train = data.drop(target, axis=1)
cv = StratifiedKFold(5)
scorer = rev_weighted_quad_kappa
predictors = [
LabelsClipper(regressor=LinearRegression()),
]
binner = CustomBinaryBinner({col: {'bins': 7} for col in continuous})
# BINNER_CONFIG = [{ col: {'bins': 3} for col in continuous },
# { col: {'bins': 5} for col in continuous },
# { col: {'bins': 7} for col in continuous },
# { col: {'values': [train[col].max()]} for col in continuous }]
BOX_COX = BOX_COX_P
precision = 4
elif dataset == 'boston':
data, labels, continuous, discrete, dummy, categorical, target, missing = get_boston(
missing=MISSING)
train = data.drop(target, axis=1)
cv = KFold(5, shuffle=True, random_state=0)
scorer = rmse
predictors = [
LinearRegression(),
# SVR(),
# RandomForestRegressor(),
# DecisionTreeRegressor(),
# KNeighborsRegressor(n_neighbors=5),
]
binner = CustomBinner(
{col: {
'bins': 7
}
for col in continuous + discrete})
def get_test_config_boston(missing=True):
data, labels, continuous, discrete, dummy, categorical, target, missing = get_boston(
test=False, missing=missing)
test_data, test_labels = get_boston(test=True, missing=missing)[0:2]
test = test_data.drop(target, axis=1)
scorer = rmse
model = Pipeline([('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 = {
# 0
'XGBRegressor_best': {
'params': {
'binner2':
None,
'boxcox':
BoxCoxTransformer(lambdas_per_column={
'age': 2,
'tax': 0,
'lstat': 0
}),
'clipper':
OutliersClipper(columns=[
'crim', 'zn', 'nox', 'indus', 'rm', 'age', 'tax',
'ptratio', 'b', 'lstat', 'dis'
]),
'combinations':
None,
'dropper__drop': [],
'main_imputer':
ModelBasedFullImputer(
columns=[
'crim', 'zn', 'nox', 'indus', 'rm', 'age', 'tax',
'ptratio', 'b', 'dis'
],
model=DecisionTreeRegressor(max_depth=None)),
'poly':
PolynomialsAdder(
powers_per_column={
'crim': [2, 3],
'zn': [2, 3],
'nox': [2, 3],
'indus': [2, 3],
'rm': [2, 3],
'age': [2, 3],
'tax': [2, 3],
'ptratio': [2, 3],
'b': [2, 3],
'lstat': [2, 3],
'dis': [2, 3]
}),
'predictor':
XGBRegressor(learning_rate=0.05, max_depth=6,
n_estimators=500),
'reduce_dim':
None,
'scaler':
None,
'simple_imputer':
FillNaTransformer(from_dict={},
mean=[],
median=[
'crim', 'zn', 'nox', 'indus', 'rm',
'age', 'tax', 'ptratio', 'b', 'dis'
],
nan_flag=[
'crim', 'zn', 'nox', 'indus', 'rm',
'age', 'tax', 'ptratio', 'b', 'dis'
],
zero=[])
},
'score': 3.7821358047127682,
'std': 0.4967512627490983
},
# 0
'Lasso_best': {
'params': {
'binner2':
None,
'boxcox':
BoxCoxTransformer(lambdas_per_column={
'age': 2,
'tax': 0,
'lstat': 0
}),
'clipper':
OutliersClipper(columns=[
'crim', 'zn', 'nox', 'indus', 'rm', 'age', 'tax',
'ptratio', 'b', 'lstat', 'dis'
]),
'combinations':
FeatureProduct(columns=[
'crim', 'zn', 'nox', 'indus', 'rm', 'age', 'tax',
'ptratio', 'b', 'lstat', 'dis'
]),
'dropper__drop': [
'crim_nan', 'zn_nan', 'nox_nan', 'indus_nan', 'rm_nan',
'age_nan', 'tax_nan', 'ptratio_nan', 'b_nan', 'dis_nan'
],
'main_imputer':
ModelBasedFullImputer(
columns=[
'crim', 'zn', 'nox', 'indus', 'rm', 'age', 'tax',
'ptratio', 'b', 'dis'
],
model=DecisionTreeRegressor(max_depth=None)),
'poly':
None,
'predictor':
Lasso(alpha=0.01),
'reduce_dim':
None,
'scaler':
RobustScaler(),
'simple_imputer':
FillNaTransformer(from_dict={},
mean=[],
median=[
'crim', 'zn', 'nox', 'indus', 'rm',
'age', 'tax', 'ptratio', 'b', 'dis'
],
nan_flag=[
'crim', 'zn', 'nox', 'indus', 'rm',
'age', 'tax', 'ptratio', 'b', 'dis'
],
zero=[])
},
'score': 3.993797473454735,
'std': 0.5808956921355953
},
# 0
'LinearRegression_best': {
'params': {
'binner2':
None,
'boxcox':
BoxCoxTransformer(lambdas_per_column={
'age': 2,
'tax': 0,
'lstat': 0
}),
'clipper':
OutliersClipper(columns=[
'crim', 'zn', 'nox', 'indus', 'rm', 'age', 'tax',
'ptratio', 'b', 'lstat', 'dis'
]),
'combinations':
None,
'dropper__drop': [
'crim_nan', 'zn_nan', 'nox_nan', 'indus_nan', 'rm_nan',
'age_nan', 'tax_nan', 'ptratio_nan', 'b_nan', 'dis_nan'
],
'main_imputer':
ModelBasedFullImputer(
columns=[
'crim', 'zn', 'nox', 'indus', 'rm', 'age', 'tax',
'ptratio', 'b', 'dis'
],
model=DecisionTreeRegressor(max_depth=8)),
'poly':
PolynomialsAdder(
powers_per_column={
'crim': [2, 3],
'zn': [2, 3],
'nox': [2, 3],
'indus': [2, 3],
'rm': [2, 3],
'age': [2, 3],
'tax': [2, 3],
'ptratio': [2, 3],
'b': [2, 3],
'lstat': [2, 3],
'dis': [2, 3]
}),
'predictor':
LinearRegression(),
'reduce_dim':
None,
'scaler':
None,
'simple_imputer':
FillNaTransformer(from_dict={},
mean=[],
median=[
'crim', 'zn', 'nox', 'indus', 'rm',
'age', 'tax', 'ptratio', 'b', 'dis'
],
nan_flag=[
'crim', 'zn', 'nox', 'indus', 'rm',
'age', 'tax', 'ptratio', 'b', 'dis'
],
zero=[])
},
'score': 4.514645815970899,
'std': 0.7631593234069367
},
'DecisionTreeRegressor_base': {
'params': {
'predictor':
DecisionTreeRegressor(criterion='mse',
max_depth=4,
max_features=None,
max_leaf_nodes=None,
min_impurity_decrease=0.0,
min_impurity_split=None,
min_samples_leaf=1,
min_samples_split=2,
min_weight_fraction_leaf=0.0,
presort=False,
random_state=None,
splitter='best'),
'scaler':
None,
'simple_imputer':
FillNaTransformer(from_dict={},
mean=[],
median=[],
nan_flag=[],
zero=[
'crim', 'zn', 'nox', 'indus', 'rm',
'age', 'tax', 'ptratio', 'b', 'dis'
])
},
'score': 5.5088106991425985,
'std': 0.293662905734789
},
'KNeighborsRegressor_base': {
'params': {
'predictor':
KNeighborsRegressor(algorithm='auto',
leaf_size=30,
metric='minkowski',
metric_params=None,
n_jobs=1,
n_neighbors=7,
p=2,
weights='uniform'),
'scaler':
RobustScaler(copy=True,
quantile_range=(25.0, 75.0),
with_centering=True,
with_scaling=True),
'simple_imputer':
FillNaTransformer(from_dict={},
mean=[
'crim', 'zn', 'nox', 'indus', 'rm',
'age', 'tax', 'ptratio', 'b', 'dis'
],
median=[],
nan_flag=[],
zero=[])
},
'score': 5.859771905373064,
'std': 0.90721907618626
},
'LinearRegression_base': {
'params': {
'predictor':
LinearRegression(copy_X=True,
fit_intercept=True,
n_jobs=1,
normalize=False),
'scaler':
RobustScaler(copy=True,
quantile_range=(25.0, 75.0),
with_centering=True,
with_scaling=True),
'simple_imputer':
FillNaTransformer(from_dict={},
mean=[],
median=[
'crim', 'zn', 'nox', 'indus', 'rm',
'age', 'tax', 'ptratio', 'b', 'dis'
],
nan_flag=[],
zero=[])
},
'score': 5.494688479501426,
'std': 0.5377531716144219
},
'Lasso_base': {
'params': {
'predictor':
Lasso(alpha=0.01),
'scaler':
RobustScaler(copy=True,
quantile_range=(25.0, 75.0),
with_centering=True,
with_scaling=True),
'simple_imputer':
FillNaTransformer(from_dict={},
mean=[],
median=[
'crim', 'zn', 'nox', 'indus', 'rm',
'age', 'tax', 'ptratio', 'b', 'dis'
],
nan_flag=[],
zero=[])
},
'score': 0,
'std': 0
},
# 'XGBRegressor_base': {'params': {
# 'predictor': XGBRegressor(max_depth=4),
# 'scaler': None,
# 'simple_imputer': FillNaTransformer(from_dict={},
# mean=['crim', 'zn', 'nox', 'indus', 'rm', 'age', 'tax', 'ptratio', 'b', 'dis'],
# median=[], nan_flag=[], zero=[])},
# 'score': 4.088217989236429,
# 'std': 0.5303490714753816,
# },
'XGBRegressor_tuned_base': {
'params': {
'predictor':
XGBRegressor(learning_rate=0.05, max_depth=6,
n_estimators=500),
'scaler':
None,
'simple_imputer':
FillNaTransformer(from_dict={},
mean=[
'crim', 'zn', 'nox', 'indus', 'rm',
'age', 'tax', 'ptratio', 'b', 'dis'
],
median=[],
nan_flag=[],
zero=[])
},
'score': 3.942697483564859,
'std': 0.6029251513214098
},
'DecisionTreeRegressor_best': {
'params': {
'binner2':
None,
'boxcox':
BoxCoxTransformer(lambdas_per_column={
'age': 2,
'tax': 0,
'lstat': 0
}),
'clipper':
OutliersClipper(columns=[
'crim', 'zn', 'nox', 'indus', 'rm', 'age', 'tax',
'ptratio', 'b', 'lstat', 'dis'
]),
'combinations':
None,
'dropper__drop': [],
'main_imputer':
ModelBasedFullImputer(columns=[
'crim', 'zn', 'nox', 'indus', 'rm', 'age', 'tax',
'ptratio', 'b', 'dis'
],
model=DecisionTreeRegressor(
criterion='mse',
max_depth=None,
max_features=None,
max_leaf_nodes=None,
min_impurity_decrease=0.0,
min_impurity_split=None,
min_samples_leaf=1,
min_samples_split=2,
min_weight_fraction_leaf=0.0,
presort=False,
random_state=None,
splitter='best')),
'poly':
None,
'predictor':
DecisionTreeRegressor(criterion='mse',
max_depth=8,
max_features=None,
max_leaf_nodes=None,
min_impurity_decrease=0.0,
min_impurity_split=None,
min_samples_leaf=1,
min_samples_split=2,
min_weight_fraction_leaf=0.0,
presort=False,
random_state=None,
splitter='best'),
'reduce_dim':
None,
'scaler':
None,
'simple_imputer':
FillNaTransformer(from_dict={},
mean=[],
median=[
'crim', 'zn', 'nox', 'indus', 'rm',
'age', 'tax', 'ptratio', 'b', 'dis'
],
nan_flag=[
'crim', 'zn', 'nox', 'indus', 'rm',
'age', 'tax', 'ptratio', 'b', 'dis'
],
zero=[])
},
'score': 4.840076015850126,
'std': 0.718619669114889
},
}
return data, test, test_labels, scorer, model, params, target
