from typing import Tuple, Union
import numpy as np
from sklearn import linear_model, metrics
[docs]def link_sign_direction_prediction_logistic_function(embeddings: np.ndarray, train_X: np.ndarray, train_y: np.ndarray, test_X: np.ndarray, test_y: np.ndarray, class_weight: Union[dict, str] = None) -> Tuple[float, float, float, float, float]:
"""
link_sign_prediction_logistic_function [summary]
Link sign prediction is a multi-class classification machine learning task.
It will return the metrics for link sign direction prediction (i.e., Accuracy, Macro-F1, and Micro-F1).
Args:
embeddings (np.ndarray): The embeddings for signed graph.
train_X (np.ndarray): The indices for training data (e.g., [[0, 1], [0, 2]])
train_y (np.ndarray): The sign for training data (e.g., [[1, -1]])
test_X (np.ndarray): The indices for test data (e.g., [[1, 2]])
test_y (np.ndarray): The sign for test data (e.g., [[1]])
class_weight (Union[dict, str], optional): Weights associated with classes in the form ``{class_label: weight}``.
If not given, all classes are supposed to have weight one.. Defaults to None.
Returns:
[type]: The metrics for link sign prediction task.
Tuple[float,float,float]: Accuracy, Macro-F1, Micro-F1
"""
train_X1 = []
test_X1 = []
for i, j in train_X:
train_X1.append(np.concatenate([embeddings[i], embeddings[j]]))
for i, j in test_X:
test_X1.append(np.concatenate([embeddings[i], embeddings[j]]))
logistic_function = linear_model.LogisticRegression(
solver='lbfgs', max_iter=1000)
logistic_function.fit(train_X1, train_y)
pred = logistic_function.predict(test_X1)
accuracy = metrics.accuracy_score(test_y, pred)
f1_macro = metrics.f1_score(test_y, pred, average='macro')
f1_micro = metrics.f1_score(test_y, pred, average='micro')
return accuracy, f1_macro, f1_micro