from hpsklearn import HyperoptEstimator, svc
from sklearn import svm

# Load Data
# ...

if __name__ == "__main__":
    if use_hpsklearn:
        estim = HyperoptEstimator(classifier=svc("mySVC"))
    else:
        estim = svm.SVC()
    
    estim.fit(X_train, y_train)
    
    print(estim.score(X_test, y_test))
# <<show score here>>

from hpsklearn import HyperoptEstimator, sgd_classifier
from hyperopt import hp
import numpy as np

sgd_penalty = "l2"
sgd_loss = hp.pchoice("loss", [(0.50, "hinge"), (0.25, "log"), (0.25, "huber")])
sgd_alpha = hp.loguniform("alpha", low=np.log(1e-5), high=np.log(1))

if __name__ == "__main__":
    estim = HyperoptEstimator(classifier=sgd_classifier("my_sgd", penalty=sgd_penalty, loss=sgd_loss, alpha=sgd_alpha))
    estim.fit(X_train, y_train)

from hpsklearn import HyperoptEstimator, any_classifier, any_preprocessing
from sklearn.datasets import load_iris
from hyperopt import tpe
import numpy as np

# Download the data and split into training and test sets

iris = load_iris()

X = iris.data
y = iris.target

test_size = int(0.2 * len(y))
np.random.seed(13)
indices = np.random.permutation(len(X))
X_train = X[indices[:-test_size]]
y_train = y[indices[:-test_size]]
X_test = X[indices[-test_size:]]
y_test = y[indices[-test_size:]]


if __name__ == "__main__":
    # Instantiate a HyperoptEstimator with the search space and number of evaluations
    estim = HyperoptEstimator(classifier=any_classifier("my_clf"),
                              preprocessing=any_preprocessing("my_pre"),
                              algo=tpe.suggest,
                              max_evals=100,
                              trial_timeout=120)
    
    # Search the hyperparameter space based on the data
    estim.fit(X_train, y_train)
    
    # Show the results
    print(estim.score(X_test, y_test))
    # 1.0
    
    print(estim.best_model())
    # {'learner': ExtraTreesClassifier(bootstrap=False, class_weight=None, criterion='gini',
    #           max_depth=3, max_features='log2', max_leaf_nodes=None,
    #           min_impurity_decrease=0.0, min_impurity_split=None,
    #           min_samples_leaf=1, min_samples_split=2,
    #           min_weight_fraction_leaf=0.0, n_estimators=13, n_jobs=1,
    #           oob_score=False, random_state=1, verbose=False,
    #           warm_start=False), 'preprocs': (), 'ex_preprocs': ()}

from hpsklearn import HyperoptEstimator, extra_tree_classifier
from sklearn.datasets import load_digits
from hyperopt import tpe
import numpy as np

# Download the data and split into training and test sets

digits = load_digits()

X = digits.data
y = digits.target

test_size = int(0.2 * len(y))
np.random.seed(13)
indices = np.random.permutation(len(X))
X_train = X[indices[:-test_size]]
y_train = y[indices[:-test_size]]
X_test = X[indices[-test_size:]]
y_test = y[indices[-test_size:]]


if __name__ == "__main__":
    # Instantiate a HyperoptEstimator with the search space and number of evaluations
    estim = HyperoptEstimator(classifier=extra_tree_classifier("my_clf"),
                              preprocessing=[],
                              algo=tpe.suggest,
                              max_evals=10,
                              trial_timeout=300)

    # Search the hyperparameter space based on the data
    estim.fit(X_train, y_train)

    # Show the results
    print(estim.score(X_test, y_test))
    # 0.962785714286

    print(estim.best_model())
    # {'learner': ExtraTreesClassifier(bootstrap=True, class_weight=None, criterion='entropy',
    #           max_depth=None, max_features=0.959202875857,
    #           max_leaf_nodes=None, min_impurity_decrease=0.0,
    #           min_impurity_split=None, min_samples_leaf=1,
    #           min_samples_split=2, min_weight_fraction_leaf=0.0,
    #           n_estimators=20, n_jobs=1, oob_score=False, random_state=3,
    #           verbose=False, warm_start=False), 'preprocs': (), 'ex_preprocs': ()}