зеркало из https://github.com/mozilla/taar.git
more tests updated with better mocking
This commit is contained in:
Victor Ng
Родитель
24e4b59c64
Коммит
9cefd94ceb
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@ -7,6 +7,7 @@ from itertools import groupby
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from scipy.spatial import distance
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from srgutil.interfaces import IMozLogging
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import numpy as np
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from .lazys3 import LazyJSONLoader
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FLOOR_DISTANCE_ADJUSTMENT = 0.001
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@ -14,6 +15,7 @@ CATEGORICAL_FEATURES = ["geo_city", "locale", "os"]
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CONTINUOUS_FEATURES = ["subsession_length", "bookmark_count", "tab_open_count", "total_uri", "unique_tlds"]
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S3_BUCKET = 'telemetry-parquet'
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DONOR_LIST_KEY = 'taar/similarity/donors.json'
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LR_CURVES_SIMILARITY_TO_PROBABILITY = 'taar/similarity/lr_curves.json'
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@ -38,19 +40,35 @@ class SimilarityRecommender(AbstractRecommender):
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def __init__(self, ctx):
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self._ctx = ctx
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if 'similarity_donors_pool' in self._ctx:
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self._donors_pool = self._ctx['similarity_donors_pool']
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else:
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self._donors_pool = LazyJSONLoader(S3_BUCKET, DONOR_LIST_KEY)
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if 'similarity_lr_curves' in self._ctx:
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self._lr_curves = self._ctx['similarity_lr_curves']
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else:
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self._lr_curves = LazyJSONLoader(S3_BUCKET, LR_CURVES_SIMILARITY_TO_PROBABILITY)
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self.logger = self._ctx[IMozLogging].get_logger('taar')
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self._init_from_ctx()
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@property
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def donors_pool(self):
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return self._donors_pool.get()[0]
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@property
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def lr_curves(self):
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return self._lr_curves.get()[0]
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def _init_from_ctx(self):
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# Download the addon donors list.
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cache = self._ctx['cache']
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self.donors_pool = cache.get_s3_json_content(S3_BUCKET, DONOR_LIST_KEY)
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if self.donors_pool is None:
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self.logger.error("Cannot download the donor list: {}".format(DONOR_LIST_KEY))
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# Download the probability mapping curves from similarity to likelihood of being a good donor.
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self.lr_curves = cache.get_s3_json_content(S3_BUCKET, LR_CURVES_SIMILARITY_TO_PROBABILITY)
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if self.lr_curves is None:
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self.logger.error("Cannot download the lr curves: {}".format(LR_CURVES_SIMILARITY_TO_PROBABILITY))
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self.build_features_caches()
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@ -39,7 +39,6 @@ def install_none_mock_data(ctx):
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ITEM_MATRIX_CONFIG[0],
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ITEM_MATRIX_CONFIG[1])
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# Don't reuse connections with moto. badness happens
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conn = boto3.resource('s3', region_name='us-west-2')
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conn.create_bucket(Bucket=ADDON_MAPPING_CONFIG[0])
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@ -55,7 +54,6 @@ def install_mock_data(ctx):
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Overload the 'real' addon model and mapping URLs responses so that
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we always the fixture data at the top of this test module.
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"""
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conn = boto3.resource('s3', region_name='us-west-2')
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addon_space = [{"id": "addon1.id", "name": "addon1.name", "isWebextension": True},
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{"id": "addon2.id", "name": "addon2.name", "isWebextension": True},
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@ -63,9 +61,6 @@ def install_mock_data(ctx):
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{"id": "addon4.id", "name": "addon4.name", "isWebextension": True},
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{"id": "addon5.id", "name": "addon5.name", "isWebextension": True}]
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conn.create_bucket(Bucket=ITEM_MATRIX_CONFIG[0])
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conn.create_bucket(Bucket=ADDON_MAPPING_CONFIG[0])
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fake_addon_matrix = []
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for i, addon in enumerate(addon_space):
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row = {"id": positive_hash(addon['id']), "features": [0, 0.2, 0.0, 0.1, 0.15]}
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@ -77,7 +72,12 @@ def install_mock_data(ctx):
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java_hash = positive_hash(addon['id'])
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fake_mapping[str(java_hash)] = addon
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conn = boto3.resource('s3', region_name='us-west-2')
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conn.create_bucket(Bucket=ITEM_MATRIX_CONFIG[0])
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conn.Object(ITEM_MATRIX_CONFIG[0], ITEM_MATRIX_CONFIG[1]).put(Body=json.dumps(fake_addon_matrix))
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conn = boto3.resource('s3', region_name='us-west-2')
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conn.create_bucket(Bucket=ADDON_MAPPING_CONFIG[0])
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conn.Object(ADDON_MAPPING_CONFIG[0], ADDON_MAPPING_CONFIG[1]).put(Body=json.dumps(fake_mapping))
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ctx['collaborative_addon_mapping'] = LazyJSONLoader(ctx,
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@ -5,12 +5,14 @@
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import json
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import six
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import pytest
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import numpy as np
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import scipy.stats
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from taar.recommenders.lazys3 import LazyJSONLoader
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from taar.cache import JSONCache, Clock
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import boto3
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from moto import mock_s3
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from taar.recommenders.similarity_recommender import S3_BUCKET
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from taar.recommenders.similarity_recommender import \
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CATEGORICAL_FEATURES, CONTINUOUS_FEATURES, DONOR_LIST_KEY, LR_CURVES_SIMILARITY_TO_PROBABILITY, \
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SimilarityRecommender
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@ -55,68 +57,83 @@ def generate_a_fake_taar_client():
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}
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class MockNoDataUtils:
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def get_s3_json_content(self, *args, **kwargs):
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return None
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def install_no_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=S3_BUCKET)
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conn.Object(S3_BUCKET, DONOR_LIST_KEY).put(Body="")
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conn.Object(S3_BUCKET, LR_CURVES_SIMILARITY_TO_PROBABILITY).put(Body="")
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ctx['similarity_donors_pool'] = LazyJSONLoader(ctx,
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S3_BUCKET,
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DONOR_LIST_KEY)
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ctx['similarity_lr_curves'] = LazyJSONLoader(ctx,
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S3_BUCKET,
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LR_CURVES_SIMILARITY_TO_PROBABILITY)
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return ctx
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class MockCategoricalData:
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def install_categorical_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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cat_data = json.loads(json.dumps(CATEGORICAL_FEATURE_FIXTURE_DATA))
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lrs_data = json.loads(json.dumps(generate_fake_lr_curves(1000)))
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conn.create_bucket(Bucket=S3_BUCKET)
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conn.Object(S3_BUCKET, DONOR_LIST_KEY).put(Body=json.dumps(CATEGORICAL_FEATURE_FIXTURE_DATA))
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def get_s3_json_content(self, bucket, key):
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if key == DONOR_LIST_KEY:
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return self.cat_data
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if key == LR_CURVES_SIMILARITY_TO_PROBABILITY:
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return self.lrs_data
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conn.Object(S3_BUCKET, LR_CURVES_SIMILARITY_TO_PROBABILITY).put(Body=json.dumps(generate_fake_lr_curves(1000)))
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ctx['similarity_donors_pool'] = LazyJSONLoader(ctx,
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S3_BUCKET,
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DONOR_LIST_KEY)
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ctx['similarity_lr_curves'] = LazyJSONLoader(ctx,
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S3_BUCKET,
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LR_CURVES_SIMILARITY_TO_PROBABILITY)
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return ctx
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class MockContinuousData:
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def install_continuous_data(ctx):
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ctx = ctx.child()
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cts_data = json.dumps(CONTINUOUS_FEATURE_FIXTURE_DATA)
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lrs_data = json.dumps(generate_fake_lr_curves(1000))
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cts_data = json.loads(json.dumps(CONTINUOUS_FEATURE_FIXTURE_DATA))
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lrs_data = json.loads(json.dumps(generate_fake_lr_curves(1000)))
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conn = boto3.resource('s3', region_name='us-west-2')
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def get_s3_json_content(self, bucket, key):
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if key == DONOR_LIST_KEY:
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return self.cts_data
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if key == LR_CURVES_SIMILARITY_TO_PROBABILITY:
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return self.lrs_data
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@pytest.fixture
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def cat_test_ctx(test_ctx):
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ctx = test_ctx
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ctx['utils'] = MockCategoricalData()
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ctx['clock'] = Clock()
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ctx['cache'] = JSONCache(ctx)
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return ctx.child()
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@pytest.fixture
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def cts_test_ctx(test_ctx):
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ctx = test_ctx
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ctx['utils'] = MockContinuousData()
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ctx['clock'] = Clock()
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ctx['cache'] = JSONCache(ctx)
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return ctx.child()
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conn.create_bucket(Bucket=S3_BUCKET)
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conn.Object(S3_BUCKET, DONOR_LIST_KEY).put(Body=cts_data)
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conn.Object(S3_BUCKET, LR_CURVES_SIMILARITY_TO_PROBABILITY).put(Body=lrs_data)
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ctx['similarity_donors_pool'] = LazyJSONLoader(ctx,
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S3_BUCKET,
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DONOR_LIST_KEY)
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ctx['similarity_lr_curves'] = LazyJSONLoader(ctx,
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S3_BUCKET,
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LR_CURVES_SIMILARITY_TO_PROBABILITY)
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return ctx
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@mock_s3
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def test_soft_fail(test_ctx):
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# Create a new instance of a SimilarityRecommender.
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ctx = test_ctx
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ctx['utils'] = MockNoDataUtils()
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ctx['clock'] = Clock()
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ctx['cache'] = JSONCache(ctx)
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ctx = install_no_data(test_ctx)
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r = SimilarityRecommender(ctx)
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# Don't recommend if the source files cannot be found.
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assert not r.can_recommend({})
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def test_can_recommend(cts_test_ctx):
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@mock_s3
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def test_can_recommend(test_ctx):
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# Create a new instance of a SimilarityRecommender.
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ctx = cts_test_ctx
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ctx = install_continuous_data(test_ctx)
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r = SimilarityRecommender(ctx)
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# Test that we can't recommend if we have not enough client info.
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@ -140,13 +157,12 @@ def test_can_recommend(cts_test_ctx):
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assert not r.can_recommend(profile_without_x)
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def test_recommendations(cts_test_ctx):
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@mock_s3
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def test_recommendations(test_ctx):
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# Create a new instance of a SimilarityRecommender.
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ctx = cts_test_ctx
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ctx = install_continuous_data(test_ctx)
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r = SimilarityRecommender(ctx)
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# TODO: clobber the SimilarityRecommender::lr_curves
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recommendation_list = r.recommend(generate_a_fake_taar_client(), 1)
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assert isinstance(recommendation_list, list)
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@ -159,25 +175,28 @@ def test_recommendations(cts_test_ctx):
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assert type(weight) == np.float64
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def test_recommender_str(cts_test_ctx):
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@mock_s3
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def test_recommender_str(test_ctx):
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# Tests that the string representation of the recommender is correct.
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ctx = cts_test_ctx
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ctx = install_continuous_data(test_ctx)
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r = SimilarityRecommender(ctx)
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assert str(r) == "SimilarityRecommender"
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def test_get_lr(cts_test_ctx):
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@mock_s3
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def test_get_lr(test_ctx):
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# Tests that the likelihood ratio values are not empty for extreme values and are realistic.
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ctx = cts_test_ctx
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ctx = install_continuous_data(test_ctx)
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r = SimilarityRecommender(ctx)
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assert r.get_lr(0.0001) is not None
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assert r.get_lr(10.0) is not None
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assert r.get_lr(0.001) > r.get_lr(5.0)
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def test_compute_clients_dist(cts_test_ctx):
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@mock_s3
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def test_compute_clients_dist(test_ctx):
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# Test the distance function computation.
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ctx = cts_test_ctx
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ctx = install_continuous_data(test_ctx)
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r = SimilarityRecommender(ctx)
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test_clients = [
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{
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@ -229,9 +248,10 @@ def test_compute_clients_dist(cts_test_ctx):
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assert per_client_test[0] >= per_client_test[1] >= per_client_test[2]
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def test_distance_functions(cts_test_ctx):
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@mock_s3
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def test_distance_functions(test_ctx):
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# Tests the similarity functions via expected output when passing modified client data.
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ctx = cts_test_ctx
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ctx = install_continuous_data(test_ctx)
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r = SimilarityRecommender(ctx)
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# Generate a fake client.
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@ -271,9 +291,10 @@ def test_distance_functions(cts_test_ctx):
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assert abs((j_c + 0.01) * j_d) != 0.0
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def test_weights_continuous(cts_test_ctx):
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@mock_s3
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def test_weights_continuous(test_ctx):
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# Create a new instance of a SimilarityRecommender.
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ctx = cts_test_ctx
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ctx = install_continuous_data(test_ctx)
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r = SimilarityRecommender(ctx)
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# In the ensemble method recommendations should be a sorted list of tuples
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@ -303,7 +324,8 @@ def test_weights_continuous(cts_test_ctx):
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assert rec0_weight > rec1_weight > 1.0
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def test_weights_categorical(cat_test_ctx, cts_test_ctx):
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@mock_s3
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def test_weights_categorical(test_ctx):
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'''
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This should get :
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["{test-guid-1}", "{test-guid-2}", "{test-guid-3}", "{test-guid-4}"],
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@ -313,9 +335,10 @@ def test_weights_categorical(cat_test_ctx, cts_test_ctx):
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'''
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# Create a new instance of a SimilarityRecommender.
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ctx = cat_test_ctx
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ctx2 = cts_test_ctx
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wrapped = ctx2.wrap(ctx)
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cat_ctx = install_categorical_data(test_ctx)
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cts_ctx = install_continuous_data(test_ctx)
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wrapped = cts_ctx.wrap(cat_ctx)
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r = SimilarityRecommender(wrapped)
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# In the ensemble method recommendations should be a sorted list of tuples
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@ -1,13 +0,0 @@
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from taar.recommenders import utils
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def test_fetch_json():
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""" Just test a URL that we know will fail """
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jdata = utils.fetch_json("http://127.0.0.1:9001/some-nonexistant-url-foo.json")
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assert jdata is None
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def test_get_s3_json_content():
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""" Just test an S3 bucket and key that doesn't exist """
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jdata = utils.get_s3_json_content("taar_not_my_bucket", "this/is/not/a/valid/path")
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assert jdata is None
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