from __future__ import annotations
import importlib
import uuid
from typing import TYPE_CHECKING
import gamspy as gp
import gamspy.formulations.utils as utils
from gamspy.exceptions import ValidationError
from gamspy.formulations.ml.decision_tree_struct import DecisionTreeStruct
from gamspy.formulations.ml.regression_tree import RegressionTree
if TYPE_CHECKING:
from sklearn.ensemble import RandomForestRegressor
from sklearn.tree import DecisionTreeRegressor
[docs]
class RandomForest:
"""
Formulation generator for Random Forests in GAMSPy.
Parameters
----------
container : Container
Container that will contain the new variable and equations.
ensemble: RandomForestRegressor | None
- A fitted `sklearn.ensemble.RandomForestRegressor` instance,
- If `sklearn.ensemble.RandomForestRegressor` is not utilized, the ensembled trees information
can be supplied via a list of `DecisionTreeStruct` dataclasse instances, which represents
the same components as those in `sklearn.tree`.
See :meth:`DecisionTreeStruct <gamspy.formulations.DecisionTreeStruct>` for details on required attributes.
name_prefix : str | None
Prefix for generated GAMSPy symbols, by default None which means
random prefix. Using the same name_prefix in different formulations causes name
conflicts. Do not use the same name_prefix again.
Examples
--------
>>> import gamspy as gp
>>> import numpy as np
>>> from gamspy.math import dim
>>> np.random.seed(42)
>>> m = gp.Container()
>>> in_data = np.random.randint(0, 10, size=(5, 2))
>>> out_data = np.random.randint(1, 3, size=(5, 1))
>>> tree1_attribute = {
... "capacity": 7,
... "children_left": np.array([ 1, -1, 3, -1, 5, -1, -1]),
... "children_right": np.array([ 2, -1, 4, -1, 6, -1, -1]),
... "feature": np.array([ 1, -2, 0, -2, 1, -2, -2]),
... "n_features": 2,
... "threshold": np.array([ 2. , -2. , 5.5, -2. , 8.5, -2. , -2. ]),
... "value": np.array([[1.6 ],[1. ],[1.75],[2. ],[1.5 ],[1. ],[2. ]])
... }
>>> tree2_attribute = {
... "capacity": 3,
... "children_left": np.array([ 1, -1, -1]),
... "children_right": np.array([ 2, -1, -1]),
... "feature": np.array([ 0, -2, -2]),
... "n_features": 2,
... "threshold": np.array([ 1.5, -2. , -2. ]),
... "value": np.array([[1.4],[1. ],[2. ]])
... }
>>> forest = [gp.formulations.DecisionTreeStruct(**tree1_attribute), gp.formulations.DecisionTreeStruct(**tree2_attribute)]
>>> dt_model = gp.formulations.RandomForest(m, forest)
>>> x = gp.Variable(m, "x", domain=dim((5, 2)), type="positive")
>>> x.up[:, :] = 10
>>> y, eqns = dt_model(x)
>>> set_of_samples = y.domain[0]
>>> set_of_samples.name
'DenseDim5_1'
"""
def __init__(
self,
container: gp.Container,
ensemble: RandomForestRegressor | list[DecisionTreeStruct],
name_prefix: str | None = None,
):
if not isinstance(container, gp.Container):
raise ValidationError(f"{container} is not a gp.Container.")
def _validate_ensemble(
ensemble,
) -> list[DecisionTreeRegressor | DecisionTreeStruct]:
if isinstance(ensemble, list) and all(
isinstance(item, DecisionTreeStruct) for item in ensemble
):
return ensemble
else:
try:
sklearn_forest = importlib.import_module(
"sklearn.ensemble"
)
if isinstance(
ensemble, sklearn_forest.RandomForestRegressor
):
if not hasattr(ensemble, "estimators_"):
raise ValidationError(
f"{ensemble} must be a trained/fitted instance of >sklearn.ensemble.RandomForestRegressor<."
)
return ensemble.estimators_
else:
raise ValidationError(
f"{ensemble} must be an instance of either >sklearn.ensemble.RandomForestRegressor< or a list of >DecisionTreeStruct<"
)
except ModuleNotFoundError:
raise ValidationError(
">sklearn.ensemble< module not found."
) from None
self.container = container
if name_prefix is None:
name_prefix = str(uuid.uuid4()).split("-")[0]
self._name_prefix = name_prefix
self._list_of_trees: list[RegressionTree] = []
for tree in _validate_ensemble(ensemble):
rt_instance = RegressionTree(
self.container,
regressor=tree,
name_prefix=self._name_prefix,
)
self._list_of_trees.append(rt_instance)
[docs]
def __call__(
self,
input: gp.Parameter | gp.Variable,
M: float | None = None,
) -> tuple[gp.Variable, list[gp.Equation]]:
rf_out_list: list[gp.Variable] = []
rf_eqn_list: list[gp.Equation] = []
previous_value = gp.get_option("DOMAIN_VALIDATION")
gp.set_options({"DOMAIN_VALIDATION": 0})
for regression_tree in self._list_of_trees:
dt_out, dt_eqn, set_of_output_dim = regression_tree(
input, M, is_ensemble=True
)
rf_out_list.append(dt_out)
rf_eqn_list += dt_eqn
set_of_samples = input.domain[0]
out = gp.Variable._constructor_bypass(
self.container,
name=utils._generate_name("v", self._name_prefix, "real_output"),
domain=[set_of_samples, set_of_output_dim],
)
rf_eqn = gp.Equation._constructor_bypass(
self.container,
name=utils._generate_name("e", self._name_prefix, "rf_eqn"),
domain=[set_of_samples, set_of_output_dim],
description="predicted out times number of estimators should be equal to the random forest out",
)
self.container._synch_with_gams(gams_to_gamspy=True)
rf_eqn[...] = len(self._list_of_trees) * out == sum(rf_out_list)
rf_eqn_list.append(rf_eqn)
gp.set_options({"DOMAIN_VALIDATION": previous_value})
return out, rf_eqn_list
