ipss 0.3.0

Python implementation of Integrated Path Stability Selection (IPSS)

Integrated path stability selection (IPSS)

Integrated path stability selection (IPSS) is a general method for improving feature selection algorithms that yields more robust, accurate, and interpretable models. IPSS does this by allowing users to control the expected number of falsely selected features, E(FP), while producing far more true positives than other versions of stability selection. This Python implementation of IPSS applied to L1-regularized linear and logistic regression is intended for researchers and practitioners alike, requiring only the X and y data and specification of E(FP).

Associated paper

arXiv:

Installation

Dependencies

pip install joblib numpy scikit-learn scipy

Installing IPSS

To install from PyPI:

pip install ipss

To clone from GitHub:

git clone [e-mail]:omelikechi/ipss.git

Or clone from GitHub using HTTPS:

git clone https://github.com/omelikechi/ipss.git

Usage

Given an n-by-p matrix of features, X (n = number of samples, p = number of features), an n-by-1 vector of responses, y, and a target number of expected false positives, EFP:

from ipss import ipss

# Load data X and y
# Specify expected number of false positives (EFP)
# Run IPSS:
result = ipss(X, y, EFP)

# Result analysis
print(result['selected_features'])  # features selected by IPSS

Results

result is a dictionary containing:

• alphas: Grid of regularization parameters (array of shape (n_alphas,)).
• average_select: Average number of features selected at each regularization (array of shape (n_alphas,)).
• scores: IPSS score for each feature (array of shape (p,)).
• selected_features: Indices of features selected by IPSS (list of ints).
• stability_paths: Estimated selection probabilities at each regularization (array of shape (n_alphas, p))
• stop_index: Index of regularization value at which IPSS threshold is passed (int).
• threshold: The calculated threshold value tau = Integral value / EFP (scalar).

Full ist of arguments

ipss takes the following arguments (only X and y are required, and typically only EFP is specified):

• X: Features (array of shape (n,p)).
• y: Responses (array of shape (n,) or (n, 1)). IPSS automatically detects if y is continuous or binary.
• EFP: Target expected number of false positives (positive scalar; default is 1).
• cutoff: Together with EFP, determines IPSS threshold (positive scalar; default is 0.05).
• B: Number of subsampling steps (int; default is 50).
• n_alphas: Number of values in regularization grid (int; default is 25).
• q_max: Max number of features selected (int; default is None, in which case q_max = p/2).
• Z_sparse: If True, tensor of subsamples, Z, is sparse (default is False).
• lars: Implements least angle regression (LARS) for linear regression if True, lasso otherwise (default is False).
• selection_function: Function to apply to the stability paths. If a positive int, m, function is h_m(x) = (2x - 1)**m if x >= 0.5 and 0 if x < 0.5 (int, callable, or None; default is None, in which case function is h_2 if y is binary, or h_3 if continuous).
• with_stability: If True, uses a stability measure in selection process (default is False).
• delta: Determines scaling of regularization interval (scalar; default is 1).
• standardize_X: If True, standardizes all features (default is True).
• center_y: If True, centers y when it is continuous (default is True).

Examples

Examples are available in the examples folder. These include

• A simple example in which features are simulated independently from a standard normal distribution.
• An example using prostate cancer data, as detailed in the associated paper.
• An example using colon cancer data, as detailed in the associated paper.