'''
This module builds a a base class for regression problems, such as least squares
or k-nearest neighbors regressions. The preprocessing (if applicable)
is done at this class level.
'''
import numpy as np
from src.helperfunctions.preprocessing import train_test_split
from src.helperfunctions.preprocessing import scale_and_center
[docs]class Regression:
"""
A class used to represent a regression algorithm.
Parameters
-----------
features : numpy.ndarray
Design matrix of explanatory variables.
output : numpy.ndarray
Labels of data corresponding to feature matrix.
split_proportion : float
Proportion of data to use for training; between 0 and 1.
standardized : bool
Whether to center/scale the data (train/test done separately).
True by default.
Attributes
-----------
sample_size : int
The sample size of all given data (train and test).
train_size : int
The sample size of the training data.
test_size : int
The sample size of the test data.
train_rows : numpy.ndarray
The list of indices for the train split.
test_rows : numpy.ndarray
The list of indices for the test split.
train_features : numpy.ndarray
The train design matrix.
test_features : numpy.ndarray
The test design matrix.
train_output : numpy.ndarray
The train output data.
test_output : numpy.ndarray
The test output data.
dimension : int
The number of dimensions of the data, or columns of design matrix.
Does not include output.
"""
def __init__(self, features, output, split_proportion=0.75, standardized=True):
train_test_split_data = train_test_split(features, output, split_proportion)
self.sample_size = train_test_split_data.sample_size
self.train_size = train_test_split_data.train_size
self.test_size = train_test_split_data.test_size
self.train_rows = train_test_split_data.train_rows
self.test_rows = train_test_split_data.test_rows
self.train_features = train_test_split_data.train_features
self.test_features = train_test_split_data.test_features
self.train_output = train_test_split_data.train_output
self.test_output = train_test_split_data.test_output
self.dimension = self.train_features.shape[1]
if standardized:
self.standardize()
self.standardized = True
else:
self.standardized = False
[docs] def standardize(self):
'''
Separately scale/center the train and test data so each feature
(column of observations) has 0 mean and unit variance.
'''
self.train_features = scale_and_center(self.train_features)
self.test_features = scale_and_center(self.test_features)