Kaggle(47) - タイタニック生存予測 - 正解率99%のノート

Kaggle(47) - タイタニック生存予測 - 正解率99%のノート

タイタニックの生存予測で99%の正解率をうたっているノートがあったので試しに実行してみました。

A Data Science Framework: To Achieve 99% Accuracy

正解率99%のソースを実行

最終更新が3年前のため、動作しない箇所がありましたので少々修正を行っています。

下記が動作を確認できたソース全体となります。

[ソース]

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# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python

#load packages
import sys #access to system parameters https://docs.python.org/3/library/sys.html
print("Python version: {}". format(sys.version))

import pandas as pd #collection of functions for data processing and analysis modeled after R dataframes with SQL like features
print("pandas version: {}". format(pd.__version__))

import matplotlib #collection of functions for scientific and publication-ready visualization
print("matplotlib version: {}". format(matplotlib.__version__))

import numpy as np #foundational package for scientific computing
print("NumPy version: {}". format(np.__version__))

import scipy as sp #collection of functions for scientific computing and advance mathematics
print("SciPy version: {}". format(sp.__version__))

import IPython
from IPython import display #pretty printing of dataframes in Jupyter notebook
print("IPython version: {}". format(IPython.__version__))

import sklearn #collection of machine learning algorithms
print("scikit-learn version: {}". format(sklearn.__version__))

#misc libraries
import random
import time

#ignore warnings
import warnings
warnings.filterwarnings('ignore')
print('-'*25)

# Input data files are available in the "../input/" directory.
# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory

from subprocess import check_output
print(check_output(["ls", "../input"]).decode("utf8"))

# Any results you write to the current directory are saved as output.

#Common Model Algorithms
from sklearn import svm, tree, linear_model, neighbors, naive_bayes, ensemble, discriminant_analysis, gaussian_process
from xgboost import XGBClassifier

#Common Model Helpers
from sklearn.preprocessing import OneHotEncoder, LabelEncoder
from sklearn import feature_selection
from sklearn import model_selection
from sklearn import metrics

#Visualization
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.pylab as pylab
import seaborn as sns
# from pandas.tools.plotting import scatter_matrix
from pandas import plotting

#Configure Visualization Defaults
#%matplotlib inline = show plots in Jupyter Notebook browser
%matplotlib inline
mpl.style.use('ggplot')
sns.set_style('white')
pylab.rcParams['figure.figsize'] = 12,8

#import data from file: https://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html
data_raw = pd.read_csv('/kaggle/input/titanic//train.csv')


#a dataset should be broken into 3 splits: train, test, and (final) validation
#the test file provided is the validation file for competition submission
#we will split the train set into train and test data in future sections
data_val = pd.read_csv('/kaggle/input/titanic//test.csv')


#to play with our data we'll create a copy
#remember python assignment or equal passes by reference vs values, so we use the copy function: https://stackoverflow.com/questions/46327494/python-pandas-dataframe-copydeep-false-vs-copydeep-true-vs
data1 = data_raw.copy(deep = True)

#however passing by reference is convenient, because we can clean both datasets at once
data_cleaner = [data1, data_val]

#preview data
print (data_raw.info()) #https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.info.html
data_raw.sample(10) #https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.sample.html

print('Train columns with null values:\n', data1.isnull().sum())
print("-"*10)

print('Test/Validation columns with null values:\n', data_val.isnull().sum())
print("-"*10)

data_raw.describe(include = 'all')


###COMPLETING: complete or delete missing values in train and test/validation dataset
for dataset in data_cleaner:
#complete missing age with median
dataset['Age'].fillna(dataset['Age'].median(), inplace = True)

#complete embarked with mode
dataset['Embarked'].fillna(dataset['Embarked'].mode()[0], inplace = True)

#complete missing fare with median
dataset['Fare'].fillna(dataset['Fare'].median(), inplace = True)

#delete the cabin feature/column and others previously stated to exclude in train dataset
drop_column = ['PassengerId','Cabin', 'Ticket']
data1.drop(drop_column, axis=1, inplace = True)

print(data1.isnull().sum())
print("-"*10)
print(data_val.isnull().sum())

###CREATE: Feature Engineering for train and test/validation dataset
for dataset in data_cleaner:
#Discrete variables
dataset['FamilySize'] = dataset ['SibSp'] + dataset['Parch'] + 1

dataset['IsAlone'] = 1 #initialize to yes/1 is alone
dataset['IsAlone'].loc[dataset['FamilySize'] > 1] = 0 # now update to no/0 if family size is greater than 1

#quick and dirty code split title from name: http://www.pythonforbeginners.com/dictionary/python-split
dataset['Title'] = dataset['Name'].str.split(", ", expand=True)[1].str.split(".", expand=True)[0]

#Continuous variable bins; qcut vs cut: https://stackoverflow.com/questions/30211923/what-is-the-difference-between-pandas-qcut-and-pandas-cut
#Fare Bins/Buckets using qcut or frequency bins: https://pandas.pydata.org/pandas-docs/stable/generated/pandas.qcut.html
dataset['FareBin'] = pd.qcut(dataset['Fare'], 4)

#Age Bins/Buckets using cut or value bins: https://pandas.pydata.org/pandas-docs/stable/generated/pandas.cut.html
dataset['AgeBin'] = pd.cut(dataset['Age'].astype(int), 5)

#cleanup rare title names
#print(data1['Title'].value_counts())
stat_min = 10 #while small is arbitrary, we'll use the common minimum in statistics: http://nicholasjjackson.com/2012/03/08/sample-size-is-10-a-magic-number/
title_names = (data1['Title'].value_counts() < stat_min) #this will create a true false series with title name as index

#apply and lambda functions are quick and dirty code to find and replace with fewer lines of code: https://community.modeanalytics.com/python/tutorial/pandas-groupby-and-python-lambda-functions/
data1['Title'] = data1['Title'].apply(lambda x: 'Misc' if title_names.loc[x] == True else x)
print(data1['Title'].value_counts())
print("-"*10)

#preview data again
data1.info()
data_val.info()
data1.sample(10)

#CONVERT: convert objects to category using Label Encoder for train and test/validation dataset

#code categorical data
label = LabelEncoder()
for dataset in data_cleaner:
dataset['Sex_Code'] = label.fit_transform(dataset['Sex'])
dataset['Embarked_Code'] = label.fit_transform(dataset['Embarked'])
dataset['Title_Code'] = label.fit_transform(dataset['Title'])
dataset['AgeBin_Code'] = label.fit_transform(dataset['AgeBin'])
dataset['FareBin_Code'] = label.fit_transform(dataset['FareBin'])

#define y variable aka target/outcome
Target = ['Survived']

#define x variables for original features aka feature selection
data1_x = ['Sex','Pclass', 'Embarked', 'Title','SibSp', 'Parch', 'Age', 'Fare', 'FamilySize', 'IsAlone'] #pretty name/values for charts
data1_x_calc = ['Sex_Code','Pclass', 'Embarked_Code', 'Title_Code','SibSp', 'Parch', 'Age', 'Fare'] #coded for algorithm calculation
data1_xy = Target + data1_x
print('Original X Y: ', data1_xy, '\n')

#define x variables for original w/bin features to remove continuous variables
data1_x_bin = ['Sex_Code','Pclass', 'Embarked_Code', 'Title_Code', 'FamilySize', 'AgeBin_Code', 'FareBin_Code']
data1_xy_bin = Target + data1_x_bin
print('Bin X Y: ', data1_xy_bin, '\n')

#define x and y variables for dummy features original
data1_dummy = pd.get_dummies(data1[data1_x])
data1_x_dummy = data1_dummy.columns.tolist()
data1_xy_dummy = Target + data1_x_dummy
print('Dummy X Y: ', data1_xy_dummy, '\n')

data1_dummy.head()

print('Train columns with null values: \n', data1.isnull().sum())
print("-"*10)
print (data1.info())
print("-"*10)

print('Test/Validation columns with null values: \n', data_val.isnull().sum())
print("-"*10)
print (data_val.info())
print("-"*10)

data_raw.describe(include = 'all')

#split train and test data with function defaults
#random_state -> seed or control random number generator: https://www.quora.com/What-is-seed-in-random-number-generation
train1_x, test1_x, train1_y, test1_y = model_selection.train_test_split(data1[data1_x_calc], data1[Target], random_state = 0)
train1_x_bin, test1_x_bin, train1_y_bin, test1_y_bin = model_selection.train_test_split(data1[data1_x_bin], data1[Target] , random_state = 0)
train1_x_dummy, test1_x_dummy, train1_y_dummy, test1_y_dummy = model_selection.train_test_split(data1_dummy[data1_x_dummy], data1[Target], random_state = 0)

print("Data1 Shape: {}".format(data1.shape))
print("Train1 Shape: {}".format(train1_x.shape))
print("Test1 Shape: {}".format(test1_x.shape))

train1_x_bin.head()

#Discrete Variable Correlation by Survival using
#group by aka pivot table: https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.groupby.html
for x in data1_x:
if data1[x].dtype != 'float64' :
print('Survival Correlation by:', x)
print(data1[[x, Target[0]]].groupby(x, as_index=False).mean())
print('-'*10, '\n')

#using crosstabs: https://pandas.pydata.org/pandas-docs/stable/generated/pandas.crosstab.html
print(pd.crosstab(data1['Title'],data1[Target[0]]))

#IMPORTANT: Intentionally plotted different ways for learning purposes only.

#optional plotting w/pandas: https://pandas.pydata.org/pandas-docs/stable/visualization.html

#we will use matplotlib.pyplot: https://matplotlib.org/api/pyplot_api.html

#to organize our graphics will use figure: https://matplotlib.org/api/_as_gen/matplotlib.pyplot.figure.html#matplotlib.pyplot.figure
#subplot: https://matplotlib.org/api/_as_gen/matplotlib.pyplot.subplot.html#matplotlib.pyplot.subplot
#and subplotS: https://matplotlib.org/api/_as_gen/matplotlib.pyplot.subplots.html?highlight=matplotlib%20pyplot%20subplots#matplotlib.pyplot.subplots

#graph distribution of quantitative data
plt.figure(figsize=[16,12])

plt.subplot(231)
plt.boxplot(x=data1['Fare'], showmeans = True, meanline = True)
plt.title('Fare Boxplot')
plt.ylabel('Fare ($)')

plt.subplot(232)
plt.boxplot(data1['Age'], showmeans = True, meanline = True)
plt.title('Age Boxplot')
plt.ylabel('Age (Years)')

plt.subplot(233)
plt.boxplot(data1['FamilySize'], showmeans = True, meanline = True)
plt.title('Family Size Boxplot')
plt.ylabel('Family Size (#)')

plt.subplot(234)
plt.hist(x = [data1[data1['Survived']==1]['Fare'], data1[data1['Survived']==0]['Fare']],
stacked=True, color = ['g','r'],label = ['Survived','Dead'])
plt.title('Fare Histogram by Survival')
plt.xlabel('Fare ($)')
plt.ylabel('# of Passengers')
plt.legend()

plt.subplot(235)
plt.hist(x = [data1[data1['Survived']==1]['Age'], data1[data1['Survived']==0]['Age']],
stacked=True, color = ['g','r'],label = ['Survived','Dead'])
plt.title('Age Histogram by Survival')
plt.xlabel('Age (Years)')
plt.ylabel('# of Passengers')
plt.legend()

plt.subplot(236)
plt.hist(x = [data1[data1['Survived']==1]['FamilySize'], data1[data1['Survived']==0]['FamilySize']],
stacked=True, color = ['g','r'],label = ['Survived','Dead'])
plt.title('Family Size Histogram by Survival')
plt.xlabel('Family Size (#)')
plt.ylabel('# of Passengers')
plt.legend()

#we will use seaborn graphics for multi-variable comparison: https://seaborn.pydata.org/api.html

#graph individual features by survival
fig, saxis = plt.subplots(2, 3,figsize=(16,12))

sns.barplot(x = 'Embarked', y = 'Survived', data=data1, ax = saxis[0,0])
sns.barplot(x = 'Pclass', y = 'Survived', order=[1,2,3], data=data1, ax = saxis[0,1])
sns.barplot(x = 'IsAlone', y = 'Survived', order=[1,0], data=data1, ax = saxis[0,2])

sns.pointplot(x = 'FareBin', y = 'Survived', data=data1, ax = saxis[1,0])
sns.pointplot(x = 'AgeBin', y = 'Survived', data=data1, ax = saxis[1,1])
sns.pointplot(x = 'FamilySize', y = 'Survived', data=data1, ax = saxis[1,2])

#graph distribution of qualitative data: Pclass
#we know class mattered in survival, now let's compare class and a 2nd feature
fig, (axis1,axis2,axis3) = plt.subplots(1,3,figsize=(14,12))

sns.boxplot(x = 'Pclass', y = 'Fare', hue = 'Survived', data = data1, ax = axis1)
axis1.set_title('Pclass vs Fare Survival Comparison')

sns.violinplot(x = 'Pclass', y = 'Age', hue = 'Survived', data = data1, split = True, ax = axis2)
axis2.set_title('Pclass vs Age Survival Comparison')

sns.boxplot(x = 'Pclass', y ='FamilySize', hue = 'Survived', data = data1, ax = axis3)
axis3.set_title('Pclass vs Family Size Survival Comparison')

#graph distribution of qualitative data: Sex
#we know sex mattered in survival, now let's compare sex and a 2nd feature
fig, qaxis = plt.subplots(1,3,figsize=(14,12))

sns.barplot(x = 'Sex', y = 'Survived', hue = 'Embarked', data=data1, ax = qaxis[0])
axis1.set_title('Sex vs Embarked Survival Comparison')

sns.barplot(x = 'Sex', y = 'Survived', hue = 'Pclass', data=data1, ax = qaxis[1])
axis1.set_title('Sex vs Pclass Survival Comparison')

sns.barplot(x = 'Sex', y = 'Survived', hue = 'IsAlone', data=data1, ax = qaxis[2])
axis1.set_title('Sex vs IsAlone Survival Comparison')

#more side-by-side comparisons
fig, (maxis1, maxis2) = plt.subplots(1, 2,figsize=(14,12))

#how does family size factor with sex & survival compare
sns.pointplot(x="FamilySize", y="Survived", hue="Sex", data=data1,
palette={"male": "blue", "female": "pink"},
markers=["*", "o"], linestyles=["-", "--"], ax = maxis1)

#how does class factor with sex & survival compare
sns.pointplot(x="Pclass", y="Survived", hue="Sex", data=data1,
palette={"male": "blue", "female": "pink"},
markers=["*", "o"], linestyles=["-", "--"], ax = maxis2)

#how does embark port factor with class, sex, and survival compare
#facetgrid: https://seaborn.pydata.org/generated/seaborn.FacetGrid.html
e = sns.FacetGrid(data1, col = 'Embarked')
e.map(sns.pointplot, 'Pclass', 'Survived', 'Sex', ci=95.0, palette = 'deep')
e.add_legend()

#plot distributions of age of passengers who survived or did not survive
a = sns.FacetGrid( data1, hue = 'Survived', aspect=4 )
a.map(sns.kdeplot, 'Age', shade= True )
a.set(xlim=(0 , data1['Age'].max()))
a.add_legend()

#histogram comparison of sex, class, and age by survival
h = sns.FacetGrid(data1, row = 'Sex', col = 'Pclass', hue = 'Survived')
h.map(plt.hist, 'Age', alpha = .75)
h.add_legend()

#pair plots of entire dataset
pp = sns.pairplot(data1, hue = 'Survived', palette = 'deep', size=1.2, diag_kind = 'kde', diag_kws=dict(shade=True), plot_kws=dict(s=10) )
pp.set(xticklabels=[])

#correlation heatmap of dataset
def correlation_heatmap(df):
_ , ax = plt.subplots(figsize =(14, 12))
colormap = sns.diverging_palette(220, 10, as_cmap = True)

_ = sns.heatmap(
df.corr(),
cmap = colormap,
square=True,
cbar_kws={'shrink':.9 },
ax=ax,
annot=True,
linewidths=0.1,vmax=1.0, linecolor='white',
annot_kws={'fontsize':12 }
)

plt.title('Pearson Correlation of Features', y=1.05, size=15)

correlation_heatmap(data1)

#Machine Learning Algorithm (MLA) Selection and Initialization
MLA = [
#Ensemble Methods
ensemble.AdaBoostClassifier(),
ensemble.BaggingClassifier(),
ensemble.ExtraTreesClassifier(),
ensemble.GradientBoostingClassifier(),
ensemble.RandomForestClassifier(),

#Gaussian Processes
gaussian_process.GaussianProcessClassifier(),

#GLM
linear_model.LogisticRegressionCV(),
linear_model.PassiveAggressiveClassifier(),
linear_model.RidgeClassifierCV(),
linear_model.SGDClassifier(),
linear_model.Perceptron(),

#Navies Bayes
naive_bayes.BernoulliNB(),
naive_bayes.GaussianNB(),

#Nearest Neighbor
neighbors.KNeighborsClassifier(),

#SVM
svm.SVC(probability=True),
svm.NuSVC(probability=True),
svm.LinearSVC(),

#Trees
tree.DecisionTreeClassifier(),
tree.ExtraTreeClassifier(),

#Discriminant Analysis
discriminant_analysis.LinearDiscriminantAnalysis(),
discriminant_analysis.QuadraticDiscriminantAnalysis(),

#xgboost: http://xgboost.readthedocs.io/en/latest/model.html
XGBClassifier()
]

#split dataset in cross-validation with this splitter class: http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.ShuffleSplit.html#sklearn.model_selection.ShuffleSplit
#note: this is an alternative to train_test_split
cv_split = model_selection.ShuffleSplit(n_splits = 10, test_size = .3, train_size = .6, random_state = 0 ) # run model 10x with 60/30 split intentionally leaving out 10%

#create table to compare MLA metrics
MLA_columns = ['MLA Name', 'MLA Parameters','MLA Train Accuracy Mean', 'MLA Test Accuracy Mean', 'MLA Test Accuracy 3*STD' ,'MLA Time']
MLA_compare = pd.DataFrame(columns = MLA_columns)

#create table to compare MLA predictions
MLA_predict = data1[Target]

#index through MLA and save performance to table
row_index = 0
for alg in MLA:

#set name and parameters
MLA_name = alg.__class__.__name__
MLA_compare.loc[row_index, 'MLA Name'] = MLA_name
MLA_compare.loc[row_index, 'MLA Parameters'] = str(alg.get_params())

#score model with cross validation: http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.cross_validate.html#sklearn.model_selection.cross_validate
cv_results = model_selection.cross_validate(alg, data1[data1_x_bin], data1[Target], cv = cv_split)
MLA_compare.loc[row_index, 'MLA Time'] = cv_results['fit_time'].mean()
# MLA_compare.loc[row_index, 'MLA Train Accuracy Mean'] = cv_results['train_score'].mean()
MLA_compare.loc[row_index, 'MLA Test Accuracy Mean'] = cv_results['test_score'].mean()
#if this is a non-bias random sample, then +/-3 standard deviations (std) from the mean, should statistically capture 99.7% of the subsets
MLA_compare.loc[row_index, 'MLA Test Accuracy 3*STD'] = cv_results['test_score'].std()*3 #let's know the worst that can happen!

#save MLA predictions - see section 6 for usage
alg.fit(data1[data1_x_bin], data1[Target])
MLA_predict[MLA_name] = alg.predict(data1[data1_x_bin])

row_index+=1

#print and sort table: https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.sort_values.html
MLA_compare.sort_values(by = ['MLA Test Accuracy Mean'], ascending = False, inplace = True)
MLA_compare
#MLA_predict

#barplot using https://seaborn.pydata.org/generated/seaborn.barplot.html
sns.barplot(x='MLA Test Accuracy Mean', y = 'MLA Name', data = MLA_compare, color = 'm')

#prettify using pyplot: https://matplotlib.org/api/pyplot_api.html
plt.title('Machine Learning Algorithm Accuracy Score \n')
plt.xlabel('Accuracy Score (%)')
plt.ylabel('Algorithm')

#IMPORTANT: This is a handmade model for learning purposes only.
#However, it is possible to create your own predictive model without a fancy algorithm :)

#coin flip model with random 1/survived 0/died

#iterate over dataFrame rows as (index, Series) pairs: https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.iterrows.html
for index, row in data1.iterrows():
#random number generator: https://docs.python.org/2/library/random.html
if random.random() > .5: # Random float x, 0.0 <= x < 1.0
#data1.set_value(index, 'Random_Predict', 1) #predict survived/1
data1.at[index, 'Random_Predict']= 1 #predict survived/1
else:
#data1.set_value(index, 'Random_Predict', 0) #predict died/0
data1.at[index, 'Random_Predict']= 0 #predict died/0

#score random guess of survival. Use shortcut 1 = Right Guess and 0 = Wrong Guess
#the mean of the column will then equal the accuracy
data1['Random_Score'] = 0 #assume prediction wrong
data1.loc[(data1['Survived'] == data1['Random_Predict']), 'Random_Score'] = 1 #set to 1 for correct prediction
print('Coin Flip Model Accuracy: {:.2f}%'.format(data1['Random_Score'].mean()*100))

#we can also use scikit's accuracy_score function to save us a few lines of code
#http://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html#sklearn.metrics.accuracy_score
print('Coin Flip Model Accuracy w/SciKit: {:.2f}%'.format(metrics.accuracy_score(data1['Survived'], data1['Random_Predict'])*100))

#group by or pivot table: https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.groupby.html
pivot_female = data1[data1.Sex=='female'].groupby(['Sex','Pclass', 'Embarked','FareBin'])['Survived'].mean()
print('Survival Decision Tree w/Female Node: \n',pivot_female)

pivot_male = data1[data1.Sex=='male'].groupby(['Sex','Title'])['Survived'].mean()
print('\n\nSurvival Decision Tree w/Male Node: \n',pivot_male)

#handmade data model using brain power (and Microsoft Excel Pivot Tables for quick calculations)
def mytree(df):

#initialize table to store predictions
Model = pd.DataFrame(data = {'Predict':[]})
male_title = ['Master'] #survived titles

for index, row in df.iterrows():

#Question 1: Were you on the Titanic; majority died
Model.loc[index, 'Predict'] = 0

#Question 2: Are you female; majority survived
if (df.loc[index, 'Sex'] == 'female'):
Model.loc[index, 'Predict'] = 1

#Question 3A Female - Class and Question 4 Embarked gain minimum information

#Question 5B Female - FareBin; set anything less than .5 in female node decision tree back to 0
if ((df.loc[index, 'Sex'] == 'female') &
(df.loc[index, 'Pclass'] == 3) &
(df.loc[index, 'Embarked'] == 'S') &
(df.loc[index, 'Fare'] > 8)

):
Model.loc[index, 'Predict'] = 0

#Question 3B Male: Title; set anything greater than .5 to 1 for majority survived
if ((df.loc[index, 'Sex'] == 'male') &
(df.loc[index, 'Title'] in male_title)
):
Model.loc[index, 'Predict'] = 1
return Model

#model data
Tree_Predict = mytree(data1)
print('Decision Tree Model Accuracy/Precision Score: {:.2f}%\n'.format(metrics.accuracy_score(data1['Survived'], Tree_Predict)*100))

#Accuracy Summary Report with http://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html#sklearn.metrics.classification_report
#Where recall score = (true positives)/(true positive + false negative) w/1 being best:http://scikit-learn.org/stable/modules/generated/sklearn.metrics.recall_score.html#sklearn.metrics.recall_score
#And F1 score = weighted average of precision and recall w/1 being best: http://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html#sklearn.metrics.f1_score
print(metrics.classification_report(data1['Survived'], Tree_Predict))

#Plot Accuracy Summary
#Credit: http://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html
import itertools
def plot_confusion_matrix(cm, classes,
normalize=False,
title='Confusion matrix',
cmap=plt.cm.Blues):
"""
This function prints and plots the confusion matrix.
Normalization can be applied by setting `normalize=True`.
"""
if normalize:
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
print("Normalized confusion matrix")
else:
print('Confusion matrix, without normalization')

print(cm)

plt.imshow(cm, interpolation='nearest', cmap=cmap)
plt.title(title)
plt.colorbar()
tick_marks = np.arange(len(classes))
plt.xticks(tick_marks, classes, rotation=45)
plt.yticks(tick_marks, classes)

fmt = '.2f' if normalize else 'd'
thresh = cm.max() / 2.
for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
plt.text(j, i, format(cm[i, j], fmt),
horizontalalignment="center",
color="white" if cm[i, j] > thresh else "black")

plt.tight_layout()
plt.ylabel('True label')
plt.xlabel('Predicted label')

# Compute confusion matrix
cnf_matrix = metrics.confusion_matrix(data1['Survived'], Tree_Predict)
np.set_printoptions(precision=2)

class_names = ['Dead', 'Survived']
# Plot non-normalized confusion matrix
plt.figure()
plot_confusion_matrix(cnf_matrix, classes=class_names,
title='Confusion matrix, without normalization')

# Plot normalized confusion matrix
plt.figure()
plot_confusion_matrix(cnf_matrix, classes=class_names, normalize=True,
title='Normalized confusion matrix')

#base model
dtree = tree.DecisionTreeClassifier(random_state = 0)
base_results = model_selection.cross_validate(dtree, data1[data1_x_bin], data1[Target], cv = cv_split)
dtree.fit(data1[data1_x_bin], data1[Target])

print('BEFORE DT Parameters: ', dtree.get_params())
#print("BEFORE DT Training w/bin score mean: {:.2f}". format(base_results['train_score'].mean()*100))
print("BEFORE DT Test w/bin score mean: {:.2f}". format(base_results['test_score'].mean()*100))
print("BEFORE DT Test w/bin score 3*std: +/- {:.2f}". format(base_results['test_score'].std()*100*3))
#print("BEFORE DT Test w/bin set score min: {:.2f}". format(base_results['test_score'].min()*100))
print('-'*10)

#tune hyper-parameters: http://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html#sklearn.tree.DecisionTreeClassifier
param_grid = {'criterion': ['gini', 'entropy'], #scoring methodology; two supported formulas for calculating information gain - default is gini
#'splitter': ['best', 'random'], #splitting methodology; two supported strategies - default is best
'max_depth': [2,4,6,8,10,None], #max depth tree can grow; default is none
#'min_samples_split': [2,5,10,.03,.05], #minimum subset size BEFORE new split (fraction is % of total); default is 2
#'min_samples_leaf': [1,5,10,.03,.05], #minimum subset size AFTER new split split (fraction is % of total); default is 1
#'max_features': [None, 'auto'], #max features to consider when performing split; default none or all
'random_state': [0] #seed or control random number generator: https://www.quora.com/What-is-seed-in-random-number-generation
}

#print(list(model_selection.ParameterGrid(param_grid)))

#choose best model with grid_search: #http://scikit-learn.org/stable/modules/grid_search.html#grid-search
#http://scikit-learn.org/stable/auto_examples/model_selection/plot_grid_search_digits.html
tune_model = model_selection.GridSearchCV(tree.DecisionTreeClassifier(), param_grid=param_grid, scoring = 'roc_auc', cv = cv_split)
tune_model.fit(data1[data1_x_bin], data1[Target])

#print(tune_model.cv_results_.keys())
#print(tune_model.cv_results_['params'])
print('AFTER DT Parameters: ', tune_model.best_params_)
#print(tune_model.cv_results_['mean_train_score'])
#print("AFTER DT Training w/bin score mean: {:.2f}". format(tune_model.cv_results_['mean_train_score'][tune_model.best_index_]*100))
#print(tune_model.cv_results_['mean_test_score'])
print("AFTER DT Test w/bin score mean: {:.2f}". format(tune_model.cv_results_['mean_test_score'][tune_model.best_index_]*100))
print("AFTER DT Test w/bin score 3*std: +/- {:.2f}". format(tune_model.cv_results_['std_test_score'][tune_model.best_index_]*100*3))
print('-'*10)

#duplicates gridsearchcv
#tune_results = model_selection.cross_validate(tune_model, data1[data1_x_bin], data1[Target], cv = cv_split)

#print('AFTER DT Parameters: ', tune_model.best_params_)
#print("AFTER DT Training w/bin set score mean: {:.2f}". format(tune_results['train_score'].mean()*100))
#print("AFTER DT Test w/bin set score mean: {:.2f}". format(tune_results['test_score'].mean()*100))
#print("AFTER DT Test w/bin set score min: {:.2f}". format(tune_results['test_score'].min()*100))
#print('-'*10)

#base model
print('BEFORE DT RFE Training Shape Old: ', data1[data1_x_bin].shape)
print('BEFORE DT RFE Training Columns Old: ', data1[data1_x_bin].columns.values)

# print("BEFORE DT RFE Training w/bin score mean: {:.2f}". format(base_results['train_score'].mean()*100))
print("BEFORE DT RFE Test w/bin score mean: {:.2f}". format(base_results['test_score'].mean()*100))
print("BEFORE DT RFE Test w/bin score 3*std: +/- {:.2f}". format(base_results['test_score'].std()*100*3))
print('-'*10)

#feature selection
dtree_rfe = feature_selection.RFECV(dtree, step = 1, scoring = 'accuracy', cv = cv_split)
dtree_rfe.fit(data1[data1_x_bin], data1[Target])

#transform x&y to reduced features and fit new model
#alternative: can use pipeline to reduce fit and transform steps: http://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html
X_rfe = data1[data1_x_bin].columns.values[dtree_rfe.get_support()]
rfe_results = model_selection.cross_validate(dtree, data1[X_rfe], data1[Target], cv = cv_split)

#print(dtree_rfe.grid_scores_)
print('AFTER DT RFE Training Shape New: ', data1[X_rfe].shape)
print('AFTER DT RFE Training Columns New: ', X_rfe)

# print("AFTER DT RFE Training w/bin score mean: {:.2f}". format(rfe_results['train_score'].mean()*100))
print("AFTER DT RFE Test w/bin score mean: {:.2f}". format(rfe_results['test_score'].mean()*100))
print("AFTER DT RFE Test w/bin score 3*std: +/- {:.2f}". format(rfe_results['test_score'].std()*100*3))
print('-'*10)


#tune rfe model
rfe_tune_model = model_selection.GridSearchCV(tree.DecisionTreeClassifier(), param_grid=param_grid, scoring = 'roc_auc', cv = cv_split)
rfe_tune_model.fit(data1[X_rfe], data1[Target])

#print(rfe_tune_model.cv_results_.keys())
#print(rfe_tune_model.cv_results_['params'])
print('AFTER DT RFE Tuned Parameters: ', rfe_tune_model.best_params_)
#print(rfe_tune_model.cv_results_['mean_train_score'])
# print("AFTER DT RFE Tuned Training w/bin score mean: {:.2f}". format(rfe_tune_model.cv_results_['mean_train_score'][tune_model.best_index_]*100))
#print(rfe_tune_model.cv_results_['mean_test_score'])
print("AFTER DT RFE Tuned Test w/bin score mean: {:.2f}". format(rfe_tune_model.cv_results_['mean_test_score'][tune_model.best_index_]*100))
print("AFTER DT RFE Tuned Test w/bin score 3*std: +/- {:.2f}". format(rfe_tune_model.cv_results_['std_test_score'][tune_model.best_index_]*100*3))
print('-'*10)

#Graph MLA version of Decision Tree: http://scikit-learn.org/stable/modules/generated/sklearn.tree.export_graphviz.html
import graphviz
dot_data = tree.export_graphviz(dtree, out_file=None,
feature_names = data1_x_bin, class_names = True,
filled = True, rounded = True)
graph = graphviz.Source(dot_data)
graph

#compare algorithm predictions with each other, where 1 = exactly similar and 0 = exactly opposite
#there are some 1's, but enough blues and light reds to create a "super algorithm" by combining them
correlation_heatmap(MLA_predict)

#why choose one model, when you can pick them all with voting classifier
#http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.VotingClassifier.html
#removed models w/o attribute 'predict_proba' required for vote classifier and models with a 1.0 correlation to another model
vote_est = [
#Ensemble Methods: http://scikit-learn.org/stable/modules/ensemble.html
('ada', ensemble.AdaBoostClassifier()),
('bc', ensemble.BaggingClassifier()),
('etc',ensemble.ExtraTreesClassifier()),
('gbc', ensemble.GradientBoostingClassifier()),
('rfc', ensemble.RandomForestClassifier()),

#Gaussian Processes: http://scikit-learn.org/stable/modules/gaussian_process.html#gaussian-process-classification-gpc
('gpc', gaussian_process.GaussianProcessClassifier()),

#GLM: http://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
('lr', linear_model.LogisticRegressionCV()),

#Navies Bayes: http://scikit-learn.org/stable/modules/naive_bayes.html
('bnb', naive_bayes.BernoulliNB()),
('gnb', naive_bayes.GaussianNB()),

#Nearest Neighbor: http://scikit-learn.org/stable/modules/neighbors.html
('knn', neighbors.KNeighborsClassifier()),

#SVM: http://scikit-learn.org/stable/modules/svm.html
('svc', svm.SVC(probability=True)),

#xgboost: http://xgboost.readthedocs.io/en/latest/model.html
('xgb', XGBClassifier())
]

#Hard Vote or majority rules
vote_hard = ensemble.VotingClassifier(estimators = vote_est , voting = 'hard')
vote_hard_cv = model_selection.cross_validate(vote_hard, data1[data1_x_bin], data1[Target], cv = cv_split)
vote_hard.fit(data1[data1_x_bin], data1[Target])

# print("Hard Voting Training w/bin score mean: {:.2f}". format(vote_hard_cv['train_score'].mean()*100))
print("Hard Voting Test w/bin score mean: {:.2f}". format(vote_hard_cv['test_score'].mean()*100))
print("Hard Voting Test w/bin score 3*std: +/- {:.2f}". format(vote_hard_cv['test_score'].std()*100*3))
print('-'*10)

#Soft Vote or weighted probabilities
vote_soft = ensemble.VotingClassifier(estimators = vote_est , voting = 'soft')
vote_soft_cv = model_selection.cross_validate(vote_soft, data1[data1_x_bin], data1[Target], cv = cv_split)
vote_soft.fit(data1[data1_x_bin], data1[Target])

# print("Soft Voting Training w/bin score mean: {:.2f}". format(vote_soft_cv['train_score'].mean()*100))
print("Soft Voting Test w/bin score mean: {:.2f}". format(vote_soft_cv['test_score'].mean()*100))
print("Soft Voting Test w/bin score 3*std: +/- {:.2f}". format(vote_soft_cv['test_score'].std()*100*3))
print('-'*10)

#WARNING: Running is very computational intensive and time expensive.
#Code is written for experimental/developmental purposes and not production ready!

#Hyperparameter Tune with GridSearchCV: http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html
grid_n_estimator = [10, 50, 100, 300]
grid_ratio = [.1, .25, .5, .75, 1.0]
grid_learn = [.01, .03, .05, .1, .25]
grid_max_depth = [2, 4, 6, 8, 10, None]
grid_min_samples = [5, 10, .03, .05, .10]
grid_criterion = ['gini', 'entropy']
grid_bool = [True, False]
grid_seed = [0]

grid_param = [
[{
#AdaBoostClassifier - http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.AdaBoostClassifier.html
'n_estimators': grid_n_estimator, #default=50
'learning_rate': grid_learn, #default=1
#'algorithm': ['SAMME', 'SAMME.R'], #default=’SAMME.R
'random_state': grid_seed
}],

[{
#BaggingClassifier - http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.BaggingClassifier.html#sklearn.ensemble.BaggingClassifier
'n_estimators': grid_n_estimator, #default=10
'max_samples': grid_ratio, #default=1.0
'random_state': grid_seed
}],

[{
#ExtraTreesClassifier - http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesClassifier.html#sklearn.ensemble.ExtraTreesClassifier
'n_estimators': grid_n_estimator, #default=10
'criterion': grid_criterion, #default=”gini”
'max_depth': grid_max_depth, #default=None
'random_state': grid_seed
}],

[{
#GradientBoostingClassifier - http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#sklearn.ensemble.GradientBoostingClassifier
#'loss': ['deviance', 'exponential'], #default=’deviance’
'learning_rate': [.05], #default=0.1 -- 12/31/17 set to reduce runtime -- The best parameter for GradientBoostingClassifier is {'learning_rate': 0.05, 'max_depth': 2, 'n_estimators': 300, 'random_state': 0} with a runtime of 264.45 seconds.
'n_estimators': [300], #default=100 -- 12/31/17 set to reduce runtime -- The best parameter for GradientBoostingClassifier is {'learning_rate': 0.05, 'max_depth': 2, 'n_estimators': 300, 'random_state': 0} with a runtime of 264.45 seconds.
#'criterion': ['friedman_mse', 'mse', 'mae'], #default=”friedman_mse”
'max_depth': grid_max_depth, #default=3
'random_state': grid_seed
}],

[{
#RandomForestClassifier - http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html#sklearn.ensemble.RandomForestClassifier
'n_estimators': grid_n_estimator, #default=10
'criterion': grid_criterion, #default=”gini”
'max_depth': grid_max_depth, #default=None
'oob_score': [True], #default=False -- 12/31/17 set to reduce runtime -- The best parameter for RandomForestClassifier is {'criterion': 'entropy', 'max_depth': 6, 'n_estimators': 100, 'oob_score': True, 'random_state': 0} with a runtime of 146.35 seconds.
'random_state': grid_seed
}],

[{
#GaussianProcessClassifier
'max_iter_predict': grid_n_estimator, #default: 100
'random_state': grid_seed
}],

[{
#LogisticRegressionCV - http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegressionCV.html#sklearn.linear_model.LogisticRegressionCV
'fit_intercept': grid_bool, #default: True
#'penalty': ['l1','l2'],
'solver': ['newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga'], #default: lbfgs
'random_state': grid_seed
}],

[{
#BernoulliNB - http://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.BernoulliNB.html#sklearn.naive_bayes.BernoulliNB
'alpha': grid_ratio, #default: 1.0
}],

#GaussianNB -
[{}],

[{
#KNeighborsClassifier - http://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#sklearn.neighbors.KNeighborsClassifier
'n_neighbors': [1,2,3,4,5,6,7], #default: 5
'weights': ['uniform', 'distance'], #default = ‘uniform’
'algorithm': ['auto', 'ball_tree', 'kd_tree', 'brute']
}],

[{
#SVC - http://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC
#http://blog.hackerearth.com/simple-tutorial-svm-parameter-tuning-python-r
#'kernel': ['linear', 'poly', 'rbf', 'sigmoid'],
'C': [1,2,3,4,5], #default=1.0
'gamma': grid_ratio, #edfault: auto
'decision_function_shape': ['ovo', 'ovr'], #default:ovr
'probability': [True],
'random_state': grid_seed
}],

[{
#XGBClassifier - http://xgboost.readthedocs.io/en/latest/parameter.html
'learning_rate': grid_learn, #default: .3
'max_depth': [1,2,4,6,8,10], #default 2
'n_estimators': grid_n_estimator,
'seed': grid_seed
}]
]

start_total = time.perf_counter() #https://docs.python.org/3/library/time.html#time.perf_counter
for clf, param in zip (vote_est, grid_param): #https://docs.python.org/3/library/functions.html#zip

#print(clf[1]) #vote_est is a list of tuples, index 0 is the name and index 1 is the algorithm
#print(param)
start = time.perf_counter()
best_search = model_selection.GridSearchCV(estimator = clf[1], param_grid = param, cv = cv_split, scoring = 'roc_auc')
best_search.fit(data1[data1_x_bin], data1[Target])
run = time.perf_counter() - start

best_param = best_search.best_params_
print('The best parameter for {} is {} with a runtime of {:.2f} seconds.'.format(clf[1].__class__.__name__, best_param, run))
clf[1].set_params(**best_param)


run_total = time.perf_counter() - start_total
print('Total optimization time was {:.2f} minutes.'.format(run_total/60))

print('-'*10)

#Hard Vote or majority rules w/Tuned Hyperparameters
grid_hard = ensemble.VotingClassifier(estimators = vote_est , voting = 'hard')
grid_hard_cv = model_selection.cross_validate(grid_hard, data1[data1_x_bin], data1[Target], cv = cv_split)
grid_hard.fit(data1[data1_x_bin], data1[Target])

# print("Hard Voting w/Tuned Hyperparameters Training w/bin score mean: {:.2f}". format(grid_hard_cv['train_score'].mean()*100))
print("Hard Voting w/Tuned Hyperparameters Test w/bin score mean: {:.2f}". format(grid_hard_cv['test_score'].mean()*100))
print("Hard Voting w/Tuned Hyperparameters Test w/bin score 3*std: +/- {:.2f}". format(grid_hard_cv['test_score'].std()*100*3))
print('-'*10)

#Soft Vote or weighted probabilities w/Tuned Hyperparameters
grid_soft = ensemble.VotingClassifier(estimators = vote_est , voting = 'soft')
grid_soft_cv = model_selection.cross_validate(grid_soft, data1[data1_x_bin], data1[Target], cv = cv_split)
grid_soft.fit(data1[data1_x_bin], data1[Target])

# print("Soft Voting w/Tuned Hyperparameters Training w/bin score mean: {:.2f}". format(grid_soft_cv['train_score'].mean()*100))
print("Soft Voting w/Tuned Hyperparameters Test w/bin score mean: {:.2f}". format(grid_soft_cv['test_score'].mean()*100))
print("Soft Voting w/Tuned Hyperparameters Test w/bin score 3*std: +/- {:.2f}". format(grid_soft_cv['test_score'].std()*100*3))
print('-'*10)

#prepare data for modeling
print(data_val.info())
print("-"*10)
#data_val.sample(10)

#handmade decision tree - submission score = 0.77990
data_val['Survived'] = mytree(data_val).astype(int)

data_val['Survived'] = grid_hard.predict(data_val[data1_x_bin])

#submit file
submit = data_val[['PassengerId','Survived']]
submit.to_csv("submit.csv", index=False)

print('Validation Data Distribution: \n', data_val['Survived'].value_counts(normalize = True))
submit.sample(10)

[結果]

????

正解率77.75%となりました。

ごく平凡な結果です。修正した箇所がよくなかったのかもしれません。


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