# 'houses',
'heart'
]
MISSING = True
outliers = [
0,
# 'clip',
# 'remove'
]
model_results = {}
for dataset in sets:
print("Performing tests for ", dataset)
if dataset == 'prudential':
data, labels, continuous, discrete, dummy, categorical, target, missing = get_prudential(
)
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':
def get_test_config_prudential():
data, labels, continuous, discrete, dummy, categorical, target, missing = get_prudential(
test=False)
test_data, test_labels = get_prudential(test=True)[0:2]
test = test_data.drop(target, axis=1)
scorer = rev_weighted_quad_kappa
one_hot = CustomOneHotEncoder(columns=categorical)
model = Pipeline([('onehot', one_hot), ('clipper', None), ('binner', None),
('binner2', None), ('simple_imputer', None),
('main_imputer', None),
('dropper', FeatureDropper(drop=[])), ('poly', None),
('combinations', None), ('boxcox', None),
('scaler', None), ('reduce_dim', None),
('predictor', None)])
params = {
# 'DecisionTreeClassifier_base': {'params': {'predictor': LabelsClipper(regressor=DecisionTreeClassifier()),
# 'scaler': RobustScaler(),
# 'simple_imputer': FillNaTransformer(from_dict={}, mean=[],
# median=['Product_Info_4', 'Ins_Age', 'Ht', 'Wt', 'BMI', 'Employment_Info_1', 'Employment_Info_4', 'Employment_Info_6', 'Insurance_History_5', 'Family_Hist_2', 'Family_Hist_3', 'Family_Hist_4', 'Family_Hist_5', 'Medical_History_1', 'Medical_History_10', 'Medical_History_15', 'Medical_History_24', 'Medical_History_32'],
# nan_flag=[], zero=[])},
# 'score': 0.5894217150410359,
# 'std': 0.007355958627832262},
# 'DecisionTreeRegressor': {'params': {'predictor': LabelsClipper(regressor=DecisionTreeRegressor()),
# 'scaler': None,
# 'simple_imputer': FillNaTransformer(from_dict={},
# mean=['Product_Info_4', 'Ins_Age', 'Ht', 'Wt', 'BMI', 'Employment_Info_1', 'Employment_Info_4', 'Employment_Info_6', 'Insurance_History_5', 'Family_Hist_2', 'Family_Hist_3', 'Family_Hist_4', 'Family_Hist_5', 'Medical_History_1', 'Medical_History_10', 'Medical_History_15', 'Medical_History_24', 'Medical_History_32'],
# median=[], nan_flag=[], zero=[])},
# 'score': 0.5686751743396636,
# # 'std': 0.0060008388054878},
# 'LinearRegression': {'params': {'predictor': LabelsClipper(regressor=LinearRegression()),
# 'scaler': RobustScaler(),
# 'simple_imputer': FillNaTransformer(from_dict={},
# mean=['Product_Info_4', 'Ins_Age', 'Ht', 'Wt', 'BMI', 'Employment_Info_1', 'Employment_Info_4', 'Employment_Info_6', 'Insurance_History_5', 'Family_Hist_2', 'Family_Hist_3', 'Family_Hist_4', 'Family_Hist_5', 'Medical_History_1', 'Medical_History_10', 'Medical_History_15', 'Medical_History_24', 'Medical_History_32'],
# median=[], nan_flag=[], zero=[])},
# 'score': 0.45392704971818426,
# 'std': 0.003043510387390882},
# 'XGBRegressor_base': {'params': {
# 'predictor': LabelsClipper(regressor=XGBRegressor(max_depth=8)),
# 'scaler': RobustScaler(),
# 'simple_imputer': FillNaTransformer(from_dict={},
# mean=['Product_Info_4', 'Ins_Age', 'Ht', 'Wt', 'BMI', 'Employment_Info_1', 'Employment_Info_4', 'Employment_Info_6', 'Insurance_History_5', 'Family_Hist_2', 'Family_Hist_3', 'Family_Hist_4', 'Family_Hist_5', 'Medical_History_1', 'Medical_History_10', 'Medical_History_15', 'Medical_History_24', 'Medical_History_32'],
# median=[], nan_flag=[], zero=[])},
# 'score': 0.40115420082450576,
# 'std': 0.00512923799723226},
'XGBRegressor_tuned_base': {
'params': {
'predictor':
LabelsClipper(regressor=XGBRegressor(n_jobs=7,
colsample_bytree=1,
learning_rate=0.07,
max_depth=16,
n_estimators=1000)),
'scaler':
RobustScaler(),
'simple_imputer':
FillNaTransformer(
from_dict={},
mean=[
'Product_Info_4', 'Ins_Age', 'Ht', 'Wt', 'BMI',
'Employment_Info_1', 'Employment_Info_4',
'Employment_Info_6', 'Insurance_History_5',
'Family_Hist_2', 'Family_Hist_3', 'Family_Hist_4',
'Family_Hist_5', 'Medical_History_1',
'Medical_History_10', 'Medical_History_15',
'Medical_History_24', 'Medical_History_32'
],
median=[],
nan_flag=[],
zero=[])
},
'score': 0.397178540923399,
'std': 0.0052540614137706766
},
# 'LinearRegression_best': {'params': {'binner': CustomBinner(configuration={'Medical_History_1': {'values': [0, 240], 'bins': [0, 1, 10, 30, 100, 200, 240]}, 'Medical_History_10': {'values': [0, 240], 'bins': [0, 1, 2, 240]}, 'Medical_History_15': {'values': [0, 240], 'bins': [0, 1, 2, 240]}, 'Medical_History_24': {'values': [0, 240], 'bins': [0, 1, 2, 240]}, 'Medical_History_32': {'values': [0, 240], 'bins': [0, 1, 2, 240]}},
# drop=False, nan=False),
# 'binner2': CustomBinaryBinner(configuration={'Product_Info_4': {'bins': 7}, 'Ins_Age': {'bins': 7}, 'Ht': {'bins': 7}, 'Wt': {'bins': 7}, 'BMI': {'bins': 7}, 'Employment_Info_1': {'bins': 7}, 'Employment_Info_4': {'bins': 7}, 'Employment_Info_6': {'bins': 7}, 'Insurance_History_5': {'bins': 7}, 'Family_Hist_2': {'bins': 7}, 'Family_Hist_3': {'bins': 7}, 'Family_Hist_4': {'bins': 7}, 'Family_Hist_5': {'bins': 7}},
# drop=False, nan=False),
# 'boxcox': BoxCoxTransformer(lambdas_per_column={'Wt': 0.5}),
# 'combinations': FeatureProduct(columns=['Product_Info_4', 'Ins_Age', 'Ht', 'Wt', 'BMI', 'Employment_Info_1', 'Employment_Info_4', 'Employment_Info_6', 'Insurance_History_5', 'Family_Hist_2', 'Family_Hist_3', 'Family_Hist_4', 'Family_Hist_5']),
# 'dropper__drop': [],
# 'main_imputer': ModelBasedFullImputer(columns=['Product_Info_4', 'Ins_Age', 'Ht', 'Wt', 'BMI', 'Employment_Info_1', 'Employment_Info_4', 'Employment_Info_6', 'Insurance_History_5', 'Family_Hist_2', 'Family_Hist_3', 'Family_Hist_4', 'Family_Hist_5', 'Medical_History_1', 'Medical_History_10', 'Medical_History_15', 'Medical_History_24', 'Medical_History_32'],
# model=XGBRegressor()),
# 'poly': PolynomialsAdder(powers_per_column={'Product_Info_4': [2, 3], 'Ins_Age': [2, 3], 'Ht': [2, 3], 'Wt': [2, 3], 'BMI': [2, 3], 'Employment_Info_1': [2, 3], 'Employment_Info_4': [2, 3], 'Employment_Info_6': [2, 3], 'Insurance_History_5': [2, 3], 'Family_Hist_2': [2, 3], 'Family_Hist_3': [2, 3], 'Family_Hist_4': [2, 3], 'Family_Hist_5': [2, 3]}),
# 'predictor': LabelsClipper(regressor=LinearRegression(copy_X=True, fit_intercept=True, n_jobs=4, normalize=False)),
# 'reduce_dim': SelectFromModel(estimator=Lasso(alpha=0.0001)),
# 'scaler': RobustScaler(copy=True, quantile_range=(25.0, 75.0), with_centering=True,
# with_scaling=True),
# 'simple_imputer': FillNaTransformer(from_dict={},
# mean=['Product_Info_4', 'Ins_Age', 'Ht', 'Wt', 'BMI', 'Employment_Info_1', 'Employment_Info_4', 'Employment_Info_6', 'Insurance_History_5', 'Family_Hist_2', 'Family_Hist_3', 'Family_Hist_4', 'Family_Hist_5', 'Medical_History_1', 'Medical_History_10', 'Medical_History_15', 'Medical_History_24', 'Medical_History_32'],
# median=[],
# nan_flag=['Product_Info_4', 'Ins_Age', 'Ht', 'Wt', 'BMI', 'Employment_Info_1', 'Employment_Info_4', 'Employment_Info_6', 'Insurance_History_5', 'Family_Hist_2', 'Family_Hist_3', 'Family_Hist_4', 'Family_Hist_5', 'Medical_History_1', 'Medical_History_10', 'Medical_History_15', 'Medical_History_24', 'Medical_History_32'],
# zero=[])},
# 'score': 0.4226586420535899,
# 'std': 0.005930880816785933},
'XGBRegressor_best': {
'params': {
'binner':
CustomBinner(configuration={
'Medical_History_1': {
'values': [0, 240],
'bins': [0, 1, 10, 30, 100, 200, 240]
},
'Medical_History_10': {
'values': [0, 240],
'bins': [0, 1, 2, 240]
},
'Medical_History_15': {
'values': [0, 240],
'bins': [0, 1, 2, 240]
},
'Medical_History_24': {
'values': [0, 240],
'bins': [0, 1, 2, 240]
},
'Medical_History_32': {
'values': [0, 240],
'bins': [0, 1, 2, 240]
}
},
drop=False,
nan=False),
'binner2':
None,
'boxcox':
BoxCoxTransformer(lambdas_per_column={'Wt': 0.5}),
'combinations':
FeatureProduct(columns=[
'Product_Info_4', 'Ins_Age', 'Ht', 'Wt', 'BMI',
'Employment_Info_1', 'Employment_Info_4',
'Employment_Info_6', 'Insurance_History_5',
'Family_Hist_2', 'Family_Hist_3', 'Family_Hist_4',
'Family_Hist_5'
]),
'dropper__drop': [
'Product_Info_4_nan', 'Ins_Age_nan', 'Ht_nan', 'Wt_nan',
'BMI_nan', 'Employment_Info_1_nan',
'Employment_Info_4_nan', 'Employment_Info_6_nan',
'Insurance_History_5_nan', 'Family_Hist_2_nan',
'Family_Hist_3_nan', 'Family_Hist_4_nan',
'Family_Hist_5_nan', 'Medical_History_1_nan',
'Medical_History_10_nan', 'Medical_History_15_nan',
'Medical_History_24_nan', 'Medical_History_32_nan'
],
'main_imputer':
HotDeckFullImputer(col_k_pairs=[('Product_Info_4', None),
('Ins_Age', None),
('Ht', None), ('Wt', None),
('BMI', None),
('Employment_Info_1', None),
('Employment_Info_4', None),
('Employment_Info_6', None),
('Insurance_History_5', None),
('Family_Hist_2', None),
('Family_Hist_3', None),
('Family_Hist_4', None),
('Family_Hist_5', None),
('Medical_History_1', None),
('Medical_History_10', None),
('Medical_History_15', None),
('Medical_History_24', None),
('Medical_History_32', None)],
default_k=5),
'poly':
None,
'predictor':
LabelsClipper(regressor=XGBRegressor(n_jobs=7, max_depth=8)),
'reduce_dim':
SelectFromModel(estimator=Lasso(alpha=0.0001)),
'scaler':
RobustScaler(),
'simple_imputer':
FillNaTransformer(
from_dict={},
mean=[
'Product_Info_4', 'Ins_Age', 'Ht', 'Wt', 'BMI',
'Employment_Info_1', 'Employment_Info_4',
'Employment_Info_6', 'Insurance_History_5',
'Family_Hist_2', 'Family_Hist_3', 'Family_Hist_4',
'Family_Hist_5', 'Medical_History_1',
'Medical_History_10', 'Medical_History_15',
'Medical_History_24', 'Medical_History_32'
],
median=[],
nan_flag=[
'Product_Info_4', 'Ins_Age', 'Ht', 'Wt', 'BMI',
'Employment_Info_1', 'Employment_Info_4',
'Employment_Info_6', 'Insurance_History_5',
'Family_Hist_2', 'Family_Hist_3', 'Family_Hist_4',
'Family_Hist_5', 'Medical_History_1',
'Medical_History_10', 'Medical_History_15',
'Medical_History_24', 'Medical_History_32'
],
zero=[])
},
'score': 0.4013165044558284,
'std': 0.005226847144375746
},
# 'XGBRegressor_tuned_best': {'params': {'binner': CustomBinner(configuration={'Medical_History_1': {'values': [0, 240], 'bins': [0, 1, 10, 30, 100, 200, 240]}, 'Medical_History_10': {'values': [0, 240], 'bins': [0, 1, 2, 240]}, 'Medical_History_15': {'values': [0, 240], 'bins': [0, 1, 2, 240]}, 'Medical_History_24': {'values': [0, 240], 'bins': [0, 1, 2, 240]}, 'Medical_History_32': {'values': [0, 240], 'bins': [0, 1, 2, 240]}},
# drop=False, nan=False),
# 'binner2': None,
# 'boxcox': BoxCoxTransformer(lambdas_per_column={'Wt': 0.5}),
# 'combinations': FeatureProduct(columns=['Product_Info_4', 'Ins_Age', 'Ht', 'Wt', 'BMI', 'Employment_Info_1', 'Employment_Info_4', 'Employment_Info_6', 'Insurance_History_5', 'Family_Hist_2', 'Family_Hist_3', 'Family_Hist_4', 'Family_Hist_5']),
# 'dropper__drop': ['Product_Info_4_nan','Ins_Age_nan','Ht_nan','Wt_nan','BMI_nan','Employment_Info_1_nan','Employment_Info_4_nan','Employment_Info_6_nan','Insurance_History_5_nan','Family_Hist_2_nan','Family_Hist_3_nan','Family_Hist_4_nan','Family_Hist_5_nan','Medical_History_1_nan','Medical_History_10_nan','Medical_History_15_nan','Medical_History_24_nan','Medical_History_32_nan'],
# 'main_imputer': HotDeckFullImputer(col_k_pairs=[('Product_Info_4', None), ('Ins_Age', None), ('Ht', None), ('Wt', None), ('BMI', None), ('Employment_Info_1', None), ('Employment_Info_4', None), ('Employment_Info_6', None), ('Insurance_History_5', None), ('Family_Hist_2', None), ('Family_Hist_3', None), ('Family_Hist_4', None), ('Family_Hist_5', None), ('Medical_History_1', None), ('Medical_History_10', None), ('Medical_History_15', None), ('Medical_History_24', None), ('Medical_History_32', None)],
# default_k=5),
# 'poly': None,
# 'predictor': LabelsClipper(regressor=XGBRegressor(
# colsample_bytree=1,learning_rate=0.07,
# max_depth=16, n_estimators=1000)),
# 'reduce_dim': SelectFromModel(estimator=Lasso(alpha=0.0001)),
# 'scaler': RobustScaler(),
# 'simple_imputer': FillNaTransformer(from_dict={},
# mean=['Product_Info_4', 'Ins_Age', 'Ht', 'Wt', 'BMI', 'Employment_Info_1', 'Employment_Info_4', 'Employment_Info_6', 'Insurance_History_5', 'Family_Hist_2', 'Family_Hist_3', 'Family_Hist_4', 'Family_Hist_5', 'Medical_History_1', 'Medical_History_10', 'Medical_History_15', 'Medical_History_24', 'Medical_History_32'],
# median=[],
# nan_flag=['Product_Info_4', 'Ins_Age', 'Ht', 'Wt', 'BMI', 'Employment_Info_1', 'Employment_Info_4', 'Employment_Info_6', 'Insurance_History_5', 'Family_Hist_2', 'Family_Hist_3', 'Family_Hist_4', 'Family_Hist_5', 'Medical_History_1', 'Medical_History_10', 'Medical_History_15', 'Medical_History_24', 'Medical_History_32'],
# zero=[])},
# 'score': 0.4013165044558284,
# 'std': 0.005226847144375746}
}
return data, test, test_labels, scorer, model, params, target