d <- na.omit(read.csv('https://stats.dip.jp/01_ds/data/titanic_data_jp.csv'))
n <- nrow(d)

library(DT)
datatable(d, options = list(pageLength = 5))

COL <- c(rgb(255,   0,   0,  105, max = 255),
         rgb(  0,   0, 255,  105, max = 255),
         rgb(  0, 155,   0,  105, max = 255),
         rgb(100, 100, 100,   55, max = 255))

library(rpart)
library(rpart.plot)
tree <- rpart(生死 ~ 年齢 + 性別 + 客室等級 + 運賃 + 兄弟配偶者数 + 親子数 + 乗船地,
              data = d, method = 'class', cp = 0.04)
rpart.plot(tree, type = 5)

rpart.plot(tree, branch.type = 5)

plotcp(tree)

plotcp(tree)

tree2 <- prune(tree, cp = 0.5)

rpart.plot(tree2, branch.type = 5)

tree3 <- prune(tree, cp = 0.07)

rpart.plot(tree3, branch.type = 5)

#rpart.predict(tree, rules = T,
#              newdata = data.frame(年齢         = 5,
#                                   性別         = '女性', 
#                                   客室等級     = '3等', 
#                                   運賃         = 10,
#                                   兄弟配偶者数 = 5,
#                                   親子数       = 7,
#                                   乗船地       = 'S'))

d.new <- data.frame(年齢         = 5,
                    性別         = '女性', 
                    客室等級     = '3等', 
                    運賃         = 10,
                    兄弟配偶者数 = 5,
                    親子数       = 7,
                    乗船地       = 'S')

rpart.predict(tree, rules = T, newdata = d.new)

##        死亡      生存                     
## 1 0.2452107 0.7547893 because 性別 is 女性

xs <- c(1, 2, 1, 2)
ys <- c(1, 1, 2, 2)

d0 <- data.frame(x = c(xs, xs,   xs + 5, xs + 5),
                 y = c(ys, ys + 5, ys + 3, ys + 9),
                 z = c(rep(1, 4), rep(2, 4), rep(3, 4), rep(4, 4)))

matplot(x = d0$x, y = d0$y, pch = 1, cex = 2, col = COL[1])
text(d0$x, d0$y, d0$z)

tree <- rpart(z ~ x + y, data = d0, minsplit = 2, minbucket = 1)
rpart.plot(tree)

matplot(x = d0$x, y = d0$y, pch = 1, cex = 2, col = COL[1])
segments(4, 0, 4,12, lty = 3, col = COL[2])
segments(4, 8, 8, 8, lty = 3, col = COL[2])
segments(0, 4, 4, 4, lty = 3, col = COL[2])
text(d0$x, d0$y, d0$z)

plotcp(tree)

tree <- rpart(年齢 ~ 性別 + 客室等級 + 運賃 + 兄弟配偶者数 + 親子数 + 乗船地, data = d)

rpart.plot(tree, type = 5)

rpart.plot(tree, branch.type = 5)

import pandas as pd
from sklearn import tree
from sklearn.tree import plot_tree

d = pd.read_csv('https://stats.dip.jp/01_ds/data/titanic_data.csv').dropna(axis = 'rows')
d

##      PassengerId  Survived  Pclass       Name  ... SibSp Parch     Fare  Embarked
## 0              1         0       3     Braund  ...     1     0   7.2500         S
## 1              2         1       1    Cumings  ...     1     0  71.2833         C
## 2              3         1       3  Heikkinen  ...     0     0   7.9250         S
## 3              4         1       1   Futrelle  ...     1     0  53.1000         S
## 4              5         0       3      Allen  ...     0     0   8.0500         S
## ..           ...       ...     ...        ...  ...   ...   ...      ...       ...
## 886          887         0       2   Montvila  ...     0     0  13.0000         S
## 887          888         1       1     Graham  ...     0     0  30.0000         S
## 888          889         0       3   Johnston  ...     1     2  23.4500         S
## 889          890         1       1       Behr  ...     0     0  30.0000         C
## 890          891         0       3     Dooley  ...     0     0   7.7500         Q
## 
## [889 rows x 10 columns]

d.columns

## Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',
##        'Parch', 'Fare', 'Embarked'],
##       dtype='object')

y = d['Survived']
x = d[['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked']]

x = pd.get_dummies(x)
y

## 0      0
## 1      1
## 2      1
## 3      1
## 4      0
##       ..
## 886    0
## 887    1
## 888    0
## 889    1
## 890    0
## Name: Survived, Length: 889, dtype: int64

x

##      Pclass  SibSp  ...  Embarked_       Q  Embarked_       S
## 0         3      1  ...              False               True
## 1         1      1  ...              False              False
## 2         3      0  ...              False               True
## 3         1      1  ...              False               True
## 4         3      0  ...              False               True
## ..      ...    ...  ...                ...                ...
## 886       2      0  ...              False               True
## 887       1      0  ...              False               True
## 888       3      1  ...              False               True
## 889       1      0  ...              False              False
## 890       3      0  ...               True              False
## 
## [889 rows x 98 columns]

model = tree.DecisionTreeClassifier(max_depth = 3, random_state = 0)
model.fit(x, y)

DecisionTreeClassifier(max_depth=3, random_state=0)

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plot_tree(model, feature_names = x.columns.tolist(), class_names = ['male', 'female'], filled = True)