sklearn kneighbours Speicherfehler python

Question

Ich arbeite an einem Windows 7 8GB RAM.

Dies ist vectorizer ich frei, eine Textspalte in meiner 52MB Trainingsdatenmenge

vec = CountVectorizer(analyzer='word',stop_words='english',decode_error='ignore',binary=True) 

Ich mag berechnen 5 nächste Nachbarn mit diesem Datensatz für eine 18MB Testset vektorisiert verwenden.

nbrs = NearestNeighbors(n_neighbors=5).fit(vec.transform(data['clean_sum'])) 
vectors = vec.transform(data_test['clean_sum']) 
distances,indices = nbrs.kneighbors(vectors) 

Dies ist der Stack-Trace -

Traceback (most recent call last): 
    File "cr_nearness.py", line 224, in <module> 
    distances,indices = nbrs.kneighbors(vectors) 
    File "C:\Anaconda2\lib\site-packages\sklearn\neighbors\base.py", line 371, 
kneighbors 
    n_jobs=n_jobs, squared=True) 
    File "C:\Anaconda2\lib\site-packages\sklearn\metrics\pairwise.py", line 12 
in pairwise_distances 
    return _parallel_pairwise(X, Y, func, n_jobs, **kwds) 
    File "C:\Anaconda2\lib\site-packages\sklearn\metrics\pairwise.py", line 10 
in _parallel_pairwise 
    return func(X, Y, **kwds) 
    File "C:\Anaconda2\lib\site-packages\sklearn\metrics\pairwise.py", line 23 
n euclidean_distances 
    distances = safe_sparse_dot(X, Y.T, dense_output=True) 
    File "C:\Anaconda2\lib\site-packages\sklearn\utils\extmath.py", line 181, 
afe_sparse_dot 
    ret = ret.toarray() 
    File "C:\Anaconda2\lib\site-packages\scipy\sparse\compressed.py", line 940 
toarray 
    return self.tocoo(copy=False).toarray(order=order, out=out) 
    File "C:\Anaconda2\lib\site-packages\scipy\sparse\coo.py", line 250, in to 
y 
    B = self._process_toarray_args(order, out) 
    File "C:\Anaconda2\lib\site-packages\scipy\sparse\base.py", line 817, in _ 
ess_toarray_args 
    return np.zeros(self.shape, dtype=self.dtype, order=order) 
MemoryError 

Irgendwelche Ideen?

Answer 1

Verwenden KNN mit KD TREE

model = KNeighborsClassifier (n_neighbors = 5, algorithm = 'kd_tree'). Passen (X_train, Y_train)

das Modell standardmäßig Algorithmus = "Brute". rohe falsche nehmen zu viel Speicher. Ich denke, für Ihr Modell sollte es so

NBRs = NearestNeighbors (n_neighbors = 5, Algorithmus = 'kd_tree'). Sitz (vec.transform (data [ 'clean_sum']))

aussehen werden