set_trace()
允许在Jupyter笔记本单元中调试我们自己的代码。你知道吗
代码段\u 1
#import the KNeighborsClassifier class from sklearn
from sklearn.neighbors import KNeighborsClassifier
from IPython.core.debugger import set_trace
#import metrics model to check the accuracy
from sklearn import metrics
#Try running from k=1 through 25 and record testing accuracy
k_range = range(1,26)
scores = {}
scores_list = []
for k in k_range:
set_trace()
knn = KNeighborsClassifier(n_neighbors=k)
knn.fit(X_train,y_train)
y_pred=knn.predict(X_test)
scores[k] = metrics.accuracy_score(y_test,y_pred)
scores_list.append(metrics.accuracy_score(y_test,y_pred))
这是“KNN on Iris Datset”源代码的一部分。你知道吗
这link是整件可以100%在线复制的作品。你知道吗
问题是
是否可以调试第三方python文件,例如Jupyter笔记本单元中的classification.py
?你知道吗
特别是,是否可以调试Jupyter笔记本单元中的knn.predict()
?你知道吗
位于
/usr/local/lib/python3.6/dist-packages/sklearn/neighbors/classification.py
这件
y_pred=knn.predict(["trap", X_test])
%debug
获取此错误
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-28-054b4ff1b356> in <module>()
----> 1 y_pred=knn.predict(["trap", X_test])
2
3 get_ipython().magic('debug')
。。。你知道吗
y_pred=knn.predict(["trap", X_test])
获取此错误(已删除长数组输出)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-28-054b4ff1b356> in <module>()
----> 1 y_pred=knn.predict(["trap", X_test])
2
3 get_ipython().magic('debug')
1 frames
/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py in check_array(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)
519 "Reshape your data either using array.reshape(-1, 1) if "
520 "your data has a single feature or array.reshape(1, -1) "
--> 521 "if it contains a single sample.".format(array))
522
523 # in the future np.flexible dtypes will be handled like object dtypes
在这个错误之后,我在一个新的单元格中运行了%debug
,然后我得到了这个错误
> /usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py(521)check_array()
519 "Reshape your data either using array.reshape(-1, 1) if "
520 "your data has a single feature or array.reshape(1, -1) "
--> 521 "if it contains a single sample.".format(array))
522
523 # in the future np.flexible dtypes will be handled like object dtypes
和ipdb输入
我进入up
,pdb切换到classification.py
设置断点
然后up
,切换回来
断点不起作用
这是全部记录
> /usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py(521)check_array()
519 "Reshape your data either using array.reshape(-1, 1) if "
520 "your data has a single feature or array.reshape(1, -1) "
--> 521 "if it contains a single sample.".format(array))
522
523 # in the future np.flexible dtypes will be handled like object dtypes
ipdb> up
> /usr/local/lib/python3.6/dist-packages/sklearn/neighbors/classification.py(147)predict()
145 Class labels for each data sample.
146 """
--> 147 X = check_array(X, accept_sparse='csr')
148
1 149 neigh_dist, neigh_ind = self.kneighbors(X)
ipdb> b
Num Type Disp Enb Where
1 breakpoint keep yes at /usr/local/lib/python3.6/dist-packages/sklearn/neighbors/classification.py:149
2 breakpoint keep yes at /usr/local/lib/python3.6/dist-packages/sklearn/neighbors/classification.py:150
ipdb> up
> <ipython-input-22-be2dbe619b73>(2)<module>()
1 X = ["trap", X_test]
----> 2 y_pred=knn.predict(X)
ipdb> X = X_test
ipdb> s
事实上这是不可能的。但有一个诀窍。您可以故意将错误的参数传递给
predict
函数,这样它就会失败,您可以调用%debug
,以便逐行执行步骤。请参见下面的示例。你知道吗y_pred=knn.predict(["trap", X_test])
这将尝试执行
predict
方法,并将失败,因为您输入的是随机列表而不是数组。您可以从那里调用%debug
magic命令执行相关问题 更多 >
编程相关推荐