Meta-feature confidence interval

In this example, we will show you how to extract meta-features with confidence interval.

Began the metafeature extraction with confidence intervals process.
Now extracting metafeatures from original sample.
Done extracting metafeatures from original sample (total of 7 metafeatures).
Started data resampling with bootstrap with the following configurations:
| Total data resamples: 256
| Confidence levels used: [0.99] (total of 1).
. Random seeds:
   | For extractor model: 1234
   . For bootstrapping: None
Now extracting metafeatures from resampled data.

  0%|          | 0/256 [00:00<?, ?it/s]/home/docs/checkouts/readthedocs.org/user_builds/pymfe/envs/stable/lib/python3.7/site-packages/pymfe/_internal.py:1568: UserWarning: It is not possible make equal discretization
  warnings.warn("It is not possible make equal discretization")
/home/docs/checkouts/readthedocs.org/user_builds/pymfe/envs/stable/lib/python3.7/site-packages/pymfe/_internal.py:1568: UserWarning: It is not possible make equal discretization
  warnings.warn("It is not possible make equal discretization")

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  warnings.warn("It is not possible make equal discretization")

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  warnings.warn("It is not possible make equal discretization")

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  warnings.warn("It is not possible make equal discretization")

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  warnings.warn("It is not possible make equal discretization")

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  warnings.warn("It is not possible make equal discretization")

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100%|##########| 256/256 [00:01<00:00, 204.56it/s]
Done extracting metafeatures from resampled data.
Finished data resampling with bootstrap.
Now calculating confidence intervals... Done.
max.mean                                                        5.425000000000001              5.625000000000002
max.sd                                                         2.3759112147178905              2.564498302062089
mean.mean                                                       3.302750000000002             3.5970708333333348
mean.sd                                                        1.8715704340684498             1.9580808450547889
nr_cor_attr                                                   0.16666666666666674                            0.5
sd.mean                                                        0.8723289319973981             1.0299047593601987
sd.sd                                                          0.5017337852499149             0.6390658778822161

# 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])))