from typing import List

import cloudpickle as pickle
import numpy as np
import pandas
import sklearn.metrics as metrics


class ModelWrapper:
    def __init__(self, model_path: str):
        with open(model_path, "rb") as rf:
            self.model = pickle.load(rf)

    def predict(self, X: dict) -> List[int]:
        X = pandas.DataFrame(X)
        return self.model.predict(X).tolist()

    def score(self, X: dict, y: List[int], method: str) -> float:
        X = pandas.DataFrame(X)
        scorer = metrics.get_scorer(method)
        return scorer(self.model, X, y)

987ff22be4a99c27ecf645c260e0451c6f7335d7
<mask token>


class ModelWrapper:
    <mask token>
    <mask token>
    <mask token>

<mask token>


class ModelWrapper:
    <mask token>

    def predict(self, X: dict) ->List[int]:
        X = pandas.DataFrame(X)
        return self.model.predict(X).tolist()

    def score(self, X: dict, y: List[int], method: str) ->float:
        X = pandas.DataFrame(X)
        scorer = metrics.get_scorer(method)
        return scorer(self.model, X, y)

<mask token>


class ModelWrapper:

    def __init__(self, model_path: str):
        with open(model_path, 'rb') as rf:
            self.model = pickle.load(rf)

    def predict(self, X: dict) ->List[int]:
        X = pandas.DataFrame(X)
        return self.model.predict(X).tolist()

    def score(self, X: dict, y: List[int], method: str) ->float:
        X = pandas.DataFrame(X)
        scorer = metrics.get_scorer(method)
        return scorer(self.model, X, y)

from typing import List
import cloudpickle as pickle
import numpy as np
import pandas
import sklearn.metrics as metrics


class ModelWrapper:

    def __init__(self, model_path: str):
        with open(model_path, 'rb') as rf:
            self.model = pickle.load(rf)

    def predict(self, X: dict) ->List[int]:
        X = pandas.DataFrame(X)
        return self.model.predict(X).tolist()

    def score(self, X: dict, y: List[int], method: str) ->float:
        X = pandas.DataFrame(X)
        scorer = metrics.get_scorer(method)
        return scorer(self.model, X, y)

from typing import List

import cloudpickle as pickle
import numpy as np
import pandas
import sklearn.metrics as metrics


class ModelWrapper:
    def __init__(self, model_path: str):
        with open(model_path, "rb") as rf:
            self.model = pickle.load(rf)

    def predict(self, X: dict) -> List[int]:
        X = pandas.DataFrame(X)
        return self.model.predict(X).tolist()

    def score(self, X: dict, y: List[int], method: str) -> float:
        X = pandas.DataFrame(X)
        scorer = metrics.get_scorer(method)
        return scorer(self.model, X, y)
