# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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"""ISCO-08 Hierarchical Accuracy Measure."""

from typing import List, Set, Dict, Tuple
import evaluate
import datasets
import re


# TODO: Add BibTeX citation
_CITATION = """
@article{scikit-learn,
  title={Scikit-learn: Machine Learning in {P}ython},
  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
  journal={Journal of Machine Learning Research},
  volume={12},
  pages={2825--2830},
  year={2011}
}
"""

_DESCRIPTION = """
The ISCO-08 Hierarchical Accuracy Measure is an implementation of the measure described in [Functional Annotation of Genes Using Hierarchical Text Categorization](https://www.researchgate.net/publication/44046343_Functional_Annotation_of_Genes_Using_Hierarchical_Text_Categorization) (Kiritchenko, Svetlana and Famili, Fazel. 2005) and adapted for the ISCO-08 classification scheme by the International Labour Organization.

The measure rewards more precise classifications that correctly identify an occupation's placement down to the specific Unit group level and applies penalties for misclassifications based on the hierarchical distance between the correct and assigned categories.

"""

_KWARGS_DESCRIPTION = """
Calculates hierarchical precision, hierarchical recall and hierarchical F1 given a list of reference codes and predicted codes from the ISCO-08 taxonomy by the International Labour Organization.

Args:
    - references (List[str]): List of ISCO-08 reference codes. Each reference code should be a single token, 4-digit ISCO-08 code string.
    - predictions (List[str]): List of machine predicted or human assigned ISCO-08 codes to score. Each prediction should be a single token, 4-digit ISCO-08 code string.

Returns:
    - hierarchical_precision (`float` or `int`): Hierarchical precision score. Minimum possible value is 0. Maximum possible value is 1.0. A higher score means higher accuracy.
    - hierarchical_recall: Hierarchical recall score. Minimum possible value is 0. Maximum possible value is 1.0. A higher score means higher accuracy.
    - hierarchical_fmeasure: Hierarchical F1 score. Minimum possible value is 0. Maximum possible value is 1.0. A higher score means higher accuracy.

Examples:
    Example 1

    >>> ham = evaluate.load("danieldux/isco_hierarchical_accuracy")
    >>> results = ham.compute(reference=["1111", "1112", "1113", "1114", "1120"], predictions=["1111", "1113", "1120", "1211", "2111"])
    >>> print(results)
    {
        "accuracy": 0.2,
        "hierarchical_precision": 0.5,
        "hierarchical_recall": 0.7777777777777778,
        "hierarchical_fmeasure": 0.6086956521739131,
    }
"""

# TODO: Define external resources urls if needed
ISCO_CSV_MIRROR_URL = (
    "https://storage.googleapis.com/isco-public/tables/ISCO_structure.csv"
)
ILO_ISCO_CSV_URL = (
    "https://www.ilo.org/ilostat-files/ISCO/newdocs-08-2021/ISCO-08/ISCO-08%20EN.csv"
)


@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
class ISCO_Hierarchical_Accuracy(evaluate.Metric):
    """The ISCO-08 Hierarchical Accuracy Measure"""

    def _info(self):
        # TODO: Specifies the evaluate.EvaluationModuleInfo object
        return evaluate.MetricInfo(
            # This is the description that will appear on the modules page.
            module_type="metric",
            description=_DESCRIPTION,
            citation=_CITATION,
            inputs_description=_KWARGS_DESCRIPTION,
            features=datasets.Features(
                {
                    "references": datasets.Sequence(datasets.Value("string")),
                    "predictions": datasets.Sequence(datasets.Value("string")),
                }
                if self.config_name == "multilabel"
                else {
                    "references": datasets.Value("string"),
                    "predictions": datasets.Value("string"),
                }
            ),
            # TODO: Homepage of the module for documentation
            homepage="http://module.homepage",
            # TODO: Additional links to the codebase or references
            codebase_urls=["http://github.com/path/to/codebase/of/new_module"],
            reference_urls=["http://path.to.reference.url/new_module"],
        )

    def create_hierarchy_dict(self, file: str) -> dict:
        """
        Creates a dictionary where keys are nodes and values are dictionaries of their parent nodes with distance as weights,
        representing the group level hierarchy of the ISCO-08 structure.

        Args:
        - file: A string representing the path to the CSV file containing the 4-digit ISCO-08 codes. It can be a local path or a web URL.

        Returns:
        - A dictionary where keys are ISCO-08 unit codes and values are dictionaries of their parent codes with distances.
        """

        try:
            import requests
            import csv
        except ImportError as error:
            raise error

        isco_hierarchy = {}

        if file.startswith("http://") or file.startswith("https://"):
            response = requests.get(file)
            lines = response.text.splitlines()
        else:
            with open(file, newline="") as csvfile:
                lines = csvfile.readlines()

        reader = csv.DictReader(lines)
        for row in reader:
            unit_code = row["unit"].zfill(4)
            minor_code = unit_code[0:3]
            sub_major_code = unit_code[0:2]
            major_code = unit_code[0]

            # Assign weights, higher for closer ancestors
            weights = {minor_code: 0.75, sub_major_code: 0.5, major_code: 0.25}

            # Store ancestors with their weights
            isco_hierarchy[unit_code] = weights

        return isco_hierarchy

    def find_ancestors(self, node: str, hierarchy: Dict[str, Set[str]]) -> Set[str]:
        """
        Find the ancestors of a given node in a hierarchy.

        Args:
            node (str): The node for which to find ancestors.
            hierarchy (Dict[str, Set[str]]): A dictionary representing the hierarchy, where the keys are nodes and the values are their parents.

        Returns:
            Set[str]: A set of ancestors of the given node.
        """
        ancestors = set()
        nodes_to_visit = [node]
        while nodes_to_visit:
            current_node = nodes_to_visit.pop()
            if current_node in hierarchy:
                parents = hierarchy[current_node]
                ancestors.update(parents)
                nodes_to_visit.extend(parents)
        return ancestors

    def extend_with_ancestors(self, classes: set, hierarchy: dict) -> set:
        """
        Extend the given set of classes with their ancestors from the hierarchy.

        Args:
            classes (set): The set of classes to extend.
            hierarchy (dict): The hierarchy of classes.

        Returns:
            set: The extended set of classes including their ancestors.
        """
        extended_classes = set(classes)
        for cls in classes:
            ancestors = self.find_ancestors(cls, hierarchy)
            extended_classes.update(ancestors)
        return extended_classes

    def calculate_hierarchical_precision_recall(
        self,
        reference_codes: List[str],
        predicted_codes: List[str],
        hierarchy: Dict[str, Dict[str, float]],
    ) -> Tuple[float, float]:
        """
        Calculates the hierarchical precision and recall given the reference codes, predicted codes, and hierarchy definition.

        Args:
            reference_codes (List[str]): The list of reference codes.
            predicted_codes (List[str]): The list of predicted codes.
            hierarchy (Dict[str, Dict[str, float]]): The hierarchy definition where keys are nodes and values are dictionaries of parent nodes with distances.

        Returns:
            Tuple[float, float]: A tuple containing the hierarchical precision and recall floating point values.
        """
        extended_real = set()
        extended_predicted = set()

        # Extend the sets of reference codes with their ancestors
        for code in reference_codes:
            extended_real.add(code)
            extended_real.update(self.find_ancestors(code, hierarchy))

        # Extend the sets of predicted codes with their ancestors
        for code in predicted_codes:
            extended_predicted.add(code)
            extended_predicted.update(self.find_ancestors(code, hierarchy))

        # Calculate the intersection for recall
        correct_recall = extended_real.intersection(extended_predicted)

        # Calculate the intersection for precision
        correct_precision = set()
        for code in predicted_codes:
            if code in extended_real:
                correct_precision.add(code)
            correct_precision.update(
                self.find_ancestors(code, hierarchy).intersection(extended_real)
            )

        # Calculate hierarchical precision and recall using the size of intersections
        hP = (
            len(correct_precision) / len(extended_predicted)
            if extended_predicted
            else 0
        )
        hR = len(correct_recall) / len(extended_real) if extended_real else 0

        return hP, hR

    def hierarchical_f_measure(self, hP, hR, beta=1.0):
        """
        Calculate the hierarchical F-measure.

        Parameters:
        hP (float): The hierarchical precision.
        hR (float): The hierarchical recall.
        beta (float, optional): The beta value for F-measure calculation. Default is 1.0.

        Returns:
        float: The hierarchical F-measure.
        """
        if hP + hR == 0:
            return 0
        return (beta**2 + 1) * hP * hR / (beta**2 * hP + hR)

    def _download_and_prepare(self, dl_manager):
        """Download external ISCO-08 csv file from the ILO website for creating the hierarchy dictionary."""
        isco_csv = dl_manager.download_and_extract(ISCO_CSV_MIRROR_URL)
        print(f"ISCO CSV file downloaded")
        self.isco_hierarchy = self.create_hierarchy_dict(isco_csv)
        print("Weighted ISCO hierarchy dictionary created as isco_hierarchy")
        # print(self.isco_hierarchy)

    # Function to check if a string matches the 4-digit code pattern
    def _is_valid_code(self, code: str):
        # Regular expression pattern for a 4-digit code
        pattern = r"^\d{4}$"
        if re.match(pattern, code):
            return True
        else:
            return False

    def _validate_codes(self, codes: list, code_type):
        if not all(self._is_valid_code(code) for code in codes):
            raise ValueError(
                f"All {code_type} labels must start with a 4-digit ISCO-08 code string."
            )

    def _compute(self, predictions, references):
        """
        Computes the accuracy scores, hierarchical precision, recall, and f-measure.

        Args:
            predictions (List[str]): A list of 4-digit ISCO-08 prediction label strings.
            references (List[str]): A list of 4-digit ISCO-08 reference label strings.

        Returns:
            dict: A dictionary containing the accuracy, hierarchical precision, hierarchical recall,
                  and hierarchical f-measure scores.
        """
        # Cast all prediction labels as strings
        predictions = [str(p) for p in predictions]
        references = [str(r) for r in references]
        # Check if the first prediction label is longer than 4 characters
        if len(predictions[0]) > 4:
            # Extract the first 4 characters from each prediction label
            predictions = [str(p.split()[0]) for p in predictions]
            # Check if all prediction labels are 4-digit strings
            self._validate_codes(predictions, "prediction")
            # Repeat for reference labels
            references = [str(r.split()[0]) for r in references]
            self._validate_codes(references, "reference")

        # Calculate accuracy
        accuracy = sum(i == j for i, j in zip(predictions, references)) / len(
            predictions
        )
        # Calculate hierarchical precision, recall and f-measure
        hP, hR = self.calculate_hierarchical_precision_recall(
            references, predictions, self.isco_hierarchy
        )
        hF = self.hierarchical_f_measure(hP, hR)

        return {
            "accuracy": accuracy,
            "hierarchical_precision": hP,
            "hierarchical_recall": hR,
            "hierarchical_fmeasure": hF,
        }