<s> from cesium import build_model <EOL> from nose . tools import with_setup <EOL> import os <EOL> from os . path import join as pjoin <EOL> import shutil <EOL> import tempfile <EOL> from sklearn . externals import joblib <EOL> from sklearn . grid_search import GridSearchCV <EOL> from sklearn . ensemble import RandomForestClassifier , RandomForestRegressor <EOL> from sklearn . linear_model import LinearRegression , SGDClassifier , RidgeClassifierCV , ARDRegression , BayesianRidge <EOL> import xarray as xr <EOL> DATA_PATH = pjoin ( os . path . dirname ( __file__ ) , "<STR_LIT:data>" ) <EOL> MODEL_TYPES = [ '<STR_LIT>' , '<STR_LIT>' , <EOL> '<STR_LIT>' , '<STR_LIT>' , '<STR_LIT>' , <EOL> '<STR_LIT>' , '<STR_LIT>' ] <EOL> def setup ( module ) : <EOL> module . TEMP_DIR = tempfile . mkdtemp ( ) <EOL> def teardown ( module ) : <EOL> shutil . rmtree ( module . TEMP_DIR ) <EOL> def remove_output ( ) : <EOL> fnames = [ "<STR_LIT>" ] <EOL> for fname in fnames : <EOL> try : <EOL> os . remove ( pjoin ( TEMP_DIR , fname ) ) <EOL> except OSError : <EOL> pass <EOL> @ with_setup ( teardown = remove_output ) <EOL> def test_build_model ( ) : <EOL> """<STR_LIT>""" <EOL> for model_type in MODEL_TYPES : <EOL> if '<STR_LIT>' in model_type : <EOL> fset = xr . open_dataset ( pjoin ( DATA_PATH , "<STR_LIT>" ) ) <EOL> elif '<STR_LIT>' in model_type : <EOL> fset = xr . open_dataset ( pjoin ( DATA_PATH , "<STR_LIT>" ) ) <EOL> else : <EOL> raise ValueError ( "<STR_LIT>" ) <EOL> model_path = pjoin ( TEMP_DIR , "<STR_LIT>" ) <EOL> build_model . create_and_pickle_model ( fset , model_type , model_path ) <EOL> model = joblib . load ( model_path ) <EOL> assert isinstance ( model , <EOL> build_model . MODELS_TYPE_DICT [ model_type ] ) <EOL> os . remove ( pjoin ( TEMP_DIR , "<STR_LIT>" ) ) <EOL> @ with_setup ( teardown = remove_output ) <EOL> def test_fit_existing_model ( ) : <EOL> """<STR_LIT>""" <EOL> fset = xr . open_dataset ( pjoin ( DATA_PATH , "<STR_LIT>" ) ) <EOL> model = build_model . MODELS_TYPE_DICT [ '<STR_LIT>' ] ( ) <EOL> model = build_model . build_model_from_featureset ( fset , model ) <EOL> assert isinstance ( model , RandomForestClassifier ) <EOL> @ with_setup ( teardown = remove_output ) <EOL> def test_fit_existing_model_optimize ( ) : <EOL> """<STR_LIT>""" <EOL> fset = xr . open_dataset ( pjoin ( DATA_PATH , "<STR_LIT>" ) ) <EOL> model = build_model . MODELS_TYPE_DICT [ '<STR_LIT>' ] ( ) <EOL> model = build_model . MODELS_TYPE_DICT [ '<STR_LIT>' ] ( ) <EOL> model_options = { "<STR_LIT>" : "<STR_LIT>" , <EOL> "<STR_LIT>" : True } <EOL> params_to_optimize = { "<STR_LIT>" : [ <NUM_LIT:10> , <NUM_LIT:50> , <NUM_LIT:100> ] , <EOL> "<STR_LIT>" : [ <NUM_LIT:2> , <NUM_LIT:5> ] , <EOL> "<STR_LIT>" : [ "<STR_LIT>" , <NUM_LIT:3> ] } <EOL> model = build_model . build_model_from_featureset ( fset , model , None , <EOL> model_options , <EOL> params_to_optimize ) <EOL> assert hasattr ( model , "<STR_LIT>" ) <EOL> assert hasattr ( model , "<STR_LIT>" ) <EOL> assert isinstance ( model , GridSearchCV ) <EOL> assert isinstance ( model . best_estimator_ , RandomForestClassifier ) <EOL> @ with_setup ( teardown = remove_output ) <EOL> def test_fit_optimize ( ) : <EOL> """<STR_LIT>""" <EOL> fset = xr . open_dataset ( pjoin ( DATA_PATH , "<STR_LIT>" ) ) <EOL> model = build_model . MODELS_TYPE_DICT [ '<STR_LIT>' ] ( ) <EOL> model_options = { "<STR_LIT>" : "<STR_LIT>" , "<STR_LIT>" : True } <EOL> model = build_model . MODELS_TYPE_DICT [ '<STR_LIT>' ] ( ** model_options ) <EOL> feature_df = build_model . rectangularize_featureset ( fset ) <EOL> params_to_optimize = { "<STR_LIT>" : [ <NUM_LIT:10> , <NUM_LIT:50> , <NUM_LIT:100> ] , <EOL> "<STR_LIT>" : [ <NUM_LIT:2> , <NUM_LIT:5> ] , <EOL> "<STR_LIT>" : [ "<STR_LIT>" , <NUM_LIT:3> ] } <EOL> model = build_model . fit_model_optimize_hyperparams ( feature_df , <EOL> fset [ '<STR_LIT:target>' ] , model , <EOL> params_to_optimize ) <EOL> assert hasattr ( model , "<STR_LIT>" ) <EOL> assert hasattr ( model , "<STR_LIT>" ) <EOL> assert isinstance ( model , GridSearchCV ) <EOL> assert isinstance ( model . best_estimator_ , RandomForestClassifier ) </s>
