Enhance serving evaluation endpoints (#595)

* Include metric_user_results in evaluation response, added eval json endpoint

* Remove query from response

* Utilize mapped inversed user id map to get original id in response

* Update serving test case to remove 'query' and add 'user_result' in response

* simplify user ID mapping

* Combined evaluation and evaluation_json endpoints

* Updated abort responses to show plaintext instead of html

* Added unit test cases

* Updated error response for empty data

* Added unit tests for provided data evaluation

* Update app.py

* Update test_app.py

---------

Co-authored-by: Quoc-Tuan Truong <tqtg@users.noreply.github.com>

cornac/serving/app.py

@@ -26,7 +26,7 @@ from cornac.eval_methods import BaseMethod
 from cornac.metrics import *
 
 try:
-    from flask import Flask, jsonify, request
+    from flask import Flask, jsonify, request, abort, make_response
 except ImportError:
     exit("Flask is required in order to serve models.\n" + "Run: pip3 install Flask")
 
@@ -185,7 +185,6 @@ def add_feedback():
     return jsonify(data), 200
 
 
-# curl -X POST -H "Content-Type: application/json" -d '{"metrics": ["RMSE()", "NDCG(k=10)"]}' "http://localhost:8080/evaluate"
 @app.route("/evaluate", methods=["POST"])
 def evaluate():
     global model, train_set, metric_classnames
@@ -197,20 +196,59 @@ def evaluate():
         return "Unable to evaluate. 'train_set' is not provided", 400
 
     query = request.json
+    validate_query(query)
 
-    query_metrics = query.get("metrics")
-    rating_threshold = query.get("rating_threshold", 1.0)
     exclude_unknowns = (
         query.get("exclude_unknowns", "true").lower() == "true"
     )  # exclude unknown users/items by default, otherwise specified
+
+    if "data" in query:
+        data = query.get("data")
+    else:
+        data = []
+        data_fpath = "data/feedback.csv"
+        if os.path.exists(data_fpath):
+            reader = Reader()
+            data = reader.read(data_fpath, fmt="UIR", sep=",")
+
+    if not data:
+        response = make_response("No feedback has been provided so far. No data available to evaluate the model.")
+        response.status_code = 400
+        abort(response)
+
+    test_set = Dataset.build(
+        data,
+        fmt="UIR",
+        global_uid_map=train_set.uid_map,
+        global_iid_map=train_set.iid_map,
+        exclude_unknowns=exclude_unknowns,
+    )
+
+    return process_evaluation(test_set, query, exclude_unknowns)
+
+
+def validate_query(query):
+    query_metrics = query.get("metrics")
+
+    if not query_metrics:
+        response = make_response("metrics is required")
+        response.status_code = 400
+        abort(response)
+    elif not isinstance(query_metrics, list):
+        response = make_response("metrics must be an array of metrics")
+        response.status_code = 400
+        abort(response)
+
+
+def process_evaluation(test_set, query, exclude_unknowns):
+    global model, train_set
+    
+    rating_threshold = query.get("rating_threshold", 1.0)
     user_based = (
         query.get("user_based", "true").lower() == "true"
     )  # user_based evaluation by default, otherwise specified
 
-    if query_metrics is None:
-        return "metrics is required", 400
-    elif not isinstance(query_metrics, list):
-        return "metrics must be an array of metrics", 400
+    query_metrics = query.get("metrics")
 
     # organize metrics
     metrics = []
@@ -226,24 +264,6 @@ def evaluate():
 
     rating_metrics, ranking_metrics = BaseMethod.organize_metrics(metrics)
 
-    # read data
-    data = []
-    data_fpath = "data/feedback.csv"
-    if os.path.exists(data_fpath):
-        reader = Reader()
-        data = reader.read(data_fpath, fmt="UIR", sep=",")
-
-    if not len(data):
-        raise ValueError("No data available to evaluate the model.")
-
-    test_set = Dataset.build(
-        data,
-        fmt="UIR",
-        global_uid_map=train_set.uid_map,
-        global_iid_map=train_set.iid_map,
-        exclude_unknowns=exclude_unknowns,
-    )
-
     # evaluation
     result = BaseMethod.eval(
         model=model,
@@ -258,10 +278,17 @@ def evaluate():
         verbose=False,
     )
 
+    # map user index back into the original user ID
+    metric_user_results = {}
+    for metric, user_results in result.metric_user_results.items():
+        metric_user_results[metric] = {
+            train_set.user_ids[int(k)]: v for k, v in user_results.items()
+        }
+
     # response
     response = {
         "result": result.metric_avg_results,
-        "query": query,
+        "user_result": metric_user_results,
     }
 
     return jsonify(response), 200

tests/cornac/serving/test_app.py

@@ -96,9 +96,10 @@ def test_evaluate_json(client):
     response = client.post('/evaluate', json=json_data)
     # assert response.content_type == 'application/json'
     assert response.status_code == 200
-    assert len(response.json['query']['metrics']) == 2
     assert 'RMSE' in response.json['result']
     assert 'Recall@5' in response.json['result']
+    assert 'RMSE' in response.json['user_result']
+    assert 'Recall@5' in response.json['user_result']
 
 
 def test_evalulate_incorrect_get(client):
@@ -110,3 +111,52 @@ def test_evalulate_incorrect_post(client):
     response = client.post('/evaluate')
     assert response.status_code == 415  # bad request, expect json
 
+
+def test_evaluate_missing_metrics(client):
+    json_data = {
+        'metrics': []
+    }
+    response = client.post('/evaluate', json=json_data)
+    assert response.status_code == 400
+    assert response.data == b'metrics is required'
+
+
+def test_evaluate_not_list_metrics(client):
+    json_data = {
+        'metrics': 'RMSE()'
+    }
+    response = client.post('/evaluate', json=json_data)
+    assert response.status_code == 400
+    assert response.data == b'metrics must be an array of metrics'
+
+
+def test_recommend_missing_uid(client):
+    response = client.get('/recommend?k=5')
+    assert response.status_code == 400
+    assert response.data == b'uid is required'
+
+
+def test_evaluate_use_data(client):
+    json_data = {
+        'metrics': ['RMSE()', 'Recall(k=5)'],
+        'data': [['930', '795', 5], ['195', '795', 3]]
+    }
+    response = client.post('/evaluate', json=json_data)
+    # assert response.content_type == 'application/json'
+    assert response.status_code == 200
+    assert 'RMSE' in response.json['result']
+    assert 'Recall@5' in response.json['result']
+    assert 'RMSE' in response.json['user_result']
+    assert 'Recall@5' in response.json['user_result']
+
+
+def test_evaluate_use_data_empty(client):
+    json_data = {
+        'metrics': ['RMSE()', 'Recall(k=5)'],
+        'data': []
+    }
+    response = client.post('/evaluate', json=json_data)
+    assert response.status_code == 400
+    assert response.data == b"No feedback has been provided so far. No data available to evaluate the model."
+
+