train_boost.py

# Import the linear regression class
from sklearn.linear_model import LinearRegression 
from sklearn.linear_model import LogisticRegression
# Sklearn also has a helper that makes it easy to do cross validation
from sklearn.cross_validation import KFold


#import random forest classifier
from sklearn import cross_validation
from sklearn.ensemble import RandomForestClassifier

# Sklearn also has a helper that makes it easy to do cross validation
import numpy as np
import pandas
from sklearn import cross_validation

#import re for regular expression
import re

import operator

#for univariate feature selection (select the best features)
from sklearn.feature_selection import SelectKBest, f_classif

import matplotlib.pyplot as plt


from sklearn.ensemble import GradientBoostingClassifier



# We can use the pandas library in python to read in the csv file.
# This creates a pandas dataframe and assigns it to the titanic variable.
titanic = pandas.read_csv("titanic_train.csv")

#fill in missing values of age with the median age
titanic["Age"]=titanic["Age"].fillna(titanic["Age"].median())


# Find all the unique genders -- the column appears to contain only male and female.
print(titanic["Sex"].unique())

# Replace all the occurences of male with the number 0 and female with 1
titanic.loc[titanic["Sex"] == "male", "Sex"] = 0
titanic.loc[titanic["Sex"] == "female", "Sex"] = 1



# Find all the unique values for "Embarked".
print(titanic["Embarked"].unique())

#fill the null values with the most common value (in this case 'S')
titanic["Embarked"] = titanic["Embarked"].fillna("S")

titanic.loc[titanic["Embarked"]=="S", "Embarked"]=0
titanic.loc[titanic["Embarked"]=="C", "Embarked"]=1
titanic.loc[titanic["Embarked"]=="Q", "Embarked"]=2


#can also make new predictors
# Generating a familysize column
titanic["FamilySize"] = titanic["SibSp"] + titanic["Parch"]

# The .apply method generates a new series
# lambda will perform an inline function, so given x return the length of x and apply this
# to each row in "Name"
titanic["NameLength"] = titanic["Name"].apply(lambda x: len(x))



predictors = ["Pclass", "Sex", "Age", "SibSp", "Parch", "Fare", "Embarked"]



# A function to get the title from a name.
def get_title(name):
    # Use a regular expression to search for a title.  Titles always consist of capital and lowercase letters, and end with a period.
    title_search = re.search(' ([A-Za-z]+)\.', name)
    # If the title exists, extract and return it.
    if title_search:
        return title_search.group(1)
    return ""

# Get all the titles and print how often each one occurs.
titles = titanic["Name"].apply(get_title)
print(pandas.value_counts(titles))

# Map each title to an integer.  Some titles are very rare, and are compressed into the same codes as other titles.
title_mapping = {"Mr": 1, "Miss": 2, "Mrs": 3, "Master": 4, "Dr": 5, "Rev": 6, "Major": 7, "Col": 7, "Mlle": 8, "Mme": 8, "Don": 9, "Lady": 10, "Countess": 10, "Jonkheer": 10, "Sir": 9, "Capt": 7, "Ms": 2}
for k,v in title_mapping.items():
    titles[titles == k] = v

# Verify that we converted everything.
print(pandas.value_counts(titles))

# Add in the title column.
titanic["Title"] = titles







# A dictionary mapping family name to id
family_id_mapping = {}

# A function to get the id given a row
def get_family_id(row):
    # Find the last name by splitting on a comma
    last_name = row["Name"].split(",")[0]
    # Create the family id
    family_id = "{0}{1}".format(last_name, row["FamilySize"])
    # Look up the id in the mapping
    if family_id not in family_id_mapping:
        if len(family_id_mapping) == 0:
            current_id = 1
        else:
            # Get the maximum id from the mapping and add one to it if we don't have an id
            current_id = (max(family_id_mapping.items(), key=operator.itemgetter(1))[1] + 1)
        family_id_mapping[family_id] = current_id
    return family_id_mapping[family_id]

# Get the family ids with the apply method
family_ids = titanic.apply(get_family_id, axis=1)

# There are a lot of family ids, so we'll compress all of the families under 3 members into one code.
family_ids[titanic["FamilySize"] < 3] = -1

# Print the count of each unique id.
print(pandas.value_counts(family_ids))

titanic["FamilyId"] = family_ids


#predictors = ["Pclass", "Sex", "Age", "SibSp", "Parch", "Fare", "Embarked", "FamilySize", "Title", "FamilyId"]







# The algorithms we want to ensemble.
# We're using the more linear predictors for the logistic regression, and everything with the gradient boosting classifier.
algorithms = [
    [GradientBoostingClassifier(random_state=1, n_estimators=25, max_depth=3), ["Pclass", "Sex", "Age", "Fare", "Embarked", "FamilySize", "Title", "FamilyId"]],
    [LogisticRegression(random_state=1), ["Pclass", "Sex", "Fare", "FamilySize", "Title", "Age", "Embarked"]]
]

# Initialize the cross validation folds
kf = KFold(titanic.shape[0], n_folds=3, random_state=1)

predictions = []
for train, test in kf:
    train_target = titanic["Survived"].iloc[train]
    full_test_predictions = []
    # Make predictions for each algorithm on each fold
    for alg, predictors in algorithms:
        # Fit the algorithm on the training data.
        alg.fit(titanic[predictors].iloc[train,:], train_target)
        # Select and predict on the test fold.  
        # The .astype(float) is necessary to convert the dataframe to all floats and avoid an sklearn error.
        test_predictions = alg.predict_proba(titanic[predictors].iloc[test,:].astype(float))[:,1]
        full_test_predictions.append(test_predictions)
    # Use a simple ensembling scheme -- just average the predictions to get the final classification.
    test_predictions = (full_test_predictions[0] + full_test_predictions[1]) / 2
    # Any value over .5 is assumed to be a 1 prediction, and below .5 is a 0 prediction.
    test_predictions[test_predictions <= .5] = 0
    test_predictions[test_predictions > .5] = 1
    predictions.append(test_predictions)

# Put all the predictions together into one array.
predictions = np.concatenate(predictions, axis=0)

# Compute accuracy by comparing to the training data.
accuracy = sum(predictions[predictions == titanic["Survived"]]) / len(predictions)
print(accuracy)