mlessentials/Chapter08/Examples/tuning_using_randomizedsearchcv.ipynb

In [0]:

from sklearn import datasets, model_selection, linear_model

# load the data
diabetes = datasets.load_diabetes()

# target
y = diabetes.target

# features
X = diabetes.data

# initialise the ridge regression
reg = linear_model.Ridge()

In [0]:

from scipy import stats

# alpha ~ gamma(1,1)
param_dist = {'alpha': stats.gamma(a=1, loc=1, scale=2)}

In [9]:

# set up the random search to sample 100 values and score on negative mean squared error
rscv = model_selection.RandomizedSearchCV(estimator=reg, param_distributions=param_dist, n_iter=100, scoring='neg_mean_squared_error')

# start the search
rscv.fit(X,y)

Out [9]:

/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.
  warnings.warn(CV_WARNING, FutureWarning)

RandomizedSearchCV(cv='warn', error_score='raise-deprecating',
                   estimator=Ridge(alpha=1.0, copy_X=True, fit_intercept=True,
                                   max_iter=None, normalize=False,
                                   random_state=None, solver='auto',
                                   tol=0.001),
                   iid='warn', n_iter=100, n_jobs=None,
                   param_distributions={'alpha': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7f6f66ae0550>},
                   pre_dispatch='2*n_jobs', random_state=None, refit=True,
                   return_train_score=False, scoring='neg_mean_squared_error',
                   verbose=0)

In [10]:

import pandas as pd

# convert the results dictionary to a pandas data frame
results = pd.DataFrame(rscv.cv_results_)

# show the top 5 hyperparamaterizations
print(results.loc[:,['params','rank_test_score']].sort_values('rank_test_score').head(5))

                           params  rank_test_score
47   {'alpha': 1.082807253775902}                1
72  {'alpha': 1.0996845885124735}                2
18  {'alpha': 1.1006233499723193}                3
52  {'alpha': 1.1012596885127424}                4
59  {'alpha': 1.1267717423087455}                5