"""re-write exploring DC

"""

import os

import pandas as pd

import numpy as np

import urllib

u=urllib.urlopen('http://archive.ics.uci.edu/ml/datasets/Drug+consumption+%28quantified%29')

text=u.read()

import re

matches=re.findall(r"\d+\.\s\w+", text)

del(matches[0:3])

del(matches[12])

for i in range(0, len(matches)):

matches[i]=re.sub(r"\d+\.\s","",matches[i])

"""#local import

os.chdir('C:\Users\Alissa\Documents\Springboard\Capstone1\data\DrugConsumption') #cd and import data

data=pd.read_csv('drug_consumption.txt',header=None,names=matches) #also header=None"""

#web import

url = "http://archive.ics.uci.edu/ml/machine-learning-databases/00373/drug_consumption.data"

data = pd.read_csv(url,names=matches)

#print(data.head())

#make drug use binary instead #code_dict={CL0:"Never Used", CL1:"Used over a Decade Ago",CL2:"Used in Last Decade",CL3:"Used in Last Year",CL4:"Used in Last Month", CL5:"Used in Last Week",CL6:"Used in Last Day"}

#month based binary variable

data=data.replace(['CL0','CL1','CL2','CL3'],0)

data=data.replace(['CL4','CL5','CL6'],1)

#1-13 are attributes 14-32 are drugs

attribute=data.iloc[:,:13]

#attribute.columns=[0,1,2,3,4,5,6,7,8,9,10,11,12]

drugs=data.iloc[:,13:] #CL4, CL5, CL6 are use in last month, week, day respectively

#drugs.columns=[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18]

print(attribute.head())

print(drugs.head())

coormat=data.corr()

#To try: XG Boost or boosting - tree tech.

#"""

# Compare Algorithms

import matplotlib.pyplot as plt

from sklearn import model_selection

from sklearn.linear_model import LogisticRegression

from sklearn.tree import DecisionTreeClassifier

from sklearn.neighbors import KNeighborsClassifier

from sklearn.discriminant_analysis import LinearDiscriminantAnalysis

from sklearn.naive_bayes import GaussianNB

from sklearn.svm import SVC

# prepare configuration for cross validation test harness

seed = 7

# prepare models

models = []

models.append(('LR', LogisticRegression()))

models.append(('LDA', LinearDiscriminantAnalysis()))

models.append(('KNN', KNeighborsClassifier()))

models.append(('CART', DecisionTreeClassifier()))

models.append(('NB', GaussianNB()))

models.append(('SVM', SVC()))

#rename data

X=attribute

Y=drugs.iloc[:,:17]

# evaluate each model in turn

results = []

names = []

scoring = 'accuracy'

count=0

#n=0

#val=[]

#best2=[]

for name in Y.columns:

y = Y[name]

print(name)

for name, model in models:

kfold = model_selection.KFold(n_splits=10, random_state=seed)

cv_results = model_selection.cross_val_score(model, X, y, cv=kfold, scoring=scoring) #ValueError: bad input shape (1696, 19)

results.append(cv_results)

names.append(name)

msg = "%s: %f (%f)" % (name, cv_results.mean(), cv_results.std())

print(msg)

# for i in xrange(0,5):

# val[i]=results[6*n].mean()+ results[6*n].std()

# best2=val.sort_values().head(2)

# # boxplot algorithm comparison

# fig = plt.figure()

# fig.suptitle('Algorithm Comparison')

# ax = fig.add_subplot(111)

# plt.boxplot(results[count]) #list call by row - not working

# ax.set_xticklabels(names)

# plt.show()

# count=count+1

#"""