Column Name Model¶

Contains class for column name data labeling model.

class dataprofiler.labelers.column_name_model.ColumnNameModel(label_mapping: dict, parameters: Optional[dict] = None)¶

Bases: dataprofiler.labelers.base_model.BaseModel

Class for column name data labeling model.

Initialize function for ColumnNameModel.

Parameters

parameters (dict) –

Contains all the appropriate parameters for the model. Possible parameters are:

max_length, max_num_chars, dim_embed

Returns

None

reset_weights() → None¶: Reset weights function.

predict(data: Union[pandas.core.frame.DataFrame, pandas.core.series.Series, numpy.ndarray], batch_size: Optional[int] = None, show_confidences: bool = False, verbose: bool = True) → dict¶

Apply the process.cdist for similarity score on input list of strings.

Parameters

data (iterator) – list of strings to predict upon
batch_size (N/A) – does not impact this model and should be fixed to not be required.
show_confidences – Parameter disabled. Confidence values returned by default.
verbose (bool) – Flag to determine whether to print status or not

Returns

char level predictions and confidences

Return type

dict

classmethod load_from_disk(dirpath: str) → dataprofiler.labelers.column_name_model.ColumnNameModel¶

Load whole model from disk with weights.

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

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.

requires_zero_mapping: bool = False¶

property reverse_label_mapping: dict¶

Return reversed order of current labels.

Useful for when needed to extract Labels via indices.

set_label_mapping(label_mapping: list[str] | dict[str, int]) → None¶

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: Any) → None¶: Set the parameters if they exist given kwargs.