utils.py

# -*- coding: utf-8 -*-
# @Time   : 2020/7/17
# @Author : Shanlei Mu
# @Email  : slmu@ruc.edu.cn

"""
recbole.utils.utils
################################
"""

import os
import datetime
import importlib
import random
import torch
import numpy as np
from recbole.utils.enum_type import ModelType


def get_local_time():
    r"""Get current time

    Returns:
        str: current time
    """
    cur = datetime.datetime.now()
    cur = cur.strftime('%b-%d-%Y_%H-%M-%S')

    return cur


def ensure_dir(dir_path):
    r"""Make sure the directory exists, if it does not exist, create it

    Args:
        dir_path (str): directory path

    """
    if not os.path.exists(dir_path):
        os.makedirs(dir_path)


def get_model(model_name):
    r"""Automatically select model class based on model name

    Args:
        model_name (str): model name

    Returns:
        Recommender: model class
    """
    model_submodule = [
        'general_recommender',
        'context_aware_recommender',
        'sequential_recommender',
        'knowledge_aware_recommender'
    ]

    model_file_name = model_name.lower()
    for submodule in model_submodule:
        module_path = '.'.join(['...model', submodule, model_file_name])
        if importlib.util.find_spec(module_path, __name__):
            model_module = importlib.import_module(module_path, __name__)

    model_class = getattr(model_module, model_name)
    return model_class


def get_trainer(model_type, model_name):
    r"""Automatically select trainer class based on model type and model name

    Args:
        model_type (ModelType): model type
        model_name (str): model name

    Returns:
        Trainer: trainer class
    """
    try:
        return getattr(importlib.import_module('recbole.trainer'), model_name + 'Trainer')
    except AttributeError:
        if model_type == ModelType.KNOWLEDGE:
            return getattr(importlib.import_module('recbole.trainer'), 'KGTrainer')
        elif model_type == ModelType.TRADITIONAL:
            return getattr(importlib.import_module('recbole.trainer'), 'TraditionalTrainer')
        else:
            return getattr(importlib.import_module('recbole.trainer'), 'Trainer')


def early_stopping(value, best, cur_step, max_step, bigger=True):
    r""" validation-based early stopping

    Args:
        value (float): current result
        best (float): best result
        cur_step (int): the number of consecutive steps that did not exceed the best result
        max_step (int): threshold steps for stopping
        bigger (bool, optional): whether the bigger the better

    Returns:
        tuple:
        - float,
          best result after this step
        - int,
          the number of consecutive steps that did not exceed the best result after this step
        - bool,
          whether to stop
        - bool,
          whether to update
    """
    stop_flag = False
    update_flag = False
    if bigger:
        if value > best:
            cur_step = 0
            best = value
            update_flag = True
        else:
            cur_step += 1
            if cur_step > max_step:
                stop_flag = True
    else:
        if value < best:
            cur_step = 0
            best = value
            update_flag = True
        else:
            cur_step += 1
            if cur_step > max_step:
                stop_flag = True
    return best, cur_step, stop_flag, update_flag


def calculate_valid_score(valid_result, valid_metric=None):
    r""" return valid score from valid result

    Args:
        valid_result (dict): valid result
        valid_metric (str, optional): the selected metric in valid result for valid score

    Returns:
        float: valid score
    """
    if valid_metric:
        return valid_result[valid_metric]
    else:
        return valid_result['Recall@10']


def dict2str(result_dict):
    r""" convert result dict to str

    Args:
        result_dict (dict): result dict

    Returns:
        str: result str
    """

    result_str = ''
    for metric, value in result_dict.items():
        result_str += str(metric) + ' : ' + '%.04f' % value + '    '
    return result_str


def init_seed(seed, reproducibility):
    r""" init random seed for random functions in numpy, torch, cuda and cudnn

    Args:
        seed (int): random seed
        reproducibility (bool): Whether to require reproducibility
    """
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)
    if reproducibility:
        torch.backends.cudnn.benchmark = False
        torch.backends.cudnn.deterministic = True
    else:
        torch.backends.cudnn.benchmark = True
        torch.backends.cudnn.deterministic = False