2018 동계 머신러닝 세미나

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2019년도 모바일 엑스 동아리 기계학습 세미나 1주차 자료 : 파일:2019 MobileX ML ch01 sem01(머신러닝환경구축).key.zip

# Jupiter Tutorial
## 1.What is Jupyter Notebook?
## 2.Live Code Example
## 3.Equation
## 4.Visualization
### 4.1 image
### 4.2 video 
### 4.3 table
### 4.4 chart
## 5.Performance check

1. What is Jupiter Notebook?

The Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and explanatory text. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, machine learning and much more.

2. Live Code Example

3+4

def add(a,b):
	return a+b

add(3,4)

3. Equation 

#sqrt(x^2+y^2+z^2)

This expression $\sqrt{x^2 + y^2 + z^2}$ is an example of a TeX inline equation

[More Examples click here](http://jupyter-notebook.readthedocs.io/en/stable/examples/Notebook/Typesetting%20Equations.html)


4. Image

from IPython.display import Image
Image (url="http://localhost:8888/static/base/images/logo.png?v=641991992878ee24c6f3826e81054a0f",width=200,height=50)

Video

from IPython.display import YouTubeVideo
YouTubeVideo ('vJwrhhL5f8Y')

Table

import pandas as pd

url="https://raw.githubusercontent.com/cs109/2014_data/master/countries.csv"
pd.read_csv(url, nrows=10)


Chart

import matplotlib.pyplot as plt
plt.plot([1,2,3,4])
plt.ylabel('result')
plt.show()

5. Performance check

%timeit 3+4

%tiemit add(3,4)

%%timeit
a=[1,2,3]
a=[x+1 for x in a]

%%timeit
b=[1,2,3]
for i in range(len(b)):
	b[i]= b[i]+i

import pandas as pd

train = pd.read_csv('input/train.csv')
test = pd.read_csv('input/test.csv')

import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
sns.set()  # setting seaborn default for plots

def bar_chart(feature) :
    survived = train[train['Survived']==1][feature].value_counts()
    dead = train[train["Survived"]==0][feature].value_counts()
    df = pd.DataFrame( [survived, dead] )
    df.index = [ 'Survived', 'Dead' ]
    df.plot(kind='bar', stacked=True, figsize=(10, 5))

Pclass1 = train[train['Pclass']==1]['Embarked'].value_counts()
Pclass2 = train[train['Pclass']==2]['Embarked'].value_counts()
Pclass3 = train[train['Pclass']==3]['Embarked'].value_counts()
df = pd.DataFrame([Pclass1, Pclass2, Pclass3])
df.index = ['1st class','2nd class', '3rd class']
df.plot(kind='bar',stacked=True, figsize=(10,5))

for dataset in train_test_data:
    dataset['Embarked'] = dataset['Embarked'].fillna('S')

embarked_mapping = {"S": 0, "C": 1, "Q": 2}
for dataset in train_test_data:
    dataset['Embarked'] = dataset['Embarked'].map(embarked_mapping)

train["Fare"].fillna(train.groupby("Pclass")["Fare"].transform("median"), inplace=True)
test["Fare"].fillna(test.groupby("Pclass")["Fare"].transform("median"), inplace=True)
train.head(50)

facet = sns.FacetGrid(train, hue="Survived",aspect=4)
facet.map(sns.kdeplot,'Fare',shade= True)
facet.set(xlim=(0, train['Fare'].max()))
facet.add_legend()

for dataset in train_test_data:
    dataset.loc[ dataset['Fare'] <= 17, 'Fare'] = 0,
    dataset.loc[(dataset['Fare'] > 17) & (dataset['Fare'] <= 30), 'Fare'] = 1,
    dataset.loc[(dataset['Fare'] > 30) & (dataset['Fare'] <= 100), 'Fare'] = 2,
    dataset.loc[ dataset['Fare'] > 100, 'Fare'] = 3

Pclass1 = train[train['Pclass']==1]['Cabin'].value_counts()
Pclass2 = train[train['Pclass']==2]['Cabin'].value_counts()
Pclass3 = train[train['Pclass']==3]['Cabin'].value_counts()
df = pd.DataFrame([Pclass1, Pclass2, Pclass3])
df.index = ['1st class','2nd class', '3rd class']
df.plot(kind='bar',stacked=True, figsize=(10,5))

cabin_mapping = {"A": 0, "B": 0.4, "C": 0.8, "D": 1.2, "E": 1.6, "F": 2, "G": 2.4, "T": 2.8}
for dataset in train_test_data:
    dataset['Cabin'] = dataset['Cabin'].map(cabin_mapping)

train["Cabin"].fillna(train.groupby("Pclass")["Cabin"].transform("median"), inplace=True)
test["Cabin"].fillna(test.groupby("Pclass")["Cabin"].transform("median"), inplace=True)

facet = sns.FacetGrid(train, hue="Survived",aspect=4)
facet.map(sns.kdeplot,'FamilySize',shade= True)
facet.set(xlim=(0, train['FamilySize'].max()))
facet.add_legend()
plt.xlim(0) 

family_mapping = {1: 0, 2: 0.4, 3: 0.8, 4: 1.2, 5: 1.6, 6: 2, 7: 2.4, 8: 2.8, 9: 3.2, 10: 3.6, 11: 4}
for dataset in train_test_data:
    dataset['FamilySize'] = dataset['FamilySize'].map(family_mapping)

features_drop = ['Ticket', 'SibSp', 'Parch']
train = train.drop(features_drop, axis=1)
test = test.drop(features_drop, axis=1)
train = train.drop(['PassengerId'], axis=1)
train_data = train.drop('Survived', axis=1)
target = train['Survived']

from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC

import numpy as np

from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
k_fold = KFold(n_splits=10, shuffle=True, random_state=0)

clf = KNeighborsClassifier(n_neighbors = 13)
scoring = 'accuracy'
score = cross_val_score(clf, train_data, target, cv=k_fold, n_jobs=1, scoring=scoring)
print(score)

clf = DecisionTreeClassifier()
scoring = 'accuracy'
score = cross_val_score(clf, train_data, target, cv=k_fold, n_jobs=1, scoring=scoring)
print(score)

clf  = RandomForestClassifier(n_estimators=13)
scoring = 'accuracy'
score = cross_val_score(clf, train_data, target, cv=k_fold, n_jobs=1, scoring=scoring)
print(score)

clf = GaussianNB()
scoring = 'accuracy'
score = cross_val_score(clf, train_data, target, cv=k_fold, n_jobs=1, scoring=scoring)
print(score)

clf = SVC()
scoring = 'accuracy'
score = cross_val_score(clf, train_data, target, cv=k_fold, n_jobs=1, scoring=scoring)
print(score)

clf = SVC()
clf.fit(train_data, target)

test_data = test.drop("PassengerId", axis=1).copy()
prediction = clf.predict(test_data)

submission = pd.DataFrame({
        "PassengerId": test["PassengerId"],
        "Survived": prediction
    })

submission.to_csv('submission.csv', index=False)
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