<reponame>kubistika/presidio
from typing import Dict


class AnalysisExplanation:
    """
    Hold tracing information to explain why PII entities were identified as such.

    :param recognizer: name of recognizer that made the decision
    :param original_score: recognizer's confidence in result
    :param pattern_name: name of pattern
            (if decision was made by a PatternRecognizer)
    :param pattern: regex pattern that was applied (if PatternRecognizer)
    :param validation_result: result of a validation (e.g. checksum)
    :param textual_explanation: Free text for describing
            a decision of a logic or model
    """

    def __init__(
        self,
        recognizer: str,
        original_score: float,
        pattern_name: str = None,
        pattern: str = None,
        validation_result: float = None,
        textual_explanation: str = None,
    ):

        self.recognizer = recognizer
        self.pattern_name = pattern_name
        self.pattern = pattern
        self.original_score = original_score
        self.score = original_score
        self.textual_explanation = textual_explanation
        self.score_context_improvement = 0
        self.supportive_context_word = ""
        self.validation_result = validation_result

    def __repr__(self):
        """Create string representation of the object."""
        return str(self.__dict__)

    def set_improved_score(self, score: float) -> None:
        """Update the score and calculate the difference from the original score."""
        self.score = score
        self.score_context_improvement = self.score - self.original_score

    def set_supportive_context_word(self, word: str) -> None:
        """Set the context word which helped increase the score."""
        self.supportive_context_word = word

    def append_textual_explanation_line(self, text: str) -> None:
        """Append a new line to textual_explanation field."""
        if self.textual_explanation is None:
            self.textual_explanation = text
        else:
            self.textual_explanation = "{}\n{}".format(self.textual_explanation, text)

    def to_dict(self) -> Dict:
        """
        Serialize self to dictionary.

        :return: a dictionary
        """
        return self.__dict__

from typing import Dict


class AnalysisExplanation:
    """
    Hold tracing information to explain why PII entities were identified as such.

    :param recognizer: name of recognizer that made the decision
    :param original_score: recognizer's confidence in result
    :param pattern_name: name of pattern
            (if decision was made by a PatternRecognizer)
    :param pattern: regex pattern that was applied (if PatternRecognizer)
    :param validation_result: result of a validation (e.g. checksum)
    :param textual_explanation: Free text for describing
            a decision of a logic or model
    """

    def __init__(
        self,
        recognizer: str,
        original_score: float,
        pattern_name: str = None,
        pattern: str = None,
        validation_result: float = None,
        textual_explanation: str = None,
    ):

        self.recognizer = recognizer
        self.pattern_name = pattern_name
        self.pattern = pattern
        self.original_score = original_score
        self.score = original_score
        self.textual_explanation = textual_explanation
        self.score_context_improvement = 0
        self.supportive_context_word = ""
        self.validation_result = validation_result

    def __repr__(self):
        """Create string representation of the object."""
        return str(self.__dict__)

    def set_improved_score(self, score: float) -> None:
        """Update the score and calculate the difference from the original score."""
        self.score = score
        self.score_context_improvement = self.score - self.original_score

    def set_supportive_context_word(self, word: str) -> None:
        """Set the context word which helped increase the score."""
        self.supportive_context_word = word

    def append_textual_explanation_line(self, text: str) -> None:
        """Append a new line to textual_explanation field."""
        if self.textual_explanation is None:
            self.textual_explanation = text
        else:
            self.textual_explanation = "{}\n{}".format(self.textual_explanation, text)

    def to_dict(self) -> Dict:
        """
        Serialize self to dictionary.

        :return: a dictionary
        """
        return self.__dict__
Hold tracing information to explain why PII entities were identified as such.      :param recognizer: name of recognizer that made the decision     :param original_score: recognizer's confidence in result     :param pattern_name: name of pattern             (if decision was made by a PatternRecognizer)     :param pattern: regex pattern that was applied (if PatternRecognizer)     :param validation_result: result of a validation (e.g. checksum)     :param textual_explanation: Free text for describing             a decision of a logic or model Create string representation of the object. Update the score and calculate the difference from the original score. Set the context word which helped increase the score. Append a new line to textual_explanation field. Serialize self to dictionary.          :return: a dictionary