<jupyter_start><jupyter_text>Core Model Results   Feature Scores<jupyter_code>from emat.analysis import feature_scores
feature_scores(
    scope=scope,
    design='ulhs',
    db=db,
)<jupyter_output><empty_output><jupyter_text>Scatter Plot Matrix<jupyter_code>from emat.analysis import display_experiments
figs = display_experiments(scope, 'ulhs', db=db)<jupyter_output><empty_output><jupyter_text>Interactive Visualizer<jupyter_code>from emat.analysis import Visualizer
viz = Visualizer(
    data='ulhs', 
    scope=scope, 
    reference_point='ref',
    db=db,
)
measures_1 = [
    'DVMTPerCapita', 'AirPollutionEm', 'FuelUse', 'UrbanTransitTrips',
    'UrbanHvyTrk_TotalDelay', 'UrbanHhCO2e', 'UrbanComSvcCO2e', 'UrbanWalkTrips',
    'UrbanVehOperatingCost', 'UrbanHhCO2eReduction',
]<jupyter_output><empty_output><jupyter_text>One Dimension Selectors<jupyter_code>viz.complete(measures=measures_1)<jupyter_output><empty_output><jupyter_text>Scatter Plot Matrix<jupyter_code>viz.splom(
    rows=measures_1,
    cols=['Income', 'FuelCost', 'ValueOfTime', 'LandUse'],
)<jupyter_output><empty_output><jupyter_text>Metamodel<jupyter_code>mm = emat.create_metamodel(scope, design_name='ulhs', db=db, include_measures=measures_1)<jupyter_output><empty_output><jupyter_text>Cross Validation<jupyter_code>mm.cross_val_scores()
mm_design = mm.design_experiments(n_samples=(140, 120), sampler='ulhs', jointly=False)
mm_design
mm_results = mm.run_experiments(mm_design)<jupyter_output><empty_output><jupyter_text>Interactive Visualizer<jupyter_code>viz1 = Visualizer(data=mm_results, scope=scope, db=db, reference_point='ref')<jupyter_output><empty_output><jupyter_text>One Dimensional Selectors<jupyter_code>viz1.complete(measures=measures_1)
viz1.two_way(x='FuelUse',y='Transit')
viz1.new_box('Breathable', upper_bounds={'AirPollutionEm':850_000})<jupyter_output><empty_output><jupyter_text>PRIM<jupyter_code>prim_all = viz1.prim(target="Breathable")
prim_all.tradeoff_selector()
prim_levers = viz1.prim(target="Breathable", data='levers')
prim_levers.tradeoff_selector()<jupyter_output><empty_output><jupyter_text>Policy Contrast<jupyter_code>import importlib
import emat.analysis.contrast
importlib.reload(emat.analysis.contrast)
from emat.analysis.contrast import *
ab = AB_Viewer(mm, background=250)
ab.interface
ab.get_figures(measures_1[:6])<jupyter_output><empty_output>
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