hgboost

hgboost_regression_import_titanic.py

##########################################
# Import Titanc dataset and preprocessing.
##########################################
df = hgb.import_example(data='titanic')

print(df)

#      PassengerId  Survived  Pclass  ...     Fare Cabin  Embarked
# 0              1         0       3  ...   7.2500   NaN         S
# 1              2         1       1  ...  71.2833   C85         C
# 2              3         1       3  ...   7.9250   NaN         S
# 3              4         1       1  ...  53.1000  C123         S
# 4              5         0       3  ...   8.0500   NaN         S
# ..           ...       ...     ...  ...      ...   ...       ...
# 886          887         0       2  ...  13.0000   NaN         S
# 887          888         1       1  ...  30.0000   B42         S
# 888          889         0       3  ...  23.4500   NaN         S
# 889          890         1       1  ...  30.0000  C148         C
# 890          891         0       3  ...   7.7500   NaN         Q

# [891 rows x 12 columns]
##################################################################
# Set Target value: Age 
y = df['Age'].values

print(y)
# [22.   38.   26.   35.   35.   54.    2.   27.   14. ... ]
##################################################################
# Remove features
df.drop(['Age', 'PassengerId', 'Name'], axis=1, inplace=True)
##################################################################
# One-hot encoding
X = hgb.preprocessing(df, verbose=0)
##################################################################
# Remove missing values
import numpy as np
I = ~np.isnan(y)
X = X.loc[I, :]
y = y[I]

##################################################################
print(X)

#      Survived_1.0  Pclass_1.0  ...  Embarked_Q  Embarked_S
# 0           False       False  ...       False        True
# 1            True        True  ...       False       False
# 2            True       False  ...       False        True
# 3            True        True  ...       False        True
# 4           False       False  ...       False        True
# ..            ...         ...  ...         ...         ...
# 885         False       False  ...        True       False
# 886         False       False  ...       False        True
# 887          True        True  ...       False        True
# 889          True        True  ...       False       False
# 890         False       False  ...        True       False

# [714 rows x 203 columns]