learningmodels.scikit¶
Module Contents¶
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class
learningmodels.scikit.GaussianProcessRegressorModel(units=None, **kwargs)¶ Learns the duration of a task from data using scikit-learn’s GaussianProcessRegressor
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model¶ GaussianProcessRegressor – The underlying model used to predict the data
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units¶ TimeUnits, optional – The time units the resulting durations should be in. Defaults to TimeUnits.seconds
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is_trained¶ bool – A boolean value indicating if the model has been trained.
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ordering¶ list[str] – The ordering of the input data used to construct input data
Parameters: units (TimeUnits, optional) – The time units the resulting durations should be in. Defaults to TimeUnits.seconds Keyword Arguments: kernel – The kernel to use in the regressor model. Defaults to ConstantKernel() + Matern(length_scale=1, nu=3 / 2) + WhiteKernel(noise_level=1) -
__init__(units=None, **kwargs)¶
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train(input_data, durations, ordering=None)¶ Trains the model from input data and durations
Note
If a Pandas DataFrame is used for the input data, the ordering of the data will be determined by the ordering of the colunms. If a pandas DataFrame is not used, then the ordering will need to be provided. Each Task must provide data as a dictionary in which the keys are the same as the names in the ordering/column names of the DataFrame
Parameters: - input_data (array-like) – The data to train the data from
- durations (array-like) – The durations associated with the data
- ordering (list[str], optional) – The ordering of the data
Raises: ValueError– When a non-DataFrame is provided as the input_data and no ordering is provided
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predict(input_data)¶ Predicts the duration of a task given its data
Parameters: - input_data (dict) – A dict containing the data necessary to predict the duration. The format must be as
- pairs in which the key is the name of the data and the value is its value. (key-value) –
Returns: The estimated duration of the task.
Return type:
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