import pandas as pd
import numpy as np
import pylab as plt

# Set the global default size of matplotlib figures
plt.rc('figure', figsize=(10, 5))

# Size of matplotlib figures that contain subplots
fizsize_with_subplots = (10, 10)

# Size of matplotlib histogram bins
bin_size = 10

"""
Explanation: This notebook was prepared by Donne Martin. Source and license info is on GitHub.
Kaggle Machine Learning Competition: Predicting Titanic Survivors

Competition Site
Description
Evaluation
Data Set
Setup Imports and Variables
Explore the Data
Feature: Passenger Classes
Feature: Sex
Feature: Embarked
Feature: Age
Feature: Family Size
Final Data Preparation for Machine Learning
Data Wrangling Summary
Random Forest: Training
Random Forest: Predicting
Random Forest: Prepare for Kaggle Submission
Support Vector Machine: Training
Support Vector Machine: Predicting

Competition Site
Description, Evaluation, and Data Set taken from the competition site.
Description

The sinking of the RMS Titanic is one of the most infamous shipwrecks in history.  On April 15, 1912, during her maiden voyage, the Titanic sank after colliding with an iceberg, killing 1502 out of 2224 passengers and crew. This sensational tragedy shocked the international community and led to better safety regulations for ships.
One of the reasons that the shipwreck led to such loss of life was that there were not enough lifeboats for the passengers and crew. Although there was some element of luck involved in surviving the sinking, some groups of people were more likely to survive than others, such as women, children, and the upper-class.
In this challenge, we ask you to complete the analysis of what sorts of people were likely to survive. In particular, we ask you to apply the tools of machine learning to predict which passengers survived the tragedy.
Evaluation
The historical data has been split into two groups, a 'training set' and a 'test set'.  For the training set, we provide the outcome ( 'ground truth' ) for each passenger.  You will use this set to build your model to generate predictions for the test set.
For each passenger in the test set, you must predict whether or not they survived the sinking ( 0 for deceased, 1 for survived ).  Your score is the percentage of passengers you correctly predict.
The Kaggle leaderboard has a public and private component.  50% of your predictions for the test set have been randomly assigned to the public leaderboard ( the same 50% for all users ).  Your score on this public portion is what will appear on the leaderboard.  At the end of the contest, we will reveal your score on the private 50% of the data, which will determine the final winner.  This method prevents users from 'overfitting' to the leaderboard.
Data Set
| File Name        | Available Formats |
|------------------|-------------------|
| train            | .csv (59.76 kb)   |
| gendermodel      | .csv (3.18 kb)    |
| genderclassmodel | .csv (3.18 kb)    |
| test             | .csv (27.96 kb)   |
| gendermodel      | .py (3.58 kb)     |
| genderclassmodel | .py (5.63 kb)     |
| myfirstforest    | .py (3.99 kb)     |
<pre>
VARIABLE DESCRIPTIONS:
survival        Survival
                (0 = No; 1 = Yes)
pclass          Passenger Class
                (1 = 1st; 2 = 2nd; 3 = 3rd)
name            Name
sex             Sex
age             Age
sibsp           Number of Siblings/Spouses Aboard
parch           Number of Parents/Children Aboard
ticket          Ticket Number
fare            Passenger Fare
cabin           Cabin
embarked        Port of Embarkation
                (C = Cherbourg; Q = Queenstown; S = Southampton)

SPECIAL NOTES:
Pclass is a proxy for socio-economic status (SES)
 1st ~ Upper; 2nd ~ Middle; 3rd ~ Lower

Age is in Years; Fractional if Age less than One (1)
 If the Age is Estimated, it is in the form xx.5

With respect to the family relation variables (i.e. sibsp and parch)
some relations were ignored.  The following are the definitions used
for sibsp and parch.

Sibling:  Brother, Sister, Stepbrother, or Stepsister of Passenger Aboard Titanic
Spouse:   Husband or Wife of Passenger Aboard Titanic (Mistresses and Fiances Ignored)
Parent:   Mother or Father of Passenger Aboard Titanic
Child:    Son, Daughter, Stepson, or Stepdaughter of Passenger Aboard Titanic

Other family relatives excluded from this study include cousins,
nephews/nieces, aunts/uncles, and in-laws.  Some children travelled
only with a nanny, therefore parch=0 for them.  As well, some
travelled with very close friends or neighbors in a village, however,
the definitions do not support such relations.
</pre>

Setup Imports and Variables
End of explanation
"""


df_train = pd.read_csv('../data/titanic/train.csv')
df_train.head()

df_train.tail()

"""
Explanation: Explore the Data
Read the data:
End of explanation
"""


df_train.dtypes

"""
Explanation: View the data types of each column:
End of explanation
"""


df_train.info()

"""
Explanation: Type 'object' is a string for pandas, which poses problems with machine learning algorithms.  If we want to use these as features, we'll need to convert these to number representations.
Get some basic information on the DataFrame:
End of explanation
"""


df_train.describe()

"""
Explanation: Age, Cabin, and Embarked are missing values.  Cabin has too many missing values, whereas we might be able to infer values for Age and Embarked.
Generate various descriptive statistics on the DataFrame:
End of explanation
"""


# Set up a grid of plots
fig = plt.figure(figsize=fizsize_with_subplots) 
fig_dims = (3, 2)

# Plot death and survival counts
plt.subplot2grid(fig_dims, (0, 0))
df_train['Survived'].value_counts().plot(kind='bar', 
                                         title='Death and Survival Counts')

# Plot Pclass counts
plt.subplot2grid(fig_dims, (0, 1))
df_train['Pclass'].value_counts().plot(kind='bar', 
                                       title='Passenger Class Counts')

# Plot Sex counts
plt.subplot2grid(fig_dims, (1, 0))
df_train['Sex'].value_counts().plot(kind='bar', 
                                    title='Gender Counts')
plt.xticks(rotation=0)

# Plot Embarked counts
plt.subplot2grid(fig_dims, (1, 1))
df_train['Embarked'].value_counts().plot(kind='bar', 
                                         title='Ports of Embarkation Counts')

# Plot the Age histogram
plt.subplot2grid(fig_dims, (2, 0))
df_train['Age'].hist()
plt.title('Age Histogram')

"""
Explanation: Now that we have a general idea of the data set contents, we can dive deeper into each column.  We'll be doing exploratory data analysis and cleaning data to setup 'features' we'll be using in our machine learning algorithms.
Plot a few features to get a better idea of each:
End of explanation
"""


pclass_xt = pd.crosstab(df_train['Pclass'], df_train['Survived'])
pclass_xt

"""
Explanation: Next we'll explore various features to view their impact on survival rates.
Feature: Passenger Classes
From our exploratory data analysis in the previous section, we see there are three passenger classes: First, Second, and Third class.  We'll determine which proportion of passengers survived based on their passenger class.
Generate a cross tab of Pclass and Survived:
End of explanation
"""


# Normalize the cross tab to sum to 1:
pclass_xt_pct = pclass_xt.div(pclass_xt.sum(1).astype(float), axis=0)

pclass_xt_pct.plot(kind='bar', 
                   stacked=True, 
                   title='Survival Rate by Passenger Classes')
plt.xlabel('Passenger Class')
plt.ylabel('Survival Rate')

"""
Explanation: Plot the cross tab:
End of explanation
"""


sexes = sorted(df_train['Sex'].unique())
genders_mapping = dict(zip(sexes, range(0, len(sexes) + 1)))
genders_mapping

"""
Explanation: We can see that passenger class seems to have a significant impact on whether a passenger survived.  Those in First Class the highest chance for survival.
Feature: Sex
Gender might have also played a role in determining a passenger's survival rate.  We'll need to map Sex from a string to a number to prepare it for machine learning algorithms.
Generate a mapping of Sex from a string to a number representation:
End of explanation
"""


df_train['Sex_Val'] = df_train['Sex'].map(genders_mapping).astype(int)
df_train.head()

"""
Explanation: Transform Sex from a string to a number representation:
End of explanation
"""


sex_val_xt = pd.crosstab(df_train['Sex_Val'], df_train['Survived'])
sex_val_xt_pct = sex_val_xt.div(sex_val_xt.sum(1).astype(float), axis=0)
sex_val_xt_pct.plot(kind='bar', stacked=True, title='Survival Rate by Gender')

"""
Explanation: Plot a normalized cross tab for Sex_Val and Survived:
End of explanation
"""


# Get the unique values of Pclass:
passenger_classes = sorted(df_train['Pclass'].unique())

for p_class in passenger_classes:
    print 'M: ', p_class, len(df_train[(df_train['Sex'] == 'male') & 
                             (df_train['Pclass'] == p_class)])
    print 'F: ', p_class, len(df_train[(df_train['Sex'] == 'female') & 
                             (df_train['Pclass'] == p_class)])

"""
Explanation: The majority of females survived, whereas the majority of males did not.
Next we'll determine whether we can gain any insights on survival rate by looking at both Sex and Pclass.
Count males and females in each Pclass:
End of explanation
"""


# Plot survival rate by Sex
females_df = df_train[df_train['Sex'] == 'female']
females_xt = pd.crosstab(females_df['Pclass'], df_train['Survived'])
females_xt_pct = females_xt.div(females_xt.sum(1).astype(float), axis=0)
females_xt_pct.plot(kind='bar', 
                    stacked=True, 
                    title='Female Survival Rate by Passenger Class')
plt.xlabel('Passenger Class')
plt.ylabel('Survival Rate')

# Plot survival rate by Pclass
males_df = df_train[df_train['Sex'] == 'male']
males_xt = pd.crosstab(males_df['Pclass'], df_train['Survived'])
males_xt_pct = males_xt.div(males_xt.sum(1).astype(float), axis=0)
males_xt_pct.plot(kind='bar', 
                  stacked=True, 
                  title='Male Survival Rate by Passenger Class')
plt.xlabel('Passenger Class')
plt.ylabel('Survival Rate')

"""
Explanation: Plot survival rate by Sex and Pclass:
End of explanation
"""


df_train[df_train['Embarked'].isnull()]

"""
Explanation: The vast majority of females in First and Second class survived.  Males in First class had the highest chance for survival.
Feature: Embarked
The Embarked column might be an important feature but it is missing a couple data points which might pose a problem for machine learning algorithms:
End of explanation
"""


# Get the unique values of Embarked
embarked_locs = sorted(df_train['Embarked'].unique())

embarked_locs_mapping = dict(zip(embarked_locs, 
                                 range(0, len(embarked_locs) + 1)))
embarked_locs_mapping

"""
Explanation: Prepare to map Embarked from a string to a number representation:
End of explanation
"""


df_train['Embarked_Val'] = df_train['Embarked'] \
                               .map(embarked_locs_mapping) \
                               .astype(int)
df_train.head()

"""
Explanation: Transform Embarked from a string to a number representation to prepare it for machine learning algorithms:
End of explanation
"""


df_train['Embarked_Val'].hist(bins=len(embarked_locs), range=(0, 3))
plt.title('Port of Embarkation Histogram')
plt.xlabel('Port of Embarkation')
plt.ylabel('Count')
plt.show()

"""
Explanation: Plot the histogram for Embarked_Val:
End of explanation
"""


if len(df_train[df_train['Embarked'].isnull()] > 0):
    df_train.replace({'Embarked_Val' : 
                   { embarked_locs_mapping[nan] : embarked_locs_mapping['S'] 
                   }
               }, 
               inplace=True)

"""
Explanation: Since the vast majority of passengers embarked in 'S': 3, we assign the missing values in Embarked to 'S':
End of explanation
"""


embarked_locs = sorted(df_train['Embarked_Val'].unique())
embarked_locs

"""
Explanation: Verify we do not have any more NaNs for Embarked_Val:
End of explanation
"""


embarked_val_xt = pd.crosstab(df_train['Embarked_Val'], df_train['Survived'])
embarked_val_xt_pct = \
    embarked_val_xt.div(embarked_val_xt.sum(1).astype(float), axis=0)
embarked_val_xt_pct.plot(kind='bar', stacked=True)
plt.title('Survival Rate by Port of Embarkation')
plt.xlabel('Port of Embarkation')
plt.ylabel('Survival Rate')

"""
Explanation: Plot a normalized cross tab for Embarked_Val and Survived:
End of explanation
"""


# Set up a grid of plots
fig = plt.figure(figsize=fizsize_with_subplots) 

rows = 2
cols = 3
col_names = ('Sex_Val', 'Pclass')

for portIdx in embarked_locs:
    for colIdx in range(0, len(col_names)):
        plt.subplot2grid((rows, cols), (colIdx, portIdx - 1))
        df_train[df_train['Embarked_Val'] == portIdx][col_names[colIdx]] \
            .value_counts().plot(kind='bar')

"""
Explanation: It appears those that embarked in location 'C': 1 had the highest rate of survival.  We'll dig in some more to see why this might be the case.  Below we plot a graphs to determine gender and passenger class makeup for each port:
End of explanation
"""


df_train = pd.concat([df_train, pd.get_dummies(df_train['Embarked_Val'], prefix='Embarked_Val')], axis=1)

"""
Explanation: Leaving Embarked as integers implies ordering in the values, which does not exist.  Another way to represent Embarked without ordering is to create dummy variables:
End of explanation
"""


df_train[df_train['Age'].isnull()][['Sex', 'Pclass', 'Age']].head()

"""
Explanation: Feature: Age
The Age column seems like an important feature--unfortunately it is missing many values.  We'll need to fill in the missing values like we did with Embarked.
Filter to view missing Age values:
End of explanation
"""


# To keep Age in tact, make a copy of it called AgeFill 
# that we will use to fill in the missing ages:
df_train['AgeFill'] = df_train['Age']

# Populate AgeFill
df_train['AgeFill'] = df_train['AgeFill'] \
                        .groupby([df_train['Sex_Val'], df_train['Pclass']]) \
                        .apply(lambda x: x.fillna(x.median()))

"""
Explanation: Determine the Age typical for each passenger class by Sex_Val.  We'll use the median instead of the mean because the Age histogram seems to be right skewed.
End of explanation
"""


len(df_train[df_train['AgeFill'].isnull()])

"""
Explanation: Ensure AgeFill does not contain any missing values:
End of explanation
"""


# Set up a grid of plots
fig, axes = plt.subplots(2, 1, figsize=fizsize_with_subplots)

# Histogram of AgeFill segmented by Survived
df1 = df_train[df_train['Survived'] == 0]['Age']
df2 = df_train[df_train['Survived'] == 1]['Age']
max_age = max(df_train['AgeFill'])
axes[0].hist([df1, df2], 
             bins=max_age / bin_size, 
             range=(1, max_age), 
             stacked=True)
axes[0].legend(('Died', 'Survived'), loc='best')
axes[0].set_title('Survivors by Age Groups Histogram')
axes[0].set_xlabel('Age')
axes[0].set_ylabel('Count')

# Scatter plot Survived and AgeFill
axes[1].scatter(df_train['Survived'], df_train['AgeFill'])
axes[1].set_title('Survivors by Age Plot')
axes[1].set_xlabel('Survived')
axes[1].set_ylabel('Age')

"""
Explanation: Plot a normalized cross tab for AgeFill and Survived:
End of explanation
"""


for pclass in passenger_classes:
    df_train.AgeFill[df_train.Pclass == pclass].plot(kind='kde')
plt.title('Age Density Plot by Passenger Class')
plt.xlabel('Age')
plt.legend(('1st Class', '2nd Class', '3rd Class'), loc='best')

"""
Explanation: Unfortunately, the graphs above do not seem to clearly show any insights.  We'll keep digging further.
Plot AgeFill density by Pclass:
End of explanation
"""


# Set up a grid of plots
fig = plt.figure(figsize=fizsize_with_subplots) 
fig_dims = (3, 1)

# Plot the AgeFill histogram for Survivors
plt.subplot2grid(fig_dims, (0, 0))
survived_df = df_train[df_train['Survived'] == 1]
survived_df['AgeFill'].hist(bins=max_age / bin_size, range=(1, max_age))

# Plot the AgeFill histogram for Females
plt.subplot2grid(fig_dims, (1, 0))
females_df = df_train[(df_train['Sex_Val'] == 0) & (df_train['Survived'] == 1)]
females_df['AgeFill'].hist(bins=max_age / bin_size, range=(1, max_age))

# Plot the AgeFill histogram for first class passengers
plt.subplot2grid(fig_dims, (2, 0))
class1_df = df_train[(df_train['Pclass'] == 1) & (df_train['Survived'] == 1)]
class1_df['AgeFill'].hist(bins=max_age / bin_size, range=(1, max_age))

"""
Explanation: When looking at AgeFill density by Pclass, we see the first class passengers were generally older then second class passengers, which in turn were older than third class passengers.  We've determined that first class passengers had a higher survival rate than second class passengers, which in turn had a higher survival rate than third class passengers.
End of explanation
"""


df_train['FamilySize'] = df_train['SibSp'] + df_train['Parch']
df_train.head()

"""
Explanation: In the first graph, we see that most survivors come from the 20's to 30's age ranges and might be explained by the following two graphs.  The second graph shows most females are within their 20's.  The third graph shows most first class passengers are within their 30's.
Feature: Family Size
Feature enginering involves creating new features or modifying existing features which might be advantageous to a machine learning algorithm.
Define a new feature FamilySize that is the sum of Parch (number of parents or children on board) and SibSp (number of siblings or spouses):
End of explanation
"""


df_train['FamilySize'].hist()
plt.title('Family Size Histogram')

"""
Explanation: Plot a histogram of FamilySize:
End of explanation
"""


# Get the unique values of Embarked and its maximum
family_sizes = sorted(df_train['FamilySize'].unique())
family_size_max = max(family_sizes)

df1 = df_train[df_train['Survived'] == 0]['FamilySize']
df2 = df_train[df_train['Survived'] == 1]['FamilySize']
plt.hist([df1, df2], 
         bins=family_size_max + 1, 
         range=(0, family_size_max), 
         stacked=True)
plt.legend(('Died', 'Survived'), loc='best')
plt.title('Survivors by Family Size')

"""
Explanation: Plot a histogram of AgeFill segmented by Survived:
End of explanation
"""


df_train.dtypes[df_train.dtypes.map(lambda x: x == 'object')]

"""
Explanation: Based on the histograms, it is not immediately obvious what impact FamilySize has on survival.  The machine learning algorithms might benefit from this feature.
Additional features we might want to engineer might be related to the Name column, for example honorrary or pedestrian titles might give clues and better predictive power for a male's survival.
Final Data Preparation for Machine Learning
Many machine learning algorithms do not work on strings and they usually require the data to be in an array, not a DataFrame.
Show only the columns of type 'object' (strings):
End of explanation
"""


df_train = df_train.drop(['Name', 'Sex', 'Ticket', 'Cabin', 'Embarked'], 
                         axis=1)

"""
Explanation: Drop the columns we won't use:
End of explanation
"""


df_train = df_train.drop(['Age', 'SibSp', 'Parch', 'PassengerId', 'Embarked_Val'], axis=1)
df_train.dtypes

"""
Explanation: Drop the following columns:
* The Age column since we will be using the AgeFill column instead.
* The SibSp and Parch columns since we will be using FamilySize instead.
* The PassengerId column since it won't be used as a feature.
* The Embarked_Val as we decided to use dummy variables instead.
End of explanation
"""


train_data = df_train.values
train_data

"""
Explanation: Convert the DataFrame to a numpy array:
End of explanation
"""


def clean_data(df, drop_passenger_id):
    
    # Get the unique values of Sex
    sexes = sorted(df['Sex'].unique())
    
    # Generate a mapping of Sex from a string to a number representation    
    genders_mapping = dict(zip(sexes, range(0, len(sexes) + 1)))

    # Transform Sex from a string to a number representation
    df['Sex_Val'] = df['Sex'].map(genders_mapping).astype(int)
    
    # Get the unique values of Embarked
    embarked_locs = sorted(df['Embarked'].unique())

    # Generate a mapping of Embarked from a string to a number representation        
    embarked_locs_mapping = dict(zip(embarked_locs, 
                                     range(0, len(embarked_locs) + 1)))
    
    # Transform Embarked from a string to dummy variables
    df = pd.concat([df, pd.get_dummies(df['Embarked'], prefix='Embarked_Val')], axis=1)
    
    # Fill in missing values of Embarked
    # Since the vast majority of passengers embarked in 'S': 3, 
    # we assign the missing values in Embarked to 'S':
    if len(df[df['Embarked'].isnull()] > 0):
        df.replace({'Embarked_Val' : 
                       { embarked_locs_mapping[nan] : embarked_locs_mapping['S'] 
                       }
                   }, 
                   inplace=True)
    
    # Fill in missing values of Fare with the average Fare
    if len(df[df['Fare'].isnull()] > 0):
        avg_fare = df['Fare'].mean()
        df.replace({ None: avg_fare }, inplace=True)
    
    # To keep Age in tact, make a copy of it called AgeFill 
    # that we will use to fill in the missing ages:
    df['AgeFill'] = df['Age']

    # Determine the Age typical for each passenger class by Sex_Val.  
    # We'll use the median instead of the mean because the Age 
    # histogram seems to be right skewed.
    df['AgeFill'] = df['AgeFill'] \
                        .groupby([df['Sex_Val'], df['Pclass']]) \
                        .apply(lambda x: x.fillna(x.median()))
            
    # Define a new feature FamilySize that is the sum of 
    # Parch (number of parents or children on board) and 
    # SibSp (number of siblings or spouses):
    df['FamilySize'] = df['SibSp'] + df['Parch']
    
    # Drop the columns we won't use:
    df = df.drop(['Name', 'Sex', 'Ticket', 'Cabin', 'Embarked'], axis=1)
    
    # Drop the Age column since we will be using the AgeFill column instead.
    # Drop the SibSp and Parch columns since we will be using FamilySize.
    # Drop the PassengerId column since it won't be used as a feature.
    df = df.drop(['Age', 'SibSp', 'Parch'], axis=1)
    
    if drop_passenger_id:
        df = df.drop(['PassengerId'], axis=1)
    
    return df

"""
Explanation: Data Wrangling Summary
Below is a summary of the data wrangling we performed on our training data set.  We encapsulate this in a function since we'll need to do the same operations to our test set later.
End of explanation
"""


from sklearn.ensemble import RandomForestClassifier

clf = RandomForestClassifier(n_estimators=100)

"""
Explanation: Random Forest: Training
Create the random forest object:
End of explanation
"""


# Training data features, skip the first column 'Survived'
train_features = train_data[:, 1:]

# 'Survived' column values
train_target = train_data[:, 0]

# Fit the model to our training data
clf = clf.fit(train_features, train_target)
score = clf.score(train_features, train_target)
"Mean accuracy of Random Forest: {0}".format(score)

"""
Explanation: Fit the training data and create the decision trees:
End of explanation
"""


df_test = pd.read_csv('../data/titanic/test.csv')
df_test.head()

"""
Explanation: Random Forest: Predicting
Read the test data:
End of explanation
"""


# Data wrangle the test set and convert it to a numpy array
df_test = clean_data(df_test, drop_passenger_id=False)
test_data = df_test.values

"""
Explanation: Note the test data does not contain the column 'Survived', we'll use our trained model to predict these values.
End of explanation
"""


# Get the test data features, skipping the first column 'PassengerId'
test_x = test_data[:, 1:]

# Predict the Survival values for the test data
test_y = clf.predict(test_x)

"""
Explanation: Take the decision trees and run it on the test data:
End of explanation
"""


df_test['Survived'] = test_y
df_test[['PassengerId', 'Survived']] \
    .to_csv('../data/titanic/results-rf.csv', index=False)

"""
Explanation: Random Forest: Prepare for Kaggle Submission
Create a DataFrame by combining the index from the test data with the output of predictions, then write the results to the output:
End of explanation
"""


from sklearn import metrics
from sklearn.cross_validation import train_test_split

# Split 80-20 train vs test data
train_x, test_x, train_y, test_y = train_test_split(train_features, 
                                                    train_target, 
                                                    test_size=0.20, 
                                                    random_state=0)
print (train_features.shape, train_target.shape)
print (train_x.shape, train_y.shape)
print (test_x.shape, test_y.shape)

"""
Explanation: Evaluate Model Accuracy
Submitting to Kaggle will give you an accuracy score.  It would be helpful to get an idea of accuracy without submitting to Kaggle.
We'll split our training data, 80% will go to "train" and 20% will go to "test":
End of explanation
"""


clf = clf.fit(train_x, train_y)
predict_y = clf.predict(test_x)

from sklearn.metrics import accuracy_score
print ("Accuracy = %.2f" % (accuracy_score(test_y, predict_y)))

"""
Explanation: Use the new training data to fit the model, predict, and get the accuracy score:
End of explanation
"""


from IPython.core.display import Image 
Image(filename='../data/confusion_matrix.png', width=800)

"""
Explanation: View the Confusion Matrix:
|      | condition True | condition false|
|------|----------------|---------------|
|prediction true|True Positive|False positive|
|Prediction False|False Negative|True Negative|
End of explanation
"""


model_score = clf.score(test_x, test_y)
print ("Model Score %.2f \n" % (model_score))

confusion_matrix = metrics.confusion_matrix(test_y, predict_y)
print ("Confusion Matrix ", confusion_matrix)

print ("          Predicted")
print ("         |  0  |  1  |")
print ("         |-----|-----|")
print ("       0 | %3d | %3d |" % (confusion_matrix[0, 0],
                                   confusion_matrix[0, 1]))
print ("Actual   |-----|-----|")
print ("       1 | %3d | %3d |" % (confusion_matrix[1, 0],
                                   confusion_matrix[1, 1]))
print ("         |-----|-----|")

"""
Explanation: Get the model score and confusion matrix:
End of explanation
"""


from sklearn.metrics import classification_report
print(classification_report(test_y, 
                            predict_y, 
                            target_names=['Not Survived', 'Survived']))

"""
Explanation: Display the classification report:
$$Precision = \frac{TP}{TP + FP}$$ 
$$Recall = \frac{TP}{TP + FN}$$ 
$$F1 = \frac{2TP}{2TP + FP + FN}$$
End of explanation
"""


