Swarmauri Measurement Mutual Information
Mutual-information measurement plugin for Swarmauri pipelines. Computes the average mutual information (in bits) between every feature column and a target column, letting you rank signal strength before training models.
Features
- Wraps
sklearn.feature_selection.mutual_info_classifbehind the standardMeasurementBaseAPI. - Supports Pandas DataFrame inputs; automatically excludes the target column from the feature set.
- Returns the average mutual information across all features (in bits) for quick screening.
Prerequisites
- Python 3.10 or newer.
scikit-learnandpandasinstalled (pulled in as dependencies of this package).- Clean, pre-processed categorical data (encode non-numeric columns before calling) since
mutual_info_classifexpects numerical inputs.
Installation
# pip
pip install swarmauri_measurement_mutualinformation
# poetry
poetry add swarmauri_measurement_mutualinformation
# uv (pyproject-based projects)
uv add swarmauri_measurement_mutualinformation
Quickstart
import pandas as pd
from swarmauri_measurement_mutualinformation import MutualInformationMeasurement
# Example dataset
frame = pd.DataFrame(
{
"feature_a": [0, 1, 1, 0, 1, 0],
"feature_b": [5.1, 5.0, 4.9, 5.2, 5.1, 5.0],
"target": [0, 1, 1, 0, 1, 0],
}
)
mi = MutualInformationMeasurement()
avg_mi = mi.calculate(frame, target_column="target")
print(f"Average mutual information: {avg_mi:.4f} bits")
Per-Feature Scores
If you need the individual MI score per feature, compute it directly and inspect the array:
import pandas as pd
from sklearn.feature_selection import mutual_info_classif
frame = pd.DataFrame(
{
"feat1": [0, 1, 1, 0, 1, 0],
"feat2": [5.1, 5.0, 4.9, 5.2, 5.1, 5.0],
"target": [0, 1, 1, 0, 1, 0],
}
)
scores = mutual_info_classif(frame[["feat1", "feat2"]], frame["target"])
for column, score in zip(["feat1", "feat2"], scores):
print(column, score)
Use the per-feature scores to filter low-signal columns before passing the DataFrame back through Swarmauri.
Tips
- Normalize or discretize continuous features when comparing very different scales; mutual information is sensitive to distribution assumptions.
- Handle missing values before calling
calculate;mutual_info_classifdoes not accept NaNs. - Binary targets work out of the box; for multi-class targets, ensure
target_columncontains integer encodings.
Want to help?
If you want to contribute to swarmauri-sdk, read up on our guidelines for contributing that will help you get started.
1 maintainer
Metadata
1 maintainer
Release files for swarmauri_measurement_mutualinformation 0.9.3
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