<s> from sklearn . ensemble import RandomForestClassifier as randomforest <EOL> from sklearn . svm import SVC <EOL> from sklearn . base import ClassifierMixin <EOL> from sklearn . pipeline import Pipeline <EOL> from sklearn . preprocessing import StandardScaler <EOL> from sklearn . feature_selection import VarianceThreshold <EOL> from oddt . scoring . models . neuralnetwork import _ffnet_sklearned <EOL> __all__ = [ '<STR_LIT>' , '<STR_LIT>' , '<STR_LIT>' ] <EOL> class neuralnetwork ( ClassifierMixin ) : <EOL> def __init__ ( self , * args , ** kwargs ) : <EOL> """<STR_LIT>""" <EOL> local_kwargs = { key : kwargs . pop ( key ) for key in kwargs . keys ( ) if key != '<STR_LIT>' and len ( key . split ( '<STR_LIT>' , <NUM_LIT:1> ) ) == <NUM_LIT:1> } <EOL> self . pipeline = Pipeline ( [ ( '<STR_LIT>' , VarianceThreshold ( ) ) , <EOL> ( '<STR_LIT>' , StandardScaler ( ) ) , <EOL> ( '<STR_LIT>' , _ffnet_sklearned ( * args , ** local_kwargs ) ) <EOL> ] ) . set_params ( ** kwargs ) <EOL> def get_params ( self , deep = True ) : <EOL> return self . pipeline . get_params ( deep = deep ) <EOL> def set_params ( self , ** kwargs ) : <EOL> return self . pipeline . set_params ( ** kwargs ) <EOL> def fit ( self , descs , target_values , ** kwargs ) : <EOL> self . pipeline . fit ( descs , target_values , ** kwargs ) <EOL> return self <EOL> def predict ( self , descs ) : <EOL> return self . pipeline . predict ( descs ) <EOL> def score ( self , descs , target_values ) : <EOL> return self . pipeline . score ( descs , target_values ) <EOL> class svm ( ClassifierMixin ) : <EOL> def __init__ ( self , * args , ** kwargs ) : <EOL> """<STR_LIT>""" <EOL> local_kwargs = { key : kwargs . pop ( key ) for key in kwargs . keys ( ) if key != '<STR_LIT>' and len ( key . split ( '<STR_LIT>' , <NUM_LIT:1> ) ) == <NUM_LIT:1> } <EOL> self . pipeline = Pipeline ( [ ( '<STR_LIT>' , VarianceThreshold ( ) ) , <EOL> ( '<STR_LIT>' , StandardScaler ( ) ) , <EOL> ( '<STR_LIT>' , SVC ( * args , ** local_kwargs ) ) <EOL> ] ) . set_params ( ** kwargs ) <EOL> def get_params ( self , deep = True ) : <EOL> return self . pipeline . get_params ( deep = deep ) <EOL> def set_params ( self , ** kwargs ) : <EOL> return self . pipeline . set_params ( ** kwargs ) <EOL> def fit ( self , descs , target_values , ** kwargs ) : <EOL> self . pipeline . fit ( descs , target_values , ** kwargs ) <EOL> return self <EOL> def predict ( self , descs ) : <EOL> return self . pipeline . predict ( descs ) <EOL> def score ( self , descs , target_values ) : <EOL> return self . pipeline . score ( descs , target_values ) </s>
