<reponame>entelecheia/eKorpKit
import logging
import pandas as pd
from .base import BaseSentimentAnalyser

log = logging.getLogger(__name__)


class HIV4SA(BaseSentimentAnalyser):
    """
    A class for sentiment analysis using the HIV4 lexicon.
    """

    def __init__(self, **kwargs):
        super().__init__(**kwargs)

    def _get_score(self, tokens, lexicon_features, feature="polarity"):
        """Get score for features.

        :returns: int
        """
        lxfeat_names = self._features.get(feature).get("lexicon_features")
        lxfeat = pd.DataFrame.from_dict(lexicon_features, orient="index")
        score = {}
        if feature == "polarity":
            lxfeat["pos"] = lxfeat.apply(
                lambda x: 1 * x["count"] if x["Positiv"] else 0, axis=1
            )
            lxfeat["neg"] = lxfeat.apply(
                lambda x: 1 * x["count"] if x["Negativ"] else 0, axis=1
            )
            lxfeat_agg = lxfeat.agg({"pos": "sum", "neg": "sum"})
            polarity = (lxfeat_agg["pos"] - lxfeat_agg["neg"]) / (
                lxfeat_agg["pos"] + lxfeat_agg["neg"] + self.EPSILON
            )
            polarity2 = (lxfeat_agg["pos"] - lxfeat_agg["neg"]) / (
                len(tokens) + self.EPSILON
            )
            subjectivity = (lxfeat_agg["pos"] + lxfeat_agg["neg"]) / (
                len(tokens) + self.EPSILON
            )
            score["positive"] = lxfeat_agg["pos"] / (len(tokens) + self.EPSILON)
            score["negative"] = lxfeat_agg["neg"] / (len(tokens) + self.EPSILON)
            score["polarity"] = polarity
            score["polarity2"] = polarity2
            score["subjectivity"] = subjectivity
        elif isinstance(lxfeat_names, str):
            lxfeat[feature] = lxfeat.apply(
                lambda x: 1 * x["count"] if x[lxfeat_names] else 0, axis=1
            )
            lxfeat_agg = lxfeat.agg({feature: "sum"})
            feat_score = lxfeat_agg[feature] / (len(tokens) + self.EPSILON)
            score[feature] = feat_score

        return score

    def _assign_class(self, score, feature="polarity"):
        """Assign class to a score.

        :returns: str
        """
        labels = self._features.get(feature).get("labels")
        if labels:
            score["label"] = ""
            for label, thresh in labels.items():
                if isinstance(thresh, str):
                    thresh = eval(thresh)
                if score[feature] >= thresh[0] and score[feature] <= thresh[1]:
                    score["label"] = label
        return score


class LMSA(BaseSentimentAnalyser):
    """
    A class for sentiment analysis using the LM lexicon.
    """

    def __init__(self, **kwargs):
        super().__init__(**kwargs)

    def _get_score(self, tokens, lexicon_features, feature="polarity"):
        """Get score for features.

        :returns: int
        """
        lxfeat_names = self._features.get(feature).get("lexicon_features")
        lxfeat = pd.DataFrame.from_dict(lexicon_features, orient="index")
        score = {}
        if lxfeat.empty:
            return score
        if feature == "polarity":
            lxfeat["pos"] = lxfeat.apply(
                lambda x: 1 * x["count"] if x["Positive"] > 0 else 0, axis=1
            )
            lxfeat["neg"] = lxfeat.apply(
                lambda x: 1 * x["count"] if x["Negative"] > 0 else 0, axis=1
            )
            lxfeat_agg = lxfeat.agg({"pos": "sum", "neg": "sum"})
            polarity = (lxfeat_agg["pos"] - lxfeat_agg["neg"]) / (
                lxfeat_agg["pos"] + lxfeat_agg["neg"] + self.EPSILON
            )
            polarity2 = (lxfeat_agg["pos"] - lxfeat_agg["neg"]) / (
                len(tokens) + self.EPSILON
            )
            subjectivity = (lxfeat_agg["pos"] + lxfeat_agg["neg"]) / (
                len(tokens) + self.EPSILON
            )
            score["positive"] = lxfeat_agg["pos"] / (len(tokens) + self.EPSILON)
            score["negative"] = lxfeat_agg["neg"] / (len(tokens) + self.EPSILON)
            score["num_tokens"] = len(tokens)
            score[feature] = polarity
            score['polarity2'] = polarity2
            score["subjectivity"] = subjectivity
        elif isinstance(lxfeat_names, str):
            lxfeat[feature] = lxfeat.apply(
                lambda x: 1 * x["count"] if x[lxfeat_names] > 0 else 0, axis=1
            )
            lxfeat_agg = lxfeat.agg({feature: "sum"})
            feat_score = lxfeat_agg[feature] / (len(tokens) + self.EPSILON)
            score[feature] = feat_score

        return score

    def _assign_class(self, score, feature="polarity"):
        """Assign class to a score.

        :returns: str
        """
        label_key = feature + "_label"
        labels = self._features.get(feature).get("labels")
        if labels:
            score[label_key] = ""
            for label, thresh in labels.items():
                if isinstance(thresh, str):
                    thresh = eval(thresh)
                if score[feature] >= thresh[0] and score[feature] <= thresh[1]:
                    score[label_key] = label
        return score

import logging
import pandas as pd
from .base import BaseSentimentAnalyser

log = logging.getLogger(__name__)


class HIV4SA(BaseSentimentAnalyser):
    """
    A class for sentiment analysis using the HIV4 lexicon.
    """

    def __init__(self, **kwargs):
        super().__init__(**kwargs)

    def _get_score(self, tokens, lexicon_features, feature="polarity"):
        """Get score for features.

        :returns: int
        """
        lxfeat_names = self._features.get(feature).get("lexicon_features")
        lxfeat = pd.DataFrame.from_dict(lexicon_features, orient="index")
        score = {}
        if feature == "polarity":
            lxfeat["pos"] = lxfeat.apply(
                lambda x: 1 * x["count"] if x["Positiv"] else 0, axis=1
            )
            lxfeat["neg"] = lxfeat.apply(
                lambda x: 1 * x["count"] if x["Negativ"] else 0, axis=1
            )
            lxfeat_agg = lxfeat.agg({"pos": "sum", "neg": "sum"})
            polarity = (lxfeat_agg["pos"] - lxfeat_agg["neg"]) / (
                lxfeat_agg["pos"] + lxfeat_agg["neg"] + self.EPSILON
            )
            polarity2 = (lxfeat_agg["pos"] - lxfeat_agg["neg"]) / (
                len(tokens) + self.EPSILON
            )
            subjectivity = (lxfeat_agg["pos"] + lxfeat_agg["neg"]) / (
                len(tokens) + self.EPSILON
            )
            score["positive"] = lxfeat_agg["pos"] / (len(tokens) + self.EPSILON)
            score["negative"] = lxfeat_agg["neg"] / (len(tokens) + self.EPSILON)
            score["polarity"] = polarity
            score["polarity2"] = polarity2
            score["subjectivity"] = subjectivity
        elif isinstance(lxfeat_names, str):
            lxfeat[feature] = lxfeat.apply(
                lambda x: 1 * x["count"] if x[lxfeat_names] else 0, axis=1
            )
            lxfeat_agg = lxfeat.agg({feature: "sum"})
            feat_score = lxfeat_agg[feature] / (len(tokens) + self.EPSILON)
            score[feature] = feat_score

        return score

    def _assign_class(self, score, feature="polarity"):
        """Assign class to a score.

        :returns: str
        """
        labels = self._features.get(feature).get("labels")
        if labels:
            score["label"] = ""
            for label, thresh in labels.items():
                if isinstance(thresh, str):
                    thresh = eval(thresh)
                if score[feature] >= thresh[0] and score[feature] <= thresh[1]:
                    score["label"] = label
        return score


class LMSA(BaseSentimentAnalyser):
    """
    A class for sentiment analysis using the LM lexicon.
    """

    def __init__(self, **kwargs):
        super().__init__(**kwargs)

    def _get_score(self, tokens, lexicon_features, feature="polarity"):
        """Get score for features.

        :returns: int
        """
        lxfeat_names = self._features.get(feature).get("lexicon_features")
        lxfeat = pd.DataFrame.from_dict(lexicon_features, orient="index")
        score = {}
        if lxfeat.empty:
            return score
        if feature == "polarity":
            lxfeat["pos"] = lxfeat.apply(
                lambda x: 1 * x["count"] if x["Positive"] > 0 else 0, axis=1
            )
            lxfeat["neg"] = lxfeat.apply(
                lambda x: 1 * x["count"] if x["Negative"] > 0 else 0, axis=1
            )
            lxfeat_agg = lxfeat.agg({"pos": "sum", "neg": "sum"})
            polarity = (lxfeat_agg["pos"] - lxfeat_agg["neg"]) / (
                lxfeat_agg["pos"] + lxfeat_agg["neg"] + self.EPSILON
            )
            polarity2 = (lxfeat_agg["pos"] - lxfeat_agg["neg"]) / (
                len(tokens) + self.EPSILON
            )
            subjectivity = (lxfeat_agg["pos"] + lxfeat_agg["neg"]) / (
                len(tokens) + self.EPSILON
            )
            score["positive"] = lxfeat_agg["pos"] / (len(tokens) + self.EPSILON)
            score["negative"] = lxfeat_agg["neg"] / (len(tokens) + self.EPSILON)
            score["num_tokens"] = len(tokens)
            score[feature] = polarity
            score['polarity2'] = polarity2
            score["subjectivity"] = subjectivity
        elif isinstance(lxfeat_names, str):
            lxfeat[feature] = lxfeat.apply(
                lambda x: 1 * x["count"] if x[lxfeat_names] > 0 else 0, axis=1
            )
            lxfeat_agg = lxfeat.agg({feature: "sum"})
            feat_score = lxfeat_agg[feature] / (len(tokens) + self.EPSILON)
            score[feature] = feat_score

        return score

    def _assign_class(self, score, feature="polarity"):
        """Assign class to a score.

        :returns: str
        """
        label_key = feature + "_label"
        labels = self._features.get(feature).get("labels")
        if labels:
            score[label_key] = ""
            for label, thresh in labels.items():
                if isinstance(thresh, str):
                    thresh = eval(thresh)
                if score[feature] >= thresh[0] and score[feature] <= thresh[1]:
                    score[label_key] = label
        return score
A class for sentiment analysis using the HIV4 lexicon. Get score for features.          :returns: int Assign class to a score.          :returns: str A class for sentiment analysis using the LM lexicon. Get score for features.          :returns: int Assign class to a score.          :returns: str