CatBoostMultiSegmentModel

class CatBoostMultiSegmentModel(iterations: Optional[int] = None, depth: Optional[int] = None, learning_rate: Optional[float] = None, logging_level: Optional[str] = 'Silent', l2_leaf_reg: Optional[float] = None, thread_count: Optional[int] = None, **kwargs)

Bases: etna.models.mixins.MultiSegmentModelMixin, etna.models.mixins.NonPredictionIntervalContextIgnorantModelMixin, etna.models.base.NonPredictionIntervalContextIgnorantAbstractModel

Class for holding Catboost model for all segments.

Examples

>>> from etna.datasets import generate_periodic_df
>>> from etna.datasets import TSDataset
>>> from etna.models import CatBoostMultiSegmentModel
>>> from etna.transforms import LagTransform
>>> classic_df = generate_periodic_df(
...     periods=100,
...     start_time="2020-01-01",
...     n_segments=4,
...     period=7,
...     sigma=3
... )
>>> df = TSDataset.to_dataset(df=classic_df)
>>> ts = TSDataset(df, freq="D")
>>> horizon = 7
>>> transforms = [
...     LagTransform(in_column="target", lags=[horizon, horizon+1, horizon+2])
... ]
>>> ts.fit_transform(transforms=transforms)
>>> future = ts.make_future(horizon, transforms=transforms)
>>> model = CatBoostMultiSegmentModel()
>>> model.fit(ts=ts)
CatBoostMultiSegmentModel(iterations = None, depth = None, learning_rate = None,
logging_level = 'Silent', l2_leaf_reg = None, thread_count = None, )
>>> forecast = model.forecast(future)
>>> forecast.inverse_transform(transforms)
>>> pd.options.display.float_format = '{:,.2f}'.format
>>> forecast[:, :, "target"].round()
segment    segment_0 segment_1 segment_2 segment_3
feature       target    target    target    target
timestamp
2020-04-10      9.00      9.00      4.00      6.00
2020-04-11      5.00      2.00      7.00      9.00
2020-04-12     -0.00      4.00      7.00      9.00
2020-04-13      0.00      5.00      9.00      7.00
2020-04-14      1.00      2.00      1.00      6.00
2020-04-15      5.00      7.00      4.00      7.00
2020-04-16      8.00      6.00      2.00      0.00

Create instance of CatBoostMultiSegmentModel with given parameters.

Parameters

• iterations (Optional[int]) – The maximum number of trees that can be built when solving machine learning problems. When using other parameters that limit the number of iterations, the final number of trees may be less than the number specified in this parameter.

• depth (Optional[int]) –

  Depth of the tree. The range of supported values depends on the processing unit type and the type of the selected loss function:

  • CPU — Any integer up to 16.

  • GPU — Any integer up to 8 pairwise modes (YetiRank, PairLogitPairwise and QueryCrossEntropy) and up to 16 for all other loss functions.

• learning_rate (Optional[float]) – The learning rate. Used for reducing the gradient step. If None the value is defined automatically depending on the number of iterations.

• logging_level (Optional[str]) –

  The logging level to output to stdout. Possible values:

  • Silent — Do not output any logging information to stdout.

  • Verbose — Output the following data to stdout:

    • optimized metric

    • elapsed time of training

    • remaining time of training

  • Info — Output additional information and the number of trees.

  • Debug — Output debugging information.

• l2_leaf_reg (Optional[float]) – Coefficient at the L2 regularization term of the cost function. Any positive value is allowed.

• thread_count (Optional[int]) –

  The number of threads to use during the training.

  • For CPU. Optimizes the speed of execution. This parameter doesn’t affect results.

  • For GPU. The given value is used for reading the data from the hard drive and does not affect the training. During the training one main thread and one thread for each GPU are used.

Inherited-members

Methods

fit(ts)	Fit model.
forecast(ts[, return_components])	Make predictions.
get_model()	Get internal model that is used inside etna class.
load(path)	Load an object.
params_to_tune()	Get default grid for tuning hyperparameters.
predict(ts[, return_components])	Make predictions with using true values as autoregression context if possible (teacher forcing).
save(path)	Save the object.
set_params(**params)	Return new object instance with modified parameters.
to_dict()	Collect all information about etna object in dict.

Attributes

context_size

Context size of the model.

params_to_tune() → Dict[str, etna.distributions.distributions.BaseDistribution]

Get default grid for tuning hyperparameters.

This grid tunes parameters: learning_rate, depth, random_strength, l2_leaf_reg. Other parameters are expected to be set by the user.

Returns

Grid to tune.

Return type

Dict[str, etna.distributions.distributions.BaseDistribution]