Question

几个月前，我已经在Scikit-learn中训练了一个SVM：

# Create standardizer
standardizer = StandardScaler()

# Create logistic regression
lsvc = SVC(C=0.1, cache_size=200, class_weight=None, coef0=0.0,
  decision_function_shape='ovr', degree=3, gamma='auto', kernel='linear',
  max_iter=-1, probability=False, random_state=None, shrinking=True,
  tol=0.001, verbose=False)

# Create a pipeline that standardizes, then runs Support Vector Machine
svc_pipeline = make_pipeline(standardizer,lsvc)

我已经像这样腌制了模型：

# Save Trained Model
with open('WF_SVC_Final.pkl', 'wb') as fid:
    pickle.dump(svc_pipeline, fid)

现在，我已经像这样加载了腌制的模型：

WF_SVC_Final = pickle.load(open('WF_SVC_Final.pkl', 'rb'))

我可以通过以下方法使用腌制的模型对新数据进行分类：

WF_SVC_Final.predict(x)

但是我试图通过.coef_属性查看/检查腌制模型的系数，但是由于某些原因，这是行不通的：

WF_SVC_Final.coef_

我遇到以下错误：

AttributeError：“管道”对象没有属性“ coef _”

有人知道如何解决这个问题吗？谢谢

Answer 1

您快到了，只需要在管道内部调用before_action :do_stuff, if: [:condition_1?, :condition_2?], only: :show，并在其顶部调用named_steps。我已经修改了您的代码，如下所示：

coef

现在，当我们打印import pandas as pd import numpy as np from sklearn.datasets import make_classification from sklearn.preprocessing import StandardScaler from sklearn.svm import SVC from sklearn.pipeline import make_pipeline from sklearn.model_selection import train_test_split import pickle X, y = make_classification(n_samples=1000, n_classes=2, n_informative=4, weights=[0.7, 0.3], random_state=0) standardizer = StandardScaler() # Create support vector classifier lsvc = SVC(C=0.1, cache_size=200, class_weight=None, coef0=0.0, decision_function_shape='ovr', degree=3, gamma='auto', kernel='linear', max_iter=-1, probability=False, random_state=None, shrinking=True, tol=0.001, verbose=False) # Create a pipeline that standardizes, then runs Support Vector Machine svc_pipeline = make_pipeline(standardizer,lsvc) x_train, x_test, y_train, y_test = train_test_split(X,y, test_size=0.33, random_state=42) svc_pipeline.fit(x_train,y_train) with open('WF_SVC_Final.pkl', 'wb') as fid: pickle.dump(svc_pipeline, fid) WF_SVC_Final = pickle.load(open('WF_SVC_Final.pkl', 'rb')) coefficients = WF_SVC_Final.named_steps["svc"].coef_ #since svc is the name of the estimator we call it here时，我们会得到

coefficients

希望这会有所帮助！

在scikit-learn中查看腌制训练模型的系数/权重

1 个答案: