def __init__(self,
filter: Filter,
team: Team,
base_uri: str = "/") -> None:
self._funnel_correlation = FunnelCorrelation(filter,
team,
base_uri=base_uri)
super().__init__(team, filter)
def test_correlation_with_properties_raises_validation_error(self):
filters = {
"events": [
{
"id": "user signed up",
"type": "events",
"order": 0
},
{
"id": "paid",
"type": "events",
"order": 1
},
],
"insight":
INSIGHT_FUNNELS,
"date_from":
"2020-01-01",
"date_to":
"2020-01-14",
"funnel_correlation_type":
"properties",
# "funnel_correlation_names": ["$browser"], missing value
}
filter = Filter(data=filters)
correlation = FunnelCorrelation(filter, self.team)
_create_person(distinct_ids=[f"user_1"],
team_id=self.team.pk,
properties={"$browser": "Positive"})
_create_event(
team=self.team,
event="user signed up",
distinct_id=f"user_1",
timestamp="2020-01-02T14:00:00Z",
)
_create_event(
team=self.team,
event="rick",
distinct_id=f"user_1",
timestamp="2020-01-03T14:00:00Z",
)
_create_event(
team=self.team,
event="paid",
distinct_id=f"user_1",
timestamp="2020-01-04T14:00:00Z",
)
with self.assertRaises(ValidationError):
correlation._run()
filter = filter.with_data(
{"funnel_correlation_type": "event_with_properties"})
# missing "funnel_correlation_event_names": ["rick"],
with self.assertRaises(ValidationError):
FunnelCorrelation(filter, self.team)._run()
def __init__(self, filter: Filter, team: Team, base_uri: str = "/") -> None:
# Filtering on persons / groups properties can be pushed down to funnel_actors CTE
new_correlation_filter = filter.with_data(
{
"properties": filter.property_groups.combine_properties(
PropertyOperatorType.AND, filter.correlation_property_values or []
).to_dict()
}
)
self._funnel_correlation = FunnelCorrelation(new_correlation_filter, team, base_uri=base_uri)
super().__init__(team, filter)
def track_correlations_by_events(self):
filter = Filter(
data={"events": [{"id": "user signed up"}, {"id": "insight analyzed"}], **SHORT_DATE_RANGE,},
team=self.team,
)
FunnelCorrelation(filter, self.team).run()
def calculate_funnel_correlation(self, request: Request) -> Dict[str, Any]:
team = self.team
filter = Filter(request=request)
result = FunnelCorrelation(filter=filter, team=team).run()
return {"result": result}
def calculate_funnel_correlation(self, request: Request) -> Dict[str, Any]:
team = self.team
filter = Filter(request=request)
base_uri = request.build_absolute_uri("/")
result = FunnelCorrelation(filter=filter, team=team, base_uri=base_uri).run()
return {"result": result}
class _FunnelPropertyCorrelationActors(ActorBaseQuery):
_filter: Filter
def __init__(self, filter: Filter, team: Team, base_uri: str = "/") -> None:
# Filtering on persons / groups properties can be pushed down to funnel_actors CTE
new_correlation_filter = filter.with_data(
{
"properties": filter.property_groups.combine_properties(
PropertyOperatorType.AND, filter.correlation_property_values or []
).to_dict()
}
)
self._funnel_correlation = FunnelCorrelation(new_correlation_filter, team, base_uri=base_uri)
super().__init__(team, filter)
@cached_property
def aggregation_group_type_index(self):
return self._filter.aggregation_group_type_index
def actor_query(self, limit_actors: Optional[bool] = True, extra_fields: Optional[List[str]] = None):
if not self._filter.correlation_property_values:
raise ValidationError("Property Correlation expects atleast one Property to get persons for")
funnel_persons_query, funnel_persons_params = self._funnel_correlation.get_funnel_actors_cte()
conversion_filter = (
f'funnel_actors.steps {"=" if self._filter.correlation_persons_converted else "<>"} target_step'
if self._filter.correlation_persons_converted is not None
else ""
)
recording_event_select_statement = (
", any(funnel_actors.matching_events) AS matching_events" if self._filter.include_recordings else ""
)
query = f"""
WITH
funnel_actors AS ({funnel_persons_query}),
%(target_step)s AS target_step
SELECT
funnel_actors.actor_id AS actor_id
{recording_event_select_statement}
FROM funnel_actors
WHERE {conversion_filter}
GROUP BY funnel_actors.actor_id
ORDER BY actor_id
{"LIMIT %(limit)s" if limit_actors else ""}
{"OFFSET %(offset)s" if limit_actors else ""}
"""
params = {
**funnel_persons_params,
"target_step": len(self._filter.entities),
"limit": self._filter.correlation_person_limit,
"offset": self._filter.correlation_person_offset,
}
return query, params
def track_correlations_by_event_properties_materialized(self):
filter = Filter(
data={
"events": [{"id": "user signed up"}, {"id": "insight analyzed"}],
**SHORT_DATE_RANGE,
"funnel_correlation_type": FunnelCorrelationType.EVENT_WITH_PROPERTIES,
"funnel_correlation_event_names": ["$autocapture"],
},
team=self.team,
)
FunnelCorrelation(filter, self.team).run()
def track_correlations_by_properties(self):
filter = Filter(
data={
"events": [{"id": "user signed up"}, {"id": "insight analyzed"}],
**SHORT_DATE_RANGE,
"funnel_correlation_type": FunnelCorrelationType.PROPERTIES,
"funnel_correlation_names": ["$browser"],
},
team=self.team,
)
with no_materialized_columns():
FunnelCorrelation(filter, self.team).run()
def test_events_within_conversion_window_for_correlation(self):
filters = {
"events": [
{
"id": "user signed up",
"type": "events",
"order": 0
},
{
"id": "paid",
"type": "events",
"order": 1
},
],
"insight":
INSIGHT_FUNNELS,
"funnel_window_interval":
"10",
"funnel_window_interval_unit":
"minute",
"date_from":
"2020-01-01",
"date_to":
"2020-01-14",
"funnel_correlation_type":
"events",
}
filter = Filter(data=filters)
correlation = FunnelCorrelation(filter, self.team)
_create_person(distinct_ids=["user_successful"], team_id=self.team.pk)
_create_event(
team=self.team,
event="user signed up",
distinct_id="user_successful",
timestamp="2020-01-02T14:00:00Z",
)
_create_event(
team=self.team,
event="positively_related",
distinct_id="user_successful",
timestamp="2020-01-02T14:02:00Z",
)
_create_event(
team=self.team,
event="paid",
distinct_id="user_successful",
timestamp="2020-01-02T14:06:00Z",
)
_create_person(distinct_ids=["user_dropoff"], team_id=self.team.pk)
_create_event(
team=self.team,
event="user signed up",
distinct_id="user_dropoff",
timestamp="2020-01-02T14:00:00Z",
)
_create_event(
team=self.team,
event="NOT_negatively_related",
distinct_id="user_dropoff",
timestamp=
"2020-01-02T14:15:00Z", # event happened outside conversion window
)
result = correlation._run()[0]
odds_ratios = [item.pop("odds_ratio")
for item in result] # type: ignore
expected_odds_ratios = [4]
for odds, expected_odds in zip(odds_ratios, expected_odds_ratios):
self.assertAlmostEqual(odds, expected_odds)
self.assertEqual(
result,
[
{
"event": "positively_related",
"success_count": 1,
"failure_count": 0,
# "odds_ratio": 4.0,
"correlation_type": "success",
},
],
)
def test_basic_funnel_correlation_with_properties(self):
filters = {
"events": [
{
"id": "user signed up",
"type": "events",
"order": 0
},
{
"id": "paid",
"type": "events",
"order": 1
},
],
"insight":
INSIGHT_FUNNELS,
"date_from":
"2020-01-01",
"date_to":
"2020-01-14",
"funnel_correlation_type":
"properties",
"funnel_correlation_names": ["$browser"],
}
filter = Filter(data=filters)
correlation = FunnelCorrelation(filter, self.team)
for i in range(10):
_create_person(distinct_ids=[f"user_{i}"],
team_id=self.team.pk,
properties={"$browser": "Positive"})
_create_event(
team=self.team,
event="user signed up",
distinct_id=f"user_{i}",
timestamp="2020-01-02T14:00:00Z",
)
_create_event(
team=self.team,
event="paid",
distinct_id=f"user_{i}",
timestamp="2020-01-04T14:00:00Z",
)
for i in range(10, 20):
_create_person(distinct_ids=[f"user_{i}"],
team_id=self.team.pk,
properties={"$browser": "Negative"})
_create_event(
team=self.team,
event="user signed up",
distinct_id=f"user_{i}",
timestamp="2020-01-02T14:00:00Z",
)
if i % 2 == 0:
_create_event(
team=self.team,
event="negatively_related",
distinct_id=f"user_{i}",
timestamp="2020-01-03T14:00:00Z",
)
# One Positive with failure
_create_person(distinct_ids=[f"user_fail"],
team_id=self.team.pk,
properties={"$browser": "Positive"})
_create_event(
team=self.team,
event="user signed up",
distinct_id=f"user_fail",
timestamp="2020-01-02T14:00:00Z",
)
# One Negative with success
_create_person(distinct_ids=[f"user_succ"],
team_id=self.team.pk,
properties={"$browser": "Negative"})
_create_event(
team=self.team,
event="user signed up",
distinct_id=f"user_succ",
timestamp="2020-01-02T14:00:00Z",
)
_create_event(
team=self.team,
event="paid",
distinct_id=f"user_succ",
timestamp="2020-01-04T14:00:00Z",
)
result = correlation._run()[0]
odds_ratios = [item.pop("odds_ratio")
for item in result] # type: ignore
# Success Total = 11, Failure Total = 11
#
# Browser::Positive
# Success: 10
# Failure: 1
# Browser::Negative
# Success: 1
# Failure: 10
prior_count = 1
expected_odds_ratios = [
((10 + prior_count) / (1 + prior_count)) *
((11 - 1 + prior_count) / (11 - 10 + prior_count)),
((1 + prior_count) / (10 + prior_count)) *
((11 - 10 + prior_count) / (11 - 1 + prior_count)),
]
for odds, expected_odds in zip(odds_ratios, expected_odds_ratios):
self.assertAlmostEqual(odds, expected_odds)
self.assertEqual(
result,
[
{
"event": "$browser::Positive",
"success_count": 10,
"failure_count": 1,
# "odds_ratio": 121/4,
"correlation_type": "success",
},
{
"event": "$browser::Negative",
"success_count": 1,
"failure_count": 10,
# "odds_ratio": 4/121,
"correlation_type": "failure",
},
],
)
def test_are_results_insignificant(self):
# Same setup as above test: test_discarding_insignificant_events
contingency_tables = [
EventContingencyTable(
event="negatively_related",
visited=EventStats(success_count=0, failure_count=5),
success_total=10,
failure_total=10,
),
EventContingencyTable(
event="positively_related",
visited=EventStats(success_count=5, failure_count=0),
success_total=10,
failure_total=10,
),
EventContingencyTable(
event="low_sig_negatively_related",
visited=EventStats(success_count=0, failure_count=2),
success_total=10,
failure_total=10,
),
EventContingencyTable(
event="low_sig_positively_related",
visited=EventStats(success_count=1, failure_count=0),
success_total=10,
failure_total=10,
),
]
# Discard both low_sig due to %
FunnelCorrelation.MIN_PERSON_PERCENTAGE = 0.11
FunnelCorrelation.MIN_PERSON_COUNT = 25
result = [
1 for contingency_table in contingency_tables if
not FunnelCorrelation.are_results_insignificant(contingency_table)
]
self.assertEqual(len(result), 2)
# Discard one low_sig due to %
FunnelCorrelation.MIN_PERSON_PERCENTAGE = 0.051
FunnelCorrelation.MIN_PERSON_COUNT = 25
result = [
1 for contingency_table in contingency_tables if
not FunnelCorrelation.are_results_insignificant(contingency_table)
]
self.assertEqual(len(result), 3)
# Discard both due to count
FunnelCorrelation.MIN_PERSON_PERCENTAGE = 0.5
FunnelCorrelation.MIN_PERSON_COUNT = 3
result = [
1 for contingency_table in contingency_tables if
not FunnelCorrelation.are_results_insignificant(contingency_table)
]
self.assertEqual(len(result), 2)
# Discard one due to count
FunnelCorrelation.MIN_PERSON_PERCENTAGE = 0.5
FunnelCorrelation.MIN_PERSON_COUNT = 2
result = [
1 for contingency_table in contingency_tables if
not FunnelCorrelation.are_results_insignificant(contingency_table)
]
self.assertEqual(len(result), 3)
# Discard everything due to %
FunnelCorrelation.MIN_PERSON_PERCENTAGE = 0.5
FunnelCorrelation.MIN_PERSON_COUNT = 100
result = [
1 for contingency_table in contingency_tables if
not FunnelCorrelation.are_results_insignificant(contingency_table)
]
self.assertEqual(len(result), 0)
# Discard everything due to count
FunnelCorrelation.MIN_PERSON_PERCENTAGE = 0.5
FunnelCorrelation.MIN_PERSON_COUNT = 6
result = [
1 for contingency_table in contingency_tables if
not FunnelCorrelation.are_results_insignificant(contingency_table)
]
self.assertEqual(len(result), 0)
class _FunnelEventsCorrelationActors(ActorBaseQuery):
_filter: Filter
def __init__(self,
filter: Filter,
team: Team,
base_uri: str = "/") -> None:
self._funnel_correlation = FunnelCorrelation(filter,
team,
base_uri=base_uri)
super().__init__(team, filter)
@cached_property
def aggregation_group_type_index(self):
return self._filter.aggregation_group_type_index
def actor_query(self, limit_actors: Optional[bool] = True):
if not self._filter.correlation_person_entity:
raise ValidationError("No entity for persons specified")
assert isinstance(self._filter.correlation_person_entity, Entity)
funnel_persons_query, funnel_persons_params = self._funnel_correlation.get_funnel_actors_cte(
)
prop_filters = self._filter.correlation_person_entity.property_groups
# TRICKY: We use "events" as an alias here while the eventquery uses "e" by default
event_query = FunnelEventQuery(self._filter, self._team.pk)
event_query.EVENT_TABLE_ALIAS = "events"
prop_query, prop_params = event_query._get_prop_groups(prop_filters)
conversion_filter = (
f'AND actors.steps {"=" if self._filter.correlation_persons_converted else "<>"} target_step'
if self._filter.correlation_persons_converted is not None else "")
event_join_query = self._funnel_correlation._get_events_join_query()
recording_event_select_statement = (
", any(actors.matching_events) AS matching_events"
if self._filter.include_recordings else "")
query = f"""
WITH
funnel_actors as ({funnel_persons_query}),
toDateTime(%(date_to)s) AS date_to,
toDateTime(%(date_from)s) AS date_from,
%(target_step)s AS target_step,
%(funnel_step_names)s as funnel_step_names
SELECT
actors.actor_id AS actor_id
{recording_event_select_statement}
FROM events AS event
{event_join_query}
AND event.event = %(target_event)s
{conversion_filter}
{prop_query}
GROUP BY actor_id
ORDER BY actor_id
{"LIMIT %(limit)s" if limit_actors else ""}
{"OFFSET %(offset)s" if limit_actors else ""}
"""
params = {
**funnel_persons_params,
**prop_params,
"target_event": self._filter.correlation_person_entity.id,
"funnel_step_names": [entity.id for entity in self._filter.events],
"target_step": len(self._filter.entities),
"limit": self._filter.correlation_person_limit,
"offset": self._filter.correlation_person_offset,
}
return query, params
def test_no_divide_by_zero_errors(self):
filters = {
"events": [
{
"id": "user signed up",
"type": "events",
"order": 0
},
{
"id": "paid",
"type": "events",
"order": 1
},
],
"insight":
INSIGHT_FUNNELS,
"date_from":
"2020-01-01",
"date_to":
"2020-01-14",
}
filter = Filter(data=filters)
correlation = FunnelCorrelation(filter, self.team)
for i in range(2):
_create_person(distinct_ids=[f"user_{i}"],
team_id=self.team.pk,
properties={"$browser": "Positive"})
_create_event(
team=self.team,
event="user signed up",
distinct_id=f"user_{i}",
timestamp="2020-01-02T14:00:00Z",
)
# failure count for this event is 0
_create_event(
team=self.team,
event="positive",
distinct_id=f"user_{i}",
timestamp="2020-01-03T14:00:00Z",
)
_create_event(
team=self.team,
event="paid",
distinct_id=f"user_{i}",
timestamp="2020-01-04T14:00:00Z",
)
for i in range(2, 4):
_create_person(distinct_ids=[f"user_{i}"],
team_id=self.team.pk,
properties={"$browser": "Negative"})
_create_event(
team=self.team,
event="user signed up",
distinct_id=f"user_{i}",
timestamp="2020-01-02T14:00:00Z",
)
if i % 2 == 0:
# success count for this event is 0
_create_event(
team=self.team,
event="negatively_related",
distinct_id=f"user_{i}",
timestamp="2020-01-03T14:00:00Z",
)
results = correlation._run()
self.assertFalse(results[1])
result = results[0]
odds_ratios = [item.pop("odds_ratio")
for item in result] # type: ignore
expected_odds_ratios = [9, 1 / 3]
for odds, expected_odds in zip(odds_ratios, expected_odds_ratios):
self.assertAlmostEqual(odds, expected_odds)
self.assertEqual(
result,
[
{
"event": "positive",
"success_count": 2,
"failure_count": 0,
# "odds_ratio": 9.0,
"correlation_type": "success",
},
{
"event": "negatively_related",
"success_count": 0,
"failure_count": 1,
# "odds_ratio": 1 / 3,
"correlation_type": "failure",
},
],
)
class _FunnelPropertyCorrelationActors(ActorBaseQuery):
_filter: Filter
def __init__(self,
filter: Filter,
team: Team,
base_uri: str = "/") -> None:
self._funnel_correlation = FunnelCorrelation(filter,
team,
base_uri=base_uri)
super().__init__(team, filter)
@cached_property
def aggregation_group_type_index(self):
return self._filter.aggregation_group_type_index
def actor_query(self,
limit_actors: Optional[bool] = True,
extra_fields: Optional[List[str]] = None):
if not self._filter.correlation_property_values:
raise ValidationError(
"Property Correlation expects atleast one Property to get persons for"
)
funnel_persons_query, funnel_persons_params = self._funnel_correlation.get_funnel_actors_cte(
)
conversion_filter = (
f'funnel_actors.steps {"=" if self._filter.correlation_persons_converted else "<>"} target_step'
if self._filter.correlation_persons_converted is not None else "")
actor_join_subquery, actor_join_subquery_params = self._get_actor_subquery(
)
group_filters, group_filters_params = self._get_group_filters()
recording_event_select_statement = (
", any(funnel_actors.matching_events) AS matching_events"
if self._filter.include_recordings else "")
query = f"""
WITH
funnel_actors AS ({funnel_persons_query}),
%(target_step)s AS target_step
SELECT
funnel_actors.actor_id AS actor_id
{recording_event_select_statement}
FROM funnel_actors
{actor_join_subquery}
WHERE {conversion_filter}
{group_filters}
GROUP BY funnel_actors.actor_id
ORDER BY actor_id
{"LIMIT %(limit)s" if limit_actors else ""}
{"OFFSET %(offset)s" if limit_actors else ""}
"""
params = {
**funnel_persons_params,
**actor_join_subquery_params,
**group_filters_params,
"target_step": len(self._filter.entities),
"limit": self._filter.correlation_person_limit,
"offset": self._filter.correlation_person_offset,
}
return query, params
def _get_actor_subquery(self) -> Tuple[str, Dict[str, Any]]:
if self.is_aggregating_by_groups:
actor_join_subquery, actor_join_subquery_params = GroupsJoinQuery(
self._filter, self._team.pk,
join_key="funnel_actors.actor_id").get_join_query()
else:
person_query, actor_join_subquery_params = ClickhousePersonQuery(
self._filter,
self._team.pk,
entity=Entity({
"id":
"person",
"type":
"events",
"properties":
self._filter.correlation_property_values
}),
).get_query()
actor_join_subquery = f"""
JOIN ({person_query}) person
ON person.id = funnel_actors.actor_id
"""
return actor_join_subquery, actor_join_subquery_params
def _get_group_filters(self):
if self.is_aggregating_by_groups:
conditions, params = [""], {}
properties = self._filter.correlation_property_values
if properties:
for index, property in enumerate(properties):
if property.type != "group":
continue
expr, prop_params = prop_filter_json_extract(
property,
index,
prepend=f"group_type_{property.group_type_index}",
prop_var=
f"group_properties_{property.group_type_index}",
allow_denormalized_props=True,
)
conditions.append(expr)
params.update(prop_params)
return " ".join(conditions), params
else:
return "", {}
def test_basic_funnel_correlation_with_events(self):
filters = {
"events": [
{
"id": "user signed up",
"type": "events",
"order": 0
},
{
"id": "paid",
"type": "events",
"order": 1
},
],
"insight":
INSIGHT_FUNNELS,
"date_from":
"2020-01-01",
"date_to":
"2020-01-14",
"funnel_correlation_type":
"events",
}
filter = Filter(data=filters)
correlation = FunnelCorrelation(filter, self.team)
for i in range(10):
_create_person(distinct_ids=[f"user_{i}"], team_id=self.team.pk)
_create_event(
team=self.team,
event="user signed up",
distinct_id=f"user_{i}",
timestamp="2020-01-02T14:00:00Z",
)
if i % 2 == 0:
_create_event(
team=self.team,
event="positively_related",
distinct_id=f"user_{i}",
timestamp="2020-01-03T14:00:00Z",
)
_create_event(
team=self.team,
event="paid",
distinct_id=f"user_{i}",
timestamp="2020-01-04T14:00:00Z",
)
for i in range(10, 20):
_create_person(distinct_ids=[f"user_{i}"], team_id=self.team.pk)
_create_event(
team=self.team,
event="user signed up",
distinct_id=f"user_{i}",
timestamp="2020-01-02T14:00:00Z",
)
if i % 2 == 0:
_create_event(
team=self.team,
event="negatively_related",
distinct_id=f"user_{i}",
timestamp="2020-01-03T14:00:00Z",
)
result = correlation._run()[0]
odds_ratios = [item.pop("odds_ratio")
for item in result] # type: ignore
expected_odds_ratios = [11, 1 / 11]
for odds, expected_odds in zip(odds_ratios, expected_odds_ratios):
self.assertAlmostEqual(odds, expected_odds)
self.assertEqual(
result,
[
{
"event": "positively_related",
"success_count": 5,
"failure_count": 0,
# "odds_ratio": 11.0,
"correlation_type": "success",
},
{
"event": "negatively_related",
"success_count": 0,
"failure_count": 5,
# "odds_ratio": 1 / 11,
"correlation_type": "failure",
},
],
)
self.assertEqual(
len(self._get_people_for_event(filter, "positively_related")), 5)
self.assertEqual(
len(
self._get_people_for_event(filter,
"positively_related",
success=False)), 0)
self.assertEqual(
len(
self._get_people_for_event(filter,
"negatively_related",
success=False)), 5)
self.assertEqual(
len(self._get_people_for_event(filter, "negatively_related")), 0)
# Now exclude positively_related
filter = filter.with_data(
{"funnel_correlation_exclude_event_names": ["positively_related"]})
correlation = FunnelCorrelation(filter, self.team)
result = correlation._run()[0]
odds_ratio = result[0].pop("odds_ratio") # type: ignore
expected_odds_ratio = 1 / 11
self.assertAlmostEqual(odds_ratio, expected_odds_ratio)
self.assertEqual(
result,
[
{
"event": "negatively_related",
"success_count": 0,
"failure_count": 5,
# "odds_ratio": 1 / 11,
"correlation_type": "failure",
},
],
)
# Getting specific people isn't affected by exclude_events
self.assertEqual(
len(self._get_people_for_event(filter, "positively_related")), 5)
self.assertEqual(
len(
self._get_people_for_event(filter,
"positively_related",
success=False)), 0)
self.assertEqual(
len(
self._get_people_for_event(filter,
"negatively_related",
success=False)), 5)
self.assertEqual(
len(self._get_people_for_event(filter, "negatively_related")), 0)
def test_correlation_with_multiple_properties(self):
filters = {
"events": [
{
"id": "user signed up",
"type": "events",
"order": 0
},
{
"id": "paid",
"type": "events",
"order": 1
},
],
"insight":
INSIGHT_FUNNELS,
"date_from":
"2020-01-01",
"date_to":
"2020-01-14",
"funnel_correlation_type":
"properties",
"funnel_correlation_names": ["$browser", "$nice"],
}
filter = Filter(data=filters)
correlation = FunnelCorrelation(filter, self.team)
# 5 successful people with both properties
for i in range(5):
_create_person(distinct_ids=[f"user_{i}"],
team_id=self.team.pk,
properties={
"$browser": "Positive",
"$nice": "very"
})
_create_event(
team=self.team,
event="user signed up",
distinct_id=f"user_{i}",
timestamp="2020-01-02T14:00:00Z",
)
_create_event(
team=self.team,
event="paid",
distinct_id=f"user_{i}",
timestamp="2020-01-04T14:00:00Z",
)
# 10 successful people with some different properties
for i in range(5, 15):
_create_person(distinct_ids=[f"user_{i}"],
team_id=self.team.pk,
properties={
"$browser": "Positive",
"$nice": "not"
})
_create_event(
team=self.team,
event="user signed up",
distinct_id=f"user_{i}",
timestamp="2020-01-02T14:00:00Z",
)
_create_event(
team=self.team,
event="paid",
distinct_id=f"user_{i}",
timestamp="2020-01-04T14:00:00Z",
)
# 5 Unsuccessful people with some common properties
for i in range(15, 20):
_create_person(distinct_ids=[f"user_{i}"],
team_id=self.team.pk,
properties={
"$browser": "Negative",
"$nice": "smh"
})
_create_event(
team=self.team,
event="user signed up",
distinct_id=f"user_{i}",
timestamp="2020-01-02T14:00:00Z",
)
# One Positive with failure, no $nice property
_create_person(distinct_ids=[f"user_fail"],
team_id=self.team.pk,
properties={"$browser": "Positive"})
_create_event(
team=self.team,
event="user signed up",
distinct_id=f"user_fail",
timestamp="2020-01-02T14:00:00Z",
)
# One Negative with success, no $nice property
_create_person(distinct_ids=[f"user_succ"],
team_id=self.team.pk,
properties={"$browser": "Negative"})
_create_event(
team=self.team,
event="user signed up",
distinct_id=f"user_succ",
timestamp="2020-01-02T14:00:00Z",
)
_create_event(
team=self.team,
event="paid",
distinct_id=f"user_succ",
timestamp="2020-01-04T14:00:00Z",
)
result = correlation._run()[0]
# Success Total = 5 + 10 + 1 = 16
# Failure Total = 5 + 1 = 6
# Add 1 for priors
odds_ratios = [item.pop("odds_ratio")
for item in result] # type: ignore
expected_odds_ratios = [
(16 / 2) * ((7 - 1) / (17 - 15)),
(11 / 1) * ((7 - 0) / (17 - 10)),
(6 / 1) * ((7 - 0) / (17 - 5)),
(1 / 6) * ((7 - 5) / (17 - 0)),
(2 / 6) * ((7 - 5) / (17 - 1)),
(2 / 2) * ((7 - 1) / (17 - 1)),
]
# (success + 1) / (failure + 1)
for odds, expected_odds in zip(odds_ratios, expected_odds_ratios):
self.assertAlmostEqual(odds, expected_odds)
expected_result = [
{
"event": "$browser::Positive",
"success_count": 15,
"failure_count": 1,
# "odds_ratio": 24,
"correlation_type": "success",
},
{
"event": "$nice::not",
"success_count": 10,
"failure_count": 0,
# "odds_ratio": 11,
"correlation_type": "success",
},
{
"event": "$nice::very",
"success_count": 5,
"failure_count": 0,
# "odds_ratio": 3.5,
"correlation_type": "success",
},
{
"event": "$nice::smh",
"success_count": 0,
"failure_count": 5,
# "odds_ratio": 0.0196078431372549,
"correlation_type": "failure",
},
{
"event": "$browser::Negative",
"success_count": 1,
"failure_count": 5,
# "odds_ratio": 0.041666666666666664,
"correlation_type": "failure",
},
{
"event": "$nice::",
"success_count": 1,
"failure_count": 1,
# "odds_ratio": 0.375,
"correlation_type": "failure",
},
]
self.assertEqual(result, expected_result)
# _run property correlation with filter on all properties
filter = filter.with_data({"funnel_correlation_names": ["$all"]})
correlation = FunnelCorrelation(filter, self.team)
new_result = correlation._run()[0]
odds_ratios = [item.pop("odds_ratio")
for item in new_result] # type: ignore
new_expected_odds_ratios = expected_odds_ratios[:-1]
new_expected_result = expected_result[:-1]
# When querying all properties, we don't consider properties that don't exist for part of the data
# since users aren't explicitly asking for that property. Thus,
# We discard $nice:: because it's an empty result set
for odds, expected_odds in zip(odds_ratios, new_expected_odds_ratios):
self.assertAlmostEqual(odds, expected_odds)
self.assertEqual(new_result, new_expected_result)
filter = filter.with_data(
{"funnel_correlation_exclude_names": ["$browser"]})
# search for $all but exclude $browser
correlation = FunnelCorrelation(filter, self.team)
new_result = correlation._run()[0]
odds_ratios = [item.pop("odds_ratio")
for item in new_result] # type: ignore
new_expected_odds_ratios = expected_odds_ratios[
1:4] # choosing the $nice property values
new_expected_result = expected_result[1:4]
for odds, expected_odds in zip(odds_ratios, new_expected_odds_ratios):
self.assertAlmostEqual(odds, expected_odds)
self.assertEqual(new_result, new_expected_result)
def test_funnel_correlation_with_event_properties_autocapture(self):
filters = {
"events": [
{
"id": "user signed up",
"type": "events",
"order": 0
},
{
"id": "paid",
"type": "events",
"order": 1
},
],
"insight":
INSIGHT_FUNNELS,
"date_from":
"2020-01-01",
"date_to":
"2020-01-14",
"funnel_correlation_type":
"event_with_properties",
"funnel_correlation_event_names": ["$autocapture"],
}
filter = Filter(data=filters)
correlation = FunnelCorrelation(filter, self.team)
# Need a minimum of 3 hits to get a correlation result
for i in range(6):
_create_person(distinct_ids=[f"user_{i}"], team_id=self.team.pk)
_create_event(
team=self.team,
event="user signed up",
distinct_id=f"user_{i}",
timestamp="2020-01-02T14:00:00Z",
)
_create_event(
team=self.team,
event="$autocapture",
distinct_id=f"user_{i}",
elements=[
Element(nth_of_type=1,
nth_child=0,
tag_name="a",
href="/movie")
],
timestamp="2020-01-03T14:00:00Z",
properties={
"signup_source": "email",
"$event_type": "click"
},
)
# Test two different types of autocapture elements, with different counts, so we can accurately test results
if i % 2 == 0:
_create_event(
team=self.team,
event="$autocapture",
distinct_id=f"user_{i}",
elements=[
Element(nth_of_type=1,
nth_child=0,
tag_name="button",
text="Pay $10")
],
timestamp="2020-01-03T14:00:00Z",
properties={
"signup_source": "facebook",
"$event_type": "submit"
},
)
_create_event(
team=self.team,
event="paid",
distinct_id=f"user_{i}",
timestamp="2020-01-04T14:00:00Z",
)
# Atleast one person that fails, to ensure we get results
_create_person(distinct_ids=[f"user_fail"], team_id=self.team.pk)
_create_event(
team=self.team,
event="user signed up",
distinct_id=f"user_fail",
timestamp="2020-01-02T14:00:00Z",
)
result = correlation._run()[0]
# $autocapture results only return elements chain
self.assertEqual(
result,
[
{
"event":
'$autocapture::elements_chain::click__~~__a:href="/movie"nth-child="0"nth-of-type="1"',
"success_count": 6,
"failure_count": 0,
"odds_ratio": 14.0,
"correlation_type": "success",
},
{
"event":
'$autocapture::elements_chain::submit__~~__button:nth-child="0"nth-of-type="1"text="Pay $10"',
"success_count": 3,
"failure_count": 0,
"odds_ratio": 2.0,
"correlation_type": "success",
},
],
)
self.assertEqual(
len(
self._get_people_for_event(filter, "$autocapture",
{"signup_source": "facebook"})), 3)
self.assertEqual(
len(
self._get_people_for_event(filter, "$autocapture",
{"$event_type": "click"})), 6)
self.assertEqual(
len(
self._get_people_for_event(
filter,
"$autocapture",
[
{
"key": "tag_name",
"operator": "exact",
"type": "element",
"value": "button"
},
{
"key": "text",
"operator": "exact",
"type": "element",
"value": "Pay $10"
},
],
)),
3,
)
self.assertEqual(
len(
self._get_people_for_event(
filter,
"$autocapture",
[
{
"key": "tag_name",
"operator": "exact",
"type": "element",
"value": "a"
},
{
"key": "href",
"operator": "exact",
"type": "element",
"value": "/movie"
},
],
)),
6,
)
def test_discarding_insignificant_events(self):
filters = {
"events": [
{
"id": "user signed up",
"type": "events",
"order": 0
},
{
"id": "paid",
"type": "events",
"order": 1
},
],
"insight":
INSIGHT_FUNNELS,
"date_from":
"2020-01-01",
"date_to":
"2020-01-14",
"funnel_correlation_type":
"events",
}
filter = Filter(data=filters)
correlation = FunnelCorrelation(filter, self.team)
for i in range(10):
_create_person(distinct_ids=[f"user_{i}"], team_id=self.team.pk)
_create_event(
team=self.team,
event="user signed up",
distinct_id=f"user_{i}",
timestamp="2020-01-02T14:00:00Z",
)
if i % 2 == 0:
_create_event(
team=self.team,
event="positively_related",
distinct_id=f"user_{i}",
timestamp="2020-01-03T14:00:00Z",
)
if i % 10 == 0:
_create_event(
team=self.team,
event="low_sig_positively_related",
distinct_id=f"user_{i}",
timestamp="2020-01-03T14:20:00Z",
)
_create_event(
team=self.team,
event="paid",
distinct_id=f"user_{i}",
timestamp="2020-01-04T14:00:00Z",
)
for i in range(10, 20):
_create_person(distinct_ids=[f"user_{i}"], team_id=self.team.pk)
_create_event(
team=self.team,
event="user signed up",
distinct_id=f"user_{i}",
timestamp="2020-01-02T14:00:00Z",
)
if i % 2 == 0:
_create_event(
team=self.team,
event="negatively_related",
distinct_id=f"user_{i}",
timestamp="2020-01-03T14:00:00Z",
)
if i % 5 == 0:
_create_event(
team=self.team,
event="low_sig_negatively_related",
distinct_id=f"user_{i}",
timestamp="2020-01-03T14:00:00Z",
)
# Total 10 positive, 10 negative
# low sig count = 1 and 2, high sig count >= 5
# Thus, to discard the low sig count, % needs to be >= 10%, or count >= 2
# Discard both due to %
FunnelCorrelation.MIN_PERSON_PERCENTAGE = 0.11
FunnelCorrelation.MIN_PERSON_COUNT = 25
result = correlation._run()[0]
self.assertEqual(len(result), 2)
def test_funnel_correlation_with_event_properties(self):
filters = {
"events": [
{
"id": "user signed up",
"type": "events",
"order": 0
},
{
"id": "paid",
"type": "events",
"order": 1
},
],
"insight":
INSIGHT_FUNNELS,
"date_from":
"2020-01-01",
"date_to":
"2020-01-14",
"funnel_correlation_type":
"event_with_properties",
"funnel_correlation_event_names":
["positively_related", "negatively_related"],
}
filter = Filter(data=filters)
correlation = FunnelCorrelation(filter, self.team)
for i in range(10):
_create_person(distinct_ids=[f"user_{i}"], team_id=self.team.pk)
_create_event(
team=self.team,
event="user signed up",
distinct_id=f"user_{i}",
timestamp="2020-01-02T14:00:00Z",
)
if i % 2 == 0:
_create_event(
team=self.team,
event="positively_related",
distinct_id=f"user_{i}",
timestamp="2020-01-03T14:00:00Z",
properties={
"signup_source": "facebook" if i % 4 == 0 else "email",
"blah": "value_bleh"
},
)
# source: email occurs only twice, so would be discarded from result set
_create_event(
team=self.team,
event="paid",
distinct_id=f"user_{i}",
timestamp="2020-01-04T14:00:00Z",
)
for i in range(10, 20):
_create_person(distinct_ids=[f"user_{i}"], team_id=self.team.pk)
_create_event(
team=self.team,
event="user signed up",
distinct_id=f"user_{i}",
timestamp="2020-01-02T14:00:00Z",
)
if i % 2 == 0:
_create_event(
team=self.team,
event="negatively_related",
distinct_id=f"user_{i}",
timestamp="2020-01-03T14:00:00Z",
properties={
"signup_source": "shazam" if i % 6 == 0 else "email"
},
)
# source: shazam occurs only once, so would be discarded from result set
result = correlation._run()[0]
odds_ratios = [item.pop("odds_ratio")
for item in result] # type: ignore
expected_odds_ratios = [11, 5.5, 2 / 11]
for odds, expected_odds in zip(odds_ratios, expected_odds_ratios):
self.assertAlmostEqual(odds, expected_odds)
self.assertEqual(
result,
[
{
"event": "positively_related::blah::value_bleh",
"success_count": 5,
"failure_count": 0,
# "odds_ratio": 11.0,
"correlation_type": "success",
},
{
"event": "positively_related::signup_source::facebook",
"success_count": 3,
"failure_count": 0,
# "odds_ratio": 5.5,
"correlation_type": "success",
},
{
"event": "negatively_related::signup_source::email",
"success_count": 0,
"failure_count": 3,
# "odds_ratio": 0.18181818181818182,
"correlation_type": "failure",
},
],
)
self.assertEqual(
len(
self._get_people_for_event(filter, "positively_related",
{"blah": "value_bleh"})), 5)
self.assertEqual(
len(
self._get_people_for_event(filter, "positively_related",
{"signup_source": "facebook"})), 3)
self.assertEqual(
len(
self._get_people_for_event(filter, "positively_related",
{"signup_source": "facebook"},
False)), 0)
self.assertEqual(
len(
self._get_people_for_event(filter, "negatively_related",
{"signup_source": "email"}, False)),
3)
def test_funnel_correlation_with_event_properties_exclusions(self):
filters = {
"events": [
{
"id": "user signed up",
"type": "events",
"order": 0
},
{
"id": "paid",
"type": "events",
"order": 1
},
],
"insight":
INSIGHT_FUNNELS,
"date_from":
"2020-01-01",
"date_to":
"2020-01-14",
"funnel_correlation_type":
"event_with_properties",
"funnel_correlation_event_names": ["positively_related"],
"funnel_correlation_event_exclude_property_names":
["signup_source"],
}
filter = Filter(data=filters)
correlation = FunnelCorrelation(filter, self.team)
# Need more than 2 events to get a correlation
for i in range(3):
_create_person(distinct_ids=[f"user_{i}"], team_id=self.team.pk)
_create_event(
team=self.team,
event="user signed up",
distinct_id=f"user_{i}",
timestamp="2020-01-02T14:00:00Z",
)
_create_event(
team=self.team,
event="positively_related",
distinct_id=f"user_{i}",
timestamp="2020-01-03T14:00:00Z",
properties={
"signup_source": "facebook",
"blah": "value_bleh"
},
)
_create_event(
team=self.team,
event="paid",
distinct_id=f"user_{i}",
timestamp="2020-01-04T14:00:00Z",
)
# Atleast one person that fails, to ensure we get results
_create_person(distinct_ids=[f"user_fail"], team_id=self.team.pk)
_create_event(
team=self.team,
event="user signed up",
distinct_id=f"user_fail",
timestamp="2020-01-02T14:00:00Z",
)
result = correlation._run()[0]
self.assertEqual(
result,
[
{
"event": "positively_related::blah::value_bleh",
"success_count": 3,
"failure_count": 0,
"odds_ratio": 8,
"correlation_type": "success",
},
# missing signup_source, as expected
],
)
self.assertEqual(
len(
self._get_people_for_event(filter, "positively_related",
{"blah": "value_bleh"})), 3)
# If you search for persons with a specific property, even if excluded earlier, you should get them
self.assertEqual(
len(
self._get_people_for_event(filter, "positively_related",
{"signup_source": "facebook"})), 3)
