Char Load Tf Model

Contains class for training data labeler model.

class dataprofiler.labelers.char_load_tf_model.CharLoadTFModel(model_path: str, label_mapping: dict, parameters: Optional[dict] = None)

Bases: dataprofiler.labelers.base_model.BaseTrainableModel

For training data labeler model.

Initialize Loadable TF Model.

Parameters

  • model_path (str) – path to model to load

  • label_mapping (dict) – maps labels to their encoded integers

  • parameters (dict) –

    Contains all the appropriate parameters for the model. Must contain num_labels. Other possible parameters are:

    max_length, max_char_encoding_id, dim_embed, size_fc dropout, size_conv, num_fil, optimizer, default_label

Returns

None

requires_zero_mapping: bool = False
set_label_mapping(label_mapping: list[str] | dict[str, int]) → None

Set the labels for the model.

Parameters

label_mapping (dict) – label mapping of the model

Returns

None

save_to_disk(dirpath: str) → None

Save whole model to disk with weights.

Parameters

dirpath (str) – directory path where you want to save the model to

Returns

None

classmethod load_from_disk(dirpath: str) → dataprofiler.labelers.char_load_tf_model.CharLoadTFModel

Load whole model from disk with weights.

Parameters

dirpath (str) – directory path where you want to load the model from

Returns

loaded CharLoadTFModel

Return type

CharLoadTFModel

reset_weights() → None

Reset the weights of the model.

Returns

None

fit(train_data: DataArray, val_data: DataArray = None, batch_size: int = None, epochs: int = None, label_mapping: dict[str, int] = None, reset_weights: bool = False, verbose: bool = True) → tuple[dict, float | None, dict]

Train the current model with the training data and validation data.

Parameters

  • train_data (Union[list, np.ndarray]) – Training data used to train model

  • val_data (Union[list, np.ndarray]) – Validation data used to validate the training

  • batch_size (int) – Used to determine number of samples in each batch

  • label_mapping (Union[dict, None]) – maps labels to their encoded integers

  • reset_weights (bool) – Flag to determine whether to reset the weights or not

  • verbose (bool) – Flag to determine whether to print status or not

Returns

history, f1, f1_report

Return type

Tuple[dict, float, dict]

predict(data: Union[pandas.core.frame.DataFrame, pandas.core.series.Series, numpy.ndarray], batch_size: int = 32, show_confidences: bool = False, verbose: bool = True) → dict

Run model and get predictions.

Parameters

  • data (Union[list, numpy.ndarray]) – text input

  • batch_size (int) – number of samples in the batch of data

  • show_confidences – 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

details() → None

Print the relevant details of the model.

Details include summary, parameters, label mapping.

add_label(label: str, same_as: str | None = None) → 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: str) → type[BaseModel] | None

Get subclasses.

get_parameters(param_list: list[str] | None = None) → dict

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

Parameters

param_list (List[str]) – list of parameters to retrieve from the model.

Returns

dict of parameters

classmethod help() → None

Help describe alterable parameters.

Returns

None

property label_mapping: dict

Return mapping of labels to their encoded values.

property labels: list

Retrieve the label.

Returns

list of labels

property num_labels: int

Return max label mapping.

property reverse_label_mapping: dict

Return reversed order of current labels.

Useful for when needed to extract Labels via indices.

set_params(**kwargs: Any) → None

Set the parameters if they exist given kwargs.