def test_categorical_cast():
cat = Categorical("c", ['A','B','C'], "sum", {}, coltype="SMALLINT")
assert list(map(str, cat.get_columns())) == [
"sum((c = 'A')::INT)::SMALLINT",
"sum((c = 'B')::INT)::SMALLINT",
"sum((c = 'C')::INT)::SMALLINT"
]
def test_categorical_nones():
d1 = Categorical('col', {
'vala': 'a',
'valb': 'b',
'valc': 'c',
'_NULL': None
}, [], {}).quantities
d2 = Compare('col',
'=', {
'vala': 'a',
'valb': 'b',
'valc': 'c'
}, [], {},
op_in_name=False,
include_null=True).quantities
assert d1 == d2
d3 = Categorical('col', ['a', 'b', 'c', None], [], {}).quantities
assert sorted(d1.values()) == sorted(d2.values())
def test_categorical_same_as_compare():
d1 = Categorical("col", {
"vala": "a",
"valb": "b",
"valc": "c"
}, [], {}).quantities
d2 = Compare("col", "=", {
"vala": "a",
"valb": "b",
"valc": "c"
}, [], {}).quantities
assert sorted(d1.values()) == sorted(d2.values())
d3 = Categorical("col", {
"vala": "a",
"valb": "b",
"valc": "c"
}, [], {},
op_in_name=True).quantities
assert d2 == d3
def test_categorical_same_as_compare():
d1 = Categorical('col', {
'vala': 'a',
'valb': 'b',
'valc': 'c'
}, [], {}).quantities
d2 = Compare('col', '=', {
'vala': 'a',
'valb': 'b',
'valc': 'c'
}, [], {}).quantities
assert sorted(d1.values()) == sorted(d2.values())
d3 = Categorical('col', {
'vala': 'a',
'valb': 'b',
'valc': 'c'
}, [], {},
op_in_name=True).quantities
assert d2 == d3
def test_index_column_lookup(test_engine):
aggregations = [
SpacetimeAggregation(
prefix="prefix1",
aggregates=[
Categorical(
col="cat_one",
function="sum",
choices=["good", "bad", "inbetween"],
impute_rules={"coltype": "categorical", "all": {"type": "zero"}},
)
],
groups=["entity_id"],
intervals=["all"],
date_column="knowledge_date",
output_date_column="as_of_date",
dates=["2013-09-30", "2014-09-30"],
state_table="states",
state_group="entity_id",
schema="features",
from_obj="data",
),
SpacetimeAggregation(
prefix="prefix2",
aggregates=[
Aggregate(
quantity="quantity_one",
function="count",
impute_rules={"coltype": "aggregate", "all": {"type": "zero"}},
)
],
groups=["entity_id", "zip_code"],
intervals=["all"],
date_column="knowledge_date",
output_date_column="as_of_date",
dates=["2013-09-30", "2014-09-30"],
state_table="states",
state_group="entity_id",
schema="features",
from_obj="data",
),
]
features_schema_name = "features"
feature_generator = FeatureGenerator(
db_engine=test_engine,
features_schema_name=features_schema_name,
)
lookup = feature_generator.index_column_lookup(aggregations)
assert lookup == {
"prefix1_aggregation_imputed": ["as_of_date", "entity_id"],
"prefix2_aggregation_imputed": ["as_of_date", "entity_id", "zip_code"],
}
def _build_categoricals(self, categorical_config, impute_rules):
# TODO: only include null flag where necessary
return [
Categorical(col=categorical['column'],
choices=self._build_choices(categorical),
function=categorical['metrics'],
impute_rules=dict(impute_rules,
coltype='categorical',
**categorical.get('imputation', {})),
include_null=True)
for categorical in categorical_config
]
def _build_categoricals(self, categorical_config, impute_rules):
# TODO: only include null flag where necessary
return [
Categorical(
col=categorical["column"],
choices=self._build_choices(categorical),
function=categorical["metrics"],
impute_rules=dict(impute_rules,
coltype="categorical",
**categorical.get("imputation", {})),
include_null=True,
) for categorical in categorical_config
]
def test_categorical_nones():
d1 = Categorical("col", {
"vala": "a",
"valb": "b",
"valc": "c",
"_NULL": None
}, [], {}).quantities
d2 = Compare(
"col",
"=",
{
"vala": "a",
"valb": "b",
"valc": "c"
},
[],
{},
op_in_name=False,
include_null=True,
).quantities
assert d1 == d2
d3 = Categorical("col", ["a", "b", "c", None], [], {}).quantities
assert sorted(d1.values()) == sorted(d3.values())
def test_generate_table_tasks(test_engine):
test_engine.execute('create schema features')
aggregations = [
SpacetimeAggregation(
prefix="prefix1",
aggregates=[
Categorical(
col="cat_one",
function="sum",
choices=["good", "bad", "inbetween"],
impute_rules={"coltype": "categorical", "all": {"type": "zero"}},
)
],
groups=["entity_id"],
intervals=["all"],
date_column="knowledge_date",
output_date_column="as_of_date",
dates=["2013-09-30", "2014-09-30"],
state_table="states",
state_group="entity_id",
schema="features",
from_obj="data",
),
SpacetimeAggregation(
prefix="prefix2",
aggregates=[
Aggregate(
quantity="quantity_one",
function="count",
impute_rules={"coltype": "aggregate", "all": {"type": "zero"}},
)
],
groups=["entity_id"],
intervals=["all"],
date_column="knowledge_date",
output_date_column="as_of_date",
dates=["2013-09-30", "2014-09-30"],
state_table="states",
state_group="entity_id",
schema="features",
from_obj="data",
),
]
features_schema_name = "features"
table_tasks = FeatureGenerator(
db_engine=test_engine,
features_schema_name=features_schema_name,
).generate_all_table_tasks(aggregations, task_type="aggregation")
for table_name, task in table_tasks.items():
assert "DROP TABLE" in task["prepare"][0]
assert "CREATE TABLE" in str(task["prepare"][1])
assert "CREATE INDEX" in task["finalize"][0]
assert isinstance(task["inserts"], list)
# build the aggregation tables to check the imputation tasks
FeatureGenerator(
db_engine=test_engine,
features_schema_name=features_schema_name,
).process_table_tasks(table_tasks)
table_tasks = FeatureGenerator(
db_engine=test_engine,
features_schema_name=features_schema_name,
).generate_all_table_tasks(aggregations, task_type="imputation")
for table_name, task in table_tasks.items():
assert "DROP TABLE" in task["prepare"][0]
assert "CREATE TABLE" in str(task["prepare"][1])
assert "CREATE INDEX" in task["finalize"][0]
assert isinstance(task["inserts"], list)
def test_generate_table_tasks():
aggregations = [
SpacetimeAggregation(prefix='prefix1',
aggregates=[
Categorical(
col='cat_one',
function='sum',
choices=['good', 'bad', 'inbetween'],
impute_rules={
'coltype': 'categorical',
'all': {
'type': 'zero'
}
})
],
groups=['entity_id'],
intervals=['all'],
date_column='knowledge_date',
output_date_column='as_of_date',
dates=['2013-09-30', '2014-09-30'],
state_table='states',
state_group='entity_id',
schema='features',
from_obj='data'),
SpacetimeAggregation(prefix='prefix2',
aggregates=[
Aggregate(quantity='quantity_one',
function='count',
impute_rules={
'coltype': 'aggregate',
'all': {
'type': 'zero'
}
})
],
groups=['entity_id'],
intervals=['all'],
date_column='knowledge_date',
output_date_column='as_of_date',
dates=['2013-09-30', '2014-09-30'],
state_table='states',
state_group='entity_id',
schema='features',
from_obj='data')
]
with testing.postgresql.Postgresql() as postgresql:
engine = create_engine(postgresql.url())
setup_db(engine)
features_schema_name = 'features'
table_tasks = FeatureGenerator(
db_engine=engine, features_schema_name=features_schema_name
).generate_all_table_tasks(aggregations, task_type='aggregation')
for table_name, task in table_tasks.items():
assert 'DROP TABLE' in task['prepare'][0]
assert 'CREATE TABLE' in str(task['prepare'][1])
assert 'CREATE INDEX' in task['finalize'][0]
assert isinstance(task['inserts'], list)
# build the aggregation tables to check the imputation tasks
FeatureGenerator(
db_engine=engine,
features_schema_name=features_schema_name).process_table_tasks(
table_tasks)
table_tasks = FeatureGenerator(
db_engine=engine, features_schema_name=features_schema_name
).generate_all_table_tasks(aggregations, task_type='imputation')
for table_name, task in table_tasks.items():
assert 'DROP TABLE' in task['prepare'][0]
assert 'CREATE TABLE' in str(task['prepare'][1])
assert 'CREATE INDEX' in task['finalize'][0]
assert isinstance(task['inserts'], list)
def test_index_column_lookup():
aggregations = [
SpacetimeAggregation(prefix='prefix1',
aggregates=[
Categorical(
col='cat_one',
function='sum',
choices=['good', 'bad', 'inbetween'],
impute_rules={
'coltype': 'categorical',
'all': {
'type': 'zero'
}
})
],
groups=['entity_id'],
intervals=['all'],
date_column='knowledge_date',
output_date_column='as_of_date',
dates=['2013-09-30', '2014-09-30'],
state_table='states',
state_group='entity_id',
schema='features',
from_obj='data'),
SpacetimeAggregation(prefix='prefix2',
aggregates=[
Aggregate(quantity='quantity_one',
function='count',
impute_rules={
'coltype': 'aggregate',
'all': {
'type': 'zero'
}
})
],
groups=['entity_id', 'zip_code'],
intervals=['all'],
date_column='knowledge_date',
output_date_column='as_of_date',
dates=['2013-09-30', '2014-09-30'],
state_table='states',
state_group='entity_id',
schema='features',
from_obj='data')
]
with testing.postgresql.Postgresql() as postgresql:
engine = create_engine(postgresql.url())
setup_db(engine)
features_schema_name = 'features'
feature_generator = FeatureGenerator(
db_engine=engine, features_schema_name=features_schema_name)
lookup = feature_generator.index_column_lookup(aggregations)
assert lookup == {
'prefix1_aggregation_imputed': [
'as_of_date',
'entity_id',
],
'prefix2_aggregation_imputed':
['as_of_date', 'entity_id', 'zip_code']
}
