#!/usr/bin/env python3

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## WISCONSIN BREAST CANCER MACHINE LEARNING ##

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#

# Project by Raul Eulogio

#

# Project found at: https://www.inertia7.com/projects/3

#

"""

Kth Nearest Neighbor Classification

"""

# Import Packages -----------------------------------------------

import sys, os

import pandas as pd

import helper_functions as hf

from data_extraction import training_set, class_set

from data_extraction import test_set, test_class_set

from sklearn.neighbors import KNeighborsClassifier

from sklearn.model_selection import cross_val_score

from produce_model_metrics import produce_model_metrics

# Fitting model

fit_knn = KNeighborsClassifier(n_neighbors=3)

# Training model

fit_knn.fit(training_set,

class_set)

# ---------------------------------------------------------------

if __name__ == '__main__':

# Print model parameters ------------------------------------

print(fit_knn, '\n')

# Optimal K -------------------------------------------------

# Inspired by:

# https://kevinzakka.github.io/2016/07/13/k-nearest-neighbor/

myKs = []

for i in range(0, 50):

if (i % 2 != 0):

myKs.append(i)

cross_vals = []

for k in myKs:

knn = KNeighborsClassifier(n_neighbors=k)

scores = cross_val_score(knn,

training_set,

class_set,

cv = 10,

scoring='accuracy')

cross_vals.append(scores.mean())

MSE = [1 - x for x in cross_vals]

optimal_k = myKs[MSE.index(min(MSE))]

print("Optimal K is {0}".format(optimal_k), '\n')

# Initialize function for metrics ---------------------------

fit_dict_knn = produce_model_metrics(fit_knn,

test_set,

test_class_set,

'knn')

# Extract each piece from dictionary

predictions_knn = fit_dict_knn['predictions']

accuracy_knn = fit_dict_knn['accuracy']

auc_knn = fit_dict_knn['auc']

# Test Set Calculations -------------------------------------

# Test error rate

test_error_rate_knn = 1 - accuracy_knn

# Confusion Matrix

test_crosstb = hf.create_conf_mat(test_class_set,

predictions_knn)

print('Cross Validation:')

hf.cross_val_metrics(fit_knn,

training_set,

class_set,

'knn',

print_results = True)

print('Confusion Matrix:')

print(test_crosstb, '\n')

print("Here is our accuracy for our test set:\n {0: .3f}"\

.format(accuracy_knn))

print("The test error rate for our model is:\n {0: .3f}"\

.format(test_error_rate_knn))