# https://atlas.nomic.ai/data/derek2/boru-subreddit-neural-search/map import os import pandas as pd import nomic from nomic import atlas from nomic.dataset import AtlasClass import numpy as np from src.my_logger import setup_logger NOMIC_KEY = os.getenv('NOMIC_KEY') nomic.login(NOMIC_KEY) logger = setup_logger(__name__) def count_words(text): words = text.split() return len(words) def build_nomic(dataset): df = dataset['train'].to_pandas() non_embedding_columns = ['date_utc', 'title', 'flair', 'content', 'poster', 'permalink', 'id', 'word_count', 'score', 'score_percentile'] # Calculate the 0th, 10th, 20th, ..., 90th percentiles for the 'score' column percentiles = df['score'].quantile([0, .1, .2, .3, .4, .5, .6, .7, .8, .9]).tolist() # Ensure the bins are unique and include the maximum score bins = sorted(set(percentiles + [df['score'].max()])) # Define the labels for the percentile ranges # The number of labels should be one less than the number of bins labels = [int(i * 10) for i in range(len(bins) - 1)] # Add a 'percentile_ranges' column to the DataFrame # This assigns each score to its corresponding percentile range df['score_percentile'] = pd.cut(df['score'], bins=bins, labels=labels, include_lowest=True) df['word_count'] = df['content'].apply(count_words) logger.info(f"Trying to delete old version of nomic Atlas...") try: ac = AtlasClass() atlas_id = ac._get_dataset_by_slug_identifier("derek2/boru-subreddit-neural-search")['id'] ac._delete_project_by_id(atlas_id) logger.info(f"Succeeded in deleting old version of nomic Atlas.") except: logger.info(f"Failed to delete old version of nomic Atlas.") # Create Atlas project logger.info(f"Trying to create new version of Atlas...") project = atlas.map_data(embeddings=np.stack(df['embedding'].values), data=df[non_embedding_columns].to_dict(orient='records'), id_field='id', identifier='BORU Subreddit Neural Search', ) logger.info(f"Succeeded in creating new version of nomic Atlas.")