Python sci-kit学习KNN网格搜索交叉验证错误
我正在尝试重新创建KNN模型以预测汽车目的地。 https://github.com/carlosbkm/car-destination-prediction 该代码不适用于网格搜索交叉验证： https://github.com/carlosbkm/car-destination-prediction/blob/master/k-nearest-model.ipynb 起初geodash无法正常工作，所以我将其切换为geodash2，没有问题。 当我尝试拟合模型时，我得到了。 TypeError：/的不支持的操作数类型：“ str”和“ int”
当我尝试将X和y拟合为网格搜索交叉验证时，出现错误。 问题来自
def cv_optimize(clf, parameters, X, y, n_jobs=1, n_folds=5, score_func=None):
if score_func:
gs = GridSearchCV(clf, param_grid=parameters, cv=n_folds, n_jobs=n_jobs, scoring=score_func)
else:
gs = GridSearchCV(clf, param_grid=parameters, n_jobs=n_jobs, cv=n_folds)
gs.fit(X, y)
print ("BEST", gs.best_params_, gs.best_score_, gs.cv_results_)
best = gs.best_estimator_
return best
我无法使模型适合X和y： gs.fit（X，y）
我试图将X和y设置为浮点数，但没有改变
当我执行此操作时：
# Create a k-Nearest Neighbors Regression estimator
knn_estimator = KNeighborsRegressor()
#knn_parameters = {"n_neighbors": [1,2,5,10,20,50,100]}
knn_parameters = {"n_neighbors": [1,2,5]}
knn_best = cv_optimize(knn_estimator, knn_parameters, X_train, y_train, score_func='neg_mean_squared_error')
我得到：
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-124-34b56429c6b5> in <module>()
4 #knn_parameters = {"n_neighbors": [1,2,5,10,20,50,100]}
5 knn_parameters = {"n_neighbors": [1,2,5]}
----> 6 knn_best = cv_optimize(knn_estimator, knn_parameters, X_train, y_train, score_func='neg_mean_squared_error')
<ipython-input-116-1a00f84f1047> in cv_optimize(clf, parameters, X, y, n_jobs, n_folds, score_func)
6 else:
7 gs = GridSearchCV(clf, param_grid=parameters, n_jobs=n_jobs, cv=n_folds)
----> 8 gs.fit(X, y)
9 print ("BEST", gs.best_params_, gs.best_score_, gs.cv_results_)
10 best = gs.best_estimator_
~/anaconda3/envs/datascience/lib/python3.6/site-packages/sklearn/model_selection/_search.py in fit(self, X, y, groups)
943 train/test set.
944 """
--> 945 return self._fit(X, y, groups, ParameterGrid(self.param_grid))
946
947
~/anaconda3/envs/datascience/lib/python3.6/site-packages/sklearn/model_selection/_search.py in _fit(self, X, y, groups, parameter_iterable)
562 return_times=True, return_parameters=True,
563 error_score=self.error_score)
--> 564 for parameters in parameter_iterable
565 for train, test in cv_iter)
566
~/anaconda3/envs/datascience/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py in __call__(self, iterable)
756 # was dispatched. In particular this covers the edge
757 # case of Parallel used with an exhausted iterator.
--> 758 while self.dispatch_one_batch(iterator):
759 self._iterating = True
760 else:
~/anaconda3/envs/datascience/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py in dispatch_one_batch(self, iterator)
606 return False
607 else:
--> 608 self._dispatch(tasks)
609 return True
610
~/anaconda3/envs/datascience/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py in _dispatch(self, batch)
569 dispatch_timestamp = time.time()
570 cb = BatchCompletionCallBack(dispatch_timestamp, len(batch), self)
--> 571 job = self._backend.apply_async(batch, callback=cb)
572 self._jobs.append(job)
573
~/anaconda3/envs/datascience/lib/python3.6/site-packages/sklearn/externals/joblib/_parallel_backends.py in apply_async(self, func, callback)
107 def apply_async(self, func, callback=None):
108 """Schedule a func to be run"""
--> 109 result = ImmediateResult(func)
110 if callback:
111 callback(result)
~/anaconda3/envs/datascience/lib/python3.6/site-packages/sklearn/externals/joblib/_parallel_backends.py in __init__(self, batch)
324 # Don't delay the application, to avoid keeping the input
325 # arguments in memory
--> 326 self.results = batch()
327
328 def get(self):
~/anaconda3/envs/datascience/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py in __call__(self)
129
130 def __call__(self):
--> 131 return [func(*args, **kwargs) for func, args, kwargs in self.items]
132
133 def __len__(self):
~/anaconda3/envs/datascience/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py in <listcomp>(.0)
129
130 def __call__(self):
--> 131 return [func(*args, **kwargs) for func, args, kwargs in self.items]
132
133 def __len__(self):
~/anaconda3/envs/datascience/lib/python3.6/site-packages/sklearn/model_selection/_validation.py in _fit_and_score(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, error_score)
258 else:
259 fit_time = time.time() - start_time
--> 260 test_score = _score(estimator, X_test, y_test, scorer)
261 score_time = time.time() - start_time - fit_time
262 if return_train_score:
~/anaconda3/envs/datascience/lib/python3.6/site-packages/sklearn/model_selection/_validation.py in _score(estimator, X_test, y_test, scorer)
286 score = scorer(estimator, X_test)
287 else:
--> 288 score = scorer(estimator, X_test, y_test)
289 if hasattr(score, 'item'):
290 try:
~/anaconda3/envs/datascience/lib/python3.6/site-packages/sklearn/metrics/scorer.py in __call__(self, estimator, X, y_true, sample_weight)
89 super(_PredictScorer, self).__call__(estimator, X, y_true,
90 sample_weight=sample_weight)
---> 91 y_pred = estimator.predict(X)
92 if sample_weight is not None:
93 return self._sign * self._score_func(y_true, y_pred,
~/anaconda3/envs/datascience/lib/python3.6/site-packages/sklearn/neighbors/regression.py in predict(self, X)
151
152 if weights is None:
--> 153 y_pred = np.mean(_y[neigh_ind], axis=1)
154 else:
155 y_pred = np.empty((X.shape[0], _y.shape[1]), dtype=np.float64)
~/anaconda3/envs/datascience/lib/python3.6/site-packages/numpy/core/fromnumeric.py in mean(a, axis, dtype, out, keepdims)
2907
2908 return _methods._mean(a, axis=axis, dtype=dtype,
-> 2909 out=out, **kwargs)
2910
2911
~/anaconda3/envs/datascience/lib/python3.6/site-packages/numpy/core/_methods.py in _mean(a, axis, dtype, out, keepdims)
71 if isinstance(ret, mu.ndarray):
72 ret = um.true_divide(
---> 73 ret, rcount, out=ret, casting='unsafe', subok=False)
74 if is_float16_result and out is None:
75 ret = arr.dtype.type(ret)
TypeError: unsupported operand type(s) for /: 'str' and 'int'