# -*- coding: utf-8 -*-
"""Decode an elemental encoding."""
from typing import Callable, Union
import numpy as np
from loguru import logger
from numpy.typing import ArrayLike
from scipy.spatial.distance import cdist
from .data.coding_data import get_coding_dict
def _get_coding_data(coding_dict):
try:
coding_data = np.fromiter(coding_dict.values(), dtype=float)
except ValueError:
coding_data = np.vstack(coding_dict.values())
coding_shape = coding_data.shape
if len(coding_shape) == 1:
coding_data = coding_data.reshape(-1, 1)
return coding_data
[docs]def decode(
encoding: Union[int, float, np.ndarray, list, tuple],
property: str,
metric: Union[str, Callable] = "euclidean",
) -> str:
"""Decode an elemental encoding.
Args:
encoding (Union[int, float, np.ndarray]): Numerical encoding of an element
property (str): Property that was used for encoding, e.g. "mod_pettifor"
metric (Union[str, callable] optional): Metric to measure distance between (noisy) input encoding
and tabulated encodings.
If a string, the distance function can be 'braycurtis', 'canberra', 'chebyshev',
'cityblock', 'correlation', 'cosine', 'dice', 'euclidean', 'hamming', 'jaccard',
'jensenshannon', 'kulsinski', 'kulczynski1', 'mahalanobis', 'matching', 'minkowski',
'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath',
'sqeuclidean', 'yule'. Defaults to "euclidean".
Returns:
str: Element symbol
Examples:
>>> decode(80, "mod_pettifor")
'Tl'
"""
if isinstance(encoding, (int, float)):
encoding = np.array([encoding])
elif isinstance(encoding, (list, tuple)):
encoding = np.array(encoding)
coding_dict = get_coding_dict(property)
coding_data = _get_coding_data(coding_dict)
distance = cdist(coding_data, encoding.reshape(1, -1), metric=metric)
matching_index = distance.argmin()
logger.debug(
f"Matching distance: {distance[matching_index]}, mean distance: {distance.mean()}, std: {distance.std()}"
)
return list(coding_dict.keys())[matching_index]
[docs]def decode_many(
encoding: ArrayLike,
property: str,
metric: Union[str, Callable] = "euclidean",
) -> np.array:
"""Decode a collecgtio of elemental encodings.
Args:
encoding (ArrayLike): Numerical encodings of elements
property (str): Property that was used for encoding, e.g. "mod_pettifor"
metric (Union[str, callable] optional): Metric to measure distance between (noisy) input encoding
and tabulated encodings.
If a string, the distance function can be 'braycurtis', 'canberra', 'chebyshev',
'cityblock', 'correlation', 'cosine', 'dice', 'euclidean', 'hamming', 'jaccard',
'jensenshannon', 'kulsinski', 'kulczynski1', 'mahalanobis', 'matching', 'minkowski',
'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath',
'sqeuclidean', 'yule'. Defaults to "euclidean".
Returns:
np.array: Numpy array of element symbols
Examples:
>>> decode([80, 90], "mod_pettifor")
['Tl', 'Sb']
"""
encoding = np.array(encoding)
coding_dict = get_coding_dict(property)
coding_data = _get_coding_data(coding_dict)
distance = cdist(coding_data, encoding.reshape(-1, coding_data.shape[1]), metric=metric)
matching_index = distance.argmin(axis=0)
logger.debug(
f"Matching distance: {distance[matching_index]}, mean distance: {distance.mean()}, std: {distance.std()}"
)
keys = list(coding_dict.keys())
return [keys[i] for i in matching_index]