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#!/usr/bin/env python

'''
TODO
1. Invoke tearDown even if the test method fails
2. Catch and report setUp errors
3. Create TestSuite from a TestCase class
'''
class TestCase(object):
    def __init__(self, name):

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# Download data set via:
#   http://archive.ics.uci.edu/ml/datasets/Statlog+%28German+Credit+Data%29

import numpy as np
import pandas as pd
from sklearn import tree
from sklearn import preprocessing
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix
from sklearn_pandas import DataFrameMapper

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# Download data set via:
#   http://archive.ics.uci.edu/ml/datasets/Statlog+%28German+Credit+Data%29
#
# Note, the example below uses the pre-processed data that is used in the book 'Machine Learning with R' by Brett Lantz 

library(C50)

df <- read.csv("credit.csv", stringsAsFactors=TRUE)
set.seed(12345)
df_rand <- df[order(runif(1000)),]

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# Download data set via:
#   http://archive.ics.uci.edu/ml/datasets/SMS+Spam+Collection

import numpy as np
import pandas as pd
import string

from nltk import word_tokenize
from nltk.stem.porter import PorterStemmer

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# Download data set via:
#   http://archive.ics.uci.edu/ml/datasets/SMS+Spam+Collection

# import libraries
library(caret)
library(e1071)
library(tm)
library(SnowballC)

# read in the dataset