meanShiftTitanic.py

import numpy as np
from sklearn.cluster import MeanShift, KMeans
from sklearn import preprocessing, cross_validation
import pandas as pd
import matplotlib.pyplot as plt


'''
Pclass Passenger Class (1 = 1st; 2 = 2nd; 3 = 3rd)
survival Survival (0 = No; 1 = Yes)
name Name
sex Sex
age Age
sibsp Number of Siblings/Spouses Aboard
parch Number of Parents/Children Aboard
ticket Ticket Number
fare Passenger Fare (British pound)
cabin Cabin
embarked Port of Embarkation (C = Cherbourg; Q = Queenstown; S = Southampton)
boat Lifeboat
body Body Identification Number
home.dest Home/Destination
'''


# https://pythonprogramming.net/static/downloads/machine-learning-data/titanic.xls
df = pd.read_excel('titanic.xls')

original_df = pd.DataFrame.copy(df)
df.drop(['body','name'], 1, inplace=True)
df.fillna(0,inplace=True)

def handle_non_numerical_data(df):
    
    # handling non-numerical data: must convert.
    columns = df.columns.values

    for column in columns:
        text_digit_vals = {}
        def convert_to_int(val):
            return text_digit_vals[val]

        #print(column,df[column].dtype)
        if df[column].dtype != np.int64 and df[column].dtype != np.float64:
            
            column_contents = df[column].values.tolist()
            #finding just the uniques
            unique_elements = set(column_contents)
            # great, found them. 
            x = 0
            for unique in unique_elements:
                if unique not in text_digit_vals:
                    # creating dict that contains new
                    # id per unique string
                    text_digit_vals[unique] = x
                    x+=1
            # now we map the new "id" vlaue
            # to replace the string. 
            df[column] = list(map(convert_to_int,df[column]))

    return df

df = handle_non_numerical_data(df)
df.drop(['ticket','home.dest'], 1, inplace=True)

X = np.array(df.drop(['survived'], 1).astype(float))
X = preprocessing.scale(X)
y = np.array(df['survived'])

clf = MeanShift()
clf.fit(X)

labels = clf.labels_
cluster_centers = clf.cluster_centers_

#adding a new column to original dataframe
original_df['cluster_group']=np.nan

#iterating thru the labels and populating the labels to the empty column
for i in range(len(X)):
    #with iloc we are referenceing the index or the row of the dataframe where column is cluster group and we are puttin the coresponding labls in there
    original_df['cluster_group'].iloc[i] = labels[i]

#check the survival rates for each of the groups we happen to find
n_clusters_ = len(np.unique(labels))
survival_rates = {}
for i in range(n_clusters_):
    temp_df = original_df[ (original_df['cluster_group']==float(i)) ]
    #print(temp_df.head())

    survival_cluster = temp_df[  (temp_df['survived'] == 1) ]

    survival_rate = len(survival_cluster) / len(temp_df)
    #print(i,survival_rate)
    survival_rates[i] = survival_rate
    
#each cluster group is a different class ticket people note .describe() gives stats on the dataframe
print(original_df[ (original_df['cluster_group']==1) ].describe())
print(survival_rates)