sklearn 的 `RandomizedSearchCV` 不适用于 `np.random.RandomState`
sklearn's `RandomizedSearchCV` not working with `np.random.RandomState`
我正在尝试优化管道并想尝试给 RandomizedSearchCV 一个 np.random.RandomState 对象。我不能让它工作，但我可以给它其他发行版。
有什么特殊的语法可以用来给 RandomSearchCV 一个 np.random.RandomState(0).uniform(0.1,1.0)?
from scipy import stats
import numpy as np
from sklearn.neighbors import KernelDensity
from sklearn.grid_search import RandomizedSearchCV
# Generate data
x = np.random.normal(5,1,size=int(1e3))
# Make model
model = KernelDensity()
# Gridsearch for best params
# This one works
search_params = RandomizedSearchCV(model, param_distributions={"bandwidth":stats.uniform(0.1, 1)}, n_iter=30, n_jobs=2)
search_params.fit(x[:, None])
# RandomizedSearchCV(cv=None, error_score='raise',
# estimator=KernelDensity(algorithm='auto', atol=0, bandwidth=1.0, breadth_first=True,
# kernel='gaussian', leaf_size=40, metric='euclidean',
# metric_params=None, rtol=0),
# fit_params={}, iid=True, n_iter=30, n_jobs=2,
# param_distributions={'bandwidth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x106ab7da0>},
# pre_dispatch='2*n_jobs', random_state=None, refit=True,
# scoring=None, verbose=0)
# This one doesn't work :(
search_params = RandomizedSearchCV(model, param_distributions={"bandwidth":np.random.RandomState(0).uniform(0.1, 1)}, n_iter=30, n_jobs=2)
# TypeError: object of type 'float' has no len()
您观察到的是预期的，因为 np.random.RandomState() 类型对象的 class 方法 uniform 在调用时立即绘制样本.
与此相比，您对 scipy 的 stats.uniform() 的使用创建了一个尚未从 采样的分布。 （虽然我不确定它是否像您预期的那样工作；请注意参数）。
如果您想合并基于 np.random.RandomState() 的内容，您必须构建自己的 class，如 docs 中所述：
This example uses the scipy.stats module, which contains many useful distributions for sampling parameters, such as expon, gamma, uniform or randint. In principle, any function can be passed that provides a rvs (random variate sample) method to sample a value. A call to the rvs function should provide independent random samples from possible parameter values on consecutive calls.
我正在尝试优化管道并想尝试给 RandomizedSearchCV 一个 np.random.RandomState 对象。我不能让它工作，但我可以给它其他发行版。
有什么特殊的语法可以用来给 RandomSearchCV 一个 np.random.RandomState(0).uniform(0.1,1.0)?
from scipy import stats
import numpy as np
from sklearn.neighbors import KernelDensity
from sklearn.grid_search import RandomizedSearchCV
# Generate data
x = np.random.normal(5,1,size=int(1e3))
# Make model
model = KernelDensity()
# Gridsearch for best params
# This one works
search_params = RandomizedSearchCV(model, param_distributions={"bandwidth":stats.uniform(0.1, 1)}, n_iter=30, n_jobs=2)
search_params.fit(x[:, None])
# RandomizedSearchCV(cv=None, error_score='raise',
# estimator=KernelDensity(algorithm='auto', atol=0, bandwidth=1.0, breadth_first=True,
# kernel='gaussian', leaf_size=40, metric='euclidean',
# metric_params=None, rtol=0),
# fit_params={}, iid=True, n_iter=30, n_jobs=2,
# param_distributions={'bandwidth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x106ab7da0>},
# pre_dispatch='2*n_jobs', random_state=None, refit=True,
# scoring=None, verbose=0)
# This one doesn't work :(
search_params = RandomizedSearchCV(model, param_distributions={"bandwidth":np.random.RandomState(0).uniform(0.1, 1)}, n_iter=30, n_jobs=2)
# TypeError: object of type 'float' has no len()
您观察到的是预期的，因为 np.random.RandomState() 类型对象的 class 方法 uniform 在调用时立即绘制样本.
与此相比，您对 scipy 的 stats.uniform() 的使用创建了一个尚未从 采样的分布。 （虽然我不确定它是否像您预期的那样工作；请注意参数）。
如果您想合并基于 np.random.RandomState() 的内容，您必须构建自己的 class，如 docs 中所述：
This example uses the scipy.stats module, which contains many useful distributions for sampling parameters, such as expon, gamma, uniform or randint. In principle, any function can be passed that provides a rvs (random variate sample) method to sample a value. A call to the rvs function should provide independent random samples from possible parameter values on consecutive calls.