酸洗从 sklearn 管道中提取的 RandomForestClassifier 在加载到另一个笔记本时似乎会导致ModuleNotFoundError
。 错误的模块确实存在,但找不到: sklearn.ensemble._forest
。
from sklearn.ensemble import RandomForestClassifier
from sklearn.pipeline import make_pipeline
import pickle
import numpy as np
pipeline = make_pipeline(
# Other steps in pipeline as well
RandomForestClassifier(),
)
# Create some fake data
X_train = np.array([[2,8,5],[4,7,2],[1,9,4]])
y_train = np.array([26, 29, 18])
# Train the model
pipeline.fit(X_train, y_train)
# Pickle the model
model = pipeline.named_steps['randomforestclassifier']
outfile = open("model.pkl", "wb")
pickle.dump(model, outfile)
outfile.close()
在另一个笔记本中:
from sklearn.ensemble import RandomForestClassifier
import pickle
# Attempt to load the pickled model in another file / notebook:
infile = open("model.pkl", "rb")
model = pickle.load(infile)
infile.close()
# It's lonely over here
model
RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,
criterion='gini', max_depth=None, max_features='auto',
max_leaf_nodes=None, max_samples=None,
min_impurity_decrease=0.0, min_impurity_split=None,
min_samples_leaf=1, min_samples_split=2,
min_weight_fraction_leaf=0.0, n_estimators=100,
n_jobs=None, oob_score=False, random_state=None,
verbose=0, warm_start=False)
---------------------------------------------------------------------------
ModuleNotFoundError Traceback (most recent call last)
<ipython-input-20-c8d1783e8b58> in <module>
5 # Attempt to load the pickled model in another file / notebook:
6 infile = open("model.pkl", "rb")
----> 7 model = pickle.load(infile)
8 infile.close()
ModuleNotFoundError: No module named 'sklearn.ensemble._forest'
System:
python: 3.7.4 (default, Oct 9 2019, 16:55:50) [GCC 7.4.0]
executable: /usr/local/bin/python3.7
machine: Linux-4.15.0-88-generic-x86_64-with-debian-buster-sid
Python deps:
pip: 19.3
setuptools: 40.8.0
sklearn: 0.21.3
numpy: 1.17.2
scipy: 1.3.1
Cython: None
pandas: 0.25.1
编辑:修复说明
似乎无法从sklearn.ensemble._forest
导入。 这是故意的吗?
你能告诉你使用哪个版本的 scikit-learn 来腌制管道,以及你想取消哪个版本。 我假设你试图在 0.21.3 中解压,同时在 0.22.1 中进行酸洗
然后你就可以更新了。 但是,请注意,我们不将其视为错误,因为我们不支持跨不同 scikit-learn 版本的 pickling/unpickling
这正是问题所在。 我没有意识到我在不同的内核中启动了新笔记本。 非常感谢您的及时回复。