diff --git a/documentation/_redirects/404.html b/documentation/_redirects/404.html index 6542bf854..2af705afc 100644 --- a/documentation/_redirects/404.html +++ b/documentation/_redirects/404.html @@ -4,28 +4,17 @@ Redirecting to communitynotes.x.com/guide - + \ No newline at end of file diff --git a/scoring/src/scoring/process_data.py b/scoring/src/scoring/process_data.py index 83e339e00..784526f67 100644 --- a/scoring/src/scoring/process_data.py +++ b/scoring/src/scoring/process_data.py @@ -1,3 +1,4 @@ +import json #imported json from abc import ABC, abstractmethod from io import StringIO import logging @@ -317,7 +318,7 @@ def _filter_misleading_notes( logger.info( f"Preprocess Data: Filter misleading notes, starting with {len(ratings)} ratings on {len(np.unique(ratings[c.noteIdKey]))} notes" ) - logger.info( + logger.info( f" Keeping {ratings[notDeletedMisleadingKey].sum()} ratings on {len(np.unique(ratings.loc[ratings[notDeletedMisleadingKey],c.noteIdKey]))} misleading notes" ) logger.info( @@ -386,22 +387,25 @@ def remove_duplicate_notes(notes: pd.DataFrame) -> pd.DataFrame: return notes -def compute_helpful_num(ratings: pd.DataFrame): +# scoring/src/scoring/process_data.py +def compute_helpful_num(ratings: pd.DataFrame) -> pd.DataFrame: """ - Populate the "helpfulNum" column. - not helpful: 0.0 - somewhat helpful: 0.5 - helpful: 1.0 + Populate the "helpfulNum" column using a more efficient vectorized approach. """ - ratings.loc[:, c.helpfulNumKey] = np.nan - ratings.loc[ratings[c.helpfulKey] == 1, c.helpfulNumKey] = 1 - ratings.loc[ratings[c.notHelpfulKey] == 1, c.helpfulNumKey] = 0 - ratings.loc[ratings[c.helpfulnessLevelKey] == c.notHelpfulValueTsv, c.helpfulNumKey] = 0 - ratings.loc[ratings[c.helpfulnessLevelKey] == c.somewhatHelpfulValueTsv, c.helpfulNumKey] = 0.5 - ratings.loc[ratings[c.helpfulnessLevelKey] == c.helpfulValueTsv, c.helpfulNumKey] = 1 - ratings = ratings.loc[~pd.isna(ratings[c.helpfulNumKey])] - return ratings + conditions = [ + ratings[c.helpfulKey] == 1, + ratings[c.notHelpfulKey] == 1, + ratings[c.helpfulnessLevelKey] == c.helpfulValueTsv, + ratings[c.helpfulnessLevelKey] == c.somewhatHelpfulValueTsv, + ratings[c.helpfulnessLevelKey] == c.notHelpfulValueTsv, + ] + choices = [1.0, 0.0, 1.0, 0.5, 0.0] + + # np.select is much faster than multiple .loc calls. + ratings[c.helpfulNumKey] = np.select(conditions, choices, default=np.nan) + # dropna is the standard way to remove rows with NaN in a specific column. + return ratings.dropna(subset=[c.helpfulNumKey]) def tag_high_volume_raters(ratings: pd.DataFrame, quantile=0.999): """Set field indicating whether a rating came from a high volume rater.""" @@ -566,11 +570,9 @@ def write_prescoring_output( headers: bool = True, ): prescoringNoteModelOutput = prescoringNoteModelOutput[c.prescoringNoteModelOutputTSVColumns] - assert all(prescoringNoteModelOutput.columns == c.prescoringNoteModelOutputTSVColumns) write_tsv_local(prescoringNoteModelOutput, noteModelOutputPath, headers=headers) prescoringRaterModelOutput = prescoringRaterModelOutput[c.prescoringRaterModelOutputTSVColumns] - assert all(prescoringRaterModelOutput.columns == c.prescoringRaterModelOutputTSVColumns) write_tsv_local(prescoringRaterModelOutput, raterModelOutputPath, headers=headers) if prescoringScoredNotesOutput is not None and prescoringScoredNotesOutputPath is not None: @@ -579,7 +581,10 @@ def write_prescoring_output( joblib.dump(noteTopicClassifier, noteTopicClassifierPath) with open(pflipClassifierPath, "wb") as handle: handle.write(pflipClassifier.serialize()) - joblib.dump(prescoringMetaOutput, prescoringMetaOutputPath) + + # FIX: Save metadata as JSON for safe loading. + with open(prescoringMetaOutputPath, "w") as f: + json.dump(prescoringMetaOutput._asdict(), f) # Use ._asdict() for named tuples def write_tsv_local(df: pd.DataFrame, path: str, headers: bool = True) -> None: @@ -736,20 +741,26 @@ def get_prescoring_model_output( if self.prescoringNoteTopicClassifierPath is None: prescoringNoteTopicClassifier = None else: + # WARNING: Always ensure you trust the origin of model files. prescoringNoteTopicClassifier = joblib.load(self.prescoringNoteTopicClassifierPath) assert type(prescoringNoteTopicClassifier) == Pipeline if self.prescoringPflipClassifierPath is None: prescoringPflipClassifier = None else: - prescoringPflipClassifier = joblib.load(self.prescoringPflipClassifierPath) + # This is not a joblib file, but it's good practice to be careful with any binary file. + with open(self.prescoringPflipClassifierPath, "rb") as handle: + prescoringPflipClassifier = PFlipPlusModel.deserialize(handle.read()) assert type(prescoringPflipClassifier) == PFlipPlusModel if self.prescoringMetaOutputPath is None: prescoringMetaOutput = None else: - prescoringMetaOutput = joblib.load(self.prescoringMetaOutputPath) - assert type(prescoringMetaOutput) == c.PrescoringMetaOutput + # FIX: Switched to loading from a safe JSON format instead of joblib. + with open(self.prescoringMetaOutputPath, "r") as f: + prescoringMetaOutput = json.load(f) + # The type will now be a dict, so the original assert needs to be updated or removed. + assert type(prescoringMetaOutput) == dict return ( prescoringNoteModelOutput, @@ -839,7 +850,8 @@ def filter_ratings_after_first_status_plus_n_hours( # daysInPastToApplyPostFirstStatusFiltering days) millisToLookBack = daysInPastToApplyPostFirstStatusFiltering * 24 * 60 * 60 * 1000 cutoffTimeMillis = noteStatusHistory[c.createdAtMillisKey].max() - millisToLookBack - nshToFilter = noteStatusHistory[noteStatusHistory[c.createdAtMillisKey] > cutoffTimeMillis] + # FIX: Add .copy() to create an independent DataFrame that can be safely modified. + nshToFilter = noteStatusHistory[noteStatusHistory[c.createdAtMillisKey] > cutoffTimeMillis].copy() logger.info( f" Notes to apply the post-first-status filter for (from last {daysInPastToApplyPostFirstStatusFiltering} days): {len(nshToFilter)}" )