softpandas 0.1

An easy to use semantic (soft) querying on pandas dataframes.

SoftPandas - Semantic Querying for Pandas

Example Usage:

1. Let's say we want to get all red and black swim shorts that cost less than 600$

python test.py

Or play with this code:

import pandas as pd

from core.data_types import InputDataType
from core.soft_dataframe import SoftDataFrame
from embedders.clip_embedder import OpenClipEmbedder
from embedders.sentence_transformer_embedder import SentenceTransformerEmbedder
from sklearn.metrics.pairwise import cosine_similarity

lang_model = SentenceTransformerEmbedder('thenlper/gte-small',
                                metric=cosine_similarity, threshold=0.82, device="cpu")


vision_model = OpenClipEmbedder('ViT-B-32-256', metric=cosine_similarity,
                                threshold=0.25, pretrained="datacomp_s34b_b86k")

df = pd.read_csv("sample_data/men-swimwear.csv")
df = SoftDataFrame(df, soft_columns={'NAME': InputDataType.text,
                                     'DESCRIPTION & COLOR': InputDataType.text,
                                     'FABRIC': InputDataType.text},
                   models={InputDataType.text: lang_model, InputDataType.image: vision_model}
                   )

relevant_price_items = df.query("PRICE < 600")
df_filtered_desc = relevant_price_items.soft_query("'DESCRIPTION & COLOR' ~= 'red and black swim shorts'")
df = df_filtered_desc.add_soft_columns({'IMAGE': InputDataType.image}, inplace=False)

df_filtered_image = df.soft_query("'IMAGE' ~= 'red and black swim shorts'")
print(df_filtered_image)

2. Saving and loading:

relevant_price_items.to_pickle("relevant_items.p")
a = pd.read_pickle("relevant_items.p")

TODO:

1. Add saving methods for SoftDataFrame
2. Method for adding new columns
3. Add dealing with Nans
   • if a column is Nan, just ignore it
   • If value isn't there, it shouldn't pass condition - similar to normal querying
4. Batching of initial encoding -
   • don't do it one by one
   • use device (cuda, mps, tpu, etc.)
5. make into a package
   • requirements file
   • Add image

Long Term Goals:

1. Add automatic feature extraction from images into new columns
   • allows hard querying using visual data!
2. Add ability to soft query based on Image
3. Expand to more modalities