Code Documentation: Feature Normalizers

mastml.legos.feature_normalizers Module

This module contains a collection of classes and methods for normalizing features. Also included is connection with scikit-learn methods. See http://scikit-learn.org/stable/modules/classes.html#module-sklearn.preprocessing for more info.

Functions

dataframify(transform)	Method which is a decorator transforms output of scikit-learn feature normalizers from array to dataframe.
wraps(wrapped[, assigned, updated])	Decorator factory to apply update_wrapper() to a wrapper function

Classes

BaseEstimator	Base class for all estimators in scikit-learn.
Binarizer(*[, threshold, copy])	Binarize data (set feature values to 0 or 1) according to a threshold.
MaxAbsScaler(*[, copy])	Scale each feature by its maximum absolute value.
MeanStdevScaler([features, mean, stdev])	Class designed to normalize input data to a specified mean and standard deviation
MinMaxScaler([feature_range, copy, clip])	Transform features by scaling each feature to a given range.
Normalizer([norm, copy])	Normalize samples individually to unit norm.
OneHotEncoder(*[, categories, drop, sparse, …])	Encode categorical features as a one-hot numeric array.
QuantileTransformer(*[, n_quantiles, …])	Transform features using quantiles information.
RobustScaler(*[, with_centering, …])	Scale features using statistics that are robust to outliers.
StandardScaler(*[, copy, with_mean, with_std])	Standardize features by removing the mean and scaling to unit variance
TransformerMixin	Mixin class for all transformers in scikit-learn.

Class Inheritance Diagram