SSH-Master-Thesis/ethan_data_processing_scripts/nearestneighbors.py

#!/usr/bin/python3
from sklearn.neighbors import NearestNeighbors, KNeighborsClassifier
from sklearn.ensemble import RandomForestClassifier
import numpy as np
import sys
from Vector import *


def main():
    # a test of this method using an arbitrarily generated list of 5 vectors with
    # 3 features each
    # nearestNeighbors([[1, 1, 0], [1, 0, 0], [0, 0, 0], [0, 5, 5]], [[1, 1, 4]])
    print(len(sys.argv))
    if len(sys.argv) != 5:
        print("Usage: nearestneighbors.py datafile.bin classificationsfile.bin " \
              "testdatafile.bin -(p/e)")
        exit()
    data = readPickledData(sys.argv[1])
    classifcations = readPickledData(sys.argv[2])
    testdata = readPickledData(sys.argv[3])
    newdata, newtest = [], []
    for d in data:
        newdata.append(d.features)
    for d in testdata:
        newtest.append(d.features)
    print(newdata)
    print(classifcations)
    print(newtest)
    kNearestNeighbors(newdata, classifcations, newtest)
    # print("Random Forest:")
    # randomForest(newdata, classifcations, newtest)
    # kNearestNeighbors([[1, 1, 0], [1, 0, 0], [0, 0, 0], [0, 5, 5]],
    #                    ["three", 2, 3, "5"], [[1, 1, 0], [0, 5, 5]])

def kNearestNeighbors(data: list, classifications: list):
    folds = KFold(n_splits=5)
    for train_index, test_index in folds.split(data):
        kn = KNeighborsClassifier(n_neighbors=2)
        kn.fit(data[train_index], classifications[train_index])
        p = kn.predict(test_data[test_index])
        print("Predictions, matching test_data by index: ")
        print(test_data[test_index])
        print(p)
        writestr = "Predictions, matching test_data by index:\n" + str(test_data) \
                   + "\n" + str(p)
    # if sys.argv[4][1] == 'p':
    #     pickle.dump((test_data, p), open("results.bin", "wb"))
    # else:
    #     with open("results.txt", "w+") as file:
    #         file.write(writestr)

def nearestNeighbors(data: list, test_data: list):
    x = np.array(data)
    nbrs = NearestNeighbors(n_neighbors=1, algorithm='ball_tree').fit(x)
    dist, indicies = nbrs.kneighbors(test_data)
    print("Indicies:")
    print(indicies)
    print("Distances:")
    print(dist)
    return indicies, dist


def randomForest(data: list, classifications: list, test_data: list):
    rfc = RandomForestClassifier(n_estimators=len(data))
    rfc.fit(data, classifications)
    print(rfc.predict(test_data))


if __name__ == '__main__':
    main()