import numpy as np
from sklearn.preprocessing import FunctionTransformer
from ..utils import check_Xs
[docs]
class SelectIncompleteSamples(FunctionTransformer):
r"""
Remove complete samples from a multi-modal dataset. Apply `FunctionTransformer` (from `Scikit-learn`)
with `select_incomplete_samples` as a function.
Example
--------
>>> import numpy as np
>>> import pandas as pd
>>> from imml.preprocessing import SelectIncompleteSamples
>>> from imml.ampute import Amputer
>>> Xs = [pd.DataFrame(np.random.default_rng(42).random((20, 10))) for i in range(3)]
>>> Xs = Amputer(p=0.2, mechanism="mcar", random_state=42).fit_transform(Xs)
>>> transformer = SelectIncompleteSamples()
>>> transformer.fit_transform(Xs)
"""
def __init__(self):
super().__init__(select_incomplete_samples)
[docs]
def select_incomplete_samples(Xs: list):
r"""
Remove complete samples from a multi-modal dataset.
Parameters
----------
Xs : list of array-likes objects
- Xs length: n_mods
- Xs[i] shape: (n_samples, n_features)
A list of different mods.
Returns
-------
transformed_Xs : list of array-likes objects, shape (n_samples, n_features_i)
The transformed data.
Example
--------
>>> import numpy as np
>>> import pandas as pd
>>> from imml.preprocessing import select_incomplete_samples
>>> from imml.ampute import Amputer
>>> Xs = [pd.DataFrame(np.random.default_rng(42).random((20, 10))) for i in range(3)]
>>> Xs = Amputer(p=0.2, mechanism="mcar", random_state=42).fit_transform(Xs)
>>> select_incomplete_samples(Xs)
"""
Xs = check_Xs(Xs, ensure_all_finite='allow-nan')
mask = np.stack([np.isnan(X).any(axis=1) for X in Xs], axis=1)
mask = mask.any(axis=1)
transformed_Xs = [X[mask] for X in Xs]
return transformed_Xs