# Path: bartpy/model.py
# class Model:
# 
#     def __init__(self,
#                  data: Optional[Data],
#                  sigma: Sigma,
#                  trees: Optional[List[Tree]]=None,
#                  n_trees: int=50,
#                  alpha: float=0.95,
#                  beta: float=2.,
#                  k: int=2.,
#                  initializer: Initializer=SklearnTreeInitializer()):
# 
#         self.data = deepcopy(data)
#         self.alpha = float(alpha)
#         self.beta = float(beta)
#         self.k = k
#         self._sigma = sigma
#         self._prediction = None
#         self._initializer = initializer
# 
#         if trees is None:
#             self.n_trees = n_trees
#             self._trees = self.initialize_trees()
#             if self._initializer is not None:
#                 self._initializer.initialize_trees(self.refreshed_trees())
#         else:
#             self.n_trees = len(trees)
#             self._trees = trees
# 
#     def initialize_trees(self) -> List[Tree]:
#         trees = [Tree([LeafNode(Split(deepcopy(self.data)))]) for _ in range(self.n_trees)]
#         for tree in trees:
#             tree.update_y(tree.update_y(self.data.y.values / self.n_trees))
#         return trees
# 
#     def residuals(self) -> np.ndarray:
#         return self.data.y.values - self.predict()
# 
#     def unnormalized_residuals(self) -> np.ndarray:
#         return self.data.y.unnormalized_y - self.data.y.unnormalize_y(self.predict())
# 
#     def predict(self, X: np.ndarray=None) -> np.ndarray:
#         if X is not None:
#             return self._out_of_sample_predict(X)
#         return np.sum([tree.predict() for tree in self.trees], axis=0)
# 
#     def _out_of_sample_predict(self, X: np.ndarray) -> np.ndarray:
#         if type(X) == pd.DataFrame:
#             X: pd.DataFrame = X
#             X = X.values
#         return np.sum([tree.predict(X) for tree in self.trees], axis=0)
# 
#     @property
#     def trees(self) -> List[Tree]:
#         return self._trees
# 
#     def refreshed_trees(self) -> Generator[Tree, None, None]:
#         if self._prediction is None:
#             self._prediction = self.predict()
#         for tree in self._trees:
#             self._prediction -= tree.predict()
#             tree.update_y(self.data.y.values - self._prediction)
#             yield tree
#             self._prediction += tree.predict()
# 
#     @property
#     def sigma_m(self) -> float:
#         return 0.5 / (self.k * np.power(self.n_trees, 0.5))
# 
#     @property
#     def sigma(self) -> Sigma:
#         return self._sigma
# 
# Path: bartpy/samplers/sampler.py
# class Sampler(ABC):
# 
#     @abstractmethod
#     def step(self, model: Model, tree: Tree) -> bool:
#         raise NotImplementedError()
# 
# Path: bartpy/samplers/schedule.py
# class SampleSchedule:
#     """
#     The SampleSchedule class is responsible for handling the ordering of sampling within a Gibbs step
#     It is useful to encapsulate this logic if we wish to expand the model
# 
#     Parameters
#     ----------
#     tree_sampler: TreeMutationSampler
#         How to sample tree mutation space
#     leaf_sampler: LeafNodeSampler
#         How to sample leaf node predictions
#     sigma_sampler: SigmaSampler
#         How to sample sigma values
#     """
# 
#     def __init__(self,
#                  tree_sampler: TreeMutationSampler,
#                  leaf_sampler: LeafNodeSampler,
#                  sigma_sampler: SigmaSampler):
#         self.leaf_sampler = leaf_sampler
#         self.sigma_sampler = sigma_sampler
#         self.tree_sampler = tree_sampler
# 
#     def steps(self, model: Model) -> Generator[Tuple[Text, Callable[[], float]], None, None]:
#         """
#         Create a generator of the steps that need to be called to complete a full Gibbs sample
# 
#         Parameters
#         ----------
#         model: Model
#             The model being sampled
# 
#         Returns
#         -------
#         Generator[Callable[[Model], Sampler], None, None]
#             A generator a function to be called
#         """
#         for tree in model.refreshed_trees():
#             yield "Tree", lambda: self.tree_sampler.step(model, tree)
# 
#             for leaf_node in tree.leaf_nodes:
#                 yield "Node", lambda: self.leaf_sampler.step(model, leaf_node)
#         yield "Node", lambda: self.sigma_sampler.step(model, model.sigma)
# 
# Path: bartpy/trace.py
# class TraceLogger():
# 
#     def __init__(self,
#                  f_tree_mutation_log: Callable[[TreeMutation], Any]=lambda x: x is not None,
#                  f_model_log: Callable[[Model], Any]=lambda x: deep_copy_model(x),
#                  f_in_sample_prediction_log: Callable[[np.ndarray], Any]=lambda x: x):
#         self.f_tree_mutation_log = f_tree_mutation_log
#         self.f_model_log = f_model_log
#         self.f_in_sample_prediction_log = f_in_sample_prediction_log
# 
#     def __getitem__(self, item: str):
#         if item == "Tree":
#             return self.f_tree_mutation_log
#         if item == "Model":
#             return self.f_model_log
#         if item == "In Sample Prediction":
#             return self.f_in_sample_prediction_log
#         if item in ["Node", "Sigma"]:
#             return lambda x: None
#         else:
#             raise KeyError("No method for key {}".format(item))


from collections import defaultdict
from typing import List, Mapping, Union, Any, Type
from tqdm import tqdm
from bartpy.model import Model
from bartpy.samplers.sampler import Sampler
from bartpy.samplers.schedule import SampleSchedule
from bartpy.trace import TraceLogger
import numpy as np



Chain = Mapping[str, Union[List[Any], np.ndarray]]


class ModelSampler(Sampler):

    def __init__(self,

# Path: bartpy/model.py
# class Model:
# 
#     def __init__(self,
#                  data: Optional[Data],
#                  sigma: Sigma,
#                  trees: Optional[List[Tree]]=None,
#                  n_trees: int=50,
#                  alpha: float=0.95,
#                  beta: float=2.,
#                  k: int=2.,
#                  initializer: Initializer=SklearnTreeInitializer()):
# 
#         self.data = deepcopy(data)
#         self.alpha = float(alpha)
#         self.beta = float(beta)
#         self.k = k
#         self._sigma = sigma
#         self._prediction = None
#         self._initializer = initializer
# 
#         if trees is None:
#             self.n_trees = n_trees
#             self._trees = self.initialize_trees()
#             if self._initializer is not None:
#                 self._initializer.initialize_trees(self.refreshed_trees())
#         else:
#             self.n_trees = len(trees)
#             self._trees = trees
# 
#     def initialize_trees(self) -> List[Tree]:
#         trees = [Tree([LeafNode(Split(deepcopy(self.data)))]) for _ in range(self.n_trees)]
#         for tree in trees:
#             tree.update_y(tree.update_y(self.data.y.values / self.n_trees))
#         return trees
# 
#     def residuals(self) -> np.ndarray:
#         return self.data.y.values - self.predict()
# 
#     def unnormalized_residuals(self) -> np.ndarray:
#         return self.data.y.unnormalized_y - self.data.y.unnormalize_y(self.predict())
# 
#     def predict(self, X: np.ndarray=None) -> np.ndarray:
#         if X is not None:
#             return self._out_of_sample_predict(X)
#         return np.sum([tree.predict() for tree in self.trees], axis=0)
# 
#     def _out_of_sample_predict(self, X: np.ndarray) -> np.ndarray:
#         if type(X) == pd.DataFrame:
#             X: pd.DataFrame = X
#             X = X.values
#         return np.sum([tree.predict(X) for tree in self.trees], axis=0)
# 
#     @property
#     def trees(self) -> List[Tree]:
#         return self._trees
# 
#     def refreshed_trees(self) -> Generator[Tree, None, None]:
#         if self._prediction is None:
#             self._prediction = self.predict()
#         for tree in self._trees:
#             self._prediction -= tree.predict()
#             tree.update_y(self.data.y.values - self._prediction)
#             yield tree
#             self._prediction += tree.predict()
# 
#     @property
#     def sigma_m(self) -> float:
#         return 0.5 / (self.k * np.power(self.n_trees, 0.5))
# 
#     @property
#     def sigma(self) -> Sigma:
#         return self._sigma
# 
# Path: bartpy/samplers/sampler.py
# class Sampler(ABC):
# 
#     @abstractmethod
#     def step(self, model: Model, tree: Tree) -> bool:
#         raise NotImplementedError()
# 
# Path: bartpy/samplers/schedule.py
# class SampleSchedule:
#     """
#     The SampleSchedule class is responsible for handling the ordering of sampling within a Gibbs step
#     It is useful to encapsulate this logic if we wish to expand the model
# 
#     Parameters
#     ----------
#     tree_sampler: TreeMutationSampler
#         How to sample tree mutation space
#     leaf_sampler: LeafNodeSampler
#         How to sample leaf node predictions
#     sigma_sampler: SigmaSampler
#         How to sample sigma values
#     """
# 
#     def __init__(self,
#                  tree_sampler: TreeMutationSampler,
#                  leaf_sampler: LeafNodeSampler,
#                  sigma_sampler: SigmaSampler):
#         self.leaf_sampler = leaf_sampler
#         self.sigma_sampler = sigma_sampler
#         self.tree_sampler = tree_sampler
# 
#     def steps(self, model: Model) -> Generator[Tuple[Text, Callable[[], float]], None, None]:
#         """
#         Create a generator of the steps that need to be called to complete a full Gibbs sample
# 
#         Parameters
#         ----------
#         model: Model
#             The model being sampled
# 
#         Returns
#         -------
#         Generator[Callable[[Model], Sampler], None, None]
#             A generator a function to be called
#         """
#         for tree in model.refreshed_trees():
#             yield "Tree", lambda: self.tree_sampler.step(model, tree)
# 
#             for leaf_node in tree.leaf_nodes:
#                 yield "Node", lambda: self.leaf_sampler.step(model, leaf_node)
#         yield "Node", lambda: self.sigma_sampler.step(model, model.sigma)
# 
# Path: bartpy/trace.py
# class TraceLogger():
# 
#     def __init__(self,
#                  f_tree_mutation_log: Callable[[TreeMutation], Any]=lambda x: x is not None,
#                  f_model_log: Callable[[Model], Any]=lambda x: deep_copy_model(x),
#                  f_in_sample_prediction_log: Callable[[np.ndarray], Any]=lambda x: x):
#         self.f_tree_mutation_log = f_tree_mutation_log
#         self.f_model_log = f_model_log
#         self.f_in_sample_prediction_log = f_in_sample_prediction_log
# 
#     def __getitem__(self, item: str):
#         if item == "Tree":
#             return self.f_tree_mutation_log
#         if item == "Model":
#             return self.f_model_log
#         if item == "In Sample Prediction":
#             return self.f_in_sample_prediction_log
#         if item in ["Node", "Sigma"]:
#             return lambda x: None
#         else:
#             raise KeyError("No method for key {}".format(item))


# Path: bartpy/samplers/modelsampler.py
from collections import defaultdict
from typing import List, Mapping, Union, Any, Type
from tqdm import tqdm
from bartpy.model import Model
from bartpy.samplers.sampler import Sampler
from bartpy.samplers.schedule import SampleSchedule
from bartpy.trace import TraceLogger
import numpy as np




Chain = Mapping[str, Union[List[Any], np.ndarray]]


class ModelSampler(Sampler):

    def __init__(self,

                 schedule: SampleSchedule,