<s> import os <EOL> import xarray as xr <EOL> from sklearn . ensemble import RandomForestClassifier , RandomForestRegressor <EOL> from sklearn . linear_model import LinearRegression , SGDClassifier , RidgeClassifierCV , ARDRegression , BayesianRidge <EOL> from sklearn . externals import joblib <EOL> from sklearn import grid_search <EOL> __all__ = [ '<STR_LIT>' , '<STR_LIT>' , <EOL> '<STR_LIT>' , '<STR_LIT>' , <EOL> '<STR_LIT>' ] <EOL> MODELS_TYPE_DICT = { '<STR_LIT>' : RandomForestClassifier , <EOL> '<STR_LIT>' : RandomForestRegressor , <EOL> '<STR_LIT>' : SGDClassifier , <EOL> '<STR_LIT>' : LinearRegression , <EOL> '<STR_LIT>' : RidgeClassifierCV , <EOL> '<STR_LIT>' : ARDRegression , <EOL> '<STR_LIT>' : BayesianRidge } <EOL> def rectangularize_featureset ( featureset ) : <EOL> """<STR_LIT>""" <EOL> featureset = featureset . drop ( [ coord for coord in [ '<STR_LIT:target>' , '<STR_LIT:class>' ] <EOL> if coord in featureset ] ) <EOL> feature_df = featureset . to_dataframe ( ) <EOL> if '<STR_LIT>' in featureset : <EOL> feature_df = feature_df . unstack ( level = '<STR_LIT>' ) <EOL> if len ( featureset . channel ) == <NUM_LIT:1> : <EOL> feature_df . columns = [ pair [ <NUM_LIT:0> ] for pair in feature_df . columns ] <EOL> else : <EOL> feature_df . columns = [ '<STR_LIT:_>' . join ( [ str ( el ) for el in pair ] ) <EOL> for pair in feature_df . columns ] <EOL> return feature_df . loc [ featureset . name ] <EOL> def fit_model_optimize_hyperparams ( data , targets , model , params_to_optimize , <EOL> cv = None ) : <EOL> """<STR_LIT>""" <EOL> optimized_model = grid_search . GridSearchCV ( model , params_to_optimize , cv = cv ) <EOL> optimized_model . fit ( data , targets ) <EOL> return optimized_model <EOL> def build_model_from_featureset ( featureset , model = None , model_type = None , <EOL> model_options = { } , params_to_optimize = None , <EOL> cv = None ) : <EOL> """<STR_LIT>""" <EOL> if model is None : <EOL> if model_type : <EOL> model = MODELS_TYPE_DICT [ model_type ] ( ** model_options ) <EOL> else : <EOL> raise ValueError ( "<STR_LIT>" ) <EOL> feature_df = rectangularize_featureset ( featureset ) <EOL> if params_to_optimize : <EOL> model = fit_model_optimize_hyperparams ( feature_df , featureset [ '<STR_LIT:target>' ] , <EOL> model , params_to_optimize , cv ) <EOL> else : <EOL> model . fit ( feature_df , featureset [ '<STR_LIT:target>' ] ) <EOL> return model <EOL> def create_and_pickle_model ( featureset , model_type , output_path , <EOL> model_options = { } , params_to_optimize = None ) : <EOL> """<STR_LIT>""" <EOL> model = build_model_from_featureset ( featureset , model_type = model_type , <EOL> model_options = model_options , <EOL> params_to_optimize = params_to_optimize ) <EOL> joblib . dump ( model , output_path , compress = <NUM_LIT:3> ) <EOL> return model </s>
