问题描述
我接受，我是 Python 的新手。
我从 https://sds-platform-private.s3-us-east-2.amazonaws.com/uploads/P14-Part3-Classification.zip 得到了 KNN 算法代码
我将数据集行修改为
dataset = pd.read_csv('Friday-WorkingHours-Afternoon-DDos.pcap_ISCX.csv')
这是我的 CICIDS IDS 2017 数据集的链接。
我使用 jupyter notebook 来运行代码。
关于执行代码片段：
X_set,y_set = X_train,y_train
X1,X2 = np.meshgrid(np.arange(start = X_set[:,0].min() - 1,stop = X_set[:,0].max() + 1,step = 0.01),np.arange(start = X_set[:,1].min() - 1,1].max() + 1,step = 0.01))
plt.contourf(X1,X2,classifier.predict(np.array([X1.ravel(),X2.ravel()]).T).reshape(X1.shape),alpha = 0.75,cmap = ListedColormap(('red','green')))
plt.xlim(X1.min(),X1.max())
plt.ylim(X2.min(),X2.max())
for i,j in enumerate(np.unique(y_set)):
plt.scatter(X_set[y_set == j,0],X_set[y_set == j,1],c = ListedColormap(('red','green'))(i),label = j)
plt.title('K-NN (Training set)')
plt.xlabel('Xaxis')
plt.ylabel('Yaxis')
plt.legend()
plt.show()
I get this error:
---------------------------------------------------------------------------
MemoryError Traceback (most recent call last)
<ipython-input-8-db5b0d75197c> in <module>
3 X1,4 np.arange(start = X_set[:,step = 0.01))
----> 5 plt.contourf(X1,6 alpha = 0.75,'green')))
7 plt.xlim(X1.min(),X1.max())
~\anaconda3\lib\site-packages\sklearn\neighbors\_classification.py in predict(self,X)
173 X = check_array(X,accept_sparse='csr')
174
--> 175 neigh_dist,neigh_ind = self.kneighbors(X)
176 classes_ = self.classes_
177 _y = self._y
~\anaconda3\lib\site-packages\sklearn\neighbors\_base.py in kneighbors(self,X,n_neighbors,return_distance)
660 delayed_query = delayed(_tree_query_parallel_helper)
661 parallel_kwargs = {"prefer": "threads"}
--> 662 chunked_results = Parallel(n_jobs,**parallel_kwargs)(
663 delayed_query(
664 self._tree,X[s],return_distance)
~\anaconda3\lib\site-packages\joblib\parallel.py in __call__(self,iterable)
1046 # remaining jobs.
1047 self._iterating = False
-> 1048 if self.dispatch_one_batch(iterator):
1049 self._iterating = self._original_iterator is not None
1050
~\anaconda3\lib\site-packages\joblib\parallel.py in dispatch_one_batch(self,iterator)
864 return False
865 else:
--> 866 self._dispatch(tasks)
867 return True
868
~\anaconda3\lib\site-packages\joblib\parallel.py in _dispatch(self,batch)
782 with self._lock:
783 job_idx = len(self._jobs)
--> 784 job = self._backend.apply_async(batch,callback=cb)
785 # A job can complete so quickly than its callback is
786 # called before we get here,causing self._jobs to
~\anaconda3\lib\site-packages\joblib\_parallel_backends.py in apply_async(self,func,callback)
206 def apply_async(self,callback=None):
207 """Schedule a func to be run"""
--> 208 result = ImmediateResult(func)
209 if callback:
210 callback(result)
~\anaconda3\lib\site-packages\joblib\_parallel_backends.py in __init__(self,batch)
570 # Don't delay the application,to avoid keeping the input
571 # arguments in memory
--> 572 self.results = batch()
573
574 def get(self):
~\anaconda3\lib\site-packages\joblib\parallel.py in __call__(self)
260 # change the default number of processes to -1
261 with parallel_backend(self._backend,n_jobs=self._n_jobs):
--> 262 return [func(*args,**kwargs)
263 for func,args,kwargs in self.items]
264
~\anaconda3\lib\site-packages\joblib\parallel.py in <listcomp>(.0)
260 # change the default number of processes to -1
261 with parallel_backend(self._backend,kwargs in self.items]
264
~\anaconda3\lib\site-packages\sklearn\neighbors\_base.py in _tree_query_parallel_helper(tree,*args,**kwargs)
490 under PyPy.
491 """
--> 492 return tree.query(*args,**kwargs)
493
494
sklearn\neighbors\_binary_tree.pxi in sklearn.neighbors._kd_tree.BinaryTree.query()
~\anaconda3\lib\site-packages\sklearn\utils\validation.py in inner_f(*args,**kwargs)
70 FutureWarning)
71 kwargs.update({k: arg for k,arg in zip(sig.parameters,args)})
---> 72 return f(**kwargs)
73 return inner_f
74
~\anaconda3\lib\site-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,estimator)
596 array = array.astype(dtype,casting="unsafe",copy=False)
597 else:
--> 598 array = np.asarray(array,order=order,dtype=dtype)
599 except ComplexWarning:
600 raise ValueError("Complex data not supported\n"
~\anaconda3\lib\site-packages\numpy\core\_asarray.py in asarray(a,order)
81
82 """
---> 83 return array(a,copy=False,order=order)
84
85
MemoryError: Unable to allocate 2.41 GiB for an array with shape (161771019,2) and data type float64```
WHAT COULD GO WRONG?
解决方法
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