diff --git a/pymfe/complexity.py b/pymfe/complexity.py
index d4eb9e9..c626bb1 100644
--- a/pymfe/complexity.py
+++ b/pymfe/complexity.py
@@ -813,7 +813,7 @@ class MFEComplexity:
         y: np.ndarray,
         ovo_comb: t.Optional[np.ndarray] = None,
         cls_inds: t.Optional[np.ndarray] = None,
-    ) -> float:
+    ) -> np.ndarray:
         """Volume of the overlapping region.
 
         This measure calculates the overlap of the distributions of
@@ -840,8 +840,8 @@ class MFEComplexity:
 
         Returns
         -------
-        float
-            Volume of the overlapping region.
+        :obj:`np.ndarray`
+            Volume of the overlapping region for each OVO combination.
 
         References
         ----------
@@ -864,7 +864,7 @@ class MFEComplexity:
             ovo_comb = sub_dic["ovo_comb"]
             cls_inds = sub_dic["cls_inds"]
 
-        f4 = np.zeros(ovo_comb.shape[0], dtype=float)
+        f2 = np.zeros(ovo_comb.shape[0], dtype=float)
 
         for ind, (cls_id_1, cls_id_2) in enumerate(ovo_comb):
             N_cls_1 = N[cls_inds[cls_id_1], :]
@@ -875,11 +875,11 @@ class MFEComplexity:
             minmax = cls._calc_minmax(N_cls_1, N_cls_2)
             maxmin = cls._calc_maxmin(N_cls_1, N_cls_2)
 
-            f4[ind] = np.prod(
+            f2[ind] = np.prod(
                 np.maximum(0.0, minmax - maxmin) / (maxmax - minmin)
             )
 
-        return f4
+        return f2
 
     @classmethod
     def ft_f3(
@@ -2499,7 +2499,7 @@ class MFEComplexity:
         cls_inds: t.Optional[np.ndarray] = None,
         N_scaled: t.Optional[np.ndarray] = None,
         norm_dist_mat: t.Optional[np.ndarray] = None,
-    ) -> np.ndarray:
+    ) -> float:
         """Clustering coefficient.
 
         The clustering coefficient of a vertex `v_i` is given by the
@@ -2552,7 +2552,7 @@ class MFEComplexity:
 
         Returns
         -------
-        :obj:`np.ndarray`
+        float
             Clustering coefficient of given data.
 
         References
diff --git a/pymfe/mfe.py b/pymfe/mfe.py
index 3c0e9f9..5740416 100644
--- a/pymfe/mfe.py
+++ b/pymfe/mfe.py
@@ -1345,6 +1345,129 @@ class MFE:
 
         return res_names, res_vals
 
+    def extract_metafeature_names(
+        self, supervised: bool = True
+    ) -> t.Tuple[str, ...]:
+        """Extract the pre-configured meta-feature names.
+
+        Parameters
+        ----------
+        supervised : bool, optional
+            If True, extract the meta-feature names assuming that `y` (data
+            labels) is given alongside `X` (independent attributes).
+
+            If there is some data fit into the MFE model, this method checks
+            wether `y` was fitted or not. Therefore, setting `supervised=True`
+            while fitting only `X` has no effect, and only unsupervised
+            meta-feature names will be returned.
+
+        Returns
+        -------
+        tuple
+            Tuple with meta-feature names to be extracted as values.
+        """
+        if self.X is not None:
+            custom_args_ft = self._custom_args_ft
+            precomp_args_ft = self._precomp_args_ft
+            postprocess_args_ft = self._postprocess_args_ft
+
+        else:
+            # No data fit, assume given arguments.
+            given_arguments = {
+                "X",
+                "N",
+                "C",
+                "num_cv_folds",
+                "shuffle_cv_folds",
+                "lm_sample_frac",
+                "score",
+                "random_state",
+                "cat_cols",
+                "hypparam_model_dt",
+            }
+
+            if supervised:
+                given_arguments.add("y")
+                given_arguments.add("dt_model")
+
+            postprocess_args_ft = {
+                "inserted_group_dep": self.inserted_group_dep,
+            }
+
+            custom_args_ft = dict.fromkeys(given_arguments, None)
+            precomp_args_ft = {}
+
+        metafeat_names = []  # type: t.List[str]
+
+        for cur_metadata in self._metadata_mtd_ft:
+            (
+                ft_mtd_name,
+                ft_mtd_callable,
+                ft_mtd_args,
+                ft_mandatory,
+            ) = cur_metadata
+
+            ft_name_without_prefix = _internal.remove_prefix(
+                value=ft_mtd_name, prefix=_internal.MTF_PREFIX
+            )
+
+            try:
+                # Test if meta-feature can be extracted.
+                _internal.build_mtd_kwargs(
+                    mtd_name=ft_name_without_prefix,
+                    mtd_args=ft_mtd_args,
+                    mtd_mandatory=ft_mandatory,
+                    user_custom_args=None,
+                    inner_custom_args=custom_args_ft,
+                    precomp_args=precomp_args_ft,
+                    suppress_warnings=True,
+                )
+
+            except RuntimeError:
+                continue
+
+            ft_has_length = _internal.array_is_returned(ft_mtd_callable)
+
+            if self._metadata_mtd_sm and ft_has_length:
+                for cur_metadata_sm in self._metadata_mtd_sm:
+                    sm_mtd_name, sm_mtd_callable, _, _ = cur_metadata_sm
+
+                    try:
+                        summarized_val_len = len(sm_mtd_callable([0]))
+
+                    except TypeError:
+                        summarized_val_len = 0
+
+                    if summarized_val_len > 0:
+                        metafeat_names += [
+                            ".".join(
+                                (ft_name_without_prefix, sm_mtd_name, str(i))
+                            )
+                            for i in range(summarized_val_len)
+                        ]
+
+                    else:
+                        metafeat_names.append(
+                            ".".join((ft_name_without_prefix, sm_mtd_name))
+                        )
+
+            else:
+                metafeat_names.append(ft_name_without_prefix)
+
+        fake_vals = len(metafeat_names) * [0]
+
+        with warnings.catch_warnings():
+            warnings.filterwarnings("ignore")
+
+            _internal.post_processing(
+                results=(metafeat_names, fake_vals, fake_vals),
+                groups=self.groups,
+                suppress_warnings=True,
+                **postprocess_args_ft,
+            )
+
+        return tuple(sorted(metafeat_names))
+
     def _extract_with_bootstrap(
         self,
         extractor: "MFE",

Method for extracting meta-feature names before extraction
**Is your feature request related to a problem? Please describe.**
Sometimes it is convenient to extract the list of meta-feature names before the meta-feature extraction.

```python
#An example to motivate the requested feature
from pymfe.mfe import MFE
import pandas as pd

number_of_datasets = 64
extractor = MFE(features="all", summary=["mean", "histogram", "max"])

# Init meta-dataset (but we need the meta-feature names...)
X, y = get_dataset_from_id(0)
mtf_names, mtf_values_dt_0 = extractor.fit(X, y).extract()  # Ugly, inconvenient...
meta_dataset = pd.DataFrame(index=range(number_of_datasets), columns=len(mtf_names))

meta_dataset.loc[0, :] = mtf_values_dt_0  # Not visually satisfying...
for i in range(1, number_of_datasets):
    meta_dataset.loc[i, :] = extractor.fit(X, y).extract()[1]  # Repeated line of code...
```

For instance, if the goal is to construct a meta-dataset using a pandas dataframe, it is convenient to get the meta-feature names in order to correctly set the data frame column names even before the meta-feature extraction starts. If the meta-dataset is simply a numpy array, then the number of extracted meta-features is sufficient to correctly initiate the number of columns in the array, which also is a statistic that can be naturally extracted from the list of meta-feature names (e.g. `len(meta_feat_names)`). Since in this case we're iterating over various base datasets in order to fill that meta-dataset structure, it is highly inconvenient to make a manual extraction before the iterating process starts (see example above) in order to just prepare the meta-dataset structure.

See section below for concrete examples.

**Describe the solution you'd like**
I propose a method '`.extract_metafeature_names()`' that works even before any data is fit, using only the information got while instantiating a MFE model.

Concrete usage examples would be:

```python
# A pandas data-frame example
from pymfe.mfe import MFE
import pandas as pd

number_of_datasets = 64
extractor = MFE(features="all", summary=["mean", "histogram", "max"])
mtf_names = extractor.extract_metafeature_names()  # To be implemented...

# Init meta-dataset with the correct number of columns and column names
meta_dataset = pd.DataFrame(index=range(number_of_datasets), columns=mtf_names)

for i in range(number_of_datasets):
    X, y = get_dataset_from_id(i)
    meta_dataset.loc[i, :] = extractor.fit(X, y).extract()[1]
```
```python
# A numpy array example
from pymfe.mfe import MFE
import numpy as np

number_of_datasets = 64
extractor = MFE(features="all", summary=["mean", "histogram", "max"])
mtf_names = extractor.extract_metafeature_names()  # To be implemented...

# Init meta-dataset with the correct number of columns
meta_dataset = np.full((number_of_datasets, len(mtf_names)), fill_value=np.nan)

for i in range(number_of_datasets):
    X, y = get_dataset_from_id(i)
    meta_dataset[i, :] = extractor.fit(X, y).extract()[1]
```

**Describe alternatives you've considered**
None.

**Additional context**
None.

diff --git a/tests/test_architecture.py b/tests/test_architecture.py
index 565d6b8..6fc6261 100644
--- a/tests/test_architecture.py
+++ b/tests/test_architecture.py
@@ -243,6 +243,102 @@ class TestArchitecture:
         assert (len(res) == len(_internal.VALID_GROUPS)
                 and not set(res).symmetric_difference(_internal.VALID_GROUPS))
 
+    @pytest.mark.parametrize("groups, summary", [
+        ("statistical", "all"),
+        ("general", "all"),
+        ("landmarking", "all"),
+        ("relative", "all"),
+        ("model-based", "all"),
+        ("info-theory", "all"),
+        ("statistical", ("mean", "sd")),
+        ("general", ("mean", "sd")),
+        ("landmarking", ("mean", "sd")),
+        ("model-based", ("mean", "sd")),
+        ("general", ("mean", "histogram")),
+        ("landmarking", ("mean", "histogram")),
+        ("model-based", ("mean", "histogram")),
+        ("general", ("quantiles", "histogram")),
+        ("landmarking", ("quantiles", "histogram")),
+        ("model-based", ("quantiles", "histogram")),
+        (["general", "relative"], ("mean", "sd")),
+        (["general", "relative"], ("quantiles", "histogram")),
+        (["landmarking", "relative"], ("mean", "sd")),
+        (["landmarking", "relative"], ("quantiles", "histogram")),
+        (["statistical", "landmarking", "relative"], ("mean", "sd")),
+        ("all", "all"),
+    ])
+    def test_extract_metafeature_names_supervised(self, groups, summary):
+        """Test .extract_metafeature_names method."""
+        X, y = utils.load_xy(0)
+
+        mfe = MFE(groups=groups, summary=summary)
+
+        mtf_names_1 = mfe.extract_metafeature_names(supervised=True)
+        mtf_names_2 = mfe.fit(X.values, y.values).extract(suppress_warnings=True)[0]
+
+        assert mtf_names_1 == tuple(mtf_names_2)
+
+    @pytest.mark.parametrize("groups, summary", [
+        ("statistical", "all"),
+        ("general", "all"),
+        ("landmarking", "all"),
+        ("relative", "all"),
+        ("model-based", "all"),
+        ("info-theory", "all"),
+        ("statistical", ("mean", "sd")),
+        ("general", ("mean", "sd")),
+        ("landmarking", ("mean", "sd")),
+        ("model-based", ("mean", "sd")),
+        ("general", ("mean", "histogram")),
+        ("landmarking", ("mean", "histogram")),
+        ("model-based", ("mean", "histogram")),
+        ("general", ("quantiles", "histogram")),
+        ("landmarking", ("quantiles", "histogram")),
+        ("model-based", ("quantiles", "histogram")),
+        (["general", "relative"], ("mean", "sd")),
+        (["general", "relative"], ("quantiles", "histogram")),
+        (["landmarking", "relative"], ("mean", "sd")),
+        (["landmarking", "relative"], ("quantiles", "histogram")),
+        (["statistical", "landmarking", "relative"], ("mean", "sd")),
+        ("all", "all"),
+    ])
+    def test_extract_metafeature_names_unsupervised_01(self, groups, summary):
+        """Test .extract_metafeature_names method."""
+        X, _ = utils.load_xy(0)
+
+        mfe = MFE(groups=groups, summary=summary)
+
+        mtf_names_1 = mfe.extract_metafeature_names(supervised=False)
+        mtf_names_2 = mfe.fit(X.values).extract(suppress_warnings=True)[0]
+
+        assert mtf_names_1 == tuple(mtf_names_2)
+
+    @pytest.mark.parametrize("groups, summary", [
+        ("general", "all"),
+        ("statistical", ("mean", "sd")),
+        (["general", "relative"], ("mean", "sd")),
+        (["general", "relative"], ("quantiles", "histogram")),
+        (["landmarking", "relative"], ("mean", "sd")),
+        (["landmarking", "relative"], ("quantiles", "histogram")),
+        (["statistical", "landmarking", "relative"], ("mean", "sd")),
+        ("all", "all"),
+    ])
+    def test_extract_metafeature_names_unsupervised_02(self, groups, summary):
+        """Test .extract_metafeature_names method."""
+        X, _ = utils.load_xy(0)
+
+        mfe = MFE(groups=groups, summary=summary)
+
+        mtf_names_1 = mfe.fit(X.values).extract(suppress_warnings=True)[0]
+        # Note: by default, .extract_metafeature_names should check wether
+        # 'y' was fitted or not if .fit was called before. Therefore, here,
+        # supervised=True is expected to be ignored and behave like
+        # supervised=False.
+        mtf_names_2 = mfe.extract_metafeature_names(supervised=True)
+        mtf_names_3 = mfe.extract_metafeature_names(supervised=False)
+
+        assert tuple(mtf_names_1) == mtf_names_2 == mtf_names_3
+
     @pytest.mark.parametrize("groups", [
         "statistical",
         "general",

pip install -e .[dev]
parse_log_pytest
requirements.txt
pytest pytest-cov
3.7
requirements.txt requirements-dev.txt requirements-docs.txt
pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning
alabaster==0.7.13
astroid==2.15.8
Babel==2.14.0
certifi @ file:///croot/certifi_1671487769961/work/certifi
charset-normalizer==3.4.1
coverage==7.2.7
cycler==0.11.0
dill==0.3.7
docutils==0.17.1
exceptiongroup==1.2.2
flake8==5.0.4
fonttools==4.38.0
idna==3.10
imagesize==1.4.1
importlib-metadata==4.2.0
iniconfig==2.0.0
isort==5.11.5
Jinja2==3.1.6
joblib==1.3.2
kiwisolver==1.4.5
lazy-object-proxy==1.9.0
liac-arff==2.5.0
MarkupSafe==2.1.5
matplotlib==3.5.3
mccabe==0.7.0
mypy==1.4.1
mypy-extensions==1.0.0
numpy==1.21.6
numpydoc==1.5.0
packaging==24.0
pandas==1.3.5
patsy==1.0.1
Pillow==9.5.0
platformdirs==4.0.0
pluggy==1.2.0
pycodestyle==2.9.1
pyflakes==2.5.0
Pygments==2.17.2
pylint==2.17.7
-e git+https://github.com/ealcobaca/pymfe.git@c8f5d4a5ae342d7a630620aebd196dc651c35531#egg=pymfe
pyparsing==3.1.4
pytest==7.4.4
pytest-cov==4.1.0
python-dateutil==2.9.0.post0
pytz==2025.2
requests==2.31.0
scikit-learn==1.0.2
scipy==1.7.3
seaborn==0.12.2
six==1.17.0
snowballstemmer==2.2.0
Sphinx==4.3.2
sphinx-gallery==0.14.0
sphinx-rtd-theme==1.3.0
sphinxcontrib-applehelp==1.0.2
sphinxcontrib-devhelp==1.0.2
sphinxcontrib-htmlhelp==2.0.0
sphinxcontrib-jquery==4.1
sphinxcontrib-jsmath==1.0.1
sphinxcontrib-qthelp==1.0.3
sphinxcontrib-serializinghtml==1.1.5
statsmodels==0.13.5
texttable==1.7.0
threadpoolctl==3.1.0
tomli==2.0.1
tomlkit==0.12.5
typed-ast==1.5.5
typing_extensions==4.7.1
urllib3==2.0.7
wrapt==1.16.0
zipp==3.15.0

name: pymfe
channels:
  - defaults
  - https://repo.anaconda.com/pkgs/main
  - https://repo.anaconda.com/pkgs/r
  - conda-forge
dependencies:
  - _libgcc_mutex=0.1=main
  - _openmp_mutex=5.1=1_gnu
  - ca-certificates=2025.2.25=h06a4308_0
  - certifi=2022.12.7=py37h06a4308_0
  - ld_impl_linux-64=2.40=h12ee557_0
  - libffi=3.4.4=h6a678d5_1
  - libgcc-ng=11.2.0=h1234567_1
  - libgomp=11.2.0=h1234567_1
  - libstdcxx-ng=11.2.0=h1234567_1
  - ncurses=6.4=h6a678d5_0
  - openssl=1.1.1w=h7f8727e_0
  - pip=22.3.1=py37h06a4308_0
  - python=3.7.16=h7a1cb2a_0
  - readline=8.2=h5eee18b_0
  - setuptools=65.6.3=py37h06a4308_0
  - sqlite=3.45.3=h5eee18b_0
  - tk=8.6.14=h39e8969_0
  - wheel=0.38.4=py37h06a4308_0
  - xz=5.6.4=h5eee18b_1
  - zlib=1.2.13=h5eee18b_1
  - pip:
      - alabaster==0.7.13
      - astroid==2.15.8
      - babel==2.14.0
      - charset-normalizer==3.4.1
      - coverage==7.2.7
      - cycler==0.11.0
      - dill==0.3.7
      - docutils==0.17.1
      - exceptiongroup==1.2.2
      - flake8==5.0.4
      - fonttools==4.38.0
      - idna==3.10
      - imagesize==1.4.1
      - importlib-metadata==4.2.0
      - iniconfig==2.0.0
      - isort==5.11.5
      - jinja2==3.1.6
      - joblib==1.3.2
      - kiwisolver==1.4.5
      - lazy-object-proxy==1.9.0
      - liac-arff==2.5.0
      - markupsafe==2.1.5
      - matplotlib==3.5.3
      - mccabe==0.7.0
      - mypy==1.4.1
      - mypy-extensions==1.0.0
      - numpy==1.21.6
      - numpydoc==1.5.0
      - packaging==24.0
      - pandas==1.3.5
      - patsy==1.0.1
      - pillow==9.5.0
      - platformdirs==4.0.0
      - pluggy==1.2.0
      - pycodestyle==2.9.1
      - pyflakes==2.5.0
      - pygments==2.17.2
      - pylint==2.17.7
      - pyparsing==3.1.4
      - pytest==7.4.4
      - pytest-cov==4.1.0
      - python-dateutil==2.9.0.post0
      - pytz==2025.2
      - requests==2.31.0
      - scikit-learn==1.0.2
      - scipy==1.7.3
      - seaborn==0.12.2
      - six==1.17.0
      - snowballstemmer==2.2.0
      - sphinx==4.3.2
      - sphinx-gallery==0.14.0
      - sphinx-rtd-theme==1.3.0
      - sphinxcontrib-applehelp==1.0.2
      - sphinxcontrib-devhelp==1.0.2
      - sphinxcontrib-htmlhelp==2.0.0
      - sphinxcontrib-jquery==4.1
      - sphinxcontrib-jsmath==1.0.1
      - sphinxcontrib-qthelp==1.0.3
      - sphinxcontrib-serializinghtml==1.1.5
      - statsmodels==0.13.5
      - texttable==1.7.0
      - threadpoolctl==3.1.0
      - tomli==2.0.1
      - tomlkit==0.12.5
      - typed-ast==1.5.5
      - typing-extensions==4.7.1
      - urllib3==2.0.7
      - wrapt==1.16.0
      - zipp==3.15.0
prefix: /opt/conda/envs/pymfe

tests/test_architecture.py::TestArchitecture::test_extract_metafeature_names_supervised[statistical-all] tests/test_architecture.py::TestArchitecture::test_extract_metafeature_names_supervised[general-all] tests/test_architecture.py::TestArchitecture::test_extract_metafeature_names_supervised[landmarking-all] tests/test_architecture.py::TestArchitecture::test_extract_metafeature_names_supervised[relative-all] tests/test_architecture.py::TestArchitecture::test_extract_metafeature_names_supervised[model-based-all] tests/test_architecture.py::TestArchitecture::test_extract_metafeature_names_supervised[info-theory-all] tests/test_architecture.py::TestArchitecture::test_extract_metafeature_names_supervised[statistical-summary6] tests/test_architecture.py::TestArchitecture::test_extract_metafeature_names_supervised[general-summary7] tests/test_architecture.py::TestArchitecture::test_extract_metafeature_names_supervised[landmarking-summary8] tests/test_architecture.py::TestArchitecture::test_extract_metafeature_names_supervised[model-based-summary9] tests/test_architecture.py::TestArchitecture::test_extract_metafeature_names_supervised[general-summary10] tests/test_architecture.py::TestArchitecture::test_extract_metafeature_names_supervised[landmarking-summary11] tests/test_architecture.py::TestArchitecture::test_extract_metafeature_names_supervised[model-based-summary12] tests/test_architecture.py::TestArchitecture::test_extract_metafeature_names_supervised[general-summary13] tests/test_architecture.py::TestArchitecture::test_extract_metafeature_names_supervised[landmarking-summary14] tests/test_architecture.py::TestArchitecture::test_extract_metafeature_names_supervised[model-based-summary15] tests/test_architecture.py::TestArchitecture::test_extract_metafeature_names_supervised[groups16-summary16] tests/test_architecture.py::TestArchitecture::test_extract_metafeature_names_supervised[groups17-summary17] tests/test_architecture.py::TestArchitecture::test_extract_metafeature_names_supervised[groups18-summary18] tests/test_architecture.py::TestArchitecture::test_extract_metafeature_names_supervised[groups19-summary19] tests/test_architecture.py::TestArchitecture::test_extract_metafeature_names_supervised[groups20-summary20] tests/test_architecture.py::TestArchitecture::test_extract_metafeature_names_supervised[all-all] tests/test_architecture.py::TestArchitecture::test_extract_metafeature_names_unsupervised_01[statistical-all] tests/test_architecture.py::TestArchitecture::test_extract_metafeature_names_unsupervised_01[general-all] tests/test_architecture.py::TestArchitecture::test_extract_metafeature_names_unsupervised_01[landmarking-all] tests/test_architecture.py::TestArchitecture::test_extract_metafeature_names_unsupervised_01[relative-all] tests/test_architecture.py::TestArchitecture::test_extract_metafeature_names_unsupervised_01[model-based-all] tests/test_architecture.py::TestArchitecture::test_extract_metafeature_names_unsupervised_01[info-theory-all] tests/test_architecture.py::TestArchitecture::test_extract_metafeature_names_unsupervised_01[statistical-summary6] tests/test_architecture.py::TestArchitecture::test_extract_metafeature_names_unsupervised_01[general-summary7] tests/test_architecture.py::TestArchitecture::test_extract_metafeature_names_unsupervised_01[landmarking-summary8] tests/test_architecture.py::TestArchitecture::test_extract_metafeature_names_unsupervised_01[model-based-summary9] tests/test_architecture.py::TestArchitecture::test_extract_metafeature_names_unsupervised_01[general-summary10] tests/test_architecture.py::TestArchitecture::test_extract_metafeature_names_unsupervised_01[landmarking-summary11] tests/test_architecture.py::TestArchitecture::test_extract_metafeature_names_unsupervised_01[model-based-summary12] tests/test_architecture.py::TestArchitecture::test_extract_metafeature_names_unsupervised_01[general-summary13] tests/test_architecture.py::TestArchitecture::test_extract_metafeature_names_unsupervised_01[landmarking-summary14] tests/test_architecture.py::TestArchitecture::test_extract_metafeature_names_unsupervised_01[model-based-summary15] tests/test_architecture.py::TestArchitecture::test_extract_metafeature_names_unsupervised_01[groups16-summary16] tests/test_architecture.py::TestArchitecture::test_extract_metafeature_names_unsupervised_01[groups17-summary17] tests/test_architecture.py::TestArchitecture::test_extract_metafeature_names_unsupervised_01[groups18-summary18] tests/test_architecture.py::TestArchitecture::test_extract_metafeature_names_unsupervised_01[groups19-summary19] tests/test_architecture.py::TestArchitecture::test_extract_metafeature_names_unsupervised_01[groups20-summary20] tests/test_architecture.py::TestArchitecture::test_extract_metafeature_names_unsupervised_01[all-all] tests/test_architecture.py::TestArchitecture::test_extract_metafeature_names_unsupervised_02[general-all] tests/test_architecture.py::TestArchitecture::test_extract_metafeature_names_unsupervised_02[statistical-summary1] tests/test_architecture.py::TestArchitecture::test_extract_metafeature_names_unsupervised_02[groups2-summary2] tests/test_architecture.py::TestArchitecture::test_extract_metafeature_names_unsupervised_02[groups3-summary3] tests/test_architecture.py::TestArchitecture::test_extract_metafeature_names_unsupervised_02[groups4-summary4] tests/test_architecture.py::TestArchitecture::test_extract_metafeature_names_unsupervised_02[groups5-summary5] tests/test_architecture.py::TestArchitecture::test_extract_metafeature_names_unsupervised_02[groups6-summary6] tests/test_architecture.py::TestArchitecture::test_extract_metafeature_names_unsupervised_02[all-all]
tests/test_architecture.py::TestArchitecture::test_ohe_full_encoding_missing_value
tests/test_architecture.py::TestArchitecture::test_summary_valid1 tests/test_architecture.py::TestArchitecture::test_summary_valid2 tests/test_architecture.py::TestArchitecture::test_summary_invalid1 tests/test_architecture.py::TestArchitecture::test_summary_invalid2 tests/test_architecture.py::TestArchitecture::test_postprocessing_valid tests/test_architecture.py::TestArchitecture::test_preprocessing_valid tests/test_architecture.py::TestArchitecture::test_feature_detection tests/test_architecture.py::TestArchitecture::test_get_groups tests/test_architecture.py::TestArchitecture::test_metafeature_description tests/test_architecture.py::TestArchitecture::test_metafeature_description_exceptions tests/test_architecture.py::TestArchitecture::test_default_alias_groups tests/test_architecture.py::TestArchitecture::test_parse_valid_metafeatures[statistical] tests/test_architecture.py::TestArchitecture::test_parse_valid_metafeatures[general] tests/test_architecture.py::TestArchitecture::test_parse_valid_metafeatures[landmarking] tests/test_architecture.py::TestArchitecture::test_parse_valid_metafeatures[relative] tests/test_architecture.py::TestArchitecture::test_parse_valid_metafeatures[model-based] tests/test_architecture.py::TestArchitecture::test_parse_valid_metafeatures[info-theory] tests/test_architecture.py::TestArchitecture::test_parse_valid_metafeatures[groups6] tests/test_architecture.py::TestArchitecture::test_parse_valid_metafeatures[groups7] tests/test_architecture.py::TestArchitecture::test_parse_valid_metafeatures[groups8] tests/test_architecture.py::TestArchitecture::test_parse_valid_metafeatures[groups9] tests/test_architecture.py::TestArchitecture::test_no_cat_transformation tests/test_architecture.py::TestArchitecture::test_gray_encoding_missing_value tests/test_architecture.py::TestArchitecture::test_one_hot_encoding_01 tests/test_architecture.py::TestArchitecture::test_one_hot_encoding_02 tests/test_architecture.py::TestArchitecture::test_one_hot_encoding_03 tests/test_architecture.py::TestArchitecture::test_one_hot_encoding_04 tests/test_architecture.py::TestArchitecture::test_extract_with_confidence[0.95] tests/test_architecture.py::TestArchitecture::test_extract_with_confidence[0.99] tests/test_architecture.py::TestArchitecture::test_extract_with_confidence_invalid1 tests/test_architecture.py::TestArchitecture::test_extract_with_confidence_invalid2 tests/test_architecture.py::TestArchitecture::test_extract_with_confidence_invalid3 tests/test_architecture.py::TestArchitecture::test_extract_with_confidence_time[True] tests/test_architecture.py::TestArchitecture::test_extract_with_confidence_time[False] tests/test_architecture.py::TestArchitecture::test_extract_with_confidence_multiple_conf_level tests/test_architecture.py::TestArchitecture::test_extract_with_confidence_random_state1 tests/test_architecture.py::TestArchitecture::test_extract_with_confidence_random_state2 tests/test_architecture.py::TestArchitecture::test_extract_with_confidence_random_state3 tests/test_architecture.py::TestArchitecture::test_extract_from_model tests/test_architecture.py::TestArchitecture::test_extract_from_model_invalid1 tests/test_architecture.py::TestArchitecture::test_extract_from_model_invalid2 tests/test_architecture.py::TestArchitecture::test_extract_from_model_invalid3 tests/test_architecture.py::TestArchitecture::test_extract_from_model_invalid4 tests/test_architecture.py::TestArchitectureWarnings::test_feature_warning1 tests/test_architecture.py::TestArchitectureWarnings::test_feature_warning2 tests/test_architecture.py::TestArchitectureWarnings::test_mem_err_precompute tests/test_architecture.py::TestArchitectureWarnings::test_mem_err_postprocess tests/test_architecture.py::TestArchitectureWarnings::test_postprocessing_invalid1 tests/test_architecture.py::TestArchitectureWarnings::test_postprocessing_invalid2 tests/test_architecture.py::TestArchitectureWarnings::test_preprocessing_invalid
pymfe/mfe.py pymfe/complexity.py
pymfe/mfe.py pymfe/complexity.py