import pandas as pd

# do not worry about circular dependencies, this class will never call something else
from Scripts.DataCleaning.legacy.filter import PipelineApplier

class RDF_mask_task_dataset():
    """
        Write the CSV for the third task, which is "Predicting a masked component within an RDF triple".
        The CSV is like: for each RDF there will be 3 rows, where every time one of the componments is missing.
        CSV Composition: ["MovieID","IncompleteRDF","Missing","RDF"]
    """
    def __init__(self, output_path:str):
     
        # this methods will only be used by this class, but they belong in a lower level
        self._build_triple = PipelineApplier.build_triple
        self._build_incomplete_triple = PipelineApplier.build_incomplete_triple

        self.output =  open(output_path, "w")
        # then the first row as header
        header = ["MovieID","IncompleteRDF","Missing","RDF"]
        self.output.write(",".join(header) + "\n")

    def close(self):
        self.output.close()

    def write(self, RDF: pd.DataFrame):
        rdf_complete = self._build_triple(RDF)

        rdf_without_subject = self._build_incomplete_triple(RDF.drop(columns=["SubjectURI"]))
        rdf_without_relationship = self._build_incomplete_triple(RDF.drop(columns=["RelationshipURI"]))
        rdf_without_object = self._build_incomplete_triple(RDF.drop(columns=["ObjectURI"]))
        ####
        df_subject = pd.DataFrame({
            "MovieID": RDF["MovieID"],
            "IncompleteRDF": rdf_without_subject,
            "Missing": RDF["SubjectURI"],
            "RDF": rdf_complete,
        })

        df_relationship = pd.DataFrame({
            "MovieID": RDF["MovieID"],
            "IncompleteRDF": rdf_without_relationship,
            "Missing": RDF["RelationshipURI"],
            "RDF": rdf_complete,
        })

        df_object = pd.DataFrame({
            "MovieID": RDF["MovieID"],
            "IncompleteRDF": rdf_without_object,
            "Missing": RDF["ObjectURI"],
            "RDF": rdf_complete,
        })


        output_df = pd.concat([df_subject, df_relationship, df_object], ignore_index=True)
        output_df.to_csv(self.output, index=False, header=False)