def inputs(self):
return {
"pathTocontentMeta": File_Txt(self.node.inputs[0]),
"pathTotaxonomy": Pandas_Dataframe(self.node.inputs[1]),
"root_path": File_Txt(self.node.inputs[2]),
"path_to_corpus": File_Txt(self.node.inputs[3])
}
def inputs(self):
return {
"localpathTocontentMeta":
ReadDaggitTask_Folderpath(self.node.inputs[0]),
"pathTotaxonomy":
Pandas_Dataframe(self.node.inputs[1]),
"root_path":
File_Txt(self.node.inputs[2]),
"path_to_corpus":
File_Txt(self.node.inputs[3])
}
def inputs(self):
return {
"timestamp_folder": File_Txt(self.node.inputs[0]),
"pathTocredentials":
ReadDaggitTask_Folderpath(self.node.inputs[1]),
"categoryLookup": ReadDaggitTask_Folderpath(self.node.inputs[2])
}
def outputs(self):
return {
"model": File_Txt(self.node.outputs[0]),
"model_weights": Pickle_Obj(self.node.outputs[1]),
"predictions": Pandas_Dataframe(self.node.outputs[2]),
"report": Pickle_Obj(self.node.outputs[3]),
}
def inputs(self):
return {
"pathTotaxonomy": Pandas_Dataframe(self.node.inputs[0]),
"categoryLookup": ReadDaggitTask_Folderpath(self.node.inputs[1]),
"timestamp_folder": File_Txt(self.node.inputs[2]),
"pathTocredentials": ReadDaggitTask_Folderpath(self.node.inputs[3])
}
def outputs(self):
"""
Function that the ContentToTextRead operator defines while returning graph outputs
:returns: Returns the path to timestamp folder in which auto tagging results get generated
"""
return {"timestamp_folder": File_Txt(self.node.outputs[0])}
def outputs(self):
"""
Function that the OcrTextExtraction operator defines while returning graph outputs
:returns: Returns the path to the folder in which text extraction results get generated
"""
return {"path_to_result_folder": File_Txt(self.node.outputs[0])}
def outputs(self):
return {
"path_to_timestampFolder": File_Txt(self.node.outputs[0]),
"path_to_distMeasure": File_Txt(self.node.outputs[1]),
"path_to_domain_level": File_Txt(self.node.outputs[2])
}
def inputs(self):
return {"path_to_timestampFolder": File_Txt(self.node.inputs[0])}
def outputs(self):
return {
"root_path": File_Txt(self.node.outputs[0]),
"path_to_corpus": File_Txt(self.node.outputs[1])
}
def outputs(self):
return {"cross_tabs": File_Txt(self.node.outputs[0])}
def outputs(self):
return {"pathTocontentMeta": File_Txt(self.node.outputs[0])}
def inputs(self):
return {
"pathTotaxonomy": Pandas_Dataframe(self.node.inputs[0]),
"path_to_contentKeywords": File_Txt(self.node.inputs[1])
}
def outputs(self):
return {
"metrics": File_Txt(self.node.outputs[0]),
"topk_predictions": Pandas_Dataframe(self.node.outputs[1]),
"result_df": Pandas_Dataframe(self.node.outputs[2])
}
def inputs(self):
return {
"path_to_timestampFolder": File_Txt(self.node.inputs[0]),
"path_to_observedtag": File_Txt(self.node.inputs[1]),
"path_to_predictedtag": File_Txt(self.node.inputs[2])
}
def outputs(self):
return {"path_to_predictedTags": File_Txt(self.node.outputs[0])}
def inputs(self):
return {
"pathTocredentials":
ReadDaggitTask_Folderpath(self.node.inputs[0]),
"pathTocontentMeta": File_Txt(self.node.inputs[1])
}
def outputs(self):
return {
"path_to_agg_precision": File_Txt(self.node.outputs[0]),
"path_to_nonagg_precision": File_Txt(self.node.outputs[1])
}
def inputs(self):
return {
"pathTocontentMeta": File_Txt(self.node.inputs[0]),
"pathTotaxonomy": Pandas_Dataframe(self.node.inputs[1]),
"path_to_timestampFolder": File_Txt(self.node.inputs[2])
}
def inputs(self):
return {"timestamp_folder": File_Txt(self.node.inputs[0])}
def outputs(self):
return {
"report": File_Txt(self.node.outputs[0]),
"model": Pickle_Obj(self.node.outputs[1])
}
def inputs(self):
return {"path_to_contentKeywords": File_Txt(self.node.inputs[0])}
