def test_signal_based_update(self):
"""
Turning on signals will automatically update objects in the autocompleter
"""
signal_registry.register(Stock)
aapl = Stock(symbol='AAPL', name='Apple', market_cap=50)
aapl.save()
autocomp = Autocompleter("stock")
matches = autocomp.suggest('aapl')
self.assertEqual(len(matches), 1)
aapl.symbol = 'XYZ'
aapl.name = 'XYZ & Co.'
aapl.save()
matches = autocomp.suggest('aapl')
self.assertEqual(len(matches), 0)
matches = autocomp.suggest('xyz')
self.assertEqual(len(matches), 1)
aapl.delete()
keys = self.redis.keys('djac.test.stock*')
self.assertEqual(len(keys), 0)
signal_registry.unregister(Stock)
def get(self):
# logging.info("request start \n{}".format(time.clock() * 1000 % 1000))
self.response.headers['Content-Type'] = 'text/plain; charset=utf-8'
query = self.request.get('q')
logging.info(u"Autocomplete search for '{}'".format(query))
autocompleter = Autocompleter()
# logging.info("before autocompleter.get_results \n{}".format(time.clock() * 1000 % 1000))
results = autocompleter.get_results(query)
# logging.info("after autocompleter.get_results \n{}".format(time.clock() * 1000 % 1000))
json_results = []
for book in results:
if book is None:
logging.warning(
u"Autocompleter result for '{}' returned None.".format(
query))
continue
assert isinstance(book, BookRecord)
suggestion_map = {
'author': book.author,
'title': book.title,
'year': book.year,
'count': book.count,
'item_ids': book.key.string_id()
}
json_results.append(suggestion_map)
json_object = {
'query': query,
'version': AutocompleteJson.CURRENT_VERSION,
'status': 'completed',
'suggestions': json_results
}
# logging.info("before json.dump \n{}".format(time.clock() * 1000 % 1000))
self.response.write(json.dumps(json_object, indent=2))
def __init__(self, root, filename, text):
self.textEditor = text
self.root = root
self.StepType = {}
self.StepDetail = {}
self.StepDelete = {}
self.StepDTButton = {}
self.StepDTTable = {}
self.sImages = {}
self.sExamplesTable = None
self.AddBtn = None
self.tags = None
self.scrollFrame = None
self.filename = filename
self.SubmitBtn = None
self.CancelBtn = None
self.scenarioname = None
self.scenarioType = None
self.AllStatements = []
oXml = cXml(self.filename)
sKeys = self.read_config("XML", "keys")
self.autocompl = Autocompleter()
df = self.autocompl.import_xml(filename)
self.new_df = self.autocompl.process_data(df)
self.model_tf, self.tfidf_matrice = self.autocompl.calc_matrice(self.new_df)
def test_hashing_order(self):
"""
Facets with identical key/values in different order should still have same hash
"""
facet_1 = [{
'type':
'or',
'facets': [{
'key': 'sector',
'value': 'Technology'
}, {
'value': 'Software',
'key': 'industry'
}]
}]
facet_2 = [{
'type':
'or',
'facets': [{
'key': 'industry',
'value': 'Software'
}, {
'key': 'sector',
'value': 'Technology'
}]
}]
facet_1_hash = Autocompleter.hash_facets(facet_1)
facet_2_hash = Autocompleter.hash_facets(facet_2)
self.assertEqual(facet_1_hash, facet_2_hash)
def test_hash_identical_values_different_facet_type(self):
"""
Facets with same key/values but different facet type in different order shouldn't have same hash
"""
facet_1 = [{
'type':
'and',
'facets': [{
'key': 'sector',
'value': 'Technology'
}, {
'key': 'industry',
'value': 'Software'
}]
}]
facet_2 = [{
'type':
'or',
'facets': [{
'key': 'industry',
'value': 'Software'
}, {
'key': 'sector',
'value': 'Technology'
}]
}]
facet_1_hash = Autocompleter.hash_facets(facet_1)
facet_2_hash = Autocompleter.hash_facets(facet_2)
self.assertNotEqual(facet_1_hash, facet_2_hash)
def test_signal_based_update(self):
"""
Turning on signals will automatically update objects in the autocompleter
"""
signal_registry.register(Stock)
aapl = Stock(symbol='AAPL', name='Apple', market_cap=50)
aapl.save()
autocomp = Autocompleter("stock")
matches = autocomp.suggest('aapl')
self.assertEqual(len(matches), 1)
aapl.symbol = 'XYZ'
aapl.name = 'XYZ & Co.'
aapl.save()
matches = autocomp.suggest('aapl')
self.assertEqual(len(matches), 0)
matches = autocomp.suggest('xyz')
self.assertEqual(len(matches), 1)
aapl.delete()
keys = self.redis.keys('djac.stock*')
self.assertEqual(len(keys), 0)
signal_registry.unregister(Stock)
def test_hashing_order(self):
"""
Facets with identical key/values in different order should still have same hash
"""
facet_1 = [
{
'type': 'or',
'facets': [
{'key': 'sector', 'value': 'Technology'},
{'key': 'industry', 'value': 'Software'}
]
}
]
facet_2 = [
{
'type': 'or',
'facets': [
{'key': 'industry', 'value': 'Software'},
{'key': 'sector', 'value': 'Technology'}
]
}
]
facet_1_hash = Autocompleter.hash_facets(facet_1)
facet_2_hash = Autocompleter.hash_facets(facet_2)
self.assertEqual(facet_1_hash, facet_2_hash)
def test_hash_identical_values_different_facet_type(self):
"""
Facets with same key/values but different facet type in different order shouldn't have same hash
"""
facet_1 = [
{
'type': 'and',
'facets': [
{'key': 'sector', 'value': 'Technology'},
{'key': 'industry', 'value': 'Software'}
]
}
]
facet_2 = [
{
'type': 'or',
'facets': [
{'key': 'industry', 'value': 'Software'},
{'key': 'sector', 'value': 'Technology'}
]
}
]
facet_1_hash = Autocompleter.hash_facets(facet_1)
facet_2_hash = Autocompleter.hash_facets(facet_2)
self.assertNotEqual(facet_1_hash, facet_2_hash)
def get(self):
# logging.info("request start \n{}".format(time.clock() * 1000 % 1000))
self.response.headers['Content-Type'] = 'text/plain; charset=utf-8'
query = self.request.get('q')
logging.info(u"Autocomplete search for '{}'".format(query))
autocompleter = Autocompleter()
# logging.info("before autocompleter.get_results \n{}".format(time.clock() * 1000 % 1000))
results = autocompleter.get_results(query)
# logging.info("after autocompleter.get_results \n{}".format(time.clock() * 1000 % 1000))
json_results = []
for book in results:
if book is None:
logging.warning(u"Autocompleter result for '{}' returned None."
.format(query))
continue
assert isinstance(book, BookRecord)
suggestion_map = {'author': book.author, 'title': book.title,
'year': book.year, 'count': book.count,
'item_ids': book.key.string_id()}
json_results.append(suggestion_map)
json_object = {
'query': query,
'version': AutocompleteJson.CURRENT_VERSION,
'status': 'completed',
'suggestions': json_results
}
# logging.info("before json.dump \n{}".format(time.clock() * 1000 % 1000))
self.response.write(json.dumps(json_object, indent=2))
class MixedFacetProvidersMatchingTestCase(AutocompleterTestCase):
fixtures = ['stock_test_data_small.json', 'indicator_test_data_small.json']
def setUp(self):
super(MixedFacetProvidersMatchingTestCase, self).setUp()
self.autocomp = Autocompleter('facet_stock_no_facet_ind')
self.autocomp.store_all()
def test_autocompleter_with_facet_and_non_facet_providers(self):
"""
Autocompleter with facet and non-facet providers works correctly
"""
registry.set_autocompleter_setting('facet_stock_no_facet_ind', 'MAX_RESULTS', 100)
facets = [
{
'type': 'and',
'facets': [{'key': 'sector', 'value': 'Financial Services'}]
}
]
matches = self.autocomp.suggest('a')
facet_matches = self.autocomp.suggest('a', facets=facets)
# because we are using the faceted stock provider in the 'facet_stock_no_facet_ind' AC,
# we expect using facets will decrease the amount of results when searching.
self.assertEqual(len(matches['faceted_stock']), 25)
self.assertEqual(len(facet_matches['faceted_stock']), 2)
# since the indicator provider does not support facets,
# we expect the search results from both a facet and non-facet search to be the same.
self.assertEqual(len(matches['ind']), 16)
self.assertEqual(len(matches['ind']), len(facet_matches['ind']))
registry.del_autocompleter_setting('facet_stock_no_facet_ind', 'MAX_RESULTS')
def test_rounding_works_correctly(self):
"""
Rounding works correctly
"""
self.assertEqual(1, Autocompleter.normalize_rounding(.51))
self.assertEqual(0, Autocompleter.normalize_rounding(.49))
self.assertEqual(-1, Autocompleter.normalize_rounding(-.51))
self.assertEqual(0, Autocompleter.normalize_rounding(-.49))
def test_rounding_half(self):
"""
Rounding a number that ends in .5 should produce a number with a greater absolute value
"""
self.assertEqual(1, Autocompleter.normalize_rounding(.5))
self.assertEqual(2, Autocompleter.normalize_rounding(1.5))
self.assertEqual(-1, Autocompleter.normalize_rounding(-.5))
self.assertEqual(-2, Autocompleter.normalize_rounding(-1.5))
def test_rounding_works_correctly(self):
"""
Rounding works correctly
"""
self.assertEqual(1, Autocompleter.normalize_rounding(.51))
self.assertEqual(0, Autocompleter.normalize_rounding(.49))
self.assertEqual(-1, Autocompleter.normalize_rounding(-.51))
self.assertEqual(0, Autocompleter.normalize_rounding(-.49))
def test_rounding_half(self):
"""
Rounding a number that ends in .5 should produce a number with a greater absolute value
"""
self.assertEqual(1, Autocompleter.normalize_rounding(.5))
self.assertEqual(2, Autocompleter.normalize_rounding(1.5))
self.assertEqual(-1, Autocompleter.normalize_rounding(-.5))
self.assertEqual(-2, Autocompleter.normalize_rounding(-1.5))
def suggest(request, name):
if settings.SUGGEST_PARAMETER_NAME in request.GET:
term = request.GET[settings.SUGGEST_PARAMETER_NAME]
ac = Autocompleter(name)
results = ac.suggest(term)
json_response = json.dumps(results)
return HttpResponse(json_response, content_type='application/json')
return HttpResponseServerError("Search parameter not found.")
def test_exact_matches_not_stored_by_default(self):
"""
Exact matches are not stored by default
"""
autocomp = Autocompleter("stock")
autocomp.store_all()
keys = self.redis.keys('djac.test.stock.e.*')
self.assertEqual(len(keys), 0)
self.assertFalse(self.redis.exists('djac.test.stock.es'))
autocomp.remove_all()
def get_autocomplete():
query = request.args.get('query')
autocompleter = Autocompleter()
results = autocompleter.autocomplete(query)
formatted = []
for result in results:
document = {"value": result, "data": "AE"}
formatted.append(document)
return jsonify({"suggestions": formatted})
def test_multiple_facets_hashing_order(self):
"""
A facet list with multiple facets should have same hash when key/values are identical regardless of order
"""
facet_1 = [{
'type':
'or',
'facets': [{
'value': 'Technology',
'key': 'sector'
}, {
'value': 'Software',
'key': 'industry'
}]
}, {
'type':
'and',
'facets': [{
'key': 'sector',
'value': 'Energy'
}, {
'key': 'industry',
'value': 'Oil & Gas Integrated'
}]
}]
facet_2 = [
{
'type':
'and',
'facets': [{
'key': 'industry',
'value': 'Oil & Gas Integrated'
}, {
'key': 'sector',
'value': 'Energy'
}]
},
{
'type':
'or',
'facets': [{
'value': 'Technology',
'key': 'sector'
}, {
'key': 'industry',
'value': 'Software'
}]
},
]
facet_1_hash = Autocompleter.hash_facets(facet_1)
facet_2_hash = Autocompleter.hash_facets(facet_2)
self.assertEqual(facet_1_hash, facet_2_hash)
class DictProviderMatchingTestCase(AutocompleterTestCase):
def setUp(self):
super(DictProviderMatchingTestCase, self).setUp()
self.autocomp = Autocompleter("metric")
self.autocomp.store_all()
def tearDown(self):
self.autocomp.remove_all()
def test_basic_match(self):
matches = self.autocomp.suggest('m')
self.assertEqual(len(matches), 1)
class DictProviderMatchingTestCase(AutocompleterTestCase):
def setUp(self):
super(DictProviderMatchingTestCase, self).setUp()
self.autocomp = Autocompleter("metric")
self.autocomp.store_all()
def tearDown(self):
self.autocomp.remove_all()
def test_basic_match(self):
matches = self.autocomp.suggest('m')
self.assertEqual(len(matches), 1)
def test_remove_intermediate_results_exact_suggest(self):
"""
After exact_suggest call, all intermediate result sets are removed
"""
setattr(auto_settings, 'MAX_EXACT_MATCH_WORDS', 2)
autocomp = Autocompleter('stock')
autocomp.store_all()
autocomp.exact_suggest('aapl')
keys = self.redis.keys('djac.results.*')
self.assertEqual(len(keys), 0)
setattr(auto_settings, 'MAX_EXACT_MATCH_WORDS', 0)
def test_store_all_facet_data(self):
"""
Calling store_all stores all facet data
"""
autocomp = Autocompleter("faceted_stock")
autocomp.store_all()
facet_set_name = base.FACET_SET_BASE_NAME % ('faceted_stock', 'sector', 'Technology',)
set_length = self.redis.zcard(facet_set_name)
self.assertEqual(set_length, 12)
facet_map_name = base.FACET_MAP_BASE_NAME % ('faceted_stock',)
keys = self.redis.hkeys(facet_map_name)
self.assertEqual(len(keys), 104)
def test_facet_identity_hash(self):
"""
Hashing a facet should equal the hash of an earlier call
"""
facets = [
{
'type': 'or',
'facets': [{'key': 'sector', 'value': 'Technology'}]
}
]
first_hash = Autocompleter.hash_facets(facets)
second_hash = Autocompleter.hash_facets(facets)
self.assertEqual(first_hash, second_hash)
def test_store_and_remove_all_basic(self):
"""
Storing and removing items all at once works for a dictionary obj autocompleter.
"""
autocomp = Autocompleter("stock")
autocomp.store_all()
keys = self.redis.hkeys('djac.test.stock')
self.assertEqual(len(keys), 104)
autocomp.remove_all()
keys = self.redis.keys('djac.test.stock*')
self.assertEqual(len(keys), 0)
def get(self):
result = u"<p>Behold autocomplete stuff:</p>"
autocompleter = Autocompleter()
query = self.request.get('q', default_value=u"Tolkien")
suggestions = autocompleter.get_results(query)
result += u"<pre>"
for suggestion in suggestions:
assert isinstance(suggestion, BookRecord)
result += u"{} - {} ({})\n".format(suggestion.author, suggestion.title, suggestion.year)
result += u"</pre>"
render_html(self, "admin_generic.html", u"Testing BQ",
result)
def test_store_and_remove_all_basic(self):
"""
Storing and removing items all the once works for a single-model autocompleter.
"""
autocomp = Autocompleter("stock")
autocomp.store_all()
keys = self.redis.hkeys('djac.stock')
self.assertEqual(len(keys), 101)
autocomp.remove_all()
keys = self.redis.keys('djac.stock*')
self.assertEqual(len(keys), 0)
class CalcAutocompleteProviderTestCase(AutocompleterTestCase):
fixtures = ['indicator_test_data_small.json']
def setUp(self):
super(CalcAutocompleteProviderTestCase, self).setUp()
self.autocomp = Autocompleter("metric_aliased")
self.autocomp.store_all()
def tearDown(self):
self.autocomp.remove_all()
def test_one_way_alias_list_creation(self):
"""
Test that oneway alias lists are created properly
"""
provider = registry._providers_by_ac["metric_aliased"][0]
aliases = provider.get_norm_phrase_aliases()
self.assertTrue('revenue' in aliases)
self.assertFalse('turnover' in aliases)
def test_one_way_aliasing(self):
"""
Aliases in get_one_way_phrase_aliases are not aliased both ways.
"""
matches = self.autocomp.suggest('revenue')
self.assertEqual(len(matches), 1)
matches = self.autocomp.suggest('Turnover')
self.assertEqual(len(matches), 2)
def test_one_way_with_two_way_alias_list_creation(self):
"""
Two way and one way aliases are both included/treated properly
"""
provider = registry._providers_by_ac["metric_aliased"][0]
aliases = provider.get_norm_phrase_aliases()
self.assertTrue('ev' in aliases)
self.assertTrue('enterprise value' in aliases)
self.assertTrue('revenue' in aliases)
self.assertFalse('turnover' in aliases)
def test_one_way_with_two_way_aliasing(self):
"""
Aliases in get_one_way_phrase_aliases are not aliased both ways.
"""
rev_matches = self.autocomp.suggest('revenue')
turn_matches = self.autocomp.suggest('Turnover')
self.assertFalse(rev_matches == turn_matches)
ev_matches = self.autocomp.suggest('EV')
ent_val_matches = self.autocomp.suggest('Enterprise Value')
self.assertEqual(ev_matches, ent_val_matches)
def test_facet_match_with_move_exact_matches(self):
"""
Exact matching still works with facet suggest
"""
setattr(auto_settings, 'MAX_EXACT_MATCH_WORDS', 10)
temp_autocomp = Autocompleter('faceted_stock')
temp_autocomp.store_all()
facets = [
{
'type': 'or',
'facets': [
{'key': 'sector', 'value': 'Technology'},
{'key': 'industry', 'value': 'Software'}
]
}
]
matches = temp_autocomp.suggest('Ma', facets=facets)
setattr(auto_settings, 'MOVE_EXACT_MATCHES_TO_TOP', True)
matches2 = temp_autocomp.suggest('Ma', facets=facets)
self.assertNotEqual(matches[0]['search_name'], matches2[0]['search_name'])
# Must set the setting back to where it was as it will persist
setattr(auto_settings, 'MOVE_EXACT_MATCHES_TO_TOP', False)
temp_autocomp.remove_all()
def test_facet_identity_hash(self):
"""
Hashing a facet should equal the hash of an earlier call
"""
facets = [{
'type': 'or',
'facets': [{
'key': 'sector',
'value': 'Technology'
}]
}]
first_hash = Autocompleter.hash_facets(facets)
second_hash = Autocompleter.hash_facets(facets)
self.assertEqual(first_hash, second_hash)
class MultiExactMatchTestCase(AutocompleterTestCase):
fixtures = ['stock_test_data_small.json', 'indicator_test_data_small.json']
def setUp(self):
super(MultiExactMatchTestCase, self).setUp()
setattr(auto_settings, 'MAX_EXACT_MATCH_WORDS', 10)
self.autocomp = Autocompleter("mixed")
self.autocomp.store_all()
def tearDown(self):
setattr(auto_settings, 'MAX_EXACT_MATCH_WORDS', 0)
self.autocomp.remove_all()
def test_exact_suggest(self):
"""
Exact matching works in multi-provider autocompleters
"""
matches = self.autocomp.exact_suggest('ma')
self.assertEqual(len(matches['stock']), 1)
matches = self.autocomp.exact_suggest('US Unemployment Rate')
self.assertEqual(len(matches['ind']), 1)
def test_move_exact_matches_to_top(self):
"""
MOVE_EXACT_MATCHES_TO_TOP works in multi-provider autocompleters
"""
matches = self.autocomp.suggest('Ma')
setattr(auto_settings, 'MOVE_EXACT_MATCHES_TO_TOP', True)
matches2 = self.autocomp.suggest('Ma')
self.assertNotEqual(matches['stock'][0]['search_name'], matches2['stock'][0]['search_name'])
setattr(auto_settings, 'MOVE_EXACT_MATCHES_TO_TOP', False)
class IndicatorAliasedMatchTestCase(AutocompleterTestCase):
fixtures = ['indicator_test_data_small.json']
def setUp(self):
self.autocomp = Autocompleter("indicator_aliased")
self.autocomp.store_all()
super(IndicatorAliasedMatchTestCase, self).setUp()
def tearDown(self):
self.autocomp.remove_all()
def test_aliasing(self):
"""
Various permutations of aliased matching work
"""
matches = self.autocomp.suggest('us consumer price index')
self.assertNotEqual(len(matches), 0)
matches = self.autocomp.suggest('united states consumer price index')
self.assertNotEqual(len(matches), 0)
matches = self.autocomp.suggest('us cpi')
self.assertNotEqual(len(matches), 0)
matches = self.autocomp.suggest('united states consumer price index')
self.assertNotEqual(len(matches), 0)
def handle(self, *args, **options):
# Configure loggingin
level = {
0: logging.WARN,
1: logging.INFO,
2: logging.DEBUG
}[options.get('verbosity', 0)]
logging.basicConfig(level=level,
format="%(name)s: %(levelname)s: %(message)s")
self.log = logging.getLogger('commands.autocompleter_init')
autocomp = Autocompleter(options["name"])
if options['remove']:
self.log.info("Removing all objects for autocompleter: %s" %
(options['name']))
autocomp.remove_all()
if options['store']:
delete_old = options['delete_old']
self.log.info("Storing all objects for autocompleter: %s" %
(options['name']))
autocomp.store_all(delete_old=delete_old)
if options['clear_cache']:
self.log.info("Clearing cache for autocompleter: %s" %
(options['name']))
autocomp.clear_cache()
def get(self):
result = u"<p>Behold autocomplete stuff:</p>"
autocompleter = Autocompleter()
query = self.request.get('q', default_value=u"Tolkien")
suggestions = autocompleter.get_results(query)
result += u"<pre>"
for suggestion in suggestions:
assert isinstance(suggestion, BookRecord)
result += u"{} - {} ({})\n".format(suggestion.author,
suggestion.title,
suggestion.year)
result += u"</pre>"
render_html(self, "admin_generic.html", u"Testing BQ", result)
class MultiExactMatchTestCase(AutocompleterTestCase):
fixtures = ['stock_test_data_small.json', 'indicator_test_data_small.json']
def setUp(self):
super(MultiExactMatchTestCase, self).setUp()
setattr(auto_settings, 'MAX_EXACT_MATCH_WORDS', 10)
self.autocomp = Autocompleter("mixed")
self.autocomp.store_all()
def tearDown(self):
setattr(auto_settings, 'MAX_EXACT_MATCH_WORDS', 0)
self.autocomp.remove_all()
def test_exact_suggest(self):
"""
Exact matching works in multi-provider autocompleters
"""
matches = self.autocomp.exact_suggest('ma')
self.assertEqual(len(matches['stock']), 1)
matches = self.autocomp.exact_suggest('US Unemployment Rate')
self.assertEqual(len(matches['ind']), 1)
def test_move_exact_matches_to_top(self):
"""
MOVE_EXACT_MATCHES_TO_TOP works in multi-provider autocompleters
"""
matches = self.autocomp.suggest('Ma')
setattr(auto_settings, 'MOVE_EXACT_MATCHES_TO_TOP', True)
matches2 = self.autocomp.suggest('Ma')
self.assertNotEqual(matches['stock'][0]['search_name'],
matches2['stock'][0]['search_name'])
setattr(auto_settings, 'MOVE_EXACT_MATCHES_TO_TOP', False)
def test_exact_matches_stored_when_turned_on(self):
"""
We store exact matches when MAX_EXACT_MATCH_WORDS is turned on
"""
setattr(auto_settings, 'MAX_EXACT_MATCH_WORDS', 10)
autocomp = Autocompleter("stock")
autocomp.store_all()
keys = self.redis.keys('djac.test.stock.e.*')
self.assertNotEqual(len(keys), 0)
self.assertTrue(self.redis.exists('djac.test.stock.es'))
autocomp.remove_all()
# Must set the setting back to where it was as it will persist
setattr(auto_settings, 'MAX_EXACT_MATCH_WORDS', 0)
def get(self, request, name):
if settings.SUGGEST_PARAMETER_NAME in request.GET:
term = request.GET[settings.SUGGEST_PARAMETER_NAME]
ac = Autocompleter(name)
if settings.FACET_PARAMETER_NAME in request.GET:
facets = request.GET[settings.FACET_PARAMETER_NAME]
facets = json.loads(facets)
if not self.validate_facets(facets):
return HttpResponseBadRequest('Malformed facet parameter.')
results = ac.suggest(term, facets=facets)
else:
results = ac.suggest(term)
json_response = json.dumps(results)
return HttpResponse(json_response, content_type='application/json')
return HttpResponseServerError('Search parameter not found.')
def save_consolidated_autocomplete_data(data_slice, offset):
consolidated_records = _ConsolidationHashMap.query().fetch(1000)
consolidated_books = {}
for rec in consolidated_records:
consolidated_books.update(rec.hashmap)
logging.info("Running save subprocess for {} records, offset={}. "
"consolidated_books={}".format(len(data_slice), offset,
len(consolidated_books)))
autocompleter = Autocompleter()
docs = []
records = []
for book_hash in consolidated_books:
book = consolidated_books[book_hash]
# assert isinstance(book, tuple)
assert len(book) == 3
index = book[0] - offset
if not (0 <= index < len(data_slice)):
continue # not found in slice - a sibling process will take care of this
row = data_slice[index]
year = None
try:
year = int(unicode(row[3]))
except:
pass
doc, record = Autocompleter.create_instances_to_be_saved(
book[1], # item_ids
row[1], # author
row[2], # title
year, # year
book[2] # count
)
docs.append(doc)
if len(docs) >= 200:
autocompleter.index.put(docs)
logging.info("{} docs were put into index".format(len(docs)))
docs = []
records.append(record)
if len(records) >= 200:
ndb.put_multi(records)
logging.info("{} records were put into storage".format(
len(records)))
records = []
autocompleter.index.put(docs)
logging.info("{} docs were put into index".format(len(docs)))
ndb.put_multi(records)
logging.info("{} records were put into storage".format(len(records)))
class StockExactMatchTestCase(AutocompleterTestCase):
fixtures = ['stock_test_data_small.json']
def setUp(self):
super(StockExactMatchTestCase, self).setUp()
setattr(auto_settings, 'MAX_EXACT_MATCH_WORDS', 10)
self.autocomp = Autocompleter("stock")
self.autocomp.store_all()
def tearDown(self):
setattr(auto_settings, 'MAX_EXACT_MATCH_WORDS', 0)
self.autocomp.remove_all()
def test_exact_suggest(self):
"""
Exact matching works
"""
matches_symbol = self.autocomp.exact_suggest('ma')
self.assertEqual(len(matches_symbol), 1)
def test_move_exact_matches_to_top_setting(self):
"""
MOVE_EXACT_MATCHES_TO_TOP works
"""
matches = self.autocomp.suggest('Ma')
setattr(auto_settings, 'MOVE_EXACT_MATCHES_TO_TOP', True)
matches2 = self.autocomp.suggest('Ma')
self.assertNotEqual(matches[0]['search_name'], matches2[0]['search_name'])
# Must set the setting back to where it was as it will persist
setattr(auto_settings, 'MOVE_EXACT_MATCHES_TO_TOP', False)
def test_exact_caching(self):
"""
Exact caching works
"""
matches = self.autocomp.exact_suggest('aapl')
setattr(auto_settings, 'CACHE_TIMEOUT', 3600)
for i in range(0, 10):
matches2 = self.autocomp.exact_suggest('aapl')
self.assertEqual(len(matches), len(matches2))
# Must set the setting back to where it was as it will persist
setattr(auto_settings, 'CACHE_TIMEOUT', 0)
class StockExactMatchTestCase(AutocompleterTestCase):
fixtures = ['stock_test_data_small.json']
def setUp(self):
super(StockExactMatchTestCase, self).setUp()
setattr(auto_settings, 'MAX_EXACT_MATCH_WORDS', 10)
self.autocomp = Autocompleter("stock")
self.autocomp.store_all()
def tearDown(self):
setattr(auto_settings, 'MAX_EXACT_MATCH_WORDS', 0)
self.autocomp.remove_all()
def test_exact_suggest(self):
"""
Exact matching works
"""
matches_symbol = self.autocomp.exact_suggest('ma')
self.assertEqual(len(matches_symbol), 1)
def test_move_exact_matches_to_top_setting(self):
"""
MOVE_EXACT_MATCHES_TO_TOP works
"""
matches = self.autocomp.suggest('Ma')
setattr(auto_settings, 'MOVE_EXACT_MATCHES_TO_TOP', True)
matches2 = self.autocomp.suggest('Ma')
self.assertNotEqual(matches[0]['search_name'],
matches2[0]['search_name'])
# Must set the setting back to where it was as it will persist
setattr(auto_settings, 'MOVE_EXACT_MATCHES_TO_TOP', False)
def test_exact_caching(self):
"""
Exact caching works
"""
matches = self.autocomp.exact_suggest('aapl')
setattr(auto_settings, 'CACHE_TIMEOUT', 3600)
for i in range(0, 10):
matches2 = self.autocomp.exact_suggest('aapl')
self.assertEqual(len(matches), len(matches2))
# Must set the setting back to where it was as it will persist
setattr(auto_settings, 'CACHE_TIMEOUT', 0)
def test_provider_specific_max_exact_match_words_setting(self):
"""
We can store exact matches for 1 individual provider, and not others
"""
setattr(auto_settings, 'MAX_EXACT_MATCH_WORDS', 10)
registry.set_provider_setting(IndicatorAutocompleteProvider, 'MAX_EXACT_MATCH_WORDS', 0)
autocomp = Autocompleter("mixed")
autocomp.store_all()
keys = self.redis.keys('djac.test.stock.e.*')
self.assertNotEqual(len(keys), 0)
self.assertTrue(self.redis.exists('djac.test.stock.es'))
keys = self.redis.keys('djac.test.ind.e.*')
self.assertEqual(len(keys), 0)
self.assertFalse(self.redis.exists('djac.test.ind.es'))
autocomp.remove_all()
registry.del_provider_setting(IndicatorAutocompleteProvider, 'MAX_EXACT_MATCH_WORDS')
setattr(auto_settings, 'MAX_EXACT_MATCH_WORDS', 0)
def test_store_and_remove_all_multi(self):
"""
Storing and removing items all the once works for a multi-model autocompleter.
"""
autocomp = Autocompleter("mixed")
autocomp.store_all()
keys = self.redis.hkeys('djac.stock')
self.assertEqual(len(keys), 101)
keys = self.redis.hkeys('djac.ind')
self.assertEqual(len(keys), 100)
autocomp.remove_all()
keys = self.redis.keys('djac.stock*')
self.assertEqual(len(keys), 0)
keys = self.redis.keys('djac.ind*')
self.assertEqual(len(keys), 0)
keys = self.redis.keys('djac.mixed*')
self.assertEqual(len(keys), 0)
def test_hashing_facet_type(self):
"""
Facet list with same sub facets but different type should not have equal hashes
"""
and_facet = [
{
'type': 'and',
'facets': [{'key': 'sector', 'value': 'Technology'}]
}
]
or_facet = [
{
'type': 'or',
'facets': [{'key': 'sector', 'value': 'Technology'}]
}
]
and_hash = Autocompleter.hash_facets(and_facet)
or_hash = Autocompleter.hash_facets(or_facet)
self.assertNotEqual(and_hash, or_hash)
def test_multiple_facets_hashing_order(self):
"""
A facet list with multiple facets should have same hash when key/values are identical regardless of order
"""
facet_1 = [
{
'type': 'or',
'facets': [
{'key': 'sector', 'value': 'Technology'},
{'key': 'industry', 'value': 'Software'}
]
},
{
'type': 'and',
'facets': [
{'key': 'sector', 'value': 'Energy'},
{'key': 'industry', 'value': 'Oil & Gas Integrated'}
]
}
]
facet_2 = [
{
'type': 'and',
'facets': [
{'key': 'industry', 'value': 'Oil & Gas Integrated'},
{'key': 'sector', 'value': 'Energy'}
]
},
{
'type': 'or',
'facets': [
{'key': 'sector', 'value': 'Technology'},
{'key': 'industry', 'value': 'Software'}
]
},
]
facet_1_hash = Autocompleter.hash_facets(facet_1)
facet_2_hash = Autocompleter.hash_facets(facet_2)
self.assertEqual(facet_1_hash, facet_2_hash)
def decompress(self, value):
"""
Decompress the field's DB value to both widgets <input> fields.
returns [`display_name_field`, `database_field`]
"""
if not value:
# if DB field is empty, return blank values.
return [None, None]
provider = self._get_provider()
object_data = Autocompleter(self.autocompleter_name).get_provider_result_from_id(
provider_name=provider.provider_name,
object_id=self._get_object_id(value, provider)
)
if not object_data:
raise forms.ValidationError('Unable to retrieve data for "{}"'.format(value))
# show the `display_name_field` value in the search field.
name = object_data.get(self.display_name_field)
return [name, value]
def decompress(self, value):
"""
Decompress the field's DB value to both widgets <input> fields.
returns [`display_name_field`, `database_field`]
"""
if not value:
# if DB field is empty, return blank values.
return [None, None]
provider = self._get_provider()
object_data = Autocompleter(
self.autocompleter_name).get_provider_result_from_id(
provider_name=provider.provider_name,
object_id=self._get_object_id(value, provider))
if not object_data:
raise forms.ValidationError(
'Unable to retrieve data for "{}"'.format(value))
# show the `display_name_field` value in the search field.
name = object_data.get(self.display_name_field)
return [name, value]
def test_hashing_facet_type(self):
"""
Facet list with same sub facets but different type should not have equal hashes
"""
and_facet = [{
'type': 'and',
'facets': [{
'key': 'sector',
'value': 'Technology'
}]
}]
or_facet = [{
'type': 'or',
'facets': [{
'key': 'sector',
'value': 'Technology'
}]
}]
and_hash = Autocompleter.hash_facets(and_facet)
or_hash = Autocompleter.hash_facets(or_facet)
self.assertNotEqual(and_hash, or_hash)
def test_facet_mismatch_with_move_exact_matches(self):
"""
Exact matching shouldn't move an object that doesn't have a matching facet value
"""
# This test case depends on very specific data, which is why this test
# issues multiple asserts to check our assumptions
setattr(auto_settings, 'MAX_EXACT_MATCH_WORDS', 10)
temp_autocomp = Autocompleter('faceted_stock')
temp_autocomp.store_all()
facets = [
{
'type': 'and',
'facets': [
{'key': 'sector', 'value': 'Healthcare'},
{'key': 'industry', 'value': 'Healthcare Plans'},
]
}
]
# When gathering suggestions for 'Un', based on the stock_data_small.json fixture,
# the only match should be UnitedHealth Group Inc. when using the Healthcare sector facet
matches = temp_autocomp.suggest('Un', facets=facets)
self.assertEqual(len(matches), 1)
self.assertEqual(matches[0]['search_name'], "UNH")
# When MOVE_EXACT_MATCHES_TO_TOP is set to True and not using facets,
# we are expecting Unilever to be moved to the top.
setattr(auto_settings, 'MOVE_EXACT_MATCHES_TO_TOP', True)
matches = temp_autocomp.suggest('Un')
self.assertEqual(matches[0]['search_name'], "UN")
# When MOVE_EXACT_MATCHES_TO_TOP is set to True and we are using the
# Healthcare sector facet, we are expecting to see UnitedHealth group
# since Unilever belongs to the Consumer Defensive sector
matches = temp_autocomp.suggest('Un', facets=facets)
self.assertEqual(matches[0]['search_name'], "UNH")
# Must set the setting back to where it was as it will persist
setattr(auto_settings, 'MOVE_EXACT_MATCHES_TO_TOP', False)
temp_autocomp.remove_all()
def test_remove_intermediate_results_suggest(self):
"""
After suggest call, all intermediate result sets are removed
"""
autocomp = Autocompleter('stock')
autocomp.store_all()
autocomp.suggest('aapl')
keys = self.redis.keys('djac.results.*')
self.assertEqual(len(keys), 0)
def test_exact_matches_not_stored_by_default(self):
"""
Exact matches are not stored by default
"""
autocomp = Autocompleter("stock")
autocomp.store_all()
keys = self.redis.keys('djac.test.stock.e.*')
self.assertEqual(len(keys), 0)
self.assertFalse(self.redis.exists('djac.test.stock.es'))
autocomp.remove_all()
def test_orphan_removal(self):
"""
test orphan removal
"""
signal_registry.register(Indicator)
autocomp = Autocompleter("indicator")
autocomp.store_all()
unemployment = Indicator.objects.get(internal_name='unemployment_rate')
unemployment.name = 'free parking'
unemployment.save()
self.assertTrue(autocomp.suggest('free parking')[0]['id'] == 1)
self.assertTrue(len(autocomp.suggest('US Unemployment Rate')) == 0)
autocomp.remove_all()
signal_registry.unregister(Indicator)
def test_store_and_remove_all_basic(self):
"""
Storing and removing items all at once works for a dictionary obj autocompleter.
"""
autocomp = Autocompleter("stock")
autocomp.store_all()
keys = self.redis.hkeys('djac.test.stock')
self.assertEqual(len(keys), 101)
autocomp.remove_all()
keys = self.redis.keys('djac.test.stock*')
self.assertEqual(len(keys), 0)
def test_dict_store_and_remove_all_basic(self):
"""
Storing and removing items all at once works for a single-model autocompleter.
"""
autocomp = Autocompleter("metric")
autocomp.store_all()
keys = self.redis.hkeys('djac.test.metric')
self.assertEqual(len(keys), 8)
autocomp.remove_all()
keys = self.redis.keys('djac.test.metric')
self.assertEqual(len(keys), 0)
