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Bounds Handling in Linear Tree #152

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Bounds Handling in Linear Tree #152

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13 changes: 13 additions & 0 deletions src/omlt/linear_tree/lt_definition.py
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Original file line number Diff line number Diff line change
Expand Up @@ -54,6 +54,7 @@ def __init__(
self.__model = lt_regressor
self.__scaling_object = scaling_object

is_scaled = True
# Process input bounds to insure scaled input bounds exist for formulations
if scaled_input_bounds is None:
if unscaled_input_bounds is not None and scaling_object is not None:
Expand All @@ -72,13 +73,15 @@ def __init__(
# input bounds = unscaled input bounds
elif unscaled_input_bounds is not None and scaling_object is None:
scaled_input_bounds = unscaled_input_bounds
is_scaled = False
elif unscaled_input_bounds is None:
raise ValueError(
"Input Bounds needed to represent linear trees as MIPs"
)

self.__unscaled_input_bounds = unscaled_input_bounds
self.__scaled_input_bounds = scaled_input_bounds
self.__is_scaled = is_scaled

self.__splits, self.__leaves, self.__thresholds = _parse_tree_data(
lt_regressor, scaled_input_bounds
Expand All @@ -97,6 +100,16 @@ def scaled_input_bounds(self):
"""Returns dict containing scaled input bounds"""
return self.__scaled_input_bounds

@property
def unscaled_input_bounds(self):
"""Returns dict containing unscaled input bounds"""
return self.__unscaled_input_bounds

@property
def is_scaled(self):
"""Returns bool indicating whether model is scaled"""
return self.__is_scaled

@property
def splits(self):
"""Returns dict containing split information"""
Expand Down
45 changes: 29 additions & 16 deletions src/omlt/linear_tree/lt_formulation.py
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Original file line number Diff line number Diff line change
Expand Up @@ -229,7 +229,8 @@ def _add_gdp_formulation_to_block(

"""
leaves = model_definition.leaves
input_bounds = model_definition.scaled_input_bounds
scaled_input_bounds = model_definition.scaled_input_bounds
unscaled_input_bounds = model_definition.unscaled_input_bounds
n_inputs = model_definition.n_inputs

# The set of leaves and the set of features
Expand All @@ -242,17 +243,25 @@ def _add_gdp_formulation_to_block(

# Use the input_bounds and the linear models in the leaves to calculate
# the lower and upper bounds on the output variable. Required for Pyomo.GDP
output_bounds = _build_output_bounds(model_definition, input_bounds)
scaled_output_bounds = _build_output_bounds(model_definition, scaled_input_bounds)
unscaled_output_bounds = _build_output_bounds(
model_definition, unscaled_input_bounds
)

# Ouptuts are automatically scaled based on whether inputs are scaled
block.outputs.setub(output_bounds[1])
block.outputs.setlb(output_bounds[0])
block.scaled_outputs.setub(output_bounds[1])
block.scaled_outputs.setlb(output_bounds[0])

block.intermediate_output = pe.Var(
tree_ids, bounds=(output_bounds[0], output_bounds[1])
)
block.outputs.setub(unscaled_output_bounds[1])
block.outputs.setlb(unscaled_output_bounds[0])
block.scaled_outputs.setub(scaled_output_bounds[1])
block.scaled_outputs.setlb(scaled_output_bounds[0])

if model_definition.is_scaled is True:
block.intermediate_output = pe.Var(
tree_ids, bounds=(scaled_output_bounds[0], scaled_output_bounds[1])
)
else:
block.intermediate_output = pe.Var(
tree_ids, bounds=(unscaled_output_bounds[0], unscaled_output_bounds[1])
)

# Create a disjunct for each leaf containing the bound constraints
# and the linear model expression.
Expand Down Expand Up @@ -302,7 +311,8 @@ def _add_hybrid_formulation_to_block(block, model_definition, input_vars, output
output_vars -- output variable of the linear tree model
"""
leaves = model_definition.leaves
input_bounds = model_definition.scaled_input_bounds
scaled_input_bounds = model_definition.scaled_input_bounds
unscaled_input_bounds = model_definition.unscaled_input_bounds
n_inputs = model_definition.n_inputs

# The set of trees
Expand All @@ -318,13 +328,16 @@ def _add_hybrid_formulation_to_block(block, model_definition, input_vars, output

# Use the input_bounds and the linear models in the leaves to calculate
# the lower and upper bounds on the output variable. Required for Pyomo.GDP
output_bounds = _build_output_bounds(model_definition, input_bounds)
scaled_output_bounds = _build_output_bounds(model_definition, scaled_input_bounds)
unscaled_output_bounds = _build_output_bounds(
model_definition, unscaled_input_bounds
)

# Ouptuts are automatically scaled based on whether inputs are scaled
block.outputs.setub(output_bounds[1])
block.outputs.setlb(output_bounds[0])
block.scaled_outputs.setub(output_bounds[1])
block.scaled_outputs.setlb(output_bounds[0])
block.outputs.setub(scaled_output_bounds[1])
block.outputs.setlb(scaled_output_bounds[0])
block.scaled_outputs.setub(unscaled_output_bounds[1])
block.scaled_outputs.setlb(unscaled_output_bounds[0])

# Create the intermeditate variables. z is binary that indicates which leaf
# in tree t is returned. intermediate_output is the output of tree t and
Expand Down
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