from __future__ import print_function
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import classification_report
from sklearn import datasets
from skimage import exposure
import numpy as np
from timeit import default_timer as timer
# load the MNIST digits dataset
mnist = datasets.load_digits()

# Training and testing split,
# 75% for training and 25% for testing
(trainData, testData, trainLabels, testLabels) = train_test_split(np.array(mnist.data), mnist.target, test_size=0.25, random_state=42)

# take 10% of the training data and use that for validation
(trainData, valData, trainLabels, valLabels) = train_test_split(trainData, trainLabels, test_size=0.1, random_state=84)

# Checking sizes of each data split
print("training data points: {}".format(len(trainLabels)))
print("validation data points: {}".format(len(valLabels)))
print("testing data points: {}".format(len(testLabels)))


# initialize the values of k for our k-Nearest Neighbor classifier along with the
# list of accuracies for each value of k
kVals = range(1, 30, 2)
accuracies = []

# loop over kVals
t0 = timer()
for k in range(1, 30, 2):
    # train the classifier with the current value of `k`
    model = KNeighborsClassifier(n_neighbors=k)
    model.fit(trainData, trainLabels)

    # evaluate the model and print the accuracies list
    score = model.score(valData, valLabels)
    print("k=%d, accuracy=%.2f%%" % (k, score * 100))
    accuracies.append(score)

# largest accuracy
# np.argmax returns the indices of the maximum values along an axis
i = np.argmax(accuracies)
print("k=%d achieved highest accuracy of %.2f%% on validation data" % (kVals[i],
    accuracies[i] * 100))


# Now that I know the best value of k, re-train the classifier
model = KNeighborsClassifier(n_neighbors=kVals[i])
model.fit(trainData, trainLabels)
t1 = timer()
# Predict labels for the test set
predictions = model.predict(testData)

# Evaluate performance of model for each of the digits
print("EVALUATION ON TESTING DATA")
print(classification_report(testLabels, predictions))
print ("Execution time:"+str(t1-t0)+"s")