fastHDMI 1.23.26

Use fast FFT-based mutual information screening for large datasets. Works well on MRI brain imaging data. Developed by Kai Yang, [GPG Public key Fingerprint: CC02CF153594774CF956691492B2600D18170329](https://keys.openpgp.org/vks/v1/by-fingerprint/CC02CF153594774CF956691492B2600D18170329)

fastHDMI -- fast High-Dimensional Mutual Information estimation

Kai Yang

<kai.yang2 "at" mail.mcgill.ca>

License :: OSI Approved :: GNU Affero General Public License v3 or later (AGPLv3+)

GPG Public key Fingerprint: CC02CF153594774CF956691492B2600D18170329

This packages uses FFT-based mutual information screening and accelerated gradient method for important variables from (potentially very) high-dimensional large datasets. version 1.23.23 is a version with only the README file updated to illustrate the functions more clearly

Consider the sizes of the datafiles, the most commonly-used functions are the functions run in parallel -- all functions running in parallel will has _parallel suffix; and they all have arguments:

• core_num: number of CPU cores used for multiprocessing; the default option is to use all the cores available, considering the job is most likely running on a server instead of a PC
• multp: job multiplier, the job to be run in parallel will be first divided into core_num * multp sub-jobs -- as equal as possible, then at each time, one core will take one subjob.
• verbose: how verbal the function will be, with 0 being least verbal and increases wrt. the number decalred here

The function implementing our propsoed FFT-based mutual information estimation will have the following arguments:

• N: the grid size for 1-D FFT; with N=500 as the default value
• a_N, a_N: similar to above, the grid size for 2-D FFT; with 300 as the default values
• kernel and bw specify the kernel and bandwidth used for KDE
• norm is the norm used for KDE -- this option only takes effects for 2-D KDE

The screening functions and their arguments:

• For plink files:

• arguments bed_file, bim_file, fam_file are the location of the plink files;
• arguments outcome, outcome_iid are the outcome values and the iids for the outcome. For genetic data, it is usual that the order of SNP iid and the outcome iid don't match. While SNP iid can be obtained from the plink1 files, outcome iid here is to be declared separately. outcome_iid should be a list of strings or a one-dimensional numpy string array.
• continuous_screening_plink, continuous_screening_plink_parallel for screening on continuous outcomes with continuous covariates
• binary_screening_plink, binary_screening_plink_parallel for screening on binary outcomes with continuous covariates
• clump_plink_parallel for clumping -- starting from the first covariate (i.e., the first column on the left of the datafile), clumping will remove all subsequent covariates with a mutual information higher than what the clumping_threshold declares with the one it looks at

• For csv files:

• argument _usecols is a list of column labels to be used, the first element should be the outcome. Returned mutual information calculation results match _usecols.
• Note that it is assumed the left first column should be the outcome; if not, use _usecols to set the first element to be the outcome column label.
• csv_engine can use dask for low memory situations, or pandas's read_csv engines, or fastparquet engine for a created parquet file for faster speed. If fastparquet is chosen, declare parquet_file as the filepath to the parquet file; if dask is chosen to read very large CSV, it might need to specify a larger sample.
• binary_screening_csv, binary_screening_csv_parallel for screening on binary outcomes with continuous covariates
• binary_skMI_screening_csv_parallel, continuous_skMI_screening_csv_parallel for screening using mutual information estimation provided by skLearn, i.e., sklearn.metrics.mutual_info_score, sklearn.feature_selection.mutual_info_classif
• Pearson_screening_csv_parallel for screening using Pearson correlation
• continuous_screening_csv, continuous_screening_csv_parallel for screening on continuous outcomes with continuous covariates
• clump_continuous_csv_parallel similar to above

A share_memory option is added for multiprocess computing. As a feature, it can be applied on large .csv data in parallel in a memory-efficient manner and use FFT for KDE to estimate the mutual information extremely fast. A tqdm progress bar is now added to be more useful on cloud computing platforms. verbose option can take values of 0,1,2, with 2 being most verbal; 1 being only show progress bar, and 0 being not verbal at all.

• For DataFrame files:

• binary_screening_dataframe, binary_screening_dataframe_parallel for screening on binary outcomes with continuous covariates
• binary_skMI_screening_dataframe_parallel, continuous_skMI_screening_dataframe_parallel for screening using mutual information estimation provided by skLearn, i.e., sklearn.metrics.mutual_info_score, sklearn.feature_selection.mutual_info_classif
• Pearson_screening_dataframe_parallel for screening using Pearson correlation
• continuous_screening_dataframe, continuous_screening_dataframe_parallel for screening on continuous outcomes with continuous covariates
• clump_continuous_dataframe_parallel similar to above

• For numpy arrays:

• binary_screening_array, binary_screening_array_parallel for screening on binary outcomes with continuous covariates
• continuous_screening_array, continuous_screening_array_parallel for screening on continuous outcomes with continuous covariates
• binary_skMI_array_parallel, continuous_skMI_array_parallel for screening using mutual information estimation provided by skLearn, i.e., sklearn.metrics.mutual_info_score, sklearn.feature_selection.mutual_info_classif
• continuous_Pearson_array_parallel for screening using Pearson correlation