如何进行FM算法原理分析与实践

from sklearn.datasets import load_irisfrom sklearn.model_selection import train_test_splitfrom pyfm import pylibfmfrom sklearn.feature_extraction import DictVectorizer
def load_data():    """    调用sklearn的iris数据集，筛选正负样本并构造切分训练测试数据集    """    iris_data = load_iris()    X = iris_data['data']    y = iris_data['target'] == 2    data = [ {v: k for k, v in dict(zip(i, range(len(i)))).items()}  for i in X]    X_train,X_test,y_train, y_test = train_test_split(data,y, test_size=0.3, random_state=0)    return X_train,X_test,y_train, y_test
X_train,X_test,y_train, y_test = load_data()
v = DictVectorizer()X_train = v.fit_transform(X_train)X_test = v.transform(X_test)
fm = pylibfm.FM(num_factors=2,                 num_iter=200,                 verbose=True,                 task="classification",                 initial_learning_rate=0.001,                 learning_rate_schedule="optimal")
fm.fit(X_train, y_train)
y_preds = fm.predict(X_test)y_preds_label = y_preds > 0.5from sklearn.metrics import log_loss,accuracy_scoreprint ("Validation log loss: %.4f" % log_loss(y_test, y_preds))print ("accuracy: %.4f" % accuracy_score(y_test, y_preds_label))

Training log loss: 0.12161Validation log loss: 0.1868accuracy: 0.9778