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);

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);

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

• see https://www.kaggle.com/c/titanic#evaluation for file format

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