简介
2016年11月的时候决定开始入坑机器学习 首先照着Kaggle上第一个题目《泰坦尼克号生还者分析》的官方示例敲了一遍。
2017年2月更新:用pandas重新整理了数据，计算了详细的正确率，试用了scikit-learn中的LinearRegression
题目介绍在这：Titanic: Machine Learning from Disaster
下面是数据集表格样式，每个人有12个属性
不是算法的算法
官方示例就是按几个属性分类，比如年龄，性别，票价(.....)
然后对每个属性内所有人的生还数据（0或者1）加一起求平均。
英文注释都是官方文档的说明 我就当入门教程学了，也全打了上去 代码如下：
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193# -*- coding: utf-8 -*-
"""
Created on Sun Oct 30 15:28:22 2016
@author: thinkwee
"""
import csv as csv
import numpy as np
from glue import qglue
test_file=(open(r'文件目录略', 'r'))
test_file_object = csv.reader(open(r'文件目录略', 'r'))
testheader = next(test_file_object)
predictions_file = open(r"文件目录略", "w")
predictions_file_object = csv.writer(predictions_file)
p = csv.writer(predictions_file)
p.writerow(["PassengerId", "Survived"])
csv_file_object = csv.reader(open(r'文件目录略', 'r'))
trainheader = next(csv_file_object) # The next() command just skips the
# first line which is a header
data=[] # Create a variable called 'data'.
for row in csv_file_object: # Run through each row in the csv file,
data.append(row) # adding each row to the data variable
print(type(data))
data = np.array(data) # Then convert from a list to an array
# Be aware that each item is currently
# a string in this format
number_passengers = np.size(data[0::,1].astype(np.float))
number_survived = np.sum(data[0::,1].astype(np.float))
proportion_survivors = number_survived / number_passengers
women_only_stats = data[0::,4] == "female" # This finds where all
# the elements in the gender
# column that equals “female”
men_only_stats = data[0::,4] != "female" # This finds where all the
# elements do not equal
# female (i.e. male)
# Using the index from above we select the females and males separately
women_onboard = data[women_only_stats,1].astype(np.float)
men_onboard = data[men_only_stats,1].astype(np.float)
# Then we finds the proportions of them that survived
proportion_women_survived = \
np.sum(women_onboard) / np.size(women_onboard)
proportion_men_survived = \
np.sum(men_onboard) / np.size(men_onboard)
# and then print it out
print ('Proportion of women who survived is %s' % proportion_women_survived)
print ('Proportion of men who survived is %s' % proportion_men_survived)
# The script will systematically will loop through each combination
# and use the 'where' function in python to search the passengers that fit that combination of variables.
# Just like before, you can ask what indices in your data equals female, 1st class, and paid more than $30.
# The problem is that looping through requires bins of equal sizes, i.e. $0-9, $10-19, $20-29, $30-39.
# For the sake of binning let's say everything equal to and above 40 "equals" 39 so it falls in this bin.
# So then you can set the bins
# So we add a ceiling
fare_ceiling = 40
# then modify the data in the Fare column to = 39, if it is greater or equal to the ceiling
data[ data[0::,9].astype(np.float) >= fare_ceiling, 9 ] = fare_ceiling - 1.0
fare_bracket_size = 10
number_of_price_brackets = fare_ceiling // fare_bracket_size
# Take the length of an array of unique values in column index 2
number_of_classes = len(np.unique(data[0::,2]))
number_of_age_brackets=8
# Initialize the survival table with all zeros
survival_table = np.zeros((2, number_of_classes,
number_of_price_brackets,
number_of_age_brackets))
#Now that these are set up,
#you can loop through each variable
#and find all those passengers that agree with the statements
for i in range(number_of_classes): #loop through each class
for j in range(number_of_price_brackets): #loop through each price bin
for k in range(number_of_age_brackets): #loop through each age bin
women_only_stats_plus = data[ #Which element
(data[0::,4] == "female") #is a female
&(data[0::,2].astype(np.float) #and was ith class
== i+1)
&(data[0:,9].astype(np.float) #was greater
>= j*fare_bracket_size) #than this bin
&(data[0:,9].astype(np.float) #and less than
< (j+1)*fare_bracket_size)
&(data[0:,5].astype(np.float)>=k*10)
&(data[0:,5].astype(np.float)<(k+1)*10)#the next bin
, 1] #in the 2nd col
men_only_stats_plus = data[ #Which element
(data[0::,4] != "female") #is a male
&(data[0::,2].astype(np.float) #and was ith class
== i+1)
&(data[0:,9].astype(np.float) #was greater
>= j*fare_bracket_size) #than this bin
&(data[0:,9].astype(np.float) #and less than
< (j+1)*fare_bracket_size)#the next bin
&(data[0:,5].astype(np.float)>=k*10)
&(data[0:,5].astype(np.float)<(k+1)*10)
, 1]
survival_table[0,i,j,k] = np.mean(women_only_stats_plus.astype(np.float))
survival_table[1,i,j,k] = np.mean(men_only_stats_plus.astype(np.float))
#if nan then the type will change to string from float so this sentence can set nan to 0.
survival_table[ survival_table != survival_table ] = 0.
#Notice that data[ where function, 1] means
#it is finding the Survived column for the conditional criteria which is being called.
#As the loop starts with i=0 and j=0,
#the first loop will return the Survived values for all the 1st-class females (i + 1)
#who paid less than 10 ((j+1)*fare_bracket_size)
#and similarly all the 1st-class males who paid less than 10.
#Before resetting to the top of the loop,
#we can calculate the proportion of survivors for this particular
#combination of criteria and record it to our survival table
#官方示例中将概率大于0.5的视为生还，这里我们略过
#直接打印详细概率
#survival_table[ survival_table < 0.5 ] = 0
#survival_table[ survival_table >= 0.5 ] = 1
#Then we can make the prediction
for row in test_file_object: # We are going to loop
# through each passenger
# in the test set
for j in range(number_of_price_brackets): # For each passenger we
# loop thro each price bin
try: # Some passengers have no
# Fare data so try to make
row[8] = float(row[8]) # a float
except: # If fails: no data, so
bin_fare = 3 - float(row[1]) # bin the fare according Pclass
break # Break from the loop
if row[8] > fare_ceiling: # If there is data see if
# it is greater than fare
# ceiling we set earlier
bin_fare = number_of_price_brackets-1 # If so set to highest bin
break # And then break loop
if row[8] >= j * fare_bracket_size\
and row[8] < \
(j+1) * fare_bracket_size: # If passed these tests
# then loop through each bin
bin_fare = j # then assign index
break
for j in range(number_of_age_brackets):
try:
row[4] = float(row[4])
except:
bin_age = -1
break
if row[4] >= j * 10\
and row[4] < \
(j+1) * 10: # If passed these tests
# then loop through each bin
bin_age = j # then assign index
break
if row[3] == 'female': #If the passenger is female
p.writerow([row[0], "%f %%" % \
(survival_table[0, int(row[1])-1, bin_fare,bin_age]*100)])
else: #else if male
p.writerow([row[0], "%f %%" % \
(survival_table[1, int(row[1])-1, bin_fare,bin_age]*100)])
# Close out the files.
test_file.close()
predictions_file.close()
多元线性回归
之后买了西瓜书，我把这个例题改成了线性回归模型： 假设每一个人生还可能与这个人的性别，价位，舱位，年龄四个属性成线性关系， 我们就利用最小二乘法找到一组线性系数，是所有样本到这个线性函数直线上的距离最小 用均方误差作为性能度量，均方误差是线性系数的函数 对线性系数w求导，可以得到w最优解的闭式
关键公式是 ** \[ w^*=(X^TX)^{-1}X^Ty \] **
- X:数据集矩阵，每一行对应一个人的数据，每一行最后添加一个1， 假如训练集有m个人，n个属性，则矩阵大小为m*(n+1)
- w:线性系数
- y:生还结果 \[ y=w^T*x \]
写的时候把年龄中缺失值全删除了，官方给了891条数据，我分了193条用于验证计算正确率，最后正确率是75.155280 %
代码如下
1 | train1=train.dropna(subset=(['Age']),axis=0) |
scikit-learn中的多元线性回归
试了一下scikit,增加了几个属性，一样的数据，但是好像有些属性不太好，导致正确率下降至64.375000 %
如果再模型的fit阶段出现错误，请检查你fit的x,y数据集是否出现了空元素，无限大元素，或者各个属性的长度不一致，可以用info()做一个概览
1 | train=train.dropna(subset=['Age','Embarked'],axis=0) |