Prepare PyPI release 0.1.0 (#11)

* Update PyGaggle version and clean up

* Update docs

chatty_goose/pipeline/retrieval_pipeline.py

@@ -15,63 +15,63 @@ class RetrievalPipeline:
     
     Parameters:
         searcher (SimpleSearcher): Pyserini searcher for Lucene index
-        retrievers (List[CQR]): List of CQR retrievers to use for first-stage retrieval
+        reformulators (List[CQR]): List of CQR methods to use for first-stage retrieval
         searcher_num_hits (int): number of hits returned by searcher - default 10
         early_fusion (bool): flag to perform fusion before second-stage retrieval - default True
         reranker (Reranker): optional reranker for second-stage retrieval
         reranker_query_index (int): retriever index to use for reranking query - defaults to last retriever
-        reranker_query_retriever (CQR): retriever for generating reranker query,
-                                        overrides reranker_query_index if provided
+        reranker_query_reformulator (CQR): CQR method for generating reranker query,
+                                           overrides reranker_query_index if provided
     """
 
     def __init__(
         self,
         searcher: SimpleSearcher,
-        retrievers: List[CQR],
+        reformulators: List[CQR],
         searcher_num_hits: int = 10,
         early_fusion: bool = True,
         reranker: Reranker = None,
         reranker_query_index: int = -1,
-        reranker_query_retriever: CQR = None,
+        reranker_query_reformulator: CQR = None,
     ):
         self.searcher = searcher
-        self.retrievers = retrievers
+        self.reformulators = reformulators
         self.searcher_num_hits = int(searcher_num_hits)
         self.early_fusion = early_fusion
         self.reranker = reranker
         self.reranker_query_index = reranker_query_index
-        self.reranker_query_retriever = reranker_query_retriever
+        self.reranker_query_reformulator = reranker_query_reformulator
 
     def retrieve(self, query) -> List[JSimpleSearcherResult]:
-        retriever_hits = []
-        retriever_queries = []
-        for retriever in self.retrievers:
-            new_query = retriever.rewrite(query)
+        cqr_hits = []
+        cqr_queries = []
+        for cqr in self.reformulators:
+            new_query = cqr.rewrite(query)
             hits = self.searcher.search(new_query, k=self.searcher_num_hits)
-            retriever_hits.append(hits)
-            retriever_queries.append(new_query)
+            cqr_hits.append(hits)
+            cqr_queries.append(new_query)
 
-        # Merge results from multiple retrievers if required
+        # Merge results from multiple CQR methods if required
         if self.early_fusion or self.reranker is None:
-            retriever_hits = reciprocal_rank_fusion(retriever_hits)
+            cqr_hits = reciprocal_rank_fusion(cqr_hits)
 
         # Return results if no reranker
         if self.reranker is None:
-            return retriever_hits
+            return cqr_hits
 
         # Get query for reranker
-        if self.reranker_query_retriever is None:
-            rerank_query = retriever_queries[self.reranker_query_index]
+        if self.reranker_query_reformulator is None:
+            rerank_query = cqr_queries[self.reranker_query_index]
         else:
-            rerank_query = self.reranker_query_retriever.rewrite(query)
+            rerank_query = self.reranker_query_reformulator.rewrite(query)
 
         # Rerank results
         if self.early_fusion:
-            results = self.rerank(rerank_query, retriever_hits[:self.searcher_num_hits])
+            results = self.rerank(rerank_query, cqr_hits[:self.searcher_num_hits])
         else:
-            # Rerank all retriever results and fuse together
+            # Rerank all CQR results and fuse together
             results = []
-            for hits in retriever_hits:
+            for hits in cqr_hits:
                 results = self.rerank(rerank_query, hits)
             results = reciprocal_rank_fusion(results)
         return results
@@ -91,8 +91,8 @@ def rerank(self, query, hits):
         return reranked_hits
 
     def reset_history(self):
-        for retriever in self.retrievers:
-            retriever.reset_history()
+        for cqr in self.reformulators:
+            cqr.reset_history()
 
-        if self.reranker_query_retriever:
-            self.reranker_query_retriever.reset_history()
+        if self.reranker_query_reformulator:
+            self.reranker_query_reformulator.reset_history()

chatty_goose/settings.py

@@ -8,7 +8,7 @@
 class SearcherSettings(BaseSettings):
     """Settings for Anserini searcher"""
 
-    index_path: str  # Lucene index path
+    index_path: str  # Pre-built index name or path to Lucene index 
     k1: float = 0.82  # BM25 k parameter
     b: float = 0.68  # BM25 b parameter
     rm3: bool = False  # use RM3

chatty_goose/util.py

@@ -1,4 +1,5 @@
 import logging
+from os import path
 from typing import Dict, List, Tuple
 
 from pygaggle.rerank.transformer import MonoBERT
@@ -49,7 +50,10 @@ def build_bert_reranker(
 
 
 def build_searcher(settings: SearcherSettings) -> SimpleSearcher:
-    searcher = SimpleSearcher(settings.index_path)
+    if path.isdir(settings.index_path):
+        searcher = SimpleSearcher(settings.index_path)
+    else:
+        searcher = SimpleSearcher.from_prebuilt_index(settings.index_path)
     searcher.set_bm25(float(settings.k1), float(settings.b))
     logging.info(
         "Initializing BM25, setting k1={} and b={}".format(settings.k1, settings.b)

docs/cqr_experiments.md

@@ -2,31 +2,24 @@
 
 ## Data Preparation
 
-1. Download the pre-built CAsT 2019 index using Pyserini. This will download the entire index to `~/.cache/pyserini`.
+1. Download either the [training](https://github.com/daltonj/treccastweb/blob/master/2019/data/training/train_topics_v1.0.json) and [evaluation](https://github.com/daltonj/treccastweb/blob/master/2019/data/evaluation/evaluation_topics_v1.0.json) input query JSON files. These files can be found under `data/treccastweb/2019/data` if you cloned the submodules for this repo.
 
-```
-from pyserini.search import SimpleSearcher
-SimpleSearcher.from_prebuilt_index('cast2019')
-```
-
-2. Download either the [training](https://github.com/daltonj/treccastweb/blob/master/2019/data/training/train_topics_v1.0.json) and [evaluation](https://github.com/daltonj/treccastweb/blob/master/2019/data/evaluation/evaluation_topics_v1.0.json) input query JSON files. These files can be found under `data/treccastweb/2019/data` if you cloned the submodules for this repo.
-
-3. Download the evaluation answer files for [training](https://github.com/daltonj/treccastweb/blob/master/2019/data/training/train_topics_mod.qrel) or [evaluation](https://trec.nist.gov/data/cast/2019qrels.txt). The training answer file is found under `data/treccastweb/2019/data`.
+2. Download the evaluation answer files for [training](https://github.com/daltonj/treccastweb/blob/master/2019/data/training/train_topics_mod.qrel) or [evaluation](https://trec.nist.gov/data/cast/2019qrels.txt). The training answer file is found under `data/treccastweb/2019/data`.
 
 ## Run CQR retrieval
 
-The following command is for HQE, but you can also run other CQR methods using `t5` or `fusion` instead of `hqe` as the input to the `--experiment` flag.
+The following command is for HQE, but you can also run other CQR methods using `t5` or `fusion` instead of `hqe` as the input to the `--experiment` flag. Running the command for the first time will download the CAsT 2019 index (or whatever index is specified for the `--index` flag). It is also possible to supply a path to a local directory containing the index.
 
 ```shell=bash
 python -m experiments.run_retrieval \
       --experiment hqe \
       --hits 1000 \
-      --index $anserini_index_path \
+      --index cast2019 \
       --qid_queries $input_query_json \
       --output ./output/hqe_bm25 \
 ```
 
-Running the experiment will output the retrieval results at the specified location in TSV format. By default, this will perform retrieval using only BM25, but you can add the `--rerank` flag to further rerank these results using BERT. For other command line arguments, see [run_retrieval.py](experiments/run_retrieval.py).
+The experiment will output the retrieval results at the specified location in TSV format. By default, this will perform retrieval using only BM25, but you can add the `--rerank` flag to further rerank these results using BERT. For other command line arguments, see [run_retrieval.py](experiments/run_retrieval.py).
 
 ## Evaluate CQR results
 

examples/messenger/README.md

@@ -9,7 +9,7 @@ This guide is based on ParlAI's [chat service tutorial](https://parl.ai/docs/tut
 3. Run the webhook server and Chatty Goose agent using our provided configuration. This assumes you have the ParlAI Python package installed and are inside the `chatty-goose` root repository folder.
 
 ```
-python3.7 -m parlai.chat_service.services.messenger.run --config-path examples/messenger/config.yml
+python -m parlai.chat_service.services.messenger.run --config-path examples/messenger/config.yml
 ```
 
 4. Add the webhook URL outputted from the above command as a callback URL for the Messenger App settings, and set the verify token to `Messenger4ParlAI`. For Heroku, this URL should look like `https://firstname-parlai-messenger-chatbot.herokuapp.com/webhook`.

experiments/run_retrieval.py

@@ -116,9 +116,9 @@ def run_experiment(rp: RetrievalPipeline):
     )
     searcher = build_searcher(searcher_settings)
 
-    # Initialize retrievers and reranker
-    retrievers = []
-    reranker_query_retriever = None
+    # Initialize CQR and reranker
+    reformulators = []
+    reranker_query_reformulator = None
     reranker = build_bert_reranker(device=args.reranker_device) if args.rerank else None
 
     if experiment == CQRType.HQE or experiment == CQRType.FUSION:
@@ -131,7 +131,7 @@ def run_experiment(rp: RetrievalPipeline):
             verbose=args.verbose,
         )
         hqe_bm25 = HQE(searcher, hqe_bm25_settings)
-        retrievers.append(hqe_bm25)
+        reformulators.append(hqe_bm25)
 
     if experiment == CQRType.T5 or experiment == CQRType.FUSION:
         # Initialize T5
@@ -143,7 +143,7 @@ def run_experiment(rp: RetrievalPipeline):
             verbose=args.verbose,
         )
         t5 = T5_NTR(t5_settings, device=args.t5_device)
-        retrievers.append(t5)
+        reformulators.append(t5)
 
     if experiment == CQRType.HQE:
         hqe_bert_settings = HQESettings(
@@ -153,14 +153,14 @@ def run_experiment(rp: RetrievalPipeline):
             R_sub=args.R1_sub,
             filter=PosFilter(args.filter),
         )
-        reranker_query_retriever = HQE(searcher, hqe_bert_settings)
+        reranker_query_reformulator = HQE(searcher, hqe_bert_settings)
 
     rp = RetrievalPipeline(
         searcher,
-        retrievers,
+        reformulators,
         searcher_num_hits=args.hits,
         early_fusion=not args.late_fusion,
         reranker=reranker,
-        reranker_query_retriever=reranker_query_retriever,
+        reranker_query_reformulator=reranker_query_reformulator,
     )
     run_experiment(rp)

requirements.txt

@@ -1,5 +1,5 @@
 coloredlogs
 parlai==1.1.0
 pydantic>=1.5
-pygaggle==0.0.2
+pygaggle==0.0.3.1
 spacy>=2.2.4

setup.py

@@ -6,7 +6,7 @@
 with open("requirements.txt") as f:
     requirements = f.read().splitlines()
 
-excluded = ["data*", "experiments*"]
+excluded = ["data*", "examples*", "experiments*"]
 
 
 setuptools.setup(
@@ -17,7 +17,7 @@
     description="A conversational passage retrieval toolkit",
     long_description=long_description,
     long_description_content_type="text/markdown",
-    url="https://github.com/jacklin64/chatty-goose",
+    url="https://github.com/castorini/chatty-goose",
     install_requires=requirements,
     packages=setuptools.find_packages(exclude=excluded),
     classifiers=[