sparkocr.transformers.visual_document_classifier_v3#

Classes#

class VisualDocumentClassifierV3(classname='com.johnsnowlabs.ocr.transformers.VisualDocumentClassifierv3', java_model=None)#

Bases: pyspark.ml.wrapper.JavaTransformer, pyspark.ml.param.shared.HasInputCols, pyspark.ml.param.shared.HasOutputCol, pyspark.ml.util.JavaMLReadable, pyspark.ml.util.JavaMLWritable

Base class for :py:class:`Transformer`s that wrap Java/Scala implementations. Subclasses should ensure they have the transformer Java object available as _java_obj.

evalSize#

inputCols#

labels#

name = 'VisualDocumentClassifierV3'#

outputCol#

trainBatchSize#

trainEpochs#

uid = ''#

vocabulary = []#

clear(param: pyspark.ml.param.Param) → None#

Clears a param from the param map if it has been explicitly set.

copy(extra: pyspark.ml._typing.ParamMap | None = None) → JP#

Creates a copy of this instance with the same uid and some extra params. This implementation first calls Params.copy and then make a copy of the companion Java pipeline component with extra params. So both the Python wrapper and the Java pipeline component get copied.

Parameters:

extra (dict, optional) – Extra parameters to copy to the new instance

Returns:

Copy of this instance

Return type:

JavaParams

explainParam(param: str | Param) → str#

Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.

explainParams() → str#

Returns the documentation of all params with their optionally default values and user-supplied values.

extractParamMap(extra: pyspark.ml._typing.ParamMap | None = None) → pyspark.ml._typing.ParamMap#

Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values < user-supplied values < extra.

Parameters:

extra (dict, optional) – extra param values

Returns:

merged param map

Return type:

dict

fit(dataset, base_model='dit-base-224-p16-500k-62d53a.pth', validate=False, atol=0.01, base_model_path=None)#

getInputCols() → List[str]#

Gets the value of inputCols or its default value.

getOrDefault(param: str) → Any#

getOrDefault(param: Param[T]) → T

Gets the value of a param in the user-supplied param map or its default value. Raises an error if neither is set.

getOutputCol() → str#

Gets the value of outputCol or its default value.

getParam(paramName: str) → Param#

Gets a param by its name.

hasDefault(param: str | Param[Any]) → bool#

Checks whether a param has a default value.

hasParam(paramName: str) → bool#

Tests whether this instance contains a param with a given (string) name.

isDefined(param: str | Param[Any]) → bool#

Checks whether a param is explicitly set by user or has a default value.

isSet(param: str | Param[Any]) → bool#

Checks whether a param is explicitly set by user.

static load(folder)#

Loads a onnx graph and setups model :param folder: location of onnx model :return:

static loadONNXModel(model_path, spark_session)#

Loads a onnx graph and setups model :param folder: location of onnx model :param spark_session: Spark session :return:

static pretrained(name, lang='en', remote_loc=None)#

classmethod read() → JavaMLReader[RL]#

Returns an MLReader instance for this class.

save(path: str) → None#

Save this ML instance to the given path, a shortcut of ‘write().save(path)’.

saveModel(model_path)#

Saves model :param model_path: location of saved model :return:

set(param: Param, value: Any) → None#

Sets a parameter in the embedded param map.

setEvalSize(value)#

Sets the value of evalSize.

setInputCols(value)#

Sets the value of inputCols.

setLabels(value)#

Sets document class labels.

setOutputCol(value)#

Sets the value of outputCol.

setTrainBatchSize(value)#

Sets the value of outputCol.

setTrainEpochs(value)#

Sets the value of trainEpochs.

transform(dataset: pyspark.sql.dataframe.DataFrame, params: pyspark.ml._typing.ParamMap | None = None) → pyspark.sql.dataframe.DataFrame#

Transforms the input dataset with optional parameters.

New in version 1.3.0.

Parameters:

• dataset (pyspark.sql.DataFrame) – input dataset

• params (dict, optional) – an optional param map that overrides embedded params.

Returns:

transformed dataset

Return type:

pyspark.sql.DataFrame

write() → JavaMLWriter#

Returns an MLWriter instance for this ML instance.