# import all necessary libraries

import pandas

from pandas.tools.plotting import scatter_matrix

from sklearn import cross_validation

from sklearn.metrics import matthews_corrcoef

from sklearn.metrics import classification_report

from sklearn.metrics import confusion_matrix

from sklearn.metrics import accuracy_score

# load the dataset (local path)

url = "data.csv"

# feature names

features = ["MDVP:Fo(Hz)","MDVP:Fhi(Hz)","MDVP:Flo(Hz)","MDVP:Jitter(%)","MDVP:Jitter(Abs)","MDVP:RAP","MDVP:PPQ","Jitter:DDP","MDVP:Shimmer","MDVP:Shimmer(dB)","Shimmer:APQ3","Shimmer:APQ5","MDVP:APQ","Shimmer:DDA","NHR","HNR","RPDE","DFA","spread1","spread2","D2","PPE","status"]

dataset = pandas.read_csv(url, names = features)

# store the dataset as an array for easier processing

array = dataset.values

# X stores feature values

X = array[:,0:22]

# Y stores "answers", the flower species / class (every row, 4th column)

Y = array[:,22]

validation_size = 0.3

# randomize which part of the data is training and which part is validation

seed = 7

# split dataset into training set (80%) and validation set (20%)

X_train, X_validation, Y_train, Y_validation = cross_validation.train_test_split(X, Y, test_size = validation_size, random_state = seed)

# 10-fold cross validation to estimate accuracy (split data into 10 parts; use 9 parts to train and 1 for test)

num_folds = 10

num_instances = len(X_train)

seed = 7

# use the 'accuracy' metric to evaluate models (correct / total)

scoring = 'accuracy'

predictions = []

for instance in X_validation:

    predictions.append(1)

print(accuracy_score(Y_validation, predictions)*100)

print(matthews_corrcoef(Y_validation, predictions))