tffs 1.1.2

Feature selection based on top frequency

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:

Parameter	Type	Description
df	pandas.DataFrame	The input dataset containing features and target variables. The first column should be the class label.
number_of_runs	int	The number of times a Random Forest model is built to compute feature importance.
percent	float	The percentage of top important features to retain (e.g., percent=20 keeps the top 20% most important features).
n_estimators	int	The number of decision trees in the Random Forest model.

📤 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

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