hook,
             **extra_options,
         }
 

service/utils/rabbitmq.py

@@ -52,7 +52,7 @@ def callback(ch, method, properties, body):
     channel.start_consuming()
 
 
-def gen_rabbitmq_hook(job_id):
+def gen_calibrate_rabbitmq_hook(job_id):
     connection = pika.BlockingConnection(
         conn_config,
     )
@@ -63,8 +63,42 @@ def hook(progress, loss):
             exchange="",
             routing_key="simulation-status",
             body=json.dumps(
-                {"job_id": job_id, "progress": progress, "loss": str(loss)}
+                {
+                    "job_id": job_id,
+                    "type": "calibrate",
+                    "progress": progress,
+                    "loss": str(loss),
+                }
             ),
         )
 
     return hook
+
+
+class OptimizeHook:
+    def __init__(self, job_id, total_possible_iterations):
+        connection = pika.BlockingConnection(
+            conn_config,
+        )
+        self.channel = connection.channel()
+        self.job_id = job_id
+        self.type = "optimize"
+        self.result = []
+        self.step = 0
+        self.total_possible_iterations = total_possible_iterations
+
+    def __call__(self, current_results):
+        self.step += 1
+        self.channel.basic_publish(
+            exchange="",
+            routing_key="simulation-status",
+            body=json.dumps(
+                {
+                    "job_id": self.job_id,
+                    "progress": self.step,
+                    "type": self.type,
+                    "current_results": current_results.tolist(),
+                    "total_possible_iterations": self.total_possible_iterations,
+                }
+            ),
+        )