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added Regressor Classifier #1191 #1193

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22 changes: 21 additions & 1 deletion tpot/config/classifier.py
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Original file line number Diff line number Diff line change
Expand Up @@ -24,7 +24,7 @@
"""

import numpy as np

from sklearn.gaussian_process.kernels import WhiteKernel, Matern, RBF, DotProduct, RationalQuadratic, ExpSineSquared, ConstantKernel
# Check the TPOT documentation for information on the structure of config dicts

classifier_config_dict = {
Expand Down Expand Up @@ -124,6 +124,26 @@
'learning_rate_init': [1e-3, 1e-2, 1e-1, 0.5, 1.]
},

'sklearn.gaussian_process.GaussianProcessClassifier': {
'kernel' : [1.0*RBF(length_scale=0.5, length_scale_bounds=(1e-05, 100000.0)),
1.0*RationalQuadratic(length_scale=0.5, alpha=0.1),
1.0*ExpSineSquared(length_scale=0.5, periodicity=3.0,
length_scale_bounds=(1e-05, 100000.0),
periodicity_bounds=(1.0, 10.0)),
ConstantKernel(0.1, (0.01, 10.0))*(DotProduct(sigma_0=1.0, sigma_0_bounds=(0.1, 10.0)) ** 2),
1.0**2*Matern(length_scale=0.5, length_scale_bounds=(1e-05, 100000.0),
nu=0.5)],
'alpha': [5e-9,1e-3, 1e-2, 1e-1, 1., 10., 100.],
'normalize_y' : [True, False],
'optimizer' : ['fmin_l_bfgs_b']
},

'sklearn.ensemble.AdaBoostClassifier': {
'n_estimators': [100],
'learning_rate': [1e-3, 1e-2, 1e-1, 0.5, 1.],
'loss': ["linear", "square", "exponential"]
},

# Preprocesssors
'sklearn.preprocessing.Binarizer': {
'threshold': np.arange(0.0, 1.01, 0.05)
Expand Down
21 changes: 20 additions & 1 deletion tpot/config/regressor.py
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Original file line number Diff line number Diff line change
Expand Up @@ -24,7 +24,7 @@
"""

import numpy as np

from sklearn.gaussian_process.kernels import WhiteKernel, Matern, RBF, DotProduct, RationalQuadratic, ExpSineSquared, ConstantKernel
# Check the TPOT documentation for information on the structure of config dicts

regressor_config_dict = {
Expand Down Expand Up @@ -117,6 +117,25 @@
'power_t': [0.5, 0.0, 1.0, 0.1, 100.0, 10.0, 50.0]
},

'sklearn.gaussian_process.GaussianProcessRegressor': {
'kernel' : [1.0*RBF(length_scale=0.5, length_scale_bounds=(1e-05, 100000.0)),
1.0*RationalQuadratic(length_scale=0.5, alpha=0.1),
1.0*ExpSineSquared(length_scale=0.5, periodicity=3.0,
length_scale_bounds=(1e-05, 100000.0),
periodicity_bounds=(1.0, 10.0)),
ConstantKernel(0.1, (0.01, 10.0))*(DotProduct(sigma_0=1.0, sigma_0_bounds=(0.1, 10.0)) ** 2),
1.0**2*Matern(length_scale=0.5, length_scale_bounds=(1e-05, 100000.0),
nu=0.5)],
'alpha': [5e-9,1e-3, 1e-2, 1e-1, 1., 10., 100.],
'normalize_y' : [True, False],
'optimizer' : ['fmin_l_bfgs_b']
},

'sklearn.ensemble.AdaBoostClassifier': {
'n_estimators': [100],
'learning_rate': [1e-3, 1e-2, 1e-1, 0.5, 1.],
'loss': ["linear", "square", "exponential"]
},
# Preprocessors
'sklearn.preprocessing.Binarizer': {
'threshold': np.arange(0.0, 1.01, 0.05)
Expand Down