<reponame>ValentineNauli/My_MachineLearning
#AUTHOR : <NAME>

"""
You have been given a stack of documents that have already been processed and some that have not. 
Your task is to classify these documents into one of eight categories: [1,2,3,...8]. 
You look at the specifications on what a document must contain and are baffled by the jargon. 
However, you notice that you already have a large amount of documents which have already been correctly categorize (training data). 
You decide to use Machine Learning on this data in order to categorize the uncategorized documents.

Access the document from here -> https://s3.amazonaws.com/hr-testcases/597/assets/trainingdata.txt

The first line in the input file will contain T the number of documents. 
T lines will follow each containing a series of space seperated words which represents the processed document.

For each document output a number between 1-8 which you believe this document should be categorized as.

->Sample input :
3
This is a document
this is another document
documents are seperated by newlines

->Output :
1
4
8
"""

import sys
from sklearn.feature_extraction import text
from sklearn import pipeline
from sklearn import linear_model
import numpy


def make_model():
    clf = pipeline.Pipeline([
        ('vect',
         text.TfidfVectorizer(stop_words='english', ngram_range=(1, 1),
                              strip_accents='ascii', lowercase=True)),
        ('clf',
         linear_model.SGDClassifier(class_weight='balanced'))
    ])
    return clf


def run():
    known = [('Business means risk!', 1)]
    xs, ys = load_data('trainingdata.txt')
    mdl = make_model()
    mdl.fit(xs, ys)
    txs = list(line for line in sys.stdin)[1:]
    for y, x in zip(mdl.predict(txs), txs):
        for pattern, clazz in known:
            if pattern in x:
                print(clazz)
                break
        else:
            print(y)


def load_data(filename):
    with open(filename, 'r') as data_file:
        sz = int(data_file.readline())
        xs = numpy.zeros(sz, dtype=numpy.object)
        ys = numpy.zeros(sz, dtype=numpy.int)
        for i, line in enumerate(data_file):
            idx = line.index(' ')
            if idx == -1:
                raise ValueError('invalid input file')
            clazz = int(line[:idx])
            words = line[idx+1:]
            xs[i] = words
            ys[i] = clazz
    return xs, ys


if __name__ == '__main__':
    run()
#AUTHOR : <NAME>

"""
You have been given a stack of documents that have already been processed and some that have not. 
Your task is to classify these documents into one of eight categories: [1,2,3,...8]. 
You look at the specifications on what a document must contain and are baffled by the jargon. 
However, you notice that you already have a large amount of documents which have already been correctly categorize (training data). 
You decide to use Machine Learning on this data in order to categorize the uncategorized documents.

Access the document from here -> https://s3.amazonaws.com/hr-testcases/597/assets/trainingdata.txt

The first line in the input file will contain T the number of documents. 
T lines will follow each containing a series of space seperated words which represents the processed document.

For each document output a number between 1-8 which you believe this document should be categorized as.

->Sample input :
3
This is a document
this is another document
documents are seperated by newlines

->Output :
1
4
8
"""

import sys
from sklearn.feature_extraction import text
from sklearn import pipeline
from sklearn import linear_model
import numpy


def make_model():
    clf = pipeline.Pipeline([
        ('vect',
         text.TfidfVectorizer(stop_words='english', ngram_range=(1, 1),
                              strip_accents='ascii', lowercase=True)),
        ('clf',
         linear_model.SGDClassifier(class_weight='balanced'))
    ])
    return clf


def run():
    known = [('Business means risk!', 1)]
    xs, ys = load_data('trainingdata.txt')
    mdl = make_model()
    mdl.fit(xs, ys)
    txs = list(line for line in sys.stdin)[1:]
    for y, x in zip(mdl.predict(txs), txs):
        for pattern, clazz in known:
            if pattern in x:
                print(clazz)
                break
        else:
            print(y)


def load_data(filename):
    with open(filename, 'r') as data_file:
        sz = int(data_file.readline())
        xs = numpy.zeros(sz, dtype=numpy.object)
        ys = numpy.zeros(sz, dtype=numpy.int)
        for i, line in enumerate(data_file):
            idx = line.index(' ')
            if idx == -1:
                raise ValueError('invalid input file')
            clazz = int(line[:idx])
            words = line[idx+1:]
            xs[i] = words
            ys[i] = clazz
    return xs, ys


if __name__ == '__main__':
    run()
#AUTHOR : <NAME> You have been given a stack of documents that have already been processed and some that have not. 
 Your task is to classify these documents into one of eight categories: [1,2,3,...8]. 
 You look at the specifications on what a document must contain and are baffled by the jargon. 
 However, you notice that you already have a large amount of documents which have already been correctly categorize (training data). 
 You decide to use Machine Learning on this data in order to categorize the uncategorized documents.
 
 Access the document from here -> https://s3.amazonaws.com/hr-testcases/597/assets/trainingdata.txt
 
 The first line in the input file will contain T the number of documents. 
 T lines will follow each containing a series of space seperated words which represents the processed document.
 
 For each document output a number between 1-8 which you believe this document should be categorized as.
 
 ->Sample input :
 3
 This is a document
 this is another document
 documents are seperated by newlines
 
 ->Output :
 1
 4
 8