def get_test_config_houses():
data, labels, continuous, discrete, dummy, categorical, target, missing = get_houses(test=False)
test_data, test_labels = get_houses(test=True)[0:2]
train = data.drop(target, axis=1)
test = test_data.drop(target, axis=1)
scorer = rmse
binner = CustomBinaryBinner({ col: {'values': [train[col].max()]} for col in continuous + discrete })
one_hot = CustomOneHotEncoder(columns=categorical)
model = Pipeline([
('onehot', one_hot),
('clipper', None),
('binner', None),
('binner2', None),
('simple_imputer', None),
('zero_filler', ZeroFiller()),
('main_imputer', None),
('dropper', FeatureDropper(drop=[])),
('poly', None),
('combinations', None),
('boxcox', None),
('scaler', None),
('reduce_dim', None),
('predictor', None)
])
params = {
'LinearRegression_best': {
'params': {'binner2': binner,
'boxcox': BoxCoxTransformer(lambdas_per_column={'BsmtFinSF1': 0, 'LowQualFinSF': 0, 'GrLivArea': 0, 'WoodDeckSF': 0}),
'clipper': None,
'combinations': FeatureProduct(columns=['LotFrontage', 'BsmtFinSF1', 'MasVnrArea', '1stFlrSF', 'GarageArea', 'TotalBsmtSF', 'GrLivArea']),
'dropper__drop': ['LotFrontage_nan', 'MasVnrArea_nan', 'GarageYrBlt_nan'],
'main_imputer': ModelBasedFullImputer(columns=['LotFrontage', 'MasVnrArea', 'GarageYrBlt'],
model=LinearRegression(copy_X=True, fit_intercept=True, n_jobs=7, normalize=False)),
'poly': PolynomialsAdder(powers_per_column={'LotFrontage': [3], 'BsmtFinSF1': [3], 'MasVnrArea': [3], '1stFlrSF': [3], 'GarageArea': [3], 'TotalBsmtSF': [3], 'GrLivArea': [3]}),
'predictor': LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False),
'reduce_dim': PCA(copy=True, iterated_power='auto', n_components=80, random_state=None,
svd_solver='auto', tol=0.0, whiten=False),
'scaler': RobustScaler(copy=True, quantile_range=(25.0, 75.0), with_centering=True,
with_scaling=True),
'simple_imputer': FillNaTransformer(from_dict={}, mean=[], median=[],
nan_flag=['LotFrontage', 'MasVnrArea', 'GarageYrBlt'],
zero=['LotFrontage', 'MasVnrArea', 'GarageYrBlt'])},
'score': 23615.61547841841,
'std': 1575.0296698711768},
# same as baseline
'XGBRegressor_best': {'params': {'binner2': None,
'boxcox': None,
'clipper': None,
'combinations': None,
'dropper__drop': ['LotFrontage_nan', 'MasVnrArea_nan', 'GarageYrBlt_nan'],
'main_imputer': None,
'poly': None,
'predictor': XGBRegressor(
colsample_bytree=1, learning_rate=0.07,
max_depth=3, n_estimators=1000, n_jobs=7),
'reduce_dim': None,
'scaler': None,
'simple_imputer': FillNaTransformer(from_dict={},
mean=['LotFrontage', 'MasVnrArea', 'GarageYrBlt'], median=[],
nan_flag=['LotFrontage', 'MasVnrArea', 'GarageYrBlt'], zero=[])},
'score': 25011.258367120317,
'std': 1390.9293424644447},
'DecisionTreeRegressor_base': {'params': {'predictor': DecisionTreeRegressor(),
'scaler': None,
'simple_imputer': FillNaTransformer(from_dict={},
mean=['LotFrontage', 'MasVnrArea', 'GarageYrBlt'], median=[],
nan_flag=[], zero=[])},
'score': 41942.13985251157,
'std': 4160.532009551353},
'KNeighborsRegressor_base': {'params': {'predictor': KNeighborsRegressor(n_neighbors=7, n_jobs=7),
'scaler': None,
'simple_imputer': FillNaTransformer(from_dict={}, mean=[], median=[], nan_flag=[],
zero=['LotFrontage', 'MasVnrArea', 'GarageYrBlt'])},
'score': 49074.08623384354,
'std': 4863.286944918721},
'LinearRegression_base': {'params': {'predictor': LinearRegression(n_jobs=7),
'scaler': None,
'simple_imputer': FillNaTransformer(from_dict={},
mean=['LotFrontage', 'MasVnrArea', 'GarageYrBlt'], median=[],
nan_flag=[], zero=[])},
'score': 58196.855144405956,
'std': 28243.597268210826},
'XGBRegressor_base': {'params': {
'predictor': XGBRegressor(max_depth=3, n_jobs=7),
'scaler': None,
'simple_imputer': FillNaTransformer(from_dict={}, mean=[], median=[], nan_flag=[],
zero=['LotFrontage', 'MasVnrArea', 'GarageYrBlt'])},
'score': 26368.673265668913,
'std': 1310.375215389942},
'XGBRegressor_tuned_base': {'params': {'predictor': XGBRegressor(
colsample_bytree=1,learning_rate=0.07,
max_depth=3, n_estimators=1000, n_jobs=7),
'scaler': None,
'simple_imputer': FillNaTransformer(from_dict={},
mean=['LotFrontage', 'MasVnrArea', 'GarageYrBlt'], median=[],
nan_flag=[], zero=[])},
'score': 24894.281304255797,
'std': 1177.011047285853},
'DecisionTreeRegressor_best': {'params': {'binner': None,
'binner2': binner,
'clipper': OutliersClipper(columns=['LotFrontage', 'LotArea', 'MasVnrArea', 'BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', '1stFlrSF', '2ndFlrSF', 'LowQualFinSF', 'GrLivArea', 'GarageArea', 'WoodDeckSF', 'OpenPorchSF', 'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'MiscVal']),
'combinations': FeatureProduct(columns=['LotFrontage', 'BsmtFinSF1', 'MasVnrArea', '1stFlrSF', 'GarageArea', 'TotalBsmtSF', 'GrLivArea']),
'dropper__drop': ['LotFrontage_nan', 'MasVnrArea_nan', 'GarageYrBlt_nan'],
'main_imputer': HotDeckFullImputer(col_k_pairs=[('LotFrontage', None), ('MasVnrArea', None), ('GarageYrBlt', None)], default_k=5),
'poly': PolynomialsAdder(powers_per_column={'LotFrontage': [2], 'LotArea': [2], 'MasVnrArea': [2], 'BsmtFinSF1': [2], 'BsmtFinSF2': [2], 'BsmtUnfSF': [2], 'TotalBsmtSF': [2], '1stFlrSF': [2], '2ndFlrSF': [2], 'LowQualFinSF': [2], 'GrLivArea': [2], 'GarageArea': [2], 'WoodDeckSF': [2], 'OpenPorchSF': [2], 'EnclosedPorch': [2], '3SsnPorch': [2], 'ScreenPorch': [2], 'PoolArea': [2], 'MiscVal': [2]}),
'predictor': DecisionTreeRegressor(),
'reduce_dim': SelectFromModel(estimator=RandomForestRegressor(max_depth=8)),
'simple_imputer': FillNaTransformer(from_dict={},
mean=['LotFrontage', 'MasVnrArea', 'GarageYrBlt'], median=[],
nan_flag=['LotFrontage', 'MasVnrArea', 'GarageYrBlt'], zero=[])},
'score': 37866.96889728187,
'std': 5359.25597193946},
'Lasso_best': {'params': {
'binner': None,
'binner2': binner,
'clipper': None,
'combinations': FeatureProduct(
columns=['LotFrontage', 'BsmtFinSF1', 'MasVnrArea', '1stFlrSF', 'GarageArea', 'TotalBsmtSF', 'GrLivArea']),
'dropper__drop': ['LotFrontage_nan', 'MasVnrArea_nan', 'GarageYrBlt_nan'],
'main_imputer': HotDeckFullImputer(col_k_pairs=[('LotFrontage', None), ('MasVnrArea', None), ('GarageYrBlt', None)],
default_k=7),
'poly': None,
'predictor': Lasso(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=1000,
normalize=False, positive=False, precompute=False, random_state=None,
selection='cyclic', tol=0.0001, warm_start=False),
'reduce_dim': PCA(copy=True, iterated_power='auto', n_components=80, random_state=None,
svd_solver='auto', tol=0.0, whiten=False),
'simple_imputer': FillNaTransformer(from_dict={},
mean=['LotFrontage', 'MasVnrArea', 'GarageYrBlt'], median=[],
nan_flag=['LotFrontage', 'MasVnrArea', 'GarageYrBlt'], zero=[])},
'score': 24138.931080813963,
'std': 639.0998169991468} ,
'Lasso_base': {'params': {
'predictor': Lasso(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=1000,
normalize=False, positive=False, precompute=False, random_state=None,
selection='cyclic', tol=0.0001, warm_start=False),
'scaler': RobustScaler(),
'simple_imputer': FillNaTransformer(from_dict={},
mean=['LotFrontage', 'MasVnrArea', 'GarageYrBlt'])},
'score': 0,
'std': 0}
}
return data, test, test_labels, scorer, model, params, target
# KNeighborsRegressor(n_neighbors=5),
]
binner = CustomBinner(
{col: {
'bins': 7
}
for col in continuous + discrete})
# BINNER_CONFIG = [{ col: {'bins': 3} for col in continuous + discrete },
# { col: {'bins': 5} for col in continuous + discrete },
# { col: {'bins': 7} for col in continuous + discrete },
# { col: {'values': [train[col].max()]} for col in continuous + discrete }]
BOX_COX = BOX_COX_B
precision = 3
elif dataset == 'houses':
data, labels, continuous, discrete, dummy, categorical, target, missing = get_houses(
)
cv = KFold(5, shuffle=True, random_state=0)
scorer = rmse
precision = 0
predictors = [
LinearRegression(),
# DecisionTreeRegressor(),
# KNeighborsRegressor(n_neighbors=5),
]
train = data.drop(target, axis=1)
binner = CustomBinaryBinner({
col: {
'values': [train[col].max()]
}
for col in continuous + discrete
})