Base Model¶

Contains abstract classes for labeling data.

class dataprofiler.labelers.base_model.AutoSubRegistrationMeta(clsname, bases, attrs)¶

Bases: abc.ABCMeta

For registering subclasses.

Create auto registration object and return new class.

mro()¶: Return a type’s method resolution order.

register(subclass)¶

Returns the subclass, to allow usage as a class decorator.

class dataprofiler.labelers.base_model.BaseModel(label_mapping, parameters)¶

Bases: object

For labeling data.

Initialize Base Model.

Only model and model parameters are stored here. :param parameters: Contains all the appropriate parameters for the model.

Must contain num_labels.

Returns: None

requires_zero_mapping = False¶

property label_mapping¶: Return mapping of labels to their encoded values.

property reverse_label_mapping¶

Return reversed order of current labels.

Useful for when needed to extract Labels via indices.

property labels¶

Retrieve the label.

Returns: list of labels

property num_labels¶: Return max label mapping.

classmethod get_class(class_name)¶: Get subclasses.

get_parameters(param_list=None)¶

Return a dict of parameters from the model given a list.

Parameters: param_list (list) – list of parameters to retrieve from the model.
Returns: dict of parameters

set_params(**kwargs)¶: Set the parameters if they exist given kwargs.

add_label(label, same_as=None)¶

Add 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

set_label_mapping(label_mapping)¶

Set 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

classmethod help()¶

Help describe alterable parameters.

Returns: None

abstract reset_weights()¶

Reset the weights of the model.

Returns: None

abstract predict(data, batch_size, show_confidences, verbose)¶

Predict the data with the current model.

Parameters

data (iterator of data to process) – model input data to predict on
batch_size (int) – number of samples in the batch of data
show_confidences (bool) – whether user wants prediction confidences
verbose (bool) – Flag to determine whether to print status or not

Returns

char level predictions and confidences

Return type

dict

abstract classmethod load_from_disk(dirpath)¶

Load whole model from disk with weights.

Parameters: dirpath (str) – directory path where you want to load the model from
Returns: None

abstract save_to_disk(dirpath)¶

Save whole model to disk with weights.

Parameters: dirpath (str) – directory path where you want to save the model to
Returns: None

class dataprofiler.labelers.base_model.BaseTrainableModel(label_mapping, parameters)¶

Bases: dataprofiler.labelers.base_model.BaseModel

Contains abstract method for training models.

Initialize Base Model.

Only model and model parameters are stored here. :param parameters: Contains all the appropriate parameters for the model.

Must contain num_labels.

Returns: None

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.

Parameters

train_data (Union[pd.DataFrame, pd.Series, np.ndarray]) – Training data used to train model
val_data (Union[pd.DataFrame, pd.Series, np.ndarray]) – Validation data used to validate the training
batch_size (int) – Used to determine number of samples in each batch
epochs (int) – Used to determine how many epochs to run
label_mapping (dict) – Mapping of the labels
reset_weights (bool) – Flag to determine whether or not to reset the model’s weights

Returns

None

add_label(label, same_as=None)¶

Add 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

classmethod get_class(class_name)¶: Get subclasses.

get_parameters(param_list=None)¶

Return a dict of parameters from the model given a list.

Parameters: param_list (list) – list of parameters to retrieve from the model.
Returns: dict of parameters

classmethod help()¶

Help describe alterable parameters.

Returns: None

property label_mapping¶: Return mapping of labels to their encoded values.

property labels¶

Retrieve the label.

Returns: list of labels

abstract classmethod load_from_disk(dirpath)¶

Load whole model from disk with weights.

Parameters: dirpath (str) – directory path where you want to load the model from
Returns: None

property num_labels¶: Return max label mapping.

abstract predict(data, batch_size, show_confidences, verbose)¶

Predict the data with the current model.

Parameters

data (iterator of data to process) – model input data to predict on
batch_size (int) – number of samples in the batch of data
show_confidences (bool) – whether user wants prediction confidences
verbose (bool) – Flag to determine whether to print status or not

Returns

char level predictions and confidences

Return type

dict

requires_zero_mapping = False¶

abstract reset_weights()¶

Reset the weights of the model.

Returns: None

property reverse_label_mapping¶

Return reversed order of current labels.

Useful for when needed to extract Labels via indices.

abstract save_to_disk(dirpath)¶

Save whole model to disk with weights.

Parameters: dirpath (str) – directory path where you want to save the model to
Returns: None

set_label_mapping(label_mapping)¶

Set 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

set_params(**kwargs)¶: Set the parameters if they exist given kwargs.