from cesamo import CESAMOEncoder
from entity_embedding import EntityEmbeddingEncoder
from pattern_preserving import SimplePPEncoder, AgingPPEncoder, GeneticPPEncoder
Encoders = {'Ordinal': ce.OrdinalEncoder(),
'Polynomial': ce.PolynomialEncoder(),
'OneHot': ce.OneHotEncoder(),
'BackwardDifference': ce.BackwardDifferenceEncoder(),
'Helmert': ce.HelmertEncoder(),
'EntityEmbedding': EntityEmbeddingEncoder(),
'TargetEnc': ce.TargetEncoder(),
'WOE': ce.WOEEncoder(),
'CENG': CENGEncoder(verbose = 0),
'GeneticPP': GeneticPPEncoder(),
'AgingPP': AgingPPEncoder(),
'SimplePP': SimplePPEncoder(),
'CESAMOEncoder': CESAMOEncoder()}
"""END: Import encoders"""
"""START: Import models"""
try:
import sklearn.linear_model as lm
import sklearn.svm as svm
from sklearn.neighbors import KNeighborsClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.ensemble import RandomForestClassifier
from sklearn.neural_network import MLPClassifier
from sklearn.gaussian_process.kernels import RBF
rbf_kernel = 1.0 * RBF(1.0)
except:
from entity_embedding import EntityEmbeddingEncoder
from cesamo import CESAMOEncoder
Encoders = {
'Ordinal': ce.OrdinalEncoder(),
'Polynomial': ce.PolynomialEncoder(),
'OneHot': ce.OneHotEncoder(),
'BackwardDifference': ce.BackwardDifferenceEncoder(),
'Helmert': ce.HelmertEncoder(),
'EntityEmbedding': EntityEmbeddingEncoder(),
'TargetEnc': ce.TargetEncoder(),
'WOE': ce.WOEEncoder(),
'CENG': CENGEncoder(verbose=0),
'GeneticPP': GeneticPPEncoder(num_predictors=2),
'AgingPP': AgingPPEncoder(num_predictors=2),
'SimplePP': SimplePPEncoder(num_predictors=2),
'CESAMOEncoder': CESAMOEncoder()
}
if target_flag == 0:
del Encoders['EntityEmbedding']
del Encoders['TargetEnc']
del Encoders['WOE']
"""END: Import encoders"""
import time
def apply_encoder(X, y, encoder, target_flag):
tic = time.perf_counter()
'Helmert':
ce.HelmertEncoder(),
'EntityEmbedding':
EntityEmbeddingEncoder(),
'TargetEnc':
ce.TargetEncoder(),
'WOE':
ce.WOEEncoder(),
'CENG':
CENGEncoder(verbose=0),
'GeneticPP':
GeneticPPEncoder(estimator_name='LinearRegression', num_predictors=2),
'AgingPP':
AgingPPEncoder(estimator_name='LinearRegression', num_predictors=2),
'SimplePP':
SimplePPEncoder(estimator_name='LinearRegression', num_predictors=2),
'CESAMOEncoder':
CESAMOEncoder()
}
if target_flag == 0:
del Encoders['EntityEmbedding']
del Encoders['TargetEnc']
del Encoders['WOE']
"""END: Import encoders"""
import time
def apply_encoder(X, y, encoder, target_flag):