import os
import pandas as pd
from sklearn.model_selection import ShuffleSplit
import rampwf as rw
problem_title = 'Boston housing price regression'
_target_column_name = 'medv'
# A type (class) which will be used to create wrapper objects for y_pred
Predictions = rw.prediction_types.make_regression()
# An object implementing the workflow
workflow = rw.workflows.Regressor()
score_types = [
rw.score_types.RMSE(),
rw.score_types.RelativeRMSE(name='rel_rmse'),
rw.score_types.NormalizedRMSE(name='n_rmse'),
]
def get_cv(X, y):
cv = ShuffleSplit(n_splits=2, test_size=0.2, random_state=57)
return cv.split(X)
def _read_data(path, f_name):
data = pd.read_csv(os.path.join(path, 'data', f_name))
y_array = data[_target_column_name].values
X_array = data.drop([_target_column_name], axis=1).values
return X_array, y_array
def get_train_data(path='.'):
f_name = 'train.csv'
return _read_data(path, f_name)
def get_test_data(path='.'):
f_name = 'test.csv'
return _read_data(path, f_name)
