Welcome to knockpy’s documentation!

Knockoffs are a powerful tool which can be used in combination with nearly any machine learning algorithm to control the false discovery rate (FDR) in feature selection. Knockoffs were initially developed in Barber and Candes 2015 and Candes et al 2018.

Knockpy is a python implementation of the knockoffs framework which makes it easy to apply knockoff-based inference in only a few lines of code. Knockpy is also built to be modular, so researchers and analysts can easily layer functionality on top of it. See usage for more details!

Contents:

• Getting Started
  • Installation
  • What if installation fails?
  • Quickstart
• Tutorials
  • A quick review of knockoffs
  • The KnockoffFilter class
  • Constructing knockoffs
    • Gaussian knockoffs galore
    • Metropolized knockoff sampling
  • Knockoff feature statistics
    • Built-in feature statistics
    • Masked likelihood ratio (MLR) statistics
    • Custom feature statistics with FeatureStatistic
  • Other functionality: group knockoffs and graphical model discovery
    • Group knockoffs
    • Gaussian graphical models
  • To-dos
• MRC Knockoffs Primer
• API Reference
  • The KnockoffFilter
    • KnockoffFilter
  • Feature Statistics
    • DeepPinkStatistic
    • FeatureStatistic
    • LassoStatistic
    • MargCorrStatistic
    • OLSStatistic
    • RandomForestStatistic
    • RidgeStatistic
    • calc_lars_path()
    • calc_mse()
    • combine_Z_stats()
    • compute_residual_variance()
    • data_dependent_threshhold()
    • fit_lasso()
    • fit_ridge()
    • parse_logistic_flag()
    • parse_y_dist()
    • MLR_FX_Spikeslab
    • MLR_Spikeslab
    • MLR_Spikeslab_Splines
    • OracleMLR
  • Gaussian and Fixed-X Knockoff Samplers
    • FXSampler
    • GaussianSampler
    • KnockoffSampler
    • produce_FX_knockoffs()
  • Metropolized Samplers
    • ARTKSampler
    • BlockTSampler
    • GibbsGridSampler
    • MetropolizedKnockoffSampler
    • gaussian_log_likelihood()
    • get_ordering()
    • t_log_likelihood()
    • t_markov_loglike()
    • t_mvn_loglike()
  • S-matrix computation
    • compute_smatrix()
    • compute_smatrix_factored()
    • divide_computation()
    • merge_groups()
    • parse_method()
    • cholupdate()
    • maxent_loss()
    • mmi_loss()
    • mvr_loss()
    • solve_ciknock()
    • solve_maxent()
    • solve_maxent_factored()
    • solve_mmi()
    • solve_mvr()
    • solve_mvr_factored()
    • calc_min_group_eigenvalue()
    • solve_SDP()
    • solve_equicorrelated()
    • solve_group_SDP()
  • Kpytorch
    • DeepPinkModel
    • MVRLoss
    • PSGDSolver
    • block_diag_sparse()
    • solve_mrc_psgd()
  • Gaussian graphical model discovery
    • KnockoffGGM
    • compute_ggm_threshold()
    • discovered_edges()
  • Quickly creating data-generating processes
    • AR1()
    • DGP
    • DirichletCorr()
    • ErdosRenyi()
    • FactorModel()
    • NestedAR1()
    • PartialCorr()
    • UniformDot()
    • Wishart()
    • block_equi_graph()
    • construct_gibbs_grid()
    • coords2num()
    • cov2blocks()
    • create_correlation_tree()
    • create_grouping()
    • create_sparse_coefficients()
    • graph2cliques()
    • num2coords()
    • sample_ar1t()
    • sample_block_tmvn()
    • sample_gibbs()
    • sample_response()
  • Utility functions
    • apply_pool()
    • blockdiag_to_blocks()
    • calc_group_sizes()
    • calc_mineig()
    • chol2inv()
    • cov2corr()
    • estimate_covariance()
    • estimate_factor()
    • fetch_group_nonnulls()
    • permute_matrix_by_groups()
    • preprocess_groups()
    • random_permutation_inds()
    • scale_until_PSD()
    • shift_until_PSD()
    • srand()

Indices and tables

• Index

• Module Index

• Search Page