我已经使用 auto-sklearn 安装了一个分类模型,并设法将其保存到带有 pickle 的文件中。
x = automl.show_models()
results = {"ensemble": x}
pickle.dump(results, open('file.pickle','wb'))
我还设法重新加载了模型。
automl = pickle.load(open('file.pickle','rb'))
但我无法使用重新加载的模型对新数据进行预测。
当我运行时:
y_hat = automl.predict(X_test)
我收到以下错误:
AttributeError: 'str' object has no attribute 'predict'
最佳答案
不正确:
x = automl.show_models()
results = {"ensemble": x} # <---
pickle.dump(results, open('file.pickle','wb'))
正确:x = automl.show_models()
#results = {"ensemble": x}
results = autml # the classifier/regressor itself
pickle.dump(results, open('file.pickle','wb'))
鸢尾花的示例代码:from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from autosklearn.classification import AutoSklearnClassifier
import pickle
# dataset:
X, y = load_iris(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2)
# train model:
classifier = AutoSklearnClassifier(
time_left_for_this_task=30,
per_run_time_limit=60,
memory_limit=1024*12) # depends on your computer
classifier.fit(X_train, y_train)
# save model
with open('iris-classifier.pkl', 'wb') as f:
pickle.dump(classifier, f)
# load model
with open('iris-classifier.pkl', 'rb') as f:
loaded_classifier = pickle.load(f)
# predict
y_true = y_test
y_pred = loaded_classifier.predict(X_test)
print('iris classifier: accuracy:', accuracy_score(y_true, y_pred))
# iris classifier: accuracy: 0.9333333333333333
关于python - 自动 sklearn : How to load pickle file and run predict(),我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/61703174/