import sys
from typing import Tuple

import numpy
from sklearn.metrics import accuracy_score, confusion_matrix


class Model(object):
    def __init__(self, save_path: str = "", name: str = "Not Specified"):
        self.model = None
        self.save_path = save_path
        self.name = name
        self.trained = False

    def train(
        self,
        x_train: numpy.ndarray,
        y_train: numpy.ndarray,
        x_val: numpy.ndarray = None,
        y_val: numpy.ndarray = None,
    ) -> None:
        raise NotImplementedError()

    def predict(self, samples: numpy.ndarray) -> Tuple:
        results = [self.predict_one(sample) for sample in samples]
        return tuple(results)

    def predict_one(self, sample) -> int:
        raise NotImplementedError()

    def restore_model(self, load_path: str = None) -> None:
        to_load = load_path or self.save_path
        if to_load is None:
            sys.stderr.write("Provide a path to load from or save_path of the model\n")
            sys.exit(-1)
        self.load_model(to_load)
        self.trained = True

    def load_model(self, to_load: str) -> None:
        raise NotImplementedError()

    def save_model(self) -> None:
        raise NotImplementedError()

    def evaluate(self, x_test: numpy.ndarray, y_test: numpy.ndarray) -> None:
        predictions = self.predict(x_test)
        print(y_test)
        print(predictions)
        print("Accuracy:%.3f\n" % accuracy_score(y_pred=predictions, y_true=y_test))
        print("Confusion matrix:", confusion_matrix(y_pred=predictions, y_true=y_test))