我有随机森林模型的网格搜索实现。
<table *ngIf="visibleAttachments().length > 0">
<thead>
<tr>
<th>File</th>
<th>Description</th>
<th>Date</th>
</tr>
</thead>
<tbody>
<tr *ngFor="let attachment of visibleAttachments()">
<td>{{ attachment.file }}</td>
<td>{{ attachment.description }}</td>
<td>{{ attachment.date }}</td>
</tr>
</tbody>
</table>
在我将其用于此网格搜索之前，我已将完全相同的数据集用于许多其他任务，因此数据不应存在任何问题。另外，为了测试目的，我首先使用LinearRegression来查看整个管道是否顺利运行，它的工作原理。然后我切换到RandomForestRegressor并设置一个非常少量的估算器来再次测试它。他们发生了一件非常奇怪的事情，我会附上详细的信息。性能显着下降，我不知道发生了什么。没有理由花30分钟+来运行一次小网格搜索。
train_X, test_X, train_y, test_y = train_test_split(features, target, test_size=.10, random_state=0)
# A bit performance gains can be obtained from standarization
train_X, test_X = standarize(train_X, test_X)
tuned_parameters = [{
'n_estimators': [5],
'criterion': ['mse', 'mae'],
'random_state': [0]
}]
scores = ['neg_mean_squared_error', 'neg_mean_absolute_error']
for n_fold in [5]:
for score in scores:
print("# Tuning hyper-parameters for %s with %d-fold" % (score, n_fold))
start_time = time.time()
print()
# TODO: RandomForestRegressor
clf = GridSearchCV(RandomForestRegressor(verbose=2), tuned_parameters, cv=n_fold,
scoring=score, verbose=2, n_jobs=-1)
clf.fit(train_X, train_y)
... Rest omitted
上面的日志打印几秒钟，然后事情似乎从这里开始......
Fitting 5 folds for each of 2 candidates, totalling 10 fits
[CV] criterion=mse, n_estimators=5, random_state=0 ...................
building tree 1 of 5
[CV] criterion=mse, n_estimators=5, random_state=0 ...................
building tree 1 of 5
[CV] criterion=mse, n_estimators=5, random_state=0 ...................
building tree 1 of 5
[CV] criterion=mse, n_estimators=5, random_state=0 ...................
building tree 1 of 5
[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 1.0s remaining: 0.0s
building tree 2 of 5
[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 1.0s remaining: 0.0s
building tree 2 of 5
[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 1.1s remaining: 0.0s
building tree 2 of 5
[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 1.1s remaining: 0.0s
building tree 2 of 5
building tree 3 of 5
building tree 3 of 5
building tree 3 of 5
building tree 3 of 5
building tree 4 of 5
building tree 4 of 5
building tree 4 of 5
building tree 4 of 5
building tree 5 of 5
building tree 5 of 5
building tree 5 of 5
building tree 5 of 5
[Parallel(n_jobs=1)]: Done 5 out of 5 | elapsed: 5.0s finished
[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.0s remaining: 0.0s
[Parallel(n_jobs=1)]: Done 5 out of 5 | elapsed: 5.0s finished
[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.0s remaining: 0.0s
[Parallel(n_jobs=1)]: Done 5 out of 5 | elapsed: 5.0s finished
[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.0s remaining: 0.0s
[Parallel(n_jobs=1)]: Done 5 out of 5 | elapsed: 0.2s finished
[Parallel(n_jobs=1)]: Done 5 out of 5 | elapsed: 5.0s finished
[Parallel(n_jobs=1)]: Done 5 out of 5 | elapsed: 0.3s finished
[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.1s remaining: 0.0s
[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.1s remaining: 0.0s
[Parallel(n_jobs=1)]: Done 5 out of 5 | elapsed: 0.3s finished
[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.1s remaining: 0.0s
[Parallel(n_jobs=1)]: Done 5 out of 5 | elapsed: 0.2s finished
[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.1s remaining: 0.0s
[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.1s remaining: 0.0s
[Parallel(n_jobs=1)]: Done 5 out of 5 | elapsed: 0.8s finished
[CV] .... criterion=mse, n_estimators=5, random_state=0, total= 5.3s
[CV] criterion=mse, n_estimators=5, random_state=0 ...................
[Parallel(n_jobs=1)]: Done 5 out of 5 | elapsed: 0.8s finished
[CV] .... criterion=mse, n_estimators=5, random_state=0, total= 5.3s
building tree 1 of 5
[CV] criterion=mae, n_estimators=5, random_state=0 ...................
[Parallel(n_jobs=1)]: Done 5 out of 5 | elapsed: 0.9s finished
[CV] .... criterion=mse, n_estimators=5, random_state=0, total= 5.3s
building tree 1 of 5
[CV] criterion=mae, n_estimators=5, random_state=0 ...................
building tree 1 of 5
[Parallel(n_jobs=1)]: Done 5 out of 5 | elapsed: 0.9s finished
[CV] .... criterion=mse, n_estimators=5, random_state=0, total= 5.3s
[CV] criterion=mae, n_estimators=5, random_state=0 ...................
building tree 1 of 5
[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 1.0s remaining: 0.0s
building tree 2 of 5
building tree 3 of 5
building tree 4 of 5
building tree 5 of 5
[Parallel(n_jobs=1)]: Done 5 out of 5 | elapsed: 5.3s finished
[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.0s remaining: 0.0s
[Parallel(n_jobs=1)]: Done 5 out of 5 | elapsed: 0.2s finished
[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.1s remaining: 0.0s
[Parallel(n_jobs=1)]: Done 5 out of 5 | elapsed: 0.5s finished
[CV] .... criterion=mse, n_estimators=5, random_state=0, total= 5.6s
[CV] criterion=mae, n_estimators=5, random_state=0 ...................
building tree 1 of 5
这些线路的成本超过20分钟。
BTW，对于每次GridSearchCV运行，线性回归的成本不到1秒。你知道为什么性能下降那么多吗？
任何建议和评论都表示赞赏。谢谢。
答案 0 :(得分：1)
尝试为RandomForestRegressor设置max_depth。这应该减少装配时间。默认情况下为max_depth=None。
例如：
tuned_parameters = [{
'n_estimators': [5],
'criterion': ['mse', 'mae'],
'random_state': [0],
'max_depth': [4],
}]
修改：此外，默认情况下RandomForestRegressor有n_jobs=1。它将使用此设置一次构建一个树。尝试设置n_jobs=-1。
此外，您可以指定多个指标，而不是将scoring参数循环到GridSearchCV。执行此操作时，您还必须指定要GridSearchCV选择的指标作为refit的值。然后，您可以在拟合后访问cv_results_字典中的所有分数。
clf = GridSearchCV(RandomForestRegressor(verbose=2),tuned_parameters,
cv=n_fold, scoring=scores, refit='neg_mean_squared_error',
verbose=2, n_jobs=-1)
clf.fit(train_X, train_y)
results = clf.cv_results_
print(np.mean(results['mean_test_neg_mean_squared_error']))
print(np.mean(results['mean_test_neg_mean_absolute_error']))