35 lines
1.6 KiB
Python
35 lines
1.6 KiB
Python
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import src.utils.embedding_utils as embedding_utils
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from src.investment.code_funcs import get_proc_crosswalk, get_clean_value
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from crosswalk.crosswalk_utils import CrosswalkBuilder
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import faiss
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import pickle
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import os
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import pandas as pd
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import numpy as np
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from sentence_transformers import SentenceTransformer
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# Load model
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roberta_model = SentenceTransformer("all-roberta-large-v1")
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# If running for the first time
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mapping_dir = "crosswalk/mapping_csvs/proc_cd"
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pkl_dir = "embeddings"
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for filename in os.listdir(mapping_dir):
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if filename.endswith(".csv"):
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print(f"Creating embeddings for {filename}")
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stripped_filename = filename.strip(".csv")
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mapping_df = pd.read_csv(os.path.join(mapping_dir, filename))
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proc_crosswalk = CrosswalkBuilder().from_df(mapping_df, from_col="Code", to_col="Description")
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proc_choices = [get_clean_value(x) for x in proc_crosswalk.mapping.values()]
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os.makedirs(os.path.join(pkl_dir, stripped_filename), exist_ok=True)
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proc_index = embedding_utils.create_faiss_index(choices=proc_choices,
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model=roberta_model,
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save_path=os.path.join(pkl_dir, stripped_filename, "index.bin"),
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embedding_path=os.path.join(pkl_dir, stripped_filename, "embeddings.npy"),
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choices_path=os.path.join(pkl_dir, stripped_filename, "choices.pkl")
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)
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