Python机器学习泰坦尼克号生存者预测

# Kaggle Competition Titanic Machine Learning From Disaster. # A Predictive Model based on Gender, and Class # AstroDave Remake by AGC import csv as csv import numpy as np # Load in the train.csv file # The 'rb' allows for reading only privileges. csv_file_object = csv.reader(open('train.csv', 'rb')) # Skip the fist line of the file because it is a header. # We only want the data in the file for analysis. header = csv_file_object.next() # Create a bin to store our data data = [] # Skip through each row in the csv file for row in csv_file_object: #add each row to the data variable data.append(row) # Then convert from a list to a NumPy array data = np.array(data) # In order to analyze the price column I need to bin up that data # here are my binning parameters the problem we face is some of the fares are very large # So we can either have a lot of bins with nothing in them or we can just absorb some # information and just say anything over 30 is just in the last bin so we add a ceiling fare_ceiling = 40 data[data[0::,8].astype(np.float) >= fare_ceiling, 8] = fare_ceiling - 1.0 fare_bracket_size = 10 number_of_price_brackets = fare_ceiling / fare_bracket_size number_of_classes = 3 # There were 1st, 2nd and 3rd classes on board # This reference table will show we the proportion of survivors as a function of # Gender, class and ticket fare. survival_table = np.zeros([2,number_of_classes,number_of_price_brackets],float) # I can now find the stats of all the women and men on board for i in xrange(number_of_classes): for j in xrange(number_of_price_brackets): women_only_stats = data[ (data[0::,3] == "female") \ & (data[0::,1].astype(np.float) == i+1) \ & (data[0:,8].astype(np.float) >= j*fare_bracket_size) \ & (data[0:,8].astype(np.float) < (j+1)*fare_bracket_size), 0] men_only_stats = data[ (data[0::,3] != "female") \ & (data[0::,1].astype(np.float) == i+1) \ & (data[0:,8].astype(np.float) >= j*fare_bracket_size) \ & (data[0:,8].astype(np.float) < (j+1)*fare_bracket_size), 0] #if i == 0 and j == 3: survival_table[0,i,j] = np.mean(women_only_stats.astype(np.float)) # Women stats survival_table[1,i,j] = np.mean(men_only_stats.astype(np.float)) # Men stats # Since in python if it tries to find the mean of an array with nothing in it # such that the denominator is 0, then it returns NaN, we can convert these to 0 # by just saying where does the array not equal the array, and set these to 0. survival_table[ survival_table != survival_table ] = 0. # Now I have my proportion of survivors, simply round them such that if < 0.5 # they don't survive and >1 they do survival_table[ survival_table < 0.5 ] = 0 survival_table[ survival_table >= 0.5 ] = 1 # Now I have my indicator I can read in the test file and write out # if a women then survived(1) if a man then did not survived (0) # 1st Read in test test_file_obect = csv.reader(open('test.csv', 'rb')) open_file_object = csv.writer(open("genderclasspricebasedmodelpy.csv", "wb")) header = test_file_obect.next() # First thing to do is bin up the price file for row in test_file_obect: for j in xrange(number_of_price_brackets): # If there is no fare then place the price of the ticket # According to class try: row[7] = float(row[7]) # No fare recorded will come up as a string so # try to make it a float except: # If fails then just bin the fare according to the class bin_fare = 3-float(row[0]) break # Break from the loop and move to the next row if row[7] > fare_ceiling: # Otherwise now test to see if it is higher # than the fare ceiling we set earlier bin_fare = number_of_price_brackets-1 break # And then break to the next row if row[7] >= j*fare_bracket_size\ and row[7] < (j+1)*fare_bracket_size: # If passed these tests then loop through # each bin until you find the right one # append it to the binned_price # and move to the next loop bin_fare = j break # Now I have the bin fare, the class and whether female or male we can # just cross ref their details with our 'survival table if row[2] == 'female': row.insert(0,int(survival_table[0,float(row[0])-1,bin_fare])) # Insert the prediction # at the start of the row open_file_object.writerow(row) # Write the row to the file else: row.insert(0,int(survival_table[1,float(row[0])-1,bin_fare])) # Insert the prediction # at the start of the row open_file_object.writerow(row)