temperature-based-fertility-prediction-thesis/code/utils/utils.py

import sys
import inspect
import hashlib
import logging
from functools import partial
import random
from typing import Callable
import importlib

import numpy as np


def get_logger(module_name: str, filename: str = "main.log") -> logging.Logger:
    """
    Returns a logger for the given module name and filename.
    :param module_name: name of the module, as string
    :param filename: name of the logging file, as string
    :return: the logger, as logging.Logger object
    """
    logger = logging.getLogger(module_name)
    logger.setLevel(logging.DEBUG)
    logger.propagate = False
    formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')

    file_handler = logging.FileHandler(filename)
    file_handler.setLevel(logging.DEBUG)
    file_handler.setFormatter(formatter)

    logger.addHandler(file_handler)

    # add system out handler
    stream_handler = logging.StreamHandler(sys.stdout)
    stream_handler.setLevel(logging.INFO)
    stream_handler.setFormatter(formatter)

    logger.addHandler(stream_handler)

    return logger


def get_variable_from_module(module_path: str, variable_name: str):
    """
    Get a variable from a module by its name during runtime

    Args:
        module_path: module to fetch variable from
        variable_name: variable to fetch from module

    Returns:
        variable from module

    """
    module = importlib.import_module(module_path)
    variable = getattr(module, variable_name)
    if variable is None:
        raise ValueError(f"Variable {variable_name} not found in module {module_path}")
    return variable


def get_callable_name(callable_obj):
    """
    Get the name of the callable, handling `functools.partial`.

    :param callable_obj: callable object
    :return: name of the callable
    """
    if isinstance(callable_obj, partial):
        func_name = callable_obj.func.__name__
        args = ", ".join(object_to_string(arg) for arg in callable_obj.args)
        kwargs = ", ".join(f"{object_to_string(k)}={object_to_string(v)!r}" for k, v in callable_obj.keywords.items())
        return f"partial({func_name}, {args}, {kwargs})"
    else:
        if hasattr(callable_obj, '__name__'):
            return callable_obj.__name__
        elif hasattr(callable_obj, '__class__'):
            return callable_obj.__class__.__name__
        elif hasattr(callable_obj, '__hash__'):
            return callable_obj.__hash__
        else:
            raise ValueError(f"Could not determine name of callable object {callable_obj}")


def object_to_string(value, skip_types=None):
    """
    Convert any object to a string representation that avoids memory addresses.
    Handles complex data types recursively.

    :param value: object to convert
    :param skip_types: types to skip during conversion
    :return: string representation of the object
    """
    if skip_types is None:
        skip_types = []

    if any(isinstance(value, t) for t in skip_types):
        return 'skipped_type'
    elif isinstance(value, (str, int, float, bool)):  # Handle primitive data types directly
        return repr(value)
    elif isinstance(value, dict):
        return '{' + ', '.join(f"{k}: {object_to_string(v, skip_types)}" for k, v in value.items()) + '}'
    elif isinstance(value, (list, tuple)):
        return '[' + ', '.join(object_to_string(item, skip_types) for item in value) + ']'
    elif isinstance(value, partial):
        return get_callable_name(value)
    elif inspect.isclass(value):
        return f"<class '{value.__name__}'>"
    elif hasattr(value,
                 '__class__') and not value.__class__ != "function":  # Correct handling for instances of classes, but not functions
        return f"<instance of class '{value.__class__.__name__}'>"
    elif isinstance(value, Callable):
        return f"<callable '{get_callable_name(value)}'>"
    else:
        return repr(value)


def get_config_id(configuration: dict) -> str:
    """
    Generate a somewhat unique human-readable model name from the model and training parameters.
    :param configuration: dictionary containing model and training parameters
    :return: human-readable model name
    """
    adjectives = ["autumn", "hidden", "bitter", "misty", "silent", "empty", "dry", "dark", "summer", "icy", "delicate",
                  "quiet", "white", "black", "blue", "green", "red", "yellow",
                  "purple", "orange", "pink", "golden", "silver", "crimson", "violet", "azure", "amber", "sapphire",
                  "emerald", "ruby", "pearl", "topaz", "onyx", "turquoise", "citrine", ]
    nouns = ["waterfall", "river", "breeze", "moon", "rain", "wind", "sea", "morning", "snow", "lake", "sunset", "pine",
             "shadow", "leaf", "dawn", "glitter", "forest", "cloud", "sky", "sun", "butterfly",
             "flower", "bird", "mountain", "valley", "ocean", "star", "night", "dream", "whisper", "echo", "horizon",
             "wave", "petal", "dew", "mist"]

    # also add dataset config, but skip functions
    base_name = object_to_string(configuration)

    # hash long name
    basename_hash = hashlib.md5(base_name.encode()).hexdigest()

    # select adjective and noun based on hash
    random.seed(int(basename_hash, 16))
    model_name = f"{random.choice(adjectives)}_{random.choice(nouns)}_{basename_hash[:5]}"

    return model_name


def convert_for_json(obj):
    if isinstance(obj, dict):
        return {k: convert_for_json(v) for k, v in obj.items()}
    elif isinstance(obj, list):
        return [convert_for_json(v) for v in obj]
    elif isinstance(obj, np.generic):
        return obj.item()
    else:
        return obj