Prototype using new LensKit pipeline abstraction

src/poprox_recommender/default.py

@@ -8,10 +8,10 @@
 from poprox_recommender.components.filters import TopicFilter
 from poprox_recommender.components.joiners import Fill
 from poprox_recommender.components.rankers.topk import TopkRanker
-from poprox_recommender.components.samplers import UniformSampler
+from poprox_recommender.components.samplers.uniform import UniformSampler
 from poprox_recommender.components.scorers import ArticleScorer
+from poprox_recommender.lkpipeline import Pipeline, PipelineState
 from poprox_recommender.model import get_model
-from poprox_recommender.pipeline import PipelineState, RecommendationPipeline
 
 logger = logging.getLogger(__name__)
 logger.setLevel(logging.DEBUG)
@@ -25,22 +25,25 @@ def select_articles(
     algo_params: dict[str, Any] | None = None,
 ) -> PipelineState:
     """
-    Select articles with default recommender configuration.
+    Select articles with default recommender configuration.  It returns a
+    pipeline state whose ``default`` is the final list of recommendations.
     """
     pipeline = None
 
     pipeline = personalized_pipeline(num_slots, algo_params)
+    assert pipeline is not None
 
-    inputs = {
-        "candidate": candidate_articles,
-        "clicked": clicked_articles,
-        "profile": interest_profile,
-    }
+    recs = pipeline.node("recommender")
+    topk = pipeline.node("ranker", missing="none")
+    if topk is None:
+        wanted = (recs,)
+    else:
+        wanted = (topk, recs)
 
-    return pipeline(inputs)
+    return pipeline.run_all(*wanted, candidate=candidate_articles, clicked=clicked_articles, profile=interest_profile)
 
 
-def personalized_pipeline(num_slots: int, algo_params: dict[str, Any] | None = None) -> RecommendationPipeline | None:
+def personalized_pipeline(num_slots: int, algo_params: dict[str, Any] | None = None) -> Pipeline | None:
     """
     Create the default personalized recommendation pipeline.
 
@@ -82,23 +85,31 @@ def personalized_pipeline(num_slots: int, algo_params: dict[str, Any] | None = N
         logger.info("Recommendations will be ranked with plain top-k.")
         ranker = topk_ranker
 
-    pipeline = RecommendationPipeline(name=diversify)
+    # TODO put pipeline name back in
+    pipeline = Pipeline()
+
+    # Define pipeline inputs
+    candidates = pipeline.create_input("candidate", ArticleSet)
+    clicked = pipeline.create_input("clicked", ArticleSet)
+    profile = pipeline.create_input("profile", InterestProfile)
 
     # Compute embeddings
-    pipeline.add(article_embedder, inputs=["candidate"], output="candidate")
-    pipeline.add(article_embedder, inputs=["clicked"], output="clicked")
-    pipeline.add(user_embedder, inputs=["clicked", "profile"], output="profile")
+    e_cand = pipeline.add_component("candidate-embedder", article_embedder, article_set=candidates)
+    e_click = pipeline.add_component("history-emberdder", article_embedder, article_set=clicked)
+    e_user = pipeline.add_component("user-embedder", user_embedder, clicked_articles=e_click, interest_profile=profile)
 
     # Score and rank articles with diversification/calibration reranking
-    pipeline.add(article_scorer, inputs=["candidate", "profile"], output="candidate")
-    pipeline.add(ranker, inputs=["candidate", "profile"], output="reranked")
-
-    # Output the plain descending-by-score ranking for comparison
-    pipeline.add(topk_ranker, inputs=["candidate", "profile"], output="ranked")
+    o_scored = pipeline.add_component("scorer", article_scorer, candidate_articles=e_cand, interest_profile=e_user)
+    o_topk = pipeline.add_component("ranker", topk_ranker, candidate_articles=o_scored, interest_profile=e_user)
+    if ranker is topk_ranker:
+        o_rank = o_topk
+    else:
+        o_rank = pipeline.add_component("reranker", ranker, candidate_articles=o_scored, interest_profile=e_user)
 
     # Fallback in case not enough articles came from the ranker
-    pipeline.add(topic_filter, inputs=["candidate", "profile"], output="topical")
-    pipeline.add(sampler, inputs=["topical", "candidate"], output="sampled")
-    pipeline.add(fill, inputs=["reranked", "sampled"], output="recs")
+    # TODO: make this lazy so the sampler only runs if the reranker isn't enough
+    o_filtered = pipeline.add_component("topic-filter", topic_filter, candidate=candidates, interest_profile=profile)
+    o_sampled = pipeline.add_component("sampler", sampler, candidate=o_filtered, backup=candidates)
+    pipeline.add_component("recommender", fill, candidates1=o_rank, candidates2=o_sampled)
 
     return pipeline

src/poprox_recommender/evaluation/offline.py

@@ -9,6 +9,7 @@
 from tqdm import tqdm
 
 from poprox_recommender.data.mind import TEST_REC_COUNT, MindData
+from poprox_recommender.lkpipeline import PipelineState
 from poprox_recommender.logging_config import setup_logging
 from poprox_recommender.pipeline import RecommendationPipeline
 
@@ -25,7 +26,6 @@
 from poprox_recommender.default import personalized_pipeline
 from poprox_recommender.evaluation.metrics import rank_biased_overlap
 from poprox_recommender.paths import model_file_path, project_root
-from poprox_recommender.pipeline import PipelineState
 
 logger = logging.getLogger("poprox_recommender.test_offline")
 
@@ -45,27 +45,23 @@ def custom_encoder(obj):
         return str(obj)
 
 
-def recsys_metric(
-    mind_data: MindData,
-    request: RecommendationRequest,
-    pipeline_state: PipelineState,
-):
+def recsys_metric(mind_data: MindData, request: RecommendationRequest, state: PipelineState):
     # recommendations {account id (uuid): LIST[Article]}
     # use the url of Article
+    final = state["recommender"]
 
-    recs = pd.DataFrame({"item": [a.article_id for a in pipeline_state.recs]})
+    recs = pd.DataFrame({"item": [a.article_id for a in final.articles]})
     truth = mind_data.user_truth(request.interest_profile.profile_id)
 
     # RR should look for *clicked* articles, not just all impression articles
     single_rr = topn.recip_rank(recs, truth[truth["rating"] > 0])
     single_ndcg5 = topn.ndcg(recs, truth, k=5)
     single_ndcg10 = topn.ndcg(recs, truth, k=10)
 
-    ranked = pipeline_state.elements.get("ranked", None)
-    reranked = pipeline_state.elements.get("reranked", None)
-    if ranked and reranked:
-        single_rbo5 = rank_biased_overlap(ranked, reranked, k=5)
-        single_rbo10 = rank_biased_overlap(ranked, reranked, k=10)
+    if "ranker" in state:
+        topk = state["ranker"]
+        single_rbo5 = rank_biased_overlap(topk, final, k=5)
+        single_rbo10 = rank_biased_overlap(topk, final, k=10)
     else:
         single_rbo5 = None
         single_rbo10 = None
@@ -115,7 +111,7 @@ def recsys_metric(
                 "clicked": ArticleSet(articles=request.past_articles),
                 "profile": request.interest_profile,
             }
-            outputs = pipeline(inputs)
+            state = pipeline.run_all(**inputs)
             if request.interest_profile.click_history.article_ids:
                 personalized = 1
             else:
@@ -125,7 +121,7 @@ def recsys_metric(
             raise e
 
         logger.debug("measuring for user %s", request.interest_profile.profile_id)
-        single_ndcg5, single_ndcg10, single_rr, single_rbo5, single_rbo10 = recsys_metric(mind_data, request, outputs)
+        single_ndcg5, single_ndcg10, single_rr, single_rbo5, single_rbo10 = recsys_metric(mind_data, request, state)
         user_csv.writerow(
             [
                 request.interest_profile.profile_id,

src/poprox_recommender/handler.py

@@ -2,10 +2,7 @@
 import logging
 
 from poprox_concepts import ArticleSet
-from poprox_concepts.api.recommendations import (
-    RecommendationRequest,
-    RecommendationResponse,
-)
+from poprox_concepts.api.recommendations import RecommendationRequest, RecommendationResponse
 from poprox_recommender.default import select_articles
 from poprox_recommender.topics import user_topic_preference
 
@@ -62,7 +59,7 @@ def generate_recs(event, context):
     logger.info("Constructing response...")
     resp_body = RecommendationResponse.model_validate(
         {
-            "recommendations": {profile.profile_id: outputs.recs},
+            "recommendations": {profile.profile_id: outputs.default.articles},
         }
     )