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_partial_dependence_container.py
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_partial_dependence_container.py
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from pandas.api.types import is_numeric_dtype
from .._plot_container import PlotContainer
class PartialDependenceContainer(PlotContainer):
info = {
'name': "Partial Dependence",
'plotType': 'PartialDependence',
'plotCategory': 'Dataset Level',
'requiredParams': ['model', 'variable']
}
options_category = 'PartialDependence'
options = {
'grid_type': { 'default': 'quantile', 'desc': 'grid type "quantile" or "uniform"'},
'grid_points': { 'default': 101, 'desc': 'Maximum number of points for profile' },
'N': { 'default': 500, 'desc': 'Number of observations to use. None for all.' }
}
def _fit(self, model, variable):
if not variable.variable in model.variables:
raise Exception('Variable is not a column of explainer')
if is_numeric_dtype(model.explainer.data[variable.variable]):
self.plot_component = 'LinearDependence'
profile = model.explainer.model_profile(
type='partial',
variables=variable.variable,
variable_type='numerical',
grid_points=self.get_option('grid_points'),
variable_splits_type=self.get_option('grid_type'),
N=self.get_option('N'),
verbose=False
)
else:
self.plot_component = 'CategoricalDependence'
profile = model.explainer.model_profile(
type='partial',
variables=variable.variable,
variable_type='categorical',
N=self.get_option('N'),
verbose=False
)
self.data = {
'x': profile.result['_x_'].tolist(),
'y': profile.result['_yhat_'].tolist(),
'variable': variable.variable,
'base': profile.mean_prediction
}