模型持久化

HiClass 完全兼容 Pickle。 Pickle 可以用于轻松地将机器学习模型存储在磁盘上。 在本示例中，我们演示了如何使用 pickle 来存储和加载训练好的分类器。

输出:

[['Animal' 'Reptile' 'Lizard']
 ['Animal' 'Reptile' 'Snake']
 ['Animal' 'Mammal' 'Cow']
 ['Animal' 'Mammal' 'Sheep']]

import pickle

from sklearn.linear_model import LogisticRegression

from hiclass import LocalClassifierPerLevel

# Define data
X_train = [[1, 2], [3, 4], [5, 6], [7, 8]]
X_test = [[7, 8], [5, 6], [3, 4], [1, 2]]
Y_train = [
    ["Animal", "Mammal", "Sheep"],
    ["Animal", "Mammal", "Cow"],
    ["Animal", "Reptile", "Snake"],
    ["Animal", "Reptile", "Lizard"],
]

# Use Logistic Regression classifiers for every level in the hierarchy
lr = LogisticRegression()
classifier = LocalClassifierPerLevel(local_classifier=lr)

# Train local classifier per level
classifier.fit(X_train, Y_train)

# Save the model to disk
filename = "trained_model.sav"
pickle.dump(classifier, open(filename, "wb"))

# Some time in the future...

# Load the model from disk
loaded_model = pickle.load(open(filename, "rb"))

# Predict
predictions = loaded_model.predict(X_test)
print(predictions)