magcl_eval.py

import numpy as np
import functools


from sklearn.metrics import accuracy_score
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.multiclass import OneVsRestClassifier
from sklearn.preprocessing import normalize, OneHotEncoder

import tensorlayerx as tlx

def prob_to_one_hot(y_pred):
    ret = np.zeros(y_pred.shape, np.bool8)
    indices = np.argmax(y_pred, axis=1)
    for i in range(y_pred.shape[0]):
        ret[i][indices[i]] = True
    return ret


def evaluate(embeddings, y, train_mask, test_mask, split='random', ratio=0.1):
    X = embeddings
    Y = tlx.convert_to_numpy(y)
    Y = Y.reshape(-1, 1)
    onehot_encoder = OneHotEncoder(categories='auto').fit(Y)
    Y = onehot_encoder.transform(Y).toarray().astype(np.bool8)

    X = normalize(X, norm='l2')

    if split == 'random':
        X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=1 - ratio)
    elif split == 'public':
        X_train = X[train_mask]
        X_test = X[test_mask]
        y_train = Y[train_mask]
        y_test = Y[test_mask]

    logreg = LogisticRegression(solver='liblinear')
    c = 2.0 ** np.arange(-10, 10)

    clf = GridSearchCV(estimator=OneVsRestClassifier(logreg),
                       param_grid=dict(estimator__C=c), n_jobs=8, cv=5,
                       verbose=0)
    clf.fit(X_train, y_train)

    y_pred = clf.predict_proba(X_test)
    y_pred = prob_to_one_hot(y_pred)

    acc = accuracy_score(y_test, y_pred)

    return acc