Echo Pod KNN


In [1]:
import pandas
import numpy as np
import scipy.stats as sp
from sklearn import neighbors
from sklearn.neighbors import DistanceMetric
from pprint import pprint

titanic_data = pandas.read_csv("train.csv", header=0)
titanic_data.head(5)


Out[1]:
PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked
0 1 0 3 Braund, Mr. Owen Harris male 22.0 1 0 A/5 21171 7.2500 NaN S
1 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 0 PC 17599 71.2833 C85 C
2 3 1 3 Heikkinen, Miss. Laina female 26.0 0 0 STON/O2. 3101282 7.9250 NaN S
3 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 0 113803 53.1000 C123 S
4 5 0 3 Allen, Mr. William Henry male 35.0 0 0 373450 8.0500 NaN S

Removing Excess Columns


In [2]:
titanic_data.drop(['Name', 'Ticket', 'Cabin'], axis=1, inplace=True)
titanic_data.head(1)


Out[2]:
PassengerId Survived Pclass Sex Age SibSp Parch Fare Embarked
0 1 0 3 male 22.0 1 0 7.25 S

In [3]:
titanic_data.describe()


Out[3]:
PassengerId Survived Pclass Age SibSp Parch Fare
count 891.000000 891.000000 891.000000 714.000000 891.000000 891.000000 891.000000
mean 446.000000 0.383838 2.308642 29.699118 0.523008 0.381594 32.204208
std 257.353842 0.486592 0.836071 14.526497 1.102743 0.806057 49.693429
min 1.000000 0.000000 1.000000 0.420000 0.000000 0.000000 0.000000
25% 223.500000 0.000000 2.000000 20.125000 0.000000 0.000000 7.910400
50% 446.000000 0.000000 3.000000 28.000000 0.000000 0.000000 14.454200
75% 668.500000 1.000000 3.000000 38.000000 1.000000 0.000000 31.000000
max 891.000000 1.000000 3.000000 80.000000 8.000000 6.000000 512.329200

In [4]:
titanic_data.info()


<class 'pandas.core.frame.DataFrame'>
RangeIndex: 891 entries, 0 to 890
Data columns (total 9 columns):
PassengerId    891 non-null int64
Survived       891 non-null int64
Pclass         891 non-null int64
Sex            891 non-null object
Age            714 non-null float64
SibSp          891 non-null int64
Parch          891 non-null int64
Fare           891 non-null float64
Embarked       889 non-null object
dtypes: float64(2), int64(5), object(2)
memory usage: 62.7+ KB

Pre-Processing

Taking discrete values and making them integers.


In [5]:
titanic_data['Embarked'].unique()
titanic_data['Port'] = titanic_data['Embarked'].map({'C':1, 'S':2, 'Q':3}).astype(float)
titanic_data['Sex'].unique()
titanic_data['Gender'] = titanic_data['Sex'].map({'female': 0, 'male': 1}).astype(int)
titanic_data = titanic_data.drop(['Sex', 'Embarked'], axis=1)
titanic_data.head(5)


Out[5]:
PassengerId Survived Pclass Age SibSp Parch Fare Port Gender
0 1 0 3 22.0 1 0 7.2500 2.0 1
1 2 1 1 38.0 1 0 71.2833 1.0 0
2 3 1 3 26.0 0 0 7.9250 2.0 0
3 4 1 1 35.0 1 0 53.1000 2.0 0
4 5 0 3 35.0 0 0 8.0500 2.0 1

In [6]:
cols = titanic_data.columns.tolist()
print(cols)
# cols = [cols[1]] + cols[0:1] + cols[2:]
# print(cols)
titanic_data = titanic_data[cols]
# print(titanic_data.head(5))
train_data = titanic_data[cols[2: ]]
train_target = titanic_data[cols[1]]
# print(train_target.head(5))
print(train_data, train_target)

pprint('column_list: {0}'.format(cols))


['PassengerId', 'Survived', 'Pclass', 'Age', 'SibSp', 'Parch', 'Fare', 'Port', 'Gender']
     Pclass   Age  SibSp  Parch      Fare  Port  Gender
0         3  22.0      1      0    7.2500   2.0       1
1         1  38.0      1      0   71.2833   1.0       0
2         3  26.0      0      0    7.9250   2.0       0
3         1  35.0      1      0   53.1000   2.0       0
4         3  35.0      0      0    8.0500   2.0       1
5         3   NaN      0      0    8.4583   3.0       1
6         1  54.0      0      0   51.8625   2.0       1
7         3   2.0      3      1   21.0750   2.0       1
8         3  27.0      0      2   11.1333   2.0       0
9         2  14.0      1      0   30.0708   1.0       0
10        3   4.0      1      1   16.7000   2.0       0
11        1  58.0      0      0   26.5500   2.0       0
12        3  20.0      0      0    8.0500   2.0       1
13        3  39.0      1      5   31.2750   2.0       1
14        3  14.0      0      0    7.8542   2.0       0
15        2  55.0      0      0   16.0000   2.0       0
16        3   2.0      4      1   29.1250   3.0       1
17        2   NaN      0      0   13.0000   2.0       1
18        3  31.0      1      0   18.0000   2.0       0
19        3   NaN      0      0    7.2250   1.0       0
20        2  35.0      0      0   26.0000   2.0       1
21        2  34.0      0      0   13.0000   2.0       1
22        3  15.0      0      0    8.0292   3.0       0
23        1  28.0      0      0   35.5000   2.0       1
24        3   8.0      3      1   21.0750   2.0       0
25        3  38.0      1      5   31.3875   2.0       0
26        3   NaN      0      0    7.2250   1.0       1
27        1  19.0      3      2  263.0000   2.0       1
28        3   NaN      0      0    7.8792   3.0       0
29        3   NaN      0      0    7.8958   2.0       1
..      ...   ...    ...    ...       ...   ...     ...
861       2  21.0      1      0   11.5000   2.0       1
862       1  48.0      0      0   25.9292   2.0       0
863       3   NaN      8      2   69.5500   2.0       0
864       2  24.0      0      0   13.0000   2.0       1
865       2  42.0      0      0   13.0000   2.0       0
866       2  27.0      1      0   13.8583   1.0       0
867       1  31.0      0      0   50.4958   2.0       1
868       3   NaN      0      0    9.5000   2.0       1
869       3   4.0      1      1   11.1333   2.0       1
870       3  26.0      0      0    7.8958   2.0       1
871       1  47.0      1      1   52.5542   2.0       0
872       1  33.0      0      0    5.0000   2.0       1
873       3  47.0      0      0    9.0000   2.0       1
874       2  28.0      1      0   24.0000   1.0       0
875       3  15.0      0      0    7.2250   1.0       0
876       3  20.0      0      0    9.8458   2.0       1
877       3  19.0      0      0    7.8958   2.0       1
878       3   NaN      0      0    7.8958   2.0       1
879       1  56.0      0      1   83.1583   1.0       0
880       2  25.0      0      1   26.0000   2.0       0
881       3  33.0      0      0    7.8958   2.0       1
882       3  22.0      0      0   10.5167   2.0       0
883       2  28.0      0      0   10.5000   2.0       1
884       3  25.0      0      0    7.0500   2.0       1
885       3  39.0      0      5   29.1250   3.0       0
886       2  27.0      0      0   13.0000   2.0       1
887       1  19.0      0      0   30.0000   2.0       0
888       3   NaN      1      2   23.4500   2.0       0
889       1  26.0      0      0   30.0000   1.0       1
890       3  32.0      0      0    7.7500   3.0       1

[891 rows x 7 columns] 0      0
1      1
2      1
3      1
4      0
5      0
6      0
7      0
8      1
9      1
10     1
11     1
12     0
13     0
14     0
15     1
16     0
17     1
18     0
19     1
20     0
21     1
22     1
23     1
24     0
25     1
26     0
27     0
28     1
29     0
      ..
861    0
862    1
863    0
864    0
865    1
866    1
867    0
868    0
869    1
870    0
871    1
872    0
873    0
874    1
875    1
876    0
877    0
878    0
879    1
880    1
881    0
882    0
883    0
884    0
885    0
886    0
887    1
888    0
889    1
890    0
Name: Survived, dtype: int64
("column_list: ['PassengerId', 'Survived', 'Pclass', 'Age', 'SibSp', 'Parch', "
 "'Fare', 'Port', 'Gender']")

In [7]:
df_test = pandas.read_csv('test.csv')

df_test = df_test.drop(['Name', 'Ticket', 'Cabin'], axis=1)


df_test['Gender'] = df_test['Sex'].map({'female': 0, 'male': 1}).astype(int)
df_test['Port'] = df_test['Embarked'].map({'C':1, 'S':2, 'Q':3}).astype(int)


ids = df_test.PassengerId.values

df_test = df_test.drop(['Sex', 'Embarked', 'PassengerId'], axis=1)
df_test.Fare.fillna(np.mean(df_test.Fare), inplace=True)
df_test.Age.fillna(np.mean(df_test.Age), inplace=True)
df_test.info()
train_data.info()
test_data = df_test.values
test_data


<class 'pandas.core.frame.DataFrame'>
RangeIndex: 418 entries, 0 to 417
Data columns (total 7 columns):
Pclass    418 non-null int64
Age       418 non-null float64
SibSp     418 non-null int64
Parch     418 non-null int64
Fare      418 non-null float64
Gender    418 non-null int64
Port      418 non-null int64
dtypes: float64(2), int64(5)
memory usage: 22.9 KB
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 891 entries, 0 to 890
Data columns (total 7 columns):
Pclass    891 non-null int64
Age       714 non-null float64
SibSp     891 non-null int64
Parch     891 non-null int64
Fare      891 non-null float64
Port      889 non-null float64
Gender    891 non-null int64
dtypes: float64(3), int64(4)
memory usage: 48.8 KB
Out[7]:
array([[  3.        ,  34.5       ,   0.        , ...,   7.8292    ,
          1.        ,   3.        ],
       [  3.        ,  47.        ,   1.        , ...,   7.        ,
          0.        ,   2.        ],
       [  2.        ,  62.        ,   0.        , ...,   9.6875    ,
          1.        ,   3.        ],
       ..., 
       [  3.        ,  38.5       ,   0.        , ...,   7.25      ,
          1.        ,   2.        ],
       [  3.        ,  30.27259036,   0.        , ...,   8.05      ,
          1.        ,   2.        ],
       [  3.        ,  30.27259036,   1.        , ...,  22.3583    ,
          1.        ,   1.        ]])

Normalize & Fill


In [8]:
titanic_data.Age.fillna(np.mean(titanic_data.Age), inplace=True)
titanic_data.Port.fillna(3.0, inplace=True)

train_data = titanic_data[cols[2: ]]
train_target = titanic_data[cols[1]]

print(titanic_data.Age)                        
titanic_data.info()


0      22.000000
1      38.000000
2      26.000000
3      35.000000
4      35.000000
5      29.699118
6      54.000000
7       2.000000
8      27.000000
9      14.000000
10      4.000000
11     58.000000
12     20.000000
13     39.000000
14     14.000000
15     55.000000
16      2.000000
17     29.699118
18     31.000000
19     29.699118
20     35.000000
21     34.000000
22     15.000000
23     28.000000
24      8.000000
25     38.000000
26     29.699118
27     19.000000
28     29.699118
29     29.699118
         ...    
861    21.000000
862    48.000000
863    29.699118
864    24.000000
865    42.000000
866    27.000000
867    31.000000
868    29.699118
869     4.000000
870    26.000000
871    47.000000
872    33.000000
873    47.000000
874    28.000000
875    15.000000
876    20.000000
877    19.000000
878    29.699118
879    56.000000
880    25.000000
881    33.000000
882    22.000000
883    28.000000
884    25.000000
885    39.000000
886    27.000000
887    19.000000
888    29.699118
889    26.000000
890    32.000000
Name: Age, dtype: float64
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 891 entries, 0 to 890
Data columns (total 9 columns):
PassengerId    891 non-null int64
Survived       891 non-null int64
Pclass         891 non-null int64
Age            891 non-null float64
SibSp          891 non-null int64
Parch          891 non-null int64
Fare           891 non-null float64
Port           891 non-null float64
Gender         891 non-null int64
dtypes: float64(3), int64(6)
memory usage: 62.7 KB

SciKit Learn


In [9]:
model = neighbors.KNeighborsClassifier()
print(train_data.values)
train_data.info()
print(train_target.values)
print(train_data.values)
model.fit(train_data.values, train_target.values)
print(model.fit)

output = model.predict(test_data).astype(int)
print(output)

result = np.c_[ids.astype(int), output]
print(result)

df_result = pandas.DataFrame(result[:,0:2], columns=['PassengerId', 'Survived'])
df_result.to_csv('titanic.csv', index=False)


[[  3.          22.           1.         ...,   7.25         2.           1.        ]
 [  1.          38.           1.         ...,  71.2833       1.           0.        ]
 [  3.          26.           0.         ...,   7.925        2.           0.        ]
 ..., 
 [  3.          29.69911765   1.         ...,  23.45         2.           0.        ]
 [  1.          26.           0.         ...,  30.           1.           1.        ]
 [  3.          32.           0.         ...,   7.75         3.           1.        ]]
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 891 entries, 0 to 890
Data columns (total 7 columns):
Pclass    891 non-null int64
Age       891 non-null float64
SibSp     891 non-null int64
Parch     891 non-null int64
Fare      891 non-null float64
Port      891 non-null float64
Gender    891 non-null int64
dtypes: float64(3), int64(4)
memory usage: 48.8 KB
[0 1 1 1 0 0 0 0 1 1 1 1 0 0 0 1 0 1 0 1 0 1 1 1 0 1 0 0 1 0 0 1 1 0 0 0 1
 0 0 1 0 0 0 1 1 0 0 1 0 0 0 0 1 1 0 1 1 0 1 0 0 1 0 0 0 1 1 0 1 0 0 0 0 0
 1 0 0 0 1 1 0 1 1 0 1 1 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 1 0 1 0
 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 1 0 0 0 0 1 0 0 1 0 0 0 0 1 1 0 0 0 1 0
 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 1 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1
 0 1 1 0 0 1 0 1 1 1 1 0 0 1 0 0 0 0 0 1 0 0 1 1 1 0 1 0 0 0 1 1 0 1 0 1 0
 0 0 1 0 1 0 0 0 1 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 1 1 0 0 0 0 0 0 1 1 1 1
 1 0 1 0 0 0 0 0 1 1 1 0 1 1 0 1 1 0 0 0 1 0 0 0 1 0 0 1 0 1 1 1 1 0 0 0 0
 0 0 1 1 1 1 0 1 0 1 1 1 0 1 1 1 0 0 0 1 1 0 1 1 0 0 1 1 0 1 0 1 1 1 1 0 0
 0 1 0 0 1 1 0 1 1 0 0 0 1 1 1 1 0 0 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0 1 1 1 1
 1 0 0 0 0 1 1 0 0 0 1 1 0 1 0 0 0 1 0 1 1 1 0 1 1 0 0 0 0 1 1 0 0 0 0 0 0
 1 0 0 0 0 1 0 1 0 1 1 0 0 0 0 0 0 0 0 1 1 0 1 1 1 1 0 0 1 0 1 0 0 1 0 0 1
 1 1 1 1 1 1 0 0 0 1 0 1 0 1 1 0 1 0 0 0 0 0 0 0 0 1 0 0 1 1 0 0 0 0 0 1 0
 0 0 1 1 0 1 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 1 1 0 1 1 0 1 1 0 0 1 0
 1 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 1 0 1 0 1 1 0 0 1 0 0 1 1 0 1 1 0 0 1 1
 0 1 0 1 1 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 1 1 0 1 1 1 0 0 0 1 0 1 0 0 0 1
 0 0 0 0 1 0 0 1 1 0 0 0 1 0 0 1 1 1 0 0 1 0 0 1 0 0 1 0 0 1 1 0 0 0 0 1 0
 0 1 0 1 0 0 1 0 0 0 0 0 1 0 1 1 1 0 1 0 1 0 1 0 1 0 0 0 0 0 0 1 0 0 0 1 0
 0 0 0 1 1 0 0 1 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 1 0 0 1 1 0
 0 0 0 1 1 1 1 1 0 1 0 0 0 1 1 0 0 1 0 0 0 1 0 1 1 0 0 1 0 0 0 0 0 0 1 0 0
 1 0 1 0 1 0 0 1 0 0 1 1 0 0 1 1 0 0 0 1 0 0 1 1 0 1 0 0 0 0 0 0 0 0 1 0 0
 1 0 1 1 1 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1 1 0 0 0 1 1 1 1 0 0 0 0 1 0 0 0 0
 0 0 0 0 0 0 1 1 0 1 0 0 0 1 1 1 1 1 0 0 0 1 0 0 1 1 0 0 1 0 0 0 0 0 0 1 0
 0 0 1 0 1 1 1 1 0 0 0 1 0 0 1 1 0 0 1 0 1 0 0 1 1 0 0 0 1 1 0 0 0 0 0 0 1
 0 1 0]
[[  3.          22.           1.         ...,   7.25         2.           1.        ]
 [  1.          38.           1.         ...,  71.2833       1.           0.        ]
 [  3.          26.           0.         ...,   7.925        2.           0.        ]
 ..., 
 [  3.          29.69911765   1.         ...,  23.45         2.           0.        ]
 [  1.          26.           0.         ...,  30.           1.           1.        ]
 [  3.          32.           0.         ...,   7.75         3.           1.        ]]
<bound method SupervisedIntegerMixin.fit of KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
           metric_params=None, n_jobs=1, n_neighbors=5, p=2,
           weights='uniform')>
[0 0 0 0 0 0 0 1 0 1 0 0 1 0 1 1 0 0 0 0 0 1 1 1 1 0 1 0 0 0 1 1 0 0 1 0 0
 0 0 1 0 0 0 0 1 0 0 0 1 0 1 0 1 1 0 0 0 0 0 1 0 1 0 0 1 0 0 0 0 1 0 0 0 0
 1 0 0 0 0 0 1 1 1 0 0 0 1 0 0 1 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0
 0 1 0 1 0 0 1 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0
 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 1 0 1
 0 1 0 0 0 1 0 1 0 0 0 1 0 0 0 0 1 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0
 0 0 0 1 0 0 0 0 1 1 0 0 1 0 1 0 0 0 0 0 1 0 0 1 0 0 0 0 1 0 1 0 0 0 0 0 0
 0 0 0 0 1 0 0 0 0 0 0 1 0 1 0 0 1 0 0 0 0 0 1 0 1 1 0 0 1 0 0 0 0 0 1 0 0
 1 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 1 0 1 0 1 1 0 0 0 1 1 0 0 1 0 0 0 0 0
 0 1 0 1 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0 0 1 1 1 1 0 0 0 0 0 0 0 1 0 0 0 0 0
 0 1 0 1 1 1 0 0 0 0 0 0 1 0 0 1 0 0 0 0 1 0 0 0 1 1 0 1 0 0 1 0 1 0 0 0 0
 1 0 1 0 1 0 0 1 0 0 0]
[[ 892    0]
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