scikit-learn中用于聚类的超参数评估的网格搜索 [英] Grid search for hyperparameter evaluation of clustering in scikit-learn
问题描述
我正在对大约100条记录(未标记)的样本进行聚类,并尝试使用grid_search评估具有各种超参数的聚类算法。我正在使用 silhouette_score
得分,效果很好。
I'm clustering a sample of about 100 records (unlabelled) and trying to use grid_search to evaluate the clustering algorithm with various hyperparameters. I'm scoring using silhouette_score
which works fine.
我的问题是我不需要使用 GridSearchCV
/ RandomizedSearchCV
的交叉验证方面,但是我找不到简单的 GridSearch
/ RandomizedSearch
。我可以编写自己的对象,但是 ParameterSampler
和 ParameterGrid
对象非常有用。
My problem here is that I don't need to use the cross-validation aspect of the GridSearchCV
/RandomizedSearchCV
, but I can't find a simple GridSearch
/RandomizedSearch
. I can write my own but the ParameterSampler
and ParameterGrid
objects are very useful.
下一步是继承 BaseSearchCV
并实现我自己的 _fit()
方法,但是认为值得一问的是,是否有更简单的方法来做到这一点,例如通过将某些内容传递给 cv
参数?
My next step will be to subclass BaseSearchCV
and implement my own _fit()
method, but thought it was worth asking is there a simpler way to do this, for example by passing something to the cv
parameter?
def silhouette_score(estimator, X):
clusters = estimator.fit_predict(X)
score = metrics.silhouette_score(distance_matrix, clusters, metric='precomputed')
return score
ca = KMeans()
param_grid = {"n_clusters": range(2, 11)}
# run randomized search
search = GridSearchCV(
ca,
param_distributions=param_dist,
n_iter=n_iter_search,
scoring=silhouette_score,
cv= # can I pass something here to only use a single fold?
)
search.fit(distance_matrix)
clusteval
库将帮助您评估数据并找到最佳的簇数。该库包含五种可用于评估聚类的方法。 剪影, dbindex ,衍生物,* dbscan *和 hdbscan 。
The clusteval
library will help you to evaluate the data and find the optimal number of clusters. This library contains five methods that can be used to evaluate clusterings; silhouette, dbindex, derivative, *dbscan *and hdbscan.
pip install clusteval
取决于数据,可以选择评估方法。
Depending on your data, the evaluation method can be chosen.
# Import library
from clusteval import clusteval
# Set parameters, as an example dbscan
ce = clusteval(method='dbscan')
# Fit to find optimal number of clusters using dbscan
results= ce.fit(X)
# Make plot of the cluster evaluation
ce.plot()
# Make scatter plot. Note that the first two coordinates are used for plotting.
ce.scatter(X)
# results is a dict with various output statistics. One of them are the labels.
cluster_labels = results['labx']
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