我第一次尝试使用 hyperopt 调整 KNeighbors 参数，但我遇到了一个奇怪的错误。不知道问题出在哪里，但希望得到解决。以下是有关此问题的更多详细信息：
代码：
from sklearn.neighbors import KNeighborsRegressor
from hyperopt import fmin, tpe, hp, STATUS_OK, Trials
space4knn = {
'n_neighbors': hp.choice('n_neighbors', range(1,50)),
'weights': hp.choice('weights',['uniform','distance']),
'algorithm': hp.choice('algorithm',['auto', 'ball_tree', 'kd_tree', 'brute']),
'leaf_size': hp.quniform('leaf_size',1,50,1),
'metric': hp.choice('metric',['minkowski','mahalanobis','chebyshev','seuclidean']),
'p': hp.quniform('p',1,15,1),
'V': hp.quniform('V',1,15,1)
}
def score(params):
print("Training with params: ")
print(params)
knn = KNeighborsRegressor(params)
knn.fit(X_train[X_train['date_block_num']<33].drop(tc+['ID','target'],axis = 1),\
X_train[X_train['date_block_num']<33]['target'])
y_pred = knn.predict(X_train[X_train['date_block_num']==33].drop(tc+['ID', 'target'],axis = 1))
y_pred = np.where( y_pred > 20, 20, np.where(y_pred < 0, 0, y_pred))
y = X_train[X_train['date_block_num']==33]['target']
error = np.sqrt(mean_squared_error(np.where( y > 20, 20, np.where(y < 0, 0, y)), np.round(y_pred)))
# TODO: Add the importance for the selected features
print("\tScore {0}\n\n".format(1-error))
return {'loss': error, 'status': STATUS_OK}
best = fmin(score, space4knn, algo=tpe.suggest,
# trials=trials,
max_evals=100)
print("The best hyperparameters are: ", "\n")
print(best)
错误：
--------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-19-88db883141df> in <module>()
34 best = fmin(score, space4knn, algo=tpe.suggest,
35 # trials=trials,
---> 36 max_evals=100)
37
38 print("The best hyperparameters are: ", "\n")
C:\Anaconda3\lib\site-packages\hyperopt\fmin.py in fmin(fn, space, algo, max_evals, trials, rstate, allow_trials_fmin, pass_expr_memo_ctrl, catch_eval_exceptions, verbose, return_argmin, points_to_evaluate, max_queue_len, show_progressbar)
405 show_progressbar=show_progressbar)
406 rval.catch_eval_exceptions = catch_eval_exceptions
--> 407 rval.exhaust()
408 if return_argmin:
409 return trials.argmin
C:\Anaconda3\lib\site-packages\hyperopt\fmin.py in exhaust(self)
260 def exhaust(self):
261 n_done = len(self.trials)
--> 262 self.run(self.max_evals - n_done, block_until_done=self.asynchronous)
263 self.trials.refresh()
264 return self
C:\Anaconda3\lib\site-packages\hyperopt\fmin.py in run(self, N, block_until_done)
225 else:
226 # -- loop over trials and do the jobs directly
--> 227 self.serial_evaluate()
228
229 try:
C:\Anaconda3\lib\site-packages\hyperopt\fmin.py in serial_evaluate(self, N)
139 ctrl = base.Ctrl(self.trials, current_trial=trial)
140 try:
--> 141 result = self.domain.evaluate(spec, ctrl)
142 except Exception as e:
143 logger.info('job exception: %s' % str(e))
C:\Anaconda3\lib\site-packages\hyperopt\base.py in evaluate(self, config, ctrl, attach_attachments)
842 memo=memo,
843 print_node_on_error=self.rec_eval_print_node_on_error)
--> 844 rval = self.fn(pyll_rval)
845
846 if isinstance(rval, (float, int, np.number)):
<ipython-input-19-88db883141df> in score(params)
17 knn = KNeighborsRegressor(params)
18
---> 19 knn.fit(X_train[X_train['date_block_num']<33].drop(tc+['ID','target'],axis
= 1), X_train[X_train['date_block_num']<33]['target'])
20
21 y_pred = knn.predict(X_train[X_train['date_block_num']==33].drop(tc+['ID', 'target'],axis = 1))
~\AppData\Roaming\Python\Python36\site-packages\sklearn\neighbors\base.py in fit(self, X, y)
871 X, y = check_X_y(X, y, "csr", multi_output=True)
872 self._y = y
--> 873 return self._fit(X)
874
875
~\AppData\Roaming\Python\Python36\site-packages\sklearn\neighbors\base.py in _fit(self, X)
237 # and KDTree is generally faster when available
238 if ((self.n_neighbors is None or
--> 239 self.n_neighbors < self._fit_X.shape[0] // 2) and
240 self.metric != 'precomputed'):
241 if self.effective_metric_ in VALID_METRICS['kd_tree']:
TypeError: '<' not supported between instances of 'dict' and 'int'
部分数据：
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<th>shop_avg_item_cnt_per_category_lag_6</th>
<th>target_lag_12</th>
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<th>category_sum_item_cnt_day_lag_12</th>
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<td>1.203154</td>
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<td>...</td>
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<td>191.044051</td>
<td>24806.0</td>
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<p>5 rows × 58 columns</p>
</div>所有数据类型都是 float32。错误是来自 KNeignoborsRegressor 还是来自数据或 hyperopt 以及如何解决？谢谢。