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python-3.x - 为什么在为 RandomizedSearchCV 的参数“param_distributions”提供字典列表时出现错误？

问题描述

我可以在文档中看到该参数param_distribution接受 dict 或 dict 列表。当我传递字典时，我的代码在这里有效，但是一旦传递字典列表就会出现错误。

from sklearn.model_selection import train_test_split
from sklearn.model_selection import RandomizedSearchCV
from sklearn.linear_model import LogisticRegression
from sklearn.datasets import load_breast_cancer
import pandas as pd
import numpy as np

breast_cancer = load_breast_cancer()
df = pd.DataFrame(load_breast_cancer().data, columns = breast_cancer.feature_names)
df['target'] = pd.Series(load_breast_cancer().target)
df.head()

Xi = df.iloc[:,:-1]
Yi = df.iloc[:,-1]

x_train1, x_test1, y_train1, y_test1 = train_test_split(Xi, Yi, train_size = 0.9)
dist = [{'C': np.random.uniform(34,89,4)}, {"C": np.random.uniform(2, 16, 5)}]    # {"C": uniform(4, 97)}
rcv = RandomizedSearchCV(estimator = LogisticRegression(), cv = 5, scoring= 'roc_auc', n_jobs= 5,
                         param_distributions= dist, n_iter = 10)

rcv.fit(x_train1, y_train1)

输出：

AttributeError Traceback（最近一次调用最后一次）

AttributeError：“列表”对象没有属性“值”

当我用单个字典替换这个字典列表时，我的代码工作正常，例如

dist = {'C': np.random.uniform(34,89,45)}

rcv = RandomizedSearchCV(estimator = LogisticRegression(), cv = 5, scoring= 'roc_auc', n_jobs= 5,
                         param_distributions= dist, n_iter = 20)

rcv.fit(x_train1, y_train1)

输出：

RandomizedSearchCV(cv=5, error_score='raise-deprecating',
                   estimator=LogisticRegression(C=1.0, class_weight=None,
                                                dual=False, fit_intercept=True,
                                                intercept_scaling=1,
                                                l1_ratio=None, max_iter=100,
                                                multi_class='warn', n_jobs=None,
                                                penalty='l2', random_state=None,
                                                solver='warn', tol=0.0001,
                                                verbose=0, warm_start=False),
                   iid='warn', n_iter=20, n_jobs=5,
                   param_distributions...
       68.32247988, 53.2886396 , 64.71957325, 53.42115708, 66.06577109,
       54.09200687, 87.22769322, 81.02240252, 55.25783926, 84.31009298,
       71.13884939, 85.74823239, 87.23400718, 54.48527833, 59.49131351,
       63.59157499, 38.9348315 , 51.5738502 , 82.72414647, 75.27901268,
       42.63960409, 40.65314118, 56.97608301, 66.41059041, 58.37528729])},
                   pre_dispatch='2*n_jobs', random_state=None, refit=True,
                   return_train_score=False, scoring='roc_auc', verbose=0)

标签： python-3.xmachine-learningscikit-learnlogistic-regression

解决方案

正如@SergeyBushmanov 所建议的，上面的代码适用于sklearn 的v0.22.2 版本。我还不得不调整n_iter参数的值来逃避警告信息。在将参数设置为 20 之前，因为出现了这些警告。这些警告也是合法的，因为我有两个 hyperparameter("C") dist = [{'C': np.random.uniform(34,89,4)}, {"C": np.random.uniform(2, 16, 5)}]。现在，总共有 4x5=20 种超参数组合可供尝试。n_iter指定要尝试的组合数。如果n_iter = 10，则表示在 20 个中，RandomSearchCV 将尝试随机 10 个超参数值的组合。

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