Merge branch 'develop' into feature/QUANT-1196-solve-all-the-things

.docker-compose/Dockerfile

@@ -9,12 +9,13 @@ RUN cd Cbc-2.9.8 && \
     ./configure && \
     make && \
     make install
-RUN python -m pip install fastapi[all]
+RUN python -m pip install pydantic
+RUN python -m pip install pySqsListener
 
 # Bundle app source
 COPY . /app
 
 WORKDIR /app/app
 
-# CMD tail -f /dev/null
-CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "80"]
+# CMD [ "python", "main.py" ]
+CMD tail -f /dev/null

Dockerfile

@@ -9,13 +9,13 @@ RUN cd Cbc-2.9.8 && \
     ./configure && \
     make && \
     make install
-RUN python -m pip install fastapi[all]
-
+RUN python -m pip install pydantic
+RUN python -m pip install pySqsListener
 
 # Bundle app source
 COPY . /app
 
 WORKDIR /app/app
 
-# CMD tail -f /dev/null
-CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "80"]
+# CMD [ "python", "main.py" ]
+CMD tail -f /dev/null

app/helpers/aws_helper.py

@@ -0,0 +1,37 @@
+import boto3
+import os
+import json
+
+session = boto3.Session(
+  aws_access_key_id=os.environ['SOLVER_AWS_ACCESS_KEY_ID'],
+  aws_secret_access_key=os.environ['SOLVER_AWS_SECRET_ACCESS_KEY']
+)
+
+s3 = session.resource('s3', region_name=os.environ['SOLVER_AWS_REGION'])
+sqs = session.client('sqs', region_name=os.environ['SOLVER_AWS_REGION'])
+
+def get_key_from_message(message):
+  body = json.loads(message['Body'])
+  return body['Records'][0]['s3']['object']['key']
+
+def get_object(key, bucket):
+  return s3.Object(
+    bucket_name=bucket,
+    key=key
+  ).get()['Body'].read()
+
+def file_stream_upload(buffer, name, bucket):
+  return s3.Bucket(bucket).upload_fileobj(buffer, name)
+
+def receive_message(queue, message_num=1, wait_time=1):
+  return sqs.receive_message(
+    QueueUrl=queue,
+    MaxNumberOfMessages=message_num,
+    WaitTimeSeconds=wait_time
+  )
+
+def delete_message(queue, receipt):
+  return sqs.delete_message(
+    QueueUrl=queue,
+    ReceiptHandle=receipt
+  )

app/helpers/csv_helper.py

@@ -0,0 +1,5 @@
+import csv
+import io
+
+def file_stream_reader(f):
+  return csv.reader(io.StringIO(f.read().decode('ascii')))

app/helpers/service_helper.py

@@ -0,0 +1,51 @@
+import csv
+import io
+import re
+
+def items_csv_to_dict(items_csv_reader):
+  items = []
+  headers = []
+
+  # get headers and items
+  for key, row in enumerate(items_csv_reader):
+    if key == 0:
+      headers = row
+    else:
+      item = { 'attributes': [] }
+
+      for key, col in enumerate(headers):
+        if key == 0:
+          item[col] = row[key]
+        elif col == 'b_param':
+          item[col] = row[key]
+        elif key > 1:
+          item['attributes'].append({
+            'id': col,
+            'value': row[key],
+            'type': 'metadata'
+          })
+
+      items.append(item)
+
+  return items
+
+def solution_to_file(buffer, total_form_items, forms):
+  wr = csv.writer(buffer, dialect='excel', delimiter=',')
+
+  # write header row for first row utilizing the total items all forms will have
+  # and the cut score as the last item
+  header = [x + 1 for x in range(total_form_items)] + ['cut score']
+  wr.writerow(header)
+
+  # add each form as row to processed csv
+  for form in forms:
+    # provide generated items and cut score
+    row = form.items + [form.cut_score]
+    wr.writerow(row)
+
+  buff2 = io.BytesIO(buffer.getvalue().encode())
+
+  return buff2
+
+def key_to_uuid(key):
+  return re.split("_", key)[0]

app/helpers/tar_helper.py

@@ -0,0 +1,9 @@
+import io
+import tarfile
+
+def raw_to_tar(raw_object):
+  tarball = io.BytesIO(raw_object)
+  return tarfile.open(fileobj=tarball, mode='r:gz')
+
+def extract_file_from_tar(tar, file_name):
+  return tar.extractfile(tar.getmember(file_name))

app/main.py

@@ -1,39 +1,27 @@
-from fastapi import FastAPI, __version__
-from pydantic import BaseModel
-from typing import Set, List, Optional, Dict
-from random import randint
+import os
+import sys
 
-from models.solver_run import SolverRun
-from models.solution import Solution
-from models.form import Form
+from services.loft_service import LoftService
+from helpers import aws_helper
+from sqs_listener import SqsListener
+from sqs_listener.daemon import Daemon
 
-app = FastAPI()
+print("Starting Solver Service (v0.3.1)...")
 
-@app.get("/")
-async def root():
-  return {"message": "Welcome to Measures LOFT solver service. v0.1"}
+# # listen to the solver queue
+while True:
+  msg = aws_helper.receive_message(os.environ['SOLVER_SQS_INGEST_QUEUE'])
 
-@app.get("/healthcheck")
-async def health():
-  content = {
-    "maintainer": "Meazure Horizon Team",
-    "git_repo": "https://github.com/yardstick/measure-solver",
-    "server": "OK",
-    "fastapi version": __version__,
-    "app version": "0.1.1"
-  }
-  return content
+  # get the item from the queue
+  for message in msg.get("Messages", []):
+    # for now the solver service only supports Loft types
+    # this is here to allow us to create an extensible way to
+    # gather/manage/process data based on the particular needs
+    service = LoftService(message)
+    service.process()
 
-@app.get('/readycheck')
-async def ready():
-  return 'OK'  # just means we're on air
+    # delete message once process is complete
+    response = aws_helper.delete_message(os.environ['SOLVER_SQS_INGEST_QUEUE'], message['ReceiptHandle'])
 
-@app.post('/solve/')
-async def solve(solver_run: SolverRun):
-  response = Solution(
-    response_id=randint(100,5000),
-    forms=[Form(
-      items=[item.id for item in solver_run.items]
-    )]
-  )
-  return response
+    # probably convert to logging, but maybe keep this to some extent
+    print("MESSAGE PROCESSED: ", message['MessageId'])

app/models/attribute.py

@@ -2,6 +2,6 @@ from pydantic import BaseModel
 from typing import Optional
 
 class Attribute(BaseModel):
-  value: str
+  value: Optional[str]
   type: Optional[str]
   id: str

app/models/form.py

@@ -5,3 +5,4 @@ from models.item import Item
 
 class Form(BaseModel):
   items: List[int]
+  cut_score: float

app/models/item.py

@@ -6,3 +6,4 @@ from models.attribute import Attribute
 class Item(BaseModel):
   id: int
   attributes: List[Attribute]
+  b_param: int

app/models/objective_function.py

@@ -1,5 +1,5 @@
 from pydantic import BaseModel
-from typing import Dict, List
+from typing import Dict, List, AnyStr
 
 from models.target import Target
 
@@ -9,5 +9,5 @@ class ObjectiveFunction(BaseModel):
   # likely with models representing each objective function type
   tif_targets: List[Target]
   tcc_targets: List[Target]
-  objective: "minimize"
+  objective: AnyStr = "minimize"
   weight: Dict = {'tif': 1, 'tcc': 1}

app/models/solver_run.py

@@ -11,7 +11,8 @@ class SolverRun(BaseModel):
   items: List[Item]
   constraints: List[Constraint]
   irt_model: IRTModel
-  objective_fuction: ObjectiveFunction
+  objective_function: ObjectiveFunction
   total_form_items: int
+  theta_cut_score: float = 0.00
   advanced_options: Optional[AdvancedOptions]
   engine: str

app/services/base.py

@@ -0,0 +1,4 @@
+class Base:
+  def __init__(self, source, ingest_type='message'):
+    self.ingest_type = ingest_type
+    self.source = source

app/services/loft_service.py

@@ -0,0 +1,62 @@
+import os
+import json
+import random
+import io
+
+from helpers import aws_helper, tar_helper, csv_helper, service_helper
+
+from models.solver_run import SolverRun
+from models.solution import Solution
+from models.form import Form
+
+from services.base import Base
+
+class LoftService(Base):
+  def process(self):
+    self.solver_run = SolverRun.parse_obj(self.retreive_attributes_from_message())
+    self.solution = self.generate_solution()
+    self.result = self.stream_to_s3_bucket()
+
+  def retreive_attributes_from_message(self):
+    # get s3 object
+    self.key = aws_helper.get_key_from_message(self.source)
+    s3_object = aws_helper.get_object(self.key, os.environ['SOLVER_INGEST_BUCKET'])
+
+    # convert to tar
+    self.tar = tar_helper.raw_to_tar(s3_object)
+
+    # get attributes file and convert to dict
+    attributes = json.loads(tar_helper.extract_file_from_tar(self.tar , 'solver_run_attributes.json').read())
+
+    # get items file and convert to dict
+    items_csv = tar_helper.extract_file_from_tar(self.tar , 'items.csv')
+    items_csv_reader = csv_helper.file_stream_reader(items_csv)
+
+    # add items to attributes dict
+    attributes['items'] = service_helper.items_csv_to_dict(items_csv_reader)
+
+    return attributes
+
+  def generate_solution(self):
+    # temporary data for mocks
+    form_count = 10
+
+    # items will be generated from real solver process, this is for mock purposes
+    # real solver will return N forms and process a cut score, this is for mock purposes
+    return Solution(
+      response_id=random.randint(100,5000),
+      forms=[
+        Form(
+          items=[item.id for item in random.sample(self.solver_run.items, self.solver_run.total_form_items)],
+          cut_score=120
+        ) for x in range(form_count)
+      ]
+    )
+
+  def stream_to_s3_bucket(self):
+    # setup writer buffer and write processed forms to file
+    buffer = io.StringIO()
+    solution_file = service_helper.solution_to_file(buffer, self.solver_run.total_form_items, self.solution.forms)
+
+    # upload generated file to s3 and return result
+    return aws_helper.file_stream_upload(solution_file, f'{service_helper.key_to_uuid(self.key)}.csv', os.environ['MEASURE_PROCESSED_BUCKET'])

docker-compose.override.yml

@@ -1,2 +0,0 @@
-override_file_placeholder:
-  image: busybox

docker-compose.yml

@@ -1,9 +0,0 @@
-measure-solver:
-  build: /apps/measure-solver/.docker-compose
-  environment:
-    VIRTUAL_HOST: yssolver.localhost
-    MEASURE_BASE_URL: http://admin.localhost
-  dns:
-    - 172.17.0.1
-  volumes:
-    - /apps/measure-solver:/app

docker_tag.txt

@@ -1 +0,0 @@
-QUANT-987