import os
import pandas as pd
import time
import json
from sklearn.metrics import precision_score, recall_score, f1_score
from utils.biz_utils import get_unique_words_text, get_beginning_common_words, remove_special_characters
from utils.sql_query_util import query_document_fund_mapping
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,
        ground_truth_sheet_name: str = "Sheet1",
        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
        self.ground_truth_sheet_name = ground_truth_sheet_name

        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, strict_model: bool = False):
        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}
        ]

        missing_error_list, metrics_list = self.calculate_metrics(strict_model=strict_model)

        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 calculate_metrics(self, strict_model: bool = False):
        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=self.ground_truth_sheet_name
        )
        if self.data_type == "page_filter":
            ground_truth_df = ground_truth_df[ground_truth_df["Checked"] == 1]
        elif self.data_type == "data_extraction":
            ground_truth_df = ground_truth_df[ground_truth_df["rawname_checked"] == 1]
        elif self.data_type in ["investment_mapping", "document_mapping_in_db"]:
            ground_truth_df = ground_truth_df[ground_truth_df["mapping_checked"] == 1]
        else:
            logger.error(f"Invalid data type: {self.data_type}")
            return [], []

        tor_true = []
        tor_pred = []

        ter_true = []
        ter_pred = []

        ogc_true = []
        ogc_pred = []

        performance_fee_true = []
        performance_fee_pred = []
        
        investment_mapping_true = []
        investment_mapping_pred = []

        missing_error_list = []
        data_point_list = ["tor", "ter", "ogc", "performance_fee"]

        if self.data_type == "page_filter":
            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_page_filter_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)
        elif self.data_type == "data_extraction":
            prediction_doc_id_list = prediction_df["doc_id"].unique().tolist()
            ground_truth_doc_id_list = ground_truth_df["doc_id"].unique().tolist()
            # get intersection of doc_id_list
            doc_id_list = list(
                set(prediction_doc_id_list) & set(ground_truth_doc_id_list)
            )
            # order by doc_id
            doc_id_list.sort()

            for doc_id in doc_id_list:
                prediction_data = prediction_df[prediction_df["doc_id"] == doc_id]
                ground_truth_data = ground_truth_df[ground_truth_df["doc_id"] == doc_id]
                for data_point in data_point_list:
                    true_data, pred_data, missing_error_data = (
                        self.get_data_extraction_true_pred_data(
                            doc_id, ground_truth_data, 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.extend(missing_error_data)
        elif self.data_type == "investment_mapping":
            prediction_doc_id_list = prediction_df["doc_id"].unique().tolist()
            ground_truth_doc_id_list = ground_truth_df["doc_id"].unique().tolist()
            # get intersection of doc_id_list
            doc_id_list = list(
                set(prediction_doc_id_list) & set(ground_truth_doc_id_list)
            )
            # order by doc_id
            doc_id_list.sort()

            for doc_id in doc_id_list:
                prediction_data = prediction_df[prediction_df["doc_id"] == doc_id]
                ground_truth_data = ground_truth_df[ground_truth_df["doc_id"] == doc_id]
                for data_point in data_point_list:
                    true_data, pred_data, missing_error_data = self.get_investment_mapping_true_pred_data(
                        doc_id, ground_truth_data, prediction_data, data_point
                    )
                    investment_mapping_true.extend(true_data)
                    investment_mapping_pred.extend(pred_data)
                    missing_error_list.extend(missing_error_data)
        elif self.data_type == "document_mapping_in_db":
            prediction_doc_id_list = prediction_df["doc_id"].unique().tolist()
            ground_truth_doc_id_list = ground_truth_df["doc_id"].unique().tolist()
            # get intersection of doc_id_list
            doc_id_list = list(
                set(prediction_doc_id_list) & set(ground_truth_doc_id_list)
            )
            # order by doc_id
            doc_id_list.sort()

            for doc_id in doc_id_list:
                prediction_data = prediction_df[prediction_df["doc_id"] == doc_id]
                ground_truth_data = ground_truth_df[ground_truth_df["doc_id"] == doc_id]
                true_data, pred_data, missing_error_data = self.get_document_mapping_in_db_true_pred_data(
                    doc_id, ground_truth_data, prediction_data, strict_mode=strict_model
                )
                investment_mapping_true.extend(true_data)
                investment_mapping_pred.extend(pred_data)
                missing_error_list.extend(missing_error_data)

        metrics_list = []
        if self.data_type in ["investment_mapping", "document_mapping_in_db"]:
            if len(investment_mapping_true) == 0 and len(investment_mapping_pred) == 0:
                investment_mapping_true.append(1)
                investment_mapping_pred.append(1)
            precision, recall, f1 = self.get_specific_metrics(investment_mapping_true, investment_mapping_pred)
            investment_mapping_support = self.get_support_number(investment_mapping_true)
            metrics_list.append(
                {
                    "Data_Point": "Investment Mapping",
                    "Precision": precision,
                    "Recall": recall,
                    "F1": f1,
                    "Support": investment_mapping_support,
                }
            )
            logger.info(
                        f"Investment mapping Precision: {precision}, Recall: {recall}, F1: {f1}, Support: {investment_mapping_support}"
                    )
        else:
            for data_point in data_point_list:
                if data_point == "tor":
                    precision, recall, f1 = self.get_specific_metrics(tor_true, tor_pred)
                    tor_support = self.get_support_number(tor_true)
                    metrics_list.append(
                        {
                            "Data_Point": data_point,
                            "Precision": precision,
                            "Recall": recall,
                            "F1": f1,
                            "Support": tor_support,
                        }
                    )
                    logger.info(
                        f"TOR Precision: {precision}, Recall: {recall}, F1: {f1}, Support: {tor_support}"
                    )
                elif data_point == "ter":
                    precision, recall, f1 = self.get_specific_metrics(ter_true, ter_pred)
                    ter_support = self.get_support_number(ter_true)
                    metrics_list.append(
                        {
                            "Data_Point": data_point,
                            "Precision": precision,
                            "Recall": recall,
                            "F1": f1,
                            "Support": ter_support,
                        }
                    )
                    logger.info(
                        f"TER Precision: {precision}, Recall: {recall}, F1: {f1}, Support: {ter_support}"
                    )
                elif data_point == "ogc":
                    precision, recall, f1 = self.get_specific_metrics(ogc_true, ogc_pred)
                    ogc_support = self.get_support_number(ogc_true)
                    metrics_list.append(
                        {
                            "Data_Point": data_point,
                            "Precision": precision,
                            "Recall": recall,
                            "F1": f1,
                            "Support": ogc_support,
                        }
                    )
                    logger.info(
                        f"OGC Precision: {precision}, Recall: {recall}, F1: {f1}, Support: {ogc_support}"
                    )
                elif data_point == "performance_fee":
                    precision, recall, f1 = self.get_specific_metrics(
                        performance_fee_true, performance_fee_pred
                    )
                    performance_fee_support = self.get_support_number(performance_fee_true)
                    metrics_list.append(
                        {
                            "Data_Point": data_point,
                            "Precision": precision,
                            "Recall": recall,
                            "F1": f1,
                            "Support": performance_fee_support,
                        }
                    )
                    logger.info(
                        f"Performance Fee Precision: {precision}, Recall: {recall}, F1: {f1}, Support: {performance_fee_support}"
                    )

            # 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_support_number(self, true_data: list):
        # get the count which true_data is 1
        return sum(true_data)

    def get_page_filter_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_data_extraction_true_pred_data(
        self,
        doc_id,
        ground_truth_data: pd.DataFrame,
        prediction_data: pd.DataFrame,
        data_point: str,
    ):
        dp_prediction = prediction_data[prediction_data["datapoint"] == data_point]
        dp_prediction = self.modify_data(dp_prediction)
        pred_simple_raw_names = dp_prediction["simple_raw_name"].unique().tolist()
        pred_simple_name_unique_words_list = (
            dp_prediction["simple_name_unique_words"].unique().tolist()
        )

        dp_ground_truth = ground_truth_data[
            ground_truth_data["datapoint"] == data_point
        ]
        dp_ground_truth = self.modify_data(dp_ground_truth)
        gt_simple_raw_names = dp_ground_truth["simple_raw_name"].unique().tolist()
        gt_simple_name_unique_words_list = (
            dp_ground_truth["simple_name_unique_words"].unique().tolist()
        )

        true_data = []
        pred_data = []

        missing_error_data = []

        if len(dp_ground_truth) == 0 and len(dp_prediction) == 0:
            true_data.append(1)
            pred_data.append(1)
            return true_data, pred_data, missing_error_data

        for index, prediction in dp_prediction.iterrows():
            pred_page_index = prediction["page_index"]
            pred_raw_name = prediction["raw_name"]
            pred_simple_raw_name = prediction["simple_raw_name"]
            pred_simple_name_unique_words = prediction["simple_name_unique_words"]
            pred_data_point_value = prediction["value"]
            pred_investment_type = prediction["investment_type"]

            find_raw_name_in_gt = [
                gt_raw_name
                for gt_raw_name in gt_simple_raw_names
                if (
                    gt_raw_name in pred_simple_raw_name
                    or pred_simple_raw_name in gt_raw_name
                )
                and gt_raw_name.endswith(pred_simple_raw_name.split()[-1])
            ]

            if (
                pred_simple_name_unique_words in gt_simple_name_unique_words_list
                or len(find_raw_name_in_gt) > 0
            ):
                # get the ground truth data with the same unique words
                if pred_simple_name_unique_words in gt_simple_name_unique_words_list:
                    gt_data_df = dp_ground_truth[
                        dp_ground_truth["simple_name_unique_words"]
                        == pred_simple_name_unique_words
                    ]
                    if len(gt_data_df) > 1:
                        if (
                            len(gt_data_df[gt_data_df["page_index"] == pred_page_index])
                            == 0
                        ):
                            gt_data = gt_data_df.iloc[0]
                        else:
                            gt_data = gt_data_df[
                                gt_data_df["page_index"] == pred_page_index
                            ].iloc[0]
                    elif len(gt_data_df) == 1:
                        gt_data = gt_data_df.iloc[0]
                    else:
                        gt_data = None
                else:
                    gt_data_df = dp_ground_truth[
                        dp_ground_truth["simple_raw_name"] == find_raw_name_in_gt[0]
                    ]
                    if len(gt_data_df) > 1:
                        if (
                            len(gt_data_df[gt_data_df["page_index"] == pred_page_index])
                            == 0
                        ):
                            gt_data = gt_data_df.iloc[0]
                        else:
                            gt_data = gt_data_df[
                                gt_data_df["page_index"] == pred_page_index
                            ].iloc[0]
                    elif len(gt_data_df) == 1:
                        gt_data = gt_data_df.iloc[0]
                    else:
                        gt_data = None
                if gt_data is None:
                    gt_data_point_value = None
                else:
                    gt_data_point_value = gt_data["value"]
                if (
                    gt_data_point_value is not None
                    and pred_data_point_value == gt_data_point_value
                ):
                    true_data.append(1)
                    pred_data.append(1)
                else:
                    true_data.append(0)
                    pred_data.append(1)
                    error_data = {
                        "doc_id": doc_id,
                        "data_point": data_point,
                        "page_index": pred_page_index,
                        "pred_raw_name": pred_raw_name,
                        "investment_type": pred_investment_type,
                        "error_type": "data value incorrect",
                        "error_value": pred_data_point_value,
                        "correct_value": gt_data_point_value,
                    }
                    missing_error_data.append(error_data)
            else:
                # If data point is performance fees, and value is 0,
                # then it's correct
                pred_value_num = None
                try:
                    pred_value_num = float(pred_data_point_value)
                except:
                    pass
                if data_point == "performance_fee" and pred_value_num == 0:
                    true_data.append(1)
                    pred_data.append(1)
                else:
                    true_data.append(0)
                    pred_data.append(1)
                    error_data = {
                        "doc_id": doc_id,
                        "data_point": data_point,
                        "page_index": pred_page_index,
                        "pred_raw_name": pred_raw_name,
                        "investment_type": pred_investment_type,
                        "error_type": "raw name incorrect",
                        "error_value": pred_raw_name,
                        "correct_value": "",
                    }
                    missing_error_data.append(error_data)

        for index, ground_truth in dp_ground_truth.iterrows():
            gt_page_index = ground_truth["page_index"]
            gt_raw_name = ground_truth["raw_name"]
            gt_simple_raw_name = ground_truth["simple_raw_name"]
            gt_simple_name_unique_words = ground_truth["simple_name_unique_words"]
            gt_data_point_value = ground_truth["value"]
            gt_investment_type = ground_truth["investment_type"]

            find_raw_name_in_pred = [
                pred_raw_name
                for pred_raw_name in pred_simple_raw_names
                if (
                    gt_simple_raw_name in pred_raw_name
                    or pred_raw_name in gt_simple_raw_name
                )
                and pred_raw_name.endswith(gt_simple_raw_name.split()[-1])
            ]

            if (
                gt_simple_name_unique_words not in pred_simple_name_unique_words_list
                and len(find_raw_name_in_pred) == 0
            ):
                gt_value_num = None
                try:
                    gt_value_num = float(gt_data_point_value)
                except:
                    pass
                # If data point is performance fees, and value is 0,
                # then it's correct
                if data_point == "performance_fee" and gt_value_num == 0:
                    true_data.append(1)
                    pred_data.append(1)
                else:
                    true_data.append(1)
                    pred_data.append(0)
                    error_data = {
                        "doc_id": doc_id,
                        "data_point": data_point,
                        "page_index": gt_page_index,
                        "pred_raw_name": "",
                        "investment_type": gt_investment_type,
                        "error_type": "raw name missing",
                        "error_value": "",
                        "correct_value": gt_raw_name,
                    }
                    missing_error_data.append(error_data)

        return true_data, pred_data, missing_error_data

    def get_investment_mapping_true_pred_data(
        self,
        doc_id,
        ground_truth_data: pd.DataFrame,
        prediction_data: pd.DataFrame,
        data_point: str,
    ):
        dp_prediction = prediction_data[prediction_data["datapoint"] == data_point]
        dp_prediction = self.modify_data(dp_prediction)
        pred_simple_raw_names = dp_prediction["simple_raw_name"].unique().tolist()
        pred_simple_name_unique_words_list = (
            dp_prediction["simple_name_unique_words"].unique().tolist()
        )

        dp_ground_truth = ground_truth_data[
            ground_truth_data["datapoint"] == data_point
        ]
        dp_ground_truth = self.modify_data(dp_ground_truth)
        gt_simple_raw_names = dp_ground_truth["simple_raw_name"].unique().tolist()
        gt_simple_name_unique_words_list = (
            dp_ground_truth["simple_name_unique_words"].unique().tolist()
        )
        
        compare_data_list = []
        for index, ground_truth in dp_ground_truth.iterrows():
            gt_page_index = ground_truth["page_index"]
            gt_raw_name = ground_truth["raw_name"]
            gt_simple_raw_name = ground_truth["simple_raw_name"]
            gt_simple_name_unique_words = ground_truth["simple_name_unique_words"]
            gt_investment_type = ground_truth["investment_type"]

            find_raw_name_in_pred = [
                pred_raw_name
                for pred_raw_name in pred_simple_raw_names
                if (
                    gt_simple_raw_name in pred_raw_name
                    or pred_raw_name in gt_simple_raw_name
                )
                and pred_raw_name.endswith(gt_simple_raw_name.split()[-1])
            ]

            if (
                gt_simple_name_unique_words in pred_simple_name_unique_words_list
                or len(find_raw_name_in_pred) > 0
            ):
                # get the ground truth data with the same unique words
                if gt_simple_name_unique_words in pred_simple_name_unique_words_list:
                    pred_data_df = dp_prediction[
                        dp_prediction["simple_name_unique_words"]
                        == gt_simple_name_unique_words
                    ]
                    if len(pred_data_df) > 1:
                        if (
                            len(pred_data_df[pred_data_df["page_index"] == gt_page_index])
                            == 0
                        ):
                            pred_data = pred_data_df.iloc[0]
                        else:
                            pred_data = pred_data_df[
                                pred_data_df["page_index"] == gt_page_index
                            ].iloc[0]
                    elif len(pred_data_df) == 1:
                        pred_data = pred_data_df.iloc[0]
                    else:
                        pred_data = None
                else:
                    pred_data_df = dp_prediction[
                        dp_prediction["simple_raw_name"] == find_raw_name_in_pred[0]
                    ]
                    if len(pred_data_df) > 1:
                        if (
                            len(pred_data_df[pred_data_df["page_index"] == gt_page_index])
                            == 0
                        ):
                            pred_data = pred_data_df.iloc[0]
                        else:
                            pred_data = pred_data_df[
                                pred_data_df["page_index"] == gt_page_index
                            ].iloc[0]
                    elif len(pred_data_df) == 1:
                        pred_data = pred_data_df.iloc[0]
                    else:
                        pred_data = None
                if pred_data is not None:
                    compare_data = {"raw_name": gt_raw_name,
                                    "investment_type": gt_investment_type,
                                    "gt_investment_id": ground_truth["investment_id"],
                                    "gt_investment_name": ground_truth["investment_name"],
                                    "pred_investment_id": pred_data["investment_id"],
                                    "pred_investment_name": pred_data["investment_name"]}
                    compare_data_list.append(compare_data)
        
        true_data = []
        pred_data = []
        missing_error_data = []
        
        for compare_data in compare_data_list:
            if compare_data["gt_investment_id"] == compare_data["pred_investment_id"]:
                true_data.append(1)
                pred_data.append(1)
            else:
                true_data.append(1)
                pred_data.append(0)
                error_data = {
                    "doc_id": doc_id,
                    "data_point": data_point,
                    "raw_name": compare_data["raw_name"],
                    "investment_type": compare_data["investment_type"],
                    "error_type": "mapping missing",
                    "error_id": compare_data["pred_investment_id"],
                    "error_name": compare_data["pred_investment_name"],
                    "correct_id": compare_data["gt_investment_id"],
                    "correct_name": compare_data["gt_investment_name"]
                }
                missing_error_data.append(error_data)
        
        for index, prediction in dp_prediction.iterrows():
            pred_raw_name = prediction["raw_name"]
            pred_investment_id = prediction["investment_id"]
            pred_investment_name = prediction["investment_name"]
            pred_investment_type = prediction["investment_type"]
            gt_data_df = dp_ground_truth[dp_ground_truth["investment_id"] == pred_investment_id]
            if len(gt_data_df) == 0:
                true_data.append(0)
                pred_data.append(1)
                error_data = {
                    "doc_id": doc_id,
                    "data_point": data_point,
                    "raw_name": pred_raw_name,
                    "investment_type": pred_investment_type,
                    "error_type": "mapping incorrect",
                    "error_id": pred_investment_id,
                    "error_name": pred_investment_name,
                    "correct_id": "",
                    "correct_name": ""
                }
                missing_error_data.append(error_data)
        return true_data, pred_data, missing_error_data
    
    def get_document_mapping_in_db_true_pred_data(
        self,
        doc_id,
        ground_truth_data: pd.DataFrame,
        prediction_data: pd.DataFrame,
        strict_mode: bool = False,
    ):
        """
        EMEA AR Mapping Metrics based on document mapping in DB
        1. Make ground truth manually
            According to fund name/ share name in document mapping,
            Find relevant data in document data extraction, input mapping id
        2. Metrics calculation
            Recall:
                Based on each document:
                    a. Ground truth data
                    According to the mapping id in document mapping,
                    filter relevant document data extraction records.
                    b. Prediction data
                    Get document mapping by fund/ share raw name from PDF document.
                        mapping correct: true 1 pred 1
                        mapping error
                            mapping is empty:
                            true 1 pred 0 --- hurt recall
                            mapping is incorrect: other fund/ share id:
                                true 1 pred 0 --- hurt recall
                                if incorrect mapping in document mapping:
                                true 0 pred 1 --- hurt precision
        """
        document_mapping_data = query_document_fund_mapping(doc_id)
        if len(document_mapping_data) == 0:
            return [1], [1], []
        fund_id_list = document_mapping_data["FundId"].unique().tolist()
        share_id_list = document_mapping_data["SecId"].unique().tolist()
        id_list = fund_id_list + share_id_list
        
        # get dp_ground_truth which investment_id in id_list
        dp_ground_truth = ground_truth_data[
            ground_truth_data["investment_id"].isin(id_list)
        ]
        
        dp_ground_truth = self.modify_data(dp_ground_truth)
        # only get the columns: doc_id, raw_name, simple_raw_name, simple_name_unique_words, 
        # investment_type, investment_id, investment_name 
        # from dp_ground_truth
        dp_ground_truth = dp_ground_truth[["doc_id", "page_index", "raw_name", "simple_raw_name",
                                           "simple_name_unique_words", "investment_type", 
                                           "investment_id", "investment_name"]]
        dp_ground_truth.drop_duplicates(inplace=True)
        dp_ground_truth.reset_index(drop=True, inplace=True)
        
        # fillnan for dp_prediction investment_id to be "" if it is nan
        prediction_data["investment_id"].fillna("", inplace=True)
        prediction_data["investment_name"].fillna("", inplace=True)
        dp_prediction = self.modify_data(prediction_data)
        dp_prediction = dp_prediction[["doc_id", "page_index", "raw_name", "simple_raw_name",
                                       "simple_name_unique_words", "investment_type",
                                       "investment_id", "investment_name"]]
        dp_prediction.drop_duplicates(inplace=True)
        dp_prediction.reset_index(drop=True, inplace=True)
        # pred_simple_raw_names = dp_prediction["simple_raw_name"].unique().tolist()
        # pred_simple_name_unique_words_list = (
        #     dp_prediction["simple_name_unique_words"].unique().tolist()
        # )

        compare_data_list = []
        gt_investment_id_list = []
        for index, ground_truth in dp_ground_truth.iterrows():
            gt_page_index = ground_truth["page_index"]
            gt_raw_name = ground_truth["raw_name"]
            gt_simple_raw_name = ground_truth["simple_raw_name"]
            gt_simple_name_unique_words = ground_truth["simple_name_unique_words"]
            gt_investment_type = ground_truth["investment_type"]
            gt_investment_id = ground_truth["investment_id"]
            gt_investment_name = ground_truth["investment_name"]
            
            # get pred_simple_raw_names by gt_page_index
            pred_page_data = dp_prediction[dp_prediction["page_index"] == gt_page_index]
            if len(pred_page_data) > 0:
                pred_simple_raw_names = pred_page_data["simple_raw_name"].unique().tolist()
                pred_simple_name_unique_words_list = (
                    pred_page_data["simple_name_unique_words"].unique().tolist()
                )
            else:
                pred_simple_raw_names = []
                pred_simple_name_unique_words_list = []
            
            if gt_investment_id in gt_investment_id_list:
                continue
            find_raw_name_in_pred = [
                pred_raw_name
                for pred_raw_name in pred_simple_raw_names
                if (
                    gt_simple_raw_name in pred_raw_name
                    or pred_raw_name in gt_simple_raw_name
                )
                and pred_raw_name.endswith(gt_simple_raw_name.split()[-1])
            ]

            if (
                gt_simple_name_unique_words in pred_simple_name_unique_words_list
                or len(find_raw_name_in_pred) > 0
            ):
                # get the ground truth data with the same unique words
                if gt_simple_name_unique_words in pred_simple_name_unique_words_list:
                    pred_data_df = dp_prediction[
                        dp_prediction["simple_name_unique_words"]
                        == gt_simple_name_unique_words
                    ]
                    if len(pred_data_df) > 1:
                        if (
                            len(pred_data_df[pred_data_df["page_index"] == gt_page_index])
                            == 0
                        ):
                            pred_data = pred_data_df.iloc[0]
                        else:
                            pred_data = pred_data_df[
                                pred_data_df["page_index"] == gt_page_index
                            ].iloc[0]
                    elif len(pred_data_df) == 1:
                        pred_data = pred_data_df.iloc[0]
                    else:
                        pred_data = None
                else:
                    pred_data_df = dp_prediction[
                        dp_prediction["simple_raw_name"] == find_raw_name_in_pred[0]
                    ]
                    if len(pred_data_df) > 1:
                        if (
                            len(pred_data_df[pred_data_df["page_index"] == gt_page_index])
                            == 0
                        ):
                            pred_data = pred_data_df.iloc[0]
                        else:
                            pred_data = pred_data_df[
                                pred_data_df["page_index"] == gt_page_index
                            ].iloc[0]
                    elif len(pred_data_df) == 1:
                        pred_data = pred_data_df.iloc[0]
                    else:
                        pred_data = None
                if pred_data is not None:
                    compare_data = {"raw_name": gt_raw_name,
                                    "investment_type": gt_investment_type,
                                    "gt_investment_id": gt_investment_id,
                                    "gt_investment_name": gt_investment_name,
                                    "pred_investment_id": pred_data["investment_id"],
                                    "pred_investment_name": pred_data["investment_name"]}
                    gt_investment_id_list.append(gt_investment_id)
                    compare_data_list.append(compare_data)
            else:
                if strict_mode:
                    compare_data = {"raw_name": gt_raw_name,
                                    "investment_type": gt_investment_type,
                                    "gt_investment_id": gt_investment_id,
                                    "gt_investment_name": gt_investment_name,
                                    "pred_investment_id": "",
                                    "pred_investment_name": ""}
                    compare_data_list.append(compare_data)
        
        true_data = []
        pred_data = []
        missing_error_data = []
        
        for compare_data in compare_data_list:
            gt_investment_id = compare_data["gt_investment_id"]
            pred_investment_id = compare_data["pred_investment_id"]
            if gt_investment_id == pred_investment_id:
                true_data.append(1)
                pred_data.append(1)
            else:
                true_data.append(1)
                pred_data.append(0)
                if pred_investment_id is not None and len(pred_investment_id) > 0:
                    if pred_investment_id in id_list:
                        true_data.append(0)
                        pred_data.append(1)
                    error_data = {
                        "doc_id": doc_id,
                        "raw_name": compare_data["raw_name"],
                        "investment_type": compare_data["investment_type"],
                        "error_type": "mapping incorrect",
                        "error_id": pred_investment_id,
                        "error_name": compare_data["pred_investment_name"],
                        "correct_id": compare_data["gt_investment_id"],
                        "correct_name": compare_data["gt_investment_name"]
                    }
                else:
                    error_data = {
                        "doc_id": doc_id,
                        "raw_name": compare_data["raw_name"],
                        "investment_type": compare_data["investment_type"],
                        "error_type": "mapping missing",
                        "error_id": "",
                        "error_name": "",
                        "correct_id": compare_data["gt_investment_id"],
                        "correct_name": compare_data["gt_investment_name"]
                    }
                missing_error_data.append(error_data)
        
        return true_data, pred_data, missing_error_data
    
    def modify_data(self, data: pd.DataFrame):
        data["simple_raw_name"] = ""
        data["simple_name_unique_words"] = ""
        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
                simple_raw_name = remove_special_characters(simple_raw_name)
                temp_splits = [word for word in simple_raw_name.split() 
                               if word.lower() not in ["class"]]
                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