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Merge pull request #106 from SDSRV-IDP/trungpt/not_required_fix

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Trungpt/not required fix
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Đỗ Xuân Tân 2024-04-04 14:03:26 +07:00 committed by GitHub Enterprise
commit d2f5052472
1 changed files with 96 additions and 68 deletions
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162
cope2n-api/fwd_api/utils/accuracy.py
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@ -16,8 +16,10 @@ import redis
from fwd import settings
from ..models import SubscriptionRequest, Report, ReportFile
import json
from typing import Union, List, Dict
valid_keys = ["retailername", "sold_to_party", "invoice_no", "purchase_date", "imei_number"]
optional_keys = ['invoice_no']
class ReportAccumulateByRequest:
def __init__(self, sub):
@ -533,6 +535,13 @@ def first_of_list(the_list):
return None
return the_list[0]
def _feedback_invoice_no_exist(feedback_result):
invoice_no = feedback_result.get("invoice_no", None)
if invoice_no in ["", [], None]:
return False
else:
return True
def extract_report_detail_list(report_detail_list, lower=False, in_percent=True):
data = []
for report_file in report_detail_list:
@ -549,7 +558,7 @@ def extract_report_detail_list(report_detail_list, lower=False, in_percent=True)
"Invoice_Number_User": report_file.feedback_result.get("invoice_no", None) if report_file.feedback_result else None,
"Invoice_Number_OCR": report_file.predict_result.get("invoice_no", None),
"Invoice_Number Revised": report_file.reviewed_result.get("invoice_no", None) if report_file.reviewed_result else None,
"Invoice_Number_Accuracy": first_of_list(report_file.feedback_accuracy.get("invoice_no", [None])),
"Invoice_Number_Accuracy": first_of_list(report_file.feedback_accuracy.get("invoice_no", [None])) if _feedback_invoice_no_exist(report_file.feedback_result) else None,
"Invoice_Purchase Date_Consumer": report_file.feedback_result.get("purchase_date", None) if report_file.feedback_result else None,
"Invoice_Purchase Date_OCR": format_purchase_date_ocr_for_report(report_file.predict_result.get("purchase_date", [])),
"Invoice_Purchase Date Revised": report_file.reviewed_result.get("purchase_date", None) if report_file.reviewed_result else None,
@ -644,57 +653,60 @@ def predict_result_to_ready(result):
dict_result["invoice_no"] = result.get("content", {}).get("document", [{}])[0].get("content", [{}, {}, {}, {}, {}])[4].get("value", None)
return dict_result
def align_fine_result(ready_predict, fine_result):
# print(f"[DEBUG]: fine_result: {fine_result}")
# print(f"[DEBUG]: ready_predict: {ready_predict}")
if fine_result:
if fine_result["purchase_date"] and len(ready_predict["purchase_date"]) == 0:
ready_predict["purchase_date"] = [None]
if fine_result["retailername"] and not ready_predict["retailername"]:
ready_predict["retailername"] = [None]
if ready_predict.get("invoice_no", None) and not fine_result.get("invoice_no", None):
fine_result["invoice_no"] = [None]
fine_result["purchase_date"] = [fine_result["purchase_date"] for _ in range(len(ready_predict["purchase_date"]))]
return ready_predict, fine_result
def update_temp_accuracy(accuracy, acc, keys):
for key in keys:
accuracy[key].add(acc[key])
return accuracy
def calculate_accuracy(key_name, inference, target):
def _accuracy_calculate_formatter(inference, target):
"""_summary_
format type of inference, and target from str/None to List of str/None.
Make both list inference and target to be the same length.
"""
if not isinstance(inference, list):
inference = [] if inference is None else [inference]
if not isinstance(target, list):
target = [] if target is None else [target]
length = max(len(target), len(inference))
target = target + (length - len(target))*[None]
inference = inference + (length - len(inference))*[None]
return inference, target
def _acc_will_be_ignored(key_name, _target, type):
is_optional_key = key_name in optional_keys
is_empty_target = _target in [[], None, '']
if is_optional_key and is_empty_target and type == 'feedback':
return True
else:
return False
def calculate_accuracy(key_name: str, inference: Dict[str, Union[str, List]], target: Dict[str, Union[str, List]], type: str):
"""_summary_
NOTE: This has been changed to return accuracy = None if
Args:
key_name (string): key to calculate accuracy on, ex: retailername
inference (dict): result from ocr, refined to align with the target down below
target (dict): result of type
is_optional_keyname: default is set to False (which mean this is not an optional keyname)
currently we have invoice_no is an optional keyname.
"""
acc = []
data = []
if not target or not inference:
return acc, data
if not isinstance(inference[key_name], list):
if inference[key_name] is None:
inference[key_name] = []
else:
inference[key_name] = [inference[key_name]]
if not isinstance(target[key_name], list):
if target[key_name] is None:
target[key_name] = []
else:
target[key_name] = [target[key_name]]
# Realign lenght for mis predicted/feedback/reivew result
if len(target[key_name]) == 0 and len(inference[key_name]) > 0:
target[key_name] = [None for _ in range(len(inference[key_name]))]
elif len(inference[key_name]) == 0 and len(target[key_name]) > 0:
target[key_name] = [None for _ in range(len(inference[key_name]))]
for i, v in enumerate(inference[key_name]):
# TODO: target[key_name][i] is None, ""
x = post_processing_str(key_name, inference[key_name][i], is_gt=False)
y = post_processing_str(key_name, target[key_name][i], is_gt=True)
_inference = inference[key_name]
_target = target[key_name]
_will_acc_be_ignored = _acc_will_be_ignored(key_name, _target, type)
_inference = _accuracy_calculate_formatter(_inference)
_target = _accuracy_calculate_formatter(_target)
for i, v in enumerate(_inference):
# TODO: target[i] is None, ""
x = post_processing_str(key_name, _inference[i], is_gt=False)
y = post_processing_str(key_name, _target[i], is_gt=True)
score = eval_ocr_metric(
[x],
@ -705,7 +717,8 @@ def calculate_accuracy(key_name, inference, target):
# "line_acc",
# "one_minus_ned_word",
])
acc.append(list(score.values())[0])
if not _will_acc_be_ignored:
acc.append(list(score.values())[0])
data.append([x, y])
return acc, data
@ -821,30 +834,43 @@ def calculate_a_request(report, request):
if status != 200:
continue
image.feedback_accuracy = att["acc"]["feedback"] # dict {key: [values]}
image.reviewed_accuracy = att["acc"]["reviewed"] # dict {key: [values]}
image.is_bad_image_quality = att["is_bad_image"]
image.is_bad_image_quality = att["is_bad_image"] # is_bad_image=avg_acc<threshold (avg_acc=feedback_acc)
if att["is_reviewed"]==1: # Image is already reviewed
image.reviewed_accuracy = att["acc"]["reviewed"] # dict {key: [values]}
if not image.doc_type:
# try to revert doc type from filename
_doc_type = image.file_name.split("_")[1]
if _doc_type in ["imei", "invoice"]:
image.doc_type = _doc_type
image.save()
_sub = "NA"
if request.redemption_id:
_sub = map_subsidiary_short_to_long(request.redemption_id[:2])
else:
print(f"[WARM]: empty redemption_id, check request: {request.request_id}")
print(f"[WARN]: empty redemption_id, check request: {request.request_id}")
# Little trick to replace purchase date to normalized
if len(att["normalized_data"]["feedback"].get("purchase_date", [])) > 0:
image.predict_result["purchase_date"] = [att["normalized_data"]["feedback"]["purchase_date"][i][0] for i in range(len(att["normalized_data"]["feedback"]["purchase_date"]))]
image.predict_result["purchase_date"] = [value_pair[0] for value_pair in att["normalized_data"]["feedback"]["purchase_date"]]
image.feedback_result["purchase_date"] = att["normalized_data"]["feedback"]["purchase_date"][fb_max_indexes["purchase_date"]][1]
if len(att["normalized_data"]["reviewed"].get("purchase_date", [])) > 0:
image.predict_result["purchase_date"] = [att["normalized_data"]["reviewed"]["purchase_date"][i][0] for i in range(len(att["normalized_data"]["reviewed"]["purchase_date"]))]
image.predict_result["purchase_date"] = [value_pair[0] for value_pair in att["normalized_data"]["reviewed"]["purchase_date"]]
image.reviewed_result["purchase_date"] = att["normalized_data"]["reviewed"]["purchase_date"][rv_max_indexes["purchase_date"]][1]
# if request.is_reviewed:
# att["is_reviewed"] = 1
request_att["is_reviewed"].append(att["is_reviewed"])
if att["is_reviewed"] == -1: # -1 means "not required"
att["acc"]["reviewed"] = {}
reviewed_result = {}
reason = None
counter_measure = None
else:
if att["is_reviewed"] == 1:
reviewed_result = image.reviewed_result
reason = image.reason
counter_measure = image.counter_measures
new_report_file = ReportFile(report=report,
subsidiary=_sub,
correspond_request_id=request.request_id,
@ -853,15 +879,15 @@ def calculate_a_request(report, request):
doc_type=image.doc_type,
predict_result=image.predict_result,
feedback_result=image.feedback_result,
reviewed_result=image.reviewed_result,
reviewed_result=reviewed_result,
feedback_accuracy=att["acc"]["feedback"],
reviewed_accuracy=att["acc"]["reviewed"],
acc=att["avg_acc"],
is_bad_image=att["is_bad_image"],
is_reviewed= review_status_map(att["is_reviewed"]),
time_cost=image.processing_time,
bad_image_reason=image.reason,
counter_measures=image.counter_measures,
bad_image_reason=reason,
counter_measures=counter_measure,
error="|".join(att["err"]),
review_status=att["is_reviewed"],
)
@ -890,17 +916,17 @@ def calculate_a_request(report, request):
request_att["acc"]["feedback"]["sold_to_party"] += _att["acc"]["feedback"]["sold_to_party"]
request_att["acc"]["feedback"]["invoice_no"] += _att["acc"]["feedback"]["invoice_no"]
request_att["acc"]["reviewed"]["imei_number"] += _att["acc"]["reviewed"]["imei_number"]
request_att["acc"]["reviewed"]["purchase_date"] += _att["acc"]["reviewed"]["purchase_date"]
request_att["acc"]["reviewed"]["retailername"] += _att["acc"]["reviewed"]["retailername"]
request_att["acc"]["reviewed"]["sold_to_party"] += _att["acc"]["reviewed"]["sold_to_party"]
request_att["acc"]["reviewed"]["invoice_no"] += _att["acc"]["reviewed"]["invoice_no"]
request_att["acc"]["reviewed"]["imei_number"] += _att["acc"]["reviewed"]["imei_number"] if _att["is_reviewed"]==1 else []
request_att["acc"]["reviewed"]["purchase_date"] += _att["acc"]["reviewed"]["purchase_date"] if _att["is_reviewed"]==1 else []
request_att["acc"]["reviewed"]["retailername"] += _att["acc"]["reviewed"]["retailername"] if _att["is_reviewed"]==1 else []
request_att["acc"]["reviewed"]["sold_to_party"] += _att["acc"]["reviewed"]["sold_to_party"] if _att["is_reviewed"]==1 else []
request_att["acc"]["reviewed"]["invoice_no"] += _att["acc"]["reviewed"]["invoice_no"] if _att["is_reviewed"]==1 else []
request_att["acc"]["acumulated"]["imei_number"] += _att["acc"]["reviewed"]["imei_number"] if _att["acc"]["reviewed"]["imei_number"] else _att["acc"]["feedback"]["imei_number"]
request_att["acc"]["acumulated"]["purchase_date"] += _att["acc"]["reviewed"]["purchase_date"] if _att["acc"]["reviewed"]["purchase_date"] else _att["acc"]["feedback"]["purchase_date"]
request_att["acc"]["acumulated"]["retailername"] += _att["acc"]["reviewed"]["retailername"] if _att["acc"]["reviewed"]["retailername"] else _att["acc"]["feedback"]["retailername"]
request_att["acc"]["acumulated"]["sold_to_party"] += _att["acc"]["reviewed"]["sold_to_party"] if _att["acc"]["reviewed"]["sold_to_party"] else _att["acc"]["feedback"]["sold_to_party"]
request_att["acc"]["acumulated"]["invoice_no"] += _att["acc"]["reviewed"]["invoice_no"] if _att["acc"]["reviewed"]["invoice_no"] else _att["acc"]["feedback"]["invoice_no"]
request_att["acc"]["acumulated"]["imei_number"] += _att["acc"]["reviewed"]["imei_number"] if _att["acc"]["reviewed"]["imei_number"] and _att["is_reviewed"]==1 else _att["acc"]["feedback"]["imei_number"]
request_att["acc"]["acumulated"]["purchase_date"] += _att["acc"]["reviewed"]["purchase_date"] if _att["acc"]["reviewed"]["purchase_date"] and _att["is_reviewed"]==1 else _att["acc"]["feedback"]["purchase_date"]
request_att["acc"]["acumulated"]["retailername"] += _att["acc"]["reviewed"]["retailername"] if _att["acc"]["reviewed"]["retailername"] and _att["is_reviewed"]==1 else _att["acc"]["feedback"]["retailername"]
request_att["acc"]["acumulated"]["sold_to_party"] += _att["acc"]["reviewed"]["sold_to_party"] if _att["acc"]["reviewed"]["sold_to_party"] and _att["is_reviewed"]==1 else _att["acc"]["feedback"]["sold_to_party"]
request_att["acc"]["acumulated"]["invoice_no"] += _att["acc"]["reviewed"]["invoice_no"] if _att["acc"]["reviewed"]["invoice_no"] and _att["is_reviewed"]==1 else _att["acc"]["feedback"]["invoice_no"]
if image.reason not in settings.ACC_EXCLUDE_RESEASONS:
request_att["bad_images"] += int(_att["is_bad_image"])
@ -926,33 +952,35 @@ def calculate_subcription_file(subcription_request_file):
return 400, att
inference_result = copy.deepcopy(subcription_request_file.predict_result)
inference_result, feedback_result = align_fine_result(inference_result, copy.deepcopy(subcription_request_file.feedback_result))
inference_result, reviewed_result = align_fine_result(inference_result, copy.deepcopy(subcription_request_file.reviewed_result))
feedback_result = copy.deepcopy(subcription_request_file.feedback_result)
reviewed_result = copy.deepcopy(subcription_request_file.reviewed_result)
for key_name in valid_keys:
try:
att["acc"]["feedback"][key_name], att["normalized_data"]["feedback"][key_name] = calculate_accuracy(key_name, inference_result, feedback_result)
att["acc"]["reviewed"][key_name], att["normalized_data"]["reviewed"][key_name] = calculate_accuracy(key_name, inference_result, reviewed_result)
att["acc"]["feedback"][key_name], att["normalized_data"]["feedback"][key_name] = calculate_accuracy(key_name, inference_result, feedback_result, "feedback")
att["acc"]["reviewed"][key_name], att["normalized_data"]["reviewed"][key_name] = calculate_accuracy(key_name, inference_result, reviewed_result, "reviewed")
except Exception as e:
att["err"].append(str(e))
# print(f"[DEBUG]: predict_result: {subcription_request_file.predict_result}")
# print(f"[DEBUG]: e: {e} -key_name: {key_name}")
subcription_request_file.feedback_accuracy = att["acc"]["feedback"]
subcription_request_file.reviewed_accuracy = att["acc"]["reviewed"]
avg_reviewed = calculate_avg_accuracy(att["acc"], "reviewed", ["retailername", "sold_to_party", "invoice_no", "purchase_date", "imei_number"])
avg_feedback = calculate_avg_accuracy(att["acc"], "feedback", ["retailername", "sold_to_party", "invoice_no", "purchase_date", "imei_number"])
avg_reviewed = calculate_avg_accuracy(att["acc"], "reviewed", valid_keys)
avg_feedback = calculate_avg_accuracy(att["acc"], "feedback", valid_keys)
if avg_feedback is not None or avg_reviewed is not None:
avg_acc = 0
if avg_feedback is not None:
avg_acc = avg_feedback
if avg_feedback < settings.NEED_REVIEW:
att["is_reviewed"] = 0
if avg_reviewed is not None:
else:
att["is_reviewed"] = -1
if avg_reviewed is not None and att["is_reviewed"]!=-1:
avg_acc = avg_reviewed
att["is_reviewed"] = 1
# Little trick to overcome issue caused by misleading manually review process
if subcription_request_file.reason or subcription_request_file.counter_measures:
if (subcription_request_file.reason or subcription_request_file.counter_measures) and att["is_reviewed"]!=-1:
att["is_reviewed"] = 1
att["avg_acc"] = avg_acc