From 3ca96a852fc08bb128353544ee6a889423a04d7c Mon Sep 17 00:00:00 2001
From: ayush chaurasia <ayush.chaurarsia@gmail.com>
Date: Mon, 15 Apr 2024 17:02:58 +0530
Subject: [PATCH] remove test file

---
 test.py | 121 --------------------------------------------------------
 1 file changed, 121 deletions(-)
 delete mode 100644 test.py

diff --git a/test.py b/test.py
deleted file mode 100644
index 09510ab9..00000000
--- a/test.py
+++ /dev/null
@@ -1,121 +0,0 @@
-import json
-from tqdm import tqdm
-import pandas as pd
-import time
-from llama_index.core import ServiceContext, VectorStoreIndex, StorageContext
-from llama_index.core.schema import TextNode
-from llama_index.embeddings.openai import OpenAIEmbedding
-from llama_index.vector_stores.lancedb import LanceDBVectorStore
-from lancedb.rerankers import Reranker, CohereReranker
-from llama_index.embeddings.huggingface import HuggingFaceEmbedding
-
-import wandb
-
-
-TRAIN_DATASET_FPATH = './finetune-embedding/data/train_dataset.json'
-VAL_DATASET_FPATH = './finetune-embedding/data/val_dataset.json'
-
-with open(TRAIN_DATASET_FPATH, 'r+') as f:
-    train_dataset = json.load(f)
-
-with open(VAL_DATASET_FPATH, 'r+') as f:
-    val_dataset = json.load(f)
-
-def run_query(tbl, query, vector_query, fts_query=None, reranker=None, top_k=5):
-    if reranker is None:
-        return tbl.search(vector_query).limit(2*top_k)
-    elif fts_query is None:
-        results = tbl.search(vector_query).rerank(reranker=reranker, query_string=query).limit(2*top_k)
-    else:
-        results = tbl.search((vector_query, fts_query)).rerank(reranker=reranker).limit(2*top_k)
-    return results
-       
-    
-def evaluate(
-    dataset,
-    embed_model,
-    top_k=5,
-    verbose=False,
-    reranker: Reranker=None,
-    query_type="vector"
-):
-    """
-    Evaluate the retrieval performance of the given dataset using the given embedding model.
-
-    Args:
-    - dataset (dict): The dataset to evaluate. It should have the following keys:
-        - corpus (dict): A dictionary of document IDs and their corresponding text.
-        - queries (dict): A dictionary of query IDs and their corresponding text.
-        - relevant_docs (dict): A dictionary of query IDs and their corresponding relevant document IDs.
-    - embed_model (str): The embedding model to use.
-    - top_k (int): The number of documents to retrieve.
-    - verbose (bool): Whether to print the evaluation results.
-    - reranker (Reranker): The reranker to use.
-    - query_type (str): The type of query to use. It should be either "vector" or "hybrid".
-           
-    """
-    corpus = dataset['corpus']
-    queries = dataset['queries']
-    relevant_docs = dataset['relevant_docs']
-
-    service_context = ServiceContext.from_defaults(embed_model=embed_model)
-    nodes = [TextNode(id_=id_, text=text) for id_, text in corpus.items()] 
-
-    vector_store = LanceDBVectorStore(uri=f"/tmp/lancedb_hybrid_benc-{time.time()}")
-    storage_context = StorageContext.from_defaults(vector_store=vector_store)
-    index = VectorStoreIndex(
-        nodes, 
-        service_context=service_context, 
-        storage_context=storage_context,
-        show_progress=True
-    )
-    tbl = vector_store._connection.open_table(vector_store.table_name)
-    # id: string
-    # doc_id: null
-    # vector: fixed_size_list<item: float>[1536]
-    # child 0, item: float
-    # text: string
-    tbl.create_fts_index("text", replace=True)
-    eval_results = []
-    for query_id, query in tqdm(queries.items()):
-        vector_query = embed_model.get_text_embedding(query)
-        if query_type == "vector":
-            rs = run_query(tbl, query, vector_query, reranker=reranker, top_k=top_k)
-        elif query_type == "hybrid":
-            fts_query = query
-            rs = run_query(tbl, query, vector_query, fts_query, reranker=reranker, top_k=top_k)
-        else:
-            raise ValueError(f"Invalid query_type: {query_type}")
-        try:
-            retrieved_ids = rs.to_pandas()["id"].tolist()[:top_k]
-        except Exception as e:
-            print(f"Error: {e}")
-            continue
-        expected_id = relevant_docs[query_id][0]
-        is_hit = expected_id in retrieved_ids  # assume 1 relevant doc
-        
-        eval_result = {
-            'is_hit': is_hit,
-            'retrieved': retrieved_ids,
-            'expected': expected_id,
-            'query': query_id,
-        }
-        eval_results.append(eval_result)
-    return eval_results
-
-rerankers = [None, CohereReranker(), CohereReranker(model_name="rerank-english-v3.0")]
-query_type = ["vector", "hybrid"]
-top_ks = [3]
-
-for top_k in top_ks:
-    for qt in query_type:
-        for reranker in rerankers:
-            wandb.init(project="cohere-v3-hf-embed", name=f"{reranker}_{qt}_top@{top_k}")
-            embed = HuggingFaceEmbedding("sentence-transformers/all-MiniLM-L6-v2") #OpenAIEmbedding()
-            results = evaluate(val_dataset, embed, reranker=reranker, query_type=qt)
-            df = pd.DataFrame(results)
-            hit_rate = df['is_hit'].mean()
-            print(f"Reranker: {reranker}, Query Type: {qt}, Hit Rate: {hit_rate}")
-            wandb.log({"hit_rate": hit_rate})
-            wandb.finish()
-