536 lines
22 KiB
Python
536 lines
22 KiB
Python
import os
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import pandas as pd
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import time
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import json
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from sklearn.metrics import precision_score, recall_score, f1_score
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from utils.biz_utils import get_unique_words_text, get_beginning_common_words
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from utils.logger import logger
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class Metrics:
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def __init__(
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self,
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data_type: str,
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prediction_file: str,
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prediction_sheet_name: str = "Sheet1",
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ground_truth_file: str = None,
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ground_truth_sheet_name: str = "Sheet1",
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output_folder: str = None,
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) -> None:
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self.data_type = data_type
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self.prediction_file = prediction_file
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self.prediction_sheet_name = prediction_sheet_name
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self.ground_truth_file = ground_truth_file
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self.ground_truth_sheet_name = ground_truth_sheet_name
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if output_folder is None or len(output_folder) == 0:
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output_folder = r"/data/emea_ar/output/metrics/"
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os.makedirs(output_folder, exist_ok=True)
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time_stamp = time.strftime("%Y%m%d%H%M%S", time.localtime())
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self.output_file = os.path.join(
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output_folder,
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f"metrics_{data_type}_{time_stamp}.xlsx",
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)
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def get_metrics(self):
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if (
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self.prediction_file is None
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or len(self.prediction_file) == 0
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or not os.path.exists(self.prediction_file)
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):
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logger.error(f"Invalid prediction file: {self.prediction_file}")
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return []
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if (
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self.ground_truth_file is None
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or len(self.ground_truth_file) == 0
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or not os.path.exists(self.ground_truth_file)
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):
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logger.error(f"Invalid ground truth file: {self.ground_truth_file}")
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return []
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metrics_list = [
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{"Data_Point": "NAN", "Precision": 0, "Recall": 0, "F1": 0, "Support": 0}
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]
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missing_error_list, metrics_list = self.calculate_metrics()
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missing_error_df = pd.DataFrame(missing_error_list)
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missing_error_df.reset_index(drop=True, inplace=True)
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metrics_df = pd.DataFrame(metrics_list)
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metrics_df.reset_index(drop=True, inplace=True)
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with pd.ExcelWriter(self.output_file) as writer:
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missing_error_df.to_excel(writer, sheet_name="Missing_Error", index=False)
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metrics_df.to_excel(writer, sheet_name="Metrics", index=False)
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return missing_error_list, metrics_list, self.output_file
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def calculate_metrics(self):
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prediction_df = pd.read_excel(
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self.prediction_file, sheet_name=self.prediction_sheet_name
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)
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ground_truth_df = pd.read_excel(
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self.ground_truth_file, sheet_name=self.ground_truth_sheet_name
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)
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ground_truth_df = ground_truth_df[ground_truth_df["Checked"] == 1]
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tor_true = []
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tor_pred = []
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ter_true = []
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ter_pred = []
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ogc_true = []
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ogc_pred = []
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performance_fee_true = []
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performance_fee_pred = []
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missing_error_list = []
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data_point_list = ["tor", "ter", "ogc", "performance_fee"]
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if self.data_type == "page_filter":
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for index, row in ground_truth_df.iterrows():
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doc_id = row["doc_id"]
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# get first row with the same doc_id
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prediction_data = prediction_df[prediction_df["doc_id"] == doc_id].iloc[
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0
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]
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for data_point in data_point_list:
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true_data, pred_data, missing_error_data = (
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self.get_page_filter_true_pred_data(
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doc_id, row, prediction_data, data_point
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)
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)
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if data_point == "tor":
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tor_true.extend(true_data)
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tor_pred.extend(pred_data)
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elif data_point == "ter":
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ter_true.extend(true_data)
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ter_pred.extend(pred_data)
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elif data_point == "ogc":
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ogc_true.extend(true_data)
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ogc_pred.extend(pred_data)
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elif data_point == "performance_fee":
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performance_fee_true.extend(true_data)
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performance_fee_pred.extend(pred_data)
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missing_error_list.append(missing_error_data)
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else:
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prediction_doc_id_list = prediction_df["doc_id"].unique().tolist()
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ground_truth_doc_id_list = ground_truth_df["doc_id"].unique().tolist()
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# get intersection of doc_id_list
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doc_id_list = list(
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set(prediction_doc_id_list) & set(ground_truth_doc_id_list)
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)
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# order by doc_id
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doc_id_list.sort()
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for doc_id in doc_id_list:
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prediction_data = prediction_df[prediction_df["doc_id"] == doc_id]
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ground_truth_data = ground_truth_df[ground_truth_df["doc_id"] == doc_id]
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for data_point in data_point_list:
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true_data, pred_data, missing_error_data = (
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self.get_data_extraction_true_pred_data(
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doc_id, ground_truth_data, prediction_data, data_point
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)
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)
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if data_point == "tor":
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tor_true.extend(true_data)
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tor_pred.extend(pred_data)
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elif data_point == "ter":
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ter_true.extend(true_data)
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ter_pred.extend(pred_data)
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elif data_point == "ogc":
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ogc_true.extend(true_data)
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ogc_pred.extend(pred_data)
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elif data_point == "performance_fee":
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performance_fee_true.extend(true_data)
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performance_fee_pred.extend(pred_data)
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missing_error_list.extend(missing_error_data)
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metrics_list = []
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for data_point in data_point_list:
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if data_point == "tor":
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precision, recall, f1 = self.get_specific_metrics(tor_true, tor_pred)
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tor_support = self.get_support_number(tor_true)
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metrics_list.append(
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{
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"Data_Point": data_point,
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"Precision": precision,
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"Recall": recall,
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"F1": f1,
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"Support": tor_support,
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}
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)
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logger.info(
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f"TOR Precision: {precision}, Recall: {recall}, F1: {f1}, Support: {tor_support}"
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)
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elif data_point == "ter":
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precision, recall, f1 = self.get_specific_metrics(ter_true, ter_pred)
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ter_support = self.get_support_number(ter_true)
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metrics_list.append(
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{
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"Data_Point": data_point,
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"Precision": precision,
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"Recall": recall,
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"F1": f1,
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"Support": ter_support,
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}
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)
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logger.info(
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f"TER Precision: {precision}, Recall: {recall}, F1: {f1}, Support: {ter_support}"
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)
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elif data_point == "ogc":
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precision, recall, f1 = self.get_specific_metrics(ogc_true, ogc_pred)
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ogc_support = self.get_support_number(ogc_true)
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metrics_list.append(
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{
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"Data_Point": data_point,
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"Precision": precision,
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"Recall": recall,
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"F1": f1,
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"Support": ogc_support,
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}
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)
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logger.info(
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f"OGC Precision: {precision}, Recall: {recall}, F1: {f1}, Support: {ogc_support}"
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)
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elif data_point == "performance_fee":
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precision, recall, f1 = self.get_specific_metrics(
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performance_fee_true, performance_fee_pred
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)
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performance_fee_support = self.get_support_number(performance_fee_true)
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metrics_list.append(
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{
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"Data_Point": data_point,
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"Precision": precision,
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"Recall": recall,
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"F1": f1,
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"Support": performance_fee_support,
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}
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)
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logger.info(
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f"Performance Fee Precision: {precision}, Recall: {recall}, F1: {f1}, Support: {performance_fee_support}"
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)
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# get average metrics
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precision_list = [metric["Precision"] for metric in metrics_list]
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recall_list = [metric["Recall"] for metric in metrics_list]
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f1_list = [metric["F1"] for metric in metrics_list]
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metrics_list.append(
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{
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"Data_Point": "Average",
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"Precision": sum(precision_list) / len(precision_list),
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"Recall": sum(recall_list) / len(recall_list),
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"F1": sum(f1_list) / len(f1_list),
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"Support": sum([metric["Support"] for metric in metrics_list]),
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}
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)
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return missing_error_list, metrics_list
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def get_support_number(self, true_data: list):
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# get the count which true_data is 1
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return sum(true_data)
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def get_page_filter_true_pred_data(
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self,
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doc_id,
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ground_truth_data: pd.Series,
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prediction_data: pd.Series,
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data_point: str,
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):
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ground_truth_list = ground_truth_data[data_point]
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if isinstance(ground_truth_list, str):
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ground_truth_list = json.loads(ground_truth_list)
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prediction_list = prediction_data[data_point]
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if isinstance(prediction_list, str):
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prediction_list = json.loads(prediction_list)
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true_data = []
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pred_data = []
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missing_error_data = {
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"doc_id": doc_id,
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"data_point": data_point,
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"missing": [],
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"error": [],
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}
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missing_data = []
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error_data = []
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if len(ground_truth_list) == 0 and len(prediction_list) == 0:
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true_data.append(1)
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pred_data.append(1)
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return true_data, pred_data, missing_error_data
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for prediction in prediction_list:
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if prediction in ground_truth_list:
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true_data.append(1)
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pred_data.append(1)
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else:
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true_data.append(0)
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pred_data.append(1)
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error_data.append(prediction)
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for ground_truth in ground_truth_list:
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if ground_truth not in prediction_list:
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true_data.append(1)
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pred_data.append(0)
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missing_data.append(ground_truth)
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missing_error_data = {
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"doc_id": doc_id,
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"data_point": data_point,
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"missing": missing_data,
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"error": error_data,
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}
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return true_data, pred_data, missing_error_data
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def get_data_extraction_true_pred_data(
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self,
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doc_id,
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ground_truth_data: pd.DataFrame,
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prediction_data: pd.DataFrame,
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data_point: str,
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):
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dp_prediction = prediction_data[prediction_data["datapoint"] == data_point]
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dp_prediction = self.modify_data(dp_prediction)
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pred_simple_raw_names = dp_prediction["simple_raw_name"].unique().tolist()
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pred_simple_name_unique_words_list = (
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dp_prediction["simple_name_unique_words"].unique().tolist()
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)
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dp_ground_truth = ground_truth_data[
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ground_truth_data["datapoint"] == data_point
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]
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dp_ground_truth = self.modify_data(dp_ground_truth)
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gt_simple_raw_names = dp_ground_truth["simple_raw_name"].unique().tolist()
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gt_simple_name_unique_words_list = (
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dp_ground_truth["simple_name_unique_words"].unique().tolist()
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)
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true_data = []
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pred_data = []
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missing_error_data = []
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if len(dp_ground_truth) == 0 and len(dp_prediction) == 0:
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true_data.append(1)
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pred_data.append(1)
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return true_data, pred_data, missing_error_data
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for index, prediction in dp_prediction.iterrows():
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pred_page_index = prediction["page_index"]
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pred_raw_name = prediction["raw_name"]
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pred_simple_raw_name = prediction["simple_raw_name"]
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pred_simple_name_unique_words = prediction["simple_name_unique_words"]
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pred_data_point_value = prediction["value"]
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pred_investment_type = prediction["investment_type"]
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find_raw_name_in_gt = [
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gt_raw_name
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for gt_raw_name in gt_simple_raw_names
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if (
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gt_raw_name in pred_simple_raw_name
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or pred_simple_raw_name in gt_raw_name
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)
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and gt_raw_name.endswith(pred_simple_raw_name.split()[-1])
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]
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if (
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pred_simple_name_unique_words in gt_simple_name_unique_words_list
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or len(find_raw_name_in_gt) > 0
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):
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# get the ground truth data with the same unique words
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if pred_simple_name_unique_words in gt_simple_name_unique_words_list:
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gt_data_df = dp_ground_truth[
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dp_ground_truth["simple_name_unique_words"]
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== pred_simple_name_unique_words
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]
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if len(gt_data_df) > 1:
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if (
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len(gt_data_df[gt_data_df["page_index"] == pred_page_index])
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== 0
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):
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gt_data = gt_data_df.iloc[0]
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else:
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gt_data = gt_data_df[
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gt_data_df["page_index"] == pred_page_index
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].iloc[0]
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elif len(gt_data_df) == 1:
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gt_data = gt_data_df.iloc[0]
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else:
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gt_data = None
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else:
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gt_data_df = dp_ground_truth[
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dp_ground_truth["simple_raw_name"] == find_raw_name_in_gt[0]
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]
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if len(gt_data_df) > 1:
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if (
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len(gt_data_df[gt_data_df["page_index"] == pred_page_index])
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== 0
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):
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gt_data = gt_data_df.iloc[0]
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else:
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gt_data = gt_data_df[
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gt_data_df["page_index"] == pred_page_index
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].iloc[0]
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elif len(gt_data_df) == 1:
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gt_data = gt_data_df.iloc[0]
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else:
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gt_data = None
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if gt_data is None:
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gt_data_point_value = None
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else:
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gt_data_point_value = gt_data["value"]
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if (
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gt_data_point_value is not None
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and pred_data_point_value == gt_data_point_value
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):
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true_data.append(1)
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pred_data.append(1)
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else:
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true_data.append(0)
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pred_data.append(1)
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error_data = {
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"doc_id": doc_id,
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"data_point": data_point,
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"page_index": pred_page_index,
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"pred_raw_name": pred_raw_name,
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"investment_type": pred_investment_type,
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"error_type": "data value incorrect",
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"error_value": pred_data_point_value,
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"correct_value": gt_data_point_value,
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}
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missing_error_data.append(error_data)
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else:
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# If data point is performance fees, and value is 0,
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# then it's correct
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pred_value_num = None
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try:
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pred_value_num = float(pred_data_point_value)
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except:
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pass
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if data_point == "performance_fee" and pred_value_num == 0:
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true_data.append(1)
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pred_data.append(1)
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else:
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true_data.append(0)
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pred_data.append(1)
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error_data = {
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"doc_id": doc_id,
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"data_point": data_point,
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"page_index": pred_page_index,
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"pred_raw_name": pred_raw_name,
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"investment_type": pred_investment_type,
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"error_type": "raw name incorrect",
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"error_value": pred_raw_name,
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"correct_value": "",
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}
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missing_error_data.append(error_data)
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for index, ground_truth in dp_ground_truth.iterrows():
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gt_page_index = ground_truth["page_index"]
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gt_raw_name = ground_truth["raw_name"]
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gt_simple_raw_name = ground_truth["simple_raw_name"]
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gt_simple_name_unique_words = ground_truth["simple_name_unique_words"]
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gt_data_point_value = ground_truth["value"]
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gt_investment_type = ground_truth["investment_type"]
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find_raw_name_in_pred = [
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pred_raw_name
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for pred_raw_name in pred_simple_raw_names
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if (
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gt_simple_raw_name in pred_raw_name
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or pred_raw_name in gt_simple_raw_name
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)
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and pred_raw_name.endswith(gt_simple_raw_name.split()[-1])
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]
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if (
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gt_simple_name_unique_words not in pred_simple_name_unique_words_list
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and len(find_raw_name_in_pred) == 0
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):
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gt_value_num = None
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try:
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gt_value_num = float(gt_data_point_value)
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except:
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pass
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# If data point is performance fees, and value is 0,
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# then it's correct
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if data_point == "performance_fee" and gt_value_num == 0:
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true_data.append(1)
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pred_data.append(1)
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else:
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true_data.append(1)
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pred_data.append(0)
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error_data = {
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"doc_id": doc_id,
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"data_point": data_point,
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"page_index": gt_page_index,
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"pred_raw_name": "",
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"investment_type": gt_investment_type,
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"error_type": "raw name missing",
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"error_value": "",
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"correct_value": gt_raw_name,
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}
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missing_error_data.append(error_data)
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return true_data, pred_data, missing_error_data
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def modify_data(self, data: pd.DataFrame):
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data["simple_raw_name"] = ""
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data["simple_name_unique_words"] = ""
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page_index_list = data["page_index"].unique().tolist()
|
|
for pagex_index in page_index_list:
|
|
page_data = data[data["page_index"] == pagex_index]
|
|
raw_name_list = page_data["raw_name"].unique().tolist()
|
|
beginning_common_words = get_beginning_common_words(raw_name_list)
|
|
for raw_name in raw_name_list:
|
|
if (
|
|
beginning_common_words is not None
|
|
and len(beginning_common_words) > 0
|
|
):
|
|
simple_raw_name = raw_name.replace(
|
|
beginning_common_words, ""
|
|
).strip()
|
|
if len(simple_raw_name) == 0:
|
|
simple_raw_name = raw_name
|
|
else:
|
|
simple_raw_name = raw_name
|
|
temp_splits = [word for word in simple_raw_name.split()
|
|
if word.lower() not in ["class", "usd"]]
|
|
if len(temp_splits) > 0:
|
|
simple_raw_name = " ".join(
|
|
word
|
|
for word in simple_raw_name.split()
|
|
if word.lower() not in ["class"]
|
|
)
|
|
simple_raw_name_splits = simple_raw_name.split()
|
|
if len(simple_raw_name_splits) > 2 and \
|
|
simple_raw_name_splits[-1] == "USD":
|
|
simple_raw_name = " ".join(simple_raw_name_splits[:-1])
|
|
# set simple_raw_name which with the same page and same raw_name
|
|
data.loc[
|
|
(data["page_index"] == pagex_index)
|
|
& (data["raw_name"] == raw_name),
|
|
"simple_raw_name",
|
|
] = simple_raw_name
|
|
data.loc[
|
|
(data["page_index"] == pagex_index)
|
|
& (data["raw_name"] == raw_name),
|
|
"simple_name_unique_words",
|
|
] = get_unique_words_text(simple_raw_name)
|
|
return data
|
|
|
|
def get_specific_metrics(self, true_data: list, pred_data: list):
|
|
precision = precision_score(true_data, pred_data)
|
|
recall = recall_score(true_data, pred_data)
|
|
f1 = f1_score(true_data, pred_data)
|
|
return precision, recall, f1
|
|
|
|
def get_datapoint_metrics(self):
|
|
pass
|