TFFS

Description

TFFS (Feature Selection based on Top Frequency) is a feature selection method that leverages Random Forest to identify the most frequently important features across multiple model runs. This method helps in reducing dimensionality while retaining significant features for better model performance.

Installation

pip install tffs

🔥 Functionality

The library provides multiple feature selection functions that combine TFFS with classical selection techniques:

🏷 Core Function:

get_frequency_of_feature_by_percent(df, number_of_runs, percent, n_estimators)

📌 Parameters

The function get_frequency_of_feature_by_percent() accepts the following parameters:

📤 Return

The function returns:

• A NumPy array containing the indices of the selected features that are among the top percent% most important features across multiple Random Forest runs.

🔄 Example Return:

array([0, 2, 4, 7, 9])

📌 Example Usage

import pandas as pd
from tffs import get_features_by_forward_and_tffs

# Create a sample DataFrame
data = pd.DataFrame({
    'class': [0, 1, 0, 1, 2, 0, 1, 2, 0, 1],
    'feature_1': [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0],
    'feature_2': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
    'feature_3': [5, 6, 7, 8, 9, 10, 11, 12, 13, 14],
    'feature_4': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
    'feature_5': [5, 6, 7, 8, 9, 10, 11, 12, 13, 14],
    'feature_6': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
    'feature_7': [2, 3, 4, 5, 6, 7, 8, 9, 10, 11],
    'feature_8': [3, 4, 5, 6, 7, 8, 9, 10, 11, 12],
    'feature_9': [4, 5, 6, 7, 8, 9, 10, 11, 12, 13]
})

# Run the function
selected_features = get_features_by_forward_and_tffs(
    data,
    percent_tffs=50,
    number_run=10,
    n_estimators=100,
    percent_forward=30
)

print("Selected features:", selected_features)

Author

Vu Thi Kieu Anh

© 2025