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models.py
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models.py
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# Machine Learning
from sklearn.neural_network import MLPRegressor, MLPClassifier
from sklearn.svm import SVR, SVC
from sklearn.tree import DecisionTreeRegressor, DecisionTreeClassifier
from sklearn.kernel_ridge import KernelRidge
from sklearn.neighbors import (KNeighborsRegressor, KNeighborsClassifier,
RadiusNeighborsRegressor, RadiusNeighborsClassifier)
from sklearn.ensemble import (RandomForestRegressor, RandomForestClassifier,
AdaBoostRegressor, AdaBoostClassifier,
GradientBoostingRegressor, GradientBoostingClassifier,
BaggingRegressor, BaggingClassifier,
ExtraTreesRegressor, ExtraTreesClassifier)
from sklearn.linear_model import (BayesianRidge, RidgeClassifier,
SGDRegressor, SGDClassifier,
LinearRegression, LogisticRegression,
Lasso, ElasticNet)
regression_options = {
'MLPRegressor': {
'model':MLPRegressor(learning_rate = 'adaptive',
max_iter=500,
learning_rate_init=.005),
'name':'MLP NN'
},
'RandomForestRegressor': {
'model':RandomForestRegressor(n_estimators = 20,
max_features = 2),
'name':'Random Forest'
},
'BayesianRidge': {
'model':BayesianRidge(),
'name':'Bayesian Ridge'
},
'Lasso': {
'model':Lasso(),
'name':'Lasso Regressor'
},
'GradientBoostingRegressor': {
'model':GradientBoostingRegressor(max_features=2),
'name':'Gradient Boost'
},
'ElasticNet': {
'model':ElasticNet(selection='random'),
'name':'Elastic Net Regressor'
},
'KernelRidge': {
'model':KernelRidge(),
'name':'Kernel Ridge'
},
'SVR': {
'model':SVR(),
'name':'SVR'
},
'BaggingRegressor': {
'model':BaggingRegressor(),#base_estimator = LinearRegression()),
'name':'Bagging Regressor'
},
'ExtraTreesRegressor': {
'model':ExtraTreesRegressor(),
'name':'Extra Trees Regressor'
},
'KNeighborsRegressor': {
'model':KNeighborsRegressor(),
'name':'K Neighbors Regressor'
},
'DecisionTreeRegressor': {
'model':DecisionTreeRegressor(),
'name':'Decision Tree Regressor'
},
'AdaBoostRegressor': {
'model':AdaBoostRegressor(),#base_estimator = LinearRegression()),
'name':'AdaBoost'
},
'LinearRegression': {
'model':LinearRegression(),
'name':'Linear Regression'
},
'SGDRegressor': {
'model':SGDRegressor(max_iter=1000),
'name':'Stochastic Gradient Descent'
},
}
classification_options = {
'MLPClassifier': {
'model':MLPClassifier(learning_rate = 'adaptive',
max_iter=500,
learning_rate_init=.005),
'name':'MLP NN'
},
'RandomForestClassifier': {
'model':RandomForestClassifier(n_estimators = 20,
max_features = 2),
'name':'Random Forest'
},
'RidgeClassifier': {
'model':RidgeClassifier(),
'name':'Ridge Classifier'
},
'GradientBoostingClassifier': {
'model':GradientBoostingClassifier(max_features=2),
'name':'Gradient Boost'
},
'SVC': {
'model':SVC(),
'name':'SVC'
},
'BaggingClassifier': {
'model':BaggingClassifier(),#base_estimator = LinearRegression()),
'name':'Bagging Classifier'
},
'ExtraTreesClassifier': {
'model':ExtraTreesClassifier(),
'name':'Extra Trees Classifier'
},
'KNeighborsClassifier': {
'model':KNeighborsClassifier(),
'name':'K Neighbors Classifier'
},
'DecisionTreeClassifier': {
'model':DecisionTreeClassifier(),
'name':'Decision Tree Classifier'
},
'AdaBoostClassifier': {
'model':AdaBoostClassifier(),#base_estimator = LinearRegression()),
'name':'AdaBoost'
},
'LogisticRegression': {
'model':LogisticRegression(),
'name':'Logistic Regression'
},
'SGDClassifier': {
'model':SGDClassifier(max_iter=1000),
'name':'Stochastic Gradient Descent'
},
}