NearestNeighborClassifier.py

# =================================================================================================
# NearestNeighborClassifier.py
# Name: Alycia Wong
# Date: June 2020
# Description: Use this to brush up on Python syntax (I reccomend Google 🤪). 
# Process and graph a CSV file containing biomedical data that relates hemoglobin levels, glucose 
# levels, and chronic kidney disease (CKD). Create n number of random test cases and determine 
# whether the case is likely to have CKD depending on the classification of the nearest point. 
# Bonus: graph a line connecting the test case to its nearest neighbor.
# Except for graphing, most of these can be one line functions.
# It may be easiest to create a class to work with the array.
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# Import statements
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import matplotlib.pyplot as plt
import numpy as np

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# Functions
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# openCSVFile function takes in no arguments and parses/organizes data from a CSV file into a 
# 2-D numpy array with the columns being: hemoglobin, glucose, classification and each row being 
# a case.
# Note: np.array[row][column]
def openCSVFile():
    dataArr = np.zeros((159,3))
    return dataArr

# normalizeData function takes in a 2D numpy array and scales down the first and second columns to 
# range from 0-1 and outputs a 2D array with the normalized data. 
def normalizeData(dataArr):
    return dataArr

# graphCSVFile void function takes in a 2D numpy array and graphs with: the first column 
# (hemoglobin) as the x-axis and second column (glucose) as the y-axis. The third column 
# (classification) is used to determine the color of the points on the graph.
def graphCSVFile(dataArr):
    return

# findDistance function is either:
# a) takes in an array and a point and returns an array of distances or the minimum distance or
# b) takes in cartesian coordinates and uses a simple use of the distance formula to return the 
# distance between the two points.
def findDistance():
    return 

# createTestCase function creates two random test cases (hemoglobin and glucose) from 0-1 and 
# either: 
# a) adds the points to the inputted array as a new row
# b) returns two points
# c) creates a new 1D array with the two points
# and
# a) creates a classification for the points using the findDistance function
# b) return the points without alteration
def createTestCase():
    return 

# nearestNeighborIndex function takes in a 2D array of cartesian points and a randomly generated test
# case. Returns the index of the closest point in the 2D array to the test case. 
# Method 1: Use indices and the distance function to create an array of points and return the index
# of the closest point. 
# Method 2: Use KDTree 
# If you do this, you will not need to use the distance formula since this uses a spatial library
# Returns the index of the closest point in the 2D array to the test case.
def nearestNeighborIndex(dataArr, testCase):
    index = 0
    return index

# graphNearestNeighbor void function takes in a 2D numpy array (and a cartesian coordinate
# depending on createTestCase) and graphs the first column (hemoglobin) as the x-axis and the
# second column (glucose) as the y-axis the third column (classification) determines the color of 
# the points. A  randomly generated test case is graphed as a distinct point with a line connecting
# it to the nearest neighbor whose classification it takes on. A legend is generated in a
# reasonable position.
def graphNearestNeighbor(dataArr):
    return

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# Main Script
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# mainDriver function takes in no inputs and graphs both the orginial CSV file and the test case. 
# This function returns 0.
def mainDriver():
    return 0

# mainDriver()