hpsklearn调参实例

from __future__ import print_function
# import numpy as np
from sklearn import datasets
from sklearn.cross_validation import train_test_split
from hyperopt import tpe
import hpsklearn
import sys

def test_demo_iris():

iris = datasets.load_iris()
X_train, X_test, y_train, y_test = train_test_split(
iris.data, iris.target, test_size=.25, random_state=1)

estimator = hpsklearn.HyperoptEstimator(
preprocessing=hpsklearn.components.any_preprocessing('pp'),
classifier=hpsklearn.components.any_classifier('clf'),
algo=tpe.suggest,
trial_timeout=15.0,  # seconds
max_evals=10,
seed=1
)

# /BEGIN `Demo version of estimator.fit()`
print('', file=sys.stderr)
print('====Demo classification on Iris dataset====', file=sys.stderr)

iterator = estimator.fit_iter(X_train, y_train)
next(iterator)

n_trial = 0
while len(estimator.trials.trials) < estimator.max_evals:
iterator.send(1)  # -- try one more model
n_trial += 1
print('Trial', n_trial, 'loss:', estimator.trials.losses()[-1],
file=sys.stderr)
# hpsklearn.demo_support.scatter_error_vs_time(estimator)
# hpsklearn.demo_support.bar_classifier_choice(estimator)

estimator.retrain_best_model_on_full_data(X_train, y_train)

# /END Demo version of `estimator.fit()`

print('Test accuracy:', estimator.score(X_test, y_test), file=sys.stderr)
print('====End of demo====', file=sys.stderr)

def test_demo_boston():

boston = datasets.load_boston()
X_train, X_test, y_train, y_test = train_test_split(
boston.data, boston.target, test_size=.25, random_state=1)

estimator = hpsklearn.HyperoptEstimator(
preprocessing=hpsklearn.components.any_preprocessing('pp'),
regressor=hpsklearn.components.any_regressor('reg'),
algo=tpe.suggest,
trial_timeout=15.0,  # seconds
max_evals=10,
seed=1
)

# /BEGIN `Demo version of estimator.fit()`
print('', file=sys.stderr)
print('====Demo regression on Boston dataset====', file=sys.stderr)

iterator = estimator.fit_iter(X_train, y_train)
next(iterator)

n_trial = 0
while len(estimator.trials.trials) < estimator.max_evals:
iterator.send(1)  # -- try one more model
n_trial += 1
print('Trial', n_trial, 'loss:', estimator.trials.losses()[-1],
file=sys.stderr)
# hpsklearn.demo_support.scatter_error_vs_time(estimator)
# hpsklearn.demo_support.bar_classifier_choice(estimator)

estimator.retrain_best_model_on_full_data(X_train, y_train)

# /END Demo version of `estimator.fit()`

print('Test R2:', estimator.score(X_test, y_test), file=sys.stderr)
print('====End of demo====', file=sys.stderr)