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Add type filtering on sti operation for single tables, bug fixes and … #1

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Add type filtering on sti operation for single tables, bug fixes and … #1

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d4ac198
Add type filtering on sti operation for single tables, bug fixes and …
Riccardo-Ghilotti Sep 13, 2024
d6279ca
transitive closure prototype, STI with type filtering on multiple tab…
Riccardo-Ghilotti Oct 18, 2024
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1,582 changes: 811 additions & 771 deletions api/app.py
100755 → 100644
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251 changes: 127 additions & 124 deletions api/process/computation.py
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Original file line number Diff line number Diff line change
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import asyncio
import os
import sys
import time
import traceback

import redis
from keras.models import load_model

import utils.utils as utils
from phases.data_preparation import DataPreparation
from phases.featuresExtractionRevision import FeaturesExtractionRevision
from phases.feauturesExtraction import FeauturesExtraction
from phases.lookup import Lookup
from phases.prediction import Prediction
from phases.decision import Decision
from wrapper.lamAPI import LamAPI
from wrapper.Database import MongoDBWrapper # MongoDB database wrapper



async def main():
start = time.time()

pn_neural_path = "./process/ml_models/Linker_PN_100.h5"
rn_neural_path = "./process/ml_models/Linker_RN_100.h5"

pn_model = load_model(pn_neural_path)
rn_model = load_model(rn_neural_path)


REDIS_ENDPOINT = os.environ["REDIS_ENDPOINT"]
REDIS_JOB_DB = int(os.environ["REDIS_JOB_DB"])
LAMAPI_HOST = os.environ["LAMAPI_ENDPOINT"]
LAMAPI_TOKEN = os.environ["LAMAPI_TOKEN"]


job_active = redis.Redis(host=REDIS_ENDPOINT, db=REDIS_JOB_DB)

# Initialize MongoDB wrapper and get collections for different data models
mongoDBWrapper = MongoDBWrapper()
log_c = mongoDBWrapper.get_collection("log")
row_c = mongoDBWrapper.get_collection("row")
candidate_scored_c = mongoDBWrapper.get_collection("candidateScored")
cea_c = mongoDBWrapper.get_collection("cea")
cpa_c = mongoDBWrapper.get_collection("cpa")
cta_c = mongoDBWrapper.get_collection("cta")
cea_prelinking_c = mongoDBWrapper.get_collection("ceaPrelinking")

data = row_c.find_one_and_update({"status": "TODO"}, {"$set": {"status": "DOING"}})

if data is None:
print("No data to process", flush=True)
job_active.set("STOP", "")
sys.exit(0)

rows_data = data["rows"]
kg_reference = data["kgReference"]
limit = data["candidateSize"]
column_metadata = data["column"]
target = data["target"]
_id = data["_id"]
dataset_name = data["datasetName"]
table_name = data["tableName"]
page = data["page"]
header = data["header"]

lamAPI = LamAPI(LAMAPI_HOST, LAMAPI_TOKEN, mongoDBWrapper, kg=kg_reference)

obj_row_update = {"status": "DONE", "time": None}
dp = DataPreparation(header, rows_data, lamAPI)

try:
column_metadata, target = await dp.compute_datatype(column_metadata, target)
if target["SUBJ"] is not None:
column_metadata[str(target["SUBJ"])] = "SUBJ"
obj_row_update["column"] = column_metadata
obj_row_update["metadata"] = {
"column": [{"idColumn": int(id_col), "tag": column_metadata[id_col]} for id_col in column_metadata]
}
obj_row_update["target"] = target

metadata = {
"datasetName": dataset_name,
"tableName": table_name,
"kgReference": kg_reference,
"page": page
}

collections = {
"ceaPrelinking": cea_prelinking_c,
"cea": cea_c,
"cta": cta_c,
"cpa": cpa_c,
"candidateScored": candidate_scored_c
}
dp.rows_normalization()
l = Lookup(data, lamAPI, target, log_c, kg_reference, limit)
await l.generate_candidates()
rows = l.get_rows()
features = await FeauturesExtraction(rows, lamAPI).compute_feautures()
Prediction(rows, features, pn_model).compute_prediction("rho")
cea_preliking_data = utils.get_cea_pre_linking_data(metadata, rows)
revision = FeaturesExtractionRevision(rows)
features = revision.compute_features()
Prediction(rows, features, rn_model).compute_prediction("rho'")
storage = Decision(metadata, cea_preliking_data, rows, revision._cta, revision._cpa_pair, collections)
storage.store_data()
end = time.time()
execution_time = round(end - start, 2)
obj_row_update["time"] = execution_time
row_c.update_one({"_id": _id}, {"$set": obj_row_update})
print("End", flush=True)
except Exception as e:
log_c.insert_one({
"datasetName": dataset_name,
"tableName": table_name,
"error": str(e),
"stackTrace": traceback.format_exc()
})


# Run the asyncio event loop
asyncio.run(main())
import asyncio
import os
import sys
import time
import traceback

import redis
from keras.models import load_model

import utils.utils as utils
from phases.data_preparation import DataPreparation
from phases.featuresExtractionRevision import FeaturesExtractionRevision
from phases.feauturesExtraction import FeauturesExtraction
from phases.lookup import Lookup
from phases.prediction import Prediction
from phases.decision import Decision
from wrapper.lamAPI import LamAPI
from wrapper.Database import MongoDBWrapper # MongoDB database wrapper



async def main():
start = time.time()

pn_neural_path = "./process/ml_models/Linker_PN_100.h5"
rn_neural_path = "./process/ml_models/Linker_RN_100.h5"

pn_model = load_model(pn_neural_path)
rn_model = load_model(rn_neural_path)


REDIS_ENDPOINT = os.environ["REDIS_ENDPOINT"]
REDIS_JOB_DB = int(os.environ["REDIS_JOB_DB"])
LAMAPI_HOST = os.environ["LAMAPI_ENDPOINT"]
LAMAPI_TOKEN = os.environ["LAMAPI_TOKEN"]


job_active = redis.Redis(host=REDIS_ENDPOINT, db=REDIS_JOB_DB)

# Initialize MongoDB wrapper and get collections for different data models
mongoDBWrapper = MongoDBWrapper()
log_c = mongoDBWrapper.get_collection("log")
row_c = mongoDBWrapper.get_collection("row")
candidate_scored_c = mongoDBWrapper.get_collection("candidateScored")
cea_c = mongoDBWrapper.get_collection("cea")
cpa_c = mongoDBWrapper.get_collection("cpa")
cta_c = mongoDBWrapper.get_collection("cta")
cea_prelinking_c = mongoDBWrapper.get_collection("ceaPrelinking")



data = row_c.find_one_and_update({"status": "TODO"}, {"$set": {"status": "DOING"}})

if data is None:
print("No data to process", flush=True)
job_active.set("STOP", "")
sys.exit(0)

rows_data = data["rows"]
kg_reference = data["kgReference"]
limit = data["candidateSize"]
column_metadata = data["column"]
target = data["target"]
_id = data["_id"]
dataset_name = data["datasetName"]
table_name = data["tableName"]
page = data["page"]
header = data["header"]


lamAPI = LamAPI(LAMAPI_HOST, LAMAPI_TOKEN, mongoDBWrapper, kg=kg_reference)

obj_row_update = {"status": "DONE", "time": None}
dp = DataPreparation(header, rows_data, lamAPI)

try:
column_metadata, target = await dp.compute_datatype(column_metadata, target, data["NorL_types"], data["l_types"])
if target["SUBJ"] is not None:
column_metadata[str(target["SUBJ"])] = "SUBJ"
obj_row_update["column"] = column_metadata
obj_row_update["metadata"] = {
"column": [{"idColumn": int(id_col), "tag": column_metadata[id_col]} for id_col in column_metadata]
}
obj_row_update["target"] = target

metadata = {
"datasetName": dataset_name,
"tableName": table_name,
"kgReference": kg_reference,
"page": page
}

collections = {
"ceaPrelinking": cea_prelinking_c,
"cea": cea_c,
"cta": cta_c,
"cpa": cpa_c,
"candidateScored": candidate_scored_c
}
dp.rows_normalization()
l = Lookup(data, lamAPI, target, log_c, kg_reference, limit)
await l.generate_candidates()
rows = l.get_rows()
features = await FeauturesExtraction(rows, lamAPI).compute_feautures()
Prediction(rows, features, pn_model).compute_prediction("rho")
cea_preliking_data = utils.get_cea_pre_linking_data(metadata, rows)
revision = FeaturesExtractionRevision(rows)
features = revision.compute_features()
Prediction(rows, features, rn_model).compute_prediction("rho'")
storage = Decision(metadata, cea_preliking_data, rows, revision._cta, revision._cpa_pair, collections)
storage.store_data()
end = time.time()
execution_time = round(end - start, 2)
obj_row_update["time"] = execution_time
row_c.update_one({"_id": _id}, {"$set": obj_row_update})
print("End", flush=True)
except Exception as e:
log_c.insert_one({
"datasetName": dataset_name,
"tableName": table_name,
"error": str(e),
"stackTrace": traceback.format_exc()
})


# Run the asyncio event loop
asyncio.run(main())
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