Supervised usecases¶
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class
previsionio.supervised.
Classification
(**usecase_info)¶ Bases:
previsionio.supervised.Supervised
A (binary) classification usecase for a categorical target with exactly 2 modalities using a basic dataset.
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predict_proba
(df, use_best_single=False, confidence=False) → pandas.core.frame.DataFrame¶ Get the predictions for a dataset stored in the current active [client] workspace using the best model of the usecase with a Scikit-learn style blocking prediction mode, and returns the probabilities.
Warning
For large dataframes and complex (blend) models, this can be slow (up to 1-2 hours). Prefer using this for simple models and small dataframes, or use option
use_best_single = True
.Parameters: Returns: Prediction probabilities data (as
pandas
dataframe) and prediction job ID.Return type:
-
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class
previsionio.supervised.
ClassificationImages
(**usecase_info)¶ Bases:
previsionio.supervised.SupervisedImages
A (binary) classification usecase for a categorical target with exactly 2 modalities using an image dataset.
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class
previsionio.supervised.
MultiClassification
(**usecase_info)¶ Bases:
previsionio.supervised.Supervised
A multiclassification usecase for a categorical target with strictly more than 2 modalities using a basic dataset.
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class
previsionio.supervised.
MultiClassificationImages
(**usecase_info)¶ Bases:
previsionio.supervised.SupervisedImages
A multiclassification usecase for a categorical target with strictly more than 2 modalities using an image dataset.
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class
previsionio.supervised.
Regression
(**usecase_info)¶ Bases:
previsionio.supervised.Supervised
A regression usecase for a numerical target using a basic dataset.
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class
previsionio.supervised.
RegressionImages
(**usecase_info)¶ Bases:
previsionio.supervised.SupervisedImages
A regression usecase for a numerical target using an image dataset.
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class
previsionio.supervised.
Supervised
(**usecase_info)¶ Bases:
previsionio.usecase.BaseUsecase
A supervised usecase.
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classmethod
fit
(name, dataset, column_config, metric=None, holdout_dataset=None, training_config=<previsionio.usecase_config.TrainingConfig object>, type_problem=None, **kwargs)¶ Start a supervised usecase training with a specific training configuration (on the platform).
Parameters: - name (str) – Name of the usecase to create
- dataset (
Dataset
,DatasetImages
) – Reference to the dataset object to use for as training dataset - column_config (
ColumnConfig
) – Column configuration for the usecase (see the documentation of theColumnConfig
resource for more details on each possible column types) - metric (str, optional) – Specific metric to use for the usecase (default:
None
) - holdout_dataset (
Dataset
, optional) – Reference to a dataset object to use as a holdout dataset (default:None
) - training_config (
TrainingConfig
) – Specific training configuration (see the documentation of theTrainingConfig
resource for more details on all the parameters) - type_problem (str, optional) – Specific problem type to train (default:
None
)
Returns: Newly created supervised usecase object
Return type:
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classmethod
from_id
(_id, version=1)¶ Get a supervised usecase from the platform by its unique id.
Parameters: Returns: Fetched usecase
Return type: Raises: PrevisionException
– Invalid problem type or any error while fetching data from the platform or parsing result
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classmethod
from_name
(name, raise_if_non_unique=False, partial_match=False)¶ Get a supervised usecase from the platform by its name.
Parameters: - name (str) – Name of the usecase to retrieve
- raise_if_non_unique (bool, optional) – Whether or not to raise an error if
duplicates are found (default:
False
) - partial_match (bool, optional) – If true, usecases with a name containing
the requested name will also be returned; else, only perfect matches
will be found (default:
False
)
Raises: PrevisionException
– Error if duplicates are found and theraise_if_non_unique
is enabledReturns: Fetched usecase
Return type:
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new_version
(**fit_params)¶ Start a supervised usecase training to create a new version of the usecase (on the platform): the training config is copied from the current version and then overridden for the given parameters.
Parameters: fit_params (kwargs) – Training config parameters to change for the new version (compared to the current version) Returns: Newly created supervised usecase object (new version) Return type: Supervised
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classmethod
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class
previsionio.supervised.
SupervisedImages
(**usecase_info)¶ Bases:
previsionio.supervised.Supervised
A supervised usecase with an image dataset.
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class
previsionio.timeseries.
TimeSeries
(**usecase_info)¶ Bases:
previsionio.usecase.BaseUsecase
A TimeSeries usecase.
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model_class
¶ alias of
previsionio.model.RegressionModel
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class
previsionio.timeseries.
TimeWindow
(derivation_start, derivation_end, forecast_start, forecast_end)¶ Bases:
previsionio.usecase_config.UsecaseConfig
A time window object for representing either feature derivation window periods or forecast window periods