Code Documentation: Feature Selectors

mastml.legos.feature_selectors Module

This module contains a collection of classes and methods for selecting features, and interfaces with scikit-learn feature selectors. More information on scikit-learn feature selectors is available at:

http://scikit-learn.org/stable/modules/classes.html#module-sklearn.feature_selection

Functions

cov(m[, y, rowvar, bias, ddof, fweights, …])	Estimate a covariance matrix, given data and weights.
dataframify_new_column_names(transform, name)	Method which transforms output of scikit-learn feature selectors to dataframe, and adds column names
dataframify_selector(transform)	Method which transforms output of scikit-learn feature selectors from array to dataframe.
fitify_just_use_values(fit)	Method which enables a feature selector fit method to operate on dataframes
pearsonr(x, y)	Pearson correlation coefficient and p-value for testing non-correlation.
root_mean_squared_error(y_true, y_pred)	Method that calculates the root mean squared error (RMSE)
wraps(wrapped[, assigned, updated])	Decorator factory to apply update_wrapper() to a wrapper function

Classes

BaseEstimator	Base class for all estimators in scikit-learn.
EnsembleModelFeatureSelector(estimator, …)	Class custom-written for MAST-ML to conduct selection of features with ensemble model feature importances
MASTMLFeatureSelector(estimator, …[, …])	Class custom-written for MAST-ML to conduct forward selection of features with flexible model and cv scheme
PCA([n_components, copy, whiten, …])	Principal component analysis (PCA).
PearsonSelector(threshold_between_features, …)	Class custom-written for MAST-ML to conduct selection of features based on Pearson correlation coefficent between features and target.
SequentialFeatureSelector(estimator[, …])	Sequential Feature Selection for Classification and Regression.
TransformerMixin	Mixin class for all transformers in scikit-learn.
constructor	alias of sklearn.feature_selection._variance_threshold.VarianceThreshold

Class Inheritance Diagram