virtual_ensembles_predict

Apply the model to the given dataset.

Note

The model prediction results will be correct only if the data parameter with feature values contains all the features used in the model. Typically, the order of these features must match the order of the corresponding columns that is provided during the training. But if feature names are provided both during the training and when applying the model, they can be matched by names instead of columns order. Feature names can be specified if the data parameter has one of the following types:

Method call format

predict(data,
    prediction_type='VirtEnsembles',
    ntree_end=0,
    virtual_ensembles_count=10,
    thread_count=-1 (the number of threads is equal to the number of processor cores),
    verbose=None)

Parameters

data

Description

Feature values data.

The format depends on the number of input objects:

  • Multiple — Matrix-like data of shape (object_count, feature_count)
  • Single — An array

Possible types

For multiple objects:

  • catboost.Pool
  • list of lists
  • numpy.ndarray of shape (object_count, feature_count)
  • pandas.DataFrame
  • pandas.SparseDataFrame
  • pandas.Series
  • catboost.FeaturesData
  • scipy.sparse.spmatrix (all subclasses except dia_matrix)

For a single object:

  • list of feature values
  • one-dimensional numpy.ndarray with feature values

Default value

Required parameter

prediction_type

Description

Required prediction type. Supported prediction types: VirtEnsembles, TotalUncertainty

Possible types

string

Default value

VirtEnsembles

ntree_end

Description

To reduce the number of trees to use when the model is applied or the metrics are calculated, set the range of the tree indices to [ntree_start; ntree_end) and the step of the trees to use to eval_period.

This parameter defines the index of the first tree not to be used when applying the model or calculating the metrics (the exclusive right border of the range). Indices are zero-based.

Possible types
int

Default value
0 (the index of the last tree to use equals to the number of trees in the model minus one)

thread_count

Description

The number of threads to use during the training.

Optimizes the speed of execution. This parameter doesn't affect results.

Possible types

int

Default value

-1 (the number of threads is equal to the number of processor cores)

verbose

Description

Output the measured evaluation metric to stderr.

Possible types

bool

Default value

None

Return value

prediction_type VirtEnsembles

Each virtual ensemble can be consider as truncated model. Returns virtual_ensembles_count predictions from each virtual ensemble. The return value type depends on the number of input objects and model type:

  • Single object — Return numpy.ndarray one-dimensional or two-dimensional numpy.ndarray of shape (virtual_ensembles_count) or (virtual_ensembles_count, single document predict size) of virtual_ensemble.predict(document, prediction_type='RawFormulaVal') results. For model learned with RMSEWithUncertainty for virtual ensembles predictions used prediction_type='RMSEWithUncertainty' instead of prediction_type='RawFormulaVal'.
  • Multiple objects — two-dimensional or three-dimensional numpy.ndarray of shape (number_of_objects, virtual_ensembles_count) or (number_of_objects, virtual_ensembles_count, single document predict size) similarly to Single object predict type.

prediction_type TotalUncertainty

  • Single object

    • Regression (not RMSEWithUncertainty): one-dimensional numpy.ndarray [Mean Predictions, Knowledge Uncertainty]
    • RMSEWithUncertainty: one-dimensional numpy.ndarray [Mean Predictions, Knowledge Uncertainty, Data Uncertainty]
    • Classification: one-dimensional numpy.ndarray [Data Uncertainty, Total Uncertainty]
  • Multiple objects

    Return two-dimensional numpy.ndarray of shape (number_of_objects, 2) or (number_of_objects, 3) similarly to single object return type.prediction_type VirtEnsembles:

Additional information is available in the article.