scikit learn GridSearchCV on KNeighbors

Question

tI не удается использовать GridSearchCV с использованием классификаторов KNeighbors. Я получаю следующее сообщение об ошибке при выдаче grid.fit (dataImp, у):

TypeError: «Init() получила неожиданный аргумент ключевого слова„р“»

Ошибки при используются способность восстановления любых данных , Данные, на которые даны ссылки, являются лишь фиктивными данными для теста.

Код для воспроизведения ниже:

from sklearn.grid_search import GridSearchCV 
from sklearn import cross_validation 
from sklearn import neighbors 
import numpy as np 

dataImpNew = np.transpose(np.atleast_2d(np.arange(20.)))*np.arange(20.) 
yNew  = np.sign(np.arange(-5.5,14)) 
nFolds = 4 
random_state = 1234 
metrics  = ['minkowski','euclidean','manhattan'] 
weights  = ['uniform','distance'] #10.0**np.arange(-5,4) 
numNeighbors = np.arange(5,10) 
param_grid = dict(metric=metrics,weights=weights,n_neighbors=numNeighbors) 
cv   = cross_validation.StratifiedKFold(yNew,nFolds) 
grid = GridSearchCV(neighbors.KNeighborsClassifier(),param_grid=param_grid,cv=cv) 
grid.fit(dataImpNew,yNew) 

Полного Trackback:

Traceback (most recent call last): 
    File "/home/pjvalla/testDir/test.py", line 25, in <module> 
grid.fit(dataImpNew,yNew) 
    File "/usr/lib/python2.7/dist-packages/sklearn/grid_search.py", line 596, in fit 
return self._fit(X, y, ParameterGrid(self.param_grid)) 
File "/usr/lib/python2.7/dist-packages/sklearn/grid_search.py", line 378, in _fit 
for parameters in parameter_iterable 
    File "/usr/lib/python2.7/dist-packages/joblib/parallel.py", line 653, in __call__ 
self.dispatch(function, args, kwargs) 
    File "/usr/lib/python2.7/dist-packages/joblib/parallel.py", line 400, in dispatch 
job = ImmediateApply(func, args, kwargs) 
    File "/usr/lib/python2.7/dist-packages/joblib/parallel.py", line 138, in __init__ 
self.results = func(*args, **kwargs) 
    File "/usr/lib/python2.7/dist-packages/sklearn/cross_validation.py", line 1239, in _fit_and_score 
estimator.fit(X_train, y_train, **fit_params) 
    File "/usr/lib/python2.7/dist-packages/sklearn/neighbors/base.py", line 628, in fit 
return self._fit(X) 
    File "/usr/lib/python2.7/dist-packages/sklearn/neighbors/base.py", line 217, in _fit 
**self.effective_metric_kwds_) 
    File "binary_tree.pxi", line 1062, in sklearn.neighbors.kd_tree.BinaryTree.__init__ (sklearn/neighbors/kd_tree.c:8380) 
    File "dist_metrics.pyx", line 280, in sklearn.neighbors.dist_metrics.DistanceMetric.get_metric (sklearn/neighbors/dist_metrics.c:4066) 
TypeError: __init__() got an unexpected keyword argument 'p' 

Answer 1

работают для меня, хотя мне пришлось переименовать dataImpNew и yNew (удаление 'новые' части):

In [4]: %cpaste 
Pasting code; enter '--' alone on the line to stop or use Ctrl-D. 
:from sklearn.grid_search import GridSearchCV 
:from sklearn import cross_validation 
:from sklearn import neighbors 
:import numpy as np 
: 
:dataImp = np.transpose(np.atleast_2d(np.arange(20.)))*np.arange(20.) 
:y  = np.sign(np.arange(-5.5,14)) 
:nFolds = 4 
:random_state = 1234 
:metrics  = ['minkowski','euclidean','manhattan'] 
:weights  = ['uniform','distance'] #10.0**np.arange(-5,4) 
:numNeighbors = np.arange(5,10) 
:param_grid = dict(metric=metrics,weights=weights,n_neighbors=numNeighbors) 
:cv   = cross_validation.StratifiedKFold(y,nFolds) 
:grid = GridSearchCV(neighbors.KNeighborsClassifier(),param_grid=param_grid,cv=cv) 
:grid.fit(dataImp,y) 
: 
:<EOF> 
Out[4]: 
GridSearchCV(cv=sklearn.cross_validation.StratifiedKFold(labels=[-1. -1. -1. -1. -1. -1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 
    1. 1.], n_folds=4, shuffle=False, random_state=None), 
     estimator=KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski', 
      metric_params=None, n_neighbors=5, p=2, weights='uniform'), 
     fit_params={}, iid=True, loss_func=None, n_jobs=1, 
     param_grid={'n_neighbors': array([5, 6, 7, 8, 9]), 'metric': ['minkowski', 'euclidean', 'manhattan'], 'weights': ['uniform', 'distance']}, 
     pre_dispatch='2*n_jobs', refit=True, score_func=None, scoring=None, 
     verbose=0) 

Можете ли вы опубликовать полную статистику стека?