05.04 - PARTICIPATE IN KAGGLE

05.04 - PARTICIPATE IN KAGGLE#

!wget --no-cache -O init.py -q https://raw.githubusercontent.com/rramosp/ai4eng.v1/main/content/init.py
import init; init.init(force_download=False); init.get_weblink()

import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
import local.lib.mlutils
import pandas as pd
from tensorflow.keras import Sequential
from tensorflow.keras.layers import Dense, Dropout, Flatten
%matplotlib inline

We use Titanic data in Kaggle#

  • Register to Kaggle

  • Enter the competition Titanic Data at Kaggle

  • Download the train.csv and test.csv files

  • UPLOAD THE FILES to your notebook environment (in Colab, open the Files tab and upload)

d = pd.read_csv("train.csv")
print (d.shape)

(891, 12)

d.head()
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

Understand NaN values are present

for i in d.columns:
    print ("%20s"%i, np.sum(d[i].isna()))

         PassengerId 0
            Survived 0
              Pclass 0
                Name 0
                 Sex 0
                 Age 177
               SibSp 0
               Parch 0
              Ticket 0
                Fare 0
               Cabin 687
            Embarked 2

d.Embarked.value_counts()

S    644
C    168
Q     77
Name: Embarked, dtype: int64

plt.hist(d.Age.dropna().values, bins=30);
../_images/9dbe3e3f7e802e005abd09a1ad3ee40a838d0636919b5575e0589775f379d017.png

Remove uninformative columns

del(d["PassengerId"])
del(d["Name"])
del(d["Ticket"])
del(d["Cabin"])

Fix NaN values

  • observe the different filling policies we decide to have

d["Embarked"] = d.Embarked.fillna("N")
d["Age"]      = d.Age.fillna(d.Age.mean())
d.head()
Survived Pclass Sex Age SibSp Parch Fare Embarked
0 0 3 male 22.0 1 0 7.2500 S
1 1 1 female 38.0 1 0 71.2833 C
2 1 3 female 26.0 0 0 7.9250 S
3 1 1 female 35.0 1 0 53.1000 S
4 0 3 male 35.0 0 0 8.0500 S 
plt.hist(d.Age.dropna().values, bins=30);
../_images/0b958dcb1487bba4b0042aa7a92f910e5e7608ed039f9b3b07bf55108176a968.png

Turn categorical columns to a one_hot encoding

def to_onehot(x):
    values = np.unique(x)
    r = np.r_[[np.argwhere(i==values)[0][0] for i in x]]
    return np.eye(len(values))[r].astype(int)
    
k = to_onehot(d.Embarked.values)
k[:5]

array([[0, 0, 0, 1],
       [1, 0, 0, 0],
       [0, 0, 0, 1],
       [0, 0, 0, 1],
       [0, 0, 0, 1]])

def replace_columns_with_onehot(d, col):
    k = to_onehot(d[col].values)
    r = pd.DataFrame(k, columns=["%s_%d"%(col, i) for i in range(k.shape[1])], index=d.index).join(d)
    del(r[col])
    return r 

d.head()
Survived Pclass Sex Age SibSp Parch Fare Embarked
0 0 3 male 22.0 1 0 7.2500 S
1 1 1 female 38.0 1 0 71.2833 C
2 1 3 female 26.0 0 0 7.9250 S
3 1 1 female 35.0 1 0 53.1000 S
4 0 3 male 35.0 0 0 8.0500 S 
d = replace_columns_with_onehot(d, "Embarked")
d.head()
Embarked_0 Embarked_1 Embarked_2 Embarked_3 Survived Pclass Sex Age SibSp Parch Fare
0 0 0 0 1 0 3 male 22.0 1 0 7.2500
1 1 0 0 0 1 1 female 38.0 1 0 71.2833
2 0 0 0 1 1 3 female 26.0 0 0 7.9250
3 0 0 0 1 1 1 female 35.0 1 0 53.1000
4 0 0 0 1 0 3 male 35.0 0 0 8.0500 
d = replace_columns_with_onehot(d, "Sex")
d.head()
Sex_0 Sex_1 Embarked_0 Embarked_1 Embarked_2 Embarked_3 Survived Pclass Age SibSp Parch Fare
0 0 1 0 0 0 1 0 3 22.0 1 0 7.2500
1 1 0 1 0 0 0 1 1 38.0 1 0 71.2833
2 1 0 0 0 0 1 1 3 26.0 0 0 7.9250
3 1 0 0 0 0 1 1 1 35.0 1 0 53.1000
4 0 1 0 0 0 1 0 3 35.0 0 0 8.0500 
d.shape, d.values.sum()

((891, 12), 60142.86312352941)

Put all transformations together#

def clean_titanic(d):
    del(d["PassengerId"])
    del(d["Name"])
    del(d["Ticket"])
    del(d["Cabin"])
    d["Embarked"] = d.Embarked.fillna("N")
    d["Fare"]     = d.Fare.fillna(d.Fare.mean())
    d["Age"]      = d.Age.fillna(d.Age.mean())
    d = replace_columns_with_onehot(d, "Embarked")
    d = replace_columns_with_onehot(d, "Sex")
    return d

transform train and test data together

  • observe that test data does not have a Survival column. This is the result to submit to Kaggle

dtr = pd.read_csv("train.csv")
dts = pd.read_csv("test.csv")
lentr = len(dtr)
dtr.shape, dts.shape

((891, 12), (418, 11))

dts.head()
PassengerId Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked
0 892 3 Kelly, Mr. James male 34.5 0 0 330911 7.8292 NaN Q
1 893 3 Wilkes, Mrs. James (Ellen Needs) female 47.0 1 0 363272 7.0000 NaN S
2 894 2 Myles, Mr. Thomas Francis male 62.0 0 0 240276 9.6875 NaN Q
3 895 3 Wirz, Mr. Albert male 27.0 0 0 315154 8.6625 NaN S
4 896 3 Hirvonen, Mrs. Alexander (Helga E Lindqvist) female 22.0 1 1 3101298 12.2875 NaN S

get data ready for training

source_cols = [i for i in dtr.columns if i!="Survived"]
all_data = pd.concat((dtr[source_cols], dts[source_cols]))
all_data.index = range(len(all_data))
all_data = clean_titanic(all_data)

Xtr, ytr = all_data.iloc[:lentr].values, dtr["Survived"].values
Xts      = all_data.iloc[lentr:].values

print (Xtr.shape, ytr.shape)
print (Xts.shape)

(891, 11) (891,)
(418, 11)

cross validate for model selection

from sklearn.ensemble import RandomForestClassifier
from sklearn.svm import SVC
from sklearn.model_selection import cross_val_score
rf = RandomForestClassifier()
print (cross_val_score(rf, Xtr, ytr))

svc = SVC()
print (cross_val_score(svc, Xtr, ytr))

[0.77094972 0.81460674 0.84831461 0.79213483 0.83707865]
[0.59217877 0.71348315 0.69101124 0.68539326 0.69101124]

now train with full dataset and generate submission for Kaggle

rf.fit(Xtr, ytr)
preds_ts = rf.predict(Xts)
preds_ts

array([0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1,
       1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1,
       1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1,
       1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0,
       1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0,
       0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1,
       0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1,
       1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0,
       0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0,
       1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1,
       0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1,
       0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1,
       0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0,
       1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0,
       0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0,
       1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1,
       0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1])

get predictions ready to submit to Kaggle

submission = pd.DataFrame([dts.PassengerId, pd.Series(preds_ts, name="Survived")]).T
submission.head()
PassengerId Survived
0 892 0
1 893 0
2 894 0
3 895 1
4 896 0 
submission.to_csv("titanic_kaggle.csv", index=False)

!head titanic_kaggle.csv

PassengerId,Survived
892,0
893,0
894,0
895,1
896,0
897,0
898,0
899,0
900,1
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