Base Model¶
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class
dataprofiler.labelers.base_model.
AutoSubRegistrationMeta
(clsname, bases, attrs)¶ Bases:
abc.ABCMeta
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mro
()¶ Return a type’s method resolution order.
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register
(subclass)¶ Register a virtual subclass of an ABC.
Returns the subclass, to allow usage as a class decorator.
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class
dataprofiler.labelers.base_model.
BaseModel
(label_mapping, parameters)¶ Bases:
object
Base Model Initializer. Only model and model parameters are stored here :param parameters: Contains all the appropriate parameters for the model.
Must contain num_labels.
- Returns
None
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requires_zero_mapping
= False¶
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property
label_mapping
¶ mapping of labels to their encoded values
- Type
return
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property
reverse_label_mapping
¶ Reversed order of current labels, useful for when needed to extract Labels via indices
- Type
return
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property
labels
¶ Retrieves the label :return: list of labels
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property
num_labels
¶
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classmethod
get_class
(class_name)¶
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get_parameters
(param_list=None)¶ Returns a dict of parameters from the model given a list. :param param_list: list of parameters to retrieve from the model. :type param_list: list :return: dict of parameters
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set_params
(**kwargs)¶ Given kwargs, set the parameters if they exist.
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add_label
(label, same_as=None)¶ Adds a label to the data labeler.
- Parameters
label (str) – new label being added to the data labeler
same_as (str) – label to have the same encoding index as for multi-label to single encoding index.
- Returns
None
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set_label_mapping
(label_mapping)¶ Sets the labels for the model
- Parameters
label_mapping (Union[list, dict]) – label mapping of the model or list of labels to be converted into the label mapping
- Returns
None
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classmethod
help
()¶ Help function describing alterable parameters.
- Returns
None
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abstract
reset_weights
()¶ Reset the weights of the model.
- Returns
None
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abstract
predict
(data, batch_size, show_confidences, verbose)¶ Predict the data with the current model :param data: model input data to predict on :type data: iterator of data to process :param batch_size: number of samples in the batch of data :type batch_size: int :param show_confidences: whether user wants prediction confidences :type show_confidences: bool :param verbose: Flag to determine whether to print status or not :type verbose: bool :return: char level predictions and confidences :rtype: dict
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abstract classmethod
load_from_disk
(dirpath)¶ Loads whole model from disk with weights :param dirpath: directory path where you want to load the model from :type dirpath: str :return: None
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abstract
save_to_disk
(dirpath)¶ Saves whole model to disk with weights :param dirpath: directory path where you want to save the model to :type dirpath: str :return: None
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class
dataprofiler.labelers.base_model.
BaseTrainableModel
(label_mapping, parameters)¶ Bases:
dataprofiler.labelers.base_model.BaseModel
Base Model Initializer. Only model and model parameters are stored here :param parameters: Contains all the appropriate parameters for the model.
Must contain num_labels.
- Returns
None
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abstract
fit
(train_data, val_data, batch_size=32, epochs=1, label_mapping=None, reset_weights=False)¶ Train the current model with the training data and validation data :param train_data: Training data used to train model :type train_data: Union[pd.DataFrame, pd.Series, np.ndarray] :param val_data: Validation data used to validate the training :type val_data: Union[pd.DataFrame, pd.Series, np.ndarray] :param batch_size: Used to determine number of samples in each batch :type batch_size: int :param epochs: Used to determine how many epochs to run :type epochs: int :param label_mapping: Mapping of the labels :type label_mapping: dict :param reset_weights: Flag to determine whether or not to reset the
model’s weights
- Returns
None
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add_label
(label, same_as=None)¶ Adds a label to the data labeler.
- Parameters
label (str) – new label being added to the data labeler
same_as (str) – label to have the same encoding index as for multi-label to single encoding index.
- Returns
None
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classmethod
get_class
(class_name)¶
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get_parameters
(param_list=None)¶ Returns a dict of parameters from the model given a list. :param param_list: list of parameters to retrieve from the model. :type param_list: list :return: dict of parameters
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classmethod
help
()¶ Help function describing alterable parameters.
- Returns
None
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property
label_mapping
¶ mapping of labels to their encoded values
- Type
return
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property
labels
¶ Retrieves the label :return: list of labels
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abstract classmethod
load_from_disk
(dirpath)¶ Loads whole model from disk with weights :param dirpath: directory path where you want to load the model from :type dirpath: str :return: None
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property
num_labels
¶
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abstract
predict
(data, batch_size, show_confidences, verbose)¶ Predict the data with the current model :param data: model input data to predict on :type data: iterator of data to process :param batch_size: number of samples in the batch of data :type batch_size: int :param show_confidences: whether user wants prediction confidences :type show_confidences: bool :param verbose: Flag to determine whether to print status or not :type verbose: bool :return: char level predictions and confidences :rtype: dict
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requires_zero_mapping
= False¶
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abstract
reset_weights
()¶ Reset the weights of the model.
- Returns
None
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property
reverse_label_mapping
¶ Reversed order of current labels, useful for when needed to extract Labels via indices
- Type
return
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abstract
save_to_disk
(dirpath)¶ Saves whole model to disk with weights :param dirpath: directory path where you want to save the model to :type dirpath: str :return: None
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set_label_mapping
(label_mapping)¶ Sets the labels for the model
- Parameters
label_mapping (Union[list, dict]) – label mapping of the model or list of labels to be converted into the label mapping
- Returns
None
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set_params
(**kwargs)¶ Given kwargs, set the parameters if they exist.