在 python 中尝试使用 hyperopt 调整最近邻居时出错
Error while trying to tune nearest neighbors with hyperopt in python
我第一次尝试使用 hyperopt 调整 KNeighbors 参数，但出现了一个奇怪的错误。不确定问题出在哪里，但希望得到修复。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：
<div>
<style scoped="">
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table class="dataframe" border="1">
<thead>
<tr style="text-align: right;">
<th></th>
<th>shop_avg_item_price_per_category</th>
<th>shop_sum_item_cnt_day_lag_1</th>
<th>shop_avg_item_cnt_day_lag_1</th>
<th>category_avg_item_price_lag_1</th>
<th>shop_avg_item_price_per_category_lag_1</th>
<th>avg_item_cnt_day_lag_2</th>
<th>shop_sum_item_cnt_day_lag_2</th>
<th>shop_avg_item_cnt_day_lag_2</th>
<th>category_avg_item_price_lag_2</th>
<th>shop_avg_item_price_per_category_lag_2</th>
<th>shop_sum_item_cnt_per_category_lag_2</th>
<th>shop_avg_item_cnt_per_category_lag_2</th>
<th>item_price_lag_3</th>
<th>avg_item_price_lag_3</th>
<th>sum_item_cnt_day_lag_3</th>
<th>avg_item_cnt_day_lag_3</th>
<th>shop_sum_item_cnt_day_lag_3</th>
<th>shop_avg_item_cnt_day_lag_3</th>
<th>category_avg_item_price_lag_3</th>
<th>category_sum_item_cnt_day_lag_3</th>
<th>category_avg_item_cnt_day_lag_3</th>
<th>shop_avg_item_price_per_category_lag_3</th>
<th>shop_sum_item_cnt_per_category_lag_3</th>
<th>shop_avg_item_cnt_per_category_lag_3</th>
<th>item_price_lag_4</th>
<th>...</th>
<th>shop_avg_item_price_per_category_lag_4</th>
<th>shop_sum_item_cnt_per_category_lag_4</th>
<th>shop_avg_item_cnt_per_category_lag_4</th>
<th>item_price_lag_6</th>
<th>sum_item_cnt_day_lag_6</th>
<th>avg_item_cnt_day_lag_6</th>
<th>shop_sum_item_cnt_day_lag_6</th>
<th>shop_avg_item_cnt_day_lag_6</th>
<th>category_avg_item_price_lag_6</th>
<th>category_sum_item_cnt_day_lag_6</th>
<th>category_avg_item_cnt_day_lag_6</th>
<th>shop_avg_item_price_per_category_lag_6</th>
<th>shop_sum_item_cnt_per_category_lag_6</th>
<th>shop_avg_item_cnt_per_category_lag_6</th>
<th>target_lag_12</th>
<th>sum_item_cnt_day_lag_12</th>
<th>avg_item_cnt_day_lag_12</th>
<th>shop_sum_item_cnt_day_lag_12</th>
<th>shop_avg_item_cnt_day_lag_12</th>
<th>category_avg_item_price_lag_12</th>
<th>category_sum_item_cnt_day_lag_12</th>
<th>category_avg_item_cnt_day_lag_12</th>
<th>shop_avg_item_price_per_category_lag_12</th>
<th>shop_sum_item_cnt_per_category_lag_12</th>
<th>shop_avg_item_cnt_per_category_lag_12</th>
</tr>
</thead>
<tbody>
<tr>
<th>4488710</th>
<td>1255.026825</td>
<td>1322.0</td>
<td>0.156025</td>
<td>1327.493573</td>
<td>1326.642773</td>
<td>0.044444</td>
<td>862.0</td>
<td>0.106564</td>
<td>1394.281350</td>
<td>1393.509145</td>
<td>140.0</td>
<td>0.571429</td>
<td>1461.228571</td>
<td>1393.537888</td>
<td>6.0</td>
<td>0.130435</td>
<td>795.0</td>
<td>0.098893</td>
<td>1354.019638</td>
<td>14113.0</td>
<td>1.203154</td>
<td>1350.446936</td>
<td>191.0</td>
<td>0.749020</td>
<td>1461.228571</td>
<td>...</td>
<td>1252.440779</td>
<td>257.0</td>
<td>0.992278</td>
<td>1461.228571</td>
<td>3.0</td>
<td>0.065217</td>
<td>807.0</td>
<td>0.096014</td>
<td>1236.387840</td>
<td>7497.0</td>
<td>0.681917</td>
<td>1235.285393</td>
<td>138.0</td>
<td>0.577406</td>
<td>1.0</td>
<td>7.0</td>
<td>0.155556</td>
<td>1146.0</td>
<td>0.14122</td>
<td>1201.195942</td>
<td>8983.0</td>
<td>0.849456</td>
<td>1202.996944</td>
<td>114.0</td>
<td>0.485106</td>
</tr>
<tr>
<th>4488711</th>
<td>201.385813</td>
<td>1322.0</td>
<td>0.156025</td>
<td>202.264922</td>
<td>202.541912</td>
<td>1.022222</td>
<td>862.0</td>
<td>0.106564</td>
<td>196.136342</td>
<td>195.771183</td>
<td>49.0</td>
<td>0.022940</td>
<td>262.545138</td>
<td>234.362681</td>
<td>24.0</td>
<td>0.521739</td>
<td>795.0</td>
<td>0.098893</td>
<td>199.127951</td>
<td>24173.0</td>
<td>0.254233</td>
<td>198.815861</td>
<td>44.0</td>
<td>0.021287</td>
<td>262.545138</td>
<td>...</td>
<td>195.339467</td>
<td>56.0</td>
<td>0.025998</td>
<td>262.545138</td>
<td>41.0</td>
<td>0.891304</td>
<td>807.0</td>
<td>0.096014</td>
<td>191.044051</td>
<td>24806.0</td>
<td>0.228500</td>
<td>190.384669</td>
<td>92.0</td>
<td>0.038983</td>
<td>0.0</td>
<td>0.0</td>
<td>0.000000</td>
<td>0.0</td>
<td>0.00000</td>
<td>199.562656</td>
<td>0.0</td>
<td>0.000000</td>
<td>199.726151</td>
<td>0.0</td>
<td>0.000000</td>
</tr>
<tr>
<th>4488712</th>
<td>356.645697</td>
<td>1322.0</td>
<td>0.156025</td>
<td>356.371940</td>
<td>354.899145</td>
<td>0.600000</td>
<td>862.0</td>
<td>0.106564</td>
<td>341.460339</td>
<td>338.698243</td>
<td>42.0</td>
<td>0.046823</td>
<td>518.387881</td>
<td>530.473848</td>
<td>25.0</td>
<td>0.543478</td>
<td>795.0</td>
<td>0.098893</td>
<td>349.472332</td>
<td>6950.0</td>
<td>0.171495</td>
<td>348.349523</td>
<td>29.0</td>
<td>0.032917</td>
<td>518.387881</td>
<td>...</td>
<td>341.946769</td>
<td>24.0</td>
<td>0.028302</td>
<td>518.387881</td>
<td>14.0</td>
<td>0.304348</td>
<td>807.0</td>
<td>0.096014</td>
<td>336.559242</td>
<td>8623.0</td>
<td>0.224768</td>
<td>333.367923</td>
<td>21.0</td>
<td>0.025180</td>
<td>0.0</td>
<td>0.0</td>
<td>0.000000</td>
<td>0.0</td>
<td>0.00000</td>
<td>352.298165</td>
<td>0.0</td>
<td>0.000000</td>
<td>352.452030</td>
<td>0.0</td>
<td>0.000000</td>
</tr>
<tr>
<th>4488713</th>
<td>201.385813</td>
<td>1322.0</td>
<td>0.156025</td>
<td>202.264922</td>
<td>202.541912</td>
<td>1.800000</td>
<td>862.0</td>
<td>0.106564</td>
<td>196.136342</td>
<td>195.771183</td>
<td>49.0</td>
<td>0.022940</td>
<td>221.330209</td>
<td>205.940671</td>
<td>58.0</td>
<td>1.260870</td>
<td>795.0</td>
<td>0.098893</td>
<td>199.127951</td>
<td>24173.0</td>
<td>0.254233</td>
<td>198.815861</td>
<td>44.0</td>
<td>0.021287</td>
<td>221.330209</td>
<td>...</td>
<td>195.339467</td>
<td>56.0</td>
<td>0.025998</td>
<td>221.330209</td>
<td>87.0</td>
<td>1.891304</td>
<td>807.0</td>
<td>0.096014</td>
<td>191.044051</td>
<td>24806.0</td>
<td>0.228500</td>
<td>190.384669</td>
<td>92.0</td>
<td>0.038983</td>
<td>0.0</td>
<td>299.0</td>
<td>6.644444</td>
<td>1146.0</td>
<td>0.14122</td>
<td>179.841439</td>
<td>33489.0</td>
<td>0.310860</td>
<td>178.901545</td>
<td>175.0</td>
<td>0.073099</td>
</tr>
<tr>
<th>4488714</th>
<td>356.645697</td>
<td>1322.0</td>
<td>0.156025</td>
<td>356.371940</td>
<td>354.899145</td>
<td>0.333333</td>
<td>862.0</td>
<td>0.106564</td>
<td>341.460339</td>
<td>338.698243</td>
<td>42.0</td>
<td>0.046823</td>
<td>245.228381</td>
<td>218.896182</td>
<td>33.0</td>
<td>0.717391</td>
<td>795.0</td>
<td>0.098893</td>
<td>349.472332</td>
<td>6950.0</td>
<td>0.171495</td>
<td>348.349523</td>
<td>29.0</td>
<td>0.032917</td>
<td>245.228381</td>
<td>...</td>
<td>341.946769</td>
<td>24.0</td>
<td>0.028302</td>
<td>245.228381</td>
<td>46.0</td>
<td>1.000000</td>
<td>807.0</td>
<td>0.096014</td>
<td>336.559242</td>
<td>8623.0</td>
<td>0.224768</td>
<td>333.367923</td>
<td>21.0</td>
<td>0.025180</td>
<td>1.0</td>
<td>61.0</td>
<td>1.355556</td>
<td>1146.0</td>
<td>0.14122</td>
<td>338.532581</td>
<td>6094.0</td>
<td>0.199738</td>
<td>333.080366</td>
<td>52.0</td>
<td>0.076696</td>
</tr>
</tbody>
</table>
<p>5 rows × 58 columns</p>
</div>
所有数据类型均为 float32。
错误是来自 KNeignoborsRegressor 还是来自数据或 hyperopt 以及如何解决？谢谢
声明 knn 模型对象时传递“**params”而不是“params”。如果您只传递“params”，那么它将作为字典对象传递，并且不会传递属性。
knn = KNeighborsRegressor(**params)
我第一次尝试使用 hyperopt 调整 KNeighbors 参数，但出现了一个奇怪的错误。不确定问题出在哪里，但希望得到修复。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：
<div>
<style scoped="">
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table class="dataframe" border="1">
<thead>
<tr style="text-align: right;">
<th></th>
<th>shop_avg_item_price_per_category</th>
<th>shop_sum_item_cnt_day_lag_1</th>
<th>shop_avg_item_cnt_day_lag_1</th>
<th>category_avg_item_price_lag_1</th>
<th>shop_avg_item_price_per_category_lag_1</th>
<th>avg_item_cnt_day_lag_2</th>
<th>shop_sum_item_cnt_day_lag_2</th>
<th>shop_avg_item_cnt_day_lag_2</th>
<th>category_avg_item_price_lag_2</th>
<th>shop_avg_item_price_per_category_lag_2</th>
<th>shop_sum_item_cnt_per_category_lag_2</th>
<th>shop_avg_item_cnt_per_category_lag_2</th>
<th>item_price_lag_3</th>
<th>avg_item_price_lag_3</th>
<th>sum_item_cnt_day_lag_3</th>
<th>avg_item_cnt_day_lag_3</th>
<th>shop_sum_item_cnt_day_lag_3</th>
<th>shop_avg_item_cnt_day_lag_3</th>
<th>category_avg_item_price_lag_3</th>
<th>category_sum_item_cnt_day_lag_3</th>
<th>category_avg_item_cnt_day_lag_3</th>
<th>shop_avg_item_price_per_category_lag_3</th>
<th>shop_sum_item_cnt_per_category_lag_3</th>
<th>shop_avg_item_cnt_per_category_lag_3</th>
<th>item_price_lag_4</th>
<th>...</th>
<th>shop_avg_item_price_per_category_lag_4</th>
<th>shop_sum_item_cnt_per_category_lag_4</th>
<th>shop_avg_item_cnt_per_category_lag_4</th>
<th>item_price_lag_6</th>
<th>sum_item_cnt_day_lag_6</th>
<th>avg_item_cnt_day_lag_6</th>
<th>shop_sum_item_cnt_day_lag_6</th>
<th>shop_avg_item_cnt_day_lag_6</th>
<th>category_avg_item_price_lag_6</th>
<th>category_sum_item_cnt_day_lag_6</th>
<th>category_avg_item_cnt_day_lag_6</th>
<th>shop_avg_item_price_per_category_lag_6</th>
<th>shop_sum_item_cnt_per_category_lag_6</th>
<th>shop_avg_item_cnt_per_category_lag_6</th>
<th>target_lag_12</th>
<th>sum_item_cnt_day_lag_12</th>
<th>avg_item_cnt_day_lag_12</th>
<th>shop_sum_item_cnt_day_lag_12</th>
<th>shop_avg_item_cnt_day_lag_12</th>
<th>category_avg_item_price_lag_12</th>
<th>category_sum_item_cnt_day_lag_12</th>
<th>category_avg_item_cnt_day_lag_12</th>
<th>shop_avg_item_price_per_category_lag_12</th>
<th>shop_sum_item_cnt_per_category_lag_12</th>
<th>shop_avg_item_cnt_per_category_lag_12</th>
</tr>
</thead>
<tbody>
<tr>
<th>4488710</th>
<td>1255.026825</td>
<td>1322.0</td>
<td>0.156025</td>
<td>1327.493573</td>
<td>1326.642773</td>
<td>0.044444</td>
<td>862.0</td>
<td>0.106564</td>
<td>1394.281350</td>
<td>1393.509145</td>
<td>140.0</td>
<td>0.571429</td>
<td>1461.228571</td>
<td>1393.537888</td>
<td>6.0</td>
<td>0.130435</td>
<td>795.0</td>
<td>0.098893</td>
<td>1354.019638</td>
<td>14113.0</td>
<td>1.203154</td>
<td>1350.446936</td>
<td>191.0</td>
<td>0.749020</td>
<td>1461.228571</td>
<td>...</td>
<td>1252.440779</td>
<td>257.0</td>
<td>0.992278</td>
<td>1461.228571</td>
<td>3.0</td>
<td>0.065217</td>
<td>807.0</td>
<td>0.096014</td>
<td>1236.387840</td>
<td>7497.0</td>
<td>0.681917</td>
<td>1235.285393</td>
<td>138.0</td>
<td>0.577406</td>
<td>1.0</td>
<td>7.0</td>
<td>0.155556</td>
<td>1146.0</td>
<td>0.14122</td>
<td>1201.195942</td>
<td>8983.0</td>
<td>0.849456</td>
<td>1202.996944</td>
<td>114.0</td>
<td>0.485106</td>
</tr>
<tr>
<th>4488711</th>
<td>201.385813</td>
<td>1322.0</td>
<td>0.156025</td>
<td>202.264922</td>
<td>202.541912</td>
<td>1.022222</td>
<td>862.0</td>
<td>0.106564</td>
<td>196.136342</td>
<td>195.771183</td>
<td>49.0</td>
<td>0.022940</td>
<td>262.545138</td>
<td>234.362681</td>
<td>24.0</td>
<td>0.521739</td>
<td>795.0</td>
<td>0.098893</td>
<td>199.127951</td>
<td>24173.0</td>
<td>0.254233</td>
<td>198.815861</td>
<td>44.0</td>
<td>0.021287</td>
<td>262.545138</td>
<td>...</td>
<td>195.339467</td>
<td>56.0</td>
<td>0.025998</td>
<td>262.545138</td>
<td>41.0</td>
<td>0.891304</td>
<td>807.0</td>
<td>0.096014</td>
<td>191.044051</td>
<td>24806.0</td>
<td>0.228500</td>
<td>190.384669</td>
<td>92.0</td>
<td>0.038983</td>
<td>0.0</td>
<td>0.0</td>
<td>0.000000</td>
<td>0.0</td>
<td>0.00000</td>
<td>199.562656</td>
<td>0.0</td>
<td>0.000000</td>
<td>199.726151</td>
<td>0.0</td>
<td>0.000000</td>
</tr>
<tr>
<th>4488712</th>
<td>356.645697</td>
<td>1322.0</td>
<td>0.156025</td>
<td>356.371940</td>
<td>354.899145</td>
<td>0.600000</td>
<td>862.0</td>
<td>0.106564</td>
<td>341.460339</td>
<td>338.698243</td>
<td>42.0</td>
<td>0.046823</td>
<td>518.387881</td>
<td>530.473848</td>
<td>25.0</td>
<td>0.543478</td>
<td>795.0</td>
<td>0.098893</td>
<td>349.472332</td>
<td>6950.0</td>
<td>0.171495</td>
<td>348.349523</td>
<td>29.0</td>
<td>0.032917</td>
<td>518.387881</td>
<td>...</td>
<td>341.946769</td>
<td>24.0</td>
<td>0.028302</td>
<td>518.387881</td>
<td>14.0</td>
<td>0.304348</td>
<td>807.0</td>
<td>0.096014</td>
<td>336.559242</td>
<td>8623.0</td>
<td>0.224768</td>
<td>333.367923</td>
<td>21.0</td>
<td>0.025180</td>
<td>0.0</td>
<td>0.0</td>
<td>0.000000</td>
<td>0.0</td>
<td>0.00000</td>
<td>352.298165</td>
<td>0.0</td>
<td>0.000000</td>
<td>352.452030</td>
<td>0.0</td>
<td>0.000000</td>
</tr>
<tr>
<th>4488713</th>
<td>201.385813</td>
<td>1322.0</td>
<td>0.156025</td>
<td>202.264922</td>
<td>202.541912</td>
<td>1.800000</td>
<td>862.0</td>
<td>0.106564</td>
<td>196.136342</td>
<td>195.771183</td>
<td>49.0</td>
<td>0.022940</td>
<td>221.330209</td>
<td>205.940671</td>
<td>58.0</td>
<td>1.260870</td>
<td>795.0</td>
<td>0.098893</td>
<td>199.127951</td>
<td>24173.0</td>
<td>0.254233</td>
<td>198.815861</td>
<td>44.0</td>
<td>0.021287</td>
<td>221.330209</td>
<td>...</td>
<td>195.339467</td>
<td>56.0</td>
<td>0.025998</td>
<td>221.330209</td>
<td>87.0</td>
<td>1.891304</td>
<td>807.0</td>
<td>0.096014</td>
<td>191.044051</td>
<td>24806.0</td>
<td>0.228500</td>
<td>190.384669</td>
<td>92.0</td>
<td>0.038983</td>
<td>0.0</td>
<td>299.0</td>
<td>6.644444</td>
<td>1146.0</td>
<td>0.14122</td>
<td>179.841439</td>
<td>33489.0</td>
<td>0.310860</td>
<td>178.901545</td>
<td>175.0</td>
<td>0.073099</td>
</tr>
<tr>
<th>4488714</th>
<td>356.645697</td>
<td>1322.0</td>
<td>0.156025</td>
<td>356.371940</td>
<td>354.899145</td>
<td>0.333333</td>
<td>862.0</td>
<td>0.106564</td>
<td>341.460339</td>
<td>338.698243</td>
<td>42.0</td>
<td>0.046823</td>
<td>245.228381</td>
<td>218.896182</td>
<td>33.0</td>
<td>0.717391</td>
<td>795.0</td>
<td>0.098893</td>
<td>349.472332</td>
<td>6950.0</td>
<td>0.171495</td>
<td>348.349523</td>
<td>29.0</td>
<td>0.032917</td>
<td>245.228381</td>
<td>...</td>
<td>341.946769</td>
<td>24.0</td>
<td>0.028302</td>
<td>245.228381</td>
<td>46.0</td>
<td>1.000000</td>
<td>807.0</td>
<td>0.096014</td>
<td>336.559242</td>
<td>8623.0</td>
<td>0.224768</td>
<td>333.367923</td>
<td>21.0</td>
<td>0.025180</td>
<td>1.0</td>
<td>61.0</td>
<td>1.355556</td>
<td>1146.0</td>
<td>0.14122</td>
<td>338.532581</td>
<td>6094.0</td>
<td>0.199738</td>
<td>333.080366</td>
<td>52.0</td>
<td>0.076696</td>
</tr>
</tbody>
</table>
<p>5 rows × 58 columns</p>
</div>
所有数据类型均为 float32。 错误是来自 KNeignoborsRegressor 还是来自数据或 hyperopt 以及如何解决？谢谢
声明 knn 模型对象时传递“**params”而不是“params”。如果您只传递“params”，那么它将作为字典对象传递，并且不会传递属性。
knn = KNeighborsRegressor(**params)