Merge branch 'dev.etl' into dev
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GassiGiuseppe
145
Scripts/DataCleaning/legacy/pipeline.py
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145
Scripts/DataCleaning/legacy/pipeline.py
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import re
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from Scripts.Libs.CleaningPipeline.sql_endpoint import SqlEndpoint
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from Scripts.DataCleaning.legacy.filter import PipelineApplier
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# tasks dataset builder
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from Scripts.DataCleaning.data_output_models.rdf_mask_task import RDF_mask_task_dataset
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from Scripts.DataCleaning.data_output_models.bpe_corpus import BPE_corpus
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from Scripts.DataCleaning.data_output_models.rdf_text_tasks import RDF_text_task_dataset
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from Scripts.DataCleaning.data_output_models.rdf_completation_task import RDF_completation_task_dataset
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from Scripts.DataCleaning.data_output_models.debug_csv import Debug_csv
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import pandas as pd
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class Pipeline():
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def __init__(self):
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self.sql_endpoint = SqlEndpoint()
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# classes to manage taskes' datasets
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self.task_rdf_mask = RDF_mask_task_dataset(mask_task_dataset_path)
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self.task_bpe_corpus = BPE_corpus(bpe_corpus_path)
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self.task_rdf_text = RDF_text_task_dataset(text_to_rdf_task_dataset_path)
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self.task_rdf_completation = RDF_completation_task_dataset(completation_rdf_task_dataset_path)
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# prepare the filter
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# the filter applier needs to know the frequence of Movies and Relationship among all the Dataset
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self.filter_applier = PipelineApplier()
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MOVIE_COUNT = self.sql_endpoint.get_movies_id_count()
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REL_COUNT = self.sql_endpoint.get_relationship_count()
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self.filter_applier.generate_frequency_movie_filter(MOVIE_COUNT,50,3000)
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self.filter_applier.generate_frequency_relationship_filter(REL_COUNT, 50, 2395627) # from 2718 to 3069
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# prepare the filter on the relationshipURI you want to delete:
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relationship_uri_banned_list = [
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"dbp-dbp:wikiPageUsesTemplate","w3:2000/01/rdf-schema#label","dbp-dbo:abstract",
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"dbp-dbo:wikiPageID","dbp-dbo:wikiPageRevisionID", "dbp-dbo:wikiPageDisambiguates",
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"w3:2002/07/owl#sameAs","dbp-dbp:image","dbp-dbo:wikiPageLength", "w3:2000/01/rdf-schema#comment",
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"dbp-dbo:thumbnail", "foaf:depiction", "w3:1999/02/22-rdf-syntax-ns#type",
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"dbp-dbp:id","dbp-dbp:totalWidth", "w3:ns/prov#wasDerivedFrom", "dbp-dbp:n", "dbp-dbp:alt",
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"dbp-dbo:soundRecording"
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]
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self.filter_applier.generate_list_relationship_filter(relationship_uri_banned_list)
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def execute_task_bpe_corpus(self):
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for RDF in self._get_cleaned_movie_rows():
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RDF = self.filter_applier.rebuild_by_movie(RDF)
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RDF = RDF[["Triple","Abstract"]]
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self.task_bpe_corpus.write_from_df(RDF)
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self._end_file_handler()
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def execute_task_rdf_mask(self):
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for RDF in self._get_cleaned_movie_rows():
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self.task_rdf_mask.write(RDF)
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self._end_file_handler()
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def execute_tasks_rdf_text(self):
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for RDF in self._get_cleaned_movie_rows():
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RDF = self.filter_applier.rebuild_by_movie(RDF)
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self.task_rdf_text.write(RDF)
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self._end_file_handler()
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def execute_task_rdf_completation(self):
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for RDF in self._get_cleaned_movie_rows():
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RDF["Triple"] = self.filter_applier.build_triple(RDF)
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self.task_rdf_completation.write(RDF[["MovieID","Triple"]])
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self._end_file_handler()
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def execute_all_task(self):
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for RDF in self._get_cleaned_movie_rows():
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self.task_rdf_mask.write(RDF)
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RDF["Triple"] = self.filter_applier.build_triple(RDF)
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self.task_rdf_completation.write(RDF[["MovieID","Triple"]])
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RDF = self.filter_applier.group_by_movie_from_triple(RDF[["MovieID","Triple","Abstract"]])
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self.task_rdf_text.write(RDF)
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self.task_bpe_corpus.write_from_df(RDF[["Triple","Abstract"]])
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self._end_file_handler()
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def _end_file_handler(self):
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self.task_bpe_corpus.close()
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self.task_rdf_mask.close()
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self.task_rdf_text.close()
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self.task_rdf_completation.close()
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def _get_cleaned_movie_rows(self):
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for RDF in self.sql_endpoint.get_abbreviated_dataset_by_movie_id():
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RDF = self.filter_applier.drop_na_from_dataset(RDF)
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RDF = self.filter_applier.filter_by_frequency_movie_id(RDF)
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RDF = self.filter_applier.filter_by_frequency_relationship(RDF)
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# other filter
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#
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RDF = self.filter_applier.delete_relationship_by_list_filter(RDF)
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# regex on ObjectURI
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RDF = self.filter_applier.regex_on_objects(RDF)
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if RDF.empty:
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continue
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RDF = self.filter_applier.rdf_add_special_token(RDF) # WARNING, THIS MUST BE DONE AFTER FILTER BY FREQUENCE
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yield RDF
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def use_toy_dataset(self):
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# CHOOSEN MOVIE:
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# The Dark Knight : 117248
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# Inception : 147074
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# The Avengers : 113621
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# Cast Away : 1123
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# The Departed : 117586
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# American Psycho : 90177
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# Avatar : 71587
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# Django Unchained : 138952
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# Spirited Away : 144137
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# Knives Out : 148025
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movie_list = [117248, 147074, 113621, 1123, 117586, 90177, 71587, 138952, 144137, 148025]
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self.sql_endpoint.movie_ids = movie_list
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def generate_csv_debug_file(self, debug_path:str):
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debug_csv = Debug_csv(debug_path)
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for RDF in self._get_cleaned_movie_rows():
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debug_csv.write(RDF)
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debug_csv.close()
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# there are a lot of settings to manage
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# you only need to change settings:
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# in the init for file paths, frequency filter limit, banned reletionshipURI
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# in the use_toy_dataset , to change the toy dataset
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# in _get_cleaned_movie_rows: to change how the pipeline behave
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#pipeline = Pipeline()
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pipeline.use_toy_dataset()
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# pipeline.execute_task_bpe_corpus()
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# pipeline.execute_task_rdf_mask()
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# pipeline.execute_tasks_rdf_text()
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# pipeline.execute_task_rdf_completation()
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# pipeline.execute_all_task()
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pipeline.generate_csv_debug_file("Assets/Dataset/Tmp/debug.csv")
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