from sklearn.datasets import load_iris
from sklearn.pipeline import Pipeline
from sklearn.linear_model import LogisticRegression, RidgeClassifier
from sklearn.svm import SVC
from sklearn.model_selection import RandomizedSearchCV
from sklearn.utils.fixes import loguniform
iris = load_iris()
pipeline = Pipeline([('clf', SVC())])
search_dists = [
    {'clf': [LogisticRegression(random_state=42)],
     'clf__C': loguniform(1e-2, 1e1),
     'clf__penalty': ['l1', 'l2']
    },
    {'clf': [RidgeClassifier(random_state=42)],
     'clf__alpha': loguniform(1e-2, 1e0)
    },
    {'clf': [SVC(random_state=42)],
     'clf__C': loguniform(1e-2, 1e2), 
     'clf__gamma': loguniform(1e-3, 1e0),
    }]
rand_search_cv = RandomizedSearchCV(pipeline, search_dists, n_jobs=-1, random_state=42)
rand_res = rand_search_cv.fit(iris.data, iris.target)

from sklearn import set_config
set_config(display='diagram')
# diplays HTML representation in a jupyter context of the initial `pipeline`
rand_res

from sklearn import set_config
set_config(display='diagram')
# diplays HTML representation in a jupyter context of the initial `pipeline`
rand_res.best_estimator_