code.py

#basic guessing game
import csv
import random
import re
import numpy

import matplotlib.pyplot as plt

#num_to_guess = random.randint(0,100)
#
##print "Youre trying to guess {}".format(num_to_guess)
##3 options
##1 - guess = num_to_guess, user wins, guess < num_to_guess, print too little, guess > num_to_guess, print too high
#num_guesses = 0
#guessed = False
#while guessed == False:
#    num_guesses += 1
#    guess = int(raw_input('Enter your number: '))
#
#    if (guess == num_to_guess) or (5 < 7) or ____:
#        print "You got it"
#        print "It took you {num_guesses} guesses to guess the number {num_to_guess}.".format(num_guesses=num_guesses, num_to_guess=num_to_guess)
#        guessed = True
#    
#    elif guess > num_to_guess * 2:
#        print "You guessed WAY too high"
#     
#    elif guess > num_to_guess:
#        print "You guessed too high"
#        
#    else:
#        print "You guessed too low"
#
#
#print "You won!"
#elif guess <= num_to_guess:
#    print "You guessed too low"
#
ages = []
myfile = open('/Users/Ben/Desktop/train.csv')
for line in csv.DictReader(myfile):
    if line['Age'] != '':
        ages.append(float(line['Age']))

average = numpy.mean(ages)


classes = []
sexes = []
survived = []
myfile = open('/Users/Ben/Desktop/train.csv')
for line in csv.DictReader(myfile):
    if line['Pclass'] != '' and line['Sex'] != '':
        if line['Survived'] == '1':
            classes.append(float(line['Pclass']))
            if line['Sex'] == 'male':
                sexes.append(1)
            else:
                sexes.append(0)
            survived.append('g')
        else:
            classes.append(float(line['Pclass']))
            if line['Sex'] == 'male':
                sexes.append(1)
            else:
                sexes.append(0)
            survived.append('r')

plt.scatter(classes, sexes, color=survived)

def predict(datapoint):
    #predict takes a datapoint and predicts whether the person will survive
    #first idea: > 18, no < 18, yes
    if line['Age'] != '':
        age = float(datapoint['Age'])
    else:
        age = average
    if line['Sex'] != '':
        if line['Sex'] == 'male':
            male = True
        else:
            male = False
    #2 vars, age, is the age, male is a bool if the passenger was male
#    if line['Pclass'] == '1':
#        return True
    if age < 18:
        if male == True:
            return True
        elif male == False:
            return False
    elif age > 65:
        return False
        
    else:
        if male == True:
            return False
        else:
            return True
    
    
train_datapoints = []
train_results = []
test_datapoints = []
test_results = []
myfile = open('/Users/Ben/Desktop/train.csv')
for line in csv.DictReader(myfile):
    
    if line['Age'] != '':
        age = float(line['Age'])
    else:
        age = average
    if line['Sex'] != '':
        if line['Sex'] == 'male':
            sex = 1
        else:
            sex = 0
    

    datapoint = [age, sex, int(line['Pclass']), int(line['SibSp'])]
    if random.randint(1,10) > 8:
        test_datapoints.append(datapoint)
        if line['Survived']== '1':
            test_results.append(1)
        else:
            test_results.append(0)
        
    else:
        train_datapoints.append(datapoint)
        if line['Survived']== '1':
            train_results.append(1)
        else:
            train_results.append(0)
        
    print datapoint
    print '-----'
print len(test_results), len(train_results)

from sklearn.linear_model import LinearRegression
classifier = LinearRegression()

#from sklearn.neighbors import KNeighborsClassifier
#classifier = KNeighborsClassifier(n_neighbors=4, weights='distance')

#from sklearn.tree import DecisionTreeClassifier
#classifier = DecisionTreeClassifier()

classifier.fit(train_datapoints, train_results)

predictions = classifier.predict(test_datapoints)
answers = []
for x in range(len(predictions)):
    pred = predictions[x]
    correct = test_results[x]
    if pred <= .1:
        pred = 0
    else:
        pred = 1
    answers.append(pred)
#    if pred == correct:
#        answers.append(True)
#    else:
#        answers.append(False)
        
print predictions

from sklearn.metrics import confusion_matrix
print confusion_matrix(answers, test_results)
#corrects =  answers.count(True)
#wrongs =  answers.count(False)
#
#print corrects/float(corrects + wrongs)