зеркало из https://github.com/mozilla/taar.git
117 строки
3.4 KiB
Python
117 строки
3.4 KiB
Python
# This Source Code Form is subject to the terms of the Mozilla Public
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# License, v. 2.0. If a copy of the MPL was not distributed with this
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# file, You can obtain one at http://mozilla.org/MPL/2.0/.
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"""
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Test cases for the TAAR Hybrid recommender
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"""
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from taar.recommenders.hybrid_recommender import CuratedRecommender
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from taar.recommenders.hybrid_recommender import HybridRecommender
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from taar.recommenders.ensemble_recommender import EnsembleRecommender
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from taar.recommenders.s3config import TAAR_WHITELIST_BUCKET, TAAR_WHITELIST_KEY
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# from taar.recommenders.hybrid_recommender import ENSEMBLE_WEIGHTS
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from .test_ensemblerecommender import install_mock_ensemble_data
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from .mocks import MockRecommenderFactory
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import json
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from moto import mock_s3
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import boto3
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def install_no_curated_data(ctx):
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ctx = ctx.child()
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conn = boto3.resource("s3", region_name="us-west-2")
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conn.create_bucket(Bucket=TAAR_WHITELIST_BUCKET)
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conn.Object(TAAR_WHITELIST_BUCKET, TAAR_WHITELIST_KEY).put(Body="")
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return ctx
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def install_mock_curated_data(ctx):
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mock_data = []
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for i in range(20):
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mock_data.append(str(i) * 16)
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ctx = ctx.child()
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conn = boto3.resource("s3", region_name="us-west-2")
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conn.create_bucket(Bucket=TAAR_WHITELIST_BUCKET)
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conn.Object(TAAR_WHITELIST_BUCKET, TAAR_WHITELIST_KEY).put(
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Body=json.dumps(mock_data)
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)
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return ctx
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def install_ensemble_fixtures(ctx):
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ctx = install_mock_ensemble_data(ctx)
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factory = MockRecommenderFactory()
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ctx["recommender_factory"] = factory
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ctx["recommender_map"] = {
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"collaborative": factory.create("collaborative"),
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"similarity": factory.create("similarity"),
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"locale": factory.create("locale"),
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}
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ctx["ensemble_recommender"] = EnsembleRecommender(ctx.child())
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return ctx
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@mock_s3
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def test_curated_can_recommend(test_ctx):
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ctx = install_no_curated_data(test_ctx)
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r = CuratedRecommender(ctx)
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# CuratedRecommender will always recommend something no matter
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# what
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assert r.can_recommend({})
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assert r.can_recommend({"installed_addons": []})
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@mock_s3
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def test_curated_recommendations(test_ctx):
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ctx = install_mock_curated_data(test_ctx)
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r = CuratedRecommender(ctx)
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# CuratedRecommender will always recommend something no matter
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# what
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for LIMIT in range(1, 5):
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guid_list = r.recommend({"client_id": "000000"}, limit=LIMIT)
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# The curated recommendations should always return with some kind
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# of recommendations
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assert len(guid_list) == LIMIT
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@mock_s3
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def test_hybrid_recommendations(test_ctx):
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# verify that the recommendations mix the curated and
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# ensemble results
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ctx = install_mock_curated_data(test_ctx)
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ctx = install_ensemble_fixtures(ctx)
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r = HybridRecommender(ctx)
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# Test that we can generate lists of results
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for LIMIT in range(4, 8):
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guid_list = r.recommend({"client_id": "000000"}, limit=LIMIT)
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# The curated recommendations should always return with some kind
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# of recommendations
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assert len(guid_list) == LIMIT
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# Test that the results are actually mixed
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guid_list = r.recommend({"client_id": "000000"}, limit=4)
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# A mixed list will have two recommendations with weight > 1.0
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# (ensemble) and 2 with exactly weight 1.0 from the curated list
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assert guid_list[0][1] > 1.0
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assert guid_list[1][1] > 1.0
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assert guid_list[2][1] == 1.0
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assert guid_list[3][1] == 1.0
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