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Add module for gradient boosting for regression and classification, resolves #1 #16

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937da12
Updated regression for compatilibility
NavaneeshGangala Oct 19, 2019
8ea76be
Add module for gradient boosting for regression and classification, …
NavaneeshGangala Oct 19, 2019
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15 changes: 12 additions & 3 deletions hunga_bunga/classification.py
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Original file line number Diff line number Diff line change
Expand Up @@ -24,9 +24,10 @@
from sklearn.base import ClassifierMixin
from sklearn.base import RegressorMixin
from sklearn.base import is_classifier
from sklearn.ensemble import GradientBoostingClassifier

from core import *
from params import *
from hunga_bunga.core import *
from hunga_bunga.params import *


linear_models_n_params = [
Expand Down Expand Up @@ -157,7 +158,11 @@
(ExtraTreesClassifier,
{'n_estimators': n_estimators, 'max_features': max_features, 'max_depth': max_depth,
'min_samples_split': min_samples_split, 'min_samples_leaf': min_samples_leaf, 'min_impurity_split': min_impurity_split, 'warm_start': warm_start,
'criterion': ['gini', 'entropy']})
'criterion': ['gini', 'entropy']}),

(GradientBoostingClassifier,
{'n_estimators': n_estimators, 'max_features': max_features, 'max_depth': max_depth, 'min_samples_split': min_samples_split,
'min_samples_leaf': min_samples_leaf, 'min_impurity_split': min_impurity_split, 'warm_start': warm_start})
]

tree_models_n_params_small = [
Expand All @@ -171,6 +176,10 @@
}),

(ExtraTreesClassifier,
{'n_estimators_small': n_estimators_small, 'max_features_small': max_features_small, 'max_depth_small': max_depth_small,
'min_samples_split': min_samples_split, 'min_samples_leaf': min_samples_leaf}),

(GradientBoostingClassifier,
{'n_estimators_small': n_estimators_small, 'max_features_small': max_features_small, 'max_depth_small': max_depth_small,
'min_samples_split': min_samples_split, 'min_samples_leaf': min_samples_leaf})
]
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16 changes: 12 additions & 4 deletions hunga_bunga/regression.py
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Original file line number Diff line number Diff line change
Expand Up @@ -19,10 +19,10 @@
from sklearn.gaussian_process.kernels import RBF, ConstantKernel, DotProduct, WhiteKernel
from sklearn.ensemble import AdaBoostRegressor, ExtraTreesRegressor, RandomForestRegressor
from sklearn.linear_model import LinearRegression, Ridge, Lasso, ElasticNet, Lars, LassoLars, OrthogonalMatchingPursuit, BayesianRidge, ARDRegression, SGDRegressor, PassiveAggressiveRegressor, RANSACRegressor, HuberRegressor
from sklearn.ensemble import GradientBoostingRegressor


from core import *
from params import *
from hunga_bunga.core import *
from hunga_bunga.params import *


linear_models_n_params = [
Expand Down Expand Up @@ -271,6 +271,10 @@
{'n_estimators': n_estimators, 'max_features': max_features, 'max_depth': max_depth, 'min_samples_split': min_samples_split,
'min_samples_leaf': min_samples_leaf, 'min_impurity_split': min_impurity_split, 'warm_start': warm_start,
'criterion': ['mse', 'mae']}),

(GradientBoostingRegressor,
{'n_estimators': n_estimators, 'max_features': max_features, 'max_depth': max_depth, 'min_samples_split': min_samples_split,
'min_samples_leaf': min_samples_leaf, 'min_impurity_split': min_impurity_split, 'warm_start': warm_start}),

]

Expand All @@ -282,7 +286,11 @@
(ExtraTreesRegressor,
{'n_estimators': n_estimators_small, 'max_features': max_features_small, 'max_depth': max_depth_small, 'min_samples_split': min_samples_split,
'min_samples_leaf': min_samples_leaf,
'criterion': ['mse', 'mae']})
'criterion': ['mse', 'mae']}),

(GradientBoostingRegressor,
{'n_estimators': n_estimators_small, 'max_features': max_features_small, 'max_depth': max_depth_small, 'min_samples_split': min_samples_split,
'min_samples_leaf': min_samples_leaf})
]


Expand Down