def test_extract_with_confidence_output_dictionary_unsupervised(self):
X, _ = load_xy(2)
extractor = MFE(groups="general").fit(X.values)
res = extractor.extract_with_confidence(
3, arguments_extract=dict(out_type=dict))
assert isinstance(res, dict)
assert len(res) == 3
def test_extract_with_time_and_with_confidence_output_dictionary(self):
X, y = load_xy(2)
extractor = MFE(groups="general",
measure_time="total").fit(X.values, y.values)
res = extractor.extract_with_confidence(
3, arguments_extract=dict(out_type=dict))
assert isinstance(res, dict)
assert len(res) == 4
def test_invalid_output_type_with_confidence_unsupervised(self):
X, _ = load_xy(2)
extractor = MFE(groups="general").fit(X.values)
with pytest.raises(TypeError):
res = extractor.extract_with_confidence(
3, arguments_extract=dict(out_type=set))
interval.
"""
# Load a dataset
import sklearn.tree
from sklearn.datasets import load_iris
from pymfe.mfe import MFE
data = load_iris()
y = data.target
X = data.data
# You can also extract your meta-features with confidence intervals using
# bootstrap. Keep in mind that this method extracts each meta-feature several
# times, and may be very expensive depending mainly on your data and the
# number of meta-feature extract methods called.
# Extract meta-features with confidence interval
mfe = MFE(features=["mean", "nr_cor_attr", "sd", "max"])
mfe.fit(X, y)
ft = mfe.extract_with_confidence(
sample_num=256,
confidence=0.99,
verbose=1,
)
print("\n".join("{:50} {:30} {:30}".format(x, y[0], y[1])
for x, y in zip(ft[0], ft[2])))
def test_bootstrap_extractor_extract_with_confidence_without_data(self):
mfe = MFE()
bootstrap_extractor = _bootstrap.BootstrapExtractor(extractor=mfe)
with pytest.raises(TypeError):
mfe.extract_with_confidence()
def test_extract_with_confidence_without_data(self):
mfe = MFE()
with pytest.raises(TypeError):
mfe.extract_with_confidence()
