序列化时 MLeap 使用 Scikit-learn 中断:对象没有属性 'input_features'
MLeap broken with Skicit-learn when serialising: object has no attribute 'input_features'
我在尝试序列化模型时遇到 MLeap 0.16 和 Python 3 的问题。
这是我的代码:
from mleap.sklearn.logistic import LogisticRegression
from sklearn.datasets import load_iris
X, y = load_iris(return_X_y=True)
clf = LogisticRegression(random_state=0).fit(X, y)
clf.serialize_to_bundle("path", "irismodel")
错误:
AttributeError: 'LogisticRegression' object has no attribute 'input_features'
有没有人找到解决方法?
我找到了解决方案。
clf.mlinit(input_features="features", prediction_column="prediction")
丢失了。
您也可以使用管道来做到这一点:
from mleap.sklearn.logistic import LogisticRegression
from sklearn.datasets import load_iris
from mleap.sklearn.pipeline import Pipeline
X, y = load_iris(return_X_y=True)
logistic = LogisticRegression(random_state=0)
logistic.mlinit(input_features="features", prediction_column="prediction")
pipeline = Pipeline([("log", logistic)])
clf = pipeline.fit(X, y)
clf.mlinit()
clf.serialize_to_bundle("/dbfs/endpath", "model.json")
我在尝试序列化模型时遇到 MLeap 0.16 和 Python 3 的问题。 这是我的代码:
from mleap.sklearn.logistic import LogisticRegression
from sklearn.datasets import load_iris
X, y = load_iris(return_X_y=True)
clf = LogisticRegression(random_state=0).fit(X, y)
clf.serialize_to_bundle("path", "irismodel")
错误:
AttributeError: 'LogisticRegression' object has no attribute 'input_features'
有没有人找到解决方法?
我找到了解决方案。
clf.mlinit(input_features="features", prediction_column="prediction")
丢失了。
您也可以使用管道来做到这一点:
from mleap.sklearn.logistic import LogisticRegression
from sklearn.datasets import load_iris
from mleap.sklearn.pipeline import Pipeline
X, y = load_iris(return_X_y=True)
logistic = LogisticRegression(random_state=0)
logistic.mlinit(input_features="features", prediction_column="prediction")
pipeline = Pipeline([("log", logistic)])
clf = pipeline.fit(X, y)
clf.mlinit()
clf.serialize_to_bundle("/dbfs/endpath", "model.json")