scikit-rare 0.9.2
A Scikit Learn compatible implementation of RARE Algorithm
Requires Python: >=3.6

# scikit-RARE

**RARE: Relevant Association Rare-variant-bin Evolver** (see [citation](#citiation)) is an evolutionary algorithm approach to binning rare variants as a rare variant association analysis tool. scikit-RARE is scikit compatible pypi package for the RARE algotithm.

RARE constructs bins of rare variant features with relevant association to class (univariate and/or multivariate interactions) through the following steps:

1. Random bin initializaiton or expert knowledge input
2. Repeated evolutionary cycles consisting of:
   - Candidate bin evaluation with univariate scoring (chi-square test) or Relief-based scoring (MultiSURF algorithm); note: new scoring options currently under testing
   - Genetic operations (parent selection, crossover, and mutation) to generate the next generation of candidate bins
3. Final bin evaluation and summary of top bins

## Installation
We can easily install scikit-rare using the following command:
```
pip install scikit-rare
```

## Parameters for RARE Class:
1) given_starting_point: whether or not expert knowledge is being inputted (True or False)
2) amino_acid_start_point: if RARE is starting with expert knowledge, input the list of features here; otherwise None
3) amino_acid_bins_start_point: if RARE is starting with expert knowledge, input the list of bins of features here; otherwise None
4) iterations: the number of evolutionary cycles RARE will run
5) original_feature_matrix: the dataset 
6) label_name: label for the class/endpoint column in the dataset (e.g., 'Class')
7) rare_variant_MAF_cutoff: the minor allele frequency cutoff separating common features from rare variant features
8) set_number_of_bins: the population size of candidate bins
9) min_features_per_group: the minimum number of features in a bin
10) max_number_of_groups_with_feature: the maximum number of bins containing a feature
11) scoring_method: 'Univariate', 'Relief', or 'Relief only on bin and common features'
12) score_based_on_sample: if Relief scoring is used, whether or not bin evaluation is done based on a sample of instances rather than the whole dataset
13) score_with_common_variables: if Relief scoring is used, whether or not common features should be used as context for evaluating rare variant bins
14) instance_sample_size: if bin evaluation is done based on a sample of instances, input the sample size here
15) crossover_probability: the probability of each feature in an offspring bin to crossover to the paired offspring bin (recommendation: 0.5 to 0.8)
16) mutation_probability: the probability of each feature in a bin to be deleted (a proportionate probability is automatically applied on each feature outside the bin to be added (recommendation: 0.05 to 0.5 depending on situation and number of iterations run)
17) elitism_parameter: the proportion of elite bins in the current generation to be preserved for the next evolutionary cycle (recommendation: 0.2 to 0.8 depending on conservativeness of approach and number of iterations run)
18) random_seed: the seed value needed to generate a random number
19)bin_size_variability_constraint: sets the max bin size of children to be n times the size of their sibling (recommendation: 2, with larger or smaller values the population would trend heavily towards small or large bins without exploring the search space)
20) max_features_per_bin: sets a max value for the number of features per bin

## Parameters for RARE Methods
### RARE Variant Data Simulators (RVDSs)
**RARE Variant Data Simulators (RVDSs)** are functions that create simulated data for testing/evaluating RARE.
1) The RVDS for Univariate Association Bin (called RVDS_One_Bin) creates a dataset such that no rare variant feature is 100% predictive of class, but an additive bin of features is fully penetrant to class.
2) The RVDS for Epistatic Interaction Bin creates a dataset such that no rare variant feature or bin of rare variant features is predictive of class, but an epistatic interaction between a common feature and an additive bin of rare variant features is 100% predictive of class.

### Parameters for RVDS for Univariate Association Bin:
1) number_of_instances: number of instances (i.e., rows) desired in the simulated dataset
2) number_of_features: the total number of rare variant features that should be in the simulated dataset
3) number_of_features_in_bin: of the number_of_features, how many rare variant features should be binned additively for univariate association to class
4) rare_variant_MAF_cutoff: the minor allele frequency that all rare variant features but be below
5) endpoint_cutoff_parameter: "mean" or "median" (recommended "mean")
6) endpoint_variation_probability: how much noise is desired in the dataset (0 produces a bin with a 100% clear signal, 0.5 can be used as a negative control where class value is randomly assigned)

### Parameters for RVDS for Epistatic Interaction Bin:
1) number_of_instances: number of instances (i.e., rows) desired in the simulated dataset
2) number_of_rare_features: the total number of rare variant features that should be in the simulated dataset
3) number_of_features_in_bin: of the number_of_features, how many rare variant features should be binned additively for univariate association to class
4) rare_variant_MAF_cutoff: the minor allele frequency that all rare variant features but be below
5) common_feature_genotype_frequencies_list: a list with the genotype frequencies of each of the common feature genotypes (BB, Bb, bb). Should be of the form [0.25, 0.5, 0.25], where 0.25 is the frequency of the BB genotype, 0.5 is the frequency of the Bb genotype, and 0.25 is the frequency of the bb genotype. [0.25, 0.5, 0.25] is recommended
6) genotype_cutoff_metric: "mean" or "median" (recommended "mean")
7) endpoint_variation_probability: how much noise is desired in the dataset (0 produces a bin that interacts with a common feature to be fully penetrant, 0.5 can be used as a negative control where class value is randomly assigned)
8) list of MLGs_predicting_disease: which of the nine MLGs (AABB, AaBB, aaBB, AABb, AaBb, aaBb, AAbb, Aabb, aabb) correspond to a value of 1 in the class column. [AABB, aaBB, AaBb, AAbb, aabb] should be paired with [0.25, 0.5, 0.25] for the common feature genotype frequencies list to create a dataset with pure, strict epistasis
9) print_summary: whether or not a summary of the simulated datasets with penetrance and frequency values for each of the bin genotypes, common feature genotypes, and MLGs should be printed (True or False)

## Citation
Dasariraju, S., & Urbanowicz, R. J. (2021). RARE: Evolutionary feature engineering for rare-variant bin discovery. Proceedings of the Genetic and Evolutionary Computation Conference Companion, 1335–1343. https://doi.org/10.1145/3449726.3463174