support calculate page filter metrics.

configuration/datapoint_exclude_keyword.json

@@ -0,0 +1,17 @@
+{
+  "ISIN": {
+    "english": []
+  },
+  "ter": {
+    "english": []
+  },
+  "tor": {
+    "english": []
+  },
+  "ogc": {
+    "english": ["operating expenses paid"]
+  },
+  "performance_fee": {
+    "english": []
+  }
+}

configuration/datapoint_keyword.json

@@ -299,7 +299,6 @@
       "Ongoing Fund Charge",
       "Operating Charge",
       "Operating Charges",
-      "Operating expenses",
       "Operating, Administrative and Servicing Expenses"
     ],
     "spanish": [
@@ -340,8 +339,12 @@
       "Performance Fees",
       "performance-based fee",
       "performance-related fee",
-      "with performance)",
+      "Performance- related Fee",
-      "with performance fee)"
+      "perform- mance fees",
+      "per- formance fees",
+      "with performance",
+      "with performance fee",
+      "de Performance"
     ],
     "spanish": [
       "Comisión de Gestión sobre Resultados",

core/metrics.py

@@ -0,0 +1,237 @@
+import os
+import pandas as pd
+import time
+import json
+from sklearn.metrics import precision_score, recall_score, f1_score
+from utils.logger import logger
+
+
+class Metrics:
+    def __init__(
+        self,
+        data_type: str,
+        prediction_file: str,
+        prediction_sheet_name: str = "Sheet1",
+        ground_truth_file: str = None,
+        output_folder: str = None,
+    ) -> None:
+        self.data_type = data_type
+        self.prediction_file = prediction_file
+        self.prediction_sheet_name = prediction_sheet_name
+        self.ground_truth_file = ground_truth_file
+
+        if output_folder is None or len(output_folder) == 0:
+            output_folder = r"/data/emea_ar/output/metrics/"
+
+        os.makedirs(output_folder, exist_ok=True)
+        time_stamp = time.strftime("%Y%m%d%H%M%S", time.localtime())
+        self.output_file = os.path.join(
+            output_folder,
+            f"metrics_{data_type}_{time_stamp}.xlsx",
+        )
+
+    def get_metrics(self):
+        if (
+            self.prediction_file is None
+            or len(self.prediction_file) == 0
+            or not os.path.exists(self.prediction_file)
+        ):
+            logger.error(f"Invalid prediction file: {self.prediction_file}")
+            return []
+        if (
+            self.ground_truth_file is None
+            or len(self.ground_truth_file) == 0
+            or not os.path.exists(self.ground_truth_file)
+        ):
+            logger.error(f"Invalid ground truth file: {self.ground_truth_file}")
+            return []
+
+        metrics_list = [
+            {"Data_Point": "NAN", "Precision": 0, "Recall": 0, "F1": 0, "Support": 0}
+        ]
+        if self.data_type == "page_filter":
+            missing_error_list, metrics_list = self.get_page_filter_metrics()
+        elif self.data_type == "datapoint":
+            missing_error_list, metrics_list = self.get_datapoint_metrics()
+        else:
+            logger.error(f"Invalid data type: {self.data_type}")
+
+        missing_error_df = pd.DataFrame(missing_error_list)
+        missing_error_df.reset_index(drop=True, inplace=True)
+        
+        metrics_df = pd.DataFrame(metrics_list)
+        metrics_df.reset_index(drop=True, inplace=True)
+        
+        with pd.ExcelWriter(self.output_file) as writer:
+            missing_error_df.to_excel(writer, sheet_name="Missing_Error", index=False)
+            metrics_df.to_excel(writer, sheet_name="Metrics", index=False)
+        return missing_error_list, metrics_list, self.output_file
+
+    def get_page_filter_metrics(self):
+        prediction_df = pd.read_excel(self.prediction_file, sheet_name=self.prediction_sheet_name)
+        ground_truth_df = pd.read_excel(self.ground_truth_file, sheet_name="Sheet1")
+        ground_truth_df = ground_truth_df[ground_truth_df["Checked"] == 1]
+
+        tor_true = []
+        tor_pred = []
+
+        ter_true = []
+        ter_pred = []
+
+        ogc_true = []
+        ogc_pred = []
+
+        performance_fee_true = []
+        performance_fee_pred = []
+
+        missing_error_list = []
+        data_point_list = ["tor", "ter", "ogc", "performance_fee"]
+
+        for index, row in ground_truth_df.iterrows():
+            doc_id = row["doc_id"]
+            # get first row with the same doc_id
+            prediction_data = prediction_df[prediction_df["doc_id"] == doc_id].iloc[0]
+            for data_point in data_point_list:
+                true_data, pred_data, missing_error_data = self.get_true_pred_data(
+                    doc_id, row, prediction_data, data_point
+                )
+                if data_point == "tor":
+                    tor_true.extend(true_data)
+                    tor_pred.extend(pred_data)
+                elif data_point == "ter":
+                    ter_true.extend(true_data)
+                    ter_pred.extend(pred_data)
+                elif data_point == "ogc":
+                    ogc_true.extend(true_data)
+                    ogc_pred.extend(pred_data)
+                elif data_point == "performance_fee":
+                    performance_fee_true.extend(true_data)
+                    performance_fee_pred.extend(pred_data)
+                missing_error_list.append(missing_error_data)
+
+        metrics_list = []
+        for data_point in data_point_list:
+            if data_point == "tor":
+                precision, recall, f1 = self.get_specific_metrics(tor_true, tor_pred)
+                metrics_list.append(
+                    {
+                        "Data_Point": data_point,
+                        "Precision": precision,
+                        "Recall": recall,
+                        "F1": f1,
+                        "Support": len(tor_true),
+                    }
+                )
+                logger.info(
+                    f"TOR Precision: {precision}, Recall: {recall}, F1: {f1}, Support: {len(tor_true)}"
+                )
+            elif data_point == "ter":
+                precision, recall, f1 = self.get_specific_metrics(ter_true, ter_pred)
+                metrics_list.append(
+                    {
+                        "Data_Point": data_point,
+                        "Precision": precision,
+                        "Recall": recall,
+                        "F1": f1,
+                        "Support": len(ter_true),
+                    }
+                )
+                logger.info(
+                    f"TER Precision: {precision}, Recall: {recall}, F1: {f1}, Support: {len(ter_true)}"
+                )
+            elif data_point == "ogc":
+                precision, recall, f1 = self.get_specific_metrics(ogc_true, ogc_pred)
+                metrics_list.append(
+                    {
+                        "Data_Point": data_point,
+                        "Precision": precision,
+                        "Recall": recall,
+                        "F1": f1,
+                        "Support": len(ogc_true),
+                    }
+                )
+                logger.info(
+                    f"OGC Precision: {precision}, Recall: {recall}, F1: {f1}, Support: {len(ogc_true)}"
+                )
+            elif data_point == "performance_fee":
+                precision, recall, f1 = self.get_specific_metrics(
+                    performance_fee_true, performance_fee_pred
+                )
+                metrics_list.append(
+                    {
+                        "Data_Point": data_point,
+                        "Precision": precision,
+                        "Recall": recall,
+                        "F1": f1,
+                        "Support": len(performance_fee_true),
+                    }
+                )
+                logger.info(
+                    f"Performance Fee Precision: {precision}, Recall: {recall}, F1: {f1}, Support: {len(performance_fee_true)}"
+                )
+
+        # get average metrics
+        precision_list = [metric["Precision"] for metric in metrics_list]
+        recall_list = [metric["Recall"] for metric in metrics_list]
+        f1_list = [metric["F1"] for metric in metrics_list]
+        metrics_list.append(
+            {
+                "Data_Point": "Average",
+                "Precision": sum(precision_list) / len(precision_list),
+                "Recall": sum(recall_list) / len(recall_list),
+                "F1": sum(f1_list) / len(f1_list),
+                "Support": sum([metric["Support"] for metric in metrics_list]),
+            }
+        )
+        return missing_error_list, metrics_list
+
+    def get_true_pred_data(
+        self, doc_id, ground_truth_data: pd.Series, prediction_data: pd.Series, data_point: str
+    ):
+        ground_truth_list = ground_truth_data[data_point]
+        if isinstance(ground_truth_list, str):
+            ground_truth_list = json.loads(ground_truth_list)
+        prediction_list = prediction_data[data_point]
+        if isinstance(prediction_list, str):
+            prediction_list = json.loads(prediction_list)
+
+        true_data = []
+        pred_data = []
+        
+        
+        missing_error_data = {"doc_id": doc_id, "data_point": data_point, "missing": [], "error": []}
+        
+        missing_data = []
+        error_data = []
+
+        if len(ground_truth_list) == 0 and len(prediction_list) == 0:
+            true_data.append(1)
+            pred_data.append(1)
+            return true_data, pred_data, missing_error_data
+        
+        for prediction in prediction_list:
+            if prediction in ground_truth_list:
+                true_data.append(1)
+                pred_data.append(1)
+            else:
+                true_data.append(0)
+                pred_data.append(1)
+                error_data.append(prediction)
+
+        for ground_truth in ground_truth_list:
+            if ground_truth not in prediction_list:
+                true_data.append(1)
+                pred_data.append(0)
+                missing_data.append(ground_truth)
+        missing_error_data = {"doc_id": doc_id, "data_point": data_point, "missing": missing_data, "error": error_data}
+        
+        return true_data, pred_data, missing_error_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

core/page_filter.py

@@ -22,7 +22,7 @@ class FilterPages:
             self.document_mapping_info_df = document_mapping_info_df
         self.get_configuration_from_file()
         self.doc_info = self.get_doc_info()
-        self.datapoint_config = self.get_datapoint_config()
+        self.datapoint_config, self.datapoint_exclude_config = self.get_datapoint_config()
 
     def get_pdf_page_text_dict(self) -> dict:
         pdf_util = PDFUtil(self.pdf_file)
@@ -33,12 +33,15 @@ class FilterPages:
         language_config_file = r"./configuration/language.json"
         domicile_datapoint_config_file = r"./configuration/domicile_datapoints.json"
         datapoint_keywords_config_file = r"./configuration/datapoint_keyword.json"
+        datapoint_exclude_keywords_config_file = r"./configuration/datapoint_exclude_keyword.json"
         with open(language_config_file, "r", encoding="utf-8") as file:
             self.language_config = json.load(file)
         with open(domicile_datapoint_config_file, "r", encoding="utf-8") as file:
             self.domicile_datapoint_config = json.load(file)
         with open(datapoint_keywords_config_file, "r", encoding="utf-8") as file:
             self.datapoint_keywords_config = json.load(file)
+        with open(datapoint_exclude_keywords_config_file, "r", encoding="utf-8") as file:
+            self.datapoint_exclude_keywords_config = json.load(file)
 
     def get_doc_info(self) -> dict:
         if len(self.document_mapping_info_df) == 0:
@@ -77,18 +80,30 @@ class FilterPages:
         if self.domicile_datapoint_config[domicile].get(document_type, None) is None:
             document_type = "ar"
         datapoint_list =  self.domicile_datapoint_config[domicile][document_type]
+        datapoint_keywords = self.get_keywords("include", datapoint_list, language)
+        datapoint_exclude_keywords = self.get_keywords("exclude", datapoint_list, language)
+        return datapoint_keywords, datapoint_exclude_keywords
+    
+    def get_keywords(self, keywords_type: str, datapoint_list: list, language: str) -> dict:
+        if keywords_type == "include":
+            config = self.datapoint_keywords_config
+        elif keywords_type == "exclude":
+            config = self.datapoint_exclude_keywords_config
+        else:
+            config = self.datapoint_keywords_config
         datapoint_keywords = {}
+        
         for datapoint in datapoint_list:
-            keywords = self.datapoint_keywords_config.get(datapoint, {}).get(language, [])
+            keywords = config.get(datapoint, {}).get(language, [])
             if len(keywords) > 0:
                 keywords = self.optimize_keywords_regex(keywords)
             datapoint_keywords[datapoint] = keywords
             if language != "english":
-                english_keywords = self.datapoint_keywords_config.get(datapoint, {}).get("english", [])
+                english_keywords = config.get(datapoint, {}).get("english", [])
                 if len(english_keywords) > 0:
                     english_keywords = self.optimize_keywords_regex(english_keywords)
                     datapoint_keywords[datapoint] += english_keywords
-        return datapoint_keywords
+        return datapoint_keywords   
     
     def optimize_keywords_regex(self, keywords: list) -> list:
         new_keywords = []
@@ -110,15 +125,78 @@ class FilterPages:
         }
         """
         result = {"doc_id": self.doc_id}
+        result_details = []
         for datapoint in self.datapoint_config.keys():
             result[datapoint] = []
         for page_num, page_text in self.page_text_dict.items():
-            text = clean_text(page_text)
+            text = "\n" + clean_text(page_text) + "\n"
             for datapoint, keywords in self.datapoint_config.items():
                 # idx = idx & np.array([re.findall(r'\b' + word + r'\d*\b', page) != [] for page in self.pages_clean])
+                find_datapoint = False
                 for keyword in keywords:
-                    search_regex = r"\b{0}\d*\b\s*".format(keyword)
+                    search_iter = self.search_keyword(text, keyword)
-                    if re.search(search_regex, text, re.IGNORECASE):
+                    for search in search_iter:
+                        search_text = search.group().strip()
+                        exclude_search_list = self.search_exclude_keywords(text, datapoint)
+                        if exclude_search_list is not None:
+                            need_exclude = False
+                            for exclude_search_text in exclude_search_list:
+                                if search_text in exclude_search_text:
+                                    need_exclude = True
+                                    break
+                            if need_exclude:
+                                continue
+                        
+                        is_valid = self.search_in_sentence_is_valid(search_text, text)
+                        if not is_valid:
+                            continue
                         result[datapoint].append(page_num)
+                        detail = {
+                            "doc_id": self.doc_id,
+                            "datapoint": datapoint,
+                            "page_num": page_num,
+                            "keyword": keyword,
+                            "text": search_text
+                        }
+                        result_details.append(detail)
+                        find_datapoint = True
                         break
-        return result
+                    if find_datapoint:
+                        break
+        return result, result_details
+    
+    def search_in_sentence_is_valid(self, 
+                                    search_text: str,
+                                    text: str):
+        search_text_regex = add_slash_to_text_as_regex(search_text)
+        search_regex = r"\n.*{0}.*\n".format(search_text_regex)
+        search_iter = re.finditer(search_regex, text, re.IGNORECASE)
+        is_valid = False
+        lower_word_count_threshold = 7
+        for search in search_iter:
+            lower_word_count = 0
+            if search is not None:
+                # get the word count in search text which start with lower case
+                search_text = search.group().strip()
+                search_text_split = search_text.split()
+                for split in search_text_split:
+                    if split[0].islower():
+                        lower_word_count += 1
+                if lower_word_count < lower_word_count_threshold:
+                    is_valid = True
+                    break
+        return is_valid
+
+    def search_keyword(self, text: str, keyword: str):
+        search_regex = r"\b{0}\d*\W*\s*\b".format(keyword)
+        return re.finditer(search_regex, text, re.IGNORECASE)
+    
+    def search_exclude_keywords(self, text: str, datapoint: str):
+        exclude_keywords = self.datapoint_exclude_config.get(datapoint, [])
+        search_list = []
+        for keyword in exclude_keywords:
+            search_iter = self.search_keyword(text, keyword)
+            
+            for search in search_iter:
+                search_list.append(search.group())
+        return search_list

main.py

@@ -8,6 +8,7 @@ from utils.logger import logger
 from utils.pdf_download import download_pdf_from_documents_warehouse
 from utils.sql_query_util import query_document_fund_mapping
 from core.page_filter import FilterPages
+from core.metrics import Metrics
 
 
 class EMEA_AR_Parsing:
@@ -17,7 +18,7 @@ class EMEA_AR_Parsing:
         os.makedirs(self.pdf_folder, exist_ok=True)
         self.pdf_file = self.download_pdf()
         self.document_mapping_info_df = query_document_fund_mapping(doc_id)
-        self.datapoint_page_info = self.get_datapoint_page_info()
+        self.datapoint_page_info, self.result_details = self.get_datapoint_page_info()
 
     def download_pdf(self) -> str:
         pdf_file = download_pdf_from_documents_warehouse(self.pdf_folder, self.doc_id)
@@ -27,26 +28,51 @@ class EMEA_AR_Parsing:
         filter_pages = FilterPages(
             self.doc_id, self.pdf_file, self.document_mapping_info_df
         )
-        datapoint_page_info = filter_pages.start_job()
+        datapoint_page_info, result_details = filter_pages.start_job()
-        return datapoint_page_info
+        return datapoint_page_info, result_details
 
 
 def filter_pages(doc_id: str, pdf_folder: str) -> None:
     logger.info(f"Parsing EMEA AR for doc_id: {doc_id}")
     emea_ar_parsing = EMEA_AR_Parsing(doc_id, pdf_folder)
-    return emea_ar_parsing.datapoint_page_info
+    return emea_ar_parsing.datapoint_page_info, emea_ar_parsing.result_details
 
 
-def batch_filter_pdf_files(pdf_folder: str, output_folder: str) -> None:
+def batch_filter_pdf_files(
+    pdf_folder: str,
+    doc_data_excel_file: str = None,
+    output_folder: str = r"/data/emea_ar/output/filter_pages/",
+    special_doc_id_list: list = None,
+) -> None:
     pdf_files = glob(pdf_folder + "*.pdf")
+    doc_list = []
+    if special_doc_id_list is not None and len(special_doc_id_list) > 0:
+        doc_list = special_doc_id_list
+    if (
+        len(doc_list) == 0
+        and doc_data_excel_file is not None
+        and len(doc_data_excel_file) > 0
+        and os.path.exists(doc_data_excel_file)
+    ):
+        doc_data_df = pd.read_excel(doc_data_excel_file)
+        doc_data_df = doc_data_df[doc_data_df["Checked"] == 1]
+        doc_list = [str(doc_id) for doc_id in doc_data_df["doc_id"].tolist()]
     result_list = []
+    result_details = []
     for pdf_file in tqdm(pdf_files):
         pdf_base_name = os.path.basename(pdf_file)
         doc_id = pdf_base_name.split(".")[0]
-        datapoint_page_info = filter_pages(doc_id=doc_id, pdf_folder=pdf_folder)
+        if doc_list is not None and doc_id not in doc_list:
-        result_list.append(datapoint_page_info)
+            continue
+        doc_datapoint_page_info, doc_result_details = filter_pages(doc_id=doc_id, pdf_folder=pdf_folder)
+        result_list.append(doc_datapoint_page_info)
+        result_details.extend(doc_result_details)
+    
     result_df = pd.DataFrame(result_list)
     result_df.reset_index(drop=True, inplace=True)
+    
+    result_details_df = pd.DataFrame(result_details)
+    result_details_df.reset_index(drop=True, inplace=True)
 
     logger.info(f"Saving the result to {output_folder}")
     os.makedirs(output_folder, exist_ok=True)
@@ -56,10 +82,54 @@ def batch_filter_pdf_files(pdf_folder: str, output_folder: str) -> None:
         f"datapoint_page_info_{len(result_df)}_documents_{time_stamp}.xlsx",
     )
     with pd.ExcelWriter(output_file) as writer:
-        result_df.to_excel(writer, index=False)
+        result_df.to_excel(writer, index=False, sheet_name="dp_page_info")
+        result_details_df.to_excel(writer, index=False, sheet_name="dp_page_info_details")
+
+    if len(special_doc_id_list) == 0:
+        logger.info(f"Calculating metrics for {output_file}")
+        metrics_output_folder = r"/data/emea_ar/output/metrics/"
+        missing_error_list, metrics_list, metrics_file = get_metrics(
+            data_type="page_filter",
+            prediction_file=output_file,
+            prediction_sheet_name="dp_page_info",
+            ground_truth_file=doc_data_excel_file,
+            output_folder=metrics_output_folder,
+        )
+        return missing_error_list, metrics_list, metrics_file
+
+
+def get_metrics(
+    data_type: str,
+    prediction_file: str,
+    prediction_sheet_name: str,
+    ground_truth_file: str,
+    output_folder: str = None
+) -> None:
+    metrics = Metrics(
+        data_type=data_type,
+        prediction_file=prediction_file,
+        prediction_sheet_name=prediction_sheet_name,
+        ground_truth_file=ground_truth_file,
+        output_folder=output_folder
+    )
+    missing_error_list, metrics_list, metrics_file = metrics.get_metrics()
+    return missing_error_list, metrics_list, metrics_file
 
 
 if __name__ == "__main__":
     pdf_folder = r"/data/emea_ar/small_pdf/"
-    output_folder = r"/data/emea_ar/output/filter_pages/"
+    page_filter_ground_truth_file = (
-    batch_filter_pdf_files(pdf_folder, output_folder)
+        r"/data/emea_ar/ground_truth/page_filter/datapoint_page_info_88_documents.xlsx"
+    )
+    prediction_output_folder = r"/data/emea_ar/output/filter_pages/"
+    metrics_output_folder = r"/data/emea_ar/output/metrics/"
+    special_doc_id_list = []
+    batch_filter_pdf_files(
+        pdf_folder, page_filter_ground_truth_file, prediction_output_folder, special_doc_id_list
+    )
+
+    # data_type = "page_filter"
+    # prediction_file = r"/data/emea_ar/output/filter_pages/datapoint_page_info_73_documents_20240903145002.xlsx"
+    # missing_error_list, metrics_list, metrics_file = get_metrics(
+    #     data_type, prediction_file, page_filter_ground_truth_file, metrics_output_folder
+    # )

utils/biz_utils.py

@@ -15,8 +15,8 @@ def add_slash_to_text_as_regex(text: str):
 
 
 def clean_text(text: str) -> str:
-    text = text.lower()
+    # text = text.lower()
     # update the specical character which begin with \u, e.g \u2004 or \u00a0 to be space
-    text = re.sub(r"\\u[0-9a-z]{4}", ' ', text)
+    text = re.sub(r"\\u[A-Z0-9a-z]{4}", ' ', text)
     text = re.sub(r"( ){2,}", ' ', text.strip())
     return text