from sklearn import datasets, svm, neighbors

from experiments.base import CompleteExperiment


class SwissRollExperiment(CompleteExperiment):
    title = 'iso-swiss-roll'
    plotting = True
    reduction_method = 'isomap'
    reduction_params = {'k': 7}
    knn = neighbors.KNeighborsRegressor(n_neighbors=1, n_jobs=-1)

    learner = svm.SVR
    learning_parameters = [
        {'C': (1, 10, 100), 'gamma': (.01, .1), 'kernel': ('rbf',)}
    ]

    displaying_cycle_components = (2, 1)
    learning_cycle_components = (2, 1)

    def _load_data(self):
        self.data, self.target = datasets.make_swiss_roll(1000, random_state=0)
        self.feature_names = ['A', 'B', 'C']


if __name__ == '__main__':
    SwissRollExperiment().start()

from sklearn import datasets, svm, neighbors
from experiments.base import CompleteExperiment


class SwissRollExperiment(CompleteExperiment):
    title = 'iso-swiss-roll'
    plotting = True
    reduction_method = 'isomap'
    reduction_params = {'k': 7}
    knn = neighbors.KNeighborsRegressor(n_neighbors=1, n_jobs=-1)

    learner = svm.SVR
    learning_parameters = [
        {'C': (1, 10, 100), 'gamma': (.01, .1), 'kernel': ('rbf',)}
    ]

    def _load_data(self):
        self.data, self.target = datasets.make_swiss_roll(1000, random_state=0)
        self.feature_names = ['A', 'B', 'C']


if __name__ == '__main__':
    SwissRollExperiment().start()

Update experiments/diverse/iso_1_swiss_roll