from rs_helper.core import Prediction
from rs_helper.core.model import Model
from rs_helper.core.distributed_models import EmbeddingModel, DAN, FastTextWrapper
from joblib import load
import os
from typing import Dict, List
from nltk.tokenize import word_tokenize
from rs_helper.core import LabelMap


class KNNModel(Model):
    """
    Class for nearest neighbor classification in vector space against clean dataset data
    """

    def __init__(self, path_to_model: str, embedding_model: EmbeddingModel) -> None:
        """
        Constructor of KNN classifier.

        :param path_to_model: Path to the classifier
        :type path_to_model: str
        :param embedding_model: The embedding model to generate the vector representations
        :type embedding_model: EmbeddingModel
        """
        if not (isinstance(embedding_model, DAN) or isinstance(embedding_model, FastTextWrapper)):
            raise ValueError("Embedding model must be of type DAN or FastTextWrapper")

        super().__init__(path_to_model)
        self.embedding_model = embedding_model
        self.initialize()

    def initialize(self) -> None:
        """
        Initializes the model by loading the model from the supplied path

        :return: None
        """
        self.model = load(self.path)

    def predict(self, text: str) -> Prediction:
        """
        :param text: Text to predict.
        :type text: str

        :return: The final prediction
        :rtype: Prediction
        """
        x = self.embedding_model.inference(word_tokenize(text), sentence_level=True) \
            if isinstance(self.embedding_model, FastTextWrapper) \
            else self.embedding_model.inference(word_tokenize(text))
        prediction = self.model.predict_proba(x)
        lm = LabelMap(os.path.join(os.path.dirname(self.path), "label_map.json"))

        classes = list()
        values = list()
        for i, y in enumerate(prediction[0]):
            classes.append(lm.get_name(i))
            values.append(y)

        return Prediction(classes=classes, values=values)

    def normalize_result(self, prediction: Prediction) -> Prediction:
        pass

18259c1aefbb3eca27d2debb357cb40af19b6bc6
<mask token>


class KNNModel(Model):
    <mask token>
    <mask token>

    def initialize(self) ->None:
        """
        Initializes the model by loading the model from the supplied path

        :return: None
        """
        self.model = load(self.path)
    <mask token>

    def normalize_result(self, prediction: Prediction) ->Prediction:
        pass

<mask token>


class KNNModel(Model):
    <mask token>

    def __init__(self, path_to_model: str, embedding_model: EmbeddingModel
        ) ->None:
        """
        Constructor of KNN classifier.

        :param path_to_model: Path to the classifier
        :type path_to_model: str
        :param embedding_model: The embedding model to generate the vector representations
        :type embedding_model: EmbeddingModel
        """
        if not (isinstance(embedding_model, DAN) or isinstance(
            embedding_model, FastTextWrapper)):
            raise ValueError(
                'Embedding model must be of type DAN or FastTextWrapper')
        super().__init__(path_to_model)
        self.embedding_model = embedding_model
        self.initialize()

    def initialize(self) ->None:
        """
        Initializes the model by loading the model from the supplied path

        :return: None
        """
        self.model = load(self.path)
    <mask token>

    def normalize_result(self, prediction: Prediction) ->Prediction:
        pass

<mask token>


class KNNModel(Model):
    """
    Class for nearest neighbor classification in vector space against clean dataset data
    """

    def __init__(self, path_to_model: str, embedding_model: EmbeddingModel
        ) ->None:
        """
        Constructor of KNN classifier.

        :param path_to_model: Path to the classifier
        :type path_to_model: str
        :param embedding_model: The embedding model to generate the vector representations
        :type embedding_model: EmbeddingModel
        """
        if not (isinstance(embedding_model, DAN) or isinstance(
            embedding_model, FastTextWrapper)):
            raise ValueError(
                'Embedding model must be of type DAN or FastTextWrapper')
        super().__init__(path_to_model)
        self.embedding_model = embedding_model
        self.initialize()

    def initialize(self) ->None:
        """
        Initializes the model by loading the model from the supplied path

        :return: None
        """
        self.model = load(self.path)

    def predict(self, text: str) ->Prediction:
        """
        :param text: Text to predict.
        :type text: str

        :return: The final prediction
        :rtype: Prediction
        """
        x = self.embedding_model.inference(word_tokenize(text),
            sentence_level=True) if isinstance(self.embedding_model,
            FastTextWrapper) else self.embedding_model.inference(word_tokenize
            (text))
        prediction = self.model.predict_proba(x)
        lm = LabelMap(os.path.join(os.path.dirname(self.path),
            'label_map.json'))
        classes = list()
        values = list()
        for i, y in enumerate(prediction[0]):
            classes.append(lm.get_name(i))
            values.append(y)
        return Prediction(classes=classes, values=values)

    def normalize_result(self, prediction: Prediction) ->Prediction:
        pass

from rs_helper.core import Prediction
from rs_helper.core.model import Model
from rs_helper.core.distributed_models import EmbeddingModel, DAN, FastTextWrapper
from joblib import load
import os
from typing import Dict, List
from nltk.tokenize import word_tokenize
from rs_helper.core import LabelMap


class KNNModel(Model):
    """
    Class for nearest neighbor classification in vector space against clean dataset data
    """

    def __init__(self, path_to_model: str, embedding_model: EmbeddingModel
        ) ->None:
        """
        Constructor of KNN classifier.

        :param path_to_model: Path to the classifier
        :type path_to_model: str
        :param embedding_model: The embedding model to generate the vector representations
        :type embedding_model: EmbeddingModel
        """
        if not (isinstance(embedding_model, DAN) or isinstance(
            embedding_model, FastTextWrapper)):
            raise ValueError(
                'Embedding model must be of type DAN or FastTextWrapper')
        super().__init__(path_to_model)
        self.embedding_model = embedding_model
        self.initialize()

    def initialize(self) ->None:
        """
        Initializes the model by loading the model from the supplied path

        :return: None
        """
        self.model = load(self.path)

    def predict(self, text: str) ->Prediction:
        """
        :param text: Text to predict.
        :type text: str

        :return: The final prediction
        :rtype: Prediction
        """
        x = self.embedding_model.inference(word_tokenize(text),
            sentence_level=True) if isinstance(self.embedding_model,
            FastTextWrapper) else self.embedding_model.inference(word_tokenize
            (text))
        prediction = self.model.predict_proba(x)
        lm = LabelMap(os.path.join(os.path.dirname(self.path),
            'label_map.json'))
        classes = list()
        values = list()
        for i, y in enumerate(prediction[0]):
            classes.append(lm.get_name(i))
            values.append(y)
        return Prediction(classes=classes, values=values)

    def normalize_result(self, prediction: Prediction) ->Prediction:
        pass

from rs_helper.core import Prediction
from rs_helper.core.model import Model
from rs_helper.core.distributed_models import EmbeddingModel, DAN, FastTextWrapper
from joblib import load
import os
from typing import Dict, List
from nltk.tokenize import word_tokenize
from rs_helper.core import LabelMap


class KNNModel(Model):
    """
    Class for nearest neighbor classification in vector space against clean dataset data
    """

    def __init__(self, path_to_model: str, embedding_model: EmbeddingModel) -> None:
        """
        Constructor of KNN classifier.

        :param path_to_model: Path to the classifier
        :type path_to_model: str
        :param embedding_model: The embedding model to generate the vector representations
        :type embedding_model: EmbeddingModel
        """
        if not (isinstance(embedding_model, DAN) or isinstance(embedding_model, FastTextWrapper)):
            raise ValueError("Embedding model must be of type DAN or FastTextWrapper")

        super().__init__(path_to_model)
        self.embedding_model = embedding_model
        self.initialize()

    def initialize(self) -> None:
        """
        Initializes the model by loading the model from the supplied path

        :return: None
        """
        self.model = load(self.path)

    def predict(self, text: str) -> Prediction:
        """
        :param text: Text to predict.
        :type text: str

        :return: The final prediction
        :rtype: Prediction
        """
        x = self.embedding_model.inference(word_tokenize(text), sentence_level=True) \
            if isinstance(self.embedding_model, FastTextWrapper) \
            else self.embedding_model.inference(word_tokenize(text))
        prediction = self.model.predict_proba(x)
        lm = LabelMap(os.path.join(os.path.dirname(self.path), "label_map.json"))

        classes = list()
        values = list()
        for i, y in enumerate(prediction[0]):
            classes.append(lm.get_name(i))
            values.append(y)

        return Prediction(classes=classes, values=values)

    def normalize_result(self, prediction: Prediction) -> Prediction:
        pass
