ENH avoid checking columns where training data is all nan in KNNImputer #29060
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This code script shows efficiency improvement if one column of data is all nan. import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.impute import KNNImputer
import pandas as pd
calhousing = fetch_california_housing()
X = pd.DataFrame(calhousing.data, columns=calhousing.feature_names)
y = pd.Series(calhousing.target, name='house_value')
rng = np.random.RandomState(42)
density = 10
mask = rng.randint(density, size=X.shape) == 0
X_na = X.copy()
X_na.values[mask] = np.nan
X_na.values[:, 0] = np.nan # one column is all nanThis PR Main |
adrinjalali
approved these changes
Aug 13, 2024
|
maybe @adam2392 or @OmarManzoor could have a look? |
OmarManzoor
approved these changes
Aug 15, 2024
MarcBresson
pushed a commit
to MarcBresson/scikit-learn
that referenced
this pull request
Sep 2, 2024
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Reference Issues/PRs
What does this implement/fix? Explain your changes.
In
KNNImputer, columns where training data is all nan will be removed or impute with 0.Therefore, we only need to check data with valid columns using
valid_mask.This can avoid computing pairwise distance when data with valid columns has no missing values.
Any other comments?
This could potentially save some memory.