etna.models.CatBoostMultiSegmentModel#

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

Bases: MultiSegmentModelMixin, NonPredictionIntervalContextIgnorantModelMixin, 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
>>> df = generate_periodic_df(
...     periods=100,
...     start_time="2020-01-01",
...     n_segments=4,
...     period=7,
...     sigma=3
... )
>>> 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 (int | None) – 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 (int | None) –

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

  • logging_level (str | None) –

    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 (float | None) – Coefficient at the L2 regularization term of the cost function. Any positive value is allowed.

  • thread_count (int | None) –

    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.

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

This class stores its __init__ parameters as attributes.

context_size

Context size of the model.
fit(ts: TSDataset) → MultiSegmentModelMixin[source]#

Fit model.

Parameters:

ts (TSDataset) – Dataset with features

Returns:

Model after fit

Return type:

MultiSegmentModelMixin

forecast(ts: TSDataset, return_components: bool = False) → TSDataset[source]#

Make predictions.

Parameters:

  • ts (TSDataset) – Dataset with features

  • return_components (bool) – If True additionally returns forecast components

Returns:

Dataset with predictions

Return type:

TSDataset

get_model() → Any[source]#

Get internal model that is used inside etna class.

Internal model is a model that is used inside etna to forecast segments, e.g. catboost.CatBoostRegressor or sklearn.linear_model.Ridge.

Returns:

Internal model

Return type:

Any

classmethod load(path: Path) → Self[source]#

Load an object.

Warning

This method uses dill module which is not secure. It is possible to construct malicious data which will execute arbitrary code during loading. Never load data that could have come from an untrusted source, or that could have been tampered with.

Parameters:

path (Path) – Path to load object from.

Returns:

Loaded object.

Return type:

Self

params_to_tune() → Dict[str, BaseDistribution][source]#

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, BaseDistribution]

predict(ts: TSDataset, return_components: bool = False) → TSDataset[source]#

Make predictions with using true values as autoregression context if possible (teacher forcing).

Parameters:

  • ts (TSDataset) – Dataset with features

  • return_components (bool) – If True additionally returns prediction components

Returns:

Dataset with predictions

Return type:

TSDataset

save(path: Path)[source]#

Save the object.

Parameters:

path (Path) – Path to save object to.

set_params(**params: dict) → Self[source]#

Return new object instance with modified parameters.

Method also allows to change parameters of nested objects within the current object. For example, it is possible to change parameters of a model in a Pipeline.

Nested parameters are expected to be in a <component_1>.<...>.<parameter> form, where components are separated by a dot.

Parameters:

**params (dict) – Estimator parameters

Returns:

New instance with changed parameters

Return type:

Self

Examples

>>> from etna.pipeline import Pipeline
>>> from etna.models import NaiveModel
>>> from etna.transforms import AddConstTransform
>>> model = NaiveModel(lag=1)
>>> transforms = [AddConstTransform(in_column="target", value=1)]
>>> pipeline = Pipeline(model, transforms=transforms, horizon=3)
>>> pipeline.set_params(**{"model.lag": 3, "transforms.0.value": 2})
Pipeline(model = NaiveModel(lag = 3, ), transforms = [AddConstTransform(in_column = 'target', value = 2, inplace = True, out_column = None, )], horizon = 3, )
to_dict()[source]#

Collect all information about etna object in dict.

property context_size: int[source]#

Context size of the model. Determines how many history points do we ask to pass to the model.

Zero for this model.