# Source code for getml.predictors.xgboost_regressor

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"""
A gradient boosting model for predicting regression problems.
"""

from dataclasses import dataclass

from .predictor import _Predictor
from .xgboost_classifier import _validate_xgboost_parameters

# ------------------------------------------------------------------------------

[docs]@dataclass(repr=False)
class XGBoostRegressor(_Predictor):
"""Gradient boosting regressor based on xgboost <https://xgboost.readthedocs.io/en/latest/>_.

XGBoost is an implementation of the gradient tree boosting algorithm that
is widely recognized for its efficiency and predictive accuracy.

Gradient tree boosting trains an ensemble of decision trees by training
each tree to predict the *prediction error of all previous trees* in the
ensemble:

.. math::

\\min_{\\nabla f_{t,i}} \\sum_i L(f_{t-1,i} + \\nabla f_{t,i}; y_i),

where :math:\\nabla f_{t,i} is the prediction generated by the
newest decision tree for sample :math:i and :math:f_{t-1,i} is
the prediction generated by all previous trees, :math:L(...) is
the loss function used and :math:y_i is the :ref:target
<annotating_roles_target> we are trying to predict.

XGBoost implements this general approach by adding two specific components:

1. The loss function :math:L(...) is approximated using a Taylor series.

2. The leaves of the decision tree :math:\\nabla f_{t,i} contain weights
that can be regularized.

These weights are calculated as follows:

.. math::

w_l = -\\frac{\\sum_{i \\in l} g_i}{ \\sum_{i \\in l} h_i + \\lambda},

where :math:g_i and :math:h_i are the first and second order derivative
of :math:L(...) w.r.t. :math:f_{t-1,i}, :math:w_l denotes the weight
on leaf :math:l and :math:i \\in l denotes all samples on that leaf.

:math:\\lambda is the regularization parameter reg_lambda.
This hyperparameter can be set by the users or the hyperparameter
optimization algorithm to avoid overfitting.

Args:
booster (string, optional):

Which base classifier to use.

Possible values:

* 'gbtree': normal gradient boosted decision trees
* 'gblinear': uses a linear model instead of decision trees
Please also refer to the remarks on *rate_drop* for further
explanation on 'dart'.

colsample_bylevel (float, optional):

Subsample ratio for the columns used, for each level
inside a tree.

Note that XGBoost grows its trees level-by-level, not
node-by-node.
At each level, a subselection of the features will be randomly
picked and the best
feature for each split will be chosen. This hyperparameter
determines the share of features randomly picked at each level.
When set to 1, then now such sampling takes place.

*Decreasing* this hyperparameter reduces the
likelihood of overfitting.

Range: (0, 1]

colsample_bytree (float, optional):

Subsample ratio for the columns used, for each tree.
This means that for each tree, a subselection
of the features will be randomly chosen. This hyperparameter
determines the share of features randomly picked for each tree.

*Decreasing* this hyperparameter reduces the
likelihood of overfitting.

Range: (0, 1]

gamma (float, optional):

Minimum loss reduction required for any update
to the tree. This means that every potential update
will first be evaluated for its improvement to the loss
function. If the improvement exceeds gamma,
the update will be accepted.

*Increasing* this hyperparameter reduces the
likelihood of overfitting.

Range: [0, :math:\\infty]

learning_rate (float, optional):

Learning rate for the gradient boosting algorithm.
When a new tree :math:\\nabla f_{t,i} is trained,
it will be added to the existing trees
:math:f_{t-1,i}. Before doing so, it will be
multiplied by the *learning_rate*.

*Decreasing* this hyperparameter reduces the
likelihood of overfitting.

Range: [0, 1]

max_delta_step (float, optional):

The maximum delta step allowed for the weight estimation
of each tree.

*Decreasing* this hyperparameter reduces the
likelihood of overfitting.

Range: [0, :math:\\infty)

max_depth (int, optional):

Maximum allowed depth of the trees.

*Decreasing* this hyperparameter reduces the
likelihood of overfitting.

Range: [0, :math:\\infty]

min_child_weights (float, optional):

Minimum sum of weights needed in each child node for a
split. The idea here is that any leaf should have
a minimum number of samples in order to avoid overfitting.
This very common form of regularizing decision trees is
slightly
modified to refer to weights instead of number of samples,
but the basic idea is the same.

*Increasing* this hyperparameter reduces the
likelihood of overfitting.

Range: [0, :math:\\infty]

n_estimators (int, optional):

Number of estimators (trees).

*Decreasing* this hyperparameter reduces the
likelihood of overfitting.

Range: [10, :math:\\infty]

normalize_type (string, optional):

This determines how to normalize trees during 'dart'.

Possible values:

* 'tree': a new tree has the same weight as a single
dropped tree.

* 'forest': a new tree has the same weight as a the sum of
all dropped trees.

Please also refer to the remarks on
*rate_drop* for further explanation.

Will be ignored if booster is not set to 'dart'.

n_jobs (int, optional):

Number of parallel threads. When set to zero, then
the optimal number of threads will be inferred automatically.

Range: [0, :math:\\infty]

objective (string, optional):

Specify the learning task and the corresponding
learning objective.

Possible values:

* 'reg:squarederror'
* 'reg:tweedie'

one_drop (bool, optional):

If set to True, then at least one tree will always be
dropped out. Setting this hyperparameter to *true* reduces
the likelihood of overfitting.

Please also refer to the remarks on
*rate_drop* for further explanation.

Will be ignored if booster is not set to 'dart'.

rate_drop (float, optional):

Dropout rate for trees - determines the probability
that a tree will be dropped out. Dropout is an
algorithm that enjoys considerable popularity in
the deep learning community. It means that every node can
be randomly removed during training.

This approach
can also be applied to gradient boosting, where it
means that every tree can be randomly removed with
a certain probability. Said probability is determined
by *rate_drop*. Dropout for gradient boosting is
referred to as the 'dart' algorithm.

*Increasing* this hyperparameter reduces the
likelihood of overfitting.

Will be ignored if booster is not set to 'dart'.

reg_alpha(float, optional):

L1 regularization on the weights.

*Increasing* this hyperparameter reduces the
likelihood of overfitting.

Range: [0, :math:\\infty]

reg_lambda (float, optional):

L2 regularization on the weights. Please refer to
the introductory remarks to understand how this

*Increasing* this hyperparameter reduces the
likelihood of overfitting.

Range: [0, :math:\\infty]

sample_type (string, optional):

Possible values:

* 'uniform': every tree is equally likely to be dropped
out

* 'weighted': the dropout probability will be proportional
to a tree's weight

Please also refer to the remarks on
*rate_drop* for further explanation.

Will be ignored if booster is not set to 'dart'.

silent (bool, optional):

In silent mode, XGBoost will not print out information on
the training progress.

skip_drop (float, optional):

Probability of skipping the dropout during a given
iteration. Please also refer to the remarks on
*rate_drop* for further explanation.

*Increasing* this hyperparameter reduces the
likelihood of overfitting.

Will be ignored if booster is not set to 'dart'.

Range: [0, 1]

subsample (float, optional):

Subsample ratio from the training set. This means
that for every tree a subselection of *samples*
from the training set will be included into training.
Please note that this samples *without* replacement -
the common approach for random forests is to sample
*with* replace.

*Decreasing* this hyperparameter reduces the
likelihood of overfitting.

Range: (0, 1]

Raises:
TypeError: If any of the input arguments does not match its
expected type.

"""

booster: str = "gbtree"
colsample_bylevel: float = 1.0
colsample_bytree: float = 1.0
gamma: float = 0.0
learning_rate: float = 0.1
max_delta_step: float = 0.0
max_depth: int = 3
min_child_weights: float = 1.0
n_estimators: int = 100
normalize_type: str = "tree"
num_parallel_tree: int = 1
n_jobs: int = 1
objective: str = "reg:squarederror"
one_drop: bool = False
rate_drop: float = 0.0
reg_alpha: float = 0.0
reg_lambda: float = 1.0
sample_type: str = "uniform"
silent: bool = True
skip_drop: float = 0.0
subsample: float = 1.0

# ----------------------------------------------------------------

[docs]    def validate(self, params=None):
"""Checks both the types and the values of all instance
variables and raises an exception if something is off.

Args:
params (dict, optional): A dictionary containing
the parameters to validate. If not is passed,
the own parameters will be validated.

Examples:

.. code-block:: python

x = getml.predictors.XGBoostRegressor()
x.gamma = 200
x.validate()

Raises:
KeyError: If an unsupported instance variable is
encountered.

TypeError: If any instance variable is of wrong type.

ValueError: If any instance variable does not match its
possible choices (string) or is out of the expected
bounds (numerical).

Note:

This method is called at end of the __init__ constructor
and every time before the predictor - or a class holding
it as an instance variable - is send to the getML engine.
"""

# ------------------------------------------------------------

if params is None:
params = self.__dict__
else:
params = {**self.__dict__, **params}

if not isinstance(params, dict):
raise ValueError("params must be None or a dictionary!")

_validate_xgboost_parameters(params)

# ------------------------------------------------------------

if params["objective"] not in ["reg:squarederror", "reg:tweedie", "reg:linear"]:
raise ValueError(
"""'objective' supported in XGBoostRegressor
are 'reg:squarederror', 'reg:tweedie',
and 'reg:linear'"""
)

# ------------------------------------------------------------------------------