hybridtffs 0.1.7

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get_features_by_backward_and_tffs

Description

The get_features_by_backward_and_tffs function performs feature selection based on Backward Selection combined with Feature Selection based on Top Frequency (TFFS). This function helps identify important features in a dataset by leveraging the TFFS algorithm from the pypi-tffs repository and Backward Selection.

Parameters

Parameter	Data Type	Description
data	pd.DataFrame	The input dataset containing features to be selected. The first column should represent the class label for the classification process.
percent_tffs	float	The percentage of features to retain after applying TFFS. This value should be greater than percent_backward to ensure effective selection.
number_run	int	The number of times the Random Forest model is built during the TFFS process.
n_estimators	int	The number of decision trees in the Random Forest model used to assess feature importance.
percent_backward	float	The percentage of features to retain after applying Backward Selection.

Workflow

1. Feature Selection based on Top Frequency (TFFS):

   • Apply the TFFS algorithm from pypi-tffs to identify features that frequently appear as important across multiple Random Forest model runs.
   • Retain the top percent_tffs% most frequently selected features.

2. Backward Selection:

   • Iteratively remove the least important features and evaluate model performance to identify those with the least impact.

3. Backward Selection:

   • From the set of features selected in the TFFS step, retain percent_backward% of the most impactful features.

Feature Selection Methods

In addition to Backward Selection, this package includes multiple classical feature selection methods combined with TFFS:

• Forward Selection (get_features_by_forward_and_tffs)
• Recursive Feature Elimination (RFE) (get_features_by_recursive_and_tffs)
• Pearson Correlation (P.C.) (get_features_by_pc_and_tffs)
• Mutual Information (M.I.) (get_features_by_mi_and_tffs)
• Fish Score (get_features_by_fs_and_tffs)
• Lasso Regression (get_features_by_lasso_and_tffs)

Example Usage

import pandas as pd
from hybridtffs import get_features_by_backward_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_backward_and_tffs(
    data,
    percent_tffs=50,
    number_run=10,
    n_estimators=100,
    percent_backward=30
)

print("Selected features:", selected_features)

Author

Vu Thi Kieu Anh

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