并行训练

较大的数据集需要更多的时间进行训练。 虽然默认情况下HiClass中的模型是使用单核进行训练的， 但可以通过利用Ray库1并行训练每个本地分类器。 如果未安装Ray，则并行性默认为Joblib。 在此示例中，我们演示了如何通过将参数n_jobs设置为使用所有可用核心来并行训练分层分类器。训练是在来自Kaggle2的模拟数据集上进行的。

1

https://www.ray.io/

2

https://www.kaggle.com/datasets/kashnitsky/hierarchical-text-classification

Pipeline(steps=[('count', CountVectorizer()), ('tfidf', TfidfTransformer()),
                ('lcppn',
                 LocalClassifierPerParentNode(local_classifier=LogisticRegression(max_iter=1000),
                                              n_jobs=2))])

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import sys
from os import cpu_count
from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import Pipeline

from hiclass import LocalClassifierPerParentNode
from hiclass.datasets import load_hierarchical_text_classification

# Load train and test splits
X_train, X_test, Y_train, Y_test = load_hierarchical_text_classification()

# We will use logistic regression classifiers for every parent node
lr = LogisticRegression(max_iter=1000)

pipeline = Pipeline(
    [
        ("count", CountVectorizer()),
        ("tfidf", TfidfTransformer()),
        (
            "lcppn",
            LocalClassifierPerParentNode(local_classifier=lr, n_jobs=cpu_count()),
        ),
    ]
)

# Fixes bug AttributeError: '_LoggingTee' object has no attribute 'fileno'
# This only happens when building the documentation
# Hence, you don't actually need it for your code to work
sys.stdout.fileno = lambda: False

# Now, let's train the local classifier per parent node
pipeline.fit(X_train, Y_train)