"""
Graph Representation Learning via Hard Attention Networks in DGL using Adam optimization.
References
----------
Paper: https://arxiv.org/abs/1907.04652
"""

import numpy as np
import torch
import torch.nn as nn


class EarlyStopping:
    def __init__(self, patience=10):
        self.patience = patience
        self.counter = 0
        self.best_score = None
        self.early_stop = False

    def step(self, acc, model):
        score = acc
        if self.best_score is None:
            self.best_score = score
            self.save_checkpoint(model)
        elif score < self.best_score:
            self.counter += 1
            print(
                f"EarlyStopping counter: {self.counter} out of {self.patience}"
            )
            if self.counter >= self.patience:
                self.early_stop = True
        else:
            self.best_score = score
            self.save_checkpoint(model)
            self.counter = 0
        return self.early_stop

    def save_checkpoint(self, model):
        """Saves model when validation loss decrease."""
        torch.save(model.state_dict(), "es_checkpoint.pt")
