"""
CatBoost model
--------------
CatBoost based regression model.
This implementation comes with the ability to produce probabilistic forecasts.
"""
from typing import Dict, List, Optional, Sequence, Tuple, Union
import numpy as np
from catboost import CatBoostRegressor, Pool
from darts.logging import get_logger
from darts.models.forecasting.regression_model import RegressionModel, _LikelihoodMixin
from darts.timeseries import TimeSeries
logger = get_logger(__name__)
[docs]class CatBoostModel(RegressionModel, _LikelihoodMixin):
def __init__(
self,
lags: Union[int, list] = None,
lags_past_covariates: Union[int, List[int]] = None,
lags_future_covariates: Union[Tuple[int, int], List[int]] = None,
output_chunk_length: int = 1,
output_chunk_shift: int = 0,
add_encoders: Optional[dict] = None,
likelihood: str = None,
quantiles: List = None,
random_state: Optional[int] = None,
multi_models: Optional[bool] = True,
use_static_covariates: bool = True,
**kwargs,
):
"""CatBoost Model
Parameters
----------
lags
Lagged target `series` values used to predict the next time step/s.
If an integer, must be > 0. Uses the last `n=lags` past lags; e.g. `(-1, -2, ..., -lags)`, where `0`
corresponds the first predicted time step of each sample. If `output_chunk_shift > 0`, then
lag `-1` translates to `-1 - output_chunk_shift` steps before the first prediction step.
If a list of integers, each value must be < 0. Uses only the specified values as lags.
If a dictionary, the keys correspond to the `series` component names (of the first series when
using multiple series) and the values correspond to the component lags (integer or list of integers). The
key 'default_lags' can be used to provide default lags for un-specified components. Raises and error if some
components are missing and the 'default_lags' key is not provided.
lags_past_covariates
Lagged `past_covariates` values used to predict the next time step/s.
If an integer, must be > 0. Uses the last `n=lags_past_covariates` past lags; e.g. `(-1, -2, ..., -lags)`,
where `0` corresponds to the first predicted time step of each sample. If `output_chunk_shift > 0`, then
lag `-1` translates to `-1 - output_chunk_shift` steps before the first prediction step.
If a list of integers, each value must be < 0. Uses only the specified values as lags.
If a dictionary, the keys correspond to the `past_covariates` component names (of the first series when
using multiple series) and the values correspond to the component lags (integer or list of integers). The
key 'default_lags' can be used to provide default lags for un-specified components. Raises and error if some
components are missing and the 'default_lags' key is not provided.
lags_future_covariates
Lagged `future_covariates` values used to predict the next time step/s. The lags are always relative to the
first step in the output chunk, even when `output_chunk_shift > 0`.
If a tuple of `(past, future)`, both values must be > 0. Uses the last `n=past` past lags and `n=future`
future lags; e.g. `(-past, -(past - 1), ..., -1, 0, 1, .... future - 1)`, where `0` corresponds the first
predicted time step of each sample. If `output_chunk_shift > 0`, the position of negative lags differ from
those of `lags` and `lags_past_covariates`. In this case a future lag `-5` would point at the same
step as a target lag of `-5 + output_chunk_shift`.
If a list of integers, uses only the specified values as lags.
If a dictionary, the keys correspond to the `future_covariates` component names (of the first series when
using multiple series) and the values correspond to the component lags (tuple or list of integers). The key
'default_lags' can be used to provide default lags for un-specified components. Raises and error if some
components are missing and the 'default_lags' key is not provided.
output_chunk_length
Number of time steps predicted at once (per chunk) by the internal model. It is not the same as forecast
horizon `n` used in `predict()`, which is the desired number of prediction points generated using a
one-shot- or autoregressive forecast. Setting `n <= output_chunk_length` prevents auto-regression. This is
useful when the covariates don't extend far enough into the future, or to prohibit the model from using
future values of past and / or future covariates for prediction (depending on the model's covariate
support).
output_chunk_shift
Optionally, the number of steps to shift the start of the output chunk into the future (relative to the
input chunk end). This will create a gap between the input (history of target and past covariates) and
output. If the model supports `future_covariates`, the `lags_future_covariates` are relative to the first
step in the shifted output chunk. Predictions will start `output_chunk_shift` steps after the end of the
target `series`. If `output_chunk_shift` is set, the model cannot generate autoregressive predictions
(`n > output_chunk_length`).
add_encoders
A large number of past and future covariates can be automatically generated with `add_encoders`.
This can be done by adding multiple pre-defined index encoders and/or custom user-made functions that
will be used as index encoders. Additionally, a transformer such as Darts' :class:`Scaler` can be added to
transform the generated covariates. This happens all under one hood and only needs to be specified at
model creation.
Read :meth:`SequentialEncoder <darts.dataprocessing.encoders.SequentialEncoder>` to find out more about
``add_encoders``. Default: ``None``. An example showing some of ``add_encoders`` features:
.. highlight:: python
.. code-block:: python
def encode_year(idx):
return (idx.year - 1950) / 50
add_encoders={
'cyclic': {'future': ['month']},
'datetime_attribute': {'future': ['hour', 'dayofweek']},
'position': {'past': ['relative'], 'future': ['relative']},
'custom': {'past': [encode_year]},
'transformer': Scaler(),
'tz': 'CET'
}
..
likelihood
Can be set to 'quantile', 'poisson' or 'gaussian'. If set, the model will be probabilistic,
allowing sampling at prediction time. When set to 'gaussian', the model will use CatBoost's
'RMSEWithUncertainty' loss function. When using this loss function, CatBoost returns a mean
and variance couple, which capture data (aleatoric) uncertainty.
This will overwrite any `objective` parameter.
quantiles
Fit the model to these quantiles if the `likelihood` is set to `quantile`.
random_state
Control the randomness in the fitting procedure and for sampling.
Default: ``None``.
multi_models
If True, a separate model will be trained for each future lag to predict. If False, a single model is
trained to predict at step 'output_chunk_length' in the future. Default: True.
use_static_covariates
Whether the model should use static covariate information in case the input `series` passed to ``fit()``
contain static covariates. If ``True``, and static covariates are available at fitting time, will enforce
that all target `series` have the same static covariate dimensionality in ``fit()`` and ``predict()``.
**kwargs
Additional keyword arguments passed to `catboost.CatBoostRegressor`.
Examples
--------
>>> from darts.datasets import WeatherDataset
>>> from darts.models import CatBoostModel
>>> series = WeatherDataset().load()
>>> # predicting atmospheric pressure
>>> target = series['p (mbar)'][:100]
>>> # optionally, use past observed rainfall (pretending to be unknown beyond index 100)
>>> past_cov = series['rain (mm)'][:100]
>>> # optionally, use future temperatures (pretending this component is a forecast)
>>> future_cov = series['T (degC)'][:106]
>>> # predict 6 pressure values using the 12 past values of pressure and rainfall, as well as the 6 temperature
>>> # values corresponding to the forecasted period
>>> model = CatBoostModel(
>>> lags=12,
>>> lags_past_covariates=12,
>>> lags_future_covariates=[0,1,2,3,4,5],
>>> output_chunk_length=6
>>> )
>>> model.fit(target, past_covariates=past_cov, future_covariates=future_cov)
>>> pred = model.predict(6)
>>> pred.values()
array([[1006.4153701 ],
[1006.41907237],
[1006.30872957],
[1006.28614154],
[1006.22355514],
[1006.21607546]])
"""
kwargs["random_state"] = random_state # seed for tree learner
self.kwargs = kwargs
self._median_idx = None
self._model_container = None
self._rng = None
self.likelihood = likelihood
self.quantiles = None
self._output_chunk_length = output_chunk_length
likelihood_map = {
"quantile": None,
"poisson": "Poisson",
"gaussian": "RMSEWithUncertainty",
"RMSEWithUncertainty": "RMSEWithUncertainty",
}
available_likelihoods = list(likelihood_map.keys())
if likelihood is not None:
self._check_likelihood(likelihood, available_likelihoods)
self._rng = np.random.default_rng(seed=random_state) # seed for sampling
if likelihood == "quantile":
self.quantiles, self._median_idx = self._prepare_quantiles(quantiles)
self._model_container = self._get_model_container()
else:
self.kwargs["loss_function"] = likelihood_map[likelihood]
# suppress writing catboost info files when user does not specifically ask to
if "allow_writing_files" not in kwargs:
kwargs["allow_writing_files"] = False
super().__init__(
lags=lags,
lags_past_covariates=lags_past_covariates,
lags_future_covariates=lags_future_covariates,
output_chunk_length=output_chunk_length,
output_chunk_shift=output_chunk_shift,
add_encoders=add_encoders,
multi_models=multi_models,
model=CatBoostRegressor(**kwargs),
use_static_covariates=use_static_covariates,
)
[docs] def fit(
self,
series: Union[TimeSeries, Sequence[TimeSeries]],
past_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None,
future_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None,
val_series: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None,
val_past_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None,
val_future_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None,
max_samples_per_ts: Optional[int] = None,
n_jobs_multioutput_wrapper: Optional[int] = None,
sample_weight: Optional[Union[TimeSeries, Sequence[TimeSeries], str]] = None,
val_sample_weight: Optional[
Union[TimeSeries, Sequence[TimeSeries], str]
] = None,
verbose: Optional[Union[int, bool]] = 0,
**kwargs,
):
"""
Fits/trains the model using the provided list of features time series and the target time series.
Parameters
----------
series
TimeSeries or Sequence[TimeSeries] object containing the target values.
past_covariates
Optionally, a series or sequence of series specifying past-observed covariates
future_covariates
Optionally, a series or sequence of series specifying future-known covariates
val_series
TimeSeries or Sequence[TimeSeries] object containing the target values for evaluation dataset
val_past_covariates
Optionally, a series or sequence of series specifying past-observed covariates for evaluation dataset
val_future_covariates : Union[TimeSeries, Sequence[TimeSeries]]
Optionally, a series or sequence of series specifying future-known covariates for evaluation dataset
max_samples_per_ts
This is an integer upper bound on the number of tuples that can be produced
per time series. It can be used in order to have an upper bound on the total size of the dataset and
ensure proper sampling. If `None`, it will read all of the individual time series in advance (at dataset
creation) to know their sizes, which might be expensive on big datasets.
If some series turn out to have a length that would allow more than `max_samples_per_ts`, only the
most recent `max_samples_per_ts` samples will be considered.
n_jobs_multioutput_wrapper
Number of jobs of the MultiOutputRegressor wrapper to run in parallel. Only used if the model doesn't
support multi-output regression natively.
sample_weight
Optionally, some sample weights to apply to the target `series` labels. They are applied per observation,
per label (each step in `output_chunk_length`), and per component.
If a series or sequence of series, then those weights are used. If the weight series only have a single
component / column, then the weights are applied globally to all components in `series`. Otherwise, for
component-specific weights, the number of components must match those of `series`.
If a string, then the weights are generated using built-in weighting functions. The available options are
`"linear"` or `"exponential"` decay - the further in the past, the lower the weight. The weights are
computed globally based on the length of the longest series in `series`. Then for each series, the weights
are extracted from the end of the global weights. This gives a common time weighting across all series.
val_sample_weight
Same as for `sample_weight` but for the evaluation dataset.
verbose
An integer or a boolean that can be set to 1 to display catboost's default verbose output
**kwargs
Additional kwargs passed to `catboost.CatboostRegressor.fit()`
"""
if self.likelihood == "quantile":
# empty model container in case of multiple calls to fit, e.g. when backtesting
self._model_container.clear()
for quantile in self.quantiles:
this_quantile = str(quantile)
# translating to catboost argument
self.kwargs["loss_function"] = f"Quantile:alpha={this_quantile}"
self.model = CatBoostRegressor(**self.kwargs)
super().fit(
series=series,
past_covariates=past_covariates,
future_covariates=future_covariates,
val_series=val_series,
val_past_covariates=val_past_covariates,
val_future_covariates=val_future_covariates,
max_samples_per_ts=max_samples_per_ts,
n_jobs_multioutput_wrapper=n_jobs_multioutput_wrapper,
sample_weight=sample_weight,
val_sample_weight=val_sample_weight,
verbose=verbose,
**kwargs,
)
self._model_container[quantile] = self.model
return self
super().fit(
series=series,
past_covariates=past_covariates,
future_covariates=future_covariates,
val_series=val_series,
val_past_covariates=val_past_covariates,
val_future_covariates=val_future_covariates,
max_samples_per_ts=max_samples_per_ts,
n_jobs_multioutput_wrapper=n_jobs_multioutput_wrapper,
sample_weight=sample_weight,
val_sample_weight=val_sample_weight,
verbose=verbose,
**kwargs,
)
return self
def _predict_and_sample(
self,
x: np.ndarray,
num_samples: int,
predict_likelihood_parameters: bool,
**kwargs,
) -> np.ndarray:
"""Override of RegressionModel's method to allow for the probabilistic case"""
if self.likelihood in ["gaussian", "RMSEWithUncertainty"]:
return self._predict_and_sample_likelihood(
x, num_samples, "normal", predict_likelihood_parameters, **kwargs
)
elif self.likelihood is not None:
return self._predict_and_sample_likelihood(
x, num_samples, self.likelihood, predict_likelihood_parameters, **kwargs
)
else:
return super()._predict_and_sample(
x, num_samples, predict_likelihood_parameters, **kwargs
)
def _likelihood_components_names(
self, input_series: TimeSeries
) -> Optional[List[str]]:
"""Override of RegressionModel's method to support the gaussian/normal likelihood"""
if self.likelihood == "quantile":
return self._quantiles_generate_components_names(input_series)
elif self.likelihood == "poisson":
return self._likelihood_generate_components_names(input_series, ["lamba"])
elif self.likelihood in ["gaussian", "RMSEWithUncertainty"]:
return self._likelihood_generate_components_names(
input_series, ["mu", "sigma"]
)
else:
return None
def _add_val_set_to_kwargs(
self,
kwargs: Dict,
val_series: Sequence[TimeSeries],
val_past_covariates: Optional[Sequence[TimeSeries]],
val_future_covariates: Optional[Sequence[TimeSeries]],
val_sample_weight: Optional[Union[Sequence[TimeSeries], str]],
max_samples_per_ts: int,
) -> dict:
# CatBoostRegressor requires sample weights to be passed with a validation set `Pool`
kwargs = super()._add_val_set_to_kwargs(
kwargs=kwargs,
val_series=val_series,
val_past_covariates=val_past_covariates,
val_future_covariates=val_future_covariates,
val_sample_weight=val_sample_weight,
max_samples_per_ts=max_samples_per_ts,
)
val_set_name, val_weight_name = self.val_set_params
val_sets = kwargs[val_set_name]
# CatBoost requires eval set Pool with sample weights -> remove from kwargs
val_weights = kwargs.pop(val_weight_name)
val_pools = []
for i, val_set in enumerate(val_sets):
val_pools.append(
Pool(
data=val_set[0],
label=val_set[1],
weight=val_weights[i] if val_weights is not None else None,
)
)
kwargs[val_set_name] = val_pools
return kwargs
@property
def supports_probabilistic_prediction(self) -> bool:
return self.likelihood is not None
@property
def supports_val_set(self) -> bool:
return True
@property
def val_set_params(self) -> Tuple[Optional[str], Optional[str]]:
return "eval_set", "eval_sample_weight"
@property
def min_train_series_length(self) -> int:
# Catboost requires a minimum of 2 train samples, therefore the min_train_series_length should be one more than
# for other regression models
return max(
3,
(
-self.lags["target"][0] + self.output_chunk_length + 1
if "target" in self.lags
else self.output_chunk_length
),
)